Keywords: Water scarcity, water management, smart water, machine learning, GIS, rainwater harvesting, water quality, climate change, Palestine V Pénurie d'eau, gestion de l'eau, eau intelligente, apprentissage automatique, SIG, collecte de l'eau de pluie, qualité de

This thesis aims at establishing a smart management system to address the water scarcity in Palestine with emphasis on the assessment of water scarcity, protection of conventional water resources, adoption of rainwater harvesting (RWH), and use of dynamic smart water management. The thesis starts by investigating the state of the art and gaps of previous researches in addressing the water scarcity. Accordingly, a methodology is proposed to address these gaps with emphasis on the West Bank (Palestine). It includes (i) the assessment of water scarcity, (ii) the protection of conventional water resources using the GIS, water quality index and Machine Learning-based water quality prediction, (iii) the adoption of potential RWH for addressing the water scarcity, and (iv) the adoption of smart RWH system to promote the water security. Results indicate that seven out of the eleven West Bank governorates are suffering from extreme to acute water scarcity in 2020. Around 20% of the wells in the urban areas had experienced potability-related contamination. More than 95% of the potability-related contaminated samples are found in the Eocene Aquifer. Concerning groundwater nitrate contamination, Random Forest model successfully attained a maximum and average prediction accuracy of 91.70% and 88.54%, respectively. All water-contaminated samples were observed in areas with improper practices such as the use of cesspits, fertilizers, and pesticides. The smart RWH system showed a capability to cover 41% of the domestic water needs. The smart dual water system shows higher reliability in addressing the water demand compared to the smart RWH system solely. By adopting the dynamic management and a new supply agenda, the smart dual system showed a 100% capacity to address the water scarcity at all altitude levels in the study area. They also contributed to getting the best performance by using smaller storage tank size which could promote the city's socioeconomic development.

The knowledge-based framework and results presented in this thesis forms a step forward in the water engineering and sciences. However, this research shows some limitations including the dependency to downscaled global climate change models, and the lack of heavy metals characterization in the water quality aspects. Future research could focus on (i) considering a local climate change model to estimate the projected rainfall and water availability, and (ii) investigating the social acceptance for the adoption of the proposed smart system.

IX

List of Figures

Figure 1.1. KIM-based interpolated vs. observed groundwater levels in an aquifer in piedmont plains, China [START_REF] Xiao | Geostatistical interpolation model selection based on ArcGIS and spatio-temporal variability analysis of groundwater level in piedmont plains, northwest China[END_REF] 3.1. Domestic water scarcity values and the associated levels [START_REF] Huang | Supply and Demand Analysis of Water Resources Based on System Dynamics Model[END_REF] .... Table 3.2 4.1. Identification of potability and palatability groundwater quality statuses (PoGWQSs, PaGWQSs), and grades (PoGWQGs, and PaGWQGs) [START_REF] Brown | A Water Quality Index: Do We Dare? Water Sewage Works[END_REF] ............................... Table 4.2. Vn, Vi, and Vs values of water quality parameters (PWA, 2020;WHO, 2011;and Keršulien et al., 2010) 5.5. Confusion matrix for the RFA prediction model (trial 10, Table 5.4 Chapter 0. General Introduction

XI

List of Tables

Main water challenges of arid and semi-arid areas

Given the increasing population growth and water demand, the sustainable management of water resources forms a necessity for most of the arid and semi-arid regions, especially in Palestine [START_REF] Alawna | Rooftop Rainwater Harvesting to Alleviate Domestic Water Shortage in the West Bank, Palestine[END_REF]and Durán-Sánchez et al., 2018). Such management aims to satisfy the existing and future water demand and to address the water scarcity without causing the depletion and contamination to the water bodies (Durán-Sánchez et al., 2018). In the West Bank, Palestine, water scarcity is the main challenge that threaten the sustainable development [START_REF] Alawna | Rooftop Rainwater Harvesting to Alleviate Domestic Water Shortage in the West Bank, Palestine[END_REF]and PWA. 2016). This scarcity has been deteriorated due to several factors including: the population growth and the associated increasing water demand, the water pollution, and the climate change impacts that imposed a terrific stress on the conventional water supplies (Judeh and Shahrour, 2021;and PWA, 2016). The existing political situation deepens the water scarcity since the Israeli Occupation controls the Palestinians' accessibility to their water resources [START_REF] Judeh | Assessment of Water Governance in the West Bank, Palestine[END_REF]. Such control along with the Palestinians' dominant dependency to the conventional water resource (mainly to groundwater that contribute with more than 90% of the water supply) obstruct the alleviation of water scarcity through the adoption of sustainable management of water resources that relies on multi-sources of water supply (PWA, 2017). Thus, this research focuses on addressing the Palestinian water scarcity by the adoption of proper and sustainable water management practices. Such practices involve the monitoring and protection and of existing water resources, the adoption of alternative water resources (rainwater harvesting, RWH), and the adoption of smart water systems to improve the performance of the management of water resources.

Water scarcity

Water scarcity is a serious hazard that threaten the human life, and the agricultural, industrial, and urban development [START_REF] Hasan | The Effect of Temperature and Rainfall Changes on Biophysical and Socio-Economic Status of People in Northern Jordan Valley Drylands, Palestine[END_REF][START_REF] Chaudhary | Assessment of groundwater quality for drinking and irrigation purposes in arid areas of Rajasthan, India[END_REF][START_REF] Kahil | Modeling water scarcity and droughts for policy adaptation to climate change in arid and semiarid regions[END_REF]. Scarcity can be seen in the physical in-availability of water resources, in the nonpotability of available water due to the various contaminants reaching the different water bodies, or in the inaccessibility to water resources due to political, technical and institutional failure (Judeh and Shahrour, 2021). Such scarcity contrasting the recommendation of the World Health Organizations (WHO), and the United Nations (UN) Sustainable Development Goals-SDG 6 "ensure availability and sustainable management of water and sanitation for all" (UN, 2021). Scholars confirm the responsibility of such scarcity in the spread of infectious diseases (e.g., SARS, Ebola, and influenza). Such diseases could be eliminated by adopting the needed water, sanitation and hygiene (WASH) measures [START_REF] Anser | Identifying the Potential Causes, Consequences, and Prevention of Communicable Diseases (Including COVID-19)[END_REF][START_REF] Adams | Water Insecurity and Urban Poverty in the Global South: Implications for Health and Human Biology[END_REF][START_REF] Zakar | Climate Change-Induced Water Scarcity: A Threat to Human Health*[END_REF]. Water scarcity is also linked to other diseases such as cholera, typhoid fever, salmonellosis, and dysentery [START_REF] Anser | Identifying the Potential Causes, Consequences, and Prevention of Communicable Diseases (Including COVID-19)[END_REF][START_REF] Adams | Water Insecurity and Urban Poverty in the Global South: Implications for Health and Human Biology[END_REF][START_REF] Zakar | Climate Change-Induced Water Scarcity: A Threat to Human Health*[END_REF]. Agriculture and food security are also adversely affected by the water scarcity since it restricts the ability to address the increasing agricultural water demand (e.g. irrigated agriculture as well as livestock) [START_REF] Shadeed | Rainwater Harvesting for Sustainable Agriculture in High Water-Poor Areas in the West Bank, Palestine[END_REF][START_REF] Sullivan | The water poverty index: Development and application at the community scale[END_REF]. Scarcity also impacts the food processing industries [START_REF] Sullivan | The water poverty index: Development and application at the community scale[END_REF]. Such deterioration in human health, agricultural and industrial development significantly threaten the sustainable urban development [START_REF] Chaudhary | Assessment of groundwater quality for drinking and irrigation purposes in arid areas of Rajasthan, India[END_REF][START_REF] Sullivan | The water poverty index: Development and application at the community scale[END_REF]. As a result of the water scarcity, more than two billion people have insufficient access to water supply (Gonçalves et al., 2019). In the West Bank, the domestic water supply-demand gap was estimated at 58 million cubic meters per year (MCM/y) in 2018 (Judeh and Shahrour, 2021). In 2019, around 60% of Palestinians are suffering from high to very high domestic water poverty [START_REF] Shadeed | Developing a GIS-Based Water Poverty and Rainwater Harvesting Suitability Maps for Domestic Use in the Dead Sea Region (West Bank, Palestine)[END_REF]. On the other hand, about 61% of the West Bank governorates are characterized by high to very high agricultural water poverty [START_REF] Shadeed | Rainwater Harvesting for Sustainable Agriculture in High Water-Poor Areas in the West Bank, Palestine[END_REF]. This in turn adversely impacted the water-food nexus in most of the Palestine governorates [START_REF] Shadeed | Rainwater Harvesting for Sustainable Agriculture in High Water-Poor Areas in the West Bank, Palestine[END_REF].

According to the UN' World Water Development Report, water scarcity is a major issue in today's world of 7.7 billion people. Such issue is expected to be more complicated by 2050 since the world population will increase by around 33% to reach around 10.2 billion. Most of the population growth is expected to occur in the developing countries (e.g. Palestine) (World Water Assessment Programme, 2018). By 2030, half of the world's population are expected to live in countries facing water scarcity status. This is expected to contribute to the displacement of 700 million people from the water-scare countries to the water-secured ones (Gonçalves et al., 2019). By 2050, around 90% of the world's population in the developing countries, especially in the arid and semi-arid areas will lack access to safe water supply (Gonçalves et al., 2019). Global water demand is expected to dramatically grow over the coming three decades among the domestic, agricultural, and industrial sectors. (World Water Assessment Programme, 2018). The global domestic water demand is expected to increase worldwide over the period 2020-2050, where the highest increase (300%) will be recorded in Africa and Asia [START_REF] Wada | Modeling global water use for the 21st century: The Water Futures and Solutions (WFaS) initiative and its approaches[END_REF]. Global agricultural water demand is expected to increase by around 60% by 2050 (World Water Assessment Programme, 2018;[START_REF] Wada | Modeling global water use for the 21st century: The Water Futures and Solutions (WFaS) initiative and its approaches[END_REF]. Considering the industry, global water demand is expected to increase by around 400% in 2050. Such growth will be spatially variated between 250% in Asia and 800% in Africa (World Water Assessment Programme, 2018;[START_REF] Wada | Modeling global water use for the 21st century: The Water Futures and Solutions (WFaS) initiative and its approaches[END_REF]. In the West Bank, Palestine, population are expected to increase by around 75% between 2020 (around 2.7 million capita) and 2050 (around 4.7 million capita) [START_REF] Courbage | Palestine 2030 -Demographic Change: Opportunities for Development[END_REF]. This population growth is associated with significant growth in the domestic water needs from 110 MCM/year (for the current status) to 175 MCM/year (for the year 2050) (Judeh and Shahrour, 2021). Such dramatic increase in the water needs forms significant stresses on the limited conventional water resources (surface and groundwater) in Palestine (Judeh and Shahrour, 2021).

Climate change

Beside to the increasing water needs, climate change forms a major challenge to the sustainable management and mitigation of water scarcity [START_REF] Stringer | Climate change impacts on water security in global drylands[END_REF][START_REF] Farsani | Assessment of the impact of climate change on spatiotemporal variability of blue and green water resources under CMIP3 and CMIP5 models in a highly mountainous watershed[END_REF]Gonçalves et al., 2019;[START_REF] Abbaspour | Assessing the impact of climate change on water resources in Iran[END_REF]. The main driver of climate change is increasing greenhouse gas emissions from anthropogenic activities [START_REF] Lu | Impacts of climate change on water resources and grain production[END_REF]. As a result of the increase in the atmospheric concentration of greenhouse gas emissions, the global average surface temperature has risen by 0.6 °C to 0.8 °C over the past century [START_REF] Lu | Impacts of climate change on water resources and grain production[END_REF]. Such warming is 20-40% greater in the arid and semi-arid regions compared to the humid ones (MoFA Netherlands, 2018). Climate change has increased the incidents of extreme weather including, tropical storms, flash floods, droughts, and changes in precipitation [START_REF] Bai | Impact of land use and climate change on water-related ecosystem services in Kentucky, USA[END_REF][START_REF] Tegegne | Comparison of hydrological models for the assessment of water resources in a data-scarce region, the Upper Blue Nile River Basin[END_REF]. In the arid to semi-arid areas, the water balance is usually negative since the average annual precipitation is generally close or even below the potential annual evaporation [START_REF] Heyns | Water conservation in arid and semi-arid regions[END_REF]. The high evaporation rate combined with unmanaged precipitation have led to a reduction in the levels of surface water resources (i.e., lakes and rivers) [START_REF] Middleton | Climate hazards in drylands: A review[END_REF]). Yet, groundwater storage has continuously declined due to recharge and extraction imbalance (Heiß et al., 2020). In the Mediterranean region (including Palestine), [START_REF] Hochman | The seasons' length in 21st century CMIP5 projections over the eastern Mediterranean[END_REF] predicted changes in the length of seasons by the end of this century; the summer is expected to be longer by 49 %, while the winter season will be shortened by 56%. By 2100, global and regional climate models expected an increase in temperature between 2˚C to 2.7˚C in the region. On the other hand, annual precipitation is expected to experience a reduction ranging from 3% to 10% [START_REF] Christensen | Climate phenomena and their relevance for future regional climate change[END_REF]. Subsequently, the water resources availability is expected to decrease by around 26% [START_REF] Menzel | GLOWA Jordan River Phase II Final Report: An Integrated Approach to Sustainable Management of Water Resources Under Global Change[END_REF]. Other environmental changes might arise including the deficits in hydrological regimes, and the imbalances in natural production [START_REF] Freij | Climate Change and the Vulnerable Occupied Palestinian Territories[END_REF][START_REF] Christensen | Climate phenomena and their relevance for future regional climate change[END_REF]. Thus, implying the climate change impacts in the sustainable management of water resources in Palestine is of high importance (Durán-Sánchez et al., 2018).

Water pollution

Beside to the above mentioned reasons, the increased water scarcity could be attributed to several factors including: the water resources pollution, and the unaware use, allocation and management of the water resources [START_REF] Shadeed | Developing a GIS-Based Water Poverty and Rainwater Harvesting Suitability Maps for Domestic Use in the Dead Sea Region (West Bank, Palestine)[END_REF][START_REF] Chitsaz | Water scarcity management in arid regions based on an extended multiple criteria technique[END_REF]. According to the UN' World Water Development Report, the pollution of water resources is becoming worse, especially in the last few decades (World Water Assessment Programme, 2018). Such pollution is originated from point (from industry) and non-point (from agriculture and urban areas) sources (World Water Assessment Programme, 2018;and UNEP, 2016). Worldwide, around 730 million tons of sewage and other contaminating effluents are discharged into the water per year, where the industry is the main contributor (around 300 to 400 million tons per year) (World Water Assessment Programme, 2018). These loads affect most of the surface water quality (e.g. lakes and rivers), especially in Middle East (including Palestine) [START_REF] Almasri | Identification of the Nitrogen Sources in the Eocene Aquifer Area (Palestine)[END_REF]and UN, 2011). Moreover, many studies documented the deterioration of the groundwater resources quality worldwide [START_REF] Chaudhary | Assessment of groundwater quality for drinking and irrigation purposes in arid areas of Rajasthan, India[END_REF][START_REF] Macdonald | Groundwater quality and depletion in the Indo-Gangetic basin mapped from in situ observations[END_REF][START_REF] Gleeson | Water supply and access to safe water in developing arid countries[END_REF]. Such deterioration is attributed to the low natural recharge, extensive use of pesticides in agricultural areas, heavy abstraction, and other anthropogenic activities [START_REF] Adimalla | Groundwater quality for drinking and irrigation purposes and potential health risks assessment: a case study from semi-arid region of South India[END_REF]. High levels of various minerals (e.g. Nitrate, NO3) were found in the groundwater aquifers [START_REF] Ayadi | Statistical and geochemical assessment of groundwater quality in Teboursouk area (Northwestern Tunisian Atlas)[END_REF][START_REF] El Alfy | Groundwater characteristics and pollution assessment using integrated hydrochemical investigations GIS and multivariate geostatistical techniques in arid areas[END_REF][START_REF] Krishnakumar | Assessment of groundwater quality in and around Vedaraniyam, South India[END_REF]. The increasing temperature of surface and groundwater resources has altered some biological and chemical processes and in turn impact the quality of water resources (Heiß et al., 2020). Subsequently, around 1.8 billion people drink a water contaminated by feces. Such contaminated water along with the poor hygiene and sanitation cause the death of around 1000 children each day (UN, 2018;UN, 2015a;UN, 2011). In the next few decades, water pollution is expected to intensify which form a threat to the world sustainable development (UNEP, 2016). In which, effluents from wastewater and industrial effluents are projected to significant increase due to the rapid urbanization (UNEP, 2016). Moreover, the increasing fertilizes-based agriculture is a significant source of nitrogen and phosphorus loading (UNEP, 2016). In 2050, the nitrogen and phosphorus effluents are projected to increase by around 180 % and 150 % respectively (UNEP, 2016). The list of water pollutants (with concentrations higher than the WHO standards) is increasing. These pollutants comprise the fragrances, pharmaceuticals, caffeine, hormones, personal care products, detergents, cleaning agents, and flame retardants (Sauve and Desrosiers, 2014). Such pollutants will be driven by the increasing population and economic growth and the lack of wastewater treatment facilities. Low and middle-income countries are the most vulnerable to these pollutants (Sauve and Desrosiers, 2014). In the West Bank, Palestine, around 25 MCM/y of untreated sewage is directly disposed in wadis and streams (UNEP, 2020). The Israeli settlers heavily contribute to the degradation of water resources and the environment in the West Bank, by the release of untreated wastewater into the natural streams and surrounding areas (ARIJ, 2015). Many Wadis in the region have become wastewater channels as in the case of Wadi Al Samin near Hebron, Wadi Al Zomer near Nablus, and Wadi Al Nar near Bethlehem (ARIJ, 2015). The discharged wastewater infiltrates into the soil and reaches the groundwater basins causing contamination of groundwater [START_REF] Mahmoud | Groundwater Quality of Drinking Water Wells in the West Bank, Palestine[END_REF][START_REF] Almasri | Identification of the Nitrogen Sources in the Eocene Aquifer Area (Palestine)[END_REF]. Moreover, the extensive use of fertilizers and pesticides cause the contamination of groundwater (mainly by NO3) [START_REF] Almasri | Identification of the Nitrogen Sources in the Eocene Aquifer Area (Palestine)[END_REF].

Alternative water resources

Given the elevated stresses on surface and groundwater, adopting alternative water resources is of high significant to promote the water security (Karimidastenaei et al., 2022;[START_REF] Yu | Fog Harvesting Devices Inspired from Single to Multiple Creatures: Current Progress and Future Perspective[END_REF]and Judeh et al., 2021). Several alternative resources are adopted worldwide to support the conventional water resources (e.g. water desalination, RWH, and fog water collection) (Karimidastenaei et al., 2022;and Yu et al., 2022). However, the Israeli Occupation totally restricts the Palestinian access to their fresh and saline surface water [START_REF] Judeh | Assessment of Water Governance in the West Bank, Palestine[END_REF]. Moreover, fog intensity in the West Bank is insufficient and highly temporally variated (PMA, 2018). Thus, RWH seems to be the most suitable supporter to the existing water supplies in the West Bank, where the long-term average annual rainfall is around 450 mm [START_REF] Zabidi | A Review of Roof and Pond Rainwater Harvesting Systems for Water Security: The Design, Performance and Way Forward[END_REF][START_REF] Shadeed | Developing a GIS-Based Water Poverty and Rainwater Harvesting Suitability Maps for Domestic Use in the Dead Sea Region (West Bank, Palestine)[END_REF].

Smart water

RWH systems consist of a collection catchment, conveyance, and storage tanks. It could be simply constructed and maintained. Harvested water contributes in reducing the burden on the surface and groundwater resources [START_REF] Zabidi | A Review of Roof and Pond Rainwater Harvesting Systems for Water Security: The Design, Performance and Way Forward[END_REF]. However, the RWH yield is not secured at all times, since it is dependent to the rainfall volume and intensity, and to the system efficiency. In addition, the quality of RWH is not well secured (Judeh and Shahrour, 2021;[START_REF] Zabidi | A Review of Roof and Pond Rainwater Harvesting Systems for Water Security: The Design, Performance and Way Forward[END_REF]. Thus, control plans for enhancing the systems efficiency and the harvested water quality are needed (Judeh and Shahrour, 2021;[START_REF] Zabidi | A Review of Roof and Pond Rainwater Harvesting Systems for Water Security: The Design, Performance and Way Forward[END_REF]. Scholars proposed the adoption of smart systems (based on real-time and historical data) to address the limitations of engineering systems and to improve their performance [START_REF] Petrolo | Towards a Smart City Based on Cloud of Things, a Survey on the Smart City Vision and Paradigms[END_REF][START_REF] Castro | Smart Lighting Solutions for Smart Cities[END_REF][START_REF] Kyriazis | Sustainable Smart City IoT Applications: Heat and Electricity Management \& Eco-Conscious Cruise Control for Public Transportation[END_REF][START_REF] Stratigea | The Concept of 'Smart' Cities -Towards Community Development[END_REF]. Such systems have been widely used in the water-related applications including; the monitoring of water leakage in urban networks [START_REF] Mashhadi | Groundwater Contamination in Sub-Saharan Africa: Implications for Groundwater Protection in Developing Countries[END_REF][START_REF] Farah | Leakage Detection Using Smart Water System: Combination of Water Balance and Automated Minimum Night Flow[END_REF], and water quality [START_REF] Pasika | Long-term changes in nitrogen and phosphorus emission into the Vistula and Oder catchments (Poland)-modeling (MONERIS) studies[END_REF][START_REF] Prasad | Smart Water Quality Monitoring System[END_REF][START_REF] Dong | A Survey of Smart Water Quality Monitoring System[END_REF], and the promoting of water resources management [START_REF] Ramos | Smart Water Management towards Future Water Sustainable Networks[END_REF][START_REF] Ntuli | A Simple Security Architecture for Smart Water Management System[END_REF][START_REF] Lee | Smart Water Grid: The Future Water Management Platform[END_REF]. The affordability of smart systems is flexible since they could be designed considering the economic status of the beneficiaries. The cost of the system ranges from the cheapest (e.g. full rely on crowdsourcing) to the most expensive (e.g. full rely on complex smart sensors). Concerning RWH, the use of smart systems is limited and lacks the integrity. The systems were designed considering a single-aspect upgrade on either water quantity (e.g. leak control) or water quality (e.g. pH control) [START_REF] Ranjan | The Internet of Things (IOT) Based Smart Rain Water Harvesting System[END_REF][START_REF] Behzadian | Can Smart Rainwater Harvesting Schemes Result in the Improved Performance of Integrated Urban Water Systems?[END_REF]. Thus, there is a need to propose and dynamically manage a comprehensive smart RWH systems (considering quantity and quality control simultaneously). The effect of these systems to the mitigation of water scarcity is also needed to be investigated.

Scientific questions

The scientific question for this research is: could the integration of the conventional water resources protection, rainwater harvesting adoption, and dynamic smart water management contribute to addressing the water scarcity in Palestine?

To deal with this question, this research aims at establishing a smart management system to address the water scarcity in Palestine with emphasis on: the assessment of water scarcity, the protection of conventional water resources, the adoption of RWH, the implication of demographic and climate change effects, and the use of dynamic smart water management. Accordingly, literature review will be prepared to investigate the needed scientific contributions in this research in order to address the water scarcity and its drivers. Such contributions are unprecedentedly introduced at each phase of this research, including:

(i) Adopting the supply demand balance index (SDBI) to assess the current and future water scarcity in arid and semi-arid regions on light of the demographic and climate change impacts. (ii) Proposing and adopting an adjusted water quality index (WQI) to perform an accurate, efficient, and informative groundwater potability and palatability assessment. Such assessment contributes in delineating the vulnerable areas for groundwater contamination. (iii) Proposing a new approach combining Geographic Information System (GIS), statistical analysis and Machine Learning prediction models for the comprehensive management of the Groundwater Nitrate Contamination (GNC). Such approach along with the WQI are used to form a fundamental step in performing an efficient plan toward the protection of conventional water resources in the arid and semi-arid areas. They help in making the needed decisions and taking the required action for preserving the water quality in such areas. (iv) Investigating the role of potential RWH in addressing the current and future water scarcity considering the demographic and climate changes. (v) Proposing an innovative smart RWH system and a smart dual water supply system to secure the sufficiency and potability of the water supply and the reliability of the used systems.

After that, such contributions will be used as inputs in the research methodology that employed to develop and realize the research. This thesis is presented in seven chapters:

Chapter 1 presents the state of the art concerning how previous researches addressed the water scarcity and its components. Accordingly, the chapter highlights the limitations/gaps of previous researches and introduces the contributions of this research to fill this gap.

Chapter 2 describes the methodology of this research. Based on the state of the art, the most suitable methods are picked, integrated and justified in order to introduce an integrated smart management of water scarcity in arid and semi-arid areas. Such methodology targets the West Bank (Palestine) and implies the assessment of water scarcity through the use of SDBI, the protection of conventional water resources using the WQI and the Machine Learning-based water quality prediction, the adoption of potential RWH for addressing the water scarcity, and the adoption of smart RWH system and smart dual water supply system to promote the water security in study area.

Chapter 3 presents the first issue entitled "Assessment of Current and Forecasted Water Scarcity in the Arid and Semi-Arid Areas". Such assessment is carried out considering the demographic and climate change effects. This application was targeted the West Bank as case study. The detailed review, methodology, results and discussion concerning the application are provided in the chapter.

Chapter 4 presents the second issue entitled "GIS-Based Spatiotemporal Mapping of the Groundwater Potability and Palatability Indices in Arid and Semi-Arid Areas". This application was targeted the West Bank as case study. The detailed review, methodology, results and discussion concerning the application are provided in the chapter.

Chapter 5 introduces the third issue in the thesis entitled "Use of GIS, Statistics and Machine Learning for Groundwater Quality Management: Application to Nitrate Contamination". On light of the Chapter 4 findings (northern parts of the West Bank is vulnerable for frequent water contamination events), this application was targeted the Eocene Aquifer (located to the north of the West Bank) as case study. Integrating the methods and outcomes of Chapters 4 and 5 contribute in formulating water resources protection plans and in taking the needed actions.

Chapter 6 presents the fourth issue in the thesis entitled "Rainwater Harvesting to Address Water Scarcity in Arid and Semi-Arid Areas: Application to Arid and Semi-Arid Areas". The West Bank is selected as a case study for this application. The potential ability of RWH to bridge the current and future water scarcity is conducted in light of the demographic and climate change effects. This helps in promoting more water-sustainable systems and plans.

Chapter 7 introduces the fifth issue in the thesis entitled: "Smart Rainwater Harvesting for Sustainable Potable Water Supply in Arid and Semi-Arid Areas". This application was targeted the Jenin City as a case study. It proposes a smart system (based on Internet of Things (IoT) and crowdsourcing) to unprecedentedly secure the potability of RWH. It also employs the dynamic management to imply the smart RWH system within the framework of the smart dual water supply system. Such system shows its high efficiency and reliability in addressing the domestic water scarcity in the selected study area.

Chapter 1. State of the Art

This chapter presents a state of the art about water scarcity management with focus on: (i) water scarcity assessment, (ii) protection of conventional water resources, and (iii) alternative and nonconventional water resources. It also discusses the role of Machine Learning and smart water systems in addressing the challenges of water scarcity management. Finally, it highlights the research gap and the contributions of this research to fill this gap.

Water scarcity management

The management of water scarcity is essential to ensure the optimum utilization of scarce water in terms of quality, quantity, and sustainability (Sheffield et al., 2018m;[START_REF] Hering | Water resources management: what should be integrated?[END_REF]. Such management contributes to mitigating the severity of water scarcity (Sheffield et al., 2018;[START_REF] Hering | Water resources management: what should be integrated?[END_REF]. The process of water scarcity management includes several phases including monitoring, controlling, and developing of water resources (Sheffield et al., 2018m;[START_REF] Hering | Water resources management: what should be integrated?[END_REF].

Management of water scarcity has evolved over time. In the early civilizations, it was structure-based, representing a supply-oriented approach (Sheffield et al., 2018). In which, some studies focused on the quantity aspect of water scarcity management such as [START_REF] Sisay | Hydrological modelling of ungauged urban watershed using SWAT model[END_REF], [START_REF] Asati | Comparative study of stream flow prediction models[END_REF], [START_REF] Bhadra | Rainfall-runoff modeling: comparison of two approaches with different data requirements[END_REF], and [START_REF] Ahmad | System dynamics modeling of reservoir operations for flood management[END_REF]. Later, scholars gave particular interest in the quality of water and its impact on water scarcity like [START_REF] Gholami | Groundwater quality modeling using self-organizing map (SOM) and geographic information system (GIS) on the Caspian southern coasts[END_REF], [START_REF] Rabeiy | Assessment and modeling of groundwater quality using WQI and GIS in Upper Egypt area[END_REF], Criollo et al., 2017, Zheng et al., (2017[START_REF] Wellen | Evaluation of the current state of distributed watershed nutrient water quality modeling[END_REF]. As a response to the increasing water scarcity, special interest has been given to rainwater harvesting (RWH) and non-conventional water resources [START_REF] Toosi | Annual and seasonal reliability of urban rainwater harvesting system under climate change[END_REF][START_REF] Zhang | Impacts of climate change on urban rainwater harvesting systems[END_REF]and Sheffield et al., 2018).

Under the growing stress on water resources due to population growth, climate change, urbanization, and socioeconomic development, the smart water scarcity management has emerged [START_REF] Barbieri | Climate change and its effect on groundwater quality[END_REF][START_REF] Patil | Climate change scenarios and its effect on groundwater level in the Hiranyakeshi watershed[END_REF][START_REF] Nguyen | New approach of water quantity vulnerability assessment using satellite images and GIS-based model: An application to a case study in Vietnam[END_REF][START_REF] Chawla | A review of remote sensing applications for water security: Quantity, quality, and extremes[END_REF][START_REF] Hering | Water resources management: what should be integrated?[END_REF]. At present, in the light of the digital revolution, there is an increasing interest in introducing new technologies and tools in water scarcity management such as smart systems, the internet of things (IoT), and Machine Learning [START_REF] Li | Combining Spatial Analysis and a Drinking Water Quality Index to Evaluate Monitoring Data[END_REF]. For example, [START_REF] Zhou | Real-time management of groundwater resources based on wireless sensors networks[END_REF] developed a simulation-optimization model to estimate the optimal pumping rates of groundwater. The model was integrated with wireless sensors network to real-time monitor the quality of groundwater and facilitate decision-making. [START_REF] Pasika | Long-term changes in nitrogen and phosphorus emission into the Vistula and Oder catchments (Poland)-modeling (MONERIS) studies[END_REF] developed a smart costeffective water quality monitoring system using IoT. [START_REF] Farah | Leakage Detection Using Smart Water System: Combination of Water Balance and Automated Minimum Night Flow[END_REF] presented a smart water leakage detection system based on hydraulic sensors and artificial neural networks (ANN).

To conclude, water scarcity management is a multidimensional issue that needs to be adopted considering all of its contributors (e.g. limited water availability, restricted water accessibility, water pollution, dependency to conventional water resources, climate change effects to the water resources, and the increasing water demand due to population growth) [START_REF] Patil | Climate change scenarios and its effect on groundwater level in the Hiranyakeshi watershed[END_REF]. However, such contributors differ spatially, and need to be identified prior the formulating the proper management plans [START_REF] Patil | Climate change scenarios and its effect on groundwater level in the Hiranyakeshi watershed[END_REF]. Considering the Palestinian case, there is a persistent need for adopting an efficient water scarcity management (by considering the water scarcity assessment, the protection of conventional water resources, the adoption of alternative and nonconventional water resources, and the adoption of smart water supply systems) in order to deal with the threaten challenges face the Palestinian water sector.

Water scarcity assessment

Given the dramatic increase in the population growth, the expansion agricultural and industrial activates, water scarcity becomes of serious concern [START_REF] He | Index-based groundwater vulnerability and water quality assessment in the arid region of Tata city (Morocco)[END_REF][START_REF] Liu | Water Scarcity Assessments in the Past, Present, and Future[END_REF][START_REF] Ercin | Water Footprint Scenarios for 2050: A Global Analysis[END_REF]. It threatens the human health, the water-food-energy-ecosystem nexus, and the sustainable development [START_REF] He | Index-based groundwater vulnerability and water quality assessment in the arid region of Tata city (Morocco)[END_REF][START_REF] Liu | Water Scarcity Assessments in the Past, Present, and Future[END_REF][START_REF] Ercin | Water Footprint Scenarios for 2050: A Global Analysis[END_REF]. Global statistics indicate that two billion people experience an extreme water scarcity conditions (UN, 2018). Studies show that more than half of the global population are expected to suffer from water scarcity by 2050 (UN, 2015a). Addressing water scarcity has been targeted as one of the main United Nation (UN) Sustainable Development Goals (mainly SDG 6) ( UN, 2018;and UN, 2015a). Various definitions for water scarcity is introduced globally considering the water quantity, quality or both (Table 1.1).

Table 1.1. Water scarcity definitions

Definition

Reference The adverse effect of extensive and unmanaged water utilization with respect to the water availability [START_REF] Kummu | The World's Road to Water Scarcity: Shortage and Stress in the 20th Century and Pathways towards Sustainability[END_REF] The ratio of sectoral freshwater utilization to the total available fresh water [START_REF] Van Vliet | Quality Matters for Water Scarcity[END_REF] The volumetric shortage in freshwater availability (CEO, 2014) Water scarcity happens once the water demand exceeds the water availability during a certain period of the year (e.g. once deteriorated water quality limits its utilization). [START_REF] Wang | A Review of Water Stress and Water Footprint Accounting[END_REF] The ratio of total fresh water withdrawn by all sectors to the water availability in a particular country or region [START_REF] Vanham | Physical Water Scarcity Metrics for Monitoring Progress towards SDG Target 6.4: An Evaluation of Indicator 6.4.2 "Level of Water Stress[END_REF] One of the most convenient definitions for water scarcity is what stated by [START_REF] Taylor | Rethinking Water Scarcity: The Role of Storage[END_REF]: "Water Scarcity is the insufficiency of available freshwater to address the needed water demand".

Water scarcity assessment is stated as the core step for formulating an efficient strategy for addressing such scarcity (Jafari-Shalamzari and Zhang, 2018;and Liu et al., 2017). Various indices are used to evaluate water scarcity. Table 1.2 illustrates the different indices and their elements.

Water quality

Criticality ratio

The water use ratio to the total water availability Yes No [START_REF] Hussain | A comparative Appraisal of Classical and Holistic Water Scarcity Indicators[END_REF][START_REF] Oki | Global Hydrological Cycles and World Water Resources[END_REF] Water poverty index

The arithmetic weighted average of five indicator (water availability, Yes Yes [START_REF] Shadeed | Developing a GIS-Based Water Poverty and Rainwater Harvesting Suitability Maps for Domestic Use in the Dead Sea Region (West Bank, Palestine)[END_REF][START_REF] Sullivan | The water poverty index: Development and application at the community scale[END_REF] accessibility, environment, use, and capacity)

Falkenmark index

The per capita availability of water Yes No [START_REF] Nkiaka | Water security assessment in ungauged regions using the water balance and water footprint concepts and satellite observations[END_REF][START_REF] Falkenmark | Present and Future Water Requirements for Feeding Humanity[END_REF]

Cumulative abstraction to demand ratio

The ratio of the cumulative daily water abstraction to the cumulative daily water demand

Yes

No [START_REF] Liu | Water Scarcity Assessments in the Past, Present, and Future[END_REF][START_REF] Hanasaki | An integrated model for the assessment of global water resources -Part 2: Applications and assessments[END_REF] • Criticality Ratio The criticality ratio index, is a commonly adopted indicator used for the assessment of water scarcity [START_REF] Hussain | A comparative Appraisal of Classical and Holistic Water Scarcity Indicators[END_REF][START_REF] Oki | Global Hydrological Cycles and World Water Resources[END_REF]. It compares the used amount of water to the total availability of renewable water resources. Water use is implying the water consumption and water withdrawals [START_REF] Hussain | A comparative Appraisal of Classical and Holistic Water Scarcity Indicators[END_REF][START_REF] Oki | Global Hydrological Cycles and World Water Resources[END_REF]. This index defines the water consumption as the water evaporation volumes from the different sources of water. In addition, water withdrawal implies the volumes of water that is withdrawn from these sources. However, the unilateral considering of water consumption or water withdrawal as a physical meaning of the water use could come up with unrealistic assessment of the water scarcity in this indicator [START_REF] Hussain | A comparative Appraisal of Classical and Holistic Water Scarcity Indicators[END_REF][START_REF] Oki | Global Hydrological Cycles and World Water Resources[END_REF].

• Water Poverty Index It introduces a relationship combining the physical availability of water resources, their abstraction possibility, and the welfare level of the targeted community [START_REF] Sullivan | The water poverty index: Development and application at the community scale[END_REF]. The index involves five indicators: water resources availability; water resources accessibility; people's capacity toward the efficient management of water resources; the use of water resources (domestic, agricultural, and industrial); water-related environmental integrity (e.g. effect to the ecosystem) [START_REF] Shadeed | Developing a GIS-Based Water Poverty and Rainwater Harvesting Suitability Maps for Domestic Use in the Dead Sea Region (West Bank, Palestine)[END_REF]. Such index is estimated using an arithmetic weighted average of the five indicators. Each indicator is standardized to a value ranges between 0 and 100. The resulting index lies also between 0 (representing the extreme water poverty conditions) and 100 (representing that there is no water poverty conditions) [START_REF] Shadeed | Developing a GIS-Based Water Poverty and Rainwater Harvesting Suitability Maps for Domestic Use in the Dead Sea Region (West Bank, Palestine)[END_REF][START_REF] Sullivan | The water poverty index: Development and application at the community scale[END_REF]. Despite the comprehensiveness of this index, its application is restricted by its complexity and its dependency to massive volume of data [START_REF] Shadeed | Developing a GIS-Based Water Poverty and Rainwater Harvesting Suitability Maps for Domestic Use in the Dead Sea Region (West Bank, Palestine)[END_REF][START_REF] Sullivan | The water poverty index: Development and application at the community scale[END_REF].

• The Falkenmark Index

The Falkenmark index is a commonly used indicator for the characterization of water scarcity. It implies the ratio of the available water volume in a specific area per year to the number of people living in the area. Accordingly, four water scarcity classes are identified (Table 1.3) [START_REF] Falkenmark | Present and Future Water Requirements for Feeding Humanity[END_REF]. Despite its common application, the Falkenmark index considers only the water supply in its scarcity assessment [START_REF] Wang | A Review of Water Stress and Water Footprint Accounting[END_REF]. The index ignores the supply temporal variability and the inclusion of water demand. In which, the index thresholds don't reveal the real spatial distribution of water demand among the areas where the index is adopted [START_REF] Wang | A Review of Water Stress and Water Footprint Accounting[END_REF].

• Integrated Water Quantity-Quality-Environment Flow Index This multi-dimensional and complex index integrates both quantity-based and quality-based indicators [START_REF] Liu | Assessing water scarcity by simultaneously considering environmental flow requirements, water quantity, and water quality[END_REF]. The quantity-based index is estimated following the criticality ratio index while the quality-based indicator is estimated through the ratio of contaminated water to the total available water [START_REF] Liu | Assessing water scarcity by simultaneously considering environmental flow requirements, water quantity, and water quality[END_REF]. The index has a threshold of 1.0, exceeding this threshold for the quantity-based indicator indicates an acute water scarcity (in water utilization). Moreover, breaking the threshold for the quality-based indicator indicates an extreme water contamination condition [START_REF] Liu | Assessing water scarcity by simultaneously considering environmental flow requirements, water quantity, and water quality[END_REF].

• The IWMII Indicator A slightly complicated water scarcity index is developed by IWMII [START_REF] Liu | Water Scarcity Assessments in the Past, Present, and Future[END_REF]. Such index combines both the physical and economic dimensions of water scarcity. The index considers the ratio of water supply to the available and renewable freshwater resource. It also considers the country's potential to upgrade water infrastructure and to enhance the efficiency of irrigational water use [START_REF] Liu | Water Scarcity Assessments in the Past, Present, and Future[END_REF]. However, the index is less commonly used comparing to other water scarcity indices. This could be attributed to its complexity and need for massive data that make it a time-consuming approach [START_REF] Liu | Water Scarcity Assessments in the Past, Present, and Future[END_REF]. Moreover, it has less intuitive interpretation with respect to other indices. Therefore, it is considered less attractive, informative, and understandable to the end-users and decision makers [START_REF] Liu | Water Scarcity Assessments in the Past, Present, and Future[END_REF].

• Cumulative Abstraction to Demand Ratio

The Cumulative Abstraction to Demand Index is based on the findings of the global hydrological models (water abstraction and discharge from water resources in a daily basis) [START_REF] Hanasaki | An integrated model for the assessment of global water resources -Part 2: Applications and assessments[END_REF]. This index is estimated as the annual ratio of the cumulative daily water abstraction to the cumulative daily water demand [START_REF] Hanasaki | An integrated model for the assessment of global water resources -Part 2: Applications and assessments[END_REF]. However, various studies adopted this index on monthly scale [START_REF] Liu | Water Scarcity Assessments in the Past, Present, and Future[END_REF][START_REF] Hanasaki | An integrated model for the assessment of global water resources -Part 2: Applications and assessments[END_REF]. Therefore, it is considered a powerful approach for clarifying the climate change effect on water resources. The complex computational and modeling tasks along with the high demand for input data have restricted the adoption of this index for water scarcity assessment [START_REF] Liu | Water Scarcity Assessments in the Past, Present, and Future[END_REF][START_REF] Hanasaki | An integrated model for the assessment of global water resources -Part 2: Applications and assessments[END_REF].

On light of the stated literature, the presented scarcity indices (criticality ratio, water poverty index, the falkenmark index, integrated water quantity-quality-environment flow index, the IWMII indicator, and cumulative abstraction to demand ratio) are complex [START_REF] Hussain | A comparative Appraisal of Classical and Holistic Water Scarcity Indicators[END_REF][START_REF] Nkiaka | Water security assessment in ungauged regions using the water balance and water footprint concepts and satellite observations[END_REF][START_REF] Liu | Water Scarcity Assessments in the Past, Present, and Future[END_REF][START_REF] Liu | Assessing water scarcity by simultaneously considering environmental flow requirements, water quantity, and water quality[END_REF][START_REF] Falkenmark | Present and Future Water Requirements for Feeding Humanity[END_REF]. They require massive data that could be unavailable. Moreover, the dependency on great amount of data could produce unrealistic and uncertain results [START_REF] Hussain | A comparative Appraisal of Classical and Holistic Water Scarcity Indicators[END_REF][START_REF] Nkiaka | Water security assessment in ungauged regions using the water balance and water footprint concepts and satellite observations[END_REF][START_REF] Liu | Water Scarcity Assessments in the Past, Present, and Future[END_REF][START_REF] Liu | Assessing water scarcity by simultaneously considering environmental flow requirements, water quantity, and water quality[END_REF][START_REF] Falkenmark | Present and Future Water Requirements for Feeding Humanity[END_REF]. To avoid such complexity and uncertainty, this research adopts a new index (introduced in 2017) called "supply demand balance index (SDBI)" for the water scarcity assessment [START_REF] Huang | Supply and Demand Analysis of Water Resources Based on System Dynamics Model[END_REF]. SDBI is a simple approach for the water scarcity assessment. It is estimated through the ratio of total water supply (TWS) to the total water demand (TWD) for a specific population in a specific area [START_REF] Huang | Supply and Demand Analysis of Water Resources Based on System Dynamics Model[END_REF]. SDBI is the only index that directly considers freshwater utilization and citizens' water needs. Its single value has the ability to characterizes the level of water scarcity on a scale ranging from "extreme" to "no" water shortage [START_REF] Huang | Supply and Demand Analysis of Water Resources Based on System Dynamics Model[END_REF]. It can efficiently consider the water quality issue by ignoring the contaminated water volume during the estimation of TWS. Thus, it is considered one of the easiest and most efficient tools for assessing the water scarcity, especially in urban areas [START_REF] Huang | Supply and Demand Analysis of Water Resources Based on System Dynamics Model[END_REF].

Protection of conventional water resources

Protection of conventional water resources implies the preservation of both water quality and quantity, among the conventional water bodies (e.g. surface and groundwater) [START_REF] Yang | Development and application of a new package for MODFLOW-LGR-MT3D for simulating regional groundwater and salt dynamics with subsurface drainage systems[END_REF][START_REF] Almasri | Identification of the Nitrogen Sources in the Eocene Aquifer Area (Palestine)[END_REF]. Several physiochemical and biological pollutants threaten the quality of water resources and restrict their suitability for different uses (e.g. domestic and agricultural) [START_REF] Mester | Assessment of Groundwater Quality Changes in the Rural Environment of the Hungarian Great Plain Based on Selected Water Quality Indicators[END_REF][START_REF] Banda | A Review of the Existing Water Quality Indices (WQIs)[END_REF]. Moreover, unmanaged and extensive use of water resources could lead to the decline of water levels and to the depletion of such resources [START_REF] O'neill | Assessment of the ParFlow-CLM CONUS 1.0 integrated hydrologic model: Evaluation of hyper-resolution water balance components across the contiguous United States[END_REF]. Protection of water resources have to be achieved relying on the outcomes of scientific-based approaches that then converted to plans, strategies and actions at the different levels [START_REF] Lee | Development and Application of a QGIS-Based Model to Estimate Monthly Streamflow[END_REF][START_REF] Mester | Assessment of Groundwater Quality Changes in the Rural Environment of the Hungarian Great Plain Based on Selected Water Quality Indicators[END_REF]. Such approaches could recognize and monitor the water quality and quantity parameters among the water bodies, and compare them to standards [START_REF] Pham | A Method to Represent a Well in a Three-Dimensional Discrete Fracture Network Model[END_REF][START_REF] Mester | Assessment of Groundwater Quality Changes in the Rural Environment of the Hungarian Great Plain Based on Selected Water Quality Indicators[END_REF][START_REF] Ranjith | Water Quality Model for Streams: A Review[END_REF][START_REF] Anayah | Trends and Occurrences of Nitrate in the Groundwater of the West Bank, Palestine[END_REF]). This could be followed by performing a causal-effect relation for the water resources pollution and depletion [START_REF] Lee | Development and Application of a QGIS-Based Model to Estimate Monthly Streamflow[END_REF][START_REF] Almasri | Identification of the Nitrogen Sources in the Eocene Aquifer Area (Palestine)[END_REF]. This contributes to taking the needed actions to manage, mitigate or even eliminate the risks of pollution and depletion of water resources [START_REF] Almasri | Identification of the Nitrogen Sources in the Eocene Aquifer Area (Palestine)[END_REF].

Various approaches are adopted for protecting the quality (e.g. pollution) and quantity (e.g. flow and levels) of water resources including; descriptive statistical assessment [START_REF] Anayah | Trends and Occurrences of Nitrate in the Groundwater of the West Bank, Palestine[END_REF], water quality and quantity indices [START_REF] Banda | A Review of the Existing Water Quality Indices (WQIs)[END_REF][START_REF] Shadeed | Rainwater Harvesting for Sustainable Agriculture in High Water-Poor Areas in the West Bank, Palestine[END_REF][START_REF] Shadeed | Developing a GIS-Based Water Poverty and Rainwater Harvesting Suitability Maps for Domestic Use in the Dead Sea Region (West Bank, Palestine)[END_REF][START_REF] Poonam | Water Quality Indices-Important Tools for Water Quality Assessment: A Review[END_REF][START_REF] Li | Water Quality Indices[END_REF][START_REF] Ocampo-Duque | Water Quality Analysis in Rivers with Non-Parametric Probability Distributions and Fuzzy Inference Systems: Application to the Cauca River, Colombia[END_REF], water quality and quantity mapping [START_REF] Evans | Groundwater Level Mapping Tool: An open source web application for assessing groundwater sustainability[END_REF], and water quality and quantity modeling [START_REF] Lee | Development and Application of a QGIS-Based Model to Estimate Monthly Streamflow[END_REF][START_REF] Pham | A Method to Represent a Well in a Three-Dimensional Discrete Fracture Network Model[END_REF][START_REF] Ranjith | Water Quality Model for Streams: A Review[END_REF].

• Descriptive statistical assessment

Descriptive statistical assessment is the simplest approach for assessing the water resources quality and quantity [START_REF] Anayah | Trends and Occurrences of Nitrate in the Groundwater of the West Bank, Palestine[END_REF]. Firstly, records for the threatening water quality parameters and for the water levels are obtained (e.g. from field and laboratory works, or water datasets) [START_REF] Anayah | Trends and Occurrences of Nitrate in the Groundwater of the West Bank, Palestine[END_REF]. Then, the statistical analysis employs the collected records to perform the descriptive assessment of water resources quality and quantity [START_REF] Shadeed | Developing a GIS-Based Water Poverty and Rainwater Harvesting Suitability Maps for Domestic Use in the Dead Sea Region (West Bank, Palestine)[END_REF][START_REF] Anayah | Trends and Occurrences of Nitrate in the Groundwater of the West Bank, Palestine[END_REF]. This approach enables the temporal analysis of water pollution and availability. Boxplots of the water records (e.g. minimum, 1 st quartile (Q1), median, mean, 3 rd quartile (Q3), maximum) are adopted to present the assessment results. However, this approach misses the spatial distribution/variability of water pollution and availability [START_REF] Shadeed | Developing a GIS-Based Water Poverty and Rainwater Harvesting Suitability Maps for Domestic Use in the Dead Sea Region (West Bank, Palestine)[END_REF][START_REF] Anayah | Trends and Occurrences of Nitrate in the Groundwater of the West Bank, Palestine[END_REF]. Moreover, it relies on a single parameter assessment by considering one of the water contaminants or one of the water availability determinates (e.g. water level) [START_REF] Shadeed | Developing a GIS-Based Water Poverty and Rainwater Harvesting Suitability Maps for Domestic Use in the Dead Sea Region (West Bank, Palestine)[END_REF][START_REF] Anayah | Trends and Occurrences of Nitrate in the Groundwater of the West Bank, Palestine[END_REF]. Therefore, it has a weak ability to construct the needed causal effect relationships for the water-related issues [START_REF] Judeh | Use of GIS, Statistics and Machine Learning for Groundwater Quality Management: Application to Nitrate Contamination[END_REF][START_REF] Shadeed | Developing a GIS-Based Water Poverty and Rainwater Harvesting Suitability Maps for Domestic Use in the Dead Sea Region (West Bank, Palestine)[END_REF][START_REF] Anayah | Trends and Occurrences of Nitrate in the Groundwater of the West Bank, Palestine[END_REF].

• Water quality and quantity indices

Water quality and quantity indices synthesize the water quality and quantity records into a precise, informative, and easily understood format [START_REF] Singha | Assessing Ground Water Quality Using GIS[END_REF]. Each index has a single value that can describe the overall water quality/quantity conditions in a clearly communicated manner to all beneficiaries (e.g. decision makers, service providers, and end-users) [START_REF] Al-Omran | Drinking water quality assessment and water quality index of Riyadh, Saudi Arabia[END_REF]. Both indices help in performing the protection plans for the water bodies [START_REF] Mester | Assessment of Groundwater Quality Changes in the Rural Environment of the Hungarian Great Plain Based on Selected Water Quality Indicators[END_REF]. Scholars adopted various water quantity/availability indices including: water vulnerability index and water poverty index [START_REF] Shadeed | Rainwater Harvesting for Sustainable Agriculture in High Water-Poor Areas in the West Bank, Palestine[END_REF][START_REF] Shadeed | Developing a GIS-Based Water Poverty and Rainwater Harvesting Suitability Maps for Domestic Use in the Dead Sea Region (West Bank, Palestine)[END_REF]. Concerning the quality aspects, scholar adopted several water quality indices (WQI) for assessing the water resources quality including: the Canadian Council of Ministers of the Environment WQI [START_REF] Neary | Canadian Environmental Quality Guidelines[END_REF], universal WQI [START_REF] Boyacioglu | Utilization of the Water Quality Index Method as a Classification Tool[END_REF][START_REF] Boyacioglu | Development of a Water Quality Index Based on a European Classification Scheme[END_REF], the Horton model of WQI [START_REF] Horton | An Index-Number System for Rating Water Quality[END_REF], Vaal WQI [START_REF] Banda | A Review of the Existing Water Quality Indices (WQIs)[END_REF], Modified National Sanitation Foundation WQI [START_REF] Poonam | Water Quality Indices-Important Tools for Water Quality Assessment: A Review[END_REF], fuzzy-based WQI [START_REF] Ocampo-Duque | Water Quality Analysis in Rivers with Non-Parametric Probability Distributions and Fuzzy Inference Systems: Application to the Cauca River, Colombia[END_REF]), Bhargava's WQI [START_REF] Li | Water Quality Indices[END_REF], Scottish Research Development Department WQI [START_REF] Banda | A Review of the Existing Water Quality Indices (WQIs)[END_REF], Martínez de Bascarón WQI [START_REF] Banda | A Review of the Existing Water Quality Indices (WQIs)[END_REF]), Liou's WQI [START_REF] Banda | A Review of the Existing Water Quality Indices (WQIs)[END_REF][START_REF] Liou | A Generalized Water Quality Index for Taiwan[END_REF], Oregon WQI [START_REF] Cude | Oregon Water Quality Index: A Tool for Evaluating Water Quality Management Effectiveness[END_REF][START_REF] Dunnette | A Geographically Variable Water Quality Index Used in Oregon[END_REF]. However, such quality indices are rigorous to the input parameters [START_REF] Mester | Assessment of Groundwater Quality Changes in the Rural Environment of the Hungarian Great Plain Based on Selected Water Quality Indicators[END_REF][START_REF] Banda | A Review of the Existing Water Quality Indices (WQIs)[END_REF]. In contrast, the weighted arithmetic water quality index (WAWQI) method has flexibility in the selection of parameters [START_REF] Mester | Assessment of Groundwater Quality Changes in the Rural Environment of the Hungarian Great Plain Based on Selected Water Quality Indicators[END_REF][START_REF] Banda | A Review of the Existing Water Quality Indices (WQIs)[END_REF]. It characterizes the water quality status based on the most common pollutants in the study area [START_REF] Mester | Assessment of Groundwater Quality Changes in the Rural Environment of the Hungarian Great Plain Based on Selected Water Quality Indicators[END_REF][START_REF] Banda | A Review of the Existing Water Quality Indices (WQIs)[END_REF]. However, the WAWQI weighting system for the input parameters has a high level of uncertainty which could lead to unrealistic outcomes [START_REF] Mester | Assessment of Groundwater Quality Changes in the Rural Environment of the Hungarian Great Plain Based on Selected Water Quality Indicators[END_REF][START_REF] Banda | A Review of the Existing Water Quality Indices (WQIs)[END_REF].

• Water quality and quantity mapping Water quality and quantity mapping are the most adopted approach for the spatiotemporal analysis of water resources pollution and availability, respectively [START_REF] Evans | Groundwater Level Mapping Tool: An open source web application for assessing groundwater sustainability[END_REF][START_REF] Shadeed | Developing a GIS-Based Water Poverty and Rainwater Harvesting Suitability Maps for Domestic Use in the Dead Sea Region (West Bank, Palestine)[END_REF][START_REF] Anayah | Trends and Occurrences of Nitrate in the Groundwater of the West Bank, Palestine[END_REF]. Water quality mapping mainly employs Geographic Information System (GIS) along with and statistical descriptive analysis and/or the WQI to carry out the contamination mapping. This approach enables the examination of the various factors that could affect the level of water contamination (e.g. soil type, water flow direction, conductivity, land use, and anthropogenic activities) [START_REF] Anayah | Trends and Occurrences of Nitrate in the Groundwater of the West Bank, Palestine[END_REF].

Water quantity mapping could be achieved using various determinates such as water flow and water levels [START_REF] Evans | Groundwater Level Mapping Tool: An open source web application for assessing groundwater sustainability[END_REF]. Water level mapping enables the spatiotemporal visualization and quantification of water levels and volumes within the water resources [START_REF] Evans | Groundwater Level Mapping Tool: An open source web application for assessing groundwater sustainability[END_REF]. It is mainly estimated based on the interpolation of spatiotemporally distributed data (e.g. water level, and depth to water table). Such approach also enables the spatial estimation of water storage at a time steps [START_REF] Evans | Groundwater Level Mapping Tool: An open source web application for assessing groundwater sustainability[END_REF]. Various approaches are used for the spatiotemporal interpolation of water-related data, where Kriging Interpolation Method (KIM) is one of the most commonly used [START_REF] Xiao | Geostatistical interpolation model selection based on ArcGIS and spatio-temporal variability analysis of groundwater level in piedmont plains, northwest China[END_REF]. KIM enables the performing of uncertainty analysis for the resulted interpolated maps. [START_REF] Xiao | Geostatistical interpolation model selection based on ArcGIS and spatio-temporal variability analysis of groundwater level in piedmont plains, northwest China[END_REF] adopted Ordinary Kriging (OK), Simple Kriging (SK) and Universal Kriging (UK) interpolation to predict the unknown depths to water table in an aquifer in Piedmont Plains, northwest China. It is found that interpolated water levels are matched to the actual water levels in almost all types of KIM interpolation (Figure 1.1). However, this mapping could only characterize the existing water quantity and quality status. In order to perform forecasting models for such status, mapping should be coupled with the Machine Learning approaches. • Water quality and quantity modeling Water quality and quantity models are robust tools that lead to the formulation of efficient plans for the protection of water resources (Raazia and Dar, 2022;[START_REF] Ejigu | Overview of water quality modeling[END_REF][START_REF] Medici | Review of Modeling Approaches to Groundwater Flow in Deformed Carbonate Aquifers[END_REF]. Water quality modeling implies mathematical/numerical simulations and representations of contaminants fate, transport, and degradation within the different water bodies [START_REF] Ejigu | Overview of water quality modeling[END_REF]. Such models involve three phases which are (i) model development, (ii) calibration, and (iii) validation [START_REF] Ejigu | Overview of water quality modeling[END_REF]. Scholars introduced and adopted various models for the water quality modeling such as TOMCAT, MT3D, SIMCAT, ECM catchment, MIKE-11, DRAINMOD, MONERIS, TOPCAT-NP, and QUAL2K (Table 1.4) Water quantity modeling requires mathematical simulation and representation of the water levels and flow through the surface and groundwater as a result of the hydrological cycle components (e.g. precipitation, runoff, evaporation, and transpiration) (Figure 1.2) (Raazia and Dar, 2022;[START_REF] Tao | Finite Difference Modelings of Groundwater Flow for Constructing Artificial Recharge Structures[END_REF][START_REF] Medici | Review of Modeling Approaches to Groundwater Flow in Deformed Carbonate Aquifers[END_REF]. Several models have been used for the surface water flow modeling such as HEC-RAS [START_REF] Costabile | Performances of the New HEC-RAS Version 5 for 2-D Hydrodynamic-Based Rainfall-Runoff Simulations at Basin Scale: Comparison with a State-of-the Art Model[END_REF], QGIS [START_REF] Lee | Development and Application of a QGIS-Based Model to Estimate Monthly Streamflow[END_REF], and HEC-HMS [START_REF] Hamdan | Rainfall-Runoff Modeling Using the HEC-HMS Model for the Al-Adhaim River Catchment, Northern Iraq[END_REF]. Groundwater flow modeling are also widely used including MODFLOW [START_REF] Behera | Assessment of Groundwater Flow Dynamics Using MODFLOW in Shallow Aquifer System of Mahanadi Delta (East Coast), India[END_REF], Parflow [START_REF] Tran | Development of a Deep Learning Emulator for a Distributed Groundwater-Surface Water Model: ParFlow-ML[END_REF], and DFNWorks [START_REF] Pham | A Method to Represent a Well in a Three-Dimensional Discrete Fracture Network Model[END_REF]. [START_REF] Levizzani | Satellite Remote Sensing of Precipitation and the Terrestrial Water Cycle in a Changing Climate[END_REF] However, water quality and quantity models require massive input data, and have a level of uncertainty due to the implying assumptions [START_REF] Lee | Development and Application of a QGIS-Based Model to Estimate Monthly Streamflow[END_REF][START_REF] Pham | A Method to Represent a Well in a Three-Dimensional Discrete Fracture Network Model[END_REF][START_REF] Ranjith | Water Quality Model for Streams: A Review[END_REF].

Alternative and nonconventional water resources

The elevated stresses on the availability, accessibility and quality of the conventional water resources (e.g. surface and groundwater) urge the need to use alternative (e.g. RWH) and nonconventional (water desalination, and fog water collection) water resources (Karimidastenaei et al., 2022;and Yu et al., 2022). Such resources are employed to mitigate the increasing water scarcity, and to decrease the burden on the conventional water resources in different countries worldwide such as Gulf countries, Chile and Palestine [START_REF] Charcosset | Classical and Recent Developments of Membrane Processes for Desalination and Natural Water Treatment[END_REF]Karimidastenaei et al., 2022;[START_REF] Yu | Fog Harvesting Devices Inspired from Single to Multiple Creatures: Current Progress and Future Perspective[END_REF][START_REF] Curto | A Review of the Water Desalination Technologies[END_REF][START_REF] Zabidi | A Review of Roof and Pond Rainwater Harvesting Systems for Water Security: The Design, Performance and Way Forward[END_REF].

Water desalination (removing of impurities and salts from saline water to produce fresh water) produces water that meet the global water quality standards [START_REF] Charcosset | Classical and Recent Developments of Membrane Processes for Desalination and Natural Water Treatment[END_REF][START_REF] Curto | A Review of the Water Desalination Technologies[END_REF]. It also reduces the pressure on freshwater resources. On the other hand, water desalination is very costly due to the need to construct and the operate the water desalination plants. The operation of the plants is high energy-consuming [START_REF] Charcosset | Classical and Recent Developments of Membrane Processes for Desalination and Natural Water Treatment[END_REF][START_REF] Curto | A Review of the Water Desalination Technologies[END_REF]. The energy cost accounts for around 50% of the total cost required for producing desalinated water [START_REF] Charcosset | Classical and Recent Developments of Membrane Processes for Desalination and Natural Water Treatment[END_REF][START_REF] Curto | A Review of the Water Desalination Technologies[END_REF]. Desalination also has negative environmental impact since it could produce many chemicals including hydrochloric acid, carbon dioxide, and chlorine that could be dangerous in high concentrations [START_REF] Charcosset | Classical and Recent Developments of Membrane Processes for Desalination and Natural Water Treatment[END_REF][START_REF] Curto | A Review of the Water Desalination Technologies[END_REF]. Fog water collection (capturing and storing the water from wind-driven fogs) is cheap, easy to be installed and maintained, and consume limited or no energy. It produces water with good quality, especially in non-industrial areas (Karimidastenaei et al., 2022;and Yu et al., 2022). However, fog water collection requires very specific climatologic conditions where the fog is intensive. In addition, it could not be adopted as a solely source of water supply and it is unlikely to be adopted of national scale. In case that the end-users are not close to collection area, pipelines are needed to transport the water which could make the system uneconomic and hydraulically difficult (Karimidastenaei et al., 2022;and Yu et al., 2022). In the West Bank, access to the surface water (fresh and saline) is almost totally restricted by the Israeli occupation. Moreover, this low-income country could face serious difficulties in financing the construction and operation of the water desalination plants [START_REF] Judeh | Assessment of Water Governance in the West Bank, Palestine[END_REF]. The fog collection is not common in the West Bank since the fog intensity is low (PMA, 2018). Therefore, and given the West Bank is characterized by a reasonable long-term average annual rainfall of about 450 mm, RWH (capturing and soring the rainwater, rather than allowing it to run off) considered a good supporter to the existing water supplies [START_REF] Zabidi | A Review of Roof and Pond Rainwater Harvesting Systems for Water Security: The Design, Performance and Way Forward[END_REF][START_REF] Shadeed | Developing a GIS-Based Water Poverty and Rainwater Harvesting Suitability Maps for Domestic Use in the Dead Sea Region (West Bank, Palestine)[END_REF].

Role of Machine Learning and smart water systems in the water scarcity management

According to sections 1.1.1., 1.1.2 and 1.1.3, the adopted methods for characterizing, managing and addressing the water scarcity have some limitations (e.g. complexity, uncertainty, and inefficiency). Thus, the following section will present the role of Machine Learning and smart water systems in addressing the limitations of these methods.

Machine learning

Machine Learning, a part of artificial intelligence, is an approach that uses computer algorithms (through the collected data and experience) to solve complex problems [START_REF] Breiman | Random Forests[END_REF]and Friedl et al., 1999). Machine Learning methods have powerful capability in predicting the water resources quantity and quality compared to the descriptive statistical assessment, water indices, water mapping and water modeling [START_REF] Breiman | Random Forests[END_REF]and Friedl et al., 1999). They are capable to detect pollution levels as well as the sources of pollution among the water bodies [START_REF] Band | Comparative Analysis of Artificial Intelligence Models for Accurate Estimation of Groundwater Nitrate Concentration[END_REF][START_REF] Uddameri | Tree-Based Modeling Methods to Predict Nitrate Exceedances in the Ogallala Aquifer in Texas[END_REF][START_REF] Canion | Predictive Modeling of Elevated Groundwater Nitrate in a Karstic Spring-Contributing Area Using Random Forests and Regression-Kriging[END_REF][START_REF] Knoll | Large Scale Prediction of Groundwater Nitrate Concentrations from Spatial Data Using Machine Learning[END_REF][START_REF] Ransom | A Hybrid Machine Learning Model to Predict and Visualize Nitrate Concentration throughout the Central Valley Aquifer, California, USA[END_REF][START_REF] Mair | Logistic Regression Modeling to Assess Groundwater Vulnerability to Contamination in Hawaii, USA[END_REF]. Scholars adopted several Machine Learning approaches (e.g. random forest algorithm (RFA), multiple linear regression, decision tree, support vector machine (SVM), boosted regression trees, logistic regression, cubist, and ANN) for the prediction of water resources contamination [START_REF] Band | Comparative Analysis of Artificial Intelligence Models for Accurate Estimation of Groundwater Nitrate Concentration[END_REF][START_REF] Uddameri | Tree-Based Modeling Methods to Predict Nitrate Exceedances in the Ogallala Aquifer in Texas[END_REF][START_REF] Canion | Predictive Modeling of Elevated Groundwater Nitrate in a Karstic Spring-Contributing Area Using Random Forests and Regression-Kriging[END_REF][START_REF] Knoll | Large Scale Prediction of Groundwater Nitrate Concentrations from Spatial Data Using Machine Learning[END_REF][START_REF] Ransom | A Hybrid Machine Learning Model to Predict and Visualize Nitrate Concentration throughout the Central Valley Aquifer, California, USA[END_REF][START_REF] Mair | Logistic Regression Modeling to Assess Groundwater Vulnerability to Contamination in Hawaii, USA[END_REF]. Concerning the water quantity, Machine Learning is widely used for the protection of water resources quantity [START_REF] Xuanhui | Short-term prediction of groundwater level using improved random forest regression with a combination of random features[END_REF]. It facilitates the monitoring of water levels, flow, and directions [START_REF] Xuanhui | Short-term prediction of groundwater level using improved random forest regression with a combination of random features[END_REF][START_REF] Emamgholizadeh | Prediction the groundwater level of bastam plain (Iran) by artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS)[END_REF]. Examples of the artificial intelligence approaches that adopted for the monitoring of water levels are shown in Table 1.5.

Table 1.5. Artificial intelligence models for water level/flow monitoring

Methods

Target Reference RFA To predict the variation in water level [START_REF] Valipour | Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez dam reservoir[END_REF] ANN to come up with accurate predictions without increasing the needed computational time [START_REF] Emamgholizadeh | Prediction the groundwater level of bastam plain (Iran) by artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS)[END_REF] Canonical Correlation Forest Algorithm (CCFA)

Addressing the data-scarcity and performing an efficient water level prediction model [START_REF] Xuanhui | Short-term prediction of groundwater level using improved random forest regression with a combination of random features[END_REF] SVM to overcome the variation in groundwater level prediction [START_REF] Yoon | A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer[END_REF] Machine learning methods are highly recommended for massive spatiotemporal data [START_REF] Band | Comparative Analysis of Artificial Intelligence Models for Accurate Estimation of Groundwater Nitrate Concentration[END_REF]. The accuracy of such models is dependent on the quality of input data and the complexity of related phenomena [START_REF] Band | Comparative Analysis of Artificial Intelligence Models for Accurate Estimation of Groundwater Nitrate Concentration[END_REF]. Thus, performing a benchmarking analysis is important to identify the most suitable Machine Learning method [START_REF] Band | Comparative Analysis of Artificial Intelligence Models for Accurate Estimation of Groundwater Nitrate Concentration[END_REF]. This contributes to reducing the complexity of Machine Learning methods by decreasing the number of input parameters [START_REF] Band | Comparative Analysis of Artificial Intelligence Models for Accurate Estimation of Groundwater Nitrate Concentration[END_REF]. It also contributes to enhancing the accuracy for the prediction models [START_REF] Band | Comparative Analysis of Artificial Intelligence Models for Accurate Estimation of Groundwater Nitrate Concentration[END_REF].

Smart water supply systems

The smart water system employs the IoT-based sensors and crowdsourcing across for gathering the water-related data. This contributes to performing a real-time monitoring and control among the several water grid components [START_REF] Ntuli | A Simple Security Architecture for Smart Water Management System[END_REF]. Scholars adopted certain architecture for the smart water systems [START_REF] Shahrour | Satellite remote sensing for water resources management: Potential for supporting sustainable development in data-poor regions[END_REF]and Li et al., 2020). It involves six layers: physical layer, monitoring layer, data transfer layer, data processing layer, control layer, and smart services layers (Figure 1.3) [START_REF] Shahrour | Satellite remote sensing for water resources management: Potential for supporting sustainable development in data-poor regions[END_REF]and Luciani et al., 2018). Smart systems have been adopted to monitor and address the water related-challenges (Li et al., 2020). They efficiently contribute to monitoring the surface water flow [START_REF] Fell | Advancing surface water flow measurement with lowcost smart sensors[END_REF] and quality [START_REF] Budiarti | Big Data Technologies using SVM (Case Study: Surface Water Classification on Regional Water Utility Company in Surabaya)[END_REF][START_REF] Yan | An evaluation model of water quality based on DSA-ELM method[END_REF], the groundwater levels [START_REF] Akbari | Assessing early warning for desertification hazard based on E-SMART indicators in arid regions of northeastern Iran[END_REF], the groundwater quality [START_REF] Jha | Cloud-Based Smart Water Quality Monitoring System using IoT Sensors and Machine Learning[END_REF], and the performance of the water desalination plants [START_REF] Alshehri | Cloud and IoT based Smart Architecture for Desalination Water Treatment[END_REF]. In addition, they are used for optimizing the performance of the municipal water distribution networks (Table 1.6). To do so, several smart sensors and devices are installed within the distribution networks (Table 1.7) [START_REF] Li | Combining Spatial Analysis and a Drinking Water Quality Index to Evaluate Monitoring Data[END_REF][START_REF] Helmbrecht | Smart Solution to Improve Water-energy Nexus for Water Supply Systems[END_REF]. Performing a real-time monitoring for water flow and water levels among the components of the water distribution network can assist in optimizing the water supply volumes and in detecting, localizing, reducing, and eliminating the water leak. [START_REF] Li | Combining Spatial Analysis and a Drinking Water Quality Index to Evaluate Monitoring Data[END_REF] Securing drinking water quality Smart real-time monitoring for the probable water contaminants (e.g. free chlorine, coliforms, pH, and turbidity) assists in mitigating and addressing such contaminations. [START_REF] Di Nardo | An Automated Tool for Smart Water Network Partitioning[END_REF] Promoting the system life cycle Monitoring the status and performance of system' components (e.g. pipelines, pumps, valves, and storage tank) can contribute to performing the needed maintenance or replacement of the system components. This will promote the life of the system. [START_REF] Schilean | Choosing a water distribution pipe rehabilitation solution using the analytical network process method[END_REF][START_REF] Romano | Adaptive water demand forecasting for near real-time management of smart water distribution systems[END_REF]

Minimizing energy loss

Monitoring and reducing the water leak volumes as well as the water volumes to be treated, pumped, and transported can contribute to minimizing the system energy demand. [START_REF] Helmbrecht | Smart Solution to Improve Water-energy Nexus for Water Supply Systems[END_REF][START_REF] Ton | Department of Energy's Microgrid Initiative[END_REF] Optimizing pressure supply

Real-time monitoring of the water pressure can help in detecting any pressure deficit in the water distribution network.

(McKenna and

Keane, 2016) Concerning RWH, smart technologies are adopted to address the challenges of the conventional roof RWH systems [START_REF] Ranjan | The Internet of Things (IOT) Based Smart Rain Water Harvesting System[END_REF][START_REF] Behzadian | Can Smart Rainwater Harvesting Schemes Result in the Improved Performance of Integrated Urban Water Systems?[END_REF]. Such systems have several shortcomings including: (i) the un-potability of the harvested water [START_REF] Dao | Assessment of Rainwater Harvesting and Maintenance Practice for Better Drinking Water Quality in Rural Areas[END_REF][START_REF] Palermo | Optimizing Rainwater Harvesting Systems for Non-Potable Water Uses and Surface Runoff Mitigation BT-Numerical Computations: Theory and Algorithms[END_REF][START_REF] Rostad | Harvesting Rooftop Runoff to Flush Toilets: Drawing Conclusions from Four Major U.S. Cities[END_REF], (ii) the uncontrolled filling and emptying process of the RWH storage tanks, and (iii) the inability of water leakage detection [START_REF] Behzadian | Can Smart Rainwater Harvesting Schemes Result in the Improved Performance of Integrated Urban Water Systems?[END_REF]. Smart IoT-based sensors are adopted to monitor the RWH level in the storage tank [START_REF] Behzadian | Can Smart Rainwater Harvesting Schemes Result in the Improved Performance of Integrated Urban Water Systems?[END_REF]. This contributes to securing a reliable non-potable water supply, and in detecting water leak [START_REF] Behzadian | Can Smart Rainwater Harvesting Schemes Result in the Improved Performance of Integrated Urban Water Systems?[END_REF]. Moreover, smart IoT-based water quality sensors are used to monitor the harvested water quality (based on pH values) [START_REF] Ranjan | The Internet of Things (IOT) Based Smart Rain Water Harvesting System[END_REF].

However, smart interventions to address the RWH-related challenges (e.g. water pollution, shortage, and leak) are separately adopted. Thus, introducing a comprehensive smart RWH system (on light of the smart system architecture shown in Figure 1.3) is needed to address the RWHrelated challenges and overcome the water scarcity.

Research gaps, objectives and contributions

According to the literature review, the elements of water scarcity management are fragmentally used. The adopted management systems lack the reasonable transition from the assessment of water scarcity, the prevention of deepen the scarcity, and reaching to the mitigation/elimination of such scarcity. The adopted indices (criticality ratio, water poverty index, the falkenmark index, integrated water quantity-quality-environment flow index, the IWMII indicator, and cumulative abstraction to demand ratio) for the water scarcity assessment don't simultaneously consider the water availability, quality and citizens' water needs. In addition, they don't consider the climate and demographic impacts. The adopted methods for the protection of the water resources have several limitations including their complexity, uncertainty, dependency to massive input data, the inability to perform clear causal effect relationships, and the inability to predict the levels of water pollution along with their sources [START_REF] Evans | Groundwater Level Mapping Tool: An open source web application for assessing groundwater sustainability[END_REF][START_REF] Shadeed | Developing a GIS-Based Water Poverty and Rainwater Harvesting Suitability Maps for Domestic Use in the Dead Sea Region (West Bank, Palestine)[END_REF][START_REF] Anayah | Trends and Occurrences of Nitrate in the Groundwater of the West Bank, Palestine[END_REF]. The RWH adoption for supporting the conventional water resources lacks the consideration of climate change impacts. Moreover, RWH role in mitigating/eliminating the water scarcity is missed. Finally, scholars don't introduce any smart RWH system to simultaneously address the limitations of conventional RWH system (e.g. system reliability, water leaks and pollution).

These research gaps constitute the ore of this research with emphasis on the following question: could the integration of the conventional water resources protection, rainwater harvesting adoption, and dynamic smart water management contribute to addressing the water scarcity in Palestine?

To deal with this question, this research aims at establishing a smart management system to address the water scarcity in Palestine with emphasis on: the assessment of water scarcity, the protection of conventional water resources, the adoption of RWH, the implication of demographic and climate change effects, and the use of dynamic smart water management (Figure 1.4).

Accordingly, this research aims at achieving the following contributions:

(i)
Adopting the supply demand balance index (SDBI) to assess the current and future water scarcity in arid and semi-arid regions on light of the demographic and climate change impacts. (ii) Proposing and adopting an adjusted water quality index (WQI) to perform an accurate, efficient, and informative groundwater potability and palatability assessment. Such assessment contributes to delineating the vulnerable areas for groundwater contamination. (iii) Proposing a new approach combining Geographic Information System (GIS), statistical analysis and Machine Learning prediction models for the comprehensive management of the Groundwater Nitrate Contamination (GNC). Such approach along with the WQI are used to form a fundamental step in performing an efficient plan toward the protection of conventional water resources in the arid and semi-arid areas. They help in making the needed decisions and taking the required action for controlling the deepen of water scarcity. (iv) Investigating the role of potential RWH in addressing the current and future water scarcity considering the demographic and climate changes.

(v) Proposing an innovative smart RWH system and a smart dual water supply system to secure the sufficiency and potability of the water supply and the reliability of the used systems. This will be followed by investigating the role of smart systems in mitigating the water scarcity level.

Conclusion

This chapter presented the state of the art about the water scarcity in Palestine with a focus on the smart management of such scarcity. It combined the water scarcity assessment, the protection of the water resources, and the adoption of smart RWH system. This next chapter will present the methodology adapted for the smart management of the water scarcity. Then, vulnerable case studies that form a good example of the arid and semi-arid areas with limited water availability will be picked and characterized. After that, applications of the different scientific contributions will be presented in order to achieve the research objectives. Finally, some recommendations will be presented.

Chapter 2. Research Methodology

Introduction

This chapter presents the research methodology of this research. It has been designed to answer the research question "could the integration of conventional water resources protection, rainwater harvesting adoption, and dynamic smart water contribute to addressing water scarcity in Palestine considering the demographic, political and climate change challenges?" and to achieve the research objective "establishing a smart management system to address the water scarcity in Palestine with emphasis on: the assessment of water scarcity, the protection of conventional water resources, the adoption of RWH, the implication of demographic and climate change effects, and the use of dynamic smart water management".

Since the research is carried out considering the Palestinian water context, this chapter starts by presenting the features (e.g. geographical setting and demographic composition) and the waterrelated challenges of the selected case study (West Bank, Palestine). Then, the materials and methods are presented with emphasis on the options to deal with each research question (at each phase), the selection and justification of the most suitable options, and the contribution of this research at the several phases.

Study area

This research concerns the West Bank (Palestine), which is an arid and semi-arid area with water scarcity. It is also governed by a specific political status that directly affects the water security.

Geographical extent and main characteristics

The West Bank, Palestine, is the landlocked area located to the West of Jordan (Figure 2.1). It covers 11 governorates with 5860 km 2 and around 3.12 million inhabitants (PCBS, 2020a).

The maximum (1022 m above mean sea level, MSL) and minimum (410 m below MSL) ground surface elevation are located in Hebron and Jericho governorates, respectively (UNEP, 2003). The land-use map shows three main classes: rough grazing, agricultural areas, and built-up areas (urban and rural), which account for 63%, 32%, and 5% of the study area, respectively [START_REF] Shadeed | Developing a GIS-Based Water Poverty and Rainwater Harvesting Suitability Maps for Domestic Use in the Dead Sea Region (West Bank, Palestine)[END_REF]. Soil textures range from clay (47%), clay loam (31%), loamy (9%), sandy loam (8%) to bare rock (5%) [START_REF] Shadeed | Developing a GIS-Based Water Poverty and Rainwater Harvesting Suitability Maps for Domestic Use in the Dead Sea Region (West Bank, Palestine)[END_REF]. The West Bank is characterized by a Mediterranean climate with a high seasonal deviation [START_REF] Shadeed | Rainwater Harvesting for Sustainable Agriculture in High Water-Poor Areas in the West Bank, Palestine[END_REF]. The average rainfall is about 450 mm/year [START_REF] Shadeed | Rainwater Harvesting for Sustainable Agriculture in High Water-Poor Areas in the West Bank, Palestine[END_REF]. Approximately 80% of the West Bank ranges from arid to semi-arid, while the rest (20%) is classified as sub-humid [START_REF] Shadeed | Developing a GIS-Based Water Poverty and Rainwater Harvesting Suitability Maps for Domestic Use in the Dead Sea Region (West Bank, Palestine)[END_REF]. Thus, the West Bank is internationally classified as an arid to the semi-arid region (Judeh et al., 2021). In 1995, the Oslo II Agreement has put 80% of the shared water resources under the control of the Israeli Occupation. The Interim Agreement was intended for five years; however, it is still the main agreement regulating shared water resources. Oslo Accord and Oslo II Agreement divided the West Bank into three parts: Areas A, B, and C. Area A is under the Palestinian Authority civil and security control. It accounts for less than 18% of the West Bank area. Area B is under Palestinian civil control and joint Israeli Occupation and Palestinian security control. It represents 21% of the West Bank. Area C represents 61% of the total area of the West Bank, includes the most fertile agricultural areas in the West Bank and the majority of water resources, and is under complete Israeli Occupation control (PWA, 2017;and UN, 2015b).

The Israeli Occupation has limited the access of Palestinians to their water resources and restricted the Palestinian's ability to improve the water availability [START_REF] Judeh | Assessment of Water Governance in the West Bank, Palestine[END_REF]. It has imposed many restrictions on access to water resources, building new water installations (such as deep groundwater wells and dams in Area C, construction of wastewater treatment plants and desalination plants), and maintenance of existing ones (Human Rights Council, 2021;[START_REF] Judeh | Assessment of Water Governance in the West Bank, Palestine[END_REF]. As a result, Palestinians lack appropriate water infrastructure; suffer from irregular water supply, and are at high risk of water scarcity (PWA, 2013).

The main water source in Palestine is the groundwater. It represents more than 90% of the water supply in the West Bank and Gaza strip [START_REF] Almasri | Identification of the Nitrogen Sources in the Eocene Aquifer Area (Palestine)[END_REF]. The quality of the groundwater has deteriorated (mainly polluted by Nitrate, NO3) during the past decades especially in the coastal aquifer in Gaza strip, where water is not safe for domestic use, which threatened the public health of many Palestinians (PWA, 2017;and PWA, 2011). Consequently, assessing the existing and forecasted Palestinian water challenges is the core of this research. Such assessment is carried out based on the literature review (e.g. papers, books, and reports) with a focus on the following issues (

Water resources availability and accessibility

The main water resources in Palestine are: (i) surface water represented by The Jordan river and wadis, (ii) groundwater aquifers including the eastern basin, western basin, north-eastern basin, and coastal aquifer, (iii) non-conventional water resources (e.g. desalinated water), and (iv) rainfall.

• Surface water

The surface water resources include the Jordan River and ephemeral wadis [START_REF] Judeh | Assessment of Water Governance in the West Bank, Palestine[END_REF]. The Jordan river is the major surface water resource in the region, that shared among Palestine, Jordan, Syria, Lebanon, and the Israeli Occupation (PWA, 2013). The river originates from the Hasbani in the south of Lebanon, and the Banias and the Dan at Jabl el-Shaykh (Mount Hermon). It flows from Jabl el-Shaykh in the north (2200 m above MSL) into Lake Tiberius, continues to join with the Yarmouk River at the Triangle in the south, and discharges into the Dead Sea, which is the lowest land-based elevation on Earth (425 m below MSL) (PWA, 2013). The Israeli Occupation has the biggest share of the river's water (Human Rights Council, 2021), while the Palestinian have been deprived of their rights to the utilization of the Jordan River's water by the Israeli Occupation [START_REF] Isaac | The Intensifying Water Crisis in Palestine[END_REF]. The water input from the Lower Jordan River into the Dead Sea has dramatically decreased over years from approximately 1400 MCM/y to 30 MCM/y (PWA, 2013). This is mainly due to the diversion of water from the Jordan River to the National Israeli Water Carrier (more than 500 MCM/y) and the construction of dams upstream, in addition to natural factors. The reduced water discharge into the Dead Sea over the past century has led to a major decline in the water level of the Dead Sea of around 1 meter/year, consequently, the surface area has shrunk by almost the half (UNEP, 2020) Another important potential source of the water in Palestine is Wadis related to runoff water flows during the rainy season. Wadis are classified based on the direction of flow into the eastern wadis towards the Dead Sea and the western wadis towards the Mediterranean. The floodwater long-term average annual flow in wadis is about 165 MCM/y in the West Bank (PWA, 2017;and PWA, 2011). In Gaza Strip, Wadi Gaza is the main wadi and it is located in the east. Except for periods of heavy rainfall, the wadi is mostly dry because the Israeli Occupation traps the natural flow that feeds the wadi for its irrigation purposes (UNEP, 2020).

• Groundwater

The main water source in Palestine is groundwater. Over 90% of the freshwater supply in the country comes from aquifers (UN, 2016). Four major aquifers exist in Palestine; three in the West Bank and One in Gaza Strip (Figure 2.3). The largest basin in the West Bank is the Western Basin with an annual yield that varies from 318 to 430 MCM/y (PWA, 2013). The Palestinian utilization of this basin according to Oslo Acord is limited to 22 MCM/y, while the Israeli Occupation heavily exploited this basin at a very high rate of 340 to 430 MCM/y (PWA, 2017;and PWA, 2011). The second basin in the West Bank is the Northeastern Basin. It has an annual yield of about 135 to187 MCM/y. The Palestinian utilization of this basin is around 42 MCM/y, while Israeli Occupation exploitation is about 103 MCM/y [START_REF] Mizyed | Climate change challenges to groundwater resources: Palestine as a case study[END_REF]. The Third basin is the Eastern Basin. It is divided into three sub-aquifers: Jordan Valley, Northeastern Tip, and Mountainous Heights. The annual yield of this basin is about 125 to 197 MCM/y. The allocation of this basin is 42 MCM/y for the Palestinians and 103 MCM/y for the Israelis [START_REF] Mizyed | Climate change challenges to groundwater resources: Palestine as a case study[END_REF]. Gaza Coastal Aquifer represents the only source of water in the strip. The water-bearing strata thickness ranges between 120 to 150 m in the west and along the coast and a few meters in the eastern region. The annual average recharge rate of the coastal aquifer is about 55 to 60 MCM/y (PWA, 2017;and PWA, 2011). 2.1 shows the water allocation and utilization according to Article 40 of the Oslo Acord [START_REF] Mizyed | Climate change challenges to groundwater resources: Palestine as a case study[END_REF]and PWA, 2013). There are 383 groundwater wells in the West Bank (Figure 2.4). 119 wells are not in service and require rehabilitation and others have dried up (PWA, 2013). The Israeli Occupation restrictions limit the rehabilitation and the maintenance of the Palestinian wells and drilling new deep wells (Human Rights Council, 2021;[START_REF] Judeh | Assessment of Water Governance in the West Bank, Palestine[END_REF]. The Israeli Occupation owns 39 wells in the West Bank. The estimated average annual groundwater pumping of these wells is about 54 MCM/y. Inside the Green Line, the Israeli Occupation has not less than 500 wells most of which are tapping the Western Basin, with a total annual abstraction rate greater than the recharge rates. , 2018;and PWA, 2011). Groundwater level depends on various factors such as the meteorological conditions and the recharge and abstraction rates. In the case of the West Bank, the decline is mainly attributed to droughts and the intensive exploitation of groundwater by the Israeli Occupation (MoFA Netherlands, 2018), while in Gaza strip the intensive local pumping is the major reason behind serious declines in water levels [START_REF] Al-Najjar | Statistical modeling of spatial and temporal vulnerability of groundwater level in the Gaza Strip (Palestine)[END_REF]. Springs discharge is another form of groundwater abstraction from aquifer systems in the West Bank. Approximately 300 springs exist in the West Bank, the majority of the springs are small and have a discharge rate below 0.1 liter/second (PWA, 2013). These springs have a longterm annual discharge of about 54 MCM/y. In recent years, the total annual average discharge has dropped to about half of the long-term annual average mainly due to droughts (UNEP, 2020). The Dead Sea springs are under the control of the Israeli Occupation, the overall yearly discharge of these springs reaches 110 MCM/y (PWA, 2017;and PWA, 2011).

• Non-conventional water resources Desalinated water is an example of the non-conventional water resource adopted in Gaza Strip (PWA, 2013). Desalination has become the main source of domestic water in the strip since (i) the intensive local pumping has caused serious deterioration of the Coastal Aquifer and (ii) the intrusion of saltwater from the Mediterranean Sea has amplified the situation leading to water salinity. A total of 286 desalination plants exist in Gaza (European Investment Bank, 2019). In the West Bank, most of the brackish water desalination plants are small-scale commercial projects (PWA, 2013). The desalination process is very expensive and energy-intensive. The acute shortage in electricity and other energy types in the country has limited the ability of desalination plants to obtain their capacity and caused others to stop operating (Human Rights Council, 2021).

Due to the incapacity of existing water resources (conventional and nonconventional) to address the water shortage status in Palestine, Palestinian Government is obliged to purchase water from water companies belong to the Israeli Occupation (e.g. Mekorot) [START_REF] Judeh | Assessment of Water Governance in the West Bank, Palestine[END_REF]. In 2018, the Palestinian government purchased 85.7 MCM, of which 73.7 MCM were supplied to communities in the West Bank while the rest were supplied to Gaza strip communities (PCBS, 2021a). Most of this Purchased water has been extracted from the West Bank's Mountain aquifers (WASH Custer-State of Palestine, 2021).

• Rainfall

The long-term average annual rainfall in the West Bank and Gaza strip is about 454 mm and 356 mm, respectively (PWA, 2013). The mean annual rainfall in the country has an extreme spatial distribution due to the location of Palestine in a transitional climatic zone between the arid, semiarid and Mediterranean zones (MoFA Netherlands, 2018). The mean annual rainfall in the West Bank varies from 700 mm in the west to less than 160 mm in the arid eastern part (Figure 2.6). In Gaza strip, the coastal climate is dominant, the mean annual rainfall decreases from about 520 mm in the north to 220 mm in the south (PWA, 2017;and PWA, 2011). The uncertainty of surface and groundwater supply (mainly due to political aspects) in Palestine makes rainwater harvesting (RWH) an urgent and sustainable option to address the existing domestic water supply-demand gap [START_REF] Shadeed | Developing a GIS-Based Water Poverty and Rainwater Harvesting Suitability Maps for Domestic Use in the Dead Sea Region (West Bank, Palestine)[END_REF]. Despite that, the exiting RWH plans and projects need to be optimized to promote the water security in Palestine [START_REF] Shadeed | Developing a GIS-Based Water Poverty and Rainwater Harvesting Suitability Maps for Domestic Use in the Dead Sea Region (West Bank, Palestine)[END_REF].

The harvesting of rainwater for agricultural and domestic use is an ancient practice in Palestine. It dates back to 4000 years ago [START_REF] Shadeed | Rainwater harvesting to alleviate water scarcity in dry conditions: A case study in Faria Catchment, Palestine[END_REF]. Different harvesting methods are used in the West Bank including cisterns, dams such as the Al' Auja dam which has a storage capacity of 700,000 m 3 , and agricultural ponds like those in Jordan Valley and Marj Ibn A'mer (PWA, 2017;and PWA, 2011). However, the harvesting of the surface runoff water in wadis is not developed enough in Palestine because of the required high investments and the Israeli Occupation restrictions. However, many communities in the West Bank rely on the RWH. Water harvesting is performed using a very common and traditional technique based on the rooftop runoff harvesting (UNEP, 2020). To maximize the benefit of these randomized individual practices, more efforts should be done by the government and local authorities especially in the northwestern part of the West Bank because it has very high suitability for domestic RWH as illustrated in Figure 2.7 [START_REF] Shadeed | Developing a GIS-Based Water Poverty and Rainwater Harvesting Suitability Maps for Domestic Use in the Dead Sea Region (West Bank, Palestine)[END_REF]. On light of the water shortage conditions that threaten the Palestinian water sector, there is an urge need to (i) spatiotemporally assess the severity of water scarcity based on scientific-based approaches, (ii) optimize the sustainable use of the conventional water resources (rain, surface, and groundwater), (iii) adopt alternative water supply options, (iv) adopt a comprehensive water resources management plans.

Water resources quality

Water scarcity in Palestine is driven by both water quantity and water quality [START_REF] Almasri | Identification of the Nitrogen Sources in the Eocene Aquifer Area (Palestine)[END_REF]. The declining of the water resources quality is a consequence of human activities such as unmanaged agricultural practices as well as natural phenomena such as droughts [START_REF] Jebreen | Recharge, geochemical processes and water quality in karst aquifers: Central West Bank, Palestine[END_REF]. Although these influences are usually coupled, this section will focus on the human influence, and the latter will be discussed in the next section.

The discharge of untreated wastewater into the environment is a major source of water resources contamination in Palestine [START_REF] Almasri | Identification of the Nitrogen Sources in the Eocene Aquifer Area (Palestine)[END_REF]. About 25 MCM/y of untreated sewage is disposed of directly in wadis and streams in the West Bank due to lack of wastewater infrastructure and wastewater treatment plants (UNEP, 2020). Many Wadis in the region have become wastewater channels as in the case of Wadi Al Samin near Hebron, Wadi Al Zomer near Nablus, and Wadi Al Nar near Bethlehem. The Israeli settlers also contribute to the degradation of water resources and the environment in the West Bank, by the release of untreated wastewater into the natural streams and surrounding areas (ARIJ, 2015). The discharged wastewater infiltrates into the soil and reaches the groundwater basins causing contamination of groundwater [START_REF] Mahmoud | Groundwater Quality of Drinking Water Wells in the West Bank, Palestine[END_REF][START_REF] Almasri | Identification of the Nitrogen Sources in the Eocene Aquifer Area (Palestine)[END_REF].

The quality of the groundwater in the West Bank is still acceptable. However, high levels of NO3 and Chloride (Cl) have been found in some areas (PWA, 2013). In Tulkarm and Qalqiliya, elevated levels of NO3 above the WHO limits have been identified in some wells (Figure 2.8) due to infiltration of wastewater and pesticides from poor agricultural practices (PWA, 2013;and WHO, 2011). In the Jordan Valley area, most wells have high concentrations of Cl that exceed the WHO guidelines, while the NO3 concentration is acceptable (PWA, 2017;and PWA, 2016). Worse water quality status is found in Gaza Strip where the groundwater is undrinkable (UNEP, 2020). Poor agricultural practices such as intensive use of agricultural pesticides combined with the infiltration of wastewater into the Coastal Aquifer have increased the concentration of NO3 (UNEP, 2020). The levels of NO3 exceed 300 mg/L in residential areas (served by cesspits) and decrease to 50 mg/L in non-residential areas (ARIJ, 2015). The recommended concentration of NO3 by the WHO is below 50 mg/L (WHO, 2011). The excessive local pumping of the groundwater from the Coastal Aquifer has increased the level of salinity and Cl beyond the WHO guidelines [START_REF] Efron | The public health impacts of Gaza's water crisis: analysis and policy options[END_REF]. This could be referred to the infiltration of seawater and brine from the Mediterranean and deeper layers as a consequence of this practice [START_REF] Efron | The public health impacts of Gaza's water crisis: analysis and policy options[END_REF]. The concentration of Cl varies from 100 to more than 2000 mg/l (Figure 2.9). Subsequently, more than 97% of the water pumped from the Coastal Aquifer is not suitable for human consumption [START_REF] Efron | The public health impacts of Gaza's water crisis: analysis and policy options[END_REF].

Effect of climate change and demographic growth on water resources

Palestine is located in the Middle East region in a transitional climatic zone [START_REF] Al-Najjar | Stochastic timeseries models for drought assessment in the Gaza Strip (Palestine)[END_REF]MoFA Netherlands, 2018;[START_REF] Ighbareyeh | Assessing crop yield sustainability under the climatic and bioclimatic change in the area of Palestine[END_REF]. Its climate is characterized by cool wet winters and hot dry summers, locally modified by altitude and latitude (MoFA Netherlands, 2018). Based on the De Martonne index of aridity, the country is classified into different zones: arid to hyper-arid in the eastern and southern parts, semi-arid to sub-humid in the western part, and semi coastal area [START_REF] Richard | Analysis of climatic variability and its environmental impacts across the occupied Palestinian territory[END_REF]. All of these fluctuations in addition to the unstable political situation have made the country extremely vulnerable to climate change [START_REF] Ighbareyeh | Assessing crop yield sustainability under the climatic and bioclimatic change in the area of Palestine[END_REF].

The climate changes are evident in Palestine [START_REF] Al-Najjar | Stochastic timeseries models for drought assessment in the Gaza Strip (Palestine)[END_REF]MoFA Netherlands, 2018;[START_REF] Ighbareyeh | Assessing crop yield sustainability under the climatic and bioclimatic change in the area of Palestine[END_REF]. It can be seen through the rising in annual average temperature, the reduction in rainfall, and the increase in droughts [START_REF] Al-Najjar | Stochastic timeseries models for drought assessment in the Gaza Strip (Palestine)[END_REF]MoFA Netherlands, 2018;[START_REF] Ighbareyeh | Assessing crop yield sustainability under the climatic and bioclimatic change in the area of Palestine[END_REF]. The mean annual temperature of the country has shown a clear increasing trend during the period 1843-2013 [START_REF] Hasan | The Effect of Temperature and Rainfall Changes on Biophysical and Socio-Economic Status of People in Northern Jordan Valley Drylands, Palestine[END_REF]. The probability of very hot summer days has increased [START_REF] Al-Najjar | Stochastic timeseries models for drought assessment in the Gaza Strip (Palestine)[END_REF]MoFA Netherlands, 2018;[START_REF] Ighbareyeh | Assessing crop yield sustainability under the climatic and bioclimatic change in the area of Palestine[END_REF]. The average annual rainfall of the West Bank and Gaza Strip has shown a decreasing trend during the period 2001-2011. A similar trend was observed at Jerusalem station for the rainy season from 1964 to 2012 [START_REF] Richard | Analysis of climatic variability and its environmental impacts across the occupied Palestinian territory[END_REF]. The increase in the incidence of drought occurrence is also evident. The droughts events have increased in the country by over 80% during the past decade as illustrated in Due to the temperature rising, evaporation will increase and water needs will grow. The aridity and desertification are expected to increase [START_REF] Mizyed | Climate change challenges to groundwater resources: Palestine as a case study[END_REF]. The reduction in the precipitation amounts and changes in its patterns will affect the availability of the water resources (e.g. reduce groundwater recharge) [START_REF] Mizyed | Climate change challenges to groundwater resources: Palestine as a case study[END_REF]. [START_REF] Mizyed | Climate change challenges to groundwater resources: Palestine as a case study[END_REF] used hydrological modeling to quantify the impact of changes in precipitation and temperature on the natural recharge of the groundwater. The simulation was based on different scenarios: (a) rise in monthly average temperature by 2˚C to 3˚C, (b) decrease in annual precipitation by 3%, 6%, 10%, and 15%. All scenarios are based on [START_REF] Christensen | Climate phenomena and their relevance for future regional climate change[END_REF] predictions in the Mediterranean region. Table 2.2 shows the relative changes in the natural recharge of the three main aquifers in the West Bank under different climate change scenarios. A spatial variation in the impact of climate change on the natural recharge could be observed: a reduction by 10 % in the annual precipitation will reduce the natural recharge by 14% to 24%, with the highest reduction in the Eastern Aquifer recharge which is located in the drier area [START_REF] Mizyed | Climate change challenges to groundwater resources: Palestine as a case study[END_REF]. The effect of the temperature increase is also recognized, but less significant than the rainfall reduction. Al-Najjar (2021) modeled the impact of the climate change on the groundwater level in the Coastal Aquifer in Gaza Strip. The study first forecasted the climate parameters during the next 20 years; the average annual rainfall is predicted to decrease by 5.2%, the temperature is expected to rise by about 1 °C, and the relative humidity is most likely to decrease by 8%. Simultaneously, the sunshine hours and potential evaporation will increase by about 5 hours and 111 mm, respectively. The abstraction rate of the groundwater is predicted to be doubled by 2040. Then, the researchers analyzed the combined effect of the climate change and the over-pumping on the groundwater storage. The result of the simulation is presented in Figure 2.11. It shows a drop in the groundwater levels between 1.13 and 28 m in 2040. The quality of the water resources is also jeopardized by the climate change (UNDP, 2010). The levels of salinity of the groundwater will increase due to the reduction in the precipitation which will reduce the recharge of the groundwater [START_REF] Smithers | National adaptation plan to climate changes[END_REF]and UNDP, 2010). The case of the Coastal Aquifer is very critical as it has high permeability and a shallow water table. Under the projected rise of the sea level by 0.1 to 0.4 m by 2100, the intrusion of the seawater into the aquifer will increase, which will raise the salinity of the Coastal Aquifer. [START_REF] Abd-Elhamid | Impact of Over-Pumping and Sea Level Rise on Seawater Intrusion in Gaza Aquifer (Palestine)[END_REF] simulated the current and future intrusion of seawater into the Gaza Coastal Aquifer. They showed a severe seawater intrusion into the aquifer which makes the groundwater non-drinkable. The future seawater intrusion was simulated under different scenarios. An extra intrusion of 250 m in the coastal aquifer is predicted under a reduction of 0.5 m in the water table because of over-abstraction. While a 100 cm rise in the sea level will result in about 500 m of additional seawater intrusion. Yet, the combined effect of over-pumping and rise in sea level is expected to increase the intrusion by about 1 km into the aquifer.

The climate change will exacerbate the water stress with a deterioration of the water quality, reduction in the water supply, and increase in the domestic and agricultural water demand [START_REF] Al-Najjar | Statistical modeling of spatial and temporal vulnerability of groundwater level in the Gaza Strip (Palestine)[END_REF][START_REF] Mizyed | Climate change challenges to groundwater resources: Palestine as a case study[END_REF][START_REF] Abd-Elhamid | Impact of Over-Pumping and Sea Level Rise on Seawater Intrusion in Gaza Aquifer (Palestine)[END_REF]. The stability of the country and the socio-economic conditions will be negatively affected by the climate change. Thus, there is a need for adaptation strategies to the climate change [START_REF] Iglesias | Re-thinking water policy priorities in the Mediterranean region in view of climate change[END_REF].

Materials and methods

The research framework adopted in this research consists of four phases (Figure 2.12). The first phase targets the assessment of the current and future water scarcity in the West Bank. The second one concerns the protection of the quality of the water resources. The third one assesses the potential capacity of RWH to address the water scarcity. The last phase proposes a smart RWH system and smart dual water supply system to promote the domestic water security. This chapter briefly discusses the four phases. The detailed description will be presented in the application chapters. This section presents the methodology used for assessing the water scarcity in the West Bank (Figure 2.13). According to literature review, scholars adopted several methods for water scarcity assessment including criticality ratio, water poverty index, the falkenmark index, integrated water quantity-quality-environment flow index, the International Water Management Institute Indicator (IWMII), and cumulative abstraction to demand ratio [START_REF] Hussain | A comparative Appraisal of Classical and Holistic Water Scarcity Indicators[END_REF][START_REF] Nkiaka | Water security assessment in ungauged regions using the water balance and water footprint concepts and satellite observations[END_REF]Liu et al., 2017;[START_REF] Liu | Assessing water scarcity by simultaneously considering environmental flow requirements, water quantity, and water quality[END_REF][START_REF] Falkenmark | Present and Future Water Requirements for Feeding Humanity[END_REF]. The used indices are complex. They require massive input data, and could come up with misleading uncertain results [START_REF] Hussain | A comparative Appraisal of Classical and Holistic Water Scarcity Indicators[END_REF][START_REF] Nkiaka | Water security assessment in ungauged regions using the water balance and water footprint concepts and satellite observations[END_REF][START_REF] Liu | Water Scarcity Assessments in the Past, Present, and Future[END_REF][START_REF] Liu | Assessing water scarcity by simultaneously considering environmental flow requirements, water quantity, and water quality[END_REF][START_REF] Falkenmark | Present and Future Water Requirements for Feeding Humanity[END_REF].

To overcome these limitations, this research adopts the supply demand balance index (SDBI) to assess the existing domestic water scarcity. This index is simple and have a single value resulted from dividing the water supply to the water demand [START_REF] Huang | Supply and Demand Analysis of Water Resources Based on System Dynamics Model[END_REF]. The value can assess the water scarcity level on a scale ranging from "extreme" to "no" water shortage. It can simultaneously consider freshwater utilization, water quality, and citizens' water needs. The SDBI simplicity enables to consider the climate change and demographic effects on the future water scarcity [START_REF] Huang | Supply and Demand Analysis of Water Resources Based on System Dynamics Model[END_REF]. This research investigates the climate change effects on the water resources availability till 2050 as well as the impact of the population growth on the domestic water demand. The SDBI, Geographic Information System (GIS), climate change and demographic growth models are integrated to assess the future water scarcity. The detailed methodology for the assessment of existing and projected water scarcity in the West Bank is presented in Section 3.2. According to the state of art, scholars adopted several methods for protecting the groundwater quality including the descriptive statistical assessment [START_REF] Judeh | Use of GIS, Statistics and Machine Learning for Groundwater Quality Management: Application to Nitrate Contamination[END_REF][START_REF] Anayah | Trends and Occurrences of Nitrate in the Groundwater of the West Bank, Palestine[END_REF], the water quality indices (WQI) [START_REF] Banda | A Review of the Existing Water Quality Indices (WQIs)[END_REF][START_REF] Shadeed | Rainwater Harvesting for Sustainable Agriculture in High Water-Poor Areas in the West Bank, Palestine[END_REF][START_REF] Shadeed | Developing a GIS-Based Water Poverty and Rainwater Harvesting Suitability Maps for Domestic Use in the Dead Sea Region (West Bank, Palestine)[END_REF][START_REF] Poonam | Water Quality Indices-Important Tools for Water Quality Assessment: A Review[END_REF][START_REF] Li | Water Quality Indices[END_REF][START_REF] Ocampo-Duque | Water Quality Analysis in Rivers with Non-Parametric Probability Distributions and Fuzzy Inference Systems: Application to the Cauca River, Colombia[END_REF], the water quality mapping (Judeh et al., 2021), and the water quality modeling [START_REF] Pham | A Method to Represent a Well in a Three-Dimensional Discrete Fracture Network Model[END_REF][START_REF] Ranjith | Water Quality Model for Streams: A Review[END_REF]. These methods have several limitations: complexity (e.g. water quality modeling), uncertainty (e.g. WQI and water quality mapping), dependency on massive input data (e.g. water quality modeling), and the inability to predict the levels of water pollution along with their sources (e.g. all listed methods) [START_REF] Shadeed | Developing a GIS-Based Water Poverty and Rainwater Harvesting Suitability Maps for Domestic Use in the Dead Sea Region (West Bank, Palestine)[END_REF][START_REF] Anayah | Trends and Occurrences of Nitrate in the Groundwater of the West Bank, Palestine[END_REF].

To address these limitations, this research proposes to combine four methods (Figure 2.14). The first method adjusts and employs a new GIS-based Groundwater Quality Index (GWQI) for the groundwater potability and palatability assessment and mapping. This adjusted index contributes in addressing the complexity and uncertainty of previously-used indices. It contributes in properly identifying the hotspots for groundwater contamination and the most threatening contaminants. The second method uses a GIS-based descriptive statistical assessment for constructing a causal-effect relation for groundwater contamination with a focus on the resulted hotspot in the first method [START_REF] Judeh | Use of GIS, Statistics and Machine Learning for Groundwater Quality Management: Application to Nitrate Contamination[END_REF]. Such relation helps in investigating the main sources and factors affecting the groundwater contamination including: (i) well depth, (ii) well use, (iii) anthropogenic on-ground activities, (iv) soil type, (v) land use, (vi) groundwater flow, (vii) aquifer conductivity, and (viii) watersheds. The third method employs such factors and sources for realizing a Machine Learning-based Random Forest model (RFA) for predicting the groundwater contamination [START_REF] Judeh | Use of GIS, Statistics and Machine Learning for Groundwater Quality Management: Application to Nitrate Contamination[END_REF]. The adoption of RFA will contribute in addressing the prediction limitations/incapability of the other methods used for water quality protection. The fourth method employs climate change model (Soil-Vegetation-Atmosphere Model, SVAT) for assessing the change in groundwater storage till 2050 [START_REF] Menzel | GLOWA Jordan River Phase II Final Report: An Integrated Approach to Sustainable Management of Water Resources Under Global Change[END_REF].

The integration of the four methods helps in providing a sustainable plan for the groundwater protection through:

(i)
The allocation of hotspots for groundwater pollution, and the identification of the most threatening sources for such contamination. Instructions and recommendation for protecting such spots is presented (with focus on the man-made activities).

(ii)

The forecasting of groundwater storage changes till 2050 based on climate change scenarios. Adopting alternative water resources is introduced accordingly. This will decrease the burden on groundwater and will preserve its level (from declining or even depletion). This section presents the methodology used for investigating the role of alternative water resources in addressing the water scarcity in West Bank (Figure 2.15). Previous researches focused on the estimation of the potential water volumes that the alternative water resources can produce. The effect of these volumes to the water scarcity level was not investigated. This research employs the GIS and Gould and Nissen-Petersen's equation to estimate the potential RWH volumes in the current status. After that, climate change impact on the rainfall is investigated. Finally, the current and future RWH volumes are implied within the SDBI. This contributes to investigate the role of the harvested water in alleviating the water scarcity. The conventional RWH systems have several limitations. They don't control the filling and emptying process of the storage tanks. Moreover, the water leakage throughout the system is not controlled [START_REF] Behzadian | Can Smart Rainwater Harvesting Schemes Result in the Improved Performance of Integrated Urban Water Systems?[END_REF]. To overcome these limitations, this research proposes a smart RWH system (Figure 2.16), which employs smart sensors and crowdsourcing to monitor and control the harvested water' potability, level, and leak. The dynamic management is adopted to optimize the size of the storage tank. The smart RWH system is integrated with the municipal water supply. The smart dual water supply system is designed considering the sharing of water at a neighborhood level. A reliability analysis for assessing the system' performance is performed. Finally, the role of the system in addressing the water scarcity in the West Bank is investigated.

Conclusion

This chapter presented an innovative framework for the smart management of the water scarcity. It combines the smart technology and Machine Learning for the assessment, preservation, mitigation and reduction of the water scarcity. The framework considers the water scarcity main causes: limited water availability, restricted water accessibility, water pollution, increase in the water demand, and the climate change.

The proposed framework is applied to the West Bank. The use of GIS and SDBI for the assessment water scarcity will be presented in Chapter 3. The use of GIS, statistics, and the new adjusted GWQI for the protection of the conventional water resources will be presented in chapters 4 and 5. The adoption of the Gould and Nissen-Petersen's equation and GIS to assesses the potential capacity of RWH in addressing water scarcity will be discussed in chapter 6. The use of the smart systems to address the water security will be described in. chapter 7.

Chapter 3. Assessment of Current and Forecasted

Water Scarcity in the Arid and Semi-Arid Areas

Introduction

This chapter aims at assessing the domestic water scarcity in arid and semi-arid areas with emphasis on the West Bank in Palestine. Domestic water scarcity is defined as the inability of available freshwater to satisfy the needed domestic water demand [START_REF] Taylor | Rethinking Water Scarcity: The Role of Storage[END_REF]. According to the World Health Organization (WHO), water scarcity adversely affects the water consumption and hygiene [START_REF] Howard | Domestic Water Quantity, Service Level and Health[END_REF]. Areas suffering from water scarcity are classified as risky to human health [START_REF] Howard | Domestic Water Quantity, Service Level and Health[END_REF]. Water scarcity increases the spread of infectious diseases (e.g. Influenza, Ebola, and SARS) [START_REF] Anser | Identifying the Potential Causes, Consequences, and Prevention of Communicable Diseases (Including COVID-19)[END_REF][START_REF] Adams | Water Insecurity and Urban Poverty in the Global South: Implications for Health and Human Biology[END_REF][START_REF] Zakar | Climate Change-Induced Water Scarcity: A Threat to Human Health*[END_REF]. The United Nation (UN) Sustainable Development Goal (SDG-6.4) expressed the necessity of decreasing the number of people affected by domestic water scarcity (UN, 2021). Domestic water scarcity is of increasing concern, especially in arid and semi-arid areas [START_REF] Shadeed | Developing a GIS-Based Water Poverty and Rainwater Harvesting Suitability Maps for Domestic Use in the Dead Sea Region (West Bank, Palestine)[END_REF]. This could be attributed to the increasing water demand and the vulnerability of water resources under climate change [START_REF] Shadeed | Developing a GIS-Based Water Poverty and Rainwater Harvesting Suitability Maps for Domestic Use in the Dead Sea Region (West Bank, Palestine)[END_REF]The Arab Water Council, 2009;[START_REF] Menzel | GLOWA Jordan River Phase II Final Report: An Integrated Approach to Sustainable Management of Water Resources Under Global Change[END_REF]. In the West Bank, the domestic water supply-demand gap reached 40% of the water demand in 2018 (PCBS, 2021b).

Assessing the water scarcity constitutes a vital step in addressing water scarcity challenges (Jafari-Shalamzari and Zhang, 2018;and Liu et al., 2017). Various indices are used to evaluate the domestic water scarcity. The Falkenmark index uses the per capita water availability as an indicator of water scarcity [START_REF] Falkenmark | Present and Future Water Requirements for Feeding Humanity[END_REF][START_REF] Falkenmark | Macro-Scale Water Scarcity Requires Micro-Scale Approaches[END_REF]. [START_REF] Damkjaer | The Measurement of Water Scarcity: Defining a Meaningful Indicator[END_REF][START_REF] Oki | Global Hydrological Cycles and World Water Resources[END_REF] proposed to use the criticality ratio index to estimate water scarcity. [START_REF] Shadeed | Developing a GIS-Based Water Poverty and Rainwater Harvesting Suitability Maps for Domestic Use in the Dead Sea Region (West Bank, Palestine)[END_REF][START_REF] Jafari-Shalamzari | Assessing Water Scarcity Using the Water Poverty Index (WPI) in Golestan Province of Iran[END_REF]and Liu et al., 2018) used water poverty for water scarcity assessment. This indicator considers the average of the following elements: water availability, accessibility, capacity, use, and environment. According to [START_REF] Huang | Supply and Demand Analysis of Water Resources Based on System Dynamics Model[END_REF], the supply demand balance index (SDBI) is the only index that directly considers both water availability and citizens' water needs. It provides a simple way of assessing the domestic water scarcity by estimating the ratio of the water supply to the water demand. It has a single value that characterizes the water scarcity level on a scale ranging from "extreme" to "no" water shortage [START_REF] Huang | Supply and Demand Analysis of Water Resources Based on System Dynamics Model[END_REF].

Climate change influences the availability of water resources [START_REF] Urama | Impacts of Climate Change on Water Resources in Africa: The Role of Adaptation[END_REF]. The projected water demand is affected by the demographic changes [START_REF] Johannsen | Future of Water Supply and Demand in the Middle Drâa Valley, Morocco, under Climate and Land Use Change[END_REF]. Consequently, assessing the domestic water scarcity could be viewed as a dynamic process [START_REF] Johannsen | Future of Water Supply and Demand in the Middle Drâa Valley, Morocco, under Climate and Land Use Change[END_REF]. However, the literature review shows that the assessment of the domestic water scarcity through SDBI does not properly consider the climate change.

The contribution of this chapter could be summarized by the use of the SDBI to assess the current and projected domestic water scarcity in the West Bank. It is organized as follows. First, it presents the research methodology for the estimation of SDBI. It describes the case study with emphasis on data collection, and finally, it discusses the resulted scarcity levels in the West Bank, Palestine.

Materials and methods

Methodology

The methodology adopted in this research is illustrated in Figure 3.1. It includes three phases. It starts by identifying and quantifying the total water supply (TWS) in million cubic meters per year (MCM/y), the total water demand (TWD) in MCM/y. The second phase estimates the effect of climate change and population growth to the TWS and TWD (till 2050), respectively. The last phase employs the SDBI (by implying the current and projected TWS and TWD) to assess the current and projected domestic water scarcity in the study area.

Assessment of TWS and TWD

The assessment of domestic water scarcity starts by identifying and quantifying the sources of water supply (e.g. surface water, groundwater, desalination and etc.). Accordingly, the current TWS is estimated. The domestic water consumption rate (DWCR) is specified considering the recommendation of WHO [START_REF] Howard | Domestic Water Quantity, Service Level and Health[END_REF]. Such rates, along with the population statistics, are used to estimate the TWD. Where, TWDi is the TWD for the i th year in (MCM/y) POPi is the population for the i th year in (capita)

Impacts of climate and demographic change to TWS and TWD

Scholars adopted various models to characterize the effect of climate change to water resources availability. Such models include (i) Water Balance Model developed by the Research Center for Climate Change (RCCC-WBM) [START_REF] Qiao | Impact of Climate Change on Water Availability in Water Source Areas of the South-to-North Water Diversion Project in China[END_REF], (ii) Hydrologiska Byrans Vattenbalansavdelning model (HBV) [START_REF] Versini | Climate Change Impact on Water Resources Availability: Case Study of the Llobregat River Basin (Spain)[END_REF], (iii) Soil and Water Assessment Tool (SWAT) [START_REF] Emami | Modeling the Impact of Climate Change on Water Availability in the Zarrine River Basin and Inflow to the Boukan Dam, Iran[END_REF], and (iv) Soil-Vegetation-Atmosphere Model (SVAT) [START_REF] Menzel | GLOWA Jordan River Phase II Final Report: An Integrated Approach to Sustainable Management of Water Resources Under Global Change[END_REF]. According to [START_REF] Petropoulos | An Overview of the Use of the SimSphere Soil Vegetation Atmosphere Transfer (SVAT) Model for the Study of Land-Atmosphere Interactions[END_REF], the SVAT has the following strengths compared to the other models: (i) consideration of fine time-step analysis which goes in line with the time-steps of the physical processes simulations, (ii) flexibility of the input parameters and their time-steps records, and (iii) consideration of the under-ground layer (e.g. soil layer), onground layer (e.g. vegetation layer), and over-ground layer (atmospheric layer) [START_REF] Petropoulos | An Overview of the Use of the SimSphere Soil Vegetation Atmosphere Transfer (SVAT) Model for the Study of Land-Atmosphere Interactions[END_REF]. This model was used for the determination of TWS in MCM/y in 2050 [START_REF] Menzel | GLOWA Jordan River Phase II Final Report: An Integrated Approach to Sustainable Management of Water Resources Under Global Change[END_REF]. The model has a resolution of 18 x 18 km 2 and involves rainfall, climatic, vegetation, landscape and soil parameters as inputs. Rainfall parameters include the rainfall depth, patterns and spatial distribution. Climatic and vegetation parameters involve the solar radiation and the leaf area index, respectively. Landscape parameters imply the elevation, slope and land-use, while the soil parameters include soil type, texture and field capacity [START_REF] Menzel | GLOWA Jordan River Phase II Final Report: An Integrated Approach to Sustainable Management of Water Resources Under Global Change[END_REF].

The schematic overview of the SVAT model is shown in Figure 3.2. First, the model considers the different inputs. Then, characterizing the infiltration, percolation, interception, transpiration, and runoff is carried out. Finally, the vertical and horizontal water flow is estimated at the different time steps [START_REF] Menzel | GLOWA Jordan River Phase II Final Report: An Integrated Approach to Sustainable Management of Water Resources Under Global Change[END_REF].

Assessment of the domestic water scarcity

Current and projected TWS and TWD are used to estimate the SDBI for 2020 and 2050 [START_REF] Huang | Supply and Demand Analysis of Water Resources Based on System Dynamics Model[END_REF]. This index is used for the assessment of domestic water scarcity. In addition, SDBI could be used at the regional and national levels [START_REF] Huang | Supply and Demand Analysis of Water Resources Based on System Dynamics Model[END_REF]. It is estimated by the ratio between TWS and TWD:

𝑆𝑆𝑆𝑆𝑆𝑆𝐼𝐼 𝑖𝑖 = 𝑇𝑇𝑇𝑇𝑆𝑆 𝑖𝑖 𝑇𝑇𝑇𝑇𝐷𝐷 𝑖𝑖 (Equ. 3.2).
Where, SDBIi: supply demand balance index for the i th year TWSi: total water supply for the i th year in (MCM/y) TWDi: total water demand for the i th year in (MCM/y) According to [START_REF] Huang | Supply and Demand Analysis of Water Resources Based on System Dynamics Model[END_REF], five domestic water scarcity levels are identified, as summarized in Table 3.1. The proposed methodology is applied to the West Bank, Palestine. Data are collected from different sources. The historical and current population statistics are collected from the Palestinian Central Bureau of Statistics (PCBS) (PCBS, 2021c). WHO recommended a DWCR of about 100 liters per capita per day (l/c/d) [START_REF] Howard | Domestic Water Quantity, Service Level and Health[END_REF]. Accordingly, Equ. 3.1 is used for the estimation of historical and current TWD for the whole West Bank. POPp (in 2050) is estimated using a dynamic population projection model [START_REF] Courbage | Palestine 2030 -Demographic Change: Opportunities for Development[END_REF]; which considers several determinants such as age, sex, fertility, international migration trends, and mortality rates. Equ. 3.1 is used for the determination of the TWD in 2020 and 2050 on a governorate scale. Table 3.2 summarizes the results for the eleven West Bank governorates. TWD in 2020 varies between 1.9 MCM/y (Jericho) and 27.8 MCM/y (Hebron), with an average value of 9.9 MCM/y. In 2050, the TWD is expected to vary between 3.2 MCM/y (Jericho) and 50.9 MCM/y (Hebron), with an average value of 15.7 MCM/y, which is about 58% higher than that in 2020. By characterizing the probable sources of water in the West Bank, it is found that water desalination and surface water have no contribution to the TWS (PCBS, 2021a;and PCBS, 2020b). Groundwater (through the pumping wells and the springs) is the only adopted source of water to supply citizens by their needs. Therefore, TWS is quantified through the estimation of the supplied (by services providers) groundwater volumes in the study area. Such volumes are gained from PCBS database (PCBS, 2021b). The variation in TWS in the West Bank governorates in 2020 is illustrated in Table 3.3. The lowest TWS is recorded in Ramallah & Al-Bireh with around 1.1 MCM/y, while the highest TWS is in Nablus (8.5 MCM/y). The rate of change in TWS between 2020 and 2050 are estimated using the downscaled SVAT model developed by GLOWA [START_REF] Menzel | GLOWA Jordan River Phase II Final Report: An Integrated Approach to Sustainable Management of Water Resources Under Global Change[END_REF]. The GIS-based Raster Calculator is then used to estimate the values of TWS in 2050. However, downscaled SVAT model involves uncertainties [START_REF] Gunkel | New Insights into The Natural Variability of Water Resources in The Lower Jordan River Basin[END_REF][START_REF] Menzel | GLOWA Jordan River Phase II Final Report: An Integrated Approach to Sustainable Management of Water Resources Under Global Change[END_REF]. To mitigate this shortcoming, available data at the highest spatial and temporal resolution were used [START_REF] Gunkel | New Insights into The Natural Variability of Water Resources in The Lower Jordan River Basin[END_REF][START_REF] Menzel | GLOWA Jordan River Phase II Final Report: An Integrated Approach to Sustainable Management of Water Resources Under Global Change[END_REF]. Calibration of SVAT model was carried out using historical and current TWS records (in 2015 and 2020, respectively) (PCBS, 2021b). Due to limited data availability, this calibration is conducted for Jenin governorate. Actual TWS in 2015 (3.63 MCM/y) is used to predict the TWS in 2020 (using SVAT) for Jenin governorate. It is found that the predicted TWS in 2020 equals around 3.68 MCM/y. Such prediction is significantly close to the actual TWS in 2020 which equal to 3.70 MCM/y (with a relative error of about 0.005) (PCBS, 2021b). This slight relative error provides a considerable level of confidence by the used model.

Results and discussion

This section presents and discusses the SDBI values and the domestic water scarcity levels in the West Bank governorates for the years 2020 and 2050 (Table 3.4). In 2020, the status "no water shortage" is observed in Jericho, Qalqilya, and Tubas, with a SDBI of about 1.5, 1.2, and 1.1, respectively. A slight water shortage is observed in Tulkarm, while the rest of the West Bank governorates experienced an extreme to acute water shortage. Results of the current water scarcity in the West Bank are compatible with the results of other scholars [START_REF] Jabari | Assessment of the Urban Water Security in a Severe Water Stress Area-Application to Palestinian Cities[END_REF][START_REF] Shadeed | Developing a GIS-Based Water Poverty and Rainwater Harvesting Suitability Maps for Domestic Use in the Dead Sea Region (West Bank, Palestine)[END_REF]. All governorates (except for Tubas, Ramallah & Al-Bireh, and Jerusalem) have water scarcity/poverty categories, which are similar to the findings of [START_REF] Shadeed | Developing a GIS-Based Water Poverty and Rainwater Harvesting Suitability Maps for Domestic Use in the Dead Sea Region (West Bank, Palestine)[END_REF]. Compared to [START_REF] Jabari | Assessment of the Urban Water Security in a Severe Water Stress Area-Application to Palestinian Cities[END_REF], compatible water scarcity/security categories exist in all governorates except Tulkarm and Jericho. The gap in characterizing the water status in some governorates could be attributed to the approaches and indicators adopted by scholars. For example, [START_REF] Shadeed | Developing a GIS-Based Water Poverty and Rainwater Harvesting Suitability Maps for Domestic Use in the Dead Sea Region (West Bank, Palestine)[END_REF] adopted the weighted arithmetic average method (WAAM) by considering the water availability, accessibility, capacity, use, and environment for assessing the water status, while [START_REF] Jabari | Assessment of the Urban Water Security in a Severe Water Stress Area-Application to Palestinian Cities[END_REF] used the same method but in considering other indicators such as water resources, water services, and water governance. Adopting WAAM with flexible input indicators to characterize the water scarcity/poverty was discussed by [START_REF] Goodarzi | Water Poverty Index (WPI) Evaluation in Borujerd-Dorood Watershed (Iran) to Reinforce Land Management Plans[END_REF][START_REF] Koirala | Application of Water Poverty Index (WPI) in Spatial Analysis of Water Stress in Koshi River Basin, Nepal[END_REF][START_REF] Sousa Cordão | Water Shortage Risk Mapping: A GIS-MCDA Approach for a Medium-Sized City in the Brazilian Semi-Arid Region[END_REF], who confirmed that the change in the input indicators could affect the estimation of the water poverty/scarcity status [START_REF] Goodarzi | Water Poverty Index (WPI) Evaluation in Borujerd-Dorood Watershed (Iran) to Reinforce Land Management Plans[END_REF][START_REF] Koirala | Application of Water Poverty Index (WPI) in Spatial Analysis of Water Stress in Koshi River Basin, Nepal[END_REF][START_REF] Sousa Cordão | Water Shortage Risk Mapping: A GIS-MCDA Approach for a Medium-Sized City in the Brazilian Semi-Arid Region[END_REF].

In 2050, lower SDBI values are expected to be recorded comparing to the year 2020. All governorates have water scarcity levels that range from extreme, acute, to moderate scarcity. This indicates that the water scarcity challenges will be severest in the future. Therefore, sustainable management of water scarcity must be adopted through the consideration of the climate and demographic impacts. This will contribute to addressing the future water challenges.

Conclusion

This chapter presented a new methodology for assessing the domestic water scarcity in arid and semi-arid areas. First, the current total water supply (TWS) and total water demand (TWD) are estimated. Then, the effects of the climate change and the population growth on the TWS and TWD are investigated in 2050. Finally, the SDBI is used to assess the current and projected domestic water scarcity in the West Bank.

Results showed that seven out of the eleven West Bank governorates are suffering from extreme to acute water scarcity in 2020. Due to the climate and demographic change impact, one more governorate is expected to suffer from extreme to acute scarcity level by 2050. This research helps decision-makers to properly evaluate the current and projected water scarcity challenges in the West Bank and to establish proper water management strategies to address the water scarcity.

Chapter 4. GIS-Based Spatiotemporal Mapping of the Groundwater Potability and Palatability Indices

in Arid and Semi-Arid Areas

Introduction

This chapter aims at assessing the quality of the groundwater resources to establish efficient water protection plans and address the water scarcity in the West Bank. Groundwater forms a major source of potable water for many countries in the world (Masindi and Foteinis, 2020;[START_REF] Almasri | Identification of the Nitrogen Sources in the Eocene Aquifer Area (Palestine)[END_REF]. It has been naturally purified by the infiltration process. Therefore, it is usually of excellent quality and requires no more than slight monitoring and treatment [START_REF] Li | Sources and Consequences of Groundwater Contamination[END_REF][START_REF] Mays | Groundwater Contamination, Subsurface Processes, and Remediation Methods: Overview of the Special Issue of Water on Groundwater Contamination and Remediation[END_REF][START_REF] Singha | Assessing Ground Water Quality Using GIS[END_REF]. Unluckily, excellent quality is no longer assured due to human activities [START_REF] Li | Sources and Consequences of Groundwater Contamination[END_REF][START_REF] Ortega | Application of Geoelectric Parameters in the Study of Vulnerability for Groundwater Contamination[END_REF][START_REF] Ahmad | Groundwater Contamination Issues in Chiniot Area, Punjab, Pakistan[END_REF][START_REF] Lee | Private Wells and Rural Health: Groundwater Contaminants of Emerging Concern[END_REF]. Indeed, urban (e.g., use of cesspits for wastewater disposal), agricultural (e.g., intensive use of fertilizers and pesticides), and industrial (e.g., unmanaged solid waste disposal) activities increase the soluble contaminants reaching groundwater [START_REF] Khan | Groundwater Contamination and Health Risk Posed by Industrial Effluent in NCR Region[END_REF][START_REF] Abul Hasan | Groundwater Contamination by Hazardous Wastes[END_REF][START_REF] Kumar | Mathematical Modelling to Establish the Influence of Pesticides on Groundwater Contamination[END_REF]Peixoto, 2020;[START_REF] Estrada | Groundwater Contamination Due to the Use of Agrochemicals in Sugar Cane Agroecosystems[END_REF][START_REF] Alaizari | Analytical Groundwater Contamination by Heavy Metals[END_REF][START_REF] Baalousha | Vulnerability, Probability and Groundwater Contamination Risk[END_REF]. Scholars have confirmed the increasing health risk associated with groundwater contamination [START_REF] Karunanidhi | Impact of Groundwater Contamination on Human Health[END_REF][START_REF] Kadam | Seasonal Assessment of Groundwater Contamination, Health Risk and Chemometric Investigation for a Hard Rock Terrain of Western India[END_REF], which could cause different diseases (e.g., hepatitis, dysentery, poisoning, blue baby syndrome, and cancers) and lead to death [START_REF] Khan | Groundwater Contamination and Health Risk Posed by Industrial Effluent in NCR Region[END_REF][START_REF] Abul Hasan | Groundwater Contamination by Hazardous Wastes[END_REF]Peixoto, 2020;[START_REF] Kubicz | Effects of Drought on Environmental Health Risk Posed by Groundwater Contamination[END_REF][START_REF] Khaiwal | Evaluation of Groundwater Contamination in Chandigarh: Source Identification and Health Risk Assessment[END_REF][START_REF] Raza | Groundwater Status in Pakistan: A Review of Contamination, Health Risks, and Potential Needs[END_REF].

The assessment of the groundwater quality is necessary for evaluating its suitability for human usage. Traditionally, researchers adopted the single parameter assessment to characterize the groundwater quality [START_REF] El Baba | Evaluation of the Groundwater Quality Using the Water Quality Index and Geostatistical Analysis in the Dier Al-Balah Governorate, Gaza Strip, Palestine[END_REF]. They compared the parameter' concentration to the drinking water standards [START_REF] El Baba | Evaluation of the Groundwater Quality Using the Water Quality Index and Geostatistical Analysis in the Dier Al-Balah Governorate, Gaza Strip, Palestine[END_REF]. This traditional approach does not consider the multi-contaminant effect on drinking water [START_REF] El Baba | Evaluation of the Groundwater Quality Using the Water Quality Index and Geostatistical Analysis in the Dier Al-Balah Governorate, Gaza Strip, Palestine[END_REF]. Thus, researchers embraced the groundwater quality index (GWQI) as a strategic tool for characterizing groundwater quality [START_REF] Kayemah | Evaluation of Groundwater Quality Using Groundwater Quality Index (GWQI) in Sharjah, UAE[END_REF][START_REF] Kumari | Evaluation of Groundwater Quality Index for Drinking Water in Tank Cascade Landscape[END_REF][START_REF] Al Aboodi | Spatial Distribution of Groundwater Quality Index in Safwan-Zubair Area[END_REF]. It considers physical, chemical, and biological water characteristics according to drinking water standards. GWQI identifies the groundwater quality using a single score [START_REF] Kayemah | Evaluation of Groundwater Quality Using Groundwater Quality Index (GWQI) in Sharjah, UAE[END_REF][START_REF] Kumari | Evaluation of Groundwater Quality Index for Drinking Water in Tank Cascade Landscape[END_REF][START_REF] Al Aboodi | Spatial Distribution of Groundwater Quality Index in Safwan-Zubair Area[END_REF]. The score can be transformed into excellent, good, satisfactory, poor, and undrinkable water. Decision-makers and end-users could easily understand this approach [START_REF] Kayemah | Evaluation of Groundwater Quality Using Groundwater Quality Index (GWQI) in Sharjah, UAE[END_REF][START_REF] Kumari | Evaluation of Groundwater Quality Index for Drinking Water in Tank Cascade Landscape[END_REF][START_REF] Al Aboodi | Spatial Distribution of Groundwater Quality Index in Safwan-Zubair Area[END_REF]. The GWQI is applied in many countries such as Mexico [START_REF] Salcedo | Application of Water Quality Index to Evaluate Groundwater Quality (Temporal and Spatial Variation) of an Intensively Exploited Aquifer (Puebla Valley, Mexico)[END_REF], Portugal [START_REF] Stigter | Application of a Groundwater Quality Index as an Assessment and Communication Tool in Agro-Environmental Policies-Two Portuguese Case Studies[END_REF], Hungary [START_REF] Mester | Assessment of Groundwater Quality Changes in the Rural Environment of the Hungarian Great Plain Based on Selected Water Quality Indicators[END_REF], India [START_REF] Selvaganapathi | Evaluation of Groundwater Quality and Water Quality Index in the Palacode and Pennagaram Taluks, Dharmapuri District, Tamil Nadu, India[END_REF] Malaysia [START_REF] Roslan | Creation of a Ground Water Quality Index for an Open Municipal Landfill Area[END_REF], Algeria [START_REF] Bouderbala | Assessment of Water Quality Index for the Groundwater in the Upper Cheliff Plain, Algeria[END_REF], Egypt [START_REF] Abdelaziz | Groundwater Quality Index Based on PCA: Wadi El-Natrun, Egypt[END_REF], and United Arab Emirates [START_REF] Kayemah | Evaluation of Groundwater Quality Using Groundwater Quality Index (GWQI) in Sharjah, UAE[END_REF].

Various methods were used to develop the water quality index (WQI) for the groundwater resources such as the Horton model of WQI [START_REF] Horton | An Index-Number System for Rating Water Quality[END_REF], Modified National Sanitation Foundation WQI [START_REF] Poonam | Water Quality Indices-Important Tools for Water Quality Assessment: A Review[END_REF], Scottish Research Development Department WQI [START_REF] Banda | A Review of the Existing Water Quality Indices (WQIs)[END_REF], Oregon WQI [START_REF] Cude | Oregon Water Quality Index: A Tool for Evaluating Water Quality Management Effectiveness[END_REF][START_REF] Dunnette | A Geographically Variable Water Quality Index Used in Oregon[END_REF], Martínez de Bascarón WQI (Banda and Kumarasamy, 2020), Bhargava's WQI [START_REF] Li | Water Quality Indices[END_REF], Canadian Council of Ministers of the Environment WQI [START_REF] Neary | Canadian Environmental Quality Guidelines[END_REF]), Liou's WQI [START_REF] Banda | A Review of the Existing Water Quality Indices (WQIs)[END_REF][START_REF] Liou | A Generalized Water Quality Index for Taiwan[END_REF], fuzzy-based WQI [START_REF] Ocampo-Duque | Water Quality Analysis in Rivers with Non-Parametric Probability Distributions and Fuzzy Inference Systems: Application to the Cauca River, Colombia[END_REF], Vaal WQI [START_REF] Banda | A Review of the Existing Water Quality Indices (WQIs)[END_REF] and universal WQI [START_REF] Boyacioglu | Utilization of the Water Quality Index Method as a Classification Tool[END_REF][START_REF] Boyacioglu | Development of a Water Quality Index Based on a European Classification Scheme[END_REF]. The weighted arithmetic water quality index (WAWQI) method has flexibility in the input parameters [START_REF] Mester | Assessment of Groundwater Quality Changes in the Rural Environment of the Hungarian Great Plain Based on Selected Water Quality Indicators[END_REF][START_REF] Banda | A Review of the Existing Water Quality Indices (WQIs)[END_REF]. It estimates the GWQI considering the most common contaminants [START_REF] Tyagi | Water Quality Assessment in Terms of Water Quality Index[END_REF].

As stated by [START_REF] Aleem | Evaluation of Groundwater Quality in the Vicinity of Khurrianwala Industrial Zone, Pakistan[END_REF], the combination of GWQI and spatial analysis has proved to be a robust tool for assessing the groundwater quality [START_REF] Alexakis | Water Quality Indices: Current and Future Trends in Evaluating Contamination of Groundwater Resources[END_REF][START_REF] Aleem | Evaluation of Groundwater Quality in the Vicinity of Khurrianwala Industrial Zone, Pakistan[END_REF]. The geographic information system (GIS) enables the use of various interpolation methods for GWQI spatial analysis, such as proximity interpolation, inverse distance weighted interpolation, and the kriging interpolation method (KIM) [START_REF] Li | Combining Spatial Analysis and a Drinking Water Quality Index to Evaluate Monitoring Data[END_REF][START_REF] Lodwick | Attribute Error and Sensitivity Analysis of Map Operations in Geographical Informations Systems: Suitability Analysis[END_REF]. Compared to other interpolation methods, the KIM method employs statistical approaches to eliminate the spatial trends in data. It also characterizes the spatial data autocorrelation by defining the optimal experimental variogram model [START_REF] Li | Combining Spatial Analysis and a Drinking Water Quality Index to Evaluate Monitoring Data[END_REF][START_REF] Lodwick | Attribute Error and Sensitivity Analysis of Map Operations in Geographical Informations Systems: Suitability Analysis[END_REF].

In the West Bank, the groundwater quality has deteriorated due to the weak sewer infrastructure, unmanaged human practices, and weak quality monitoring systems [START_REF] Almasri | Identification of the Nitrogen Sources in the Eocene Aquifer Area (Palestine)[END_REF][START_REF] Anayah | Trends and Occurrences of Nitrate in the Groundwater of the West Bank, Palestine[END_REF]. Various studies confirmed the groundwater contamination using descriptive and statistical methods [START_REF] Daghara | Quality of Drinking Water from Springs in Palestine: West Bank as a Case Study[END_REF][START_REF] Aish | Drinking Water Quality Assessment of the Middle Governorate in the Gaza Strip, Palestine[END_REF][START_REF] Anayah | Trends and Occurrences of Nitrate in the Groundwater of the West Bank, Palestine[END_REF]. However, such methods are less comprehensive than the GWQI in assessing the groundwater contamination [START_REF] Tyagi | Water Quality Assessment in Terms of Water Quality Index[END_REF]. GWQI considers the combined influence of contaminants which in turn provides decision-makers with informative results [START_REF] Tyagi | Water Quality Assessment in Terms of Water Quality Index[END_REF]. This chapter presents an adjusted WAWQI method to develop the groundwater potability (PoGWQI) and palatability (PaGWQI) indices in the West Bank. The GWQI method is used for the first time to assess the groundwater quality in the West Bank. The chapter discusses an unparalleled adjustment of the WAWQI method for developing the GWQI method. The adjusted method combines experts' opinions, step-wise assessment ratio analysis method (SWARA), and the conventional WAWQI method. The proposed approach can help decision-makers to develop strategies for protecting water resources. This section proposes a new approach for developing PoGWQI and PaGWQI (Figure 4.2). It relies on the conventional WAWQI method for developing both indices. This method has no restriction in selecting the water quality parameters included in the calculation [START_REF] Balan | An Assessment of Ground Water Quality Using Water Quality Index in Chennai Tamil Nadu, India[END_REF][START_REF] Rao | Determination of Water Quality Index of Some Areas in Guntur District Andhra Pradesh[END_REF]. It is based on the selection of the most widespread parameters that threaten water quality. The conventional WAWQI can be used according to the following steps [START_REF] Oni | The Use of Water Quality Index Method to Determine the Potability of Surface Water and Groundwater in the Vicinity of a Municipal Solid Waste Dumpsite in Nigeria[END_REF]and WHO, 2011). The first step concerns selecting the quality parameters for water potability and palatability. The second step consists of estimating the quality scores (Qn) for the water quality parameters using the following expression:

Materials and methods

Methodology

Qn = 100 ( 𝑉𝑉𝑉𝑉-𝑉𝑉𝑖𝑖 𝑉𝑉𝑉𝑉-𝑉𝑉𝑖𝑖 ) (Equ. 4.1)
where, Vn is the measured amount of the nth parameter in the analyzed water, Vi is the ideal amount nth parameter in pure water, and Vs is the standard permissible amount for the nth parameter according to the World Health Organization (WHO) (PWA, 2020;WHO, 2011;and Keršulien et al., 2010).

The third step corresponds to estimating the weights (Wn) for water quality parameters using the following formulas:

𝐾𝐾 = 1 ∑(1 𝑉𝑉𝑉𝑉) ⁄ (Equ. 4.1) 𝑊𝑊𝑊𝑊 = 𝐾𝐾 𝑉𝑉𝑉𝑉 (Equ. 4.2)
where k, is the proportionality constant.

The last concerns estimating PoGWQI and PaGWQI using the WQI formula:

𝑊𝑊𝑊𝑊𝐼𝐼 = ∑ 𝑊𝑊𝑊𝑊 𝑊𝑊𝑊𝑊 ∑ 𝑊𝑊𝑊𝑊 (Equ. 4.3)
The conventional WAWQI' weighting system uses the standard permissible parameter (Vs) to assign the importance of the water quality parameters. Since this method does not consider the impact of the water quality parameters on human health and water acceptability, it could lead to inaccurate quality indices. In some cases, WQI could show excellent water potability, while some of the water quality parameters that significantly affect water potability exceed the WHO standards. Alternatively, this research proposes the use of adjusted WAWQI (AWAWQI) method that considers experts' opinions to develop a comprehensive weighting system for the water quality parameters. The new system assigns the permeameters' weights considering the effect of quality parameters on the water potability and palatability. A close-ended questionnaire is used to assess the importance of quality parameters concerning water potability and palatability. Experts assess each parameter using the Likert scale (1, Not important, and 5, Extremely important). The questionnaire was addressed to water experts in the selected case study. The SWARA method was used to determine the weights of quality parameters (Wn′) using the following formulas [START_REF] Tyagi | Water Quality Assessment in Terms of Water Quality Index[END_REF]:

𝑉𝑉𝑠𝑠 = � 𝐴𝐴𝑠𝑠 𝑊𝑊 𝑉𝑉 𝑗𝑗 (Equ. 4.4) 𝑘𝑘𝑠𝑠 = � 1, 𝑠𝑠 = 1 𝑉𝑉𝑠𝑠 + 1, 𝑠𝑠 > 1 (Equ. 4.5) 𝑞𝑞𝑠𝑠 = � 1, 𝑠𝑠 = 1 𝑞𝑞𝑠𝑠 -1 𝑘𝑘𝑠𝑠 , 𝑠𝑠 > 1 (Equ. 4.6) 𝑊𝑊𝑊𝑊′ = 𝑞𝑞𝑠𝑠 ∑ 𝑞𝑞𝑠𝑠 𝑉𝑉 𝑘𝑘=1 (Equ. 4.7)
where Aj is the Likert scale score given by expert i n is the total number of experts.

sj, kj and qj are intermediary parameters used in the method.

Accordingly, the AWAWQI was used to develop PoGWQI and PaGWQI in this research:

𝐴𝐴𝐴𝐴𝑠𝑠𝐴𝐴𝑉𝑉𝐴𝐴𝐴𝐴𝐴𝐴 𝑊𝑊𝑊𝑊𝐼𝐼 = ∑ 𝑊𝑊𝑊𝑊 𝑊𝑊𝑊𝑊′ ∑ 𝑊𝑊𝑊𝑊′ (Equ. 4.8)
Five potability and palatability groundwater quality statuses (PoGWQS and PaGWQS) and grades (PoGWQG and PaGWQG) were identified according to [START_REF] Brown | A Water Quality Index: Do We Dare? Water Sewage Works[END_REF]. Except for grade "E", all grades were considered potable and palatable but with gradual quality (Table 4.1). The proposed weighting system considers the degree of severity of each water quality parameter on health in the local context. However, the proposed method could be combined with other methods for water quality assessment.

The proposed method is convenient for spatial water quality mapping. It could be used by decision-makers to scan the water quality at a large scale and to develop strategies for water resource protection.

Potability groundwater quality index (PoGWQI) and palatability groundwater quality index (PaGWQI) mapping

Qn values were imported into Arc GIS and then were interpolated using the KIM method, which KIM is highly recommended. In this method, the input data are biased and spatially correlated [START_REF] Li | Combining Spatial Analysis and a Drinking Water Quality Index to Evaluate Monitoring Data[END_REF]. The KIM method relies on statistics and spatial autocorrelation. Therefore, it is widely used in soil science, geology, and groundwater contamination research [START_REF] Lodwick | Attribute Error and Sensitivity Analysis of Map Operations in Geographical Informations Systems: Suitability Analysis[END_REF]. GIS-based raster calculator was used to processing the interpolated Qn and Wn′ values to develop spatiotemporal PoGWQI and PaGWQI mapping.

Water quality data from unconsidered groundwater wells are used to perform cross-validation for the resulted PoGWQI and PaGWQI maps. Such validation examines the ability and accuracy of KIM in predicting groundwater quality.

Mapping sensitivity analysis

Sensitivity of PoGWQI and PaGWQI toward the input quality parameters is performed using the map removal sensitivity approach (MRSA) [START_REF] Lam | Use of Random Forest Analysis to Quantify the Importance of the Structural Characteristics of Beta-Glucans for Prebiotic Development[END_REF][START_REF] Guio-Blanco | Spatial Prediction of Soil Water Retention in a Páramo Landscape: Methodological Insight into Machine Learning Using Random Forest[END_REF][START_REF] Kursa | Robustness of Random Forest-Based Gene Selection Methods[END_REF]. The mean absolute error (MAE) in PoGWQI and PaGWQI was calculated by removing each of their input quality parameters separately.

Causal-effect analysis

Causal-effect descriptive analysis was used to understand how PoGWQI and PaGWQI changed within the different classes of influencing factors (land-use, soil texture, aquifer, well' depths, and rainfall). Then, the relative importance of each influencing factor was estimated using the random forest (RFA) and factor importance analysis. RFA is widely applied in life science for classifying the importance of influencing factors [START_REF] Kursa | Robustness of Random Forest-Based Gene Selection Methods[END_REF]. It can characterize features' importance at all multivariate interactions included in algorithm [START_REF] Almasri | Identification of the Nitrogen Sources in the Eocene Aquifer Area (Palestine)[END_REF].

Application to the West Bank

According to various research works, the relevant water quality parameters in the West Bank concern turbidity, chloride (Cl), pH, electrical conductivity (EC), nitrate (NO3), hardness, sodium (Na), total coliform (TC), fecal coliform (FC), total dissolved solids (TDS), magnesium (Mg), calcium (Ca), potassium (K), bicarbonate (HCO3), and sulfate (SO4) [START_REF] El Baba | Evaluation of the Groundwater Quality Using the Water Quality Index and Geostatistical Analysis in the Dier Al-Balah Governorate, Gaza Strip, Palestine[END_REF][START_REF] Daghara | Quality of Drinking Water from Springs in Palestine: West Bank as a Case Study[END_REF][START_REF] Aish | Drinking Water Quality Assessment of the Middle Governorate in the Gaza Strip, Palestine[END_REF]. Due to a lack of data, this research will focus on nine parameters, including pH, HCO3, Cl, SO4, NO3, TDS, FC, hardness, and turbidity. Data was collected from the Palestinian Water Authority (PWA) database (PWA, 2020). Collected records are distributed among 79 domestic wells in the West Bank over the period 2001-2016. Quality parameters were divided into two groups according to WHO guidelines and experts' opinions [START_REF] Tyagi | Water Quality Assessment in Terms of Water Quality Index[END_REF]. The first group included parameters affecting water potability, such as FC and NO3. The second one concerned water palatability such as pH, HCO3, Cl, SO4, Hardness, TDS, and Turbidity.

Vn, Vi, and Vs of the selected water quality parameters are illustrated in Table 4.2 (PWA, 2020;WHO, 2011;and Keršulien et al., 2010). A close-ended questionnaire was addressed to 42 water-related bodies in the West Bank. These bodies are defined as the targeted population (Pop) (Table 4.3). Accordingly, the sample size for infinite population (SSIP) and the required sample size (SS) were calculated according to the Cochran formula [START_REF] Cochran | Sampling Techniques 2[END_REF].

𝑆𝑆𝑆𝑆𝐼𝐼𝑆𝑆 =

𝑍𝑍²𝑝𝑝(1 -𝑝𝑝) 𝐴𝐴² (Equ. 4.9)

𝑆𝑆𝑆𝑆 = 𝑆𝑆𝑆𝑆𝐼𝐼𝑆𝑆 1 + ( 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆-1 𝑆𝑆𝑃𝑃𝑃𝑃 ) (Equ. 4.10)
Where, Z value (given 95% confidence interval), population proportion (p) and margin of error (e) were equal to 1.96, 0.5 and 0.1 respectively. Accordingly, a SSIP of 96 and a SS of 29 were recorded. However, this questionnaire was filled out by 41 water experts from 30 waterrelated institutions to ensure more accuracy. Accordingly, and by employing SWARA, two weighting systems were developed concerning water potability and palatability (Table 4.4).

Results

Groundwater potability and palatability in the West Bank

Figure 4.3 illustrates the distribution of PoGWQGs and PaGWQGs in the West Bank. It shows that 49%, 33%, 13%, and 3% of the tested samples among all wells had A, B, C, and D-PoGWQGs, respectively. Around 2% of the tested samples were found to be unsafe for drinking (E-PoGWQG). The undrinkable samples were found in 4 wells (5.1% of all wells) in the West Bank (Figure 4.4).

Those wells are located in the northern and middle of the West Bank and have different rates of unpotable samples. Figure 4.4 shows that 67% of the samples taken from Well #1 were unpotable. Well #2, Well #3, and Well #4 have 13%, 11%, and 10% probability of providing unpotable water.

On the other hand, none of the West Bank wells had A-PaGWQG; 28%, 64%, and 3% of the tested samples had B, C, and D-PaGWQGs, respectively (Figure 4.3); 5% of unpalatable samples were recorded in two wells in the Eastern part of the study area. However, all samples (100% probability) taken from both wells were found to be unpalatable. 4.5 shows the validation results for two groundwater sources (wells) located in the middle and northern parts of the West Bank during the period 2001-2016. It is found that 14 out of the 17 PoGWQGs were correctly predicted (82.4% prediction accuracy). On the other hand, only one PaGWQG was incorrectly predicted (94.1% prediction accuracy). Figure 4.5 shows the PoGWQI and PaGWQI maps over the period 2001-2016. During the years 2001, 2007[START_REF] Boyacioglu | Utilization of the Water Quality Index Method as a Classification Tool[END_REF], 2011, 2015, and 2016, PoGWQG ranged from A, B to C. In 2003[START_REF] Susmaq | Conceptual, Steady-State and Transient-State Models of the Eocene Aquifer in the North-Eastern Aquifer Basin[END_REF], 2009, and 2012, D and E grades appeared in the far north of the West Bank. Only in 2005 did the middle part of the West Bank experience groundwater potability contamination. This spatiotemporal variation in PoGWQI refers to the flash biological groundwater contamination (mainly due to FC). Such flash contamination could be caused by a direct seepage of the wastewater (mainly from cesspits) to shallow groundwater wells. On the other hand, the southern part of the West Bank was free of groundwater potability contamination. Jenin governorate recorded the worst water potability with an E-PoGWQG of about 9%. This was followed by Tubas and Ramallah and Al Bireh, with an E-PoGWQG of about 5% and 3%, respectively. This could be related to the use of cesspits for wastewater disposal. It could also be referred to as the intensive use of agrochemicals.

PaGWQG Source #1 2003 A A B B Source #1 2004 A A C C Source #1 2005 A C B B Source #1 2007 A A B B Source #1 2009 A A C C Source #1 2010 A A C C Source #1 2011 A A B B Source #1 2012 A B B B Source #2 2001 C C C C Source #2 2003 C C C C Source #2 2004 C C C C Source #2 2005 C C C C Source #2 2007 B B C C Source #2 2010 D C C C Source #2 2011 C C C C 63 Source #2 2012 C C C B Source #2 2016 C C C C
Except for Jericho (with 100% E-PaGWQG), all governorates had zero E-PaGWQG. Generally, the palatability grades from B to D. The unpalatable water in Jericho could be caused by the saltwater intrusion from the Dead Sea. In this area, the TDS increased up to around 5300 mg/L. Results show that HCO3 was the most influencing parameter in estimating PaGWQI. This result contrasted with the experts' opinion, who considered HCO3 as the lowest influencing parameter. This high importance of HCO3 could be related to its high concentration. TDS and Cl follow it with MAE between 7-18 and 6-16 units, respectively.

Discussion

This section presents an analysis of the influence of various factors on PoGWQI and PaGWQI (e.g., land-use, soil texture, aquifer types, well depths, and the long-term average rainfall). Figure 4.8 indicates that urban areas have the worst water potability status (with 20% of E-PoGWQG) compared to the other land-use classes. This result could be attributed to the poor sewage network in this area. Around 54% of the urban areas are unserved by the sewer network (PCBS, 2021d). Most citizens rely on cesspits for wastewater disposal (PCBS, 2021d). Such cesspits cause a significant wastewater seepage to the groundwater aquifers, leading to high FC and NO3 concentrations. Agricultural areas have around 1% of E-PoGWQG. Agrochemical applications could be the main reason for groundwater contamination (Alexakis, 2020). Farmers extensively use agrochemicals to enhance their food production and to maximize their profits [START_REF] Almasri | Identification of the Nitrogen Sources in the Eocene Aquifer Area (Palestine)[END_REF]. The best water potability was recorded under rough grazing. PoGWQGs under this landuse class ranged from A (80%), B (18%) to C (2%). Residential, agricultural, and industrial humanmade activities are limited in this land-use class. The worst PaGWQGs status was observed in agricultural areas (with around 13% of E-PaGWQG). This result is due to the intensive use of agrochemical with significant SO4 and TDS concentrations. Despite the fact that clay soil has the highest water holding capacity and the lowest infiltration rate compared to other soil textures, it recorded the worst PoGWQGs. However, clay soil is the dominant soil texture in the West Bank, and it has a significant intersection with the residential and agricultural areas [START_REF] Onwuka | Multivariate Statistical Assessment and Spatial Distribution of Groundwater Contamination in Neke and Its Environs, South-Eastern Nigeria[END_REF]. By contrast, groundwater wells under sandy loam were extremely unpalatable (with E-PaGWQG of 100%). This soil texture is distributed in the Eastern part of the West Bank with direct intrusion from the Dead Sea. Consequently, the saltwater infiltrates easily to over-pumped wells [START_REF] Abd-Elhamid | Impact of Over-Pumping and Sea Level Rise on Seawater Intrusion in Gaza Aquifer (Palestine)[END_REF]. Figure 4.8 shows that almost all the unpotable water samples were found in the Eocene Aquifer (20% with E-PoGWQG). Moreover, the Alluvium Aquifer was dominated by unpalatable water (100% with E-PaGWQG). This contamination could be related to the unconfined character of the aquifers. Indeed, unconfined aquifers are vulnerable to contamination due to their direct connection with the ground surface [START_REF] Anayah | Trends and Occurrences of Nitrate in the Groundwater of the West Bank, Palestine[END_REF]. Other aquifers were almost free of water contamination.

All E-PoGWQG and E-PaGWQG samples were found in shallow wells (Below 250 m). Deeper wells were more potable and palatable. This observation indicates that well depth and groundwater contamination were negatively proportional. Various research works confirmed and discussed this negative outcome [START_REF] Anayah | Trends and Occurrences of Nitrate in the Groundwater of the West Bank, Palestine[END_REF]. On-ground contaminants are easily infiltrated and leached to the shallow wells, whereas deep wells are more protected [START_REF] Almasri | Identification of the Nitrogen Sources in the Eocene Aquifer Area (Palestine)[END_REF]. Infiltrated contaminated water follows mixing processes through its way to deep wells. Moreover, both microorganisms and soil particles degrade and filtrate contaminants before leaching into the deep wells [START_REF] Almasri | Identification of the Nitrogen Sources in the Eocene Aquifer Area (Palestine)[END_REF].

Wells with D and E-PoGWQGs were located in areas with high average annual long-term rainfall (above 400 mm). A relatively high recharge rate characterizes those areas. Therefore, contaminants are infiltrating and leaching groundwater more easily [START_REF] Almasri | Identification of the Nitrogen Sources in the Eocene Aquifer Area (Palestine)[END_REF]. Moreover, high rainfall could cause contaminants' wet deposition (e.g., NO3) that reaches the ground surface and leaches to groundwater [START_REF] Almasri | Identification of the Nitrogen Sources in the Eocene Aquifer Area (Palestine)[END_REF]. By contrast, unpalatable groundwater was found under rainfall-poor areas (mainly below 200 mm). However, those areas were located in the West Bank's eastern coastal and directly affected by the Dead Sea salinity.

The relative importance of the different factors affecting PoGWQI and PaGWQI was estimated using RFA (Figure 4.9). Results showed that factors' relative importance concerning PoGWQI deviated more compared to PaGWQI. Wells' depths and land-use had the main influence on PoGWQI with a relative importance of about 35% and 22%, respectively. This result reflects the high vulnerability of shallow wells to NO3 and FC contamination (specifically in urban areas). Both contaminants can directly reach shallow wells through wastewater seepage from cesspits.

Conclusion

An adjusted weighted arithmetic water quality index method was used to assess the groundwater quality in the West Bank. This enhances the conventional WAWQIM method by considering the experts' opinions through a close-ended questionnaire and SWARA method. AWAWQI, GIS, and KIM were combined for the first time for the spatiotemporal mapping of PoGWQI and PaGWQI. This combination enabled an in-depth analysis of the groundwater contamination, its causes, and potential consequences. Results showed that (i) around 5% of the wells in the West Bank had experienced potability-related contamination, (ii) deep wells had better PoGWQI and PaGWQI than shallow ones, (iii) water contamination was observed in areas with improper practices such as the use of cesspits, fertilizers, and pesticides. The protection of water resources in these areas requires urgent intervention to reduce (i) wastewater infiltration by installing sanitation systems, septic tanks, and sealed cesspits, and (ii) the use of chemicals and pesticides in areas impacting the groundwater resources.

The proposed method is helpful for decision-makers. It allows conducting a large-scale scan of the water quality to develop efficient water resources protection plans and to manage the water scarcity. The proposed method could be combined with other water-quality control methods to control the water quality. and the descriptive statistical analysis for the development of spatiotemporal GNC assessment. Results will be used as input for building the Machine Learning GNC prediction model. Spatiotemporal data is the core input of GNC assessment and prediction models [START_REF] Anayah | Trends and Occurrences of Nitrate in the Groundwater of the West Bank, Palestine[END_REF]. Given its ability in processing, analyzing, interpolating and visualizing spatiotemporal data, Geographic Information System (GIS) has a key role in the spatial assessment and prediction of groundwater quality [START_REF] Maqsoom | A GIS-Based DRASTIC Model and an Adjusted DRASTIC Model (DRASTICA) for Groundwater Susceptibility Assessment along the China-Pakistan Economic Corridor (CPEC) Route[END_REF]. It is widely adopted in the United States [START_REF] Schilling | A GIS-Based Groundwater Travel Time Model to Evaluate Stream Nitrate Concentration Reductions from Land Use Change[END_REF], Germany [START_REF] Rodda | The Application of a GIS-Based Decision Support System to Predict Nitrate Leaching to Groundwater in Southern Germany[END_REF]), Iran (Sheikhy Narany et al., 2014) and China [START_REF] Maqsoom | A GIS-Based DRASTIC Model and an Adjusted DRASTIC Model (DRASTICA) for Groundwater Susceptibility Assessment along the China-Pakistan Economic Corridor (CPEC) Route[END_REF].

Palestinians are facing two main water challenges: the low groundwater availability and the deterioration of its quality [START_REF] El Baba | Evaluation of the Groundwater Quality Using the Water Quality Index and Geostatistical Analysis in the Dier Al-Balah Governorate, Gaza Strip, Palestine[END_REF]and UNDP, 2013). A recent field study of the GNC in Palestine showed that the NO3 concentration in 91% of the groundwater samples exceeded the MCL [START_REF] Qrenawi | A numerical model of groundwater flow in Karewa-Alluvium aquifers of NW Indian Himalayan Region. Model[END_REF]. However, many aquifers in Palestine that are still not well characterized for recent occurrences of NO3. This chapter proposes an approach that combines GIS, statistical analysis and Machine Learning prediction models for the comprehensive management of the GNC. The application of this approach in the Eocene Aquifer, Palestine constitutes a vital issue because this aquifer accounts for 95% of the water supply in this area (PWA, 2017).

Materials and methods

Methodology

In order to outline the comprehensive management of GNC, this section introduces an unparalleled methodology. It consists of three phases: data collection and preparation, GIS-based GNC assessment, and Machine Learning-based GNC prediction (Figure 5.1).

Data collection

This phase involves the collection of spatiotemporal NO3 records for the groundwater wells in the study area. It also involves a gathering of spatial information concerning well uses, well depths, soil types, land uses, surrounding anthropogenic activities, and watersheds.

GIS-based GNC assessment

GIS and statistical descriptive analysis treat the collected records to carry out a geo-statistical assessment of GNC. GIS has the powerful of considering the spatial variations concerning GNC and considering the factors causing such variations. Statistical descriptive analysis enables the frequency detection of any abnormal event (e.g. NO3 above MCL). Boxplots of the NO3 concentration (e.g. minimum, 1 st quartile (Q1), median, mean, 3 rd quartile (Q3), maximum) were used to present the assessment results. Factors affecting GNC are specified through the examination of various factors such as land use, soil type, watershed, groundwater flow direction, well use, well depth and etc.

Kriging Interpolation Method (KIM) employs assessment results (e.g. spatiotemporal mean GNC records) to develop GIS-based spatiotemporal GNC maps. KIM has the powerful of considering both spatial autocorrelation and statistical models (Judeh et al., 2021;[START_REF] Lodwick | Attribute Error and Sensitivity Analysis of Map Operations in Geographical Informations Systems: Suitability Analysis[END_REF]. It is highly effective in cases where quality records are biased and spatially correlated [START_REF] Li | Combining Spatial Analysis and a Drinking Water Quality Index to Evaluate Monitoring Data[END_REF]. Therefore, researchers confirm its suitability for groundwater contamination and hydrogeology researches [START_REF] Lodwick | Attribute Error and Sensitivity Analysis of Map Operations in Geographical Informations Systems: Suitability Analysis[END_REF].

Machine Learning-based GNC prediction

This research employs RFA for predicting the probability to exceed the globally stated NO3 MCL thresholds. RFA is selected among other Machine Learning methods due to its ability and collectivity in integrating multiple decision tree algorithms [START_REF] Rodriguez-Galiano | Predictive Modeling of Groundwater Nitrate Pollution Using Random Forest and Multisource Variables Related to Intrinsic and Specific Vulnerability: A Case Study in an Agricultural Setting (Southern Spain)[END_REF]. It has the ability to generate repeated predictions of the same phenomenon [START_REF] Rodriguez-Galiano | Predictive Modeling of Groundwater Nitrate Pollution Using Random Forest and Multisource Variables Related to Intrinsic and Specific Vulnerability: A Case Study in an Agricultural Setting (Southern Spain)[END_REF]. Furthermore, it can quantify the relative importance of the input influencing factors [START_REF] Breiman | Random Forests[END_REF].

RFA model is built through the online platform Kaggle. GNC distribution and its influencing factors resulting from GNC assessment are used as inputs for RFA prediction model. These factors include both natural and man-made ones. Accordingly, a CSV file includes both assessment results (influencing factors) and the NO3 records from the different groundwater wells is executed using Kaggle platform. After that, data analysis, visualization and prediction were coded on the platform using Python Script. The coded prediction model is trained using randomly picked 80% of the database. The remaining 20% are used to test the prediction accuracy.

Study area

The Eocene Aquifer is an unconfined aquifer located in the northern part of the West Bank, Palestine. It has a total surface area of 430 km 2 covering areas from Jenin, Nablus and Tubas governorates (Figure 5.2) [START_REF] Khader | A Decision Tree Model to Estimate the Value of Information Provided by a Groundwater Quality Monitoring Network[END_REF][START_REF] Susmaq | Conceptual, Steady-State and Transient-State Models of the Eocene Aquifer in the North-Eastern Aquifer Basin[END_REF]. It serves 36 communities with a total population of about 225,000 capita (PCBS, 2020c). The groundwater flows from the south to the north and northeast [START_REF] Tubeileh | Modeling the Eocene Aquifer in Northern West Bank[END_REF]. Rainfall plays a major role in recharging the aquifer. The annual rainfall varies between 400 mm and 600 mm and mainly occurs in winter [START_REF] Shadeed | Developing a GIS-Based Water Poverty and Rainwater Harvesting Suitability Maps for Domestic Use in the Dead Sea Region (West Bank, Palestine)[END_REF][START_REF] Shadeed | Spatio-Temporal Drought Analysis in Arid and Semi-Arid Regions: A Case Study from Palestine[END_REF]. The West Bank climate is classified as hot and dry during the summer and cool and wet in winter [START_REF] Shadeed | Rainwater Harvesting for Sustainable Agriculture in High Water-Poor Areas in the West Bank, Palestine[END_REF].

Figure 5.2. The geographic setting of the Eocene Aquifer

The study area is characterized by intensive agricultural activities with an extensive use of agrochemicals [START_REF] Almasri | Identification of the Nitrogen Sources in the Eocene Aquifer Area (Palestine)[END_REF]. It includes about 17,000 hectares of irrigable areas including 6,500 hectares of irrigated area [START_REF] Nofal | Geo-Database of the Ministry of Agriculture; Personal Communication[END_REF]. The aquifer was exploited by 129 wells including 54 active ones (45 for agricultural and nine for domestic uses) [START_REF] Nofal | Geo-Database of the Ministry of Agriculture; Personal Communication[END_REF]. 43 out of the 54 active wells are distributed among 12 communities. Table 5.1 summarizes the statistical descriptive parameters of the groundwater NO3 in the 12 communities. Arrana, Jenin city and Ras Al-Far'a (54% of the population) have mean and median NO3 concentrations exceeding the MCL.

Furthermore, seven out of the 12 communities (82% of the population) use groundwater wells that have a maximum NO3 concentration exceeding the MCL. The ground surface elevation in the study area ranges from 100 m above mean sea level (MSL) in the north to 925 MSL in the south. The geologic cross section for the aquifer shows the following formations; limestone, dolomite and marl of Cenomanian to Turonian age, chalk and chert of Senonian age, chalk, limestone and chert of Eocene age and alluvium of Pleistocene to Recent age. However, the Eocene Aquifer overlies the Upper Cenomanian-Turnoian Aquifer, with a transition zone of chalk and chert that varies in thickness between 20 to 480 m (SUSMAQ, 2004) (Figure 5.3). Accordingly, GNC assessment and mapping in the study area is conducted. Considering RFA model, four levels of NO3 contamination are identified (Table 5.2). Levels are selected considering both: WHO guidelines, and the natural breaks of the continuous NO3 records in the database (WHO, 2003). Figure 5.5 shows the spatiotemporal distribution of GNC in the Eocene Aquifer between 1982 and 2019. A significant increase in the GNC is noticed in the southern part of the aquifer. This is due to the intensive application of agrochemicals in this part, which provides citizens in the study area by the needed fruits and vegetables. On the other hand, the middle part shows a decreasing GNC trend. This is due to the construction and rehabilitation of sewer network in Jenin city and its suburbs.

Influence of the soil type

The soil map of the Eocene Aquifer area contains five types of soil: Terra Rossa, Mediterranean Brown Forest, Alluvial, Colluvial-Alluvial and Brown Alluvial covering [START_REF] Barkat | Assessment of Complex Terminal Groundwater Aquifer for Different Use of Oued Souf Valley (Algeria) Using Multivariate Statistical Methods, Geostatistical Modeling, and Water Quality Index[END_REF][START_REF] Akbari | Assessing early warning for desertification hazard based on E-SMART indicators in arid regions of northeastern Iran[END_REF][START_REF] Ahmad | System dynamics modeling of reservoir operations for flood management[END_REF][START_REF] Ahmad | Groundwater Contamination Issues in Chiniot Area, Punjab, Pakistan[END_REF] and 7% of the study area, respectively [START_REF] Nofal | Geo-Database of the Ministry of Agriculture; Personal Communication[END_REF].

Influence of well depth

Generally, there is an inverse relationship between well depths and NO3 concentration [START_REF] Anayah | Trends and Occurrences of Nitrate in the Groundwater of the West Bank, Palestine[END_REF][START_REF] Tesoriero | Vulnerability of Arid and Semi-Arid Regions to Climate Change-Impacts and Adaptive Strategies: Perspectives on water and climate change adaptation[END_REF][START_REF] Hallberg | Nitrate in Ground Water in the United States[END_REF]and Freeze et al., 1979). Figure 5.8a shows the variation of the statistical parameters with the well depth. It clearly indicates high NO3 concentrations in the first 100 m of the aquifer. After that, NO3 concentration significantly decreases with depth and becomes insignificant below 300 m. This NO3 profile could be attributed to the following factors: denitrification in groundwater, groundwater movement and the associated NO3 transport and mixing.

The NO3 concentration is also influenced by other factors such as land use, groundwater recharge and fertilization practices. To reduce the influence of these factors, analysis was conducted on samples from five adjacent wells (distributed among an area of 2.37 km 2 ) in Qabatiya. Figure 5.8b illustrates the variation of concentrations with depth for these samples. It clearly shows that the NO3 concentration decreases with the increase in depth. The groundwater wells are used for domestic and agricultural activities. Figure 5.9a shows that the agricultural wells have higher median (49 mg/l), mean (59 mg/l), 75 th percentile (71 mg/l) and maximum (256 mg/l) values as compared to the domestic wells. The domestic wells have NO3 concentrations in the range of 0 to 49 mg/l with a median, mean and 75 th percentile that equal 16, 18 and 25 mg/l, respectively. The intensive use of agrochemicals in agriculture and cesspits for wastewater disposal are the main source of on-ground nitrogen loading in the study area [START_REF] Canion | Predictive Modeling of Elevated Groundwater Nitrate in a Karstic Spring-Contributing Area Using Random Forests and Regression-Kriging[END_REF]. Collected data was used to investigate the influence of these factors on GNC. Practices in the proximity of the sampled wells were classified into four categories: extensive presence of cesspits, extensive application of fertilizers, extensive use of cesspits and fertilizers, and neither cesspits nor fertilizers. Figure 5.9b illustrates the statistics of NO3 concentrations for the four practices. It shows that the highest concentrations are located in the category "extensive use of cesspits and fertilizers", followed by the category "extensive use of cesspits" and then the category "extensive use of fertilizers". GNC Assessment outcomes are integrated with the Machine Learning-based GNC prediction model. They provided the prediction model by the GNC distribution as well as by the main factors influencing this distribution. Such information is used as an input for the development of the prediction model. Generally, assessment results indicated that except soil type, all factors (well depth, well use, land use, watershed, and fertilization and wastewater disposal practices) can be used as inputs for the Machine Learning prediction model. RFA model was built considering these factors and executed 10 times. Table 5.4 shows that the obtained accuracy scores range between 83.3% and 91.7% with an average value of 88.5%. 6 91.7 91.7 83.3 85.4 87.5 89.6 91.6 83.3 91.7 88.5 Table 5.5 shows the RFA confusion matrix for the best trial (accuracy = 91.7). It indicates two types of errors: safe error (error of predicting worse than contamination situation) and risk error (error of predicting less than contamination level). The safe error equals 2.08%, while the risk error equals 6.22%. Figure 5.10 shows the distribution of wells with correct, safe error and risk error prediction. It is noticed that the false predicted wells are not spatially correlated. They are distributed amongst the northern, middle and southern parts of the study area. Table 5.6 illustrates that both errors in the false predicted wells are occurred on the margins of the thresholds between two successive contamination levels. Actual and predicted GNC in wells 1 and 2 are close to the threshold of 25 mg/l. Moreover, actual and predicted values are around the threshold of 50 mg/l in wells 3 and 4. This reflects the low severity of the prediction model errors. 5.7 shows the relative importance of the input factors used in the RFA prediction model. It is obvious that the well depth (with 41% relative importance) is the most influencing factor, followed by anthropogenic-related factors. This reflects the high vulnerability of shallow groundwater wells to GNC. It also highlights the severity of uncontrolled man-made practices (land use, and fertilization and wastewater disposal method) upon the GNC. On the other hand, watershed and well use have the lowest influence with relative influence of 14% and 7% respectively.

There is a difficulty in controlling the depths of existing and new wells due to political restrictions. Therefore, management options should be directed toward the anthropogenic-related factors. In which, the control of intensive use of fertilizers and cesspits are the most important ones. Decision makers are advised to embrace the land use planning as a strategic tool for mitigating GNC in the study area. Protection zones for groundwater wells have to be delineated and sources of GNC have to be prohibited in those zones.

Conclusion

This chapter presented a new approach that combines GIS, statistics and Machine Learning to assess and predict GNC in the Eocene Aquifer, Palestine. The integration of GIS and statistical analysis led to a comprehensive spatiotemporal assessment of GNC. This assessment provided the RFA model by the input factors. Assessment indicated serious NO3 contaminations of the Aquifer. GIS maps indicated an increasing GNC trends in the southern part of the study area. Results showed the necessity to use five factors (well depth, well use, land use, watershed, and fertilization and wastewater disposal practices) in the RFA predictive model of the GNC. RFA model successfully attained a maximum and average prediction accuracy of 91.70% and 88.54%, respectively.

Decisions makers could use the approach presented in this chapter to establish a knowledgebased approach for the sustainable management of the groundwater quality in Palestine. Collaboration is crucial between different stakeholders to establish groundwater monitoring programs to support the sustainable use of the groundwater through the control of agrochemicals and cesspits.

Chapter 6. Rainwater Harvesting to Address Water

Scarcity in Arid and Semi-Arid Areas

Introduction

This chapter aims to analyze the capacity of rooftops rainwater harvesting (RWH) in addressing the domestic water scarcity in arid and semi-arid areas. RWH is commonly adopted to support the insufficient conventional water resources [START_REF] Abu-Zreig | Assessment of Rooftop Rainwater Harvesting in Northern Jordan[END_REF]. It is widely used in Pakistan [START_REF] Abbas | Assessing the Potential for Rooftop Rainwater Harvesting and Its Physio and Socioeconomic Impacts, Rawal Watershed, Islamabad, Pakistan[END_REF], Kenya [START_REF] Mundia | Assessing the Reliability of Rooftop Rainwater Harvesting for Domestic Use in Western Kenya[END_REF], East Africa [START_REF] Bernard | Contribution of Rainfall on Rooftop Rainwater Harvesting and Saving on the Slopes of Mt. Elgon, East Africa[END_REF], Ghana [START_REF] Boakye | Quantifying Rooftop Rainwater Harvest Potential: Case of Takoradi Polytechnic in Takoradi, Ghana[END_REF], Afghanistan [START_REF] Rahimi | Rooftop Rainwater Harvesting and its Efficiency in Kabul New City[END_REF], Zambia [START_REF] Malambo | Rooftop Rainwater Harvesting as an Alternative Domestic Water Resource in Zambia[END_REF], Sri Lanka [START_REF] Sendanayake | Potential for Domestic Rooftop Rainwater Harvesting in the District of Colombo, Sri Lanka[END_REF], Bangladesh [START_REF] Biswas | Construction and Evaluation of Rainwater Harvesting System for Domestic Use in a Remote and Rural Area of Khulna, Bangladesh[END_REF], Iran [START_REF] Sheikh | Perception of Domestic Rainwater Harvesting by Iranian Citizens[END_REF], Egypt [START_REF] Gado | Feasibility of Rainwater Harvesting for Sustainable Water Management in Urban Areas of Egypt[END_REF][START_REF] Gado | Feasibility of Rainwater Harvesting for Sustainable Water Management in Urban Areas of Egypt[END_REF]Jordan (Abu-Zreig et al., 2019;[START_REF] Abdulla | Roof Rainwater Harvesting Systems for Household Water Supply in Jordan[END_REF]. RWH efficiency depends on many factors, including rainfall volume, patterns and distribution, catchment area, and catchment type [START_REF] Gebreyess | Water Harvesting Technologies in Semi-Arid and Arid Areas[END_REF]and Ffolliott et al., 2014).

A sustainable water resources management should consider the climate change and demographic growth [START_REF] Menzel | GLOWA Jordan River Phase II Final Report: An Integrated Approach to Sustainable Management of Water Resources Under Global Change[END_REF]. This chapter contributes to this objective through (i) investigating the ability of the potential RWH in addressing the current domestic water scarcity, (ii) analyzing the role of RWH in addressing the future water scarcity relate to the demographic and climate changes. The chapter is organized as follows. First, it presents the research methodology, including the estimation of the potential RWH. Then, it describes its application on a case study. Finally, it discusses the role of the RWH in addressing the domestic water scarcity in the West Bank, Palestine.

Materials and methods

Methodology

The methodology adopted in this research is illustrated in Figure 6.1. Geographic Information System (GIS) is used to map the rainfall and rooftops spatial distribution in 2020. A Global Climate Model (GCM) (called European Centre Hamburg Model, Version 4) estimates the spatial rainfall distribution in 2050 [START_REF] Menzel | GLOWA Jordan River Phase II Final Report: An Integrated Approach to Sustainable Management of Water Resources Under Global Change[END_REF]. This model is downscaled up to 18 x 18 km 2 resolution using a Regional Climate Model (Mesoscale Model, Version 5). These models were developed and validated by GLOWA [START_REF] Menzel | GLOWA Jordan River Phase II Final Report: An Integrated Approach to Sustainable Management of Water Resources Under Global Change[END_REF]. The projected (2050) rooftops distribution is estimated assuming a linear relationship with the population growth in the study area.

Gould and Nissen-Petersen's equation along with GIS employ the current and projected rainfall and rooftops to estimate the potential RWH [START_REF] Gould | Rainwater Catchment Systems for Domestic Supply: Design, Construction and Implementation[END_REF]. In addition, this equation considers the RWH collection efficiency and runoff rate [START_REF] Gould | Rainwater Catchment Systems for Domestic Supply: Design, Construction and Implementation[END_REF]). An average runoff and collection efficiency coefficient (C) of about 0.9 is suggested by several scholars (See equ. 6.1) [START_REF] Alawna | Rooftop Rainwater Harvesting to Alleviate Domestic Water Shortage in the West Bank, Palestine[END_REF][START_REF] Farreny | Roof Selection for Rainwater Harvesting: Quantity and Quality Assessments in Spain[END_REF][START_REF] Abdulla | Roof Rainwater Harvesting Systems for Household Water Supply in Jordan[END_REF].

RWH i = ∑ 𝐶𝐶 * 𝑅𝑅𝐹𝐹 𝑖𝑖𝑗𝑗 * 𝐴𝐴 𝑖𝑖𝑗𝑗 𝑉𝑉 𝑖𝑖=1 (Equ. 6.1)
Where, RWHi: potential RWH volume for the i th year in (m³/year) RFij: average rainfall for the j th rooftop in the i th year in (m/year) Aij: area of the j th rooftop in the i th year in (m²/year) n: number of rooftops

The current and projected RWH is integrated with the total water supply (TWS) (calculated in chapter 3) in the years 2020 and 2050, respectively. Then, current and projected supply demand balance index (SDBI) and the associated water scarcity levels (presented in chapter 3) are reestimated by considering RWH. The proposed methodology is applied to the West Bank, Palestine which is located to the west of the Dead Sea (detailed description of the study area is previously presented in Section 2.2). Data are collected from different sources. The building rooftops shapefile in 2020 is obtained from the Ministry of Local Government (MoLG) (MoLG, 2021). The projected rooftop areas in 2050 are estimated, assuming a linear relation with the population growth (this population growth is estimated in Chapter 3). Table 6.1 summarizes the current and projected rooftop areas for the eleven West Bank governorates. The authors are aware of the limitation of this assumption, which could be improved in the future. Current rainfall for the West Bank are obtained from the Palestinian Metrological Authority (PMA) (PMA, 2018). PMA has 75 spatially distributed rainfall stations in the West Bank governorates (Figure 6.2). Table 6.2 summarizes the number of rainfall satiations per km² (for each governorate). It is noticed that the station resolution (density) ranges from 0.1 (in Hebron and Jerusalem) to 0.8 (in Salfit) station per km². .3 shows a GIS-based spatial interpolation for average annual rainfall from the spatially distributed rainfall stations. This interpolation is realized at a scale of 100*100 m 2 . It is indicated that the average annual rainfall (in 2020) ranges from 166 mm/year (in the eastern part) to 662 mm/year (in the middle part), with a mean value of 520 mm/year. Table 6.3 provides the average annual rainfall for the different West Bank governorates in 2020. However, the used rainfall data could form a source of uncertainty in the rainfall-dependent models (e.g. climate change models for rainfall and water availability) [START_REF] Fraga | Effect of Rainfall Uncertainty on the Performance of Physically Based Rainfall-Runoff Models[END_REF][START_REF] Muñoz | Uncertainty in Rainfall Input Data in a Conceptual Water Balance Model: Effects on Outputs and Implications for Predictability[END_REF]. Scholars confirm the sensitivity of rainfall-dependent models to rainfall uncertainty [START_REF] Fraga | Effect of Rainfall Uncertainty on the Performance of Physically Based Rainfall-Runoff Models[END_REF][START_REF] Muñoz | Uncertainty in Rainfall Input Data in a Conceptual Water Balance Model: Effects on Outputs and Implications for Predictability[END_REF]. The highest sensitivity occurs in the rainy periods (e.g. October to March). In dry periods with few rainfall amount, the rainfall-dependent models are less sensitive to rainfall uncertainties [START_REF] Fraga | Effect of Rainfall Uncertainty on the Performance of Physically Based Rainfall-Runoff Models[END_REF][START_REF] Muñoz | Uncertainty in Rainfall Input Data in a Conceptual Water Balance Model: Effects on Outputs and Implications for Predictability[END_REF]. Such uncertainty could be addressed by performing a cross validation for the rainfall records using different approaches [START_REF] Fraga | Effect of Rainfall Uncertainty on the Performance of Physically Based Rainfall-Runoff Models[END_REF][START_REF] Muñoz | Uncertainty in Rainfall Input Data in a Conceptual Water Balance Model: Effects on Outputs and Implications for Predictability[END_REF]. The rate of change in rainfall between 2020 and 2050 are estimated using the downscaled GCM models developed by GLOWA [START_REF] Menzel | GLOWA Jordan River Phase II Final Report: An Integrated Approach to Sustainable Management of Water Resources Under Global Change[END_REF]. The GIS-based Raster Calculator is then used to estimate the values of rainfall in 2050. Figure 6.3 illustrates the distribution of rainfall in the West Bank. It indicates that all the governorates, except Jenin and Nablus, will experience in 2050 a decrease in rainfall volumes (up to 15%) comparing to the year 2020.

RWH can cover around 54% of the total water semand (TWD) in Salfit and between 40 and 48% in Nablus,Bethlehem,Qalqilya,Ramallah,and Tulkarm governorates. Tables 6.6 summarizes the impact of using four rooftops rates in the RWH (25%, 50%, 75%, and 100%) on domestic water scarcity. It shows that full or partial use of the RWH will cover the TWS in Tubas, Qalqilya, and Jericho. However, the status of 'No shortage" in Slafit requires a full adoption of the RWH. The latter is not sufficient to afford the TWS in Ramallah & Al-Bireh, and Hebron. This result argues for a national water strategy that shares the RWH capacities. Results confirm the findings of [START_REF] Alawna | Rooftop Rainwater Harvesting to Alleviate Domestic Water Shortage in the West Bank, Palestine[END_REF] and [START_REF] Shadeed | Developing a GIS-Based Water Poverty and Rainwater Harvesting Suitability Maps for Domestic Use in the Dead Sea Region (West Bank, Palestine)[END_REF] concerning the potential of RWH in the different West Bank governorates. However, [START_REF] Shadeed | Developing a GIS-Based Water Poverty and Rainwater Harvesting Suitability Maps for Domestic Use in the Dead Sea Region (West Bank, Palestine)[END_REF] focused only on the optimal allocation of the RWH in the West Bank, while [START_REF] Alawna | Rooftop Rainwater Harvesting to Alleviate Domestic Water Shortage in the West Bank, Palestine[END_REF] focused on estimating potential RWH volume. Other scholars treated separately the allocation and the estimation of potential RWH [START_REF] Ranaee | Feasibility of Rainwater Harvesting and Consumption in a Middle Eastern Semiarid Urban Area[END_REF][START_REF] Hashim | Detection of Suitable Sites for Rainwater Harvesting Planning in an Arid Region Using Geographic Information System[END_REF][START_REF] Alwan | Potential Water Harvesting Sites Identification Using Spatial Multi-Criteria Evaluation in Maysan Province, Iraq[END_REF]). This research contributed to the analysis of the capacity of the potential RWH to cover water scarcity by quantifying the influence of RWH on the current and future domestic water scarcity.

RWH efficiency at different urban scales

This section discusses the RWH efficiency at different urban scales (city, village, and camp), emphasizing the Jenin governorate in 2020. Figure 6.5 shows that the full adoption of RWH in Jenin City can change the water scarcity status from "acute water shortage" to "no water shortage". Furthermore, this full adoption can change the water scarcity status in (i) Jenin Camp from "acute water shortage" to "moderate water shortage" and (ii) in villages from "extreme water shortage" to "acute water shortage". These results indicate that the lowest RWH efficiency concerns the Jenin camp. The variation of the efficiency of the potential RWH is related to the ratio of the rooftops to the population. This ratio equals 16 m 2 /citizen in the camp compared to 29 m 2 /citizen and 48 m 2 /citizen in the villages and the city, respectively. Exploiting 65% of the rooftops is enough to cover the TWD in Jenin city. Therefore, full roof exploitation in this city could help mitigate or even eliminate the domestic water scarcity in the camp and villages. This section discusses the capacity of the adoption of RWH to address the projected domestic water scarcity in 2050. The projected RWH is expected to provide around 59 MCM/y in 2050, which is 1.5 times the projected municipal water supply. Table 6.7 summarizes the influence of the RWH on both TWS and TWD. Full adoption of the RWH is expected to increase the values of TWS from 20% (in Jericho) to 1038% (in Ramallah and Al-Bireh). This full adoption can satisfy half of the needed TWD in Salfit, and between 13 and 48% in other West Bank governorates. 6.8 summarizes the impact of projected RWH on water scarcity in the West Bank in 2050. Tulkarm and Qalqilya could reach "no water shortage" status. A "slight water shortage" could be reached in Jericho and Nablus by exploiting 50% and 100% of the roofs, respectively. Salfit, Tubas, and Jenin are expected to reach a (moderate water shortage) status by exploiting 91 50%, 50%, and 100% of their roofs. In other governorates, full adoption of the RWH will not be enough to cover the domestic water needs. A full RWH adoption efficiently addresses the domestic water scarcity challenges in Ramallah and Al-Bireh, Bethlehem and, Hebron. For these governorates, the domestic water scarcity will be improved from "extreme water shortage" to "acute water shortage". The best improvement from "extreme water shortage" to "moderate water shortage" is expected in Jenin. Likewise, a reasonable improvement from "moderate water shortage" to "no water shortage" is expected in Tulkarm and Qalqilya.

Conclusion

This chapter presented an analysis of the capacity of the rainwater harvesting in addressing the domestic water scarcity in arid and semi-arid areas. Results show that the RWH can address the current domestic water scarcity in the majority of the West Bank governorates. A national policy to share the potential RWH capacity will increase the efficiency of the RWH by a share of the excess water harvesting among the West Bank governorates. An estimation of the potential RWRH capacity in 2050 shows that it could cover about 1.5 times the conventional water supply. However, this research is subjected to the following limitations: (i) lack of accurate rooftops projection model in 2050, (ii) absence of detailed information about the roof types (e.g., concrete, clay-tiles, steel, etc.), which affect the RWH efficiency, and (iii) the absence of local climate change models for predicting future rainfall and water resources availability. This research attempted to overcome these limitations by considering (i) a proportional relationship between the projected population and rooftops, (ii) an average runoff coefficient and collection efficiency of 0.9 as suggested in the literature. This research could be improved in the future by considering (i) a local rooftop model, (ii) a local climate change model to estimate the projected rainfall and water availability, and (iii) monitoring the water demand, water supply, rainfall intensity, and the rooftops water harvesting. single-aspect upgrade [START_REF] Ranjan | The Internet of Things (IOT) Based Smart Rain Water Harvesting System[END_REF][START_REF] Behzadian | Can Smart Rainwater Harvesting Schemes Result in the Improved Performance of Integrated Urban Water Systems?[END_REF] on either water quantity or water quality. [START_REF] Behzadian | Can Smart Rainwater Harvesting Schemes Result in the Improved Performance of Integrated Urban Water Systems?[END_REF] discussed the use of Internet of Things (IoT-based sensors) to control the water level in the RWH tanks. This use secures sufficient spare in the tank to receive the runoff following storm events. It has been found that adopting the water-level monitoring in RWH tanks can pointedly mitigate urban flooding and secure non-potable water supply [START_REF] Behzadian | Can Smart Rainwater Harvesting Schemes Result in the Improved Performance of Integrated Urban Water Systems?[END_REF]. [START_REF] Ranjan | The Internet of Things (IOT) Based Smart Rain Water Harvesting System[END_REF] discussed using an IoT-based water quality sensor to control the harvested water quality. The sensor helps diverting the harvested water (based on its pH value) into two storage tanks: potable and non-potable tanks. Harvested water in the tanks is then directed to appropriate uses (e.g. drinking and irrigation). However, the use of pH to control the harvested water potability is controversial. The World Health Organization (WHO) stated the vulnerability of RWH to physical (e.g. Turbidity), chemical (e.g. Nitrate, Lead, and Zinc), and biological contamination (e.g. Coliform) [START_REF] Frichot | Maximizing Land Cover Classification Accuracies Produced by Decision Trees at Continental to Global Scales[END_REF]and WHO, 2011). The type of contamination depends on different factors including (i) the RWH surrounding activities (e.g. urban, industrial, and agricultural activities) and (ii) roofs, pipes, and storage tanks materials [START_REF] Frichot | Maximizing Land Cover Classification Accuracies Produced by Decision Trees at Continental to Global Scales[END_REF]and WHO, 2011).

Considering the challenges of the water harvesting and the limitations of the conventional water harvesting system, this chapter proposes an innovative smart water harvesting system that combines (i) water tank sharing at the neighborhood level, (ii) a dual water supply system, and (iii) a comprehensive smart monitoring of the water quality, water level and the water leakage in the system. Moreover, the chapter investigates the ability of the proposed system in addressing the domestic water scarcity.

Materials and methods

Methodology

The methodology adopted in this research involves four phases, as shown in Figure 7.1. The first phase targets the characterization of the potential causes of RWH contamination and insufficiency. Phase two employs the characterization outputs to design the smart RWH/dual system architecture. Phase three aims at performing a reliability analysis of the smart system performance. The last phase aims at investigating the impact of proposed system to the alleviation of water scarcity conditions.

. Sources for RWH contamination and inefficiency

Although RWH is less vulnerable to contamination than surface and groundwater resources, it could be exposed to contamination sources [START_REF] Wu | Quality Assessment of Rainwater and Harvested Rainwater Stored in Different Types of Cisterns[END_REF][START_REF] Mosley | Report 579: Water Quality of Rainwater Harvesting Systems[END_REF]. The first source is the atmosphere, as the rain droplets could absorb the air contaminants such as Nitrite, Carbon Dioxide, and Sulfate [START_REF] Osayemwenre | A Review of Health Hazards Associated with Rainwater Harvested from Green, Conventional and Photovoltaic Rooftops[END_REF]. It could also acquire heavy metals due to industrial emissions (e.g. Lead, Zinc, Copper, and Cadmium) [START_REF] Chubaka | Zinc, Copper, and Cadmium Content of Water from South Australian Rainwater Tanks[END_REF]. The second source is the rooftop materials (Osayemwenre and Osibote, 2021) (e.g. lead-based roofs) which are classified as a hotspot for various toxins [START_REF] Abbasi | Sources of Pollution in Rooftop Rainwater Harvesting Systems and Their Control[END_REF]. The third source is the wastes (e.g., fecal material and leaves) originated by the creatures (e.g. birds) and trees and settled on the roofs [START_REF] Abbasi | Sources of Pollution in Rooftop Rainwater Harvesting Systems and Their Control[END_REF]. These sources could cause physical, chemical, and biological contamination of the RWH [START_REF] Alim | Suitability of Roof Harvested Rainwater for Potential Potable Water Production: A Scoping Review[END_REF][START_REF] Vilane | An Assessment of the Quality of Rainwater Harvested Using Rooftop Rainwater Harvesting[END_REF]. The type of RWH contamination differs spatially [START_REF] Vilane | An Assessment of the Quality of Rainwater Harvested Using Rooftop Rainwater Harvesting[END_REF][START_REF] Tamimi | Rainwater Harvesting System: Quality and Impacts on Public Health[END_REF]. It depends on the surrounding activities that pollute the roofs and air [START_REF] Vilane | An Assessment of the Quality of Rainwater Harvested Using Rooftop Rainwater Harvesting[END_REF][START_REF] Tamimi | Rainwater Harvesting System: Quality and Impacts on Public Health[END_REF]. Monitoring and implementing treatment units for these contaminations are complex, costly, and time-consuming [START_REF] Ighalo | A Comprehensive Review of Water Quality Monitoring and Assessment in Nigeria[END_REF][START_REF] Hasan | Water Quality Indices for Rainwater Quality Assessment in Bandung Urban Region[END_REF]. Thus, characterizing the probable contaminants and their sources is a core step in constructing an efficient and financially feasible quality monitoring system. This research characterizes the RWH contamination by considering: (i) the spatial sampling of the RWH from different locations, (ii) the laboratory analysis of the collected samples, and (iii) the literature review (Figure 7.2).

Figure 7.2. Characterization of RWH contamination

The inefficiency of RWH systems in providing a reasonable water supply volume could be attributed to un-controllable and controllable factors [START_REF] Das | A Case Study of Rainwater Harvesting: Its Desing, Factors Affecting and Cost Installation of AIIMS Hospital, Raipur[END_REF][START_REF] Abu-Zreig | Assessment of Rooftop Rainwater Harvesting in Northern Jordan[END_REF]. The uncontrollable factors include the rainfall volumes and patterns and the roof area (Judeh and Shahrour, 2021). The controllable ones imply the proper sizing of the RWH storage tank, the leak in the RWH system, the efficiency of the RWH system components (e.g. pumps and valves), and the roof type (Judeh and Shahrour, 2021;[START_REF] Das | A Case Study of Rainwater Harvesting: Its Desing, Factors Affecting and Cost Installation of AIIMS Hospital, Raipur[END_REF][START_REF] Abu-Zreig | Assessment of Rooftop Rainwater Harvesting in Northern Jordan[END_REF]. Therefore, controlling and monitoring these controllable components is the key to an efficient RWH system.

Smart RWH system architecture

The smart RWH system is used to ensure (i) early detection of water contamination and water leak, (ii) the control of water contamination and water leak, (iii) satisfying the needed domestic water demand, and (iv) the wise and sustainable use of available water resources/supply. In addition, the system ensures (i) data collection, (ii) interaction with users, and (iii) the control of the smart system' equipment such as valves and pumps.

The architecture of the smart RWH system involves six layers, as illustrated in Figure 7.3. The physical layer includes the physical components of the water harvesting system, the municipal water supply, and the users. The monitoring layer includes sensors used to monitor the water quality, water flow, and the water level in the water tank. The data transfer layer uses wireless technology for data transmission from the monitoring system to the server. The data processing layer operates data cleaning, storage, analysis, and visualization. The control layer includes actuators that control the water flow, such as pumps and valves. Finally, the smart services include the detection of water contamination or water leak and the optimal tank filling. The water supply, leak, and contamination are influenced by the building's distribution, citizens' density, and the water distribution systems (Rojek and Studzinski, 2019;[START_REF] Mizuki | Intelligent Water System for Smart Cities[END_REF]. Therefore, the smart water supply system is designed to consider the physical components (Figure 7.4), which could be organized in three groups. The first involves the building roofs, gutters, shared RWH tank, household water tank, RWH distribution network (from roofs to shared RWH tank and from shared RWH tank to household water tanks), valves, overflow pipe, pump, backflow preventer, municipal water supply, and control panel. The second group involves the inlet filter, first flush diverter, RWH tank screen, over-flow pipe screen, treatment unit, discharge pipe, and flushing unit. The third group concerns the users, public authorities, service providers, and policymakers.

The building roofs are considered the catchment area for receiving the falling rainfall droplets. Such droplets are transported through the gutters (channels located around the edge of a sloping roof) to the RWH distribution network. The latter is used to transport the rainwater from the roofs to the shared RWH tank. Valves are used to control the water supply. Pumps transport the harvested rainwater from the shared tank to the household water tank installed on the top of the house. The house tank is also connected to the municipal water system. Backflow preventers are installed to protect the pump.

An inlet filter is installed to catch the large debris and prevent them from entering the pipe of the RWH distribution network. A first flush diverter is used to capture and divert the contaminated harvested water during the first rain because different substances and deposits could contaminate it on the roof. However, this diverter forms the second defense line since it captures the contaminants not captured by the inlet filter. The RWH tank screen is located at the entry point of the RWH storage tank. It has two main roles: filtering the harvested water before reaching the storage tank and preventing the pests and mosquitos from entering the tank. The overflow pipe screen (filter) is installed at the end of the overflow pipe. It prevents pets and mosquitos from entering the RWH system. A treatment unit is added to convert the non-potable into potable water. This unit will be based on the characterization of the probable RWH contamination in the study area. A discharge pipe is installed to discharge the non-potable water away from the distribution network and prevent it from reaching the household water tank. Finally, a flushing unit is added for cleaning, disinfecting, and flushing the RWH system. This layer ensures data collection using the following sensors: (i) smart water flow meter to monitor the water flow in the distribution network, (ii) water level sensor to monitor the water level in the shared RWH tank and the household's tanks, (iii) smart water quality device to monitor the water quality in the RWH system and (iv) leak detection sensor: these sensors can detect the indoor water leak for the different household equipment. In addition, sensors can perform a realtime collection and transmission of data to the RWH server (Figure 7.5).

The system also collects the water supply agenda from the water provider, the weather and air quality forecasting from the relevant authorities, and information from users about the water quality or shortage in the RWH service [START_REF] Shahrour | Satellite remote sensing for water resources management: Potential for supporting sustainable development in data-poor regions[END_REF][START_REF] Guo | Mobile Crowd Sensing and Computing: The Review of an Emerging Human-Powered Sensing Paradigm[END_REF].

Data transfer is carried out using wireless networks (e.g., WIFI, Bluetooth, and 4G). Wireless networks transmit the collected data from the IoT sensors to the server, where data are processed and analyzed. The data processing layer operates the following tasks: data cleaning, data storage, analysis, and visualization. Data cleaning is the process of guaranteeing data correctness, consistency, and usability [START_REF] Hu | Data Cleaning and Feature Selection for Gravelly Soil Liquefaction[END_REF][START_REF] Love | Making a Distinction between Data Cleaning and Central Monitoring in Clinical Trials[END_REF]. It involves detecting and removing/replacing/correcting inaccurate records from the database [START_REF] Wehbe | A BIM-Based Smart System for Fire Evacuation[END_REF]. The smart systems' actions depend highly on the input data [START_REF] Northcutt | Confident Learning: Estimating Uncertainty in Dataset Labels[END_REF]. Therefore, cleaning collecting data could significantly help the smart systems avoid inappropriate actions. Data storage implies the containment and integration of collected data in a specific location (database) [START_REF] Liu | Toward Secure Distributed Data Storage with Error Locating in Blockchain Enabled Edge Computing[END_REF]. The access, calling, and manipulation of stored data should be secured (e.g., using XQuery) [START_REF] Robie | XML Processing and Data Integration with XQuery[END_REF]. Data analysis converts the collected, cleaned, and stored data into helpful information [START_REF] Elhoseny | A Framework for Big Data Analysis in Smart Cities[END_REF][START_REF] Tang | A Hierarchical Distributed Fog Computing Architecture for Big Data Analysis in Smart Cities[END_REF], which is then used to conduct suitable actions [START_REF] Elhoseny | A Framework for Big Data Analysis in Smart Cities[END_REF][START_REF] Tang | A Hierarchical Distributed Fog Computing Architecture for Big Data Analysis in Smart Cities[END_REF]. In this research, data is statistically analyzed. Water quality records are statistically compared to the drinking water standards stated by WHO (WHO, 2011). Statistics concerning water flow, volumes, and levels in the RWH system are analyzed and compared. This analysis helps detect a water leak and allocate water resources to users. Data visualization is the graphical representation of the analyzed data [START_REF] Da Silva Lopes | Parallel T-SNE Applied to Data Visualization in Smart Cities[END_REF][START_REF] Ji | Visual IoT: Enabling Internet of Things Visualization in Smart Cities[END_REF]. It forms an effective way of communication, especially where the input data is big data (e.g. temporal, spatial, and spatiotemporal data) [START_REF] Elhoseny | A Framework for Big Data Analysis in Smart Cities[END_REF].

Data cleaning, storage, analysis, and visualization are connected to the smart platform. The platform receives real-time data from the water level sensors, water flow meters, and leak detection sensors. It can then detect the water leak in; (i) RWH system, (ii) municipal water system, (iii) indoor household equipment, and (iv) indoor water network. The platform also receives water quality records concerning the treated RWH from the water quality check unit. These real-time records will be available for the users on the platform.

• Control layer and the provided smart services Following data analysis, the smart system operates control actions of pumps and valves. For example, pumps will be automatically shut down in case of water contamination to avoid a nonpotable water supply. Valves will be used to discharge the contaminated water through the discharge pipes. Moreover, back-flushing will be automatically operated to clean and disinfect the RWH system from the contaminants. In case of detecting the water leak, pumps and valves will be temporally turned off to minimize water losses.

The smart system provides several services, including (i) the early detection of water contamination, (ii) the early detection of the water leak, (iii) the smart control and elimination of water contamination to secure potable water supply, (iv) the optimal use of available water resources (e.g., RWH and municipal water supply), (v) incidents notification to users.

Smart system reliability analysis

The smart water systems' reliability aims to assess the efficiency and capability of the system to supply the water demand [START_REF] Karim | Rainwater Harvesting Potentials in Commercial Buildings in Dhaka: Reliability and Economic Analysis[END_REF][START_REF] Fulton | A Simulation of Rainwater Harvesting Design and Demand-Side Controls for Large Hospitals[END_REF]. Scholars introduced two indices for the estimation of the systems reliability: (i) time-based reliability (Re), which designates the ratio of days with a fully met water demand in one year, (ii) and the volumetric reliability (Rv), which denotes the ratio of the annual supplied water to the annual volume of water demand [START_REF] Preeti | A Case Study on Reliability, Water Demand and Economic Analysis of Rainwater Harvesting in Australian Capital Cities[END_REF]Zhang et al., 2018b;[START_REF] Ursino | Reliability and Efficiency of Rainwater Harvesting Systems under Different Climatic and Operational Scenarios[END_REF]and;[START_REF] Basinger | A Rainwater Harvesting System Reliability Model Based on Nonparametric Stochastic Rainfall Generator[END_REF]. This research is based on a simulation-based Re and Rv dataset obtained using a python code on the Kaggle platform. The simulation is carried out in three steps. The first step consists of estimating the daily water demand (Dt) and the daily captured volume of RWH at the roofs (St). The second step targets the quantification of the daily volume of rainwater in the RWH tank (Vt), Vt-1: daily water volume in the RWH tank in the previous day St: daily captured volume of rainwater at the roof (m³) Dt: daily water demand (m³) C: tank size/capacity (m³) Xt: daily shortage in covering the needed water demand (m³) t: day (1 to 365)

• Third step: Estimation of Re and Rv Re and Rv are estimated using the following formulas [START_REF] Preeti | A Case Study on Reliability, Water Demand and Economic Analysis of Rainwater Harvesting in Australian Capital Cities[END_REF]Zhang et al., 2018b;[START_REF] Ursino | Reliability and Efficiency of Rainwater Harvesting Systems under Different Climatic and Operational Scenarios[END_REF][START_REF] Basinger | A Rainwater Harvesting System Reliability Model Based on Nonparametric Stochastic Rainfall Generator[END_REF]: 3.1, the domestic water scarcity level in the study area is estimated. Such level is characterized before and after the adoption of the smart system.

𝑅𝑅 𝑒𝑒 = 𝑁𝑁-𝑈𝑈 𝑁𝑁 *

Data collection and application

The application of the proposed methodology is carried out in a small neighborhood in Jenin city, which is located in the North of the West Bank, Palestine (Figure 7.6). Table 7.1 summarizes the characteristics of the six buildings in the neighborhood. The community receives an average annual rainfall of around 590 mm/year, while the maximum annual daily rainfall is about 62.3 mm/day (PMA, 2018). Therefore, the number of rainy days (with rainfall depth of more than 1 mm/day) is around 50 days (PMA, 2018). The temporal distribution of the daily rainfall in the study area is shown in Figure 7.7. It is noticed that most of the rainfall falls between October and April. This section assesses the probable physiochemical and biological contamination in the collected RWH samples (Table 7.3). Accordingly, it proposes the most suitable and feasible smart quality control options. Table 7.3 shows that pH, Alkalinity, and TDS are within the acceptable limits of the WHO standards for all the samples. In contrast, 20% of the samples exceed the thresholds of the WHO turbidity standards. Measured turbidity could reach 29 nephelometric turbidity units (NTU), about six times higher than the maximum allowable limit (5 NTU). This could be related to the absence of screens and filters in the practiced RWH systems in the study area [START_REF] Mahmoud | Assessment of Rainwater Harvesting for Domestic Water Supply in Palestinian Rural Areas[END_REF]. High turbidity protects the organisms (e.g. bacteria and pathogens) in the drinking water [START_REF] Muoio | Water Safety Plans and Risk Assessment: A Novel Procedure Applied to Treated Water Turbidity and Gastrointestinal Diseases[END_REF][START_REF] Alenazi | Turbidity Removal Using Natural Coagulants Derived from the Seeds of Strychnos Potatorum: Statistical and Experimental Approach[END_REF][START_REF] Soros | Turbidity Reduction in Drinking Water by Coagulation-Flocculation with Chitosan Polymers[END_REF]. In which disinfecting the water (e.g. through chlorination) becomes less efficient. Thus, consuming such water could cause nausea and headaches to users [START_REF] Muoio | Water Safety Plans and Risk Assessment: A Novel Procedure Applied to Treated Water Turbidity and Gastrointestinal Diseases[END_REF][START_REF] Alenazi | Turbidity Removal Using Natural Coagulants Derived from the Seeds of Strychnos Potatorum: Statistical and Experimental Approach[END_REF][START_REF] Soros | Turbidity Reduction in Drinking Water by Coagulation-Flocculation with Chitosan Polymers[END_REF].

Around 49% of the samples recorded FC levels higher than the WHO standards. The maximum presence of FC in the sampled water reaches 545 CFU/100 ml (around 55 times the maximum allowable limit by WHO). This could be attributed to the extensive use of cesspits for wastewater disposal (Judeh et al., 2021). Leaching from these cesspits could contaminate the underground RWH tank [START_REF] Al-Batsh | Assessment of Rainwater Harvesting Systems in Poor Rural Communities: A Case Study from Yatta Area, Palestine[END_REF][START_REF] Mahmoud | Assessment of Rainwater Harvesting for Domestic Water Supply in Palestinian Rural Areas[END_REF][START_REF] Daoud | Quality Assessment of Roof-Harvested Rainwater in the West Bank, Palestinian Authority[END_REF]. Birds, animals, and other creatures' fecal waste (on the roof or next to the storage tank) form a main source of FC in the harvested rainwater [START_REF] Al-Batsh | Assessment of Rainwater Harvesting Systems in Poor Rural Communities: A Case Study from Yatta Area, Palestine[END_REF][START_REF] Mahmoud | Assessment of Rainwater Harvesting for Domestic Water Supply in Palestinian Rural Areas[END_REF][START_REF] Daoud | Quality Assessment of Roof-Harvested Rainwater in the West Bank, Palestinian Authority[END_REF]. Elevated numbers of FC in drinking water are linked the infection by the stomach and intestinal diseases such as diarrhea and nausea [START_REF] Xu | Fecal Coliform Distribution and Health Risk Assessment in Surface Water in an Urban-Intensive Catchment[END_REF][START_REF] Wang | Modeling and Predicting Fecal Coliform Bacteria Levels in Oyster Harvest Waters along Louisiana Gulf Coast[END_REF]. The severity of such health problems might be higher and could be lifethreatening for people suffering from immune deficiencies [START_REF] Xu | Fecal Coliform Distribution and Health Risk Assessment in Surface Water in an Urban-Intensive Catchment[END_REF][START_REF] Wang | Modeling and Predicting Fecal Coliform Bacteria Levels in Oyster Harvest Waters along Louisiana Gulf Coast[END_REF]. 51% of the samples recorded a residual chlorine concentration lower than the minimum recommended concentration. Securing the minimum recommended chlorine concentration in drinking water is important [START_REF] Goyal | Analysis of Residual Chlorine in Simple Drinking Water Distribution System with Intermittent Water Supply[END_REF][START_REF] Helbling | Modeling Residual Chlorine Response to a Microbial Contamination Event in Drinking Water Distribution Systems[END_REF] to eliminate bacteria's harmful effects and prevent water recontamination during the storage phase [START_REF] Goyal | Analysis of Residual Chlorine in Simple Drinking Water Distribution System with Intermittent Water Supply[END_REF][START_REF] Helbling | Modeling Residual Chlorine Response to a Microbial Contamination Event in Drinking Water Distribution Systems[END_REF]. On the other hand, around 6% of the samples exceed the maximum allowable residual chlorine concentration. High residual chlorine concentrations can react to form hypochlorous acid and hypochlorites [START_REF] Kali | Occurrence, Influencing Factors, Toxicity, Regulations, and Abatement Approaches for Disinfection by-Products in Chlorinated Drinking Water: A Comprehensive Review[END_REF][START_REF] Hrudey | Evaluating Evidence for Association of Human Bladder Cancer with Drinking-Water Chlorination Disinfection By-Products[END_REF]. Therefore, consuming water with high chlorine concentrations could cause human health problems such as diarrhea, vomiting, stomachaches, poisoning, and bladder cancer [START_REF] Kali | Occurrence, Influencing Factors, Toxicity, Regulations, and Abatement Approaches for Disinfection by-Products in Chlorinated Drinking Water: A Comprehensive Review[END_REF][START_REF] Pickering | Effect of In-Line Drinking Water Chlorination at the Point of Collection on Child Diarrhoea in Urban Bangladesh: A Double-Blind, Cluster-Randomised Controlled Trial[END_REF][START_REF] Hrudey | Evaluating Evidence for Association of Human Bladder Cancer with Drinking-Water Chlorination Disinfection By-Products[END_REF][START_REF] Källén | Drinking Water Chlorination and Delivery Outcome-a Registry-Based Study in Sweden[END_REF]. In addition, high chlorine levels affect the water palatability since it becomes of unpleasant taste and odor [START_REF] Crider | Can You Taste It? Taste Detection and Acceptability Thresholds for Chlorine Residual in Drinking Water in Dhaka, Bangladesh[END_REF]and Wang et al., 2018).

Results of the physiochemical and biological assessment are compatible with what was found by other scholars for the southern [START_REF] Al-Batsh | Assessment of Rainwater Harvesting Systems in Poor Rural Communities: A Case Study from Yatta Area, Palestine[END_REF][START_REF] Celik | Management of Rainwater Harvesting and Its Impact on the Health of People in the Middle East: Case Study from Yatta Town, Palestine[END_REF][START_REF] Al-Salaymeh | Towards Sustainable Water Quality: Management of Rainwater Harvesting Cisterns in Southern Palestine[END_REF], Middle [START_REF] Mahmoud | Assessment of Rainwater Harvesting for Domestic Water Supply in Palestinian Rural Areas[END_REF], and northern [START_REF] Daoud | Quality Assessment of Roof-Harvested Rainwater in the West Bank, Palestinian Authority[END_REF] parts of the West Bank. 7.9 indicates the negative relationship between residual chlorine and FC among the 35 samples. Such a relationship goes in line with other researchers' findings [START_REF] Yousefi | Data on Microbiological Quality Assessment of Rural Drinking Water Supplies in Poldasht County[END_REF][START_REF] Zafarzadeh | Relationship between Turbidity and Residual Chlorine and Microbial Quality of Drinking Water[END_REF][START_REF] Farooq | Monitoring of Coliforms and Chlorine Residual in Water Distribution Network of Rawalpindi, Pakistan[END_REF]. It is found that 16 out of the 17 contaminated samples by FC are also out of the stated limits of residual chlorine. They almost fall below the minimum threshold of residual chlorine (0.2 mg/l). So, controlling the levels of residual chlorine in RWH will relatively secure the control of FC as well. The physiochemical and biological analysis shows that the control of the RWH turbidity is the first defense line for securing RWH potability in the study area. Such control could be performed by installing the inlet pipe screen, first flush diverter, filter, and overflow pipe screen. The smart monitoring of turbidity could be conducted by assessing the performance of such a defense line. This could be achieved by using (i) smart turbidity sensors and/or (ii) conventional turbidity meters and crowdsourcing.

Controlling the FC and residual chlorine levels is the most important part of the RWH quality control. According to WHO, RWH requires 2 mg/l of chlorine to be disinfected (to inactive the organisms, including FC) (WHO, 2011;[START_REF] Howard | Domestic Water Quantity, Service Level and Health[END_REF]. Thus, WHO recommended an optimal chlorination rate of about 2.5 mg/l to ensure the disinfection of existing FC (WHO, 2011;[START_REF] Howard | Domestic Water Quantity, Service Level and Health[END_REF]. Such disinfection maintains a residual chlorine concentration of a round 0.5 mg/l in the harvested water. This will secure the re-disinfection of future FC contamination during the storage and pumping phases (WHO, 2011;[START_REF] Howard | Domestic Water Quantity, Service Level and Health[END_REF]. Thus, the use and calibration of the chlorination unit are of high importance in the smart RWH system. In addition, residual chlorine sensors/meters are noticeably cheaper than other water quality sensors (including FC sensors/meters) (WHO, 2011;[START_REF] Howard | Domestic Water Quantity, Service Level and Health[END_REF]. So, this research proposes the use of these feasible sensors to control FC and residual chlorine levels through the RWH system in the study area.

Smart RWH/dual system reliability

This section discusses the reliability of the smart RWH system in supplying the residents with their potable water needs. The potential annual RWH volume at the roofs is around 507 m 3 . However, the actual annual RWH volume that could be stored and utilized depends on different factors, including the system quality (e.g., percent of the leak) and the size of the RWH storage tank. Figure 7.10 shows the effect of RWH tank size on the annual RWH and overflow volumes. It is found that the annual storage of the RWH increases with the tank size to attain a maximum of about 500 m 3 for a tank size of 112 m 3 . This size guarantees no overflow from the tank. 7.1, the annual water demand for the study area is around 1240 m 3 /year. The lowest shortage (59%) in water supply could be reached by using the optimal tank size (112.5 m 3 ). However, using lower sizes is associated with a higher shortage rate. For instance, the tank of 5 m 3 size has 83% shortage in providing the needed water demand (Figure 7.11). Figure 7.12 displays the influence of the tank size on Re and Rv of the smart RWH system. Re is around 12.6% for the 5 m 3 tank. Such a rate covers the water demand for 46 days. The maximum Re (37.5%) is recorded for the 112.5 m 3 tank, which means that the residents' water demand is covered for 137 days. Concerning Rv, the maximum recorded rate is 41%. This implies providing the residents with an average daily supply of (41 l/c/d). To overcome the water shortage with the RWH system, it is necessary to use the dual water system that combines RWH and municipal water supply [START_REF] Kang | Dual Water Distribution Network Design under Triple-Bottom-Line Objectives[END_REF]. Various researchers discussed the concept and performance of the dual supply system (Burszta-Adamiak and Spychalski, 2021; [START_REF] Rasoulkhani | Resilience-Based Infrastructure Planning and Asset Management: Study of Dual and Singular Water Distribution Infrastructure Performance Using a Simulation Approach[END_REF][START_REF] Cole | Centralized and Decentralized Strategies for Dual Water Supply: Case Study[END_REF][START_REF] Nguyen | Design of Dual Water Supply System Using Rainwater and Groundwater at Arsenic Contaminated Area in Vietnam[END_REF][START_REF] Kang | Dual Water Distribution Network Design under Triple-Bottom-Line Objectives[END_REF]. Scholars agreed on adopting two independent distribution networks; centralized and decentralized [START_REF] Burszta-Adamiak | Water Savings and Reduction of Costs through the Use of a Dual Water Supply System in a Sports Facility[END_REF][START_REF] Rasoulkhani | Resilience-Based Infrastructure Planning and Asset Management: Study of Dual and Singular Water Distribution Infrastructure Performance Using a Simulation Approach[END_REF][START_REF] Cole | Centralized and Decentralized Strategies for Dual Water Supply: Case Study[END_REF][START_REF] Nguyen | Design of Dual Water Supply System Using Rainwater and Groundwater at Arsenic Contaminated Area in Vietnam[END_REF][START_REF] Kang | Dual Water Distribution Network Design under Triple-Bottom-Line Objectives[END_REF]. This section discusses the dynamic management of the proposed dual water supply system. Such management considers the various spatial levels in the study area (e.g. high-altitude, moderate-altitude, and low-altitude locations). Figure 7.13 shows the effect of the tank size on Re and Rv for the smart dual water supply system in light of the existing municipal water supply agenda. Both reliability indices hit 100% for the low-altitude (using tank sizes of 20 m 3 ) and moderate-altitude locations (using tank sizes of 40 m 3 ). This implies fully addressing water scarcity (0% of water shortage). Concerning highaltitude locations, Re ranges between 76% (for 10 m 3 tank) and 98% (for 190 m 3 tank). In addition, Rv ranges between 85% (for 10 m 3 tank) and 99% (for 190 m 3 tank). Such results urge the need to propose and examine other municipal water supply agendas in order to achieve 100% reliability for all altitude levels. Thus, two agendas are introduced: (i) agenda A (9, 7, 5 days of water supply per 3 weeks for high-altitude, moderate-altitude, and lowaltitude locations, respectively), and agenda B (10, 7, 4 days of water supply per 3 weeks for highaltitude, moderate-altitude, and low-altitude locations, respectively). Figure 7.14 shows that agenda A could assist in reaching the full reliability (both Re and Rv) at all altitude levels in the study area. This could be realized by utilizing tank sizes of 30, 40, and 80 m 3 for the low-altitude, moderate-altitude, and high-altitude regions, respectively. On the other hand, it is found that agenda B achieves the same reliability records (100%) by utilizing smaller tank sizes. Tank sizes of 30 m 3 could be used in low-altitude areas, and 40 m 3 could be in moderatealtitude and high-altitude areas (Figure 7.15). Such reliabilities contribute to addressing the acute water scarcity in Jenin City (Table 7.4). Table 7.4. shows that under Agenda B, a "slight water scarcity" could be reached by using a 20 m 3 tank size for all altitudes. Moreover, a "no water scarcity" could be reached by using a 30, 40, and 40 m 3 tank size for the low-altitude, moderatealtitude, and high-altitude regions, respectively. These results indicate the efficiency of the dual water system in fully addressing the water scarcity in the study area compared to the smart RWH system solely. It also shows the need for rescheduling the existing municipal water supply agenda.

Conclusion

This chapter introduced and assessed the reliability of a smart RWH dual water supply system to address the potable water shortage in the arid and semi-arid areas. Results indicated the need for implying a crowdsourcing-based, automated-based treatment and check units in the smart system to control the water turbidity, fecal coliform, and residual chlorine in the harvested rainwater in Jenin city. The smart RWH system showed a capability to cover 41% of the domestic water needs.

Results indicated the efficiency and reliability of the dual water system in addressing the water demand compared to the smart RWH system solely. The dynamic management of the system (including the storage) enables hitting the best reliability by using smaller storage tank size. This could have a significant positive effect on the city's socioeconomic development. Results also showed a need for rescheduling the existing municipal water supply agenda. By adopting the dynamic management and a new supply agenda, the smart dual system showed a capacity to address the water scarcity at all altitude levels in the study area.

Future research could focus on (i) the cost-benefit analysis for the smart RWH system and (ii) the social acceptance investigation for the adoption of the proposed system.

General Conclusion and Perspectives

Water scarcity is found as a serious hazard that threaten the human life, and the agricultural, industrial, and urban development. The management of this hazard is a complex issue due to its multi-contributors: limited water availability, restricted water accessibility, water pollution, increase in the water demand, and the climate change. To address this complexity, this thesis presented an innovative framework for an effective management of the water scarcity. It combined water resources monitoring, data analysis using statistics and Machine Learning, and use of the smart technology. The proposed framework was applied to the West Bank in Palestine, which is considered as an arid to semi-arid area with vulnerable water resources. It consists of four phases. The first phase employed the supply demand balance index (SDBI) to assess the current water scarcity in the West Bank. The consideration of the impact of the climate change and demographic growth constituted an added value to the research. They enable the assessment of future water scarcity. The second phase targeted the protection of the quality of the water resources. An adjusted weighted arithmetic water quality index method (AWAWQI) and Machine Learning were used to assess, predict and preserve the water quality in the West Bank. The index facilitated conducting a large-scale scan of the water quality. It enabled an in-depth analysis of the groundwater contamination, its causes, and potential consequences. The proposed AWAWQI helped in addressing the limitations of the previously adopted weighted arithmetic water quality indices (e.g. uncertain results due to the unrealistic weighting system). Machine Learning (Random Forest) is adopted to predict the groundwater nitrate contamination (GNC) at this most vulnerable aquifer in the study area. Random Forest is an added value to the framework since it has the ability to generate repeated predictions of GNC. It helped in quantifying the relative importance of the input influencing factors. GIS-based spatiotemporal maps indicated an increasing GNC trends in the southern part of the Eocene Aquifer. The third phase combined the GIS, and Gould and Nissen-Petersen's equation to investigate the potential capacity of the rainwater harvesting (RWH) in addressing the water scarcity. The use of GIS enabled the spatiotemporal analysis of input data. The conventional RWH systems showed several limitations: unsecured quality, weak performance, and the water leakage. To overcome these limitations, the last phase proposed a smart RWH system and smart dual water supply system to promote the domestic water security.

Results indicated that seven out of the eleven West Bank governorates are suffering from extreme to acute water scarcity in 2020. One more governorate is expected to suffer from extreme to acute scarcity level by 2050. However, scarcity assessment results are dependent to the adopted downscaled global climate change models, which considers the main limitation of scarcity assessment phase. Around 20% of the wells in the urban areas had experienced potability-related contamination, with high concerns in the shallow wells. More than 95% of the potability-related contaminated samples are found in the Eocene Aquifer (located in the northern part of the West Bank). However, dropping the consideration of heavy metals (due to lack of data) is the main limitation of this resulted indices. Random Forest model successfully attained a maximum and average GNC prediction accuracy of 91.70% and 88.54%, respectively. All water-contaminated samples were observed in areas with improper practices such as the use of cesspits, fertilizers, and pesticides. The protection of water resources in these areas requires urgent intervention to reduce (i) the wastewater infiltration by installing sanitation systems, septic tanks, and sealed cesspits, and (ii) the use of chemicals and pesticides in areas impacting the groundwater resources.

Results also showed that the RWH can address the current and future domestic water scarcity in the majority of the West Bank governorates. A national policy to share the potential RWH capacity will increase the efficiency of the RWH by a share of the excess water harvesting among the West Bank governorates. The smart RWH/dual system showed its capacity to secure the harvested water potability by implying a crowdsourcing-based, automated-based treatment and check units in the system. These units enabled the control the water turbidity, fecal coliform, and residual chlorine in the harvested rainwater. Results indicated the efficiency and reliability of the smart dual water system in addressing the water demand compared to the smart RWH system solely. By adopting the dynamic management and a new supply agenda, the smart dual system showed a capacity to address the water scarcity at all altitude levels in the study area. They also contributed to getting the best performance by using smaller storage tank size which could promote the city's socioeconomic development.

The knowledge-based framework presented in this thesis forms a step forward in the management of water scarcity in arid and semi-arid areas. It proposed an efficient scarcitymitigation solutions based on recent approaches. The framework novelty stems from its ability to integrate the technical (e.g. via rainwater water simulations), sustainable (e.g. via climate change and demographic growth models), and technological (e.g. via Machine Learning and smart systems) solutions towards the sustainable mitigation of water scarcity challenges. The framework showed its ability to support the water security in conflict areas with restricted water accessibility. This would support the steadfastness of the suffering communities in their land, which in turn contribute to addressing the water-related migration as stated by the United Nations (UN, 2021;and UN, 2018).

Future research could focus on (i) considering a local climate change model to estimate the projected rainfall and water availability, and (ii) investigating the social acceptance for the adoption of the proposed smart system.

Figure 1 . 2 .Figure 1 . 3 .

 1213 Figure 1.1. KIM-based interpolated vs. observed groundwater levels in an aquifer in piedmont plains, China (Xiao et al., 2016) ................................................................................................... Figure 1.2. Hydrological cycle (Levizzani and Cattani, 2019) .................................................... Figure 1.3. Architecture of Smart systems ................................................................................... Figure 1.4. Research outline ........................................................................................................Figure 2.1. The geographical setting of the West Bank ...............................................................Figure 2.2. Assessment of water challenges in Palestine .............................................................Figure 2.3. Groundwater flow, divides and aquifers in the West Bank and Gaza Strip ..............Figure 2.4. Spatial distribution of the groundwater wells in the West Bank ...............................Figure 2.5. Decline of groundwater level in the Well L/57 located in Gaza Strip between 1966 and 2014 ........................................................................................................................................Figure 2.6. Long-term average annual rainfall in the West Bank ................................................Figure 2.7. RWH suitability map for domestic uses in the West Bank .......................................Figure 2.8. Average annual concentrations of NO3 and Cl in groundwater wells in Tulkarm and Qalqiliya ........................................................................................................................................Figure 2.9. Spatial distribution of NO3 and Cl amongst Gaza Coastal Aquifer ..........................Figure 2.10. Drought occurrence in the MENA ...........................................................................Figure 2.11. Spatiotemporal impact of climate change and over-pumping on the groundwater level of the Gaza Coastal Aquifer between 1974 and 2040 ..........................................................Figure 2.12. Overall research methodology .................................................................................Figure 2.13. Assessment of existing and projected water scarcity ..............................................Figure 2.14. Protection of conventional water resources .............................................................Figure 2.15. Investigating the role of potential RWH in addressing the current and future domestic water scarcity .................................................................................................................Figure 2.16. Methodological framework for smart RWH and smart dual water supply systems Figure 3.1. Research methodology .............................................................................................. Figure 3.2. Schematic overview of the adopted SVAT model .................................................... Figure 3.3. Historical and current statistics of population and TWD in the whole West Bank between 1997 and 2020 ................................................................................................................Figure 3.4. Variation in TWS considering climate change ..........................................................Figure 4.1. Overall methodological framework ...........................................................................Figure 4.2. Development of potability and palatability groundwater quality indexes (PoGWQI and PaGWQI) ................................................................................................................................Figure 4.3. PoGWQGs and PaGWQGs in the West Bank ...........................................................Figure 4.4. Un-potability rate over four contaminated groundwater wells in the West Bank .....Figure 4.5. Spatiotemporal mapping of PoGWQI and PaGWQI in the West Bank between 2001 and 2016 ........................................................................................................................................Figure 4.6. PoGWQGs and PaGWQGs cross the West Bank governorates ................................Figure 4.7. Sensitivity analysis of PoGWQI and PaGWQI .........................................................Figure 4.8. Factors affecting PoGWQI and PaGWQI in the West Bank .....................................Figure 4.9. Relative importance analysis for the main factors affecting PoGWQI and PaGWQI67

Figure 1 . 4 .

 14 Figure 1.1. KIM-based interpolated vs. observed groundwater levels in an aquifer in piedmont plains, China (Xiao et al., 2016) ................................................................................................... Figure 1.2. Hydrological cycle (Levizzani and Cattani, 2019) .................................................... Figure 1.3. Architecture of Smart systems ................................................................................... Figure 1.4. Research outline ........................................................................................................Figure 2.1. The geographical setting of the West Bank ...............................................................Figure 2.2. Assessment of water challenges in Palestine .............................................................Figure 2.3. Groundwater flow, divides and aquifers in the West Bank and Gaza Strip ..............Figure 2.4. Spatial distribution of the groundwater wells in the West Bank ...............................Figure 2.5. Decline of groundwater level in the Well L/57 located in Gaza Strip between 1966 and 2014 ........................................................................................................................................Figure 2.6. Long-term average annual rainfall in the West Bank ................................................Figure 2.7. RWH suitability map for domestic uses in the West Bank .......................................Figure 2.8. Average annual concentrations of NO3 and Cl in groundwater wells in Tulkarm and Qalqiliya ........................................................................................................................................Figure 2.9. Spatial distribution of NO3 and Cl amongst Gaza Coastal Aquifer ..........................Figure 2.10. Drought occurrence in the MENA ...........................................................................Figure 2.11. Spatiotemporal impact of climate change and over-pumping on the groundwater level of the Gaza Coastal Aquifer between 1974 and 2040 ..........................................................Figure 2.12. Overall research methodology .................................................................................Figure 2.13. Assessment of existing and projected water scarcity ..............................................Figure 2.14. Protection of conventional water resources .............................................................Figure 2.15. Investigating the role of potential RWH in addressing the current and future domestic water scarcity .................................................................................................................Figure 2.16. Methodological framework for smart RWH and smart dual water supply systems Figure 3.1. Research methodology .............................................................................................. Figure 3.2. Schematic overview of the adopted SVAT model .................................................... Figure 3.3. Historical and current statistics of population and TWD in the whole West Bank between 1997 and 2020 ................................................................................................................Figure 3.4. Variation in TWS considering climate change ..........................................................Figure 4.1. Overall methodological framework ...........................................................................Figure 4.2. Development of potability and palatability groundwater quality indexes (PoGWQI and PaGWQI) ................................................................................................................................Figure 4.3. PoGWQGs and PaGWQGs in the West Bank ...........................................................Figure 4.4. Un-potability rate over four contaminated groundwater wells in the West Bank .....Figure 4.5. Spatiotemporal mapping of PoGWQI and PaGWQI in the West Bank between 2001 and 2016 ........................................................................................................................................Figure 4.6. PoGWQGs and PaGWQGs cross the West Bank governorates ................................Figure 4.7. Sensitivity analysis of PoGWQI and PaGWQI .........................................................Figure 4.8. Factors affecting PoGWQI and PaGWQI in the West Bank .....................................Figure 4.9. Relative importance analysis for the main factors affecting PoGWQI and PaGWQI67

Figure 2 . 1 .

 21 Figure 1.1. KIM-based interpolated vs. observed groundwater levels in an aquifer in piedmont plains, China (Xiao et al., 2016) ................................................................................................... Figure 1.2. Hydrological cycle (Levizzani and Cattani, 2019) .................................................... Figure 1.3. Architecture of Smart systems ................................................................................... Figure 1.4. Research outline ........................................................................................................Figure 2.1. The geographical setting of the West Bank ...............................................................Figure 2.2. Assessment of water challenges in Palestine .............................................................Figure 2.3. Groundwater flow, divides and aquifers in the West Bank and Gaza Strip ..............Figure 2.4. Spatial distribution of the groundwater wells in the West Bank ...............................Figure 2.5. Decline of groundwater level in the Well L/57 located in Gaza Strip between 1966 and 2014 ........................................................................................................................................Figure 2.6. Long-term average annual rainfall in the West Bank ................................................Figure 2.7. RWH suitability map for domestic uses in the West Bank .......................................Figure 2.8. Average annual concentrations of NO3 and Cl in groundwater wells in Tulkarm and Qalqiliya ........................................................................................................................................Figure 2.9. Spatial distribution of NO3 and Cl amongst Gaza Coastal Aquifer ..........................Figure 2.10. Drought occurrence in the MENA ...........................................................................Figure 2.11. Spatiotemporal impact of climate change and over-pumping on the groundwater level of the Gaza Coastal Aquifer between 1974 and 2040 ..........................................................Figure 2.12. Overall research methodology .................................................................................Figure 2.13. Assessment of existing and projected water scarcity ..............................................Figure 2.14. Protection of conventional water resources .............................................................Figure 2.15. Investigating the role of potential RWH in addressing the current and future domestic water scarcity .................................................................................................................Figure 2.16. Methodological framework for smart RWH and smart dual water supply systems Figure 3.1. Research methodology .............................................................................................. Figure 3.2. Schematic overview of the adopted SVAT model .................................................... Figure 3.3. Historical and current statistics of population and TWD in the whole West Bank between 1997 and 2020 ................................................................................................................Figure 3.4. Variation in TWS considering climate change ..........................................................Figure 4.1. Overall methodological framework ...........................................................................Figure 4.2. Development of potability and palatability groundwater quality indexes (PoGWQI and PaGWQI) ................................................................................................................................Figure 4.3. PoGWQGs and PaGWQGs in the West Bank ...........................................................Figure 4.4. Un-potability rate over four contaminated groundwater wells in the West Bank .....Figure 4.5. Spatiotemporal mapping of PoGWQI and PaGWQI in the West Bank between 2001 and 2016 ........................................................................................................................................Figure 4.6. PoGWQGs and PaGWQGs cross the West Bank governorates ................................Figure 4.7. Sensitivity analysis of PoGWQI and PaGWQI .........................................................Figure 4.8. Factors affecting PoGWQI and PaGWQI in the West Bank .....................................Figure 4.9. Relative importance analysis for the main factors affecting PoGWQI and PaGWQI67

Figure 2 . 2 .

 22 Figure 1.1. KIM-based interpolated vs. observed groundwater levels in an aquifer in piedmont plains, China (Xiao et al., 2016) ................................................................................................... Figure 1.2. Hydrological cycle (Levizzani and Cattani, 2019) .................................................... Figure 1.3. Architecture of Smart systems ................................................................................... Figure 1.4. Research outline ........................................................................................................Figure 2.1. The geographical setting of the West Bank ...............................................................Figure 2.2. Assessment of water challenges in Palestine .............................................................Figure 2.3. Groundwater flow, divides and aquifers in the West Bank and Gaza Strip ..............Figure 2.4. Spatial distribution of the groundwater wells in the West Bank ...............................Figure 2.5. Decline of groundwater level in the Well L/57 located in Gaza Strip between 1966 and 2014 ........................................................................................................................................Figure 2.6. Long-term average annual rainfall in the West Bank ................................................Figure 2.7. RWH suitability map for domestic uses in the West Bank .......................................Figure 2.8. Average annual concentrations of NO3 and Cl in groundwater wells in Tulkarm and Qalqiliya ........................................................................................................................................Figure 2.9. Spatial distribution of NO3 and Cl amongst Gaza Coastal Aquifer ..........................Figure 2.10. Drought occurrence in the MENA ...........................................................................Figure 2.11. Spatiotemporal impact of climate change and over-pumping on the groundwater level of the Gaza Coastal Aquifer between 1974 and 2040 ..........................................................Figure 2.12. Overall research methodology .................................................................................Figure 2.13. Assessment of existing and projected water scarcity ..............................................Figure 2.14. Protection of conventional water resources .............................................................Figure 2.15. Investigating the role of potential RWH in addressing the current and future domestic water scarcity .................................................................................................................Figure 2.16. Methodological framework for smart RWH and smart dual water supply systems Figure 3.1. Research methodology .............................................................................................. Figure 3.2. Schematic overview of the adopted SVAT model .................................................... Figure 3.3. Historical and current statistics of population and TWD in the whole West Bank between 1997 and 2020 ................................................................................................................Figure 3.4. Variation in TWS considering climate change ..........................................................Figure 4.1. Overall methodological framework ...........................................................................Figure 4.2. Development of potability and palatability groundwater quality indexes (PoGWQI and PaGWQI) ................................................................................................................................Figure 4.3. PoGWQGs and PaGWQGs in the West Bank ...........................................................Figure 4.4. Un-potability rate over four contaminated groundwater wells in the West Bank .....Figure 4.5. Spatiotemporal mapping of PoGWQI and PaGWQI in the West Bank between 2001 and 2016 ........................................................................................................................................Figure 4.6. PoGWQGs and PaGWQGs cross the West Bank governorates ................................Figure 4.7. Sensitivity analysis of PoGWQI and PaGWQI .........................................................Figure 4.8. Factors affecting PoGWQI and PaGWQI in the West Bank .....................................Figure 4.9. Relative importance analysis for the main factors affecting PoGWQI and PaGWQI67

Figure 2 . 3 .

 23 Figure 1.1. KIM-based interpolated vs. observed groundwater levels in an aquifer in piedmont plains, China (Xiao et al., 2016) ................................................................................................... Figure 1.2. Hydrological cycle (Levizzani and Cattani, 2019) .................................................... Figure 1.3. Architecture of Smart systems ................................................................................... Figure 1.4. Research outline ........................................................................................................Figure 2.1. The geographical setting of the West Bank ...............................................................Figure 2.2. Assessment of water challenges in Palestine .............................................................Figure 2.3. Groundwater flow, divides and aquifers in the West Bank and Gaza Strip ..............Figure 2.4. Spatial distribution of the groundwater wells in the West Bank ...............................Figure 2.5. Decline of groundwater level in the Well L/57 located in Gaza Strip between 1966 and 2014 ........................................................................................................................................Figure 2.6. Long-term average annual rainfall in the West Bank ................................................Figure 2.7. RWH suitability map for domestic uses in the West Bank .......................................Figure 2.8. Average annual concentrations of NO3 and Cl in groundwater wells in Tulkarm and Qalqiliya ........................................................................................................................................Figure 2.9. Spatial distribution of NO3 and Cl amongst Gaza Coastal Aquifer ..........................Figure 2.10. Drought occurrence in the MENA ...........................................................................Figure 2.11. Spatiotemporal impact of climate change and over-pumping on the groundwater level of the Gaza Coastal Aquifer between 1974 and 2040 ..........................................................Figure 2.12. Overall research methodology .................................................................................Figure 2.13. Assessment of existing and projected water scarcity ..............................................Figure 2.14. Protection of conventional water resources .............................................................Figure 2.15. Investigating the role of potential RWH in addressing the current and future domestic water scarcity .................................................................................................................Figure 2.16. Methodological framework for smart RWH and smart dual water supply systems Figure 3.1. Research methodology .............................................................................................. Figure 3.2. Schematic overview of the adopted SVAT model .................................................... Figure 3.3. Historical and current statistics of population and TWD in the whole West Bank between 1997 and 2020 ................................................................................................................Figure 3.4. Variation in TWS considering climate change ..........................................................Figure 4.1. Overall methodological framework ...........................................................................Figure 4.2. Development of potability and palatability groundwater quality indexes (PoGWQI and PaGWQI) ................................................................................................................................Figure 4.3. PoGWQGs and PaGWQGs in the West Bank ...........................................................Figure 4.4. Un-potability rate over four contaminated groundwater wells in the West Bank .....Figure 4.5. Spatiotemporal mapping of PoGWQI and PaGWQI in the West Bank between 2001 and 2016 ........................................................................................................................................Figure 4.6. PoGWQGs and PaGWQGs cross the West Bank governorates ................................Figure 4.7. Sensitivity analysis of PoGWQI and PaGWQI .........................................................Figure 4.8. Factors affecting PoGWQI and PaGWQI in the West Bank .....................................Figure 4.9. Relative importance analysis for the main factors affecting PoGWQI and PaGWQI67

Figure 2 . 4 .

 24 Figure 1.1. KIM-based interpolated vs. observed groundwater levels in an aquifer in piedmont plains, China (Xiao et al., 2016) ................................................................................................... Figure 1.2. Hydrological cycle (Levizzani and Cattani, 2019) .................................................... Figure 1.3. Architecture of Smart systems ................................................................................... Figure 1.4. Research outline ........................................................................................................Figure 2.1. The geographical setting of the West Bank ...............................................................Figure 2.2. Assessment of water challenges in Palestine .............................................................Figure 2.3. Groundwater flow, divides and aquifers in the West Bank and Gaza Strip ..............Figure 2.4. Spatial distribution of the groundwater wells in the West Bank ...............................Figure 2.5. Decline of groundwater level in the Well L/57 located in Gaza Strip between 1966 and 2014 ........................................................................................................................................Figure 2.6. Long-term average annual rainfall in the West Bank ................................................Figure 2.7. RWH suitability map for domestic uses in the West Bank .......................................Figure 2.8. Average annual concentrations of NO3 and Cl in groundwater wells in Tulkarm and Qalqiliya ........................................................................................................................................Figure 2.9. Spatial distribution of NO3 and Cl amongst Gaza Coastal Aquifer ..........................Figure 2.10. Drought occurrence in the MENA ...........................................................................Figure 2.11. Spatiotemporal impact of climate change and over-pumping on the groundwater level of the Gaza Coastal Aquifer between 1974 and 2040 ..........................................................Figure 2.12. Overall research methodology .................................................................................Figure 2.13. Assessment of existing and projected water scarcity ..............................................Figure 2.14. Protection of conventional water resources .............................................................Figure 2.15. Investigating the role of potential RWH in addressing the current and future domestic water scarcity .................................................................................................................Figure 2.16. Methodological framework for smart RWH and smart dual water supply systems Figure 3.1. Research methodology .............................................................................................. Figure 3.2. Schematic overview of the adopted SVAT model .................................................... Figure 3.3. Historical and current statistics of population and TWD in the whole West Bank between 1997 and 2020 ................................................................................................................Figure 3.4. Variation in TWS considering climate change ..........................................................Figure 4.1. Overall methodological framework ...........................................................................Figure 4.2. Development of potability and palatability groundwater quality indexes (PoGWQI and PaGWQI) ................................................................................................................................Figure 4.3. PoGWQGs and PaGWQGs in the West Bank ...........................................................Figure 4.4. Un-potability rate over four contaminated groundwater wells in the West Bank .....Figure 4.5. Spatiotemporal mapping of PoGWQI and PaGWQI in the West Bank between 2001 and 2016 ........................................................................................................................................Figure 4.6. PoGWQGs and PaGWQGs cross the West Bank governorates ................................Figure 4.7. Sensitivity analysis of PoGWQI and PaGWQI .........................................................Figure 4.8. Factors affecting PoGWQI and PaGWQI in the West Bank .....................................Figure 4.9. Relative importance analysis for the main factors affecting PoGWQI and PaGWQI67

Figure 2 . 5 .

 25 Figure 1.1. KIM-based interpolated vs. observed groundwater levels in an aquifer in piedmont plains, China (Xiao et al., 2016) ................................................................................................... Figure 1.2. Hydrological cycle (Levizzani and Cattani, 2019) .................................................... Figure 1.3. Architecture of Smart systems ................................................................................... Figure 1.4. Research outline ........................................................................................................Figure 2.1. The geographical setting of the West Bank ...............................................................Figure 2.2. Assessment of water challenges in Palestine .............................................................Figure 2.3. Groundwater flow, divides and aquifers in the West Bank and Gaza Strip ..............Figure 2.4. Spatial distribution of the groundwater wells in the West Bank ...............................Figure 2.5. Decline of groundwater level in the Well L/57 located in Gaza Strip between 1966 and 2014 ........................................................................................................................................Figure 2.6. Long-term average annual rainfall in the West Bank ................................................Figure 2.7. RWH suitability map for domestic uses in the West Bank .......................................Figure 2.8. Average annual concentrations of NO3 and Cl in groundwater wells in Tulkarm and Qalqiliya ........................................................................................................................................Figure 2.9. Spatial distribution of NO3 and Cl amongst Gaza Coastal Aquifer ..........................Figure 2.10. Drought occurrence in the MENA ...........................................................................Figure 2.11. Spatiotemporal impact of climate change and over-pumping on the groundwater level of the Gaza Coastal Aquifer between 1974 and 2040 ..........................................................Figure 2.12. Overall research methodology .................................................................................Figure 2.13. Assessment of existing and projected water scarcity ..............................................Figure 2.14. Protection of conventional water resources .............................................................Figure 2.15. Investigating the role of potential RWH in addressing the current and future domestic water scarcity .................................................................................................................Figure 2.16. Methodological framework for smart RWH and smart dual water supply systems Figure 3.1. Research methodology .............................................................................................. Figure 3.2. Schematic overview of the adopted SVAT model .................................................... Figure 3.3. Historical and current statistics of population and TWD in the whole West Bank between 1997 and 2020 ................................................................................................................Figure 3.4. Variation in TWS considering climate change ..........................................................Figure 4.1. Overall methodological framework ...........................................................................Figure 4.2. Development of potability and palatability groundwater quality indexes (PoGWQI and PaGWQI) ................................................................................................................................Figure 4.3. PoGWQGs and PaGWQGs in the West Bank ...........................................................Figure 4.4. Un-potability rate over four contaminated groundwater wells in the West Bank .....Figure 4.5. Spatiotemporal mapping of PoGWQI and PaGWQI in the West Bank between 2001 and 2016 ........................................................................................................................................Figure 4.6. PoGWQGs and PaGWQGs cross the West Bank governorates ................................Figure 4.7. Sensitivity analysis of PoGWQI and PaGWQI .........................................................Figure 4.8. Factors affecting PoGWQI and PaGWQI in the West Bank .....................................Figure 4.9. Relative importance analysis for the main factors affecting PoGWQI and PaGWQI67

Figure 2 . 6 .

 26 Figure 1.1. KIM-based interpolated vs. observed groundwater levels in an aquifer in piedmont plains, China (Xiao et al., 2016) ................................................................................................... Figure 1.2. Hydrological cycle (Levizzani and Cattani, 2019) .................................................... Figure 1.3. Architecture of Smart systems ................................................................................... Figure 1.4. Research outline ........................................................................................................Figure 2.1. The geographical setting of the West Bank ...............................................................Figure 2.2. Assessment of water challenges in Palestine .............................................................Figure 2.3. Groundwater flow, divides and aquifers in the West Bank and Gaza Strip ..............Figure 2.4. Spatial distribution of the groundwater wells in the West Bank ...............................Figure 2.5. Decline of groundwater level in the Well L/57 located in Gaza Strip between 1966 and 2014 ........................................................................................................................................Figure 2.6. Long-term average annual rainfall in the West Bank ................................................Figure 2.7. RWH suitability map for domestic uses in the West Bank .......................................Figure 2.8. Average annual concentrations of NO3 and Cl in groundwater wells in Tulkarm and Qalqiliya ........................................................................................................................................Figure 2.9. Spatial distribution of NO3 and Cl amongst Gaza Coastal Aquifer ..........................Figure 2.10. Drought occurrence in the MENA ...........................................................................Figure 2.11. Spatiotemporal impact of climate change and over-pumping on the groundwater level of the Gaza Coastal Aquifer between 1974 and 2040 ..........................................................Figure 2.12. Overall research methodology .................................................................................Figure 2.13. Assessment of existing and projected water scarcity ..............................................Figure 2.14. Protection of conventional water resources .............................................................Figure 2.15. Investigating the role of potential RWH in addressing the current and future domestic water scarcity .................................................................................................................Figure 2.16. Methodological framework for smart RWH and smart dual water supply systems Figure 3.1. Research methodology .............................................................................................. Figure 3.2. Schematic overview of the adopted SVAT model .................................................... Figure 3.3. Historical and current statistics of population and TWD in the whole West Bank between 1997 and 2020 ................................................................................................................Figure 3.4. Variation in TWS considering climate change ..........................................................Figure 4.1. Overall methodological framework ...........................................................................Figure 4.2. Development of potability and palatability groundwater quality indexes (PoGWQI and PaGWQI) ................................................................................................................................Figure 4.3. PoGWQGs and PaGWQGs in the West Bank ...........................................................Figure 4.4. Un-potability rate over four contaminated groundwater wells in the West Bank .....Figure 4.5. Spatiotemporal mapping of PoGWQI and PaGWQI in the West Bank between 2001 and 2016 ........................................................................................................................................Figure 4.6. PoGWQGs and PaGWQGs cross the West Bank governorates ................................Figure 4.7. Sensitivity analysis of PoGWQI and PaGWQI .........................................................Figure 4.8. Factors affecting PoGWQI and PaGWQI in the West Bank .....................................Figure 4.9. Relative importance analysis for the main factors affecting PoGWQI and PaGWQI67

Figure 2 . 7 .

 27 Figure 1.1. KIM-based interpolated vs. observed groundwater levels in an aquifer in piedmont plains, China (Xiao et al., 2016) ................................................................................................... Figure 1.2. Hydrological cycle (Levizzani and Cattani, 2019) .................................................... Figure 1.3. Architecture of Smart systems ................................................................................... Figure 1.4. Research outline ........................................................................................................Figure 2.1. The geographical setting of the West Bank ...............................................................Figure 2.2. Assessment of water challenges in Palestine .............................................................Figure 2.3. Groundwater flow, divides and aquifers in the West Bank and Gaza Strip ..............Figure 2.4. Spatial distribution of the groundwater wells in the West Bank ...............................Figure 2.5. Decline of groundwater level in the Well L/57 located in Gaza Strip between 1966 and 2014 ........................................................................................................................................Figure 2.6. Long-term average annual rainfall in the West Bank ................................................Figure 2.7. RWH suitability map for domestic uses in the West Bank .......................................Figure 2.8. Average annual concentrations of NO3 and Cl in groundwater wells in Tulkarm and Qalqiliya ........................................................................................................................................Figure 2.9. Spatial distribution of NO3 and Cl amongst Gaza Coastal Aquifer ..........................Figure 2.10. Drought occurrence in the MENA ...........................................................................Figure 2.11. Spatiotemporal impact of climate change and over-pumping on the groundwater level of the Gaza Coastal Aquifer between 1974 and 2040 ..........................................................Figure 2.12. Overall research methodology .................................................................................Figure 2.13. Assessment of existing and projected water scarcity ..............................................Figure 2.14. Protection of conventional water resources .............................................................Figure 2.15. Investigating the role of potential RWH in addressing the current and future domestic water scarcity .................................................................................................................Figure 2.16. Methodological framework for smart RWH and smart dual water supply systems Figure 3.1. Research methodology .............................................................................................. Figure 3.2. Schematic overview of the adopted SVAT model .................................................... Figure 3.3. Historical and current statistics of population and TWD in the whole West Bank between 1997 and 2020 ................................................................................................................Figure 3.4. Variation in TWS considering climate change ..........................................................Figure 4.1. Overall methodological framework ...........................................................................Figure 4.2. Development of potability and palatability groundwater quality indexes (PoGWQI and PaGWQI) ................................................................................................................................Figure 4.3. PoGWQGs and PaGWQGs in the West Bank ...........................................................Figure 4.4. Un-potability rate over four contaminated groundwater wells in the West Bank .....Figure 4.5. Spatiotemporal mapping of PoGWQI and PaGWQI in the West Bank between 2001 and 2016 ........................................................................................................................................Figure 4.6. PoGWQGs and PaGWQGs cross the West Bank governorates ................................Figure 4.7. Sensitivity analysis of PoGWQI and PaGWQI .........................................................Figure 4.8. Factors affecting PoGWQI and PaGWQI in the West Bank .....................................Figure 4.9. Relative importance analysis for the main factors affecting PoGWQI and PaGWQI67

Figure 2 . 8 .

 28 Figure 1.1. KIM-based interpolated vs. observed groundwater levels in an aquifer in piedmont plains, China (Xiao et al., 2016) ................................................................................................... Figure 1.2. Hydrological cycle (Levizzani and Cattani, 2019) .................................................... Figure 1.3. Architecture of Smart systems ................................................................................... Figure 1.4. Research outline ........................................................................................................Figure 2.1. The geographical setting of the West Bank ...............................................................Figure 2.2. Assessment of water challenges in Palestine .............................................................Figure 2.3. Groundwater flow, divides and aquifers in the West Bank and Gaza Strip ..............Figure 2.4. Spatial distribution of the groundwater wells in the West Bank ...............................Figure 2.5. Decline of groundwater level in the Well L/57 located in Gaza Strip between 1966 and 2014 ........................................................................................................................................Figure 2.6. Long-term average annual rainfall in the West Bank ................................................Figure 2.7. RWH suitability map for domestic uses in the West Bank .......................................Figure 2.8. Average annual concentrations of NO3 and Cl in groundwater wells in Tulkarm and Qalqiliya ........................................................................................................................................Figure 2.9. Spatial distribution of NO3 and Cl amongst Gaza Coastal Aquifer ..........................Figure 2.10. Drought occurrence in the MENA ...........................................................................Figure 2.11. Spatiotemporal impact of climate change and over-pumping on the groundwater level of the Gaza Coastal Aquifer between 1974 and 2040 ..........................................................Figure 2.12. Overall research methodology .................................................................................Figure 2.13. Assessment of existing and projected water scarcity ..............................................Figure 2.14. Protection of conventional water resources .............................................................Figure 2.15. Investigating the role of potential RWH in addressing the current and future domestic water scarcity .................................................................................................................Figure 2.16. Methodological framework for smart RWH and smart dual water supply systems Figure 3.1. Research methodology .............................................................................................. Figure 3.2. Schematic overview of the adopted SVAT model .................................................... Figure 3.3. Historical and current statistics of population and TWD in the whole West Bank between 1997 and 2020 ................................................................................................................Figure 3.4. Variation in TWS considering climate change ..........................................................Figure 4.1. Overall methodological framework ...........................................................................Figure 4.2. Development of potability and palatability groundwater quality indexes (PoGWQI and PaGWQI) ................................................................................................................................Figure 4.3. PoGWQGs and PaGWQGs in the West Bank ...........................................................Figure 4.4. Un-potability rate over four contaminated groundwater wells in the West Bank .....Figure 4.5. Spatiotemporal mapping of PoGWQI and PaGWQI in the West Bank between 2001 and 2016 ........................................................................................................................................Figure 4.6. PoGWQGs and PaGWQGs cross the West Bank governorates ................................Figure 4.7. Sensitivity analysis of PoGWQI and PaGWQI .........................................................Figure 4.8. Factors affecting PoGWQI and PaGWQI in the West Bank .....................................Figure 4.9. Relative importance analysis for the main factors affecting PoGWQI and PaGWQI67

Figure 2 . 9 .

 29 Figure 1.1. KIM-based interpolated vs. observed groundwater levels in an aquifer in piedmont plains, China (Xiao et al., 2016) ................................................................................................... Figure 1.2. Hydrological cycle (Levizzani and Cattani, 2019) .................................................... Figure 1.3. Architecture of Smart systems ................................................................................... Figure 1.4. Research outline ........................................................................................................Figure 2.1. The geographical setting of the West Bank ...............................................................Figure 2.2. Assessment of water challenges in Palestine .............................................................Figure 2.3. Groundwater flow, divides and aquifers in the West Bank and Gaza Strip ..............Figure 2.4. Spatial distribution of the groundwater wells in the West Bank ...............................Figure 2.5. Decline of groundwater level in the Well L/57 located in Gaza Strip between 1966 and 2014 ........................................................................................................................................Figure 2.6. Long-term average annual rainfall in the West Bank ................................................Figure 2.7. RWH suitability map for domestic uses in the West Bank .......................................Figure 2.8. Average annual concentrations of NO3 and Cl in groundwater wells in Tulkarm and Qalqiliya ........................................................................................................................................Figure 2.9. Spatial distribution of NO3 and Cl amongst Gaza Coastal Aquifer ..........................Figure 2.10. Drought occurrence in the MENA ...........................................................................Figure 2.11. Spatiotemporal impact of climate change and over-pumping on the groundwater level of the Gaza Coastal Aquifer between 1974 and 2040 ..........................................................Figure 2.12. Overall research methodology .................................................................................Figure 2.13. Assessment of existing and projected water scarcity ..............................................Figure 2.14. Protection of conventional water resources .............................................................Figure 2.15. Investigating the role of potential RWH in addressing the current and future domestic water scarcity .................................................................................................................Figure 2.16. Methodological framework for smart RWH and smart dual water supply systems Figure 3.1. Research methodology .............................................................................................. Figure 3.2. Schematic overview of the adopted SVAT model .................................................... Figure 3.3. Historical and current statistics of population and TWD in the whole West Bank between 1997 and 2020 ................................................................................................................Figure 3.4. Variation in TWS considering climate change ..........................................................Figure 4.1. Overall methodological framework ...........................................................................Figure 4.2. Development of potability and palatability groundwater quality indexes (PoGWQI and PaGWQI) ................................................................................................................................Figure 4.3. PoGWQGs and PaGWQGs in the West Bank ...........................................................Figure 4.4. Un-potability rate over four contaminated groundwater wells in the West Bank .....Figure 4.5. Spatiotemporal mapping of PoGWQI and PaGWQI in the West Bank between 2001 and 2016 ........................................................................................................................................Figure 4.6. PoGWQGs and PaGWQGs cross the West Bank governorates ................................Figure 4.7. Sensitivity analysis of PoGWQI and PaGWQI .........................................................Figure 4.8. Factors affecting PoGWQI and PaGWQI in the West Bank .....................................Figure 4.9. Relative importance analysis for the main factors affecting PoGWQI and PaGWQI67

Figure 2 . 10 .

 210 Figure 1.1. KIM-based interpolated vs. observed groundwater levels in an aquifer in piedmont plains, China (Xiao et al., 2016) ................................................................................................... Figure 1.2. Hydrological cycle (Levizzani and Cattani, 2019) .................................................... Figure 1.3. Architecture of Smart systems ................................................................................... Figure 1.4. Research outline ........................................................................................................Figure 2.1. The geographical setting of the West Bank ...............................................................Figure 2.2. Assessment of water challenges in Palestine .............................................................Figure 2.3. Groundwater flow, divides and aquifers in the West Bank and Gaza Strip ..............Figure 2.4. Spatial distribution of the groundwater wells in the West Bank ...............................Figure 2.5. Decline of groundwater level in the Well L/57 located in Gaza Strip between 1966 and 2014 ........................................................................................................................................Figure 2.6. Long-term average annual rainfall in the West Bank ................................................Figure 2.7. RWH suitability map for domestic uses in the West Bank .......................................Figure 2.8. Average annual concentrations of NO3 and Cl in groundwater wells in Tulkarm and Qalqiliya ........................................................................................................................................Figure 2.9. Spatial distribution of NO3 and Cl amongst Gaza Coastal Aquifer ..........................Figure 2.10. Drought occurrence in the MENA ...........................................................................Figure 2.11. Spatiotemporal impact of climate change and over-pumping on the groundwater level of the Gaza Coastal Aquifer between 1974 and 2040 ..........................................................Figure 2.12. Overall research methodology .................................................................................Figure 2.13. Assessment of existing and projected water scarcity ..............................................Figure 2.14. Protection of conventional water resources .............................................................Figure 2.15. Investigating the role of potential RWH in addressing the current and future domestic water scarcity .................................................................................................................Figure 2.16. Methodological framework for smart RWH and smart dual water supply systems Figure 3.1. Research methodology .............................................................................................. Figure 3.2. Schematic overview of the adopted SVAT model .................................................... Figure 3.3. Historical and current statistics of population and TWD in the whole West Bank between 1997 and 2020 ................................................................................................................Figure 3.4. Variation in TWS considering climate change ..........................................................Figure 4.1. Overall methodological framework ...........................................................................Figure 4.2. Development of potability and palatability groundwater quality indexes (PoGWQI and PaGWQI) ................................................................................................................................Figure 4.3. PoGWQGs and PaGWQGs in the West Bank ...........................................................Figure 4.4. Un-potability rate over four contaminated groundwater wells in the West Bank .....Figure 4.5. Spatiotemporal mapping of PoGWQI and PaGWQI in the West Bank between 2001 and 2016 ........................................................................................................................................Figure 4.6. PoGWQGs and PaGWQGs cross the West Bank governorates ................................Figure 4.7. Sensitivity analysis of PoGWQI and PaGWQI .........................................................Figure 4.8. Factors affecting PoGWQI and PaGWQI in the West Bank .....................................Figure 4.9. Relative importance analysis for the main factors affecting PoGWQI and PaGWQI67

Figure 2 . 11 .

 211 Figure 1.1. KIM-based interpolated vs. observed groundwater levels in an aquifer in piedmont plains, China (Xiao et al., 2016) ................................................................................................... Figure 1.2. Hydrological cycle (Levizzani and Cattani, 2019) .................................................... Figure 1.3. Architecture of Smart systems ................................................................................... Figure 1.4. Research outline ........................................................................................................Figure 2.1. The geographical setting of the West Bank ...............................................................Figure 2.2. Assessment of water challenges in Palestine .............................................................Figure 2.3. Groundwater flow, divides and aquifers in the West Bank and Gaza Strip ..............Figure 2.4. Spatial distribution of the groundwater wells in the West Bank ...............................Figure 2.5. Decline of groundwater level in the Well L/57 located in Gaza Strip between 1966 and 2014 ........................................................................................................................................Figure 2.6. Long-term average annual rainfall in the West Bank ................................................Figure 2.7. RWH suitability map for domestic uses in the West Bank .......................................Figure 2.8. Average annual concentrations of NO3 and Cl in groundwater wells in Tulkarm and Qalqiliya ........................................................................................................................................Figure 2.9. Spatial distribution of NO3 and Cl amongst Gaza Coastal Aquifer ..........................Figure 2.10. Drought occurrence in the MENA ...........................................................................Figure 2.11. Spatiotemporal impact of climate change and over-pumping on the groundwater level of the Gaza Coastal Aquifer between 1974 and 2040 ..........................................................Figure 2.12. Overall research methodology .................................................................................Figure 2.13. Assessment of existing and projected water scarcity ..............................................Figure 2.14. Protection of conventional water resources .............................................................Figure 2.15. Investigating the role of potential RWH in addressing the current and future domestic water scarcity .................................................................................................................Figure 2.16. Methodological framework for smart RWH and smart dual water supply systems Figure 3.1. Research methodology .............................................................................................. Figure 3.2. Schematic overview of the adopted SVAT model .................................................... Figure 3.3. Historical and current statistics of population and TWD in the whole West Bank between 1997 and 2020 ................................................................................................................Figure 3.4. Variation in TWS considering climate change ..........................................................Figure 4.1. Overall methodological framework ...........................................................................Figure 4.2. Development of potability and palatability groundwater quality indexes (PoGWQI and PaGWQI) ................................................................................................................................Figure 4.3. PoGWQGs and PaGWQGs in the West Bank ...........................................................Figure 4.4. Un-potability rate over four contaminated groundwater wells in the West Bank .....Figure 4.5. Spatiotemporal mapping of PoGWQI and PaGWQI in the West Bank between 2001 and 2016 ........................................................................................................................................Figure 4.6. PoGWQGs and PaGWQGs cross the West Bank governorates ................................Figure 4.7. Sensitivity analysis of PoGWQI and PaGWQI .........................................................Figure 4.8. Factors affecting PoGWQI and PaGWQI in the West Bank .....................................Figure 4.9. Relative importance analysis for the main factors affecting PoGWQI and PaGWQI67

Figure 2 . 12 .

 212 Figure 1.1. KIM-based interpolated vs. observed groundwater levels in an aquifer in piedmont plains, China (Xiao et al., 2016) ................................................................................................... Figure 1.2. Hydrological cycle (Levizzani and Cattani, 2019) .................................................... Figure 1.3. Architecture of Smart systems ................................................................................... Figure 1.4. Research outline ........................................................................................................Figure 2.1. The geographical setting of the West Bank ...............................................................Figure 2.2. Assessment of water challenges in Palestine .............................................................Figure 2.3. Groundwater flow, divides and aquifers in the West Bank and Gaza Strip ..............Figure 2.4. Spatial distribution of the groundwater wells in the West Bank ...............................Figure 2.5. Decline of groundwater level in the Well L/57 located in Gaza Strip between 1966 and 2014 ........................................................................................................................................Figure 2.6. Long-term average annual rainfall in the West Bank ................................................Figure 2.7. RWH suitability map for domestic uses in the West Bank .......................................Figure 2.8. Average annual concentrations of NO3 and Cl in groundwater wells in Tulkarm and Qalqiliya ........................................................................................................................................Figure 2.9. Spatial distribution of NO3 and Cl amongst Gaza Coastal Aquifer ..........................Figure 2.10. Drought occurrence in the MENA ...........................................................................Figure 2.11. Spatiotemporal impact of climate change and over-pumping on the groundwater level of the Gaza Coastal Aquifer between 1974 and 2040 ..........................................................Figure 2.12. Overall research methodology .................................................................................Figure 2.13. Assessment of existing and projected water scarcity ..............................................Figure 2.14. Protection of conventional water resources .............................................................Figure 2.15. Investigating the role of potential RWH in addressing the current and future domestic water scarcity .................................................................................................................Figure 2.16. Methodological framework for smart RWH and smart dual water supply systems Figure 3.1. Research methodology .............................................................................................. Figure 3.2. Schematic overview of the adopted SVAT model .................................................... Figure 3.3. Historical and current statistics of population and TWD in the whole West Bank between 1997 and 2020 ................................................................................................................Figure 3.4. Variation in TWS considering climate change ..........................................................Figure 4.1. Overall methodological framework ...........................................................................Figure 4.2. Development of potability and palatability groundwater quality indexes (PoGWQI and PaGWQI) ................................................................................................................................Figure 4.3. PoGWQGs and PaGWQGs in the West Bank ...........................................................Figure 4.4. Un-potability rate over four contaminated groundwater wells in the West Bank .....Figure 4.5. Spatiotemporal mapping of PoGWQI and PaGWQI in the West Bank between 2001 and 2016 ........................................................................................................................................Figure 4.6. PoGWQGs and PaGWQGs cross the West Bank governorates ................................Figure 4.7. Sensitivity analysis of PoGWQI and PaGWQI .........................................................Figure 4.8. Factors affecting PoGWQI and PaGWQI in the West Bank .....................................Figure 4.9. Relative importance analysis for the main factors affecting PoGWQI and PaGWQI67

Figure 2 . 13 .

 213 Figure 1.1. KIM-based interpolated vs. observed groundwater levels in an aquifer in piedmont plains, China (Xiao et al., 2016) ................................................................................................... Figure 1.2. Hydrological cycle (Levizzani and Cattani, 2019) .................................................... Figure 1.3. Architecture of Smart systems ................................................................................... Figure 1.4. Research outline ........................................................................................................Figure 2.1. The geographical setting of the West Bank ...............................................................Figure 2.2. Assessment of water challenges in Palestine .............................................................Figure 2.3. Groundwater flow, divides and aquifers in the West Bank and Gaza Strip ..............Figure 2.4. Spatial distribution of the groundwater wells in the West Bank ...............................Figure 2.5. Decline of groundwater level in the Well L/57 located in Gaza Strip between 1966 and 2014 ........................................................................................................................................Figure 2.6. Long-term average annual rainfall in the West Bank ................................................Figure 2.7. RWH suitability map for domestic uses in the West Bank .......................................Figure 2.8. Average annual concentrations of NO3 and Cl in groundwater wells in Tulkarm and Qalqiliya ........................................................................................................................................Figure 2.9. Spatial distribution of NO3 and Cl amongst Gaza Coastal Aquifer ..........................Figure 2.10. Drought occurrence in the MENA ...........................................................................Figure 2.11. Spatiotemporal impact of climate change and over-pumping on the groundwater level of the Gaza Coastal Aquifer between 1974 and 2040 ..........................................................Figure 2.12. Overall research methodology .................................................................................Figure 2.13. Assessment of existing and projected water scarcity ..............................................Figure 2.14. Protection of conventional water resources .............................................................Figure 2.15. Investigating the role of potential RWH in addressing the current and future domestic water scarcity .................................................................................................................Figure 2.16. Methodological framework for smart RWH and smart dual water supply systems Figure 3.1. Research methodology .............................................................................................. Figure 3.2. Schematic overview of the adopted SVAT model .................................................... Figure 3.3. Historical and current statistics of population and TWD in the whole West Bank between 1997 and 2020 ................................................................................................................Figure 3.4. Variation in TWS considering climate change ..........................................................Figure 4.1. Overall methodological framework ...........................................................................Figure 4.2. Development of potability and palatability groundwater quality indexes (PoGWQI and PaGWQI) ................................................................................................................................Figure 4.3. PoGWQGs and PaGWQGs in the West Bank ...........................................................Figure 4.4. Un-potability rate over four contaminated groundwater wells in the West Bank .....Figure 4.5. Spatiotemporal mapping of PoGWQI and PaGWQI in the West Bank between 2001 and 2016 ........................................................................................................................................Figure 4.6. PoGWQGs and PaGWQGs cross the West Bank governorates ................................Figure 4.7. Sensitivity analysis of PoGWQI and PaGWQI .........................................................Figure 4.8. Factors affecting PoGWQI and PaGWQI in the West Bank .....................................Figure 4.9. Relative importance analysis for the main factors affecting PoGWQI and PaGWQI67

Figure 2 . 14 .

 214 Figure 1.1. KIM-based interpolated vs. observed groundwater levels in an aquifer in piedmont plains, China (Xiao et al., 2016) ................................................................................................... Figure 1.2. Hydrological cycle (Levizzani and Cattani, 2019) .................................................... Figure 1.3. Architecture of Smart systems ................................................................................... Figure 1.4. Research outline ........................................................................................................Figure 2.1. The geographical setting of the West Bank ...............................................................Figure 2.2. Assessment of water challenges in Palestine .............................................................Figure 2.3. Groundwater flow, divides and aquifers in the West Bank and Gaza Strip ..............Figure 2.4. Spatial distribution of the groundwater wells in the West Bank ...............................Figure 2.5. Decline of groundwater level in the Well L/57 located in Gaza Strip between 1966 and 2014 ........................................................................................................................................Figure 2.6. Long-term average annual rainfall in the West Bank ................................................Figure 2.7. RWH suitability map for domestic uses in the West Bank .......................................Figure 2.8. Average annual concentrations of NO3 and Cl in groundwater wells in Tulkarm and Qalqiliya ........................................................................................................................................Figure 2.9. Spatial distribution of NO3 and Cl amongst Gaza Coastal Aquifer ..........................Figure 2.10. Drought occurrence in the MENA ...........................................................................Figure 2.11. Spatiotemporal impact of climate change and over-pumping on the groundwater level of the Gaza Coastal Aquifer between 1974 and 2040 ..........................................................Figure 2.12. Overall research methodology .................................................................................Figure 2.13. Assessment of existing and projected water scarcity ..............................................Figure 2.14. Protection of conventional water resources .............................................................Figure 2.15. Investigating the role of potential RWH in addressing the current and future domestic water scarcity .................................................................................................................Figure 2.16. Methodological framework for smart RWH and smart dual water supply systems Figure 3.1. Research methodology .............................................................................................. Figure 3.2. Schematic overview of the adopted SVAT model .................................................... Figure 3.3. Historical and current statistics of population and TWD in the whole West Bank between 1997 and 2020 ................................................................................................................Figure 3.4. Variation in TWS considering climate change ..........................................................Figure 4.1. Overall methodological framework ...........................................................................Figure 4.2. Development of potability and palatability groundwater quality indexes (PoGWQI and PaGWQI) ................................................................................................................................Figure 4.3. PoGWQGs and PaGWQGs in the West Bank ...........................................................Figure 4.4. Un-potability rate over four contaminated groundwater wells in the West Bank .....Figure 4.5. Spatiotemporal mapping of PoGWQI and PaGWQI in the West Bank between 2001 and 2016 ........................................................................................................................................Figure 4.6. PoGWQGs and PaGWQGs cross the West Bank governorates ................................Figure 4.7. Sensitivity analysis of PoGWQI and PaGWQI .........................................................Figure 4.8. Factors affecting PoGWQI and PaGWQI in the West Bank .....................................Figure 4.9. Relative importance analysis for the main factors affecting PoGWQI and PaGWQI67

Figure 2 . 15 .

 215 Figure 1.1. KIM-based interpolated vs. observed groundwater levels in an aquifer in piedmont plains, China (Xiao et al., 2016) ................................................................................................... Figure 1.2. Hydrological cycle (Levizzani and Cattani, 2019) .................................................... Figure 1.3. Architecture of Smart systems ................................................................................... Figure 1.4. Research outline ........................................................................................................Figure 2.1. The geographical setting of the West Bank ...............................................................Figure 2.2. Assessment of water challenges in Palestine .............................................................Figure 2.3. Groundwater flow, divides and aquifers in the West Bank and Gaza Strip ..............Figure 2.4. Spatial distribution of the groundwater wells in the West Bank ...............................Figure 2.5. Decline of groundwater level in the Well L/57 located in Gaza Strip between 1966 and 2014 ........................................................................................................................................Figure 2.6. Long-term average annual rainfall in the West Bank ................................................Figure 2.7. RWH suitability map for domestic uses in the West Bank .......................................Figure 2.8. Average annual concentrations of NO3 and Cl in groundwater wells in Tulkarm and Qalqiliya ........................................................................................................................................Figure 2.9. Spatial distribution of NO3 and Cl amongst Gaza Coastal Aquifer ..........................Figure 2.10. Drought occurrence in the MENA ...........................................................................Figure 2.11. Spatiotemporal impact of climate change and over-pumping on the groundwater level of the Gaza Coastal Aquifer between 1974 and 2040 ..........................................................Figure 2.12. Overall research methodology .................................................................................Figure 2.13. Assessment of existing and projected water scarcity ..............................................Figure 2.14. Protection of conventional water resources .............................................................Figure 2.15. Investigating the role of potential RWH in addressing the current and future domestic water scarcity .................................................................................................................Figure 2.16. Methodological framework for smart RWH and smart dual water supply systems Figure 3.1. Research methodology .............................................................................................. Figure 3.2. Schematic overview of the adopted SVAT model .................................................... Figure 3.3. Historical and current statistics of population and TWD in the whole West Bank between 1997 and 2020 ................................................................................................................Figure 3.4. Variation in TWS considering climate change ..........................................................Figure 4.1. Overall methodological framework ...........................................................................Figure 4.2. Development of potability and palatability groundwater quality indexes (PoGWQI and PaGWQI) ................................................................................................................................Figure 4.3. PoGWQGs and PaGWQGs in the West Bank ...........................................................Figure 4.4. Un-potability rate over four contaminated groundwater wells in the West Bank .....Figure 4.5. Spatiotemporal mapping of PoGWQI and PaGWQI in the West Bank between 2001 and 2016 ........................................................................................................................................Figure 4.6. PoGWQGs and PaGWQGs cross the West Bank governorates ................................Figure 4.7. Sensitivity analysis of PoGWQI and PaGWQI .........................................................Figure 4.8. Factors affecting PoGWQI and PaGWQI in the West Bank .....................................Figure 4.9. Relative importance analysis for the main factors affecting PoGWQI and PaGWQI67

Figure 2 . 16 . 3 . 1 .Figure 3 . 2 .Figure 3 . 3 .

 216313233 Figure 1.1. KIM-based interpolated vs. observed groundwater levels in an aquifer in piedmont plains, China (Xiao et al., 2016) ................................................................................................... Figure 1.2. Hydrological cycle (Levizzani and Cattani, 2019) .................................................... Figure 1.3. Architecture of Smart systems ................................................................................... Figure 1.4. Research outline ........................................................................................................Figure 2.1. The geographical setting of the West Bank ...............................................................Figure 2.2. Assessment of water challenges in Palestine .............................................................Figure 2.3. Groundwater flow, divides and aquifers in the West Bank and Gaza Strip ..............Figure 2.4. Spatial distribution of the groundwater wells in the West Bank ...............................Figure 2.5. Decline of groundwater level in the Well L/57 located in Gaza Strip between 1966 and 2014 ........................................................................................................................................Figure 2.6. Long-term average annual rainfall in the West Bank ................................................Figure 2.7. RWH suitability map for domestic uses in the West Bank .......................................Figure 2.8. Average annual concentrations of NO3 and Cl in groundwater wells in Tulkarm and Qalqiliya ........................................................................................................................................Figure 2.9. Spatial distribution of NO3 and Cl amongst Gaza Coastal Aquifer ..........................Figure 2.10. Drought occurrence in the MENA ...........................................................................Figure 2.11. Spatiotemporal impact of climate change and over-pumping on the groundwater level of the Gaza Coastal Aquifer between 1974 and 2040 ..........................................................Figure 2.12. Overall research methodology .................................................................................Figure 2.13. Assessment of existing and projected water scarcity ..............................................Figure 2.14. Protection of conventional water resources .............................................................Figure 2.15. Investigating the role of potential RWH in addressing the current and future domestic water scarcity .................................................................................................................Figure 2.16. Methodological framework for smart RWH and smart dual water supply systems Figure 3.1. Research methodology .............................................................................................. Figure 3.2. Schematic overview of the adopted SVAT model .................................................... Figure 3.3. Historical and current statistics of population and TWD in the whole West Bank between 1997 and 2020 ................................................................................................................Figure 3.4. Variation in TWS considering climate change ..........................................................Figure 4.1. Overall methodological framework ...........................................................................Figure 4.2. Development of potability and palatability groundwater quality indexes (PoGWQI and PaGWQI) ................................................................................................................................Figure 4.3. PoGWQGs and PaGWQGs in the West Bank ...........................................................Figure 4.4. Un-potability rate over four contaminated groundwater wells in the West Bank .....Figure 4.5. Spatiotemporal mapping of PoGWQI and PaGWQI in the West Bank between 2001 and 2016 ........................................................................................................................................Figure 4.6. PoGWQGs and PaGWQGs cross the West Bank governorates ................................Figure 4.7. Sensitivity analysis of PoGWQI and PaGWQI .........................................................Figure 4.8. Factors affecting PoGWQI and PaGWQI in the West Bank .....................................Figure 4.9. Relative importance analysis for the main factors affecting PoGWQI and PaGWQI67

Figure 3 . 4 .

 34 Figure 1.1. KIM-based interpolated vs. observed groundwater levels in an aquifer in piedmont plains, China (Xiao et al., 2016) ................................................................................................... Figure 1.2. Hydrological cycle (Levizzani and Cattani, 2019) .................................................... Figure 1.3. Architecture of Smart systems ................................................................................... Figure 1.4. Research outline ........................................................................................................Figure 2.1. The geographical setting of the West Bank ...............................................................Figure 2.2. Assessment of water challenges in Palestine .............................................................Figure 2.3. Groundwater flow, divides and aquifers in the West Bank and Gaza Strip ..............Figure 2.4. Spatial distribution of the groundwater wells in the West Bank ...............................Figure 2.5. Decline of groundwater level in the Well L/57 located in Gaza Strip between 1966 and 2014 ........................................................................................................................................Figure 2.6. Long-term average annual rainfall in the West Bank ................................................Figure 2.7. RWH suitability map for domestic uses in the West Bank .......................................Figure 2.8. Average annual concentrations of NO3 and Cl in groundwater wells in Tulkarm and Qalqiliya ........................................................................................................................................Figure 2.9. Spatial distribution of NO3 and Cl amongst Gaza Coastal Aquifer ..........................Figure 2.10. Drought occurrence in the MENA ...........................................................................Figure 2.11. Spatiotemporal impact of climate change and over-pumping on the groundwater level of the Gaza Coastal Aquifer between 1974 and 2040 ..........................................................Figure 2.12. Overall research methodology .................................................................................Figure 2.13. Assessment of existing and projected water scarcity ..............................................Figure 2.14. Protection of conventional water resources .............................................................Figure 2.15. Investigating the role of potential RWH in addressing the current and future domestic water scarcity .................................................................................................................Figure 2.16. Methodological framework for smart RWH and smart dual water supply systems Figure 3.1. Research methodology .............................................................................................. Figure 3.2. Schematic overview of the adopted SVAT model .................................................... Figure 3.3. Historical and current statistics of population and TWD in the whole West Bank between 1997 and 2020 ................................................................................................................Figure 3.4. Variation in TWS considering climate change ..........................................................Figure 4.1. Overall methodological framework ...........................................................................Figure 4.2. Development of potability and palatability groundwater quality indexes (PoGWQI and PaGWQI) ................................................................................................................................Figure 4.3. PoGWQGs and PaGWQGs in the West Bank ...........................................................Figure 4.4. Un-potability rate over four contaminated groundwater wells in the West Bank .....Figure 4.5. Spatiotemporal mapping of PoGWQI and PaGWQI in the West Bank between 2001 and 2016 ........................................................................................................................................Figure 4.6. PoGWQGs and PaGWQGs cross the West Bank governorates ................................Figure 4.7. Sensitivity analysis of PoGWQI and PaGWQI .........................................................Figure 4.8. Factors affecting PoGWQI and PaGWQI in the West Bank .....................................Figure 4.9. Relative importance analysis for the main factors affecting PoGWQI and PaGWQI67

Figure 4 . 1 .

 41 Figure 1.1. KIM-based interpolated vs. observed groundwater levels in an aquifer in piedmont plains, China (Xiao et al., 2016) ................................................................................................... Figure 1.2. Hydrological cycle (Levizzani and Cattani, 2019) .................................................... Figure 1.3. Architecture of Smart systems ................................................................................... Figure 1.4. Research outline ........................................................................................................Figure 2.1. The geographical setting of the West Bank ...............................................................Figure 2.2. Assessment of water challenges in Palestine .............................................................Figure 2.3. Groundwater flow, divides and aquifers in the West Bank and Gaza Strip ..............Figure 2.4. Spatial distribution of the groundwater wells in the West Bank ...............................Figure 2.5. Decline of groundwater level in the Well L/57 located in Gaza Strip between 1966 and 2014 ........................................................................................................................................Figure 2.6. Long-term average annual rainfall in the West Bank ................................................Figure 2.7. RWH suitability map for domestic uses in the West Bank .......................................Figure 2.8. Average annual concentrations of NO3 and Cl in groundwater wells in Tulkarm and Qalqiliya ........................................................................................................................................Figure 2.9. Spatial distribution of NO3 and Cl amongst Gaza Coastal Aquifer ..........................Figure 2.10. Drought occurrence in the MENA ...........................................................................Figure 2.11. Spatiotemporal impact of climate change and over-pumping on the groundwater level of the Gaza Coastal Aquifer between 1974 and 2040 ..........................................................Figure 2.12. Overall research methodology .................................................................................Figure 2.13. Assessment of existing and projected water scarcity ..............................................Figure 2.14. Protection of conventional water resources .............................................................Figure 2.15. Investigating the role of potential RWH in addressing the current and future domestic water scarcity .................................................................................................................Figure 2.16. Methodological framework for smart RWH and smart dual water supply systems Figure 3.1. Research methodology .............................................................................................. Figure 3.2. Schematic overview of the adopted SVAT model .................................................... Figure 3.3. Historical and current statistics of population and TWD in the whole West Bank between 1997 and 2020 ................................................................................................................Figure 3.4. Variation in TWS considering climate change ..........................................................Figure 4.1. Overall methodological framework ...........................................................................Figure 4.2. Development of potability and palatability groundwater quality indexes (PoGWQI and PaGWQI) ................................................................................................................................Figure 4.3. PoGWQGs and PaGWQGs in the West Bank ...........................................................Figure 4.4. Un-potability rate over four contaminated groundwater wells in the West Bank .....Figure 4.5. Spatiotemporal mapping of PoGWQI and PaGWQI in the West Bank between 2001 and 2016 ........................................................................................................................................Figure 4.6. PoGWQGs and PaGWQGs cross the West Bank governorates ................................Figure 4.7. Sensitivity analysis of PoGWQI and PaGWQI .........................................................Figure 4.8. Factors affecting PoGWQI and PaGWQI in the West Bank .....................................Figure 4.9. Relative importance analysis for the main factors affecting PoGWQI and PaGWQI67

Figure 4 . 2 .

 42 Figure 1.1. KIM-based interpolated vs. observed groundwater levels in an aquifer in piedmont plains, China (Xiao et al., 2016) ................................................................................................... Figure 1.2. Hydrological cycle (Levizzani and Cattani, 2019) .................................................... Figure 1.3. Architecture of Smart systems ................................................................................... Figure 1.4. Research outline ........................................................................................................Figure 2.1. The geographical setting of the West Bank ...............................................................Figure 2.2. Assessment of water challenges in Palestine .............................................................Figure 2.3. Groundwater flow, divides and aquifers in the West Bank and Gaza Strip ..............Figure 2.4. Spatial distribution of the groundwater wells in the West Bank ...............................Figure 2.5. Decline of groundwater level in the Well L/57 located in Gaza Strip between 1966 and 2014 ........................................................................................................................................Figure 2.6. Long-term average annual rainfall in the West Bank ................................................Figure 2.7. RWH suitability map for domestic uses in the West Bank .......................................Figure 2.8. Average annual concentrations of NO3 and Cl in groundwater wells in Tulkarm and Qalqiliya ........................................................................................................................................Figure 2.9. Spatial distribution of NO3 and Cl amongst Gaza Coastal Aquifer ..........................Figure 2.10. Drought occurrence in the MENA ...........................................................................Figure 2.11. Spatiotemporal impact of climate change and over-pumping on the groundwater level of the Gaza Coastal Aquifer between 1974 and 2040 ..........................................................Figure 2.12. Overall research methodology .................................................................................Figure 2.13. Assessment of existing and projected water scarcity ..............................................Figure 2.14. Protection of conventional water resources .............................................................Figure 2.15. Investigating the role of potential RWH in addressing the current and future domestic water scarcity .................................................................................................................Figure 2.16. Methodological framework for smart RWH and smart dual water supply systems Figure 3.1. Research methodology .............................................................................................. Figure 3.2. Schematic overview of the adopted SVAT model .................................................... Figure 3.3. Historical and current statistics of population and TWD in the whole West Bank between 1997 and 2020 ................................................................................................................Figure 3.4. Variation in TWS considering climate change ..........................................................Figure 4.1. Overall methodological framework ...........................................................................Figure 4.2. Development of potability and palatability groundwater quality indexes (PoGWQI and PaGWQI) ................................................................................................................................Figure 4.3. PoGWQGs and PaGWQGs in the West Bank ...........................................................Figure 4.4. Un-potability rate over four contaminated groundwater wells in the West Bank .....Figure 4.5. Spatiotemporal mapping of PoGWQI and PaGWQI in the West Bank between 2001 and 2016 ........................................................................................................................................Figure 4.6. PoGWQGs and PaGWQGs cross the West Bank governorates ................................Figure 4.7. Sensitivity analysis of PoGWQI and PaGWQI .........................................................Figure 4.8. Factors affecting PoGWQI and PaGWQI in the West Bank .....................................Figure 4.9. Relative importance analysis for the main factors affecting PoGWQI and PaGWQI67

Figure 4 . 3 .

 43 Figure 1.1. KIM-based interpolated vs. observed groundwater levels in an aquifer in piedmont plains, China (Xiao et al., 2016) ................................................................................................... Figure 1.2. Hydrological cycle (Levizzani and Cattani, 2019) .................................................... Figure 1.3. Architecture of Smart systems ................................................................................... Figure 1.4. Research outline ........................................................................................................Figure 2.1. The geographical setting of the West Bank ...............................................................Figure 2.2. Assessment of water challenges in Palestine .............................................................Figure 2.3. Groundwater flow, divides and aquifers in the West Bank and Gaza Strip ..............Figure 2.4. Spatial distribution of the groundwater wells in the West Bank ...............................Figure 2.5. Decline of groundwater level in the Well L/57 located in Gaza Strip between 1966 and 2014 ........................................................................................................................................Figure 2.6. Long-term average annual rainfall in the West Bank ................................................Figure 2.7. RWH suitability map for domestic uses in the West Bank .......................................Figure 2.8. Average annual concentrations of NO3 and Cl in groundwater wells in Tulkarm and Qalqiliya ........................................................................................................................................Figure 2.9. Spatial distribution of NO3 and Cl amongst Gaza Coastal Aquifer ..........................Figure 2.10. Drought occurrence in the MENA ...........................................................................Figure 2.11. Spatiotemporal impact of climate change and over-pumping on the groundwater level of the Gaza Coastal Aquifer between 1974 and 2040 ..........................................................Figure 2.12. Overall research methodology .................................................................................Figure 2.13. Assessment of existing and projected water scarcity ..............................................Figure 2.14. Protection of conventional water resources .............................................................Figure 2.15. Investigating the role of potential RWH in addressing the current and future domestic water scarcity .................................................................................................................Figure 2.16. Methodological framework for smart RWH and smart dual water supply systems Figure 3.1. Research methodology .............................................................................................. Figure 3.2. Schematic overview of the adopted SVAT model .................................................... Figure 3.3. Historical and current statistics of population and TWD in the whole West Bank between 1997 and 2020 ................................................................................................................Figure 3.4. Variation in TWS considering climate change ..........................................................Figure 4.1. Overall methodological framework ...........................................................................Figure 4.2. Development of potability and palatability groundwater quality indexes (PoGWQI and PaGWQI) ................................................................................................................................Figure 4.3. PoGWQGs and PaGWQGs in the West Bank ...........................................................Figure 4.4. Un-potability rate over four contaminated groundwater wells in the West Bank .....Figure 4.5. Spatiotemporal mapping of PoGWQI and PaGWQI in the West Bank between 2001 and 2016 ........................................................................................................................................Figure 4.6. PoGWQGs and PaGWQGs cross the West Bank governorates ................................Figure 4.7. Sensitivity analysis of PoGWQI and PaGWQI .........................................................Figure 4.8. Factors affecting PoGWQI and PaGWQI in the West Bank .....................................Figure 4.9. Relative importance analysis for the main factors affecting PoGWQI and PaGWQI67

Figure 4 . 4 .

 44 Figure 1.1. KIM-based interpolated vs. observed groundwater levels in an aquifer in piedmont plains, China (Xiao et al., 2016) ................................................................................................... Figure 1.2. Hydrological cycle (Levizzani and Cattani, 2019) .................................................... Figure 1.3. Architecture of Smart systems ................................................................................... Figure 1.4. Research outline ........................................................................................................Figure 2.1. The geographical setting of the West Bank ...............................................................Figure 2.2. Assessment of water challenges in Palestine .............................................................Figure 2.3. Groundwater flow, divides and aquifers in the West Bank and Gaza Strip ..............Figure 2.4. Spatial distribution of the groundwater wells in the West Bank ...............................Figure 2.5. Decline of groundwater level in the Well L/57 located in Gaza Strip between 1966 and 2014 ........................................................................................................................................Figure 2.6. Long-term average annual rainfall in the West Bank ................................................Figure 2.7. RWH suitability map for domestic uses in the West Bank .......................................Figure 2.8. Average annual concentrations of NO3 and Cl in groundwater wells in Tulkarm and Qalqiliya ........................................................................................................................................Figure 2.9. Spatial distribution of NO3 and Cl amongst Gaza Coastal Aquifer ..........................Figure 2.10. Drought occurrence in the MENA ...........................................................................Figure 2.11. Spatiotemporal impact of climate change and over-pumping on the groundwater level of the Gaza Coastal Aquifer between 1974 and 2040 ..........................................................Figure 2.12. Overall research methodology .................................................................................Figure 2.13. Assessment of existing and projected water scarcity ..............................................Figure 2.14. Protection of conventional water resources .............................................................Figure 2.15. Investigating the role of potential RWH in addressing the current and future domestic water scarcity .................................................................................................................Figure 2.16. Methodological framework for smart RWH and smart dual water supply systems Figure 3.1. Research methodology .............................................................................................. Figure 3.2. Schematic overview of the adopted SVAT model .................................................... Figure 3.3. Historical and current statistics of population and TWD in the whole West Bank between 1997 and 2020 ................................................................................................................Figure 3.4. Variation in TWS considering climate change ..........................................................Figure 4.1. Overall methodological framework ...........................................................................Figure 4.2. Development of potability and palatability groundwater quality indexes (PoGWQI and PaGWQI) ................................................................................................................................Figure 4.3. PoGWQGs and PaGWQGs in the West Bank ...........................................................Figure 4.4. Un-potability rate over four contaminated groundwater wells in the West Bank .....Figure 4.5. Spatiotemporal mapping of PoGWQI and PaGWQI in the West Bank between 2001 and 2016 ........................................................................................................................................Figure 4.6. PoGWQGs and PaGWQGs cross the West Bank governorates ................................Figure 4.7. Sensitivity analysis of PoGWQI and PaGWQI .........................................................Figure 4.8. Factors affecting PoGWQI and PaGWQI in the West Bank .....................................Figure 4.9. Relative importance analysis for the main factors affecting PoGWQI and PaGWQI67

Figure 4 . 5 .

 45 Figure 1.1. KIM-based interpolated vs. observed groundwater levels in an aquifer in piedmont plains, China (Xiao et al., 2016) ................................................................................................... Figure 1.2. Hydrological cycle (Levizzani and Cattani, 2019) .................................................... Figure 1.3. Architecture of Smart systems ................................................................................... Figure 1.4. Research outline ........................................................................................................Figure 2.1. The geographical setting of the West Bank ...............................................................Figure 2.2. Assessment of water challenges in Palestine .............................................................Figure 2.3. Groundwater flow, divides and aquifers in the West Bank and Gaza Strip ..............Figure 2.4. Spatial distribution of the groundwater wells in the West Bank ...............................Figure 2.5. Decline of groundwater level in the Well L/57 located in Gaza Strip between 1966 and 2014 ........................................................................................................................................Figure 2.6. Long-term average annual rainfall in the West Bank ................................................Figure 2.7. RWH suitability map for domestic uses in the West Bank .......................................Figure 2.8. Average annual concentrations of NO3 and Cl in groundwater wells in Tulkarm and Qalqiliya ........................................................................................................................................Figure 2.9. Spatial distribution of NO3 and Cl amongst Gaza Coastal Aquifer ..........................Figure 2.10. Drought occurrence in the MENA ...........................................................................Figure 2.11. Spatiotemporal impact of climate change and over-pumping on the groundwater level of the Gaza Coastal Aquifer between 1974 and 2040 ..........................................................Figure 2.12. Overall research methodology .................................................................................Figure 2.13. Assessment of existing and projected water scarcity ..............................................Figure 2.14. Protection of conventional water resources .............................................................Figure 2.15. Investigating the role of potential RWH in addressing the current and future domestic water scarcity .................................................................................................................Figure 2.16. Methodological framework for smart RWH and smart dual water supply systems Figure 3.1. Research methodology .............................................................................................. Figure 3.2. Schematic overview of the adopted SVAT model .................................................... Figure 3.3. Historical and current statistics of population and TWD in the whole West Bank between 1997 and 2020 ................................................................................................................Figure 3.4. Variation in TWS considering climate change ..........................................................Figure 4.1. Overall methodological framework ...........................................................................Figure 4.2. Development of potability and palatability groundwater quality indexes (PoGWQI and PaGWQI) ................................................................................................................................Figure 4.3. PoGWQGs and PaGWQGs in the West Bank ...........................................................Figure 4.4. Un-potability rate over four contaminated groundwater wells in the West Bank .....Figure 4.5. Spatiotemporal mapping of PoGWQI and PaGWQI in the West Bank between 2001 and 2016 ........................................................................................................................................Figure 4.6. PoGWQGs and PaGWQGs cross the West Bank governorates ................................Figure 4.7. Sensitivity analysis of PoGWQI and PaGWQI .........................................................Figure 4.8. Factors affecting PoGWQI and PaGWQI in the West Bank .....................................Figure 4.9. Relative importance analysis for the main factors affecting PoGWQI and PaGWQI67

Figure 4 . 6 .

 46 Figure 1.1. KIM-based interpolated vs. observed groundwater levels in an aquifer in piedmont plains, China (Xiao et al., 2016) ................................................................................................... Figure 1.2. Hydrological cycle (Levizzani and Cattani, 2019) .................................................... Figure 1.3. Architecture of Smart systems ................................................................................... Figure 1.4. Research outline ........................................................................................................Figure 2.1. The geographical setting of the West Bank ...............................................................Figure 2.2. Assessment of water challenges in Palestine .............................................................Figure 2.3. Groundwater flow, divides and aquifers in the West Bank and Gaza Strip ..............Figure 2.4. Spatial distribution of the groundwater wells in the West Bank ...............................Figure 2.5. Decline of groundwater level in the Well L/57 located in Gaza Strip between 1966 and 2014 ........................................................................................................................................Figure 2.6. Long-term average annual rainfall in the West Bank ................................................Figure 2.7. RWH suitability map for domestic uses in the West Bank .......................................Figure 2.8. Average annual concentrations of NO3 and Cl in groundwater wells in Tulkarm and Qalqiliya ........................................................................................................................................Figure 2.9. Spatial distribution of NO3 and Cl amongst Gaza Coastal Aquifer ..........................Figure 2.10. Drought occurrence in the MENA ...........................................................................Figure 2.11. Spatiotemporal impact of climate change and over-pumping on the groundwater level of the Gaza Coastal Aquifer between 1974 and 2040 ..........................................................Figure 2.12. Overall research methodology .................................................................................Figure 2.13. Assessment of existing and projected water scarcity ..............................................Figure 2.14. Protection of conventional water resources .............................................................Figure 2.15. Investigating the role of potential RWH in addressing the current and future domestic water scarcity .................................................................................................................Figure 2.16. Methodological framework for smart RWH and smart dual water supply systems Figure 3.1. Research methodology .............................................................................................. Figure 3.2. Schematic overview of the adopted SVAT model .................................................... Figure 3.3. Historical and current statistics of population and TWD in the whole West Bank between 1997 and 2020 ................................................................................................................Figure 3.4. Variation in TWS considering climate change ..........................................................Figure 4.1. Overall methodological framework ...........................................................................Figure 4.2. Development of potability and palatability groundwater quality indexes (PoGWQI and PaGWQI) ................................................................................................................................Figure 4.3. PoGWQGs and PaGWQGs in the West Bank ...........................................................Figure 4.4. Un-potability rate over four contaminated groundwater wells in the West Bank .....Figure 4.5. Spatiotemporal mapping of PoGWQI and PaGWQI in the West Bank between 2001 and 2016 ........................................................................................................................................Figure 4.6. PoGWQGs and PaGWQGs cross the West Bank governorates ................................Figure 4.7. Sensitivity analysis of PoGWQI and PaGWQI .........................................................Figure 4.8. Factors affecting PoGWQI and PaGWQI in the West Bank .....................................Figure 4.9. Relative importance analysis for the main factors affecting PoGWQI and PaGWQI67

Figure 4 . 7 .

 47 Figure 1.1. KIM-based interpolated vs. observed groundwater levels in an aquifer in piedmont plains, China (Xiao et al., 2016) ................................................................................................... Figure 1.2. Hydrological cycle (Levizzani and Cattani, 2019) .................................................... Figure 1.3. Architecture of Smart systems ................................................................................... Figure 1.4. Research outline ........................................................................................................Figure 2.1. The geographical setting of the West Bank ...............................................................Figure 2.2. Assessment of water challenges in Palestine .............................................................Figure 2.3. Groundwater flow, divides and aquifers in the West Bank and Gaza Strip ..............Figure 2.4. Spatial distribution of the groundwater wells in the West Bank ...............................Figure 2.5. Decline of groundwater level in the Well L/57 located in Gaza Strip between 1966 and 2014 ........................................................................................................................................Figure 2.6. Long-term average annual rainfall in the West Bank ................................................Figure 2.7. RWH suitability map for domestic uses in the West Bank .......................................Figure 2.8. Average annual concentrations of NO3 and Cl in groundwater wells in Tulkarm and Qalqiliya ........................................................................................................................................Figure 2.9. Spatial distribution of NO3 and Cl amongst Gaza Coastal Aquifer ..........................Figure 2.10. Drought occurrence in the MENA ...........................................................................Figure 2.11. Spatiotemporal impact of climate change and over-pumping on the groundwater level of the Gaza Coastal Aquifer between 1974 and 2040 ..........................................................Figure 2.12. Overall research methodology .................................................................................Figure 2.13. Assessment of existing and projected water scarcity ..............................................Figure 2.14. Protection of conventional water resources .............................................................Figure 2.15. Investigating the role of potential RWH in addressing the current and future domestic water scarcity .................................................................................................................Figure 2.16. Methodological framework for smart RWH and smart dual water supply systems Figure 3.1. Research methodology .............................................................................................. Figure 3.2. Schematic overview of the adopted SVAT model .................................................... Figure 3.3. Historical and current statistics of population and TWD in the whole West Bank between 1997 and 2020 ................................................................................................................Figure 3.4. Variation in TWS considering climate change ..........................................................Figure 4.1. Overall methodological framework ...........................................................................Figure 4.2. Development of potability and palatability groundwater quality indexes (PoGWQI and PaGWQI) ................................................................................................................................Figure 4.3. PoGWQGs and PaGWQGs in the West Bank ...........................................................Figure 4.4. Un-potability rate over four contaminated groundwater wells in the West Bank .....Figure 4.5. Spatiotemporal mapping of PoGWQI and PaGWQI in the West Bank between 2001 and 2016 ........................................................................................................................................Figure 4.6. PoGWQGs and PaGWQGs cross the West Bank governorates ................................Figure 4.7. Sensitivity analysis of PoGWQI and PaGWQI .........................................................Figure 4.8. Factors affecting PoGWQI and PaGWQI in the West Bank .....................................Figure 4.9. Relative importance analysis for the main factors affecting PoGWQI and PaGWQI67

Figure 4 . 8 .

 48 Figure 1.1. KIM-based interpolated vs. observed groundwater levels in an aquifer in piedmont plains, China (Xiao et al., 2016) ................................................................................................... Figure 1.2. Hydrological cycle (Levizzani and Cattani, 2019) .................................................... Figure 1.3. Architecture of Smart systems ................................................................................... Figure 1.4. Research outline ........................................................................................................Figure 2.1. The geographical setting of the West Bank ...............................................................Figure 2.2. Assessment of water challenges in Palestine .............................................................Figure 2.3. Groundwater flow, divides and aquifers in the West Bank and Gaza Strip ..............Figure 2.4. Spatial distribution of the groundwater wells in the West Bank ...............................Figure 2.5. Decline of groundwater level in the Well L/57 located in Gaza Strip between 1966 and 2014 ........................................................................................................................................Figure 2.6. Long-term average annual rainfall in the West Bank ................................................Figure 2.7. RWH suitability map for domestic uses in the West Bank .......................................Figure 2.8. Average annual concentrations of NO3 and Cl in groundwater wells in Tulkarm and Qalqiliya ........................................................................................................................................Figure 2.9. Spatial distribution of NO3 and Cl amongst Gaza Coastal Aquifer ..........................Figure 2.10. Drought occurrence in the MENA ...........................................................................Figure 2.11. Spatiotemporal impact of climate change and over-pumping on the groundwater level of the Gaza Coastal Aquifer between 1974 and 2040 ..........................................................Figure 2.12. Overall research methodology .................................................................................Figure 2.13. Assessment of existing and projected water scarcity ..............................................Figure 2.14. Protection of conventional water resources .............................................................Figure 2.15. Investigating the role of potential RWH in addressing the current and future domestic water scarcity .................................................................................................................Figure 2.16. Methodological framework for smart RWH and smart dual water supply systems Figure 3.1. Research methodology .............................................................................................. Figure 3.2. Schematic overview of the adopted SVAT model .................................................... Figure 3.3. Historical and current statistics of population and TWD in the whole West Bank between 1997 and 2020 ................................................................................................................Figure 3.4. Variation in TWS considering climate change ..........................................................Figure 4.1. Overall methodological framework ...........................................................................Figure 4.2. Development of potability and palatability groundwater quality indexes (PoGWQI and PaGWQI) ................................................................................................................................Figure 4.3. PoGWQGs and PaGWQGs in the West Bank ...........................................................Figure 4.4. Un-potability rate over four contaminated groundwater wells in the West Bank .....Figure 4.5. Spatiotemporal mapping of PoGWQI and PaGWQI in the West Bank between 2001 and 2016 ........................................................................................................................................Figure 4.6. PoGWQGs and PaGWQGs cross the West Bank governorates ................................Figure 4.7. Sensitivity analysis of PoGWQI and PaGWQI .........................................................Figure 4.8. Factors affecting PoGWQI and PaGWQI in the West Bank .....................................Figure 4.9. Relative importance analysis for the main factors affecting PoGWQI and PaGWQI67

Figure 4 . 9 .Figure 5 . 1 .

 4951 Figure 1.1. KIM-based interpolated vs. observed groundwater levels in an aquifer in piedmont plains, China (Xiao et al., 2016) ................................................................................................... Figure 1.2. Hydrological cycle (Levizzani and Cattani, 2019) .................................................... Figure 1.3. Architecture of Smart systems ................................................................................... Figure 1.4. Research outline ........................................................................................................Figure 2.1. The geographical setting of the West Bank ...............................................................Figure 2.2. Assessment of water challenges in Palestine .............................................................Figure 2.3. Groundwater flow, divides and aquifers in the West Bank and Gaza Strip ..............Figure 2.4. Spatial distribution of the groundwater wells in the West Bank ...............................Figure 2.5. Decline of groundwater level in the Well L/57 located in Gaza Strip between 1966 and 2014 ........................................................................................................................................Figure 2.6. Long-term average annual rainfall in the West Bank ................................................Figure 2.7. RWH suitability map for domestic uses in the West Bank .......................................Figure 2.8. Average annual concentrations of NO3 and Cl in groundwater wells in Tulkarm and Qalqiliya ........................................................................................................................................Figure 2.9. Spatial distribution of NO3 and Cl amongst Gaza Coastal Aquifer ..........................Figure 2.10. Drought occurrence in the MENA ...........................................................................Figure 2.11. Spatiotemporal impact of climate change and over-pumping on the groundwater level of the Gaza Coastal Aquifer between 1974 and 2040 ..........................................................Figure 2.12. Overall research methodology .................................................................................Figure 2.13. Assessment of existing and projected water scarcity ..............................................Figure 2.14. Protection of conventional water resources .............................................................Figure 2.15. Investigating the role of potential RWH in addressing the current and future domestic water scarcity .................................................................................................................Figure 2.16. Methodological framework for smart RWH and smart dual water supply systems Figure 3.1. Research methodology .............................................................................................. Figure 3.2. Schematic overview of the adopted SVAT model .................................................... Figure 3.3. Historical and current statistics of population and TWD in the whole West Bank between 1997 and 2020 ................................................................................................................Figure 3.4. Variation in TWS considering climate change ..........................................................Figure 4.1. Overall methodological framework ...........................................................................Figure 4.2. Development of potability and palatability groundwater quality indexes (PoGWQI and PaGWQI) ................................................................................................................................Figure 4.3. PoGWQGs and PaGWQGs in the West Bank ...........................................................Figure 4.4. Un-potability rate over four contaminated groundwater wells in the West Bank .....Figure 4.5. Spatiotemporal mapping of PoGWQI and PaGWQI in the West Bank between 2001 and 2016 ........................................................................................................................................Figure 4.6. PoGWQGs and PaGWQGs cross the West Bank governorates ................................Figure 4.7. Sensitivity analysis of PoGWQI and PaGWQI .........................................................Figure 4.8. Factors affecting PoGWQI and PaGWQI in the West Bank .....................................Figure 4.9. Relative importance analysis for the main factors affecting PoGWQI and PaGWQI67

Figure 5 . 2 .Figure 5 . 3 .Figure 5 . 4 .Figure 5 . 5 .

 52535455 Figure 5.1. Methodological framework .......................................................................................Figure 5.2. The geographic setting of the Eocene Aquifer .......................................................... Figure 5.3. Geologic cross section of the Eocene Aquifer ........................................................... Figure 5.4. Annual NO3 concentration statistics in the Eocene Aquifer (Period 1982-2019) ..... Figure 5.5. Spatiotemporal distribution of GNC in the Eocene Aquifer between 1982 and 2019 ....................................................................................................................................................... Figure 5.6. Influence of soil type on NO3 concentration (period 1982-2019) .............................Figure 5.7. Influence of watersheds and their locations on the NO3 concentration (period 1982 -2019) .............................................................................................................................................Figure 5.8. Influence of well depth on the NO3 concentration .................................................... Figure 5.9. Influence of well use and anthropogenic practices on the NO3 concentration ......... Figure 5.10. Distribution of correct and false predicted wells concerning GNC .........................Figure 6.1. Research methodology ..............................................................................................Figure 6.2. Distribution of rainfall stations in the West Bank .....................................................Figure 6.3. Variation in rainfall considering climate change .......................................................Figure 6.4. Impact of the RWH on the domestic water scarcity in Jerusalem and Tulkarm governorates (in 2020) .................................................................................................................. Figure 6.5. Impact of the RWH on domestic water scarcity for the different urban scales of Jenin governorate ...................................................................................................................................Figure 7.1. Research methodology ..............................................................................................Figure 7.2. Characterization of RWH contamination ..................................................................Figure 7.3. Architecture of the smart RWH system .....................................................................Figure 7.4. Physical components of the smart RWH system .......................................................Figure 7.5. Monitoring components of the smart RWH system ..................................................Figure 7.6. BIM representation for the case study .....................................................................Figure 7.7. Temporal distribution of the daily rainfall in the study area ...................................Figure 7.8. RWH sampling locations .........................................................................................Figure 7.9. Residual chlorine VS. FC among the RWH samples ..............................................Figure 7.10. Effect of tank size to the annual utilized RWH volumes and the annual overflow107 Figure 7.11. Effect of tank size to the annual shortage through the smart RWH system ..........Figure 7.12. Effect of tank size to the Re and Rv through the smart RWH system ..................Figure 7.13. Impact of tank size on: (a) Re; (b) Rv of the dual supply system considering the existing municipal water supply agenda .....................................................................................Figure 7.14. Impact of tank size on: (a) Re; (b) Rv of the dual supply system considering agenda A ..................................................................................................................................................Figure 7.15. Impact of tank size on: (a) Re; (b) Rv of the dual supply system considering agenda B ..................................................................................................................................................

Figure 5 . 6 .

 56 Figure 5.1. Methodological framework .......................................................................................Figure 5.2. The geographic setting of the Eocene Aquifer .......................................................... Figure 5.3. Geologic cross section of the Eocene Aquifer ........................................................... Figure 5.4. Annual NO3 concentration statistics in the Eocene Aquifer (Period 1982-2019) ..... Figure 5.5. Spatiotemporal distribution of GNC in the Eocene Aquifer between 1982 and 2019 ....................................................................................................................................................... Figure 5.6. Influence of soil type on NO3 concentration (period 1982-2019) .............................Figure 5.7. Influence of watersheds and their locations on the NO3 concentration (period 1982 -2019) .............................................................................................................................................Figure 5.8. Influence of well depth on the NO3 concentration .................................................... Figure 5.9. Influence of well use and anthropogenic practices on the NO3 concentration ......... Figure 5.10. Distribution of correct and false predicted wells concerning GNC .........................Figure 6.1. Research methodology ..............................................................................................Figure 6.2. Distribution of rainfall stations in the West Bank .....................................................Figure 6.3. Variation in rainfall considering climate change .......................................................Figure 6.4. Impact of the RWH on the domestic water scarcity in Jerusalem and Tulkarm governorates (in 2020) .................................................................................................................. Figure 6.5. Impact of the RWH on domestic water scarcity for the different urban scales of Jenin governorate ...................................................................................................................................Figure 7.1. Research methodology ..............................................................................................Figure 7.2. Characterization of RWH contamination ..................................................................Figure 7.3. Architecture of the smart RWH system .....................................................................Figure 7.4. Physical components of the smart RWH system .......................................................Figure 7.5. Monitoring components of the smart RWH system ..................................................Figure 7.6. BIM representation for the case study .....................................................................Figure 7.7. Temporal distribution of the daily rainfall in the study area ...................................Figure 7.8. RWH sampling locations .........................................................................................Figure 7.9. Residual chlorine VS. FC among the RWH samples ..............................................Figure 7.10. Effect of tank size to the annual utilized RWH volumes and the annual overflow107 Figure 7.11. Effect of tank size to the annual shortage through the smart RWH system ..........Figure 7.12. Effect of tank size to the Re and Rv through the smart RWH system ..................Figure 7.13. Impact of tank size on: (a) Re; (b) Rv of the dual supply system considering the existing municipal water supply agenda .....................................................................................Figure 7.14. Impact of tank size on: (a) Re; (b) Rv of the dual supply system considering agenda A ..................................................................................................................................................Figure 7.15. Impact of tank size on: (a) Re; (b) Rv of the dual supply system considering agenda B ..................................................................................................................................................

Figure 5 . 7 .Figure 5 . 8 .

 5758 Figure 5.1. Methodological framework .......................................................................................Figure 5.2. The geographic setting of the Eocene Aquifer .......................................................... Figure 5.3. Geologic cross section of the Eocene Aquifer ........................................................... Figure 5.4. Annual NO3 concentration statistics in the Eocene Aquifer (Period 1982-2019) ..... Figure 5.5. Spatiotemporal distribution of GNC in the Eocene Aquifer between 1982 and 2019 ....................................................................................................................................................... Figure 5.6. Influence of soil type on NO3 concentration (period 1982-2019) .............................Figure 5.7. Influence of watersheds and their locations on the NO3 concentration (period 1982 -2019) .............................................................................................................................................Figure 5.8. Influence of well depth on the NO3 concentration .................................................... Figure 5.9. Influence of well use and anthropogenic practices on the NO3 concentration ......... Figure 5.10. Distribution of correct and false predicted wells concerning GNC .........................Figure 6.1. Research methodology ..............................................................................................Figure 6.2. Distribution of rainfall stations in the West Bank .....................................................Figure 6.3. Variation in rainfall considering climate change .......................................................Figure 6.4. Impact of the RWH on the domestic water scarcity in Jerusalem and Tulkarm governorates (in 2020) .................................................................................................................. Figure 6.5. Impact of the RWH on domestic water scarcity for the different urban scales of Jenin governorate ...................................................................................................................................Figure 7.1. Research methodology ..............................................................................................Figure 7.2. Characterization of RWH contamination ..................................................................Figure 7.3. Architecture of the smart RWH system .....................................................................Figure 7.4. Physical components of the smart RWH system .......................................................Figure 7.5. Monitoring components of the smart RWH system ..................................................Figure 7.6. BIM representation for the case study .....................................................................Figure 7.7. Temporal distribution of the daily rainfall in the study area ...................................Figure 7.8. RWH sampling locations .........................................................................................Figure 7.9. Residual chlorine VS. FC among the RWH samples ..............................................Figure 7.10. Effect of tank size to the annual utilized RWH volumes and the annual overflow107 Figure 7.11. Effect of tank size to the annual shortage through the smart RWH system ..........Figure 7.12. Effect of tank size to the Re and Rv through the smart RWH system ..................Figure 7.13. Impact of tank size on: (a) Re; (b) Rv of the dual supply system considering the existing municipal water supply agenda .....................................................................................Figure 7.14. Impact of tank size on: (a) Re; (b) Rv of the dual supply system considering agenda A ..................................................................................................................................................Figure 7.15. Impact of tank size on: (a) Re; (b) Rv of the dual supply system considering agenda B ..................................................................................................................................................

Figure 5 . 9 .Figure 5 . 10 .

 59510 Figure 5.1. Methodological framework .......................................................................................Figure 5.2. The geographic setting of the Eocene Aquifer .......................................................... Figure 5.3. Geologic cross section of the Eocene Aquifer ........................................................... Figure 5.4. Annual NO3 concentration statistics in the Eocene Aquifer (Period 1982-2019) ..... Figure 5.5. Spatiotemporal distribution of GNC in the Eocene Aquifer between 1982 and 2019 ....................................................................................................................................................... Figure 5.6. Influence of soil type on NO3 concentration (period 1982-2019) .............................Figure 5.7. Influence of watersheds and their locations on the NO3 concentration (period 1982 -2019) .............................................................................................................................................Figure 5.8. Influence of well depth on the NO3 concentration .................................................... Figure 5.9. Influence of well use and anthropogenic practices on the NO3 concentration ......... Figure 5.10. Distribution of correct and false predicted wells concerning GNC .........................Figure 6.1. Research methodology ..............................................................................................Figure 6.2. Distribution of rainfall stations in the West Bank .....................................................Figure 6.3. Variation in rainfall considering climate change .......................................................Figure 6.4. Impact of the RWH on the domestic water scarcity in Jerusalem and Tulkarm governorates (in 2020) .................................................................................................................. Figure 6.5. Impact of the RWH on domestic water scarcity for the different urban scales of Jenin governorate ...................................................................................................................................Figure 7.1. Research methodology ..............................................................................................Figure 7.2. Characterization of RWH contamination ..................................................................Figure 7.3. Architecture of the smart RWH system .....................................................................Figure 7.4. Physical components of the smart RWH system .......................................................Figure 7.5. Monitoring components of the smart RWH system ..................................................Figure 7.6. BIM representation for the case study .....................................................................Figure 7.7. Temporal distribution of the daily rainfall in the study area ...................................Figure 7.8. RWH sampling locations .........................................................................................Figure 7.9. Residual chlorine VS. FC among the RWH samples ..............................................Figure 7.10. Effect of tank size to the annual utilized RWH volumes and the annual overflow107 Figure 7.11. Effect of tank size to the annual shortage through the smart RWH system ..........Figure 7.12. Effect of tank size to the Re and Rv through the smart RWH system ..................Figure 7.13. Impact of tank size on: (a) Re; (b) Rv of the dual supply system considering the existing municipal water supply agenda .....................................................................................Figure 7.14. Impact of tank size on: (a) Re; (b) Rv of the dual supply system considering agenda A ..................................................................................................................................................Figure 7.15. Impact of tank size on: (a) Re; (b) Rv of the dual supply system considering agenda B ..................................................................................................................................................

Figure 6 . 1 .

 61 Figure 5.1. Methodological framework .......................................................................................Figure 5.2. The geographic setting of the Eocene Aquifer .......................................................... Figure 5.3. Geologic cross section of the Eocene Aquifer ........................................................... Figure 5.4. Annual NO3 concentration statistics in the Eocene Aquifer (Period 1982-2019) ..... Figure 5.5. Spatiotemporal distribution of GNC in the Eocene Aquifer between 1982 and 2019 ....................................................................................................................................................... Figure 5.6. Influence of soil type on NO3 concentration (period 1982-2019) .............................Figure 5.7. Influence of watersheds and their locations on the NO3 concentration (period 1982 -2019) .............................................................................................................................................Figure 5.8. Influence of well depth on the NO3 concentration .................................................... Figure 5.9. Influence of well use and anthropogenic practices on the NO3 concentration ......... Figure 5.10. Distribution of correct and false predicted wells concerning GNC .........................Figure 6.1. Research methodology ..............................................................................................Figure 6.2. Distribution of rainfall stations in the West Bank .....................................................Figure 6.3. Variation in rainfall considering climate change .......................................................Figure 6.4. Impact of the RWH on the domestic water scarcity in Jerusalem and Tulkarm governorates (in 2020) .................................................................................................................. Figure 6.5. Impact of the RWH on domestic water scarcity for the different urban scales of Jenin governorate ...................................................................................................................................Figure 7.1. Research methodology ..............................................................................................Figure 7.2. Characterization of RWH contamination ..................................................................Figure 7.3. Architecture of the smart RWH system .....................................................................Figure 7.4. Physical components of the smart RWH system .......................................................Figure 7.5. Monitoring components of the smart RWH system ..................................................Figure 7.6. BIM representation for the case study .....................................................................Figure 7.7. Temporal distribution of the daily rainfall in the study area ...................................Figure 7.8. RWH sampling locations .........................................................................................Figure 7.9. Residual chlorine VS. FC among the RWH samples ..............................................Figure 7.10. Effect of tank size to the annual utilized RWH volumes and the annual overflow107 Figure 7.11. Effect of tank size to the annual shortage through the smart RWH system ..........Figure 7.12. Effect of tank size to the Re and Rv through the smart RWH system ..................Figure 7.13. Impact of tank size on: (a) Re; (b) Rv of the dual supply system considering the existing municipal water supply agenda .....................................................................................Figure 7.14. Impact of tank size on: (a) Re; (b) Rv of the dual supply system considering agenda A ..................................................................................................................................................Figure 7.15. Impact of tank size on: (a) Re; (b) Rv of the dual supply system considering agenda B ..................................................................................................................................................

Figure 6 . 2 .

 62 Figure 5.1. Methodological framework .......................................................................................Figure 5.2. The geographic setting of the Eocene Aquifer .......................................................... Figure 5.3. Geologic cross section of the Eocene Aquifer ........................................................... Figure 5.4. Annual NO3 concentration statistics in the Eocene Aquifer (Period 1982-2019) ..... Figure 5.5. Spatiotemporal distribution of GNC in the Eocene Aquifer between 1982 and 2019 ....................................................................................................................................................... Figure 5.6. Influence of soil type on NO3 concentration (period 1982-2019) .............................Figure 5.7. Influence of watersheds and their locations on the NO3 concentration (period 1982 -2019) .............................................................................................................................................Figure 5.8. Influence of well depth on the NO3 concentration .................................................... Figure 5.9. Influence of well use and anthropogenic practices on the NO3 concentration ......... Figure 5.10. Distribution of correct and false predicted wells concerning GNC .........................Figure 6.1. Research methodology ..............................................................................................Figure 6.2. Distribution of rainfall stations in the West Bank .....................................................Figure 6.3. Variation in rainfall considering climate change .......................................................Figure 6.4. Impact of the RWH on the domestic water scarcity in Jerusalem and Tulkarm governorates (in 2020) .................................................................................................................. Figure 6.5. Impact of the RWH on domestic water scarcity for the different urban scales of Jenin governorate ...................................................................................................................................Figure 7.1. Research methodology ..............................................................................................Figure 7.2. Characterization of RWH contamination ..................................................................Figure 7.3. Architecture of the smart RWH system .....................................................................Figure 7.4. Physical components of the smart RWH system .......................................................Figure 7.5. Monitoring components of the smart RWH system ..................................................Figure 7.6. BIM representation for the case study .....................................................................Figure 7.7. Temporal distribution of the daily rainfall in the study area ...................................Figure 7.8. RWH sampling locations .........................................................................................Figure 7.9. Residual chlorine VS. FC among the RWH samples ..............................................Figure 7.10. Effect of tank size to the annual utilized RWH volumes and the annual overflow107 Figure 7.11. Effect of tank size to the annual shortage through the smart RWH system ..........Figure 7.12. Effect of tank size to the Re and Rv through the smart RWH system ..................Figure 7.13. Impact of tank size on: (a) Re; (b) Rv of the dual supply system considering the existing municipal water supply agenda .....................................................................................Figure 7.14. Impact of tank size on: (a) Re; (b) Rv of the dual supply system considering agenda A ..................................................................................................................................................Figure 7.15. Impact of tank size on: (a) Re; (b) Rv of the dual supply system considering agenda B ..................................................................................................................................................

Figure 6 . 3 .

 63 Figure 5.1. Methodological framework .......................................................................................Figure 5.2. The geographic setting of the Eocene Aquifer .......................................................... Figure 5.3. Geologic cross section of the Eocene Aquifer ........................................................... Figure 5.4. Annual NO3 concentration statistics in the Eocene Aquifer (Period 1982-2019) ..... Figure 5.5. Spatiotemporal distribution of GNC in the Eocene Aquifer between 1982 and 2019 ....................................................................................................................................................... Figure 5.6. Influence of soil type on NO3 concentration (period 1982-2019) .............................Figure 5.7. Influence of watersheds and their locations on the NO3 concentration (period 1982 -2019) .............................................................................................................................................Figure 5.8. Influence of well depth on the NO3 concentration .................................................... Figure 5.9. Influence of well use and anthropogenic practices on the NO3 concentration ......... Figure 5.10. Distribution of correct and false predicted wells concerning GNC .........................Figure 6.1. Research methodology ..............................................................................................Figure 6.2. Distribution of rainfall stations in the West Bank .....................................................Figure 6.3. Variation in rainfall considering climate change .......................................................Figure 6.4. Impact of the RWH on the domestic water scarcity in Jerusalem and Tulkarm governorates (in 2020) .................................................................................................................. Figure 6.5. Impact of the RWH on domestic water scarcity for the different urban scales of Jenin governorate ...................................................................................................................................Figure 7.1. Research methodology ..............................................................................................Figure 7.2. Characterization of RWH contamination ..................................................................Figure 7.3. Architecture of the smart RWH system .....................................................................Figure 7.4. Physical components of the smart RWH system .......................................................Figure 7.5. Monitoring components of the smart RWH system ..................................................Figure 7.6. BIM representation for the case study .....................................................................Figure 7.7. Temporal distribution of the daily rainfall in the study area ...................................Figure 7.8. RWH sampling locations .........................................................................................Figure 7.9. Residual chlorine VS. FC among the RWH samples ..............................................Figure 7.10. Effect of tank size to the annual utilized RWH volumes and the annual overflow107 Figure 7.11. Effect of tank size to the annual shortage through the smart RWH system ..........Figure 7.12. Effect of tank size to the Re and Rv through the smart RWH system ..................Figure 7.13. Impact of tank size on: (a) Re; (b) Rv of the dual supply system considering the existing municipal water supply agenda .....................................................................................Figure 7.14. Impact of tank size on: (a) Re; (b) Rv of the dual supply system considering agenda A ..................................................................................................................................................Figure 7.15. Impact of tank size on: (a) Re; (b) Rv of the dual supply system considering agenda B ..................................................................................................................................................

Figure 6 . 4 .

 64 Figure 5.1. Methodological framework .......................................................................................Figure 5.2. The geographic setting of the Eocene Aquifer .......................................................... Figure 5.3. Geologic cross section of the Eocene Aquifer ........................................................... Figure 5.4. Annual NO3 concentration statistics in the Eocene Aquifer (Period 1982-2019) ..... Figure 5.5. Spatiotemporal distribution of GNC in the Eocene Aquifer between 1982 and 2019 ....................................................................................................................................................... Figure 5.6. Influence of soil type on NO3 concentration (period 1982-2019) .............................Figure 5.7. Influence of watersheds and their locations on the NO3 concentration (period 1982 -2019) .............................................................................................................................................Figure 5.8. Influence of well depth on the NO3 concentration .................................................... Figure 5.9. Influence of well use and anthropogenic practices on the NO3 concentration ......... Figure 5.10. Distribution of correct and false predicted wells concerning GNC .........................Figure 6.1. Research methodology ..............................................................................................Figure 6.2. Distribution of rainfall stations in the West Bank .....................................................Figure 6.3. Variation in rainfall considering climate change .......................................................Figure 6.4. Impact of the RWH on the domestic water scarcity in Jerusalem and Tulkarm governorates (in 2020) .................................................................................................................. Figure 6.5. Impact of the RWH on domestic water scarcity for the different urban scales of Jenin governorate ...................................................................................................................................Figure 7.1. Research methodology ..............................................................................................Figure 7.2. Characterization of RWH contamination ..................................................................Figure 7.3. Architecture of the smart RWH system .....................................................................Figure 7.4. Physical components of the smart RWH system .......................................................Figure 7.5. Monitoring components of the smart RWH system ..................................................Figure 7.6. BIM representation for the case study .....................................................................Figure 7.7. Temporal distribution of the daily rainfall in the study area ...................................Figure 7.8. RWH sampling locations .........................................................................................Figure 7.9. Residual chlorine VS. FC among the RWH samples ..............................................Figure 7.10. Effect of tank size to the annual utilized RWH volumes and the annual overflow107 Figure 7.11. Effect of tank size to the annual shortage through the smart RWH system ..........Figure 7.12. Effect of tank size to the Re and Rv through the smart RWH system ..................Figure 7.13. Impact of tank size on: (a) Re; (b) Rv of the dual supply system considering the existing municipal water supply agenda .....................................................................................Figure 7.14. Impact of tank size on: (a) Re; (b) Rv of the dual supply system considering agenda A ..................................................................................................................................................Figure 7.15. Impact of tank size on: (a) Re; (b) Rv of the dual supply system considering agenda B ..................................................................................................................................................

Figure 6 . 5 .

 65 Figure 5.1. Methodological framework .......................................................................................Figure 5.2. The geographic setting of the Eocene Aquifer .......................................................... Figure 5.3. Geologic cross section of the Eocene Aquifer ........................................................... Figure 5.4. Annual NO3 concentration statistics in the Eocene Aquifer (Period 1982-2019) ..... Figure 5.5. Spatiotemporal distribution of GNC in the Eocene Aquifer between 1982 and 2019 ....................................................................................................................................................... Figure 5.6. Influence of soil type on NO3 concentration (period 1982-2019) .............................Figure 5.7. Influence of watersheds and their locations on the NO3 concentration (period 1982 -2019) .............................................................................................................................................Figure 5.8. Influence of well depth on the NO3 concentration .................................................... Figure 5.9. Influence of well use and anthropogenic practices on the NO3 concentration ......... Figure 5.10. Distribution of correct and false predicted wells concerning GNC .........................Figure 6.1. Research methodology ..............................................................................................Figure 6.2. Distribution of rainfall stations in the West Bank .....................................................Figure 6.3. Variation in rainfall considering climate change .......................................................Figure 6.4. Impact of the RWH on the domestic water scarcity in Jerusalem and Tulkarm governorates (in 2020) .................................................................................................................. Figure 6.5. Impact of the RWH on domestic water scarcity for the different urban scales of Jenin governorate ...................................................................................................................................Figure 7.1. Research methodology ..............................................................................................Figure 7.2. Characterization of RWH contamination ..................................................................Figure 7.3. Architecture of the smart RWH system .....................................................................Figure 7.4. Physical components of the smart RWH system .......................................................Figure 7.5. Monitoring components of the smart RWH system ..................................................Figure 7.6. BIM representation for the case study .....................................................................Figure 7.7. Temporal distribution of the daily rainfall in the study area ...................................Figure 7.8. RWH sampling locations .........................................................................................Figure 7.9. Residual chlorine VS. FC among the RWH samples ..............................................Figure 7.10. Effect of tank size to the annual utilized RWH volumes and the annual overflow107 Figure 7.11. Effect of tank size to the annual shortage through the smart RWH system ..........Figure 7.12. Effect of tank size to the Re and Rv through the smart RWH system ..................Figure 7.13. Impact of tank size on: (a) Re; (b) Rv of the dual supply system considering the existing municipal water supply agenda .....................................................................................Figure 7.14. Impact of tank size on: (a) Re; (b) Rv of the dual supply system considering agenda A ..................................................................................................................................................Figure 7.15. Impact of tank size on: (a) Re; (b) Rv of the dual supply system considering agenda B ..................................................................................................................................................

Figure 7 . 1 .

 71 Figure 5.1. Methodological framework .......................................................................................Figure 5.2. The geographic setting of the Eocene Aquifer .......................................................... Figure 5.3. Geologic cross section of the Eocene Aquifer ........................................................... Figure 5.4. Annual NO3 concentration statistics in the Eocene Aquifer (Period 1982-2019) ..... Figure 5.5. Spatiotemporal distribution of GNC in the Eocene Aquifer between 1982 and 2019 ....................................................................................................................................................... Figure 5.6. Influence of soil type on NO3 concentration (period 1982-2019) .............................Figure 5.7. Influence of watersheds and their locations on the NO3 concentration (period 1982 -2019) .............................................................................................................................................Figure 5.8. Influence of well depth on the NO3 concentration .................................................... Figure 5.9. Influence of well use and anthropogenic practices on the NO3 concentration ......... Figure 5.10. Distribution of correct and false predicted wells concerning GNC .........................Figure 6.1. Research methodology ..............................................................................................Figure 6.2. Distribution of rainfall stations in the West Bank .....................................................Figure 6.3. Variation in rainfall considering climate change .......................................................Figure 6.4. Impact of the RWH on the domestic water scarcity in Jerusalem and Tulkarm governorates (in 2020) .................................................................................................................. Figure 6.5. Impact of the RWH on domestic water scarcity for the different urban scales of Jenin governorate ...................................................................................................................................Figure 7.1. Research methodology ..............................................................................................Figure 7.2. Characterization of RWH contamination ..................................................................Figure 7.3. Architecture of the smart RWH system .....................................................................Figure 7.4. Physical components of the smart RWH system .......................................................Figure 7.5. Monitoring components of the smart RWH system ..................................................Figure 7.6. BIM representation for the case study .....................................................................Figure 7.7. Temporal distribution of the daily rainfall in the study area ...................................Figure 7.8. RWH sampling locations .........................................................................................Figure 7.9. Residual chlorine VS. FC among the RWH samples ..............................................Figure 7.10. Effect of tank size to the annual utilized RWH volumes and the annual overflow107 Figure 7.11. Effect of tank size to the annual shortage through the smart RWH system ..........Figure 7.12. Effect of tank size to the Re and Rv through the smart RWH system ..................Figure 7.13. Impact of tank size on: (a) Re; (b) Rv of the dual supply system considering the existing municipal water supply agenda .....................................................................................Figure 7.14. Impact of tank size on: (a) Re; (b) Rv of the dual supply system considering agenda A ..................................................................................................................................................Figure 7.15. Impact of tank size on: (a) Re; (b) Rv of the dual supply system considering agenda B ..................................................................................................................................................

Figure 7 . 2 .

 72 Figure 5.1. Methodological framework .......................................................................................Figure 5.2. The geographic setting of the Eocene Aquifer .......................................................... Figure 5.3. Geologic cross section of the Eocene Aquifer ........................................................... Figure 5.4. Annual NO3 concentration statistics in the Eocene Aquifer (Period 1982-2019) ..... Figure 5.5. Spatiotemporal distribution of GNC in the Eocene Aquifer between 1982 and 2019 ....................................................................................................................................................... Figure 5.6. Influence of soil type on NO3 concentration (period 1982-2019) .............................Figure 5.7. Influence of watersheds and their locations on the NO3 concentration (period 1982 -2019) .............................................................................................................................................Figure 5.8. Influence of well depth on the NO3 concentration .................................................... Figure 5.9. Influence of well use and anthropogenic practices on the NO3 concentration ......... Figure 5.10. Distribution of correct and false predicted wells concerning GNC .........................Figure 6.1. Research methodology ..............................................................................................Figure 6.2. Distribution of rainfall stations in the West Bank .....................................................Figure 6.3. Variation in rainfall considering climate change .......................................................Figure 6.4. Impact of the RWH on the domestic water scarcity in Jerusalem and Tulkarm governorates (in 2020) .................................................................................................................. Figure 6.5. Impact of the RWH on domestic water scarcity for the different urban scales of Jenin governorate ...................................................................................................................................Figure 7.1. Research methodology ..............................................................................................Figure 7.2. Characterization of RWH contamination ..................................................................Figure 7.3. Architecture of the smart RWH system .....................................................................Figure 7.4. Physical components of the smart RWH system .......................................................Figure 7.5. Monitoring components of the smart RWH system ..................................................Figure 7.6. BIM representation for the case study .....................................................................Figure 7.7. Temporal distribution of the daily rainfall in the study area ...................................Figure 7.8. RWH sampling locations .........................................................................................Figure 7.9. Residual chlorine VS. FC among the RWH samples ..............................................Figure 7.10. Effect of tank size to the annual utilized RWH volumes and the annual overflow107 Figure 7.11. Effect of tank size to the annual shortage through the smart RWH system ..........Figure 7.12. Effect of tank size to the Re and Rv through the smart RWH system ..................Figure 7.13. Impact of tank size on: (a) Re; (b) Rv of the dual supply system considering the existing municipal water supply agenda .....................................................................................Figure 7.14. Impact of tank size on: (a) Re; (b) Rv of the dual supply system considering agenda A ..................................................................................................................................................Figure 7.15. Impact of tank size on: (a) Re; (b) Rv of the dual supply system considering agenda B ..................................................................................................................................................

Figure 7 . 3 .

 73 Figure 5.1. Methodological framework .......................................................................................Figure 5.2. The geographic setting of the Eocene Aquifer .......................................................... Figure 5.3. Geologic cross section of the Eocene Aquifer ........................................................... Figure 5.4. Annual NO3 concentration statistics in the Eocene Aquifer (Period 1982-2019) ..... Figure 5.5. Spatiotemporal distribution of GNC in the Eocene Aquifer between 1982 and 2019 ....................................................................................................................................................... Figure 5.6. Influence of soil type on NO3 concentration (period 1982-2019) .............................Figure 5.7. Influence of watersheds and their locations on the NO3 concentration (period 1982 -2019) .............................................................................................................................................Figure 5.8. Influence of well depth on the NO3 concentration .................................................... Figure 5.9. Influence of well use and anthropogenic practices on the NO3 concentration ......... Figure 5.10. Distribution of correct and false predicted wells concerning GNC .........................Figure 6.1. Research methodology ..............................................................................................Figure 6.2. Distribution of rainfall stations in the West Bank .....................................................Figure 6.3. Variation in rainfall considering climate change .......................................................Figure 6.4. Impact of the RWH on the domestic water scarcity in Jerusalem and Tulkarm governorates (in 2020) .................................................................................................................. Figure 6.5. Impact of the RWH on domestic water scarcity for the different urban scales of Jenin governorate ...................................................................................................................................Figure 7.1. Research methodology ..............................................................................................Figure 7.2. Characterization of RWH contamination ..................................................................Figure 7.3. Architecture of the smart RWH system .....................................................................Figure 7.4. Physical components of the smart RWH system .......................................................Figure 7.5. Monitoring components of the smart RWH system ..................................................Figure 7.6. BIM representation for the case study .....................................................................Figure 7.7. Temporal distribution of the daily rainfall in the study area ...................................Figure 7.8. RWH sampling locations .........................................................................................Figure 7.9. Residual chlorine VS. FC among the RWH samples ..............................................Figure 7.10. Effect of tank size to the annual utilized RWH volumes and the annual overflow107 Figure 7.11. Effect of tank size to the annual shortage through the smart RWH system ..........Figure 7.12. Effect of tank size to the Re and Rv through the smart RWH system ..................Figure 7.13. Impact of tank size on: (a) Re; (b) Rv of the dual supply system considering the existing municipal water supply agenda .....................................................................................Figure 7.14. Impact of tank size on: (a) Re; (b) Rv of the dual supply system considering agenda A ..................................................................................................................................................Figure 7.15. Impact of tank size on: (a) Re; (b) Rv of the dual supply system considering agenda B ..................................................................................................................................................

Figure 7 . 4 .

 74 Figure 5.1. Methodological framework .......................................................................................Figure 5.2. The geographic setting of the Eocene Aquifer .......................................................... Figure 5.3. Geologic cross section of the Eocene Aquifer ........................................................... Figure 5.4. Annual NO3 concentration statistics in the Eocene Aquifer (Period 1982-2019) ..... Figure 5.5. Spatiotemporal distribution of GNC in the Eocene Aquifer between 1982 and 2019 ....................................................................................................................................................... Figure 5.6. Influence of soil type on NO3 concentration (period 1982-2019) .............................Figure 5.7. Influence of watersheds and their locations on the NO3 concentration (period 1982 -2019) .............................................................................................................................................Figure 5.8. Influence of well depth on the NO3 concentration .................................................... Figure 5.9. Influence of well use and anthropogenic practices on the NO3 concentration ......... Figure 5.10. Distribution of correct and false predicted wells concerning GNC .........................Figure 6.1. Research methodology ..............................................................................................Figure 6.2. Distribution of rainfall stations in the West Bank .....................................................Figure 6.3. Variation in rainfall considering climate change .......................................................Figure 6.4. Impact of the RWH on the domestic water scarcity in Jerusalem and Tulkarm governorates (in 2020) .................................................................................................................. Figure 6.5. Impact of the RWH on domestic water scarcity for the different urban scales of Jenin governorate ...................................................................................................................................Figure 7.1. Research methodology ..............................................................................................Figure 7.2. Characterization of RWH contamination ..................................................................Figure 7.3. Architecture of the smart RWH system .....................................................................Figure 7.4. Physical components of the smart RWH system .......................................................Figure 7.5. Monitoring components of the smart RWH system ..................................................Figure 7.6. BIM representation for the case study .....................................................................Figure 7.7. Temporal distribution of the daily rainfall in the study area ...................................Figure 7.8. RWH sampling locations .........................................................................................Figure 7.9. Residual chlorine VS. FC among the RWH samples ..............................................Figure 7.10. Effect of tank size to the annual utilized RWH volumes and the annual overflow107 Figure 7.11. Effect of tank size to the annual shortage through the smart RWH system ..........Figure 7.12. Effect of tank size to the Re and Rv through the smart RWH system ..................Figure 7.13. Impact of tank size on: (a) Re; (b) Rv of the dual supply system considering the existing municipal water supply agenda .....................................................................................Figure 7.14. Impact of tank size on: (a) Re; (b) Rv of the dual supply system considering agenda A ..................................................................................................................................................Figure 7.15. Impact of tank size on: (a) Re; (b) Rv of the dual supply system considering agenda B ..................................................................................................................................................

Figure 7 . 5 .

 75 Figure 5.1. Methodological framework .......................................................................................Figure 5.2. The geographic setting of the Eocene Aquifer .......................................................... Figure 5.3. Geologic cross section of the Eocene Aquifer ........................................................... Figure 5.4. Annual NO3 concentration statistics in the Eocene Aquifer (Period 1982-2019) ..... Figure 5.5. Spatiotemporal distribution of GNC in the Eocene Aquifer between 1982 and 2019 ....................................................................................................................................................... Figure 5.6. Influence of soil type on NO3 concentration (period 1982-2019) .............................Figure 5.7. Influence of watersheds and their locations on the NO3 concentration (period 1982 -2019) .............................................................................................................................................Figure 5.8. Influence of well depth on the NO3 concentration .................................................... Figure 5.9. Influence of well use and anthropogenic practices on the NO3 concentration ......... Figure 5.10. Distribution of correct and false predicted wells concerning GNC .........................Figure 6.1. Research methodology ..............................................................................................Figure 6.2. Distribution of rainfall stations in the West Bank .....................................................Figure 6.3. Variation in rainfall considering climate change .......................................................Figure 6.4. Impact of the RWH on the domestic water scarcity in Jerusalem and Tulkarm governorates (in 2020) .................................................................................................................. Figure 6.5. Impact of the RWH on domestic water scarcity for the different urban scales of Jenin governorate ...................................................................................................................................Figure 7.1. Research methodology ..............................................................................................Figure 7.2. Characterization of RWH contamination ..................................................................Figure 7.3. Architecture of the smart RWH system .....................................................................Figure 7.4. Physical components of the smart RWH system .......................................................Figure 7.5. Monitoring components of the smart RWH system ..................................................Figure 7.6. BIM representation for the case study .....................................................................Figure 7.7. Temporal distribution of the daily rainfall in the study area ...................................Figure 7.8. RWH sampling locations .........................................................................................Figure 7.9. Residual chlorine VS. FC among the RWH samples ..............................................Figure 7.10. Effect of tank size to the annual utilized RWH volumes and the annual overflow107 Figure 7.11. Effect of tank size to the annual shortage through the smart RWH system ..........Figure 7.12. Effect of tank size to the Re and Rv through the smart RWH system ..................Figure 7.13. Impact of tank size on: (a) Re; (b) Rv of the dual supply system considering the existing municipal water supply agenda .....................................................................................Figure 7.14. Impact of tank size on: (a) Re; (b) Rv of the dual supply system considering agenda A ..................................................................................................................................................Figure 7.15. Impact of tank size on: (a) Re; (b) Rv of the dual supply system considering agenda B ..................................................................................................................................................

Figure 7 . 6 .

 76 Figure 5.1. Methodological framework .......................................................................................Figure 5.2. The geographic setting of the Eocene Aquifer .......................................................... Figure 5.3. Geologic cross section of the Eocene Aquifer ........................................................... Figure 5.4. Annual NO3 concentration statistics in the Eocene Aquifer (Period 1982-2019) ..... Figure 5.5. Spatiotemporal distribution of GNC in the Eocene Aquifer between 1982 and 2019 ....................................................................................................................................................... Figure 5.6. Influence of soil type on NO3 concentration (period 1982-2019) .............................Figure 5.7. Influence of watersheds and their locations on the NO3 concentration (period 1982 -2019) .............................................................................................................................................Figure 5.8. Influence of well depth on the NO3 concentration .................................................... Figure 5.9. Influence of well use and anthropogenic practices on the NO3 concentration ......... Figure 5.10. Distribution of correct and false predicted wells concerning GNC .........................Figure 6.1. Research methodology ..............................................................................................Figure 6.2. Distribution of rainfall stations in the West Bank .....................................................Figure 6.3. Variation in rainfall considering climate change .......................................................Figure 6.4. Impact of the RWH on the domestic water scarcity in Jerusalem and Tulkarm governorates (in 2020) .................................................................................................................. Figure 6.5. Impact of the RWH on domestic water scarcity for the different urban scales of Jenin governorate ...................................................................................................................................Figure 7.1. Research methodology ..............................................................................................Figure 7.2. Characterization of RWH contamination ..................................................................Figure 7.3. Architecture of the smart RWH system .....................................................................Figure 7.4. Physical components of the smart RWH system .......................................................Figure 7.5. Monitoring components of the smart RWH system ..................................................Figure 7.6. BIM representation for the case study .....................................................................Figure 7.7. Temporal distribution of the daily rainfall in the study area ...................................Figure 7.8. RWH sampling locations .........................................................................................Figure 7.9. Residual chlorine VS. FC among the RWH samples ..............................................Figure 7.10. Effect of tank size to the annual utilized RWH volumes and the annual overflow107 Figure 7.11. Effect of tank size to the annual shortage through the smart RWH system ..........Figure 7.12. Effect of tank size to the Re and Rv through the smart RWH system ..................Figure 7.13. Impact of tank size on: (a) Re; (b) Rv of the dual supply system considering the existing municipal water supply agenda .....................................................................................Figure 7.14. Impact of tank size on: (a) Re; (b) Rv of the dual supply system considering agenda A ..................................................................................................................................................Figure 7.15. Impact of tank size on: (a) Re; (b) Rv of the dual supply system considering agenda B ..................................................................................................................................................

Figure 7 . 7 .

 77 Figure 5.1. Methodological framework .......................................................................................Figure 5.2. The geographic setting of the Eocene Aquifer .......................................................... Figure 5.3. Geologic cross section of the Eocene Aquifer ........................................................... Figure 5.4. Annual NO3 concentration statistics in the Eocene Aquifer (Period 1982-2019) ..... Figure 5.5. Spatiotemporal distribution of GNC in the Eocene Aquifer between 1982 and 2019 ....................................................................................................................................................... Figure 5.6. Influence of soil type on NO3 concentration (period 1982-2019) .............................Figure 5.7. Influence of watersheds and their locations on the NO3 concentration (period 1982 -2019) .............................................................................................................................................Figure 5.8. Influence of well depth on the NO3 concentration .................................................... Figure 5.9. Influence of well use and anthropogenic practices on the NO3 concentration ......... Figure 5.10. Distribution of correct and false predicted wells concerning GNC .........................Figure 6.1. Research methodology ..............................................................................................Figure 6.2. Distribution of rainfall stations in the West Bank .....................................................Figure 6.3. Variation in rainfall considering climate change .......................................................Figure 6.4. Impact of the RWH on the domestic water scarcity in Jerusalem and Tulkarm governorates (in 2020) .................................................................................................................. Figure 6.5. Impact of the RWH on domestic water scarcity for the different urban scales of Jenin governorate ...................................................................................................................................Figure 7.1. Research methodology ..............................................................................................Figure 7.2. Characterization of RWH contamination ..................................................................Figure 7.3. Architecture of the smart RWH system .....................................................................Figure 7.4. Physical components of the smart RWH system .......................................................Figure 7.5. Monitoring components of the smart RWH system ..................................................Figure 7.6. BIM representation for the case study .....................................................................Figure 7.7. Temporal distribution of the daily rainfall in the study area ...................................Figure 7.8. RWH sampling locations .........................................................................................Figure 7.9. Residual chlorine VS. FC among the RWH samples ..............................................Figure 7.10. Effect of tank size to the annual utilized RWH volumes and the annual overflow107 Figure 7.11. Effect of tank size to the annual shortage through the smart RWH system ..........Figure 7.12. Effect of tank size to the Re and Rv through the smart RWH system ..................Figure 7.13. Impact of tank size on: (a) Re; (b) Rv of the dual supply system considering the existing municipal water supply agenda .....................................................................................Figure 7.14. Impact of tank size on: (a) Re; (b) Rv of the dual supply system considering agenda A ..................................................................................................................................................Figure 7.15. Impact of tank size on: (a) Re; (b) Rv of the dual supply system considering agenda B ..................................................................................................................................................

Figure 7 . 8 .

 78 Figure 5.1. Methodological framework .......................................................................................Figure 5.2. The geographic setting of the Eocene Aquifer .......................................................... Figure 5.3. Geologic cross section of the Eocene Aquifer ........................................................... Figure 5.4. Annual NO3 concentration statistics in the Eocene Aquifer (Period 1982-2019) ..... Figure 5.5. Spatiotemporal distribution of GNC in the Eocene Aquifer between 1982 and 2019 ....................................................................................................................................................... Figure 5.6. Influence of soil type on NO3 concentration (period 1982-2019) .............................Figure 5.7. Influence of watersheds and their locations on the NO3 concentration (period 1982 -2019) .............................................................................................................................................Figure 5.8. Influence of well depth on the NO3 concentration .................................................... Figure 5.9. Influence of well use and anthropogenic practices on the NO3 concentration ......... Figure 5.10. Distribution of correct and false predicted wells concerning GNC .........................Figure 6.1. Research methodology ..............................................................................................Figure 6.2. Distribution of rainfall stations in the West Bank .....................................................Figure 6.3. Variation in rainfall considering climate change .......................................................Figure 6.4. Impact of the RWH on the domestic water scarcity in Jerusalem and Tulkarm governorates (in 2020) .................................................................................................................. Figure 6.5. Impact of the RWH on domestic water scarcity for the different urban scales of Jenin governorate ...................................................................................................................................Figure 7.1. Research methodology ..............................................................................................Figure 7.2. Characterization of RWH contamination ..................................................................Figure 7.3. Architecture of the smart RWH system .....................................................................Figure 7.4. Physical components of the smart RWH system .......................................................Figure 7.5. Monitoring components of the smart RWH system ..................................................Figure 7.6. BIM representation for the case study .....................................................................Figure 7.7. Temporal distribution of the daily rainfall in the study area ...................................Figure 7.8. RWH sampling locations .........................................................................................Figure 7.9. Residual chlorine VS. FC among the RWH samples ..............................................Figure 7.10. Effect of tank size to the annual utilized RWH volumes and the annual overflow107 Figure 7.11. Effect of tank size to the annual shortage through the smart RWH system ..........Figure 7.12. Effect of tank size to the Re and Rv through the smart RWH system ..................Figure 7.13. Impact of tank size on: (a) Re; (b) Rv of the dual supply system considering the existing municipal water supply agenda .....................................................................................Figure 7.14. Impact of tank size on: (a) Re; (b) Rv of the dual supply system considering agenda A ..................................................................................................................................................Figure 7.15. Impact of tank size on: (a) Re; (b) Rv of the dual supply system considering agenda B ..................................................................................................................................................

Figure 7 . 9 .

 79 Figure 5.1. Methodological framework .......................................................................................Figure 5.2. The geographic setting of the Eocene Aquifer .......................................................... Figure 5.3. Geologic cross section of the Eocene Aquifer ........................................................... Figure 5.4. Annual NO3 concentration statistics in the Eocene Aquifer (Period 1982-2019) ..... Figure 5.5. Spatiotemporal distribution of GNC in the Eocene Aquifer between 1982 and 2019 ....................................................................................................................................................... Figure 5.6. Influence of soil type on NO3 concentration (period 1982-2019) .............................Figure 5.7. Influence of watersheds and their locations on the NO3 concentration (period 1982 -2019) .............................................................................................................................................Figure 5.8. Influence of well depth on the NO3 concentration .................................................... Figure 5.9. Influence of well use and anthropogenic practices on the NO3 concentration ......... Figure 5.10. Distribution of correct and false predicted wells concerning GNC .........................Figure 6.1. Research methodology ..............................................................................................Figure 6.2. Distribution of rainfall stations in the West Bank .....................................................Figure 6.3. Variation in rainfall considering climate change .......................................................Figure 6.4. Impact of the RWH on the domestic water scarcity in Jerusalem and Tulkarm governorates (in 2020) .................................................................................................................. Figure 6.5. Impact of the RWH on domestic water scarcity for the different urban scales of Jenin governorate ...................................................................................................................................Figure 7.1. Research methodology ..............................................................................................Figure 7.2. Characterization of RWH contamination ..................................................................Figure 7.3. Architecture of the smart RWH system .....................................................................Figure 7.4. Physical components of the smart RWH system .......................................................Figure 7.5. Monitoring components of the smart RWH system ..................................................Figure 7.6. BIM representation for the case study .....................................................................Figure 7.7. Temporal distribution of the daily rainfall in the study area ...................................Figure 7.8. RWH sampling locations .........................................................................................Figure 7.9. Residual chlorine VS. FC among the RWH samples ..............................................Figure 7.10. Effect of tank size to the annual utilized RWH volumes and the annual overflow107 Figure 7.11. Effect of tank size to the annual shortage through the smart RWH system ..........Figure 7.12. Effect of tank size to the Re and Rv through the smart RWH system ..................Figure 7.13. Impact of tank size on: (a) Re; (b) Rv of the dual supply system considering the existing municipal water supply agenda .....................................................................................Figure 7.14. Impact of tank size on: (a) Re; (b) Rv of the dual supply system considering agenda A ..................................................................................................................................................Figure 7.15. Impact of tank size on: (a) Re; (b) Rv of the dual supply system considering agenda B ..................................................................................................................................................

Figure 7 . 10 .Figure 7 . 11 .

 710711 Figure 5.1. Methodological framework .......................................................................................Figure 5.2. The geographic setting of the Eocene Aquifer .......................................................... Figure 5.3. Geologic cross section of the Eocene Aquifer ........................................................... Figure 5.4. Annual NO3 concentration statistics in the Eocene Aquifer (Period 1982-2019) ..... Figure 5.5. Spatiotemporal distribution of GNC in the Eocene Aquifer between 1982 and 2019 ....................................................................................................................................................... Figure 5.6. Influence of soil type on NO3 concentration (period 1982-2019) .............................Figure 5.7. Influence of watersheds and their locations on the NO3 concentration (period 1982 -2019) .............................................................................................................................................Figure 5.8. Influence of well depth on the NO3 concentration .................................................... Figure 5.9. Influence of well use and anthropogenic practices on the NO3 concentration ......... Figure 5.10. Distribution of correct and false predicted wells concerning GNC .........................Figure 6.1. Research methodology ..............................................................................................Figure 6.2. Distribution of rainfall stations in the West Bank .....................................................Figure 6.3. Variation in rainfall considering climate change .......................................................Figure 6.4. Impact of the RWH on the domestic water scarcity in Jerusalem and Tulkarm governorates (in 2020) .................................................................................................................. Figure 6.5. Impact of the RWH on domestic water scarcity for the different urban scales of Jenin governorate ...................................................................................................................................Figure 7.1. Research methodology ..............................................................................................Figure 7.2. Characterization of RWH contamination ..................................................................Figure 7.3. Architecture of the smart RWH system .....................................................................Figure 7.4. Physical components of the smart RWH system .......................................................Figure 7.5. Monitoring components of the smart RWH system ..................................................Figure 7.6. BIM representation for the case study .....................................................................Figure 7.7. Temporal distribution of the daily rainfall in the study area ...................................Figure 7.8. RWH sampling locations .........................................................................................Figure 7.9. Residual chlorine VS. FC among the RWH samples ..............................................Figure 7.10. Effect of tank size to the annual utilized RWH volumes and the annual overflow107 Figure 7.11. Effect of tank size to the annual shortage through the smart RWH system ..........Figure 7.12. Effect of tank size to the Re and Rv through the smart RWH system ..................Figure 7.13. Impact of tank size on: (a) Re; (b) Rv of the dual supply system considering the existing municipal water supply agenda .....................................................................................Figure 7.14. Impact of tank size on: (a) Re; (b) Rv of the dual supply system considering agenda A ..................................................................................................................................................Figure 7.15. Impact of tank size on: (a) Re; (b) Rv of the dual supply system considering agenda B ..................................................................................................................................................

Figure 7 . 12 .

 712 Figure 5.1. Methodological framework .......................................................................................Figure 5.2. The geographic setting of the Eocene Aquifer .......................................................... Figure 5.3. Geologic cross section of the Eocene Aquifer ........................................................... Figure 5.4. Annual NO3 concentration statistics in the Eocene Aquifer (Period 1982-2019) ..... Figure 5.5. Spatiotemporal distribution of GNC in the Eocene Aquifer between 1982 and 2019 ....................................................................................................................................................... Figure 5.6. Influence of soil type on NO3 concentration (period 1982-2019) .............................Figure 5.7. Influence of watersheds and their locations on the NO3 concentration (period 1982 -2019) .............................................................................................................................................Figure 5.8. Influence of well depth on the NO3 concentration .................................................... Figure 5.9. Influence of well use and anthropogenic practices on the NO3 concentration ......... Figure 5.10. Distribution of correct and false predicted wells concerning GNC .........................Figure 6.1. Research methodology ..............................................................................................Figure 6.2. Distribution of rainfall stations in the West Bank .....................................................Figure 6.3. Variation in rainfall considering climate change .......................................................Figure 6.4. Impact of the RWH on the domestic water scarcity in Jerusalem and Tulkarm governorates (in 2020) .................................................................................................................. Figure 6.5. Impact of the RWH on domestic water scarcity for the different urban scales of Jenin governorate ...................................................................................................................................Figure 7.1. Research methodology ..............................................................................................Figure 7.2. Characterization of RWH contamination ..................................................................Figure 7.3. Architecture of the smart RWH system .....................................................................Figure 7.4. Physical components of the smart RWH system .......................................................Figure 7.5. Monitoring components of the smart RWH system ..................................................Figure 7.6. BIM representation for the case study .....................................................................Figure 7.7. Temporal distribution of the daily rainfall in the study area ...................................Figure 7.8. RWH sampling locations .........................................................................................Figure 7.9. Residual chlorine VS. FC among the RWH samples ..............................................Figure 7.10. Effect of tank size to the annual utilized RWH volumes and the annual overflow107 Figure 7.11. Effect of tank size to the annual shortage through the smart RWH system ..........Figure 7.12. Effect of tank size to the Re and Rv through the smart RWH system ..................Figure 7.13. Impact of tank size on: (a) Re; (b) Rv of the dual supply system considering the existing municipal water supply agenda .....................................................................................Figure 7.14. Impact of tank size on: (a) Re; (b) Rv of the dual supply system considering agenda A ..................................................................................................................................................Figure 7.15. Impact of tank size on: (a) Re; (b) Rv of the dual supply system considering agenda B ..................................................................................................................................................

Figure 7 . 13 .

 713 Figure 5.1. Methodological framework .......................................................................................Figure 5.2. The geographic setting of the Eocene Aquifer .......................................................... Figure 5.3. Geologic cross section of the Eocene Aquifer ........................................................... Figure 5.4. Annual NO3 concentration statistics in the Eocene Aquifer (Period 1982-2019) ..... Figure 5.5. Spatiotemporal distribution of GNC in the Eocene Aquifer between 1982 and 2019 ....................................................................................................................................................... Figure 5.6. Influence of soil type on NO3 concentration (period 1982-2019) .............................Figure 5.7. Influence of watersheds and their locations on the NO3 concentration (period 1982 -2019) .............................................................................................................................................Figure 5.8. Influence of well depth on the NO3 concentration .................................................... Figure 5.9. Influence of well use and anthropogenic practices on the NO3 concentration ......... Figure 5.10. Distribution of correct and false predicted wells concerning GNC .........................Figure 6.1. Research methodology ..............................................................................................Figure 6.2. Distribution of rainfall stations in the West Bank .....................................................Figure 6.3. Variation in rainfall considering climate change .......................................................Figure 6.4. Impact of the RWH on the domestic water scarcity in Jerusalem and Tulkarm governorates (in 2020) .................................................................................................................. Figure 6.5. Impact of the RWH on domestic water scarcity for the different urban scales of Jenin governorate ...................................................................................................................................Figure 7.1. Research methodology ..............................................................................................Figure 7.2. Characterization of RWH contamination ..................................................................Figure 7.3. Architecture of the smart RWH system .....................................................................Figure 7.4. Physical components of the smart RWH system .......................................................Figure 7.5. Monitoring components of the smart RWH system ..................................................Figure 7.6. BIM representation for the case study .....................................................................Figure 7.7. Temporal distribution of the daily rainfall in the study area ...................................Figure 7.8. RWH sampling locations .........................................................................................Figure 7.9. Residual chlorine VS. FC among the RWH samples ..............................................Figure 7.10. Effect of tank size to the annual utilized RWH volumes and the annual overflow107 Figure 7.11. Effect of tank size to the annual shortage through the smart RWH system ..........Figure 7.12. Effect of tank size to the Re and Rv through the smart RWH system ..................Figure 7.13. Impact of tank size on: (a) Re; (b) Rv of the dual supply system considering the existing municipal water supply agenda .....................................................................................Figure 7.14. Impact of tank size on: (a) Re; (b) Rv of the dual supply system considering agenda A ..................................................................................................................................................Figure 7.15. Impact of tank size on: (a) Re; (b) Rv of the dual supply system considering agenda B ..................................................................................................................................................

Figure 7 . 14 .

 714 Figure 5.1. Methodological framework .......................................................................................Figure 5.2. The geographic setting of the Eocene Aquifer .......................................................... Figure 5.3. Geologic cross section of the Eocene Aquifer ........................................................... Figure 5.4. Annual NO3 concentration statistics in the Eocene Aquifer (Period 1982-2019) ..... Figure 5.5. Spatiotemporal distribution of GNC in the Eocene Aquifer between 1982 and 2019 ....................................................................................................................................................... Figure 5.6. Influence of soil type on NO3 concentration (period 1982-2019) .............................Figure 5.7. Influence of watersheds and their locations on the NO3 concentration (period 1982 -2019) .............................................................................................................................................Figure 5.8. Influence of well depth on the NO3 concentration .................................................... Figure 5.9. Influence of well use and anthropogenic practices on the NO3 concentration ......... Figure 5.10. Distribution of correct and false predicted wells concerning GNC .........................Figure 6.1. Research methodology ..............................................................................................Figure 6.2. Distribution of rainfall stations in the West Bank .....................................................Figure 6.3. Variation in rainfall considering climate change .......................................................Figure 6.4. Impact of the RWH on the domestic water scarcity in Jerusalem and Tulkarm governorates (in 2020) .................................................................................................................. Figure 6.5. Impact of the RWH on domestic water scarcity for the different urban scales of Jenin governorate ...................................................................................................................................Figure 7.1. Research methodology ..............................................................................................Figure 7.2. Characterization of RWH contamination ..................................................................Figure 7.3. Architecture of the smart RWH system .....................................................................Figure 7.4. Physical components of the smart RWH system .......................................................Figure 7.5. Monitoring components of the smart RWH system ..................................................Figure 7.6. BIM representation for the case study .....................................................................Figure 7.7. Temporal distribution of the daily rainfall in the study area ...................................Figure 7.8. RWH sampling locations .........................................................................................Figure 7.9. Residual chlorine VS. FC among the RWH samples ..............................................Figure 7.10. Effect of tank size to the annual utilized RWH volumes and the annual overflow107 Figure 7.11. Effect of tank size to the annual shortage through the smart RWH system ..........Figure 7.12. Effect of tank size to the Re and Rv through the smart RWH system ..................Figure 7.13. Impact of tank size on: (a) Re; (b) Rv of the dual supply system considering the existing municipal water supply agenda .....................................................................................Figure 7.14. Impact of tank size on: (a) Re; (b) Rv of the dual supply system considering agenda A ..................................................................................................................................................Figure 7.15. Impact of tank size on: (a) Re; (b) Rv of the dual supply system considering agenda B ..................................................................................................................................................

Figure 7 . 15 .

 715 Figure 5.1. Methodological framework .......................................................................................Figure 5.2. The geographic setting of the Eocene Aquifer .......................................................... Figure 5.3. Geologic cross section of the Eocene Aquifer ........................................................... Figure 5.4. Annual NO3 concentration statistics in the Eocene Aquifer (Period 1982-2019) ..... Figure 5.5. Spatiotemporal distribution of GNC in the Eocene Aquifer between 1982 and 2019 ....................................................................................................................................................... Figure 5.6. Influence of soil type on NO3 concentration (period 1982-2019) .............................Figure 5.7. Influence of watersheds and their locations on the NO3 concentration (period 1982 -2019) .............................................................................................................................................Figure 5.8. Influence of well depth on the NO3 concentration .................................................... Figure 5.9. Influence of well use and anthropogenic practices on the NO3 concentration ......... Figure 5.10. Distribution of correct and false predicted wells concerning GNC .........................Figure 6.1. Research methodology ..............................................................................................Figure 6.2. Distribution of rainfall stations in the West Bank .....................................................Figure 6.3. Variation in rainfall considering climate change .......................................................Figure 6.4. Impact of the RWH on the domestic water scarcity in Jerusalem and Tulkarm governorates (in 2020) .................................................................................................................. Figure 6.5. Impact of the RWH on domestic water scarcity for the different urban scales of Jenin governorate ...................................................................................................................................Figure 7.1. Research methodology ..............................................................................................Figure 7.2. Characterization of RWH contamination ..................................................................Figure 7.3. Architecture of the smart RWH system .....................................................................Figure 7.4. Physical components of the smart RWH system .......................................................Figure 7.5. Monitoring components of the smart RWH system ..................................................Figure 7.6. BIM representation for the case study .....................................................................Figure 7.7. Temporal distribution of the daily rainfall in the study area ...................................Figure 7.8. RWH sampling locations .........................................................................................Figure 7.9. Residual chlorine VS. FC among the RWH samples ..............................................Figure 7.10. Effect of tank size to the annual utilized RWH volumes and the annual overflow107 Figure 7.11. Effect of tank size to the annual shortage through the smart RWH system ..........Figure 7.12. Effect of tank size to the Re and Rv through the smart RWH system ..................Figure 7.13. Impact of tank size on: (a) Re; (b) Rv of the dual supply system considering the existing municipal water supply agenda .....................................................................................Figure 7.14. Impact of tank size on: (a) Re; (b) Rv of the dual supply system considering agenda A ..................................................................................................................................................Figure 7.15. Impact of tank size on: (a) Re; (b) Rv of the dual supply system considering agenda B ..................................................................................................................................................

  aquifers ................................................................................................................................ Table

.

  Current and projected population (POPc and POPp) and TWD ................................. Table 3.3. TWS in the different West Bank governorates in 2020 .............................................. Table 3.4. SDBI values and water scarcity levels in the West Bank governorates in 2020 and 2050............................................................................................................................................... Table

4 . 3 . 4 . 4 . 5 . 1 .

 434451 ...................................................................................................................Table Targeted water-related bodies for the groundwater quality survey ............................ Table Groundwater potability and palatability weighting systems in the West Bank .......... Table 4.5. Cross-validation results for PoGWQGs and PaGWQGs among two groundwater wells in the West Bank ........................................................................................................................... Table Descriptive statistics of the NO3 concentrations (mg/l) in different communities in the Eocene Aquifer (period 1982 -2019) ........................................................................................... Table 5.2. Levels of GNC used in Machine Learning methods ................................................... Table 5.3. Influence of land use on NO3 concentrations (period 1982-2019) ............................. Table 5.4. Prediction accuracy for RFA model over 10 execution trials ..................................... Table

5 . 6 . 5 . 7 . 6 . 1 . 6 . 2 . 6 . 3 . 6 . 4 . 6 . 5 . 6 . 6 . 6 . 7 .

 565761626364656667 ) ........................ Table Types of prediction errors for the false predicted wells ..............................................Table Importance of input factors used in the RFA prediction model .................................. Table Current and projected POP and rooftop areas ............................................................. Table Density of rainfall measurements in the West Bank ................................................... Table Average annual rainfall for the different West Bank governorates in 2020 ............... Table Contribution of RWH to TWS (in 2020) .................................................................... Table Contribution of RWH to TWD in 2020 ...................................................................... Table Impact of RWH on the domestic water scarcity level in 2020 ................................... Table Contribution of RWH to TWS and TWD in 2050 ...................................................... Table 6.8. Impact of RWH on the domestic water scarcity level in 2050 ................................... Table 7.1. Characteristics off the buildings in the case study ....................................................

Figure 1 . 1 .

 11 Figure 1.1. KIM-based interpolated vs. observed groundwater levels in an aquifer in piedmont plains, China[START_REF] Xiao | Geostatistical interpolation model selection based on ArcGIS and spatio-temporal variability analysis of groundwater level in piedmont plains, northwest China[END_REF]

Figure 1 . 2 .

 12 Figure 1.2. Hydrological cycle[START_REF] Levizzani | Satellite Remote Sensing of Precipitation and the Terrestrial Water Cycle in a Changing Climate[END_REF]

Figure 1 . 3 .

 13 Figure 1.3. Architecture of Smart systems

Figure 1 . 4 .

 14 Figure 1.4. Research outline

Figure 2 . 1 .

 21 Figure 2.1. The geographical setting of the West Bank2.2.2. Water challenges in PalestineThe scarcity of water is a feature of the Palestinians' life(PWA, 2016). The average daily water consumption per capita in the country is well below the internationally recommended rate by the World Health Organization (WHO) of 100 liters per capita per day (l/c/d)(Human Rights Council, 2021). The gap between supply and demand for domestic use in the West Bank was estimated at 58 million cubic meters per year (MCM/y) in 2018. About one-third of the Palestinian water supply is lost by leakage due to the poor and deteriorated condition of water supply grids linking the West Bank's communities(Human Rights Council, 2021). This could be attributed to the obstructions to maintenance and upgrades by the Israeli Occupation (Human Rights Council, 2021).

Figure 2

 2 their associated social, economic, and environmental consequences are discussed in the following sections.

Figure 2 . 2 .

 22 Figure 2.2. Assessment of water challenges in Palestine

Figure 2 . 3 .

 23 Figure 2.3. Groundwater flow, divides and aquifers in the West Bank and Gaza Strip More than 25 years after Oslo II Accord entered into force, the Israeli Occupation is still controlling over 80% of the shared groundwater resources in Palestine. This interim agreement was intended to five years. However, it remains the key agreement regulating the water use in Palestine (Human Rights Council, 2021). Table2.1 shows the water allocation and utilization according to Article 40 of the Oslo Acord[START_REF] Mizyed | Climate change challenges to groundwater resources: Palestine as a case study[END_REF] and PWA, 2013).

Figure 2 . 4 .

 24 Figure 2.4. Spatial distribution of the groundwater wells in the West Bank

Figure 2 . 5 .

 25 Figure 2.5. Decline of groundwater level in the Well L/57 located in Gaza Strip between 1966 and 2014

Figure 2 . 6 .

 26 Figure 2.6. Long-term average annual rainfall in the West Bank

Figure 2 . 7 .

 27 Figure 2.7. RWH suitability map for domestic uses in the West Bank

Figure 2 . 8 .

 28 Figure 2.8. Average annual concentrations of NO3 and Cl in groundwater wells in Tulkarm and Qalqiliya

Figure 2 . 9 .

 29 Figure 2.9. Spatial distribution of NO3 and Cl amongst Gaza Coastal Aquifer On light of the deteriorated water quality in the West Bank and Gaza Strip, protection plans are required for the protection of the water resources.

Figure 2 .

 2 10 (Al-Najjar et al., 2020). The increase in the incidence of drought occurrence is attributed to the reduction in precipitation and rise in evaporation as a result of temperature increase[START_REF] Al-Najjar | Statistical modeling of spatial and temporal vulnerability of groundwater level in the Gaza Strip (Palestine)[END_REF].

Figure 2 . 10 .

 210 Figure 2.10. Drought occurrence in the MENA

Figure 2 . 11 .

 211 Figure 2.11. Spatiotemporal impact of climate change and over-pumping on the groundwater level of the Gaza Coastal Aquifer between 1974 and 2040

Figure 2 . 12 .

 212 Figure 2.12. Overall research methodology 2.3.1. Assessment of the water scarcity

Figure 2 . 13 .

 213 Figure 2.13. Assessment of existing and projected water scarcity

Figure 2 . 14 .

 214 Figure 2.14. Protection of conventional water resources

Figure 2 . 15 .

 215 Figure 2.15. Investigating the role of potential RWH in addressing the current and future domestic water scarcity

Figure 2 . 16 .

 216 Figure 2.16. Methodological framework for smart RWH and smart dual water supply systems

Figure 3 . 1 .

 31 Figure 3.1. Research methodology

Figure 3 . 2 .

 32 Figure 3.2. Schematic overview of the adopted SVAT model Projected population (POPp) and DWCR in 2050 are employed to quantify the projected TWD for the year 2050.

Figure 3 .

 3 3 shows the increasing trend in population and TWD. Population increases from 1.78 million capita to 3.05 million capita, and TWD increases from 65.2 MCM/y to 111.4 MCM/y between 1997 and 2020.

Figure 3 . 3 .

 33 Figure 3.3. Historical and current statistics of population and TWD in the whole West Bank between 1997 and 2020

Figure 3 .

 3 Figure 3.4 illustrates the distribution of current and projected TWS in the West Bank. It indicates that all the governorates, except Jenin and Nablus, will experience in 2050 a decrease in TWS compared to 2020. This maximum decrease could reach 26% (in the Northern part of the West Bank).

Figure 3 . 4 .

 34 Figure 3.4. Variation in TWS considering climate change

Figure 4 .

 4 Figure 4.1 shows the methodology adopted in this research. It includes four phases: development of PoGWQI and PaGWQI, mapping of water quality indices, performing a sensitivity analysis, and constructing causal-effect analysis for understanding and prioritizing the factors affecting PoGWQI and PaGWQI.

Figure 4 . 1 .

 41 Figure 4.1. Overall methodological framework

Figure 4 . 2 .

 42 Figure 4.2. Development of potability and palatability groundwater quality indexes (PoGWQI and PaGWQI)

Figure 4 . 3 .

 43 Figure 4.3. PoGWQGs and PaGWQGs in the West Bank

Figure 4 . 4 .

 44 Figure 4.4. Un-potability rate over four contaminated groundwater wells in the West Bank

Figure 4 .

 4 Figure 4.5 indicates that PaGWQI had a more consistent trend. The eastern part of the West Bank was characterized by permanent unpalatable groundwater. PaGWQG ranges from D to E. Moreover, grade D frequently appeared (in 6 non-successive years) in the far south of the study area. This stability of PaGWQI could be related to the permanent high salinity near the Dead Sea (the Eastern part of the study area). The middle, eastern and northern parts provide almost palatable water.

Figure 4 . 5 .Figure 4 .

 454 Figure 4.5. Spatiotemporal mapping of PoGWQI and PaGWQI in the West Bank between 2001 and 2016 Figure 4.6 indicates that Jerusalem and Bethlehem governorates had the best potable water with almost 100% A-PoGWQG. It is also found that PoGWQGs range from A to D in Hebron, Qalqiliya, and Tulkarm governorates.Jenin governorate recorded the worst water potability with an E-PoGWQG of about 9%. This was followed by Tubas and Ramallah and Al Bireh, with an E-PoGWQG of about 5% and 3%, respectively. This could be related to the use of cesspits for wastewater disposal. It could also be referred to as the intensive use of agrochemicals.

Figure 4 . 6 .

 46 Figure 4.6. PoGWQGs and PaGWQGs cross the West Bank governorates 4.3.3. Analysis of indices' sensitivity The sensitivity of indices stems from the scores (values) and weights of their input parameters.Figure 4.7 indicates that PoGWQI was highly sensitive to the NO3 concentrations with a MAE of 23-37 units. FC also had a significant influence on PoGWQI with a MAE between 19-30 units.

Figure 4 .

 4 7 indicates that PoGWQI was highly sensitive to the NO3 concentrations with a MAE of 23-37 units. FC also had a significant influence on PoGWQI with a MAE between 19-30 units.

Figure 4 . 7 .

 47 Figure 4.7. Sensitivity analysis of PoGWQI and PaGWQI

Figure 4 . 8 .

 48 Figure 4.8. Factors affecting PoGWQI and PaGWQI in the West Bank

Figure 4 . 9 .

 49 Figure 4.9. Relative importance analysis for the main factors affecting PoGWQI and PaGWQI On the other hand, all factors have a close relative influence (15-25%) on PaGWQI estimation. This influence could be referred to as the multi-causes for the high values in PaGWQI input parameters. These unaccepted values in some of the input parameters, such as (pH, TDS, and HCO3) are caused by human activities (associated with land use), aquifer, or soil characteristics (e.g., mineralized formations).

Figure 5 . 1 .

 51 Figure 5.1. Methodological framework

Figure 5 . 3 .

 53 Figure 5.3. Geologic cross section of the Eocene Aquifer 5.2.3. Data collection and application to the Eocene Aquifer Two datasets are used in this research: (i) Palestinian Water Authority (PWA) dataset for 27 wells over the period 1982-2012 (PWA, 2020) and (ii) field dataset for 24 wells which were sampled during the period 2017-2019. Eight mutual wells have recorded in the datasets which have both historical (1982-2012) and recent (2017-2019) NO3 concentration values. However, the two datasets were combined into a one composite GIS-based data, which includes data for 43 wells. It includes the following indicators: well depths, coordinates of spatial location (X, Y), type (use), on-ground activities, NO3 concentration and sampling date. The collected data was manipulated under the GIS environment where wells were spatially joined by communities, watersheds, soil types and land use shapefiles.

Figure 5 .

 5 Figure 5.4 shows the key statistics of the annual NO3 concentrations for the period 1982-2019. It is noticed that the mean values are mostly above the MCL. Median values are always below the mean and close to the MCL. This observation indicates that approximately 50% of the NO3 concentrations are above the MCL. The maximum values of the annual NO3 concentrations are always higher than the MCL, approaching its up-limit value of 256 mg/l in 2012. Such concentrations clearly reflect the high groundwater contamination through the mean, median, 75 th percentile and maximum NO3 concentrations. However, Figure 5.4 considers only the temporal GNC and neglects the spatial variation.

Figure 5 . 4 .

 54 Figure 5.4. Annual NO3 concentration statistics in the Eocene Aquifer(Period 1982(Period -2019) )

Figure 5 . 5 .

 55 Figure 5.5. Spatiotemporal distribution of GNC in the Eocene Aquifer between 1982 and 2019

  Figure 5.6 illustrates the minimum, 25 th percentile, mean, median, 75 th percentile and maximum NO3 concentrations for the different soil types. It indicates that the mean NO3 concentrations in the groundwater under all soil types are equal or exceed the MCL. Since the different soil types in the study area have a similar permeabilities, they have nearly the same mean, median and 75 th percentile groundwater NO3 concentrations. This result indicates a low influence of the soil type on the groundwater NO3 concentration.

Figure 5 . 6 .Figure 5 . 7 .

 5657 Figure 5.6. Influence of soil type on NO3 concentration (period 1982-2019) 5.3.1.3. Influence of the land use Table 5.3 summarizes the key statistics of NO3 concentrations for the different land use classes. It shows that the groundwater under the discontinuous urban areas has the highest mean NO3 concentration with a value of 85 mg/l. This can be attributed to the extensive use of cesspits for wastewater disposal. Furthermore, fertilizers are used in the green spaces distributed among

Figure 5 . 8 .

 58 Figure 5.8. Influence of well depth on the NO3 concentration 5.3.1.6. Influence of the wells use and the anthropogenic practices

Figure 5 . 9 .

 59 Figure 5.9. Influence of well use and anthropogenic practices on the NO3 concentration

Figure 5 . 10 . 5 . 6 .

 51056 Figure 5.10. Distribution of correct and false predicted wells concerning GNCTable 5.6. Types of prediction errors for the false predicted wells

Figure 6 . 1 .

 61 Figure 6.1. Research methodology

Figure 6 . 2 .

 62 Figure 6.2. Distribution of rainfall stations in the West Bank

Figure 6 .

 6 Figure 6.4 shows the impact of the RWH on the domestic water scarcity level in Tulkarm and Jerusalem in 2020. The highest effect is observed in Tulkarm. Exploiting around 16% of the roofs for RWH could drop the scarcity level to "no water shortage". The rest roof areas could support other purposes such as agricultural and industrial uses. The extra RWH can also support water shortages in other governorates.

Figure 6 .

 6 Figure 6.4 shows that the RWH has low efficiency in addressing the domestic water scarcity in Jerusalem: Jerusalem faces an "extreme water shortage" after fully adopting RWH in 2020. This low performance of the RWH is due to the low ratio of rooftops to the population, which is around 10.7 m 2 /citizen, to be compared to values in the rest of the West Bank governorates, which varies between 26.5 and 42.4 m 2 /citizen.

Figure 6 . 4 .

 64 Figure 6.4. Impact of the RWH on the domestic water scarcity in Jerusalem and Tulkarm governorates (in 2020)

Figure 6 . 5 .

 65 Figure 6.5. Impact of the RWH on domestic water scarcity for the different urban scales of Jenin governorate 6.3.3. Projected capacity of RWH to address the domestic water scarcity in 2050

Figure 7 . 1 .

 71 Figure 7.1. Research methodology 7.2.1.1. Sources for RWH contamination and inefficiency

Figure 7 . 3 .

 73 Figure 7.3. Architecture of the smart RWH system

Figure 7 . 4 .

 74 Figure 7.4. Physical components of the smart RWH system

Figure 7 . 5 .

 75 Figure 7.5. Monitoring components of the smart RWH system

Figure 7 . 7 .

 77 Figure 7.7. Temporal distribution of the daily rainfall in the study area A spatially distributed RWH samples are collected from 35 residential units in Jenin city (Figure 7.8). The sampling process is carried out between October and December 2021. RWH samples are analyzed at the laboratory for various physiochemical and biological water quality parameters, including pH, Turbidity, Alkalinity, Total Dissolved Solids (TDS), FC, and Residual Chlorine. The sampling and analysis processes are carried out considering the regulations and procedures stated by the WHO (WHO, 2011). Analysis results are then compared to the WHO drinking water standards (WHO, 2011).

Figure 7 . 8 .

 78 Figure 7.8. RWH sampling locations

Figure 7 . 9 .

 79 Figure 7.9. Residual chlorine VS. FC among the RWH samples

Figure 7 . 10 .

 710 Figure 7.10. Effect of tank size to the annual utilized RWH volumes and the annual overflowAccording to Equation7.1, the annual water demand for the study area is around 1240 m 3 /year. The lowest shortage (59%) in water supply could be reached by using the optimal tank size (112.5 m 3 ). However, using lower sizes is associated with a higher shortage rate. For instance, the tank of 5 m 3 size has 83% shortage in providing the needed water demand(Figure 7.11).

Figure 7 . 11 .

 711 Figure 7.11. Effect of tank size to the annual shortage through the smart RWH system

Figure 7 . 12 .

 712 Figure 7.12. Effect of tank size to the Re and Rv through the smart RWH system

Figure 7 . 13 .

 713 Figure 7.13. Impact of tank size on: (a) Re; (b) Rv of the dual supply system considering the existing municipal water supply agenda

Figure 7 . 14 .Figure 7 . 15 .

 714715 Figure 7.14. Impact of tank size on: (a) Re; (b) Rv of the dual supply system considering agenda A

Table 1 . 1 .

 11 Water scarcity definitions .............................................................................................

Table 1 . 2 .

 12 Water scarcity indices ...................................................................................................

Table 1 . 3 .

 13 Falkenmark index classes ............................................................................................

Table 1 . 4 .

 14 Models used for water quality modeling .....................................................................

Table 1 . 5 .

 15 Artificial intelligence models for water level/flow monitoring ..................................

Table 1 . 6 .

 16 Smart interventions and their roles in water distribution networks ............................

Table 1 . 7 .

 17 Smart sensors/devices in the water distribution networks ..........................................

Table 2 . 1 .

 21 Groundwater allocation (utilization) according to Oslo Agreement ...........................

Table 2 . 2 .

 22 Relative changes in recharge under different climate change scenarios for the West Bank

Table 7 . 2 .

 72 Municipal supply capacity at the neighborhood level ...............................................

	XII

Table 7 . 3 .

 73 Physiochemical and biological analysis of RWH samples ....................................... 105

Table 7 . 4 .

 74 Impact of tank size on the domestic water scarcity levels of the dual supply system considering agenda B .................................................................................................................. 110

	XIII

Table 1 .2. Water scarcity indices Index Definition Target Reference Water quantity

 1

Table 1 .3. Falkenmark index classes Class Index value (m 3 /capita/year)

 1

	No water stresses	> 1700
	Water stresses	1000-1700
	Water scarcity	500-1000
	Absolute water scarcity	< 500

Table 1 . 4 .

 14 . Models used for water quality modeling

	Model	Contaminants	Description	Reference
	TOMCAT	Ammonium (NH4),	It considers the Monte Carlo	(Ranjith et al.,
		Dissolved Oxygen	analysis approach.	2019)
		(DO), and Biochemical
		Oxygen Demand (BOD)
	MT3D	Nitrate (NO3), Chlorine	It is a three-dimensional	(Yang et al.,
		(Cl2), and salinity	transport model.	2022)
	SIMCAT	BOD, Cl, NH4, DO, and	It considers deterministic,	(Ejigu, 2021)
		NO3	stochastic, and Monte Carlo
			approaches.
	ECM catchment Total Nitrogen (TN) and	It relies on data collection	(Zhang et al.,
		Total Phosphorous(TP)	from different sources	2018a)
			including: databases and
			questionnaires.
	MIKE-11	NO3, coliforms, DO,	It is a fully hydrodynamic	(Thu Minh et al.,
		NH4, TP, and BOD	approach.	2022)
	DRAINMOD	Salinity and TN	It considers the surface	(Wilson et al.,
			runoff, evapotranspiration,	2020)
			depth to water table, and
			drainage rates.
	MONERIS	TP, TN, and heavy	It is a semi-empirical, model	(Pastuszak et al.,
		metals	that could be integrated to	2018)
			GIS for the run-off water
			quality modeling.
	TOPCAT-NP	TP, and TN	It adopted a subsurface flow	(Adams et al.,
			and identical soil moisture	2020)
			equations
	QUAL2K	DO, pH, BOD, Algae,	It has no dynamic and	(Hoang et al.,
		TP, TN, and pathogens	stochastic characteristics	2019)

Table 1 . 6 .

 16 Smart interventions and their roles in water distribution networks

	Intervention	Clarification	Reference
	Optimizing water supply
	and minimizing water leak

Table 1 . 7 .

 17 Smart sensors/devices in the water distribution networks

	Smart sensor/device	Function	Addressed challenges
	Smart water flow sensors	Monitoring the water flow Extensive water utilization,
			Pipe burst, and water
			leakage
	Smart water quality sensor Ensuring the potability of	Water contamination, and
		water	network deterioration
	Smart water pressure	Monitoring the water	Pressure instability, and
	sensor	pressure	energy loss
	Smart pumps	Assessing pump	Pressure unbalance, and
		performance and efficiency	energy loss
	Smart valves	Multi-direction operation	Water leakage, and pipe
			burst

Table 2 .1. Groundwater allocation (utilization) according to Oslo Agreement Division Allocation (utilization in 2011) in MCM/y

 2

		Western aquifer	Northeastern aquifer	Eastern aquifer	Total
	Israeli Occupation	340 (411)	103 (103)	40 (150)	483 (664)
	Palestinian utilization	22 (25)	42 (20)	54 (42)	118 (87)
	Additional quantity for
	Palestinian	0	0	78	78
	development

Table 2 . 2 .

 22 Relative changes in recharge under different climate change scenarios for the West Bank aquifers

	Aquifer	Annual Precipitation (mm)	3˚C temperature increase	3% Precipitation reduction	Scenario 6% Precipitation reduction	10% Precipitation reduction	15% Precipitation reduction
	North eastern	546	9.1%	6.8%	13.9%	22.9%	39.7%
	Western	564	8.4%	2.4%	7.5%	14.0%	28.2%
	Eastern	327	13.2%	6.8%	14.4%	24.4%	51.0%
	Total	440	9.8%	4.5%	10.7%	18.7%	36.7%

Table 3 . 1 .

 31 Domestic water scarcity values and the associated levels[START_REF] Huang | Supply and Demand Analysis of Water Resources Based on System Dynamics Model[END_REF]

	SDBI level	SDBI value
	Extreme water scarcity	(0,0.3)
	Acute water scarcity	[0.3,0.6]
	Moderate water scarcity	(0.6,0.9)
	Slight water scarcity	[0.9,1)
	No water scarcity	≥1

Table 3 . 2 .

 32 Current and projected population (POPc and POPp) and TWD

	Governorate	POP c	POP p	TWD 2020 (MCM/y)	TWD 2050 (MCM/y)
	Jenin	332050 481000	12.1	17.6
	Tubas	64507 133000	2.4	4.9
	Tulkarm	195341 227000	7.1	8.3
	Nablus	407754 532000	14.9	19.4
	Qalqilya	119042 174000	4.3	6.4
	Salfit	80225 101000	2.9	3.7
	Ramallah & Al-Bireh 347818 602000	12.7	22.0
	Jerusalem	461666 642000	16.9	23.4
	Jericho	52355	89000	1.9	3.2
	Bethlehem	229884 354000	8.4	12.9
	Hebron	762541 1394000	27.8	50.9

Table 3 . 3 .

 33 TWS in the different West Bank governorates in 2020

	Governorate	TWS in 2020 (MCM/y)
	Jenin	3.7
	Tubas	2.7
	Tulkarm	6.6
	Nablus	8.5
	Qalqilya	5.2
	Salfit	1.5
	Ramallah & Al-Bireh	1.1
	Jerusalem	1.4
	Jericho	2.9
	Bethlehem	2.0
	Hebron	6.6

Table 3 . 4 .

 34 SDBI values and water scarcity levels in the West Bank governorates in 2020 and 2050

	Governorate	SDBI value	Domestic water scarcity level
		2020	2050	2020	2050
	Jenin	0.29	0.17	Extreme	Extreme
	Tubas	1.14	0.50	No Shortage	Acute
	Tulkarm	0.92	0.61	Slight	Moderate
	Nablus	0.57	0.33	Acute	Acute
	Qalqilya	1.20	0.62	No Shortage	Moderate
	Salfit	0.50	0.31	Acute	Acute
	Ramallah & Al-Bireh	0.08	0.04	Extreme	Extreme
	Jerusalem	0.08	0.04	Extreme	Extreme
	Jericho	1.53	0.74	No Shortage	Moderate
	Bethlehem	0.24	0.10	Extreme	Extreme
	Hebron	0.24	0.08	Extreme	Extreme

Table 4 . 1 .

 41 Identification of potability and palatability groundwater quality statuses (PoGWQSs, PaGWQSs), and grades (PoGWQGs, and PaGWQGs)[START_REF] Brown | A Water Quality Index: Do We Dare? Water Sewage Works[END_REF]

	PoGWQI/PaGWQI	PoGWQS	PaGWQS	PoGWQG/PaGWQG
	≤25	Excellent	Excellent	A
	25.01-50	Good	Good	B
	50.01-75	Satisfactory	Satisfactory	C
	75.01-100	Poor	Poor	D
	>100	Undrinkable	Unpalatable	E

Table 4 . 2 .

 42 Vn, Vi, and Vs values of water quality parameters(PWA, 2020; WHO, 2011; and Keršulien et al., 2010)

	Water Quality Parameter V n (Range)	V i	V s	Unit
	Fecal Coliform (FC)	0-120	0	10	CFU/100 ml
	Nitrate (NO3)	0-128	0	50	mg/L as NO3
	Chloride (Cl)	12-2573	0	250	mg/L
	Total Dissolved Solids (TDS)	33-5288	0	600	mg/L
	Turbidity	0-13	0	5	NTU
	pH	6.8-8.6	7	6.5-8.5	-
	Sulfate (SO4)	0-600	0	250	mg/L
	Hardness	168-1110	0	500	mg/L
	Bicarbonate (HCO3)	76-431	0	120	mg/L

Table 4 . 3 .

 43 Targeted water-related bodies for the groundwater quality survey

	Group	Total No. of Water-Related Institutions (Pop)	No. of Surveyed Institutions	No. of Respondents
	Policymakers and related public bodies	7	6	13
	Municipalities (in cities)	11	6	6
	Non-governmental organizations (NGOs)	5	4	4
	Private sector	10	5	5
	Universities and water Institutes	9	9	13

Table 4 . 4 .

 44 Groundwater potability and palatability weighting systems in the West Bank

	Parameter	Potability W n ′	Palatability W n ′
	FC	55.1	-
	NO3	44.9	-
	Cl	-	19.0
	TDS	-	17.1
	Turbidity	-	15.6
	pH	-	14.0
	SO4	-	12.8
	Hardness	-	11.5
	HCO3	-	10.0
	AWAWQI treats the scores and weights illustrated in Tables 4.3 and 4.4 to develop PoGWQI
	and PaGWQI in the West Bank.

Table 4 . 5 .

 45 Cross-validation results for PoGWQGs and PaGWQGs among two groundwater wells in the West Bank Groundwater

Source Year Actual PoGWQG Interpolated PoGWQG Actual PaGWQG Interpolated

Table 5 . 1 .

 51 Descriptive statistics of the NO3 concentrations (mg/l) in different communities in the Eocene Aquifer(period 1982 -2019)

	Communities	Population in 2019	No. of samples	Mean Median Min Max
	Al-Jalamah	2,343	41	48	50	11	64
	Arrana	2,498	8	57	59	32	71
	Al-Shuhada	2,375	4	4	4	2	5
	Az-Zababdeh	4,402	7	19	19	10	28
	Deir Abu De'if	7,278	8	11	11	1	22
	Deir Ghazaleh	1,166	39	45	46	7	80
	Jalboun	2,906	8	9	6	2	25
	Jenin city	51,557	150	70	53	0	216
	Kafr Dan	6,809	63	47	45	6	85
	Qabatiya	25,247	84	39	19	0	192
	Ras Al-Far'a	1,323	103	66	58	6	256
	Sanur	5,202	7	12	11	8	19

Table 5 . 2 .

 52 Levels of GNC used in Machine Learning methods

	NO 3 Level (mg/l)	Description
	<25	Not contaminated by NO3
	25-50	Reasonable NO3 concentration
	50-100	Contaminated by NO3
	>100	Extremely contaminated by NO3

Table 5 . 4 .

 54 Prediction accuracy for RFA model over 10 execution trials

	Trial	1	2	3	4	5	6	7	8	9	10 Mean
	Accuracy (%) 89.

Table 5 . 5 .

 55 Confusion matrix for the RFA prediction model (trial 10,Table 5.4)

Contamination Level Predicted contamination level Not contaminated by NO 3 (%) Reasonable NO 3 concentration (%) Contaminated by NO 3 (%) Extremely contaminated by NO 3 (%) Actual contamination level Not contaminated by NO 3 (%)

		27.08	2.08	0	0
	Reasonable
	NO 3 concentration	2.08	50	0	0
	(%)
	Contaminated by NO 3 (%)	0	4.14	8.37	0
	Extremely
	contaminated	0	0	0	6.25
	by NO 3 (%)

Table 5 . 7 .

 57 Importance of input factors used in the RFA prediction model

	Factors	Well	Land	Anthropogenic	Watershed Well use
		depth	use	activities
	Relative importance (%)	41	20	18	14	7

Table 6 . 1 .

 61 Current and projected POP and rooftop areas

				Rooftops	Rooftops	Rooftops
	Governorate POPc	POPp	area 2020	density	area 2050
				(km 2 )	(m 2 /capita)	(km 2 )
	Jenin	332050 481000	10.5	31.7	15.3
	Tubas	64507 133000	1.7	26.5	3.5
	Tulkarm	195341 227000	6.0	30.8	7.0
	Nablus	407754 532000	11.5	28.3	15.1
	Qalqilya	119042 174000	3.3	27.8	4.8
	Salfit	80225 101000	2.7	33.4	3.4
	Ramallah & Al-Bireh	347818 602000	11.7	33.7	20.3
	Jerusalem	461666 642000	4.9	10.7	6.8
	Jericho	52355	89000	2.2	42.4	3.8
	Bethlehem 229884 354000	7.3	31.6	11.2
	Hebron	762541 1394000	21.8	28.7	39.9

Table 6 .2. Density of rainfall measurements in the West Bank Governorate No. of rainfall stations Urban area (km²) Rainfall stations density (station/km²)

 6

	Jenin	13	23.3	0.6
	Tubas	3	5.1	0.6
	Tulkarm	6	21.4	0.3
	Nablus	9	27.6	0.3
	Qalqilya	5	6.8	0.7
	Salfit	5	6.7	0.8
	Ramallah & Al-Bireh	11	38.7	0.3
	Jerusalem	5	37.3	0.1
	Jericho	3	10.2	0.3
	Bethlehem	6	27.0	0.2
	Hebron	9	77.2	0.1

Table 6 . 3 .

 63 Average annual rainfall for the different West Bank governorates in 2020

	Governorate	Average annual rainfall in
		2020 (mm/year)
	Jenin	498
	Tubas	402
	Tulkarm	630
	Nablus	574
	Qalqilya	662
	Salfit	656
	Ramallah & Al-Bireh	579
	Jerusalem	537
	Jericho	166
	Bethlehem	518
	Hebron	487

Table 6 . 5 .

 65 Contribution of RWH to TWD in 2020

		The ratio of
	Governorate	RWH to
		TWD (%)
	Jenin	39
	Tubas	26
	Tulkarm	48
	Nablus	40
	Qalqilya	45
	Salfit	54
	Ramallah & Al-Bireh	48
	Jerusalem	14
	Jericho	17
	Bethlehem	40
	Hebron	34

Table 6 . 6 .

 66 Impact of RWH on the domestic water scarcity level in 2020

	Governorate	25%	RWH adoption rate 50% 75%	100%
	Jenin	Acute	Acute	Moderate	Moderate
	Tubas	No shortage No shortage No shortage	No shortage
	Nablus	Moderate	Moderate	Moderate	Slight
	Qalqilya	No shortage No shortage No shortage	No shortage
	Salfit	Moderate	Moderate	Slight	No shortage
	Ramallah & Al-Bireh	Extreme	Acute	Acute	Acute
	Jericho	No shortage No shortage No shortage	No shortage
	Bethlehem	Acute	Acute	Acute	Moderate
	Hebron	Acute	Acute	Acute	Acute

Table 6 . 7 .

 67 Contribution of RWH to TWS and TWD in 2050

		Increase in TWS	The ratio of
	Governorate	after adopting	RWH to TWD
		RWH (%)	(%)
	Jenin	177	41
	Tubas	49	25
	Tulkarm	60	48
	Nablus	89	42
	Qalqilya	57	43
	Salfit	141	51
	Ramallah & Al-Bireh	1038	46
	Jerusalem	250	13
	Jericho	20	17
	Bethlehem	285	36
	Hebron	296	30

Table

Table 6 . 8 .

 68 Impact of RWH on the domestic water scarcity level in 2050

	Governorate	0%	25%	RWH adoption rate 50% 75%	100%
	Jenin Jerusalem	Extreme	Extreme	Extreme	Extreme	Extreme
	Jericho	Moderate	Moderate	Slight	Slight	Slight
	Bethlehem	Extreme	Extreme	Acute	Acute	Acute
	Hebron	Extreme	Extreme	Extreme	Acute	Acute

Table 7 . 2 .

 72 Municipal supply capacity at the neighborhood level

Altitude Supply rate (m 3 /day of supply/neighborhood)* Dry season (May to Oct)

			Rainy season
			(Nov to Apr)
	high-altitude,	7	15
	moderate-altitude,	10	25
	low-altitude	22	35

* neighborhood is adjacent houses in the study area (mainly 5-15 houses)

Table 7 . 3 .

 73 Physiochemical and biological analysis of RWH samples

	Parameter	Min	Mean	Median	Max	WHO standards	Number of contaminated samples (%)
	pH	6.92	7.32	7.31	7.75	6.5-8.5	0 (0%)
	Turbidity (NTU)	0.18	3.37	0.95	28.50	≤ 5.00	7 (20%)
	Alkalinity (mg/L CaCO3)	65.00	169.74	145.00	325.00	≤ 400.00	0 (0%)
	TDS (mg/L)	72.00	185.49	175.00	302.00	≤ 600.00	0 (0%)
	FC (CFU/100 ml)	0.00	92.23	9.00	545.00	≤ 10.00	17 (48.6%)
	Residual Chlorine (mg/L)	0.00	0.27	0.17	2.10	0.2-0.8	20 (57.1%)

Table 7 . 4 .

 74 Impact of tank size on the domestic water scarcity levels of the dual supply system considering agenda B

Tank size Domestic water scarcity level Low-altitude Moderate-altitude High-altitude

	10	Moderate	Slight	Slight
	20	Slight	Slight	Slight
	30	No scarcity	Slight	Slight
	40	No scarcity	No scarcity	No scarcity
	50	No scarcity	No scarcity	No scarcity

Chapter 5. Use of GIS, Statistics and Machine Learning for Groundwater Quality Management:

Application to Nitrate Contamination

Introduction

Groundwater nitrate contamination (GNC) is a common problem that threatens the drinkability of the groundwater [START_REF] Mas-Pla | Groundwater Nitrate Pollution and Climate Change: Learnings from a Water Balance-Based Analysis of Several Aquifers in a Western Mediterranean Region (Catalonia)[END_REF]. The severity of the GNC depends on the importance of the groundwater as a main source for uses among which, the domestic use is the most challenging (PWA, 2017;[START_REF] Almasri | Analysis of Nitrate Contamination of Gaza Coastal Aquifer, Palestine[END_REF][START_REF] Khayat | Identification of Nitrate Sources in Groundwater by Δ15Nnitrateand Δ18Onitrate Isotopes: A Study of the Shallow Pleistocene Aquifer in the Jericho Area, Palestine[END_REF][START_REF] Almasri | Assessment and Management of Long-Term Nitrate Pollution of Ground Water in Agriculture-Dominated Watersheds[END_REF][START_REF] Schröder | The Effects of Nutrient Losses from Agriculture on Ground and Surface Water Quality: The Position of Science in Developing Indicators for Regulation[END_REF]. GNC severity depends on the Nitrate (NO3) concentration with reference to the maximum contaminant level (MCL). According to the World Health Organization (WHO), the MCL is equal to 50 mg/l (WHO, 2003). The use of water beyond the MCL for drinking has been linked to methemoglobinemia and cancers [START_REF] Almasri | Identification of the Nitrogen Sources in the Eocene Aquifer Area (Palestine)[END_REF][START_REF] Comly | Cyanosis in Infants Caused by Nitrates in Well Water[END_REF].

Groundwater quality management has become of great importance in all groundwaterdependent countries (UN, 2011). Given the widespread and riskiness of the GNC, it is considered as a fundamental aspect for the water resources protection (UN, 2011) particularly in areas with vulnerable groundwater quality [START_REF] Mas-Pla | Groundwater Nitrate Pollution and Climate Change: Learnings from a Water Balance-Based Analysis of Several Aquifers in a Western Mediterranean Region (Catalonia)[END_REF]). An efficient management of GNC requires GNC assessment and prediction [START_REF] Knoll | Large Scale Prediction of Groundwater Nitrate Concentrations from Spatial Data Using Machine Learning[END_REF][START_REF] Anayah | Trends and Occurrences of Nitrate in the Groundwater of the West Bank, Palestine[END_REF][START_REF] Hajhamad | Assessment of Nitrate Contamination of Groundwater Using Lumped-Parameter Models[END_REF].

Different approaches were proposed to assess the GNC: descriptive statistical assessment [START_REF] Anayah | Trends and Occurrences of Nitrate in the Groundwater of the West Bank, Palestine[END_REF], GIS-based groundwater NO3 mapping [START_REF] Ducci | An Easy-to-Use Method for Assessing Nitrate Contamination Susceptibility in Groundwater[END_REF], lumpedparameter models [START_REF] Hajhamad | Assessment of Nitrate Contamination of Groundwater Using Lumped-Parameter Models[END_REF], Geodetector-Based Frequency Ratio [START_REF] Shrestha | Assessment of Groundwater Nitrate Pollution Potential in Central Valley Aquifer Using Geodetector-Based Frequency Ratio (GFR) and Optimized-DRASTIC Methods[END_REF], Optimized-DRASTIC Methods [START_REF] Shrestha | Assessment of Groundwater Nitrate Pollution Potential in Central Valley Aquifer Using Geodetector-Based Frequency Ratio (GFR) and Optimized-DRASTIC Methods[END_REF], parametric IPNOA [START_REF] Rizeei | Assessment of Groundwater Nitrate Contamination Hazard in a Semi-Arid Region by Using Integrated Parametric IPNOA and Data-Driven Logistic Regression Models[END_REF], data-driven logistic regression models [START_REF] Rizeei | Assessment of Groundwater Nitrate Contamination Hazard in a Semi-Arid Region by Using Integrated Parametric IPNOA and Data-Driven Logistic Regression Models[END_REF], water quality index (WQI) [START_REF] Barkat | Assessment of Complex Terminal Groundwater Aquifer for Different Use of Oued Souf Valley (Algeria) Using Multivariate Statistical Methods, Geostatistical Modeling, and Water Quality Index[END_REF], and groundwater potability index (Judeh et al., 2021). Groundwater quality strategies should rely on robust and accurate models for the prediction of groundwater contamination [START_REF] Knoll | Large Scale Prediction of Groundwater Nitrate Concentrations from Spatial Data Using Machine Learning[END_REF]. The Machine Learning methods have been successfully used as predictive tools for the GNC [START_REF] Breiman | Random Forests[END_REF]and Friedl et al., 1999). Researchers adopted an algorithm to obtain an average of multiple predictions to decrease the bias and to improve the prediction accuracy [START_REF] Breiman | Random Forests[END_REF]and Friedl et al., 1999). Logistic regression, random forest (RFA), decision tree, multiple linear regression, boosted regression trees, support vector machine (SVM), cubist, and Bayesian artificial neural network (ANN) are commonly used as predictive models for the GNC [START_REF] Band | Comparative Analysis of Artificial Intelligence Models for Accurate Estimation of Groundwater Nitrate Concentration[END_REF][START_REF] Uddameri | Tree-Based Modeling Methods to Predict Nitrate Exceedances in the Ogallala Aquifer in Texas[END_REF][START_REF] Canion | Predictive Modeling of Elevated Groundwater Nitrate in a Karstic Spring-Contributing Area Using Random Forests and Regression-Kriging[END_REF][START_REF] Knoll | Large Scale Prediction of Groundwater Nitrate Concentrations from Spatial Data Using Machine Learning[END_REF][START_REF] Ransom | A Hybrid Machine Learning Model to Predict and Visualize Nitrate Concentration throughout the Central Valley Aquifer, California, USA[END_REF][START_REF] Mair | Logistic Regression Modeling to Assess Groundwater Vulnerability to Contamination in Hawaii, USA[END_REF]. The prediction capability, efficiency and accuracy of Machine Learning methods are dependent on the complexity of the case under consideration and quality of the available data. Therefore, it is important to conduct benchmarking analysis to select the best Machine Learning method [START_REF] Band | Comparative Analysis of Artificial Intelligence Models for Accurate Estimation of Groundwater Nitrate Concentration[END_REF]. A comprehensive integration of spatiotemporal assessment and spatiotemporal prediction of GNC is still missing. This research will combine the GIS-based geospatial analysis this land use class. The second highest mean NO3 concentration is observed in the groundwater under the irrigated cultivation with a value of 55 mg/l. This area is subjected to an extensive use of agrochemicals. The lowest mean value occurred under the olive groves as expected since olive trees are usually planted in the mountainous areas where either the depth to groundwater is high or contaminants are being washed away with surface runoff. Except for olive groves, all the maximum NO3 concentrations exceed the MCL. The overall maximum NO3 concentration is located in the groundwater under the discontinuous urban areas with a value of 256 mg/l.

Influence of watersheds

The Eocene Aquifer area overlaps seven different watersheds: Al-Moqatta', Al-Khodera, Marj Sanur, Iskanderon, Al-Far'a, Wadi Shobash and Al-Maleh (Figure 5.7a). The first four watersheds drain into the west to the Mediterranean Sea; while the others drain into the east to the Jordan River. Actually, the available NO3 concentration data were only distributed among four watersheds; Al-Far'a, Marj Sanur, Al-Khodera and Al-Moqatta'. Figure 5.7b illustrates the minimum, 25 th percentile, mean, median, 75 th percentile and maximum NO3 concentrations for the different watersheds for the period 1982-2019. Marj Sanur watershed was excluded from the discussion as it has unrepresentative number of NO3 concentration readings (7 readings). The groundwater wells located in Al-Far'a watershed has mean and median NO3 concentrations that exceed the MCL. It also has the highest maximum and 75 th percentile values compared to other watersheds. This is due to the intensive use of agrochemicals in the watershed as it is considered as 'the food basket' for the northern part of the West Bank and the most dominated watershed by agriculture in the West Bank [START_REF] Almasri | Identification of the Nitrogen Sources in the Eocene Aquifer Area (Palestine)[END_REF]. Mean and median NO3 concentrations are approximately equal to MCL at Al-Moqatta' watershed while the lowest concentrations were recorded at Al-Khodera watershed. Table 6.4 summarizes the impact of adopting RWH on the increase in TWS. Jericho has the lowest increase (11%), followed by Tubas, Qalqilya, Tulkarm, and Nablus. The highest increase (577%) is observed in Ramallah and Al-Bireh. These results indicate the importance of RWH in addressing the water scarcity challenges. [START_REF] Judeh | Assessment of Water Governance in the West Bank, Palestine[END_REF], RWH presents a valuable alternative to address these challenges. Table 6.5 shows that

Chapter 7. Smart Rainwater Harvesting for Sustainable Potable Water Supply in Arid and

Semi-Arid Areas

Introduction

This chapter presents a smart rainwater harvesting (RWH) system to address the water scarcity in arid and semi-arid areas, which face severe environmental challenges [START_REF] Patrão | Review of Smart City Assessment Tools[END_REF], particularly a decrease in freshwater, population growth and water resources contamination [START_REF] Preeti | A Case Study on Reliability, Water Demand and Economic Analysis of Rainwater Harvesting in Australian Capital Cities[END_REF][START_REF] Jaren | Assessing Water Poverty of Livelihood Groups in Peri-Urban Areas around Dhaka under a Changing Environment[END_REF][START_REF] Villar-Navascués | Managing Water Scarcity Futures: Identifying Factors Influencing Water Quality, Risk Perception and Daily Practices in Urban Environments after the Introduction of Desalination[END_REF][START_REF] Ayt Ougougdal | Assessment of Future Water Demand and Supply under IPCC Climate Change and Socio-Economic Scenarios, Using a Combination of Models in Ourika Watershed, High Atlas, Morocco[END_REF][START_REF] Chourabi | Understanding Smart Cities: An Integrative Framework[END_REF]. The conventional water supply systems have limited capacity to meet the water demand. For example, in the West Bank, conventional systems can provide only 60% of the domestic water demand (PCBS, 2021b).

Several authors presented the advantages of using RWH in arid and semi-arid areas [START_REF] Preeti | A Case Study on Reliability, Water Demand and Economic Analysis of Rainwater Harvesting in Australian Capital Cities[END_REF][START_REF] Ranaee | Feasibility of Rainwater Harvesting and Consumption in a Middle Eastern Semiarid Urban Area[END_REF]Judeh and Shahrour, 2021;[START_REF] Tamagnone | Rainwater Harvesting Techniques to Face Water Scarcity in African Drylands: Hydrological Efficiency Assessment[END_REF]. Conventional rooftop RWH systems include a collection catchment, conveyance and storage tanks [START_REF] Abdulla | Concept and Technology of Rainwater Harvesting: Handbook of Water Harvesting and Conservation[END_REF]. These systems have several strengths, including (i) independency, (ii) proximity of RWH storage tanks to users, (iii) ease of construction and maintenance, (iv) flood mitigation, and (v) reduction of pressure on water resources [START_REF] Abbas | Assessing the Potential for Rooftop Rainwater Harvesting and Its Physio and Socioeconomic Impacts, Rawal Watershed, Islamabad, Pakistan[END_REF][START_REF] Słyś | Centralized or Decentralized Rainwater Harvesting Systems: A Case Study[END_REF]. However, these conventional systems have some limitations [START_REF] Dao | Assessment of Rainwater Harvesting and Maintenance Practice for Better Drinking Water Quality in Rural Areas[END_REF], particularly a lack of control of the quality of the harvested water [START_REF] Dao | Assessment of Rainwater Harvesting and Maintenance Practice for Better Drinking Water Quality in Rural Areas[END_REF]. As a result, the RWH is considered an undrinkable source of water [START_REF] Palermo | Optimizing Rainwater Harvesting Systems for Non-Potable Water Uses and Surface Runoff Mitigation BT-Numerical Computations: Theory and Algorithms[END_REF][START_REF] Rostad | Harvesting Rooftop Runoff to Flush Toilets: Drawing Conclusions from Four Major U.S. Cities[END_REF]. In addition, the conventional systems don't monitor (i) the filling and emptying process of the storage tanks as well as (ii) the water leakage [START_REF] Behzadian | Can Smart Rainwater Harvesting Schemes Result in the Improved Performance of Integrated Urban Water Systems?[END_REF]. These processes are governed considering different factors: rainfall volume and intensity, tank storage capacity, and water demand [START_REF] Behzadian | Can Smart Rainwater Harvesting Schemes Result in the Improved Performance of Integrated Urban Water Systems?[END_REF].

Scholars proposed the use of the smart technology to enhance the engineering systems' efficiency and overcome their limitations [START_REF] Petrolo | Towards a Smart City Based on Cloud of Things, a Survey on the Smart City Vision and Paradigms[END_REF][START_REF] Castro | Smart Lighting Solutions for Smart Cities[END_REF][START_REF] Kyriazis | Sustainable Smart City IoT Applications: Heat and Electricity Management \& Eco-Conscious Cruise Control for Public Transportation[END_REF][START_REF] Stratigea | The Concept of 'Smart' Cities -Towards Community Development[END_REF]. The smart technology uses real-time and historical data to improve the performance and resilience of urban systems [START_REF] Petrolo | Towards a Smart City Based on Cloud of Things, a Survey on the Smart City Vision and Paradigms[END_REF][START_REF] Castro | Smart Lighting Solutions for Smart Cities[END_REF][START_REF] Kyriazis | Sustainable Smart City IoT Applications: Heat and Electricity Management \& Eco-Conscious Cruise Control for Public Transportation[END_REF][START_REF] Stratigea | The Concept of 'Smart' Cities -Towards Community Development[END_REF]. They are used in different fields such as health [START_REF] Zhao | IoT-Based Sanitizer Station Network: A Facilities Management Case Study on Monitoring Hand Sanitizer Dispenser Usage[END_REF]andCarminati et al., 2021), transportation, mobility (Bin Hariz et al., 2021;and Anagnostopoulos, 2021), indoor risk management [START_REF] Wehbe | A BIM-Based Smart System for Fire Evacuation[END_REF]Shahrour, 2021), energy (Martins et al., 2021), and environment [START_REF] Vishnu | IoT-Enabled Solid Waste Management in Smart Cities[END_REF]. They are also used to enhance the water supply [START_REF] Mudumbe | Smart Water Meter System for User-Centric Consumption Measurement[END_REF][START_REF] Savić | Smart Water, Smart Societies: The IWIDGET Project[END_REF], monitor urban water networks [START_REF] Rasekh | Smart Water Networks and Cyber Security[END_REF][START_REF] Wu | Applications of Deep Learning for Smart Water Networks[END_REF], detect water leakage [START_REF] Mashhadi | Groundwater Contamination in Sub-Saharan Africa: Implications for Groundwater Protection in Developing Countries[END_REF][START_REF] Farah | Leakage Detection Using Smart Water System: Combination of Water Balance and Automated Minimum Night Flow[END_REF], monitor water quality [START_REF] Pasika | Long-term changes in nitrogen and phosphorus emission into the Vistula and Oder catchments (Poland)-modeling (MONERIS) studies[END_REF][START_REF] Prasad | Smart Water Quality Monitoring System[END_REF][START_REF] Dong | A Survey of Smart Water Quality Monitoring System[END_REF], and enhance the water resources management [START_REF] Ramos | Smart Water Management towards Future Water Sustainable Networks[END_REF][START_REF] Ntuli | A Simple Security Architecture for Smart Water Management System[END_REF][START_REF] Lee | Smart Water Grid: The Future Water Management Platform[END_REF][START_REF] Robles | An Internet of Things-Based Model for Smart Water Management[END_REF]. Scholars used the smart technology to address the shortcomings of the conventional RWH systems at the household level [START_REF] Ranjan | The Internet of Things (IOT) Based Smart Rain Water Harvesting System[END_REF][START_REF] Behzadian | Can Smart Rainwater Harvesting Schemes Result in the Improved Performance of Integrated Urban Water Systems?[END_REF] with a focus on a the daily overflow from the RWH tank (Ot), and the daily shortage in covering the needed water demand (Xt). Finally, the last step targets the estimation of both Re and Rv.

First step: Estimation of Dt and St

The daily per capita water consumption rate (DWCR) is identified in view of the WHO recommendation (WHO, 2011). Such rates, along with the residents' statistics, are employed to estimate Dt. St is estimated using Gould and Nissen-Petersen's equation [START_REF] Gould | Rainwater Catchment Systems for Domestic Supply: Design, Construction and Implementation[END_REF]:

Where, St: the potential daily RWH volume for the shared system in the t th day (m³/day) RFt: daily rainfall for the t th day (m/day) Aj: area for the j th rooftop (m²) RC: runoff and collection efficiency coefficient for the j th rooftop (Judeh and Shahrour, 2021) t: the day (1 to 365) j: the building number n: total number of buildings • Second step: Estimation of Vt, Ot, and Xt

The estimation of Vt, Ot, and Xt is conducted using the following formulas [START_REF] Fulton | A Simulation of Rainwater Harvesting Design and Demand-Side Controls for Large Hospitals[END_REF]: However, the water supply continuity is vulnerable and ranges between 2-3 days/week (Jenin [START_REF] Municipality | Annual Report on Water Supply Service in Jenin Municipality[END_REF]. Each of the high-altitude, moderate-altitude, and low-altitude locations in the study area are separately supplied (7 days every 3 weeks). Therefore, supply volumes vary considering the altitude of the community and the season (Table 7.2). DWCR of about 100 l/c/d is specified for the study area according to WHO and Palestinian Water Authority (PWA) recommendations (PWA, 2017;[START_REF] Howard | Domestic Water Quantity, Service Level and Health[END_REF].