The data inventory is useful in examining the availability of existing knowledge (in data and models) that are needed to quantify the main dynamic cause-effect synergies and define the baseline values of SD model variables for each case study. Adding to this the inventory is necessary for integrating and harmonizing the data and models from the different sources and for the different case studies, and involves analysis and evaluation of the availability, quality and usefulness of the data and models, as well as the spatial-temporal scales and periodicities of data and model results/outputs. Using a combination of data and background models can improve the accuracy and details of the SD modelling, while, a comprehensive metadata inventory will be useful in future studies contributing to the knowledge-exchange objective of COASTAL and increasing the potential of reusing data as well as validating the results.

Executive Summary

The quantification of the physical, socio-economic and environmental coastal-rural interactions in the six representative case studies, (project objective 2), which is needed for the system dynamics modelling (WP4) is the main scope of WP2. This work package is a bridge between the qualitative and quantitative operations of COASTAL, transforming the mental maps created through local and scientific knowledge into quantitative data. This transformation is achieved through the use of existing databases and models developed by 3 rd parties, and where necessary on newly collected data. The aim is to build the scientific model constructs that are needed for the synergistic analysis of the rural -coastal interactions, and for the performing the strategic business and policy analysis that is done by WP3. The first task of WP2 is to identify the data needs, and collect the data and models to support the quantitative analysis. This quantification includes the formulation of equations to describe the interactions between system state variables, thresholds and time delays, initial conditions, and model parameter settings. This deliverable is an inventory of the necessary data and models that are needed to transform concepts into model parameters for the system dynamic models. The structure of the deliverable reflects the two periods during the project when each MAL identified and recorded the data and models needed for their quantitative Stock-Flow modelling analysis. Data and models included in the first submission of the deliverable (January 2019) are included in APPENDIX A of this report and are based on each MAL's expected needs after the completion of the sectoral workshops for each case study.

Document Updates (May-September 2020):

Since the submission of the initial inventory there has been more interaction with the stakeholders (reported in D04, Tiller et aI, 2020) and the MALs have identified a problem scope (reported in D12, DeKok et al, 2019) and have defined their basic model structure (reported in D13 Viaene et al, 2020). Thus, each MAL has had the opportunity to re-evaluate data and models needs and focus their respective inventories which are now presented in APPENDIX B (Tables B-1 to B-6). The interactions with the stakeholders were used to clearly identify each case areas problem that could be translated into system dynamic land sea interaction models by the use of quantifiable variables. This report contains a descriptive analysis of how each MAL made these connections and selected their set of data and models needed for this quantification and a table of problems identified for each MAL.

Updating the inventory at this stage can include links of the Stock-Flow variables in the structured models directly to the data and models that will be used to quantify the initial or normal values, delays, system limits, current trends and historic time series. The next WP2 deliverable D07, will outline quantitative information including available data and models that are/will be used to support/validate equations and parameter settings in the SD models that are (being) developed in the MALs. This deliverable will also summarize envisioned scenario analysis procedure and some reflections on relevant policies at international, regional and national levels according to the SD modelling and its anticipated outcomes. The Table 4- tasks defined based on the conceptual analysis of WP1, to bridge between the qualitative and the quantitative actions of COASTAL (Figure 12). This report answers to the first WP2 task, to identify the relevant data, models and their sources, which are needed and can be used to achieve the WP2 aim. As the project progresses all the MALs have the ability to identify and concentrate on problems and actions with the higher impact potential both for the stakeholders and the project partners. This process drives also a re-evaluation of the data and models needed to better describe these problems and opportunities as

Role of Deliverable

The objective of WP2 is to develop the quantitative data and scientific model constructs needed for synergistic analysis of the rural-coastal interactions identified in WP1. These need to be translated to the level of detail needed for strategic business and policy analysis (Figure 2-1). The tasks of WP2 are based on the outcomes of the conceptual analysis of the coastal-rural feedback dynamics analysed by coastal and rural stakeholders, actors, and researchers in the Multi-Actor Labs. This analysis has led to the identification of specific area problems recognised by the stakeholders which have guided the MAL leaders into defining the problem scope of each case study (DeKok, et al, 2019). To the extent possible, the focus of WP2 is on the translation and use of existing data and models developed by 3 rd parties for the quantification of the social-economic, physical, and environmental interactions in a System Dynamics (SD) models by WP4

(System Modelling). This quantification includes the formulation of equations to describe the interactions between system state variables, thresholds and time delays, initial conditions, and model parameter settings (D7, due in December 2020). and policy indicators for WP3 (Business and Policy Analysis). Generic modelling principles and algorithms will be developed to facilitate the exchange of knowledge between the case studies and reuse of model constructs, making the task of system modellers easier.

WP2 connects with WP1 and WP4 to delineate the problem context, potential causes, and spatial-temporal boundaries and resolution for the modelling based on the outcomes of the sectoral workshops with local actors and stakeholders. This information will be used to identify the data and associated models required for analysing the coastal and rural processes for each case study. In principle, field work and laboratory testing is avoided. It will be limited to fulfil the data needs for calibrating non-linear functions required to quantify the system feedback structures. The work of WP2 can be broken down into three subtasks:

• Data and Model Base: identifying the data needs, and collecting the data and models to support the quantitative analysis of the physical, socio-economic and environmental processes affecting land-sea feedback dynamics;

• Knowledge Transition: translating models and data into equations and parameters settings, allowing the quantification;

• Confidence Building: formulating tests and collecting data to assist WP4 with the scientific validation of the SD models

Data and Model Inventory

Deliverable D6 is a Data and Model Inventory -consisting of a comprehensive inventory of data and models for each case study, including problems and model boundaries, spatial-temporal scales, processes, variables and parameters that are needed for Task 2.2. (Knowledge Transition). The deliverable is structured in the form of presenting descriptive information, both on the models and the data to be used that are necessary to support the SD modelling activities of COASTAL (WP4). These system modelling activities are based on a problem-driven stock-flow modelling, referred to as SD modelling (De Kok et al., 2018). The general principle is to explain the dynamic transition behaviour of the land-sea system from the internal feedback structure of the system. The interaction of positive (reinforcing) and negative (balancing) feedback loops in the system affect the short-, mid-and long-term impacts of business and public policy decisions, which can sometimes be counterintuitive [START_REF] John | Business Dynamics: Systems Thinking and Modeling for a Complex World[END_REF]. The ultimate goal of the SD modelling is policy analysis -analysing simulated policy impacts to support the formulation of business road maps and policy guidelines (WP3). An example of a stock-flow model is shown in Kok et al., 2018).

The variable 'Pressure on Space' is an 'auxiliary' variable [START_REF] John | Business Dynamics: Systems Thinking and Modeling for a Complex World[END_REF]. This variable is a function of the stock variable 'number of tourists' and a model parameter 'tourism capacity', which is a constant in the model. Finally, we notice some variables are denoted as 'impact on… '. These refer to non-linear dependencies between variables. For these COASTAL adopts and approach based on graphical 'table functions' [START_REF] John | Business Dynamics: Systems Thinking and Modeling for a Complex World[END_REF] which are easier to interpret than mathematical equations and can be designed interactively if necessary. An example of a non-linear table function for the impact of coastal attractiveness (a dimensionless variable with range 0-100) on tourism change is shown in Figure 2-3. The rate at which the attractiveness for tourism affects the tourism change will rapidly increase and slow down again when the maximum attractiveness is approached. In principle we distinguish three supportive functions for data and models: problem analysis, model design, and model validation. The example for tourism illustrates how data can be used in multiple ways:

• to analyse historic time series of data and graphs describing the problem, so-called 'reference modes' [START_REF] John | Business Dynamics: Systems Thinking and Modeling for a Complex World[END_REF] • to set the initial values of the key system variables ('stocks')

• to determine the standard (normal) values of the rate of change of variables

• to fix constants used in the model (parameters)

• to define the shape non-linear functions, complementing human judgement

• for validation of business and policy simulations (using independent data)

This inventory contains descriptive information on the availability of data and models needed to transform the causal loops determined during the sectoral workshops and mind mapping activities of WP1 into stockflow diagrams that will be needed to create the system dynamic models for WP4. The update of the deliverable was prepared after the finalisation of the structure of the models for each case study (Viaene et al, 2020), with the intent of connecting the models directly to the inventory .

Background

This deliverable is the basis for the creation of the system dynamic models as it includes an inventory of the necessary data and models that will be needed to transform concepts into model parameters. Such an inventory is necessary in order to identify, screen and collect the data and models needed for each case study (WP2 -obj1) and to formulate a model work plan for addressing the SD modelling of interactions (WP2 -obj2). However, parallel sector workshops and detailed analysis of the mind -maps will better define the demands on model and data based on detailed formulations of each case specific problem.

Since the initial inventory was submitted (January 2019), all MALs have made great progress towards the creation of their Dynamic-System models, which has resulted in re-evaluating the data and models needed for quantitatively describing the interactions of the Land-sea systems in the respective case studies

The process has included the identification of the problems acknowledged by the stakeholders, which has Updating the inventory at this stage can include links of the Stock-Flow variables in the structured models directly to the data and models that will be used to quantify the initial or normal values, current trends and historic time series. The next WP2 deliverable D07, will outline quantitative information including available data and models that are/will be used to support/validate equations and parameter settings in the SD models that are (being) developed in the MALs. This deliverable will also summarize envisioned scenario analysis procedure and some reflections on relevant policies at international, regional and national levels according to the SD modelling and its anticipated outcomes

Approach

The data tables included in the first submission of the report (January, 2019) reflected the data and models that were anticipated (and were available) to be used for transforming the characteristics of the land-sea interactions (stemming from the stakeholder descriptions during the sectoral workshops) into quantifiable parameters, variables and their inter-dependencies as necessary for the SD models. The report includes information on possible problems, model boundaries, spatial -temporal scales and processes.

After the finalisation of the structure of the Stock-Flow models for each MAL the data and models needed to quantify the variables and interdependencies were re-assessed with the intent of creating an updated inventory which is directly linked to the quantification needs of the Stock-Flow models.

Transforming Stakeholder Concepts to Models

Participatory, multi-actor approaches are the backbone of COASTAL and activities of the MALs. In these MALs, coastal and rural business entrepreneurs, investors, administrations, economic sectors, and stakeholder networks interact directly with domain experts to co-identify business and policy solutions with a maximum potential for societal uptake. COASTAL adopts an iterative and strongly participatory multiactor approach, combining qualitative group-structuring techniques with quantitative SD modelling. This approach will help to increase the current level of understanding of the environmental, economic, and social processes affecting coastal and rural development. This multi-actor approach supported by participatory modelling techniques is used is to ensure a maximum potential for uptake by the target groups and stakeholder support and exploitation of local knowledge. The case studies for the MALs differ by their problem context, spatial scale, and social-environmental conditions. The organisation of sectoral workshops early in the project aimed at ensuring a full understanding of the local systems and in facilitating an effective information flow towards the development of qualitative and quantitative tools to be used later in the project. The stakeholder workshops produced a series of Mental Maps (6 for each MAL) that give graphical representations of each sectoral group's perceptions of the system in question and the problems and opportunities it presents (WP1 D03). The mental maps are useful in identifying ideas, investigating problems, and providing different perspectives without restrictions [START_REF] Salerno | Concept Maps for Combining Hard and Soft System Thinking in the Management of Socio-Ecosystems[END_REF]. These ideas are linked with lines and arrows, which show and describe how the different concepts are perceived to be related to each other, in terms of propositions about the phenomena based on a representational ontology and epistemology [START_REF] Scherp | Quantifying qualitative data using cognitive maps[END_REF]. These mental maps include the local descriptive knowledge and contain information relating to:

• The connections and flows between the different parts of the system in the form of arrows and nodes

• The different conceptual and categorical boundaries that shape the system in the way the different social actors categorise and shape each system

• the impacts and influences between land and sea in the way local actors interact with each other and the system This descriptive knowledge of the system is being transformed into SD models as part of the tasks of WP4, using existing scientific models and readily available data, collected for each case study in WP2. The integration of stakeholders' perceptions and knowledge into formal knowledge increases a model's perceived utility and consequently the adoption of its results (Gray, et al., 2012;[START_REF] Elsawah | A methodology for eliciting, representing, and analysing stakeholder knowledge for decision making on complex socio-ecological systems: From cognitive maps to agent-based models[END_REF]. Such integration requires the ability to capture the highly qualitative people's ways of thinking and transforming it into quantitative data to be used in decision support tools. The process of transforming the mental maps into Stock-Flow Models is not straightforward as it involves the transformation of qualitative conceptual descriptions into quantitative variables and equations and there are gaps in the process [START_REF] Kok | The potential of Fuzzy Cognitive Maps for semi-quantitative scenario development, with an example from Brazil[END_REF] resulting in several assumptions and simplifications. Nonetheless, including these qualitative constructs is important since, as Sterman (2000, p 854) states: "Omitting structures or variables known to be important because numerical data are unavailable is actually less scientific and less accurate than using your best judgment to estimate their values". The process of estimating, and making assumptions and simplifications needs to be transparent and consistent with the qualitative data, so that stakeholder engagement and social learning is supported [START_REF] Kelly | Selecting among five common modelling approaches for integrated environmental assessment and management[END_REF], while the possibility for quantification is maintained [START_REF] Elsawah | A methodology for eliciting, representing, and analysing stakeholder knowledge for decision making on complex socio-ecological systems: From cognitive maps to agent-based models[END_REF].

In COASTAL this stepwise process involved the identification of problems described by the stakeholders Drivers, Receivers and Central variables of each descriptive system. FCMs can thus be used to analyse the key feedback mechanisms and causalities of systems by means of step-wise iteration [START_REF] Tiller | Multi Actor Analysis of Land-Sea Dynamics[END_REF]. At the same time, recognising stakeholder's problems and their causes (in term of actors and actions) as well as the actor-receptors, and the challenges faced, leads each MAL in the identification of the Themes of data and 3 rd party models that are needed for the quantification.

Transparency in the modelling process is an important aspect of COASTAL, reflected by the project title ('knowledge exchange') and the consortium agreed to participate in the Open Research Data Pilot of Horizon 2020 and FAIR principles for use of metadata. The creation of a data inventory assists identifying the variety of quantitative factors that will be used to model the land-sea interactions for each case study.

In addition it is being used to examine the availability of knowledge and data that are needed to quantify the main dynamic cause-effect synergies and define the baseline values of SD model variables for each case study. Furthermore, according to the outcome analysis of the sector workshops in each case study and the definition of the main variables that represent the land-sea interactions, the data and model inventory will be used to identify data gaps and look for proxies or determine if further research is necessary to complement the data [START_REF] Luna-Reyes | Collecting and analyzing qualitative data for system dynamics: methods and models[END_REF].

The initial identified data, models for each MAL case study are listed in the APPENDIX 1 of this deliverable.

Whereas the updated inventory is included in

Case Studies

The case studies for the MALs differ by their problem context, spatial scale, and social-environmental conditions (Table 4-1). With this range of case studies and desk cases, COASTAL covers different spatial scales, time spaces, data conditions, locations, expectations, and targeted sectors. However, several main activities are shared between more than one case studies, thus the generic data are structured in a similar format following the categories of the socio-economic and environmental characteristics that shape its system. Multi-scale (20 -400 x 1000 km2) Regional (10.000 km2) Local -Regional (1000 km2)

Regional (1200 km2) Agriculture ✓ ✓ ✓ ✓ ✓ ✓ Fisheries /aquaculture ✓ ✓ ✓ ✓ ✓ industry ✓ ✓ ✓ ✓ offshore energy ✓ ✓ ✓ Tourism ✓ ✓ ✓ ✓ ✓ ✓ transport ✓ ✓ ✓ ✓

Belgian Coastal Zone (North Sea)

Description of case study

The Belgian Multi-Actor Lab focuses specifically on providing business and policy opportunities for a region suffering from intensive use of space and competition for resources, combined with a fragmented governmental context (municipal, regional and federal level). The Belgian coast and harbours are a regional responsibility of the Flemish Region, whereas the Belgian North Sea is a national (federal) responsibility.

Although the different administrative levels make an effort to address problems in an integrated way this need for collaboration complicates the practical implementation of solutions aimed at land-sea synergy.

Along the relatively short Belgian coast (67 km length) and its hinterland rural, coastal and sea-based activities such as agriculture, fisheries, agro-food industry, transport, energy production, and recreation are closely interwoven and compete for space, resources and infrastructure. A Marine Spatial Plan is in function, and an updated version covering 2020-2026 will be issued by 2020. COASTAL has the ambition to connect and reinforce the different policies for a sustainable use of marine space, exploiting new development opportunities related to blue growth. Offshore energy production is expected to create new job opportunities and the strategic specialisation of port activities such as for Ostend makes Belgium one of the leading countries in know-how related to offshore energy production, including multi-purpose use of wind farms. Meanwhile, the quality of freshwater resources is under pressure, and land-based emissions of nutrients still exceed the EU-WFD target levels and contribute to coastal eutrophication. The quantities of fresh water are under pressure during extended periods of drought, as a result of multiple demands from industry, tourism, population and agriculture. A major stressor is the increasing salinisation of inland waters, related to human waterworks, water management, and sea level rise. Limited water resources and decreasing surface water quality put pressure on the traditional activities in the rural hinterland. A combination of factors leads to increased salinisation which poses problems for traditional agriculture but might offer an opportunity for alternative forms of agriculture or aquaculture. Based on the expertise and infrastructure of coastal tourism, developing sustainable rural and/or agro-tourism can provide additional income for the hinterland. Economic and environmental opportunities are found as well in the sectoral restructuring and modernization, improved integration in the rural food chain with diversification, changes in farming practices and other new business opportunities. The available data and/or models to address the issues of the Belgian case study are presented in Appendix A Table A-1.

Data selection Process and Inventory Update (September 2020)

After careful deliberation with the local actor partners it was decided to build the systems modelling around two cases with strong land-sea interactions requiring a holistic analysis of the problem:

• Sustainable decommissioning of offshore wind parks and life cycle management;

• Climate-resilient management of water and land resources in the Oudland polder

Description of case study

Agriculture (mainly olive trees) and coastal tourism are the two major economic activities in Western Messinia, Greece. Tourism is expanding and goes hand in hand with infrastructure development (hotels, roads and airports) and can provide opportunities for diversified livelihoods, but also increases pressures on the agricultural land uses, the environment and cultural sites. These conflicts are also enhanced by the lack of a regional and local spatial plan for the coastal and rural areas. Coastal areas are also affected by agrochemicals, soil erosion, solid waste landfills, and waste waters. In particular waste products from olive production form a threat to surface and coastal water quality. Climate change is expected to increase coastal erosion and decrease the availability of freshwater, with increased risk for saltwater intrusion into coastal wetlands and aquifers. There are also plans for offshore oil and gas exploration that will have implications for the area's rich coastal biodiversity. The study area comprises several important cultural sites and Mediterranean habitats included in the reference list of the Natura 2000 initiative. The MAL will develop a number of alternative strategies for local economic development that will allow a diversification and strengthening of a sustainable local economy while minimizing the impact on the Natura 2000 sites.

Long-term planning for sustainable tourism and agriculture will take into account resilience to future climatic changes, exploiting the expertise and experience of local stakeholders. The data identified as useful for the quantification of the system parameters and their links at the time of the first submission of the deliverable are presented in Appendix A-

Data selection Process and Inventory Update (September 2020)

Although SW Messinia MAL is a relatively small and locally focused case study, there is still high complexity in terms of Land -Sea interactions and the environmental and social components of the relating system.

This complexity is highly evident in both the problems identified by the stakeholders (Table 4-3) and the System interactions identified for D12 as part of the process to creating the System Dynamic Models (De Kok, et al., 2019). The System Dynamic models created for the SW Messinia MAL were chosen to relate to the common vision agreed by our stakeholders of a Sustainable Messinia, expanding across all sectoral practices and ensuring a collaboration among them (D04, Tiller et al., 2019). Based on the problems recognized (Table 4-3) by the stakeholders we identified eight key challenges that are part of this goal:

• Improve the ecosystem status of Gialova Lagoon whilst maintaining a viable fishing activity The problems identified can either be characterized as internal, caused by competition for the use of resources such as land and water, or external to the system caused by climatic changes or national policies.

•
Many of the drivers of these problems relate to social perceptions and lifestyle choices which are difficult to quantify, whereas the lack of data monitoring has been identified by the stakeholders as a problem, which meant that for MAL 2 there was also a lack of data relating to the environmental status of the water bodies in the area. To balance this lack of data MAL2 undertook several field visits in order to define the environmental status of inland, coastal and marine water bodies. The data collected through these field visits will be subsequently used to quantify the models, and will become freely available after the end of the project, as described in the data management plan. For all other data we relied on open access databases and published papers as well as expert knowledge and inputs from our actor partners (Appendix

B Table B-2).

Sweden -Norrström/Baltic Sea

Description of case study

The Baltic Sea is one of the world's largest brackish water bodies, with a land catchment area about four times larger than the sea surface area. In the Swedish part of the Baltic catchment, the Norrström drainage basin and its adjacent and surrounding coastal zones (MAL3 in COASTAL) is a key area with a large human population. It includes the Swedish capital of Stockholm as well as agricultural and industrial activities, contributes considerable nutrient loading to the Baltic Sea, and suffers from eutrophication and harmful algae blooms resulting from such loads (HELCOM, 2017) also in the MAL3 archipelago and coastal waters.

International agreements and environmental regulations put in place since decades still have not managed to sufficiently decrease the nutrient loads from land to combat the severe eutrophication, hypoxia and algae bloom problems in the coastal and marine waters of the Baltic Sea. To overcome difficulties in managing and decreasing the nutrient loads from land to sea different physical and socio-economic aspects needs to be addressed, such as, uncertainties about biogeophysical system behaviour [START_REF] Destouni | Water quality and ecosystem management: Data-driven reality check of effects in streams and lakes[END_REF][START_REF] Levi | Data-driven analysis of nutrient inputs and transfers through nested catchments[END_REF] combined with social fairness issues [START_REF] Gren | Does Divergence of Nutrient Load Measurements Matter for Successful Mitigation of Marine Eutrophication?[END_REF], major gaps in relevant environmental monitoring [START_REF] Hannerz | Spatial Characterization of the Baltic Sea Drainage Basin and Its Unmonitored Catchments[END_REF][START_REF] Destouni | Water quality and ecosystem management: Data-driven reality check of effects in streams and lakes[END_REF], and dominant nutrient legacies from historic-to-present human activities, with unclear sector responsibility for the difficult (if not practically impossible) mitigation of their considerable contributions to current nutrient loads [START_REF] Destouni | Zones of untreatable water pollution call for better appreciation of mitigation limits and opportunities[END_REF]. How to achieve sufficient management and mitigation of the nutrient loads in the short and long term, under changing human pressures and hydro-climatic conditions, is a key problem to address in MAL3 for the sustainable development of this coastal zone and its rural and urban hinterland areas, as for the entire catchment and coastal region of the whole Baltic Sea. Furthermore, also other environmental and social challenges need to be addressed and met for achieving sustainable development in this coastal region, such as managing coastal groundwater [START_REF] Mazi | Quantifying a sustainable management space for human use of coastal groundwater under multiple change pressures[END_REF], maintaining ecosystem services [START_REF] Goldenberg | Distinction, quantification and mapping of potential and realized supply-demand of flow-dependent ecosystem services[END_REF] and enhancing human wellbeing [START_REF] Goldenberg | Increased access to nearby green-blue areas associated with greater metropolitan population well-being[END_REF] under multiple regional changes and change drivers. The available data and support models relevant to the main issues of the Swedish case study at the time of the first submission of the deliverable are presented in

Appendix A Table A-3 (

Data selection Process and Inventory Update (September 2020)

There is a high complexity of land-sea interactions and linkages between different system components in MAL3, as shown in the co-developed causal loop diagrams (CLDs) from sector workshops (presented and discussed in project Deliverable D12. Model Scope and Feedback Structure, submitted on January 2019).

System dynamics (SD) modelling in MAL3 has focused on the policy/market-driven land-, water-and nutrient/eutrophication-management problems outlined in Table 4-4, using water flow as a tracer to address scenarios and impacts of possible hydroclimatic changes and local/regional developments in the MAL3 land-sea system. Various sectors and ecosystems involved in these problems are also highlighted in this table. Considering data and model (results) availability in MAL3, two sub-models are developed as: (i)

Sub-model 1. Land-sea inter-sectoral water exchange; and (ii) Sub-model 2. Land-sea inter-sectoral waterborne nutrient exchange.

The problem areas in Table 4-4, consider regional developments that may cause sectoral competition for land and water in MAL3, mainly involving forests and forestry, agriculture and urban areas (and also possible associated agro-and urban tourism expansions). Hydroclimatic changes (with main model focus on potential precipitation changes in different directions) also affect these sectors, seawater intrusion risks, and terrestrial and coastal-marine ecosystems through changed water through flows and availability and waterborne nutrient loads. Urbanization and associated population and tourism growth scenarios also have implications for storm water runoff and associated sector and ecosystem problems as outlined in Table 4-4.

Hydroclimatic change may exacerbate both drought and waterlogging problems in agriculture and forestry, with large investment requirements for handling by drainage and irrigation infrastructure. In general, decreased water flows to the coast, possibly in combination with sea level rise on the sea side, increase the risk of seawater intrusion and limit water availability for all users of coastal groundwater. In addition to currently active sector sources of waterborne nutrient loads from land to the coast, subsurface legacy sources also contribute significantly to the total coastal nutrient loads, thereby affecting coastal-marine water quality and ecosystem status. The legacy sources and their load contributions are commonly not monitored but their influence and implications for the addressed MAL3 problems (Table 4-4) are still being accounted for in Sub-model 2. Land-sea inter-sectoral waterborne nutrient exchange.

The two developed sub-models in MAL3 can address and quantify how different types of land-and wateruses and possible shifts in these, driven by various relevant scenarios of future changes in hydroclimate, markets, or policy implementation/enforcement, can be expected to affect (i) coastal water flows, freshsea water interactions, and seawater intrusion risks; (ii) water through flows and availability for different sectors and terrestrial ecosystems; (iii) waterborne nutrient loads from/through various sectors and terrestrial water ecosystems to coastal-marine waters and ecosystems. These sub-models can also be used to assess how policy-driven such change scenarios may affect possible future regional developments in MAL3.

To quantify these sub-models, we have in MAL3 mainly used established openly available data, model results, and model equations reported in peer-reviewed scientific publications. The list of variables in the two SD sub-models for MAL3, and the sources of data and supporting model results and equations used to quantify these are presented in the Appendix B -

Description of case study

The Charente river basin in the south-western part of France covers an area of 10,500 km², mainly comprised of rural areas. The Charente River flows into the ocean in the Pertuis Charente where it is largely influenced by the tide. At the river mouth, the Charente River supplies fresh water to the oyster basin of Marennes-Oléron composed of salt marshes and oyster fields. Marennes-Oléron bay is the biggest shellfish farming area in Europe for spat production, and also the largest in terms of the number of shellfish companies (SMEs) in France. Around 50,000 tons are sold per year, including 12,000 tons of mussels. Along the coast, fish markets in La Cotinière and La Rochelle account for around 9,000 tons of fish sold, equating to somewhere in the region of 49 million euros.

The Charente river mouth is home to three major marshes, under the control of the French Conservatoire du Littoral (the body responsible for preservation of the French coastline that watches over the protection of large natural areas of major ecological and landscape interests). Its extensive coastal marshes consist of salt marshes (150 km²) crisscrossed by an intricate network of channels and ditches, providing drainage, environmental purification, and a travel corridor for wildlife (fish, otters ...). Freshwater marshes (950km²) are located on the estuary and along Charente tributaries, partially drained and converted into meadows or crops (800km²). Continued cultivation of coastal marshes poses a real risk to biodiversity, endangering local fauna and the flora.

Other key issues in the area are restoring of the functionality of rivers and wetlands, and preventing risks of flooding and submersion further downstream in the Charente region.

Agriculture is the main hinterland activity influencing the development of the Port Atlantique-La Rochelle. The global SD model of the Charente site (MA4) has been developed using the causal loop diagrams that were co-built with stakeholders during several sectoral and multi-actor workshops and is related to the problems presented in (Table 45). However, the MAL4 team had to make some modifications during the development of the pilot SD sub models -water, agriculture, shellfish farming and infrastructure -to meet the constraints of dynamic modelling: some intermediate variables were added, others removed. All the data considered as the most important/relevant by our stakeholders in order to describe the land-sea system and its interdependencies have been included in APPENDIX B Table B-4.

The water model intends to link water uses in the land-sea system (and their possible evolution) and trends in water availability and water quality both in inland and coastal waters. The goal is not to replace existing hydrological models but to use the results of previous studies as an entry into our SD models. An annual time step appeared to be the most relevant for the simulations contemplated, although it does not allow seasonal variations to be taken into account in detail. Yearly time series on water flows, water fluxes and meteorological data are used as well as simulations with a SWAT model linking land use flows and nutrients fluxes.

The agricultural sub-model and the shellfish farming model considers the current systems and practices but also their possible evolution, dealing with climate change, water scarcity and land-sea interactions.

Regarding the agricultural model, the transformation processes of the systems towards innovative systems that use less water or towards organic systems are considered. We use time series and expertise provided by CRANA and FRAB, and official statistics of the Ministry of Agriculture to qualify agricultural production, economical values and market trends. Average values are generated to qualify the main crops,conventional or organic, irrigated or non-irrigated-in the area.

A sustainable coastal system requires the determination of the optimal shellfish biomass that can be produced without endangering biodiversity, which itself depends on inland coastal uses. To highlight these interactions, the shellfish farming model takes into account the 3 years production system and its 4.5 Romania -Danube Mouths (Black Sea)

Description of case study

Due to the semi-enclosed location and size of the contributing catchment area the Black Sea is vulnerable to anthropogenic pressures and pollution sources (BSC, 2008). The nutrient regime of the Danube has undergone significant changes due to increased economic activity, use of fertilizers, waste water discharges, and use of detergents, leading to changes in the Black Sea ecosystem. Eutrophication results in decreased transparency, higher quantities of organic matter decomposition and oxygen depletion with bottom waters becoming seasonally hypoxic or even anoxic. Since the early 90s decreasing nutrient inputs resulted in signs of recovery. Today the Black Sea catchment is still under pressure from excess nutrients and contaminants due to emissions from agriculture, tourism, industry and urbanization in the Danube basin. This prevents achieving the Good Environmental Status by 2020, as required by the EU-MSFD. The increased rates of eutrophication, pollution and bioaccumulation affect both the biodiversity (including Natura 2000 sites) and fishing sectors. Mass tourism is an important growth sector for the Black Sea and eco-tourism is becoming more important in the region. Approximately 65% of the Romanian coastline is located in the Danube Delta Biosphere Reserve and subject to tourism regulations, resulting in conflicts between nature conservation and economic development. Failing to resolve these conflicts has economic and political impact. Thus, the goal of the model is to explore alternative scenarios to improve the quality of life and sustainability within Danube Delta Biosphere reserve and its marine waters (Black Sea) as one of the most impacted area along the Romanian littoral. The available data and/or models to address the issues of the Romanian case study are presented in APPENDIX A Table A-5.

Data selection Process and Inventory Update (September 2020)

Data selection was done based on outputs of the CLDs variables derived during the sectoral workshops for Agriculture, Fishery (freshwater and marine), Tourism (rural and coastal), and Rural development. During the stakeholder meetings the ecosystem management and the environmental protection were not specified as a major concern. However, several variables (e.g. pollution, water quality, biodiversity) are part of more than one CLD, and as a result different environmental issues are related to the problems identified (Table 456). Additionally, one of the strategic objectives of the Danube Delta Strategy is to keep the unique natural values through an environmental management guided by science and by strengthening local communities in the role their proactive protectors of this unique world heritage (MDRAP, 2016). Accordingly, we decided to further develop a new sub model, Ecosystem management. It requires maintaining natural capital (water quality, biodiversity) as both a provider of economic inputs and outputs.

The protection of natural systems represents not an overarching panacea for achieving economic vitality and social justice, but a necessary component of an entire system for achieving economic, social, and environmental 'sustainability', in which economic reforms and social reforms are as important (Basiago, 1999). This sub-model structure contains all relevant variables related to the environment (Danube Delta and Black Sea). These are further supplemented with some new ones like Freshwater Quality and Black Sea water Quality, Low economic fish species, Birds. The Biodiversity stock was moved from the Tourism sub model as the most important ecosystem service of the Danube Delta Biosphere. In this phase links between sub models were created as shadow variables which refer to variables defined in other sub-models: Pollution from agriculture, Pollution from basic services and Pollution from tourism are inputs for the rate of decreasing freshwater quality. We replace the pollution from Freshwater Fishery with Freshwater quality and from Marine Fishery with Black Sea water quality. Thus, the freshwater quality in the Danube Delta is calculated as function of the upstream water quality and climate change variable related to the river flow.

The freshwater quality it is also improved through ecological restoration and management measures taken based on research and monitoring activities. The water quality in the Danube Delta is deteriorated by pollution from different sectors -agriculture, tourism, and basic services. The water quality is an important input to the increase in biodiversity which is the main ecosystem service of the biosphere reserve. Another important link is with the Black Sea water quality which is significantly influenced by the river's outflow not only due to freshwater but also pollutants. The updated data inventory is presented in APPENDIX B Table B-5. Beside international visitors, the Mar Menor has an important touristic function for the regional population (1.5 million inhabitants). The availability of water for irrigation and drinking water for tourism will be further reduced under future climate conditions. As such, the Mar Menor is strongly influenced by interactions between inland agriculture on the one side, and coastal tourism and fisheries affecting natural ecological values and socioeconomic sustainability on the other side. The need to move towards sustainable modes of agriculture, fishery and tourism is increasingly recognized and recently revived strongly due to sudden increase in contamination levels resulting in a strong drop in tourism. The available data and/or models to address the issues of the Spanish case study are presented in APPENDIX A Table A-6.

Data selection Process and Inventory Update (September 2020)

As described in detail in Deliverable 13, the System Dynamics model of the different sectors in the Mar

Menor was developed based on the causal loop diagrams that were developed in collaboration with stakeholder during the sectoral and multi-actor workshop.

Table 4-7 provides a summary of which problems identified by the stakeholders can be evaluated with the different SD sub-models. The design and set-up of the SD pilot models determined which data were finally needed and in which format. More details about the data used in the models can be found in the appendix of this deliverable. Most raster datasets that we initially planned to use in relation to runoff and nutrients modelling were finally not needed directly as input in the SD models since we use annual average values in the corresponding sub-models. This is so because the main goal of our model is not to quantify in detail the water and nutrients flow in the study area but to identify coastal and rural sectoral interactions in order to propose sustainable development solutions. Water and nutrients budget sub-models are needed as part of the SD model, but they are not intended to replace existing spatial hydrological and nutrient models and therefore we used field and model output literature data to feed our SD model. On the other hand, using yearly time series would require including many more variables in the model, making it much more complex, in order to be able to predict values for each single year in the future, which is also not our main goal. Our stakeholders are rather interested in the expected trends and gradual transitions, and would not be able to grasp a very complex model structure. Assessing trends also avoids bringing too much attention and critics in the model structure and helps focus on consensus solutions among stakeholders based on key socio-economic and ecological variables. Besides, some of the topics initially considered to be included in the model by us didn't finally play a major role in the causal loop diagram developed by stakeholders, so several variables initially considered important were discarded and not included in the model, such as fish catch limits, livestock heads, number of public schools, etc. The main issue we faced was due to contradicting reported values for some important model input variables, especially in relation to the water and nutrients balance. We finally solved this by systematically using reliable official, government, nongovernment and scientific data coming from the latest existing reports, while also documenting the different data estimates from other informal sources. The fact that we use mean annual values also helps decreasing the uncertainty in selecting data coming from different studies. We also changed the time period of the study to start before the Tagus-Segura water transfer was opened and thus be able to assess its influence and to test the robustness of the model under contrasting scenarios. Besides, instead of using different data for each municipality as initially considered as option, we decided to spatially aggregate input data values for the entire study area, focussing on coastal and rural processes and interactions. On the other hand, many variables were used as inputs and for validation purposes, as initially expected, such as: the extent of (irrigated) cropland areas; the amount of irrigation water used from the aquifer versus the water from Tajo-Segura transfer; the population working in each sector; the amount of urban wastewater effluents used for irrigation, the water consumption by hectare of irrigated croplands, etc. One of the main challenges was to be able to quantify the degradation of the Mar Menor over time since it went through a rapid and recurrent ecological collapse starting in 2016. The amount and complexity of ecological processes occurring at different scales and realms within the lagoon made it impractical to develop an accurate model of ecological processes within the lagoon. Therefore, we had to simplify the model equations and calibrate the model outputs based on observed patterns and identifying the most important causes and drivers.

Finally, new variables, such as the ones related to the renewable energy sector were included in the model given that it was considered by stakeholders as a potentially important sector providing possible synergies with agriculture. The updated inventory is presented in APPENDIX B,

Discussion

The different themes of available data and/or models for the SD modelling of land-sea interactions in each case study cover the full spectrum of socioeconomic and ecological characteristics of each area in question.

The data and models to be used through the next task in WP2 (Task 2.2) consist of data and models found in existing databases and reported model developments, provided and used by the actors and stakeholders participating in the Multi-Actor Labs (coastal and rural business entrepreneurs, agencies and administrations) and also some newly generated/derived data and models by the partners of COASTAL. The resulting combination of primary (generated by the partners of COASTAL), and secondary data (e.g.

National Censuses) as well as model outputs (background models, developed by 3 rd parties), presents both an asset and a problem with respect to data management and analysis. The use of both data and background models has the potential of increasing the level of detail and accuracy of the developed SD modelling, while, the resulting metadata inventory will be useful in socioeconomic, ecological, and integrated studies, as well as in policy and sustainability assessments. Thus, the inventory in WP2 contributes to the knowledge-exchange objective of COASTAL and increasing the potential of reusing data as well as validating the results. On the other hand, combining heterogeneous databases and models (and their results/outputs) presents a series of concerns that need to be confronted.

• The use of European secondary data sources and statistical databases compared to localised studies has the advantage of covering large areas, including all case studies for some types of data (Corinne and Eurostat data), but the level of detail and the scale of the collected data might not be enough for the needs of COASTAL case studies.

• Different country wide data bases might offer information at finer scales but the collection and presentation of data are often based on different definitions making the data incompatible if not acknowledged and harmonised.

• Social and Environmental databases relate to different scales and boundaries in their collection, presentation and analyses, which affects both qualitative and quantitative system characteristics (Gibson et al 2010[START_REF] Cumming | Scale mismatches in social-ecological systems: causes, consequences, and solutions[END_REF]. These differences affect also the considered spatial and temporal event/representation scales, but also the way stakeholders perceive and identify the issues of an area in question [START_REF] Metcalf | Modelling Boundaries of Concern among Conflicting Stakeholders[END_REF].

This inventory is the first step in the process of integrating and harmonizing the data and models from the different sources and for the different case studies, and involves analysis and evaluation of the availability, quality and usefulness of the data and models, as well as the spatial-temporal scales and periodicities of data and model results/outputs. Additionally, concepts and definitions also need to be harmonised and case study boundaries need to be clarified and agreed upon for each case study. Furthermore, essential differences at macro, meso and micro levels need to be handled given the variation of data and model The data selection process for each MAL has been described as part of their case study analysis within this inventory.

Conclusions

Mental Models are by nature [START_REF] Elsawah | A methodology for eliciting, representing, and analysing stakeholder knowledge for decision making on complex socio-ecological systems: From cognitive maps to agent-based models[END_REF] context-specific and dynamic; people build their mental models based on a particular situation at a particular point of time. As time passes and context changes, people perceive new information, and update their mental models. As the produced SD modelling in COASTAL is to be based on the sector and MAL workshop outcomes, this inventory will be updated over time in order to fulfil the needs of WP2, and also WP4. These changes are evident also in the description of the steps taken for quantification by each MAL, but also in the identified data and models included in the inventory at each step (APPENDICES A and B).

  research and innovation program under grant agreement N° 773782 D6 Data and Model Inventory TABLES OF DATA AND MODEL INVENTORIES OF EACH MAL (DECEMBER 2018) ......................................................... 45 COASTAL STOCK-FLOW MODELS INVENTORY OF DATA AND THIRD PARTY MODELS. (SEPTEMBER 2020) ...................... 65 This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement N° 773782 D6 Data and Model Inventory List of Tables

1Figure 1 - 2

 12 Figure 1-2 Workflow in COASTAL for designing, testing and applying SD-based tools together with the Multi-Actor Labs. ................................................................................................................................

Figure 2 - 1

 21 Figure 2-1 General workflow in the COASTAL project (https://h2020-coastal.eu). ...................................

Figure 2 - 2 Figure 2 - 3

 2223 Figure 2-2 Example of a stock-flow model for coastal tourism, used to clarify the principles of SD modelling during the kickoff meeting (De Kok et al., 2018). ............................................................... Figure 2-3 Example of a non-linear table function for the impact of coastal attractiveness on tourism change. ...............................................................................................................................................

  dynamic systems. As a result the inventory is treated as a live document where both anticipated and actual data needs are being recorded. The initial inventories of anticipated data and model needs for each MAL case study are listed in the Appendix A: COASTAL Stock-Flow models Inventory of Data and Third Party Models. (Tables A-1 to A-6). The updated data and model inventories which are directly connected to particular models and variables are listed in APPENDIX B (Tables B-1 to B6).

  Figure 1-2 Workflow in COASTAL for designing, testing and applying SD-based tools together with the Multi-Actor Labs.

Figure 2 - 1

 21 Figure 2-1 General workflow in the COASTAL project (https://h2020-coastal.eu).

Figure 4 .

 4 Figure 2-2 Example of a stock-flow model for coastal tourism, used to clarify the principles of SD modelling during the kickoff meeting (De Kok et al., 2018).

Figure 2 - 3

 23 Figure 2-3 Example of a non-linear table function for the impact of coastal attractiveness on tourism change.

  led to the establishment of a Problem Scope for each MAL (Deliverable D12, DeKok et al, 2019), the completion of the first round of MAL workshops and the creation of the Fuzzy Cognitive Maps (Deliverable D04, Tiller et alI, 2020) and finally the generation of the Pilot Models and Structures for all MALs (D13, Viaene et al, 2020)

  that guided the definition of each model's problem scope (Viaene et al, 2020). In addition, prior to creating This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement N° 773782 D6 Data and Model Inventory the Dynamic system models (quantitative), the purely qualitative mental maps were transformed into semiquantitative Fuzzy Cognitive Maps (FCMs) (WP1, D04 Tiller et al, 2020) used to distinguish between the

  Appendix 2: COASTAL Stock-Flow models Inventory of Data and Third Party Models. This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement N° 773782 D6 Data and Model Inventory

D6

  Data and Model Inventory Data availability is not considered a determining factor or limitation for the modelling of the Blue Industry model (decommissioning) by both the R&D and local actor partners. Statistics and reports are publicly available and accessible online. The challenge with the modelling is in the uncertain driving factors (for example innovation) for which projections need to be developed. Also for the second model case (polder management) concerns on data availability are mainly related to uncertain effects of drivers such as politics and lifestyle. Physical processes such as the prevailing hydrology are quite well understood and field data are being collected as well as a new water balance for the region will be developed, commissioned by the local actor partner VLM (Flemish Land Agency) as part of the new Regulatory Framework to ensure climate resilience of the polder. Detailed future land-use data will be obtained at a 100 m resolution from urbanisation scenarios derived with the VITO land-use change model (RuimteModel) for the period 2020-2060.The updated data and model inventory used in the Stock -Flow models of the Belgian case study is included in Appendix B, TableB-1

  Reduce the use of pesticides/fertilizers in olive groves in compliance with the Green Deal • Expand of the tourism season and manage the numbers of beach goers in high season • Identify opportunities for fishers and farmers to better connect with the tourism industry research and innovation program under grant agreement N° 773782 D6 Data and Model Inventory • Plan for climate change including potential sea level rise and increased dry spells • Manage the increasing and seasonal water demand • Reduce the bulk sales of olive oil. • Manage the pressures for land use change in order to maintain the Identity of the area The problems identified and the challenges recognized were grouped into 3 System Dynamic models for quantification the design of which was based on the CLDs co-created with the stakeholders during the sectoral (D03 Tiller, et al, 2018) and MAL workshops (D04, Tiller et al 2019).

  received funding from the European Union's Horizon 2020 research and innovation program under grant agreement N° 773782 D6 -Model and Data Inventory 4.4 France -Charrente River Basin (Atlantic Region)

  Industrial activity is dominated by agri-food and wine production, around the cities of Cognac and Angoulême. At the same time, the two major trade ports in the area rely heavily on local agricultural produce. Port La Rochelle, which handles an average of 4 million tons of cereal pear year (and still growing), is the only deep-water harbor on the Atlantic coast with direct sea access. New opportunities for agriculture lay in the development of organic farming and viticulture, and related chain values. In the Charente case study, water in both coastal and inland areas is a prominent issue both in terms of quality (i.e. pollution by nitrate and pesticides notably with regard to the supply of drinking water) and quantity (impact on natural environments and availability of drinking water). In addition, particular concern relating to the availability of water has been enhanced by climate changes over the last decade. Most inland watercourses suffer from droughts and periodic low water levels. In this particular region, activities carried out inland (irrigation of crops, use of pesticides -particularly on vines used for Cognac production -and domestic use) have a significant impact on water resources. This impact is also felt downstream, in coastal areas, in significant sectors for the local economy such as tourism and shellfish farming. The region is also characterized by a contrast between the densely populated coastal fringe and inland rural areas with low population density. In addition, the continuous increase of residential elderly population on coastal zones has an important effect on land prices and changes of demand for products and services. Tourism is very important for the region's economy, with 8 million overnight stays recorded in 2016, mainly in the coastal area and during the summer months. All these inter-connections mean that any significant change in land use and economic activities will impact activities and employment in a number of sectors. The available data and/or models to model the issues of the French case study are presented in Appendix A TableA-4 4.4.2 Data selection Process and Inventory Update (September 2020)

  dependence on the environment. The shellfish farming association (CRC) and Ifremer (research institute) are our main sources of data and expertise for the SD Shellish farming model at a yearly time step.The infrastructure model aims at representing the development of residential coastal housing, the interaction of ports development with agricultural activity. Official statistics (government) and the management of local ports provide yearly time series and expertise as they were participants in all workshops. Water treatment plants and drinking water treatment plants will be included further with variables shared with the water sub-model, using data from the river basin agency.A local data management plan has been initiated, allowing to describe for each variable the data source and to store it in an interoperable form (xls and csv). This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement N° 773782 D6 Data and Model Inventory

  availability and sources. Transparency in the harmonisation process as well as ability to trace back the steps taken are also important for the validity of research outcomes. research and innovation program under grant agreement N° 773782 D6 Data and Model Inventory

Table 4 -

 4 1 Characteristics of the 6 case studies

						Danube
	Socioeconomic	Belgian	SW	Norrström	Charente	Mouths	Mar Menor
	aspects	Coastal Zone	Messinia	/ Baltic	River Basin	-Black	Lagoon
						Sea
	Scale	Regional (1000 km2)	Local-Regional (< 200 km2)

Table 4 -

 4 2 Problems identified by MAL1 Stakeholders and the related models

	Problem Description	What/Who Causes problem	Who is affected	Land/sea interactions relevant? (Y/N)	Which model deals with this problem? (D13)
			Regional economy,
	Waste, logistics, infrastructure and skilled labor	Decommissioning and repowering of aged wind parks	administrators marine environment, regional population (employment), port	Y	Port and offshore activities
	Climate change (sea level rise, flooding frequency, droughts)	Global population, economic activities and transport	Global population, marine environment, coastal population	Y	Port and offshore activities
		Sea level rise, droughts			Climate resilience
	Climate change -water resources	and salinization, use of water resources,	Regional farming	Y	and polder management
		trafficability			(Oudland Polder)
	Land use change -gentrification	Urbanization and gentrification of farming land leading to loss of food producing capacity	Regional farming activities, and population	Y	Climate resilience and polder management (Oudland Polder)

This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement N° 773782 D6 -Model and Data Inventory 4.

2 Greece -South West Messina (Eastern Mediterranean Region)

Table 4

 4

	-3 Problems identified by MAL2 Stakeholders and related models
				land/sea
	Problem Description	What/Who Causes problem	Who is affected	interactions relevant?
				(Y/

N) Which model deals with this problem? (D13) Lack of cooperatives (obstacle for: provision of knowhow and services such as smart agriculture; ISO exports;

	D6 Data and Model Inventory
	Increased need for Irrigation High seasonality in tourism Tourist practices Climate Change -Increasing temperature, changes in precipitation events	resources Fishers Farmers, Local Population, Ecosystem Ecosystem; Water resources; Local community; Tourism;	N Y	Integrated Farming Tourism
		Increased number of Tourism, Waste	Ecosystem, Fishers,
	Bulk sales of Olive Oil Litter Lack of Marine Fishing Shelters	non-professional farmers; Lack of cooperatives; Insecurity; Lack of vision Management, Population Behavior Lack of Marine spatial Governance Planning, National Policies, Regional	Olive-oil price; Branding potential; Biodiversity, Local Population Profit from olive-oil sales; Biodiversity, Water Fishers	N Y N	Tourism Integrated Farming n/a
	Agro-Chemical Runoff	Conventional Farming Practices	resources, Olive-oil quality; Ecosystem; Water resources;	Y	Integrated Farming, Lagoon Salinity & Tourism
			Tourism; Fishing;
		Lack Of Spatial	Farmers, Local
	Land Use Change Conflicts	new hotels. Planning, Building of	Ecosystem Population,	Y	Tourism
	Poor Natura 2000 management	Local and Regional Governance, Unstable Policies -National Natura Management	Lagoon fishers; Water resources; Tourism; Ecosystem;	Y	Lagoon Salinity
		Past management
		efforts and man-made
		constructions; Climate
		change; Lack of water
	Increased lagoon salinity	management; Increased need for groundwater resources	Fishers; Tourism; Ecosystem;	Y	Lagoon Salinity
		(irrigation and
	Poor Ecosystem Status of Gialova Lagoon	domestic use) during Land fragmentation, farmers' mentality, poor cooperative management in the past, lack of vision the dry period -Poor Natura Mananagement Lack of management plan, farming practices	Farmers' income, Water resources, Exports, Local economy Ecosystem; Fishers	N Y	Integrated farming Lagoon Salinity
	Ageing population (Youth is not interested in agriculture) Waste and wastewater management Increase of diseases leading to increased use of pesticides Lack of monitoring and data	Insecurity in the farm; Job opportunities in tourism provide a more attractive System capacity vs number of visitors; lifestyle; Increased humidity and temperatures Poor organization; lack of instruments; political will	Ecosystem; Water Agricultural sector; promotion of agri-resources; Local community; Tourism; Fishers tourism Overall system (e.g. etc.) Farmers, Fishers, Ecosystem difficult to apply water management; smart agriculture; limited data for	Y Y Y Y	Integrated farming Tourism Integrated Farming Lagoon Salinity, Integrated Farming
	Increased occurrence of Invasive Species Liquid wastes from olive-mills	Climate Change -Sea temperature Outdated Olive oil production practices	Fishers Fishers; Tourism; Ecosystem; Water	N Y	Tourism Integrated Farming

This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement N° 773782 D6 Data and Model Inventory research and innovation program under grant agreement N° 773782 This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement N° 773782 D6 Data and Model Inventory

  Table of data and model themes and main descriptors for the Swedish

(Norrström/Baltic Sea) Case Study -The list was prepared on December 2018 based on an overall compilation of available and relevant data and support models).

Table B

 B

	-3 Further details, information, and explanations
	of this quantification are reported in project Deliverable D13. Pilot SD Models for Coastal-Rural Interactions

-Case Study Level, submitted in July 2020. This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement N° 773782 D6 Data and Model Inventory

Table 4 -

 4 4 Problems identified by MAL3 Stakeholders and the related models

				land/sea	Which model
	Problem Description	What/Who Causes problem	Who is affected?	interactions relevant?	deals with this problem?
				(Y/N)	(D13)
			-Urban and agriculture sectors -Forests and forestry -Wood and paper industries -Users of coastal		Sub-model 1: land-sea inter-sectoral and coastal water exchange
	Land competition among urban, agriculture and forestry sectors	-Land availability	groundwater (through water flow changes and associated seawater intrusion risk) -Coastal-marine ecosystems (through water flow changes and associated nutrient loads to coast-sea)	Y	& Sub-model 2: land-sea inter-sectoral and coastal waterborne nutrient exchange
	Water availability for terrestrial ecosystems (through surface and subsurface water flows)	-Precipitation change (decrease) -Urban and agricultural sector	-Terrestrial ecosystems	N	Sub-model 1: land-sea inter-sectoral and coastal water exchange
		developments
					Sub-model 1:
	Urban expansion, population and tourism growth, associated water availability and supply	-Urban expansion -Associated urban tourism expansion	-Urban residents, and tourists -Municipalities -Water and wastewater utilities -Terrestrial and aquatic (freshwater and coastal-marine) ecosystems -Users of coastal groundwater	Y	land-sea inter-sectoral and coastal water exchange & Sub-model 2: land-sea inter-sectoral and coastal waterborne
					nutrient
					exchange

Table 4 -

 4 5 Problems identified by MAL4 Stakeholders and the related models

	Problem Description	What/Who Causes problem	Who is affected	land/sea int eractions rel evant? (Y/N)	Which model deals with this problem? (D13)
	Significant environmental pressures from economic activities mainly agriculture, and tourism on water resources	Agriculture, Tourism, Increase of coastal population	water resources quality and quantity, Shellfish farming activities, Drinking water supply	Y	Sub model agriculture Sub model water Sub model shellfish farming
			Water resources,
			Sustainable		Sub model
			Agriculture especially		agriculture
	Competition for water and	Urbanization,	Organic farming,		Sub model water
	space	Tourism	Livestock farming,	Y	Sub model
			Preservation of		shellfish farming
			Natural areas,		Sub model
			Shellfish farming		Infrastructure
					Sub model
	Land-sea interdependencies	High dependence of downstream activities on upstream activities	Water resources, Agriculture, Natural areas, Shellfish farming,	Y	agriculture Sub model water Sub model shellfish farming
			Ports		Sub model
					Infrastructure
			Water treatment
			plants(waste and		Sub model water
	Development of urban coastal population and tourism	Increase of residential elderly population, Increase of tourism	drinking waters), Local supply chains and services, Spatial repartition of	Y	Sub model agriculture Sub model
			population and		shellfish farming
			activities
	Agriculture is confronted to limitation of water availability especially in	Climate change, Droughts, Current agricultural	Irrigated agriculture, Water resources	Y	Sub model agriculture
	summer	business model			Sub model water
			Development of a
		The two major ports in	more sustainable		Sub model
	Sustainable development	the area rely on local	agriculture less		infrastructure
	of port activities	agricultural produce	focused on export vs	Y
		for a sizeable portion	intensive		Sub model
		of their business	monoculture		agriculture
			dedicated to export

Impact of water quality on shellfish production (frequency of mortality, spat capture rate, viruses)

	D6 Data and Model Inventory D6 -Model and Data Inventory
			Shellfish farming,
	Sea level rise, salinity,	Flatness of the coast, Presence of important	Marshes and natural areas		Sub model water
	sea water intrusion	wetlands Effects of climate change	Coastal activities (agriculture, tourism, urbanization)	Y	Sub model infrastructure
			Traditional shellfish		Sub model
		Inland activities, Urbanization	farming	Y	shellfish farming
					Sub model water

This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement N° 773782

Table 4 -

 4 6 Problems identified by MAL5 Stakeholders and the related models

	Problem Description	What/Who Causes	Who is affected	land/sea	Which model
		problem		interactions	deals with this
				relevant?	problem? (D13)
				(Y/N)
	Lack of the area's	Incoherent	Business area	Y	Tourism
	sustainable development	governmental and local administrations and excessive bureaucracy	(business planning, lack of facilities for investors and of compensatory		Freshwater Fishery Marine fishery Ecosystem management
			measures,
			tourism), Social
			area (health,
			incomes,
			protection, jobs)
			Ecosystem services
			(pollution from
			different sectors)
	Conflict

between Marine Protected Area (MPA) and the exploitation of resources

	D6 Data and Model Inventory D6 Data and Model Inventory
	Competition between	correlation with Lack of decision and	Ecosystem services -	Y	Tourism
	operators of Mass tourism and niche	applied research harmonization	biodiversity		Ecosystem management
	tourism in Danube Delta
	Rural out migration and	low economic	Tourism,	N	Rural
	Aging	opportunities,	Agriculture,		development
			Fishery,
	Aquaculture units development Lack of farmers' cooperation	Low Water quality -Low awareness on High nutrient cooperation benefits concentration	Development Industry Integrated production Closing the loop on	y Y	Marine Fishery Agriculture Rural development
	Waste management and reduced recycling Fishing restrictions Soil quality	Improper infrastructure schools, Freshwater fish stock mapping connectivity, need for agrochemical Land degradation,	Fishermen Local population, Rural economy production Environment resources use Farmers welfare Agriculture	Y Y Y	Freshwater fishery Rural development Tourism Agriculture
	Low development of rural	Marine fish stock Low entrepreneurial	Local inhabitants	Y	Marine fishery Rural
	business	initiatives; low capacity	Local administrations		development
	Fish migration to greater	building initiatives	Fishermen, Local (low tax revenues)	Y	Marine fishery
	depths due to increased Seawater temperature Need for knowledge and innovation Lack of facilities for fish processing	Development, Rural Farmers Economy Local population Lack of infrastructure Development of the Low share of farmers Climate Change receiving support for Small scale enhance economic, area and migration operational groups to or participation in difficulty to adapt knowledge exchange subsistence fishery advice, training,	Y Y	Agriculture Rural Rural development development
		environmental,
	Shortage of qualified workforce for economic activities Capital access Lack of functional, specialized market -Bursa de peste Lack	Abolition of vocational schools climate and resource efficiency performance Decreased knowledge There is a project which is not functional. It was proposed e-bursa de on funding schemes; lack of associative forms	Fishermen Area's development Tourism operators Fishermen Quality of life -Farmers Fishermen fishermen welfare	Y N y	Freshwater and Marine fisheries Tourism, Rural development Agriculture Agriculture Freshwater and Rural Tourism Marine fisheries
	Applied research for Clogged canals particular species for aquaculture	Incoherent governmental and peste (fish market) instead of classical one. local administrations There is no and excessive bureaucracy Water and Danube Delta administration research production and between fish cooperation	Fishermen Local people Business Biodiversity Water availability Fishermen development	Y Y Y	Freshwater Fishery Freshwater fishery Tourism Marine fishery
	Water quality (pollution)	Direct discharges of	Ecosystem services -	y	Ecosystem
	Illegal, Unreported and in the Danube Delta	Lack of regulation due waste water	Fishermen biodiversity	Y	Freshwater Fishery management
	Unregulated (IUU) fishing Water availability Aquaculture units development	to unrecognized problem at the Climate change -authorities' level Lack of legislation and investments Lack of technology droughts	Ecosystem services Farmers Fishermen Business Fishery Tourism Naval transport	Y y	Marine fishery Agriculture Freshwater Fishery Marine fishery Tourism Freshwater Fishery
		due to absence of
	research and innovation program under grant agreement N° 773782 773782

This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement N°

of exploiting the relative advantage of the rich Cultural heritage of the area

	D6 -Model and Data Inventory
		Decreased investment	Tourism	n	Rural
		in Promotion of			development
		cultural heritage in the			Rural tourism
		area
	Local people are paying for	Lack of awareness,	Sustainable	y	Rural
	solid waste brought from	solid waste	development		development
	upstream	management, mass
		tourism
	Increased presence of low	Clogged canals	Biodiversity	Y	Ecosystem
	financial value fish				management

This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement N° 773782 4.

6 Spain -Mar Menor Coastal Lagoon (Western Mediterranean) 4.6.1 Description of case study

  The Mar Menor coastal lagoon (135 km2) is located in the Region of Murcia (SE Spain). The area is characterized by multiple environmental, social-cultural and economic interests, often competing for scarce resources, water being the most important. There is a high potential for complementarity, win-win scenarios and development of sustainable business cases based on public-private collaboration, efficient use of water, and innovative farming practices and a transition to sustainable models of tourism and agriculture. The catchment draining into the Mar Menor covers an area of 1.255 km2 and is mainly covered by intensive irrigated agriculture and tree crops. The intensive and highly profitable irrigated agriculture depends on scarce low quality groundwater and water from inland inter-basin water transfers. Agriculture provides labour and income to the region, but forms a source of excessive nutrients and contamination into the Mar Menor coastal lagoon. The resulting poor water quality affects the ecology of the lagoon with severe implications for its potential function for tourism and fisheries. The coastal lagoon forms part of a Specially Protected Area of Mediterranean Importance (SPAMI). The Mar Menor is one of the hotspots for tourism in the Region of Murcia, with a total number of 346,000 tourists and 1.4 million over-night stays in 2016.

Table 4

 4

	-7 Problems identified by MAL6 Stakeholders and the related models
	Problem Description	What/Who Causes problem	Who is affected	land/sea interactions relevant? (Y/N)	Which model deals with this problem? (D13)
		Water scarcity, high
	Water availability	demand of water by agriculture, high dependency on the Tagus-Segura water transfer, climate	Mostly farmers but eventually local populations and tourism sector.
		change uncertainty.

Table A -

 A Data and model themes and main descriptors of the Greek Case Study (as anticipated on December 2018 after preliminary analysis of Stakeholder concept maps) 3 Data and model themes and main descriptors for the Swedish (Norrström/Baltic Sea) (as anticipated on December 2018 after preliminary analysis of Stakeholder concept maps)

	D6 Data and Model Inventory D6 Data and Model Inventory D6 Data and Model Inventory D6 Data and Model Inventory
	Coastal economy Demography Tourism Transport Infrastracture Infrastracture Social Social	Harbor shipping Population Size, Gender, etc. Statistics Congestion road network Annual / Seasonal Water Age, Demand Waste sites (location number condition) cultural/archeological sites	freight & person transport Total Population multiple (hotel nights, origin country, trends, …) Loss hours identifier Monthly water demand identifier Name / location	Flemish Region Federal -Belgium Flemish Region Flemish Region Region Regional catchment Region Region
	Energy Energy Energy Transport Transport Transport Transport Infrastracture Infrastracture Tourism Tourism Infrastracture Tourism	Energy renewable Social-econ impacts of Use, share offshore energy Offshore energy Shipping Road Traffic on main roads Volume, congestion, travel time Consumption per household added value, govt income, jobs Projects, Power Supply, Jobs Transport Demand transport indicators, Transport Projections Vehicle km Ports arrivals, length of stay Number of beds/ average stay/trends location, number and size of olive oil mills Programmes Statistics multiple (hotel nights, origin country, trends)) and infrastructure of adult Adult educational activities Tourist carrying capacity tourist accommodation	Federal -Belgium Belgian North Sea (BNS) Flemish Region Federal -Belgium Region Region Region Messinia Region Federal -Belgium Region Region
	Fisheries Aquaculture Fisheries Aquaculture Fisheries Aquaculture Infrastracture Tourism Tourism Table Α-2 Main Theme Infrastracture Tourism Fisheries Agriculture Land Use / Land Cover Aquaculture Fisheries Aquaculture Agriculture Land Use / Land Cover Main Theme Land Use Agriculture Agriculture Land Use / Land Cover Rural and Environment Agriculture Land Use / Land Cover Rural and	& & & /farmers education Fisheries & aquaculture Fisheries & aquaculture Fisheries & aquaculture Participation of population in adult education programms Tourist carrying capacity Tourist carrying capacity Sub Theme Tourist carrying capacity Tourist carrying capacity Census and/or tri-annual FSS Corine 2000/ 2012 & Fisheries & aquaculture & Fisheries & aquaculture Farm Accounts Corine 2000/ 2012 Sub Theme Land use status report RURA irrigated area percentage of organic crops land cover Land Water quality Unit size Burnt areas Land Water quality	oyster production Production Adult educational activities camping piches Hotels Variable/information key characteristics and trends Number of beds and camping nights sites fleet size, landings, economic value, fuel costs, … (number/size of holdings, no of employees) code FishFrame Regional Database economic indicators (economic accounts etc) code Variable long term trends of commercial fisheries (landings, fleet size, …) 18 indicators, 5 themes (open space, residential, work, mobility, environment) total irrigated area Input and output nutrients for Code municipal wastewater treatment property size plants Code Population connected to sewage	Belgium Belgium, global Region Region Region Geographical Coverage Region Region Flemish Region Regional EU Regional Geographical Coverage Flemish Region Flemish Region Regional Whole Sweden country Regional Regional Whole Sweden country
	Land Use Land Use Agriculture Agriculture Regulations Environment Rural and Agriculture Regulations Environment	Open Space Platform Dynamic Land Crops Crops Habitats directive (Natura 2000) Land Water quality use projections Crops Birds directive (Natura 2000)	Examining scenarios Production olive oil Total crops input network Name General diagnostic test model Land use type Total crops output Name (including data sources for Norrström) for legacy sources of	Flemish Region Regional Regional Greece Norrström catchment Flemish Region Regional and whole Sweden Greece country
	Rural Environment Rural Environment Agriculture Agriculture Rural and Land Environment Agriculture Rural and Land Environment Rural and Environment	Biological Evaluation & Natura 2000 Flanders Nature Report (NARA) Employment Employment Runoff Subsidies Variables related to water balance Land Water quality	Online Map Viewer key indicators & trends Total labor input Agricultural holding nitrogen and phosphorus (and River runoff other types of pollutants) Subsidies Nitrogen and phosphorus Evapotranspiration and runoff concentrations and loads-data (data and general model) inventory and analysis with given	Flemish Region Regional Total regional catchment Regional Regional Norrström catchment Total regional catchment and whole Sweden country
	Rural Environment Agriculture Agriculture Rural and Land Environment Rural and	Flanders Report (MIRA) Agricultural product Environment uptakes from irrigation River Land Water quality	key indicators & trends Farm Net Income water uptake for irrigation River network data sources Various variables	Flemish Region Regional Regional Total regional catchment
	Rural Environment Rural Environment Agriculture Agriculture Rural Environment Environment Rural and Agriculture Rural Environment Environment Rural and	Natura 2000 habitats, species & projects Spatial Environment Explorer (Map Viewer) Total Livestock units Land soil characteristics Land Water quantity Water use dessertification Land Runoff	Online Map Viewer Online Map Viewer Livestock Unit Irrigated land use soil physical features and type Various variables Irrigated water use soil quality Water discharge related to nitrogen	Flemish Region Regional Total regional catchment Total regional catchment Norrström catchment
	Spatial planning Spatial planning Climate and Weather Rural Environment Environment Spatial planning Climate and Weather Rural Environment Environment Rural and	Coastal atlas Local implementation plans spatial Climate models fertilizers pesticides usage (tons bought) Marine atlas & MSP Climate models Inland water quality Land Runoff	activities Various hydroclimatic outputs from climate models and phosphorus loads-data inventory and analysis with given data sources activities & zoning Surface temperature and precipitation T, pH, cond, DO, Turb, N, P Total river runoff	Belgian Coastal Zone National and whole Sweden national -Greece country West Flanders Belgian Coastal Zone Total regional catchment 10 sites across the Region Norrström catchment
	Spatial planning Tourism Social Climate and Weather Rural Environment Environment Rural and	Marine spatial plan overnight stays education climate data flood, rainfall data, drought Dams and lagoons Land Water use	total population Name Total water extraction	National -Greece Flemish Region Gialova region Whole Sweden country

research and innovation program under grant agreement N° 773782 This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement N° 773782 rainfall, temp, RH, WS, Sunshine duration Regional research and innovation program under grant agreement N° 773782

This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement N° 773782

ACKNOWLEDGEMENTS

This report is compiled for the H2020 COASTAL project (Grant Agreement No. 773782). Colleagues at partner organisations have contributed information on specific details relating to data protection and the named individuals with the relevant responsibilities. Partners with relevant authority have signed the declarations of compliance with EU and national laws and regulations.

This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement N° 773782 D6 -Model and Data Inventory This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement N° 773782 D6 Data and Model Inventory update inventory of COASTAL Stock-Flow models Inventory of Data and Third Party Models are included as Tables in Appendix B. This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement N° 773782 D6 Data and Model Inventory This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement N° 773782 D6 Data and Model Inventory D6 -Model and Data Inventory This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement N° 773782 Land Use Corine Land Cover/orchards Code France Land Use Corine Land Cover/anthropic areas Code France

D6 -Model and Data Inventory This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement N° 773782

Code France Land Use Corine Land Cover/heterogeneous agricultural area Code France Land Use Corine Land Cover/great culture Code France Land Use Corine Land Cover/vineyards Code France Land Use Corine Land Cover/grasslands Code France Land Use Corine Land Cover/forests Code France Land Use Corine Land Cover/heathlands Code France

Appendix A

Tables of Data and Model Inventories of each MAL (December 2018)