Jakub Gregor
email: gregor@iba.muni.cz

Jana Borůvková

Richard Hůlek

Jiří Kalina

Kateřina Šebková

Jiří Jarkovský

Ladislav Dušek

Jana Klánová

GMP Data Warehouse -a Supporting Tool of Effectiveness Evaluation of the Stockholm Convention on Persistent Organic Pollutants

Keywords: Stockholm Convention, Global Monitoring Plan, POPs, Database, Data collection, Visualisation, Analysis, Standardisation, Data Structure

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

The Stockholm Convention on Persistent Organic Pollutants was adopted on 22 May 2001 in Stockholm, Sweden, and entered into force on 17 May 2004. The convention is focused on selected persistent organic pollutants (POPs) -chemicals that represent a significant risk for the environment and living organisms, including the humans.

Although POPs form a heterogeneous group from the chemical point of view, their common characteristics include acute or chronic toxicity and high resistance to transformation processes, which makes them capable of long-range transport and accumulation in tissues of the living organisms.

The Stockholm Convention (SC) and its annexes currently (2016) contain 26 selected POPs (or their groups), for which the contracting Parties must adopt measures to eliminate or reduce their production and use or minimise the unintentional releases. In other words, the list contains both chemicals that were or have been intentionally produced and used (e.g. DDT and other POP pesticides in agriculture, polychlorinated biphenyls in industry) and chemicals that are unintentionally formed and released during anthropogenic processes (e.g. production of dioxins during combustion processes).

Naturally, adoption and application of (legal/technical) measures for the reduction of environmental burden by POPs is not the only step that should be implemented; these measures should be also continuously evaluated in terms of their feasibility and effectiveness. Effectiveness evaluation of the Stockholm Convention is defined in its Article 16. Among others, this article requires establishment and operation of the Global Monitoring Plan for Persistent Organic Pollutants (GMP) -a tool for the collection of global data on POPs levels, assessment of their spatial and temporal trends and thus generating information on whether the environmental burden by POPs decrease and measures adopted by the Convention are effective in reality.

Stockholm Convention's Global Monitoring Plan for POPs

GMP implementation was officially endorsed in 2007 by the decision. SC-3/19, of the 3rd meeting of the Conference of the Parties to the Stockholm Convention. Main objective of the GMP is global collection of available data on POPs concentrations in selected matrices -ambient air, human milk, human blood, and water (for hydrophilic substances). Article 16 of the SC expects collection of data from existing environmental monitoring programmes however it allows for capacity building assistance and establishment of new monitoring programmes only in those regions with data gaps.

Request for harmonised and standardised data collection framework and their assessment is set up in a document entitled "Guidance on the Global Monitoring Plan for Persistent Organic Pollutants" [START_REF]Guidance on the Global Monitoring Plan for Persistent Organic Pollutants[END_REF]. The first draft document was approved simultaneously with the establishment of the GMP framework in 2007 and has undergone several updates since then, due to the addition of several chemicals to annexes of the Convention and need for detailed updated data handling and assessment guidelines.

Data collection for GMP purposes is performed in a six-year interval and the results including interpretations, conclusions, and recommendations are published in five regional reports (UN regions) and one global monitoring report. The reports are prepared by Regional Organisation Groups (ROGs) and a Global Coordination Group (GCG). These bodies consist of experts, representatives nominated by the Parties to the SC, with a significant knowledge in environmental monitoring and data management.

GMP Data Warehouse development and tools

It is noteworthy that the collection of data from different sources (i.e. monitoring programmes) is not a trivial issue. Each national or international programme has a specific purpose, different design and methods of sampling and chemical analyses, the data are collected and stored in different forms and structures. Hence, their global comparison is a challenge that emerged immediately after the first data collection campaign in 2008. It is therefore necessary to set up a really robust and flexible methodology that allows merging such heterogeneous data together and performs their comparison, assessment and analysis. The published monitoring reports of 2008 showed that there is a strong need for a standardised electronic tool for both data collection and their presentation.

Establishment of a single data warehouse, in which all GMP data would be stored, is proposed in Chapter 6.5.2 of the GMP Guidance document. The GCG, ROGs, and Secretariat of the SC considered, supported and approved establishment of such at their meetings in 2011-2013. The concept and development of the electronic Global Monitoring Plan Data Warehouse (GMP DWH) was endorsed by the decision of the Conference of the Parties to the Stockholm Convention SC-6/23 at its 6th meeting in May 2013. Consequently, they authorised two research departments of the Masaryk University (Brno, Czech Republic) to design and develop a comprehensive information system for the collection, analysis and visualisation of GMP data in accordance with the following principles:

• fully electronic data processing • standardised parametric data structure, standardised predefined code lists • defined processes for the data input, validation, approval, and publication • defined hierarchy of users and user rights with respect to access to the system and data processing • connection of the database with visualisation and export tools • online access to all tools and functions • system of user support, help desk Goal of the GMP DWH is to provide long-term reliable and cost-effective information and services to global community, support POPs monitoring activities and data management under the Stockholm Convention and offer tools for collection, storage, organisation, comparison, analysis, and evaluation of performance in relation to monitoring programmes on POPs. The objectives of the online GMP DWH are therefore twofold:

• provide user friendly tools for storage and analyses of data from international monitoring activities under the Global Monitoring Plan of the Stockholm Convention on Persistent Organic Pollutants and make POPs data visualisation available for regions and programmes that require support in data management, and • contribute to the effectiveness evaluation of the Stockholm Convention by compiling and visualising results of global monitoring activities on POPs.

Development of the GMP DWH system was completed in 2014 and the system was handed over to the global community and main user (Secretariat of the Stockholm Convention). The GMP DWH structure has been designed to incorporate state of the art knowledge and expertise in building knowledge-based infrastructures. It encompasses data input, storage, processing (compiling and archiving) of both primary data as well as aggregated data, including supplementary data in cases where no primary data are made available. The system holds data on POPs in four core matrices: air, human milk, human blood, and water. By respecting the requirements of uniform and harmonised presentation of data, all outputs of the GMP DWH are shown on the visualisation portal (http://visualization.pops-gmp.org/2014/). The GMP DWH architecture consists of three layers guiding the data flow from the initial upload to the final publication. Each layer is connected with appropriate tools and processes of the data flow, user administration, security, user support etc. (Fig. 1) Data repository and data visualisation are the main parts of the system accessible by a wider group of users. No additional software is required to use the system, because it is implemented fully online and is accessible through standard internet browsers (Internet Explorer, Mozilla Firefox, Google Chrome). It is highly recommended to use their recent versions, which are freely available and ensure proper functioning, particularly for visualisations and graphic outputs.

Data repository

The data repository refers to a database in which all data are stored and administered. Data are collected and inserted in a predefined structure, which is partly identical for all monitored environmental matrices (ambient air, human milk, human blood, water); however, it also reflects certain specificities of the individual matrices and nature of the data (primary vs aggregated). The individual items of data structure are logically grouped into three levels (Fig. 2). The top level ("Site") defines and describes the place in which sampling was performed. The middle level ("Sampling attributes") contains information about the sampling period and employed methods. The third level ("Measurement") provides data about measured chemical substances, their concentrations, statistical variability etc. Such predefined structure ensures a full comparability of collected data and also prevents gaps in their completeness. The GMP DWH is designed to store aggregated data with one-year granularity. This consensus works under the assumption that a large part of data from the monitoring programmes are not available in their primary format and that POPs concentration show, particularly in temperate regions, fluctuations due to seasonal variations in the course of the year. However, data providers are allowed to send/upload both primary or aggregated data using online forms or MS Excel sheets of a predefined structure. The data sets are subsequently processed, validated and aggregated (where applicable) by the GMP DWH administrators. The data insertion process is shown in Fig. 3.

Validated data are subject to approval process of the relevant geopolitical expert group ROGs; it is only relevant ROG members are allowed to approve particular data records for publication (in monitoring reports, online visualisation, data exports etc.) or reject any public use of these data in relation to the GMP.

The previous paragraphs imply that the system is accessed by various users and groups of users in terms of data management (data providers, administrators, ROG members) and geographical affiliation (individual UN Regional Groups). Management of users and user rights is therefore one of the crucial points in the system design. Access rights must be precisely defined for each individual user so that he/she could only view and process those data records that fall under his/her competence from both of the above-mentioned points of view. Data flow and the definition of user roles and rights are further complicated by the fact that the same user can be, for example, a representative of data-providing institution and a ROG member at the same time, or a European institution may provide data to ROG Africa etc. Development of the processes and rules for the management of users, their roles and rights (Fig. 4) was therefore another significant issue. This is closely linked to data security, which is guaranteed by a data security management certificate according to the ISO/IEC 27001:2014.

There are also other very important parts of the system, such as the statistical and analytical tools for data validation, correct aggregation, and last but not least, the assessment of time trends in POPs levels. For this purpose, a special R package [START_REF] Kalina | genasis: Global ENvironmental ASsessment Information System (GENASIS) computational tools[END_REF] was developed in cooperation with the Environment Canada, which is able to perform all necessary steps. The software is described in details on a special website (http://www.genasis.cz/time-series/), which also provides a practical demonstration of the package [START_REF] Kalina | Time series assessment in the Era of Stockholm Convention & GMP[END_REF]. See Chapter 3.3 for details.

Data visualisation

Work with large data sets often requires some type of visualisation in order to understand their nature and distribution, to compare different data sets, or to identify outliers. The website http://visualization.pops-gmp.org/2014/ [START_REF] Hůlek | Global Monitoring Plan of the Stockholm Convention on Persistent Organic Pollutants: visualisation and on-line analysis of global levels of chemicals in air, water, breast milk and blood[END_REF] was designed to provide such basic overview of data in the GMP DWH; moreover, it also offers advanced analytical and mapping tools that enable the user to browse and to analyse data from different points of view:

• "Spatial distribution" -a map overview of sites for which data are available. The sites can be stratified according to a set of criteria. • "Data availability" -a plot describing data availability in time (years in which sampling ran) and for individual POPs (compounds that were analysed).

• "Summary statistics" -assessment of measured concentrations of a particular compound at individual sampling sites and years by means of descriptive statistics.

The user can switch between various types of central value (mean, median) and variability (percentiles, min-max, standard deviation).

• "Time series" -three different views on time trends of POPs levels globally. Two of them are maps that display statistical assessment of long-term trends, i.e. whether the trend is increasing, decreasing, or statistically insignificant. The third tool is a plot that shows annual values of POPs concentrations in time, trend regression and outputs of statistical tests.

• "Data exports" -this provides an overall summary of selected dataset i.e. contributing monitoring programmes, sites, and analytical methods included in the current data set, and also tools for export of the dataset to files that may be processed in MS Excel.

The above-mentioned modules (Fig. 5) are further enriched by other functions to facilitate the work with the system. Graphical outputs can be directly adjusted according to user needs (ordering values, type of values) and they can also be exported into a PNG format in order to be used in further work. Each combination of filters and each view on the selected data set are defined by a unique code which is included in the URL of a particular view. This significantly enhances linking and sharing of information through various internet communication channels.

Employed information technologies and approaches

The GENASIS system [START_REF] Hůlek | GENASIS System Architecture: on the Way from Environmental Data Repository towards a Research Infrastructure[END_REF] was used as a platform for handling primary data and their transformation into the form and structure required by the GMP Guidance [START_REF]Guidance on the Global Monitoring Plan for Persistent Organic Pollutants[END_REF] and GMP DWH standards (see Chapter 3.1). An OpenCPU technology was used for integration of the R software environment into the GENASIS infrastructure [START_REF] Hůlek | Integration of R Statistical Environment into ICT Infrastructure of GMP and GENASIS[END_REF]. The R package developed in cooperation between the Masaryk University and Environment Canada [START_REF] Kalina | genasis: Global ENvironmental ASsessment Information System (GENASIS) computational tools[END_REF] ensured harmonisation of data on the level of data standards and metadata.

In particular, this tool was used for the recalculation of air-passive sampling data to volume-based values, which ensured their comparability with data from the air-active monitoring programmes. Further steps involved the calculation of derived parameters required by the GMP Guidance (sums of related compounds, toxicity equivalents -TEQs) and the summation of individual sampled fractions (for air data). Another (unpublished) R package was used for the annual aggregation of primary data. The data visualisation tool employs its own database layer, which is updated in 24hour intervals on the basis of the central data warehouse content. The data and data views are partially pre-prepared and pre-calculated to enable a faster and more fluent operation, calculations and loading. Almost all charts displayed within the data visualisation are custom-made and have been implemented using the D3.js library, since common available visualisation libraries do not contain required types of charts.

Three different servers technologies are involved in the data visualisation: application/web server (Apache), ArcGIS Server and OpenCPU R-server. The application server provides web services and ensures the system security. The map server employs the ArcGIS for Server and ArcGIS for Javascript technologies to create map compositions that obtain information on sites from the database and display sites that correspond to the user's selection in map windows of the web application. The Rserver performs real-time calculations of time trends for selected sites, compounds and period [START_REF] Kalina | Three Levels of R Language Involvement in Global Monitoring Plan Warehouse Architecture[END_REF] (Fig. 6). Fig. 6. Procedures and software tools employed in processing of the POPs data on their way from the primary database (GENASIS platform) to the GMP data visualisation

GMP DWH operation and user support

The final version of the GMP DWH was provided to the main user (Secretariat of the Stockholm Convention) in 2014, in which the second GMP data collection took place. In contrast to the first data collection period, these data were collected digitally in a parametric and structured form, which enabled both standardisation and comparability of outputs that were presented in regional monitoring reports. Monitoring reports were adopted and data visualisation was made publicly available at the 7th meeting of the Conference of the Parties to the Stockholm Convention in May 2015.

All tools and parts of the GMP DWH are accessible through the web portal www.pops-gmp.org/dwh. The website contains all important information about the system, links to individual tools, background information, user guides, overview of data structure and reporting spreadsheets (Fig. 7).

Providing user support during implementation of the GMP data tool

Help desk was introduced as soon as the first version of the GMP DWH emerged in 2013. This service supported all GMP DWH users and other relevant persons interested in the project. The service is available on weekdays from 8am to 4pm CET via email and phone. Help desk operators either immediately provide requested information or assistance, or in case of a more complicated request, allocate the task to another expert/data manager. The help desk performance is in agreement with requirements of the international standard ISO/IEC 20000-1:2012 and guarantees that response will be provided within 24 hours.

The main tasks of the service include:

• technical assistance to all GMP Data Warehouse (GMP DWH) users • transfer of requests to appropriate DWH managers • administration of user accounts and associated rights • contact point for data files from the providers, their formal validation and transfer to DWH managers • contact point for error reports and suggestions for further software and services development

The list below shows the range and types of requested hotline services and support in 2014 (a period in which the data collection, validation, and approval were carried out and the visualisation tool was not yet publicly available):

• Reception and validation of data files from the Environment Canada, communication with persons responsible for data transfer (3 persons). Most issues discussed were related to correct data format and terminology (~ 10 requests) • Additional correction in data already provided and entered in the database (~ 10 requests) • Providing ROG members and their consultants with access to the database and visualisation, preparation of instructions and user guides (43 user accounts) • Support to users experiencing technical problems with the visualisation tool. These technical issues were caused by older versions of web browsers used by users. (~ 5 requests) • Identification of bugs in the visualisation tool in cooperation with the Secretariat members and other users (~ 15 requests)

Attendance of the GMP data visualisation website

Google Analytics were employed for the assessment of attendance of the GMP visualisation website.

Current use of the GMP DWH

Functional and content updates of the system were temporarily discontinued on the date of its official publication and of publication of regional monitoring reports in May 2015. In the period preceding the final publication, the data visualisation module was mainly used by ROG members and consultants for data approval and compilation of the monitoring reports. It now serves as a supplementary and supporting tool to the monitoring reports that enhances browsing, viewing and analysing the global POPs data. It is also presented as a reference tool in the communication with new data providers and partners in order to identify new potential data sources worldwide.

Conclusion

A multimodular online GMP DWH was developed for data collection, processing and reporting for current and future GMP data collection rounds. The system is based on fully parametric data sheets to improve the quality of collected global data sets on POPs concentrations, to determine their fate in the environment over time and to strengthen the responsibility and visibility of data providers. The developed online tool for the Global Monitoring Plan is capable of visualising information available globally or from regional and country perspective. The GMP DWH is accessible for anyone with internet connection on a dedicated portal www.pops-gmp.org/dwh. The system is customised to function in all standard internet browsers (Internet Explorer, Mozilla Firefox, Google Chrome).

Fig. 1 .Fig. 2 .

 12 Fig. 1. GMP Data Warehouse architecture

Fig. 3 .Fig. 4 .

 34 Fig. 3. The process of data insertion into the GMP DWH

Fig. 5 .

 5 Fig. 5. Example of graphical outputs in the GMP DWH visualisation tool

Fig. 7 .

 7 Fig. 7. Web portal of the GMP DWH (www.pops-gmp.org/dwh)

Table 1 .

 1 Table1summarises the main characteristics of users' behaviour when browsing the website during the first year from publication (from 1 May 2015 to 1 May 2016). The data indicate, among others, a relatively long duration of an average session and a high number of pages viewed. The highest attendance peaks were recorded on 11 June and 28 July 2016, when the GMP DWH was presented at the BRS Conventions' Twitter account and newsletter, respectively. Users' behaviour on the GMP data visualisation website in the first year of public operation (assessed by Google Analytics)

	Parameter	Counts
	Sessions	2,025
	Users	818
	Page views	30,012
	Pages per session	14.82
	Average session duration	11 min 8 sec


Refiz Duro
email: refiz.duro@ait.ac.at

Mert Gençtürk

Gerald Schimak

Peter Kutschera

Denis Havlik

Katharina Kutschera

Framework for Enabling Technical and Organizational Interoperability in the Management of Environmental Crises and Disasters

Keywords: Interoperability, Crisis mapping, Crisis management

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

The lack of interoperability among systems, sensor networks and in communication is one of the major obstacles for effective crisis and disaster management (CDM), especially in heterogeneous and multi-user environments. Accessing data provided by the first responders in the field, having a joint operational picture of a crisis by accessing sensor data and maps, and having a possibility of real-time effective communication with other crisis managers (organizations) are all examples of needs and activities a crisis responder experiences. However, the presence of significant diversity in the CDM domain in general creates barriers halting the efficient cooperation. Typical issues include usage of different hardware and software solutions, conforming to different (or incompatible) data and communication standards, not fully adjusting to the legal and administrative policies and procedures of involved organizations and responders, or considering the defined (data) security requirements. An example is a response to the Haiti earthquake in 2010, where lack of interoperability of equipment and procedures among the European field hospitals caused less efficient cooperation and response [START_REF] Alexander | Integrated Emergency Management for Mass Casualty Emergencies[END_REF]. Similar story goes for Hurricane Katrina in 2005, in which a higher level of data interoperability would have enhanced evacuation cooperation between the governmental and voluntary organizations in matching parents with missing children [START_REF] Lent | Facing The Challenge Of Data Interoperability[END_REF]. Moreover, the frequency of disasters is expected to increase in the forthcoming future mostly due to environmental changes [START_REF] Alfieri | Global warming increases the frequency of river floods in Europe[END_REF]. Floods are just one type of a crisis that in its nature often is transboundary, spanning several neighbouring countries or regions. For example, the Danube River is known for flooding Central and Eastern Europe regions for centuries, and several times in the recent two decades only [START_REF] Blöschl | The June 2013 flood in the Upper Danube Basin, and comparisons with the 2002, 1954 and 1899 floods[END_REF], bringing devastation and loss of lives, together with an economic burden on the affected population and governments. Such situation calls for an enhanced level of cooperation, where linguistic, law and information exchange barriers are no longer an obstacle [START_REF] Boin | Managing Transboundary Crises: Identifying the Building Blocks of an Effective Response System[END_REF]. These are just some examples among a myriad implying a necessity for a solution involving "the capability to communicate, execute programs, or transfer data among various functional units in a manner that requires the user to have little or no knowledge of the unique characteristics of those units" (OGC's definition for interoperability 1 ). In other words, in a fully interoperable response, every organization would have the possibility to use any software, data and communication formats and language that best suits their procedures and methods of operation while maintaining their independence, and at the same being able to take part in a joint crisis response.

The currently ongoing C2-SENSE2 project [START_REF]C2-SENSE project web site[END_REF] is meeting this demand by developing a framework where all relevant entities (organizations, rescue services, etc.) that need to cooperate during crises are collaborating. Collaboration means facilitating communication, information, and data exchange among actors, regardless if their systems are proprietary, or open. This reduces reaction time, increases the effectiveness of the management, and thereby saves lives and resources. The C2-SENSE Framework encompasses two parts:

1) components related to the physical nature of the Framework meaning software and hardware, and

2) the profiling approach, i.e., incorporation of rules orchestrating the functionalities of those components and rules for achieving interoperability.

The methodology and the approach in the second part are flexible enough to allow for implementation not only in crisis and disaster domain, but also in other domains where such functionality is required.

We present the Framework and its Collaboration Environment in Chapter 2, where the main software components necessary for interoperability processes are described in detail. In Chapter 3, the profiling approach is described, while an example of implementation is given in Chapter 4. The discussion and conclusion on the still ongoing work is provided in the final chapter.

The Framework and Collaboration Environment

Collaboration occurs whenever humans and/or computer applications work together to accomplish a common goal. In the domain of crisis and emergency management, it is critical that a sufficient level of collaborative effort and alignment between the crisis responders is achieved. In order to process and align the activities and procedures performed by the responders, applications and different Command and Control and Sensor Systems, the C2-SENSE Framework provides Collaboration Environment. It allows for critical functionalities and activities such as real-time data and information exchange, communication between entities involved in response activities, and alignment of operations and procedures during crises.

Collaboration Environment incorporates a set of software components. One group of these components are intended for decision making activities, covering functionalities such as GUI for crisis mapping, or data and sensor management. The second group of components are developed with the sole purpose of managing profiles, thus allowing for functionalities to achieve the interoperability at the level of seamless information and data exchange and procedure alignment, a process more described in Chapter 3.

Components and Applications for Decision Making

The following software components and tools are part of the Collaboration Environment important for the decision making in critical situations, as well as in the preparation phases.

Emergency Maps Tool (EMT):

The main purpose of the Emergency Maps Tool is to provide the end user or the network of users (e.g., crisis managers, responders in the field) with a collaboration tool for representation and sharing available geo-referenced data and information. This tool mashes up the emergency geospatial data from the C2-SENSE data repository, map layers and data from external Map Services. The tool displays the emergency area geographically to the related authorized users. The EMT is, however, not only about data on maps, but it also includes widgets that are useful and required in emergencies. These are widgets for displaying timeseries sensor data (e.g., water level development for the last 24 hours), monitoring exchanged messages among the C2-SENSE responders, or monitoring the latest incoming values regardless of their source (temperature data, showing the last position of an ambulance, etc.). The EMT tool is easily expandable to include new widgets and features. A screenshot is given in Fig. 1.

Object of Interest Data Repository (OOI): All the relevant data that have to be available to other Collaboration Environment components are stored in the Object of Interest Data Repository [START_REF] Havlik | Training Support for Crisis Managers with Elements of Serious Gaming[END_REF]. Fast response times are required during crises, implying quick data access and repository response and tight coupling between the OOI repository and technologies for reliable and fast data access and exchange by using the available technologies (e.g., Apache Kafka 3 ). The sources of data can be anything from sensors measuring environmental parameters (air quality, temperature), to sensors tracking location of ambulances. To allow for effective and accurate applications of the OOI data, the data can be further enriched with meta-information (e.g., data are georeferenced). Moreover, other type of sources, as are texts in exchanged messages or in social media can also be stored as relevant data in the OOI data repository. The component is working in the background without any direct interaction with the end user (e.g., crisis responder).

Fig. 1. Emergency Maps Tool with the map, line graph, and last values table

Sensor Management Tool (SMT): The purpose of the Sensor Management Tool is to provide an overview over available sensors in the field to crisis mangers and to allow them some basic configuration in a generic way without the need to know all the details about the sensors. As the configuration needs to be agnostic in respect to the sensor type, the possibilities of the tool are intentionally limited to very basic and generic commands. These include sending information on sensor identification (ID), capabilities and location, controlling and adjusting the data-sampling rate (off, low, medium and high), or re-locating a sensor (in case of autonomous sensor platforms).

SMT encompasses a GUI for sending commands and for visualizing responses (see Fig. 2). Visualization functionalities can also be done with the EMT. The component defines messages for the ESB, and information/data on how sensors can react by providing a reference implementation. This is an optional implementation of the inter-face to sensors and sensor network adapters. Finally, SMT is not planned as a replacement for any sensor specific configuration tools, but as a complementary tool to be used without any prior knowledge of the actual sensors used.

Fig. 2. Sensor Management Tool user interface for sending commands

GIS Server:

The GIS Server is used to provide the data from the OOI in a standard conform way to clients already used within a specific organization. The clients range from dedicated GIS solutions (e.g., QGIS) to broadly used applications (e.g., Google Earth). The component primarily makes the data available to the end users who are connected to the C2-SENSE Framework and authorized to access the data. The data, however, can also be made available to end users in case the data are open. Moreover, by using the GIS Server, it is possible to visualize crisis related data on standard GIS clients making the information available to everyone without the need to use a specialized tool like EMT.

LimitChecker: Crisis responders require automatic provision of warnings and alarms during crises. This is specifically important when many events occur simultaneously, and when there are large amounts of data coming in, making having an overview of the crisis very hard. LimitChecker provides automatic monitoring functionality. This C2-SENSE component monitors relevant sensor data (i.e., water levels) that are streamlined in the C2-SENSE data repository, and compares them to pre-defined threshold values. In the case of exceedance, a warning or an alarm are sent to the system and relevant end users. Moreover, the LimitChecker component also provides data modelling functionality, where timeseries sensor data for a chosen period are analysed to predict the development of, e.g., water levels, for the next few hours.

Due to the flexible component architecture, which includes free statistical software R sub-component, LimitChecker can easily be used and extended to serve other, more complex, data analysis tasks (e.g., combining ground-based sensor data with weather forecast).

Components and Applications for Profile Management

Management of profiles defined in Chapter 3 is done by four software components described in the following. The management process is given in figure 3.

Fig. 3. Profile management process

Profile Definition Tool (PDT):

The main purpose is to provide users with an online tool to create, to update and maintain profiles in an easy way. It provides users a graphical user interface to define a profile as an ad hoc workflow describing the order of the tasks, messages to be exchanged within tasks, and documents that constitute the message content. The aim is to lighten the workload of domain experts and help them to handle complex tasks easily. After profiles are defined, PDT produces automatically both human readable Word documents, which addresses the versioning problem and helps to keep documents always up-to-date; and machine processable XML documents, which enables software systems to understand the profiles. The schema definition of this XML document (XSD) is referred as Emergency Profile Definition Language (EPDL).

Profile Specialization Tool (PST): Profiles defined through PST are generic and independent from organizations and specific incidents. These generic profiles contain enough structure to prevent chaotic response to crises situations such as the initial activities, control and data flow structure, and resources needed to start managing a variety of crisis situations. Profile Specialization Tool is used to customize the generic profiles to specific organizations and specific incidents by considering the existing procedures and operations of the organizations involved. It is used to create a machine readable and executable process definition from the graphical definition by using a business process specification language, namely BPMN 4 . The BPMN documents created by the tool are then sent to Profile Execution Engine for execution.

Profile Execution Engine (PEE):

This is a tool built on Alfresco Activiti, which is an enterprise Business Process Management (BPM) solution targeted at business people and developers. PEE is used to execute emergency web processes defined as business rules in BPMN format by invoking corresponding Web services automatically.

Profile Monitoring Tool (PMT): Profile Monitoring Tool, a graphical user interface, makes it possible to visually trace the execution of a specific instance of profiles by PEE. The aim is to enable users to track the status, bottlenecks and incomplete tasks to take necessary measures.

Profiling Approach

The second part of the C2-SENSE framework is the application of profiles. Profiles define a standard set of messages and documents, business rules, processes, constraints and choreographies that allow even a new entity to join an already existing response network, without any additional integration efforts, as long as the entity conforms to one or more predefined profiles. In cases where entities do not conform to any profile or standard specification (meaning that they have their own proprietary document formats, workflows, business rules, etc.), adapters should be implemented for every two entities so that they can be interoperable. This operation should be repeated every time a new entity joins the network. In this regard, profiling approach helps in improving interoperability as it eliminates the need for prior bilateral agreements between entities involved in crisis response, and who wish to exchange information and data by defining standard specifications for achieving specific goals. In other words, these are sets of rules orchestrating the functionalities of the Framework.

A profile-based approach has already been successfully implemented in domains such as eHealth [START_REF]Integrating the Healthcare Enterprise Profiles[END_REF] addressing three layers of the Interoperability Stack. "Communication Layer" covers the transport and communication layer protocols, "Document Layer" addresses the content format of the messages and documents exchanged among the applications, while the "Business Process Layer" addresses the choreography of the activities to be executed by the participants [START_REF] Namli | Testing conformance and interoperability of eHealth applications[END_REF]. For the CDM domain, however, organizational aspects including policies, procedures, strategies and operations are critical. Therefore, the Interoperability Stack shown in Fig. 4 has been proposed for the domain [START_REF] Tolk | Introducing a Reference Model for Measures of Merit for Coalition Interoperability[END_REF].

The first full implementation of the Stack is deployed through the C2-SENSE Emergency Interoperability Framework [START_REF] Gençtürk | Profiling Approach for the Interoperability of Command & Control Systems with Sensing Systems in Emergency Management[END_REF]. The profiles developed in the project are addressing all the layers, exposing available applications and implementing the missing technologies, and making them available to the crisis response community. In the following, we provided details on profiles currently available in the Framework, while keeping in mind that the new ones can be added upon the need and purpose of the collaboration (e.g., data exchange for environmental monitoring, maritime surveillance [START_REF] Gençtürk | Interoperability Profiles for Disaster Managmenet and Maritime Surveillance[END_REF], etc.).

Emergency Situation Map profile: This profile describes how a user or an organization is provided with a common operational picture of the emergency area using a realtime data and geographical maps. It describes how to get the data, maps and mash-ups. The objectives of implementing this profile are: 1. Getting the overview of the current situation in the emergency area. The profile supports a process of providing C2-SENSE users with a common operational picture with maps and/or data with access determined by the Service Level Agreements (SLA). The intended scope for this profile includes pre-emergency, during and after emergency on-site coordination centres, local organizations, national organizations and non-governmental organizations. The activities specified in this profile are intended to provide a common ground for a decision support between organizations taking part in emergency operations. It is expected that all parties have necessary infrastructure/devices, to enable them to acquire maps and data, and to view them. This profile supports a process of providing users with a common operational picture with maps and/or data. The intended scope for this profile includes: pre-emergency, during and after emergency situations on-site coordination centres, local organizations, national organizations and non-governmental organizations The activities specified in this profile are intended to provide a common ground for a decision support between organizations taking part in emergency operations. It is expected that the parties have necessary infrastructure/devices, to enable them to acquire maps and data, to view them and to run simulations on them. The access is determined by the SLA agreements.

Sensor Measurement Profile: The profile describes how sensors transmit their measurements to the C2 Systems, where organizations can analyse the observations. This is important across the complete emergency incident life cycle, including preparedness, initial and on-going response, recovery and demobilization/release of sensors.

The objective of implementing this profile is to expect that all sensors are sending measurements of their observations according to their configuration.

This profile supports the process of sensors that send measurements to C2 Systems. The intended scope for this profile includes (during and after emergencies) emergency managers among on-site coordination centres, local organizations, national organizations and non-governmental organizations. It is assumed, (1) that the sensor infrastructure is ready to run and (2) that sensors can send their measurements. That means that configuration (including type of protocol, physical connection, etc.) is a prerequisite and thus not part of this profile.

Sensor Management Profile: This one describes how organizations can configure sensors to gain observations from areas of interest. This is important across the complete emergency incident life cycle, including preparedness, pre-staging of sensors, initial and on-going response, recovery and demobilization/release of sensors. The objective of implementing this profile is to expect that sensors are available and that they need to respond and adapt to emergency incidents, such as querying and finding out about available sensors, and configuring sensors to send the requested observations (data).

The profile supports a process of discovering, ordering and deploying sensors, which are needed in emergencies. The intended scope for this profile includes (during and after emergencies) emergency managers among on-site coordination centres, local organizations, national organizations and non-governmental organizations. It is assumed, that the sensor infrastructure is ready to run, i.e., low-level configuration (including type of protocol, physical connection, etc.) is a prerequisite and thus not part of this profile.

Collaboration Environment in a Real Life Example

We show here a simplified example in order to see how the physical components and the profiling approach harmonize the data exchange among two or more entities (organizations, responders, etc.). The example is a part of a larger crisis scenario involving floods.

Entity A has only limited overview of the situation in field: reports from police and firefighters provide last measurements on water levels and the extent of flooding, which can be mapped into their Geographical Information System (GIS). These are, however, not real-time and can be obsolete due to rapidly changing situation on the ground. Besides reports, Entity A can have direct access to water level measurements provided by the sensors in-situ owned by Entity B. Accessing sensor data would provide real-time situation data and an overview of the situation that would manifold the effectiveness of decision making for Entity A. The obstacles are, however, which protocols to use in order to access the data, how to get the correct permissions, and finally, how to align the data formats to be embedded into their GIS system. Since the data are in the possession of Entity B, some kind of exchange and alignment rules and agreements need to be put in place in order to bypass the mentioned obstacles. The following process is applied when exchanging the data between Entities A and B (Fig. 5):

1) Register as a new organization (in the C2-SENSE system),

2) Choose a default profile for data exchange(i.e., Emergency Situation Map profile).

The profile defines the needed input, such as the data format and communication protocol. Normally (without profiles), Entity A and Entity B are supposed to first understand their proprietary formats, protocols, rules etc., update their internal systems according to specifications of the other, and then exchange information. However, with the profiles, they just either update their internal system according to standard specifications in the profile, or use some adapters in C2-SENSE framework, which makes them profile compliant.

3) Specialize the chosen profile by providing the network access point (e.g., URL) of the receiver/sender of the data, 4) Activate the specialized profile.

Fig. 5.

Steps in the alignment process in the profiling approach for one entity After the final step, it is possible for Entity A to receive the data from Entity B based on the specifications defined in business process model and notation, and apply further functionalities provided by the components in Collaboration Environment (e.g., decision making, etc.) The process of specializing a chosen profile, however, needs to be executed by both actors for their respective specifications. Entity B sends the data in JSON format, while Entity A can only treat XML formats. The Framework would then consider this and make the appropriate transformations needed for these two actors to exchange data and information. Transformation can be done at semantic level as well. The framework provides a semantic interoperability suite allowing for semantic transformation for organizations using different data models, or the same standard but different versions. What this example shows is the interaction between the physical components that offer data integration and transformation and the rules governing the data exchange between these components. Without the profiles, the system would not know which format is received, nor which format is accepted and used by the receiving actor.

Very similar actions are planned as a part of the real flood scenario in the C2-SENSE project's pilot due in 2017 [START_REF] Gençtürk | Profiling Approach for the Interoperability of Command & Control Systems with Sensing Systems in Emergency Management[END_REF]. The results will be reported in a subsequent publication.

Discussion and Conclusion

The novel profiling approach presented here is based on the successful application of the conventional profiling approach in eHealth domain [START_REF]Integrating the Healthcare Enterprise Profiles[END_REF]. However, the conventional one addresses only the first three layers of the Interoperability Stack, covering the transport and communication layer protocols, addressing the content format of the messages and documents exchanged among the applications, and addressing the choreography of the activities to be executed by the participants [START_REF] Namli | Testing conformance and interoperability of eHealth applications[END_REF]. For the emergency management domain, however, organizational aspects, such as policies, procedures, operations and strategies are as important as technical aspects of interoperability. Absence of those can result in loss of time and resources, and potential wrong decision-making. The C2-SENSE Framework offers a solution in form of its software components and profiles. The defined and developed profiles in C2-SENSE are addressing additional layers of the Interoperability Stack defined in [START_REF] Tolk | Introducing a Reference Model for Measures of Merit for Coalition Interoperability[END_REF], thus covering these lacking aspects. The solution is flexible enough to be applied for the Emergency Management domain, or to be adjusted for any other domain that incorporates sensor and sensor network data, data exchanges, crisis mapping, and collaboration in multi-user environments. One such domain is Maritime Surveillance where illegal activities such as smuggling or piracy, or maritime vessel traffic management and protection are of critical importance for law enforcement. Implementing the identified profiles in such scenarios can serve for their improvement [START_REF] Gençtürk | Interoperability Profiles for Disaster Managmenet and Maritime Surveillance[END_REF].

This implies potential for introducing the Framework in domains dealing with environmental challenges, being that of real-time monitoring, or mitigating. Since the world today awaits more disasters related to climate changes (e.g., floods, droughts, landslides, air pollution), it is important to provide necessary tools for not only treating the crises when they occur, but also for monitoring the events leading to a disastrous situations. Moreover, such solutions need to have the strength of being easily implementable, adjustable to all the needs of different end-users (e.g., crisis responder, police, volunteer [START_REF] Whittaker | A review of informal volunteerism in emergencies and disasters: Definition, opportunities and challenges[END_REF]), adjustable to new research and technological approaches (e.g., citizen science [14], crowdtasking [START_REF] Middelhoff | Crowdsourcing and crowdtasking in crisis management: Lessons learned from a field experiment simulating a flooding in the city of the Hague[END_REF]), and potentially globally affordable and available. It is expected that the profiling approach of the C2-SENSE Framework, with its provided software components in combination with the set of rules orchestrating the functionalities of those components and rules for achieving interoperability, is a solution fulfilling those needs.

2 .

 2 Identifying emergency characteristics/attributes/ hallmarks. 3. Identifying the critical emergency areas. 4. Identifying location and information related to emergency resources. 5. Identifying location and information related to rescue targets. 6. Comparing different information related to maps (e.g., elevation) or data (water levels at different locations). 7. Communicating the findings to other C2-SENSE and local parties.

Fig. 4 .

 4 Fig. 4. Interoperability Stack for Emergency Management Situation Analysis profile: This profile describes how a user or an organization is provided with a possibility to simulate the situation on the ground for a given set of data. The profile describes how to get the data, maps, mash-ups and simulation results. The objectives of implementing this profile are: 1. Acquiring an overview of the possible situations based on the current situation (data) in the emergency area. 2. Identifying the potentially critical emergency areas. 3. Identifying the potentially critical events. 4. Communicating the findings to other parties.This profile supports a process of providing users with a common operational picture with maps and/or data. The intended scope for this profile includes: pre-emergency, during and after emergency situations on-site coordination centres, local organizations, national organizations and non-governmental organizations The activities specified in this

http://www.opengeospatial.org/ogc/glossary/i

C2-SENSE: Interoperability Profiles for Command/Control Systems and Sensor Systems in Emergency Management

https://kafka.apache.org/

BPMN, Business Process Model and Notation

Acknowledgement. The research leading to this paper has been performed in the scope of the C2-SENSE project. C2-SENSE has received funding from the European Community's Seven Framework Programme (FP7/2007-2013) under grant agreement number 607729.


Nigel W T Quinn
email: nwquinn@lbl.gov

PhD, P.E D F Wre

John Asce

Cronin

Use of the Hydro-Salinity, Crop Production Optimization Model APSIDE to Validate Results from an Updated Regional Flow Model of the San Joaquin River Basin

Keywords: Salinity, Crop production, Optimization, Drainage, Irrigation

 which includes the San Joaquin Basin. The APSIDE model was updated using recent aquifer and climate data and provided common initial conditions to allow a 53 year comparative simulation of the models. Model outputs for individual water districts for parameters such as deep percolation and upflux in APSIDE were compared to identical drained subareas within the CVHM model, The comparison showed that the APSIDE model produced lower values of deep percolation and upflux than CVHM. CVHM's deep percolation values were 18 % higher in Panoche WD, 40 % higher in Broadview WD, 68 % higher in San Luis WD, and 46 % higher in Pacheco WD. Unlike the CVHM model that assumes fixed levels of irrigation and drainage technology and static average water district irrigation efficiency APSIDE will substitute more cost effective irrigation and drainage technologies based on the calculated future benefit stream relative to the cost of production and impact of salinity on crop yields. An unpublished recent update to the current CVHM model (CVHM-2) which substitutes actual irrigation diversion records from delivery canals rather than usually-reliable Agency records -produced water district irrigation diversions that were approximately 50% of the previously provided diversion data. The new model produces water district aquifer recharge estimates that correlate closely with APSIDE model output. This study demonstrates the successful use of a complementary agricultural production optimization and hydro-salinity simulation model to help validate a radical and important update to a widely distributed and well-accepted regional flow groundwater model.

Introduction

In California, agriculture is the largest user of water, an important source of employment and income for many regions, and source of tax revenues for the state. Statewide irrigated agriculture revenues are approximately $22 billion per year (www.opr.ca.gov) supporting 300,000 to 450,000 jobs. The population of California is forecast to exceed 65 million by 2050, implying significant increases in urban water demand and use. Further complicating the picture is an increasing awareness of the importance of the Sacramento-San Joaquin Delta as a key ecosystem for native fish and the importance of water quality, particularly salinity, in developing long-term sustainability plans for the Region. The use of surface and groundwater simulation models that incorporate economics can be useful for policy analysis and to help balance competing uses for water supply and protecting water quality. An ideal integrated modeling framework incorporates the dynamics of the interaction between the environment, urban and agricultural stakeholders providing a decision analysis tool for evaluating the economic benefits and costs of water policies and policy-induced changes to the current system [START_REF] Howitt | Economic Modeling of Agriculture and Water in California using the Statewide Agricultural Production Model[END_REF]). This paper describes the use of a unique hydro-salinity and economic simulation tool APSIDE (Agricultural Production Salinity Irrigation Drainage Economics), capable of simulating agricultural production and land use in the salinity-impacted western San Joaquin Valley of California, to query the results from a widelyaccepted regional flow model of the Central Valley (Sacramento and San Joaquin Basins), developed by the US Geological Survey (USGS). This study also serves to validate preliminary results from a recent update to the USGS Central Valley Hydrologic Model (CVHM), based on raw diversion data obtained from the Water Authority charged with operating the water distribution system rather than the water agency traditionally charged with furnishing this data to the public. Hydrological models used for planning, studies around the world often achieve legacy status -the datasets used to calibrate and validate these models are rarely questioned, especially when the models are developed by science-based institutions such as the US Geological Survey in the USA. Although subsequent studies by other agencies, universities and consultants may produce anomalous results -such is the inertia of the model development and review process that these models become a de-facto "gold standard" against which other modeling efforts are compared. Some datasets such as crop coefficients used for computation of crop-based evapotranspiration are based on experiments that are decades old and crop cultivars that have since been replaced by more robust and drought-tolerant strains. Similarly, methods for flow estimation in canals and at diversion structures have been replaced by more advanced acoustic Doppler technologies that provide more accurate accounting. Greater use of complementary modeling and simulation tools can help to challenge the validity of certain datasets and assumptions made in these important legacy models leading to improved outcomes.

Background

Technological innovation does not typically succeed without being cost effectivehence the consideration of economics is paramount to guide basin-scale water quality management. The most obvious technology solutions involve: (a) increasing irrigation efficiency by re-using drain water to blend with good irrigation water; (b) growing salttolerant crops; (c) improve on-farm drainage management. One can also fallow or retire the land and sell the water, or utilize the assimilative capacity of the San Joaquin River in a coordinated fashion to discharge limited amounts of salt load without exceeding salinity objectives. A changing climate can have an effect on water availability as well as temperature, which in turn can have an effect on plant yields.. Understanding water availability and water quality, in particular, is critical to understanding long-term agricultural production in the San Joaquin Valley.

The west-side of the San Joaquin River Basin (SJRB) receives limited rainfall and relies on additional water supply from the Delta to meet the needs of irrigated agriculture. Salts are imported with irrigation water -changing water availability and water salinity can have a direct effect on yields. Applied irrigation water is subject to the processes of direct evaporation from the soil surface and transpiration from the crop. Pure water is evaporated, leaving behind the salts in irrigation water. Over time, these salts can accumulate in the soil and groundwater and can affect agricultural yields. Excess water can leach out salts in soil profile -however excess water is limited in the SJRB to effect this leaching. Schoups et al (2005) showed a steady cumulative increase in net salt in SJRB soils and groundwater -which has the potential to diminish irrigation sustainability.

To address salinization related problems on the west-side of the SJRB -drainage management measures have been suggested as follows:

• Reduction of deep percolation (the downward movement of water below the root zone, past drains to the local groundwater system) through the adoption of water conserving irrigation technologies and practices, better irrigation scheduling and changes in cropping practices. • Reuse of drain water, through the use of salt-tolerant crops and agro-forestry.

• Manipulation of the water table to meet part of the crop evapotranspiration requirements. • Conjunctive use of groundwater to meet a portion of crop needs.

• Improved instrumentation and monitoring systems to produce accurate and timely information and improve access to this information by growers. • Development and installation of real-time monitoring systems to progressively evaluate changes in soil and water quality in the terrestrial and aquatic ecosystems over time. High irrigation efficiencies may actually lead to higher concentrations of salt in deep percolation. Efficient irrigation methods are those with deep percolation of less than 10% of the irrigation applied water (Hanson et al., 2006). [START_REF] Doneen | Quality of percolating waters, I. properties of deep substrata materials in the west side of the San Joaquin Valley, California[END_REF] reported that in the SJRB the salinity of soils covered with native vegetation is generally lower than soils in irrigated areas. Irrigation water normally contains from 0.06 to 3.95 tons of salt per acre-ft of water (1 ton/acre-ft = 0.82 kg/m3) and crop requirements are between 2.03 to 3.05 acre-ft/acre (1 acre-ft/acre = 3048 m3/hectare) to fulfill evaporation requirements. Thus, this amount of irrigation water may add approximately from 0.12 to 1.29 tons of salts/acre (1 ton/acre = 2242 kg/ha) annually anywhere [START_REF] Rhoades | Reducing water quality degradation through minimized leaching management[END_REF]. Groundwater can become degraded by salinity through irrigated agricultural practices by three process 1) salt concentrated is due to the uptake of water by plant, 2) salt moves down from the unsaturated zone into groundwater (saturated zone) because of leaching and mixing of subsurface saline water with higher quality groundwater and 3) enhanced percolation of saline water into the lower zone as a result of groundwater pumping for irrigation. [START_REF] Suarez | Impact of agricultural practices on groundwater salinity[END_REF].

Modeling of Hydrosalinity in West-side Irrigated Agriculture

A state-of-the-art model known as the Central Valley Hydrologic Model, (CVHM) was developed by the US Geological Survey (USGS) and simulates the effects of hydraulic conductivity, irrigation, streamflow losses, wells, and other parameters on groundwater flow [START_REF] Faunt | Numerical model of the hydrologic landscape and Groundwater flow in California's Central Valley (Chapter C)[END_REF]). The CVHM model application uses the recently published FORTRAN hydrologic code, MODFLOW-OWHM which contains the Farm Management Process -a pre-processor that simulates agricultural irrigation hydrology. This model was created and calibrated using historic observations, calculations, and measurements of the factors affecting hydrology, including geological and meteorological data from April 1961 until December 2013. Canal diversions to individual water districts were obtained from the Central Valley Operations Office of the US Bureau of Reclamation which has been providing this data to the public for many decades as is considered the most reliable source of this information. The model divides the valley into one-mile (1.6 km) square cells which form a grid 98 cells wide and 441 cells long. The grid is also 10 layers deep in the vertical dimension, enabling the user to analyze subsurface water flow separately from surface water flow. Models such as CVHM have been used for long-term planning studies provided a suite of realistic future conditions can be developed including future hydrologic conditions, land use, agricultural production and regulatory constraints on agricultural production. However models such as CVHM assume static conditions for irrigation management and technology. Since detailed water district cropping data has typically only been publicly available in 5-7 year intervals (now available annually) models such as CVHM have typically also assumed static cropping mixes within each modeled subarea. More realistic tracking of agricultural production on the west-side of the SJRB requires a simulation tool that recognizes relationships between crop markets and costs of production, the impacts of investments in improved irrigation and drainage technologies on irrigation hydrology and soil salinity and the relationship between soil salinity and crop yield which can promote crop substitution over time to more salt tolerant crops and crop cultivars. The APSIDE (Agricultural Production-Salinity-Drainage-Econom-ics) Model (Figures 1 a and1b) fulfills this purpose allowing more realistic future irrigation hydrology projections to be made on the west-side of the SJRB. APSIDE was used in this study to develop comparative irrigation hydrology, drainage and aquifer recharge estimates over a 53 year simulation period.

APSIDE Model Features

Resource analysts face a fundamental difficulty in modeling regional production activities: simplifications used to keep a model manageable by reducing computation and data requirements also prevent it from reproducing the variety and proportions of activities actually observed [START_REF] Hatchett | A drainage economics model to evaluate policy options for management of selenium contaminated drainage[END_REF]. These simplifications also often omit important cost differences that influence production patterns. Flexibility constraints can force regional activities to approximate observed levels, but then these constraints prevent the model from adjusting very much to policy changes. APSIDE is an agricultural production optimization model, written in the GAMS language (GAMS 2008), that simulates hydrology and salinity (Figures 1a and1b) on the west-side of the SJRB. The model proved useful for understanding water resource utilization in the San Joaquin basin in response to potential future climate change and long term surface water allocations within the federal Central Valley Project service area [START_REF] Quinn | Model Integration for Assessing Future Hydroclimate Impacts on Water Resources, Agricultural Production and Environmental Quality in the San Joaquin Basin, California[END_REF]. The motivation for enhancing and updating the APSIDE model is to provide a decision tool that simulates long-term agricultural production taking into consideration the availability of groundwater resources, the cost of pumping and the impacts of salinity in groundwater pumpage. The genesis of the APSIDE model were two models -the Statewide Agricultural Production model (SWAP), which forecasts future agricultural production as a function of commodity price projections, anticipated changes in the costs of production and surface water supply availability and the Westside Agricultural Drainage Economics Model (WADE), which makes projections of future agricultural drainage and farm income resulting from policies that affect agricultural production and investments in irrigation and drainage technology [START_REF] Hatchett | A drainage economics model to evaluate policy options for management of selenium contaminated drainage[END_REF]. The agricultural production simulation algorithms utilize a technique known as Positive Mathematical Programming (PMP), which can reproduce observed activities quite precisely without a large increase in required data and without restricting the model's ability to shift activities as conditions change Howitt and Mean (1984).

The PMP technique relies on the concept of dual variables, or shadow prices, to infer otherwise unobserved cost differences among activities. A PMP model of regional crop production is implemented in two stages. The first, or calibration, stage is a traditional programming model which restricts crop acreage to observed levels. The dual values associated with the acreage constraints are the marginal changes in the objective (usually net revenue) function from small changes in the constraints. The dual values are positive when the constraints force a lower acreage of a particular crop than an unrestricted model would calculate (and negative when the constraints force a higher acreage). The second stage of PMP re-solves the first stage model after making two important changes. First, the crop acreage constraints are removed. Then the dual values from the calibration stage are used to calculate a linear marginal cost function for each crop activity. Integrating the marginal cost gives a total cost quadratic in crop acreages. The quadratic form is then appended to the objective function. The cost function intercept and slope values for each crop and region, obtained for the PMP algorithm during calibration of the APSIDE model are used to estimate proxy crop activity levels at the beginning of each year simulated by the model. The PMP algorithm will duplicate the crop mix from the restricted calibration model and will also allow smooth changes in crop levels as conditions or policies change.

Five proxy crops were considered in the APM ; alfalfa (including hay and seed crops, rice, irrigated pasture); trees, fruits and nuts (almonds, apples, apricots, olives, peaches, walnuts, pistachios, grapes, nectarines, oranges ); row crops (cotton, sugar-beets, processing tomatoes, corn, sorghum) ; grain crops (wheat, barley, oats.) and vegetable crops (beans, melons, lettuce, spinach, onions, garlic, broccoli, peas). These proxy crops are assigned average hydrologic characteristics of the group they represent. The PMP cost function were calculated separately for overspecialized and under-specialized crops grown in the study subareas. Overspecialized crops are those which are so profitable that greater quantities would be produced according to the model than would normally be observed. Hence an increment was added to the production costs of these crops to lower their activity to the observed marginal profitability levels. Conversely, incremental increases in revenue were added to the production of under-specialized crops in order to match the observed marginal values. The increments added are not fixed, but rather vary in quadratic fashion with crop acreage. For the SWAP and WADE models -the agricultural production submodels considered the summer (irrigation) season and the winter (rain and pre-irrigation) season of each year, interacting sequentially with the hydrology and salinity models. In the APSIDE model the sequential seasonal hydrology and salinity models were transformed into a discrete monthly timestep hydrosalinity submodel that solves simultaneously in order to capture more of the complexity of irrigation hydrology and water quality within the SJRB -where water supply to agriculture is subject to frequent perturbations in water quality. This has allowed the APSIDE model to be used in the past in integrated model-based planning studies of the Basin that consider future potential climate change [START_REF] Quinn | Model Integration for Assessing Future Hydroclimate Impacts on Water Resources, Agricultural Production and Environmental Quality in the San Joaquin Basin, California[END_REF]).

Comparison of Model Simulations

The APSIDE model was run for a period of 53 years for four water districts in the Grasslands Subarea on the west-side of the SJRB (Figure 3). The APSIDE model application used the same water table, cropping and irrigation water use efficiency initial conditions as the USGS CVHM [START_REF] Faunt | Numerical model of the hydrologic landscape and Groundwater flow in California's Central Valley (Chapter C)[END_REF] model. In the APSIDE model aquifer characteristic data such as aquifer hydraulic conductivity, specific yield and storativity was averaged (lumped) for each water district in the study -whereas CVHM can assign unique values for each one mile square model cell.

The APSIDE and CVHM model outputs for deep percolation and upward capilliary flow were compared. These outputs are the most important factors impacting water tables, tile drainage, soil salinity and crop yield. The comparison showed that, on average, APSIDE produced lower values of deep percolation and groundwater upward capilliary flow (upflux) than CVHM (Figure 4). On average, CVHM's deep percolation estimates were 26 % higher in Panoche WD, 12.9 % higher in Broadview WD, 45.3 % higher in San Luis WD, and 51.3 % higher in Pacheco WD (Figure 5). (Broadview gets no surface water deliveries having sold their federal water rights to an adjacent water district more than a decade ago). Upflux estimates are similarly higher for the CVHM model (Figure 6) -Pacheco WD shows the greatest difference between CVHM and APSIDE model estimates. Water deliveries (canal diversions) to each of these water districts were derived from publicly available US Bureau of Reclamation Central Valley Operations bulletins.

The APSIDE model bases water deliveries on water requirements which in turn are based on crop selection and irrigation management technology and practices. Hence differences in the APSIDE and CVHM model estimates can be ascribed either to the inability of the CVHM model to track changes in crop selection and irrigation water use efficiency resulting from changes in crop root zone salinity or to problems in the reported irrigation water deliveries (diversions) to each water district. One of the novel features of the APSIDE model is its ability to adapt irrigation and drainage technologies in response to production costs, the water saving potential of improved techniques and salt load discharge constraints that limit the export of salts to the San Joaquin River. As the cost of drainage disposal increases over time the APSIDE model substitutes more water conserving irrigation technologies such as sprinkler and drip irrigation for furrow and basin flooding techniques -improving irrigation water use efficiency over time. Using the same irrigation diversion data the APSIDE model still derived optimal yields and farm income by reducing irrigation application resulting in deep percolation rates that were as much as 50% lower than those produced by CVHM.

The deep percolation values in APSIDE and CVHM were further compared to reported data from Panoche and Pacheco Water Districts. Pacheco Water District reported year 2010 deep percolation values of 1770 acre-feet on 4080 acres of irrigated land (Pacheco Water District 2010). This corresponds to a deep percolation of 0.43 acre-feet per acre. This aligns fairly closely to APSIDE's predicted value of 0.42 acre-feet per acre for Pacheco Water District [START_REF] Westcot | Grassland Basin Irrigation and Drainage Study[END_REF]).

CVHM-2 Diversion Data Update

Difficulties

Summary and Conclusions

The time and effort involved in developing comprehensive regional surface and groundwater simulation models often confers a legacy status to these models whereby they become the "gold standard" against which subsequent modeling studies are compared. This is accompanied by a reluctance or "inertia" to revisit model assumptions or the data used to develop the model -even though these further studies might suggest fundamental problems. The use of complementary simulation models and analytical tools to test assumptions and the conceptual hydrology underlying these legacy models has significant, unrealized potential to promote better outcomes and result in more accurate decision tools. This study has provided an exemplar of the use of a simple agricultural production optimization and hydro-salinity simulation model that utilizes a unique normative calibration technique to allow realistic simulation of future changes in agricultural land use and investments in irrigation and drainage technology over long-term planning horizons. The PMP algorithm allows substitution of irrigation and drainage technologies while calibrating crop production shifts to field observationscapturing some of the socioeconomic factors known to effect on-farm decision making.

The APSIDE model can be applied to any problem involving irrigated crop production under saline conditions provided data are available for annual cropping and the costs of irrigation and drainage technology substitution are available. Soil and aquifer characteristic data including root zone and aquifer salinity data are also needed.

Figure 1a .

 1a Figure 1a. APSIDE conceptual model for flow showing parameters and variables listed in GAMS notation. Lateral flow between adjacent water districts are simulated as "equations of motion" or head-dependent fluxes between the centroids of each subarea.

Figure 1b .

 1b Figure 1b. APSIDE conceptual models for salinity showing parameters and variables listed in GAMS notation. Lateral movement of salt between adjacent water districts is driven by the hydrology and hydraulic flux between the centroids of each area.

Figure 3 .

 3 Figure 3. Water districts within the salinity impacted study area on the west-side of the SJRB, California. Results from Panoche, Pacheco, Broadview and San Luis Water Districts are the subject of the analysis in this study.

Figure 4 .

 4 Figure 4. Comparative results using APSIDE and CVHM models showing more realistic deep percolation estimates using APSIDE agricultural production optimization algorithms using PMP.

  in obtaining good model calibration for the west-side of the San Joaquin Valley in CVHM, particularly in the simulation of subsurface tile drainage, prompted the USGS to seek better quality data as part of the development of an updated simulation model of the Central Valley CVHM-2. The new model increased the number of vertical layers from 10 to 13, improved the accuracy of cropping data by incorporating data obtained directly from water districts and substituted public records from the US Bureau of Reclamation's Central Valley Operations office for raw canal turnout records Comparison of deep percolation estimates between CVHM and APSIDE models. Higher deep percolation rates raise water levels, increasing subsurface drainage discharge production and salt load export to the San Joaquin River. Water Authority responsible for daily operations along the Delta Mendota Canal -the main conveyance for federal water deliveries. These data had to be further processed to associate each irrigation turnout with a delivery point within each water district subarea. Preliminary (unpublished) model results shared by colleagues in the USGS have shown that average water diversions to the water districts that were the subject of this study decreased up almost 50%. Similarly the CVHM-2 model estimates of aquifer deep percolation were reduced by an equivalent amount. These same colleagues report that the CVHM-2 model achieved a better calibration for west-side San Joaquin Valley hydrology than the original CVHM model -much of it ascribed to the more realistic diversion data. These results correlate much more closely with the results from the APSIDE model.

	compiled by the
			Panoche WD Deep Percolation Comparison of model deep percolation estimates
	Feet Deep percolation acre-ft/acre -yr)	0 0.2 0.4 0.6 0.8 1 0.00 0.20 0.40 0.60 0.80 1.00 1.20	1990	1992 Pacheco 1994 CVHM 1996 Zonebudget 1998 Acre-Feet /Acre 2000 2002 2004 Broadview	2006 San Luis 2008	2010	2012	2014 Panoche
				Water Districts in Grasslands study area
				CVHM	APSIDE
	0.00 0.10 0.20 0.30 0.40 0.50 0.60 Figure 5. Figure 6. Comparison of groundwater upflux estimates between CVHM and APSIDE Pacheco Broadview San Luis Panoche Upflux (Are-ft/acre -yr) Water Districts in Grasslands study area Comparison of model upflux estimates CVHM APSIDE
	models -higher groundwater upflux increases precipitation of salt in the crop root zone.