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ABOT Action - Final Technical Report

1. Description

1.1. Name of beneficiary of grant contract: Research Institute for Geo-Hydrological Protection, National Research Council (IRPI-CNR)

1.2. Name and title of the Co-ordinator: Research Institute for Geo-Hydrological Protection, National Research Council (IRPI-CNR)

1.3. Name of partners in the grant agreement (if applicable):

Helmholtz – Zentrum für Umweltforschung GmbH – established in Germany

Seven Engineering Consultants – established in Greece

University of Architecture, Civil Engineering and Geodesy - established in Bulgaria

National Technical University of Athens - established in Greece

1.4. Title of the Action: “Assessment of water Balances and Optimisation based Target setting across EU River Basins (ABOT)”

1.5. Number of the Grant Agreement: No. 07.032900/2011/612888/SUB/D1

1.6. Start date and end date of the Grant Agreement: 21 December 2011 – 20 March 2013

2. Assessment of implementation of Action activities

2.1. Executive summary

The overall aim of the ABOT project is to support the European Commission’s effort to identify means and develop prevention activities to halt desertification in Europe, by focusing on complementing EU water resources balances elaborated in the framework of the System of Economic and Environmental Accounts for Water (SEEAW) and supplementing ongoing projects which tackle water scarcity, droughts and desertification. The specific objectives of the project are to:

Establish a clear overview of data availability for water balance modeling in 4 pilot River Basins across Europe: Tiber RB (Italy), Mulde RB (Germany), Ali-Efenti Pinios RB (Greece), Vit RB (Bulgaria)

Collect, process and analyze for the pilot River Basins the necessary datasets that are indispensable for the development of water accounts under the SEEAW system and feed them in the SEEAW-ECRINS framework.

Develop detailed water resources balances for the pilot River Basins based on the method applied by the SEEAW and using an analytical physical based model to accurately capture the interactions of the different components of the water cycle (common modelling approach using the WEAP software)

Identify management, technological and economic measures allowing the setting up of optimal water management in the pilot River Basins while involving local stakeholders and water managers.

Develop a library of “wish” measures that can improve the water balance and alleviate the possible deficit between availability and demand (i.e. increase supply, reduce demand), and test/simulate their impact and effectiveness against specific criteria, (e.g. water use reduction per economic activity, cost, environmental and socio-economic benefits) for the pilot River Basins.

Build and apply optimization algorithms in order to estimate possibilities for optimization of water allocation to meet demand in the selected pilot River Basins as well as the water saving potential associated to the different measures under specific

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context. Run an optimization process under specific criteria and constraints to select the optimum measures against a specific objective function.

Derive sector specific targets in the pilot River Basins regarding water saving and water efficiency which will allow the preservation and/or restoration of the natural water balance. Cross-compare these outputs with the purpose of proposing targets according to different typology of River Basins.

Run a sensitivity analysis for these proposed targets for 3 alternative futures (climate and socio-economic) in order to evaluate the robustness of the proposed interventions

Share and disseminate all results, key assumptions and uncertainties in a transparent manner with all relevant parties and involve local stakeholders in the process.

Post process the project results to provide necessary input to the Blueprint and the WS&D Policy Review.

These objectives are clearly linked to the current developments of DG Environment, DG Research, Joint Research Centre, European Environment Agency and others in relation to the 2012 Blueprint to Safeguard Europe's Waters’1, the 2007 Communication on Water Scarcity and Drought2, the development of EU water accounts under the SEEAW framework, the WFD objectives, and are complementing other ongoing projects and initiatives. Pillars of the 2007 Communication and the 2012 Blueprint are directly addressed within this project: assessment of the vulnerability of water resources to climate change and other man made pressures, indications for water efficiency targets, water allocation, fostering water saving measures, etc. The project objectives have been clearly achieved. The implementation process was built around 5 inter-related tasks which were feeding results one-to-the other: Task A is a management and methodology development task

Task A aimed at leading the project in scientific and technical success, within a robust organizational framework that supports collaboration and exchange. This task also aimed in supporting a good communication and feedback with the local RB authorities, DG Environment and other involved contractors of relevant ongoing studies. Among the first activities was drafting the conceptual methodology to be followed during the study, in a stepwise and analytical way, defining the modeling framework and design specifications, the data requirements, the scenarios to be analyzed, the optimization process, the targets to be derived, the constraints and uncertainties involved etc.

Tasks B is a data collection and analysis task Task B aimed at collecting all the necessary data to build water balances in the pilot

River Basins according to the SEEAW methodology. These data have been quality assured, processed and analyzed to achieve the required level of temporal and spatial disaggregation and were used to (a) build the water balances, (b) feed the ongoing SEEAW calculations under the ECRINS system.

Tasks C and D are modeling tasks Task C aimed at building a water management model for each pilot River Basins using

a harmonized approach and common software, the “Water Evaluation and Planning System” (WEAP). The model developed water balances as well as additional functionalities in order to run simulations and scenarios for optimal water allocation representing the physical system. Local stakeholders were involved in this process, while the model output also fed the SEEAW standard framework.

Task D aimed at identifying management, technological and economic measures (library of “wish” measures) and test/simulate their impact and effectiveness against specific criteria (e.g. water use reduction per economic activity, cost, environmental

1 http://ec.europa.eu/environment/water/blueprint/index_en.htm 2 COM(2007) 414 final of 18.07.2007

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and socio-economic benefits) for the pilot River Basins within the WEAP models developed in task C. Furthermore, within this task built and applied optimization algorithms in order to optimize water allocation and meet demand in the selected pilot River Basins while maximizing the selected objective function. The optimal measures were cross-compared among the 4 Pilot River Basins.

Task E is a policy related and dissemination task Task E aimed at deriving indicative targets for reducing the vulnerability of water

resources in the pilot River Basins. These sector specific targets focused on water saving and water efficiency will allow the preservation and/or restoration of the natural water balance. Their robustness was assessed against alternative future scenarios. The overall goal was to propose targets according to different typology of River Basins. The results were post-processed in order to provide input to the water policy process.

2.2. Activities and results

Please list all the activities in line with Annex 1 of the contract

Activity A1

Title of the activity: Inception report and detailed project strategy

Topics/activities covered: The inception report is meant to refine the description of work and specify in detail the activities, commitments and responsibilities of each partner within each task and work package, as well as close possible gaps in the overall implementation methodology.

Results of this activity: The  detailed  description  and  results  of Activity A1  are  presented  in  the Deliverable D.A1: Inception Report, which was sent to DG ENV in Month 3 (31/03/2012) The task was successfully completed with the production of a detailed Inception Report, specifying the activities commitments and responsibilities of each partner within each task, and providing a timetable. A layout of all the ABOT tasks and activities is presented in Figure A1. Prior to the release of the inception report two meetings took place: A kick-off meeting of the consortium in Athens, on 16-17/01/2012 to further discuss the details of the project activities, and a kick-off meeting with the Commission (Brussels, 16/02/2012), where a presentation of the DG service contract on water accounts (ENV.D.1/SER/2011/0036) was also held. DG ENV needs as communicated during this meeting were picked-up by the consortium and are reflected in the Inception Report. The lead partner IRPI-CNR has been responsible for this activity, while all partners have significantly contributed. NTUA and SEVEN facilitated linking with other ongoing projects (ETC/ICM and the SEEAW work, i-adapt, ClimWatAdapt, WSD Gap Analysis).

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 Figure A1: Overall structure of the project, tasks and activities 

Activity A2

Title of the activity: Administrative coordination

Topics/activities covered: This activity aimed at ensuring sound legal, contractual and administrative management of the project, in compliance with the contractual obligations, good management practices and the provisions of the Grant Agreement. Results of this activity: The detailed description and results of Activity A2 are presented  in the Deliverables D.A2.1‐D.A2.5: Quarterly  Progress  Reports  and  Final  Report  (which were  periodically  sent  to DG ENV) and D.A2.6: Project Website (available online: www.abot.it ) The results and milestones of this activity include: 1) representation of the consortium in contact with the DG ENV project officer(s) and various other scientific and policy bodies; 2) coordination of the knowledge management issues; 3) oversight of ethical and gender aspects within the project; 4) coordination of the research and policy related activities and monitoring the fulfilment of the project's objectives and deliverables; 5) coordination of the project meetings; 6) supervision of the involvement of the stakeholders; 7) Reporting to the Commission: Quarterly interim/midterm technical implementation reports to update the Commission on the progress of the technical and financial activities, Final technical implementation report and financial statement, including a consolidated statement and a breakdown between each beneficiary the latest one month after the end of the grant agreement; 8) Dissemination of results, including the development of a dedicated website (www.aboit.it) launched early in the project and frequently updated.

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The website (Figure A2) will be active for at least 2 more years after the project completion, serving as an important communication and dissemination tool. The website is also linked to other webpages of the partners in order to increase the project’s visibility.The website contains the following sections: - “Project”: provides general information about the project, its scope and specific objectives. Important news and events will also appear in this section.

- “Context”: provides information about the characteristics and main issues confronted in the 4 Pilot River Basins

- “Approach”: provides an overview of the tasks and activities to be implemented

- “Partners”: provides an overview of the partners of the consortium

- “Downloads”: provides material available for downloading, such as leaflets, reports, presentations, for public dissemination   

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 Figure A2:  The ABOT project website (current view) 

Activity A3

Title of the activity: Organization of the Stakeholder Forum and meetings

Topics/activities covered:

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This activity aimed to: (1) engage local stakeholder, (2) set up an Advisory Group, (3) plan and properly coordinate the necessary meetings. Results of this activity: Detailed  information on  the  results of Activity A3  is presented  in  the agendas and  internal minutes of the various meetings (D.A3) distributed among the consortium partners. A group of stakeholders has already been identified for each pilot river basin in the Inception phase. One of the goals of the project is to grasp the proposed response measures to reality by seeking the advice and input of the expert local rather than arbitrarily selecting random solutions. During the project the partners were in close communication with the local stakeholders who facilitated them in a) collecting the necessary data; b) providing technical advise on modelling aspects and feedback on whether the models seem to capture the hydrological reality and the water balance conditions in the pilot basins; c) providing input on the water allocation priorities in the area; d) helping in the selection of response measures to be applied by providing input on the costs and benefits, and the feasibility of implementation/penetration of these solutions in the area; e) providing input on the future scenarios; e) assessing the feasibility of the optimization outputs regarding the indicative targets. This close collaboration has been beneficial on both ends: the consortium managed to better tailor the activities and the “problem to be solved” to the real needs of the area and select the potential measures to be applied on a “realistic” basis, cross-checked with the stakeholders. The work was in turn beneficial to the stakeholders as well, as they now have in their hands operational decision making tools to use them for optimal water allocation, balancing the demand with the availability or other management issues. An Advisory Group consisting of international experts in areas of interest to the project have been consulted during the project duration on specific issues (e.g. modelling issues, selection of measures aligned with the Policy-Blueprint priorities, estimation of costs, etc.). The project meetings (general partners’ meetings, bilateral meetings, coordination meetings with the DG ENV and other beneficiaries of the call, etc.) have been frequent and beneficial. Along the course of the project the following meeting were organised:

‐ 3 Coordination meetings with DG ENV and the beneficiaries of the Desertification 2011 call

‐ 4 General partners’ meeting (including the kick-off and the final) ‐ 2 bilateral work meetings among the project partners to address specific issues (e.g.

modelling, managerial, etc.) ‐ 8 bilateral meetings with stakeholders with the purpose to collect data, seek expert

advice on various project aspects (as described previously), present the measures to be tested etc.

‐ Multiple SKYPE meeting among the partners, or between partners-stakeholders and/or advisory group participants.

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Activity B1 and B2

Title of the activities: Data collection and analysis

Processing data and feeding SEEAW-ECRINS

Topics/activities covered: The detailed description and results of Activity B3 are presented  in the Deliverables D.B2.1: Database  for  each pilot RB  in  the  proper  format  to directly  feed  ECRINS‐SEEAW  including metadata and D.B2.2: Report on  the methodologies used  to analyze and process  the data, including  methodologies  for  gap  filling,  disaggregation  techniques,  proxy  calculations  as applicable which are attached as Annexes to this report.  Activities B1 and B2 (under Task B) are close interrelated and aimed at collecting all the necessary data to build water balances in the pilot River Basins (Tiber, Mulde, Ali-Efenti Pinios, Vit) according to the SEEAW methodology, but also additional data that may be required to run the water allocation optimization and set targets (e.g. data on the theoretical effectiveness of response measure) as proposed in the following Tasks C-E. The data have been quality assured, processed and analyzed to achieve the required level of temporal and spatial disaggregation and were used to (a) build the water balances, (b) feed the ongoing SEEAW calculations under the ECRINS system. Results of this activity: As a fits step a thorough data screening has been undertaken to identify and collect data, in close communication also with the stakeholders, including:

Hydrometeorology (rainfall, evapotranspiration, discharge, storage, groundwater level etc.)

Water abstraction and water use and consumption (per source and economic sector according to NACE classification)

Water returns (per sector and recipient) Land uses Soil and geology Economic information related to the optimization (e.g. operational costs, production

yields, water prices etc.) Additional information (on impacts, response measures cost/benefits, economic-

policy instruments etc.)  The data have been analyzed following common specifications across the 4 pilot RBs so that the underlying assumptions and constraints are harmonized and allow the comparison of the results. In case of data gaps proxy calculations have been applied, and the methodology will be commonly agreed across the pilots. Following the assessment and the analysis of the available data in the activity B1, the best approach to use these data within the modeling framework of the SEEAW-ECRINS system developed by the EEA (under the Nopolu) and the DG ENV Contractor has been investigated. For this purpose, a Database in the format requested by the EEA has been compiled for each RB and sent to the EEA in Month 4. A layer of streamflow gauging stations for each pilot river basin has been produced for snapping in ECRINS and daily streamflow data have been processed as priority. Data on abstraction have been disaggregated to the minimum possible scale so that they can be re-processed in ECRINS as fit while maintaining statistical representativity. In case of data gaps coherent methodologies for filling and using proxy calculations have been developed across the pilots, as sited previously. The streamflow data for the four pilot case study basins show large differences with regard to availability and temporal resolution. Daily streamflow data are available for the Tiber river basin in Italy (for 7 stations in the period between 2000 and 2011), the Mulde river basin in Germany (for 7 stations in the period between 1994 and 2009). For the Ali-Efenti Pinios river

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basin in Greece monthly streamflow data are available (for 6 stations in the period between 1973 and 1995). For the Vit river basin in Bulgaria, streamflow data from the most downstream station (Tarnene) are available in a monthly time step for the period between 2000 and 2009 and for 2011. All data are provided in the format indicated by the EEA for processing in the SEEAW-ECRINS. The areas of the gauged sub-basins of the considered part of the Tiber river basin vary between 933 and 5,279 km². The gauged sub-basins of the Pinios river basin show areas between 137 and 2,789 km2. For the Mulde river basin, the areas of the gauged sub-basins vary between 586 and 6,170 km². The area for the gauged Vit river basin is 2,236 km2. The abstraction data for the four pilot case study basins show large differences with regard to availability and degree of detail. Mostly, it was not possible to get the data in the required detail (and thus format) of SEEAW-ECRINS. Reasons for this are data protection laws and limited data inventory.

For the Tiber river basin, abstraction data from reservoirs (mostly for irrigation) are available, mostly for periods of different lengths between 1998 and 2011. Monthly abstraction data from wells and springs are provided for the period between 2008 and 2011.

For the Mulde river basin in Germany, the provided data are based on three public water abstraction reports and one non-public water abstraction report of the Saxonian State Agency for Statistics (STALA). Data for the public sector are available as yearly values for the time steps 2001, 2004, and 2007. Data for the non-public/private sector are available in public reports as yearly values for the time step 2007. The data itself is divided into water abstraction, water distribution/use, waste water treatment, and sewage disposal. It is available on the level of second order administration (while 1st order would be the smallest unit and the federal state the fourth order), catchment scale and by economical branches (for the non-public sector). In this report only the catchment scale as the most significant one is presented. Further data might be found in the related SEEAW tables. The STALA inquires the provided abstraction data for the public sector in a three-year cycle.

For the Ali-Efenti Pinios river basin the available data are at annual scale, and for selected years (case of scare data, limited availability) within the period 1980-2001. The data are provided at the municipality level for all uses (domestic, agriculture, industry, livestock). The data have been correlated to census data and thus values for the missing years have been filled by interpolarion/regression.

For the Vit river basin annual data on the quantity of the supplied potable water, for each agglomeration within the catchment, for the period 2000-2009 are available, provided by the Regional Water Companies (RWC). The same set of data on monthly base was provided for year 2009. In addition, the RWC provided annual data on accounted water consumption of different users (households, business units) within agglomerations for the period 2000-2009 and corresponding monthly data for year 2009. Similar set of data was requested additionally for year 2011 from RWC Pleven for validation of the water balance model. Inflow, storage volume and abstraction data for the most significant reservoirs in the Vit river basin were provided on monthly base for the period 2009-2011. Data on the purpose of water use (e.g industrial supply, environmental outflow, water power plants etc.) were also provided at monthly base, for each reservoir, for the period 2009-2011. For some non-significant reservoirs the data set is not completed for year 2011. Data on the type of the groundwater bodies within the Vit catchment, information about the permitted abstraction of groundwater, as well as information about the permitted abstraction of surface water were provided by the Danube River Basin Directorate (DRBD). Different methods have been used to analyse and process the data, including methodologies for gap filling, disaggregation techniques, proxy calculations.

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In the Mulde RB The used time series show no gaps, so no methods for gap filling were used. Water use and consumption data were aggregated from county level to (sub)catchment level.

In the Tiber RB the missing data periods in hydrometric time series are identified by -9999 value. Different methodologies for gap filling are used depending on the missing data period length: a) Missing data period shorter than 2 hours: a linear interpolation is carried out; b) Missing data period longer than 2 hours: a linear interpolation is considered for periods without flood events occurrence. Otherwise, the correction procedure is based on the comparison between the dimensionless stage hydrograph recorded by the hydrometric station of interest and the ones observed by neighboring hydrometric stations located at some distance away, if available. When the trend of the compared dimensionless stage hydrographs is found quite similar, the missing data period of the hydrometer of interest can be filled through the records of the neighboring gauged sites. The streamflow time series are obtained by applying accurate stage-discharge relationships to the recorded stage time series. Daily discharge data of well fields and springs have been downloaded from the Arpa Umbria website http://www.arpa.umbria.it (last access April 2012). These data have been aggregated at monthly time scale (Mm3/month). No correction procedures have been applied and missing data period have been not filled and are identified by -999 value. Irrigation districts managers have provided only seasonal supply data (irrigation period: June-September). Since monthly data of the supplied volumes are not available, we carried out a breakdown of the seasonal data to a monthly time scale. The disaggregation process takes into account: the irrigated areas of each crop type (A1, A2, …, An) and the monthly crop coefficients (Kc1, Kc2, …, Kcn). The monthly crop coefficients have been determined as the mean of the daily Kc values, following the FAO Paper 56 Tables 11 and 12 (for L and Kc values respectively). During the 4 months of the irrigation period, the supplied monthly volumes (V1, V2, V3, V4) were obtained as a percentage of the total volume (Vtot) using adequate formulas

In the Ali-Efenti-Pinios RB the streamflow data had no gaps. Regarding the water abstraction data which were available at annual scale, and for selected years (case of scare data, limited availability) within the period 1980-2001 gap filling was performed. The data are provided at the municipality level for all uses (domestic, agriculture, industry, livestock). The data have been correlated to census data and thus values for the missing years have been filled by interpolation/regression. To dissagregate the annual to monthly values, seasonal correction factors have been applied based on the irrigation demands of the crops (in this process the irrigated areas of each crop type and the monthly crop coefficient Kc have been used).

In the Vit RB the used time series for hydrometric and abstraction data show no gaps, so no methods for gap filling were used. The collected data concerning the water abstraction are directly used, i.e they are neither aggregated nor disaggregated. The water for irrigation that is abstracted from the reservoirs is calculated on the basis of the data for the irrigated land and the potential evapotranspiration of the crops; the irrigation efficiency and the losses of the irrigation system.

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Activity C1

Title of the activity: Development of water balances

Topics/activities covered: The purpose of this task was to build a robust water management model for each pilot River Basin using a harmonized approach and common software, the “Water Evaluation and Planning System” (WEAP) across the ABOT pilot Riber Basins, namely: Tiber (Italy), Mulde (Germany) Ali-Efenti Pinios (Greece), Vit (Bulgaria). The models produced water balances as well as additional functionalities in order to run simulations and scenarios for optimal water allocation representing the physical system with the optimal goal of setting targets (to feed Task D, E in the next sessions). Local stakeholders has been involved in this process, while the model outputs were used to feed the water accounts under the SEEAW standard framework and the SEEAW standard tables on physical supply and use. Results of this activity: The detailed description and results of Activity C1 are presented in the Deliverables D.C1.1 ‐ Analytical water  balance model  for  each  pilot  RBs  accurately  set‐up  and  calibrated  AND D.C1.2 ‐ Policy Brief on the water balances in the pilot RBs which are attached as Annexes to this report. The modeling platform proposed for developing detailed water balances is the Water Evaluation and Panning (WEAP) system (http://www.weap21.org/), developed by the Stockholm Environment Institute's U.S. Centre. WEAP is a generic, integrated water resource planning software tool that provides a comprehensive, flexible and user-friendly framework for planning and policy analysis, which was proved to be suited to support the proposed project. The following steps have been implemented across all the 4 pilots RBS:

Detailed water balances have been developed using physical based hydrological models in the pilot RBs and following the methodology applied by the SEEAW at monthly scale and catchment level, using the data collected in Task B and the WEAP softwater

The detailed water balance models represent the salient feature of the river basins in their actual spatial orientation. These applications include all major surface water and groundwater resources, distributed representations of demand, return, losses, transfers etc. and objects to represent the major water management infrastructure (reservoirs, WWTPs, desalination plants, etc.).

The problem of water scarcity has been mapped as an imbalance between demand and availability (i.e. unmet demand) in the sub-catchments where it is relevant and per economic sector

The accurateness of the water balance has been subject to local stakeholders’ and water managers’ expert knowledge, and presented/discussed with them.

The results of the models have been used to feed the standard SEEAW tables (asset accounts, physical supply and use accounts) in the next activity C2m, and to also produce a relevant Policy Brief.

All the pilots have followed the same modelling approach, using common features on the WEAP software. The detailed models schemas for each River Basin are presented in Figures C1-C4.

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Figure  C1:  Tiber  River  Basin:  WEAP  scheme  showing  water  consumers  and  their interconnections 

Figure C2: WEAP scheme of the Mulde River Basin model 

Figure C3: Schematic representation of the WEAP model for the Ali‐Efenti basin 

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Figure C3: Schematic representation of the WEAP model for the Vit basin 

Tiber River Basin: The WEAP model was applied for simulating the Tiber River basin where, due to natural water shortage, the river is not the only water supply, Water supply is also provided by the Montedoglio, Casanuova, and the small Arezzo (or reservoirs (not yet in operation) reservoirs and with additional very small reservoirs used for agricultural purposes, the groundwater, springs and creeks. Most of the users are urban municipalities and agriculture districts, while few industries are present. The water demands sites in the study area are represented in WEAP by seventeen irrigation schemes implemented as “catchments”, 24 urban nodes and 3 industrial nodes simulated as “demand sites”. 2008 is selected as the ‘Current Accounts’ year or base year, whereas the entire project period is set to 2008-2011. Based on observed data, these four years are considered as representative. In particular, 2008 data are used for the model calibration, and 2009-2011 data for model validation purposes. The calibration phase is based on the comparison, on the one hand, between the streamflow calculated by the model and the one observed at the gauged stations located along the main streams and, on the other hand, between the simulated stored volumes in the main reservoirs and the observed ones. The modeling time step interval in WEAP is monthly and, analogously, the simulated results. The monthly and annual unmet demand for the four years of simulation are presented in Figure C5 

 Figure C5: Monthly (left) and annual (right) variations of the unmet demand 

As a conclusion, The WEAP model was successfully applied for a typical Italian watershed as Tiber River basin with a complex scheme of natural water resources and different type of water consumers. The model shows its flexibility and applicability even for a watershed with limited available information, nevertheless it could be further tuned when more detailed information is collected by the responsible stakeholders. The calibration and validation results

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(monthly values) are in good accordance with the observed data demonstrating the confidentiality for the model application for analyzing future scenarios.

Mulde River Basin: The WEAP model for the Mulde River Basin was spatially divided in the three main catchments “Zwickauer Mulde”, “Freiberger Mulde”, and “Vereinigte Mulde”. Additionally, the hydrology was modeled on the basis of reservoir catchment areas for all reservoirs within the whole basin. That means that each reservoir will get its own catchment (excluding the catchments of upstream reservoirs) and a corresponding parameterization. The reason for this decision was the water supply situation. As about 65% of the water use is supplied by reservoirs, and the rest is mainly taken from groundwater, we figured those as most important points in our water balance model. In order to generate adequate reservoir water levels it is important to model catchments hydrodynamics and hydrology as best as possible what is most appropriate by using the soil moisture method. The differentiation of the demand network had to be done on a different data base and led therefore to a different allocation. Water use data was available for administrative units in 15 regions. Based on that data two sectors were applied for each region: The public sector which accounts for households and minor trading activities as well as offices and the private sector which is diverted into industries and some agriculture. The fact that water is supplied from 18 “collectives” while the regions are officially in charge of the supplying made the data processing partially very difficult. The situation gets even more difficult considering the sources as the water is taken from 15 reservoirs, groundwater, the river (and the suppliers are mostly interconnected). The model was calibrated for the period of 2000 - 2004 in which main dry, wet and average years occurred. Three stream gauges were used as calibration reference (one at the outlet of each main catchment (Zwickauer, Freiberger and Vereinigte Mulde)). The WEAP model was setup and calibrated on a monthly time scale. The aim of the calibration was the best fitting of the modeled flow to the observed flow. This was done by adjusting corresponding parameters within a physical meaningful range. It turned out that the groundwater parameterization was very sensitive (in addition to the snow and surface flow related parameters). The model was also very sensitive to the runoff resistant factor which is used to control surface runoff response. The model validation was done for the period of 2005 and 2006. The results are satisfactory providing a coefficient of determination (r²) of 0.66 in contrast to r²=0.78 in the calibration period. The results showed that there is no unmet demand in the Mulde, i.e. the basin is well managed. In conclusion, it was possible to setup a WEAP model for the Mulde river basin in accordance with the project aims, the data availability and specific limitations. The model shows satisfying calibration and validation results although a conflict occurred between the model parameterization (snow melt and accumulation) and the monthly time scale. The quality of results was also affected by the large number of reservoirs in the catchment (and the lack of data of their management) which are influencing the stream flows. Therefore it would be most interesting to access data on reservoir management for further detailed modeling. Nevertheless the model has been proven to be valid for further scenarios application and water management analysis. Ali-Efenti Pinios River Basin: A detailed water balance model has been set up for the Ali-Efenti River Basin in northeastern Pinios using the WEAP software at monthly timestep for the period 1980-2010. The 1980-1994 period has been used for model calibration and validation, while the 1995-2010 period represents the baseline scenario. In order to set up the node-based dissagregated WEAP model, a detailed analysis of the study areas has been implemented to post-process all the data collected in Task B and create the necessary input data for the model. The model comprises of 23 sub-catchments, 8 groundwater bodies, 6 springs, 46 runoff/infiltration links (carrying runoff and infiltration from catchments to rivers and groundwater bodies), 57 demand sites

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(50 for domestic, irrigation, livestock and industrial water users, and another 7 “dummy” nodes), 6 WWTPs, 139 transmission links (transmitting water from a surface or groundwater withdrawal node to a user), 70 return flow links (directing the water that is not consumed in a demand side to a WWTP, surface or groundwater body). The model has been calibrated for the period 1980-1992, using observed streamflow data at 6 gauging stations (period of calibration varies among the stations).The purpose of the calibration was to achieve a better representation of the catchment physical processes. The selected parameters to be calibrated are the “% of effective precipitation”, the “infiltration fraction” per catchment, and the “groundwater outflow” from the river bed to the groundwater. The exact values of these parameters present some uncertainty in the model due to the simplified RR model used within the WEAP which lacks snow accumulation and snowmelt routines, and the presence of karstic aquifers in the basin and associated lag-time in their discharge through the springs. The objective function to maximise was selected to include three goodness-of-fit metrics, namely the efficiency EFF (Nash-Sutcliffe), the correlation factor r and the BIAS. To further assess the calibration results the model has been validated for the period 1988-1994, using observed streamflow data at the 6 gauging stations (period of verification varies among the stations). A detailed water balance model has been developed for the Ali-Efenti basin in Pinios, allowing the representation of the components of the hydrological cycle and catchment process along with the water demand and use aspects in the catchment. All model features have been calculated at monthly timestep, for each of the 23 sub-catchments and 50 demand sites, allowing the identification of opening and closing stock, and exchange in flows. Figures C6-C8 present the inflows and outflows per sub-catchment for the dry year 2007, the normal year 1997 and the wet year 2010.

  Figure C6: Land Class Inflows and Outflows (mio m3) for the dry year 2007 in the sub‐catchments of the Ali‐Efenti basin 

 Figure C7: Land Class Inflows and Outflows (mio m3) for the normal year 1997 in the 23 sub‐catchments of the Ali‐Efenti basin 

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 Figure C8: Land Class Inflows and Outflows (mio m3) for the wet year 2010 in the 23 sub‐catchments of the Ali‐Efenti basin 

Based on the model results the balance between demand and availability is negative, resulting in unmet demand in all the 23 subcatchments every year, mainly for irrigation purposes. Unmet demand for industrial and livestock activities has also experienced during the years 2004-2008 but at a much lower level than in irrigation. The total annual unmet demand in the Ali-Efenti Basin ranges from as low as 5 mio m3 to as high as 114 mio m3, with an average value of 33 mio m3 over the 16-year period. This unmet demand is mainly attributed to irrigation, yet the industry and livestock sectors are also affected during some years (Figures C9-11).

 Figure C9: Unmet demand (mio m3) for irrigation per year (form 1995‐2010) in the 23 sub‐catchments of the Ali‐Efenti basin 

 Figure C10: Unmet demand (mio m3) for industry per year (form 1995‐2010) in the 23 sub‐catchments of the Ali‐Efenti basin  

 Figure C11: Unmet demand (mio m3) for livestock per year (form 1995‐2010) in the 23 sub‐catchments of the Ali‐Efenti basin 

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The Reliability (%) of the system in supplying the requested demand (i.e the percent of the timesteps in which a demand site’s demand was fully satisfied) ranges among the uses. As domestic use is priority 1, the water allocation to this use has a reliability of 100%. Reliability in the provision of water to the livestock sector is a bit lower around 98% and for the industry around 95%. Yet, the reliability in irrigation water supply highly varies and is some cases as low as 70%. Regarding the application of the WEAP model to derive water balances in the Ali-Efenti basin we can conclude: The WEAP model is successfully applied for a water stressed catchment as the Ali-Efenti River basin with a complex scheme of hydrological features, different type of water users, and many connections from multiple sources to users to return nodes. The WEAP model can be successfully used for the production on detailed water balances at monthly level, capturing all the salient features of the hydrological cycle and water demand/use aspects. It can easily support the production of Water Accounts under the SEEAW methodology, where many of the requested parameters in the SEEAW table cannot simply be obtained as products of reporting but require the set-up and output of a detailed water management model. The WEAP model shows its flexibility and applicability even for a watershed with limitation in the available information. Further tuning and updating of the model could is feasible and easy should more detailed information be available by the responsible stakeholders. The visualization tools of WEAP can facilitate the production of relevant graphs and the different selection options allows for an easy export of the desired outputs. The scenario building functions can facilitate the evaluation of alternative options and scenarios (to be further investigated in Task E). The model is robust and the API (analytical programming interface) of WEAP allows linking with external models and programmes, such as MATLAB, in order to enhance some of the model functions, e.g. run calibration with more powerful algorithms. Vit River Basin: The WEAP software was applied for modeling of the complicated situation of the Vit watershed, where, due to natural water shortage, the river is not the only water source, but it is combined with underground waters, creeks and transferred waters from neighboring basins. In addition to this, Vit watershed has variety of water consumers irregularly spread over the basin territory, for which the quality and frequency of the monitoring information differs among different management institutions. The developed scheme comprises of 77 settlements (design nodes). The water supply and sewerage systems of 47 of them are managed by Pleven water operator. The systems of the rest 30 are managed by Lovech water operator. According to the number of their citizens the settlements are classified as: one big town with more than 100 000 citizens - Pleven, one relatively big town between 10 000 and 100 000 - Teteven, eleven settlements between 2 and 10 000 and 64 villages with less than 2000 citizens. Five big reservoirs with economic significance are selected - Sopot, Telish, Gorni Dabnik, Dolni Dabnik and Krushovitsa. On the territory there are 3 acting hydroelectric power stations or power houses - Rakita, Telish and Gorni Dabnik. Two bid complex industrial water consumers are included in the modeling scheme named as industry Pleven and industry Dolna Mitropolia, which are the two settlements with active industrial activity. Additionally 12 design nodes simulating pumping stations, controllers of inflows and outflows of the reservoirs are included. Regarding the water consumption for irrigation, the required collected information consists of areas, crops and the type of irrigation.

The model was calibrated for year 2009, which is a year with relatively average annual flow and validated for year 2011, which is a relatively dry one. The lack of detailed information for different water consumers as settlements, reservoirs, irrigation etc., limited the period of the simulations. The modeling time step interval in WEAP is a month and respectively, the simulated results are monthly values. The goal of the calibration is to adjust the model so that the simulated flow resembles the observed flow data as closely as possible – procedure requiring adjustment of various parameters within WEAP model reflecting the real hydrological, climatic, water demand and consumption or anthropogenic conditions in the

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studied catchment area of Vit basin. Based on the model results the monthly unmet demand for the years 2009 and 2011 is presented in Figures C12-13. For the year 2009 the monthly unmet demand ranges from 164 (in January) to 221 m3/month (in November). For the year 2011 the monthly unmet demand ranges from 76 (in August) to 677 m3/month (in December).

Pleven

Unmet DemandScenario: Reference, All months (12)

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 Figure C12: Monthly values of the unmet demand for Iskar for the year 2009 

Pleven

Unmet DemandScenario: Reference, All months (12)

Jan2011

Feb2011

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Figure C13: Monthly values of the unmet demand for Iskar for the year 2011 

In conclusion, the WEAP model is successfully applied for a typical Bulgarian watershed as Vit basin with complicated scheme of natural water sources and different type of water consumers. The WEAP model showed its flexibility and applicability even for a watershed with limited available information, and can be further tuned when more detailed information is collected by the responsible stakeholders.

The outputs of the activity C1 are detailed water balances at monthly level, capturing all the salient features of the hydrological cycle and water demand/use aspects in each pilot RB which are the corner stone for the development of all the following activities of the project. As a global conclusion the WEAP model can easily support the production of Water Accounts under the SEEAW methodology, where many of the requested parameters in the SEEAW table cannot simply be obtained as products of reporting but require the set-up and output of a detailed water management model (to be further investigated in Task C2). The scenario building functions can facilitate the evaluation of alternative options and scenarios (to be further investigated in Task E). A Policy Brief has also been produced as a result of the Activity C1. The main conclusions of the brief indicate that: The use of the WEAP modeling approach allows estimation of water use efficiency not only for current or past periods, but also for future forecasts. It is advisable to apply the models at smaller units like river basin, because of achieving higher accuracy. The Water Exploitation Index (WEI and WEI+) gives good information for assessment of the water stress at annual time scale and WEAP can support its calculation, but on tops of this WEAP calculates another two indicators for assessing the water stress at smaller time scales (monthly) – unmet demand and demand site reliability. All these indicators, however, does not show whether the water is used in an efficient way. So, additional sector specific

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indicators, like water used per capita per day, avoidable water losses in the urban water supply network, irrigation efficiency, etc. were applied. These two sets of the indicators serve as a base for selection of appropriate mitigation measures. These steps will be elaborated in ABOT project within the framework of the next tasks.  

Activity C2

Title of the activity: Sharing methodologies with SEEAW-ECRINS Topics/activities covered:

This activity had the purpose of feeding the water balances (output of the Activity C1) to the SEEAW-ECRINS system by using the standard SEEAW tables of asset accounts and physical supply and use, and exchange on methodologies used to represent some salient features of the modelling process. It is important to emphasize here that by studying the SEEAW tables and the proposed methodology, the partners have identified that some elements/parameters of the tables are very difficult to fill based on observed and measured data (e.g. soil water, flows between the water resources from one water body to another). The aspiration here is thus that the analytical water balance modeling in WEAP will provide as output some of these very challenging parameters to feed the SEEAW tables.

Results of this activity: The detailed description and  results of Activity C2 are presented  in  the Deliverables D.C2  ‐ Report on the water balances  (following the standard SEEAW  format) which  is attached as Annex to this report.  To assess the available water resources and the socio-economic water use within studied basin, two tools have been used in combination: (1) the System of Economic and Environmental Accounts for Water (SEEAW); (2) the Water Evaluation and Planning System (WEAP). The SEEAW was developed by the United Nations Statistics Division (UNSD) in collaboration with the London Group on Environmental Accounting with purpose to encourage the countries to launch integrated overall water management approach through establishing operational framework that integrates economic and hydrological information. This system is an attempt to provide the ‘missing link’ in many river basins for water management. They intend to specify how much water flows in and out of a river basin and how much water can realistically be expected to be available before allocation takes place. Water accounts fill a gap by bringing together knowledge that so far was only available in a scattered and piecemeal manner. A key element in the SEEAW concept is setting of tables for physical water supply and use coupling hydrological and economic information. Populating these standard tables, however, requires adoption of additional tools since the existing information is often:

• Not enough complete – usually the flow measurements are scarce, as either the water is measured at the starting point or at the end point thus making hard to evaluate losses through physical leakages or evaporation; some common examples are:

o often within industrial unit only the water supply is monitored not the wastewater discharge, thus making difficult to estimate the consumption within the unit;

o often for big consumers the consumed water is recorded based on upper permitted limit not on really consumed water; If the price of water is relatively low, this further encourages such an approach;

• The available data sets can’t be used directly, since the system boundary, where they have been collected do not match with the boundaries of the river basin.

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Usually the data are available at the level of administrative units (settlements, districts) or business units (water supply operators or producers).

• Some important issues on water use cannot be measured directly, like:

o Evaporation;

o Evapotranspiration;

o Water exchange between surface and ground waters;

In order to facilitate the filling of the SEEAW Tables for the case studies within ABOT Project modelling using WEAP Software was applied. WEAP is as a generic, integrated water resource planning software tool, and provides a system for simulating the hydrological parameters of the water cycle, water demand and water supply, thus enabling the development of mass balance models on a link-node architecture. For each of the 4 ABOT pilot river basins the SEEAW tables are filled for three different years, selected within the simulation period, in order to capture a dry, a wet and a normal year characterized by average climatic conditions. The following tables have been filled, matching WEAP parameters and outputs to the tables’ fields and attributes:

• Table 3.1 Physical Supply Table; • Table 3.2 Physical Use Table; • Table 6.1 Assets Accounts; • Table 6.2 Matrix of flows between water resources;

The number of table reflects the original number as shown in SEEAW Draft Manual. The codes of the respective economic users are as follows:

• ISIC 1-3 includes agriculture, forestry and fishing; • ISIC 5-33, 41-43 includes mining and quarrying, manufacturing and construction; • ISIC 35 includes electricity, gas, steam and air conditioning supply; • ISIC 36 includes water collection, treatment and supply; • ISIC 37 includes sewerage • ISIC 38,39,45-99 corresponds to the service indutsries;

In the section below, selected tables for each RB are presented:

21

22

23

24

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The purpose of Task C2, namely testing of the procedure of filling the account tables, has been fulfilled. This activity appeared as a challenging one. Below, the main shortcomings are summarized:

1. In the Physical Use and Physical Supply tables there should be a spatial item concerning the reservoirs. The issue is that the reservoirs are also an economic unit, although they “do not produce”. The storage of water in such amount demands significant investment costs, as well as O&M Costs. The construction and operation of dams have also notable environment effect.

2. In complex systems, where there are cascade transfers of water, one and the same amount of water is considered several times. For instance in the case of VIT Basin the water taken from PH Rakita once is recorded as abstracted water from Vit river and afterwards is again recorded as abstracted water from Gorni Dabnik Dam (see the figure below).

3. Whenever there is imported water from “rest of the world” as it is the case with potable water supply for VIT Basin, in the way the tables are constructed it is not clear actually which economic unit imports water. It also appears that this figure is not considered when calculating row 3 “total use of water”. As a result in row 7 may appear negative value.

4. In Assets accounts table and in Matrix flow table there is no option to consider “imported water” from rest of the world. In Vit Basin for example surface water from another territory is transferred through the potable water distribution network (as it is the case with Vit Basin)

5. In table 6.1 Asset Accounts the opening stock for EA 1313 Rivers can hardly be measured. Also the precipitation over the rivers is a detail that will not bring much knowledge to the issue. The surface of the river is most probably less than 0,5% of the whole territory of the catchment. The mistake of measurement through satellite image is too big.

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6. The SEEAW tables are, in principle, designed to be filled by statistically collected data. Yet, many of the requested parameters in the SEEAW tables cannot simply be obtained as products of reporting but require the set-up and output of a detailed water management model. For the purpose of developing EU water balances and capturing the quantitative status of the water resources the tables are too detailed and some of the requested information is practically not policy relevant and very difficult to obtain. Thus, aggregations some parameters of the tables, and deriving, on this basis, meaningful indicators to be reported is advised.

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Activity D1

Title of the activity: Simulating response measures Topics/activities covered: The recent assessment of the WFD RBMPs showed that water supply measures (in 30-40% of RBMPs) are significantly stronger reflected in the screened set of plans than restrictions of pressures (e.g. new water-demanding urban or agricultural developments) or measures to ensure the achievement of the environmental WFD objectives under water scarcity and drought (WS&D) conditions. Furthermore, evidence on the impacts of the applied response measures is limited and no concrete conclusion can be drawn on their effectiveness. This activity targeted the simulation of response measures focused on demand reduction and water saving (and bundle of them), and tested their application on the physical models of the pilot RBs (developed in Task C) in order to assess their true potential under specific conditions and cases. The aim of this task was two fold: to (a) identify measures (interventions) covering management, technological and economic aspects, and (b) test/simulate their impact and effectiveness against specific criteria for the ABOT pilot River Basins. In order to design water saving measures in the various sectors cost-effective intervention curves were created for each case study, which include combinations of water saving measures, and embed within them the partner’s expertise and knowledge of the areas – beyond what is modeled in WEAP21. Stakeholder’s advice regarding the selection of the measures to be tested (i.e. which ones are applicable and attractive to them) has been incorporated. This activity provided input to the following one (Activity D2) which developed an optimization algorithm to optimize the measures allocation on the basis of these curves.

Results of this activity: The detailed description and results of Activity D1 are presented  in the Deliverable D.D2.1  ‐ Report on the water allocation optimization for each pilot RB which is attached as Annex to this report. In order to implement water saving measures in various sectors, cost-benefit curves have been developed for each pilot RB. The higher water demand in the pilots is tied to the agricultural and urban use; therefore, the study focused on these two sectors, while the industrial use was neglected. With regards to the urban sector, different water saving measures have been analysed (low flow taps, dual flush toilet, efficient washing-machine and efficient dishwasher), as well as additional leakage reduction measures (monitoring and leakage repairs, pressure control, and replacement of old pipes). Rainwater harvesting and greywater reuses where also examined were applicable. For all these measures cost-benefit curves have been developed for each pilot, and then merged into a final cost-benefit curve for each pilot assuming that different percentages of population apply them (i.e. degree of penetration). The relevant parameters in the WEAP models that were modified to capture these savings (and associated costs) are the ‘DMS’ (defined in WEAP as the % reduction in total monthly demand due to demand side management programs) and the ‘loss rate’ reduction. Thus, the effect/impact of these interventions on the physical system and the water balance of the catchment could be assessed. With regards to the agricultural sector measures to both increase conveyance efficiency and irrigation method efficiency have been examined. These include: 1) monitoring and leakage repairs, pressure control; 2) switching for open to closed canals; 2) changing of irrigation method (switch from sprinkler to drip irrigation, including precision agriculture). The WEAP control parameter that was modified based on the results of curves is the ‘irrigation fraction’ defined as the product of ‘conveyance efficiency’ * ‘irrigation method efficiency’. The ‘irrigation method efficiency’ depends on the type of the irrigation plant (i.e 75% for sprinklers, 95% for drip irrigation). In order to decrease the unmet demand, the WEAP irrigation fraction control variable should be increased. The results of the intervention curves

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have been integrated in the models, thus the effect/impact of these interventions on the physical system and the balance between availability, supply and demand was assessed for each pilot catchments. For the Tiber River Basin, regarding the urban sector, four different water saving measures have been analyzed: 1) Low flow taps; 2) Dual flush toilet; 3) Efficient washing-machine; 4) Efficient dishwasher. For the construction of cost-benefit curve, only the first two measures are taken into account considering that the last two were found to be less convenient from the economic point of view. In particular, two different curves, one for measure 1 (Figure D1-left) and one for measure 2 (Figure D1-right), have been arranged and, then, they have been combined in order to obtain a unique curve for the DSM savings parameter of the WEAP Tiber model. In order to define the final cost-benefit curve for WEAP DSM saving control parameter, a combination of the curves referred to the two measures above mentioned has been considered by assuming that different percentages of population apply Measure 1 or/and Measure 2. The final curve is shown in Figure D2. Two different water saving measures have been additionally analyzed: 1) Monitoring and leakage repairs, pressure control, 2) Replacement of old pipes. Two different curves, one for Measure 1 and one for Measure 2, have been identified and have been combined in order to obtain a unique cost-benefit curve for the loss rate parameter (Figure D3). The parameter ‘loss rate’ has been used in the WEAP Tiber model to simulate this effect.  

 Figure D1: DSM saving control parameter: (left) cost‐benefit curve for measure 1 (low‐flow taps); (right) cost‐benefit curve for measure 2 (dual flash toilets) 

 

 Figure D2: DSM saving control parameter: cost‐benefit curve combined from both measures 1 and 2 

Figure D3: loss rate control parameter: cost‐benefit curve combined from measure 1 and 2

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Regarding the agricultural sector, in the pilot area of Tiber River Basin the conveyance efficiency can be assumed (as suggested by the agricultural manager Consortia) equal to 70% (considering the ‘conveyance losses’ equal to 30%).Within the study area, all the agricultural nodes (except two of them supplied by Arezzo Reservoir) are irrigated with sprinkler plants and the thus the resulting irrigation fraction (IF) is 52.5%. The two agricultural nodes supplied by Arezzo Reservoir are irrigated with drip plants and therefore, the irrigation fraction for the Arezzo area (IF Ar) is 66.5%. In order to decrease the unmet demand (i.e. increase the WEAP irrigation fraction control variable) two different water saving measures have been analyzed: 1) Monitoring and leakage repairs, pressure control; 2) Replacement of irrigation plants (switch from sprinkler to drip irrigation). Two different curves, one for measure 1 and one for measure 2, have been built and, then, they have been combined in order to obtain a unique curve for the irrigation fraction parameter (Figure D4).  

  Figure D4:  Irrigation  fraction control parameter: cost‐benefit curve combining measure 1 and 2. (left: Arezzo area; right: remaining of the basin) 

In contrast to the other ABOT pilot RBs, the Mulde River Basin already has a very advanced water resource management system which is capable to completely cover both actual and potential regional water demands. Therefore it turned out to be impractical to apply optimization to select additional measures to decrease unmet demand – since such a demand was not identified in the simulations. Since the water demand and supply sector in the Mulde Basin is highly and efficiently managed additional management measures would maybe provide a very inappropriate cost-effectiveness balance. Also a network maintenance or pipe replacement seems to be non-efficient as the overall water loss is very small in the systems. As there is no recognizable irrigation, the agricultural water demand is comparably small and therefore included into the reported water use from the non-public sector which is the main water consumer due to the conglomeration with any other sector besides the public water sector. The second recognized demanding unit is the public sector which accounts for ~1/3 of the total water demand. Even though the private sector is the main water user, it is very difficult to consider corresponding measures to decrease its demand as economic consequences can hardly be overseen - and the water use is diver in any branch. As agriculture also has no water issue in the catchment, only the public sector could be the one to implement any water saving measures. Although we could not show the need of measures, we assessed and discussed the feasibility of water use reduction based on rainwater harvesting as this would also bring beneficial side effects regarding the buffer of high amounts of runoff in urban areas and some feedback on floods. As those areas are highly sealed and sewage networks are usually prepared to collect runoff up to the 10 year return flood period, the largely spread collection of rainwater would provide positive effects on such flushes storing large amounts of rainfall on the buildings and releasing them slowly. A rather meaningful measure would be a way to regionalize the water supply and therefore reduce supply costs by installing rainwater harvesting systems. Other measures which were considered as potentially suitable for the region, also in accordance

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with the local administrations, have been the change of crop types to less water demanding crops (including crop rotation strategies). However, conservation tillage and no-till management strategies are already emphasized and mostly implemented in the region - which increases the soils water holding capacity and therefore decreases potential external water demand. The data base for the rainwater harvesting related calculations are statistics from the federal state of Saxony regarding population, number of houses in house classes, and average rooftop sizes (http://www.stla.sachsen.de/). Furthermore, we used average investment costs of harvesting installations in Germany leaving apart maintenance costs which would be 100€/y after ten years of installation. The potential fields of applying rain water were taken from regarding publications (web1, 2013). Considering the fact that only certain water applications can be done with rainwater without limitations, we concluded that only 45% of the domestic water use can be replaced. Thereby we considered cloth washing, gardening, cleaning and toilet flushing. Regarding the inputs (precipitation) roof tops and number of families in houses we concluded that those 45% can be saved as long as no more than 7 families live in one house. Due to the investment costs, the share among costumers and the total potential replacement, a cost-effective relation can be drawn showing that the saving per euro would be between 55.8 and 7.4 liters considering the first year of application. Correspondingly, this number can be multiplied by the number of years one would assume for the amortization (minus 100€ for each year which accounts for the maintenance). The curve in Figure D5 shows the effectiveness of such an implementation.

 Figure D5: Effectiveness of implementation. 

Although rainwater harvesting as a potentially strong measure to reduce water problems at once including symbiotically effects, it is still very difficult to implement such in reality and will most probably not happen until no certain need can be shown. The main problem in Germany is here the legal issue. Since sewage water costs in Germany are calculated by the input, a rainwater harvesting system would make it almost impossible to be checked. The other point is the high investment costs of rainwater harvesting costs. Considering only one year for the amortization, one m³ water from the system would cost between 20 Euro and 133 Euro which is much in contrast to about 2 Euro from the communal supplier. In the Pinios Ali-Efenti River Basin the water saving measures examined for the urban sector were dual flush toilets, low flow taps and showerheads, efficient washing machines, rainwater harvesting and greywater reuse. Pinios has a population equal to 190,276 people, while the average household in Greece has 2.8 persons (Eurostat data for 2005), thus 67,956 households. In order to design the optimum urban water cost-effective curve, the total number of households was divided into 5 clusters where different set of measures are applied. The

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size of each of the 5 clusters is a decision variable and all 5 should sum up to 67,956. Each cluster has 5 decision variables which are the 5 water saving measures discussed above. Therefore the total number of decision variables is 5x5 clusters=25 and 5 for the size of each cluster, a total of 30 variables. A model in MATLAB was created and optimized in order to create the urban cost-effective curve. The Matlab Global Optimization toolbox was used which incorporates NSGA-II. The cost-effective curve of urban water measures is shown below in Figure D6 based on the Annual Equivalent Cost (AEC). As shown in Figure D6 it is relatively easy and entails relatively low cost to achieve conservation up to 7.5%/cap with a cost of approximately 7.5 million euro per year. Above that level and until the maximum level (26%) of water saving the cost is increasing rapidly until the maximum cost of 30 million euro per year. This is due to the algorithm selecting relatively expensive measures, such as rainwater harvesting, greywater reuse and efficient washing machines, to decrease demand.

Figure D6: Cost‐effective curve of urban water measures relative to AEC. 

Table D1: Optimization  indicative  results  of Urban  cost‐effective  curve  in  the  Ali‐Efenti Basin (Pinios) 

Total Investment €

Investment cost (AEC) €

Saving %

Area %

Dual flush toilet

Low flow taps / Showerheads replacement

Efficient Washing Machine

Rainwater harvesting

Greywater reuse

100% 1 1 1 1 1

0% 0 0 0 0 0

0% 0 0 0 0 0

0% 0 0 0 0 0

160,585,124.39 €

29,797,086.86 € 26.03%

0% 0 0 0 0 0

79.1% 1 1 0 0 1

0% 0 0 0 0 0

20.9% 1 1 0 0 0

0% 0 0 0 0 0

72,237,489.45 €

13,403,898.75 € 16.18%

0% 0 0 0 0 0

63.4% 1 1 0 0 0

10.5% 1 1 0 0 1

4.3% 1 1 0 0 0

5.7% 1 1 0 0 1

18,849,623.88 €

3,497,608.40 € 9.23%

16.1% 1 1 0 0 0

12,401,665.20 8.34% 62.5% 1 1 0 0 0

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10.4% 1 1 0 0 0

4.9% 1 1 0 1 0

5.8% 1 1 0 0 1

€ 2,301,168.91 €

16.5% 1 1 0 0 0

62.7% 1 1 0 0 0

10.7% 0 1 0 0 0

5.0% 1 1 0 0 0

5.7% 1 0 0 0 0

4,629,525.76 € 859,023.41 € 6.79%

16.0% 1 1 0 0 0

Regarding the agricultural sector in the Ali-Efenti Basin, a first step was to calculate the irrigation efficiency (WEAP parameter ‘irrigation fraction’) which takes into account the conveyance method (closed pressurized pipe or open channel), and the method of irrigation (drip irrigation, furrow or surface methods). On this basis, measures would be applied to maximize efficiency gains and minimized losses. The assessment of this coefficient was based on several recent studies for the case study area and exchanges with the stakeholders and is presented in Table D2. As seen below the small individual networks which are drip irrigated have the highest efficiency and that is due to the conveyance efficiency is very high (95%). High efficiency is also noted in new drip irrigated networks. Based in these data the combined conveyance and irrigation efficiency of each Prefecture (Karditsa, Trikala) has been calculated. Thus, Karditsa’s combined efficiency in the current situation is estimated to be 75.4% and Trikala’s combined efficiency is 77.6%. Table D2: Aggregated table for conveyance and irrigation efficiency 

Conveyance and Irrigation Efficiency Drip Sprinkler Furrow Collective Networks 76.0% 68.0% 52.0% Small individual networks 90.3% 80.8% 61.8% Closed Pipes New Networks 90.3% 80.8% 61.8% Collective Networks 57.0% 51.0% 39.0% Open Channels Small individual networks - - -

The cost-effective curve for irrigation focused on finding the optimum trade-off between the various conveyance and irrigation methods (including precision agriculture). In other words the investigation focuses to how much the efficiency would be improved in the prefecture if a different “mix” of conveyance and irrigation methods are used, looking at which combination delivers the highest efficiency with minimum cost. For each Prefecture an optimum cost-effective curve is calculated. The percentages that represent collective and individual networks remain constant. So the decision variables are the conveyance methods and the irrigation methods. Every transaction from one method (conveyance or irrigation) to another has different effectiveness and different cost. The transactions examined were only those which could improve the efficiency (i.e. the case of moving from closed pipes to open channels was not taken in to account). The transactions from one method to one other are subject of constraints and cannot exceed their initial value. As before, a model in MATLAB was created and optimized in order to create the agriculture cost-effective intervention curves for both prefectures. The Matlab Global Optimization was used which incorporates NSGA-II. The total decision variables were 15, one for each transaction and two for precision irrigation. The figures below represent the cost-effective curves for both prefectures. Figure D7 shows Karditsa’s and Trikala’s cost-effective curves based on total investment cost, while Figure D8 represents both curves based on annual equivalent cost (AEC). Both agriculture intervention

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curves indicate that in order to improve the combined efficiency of irrigation networks a high percentage of drip irrigation and precision agriculture is required.

Figure D7: Karditsa and Trikala cost‐effective curve. 

Figure D8: Karditsa (top) and Trikala (bottom) cost‐effective curve based on AEC. 

In the Vit River Basin the unmet demand is observed only for potable water consumers. These users are of highest priority not only in terms of water quantity, but also in terms of water quality. As for the other users (irrigation, industry) there is no unmet demand. With regards to the urban sector, the urban cost-benefit curves have been developed in two directions: (a) decreasing the amount of the supplied water through reducing the physical losses; (b) decreasing the urban water demand through introduction of water efficient household devices.

For (a) decreasing the urban water supply (current water losses are around 18% due to leaking pipes and commercial losses) three measures have been studied for achieving cost-effective decreasing of physical losses:1) Installation of Pressure Reducing Valves (PRV); 2) Active Leakage Control (ALC); 3) Replacement of old pipes. These measures have been considered for the town of Pleven, since the analysis showed that it has the biggest share in the potable water supply distribution and town present 60% of the Non-Revenue Water (NRW) of the whole watershed. The cost-effect analysis on applying each measure solely has shown that measure 3 is the most expensive. Therefore it is recommended measures 1 and 2 are

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implemented first and the rest of the physical losses to be eliminated through pipe replacement. Figure D9 presents the resulting compound cost-effective curve.

Figure D9: Compound cost‐effect curve of water supply reducing measures 

For (b) reducing the urban water demand, the use of more modern water appliances has been considered by implementing two levels of retrofitting: First Level of Retrofitting (FLR) can be achieved by changing with more efficient showerhead (12 to 8 litres/min) and more efficient taps (12 to 10 litres). This action may lead to a rough 20-25 % saving at a reduced cost. Second Level of Retrofitting (SLR) can be reached with fixture changes as toilets (3/6 l dual flush), reduction of bath size and investments in more efficient white goods (marginal part of the saving, for a high cost, which explain the high variability of cost mention on this line). With such changes one could save around 35-45% of water as compared to the base case. Based on these, three variants of changing water consumption have been developed for the town of Pleven: Case 1: Up to 70% of population of the town of Pleven is applying FLR Case 2: Up to 30% of population in Pleven is applying both FLR and SLR Case 3: 70% of population has applied FLR, and the last 30% of them have also applied SLR Figure D10 shows the respective cost-effective curves. The third case was implemented in the model optimization.

Figure D10: Cost‐effect curves of water demand reducing measures for the town of Pleven 

With regards to the agricultural sector, two measures are proposed to reduce irrigation water use: (1) increasing of the irrigation efficiency; (2) rehabilitation of the irrigation system. Data about the irrigation are provided by “Irrigation Systems”-Pleven. Irrigation from individual wells or direct river abstraction is not taken into account, since there is no reliable information

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about them; these abstractions however are insignificant according to expert evaluation. The average coefficient of irrigation efficiency of the fields is currently 0.76. The cost-effective curve for irrigation investigates and tries to find the optimum trade-off harmonizing three irrigation methods (drip, sprinkler, furrow) for the four main crops (vegetables/strawberries, maize-sunflower, melon, fruits). Тhe decision variables are the irrigation methods. Every transaction from one irrigation method to another has different effectiveness and different cost. The transactions examined were only those which could improve the efficiency ( i.e. the case of moving from drip irrigation to sprinkler irrigation has not taken into account). A model in MATLAB was created and optimized in order to create the agriculture cost-effective curves. The Matlab Global optimization was used which incorporates the NSGA-II multi-objective algorithm. Figure D11 presents the cost-effective curve based on total investment cost.

Figure D11: Irrigation cost‐effect curve for VIT Basin 

The irrigation network in Vit basin is in a very bad condition compared to the design status. All pump stations don’t function and the trace of the existing channels was changed in the course of time in a way to deliver water by gravity. Updated blueprints of the system reflecting the changes that has happened over the time does not exist. Therefore it is impossible to make up-to-date evaluation of the investments for replacement of the open cannels with closed channels or pipes. The necessary investment costs for decreasing the water losses from leakages in the irrigation system are defined based on the expert evaluation of the operating company. The results are presented in Figure D12.

Figure D12: Cost‐effect curve of physical leakage reductions from the irrigation network 

Concluding remarks for this activity across the 4 pilots suggest that: - The generation of reliable intervention curves that represent the tradeoff between

investment cost and water saving effect requires extensive data related to the target domain

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or engineering object (water distribution network, irrigation system, etc.), on the effect of each measure, as well as up-to-date information on the operation, management and investment costs. The approach however is flexible and robust enough to be able to incorporate engineering judgment and domain and case-specific knowledge into the optimisation process.

- When more than one measure is relevant for the same object (e.g water supply network, irrigation water network, etc.), depending on the mutual interrelations between the effects of these measures, two approaches are possible: 1) when the effects of the measures are interrelated, they should be prioritized by expert judgment 2) if the application of one of the measures does not influence the effect of the application of the others, then (direct) optimization is an useful tool for performing the prioritization analysis.

Activity D2

Title of the activity: Optimize water allocation Topics/activities covered: Within this task optimization algorithms were developed and applied in order to optimize water allocation and meet demand in the selected pilot River Basins while maximizing the selected objective function. The goal was to optimize the measures allocation on the basis of the cost-benefit curves built in the previous Activity D1. The WEAP water balance models developed in Task C were nested within a large decision support structure, coupled with MATLAB, capable of supporting policy analysis. Individual optimization was run for each of the 4 pilot RBs following a common process and with similar objective function in order to define the optimal bundle of measures to be applied in each case rendering the maximum benefit. Results of this activity: The detailed description and results of Activity D1 are presented  in the Deliverable D.D2.1  ‐ Report on the water allocation optimization for each pilot RB, and D.D2.2 ‐ Policy Brief on the effectiveness  and  cost‐benefit  of  response measure which  are  attached  as  Annex  to  this report. The modeling platform used for this work is the Water Evaluation and Planning (WEAP) system (http://www.weap21.org), developed by the Stockholm Environment Institute's U.S. Centre coupled with the MATLAB mathematical environment. WEAP is a generic, integrated water resource planning software tool that provides a comprehensive, flexible and user-friendly framework for planning and policy analysis. The WEAP water balance models developed in Task C, while they are the core part of the overall analytical framework, are not individually sufficient to support the sort of policy analysis called for under the proposed project. The models were nested within a decision support structure which is presented in Figure D13. The key characteristic of that structure is the implementation of response measures routines and optimization algorithms.

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Figure D13: Decision support structure used in Task D 

Figure D14 presents the flow chart used in order to optimize interventions (as defined on the basis of the cost-benefit curves of the previous Activity D1) in the pilot river basins. More specifically the decision variables in this chart represent the potential set of interventions and the optimal policy is the optimization objectives. Briefly the main loop of the iterative process begins with the transformation of decision variables to an appropriate format readable by WEAP, then starting the connection with WEAP, passing the decision variables to be implemented into WEAP and calculating the results. After that results are exported to Matlab where the objective functions are calculated. Then the optimization algorithm generates a new set of decision variables and the iterative process continues. This procedure is continued until stopping criteria are satisfied. The procedure was successfully implemented in the four basins, and it was concluded that WEAP21, coupled with Evolutionary Programming (GA) optimisation in Matlab, provides a robust and powerful tool for water scarcity management and cost effective interventions optioneering. Specific conclusions and key messages from the optimisation Task for each river basin are briefly presented bellow.

Figure  D14:  Flow  Chart  which  describes  the  methodology  used  to  optimize  the hydrosystem 

For the Tiber River Basin the Pareto front of optimization, where the objective functions are cost and unmet demand minimization, is presented in Figure D15. The default situation

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(current) with zero investment cost has unmet demand equal to 28.72 Mm3. It is observed that the maximum optimal investment can reduce the unmet demand from 28.72 Mm3 to 22.52 Mm3. The obtained results may be useful for managers and stakeholders in order to pursue a policy of water-saving. The unmet demand could be reduced from 28.72 Mm3 to 24.89 Mm3

with an investment cost of 15.80 M€. To this end, the ‘loss rate’ has to be decreased from 40% to 39.65% (cost: 0.62 M€), the ‘irrigation fraction’ has to be increased from 52.5% to 55.69% (cost: 0.72 M€), the ‘irrigation fraction in Arazzo’ has to be increased from 66.5% to 69.44% (cost: 0.07 M€) and the ‘DSMs’ has to be increased from 0% to 7.99% (cost: 14.39 M€). It is important to emphasize that the results are strongly affected by the assumed cost-benefit curves.

Figure D15: Pareto front: unmet demand (Mm3) versus investment cost (M€).

The Mulde River Basin is an efficiently managed region in terms of water resources, which already implements several measures that have effectively addressed past water problems. In that respect, the water management system in the Mulde River Basin could maybe employed as demonstrative case for efficient management particularly since even the Central (dryer) part of the catchment is sufficiently supplied in dry periods and no unmet demand has been recorded. A controversial discussion can be hold upon the potential impact of climate change scenarios especially on agricultural water demand. On one hand, no issue with irrigation demands could be found based on the WEAP model. On the other hand, the model was very limited in this regard as it is running on a monthly time scale and can therefore not evolve any detailed temporal consequences. In that context it might be interesting to setup a daily time scale model for the region to simulate sub-monthly drought periods - although it would be very possible that the corresponding uncertainties of such a model would achieve a dimension that further outputs become not useful or explainable. The investigation of water saving technologies and strategies resulted in several potential measures. But as no unmet demand was found for the region (despite considering the worst-case climate change scenario) an optimization approach was considered unrealistic and the focus moved towards suitable measures for improving the water supply situation, carrying positive side effects. Rainwater harvesting was found to be the most applicable measure although it will be not be an realistic option until serious water shortage would occur (due to legal and economic reasons). For rainwater harvesting, a cost-effective relation has be drawn showing that the saving per euro would be between 55.8 and 7.4 lt. In the Ali-Efenti River Basin in Pinios two optimisation scenarios were applied: in scenario A the objective functions are cost and unmet demand minimization; scenario B has the same objectives as scenario A but the decision variables used are different. In this case an extra decision variable is used, deficit irrigation. The default situation (current) with zero investment cost has an unmet demand equal to 117 hm3 and supply delivered of 370 hm3. The results of scenario A (Figure D16) indicated that the minimum achieved unmet demand is about 94 hm3 with an investment cost about 13 million euro per year; the supply delivered in this case is close to 306 hm3. After that point the improvement is minimum and reaches 93.9 hm3 with an investment cost of appr. 25 million euro per year. For that reason it is suggested

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that investments above 13 million euro could be disregarded. In the best case unmet demand is reduced by 20% and the supply delivered by 17%. As it was anticipated there is a reduction also to supplied water and mainly this is because of the suggested improvement of irrigation conveyance and irrigation methods. The optimization results are presented in the Figure D15. The use of urban water saving measures does not contribute significantly to the reduction of unmet demand and this is because the urban cost-effective curve has a smaller slope above the water saving level of 7.2%/cap and because the urban water use is relatively small compared to agriculture.

Figure D16: Pareto front of optimization scenario A (left) and B (right) and unmet demand for each solution. 

The results of scenario B (Figure D16) indicated that the maximum optimal investment can reduce the unmet demand by 75% (30 hm3). Another notable output is that investments ranging from zero to 11.5 million euros per year reduce the unmet demand with higher rates (the slope of the curve at this part is steeper). This is probably caused by the combination of high deficit irrigation and small infrastructure investments. After the investment cost of 11.5 million euro per year improvement is minimum. It is worth noticing that the algorithm doesn’t choose to improve the decision variable “water saving urban”. This is happening probably due to the high investment cost. Also the algorithm chooses to make small improvements to the “irrigation fraction” variables that represent the combined agriculture efficiency. Finally the algorithm decides to use extensively deficit irrigation due to the lower cost and higher benefit. In this case the key factor was the farmer’s loss income which was incorporated in the investment cost. In the Vit River Basin the optimization scenario investigated opt to reduce the amount of the abstracted water in a cost-effective way. Figure D17 demonstrates the Pareto front of the optimization scenario where the objective functions are cost and water abstraction minimization. The default situation (current state) with zero investment cost corresponds to water abstraction equal to 257 million m3. When the investments are relatively low up to 1.5 million EUR the corresponding amount of the abstracted water equals to 222 million m3, e.g. the water saved is 35 million m3 or 14% of the initial amount of the abstracted water. After this point the effect of the measures is insignificant – from 222 million m3 to 216 million m3, while the investment costs increase significantly to 25 million EUR. If “zero” measures are applied along the ordinate the abstracted volumes reflect the current state. The main effect upon the decreasing of water abstraction is connected with the decreasing of physical leakages in Pleven and in the irrigation system. The graph shows that if the water losses in the town of Pleven decrease with 25.25% then the unmet demand equals zero. The strong influence of the water losses in the irrigation system is connected with the issue that the irrigation network is also used for transport of water for hydropower production, which is actually the biggest user of water within the catchment.

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Figure 1: Pareto front of optimization scenario A and supply delivered for each solution. 

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Activity E1 and Activity E2

Title of the activities: Derive indicative targets, Assessing targets’ robustness under alternative future

Topics/activities covered: These two tasks are closely interlinked and aim at deriving indicative targets for reducing the vulnerability of water resources in the pilot River Basins and bridging the gap between availability and demand (i.e. eliminating unmet demand). The robustness and the sensitivity of the solutions found during the optimization in Task D has been evaluated against three future scenarios and compared with a baseline scenario. The baseline scenario is a projection of the historic period to the future (2015-2030), and maintains the same climate and socio-economic characteristics with the simulated period of the models. The three future scenarios have been developed for the period 2015-2030 and include a pure climate change scenario (based on the IPCC A1B scenario), a pure socio-economic scenario (based on the GEO4 Economy-First scenario and relevant national reports), and a combined climate + socio-economic change scenario (merging the two previous ones). These scenarios are described in detail in the next sections. The sector specific targets focus on the urban and agricultural sector and concern the setting of targets for increased efficiency, water saving and demand reduction on the basis of the measures adopted in Task D. The initial targets (as resulted from the optimization process) have been assessed under the three future scenarios in order to assign a metric of robustness, and evaluate whether the proposed interventions can maintain their overall performance and the targets are realistic under future conditions. Results of this activity: The detailed description and results of Activities E1adn E2 are presented  in  the Deliverable D.E2.1 ‐ Report indicative targets for reducing the vulnerability of water resources in the pilot RBs which is attached as Annex to this report. 

In the Tiber River Basin, the climate change (CC) scenario refers to the year 2030. For the simulation of the future climatic conditions, monthly precipitations and temperatures are modified by applying monthly anomalies to the average year of the baseline period (years: 2008-2011). In particular, the emission IPCC scenario A1B is used and the anomalies for the area of the Tiber River basin are calculated relative to the 1961-1990 mean of the twentieth century simulation. The CC scenario is implemented in the WEAP model considering as year of simulation 2030 and, as for the baseline scenario, a monthly modeling time step interval. The socio-economic (SE) scenario refers to the year 2030 and it examines the case that climate remains similar to that of the 2008-2011 period while the socio-economic conditions change. As suggested in the “Report on the irrigation in Umbria” document (INEA - National Institute of Agricultural Economics, 2008), the existing agricultural districts will be extended in the future. In particular, two different hypothesis are made on the irrigated area increase: 1) for the existing districts located in the upper Tiber River basin (managed by “Alto Tevere” consortium) an increase of 25% is assumed; 2) for the districts located in the southern part (managed by “Bonificazione Umbra” authority) an increase of 100% is considered. In addition to the assumptions on the increase of the irrigated area, hypothesis about the change of the percentages of crops are made. In particular, the agricultural consortia suggested that, recently, the biomass power plants are spreading and the biomass corn used for fuel is required. For this reason, in the Socio-Economic scenario an increase of the percentage of cultivated corn is assumed for the year 2030. In the Upper Tiber River basin the main crop in 2030 will still be the tobacco, while in the agricultural districts managed by “Bonificazione Umbra” authority the corn will continue to prevail. As regards the urban nodes, the population in the year 2030 is calculated by a linear interpolation of the present population in 2001, estimating by the census, and the one projected in the “Regional Water Supply Plan”

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for the years 2025 and 2040. Finally, a combined CC+SE has been applied as a combination of the previous two. The comparison of the model results is carried out for the 4 considered scenarios (baseline, CC, SE, CC-SE) simulated in the default situation (current) with zero investment cost. Figure E1 compares the unmet demand that is almost entirely urban (the agricultural one is negligible) for all the selected scenarios at monthly scale (monthly averages for the simulated periods). It is worth noting that for the baseline scenario the unmet demand is calculated as an average value referring to the years 2008, 2009 and 2011. Considering the analysis described in the report relative to “Deliverable: D.D2.1-Optimize water allocation”, the total unmet demand for the baseline scenario is 28.72 Mm3. 12 Mm3 of these refer to the year 2010 when a fault to the Montedoglio dam, supplying several municipalities, occurred. For this reason, in order to evaluate the climate and socio-economic change effects, the unmet demand in the year 2030 is compared with the one referring to the average baseline year, not including the year 2010. It is observed that the unmet demand increases, especially during the months May-October. The impact of climate is more pronounced than the socio-economic impact.  

 Figure E1: Unmet demand compared for the selected scenarios. 

Figure E2 presents the Pareto front of the optimization implemented for the combined CC+SE scenario, which is the more sever one. The objective functions are the cost and unmet demand minimization. As it can be seen, the default situation (current) with zero investment cost has an average unmet demand equal to 9.87 Mm3. It is observed that the unmet demand can be reduced from 9.87 Mm3 to 7.68 Mm3 (21.5% reduction) with a minimum investment cost of 30.05 M€.

 Figure  E2:  Pareto  front: Unmet  demand  (Mm3)  vs.  investment  cost  (M€)  for  the  CC‐SE scenario. 

In conclusion: - The investigated socio-economic (SE) and climate change (CC) scenarios indicate that in

the future years the unmet demand will increase due to modified climatic conditions and the increase of the population. The increase of the irrigated area, on the contrary, does not

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affect the agricultural unmet demand which is negligible for each scenario. This is due to the fact that the groundwater resources, which supply the urban nodes, are more affected by the climate change than the surface resources, which usually supply the irrigated nodes.

- The shape of the Pareto front calculated for the CC-SE scenario suggests that the unmet demand, which is expected to increase in the future, could be reduced of about 21.5% with a relative low cost by applying the selected water saving measures. To further reduce the unmet demand, the cost increases significantly. This translates into applying measures which target mainly the urban sector (introducing retrofitting to achieve an 11% water saving), and secondarily the increase of irrigation efficiency by 3-4%.

- Despite the not negligible unmet demand for each scenario, in the Montedoglio reservoir there is enough stored water which could satisfy the water demand if it could be transferred to the urban nodes where the unmet demand is observed. Therefore, it suggests that a big reservoir as Montedoglio could be very useful in the future to address the water resources management in the study area.

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In the case of the Mulde River Basin it is was found, as mentioned before, that there is no unmet demand because of efficient management. Hence, the three relevant scenarios were implemented in order to i) check their impact on the system to investigate its “robustness”, ii) to gain relevant knowledge for further work on that case and finally iii) evolve potential recommendations for local administrations. The scenarios which were most reliable due to the good data and knowledge background were a A1B climate change (CC) and a population change scenario (SE). As simulation period we applied the years 2006 until 2030 and the considered base line scenario was setup without considering any change and cycling the available measured date respectively. On the top as third analysis we considered both scenarios combined (CC-SE) The climate change data was provided by the regional climate office of Saxony (Re Kis – Regional Climate Information Center) which provides regionalized climate change data for all IPCC scenarios. The measured background data applied for all provided simulations and regionalization were supplied by the German Climate Service (DWD – Deutscher Wetterdienst) and the Czech Hydro-Meteorological Service (CHMI). The applied simulation approaches were: WETTREG, REMO, STAR and CCLM (cp. http://www.wetterdienst.de/Klima/Klimawandel/Klimaszenarien/). In our case the most convenient dataset for the simulation period was WETTREG as they had some additional data for the downscaling. A strong impact of the climate change considerations was found on the stream flow. We compared the scenario with the reference and found that the total flows where largely and increasingly different during the period. Nevertheless, the absolute comparison on monthly scale is of limited significance as the applied reference were historical data. Therefore the more realistic comparison is achieved by using average changes (Figure E3). It can be observed that the strongest impact occurs in April with -6.5 m³/s and almost no impact in winter. This can easily be explained by the fact the streamflow in winter is dominated by low flow conditions which is fed by the less affected groundwater.

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 Figure E3: Impact of climate change on streamflow relative to the reference scenario for the periods 2007‐2030 (monthly average) 

The fact that still no unmet demand can be observed in our catchment can again be explained by the strong supplier network which links the reservoirs as main water sources. Figure E4 demonstrates the average effect of climate change relative to the reference. Although there is an average decrease in the reservoir mainly in summer, the amount accounts only for a few percent of the total storage capacity. A similar effect was observed for the groundwater storage.

 Figure  E4:  Average  water  volume  change  in  the  Eibenstock  reservoir  relative  to  the reference scenario 

Regarding the SE scenario, the new regionalized population prognosis for the Free State of Saxony has been published in November 2010. This prognosis shows the expected population development until 2025, and was used as a basis in this study. The projections are based on assumptions of fertility (birth rates), mortality (death rates) and migration behavior (net-migration), which result from census data/trends of the last 5 years. According to the utilized prognosis variant, the total population for the Free State of Saxony will decrease until 2025/30 of -8.7% compared to 2011, which represents our 1st scenario. Fertility and migration are very important factors of the population dynamics and development. By assuming an increase of the birth rate, a further liberalization of the labor market and a resulting increasing migration of people from neighboring countries such as Poland or Czech Republic or countries affected by the financial crisis (Italy, Spain, Greece), we developed also a population increase projection variant of +8.7% for Saxony for the year 2025/30. This is our 2nd scenario. This assumption might be rather high, even in comparison with the one positive demographic scenario published by the German Federal Ministry for Labor and Social Affairs, which assumes no increase, but a much slighter decrease of the population (http://www.der-demograf.de/index.php?id=103). However, we decided to use this number because of the high degree of uncertainty in such kind of population scenarios on the one hand and to test the model/system behavior on the other. The impact of population

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change is relatively low in comparison to the climate change scenario. Figure E4 demonstrates the demand change in the year 2030 regarding the population dynamics considered. A considerable effect on streamflow or other resource dynamics can not be shown. The average effect on the stream flow at the basin outlet was simulated by only +/- 0.01 m³/sec.

 

Figure E4: Demand differences of the three scenarios: Reference, population growth and population decrease 

For the combined scenario we accessed only a combination of population increase and climate change as the impact of population dynamics is not very effective anyway and a decrease in availability and increase in demand seemed to be the more interesting analysis. By means of the main supplier, the reservoir volume is again a good indicator on the scenario impact. Looking at the target year 2030 only, it can be seen that the impact is much stronger as if the whole period would have been averaged per month, yet it is still about 1% of the reservoir storage capacity.  In conclusion: - The A1B scenario was implemented to the WEAP model for the Mulde river basin.

Despite the fact that this is the worst case scenario with relatively large impact on the climate, it had only a low impact to the water supply and demand situation of the basin. In addition, two population change scenarios have been applied that assume for the Free State of Saxony i) a population decrease until 2025/30 of -8.7% compared to 2011, which represents a very realistic scenario. By assuming an increase of the birth rate, a further liberalization of the labor market and a resulting increasing migration of people from neighboring countries such as Poland or Czech Republic or countries affected by the financial crisis (Italy, Spain, Greece), we developed also a population increase projection variant of +8.7% for Saxony for the year 2025/30. This is our second scenario. We decided to use this number because of the high degree of uncertainty in such kind of population scenarios on the one hand and to test the model/system behavior on the other. The impact of these scenarios on water supply and demand was even lower than the effects of the climate change scenarios.

- All scenario simulations (single and combined) had very low impact on water supply conditions in the Mulde river basin and did not lead to an unmet demand. This can again be explained by the strong supplier network which links the reservoirs as main water sources – a simulation of response measures was not necessary in the Mulde river basin. In the most realistic case of a continued population decrease, the water supplier system of the basin will be over-dimensioned and not be efficient anymore because of too high maintenance costs.

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For the Ali-Efenti River Basin in Pinios the climate change A1B scenario was applied. Date have been obtained from the National Onservatory of Athens, and have been corrected by comparing the baseline data of the climate model with observed data from raingauge station for the period 1980-2010. The socio-economic (SE) scenario examines the case that climate remains similar to the 1995-2010 period and the only changes are of socio-economic nature. In this study the GEO4 scenario “Economy First” was considered as developed in the EU Project SCENES. This scenario was downscaled and “translated” for Greece, and the resulting scenario was named G1: Confident & Competitive Greece. To capture and quantify the socioeconomic conditions described in the G1 scenario, the International Futures (IFs) was used. This software was developed at Frederick S. Pardee Center for International Futures, School of International Studies, Josef Korbel of University of Denver. G1 was further refined for the Ali-Efenti basin to derive the socio-economic scenario (SE) that we applied here. Scenario SE is geographically limited to the study area. It focuses on the correlation between socio-economic activities in the study area with the demand for water. The time horizon of the scenario is 2030. The organization of the scenario is based on the individual water uses. More specifically, for each water use the primary factors that determine the demand are identified and reasonable assumptions about how these factors will change in the future have been adopted. In addition there are reasonable assumptions for water related infrastructure, utilization of water resources and water treatment facilities. On top to the socio-economic changes derived from IFs an additional assumption was done in the SE scenario concerning crop change. Since this scenario is a strong economic scenario, with liberalization, free markets and driven by the driver to increase agricultural income, it is highly likely that highly marketable crops with a positive import-export balance will prevail and thus replace less profitable crops. Of course, these crops need to be compatible with the prevailing climate and soil conditions in the area. Thus, integrating the expert view of the local stakeholders about potential future swifts in the cultivated crops, it was considered that 15% of the land currently cultivated with cotton will switch to the cultivation of aloe vera. Also, 10% of maize cultivated land will switch to cultivating kiwi and 5% of the maize cultivated land will switch to cultivating broccoli. Finally, on the combination of the above two the CC-SE scenario was also built. In order to evaluate the robustness and the sensitivity of the solutions found during the optimization (Task D) and further define indicative targets, six solutions of the optimisation (derived in the previous activity D2) have been assessed (Solution No.1 represents the case of not applying any measures. The decision variables used are the urban water saving (Wsu), the Karditsa irrigation combined conveyance efficiency and irrigation method efficiency (Karditsa irrigation fraction), the Trikala irrigation combined conveyance efficiency and irrigation method efficiency (Trikala irrigation fraction) and the deficit irrigation (DefIrr). These 6 solution where evaluated under the 3 future change scenario described in the previous section. Figure E5 compares the average unmet irrigation demand for the selected six solutions under each scenario and in comparison to the baseline It is observed that for all six solutions the climate change scenario (CC) has not major effect in the unmet irrigation demand. We can observe that climate is expected to increase the unmet demand by a small percentage when compared to the baseline scenario (which does not include cc) ranging from 2-29%. We need to point out here that the baseline scenario included some very dry years.  

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 Figure E5 Agriculture unmet demand  (mio m3)  compared  for  the  selected  solutions and scenarios. 

Another notable result is the reduction of unmet demand observed between the baseline and the socio-economic (SE) scenario. This reduction is at the range of 33% for solutions No.2-No.6. Even for solution No.1, which does not include any measures, a 66% reduction of the unmet demand is observed. This is strongly attributed to the sole effect of changing crops in the area. The major reason for this reduction is the reform of cultivated land and the introduction of new crops with lower water demand. This in fact is a very valuable result, indicating that changing the type of crops in the area alone can render important water savings and reduce the unmet irrigation demand. On top of that, these specific crops are highly profitable and marketable. When evaluating the robustness of the solutions against the combined CC+SE scenario, we observe that unmet demand reduces from 17-31% in solutions No.2-No.6. The maximum reduction is observed in solutions No.5 and No.6. Again, the incorporation of changing crop types in this scenario plays an important role. Further analysis has been implemented regarding the breakdown of the costs of these interventions into operational cost (Figure E7) and cost associated with the loss of the farmers’ income due to deficit irrigation. On the other hand, when examining the SE and the CC-SE scenarios we can observe that the farmers’ income will increase. For all solutions the comparison between baseline and SE scenarios has an average increase of farmers’ income equal to 30 million euros (Figure E8). This is also noted when comparing baseline and CC-SE scenarios, but in this case the increase is approximately 25 million euros for all the solutions. This is attributed to the fact that the new crops (aloe vera, kiwi, broccoli) are more profitable and increase the farmers’ income.  

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 Figure E7: Agriculture Unmet Demand and Total  Investment cost Trade off compared  for the selected solutions and scenarios. 

 Figure  E8:  Farmer's  income  and  unmet  demand  trade  off  compared  for  the  selected solutions and scenarios. 

In conclusion: - The response measures seem to be suitable for reducing unmet irrigation demand as in all

scenarios a reduction of the unmet demand is observed. The original unmet demand of 32.95 mio m3 can be reduced by 72-84% in the baseline scenario depending on the selected solutions No.2-No.6.

- On this basis, the proposed indicative targets are to increase the irrigation efficiency in the Karditsa agricultural area (currently 75.4%) to as high as 82.29% and in the Trikala area (currently 77.6%) to as high as 85.96%. Furthermore, increase the water savings in the urban sector by 5.94% at the highest. These interventions based on the selected solutions to apply and degree of improvement, require a total cost in the range of 6.5 to 16 million euros AEC (at current prices).

- Significant reduction of agriculture unmet demand is achievable with small interventions and low investment cost.

- The investigated solutions will in fact render additional reduction of the unmet demand under a Markets-First socio-economic scenario which incorporates changing in the crops to more profitable and less water demanding ones (i.e. 15% of cotton cultivation replaced by aloe vera; 5% of maize cultivation replaced by broccoli; 10% of maize cultivation replaced by kiwi). These crop changes will also lead to an increase in the farmers’ income in the range of 25-30 million euros. Thus, the key to increase farmers’ income and simultaneously reduce the unmet demand is the reform of cultivated land.

- Even with zero investment cost (without applying any of the selected solutions), and only by reforming the cultivated land a reduction of unmet demand from 32.95 hm3 to 11.32 hm3 can be achieved.

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In the Vit River Basin the climate change scenario in 2030 was constructed according to HADCM3 model database from the IPCC (http://www.ipcc-data.org/gcm/monthly/SRES_AR4/ index.html). The WEAP simulation using the future climatic data reveals that the different annual distribution of precipitation and the increased temperature (average annual increased is 1.8 oC) leads to significant diminishing of the river flow at last gauging station (Tarnene), especially during the summer and autumn seasons when needs for water supply and irrigation purposes are biggest (Figure E9). The results of climatic scenario are negative and show threat of decrease of flow in river system, which would lead to some deficit in water demand during the hot months.

 Figure E9: Simulated monthly values for the climatic scenario in 2030 (in blue) and the measured average monthly flows in 2009 (in red) at Tarnene station 

In the frame of the project several possible socio-economic scenarios that could occur in the Vit basin in the next 20 years were discussed. According to the collected information and considering the recent social and economic development of the region it is not realistic to expect significant increase of population in the area (on the contrary the population is constantly decreasing) or the industry production with a big additional water demand. On the other hand the irrigation activities, by the way almost destroyed during the period of political and economic transition in the country, would and should be renovated because of numerous reasons. Therefore, it is believed that the relevant socio-economic scenario for Vit watershed is a scenario with increased water demand for irrigation. Three alternative options (A, B, C) were developed representing a multiplication of the irrigated fields by 2.5, 5 and 10 times more. Running these options in WEAP we observe that: option A shows there are still not unmet demand and among the reservoirs only in the Gorni dabnik has some visible decrease of water volume; option B shows appearance of a big unmet demand and all reservoirs have visible decrease of water volume (especially the Gorni dabnik dam is reaching critical water levels in summer); option C shows the appearance of a significant unmet demand and all reservoirs have visible and bigger decrease of water volume. Again the Gorni dabnik dam is strongly impacted reaching very critical water levels in summer and autumn. Finally, the combined CC-SE scenario (option C of the SE+CC) is the most severe among all with a big decrease of river flow, a big decrease of water volume in the reservoirs and a very significant unmet demand.  In order to evaluate the solutions found during the optimization (Task D) all pareto points found in the previous task are assessed. It is observed that for all solutions the climate change scenario (CC) has not significant effect in unmet demand and diminishes the water abstraction probably because the baseline scenario includes some relatively dry year. Single Pareto fronts

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for Water abstraction and Unmet demand for the combined CC-SE compared with Total investment costs in millions are presented in Figure E10.

Water abstraction for the combined CC‐SE scenario (in million €) 

Unmet demand for the combined SE‐CC scenario (in million €) 

Figure E10: Pareto fronts for Water abstraction and Unmet demand for the combined CC‐SE scenarios compared with Total investment costs (in million €) 

Figure E11 and Figure E12 present the comparison of the water abstractions for the baseline, SE and combined CC-SE scenario in million €, and the comparison of the unmet demand for the baseline, socio-economic and mixed scenarios in million € respectively. Obviously the most unfavorable scenario is the SE (option C) where the water abstraction is increasing about 2-3 times. A bit more favorable is the combined CC-SE scenario because, as it was mentioned above, the climatic impact is relatively not significant regarding the water abstraction. On the other hand the unmet demand appears as a water problem in both scenarios as opposed to the baseline where there is at present a relatively small unmet demand.

 Figure  E11:  Comparison  of  the water  abstractions  for  the  baseline,  socio‐economic  and mixed scenarios (in million €) 

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Figure E12: Comparison of the unmet demand for the baseline, socio‐economic and mixed scenarios (in million €) 

In conclusion: - The response measures are suitable for reducing the water abstraction. Significant

reduction of water abstraction is achievable with interventions and relatively low investment cost.

- The optimization solutions have a positive influence on the reduction of the unmet demand - Scenarios SE and the combined CC-SE indicate an increase of unmet demand as response

to the expansion of irrigation fields compare to the existing ones. Yet, the modeled agricultural expansion in the study is still lower than the national projected one.

Activity E3

Title of the activity: Policy Briefing and Dissemination Topics/activities covered:

The purpose of this activity was to disseminate the results of Task E on the robustness of the proposed interventions under future scenario and the indicative targets developed in the 4 ABOT Pilot RBs to the general public and most importantly to the policy makers. For this purpose, a policy brief has been drafted, targeted to the specific audience, and aspiring to provide input to the different post-Blueprint activities that relate to water efficiency, cost-benefit and target setting. Furthermore, for the further dissemination of the project activities, publicity material has been developed and distributed, and the publication of relevant papers in conferences and peer-reviewed journals is planned in the following months.

Results of this activity: The  detailed  description and  results of Activity  E3  are presented  in  the Deliverable D.E3  ‐ Policy Brief on indicative targets according to different typology of RBs which is attached as Annex to this report. 

A number of key messages were derived from the work described above – and are summarise bellow in a policy brief context. The most important are:

All pilot areas investigated three future scenarios: a pure climate change scenario (CC) based on the IPCC A1B, a socio-economic scenario (SE) based on the GEO4 SCENES “Markets-First” or national studies, and a combined one CC+SE. The results indicated that under these scenarios the unmet demand will increase in the future (with the exception of Mulde were there is no unmet demand). In some cases

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the impact of the climate change on the water demand was more pronounced (i.e. Tiber RB, Mulde RB), while in others was negligible, with socio-economic change being the main driver of the changes of the unmet demand (i.e. Ali-Efenti Pinios RB, Vit RB).

The combined CC+SE was found to be the most influential scenario (as expected). o In the Tiber Basin the most affected sector is the urban, while irrigation

demand is not impacted (even when the irrigated area expands) due to the fact that the groundwater resources, which supply the urban nodes, are more affected by the climate change than the surface resources, which usually supply the irrigated nodes. In the case of the Ali-Efenti the agriculture is the most impacted sector but in a positive way: The implemented SE scenario draws on the GEO4 SCENES Markets-First scenario which assumes a strong economy, market opportunities and liberalisation. Downscaling this to the Ali-Efenti area led to the replacement of 15% of the existing cotton and maize cultivations with highly marketable crops (aloe vera, broccoli, kiwi) which increase the farmers’ income and are less water demanding. Thus, on the basis of this scenario, the unmet demand for irrigation is expected to reduce in the future, while at the same time farners’ income will increase if these land use changes are applied.

o In the case of Mulde, the CC+SE scenario had very low impact on water supply conditions and did not lead to an unmet demand. This can again be explained by the strong supplier network which links the reservoirs as main water sources – a simulation of response measures was not necessary in the Mulde river basin. In the most realistic case of a continued population decrease, the water supplier system of the basin will be over-dimensioned and not be efficient anymore because of too high maintenance costs.

o Finally, in the case of VIT river basin the CC-SE scenario indicate an increase of unmet demand as response to the expansion of irrigation fields compare to the existing ones. Yet, the modeled expansion in the study is still lower than the national projected one

The response measures that resulted from the optimization (in Task D) seem to be suitable intervention as in all scenarios a reduction of the unmet demand is observed when these are applied. They are robust and can meet the increasing needs as imposed by the future climate and socio-economic conditions. Thus, on their basis, the following indicative targets are proposed:

o Tiber river basin: maximum reduction of unmet demand equal to 21.6%. The key interventions relate to the reduction of the urban water demand (i.e. low flow taps), and the increase the urban water supply (i.e. reduce loss rate by monitoring or leakage repairs) and interventions to improve irrigation water conveyance and irrigation method efficiency.

o Ali-Efenti Pinios river basin: maximum reduction of unmet irrigation demand equal to 18% if only demand reduction interventions are taken into account. On the other hand 75% reduction of unmet irrigation demand is achievable if deficit irrigation practice is also taken into account, but associates with farmers’ loss of income (due to reductions total crop yield). Another possible target is switching crops. As indicated by running future scenarios a 15% replacement of cotton and maize with aloe vera, kiwi and broccoli can increase farmers’ income (more profitable crops) while significantly reducing the unmet demand. This intervention though requires careful and coordinating planning and proper institutional settings.

o Mulde pilot river basin: it is already an efficiently managed region in terms of water resources, with no unmet demand, which already implements several measures that have effectively addressed past water problems. In that respect, the water management system in the Mulde River Basin could maybe employed as demonstrative case for efficient management. Rainwater

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harvesting was found to be the most applicable measure although it will be not be a realistic option until serious water shortage would occur (due to legal and economic reasons). For rainwater harvesting, a cost-effective relation has be drawn showing that the saving per euro would be between 55.8 and 7.4 lt.

o Vit pilot river basin: 14% of the abstracted water could be saved with low investment cost. The most effective the measure was to reduce leakages in the municipal distribution network by applying active leakage control and installation of pressure reducing valves. The results indicate that if the water losses in the town of Pleven decrease by 25.25% then the unmet demand equals zero. The strong influence of water losses in the irrigation system is due to the fact that the irrigation network is also used for the transportation of water for hydropower production, thus this requires cheerful consideration and future planning.

2.3. If applicable - Activities that have not taken place

N/A

2.4. What is your assessment of the results of the Action? Include observations on the performance and the achievement of outputs, outcomes, impact and risks in relation to specific and overall objectives, and whether the Action has had any unforeseen positive or negative results. (Please quantify where possible).

The ABOT project has contributed towards achieving immediate water policy objectives by providing: - Direct input into the System of Economic and Environmental Accounts for Water (SEEAW)3 under the “European Catchment and River network system (ECRINS)4 reference system. - Detailed physically based water resources balance for 4 Pilot River Basins across Europe, covering a typology of cases (Italy, Germany, Greece, Bulgaria) that can be used for multiple objectives (e.g. calculating WS&D indicators, assessing environmental requirements etc.). - Cross-assessment of the impact and effectiveness of specific response measures in the 4 Pilot River Basins that can be used to provide input on the post-Blueprint relevant activities - Estimation of the possibilities for optimization of water allocation under specific context for a typology of River Basins that can be used as input to the WS&D Policy Review, the Bluprint activities on cost-benefits and measures - Definition of sector specific targets in the pilot River Basins regarding water saving, water demand reduction, water efficiency for the agricultural and urban sectors. DG ENV and EEA have identified a set of building blocks to feed into the Policy Review of the Strategy for Water Scarcity and Droughts, which have been integrated in the recent Communication "Blueprint to safeguard European waters". Among them and of a great importance is the development of water accounts for Europe under the SEEAW implemented under the ECRINS-NOPOLU system. This work is implemented in the EEA and currently supported by the DG ENV relevant service contract. The ABOT pilots had already in Month 4 sent relevant data to the EEA and further contributed to the SEEAW work by:

a) Feeding datasets of improved quality (irreplaceable and replaceable data) to the SEEAW NOPOLU-ECRINS system. The pilot areas in this proposal have been

3 http://unstats.un.org/unsd/envaccounting/seeaw.asp 4 http://eea.eionet.europa.eu/Public/irc/eionet-circle/ecrins/home

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strategically selected to complement areas where data are currently missing in the SEEAW platform (i.e. Italy, Greece, Germany and Bulgaria).

b) Calculating water balances based on a hydrological water balance model (common for the 4 Pilot River Basin) so that additional input on challenging simulations of the hydrological cycle (e.g. Surface-Groundwater interaction) can be transferred as knowledge and implemented in Nopolu.

c) Helping to assess the uncertainty involved in the SEEAW-ECRINS calculations.  Furthermore, the project’s interventions contribute to the long-term development objectives, with positive primary and secondary long-term effects: This project assessed the water balance in 4 EU Pilot River Basins and furthermore the impacts and effectiveness that arise from the implementation of various responses measures under a common harmonised methodology. The measures to be assessed include both increase supply and reduce demand measures incorporating various instruments (economic, technological etc.). It is currently recognised that Europe cannot increase its water supply endless; we must also reduce the demand. A more sustainable approach to water resource management across Europe is needed. Such an approach will require a marked shift towards demand-side management, implying a key role for measures focusing on water conservation and increased water use efficiency, but also clear demos of how such measures stand against the traditional increase-supply interventions. The ABOT project is providing such scientifically based demonstrations under a typology of cases to evaluate trade-offs and contribute on the long-run towards safeguarding Europe’s waters and reducing the vulnerability of the water resources while preserving welfare. Next to this main impact, additional ones arise:

- Developing a cluster of 4 Pilot River Basins that represent different geo-political conditions, yet implement harmonized approach towards assessing water resources adaptation issues is an important corner stone into building a basis to be used in the future for other pilot demonstrations activities or for extrapolation of results. - Development and availability of a generic harmonized model with many capabilities (with a user-friendly interface) for 4 Pilot River Basins that can be in the future used for other applications (e.g. piloting water-energy efficient use). - Making it possible to further research on the adaptive capacity of 4 water systems, which is the least researched among the components of drought vulnerability. - Engaging the local stakeholders and RB authorities can create an open dialog beneficial for both the policy makers and water managers contributing to improving EU early drought preparedness and risk reduction. - Modeling the possibilities for optimization of water allocation in the 4 Pilot River Basins can provide a powerful decision support tool to the local authorities which can further help them in redefining their targets and interventions in the 2nd round of the WFD RBMPs and in building DMPs as supplementary measures (where applicable). - Dissemination of the problem results contributes to public awareness rising across at least 4 diverse communities and stimulates interactions and exchange of experience. In order to simulate and evaluate the effectiveness of response measures a new methodology for designing cost-effective intervention curves was developed and applied. The method allows for the inclusion of a variety of water saving measures for each sector (i.e. domestic, agriculture, industry) and is flexible and robust enough to be able to incorporate engineering judgment and domain and case-specific knowledge into the optimisation process. A possible risk associated with the results obtained, is the quantification of investment costs, due to the fact that in a fast growing technology driven future the cost of each investment could change rapidly – including the possible emergence of another more efficient technology in, say, the next decade. However this risk can be overcame because the methodological framework developed in ABOT can handle these changes, by incorporating their effect within the intervention curves – leaving the remaining method and optimisation strategy intact.

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  2.5. Describe if the Action will continue after the support from the European Union has

ended. Are there any follow up activities envisaged? What will ensure the sustainability of the Action?

Elemesnt of the action will be pursued for continuation, The ABOT website will remain operational for the next two years at least. The organization of workshops with the local stakeholder will certainly continue in the future. The dialogue with the local authorities will continue due to many common projects and topics. Whereby, we are able to share our experience and to provide suggestions. The results of the ABOT project will be presented to the local stakeholder who have already shown a keen interest and were involved in the framework of the project. The developed tools (WEAP water balance models) can be used by the stakeholders to address in a proper way the water resources management in the study areas and can provide a significant input to the drafting to the 2nd round of the RBMPs. The different measures, urban (i.e. losses reduction, replacement of old pipes and application of water saving measures) and agricultural (i.e. increase of the irrigation efficiency) have been identified and analysed based on costs and benefits in order to decrease the unmet demand. These measures can be further explored for inclusion in the updated PoMs in the WFD 2nd round. The partners plan to open the dialogue with the stakeholders on this extend and support them if necessary. Finally, we will continue the discussion with the stakeholders regarding data collection and reporting and provide advise to them on ways to optimise the reporting with regards to the requirements on the European scale. The updating of the water balance models with new data will most possibly continue since it is a common interest of the partners and the stakeholders. Should the stakeholders provide additional data the partners could update the water balance models. It worth noticing here that the pilot RBs of ABOT are used by the partners on the basis of different projects, and it is interesting to them to update the models in order to support additional applications and assessments, e.g. related to ecosystem services. The consortium is also very interested in supporting the relevant CIS process which will follow the Blueprint. Different products and finding of the project can be provided for inclusion in the relevant Guidance Documents of water accounts, cost-benefits and targets, etc. Especially, for the Water Accounts the partners are keen to provide input to the relevant policy review process and the development of a Guidance Document since they have obtained valuable knowledge. The partners plan to publish many of the project outputs in conference and peer-reviewed journals in the future months. The methodological framework for the design of the intervention curves and their use in optimisation developed in this study is one of the streamlined publications and will potentially be applied in other case studies or projects of the partners. Finally, the project has helped to intensify the related discussions and contacts and may also be facilitate the setup of future projects in this direction. We are always interested in following-up projects and therefore intensify contacts and collaborations on a regional, national and international level.

2.6. How and by whom have the activities been monitored /evaluated? Please summarise the results of the feedback received.

Each activity has been carried out by the scientific staff and it has been internally reviewed by task leaders. Finally, the Action has been discussed with the other partners during the project meetings and through a continuous exchange of comments, suggestions

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and information. The further discussions (during the development phase, as well on the outputs) with the stakeholders and participant of the advisory committee has ensured the further evaluation. Finally, it worth mentioning that many of the partners are involved, through their work, in the water policy area and thus ensured that the activities are relevant and tailored to the wider EU water policy as presented in the Blueprint.

2.7. What has your organisation/partner learned from the Action and how has this learning been utilised and disseminated?

This Action allowed all partners to interact with the stakeholder and to undertake with them a very fruitful collaboration that is advisable to be continued in the future. We have strengthened our skills in participatory techniques. New young colleagues were also involved and learned how to communicate with stakeholders and how important their involvement is. The new experience about the responsibility of different ‘”water agencies”, their databases and their inter-relationships would support the future activities in other national basins. The deeper “studying” of the SEEAW methodology and our practical exercise to apply it in 4 RBs gave us valuable insight about the possibilities and the short-coming of this methodology and the data collection around it. This knowledge will be disseminated through scientific papers, but also to the policy area through our policy briefs, and the CIS WG – experts who will develop the relevant Guidance Document as proposed by the Bleuprint. The partners gained additional modeling skills and WEAP specific skills, and they plan to further utilize the developed models in other projects to expand to additional applications (e.g. ecosystem services) After working on the issue of developing design curves for the response measures a key lesson that was identified was that when more than one measure is relevant for the same intervention domain (e.g water supply network, irrigation water network, etc.), depending on the mutual interrelations between the effects of these measures, two approaches are possible: 1) when the effects of the measures are interrelated, they should be prioritized by expert judgment 2) if the application of one of the measures does not influence the effect of the application of the others, then (direct) optimization is the preferred option. Both approaches were incorporated in this action. This will be communicated to authortities which will work towards the 2nd round of the RBMPs. From the work undertaken it has been proven that the coupling of WEAP21 and Matlab’s Evolutionary Programming (GA) optimisation toolbox is possible with basic programming skills and allows for the development of customised, flexible, ad-hoc, decision support systems. It also emerged that the proposed and tested simulation-optimization scheme is useful for the assessment of the efficiency of different response measures and that significant efficiencies are in fact possible for most contexts (3 out of 4 catchments). These results have also been disseminated to the national authorities to be used as input in the next round of WFD RBMPs. It is important to emphasize that the project findings have been disseminated to the local stakeholders along the way, and policy briefs have been prepared for further dissemination in the EU water policy arena. Additional dissemination activities include a presentation during a session in the World water day in Sofia, submission of publication to the International conference CCWI 2013 (abstract already accepted). A wider dissemination in research and conference papers is planned from July 2013 onwards. Some methodological approaches and results could be used for further scientific studies and thesis extending knowledge and approving the methodological approaches.

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3. Project Management, Co-ordination and other Co-operation

3.1. How do you assess the project management?

The overall project coordination was a challenging task concerning all the management’s aspects, including the scientific, administrative and financial ones. The management activities also concerned the internal communication among the partners and with DG Environment, supervision of the implementation of the work plan and Quality Assurance. The Action coordination has been successfully developed mainly thanks to the significant contribution of all the partners. DG ENV has facilitated the process by promptly responding to any issues raised by the consortium, and by further coordinating meeting with the other beneficiaries of the call facilitating thus exchange of experiences. Each partner has organized meetings with the local stakeholders, in charge of the water resources management and planning in the study area, in order to exchange with them along the project, and also seek their expert opinios. The results of the Action will be disseminated to them in the next months aspiring to continue the collaboration and exchange.

3.2. How do you assess the relationship between the formal partners of this grant agreement (if applicable)?

The Consortium of the Action represents a close-knit group of scientific institutions who have already all collaborated in a previous project co-funded by EC (Monitoring, forecasting and best practices for FLOOD Mitigation and prevEntion in the CADSES region – FLOODMED, partially co-financed by the European Union under the INTERREG IIIB CADSES programme. Contract number 5D214). Bilaterally the partners have collaborated in the framework of additional EU projects (e.g. LIFE+ project FLIRE). The relationship between the partners has further improved given rise to a very fruitful collaboration.

3.3. Is the partnership to continue? If so, how? If not, why?

The future continuation of the partnership is advisable and, if possible, it will be extended within the context of projects co-financed by the European Union.

3.4. Where applicable, describe your relationship with any other organisations involved in implementing the grant agreement:

• Associate(s) (if any) • Sub-contractor(s) (if any) N/A

3.5. Where applicable, outline any links and synergies you have developed with other actions.

N/A

4. Visibility The EU contribution, fundamental to support the Action activities, is mentioned in the dedicated project website (www.abot.it). In particular, it is clearly indicated that 75% of the budget of the Action is funded by the EC, while 25% is co-financed by the partners. Moreover, in all the scientific publications that may possibly be developed on the topic investigated within the project and on the obtained outcomes the EC contribution will be mentioned. We fully agree that the final technical report of ABOT Action is published on DG ENV website.

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5. Comments on Financial implementation Eligible Costs incurred to support the developed activities within ABOT Action ‘Personnel’ (direct cost): 327,822.06 € ‘Travel and subsistence’ (direct cost): 28,615.40 € ‘Other direct costs’ (direct cost): 5,569.52 € ‘Indirect costs/Overheads’: 25,340.47 € TOTAL ELIGIBLE COSTS: 387,347.44 € Of the total eligible costs, 289,745.85 € (74.80%) are provided by EU contribution and 97,601.59 € (25.20%) are co-financed by the beneficiaries. Only IRPI-CNR has reported costs including VAT because it is not recoverable (a declaration on VAT non-recoverability is provided). All the other co-beneficiaries (UFZ, SEVEN, UACEG and NTUA) have reported costs without VAT.

The ‘personnel’ costs supported the activities of all staff members, both permanent and temporary, involved in the Action.

The ‘travel and subsistence’ costs allowed the partners to meet for planning, discussing, addressing and finalizing the activities. In particular, the following meeting were held:

- ABOT Kick-off Meeting, Athens, Greece, 16-17 January 2012 - ABOT Partner's meeting, Sofia, Bulgaria, 5-7 June 2012 - ABOT Partners Meeting, Athens, Greece, 9-11 December 2012 - ABOT Final Partners Meeting, Perugia, Italy, 11-13 March 2013

Moreover, the participation of the co-beneficiaries in the three Coordination Meetings of the 2011 Halting Desertification in Europe Pilot Projects, DG ENVIRONMENT, Brussels was also supported. Finally, bilateral meetings between the partners were also held.

The ‘other direct costs’ supported the purchase of ‘2 years academic WEAP software license, consumables for dissemination, some expenditures for partners’ meetings organization, etc.

------------------------------------------------------------------------------------------------------------------------------ Name and title of the Co-ordinator: Tommaso Moramarco Signature:

Location: Perugia, Italy Date report due: Sunday, 21/04/2013 Date report sent: Monday, 22/04/2013