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D11.1 Prototype design of drought Decision Support Systems

IMPREX has received funding from the European Union Horizon 2020 Research and Innovation Programme under Grant agreement N° 641811

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Dissemination level of this document

X PU Public

PP Restricted to other programme participants (including the Commission Services)

RE Restricted to a group specified by the consortium (including the European Commission Services)

CO Confidential, only for members of the consortium (including the European Commission Services)

Versioning and Contribution History

Version Date Modified by Modification reasons

v.01 30/12/2016 A. de Tomás Draft definition of deliverable structure and contents.

Deliverable Prototype design of drought DSS

Related Work Package: WP11: Sectorial survey: Agriculture and Droughts

Deliverable lead: FutureWater

Author(s): Johannes Hunink, Alberto de Tomás, Yu Li, Andrea Castelletti, Matteo Giuliani, Federico Giudici, Sara Suárez, Joaquín Andreu, Aristeidis Koutroulis, Manolis Grillakis, Abel Solera

Contact for queries [email protected]

Grant Agreement Number: n° 641811

Instrument: HORIZON 2020

Start date of the project: 01.10.2015

Duration of the project: 48 months

Website: www.IMPREX.eu

Abstract This report presents the needs and expectations for Weather and Climate services of the agricultural sector with a focus on drought issues and irrigation. It also presents how the different involved case studies of IMPREX plan to respond to these needs by designing a drought Decision Support System for a targeted end-user.

http://www.powerstep.eu/

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v.02 20/01/2017 J.E. Hunink Integration of contribution of survey results, including figures from Yu Li and feedback from Aristeidis Koutroulis

v.03 30/01/2017 J.E. Hunink Integration of contribution from all partners (POLMIL, TUC, UPV and FW)

v.04 09/02/2017 B vd Hurk, Laurent Pouget

Review

v.05 15/03/2017 M. Giuliani, Y. Li, A. Castelletti

Revision of Lake Como chapter, update of survey results, overall review

v.06 J.E. Hunink Inclusion of all feedback v.04 and v.05, as well as from Linus Magnusson (ECMWF). Included updated section of TUC. Added executive summary and summary for Ch2 and Ch3.

v.07 23/06/2017 J.E. Hunink Revision after preliminary feedback EU reviewers


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Table of Contents

List of figures ................................................................................................................ 6

List of tables ................................................................................................................. 8

Executive summary .................................................................................................... 10

1 Introduction .......................................................................................................... 12 1.1 Rationale ...................................................................................................... 12 1.2 This deliverable in context ............................................................................ 13

2 Stakeholder survey .............................................................................................. 15 Summary ................................................................................................................. 15 2.1 Introduction ................................................................................................... 16 2.2 Analysis of the survey responses .................................................................. 16

2.2.1 Profile of the respondents ...................................................................... 16 2.2.2 Current use of W&C services ................................................................. 18 2.2.3 Current applications of W&C services .................................................... 20 2.2.4 Expectations on W&C services .............................................................. 21

2.3 Discussion .................................................................................................... 25

3 Case studies: current practice, user requirements and proof of concept .............. 27 Summary ................................................................................................................. 27 3.1 Segura River Basin, Spain ............................................................................ 28

3.1.1 Background ........................................................................................... 28 3.1.2 Current practice ..................................................................................... 30 3.1.3 User requirements ................................................................................. 34 3.1.4 Preliminary design specifications ........................................................... 35 3.1.5 Proof of concept .................................................................................... 37 3.1.6 Testing plan ........................................................................................... 42

3.2 Messara Valley, Greece................................................................................ 45 3.2.1 Background ........................................................................................... 45 3.2.2 Current practice ..................................................................................... 46 3.2.3 User requirements ................................................................................. 47 3.2.4 Preliminary design specifications ........................................................... 47 3.2.5 Proof of concept .................................................................................... 50 3.2.6 Testing plan ........................................................................................... 53

3.3 Lake Como and Adda River, Italy ................................................................. 55 3.3.1 Background ........................................................................................... 55 3.3.2 Current practice ..................................................................................... 56 3.3.3 User requirements ................................................................................. 59 3.3.4 Preliminary design specifications ........................................................... 59 3.3.5 Proof of concept .................................................................................... 65 3.3.6 Testing plan ........................................................................................... 75

3.4 Jucar River basin, Spain ............................................................................... 76 3.4.1 Background ........................................................................................... 76 3.4.2 Current practice ..................................................................................... 79 3.4.3 User requirements ................................................................................. 83 3.4.4 Preliminary design specifications ........................................................... 84 3.4.5 Proof of concept .................................................................................... 88 3.4.6 Testing plan ........................................................................................... 91

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4 Conclusions ......................................................................................................... 93

5 References .......................................................................................................... 94

Annex A – Survey on Weather and Climate services in the agricultural sector ............ 99

Annex B – Supporting material for the identification of the best forecast in the Lake Como system ............................................................................................................ 100

Annex C – Supporting material for the design of a basin-customized drought index in the Lake Como system ............................................................................................. 105


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List of figures

Figure 1. Respondents classified in 3 categories: Irrigators, Water authorities and Agricultural Research 17

Figure 2. Temporal and spatial resolution, and lead times of the currently used W/C products, per group of respondents 19

Figure 3. Score (1-10) of the quality of the used forecasts 20

Figure 4. Current applications of the used W/C services (up) and frequency of use (down) 21

Figure 5. Reasons why part of the respondents don´t use W&C services today 22

Figure 6. Interest in forecast information, per group 22

Figure 7. Interest in spatial resolution (a), temporal resolution (b) and lead-time (c) for improved W&C services, per group 24

Figure 8. Importance of measurement issues agricultural water management 25

Figure 9. Study case basins location. Schematic of the water transfer infrastructure (pipes, pump-turbines, lifting station, tunnels, aqueducts). Red squares delimits upper Tagus and upper Segura river basins. Grey square delimits Segura river basin. Source: (Confederación Hidrográfica del Segura (CHS), 2016). 30

Figure 10. Drought index since 2004 - Jan-2017 of the internal water resources of the Segura basin 31

Figure 11. Prototype of the DSS interface. Predicted reservoir inflows are translated into probabilities for the different drought levels over the following months 36

Figure 12. Upper Tagus (top) and Upper Segura (bottom) monthly precipitation climatology (period 1980-2010). Box-plots represent ECMWF 15 ensembles. AEMET values represent the mean of 4 meteorological stations from the Spanish Meteorological Service in the area. 38

Figure 13. Upper Tagus hydrographs of monthly reservoir inflow for the period 1982-2012. The blue-shaded areas show the 5-95 percentiles and the blue line the mean of the ensembles of ECMWF-Sys4 (additional bias corrected), ref_run is the simulated discharge of ERA-Interim, UF is the user forecast (SCRATS) simulated discharge. Vertical red dashed lines indicates drought periods. 41

Figure 14. Detail of Figure 13 for the period 2002-2012 for the Upper Tagus hydrographs of monthly reservoir inflow. Vertical red dashed lines indicates a drought period (2004-2010). 42

Figure 15. Monthly averages of reservoir inflows for each initialisation month (Jan, Apr, Jul, Oct) and leadtime (FP1, FP2, FP3). 5-95 percentiles and mean are the simulated discharges of ECMWF-Sys4, ref_run is the simulated discharge of ERA-Interim, UF is the user forecast (SCRATS) forecasted discharge. 44

Figure 16. Geographical overview of the Messara study site, Crete, Greece. 45

Figure 17. Connection of raw forecast data, processes involved and front-end information provision 48

Figure 18. Overview of the Messara drought decision support system. 50

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Figure 19. Basin scale ECMWF monthly precipitation and temperature data and the respective observations (left). Scatter plot of observed and ECMWF hindcast monthly climatological averages for one month lead-time (right). 51

Figure 20. Basin scale Met Office monthly precipitation and temperature data and the respective observations (left). Scatter plot of observed and Met Office hindcast monthly climatological averages for one month lead-time (right). 51

Figure 21. Effect of the bias correction on the climatological 10 day averages for the 1 month lead time forecasts of ECMFW and MetOffice systems (y-axis), comparing to the observations (x-axis). 52

Figure 22. The 25-year simulated runoff comparison to observations (upper). In the middle panel, the difference [Observed –Simulated] in m3/s. Cumulative difference in [mm] (lower panel). 53

Figure 23. Ten-day climatological observed and simulated runoff. 53

Figure 24: Map of the Lake Como basin. 55

Figure 25. Comparison between observed and simulated release trajectory. 56

Figure 26: Historical correlation between water diversions with lake release and precipitation events. 57

Figure 27: Trend analysis of the daily inflows over the time horizon 1946–2010: the average is computed by means of a moving window that includes data over consecutive days in the same year and over the same days in consecutive years, with the window progressively shifted ahead to identify long-term trends. In the figure, each line represents a 20-years moving average, from the 1946–1966 (light blue) to the 1990–2010 (dark blue) time horizons. 58

Figure 28: Overview of the drought Decision Support System for the Lake Como basin. 59

Figure 29: Flowchart of the Information Selection and Assessment (ISA) framework (Giuliani et al., 2015). 61

Figure 30: Schematic representation of the integrated simulation model of the Lake Como basin. 63

Figure 31: A mock-up of the open data geo-information portal. 65

Figure 32: Performance obtained by the Perfect Operating Policies, Basic Operating Policies, and two Improved Operating Policies relying on SWE and alpine hydropower storage as surrogates of the actual system state. 67

Figure 33: Analysis of the Lake Como storage trajectory in dry years under the selected Improved Operating Policy conditioned on observations of SWE. As a reference, the trajectory of the target POP and the selected BOP are also illustrated. 68

Figure 34: Comparison of the trajectories in 2013 of the Virtual Snow Index σ with the snow height measured at Oga San Colombano (left panel) and with the freezing level (right panel). 70

Figure 35: Performance obtained by different Lake Como operating policies informed with ground observations (P1 - green circles), SWE estimated by ARPA (P2 - cyan


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circles), or virtual snow indexes (P3 - red circles). The performance of these solutions is contrasted with the upper bound of the system performance (black squares) and the baseline operating policies (blue circles). 70

Figure 36: Performance obtained by the Perfect Operating Policies, Basic Operating Policies, and two Improved Operating Policies relying on streamflow forecasts on different lead times (51 days and 7 days). 72

Figure 37: Analysis of the Lake Como storage trajectory in dry years under the selected Improved Operating Policy conditioned on observations of 51-days ahead inflow forecasts. As a reference, the trajectory of the target POP and the selected BOP are also illustrated. 73

Figure 38: Comparison between the water deficit at the cell level produced by the agricultural model (red dashed line) and by the constructed wrapper (blue line). 74

Figure 39: Correlation map of spring precipitation (April, May, June) with winter SSTs (January, February, March) for the positive phase of the Multivariate ENSO Index (left panel) and negative phase of North Atlantic Oscillation (right panel). 75

Figure 40: Results of the automatic selection of valuable information over the observations of state surrogates’ dataset in terms of average cumulative performance of the model describing the optimal release trajectory of the target Perfect Operating Policy. 102

Figure 41: Results of the automatic selection of valuable information over the inflow forecast’ dataset in terms of average cumulative performance of the model describing the optimal release trajectory of the target Perfect Operating Policy. 103

Figure 42: Flowchart of the adopted crowdsourcing procedure. 104

Figure 43: Results of drought detection in the Lake Como basin according to different drought indexes with multiple time aggregations, from one week (1w) to one year (52w). Each colored line corresponds to a drought event detected by the corresponding index. 105 List of tables

Table 1. Ranked interests in different type of improved forecast information (scores 1-6). Rank averages per group 23

Table 2. Tajo-Segura Water Transfer exploitation rule. 33

Table 3. Relations of the current month with preceding months discharges (persistence). 33

Table 4. SPHY model performance for the upper Tagus and Fuensanta stations and the calibration (1980-2000) and validation periods (2000-2010). 38

Table 5. Performance statistics for the period 2002-2012. CRPS colour scale: green represents low values, red represents high values and white represents values in-between. Correlation coefficient colour scale: blue represents high values, red represents low values and white represents values in-between. 43

Table 6: Technical details of the design specifications 49

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Table 7: Set of observational data as system state surrogates. 101

Table 8: Set of inflow forecasts over different lead times. 101

Table 9: List of candidate variables for the Q-WEISS experiments. 106


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Executive summary

A major threat to the agricultural sector in Europe is the potential for an increased occurrence of droughts, affecting the local and regional food security and economies. Weather and Climate services (W&Cs) focusing on drought risk and agriculture can enable a more systemic approach to risk management, leading to better informed strategic decisions at various levels for a range of users. However, use of W&C for drought risk by decision makers acting on the regional level (agricultural policy and water resources management) so far is limited. At this level for W&Cs to be useful, they need to be co-developed with the respective end-user. Thus, the first step is to map the current use and needs for W&Cs in the agricultural sector. For this purpose, the IMPREX WP11 partners consulted with stakeholders in the case study areas, especially those affected by water availability and droughts at the monthly and inter-annual time scale, and at the regional spatial scale, i.e. irrigated agriculture. Decisions affecting water allocation can potentially be improved for these stakeholders and IMPREX should reveal whether there are clear business cases for W&Cs. Besides, a survey was developed and conducted among a slightly wider group of stakeholders – including actors that are critical for the early adoption of new tools and technologies: the research and advisory community. The outcomes were analyzed and provide a useful picture of what the sector requires in terms of W&Cs and drought issues. The survey has been analysed distinguishing between 3 stakeholder groups: (1) irrigators and irrigators´ associations, (2) water resources planning authorities and partnerships responsible for water allocation at the regional level, and (3) the agricultural research and advisory community. The survey confirmed that the sector is quite used to include weather forecast information in their decision-making, but mostly on a day-to-day or weekly basis. Seasonal and long-term predictions are currently not or hardly used. The survey has indicated that there is great interest in seasonal predictions of precipitation to better foresee changes in water availability, and temperature to assess irrigation water requirements. Currently, most respondents use forecasts with a lead-time of a couple of days. Especially group 2 involved in regional planning indicated that they would like to see this increased to a couple of weeks or a couple of months. Also, this group indicates it would like to see more services with a monthly resolution. Group 1 and 2 (irrigators and research/advisory) also indicated their interest in short-range, even hourly information. This however does not relate to drought issues but to extreme rainfall or hail events. Overall, the stakeholder consultation, backed-up by the survey, confirms that the key target group for IMPREX are the organizations or authorities involved in the regional planning and allocation of water (group 2 of the survey). This group considers there is scope for including seasonal forecasts in their decision-making process. Thus, IMPREX will work directly with this target group, incorporating improved seasonal hydrometerological predictions in management procedures and decision support tools. The principle is to use existing tools in each of the case study basin but enhance them or integrate them by studying the added value of improved forecasts by the IMPREX climate partners. For four case study basins, this deliverable presents the current practice in drought management. A preliminary design based on the end-user-requirements is presented

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of the drought Decision Support System (dDSS). For all case studies, stakeholder meetings took place to assess their needs and requirements for the dDSS. Then this system was evaluated (“proof of concept”) in terms of skill and operational value. Based on the work so far, the next steps are summarized for the case studies: including improved forecasts, drought indexes, climate variability signals, and climate change impacts. For a summary of these activities please check the Summary of Chapter 3.


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1 Introduction

1.1 Rationale

A major threat to the agricultural sector in Europe is the potential for an increased occurrence of droughts, affecting the local and regional food security and economies. Droughts are periods with a temporary decrease of the average water availability. Rainfall deficiency is the main trigger of a drought period but the severity, duration and intensity of a drought depends on how the drought is managed and its propagation to the hydrological and agricultural productive system. Thus, the agricultural sector needs timely information on droughts to anticipate and take actions to reduce impacts, as for example reduced crop yields, drought emergency infrastructure, etc. Weather and Climate services (W&Cs) integrate weather and climate forecasts into customised tools. These services generally enable a more systemic approach to risk management, leading to weather & climate-smart, strategic decisions at various levels for a range of users (businesses, the public sector, and individuals) and supporting EU mitigation and adaptation policies. Some W&Cs focusing on drought risk in the agricultural sector are already available and used by some part of the sector on different decision-making levels. On the pan-European level, the European Drought Observatory provides a few indicators that are related to agriculture (soil moisture, vegetation greenness, etc), and a more tailored product to the agricultural sector is provided by JRC on a regular basis: the MARS Agricultural Drought Bulletin. These products are used already by decision-makers at the European level and large businesses as insurance. However, use of W&C for seasonal drought risk by decision makers acting on the regional level (agricultural policy and water resources management) so far is limited. At this level, W&Cs require some level of tailoring to the local situation, especially for irrigated agriculture. In several drought-prone Mediterranean basins, for drought risk monitoring water authorities developed their site-specific hydrological drought indices based on observed data and with a well-understood relationship with the dependent services. However, drought-impact forecasting at the regional scale for the agricultural sector is limited. Thus, for W&Cs to be useful for regional decision making on drought and agriculture, they need to be co-developed with the respective end-user. The IMPREX project investigates the value of improved predictions of hydro-meteorological extremes at short-, medium- and long-range in a number of sectors, including the agricultural sector (Van den Hurk et al., 2016). By means of several representative case studies, the needs and expectations of the sector are assessed and improved services are being evaluated with end-users. These case studies concern different geographical areas in Europe (south-eastern Spain, northern Italy, Greece, Netherlands), and are

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supported by close collaborations between scientists and stakeholders in the agricultural sector.

1.2 This deliverable in context

Generally, the agricultural sector is most concerned with drought risk at the seasonal and inter-annual timescale. However, decadal trends and climate change can exacerbate drought risk on the long term. Short-term forecasts on the other hand are relatively common and often used in the agricultural sector. To assess the current use and needs of W&Cs in the agricultural sector, stakeholder consultations took place, backed-up by a survey. The outcomes of this survey are presented in Chapter 2. IMPREX should bring drought predictions into local practice for the agricultural sector (Task 11.2). The objective is to incorporate seasonal predictions from the IMPREX partners in management procedures and tools. Currently, in several basins, Drought Management Plans and monitoring systems exist that can be enhanced by using these seasonal predictions and as such better anticipate to drought. Chapter 3 presents current practice (Task 11.1), user requirements and the proof of concept of a preliminary design of a drought Decision Support System (dDSS) for the four case studies. The dDSS developed in IMPREX for the case study areas focus on irrigated agriculture, as for irrigation, seasonal climate forecasts require advanced hydrological and water allocation tools, while for rainfed agriculture climate information is sufficient and there is less room for changing management practices. However, several activities in WP11 deal with rainfed agriculture and will be presented in deliverables further on in the project. A short overview:

- The current use of drought indices in the case study basins is detailed in this deliverable D11.1, but the in-depth exploration of new relationship between drought indices and agricultural impacts (also rainfed agriculture) is ongoing work (Task 11.1) and will be presented in a later phase (month 36) in D11.2

- The incorporation of relationships between large-scale climate variability indices (NAO, ENSO, etc) and agricultural impacts in rainfed agriculture is also ongoing work (Task 5.1 and Task 11.4). First outcomes of this are presented for the Lake Como case study in this deliverable. The outcomes of this task will be presented in D11.4 (month 48)

- The climate change timescale will be covered in Task 11.5: for the case study basins, climate change forcings (RCMs) will be used to assess the influence of increased climate variability on drought and agriculture related water accounting indicators. The framework used makes a clear distinction between rainfed and irrigated agriculture and several indicators will be presented that focus on the relationship between both (as was also presented during the poster session at the GA in Reading, UK). The same hydrological models will be used as in the


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dDSS (presented in this deliverable). The final results of this work will be presented in D11.5 (month 48).

- Then, in Task 11.3 a drought risk management instrument is being co-developed with end-users for two of the case study basins (Rhine-Meuse and Jucar), based on cost-efficiency of mitigation measures and probabilities of joint occurrence. This will be presented in D11.3 (month 36).

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2 Stakeholder survey

Summary

To map the current use and needs for weather and climate services in the agricultural sector, the IMPREX WP11 partners consulted with key stakeholders in the basins to assess their needs for enhanced weather and climate services focusing on drought. Based on the positive experience in the Hydropower WP8, it was decided to extend this consultation by developing and conducting a survey among these same stakeholders and several others in the case study basins. The survey was exactly the same for all basins and available in different languages – allowing good comparison among the cases. The outcomes were analyzed and provide a useful picture of what the agricultural sector in these basins require in terms of climate services and drought. The survey has been analysed distinguishing between 3 stakeholder groups: (1) irrigators and irrigators´ associations, (2) water resources planning authorities and partnerships of water users responsible for water allocation, and (3) agricultural research groups or researchers that take a leading role locally in advising the sector on the use of climate information and agriculture. Overall, group 1 and 3 seemed to have roughly the same interests, while group 2 generally responded distinctly. Clearly, the sector is used to include weather forecast information in their decision-making, but mostly on a day-to-day or weekly basis. Seasonal and long-term predictions are currently not or hardly used. The survey has indicated that there is great interest in seasonal predictions of precipitation to better foresee changes in water availability, and temperature to assess irrigation water requirements. Currently, most respondents use forecasts with a lead-time of a couple of days. Especially group 2 involved in regional planning indicated that they would like to see this increased to a couple of weeks or a couple of months. Also, this group indicates it would like to see more services with a monthly resolution. Group 1 and 2 (irrigators and research/advisory) also indicated their interest in short-range, even hourly information. This however does not relate to drought issues but to extreme rainfall or hail events. Climate projections receive moderate interest by group 2 (planning). Overall, the survey confirms that the key target group for IMPREX are the organizations or authorities involved in the regional planning and allocation of water (group 2 of the survey). This group considers there is scope for including seasonal forecasts in their decision making process. Thus, IMPREX will work directly with this target group to co-develop the drought decision support systems providing tailored information, i.e. predictions of local drought indices and drought risk assessments.


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2.1 Introduction

Within WP11, the needs for W&Cs in the agricultural sector will be evaluated. This requires good interaction and a participatory approach in the development of these services. A first step, is to assess what is currently used by the sector and evaluate the expectations for improved or new W&C services. This survey assesses the type of forecasting information that is currently being used, and which type of information and service stakeholders expect to be most beneficial. The IMPREX WP11 partners have developed and conducted this survey to map and analyze the needs of users that are involved in irrigation management and planning in the agricultural sector. The survey was distributed among the different stakeholders of the Mediterranean case studies, where drought and water scarcity are common issues. Also, related organizations (research, authorities, irrigators), not directly involved in IMPREX were contacted and several of these responded to the survey. Similar to the survey carried out for the Hydropower sector in WP8, the survey was structured as follows:

- Profile of respondents - Reasons for not using W&C services - Current use of W&C services - Current applications - Expectations on improvements - Other issues

Please check the Annex for the full survey.

2.2 Analysis of the survey responses

2.2.1 Profile of the respondents

In total, 22 respondents filled in the survey. The respondents were related to one of the four Mediterranean case study basins:

- Jucar River Basin, Spain - Messara River Basin, Greece - Segura River Basin, Spain - Lake Como River Basin, Italy

The respondents filled in the name of the organization they are working for. These organizations were classified according to the following three major categories (see Figure 1):

1. Irrigators and irrigators´ associations 2. Water authorities and partnerships responsible for water allocation 3. Agricultural research and advisory community

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Figure 1. Respondents classified in 3 categories: Irrigators, Water authorities and

Agricultural Research

Group 1: Irrigators and irrigators´ associations Irrigators are often organized in water user associations, or “irrigation communities”. These organizations are made up of farmers who share a water concession, for either a surface water supply, or a groundwater extraction permit. In Spain, this type of associations is regulated by the Spanish Water Law, and the River Basin Authority approves their statute. These non-profit organizations collect the water fees from their members and coordinate, distribute and control the allocation to the irrigators. They also construct and maintain the needed infrastructure for water distribution. On Crete (Greece), irrigators are organized in so-called “Organizations of Land Degradation” which are in charge of the distribution, the pricing of the water and the maintenance of the irrigation network. These organizations are governmental private entities governed by a council of 7 members. They cover specific areas (irrigation zones) and consist of the farmers included in these zones usually from a few hundreds to thousands. Group 2: Water authorities and water user partnerships In the Mediterranean study basins (Jucar, Segura, Messara), the River Basin Authority or the Directorate of Water decides on the water allocated to the different water user organizations. These decisions take place generally each season and depend on water rights, and water availability in storage reservoirs. In Spain, different water user associations can also be united into one larger organization, which allows them to negotiate the water allocation process in a more coordinated way. These larger organizations (different water users that compete for the same water source) are included in this group of the respondents, as they decide internally on the allocation to each of their member organizations. So, this group of respondents includes both the public organizations that take the final decisions on the water allocations, as well as the non-profit organizations that negotiate at the same level for the regional water planning decisions, representing the interests of a large group of water users.

31.8%

27.3%

40.9%

Irrigators

Water authorities

Agricultural research


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Group 3: Agricultural Research and Advisory In the Mediterranean area, public applied research institutes play a key role in the development, transfer and adoption of new technologies in agriculture. In the study areas, agriculture is an economic sector that is very much influenced by public finance and subsidies. Technological progress and developments are mainly driven by public applied research programmes. This includes: new irrigation technologies, irrigation advisory support, public extension services, etc. As an example, in the Segura River Basin, there are 4 public research institutes that carry out applied research for the agricultural sector. Most of their funding comes either from national R&D budgets, or regional budgets for agriculture. Often, part of their funding comes from the sector itself. The agro-meteorological station network and irrigation advisory support is also managed by this type of institutes. Therefore, these public entities (universities, research councils, technology centres) and the experts working in water-related issues, constitute a critical stakeholder group for the survey, as they often work directly with early adopters on new services and tools. For this survey, only people from these institutes were included that work principally on the use of climate information in agriculture, and that have a close network of farmers and stakeholders with which they collaborate on these particular issues.

2.2.2 Current use of W&C services

The first questions in the survey assessed how the respondents use W&C nowadays. Most of the respondents (17 out of 21) has indicated that they currently make use of weather and climate predictions. All the respondents using predictions obtain them from free public data sources, mostly weather/climate service centres. The survey gathered information on the type of information that is used currently, see Figure 2:

- Group 1 uses daily resolution data, at municipal level, with a few days of lead-time. They use mostly precipitation (P) and temperature (T) forecasts.

- Group 2 uses both daily as well as monthly/seasonal forecasts. They use data at municipal but also at provincial level. Lead times range from a couple of days to a couple of seasons. Forecasts used include Humidity and Wind speed forecasts, besides P and T – possibly for calculating reference evapotranspiration rates.

- Group 3 indicated to use forecasts with similar characteristics as the Irrigators group 1. One notable difference is that 2 (out of 7 in this group currently using W&C) of the respondents also indicated to use hourly information.

None of the respondents indicated to use national or global W/C services.

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Figure 2. Temporal and spatial resolution, and lead times of the currently used

W/C products, per group of respondents

The respondents considered the quality of the forecasts reasonably good (see Figure 3). Some of the respondents were dissatisfied with the quality (4 respondents with scores of 6 or below). Two of the respondents gave a very high score (9). The survey further indicated that the main criterion in judging the quality of the forecast products is “whether it correctly predicts the occurrence of the events most of the time”, e.g. whether it correctly predicts a decrease in water availability yes/no or increase in temperature yes/no. In total, 88% of the respondents found this the most important criterion, opposed to the second option “whether it correctly predicts the magnitude of the events most of the time (12%). The above suggests that the primary issue of concern is whether the W&C is able to predict the sign of the change in the variable of interest (negative vs positive deviations of the mean), while the stakeholders consider it secondary if the system is able to predict the exact size/volume/degree of change.

Not used A couple of hours A couple of days A couple of months A couple of seasons Don’t know

Not used Municipal level Provincial level National level Global level Don’t know

(a)

(b)

(c)

Not used Hourly Daily Monthly Seasonally Don’t know

Precipitation forecasts

Temperature forecasts

Solar radiation forecasts

Others

Humidity forecasts

Wind speed forecasts




Others

Humidity forecasts





Others

Humidity forecasts


1 2 3 4 5

1 2 3 4 5

1 2 3 4 6 751 2 3 4 5

1 2 3 4 6 75

1 2 3 4 6 751 2 3 4 5

1 2 3 4 5

1 2 3 4 5

Nr. of respondents

Nr. of respondents

Nr. of respondents

Irrigators Water authorities Agricultural research


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Figure 3. Score (1-10) of the quality of the used forecasts

2.2.3 Current applications of W&C services

The survey included two questions on the purpose the W&C applications are used for currently. There were four options from which the respondents could choose (see Figure 4):

1. “For irrigation water management” – most of the respondents indicated they use their forecasts for this purpose. Most likely they use them for irrigation scheduling and related management processes (fertilizer inputs, planning of on-farm water storage, groundwater pumping).

2. “To decide on which crop to grow and when” – only one respondent indicated they use the forecasts for crop scheduling

3. “To monitor possible droughts” – two of the respondents indicated they use the forecasts for more drought monitoring.

4. “To anticipate extreme rainfall events”. This option was included to evaluate whether flooding (water excess can be harming crop production, but also complicate farming operations on the field) could be of concern for irrigators. Only one respondent indicated this to be the current application.

It must be noted that “irrigation water management” is a rather broad term, and some respondents may have understood that it entails also irrigation water planning (seasonal timescale). Another weakness of this question is that it did not specify for other extreme events that may be of concern to the sector, but are out of scope in IMPREX, as:

- Hail – for certain fruit crops a key concern, causing considerable economic damage in the Mediterranean region

1 2 3 4 5 6 7 8 9 10

Score

0

1

2

3

4

5

6

7

8

Nr.

of re

spon

den

ts

21

- Heat waves – the impact on crop production is still less understood but an increasing problem in Mediterranean areas, mostly for fruit trees and grapes

Figure 4. Current applications of the used W/C services (up) and frequency of use

(down)

The majority of the respondents reported a frequency of using forecasts to be ‘once per day’ or ‘once per week’ (see Figure 4), which is consistent with the abovementioned use of forecast for irrigation management decisions.

2.2.4 Expectations on W&C services

The survey included several questions on the expectations of W&C services. These questions were also responded by the respondents that did indicate they currently don´t use any forecasts. In total, there are five respondents (from different stakeholder groups) who do not use the forecasts, and the main reasons of not adopting it are (see Figure 5):

1. lack of tools to make the forecast information operational; 2. mismatch of the desired forecast information and the forecast available; 3. poor quality of the forecast for the interested lead-time;

76%

6%

12%

6%

For irrigation water managementTo decide on which crops to grow, and whenTo monitor possible droughtsTo anticipate extreme rainfall events

24%

41%

29%

6%

Several times per dayOnce per dayOnce per weekOnce per month


22

Figure 5. Reasons why part of the respondents don´t use W&C services today

Then, the respondents indicated their interest in the type of information they would like to see in future improved W&C services. A wide range of options were available, targeted to the agricultural sector (crop yield forecast, soil moisture, etc). Figure 6 shows the outcomes.

Figure 6. Interest in forecast information, per group

In general, all listed forecast information received varying degrees of interests (from ‘little interested’ to ‘Highly interested’). Some patterns can be observed per group:

- Irrigators seem to be interested especially in short-range precipitation forecasts, but also forecasts on heat waves. They indicate to have little interest in decadal climate projections

- The water authorities instead, show similar interests as the irrigators, but are also interested in drought indices, long-term forecasts as well as the climatic projections

- Agricultural researchers indicate similar interests as the irrigators, but also include drought indices, seasonal and climate projections

we do not need it

lack of tools to make theforecast operational

lack of human resource toproduce forecast data

lack of human resource toprocess forecast data

we cannot get what we wantfrom currently available forecast

difficulties to obtain/access thedesired forecast information

poor forecast quality for the leadtimewe are interested in

we do not have budgetto buy them

our peers are not using it

we do not know what is avaiable

Strongly disagree Partly disagree Partly agree Strongly agree No opinion

Nr. of respondents

Not used Little interested Very interested Highly interested No opinion

Irrigators Water authorities

Nr. of respondents

Agricultural researchStorm forecastFlood forecast

Short-range precipitation forecastMedium-range precipitation forecast

Short-range temperature forecastMedium-rangetemperature forecast

Heat wave forecastCrop yield forecast

Frost yield forecastSoil moisture yield forecast

Meteorological drought indexClimate risk index

Sub-seasonal to seasonal climate forecastsDecadal climate projection

0 2 4 6 80 1 2 3 4 5 6 0 1 2 3 4 5 6

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The respondents were asked to rank their interest for several options of improved forecast information using scores from 1 to 6. For the purpose of this deliverable, only the average rank per group and type of information is shown, see Table 1.

Table 1. Ranked interests in different type of improved forecast information (scores 1-6). Rank averages per group

Overall, the option “better probabilistic weather forecasts” received the highest rank, especially for the irrigators. Also “better forecasts of weather extremes” received a high rank from the irrigators. For the water authorities, “more scenarios of weather forecasts” (i.e. probabilistic forecasts) was considered the most important. For the researchers, not a clear preference can be distinguished. Please note: this question was not fully understood by some of the respondents (as was clear from a few emails), so the outcomes need to be taken with caution. The survey also included questions on the desired temporal and spatial resolutions, and lead-time of the improved W&C services. Again, the three different groups are discussed separately (see Figure 7):

- The irrigators are interested in high-resolution information (5x5 km) on a daily time step. Surprisingly none of the respondents replied they were interested in hourly information. Lead times of a couple of days to a couple of weeks

- Water authorities indicated they are interested also coarser information (approx. 10x10km – 100x100 km). Temporal resolution either monthly or seasonal, and lead-time from a couple of weeks to monthly

- The researchers indicate they are interested in hourly information, in fact more than half of the respondents indicated this. On lead-time, there is not a clear signal.

IrrigatorsWater

authoritiesAgricultural

researchBetter forecasts of weather extremes 4.0 3.5 3.8Better probalistic weather forecasts 5.8 3.8 4.0More scenarios of weather forecasts 2.8 4.2 2.3Weather forecasts for longer lead times 3.0 3.5 3.8Higher spatial resolution of the weather forecasts 3.2 3.2 3.8Higher temporal resolution of the weather forecasts 2.2 2.8 3.5


24

Figure 7. Interest in spatial resolution (a), temporal resolution (b) and lead-time

(c) for improved W&C services, per group

The survey included several optional questions (3). Outcomes of the first one is shown in Figure 8, on the importance of measurement issues to agricultural water management, it can be observed that “Improving precipitation measurements”, and estimation methods for irrigation water requirements” are considered to be ‘Extremely important’.

50%

17%

17%

17%

33%

33%

17%

17%

63%

38%

63%

38%

60%

40%

57%

43%

50% 50%

40%

60%

14%

43% 14%

14%

14%

a couple of hours a couple of days a couple weeks a couple of months Not sure

hourly daily monthly seasonally Not sure

about 5x5 km about 10x10 km about 40x40 km more than 100x100 km would be fine Not sure

Irrigators Water authorities Agricultural research

(b)

(c)

25

Figure 8. Importance of measurement issues agricultural water management

A second optional question was related to the type of applications and decision support systems efforts could be directed to. The third optional question was on dissemination, communication and stakeholder engagement. For these two questions, no clear preference was found towards one or more of the options presented. See Annex A for the full outcomes of the survey.

2.3 Discussion

Since many decades, researchers and the agricultural sector have been developing different types of W&Cs for farmers and irrigators that incorporate agrometeorological forecasting information and advice farmers on irrigation scheduling. Some of these tools have been successfully adopted in some areas, others not. Most of these existing tools use short-range (lead-time of a few hours, days) information. Also, the use of historic climate information is very common, for example to estimate crop water requirements. However, the use of forecasting information on other time-scales and lead-times is limited. The survey confirms that there is interest, especially for seasonal predictions. More specifically, when comparing current use with expectations, the survey demonstrated:

- Lead-time: currently, most respondents use forecasts with a lead-time of a couple of days. Part of the respondents (especially those involved in regional planning) indicated that they would like to see this increased to a couple of weeks or a couple of months, especially for the water authorities

- Temporal resolution: currently most of the respondents use services with a daily resolution. There seems to be interest in more services with a monthly resolution, especially for the water authorities. Researchers also indicated their interest in hourly resolution.


26

- Spatial resolution: most of the respondents indicated that they currently use forecasts at the municipal or provincial level. This probably corresponds to forecasts of around 10x10 km or coarser. The resolution they would like to see in W&C services is finer, between 5x5 – 10x10 km

The above is likely to depend very much on the application the respondents have in mind. It appeared that currently most respondents use W&C services for irrigation water management. The shift in interest towards more long-range and seasonal W&C services might indicate that the sector considers there is scope to integrate this information for seasonal planning of resources and operations. The type of information that is of interest is principally precipitation, but also temperature (heat waves) and other variables relevant for the estimation of irrigation water requirements. On the other hand, the researchers have indicated they have preference towards having access to information on hourly basis, for better anticipation to extreme events (hail and rainfall). The reason they are interested in this type of information is because this hazard concerns a major economic threat to farmers. Hail can cause considerable damage to fruit crops in Mediterranean areas and is generally difficult to foresee. Farmers use various strategies to cope with this risk: insurance, nets, hail cannons and similar. Extreme rainfall events often complicate operations on the field, but can also cause damage to crops due to pests and diseases and water logging. These risks are not studied in the IMPREX project but can be valuable opportunities for further research in W&C services and applications. The survey has been distributed among the IMPREX network and especially brought under attention of the stakeholders in the Mediterranean case study basins. For all respondents (even the researchers), irrigation water management and planning belonged to their core activities. It has to be noted though that the agricultural sector entails more businesses that are indirectly dependent on water resources and W&C services in the sector. An example:

- Businesses providing inputs to the agricultural sector (fertilizers, machinery, etc) - Agricultural insurance - Food industrial sector - Distribution and logistics - Retail

IMPREX focuses on the stakeholders that are directly involved in water resources decision making – therefore this part of the sector was not integrated in the survey. It could however be interesting to evaluate how drought issues are of concern for other indirect activities in the sector.

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3 Case studies: current practice, user requirements and proof of concept

Summary

IMPREX should bring drought predictions into local practice for the agricultural sector in several case study areas. The objective is to incorporate improved seasonal hydrometerological predictions in management procedures and decision support tools. These tools are co-developed with end-users to make sure they are useful today, but they will likely have an even higher operational value in a drier future. For four case study basins, this deliverable presents the current practice in drought management. A preliminary design based on the end-user-requirements is presented of the drought Decision Support System (dDSS). For all case studies, stakeholder meetings took place to assess their needs and requirements for the dDSS. Then this system is evaluated (“proof of concept”) in terms of skill and operational value. Based on the work so far, the next steps are summarized for the case studies: including improved forecasts, drought indexes, climate variability signals, and climate change impacts. The dDSS of the Segura case study (SE-Spain) targets two stakeholders and their corresponding management system: (1) the River Basin Authority that monitors several hydrological drought indicators and takes actions based on these as stipulated in the Drought Management Plan (DMP), and (2) Irrigators´ Association representing all irrigators that rely on water from an external water transfer. For both stakeholders/management systems, a climate model-based seasonal forecasting system was developed and is being tested comparing it with current practice. Preliminary outcomes show that there is scope for improvement: the ERA-INTERIM forcings show to be sub-optimal for the initializations and more efforts should be paid to bias correcting the seasonal forecasts. At the same time, it seems in comes cases abrupt changes in drought status might be better captured by the dDSS than forecasts that are currently done by the irrigators based on an analogue method. The dDSS for the Messara valley (Crete, Greece) will collect seasonal climate and hydrological forecasts targeting a wide range of stakeholders. A lack of coordination in water resources management and overexploitation of the aquifer creates tension amongst the users. The interactions with the users and stakeholders revealed that although long range and seasonal predictions are not currently used, there is a strong interest for a transparent dDSS including seasonal forecasts of the relevant variables. The dDSS is under development and includes (a) sources of skilful short to medium term forecast information, (b) tailored drought monitoring and forecasting indices for the local groundwater aquifer and rain-fed agriculture, and (c) seasonal inflow forecasts for the local dam through hydrologic simulation to support management of freshwater resources and drought impacts on irrigated agriculture. First outcomes are promising but still further work on bias-correction and tailoring is needed. Next steps also include analysing the system response under climate change.


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For the dDSS of the Lake Como system a rigorous benchmarking analysis and assessment of the operational value of various forecast products were carried out. For the benchmarking, the performance of current system is evaluated via model simulations given observed boundary conditions. The integrated water system model is constructed in a way to embrace the main hydrological mechanism as well as the stakeholders’ decision-making processes. Then, a number of forecast information sources are routed into the Lake Como dDSS and assessed for their operational value using the Information Selection and Assessment framework (Giuliani et al., 2015). The candidate information includes both forecast products from WP3/4 partners, drought indexes, and indicators describing low frequency climatic signals (e.g., El Nino Southern Oscillation). Preliminary results reported here already demonstrate improved performances of the system informed by better forecast or additional information in the management design. The following sections describes the work in more detail for the four case study areas:

1. Segura river basin, SE-Spain 2. Messara Valley, Crete, Greece 3. Lake Como, Northern Italy 4. Jucar River Basin, Spain

The WP11 activities by UPV in the Jucar River Basin have just started, as they have just been able to extend their team. The results will be added as Annex to this deliver-able in 6 months time. The UPV has been actively part of the survey, and many of the responses were coming from stakeholders in the Jucar River Basin (previous section).

Each of the case-study sections has the following sub-sections:

1. Background – with a short description of the study area and the problem scope 2. Current practice – what is currently being used and done related to drought

management and forecasting 3. User requirements – outcomes from the stakeholder consultations 4. Preliminary design requirements – the drought Decision Support System to

be developed, what are the specifications responding to the user needs 5. Proof of concept – first performance validation of the system 6. Testing plan – next steps

3.1 Segura River Basin, Spain

3.1.1 Background

Study area description: The Segura River Basin (SRB) is located in the south-eastern corner of the Iberian Peninsula with an extension of 20,234 km2 (Figure 9). This semi-arid Mediterranean region is characterized by a mean annual temperature of 18 °C and mean annual precipitation ranging between 300 mm in the downstream areas and 600 mm upstream. Most of the rainfall falls in a few intensive rainfall events that take place in

29

spring and autumn. The temperature conditions make the area suitable for profitable agriculture (fruit trees, horticultural, etc.) in spite of having the lowest percentage of renewable water resources of Spain and recurrent drought episodes. The River Basin Authority of the Segura Basin (Confederación Hidrográfica del Segura – CHS) is in charge of managing most of the water-related infrastructure and distribution of the water resources in the basin. During drought periods, CHS has to take decisions on the use of alternative water resources, as groundwater and desalinization, and their allocation across the basin taking into account water rights, water demands and environmental commitments. Part of the water demand of the Segura Basin is met with water from the Tagus basin in Central Spain through an infrastructure of about 300 km long. The Tajo-Segura Water Transfer (TSWT) (Figure 1) provides more than one-third of the total water demand for irrigation from the upper Tagus river basin – in total about 80,000 ha. SCRATS (Sindicato Central de Regantes del Acueducto Tajo-Segura) is a consortium of 80 irrigation association that rely fully or partly on the volume transferred to the Segura basin. This consortium represents their interests (in total 80,000 irrigators) and negotiates with the public authorities on their rights and allocations. This water transfer is said to contribute more than 2 billion euros per year to the Spanish GDP.


30

Figure 9. Study case basins location. Schematic of the water transfer infrastructure (pipes, pump-turbines, l if ting station, tunnels, aqueducts). Red

squares delimits upper Tagus and upper Segura r iver basins. Grey square delimits Segura river basin.

The SRB has a total of 15 storage reservoirs with a maximum storage volume of 1070 hm3, from which Fuensanta and Cenajo reservoirs represent together 60% of the total capacity. There are 2 main desalinization plants in the area. Furthermore, farmers make use of small desalination equipment to reduce the salinity of the low-quality pumped groundwater (brackish groundwater) and mix it with better quality surface water (i.e., Tajo-Segura Water Transfer) resources to get adequate water for irrigation. Finally, reclaimed water constitutes the last source of available water for meeting agricultural water demand. In summary, the SRB is a highly regulated system, ruled by a complex and controversial combination of water use rights, transfers and prices. Problem scope: Drought events are recurrent in the SRB. Over the last 35 years there were 4 drought periods (1980 – 1983; 1990 – 1995; and 2006 – 2008, 2015-current). The impacts are considerable: for example, the severe drought in 1994 led to an 11-19% reduction of production and a 14% reduction of irrigated area compared to average (CHS, 2007). Production losses due to the lack of irrigation water, in the last year of the extended drought period, amounted to 120 million euros. This reflects the direct economic effects of long lasting drought periods in the region, but of the same significance are the effects on other socio-economic factors, in particular direct employment, as well as induced employment in other sectors. Drought Management Plans (DMP) are in place for the Segura River Basin. Based on a number of hydrological drought indices, drought mitigation activities are coordinated and implemented. These measures should minimize the impact of droughts, especially on the agricultural sector which is normally affected first. Better predictions on drought episodes and particularly these drought indices could improve decision making on these measures. The water received from the external water transfer depends on a hydrological drought index calculated from reservoir storage. For decision making on the amount of water to transfer, this index is regularly forecasted using a simple statistical analogue method. The irrigators that rely 100% on this transfer could benefit considerably from enhanced forecasts of this drought index. This requires skill full seasonal hydrologic forecasts to be able to build the drought index for the irrigators and the DMP. This could benefit the River Basin Authority and the agricultural sector as a whole – taking better informed measures and decisions on water transfers.

3.1.2 Current practice

Current practice of stakeholder 1: River Basin Authority The drought mitigation measures in the Segura basin are based on a number of drought indicators that are mainly derived from available water in storage reservoirs. The water resources depend on the internal water resources (reservoir inflow from the basin itself) and water received from the external transfer from the Tajo. There is a drought index for both water sources. The drought indices consider 4 drought levels:

31

- Green – no drought - Yellow – pre-alert phase - Orange – alert phase - Red – emergency phase

The drought index is updated monthly on the website of the River Basin Authority (see Figure 10). Depending on the drought level, the PES (Confederación Hidrográfica del Segura (CHS), 2007) defines a set of measures:

• Strategic measures, activated under normal and pre-alert situations, aim at increasing availability, reducing demands and improving efficiency. They prepare the measures that must be activated in phases of less resource availa-bility.

• Tactical measures, activated under alert situations, aim at improving water management by reducing large consumptions putting into practice voluntary water saving practices (public campaigns to raise awareness and promote a sustainable use).

• Emergency measures, activated under emergency situations, aim at managing efficiently available resources by putting into practice restrictions on minority consumptions on a first stage, or major restrictions on later stages.

Several of these measures have considerable impacts on society and the regional economy. Clearly there can be a considerable benefit if drought can be forecasted with a reasonable level of skill: measures can be anticipated even better or unnecessary actions can be prevented.

Figure 10. Drought index since 2004 - Jan-2017 of the internal water resources of

the Segura basin


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Current practice of stakeholder 2: Tajo-Segura Transfer Irrigators A large share of the irrigators in the Segura Basin depend fully on the water received from the external water transfer from the Tagus river (Tajo-Segura Water Transfer (TSWT)) and are represented by the consortium SCRATS. They negotiate with the public administration on their water rights, water allocations and alternative water resources. They have a major stake in the water transfer: limited or no water transfer incurs high costs to this stakeholder and forces them to purchase alternative resources of lower water quality that potentially harm their crop assets. The decisions on the amount of water to be transferred take place periodically, depending on the water availability in the two storage reservoirs (Buendía and Entrepeñas) in the upper Tagus basin (central Spain). The storage capacity of these reservoirs is 1000 hm3, but the maximum transfer volume is limited to 600 hm3, from which 400 hm3 are allocated to agriculture (10% is assumed to be lost due to distribution losses and 140 hm3 are allocated to urban supply). Likewise, from the 400 hm3, 50 hm3 are assigned to the Jucar basin and 15 hm3 to water users in Andalusia. This transferred volume can be randomly distributed throughout the year not exceeding the transferable maximum total amount and giving always preference to the water demands of the users in the Tagus basin itself. The TSWT has transferred during since it started operating (year 1979) an average of 334 hm3 per year, following the exploitation rule described in Table 2. The water transfer decisions depend on the reservoir inflow for the forthcoming months. A decision rule exists that takes into account a regression-based seasonal forecast of the reservoir inflow. The water balance accounting for each projected month is calculated following Equation 1 , (given Vi, Qacc and annual maximum diversion):

Equation 1

Vi+1 = Vi + Qi – Vti – Ei – DT where,

- Vi+1 is the projected storage volume in Entrepeñas and Buendía (E+B) for the month i+1,

- Vi is the storage volume in E+B for month i (given value), - Qi is the projected discharge (inflow) at the reservoirs E+B for month I (based

on Table 3), - Vti, is the max. transf. volume for month i from the tabulated values of Table 2, - Ei , is the evapotranspirated water volume from the reservoirs for month i

(tabulated value), - DT, is the Tagus water demand to be satisfy for month i (tabulated value).

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Table 2. Tajo-Segura Water Transfer exploitation rule.

Levels Decision rules Max. transf. vol. (hm3/month)

Level 1 – ordinary situation Vi > 1500 hm3 or Qacc > 1000 hm3 68

Level 2 – abnormal situation Vi < 1500 hm3 (>Table 2) and Qacc < 1000 hm3 38

Level 3 – abnormal situation (Council of Ministers approval)

Vi < tabulated values per month that the limiting values

23

Level 4 – absence of surplus Vi < 240 hm3 0

Currently, forecasts of Qi are estimated from multiple regressions of river discharges from preceding months according to the procedure explained in the document: Exploitation rule of the TSWT (MMA, 1997). These stepwise regressions relate current river discharge with preceding month discharges at the same location, explaining current discharge as the sum of explanatory variables (up to 3) from preceding discharges (6 months). In summary, the regression coefficients are obtained modifying at each step the number and selection of preceding months in order to get all months statistically significant and maximizing the variance explained. Table 3 summarizes the variables (relations with preceding months) which explain the current month discharge as well as the variance explained by the relation: Table 3. Relations of the current month with preceding months discharges (persistence).

Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep R2 Oct x x x 0.56 Nov x 0.64 Dec x x 0.3 Jan x 0.51 Feb x 0.58 Mar x x 0.66 Apr x 0.66 May x x 0.59 Jun x 0.72 Jul x x 0.81 Aug x x x 0.81 Sep x 0.46 As an example, from Table 3 it is observed that discharge for October depends on discharges from April, May and September, with a moderate explained variance


34

(R2=0.56), while January and April just depend on preceding months (December and March, respectively) with a similar explained variance (R2=0.51 and R2=0.66, respectively). Discharge for July depends not only on preceding discharge of June, but also from discharge of April, achieving a better explained variance (R2=0.81). Relations are established from analysing a time series of 84 years (from 1912). However, it must be pointed out that there is a remarkably decreased rainfall contribution in most of the major river basins in Spain from the 80´s, which might lead to outdated specified coefficients or relations. The document establishes using Q80 values of forecasted discharges, as a conservative recommendation. Consultations with the stakeholder has revealed that they find this forecasting system has considerable limitations. It is under-performing is certain conditions and they have already tested improvements but without success.

3.1.3 User requirements

As presented previously, we will focus on two main stakeholders in the basin: 1. The Segura River Basin Authority 2. Irrigators relying on the Tajo-Segura transfer (SCRATS)

With both stakeholders, a physical meeting was organized with the key experts and decision-makers and afterwards regular contact via e-mail on issues and questions on the current practice (previous section). 1. Segura River Basin Authority The interaction took place with the technical staff and head of the planning department. They have indicated that a seasonal forecast of water availability and the drought indices could potentially improve their decision-making. The drought indices they use currently perform well and in principle they see no need to change them. However, they see scope in studying the usefulness and skill of forecasts of these indices over the following months or next year. In some occasions, the users and general public have requested this type of forecasts and they have not been able to respond. They stress that currently the lack of budget and capacity to actually integrate this type of tools in their day-to-day management. In short:

- Use of existing hydrological drought index, no new one - Lead-time – at least 6 months - Reservoir inflow forecasts, but also assumptions needed on water release from

reservoir 2. Irrigators (SCRATS) A meeting took place with the director of the consortium SCRATS and various experts. Also the expert responsible for carrying out the seasonal forecasts was present and afterwards some interaction has taken place on their current practice.

The currently used regression-based forecasts are not found fully satisfactory for making longer-term forecasts. Also on the transition from wet to dry periods, the methods under-

35

perform. This is actually essential to them so they have been looking for improvements.

In short, the conclusions of their expectations are:

- They would like to see a lead-time of about 6-months: that gives margin to negotiate with politicians on the water transfer and other actions

- They are interested in a probabilistic presentation of the forecasts: some initial ideas shown were found attractive.

- They would like to see how such system compares to the forecasts they carry out currently, especially during critical situations.

- Variable of interest: reservoir inflow to the two reservoirs in the Upper Tagus, and the corresponding drought level

3.1.4 Preliminary design specifications

3.1.4.1 Forecasting system A climate-model-based seasonal hydrologic forecasting (CM-SHF) system (Yuan et al., 2015) is built for:

- The Upper Segura - The Upper Tagus

The forecasted reservoir inflows will be used to calculate probabilistic forecasts of the drought indices used by the respective targeted end-users (River Basin Authority for Segura; Irrigators for Tagus). A few specifications of the CM-SHF system are listed here:

- It uses the Spatial Processes in HYdrology model (SPHY) (Terink et al., 2015) - First setup uses the 15 ensembles of the ECMWF Seasonal Forecast Systems

4 (Molteni et al., 2011), - Outputs of interest are monthly river discharge inflows for the main reservoirs

o Buendía and Entrepeñas for Tajo o Fuensanta and Cenajo for Segura

- The first setup focuses on 4 seasons, with lead-time of 3 months. Initialisation months are January, April, July and October. These initializations months were chosen because the user-forecast (see current practice section) is also done in these months.

- The model is set up for the upper Tagus basin (5x5 km) and the upper Segura basin (2x2 km)

- ERA-INTERIM was used for the climatological forcing

The SPHY model was calibrated for the 1980-2000 period (using 1979 as a spin-up year) against discharge observations at four stations (two per basin) located at the major storage reservoirs: Entrepeñas and Buendía in the Tagus basin and Fuensanta and Cenajo in the Segura basin. The calibration took place using the SPOTPY tool (Houska et al., 2015) and the Simulated Annealing algorithm (Bertsimas and Tsitsiklis, 1993) on 9 parameters that were found to be sensitive. RMSE of monthly discharge is chosen as the statistic for the objective function. The ERA-Interim precipitation dataset is bias corrected/downscaled using the best available gridded climatological observation dataset for Spain (Spain02) (Herrera et al.,


36

2016). The adjustment is with a gridded correction factor, calculated by dividing the monthly climatology of Spain02 with ERA-INTERIM. Temperature forcings are corrected by applying a lapse rate to the Digital Elevation Model. 3.1.4.2 User interface The user interface will consist of a web-based application showing the evolution of the selected indicator corresponding to each stakeholder. Predicted reservoir inflows will be translated into probabilities representing different levels (pre-alert, alert, emergency). There will be two final prototypes of the drought decisions support system:

- For the Segura, with end-user Segura River Basin Authority. The probabilities of predicted monthly drought indices of the internal water resources, based on the current procedures to calculate these indices

- For the Tagus, with end-user the Irrigators (SCRATS). This system will show the probabilities of predicted levels of monthly max. transferred volumes, as established in Table 2 and obtained from iterative calculations of Equation 1, in order for them to know how much available water can count on for irrigation for the forthcoming months, on the basis of the drought level of the system.

Figure 11. Prototype of the DSS interface. Predicted reservoir inflows are

translated into probabil ities for the different drought levels over the following months

Jan Feb Mar Apr JunMay

Rese

rvoi

r inf

low

(hm

3/m

onth

)

Today

37

3.1.5 Proof of concept

The gridded bias adjustment maps were calculated by dividing the monthly climatology of Spain02 with ERA-Interim. The mean bias adjustment ratio was 1.7 for the Upper Segura and 1.4 for the Upper Tagus. These bias adjustment maps were applied to ERA- Interim and the ECMWF-Sys4 dataset, assuming that both datasets would be similar in terms of bias. First outputs show that the ECMWF-Sys4 simulations lead to a higher mean bias in the streamflow than the reference simulation with ERA-Interim. This suggests that not the same bias adjustment factors can be used for the ECMWF-Sys4 dataset as for the ERA- Interim forcings. In fact, analysing all datasets together (Figure 12), it should be noticed that the ERA-Interim monthly precipitation means do not correlate with the ECMWF-Sys4 means, thus confirming both datasets different bias ratios. There is a general underestimation of precipitation in both basins when comparing the ERA-Interim and ECMWF-Sys4 datasets with the Spain02 dataset (taken as a reference), being larger for the upper Segura. In general, the ERA-Interim values are below the mean value of the ECMWF-Sys4 ensembles, being even out of the range of the 15 ensembles climatology for some months: Jan, Mar, May, Jun, Jul for the upper Tagus, and Mar for the upper Segura.


38

Figure 12. Upper Tagus (top) and Upper Segura (bottom) monthly precipitation

climatology (period 1980-2010). Box-plots represent ECMWF 15 ensembles. AEMET values represent the mean of 4 meteorological stations from the Spanish

Meteorological Service in the area.

The validation of the SPHY model after calibration (using ERA- Interim) is analysed (monthly means of discharge flows) for the period 2000-2010 on the upper Tagus (Buendía and Entrepeñas stations together) and the upper Segura (Fuensanta station). The model performance statistics (Mean Error, Root Mean Square Error, Percent Bias, Nash-Sutcliffe Efficiency and Correlation Coefficient) for both the calibration and validation periods are shown in Table 4. There is almost no bias in the validation period of the Upper Tagus simulations (ME=0, PBIAS=0.2) while the Fuensanta results show an underestimation around 20% of discharge flows (ME=-1.1, PBIAS=-19.5). On the contrary, the model performs slightly better for the Fuensanta station than for the Upper Tagus, with higher values of R (0.8 and 0.5, respectively).

Table 4. SPHY model performance for the upper Tagus and Fuensanta stations and the calibration (1980-2000) and validation periods (2000-2010).

Upper Tagus Fuensanta Calibration Validation Calibration Validation

ME (m3/s) 0.2 0.0 -1.3 -1.1

RMSE (m3/s) 16.4 20.4 4.0 4.6

PBIAS (%) 0.8 0.2 -25.0 -19.5

R 0.6 0.5 0.6 0.8

In general, looking at the hydrographs of the upper Tagus system (Entrepeñas + Buendía) (Figure 13 and Figure 14), the model reference run forced with the ERA-Interim dataset is capable of distinguishing between wet and dry periods and the simulated curves represent peak-flows very well. Nevertheless, there is a mismatch between observations and simulations, especially after precipitation events, when the

39

simulated flows decrease at a lower rate than the observed ones. The selection of RMSE as the objective function for the calibration of the model might lead to favour high peaks rather than low peaks, as seen for instance when comparing the wet period of 1984-1990 with the dry period of 1990-1996 in Figure 13.

Figure 13 and Figure 14 show the mean and 5-95 percentiles of the 15 ensembles. For these figures, the ensembles were corrected with the mean a-posteriori bias found comparing simulations with streamflow observations (0.6 value). This is due to the fact that the ECMWF -Sys4 dataset was corrected using the same bias factor as for the ERA-Interim dataset, which should have been done separately, as shown in Figure 12 and explained at the beginning of this section. The ensembles mean value seems to follow reasonably well the observed values but peakflows are in general relatively high. This needs to be further analysed and a proper bias correction of the ECMWF-Sys4 dataset should be carried out (of the forcings or simply a-posteriori of the streamflow predictions). On the contrary, the reference ERA- Interim run shows a slow recession after rainfall events, while the ECMWF-Sys4 simulations appears to respond faster which corresponds better to the observed response.

Looking in detail at the drought period in Figure 14 (2004-2010) and comparing SPHY-ECMWFSys4 with the regression-based user forecast from SCRATS, both systems seem to perform similar, although the user forecast tends to fail in representing high peaks. That is the case of the periods Jan-Apr 2008, 2009 and 2010. The user-forecast is purely based on antecedent flows (Table 3), so in the transition period (wet-to-dry and dry-to-wet) such a method is likely to underperform. Clearly this can be an opportunity for the deterministic climate model-based approach we target in IMPREX: failing to represent these transitions during such critical periods might lead to wrong management and planning decisions, and in major economic losses by implementing excessive and unnecessary drought mitigation measures.

Table 5 summarizes the statistics of the hydrographs shown in Figure 15, where the monthly averages of reservoir inflows (simulated and forecasted) are analysed for each initialisation month (Jan, Apr, Jul, Oct) and lead time (FP1 = 1 month, FP2 = 2 months, FP3 = 3 months). The continuous ranked probability score (CRPS) (Hersbach, 2000) was assessed for the SPHY-Sys4 system, while the MAE was calculated for ref_run system (simulated discharge of ERA-Interim) and the user forecast system (forecasted discharge based on multi-regressions by SCRATS). For the SPHY-Sys4 system values in Table 5 and Figure 15, a-posteriori bias correction (0.6 value) was carried out. Both the SPHY-Sys4 system and the user forecast system present similar performance, when comparing CRPS to MAE, being slightly better for the latter for initialisation months April, July and October and all lead times. January (FP1) seems to be better represented by SPHY-Sys4. The ref_run system´s worse results are likely due to the lack of a-posteriori bias correction adjustment on this dataset. There must be noticed the low yearly variability of the user forecast results in general, which may explain its better performance on low inflows such as during the dry season.


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Regarding the correlation coefficients in Table 5, the user forecasts seem to achieve a better score in all initialisation months for a lead-time of 1 month (FP1). July is the start of the dry season in the region with precipitation rates close to zero, therefore explaining the very small variability of the ensembles in that period. Despite the absence of water in the system during that period, SPHY-ECMWFSys4 seems to properly represent low flows according to the correlation values for such initialisation month and all lead times. October, which is the start of the hydrologic year, and therefore important in terms of planning, does not obtain good correlation values for the SPHY-ECMWFSys4 system, neither for the user forecast FP2 and FP3. The next section summarizes the next steps to further develop and improve the dDSS .

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Figure 13. Upper Tagus hydrographs of monthly reservoir inflow for the period 1982-2012. The blue-shaded areas show the 5-95 percentiles and the blue line the mean of the ensembles of ECMWF-Sys4 (addit ional bias corrected), ref_run is the simulated discharge

of ERA-Interim, UF is the user forecast (SCRATS) simulated discharge. Vertical red dashed lines indicates drought periods.


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3.1.6 Testing plan

Up to month 36 (Sep-2018), the following activities will be carried out related to the work presented here:

- Presentation of preliminary design and outcomes to the two potential end-users - Collect feedback from the end-users and adjust design where necessary - Evaluate seasonal hindcasts of MetOffice, extent lead-time to 6 months, and

test improvements in the bias-correction methods - Include large-scale climate variability indices (related to task 11.4) in the

forecasting system - Develop the calculation of drought indices from the reservoir inflows - Develop the interface of the drought decision support system - Present improvements to stakeholders and collect feedback - Assess how the system is impacted by climate change (related to task 11.5 on

Water Accounting)

Figure 14. Detail of Figure 13 for the period 2002-2012 for the Upper Tagus hydrographs of monthly reservoir inflow. Vertical red dashed lines indicates a

drought period (2004-2010).

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ref_run MAE FP1 FP2 FP3

1 16.82 8.19 13.11 4 15.16 10.06 11.85 7 15.00 13.27 11.07

10 9.98 10.86 14.99

Corr. FP1 FP2 FP3 1 0.69 0.88 0.66 4 0.55 0.81 0.79 7 0.76 0.77 0.69

10 0.27 -0.04 0.39 SPHY-ECMWFSys4

CRPS FP1 FP2 FP3 1 10.63 11.21 11.27 4 17.93 13.09 5.82 7 6.72 6.24 5.41 10 5.50 6.65 8.23

Corr. FP1 FP2 FP3 1 0.67 0.67 0.30 4 0.48 0.73 0.55 7 0.76 0.77 0.69 10 0.26 -0.29 0.04

user forecast MAE FP1 FP2 FP3

1 16.00 11.96 10.22 4 12.95 9.82 7.90 7 2.38 1.28 2.28

10 1.78 5.59 10.47

Corr. FP1 FP2 FP3 1 0.53 0.44 0.72 4 0.76 0.63 0.22 7 0.78 0.87 0.55

10 0.72 0.11 0.07 Table 5. Performance statistics for the period 2002-2012. CRPS colour scale: green represents low values, red represents high values and white represents values in-between. Correlation coefficient colour scale: blue represents high

values, red represents low values and white represents values in-between.


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Figure 15. Monthly averages of reservoir inflows for each initialisation month (Jan, Apr, Jul, Oct) and leadtime (FP1, FP2, FP3). 5-95 percentiles and mean are the simulated discharges of ECMWF-Sys4, ref_run is the simulated discharge of ERA-Interim, UF is the user forecast (SCRATS)

forecasted discharge.

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Deliverable n° 11.1

3.2 Messara Valley, Greece

3.2.1 Background

Study area description: The Messara valley encompasses an area of 400 km2 located in the central-south area of Crete, Greece. About 250km2 of the total valley area are cultivated while the remaining area (higher grounds) is used for livestock. The main land-use activities are olive growing and grape vine cultivation. The remainder of the cultivated land is used for vegetable, fruit and cereal growing. The Messara Valley has remained rural with a small population of about 40 000 inhabitants. The main source of irrigation and domestic water supply is groundwater as well as the recently constructed Faneromeni dam at the outlet of the neighbouring Koutsoulidis basin (Figure 16) with a capacity of 18 Mm3. The downstream region of Timpaki is a highly-exploited area concerning the greenhouse cultivations, because of the favorable climatic conditions year-round. The growth of agriculture in Messara plain has a strong impact on the water resources and ecosystem services of the area by substantially increasing water demand. The economy of the region is based on agriculture with intensive cultivation of mainly olive trees, grapes, citrus, and vegetables in greenhouses. The overexploitation of the aquifer has reduced water availability, as groundwater is a major resource for irrigation. Soil degradation and salinization are important issues of the wider area.

Figure 16. Geographical overview of the Messara study site, Crete, Greece.


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Greenhouse cultivation is based on intensive farming practices and is less affected by weather variability. The rest of the cultivations (mainly olive trees and grapes) are partly irrigated and more exposed to weather extremes (floods and droughts). Thus, agricultural treatment for greenhouses is based on standard procedures while for the other cultivations a sensitivity to day-to-day weather during the growing season exists. Problem scope: The wider area of the Messara Valley is threatened with desertification. Groundwater overexploitation due to intensive agriculture led to a dramatic drop in the mean annual groundwater level during the last 30 years. The groundwater level dropdown started with the introduction of pumping of the groundwater store for drip-irrigation of the main crop which is olive trees. The problem is exacerbated due to illegal water extraction, excessive irrigation, faulty pumping schemes and prolonged dry climatic conditions. Another issue of concern is the seawater intrusion in coastal aquifers, such as the nearby coastal Timbaki area. The recently operating dam of Faneromeni added a capacity of 18Mm3 to the system and proved vital during the last two extremely dry hydrological years (2014-2016) that where almost drained to support greenhouse and olive tree irrigation. The Water Management Plan (WMP) of the Region of Crete1 was recently developed but does not include information and guidance on monitoring and management of droughts. In the view of the ongoing WMP revision for the 2015-2021 period one of the major challenges is the drought assessment (including impact of climate change on water resources), incorporation of these studies in the first revision of the Plan, and the development of a number of actions and decision support systems for the monitoring and mitigation of drought. Overall, there is a lack of coordination in the water sector of this area, which undermines effective water resources management. By calling the relevant actors together, IMPREX will advance their knowledge on hydrological and agricultural droughts and introduce a simple but informative decision support system using seasonal forecasts in the local water management.


The current monitoring of meteorological drought is based on the local network of meteorological stations. The Department of Hydro-Economy of the Region of Crete collects the data. The number of operating monitoring stations is decreasing due to budget constraints. Groundwater level of the local overexploited aquifer is also monitored by several gauges. No operational water resources monitoring tool exists. Currently, no Weather and Climate service is implemented at any decision-level in the area. At the same time, especially farmers use weather observations to some extent. Stakeholder consultations within IMPREX revealed that:

• Weather observations are the data that are most often used, followed by short term (1-5 days) weather predictions and observational averages.

• Rainfall and temperature are the most “popular” variables. Reservoir and groundwater level (being the main sources for irrigation) are important indicators of water availability.

1 According to the guidelines of the Water Framework Directive (2000/60/EC)

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Overall, the use of weather and climate information to support coordinated water resources management at the regional level is very limited.


In the Messara Valley, there are several stakeholders involved in water resources management and agriculture:

(a) farmers cultivating olive trees, grapes, citrus, and vegetables in green-houses, (b) Agro-industrial cooperatives providing services to farmers mostly involved in

product/commodity exploitation, (c) agronomists and management committees (e.g. for the local dam) providing consultation

to farmers, (d) Local Organizations of Land Reclamation that are responsible for the distribution, the

pricing of the water and the maintenance of the irrigation network, (e) the Directorate of Water of the Decentralized Administration of Crete, the main

instrument of policy development and application in the Region of Crete according to the European Water Framework Directive (2000/60/EC,

(f) The Organisation for the Development of Crete S.A., a governmental organisation responsible for studding, planning, constructing and managing of hydraulic infrastructures.

Consultations with these stakeholders revealed that:

• They find that the reliability and the skill of the current weather forecasts is very limited. The increase in the skill of short range weather predictions was of great interest.

• Regarding the provision of hydrological data there is an issue with the spatial resolution but also with the fact that there is no official agency responsible for collecting and quality controlling the local hydro-meteorological data in a systematic way. Thus, the interest of the increase of the observational coverage was high.

• Currently, long range and seasonal/annual predictions are not used, but stakeholders expressed there was a strong interest for this type of forecasting information.


This section summarizes the overall system configuration for the drought decision support system, meeting the user requirements listed in the previous section. The first section describes the forecasting system, the second the user interface. These specifications are preliminary: they will likely be modified over the course of the project, based on performance and end-user feedback.

3.2.4.1 Forecasting system The drought DSS that has been designed and is being built consists of:

- A back-end that carries out the forecast data preparation and the hydrological simulation and index estimation based on the forecast data.


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- A front-end that visualizes the outputs. The back-end consists of three modules:

1. The first module overtakes the pre-processing of the forecast data. The data are acquired, spatially downscaled to the regions of interest and finally are bias adjusted to fit the local climate according to the difference between the observations and the hindcast data.

2. The second module is the hydrological simulation of the Koutsoulidis watershed runoff (Figure 16) that is the main source of inflow to the Faneromeni Dam. The simulation will provide a frequently updated summary result from different forecasting systems for various lead times. The simulations will be repeated as soon new forecasts will be released.

3. The third module consists of the estimation of the seasonal overview of drought indices as proxies to the groundwater level of Messara watershed.

The produced information is accessible in a simple but informative way through the front-end system. A schematic illustration of the system is given in Figure 17, which shows a representation of the linkage between the provided meteorological forecast data, the hydrological processes and the produced end-user information.

Figure 17. Connection of raw forecast data, processes involved and front-end information

provision

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The seasonal forecast data that are currently tested are the ECMWF system 4 and the UK MetOffice Unified Model data, and additional forecast products are anticipated. Precipitation and temperature data are initially downscaled and adjusted for potential biases towards local observations, prior to their use to the back-end system. The adjusted data drive the hydrological model and are also used to estimate the local drought indices. The results are then statistically post processed to feed into the final information that will be visualized into the front-end system. Table 6 provides technical details about the system components.

Table 6: Technical details of the design specifications

Forecast data specifications 1 Variables Precipitation,

2 Temporal resolution Daily 3 Spatial resolution 0.7 degrees Data pre-processing 1 Downscaling Bilinear interpolation on

2 Bias correction Quantile mapping Hydrological model specifications

1 Model HBV conceptual model 2 Simulation timestep daily 3 Spatial resolution Basin scale (120 Km2) Drought Indices specifications 1 Indices SPI type indices 2 Temporal resolution Month or months 3 Spatial resolution Basin scale (120-400

3.2.4.2 User interface

One of the targets for the users of the specific site of study is the introduction of seasonal hydro-meteorological forecasts in local water management and the demonstration of available weather/climate/hydrological sources of information. Figure 18 provides an overview of the Messara drought decision support system. This design was an output from the interaction during the stakeholder surveys. It consists of three pillars of information. The first will provide specific sources and guidance for additional available weather and climate information not familiar to the local users. The second will be an operational seasonal forecasting of the local dam storage level as an output of hydrologic simulation forced by locally adjusted seasonal forecasts. The system will also provide operational, locally adjusted information for precipitation and meteorological and groundwater drought indices.


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Figure 18. Overview of the Messara drought decision support system.


A first evaluation of the drought forecasting system has been carried using hindcasts from ECMWF and MetOffice. Fifteen members of the ECMWF hindcast and 3 of the Met Office Uni-fied model data are spatially downscaled to watershed level. The downscaling process is based on the bilinear interpolation on basin level of the precipitation and temperature data. The data are then tested for their proximity to the observations for lead times of 1 to 5 months (Figure 19 and Figure 20). In the case of precipitation, an overall underestimation is exhibited in all lead times, for both systems. As an example, the 10-day wetness is shown in Figure 19 and Figure 20 (upper right), where the 1 month lead time data are presented against the observations. While the climatological 10 day averages of the modelled precipitation exhibits linear correla-tion to the respective observations, there is a significant underestimation of the precipitation amount that approximates the 50% and 80% for the ECMWF and MetOffice cases respectively. For the respective temperature data, a warm bias is present in all realizations especially in the winter season in both systems. This is probably caused by the coarse model resolution that cannot adequately simulate the mountainous climate of the watershed. As an example, the one month lead time temperature data are shown in Figure 19 and Figure 20 (lower right panels). The constant warm bias is expressed by the constant offset of the data from the bisectrix, which in the case of ECMWF tends to me more pronounced in the period between September and December months, while Met Office seems to better depict the seasonal cycle of temperature ,with respect to the constant bias.

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Figure 19. Basin scale ECMWF monthly precipitation and temperature data and the respective observations (left). Scatter plot of observed and ECMWF hindcast monthly

climatological averages for one month lead-time (right).

Figure 20. Basin scale Met Office monthly precipitation and temperature data and the

respective observations (left). Scatter plot of observed and Met Office hindcast monthly climatological averages for one month lead-time (right).


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In order to reduce the statistical difference between the modelled data and the observations,. The correction is applied after the downscaling process. Then, a formal correction in the cumulative density function follows using the quantile mapping approach. Early results of the bias correction effect is demonstrated on the 10 day aggregate values of both system data against the respective observations (Figure 21). At a climatological perspective, the correction removed the statistical difference from the simulated data. The assessment of the bias correction on the forecast skill is an ongoing task.

Figure 21. Effect of the bias correction on the climatological 10 day averages for the 1 month lead time forecasts of ECMFW and MetOffice systems (y-axis), comparing to the

observations (x-axis).

For the hydrological model calibration of the Koutsoulidis watershed, emphasis has been given to the realistic representation of the runoff volumes and the seasonal patterns. The calibration is performed against the observational data. To this time the Nash-Sutcliffe of the daily time step simulation coefficient is estimated at 0.7. Figure 22 presents the 10-day average observed and simulated runoff for the period between 1973 to 1997. Additionally the 10-day difference between the simulated and the observed runoff is shown. According to the latest calibration status, the difference ranges between +-1 m3/s while in high flow periods the difference may reach the +-4 m3/s. the cumulative difference in the 25 year simulation is found to reach the 200mm, or 8 mm/year, which is assessed as noteworthy.

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Figure 22. The 25-year simulated runoff comparison to observations (upper). In the

middle panel, the difference [Observed –Simulated] in m3/s. Cumulative difference in [mm] (lower panel).

Figure 23 shows the climatological 10-day average for observed and simulated (HBV) runoff.

Figure 23. Ten-day climatological observed and simulated runoff.

3.2.6 Testing plan

For the following 6 months, TUC plans to:


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- Finnish the HYPE hydrological model setup as an alternative for the Koutoulidis basin runoff simulation.

- Assess the ability of hindcast data to estimate the runoff through the hydrological modelling in both hydrological models.

- Complete the forecast data verification over the case study area. . - Stakeholder meetings to receive feedback on first outcomes

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3.3 Lake Como and Adda River, Italy

3.3.1 Background

Description of the area: The Adda River, in northern Italy, is the fourth longest Italian river and a tributary of the Po River. It flows into Lake Como, a regulated lake with an active storage capacity of 254 Mm3 draining a 3500 km2 catchment (Figure 24). The hydro-meteorological regime of the basin is typical of alpine regions, characterized by dry periods in winter and summer, and peaks in late spring and autumn fed by snowmelt and rainfall, respectively. Downstream from the lake, the Adda River serves eight run-of-the-river hydroelectric power plants and a dense network of irrigation canals connecting four irrigation districts. The operations of the lake must cope with the necessity of saving water to respond to the agricultural and hydro-electrical demands, which have a peak during the summer period, and, at the same time, must guarantee safety against flood events along the lake shores, particularly in the city of Como. In addition, there are important environmental interests related to the protection of the ecological conditions both of the lake and of the downstream stretch of the Adda River. All those components challenge the operations of Lake Como. This is further complicated by the limited active capacity of the lake (i.e., 254 Mm3 which is roughly 1/20 of the yearly inflow volume), as well as the small lake outlet that allows a release of 900-1,000 m3/s while the inflow can reach 1,800 to 2,000 m3/s, leading to rapid rising and filling of the lake level (at 3-5 days time scale) in the case of large flood events.

Figure 24: Map of the Lake Como basin.

Problem scope: Historically, water availability has not been a major limiting factor for the development of regional water-related activities, and board irrigation is predominantly adopted. In addition, numerous wells, originally constructed for complementing surface water supply during droughts,


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are now regularly operated. Major cultivated crops are maize and temporary grasslands, while minor cultivars include rice, soybean, wheat, tomato, and barley. Crop productions are very high, with yields of 12 t/ha for maize and 50 t/ha for temporary grasslands (Pieri and Pretolani, 2013). Such maize and grassland dominated cropping patterns are widely scattered over the Po valley due to the livestock-oriented nature of agricultural production systems in the area. The prosperity of this agricultural area is mostly supported by the irrigation supply system and thus by the operations of Lake Como. The lake operations can strongly benefit from improved medium- to long-term forecast products for timely activating drought management strategies and effective water supply hedging rules.


Modeling the benchmark operations of Lake Como requires the identification of the current operating strategy. The lake is regulated within a discretionary operating space by a human operator: when the lake level crosses the upper or the lower bound of this space the operator must respectively open or close completely the dam gates. Assuming a rational attitude, we modeled this historical regulation as an optimal operating policy dependent on the day of the year and on the lake level. The policy is obtained by solving a two-objective (floods and water supply) policy design problem yielding a set of Pareto optimal alternatives. The representative baseline is selected as the Pareto optimal solution having the smallest Euclidean distance from the performance of the historical operations, thus capturing the lake operator’s preferences among the two primary objectives. The comparison of the observed and the simulated trajectories of lake releases reported in Figure 25 shows that our baseline is effectively capturing the historical operations (Giuliani et al., 2016a).

Figure 25. Comparison between observed and simulated release trajectory.

We further validated our behavioral model, namely the assumption of rational decisions conditioned upon the use of a basic information set (i.e., day of the year and lake level) and the selected tradeoff between flood control and water supply, by modeling the historical release time series through Extremely Randomized Trees (Denaro et al., 2017): numerical results reveal that more than 60% of the variance of the historical releases can be explained by a tree-based model using as input the day of the year, which informs about the seasonality of the

0

100

200

300

400

500

600

700

800

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rele

ase

rate

[m3

/s]

Jan-2005Jan-1996

Jan-1997Jan-1998

Jan-1999Jan-2000

Jan-2001Jan-2002

Jan-2003Jan-2004

observations

simulation

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downstream water demand, and lake inflow patterns. An additional 20% of the variance in the historical releases is explained by a model that, in addition to the day of the year, also considers the reservoir level as input. This has the twofold role of informing about the flood buffer potential and the water availability useful for water supply. The resulting model explains around 85% of the historical lake release time series, with the remaining 15% that can be (arguably) attributed to the sporadic use of flood forecasts and other contingencies related to minor objectives such as navigation or environmental protection (Todini, 2014). For the downstream agricultural regions, the current irrigation management is based on a three-level structure, which involves the farmers, the irrigation districts, and the lake operator. At the beginning of each irrigation season, the farmers negotiate the seasonal water supply with the irrigation districts. Farmers' requests are generally based on historical water rights and do not change significantly from one year to the next. Then, each irrigation district manages the water diversions from the Adda River as well as the conveyance and distribution of the diverted flows to the individual farmers. Such distribution is organized according to a rotation scheme and, in each turn (i.e., 7 to 14 days depending on soil and crop characteristics), farmers receive the negotiated discharge for a fixed number of hours. The interactions between irrigation districts and the lake operator are generally limited in normal years, when the districts simply communicate to the lake operator the volume of water they diverted.

Figure 26: Historical correlation between water diversions with lake release and

precipitation events.

Given the historical water abundance, agricultural districts tend to strongly rely on irrigation and are conversely less sensitive to precipitation events. This is demonstrated in Figure 26, which contrasts the low correlation between farmers’ diversions and precipitation (i.e., -0.05) with higher correlation between farmers’ diversions and lake release (i.e., 0.47). Medium- and long-term forecast products would, therefore, be more valuable for improving the Lake Como operations, rather than for supporting watering decisions at single irrigation districts or even the farm level. For this reason, we will focus on the lake operator as the main decision maker, who manages the entire water supply system, and as the target beneficiary for IMPREX project. In addition, it is worth noting that a nonstationary hydroclimatic trend has already manifested its negative impacts in the basin as demonstrated, for example, by the alteration of the hydrological


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regime of the Lake Como inflows. This trend is illustrated in Figure 26 for the lake inflows observed over the last 60 years using the MASH tool (Anghileri et al., 2014), which enables assessing variations in the seasonal pattern of the flow represented by the 365 values of average daily flow over the year. The inflows show a clear decreasing tendency since the late ‘80s during the late spring and summer periods, which are the most critical for irrigated agriculture. Should this trend continue in the future, the reduced water availability would require a better strategic design of the release policy, and, at the same time, farmers to take action by, e.g., switching from current water demanding maize to more drought tolerant crops in order to prevent the failures experienced during the recent droughts of 2003 and 2005 (Giuliani et al., 2016). Consequently, we can assume an increasing interest by farmers to establishing information support systems to inform their actions, especially during drought events. This general interest is confirmed by the situation in other regions in Pianura Padana, where a comprehensive system for flooding forecast and drought monitoring has been launched in recent years, and farmers receive real-time update information via various channels, such as mobile phone or radio broadcast2 .

Figure 27: Trend analysis of the daily inflows over the t ime horizon 1946–2010: the

average is computed by means of a moving window that includes data over consecutive days in the same year and over the same days in consecutive years, with the window

progressively shifted ahead to identify long-term trends. In the f igure, each line represents a 20-years moving average, from the 1946–1966 (l ight blue) to the 1990–2010 (dark blue)

time horizons.

2. See the website of Po river basin authority: http://www.adbpo.gov.it/

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As mentioned before, currently no drought monitoring/forecast platform is in operation in our study area, probably because historically water abundance, rather than scarcity, was often the primary concern. Yet, during the stakeholder interactions a general interest in the weather/climate service still holds. In general, the dynamics of the irrigation network is fast and, in the absence of storage capacity, a short lead time (few hours) rainfall forecast are generally sufficient for the flood management, with real-time monitoring of the critical events. Longer lead times, e.g., week to month, could be used for planning irrigation supply and scheduling maintenance interventions. Timely and accurate forecast are greatly appreciated to reduce the risk associated to system operation, including better flood protection or more efficient water distribution. Motivated by the information supporting system used in nearby regions, there is also a strong initiative to establish a similar platform within current regions.


An overview of the envisioned drought Decision Support System (dDSS) for the Lake Como basin is represented in Figure 28. The dDSS allows performing the following tasks: 1. Benchmarking the status quo of the system by evaluating via model simulation the

current practices adopted by the stakeholders in the system (see Section 3.3.2). 2. Assessing the operational value of medium- and long-term forecast products (provided by

WP3 and WP4), drought indicators, and low frequency climatic signals (e.g., El Nino Southern Oscillation).

3. Projecting the system evolution under changing climate and evaluating projected occurrence of drought events through the computation of drought indicators, assess the climate change impacts, and explore some adaptation measures.

Figure 28: Overview of the drought Decision Support System for the Lake Como basin.


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The term forecast operational value is used in the literature to indicate the value of using a forecasting system (or more generally of using additional exogenous information, such as drought indexes or climate teleconnection) to support water management (Anghileri et al., 2016a). The operational value is hence measured in terms of system performance improvement as defined by the operating objectives of the considered problem (Laio and Tamea, 2007). Our dDSS will quantify the operational value of medium- and long-term forecasts, drought indexes, and low frequency climate signals by means of the Information Selection and Assessment (ISA) framework proposed by Giuliani et al. (2015). This evaluation methodology consists of 3 steps (Figure 28): (i) Quantification of the expected value of perfect information, i.e., the potential maximum improvement of the current operations, which generally relies on a basic information set, under the assumption of perfect knowledge of future conditions; (ii) Automatic selection of the most valuable information to improve the current operations; (iii) Evaluation of the selected information on the resulting operating policy performance.

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Figure 29: Flowchart of the Information Selection and Assessment (ISA) framework

(Giuliani et al., 2015).

However, in the absence of an institutional forecasting system/drought monitoring system, this task is particularly challenging in the Lake Como basin. In fact, we cannot evaluate the operational value of forecast products without first identifying the variables to be forecasted (e.g., meteorological forecast vs hydrological forecast) and the associated lead-time of interest. Similarly, the formulation of a proper basin-customized drought indicator, capable of accounting for the presence of snow as the main driver of the hydrological cycle as well as for the large presence of human regulation supporting the irrigated agriculture, is a second prerequisite for this task that is not available in our context. Finally, the influence of global climatic signals on the local hydrology should be also demonstrated before exploring its potential operational value.


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3.3.4.1 Forecasting system Integrated simulation model Core of the proposed dDSS (Figure 28) is an integrated simulation model of the Lake Como basin, which includes three main components as shown in Figure 30:

• Catchment model – a physically-based, fully distributed TOPKAPI-ETH hydrological model, working on a regular grid (250x250 m), which simulates the hydrological processes in the lake catchment and also accounts for the presence of reservoirs and river diversions (for details, see Anghileri et al., 2016b). For the reference situation, ground measures of precipitation, temperature and cloud cover transmissivity were spatially interpolated by means of Thiessen polygons. In addition, a correction map was applied for precipitation data based on a Swiss reanalysis gridded dataset to account for elevation-dependent patterns.

• Lake Como model – the lake dynamics is described by a mass-balance equation assuming a modelling and decision-making time step of 24 hours, where the lake releases depend on the lake operating policy (i.e., a mathematical function mapping the current system conditions, such as the day of the year and the lake level, into release decisions). According to the daily time step, the Adda River can be described by a plug-flow model to simulate the routing of the lake releases from the lake outlet to the intake of the irrigation canals. This diversion of the water from the Adda River into the irrigation canal is regulated by the water rights of the agricultural districts (Giuliani et al., 2016a).

• Agricultural districts model - the dynamic processes internal to the irrigation districts are described by three distinct modules devoted to specific tasks: i) a distributed-parameter water balance module that simulates water sources, conveyance, distribution, and soil-crop water balance (Facchi et al., 2004); ii) a heat unit module that simulates the sequence of growth stages as a function of the temperature (Neitsch et al., 2011); iii) a crop yield module that estimates the optimal and actual yields, accounting for the effects of stresses due to insufficient water supply that may have occurred during the agricultural season (Steduto et al., 2009). The water balance module partitions the irrigation district with a regular mesh of cells with a side length of 250 m, which allows the representation of the space variability of crops, soil types, meteorological inputs, and irrigation distribution.

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Figure 30: Schematic representation of the integrated simulation model of the Lake Como

basin.

Search for the “best” forecast Skilful forecasts are an asset for improving decision making, but while it is comparably easy to identify the forecast information needed to support the design of hedging rules for operation targets with fast dynamics (e.g., flood control), this task is not straightforward for operational targets with slow dynamics (e.g., water supply). For example, the key information for a reservoir operated for flood control is the time necessary to create a buffer volume for the flood (Raso et al., 2014). Instead, when the reservoir is operated with long-term operating rules, such as for water supply, it is not easy to understand which forecasted variable may be useful to design effective hedging rules (e.g., streamflow forecast at a certain point in the future, cumulative inflows over a certain lead time), and to obtain a skilful estimate of such variable (Zhao et al., 2012). When considering multi-purpose systems, the picture becomes even more intricate as operations usually need to balance short- and long-term operating targets. In this case, valuable forecasts may comprise several processes with inconsistent dynamics and disparate levels of predictability. In the absence of an institutional forecasting system for the Lake Como basin, we are exploring the potential for feature extraction methods to support the automatic selection of the most valuable forecast variables and their associated lead time using the ISA framework


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(Giuliani et al., 2015). Forecast variables and horizons are selected using feature selection techniques, which determine the most informative combination in a multi-variate regression model to the optimal reservoir releases based on perfect information. In addition, we plan to explore how this choice of forecast variable and lead time might be dependent on the considered tradeoff between the operating objectives or on the risk aversion of the lake operator. In fact, flood control can benefit from hydro-meteorological forecasts over a short lead time, which are generally sufficiently accurate to characterize the fast dynamics of the peak flow and to predict its magnitude and timing. Conversely, water supply would require medium- to long-term hydrological forecasts, which (when available) are generally less accurate. Design of basin-customized drought indexes Although drought management is largely studied in the literature, most of the traditional drought indexes fail in detecting critical events in highly regulated systems, which generally rely on ad-hoc formulations and cannot be generalized to different context. In the absence of an institutional drought management system for the Lake Como basin, we developed a novel framework for the design of a basin-customized combined drought index. This index represents a surrogate of the state of the basin and is computed by combining the available information about the water available in the system to reproduce a representative target variable for the drought conditions of the basin (e.g., water deficit). To select the relevant variables and how to combine them, we use an advanced feature extraction algorithm called Wrapper for Quasi Equally Informative Subset Selection (W-QEISS) firstly proposed by Karakaya et al. (2015) for feature classification and then extended by Taormina et al. (2016) to be used in regression problems. The W-QEISS algorithm relies on a multi-objective evolutionary algorithm to find Pareto-efficient subsets of variables by maximizing the wrapper accuracy, minimizing the number of selected variables (cardinality) and optimizing relevance and redundancy of the subset. The accuracy objective is evaluated trough the calibration of a pre-defined model (i.e. an extreme learning machine) of the water deficit for each candidate subset of variables, with the index selected from the resulting solutions identifying a suitable compromise between accuracy, cardinality, relevance, and redundancy. We constructed the combined index using all the hydrological variables from the existing monitoring system as well as the most common drought indicators at multiple time aggregations. Detection of climate teleconnection For many locations around the world, the most influential climate signal on the seasonal timescale is the El Nino Southern Oscillation (ENSO), and there are various indexes used to capture the state of ENSO and provide this information. However, the ENSO teleconnection is well defined in some locations, such as Western USA and Australia, while there is no consensus on how it can be detected and used in other river basins, particularly in Europe, Africa, and Asia. We performed the detection of climate teleconnection for the Lake Como basin by using the Nino Index Phase Analysis (NIPA) recently proposed by Zimmerman et al. (2016). NIPA utilizes the state of ENSO as captured by the Multivariate ENSO Index (MEI) to classify the years of record into four phases. Beside ENSO, we extended the search domain by including numerous large-scale climate signals, such as North Atlantic Oscillation, Pacific Decadal Oscillation, Atlantic Multidecadal Oscillation, Dipole Mode Index. Then, phase-specific antecedent Sea Surface Temperature fields are identified across the entire world and employed as predictors of the future precipitation in the Lake Como basin in a principal component regression model.

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3.3.4.2 User interface

The proposed dDSS for the Lake Como basin is motivated by a combination of the needs expressed by the stakeholders (see Section 3.3.3) and the goals of the IMPREX project. Conversely, we do not have any commitment from the local institutions for delivering a tool equipped with any specific user interface functionalities. During the interactions with our stakeholders, we proposed to share the main results created by the dDSS (e.g., hydrological observations and projections, climate change scenarios, drought indicators) in the form of an open data geo-information portal relying on a spatial data infrastructure for the integration, organization and management of these data. This portal will provide visualization tools to publicly display on the web the most important data compiled and knowledge generated in an easily accessible, explorative visual way (see example in Figure 31). In addition, all the simulation codes used for the generation of such data will be accessible in a public repository.

Figure 31: A mock-up of the open data geo-information portal.


So far, we focused our efforts in building the main components of the proposed drought Decision Support System (Figure 28), namely the integrated simulation model and benchmarking of the status quo, followed by some preliminary experiments related to the identification of the “best” (most valuable) forecasts, the design of a basin-customized drought index, and the detection of climate teleconnection. The validation of all these components and their integration is planned for the next months. Identification of the “best” forecast


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As a first step in the identification of the “best” forecast product to be used for informing the operations of Lake Como, we focused our efforts in quantifying the operational value of candidate observational data, collected from the existing monitoring system, which potentially surrogate the current system state over slow and fast dynamics (Denaro et al., 2017). Then, we repeated the procedure replacing these observational data with a set of streamflow forecasts over different lead times. We use a retrospective streamflow dataset as perfect forecast, which is expected to approximate the use of a sophisticated forecasting system (Zhao et al., 2011), while also removing possible modeling biases in the construction of the forecasts. Following the ISA procedure, we first contrast the performance of a set of Perfect Operating Policies (POPs) and a set of Basic Operating Policies (BOPs). The former are defined as ideal operating policies dependent on the current system conditions along with perfect information on future disturbances (i.e., inflows). The latter are defined as operating policies conditioned upon the day of the year and lake level, assuming that these two variables are the ones the real operator mostly considers in his daily operations of the lake (see Section 3.3.2). Figure 32 compares the performance of the POPs (black squares) and the one of the BOPs (blue circles), where the arrows indicate the direction of increasing preference, with the best solution located in the bottom-left corner of the figure. In the same figure, the purple and red circles represent the performance of the Improved Operating Policies, which will be discussed later on. Visual comparison of the black and blue Pareto fronts shows that the potential space for improvement generated by the knowledge of perfect information of the future inflows is relevant, especially for reducing the water supply deficit reported on the y-axis of the figure. It is worth noting that the shape of the POP Pareto front is sharp-cornered. This means that under the assumption of perfect inflow foresight over the entire evaluation horizon, the existing conflict between the two operating objectives would disappear. Given this sharp-cornered Pareto front, we select the almost no-conflict solution (red circled square in Figure 32) as a target solution representing a fair balance between the objectives. The gap between POPs and BOPs motivates searching for additional information which can potentially contribute in improving the system operations.

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Figure 32: Performance obtained by the Perfect Operating Policies, Basic Operating

Policies, and two Improved Operating Policies relying on SWE and alpine hydropower storage as surrogates of the actual system state.

In the second step of the ISA framework, the Iterative Input variable Selection (IIS) algorithm (Galelli and Castelletti, 2013a) is used to select the subset of most informative variables, which better characterizes the optimal sequence of release decisions of the target POP. Not surprisingly, the day of the year and the lake level are selected as the most relevant drivers, explaining up to the 65% of the optimal release sequence. The third selected variable is the SWE estimate, accounting for a R2 contribution of about 12%. The fourth selected variable is the total storage of the upstream hydropower reservoirs, which further adds another 7%. Finally, in the last step of the ISA framework, the two selected exogenous variables (i.e., SWE and hydropower storage) are incrementally included as arguments of the lake operating policies for designing two different sets of Improved Operating Policies. The IOPs conditioned upon SWE information are represented by the purple Pareto front in Figure 32, which shows a performance improvement (see the purple area) that is more emphasized in terms of water supply rather than flood control. This can be explained by the seasonal slow dynamics of snow related processes, which are more informative for hedging rules but do not contribute in describing the fast dynamics associated with flood events. The introduction of the storage of the upstream hydropower reservoirs produces a further improvement in the performance of the associated set of IOPs (red circles in Figure 31). This improvement may reflect the effect of integrating the two pieces of information thus allowing the lake operation to distinguish that part of snow melt streamflow that does not naturally flow through the river network, but is diverted


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and stored into the reservoirs to be released in later periods, and, dually, the amount that can freely reach the lake inlet. To investigate the individual operational contribution provided by the most informative state surrogate (i.e., SWE), we analyze the system dynamics in terms of trajectories of lake storage obtained via simulation under different operating policies. In each Pareto optimal set, we select the solution having the smallest Euclidean distance from the target POP, identified by the large black circles in Figure 31. Because most of the operational improvement concerns the water supply objective, we focus on the drier years in the considered horizon, namely 2006, 2007 and 2012. Figure 32 shows that the Improved Operating Policy informed by the SWE estimate produces a trajectory of lake storage that, especially from January to August, is closer to the target trajectory than the one obtained with the BOP. This difference is highlighted by the purple area in Figure 33b, and corresponds to the period where the SWE information is available and snow melt takes place (Figure 33a). The informative contribution of SWE actually lasts a bit longer because of the storing capacity of the lake, which allows to store the snow melting contribution for few months and to cover most of the summer water demand peak (see dashed red line in Figure 32b).

Figure 33: Analysis of the Lake Como storage trajectory in dry years under the selected

Improved Operating Policy condit ioned on observations of SWE. As a reference, the trajectory of the target POP and the selected BOP are also i llustrated.

The results just discussed demonstrate the value of snow information in the Lake Como basin. However, the alpine orography constrains the accurate monitoring of snow dynamics. The existing ground stations (46 over the 10,500 km2 alpine area in the Lombardy region) provide a very coarse coverage of the region and are not sufficient to reliably monitor the snow cover and the associated water content. This is instead estimated by ARPA through a hybrid procedure combining snow height and temperature data from ground stations, measures of snow density in few specific locations, satellite retrieved data of snow cover from MODIS, and model outputs for spatially interpolating these data. As a result of this complex procedure, ARPA elaborates a weekly estimate of SWE. Such reports are delivered only weekly due to the well-known

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limitations of snow products derived from optical sensors associated to the frequent satellite occlusion by cloud coverage. This limitation is particularly restrictive in the alpine region, where previous studies observed an average cloud occlusion of 63% over a five year monitoring period (Paraika and Bloschl, 2006), with critical episodes of cloud coverage lasting for more than 25 days per month in winter time. The availability on the web of large volumes of public, low-cost, and spatio-temporally dense data raises the question of whether it is possible to use such data as a supplement, or at least as a complement, to traditional monitoring systems in operational contexts. The main advantage of such public data, albeit collected for completely different purposes and with much lower quality standards, is that they can significantly increase the spatial and temporal coverage at little/no cost (Jacobs et al., 2009). This idea is part of a growing application of so called citizen science approaches to water resources systems operation (Buytaert et al., 2014) and, more generally, to diverse environmental problems (Fraternali et al., 2012). Given the value of snow information in the Lake Como basin, we explored the potential for web and crowdsourced data to retrieve relevant information on snow availability and dynamics in a river basin, and assess the utility of such information in informing the lake operations. This analysis relies on a novel crowdsourcing procedure for extracting snow-related information from public web images, either produced by users or generated by touristic webcams, and then quantifying the operational value of this information compared to other more traditional snow information, such as ground observations and a hybrid mix of satellite retrieved information, ground data, and model outputs (Giuliani et al., 2016c). To explore the operational value of this crowdsourced information, we contrasted the potential for informing the lake operation of three different snow-related data sources: daily observations of snow height from coarsely distributed ground stations; weekly SWE estimate provided by ARPA; daily values of the VSI extracted from public web images. A first qualitative analysis of the Virtual Snow Index σ can be performed by comparatively analyzing the trajectory of this VSI with respect to the snow height observations in the closest ground station (i.e., Oga San Colombano, located around 15 km far from the webcam) or with respect to some physical variables closely related to the snow dynamics. Figure 33 contrasts the historical trajectory of σ in 2013 with the trajectories of snow height observations at Oga San Colombano station (left panel) and of the freezing level (right panel). Despite some differences due to the different locations of the webcam and the ground station, the first comparison shows similar temporal patterns: most of the snowmelt occurs between April and first half of May, followed by a late snowfall at the end of May; no snow is present since late June, with the first snowfall of the next winter observed in early October. The comparison between σ and the freezing level shows a negative correlation between low values of freezing level from January to March as well as in November and December, which are associated to high values of σ. On the contrary, the freezing level increases in summer time in correspondence to low and zero values of σ. Moreover, it is worth noting the consistency in the oscillations of the two trajectories especially in winter time, when the snow accumulation is captured by increasing values of σ associated to decreasing freezing levels and, vice versa, the snow melting corresponds to decreasing values of σ and increasing freezing levels.


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Figure 34: Comparison of the trajectories in 2013 of the Virtual Snow Index σ with the snow height measured at Oga San Colombano (left panel) and with the freezing level

(right panel).

To further demonstrate the value of σ, we then quantified its operational value for informing the Lake Como operations. The performance of this set of informed operating policies (P3) is contrasted with the baseline solution, namely the traditional lake regulation conditioned on the day of the year and the lake level, and the upper bound solution, namely an ideal set of policies designed under the assumption of perfect foresight of future inflows. The same experiment is repeated using either ground observations of snow height (P1) or SWE data provided by the ARPA (P2) in order to validate the value of the VSI information with respect to traditional data sources.

Figure 35: Performance obtained by different Lake Como operating policies informed with ground observations (P1 - green circles), SWE estimated by ARPA (P2 - cyan circles), or virtual snow indexes (P3 - red circles). The performance of these solutions is contrasted

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with the upper bound of the system performance (black squares) and the baseline operating policies (blue circles).

Figure 35 illustrates the performance of the different set of solutions in terms of flood control (Jflood) and irrigation supply (Jirr), evaluated over the horizon 2013-2014. The arrows indicate the direction of increasing preference, with the best solution located in the bottom-left corner of the figure. Visual comparison of the baseline (blue circles) and upper bound solutions (black squares) shows the potential space for improvement generated by the ideal perfect information of the future inflows trajectories. Valuable snow-related information is hence expected to fill the gap between the baseline and upper bound solutions. It is interesting to observe that, beside improving the performance of the operating policies with respect to both the objectives, the use of perfect information reduces the conflict between flood control and water supply, and discovers a number of solutions close to the independent optima of the two objectives, including the selected target solution JT = (4.5; 250.6). Given the references provided by the baseline and upper bound solutions, we can assess the operational value of different snow-related information by looking at the performance of informed operating policies, represented by the green, cyan, and red circles in Figure 35. Not surprisingly, numerical results show that enlarging the information used in the lake operations by accounting for the snow dynamics in the upstream catchment is producing an improvement of the system performance. In fact, the baseline solutions are completely dominated by the sets P1, P2, and P3. These informed operating policies successfully exploit the available snow data to implicitly obtain a medium to long term forecast of the future water availability due to snow melt, which supports the daily operations of the lake balancing flood protection on the short term and water supply on the long one. Overall, the three sets of Pareto optimal solutions, obtained using different snow information, attain similar performance, thus suggesting that the VSI can be considered equivalent to the other two physically based indexes. Figure 34 also shows that policies P1 are the best for very low values of Jflood but high values of Jirr, while policies P3 result to be the best in the compromise region of the objectives space (i.e., Jflood < 10 days and Jirr < 275 m3/s) 2), which is likely including the most interesting solutions for the lake operator as they successfully balance the system tradeoffs. The results discussed in the previous section shows that there is still a large gap between the best Improved Operating Policies and the Perfect Operating Policies (see Figure 32 to Figure 35). This is possibly due to the lack of information on the timing of the inflow to the lake, as well as on the amount of water that will be available on lead times longer than the anticipation capacity provided by SWE and hydropower storage. To validate this hypothesis, we repeated the ISA procedure a second time, replacing the set of candidate state surrogates with a set of perfect inflow forecasts on different lead times (for details, see Annex B). In this case, the IIS algorithm consistently selects a combination of long (i.e., 51 days) and short (i.e., 7 days) lead time information. To quantify the operational value of the two selected forecasts, we incrementally include them as arguments of the Improved Operating Policies. The comparison of the Basic Operating Policy performance with these new IOPs is reported in Figure 36. The set of IOPs informed with the long lead time forecast (green circles in the figure) is largely better than the BOP set (blue circles) and dominates also the Improved Operating Policies informed by the selected observations of the current system conditions (i.e., SWE estimate and hydropower storage,


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represented by the dashed purple and red Pareto fronts). When the short lead time information is also considered, the corresponding IOPs (cyan circles in Figure 36) further improve, even though the marginal improvement in this case is lower than the one provided by the long lead time forecasts.

Figure 36: Performance obtained by the Perfect Operating Policies, Basic Operating

Policies, and two Improved Operating Policies relying on streamflow forecasts on different lead times (51 days and 7 days).

Finally, it is interesting to analyze the trajectories of the lake storage under these Improved Operating Policies, which provide a better understanding of the role of the selected inflow forecasts in informing the system operations. Again, we focus on the drier years in the considered horizon (i.e., 2006, 2007, and 2012), and, for each Pareto front, we consider the closest solution to the target POP (black circled solutions in Figure 36). As expected, the long lead time forecast (Figure 37a) gives valuable information for hedging: under this Improved Operating Policy, the lake storage is generally higher than under the Basic Operating Policy throughout the entire year (see the green area in Figure 37b), thus saving water towards a dry forecasted period, and is almost reproducing the target Perfect Operating Policy during the peak irrigation season (see the dashed red line in the figure).

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Figure 37: Analysis of the Lake Como storage trajectory in dry years under the selected

Improved Operating Policy condit ioned on observations of 51-days ahead inflow forecasts. As a reference, the trajectory of the target POP and the selected BOP are also i llustrated.

Design of a basin-customized drought index As mentioned, the Lake Como basin is not currently equipped with an institutional drought monitoring system. This is probably due to two main reasons: first, water availability has historically not been a major limiting factor to the economic development of this area. Rather, water governance was mostly concerned with flood risk management. Second, accurately describing drought and water scarcity in such a highly regulated basin is not an easy task, with traditional drought indexes which may provide different, and sometime inconsistent, information about the occurrence of droughts (see Annex C). We formulated a combined drought index customized on the Lake Como basin by means of feature extraction techniques (Zaniolo et al., 2017). Our procedure requires the identification of a specific proxy for the agricultural stress in the area. In our experiments, we used the agricultural model of the irrigation district to estimate a trajectory of water deficit at the cell level (see Galelli and Soncini-Sessa, 2010), computed as the positive difference between a dynamic threshold dependent on soil type, root depth, and meteorological conditions, and the soil water content in the transpirative layer. Using this deficit as target variable to capture the drought condition of the basin, we then performed a feature extraction experiment with the Wrapper for Quasi Equally Informative Subset Selection (W-QEISS). This method is able to automatically construct a model (wrapper) of the selected target variable by combining a subset of input variables selected from a generally large set of candidates. These latter, in the case of Lake Como, include both observations of hydrological variables (e.g., temperature, precipitation, lake level, Snow Water Equivalent) and several drought indexes (e.g., SPI, SPEI, SMRI), and we also explored aggregation over different time horizons (see Annex C). Preliminary results show that W-QEISS systematically builds a model with three inputs, namely the week of the year, the weekly temperature, and the SMRI index aggregated over 6 months.


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Figure 38 shows that the constructed wrapper successfully reproduces the water deficit at the cell level, attaining a coefficient of determination R2 = 0.742. The constructed combined index hence represents a good candidate solution for accurately detecting and monitoring droughts in the Lake Como basin.

Figure 38: Comparison between the water deficit at the cell level produced by the agricultural model (red dashed line) and by the constructed wrapper (blue l ine).

Detection of climate teleconnection Despite El Nino Southern Oscillation (ENSO) is the most dominant climate signal and has been proved to influence local climate and hydrology over large parts of the world (Ward et al., 2014), most of the traditional indexes look at specific areas of the Pacific Ocean and were constructed looking at the teleconnection with countries located in that part of the world. Consequently, they may fail in capturing the relationship between these climate signals and the European climate. To investigate the existence of climate teleconnection between the hydrology in the Lake Como basin and Sea Surface Temperature (SST) in different locations of the world, we extended the Nino Index Phase Analysis (NIPA) to also include other global climate signals (e.g., North Atlantic Oscillation, Pacific Decadal Oscillation, Atlantic Multidecadal Oscillation, Dipole Mode Index). We focus our initial analysis on the spring precipitation (i.e., April, March, June) in the Lake Como basin, as this represents the key time period for the operations of the lake in terms of balancing flood control and water supply. We used NIPA for predicting the spring precipitation as dependent on some selected SST observations during the previous three months (i.e., January, February, March). Preliminary results show that, despite the correlation between spring precipitation and winter SST computed across the entire time-series is equal to 0.27, we obtain a correlation of 0.41 (level of confidence = 98.6%) for the positive phase of ENSO as classified by the Multivariate ENSO Index (MEI), and also a correlation equal to 0.67 (level of confidence = 99.6%) with the negative phase of the North Atlantic Oscillation (NAO). The selected SST observations for the

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two phases are reported in Figure 39. This teleconnection has a potential for improving the seasonal predictability in the basin and, ultimately, Lake Como operations.

Figure 39: Correlation map of spring precipitation (April, May, June) with winter SSTs (January, February, March) for the positive phase of the Multivariate ENSO Index ( left

panel) and negative phase of North Atlantic Oscil lation (right panel).

3.3.6 Testing plan

The results discussed in the previous section demonstrate that, although the Lake Como basin currently lacks of an official medium- to long-term forecasting system, there is good potential for informing the lake operations with valuable observational snow data as well as with long-term streamflow forecasts. Future efforts will focus on comparatively analyzing the operational value of the state-of-the-art forecast products generated within IMPREX and contrasting their performance with the use of observational data and perfect inflow forecast. In addition, we will explore the sensitivity of our findings with respect to the selected tradeoff adopted for the operations of Lake Como in terms of balancing water supply and flood protection as this tradeoff may evolve in time responding to the changing conditions in the basin (Giuliani and Castelletti, 2016; Amigoni et al., 2016). Moreover, the preliminary results obtained in terms of formulation of a basin-customized combined drought index as well as in terms of detecting significant teleconnection between local precipitation and global climate signals motivate exploring the potential for this information in improving the seasonal predictability and in informing the lake operations. Finally, since climate change has already shown its potential negative impact in a number of situations by altering the historical hydrologic regime (see Figure 27), we plan to assess the impacts of different climate change scenarios on the Lake Como basin. The formulated drought index will allow analyzing how climate change is expected to impact on the frequency and intensity of droughts. In addition, we will explore how the operational value of forecast products and observational data, drought indexes, and global teleconnection, can change in different climate conditions and, in particular, if this information will be valuable also in more challenging (dry) futures.


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3.4 Jucar River basin, Spain

3.4.1 Background

Study area description: Jucar River Basin District (JRBD) is located in the east of the Iberian Peninsula and is composed of nine water exploitation systems that flow into the Mediterranean Sea (Figure 40). The main economic activities are tourism, irrigated agriculture (mainly citrus fruits and vegetables), hydropower, shipping and commerce, and several industrial sectors (automotive, furniture, tiles, etc.). Irrigated agriculture accounts for nearly 80% of water demand, while other sectors, including urban supply, account for 20%. The principal water exploitation system of JRBD is the Jucar River Basin (JRB) that belongs to four provinces (Valencia, Albacete, Cuenca and Teruel) with an extension of 22,186.61 km2 and an average volume of water resources of around 1,605.4 hm3/year. The inland part of the JRB has a mountainous area in the upper basin and a relatively flat area in the middle basin, in the south plateau (Meseta Sur), which supports about 100,000 hectares of relatively recently installed irrigated agriculture. The lower basin lies in the coastal plain formed by alluvial materials, which support 35,000 hectares of traditionally irrigated agricultural areas, and also around 25,000 hectares of relatively recent irrigated areas. The major part of the basin is formed by permeable materials that allow the infiltration of the rainfall reaching the aquifers. Groundwater is naturally released as base-flow in the upper and middle basin, and constitutes a large coastal aquifer in the lower basin. Currently, a significant amount of groundwater is abstracted for irrigation purposes in the middle and lower basins. In addition, the formation of wetlands is an important feature in JRBD. The Albufera is the most important wetland which lies in the coastal area between the JRB and the Turia river basin, and has an extension of 21,120 hectares (of which the Albufera lake is around 3,000 hectares) including a vast extension of rice crops. Concerning climate conditions, typically Mediterranean, the average temperature in JRBD is about 16.5oC and the average precipitation is 475.2 mm/year. The maximum temperatures are reached in summer, which is the dry season, while during the months of October and November it is quite frequent to have a short period of heavy rain due to a meteorological phenomenon called “gota fria”, a kind of “trapped” cold front. Therefore, this basin is characterised by the semi-aridity of the climate and the high intra-annual and inter-annual hydrological variability (low flows in summer, autumn floods, …) that lead to recurrent multiannual droughts. These hydrological features forced to adaptation by means of the development of different management strategies (e.g. water storage infrastructures, conjunctive use of surface and ground waters, drought management plans…) to cope with drought periods. As a consequence, this is a highly anthropized and adjusted system that has many dams and infrastructures to satisfy the needs of all sectors, being the main reservoirs Alarcón (1118 hm3), Contreras (852 hm3) and Tous (378 hm3).

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Figure 40. The different water exploitation systems of the Jucar River Basin District

In addition to infrastructures and management strategies, institutional and legal developments are quite significant. The Jucar River Basin Partnership (Confederación Hidrográfica del Júcar - CHJ) is the public-private participatory institution that manages water resources since 1936, and allocates it among urban, agricultural, hydropower and industrial users, taking into account environmental protection, and predefined water rights and priorities. Water allocation decisions are taken at different time sales and under different scenarios. In the long term, water allocation is made in the framework of JRBD Water Plans, designed by CHJ since 1998, and updated regularly (last update in 2015). At the seasonal and short term, water allocation is decided at the Water Allocation Boards of every exploitation system in JRBD taking into account the actual situation of water storages in reservoirs and aquifers. But, in alert and emergency scenarios, which are defined according to a Compounded Drought Monitoring Index (as already explained in the Segura Case Study section) water allocation decisions are taken in the sessions of the so called Permanent Drought Committee, taking into account the actual hydrological and meteorological situation, as well as the guidelines about mitigation measures included in the Drought Management Plan (Plan Especial de Sequía - PES) designed and approved by JRBD in 2007. For the final design of the measures to be


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applied in real time, a probabilistic assessment of risks using hydrological forecast for an anticipation time of 12 to 24 months is performed. More details about this procedure will be described later. It is also important to highlight that direct reuse of reclaimed wastewater is a non-conventional source of water that can provide up to 120 hm3 in the lower Jucar River Basin. Its integrated use, together with surface and groundwater, increases reliability and resiliency, hence reducing vulnerability in drought times. Problem scope: As already mentioned, drought events are also recurrent in this area, as it happened in the Segura Case of Study. A very detailed characterization of different types of droughts (from meteorological to hydrological and operational) for JRBD using multiple indices can be found in Andreu et al. (2007). In the last 50 years, the most remarkable extreme drought events have been the 1981-1986, 1992-1995, and 2005-2008 episodes, with less extreme droughts in between. There were important impacts in the event of 1992-1995 on agriculture (restrictions in water supply, reduced yields, loss of annual crops, and permanent damages to permanent crops), on water quality (deterioration of water quality in natural surface waters, reservoirs and groundwater; increased algal bloom -toxic species-; eutrophication of surface waters; increased temperature and decreased oxygen saturation levels in surface waters; increased pollution loads in surface waters; increased salinity of surface waters and groundwater), hydroelectricity (reduced production), and on environment (increased mortality of aquatic species, including endangered/protected species; migration and concentration of wildlife; increased population of invasive –exotic- aquatic species; observation of adverse impacts on populations of rare/endangered –protected- riparian and wetlands species and loss of biodiversity, and deterioration of wetlands). It has to be highlighted also that, during two months in each summer of 1994 and 1995, a stretch of almost 30 km of the Jucar River in Albacete plain dried up. In the 2005-2008 drought event, most of these impacts had only marginal importance (low-ranked impacts), showing that drought preparedness and management has greatly improved in the JRB, as explained in Andreu et al, 2013. In irrigated agriculture, restrictions were experienced, but only resulted in minor reductions of yields, even though in many areas, irrigation was more complicated (e.g., turns) and water was more expensive. In water quality and environmental systems, impacts had lower importance, even though there was some mortality of aquatic species (including endangered/protected species); and some increased species concentration near water surface. The Albufera wetland did not experience any additional deterioration. It has also to be highlighted that, management of the drought made it possible to maintain the stretch of Jucar River Basin in the Albacete plain with water flowing. But still, economic impact was important. For instance, investments in emergency measures accounted for 75 million €, and there were also additional costs to: increase the control, monitoring and management during the drought period (2.1 million €); to buy water rights to protect the environment (reduction in consumption of 116 hm3 and a total cost of 18,48 million €); pumping water from emergency wells and recycling facilities (7 million €); and direct reuse of reclaimed wastewater (estimated between 3 to 5 million €). In addition, it was a reduction in hydropower generation (40% from previous years) and in rain fed agriculture yields. The impact

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in agriculture implied a huge reduction in the GDP of this sector, 411 million € in 2006 was the biggest reduction (respect to 2004). The experience of the management of the 2005-2008 drought demonstrated de importance of proactive drought planning and management in order to cope with extreme droughts. But, in order to further improve resiliency and decrease vulnerability in front of droughts in the JRB, the following improvements would be desirable in relationship with IMPREX project:

- If better long-term predictions of future precipitation, temperatures, and flows under climate change were available, the Water Allocation in the Water Plans of JRBD could be better adjusted, as also the Drought Indicators and corresponding guidelines with sets of measures defined in the Drought Management Plans of JRBD.

- If better short term and seasonal forecasts were available for precipitation, temperatures, and flows for real time drought management, they could be incorporated in the real time probabilistic impact assessment, reducing uncertainty in the probabilistic impacts forecast that are used to decision making in design and approval of seasonal action plans and mitigation measures.

- Better short term and seasonal forecasts could also help farmers to design their crop selection and irrigation strategies for every growing season. This however, will not be addressed in this IMPREX case of study.

It is important to note however, that from the interaction with stakeholders it became very clear that these improvements will only happen in case the meteorological forecast and the climate change predictions demonstrate enough skill to reduce the uncertainty existing in the current management tools. If not, it will only introduce a new source of uncertainty in the currently used methodologies for drought planning and management in JRB and not be useful for the targeted end-users.


Long term basin planning for vulnerability reduction. The long-term planning exercise made by CHJ for the updating of Water Plans of JRBD, includes hydrological and water allocation modelling for future hydrological scenarios using decision support systems (DSS) (Andreu et al, 2009). Vulnerability indicators are produced by the DSS for every user in JRB, and they must comply with an agreed criterion in order to consider that drought vulnerability is within acceptable values, so the water plan is feasible and sustainable. Currently, hydrological scenarios are mainly based on past hydrology, using naturalized historical flows. Climate change scenarios are generated by modifying the statistical characteristics of historical flows (e.g., 9% reduction of mean historical flows), even though some research has been conducted with climate change scenarios based on scenarios provided by climatological models (see for instance Hernández-Barrios et al, 2007). It is quite obvious that if skilled climate change based predictions of future precipitation, temperatures, and flows were available, the Water Allocation in future Water Plans of JRBD could be better adjusted, and the future drought vulnerability would be better assessed.


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Long term Drought Planning. As in all Spanish basins (including the Segura River Basin preceding case of study), from the year 2000, a clearly proactive approach was introduced in the JRBD, developing a Special Drought Management Plan (DMP) approved in 2007 (Estrela and Vargas, 2012), which includes drought monitoring by means of a compounded drought operative index (CDOI) (Ortega et al, 2015), used to define drought scenarios (normality, pre-alert, alert and emergency). CDOI maps are updated monthly and displayed in the web page of CHJ and serve as a first step early warning system to trigger predefined anticipation and mitigation measures attached to each scenario in order to decrease vulnerability and increase resiliency. The set of measures includes improvements in efficiency of water uses; water saving practices; conjunctive use of surface and groundwater; water rights purchase for environmental protection; irrigation sluice water recirculation; reclaimed wastewater direct reuse; improvements in controls of water uses, water quality, and ecological status of water bodies; and revision of actions and post analysis after a drought episode. In Figure 41, the evolution of the JRB compounded drought index can be seen for the period October 2001 to October 2009.

Figure 41. Evolution of the JRB compounded drought index the period October 2001 to October 2009 (green colour is for normal situation, yellow is for pre-alert, orange for

alert and red for emergency) (source: self-elaboration with data provided by CHJ).

The Compounded drought index and the sets of measures attached to each drought situation have been designed by means of simulations of the water resources system performance using hydro-meteorological scenarios based on historical values. Thus, future updates would clearly benefit from better skilled climate change based predictions of future precipitation, temperatures, and flows. Impacts and risks assessment in Real Time Drought Management. The drought monitoring system described above provides useful information for first level early warning and action against drought, as well as for risk perception by the public. Yet, in order to manage droughts, a more elaborated and detailed information system is needed to better assess the risk and the effectiveness of the measures that can be used to modify the risks, and to mitigate the effects of the drought on both the established uses and on the environment. In order to produce such information, a DSS is used for the development and use of real-time management models able to assess in a probabilistic manner the impacts of drought and the efficacy of the measures, being applied on regular basis for the management of JRB (Andreu, 2013).

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Following the methodology of drought risk and assessment explained in Andreu and Solera (2006) and depicted in Figure 42 and using the SIMRISK module of Aquatool, probability distributions for all variables of interest are obtained (e.g., deficits in water demands, volumes in reservoir storage, deficits in ecological flows) for every month over the assumed time horizon (e.g., 12, months). Currently, the hydrological inputs to this risk assessment methodology (“multiple natural streamflow scenarios” in the figure) are obtained through stochastic modelling of the inflow variables by means of a Multivariate Aurorregresive and Moving Average model (ARMA) (Salas et al, 1980). With this type of modelling, the generated series respect, not only the basic statistics of the historic streamflow values, but also the historical drought characteristics.

Figure 42. Scheme of the risk assessment methodology used in the Jucar River basin (SIMRISK).

The DSS can show these results in tabular or graphical form, highlighting the evolution of probabilities and percentiles for water demands and for reservoir storages (see, for instance, Figure 43). Cumulative distribution functions of any state or quality variable at any time can be obtained. If the estimated risks are acceptably low, then there is no need to undertake measures. However, if the estimated risks are seen as unacceptably high, then some measures must be applied. In that case, alternatives with sets of measures are formulated, and the modification of risks and the efficiency of measures are assessed again with the DSS. This iterative procedure can be continued until, eventually, an acceptable value of risk is reached and the process ends. The approach provides a complete vision of the consequences of decisions, either concerning management or infrastructure (Andreu et al, 2013).

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Figure 43. Average (above) and probabilistic (below) forecasts for the total JRB reservoir

storage evolution in 2006 campaign without measures (blue), and with the agreed measures (red) (self elaboration).

Figure 44. Example of a probabilistic forecast of water deficits in an agricultural demand

of the JRB made in February 2002 until October 2002.

The described methodology could be employed in an improved manner if the generated series would incorporate information from skilful short-term and seasonal forecasts of precipitation and temperatures. This would decrease the uncertainties attached to the currently used generation scheme.

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UPV has a fluid and continuous relationship with the Jucar River Basin Partnership (CHJ – Confederación Hidrográfica del Júcar), which is the key stakeholder in this basin, and which in turn integrates most sectoral stakeholders (major water users, as irrigation associations, cities, and hydropower companies), as well as other interests stakeholders (the main bodies of the central administration related to water, regional and local representatives, environmental NGO’s, and general public). The way to communicate with them is by holding meetings and workshops. In these events, the user needs, the knowledge about the methodologies, the obtained results, actions taken, how to face future problems and the advances during the working period to improve water management are exchanged. As reported by UPV in WP13 (18 Month Interim report), meetings have been many:

- Multilateral (workshops or seminars): 11/11/05 (UPV-JBD-Seminar about Droughts); 14/03/2016 (EDGE project seminar); 15/06/2016 (UPV-JBD-Seminar about Droughts); 07/10/2016 (EDGE project seminar) and 21/03/2017 (Preparation of special session about JRB needs in Climateurope event)

- Bilateral with EMIVASA (urba: 29/10/2015; 04/02/2016; 11/04/2016; 21/10/2016 - Bilateral with CHJ: 11/01/2016; 20/05/2016; 20/10/2016; 15/03/2017; 30/03/2017

From these meetings and from descriptions made in previous sections, it could be concluded that the situation of this case of study with respect to drought planning and management is quite good, as acknowledged by many instances (e.g., Swabe et al., 2013; De Stefano, 2013). But, it also has been concluded that improvements can, and must, still be made in several aspects. Among them, and in summary, we can mention the following more related to IMPREX project (which are very much in line with the user requirements declared in the Segura Case of Study):

- The Jucar River Basin Partnership is interested in: o Refinement of Long-term Water Plans incorporating skilled climate change

predictions of Precipitation, Temperatures and Stream Flows in the methodology currently been used. This would produce better estimates of reliability and vulnerability indicators, and therefore, programs of measures better adapted to climate change.

o Refinement of Drought Plans, also incorporating skilled climate change predictions of Precipitation, Temperatures and Stream Flows in the methodology currently been used. This could introduce a further refinement in the Compounded Drought Index definition and estimation, reducing uncertainty, as well as earlier detection of drought episodes which could trigger or deactivate measures in a faster manner, but also in a more robust way.

o Improvement of short term and seasonal generation of future flows scenarios incorporating meteorological and seasonal forecasts (lead time at least 6 months) of Precipitation, Temperatures and Stream Flows in the probabilistic methodology currently been used for drought impacts assessment, and for testing the validity of mitigation measures in real time management of a drought episode. As described later, this can be made by two ways: one is using a hydrological model (HYPE or EVALHID) and the inputs provided by ensembles of P and T forecasts, and the other is by extending the stochastic ARMA model to an ARMAX model that incorporates as exogenous variables the P and T forecasts.


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- Stakeholders from the agricultural sector are interested in the same type of services as

CHJ, plus short and medium term forecasts of precipitation and temperature, prediction of heat waves, soil moisture, and other climatic risks. Moreover, they emphasize the necessity of improvements in probabilistic meteorological forecasts in a certain time window (hours/days in advance) and improve the methods to estimate the water needs in irrigation and measurements of precipitation. As an example, on how agricultural sector stakeholder involvement in climate services we could for instance think of a “persona” with the following profile: an agricultural engineer hired by an irrigators’ association that uses short term forecasts to advise farmers about water needs of the crops, in order to irrigate in the following days. He or she also needs to know and understand the seasonal forecasts for decisions about type of crops to grow in the season, since in some cases these decisions can be held until early spring. He/she is also advising the person representing the association (usually the president) in the bodies of CHJ where water allocation for the coming season is applied, and needs the long term forecasts (climate change) when advising that person in the bodies which update the River Basin Plans every 6 years.

- In addition, both types of stakeholders agree in the desirable spatial resolution (5x5 km) to facilitate the decision-making in agricultural planning and the management of water resources.

From these user requirements, UPV is going to address the ones related with water resources management and its impacts in agriculture, since it is not the intention to enter in the detailed agricultural short-term management. Paradoxically enough, some agricultural stakeholders have stated that with modern irrigation techniques, such as automated pressurized systems for drip irrigation, were water is applied continuously to the crop taking into account the information provided by field sensors of soil moisture, etc., they do not really feel in need of precipitation forecasts, since crop gets only the water it needs at every moment. And, on the opposite side, agricultural stakeholders that are in areas with open channel water distribution and a system of turns for irrigation (e.g., once every week), have stated that they cannot skip a turn when a forecast of precipitation is issued, since current uncertainty related with location and total amount of precipitation is too big to risk to have to wait another week to irrigate if the forecast is not met.


This section summarizes the overall system configuration for the drought decision support system, meeting the user requirements addressed from the previous section. The first part describes the forecasting system, the second the user interface. These specifications are preliminary in the sense that they will likely be modified over the course of the project, based on performance and end-user feedback. 3.4.4.1 Forecasting system The decision support system that UPV will deliver is not focused in the daily decisions about water supply of crops, which is an agronomical specialty and within the irrigation district competence. Rather it will focus on the monthly and seasonal decisions about water

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management in the basin or water resources system, and also on the decisions about measures to be applied in the basin water plans related to water allocation at the long term, improvement of resilience for droughts and adaptation to climate change. Therefore, two time horizons for the decisions will be treated: the 1 to 24 months horizon and the 6 to 25 years horizon. Eventually, time horizons of 50 years or longer will be investigated for climate change adaptation issues. Each one of the analysis and time horizons needs different weather and climate forecasting skills. The first one needs short and seasonal forecast, the second and the third need long term (climate change) forecasts. The analysis will be done in an integrated way, taking into account all elements of water resources system of the entire basin (rivers, aquifers, dams, returns, etc…), all this to be more realistic, considering all physical connections between elements and implications of any decisions in the entire system. The best way to do this is using a decision support system (DSS) for water resources system management, in order to estimate the risk of failure in supply of water to all users and the compliance with the minimal ecological flows with an anticipation period between 12 and 24 months. The forecasting system is composed of two steps, and has two options, which are explained in backwards fashion:

• The ultimate forecast of interest for decision making are the above-mentioned user requirements (i.e., forecasts of vulnerability indicators, drought indices, impacts on water uses in deterministic and probabilistic terms, modification of risks and impacts, etc.). This will be produced by means of the water resources management and allocation module SIMGES in the deterministic forecasts, and by the module SIMRISK in the probabilistic forecasts, according with the methodology already mentioned in current practice section.

• In order to feed SIMGES and SIMRISK models with hydrological scenarios, reliable hydrological forecasts (river flows) are needed. Indeed, natural flows are needed (i.e., flows that would happen if man would not produce changes due to storage and releases from reservoirs, pumping from aquifers, and diversion and return flows from consumptive uses) because Júcar basin is strongly anthropized. These flows provide a consistent baseline in order to compare the performance of different programs of measures in basin management. Two options will be available in the forecasting system in order to produce these flow forecasts: The first option is to get the flow forecast from a hydrological model, as E-HYPE, for instance. The second option is to recur to a stochastic model that uses previous values of flows and incorporates precipitation and temperature forecasts to produce flow forecast, as already mentioned in the current practice section.

With respect to the first option for naturalized flow forecasts, and as reported in WP4 (deliverable 4.2), the analysis performed by UPV about the flow forecasts, obtained from the European hydrological model E-HYPE at the five inflow points of the water resource model of the basin, has shown that E-HYPE is not capturing important characteristic of the basins, such as the importance of natural regulation provided by aquifers in the heads and in the middle part of the basin, as shown in Figure 45. This essential mismatch between E-HYPE results and real values cannot be overcome by any bias correction, since in summer months precipitation is insignificant. On the other hand, average monthly flows produced by EVALHID, are closer to the


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historical values in all sites (see Deliverable 4.2 for more details and graphs). Thus, based on the results of this comparison and the good performance of EVALHID in JRB, it was concluded that EVALHID will be the hydrological model incorporated in this first option of the forecasting system for WP11.

Figure 45. Streamflows predictions from E-HYPE versus observed streamflow in natural

regime

For the second option (stochastic model with external inputs), MASHWIN module of Aquatool will be used to analyse and calibrate the ARMAX model, as mentioned in a previous section, as well as to ultimately producing ensembles of several hundreds of forecasts to feed SIMRISK module for probabilistic assessment. 3.4.4.2 User interface Therefore, the forecasting system will be based on the software in use in JRBD (i.e., AQUATOOL, see Andreu et al 1996), a Decision Support System Shell, which provides a user-friendly interface to build and run different kind of models related to integrated water resources management at river basin scale. It includes geo-referenced databases, graphical design capabilities, graphical interfaces for data management and result analysis, and several modules for hydrological simulation, groundwater simulation, integrated management simulation and optimization, economic assessment, water quality assessment, and environmental assessment. This software is widely used by basin authorities in Spain and abroad, to assess vulnerability of water resources systems and/or to manage droughts in real time. With this tool we can process

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different types of meteorological data, simulate the river flows and estimate the baseline water supplies or the risk of water shortage, among other factors of interest in water resources management. The tool is documented, supplied with examples and can be found at http://www.upv.es/aquatool/en/index_en.html The interface of AQUATOOL for the Júcar River Basin is developed with the module SIMGES (a set of water allocation models). The model layout depicted in Figure 46 reproduces properly the functioning of the system and has been validated with observed data. This is a simplified model used by the CHJ in order to assess management rules and infrastructures functioning, but it could consider more elements or could be more simplified, depending on the requirements of the user. In order to obtain a suitable representation of the system all the elements that play an important role in relation to water resources management are taken into account: aquifers, contributions, reservoirs, demands and returns.

Figure 46. Schematic representation of the AQUATOOL system developed for the Jucar River Basin

In addition to the currently available indicators and graphs to show the results of the forecasts, some new or modified indicators and graphs will be provided by the user interface according to the findings in the development of WP11 and the homogenization of methodologies among cases of study, as for instance, tables and graphs shown in Figure 11 for the Segura Case of Study.


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Since most of the works for the comparison and subsequent decisions about the adoption of the definitive sets of precipitation and temperature forecasts are still under performance in the JRB case of study, with similar methodologies and provisional findings as depicted in the Segura case of study, we will focus on the feasibility of the first option of the flow forecasting system, i.e., the use of EVALIHD model, and in a preliminary proposal for incorporation of meteorological and climatic forecasts and predictions in a robust manner in the drought forecasting process. Figure 47 to Figure 51 show the calibration of EVALHID at five points in JRB. As already mentioned, the adjustment is quite good, and will be better illustrated in future documents.

Figure 47. Calibration of EVALHID in Molinar sub-basin

Figure 48. Calibration of EVALHID in Alarcón sub-basin

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Figure 49. Calibration of EVALHID in Contreras sub-basin

Figure 50. Calibration of EVALHID in Tous sub-basin


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Figure 51. Calibration of EVALHID in Sueca sub-basin

Climate data from ECMWF were used to feed EVALHID in order to obtain streamflows and to to compare them with the historical ones. Figures and fit scores are reported in D4.2. These scores showed the necessity of a proper bias correction of the data to continue with the process. On average, these data are more accurate and sharp in the spring and summer, presenting the major errors in winter months. Based on the above, a preliminary methodological proposal for a robust approach is made, and will be tested and developed in next months. The approach is robust in the sense that it tries to produce homogeneous outputs from the two options for flow forecast mentioned in the design specifications. And it also tries to give more confidence about the information transmission in the chain of models used from WP3 to WP5 and its use in sectoral WP’s. It begins with the comparison between data from different institutions and historical datasets to achieve a good bias correction. Then, the idea is to produce streamflows with EVALHID (calibrated hydrological model) and use the properties of these streamflows series to correct a stochastic model (MASHWIN), which will be used for the generation of multiple natural streamflows scenarios. Finally, generated series will be used for the simulation of future management with SIMRISK, which will assess the risk and the vulnerability of the system through a statistic treatment of the results in form of indicators.

This process is shown in more detail in Figure 52.

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Figure 52. Preliminary proposal of the methodology used for the drought forecast in the

Jucar River system based on different climate forecasts

3.4.6 Testing plan

During the next months until the next deliverable (month 36) a comparison between seasonal data from MetOffice with local data will be performed in order to compare the resultant bias with those of the other institutions (ECMWF and SMHI). Then, the search and test for the most appropriate method of bias correction for all of them will be undertaken in order to ensure the reliability of results and satisfy the needs of the stakeholders. In this way, we would have reliable input data that expectedly will not underestimate the impacts of droughts in the agricultural sector. With this corrected data we will use the calibrated hydrological model EVALHID and use the management model to make forecasts related to the risk of drought in future scenarios. In the same way, the idea is to assess the possible impact of climate change in the water accounting of the system, focusing in the agricultural sector. All this is intended to improve the system of drought indicators used to activate the appropriate measures (listed in the Drought Plan) and to


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avoid severe impacts and high economic losses coming from an inefficient management of water resources in drought periods. The activities rely on the improvements that preceding IMPREX work-packages will bring to the forecasts, to the downscaling processes and to the different models and approaches, as well as the synergies provided by the interaction among the transversal work-packages 6 to 13. The continuous feedback and interaction among the work packages is crucial and should make the difference to obtain meaningful results serving the water users in this case study area.

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4 Conclusions

This deliverable assesses the needs of the agricultural sector for Weather and Climate services, with a focus on irrigated agriculture. The stakeholder consultation and first results provide a first idea on the scope for improved water resources and drought risk management informed by better climate predictions from IMPREX. Current use and expectations were evaluated by means of a survey (see the summary in Chapter 2) and consultations with several stakeholders in four case study areas in the water scarce and drought-prone Mediterranean area. For specific users in each of these case study areas, a drought Decision Support System was designed, principally at the seasonal time-scale, and first results were evaluated. The following overall conclusions can be drawn from the work so far:

- The agricultural sector is already a user of short-range weather information, especially those actors that are involved in the agricultural operations and productivity in the field. Stakeholders involved in the allocation of irrigation water at the regional level also use this type of forecasts to some extent, but have indicated that they see scope to integrate improved seasonal forecasts in their decision-making process.

- Stakeholders have expressed it is critical to link these forecasts with a local impact indicator. For irrigated agriculture these are generally indicators that are related to the water availability for allocation in the storage reservoirs. Also groundwater levels can be a relevant indicator. Often these indicators are currently monitored, but not forecasted.

- The different case studies require a different setup (data, models, output visualization, etc) given the user needs, habits and area characteristics. Preliminary analysis of the dDSS and of its operational value in four case study areas has indicated that the performance may be skilful enough to be integrated in the drought management practices for certain seasons and under certain conditions. However, other products, improved forecasts, indexes and climate variability indices have to be analysed to obtain a complete and comprehensive assessment of what this operational value can be and what the final design will be.


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Annex A – Survey on Weather and Climate services in the agricultural sector

See next pages


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Annex B – Supporting material for the identification of the best forecast in the Lake Como system

The experimental setting of the Information Selection and Assessment framework is the following: • Observations of state surrogates: this set of candidate exogenous information (Table 7

collected over the period 2006-2013 includes meteorological variables (i.e., daily average precipitation over the entire watershed interpolated from punctual precipitation measures using the Thiessen method, and estimates of the zero degree isotherm, obtained interpolating levels and temperature values from ground stations), snow data (i.e., SWE estimates provided by ARPA3 as well as values of the weekly snow melting derived from the SWE estimates), and information on the hydropower reservoirs (i.e., total storage and total release from the upstream reservoirs of the two main companies operating in the basin, i.e., Enel and a2a).

• Inflow forecasts: this set of candidate exogenous information (Table 8) includes forecasts of the lake inflows for the period 2006-2013, computed over different lead times, ranging from one week to two months. At this stage of the project, we assume a retrospective streamflow dataset as perfect forecast generated from a hypothetical forecasting system, which also removes possible modeling biases in the construction of the forecasts. Real forecast products will replace the perfect forecast in the subsequent steps of development of the dDSS.

• Perfect Operating Policies: the set of POPs was designed via Deterministic Dynamic Programming (Bellman, 1957) over the evaluation horizon 2006-2013. The weighting method (Gass and Saaty, 1955) is used to convert the 2-objective problem into a single-objective one via convex combinations. The exploration of the tradeoff is performed by sampling the space of the weights used in the objectives' aggregation.

• Basic and Improved Operating Policies: we designed both the BOP and IOP sets by solving via Evolutionary Multi-Objective Direct Policy Search (Giuliani et al., 2016b), an Approximate Dynamic Programming approach that combines direct policy search, nonlinear approximating networks, and multi-objective evolutionary algorithms. In particular, we parameterized the operating policy as Gaussian radial basis functions, as they have been demonstrated to be effective in solving this type of multi-objective policy design problems (e.g., Giuliani et al., 2014; Giuliani et al., 2016b). To perform the optimization, we use the self-adaptive Borg MOEA (Hadka and Reed, 2012), which has been shown to be highly robust in solving multi-objective optimal control problems, where it met or exceeded the performance of other state-of-the-art MOEAs (Zatarain et al., 2016). Each optimization was run for 2 million function evaluations over the evaluation horizon 2006-2013. To improve solution diversity and avoid dependence on randomness, the solution set for each optimization is the result of 40 random optimization trials. The final set of Pareto optimal policies for each experiment is defined as the set of non-dominated solutions from the results of these optimization trials.

3 ARPA (Agenzia Regionale Protezione Ambiente) is the Regional Enviornmental Authority

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Table 7: Set of observational data as system state surrogates.

Variable Description Period

SWEt Snow Water Equivalent estimated by ARPA [m3]

2006-2013

Pt Precipitation measured in the catchment [mm] 2006-2013

SMt Snow melting derived from SWE [m3] 2006-2013

z t iso0 Freezing level from temperature regression [masl] 2006-2013

st,HP Total storage of upstream reservoirs [m3] 2006-2013

rt,HP Total release from upstream reservoirs [m3/s] 2006-2013

Table 8: Set of inflow forecasts over different lead times.

Variable Description Period

Lead7t Cumulative future inflow over 1 week 2006-2013

Lead14t Cumulative future inflow over 2 weeks 2006-2013

Lead21t Cumulative future inflow over 3 weeks 2006-2013

Lead30t Cumulative future inflow over 1 month 2006-2013

Lead37t Cumulative future inflow over 1 month and 1 week 2006-2013

Lead44t Cumulative future inflow over 1 month and 2 weeks 2006-2013

Lead51t Cumulative future inflow over 1 month and 3 weeks 2006-2013

Lead60t Cumulative future inflow over 2 months 2006-2013

Results of Input Variable Selection experiments Following the ISA framework, the Iterative Input variable Selection (IIS) algorithm (Galelli and Castelletti, 2013a) is used to select the subset of most informative variables, which better characterizes the optimal sequence of release decisions of the target POP. Figure 53 illustrates the results of 100 runs of the IIS algorithm over the observations of state surrogates’ dataset in terms of the average performance attained by the underlying regression model in describing the optimal release decisions sequence, measured in terms of cumulative coefficient of variation (R2). The repetition of the experiments aims at filtering the randomness associated to the construction of the extra-trees models used by the IIS algorithm (Galelli and Castelletti, 2013b). We tentatively stopped the input selection at the first four most informative variables, which allows explaining cumulatively nearly 85% of the target releases. Not surprisingly, the day of the year and the lake level are selected as the most relevant drivers, explaining up to the 65% of the optimal release sequence. The third selected variable is the SWE estimate, accounting for a R2 contribution of about 12%. The fourth selected variable is the total storage of the upstream hydropower reservoirs, which further adds another 7%. As expected, two low frequency variables are selected to describe the slow-dynamics in the basin: snow information is a key element for the lake operations as it approximates the amount


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of water stored in the upper watershed that will be made available in the next melting season, when the yearly inflow peak is reached. It is therefore a good proxy for discriminating a wet from a dry summer, triggering effective hedging strategy. The information on the total storage of upstream reservoirs is also relevant and impacts on both the operating goals: if the upstream reservoirs happen to be full in summer, the operation is informed that this retained water is potentially available in the basin and expected to reach the lake in the coming season. On the other hand, if the upstream reservoirs happen to be empty in spring or in autumn, the lake operator can rely on a bigger buffer capacity when a flood event kicks in. Figure 54 reports the results of 100 runs of the IIS algorithm over the inflow forecast’ dataset and shows that, after the day of the year and the lake level, the IIS algorithm consistently selects a combination of long (i.e., 51 days) and short (i.e., 7 days) lead time information. These variables contribute an average explained variance of 21% and 4%, with the final set of information explaining more than 90% of the target release trajectory. These results reflect the double temporal dynamics of the lake regulation objectives: insights on the total volume entering the system over some weeks are highly beneficial for water supply, but do not inform on the timing of the flood peak. This latter is better captured by the short lead time information as the time of concentration of the catchment is less than 24 hours, while the time needed to drawdown the lake level and to buffer the flood peak is around 3 days.

Figure 53: Results of the automatic selection of valuable information over the observations

of state surrogates’ dataset in terms of average cumulative performance of the model describing the optimal release trajectory of the target Perfect Operating Policy.

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Figure 54: Results of the automatic selection of valuable information over the inflow

forecast’ dataset in terms of average cumulative performance of the model describing the optimal release trajectory of the target Perfect Operating Policy.

Crowdsourced snow-related information Crowdsourced observations may act as low-cost virtual sensors in a variety of environmental contexts (Lowry and Fienen, 2013), for example, contributing to monitoring the dynamics of forests (e.g., Daume et al., 2014) or streamflow (Michelsen et al., 2016), with potential benefit in terms of the prediction of flood events and of the timely delivery of alarms (e.g., Mazzoleni et al., 2015). Given the value of snow information in the Lake Como basin, we explored the potential for web and crowdsourced data to retrieve relevant information on snow availability and dynamics in a river basin, and assess the utility of such information in informing the lake operations. This analysis relies on a novel crowdsourcing procedure (see Figure 55) for extracting snow-related information from public web images, either produced by users or generated by touristic webcams, and then quantifying the operational value of this information compared to other more traditional snow information, such as ground observations and a hybrid mix of satellite retrieved information, ground data, and model outputs (Giuliani et al., 2016c). Our procedure employs an articulated architecture (Fedorov et al., 2016), which automatically crawls content from multiple web data sources with a content acquisition pipeline integrating public webcams and user-generated photographs posted on Flickr. Next, the procedure retains only geo-tagged images containing a mountain skyline with high probability and identifies the visible mountain peaks in each image, using a digital elevation model (DEM). Then, a supervised learning classifier extracts a snow mask from each image, which distinguishes the image pixels as snow or no-snow. Finally, the resulting snow masks are post-processed to derive time series of virtual snow indexes (VSI) representing a proxy of the snow covered area. The extracted VSI (σ) is used to inform water system operations.


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Figure 55: Flowchart of the adopted crowdsourcing procedure.

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Annex C – Supporting material for the design of a basin-customized drought index in the Lake Como system

Figure 56 illustrates the results of a drought detection experiment where we look at three alternative drought indexes (i.e., SPI, SPEI, SMRI) and two SPI-type of indexes computed on the Lake Como inflow (SPIinflow) and level (SPIlevel) rather than on precipitation data, using six different time aggregations, from one week to one year. The values of these indexes (top panel) are contrasted with the trajectories of the difference between lake releases and water demand (bottom panel), where positive values indicate periods of water abundance and negative values periods of water scarcity. These results demonstrate that a single index is not able to accurately detect the beginning and the end of drought events, thus calling for the identification of a combined drought index.

Figure 56: Results of drought detection in the Lake Como basin according to different

drought indexes with multiple t ime aggregations, from one week (1w) to one year (52w). Each colored line corresponds to a drought event detected by the corresponding index.

Such combined index will combine a subset of input variables selected from a generally large set of candidates in order to accurately reproduce the status of water resources in the basin and, specifically, drought conditions. The set of candidate information includes both observations of hydrological variables (e.g., temperature, precipitation, lake level, Snow Water Equivalent) and several drought indexes (e.g., SPI, SPEI, SMRI), and, for each variable, we also explored aggregation over different time horizons (see Table 9). We use the Wrapper for Quasi Equally Informative Subset Selection (W-QEISS) to automatically construct a model (wrapper) of the selected target variable (i.e., water deficit at the cell level) by combining a subset of input variables selected from a generally large set of candidates. Specifically, the W-QEISS algorithm builds the wrapper by solving a four-objective optimization problem that aims at minimizing the number of selected features and maximizing the accuracy of a model, while optimizing two entropy-based measures of feature relevance and redundancy. We solved this


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multi-objective problem by using the self-adaptive Borg MOEA as it has been shown to be highly robust across a diverse suite of challenging multi-objective problems, where it met or exceeded the performance of other state-of-the-art MOEAs (Reed et al. 2013).

Table 9: List of candidate variables for the Q-WEISS experiments.

Variable Time aggregation (weeks)

Week of the year not aggregated

Precipitation 4

Melting 4

Snow Water Equivalent 4

SPIlevel 1, 2, 4

SPIinflow 1, 2, 4

SPI 1, 2, 4

SPEI 1, 2, 4

SRI 1, 2, 4

SMRI 4, 12, 26

SSI_sup 12, 26, 52

SSI_tot 12, 26, 52

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IMPREX has received funding from the European Union Horizon 2020 Research and

Innovation Programme under Grant Agreement N° 641811

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