Study project. Galina Shevyrina

Transcription

1 Technische Universität München Faculty of Civil, Geo and Environmental Engineering Institute of Water and Environment Chair of hydrology and River Basin Management Prof. Dr.-Ing. Markus Disse M.Sc. Eleni Bekri Development of WEAP and HYDRONOMEAS models for Alfeios River Basin, Greece. Assessment of present water allocation scheme and investigation of optimal water allocation. Study project Galina Shevyrina Munich March 12, 215 1

2 Contents List of figures... 3 List of Tables Introduction Area description WEAP model Software description Surface water hydrology Agriculture Node Catchment Crops Drinking Water Supply Node Ladhon Reservoir Hydropower production Costs and benefits Results of WEAP Scenario Scenario Scenario Comparison of scenarios Costs calculation HYDRONOMEAS model Software description Model setup Results Conclusions... 8 References

3 List of figures Figure 1 Alfeios River Basin... 7 Figure 2 Simplified representation of Alfeios River Basin (Bekri, et al., 214)... 8 Figure 3 Schematisation of Alfeios River Basin in WEAP Figure 4 Division of river network on reaches in WEAP Figure 5 WEAP-MABIA Reference evapotranspiration calculation: climatic data availability (Jabloun, et al., 212) Figure 6 Global horizontal irradiation, Patras, Greece (SolarGIS, 215) Figure 7 Crop coefficient curve (FAO, 1998) Figure 8 Soil water properties Figure 9 Runoff calculation in WEAP-MABIA Figure 1 Crops growth stages Figure 11 Cropping pattern of maize Figure 12 Cropping schedule Figure 13 Flow chart illustrating the operating mode of the irrigation module (Jabloun, и др., 212) Figure 14 Increase of drinking water demand Figure 15 Volume-elevation curve of Ladhon Reservoir Figure 16 Reservoir zones (WEAP, 215) Figure 17 Operating rules for Ladhon Reservoir (Bekri, et al., 214) Figure 18 Linearisation of volume-elevation curve of Ladhon Reservoir Figure 19 Irrigation water demand Figure 2 Yearly drinking water demand Figure 21 Irrigation water supply requirement vs water demand Figure 22 Drinking water supply requirement vs water demand Figure 23 Unmet irrigation water demand Figure 24 Potential vs simulated actual crop yield Figure 25 Relative crop yield reduction Figure 26 Unmet drinking water demand Figure 27 Unmet drinking water demand in Figure 28 Unmet drinking water demand in Figure 29 Alfeios streamflow above Erymanthos inflow in Figure 3 Alfeios streamflow below Erymanthos inflow in Figure 31 Alfeios streamflow above Erymanthos inflow in Figure 32 Alfeios streamflow below Erymanthos inflow in Figure 33 Annual turbine flow at Ladhon HPS Figure 34 Turbine flow at Ladhon HPS in Figure 35 Ladhon Reservoir storage in Figure 36 Energy generation at Ladhon HPS Figure 37 Turbine flow at Flokas HPS Figure 38 Comparision of energy generation Figure 39 Energy generation at Flokas HPS Figure 4 Comparision of energy generation Figure 41 Storage in Ladhon Reservoir in

4 Figure 42 In- and outflows of Ladhon Reservoir in Figure 43 Storage in Ladhon Reservoir in Figure 44 In- and outflows of Ladhon Reservoir in Figure 45 Unmet irrigation water demand Figure 46 Unmet irrigation water demand in Figure 47 Unmet irrigation water demand in Figure 48 Crop yield of Tomato Figure 49 Crop yield of Tomato Figure 5 Crop yield of tomato in Figure 51 Evapotranspiration in 26. Tomato 1 (Case 4) Figure 52 Depletion, RAW and TAW in 26. Tomato 1 (Case 4) Figure 53 Evapotranspiration in 26. Tomato 2 (Case 4) Figure 54 Depletion, RAW and TAW in 26. Tomato 2 (Case 4) Figure 55 Unmet drinking water demand Figure 56 Unmet drinking water demand in Figure 57 Erymanthos streamflow in Figure 58 Unmet irrigation water demand in Figure 59 Energy production at Ladhon HPS Figure 6 Turbine Flow at Ladhon HPS Figure 61 Energy production at Flokas Dam Figure 62 Relative differences in generated energy at Flokas Dam Figure 63 Turbine flow at Flokas Dam Figure 64 Relative differences in turbine flow at Flokas Dam Figure 65 Monthly energy demand Figure 66 Energy production at Ladhon Dam Figure 67 Unmet HP demand at Ladhon Dam in Figure 68 Unmet energy demand... 6 Figure 69 Headflow of Ladhon River Figure 7 Energy generation in 281 at Ladhon HPS Figure 71 Unmet energy demand in Figure 72 Simulated vs required outflow from Ladhon Reservoir in Figure 73 Irrigation supply requirement (including irrigation canal losses) Figure 74 Unmet irrigation demand Figure 75 Unmet irrigation demand in Figure 76 Unmet irrigation demand in Figure 77 Demand Site Reliability (Sc. 1) Figure 78 Demand Site Reliability (Sc. 2) Figure 79 Demand Site Reliability (Sc. 3) Figure 8 Irrigation water supply coverage (Sc. 1) Figure 81 Irrigation water supply coverage (Sc. 2) Figure 82 Irrigation water supply coverage (Sc. 3) Figure 83 Drinking water supply coverage (Scenarios 1-3) Figure 84 Crop production costs, Scenario Figure 85 Crop production costs, Scenario Figure 86 Crop production costs, Scenario

5 Figure 87 Market value, Scenario Figure 88 Market value, Scenario Figure 89 Market price. Scenario Figure 9 Schematisation of the Alfeios River Basin in HYDRONOMEAS Figure 91 Water deficit at DWS Node. Cases Figure 92 Comparison of simulated results of unmet demand Figure 93 Unmet irrigation water demand (HYDRONOMEAS) Figure 94 Unmet irrigation water demand (HYDRONOMEAS) (years with water shortages) Figure 95 Simulated unmet demand: WEAP vs HYDRONOMEAS Figure 96 Piecewise linearization of turbine flow head curve at Flokas HPS Figure 97 Hydropower generation at Flokas HPS (Cases 1-4). HYDRONOMEAS Figure 98 Yearly HP generation at Flokas Dam. HYDRONOMEAS Figure 99 Energy generation at Flokas HPS: WEAP vs HYDRONOMEAS Figure 1 Energy generation at Ladhon Dam (HYDRONOMEAS) Figure 11 Energy generation at Ladhon Dam: WEAP vs HYDRONOMEAS

6 List of Tables Table 1 Components of the schematised Alfeios River Basin system Table 2 Main reaches of the river net in the WEAP model Table 3 Climatic parameters for the agricultural area at Flokas Dam Table 4 Average monthly sunshine hours, Pyrgos Table 5 MABIA Crop Library Table 6 Yield response factor (Bekri, et al., 214) Table 7 Cropping schedule Table 8 Irrigation efficiency, (-)... 3 Table 9 Irrigation efficiency and Fw used in the model... 3 Table 1 Daily water use rate in Table 11 Increase of daily water use rate with time Table 12 Dimensions of Ladhon Reservoir Table 13 Water level vs volume of Ladhon Reservoir Table 14 Ladhon and Flokas dams parameters Table 15 Costs of agricultural production (Bekri, et al., 214) Table 16 Scenarios based on different supply priorities

7 1. Introduction The Alfeios River is a water resources system of great natural, ecological, social and economic importance for Western Greece, since it has the longest watercourse and highest flow rate in the Peloponnisos region (Figure 1). Moreover, the river basin was exposed in the last decades to a plethora of environmental stresses (e.g. hydrogeological alterations, intensively irrigated agriculture, surface and groundwater overexploitation and infrastructure developments), resulting in the degradation of its quantitative and qualitative characteristics. As in most Mediterranean countries, water resources management in Alfeios River Basin has been focused up to now on an essentially supply-driven approach. It is still characterized by a lack of effective operational strategies. Authority responsibility relationships are fragmented, and law enforcement and policy implementation are weak. Figure 1 Alfeios River Basin The present regulated water allocation puzzle entails a mixture of hydropower generation, irrigation and drinking water supply. The main water uses at the region are: hydropower production at Ladhon Dam, irrigation at Flokas, hydropower production at Flokas Dam and drinking water demand at Erymanthos. The first two water uses could be in some cases conflicting. The last two water uses are competing, since their water allocation is associated to the water volume diverted from Flokas Dam. The present water allocation priorities focus on the importance of drinking water supply firstly and irrigation at second. 7

8 The present model is based on a simplified representation of the Alfeios River Basin system (Figure 2). The river network is presented with Alfeios River itself and its three main tributaries: Lousios, Ladhon and Erymanthos. Ladhon Dam with a storage reservoir, Ladhon Reservoir, and a hydropower station situated at around 8 km downstream. A drinking water demand site with water abstraction from Erymanthos River and an irrigation area with water diversion from Alfeios River. Figure 2 Simplified representation of Alfeios River Basin (Bekri, et al., 214) The simulated models of the area were set in two different software tools, WEAP and HY- DRONOMEAS. The simulated results were analysed and compared taking into consideration the defference and the various specifications of the two software tools. 8

9 2. Area description The Alfeios River Basin has been analysed in detailes in Bekri and Yannopoulos (212) as follows. The Alfeios River is the longest (with a length of 112 km) and highest flow-rate (absolute maximum and minimum values recorded 238 and 13 m 3 /s) watercourse in the Peloponnisos region of Greece. It drains an area of 3658 km 2 and its annual water yield is estimated to be 21 million m 3 (MDDWPR, 1996). It flows for its entire length in western Peloponnisos being unevenly distributed in the regions of Arkadhia (57%), Ileia (26%), and Achaia (17%). The basin constitutes a significant ecosystem and natural resource, providing water, alluvial gravel, and lignite to these regions. Following the main flow direction, the river could be divided based on its climatic, hydrological and geospatial characteristics into three parts: (1) the upper Alfeios (25-km 2 drained area) with most significant tributaries being Xerilas, Elisson, and Lousios, (2) the middle Alfeios (348- km 2 area) with primary tributaries being Ladhon, Erymanthos, Kladheos, and Selinous, and (3) the lower Alfeios (362-km 2 area) with main tributary being Enipeus (Lestenitsas). The prevailing climate in the coastal and flat areas is the marine Mediterranean climate, whereas in the interior it changes to continental and mountainous types. Precipitation averages 11 mm annually, ranging from 8 to 16 mm with occurrences of 8 12 days. The annual basin mean air temperature is 19 C with a range of variation usually less than 16 C (MDDWPR, 1996). 9

10 3. WEAP model Software description WEAP ("Water Evaluation And Planning" system) is a software tool for integrated water resources planning that was developed by the Stockholm Environment Institute's U.S. Center (SEI). According to SEI, 214, WEAP operates on the basic principle of a water balance and can be applied to municipal and agricultural systems, a single watershed or complex transboundary river basin systems. WEAP can simulate a broad range of natural and engineered components of these systems, including rainfall runoff, baseflow, and groundwater recharge from precipitation; sectoral demand analyses; water conservation; water rights and allocation priorities, reservoir operations; hydropower generation; pollution tracking and water quality; vulnerability assessments; and ecosystem requirements. A financial analysis module also allows the user to investigate cost-benefit comparisons for projects (WEAP, 215). WEAP can operate on various timestep, from daily to yearly. For the model a monthly timestep was chosen. In general each month the calculations in WEAP follow the order (Kenov, et al., 212): a. Annual demand and monthly supply requirements for each demand site and flow requirement. b. Runoff and infiltration from catchments, assuming no irrigation inflow (yet). c. Inflows and outflows of water for every node and link in the system. This includes calculating withdrawals from supply sources to meet demand, and dispatching reservoirs. This step is solved by a linear program (LP), which attempts to optimize coverage of demand site and instream flow requirements, subject to demand priorities, supply preferences, mass balance and other constraints. d. Pollution generation by demand sites, flows and treatment of pollutants, and loadings on receiving bodies, concentrations in rivers. e. Hydropower generation. f. Capital and Operating Costs and Revenues. 1

11 Figure 3 Schematisation of Alfeios River Basin in WEAP Rivers and diversions in WEAP are made up of river nodes connected by river reaches. Other rivers may flow in (tributaries) or out (diversions) of a river. A river net (Figure 3), consisting of four rivers, Alfeios, Erymanthos, Lousios and Ladhon, was set up with network objects Rivers. Agricultural area was modelled with a network object Catchment and was named Agriculture, drinking water demand site with a Demand Site named Drinking Water Supply (DWS). Ladhon Dam was modelled as a reservoir with a hydropower station (HPS). Water quality was not taken into account in the model. Important components of the network and their representation on the scheme are listed in Table 1. 11

12 Table 1 Components of the schematised Alfeios River Basin system Object of the area Network component Name River Agricultural areas Catchment node Agriculture Alfeios Drinking water demand Demand Site node Drinking Water Supply (DWS) Erymanthos Ladhon Reservoir Reservoir node Ladhon Dam Ladhon Ladhon HPS Reservoir node Ladhon Dam Ladhon Flokas HPS Diversion of water to Agriculture Diversion of water to DWS Run-of-river Hydropower node Flokas Dam Alfeios Transmission link Irrigation Canal Alfeios Transmission link Drinking Water Allocation (DWA) Erymanthos WEAP is a simulation tool that mostly attempts to assist rather than substitute for the skilled planner (WEAP, 215). An optimisation of water allocation can be done only by defining a different demand priority system. To implement this system, relative priorities should be assigned to all demand sites and catchments, reservoir filling and hydropower generation. Priorities can range from 1 (the highest priority) to 99 (the lowest priority). In case of water shortage, higher priorities are satisfied as fully as possible before lower priorities are considered. If priorities are the same, relative shortages will be equally shared. In order to investigate the effect of the changes of the priority system of WEAP, three scenarios are set up as follows: 1. Agriculture and the DWS nodes were assigned with the highest priority. In this case WEAP should distribute water in a way that water demand of both sites will be satisfied equally. 2. DWS Node and the Operating Rules for Ladhon Reservoir were assigned with the highest priority of 1. Agriculture Node with priority of 2. The scenario shows the water conflict that can occur if Operating Rules would be strictly followed. 3. The highest priority was assigned to DWS Node, while the priorities of Agriculture Node and HP generation at Ladhon HPS were set to 2. The scenario shows a conflict that can occur in case of equal priority of irrigation water supply and energy generation. 12

13 In order to create these scenarios, the starting year for all scenarios, called Current Accounts, had to be created. The Current Accounts include the specification of supply and demand data (including definitions of reservoirs, pipelines, treatment plants, pollution generation, etc.) for the first year of the study on a monthly basis. In the model the first year of simulated period, the year 1999, was set up as Current Accounts. A step-by-step process of model development for Current Accounts and following years will be described below Surface water hydrology In this section the amounts, availability and allocation of supplies, simulated monthly river flows, including surface/groundwater interactions and instream flow requirements, hydropower generation, and tracks reservoir and groundwater storage are determined. One of the first steps is to specify the hydrologic inflows. WEAP offers four methods to project surface water hydrology: 1. The Water Year method can be chosen if historical data in a simplified form are available. For the method it is required to define standard types of water years and their sequence for the simulated period. 2. Catchments Runoff and Infiltration method provides a possibility to use Runoff/Infiltration Links to direct catchment runoff to rivers and groundwater nodes. 3. The Read From File method can be used to read in data on monthly inflows to the area from a file in ASCII format. 4. Expressions, which is the method that was used in the present model. It allows specification of inflows with various mathematical expressions. For specification of hydrological inflows a ReadFromFile function was used. The method is very similar to Read From File Method but its big advantage is that it allows WEAP to read data from a delimited text file instead of ASCII Data File Format. Stochastically simulated timeseries (Bekri, et al., 214) for the time period from 1999 till 298 were used for Alfeios River at its sources (Karytaina) and its three tributaries: Lousios, Erymanthos and Ladhon. WEAP divides the river network on several reaches as presented infigure 4. In WEAP, the inflow to a reach from upstream is defined as the amount flowing downstream from the node immediately above the reach. The flow out of a reach into the downstream node is the flow into the reach from upstream plus surface water runoff and groundwater inflows to the reach minus evaporation and outflow to groundwater (WEAP, 215). The most upstream inflows are called headflows. To consider inflows apart from headflows (i.e. surface runoff) inflows to the reaches were specified. 13

14 Figure 4 Division of river network on reaches in WEAP Due to the fact that some gauge stations are located at the upstream part of the reach and do not take into account possible flow contribution along the reach, the values of inflows for some of them were multiplied with conversion factors that were selected based on hydrologic regime (Bekri, et al., 214). The main reaches and the coefficients that were used are listed in Table 2. Table 2 Main reaches of the river net in the WEAP model River reach Coefficient Below Alfeios headflow 1. Below Lousios inflow Below Ladhon inflow 1. Below Erymanthos inflow 1. Below Lousios headflow 1. Below Ladhon headflow 1. Below Erymanthos headflow

15 Agriculture Node Catchment There are four different methods to simulate catchment processes in terms of agriculture in WEAP: a) Rainfall Runoff and b) Irrigation Demands Only versions of the Simplified Coefficient Approach, c) Soil Moisture method and d) the MABIA method. The first two methods use crop coefficients to calculate the potential evapotranspiration in the catchment and do not simulate infiltration processes or soil moisture conditions. Advantage of Rainfall Runoff Method over Irrigation Demands Only is that it simulates runoff to a river and flow to groundwater via catchment link. The most complex of the four methods is the Soil Moisture method and requires extensive soil and climate parameterization. The method not only represents the catchment with two soil layers, but also gives a possibility to take into account the potential for snow accumulation. In the model all catchment processes were modelled with the forth, MABIA, method, which was derived from the MABIA suite of software tools. MABIA simulates transpiration, evaporation, irrigation requirements and scheduling, crop growth and yields, and includes modules for estimating reference evapotranspiration and soil water capacity and it works on a daily timestep. The calculation procedure of MABIA consists of following steps: 1. Reference Evapotranspiration (ETref) 2. Soil water capacity 3. Basal Crop Coefficient (Kcb) 4. Evaporation Coefficient (Ke) 5. Potential and Actual Crop Evapotranspiration (ETc) 6. Water Balance of the Root Zone 7. Irrigation 8. Yield The MABIA method is quite flexible regarding climatic input data. The method requires daily climate data such as rainfall depth and evaporative demand of the atmosphere for the given weather conditions. Since data on the reference evapotranspiration are most of the time unavailable, the WEAP-MABIA model comes with different options to calculate reference evapotranspiration. The climatic parameters that can be defined in MABIA are presented in Figure 5. It can be seen from the chart that such data as latitude, altitude, minimum and maximum temperature are mandatory for input, but MABIA offers several possibilities for calculation of such parameters as relative air humidity, solar radiation and wind speed (Jabloun, et al., 212). The data that was used in the present model are presented in Table 3. 15

16 Figure 5 WEAP-MABIA Reference evapotranspiration calculation: climatic data availability (Jabloun, et al., 212) Evapotranspiration calculation Estimation of evapotranspiration in MABIA consists of three parts: calculation of reference, potential and actual evapotranspiration. At first, reference evapotranspiration (ET) that corresponds to the evaporation power of the atmosphere under optimal conditions, is calculated by WEAP using the Penman-Monteith equation. The following data are required for computation: mean daily temperature, relative humidity, solar radiation, wind speed, latitude and altitude of the climate measurement station. Parameters used are summarized in Table 3. Table 3 Climatic parameters for the agricultural area at Flokas Dam Variable Value Mean T, C Monthly timeseries Average annual humidity, % 69 Solar radiation, Mj/m 2 /d Wind speed, m/s 2.73 Latitude of the climate measurement station, grad Altitude of the climate measurement station, m 13 The data of daily temperature were entered based on monthly timeserie. In order to adopt data to a daily timestep, monthly values were integrated. Data on average annual solar radiation was taken 16

17 from a solar radiation map of Greece from the online resource GeoModel Solar (SolarGIS, 215). According to the map, total annual amount of solar radiation is 175 kwh/m 2 or 63 MJ/m 2 (Figure 6). Hence, assuming that average number of days in one year is 365, an average daily value of solar radiation of MJ/m 2 /d was found. Figure 6 Global horizontal irradiation, Patras, Greece (SolarGIS, 215) Data on average humidity and wind speed were taken from Hellenic National Meteorological Service for the region of Pyrgos (215). Monthly data on average sunshine hours from Pyrgos station, which is situated in the examined agricultural area, was taken and presented in Table 4. 17

18 Table 4 Average monthly sunshine hours, Pyrgos Month Average monthly sunshine hours, h January 14.7 February arch April May June July 36.4 August September October November December Secondly, potential evapotranspiration or the crop evapotranspiration under standard conditions, denoted as ETc, is calculated in WEAP. Potential evapotranspiration was defined by Penman as the amount of water transpired in a given time by a short green crop, completely shading the ground, of uniform height and with adequate water status in the soil profile (Source). In WEAP potential evapotranspiration is defined as the evapotranspiration from disease-free, well-fertilized crops, grown in large fields, under optimum soil water conditions, and achieving full production under the given climatic conditions. The value of ETc is found through multiplication reference evapotranspiration and crop coefficient Kc, which was determined experimentally (Equation 1). According to FAO 56 (FAO, 1998), standard climatic conditions can be defined as following: subhumid climate, daytime minimum relative humidity, RHmin, is 45%, calm to moderate wind speeds averaging 2 m/s. ETc = Kc ET (1) where ETc potential evapotranspiration, m; Kc crop coefficient, -; ET reference evapotranspiration, m. MABIA uses the dual Kc method. In order to predict the effects of specific wetting events on the value for the crop coefficient Kc, Kc is splitted into two separate coefficients: a) one for crop transpiration, called basal crop coefficient (Kcb), and b) one for soil evaporation (Ke). The basal crop coefficient represents actual ET conditions when the soil surface is dry, but sufficient root zone 18

19 moisture is present to support full transpiration. The Ke represents evaporation from the soil surface that is not under the crop canopy and depends on a maximum value of Kc, on the soil evaporation reduction coefficient and on the exposed and wetted soil fraction. Taking into account the division of Kc, the potential crop evapotranspiration under standard field conditions is calculated as follows: ETc = Kcb + Ke ET (2) where ETc potential evapotranspiration, m; Kcb basal crop coefficient, -; Ke evaporation coefficient, -; ET reference evapotranspiration, m. The Kcb coefficient depends on the lengths of the various growth stages. Changes in Kcb over the course of the growing season appear due to changes in physiology and vegetation cover and are represented by the crop coefficient curve (Figure 7). Figure 7 Crop coefficient curve (FAO, 1998) It can be seen in Figure 7 that the growing period is divided into four stages: initial, crop development, mid-season and late season. Three values of crop coefficient (Kcb_ini, Kcb_mid and Kcb_end) are defined in a way that they represent the average Kcb during the initial, the mid-season and the end of the late season periods respectively. Values of Kcb and Ke should not be calculated and entered by the user since they are already incorporated in a built-in CropLibrary of WEAP (24) and presented in Table 5. At third, actual crop evapotranspiration (ETa) should be calculated. Water stress may occur when precipitation and irrigation amounts are not sufficient to supply the full (ETc) requirement. In this 19

20 case potential evapotranspiration reduces to actual evapotranspiration (ETa). As it was already mentioned, selected Kcb coefficients were defined for standard climatic conditions. For climatic conditions different from standard ones, Kcb coefficients have to be adjusted with stress coefficient Ks. The value of ETa can be calculated with Equations 3 and 4. ETa = Kact ET (3) Kact = Ks Kcb + Ke (4) where ETa actual evapotranspiration, mm; Kact actual value of crop evapotranspiration coefficient, -; ET reference evapotranspiration, mm. Kcb basal crop coefficient, -; Ke evaporation coefficient, -; Ks stress coefficient, -. Stress coefficient, Ks, depends on readily available water, RAW, total available soil water in the root zone, TAW, depletion factor, p, and root zone depletion (defined as the water shortage relative to field capacity), Dr (Figure 8). In MABIA stress coefficient is estimated with the following equation (5): K s = 1, D r RAW / TAW D r TAW RAW = TAW D r (1 p)taw, D r > RAW (5) { } where Dr root zone depletion, mm, RAW readily available water, mm, TAW total available soil water in the root zone, mm, p depletion factor (the fraction of TAW that a crop can extract from the root zone without suffering water stress), -. Soil water balance In MABIA it is possible to choose between one or two bucket methods to compute the water balance of the soil. In case of one bucket method, only the top bucket is defined (the rooting zone) that includes the surface layer (the layer that is subject to drying by evaporation). In this second model the two bucket method was used. In the method, a top and a bottom buckets are defined (bottom compartment is the remainder of the soil below the rooting depth). As shown in Figure 8 2

21 it is assumed in the method that infiltration takes place at the top bucket only and the recharge of groundwater occurs from the bottom bucket only. Moreover, flow from the top to bottom bucket takes place only if the top bucket's field capacity is exceeded. The computation of the water balance is conducted in several steps. Figure 8 Soil water properties At first, MABIA calculates the available water capacity that refers to the capacity of a soil to retain water available to plants. Assuming that the soil profile as a whole is vertically homogeneous, the total available water (TAW) is calculated with Equation 6. TAW = FC WP (6) where TAW total available water [mm]; FC water content at field capacity [mm]; WP water content at wilting point [mm]. There are several options to specify required water holding properties of the soil (saturation, field capacity, wilting point) in WEAP: 1. To enter soil properties directly. 2. To use SoilProfiles function that estimates average soil water capacity (saturation, field capacity, wilting point) using one of seven available pedotransfer functions (PTF): one of the functions is based on texture class and the other six use particle size (sand, silt and clay), optionally with data on bulk density or organic matter content. The SoilProfile function can average over several soil profiles (sampling sites) and soil horizons (layers). 3. To choose a texture class from the Soil Library, a built-in library of soil data for 12 standard texture classes: Clay, Clay loam, Loam, Loamy sand, Sand, Sandy clay, Sandy clay loam, Sandy loam, Silt, Silt loam, Silty clay and Silty clay loam and 21

22 additional texture class named "Consolidated rock" which represents a rocky surface that can hold no water. The library contains following data for each texture class: saturation, field capacity, wilting point and available water capacity. In the model, due to the lack of data on soil properties, the method of Soil Library was used that required only definition of the soil type of the area. From the Land Use/Cover Area frame Statistical Survey (LUCAS) (Eurostat, 29) it was determined that the soil type of the area is sandy loam. The second step in MABIA is to compute a daily water balance, expressed in terms of depletion at the end of the day with Equation 7. D r,i = D r,i 1 P i + RO i I i CR i + ET a,i + DP i (7) where Dr,i root zone depletion at the end of day i [mm]; Dr, i-1 depletion in the root zone at the end of the previous day, i-1 [mm]; Pi precipitation on day i [mm], limited by maximum daily infiltration rate [mm]; ROi surface runoff from the soil surface on day i [mm]; Ii net irrigation depth on day i that infiltrates the soil [mm]; CRi capillary rise from the groundwater table on day i [mm]; ETa,i actual crop evapotranspiration on day i [mm]; DPi water flux out of the root zone by deep percolation on day i [mm]. If the soil water content in the root zone exceeds saturation, the amount above saturation goes to surface runoff. In case when the soil water content exceeds field capacity but stays under saturation, the difference between saturation and field capacity goes to deep percolation. Both processes considered to take place within the same day of a wetting event, and the depletion at the end of the day becomes zero. The extraction of ETa for that day occurs before percolation. Moreover, it is assumed that both, surface runoff and deep percolation, equal zero if the soil water content is below field capacity. As a result of actual evapotranspiration, the root zone depletion will increase with time in absence of rainfall events. Runoff simulation Surface runoff in WEAP is not determined by applying a process-based rainfall-runoff-model, but it is fed from three different sources: a) the remainder from effective precipitation, b) the amount of rainfall exceeding the maximum infiltration rate and c) one of the fractions diminishing irrigation efficiency (Figure 9). To partition rainfall into surface runoff and infiltration MABIA requires 22

23 the value of maximum infiltration rate. In the model typical values of mm/day was used for a texture class of sandy loam (Davis, et al., 25). Figure 9 Runoff calculation in WEAP-MABIA Crops WEAP gives a possibility to model an agricultural area by specifying various data for the planted crops. WEAP includes the Crop Library that contains information about more than 1 different crops. The following parameters are available in Crop Library: 1. The lengths of growth stages: initial stage (Lini), crop development (Ldev), mid-season (Lmid), late-season (Llate) (Figure 1). 2. The basal crop coefficient (Kcb) already described above. In the library, values of Kcb are given for standard climate conditions. 3. The depletion factor (p), the fraction of the total available water (TAW) that can be depleted from the root zone before moisture stress occurs. Values can vary from to.99 and can be different for the different crop stages. 4. The yield response factor (Ky) that describes the reduction in relative yield due to water shortage and crop evapotranspiration (ETc). The values of the factor are crop specific, they are different for different growing periods and for different growing seasons. The yield response factor will be further described below. 5. The maximum height of the crop. 6. The rooting depth (Z) that varies over the growing period and has three growth stages. 23

24 Figure 1 Crops growth stages The initial values from Crop Library for some crops that were used in the model are presented in Table 5. Missing values for Ky were replaced with the values from Table 6 (Bekri, et al., 214). 24

25 Table 5 MABIA Crop Library Alfalfa Crop Fallow Tomato Watermelons Potato Cotton Maize 1st cut. * Other cut. * Citrus Olive Stage Length, days Kcb Depl. Factor Yield Response Factor, Ky Ini. * Dev. * Mid. * Late * Total Ini. * Mid. * Late * Ini. * Mid. * Late * Ini. * Dev. * Mid. * Late * Overall Max Height, m Min Root Depth, m Max Root Depth, m `1.45 *Ini. initial, dev. development; mid. mid-season; late late-season; cut. cutting cucle(s). 25

26 Table 6 Yield response factor (Bekri, et al., 214) Crops Annual Yield response factors (Ky) Mean Min Max Alfalfa Citrus Cotton.85 Maize 1.25 Potato 1.1 Tomato 1.5 Watermelon 1.1 Olive Trees.8 Cropping schedule In WEAP each catchment branch represents a piece of land on which one or more crops are planted. In the model the specification of crops at Catchment Node was done with the help of the Crop Scheduling Wizard, which requires data on areas, planting dates and potential yields for modelled crop. In the agricultural areas at Flokas Dam citrus trees, tomatos, watermelons, olive trees, potatoes, alfalfa and maize are planted. Some of the crops are present in several types with different planting days. The planting period of alfalfa includes six cuts and the same potential yield for every cutting was assumed. The parameters used in the model are summarised in Table 7. The total area of the agricultural site is 5.752,5 ha. Crop data were taken from (Bekri, et al., 214). 26

27 Table 7 Cropping schedule Crop Cutting Planting date Harvesting date % of the total area under the crop Potential yield, t/ha Alfalfa Maize Citrus Citrus Citrus Citrus Tomato Tomato Potato Watermelon Watermelon Olive trees Olive trees Cotton For any days with no cultivation, a crop named Fallow, which is stored in the Crop Library, is automatically assumed to be planted. WEAP does not apply any irritation for fallow crop. A cropping pattern of maize is presented in Figure 11 as example. The cropping schedule at the Catchment Node is presented in Figure 12. Figure 11 Cropping pattern of maize 27

28 Cropping schedule Cotton Tomato2 Tomato1 Watermelon2 Watermelon1 Potato1 Maize Alfalafa Olive trees2 Olive trees1 Citrus 4 Citrus 3 Citrus 2 Citrus 1 27-Nov 7-Mar 15-Jun 23-Sep 1-Jan 1-Apr Figure 12 Cropping schedule Irrigation schedule When rainfall is insufficient to compensate for the water lost by evapotranspiration, irrigation should be implemented. Moreover, in order to avoid water stress, irrigation has to be applied at the right period and in the right amount. To plan a proper irrigation scheme, the soil water balance of the root zone on a daily basis has to be calculated. As was already mentioned, the amount of water that a crop can extract from its root zone, total available water (TAW), is calculated as a difference between amount of water at field capacity and wilting point. Values of TAW vary with soil physical properties and rooting depth. Although water is theoretically available until wilting point, crop begins to experience stress well before the wilting point is reached. The fraction of TAW that can be extracted by a crop from the root zone without suffering stress is called readily available water (RAW). RAW is calculated in WEAP as the product of TAW and the depletion factor, p. An irrigation schedule specifies the timing (which day) and amount (depth) of irrigation. There are four different methods to set irrigation trigger in WEAP: 1. to irrigate every N days; 2. to irrigate when soil moisture depletion is greater than or equal to a specified percent of RAW; 3. to irrigate when soil moisture depletion is greater than or equal to a specified percent TAW; 28

29 4. to irrigate when soil moisture depletion is equal to or exceeds a specified depth (in mm). Moreover, WEAP offers four different methods to determine amount of irrigation water: 1. to apply a specified percent of the current soil water depletion; 2. to apply a specified percent of RAW level, regardless of the current soil water depletion; 3. to apply a specified percent of TAW level, regardless of the current soil water depletion; 4. to apply a specified depth of water. In the present model an optimal irrigation scheme was applied: % of RAW (1% of RAW) as trigger method, and % of Depletion (1% of Depletion) as irrigation amount method. According to this schedule, irrigation would be applied at the last moment before crop stress would occur and irrigate just up to field capacity. The same irrigation scheme was chosen for all crops. Besides, it was assumed that irrigation starts on the first day of the crop season, ends on the last day of the season, and that there are no changes in irrigation scheme during the season. If there is not enough water to satisfy the irrigation demand, each calculated irrigation amount within the defined time step will be automatically reduced by the percentage of unmet irrigation demand. The algorithm is presented in Figure 12 (Jabloun, et al., 212). Figure 13 Flow chart illustrating the operating mode of the irrigation module (Jabloun, и др., 212) Irrigation canal To model an irrigation canal that diverts water from Alfeios for irrigation purposes, a transmission link from Alfeios River to Agriculturу Node was created. Losses along the irrigation canal were assumed to be 2-3 %. 29

30 Irrigation efficiency The irrigation efficiency is defined as a ratio of irrigation crop requirement to irrigation supply requirement. If it is less than 1%, then the supply requirement for irrigation will be increased. As it can be seen from Table 8, irrigation efficiency depends on type of irrigation. Although drip irrigation is considered to be the most efficient, it cannot be applied for all types of crops. Since at the agricultural areas at Flokas Dam for every crop a combination of different irrigation methods is used, average irrigation efficiencies for each crop were calculated. Irrigation methods and average efficiencies are presented in Table 9. Apart from the irrigation efficiency coefficients, coefficients of wetted fraction (Fw) that depend on irrigation type, were entered. According to FAO, standard values of coefficients for different types of irrigation are: 1 for sprinkler irrigation,.6-1. for surface irrigation and.3-.4 for trickle irrigation. Since in the modelled agricultural area for every crop several irrigation method were used, average wetted fraction coefficients were calculated for each crop. They are presented in Table 9 (FAO, 1998). Table 8 Irrigation efficiency, (-) Irrigation type Surface Irrigation Drip Irrigation Sprinkler Irrigation Min.5 Max.75 Min.8 Max.95 Min.6 Max.8 Table 9 Irrigation efficiency and F w used in the model % of total area of the crop irrigated Irr. efficiency Surface irr. Drip irr. Sprinkler irr. Min Max Alfalfa Maize Citrus Tomato Potato Watermelon Olive trees Cotton *irr. Irrigation Fw 3

31 Crop yield As mentioned above, values of potential yield for every crop are entered as shown in Table 7. Actual crop yield can differ from potential in case of unsufficient water supply. The response of yield to water supply is quantified by the yield response factor (Ky), whose values for most crops are derived on the assumption that the relationship between relative yield (Ya /Ym) and relative evapotranspiration (ETa /ETc) is linear. Moreover, the use of the derived linear relationship can be implemented only for water deficits up to 5%. The higher the yield response factor, the greater the decrease in yield for a given evapotranspiration deficit. Values of Ky for each crop are stored in the Crop Library and can be found in Table 5. In WEAP actual yield is calculated with the following equation (8): Y a = Y m (1 K y (1 ET a ET c )) (8) where Ya actual yield (corresponding to ETa) [kg/ha]; Ym maximum theoretical yield (corresponding to ETc) [kg/ha]; ETa actual crop evapotranspiration; ETc potential crop evapotranspiration; Ky yield response factor to water stress, which comes from the Crop Library. To model the agricultural areas at Flokas Dam, composed of four subregions (Pyrgos A, Pyrgos B, Epitalio and Pelopio), a single Catchment node named Agriculture Node is used. To simulate catchment processes the following parameters are required: land use (area, crops planted, soil water capacity and water balance of the root zone), climate data and costs (optionally). The simulation process is described below. 31

32 Drinking Water Supply Node Drinking water demand was set up with a Demand node named DWS Node and is divided into four parts: A, B, C and D. These four parts stand for Erymanthos-Pyrgos, Pyrgos-Katakolo, Epitalio-Alfeios-Anemochori and Skillounta-Zacharo communities respectively. In order to calculate the water demand in WEAP, the following data were entered: Water use rate: daily water use rates at every of four parts were entered separately. Estimation of drinking water needs was based on data from the 2 from the document "Predicted MAX daily water consumption of the study area". The growth of water demand with time was considered. The parameters used are summarized in Table 1 and Table 11. Monthly variation in water consumption was assumed to be proportional to the number of days of each month. Actual consumption: difference between withdrawn and return flow. Since, the return flow from the area is not discharge back to the Erymanthos River and is not of interest for the current project, no return flow was modelled and actual consumption of the area was assumed to be 1 %. Losses along the distribution system were not taken into account. Reuse rate was set to zero, since there is no water reuse in the area. Supply priority of DWS Node was set to 1 for all scenarios. Table 1 Daily water use rate in 22 Part of DWS Node Community Daily water use rate in 22, m 3 /day A Erymanthos-Pirgos B Pyrgos-Katakolo 5939 C Epitalio-Alfeios-Anemochori 933 D Skillounta-Zacharo 594 Table 11 Increase of daily water use rate with time Year Water use rate (m 3 /day) Part A Part B Part C Part D

33 DWS Node: increase of water demand with time Part A Part B Water demand, 13 m3/d Water demand, 13 m3/d Expon. (Water demand, 13 m3/d) Expon. (Water demand, 13 m3/d) Part C Part D Water demand, 13 m3/d Water demand, 13 m3/d Expon. (Water demand, 13 m3/d) Linear (Water demand, 13 m3/d) Figure 14 Increase of drinking water demand Since data on predicted drinking water demand till 24 were available, the following assumptions were made: exponential growth rates were chosen for parts A, B and C and linear for part D (Figure 14). Water diversion from the river was modelled with a transmission link from Erymanthos River to Drinking Node. No limitations in the flow rate through the link and no losses were considered Ladhon Reservoir Ladhon Reservoir is a reservoir at Ladhon Dam with total storage capacity of 56.7 million m 3. The two operation purposes of Ladhon reservoir are the satisfaction of irrigation demand at a downstream location, at Flokas, and hydropower production at HPS Ladhon, situated at around 8 km downstream. 33

34 Water level, m As it was already mentioned, Ladhon Reservoir was set up with a Reservoir Node. In order to specify dimensions of the reservoir and its operation rules, data under Physical and Operation tabs were entered. Physical tab includes following sections: Storage Capacity and Initial Storage: corresponds to the total capacity of the reservoir and the amount of water initially stored there at the beginning of the simulation. For Ladhon reservoir storage capacity equals 56.7 million m 3 and the value for initial storage was assumed to be equal 3 million m 3. Volume-Elevation Curve: the curve derived presented in Figure 15. Net Evaporation: The value of net monthly evaporation rate was calculated as difference between evaporation and precipitation on the reservoir surface. For the model stochastically simulated values were taken Volume (Million m 3 ) Figure 15 Volume-elevation curve of Ladhon Reservoir Reservoirs dimensions and operating rules were specified under the tab Operation. In WEAP a reservoir storage consists of four zones: inactive, buffer, conservation and flood-control zone (Figure 16). Under normal conditions water level never drops below the level of the top of inactive zone and the flood-control zone is normally kept vacant. The reservoir s active storage consists of the conservation and buffer pools. While the release of water from the conservation zone is restricted only by maximum hydrologic outflow (and in presence of turbines by maximum turbine flow), the release from the buffer pool is restricted according to the buffer coefficient, which determines the fraction of the water in the buffer zone available each month for release and can take values from to 1. In the model, since it was not required to set constraints on maximum water release from active storage of Ladhon Reservoir, the active storage was assumed to consist of the conservation pool only. 34

35 Figure 16 Reservoir zones (WEAP, 215) Dimensions of Ladhon Reservoir are presented in Table 12 (Bekri, et al., 214). Table 12 Dimensions of Ladhon Reservoir Water level, m Total volume/volume of a given pool, mio m 3 Area, km 2 Top of flood control storage / Top of conservation pool (maximum operation water level) / Top of inactive pool (dead storage) Area at minimum water level 38.5 The following operating rules are implied by operators at Ladhon Reservoir: target level of Ladhon Lake is 42 ± 1 m at the beginning of January, continuous filling of the lake till May with target reservoir level at 419 ± 1 m and drainage of the lake from May till January (Bekri, et al., 214). Graphical representation of the operating rules is provided in Figure

36 Figure 17 Operating rules for Ladhon Reservoir (Bekri, et al., 214) WEAP does not give the opportunity to the user to specify the operation rule of the reservoir directly or explicitly. By default it will release only as much of the storage available for release as it is needed to satisfy demand. In order to set the above mentioned operating rules in the model, a monthly variation of the maximum possible active storage of Ladhon Reservoir was defined. For this purpose monthly variation of values of top of conservation volumes was specified. From Figure 17 target water levels at the beginning of every month were derived. In order to find storage volumes corresponding to target water levels, the volume-elevation curve was linearized and three breakpoints were selected: at 14, 35 and 49 million m 3. The linearized curve and obtained equations are presented in Figure 18. Values of water levels that were derived from the graph and calculated target volumes are presented in Table 13. For the maximum water level of 42 m the corresponded target volume was not calculated, but already known value of 49 million m 3 was selected. 36

37 Water level, m Linearisation of volume-elevation curve of Ladhon Reservoir y =.3757x y =.2959x y =.6695x Storage, 1 6 m 3 Figure 18 Linearisation of volume-elevation curve of Ladhon Reservoir Table 13 Water level vs volume of Ladhon Reservoir Month Water level, m Volume, million m 3 January February March April May June July August September October November December

38 Hydropower production As it was already mentioned above, in the investigated region hydropower production takes place at Ladhon and Flokas dams. From one side Ladhon Dam has a reservoir and the HPS is incorporated into a Reservoir node. From the other side, Flokas Dam, a of run-of-river type and is set with a separate network component Run-of-river hydropower node. For both cases the computation algorithm of hydropower production in WEAP can be described as follows. The generation of hydropower is computed from the flow passing through the turbine, which is constrained by the turbine's maximum flow capacity. The joules of energy produced in a month is a function of parameters such as the mass of water passing through the turbine, the drop in elevation, the plant factor (fraction of time when hydropower is running), the generating efficiency, and a specific weight of water (Equation 9). E = V H 1 D H P H η H γ (9) where γ specific weight of water (9.86 kn/m 3 ); VH water volume through turbine [m 3 ]; DH drop in elevation [m]; PH plant factor [-]; ηh generation efficiency, [-]. The following parameters were specified under the tab Hydropower : Maximum Turbine Flow: hydropower is generated only for flows up to this value. All extra water is released through spillways and do not generate electricity. Tailwater elevation: is required to define the working water head on the turbine that is computed as the drop from the reservoir elevation to the tailwater elevation. This parameter has to be defined for river reservoirs only. For run-of-the-river power plants a value of fixed head can be entered directly. Plant factor: specifies the percentage of each month that the plant is running and can take a value from to 1. Generating Efficiency: is required in order to define the operation effectiveness of the system in converting the energy of the falling water into electricity. Hydropower Priority: determines the priority for hydropower production. Can take a value from 1 to 99 with 1 the highest possible priority. Energy Demand: Target monthly hydropower requirements. The values that were used in the model are presented in Table 14 (Bekri, et al., 214). The following assumptions were made: tailwater elevation at Ladhon HPS was assumed to stay constant in 38

39 time, generation efficiency was set to 85%, the plant factors at both dams were set to 99 in order to take possible maintenance into account. Table 14 Ladhon and Flokas dams parameters Parameter Ladhon Dam Flokas Dam Maximum Turbine Flow, m 3 /s 36 9 Tailwater elevation, m Fixed head, m Plant factor, % Generating Efficiency, % Costs and benefits In WEAP for the elements of the basin schematisation such as demand nodes, transmission links, treatment plants and reservoirs, costs and benefits can be entered. It is possible to define several types of costs and benefits: a) capital costs, b) fixed operating costs, c) variable operating costs, d) fixed operating benefits and e) variable operating benefits. While capital and fixed operating values have to be entered as an absolute value, variable operating should be provided as costs per unit of water. In the present model, costs and benefits were considered only at the Agriculture Node and Hydropower revenue. Available data concerning costs of agricultural production are given in Euro per kg of crop produced. Since WEAP does not provide the possibility to enter costs in these units, but in euro per m 3 of water, for each crop, the mean total cost of production ( /kg) was divided by the mean total water demand (m 3 /kg), and the value in Euro per m 3 was obtained. The values used for calculations and final value of overall cost production in Euro/m 3 are presented in Table 15. Table 15 Costs of agricultural production (Bekri, et al., 214) Water demand, m 3 /kg of yield Final cost of production, /kg Total yield, 1 3 t Cost of production, 1 6 Cost of production, /m 3 Final selling price, /kg Cotton Alfalfa Maize Citrus Watermelons Tomato Potato Olive Trees

40 Results of WEAP As it was already described above, various irrigation conditions (regarding irrigation efficiency and irrigation canal losses) are possible at Agriculture Node. In order to examine an effect of these conditions, four different cases were simulated: Case 1: maximum irrigation efficiency, minimum irrigation canal losses. Case 2: maximum irrigation efficiency, maximum irrigation canal losses. Case 3: minimum irrigation efficiency, minimum irrigation canal losses. Case 4: minimum irrigation efficiency, maximum irrigation canal losses. As it was already mentioned, WEAP allocates water according to the set supply priorities of the demand sites. In order to examine the effect of changing priorities, three scenarios were created. The priorities that were assigned to the demand sites in different scenarios are presented in Table 16. Table 16 Scenarios based on different supply priorities Scenario 1 Scenario 2 Scenario 3 Agriculture Node DWS Node HPP at Ladhon Dam HPP at Flokas dam Operating reservoir rules In Scenarios 1 and 2 no data on energy demand at Ladhon HPS were available. Since WEAP can take into account HP generation priority, only if the target values of HP are set, WEAP did not put emphasis on HP generation during the first two cases. The data on energy demand were obtained and analysed and Scenario 3 was built considering also the derived target hydropower production. The Scenario 3 was modelled in WEAP and in HYDRONOMEAS. The parameters such as water demand and water supply requirement do not depend on assigned priority of a site. Water demand at both sites (Figure 19 and Figure 2) remains unchanged for all scenarios. Variations from year to year can be observed as for irrigation water demand (due to changing climatic conditions), as well as for drinking water (increase of water demand with time due to the population growth). 4

41 Figure 19 Irrigation water demand hm DWS Node: yearly water demand Figure 2 Yearly drinking water demand Water demand, mio m3 The supply requirement for both sites is presented in Figure 21 and Figure 22. It can be seen from the graph (Figure 21) that the required supply for irrigation is significantly larger than water demand for all cases. Moreover, it can be observed that supply requirement for the cases with the minimum irrigation efficiency is greater than for the ones with maximum irrigation efficiency. However, the difference in the canal losses does not influence the value of supply requirement. It can be explained by the fact that by supply requirement, WEAP implies the amount of water that has to flow out of the irrigation canal. In order to track the influence of canal losses on water allocation from the river, canal inflows and outflows should be analysed. It can be seen from the graph in Figure 22 that there are no differences between water demand and water requirement due to the fact that no losses were taken into account at the DWS Node. 41