Irrig Sci (2015) 33:469–482 DOI 10.1007/s00271-015-0482-4
ORIGINAL PAPER
Water allocation under deficit irrigation using MIKE BASIN model for the mitigation of climate change Charalampos Doulgeris1,3 · Pantazis Georgiou2 · Dimitris Papadimos1 · Dimitris Papamichail2
Received: 8 August 2014 / Accepted: 7 October 2015 / Published online: 13 October 2015 © Springer-Verlag Berlin Heidelberg 2015
Abstract Irrigated agriculture is likely to be affected by the changes in temperature and precipitation patterns due to climate change. Particularly in Greece, on account of higher temperatures and reduced precipitation, the irrigation water management is essential to the viability of agriculture in areas already facing water scarcity. In this paper, two deficit distribution methods, the equal shortage (ES) and the yield stress (YS), are evaluated for the mitigation of climate change in the irrigation networks of Nestos River, Greece. The two methods were applied in the irrigation module of the MIKE BASIN model to analyze the effect of water deficit on crop yield and net profit for the periods 1980–2000, 2030–2050 and 2080–2100. In comparison with the ES method, the YS method, in which the water is by priority distributed to the crop that is more sensitive to water shortage, increases the yield in most of the crops, estimates higher net profit to farmers and secures more water for the downstream ecosystems.
Communicated by J. Ayars. * Charalampos Doulgeris
[email protected] 1
Greek Biotope/Wetland Centre, The Goulandris Natural History Museum, 14th km Thessaloniki‑Mihaniona, 57001 Thermi, Greece
2
School of Agriculture, Faculty of Agriculture, Forestry and Natural Environment, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
3
Department of Biological Sciences, School of Applied Sciences, University of Huddersfield, Queensgate, Huddersfield HD1 3DH, UK
Introduction The limited availability of water in many parts of the world is the reason for global interest of this natural resource and trial for improving management of water resources. The fact that the basic consumer of available water resources is the irrigated agriculture necessitates the water management in agricultural sector. Many mathematical models have been developed in order to assess water resources management including MIKE BASIN (DHI 1997, 2009), RIBASIM (van der Krogt 2004), WEAP (Sieber and Purkey 2005; Demertzi et al. 2013) and MIKE 11 (Madsen 2000; Doulgeris et al. 2012). These models constitute a significant element in decision support systems for water resources management. The MIKE BASIN is a modelling package for professionals working with water resources projects. The main areas of work that MIKE BASIN supports are: water allocation scenario modelling, reservoir/hydropower operation, hydrological modelling, irrigation demand and yield assessment, in-stream nutrient modelling and catchment nutrient load assessment. It has been used by many researchers (Jha and Gupta 2003; Ireson et al. 2006; Ljunggren 2007; Leemhuis et al. 2009; Bangash et al. 2012; Hassaballah et al. 2012; Fernandes et al. 2013). Jha and Gupta (2003) applied the MIKE BASIN to the Mun river basin located in northeastern Thailand using historical data of 33 years to provide a decision basis for policy makers in relation to the optimal allocation of water resources. Ireson et al. (2006) proposed the coupling of a strategic scale water resources management simulation model (MIKE BASIN) and a groundwater model (ASM) as a tool to support decision making. Leemhuis et al. (2009) developed a decision support tool (VB-WAS) to assess the impact of infrastructure on the availability of current and future
13
470
water resources by combining the MIKE BASIN model and MM5/WaSiM-ETH climate-hydrological modelling. Bangash et al. (2012) used the MIKE BASIN model as a tool for watershed planning, water resource assessment and ultimately water allocation purposes using historical data of river Francoli. Hassaballah et al. (2012) developed a methodology based on coupled simulation–optimization approach for determining filling rules for the proposed reservoir in Ethiopia with minimum impact on hydropower generation downstream using the MIKE BASIN model for the simulation of filling rules. Fernandes et al. (2013) used the MIKE BASIN model to determine whether water availability will be enough to meet present and future demands. Many previous studies showed that climate change will result in increasing temperature and negatively affecting water availability. Irrigated agriculture is expected to be affected by climate change by reducing water availability and increasing its demand for water (Yu et al. 2002; Mizyed 2009). In order to assess the risks of the global climate change, the Intergovernmental Panel on Climate Change— IPCC—concluded in the development of different models for possible future forecasts described in the special IPCC report (IPCC 2007). Future climate characteristics are dependent on the emission scenarios published by the IPCC as the Special Report on Emission Scenarios (SRES). The IPCC provides four families of climate scenarios (A1, A2, B1 and B2) to access climate change, depending on different governance and orientation toward social and environmental concerns and other aspects (IPCC 2000). The IPCC developed 40 SRES scenarios by six modelling teams. The global climate models (GCM) provide data with spatial resolution of several km and are unable to resolve important sub-grid scale features such as clouds and topography and cannot be used for local impact studies. For this reason, downscaling methods are developed to obtain localscale surface weather from regional-scale atmospheric variables that are provided by GCMs (Hewitson and Crane 1996). Stochastic weather generators could be used for the downscaling data from the GCMs to develop local climate scenarios. These generators have a new role and can serve as a computationally inexpensive tool to produce multiple-year climate change scenarios at the daily timescale (Semenov and Barrow 1997; Qian et al. 2008). Nestos River is a transboundary river situated in the Balkan area, one of the 71 transboundary rivers in Europe and has been studied by many researchers (Dafis et al. 1997; Kampragou and Mylopoulos 2004; Petalas et al. 2005; Kampragou et al. 2007; Mylopoulos et al. 2008; Boskidis et al. 2012). The delta of the river is protected by the RAMSAR treaty on wetlands and is also characterized as a national park (ACT 854/B/16-9-1996), but is covered with agricultural lands. During the 1960–1966, a diversion dam was constructed near Toxotes village, which distributes a
13
Irrig Sci (2015) 33:469–482
part of a river flow to the irrigation network of West and East Delta plain. Kampragou and Mylopoulos (2004) examined the irrigation water consumption at the Local Organization for Land Reclamation Thalassias—Kremastis (Nestos delta area). Petalas et al. (2005) analyzed the geologic, hydrologic and hydrogeologic characteristics of Nestos River basin and the effect of two large dams (Thisavros and Platanovrisi) to qualitative and quantitative regime of the river delta. The objectives of this paper are: (a) to assess the water allocation of Toxotes reservoir in Nestos irrigation networks by applying two deficit distribution methods, equal shortage (ES) and yield stress (YS), and the irrigation module of MIKE BASIN model, (b) to assess the climate change scenarios using stochastic weather generators for the downscaling data from the GCM, (c) to evaluate the effects of climate change scenarios for the periods 2030–2050 and 2080–2100 (IPCC scenario A1B) on water demand and (d) to analyze the effect of water deficit on crop yield and net profit.
Materials and methods Study area Nestos is a transboundary river with 60 % of the watershed belonging to Bulgaria (Mesta river) and 40 % to Greece (Nestos River). It springs from Rila Mountain in Bulgaria and discharges into Thracian Sea in Greece. The total area of the river basin is 5479 km2. The length of the river course (Nestos and Mesta) is 234 km (130 km is the length of Nestos River) (Petalas et al. 2005). In Greece, it is sited at Eastern Macedonia and Thrace region and is the physical boundary of the prefectures of Kavala and Xanthi (Fig. 1a). The river forms a significant ecosystem throughout its course, both in Bulgaria and Greece, and discharges in its Delta, which is a unique ecosystem protected by the Ramsar Convention, and is considered as a first priority site under EU Natura 2000 network (Dafis et al. 1997). During the 1960–1966, a diversion dam was constructed near Toxotes village, which distributes a part of a river flow to the irrigation network of West and East Delta plain. In the early of 1990s, in the Greek part, the Greek Public Power Corporation S.A. was constructed two large dams (Thisavros and Platanovrisi) upstream of the Toxotes dam, for energy production and water supply for irrigation needs. The system of the two dams regulates the river flow throughout the year and the river flow to Toxotes dam. From the Toxotes dam, water is brought to the irrigation system with two main canals (one for west delta plain and other for east delta plain). The west canal has total length 15.24 km and maximum water discharge 24 m3/s. This
Irrig Sci (2015) 33:469–482
471
Fig. 1 Nestos River basin in the Greek territory (a) and the water distribution in irrigation networks (b)
canal supply irrigation water to the Local Organization for Land Reclamation (TOEV) Chrisoupolis (Z1 irrigation network) and Crisochoriou (Z2 irrigation network). The east canal has total length 1.4 km and maximum water discharge 21 m3/s. This canal supplies irrigation water to the Local Organization for Land Reclamation (TOEV) Thalassias—Kremastis (Z3 irrigation network) (Fig. 1b). The irrigation module of MIKE BASIN MIKE BASIN is a modelling package and decision support tool for integrating water resource management in a river basin (DHI 1997, 2009). It is a basin-scale simulation model and accommodates a basin-wide representation of water availability and water demand. MIKE BASIN has been coupled with ArcGIS interface enabling the model to be user-friendly in handling spatial data. The model is structured as a network model in which the rivers and their main tributaries are represented by a network consisting of branches and nodes. The branches represent individual stream sections, while the nodes can be offtake nodes (extraction points) or diversion nodes for surface water extraction for water users or irrigation usage (Jha and Gupta 2003; DHI 2009). The nodes are connected with branches in which water is transported. The model output includes the magnitude and frequency of any water shortages as well as simulated time series of flows at all
nodes, providing information on the performance of each reservoir and water supply schemes. An irrigation node (Fig. 2) represents an irrigation area comprising one or more irrigated fields, which are drawing water from the same sources. The irrigation node represents the total irrigation requirements for the fields and optionally the crop yield. Based on the calculated requirements, water is extracted from one or more sources, e.g., river nodes and/or reservoir according to specified allocation rules. The major inputs of the model for irrigation node are the meteorological and hydrological time series, the distribution of crop and irrigated fields and some crop characteristics to compute water demand (Fig. 2). The crop water requirement (CWR) is defined by the amount of water required to compensate the evapotranspiration loss (ET) from the cropped field. The CWR depends on the local climate and the crops growing in the fields. It is computed according to the dual crop coefficient model approach and is given by (Allen et al. 1998):
ETm = (Kcb + Ke )ETo
(1)
where ETm is the crop or maximum evapotranspiration (mm day−1) under potential growing conditions with no stress caused by soil water shortage, Kcb is the basal crop coefficient that describes crop transpiration, Ke is the soil water evaporation coefficient that describes soil evaporation and ETo is the reference evapotranspiration that is
13
472
Irrig Sci (2015) 33:469–482
Fig. 2 Flowchart and input data of the irrigation node of MIKE BASIN
computed from the ASCE-standardized Penman–Monteith method (Allen et al. 1998; ASCE 2005). Water stress reduce transpiration, and thus ET, causing plant canopies to reduce stomatal opening and water loss, and ET is calculated by (Allen et al. 1998):
ETa = (Ks Kcb + Ke )ETo
(2) −1
where ETa is the actual evapotranspiration (mm day ) under actual field conditions that may include effects of water stress and Ks is the stress reduction coefficient. The dual crop coefficient approach is more complicated and more computationally intensive than the single crop coefficient approach and allows for a more accurate quantification of crop water requirement because it calculates the crop transpiration and soil evaporation, separately. The net irrigation water demand (IRn) is defined as the depth of water needed to fulfill the crop water requirements in excess of any effective precipitation (Pe), contribution of shallow ground water (GW) and change in stored soil water during the period of interest (Allen et al. 2007). In essence, IRn is the beneficially consume portion of an irrigation application. The total irrigation water demand, (IRt), includes the water required for the IRn in addition to water required to satisfy delivery and field system losses. Delivery system losses include evaporation and seepage. Field system losses include surface runoff and deep percolation. Delivery and field losses are defined by irrigation efficiency which includes application efficiency and conveyance efficiency. Effective precipitation (Pe) is that part of the total precipitation that replaces, or potentially reduces, a corresponding net quantity of required irrigation water. In this paper we used the curve number method as developed by the USDA Soil Conservation Service (1972). The curve number is a dimensionless parameter indicating the runoff response characteristics of an area. In the curve number
13
method, this parameter is related to land use, land treatment, hydrological condition, hydrological soil group and antecedent soil moisture of the area (USDA 1972; Bos et al. 2009). The MIKE BASIN uses FAO-56 soil water balance model that follows the recommendation provided in FAO56 for use with the dual crop coefficient method (Allen et al. 1998). It keeps track of the soil moisture content in a surface storage from where soil evaporation can take place and a root zone storage that provides water for transpiration. The depth of the surface storage is specified as the depth of evaporable layer, and the depth of the root zone equals the root depth at any time during the simulation (Allen et al. 1998; DHI 2009). In the MIKE BASIN the total irrigation water demand was met by the water from the sources (river, groundwater) according to the same allocation rules that applies to the irrigation node. If irrigation demands are met, the source water is distributed among the fields according to their demands. If there is insufficient water to fulfill the demands, a number of deficit management rules can be chosen to define the distribution of water (DHI 2009). When the irrigation demand exceeds the available water at the sources, deficit distribution methods are used. These methods describe how the available water should be distributed among the fields represented by the node. In MIKE BASIN three methods are available: (a) equal shortage, (b) by yield stress and (c) by priority (DHI 2009). Equal shortage (ES) In this method the fields get the same percentage of the demand covered and hence suffer the same relative shortage. Yield stress (YS) In this method the water is distributed according to how sensitive the crops are to soil water stress; thus, the crop with the highest yield response factor (ky) will be given the highest priority. If several crops
Irrig Sci (2015) 33:469–482
473
have the same yield response factor, water is distributed according to the ES method. By priority In this method the water is distributed according to the priority of the field that has been specified (arbitrarily) by the model user. In this paper the ES and the YS methods were used, since in the third method the choice of the field with the highest priority is subjective. In the deficit irrigation, the actual crop yield is less than potential yield (crop yield under optimal conditions without soil moisture stress). Crop productivity behavior with respect to deficit irrigation is expressed by the crop–water– production (CWP) function (Doorenbos and Kassam 1979; Rao et al. 1988; Georgiou et al. 2006, 2010; Georgiou and Papamichail 2008; Montazar et al. 2010 ). Doorenbos and Kassam (1979) utilized Stewart and Hagan (1973) and Stewart et al. (1976) model and developed a methodology to quantify yield of 26 crops using crop, climatic and soil data. They derived yield response factors (ky) for individual growth stages (i.e., establishment, vegetative, flowering, yield formation and ripening) and also for the total growing period. The study of Rao et al. (1988) was based on the Doorenbos and Kassam’ s model and presented two models which relate relative crop yield to relative evapotranspiration for each stage with an additive and a multiplicative way for the total growing period. In MIKE BASIN the actual crop yield is computed by the multiplicative crop–water– production function which is proposed by Doorenbos and Kassam (1979) and Rao et al. (1988) and is given by:
G ETa Ya = 1 − kyi 1 − Ym ETm i
(3)
i=1
where Ya is the actual yield (kg ha−1), Ym is the maximum crop yield under given management conditions that can be obtained when water is non-limiting (kg ha−1), ky is the yield response factor, ETa is the actual evapotranspiration (mm), ETm is the maximum evapotranspiration, and i is the index of growth stage in a growing season with a total of G growth periods. Climate change scenarios The evaluation of climate change impacts on water demand, water deficit, crop yield and net profit to the irrigation networks of Nestos River was based on the A1B scenario (a balance across all sources where balanced is defined as not relying too heavily on one particular energy source, on the assumption that similar improvement rates apply to all energy supply and end-use technologies). The meteorological data—precipitation, temperature (max and min), relative humidity, wind speed and solar
radiation—of global climate model (GCM) T63 CGCM 3.1 (Canadian Center for Climate Modelling and Analysis) for the periods 2030–2050 and 2080–2100 were used (Kim et al. 2002, 2003). The spatial resolution of T63 CGCM 3.1 is 2.8° longitude/latitude and has 31 levels in the atmosphere. The existing downscaling techniques have two broad classes: statistical and dynamical. Among the statistical downscaling techniques used by scientists to obtain stationscale climatic information, multiple regression models and stochastic weather generators have far more applications than the others, as they are computationally less demanding, simple to apply and efficient (Semenov et al. 1998; Kilsby et al. 2007; Hashmi et al. 2011). Stochastic weather generators use stochastic methods for producing synthetic series of precipitation, temperature (max and min), relative humidity, wind speed and solar radiation (Semenov et al. 1998; Stockle et al. 1999; Georgiou and Papamichail 2008). In this paper, the stochastic weather generator ClimGen (Stockle et al. 1999; Safeeq and Fares 2011) for the downscaling data from the GCM to develop local climate scenarios was used. The differences between (or ratio of) the control and future climate simulations are applied to historical observations by simply adding (or multiplying) the change factor to daily observed data. Then, in the new perturbed historical time series, we apply stochastic weather generators to produce synthetic series of data with the same statistical properties as that which was input. The synthetic series illustrate the future climate change scenario. Data sets and model calibration Water inflow in Toxotes dam for the period 1980–2000 has been estimated by the combination of a MIKE SHE model and a MIKE BASIN model established in the Nestos River basin (Doulgeris et al. 2006, 2008). Runoff and recharge of each sub-catchment in the Greek territory of Nestos River are simulated by the MIKE SHE model. In the MIKE BASIN model, runoff and recharge of sub-catchments and Nestos inflow from Bulgaria are used as inputs; the water flow in Nestos River, the inflows and outflows in Thisavros and Platanovrisi reservoirs, and the water allocation in water users are simulated. These models have been calibrated successfully against measurements of Nestos River discharge in the stations of Papades (Fig. 3a) and Temenos (Fig. 3b) and against measurements of Thisavros reservoir water level (Fig. 3c). The simulated monthly hydrograph of Nestos River outflow to Toxotes dam is the available water for allocation to Nestos irrigation networks, which is used in the present study. In future years, decrease in river’s flow is expected according to the climate change scenarios. However,
13
474
Irrig Sci (2015) 33:469–482
Fig. 3 Simulated and observed discharge of Nestos River in the stations Papades (a) and Temenos (b) and water level in Thisavros reservoir (c)
uncertainty exists about how Nestos River flow at the lower part of the catchment would be affected, since it is strongly depends on the release of flow from hydroelectric reservoirs of Thisavros and Platanovrisi. For example, the release from hydroelectric reservoirs may not change significantly during summer when high power demand appears. Therefore, the river outflow to Toxotes dam for future years is assumed the same as for the period 1980– 2000. This assumption is rationale for Nestos River basin, and furthermore, it would show the effect of climate change in irrigation water management in case the available source water remains unchanged or is slightly affected by climate change. The available meteorological data from the station of Chrisoupoli cover the period 1980–2000. The seasonal
13
percentage precipitation distribution for the period 1980– 2000 is: winter 36 %, spring 23.5 %, summer 13.5 % and autumn 27 %, while for the irrigation period the respective percentage is 24 %. The mean annual temperature is 14.3 °C. The water from diversion dam of Toxotes is allocated into three irrigation networks Z1, Z2 and Z3 (see also Fig. 1b). The total irrigated area for each network is 7378, 4489 and 2586 ha, respectively. In each irrigation network are cultivated seven crops except Z3 where are six. The seven crops are: F1 Maize, F2 Rice, F3 Cotton, F4 Beans, F5 Sugar beet, F6 Horticultural and F7 Tomato. In Z3 irrigation network, there are no beans. In Table 1 is given the distribution of the crop area in Nestos irrigation networks.
Irrig Sci (2015) 33:469–482
475
Table 1 Distribution of the crops area in the Nestos irrigation networks
Maize
Rice
Cotton
Beans
Sugar beet
Horticultural
Tomato
Total
Z1—Area (ha) Z2—Area (ha) Z3—Area (ha) Total area (ha)
4212 1973 1625 7810
291 193 3 487
1266 794 584 2644
523 597 1120
196 256 151 603
742 601 66 1409
148 75 158 380
7378 4489 2586 14,453
Distribution (%)
54.0 %
3.4 %
18.3 %
7.8 %
4.2 %
9.7 %
2.6 %
Table 2 Economic indices of the crops in the study area Maize
Rice
Cotton
Beans
Sugar beet
Horticultural
Tomato
Total
Total area (ha) Maximum yield (kg ha−1) Product price (€ kg−1) Variable cost (€ ha−1) Maximum net profit (€ ha−1)
7810 11,000 0.14 657.7 882
487 8500 0.2 68 1632
2644 3600 0.8 1165 1715
1120 3000 1.06 1335 1845
603 65,000 0.05 1517 1733
1409 35,000 0.42 5183 9517
380 52,000 0.09 3520 1160
14,453
Maximum net profit (€)
6,890,779
794,802
4,533,955
2,067,102
1,045,300
13,405,646
441,128
29,178,712
Table 3 Basal crop coefficients, maximum crop height, depletion fraction, length of crop development stages and planting date for the seven crops in the study area Maize
Rice
Cotton
Beans
Sugar beet
Horticultural
Tomato
Kcbini
0.15
1
0.15
0.15
0.15
0.15
0.15
Kcbmid
1.2
1.15
1.1
1.1
1.15
1
1.1
Kcbend Maximum crop height (m) Depletion fraction p Length of crop development stages
0.2
0.5
0.4
0.25
0.5
0.8
0.7
2 0.55 30/40/50/30
1 0.2 25/40/60/30
1.2 0.65 30/60/40/25
0.4 0.45 15/25/50/20
0.5 0.55 30/45/90/15
0.4 0.45 20/30/30/10
0.6 0.4 25/35/35/20
Planting date
21 April
10 May
21 April
10 May
21 April
30 May
15 April
The irrigation methods are basin, border and furrow. The mean efficiency for border and furrow irrigation is assumed to be equal 0.60, while for basin irrigation (for rice crop) is assumed to be equal 0.90. The effect of deficit irrigation apart from actual crop yield impacts on net profit (Georgiou et al. 2006; Georgiou and Papamichail 2008; Riegels et al. 2013). The net profit is computed from actual yield, product price, fixed cost and variable cost by the equation: n Pi · (Ya )i − (Bi + Ci ) Ai Z=
(4)
i=1
where Z is the net profit (€), P is the product price (€ kg−1), Ya is the actual yield (kg ha−1), B is the fixed cost (€ ha−1), C is the variable cost (€ ha−1), A is the cropped area (ha) and n is the number of cultivation crops. The fixed cost includes the land cost, and the variable cost is the summation of all other costs such as seed, fertilizer, pesticides, machinery, harvesting, marketing, drying and unexpected
costs. This variable cost is independent of the quantity of irrigation water applied because currently this is the standard policy in Greek agriculture. In this paper, it was assumed that farmers own the land, and thus fixed cost is equaled zero. The variable cost for each crop was computed from the data supplied by the Region of Central Macedonia, Greece (Region of Central Macedonia 2002). In Table 2 are given the product price, the variable cost and the maximum net profit (for maximum yield) for the seven crops of study area. In Table 3 are given the basal crop coefficients, the maximum crop height, the depletion fraction, the length of crop development stages and the planting date for the seven crop of study area. The yield response factors (ky) for individual growth stages (i.e., establishment, vegetative, flowering, yield formation and ripening) are given in Table 4. Table 5 shows the change of precipitation, temperature (max and min) and reference evapotranspiration for the future climate change scenario A1B in the study area. Precipitation reduced 2.85 and 10.6 % for the periods 2030–2050 and 2080–2100, respectively. Maximum and
13
476
Irrig Sci (2015) 33:469–482
Table 4 Yield response factors (ky) for individual growth stages (i.e., establishment, vegetative, flowering, yield formation and ripening) for the seven crops in the study area
Maize
Rice
Cotton
Beans
Sugar beet
Horticultural
Tomato
Establishment Vegetative Flowering Yield formation
0.00 0.40 1.50 0.50
0.00 0.30 1.10 0.40
0.00 0.20 0.5 0.40
0.00 0.20 1.10 0.75
0.00 1.00 1.00 1.00
0.00 0.45 0.80 0.70
0.00 0.40 1.10 0.80
Ripening
0.20
0.15
0.25
0.20
0.60
0.20
0.40
Table 5 Precipitation, temperature and reference evapotranspiration in climate change scenario A1B in the study area Baseline 1980–2000 Precipitation mm 446.3 Δ (%) Max temperature °C 19.1 Δ (°C) Min temperature °C 9.5 Δ (°C) Reference evapotranspiration mm 1056 Δ (%)
2030–2050
2080–2100
433.6 −2.85
399.2 −10.6
21.1 +2.0
22.8 +3.7
11.1 +1.6
12.3 +2.8
1123
1205
+6.3
+14.1
minimum temperature increased for the climate change scenarios; especially in the period 2080–2100 the increase in maximum temperature is 3.7 °C. The reference evapotranspiration will increase by 6.3 and 14.1 % for the periods 2030–2050 and 2080–2100, respectively.
Results and discussion Nestos River outflows annually a water volume of 1.3 × 109 m3 (or 41.31 m3/s) through the Toxotes dam, as
Fig. 4 Nestos River outflow to Toxotes dam and irrigation demand for the period 1980–2000
13
an average for the period 1980–2000. Irrigation consumes a small amount of the annually water volume, between 5 and 15 % with a mean of 8 %, and the remaining 92 % ends to the river’s estuary. However, a high variation of river flow exists during a year and the low-flow period is from July to September when irrigation demand appears to be high. Figure 4 shows the variation—during May to September—of irrigation demand and river’s outflow to Toxotes as average, max and min values for the period 1980–2000. In May and June is unlikely to appear water deficit for irrigation, while in July and August several days of deficit exist for certain years; in September, deficit may appear in a year of low river flow. In future years, increase in irrigation demand was estimated due to climate change. Therefore, in comparison with 1980–2000, higher deficit is expected in 2030–2050 (Fig. 5), especially during July and August. Deficit is going to increase further in 2080–2100 (Fig. 6), and it may also appear in June. It has to be mentioned that irrigation deficit is expected to increase further, if Nestos River flow during irrigation period is decreased due to climate change effects and subsequent water management upstream of Toxotes dam. Table 6 shows the irrigation demand, applied irrigation and deficit in Nestos irrigation networks. The annual irrigation demand has been calculated to 133.7 × 106 m3 for the period 1980–2000. The increase in irrigation demand due to climate change is 15.2 % for the period 2030–2050 and 26.1 % for the period 2080–2100. Applied irrigation is higher—and deficit is lower—when the ES distribution
Irrig Sci (2015) 33:469–482
477
Fig. 5 Nestos River outflow to Toxotes dam and irrigation demand for the period 2030–2050
Fig. 6 Nestos River outflow to Toxotes dam and irrigation demand for the period 2080–2100
Table 6 Average annual irrigation demand, applied irrigation and deficit based on ES and YS methods in Nestos irrigation networks Time period
Irrigation demand (106 m3)
Applied irrigation (106 m3)
Irrigation deficit (%)
ES
ES
YS
YS
1980–2000 2030–2050
133.7 153.9
109.2 107.3 18.3 19.7 126.1 123.4 18.1 19.9
2080–2100
168.6
134.6 131.6 20.1 21.9
method is applied instead of the YS method. The ES method allocates water to crops in a “fair” manner, and each crop would receive the same percentage of demand covered, while according to the YS method, the crop with the highest yield respond factor (ky) will receive water until demand is fulfilled, and therefore, the deficit in other crops would be higher. Deficit is approximately 18–20 % in ES method and 20–22 % in YS method. Also, applied irrigation is increased under climate change scenarios compared to the period 1980–2000, due to the higher supply of irrigation water when available water is in surplus in Nestos
River. Under the ES method, applied irrigation increased by 15.4 and 23.3 % for the periods 2030–2050 and 2080– 2100, respectively, compared to 1980–2000, and under the YS method the applied irrigation increased by 15.0 and 22.6 %. Figure 7 shows the irrigation demand and deficit based on ES and YS methods during the periods 1980–2000. Demand is varying during the years from 93.9 × 106 to 160.9 × 106 m3, and the relative standard deviation is 10.8 %. Deficit is varying significantly during the years from 0 % to up to 60 %; deficit is above 24 % in 1/3 of the years and above 15 % in half of the years. The irrigation demand and deficit during the period 2030–2050 is given in Fig. 8 and during the period 2080–2100 is given in Fig. 9. Irrigation demand is higher and has a lower variability compared to 1980–2000. Deficit is above 33 and 31 % in 1/3 of the years and above 16 and 21 % in half of the years in the periods 2030–2050 and 2080–2100, respectively. As mentioned above, Nestos River estuary receives a great amount of the annually water volume. However, during the low-flow period (July–September), irrigation consumes most of the available water in the Toxotes dam, and river’s estuary suffers from lack of water, especially in
13
478 Fig. 7 Irrigation demand and deficit in Nestos irrigation networks during the period 1980–2000
Fig. 8 Irrigation demand and deficit in Nestos irrigation networks during the period 2030–2050
Fig. 9 Irrigation demand and deficit in Nestos irrigation networks during the period 2080–2100
13
Irrig Sci (2015) 33:469–482
Irrig Sci (2015) 33:469–482
479
Fig. 10 Water outflow from Toxotes dam toward Nestos River estuary during July–September
August where a very low water outflow from Toxotes to estuary exists in half of the years. Climate change is going to amplify the lack of water in Nestos estuary; however, applying the YS method, more water is available to estuary compared to the ES method (Fig. 10) by 2.5, 4.5 and 5.2 % in the periods 1980–2000, 2030–2050 and 2080–2100, respectively. In any case, the water outflow from Toxotes dam toward Nestos River estuary during July–September, which are the driest months of the year, varies from 50 to 65 106 m3 (6.4–8.4 m3/s), which is higher than 10 % of the mean annual flow, i.e., 4.13 m3/s. Water deficit in irrigation affects differently the yield of various crops. Table 7 shows the yield reduction of crops in Nestos irrigation networks by applying the ES and YS deficit distribution methods for the periods 1980–2000,
Table 7 Yield reduction of crops (%) in Nestos irrigation networks based on ES and YS methods Time period and deficit distribution method Irrigation network Z1 1980–2000 ES YS 2030–2050 ES YS 2080–2100 ES YS Irrigation network Z2 1980–2000 ES YS 2030–2050 ES YS 2080–2100 ES YS Irrigation network Z3 1980–2000 ES YS 2030–2050 ES YS 2080–2100 ES YS
Fields and Crops F1: Maize
F2: Rice
F3: Cotton
F4: Beans
F5: Sugar beet
F6: Horticultural
F7: Tomato
21.77 18.56
31.90 9.43
13.81 22.60
25.88 16.58
39.64 4.01
42.89 18.24
10.30 13.81
26.07 21.46
36.66 11.05
16.08 25.99
30.36 21.29
43.17 5.80
48.61 22.59
13.13 19.87
31.15 25.03
42.04 14.42
19.12 29.14
35.73 25.91
49.58 9.04
54.19 28.61
17.78 23.95
19.92 15.99
29.60 7.48
12.67 24.11
24.21 16.99
36.59 3.89
39.77 20.57
9.55 14.89
23.44 18.44
35.28 8.52
14.46 26.90
28.90 21.48
41.33 4.38
47.54 24.68
12.53 16.55
29.04 21.67
40.30 10.42
17.52 31.25
34.57 26.47
46.51 6.16
52.89 30.00
16.70 21.31
24.41 18.99
34.03 10.22
15.45 21.79
41.65 4.12
44.84 15.85
12.15 16.56
28.73 21.70
39.50 11.74
17.97 25.54
47.52 6.98
50.58 19.67
15.34 19.15
34.55
44.81
21.55
52.66
56.43
19.28
26.29
15.02
29.15
7.55
24.91
26.49
13
480 Table 8 Net profit in Nestos irrigation networks based on the ES and YS methods
Irrig Sci (2015) 33:469–482 Time periods
ES method net profit (€)
1980–2000 2030–2050
14,770,516 12,394,410
2080–2100
9,945,543
2030–2050 and 2080–2100. When the ES method is applied, the yield reduction of crops varies from 10.3 to 42.89 % in the irrigation network Z1 for the period 1980– 2000. Since water is distributed equally to crops in the ES method, crops with low values of ky during July and August (cotton and tomato), and therefore low impact in yield under deficit irrigation, have low yield reduction. On the other hand, crops with high values of ky (rice, sugar beet, beans and horticultural) have high yield reduction. When the YS method is applied, the yield reduction in crops varies from 4.01 to 22.6 %. In this case, water is allocated first to crops that are more sensitive to water shortage (rice, sugar beet, beans and horticultural), and therefore, crops with low values of ky (cotton and tomato) receive less water and appear an higher yield reduction, compared to the ES method. Similar conclusions can be extracted for the other two irrigation networks Z2 and Z3. The yield of crops is reduced for the climate change scenarios compared to 1980–2000 for both deficit distribution methods (Table 7). If ES is the applied method during 1980–2000, yield in crops of, e.g., network Z1, is going to be reduced by 2.6 to 10 % in 2030–2050 and 6.2 to 19.8 % in 2080–2100. If YS is the applied method, yield is going to be reduced by 1.8–7 % in 2030–2050 and 5.2–12.7 % in 2080–2100. In any case, the impact of climate change in crop yield is going to affect the income of farmers. Table 8 shows the net profit from crops in Nestos irrigation networks based on the ES and YS deficit distribution methods for the periods 1980–2000, 2030–2050 and 2080–2100. When the YS method is applied, the net profit is considerable higher compared to the ES method (37.4– 64.3 %). Climate change is going to affect the income of farmers and to produce substantial income loss, namely 8.5 and 19.5 % for the periods 2030–2050 and 2080–2100, respectively, but the YS method is expected to mitigate the loss. It has to be mentioned that the YS method will reduce the crop yield of cotton and tomato and consequently impacts the income of farmers who cultivating solely cotton and tomato. However, assuming that most of the farmers cultivate two or three crops and adopting a management point of view, we may consider that the application of the YS method preserves the welfare of farmers, especially in terms of climate change. Several other mitigation measures to reduce the yield losses caused by the
13
YS method Loss (%)
−16.1 %
−32.7 %
Net profit (€) 20,294,439 18,567,144 16,338,420
Gain from YS (%) Loss (%)
−8.5 %
−19.5 %
37.4 % 49.8 % 64.3 %
climate change exist, including for example, the selection of earlier sowing dates for crops, higher irrigation depths, adoption of new varieties having much higher water use efficiency, application of more efficient water irrigation methods (e.g., drip irrigation) and replacement of water demanding crops, that are not taken into account in this paper.
Conclusions This study illustrates the application of two deficit distribution methods (management scenarios), the equal shortage (ES) and the yield stress (YS) scenarios, to the adaptation of irrigated agriculture in climate change. These management scenarios were applied in the irrigation networks of Nestos River basin (northern Greece) using the irrigation module of the MIKE BASIN model. We found that the irrigation demand will increase by 15.2 and 26.1 % in the climate change scenario A1B for the periods 2030–2050 and 2080–2100, respectively, assuming that the annual irrigation demand is 133.7 × 106 m3 for the period 1980–2000. Both management scenarios estimate a considerable variation in irrigation deficit from year to year and on average around 20 %, though deficit is slightly higher in the YS scenario. In comparison with the ES scenario, the YS scenario, in which the water is by priority distributed to the crop that is more sensitive to water shortage, increases the yield in most of the crops. Furthermore, it estimates higher net profit to farmers and mitigation of income loss due to climate change. Also, more water is available for the downstream ecosystems located in the Nestos River estuary, since less water is used for irrigation. Overall, climate change is projected to impact irrigated agriculture in the Nestos River catchment, and irrigation water management is essential to the welfare of farmers and the environmental sustainability. Acknowledgments This work was in part funded by the Hellenic Ministry of Development under the project “Development of hydroinformatic systems and tools for water resources management in water districts of Western Macedonia, Central Macedonia, Eastern Macedonia and Thrace based on the Water Framework Directive 2000/60/ EC.” The authors would also like to thank the Hellenic National Meteorological Services (Ε.Μ.Υ.) for the meteorological data of Chrisouloli station for the period 1980–2000.
Irrig Sci (2015) 33:469–482
References Allen RG, Pereira LS, Raes D, Smith M (1998) Crop evapotranspiration: guidelines for computing crop water requirements. Irrigation and drainage paper no. 56. Food and Agriculture Organization of the United Nations, Rome Allen RG, Wright JL, Pruitt WO, Pereira LS, Jensen ME (2007) Water requirements. In: Hoffman GJ, Evans RG, Jensen ME, Martin DL, Elliott RL (eds) Design and operation of farm irrigation systems. American Society of Agricultural and Biological Engineers, St. Joseph, pp 208–288 ASCE-EWRI (2005) The ASCE standardized reference evapotranspiration equation. Technical committee report to the Environmental and Water Resources Institute of the American Society of Civil Engineers from the Task Committee on standardization of reference evapotranspiration. ASCE-EWRI, Reston Bangash RF, Pasuello A, Hammond M, Schuhmacher M (2012) Water allocation assessment in low flow river under data scare conditions: a study of hydrological simulation in Mediterranean basin. Sci Total Environ 440:60–71 Bos MG, Kselik RAL, Allen RG, Molden D (2009) Water requirements for irrigation and the environment. Springer, Heidelberg Boskidis I, Gikas GD, Sylaios GK, Tsihrintzis VA (2012) Hydrologic and water quality modeling of lower Nestos river basin. Water Resour Manag 26:3023–3051 Dafis S, Papastergiadou E, Georgiou K, Babalonas D, Georgiadis T, Papageorgiou M, Lazaridou T, Tsiaoussi V (1997) Directive 92/43/EEC: the project “Habitat” in Greece: network natura 2000, life contract B4-3200/84/756, DG XI commission of the European communities. The Goulandris Mouseum of Natural History, Greek Biotope/Wetland Center, Thermi Demertzi KA, Papamichail DM, Georgiou PE, Karamouzis DN, Aschonitis VG (2013) Assessment of rural and highly seasonal tourist activity plus drought effects on reservoir operation in a semi-arid region of Greece using the WEAP model. Water Int 39:23–34 DHI (1997) MIKE BASIN—a tool for river planning and management. Danish Hydraulic Institute, Horsholm DHI (2009) MIKE BASIN—user’s guide. A GIS extension for integrated water resources management and planning. Danish Hydraulic Institute, Horsholm Doorenbos J, Kassam AH (1979) Yield response to water. FAO Irrig Drain Pap 33, p 193 Doulgeris C, Papadimos D, Alexopoulos A (2006) Water balances. Water district of East Macedonia (vol 4) and Thrace (vol 5). Technical report. Ministry of Development, Greece (in Greek) Doulgeris C, Bensasoon L, Papadimos D, Sorensen H (2008) Development and evaluation of scenarios—measures application. Water district of East Macedonia (vol IV) and Thrace (vol V). Technical report. Ministry of Development, Greece (in Greek) Doulgeris C, Georgiou P, Papadimos D, Papamichail D (2012) Ecosystem approach to water resources management using the MIKE 11 modeling system in the Strymonas River and Lake Kerkini. J Environ Manag 94:132–143 Fernandes LFS, Marques MJ, Oliveira PC, Moura JP (2013) Decision support systems in water resources in the demarcated region of Douro—case study in Pinhão river basin, Portugal. Water Environ J 28:350-357 Georgiou PΕ, Papamichail DΜ (2008) Optimization model of an irrigation reservoir for water allocation and crop planning under various weather conditions. Irrig Sci 26:487–504 Georgiou PE Antonopoulos VZ, Lekakis EH (2010) Soil water balance and distribution in a field of maize under partial root-zone drying drip irrigation. In: e-Proceedings of the international conference PRE10 “Protection and Restoration of the Environment X”, Corfu, Greece
481 Georgiou PΕ, Papamichail DΜ, Vougioukas SG (2006) Optimal irrigation reservoir operation and simultaneous multi-crop cultivation area selection using simulated annealing. Irrig Drain 55:129–144 Hashmi MZ, Shamseldin AY, Melville BV (2011) Comparison of SDSM and LARS-WG for simulation and downscaling of extreme precipitation events in a watershed. Stoch Environ Res Risk Assess 25:475–484 Hassaballah K, Jonoski A, Popescu I, Solomatine DP (2012) Modelbased optimization of downstream impact during filling of a new reservoir: case study of Mandaya/Roseires reservoirs on the Blue Nile river. Water Resour Manag 26:273–293 Hewitson BC, Crane RG (1996) Climate downscaling: techniques and application. Clim Res 7:85–95 Intergovernmental Panel on Climate Change (IPCC) (2000) IPCC special report on emission scenarios. A special report of IPCC Working Group III. Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, UK Intergovernmental Panel on Climate Change (IPCC) (2007) Climate change 2007: impacts, adaptation and vulnerability. In: Parry ML, Canziani OF, Palutikof JP, van der Linden PJ, Hanson CE (eds) Contribution of Working Group II to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press: Cambridge, UK, pp 1–976 Ireson A, Makropoulos C, Maksimovic C (2006) Water resources modelling under data scarcity: coupling MIKE BASIN and ASM groundwater model. Water Resour Manag 20:567–590 Jha MJ, Gupta AD (2003) Application of Mike Basin for water management strategies in a watershed. Water Int 28:27–35 Kampragou E, Mylopoulos YA (2004) Irrigation water consumption in TOEV Thalassias—Kremastis, in the river Nestos delta area. In: Proceedings of the international conference “Protection and Restoration of the Environment VII”, Mykonos, p 8 Kampragou E, Eleftheriadou E, Mylopoulos Y (2007) Implementing equitable water allocation in transboundary catchments: the case of river Nestos/Mesta. Water Resour Manag 21:909–918 Kilsby CG, Jones PD, Burton A, Ford AC, Fowler HJ, Harpham C, James P, Smith A, Wilby RL (2007) A daily weather generator for use in climate change studies. Environ Model Softw 22:1705–1719 Kim SJ, Flato GM, Boer GJ, McFarlane NA (2002) A coupled climate model simulation of the Last Glacial Maximum, Part 1: transient multi-decadal response. Clim Dyn 19:515–537 Kim SJ, Flato GM, Boer GJ (2003) A coupled climate model simulation of the Last Glacial Maximum, Part 2: approach to equilibrium. Clim Dyn 20:635–661 Leemhuis C, Jung G, Kasei R, Liebe J (2009) The Volta Basin water allocation system: assessing the impact of small-scale reservoir development on the water resources of the Volta basin, West Africa. Adv Geosci 21:57–62 Ljunggren M (2007) Calculation methods for water resources in small water basin—downscaling of hydrological models. M.Sc. thesis, KTH Land and Water Resources Engineering Madsen H (2000) Automatic calibration of a conceptual rainfall runoff model using multiple objectives. J Hydrol 235:276–288 Mizyed N (2009) Impacts of climate change on water resources availability and agricultural water demand in the West Bank. Water Resour Manag 23:2015–2029 Montazar A, Riazi H, Behbahani SM (2010) Conjuctive water use planning in an irrigation command area. Water Resour Manag 24:577–596 Mylopoulos Y, Kolokytha E, Kampragou E, Vagiona D (2008) A combined methodology for transboundary river basin management in Europe. Application in the Nestos-Mesta catchment area. Water Resour Manag 22:1101–1112 Petalas C, Pliakas F, Diamantis I, Kallioras A (2005) Development of an integrated conceptual model for the rational management
13
482 of the transboundary Nestos River, Greece. Environ Geol 48:941–954 Qian B, Gameda S, Hayhoe H (2008) Performance of stochastic weather generators LARS-WG and AAFC-WG for reproducing daily extremes of diverse Canadian climates. Climate Research 37:17–33 Rao NH, Sarma PBS, Chander S (1988) A simple dated water production function for use in irrigated agriculture. Agric Water Manag 13:25–32 Region of Central Macedonia (2002) Structural agricultural policy. Region of Central Macedonia, Thessaloniki, Greece (in Greek) Riegels N, Pulido-Velazquez M, Doulgeris C, Sturm V, Jensen R, Moller F, Bauer-Gottwein P (2013) A systems analysis approach to the design of efficient water pricing policies under the EU water framework directive. J Water Res Plan Manag 139:574–582 Safeeq M, Fares A (2011) Accuracy evaluation of ClimGen weather generator and daily to hourly disaggregation methods in tropical conditions. Theor Appl Climatol 106:321–341 Semenov MA, Barrow EM (1997) Use of a stochastic weather generator in the development of climate change scenarios. Clim Change 35:397–414 Semenov MA, Brooks RJ, Barrow EM, Richardson CW (1998) Comparison of the WGEN and LARS-WG stochastic weather generators for diverse climates. Clim Res 10:95–107
13
Irrig Sci (2015) 33:469–482 Sieber J, Purkey D (2005) WEAP—Water Evaluation and Planning System. User Guide for WEAP 21. SEI, Stockholm Environment Institute, Somerville Stewart JI, Hagan RM (1973) Functions to predict effects of crop water deficit. J Irrig Drain Div ASCE 99:421–439 Stewart JI, Hagan RM, Pruitt WO (1976) Water production functions and predicted irrigation programs for principle crops as required for water resources planning and increased water use efficiency. Water Science and Engineering Section, Dept. of Land, Air and Water Resources, Univ. of California, Davis Stockle CO, Campbell GS, Nelson R (1999) ClimGen manual. Biological Systems Engineering Department, Washington State University, Pullman USDA Soil Conservation Service (1972) National engineering handbook, sect. 4. USDA, Washington Van der Krogt WNM (2004) RIBASIM version 6.32. Technical reference manual. WL Delft Hydraulics, Delft Yu PS, Yang TC, Chou CC (2002) Effects of climate change on evapotranspiration from paddy fields in southern Taiwan. Clim Change 54:165–179
