This study aimed to investigate the effects of climate change scenarios on five indicators: reliability, vulnerability, resilience, sustainability, and the deficiency of the Gelevard Dam (GD) in Iran. Downscaling was performed from 2020 to 2040 in the future using the Can Ems2-GCM based on different climate scenarios and employing the support vector machines. The IHACRES model was used to simulate the inflow of GD. The cultivation pattern optimization function was performed by utilizing the LINGO software. Similarly, the flow-storage model was created using Vensim software. The results demonstrated the reduction of inflow by 15, 36, and 37% during RCP2.6, RCP4.5, and RCP8.5 scenarios, respectively. The results showed that if the optimal cultivation pattern (OCP) were to be applied, during different climatic scenarios, water supply would not be difficult in the next 11, 5, and 4 years, respectively, yet after that, water shortage would gradually appear. The findings concluded that although the implementation of OCP would improve the five indicators in all water consumption sectors, the GD reservoir would not be able to answer the demands in the future. Therefore, it would be necessary to implement practices to increase water productivity in all sectors.

  • Attention to climate change and its effect on dam operation.

  • Combined use of climate change and runoff models.

  • Combination of Vensim model and optimizer.

  • Choosing the optimal cultivation pattern based on climate change.

Water resource planners raise concerns to develop a comprehensive plan for a successful strategy to address water scarcity and meet specific needs that will emerge due to climate change in the future (Yang et al. 2008). Climate change can affect air temperature, rainfall, runoff, and water demand in the domestic, industrial, and agricultural sectors. These changes must be considered in water resource planning to better meet the future needs and expectations of society. Therefore, climate change can be a determining factor in planning and managing water resources.

Many researchers evaluated the effects of climate change on water resource systems. Recent studies report that in the future we will observe the impact of climate change on water resources. According to investigations, climate change will reduce river discharge and the system stability index (Ahmadi et al. 2015; Ehteram et al. 2018; Hakami-Kermani et al. 2020). Climate change is predicted to increase agricultural water demands and reduce system reliability (Joyce et al. 2011; Ehteram et al. 2018; Salman et al. 2020). Due to the inevitable changes, additional construction of dams and increasing the capacity of reservoirs can be a solution for sustainable water supply (Chen et al. 2016). Traynham et al. examined the capacity of the Puget Sound regional water supply system to meet future demands concerning climate change and population growth over a 75-year horizon (Traynham et al. 2011). Their findings indicated that climate change would reduce system performance in the future, thus necessitating operational policies to meet future demands. Therefore, reviewing and reassessing the procedure policies of reservoir performance in the context of climate change, and various adaptation scenarios could provide useful information for decision makers to reduce the negative effects of climate change on the reliability of water resources (Nam et al. 2017; Ranzani et al. 2018; Rehana et al. 2020; Lee & Shin 2021). In addition, water demand management keeps water scarcity at an acceptable level and can be considered as a sustainable strategy for water resources management and a means to maintain economic growth and the ecological status (Xiao-jun et al. 2014). The agricultural sector is one of the largest water consumers, which means that a reduction of any size in this sector's water demands is the most appropriate way of reducing its vulnerability (Wu et al. 2013). Thus, improving irrigation efficiency and reducing water consumption in the agricultural sector is the best way to reduce deficiencies (Zarghami et al. 2016). Optimizing the cultivation pattern as a solution to the issue of high water consumption will increase the reliability of the system and reduce the vulnerability of water resources (Zamani et al. 2017).

With the increase of the water demand in the Neka River (NR) basin, GD is constructed to store and supply sustainable water on NR. According to studies, the planning of water resources for this dam was based on the prior data, but in recent years, due to reduced rainfall, the flow of the NR has decreased. For example, the average annual flow of NR was previously reported at 112 million cubic meter (MCM), but during the past 15 years, it has decreased to less than 90 MCM per year. Worse yet, in the period of 2013–2014 and 2014–2015, the annual volume of the NR has decreased by 45 and 37 MCM per year, respectively. Therefore, studying the impacts of global warming on climatic parameters and the flow of the NR is necessary.

Figure 1 shows the NR Basin and GD. NR has a total drainage area of 1,980 km2 located in the north of Iran at latitude 36°29′ ∼ 36°42′N and longitude 23°11′ ∼ 24°44′E. The annual average rainfall in the basin is 550 mm. GD has been operational since 2020 and supplies agricultural water to 10,000 hectares of agricultural land (72 MCM) and 36 MCM for drinking, industry, and the environment, which will increase to 60 MCM over the next 20 years. In this article, we will evaluate the performance of GD under climate change conditions, and the effects of optimal cultivation pattern (OCP) on improving the yield of GD would also be evaluated.
Figure 1

Location of the NR basin in Iran.

Figure 1

Location of the NR basin in Iran.

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Meteorological data

Meteorological data used in this study consist of maximum air temperature, minimum air temperature, wind speed, sunshine hours, and rainfall, which were obtained from the Iran Meteorological Organization. To generate meteorological data for the period of 2020–2040, the time series of climate variables including monthly rainfall (mm), monthly maximum and minimum air temperatures (°C), monthly maximum and minimum relative humidity (%), monthly wind speed, and monthly sunshine hours (h) were downscaled using the support vector machine (SVM) model. SVM is a modern statistical learning theory in data-driven modeling (Zhou et al. 2017). The uniqueness of SVM is its structural risk minimization objective that balances the model's complexity against its fitting precision, instead of an empirical risk minimum (ERM) used by most intelligent algorithms that focuses mostly on fitting accuracy (Vapnik 1999). This model architecture greatly improves model generalization ability compared with ERM-based algorithms such as artificial neural networks (Zhou et al. 2017). In recent years, SVM has been used for hydrologic predictions such as precipitation (Kisi & Cimen 2012; Hou et al. 2017). For downscaling, National Centers for Environmental Prediction (NCEP) variables are used, including 26 atmospheric variables, which have a high correlation with historical climatic parameters. Then, the ability of the SVM model to simulate climatic parameters of the historical period is evaluated. Finally, the representative concentration pathway (RCP) is used to project the climatic parameters of the future period. The RCPs are based on the groupings of economic, technological, demographic, policy, and future institutional challenges of mitigation and adaptation (Babur et al. 2016). The benefit of RCPs is their better resolution that helps in performing regional and local comparative studies (Höök et al. 2010). In this study, the output from the second-generation Canadian Earth System Model (CanESM2) is utilized for downscaling. The CanESM2 is developed by the Canadian Center for Climate Modeling and Analysis (CCCma) of Environment Canada (Arora & Boer 2014). Three RCP scenarios including RCP2.6, RCP4.5, and RCP8.5 are utilized to quantify the variations in the climatic parameters over the Neka Basin for the time period of 2020–2040.

Crop water requirement estimation

The water demand at crop-planting lands was estimated using the guideline of the Food and Agriculture Organization (FAO) of the United Nations (Allen et al. 1998). The crop water requirements are calculated using the FAO Penman-Monteith equation on a monthly basis as follows:
formula
(1)
formula
(2)
formula
(3)
where ET0 is reference evapotranspiration (mm d−1), Rn is net radiation at the crop surface (MJ m−2 d−1), G is soil heat flux density (MJ m−2 d−1), U2 is the wind speed at 2 m height (m s−1), T is the mean air temperature at 2 m height (°C), es is saturation vapor pressure (kPa), ea is actual vapor pressure (kPa), es – ea is saturation vapor pressure deficit (kPa), Δ is the slope of vapor pressure curve (kPa °C−1), γ is the psychrometric constant (kPa °C−1), KC is the crop coefficient (–), and ETC is the actual crop ET (mm.d−1).

The KC coefficient is extracted from the FAO report. IR is irrigation requirement (mm d−1), Pe is effective rainfall (mm d−1), and Ea is irrigation efficiency.

Rainfall–runoff model

After downscaling climate regional data, the IHACRES rainwater runoff model is utilized to simulate runoff. The IHACRES model is used for rainfall–runoff modeling at the catchment scale. IHACRES was developed by the Institute of Hydrology and the Center for Resource and Environmental Studies at the Australian National University (Croke & Jakeman 2008). The IHACRES model requires basin size, rainfall, and air temperature or evaporation to estimate runoff (Littlewood et al. 2007). For calibrating the model, monthly time series of rainfall, runoff, and average temperature are incorporated. After calibrating the model, the inflow of the watershed reservoir is simulated considering the climate change effects based on rainfall and temperature. After calibration and validation of the IHACRES model, using the generated climatic data, the runoff of the basin during 2020–2040 is calculated. To evaluate the accuracy of the IHACRES model, root-mean-square error (RMSE), mean absolute error (MAE), and R2 indices are used (Babolhakami et al. 2020).
formula
(4)
formula
(5)
formula
(6)
where Oi is the observed values, Si is the simulated values, and n is the amount of data.

Agricultural planning optimization model

To calculate the water required for the agricultural sector, the existing cultivation pattern of the Gelevard irrigation network (GIN) is considered. Then, the area under cultivation is optimized to improve the performance of GIN by using linear optimization. To optimize the cultivation pattern, the net economic profit function is considered the objective function. Also, the minimum and maximum area under cultivation, the amount of water allocated to the agricultural sector, and the total area under cultivation are defined as objective function constraints. The cultivation pattern optimization functions are given as follows:
formula
(7)
formula
(8)
formula
(9)
formula
(10)
where C(x) is the final profit, Ai is the crop area (ha), Bi is the net profit of crop per (ha) in USD, AiMin is the minimum area under cultivation (ha), AiMax is the area under maximum cultivation (ha), ATotal is the total area under cultivation (ha), wi is the volume of crop water requirement (m3 ha−1), and WTotal is the total volume of available water (m3).

The cultivation pattern optimization function is performed using the modeling language and optimizer (LINGO) software.

Domestic and industrial water demand

The data obtained from Regional Water Company of Mazandaran province are used to estimate the demand for domestic water and industry. To determine the demand for drinking water in the upcoming years, according to the per capita water consumption and the rate of population growth in those years, the amount of drinking water demand in the coming years is calculated on a monthly basis. Furthermore, water demand in the industrial sector is estimated based on the information gathered for the study area for the base and future periods.

Environmental water demand

The water demand for the environmental sector is calculated using the Montana method (Tennant 1976). Environmental water demand is considered identical in different years. The long-term average monthly inflow to the reservoir is then calculated. In the first and second half of the year, respectively, 10 and 30% of the inflow are determined for environmental needs.

Evaporation

Evaporation is a substantial parameter in surface water equivalence calculations, especially in reservoir cases. In this study, the formula suggested by USBR (United States Bureau of Reclamation) is used to calculate evaporation.
formula
(11)
where t is the average monthly air temperature (°C) and E is evaporation (mm/month).

System dynamics

Efficient water management can be achieved using a holistic model. Modeling water resources with a dynamic system approach is a common practice (Sterman 2002; Winz et al. 2009; Xi & Poh 2013; Zarghami et al. 2016). The concept of system dynamics (SD) is based on the interaction between stocks and their flow and their feedback. Stock variables describe the state of a system, and flow variables define rates that can change stock variables. The amount of water in a reservoir is an integrated stock. A comprehensive model can help effectively manage the water supply. The SD approach with easy object-oriented programming capabilities for integrated water resources management is widely utilized. The volume of a reservoir at any time can be calculated as follows:
formula
(12)
where Inflow (t) is the inflow to the reservoir, Evaporation (t) represents evaporation from the reservoir, Spill (t) is the water spilled from the reservoir, Release (t) is the released water supply to meet the demands, and V(t0) is the initial reservoir volume. In the created SD model, the standard operating policy is used to decide on the amount of water allocation. In this method, released water is considered equal to demands if:
formula
(13)
Demand (t) is the sum of domestic demand, industry demand, agricultural demand, environmental demand, and Vmin is the minimum reservoir volume. If the upper condition is not satisfied, the released flow would be equal to:
formula
(14)
A flow-storage model is created using Vensim software to simulate the performance of GD (Figure 2). This software with a suitable and user-friendly graphical environment provides a set of powerful tools to import and manage these data. Vensim enables conceptualization, documentation, simulation, analysis, and optimization of dynamic system models (Bhatt et al. 2004). By using Equations (12)–(14) and river inflow, evaporation from the dam reservoir surface, and spill and reservoir volume in the dam, the amount of monthly water allocation to different sectors was simulated. The priorities of the water demand are set in the following order each month: domestic, environment, industry, and agriculture sectors. As mentioned earlier, the supply of water needed by the agricultural sector is the last priority. Therefore, if there is not enough water in the reservoir of the dam, the model transfers the deficiency to the agricultural sector. As shown in Figure 2, first, the amount of water released from the dam is compared with the amount of water needed for domestic (C1) and 100% of drinking needs are provided, and the rest is for the needs of environmental (C2), the needs of industry (C3), and finally for the needs of the agricultural sector (C4). For example, if the amount of released water is less than the domestic water requirement (C1), then the percentage of supplying of this sector will be less than 100%, and due to the small amount of released water, the percentage of water supply for other sectors will be zero. Based on this, the values of the five indicators were calculated for each month. This process is applied for the future period, and according to the calculation of the model's required parameters in the previous stages, the dam performance is evaluated by applying the existing and the optimal crop patterns. Hence, time series data under RCP2.6, RCP4.5, and RCP8.5 climate scenarios are the input of the Vensim model. Figure 3 shows the steps of this research.
Figure 2

Dynamic stock and flow diagram of Gelevard reservoir dam operation in Vensim.

Figure 2

Dynamic stock and flow diagram of Gelevard reservoir dam operation in Vensim.

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Figure 3

Flowchart for the proposed methodology.

Figure 3

Flowchart for the proposed methodology.

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Defining performance indices

In this study, to evaluate the performance of the reservoir, four indices including time-based reliability index (McMahon et al. 2006), vulnerability index (Loucks & van Beek 2005), resilience index (Moy et al. 1986), and water resources sustainability index (Loucks & Gladwell 1999) are used. The reliability index shows the probability of supplying the amount of water demand in the simulated time period. Vulnerability index expresses the probability of water resources (dam) being unable to respond to demand. Resilience index shows the system's ability to adapt to different conditions. The sustainability index of water resources is a function of the aforementioned indices.
formula
(15)
formula
(16)
formula
(17)
formula
(18)
where i and t represent the year and month, respectively, is the time-based reliability, is water shortage, n is the number of total time series (month) that cover the historical or simulation analysis period, and is the likely value of deficits, if they occur. is the probability that a system recovers from a period of failure, and is a summary index that measures the sustainability of water resources systems.
Deficiency of water deficit in supplying demands is expressed as follows (Loucks & van Beek 2005):
formula
(19)

Climate change modeling

In this study, observed weather data (i.e., monthly rainfall, monthly maximum and minimum air temperatures, monthly maximum and minimum relative humidity (RH), monthly wind speed, and monthly sunshine hours) for the period of 1985–2005 are used to calibrate, and the period 2006–2019 is incorporated to validate the SVM model. The results of the performance evaluation demonstrate that the performance of climatic parameters downscaling by the SVM model is in good agreement with the prior observed climatic parameters (Table 1) (Babolhakami et al. 2020).

Table 1

Performance of SVM model statistics for GD station

ParameterUnitcalibration
Validation
R2MAERMSER2MAERMSE
Tmax °C 0.98 0.80 1.04 0.97 0.93 1.13 
Tmin °C 0.99 0.53 0.68 0.99 0.65 0.83 
Sunshine hr 0.77 0.59 0.74 0.77 0.53 0.71 
Wind speed m/s 0.81 0.14 0.11 0.79 0.09 0.12 
Rainfall mm 0.72 16.7 24.7 0.70 17.9 25.6 
RH 0.70 1.73 2.51 0.68 1.49 1.83 
ParameterUnitcalibration
Validation
R2MAERMSER2MAERMSE
Tmax °C 0.98 0.80 1.04 0.97 0.93 1.13 
Tmin °C 0.99 0.53 0.68 0.99 0.65 0.83 
Sunshine hr 0.77 0.59 0.74 0.77 0.53 0.71 
Wind speed m/s 0.81 0.14 0.11 0.79 0.09 0.12 
Rainfall mm 0.72 16.7 24.7 0.70 17.9 25.6 
RH 0.70 1.73 2.51 0.68 1.49 1.83 

The downscaled maximum air temperature, minimum air temperature, and rainfall for NR Basin in all future time horizons (2020–2040) and under the CanESM2 RCP2.6, RCP4.5, and RCP8.5 scenarios by the SVM model are shown in Figure 4. The projection shows an increasing trend in the air temperature. In the future period, the average annual maximum temperature based on the scenarios of RCP2.6, RCP4.5, and RCP8.5 will increase by 0.77, 0.69, and 0.84 °C, respectively. The average annual minimum air temperature based on RCP2.6, RCP4.5, and RCP8.5 scenarios will increase by 0.63, 0.55, and 0.65 °C, respectively. In the upcoming period, the average annual rainfall based on RCP2.6, RCP4.5, and RCP8.5 scenarios will decrease by 7, 11, and 10%, respectively. The highest increase in precipitation based on the RCP2.6 scenario by 12 mm will occur in February, and the highest increase in precipitation during the scenarios of RCP4.5 and RCP8.5 by 13 and 14 mm, respectively, will occur in June. Also, the greatest decreases in precipitation under the scenarios of RCP2.6, RCP4.5, and RCP8.5 by 14, 34, and 33 mm, will occur in November, respectively (Babolhakami et al. 2020).
Figure 4

Observed and simulated monthly mean in the period of 2020–2040 by the SVM model: (a) maximum air temperature, (b) minimum air temperature, and (c) rainfall based on the PCP2.6, PCP4.5, and PCP8.5 scenarios.

Figure 4

Observed and simulated monthly mean in the period of 2020–2040 by the SVM model: (a) maximum air temperature, (b) minimum air temperature, and (c) rainfall based on the PCP2.6, PCP4.5, and PCP8.5 scenarios.

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Runoff

Historical data of precipitation, air temperature, and runoff during the years 1992–2010 are incorporated to calibrate the IHACRES model, and the period 2011–2019 is used to validate the model. To evaluate the difference between the observed and simulated values, RMSE, MAE, and R2 are calculated for both calibration and validation periods. The evaluation results of the IHACRES model are subsequently presented in Table 2. The monthly runoff simulated and observed during the calibration and validation period is shown in Figure 5. The results show that the IHACRES model has an acceptable ability to simulate the flow of the NR Basin.
Table 2

Correlation coefficient values for the calibration and validation periods (Babolhakami et al. 2020)

PeriodMAE (mm)RMSE (mm)R2 (–)
Calibration 0.91 1.24 0.94 
Validation 1.22 2.32 0.93 
PeriodMAE (mm)RMSE (mm)R2 (–)
Calibration 0.91 1.24 0.94 
Validation 1.22 2.32 0.93 
Figure 5

Comparison between the IHACRES simulated and observed runoff in the (a) validation and (b) calibration periods (Babolhakami et al. 2020).

Figure 5

Comparison between the IHACRES simulated and observed runoff in the (a) validation and (b) calibration periods (Babolhakami et al. 2020).

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Runoff will possibly decrease in the future period of 2020–2040 compared to the historical period (Figure 6). The runoff of the NR Basin in the coming period compared to the historical period will see a decrease during the majority of the months. The maximum runoff reduction will occur in July. The NR runoff in March under the RCP2.6, RCP4.5, and RCP8.5 climate scenarios will possibly decrease by 54, 56, and 53%, respectively. The average annual runoff in the historical period was 3 m3/s, but during the future period of 2020–2040, the average annual flow of the NR under the scenarios of RCP2.6, RCP4.5, and RCP8.5 will be 2.55, 1.91, and 1.89 m3/s, respectively. So, the average annual flow of the NR based on scenarios RCP2.6, RCP4.5, and RCP8.5 decreases by 15, 36, and 37%, respectively. The average annual volume of the NR at the site of GD during the years 1992–1994 was equal to 95 MCM. During the future period of 2020–2040, the average runoff at the site of GD based on the scenarios of RCP2.6, RCP4.5, and RCP8.5 will be 81, 61, and 60 MCM, respectively.
Figure 6

The average monthly runoff simulated during the climate change period (2020–2040) under the RCP2.6, RCP4.5, and RCP8.5 scenarios and during the base period (Babolhakami et al. 2020).

Figure 6

The average monthly runoff simulated during the climate change period (2020–2040) under the RCP2.6, RCP4.5, and RCP8.5 scenarios and during the base period (Babolhakami et al. 2020).

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Cultivation pattern

The cultivation pattern includes rice, canola, citrus, wheat, barley, and cotton crops. Reservoir performance simulation shows that GD will not be able to supply the required water to the existing cultivation pattern. The simulation reports that in the next period, the reservoir will not be able to supply the required water to the drinking, environment, industry, and agriculture sectors. Therefore, to improve the performance of the reservoir of GD and reduce the amount and severity of water shortages in different sections, the cultivation pattern is optimized. The OCP is determined using LINGO for different scenarios of climate change, according to the volume of available water, the yield of cultivated crops, and the total area under cultivation. Table 3 shows the OCP for RCP8.5 as an example. Generally, the results show that by changing the cultivation pattern, the area under irrigated crops will decrease and the area under cultivation of rainfed crops will increase. According to Table 3, the area under rice cultivation will also decrease by more than 50% and the area under citrus cultivation will increase by 100%. Also, the area under cultivation of dryland wheat and rapeseed crops shows up to 200% growth. In contrast, the area under barley cultivation will decrease and the area under irrigated cotton cultivation will remain unchanged.

Table 3

Existing and optimal crop pattern of GD irrigation network in RCP8.5

CropExisting crop pattern
Optimal crop pattern
%Area (ha)%Area (ha)
Wheat 10 1,000 30 3,000 
Barley 500 305 
Rice 60 6,000 22 2,195 
Canola 500 15 1,500 
Cotton 10 1,000 10 1,000 
Citrus 10 1,000 20 2,000 
CropExisting crop pattern
Optimal crop pattern
%Area (ha)%Area (ha)
Wheat 10 1,000 30 3,000 
Barley 500 305 
Rice 60 6,000 22 2,195 
Canola 500 15 1,500 
Cotton 10 1,000 10 1,000 
Citrus 10 1,000 20 2,000 

Water demand

The average demand for domestic water is 31 MCM in 2020. According to the allocation plan of the Ministry of Energy of Iran, due to population growth, domestic water demand will possibly increase to 42 MCM by 2040. The demand for agricultural water by applying the existing cropping pattern will be 35 MCM per year. Applying the optimal cropping pattern will reduce the demand for agricultural water to 17 million m3, and the agricultural water demand to 18 MCM per year. The average water demand in the environmental sector is 20 MCM per year, and annual demand for the water industry is 7 MCM. The average monthly water demand in domestic, industrial, environmental, and agricultural sectors in the next period (2020–2040) is shown in Figure 7. The results show that more than 60% of the total annual water demand will possibly be seen from May to August, and the most water stress will occur in these months.
Figure 7

Average monthly water demand in domestic, industrial, environmental, and agricultural sectors in the next period (2020–2040).

Figure 7

Average monthly water demand in domestic, industrial, environmental, and agricultural sectors in the next period (2020–2040).

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System dynamics of GD

Modeling is done in a way so as not to have a statistically significant difference between reality and model. Accordingly, several tests are used to compare simulated and observed values. It is important to mention that the forecast data of meteorological parameters have been calibrated and verified by the SVM model (Table 1). Also, the prediction of runoff values by the IHACRES model has been calibrated and validated (Table 2). To validate the Vensim model simulations, behavior repetition, unit compatibility, and structure evaluation tests are used. In the behavior repetition test, the output of the model for the historical period is simulated and compared with the measurement data. The results of this test for the period of 2006–2014 show that the model was able to predict the output value with an RMSE of 5.8 MCM per year and R2 = 0.89. In the unit compatibility test, the measurement units for each variable in the model are checked. The structure evaluation test examines the correctness of the model in terms of its compatibility with the dynamic stock and flow diagram (Figure 2). This test focuses on establishing the physical and governing laws of the model (Sterman 2002).

The performance of GD in climate change conditions and existing cultivation pattern is simulated by the GD model. The priorities of water demand are set in the following order: domestic, environment, industry, and agriculture sectors. The results show that if the current cultivation pattern continues, water shortages will occur in all sectors in the coming period. Therefore, to reduce water stress and improve the performance of GD, the optimal cultivation model is applied.

The results conclude that with the continuation of the cultivation pattern's current trend, in an optimistic climate (RCP2.6) for providing water supply, domestic, environment, industry, and agriculture will be supplied without shortage for 7 years, and in the most pessimistic climate (RCP8.5) regarding the water demand of all sectors, the supply will be sufficient only in the year 2022, and in other years, there will be water shortages in all sectors. If the optimal crop pattern model is applied, in the optimistic climate, the demand for all sectors will be met until the year 2031 without any shortages, and in the pessimistic climate, the demand will be met for 4 years and the water required for all sectors will be fully supplied.

The results of the Vensim model on the performance of GD reservoir in supplying the water required by different sectors show that if the existing cropping pattern continues, for the different climatic scenarios of RCP2.6, RCP4.5, and RCP8.5 in the next 7, 2, and 2 years, respectively, there will be no supply problem, and then there will be a water shortage crisis in the region immediately. If the OCP is applied, based on the different climatic scenarios of RCP2.6, RCP4.5, and RCP8.5, providing water for the region will not be difficult in the next 11, 5, and 4 years, respectively, and after that, water shortage will occur gradually. Therefore, applying an OCP reduces the severity of water shortage.

Table 4 depicts the values of water supply evaluation indexes to different sections and the total water demand of GD in the conditions of continuing the existing cultivation pattern and applying the OCP in climatic scenarios. These indexes are calculated based on Equations (15)–(19). Findings show that if the optimal model is applied, the five indicators of reliability, vulnerability, resilience, sustainability, and deficiency in all sectors of water consumption will be relatively improved. If the existing cropping pattern continues, the index of the reliability of water supply from the reservoir under RCP2.6, RCP4.5, and RCP8.5 climatic scenarios would be 0.67, 0.53, and 0.52, respectively, which will be improved by applying the optimal cropping pattern by 11, 11, and 10%, respectively. The vulnerability index of the requested water supply from the reservoir in the case of applying the existing cultivation pattern based on the RCP2.6, RCP4.5, and RCP8.5 climatic scenarios will be 0.16, 0.15, and 0.15, respectively, which will be possibly improved by applying the OCP by an increase of 2% in all climate scenarios. The resilience index of water demand from the reservoir will be 0.07, 0.06, and 0.06, respectively. By continuing the existing cultivation pattern based on the RCP2.6, RCP4.5, and RCP8.5 climatic scenarios, which will be improved by applying the OCP. It will reach 0.06, 0.06, and 0.05, respectively. The sustainability indexes of the GD reservoir system in the water supply of different sections based on RCP2.6, RCP4.5, and RCP8.5 climatic scenarios are 10, 7, and 7%, respectively, which will possibly have an increase of 13, 10, and 10% by applying the OCP, indicating a 3% increase. The results conclude that if the current cultivation pattern continues in the future, the average percentage of water supply demands being provided by the reservoir of GD under climatic scenarios of RCP2.6, RCP4.5, and RCP8.5 will be 74, 65, and 63%, respectively. By applying the OCP, this index will increase by 11, 14, and 13%, respectively.

Table 4

The values of water supply evaluation indicators to different sections and the total water demand of GD in the conditions of continuing the existing and optimal crop pattern in climatic scenarios

Deficiency (%)SustainabilityResilienceVulnerabilityReliabilityClimatic scenarioCrop pattern
Domestic sector   
0.22 0.04 0.29 0.80 RCP2.6 Existing 
11 0.18 0.03 0.26 0.71 RCP4.5 
11 0.19 0.03 0.27 0.72 RCP8.5 
0.26 0.04 0.31 0.87 RCP2.6 Optimal 
0.24 0.04 0.31 0.80 RCP4.5 
0.23 0.04 0.30 0.80 RCP8.5 
Environmental sector   
35 0.13 0.11 0.20 0.73 RCP2.6 Existing 
51 0.12 0.12 0.21 0.64 RCP4.5 
53 0.11 0.12 0.20 0.62 RCP8.5 
22 0.15 0.10 0.20 0.81 RCP2.6 Optimal 
34 0.16 0.11 0.24 0.73 RCP4.5 
33 0.15 0.09 0.23 0.70 RCP8.5 
Industrial sector   
30 0.13 0.09 0.19 0.71 RCP2.6 Existing 
40 0.09 0.08 0.17 0.59 RCP4.5 
41 0.09 0.08 0.17 0.57 RCP8.5 
23 0.14 0.09 0.20 0.79 RCP2.6 Optimal 
33 0.11 0.08 0.18 0.66 RCP4.5 
33 0.11 0.08 0.19 0.66 RCP8.5 
Agricultural sectors   
38 0.20 0.17 0.30 0.81 RCP2.6 Existing 
50 0.18 0.18 0.29 0.74 RCP4.5 
54 0.19 0.17 0.30 0.75 RCP8.5 
23 0.30 0.19 0.41 0.90 RCP2.6 Optimal 
35 0.27 0.21 0.40 0.85 RCP4.5 
39 0.27 0.19 0.40 0.84 RCP8.5 
Total demands   
26 0.10 0.07 0.16 0.67 RCP2.6 Existing 
35 0.07 0.06 0.15 0.53 RCP4.5 
37 0.07 0.06 0.15 0.52 RCP8.5 
15 0.13 0.06 0.18 0.78 RCP2.6 Optimal 
21 0.10 0.06 0.17 0.64 RCP4.5 
24 0.10 0.05 0.17 0.62 RCP8.5 
Deficiency (%)SustainabilityResilienceVulnerabilityReliabilityClimatic scenarioCrop pattern
Domestic sector   
0.22 0.04 0.29 0.80 RCP2.6 Existing 
11 0.18 0.03 0.26 0.71 RCP4.5 
11 0.19 0.03 0.27 0.72 RCP8.5 
0.26 0.04 0.31 0.87 RCP2.6 Optimal 
0.24 0.04 0.31 0.80 RCP4.5 
0.23 0.04 0.30 0.80 RCP8.5 
Environmental sector   
35 0.13 0.11 0.20 0.73 RCP2.6 Existing 
51 0.12 0.12 0.21 0.64 RCP4.5 
53 0.11 0.12 0.20 0.62 RCP8.5 
22 0.15 0.10 0.20 0.81 RCP2.6 Optimal 
34 0.16 0.11 0.24 0.73 RCP4.5 
33 0.15 0.09 0.23 0.70 RCP8.5 
Industrial sector   
30 0.13 0.09 0.19 0.71 RCP2.6 Existing 
40 0.09 0.08 0.17 0.59 RCP4.5 
41 0.09 0.08 0.17 0.57 RCP8.5 
23 0.14 0.09 0.20 0.79 RCP2.6 Optimal 
33 0.11 0.08 0.18 0.66 RCP4.5 
33 0.11 0.08 0.19 0.66 RCP8.5 
Agricultural sectors   
38 0.20 0.17 0.30 0.81 RCP2.6 Existing 
50 0.18 0.18 0.29 0.74 RCP4.5 
54 0.19 0.17 0.30 0.75 RCP8.5 
23 0.30 0.19 0.41 0.90 RCP2.6 Optimal 
35 0.27 0.21 0.40 0.85 RCP4.5 
39 0.27 0.19 0.40 0.84 RCP8.5 
Total demands   
26 0.10 0.07 0.16 0.67 RCP2.6 Existing 
35 0.07 0.06 0.15 0.53 RCP4.5 
37 0.07 0.06 0.15 0.52 RCP8.5 
15 0.13 0.06 0.18 0.78 RCP2.6 Optimal 
21 0.10 0.06 0.17 0.64 RCP4.5 
24 0.10 0.05 0.17 0.62 RCP8.5 

The results indicate that with the increasing air temperature and decreasing precipitation, the inflow to the dam reservoir will possibly decrease, so that in the period of 2020–2040, the inflow to the reservoir of GD under climatic scenarios of RCP2.6, RCP4.5, and RCP8.5 will be reduced by 15, 36 and 37%, respectively. Reducing the inflow to the reservoir of this dam will reduce the reliability of the reservoir in water supply, while increasing vulnerability of not meeting water demands. Also, the decrease in the river flow under climatic uncertainties reduces the capacity of the system to adapt to changing conditions, which reduces the resiliency of the system. Applying the optimal cultivation model to some extent improves the performance indicators of the dam reservoir in meeting the water needs of different sectors, but cannot stabilize the performance of the GD reservoir system. This indicates that the performance of GD in the future period will be unstable due to climate change and reduced inflow to the reservoir. So, it is suggested to review the planning for the operation of the GD reservoir or to consider other policies to reduce water demands in different sectors.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

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