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The purpose of this research is to investigate the climate change impacts in Maroon Basin, Iran. To investigate the impacts of climate change on rainfall, temperature, and inflow in Maroon Dam, a simulation of four general circulation models (GCMs) was done in three future periods 2021–2040, 2041–2060, and 2061–2080. The results showed that the projected increased temperature would significantly reduce the runoff in the basin, despite the projected increase in rainfall. The most significant decrease of the average inflow to the Maroon Dam Reservoir in the near future of the RCP4.5 and RCP8.5 scenarios in March 24 and 26.4%, the middle future in March 25.4 and 29%, and the far future in March 27 and 30.6%, respectively, is predicted. Also, the MODSIM model simulation results showed that the Maroon Dam Reservoir would face a water resources shortage in the future to provide maximum demands. The average water supply reliability in climate change scenarios showed that the maximum water supply of 85% in the period 2021–2040 and the minimum of 80.4% in 2061–2080 would occur in the RCP4.5 and RCP8.5 scenarios, respectively.

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  • To plan for water resources allocation to demands in the Maroon Basin affected by climate change, a combination of SWAT and MODSIM models was used.

  • It was found that the RCP8.5 scenario will have the most severe water stress and the worst allocation conditions.

  • Add-on of a water management model to a hydrological model results in a powerful simulation tool that can serve for sustainable river basin management.

climate change, MODSIM, optimal reservoir operation, reliability, streamflow, SWAT, water supply
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Water is a nonrenewable and vital resource that requires efficient management to distribute water resources properly. Nowadays, the effects of climate change worldwide, such as uneven distribution of rainfall, increasing drought frequency and duration, and its negative impact on water resources, are tangible. Climate change can worsen an already existing water shortage as an additional pressure factor in some regions of the world, as it may lead to increased rainfall variability and global warming (Bates et al. 2008). So, the study of the effects of climate change on water resources is much more important than identifying and studying the principles of the climate change phenomenon itself. The Intergovernmental Panel on Climate Change (IPCC 2007) has identified the impact of climate change and its negative effects as one of the main destructive drivers of available water resources management. Since significant changes in the performance of flow networks are caused by the slightest change in hydrological variables, various approaches have been proposed to reduce harmful effects (IPCC 2007). Climate change influences reservoir operations and water availability of reservoirs (Lee et al. 2016) through alterations in precipitation, temperature, and solar radiation (Farinosi et al. 2019; Liu et al. 2020). Due to the scarcity of water resources, the operation of dam reservoirs is of great importance because of the economic value resulting from the optimal use of available water resources, the increasing trend of water demands, and the lack of available water resources (Chhuon et al. 2016). In recent years, the inflow of the Maroon Basin into its reservoir has decreased due to climate change and has created problems in meeting water demands downstream (Zalaki-Badil et al. 2017). Many studies have been conducted to evaluate the optimal operation of dam reservoirs in climatic scenarios and the results showed the inflow to the dam reservoirs would be significantly reduced in the future. In a study, Dahe & Srivastava (2002) defined the water yield model as a multi-efficiency and multi-reservoir model with an acceptable deficit in annual water yield. Their results showed that the water yield model in a multi-reservoir system with single- and multi-objective conditions predicts acceptable flow compared to the simulation model. Abbaspour et al. (2009) investigated the project of Iran's water resources affected by climate change. The results showed that the probability of floods in the northern and western regions of Iran, especially in the Zagros Mountains, will increase due to changing rainfall patterns.

In other words, according to the rainfall pattern in the future, the amount of high-intensity alternate floods in this region will increase. Javadi et al. (2021) used a combination of decision-making models, numerical groundwater modeling, and clustering technique to determine suitable sites for the implementation of an artificial recharge project. This hybrid approach was employed for the Yasouj aquifer located in southwestern Iran. Eini et al. (2020) investigated the development of alternative SWAT-based models for simulating water budget components and streamflow for a karstic-influenced watershed. The results showed that karst conditions and geometry of sinkholes in karst zones did not play a major role in creation of runoff. Rahmani-Rezaeieh et al. (2020) evaluated the impacts of climate change on the floodway and floodway fringe along the Shahrchay River located at Lake Urmia Basin in Iran. The results indicated that the climate model typically underestimates the temperature and precipitation values at the historical period. In addition, a significant decrease in total runoff volume in future periods is expected and the number and magnitude of peak flows would be increased. Jafari et al. (2021) studied fully integrated numerical simulation of surface water–groundwater interactions using SWAT-MODFLOW with an improved calibration tool. The results indicated that the curve number (CN2) and GW hydraulic conductivity (K) parameters have the most significant impact on runoff and GW level simulation. Wu et al. (2023) established a novel social-ecological coupling model to meet the previous coupling coordination degree models that are empirical and very complex, without physical parameters. The research results showed that the model can well reflect changes in vegetation coverage and simplify expression of the coordinated development of social-ecological systems. Xu et al. (2023) evaluated the status of ‘Happy River’ management in the new era of China. The results showed that the follow-up work can be promoted from the aspects of ecological construction, water culture construction, water protection, and social functions. Yang et al. (2023) assessed the effects of climate factors and soil properties on ecosystem C stocks through field investigation across the Loess Plateau. The results highlight that grasslands are more predestined to store C compared with the other ecosystems, and the C stored in roots is substantial and should be considered when assessing C stocks and strongly contributes to soil organic matter formation.

Sharafati et al. (2020) evaluated the quantification and uncertainty of the impact of climate change on river discharge and sediment yield in the Dehbar River Basin in Iran. This study highlights the significant negative impact of climate change on the Dehbar River Basin, with amplification of river flows and sediment concentrations in the wet season and increased water scarcity in the dry season. Yazdandoost & Moradian (2021) studied the impacts of climate change on the streamflow of the Zarrineh River. Results on hydroclimatological changes in Zarrineh River Basin showed that the mean daily precipitation is expected to decrease from 0.94 and 0.96 mm in 2015 to 0.65 and 0.68 mm in 2050 under RCP2.6 and RCP8.5, respectively. In the case of temperature, the numbers change from 12.33 and 12.37 °C in 2015 to 14.28 and 14.32 °C in 2050. Ahn et al. (2009) evaluated the agricultural water supply capacities for the Geum River Basin in South Korea by performing a water balance analysis and considering a network of agricultural irrigation facilities using the MODSIM model. Emami & Koch (2017) evaluated the impacts of climate and demand changes on the dam operation by comparing future and historical MODSIM simulated average water budget and supply/demand ratio for the three CESM model scenarios. The results showed that the region will face more intensive water shortages in the future, owing to climatic change and increasing demands. Nguyen et al. (2022) evaluated the impacts of future changes in land use, climate, and downstream water demands on the reservoir water supply performance. The results showed that the reservoir performance can be improved by adjusting water allocation policies and best management practices in the basin. Although many studies investigated the impacts of climate change on the water cycle (Abbaspour et al. 2009), fewer studies have focused on the evaluation of the water supply of a river basin due to the impacts of climate change (Akbari et al. 2022).

The Maroon Reservoir is the main structure in the region providing water for downstream demands. In addition, the reservoir cannot meet all water demand requirements during this period. Under uncertain future, the existing operational rule curve needs to be updated due to reduced storage and considerable change in water demands. The overall aim of this study was (a) to use an adaptive optimization approach to project the range of reliability of the reservoir under possible scenarios and (b) to suggest options to improve the water supply of the reservoir.

This study was conducted in four steps:

  • (1)

    Calibration and validation of the SWAT model and do a monthly runoff comparison of two hydrostations in Maroon Basin;

  • (2)

    Prediction of the temperature and rainfall by integrating four GCM CMIP5 models in baseline and future periods and RCP4.5 and 8.5 scenarios with statistical downscaling;

  • (3)

    Prediction of the inflow to the reservoir in baseline and future periods; and

  • (4)

    Water balance analysis and assessment of watershed's water supply reliability to achieve adaptive operation of Maroon reservoir in the current situation and future periods using the MODSIM model.

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Case study area

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The Maroon Basin is located in the southwestern part of Iran in the Zagros mountainside with an area of 3,808 km2. This river is created by connecting the Shabliz, Ludab, and Saqaveh branches, and it reaches the Maroon Dam Reservoir after a distance of 120 km. The Maroon reservoir has a storage capacity of 1,274 Mm3, with a water surface area of approximately 2,500 ha. The area of the basin is located in a semi-arid climatic region. The minimum altitude in this area is 300 and the maximum altitude is 3,489 m above sea level. The annual rainfall, evaporation, and average temperature in the basin are approximately 520 mm, 2,730 mm, and 26 °C, respectively. The wet season often lasts from November to May, with a maximum of 130 mm in December, and varying seasonally in other months. The geology of Maroon Basin consists of outcrops, sandstone, conglomerate, and alternation of colored marl and silty limestone.

Evaluation of climate change

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In this study, temperature and rainfall parameters are predicted using CMIP5 models based on the IPCC Fifth Assessment Report (AR5) Climate Change scenarios. Each CMIP5 model considered varying emission rates of solar greenhouse gases, human activities, volcanic eruptions’ emission of short-term species, and natural/human aerosols known, also, as RCP-representing concentration routes. In the CMIP6 report, the socioeconomic effects are added to the climate change results of the CMIP5 report, but the basis of the meteorological effects of the reports are the same and there is no significant difference. Also, the hydrological processes have a characteristic of uncertainty, considering that the main weight of the climate change effects is in the meteorology process, so the difference between CMIP6 and CMIP5 can be ignored. Two emission scenarios were used to generate climate output data based on the Fifth Assessment Report (AR5): RCP4.5 optimistic and RCP8.5 pessimistic scenarios. This study aims to investigate the effect of climate change on rainfall, temperature, and inflow to the Maroon Dam Reservoir in three periods (2021–2040), (2041–2060), and (2061–2080), and four general circulation models (GCMs) of GFDL-CM3, EC_EARTH, Hadgem2-ES, and Miroc5 were used. By combining four GCM models, four time series for each month were weighed with the k-nearest neighbors (KNN) algorithm, thus obtaining one time series. The KNN algorithm works were based on the mean of the predicted climate parameters during the baseline period and the mean of the observed data. In this method, the weight of each model is multiplied by the changes in rainfall and temperature of the future period compared to the baseline period and the changes in temperature and rainfall in the future period are obtained:
(1)
where CVm,i is the difference between the mean of climatic parameters (temperature and rainfall) simulated in baseline periods with the model i and the related mean for month m from the mean of the observed data; Wm,i is the weight of each model i related to the long-term mean of the month m; it is the whole number of GCM models. Due to the large-scale output of GCM models, the LARS-WG downscaling model was used. In this model, changes in minimum and maximum temperature and rainfall in the RCP scenarios were entered into the LARS-WG model and using the observed data in the baseline period (2001–2018), daily data for future periods (2021–2040), (2041–2060), and (2061–2080) were generated. Table 1 shows the specifications of four models selected from the CMIP5 collection for the present study.
Table 1

Characteristics of four models selected from the CMIP5 collection for the present study

No.ModelInstitution/CountrySpatial resolution
1 GFDL-CM3 Geophysical Fluid Dynamics Laboratory, USA 2.5 × 2.0 
2 EC_EARTH EC_EARTH consortium published at Irish Centre for High-End Computing, Netherlands/Ireland 1.28 × 2.5 
3 Hadgem2-ES Met Office Hadley Centre, UK 1.875 × 1.25 
4 Miroc5 The Atmosphere and Ocean Research Institute (The University of Tokyo), National Institute for Environmental Studies, and Japan Agency for Marine-Earth Science and Technology, Japan 1.41 × 1.39 
No.ModelInstitution/CountrySpatial resolution
1 GFDL-CM3 Geophysical Fluid Dynamics Laboratory, USA 2.5 × 2.0 
2 EC_EARTH EC_EARTH consortium published at Irish Centre for High-End Computing, Netherlands/Ireland 1.28 × 2.5 
3 Hadgem2-ES Met Office Hadley Centre, UK 1.875 × 1.25 
4 Miroc5 The Atmosphere and Ocean Research Institute (The University of Tokyo), National Institute for Environmental Studies, and Japan Agency for Marine-Earth Science and Technology, Japan 1.41 × 1.39 
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SWAT and MODSIM models

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Arc SWAT 2012 extension in Arc GIS 10.3 was used for the initial configuration of the model. The waterway network was drawn using a digital elevation layer map. Hydrologic response units were created by setting three land use and soil maps and three slope classes (0–21, 21–41, and <41%) in GIS on each other. In the following, the agricultural and horticultural land use map was split into the dominant products of the region, including wheat, barley, alfalfa, and rice as crops, and apples and walnuts as horticultural products. Then, the daily rainfall and temperature data were entered into the model. The Hargreaves-Samani method was also used to calculate potential evapotranspiration. SWAT-CUP software was used to calibrate and validate the SWAT model. This software can calibrate and validate with Sufi2, GLUE, PSO, PARASOL, and MCMC methods. Studies have shown that the Sufi2 method is more efficient (Abbaspour et al. 2007; Healy & Essaid 2012). Therefore, the Sufi2 method was used in this study to analyze the sensitivity of parameters, calibration, and validation. The periods 1999–2012 and 2013–2018, respectively, were used to calibrate and validate the monthly flow data measured in Idenak and Dehno hydrostations. The performance evaluation and SWAT model simulation accuracy were performed using statistical indices of determination coefficient (R2), Nash–Sutcliffe efficiency (NSE), r-factor, and p-factor. The closer the values of R2 and NSE are to the number 1, the greater the agreement between the values simulated by the model and the observed values (Nash & Sutcliffe 1970). In the final step, the river basin (water resources) decision model, MODSIM (Labadie 1995), is applied to assess the impact of future predicted climatic change on the dam operation and water supply and demand, by incorporating the output of the previously modeled SWAT streamflow as input drivers into this model. The data used in this study are daily climatic variables including rainfall, minimum and maximum temperature, wind speed, relative humidity, sunshine, and monthly hydrometric data received from Iran Water Resources Management Company. Digital elevation map (DEM) information with the 30-m spatial resolution was downloaded from: https://earthexplorer.usgs.gov. Figure 1 shows the DEM and watershed of the Maroon Basin. Agricultural information such as the type of agricultural products, sowing and harvesting dates, fertilization, number of irrigations, volume, and source of irrigation were also received from the Ministry of Agriculture. The recorded data from the Idenak and Dehno hydrostations are used in hydrological modeling. The characteristics of the stations in the study area and details of the resolution and the time period of the collected data are shown in Tables 2 and 3, respectively. In Figure 2, the proposed modeling framework is presented.
Table 2

Characteristics of the stations in the study area

Station typeStation nameLatitudeLongitudeAltitude (m)Description
Hydrometric and Climatology Idenak 30.95 50.42 560  
Hydrometric Dehno 30.97 50.87 1,340  
Hydrometric Tange-Takab 30.68 50.33 280 Downstream of the dam reservoir 
Hydrometric Cham-Nezam 30.75 49.92 190 Downstream of the dam reservoir 
Synoptic Dehdasht 30.78 50.58 793  
Synoptic Behbahan 30.60 50.21 313  
Station typeStation nameLatitudeLongitudeAltitude (m)Description
Hydrometric and Climatology Idenak 30.95 50.42 560  
Hydrometric Dehno 30.97 50.87 1,340  
Hydrometric Tange-Takab 30.68 50.33 280 Downstream of the dam reservoir 
Hydrometric Cham-Nezam 30.75 49.92 190 Downstream of the dam reservoir 
Synoptic Dehdasht 30.78 50.58 793  
Synoptic Behbahan 30.60 50.21 313  
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Table 3

Details of resolution and time period of the collected data

Station nameParameterData collection periodJanFebMarAprMayJunJulAugSepOctNovDec
Idenak Flow (m3/s) 1999–2018 63.4 76.2 86.7 87.1 48 26.4 16.4 11.7 9.8 12 18.3 51.5 
Dehno Flow (m3/s) 1999–2018 16.9 19.5 24.8 22.3 9.5 3.9 2 1.5 1.1 1.5 2.4 11.7 
Tange-Takab Flow (m3/s) 1999–2018 45.3 47.5 57.4 41.4 26.8 23.4 31.8 34.5 32.1 29.9 29.7 30.3 
Cham-Nezam Flow (m3/s) 1999–2018 56.6 52.8 55.2 35.8 24.2 22.2 26.9 26.6 25.6 25.8 29.1 41.4 
Idenak Rainfall (mm) 1999–2020 126.2 90.6 53 52.4 11.8 0 0 2 1.2 0 52.5 129.3 
Dehdasht Rainfall (mm) 1999–2020 114.1 80.3 45.9 43.6 12.5 2.1 0 2.5 1 0 45.6 105.3 
Behbahan Rainfall (mm) 1999–2021 67.6 42.6 21.1 26.1 8.5 0 0 0 0.6 1 28.1 85.2 
Idenak Temperature (°C) 1999–2018 12.4 13.2 17.3 23.6 30.8 38.3 41 40.8 37.6 30.2 21.5 14.2 
Dehdasht Temperature (°C) 1999–2021 13.1 13.8 18.3 24.7 32.4 40.1 43.2 42.7 39.6 31.6 22.7 14.8 
Behbahan Temperature (°C) 1999–2021 14.5 15.1 20.1 27 35.6 43.9 47.4 46.8 43.4 34.6 24.9 16.2 
Idenak Evaporation (mm) 1999–2019 47 61 89 149 247 388 468 455 373 264 130 63 
Maroon Dam Reservoir storage (Mm3) 1999–2019 490.4 545.7 589.2 623.4 697.7 714.7 671.7 600.6 522.4 447.8 431.2 427.3 
Station nameParameterData collection periodJanFebMarAprMayJunJulAugSepOctNovDec
Idenak Flow (m3/s) 1999–2018 63.4 76.2 86.7 87.1 48 26.4 16.4 11.7 9.8 12 18.3 51.5 
Dehno Flow (m3/s) 1999–2018 16.9 19.5 24.8 22.3 9.5 3.9 2 1.5 1.1 1.5 2.4 11.7 
Tange-Takab Flow (m3/s) 1999–2018 45.3 47.5 57.4 41.4 26.8 23.4 31.8 34.5 32.1 29.9 29.7 30.3 
Cham-Nezam Flow (m3/s) 1999–2018 56.6 52.8 55.2 35.8 24.2 22.2 26.9 26.6 25.6 25.8 29.1 41.4 
Idenak Rainfall (mm) 1999–2020 126.2 90.6 53 52.4 11.8 0 0 2 1.2 0 52.5 129.3 
Dehdasht Rainfall (mm) 1999–2020 114.1 80.3 45.9 43.6 12.5 2.1 0 2.5 1 0 45.6 105.3 
Behbahan Rainfall (mm) 1999–2021 67.6 42.6 21.1 26.1 8.5 0 0 0 0.6 1 28.1 85.2 
Idenak Temperature (°C) 1999–2018 12.4 13.2 17.3 23.6 30.8 38.3 41 40.8 37.6 30.2 21.5 14.2 
Dehdasht Temperature (°C) 1999–2021 13.1 13.8 18.3 24.7 32.4 40.1 43.2 42.7 39.6 31.6 22.7 14.8 
Behbahan Temperature (°C) 1999–2021 14.5 15.1 20.1 27 35.6 43.9 47.4 46.8 43.4 34.6 24.9 16.2 
Idenak Evaporation (mm) 1999–2019 47 61 89 149 247 388 468 455 373 264 130 63 
Maroon Dam Reservoir storage (Mm3) 1999–2019 490.4 545.7 589.2 623.4 697.7 714.7 671.7 600.6 522.4 447.8 431.2 427.3 
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Figure 1
DEM and watershed of the Maroon Basin.
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DEM and watershed of the Maroon Basin.

Figure 1
DEM and watershed of the Maroon Basin.
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DEM and watershed of the Maroon Basin.

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Figure 2
Proposed modeling framework.
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Proposed modeling framework.

Figure 2
Proposed modeling framework.
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Proposed modeling framework.

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The present research uses the MODSIM-DSS water resources allocation model to determine the amount of water allocated to different demands in the baseline period (2002–2018) and RCP4.5 and RCP8.5 scenarios. This model is based on DSS (decision support system) and uses a one-step optimization algorithm (Shourian et al. 2008). The MODSIM model simulates the optimal water allocation in a river basin through a sequential solution of the following generalized network flow optimization problem, namely a minimization of the water allocation costs, for each period, in the constraint that the mass balance all over the network is satisfied. Mathematically, this optimization is in the following form:
(2)
(3)
(4)
where A is the set of all links in the network; N is the set of all nodes; is the set of all links originating at nodes I (i.e., outflow links); is the set of all links terminating at node i (i.e., inflow links); is the integer-valued flow rate in link ⁠; is the cost weighting factor or priority number per unit flow rate in link ⁠; and are the lower and upper bound on flow in link at time t. In this research, the inflow to the Maroon Dam, the reservoir's physical characteristics (volume–surface–height curve), evaporation rate from the reservoir surface, and time series of the demands including agricultural, environmental, potable, and industrial are the most important input data to the MODSIM model.
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Climate change based on the GCM outputs

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With the bias correction, the average monthly rainfall in the near future period (2021–2040), the middle future period (2041–2060), and the far future period (2061–2080) in the RCP8.5 scenario would increase in all months except February compared to the baseline period. Rainfall in January, November, and December will increase by 7.5, 18.4, and 5.4% in the near future period, 8.2, 18.8, and 5.8% in the middle future period, and 9, 19.2, and 6.3% in the far future period compared to the baseline period, respectively. Also, the average monthly rainfall in the RCP4.5 scenario rises compared to the baseline period in all months except January. And the highest increase in October and November by 15 and 11.5% in the near future period, 15.5 and 11.7% in the middle future period, and 16 and 12% in the far future period compared to the baseline period, respectively. The highest average temperature increases in the RCP8.5 scenario in January, February, March, and December by 14.8, 15.6, 13, and 16.9% in the near future, 25.1, 24.8, 20.7, and 22.6% in the middle future and 23.3, 34, 28.4, and 28.2% in the far future period compared to the baseline period, respectively. Also, the highest increase in average temperature in the RCP4.5 scenario in January, February, March, and December by 14.8, 13.8, 9.7, and 13.4% in the near future, 19, 17.5, 13, and 15.7% in the middle future, and 22, 20.2, 15, and 17.4% in the far future period compared to the baseline period, respectively (Figure 3).
Figure 3
Bias-corrected temperature (a), rainfall (b), and changes in the future projected monthly temperature (c), rainfall (d) in Maroon Basin under climate change.
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Bias-corrected temperature (a), rainfall (b), and changes in the future projected monthly temperature (c), rainfall (d) in Maroon Basin under climate change.

Figure 3
Bias-corrected temperature (a), rainfall (b), and changes in the future projected monthly temperature (c), rainfall (d) in Maroon Basin under climate change.
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Bias-corrected temperature (a), rainfall (b), and changes in the future projected monthly temperature (c), rainfall (d) in Maroon Basin under climate change.

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Calibration, validation, and sensitivity analysis of the SWAT model

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The analysis reveals that the inflow into the reservoir is a highly uncertain variable, which significantly influences the operational decisions for the reservoir system. Hence, in order to account for uncertainty in inflow, the reservoir operation model is solved for different exceedance probabilities of inflows. The uncertainty in inflows is represented through probability distributions such as normal, lognormal, exponential, and generalized extreme value distributions; and the best fit model is selected to obtain inflows for different exceedance probabilities. Based on the results of the sensitivity analysis of the SWAT model, 20 parameters were identified as the most sensitive parameters. This parameter is highly influenced by land use and soil type. Therefore, it has a major impact on water balance components. Some researchers, such as Yang et al. (2008), identified CN2 as the most sensitive parameter in their study. The SMTMP and SMFMN parameters are ranked after the CN2. The results showed that the 95% total prediction uncertainty bounds bracketed most of the observed data especially peak discharge values. Coefficient of variation for CN2 was small for all flood events, therefore this parameter is more sensitive than the other parameters. The SWAT model parameters' calibrated and adjusted values are shown in Table 4. After the sensitivity analysis step, the model was calibrated and validated using SWAT-CUP software and monthly flow statistics at Idenak and Dehno hydrostations (Figures 4 and 5). Finally, the model was evaluated using statistical determination coefficient (R2) indices and NSE. A summary of the monthly time step calibration and validation of the SWAT model is shown in Table 5. The results showed that the model has been an excellent simulation of runoff and flow change trends in the calibration phase. Results are similar to the findings of Moriasi et al. (2007) and Boithias et al. (2017). The results showed that the SWAT model can simulate maximum flows in the Maroon Basin with acceptable accuracy, which was similar to the findings of Zalaki-Badil et al. (2017).
Table 4

SWAT model parameters calibrated and adjusted values

NoParameterDescriptionInitial rangeCalibrated value
1 CN2 SCS runoff curve number (−0.752)–(−0.263) −0.489 
2 ALPHA_BF Baseflow alpha factor (days) (0.624)–(0.865) 0.635 
3 GW_DELAY Groundwater delay (days) (5.236)–(39.965) 26.58 
4 HRU_SLP Average slope steepness (0.332)–(0.717) 0.501 
5 RCHRG_DP Deep aquifer percolation fraction (0.623)–(0.895) 0.799 
6 SLSUBBSN Average slope length (12.301)–(41.442) 21.725 
7 CH_K2 Effective hydraulic conductivity in main channel alluvium (32.204)–(108.634) 67.23 
8 SOL_AWC Available water capacity of the soil layer (−0.425)–(0.091) −0.182 
9 SOL_K Saturated hydraulic conductivity (−0.05)–(0.599) 0.439 
10 SOL_BD Moist bulk density (0.455)–(0.899) 0.512 
11 SMTMP Snow melt base temperature (−1.325)–(2.925) −0.923 
12 SMFMN Minimum melt rate for snow during the year (occurs on winter solstice) (3.652)–(5.425) 4.223 
13 REVAPMN Threshold depth of water in the shallow aquifer for ‘return’ to occur (mm) (189.225)–(291.333) 217.44 
14 ESCO Soil evaporation compensation factor (0.245)–(0.672) 0.404 
15 SFTMP Snowfall temperature (3.325)–(6.121) 4.132 
16 ALPHA_BNK Baseflow alpha factor for bank storage (0)–(0.179) 0.043 
17 LAT_TTIME Lateral flow travel time (0)–(38.43) 18.275 
18 PLAPS Rainfall lapse rate (88.423)–(139.322) 131.025 
19 TLAPS Temperature lapse rate (−5.732)–(−0.79) −4.223 
20 PCPMM The average amount of rainfall falling in the month (mm/dd) (0.122)–(0.498) 0.351 
NoParameterDescriptionInitial rangeCalibrated value
1 CN2 SCS runoff curve number (−0.752)–(−0.263) −0.489 
2 ALPHA_BF Baseflow alpha factor (days) (0.624)–(0.865) 0.635 
3 GW_DELAY Groundwater delay (days) (5.236)–(39.965) 26.58 
4 HRU_SLP Average slope steepness (0.332)–(0.717) 0.501 
5 RCHRG_DP Deep aquifer percolation fraction (0.623)–(0.895) 0.799 
6 SLSUBBSN Average slope length (12.301)–(41.442) 21.725 
7 CH_K2 Effective hydraulic conductivity in main channel alluvium (32.204)–(108.634) 67.23 
8 SOL_AWC Available water capacity of the soil layer (−0.425)–(0.091) −0.182 
9 SOL_K Saturated hydraulic conductivity (−0.05)–(0.599) 0.439 
10 SOL_BD Moist bulk density (0.455)–(0.899) 0.512 
11 SMTMP Snow melt base temperature (−1.325)–(2.925) −0.923 
12 SMFMN Minimum melt rate for snow during the year (occurs on winter solstice) (3.652)–(5.425) 4.223 
13 REVAPMN Threshold depth of water in the shallow aquifer for ‘return’ to occur (mm) (189.225)–(291.333) 217.44 
14 ESCO Soil evaporation compensation factor (0.245)–(0.672) 0.404 
15 SFTMP Snowfall temperature (3.325)–(6.121) 4.132 
16 ALPHA_BNK Baseflow alpha factor for bank storage (0)–(0.179) 0.043 
17 LAT_TTIME Lateral flow travel time (0)–(38.43) 18.275 
18 PLAPS Rainfall lapse rate (88.423)–(139.322) 131.025 
19 TLAPS Temperature lapse rate (−5.732)–(−0.79) −4.223 
20 PCPMM The average amount of rainfall falling in the month (mm/dd) (0.122)–(0.498) 0.351 
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Table 5

Summary of the monthly time step calibration and validation of the SWAT model

StationIdenakDehno
Calibration 
NS 0.62 0.57 
R2 0.66 0.61 
r-factor 0.73 0.65 
p-factor 0.73 0.67 
PBIAS (%) 11.2 8.9 
Validation
NS 0.58 0.5 
R2 0.69 0.62 
r-factor 0.66 0.63 
p-factor 0.7 0.64 
PBIAS (%) 10.3 9.1 
StationIdenakDehno
Calibration 
NS 0.62 0.57 
R2 0.66 0.61 
r-factor 0.73 0.65 
p-factor 0.73 0.67 
PBIAS (%) 11.2 8.9 
Validation
NS 0.58 0.5 
R2 0.69 0.62 
r-factor 0.66 0.63 
p-factor 0.7 0.64 
PBIAS (%) 10.3 9.1 
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Figure 4
Comparison of the simulated vs. observed monthly runoffs in Idenak and Dehno hydrostations in the calibration phase.
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Comparison of the simulated vs. observed monthly runoffs in Idenak and Dehno hydrostations in the calibration phase.

Figure 4
Comparison of the simulated vs. observed monthly runoffs in Idenak and Dehno hydrostations in the calibration phase.
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Comparison of the simulated vs. observed monthly runoffs in Idenak and Dehno hydrostations in the calibration phase.

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Figure 5
Comparison of the simulated vs. observed monthly runoffs in Idenak and Dehno hydrostations in the validation phase.
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Comparison of the simulated vs. observed monthly runoffs in Idenak and Dehno hydrostations in the validation phase.

Figure 5
Comparison of the simulated vs. observed monthly runoffs in Idenak and Dehno hydrostations in the validation phase.
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Comparison of the simulated vs. observed monthly runoffs in Idenak and Dehno hydrostations in the validation phase.

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Climate change effects on the inflow to the Maroon Dam

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After evaluating the SWAT hydrological model and the accuracy of the results, the model was run for the RCP scenarios to investigate the effect of climate change on the inflow to the Maroon Dam Reservoir. Downscaling rainfall and temperature data were entered into the model, and the inflow was predicted for the three future periods under climate change. The results showed slight changes in the average flow in the warm months (July–August) in all scenarios compared to the baseline period. The most significant decrease of the average flow in the near future of the RCP4.5 and RCP8.5 scenarios in February will be 20.8 and 24.4%, in March 24 and 26.4%, respectively. Also, the highest decrease of the average flow in the middle future of the RCP4.5 and RCP8.5 scenarios in February is predicted to be 21 and 24.4%, in March 25.4 and 29%, and the far future in the mentioned scenario in February is predicted to be 23.5 and 27.3% and in March 27 and 30.6%, respectively (Figure 6).
Figure 6
Percent difference in the simulated and observed inflow under climate change.
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Percent difference in the simulated and observed inflow under climate change.

Figure 6
Percent difference in the simulated and observed inflow under climate change.
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Percent difference in the simulated and observed inflow under climate change.

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Simulation of water consumption in the current situation

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In this research, the inflow to the Maroon Dam Reservoir was calculated from the documented values of the Idenak station using the volumetric method (area and rainfall ratio) to determine the amount of water allocated to different demands in the current situation (2001–2018). The network simulated in the MODSIM model consists of the operation of the Maroon Dam Reservoir, the interbasin inflow (Mansour Beygi, Korreh Siah, and Sarasiab rivers), and agricultural, environmental, potable, and industrial demands. An overview of the simulated network in the MODSIM model is shown in Figure 7.
Figure 7
Overview of the simulated network of the Maroon Basin in MODSIM: (a) current situation and (b) future situation.
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Overview of the simulated network of the Maroon Basin in MODSIM: (a) current situation and (b) future situation.

Figure 7
Overview of the simulated network of the Maroon Basin in MODSIM: (a) current situation and (b) future situation.
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Overview of the simulated network of the Maroon Basin in MODSIM: (a) current situation and (b) future situation.

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By comparing the observed flow data in the Idenak and Cham-Nezam hydrostations with the simulated values, the MODSIM model is calibrated and validated. The time series of the observed and simulated flow in Idenak and Cham-Nezam stations are shown in Figure 8.
Figure 8
Time series of the observed vs. simulated streamflow in Tange-Takab and Cham-Nezam hydrostations downstream of the dam.
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Time series of the observed vs. simulated streamflow in Tange-Takab and Cham-Nezam hydrostations downstream of the dam.

Figure 8
Time series of the observed vs. simulated streamflow in Tange-Takab and Cham-Nezam hydrostations downstream of the dam.
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Time series of the observed vs. simulated streamflow in Tange-Takab and Cham-Nezam hydrostations downstream of the dam.

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The potable of Mansouriyeh, Behbahan, Aghajari, Omidiyeh, and environmental, industrial, and agricultural of north Behbahan (7,050 ha), south Behbahan (7,550 ha), Jayezan and Fajr (6,400 ha), and traditional (cultivated area of 534 ha) are the demands in the simulated network. To execute the MODSIM model, the monthly time series of the mentioned demands from 2001 to 2018 was used. The allocation priorities in this model are potable, environmental, industrial, and agricultural demands, respectively. Also, the agricultural return flow was evaluated at 15–20%.

The amount of water allocated to each demand was determined after the MODSIM model was run. The water supply reliability was used to evaluate allocations. According to the model simulation results in Figure 9, the potable water supply reliability of Behbahan, Mansuriyeh, Omidiyeh, and Aghajari and industrial demand was 98, 98.5, 98.3, 97.5, and 97% which is acceptable, respectively. The industrial demand part in the study area in the current situation is equal to 0.5 million cubic meters per year. The environmental water supply reliability was 97.5%, which indicates that this demand is fully met in most months. Also, the agricultural water supply reliability of Behbahan in the north and south are 94.6 and 95.2%, and Jayezan and Fajr irrigation network and traditional agricultural lands are 93.5 and 93%, respectively. The simulation diagram of Maroon Dam Reservoir storage and the average water supply and demand in the Maroon Basin in the current situation are shown in Figures 10 and 11, respectively. According to the results in Figure 11, the highest shortage in the water supply is in May, September, and April by 18, 15, and 14%, respectively, and in January, February, and October, all demands are fully provided.
Figure 9
Watershed's water supply reliabilities in the current situation.
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Watershed's water supply reliabilities in the current situation.

Figure 9
Watershed's water supply reliabilities in the current situation.
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Watershed's water supply reliabilities in the current situation.

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Figure 10
Variation of the Maroon reservoir storage in the current situation.
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Variation of the Maroon reservoir storage in the current situation.

Figure 10
Variation of the Maroon reservoir storage in the current situation.
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Variation of the Maroon reservoir storage in the current situation.

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Figure 11
Average water supply and demand in Maroon Basin's current situation.
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Average water supply and demand in Maroon Basin's current situation.

Figure 11
Average water supply and demand in Maroon Basin's current situation.
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Average water supply and demand in Maroon Basin's current situation.

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Simulation of water consumption in the climate change condition

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The inflow in the Maroon Dam is estimated for 2021–2040, 2041–2060, and 2061–2080 to determine the water allocated amount to different demands in the future and the scenarios by the optimistic RCP4.5 and pessimistic RCP 8.5. According to the water supply projects in the southeastern Khuzestan province, Kosar Dam will provide the potable demands of the region in the future, and no supply will be provided from the Maroon River. For this reason, the current potable water time series was also used for the future. It was also assumed that the demands of the industrial part in the study area would increase from 0.5 million cubic meters in the current situation to 8.5 million cubic meters in future periods, which is the amount of change in all climate scenarios. Three irrigation networks of Lasbid, Khaiz, and RayMakan with a cultivated area of 1,200, 2,570, and 1,250 ha were added to the MODSIM water resource allocation model in the agricultural part for future periods. The amount of water allocated for all demands was evaluated in different scenarios using water supply reliability. The results of Figure 12 showed that the dam reservoir would face water resources shortage in the future to provide maximum demands. According to the supply of future potable water from Kowsar Dam, changes in potable demands of the study area were not included in the run of climate change scenarios. The average water supply reliability in climate change scenarios showed that the maximum water supply of 85% in the period 2021–2040 and the minimum of 80.4% in 2061–2080 would occur in the RCP4.5 and RCP8.5 scenarios, respectively. Also, according to the results in Figure 12, the average of the maximum and minimum reliability in the mentioned periods and scenarios are for environmental and Raymakan irrigation network demands with 91.1 and 77.3%, respectively. The average water supply and shortage in the Maroon Basin under climate change are shown in Figure 13. These results show that the minimum water shortage of 5% occurs for RCP4.5 in January of the period 2021–2040 and the maximum of 35.1% is for RCP8.5 in March of the period 2061–2080. Also, the water supply in the RCP4.5 scenario for the periods (2021–2040), (2041–2060), and (2061–2080) will decrease by 7.3, 8.3, and 8.8%, and in the RCP8.5 scenario for the periods (2021–2040), (2041–2060), and (2061–2080) will decrease by 12.3, 12.5, and 12.8%, respectively, compared to the current situation.
Figure 12
Watershed's water supply reliabilities in climate change condition.
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Watershed's water supply reliabilities in climate change condition.

Figure 12
Watershed's water supply reliabilities in climate change condition.
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Watershed's water supply reliabilities in climate change condition.

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Figure 13
Average water supply and shortage in Maroon Basin in climate change condition.
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Average water supply and shortage in Maroon Basin in climate change condition.

Figure 13
Average water supply and shortage in Maroon Basin in climate change condition.
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Average water supply and shortage in Maroon Basin in climate change condition.

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Maintaining downstream conditions and meeting their demands in the future climate change while keeping the current reliability are essential issues. In this part, the water supply for development projects in the potable, industrial, and environmental demands that have the highest priority is maintained in the future climate change conditions. To adapt to these changes, modifications have been made only in the agricultural part as a lower priority. Reducing the downstream land cultivated area of the dam reservoir is one alternative. According to streamflow reduction in future climate change, the land cultivated area will decrease with the current cultivation pattern. Changing the cultivation pattern and replacing crops with low water requirements in the downstream lands of the dam reservoir is considered a second alternative. It should be noted that irrigation and drainage networks downstream have been built based on planning and the availability of water resources in the initial conditions of the project. Suppose part of the downstream irrigation network of the dam reservoir is not able to supply water in the future climate change conditions. In that case, it will cause dissatisfaction among the irrigation network users and have social consequences. Therefore, changing the plan cultivation pattern and water supply to all agricultural areas downstream of the dam reservoir is a more suitable option in the future. In the initial conditions of the plan, crops with high water requirements such as alfalfa, potatoes, autumn grain maize, and wheat, with 16, 16, 21, and 28% of the cultivated area, respectively, have the highest water demands from the Maroon Dam Reservoir. Changes in the cultivation pattern with a 17, 6, 5, 10, 13, and 17% decrease in the wheat, lettuce, cabbage, potato, alfalfa, and autumn grain maize cultivated area and a 13, 23, 9, 9, 6 and 8% increase in the barley, tomato, fava bean, autumn silage maize, spring silage maize, and sesame cultivated area, respectively, in RCP8.5 (2061–2080) (as the most effective of climate change), the current conditions in terms of cultivated area and water supply reliability will be maintained. In the same conditions, changes in the cultivation pattern with an 8, 1, 4, 6, and 10% decrease in the wheat, lettuce, potato, alfalfa, and autumn grain maize and a 5, 16, 2, 3, 1, and 2% increase in the barley, tomato, fava bean, autumn silage maize, spring silage maize, and sesame in RCP4.5 (2021–2040) (as the least impact of climate change), the current conditions in terms of cultivated area and water supply reliability will be maintained which is similar to the findings of Yibecal et al. (2019) and Jamshidpey & Shourian (2021). In such cases, it is possible to develop a cultivation pattern program for different periods according to the needs and demands of the community. The cultivation pattern to maintain the cultivated area and the current reliability are shown in Figure 14.
Figure 14
Cultivation pattern to stabilize the agricultural area and the current reliability.
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Cultivation pattern to stabilize the agricultural area and the current reliability.

Figure 14
Cultivation pattern to stabilize the agricultural area and the current reliability.
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Cultivation pattern to stabilize the agricultural area and the current reliability.

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Discussion

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The average annual temperature in the scenarios of RCP4.5 and RCP8.5 will increase in all months of future periods compared to the baseline period. The temperature increases in the pessimistic scenario of RCP8.5 are more than in the RCP4.5 scenario, which is similar to the findings of Azari et al. (2016). Long-term monthly changes in average temperature indicate that the most significant increase in temperature will occur in the winter months. This is despite the fact that in all months of this season the highest rainfall will be in the Maroon Basin. Based on the results of the sensitivity analysis of the SWAT model, the parameter of infiltration CN2 in average humidity conditions (CN2) had the greatest effect on the outflow of the basin. Due to the mountainous study area, SMTMP and SMFMN showed more sensitivity than other parameters, which is similar to the findings of Tuo et al. (2018), Rahman et al. (2013), Shafiei Motlagh et al. (2018), and Eslamian et al. (2018). The results of forecasting the inflow in the two scenarios showed that the increase in temperature would have a more significant effect and efficiency in reducing the flow rate efficiency, despite the increase in rainfall in the area. The average flow from January to December will decrease in the three future periods and all two scenarios compared to the baseline period. These results were similar to the findings of Maghsood et al. (2019) and Zalaki-Badil et al. (2017). In these climatic conditions, increasing winter temperatures will reduce snow storage in the heights of this basin and expect more water shortage problems in the future compared to the baseline period. The inflow to the Maroon Dam Reservoir will be the most reduced in the RCP8.5 scenario. The water requirement of plants and evapotranspiration will increase with global warming. The first reason is that evapotranspiration requires energy, and as the climate warms, more energy is needed to carry out this process, which in turn reduces the river flow and inflow to the dam reservoir. The second reason is that the decrease in flow in spring and winter shifts the growing season to winter and prolongs the sowing time. In recent years, the dominant crops of this basin and land preparation for cultivation have been driven to late winter. It has been determined that this trend will continue in the future with more intensity.

From the simulation results of the MODSIM model, it can be inferred that the period 2061–2080 will have more extreme events than the periods 2021–2040 and 2041–2060, which will reduce the inflow to the dam reservoir and not meet the maximum demands. Also, according to the results of the average reliability of different demands, RCP4.5 scenarios will have the best performance, and RCP8.5 scenarios will have the worst performance in the periods of 2020–2040, 2041–2060, and 2061–2080, respectively. In general, the lowest supply 75.2% is in the Raymakan Irrigation Network for the RCP8.5 (2061–2080). These results are fully justified considering the lowest water allocation priority of agricultural demands in the MODSIM model. According to the above results, the study basin will have its best and worst performance in 2020–2080 in the climate change scenario of RCP 4.5 and RCP8.5, respectively, which is similar to the findings of Ahn et al. (2016) and Fadaeizadeh & Shourian (2019). Water allocation to the dam's downstream demands in future periods was simulated and the reliability criteria in various scenarios were evaluated. Compared to the current situation, the average water supply to the demands would reduce by 5.6–10.3%. According to the obtained results, it can be stated that the Maroon Basin can meet different demands to 80% even in the most threatening climatic conditions. However, the dam is expected to break to supply some demands in some years, especially in the spring and autumn seasons. The change process in temperature, rainfall, inflow, and water supply parameters under climate change in the Maroon Basin is shown in Figure 15. Based on this figure, the highest reduction in the water supply will be in RCP8.5 (2061–2080) in March and the lowest reduction in water supply will be in RCP4.5 (2021–2040) in April and July, 27.9 and 1.6% lower than the current situation, respectively. In this study, the demands in the period of climate change are changed following the vision plans of the government, and the cultivation pattern for the water supply has been modified in such a way that there are no negative social consequences, and the lands downstream of the dam can be cultivated in the period of climate change according to the vision of the plan, thoroughly, which is similar to the findings of Ashraf Vaghefi et al. (2013) and Fereidoon & Koch (2018).
Figure 15
Variation in temperature, rainfall, inflow, and water supply under climate change compared to the current situation in the Maroon Basin.
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Variation in temperature, rainfall, inflow, and water supply under climate change compared to the current situation in the Maroon Basin.

Figure 15
Variation in temperature, rainfall, inflow, and water supply under climate change compared to the current situation in the Maroon Basin.
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Variation in temperature, rainfall, inflow, and water supply under climate change compared to the current situation in the Maroon Basin.

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In this research, a combination of SWAT and MODSIM models was used to plan for water resources allocation to demands in the Maroon Basin affected by climate change. To investigate the effect of climate change on basin temperature and rainfall, four GCMs were performed in the two climatic scenarios, RCP4.5 and RCP8.5, over the following three periods: near (2021–2040), middle (2041–2060), and far (2061–2080). The downscaling of rainfall and temperature data in the two scenarios was done using the LARS-WG model. Long-term monthly changes in average temperature indicate that the most significant increase in temperature will occur in the winter months. This is despite the fact that in all months of this season, the highest rainfall will be in the Maroon Basin. The Maroon Dam inflow was simulated using the SWAT hydrological model, and calibrated and validated against the monthly flow at the Idanak and Dehno hydrostations. The NSE indices at Idanak and Dehno hydrostations were 0.62 and 0.57 for the calibration and 0.58 and 0.5 for the validation stages. Then, the rainfall and temperature data were entered into the SWAT model to simulate the inflow to the dam for the following three periods. The most significant decrease of the average inflow to the Maroon Dam Reservoir in the near future of the RCP4.5 and RCP8.5 scenarios in March 24 and 26.4%, the middle future in March 25.4 and 29%, and the far future in March 27 and 30.6% is predicted, respectively. In the last step, the inflow to the Maroon Dam Reservoir in baseline and future conditions was used as one of the important inputs of the MODSIM model. At first, modeling was done for the current situation (2002–2018), and the amount of water allocated to each demand was specified. Using water supply reliability, the allocation process in this period was evaluated, and the allocation of water resources was predicted for the future. By comparing the average water supply reliability in the current situation (96.6%) and climate change scenarios, it was found that the RCP8.5 (2061–2080) scenario with 80.4% will have the most severe water stress and the worst allocation conditions. In this research, an attempt has been made to maintain the cultivated area and water supply reliability in the current situation by changing the cultivation pattern and replacing crops with low water requirements. This study also shows that the add-on of a water management model to a hydrological model results in a powerful simulation tool that can serve for sustainable river basin management and the output can be used further to support better water decision-making systems.

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No funds, grants, or other support were received.

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This research does not contain any studies with human participants or animals performed by any of the authors.

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All authors contributed to the study's conception and design. Material preparation, data collection, and analysis were performed by M.M. and checked by M.S. The first draft of the manuscript was written by M.M. and edited by M.S. and A.S. All authors read and approved the final manuscript.

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Data cannot be made publicly available; readers should contact the corresponding author for details.

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The authors declare there is no conflict.

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