Next Article in Journal
Challenges and Optimization of Building-Integrated Photovoltaics (BIPV) Windows: A Review
Next Article in Special Issue
Hydraulic Relationship between Hulun Lake and Cretaceous Confined Aquifer Using Hydrochemistry and Isotopic Data
Previous Article in Journal
Research on the Impact of Green Finance and the Digital Economy on the Energy Consumption Structure in the Context of Carbon Neutrality
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 15
Issue 22
10.3390/su152215875
sustainability-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Assessment of Climate Change’s Impact on Flow Quantity of the Mountainous Watershed of the Jajrood River in Iran Using Hydroclimatic Models

by
Farzaneh Najimi
1,
Babak Aminnejad
1,* and
Vahid Nourani
2,3
1
Department of Civil Engineering, Roudehen Branch, Islamic Azad University, Roudehen 3973188981, Iran
2
Department of Water Resources Engineering, Faculty of Civil Engineering, University of Tabriz, 29 Bahman Ave., Tabriz 5166616471, Iran
3
Faculty of Civil and Environmental Engineering, Near East University, Via Mersin 10, Nicosia 99628, Turkey
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(22), 15875; https://doi.org/10.3390/su152215875
Submission received: 25 May 2023 / Revised: 23 October 2023 / Accepted: 26 October 2023 / Published: 13 November 2023
(This article belongs to the Special Issue Climate Impacts on Water Resources: From the Glacier to the Lake)
Browse Figures
Versions Notes

Abstract

:
Rivers are the main source of fresh water in mountainous and downstream areas. It is crucial to investigate the possible threats of climate change and understand their impact on river watersheds. In this research, climate change’s impact on the mountainous watershed of the Jajrood River, upstream of Latyan Dam in Iran, was assessed by using a multivariate recursive quantile-matching nesting bias correction (MRQNBC) and the soil and water assessment tool (SWAT). Also, this study considered ten global circulation models (GCMs) from the coupled model intercomparison project phase VI (CMIP6). With a higher correlation coefficient, the MIROC6 model was selected among other models. For the future period of 2031–2060, the large-scale outputs of the MIROC6 model, corresponding to the observational data were extracted under four common socioeconomic path scenarios (SSPs 1–2.6, 2–4.5, 3–7.0, 5–8.5). The bias was corrected and downscaled by the MRQNBC method. The downscale outputs were given to the hydrological model to predict future flow. The results show that, in the period 2031–2060, the flow will be increased significantly compared to the base period (2005–2019). This increase can be seen in all scenarios. In general, changes in future flow are caused by an increase in precipitation intensity, as a result of an increase in temperature. The findings indicate that, although the results show an increase in the risk of flooding, considering the combined effects of three components, i.e., increased precipitation concentration, temperature, and reduced precipitation, climate change is intensifying the problem of water scarcity.

1. Introduction

Climate change has substantially affected the amount of water resources in mountains. Rivers are the main source of the fresh water supply for mountainous and downstream areas. Climate change affects these areas by changing the intensity and pattern of seasonal rainfall. On the one hand, due to the increase in temperature, the mountain snow melts before the melt season begins, limiting the contribution of snowmelt discharge to river runoff. On the other hand, the precipitation in these areas falls mostly in the form of rain, which affects the water storage in the long term. Climate change could have serious consequences for the downstream ecosystem, land use and urban infrastructure [1]. The upstream watershed of the Latyan dam, with an area of 670 square kilometers, is located inside the Central Alborz Mountain Range. Its highest point is Tochal Peak, with a height of nearly 3960 m, and its lowest point is the steam-bed level of Latyan Dam, at 1574 m. The average daily minimum and maximum temperature is increasing compared to the base period (1983–2018) [2], so the future temperature (in the period 2006–2050) of the watershed will be 2.25 °C warmer, compared to the base period [3]. This warming, caused by climate change, accelerates the hydrological cycle [4,5] as well as the Jajrood watershed average extreme precipitation downstream of Latyan Dam, which is predicted to increase by 14% in the coming period (2065–2046), thereby increasing the maximum amount of flow [2]. It is crucial to assess the potential climate change impact on the Jajrood River’s flow in the future. For decades, the climate crisis and its negative impacts on the hydrological cycle of watersheds have been studied by scientists around the world [6,7,8,9,10]. Regarding the Tibetan Plateau, which is the highest plateau on earth, Tian et al. (2019) [11] reported an increase in temperature, due to the impacts of climate change, on the Lhasa River basin, that will lead to a significant reduction in future flow in the middle of the century (2045–2055) and at the end of the century (2090–2100). By investigating the past climate of the Jajrood Basin, with the help of glacial evidence in the northern heights of the basin, and comparing it with the current climate, Abtahi (2013) [12] concluded that the average annual temperature and precipitation of the basin in the current period, compared to the glacial period, has increased by 4.4 degrees and decreased by 151 mm, respectively. He found that climate change over time during the Quaternary periods has caused changes in land use and in the formation of civilizations. Khosravi et al. (2019) [13] identified four climatic zones for the watershed (wet and moderate, dry and moderate, dry and hot, and wet and cold) and, by analyzing the variation in precipitation and flow, they found that, based on these four zones, there was a direct relationship between rainfall pattern and discharge. According to the fifth and sixth reports of the Intergovernmental Panel on Climate Change (IPCC), most of the studies conducted on the Jajrood watershed demonstrate the effects of the warming climate on the watershed in the coming periods [1,5]. Most of these studies conducted on the Jajrood watershed have focused on the prediction of climate variables. However, regarding the current findings and knowledge, few studies have investigated the threat of climate change’s impact on the hydrological processes of the watershed. This study is conducted with the aim of evaluating the possible threat of climate change on the flow quantity of the Jajrood watershed, using SWAT and a new method of climate data bias correction.
Regarding this aim, three objectives have been defined as follows:
  • Identification of a climate model or a group of climate models from CMIP6, so as to create the highest correlation, using observations, in simulating the climate characteristics of the watershed [14].
  • Correcting the bias of simulated climate variables using CMIP6, considering that the simulation of the impact of climate change on the Jajrood watershed by CMIP5 [3] did not correct the systematic errors of climatic variables, which led to a significant error in hydrologic simulations [15].
  • Regarding the set of new SSP scenarios in this study, these scenarios are assumptions that describe greenhouse gas emissions (GHGs) in the future. Compared to the scenarios of the representative concentration pathways (RCPs), climate changes are less considered in SSP scenarios; however, concerning greenhouse gases, they address higher CO2 emission levels. Based on the current condition of society, the new scenarios of CMIP6 describe socioeconomic drivers as the main factors. Shared socioeconomic pathway concentration projections (SSP1–2.6 to SSP5–8.5) show an acceptable trend of community growth in the 21st century [16].
The CMIP climate model is a standard experimental framework for simulating important climate indicators, such as atmospheric temperature and pollutant concentration, with the aim of reproducing important climate aspects, which have been published by IPCC since 1990. The first generation of published climate models simulated climate variables well, when compared to observations from weather stations. But they were limited in their ability to reproduce a wide range of factors and processes affecting the climate [16]. The new generation of CMIP6 models, compared to the old ones (e.g., CMIP3 and CMIP5), have better performances in simulating processes with a high resolution. One of the few studies carried out by the CMIP phase 6 in Iran is identified the damaging effects of climate change on the Amir Kabir dam watershed, using the optimal fingerprint method [17]. Gusain et al. (2020) [14] evaluated the drylands in India using CMIP6 climate models and concluded that, compared with the CMIP phase 5, the simulation of seasonal rainfall, using the sixth phase of the CMIP, shows a satisfactory improvement in recording the spatial and temporal patterns of monsoon rains over India. As a result, in this research, the MIROC6 climate model was selected from a subset of ten CMIP6 models, used to simulate climate variables.
The outputs of the climate model must be downscaled before employing the SWAT model [18]. One of the advantages of the statistical downscale method is the reduction in systematic climate model errors in the large-scale pattern [19]. Another advantage is the ability to increase the accuracy of climate model simulations before they are applied to hydrological models, which is necessary due to the coarse spatial resolution of GCMs [20,21,22,23]. Gebrechorkos et al. (2020) [6] examined the effects of greenhouse gas emissions on the hydrology of a large watershed, using climate modeling, in Ethiopia. They found that the use of GCMs with a coarse resolution in climate simulation leads to large errors and uncertainties, and that high-quality climate data are needed for local-scale adaptation and development action.
In this study, the MRQNBC method has been developed for downscaling and bias correction of the output of the MIROC6 climate model. The MRQNBC model focuses on matching distributions, time scaling, multivariate modeling, and the iteration method for minimizing the error.
Dunn et al. (2012) [24] evaluated two types of watersheds, rain-dominant and snow-dominant watersheds, in the Pacific Northwest, using the SWAT model. They found that in the mountainous snow-dominant watershed, hydrological models should be developed more accurately, due to high uncertainties caused by the selection of the model parameters. For this reason, in this study, the SWAT tool has been chosen for the hydrological modeling of the Jajrood snow-dominant watershed.
The modified outputs of the MRQNBC model are introduced as reliable inputs for the SWAT model, in order to predict the future flow (2060–2031) under four SSP scenarios. The 2005–2019 period is considered to be the base period.
By assessing the possible threat of climate change with high accuracy in the Jajrood watershed, it is expected that weather-related disasters such as heavy rains, floods, landslides and droughts will be predicted more effectively.

2. Methodology

2.1. Case Study

The Jajrood River watershed, with an area of 674 km2, is located northeast of Tehran, with a longitude of 51°51′ to 51°22′ and a latitude of 35°45′ to 36° (Figure 1). The main river of the watershed is the Jajrood, with a length of 40 km, which originates from the Alborz mountain range and then passes through the southwest of the Latyan Dam. The average annual temperature of the watershed is 7.2 °C. The average annual rainfall varies between 149 and 627 mm [12]. Nearly 90.8 percent of the land is devoted to pasture (Figure 2a). Loam dominates, as 74.7% of the soil type (Figure 2b). The climate is relatively dry and moderate. In this mountainous region, precipitation mostly occurs in the form of snowfall in late autumn and winter [25]. The Latyan Dam provides water for agricultural, irrigation and domestic water use, and for hydropower generation [25]. Since 1988, with the increasing demand for water in Greater Tehran, water stored in Lar Dam, located in the city of Amol in Mazandaran, has been transferred to Kalan hydroelectric power plant through the 20-kilometer-long Lar–Kalan tunnel, and then to the Latyan Dam reservoir. This development has increased the adjustable water volume of the dam. The Jajrood River flows from the north, along with three other distributaries flowing from the east, into the Latyan Dam (Geographical Organization of Iran).

2.2. SWAT

SWAT is a powerful tool for quantitative and qualitative simulation of a watershed system. It was developed in the 1990s by the American Agricultural Research Center for the purpose of managing water resources in large river basins [26]. In recent decades, many researchers have applied it to rainfall-runoff modeling, climate simulation, estimations of sediment transport and predictions of watershed water quality [27,28,29]. However, it is time-consuming and expensive, and also requires a lot of input data. Furthermore, it should be considered that the application of SWAT Model requires proficient skill and an exhaustive knowledge of physical processes of the watershed. The model uses a one day time-step [26]. The SWAT Model was developed to model different kinds of watersheds, such as mountainous, dry, and forested watersheds, with different purposes [6,30]. For the Jajrood watershed, the model simulates the hydrological cycles and to estimate water pollution [25,31]. The model divides the watershed into sub-watersheds. The constituent units of the sub-watershed are the hydrological response unit (HRU). HRUs are similar, regarding soil characteristics, slope, and land use [32]. Hence, dividing the sub-watershed into HRUs increases the simulation accuracy [33,34].

2.3. Input Data for Setting SWAT Model

Setting up and running the SWAT model requires several databases. The Digital Elevation Map (DEM) and land use data were extracted from the international database, http://earthexplorer.usgs.gov (accessed on 1 January 2020). After manual matching based on the existing database, they were applied to the SWAT model. To visualize land use, maps with a scale of 1:100,000 were entered into the model. Information about the soil layer was taken from the Food and Agricultural Organization (FAO) [35]. Daily weather records, consisting of precipitation, temperature, solar radiation flux, relative humidity and wind speed, were compiled from five meteorological stations of the Iran Water Resources Management Company, and the database was made by all six meteorological stations (Table 1). The model was calibrated with discharge data about the daily time-step, recorded by Roudak hydrometric station. The studied watershed has six hydrometric stations (Table 2). Roudak station is the most important station in the region, because it has relatively good data quality and long-term statistics and is located on the main trunk of the Jajroud River before the dam reservoir. Therefore, the main inflows of the Latyan dam are recorded at this station. Lavarak and Naroun stations are affected by water transfer from Lar Dam. Therefore, the recorded data of these stations are not accurate. The studied watershed is divided into 33 sub-watersheds, according to the hydrometric stations and thresholds of the river formation (Figure 2c), which are split up into 341 HRUs based on the user layer information, and the slope of the region for the four classifications (0–8, 8–20, 20–30 and 30–9999) is described in the model (Figure 2d). In this study, the snow melting module is considered to be the most important module of the SWAT model. The module calculates quantitative characteristics of snow cover, snow mass temperature and snow melt for each HRU.

2.4. SWAT Calibration

The SWAT model was calibrated in different ways. The initial performance of the model helped us to select the appropriate calibration method for the region. Due to the mountainous climate of the Jajrood region, the method of height classification of the hydrological units was chosen. For this reason, and because of the initial response of the model, six steps were taken as the final calibration method. The period of 2006–2007 was selected as the warm-up period. In the first step, to adjust precipitation and temperature in the SWAT model according to an elevated lapse rate of two, PLAPS and TLAPS were selected. Five hundred repetitions were considered and, in the 121st repetition, the optimal values of the model were obtained, with a Nash–Sutcliffe coefficient of 0.69 and a coefficient of determination of 0.82. In the second step, the snow parameters were considered for model calibration. In the next steps, the model was calibrated with soil parameters (Step 3), groundwater parameters (Step 4), the soil curve number, which shows the power of producing runoff in the basin during precipitation (Step 5), and the hydrological response unit, which is the smallest work unit in the SWAT model (Step 6). The amount of runoff entering the main body of the river, until the time it leaves the watershed area, was routed. Two types of routing, modified puls and Muskingum, were considered in the SWAT model. In the last step of the calibration, the Muskingum routing method was chosen and its coefficients were subjected to a sensitivity analysis. The test ran 200 total iterations and optimal values were obtained, meaning that the output of the model was not influenced by possible variations of the model coefficients. A total of 28 parameters were determined, according to the physical conditions of the watershed (Table 3).
A SWAT calibration and uncertainty program (SWAT-CUP) was used to determine the parameters’ uncertainty [36,37]. It has four algorithms (Glue, Parasol, Mc Mc, and SUFI-2), among which the interested optimal algorithm in this study was SUFI-2, as the output of this algorithm has fewer errors in learning and a high efficiency in model parameter identification, compared to the others [38]. The quality of the input parameters to the model, which is one of the main uncertainties in the output of the watershed model, is shown in SUFI-2 [39]. The input parameters are sampled in an initial domain with a specific objective function, the Latin hypercube sampling (LHS) method, which reduces the range of parameters that are selected for the assessments [39,40,41]. The objective function in this study is the Nash–Sutcliffe Efficiency (NSE) [42]. This downward trend continues until the highest observed data are adjusted within the 95% prediction uncertainty range (p-factor). In this case, the band thickness is the minimum that determines the quality of the fit. The r factor shows the average distance between the upper limit and the lower limit of the uncertainty band. Hence, the r factor indicates the uncertainty in the model output [43]. Finally, the periods of 2010–2014 and 2016–2019 were selected from the observed river discharge data as the calibration and validation periods, respectively. The simulated monthly flow was compared with the observed flow. The performance of the model was estimated by the coefficient of determination indicators (R2), and the bias percentage (PBIAS). PBIAS shows the percentage of the model’s bias. The coefficient of determination is a statistic that shows the relationship between two data sets, the observed and simulated data, and their differences. This describes the proximity of the points to the trend line in a scatter plot. Researchers know R2 as the coefficient of determination [11,44], being an acceptable index for evaluating the performance of the model [42,45].
R 2 = 1 − ∑ i = 1 n S i − O i 2 ∑ i = 1 n O i − O m e a n 2
P B I A S = 100 × ∑ i = 1 n O i − S i ∑ i = 1 n O i
where n is the total number of observed flows, O i and S i are the observed and simulated flows, respectively, and O m e a n is the average observed flows [11].

2.5. Preprocessing of Climate Data

It is obvious that the study of GCMs, to find the most similarity between the simulations and the regional station data, leads to improvement in the study results. By decreasing the bias, the results become more reliable. Therefore, ten GCM models were selected from the sixth phase. The climatic data of the observation period (1979–2014), for the variables of maximum temperature, minimum temperature and daily precipitation, were extracted from the database, at the address (https://esgf-node.llnl.gov/search/cmip6, accessed on 1 January 2020) for the study area [17]. In the R environment, the data were sensibly handled with the ncdf4 package, and the data format was converted from NETCDF to usable values. The climatic outputs of the GCM models were compared with each other and evaluated based on the correlation coefficient and the root-mean-square error (RMSE) indicators [45]. As a result, the closest model to the study area was obtained for climate simulation. In addition, in the SSP scenarios, wind speed data and relative humidity variables were obtained for the future period. The integration of SSPs with RCPs in CMIP6 climate models analyzed the interaction between global warming and socioeconomic conditions [46]. Four scenarios were evaluated in this study: a pessimistic scenario (SSP5–8.5), a realistic scenario (SSP3–7.0), a moderate scenario (SSP2–4.5) and an optimistic scenario (SSP1–2.6). SSP1–2.6 describes warming increasing by less than 2 °C by 2100, compared to the base period (1850–1900). SSP2–4.5 presents an increase of about 2.7 °C in temperature by 2100, compared to the period of 1850–1900. SSP3–7.0 shows CO2 emissions doubling by 2100, compared to the current levels. SSP5–8.5 describes an advanced society with no additional climate policy, but with increasing fossil fuel usage and high energy consumption, in which CO2 emissions would almost double by 2050, compared to current levels [1].

2.6. MRQNBC

Downscaling is necessary for projecting climate change and hydrology [47]. It presents regional-scale information from GCMs on a large scale. The reason for the systematic errors in representations of climate processes is our limited knowledge of the natural processes in climate models, which are not fully understood [19]. Thus, before downscaling, the raw outputs of the climate model are bias corrected [15]. The goal is to correct and reuse the error between the variable simulated by the GCM model and observational data. It is assumed that these biases remain unchanged over time [48]. This approach should be applied to more than one statistical characteristic of the climatic variables and in several time scales. The basis of bias correction is standardization. The method used in this research for bias correction was the MRQNBC approach, which has been studied in different basins [48,49,50]. This approach uses the quantile matching proposed in 2010 by Li et al. [19]. Nesting bias correction is one of the components of this approach, as presented by Johnson and Sharma in 2012 [51]. This logic is useful in the post-processing of GCM simulations, to correct biases of the mean, standard deviation, and residual first-order lag at different time scales. It is possible to bias correct simultaneously in different time nests (daily, monthly, seasonally and yearly) [52]. Mehrotra and Sharma (2012 and 2015) combined nesting logic with quantile matching [48,53], and, by using recursive logic, [50] presented a new MRQNBC method for bias correction. In this method, the statistical characteristics in different time scales are optional, and the mentioned method adapts more generally presented statistical attributes in different time scales [51]. This method is presented as a software package in the R environment, in which the quantile matches between large-scale climatic variables and observed variables is adapted in daily time series, leading to a reduction in the bias in monthly, seasonal and annual time series [17,48,49].
As scale logic alone cannot correct bias, recursive logic is suggested, which means the iteration of all bias correction steps to minimize the bias in all characteristics of the raw outputs in all time scales [50]. Hence, it is recommended to repeat the steps of this method at least three to five times to achieve the best results [51]. Figure 3 shows the process of hybrid modeling.

3. Results

3.1. Hydrological Modeling

To calibrate the SWAT model, twenty-eight hydrological parameters (Table 3) were selected. Table 4 represents eight more sensitive parameters, which control the watershed discharge, one snow parameter, two parameters for the hydrological units, two parameters for groundwater and one parameter for land use. The results show that the most sensitive parameters affecting the streamflow are snow water equivalent with 50% snow cover (SNO50COV.bsn), sediment peak rate adjustment factor for sediment routing in the sub-watershed (ADJ_PKR.bsn), soil evaporation compensation factor (ESCO.hru) and the deep aquifer percolation fraction (RCHRG_DP.gw). The snow parameter in the first rank of the table shows the necessity of simulating the flow caused by snow melting in the region, and the watershed flow is mainly controlled by snow parameter. However, to achieve accurate simulation results, all parameters were included in the model calibration.
Figure 4 shows a reasonable agreement between the simulated and observed flows. The precipitation variations have a direct relationship with the flow changes [13]. Flow is underestimated in all seasons, especially in late autumn and early winter. An overestimation of the measured flow is observed in April 2019. The highest peak flow simulated by the model is in April 2012 (30 m3/s). The SWAT model performed relatively poorly in the simulation of peak flows [11]. This is evident from the disagreement between the simulated and measured flows in 2019.
Flow simulation was also performed, for daily time scales, by the SWAT model. Because of the low accuracy of the model in simulating extreme flows, the performance of the model was estimated using the Nash coefficient, the result of which was 0.4 [42]. However, the agreement between the simulated and measured daily flows was considered appropriate. In general, for this study, the SWAT model showed a good flow simulation performance.
Table 5 shows the results of the SWAT performance evaluation. If R2 > 0.5 and PBIAS ≤ 25%, the modeling is acceptable [11,44]. According to Table 5, R2 was more than 0.8 during the calibration period, which indicates correct calibration and good watershed modeling. The PBIAS results show that the simulated flow was underestimated in both calibration and validation periods.
In general, the flow simulation by the model was evaluated well by the performance indicators. The model can therefore be run confidently to predict future flow under different climate scenarios.

3.2. Climate Modeling

Figure 5 compares the correlations of ten GCMs with observations for three variables, namely, maximum temperature, minimum temperature and precipitation, at different time scales. According to Figure 5a, the correlation coefficients (p) of the MIROC6 model, with the observations for the three variables of maximum temperature, minimum temperature and precipitation on a monthly scale, are ranked first, compared to other climate models.
In Figure 5b, the same criterion is ranked second for the seasonal scale, with a slight difference. The difference between p in this model and the leading model is not much. Therefore, due to the fact that this model ranks first and second in two different time scales and other models do not have this feature, this model was chosen to simulate regional variables.
Moreover, Figure 6 shows that the MIROC6 model has the lowest RMSE, compared to other climate models, in simulating climate variables.
Figure 7 shows the comparison of monthly average precipitation variables, maximum temperature and minimum temperature between the observed data sets and the raw and bias-corrected MIROC6 data, during the 1979–2014 period. The error of the variables simulated by the MIROC6 model was significantly reduced after bias correction was applied by MRQNBC. This indicates that the bias correction of the raw GCM data is acceptable. Accordingly, the MRQNBC method can effectively correct the bias caused by the raw GCM data for all three variables.

3.3. Future Projection of Temperature and Precipitation

Figure 8 represents the variations in average precipitation per month and the maximum and minimum monthly temperatures of the Jajrood watershed, during the 2003–2014 and 2031–2060 periods, under different scenarios. According to the most pessimistic scenario, the temperature becomes warmer all over the watershed, especially in summer. The minimum temperature increase in spring and winter is higher than in summer. The increase in maximum temperature also follows the same trend and is higher in July. The predicted changes in the minimum and maximum temperatures are consistent in different scenarios. An increase in precipitation under the SSP245, SSP320 and SSP585 scenarios occurs in winter, and its peak is in March. The highest amount of precipitation occurs in the beginning of spring (April) for the most optimistic scenario, according to observational data. In contrast, a possible decrease in precipitation, in all scenarios, occurs in summer (July). In general, there are projected precipitation decreases in all of the future scenarios. The results demonstrate that the future climate predictions from the outputs of MIROC6 model are consistent with the results of previous studies [2,3,12]. The average annual precipitation is less than 100 mm, compared with the period of 2003–2014, in all scenarios. The average annual precipitation decreases by 4.51%, 24.68%, 27.05% and 22.31% under the SSP1–2.6, SSP2–4.5, SSP3–7.0 and SSP5–8.5 scenarios, respectively. In the future period, the maximum temperature of the watershed (above 21.13 °C) will be warmer than the baseline (17.50 °C), according to the SSP scenarios. There is one more change under the most pessimistic scenario, compared to the other scenarios. The average maximum annual temperature will increase significantly under SSP1–2.6, SSP2–4.5, SSP3–7.0 and SSP5–8.5 by 0.03 °C, 1.47 °C, 1.62 °C and 3.63 °C, respectively. Similar to the predicted change in the maximum annual temperature, the average minimum annual temperature will also be higher than the baseline. The minimum annual temperature will be warmer by 0.11 °C, 0.45 °C, 0.58 °C and 1.03 °C under the four scenarios SSP1–2.6, SSP2–4.5, SSP3–7.0 and SSP5–8.5, respectively.
Fan et al. (2021) [54] measured changes in temperature, precipitation and evapotranspiration using eleven GCM models from CMIP5, for the baseline period of 1992–2011 and for the future period of 2020–2039, in eastern Iran. They found that the maximum and minimum temperature changes increased in two scenarios (RCP4.5 and RCP8.5) for almost all models. The MIROC-ESM model estimated the highest minimum and maximum temperature. Most of the models estimated a reduction in future precipitation. The lowest average precipitation was predicted by the MIROC-ESM model in the long term. Evapotranspiration increased in all models. There was a significant correlation between the findings of Fan et al. (2021) [54] and the results of the present study, for the upstream watershed of the Latyan Dam. Compared with the base period of 2005–2019, the upward trend in mean values of annual evapotranspiration will increase in the future period.

3.4. Projection of Future Discharge

Figure 9 shows the changes in annual average flow under different scenarios for the period of 2031–2060. Contrary to the predicted decrease in the rainfall, the streamflow shows a different pattern. Under the SSP126, SSP246, SSP370 and SSP585 scenarios, the flow increases by 37%, 5.91%, 4.30% and 5%, respectively, compared to the base period. The results from this study are in agreement with several prior studies [55,56,57]. The increase in future flow is associated with the increasing frequency of extreme precipitation and the decrease in low intensity precipitation events, which directly affect the discharge in the watershed [13]. The decrease in the predicted flow during in the future period is caused by a higher temperature, which increases evapotranspiration [4]. The increase in the projected flow of 2030, compared to the base period, is related to the climate policy used in the SSP scenarios, which includes the increase in carbon dioxide emissions. Factors affecting this trend include population increase, economic growth, and the rapid development of energy services [58]. Therefore, the increase in temperature caused by the industrialization of societies will play an effective role in increasing the flood risks in the watershed upstream of the Latyan Dam.

3.5. Limitations and Suggestions

The findings of the study suggest that the precipitation in the Jajrood watershed will decrease significantly in the future. This decrease will mainly be due to the increase in temperature. The upstream watershed of the Latyan dam is a mountainous watershed located in the north of Tehran (the capital city of Iran). A shortage of water resources poses severe risks to Tehran’s security and prosperity. Authorities should pay more attention to the impact of the possible threats of climate change on the watershed. Considering the mountainous nature of the watershed, the snowmelt may fill the water source temporarily, but climate change will accelerate the snow melting, and evaporation will increase remarkably. Therefore, several issues should be considered in future research. Firstly, the greatest focus should be on how to store the watershed’s water resources. Secondly, in many studies conducted on the Jajrood and other watersheds in Iran, the climatic outputs are used directly without bias correction in hydrological predictions, which creates a high level of uncertainty; this is needs to be corrected in future studies. Thirdly, the use of climate models adapted to the region is effective in producing accurate climatic predictions of the watershed and should be included in climate studies. One of the limitations of this research is the lack of hydrological and meteorological stations with high-quality statistical data. In this study, Emmame and Roudak stations, as they had longer durations of observational statistics and closer locations to the center of the watershed, were selected. Roudak station, with an area of 419 km2, has the largest runoff volume upstream of the watershed. To acquire an exhaustive knowledge of the watershed hydro cycle, the improvement and development of hydrometric stations is required. It is suggested that, to fill the station data gap in future studies the use of satellite data should be investigated.

4. Discussion

4.1. Interpreting Research Results while Considering Results of Previous Studies

The results of this research are in agreement with the results of studies conducted in other parts of Iran that have investigated hydro-climate projections.
Fallah et al. (2021) [57] simulated climate parameters, using CMIP5 and CMIP6, for the Tashk–Bakhtegan watershed, located in the center of Iran. In their research, they considered two climate models and two scenarios for each phase and concluded that the minimum and maximum temperatures will increase, for all models and scenarios. Also, precipitation reduction will occur in most scenarios and models. Their precipitation and temperature prediction results, obtained from SSP scenarios, were in accordance with the results of the present study. However, concerning runoff simulation using climatic data and the SWAT tool, GCM models showed different results under SSP and RCP scenarios. Regarding SSP scenarios in their research, the runoff showed a continuously decreasing trend in the future, which was caused largely by a larger increase in the maximum temperature than in the minimum temperature in the watershed. For the SSP585 scenario, the flow rate showed a rising trend. Meanwhile, the RCP scenarios showed an increase in runoff. The authors attributed this increase to the precipitation pattern of the fifth phase models and the intensity of precipitation in the future. Therefore, the flow prediction results under SSP5–8.5 scenarios by Fallah et al. (2021) [57] were well-correlated with the results of this study. This emphasizes the need for adaptive management of economic and societal conditions in the Jajrood watershed, such as sustainable water resources management (SWRM).
In another study, Mahdian et al. (2023) [59] evaluated the impact of climate change and anthropogenic activities on the Anzali wetland basin on the southern shores of the Caspian Sea, including streamflow and sedimentation load. They downscaled and then corrected the temperature and precipitation, which were simulated by GCM models under SSP scenarios, using quantile delta mapping (QDM). The authors concluded that, under different scenarios, precipitation and temperature will decrease and increase in the future, respectively. However, considering the impact of climate change, along with the local anthropogenic activities affecting the hydrological process of the basin, the projected streamflow will increase. The authors argue that this increase is because of deforestation and urbanization in the wetland. The findings of this research are completely in accordance with the results of the present study. According to Mahdian et al. (2023) [59], land use plays a crucial role in an increasing flow rate. Considering the Jajrood basin is mountainous and covered mostly with pasture, it is predicted that the flow rate will increase in the future. Moreover, the results of the authors are consistent with the results of this research, regarding the application of the QDM method which shows the importance of correcting the bias of climatic variables before using them in hydrological models. This affects positively the reduction in uncertainty in hydrological modeling of the Jajrood basin.
Houshmand Kouchi et al. (2019) [55] examined climate change’s impact on the runoff in the Persian Salman Dam watershed in the center of Iran by using the SWAT tool. Simulations of temperature and precipitation from three GCM models were performed under two RCP scenarios. The findings indicated a predominant increase in precipitation frequency and temperature in the future. These changes were attributed to the predicted increase in runoff in the period of 2020–2050, which will increase the risk of flooding in the watershed. This is highly correlated with the results obtained from this study. The increase in the flow rate in the period of 2031–2060 is caused by the increase in frequency of precipitation because of warming temperatures. The results are useful in providing a management plan and solution in encountering climate change.
Abbaszadeh et al. (2023) [60] assessed the effect of climate change and land use on runoff and evapotranspiration, using the SWAT model, for the Minab watershed located in the south of Iran. In this projection, precipitation under SSP scenarios increased in the period of 2021–2050 compared to 1989–2014. Evaporation and transpiration increased because of climate and land use change, which may lead to flow reduction and a lack of water resources in the watershed. Their findings showed a decrease in the flow rate as the impact of land use and land cover change in the future. Therefore, their results had a good correlation with the results of this research in predicting increasing trends of evaporation and transpiration, following the increase in temperature.
The findings of this research need to be used by decision makers. It is important to consider the peak flow time of the upcoming period. For example, when forecasts indicate a flooding risk, the emphasis is on creating and upgrading flood warning systems to increase awareness. Organizations implementing flood control should consider the watershed land-use management, and, while clearing floods on the roads in critical areas, should design and construct flood walls and embankments.

4.2. Potential Threats of Climate Change

The upstream watershed of the Latyan Dam supplies drinking water, which is essential for urban areas, especially Tehran, and agricultural areas like the Varamin plain, which lies downstream of the dam. However, the lack of hydrometric and meteorological stations with high reliability and accuracy has limited the number of studies related to this watershed. Climate change and the predicted future decrease in the average annual precipitation, compared to the period of 2003–2014, will cause a shortage of water in the Jajrood River watershed, which may have a negative impact on the access of millions of people downstream of the watershed to water sources, as well as food security, income and well-being. It is necessary for the government, along with all societal stakeholders to cooperate with each other in order to achieve the sustainable management of water resources and the improvement in sustainable water use.

5. Conclusions

One of the objectives of the study was the bias correction and downscaling of climate model outputs, to accurately assess the watershed response to the threat of climate change. Choosing the right GCM model effectively reduces the systematic error. Therefore, climate predictions with high confidence and low uncertainty should be applied to hydrological models. Among the ten GCM models of CMIP6 considered for the Jajrood watershed, the MIROC6 model was selected, due to it having the highest correlation coefficient and the lowest RMSE values, indicating that the model fits the observational data well. The model responded well at the regional scale. By understanding the physical processes of the watershed, watershed simulation can be carried out accurately and more quickly. Therefore, developing the model with the least error was another objective of this research. The SWAT model had a high ability to simulate sub-watersheds, without the quality statistics of Najarkola, Lavark and Naroun stations, and showed an acceptable performance, with an R2 more than 0.8. Therefore, it is recommended as the best option for studies related to watershed management strategies. Also, this research evaluates the consequences of climate change as a threat to the flow quantity of the Jajrood River watershed, using hydroclimate models. Changes in precipitation and flow during 2031–2060 compared to the base period were calculated under four scenarios (SSP126, SSP245, SSP370 and SSP585). According to the results, precipitation and flow will show a decreasing trend (4% to 26%) and increasing trend (5% to 37%) under all scenarios, respectively. On the one hand, increasing the intensity of precipitation may increase the risk of flooding; on the other hand, the growing water crisis is another issue which may affect the supply of water for urban areas and the agriculture sector. Gaining more knowledge of predictions of watershed flow using hydroclimatic modeling supports decision makers to evolve flood risk management plans in watersheds more coherently in the future.

Author Contributions

F.N., conceptual and operational modeling, data analysis and the writing of the original draft. B.A., supervision, review and editing. V.N., advice and guidance, review and editing. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Bhatt, I.; Huggel, C.; Insarov, G.; Morecroft, M.; Muccione, V.; Prakash, A. Summary of Mountain. Intergovernmental Panel on Climate Change Sixth Assessment Report Working Group II Report—Climate Change 2022: Impacts, Adaptation and Vulnerability; IPCC: Geneva, Switzerland, 2022. [Google Scholar]
  2. Alavi Naini, A.; Malek Mohamadi, B. The effects of global warming on extreme rainfall to floods with different return periods (case study: Jajroud watershed). Sci. Q. J. 2020, 29, 241–246. [Google Scholar]
  3. Mohseni-Bandpei, A.; Nasseri, M.; Rafiee, M.; Eslamizadeh, M.; Hashempour, Y. Impact of Climate Change on Organic Carbon Removal Efficiency in Jajrood Catchment: From Dam to Water Treatment Plant. J. Maz. Univ. Med. Sci. 2020, 30, 81–95. [Google Scholar]
  4. IPCC. Climate Change 2007: The Physical Science Basis. Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change; Cambridge University Press: Cambridge, UK, 2007; 996p.
  5. IPCC. Summary of Policy Makers Intergovernmental Panel on Climate Change Fifth Assessment Report Working Group IReport—Climate Change 2013: Natural Science Foundation; Stocker, T.F., Qin Dahe, G., Plattner, K., Tignor, M., Allen, S.K., Boschung, J., Nauels, A., Xia, Y., Bex, V., Midgley, P.M., Eds.; University of Cambridge Press: Cambridge, UK; New York, NY, USA, 2013.
  6. Gebrechorkos, S.; Bernhofer, C.; Hülsmann, S. Climate change impact assessment on the hydrology of a large river basin in Ethiopia using a local-scale climate modelling approach. Sci. Total Environ. 2020, 742, 140504. [Google Scholar] [CrossRef] [PubMed]
  7. Zhang, X.; Xu, Y.-P.; Fu, G. Uncertainties in SWAT extreme flow simulation under climate change. J. Hydrol. 2014, 515, 205–222. [Google Scholar] [CrossRef]
  8. Wen, K.; Gao, B.; Li, M. Quantifying the Impact of Future climate change on Runoff in the Amur River basin using a distributed hydrological model and CMIP6 GCM projections. Atmosphere 2021, 12, 1560. [Google Scholar] [CrossRef]
  9. Hakala, K.; Addor, N.; Teutschbein, C.; Vis, M.; Dakhlaoui, H.; Seibert, J. Hydrological modeling of climate change impacts. J. Sci. Technol. Soc. 2020. [Google Scholar] [CrossRef]
  10. Bhatta, B.; Shrestha, S.; Shrestha, P.; Talchabhadel, R. Evaluation and application of a SWAT model to assess the climate change impact on the hydrology of the Himalayan River Basin. Catena 2019, 181, 104082. [Google Scholar] [CrossRef]
  11. Tian, P.; Lu, H.; Feng, W.; Guan, Y.; Xue, Y. Large decrease in streamflow and sediment load of Qinghai-Tibetan Plateau driven by future climate change: A case study in Lhasa River Basin. Catena 2019, 187, 104340. [Google Scholar] [CrossRef]
  12. Abtahi, S.M. Investigating the paleoclimate of Jajrood watershed use of glacial evidence Persian. J. Geogr. Explor. Desert Areas 2013, 1, 185–201. [Google Scholar]
  13. Khosravi, Y.; Pari-Zanganeh, A.H.; Karimian, V.; Doustkamian, M.; Shiri, A. Analysis of climatic zoning and investigation of the effects of climatic elements on the discharge of the Jajrood river watershed Persian. J. Rain Pool Surf. Syst. 2019, 6, 19. [Google Scholar]
  14. Gusain, A.; Ghosh, S.; Karmakar, S. Added value of CMIP6 over CMIP5 models in simulating Indian summer monsoon rainfall. Atmos. Res. 2020, 232, 104680. [Google Scholar] [CrossRef]
  15. Maurer, E.P.; Hidalgo, H.G. Utility of daily vs. monthly large-scale climate data: An intercomparison of two statistical downscaling methods. Hydrol. Earth Syst. Sci. 2008, 12, 551–563. [Google Scholar] [CrossRef]
  16. IPCC. Summary of Future Global Climate: Scenario-Based Projections and Near-Term Information. Intergovernmental Panel on Climate Change Sixth Assessment Report Working Group I Report—Climate Change 2021: The Physical Science Basis; IPCC: Geneva, Switzerland, 2021.
  17. Naseri, E.; Massah Bavani, A.R.; Saadi, T.; Javadi, S. Detection and Attribution of Climate Change Effects on Inflow to Karaj Dam in the Past Periods Persian. J. Iran-Water Resour. Res. 2020, 16, 306–321. [Google Scholar]
  18. Gutmann, E.D.; Rasmussen, R.M.; Liu, C.; Ikeda, K.; Gochis, D.J.; Clark, M.P.; Dudhia, J.; Thompson, G. A Comparison of statistical and dynamical downscaling of winter precipitation over complex terrain. J. Clim. 2012, 25, 262–281. [Google Scholar] [CrossRef]
  19. Li, H.; Sheffield, J.; Wood, E.F. Bias correction of monthly precipitation and temperature fields from Intergovernmental Panel on Climate Change AR4 models using equidistant quantile matching. J. Geophys. Res. 2010, 115, D10101. [Google Scholar] [CrossRef]
  20. Xue, Y.; Janjic, Z.; Dudhia, J.; Vasic, R.; De Sales, F. A review on regional dynamical downscaling in intrapersonal to seasonal simulation/prediction and major factors that affect downscaling ability. Atmos. Res. 2014, 147, 68–85. [Google Scholar] [CrossRef]
  21. Salvi, K.; Kannan, S.; Ghosh, S. High-resolution multisite daily rainfall projections in India with statistical downscaling for climate change impacts assessment. J. Geophys. Res. Atmos. 2013, 118, 3557–3578. [Google Scholar] [CrossRef]
  22. Gebrechorkos, S.H.; Bernhofer, C.; Hülsmann, S. Impacts of projected change in climate on water balance in basins of East Africa. Sci. Total Environ. 2019, 682, 160–170. [Google Scholar] [CrossRef]
  23. Lutz, A.F.; ter Maat, H.W.; Biemans, H.; Shrestha, A.B.; Wester, P.; Immerzeel, W.W. Selecting representative climate models for climate change impact studies: An advanced envelope-based selection approach. Int. J. Clim. 2016, 36, 3988–4005. [Google Scholar] [CrossRef]
  24. Dunn, S.M.; Brown, I.; Sample, J.; Post, H. Relationships between climate, water resources, land use and diffuse pollution and the significance of uncertainty in climate change. J. Hydrol. 2012, 434–435, 19–35. [Google Scholar] [CrossRef]
  25. Osmani, H.; Motamedvaziri, B.; Moeni, A. Simulation of discharge, calibration and validation of SWAT model case study: Tehran Latyan dam upstream Persian. J. Watershed Eng. Manag. 2013, 5, 134–143. [Google Scholar]
  26. Arnold, J.G.; Srinivasan, R.; Muttiah, S.R.; Williams, J.R. Large area hydrologic modeling and assessment part I. Model development. JAWRA J. Am. Water Resour. Assoc. 1998, 34, 73–89. [Google Scholar] [CrossRef]
  27. Zakizadeh, H.; Ahmadi, H.; Zehtabian, G.; Moeini, A.; Moghaddamnia, A. A novel study of SWAT and ANN models for runoff simulation with application on dataset of metrological stations. Phys. Chem. Earth Parts A/B/C 2020, 120, 102899. [Google Scholar] [CrossRef]
  28. Marin, M.; Clinciu, I.; Tudose, N.C.; Ungurean, C.; Adorjani, A.; Mihalache, A.L.; Davidescu, A.A.; Davidesco, O.; Dinca, L.; Cacovean, H. Assessing the vulnerability of water resources in the context of climate changes in a small forested watershed using SWAT: A review. Environ. Res. 2020, 184, 109330. [Google Scholar] [CrossRef] [PubMed]
  29. Jimeno-seaz, P.; Martines-Espana, R.; Casali, J.; Perez-sanches, J.; Senet-Aparicio, J. A comparison of performance of SWAT and machine learning models for predicting sediment load in a forested Basin, Northern Spain. Catena 2020, 212, 105953. [Google Scholar] [CrossRef]
  30. Meaurio, M.; Zabaleta, A.; Uriarte, J.A.; Srinivasan, R.; Antigüedad, I. Evaluation of SWAT models performance to simulate streamflow spatial origin. The case of a small forested watershed. J. Hydrol. 2015, 525, 326–334. [Google Scholar] [CrossRef]
  31. Khalilian, S.; Shahvari, N. A SWAT Evaluation of the Effects of Climate Change on Renewable Water Resources in Salt Lake Sub-Basin, Iran. AgriEngineering 2018, 1, 44–57. [Google Scholar] [CrossRef]
  32. Arnold, J.G.; Kiniry, J.R.; Srinivasan, R.; Williams, J.R.; Haney, E.B.; Neitsch, S.L. Soil and Water Assessment Tool Input/Output File Documentation, Version 2009; Texas Water Resources Institute Technical Report; Texas Water Resources Institute: College Station, TX, USA, 2011; p. 365. [Google Scholar]
  33. Baffaut, C.; Sadler, E.J.; Ghidey, F.; Anderson, S.H. Long-term agroecosystem research in the central mississippi river basin: SWAT simulation of flow and water quality in the goodwater creek experimental watershed. J. Environ. Qual. 2015, 44, 84–96. [Google Scholar] [CrossRef]
  34. Singh, V.; Bankar, N.; Salunkhe, S.S.; Bera, A.K.; Sharma, J.R. Hydrological stream flow modelling on Tungabhadra catchment: Parameterization and uncertainty analysis using SWAT CUP. Curr. Sci. 2013, 104, 1187–1199. [Google Scholar]
  35. FAO. The State of Food Insecurity in the World (SOFI); Food and Agricultural Organization of the United Nations: Rome, Italy; World Bank: Washington, DC, USA, 2014. [Google Scholar]
  36. Arnold, J.; Moriasi, D.; Gassman, P.; Abbaspour, K.; White, M.; Srinivasan, R.; Santhi, C.; Harmel, R.; van Griensven, A.; Van Liew, M.; et al. SWAT: Model use, calibration, and validation. Trans. ASABE 2012, 55, 1491–1508. [Google Scholar] [CrossRef]
  37. Abbaspour, K.C.; Yang, J.; Maximov, I.; Siber, R.; Bogner, K.; Mieleitner, J.; Zobrist, J.; Srinivasan, R. Modelling hydrology and water quality in the pre-alpine/alpine Thur watershed using SWAT. J. Hydrol. 2007, 333, 413–430. [Google Scholar] [CrossRef]
  38. Wu, H.; Chen, B. Evaluating uncertainty estimates in distributed hydrological modeling for the Wenjing River watershed in China by GLUE, SUFI-2, and Parasol methods. Ecol. Eng. 2015, 76, 110–121. [Google Scholar] [CrossRef]
  39. Parajuli, P.B.; Jayakody, P.; Ouyang, Y. Evaluation of using remote sensing evapotranspiration data in SWAT. Water Resour. Manag. 2018, 32, 985–996. [Google Scholar] [CrossRef]
  40. Park, J.-Y.; Yu, Y.-S.; Hwang, S.-J.; Kim, C.; Kim, S.-J. SWAT modeling of best management practices for Chungju dam watershed in South Korea under future climate change scenarios. Paddy Water Environ. 2014, 12, 65–75. [Google Scholar] [CrossRef]
  41. Abbaspour, K.C.; Rouholahnejad, E.; Vaghefi, S.; Srinivasan, R.; Yang, H.; Kløve, B. A continental-scale hydrology and water quality model for Europe: Calibration and uncertainty of a high-resolution large-scale SWAT model. J. Hydrol. 2015, 524, 733–752. [Google Scholar] [CrossRef]
  42. Nash, J.E.; Sutcliffe, J.V. River flow forecasting through conceptual Models part I—A discussion of principles. J. Hydrol. 1970, 10, 282–290. [Google Scholar] [CrossRef]
  43. Ha, L.T.; Bastiaanssen, W.G.; van Griensven, A.; van Dijk, A.I.; Senay, G.B. SWAT-CUP for calibration of spatially distributed hydrological processes and ecosystem services in a Vietnamese river basin using remote sensing. Hydrol. Earth Syst. Sci. Discuss. 2017, 1–35. [Google Scholar] [CrossRef]
  44. Moriasi, D.; Arnold, J.; van Liew, M.; Bingner, R.; Harmel, R.; Veith, T. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans. ASABE 2007, 50, 885–900. [Google Scholar] [CrossRef]
  45. Nourani, V. Reply to comment on ‘Nourani V, Mogaddam AA, Nadiri AO. 2008. An ANN-based model for spatiotemporal groundwater level forecasting. Hydrological Processes 22: 5054–5066’. Hydrol. Process. 2010, 24, 370–371. [Google Scholar] [CrossRef]
  46. Riahi, K.; Van Vuuren, D.P.; Kriegler, E.; Edmonds, J.; O’Neill, B.C.; Fujimori, S.; Bauer, N.; Calvin, K.; Dellink, R.; Fricko, O.; et al. The shared socioeconomic pathways and their energy, land use, and greenhouse gas emissions implications: An overview. Glob. Environ. Change 2017, 42, 153–168. [Google Scholar] [CrossRef]
  47. Mehrotra, R.; Johnson, F.; Sharma, A. A software toolkit for correction systematic biases in climate model simulations. Environ. Model. Softw. 2018, 104, 130–152. [Google Scholar] [CrossRef]
  48. Mehrotra, R.; Sharma, A. A multivariate quantile-matching bias correction approach with auto- and cross-dependence across multiple time scales: Implications for downscaling. J. Clim. 2016, 29, 3519–3539. [Google Scholar] [CrossRef]
  49. Teutschbein, C.; Seibert, J. Is bias correction of regional climate model (RCM) simulations possible for non-stationary conditions? Hydrol. Earth Syst. Sci. 2013, 17, 5061–5077. [Google Scholar] [CrossRef]
  50. Mehrotra, R.; Sharma, A. An improved standardization procedure to remove systematic low frequency variability biases in GCM simulations. Water Resour. Res. 2012, 48, 1–8. [Google Scholar] [CrossRef]
  51. Johnson, F.; Sharma, A. A nesting model for bias correction of variability at multiple time scales in general circulation model precipitation simulations. J. Water Resour. Res. 2012, 48, 1–16. [Google Scholar] [CrossRef]
  52. Wilby, R.L.; Charles, S.P.; Zorita, E.; Timbal, B.; Whetton, P.; Mearns, L.O. Guidelines for Use of Climate Scenarios Developed from Statistical Downscaling Methods; IPCC Task Group on Scenarios for Climate Impact Assessment: Geneva, Switzerland, 2004; 27p. [Google Scholar]
  53. Mehrotra, R.; Sharma, A. Correcting for systematic biases in multiple raw GCM variables across a range of timescales. J. Hydrol. 2015, 520, 214–223. [Google Scholar] [CrossRef]
  54. Fan, G.; Sarabandi, A.; Yaghoobzadeh, M. Evaluating the climate change effects on temperature, precipitation and evapotranspiration in eastern Iran using CMIP5. Water Supply 2021, 21, 4316–4327. [Google Scholar] [CrossRef]
  55. Hoshmand Kouchi, D.; Esmaili, K.; Farid Hosseini, A.; Sanaeinejad, S.H.; Khalili, D. Simulation of climate change impacts using fifth assessment report models under RCP scenarios on water resources in the upper basin of salman farsi dam. Iran. J. Irrig. Drain. 2018, 13, 243–258. [Google Scholar]
  56. Abbaspour, K.C.; Faramarzi, M.; Ghasemi, S.S.; Yang, H. Assessing the impact of climate change on water resources in Iran. Water Resour. Res. 2009, 45, W10434. [Google Scholar] [CrossRef]
  57. Fallah Kalaki, M.; Shokri Kuchak, V.; Ramezani Etedali, H. Simulating the effects of climate change on run off using the CMIP6 and CMIP5 climate models by swat hydrological model (case study: Tashk-Bakhtegan basin). J. Iran Water Resour. Res. 2021, 17, 345–359. [Google Scholar]
  58. Riahi, K.; Schaeffer, R. Climate Change 2022, IPCC AR6 WG III Mitigation Pathways Compatible with Long-Term Goals; IPCC: Geneva, Switzerland, 2022. [Google Scholar]
  59. Mahdian, M.; Hosseinzadeh, M.; Siadatmousavi, S.M.; Chalipa, Z.; Dalavar, M.; Guo, M.; Abolfathi, S.; Noori, R. Modelling impacts of climate change and anthropogenic activities on inflows and sediment loads of wetlands: Case study of the Anzali wetland. Sci. Rep. 2023, 13, 5399. [Google Scholar] [CrossRef] [PubMed]
  60. Abbaszadeh, M.; Bazrafshan, O.; Mahdavi, R.; Sardooi, E.R.; Jamshidi, S. Modeling Future Hydrological Characteristics Based on Land Use/Land Cover and Climate Changes Using the SWAT Model. Water Resour. Manag. 2023, 37, 1–18. [Google Scholar] [CrossRef]
Sustainability 15 15875 g001
Figure 1. The Jajrood River watershed and its hydrometric and meteorological stations.
Figure 1. The Jajrood River watershed and its hydrometric and meteorological stations.
Sustainability 15 15875 g001
Sustainability 15 15875 g002
Figure 2. (a) Land use, (b) soil type, (c) sub-watershed, and (d) slope map of the Jajrood River watershed.
Figure 2. (a) Land use, (b) soil type, (c) sub-watershed, and (d) slope map of the Jajrood River watershed.
Sustainability 15 15875 g002
Sustainability 15 15875 g003
Figure 3. Flow chart of the methodology.
Figure 3. Flow chart of the methodology.
Sustainability 15 15875 g003
Sustainability 15 15875 g004
Figure 4. Changes in the monthly precipitation (mm) and simulated and measured flows (m3/s) at the Roudak station during the calibration and validation periods.
Figure 4. Changes in the monthly precipitation (mm) and simulated and measured flows (m3/s) at the Roudak station during the calibration and validation periods.
Sustainability 15 15875 g004
Sustainability 15 15875 g005
Figure 5. Comparison of correlation coefficients (p) of all models based on the observations for three climate variables on (a) monthly and (b) seasonal scales.
Figure 5. Comparison of correlation coefficients (p) of all models based on the observations for three climate variables on (a) monthly and (b) seasonal scales.
Sustainability 15 15875 g005
Sustainability 15 15875 g006aSustainability 15 15875 g006b
Figure 6. Comparison of RMSE outputs of different models for variables at different scales.
Figure 6. Comparison of RMSE outputs of different models for variables at different scales.
Sustainability 15 15875 g006aSustainability 15 15875 g006b
Sustainability 15 15875 g007
Figure 7. Comparison of monthly average precipitation variables, maximum temperature and minimum temperature between the observed data set and the raw and bias-corrected MIROC6 data, during the 1979–2014 period.
Figure 7. Comparison of monthly average precipitation variables, maximum temperature and minimum temperature between the observed data set and the raw and bias-corrected MIROC6 data, during the 1979–2014 period.
Sustainability 15 15875 g007
Sustainability 15 15875 g008aSustainability 15 15875 g008b
Figure 8. Prediction of changes in maximum temperature (°C, (a)), minimum temperature (°C, (b)) and average monthly precipitation (mm, (c)) of the Jajrood watershed during the period of 2031–2060 under different scenarios.
Figure 8. Prediction of changes in maximum temperature (°C, (a)), minimum temperature (°C, (b)) and average monthly precipitation (mm, (c)) of the Jajrood watershed during the period of 2031–2060 under different scenarios.
Sustainability 15 15875 g008aSustainability 15 15875 g008b
Sustainability 15 15875 g009aSustainability 15 15875 g009b
Figure 9. Variations of flow in the Jajrood watershed between observational data and future period predictions, under different scenarios.
Figure 9. Variations of flow in the Jajrood watershed between observational data and future period predictions, under different scenarios.
Sustainability 15 15875 g009aSustainability 15 15875 g009b
Table 1. Coordinates, altitude, and average rainfall of meteorological stations.
Table 1. Coordinates, altitude, and average rainfall of meteorological stations.
NameBase PeriodLatitudeLongitudeElevation (m)Precipitation (mm/y)
p-Emmame2005–201935.9151.582248587
p-Polur2005–201935.8552.062273609
p-Abbaspour2005–201935.7451.581482336
p-Kiga2005–201935.8651.312009690
p-Latyan2005–201935.7851.681563402
Table 2. Specifications of the hydrometric stations of the Latyan Dam watershed.
Table 2. Specifications of the hydrometric stations of the Latyan Dam watershed.
Sub-WatershedStationRiverCoordinatesElevation (m)Area (km2)
UTM-YUTM-X
15Bagh TangeEmame3973014552641221018.69
19KamarkhaniEmame3967443548159189036.85
22RoudakJajrood39665575507171710419.9
28LavarakAli abad3962250563250160097.12
23NarounAfje3965750560000175028.83
24Najar kolaGolandouk3965250557500170057.29
Table 3. Definition of selected parameters for SWAT calibration.
Table 3. Definition of selected parameters for SWAT calibration.
No.ParametersDefinition
1GW_Delay.gwGroundwater delay
2GWQMN.gwThreshold in the shallow aquifer for return flow to occur
3GW_Revap.gwGroundwater “revap” coefficient
4SHALLST.gwInitial depth of water in the shallow aquifer
5ALPHA_BF.gwBase flow alpha factor (days)
6REVAPMN.gwThreshold in the shallow aquifer for “revap” to occur
7RCHRG_DP.gwDeep aquifer percolation fraction
8CN2.mgtSCS stream flow curve number
9ADJ_PKR.bsnPeak rate adjustment factor for sediment routing in the sub basin
10MSK-CO1.bsnMuskingum channel routing (coefficient for normal flow routing)
11MSK-CO2.bsnMuskingum channel routing (coefficient for low flow routing)
12MSK-X.bsnMuskingum channel routing (weighting factor)
14SOL_AWC (relative test).bsnAvailable water capacity of the soil layer
15SOL_K (relative test).bsnSaturated hydraulic conductivity
16SOL_BD(1).bsnMoist bulk density of first soil layer (Mg/m3)
17SOL_ALB.bsnSoil albedo (dimensionless)
18SFTMP.bsnSnowfall temperature
19SMTMP.bsnSnowmelt base temperature
20SMFMX.bsnMaximum melt rate for snow during year (summer solstice)
21SMFMN.bsnMinimum melt rate for snow during year (winter solstice)
22TIMP.bsnSnowpack temperature lag factor
23SNO_SUB.bsnInitial snow water content
24SNOWCOVMX.bsnSnow water content that corresponds to 100% snow cover
25SNOW50COV.bsnSnow water equivalent that corresponds to 50% snow cover
26Plaps.subPrecipitation lapse rate
27Tlaps.subTemperature lapse rate
28ESCO.hruSoil evaporation compensation factor
Table 4. Fitted values and sensitivity rankings of model parameters.
Table 4. Fitted values and sensitivity rankings of model parameters.
Parameter NameSensitivity RanksRange UpdateFileMin. ValueMax. ValueFitted Value
SNO50COV1V.bsn0500471.
ESCO2V.hru−0.20.20.157800
RCHRG_DP3V.gw010.003000
GWQMN4V.gw050002945.000000
PLAPS5V.sub040068.500000
CN26V.mgt−0.20.20.148200
OV_N7V.hru−0.20.20.127800
MSK_CO18V.bsn0.010.01.745000
Table 5. Hydrological modeling results.
Table 5. Hydrological modeling results.
ParameterIndicesRoudak Station
Calibration (2010–2014)Validation (2016–2019)
FlowR20.850.74
PBIAS14.415
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Najimi, F.; Aminnejad, B.; Nourani, V. Assessment of Climate Change’s Impact on Flow Quantity of the Mountainous Watershed of the Jajrood River in Iran Using Hydroclimatic Models. Sustainability 2023, 15, 15875. https://doi.org/10.3390/su152215875

AMA Style

Najimi F, Aminnejad B, Nourani V. Assessment of Climate Change’s Impact on Flow Quantity of the Mountainous Watershed of the Jajrood River in Iran Using Hydroclimatic Models. Sustainability. 2023; 15(22):15875. https://doi.org/10.3390/su152215875

Chicago/Turabian Style

Najimi, Farzaneh, Babak Aminnejad, and Vahid Nourani. 2023. "Assessment of Climate Change’s Impact on Flow Quantity of the Mountainous Watershed of the Jajrood River in Iran Using Hydroclimatic Models" Sustainability 15, no. 22: 15875. https://doi.org/10.3390/su152215875

APA Style

Najimi, F., Aminnejad, B., & Nourani, V. (2023). Assessment of Climate Change’s Impact on Flow Quantity of the Mountainous Watershed of the Jajrood River in Iran Using Hydroclimatic Models. Sustainability, 15(22), 15875. https://doi.org/10.3390/su152215875

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop