This study was conducted to investigate the effects of climate change on the SWA Dam's reservoir, located in a semi-arid region. In this purpose, the Soil & Water Assessment Tool (SWAT) was used for hydrological modeling, calibration and validation were done using the SWAT Calibration and Uncertainty Program (SWAT-CUP). Future climate data were generated using the Model for Interdisciplinary Research on Climate (MIROC5) from the Coupled Model Intercomparison Project Phase 5 (CMIP5). In the hydrological modeling process, the Representative Concentration Pathway scenarios (RCP4.5 and RCP8.5) were utilized to assess influences throughout 2050-2069 and 2080-2099. So, under the impacts of the RCP4.5 scenario, the temperature is projected to rise by +3.4 °C and +3.8 °C during 2050-2069 and 2080-2099, respectively. However, considering the RCP8.5, it will rise by +4.4 °C and +6.54 °C in the same periods. Additionally, runoff is anticipated to decrease by 71.47% and 67.88% under the RCP4.5, and by 63.03% and 65.2% under the RCP8.5 scenarios during 2050-2069 and 2080-2099 accordingly. Therefore, climate change is expected to cause drought at the SWA dam, leading to future water shortages for the local population. Thus, it is recommended to take action by developing a comprehensive adaptation strategy to ensure sustainable water availability in the region.

  • The study was conducted considering RCP4.5 and RCP8.5 scenarios.

  • Precipitation in the basin will decrease due to climate change.

  • The temperature rises in the winter and spring seasons.

  • Shah Wa Arus Dam will not be able to provide water demand downstream.

  • Drought will be the main challenge for inhabitants in the area.

Climate change is a concerning issue that mostly impacts water resources at different parts of the ground. It challenges the water cycle and hydrologic components of watersheds, influencing every aspect of human life and ecosystems, including environmental-related diseases, floods, droughts, and changes in agricultural plant growth (IPCC 2022). According to the Intergovernmental Panel on Climate Change (IPCC), climate-related diseases cause thousands of deaths every year, and this number can increase if it's not taken adequate measures. There could be around 9 million climate-related deaths per year by the end of the 21st century under a high-emission scenario (IPCC 2022).

The impact of climate change continues to force people to be displaced and is a critical problem for human mobility. This is a complex event driven by factors such as rising sea levels, droughts, and rainfall variability. The impacts of drought on crop yield are crucial to be investigated for preparedness against possible hunger and disaster (Das et al. 2024). There are various perspectives and viewpoints on climate change, leading to different regulations and issues in policy formation and actual implementation (Oakes et al. 2023). It is important to note that the impacts of climate change on water resources are different according to geographical areas. For instance, the effects of climate change on the streamflow of Baltic Sea indicated that water flow increases in the winter, decreases in the summer, and flows from northern basins increase while flows from southern basins decrease (Graham 2004). Khattak et al. (2011) found that, in the upper sides of Indus River Basin, climate change causes an increase in river flow during the winter, and the basin is projected to face drought in the summer. This change was argued due to a rising temperature in the winter and spring because the rivers are dependent on melting snow.

The river flows which are dependent on melting snow are more vulnerable to temperature rise. This is due to warming temperatures in the mountainous regions. Water demands throughout the winter season are lower, if we have more river flow in the winter, we require reservoirs with larger capacity to store water during the winter and release it in seasons with higher water demands. Temperature data from seven stations in the Karakoram and Hindukush mountains during the 1961–1999 period were examined, and the result indicated a rising trend in winter and cooling in the summer season (Fowler & Archer 2006 as cited in Khattak et al. 2011). In the watershed of Dez Dam, streamflow already indicated a decreasing trend in the past and is expected to further decline in the future under the impacts of A1B, A2, and B1 scenarios (Norouzi 2020). Studies on catchments that depend on melting snow and glaciers mostly represented a decrease in runoff during the summer, and a general agreement demonstrated an increase in streamflow in the winter. The Seine and Somme watersheds were examined under the effects of climate change. The flow at the outlet of both basins reflected a decreasing trend of at least 14 and 20% throughout 2050 and 2080, respectively (Habets et al. 2013). Changes in precipitation patterns are another problem impressing water management. Based on a study conducted by Bangladesh Agriculture University, the availability of green water resources, which are essential for the ecological viability of rice farming, is greatly impacted by changes in precipitation patterns and increased climate unpredictability (Islam et al. 2024 cited in Farugue & Ali 2005; Shrestha et al. 2017; Choudhary et al. 2022). Therefore, climate change causes precipitation to vary its time and pattern that can be a questioning issue for agriculture land and water management strategies.

Afghanistan's decades of war and instability of government have caused the absence of meteorological data, poverty, and insufficient experts to manage water resources. Based on studies, approximately 80% of Afghanistan's population is engaged in agricultural products. However, changing climate patterns are causing drought in some parts of the country, and adversely affecting the economy and social welfare of the people. In addition to these issues, inadequate surface water storage and subpar reservoir management limit the amount of water availability for irrigation and exacerbate food security in Afghanistan. Snowmelt is used by many farmers to irrigate their cropland. However, snow drought, can put additional strain on the nation when it coexists with continuous conflicts, cruelty, and economic difficulties (AghaKouchak et al. 2020). Effective management of Afghanistan's water resources can help reduce poverty and address economic challenges faced by its people. However, limited data availability and a lack of comprehensive studies have created serious challenges in this area.

Rivers in Afghanistan originate primarily from higher mountains covered by snow and glaciers, which are vulnerable to temperature rise. In recent years, some studies have been conducted to analyze the effects of climate change on water resources in this country. For instance, Azizi & Asaoka (2020) found that, under the RCP4.5 and RCP8.5 scenarios, temperature indicates 1.45, 2.51, and 2.05 and 5.18 °C increase in the mid and late 21st century, respectively. Precipitation showed a declining tendency throughout the century but was positive toward the end of the century compared to the mid-century in both scenarios. Streamflow was expected to decrease under both scenarios, except for spring, which indicated an increase by mid-century (Azizi & Asaoka 2020). Climate change causes an increase in precipitation in the late autumn and winter seasons in the Balkhab River Basin (KRB). In the BRB, snowmelt has a major impact on surface runoff in the spring and early summer. A flow peak will occur in May because of snowmelt and rainfall contribution to the process (Hussainzada & Lee 2021). Dams are the main water management infrastructure which are built in the possible suitable area in the watersheds. Reservoirs of large dams are affected by climate change and require frequent investigation for adaptation of their water management strategies. Studies conducted to assess the impacts of climate change on river basins in different parts of Afghanistan. However, there is a very limited study to investigate the impact of climate change on the SWA Dam's reservoir. This research aimed to evaluate the effects of climate change on the SWA Dam's reservoir which is located in a semi-arid region. To achieve this goal, we employed the SWAT model for hydrological modeling, which is described in the following sections.

Study area

The Shah Wa Arus (SWA) Dam is located in the northwestern region of Kabul, Afghanistan (Figure 1). Its watershed has around 95 km2 area with a mean total annual precipitation of 300–450 mm. The temperature in the region ranges between 12 and 30 °C in the summer and drops to −7 °C in the winter. Snowfall on the top of mountains in the winter is the main source of river flow throughout the spring and summer. The main occupation of people living in the region is agriculture, which includes crop and livestock farming. Shakardara catchment provides water for farm irrigation, and groundwater recharge through the SWA Dam's reservoir. The basin relies primarily on snowmelt and the peak flow in the river occurs in late spring due to intense rain (Sadid et al. 2017). Many farmers living in Shakardara depend on rain-fed agriculture, making the area climate-dependent for its economic activities.
Figure 1

Location of SWA Dam's reservoir in the KRB.

Figure 1

Location of SWA Dam's reservoir in the KRB.

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SWAT model

The SWAT model is a hydrological model used to calculate the amount of water in a river basin. The USDA Agriculture Research Services developed the SWAT model (Arnold et al. 1998). This watershed scale model simulates runoff and groundwater quantity and quality in large and complex watersheds with a variety of soils, land uses, and management conditions over long periods (Emiru et al. 2021). It predicts the environmental consequences of land management techniques and climate change. This worldwide and continuously under-development model examines how agricultural practices, vegetation, soil, and climate affect the flows. In this hydrological model, the basin divides into multiple sub-basins, and each of them separates into smaller areas called hydrological response units (HRUs) which are made up of distinct homogeneous combinations of the soil and land use characteristics in each sub-basin (Arnold et al. 2012). The water balance equation is the basis for how the SWAT hydrological model operates (Neitsch et al. 2011):
(1)

In Equation (1), SWo is the initial soil water content, SWt is the final soil water content, and t is the time (day), also, runoff (Qsurf), evapotranspiration (Ea), precipitation (Rday), return flow (Qgw), and percolation (Wperc) all measured in millimeters per day (Li et al. 2012).

The required input data in the SWAT model are the digital elevation model (DEM), land use/land cover (LULC), and soil map as spatial data. Precipitation (P), Max & Min air temperature (Tmax & Tmin), wind speed (WS), solar radiation (SR), and relative humidity (RH) are weather data to simulate the PET in the model by Penman–Monteith, Priestley–Taylor, or Hargreaves methods. SWAT coupling with ArcGIS allows the management of raster, vector, and alphanumeric data. Operation of the SWAT model requires daily weather data if available. If not, the model can generate them. The USDA's soil and conservation service's curve number (CN) method and the Green and Ampt method are two alternate approaches used by the SWAT model to simulate surface runoff (USDA 1972).

Data sources

In hydrological modeling, the most important and effective parameters are the quality of input data. Hydrometeorology data scarcity is an issue for most developing countries and remote areas. In this study, P data was obtained from Tropical Rainfall Measurement Mission (TRMM_v7) for all months except for March, April, and May, which was acquired from CHIRPS dataset due to incompatibility of TRMM data to observed data which were available for a limited time. The remaining data such as Tmax & Tmin, RH, SR, and WS were acquired from the NASA POWER dataset. These data were used for streamflow simulation in the Shakardara watershed to validate the SWAT model performance in the study area. DEM which has 30 m resolution was acquired from the United States Geological Survey (USGS)'s Shuttle Radar Topography Mission (STRM) (www.earthexplorer.usgs.gov) and used as input data for the SWAT model (Figure 2(a)). Soil data clipped from UN-FAO global soil map database (Figure 2(b)), and LULC data were obtained from the ESRI 2020 LULC dataset which has a 10 m spatial resolution (Figure 3).
Figure 2

SWA Dam's Watershed: (a) DEM map and (b) soil map.

Figure 2

SWA Dam's Watershed: (a) DEM map and (b) soil map.

Close modal
Figure 3

SWA Dam's Watershed LULC map.

Figure 3

SWA Dam's Watershed LULC map.

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Calibration and validation

Calibration and validation of the model were performed using the SWAT-CUP program between 2010 and 2018. SWAT-CUP is a program for ‘sensitivity analysis, calibration, validation, and uncertainty analysis of the SWAT model’ (Arnold et al. 2012). The Sequential Uncertainty Fitting version 2 (SUFI-2) was chosen as the calibration algorithm in the SWAT-CUP program. The evaluation of model effectiveness was performed utilizing the model efficiency coefficients like NSE (Equation (3)) and R2 (Equation (2)) in both calibration and validation.
(2)
(3)

In Equations (2) and (3); is the mean value of the observed values, is the observed value, is the simulated value, is the mean value of the simulated values and n is the total count of the sample pairs (Miao et al. 2021).

The selection of parameters has a key role in the calibration and validation of hydrological models. For this purpose, the 17 most effective parameters were used for calibration and validation of the SWAT model in the SWA Dam's watershed (Table 1).

Table 1

Selected parameters for calibration and validation of the hydrological model

NOParametersDescription
CN2.mgt SCS runoff curve number 
ALPHA_BF.gw Base flow alpha factor (days) 
GW_DELAY.gw Time interval for recharge of the aquifer (days) 
GWQMN.gw Threshold depth of water in the shallow aquifer required for return flow to occur (mm) 
OV_N.hru Manning's ‘n’ value for overland flow. 
SOL_AWC (.).sol Available water capacity of the soil layer 
R__SURLAG.bsn Delay time of direct surface runoff (days) 
PPERCO.bsn Phosphorus percolation coefficient 
SFTMP.bsn Snowfall temperature 
10 R__SOL_K (.).sol Saturated soil hydraulic conductivity (mm h−1
11 SLSUBBSN.hru Average slope length (m) 
12 HRU_SLP.hru Average slope steepness 
13 ESCO.hru Soil water evaporation compensation factor (dimensionless) 
14 SMTMP.bsn Snow melt base temperature 
15 GW_REVAP.gw Groundwater revap coefficient 
16 TLAPS.sub Temperature lapse rate 
17 PLAPS.sub Precipitation lapse rate 
NOParametersDescription
CN2.mgt SCS runoff curve number 
ALPHA_BF.gw Base flow alpha factor (days) 
GW_DELAY.gw Time interval for recharge of the aquifer (days) 
GWQMN.gw Threshold depth of water in the shallow aquifer required for return flow to occur (mm) 
OV_N.hru Manning's ‘n’ value for overland flow. 
SOL_AWC (.).sol Available water capacity of the soil layer 
R__SURLAG.bsn Delay time of direct surface runoff (days) 
PPERCO.bsn Phosphorus percolation coefficient 
SFTMP.bsn Snowfall temperature 
10 R__SOL_K (.).sol Saturated soil hydraulic conductivity (mm h−1
11 SLSUBBSN.hru Average slope length (m) 
12 HRU_SLP.hru Average slope steepness 
13 ESCO.hru Soil water evaporation compensation factor (dimensionless) 
14 SMTMP.bsn Snow melt base temperature 
15 GW_REVAP.gw Groundwater revap coefficient 
16 TLAPS.sub Temperature lapse rate 
17 PLAPS.sub Precipitation lapse rate 

SCS, Soil Conservation Service.

Future climate scenarios

The General Circulation Model (GCM) was used for the preparation of future climate data. MIROC5 model from CMIP5 was employed to obtain P and Tmax and Tmin data considering RCP4.5 and RCP8.5 scenarios. ‘MIROC5 model has been cooperatively developed by the University of Tokyo, National Institute for Environmental Studies (NIES) and Japan Agency for Marine-Earth Science and Technology (JAM-STEC)’ (Nozawa et al. 2007). RCPs are standardized greenhouse gas concentration trajectories, for climate change modeling and assessment. The RCPs provide a distinct set of data, especially when it comes to the extent, level of detail, and spatial scale of information utilized in climate model projection (Van Vuuren et al. 2011). RCPs are scenarios used to estimate the variety of potential climate futures based on various socioeconomic, technological, and policy development. These scenarios provide a framework for projecting varying levels of radiative forcing, the change in energy flux in the atmosphere resulted in the emission of greenhouse gases by the year 2100 (Van Vuuren et al. 2011). Based on evaluation criteria in the SWAT calibration using model efficiency coefficients including Nash–Sutcliff Efficiency (NSE), percentage of bias (PBIAS), and coefficient of determination (R2), the MIROC5 model demonstrated the greatest agreement with observational data (Afshar et al. 2017; Rahimpour et al. 2021). The RCP4.5 and RCP8.5 scenarios were used to investigate the impact of climate change in the study area. The future climate data were downscaled and bias-corrected based on utilized input data for hydrological modeling, it was carried out using the CMhyd program. This process was performed using observed data after the calibration and validation of the developed hydrological model and obtaining acceptable results. The observed data were used as reference data, and the future climate data from both RCP4.5 and RCP8.5 scenarios were inserted into the CMhyd application for downscaling. The reliability of the future climate data was assessed by comparing it with the observed data over the same periods. For example, future climate data were bias-corrected and downscaled for the period 2010–2018 and used as input for hydrological modeling. The results were satisfactory after calibrating the model for the specified period. The comparison and evaluation were based on the observed data.

Hydrological modeling in the study area

In this study, hydrological modeling has been conducted using the SWAT model from 2010 to 2018. Calibration and validation of the model were done in the 2010–2012 and 2013–2018 periods, respectively (Figure 4). As a result of model calibration and validation in the SWA Dam's catchment, the model efficiency coefficients were obtained as R2 = 0.82, NSE = 0.81, and R2 = 0.72, NSE = 0.69 for calibration and validation accordingly. The linear relationship between observed and simulated runoff acquired as R2 = 0.73 which counts as a very good result (Figure 5). The hydrological modeling in this watershed also manifested a very good result during calibration and a good throughout the validation period.
Figure 4

Model calibration and validation.

Figure 4

Model calibration and validation.

Close modal
Figure 5

Model efficiency in the catchment.

Figure 5

Model efficiency in the catchment.

Close modal

For the simulation of streamflow in hydrological modeling, the most effective parameters are precipitation and temperature which are the main variables in climate change assessment. The temperature in the basin indicated a + 3.4 and 3.79 °C rise under the impacts of RCP4.5, and +4.39 and +6.53 °C considering RCP8.5 scenarios in the periods of 2050–2069 and 2080–2099, respectively (Table 2). This represents a rising trend of temperature throughout the century.

Table 2

Average temperature change (°C) over basin, RCP scenarios

Scenarios2000–20192050–2069Change2080–2099Change
Observed 6.23     
RCP4.5  9.63 3.4 10.02 3.79 
RCP8.5  10.62 4.39 12.76 6.53 
Scenarios2000–20192050–2069Change2080–2099Change
Observed 6.23     
RCP4.5  9.63 3.4 10.02 3.79 
RCP8.5  10.62 4.39 12.76 6.53 

The increased temperature in the area impacts the water balance elements in the future under both scenarios. Based on Table 2, under the effects of the RCP4.5 scenarios, the difference between temperature in the mid and late century is +0.39 °C. This variation is going to grow by +2.14 °C under the impact of the RCP8.5 scenario at the same period. In the SWA Dam's watershed, the fluctuation of precipitation along the year is observed. Precipitation will increase in December and January. Furthermore, unexpected precipitation rise is obtained during July, August, and September (Figure 6). The duration of precipitation is shortening and the severity of rainfall is going to rise during winter and summer.
Figure 6

Observed and downscaled precipitation in SWA watershed, RCP4.5 and RCP8.5.

Figure 6

Observed and downscaled precipitation in SWA watershed, RCP4.5 and RCP8.5.

Close modal

The accuracy of bias-corrected temperature was verified based on the reference temperature data in the same period. In this comparison, the coefficient of determination was obtained as R2 = 0.9944 and R2 = 0.9946 for RCP4.5, and RCP8.5 scenarios, respectively. Changes in water balance elements and water availability in the watershed will be evaluated under the impacts of each climate change scenario separately.

Hydrological modeling considering the RCP4.5 scenario

Hydrological modeling considering the RCP4.5 scenario indicated the precipitation to change from 493.5 mm in 2000–2019 to 325.9 and 358.7 mm during 2050–2069 and 2080–2099, respectively. PET will change from 792 mm to 898.3 and 901.9 mm accordingly. ET also rises, whereas, runoff will extremely decrease in the mentioned periods. Rainfall in the area shows an extreme drop in March, April, and May. However, oscillations are visible during the other months of the year. The rising tendency of rainfall in the watershed appears in the summer season extending to September and October. Figure 7 represents the variation in monthly precipitation over the Shakardara watershed. Snowfall going to decrease in the catchment in both periods. The streamflow will shorten its duration and extremely drop throughout the century.
Figure 7

Changes in basin values in the Shakardara, RCP4.5.

Figure 7

Changes in basin values in the Shakardara, RCP4.5.

Close modal

Hydrological modeling considering the RCP8.5 scenario

The model output in the catchment indicated the oscillation in precipitation throughout the year. It makes water management planning in the area more challenging. The P in the spring season decreases while the water demand is going to be at its maximum. In addition, precipitation has variations along the century which is going to drop from 493.5 mm in 2000–2019 to 381.9 mm during 2050–2069, then it will rise to 405.9 mm in 2080–2099. By +6.54 °C temperature rise in the catchment, the PET demonstrated an extreme change from 792 mm to 916.6 and 972.8 mm for the mentioned periods accordingly.

The runoff change in the catchment is unexpected, it was 175.74 mm in 2000–2019 and changed to 35.6 and 36.63 mm in the mid and late century, respectively. This variation represents the 20-year average; however, seasonal fluctuations in the area remain possible. Runoff simulation in other parts of the country, which are partially covered by permanent snow and glaciers, indicated no significant change in annual mean runoff. This is because glacierized regions are currently sustained by meltwater resulting from rising temperatures in the area; however, this contribution is temporary and may diminish over time. According to a research on the Kokcha sub-basin, between 1990 and 2015, nearly 15% of the glacierized land was lost, adding over 200 mm of runoff annually to the watershed's overall runoff (Joya et al. 2021). In the glacierized areas, existing streamflow prediction models are likely to underestimate future water shortages due to the inadequate representation of glacial meltwater contribution (Shokory et al. 2023). This study area which is not glacierized is projected to experience extreme water shortages in the future due to rising temperatures in the region. The variations in the rainfall, snowfall, PET, and runoff are shown in Figure 8. The temperature rise over the river basin causes variations in water balance elements. The increased amount of temperature will affect water availability in the near and far future. The result of the hydrological model considering the RCP8.5 scenario in the catchment represents changes in all components. Shifting the time and depth of rainfall in the basin, extreme decrease in snowfall, increase of PET in the first half of the year when the water demand is at its maximum, and rapid drop of runoff in the catchment cause the study area to face water shortage.
Figure 8

Changes in basin values in SWA Dam's catchment, RCP8.5.

Figure 8

Changes in basin values in SWA Dam's catchment, RCP8.5.

Close modal

It is said that, in the near and far future, the water crisis will be a major cause of human migration, especially in developing countries and arid regions. Water-related issues can be significant drivers for the movement of people. Trends in water-driven mobility can be classified as fluctuations in water quantity, hazardous water-related risks, physical impacts to water systems, and pollution of the water. When temperature increases, climate-related problems can cause drought and economic crises. Furthermore, any rise in evapotranspiration leads agriculture areas to increase water demand (Wrathall et al. 2018).

As we found in this study, climate change has a major impact on the SWA Dam's reservoir by changing its water resources. The SWA Dam will face water shortage and will not be able to store water in its reservoir. Since the economy of inhabitants in the study area depends on agricultural activities, any decrease in rainfall and snowfalls will affect agricultural products negatively. Farmers will not be able to cultivate their crops due to low rainfall, and insufficient water in the area. Therefore, without appropriate water management strategies, general harsh conditions caused by climate change impact agricultural products, and will force people to leave their lands and migrate to other regions. It is recommended to take action against climate-related challenges in the Shakardara district by implementing appropriate adaptation strategies. Since the area is situated on steep slopes, constructing small check dams to recharge groundwater during the wet season is an effective method for conserving water. Additionally, implementing rainwater harvesting, transitioning to drip and sprinkler irrigation systems, shifting crop patterns to drought-resistant and early maturing varieties, and strengthening local water user associations to ensure equitable water distribution can help mitigate potential conflicts. Another viable strategy is supplying water from neighboring regions. When combined, these strategies can help the Shakardara district effectively adapt to climate change.

In addition to modeling uncertainties, hydrological models mostly depend on precipitation and discharge data, which are either insufficient or out-of-date in Afghanistan because of decades of war and violence. This region's topography needs for detailed spatial data; mountains and snow-covered areas are two further issues that can result in major floods at unpredictable times and durations. Furthermore, satellite-derived data could not match regional occurrences. Since this study primarily used satellite-based input data due to the lack of in situ observations, it is strongly recommended to incorporate in situ data in future hydrological modeling efforts and to establish additional hydrological stations across the country.

This study was conducted to investigate the effects of climate change on the reservoir of the Shah Wa Arus Dam located in a semi-arid region. The SWAT model was utilized to simulate the streamflow in the basin. Required input data for hydrological modeling were acquired from remote sensing sources due to data deficiencies in the region. Future climate data were obtained from the MIROC5 model of GCM, and the CMhyd program was used for downscaling and bias correction of precipitation and temperature. Runoff simulation was performed considering the RCP4.5 and RCP8.5 scenarios for the periods 2050–2069 and 2080–2099. Under the impact of the RCP4.5 scenario, the temperature in the area will rise by +3.4 and +3.8 °C in 2050–2069 and 2080–2099, respectively. Precipitation will decrease by −33.96 and −27.32% throughout mentioned times. The water capacity of the river basin will drop by −71.43 and −67.78% in the mid and late centuries, respectively. However, under the RCP8.5 scenario, the temperature in the area will rise by +4.4 °C during 2050–2069 and +6.54 °C throughout 2080–2099. Precipitation will decrease by −22.61% in 2050–2069, and it will change to −17.75% at the end of the century. The water availability in the dam's reservoir will decline by −63.03 and −65.2% throughout 2050–2069 and 2080–2099, respectively. Drought will occur in the basin due to decreased streamflow in the area. Climate change will cause the SWA Dam to be unable to provide water demand downstream. Based on the results of this study, it is recommended that appropriate actions must be taken on climate change adaptation mechanisms for water management in the region, or water for Shakardara district should be supplied from other sources. Further investigations are necessary to assess the impact of climate change on available water infrastructure in the Kabul River Basin.

I would like to express my gratitude to Prof. Dr Sevinç Ozkul for her continuous support and supervision, as well as to the Institute of Natural and Applied Sciences at Dokuz Eylul University for providing the facilities necessary for this work.

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

The authors declare there is no conflict.

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