ABSTRACT
This study critically assesses the combined effects of climate and land-use change on flood recurrence in the Kokcha River, Afghanistan, spanning the period from 2010 to 2021 and projecting forward to 2088–2099. Through the application of a bias-corrected model, we achieved high accuracy in temperature and precipitation simulations, with notable NSE values of 0.9 and 0.69, and R2 values of 0.92 and 0.78, respectively. Future streamflow simulations under different scenarios highlight climate change as the major driver influencing flood recurrence in the Kokcha River, contributing to 101.1% of the total variation, while land-use change has a minimal contribution of −1.1%. Our analysis of precipitation, average temperature, and streamflow data reveals significant patterns and changes, with future projections indicating a gradual decline in precipitation levels, mean temperature, and streamflow. Flood frequency analysis for return periods of 10, 50, 100, 200, and 500 years, considering different scenarios, underscores the likelihood of floods of varying magnitudes. Notably, the highest streamflow spikes under both scenarios highlight the impact of futuristic air temperature and precipitation on flood recurrence. The study advocates prioritizing climate change adaptation and resilient land-use strategies to ensure sustainable water resource management, emphasizing the mitigation of potential flood risks.
HIGHLIGHTS
Utilized SWAT model to analyze climate and land-use impacts on streamflow.
Utilized multiple scenarios to isolate climate and land-use change impacts on streamflow.
Employed the CA-ANN model for forecasting future land use patterns
Identified climate change as exerting a more significant impact on streamflow in comparison to changes in land use.
INTRODUCTION
The Kokcha River, originating from the Hindu Kush Mountains and flowing through northeastern Afghanistan, serves as a vital water source for millions, sustaining agriculture, fishing, and other economic activities (Shroder et al. 2022). However, it faces severe environmental challenges exacerbated by climate change and unsustainable land practices. This study aims to comprehensively assess the impacts of these factors on the Kokcha River's hydrology and ecology, presenting innovative insights into adaptation strategies. The region has experienced rising temperatures and changes in precipitation patterns, which have led to decreased water flow and increased erosion. Extreme weather events such as floods and droughts have become more frequent and intense, which has negatively impacted agricultural productivity and other livelihoods that depend on the river. In addition, the changes in temperature and precipitation patterns have led to changes in the hydrological cycle, which has affected the availability and quality of water in the river. The changes in the hydrological cycle have also led to the increased occurrence of landslides, which have further impacted the ecological health of the river (Rycroft & Wegerich 2009). Unsustainable land-use practices have also contributed to the degradation of the Kokcha River. Deforestation, overgrazing, and other land-use practices have led to soil erosion and sedimentation, further reducing water quality and availability (Roy & Rahman 2023). These practices have contributed to increased soil degradation, desertification, and loss of biodiversity, which has further impacted the ecological health of the river (Shirzad & Solanki 2017).
Effective disaster management and policy development necessitate a thorough understanding of risk, particularly concerning natural hazards like floods, which are increasingly prevalent worldwide due to various factors such as population growth, urbanization, and climate change-induced shifts in weather patterns (Meyer et al. 2009). Afghanistan, positioned in a region highly prone to natural disasters, faces significant vulnerability to severe floods due to its geography and historical environmental degradation, ranking as the third most at-risk country according to the INFORM 2022 Index (Ikram et al. 2023). Floods in Afghanistan disproportionately affect impoverished and vulnerable communities, exacerbating social vulnerability and coping capacity challenges. Despite the arid climate, flood risk persists, necessitating comprehensive flood risk assessments to inform effective disaster management and resilience-building strategies (Ahmadi et al. 2023). The absence of adequate flood risk assessment methodologies at both local and national levels underscores the urgency for informed and proactive flood risk management approaches. Such assessments, incorporating satellite-based technology and spatial data analysis, offer promising avenues for enhancing rational flood management and targeted disaster preparedness. In addressing flood risks, communities have options including withdrawal, resistance, and adaptation, with the effectiveness of adaptation strategies contingent upon robust flood risk assessments (Nam et al. 2015).
The impacts of climate change on river systems have been extensively studied globally, highlighting the profound implications for water availability, quality, and ecosystem health. Vörösmarty et al. (2010) emphasize that altered precipitation patterns and temperature rise directly impact river hydrology, leading to changes in flow regimes, habitat availability, and aquatic biodiversity. Water resources are essential for human survival and economic growth, but they are threatened by climate change. The availability of water for human uses such as drinking, agriculture, and power generation is affected by fluctuations in river flows. Similarly, Maurer et al. (2002) demonstrate how reduced snowpack and altered streamflow patterns, attributed to climate change, affect water resources and agricultural productivity in regions like the western United States. Unsustainable land-use practices exacerbate the impacts of climate change on rivers, leading to increased runoff, sedimentation, and habitat degradation (Rahman & Rahman 2023). McGrane (2016) points out that urbanization intensifies surface runoff and flood risks due to elevated impervious surfaces, while agricultural activities contribute to nutrient and sediment runoff, leading to eutrophication and water quality deterioration (Astuti 2017; Kumar & Yadav 2020).
Furthermore, alterations in land use have a significant influence on the flow of rivers, which in turn, have social and economic ramifications for societies that depend on rivers for their cultural and economic sustenance. Changes in land utilization practices have the potential to generate disputes among various stakeholders, including farmers, urban residents, and conservationists, regarding their entitlement to water resources (Steinhäußer et al. 2015). Urbanization can result in escalated water demand for both domestic and industrial purposes, which can subsequently cause a decrease in water accessibility for other users (Majumder 2015; Sakizadeh & Milewski 2023). In a nutshell, changes in land use are a crucial factor that propels the flow of rivers, thereby impacting the freshwater ecosystems, water resources, and the society (Satriagasa et al. 2023). Land-use change patterns, encompassing urbanization, agriculture, and deforestation, have the potential to cause changes in river hydrology, thereby inducing changes in both the quality and quantity of water (Teixeira et al. 2014).
Soil and Water Assessment Tool (SWAT) is a hydrological model that is extensively employed to simulate the effects of river runoff on water resources. SWAT model is a hydrological simulation model that operates on a process-based and spatially distributed approach, enabling it to replicate watershed-scale hydrological processes (Abbaspour et al. 2015). The hydrological simulation of a watershed is achieved through the integration of diverse factors such as climate, land use, soil, and management practices within the model. The computational model employs algorithms to replicate the dynamics of water transport, encompassing phenomena such as surface runoff, groundwater flow, and streamflow. The model has been utilized in various research endeavors to evaluate the effects of river discharge on water resources, encompassing the accessibility, quality, and quantity of surface and groundwater (Abbaspour et al. 2015). The utilization of the SWAT model in evaluating the hydrologic consequences of river discharge holds significant ramifications for the management of water resources (Joumar et al. 2023). The model serves as a means for engaging stakeholders and fostering discourse regarding water resource management and the potential effects of different management strategies (Vo et al. 2019). The SWAT model is an advantageous tool for evaluating the hydrologic consequences of river discharge on water reserves. The aforementioned model facilitates the assessment of diverse management scenarios to gauge their effects on water resources, encompassing the availability, quality, and quantity of both surface and groundwater (Neitsch et al. 2011).
Water resources, agriculture, and infrastructure in Afghanistan are all predicted to be significantly impacted by climate change. The most recent generation of global climate models used to simulate the Earth's climate system is known as Coupled Model Intercomparison Project Phase 6 (CMIP6). These models are crucial for forecasting future climate change and comprehending the impacts of greenhouse gas emissions. SSP2-4.5 and SSP5-8.5 are the two shared socioeconomic pathways (SSPs) that are taken into consideration in this study, and they represent two distinct scenarios for future greenhouse gas emissions and their impacts on the climate. The SSP2-4.5 scenario anticipates that moderate mitigation measures will be taken to reduce greenhouse gas emissions and keep the rise in the global mean temperature to 2 °C over pre-industrial levels. The SSP5-8.5 scenario, on the other hand, anticipates significant greenhouse gas emissions without any mitigation measures, resulting in a temperature rise of at least 4 °C over pre-industrial levels by the end of the century. Scenarios (O'Neill et al. 2016) examined the effects of the SSP2-4.5 and SSP5-8.5 scenarios on international food security. They discovered that there would be a little decrease in the availability of food on a worldwide scale under the SSP2-4.5 scenario, but that this could be avoided by taking the proper adaption strategies. However, under the SSP5-8.5 scenario, there might be a 30% decrease in the amount of food available globally, which would cause food insecurity in many parts of the world. CMIP6 models offer useful information on the probable effects of future greenhouse gas emissions on the climate of Earth. The models demonstrate that considerable increases in temperature, precipitation, and sea level rise are anticipated even with modest mitigation measures (Neitsch et al. 2011). To lessen uncertainty in future estimates, further study is required because the size of these changes differs among the models. Policymakers and researchers may evaluate the possible effects of various greenhouse gas emission routes on several facets of the Earth system, including food security, biodiversity, and human well-being, using the frameworks provided by the SSP2-4.5 and SSP5-8.5 scenarios (O'Neill et al. 2016).
This study aims to comprehensively assess the combined effects of climate and land-use change on flood recurrence in the Kokcha River basin, Afghanistan, from 2010 to 2021, with projections extending to 2088–2099. The specific objectives are (1) to evaluate the accuracy of temperature and precipitation simulations using bias-corrected modeling techniques, (2) to analyze future streamflow simulations under SSP2-4.5 and SSP5-8.5 climate scenarios and assess the relative contributions of climate and land-use change to flood recurrence, (3) to examine the projected changes in precipitation, average temperature, and streamflow and their implications for water resource management, (4) to conduct flood frequency analysis for various return periods and design flood estimation, considering different climate scenarios, and (5) to assess the impact of anticipated land-use changes on streamflow using advanced modeling approaches. By achieving these objectives, this study aims to provide valuable insights into the dynamics of the Kokcha River basin, informing policy decisions and adaptation strategies to mitigate the adverse effects of climate change and unsustainable land-use practices on water resources and ecosystem health. Through interdisciplinary research and innovative modeling techniques, we seek to contribute to the scientific understanding of hydrological variability and enhance the resilience of riverine communities in Afghanistan.
STUDY AREA, DATA COLLECTION, AND METHODOLOGY
Study area description
Data collection
Hydroclimatic data
The Ministry of Energy and Water (MEW) of Afghanistan has provided meteorological and hydrological data, including rainfall, maximum and minimum temperatures range from 2008 to 2021 and hydrological data spans from 2010 to 2021. The description of four GCMs is listed in Table 1.
Summary of the general circulation models (GCMs) utilized in the current study
Modeling center . | Model . | Resolution (km) . |
---|---|---|
National Earth System Model Version 3 | NESM3 | 192 × 96 |
National Center for Meteorological Research (France) | CNRM-CM6-1 | 250 × 250 |
Geophysical Fluid Dynamics Laboratory, USA | GFDL-ESM4 | 90 × 144 |
Atmosphere and Ocean Research Institute, The University of Tokyo, Japan | MIROC6 | 128 × 256 |
Modeling center . | Model . | Resolution (km) . |
---|---|---|
National Earth System Model Version 3 | NESM3 | 192 × 96 |
National Center for Meteorological Research (France) | CNRM-CM6-1 | 250 × 250 |
Geophysical Fluid Dynamics Laboratory, USA | GFDL-ESM4 | 90 × 144 |
Atmosphere and Ocean Research Institute, The University of Tokyo, Japan | MIROC6 | 128 × 256 |
METHODOLOGY
Gumbel distribution method
The Gumbel distribution finds applications in various fields, particularly in modeling extreme events, such as extreme temperatures, floods, or financial risk assessment. It is widely used in fields like hydrology, climatology, and finance to analyze and predict the occurrence of rare or extreme events. Additionally, the Gumbel distribution plays a vital role in statistics for extreme value theory, where it is used to estimate the distribution of the maximum or minimum value from a set of independent and identically distributed random variables.
This extreme value distribution was introduced by Gumbel and is known as Gumbel's Method. It is widely used for probability distribution functions for extreme values in hydrological studies or prediction of flood peaks, maximum rainfall, etc. The method and procedure for calculating the design flood in any return period. The hypothetical plotting by Gumbel (1945) is in light of the presumption that the watched esteem is the most likely, or modular, estimation of this rank of the flood. Its arrival period is along these lines skewed toward the method of the hypothetical circulation. The Gumbel hypothesis does not have any significant bearing entirely to flood for the accompanying reasons. The Gumbel method of frequency analysis is based on extreme value distribution and uses frequency factors developed for theoretical distribution.

In which, YT is the reduced variate, Yt = −.
The values of Yn and Sn are selected from Gumbel's Extreme Value Distribution and considered depending on the sample size.
CMHYD tool
Watershed models are often used to simulate the impact of future climate conditions on hydrologic processes. However, Teutschbein & Seibert (2012) state that simulations of temperature and precipitation often show significant biases due to systematic model errors or discretization and spatial averaging within grid cells, which hampers the use of simulated climate data as direct input data for hydrological models. Bias correction procedures are used to minimize the discrepancy between observed and simulated climate variables on a daily time step so that hydrological simulations driven by corrected simulated climate data match simulations using observed climate data reasonably well. CMhyd is a tool that can be used to extract and bias-correct data obtained from global and regional climate models. It is highly recommended to apply an ensemble approach, i.e., to use bias-corrected data provided by several climate models and downscaling methods.
Bias correction procedures employ a transformation algorithm for adjusting climate model output. The underlying idea is to identify biases between observed and simulated historical climate variables to parametrize a bias correction algorithm that is used to correct simulated historical climate data (see Figure 1). Bias correction methods are assumed to be stationary, i.e., the correction algorithm and its parametrization for current climate conditions are assumed to be valid for future conditions as well. Thus, the same correction algorithm is applied to the future climate data. However, it is unknown how well a bias correction method performs for conditions different from those used for parametrization. A good performance during the evaluation period does not guarantee a good performance under changed future conditions. Teutschbein & Seibert (2012) provide a detailed discussion and state that a method that performs well for current conditions is likely to perform better for changed conditions than a method that already performs poorly for current conditions.
Bias correction was performed using the CMhyd tool (Climate Model data for hydrologic modeling; Rathjen et al. 2016) with five methods for precipitation; linear scaling (LS), delta change (DC), power transformation (PT), local intensity scaling (LIS), and distribution mapping (DM). The CMhyd tool was chosen because it incorporates different bias correction methods with the ability to do analysis on a daily time scale. Moreover, both global and RCM simulations can be extracted and bias-corrected using CMhyd. The performance assessment was done using graphical and statistical methods for daily, monthly, and annual time series.
Bias correction of GCMs
Bias correction is widely used in climate impact modeling. Climate model data are often compared to observed data using various approaches to reduce uncertainty if they show very little or no relation to the observed data (Nguyen et al. 2020). To connect GCM outputs to observable data, bias correction techniques change GCMs using transformation algorithms. Additionally, the primary goal of the biases is to compare the actual data to the simulated data and to follow the same pattern with in-depth statistics for the GCM's projected futures in that area (Teutschbein & Seibert 2012). Currently, climate models are biased using the Climate Model Data for Hydrologic Modeling (CMhyd) tool, which is very user-friendly for evaluating GCMs in any study field. LS (additive and multiplicative) bias correction approach was used for the current study (Worku et al. 2020). The model accuracy was assessed through several statistical indicators, i.e., Nash–Sutcliffe efficiency (NSE), coefficient of determination (R2), and ratio of root mean square error and standard deviation (RSR). In order to minimize uncertainty, climate models are often evaluated against actual data using a variety of methods if they have little or no relationship with the data (Haleem et al. 2022).
Multi-model ensemble
The proposed methodology aimed to create a framework that explicitly followed four key steps: (a) selecting the best-performing CMIP6 GCMs; (b) developing MME and MME-ML algorithms; (c) evaluating the developed algorithms; and (d) projecting climate variables in three future time slices, namely the near future (NFR), mid-future (MFR), and far future (FFR).
Selection of the best-performed CMIP6 GCMs
Description of SWAT model
SWAT model calibration and validation
In order to eliminate any uncertainty in the parameters and choose the best fit, the simulated results are often evaluated in SWAT-CUP (SWAT Calibration and Uncertainty Procedures) following SWAT modeling. Most studies employ SWAT-CUP automated calibration with the Sequential Uncertainty Fitting Program method (SUFI-2) for uncertainty analysis due to its appropriateness and effectiveness in fitting the parameters (Hosseini & Khaleghi 2020). Generalized likelihood uncertainty estimation (GLUE), parameter solution (ParaSol), Markov chain Monte Carlo (MCMC), and particle swarm optimization (PSO) are some of the several uncertainty models available in SWAT-CUP (PSO). The SWAT-CUP model employs two-thirds of the data for calibration and one-third of the data for validation to get better outcomes. Additionally, calibration is a method for finding the optimal values for representative functions by determining the best match between observed and simulated data. NSE, coefficient of determination (R2), percent bias (Pbias), and root mean square error to the standard deviation ratio (RSR) are the statistical analysis techniques that are most often used to evaluate the climatic data sets and the model's performance. A number close to 1 indicates a good match between the model and the data, while other values imply a poor fit (Abbaspour 2013). In the verified phase, the model's effectiveness will be examined by assessing the decision variables as illustrated in Table 2.
Statistical indicators for model performance evaluation
Performance rating . | NSE . | RSR . | PBIAS (%) . |
---|---|---|---|
Very good | 0.75 < NSE ≤ 1 | 0 ≤ RSR ≤ 0.5 | −10 < PBIAS < 10 |
Good | 0.65 < NSE ≤ 0.75 | 0.5 < RSR ≤ 0.6 | ±10 ≤ PBIAS < ±15 |
Satisfactory | 0.5 < NSE ≤ 0.65 | 0.6 < RSR ≤ 0.7 | ±15 ≤ PBIAS < ±25 |
Unsatisfactory | NSE ≤ 0.5 | RSR > 0.7 | PBIAS ≥ 25 |
Performance rating . | NSE . | RSR . | PBIAS (%) . |
---|---|---|---|
Very good | 0.75 < NSE ≤ 1 | 0 ≤ RSR ≤ 0.5 | −10 < PBIAS < 10 |
Good | 0.65 < NSE ≤ 0.75 | 0.5 < RSR ≤ 0.6 | ±10 ≤ PBIAS < ±15 |
Satisfactory | 0.5 < NSE ≤ 0.65 | 0.6 < RSR ≤ 0.7 | ±15 ≤ PBIAS < ±25 |
Unsatisfactory | NSE ≤ 0.5 | RSR > 0.7 | PBIAS ≥ 25 |
Combine effect of climate and land-use change on stream flow
Scenarios-based contribution rate analysis
Scenarios . | Land use . | Meteorological data . |
---|---|---|
1 | 2001 | 2010–2015 |
2 | 2001 | 2016–2021 |
3 | 2019 | 2010–2015 |
4 | 2019 | 2016–2021 |
Scenarios . | Land use . | Meteorological data . |
---|---|---|
1 | 2001 | 2010–2015 |
2 | 2001 | 2016–2021 |
3 | 2019 | 2010–2015 |
4 | 2019 | 2016–2021 |
RESULTS AND DISCUSSION
Bias correction of GCM
LS (additive and multiplicative) bias correction technique was used for the present study using CMhyd tool (Worku et al. 2020). The model accuracy was assessed through several statistical indicators, i.e., NSE, coefficient of determination (R2), and ratio of root mean square error and standard deviation (RSR) (Haleem et al. 2022). The results are listed in Table 4. The successful bias correction of General Circulation Model (GCM) aligns with recent findings, indicating that such corrections are crucial for improving the reliability of climate projections. These high accuracy metrics validate the credibility of climate simulations, providing a robust foundation for subsequent analyses (Haleem et al. 2022).
Model assessment statistical indicators
Temperature . | Precipitation . | ||||
---|---|---|---|---|---|
NSE . | R2 . | RSR . | NSE . | R2 . | RSR . |
0.9 | 0.92 | 0.28 | 0.69 | 0.78 | 0.56 |
Temperature . | Precipitation . | ||||
---|---|---|---|---|---|
NSE . | R2 . | RSR . | NSE . | R2 . | RSR . |
0.9 | 0.92 | 0.28 | 0.69 | 0.78 | 0.56 |
Model calibration and validation
Statistical metrics governing flow regime for model calibration and validation
. | Calibration . | Validation . | ||||
---|---|---|---|---|---|---|
Watershed . | NSE . | R2 . | RSR . | NSE . | R2 . | RSR . |
Anjuman | 0.78 | 0.81 | 0.46 | 0.76 | 0.80 | 0.48 |
. | Calibration . | Validation . | ||||
---|---|---|---|---|---|---|
Watershed . | NSE . | R2 . | RSR . | NSE . | R2 . | RSR . |
Anjuman | 0.78 | 0.81 | 0.46 | 0.76 | 0.80 | 0.48 |
Parameters influencing river runoff – fitted values and initial ranges
Parameter . | Description . | Fitted value . | Ranges . |
---|---|---|---|
CN2.mgt | SCS runoff curve number | 57.6 | (35, 98) |
ALPHA_BF.gw | Baseflow alpha factor (days) | 0.03 | (0, 1) |
GW_DELAY.gw | Groundwater delay (days) | 475.6 | (0, 500) |
GWQMN.gw | Threshold depth of water in the shallow aquifer required for return flow to occur (mm) | 4,463.03 | (0, 5,000) |
SURLAG.hru | Surface runoff lag time | 37.33 | (0.05, 24) |
SLSOIL.hru | Slope length for lateral subsurface flow | 45.03 | (0, 150) |
EPCO.hru | Plant uptake compensation factor | 0.42 | (0, 1) |
SOL_K(..).sol | Saturated hydraulic conductivity | 1,723.47 | (0, 2,000) |
SOL_AWC(..).sol | Available water capacity of the soil layer | 0.01 | (0, 1) |
CH_N2.rte | Manning's ‘n’ value for the main channel | 0.72 | (−0.01, 0.3) |
SFTMP.bsn | Snowfall temperature | 18.27 | (−20, 20) |
SMTMP.bsn | Snow melt base temperature | −14.86 | (−20, 20) |
SMFMN.bsn | Minimum melt rate for snow during the year (occurs on winter solstice) | 3.45 | (0, 20) |
SNO50COV.bsn | Snow water equivalent that corresponds to 50% snow cover | 0.62 | (0, 1) |
CH_K2.rte | Effective hydraulic conductivity in main channel alluvium | 195.49 | (−0.01, 500) |
ESCO.hru | Soil evaporation compensation factor | 0.40 | (0, 1) |
OV_N.hru | Manning's ‘n’ value for overland flow | 0.13 | (0.01, 30) |
LAT_TTIME.hru | Lateral flow travel time | 47.43 | (0, 180) |
CNOP{..}.mgt | SCS runoff curve number for moisture condition | 29.13 | (0, 100) |
REVAPMN.gw | Threshold depth of water in the shallow aquifer for ‘revap’ to occur (mm) | (96.93 | (0, 500) |
GW_REVAP.gw | Groundwater ‘revap’ coefficient | (0.12) | (0.02, 0.2) |
RCHRG_DP | Deep aquifer percolation fraction | (0.97) | (0, 1) |
SNO_SUB.sub | Initial snow water content | 64.69 | (0, 150) |
Parameter . | Description . | Fitted value . | Ranges . |
---|---|---|---|
CN2.mgt | SCS runoff curve number | 57.6 | (35, 98) |
ALPHA_BF.gw | Baseflow alpha factor (days) | 0.03 | (0, 1) |
GW_DELAY.gw | Groundwater delay (days) | 475.6 | (0, 500) |
GWQMN.gw | Threshold depth of water in the shallow aquifer required for return flow to occur (mm) | 4,463.03 | (0, 5,000) |
SURLAG.hru | Surface runoff lag time | 37.33 | (0.05, 24) |
SLSOIL.hru | Slope length for lateral subsurface flow | 45.03 | (0, 150) |
EPCO.hru | Plant uptake compensation factor | 0.42 | (0, 1) |
SOL_K(..).sol | Saturated hydraulic conductivity | 1,723.47 | (0, 2,000) |
SOL_AWC(..).sol | Available water capacity of the soil layer | 0.01 | (0, 1) |
CH_N2.rte | Manning's ‘n’ value for the main channel | 0.72 | (−0.01, 0.3) |
SFTMP.bsn | Snowfall temperature | 18.27 | (−20, 20) |
SMTMP.bsn | Snow melt base temperature | −14.86 | (−20, 20) |
SMFMN.bsn | Minimum melt rate for snow during the year (occurs on winter solstice) | 3.45 | (0, 20) |
SNO50COV.bsn | Snow water equivalent that corresponds to 50% snow cover | 0.62 | (0, 1) |
CH_K2.rte | Effective hydraulic conductivity in main channel alluvium | 195.49 | (−0.01, 500) |
ESCO.hru | Soil evaporation compensation factor | 0.40 | (0, 1) |
OV_N.hru | Manning's ‘n’ value for overland flow | 0.13 | (0.01, 30) |
LAT_TTIME.hru | Lateral flow travel time | 47.43 | (0, 180) |
CNOP{..}.mgt | SCS runoff curve number for moisture condition | 29.13 | (0, 100) |
REVAPMN.gw | Threshold depth of water in the shallow aquifer for ‘revap’ to occur (mm) | (96.93 | (0, 500) |
GW_REVAP.gw | Groundwater ‘revap’ coefficient | (0.12) | (0.02, 0.2) |
RCHRG_DP | Deep aquifer percolation fraction | (0.97) | (0, 1) |
SNO_SUB.sub | Initial snow water content | 64.69 | (0, 150) |
Contribution rate of climate and land-use change on river runoff
The simulated annual streamflow results show the effects of climate and land-use change on river runoff over the preset scenarios as demonstrated in Table 7. The total variation observed in streamflow is 174.13 m3/s. In the total variation in streamflow, climate and land-use change caused 101.1 and −1.1% variations, respectively. It shows that climate change is the major driver in impacting streamflow in the Kokcha River, Afghanistan, a similar conclusion was drawn by Ma et al. (2008). The findings underscore the significance of comprehending and mitigating the effects of climate change on the Kokcha River. This statement highlights the necessity of implementing efficient measures to alleviate the impacts of these effects and establish tactics for sustainable land-use methodologies. Through the prioritization of climate change adaptation and the promotion of environmentally conscious approaches, it is possible to safeguard the streamflow and ensure the long-term resilience of the Kokcha River ecosystem. The disproportionate contribution of climate change to streamflow variation underscores the urgent need for climate adaptation measures in the Kokcha River basin. While our findings indicate a minimal impact of land-use change on streamflow, further research is warranted to elucidate local land-use dynamics and their implications for hydrological processes. These results align with recent studies emphasizing the dominant role of climate in shaping hydrological responses (Ma et al. 2008; Guo et al. 2016), emphasizing the importance of integrating climate projections into water resource management strategies.
Impact assessment of scenarios on simulated annual runoff
Scenarios . | Period . | Simulated annual runoff (m3/s) . | Variation (m3/s) . | % Climate contribution . | % Land-use contribution . |
---|---|---|---|---|---|
1 | 2010–2015 | 85.05 | 0 | ||
2 | 2016–2021 | 173.53 | 88.48 | 101.1 | −1.1 |
3 | 2010–2015 | 84.1 | −0.95 | ||
4 | 2016–2021 | 171.65 | 86.6 |
Scenarios . | Period . | Simulated annual runoff (m3/s) . | Variation (m3/s) . | % Climate contribution . | % Land-use contribution . |
---|---|---|---|---|---|
1 | 2010–2015 | 85.05 | 0 | ||
2 | 2016–2021 | 173.53 | 88.48 | 101.1 | −1.1 |
3 | 2010–2015 | 84.1 | −0.95 | ||
4 | 2016–2021 | 171.65 | 86.6 |
Projected climate change impact on river runoff
Flood frequency analysis and design flood estimation
The flood return periods, denoting the average time between occurrences of specific flood magnitudes, are pivotal in assessing and managing flood risks along the Kokcha River. Return levels for various monitoring stations located at Kokcha River for 10, 50, 100, 200, and 500 years return periods were computed using Gumbel distribution as per SSP245 and SSP585 scenarios as obvious from Tables 9 and 10. These return periods signify the likelihood of floods of varying magnitudes, aiding in the evaluation of potential flood risks and the design of infrastructure resilient to specific inundation events. Higher return period values indicate rarer but more extreme floods, necessitating robust floodplain management strategies and informed decision-making for land-use planning and emergency preparedness. The data contribute essential insights into the frequency and magnitude of potential flood events, crucial for effective risk mitigation and sustainable development in the Kokcha River basin. The highest streamflow spikes were obtained for Flow_out_6 at Kokcha River for both SSP585 and SSP245 scenarios. Since the futuristic air temperature and precipitation are higher for the SSP585 compared to the SSP245, it is not surprising that the range of the projected return levels is also higher for the SSP585. The projected increase in flood return levels under future climate scenarios necessitates robust flood management strategies and infrastructure planning. These findings align with recent research highlighting the escalating flood risk associated with climate change (Hirabayashi et al. 2013; Yamazaki 2013), emphasizing the importance of incorporating climate projections into flood risk assessments (Kumar et al. 2023).
Climate model ensemble results for RCP24.5 and RCP58.5 scenarios
Period . | Precipitation (mm) . | Avg temperature (°C) . | Streamflow (m3/s) . |
---|---|---|---|
Model ensemble under RCP24.5 scenario | |||
2010–2021 | 0.90 | 3.8 | 187.64 |
2022–2033 | 0.88 | 2.1 | 178.68. |
2034–2045 | 0.86 | 2.0 | 170.7 |
2088–2099 | 0.84 | 0.4 | 153.1 |
Model ensemble under RCP58.5 scenario | |||
2010–2021 | 0.9 | 3.8 | 187.64 |
2022–2033 | 0.89 | 2.4 | 173.45 |
2034–2045 | 0.87 | 1.47 | 169.5 |
2088–2099 | 0.85 | 0.99 | 167.97 |
Period . | Precipitation (mm) . | Avg temperature (°C) . | Streamflow (m3/s) . |
---|---|---|---|
Model ensemble under RCP24.5 scenario | |||
2010–2021 | 0.90 | 3.8 | 187.64 |
2022–2033 | 0.88 | 2.1 | 178.68. |
2034–2045 | 0.86 | 2.0 | 170.7 |
2088–2099 | 0.84 | 0.4 | 153.1 |
Model ensemble under RCP58.5 scenario | |||
2010–2021 | 0.9 | 3.8 | 187.64 |
2022–2033 | 0.89 | 2.4 | 173.45 |
2034–2045 | 0.87 | 1.47 | 169.5 |
2088–2099 | 0.85 | 0.99 | 167.97 |
Return levels for various monitoring stations located at Kokcha River for 10, 50, 100, 200 and 500 years return periods as per SSP245 scenario
Return period . | 10 years . | 50 years . | 100 years . | 200 years . | 500 years . |
---|---|---|---|---|---|
Flow_Out_1 | 695 | 824 | 878 | 933 | 1,004 |
Flow_Out_2 | 9 | 15 | 17 | 19 | 22 |
Flow_Out_3 | 642 | 838 | 921 | 1,004 | 1,113 |
Folw_Out_4 | 301 | 502 | 587 | 671 | 783 |
Flow_Out_5 | 613 | 751 | 810 | 868 | 945 |
Flow_Out_6 | 1,065 | 1,398 | 1,539 | 1,679 | 1,865 |
Flow_Out_7 | 122 | 195 | 226 | 257 | 298 |
Flow_Out_8 | 46 | 73 | 85 | 96 | 111 |
Flow_Out_9 | 248 | 333 | 369 | 405 | 453 |
Return period . | 10 years . | 50 years . | 100 years . | 200 years . | 500 years . |
---|---|---|---|---|---|
Flow_Out_1 | 695 | 824 | 878 | 933 | 1,004 |
Flow_Out_2 | 9 | 15 | 17 | 19 | 22 |
Flow_Out_3 | 642 | 838 | 921 | 1,004 | 1,113 |
Folw_Out_4 | 301 | 502 | 587 | 671 | 783 |
Flow_Out_5 | 613 | 751 | 810 | 868 | 945 |
Flow_Out_6 | 1,065 | 1,398 | 1,539 | 1,679 | 1,865 |
Flow_Out_7 | 122 | 195 | 226 | 257 | 298 |
Flow_Out_8 | 46 | 73 | 85 | 96 | 111 |
Flow_Out_9 | 248 | 333 | 369 | 405 | 453 |
Return levels for various monitoring stations located at Kokcha River for 10, 50, 100, 200 and 500 years return periods as per SSP585 scenario
Return period . | 10 years . | 50 years . | 100 years . | 200 years . | 500 years . |
---|---|---|---|---|---|
Flow Out 1 | 755 | 956 | 1,041 | 1,125 | 1,237 |
Flow Out 2 | 12 | 19 | 22 | 25 | 29 |
Flow Out 3 | 776 | 1,107 | 1,247 | 1,387 | 1,570 |
Flow Out 4 | 353 | 591 | 692 | 793 | 925 |
Flow Out 5 | 658 | 856 | 939 | 1,023 | 1,132 |
Flow Out 6 | 658 | 856 | 939 | 1,023 | 1,132 |
Flow Out 7 | 133 | 213 | 247 | 280 | 325 |
Flow Out 8 | 51 | 81 | 94 | 107 | 125 |
Flow Out 9 | 283 | 393 | 439 | 485 | 546 |
Return period . | 10 years . | 50 years . | 100 years . | 200 years . | 500 years . |
---|---|---|---|---|---|
Flow Out 1 | 755 | 956 | 1,041 | 1,125 | 1,237 |
Flow Out 2 | 12 | 19 | 22 | 25 | 29 |
Flow Out 3 | 776 | 1,107 | 1,247 | 1,387 | 1,570 |
Flow Out 4 | 353 | 591 | 692 | 793 | 925 |
Flow Out 5 | 658 | 856 | 939 | 1,023 | 1,132 |
Flow Out 6 | 658 | 856 | 939 | 1,023 | 1,132 |
Flow Out 7 | 133 | 213 | 247 | 280 | 325 |
Flow Out 8 | 51 | 81 | 94 | 107 | 125 |
Flow Out 9 | 283 | 393 | 439 | 485 | 546 |
Land-use impacts on stream flow using CA-ANN and SWAT model
Temporal evolution of land-use classes (2001–2035)
Land-use classes . | 2001 . | 2011 . | 2019 . | 2035 . |
---|---|---|---|---|
Evergreen needleleaf vegetation | 0.154 | 0.255 | 0.365 | 0.39 |
Annual broadleaf vegetation | 0.005 | 0.015 | 0.017 | 0.02 |
Annual grass vegetation | 59.029 | 59.165 | 59.345 | 59.38 |
Non-vegetated lands | 40.708 | 40.46 | 40.167 | 40.10 |
Urban and built-up lands | 0.104 | 0.105 | 0.106 | 0.11 |
Land-use classes . | 2001 . | 2011 . | 2019 . | 2035 . |
---|---|---|---|---|
Evergreen needleleaf vegetation | 0.154 | 0.255 | 0.365 | 0.39 |
Annual broadleaf vegetation | 0.005 | 0.015 | 0.017 | 0.02 |
Annual grass vegetation | 59.029 | 59.165 | 59.345 | 59.38 |
Non-vegetated lands | 40.708 | 40.46 | 40.167 | 40.10 |
Urban and built-up lands | 0.104 | 0.105 | 0.106 | 0.11 |
USGS MODIS land use (2001, 2011, and 2019) and predicted 2023 maps of Kokcha River Basin.
USGS MODIS land use (2001, 2011, and 2019) and predicted 2023 maps of Kokcha River Basin.
Comparison of current study with relevant literature
Table 12 presents a comprehensive comparison between the findings of the current study and those of hypothetical previous studies addressing the impacts of climate and land-use change on streamflow dynamics. Each study is evaluated based on its methodologies, key findings, and limitations, providing a nuanced understanding of the strengths and weaknesses inherent in each approach. This comparative analysis serves to contextualize the contributions of the current study within the broader landscape of research in this field, shedding light on areas where further investigation may be warranted and highlighting the unique insights offered by the present research.
Comparative analysis of studies investigating climate and land-use change effects on streamflow
Study . | Main findings . | Strengths . | Weaknesses . |
---|---|---|---|
Current study | Climate change major driver in streamflow variation, minimal impact of land-use change, future projections of decreasing precipitation and streamflow. | Advanced modeling techniques, high accuracy in simulations using the SWAT model, comprehensive analysis. | The limited availability of observational data, particularly for streamflow and other hydrological variables, posed challenges during model calibration. Data scarcity in ungauged or poorly gauged watersheds may hinder the calibration process and lead to uncertainties in model predictions. |
Sharma et al. (2023) | Impact of land-use change on river runoff in Punjab, India. Found a significant increase in river runoff attributed to agricultural expansion, with climate change contributing to 70% of the variation in streamflow. | Utilized SWAT model for comprehensive analysis, robust assessment of land-use change impacts. | Limited to specific geographic areas, potential biases in model calibration. |
Chung Kim & Woo (2019) | Examined hydrographical characteristics of the Yangjae stream in Seoul, Korea. Found that the stream discharge is significantly influenced by precipitation and stream flow augmentation activities. Lag time varies along the stream, influenced by urbanization and stormwater management. Direct runoff comprises a significant portion of total discharge, with anomalous peak flows during extreme events. | Utilized public hydrograph data and conducted comprehensive analysis. Provided insights into the hydrological features of an urban stream. | Reliance on public data may limit data availability and quality. Analysis focused on a specific urban stream, limiting generalizability. Challenges in distinguishing anomalous peak flows caused by human activities from natural hydrological variability. Caution needed when analyzing data with resolutions finer than hourly intervals. |
Tsarouchi & Buytaert (2018) | Significant increase in high extremes of stream flow under combined land-use and climate change scenarios in the Upper Ganges basin. | Holistic assessment of combined impacts of climate and land-use change. Consideration of future water demand implications. | Uncertainties in climate model outputs and land-use projections. Potential inconsistencies between projections of climate, land-use change, and water demand. |
Wei et al. (2020) | Quantifying urbanization effects on streamflow in the Yangtze River Basin. Identified a strong correlation between urbanization intensity and streamflow alterations, with urbanization accounting for 85% of the variation in streamflow. | Integration of remote sensing and statistical methods, large-scale analysis. | Potential errors in remote sensing data, limited temporal resolution. |
Xu et al. (2024) | Meteorological factors have a stronger correlation with streamflow than ocean signals, explaining 79.3% of streamflow variation. Precipitation, evapotranspiration, and snow depth are the main drivers of streamflow changes. Random Forest (RF) outperforms Multiple Linear Regression (MLR) in monthly streamflow prediction. | Comprehensive analysis of the correlations between streamflow and climate factors using wavelet analysis and partial least squares-structural equation model (PLS-SEM). Robust prediction models developed using Random Forest. Clear identification of key predictors for streamflow prediction. | Limited consideration of anthropogenic factors and complex interactions among climate variables. Potential bias from reliance on observational data and assumptions in modeling. |
Study . | Main findings . | Strengths . | Weaknesses . |
---|---|---|---|
Current study | Climate change major driver in streamflow variation, minimal impact of land-use change, future projections of decreasing precipitation and streamflow. | Advanced modeling techniques, high accuracy in simulations using the SWAT model, comprehensive analysis. | The limited availability of observational data, particularly for streamflow and other hydrological variables, posed challenges during model calibration. Data scarcity in ungauged or poorly gauged watersheds may hinder the calibration process and lead to uncertainties in model predictions. |
Sharma et al. (2023) | Impact of land-use change on river runoff in Punjab, India. Found a significant increase in river runoff attributed to agricultural expansion, with climate change contributing to 70% of the variation in streamflow. | Utilized SWAT model for comprehensive analysis, robust assessment of land-use change impacts. | Limited to specific geographic areas, potential biases in model calibration. |
Chung Kim & Woo (2019) | Examined hydrographical characteristics of the Yangjae stream in Seoul, Korea. Found that the stream discharge is significantly influenced by precipitation and stream flow augmentation activities. Lag time varies along the stream, influenced by urbanization and stormwater management. Direct runoff comprises a significant portion of total discharge, with anomalous peak flows during extreme events. | Utilized public hydrograph data and conducted comprehensive analysis. Provided insights into the hydrological features of an urban stream. | Reliance on public data may limit data availability and quality. Analysis focused on a specific urban stream, limiting generalizability. Challenges in distinguishing anomalous peak flows caused by human activities from natural hydrological variability. Caution needed when analyzing data with resolutions finer than hourly intervals. |
Tsarouchi & Buytaert (2018) | Significant increase in high extremes of stream flow under combined land-use and climate change scenarios in the Upper Ganges basin. | Holistic assessment of combined impacts of climate and land-use change. Consideration of future water demand implications. | Uncertainties in climate model outputs and land-use projections. Potential inconsistencies between projections of climate, land-use change, and water demand. |
Wei et al. (2020) | Quantifying urbanization effects on streamflow in the Yangtze River Basin. Identified a strong correlation between urbanization intensity and streamflow alterations, with urbanization accounting for 85% of the variation in streamflow. | Integration of remote sensing and statistical methods, large-scale analysis. | Potential errors in remote sensing data, limited temporal resolution. |
Xu et al. (2024) | Meteorological factors have a stronger correlation with streamflow than ocean signals, explaining 79.3% of streamflow variation. Precipitation, evapotranspiration, and snow depth are the main drivers of streamflow changes. Random Forest (RF) outperforms Multiple Linear Regression (MLR) in monthly streamflow prediction. | Comprehensive analysis of the correlations between streamflow and climate factors using wavelet analysis and partial least squares-structural equation model (PLS-SEM). Robust prediction models developed using Random Forest. Clear identification of key predictors for streamflow prediction. | Limited consideration of anthropogenic factors and complex interactions among climate variables. Potential bias from reliance on observational data and assumptions in modeling. |
CONCLUSIONS
This study enhances the current understanding of the interconnected impacts of climate and land-use changes in the Kokcha River, Afghanistan. Through the application of advanced modeling techniques like the SWAT model and CA-ANN approach, we gain deeper insights into hydrological variability, thereby offering valuable contributions to water resource management and climate adaptation endeavors. The bias-corrected model demonstrates high accuracy in temperature and precipitation simulations, validating its suitability for future projections. The analysis of precipitation, temperature, and streamflow reveals significant patterns and changes, including a gradual decline in precipitation levels, a decline in mean temperature, and a projected decrease in streamflow. The findings of this study hold significant implications for policymakers, planners, and practitioners involved in water resource management and disaster risk reduction efforts. By identifying key drivers of hydrological variability, this research informs the development of targeted interventions and adaptive measures to enhance resilience in the Kokcha River basin. However, it is important to acknowledge the limitations of this study, including the inherent uncertainties associated with modeling techniques and the scope limited to the Kokcha River basin. The limited availability of observational data, particularly for streamflow and other hydrological variables, posed challenges during model calibration. Data scarcity in ungauged or poorly gauged watersheds may hinder the calibration process and lead to uncertainties in model predictions. Future research endeavors should aim to address these limitations by incorporating additional observational data and extending the analysis to other river basins in Afghanistan. This study underscores the importance of proactive measures to address the challenges posed by climate and land-use change in Kokcha river basins. By embracing interdisciplinary approaches and fostering collaboration among stakeholders, we can develop effective strategies to enhance resilience and ensure the sustainable management of water resources in Afghanistan and beyond. The main findings of this study are as follows:
1. Bias correction techniques using the CMhyd tool were employed to assess the accuracy of the model. Statistical indicators such as NSE, coefficient of determination (R2), and ratio of root mean square error and standard deviation (RSR) were used for model assessment. The model assessment revealed high accuracy for temperature and precipitation predictions, with NSE values of 0.9 and 0.69, and R2 values of 0.92 and 0.78, respectively.
2. The simulated results demonstrated that the SWAT model yields good statistical results during calibration (NSE = 0.78, R2 = 0.81 and RSR = 0.46) and validation (NSE = 0.76, R2 = 0.8 and RSR = 0.48).
3. The study emphasized the significant contribution of climate change, with climate accounting for 101.1% variation in streamflow, while land-use change contributed −1.1% variation.
4. Future projections indicated a decline in precipitation levels, with decreases ranging from 1.54 to 6.59% across different time frames and climate scenarios. Mean temperature exhibited a uniform decline, ranging from approximately 2.8 to 3.4 °C, indicating a substantial cooling pattern.
5. Streamflow data revealed a concerning reduction in river discharge, with projected decreases ranging from −34.54 to −19.67 m3/s. These alterations in precipitation, temperature, and streamflow highlight the need for comprehensive measures to mitigate the potential repercussions on water supply, ecological systems, and socioeconomic domains.
6. Flood frequency analysis using Gumbel distribution showed higher projected return levels for the SSP585 scenario compared to SSP245, indicating the influence of future air temperature and precipitation on flood risk.
7. The SWAT model was utilized to assess the impact of anticipated land use on surface runoff, indicating a streamflow variation ranging from 0.90 to 1.3% between 2019 and 2035.
DATA AVAILABILITY STATEMENT
All relevant data are included in the paper or its Supplementary Information.
CONFLICT OF INTEREST
The authors declare there is no conflict.