Assessing Water Resource Sustainability in the Kabul River Basin: A Standardized Runoff Index and Reliability, Resilience, and Vulnerability Framework Approach

Abstract

The sustainability of water resources is fundamental for basin management, especially in regions where changing hydrological conditions due to climate extremes are prevalent. This study presents a comprehensive assessment of the Kabul River Basin (KRB) sustainability using the Standardized Runoff Index (SRI) as a runoff indicator. By integrating the concepts of reliability, resilience, and vulnerability (RRV), this research aims to provide a granular understanding of water sustainability within the basin. Utilizing future climate projections derived from the mean ensemble of Global Climate Models (GCMs) of Coupled Model Intercomparison Project Phase 6 (CMIP6) under two shared socioeconomic pathways (SSP2-4.5 and SSP5-8.5), the Soil and Water Assessment Tool (SWAT) model simulated surface runoff for the KRB. Employing a grid-based approach, this study uncovers the internal variabilities within the basin, recognizing that individual grid points may present distinct water availability characteristics. Historical analysis indicated a decline in the KRB’s sustainability, attributed to increased vulnerability and reduced reliability and resilience. Future projections emphasize the basin’s susceptibility to runoff variations, particularly in downstream areas characterized by dense populations and intense agricultural activities. These findings accentuate the need for strategic water resource management within the KRB to address localized vulnerabilities and ensure sustainable water availability amidst climatic uncertainties.

1. Introduction

Climate change is increasingly acknowledged as a critical global issue, exerting significant effects on hydrological processes, including precipitation, evapotranspiration, and runoff [1,2,3]. These changes pose significant challenges to the sustainability of water resources worldwide [4,5,6]. Assessing the future conditions of river basins in light of climate variability is essential for devising robust water resource management strategies. It is particularly important in regions like Afghanistan, where hydrological conditions are rapidly evolving in response to climatic changes.

The Standardized Runoff Index (SRI), a key runoff indicator, plays a vital role in managing water resources and assessing water availability in river basins [7]. This indicator is crucial not only for characterizing current water conditions but also for its predictive capability in the context of a changing climate [8,9]. With the development of advanced modelling techniques and enhanced climate projections, the application of the SRI becomes increasingly important.

Global Climate Models (GCMs) are essential in climate change science, as they project essential climate variables for future water resource analyses [10,11,12]. The Coupled Model Intercomparison Project (CMIP) evaluates GCMs and provides improved simulations of seasonal variability in precipitation and temperature [13,14]. The latest iteration, CMIP6, incorporates factors such as population growth, ecosystems, economic development, and social dynamics, providing a more comprehensive picture of potential changes in water availability [15,16,17].

To assess water resource sustainability, this study employs the Reliability, Resilience, and Vulnerability (RRV) framework [18]. This approach measures the performance of river basins, specifically their capacity to continue functioning under variable conditions (reliability), recover from perturbations (resilience), and resist being adversely affected by such disturbances (vulnerability) [19,20,21]. In scientific literature, the approach to understanding the sustainability and performance of river basins has primarily focused on evaluations at the basin’s outlet [18,22,23,24,25]. Such studies typically offer insights into water availability at the outlet of the basin, providing a holistic view of the reservoir performance. However, this methodology potentially masks internal variabilities, underrepresenting the complexities inherent within the basin.

Given the changing climate dynamics and the diverse impacts these shifts can exert on different parts of a basin, understanding sustainability on a grid basis becomes essential. Each grid point within a basin can exhibit unique characteristics and responses to external factors. This spatial analysis is essential to identify localized vulnerabilities and stress points that might remain obscured when focusing only on the basin’s outlet [26,27].

Furthermore, while the importance of examining short-term (e.g., SRI-3) and long-term (e.g., SRI-12) impacts on water availability is well recognized [28], integrating these temporal considerations into a grid-based evaluation framework is a relatively uncharted territory in research. Such an integration can offer nuanced insights into how different regions within a basin might respond over varied time scales, fostering more targeted and effective water resource management strategies.

Afghanistan, characterized by a semi-arid to arid climate, is highly vulnerable to climate change, with annual average rainfall ranging from 200 to 500 mm. The country has repeatedly experienced prolonged droughts [6,29]. Due to years of conflict and instability, Afghanistan faces various environmental challenges, particularly in the water sector [30]. The Kabul River Basin (KRB), one of Afghanistan’s five river basins, has transboundary water resources that flow into Pakistan [31]. Irrigation water availability in the KRB relies on effective rainfall and surface runoff generated by snow in upstream mountainous regions [32]. This study is vital for Afghanistan, especially for the KRB, a densely populated watershed with a strong dependence on agriculture [33]. Any deficit or change in the reliability of water resources can severely impact the majority of people whose livelihoods depend on agriculture, as well as the national economy, which primarily relies on agriculture. Identifying the drivers of water resource sustainability and assessing the future sustainability of the KRB under climate change conditions can help inform climate change adaptation planning.

The present study aims to assess the trend in water resource sustainability in the KRB in response to runoff variation using 3-month and 12-month SRI data for the historical period (1975–2014) and two future time periods, near future (NF) (2020–2059) and far future (FF) (2060–2099), based on the RRV framework. Climate variables were generated using a mean ensemble of three downscaled CMIP6-GCMs for two SSPs (SSP2-4.5 and SSP5-8.5). A semi-distributed hydrological model called SWAT was used to calculate the runoff. To address the research gap concerning the application of the RRV framework based on the SRI runoff indicator, this study evaluates the sustainability of water resources over KRB.

2. Materials and Methods

2.1. Procedure

This study follows a four-step process, as illustrated in Figure 1. The process begins with the application of climate data, including precipitation and temperature, sourced from selected CMIP6 GCMs, alongside other crucial land surface data as an input data, to run the SWAT model. This application spans historical (1975–2014) and future periods (2020–2059, 2060–2099) across the KRB under two SSP scenarios (SSP2-4.5 and SSP5-8.5). The subsequent phase involves simulating runoff for these periods and SSP scenarios. Following this, the study calculates the SRI index from the simulated runoff data. The final step involves the evaluation of the basin’s historical performance and an assessment of future sustainability projections under different climate change scenarios.

2.2. Study Area and Datasets

The Kabul River Basin (Figure 2), which has a high population density, covers an area of 68,100 km² and is located in eastern Afghanistan [33,34,35]. The elevation in the basin ranges from 7700 m in the northern mountains (upstream) to 380 m on the eastern border with Pakistan (downstream) above sea level.

The catchment has a semi-arid climate, with an average annual precipitation of 690 mm [36]. The river originates in the Hindu Kush Mountains and spans 700 km before joining the Indus River. Over 80% of Afghanistan’s agricultural land is irrigated by surface runoff or direct rainfall, while the remaining land relies on a groundwater-based irrigation system [37]. The KRB is a critical water resource in Afghanistan, contributing approximately 26% of the country’s available water and supporting a population of around 9 million people in Afghanistan and Pakistan. The basin’s mean annual stream flow is about 24 billion cubic meters, and it irrigates an extensive area of 66,748 km². The KRB is characterized by a semi-arid and robustly continental climate, with significant climatic diversity across its sub-basins, which largely influences the hydrological and physiographic characteristics of each area [33,38].

The subdivision of the KRB into six main sub-basins—Panjshir, Logar, Laghman, Kunar, Kabul, and Chitral—is primarily based on their distinct climatic patterns, hydrological features, and physiographic properties. This division also considers the unique environmental aspects such as the distribution of glaciers, seasonal snowmelt contributions, and precipitation patterns [39]. For instance, the Panjshir, Kunar, and Chitral sub-basins dominated by mountainous terrain receive a high amount of annual rainfall and are characterized by their cold climate. It is notable for their significant snowfall, contributing to the river flow predominantly through snowmelt [40]. The Logar Sub-basin is relatively warmer and receives less rainfall, and its hydrology is influenced by both rain and snow, with a considerable amount of its water resources used for agricultural purposes. The Laghman Sub-basin is known for its moderate climate; this sub-basin has a balanced mix of rain and snow. And the terrain is a mix of hilly and flat areas, influencing the diverse hydrological processes. The Kabul Sub-basin is the most populated and urbanized sub-basin and has a relatively warmer climate with moderate rainfall [41]. Its water resources are critical for both domestic and agricultural use. The detailed characteristics of each sub-basin are presented in

Table 1. Area, annual rainfall, temperature, and elevation of Kabul River sub-basins.

No.	Sub-Basins	Annual Rainfall (mm)	Area (km2)	Temperature (°C)			Elevation (m) from m.s.l.
				Average	Max	Min	Average	Max	Min
1	Panjshir	795	12,952	5.7	20	−12	2898	5694	1032
2	Logar	480	9958	7.5	25	−10	2729	4296	1763
3	Laghman	720	6236	9.1	28	−8	2864	5432	639
4	Kunar	820	11,614	8.3	25	−6	2809	6229	492
5	Kabul	510	12,730	14.4	32	−5	1916	4692	380
6	Chitral	680	14,610	2.1	14	−22	4020	7701	1065

Note: Max = maximum, Min = minimum, and m.s.l. = mean sea level.

.

Meteorological and hydrometric data of 20 recorded stations were obtained from the National Regulatory Authority for Water Affairs in Afghanistan. These stations, strategically located throughout the Kabul River Basin, provided comprehensive data coverage from January 2009 to December 2020. In this study, we selected the ensemble of MPI-ESM1-2-LR, ACCESS-CM2, and FIO-ESM-2-0 models from the CMIP6-GCMs for our climate change assessment over Afghanistan. This selection was based on the methodology described in [10], where these models were identified as the most skilled in simulating temperature and precipitation over Afghanistan under different shared socioeconomic pathways (SSPs) and future time horizons. The CMIP6-GCMs datasets were utilized for runoff simulation and sustainability assessments in the KRB. These datasets spanned historical periods from 1975 to 2014 and projected future periods from 2020 to 2099 under two SSP scenarios (SSP2-4.5 and SSP5-8.5). These scenarios are widely used for climate change impact studies [42,43]. The SSP2-4.5 scenario is often referred to as a ‘middle-of-the-road’ pathway, where socioeconomic trends do not shift markedly from historical patterns, leading to moderate levels of greenhouse gas emissions. On the other hand, SSP5-8.5 is characterized as a ‘high-emission’ scenario, envisioning a future where economic growth is fueled by an energy-intensive lifestyle, heavily reliant on fossil fuels, leading to high greenhouse gas emissions. We employed the quantile mapping (QM) approach for downscaling GCMs to a 0.5° × 0.5° grid resolution, due to its recognized simplicity and enhanced accuracy in aligning model outputs with observed climatic data [44,45,46].

2.3. SWAT Model and Calibration

The Soil and Water Assessment Tool (SWAT) is a physically based, semi-distributed hydrologic model developed by the United States Department of Agriculture, extensively used for simulating various hydrological processes in watersheds [47]. The model operates by calculating the hydrologic cycle components, as delineated in the following equation:

$${SW}_t={SW}_0+\sum _{i=1}^t\left(R_{day}-Q_{surf}-E_a-W_{sweep}-W_{gw}\right)$$

(1)

where t represents the number of days, SW_t and SW₀ are the final and initial soil water contents (in millimeters), respectively. Daily rainfall (R_day), surface runoff (Q_surf), evapotranspiration (E_a), water seeping through the soil profile (W_sweep), and return flow (W_gw) are accounted for each day i. Surface runoff in the equation refers to the excess water that is not lost through interception, evapotranspiration, or infiltration. Supplementary Figure S1 illustrates the hydrologic processes simulated by the SWAT model.

The SWAT model’s performance was evaluated by comparing the simulated output with the measured discharge at the catchment outlet using the coefficient of determination (R²) and Nash–Sutcliffe efficiency (NSE). The required input data for the SWAT model include digital elevation model (DEM) (Figure 2), soil map (Supplementary Figure S2), land cover map (Supplementary Figure S3), and the climate data specified in Table 2. To account for the relatively low set of meteorological data, we used data interpolation and extrapolation methods, such as Inverse Distance Weighting (IDW), to generate a more comprehensive dataset for the study area. However, we acknowledge the uncertainties related to data availability, which may have implications for model calibration and validation. These uncertainties were considered when interpreting the model’s results and assessing its performance.

The watershed is divided into sub-basins, and each sub-basin is further partitioned into numerous hydrologic response units (HRUs) as the initial step in hydrologic modelling, applying the SWAT model. An HRU refers to the percentage of a sub-basin that has similar soil types, land use, and topography. In this method, each HRU is separately related to the sub-basin’s runoff, with no interactions. Therefore, to achieve a spatial resolution of the SWAT where there is an interaction between the individual grid cells, we used a grid-based SWAT [48] after calibrating the input parameters. In the SWAT grid, the KRB was divided into 74,322 grid cells with a resolution of 1 km × 1 km.

To ensure the accuracy and reliability of the SWAT model in simulating hydrological processes within the KRB, a detailed calibration and validation process was undertaken. This involved the use of the SWAT-CUP optimization tool, which employs the Sequential Uncertainty Fitting (SUFI-2) algorithm [49]. SWAT-CUP facilitates the adjustment of various SWAT parameters, considering factors such as soil properties, Hydrologic Response Units (HRUs), channel routing, and watershed characteristics that influence runoff processes [50]. For calibration, we utilized observed data from 25 recorded stations in the KRB, covering the period from 2010 to 2016. A warm-up period of one year (2009) was implemented to minimize the impact of initial conditions. Post-calibration, the model was validated over the period from 2016 to 2020. This calibration and validation process ensured that the model accurately reflected the actual hydrological conditions of the basin.

Upon successful calibration and validation, the SWAT model was then applied to simulate runoff using an ensemble of selected CMIP6-GCMs for the historical (1975–2014), near-future (2020–2059), and far-future (2060–2099) periods. This comprehensive approach allowed for a detailed assessment of long-term runoff variations and sustainability under different climate scenarios.

Table 2. SWAT model inputs.

Data Type	Sources	Data Descriptions
Topography (DEM)	Alaska Satellite Facility https://vertex.daac.asf.alaska.edu, accessed on 10 April 2022	High-resolution Digital Elevation Model (DEM) with a spatial resolution of 12.5 m × 12.5 m, used for delineating watershed topography and drainage patterns.
Soil map	Food and Agriculture Organization (FAO) https://data.apps.fao.org/map/catalog/srv/eng/catalog.search#/metadata/cc45a270-88fd-11da-a88f-000d939bc5d8, accessed on 15 April 2022	Soil map detailing soil types within the study area
Land cover	Food and Agriculture Organization (FAO) http://www.fao.org/3/i5043e/i5043e.pdf, accessed on 15 April 2022	Land cover data delineating various land use types
Meteorological data	National Water Affairs Regulatory Authority of Afghanistan [51] and CMIP6-GCMs	Climate parameters such as rainfall, temperature, wind speed, relative humidity, and solar radiation.
Discharge data	National Water Affairs Regulatory Authority	Monthly river discharge records, key for calibrating and validating the SWAT model

Table 2. SWAT model inputs.

Data Type	Sources	Data Descriptions
Topography (DEM)	Alaska Satellite Facility https://vertex.daac.asf.alaska.edu, accessed on 10 April 2022	High-resolution Digital Elevation Model (DEM) with a spatial resolution of 12.5 m × 12.5 m, used for delineating watershed topography and drainage patterns.
Soil map	Food and Agriculture Organization (FAO) https://data.apps.fao.org/map/catalog/srv/eng/catalog.search#/metadata/cc45a270-88fd-11da-a88f-000d939bc5d8, accessed on 15 April 2022	Soil map detailing soil types within the study area
Land cover	Food and Agriculture Organization (FAO) http://www.fao.org/3/i5043e/i5043e.pdf, accessed on 15 April 2022	Land cover data delineating various land use types
Meteorological data	National Water Affairs Regulatory Authority of Afghanistan [51] and CMIP6-GCMs	Climate parameters such as rainfall, temperature, wind speed, relative humidity, and solar radiation.
Discharge data	National Water Affairs Regulatory Authority	Monthly river discharge records, key for calibrating and validating the SWAT model

2.4. Calculation of SRI Index

The calculation of the SRI [7] is similar to the standardized precipitation index (SPI), with the primary difference being the utilization of runoff data instead of precipitation data. This index is designed to assess the impacts of drought on water resources, considering both the natural variability of streamflow and the influence of human activities such as water management and consumption [50,52]. The SRI index can be determined using the following equation [53]:

$$g\left(x\right)=\frac{1}{{\beta }^{\alpha \Gamma }\Gamma \left(\alpha \right)}{x}^{\alpha -1}{e}^{-\frac{x}{\beta }}, x \ge 0$$

(2)

where Γ is an ordinary gamma distribution function (GDF), α and β are shape and scale parameters that can be calculated using equations shown in Supplementary Equations (S1) and (S2), and x is the amount of runoff. The SRI index based on previous studies [52] has been divided into different categories (

Table 3. SRI index classifications.

No.	Category	SRI Value
1	Mild	−1 ≤ SRI < 0
2	Moderate	−1.5 ≤ SRI < −1
3	Severe	−2 ≤ SRI < −1.5
4	Extreme	SRI ≤ −2

).

2.5. Sustainability Assessment Using RRV Concept

The sustainability of a water resource system, especially within river basins, can be effectively assessed by understanding its Reliability, Resilience, and Vulnerability (RRV) [25]. In the context of the Kabul River Basin (KRB), the RRV indicators are derived from the variations in the Standardized Runoff Index (SRI) that indicate water availability at various grid points. This approach, termed SRI-RRV, offers a comprehensive understanding of the basin’s performance with respect to water availability.

The SRI serves as a valuable metric, revealing variations in runoff or streamflow over distinct periods. It indicates water availability and sustainability at various locations within the KRB. A positive SRI value signifies above-average runoff, hinting at better water availability, while a negative value signals potential water stress due to below-average runoff. In the SRI-RRV approach, the SRI serves as the foundation to derive the RRV indicators, which are pivotal for assessing the sustainability of the water resource system.

Reliability refers to the system’s capacity to maintain consistent water availability [41]. A reliability value closer to 1 indicates consistent water availability (as reflected by SRI), whereas a value below 1 suggests increased variations in water availability. Resilience is the system’s ability to revert to its original state following episodes of reduced water availability [54]. A resilience value closer to 1 suggests quick recovery from reduced water conditions (negative SRI values), while a value farther from 1 hints at prolonged durations of reduced water availability. Vulnerability assesses the severity of reductions in water availability [55]. A higher vulnerability value reflects pronounced or drastic reductions in water availability, while a lower value indicates milder variations.

To comprehensively evaluate the future sustainability of water resources in the KRB, this study harnesses two distinct time scales of the Standardized Runoff Index (SRI): SRI-3 (three months) and SRI-12 (twelve months). These time scales present varied insights into the runoff variations and, by extension, water availability within the basin.

SRI-3 provides an understanding of short-term variations in water availability, essential for immediate water resource management tasks, such as supporting agricultural operations that rely on consistent water supply. SRI-12 sheds light on long-term water availability dynamics, which are crucial when devising strategic plans and managing water resources at the river basin scale.

The selection of SRI thresholds, −0.1 for normal conditions as recommended by [5], and −0.7 for significant deviations from the mean (based on the 20th percentile of the time series), facilitate a detailed examination of the KRB’s water resource sustainability in the future. This dual time scale approach, combined with the SRI-RRV evaluation, yields a comprehensive and nuanced perspective on the basin’s sustainability in the face of varying runoff conditions.

The RRV indicators can be calculated as follows:

$$Rel=1-{\sum }_{j=1}^M\frac{d\left(j\right)}{T}$$

(3)

$$Res={\left\{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$M$}\right.{\sum }_{j=1}^{M}d\left(j\right)\right\}}^{-1}$$

(4)

$$Vul=\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$M$}\right.{\sum }_{i=1}^T\left\{\frac{L_{obs}\left(i\right)-L_{std}\left(i\right)}{L_{std}\left(i\right)}\times H\times (L_{obs}\left(i\right)-L_{std}(i\left)\right)\right\}$$

(5)

where M, d(j), and T are the annual number and duration of each unsatisfactory condition, respectively. L_obs(i) and L_std(i) are the SRI and the threshold (here threshold = −0.1, −0.7). H is the Heaviside function used to consider only the number of unsatisfactory conditions. If there is no failure in a year (M = 0), then H = 0, while if there is an unsatisfactory condition (M > 0), the value of H is equal to 1. The sustainability (Sus) index is then determined by standardizing each indicator and finding the aggregate value of all indicators.

$$Y=1\frac{x_i-x_{min}}{x_{max}-x_{min}}$$

(6)

where $x_i, { x}_{min}$, and $x_{max}$ represent the actual, minimum, and maximum values of the indicator before standardization, respectively. Y is the standardized value of reliability, resilience, or vulnerability. In this analysis, the focus is on using the averages of the reliability, resilience, and vulnerability indicators to compute the sustainability (SRI-RRV) index. This approach is chosen because the geometric mean, which we are using, shows greater sensitivity to variations in the individual variables compared to other types of averages, as noted by [5]. Thus, the geometric mean is utilized to represent the SRI-RRV index.

$$Sus={[R_{el}\times R_{es}\times (1-V_{ul}\left)\right]}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$3$}\right.}$$

(7)

The sustainability index is divided into five classes, as proposed by [5] and [56], and shown in

Table 4. Sustainability index classes.

No.	Category	Sustainability Index
1	Very high	0.81 ≤ Sus < 1
2	High	0.61 ≤ Sus < 81
3	Moderate	0.41 ≤ Sus < 0.61
4	Low	0.21 ≤ Sus < 0.41
5	Very low	0 ≤ Sus < 0.21

.

Additionally, we evaluated the regional variation of the trend in the sustainability condition of the KRB for the NF and FF under various Shared Socioeconomic Pathways (SSPs) and thresholds using the linear regression method. Positive and negative regression coefficients in this method indicate upward and downward trends, respectively, for p-values less than 0.1. A significant trend in terms of watershed sustainability is demonstrated by the p-value being less than 0.05 at a 95% confidence interval. This means that if the p-value is less than 0.05, we can confidently reject the null hypothesis and conclude that there is a significant trend in watershed sustainability [54]. The 95% confidence interval indicates that we can be 95% certain that the true population parameter falls within this range. In this study, the linear regression method is used to assess the regional variation of the trend in the sustainability condition of the river basin for near-future (NF) and far-future (FF) periods under various Shared Socioeconomic Pathways (SSPs) and thresholds.

3. Result

3.1. SWAT Model Calibration and Validation

The calibration and validation of the SWAT model at the Tangi Gulbahar station, conducted on a monthly basis from 2010 to 2020, are depicted in Figure 3. The model achieved an R² value of 0.84 and an NSE value of 0.74 in the calibration phase, and R² and NSE values of 0.81 and 0.7, respectively, in the validation phase. While these values might appear moderate, they are considered satisfactory and indicative of good model performance, particularly when considered within the framework of developing countries’ environments. This perspective is supported by previous research works [57,58], which acknowledge that, in such contexts, these ranges of NSE and R² values still reflect a reliable simulation of hydrological processes. Therefore, despite the moderate values, the results underscore the model’s capability in accurately simulating monthly runoff within the KRB, providing a sound basis for further exploration of hydrological variations under various climate change scenarios and their implications for water resource sustainability.

The calibrated and validated SWAT model offers valuable insights for exploring hydrological variations under various climate change scenarios. Preliminary simulations suggest notable changes in runoff patterns and water availability in the KRB under different future climate scenarios, underscoring the need for sustainable water resource management strategies in the face of climate change.

In summary, the successful calibration and validation of the SWAT model confirm its effectiveness in representing the hydrological processes within the KRB. The accuracy and reliability of the model are crucial for generating dependable projections of the basin’s future hydrological conditions under diverse climate scenarios, forming a solid foundation for further research and policy planning in the region.

3.2. Historical Pattern of RRV and Sustainability

Figure 4 presents the evaluation of reliability, resilience, vulnerability, and overall sustainability using both the SRI-3 and SRI-12 metrics over the historical period from 1975 to 2014. The Standardized Runoff Index (SRI) serves as an instrument to quantify the runoff variability, normalized against a reference period. The SRI is expressed over varying time scales, with each timeframe catering to distinct aspects of hydrological phenomena.

The choice of SRI-3, or the 3-month time scale, is instrumental in capturing short-term variations in runoff, largely influenced by seasonality and short-term climate anomalies. This is particularly relevant for understanding immediate shifts in water availability that could have ramifications on short-term water resource management decisions.

In contrast, the SRI-12, represented over a 12-month period, encapsulates longer-term, annual variability. The physical significance of the SRI-12 lies in its capacity to render insights into the overarching hydrological behavior over an entire year. By analyzing runoff patterns over this duration, one can discern patterns influenced by broader climatic cycles, persistent meteorological patterns, and other longer-term hydrological changes.

The observations indicate that both reliability (Figure 4a) and resilience (Figure 4b) showcased decreasing trends over these time scales, while vulnerability (Figure 4c) presented an upward trajectory. When compared, the average values for reliability and resilience were notably higher for the SRI-12. Conversely, the 3-month time scale exhibited a heightened average vulnerability value.

Sustainability trends, as illustrated in Figure 4d, where it is observed to follow a decreasing trend. The average values for sustainability were calculated as 0.37 ± 0.12 for SRI-3 and 0.53 ± 0.06 for SRI-12, which places the sustainability within the moderate range. This assessment highlights the differences between the two time scales and emphasizes the importance of considering these variations when evaluating water resource sustainability.

3.3. Estimation of Future Change in Reliability, Resilience, and Vulnerability

In light of the historical patterns presented in Section 2.2, the current section projects the future changes in reliability, resilience, and vulnerability concerning the 3-month SRI Index and the 12-month SRI Index (SRI-3 and SRI-12) for the KRB. This projection aids in providing a comprehensive understanding of the KRB’s performance and its likely response to future climatic shifts.

Historical trends have shown that the SRI-3 is critical in capturing short-term fluctuations in runoff due to its influence by seasonality and immediate climate anomalies. Meanwhile, the SRI-12 provides insights into the broader hydrological behavior throughout a year, capturing broader climatic cycles and more extended hydrological changes. Given the differences between these two time scales, it is vital to consider both in predicting future alterations in the KRB.

As highlighted in Section 2.2, there was a notable decrease in both reliability and resilience over the historical period, while vulnerability showed an increasing pattern. SRI-12 demonstrated superior reliability and resilience but also a heightened average vulnerability at the 3-month time scale.

Projecting these indicators into the future is crucial for understanding how various runoff conditions might respond to climate changes. This study utilized a grid-based SWAT model to simulate future runoff, which forms the basis for calculating the reliability, resilience, vulnerability, and sustainability indicators. A grid-based approach has been adopted to enable a comprehensive analysis of these factors. The focus of this analysis lies on two key thresholds: −0.1 and −0.7. These thresholds represent average and extreme runoff conditions, respectively, and are essential for assessing how different runoff scenarios might unfold under changing climatic conditions.

For the −0.1 threshold, there is a predicted decline in reliability for both SRI-3 and SRI-12, especially in the NF and FF under both SSP2-4.5 and SSP5-8.5 scenarios. The lower Kabul sub-basin is expected to experience the most significant reduction. Conversely, at the −0.7 threshold, most of the basin is predicted to witness enhanced reliability, with particular sub-basins like Panjshir, Laghman, and Chitral showing more pronounced improvements.

In terms of resilience, its spatial distribution mirrors the patterns noted for reliability. However, vulnerability patterns are inversely related. The Kabul sub-catchment, under the −0.1 threshold and SSP5-8.5 scenario, seems to be most vulnerable, suggesting an increased susceptibility to drought’s adverse impacts. On the brighter side, certain sub-catchments like Panjshir and Laghman are predicted to be less vulnerable under specific scenarios.

For the −0.1 threshold, there is a predicted decline in reliability for both SRI-3 and SRI-12, especially in the FF under the SSP5-8.5 scenario (Figure 5). The lower Kabul sub-basin has a projected reliability of lower than 0.1. Conversely, at the −0.7 threshold, most of the basin is predicted to witness enhanced reliability, with particular sub-basins like Panjshir, Laghman, and Chitral showing more pronounced improvements with reliability value of higher than 0.6.

In terms of resilience, its spatial distribution mirrors the patterns noted for reliability (Figure 6). However, vulnerability patterns are inversely related (Figure 7). The Kabul sub-catchment, under the −0.1 threshold and SSP5-8.5 scenario, projected to a vulnerability value of greater than 0.9, suggesting an increased susceptibility to drought’s adverse impacts. On the brighter side, certain sub-catchments like Panjshir and Laghman are predicted to be less vulnerable under specific scenarios.

The interplay between reliability, resilience, and vulnerability highlights the intricacies in managing water resources in the face of climate change. Assessing the impact of climate change on water resource sustainability requires a multidimensional approach.

Given the trends and predictions based on the SRI-RRV concept and grid-based runoff indicators, future water resource management strategies need to be adaptive. For rice and wheat cultivation areas, which are critically dependent on consistent water supply, these findings are particularly poignant.

The SRI-3 and SRI-12 discrepancies suggest that short-term water management decisions might need more stringent measures given the heightened vulnerability at this scale. However, long-term strategies can be a bit more flexible, relying on the overarching hydrological behavior as suggested by the SRI-12.

In conclusion, as the KRB braces for future climatic alterations, understanding these nuanced changes across time scales will be pivotal in ensuring the sustainability of its water resources and the livelihoods, especially rice and wheat cultivation, that depend on it.

3.4. Estimation of Future Category of Sustainability

Figure 8 and Figure 9 show the projected spatial sustainability classes and the percentage of grid points in each category for both SRI-3 and SRI-12 under the two SSPs, considering thresholds of −0.1 and −0.7. Using the −0.1 threshold, a majority of the basin was projected to be in the low sustainability category under SSP5-8.5. Only the upstream grid points of Panjshir, Laghman, and Chitral displayed high to very high sustainability under SSP2-4.5 for both the near-future (NF) and far-future (FF) periods.

Over 60% of the region is projected to fall into the low sustainability category under the SSP5-8.5 scenario across both SRI time scales. Notably, very low sustainability was observed for both SRI-3 and SRI-12 in downstream grid points of the Logar, Kabul, and Kunar sub-basins under SSP5-8.5. This highlighted potential concerns about water availability in these regions under the given climate scenarios.

For the −0.7 threshold, it shows projected significant improvements in the watershed’s sustainability, transitioning from very low or low levels to moderate, high, or even very high levels. This improvement is particularly notable under the SSP2-4.5 scenario, where large areas within the Chitral, Panjshir, Kunar, Laghman, and Logar sub-basins are projected to achieve high and very high sustainability conditions for both the 3-month (SRI-3) and 12-month (SRI-12) SRI time scales. For the FF, it is projected that the highest proportion of areas, about 32%, will reach very high sustainability for the SRI-12. These trends suggest that, despite drier conditions as indicated by the −0.7 threshold, certain regions within the basin show a potential for improved water availability and sustainability. This could be attributed to the basin’s inherent hydrological dynamics and possibly adaptive water resource management strategies that have been implemented or are anticipated.

Furthermore, Figure 8 reveals that, under various climate change scenarios, the majority of the basin projected lower sustainability for the short-term SRI-3 compared to the long-term SRI-12. This difference indicates that short-term or seasonal runoff variations, which are more effectively captured by the SRI-3, are likely to occur more frequently than long-term changes. Such short-term fluctuations present a significant challenge for water resource management, as they demand rapid response and adaptation to shifting water availability. This is in contrast to the long-term SRI-12, which indicates more stable and predictable patterns, allowing for strategic planning and management. Therefore, these findings highlight the need for tailored water management strategies that address both immediate and longer-term water availability concerns within the KRB.

3.5. Trend in Future Sustainability

Spatial trends in sustainability under SSP2-4.5 and SSP5-8.5 scenarios for the two thresholds are depicted in Figure 10 and Figure 11. These provide a visual representation of how sustainability trends varied across the KRB.

With the −0.1 threshold, 15% (mainly downstream) and 20% (predominantly in Kabul, lower Panjshir, Laghman, and Kunar) of the areas for SRI-3 exhibit a significant negative trend during the NF for SSP2-4.5 and SSP5-8.5, respectively. For the FF, this significant downward trend is 14% (primarily in Laghman) and 35% under SSP2-4.5 and SSP5-8.5, respectively. For SRI-12, 10% and 21% of the areas under SSP2-4.5 and SSP5-8.5, respectively, are projected to experience a significant negative trend during the NF. In contrast, 7% (particularly in lower Laghman and Kabul) and 38% (mainly in Kabul, lower Panjshir, Laghman, Kunar, and upper Chitral) of the areas are anticipated to witness a significant decreasing trend in sustainability for SSP2-4.5 and SSP5-8.5, respectively. Generally, a notable percentage of the area under different scenarios and time periods for both SRI-3 and SRI-12 shows a declining rate, with 58.4% of the area across the basin for SRI-3 under SSP5-8.5 during the NF demonstrating a diminishing trend in sustainability.

Under the −0.7 threshold, the trend in sustainability across the KRB is expected to increase significantly compared to the trend detection under the −0.1 threshold. For example, for SSP2-4.5, 12% and 13% of the areas (mainly in upper Panjshir, Logar, and Chitral) display a significant increasing trend during the FF for SRI-3 and SRI-12, respectively. For SRI-3, 15% (mainly downstream) and 20% (mainly in Kabul, lower Panjshir, Laghman, and Kunar) of the areas exhibit a significantly decreasing trend during the NF under SSP2-4.5 and SSP5-8.5, respectively. Simultaneously, under SSP2-4.5, 35% and 42% of the areas in the NF, as well as 44% and 48% of the areas in the FF, are projected to have increasing trends for SRI-3 and SRI-12, respectively. These positive trends are primarily observed in the Panjshir, Chitral, Laghman, Kunar, and Logar sub-basins, suggesting enhanced drought resilience and improved water resource management practices. Conversely, under SSP5-8.5, a decreasing trend in sustainability is more evident, particularly in the downstream areas, with 35% and 38% of the areas in the NF and FF displaying declining trends for SRI-3 and SRI-12, respectively.

In conclusion, results indicated that certain regions within the KRB might encounter severe challenges related to sustainability, especially under the more extreme SSP5-8.5 scenario during the far future. Given the projected increase in drought events, higher temperatures, and decreased water availability, the challenges for managing water resources in the basin could escalate. Therefore, emphasizing grid-based assessments and understanding water availability trends over different timeframes are essential for effective future water management practices.

4. Discussion

This research offers critical insights into the application of the Standardized Runoff Index (SRI) for conducting a Reliability–Resilience–Vulnerability (RRV) analysis at a grid scale within the Kabul River Basin (KRB). The utilization of SRI in RRV analysis, especially on a grid scale, marks a methodological advancement in understanding water resource sustainability, considering factors like snowmelt, surface runoff, and groundwater recharge [39].

In verifying the application and relevance of the Reliability, Resilience, and Vulnerability (RRV) metrics for water resource sustainability assessments, several studies offer pertinent insights. The employment of RRV metrics to measure various aspects of water resources’ system performance underscores their capability to evaluate the likelihood of system success or failure, recovery rate, and the magnitude of unsatisfactory states [24]. The application of these metrics to the Enhanced Surface Water System of Tampa Bay demonstrates their versatility in assessing system performance across diverse climatic scenarios [24]. Similarly, the integration of RRV within the Water Evaluation and Planning (WEAP) System for Tarbela Dam’s operations [22] highlights potential improvements in system reliability, resilience, and reduced vulnerability for objectives like irrigation, hydropower generation, and flood control. Their findings showcase how operational adjustments in reservoir management can significantly benefit from employing RRV metrics, especially in terms of reliability and resilience. Moreover, the effectiveness of RRV metrics in managing the hydrological dynamics of the Yorkshire water resource system [59] further exemplifies their utility in complex water resource systems under varying hydro-climatic conditions. Collectively, these studies validate the flexibility of RRV metrics across different hydrological environments, supporting the findings of our research in the Kabul River Basin (KRB), where the combination of diverse climatic and topographical conditions presents distinct challenges to water resource sustainability.

Utilizing RRV metrics, the performance of a water supply system in the context of multiple runoff water sources was assessed [25]. This study highlights the consistency of RRV metrics across various water source situations, offering a robust framework for evaluating water systems amidst diverse uncertainties. This methodology is particularly relevant to the KRB, where runoff variability is influenced by factors such as snowmelt and rainfall patterns [33]. In similar research, the adaptability of RRV metrics in different hydrological contexts was demonstrated in a study focused on watershed management in South Asia [60]. These studies collectively affirm the efficacy and comprehensive nature of the RRV metrics, reinforcing their suitability for our assessment of water resource sustainability within the SRI-RRV framework.

The SRI offers a comprehensive perspective on runoff variability, an essential aspect in basins like the KRB where snowmelt significantly contributes to runoff. By standardizing runoff, the SRI allows for a more thorough representation of the water cycle’s dynamics, capturing crucial aspects such as groundwater recharge and surface runoff [61].

The integration of runoff data into sustainability analysis, as enabled by the SRI, mirrors changes in other crucial aspects of the hydrological cycle, such as groundwater levels and soil moisture content. These elements are vital for water resource management and significantly impact agricultural practices [62]. Studies exploring the impact of climate variability on groundwater in arid regions [63], and research on soil moisture trends under changing climatic conditions [64], provide additional context to our findings. These studies emphasize the interconnectedness of hydrological components and their collective importance in sustainable water management.

The application of SRI in a grid-based RRV analysis significantly enhances the ability to capture the spatial variability of hydrological processes. This aspect is particularly vital for large river basins such as the KRB, which are characterized by diverse topographical and climatic conditions. The spatial resolution of SRI-RRV enables a more detailed assessment of water resource sustainability at a local level, which is important for site-specific adaptation and water management strategies. Previous research emphasized the importance of spatial resolution in hydrological modeling, supporting this approach [65].

Further, the spatial resolution of SRI-RRV allows for a more detailed assessment of water resource sustainability at a local level, essential for site-specific adaptation and water management strategies. For instance, the ability of spatially explicit analysis to identify potential agricultural areas for intervention is a crucial aspect of our research. This methodology is in accord with the previous findings, which demonstrated how spatial analysis is vital in pinpointing global hot spots of heat stress on agricultural crop under changing climate conditions [66].

This research also extends to evaluating future water resource sustainability under various Shared Socioeconomic Pathways (SSPs) using SRI, a novelty in itself, adding another layer of depth to this study [24,54]. By predicting potential changes in basin sustainability due to future climatic conditions, our study emphasizes the need for implementing adaptive management strategies.

Adopting different threshold values (−0.1 and −0.7) to represent varying flow regimes, this study has provided a more precise understanding of sustainability. This approach acknowledges that what is considered ‘normal’ under one scenario might be perceived as a deficit in another, allowing for a more detailed interpretation of potential future basin performance [54].

In our research, grid-based assessments are fundamental in understanding the spatial variability of water availability, a concept that is gaining importance in hydrological studies. This method contrasts traditional approaches where sustainability or performance is assessed based on runoff variations at specific points, such as outlets or reservoirs. Our study’s grid-scale assessment provides a comprehensive spatial view of the KRB, highlighting the importance of evaluating water availability and sustainability at each grid point. Such an approach is particularly vital in large basins characterized by diverse environmental and climatic conditions [67].

This research marks a significant advancement in hydrological analysis within the KRB, yet it is crucial to acknowledge its limitations. High-resolution general circulation models (GCMs) are essential for a detailed daily or seasonal evaluation of the basin, which was beyond this study’s scope. Additionally, our study’s uniform application of thresholds for both 3-month and 12-month SRI time scales might not fully capture the variability within the KRB [54]. Future research could explore region-specific thresholds in hydrological models to reflect local variability.

Despite these limitations, this study makes a significant contribution to the current understanding of water resource sustainability by applying the SRI for RRV analysis on a grid scale. This approach not only paves the way for future studies but also aids in improving water resource management in regions facing the challenges of climate change and population growth.

In conclusion, the study highlights the benefits of using SRI in RRV analysis at a grid scale. It underscores the importance of grid-based evaluations for understanding spatial variability in runoff, which is vital for developing targeted adaptation strategies. By addressing the complete water cycle, including elements like snowmelt and groundwater recharge, this research significantly contributes to sustainable water management discourse in the context of changing climate conditions.

5. Conclusions

This research provides a comprehensive evaluation of the KRB sustainability in relation to runoff variability, utilizing the concepts of reliability, resilience, and vulnerability. The assessment is conducted for both historical and future periods under a variety of climate change scenarios. The study uniquely considers potential future alterations in river basin sustainability, contingent upon climate variability, by selecting thresholds from the Standardized Runoff Index (SRI). Furthermore, two time scales, the 3-month and 12-month SRI, were employed to examine the potential repercussions of runoff variations on the KRB’s sustainability.

The findings reveal a significant decline in the KRB’s sustainability during the historical period due to increased vulnerability and decreased reliability and resilience. Moreover, future projections indicate that climate change, especially under severe scenarios, may adversely affect the basin’s sustainability. This trend is more noticeable in downstream regions, characterized by high population density and intensified agricultural activities.

Incorporating the SRI into a grid-based RRV analytical framework marks a departure from traditional precipitation-focused methodologies. The emphasis on runoff variability provides a more detailed understanding of alterations in water levels and soil moisture content. This spatial granularity is crucial for agriculture, especially in devising irrigation strategies, as it allows for a nuanced understanding of water availability at specific locations. Such precision aids in tailoring water resource strategies to specific regions within the KRB.

The application of the SRI-RRV approach in this study provides valuable insights into the historical and projected impacts of climate factors on the KRB’s sustainability. In light of these findings, the study underscores the urgent need for effective water resource management strategies and climate change adaptation plans in the KRB. Key measures or strategies to consider include:

• Enhanced Water Storage and Conservation: Developing additional water storage facilities, such as reservoirs and rainwater harvesting systems, to buffer against variability in water availability. Implementing water-saving techniques in agriculture, industry, and domestic use can significantly reduce water stress.
• Advanced Irrigation Practices: Transitioning to more efficient irrigation methods, like drip or sprinkler systems, which can reduce water wastage and improve crop yields. Tailoring irrigation schedules based on real-time data can optimize water use.
• Climate-Resilient Agriculture: Encouraging the adoption of drought-resistant crop varieties and farming practices that are less water-intensive. This will help mitigate the impact of water scarcity on food security.
• Monitoring and Forecasting Systems: Strengthening the basin’s monitoring infrastructure to better predict hydrological changes and implement timely responses. Developing early warning systems for droughts and floods can minimize their impacts.
• Policy and Institutional Strengthening: Formulating policies that promote sustainable water use and protect critical water sources. Enhancing institutional capacities for water management and climate change adaptation is also crucial.
• Public Awareness and Community Engagement: Raising awareness among local communities about water conservation and climate change impacts. Engaging communities in water management decisions can foster more sustainable practices.

By implementing these strategies, the KRB can enhance its resilience to the impacts of climate change and ensure the long-term sustainability of its water resources. These insights, with the international significance of the KRB, contribute to the global understanding of hydrological processes and are of interest to a broad international audience.