The research aims to assess the interplay between climate dynamics and snow cover changes, informing water resource management. Utilizing the SWAT model with historical and future data, hydrological responses in the Marshyangdi River Basin are examined. The model's performance confirms its ability to simulate runoff components effectively. Projections indicate a concerning decrease in snow cover area for 2030, 2050, and 2090, with declines of 2.03, 4.03, and 14.20%, respectively, attributed to global warming and human activities. Isolating climate change reveals substantial increases in stream flow, notably pre-monsoon surges under different scenarios, with projected increments of 84 and 130% under SSP245 and SSP585, respectively. Analysis of snow cover impact shows a slight increase in annual stream flow, with a 3.54% anticipated decrease during the post-monsoon period, raising concerns for water availability in the dry season. Comparing both impacts reveals no significant differences in stream flow or precipitation. Annual stream flow increases under different scenarios, driven by precipitation, with varying impacts. Climate change emerges as the dominant driver, affecting surface runoff, groundwater, and evapotranspiration. This research unravels complex relationships between climate change, snow cover, and hydrological responses, providing valuable insights for water resource management in mountainous regions like the Hindu Kush Himalaya.

  • Examines climate dynamics, snow cover changes, and hydrological responses comprehensively, improving understanding of water–climate interactions.

  • Forecasts substantial increases in stream flow, especially pre-monsoon, under different climate scenarios.

  • Provides vital insights for water resource management in mountainous areas, urging adaptive strategies for evolving hydrological conditions.

The Hindu Kush Himalayas (HKH) are renowned as Asia's water towers, serving as the origins for 10 major rivers. Snowmelt and lateral flow through groundwater are primary contributors to the lean flow of these rivers Mukherji et al. (2015). These riverine ecosystems are vital, sustaining the food and energy needs of 1.3 billion downstream people. However, ongoing climatic changes, socio-economic development, and urban migration are exerting increased pressure on natural resources, particularly water. A substantial portion of the annual stream flow in the Himalayan region is attributed to snow cover and glaciers Shrestha (2011). Snowfall and snowmelt are dominant hydrological processes in snow-fed basins Nazeer et al. (2022). During winter, extensive areas are blanketed by snow, which melts as temperatures rise toward the end of winter. In lean periods, the melting of snow and glaciers significantly contributes to water quantities. However, climate change is accelerating the melting of snow and glaciers and reducing snow accumulation due to shifts in precipitation patterns, resulting in increased variability in river discharge (Azizi & Asaoka 2020). A study by the Inventory of Carbon and Energy indicates an average decrease of 16% in the flow of water from snowmelt to rivers in high-mountain Asia between 1979–1999 and 1999–2019. Global mean surface temperature, encompassing both terrestrial and marine environments, has exhibited a consistent upward trend since 1861. Global warming is anticipated to elevate temperatures by 1.7–6.3 °C in the HKH region by the end of the 21st century (Lutz et al. 2016). The rise in minimum temperatures amplifies the snowmelt process and alters the snow cover area (SCA) by shifting the snow line to higher elevations. Higher elevations are expected to experience a greater increase in temperature. According to the IPCC AR5 assessment, there is a clear and significant decrease in both the range and extent of snow cover in the northern hemisphere. Satellite data indicate a high probability of approximately 10% reduction in snow-covered areas since the late 1960s (Brown & Robinson 2011). These consequences, coupled with future climate change impacts, are expected to exacerbate ecosystem degradation, disturbances in water quality, and the loss of snow cover, glaciers, and sea ice (Khadka et al. 2014). The snow cover and glaciers in the HKH are recognized as some of the fastest-thinning globally (Desinayak et al. 2021). Melt waters from snow and ice in the HKH region play a crucial role in providing water for downstream farming, hydropower generation, and domestic use (Scott et al. 2019). Consequently, the accelerated melting of snow cover and glaciers increases the incidence of natural hazards (Azizi & Asaoka 2020).

Numerous studies have been conducted to understand the natural hydrological response of river basins by establishing the relationship between climate and watershed characteristics in the hydrological process, utilizing rainfall runoff models (Talchabhadel & Karki 2019; Aryal et al. 2023; Ramahaimandimby et al. 2023; Devkota et al. 2024). Most research emphasizes that the application of hydrological models relies on available data and required outputs. For more accurate estimations, mathematical models are considered more realistic (Ghonchepour et al. 2021). In high mountainous areas with limited data availability, semi-distributed models or lump models are deemed more reliable (Bajracharya et al. 2018; Mishra et al. 2018; Pandey et al. 2019; Sinha et al. 2019). Researchers globally, as well as in the Nepalese Himalaya region, have successfully utilized the soil and water assessment tool (SWAT) for hydrologic modeling and water resources management within watersheds characterized by diverse climatic and topographic conditions. The SWAT model has demonstrated its efficacy in such applications. Upon calibrating the model parameters using a physically observed database, the SWAT model has shown significantly improved performance (Van Liew et al. 2007; Liu et al. 2014; Aryal et al. 2019; Marahatta et al. 2021; Talchabhadel et al. 2021; Malik et al. 2022; Devkota et al. 2024). These investigations highlight the robustness and reliability of the SWAT model in various geographical settings. In a study by Thapa et al. (2017) focusing on the Bagmati Basin, which is adjacent to the Marsyangdi River Basin (MRB), the SWAT model's outputs exhibited greater accuracy compared to alternative hydrological models such as the Hydrologiska Byrans Vattenbalansavdelning (Bergström 2006) and the block-wise use of the TOPMODEL model with Muskingum-Cunge routing (BTOPMC) (Takeuchi et al. 1999, 2007). This underscores the SWAT model's superiority in capturing the complexities of water balance components in diverse hydrological settings.

The MRB, a crucial water source in the HKH region, is currently undergoing a reduction in its SCA at a rate of 0.34% annually, as revealed by a snow cover map spanning the research region from 2000 to 2019. Given the expected alterations in snow dynamics in the future, it is imperative to develop a comprehensive understanding of the existing snow cover dynamics and their potential implications for basin hydrology. Despite the relative abundance of studies on glaciers in the Himalayas, research specifically focused on snow cover is scarce (Shrestha 2011). A conspicuous knowledge gap persists regarding the impact of changes in snow cover on basin hydrology, even though numerous studies have investigated the effects of land use and land cover (LULC) changes on watershed hydrology.

This paper endeavors to bridge this knowledge gap by projecting future snow cover in the MRB through logistic regression and establishing its impact on future river flow using a hydrological model. It is essential to establish a balanced understanding of the demand and supply dynamics at the whole river basin or sub-basin scale, considering factors such as socio-economic changes, urbanization, demographic shifts, climate variations, and alterations in agricultural practices. This is crucial for fostering sustainable water resources development. To attain this understanding, the first step is to identify and quantify the driving forces (climate and SCA change) that contribute to imbalances in resources (Mukherji et al. 2015). In this context, our study addresses these challenges by examining two key driving forces, namely climate and snow cover variation, utilizing the semi-distributed, physically based SWAT model (Arnold et al. 1998; Neitsch et al. 2011) over a snow-covered watershed. The study aims to analyze the potential effects of future changes on the hydrology of the basin in both contexts. The specific goals of this research are threefold: (i) to assess the impact of climate change on the hydrology of the basin in the near future (NF), mid-future (MF), and far future (FF); (ii) to quantify the variation in SCA in the future and its implications for river hydrology; and (iii) to evaluate the integrated impact of future SCA variation and climate change on the river hydrology of MRB. This research addresses the critical need to comprehend how shifts in snow cover could influence basin hydrology, thereby having far-reaching implications for water resource planning, development, and management strategies in the region. Establishing a robust knowledge base on the intricate relationship between snow cover fluctuations and hydrology is imperative for effectively adapting to the challenges presented by climate change in the Himalayas.

Study area

The study was conducted in the MRB, located in the central part of the Hindu Kush Himalayan (HKH) region in Nepal, as shown in Figure 1. This snow-fed basin covers a catchment area of 4,039.524 km², with elevations ranging from 349 to 7,698 m above sea level. Geographically, the basin extends from longitude 83°47′24″ E to 84°48′04″ E and latitude 27°50′42″ N to 28°54′11″ N. The watershed's outlet is marked by Hydrological Station No. 439.7, situated in Bimalnagar. The MRB spans from the northern Greater Himalaya to the southern Lesser Himalaya and encompasses four administrative districts: Manang, Lamjung, Gorkha, and Tanahu. While the majority of the basin lies on the southern slopes of the Central Himalayas, its northern section is positioned on the leeward side of the Annapurna Mountain. As a major tributary of the Narayani River, the Marsyangdi River eventually merges with the Ganges River System. The Marsyangdi River, an alpine river stretching 150 km, receives water from several tributaries, including Paundi Khola and Chundi Khola on the right, and Nagdhi Khola, Dordi Khola, Chepe Khola, and Daraundi River on the left. The substantial elevation variation from the river's source to its outlet generates high kinetic energy, which is crucial for hydroelectric energy production. The flow variations within the basin significantly impact downstream hydroelectric energy generation projects. Snow cover variation, particularly the shifting snow line, is a key factor influencing lean flow and primary energy generation for these projects. Climate change also plays a pivotal role, with rising temperatures and substantial variations in precipitation patterns altering both runoff behavior and energy generation. Currently, several prominent hydroelectric projects are operational in the region, including Lower Marsyangdi (69 MW), Upper Marsyangdi (50 MW), and Middle Marsyangdi (70 MW). Additionally, ongoing projects in tributaries such as Nyadi, Midim, Chepe, Dordi, and Daraudi contribute to the region's hydroelectric capacity. Large-scale hydropower projects like Upper Marsyangdi 1 (138 MW), Lower Manang Marsyangdi (140 MW), and Manang Marsyangdi (135 MW) are also under study and development. The MRB is one of Nepal's primary sources of hydroelectricity, underscoring its strategic importance in the country's energy landscape (Acharya 2023).
Figure 1

Location map of the study area.

Figure 1

Location map of the study area.

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Data collection

Topographic, soil, land use/cover maps, hydrological, and meteorological data were collected from various sources as outlined in Table 1. These datasets were then preprocessed for analysis. The digital elevation model (DEM) was used to delineate the watershed and generate the river stream network. Higher-resolution LULC maps included areas of mixed forest, water bodies, snow and glacier-covered regions, barren land, built-up areas, and cropland. Notably, snow cover accounted for 20% of the total area.

Table 1

List of data sources

Data (unit)Description (type)Resolution (time frame)Source
Topography DEM (spatial grid) 30 m × 30 m (for 2009) Aster Jasro, USGS 
Soil Soil classification and physical properties (spatial vector) 30 m × 30 m (for 2009) Soil and Terrain Digital Data Base 
Land use/cover Land use/cover classification (spatial grid) 30 m × 30 m (for 2000 to 2019) International Center for Integrated Mountain Development 
Precipitation Daily precipitation (time-series) 10 precipitation station (1980–2019) Department of Hydrology and Meteorology, Nepal 
Temperature Daily maximum and minimum temperature (time-series) 5 climate station (1980–2019) Department of Hydrology and Meteorology, Nepal 
River discharge Daily observed stream flow (time-series) 1 hydrological station (1988–2019) Department of Hydrology and Meteorology, Nepal 
Solar radiation Daily observed sunshine hour 1 climate station (1988–2019) Department of Hydrology and Meteorology, Nepal 
Wind speed Daily observed wind speed 1 climate station (1988–2019) Department of Hydrology and Meteorology, Nepal 
Relative humidity Daily observed relative humidity 1 climate station (1988–2019) Department of Hydrology and Meteorology, Nepal 
GCM data ACCESS-CM2, ACCESS-ESM1-5, BCC-CSM2-MR, CanESM5, EC-Earth3, EC-Earth3-Veg-LR, INM-CM4-8, INM-CM5-0, MPI-ESM1-2-HR, MRI-ESM2-0, NorESM2-LM 
Data (unit)Description (type)Resolution (time frame)Source
Topography DEM (spatial grid) 30 m × 30 m (for 2009) Aster Jasro, USGS 
Soil Soil classification and physical properties (spatial vector) 30 m × 30 m (for 2009) Soil and Terrain Digital Data Base 
Land use/cover Land use/cover classification (spatial grid) 30 m × 30 m (for 2000 to 2019) International Center for Integrated Mountain Development 
Precipitation Daily precipitation (time-series) 10 precipitation station (1980–2019) Department of Hydrology and Meteorology, Nepal 
Temperature Daily maximum and minimum temperature (time-series) 5 climate station (1980–2019) Department of Hydrology and Meteorology, Nepal 
River discharge Daily observed stream flow (time-series) 1 hydrological station (1988–2019) Department of Hydrology and Meteorology, Nepal 
Solar radiation Daily observed sunshine hour 1 climate station (1988–2019) Department of Hydrology and Meteorology, Nepal 
Wind speed Daily observed wind speed 1 climate station (1988–2019) Department of Hydrology and Meteorology, Nepal 
Relative humidity Daily observed relative humidity 1 climate station (1988–2019) Department of Hydrology and Meteorology, Nepal 
GCM data ACCESS-CM2, ACCESS-ESM1-5, BCC-CSM2-MR, CanESM5, EC-Earth3, EC-Earth3-Veg-LR, INM-CM4-8, INM-CM5-0, MPI-ESM1-2-HR, MRI-ESM2-0, NorESM2-LM 

Methodology

The methodology for this research began with the development of a hydrological model using baseline data. This was followed by simulating future snow cover variations in response to land use changes through a specialized land use change model. The accuracy and reliability of this simulation were thoroughly assessed and validated using various statistical indicators. Simultaneously, future climate change variations were investigated by analyzing multiple general circulation model (GCM) datasets. To improve the accuracy of these analyses, bias correction techniques were applied. The results were then projected under different scenarios to provide a comprehensive understanding of potential future climate change patterns. A meticulously designed, calibrated, and validated model formed the basis for optimizing model parameters. This optimization process was conducted using baseline data and rigorously tested against statistical indicators. After optimization, the model was used to evaluate both individual and integrated impacts in the context of anticipated changes. The entire methodological framework is visually presented in Figure 2 ensuring clarity and a comprehensive understanding.
Figure 2

Methodological framework.

Figure 2

Methodological framework.

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Hydrological model setup, calibration and validation

The simulation of hydrological and environmental processes within the river basin was conducted using the continuous-time, semi-distributed, process-based SWAT model, as detailed by Arnold et al. (1998). Unlike regression models, SWAT requires comprehensive information about land management practices, terrain, weather, soil properties, and vegetation within the watershed. The study area was divided into hydrologic response units (HRUs), elevation bands, and sub-basins to capture the spatial heterogeneity of the basin. Specifically, 56 sub-basins and 2,593 HRUs were generated using spatially distributed data for LULC, soil, and slope, with zero threshold values applied in a GIS environment. The slope classes were categorized into five ranges: 0–5%, 5–10%, 10–20%, 20–50%, and greater than 50%. To replicate snowmelt and orographic temperature and precipitation distribution, three elevation bands were created, each with a mean elevation of 500 m. Meteorological input for model development included time-series data as specified in Section 2.2. The auto-calibration process employed the SUFi-2 algorithm and SWAT-CUP to refine and reduce the effective range of each parameter. Global sensitivity analysis was used to assess parameter sensitivity, with the most sensitive parameters identified by the highest t-stat value and the lowest p-value (less than 0.05). Calibration involved parameterizing the model to specific conditions using the first 37 parameters. Given the sensitivity of snow parameters in the mountainous basin, seven snow parameters (SMFMX, SMFMN, TIMP, SNOCOVMX, SNO50COV, SFTMP, and SMTMP) were included. The model was calibrated using daily discharge data at the basin's outlet for the years 1988–2005 and validated for the years 2006–2015.

Performance metrics, including the coefficient of determination (R²), Nash–Sutcliffe simulation efficiency (NSE), percentage bias (PBIAS), R-factor, and P-factor, were employed to evaluate the model's performance (Table 2). The calibration process continued until acceptable performance metric values were achieved, after which the same parameters were used for validation. SWAT-CUP-derived parameter values were manually assigned in the SWAT model after calibration and validation. Additionally, the most sensitive parameters identified during auto-calibration were adjusted first during manual calibration for optimal statistical parameter values (NSE, PBIAS, and R²). The model's performance evaluation included daily and monthly simulations, assessing hydrographs, scatter plots, flow duration curves, statistical parameters, and water balance (actual vs. simulated) at daily, monthly, and annual scales. Various visual assessments were conducted, such as examining hydrograph peaks, time to peak, hydrograph shape, and base flow, to comprehensively evaluate the model's performance (Pandey et al. 2020; Devkota et al. 2024).

Table 2

Performance parameter of the model

Performance ratingR2NSEPBIAS (%)
Very good Values between 0 and 1, where larger values suggest reduced error variance, and values above 0.5 are regarded as appropriate 0.75 < NSE < 1.00 PBIAS < ±10 
Good 0.65 < NSE < 0.75 ±10 < PBIAS < ±15 
Satisfactory 0.50 < NS < 0.65 ±15 < PBIAS < ±25 
Unsatisfactory NSE < 0.50 PBIAS > ±25 
Performance ratingR2NSEPBIAS (%)
Very good Values between 0 and 1, where larger values suggest reduced error variance, and values above 0.5 are regarded as appropriate 0.75 < NSE < 1.00 PBIAS < ±10 
Good 0.65 < NSE < 0.75 ±10 < PBIAS < ±15 
Satisfactory 0.50 < NS < 0.65 ±15 < PBIAS < ±25 
Unsatisfactory NSE < 0.50 PBIAS > ±25 

Future climate projection

The analysis incorporates the latest scenarios known as shared socio-economic pathways (SSPs), as highlighted in the IPCC's Sixth Assessment Report (AR6) and the Coupled Model Intercomparison Project Phase 6 (CMIP6). Each dataset output from the GCMs represents distinct potential future scenarios. For this study, we selected the high-end emission scenario (SSP585), which reflects a fossil fuel-driven development ideology, and the middle-of-the-road scenario (SSP2), combined with the moderate emission scenario (SSP245). GCM models provide reliable insights into past, present, and future climate patterns. The baseline period from 1980 to 2009 serves as a reference for analyzing future periods, which are further divided into NF (2021–2045), MF (2046–2070), and FF (2071–2095).

Eleven GCMs were selected based on a literature review to anticipate future climate conditions in the HKH region and similar basins in Nepal. These GCM runs were evaluated using performance measures (PBIAS, RSR, and NSE) against observed data. The World Research Climate Programme provided GCM outputs in NetCDF format, which were downscaled for compatibility with the SWAT model using R-studio. A prior performance method assessed the appropriateness of each GCM, resulting in an aggregate rating and ranking based on performance scores, as shown in Table 3. Recognizing the diversity of potential scenarios depicted by different GCMs, the study employed an ensemble of the top three selected GCMs – ACCESS-ESM1-5, EC-Earth3, and CanESM5. This ensemble approach aimed to minimize uncertainties in anticipating future climatic conditions, aligning with practices in hydro-climatic studies that often utilize multiple GCMs to provide a more comprehensive understanding. Quantile mapping bias correction techniques were employed to rectify systematic distributional biases in climatic outputs derived from climate models. Among these techniques, the robust empirical quantile method proved effective for bias correction in climate data. This approach enabled the derivation of future time-series of climate variables from the raw GCMs under the SSP245 and SSP585 trajectories.

Table 3

Performance rating criteria (Moriasi et al. 2007)

PerformanceNSEPBIASRSRRating
Very good 0.75 < NSE < =1.00 PB < 10 0.00 < RSR < =0.50 
Good 0.65 < NSE < =0.75 10 < =PB < 15 0.50 < RSR < =0.60 
Satisfactory 0.50 < NSE < =0.65 15 < =PB < 25 0.60 < RSR < =0.70 
Unsatisfactory 0.4 < NSE < =0.5 25 < =PB < 35 0.70 < RSR < =0.80 
Poor NSE < =0.4 PB > =35 RSR > 0.80 
PerformanceNSEPBIASRSRRating
Very good 0.75 < NSE < =1.00 PB < 10 0.00 < RSR < =0.50 
Good 0.65 < NSE < =0.75 10 < =PB < 15 0.50 < RSR < =0.60 
Satisfactory 0.50 < NSE < =0.65 15 < =PB < 25 0.60 < RSR < =0.70 
Unsatisfactory 0.4 < NSE < =0.5 25 < =PB < 35 0.70 < RSR < =0.80 
Poor NSE < =0.4 PB > =35 RSR > 0.80 

The three selected GCMs for precipitation, maximum temperature, and minimum temperature were evaluated based on their average values. Statistical performance indicators (Table 3) reveal that ACCESS-ESM1-5 scored 2.57, EC-Earth3 scored 2.56, and CanESM5 scored 2.53. These ratings reflect the performance and suitability of each GCM in accurately capturing and simulating the specified climatic parameters. The outcomes provide a concise overview of the prioritized GCMs, guiding further considerations in the study.

Projection of future snow cover

The Dyna-CLUE model, an advanced iteration of the CLUE-S model, was specifically designed to simulate land use changes, particularly regarding snow cover variation, in a spatially explicit manner based on an empirical assessment of location suitability. This dynamic model facilitates the simulation of interactions and competitions between the temporal and spatial dynamics of various land use types, featuring two distinct modules: one for demand and the other for spatial allocation. The historical changes in land use cover, especially SCA, and the evaluation of projected demand were assessed using LULC planning scenarios or historical patterns of LULC types (Verburg & Overmars 2009). Concurrently, the second module transformed the spatial LULC requirements of the study region.

In this study, the Dyna-CLUE model was utilized to project the snow cover map at a regional scale. The iterative determination of optimal results in the model relied on four inputs: geographic features, land-use requirements (demand), conversion settings specific to each land-use type, and spatial policies and constraints. The LULC map of 2010 for the watershed served as the base map to project the yearly snow cover map up to 2100. Logistic regression models (SPSS Model) were constructed to estimate location suitability requirements based on the spatial interaction of each LULC type with a set of driving forces for LULC change. These driving factors included elevation, slope, aspect, rainfall, Tmax, Tmin, population density, distance to river, distance to road, and soil map. To facilitate the Dyna-CLUE model, all layers in the GIS environment were required to overlap properly and be converted into raster to ASCII format. In the trend scenario, no spatial policies were implemented, and the restricted region (forest to other) encompassed the Annapurna Conservation Area, where changes were not permitted. The rules of conversion between LULC types were determined by conversion parameters, including conversion flexibility and the conversion matrix. A specific LULC type could convert to other types based on its conversion flexibility, with the conversion elasticity ranging from 0 (greatest conversion possibility) to 1 (no conversion possibility) (Trisurat et al. 2010). Through a trial-and-error approach, values of conversion parameters (0.6, 1, 0.8, 1, 0.9, 0.7, 0.8, and 0.4) were allocated to forest, water body, snow and glacier, riverbed, built-up area, cropland, barren land, and grassland, respectively (et al. 2020). Water bodies and riverbeds were given high elasticity values due to their inherent resistance to transformation into another class. The conversion matrix employed in the MRB was established after a comprehensive examination of past patterns, specifically the changes that occurred between 2000 and 2010. A value of ‘1’ indicates a viable conversion, while a value of ‘0’ indicates an impossible conversion in the conversion matrix. The Dyna-CLUE model has not yet been implemented in the MRB. Therefore, conversion parameters were prepared based on an analysis of previous research on studies with comparable features and expert judgment. The following references were consulted: (Verburg & Overmars 2009; Trisurat et al. 2010; Tizora et al. 2018; et al. 2020; Khoi et al. 2021). Future demands were estimated using a simple extrapolation method based on annual historical fluctuations in the area of each LULC type, particularly snow cover variation. The receiver operating characteristic (ROC) was employed as a tool to evaluate the goodness-of-fit of logistic regression models, with the area under the curve ranging from 0.5 (complete randomness) to 1.0 (perfect match), serving as a summary indicator to assess the model's overall effectiveness. The projected LULC map (2019) and the LULC map (2019) from ICIMOD were compared to validate the model using Kappa statistics for a reliability assessment of land-use change simulations, where a Kappa statistic of 0 denotes chance agreement, and a statistic of 1 denotes perfect agreement.

Model implementation

In this study, we aimed to examine the impact of climate change on stream flow and various water balance components within a watershed. We employed a comprehensive approach, utilizing a calibrated SWAT model to simulate anticipated future conditions based on quantified climate data. We maintained the baseline snow cover map, derived from LULC data, at constant levels throughout the study. The well-calibrated SWAT model was used to estimate stream flow and water balance for three distinct time periods: NF, MF, and FF. These estimates were rigorously compared against the baseline stream flow and water balance components. To specifically assess the impact of changing snow cover, we used the 2010 SCA map as a reference and incorporated future snow cover maps for 2030, 2050, and 2090, calibrated by the Dyna-CLUE model. By maintaining climatic data consistent with the baseline period (1980–2017), we systematically compared SWAT model outcomes using future snow cover maps against the 2010 baseline information. This comparative analysis enabled us to discern the potential effects of altered snow cover on hydrological dynamics and the overall water balance in the study area. The SWAT model was supplied with projected snow cover maps for 2030, 2050, and 2090, along with corresponding climate data for the NF (2021–2045), MF (2046–2070), and FF (2071–2095) periods. Following the model run, we conducted a meticulous comparison, contrasting the projected future stream flow and water balance components with the baseline hydrology of the study region. This comprehensive evaluation aimed to gauge the integrated influence of future changes in SCA and climate change on the hydrology of the basin, providing valuable insights into potential shifts in water resources under various future scenarios.

SWAT model performance

The global sensitivity analysis conducted on the MRB indicated that among the original 37 parameters selected, PLAPS, ALPHA_BNK, CH_K2, SMFMX, TLAPS, and SMTMP emerged as the most sensitive parameters, with p-values below 0.05 and the highest t-sat values. This highlights the significant role of snowmelt in the basin's hydrological processes. The uncertainty analysis in this study produced satisfactory results, as reflected in the performance metrics. The p-factor, which represents the percentage of daily observed data within the 95% prediction uncertainty band, reached 0.8, indicating that the expected uncertainty band effectively encompasses 80% of daily observed data points. Additionally, the r-factor, representing the width of the uncertainty band, was 0.7. These results indicate reliable and accurate model performance, demonstrating a robust match between predicted and observed data. The model's performance was assessed both graphically and statistically, employing metrics such as NSE, PBIAS, and R2 (Table 2 and Figure 3).
Figure 3

Calibration and validation of model performance (a) on a daily basis and (b) on a monthly basis.

Figure 3

Calibration and validation of model performance (a) on a daily basis and (b) on a monthly basis.

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The model shows a more accurate simulation of low flows compared to high flows, a limitation acknowledged in previous studies (Mishra et al. 2018). Difficulty in replicating peak flows may be attributed to the complexity introduced by days with multiple storms, posing challenges for the existing curve number technique to predict runoff effectively. As depicted in Figure 3, a significant correlation between simulated and observed daily and monthly variables is evident during both calibration and validation stages, with NSE, PBIAS, and R2 values within acceptable ranges for daily and monthly simulations, respectively (Table 4). Combined graphical and statistical analyses affirm the model's capability to achieve acceptable runoff simulation during calibration and validation stages. These results suggest that the SWAT model can be effectively utilized for hydrological modeling in the MRB, serving as a valuable tool for assessing water resources and studying the basin's water balance.

Table 4

Model performance

Time stepProcessPeriodNSEPBAISR2
Daily Calibration 1988–2005 0.64 0.68 
Validation 2006–2015 0.67 −3.8 0.68 
Monthly Calibration 1988–2005 0.88 1.9 0.88 
Validation 2006–2015 0.85 −3.5 0.86 
Time stepProcessPeriodNSEPBAISR2
Daily Calibration 1988–2005 0.64 0.68 
Validation 2006–2015 0.67 −3.8 0.68 
Monthly Calibration 1988–2005 0.88 1.9 0.88 
Validation 2006–2015 0.85 −3.5 0.86 

Projected changes in future climate data

During the baseline period, over 75% of the average annual rainfall occurs during the monsoon season (June–September), with 16% falling in the pre-monsoon season (March–May), 4% in the post-monsoon season (October–November), and 5% in the winter season (December–February). This highlights the critical importance of monsoon rainfall in the MRB and HKH regions. The average annual precipitation for each future period shows an upward trend relative to the baseline for both scenarios. Table 5 illustrates the percentage increase in seasonal precipitation for NF, MF, and FF compared to the baseline value under both SSP245 and SSP585 scenarios, revealing a rising trend in daily precipitation from the baseline period (1988–2019) to NF, MF, and FF. Regarding temperature, the ensemble (arithmetic mean) of Tmax and Tmin from the top three selected GCM models was calculated and used for further analysis. As depicted in Table 5, the average Tmax values increase by 1.49 °C in NF, 2.51 °C in MF, and 3.24 °C in FF for SSP245, and by 1.65 °C in NF, 3.50 °C in MF, and 6.05 °C in FF for SSP585, relative to the baseline data. Meanwhile, Tmin shows an increase of 1.90 °C in NF, 2.73 °C in MF, and 3.39 °C in FF for SSP245, and 2.04 °C in NF, 3.59 °C in MF, and 5.61 °C in FF for SSP585, compared to the baseline values.

Table 5

Baseline and projected minimum and maximum temperature and precipitation

TitleSeasonBaselineSSP245
SSP585
NFMFFFNFMFFF
Change in average Tmax (°C) w.r.t. baseline Pre-monsoon 23.35 1.15 2.02 2.43 1.370 2.97 5.54 
Monsoon 25.74 2.00 2.97 4.12 1.925 4.14 6.64 
Post-monsoon 21.09 1.40 2.51 3.29 1.675 3.64 5.70 
Winter 16.14 1.20 2.38 2.81 1.546 3.08 6.00 
Annual 21.97 1.49 2.51 3.24 1.65 3.50 6.05 
Change in average Tmin (°C) w.r.t. baseline Pre-monsoon 9.61 2.04 2.76 3.35 2.04 3.62 5.83 
Monsoon 15.43 2.48 3.47 4.28 2.65 4.52 6.77 
Post-monsoon 7.82 1.92 2.98 3.61 2.19 3.86 5.88 
Winter 2.72 0.99 1.55 2.10 1.13 2.13 3.66 
Annual 9.53 1.90 2.73 3.39 2.04 3.59 5.61 
Change in average precipitation (mm) w.r.t. baseline Pre-monsoon 232.58 34.79 46.98 66.51 43.74 51.95 81.51 
Monsoon 1,106.88 20.46 44.37 48.93 36.86 65.35 115.19 
Post-monsoon 51.12 21.77 15.32 38.58 13.25 53.64 151.21 
Winter 73.87 23.42 24.06 31.79 21.50 17.97 4.20 
Annual 1,464.45 22.93 42.75 50.50 36.35 60.42 105.50 
TitleSeasonBaselineSSP245
SSP585
NFMFFFNFMFFF
Change in average Tmax (°C) w.r.t. baseline Pre-monsoon 23.35 1.15 2.02 2.43 1.370 2.97 5.54 
Monsoon 25.74 2.00 2.97 4.12 1.925 4.14 6.64 
Post-monsoon 21.09 1.40 2.51 3.29 1.675 3.64 5.70 
Winter 16.14 1.20 2.38 2.81 1.546 3.08 6.00 
Annual 21.97 1.49 2.51 3.24 1.65 3.50 6.05 
Change in average Tmin (°C) w.r.t. baseline Pre-monsoon 9.61 2.04 2.76 3.35 2.04 3.62 5.83 
Monsoon 15.43 2.48 3.47 4.28 2.65 4.52 6.77 
Post-monsoon 7.82 1.92 2.98 3.61 2.19 3.86 5.88 
Winter 2.72 0.99 1.55 2.10 1.13 2.13 3.66 
Annual 9.53 1.90 2.73 3.39 2.04 3.59 5.61 
Change in average precipitation (mm) w.r.t. baseline Pre-monsoon 232.58 34.79 46.98 66.51 43.74 51.95 81.51 
Monsoon 1,106.88 20.46 44.37 48.93 36.86 65.35 115.19 
Post-monsoon 51.12 21.77 15.32 38.58 13.25 53.64 151.21 
Winter 73.87 23.42 24.06 31.79 21.50 17.97 4.20 
Annual 1,464.45 22.93 42.75 50.50 36.35 60.42 105.50 

These projected decadal and seasonal temperature variations, particularly in maximum and minimum values, intensify the snowmelt process and shift the snow coverage line in the higher Himalaya region. The increased snowmelt rate enhances pre-monsoon flow, while the monsoon season experiences increased runoff volume from the catchment due to reduced snow coverage. These changes have multifaceted impacts, altering recharge rates and lateral flow at the catchment level, and highlight the complex interplay of phenomena resulting from the evolving climate context (Thapa et al. 2021).

Projected change in future SCA

Analyzing the observed annual SCA in 2010 from the LULC map reveals an area of 1,124.15 km², showing a reduction at a rate of 0.34% per year from 2000 to 2019. This decline is likely associated with temperature changes due to climate change. The study applies the trend of LULC change from 2000 to 2019 to simulate the future snow cover map of the MRB for 2030, 2050, and 2090. The robustness of the logistic regression model is evident, with ROC values consistently exceeding 0.8 for each land use type, underscoring the strong explanatory power of the chosen driving forces. A comparison between simulated and observed LULC maps for 2019 indicates a high level of agreement, validated by a Kappa value of 0.67, affirming the reliability of the simulated LULC data. Consequently, the calibrated Dyna-CLUE model is deemed suitable for projecting future snow cover maps. The projected SCA shows a gradual decrease in the future, with percentage variations of 2.03, 4.03, and 14.20% for 2030, 2050, and 2090, respectively, compared to the baseline snow cover map from 2010, as depicted in Figure 4. These changes, attributed to global warming and human activities such as deforestation, urbanization, and agriculture, set the stage for a future characterized by increased precipitation and temperatures in the MRB.
Figure 4

Baseline and future projected precipitation, minimum and maximum temperature, and snow cover change.

Figure 4

Baseline and future projected precipitation, minimum and maximum temperature, and snow cover change.

Close modal

Impacts of climate change

A general rise in stream flow is anticipated for the future under both SSP245 and SSP585 scenarios. Climate change is projected to increase the average annual temperature and precipitation, leading to a corresponding rise in average annual stream flow. Specifically, under the SSP245 scenario, the average annual stream flow is expected to increase by 58% (NF), 85% (MF), and 84% (FF). Under the SSP585 scenario, these increases are even more pronounced at 69% (NF), 104% (MF), and 130% (FF). These projections are supported by the greater rate of increase in rainfall in the SSP585 scenario compared to SSP245, as shown in Table 7 and Figure 5. The baseline annual stream flow is 223 m³/s as shown in Table 6. Under the SSP245 scenario, stream flow is projected to increase by 58% in NF due to a 5.5% rise in precipitation and a 4.5% decline in evapotranspiration. Under the SSP585 scenario, a 69% increase in NF stream flow is expected, driven by a 15% rise in precipitation and a 3% decline in evapotranspiration. The percentage increase in stream flow compared to the baseline is highest during the pre-monsoon season for both scenarios, with a 150% increase in FF under SSP245 and a 183% increase under SSP585. This significant rise is attributed to the recharging of groundwater during the post-monsoon season, which enhances the base flow of river discharge in the pre-monsoon season (Lamichhane & Shakya 2019).
Table 6

Seasonal and annual baseline water balance components

TimeWBCPre-monsoonMonsoonPost-monsoonAnnual
 SQ 75 718 23 831 
 Gw 17.3 68 70 221 
 LQ 82 504 41 642 
Baseline WY 177 1,295 138 1,709 
  AET 170 218 39 460 
PPT 346 1,726 79 2,246 
SF 119 476 117 223 
TimeWBCPre-monsoonMonsoonPost-monsoonAnnual
 SQ 75 718 23 831 
 Gw 17.3 68 70 221 
 LQ 82 504 41 642 
Baseline WY 177 1,295 138 1,709 
  AET 170 218 39 460 
PPT 346 1,726 79 2,246 
SF 119 476 117 223 
Table 7

Stream flow and water balance component change percentage due to snow cover change and climate change

TimeWBCCC_SSP245
CC_SSP585
Only snow cover change impact
Combine_SSP245
Combine_SSP585
Pre-monMonPost-monAnlPre-monMonPost-monAnlPre-monMonPost-monAnlPre-monMonPost-monAnlPre-monMonPost-monAnl
SCA 2030 (NF) SQ −2.7 −18.2 −33.3 −17.5 6.5 −3 −44 −3.7 26.7 9.4 17.9 11.43 45.4 −6.4 5.8 −1 56.1 9.5 −5.1 13.5 
Gw 106.4 66.3 33.3 50.7 119.5 81.3 47.9 64.4 −6.4 −2.4 −0.7 −1.69 84.1 56.3 28.1 42.6 97.8 71.9 42.8 56.6 
LQ 71.3 12.4 45.9 25.4 77.8 22.8 52 34.8 −14.4 −10.8 −12.5 −11.3 41.2 −1.6 23.9 8.4 47.1 7.8 29 16.9 
WY 42.9 −1.6 25.7 7.9 51.3 11.7 33.5 20 0.9 −1.2 1.06 47.1 −1.1 22.9 8.4 55.8 12.2 30.4 20.5 
AET −6.5 −9.7 11.3 −4.5 −3.9 −8.5 13.7 −3 −3.5 −2.5 −4.3 −2.92 −8.3 −11.3 8.5 −6.1 −5.8 −10.2 10.7 −4.6 
PPT 29.8 −4.6 44.9 5.5 36.8 6.7 41.3 15 Baseline precipitation 29.8 −4.6 44.9 5.5 36.9 6.7 41.3 15.1    
SF 150 44 76 58 122 56 83 69 1.8 −7 4.12 122 45 64 59 129 58 70 70 
SCA 2050 (MF) SQ 27 7.1 −36.2 7.2 28.5 27.9 −4.8 26.2 22 6.3 13.4 8.07 70.3 16.4 −2 21 74.8 37.6 34.2 40.7 
Gw 188.4 121.8 70 95.9 202.8 140.5 85.7 111.9 −3.1 1.5 3.4 2.27 172.5 116.9 69.2 92.5 187.5 136.2 85.6 109.1 
LQ 100.9 34.6 69.6 48.7 110.9 46.5 80.6 59.8 −12.7 −8.3 −10 −8.9 71.2 21.5 48.3 32.5 78.8 32.6 57.7 42.5 
WY 77.7 24.1 51.8 34.7 84.5 41.3 68.7 50.3 3.1 0.4 0.88 80.6 23.9 50.7 34.9 87.7 41 68.2 50.5 
AET −34.1 −45.4 −3.5 −33.8 −31.7 −42.5 2.5 −31 −3.3 −1.6 −3.8 −2.34 −34.6 −45.5 −5.1 −34.2 −32.3 −42.6 0.2 −31.4 
PPT 39.1 11.9 41.8 19.5 43.1 26.1 66.3 31.7 Baseline precipitation 39.1 11.9 41.8 19.5 43.1 26.1 66.3 31.7    
SF 147 71 98 85 164 90 116 104 3.7 1.5 −5.6 153 72 85 85 170 91 104 104 
SCA 2090 (FF) SQ 36.1 8.3 −20 9.6 56.2 76.2 72.4 72.6 9.4 −3.5 0.2 −2.18 64.9 5.8 4.3 11.5 88.5 69.5 101.6 71.5 
Gw 96.1 72.6 48.1 60.2 125.7 101.7 88.2 96.8 8.9 14.7 16.8 15.48 97.4 88.8 65.4 75.9 144.7 128.3 116.1 123.6 
LQ 86 26.6 62.7 39.8 96.5 56.1 136.2 68.5 −7.8 −0.7 −3 −1.76 63.3 23.1 48.8 32.7 70.1 54.8 118.6 62.1 
WY 65.3 18.9 40.8 27.8 82 69.7 99.3 74.2 1.3 −1.4 0.37 67.2 17.1 49.9 28.1 85.5 66.9 113.7 74.9 
AET 12.4 13 30.4 16.1 23.4 26.1 55.3 29 −3.4 1.4 −2.5 −0.67 10.3 13.8 26.5 15.5 21.7 24.9 48.8 27.3 
PPT 53.4 14.9 57.3 24.8 63.8 59.9 129.8 62.6 Baseline precipitation 53.4 14.9 57.3 24.8 63.8 59.9 129.8 62.6    
SF 150 69 95 84 183 117 156 130 7.37 2.53 −10.3 3.54 162 71 83 85 195 119 145 131 
TimeWBCCC_SSP245
CC_SSP585
Only snow cover change impact
Combine_SSP245
Combine_SSP585
Pre-monMonPost-monAnlPre-monMonPost-monAnlPre-monMonPost-monAnlPre-monMonPost-monAnlPre-monMonPost-monAnl
SCA 2030 (NF) SQ −2.7 −18.2 −33.3 −17.5 6.5 −3 −44 −3.7 26.7 9.4 17.9 11.43 45.4 −6.4 5.8 −1 56.1 9.5 −5.1 13.5 
Gw 106.4 66.3 33.3 50.7 119.5 81.3 47.9 64.4 −6.4 −2.4 −0.7 −1.69 84.1 56.3 28.1 42.6 97.8 71.9 42.8 56.6 
LQ 71.3 12.4 45.9 25.4 77.8 22.8 52 34.8 −14.4 −10.8 −12.5 −11.3 41.2 −1.6 23.9 8.4 47.1 7.8 29 16.9 
WY 42.9 −1.6 25.7 7.9 51.3 11.7 33.5 20 0.9 −1.2 1.06 47.1 −1.1 22.9 8.4 55.8 12.2 30.4 20.5 
AET −6.5 −9.7 11.3 −4.5 −3.9 −8.5 13.7 −3 −3.5 −2.5 −4.3 −2.92 −8.3 −11.3 8.5 −6.1 −5.8 −10.2 10.7 −4.6 
PPT 29.8 −4.6 44.9 5.5 36.8 6.7 41.3 15 Baseline precipitation 29.8 −4.6 44.9 5.5 36.9 6.7 41.3 15.1    
SF 150 44 76 58 122 56 83 69 1.8 −7 4.12 122 45 64 59 129 58 70 70 
SCA 2050 (MF) SQ 27 7.1 −36.2 7.2 28.5 27.9 −4.8 26.2 22 6.3 13.4 8.07 70.3 16.4 −2 21 74.8 37.6 34.2 40.7 
Gw 188.4 121.8 70 95.9 202.8 140.5 85.7 111.9 −3.1 1.5 3.4 2.27 172.5 116.9 69.2 92.5 187.5 136.2 85.6 109.1 
LQ 100.9 34.6 69.6 48.7 110.9 46.5 80.6 59.8 −12.7 −8.3 −10 −8.9 71.2 21.5 48.3 32.5 78.8 32.6 57.7 42.5 
WY 77.7 24.1 51.8 34.7 84.5 41.3 68.7 50.3 3.1 0.4 0.88 80.6 23.9 50.7 34.9 87.7 41 68.2 50.5 
AET −34.1 −45.4 −3.5 −33.8 −31.7 −42.5 2.5 −31 −3.3 −1.6 −3.8 −2.34 −34.6 −45.5 −5.1 −34.2 −32.3 −42.6 0.2 −31.4 
PPT 39.1 11.9 41.8 19.5 43.1 26.1 66.3 31.7 Baseline precipitation 39.1 11.9 41.8 19.5 43.1 26.1 66.3 31.7    
SF 147 71 98 85 164 90 116 104 3.7 1.5 −5.6 153 72 85 85 170 91 104 104 
SCA 2090 (FF) SQ 36.1 8.3 −20 9.6 56.2 76.2 72.4 72.6 9.4 −3.5 0.2 −2.18 64.9 5.8 4.3 11.5 88.5 69.5 101.6 71.5 
Gw 96.1 72.6 48.1 60.2 125.7 101.7 88.2 96.8 8.9 14.7 16.8 15.48 97.4 88.8 65.4 75.9 144.7 128.3 116.1 123.6 
LQ 86 26.6 62.7 39.8 96.5 56.1 136.2 68.5 −7.8 −0.7 −3 −1.76 63.3 23.1 48.8 32.7 70.1 54.8 118.6 62.1 
WY 65.3 18.9 40.8 27.8 82 69.7 99.3 74.2 1.3 −1.4 0.37 67.2 17.1 49.9 28.1 85.5 66.9 113.7 74.9 
AET 12.4 13 30.4 16.1 23.4 26.1 55.3 29 −3.4 1.4 −2.5 −0.67 10.3 13.8 26.5 15.5 21.7 24.9 48.8 27.3 
PPT 53.4 14.9 57.3 24.8 63.8 59.9 129.8 62.6 Baseline precipitation 53.4 14.9 57.3 24.8 63.8 59.9 129.8 62.6    
SF 150 69 95 84 183 117 156 130 7.37 2.53 −10.3 3.54 162 71 83 85 195 119 145 131 

Note: SQ, surface runoff; GW, groundwater; LQ, lateral runoff; WY, water yield; AET, actual evapotranspiration; SWE, snow water equivalent; PPT, precipitation; SF, stream flow.

Figure 5

Stream flow and water balance component change percentage due to both climate change scenarios. Note: SW, surface runoff; GW, groundwater; LQ, lateral runoff; WY, water yield; AET, actual evapotranspiration; SWE, snow water equivalent; PPT, precipitation; SF, stream flow.

Figure 5

Stream flow and water balance component change percentage due to both climate change scenarios. Note: SW, surface runoff; GW, groundwater; LQ, lateral runoff; WY, water yield; AET, actual evapotranspiration; SWE, snow water equivalent; PPT, precipitation; SF, stream flow.

Close modal

However, in future climate scenarios, the increase in precipitation patterns leads to a corresponding rise in stream flow values during this season. Despite the initially low base flow, the numeric values show a high percentage change, though the impact is not significantly pronounced. The increased water availability during the lean period, however, has a more substantial impact on downstream water resource projects. In terms of the water balance component, the average annual water yields in NF, MF, and FF, which estimate the amount of freshwater entering streams and rivers from precipitation (including rain, snow, and snowmelt), rise by 7, 34.74, and 27.81% under SSP245, and by 19.95, 50.33, and 74.18% under SSP585. This increase is influenced by future precipitation increases of 24.80% in SSP245 and 62.60% in SSP585. The two climate scenarios predict increased precipitation and reduced evapotranspiration, leading to higher surface runoff, lateral flow, and groundwater contribution in MF. However, due to the projected temperature rise, evapotranspiration is expected to increase by 16.07% under SSP245 and by 29.02% under SSP585 in FF. Under SSP245, water yield increases the most in the pre-monsoon season for each subsequent period. This is likely due to the highest expected rise in temperature and precipitation during the pre-monsoon season compared to the baseline under both scenarios for each future period. Post-monsoon and winter surface runoff is lower due to increased snow accumulation, which acts as a temporary reservoir and melts during the spring thaw. Despite a gradual decrease in snowmelt contribution, our analysis suggests a limited discernible impact on overall stream flow. The watershed, being predominantly rainfall-driven, indicates that changes in snowmelt are not significantly influencing the broader stream flow dynamics, though this aspect is not overlooked. This nuanced understanding contributes to a more comprehensive grasp of the hydrological response to climate change scenarios in the MRB.

Impacts of future snow cover variation

The impact of future changes in SCA on water balance components reveals a complex pattern. In the NF, there will be a slight increase in annual water yield, followed by a decrease in the long term. Conversely, evapotranspiration, which involves the loss of water through evaporation and plant uptake, is projected to decrease initially and then increase in the distant future. Annual surface runoff, representing water flowing over the land, is expected to rise by 11.4% in 2030 and 8.07% in 2050, but then decrease by 2.18% in 2090 (Table 7). This trend can be attributed to a reduction in snow cover, replaced by more bare ground and woodland.

The ongoing decrease in snow cover is reflected in these changes. The expansion of forested and barren land areas may enhance infiltration and evapotranspiration rates. These factors are anticipated to significantly influence the region's future water balance. In particular, lateral flow, groundwater contributions to water yield, and evapotranspiration are expected to play more substantial roles in the distant future compared to the near term. It reveals several significant trends in the MRB's water balance components for future scenarios (SCA2030, SCA2050, and SCA2090) which refer to the snow cover map of 2030, 2050, and 2090. Figure 6 illustrates the changes in the annual values of these water balance components. Seasonally, the highest surface runoff and water yield occur during the monsoon season, with the most significant percentage increases observed in the pre-monsoon period, corresponding to the peak of snowmelt. When there is a decrease in snow cover, the annual stream flow is projected to increase in the future, but at a modest rate, as indicated in Table 7. During the winter season, the rate of stream flow decreases at a maximum rate of 20.8% highlighting the impact of reduced snow cover. These findings underscore the intricate interplay between climate, land use, and snow cover, providing valuable insights into how changes in snow cover can influence water balance even in the absence of changing climate data. For a more detailed seasonal breakdown, Figure 6 offers a comprehensive overview of water balance components for each season relative to the baseline, further elucidating the dynamics of the basin's water resources.
Figure 6

Stream flow and water balance component change percentage due to snow cover change.

Figure 6

Stream flow and water balance component change percentage due to snow cover change.

Close modal

Integrated impacts of climate change and future snow cover variation

The SWAT model, calibrated with well-defined parameters, utilized projected snow cover maps (SCA) for the years 2030, 2050, and 2090, along with corresponding climate data for the periods 2021–2045 (NF), 2046–2070 (mid-future), and 2071–2095 (far future). Analyzing the impact on annual average stream flow under the SSP245 and SSP585 scenarios revealed an increase of 59% (NF), 85% (MF), 85% (FF), and 70% (NF), 104% (MF), and 131% (FF), respectively, caused by the rate of increase in the precipitation, respectively (Table 7 and Figure 7). Seasonally, stream flow exhibited a gradual increase across all seasons for each future period under both scenarios, reflecting the rising trend attributed to climate change-induced factors such as increased precipitation and temperature.
Figure 7

Stream flow and water balance component change percentage due to both climate change scenarios.

Figure 7

Stream flow and water balance component change percentage due to both climate change scenarios.

Close modal

Likewise, average annual water yield exceeded baseline levels for each future period, indicating a continuous rise in precipitation over time under both SSP245 and SSP585 scenarios, with rates of 8.41% (NF), 34.92% (MF), 28.14% (FF), and 20.5% (NF), 50.5% (MF), and 74.9% (FF), respectively. Surface runoff and groundwater flow were anticipated to increase more in the mid and NF compared to the far future, accompanied by a 15.48% increase in evapotranspiration under the SSP245 scenario. As temperatures rise and snow cover diminishes, increased evapotranspiration from forest and barren lands impacts both overland flow and groundwater recharge. Water yield exhibited an increasing trend in each season for every future period, albeit at varying rates, likely linked to precipitation variations. The combination of higher temperatures and increased precipitation may accelerate snowmelt, influencing runoff timing. Reductions in SCA and decreased snowfall rates contributed to an 11.82 and 38.36% decrease in snow water equivalent (SWE) in the far future under SSP245 and SSP585 scenarios, respectively. The decline in SWE value was particularly pronounced in post-monsoon and winter seasons compared to pre-monsoon and monsoon seasons, reflecting higher precipitation and temperature increases during the latter seasons. This shift in snowmelt dynamics underscores the changing role of snow in future river flow scenarios, driven by decreasing SCA and climate change-induced alterations in precipitation and temperature patterns.

Climate change continues to increasingly impact the HKH Region, raising concerns about its potential repercussions on river systems. These include altered flow regimes, shifts in the timing and intensity of river discharge, and implications for downstream water resource management. Understanding the dynamics of climate and projected changes in snow cover on river hydrology is crucial for informed water resource management and adaptation strategies. This paper explores historical climate patterns and anticipated changes in snow cover within the HKH Region, focusing on our study area. We developed a methodological framework using the SWAT model, coupling historical climate data, projected snow cover changes, and their integrated impact on basin water balance. The robust performance of the SWAT model, validated through calibration and validation metrics, confirms its effectiveness in simulating diverse runoff components. Our projections indicate a concerning decline in SCA by 2.03, 4.03, and 14.20% in the years 2030, 2050, and 2090, respectively.

Examining climate change alone reveals significant increases in average annual stream flow, projecting increments of 84 and 130% in FF under SSP245 and SSP585 scenarios, respectively, primarily due to expected rises in precipitation. This increase is most pronounced during the pre-monsoon season, with projected increases of 150 and 183% in FF under SSP245 and SSP585 scenarios, respectively, highlighting the season's sensitivity to future precipitation changes. Isolating the impact of projected SCA alone shows a modest increase in annual average stream flow, indicating rises of 4.1, 4, and 3.5% relative to baseline in the NF, MF, and FF periods, respectively. However, the anticipated decrease in stream flow during winter by 20.77% raises concerns for water availability during the lean period. Conversely, reduced snow cover prompts an increase in groundwater contribution and base flow, suggesting potential benefits for future groundwater-related projects. Analyzing the combined impact on annual average stream flow under SSP245 and SSP585 scenarios reveals increases of 59% (NF), 85% (MF), 85% (FF), and 70% (NF), 104% (MF), and 131% (FF), respectively, attributed to rising precipitation rates. Similarly, average annual water yield surpasses baseline levels for each future period, indicating a continuous increase in precipitation under both scenarios. The observed rise in surface runoff, groundwater, and evapotranspiration in NF and MF, accompanied by a drop in FF (SCA2090), underscores the complex interplay of rising temperatures, diminishing snow cover, and changing land use.

In conclusion, this study addresses a significant knowledge gap regarding the specific implications of future snow cover variations on MRB hydrology. By unraveling the intricate relationships between climate change, snow cover variability, and hydrological responses, our research offers valuable insights for water resource management and emphasizes the necessity for adaptive strategies in navigating evolving hydrological conditions. As we confront an uncertain climatic future, the findings from this study provide a foundation for evidence-based decision-making in sustainable water resource management, particularly in mountainous regions such as the HKH Region.

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

The authors declare there is no conflict.

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