Climate change has implications for water resources by increasing temperature, shifting precipitation patterns and altering the timing of snowfall and glacier melt, leading to shifts in the seasonality of river flows. Here, the Soil & Water Assessment Tool was run using downscaled precipitation and temperature projections from five global climate models (GCMs) and their multi-model mean to estimate the potential impact of climate change on water balance components in sub-basins of the Upper Indus Basin (UIB) under two emission (RCP4.5 and RCP8.5) and future (2020–2050 and 2070–2100) scenarios. Warming of above 6 °C relative to baseline (1974–2004) is projected for the UIB by the end of the century (2070–2100), but the spread of annual precipitation projections among GCMs is large (+16 to −28%), and even larger for seasonal precipitation (+91 to −48%). Compared to the baseline, an increase in summer precipitation (RCP8.5: +36.7%) and a decrease in winter precipitation were projected (RCP8.5: −16.9%), with an increase in average annual water yield from the nival–glacial regime and river flow peaking 1 month earlier. We conclude that predicted warming during winter and spring could substantially affect the seasonal river flows, with important implications for water supplies.

No abstract

Hydrological models play a key role to simulate and assess climate and land use/cover (LULC) change impacts on hydrology in a watershed. In this study, the impact of climate and LULC change was investigated using the Soil and Water Assessment Tool (SWAT) model. The simulated and observed streamflow showed a good agreement. Both Nash–Sutcliffe Efficiency (NSE) and coefficient of determination (R2) were found to be greater than 0.7 during the calibration (1985–2002) and validation (2003–2012) period. The water balance components were simulated with inputs from downscaled Global Climate Models (GCMs) data (i.e., future scenario (2030–2100) relative to a baseline period (1974–2004)) under RCP4.5 and RCP8.5, and hypothetical generated LULC change scenarios. All GCMs projected an increase in temperature over the Kabul River Basin (KRB), whereas there was a lack of agreement on projected precipitation among GCMs under both emission and future scenarios. Water yield (WYLD) and evapotranspiration (ET) were projected to decrease in the 21st century. Average annual WYLD was projected to increase under the agriculture-dominant scenario, whereas it decreased under forest and grassland-dominant scenarios. These results are valuable for relevant agencies and stakeholders to adopt measures to counter the negative impacts of climate and LULC change on water resources.

Several satellite-based and reanalysis products with a high spatial and temporal resolution have become available in recent decades, making it worthwhile to study the performance of multiple precipitation forcing data on hydrological modeling. This study aims to examine the veracity of five precipitation products employing a semi-distributed hydrological model, i.e., the Soil and Water Assessment Tool (SWAT) to simulate streamflow over the Chenab River Basin (CRB). The performance indices such as coefficient of determination (R2), Nash–Sutcliff efficiency (NSE) and percentage bias (PBIAS) were used to compare observed and simulated streamflow at daily and monthly scales during calibration (2015–2018) and validation (2019–2020). The hydrologic performance of European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis (ERA) 5-Land (ERA5) was very good at daily (calibration R2=0.83, NSE=0.81, PBIAS=−6%; validation R2=0.75, NSE=0.74, PBIAS=−9.6%) and monthly ( calibration R2=0.94, NSE=0.94, PBIAS=−3.3%; validation R2=0.91, NSE=0.89, PBIAS=−3.2%) scales. This study suggests that the ERA5 precipitation product was the most reliable of the five precipitation products, while the CHIRPS performance was the worst. These findings contribute to highlighting the performance of five precipitation products and reference in the selection of precipitation data as input data to the SWAT model in similar regions.

Climate change plays a key role in changing vegetation productivity dynamics, which ultimately affect the hydrological cycle of a watershed through evapotranspiration (ET). Trends and correlation analysis were conducted to investigate vegetation responses across the whole Upper Jhelum River Basin (UJRB) in the northeast of Pakistan using the normalized difference vegetation index (NDVI), climate variables, and river flow data at inter-annual/monthly scales between 1982 and 2015. The spatial variability in trends calculated with the Mann-Kendall (MK) trend test on NDVI and climate data was assessed considering five dominant land use/cover types. The inter-annual NDVI in four out of five vegetation types showed a consistent increase over the 34-year study period; the exception was for herbaceous vegetation (HV), which increased until the end of the 1990s and then decreased slightly in subsequent years. In spring, significant (p<0.05) increasing trends were found in the NDVI of all vegetation types. Minimum temperature (Tmin) showed a significant increase during spring, while maximum temperature (Tmax) decreased significantly during summer. Average annual increase in Tmin (1.54°C) was much higher than Tmax (0.37°C) over 34 years in the UJRB. Hence, Tmin appears to have an enhancing effect on vegetation productivity over the UJRB. A significant increase in NDVI, Tmin and Tmax during spring may have contributed to reductions in spring river flow by enhancing evapotranspiration observed in the watershed of UJRB. These findings provide valuable information to improve our knowledge and understanding about the interlinkages between vegetation, climate and river flow at a watershed scale.