Water shortages in Pakistan are among the most severe in the world, and its water resources are decreasing significantly due to the prevailing hydro-meteorological conditions. We assessed variations in meteorological and hydrological variables using innovative trend analysis (ITA) and traditional trend analysis methods at a practical significance level, which is also of practical interest. We developed threshold levels of hydrological variables and developed a non-parametric climate-sensitivity model of the high-altitude catchment of the western Himalayas. The runoff of Zone I decreased, while the temperature increased and the precipitation increased significantly. In Zone II, the runoff and temperature increased but the precipitation decreased. A two-dimensional visualization of the Pardé coefficient showed extreme drought events, and indicated greater sensitivity of the hydrological regime to temperature than to precipitation. The threshold levels of runoff for Zones I and II were 320 and 363 mm using the Q₈₀ fixed method, while the mean runoff amounts were estimated to be 79.95 and 55.61 mm, respectively. The transient threshold levels varied by month, and the duration of droughts in Zones I and II ranged from 26.39 to 78.98 days. The sensitivity of the hydrological regime was estimated based on a modified climate-elasticity model (ε_p = 0.11–0.23, ε_t = −0.04–2.39) for Zones I and II, respectively. These results highlight the sensitivity of the hydrological regime to temperature, which influences the melting process. However, it is important to establish thresholds for hydrological variables and understand the climate sensitivity of the hydrological regime of the entire basin, so that policy makers and water managers can make sustainable water-resource-management decisions for this region.

The present study developed a novel approach to study the climate change impact on the water resources and generation of hydropower optimally using forecasted stream flows for the Xin’anjiang water shed in China. Future flows were projected using six large-scale Global circulation models (GCMs) with RCP4.5 and RCP8.5 scenarios. A newly developed mathematical modeling using particle swarm optimization was incorporated to work out the projected optimal electricity generation from the Xin’anjiang hydropower station. The results reveal that watershed will be warmer by the end of the 21st century with a maximum increase of up to 4.9 °C for mean maximum, and 4.8 °C for mean minimum temperature. Six GCMs under Representative Concentration pathways (RCPs) showed that future precipitation is complex to predict with certainty and significant differences were observed among the different GCMs. The overall mean monthly and seasonal precipitation increase for most scenarios with the maximum increase during the 2020s and 2080s, whereas 2050s exhibited the lesser increase. Resultantly, there would be an increase in the stream flows during these periods, which was used for electricity production up to 31.41 × 10⁸ kW·h.

This study emphasizes the precipitation and the maximum and minimum temperature trend and presents the results of study in temporal and spatial scales, after performing statistical analysis of the Xin’anjiang-Fuchunjiang watershed. Statistical Mann Kendall and Theil Sen techniques were used to determine the trend and its magnitude, respectively, and for determining the start and abrupt change in the trend, Sequential Mann Kendall test has been performed. Furthermore, statistical tests were performed to determine the overall trend in the area at a regional basis. For the removal of the serial effect of the data, prewhitening technique is applied. In this study, statistical tests were performed at 1901–2013 precipitation and temperature series and then after detection of the change year precipitation data were divided into two different scenarios of 1901–1960 period and 1961–2013 period. The results showed that precipitation trend is insignificant while maximum and minimum temperature have increased during 1901–2013 period except for some stations of autumn and summer seasons.

In this study we investigated the projections of climate change and its impacts on the water resources of the Xin’anjiang watershed and optimal hydropower production using future run-offs (the decades of the 2020s, 2050s, and 2080s). The arc SWAT hydrological model and change factor downscaling technique were integrated to detect the run-offs and to downscale CMIP5 future climate variables, respectively. Optimal hydropower generation using future runoff was predicted by developing a mathematical model and by applying the particle swarm optimization technique within its paradigm. The results depict an increase of up to 5.9ºC in monthly mean maximum temperature, and 5.58ºC in minimum temperature until the 2080s. There will be a 63% increase in flow magnitudes more than the base year flow during the 2020s, whereas up to 70% and 31.40% increments have been observed for the 2050s and 2080s, respectively. The results revealed potential hydropower generation of 19.23*10⁸ kWh using 2020s runoff of rainy years. Similarly, 19.35*10⁸ kWh and 14.23*10⁸ kWh were estimated from the flows during the 2050s and 2080s, respectively.

Climate change and global warming are major events in the twenty-first century. Lower Chenab Canal (LCC) command area has been reflected as a hot spot, for impact of climate change on agriculture and water availability. The study aimed to project future changes in temperatures and rainfall in 2020s, 2050s and 2080s. Data outputs of CanESM2 GCM under RCPs scenarios RCP4.5 and RCP8.5 were used. Three weather stations data (1976−2005) was used as Statistical Downscaling Model (SDSM) inputs. The results projected an increase in mean annual Tmin and Tmax from 2020 to 2080s under both scenarios but a more distinct change by 0.59 °C in Tmax and 1.06 °C Tmin was projected in 2080s under RCP8.5. In winter and spring Tmax will increase under both scenarios in all future time series with maximum increase of 3.30 °C under RCP4.5 and 3.33 °C under RCP8.5 in spring (2080s). In summer 2080s Tmax will decrease under RCP 4.5 (− 0.88 °C) and RCP8.5 (− 1.14 °C). Spring will remain distinctive in increasing Tmin as compared to other seasons with maximum increase in 2080s by 2.48 °C (RCP4.5) and 3.00 °C RCP8.5). In autumn Tmin will decrease under both scenarios with maximum decrease by − 1.63 °C (RCP4.5) and − 0.68 °C (RCP8.5) in 2080s). Mean annual rainfall projected an increase in all future time series with a maximum increase of 52.62% under RCP4.5 and 65.13% under RCP8.5 in 2080s. An increase in rainfall was also projected in autumn, winter and summer with a major increase of 107.84% under RCP4.5 and 106.1% under RCP8.5 in winter 2020s, respectively. The autumn, winter and summer would be wetter than before and these future climate changes might have a negative effect on agriculture and water resources in LCC command area, temporally and spatially. Projected increase in climate will affect cropping season, crop growth and crop evapotranspiration in areas projecting higher temperature and vice versa. Effective adaptation measures should be applied to mitigate the effects of climate change on water resources. The study findings will help water managers and representatives in creating a background for sustainable and adaptive water management in the face of climate change.