The northern areas of Pakistan are highly vulnerable to climate change due to anthropogenic activities and deforestation, which directly affect the precipitation pattern and variations in temperature. Due to these climate fluctuations, cloud bursts, extreme events like floods and droughts, and the melting of glaciers occur. This study presents the statistical trend analysis of the Dasu watershed in northern Pakistan using a non–parametric approach. In this study, the hydroclimatic data of 16 meteorological stations from 1980 to 2022 were used and the major focus was on an annual Rabi and Kharif seasonal trend analysis. Research revealed that the rate of precipitation increased from the east to west side of the study area in the annual Kharif season, while in the Rabi season, only four stations showed an increasing trend, and the remaining showed a decreasing trend, of which shendor2 showed a significantly decreasing trend with a rate of −3.91 mm/year. On the contrary, annual temperature was declining in the east side of the study area, while three stations showed significantly increasing temperature trends in the central region of the study area. Kharif season showed a decreasing trend in the major part of the study area, while Rabi season’s temperature revealed a significantly increasing trend in most of the stations and a decreasing trend in some eastern parts of the area. Overall, the majority of the study area revealed non–significant warming trends across the annual Rabi season, while the kharif season showed a decreasing trend. Precipitation trends remained largely non–significant but increasingly variable. The findings of this research can be utilized by research institutions and farmers to modify their cropping patterns and cropping calendar to optimize crop productivities.

Pakistan, one of the world’s most water-stressed countries, is extremely vulnerable to climate change. This study analyzes the prospective effects of climate change on the rainfall in the Hub River Watershed (HRW), Sulaiman-Kirthar mountainous range of Pakistan. The projections of five global climate models (GCMs), from the Coupled Model Intercomparison Project phase 6 (CMIP6), were used. Analysis of future changes in rainfall patterns was performed under two shared socioeconomic pathways (SSPs). Results showed that the historical annual average rainfall was increasing in the HRW. The annual average rainfall is expected to decrease under SSP2–4.5 in HRW. However, under SSP5-8.5, an increasing trend over the next three decades is expected, particularly over the southern part of the HRB. The findings should further our knowledge of how climate change affects the Hub River Basin and motivate stakeholders and planners to develop the best mitigation plans.

The Upper Indus Basin (UIB) plays a crucial role in water security and socio-economic development in Pakistan. Under changing climatic conditions, the sustainable management of the water resources of the UIB needs accurate and reliable projections of hydroclimatic variables. Given the limited assessments of hydroclimatic simulations from CMIP6 models in the region, this study assessed the uncertainties associated with the historical simulations of 16 CMIP6 GCMs in the UIB. The observations of 34 in situ weather stations were used as reference, while the performances of GCMs were assessed based on widely used evaluation indices, including correlation coefficient (CC), bias, relative bias (rBIAS), root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), Taylor diagram, and the performance diagram. Results of the evaluation indices indicated that most of the considered GCMs failed to represent the observed precipitation in the UIB. Correlations between the simulations of GCMs and the reference observations were generally low; CCs ranged from −0.24 to 0.16. All GCMs exhibited negative NSE values (ranging between −2.79 and −0.51). The values of RMSE (59.36 to 98.43 mm/month) and rBIAS (9 to 96%) were also very high. Among the considered GCMs, INM-CM4-8 and EC-Earth3-Veg-LR showed comparatively lower RMSE values, moderate rBIAS, and higher CC values. Three GCMs (MRI-ESM2-0, GFDL-ESM4, and CNRM-CM6-1) performed very poorly, with high negative NSE and significant overestimations. Among the 16 GCMs, EC-Earth3-Veg-LR had the highest composite score and better performance across all considered indices. The overall findings of this study indicated that none of the CMIP6-based GCMs (in their raw form) performed satisfactorily in the UIB of Pakistan; therefore, the application of bias-correction techniques is strongly recommended before direct application of these projections for climate impact and adaptation studies in this mountainous region. The results will be useful for the hydroclimatic data users and algorithm developers of global climate models.

Analysis of the hydroclimatic variations in complex topographic and climatic regimes is important in determining the freshwater availability and its response. Although several previous studies have assessed the changing patterns of hydroclimatic variables in South Asian River basins, most of them have considered traditional statistical methods, which may inadequately reflect potential non-linear hydroclimatic trends. This study determines long-term variations in precipitation, temperature, and streamflow in the Soan River Basin of Pakistan, using three decades of in situ records (1991–2020). A non-parametric (Mann–Kendall) trend test along with an artificial neural network (ANN) approach was used to check the linear and non-linear trends. The results exhibited that the basin was getting warmer at a consistent rate, although the amount of precipitation varied significantly with location and season. The annual average amount of precipitation over the entire basin was decreasing at the rate of −7.33 mm/year. As compared to the westerly season, the trend of monsoon precipitation was less certain. Changes in streamflow patterns generally demonstrated the consequences of changing precipitation and rising temperature patterns. The annual average streamflow was decreasing at the rate of −0.47 (−1.30) m³/year, as per the results of MK (ANN). A moderate positive correlation between precipitation and streamflow indicates that precipitation mainly governed the flows in the basin. The results of the MK test and the machine-learning approach demonstrated the similar decreasing tendencies of hydroclimatic variables. However, the ANN approach more precisely demonstrates the non-linear behavior of hydroclimatic variables. It was concluded that the streamflow patterns were considerably responsive to the warming of the Soan River Basin, as well as to the changing behavior of precipitation. These findings emphasized the significance of integrating statistical and machine-learning approaches to enhance the comprehension of hydroclimatic trends. Results of this research could be applicable in sustainable management and planning of the water resources within the basin.