Climate variability and land-use change are key factors altering the hydrology of a river basin, which are strongly linked to the availability of water resources and the sustainability of the local ecosystem. This study investigated the combined and individual impacts of land-use changes (LUCs) and climate change (CC) on the hydrological processes in subbasins of the Source Region of the Yellow River (SRYR) through statistical methods and hydrological modeling based on two land-use maps for the period 1990 and 2010, and two climate periods, i.e., 1976–1995 and 1996–2014. The results revealed that the climate is anticipated to be warmer and wetter. Land-use changes were dominated by decreases in sparse grassland. However, the transformation of land-use changes varied spatially within sub-basins. The combined impacts of climate and land-use changes are more noticeable in the Maqu subbasin, where the decrease in runoff reached 18% (32.2 mm) and increase in evapotranspiration (ET) reached 10.4% (34.1 mm), followed by the Tangnaihai and Jimai subbasins. The changes in runoff and ET caused by LUC seemed to be adequate by comparison, and presented a 15.1–19.8% decline in runoff and 21.3–28% increase in ET relative to the totals. Overall, climate change has more influence on hydrological processes in all subbasins of the SRYR than LUC. It is, therefore, considered that the response to changes in hydrological processes in a subbasin can be attributed to changes in individual climate parameters and land-use classes.

Climate change strongly influences the available water resources in a watershed due to direct linkage of atmospheric driving forces and changes in watershed hydrological processes. Understanding how these climatic changes affect watershed hydrology is essential for human society and environmental processes. Coupled Model Intercomparison Project phase 6 (CMIP6) dataset of three GCM’s (BCC-CSM2-MR, INM-CM5-0, and MPI-ESM1-2-HR) with resolution of 100 km has been analyzed to examine the projected changes in temperature and precipitation over the Astore catchment during 2020–2070. Bias correction method was used to reduce errors. In this study, statistical significance of trends was performed by using the Man- Kendall test. Sen’s estimator determined the magnitude of the trend on both seasonal and annual scales at Rama Rattu and Astore stations. MPI-ESM1-2-HR showed better results with coefficient of determination (COD) ranging from 0.70–0.74 for precipitation and 0.90–0.92 for maximum and minimum temperature at Astore, Rama, and Rattu followed by INM-CM5-0 and BCC-CSM2-MR. University of British Columbia Watershed model was used to attain the future hydrological series and to analyze the hydrological response of Astore River Basin to climate change. Results revealed that by the end of the 2070s, average annual precipitation is projected to increase up to 26.55% under the SSP1–2.6, 6.91% under SSP2–4.5, and decrease up to 21.62% under the SSP5–8.5. Precipitation also showed considerable variability during summer and winter. The projected temperature showed an increasing trend that may cause melting of glaciers. The projected increase in temperature ranges from - 0.66°C to 0.50°C, 0.9°C to 1.5°C and 1.18°C to 2°C under the scenarios of SSP1–2.6, SSP2–4.5 and SSP5–8.5, respectively. Simulated streamflows presented a slight increase by all scenarios. Maximum streamflow was generated under SSP5–8.5 followed by SSP2–4.5 and SSP1–2.6. The snowmelt and groundwater contributions to streamflow have decreased whereas rainfall and glacier melt components have increased on the other hand. The projected streamflows (2020–2070) compared to the control period (1990–2014) showed a reduction of 3%–11%, 2%–9%, and 1%–7% by SSP1–2.6, SSP2–4.5, and SSP5–8.5, respectively. The results revealed detailed insights into the performance of three GCMs, which can serve as a blueprint for regional policymaking and be expanded upon to establish adaption measures.

In Pakistan, groundwater resources are depleting at an alarming rate due to intensive pumping, shifting of cropping patterns, and climate change vulnerability. The present study is aimed at investigating groundwater stress in the command area of Lower Chenab Canal (LCC) and associated branch canals. Groundwater stress is determined by considering the cropping patterns, surface water availability, groundwater levels, climatic variation, and crop water requirement (CWR) in the LCC command area. The climatic data is obtained from the Pakistan Meteorological Department (PMD) from 1990 to 2020. The records of temporal variation in cropping patterns are obtained from the Crop Reporting Service (CRS), Directorate of Agriculture, Lahore for the 1995–2020 period and classified according to Rabi season (November to April) and Kharif season (May to October). The LCC surface water flows data and groundwater levels are collected from the Punjab Irrigation Department (PID) Lahore from 2003 to 2018 and from 1995 to 2016, respectively. The CWR is estimated using the Cropwat 8.0 model and groundwater levels are estimated using the Inverse Distance Weighted (IDW) tool of ArcGIS software. It has been determined that Faisalabad, Sheikhupura, and Toba Tek Singh are highly groundwater stress cities having an average drawdown rate of 0.58 m/year. The surface water availability is also decreased from 7.75 to 4.81 billion cubic meters (Bm³) for the Kharif season whilst 4.17 to 2.63 Bm³ for the Rabi season. This study concluded that due to severe conditions in highly stressed areas, policy planners, decision-makers, and stakeholders should sincerely take some steps for maintaining groundwater levels either by capacity building workshops for the farmers or limiting the number of tubewells.

An innovative way to combat water scarcity brought on by population increase and climate change is rainwater harvesting (RWH), particularly in arid and semiarid areas. Currently, Pakistan is facing major water issues due to underprivileged water resource management, climate change, land use changes, and the sustainability of local water resources. This research aims to find out the suitable sites and options for RWH structures in the Quetta district of Pakistan by integrating the depression depth technique, Boolean analysis, and weighted linear combination (WLC) with hydrological modeling (HM), multicriteria analysis (MCA), a geographic information system (GIS), and remote sensing (RS). To find suitable sites for RWH, a collection of twelve (12) thematic layers were used, including the slope (SL), land use land cover (LULC), subarea (SA), runoff depth (RD), drainage density (DD), lineament density (LD), infiltration number (IFN), distance from built-up area (DB), distance from roads (DR), distance from lakes (DL), maximum flow distance (MFD), and topographic wetness index (TWI). The Boolean analysis and WLC approach were integrated in the GIS environment. The consistency ratio (CR) was calculated to make sure the assigned weights to thematic layers were consistent. Overall, results show that 6.36% (167.418 km²), 14.34% (377.284 km²), 16.36% (430.444 km²), 18.92% (497.663 km²), and 18.64% (490.224 km²) of the area are in the categories of very high, high, moderate, low, and very low suitability, respectively, for RWH. RWH potential is restricted to 25.35% (666.86 km²) of the area. This research also identifies the five (5) best locations for checking dams and the ten (10) best locations for percolation tanks on the streams. The conducted suitability analysis will assist stakeholders in selecting the optimal locations for RWH structures, facilitating the storage of water, and addressing the severe water scarcity prevalent in the area. This study proposes a novel approach to handle the problems of water shortage in conjunction with environmental and socioeconomic pressures in order to achieve the sustainable development goals (SDGs).

Utilization of satellite precipitation products (SPPs) for reliable flood modeling has become a necessity due to the scarcity of conventional gauging systems. Three high-resolution SPPs, i.e., Integrated Multi-satellite Retrieval for GPM (IMERG), Global Satellite Mapping of Precipitation (GSMaP), and Climate Hazards Group InfraRed Precipitation with Station (CHIRPS), data were assessed statistically and hydrologically in the sparsely gauged Chenab River basin of Pakistan. The consistency of rain gauge data was assessed by the double mass curve (DMC). The statistical metrics applied were probability of detection (POD), critical success index (CSI), false alarm ratio (FAR), correlation coefficient (CC), root mean square error (RMSE), and bias (B). The hydrologic evaluation was conducted with calibration and validation scenarios for the monsoon flooding season using the Integrated Flood Analysis System (IFAS) and flow duration curve (FDC). Sensitivity analysis was conducted using ±20 calibrating parameters. The rain gauge data have been found to be consistent with the higher coefficient of determination (R²). The mean skill scores of GSMaP were superior to those of CHIRPS and IMERG. More bias was observed during the monsoon than during western disturbances. The most sensitive parameter was the base flow coefficient (AGD), with a high mean absolute sensitivity index value. During model calibration, good values of performance indicators, i.e., R², NashSutcliffe efficiency (NSE), and percentage bias (PBIAS), were found for the used SPPs. For validation, GSMaP performed better with comparatively higher values of R² and NSE and a lower value of PBIAS. The FDC exhibited SPPs excellent performance during 20 to 40 exceedance time.