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).

Sustainable management of canal water through optimum water allocation is the need of the modern world due to the rapid rise in water demand and climatic variations. The present research was conducted at the Chaj Doab, Indus Basin Irrigation System (IBIS) of Pakistan, using the WEAP (Water Evaluation and Planning) model. Six different scenarios were developed, and the results showed that the current available surface water is not sufficient to meet crop water demands. The Lower Jhelum Canal (LJC) command area is more sensitive to water scarcity than the Upper Jhelum Canal (UJC). The future (up to 2070) climate change scenarios for RCP 4.5 and 8.5 showed a decrease in catchment reliability up to 26.80 and 26.28% for UJC as well as 27.56 and 27.31% for LJC catchment, respectively. We concluded that scenario 3 (irrigation efficiency improvement through implementation of a high efficiency irrigation system, canal lining, reduction and replacement of high delta crops with low delta crops) was sufficient to reduce the canal water deficit in order to optimize canal water allocation. Improvement in the irrigation system and cropping area should be optimized for efficient canal water management.

This study evaluated the effectiveness of soil and water conservation structures for soil erosion control by applying a semi-distributed Soil and Water Assessment Tool (SWAT) model in various small watersheds of the Chakwal and Attock districts of Pothwar, Pakistan. The validated model without soil conservation structures was applied to various ungauged small watershed sites with soil conservation stone structures. The stone bund-type structure intervention was used in the model through the modification of the Universal Soil Loss Equation (USLE) to support the practice factor (P-factor), the curve number, and the average slope length for the sub-basin (SLSUBBSN). The structures had significant effects, and the average sediment yield reduction caused by the soil conservation stone structures at these sites varied from 40% to 90%. The sediment yield and erosion reductions were also compared under conditions involving vegetation cover change. Agricultural land with winter wheat crops had a higher sediment yield than fallow land with crop residue. The fallow land facilitated sediment yield reduction, along with soil conservation structures. The slope classification analysis indicated that 60% of the agricultural area of the Chakwal and Attock districts lie in a slope range of 0–4%, where considerable potential exists for implementing soil conservation measures by installing soil conservation stone structures. The slope analysis measured the suitability of conservation structures in the semi-mountainous Pothwar area in accordance with agriculture practice on land having a slope of less than 5%. The SWAT model provides reliable performance for erosion control and watershed management in soil erosion-prone areas with steep slopes and heavy rainfall. These findings can serve as references for policymakers and planners.

The economy of Pakistan relies on the agricultural sector which mainly depends on the irrigation water generating from the upper Indus river basin. Mangla watershed is a trans-boundary basin which shares borders of India and Pakistan, it comprises five major sub-basins, i.e., Jhelum, Poonch, Kanshi, Neelum and Kunhar. The runoff production of this basin is largely controlled by snowmelt in combination with the winter precipitation in the upper part of the basin and summer monsoon. The present study focusses on the application of a statistical downscaling method to generate future climatic scenarios of climatic trends (temperature and precipitation) in Mangla watershed. Statistical Downscaling Model (SDSM) was applied to downscale the Hadley Centre Coupled Model, version 3, Global Climate Model (HadCM3-GCM) predictions of the A2 and B2 emission scenarios. The surface water analyst tool (SWAT) hydrological model was used for the future projected streamflows based on developing climate change scenarios by SDSM. The results revealed an increasing trend of annual maximum temperature (A2) at the rates of 0.4, 0.7 and 1.2 °C for the periods of 2020s, 2050s and 2080s, respectively. However, a consistent decreasing trend of temperature was observed at the high-altitude region. Similarly, the annual minimum temperature exhibited an increasing pattern at the rates of 0.3, 0.5 and 0.9 °C for the periods of 2020s, 2050s and 2080s, respectively. Furthermore, similar increases were observed for annual precipitation at the rates of 6%, 10%, and 19% during 2020, 2050 and 2080, respectively, for the whole watershed. Significant increasing precipitation trends in the future (2080) were observed in Kunhar, Neelum, Poonch and Kanshi sub-basins at the rates of 16%, 11%, 13% and 59%, respectively. Consequently, increased annual streamflow in the future at the rate of 15% was observed attributing to an increased temperature for snow melting in Mangla watershed. The similar increasing streamflow trend is consistent with the seasonal trends in terms of winter (16%), spring (19%) and summer (20%); however, autumn exhibited decreasing trend for all periods.

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.