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).

Flood events represent a major natural threat, and identifying the key factors contributing to flood occurrence has gained considerable attention in 2010 and 2022 in the Swat River, Pakistan. In this study, Google Earth Engine was utilized to extract flood-related indices for the Mohmand Dam catchment, Pakistan. Different types of datasets were used to calculate fourteen influencing parameters. These indices were processed and normalized in ArcMap 10.8 and Python to enhance their visual and analytical representation. Two multi-criteria analyses with AHP, FAHP, and five machine learning models, including logistic regression, K-nearest neighbors, random forest, support vector machine, and multi-layer perception, were applied to determine the relative importance of each parameter and produce a flood susceptibility map. The results indicate that rainfall, LULC, and soil texture are the most influential factors, each contributing 11.11% to flood susceptibility. The random forest approach demonstrated stronger predictive performance than the AHP and FAHP techniques. The flood susceptibility map reveals that approximately 31.67% (4320.40 km²) of the study area falls under high flood risk. This methodology provides valuable support for planners, policymakers, hydrologists, and disaster management authorities in developing effective flood mitigation, watershed management, and resilience strategies.

Pishin River Basin is one of the prominent river basins in Balochistan, situated in the south-eastern region of Pakistan. In view of the water scarcity in the basin, this research adopts a comprehensive approach that integrates RS-GIS and Multi-Criteria Decision Making techniques. The study uses multi-parameter datasets, including lithological, hydrological, topographical, soil, and land-use information. These datasets were acquired through a combination of geospatial and conventional data sources. The Analytical Hierarchy Process (AHP) and its fuzzy extension (FAHP) were applied to calculate the weight for each thematic layer. The AHP-based groundwater recharge model identified 14.1% of the area as very high recharge potential, 22.7% as high, 26% as moderate, 25.8% as low, and 11.3% as very low. In comparison, the Fuzzy AHP model classified 15% very high, 22.2% high, 26.3% moderate, 25.6% low, and 10.9% very low recharge potential. The model’s accuracy was validated through ROC-AUC with borehole data, resulting in 91% accuracy for the AHP model and 95% for the Fuzzy AHP model. Sensitivity analysis identified soil types, land use, and lineament density as essential factors in groundwater recharge. This research highlights the role of geospatial technology in groundwater recharge modeling. It provides spatially explicit information to support sustainable groundwater management and help achieve SDG 6 targets. Overall, key findings will offer valuable guidance for government officials and water policy decision-makers in implementing sustainable groundwater management strategies in the study area and other arid-semiarid regions.