Rainwater harvesting is an important step towards maximizing the water availability and land productivity in arid and semi-arid areas. The present study shows that the area of Ghazi Tehsil within Khyber Pakhtunkhwa Province, Pakistan, has great potential for rainwater harvesting due to its feasible climatic and topographic conditions. This area of 348 km² normally receives high rainfall annually, but, due to hilly terrain, the bulk of rainwater is lost in the runoff process. In order to enhance agricultural output for such a large area, the practice of rainwater harvesting is a sustainable and decisive approach. However, the selection of appropriate sites for rainwater harvesting on a large scale presents a critical challenge. In such areas, geospatial technology has proved very decisive in the identification of potential sites. In this study, we have used the HEC-GeoHMS tool (ArcGIS 9.3) to compute a curve number to represent the effects of rainfall against the hydrological soil group and landcover. Subsequently, the curve number was used as an input parameter in the soil conservation service runoff-curve number (SCS-CN) method to estimate surface runoff potential for different combinations of landcover and hydrological soil groups. It was observed that runoff was higher in mountainous areas and relatively low in plain areas. Finally, to identify the potential sites for rainwater harvesting, weighted overlay analysis-based related thematic map layers were further reclassified, and weights were assigned according to the technical guidelines of suggested international standards and under consideration of the study area’s topographic, hydrological, and climatic factors. As a result, about 20% of the area was found suitable, 52% less suitable, and 29% as not suitable. Furthermore, relative suitability was assigned to the results of suitable sites as an input for the identification of potential sites for different rainwater harvesting storage structures. These results show that 10% of the area was suitable for farm ponds, 5.74% for check dams, 21.5% for Nigarims, and 8.9% was found to be suitable for gully plugs. The comparison of our GIS-derived and field-based results spatially affirms that the analyzed results were agreeably overlaid in the context of spatial results for check dams, gully plugs, and Nigarims.

Islamabad is essentially the only well-planned city in Pakistan, but groundwater depletion has become a serious issue there because of the rapid increase in population, poor water management, and deforestation. The current water demand of the city is about 220 million gallons per day, with the Capital Development Authority (CDA) providing up to 70 million gallons per day. The need for water is mostly fulfilled through groundwater sources, such as water bores and commercial tube wells. Hence, identifying recharge sites for natural aquifers is a significant component of groundwater required to overcome the water crisis. Therefore, this study aims to identify potential sites for natural aquifer recharge by using analytical hierarchy process (AHP), weighted linear combination (WLC), and fuzzy logic methods. To achieve the stated objective, seven local influencing factors including soil, slope, water table, population density, land use land cover (LULC), drainage density, and elevation have been utilized in this study. AHP was utilized for the evaluation of the relative importance of the above-mentioned factors, while fuzzy logic was applied for the standardization of these factors. Finally, the AHP-WLC and fuzzy logic approaches were used to merge factor maps in order to identify suitable sites for natural aquifer recharge in Islamabad City. Two different suitability maps were constructed from both techniques, and on each of the resulting maps, the subregions were categorized into five classes: not suitable, less suitable, moderate, suitable, and most suitable. Based on the AHP-WLC results, 5% of the whole study area is deemed most suitable for natural aquifer recharge (NAR), whereas from the fuzzy logic results, 10% of the study area is marked as most suitable. In contrast, 37% and 32% of the whole study area were identified as suitable by the AHP-WLC and fuzzy logic methods, respectively. While both techniques can obtain satisfactory outcomes, the suitability map from fuzzy logic has produced more precise results. Hence, we propose to CDA-Islamabad here different sites for recharge wells based on the results of fuzzy logic. As recommended by this study, to date CDA has constructed twelve recharge wells.

Generating orchards spatial distribution maps within a heterogeneous landscape is challenging and requires fine spatial and temporal resolution images. This study examines the effectiveness of Sentinel-1 (S1) and Sentinel-2 (S2) satellite data of relatively high spatial and temporal resolutions for discriminating major orchards in the Khairpur district of the Sindh province, Pakistan using machine learning methods such as random forest (RF) and a support vector machine. A Multicollinearity test (MCT) was performed among the multi-temporal S1 and S2 variables to remove those with high correlations. Six different feature combination schemes were tested, with the fusion of multi-temporal S1 and S2 (scheme-6) outperforming all other combination schemes. The spectral separability between orchards pairs was assessed using Jeffries-Matusita (JM) distance, revealing that orchard pairs were completely separable in the multi-temporal fusion of both sensors, especially the indistinguishable pair of dates-mango. The performance difference between RF and SVM was not significant, SVM showed a slightly higher accuracy, except for scheme-4 where RF performed better. This study concludes that multi-temporal fusion of S1 and S2 data, coupled with robust ML methods, offers a reliable approach for orchard classification. Prospectively, these findings will be helpful for orchard monitoring, improvement of yield estimation and precision based agricultural practices.

Groundwater is one of the major sources of freshwater supply for drinking and domestic purposes. This study evaluates the hydrogeochemical processes, groundwater quality for human consumption, associated health risks from fluoride F⁻ and nitrate (NO₃⁻), and sources of dissolved solutes in a highland watershed in northern Pakistan. Groundwater samples (n = 51) were gathered and analyzed for a range of physicochemical parameters. To evaluate contamination, indices such as the nitrate pollution index (NPI) and fluoride pollution index (FPI) were applied, along with a composite groundwater pollution index to assess overall water quality. The findings revealed that total dissolved solid, turbidity, F⁻, and K⁺ levels exceeded health-based thresholds in 20%, 1%, 4%, and 2% of samples, respectively. Among the water sources, handpumps were identified as the most contaminated. According to the NPI and composite index, 96% and 92% of the samples did not show significant contamination, respectively. However, the FPI results highlighted that 59% of the samples exhibited low F⁻ pollution, while 41% fell under medium pollution levels. While NO₃⁻ ingestion posed no notable health risks, ${\mathrm{F}}^{-}$ exposure presented significant concerns, with 58.8% of the samples posing risks, particularly for children. The dominant hydrochemical facies were Ca-Mg-HCO₃, with the main influence on water chemistry by rock-water interactions and reverse ion exchange processes.

The Lahore–Islamabad Motorway (M-2) is a critical transportation corridor in Pakistan, where contamination in roadside dust by potentially toxic elements (PTEs) presents potential environmental and health concerns. This study evaluates the concentration, spatial distribution, and ecological risks of PTEs (Mn, Ni, Cr, Cu, Pb, Zn, Cd, Ag, Fe) in road dust along the M-2. PTE concentrations were determined using standard protocols and by analysis using an atomic absorption spectrometer. The findings indicate substantial variability in metal concentrations, with Fe (CV% = 9.35%) and Pb (CV% = 7.06%) displaying the highest consistency, whereas Ni exhibited the greatest fluctuation (CV% = 168.80%). Contamination factor analysis revealed low to moderate contamination for Ni and Fe, while Zn contamination was significant in 60% of samples. Cr and Cd exhibited persistently high contamination, and Pb was uniformly elevated across all locations. Ecological risk assessment categorized Ni, Zn, and Cu as low-risk elements, while Pb posed a substantial risk. Cd concentrations indicated high to extreme ecological hazards, emphasizing the necessity for urgent mitigation measures. Factor analysis suggested an interaction of various sources, including industrial, vehicular emissions, and construction materials. Strengthened pollution control strategies and systematic monitoring are essential for mitigating contamination and ensuring environmental sustainability along the motorway.