Floods are a widespread natural disaster with substantial economic implications and far-reaching consequences. In Northern Pakistan, the Hunza-Nagar valley faces vulnerability to floods, posing significant challenges to its sustainable development. This study aimed to evaluate flood risk in the region by employing a GIS-based Multi-Criteria Decision Analysis (MCDA) approach and big climate data records. By using a comprehensive flood risk assessment model, a flood hazard map was developed by considering nine influential factors: rainfall, regional temperature variation, distance to the river, elevation, slope, Normalized difference vegetation index (NDVI), Topographic wetness index (TWI), land use/land cover (LULC), curvature, and soil type. The analytical hierarchy process (AHP) analysis assigned weights to each factor and integrated with geospatial data using a GIS to generate flood risk maps, classifying hazard levels into five categories. The study assigned higher importance to rainfall, distance to the river, elevation, and slope compared to NDVI, TWI, LULC, curvature, and soil type. The weighted overlay flood risk map obtained from the reclassified maps of nine influencing factors identified 6% of the total area as very high, 36% as high, 41% as moderate, 16% as low, and 1% as very low flood risk. The accuracy of the flood risk model was demonstrated through the Receiver Operating Characteristics-Area Under the Curve (ROC-AUC) analysis, yielding a commendable prediction accuracy of 0.773. This MCDA approach offers an efficient and direct means of flood risk modeling, utilizing fundamental GIS data. The model serves as a valuable tool for decision-makers, enhancing flood risk awareness and providing vital insights for disaster management authorities in the Hunza-Nagar Valley. As future developments unfold, this study remains an indispensable resource for disaster preparedness and management in the Hunza-Nagar Valley region.

Highland ecologies are the most susceptible to climate change, often experiencing intensified impacts. Due to climate change and human activities, there were dramatic changes in the alpine domain of the China–Pakistan Economic Corridor (CPEC), which is a vital project of the Belt and Road Initiative (BRI). The CPEC is subjected to rapid infrastructure expansion, which may lead to potential land surface susceptibility. Hence, focusing on sustainable development goals, mainly SDG 9 (industry, innovation, and infrastructure) and SDG 13 (climate action), to evaluate the conservation and management practices for the sustainable and regenerative development of the mountainous region, this study aims to assess change detection and find climatic conditions using multispectral indices along the mountainous area of Gilgit and Hunza-Nagar, Pakistan. It has yielded practical and highly relevant implications. For sustainable and regenerative ecologies, this study utilized 30 × 30 m Landsat 5 (TM), Landsat 7 (ETM+), and Landsat-8/9 (OLI and TIRS), and meteorological data were employed to calculate the aridity index (AI). The results of the AI showed a non-significant decreasing trend (−0.0021/year, p > 0.05) in Gilgit and a significant decreasing trend (−0.0262/year, p < 0.05) in Hunza-Nagar. NDVI distribution shows a decreasing trend (−0.00469/year, p > 0.05), while NDWI has depicted a dynamic trend in water bodies. Similarly, NDBI demonstrated an increasing trend, with rates of 79.89%, 87.69%, and 83.85% from 2008 to 2023. The decreasing values of AI mean a drying trend and increasing drought risk, as the study area already has an arid and semi-arid climate. The combination of multispectral indices and the AI provides a comprehensive insight into how various factors affect the mountainous landscape and climatic conditions in the study area. This study has practical and highly relevant implications for policymakers and researchers interested in research related to land use and land cover change, environmental and infrastructure development in alpine regions.

The recent energy shortfall in Pakistan has prompted the need for the development of hydropower projects to cope with the energy and monetary crisis. Hydropower in the northern areas is available yet has not been explored too much. Focusing on the sustainable development goal (SDG) “Ensure access to affordable, reliable, sustainable and modern energy”, thirteen proposed sites were selected from upstream to downstream of the Hunza River for analysis. The head on all the proposed sites was determined by taking the elevation difference between the proposed turbine and the intake at all sites. The discharge on all proposed ungauged sites was determined using ArcGIS for watershed delineation and the area ratio method along with Soil Conservation Service–Curve Number (SCS-CN) by using gauged data of Hunza River provided by Water and Power Development Authority (WAPDA) Pakistan at Daniyor bridge Gilgit, Shimshal and the Passo tributaries of Hunza River. The Location Search Algorithm (LSA) approach used a multi-criteria decision-making tool (MDM) to make a decision matrix considering the location and constraint criteria and then normalizing the decision matrix using benefit and cost criteria, the relative weights were assigned to all criteria using a rank sum weighted method and the sites were ranked on the basis of the final score. The results revealed that Hunza River has a significant hydropower potential and based on the final score in the LSA approach, proposed site 13, site 4 and site 9 were concluded as the most promising sites among proposed alternatives. The proposed methodology could be an encouraging step for decision makers for future hydropower development in Pakistan.

The hydrology and climate of mid to high-latitude mountainous areas are significantly impacted by snow cover. Since adding or removing snow cover significantly impacts the snowpack’s capacity to operate as a reservoir for water storage, the snowfall-dominated basins of mid- to higher latitudes are anticipated to see the largest shifts in the hydrological cycle because of global warming. By moving the time slider in the historical imagery feature of Google Earth Pro, the Upper Indus Basin study area was examined from the years 1984 to 2020 to track changes in the snow cover. All observations were made with longitude and latitude at 35o, 34', 51.79" N and 74o, 34', 24.21" E, and the eye altitude at 344.46 miles. Google Earth captured pictures of all the observations on December 31st of every year. The data from 1984 to 2020 was examined keenly, and it was observed that as time goes on, global warming is showing its effects and producing climate changes, which has a negative impact on the region's snow and glacier availability. The Landsat images make it abundantly evident that the lower areas of the upper Indus Basin's snow cover are more negatively impacted than the downstream side areas due to the variation in altitude. The authors also referred to the research work by other researchers in the study to compare with their work. The study observed that some areas were utterly showing no snow in 2020 as compared to 1984 as time moved on with an increase in global warming in 36 years.

Surface water quality in Skardu, Gilgit-Baltistan, Pakistan, is of immense importance because of the city’s dependence on these resources for domestic uses, agriculture, and drinking water. The water quality index (WQI) was integrated with the Geographic Information System (GIS) to spatially envision and examine water quality data to facilitate the identification of pollution hotspots, trend analysis, and knowledge-based decision-making for effective water resource management. This study aims to evaluate the physiochemical and bacteriological parameters of the Satpara watershed and to provide the spatial distribution of these parameters. This study endeavors to achieve Sustainable Development Goal 6 (SDG 6) by identifying localities with excellent and unfit water for drinking, sanitation, and hygiene. A total of fifty-one surface water samples were collected from various parts of the Satpara watershed during the fall season of 2023. Well-established laboratory techniques were used to investigate water for parameters like Electrical Conductivity (EC), pH, turbidity, total dissolved solids (TDSs), major cations (${\mathrm{K}}^{+}$, ${\mathrm{Na}}^{+}$, ${\mathrm{Mg}}^{2+}$, ${\mathrm{Ca}}^{2+}$), major anions (${\mathrm{Cl}}^{-}$, ${\mathrm{SO}}_4^{2-}$, ${\mathrm{NO}}_3^{-}$, ${\mathrm{HCO}}_3^{-}$), and bacteriological contaminants (E. coli). Spatial distribution maps of all these parameters were created using the Inverse Distance Weighted (IDW) technique in a GIS environment. A significant variation in the quality of water was observed along the study area. The level of Escherichia coli (E. coli) contamination is above the permissible limit at various locations along the watershed, making water unsafe for direct human consumption in these areas. Some regions showed low TDS values, which could adversely affect human health and agricultural yield. From the WQI valuation, 58.82% of the collected samples were “Poor”, 31.8% were “Very poor” and 9.8% were found to be “Unfit for drinking”. The research findings emphasize the pressing need for consistent monitoring and adoption of water management strategies in Skardu City to warrant sustainable soil and water use. The spatial maps generated for various parameters and the water quality index WQI offer critical insights for targeted intercessions.

Background: Metals are required in minor amounts in the human body to perform vital functions, but beyond that level, they become toxic and cause many harmful effects. At present, polluted water is a large source of these heavy metals inside our body. Bioremediation is one of best treatments for the removal of these heavy metals from water. Objectives: The main objective of the current study was to isolate metal-reducing bacteria from soil and wastewater samples from different industries. Furthermore, the metal reducing potential of the bacteria was also evaluated under various environmental conditions (pH, temperature, incubation time, and UV exposure). Methodology: Different bacterial strains were isolated that were resistant to different concentrations of zinc (Zn) and chromium (Cr) from wastewater and soil samples from four different industries (Riaz textile mills, Sitara chemical industry, Mandiali paper mills and Siddique leather works). Morphological characterization was carried out with the help of Gram staining, spore staining and motility tests. Biochemical tests were performed, such as catalase, oxidase, and H2S production tests, starch hydrolysis tests and Simon citrate tests. Chromium reduction after UV exposure was calculated to check mutation effects on chromium reduction. Results: A total of 14 bacterial strains were isolated from the soil and wastewater samples. Six strains were Zn resistant, while eight strains were chromium resistant. A total of five strains were isolated from wastewater out of fourteen strains, while the remaining nine strains were isolated from soil samples of these industries. The bacterial strains were rod-shaped cocci and coccobacillus. The growth of bacterial strains under different environmental conditions, such as temperature, pH and incubation time, was observed, and the best growth was found at 37◦C, pH 7.0 and after 48 hrs. Maximum reduction at different concentrations was observed at pH 7.0 and 37◦C and after 48 hrs. Conclusion: In conclusion, the bacterial strains isolated from industrial wastes and soils showed significant resistivity against various concentrations of Zn and Cr and reduced it efficiently under different conditions (pH, incubation time, temperature and UV light). The use of microorganisms for bioremediation is an environmentally friendly and cost-effective approach to reduce heavy metals present in our water and soil.