The rapid urbanization and changing climate patterns in Swat, Pakistan have increased the vulnerability of urban areas to flood events. Accurate assessment of flood risk is crucial for effective urban planning and disaster management. In current research study flood hazard index was developed using analytic hierarchy process (AHP) technique in combination with the geographical information system (GIS) environment in Swat, Pakistan. The study integrates various data sources, including topographic maps, land use/land cover information, rainfall data, and infrastructure data, to develop a comprehensive flood risk assessment model. The weights obtained from the AHP analysis are combined with geospatial data using a geographic information system (GIS) to generate flood risk maps. The flood hazard levels were categorized into five distinct classes: very low, low, moderate, high, and very high. Using the GIS-AHP approach, higher weights were assigned to rainfall, distance to river, elevation, and slope in comparison to NDVI, TWI, LULC, curvature, and soil type. The flood hazard map was then reclassified for each parameter. By overlaying these maps, it was determined that 5.6% of the total area is classified as very high flood risk, 52% as high risk, 39.3% as moderate risk, and 3.1% as low risk. The developed comprehensive flood risk assessment model in current study can identify high-risk areas, prioritize mitigation measures, and aid in effective urban planning and disaster management.

Dams play a pivotal role in water resource management by storing and supplying water for a multitude of purposes. However, the looming threat of dam breach floods necessitates meticulous research and the simulation of potential failure scenarios. These endeavors are essential not only for comprehending the gravity of dam break floods but also for identifying vulnerable regions and informing emergency response strategies and land-use planning initiatives. This study employs a two-dimensional hydraulic model within the HEC-RAS (Hydrologic Engineering Center and River Analysis System) software to conduct an extensive dam breach analysis specifically focusing on the Mangla Dam located in Azad Kashmir, Pakistan. The analysis encompasses the prediction of various breach parameters, including the hydrograph of the breach flood, peak flow rates, arrival times of the flood, and the creation of inundation maps. Of primary concern is the Probable Maximum Floo, which drives the dam collapse model under unsteady flow conditions, accounting for both piping and overtopping failure scenarios. This study discerns the breach outflow hydrograph through the utility of HEC-RAS tools and evaluates hydraulic conditions at critical downstream locations. To dynamically route flood waves, the breach outflow hydrographs are harnessed. Furthermore, the HEC-RAS model is executed with breach parameters derived from five distinct empirical approaches, with ensuing outcomes subjected to rigorous comparative analysis. A comprehensive sensitivity study pertaining to breach parameters is also carried out to ascertain the sensitivity of peak flow and maximum stage. The results reveal peak flow rates of 174,850 m³/s and 177,850 m³/s in the downstream vicinity adjacent to the dam, translating into corresponding flooded areas of 379 km² and 394 km² attributable to piping and overtopping failures, respectively. The analysis of Land Use Land Cover data demonstrates that in the event of piping failure, 217 km² of agricultural land and 56 km² of urban areas would be completely submerged. Conversely, overtopping failure would inundate 220 km² of agricultural land and 59 km² of urban areas. The utilization of advanced remote sensing data, combined with flood modeling insights, equips engineers and stakeholders with invaluable knowledge. This knowledge, in turn, underpins strategic planning and well-informed decision-making processes, essential for addressing the potential global repercussions of similar catastrophes.

This study projects future water demand scenarios in the Upper Indus Basin, focusing on reference, high population growth, increased irrigation, and lower population growth scenarios. The baseline scenario indicates a significant rise in water demand from 35.74 billion cubic meters (BCMs) in 2020 to 60.28 BCM by 2035, driven by population growth and increased domestic water consumption. High population growth exacerbates this demand, reaching 62.96 BCM by 2035. This research aims to address domestic water needs under various growth scenarios, considering factors such as population growth rate and per capita consumption. The study employs integrated hydrological modeling to simulate water demand under different socioeconomic conditions. Key methods include analyzing baseline water demand, projecting future scenarios, and evaluating the impact of increased irrigation and population growth on water resources. Results reveal that without intervention, stagnant water supply management will lead to severe water shortages. Increased irrigation, influenced by a 3% growth in irrigated land, pushes agricultural water demand to 56.37 BCM by 2035. Mitigation efforts, such as a 15% reduction in domestic water consumption, could decrease overall demand to 51.23 BCM by 2035. Further reductions are explored through a 50% cut in agricultural water consumption, involving efficient irrigation techniques. The study highlights the critical role of technology and farmer awareness in achieving these reductions, despite current irrigation scheme losses of 20%. A lower population growth scenario shows a contrasting trend, with water demand decreasing to 49.11 BCM by 2035, attributed to a 1.8% population growth rate and decreased per capita consumption to 82 m³ per day. These findings underscore the importance of proactive water management strategies, technological advancements, and demographic considerations in addressing future water demand challenges in the Upper Indus Basin. This research provides proper insight into the impact of varied socioeconomic scenarios on water resources and the necessity for strategic interventions.

This study examines Islamabad's landscape changes over four decades, attributing land degradation to shifts in land use and cover. Using Landsat imagery from 1980 to 2023, it analyzes urban growth in five categories. By employing the normalized difference vegetation index (NDVI) and normalized difference built-up index (NDBI), it notes built-up areas expanding to 61% by 2023, agricultural land contraction, and fluctuating forest cover. Water bodies and bare land decrease significantly. With high accuracy values, NDVI fluctuates from +0.4523 in 1980 to +0.1596 in 2010, rebounding to +0.4422. Fluctuations in barren soil, vegetation, and built-up areas potentially contribute to temperature and rainfall changes. The study explores LULC and land surface temperature correlation. Surveyed respondents (755) express concerns about environmental changes, anticipating reduced rainfall and increased drought. Valuable for sustainable development goals, the study informs policy formulation for effective urban planning and land use control.