Flood risk susceptibility analysis in Larkana district Pakistan using multi criteria decision analysis and geospatial techniques

Flooding is one of the most devastating natural disasters in worldwide, with significant socioeconomic and environmental impacts. In such a scenario, flood susceptibility analysis is essential for successful risk management and disaster preparedness. The Larkana district located in densely populated area of Sindh province, approximately 1.8 million population with 0.32 million households, is highly susceptible to floods due to its geographical location, river systems, and hot climatic patterns with extremely high temperature in summer and mild winter. Larkana district often faces significant flood risks mainly due to monsoon rains and the overflow of the Indus River which often lead to widespread flooding. The research aims to identify and quantify key flood risk factors, including rainfall pattern, distance to river, topography, land use/land cover, soil texture, vegetation index, and drainage infrastructure. AHP was applied to prioritize these risk factors based on expert opinions and their relative significance in contributing to flood vulnerability. GIS employed for spatial analysis and mapping of risk zones, allowing for detailed visualization of high-risk areas across the district. For this study, the various data sources such as topographic data, land use and landcover information, rainfall and infrastructure data, were used to develop a comprehensive flood susceptibility model. The AHP method was employed to determine the relative weights with consistency ratio (CR) and GIS techniques to generate flood susceptibility maps by considering all nine flood risk factors. Flood risk levels were further classified into five different classes as very low, low, moderate, high and very high. By using AHP, the weights of each parameter were calculated as a percentage, and it was determined that four out of nine parameters had 79% impact on flood hazard. These factors are ranked as the most influential factors in flood hazards for study area, with rainfall, distance to river, slope, and elevation having the greatest influences, while LULC, TWI, NDVI, soil type, and curvature had 21% impacts, much less impact as compared to top ranked parameters. After that, each flood risk parameter maps were reclassified. These maps were superimposed by weighted overlay maps in order to show that 7.65% of the entire area is at very high flood risk, 63.89% is at high risk, 28.38% is at moderate risk, and 0.12% is at low risk. The findings and established complete flood susceptibility model will facilitate policymakers and disaster management authorities by identifying high-risk locations and prioritizing mitigation measures, ultimately reducing the impact of floods on local communities and infrastructure in Larkana district.