FLOOD RISK ASSESSMENT AND MANAGEMENT USING HYDROLOGICAL MODELING AND GIS TECHNIQUE

Abstract

Floods represent one of the most destructive natural hazards globally, exacerbated by climate change, rapid urbanization, and hydrometeorological extremes. Effective flood risk assessment and management require the integration of hydrological modeling, hydraulic simulation, and Geographic Information Systems (GIS) to quantify hazard, exposure, and vulnerability. This study synthesizes advanced frameworks combining semi-distributed models (HEC-HMS, SWAT) for rainfall-runoff simulation with 2D hydraulic models (HEC-RAS) for inundation mapping, leveraging high-resolution DEMs, satellite precipitation products (GPM IMERG, CHIRPS), and derived indices such as the Topographic Wetness Index (TWI). Comparative analyses highlight the strengths of different modeling architectures: SWAT excels in long-term land-use impact assessment, while HEC-HMS performs well for event-based forecasting. Machine learning approaches (Random Forest, XGBoost, RiverMamba) enhance predictive accuracy and enable real-time forecasting in data-scarce regions. Case studies, including the 2022 Pakistan mega-flood in Balochistan, demonstrate how dam failures, inadequate infrastructure, and socio-economic vulnerability amplify impacts. Multi-criteria decision analysis (MCDA/AHP) and emerging technologies like BIM-GIS integration and opportunistic sensing further support proactive management through structural (levees, dams) and non-structural (early warning systems, zoning) measures. The review underscores the need for site-specific, hybrid modeling and participatory governance to build climate-resilient flood risk strategies.