Integrating morphometric analysis and AHP–Monte Carlo framework for multi-hazard risk reduction

Frequent floods, droughts, and landslides in the Pampa River basin of Kerala, India, highlight the need for an integrated approach to multi-hazard watershed prioritization. Traditional hazard assessments often focus on individual hazards and provide deterministic prioritization outcomes without explicitly evaluating uncertainty in decision-making. This study prioritizes 31 sub-watersheds of the Pampa River basin according to their relative susceptibility to floods, droughts, and landslides using a morphometric–Analytical Hierarchy Process (AHP) framework coupled with Monte Carlo-based uncertainty analysis. Fifteen morphometric and hazard-related parameters representing linear, areal, and relief characteristics were derived from a 30 m Digital Elevation Model (DEM) using ArcGIS. AHP was employed to assign criterion weights, while a Monte Carlo simulation with 10,000 iterations was conducted to evaluate the robustness of watershed rankings under uncertainty in expert-derived weights. Results indicate that sub-watersheds W10, W14, W7, W4, and W17 consistently ranked among the highest-priority sub-watersheds across the Monte Carlo simulations, demonstrating the robustness of their prioritization under uncertainty in the AHP-derived weights.Validation using observed household-level flood, drought, and landslide damage data showed strong agreement between modeled priorities and recorded impacts, supporting the reliability of the framework. The integration of morphometric analysis, AHP, and Monte Carlo simulation provides a transparent, uncertainty-aware, and transferable framework for multi-hazard watershed prioritization, supporting disaster risk reduction, watershed management, and climate-resilient planning in data-scarce tropical river basins.