Spatial Modelling of Cyclone-Induced Flood Hazards for Disaster Risk Financing and Parametric Insurance in Sri Lanka

Abstract

Cyclone-induced compound hazards present a critical challenge for disaster risk financing (DRF) in South Asia, yet spatially-explicit and probabilistically-calibrated hazard models suitable for parametric insurance applications remain largely absent in Sri Lanka. This work-in-progress paper presents an integrated research protocol for developing and evaluating spatially-informed parametric insurance models for cyclone-related hazards. The framework combines GIS-based Fuzzy Analytic Hierarchy Process (FAHP) multi-hazard mapping, coupled with HEC-RAS 1D/2D hydrological-hydraulic simulation, Monte Carlo Probabilistic Cyclone Hazard Analysis (PCHA), and spatial lag and spatial error econometric modeling. This study focuses on five cyclone-affected districts of Sri Lanka, with Kurunegala serving as the primary analytical case study, which collectively displaced more than 95,000 people during Cyclone Ditwa (Nov–Dec 2025). This study will produce Grama Niladhari (GN)-division-level flood inundation maps, probabilistic wind speed and rainfall exceedance surfaces for return-period pricing, empirical basis risk hotspot maps, and Willingness-to-Pay (WTP) estimates. Findings will directly inform parametric trigger design, actuarial premium calibration, and sovereign disaster risk financing strategies for climate-vulnerable developing countries.