128-25 Compound Multi-Hazard Assessment Under CMIP6 Climate Scenarios: Tracking Seasonal Flood-Landslide and Drought-Fire Interactions Across Nepal

Abstract:

Climate-induced hazards in mountain systems rarely occur in isolation, instead they interact through cascading and compounding pathways that single-hazard assessments fail to capture. In Nepal, extreme monsoon rainfall can trigger landslides that dam rivers and generate secondary flooding, while prolonged dry spells create drought, desiccate vegetation fuels, and elevate fire risk. Yet, most regional studies map hazards independently, providing limited insight into how compound zones emerge under climate change. We develop a seasonal multi-hazard framework that model drought, forest fire, flood, and landslide susceptibility for historical baseline (1991-2020) and future (2021-2100). Three machine learning algorithms (Explainable Boosting Machine, Random Forest, and Extreme Gradient Boosting) were trained and evaluated under an identical protocol in which RF performed best for flood, landslide, and fire, while XGBoost performed best for drought. Drivers were interpreted with Shapley Additive Explanations (SHAP). Future susceptibility was projected using a 13-model, bias-corrected, downscaled Coupled Model Intercomparison Project (CMIP6) ensemble under Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5) for near, mid, and far future. Beyond mapping, we organize the results into two hydroclimatic regimes, water-excess (flood + landslide) and water-deficit (drought + fire) and tracked seasonal compound co-occurrence within each regime. Results under SSP5-8.5 shows flood-landslide compound zones expand most during monsoon and post-monsoon, while the deficit regime reconfigures, with stronger pre-monsoon drought and increased winter fire dominance. Compounding hazard zones across multiple seasons represent persistent multi-hazard hotspots concentrated along major population and infrastructure corridors, indicating that climate change disproportionately amplifies multi-hazard risk where human systems are most concentrated.

Compound Multi-hazard Risk; Disaster Risk Reduction; Machine Learning; Seasonal hazard assessment; SHAP interpretability

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