2026/03/17 by Soheil Radfar, Hamed Moftakhari, David F. Muñoz +4 · 1 voice
Earth and Planetary Sciences · Environmental Science · #Flood Risk Assessment and Management #Meteorological Phenomena and Simulations #Tropical and Extratropical Cyclones Research
paper · pdf · doi:10.5194/hess-30-1397-2026
openalex publication_date 2026/03/17 · openalex created_date 2026/03/18 · openalex updated_date 2026/07/30
Abstract. Modeling compound flood events requires sophisticated approaches that can capture complex nonlinear interactions between multiple flood drivers. While combining different data-driven and physics-based modeling approaches has shown promise, the criteria for classifying such combinations and the underlying terminology to describe them remain inconsistent in the literature. To establish classification criteria, we introduce a systematic framework for defining and categorizing hybrid physical-statistical modeling approaches in compound flood modeling. Hybrid compound flood models offer significant advantages in terms of prediction accuracy and computational efficiency over traditional single-model approaches, particularly in coastal regions where multiple flooding mechanisms frequently interact. We identify three categories of hybrid models: sequential, feedback, and ensemble. Through illustrative examples, we demonstrate how each category leverages the strengths of its component models while also maintaining their independence. The proposed framework enables a systematic evaluation of different hybrid modeling strategies, enhancing model comparability and supporting the development of more effective compound flood prediction tools.