Erschienen in:
Natural Hazards and Earth System Sciences, 23 (2023) 2, Seite 823-846
Sprache:
Englisch
DOI:
10.5194/nhess-23-823-2023
ISSN:
1684-9981
Entstehung:
Anmerkungen:
Beschreibung:
Abstract. Coastal river deltas are susceptible to flooding from pluvial,fluvial, and coastal flood drivers. Compound floods, which result from theco-occurrence of two or more of these drivers, typically exacerbate impactscompared to floods from a single driver. While several global flood modelshave been developed, these do not account for compound flooding. Local-scalecompound flood models provide state-of-the-art analyses but are hard toscale to other regions as these typically are based on local datasets.Hence, there is a need for globally applicable compound flood hazardmodeling. We develop, validate, and apply a framework for compound floodhazard modeling that accounts for interactions between all drivers. Itconsists of the high-resolution 2D hydrodynamic Super-Fast INundation of CoastS (SFINCS) model, which isautomatically set up from global datasets and coupled with a globalhydrodynamic river routing model and a global surge and tide model. To testthe framework, we simulate two historical compound flood events, TropicalCyclone Idai and Tropical Cyclone Eloise in the Sofala province of Mozambique, and comparethe simulated flood extents to satellite-derived extents on multiple daysfor both events. Compared to the global CaMa-Flood model, theglobally applicable model generally performs better in terms of the criticalsuccess index (−0.01–0.09) and hit rate (0.11–0.22) but worse interms of the false-alarm ratio (0.04–0.14). Furthermore, the simulated flooddepth maps are more realistic due to better floodplain connectivity andprovide a more comprehensive picture as direct coastal flooding and pluvial floodingare simulated. Using the new framework, we determine the dominant flooddrivers and transition zones between flood drivers. These vary significantlybetween both events because of differences in the magnitude of and time lagbetween the flood drivers. We argue that a wide range of plausible eventsshould be investigated to obtain a robust understanding of compound floodinteractions, which is important to understand for flood adaptation,preparedness, and response. As the model setup and coupling is automated,reproducible, and globally applicable, the presented framework is apromising step forward towards large-scale compound flood hazard modeling.