Global flood hazard map and exposed GDP inter-comparison for China

Abstract

<p>In this contribution we carried out a comprehensive comparison of flood hazard maps of 8 global flood models for the country of China based on a collective effort of the flood modelling community to participate in this study. In doing so, we assessed how differences in the simulated flood extent between the models lead to differences in simulated exposed GDP and expected annual exposed GDP. This is carried out by addressing the variation in different model structures and the variability between flood hazard maps. Our comparison uses both publicly available GFMs (GLOFRIS, ECMWF, CAMA-UT, JRC, and CIMA-UNEP) as well as industry models (Fathom, KatRisk, and JBA Risk Management) that are applied within the wider re-insurance industry. We expand upon the existing work of global flood model inter-comparison studies (e.g. Trigg et al., 2016; Bernhoffen et al., 2018) by including industry models, the pluvial flood component, and the effects of standards of protection on the flood hazard and exposure.</p><p>China is selected as our case study area because it poses many challenges to flood modelling: data scarcity; a variety of flood mechanisms spanning many climatic zones; complex topography; strong anthropogenic influence on the flood regimes, for example through river training; and a very high concentration of exposure. Moreover, China is prone to severe flood events. For example, in June 2016 alone more than 60 million people were affected by floods, resulting in an estimated damage of $22 bn (CRED 2016).</p><p>This abstract is an adaptation of “Global flood hazard map and exposed GDP comparison: a China case study” (Aerts et al., under review) and parts of this work have been presented at EGU 2019.</p>