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Journal Article

Using Climate Factors to Estimate Flood Economic Loss Risk


Hu,  Xinjia
Max Planck Institute for the Physics of Complex Systems, Max Planck Society;


Kantz,  Holger
Max Planck Institute for the Physics of Complex Systems, Max Planck Society;

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Hu, X., Wang, M., Liu, K., Gong, D., & Kantz, H. (2021). Using Climate Factors to Estimate Flood Economic Loss Risk. International Journal of Disaster Risk Science, 12(5), 731-744. doi:10.1007/s13753-021-00371-5.

Cite as: https://hdl.handle.net/21.11116/0000-0009-8CEC-0
Estimation of economic loss is essential for stakeholders to manage flood risk. Most flooding events are closely related to extreme precipitation, which is influenced by large-scale climate factors. Considering the lagged influence of climate factors, we developed a flood-risk assessment framework and used Hunan Province in China as an example to illustrate the risk assessment process. The main patterns of precipitation-as a connection between climate factors and flood economic losses-were extracted by the empirical orthogonal function (EOF) analysis. We identified the correlative climate factors through cross-correlation analysis and established a multiple stepwise linear regression model to forecast future precipitation patterns. Risk assessment was done based on the main precipitation patterns. Because the economic dataset is limited, a Monte Carlo simulation was applied to simulate 1000-year flood loss events under each precipitation regime (rainy, dry, normal years) to obtain aggregate exceedance probability (AEP) and occurrence exceedance probability (OEP) curves. We found that precipitation has a strong influence on economic loss risk, with the highest risk in rainy years. Regional economic development imbalances are the potential reason for the varying economic loss risks in different regions of Hunan Province. As the climate indices with at least several months prediction lead time are strong indicators in predicting precipitation, the framework we developed can estimate economic loss risk several months in advance.