Observation-constrained projection of flood risks and socioeconomic exposure in China

Journal article

As the planet warms, the atmosphere's water vapor holding capacity rises, leading to more intense precipitation extremes. River floods with high peak discharge or long duration can increase the likelihood of infrastructure failure and enhance ecosystem vulnerability. However, changes in the peak and duration of floods and corresponding socioeconomic exposure under climate change are still poorly understood. This study employs a bivariate framework to quantify changes in flood risks and their socioeconomic impacts in China between the past (1985–2014) and future (2071–2100) in 204 catchments. Future daily river streamflow is projected by using a cascade modeling chain based on the outputs of five bias-corrected global climate models (GCMs) under three shared socioeconomic CMIP6 pathways (SSP1-26, SSP3-70, and SSP5-85), a machine learning model and four hydrological models. We also utilize the copula function to build the joint distribution of flood peak and duration, and calculate the joint return periods of the bivariate flood hazard. Finally, the exposure of population and regional gross domestic product to floods are investigated at the national scale. Our results indicate that flood peak and duration are likely to increase in the majority of catchments by 25%–100% by the late 21st century depending on the shared socioeconomic pathway. China is projected to experience a significant increase in bivariate flood risks even under the lowest emission pathway, with 24.0 million dollars/km2 and 608 people/km2 exposed under a moderate emissions scenario (SSP3-70). These findings have direct implications for hazard mitigation and climate adaptation policies in China.

Authors: Kang, S; Yin, J; Gu, L; Yang, Y; Liu, D

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: American Geophysical Union
• Journal: Earth's Future
• Volume: 11
• Issue: 7
• Article number: e2022EF003308
• Publication date: 2023-07-05
• Acceptance date: 2023-06-09
• DOI: 10.1029/2022EF003308
• EISSN: 2328-4277
• Language: English
• Keywords: climate models; uncertainty analysis; socioeconomic exposure; flood; FFR; machine learning; hydrological simulations