Assessment of flooding and drought disaster risk in Henan, China by a multiscale approach
Cuimin Zhou, Weicheng Wu, Xinxin Ke, Yifei Song, Yecheng He, Wenjing Li, Yuan Li, Jing Rong, Peixia Song, Linqian Fu, Chunlian Mao, Meng Xie, Aohui Li, Xiaoping Song, Sicheng Li, Yingxu Song, Aiqing Chen
Abstract
Floods and droughts, common global natural disasters, threaten the human safety, food security, and socioeconomic development. This study proposed a multiscale approach using climate data, SPI, and SPEI to analyze the spatial and temporal characteristics of droughts and floods in Henan Province, China, in the past decades. At the same time, Sentinel-1 SAR data of pre- and in-flood period of the “7·20 Extreme Rainstorm” event occurring in July 17-23, 2021 were applied to assess the impacts of floods on farmland and urban environment in Pingdingshan City as a local-scale assessment and verification of the regional-scale analysis. The results show that SPEI is closer to the historical record than SPI in terms of the extent of flood events, and the identified local-scale floods in the pilot site Pingdingshan by a decision-tree classification with a high overall accuracy (OA > 95%) and Kappa Coefficient (KC > 0.85) reached 41.7 km2. Our study found that farmlands and built-up areas were most affected by flooding. It highlights the effectiveness of multiscale research for climate-related disaster assessment, offering a replicable approach for global applications. Recommendations were provided to local governments for taking disaster prevention measures and sustainable land management.