{"title":"Flood modeling prior to the instrumental era reveals limited magnitude of 1931 Yangtze flood","authors":"Ling Zhang, Zhongshi Zhang, Lu Li, Xiaoling Chen, Xijin Wang, Entao Yu, Pratik Kad, Odd Helge Otterå, Chuncheng Guo, Jianzhong Lu, Mingna Wu","doi":"10.1038/s41612-025-00908-1","DOIUrl":null,"url":null,"abstract":"<p>The global flood risk urges an improved understanding of flood magnitude and its mechanism, which needs insights from pre-instrumental flood investigations. Due to data scarcity, reconstructing pre-instrumental flood magnitudes relies on statistical downscaling, failing to capture nonlinear and dynamic characteristics. We developed a dynamical approach, NorESM-WRF-SWAT, integrating a global climate, a regional, and a hydrologic model to investigate the 1931 Yangtze River flood (the deadliest in the world) and compared it with the 1998’s. Through validation, our method outperforms the statistical method in simulating precipitations and river discharges. For the first time, we presented detailed insights into the intensity and duration of the 1931 flood, revealing a smaller magnitude but associated with an amplified loss, likely due to social vulnerability and reduced societal resilience compared to the 1998’s. While successful simulation can be interfered with by model variability, our dynamical method shows promise for simulating pre-instrumental flood and building a long-term pre-instrumental-hydrology database.</p>","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":"532 1","pages":""},"PeriodicalIF":8.5000,"publicationDate":"2025-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"npj Climate and Atmospheric Science","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1038/s41612-025-00908-1","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"METEOROLOGY & ATMOSPHERIC SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
The global flood risk urges an improved understanding of flood magnitude and its mechanism, which needs insights from pre-instrumental flood investigations. Due to data scarcity, reconstructing pre-instrumental flood magnitudes relies on statistical downscaling, failing to capture nonlinear and dynamic characteristics. We developed a dynamical approach, NorESM-WRF-SWAT, integrating a global climate, a regional, and a hydrologic model to investigate the 1931 Yangtze River flood (the deadliest in the world) and compared it with the 1998’s. Through validation, our method outperforms the statistical method in simulating precipitations and river discharges. For the first time, we presented detailed insights into the intensity and duration of the 1931 flood, revealing a smaller magnitude but associated with an amplified loss, likely due to social vulnerability and reduced societal resilience compared to the 1998’s. While successful simulation can be interfered with by model variability, our dynamical method shows promise for simulating pre-instrumental flood and building a long-term pre-instrumental-hydrology database.
期刊介绍:
npj Climate and Atmospheric Science is an open-access journal encompassing the relevant physical, chemical, and biological aspects of atmospheric and climate science. The journal places particular emphasis on regional studies that unveil new insights into specific localities, including examinations of local atmospheric composition, such as aerosols.
The range of topics covered by the journal includes climate dynamics, climate variability, weather and climate prediction, climate change, ocean dynamics, weather extremes, air pollution, atmospheric chemistry (including aerosols), the hydrological cycle, and atmosphere–ocean and atmosphere–land interactions. The journal welcomes studies employing a diverse array of methods, including numerical and statistical modeling, the development and application of in situ observational techniques, remote sensing, and the development or evaluation of new reanalyses.