Flood modeling prior to the instrumental era reveals limited magnitude of 1931 Yangtze flood

IF 8.4 1区 地球科学 Q1 METEOROLOGY & ATMOSPHERIC SCIENCES npj Climate and Atmospheric Science Pub Date : 2025-01-19 DOI:10.1038/s41612-025-00908-1
Ling Zhang, Zhongshi Zhang, Lu Li, Xiaoling Chen, Xijin Wang, Entao Yu, Pratik Kad, Odd Helge Otterå, Chuncheng Guo, Jianzhong Lu, Mingna Wu
{"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":8.4000,"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.

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
在仪器时代之前的洪水模型显示了1931年长江洪水的有限震级
全球洪水风险促使人们更好地了解洪水的震级及其机制,这需要从仪器前的洪水调查中获得见解。由于数据稀缺,重建仪器前洪水震级依赖于统计降尺度,无法捕捉非线性和动态特征。我们开发了一种动态方法,NorESM-WRF-SWAT,整合了全球气候、区域和水文模型来调查1931年长江洪水(世界上最致命的洪水),并将其与1998年的洪水进行了比较。通过验证,该方法在模拟降水和河流流量方面优于统计方法。我们首次对1931年洪水的强度和持续时间进行了详细的分析,揭示了与1998年的洪水相比,1931年的洪水规模较小,但损失却更大,这可能是由于社会脆弱性和社会恢复力的降低。虽然成功的模拟可能会受到模式变率的干扰,但我们的动态方法显示出模拟仪器前洪水和建立仪器前长期水文数据库的希望。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
npj Climate and Atmospheric Science
npj Climate and Atmospheric Science Earth and Planetary Sciences-Atmospheric Science
CiteScore
8.80
自引率
3.30%
发文量
87
审稿时长
21 weeks
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.
期刊最新文献
Atmospheric water vapor contribution to interannual variability of Northern Hemisphere summer heatwaves Enhancing the predictability limits of ENSO with physics-guided deep echo state networks Warmer temperatures lead to wetter tropical cyclones in the North Atlantic Increasing global heatwave occurrence associated with land-atmosphere interactions Precessional modulation of ENSO strength and spatial structure in a transient CGCM simulation of the past 3 million years
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1