{"title":"山洪对私人住户造成破坏的主要原因","authors":"Daniela Rodríguez Castro, Kasra Rafiezadeh Shahi, Nivedita Sairam, Melanie Fischer, Guilherme Samprogna Mohor, Annegret Thieken, Benjamin Dewals, Heidi Kreibich","doi":"10.1111/jfr3.70088","DOIUrl":null,"url":null,"abstract":"<p>Flash floods cause high numbers of casualties and enormous economic damage. Good knowledge of the damage processes is crucial for the implementation of effective flash flood risk management. However, little is known about the damage processes that occur during flash floods, despite their severity. To gain more knowledge, independent data collection initiatives were carried out in the affected areas of Belgium and Germany after the 2021 floods. The resulting datasets include 420 damaged residential buildings in the Vesdre valley in Belgium, 277 in the Ahr valley in Rhineland-Palatinate (Germany) and 332 in North Rhine-Westphalia (Germany). A total of 30 potential damage-influencing variables were harmonized across the regions, providing valuable insights into hazard characteristics, the vulnerability of exposed assets, the coping capacity of inhabitants, and socio-economic factors. Machine learning-based analysis reveals the significant importance of hazard variables, such as water depth and sediment transport, particularly for building damage. In addition to these, exposure (living area) and physical vulnerability factors (building type and wall type) also play a role in determining building damage across the affected regions. For content damage, besides water depth and living area, socio-economic vulnerability (ownership status of the building) and emergency measures were found to be important predictors. These key drivers of building and content damage from flash floods can be utilized to develop more accurate damage models, thereby improving flash flood risk assessments, enhancing risk communication, and supporting better preparedness strategies.</p>","PeriodicalId":49294,"journal":{"name":"Journal of Flood Risk Management","volume":"18 3","pages":""},"PeriodicalIF":3.0000,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jfr3.70088","citationCount":"0","resultStr":"{\"title\":\"Key Drivers of Flash Flood Damage to Private Households\",\"authors\":\"Daniela Rodríguez Castro, Kasra Rafiezadeh Shahi, Nivedita Sairam, Melanie Fischer, Guilherme Samprogna Mohor, Annegret Thieken, Benjamin Dewals, Heidi Kreibich\",\"doi\":\"10.1111/jfr3.70088\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Flash floods cause high numbers of casualties and enormous economic damage. Good knowledge of the damage processes is crucial for the implementation of effective flash flood risk management. However, little is known about the damage processes that occur during flash floods, despite their severity. To gain more knowledge, independent data collection initiatives were carried out in the affected areas of Belgium and Germany after the 2021 floods. The resulting datasets include 420 damaged residential buildings in the Vesdre valley in Belgium, 277 in the Ahr valley in Rhineland-Palatinate (Germany) and 332 in North Rhine-Westphalia (Germany). A total of 30 potential damage-influencing variables were harmonized across the regions, providing valuable insights into hazard characteristics, the vulnerability of exposed assets, the coping capacity of inhabitants, and socio-economic factors. Machine learning-based analysis reveals the significant importance of hazard variables, such as water depth and sediment transport, particularly for building damage. In addition to these, exposure (living area) and physical vulnerability factors (building type and wall type) also play a role in determining building damage across the affected regions. For content damage, besides water depth and living area, socio-economic vulnerability (ownership status of the building) and emergency measures were found to be important predictors. These key drivers of building and content damage from flash floods can be utilized to develop more accurate damage models, thereby improving flash flood risk assessments, enhancing risk communication, and supporting better preparedness strategies.</p>\",\"PeriodicalId\":49294,\"journal\":{\"name\":\"Journal of Flood Risk Management\",\"volume\":\"18 3\",\"pages\":\"\"},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2025-07-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jfr3.70088\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Flood Risk Management\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1111/jfr3.70088\",\"RegionNum\":3,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Flood Risk Management","FirstCategoryId":"93","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/jfr3.70088","RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
Key Drivers of Flash Flood Damage to Private Households
Flash floods cause high numbers of casualties and enormous economic damage. Good knowledge of the damage processes is crucial for the implementation of effective flash flood risk management. However, little is known about the damage processes that occur during flash floods, despite their severity. To gain more knowledge, independent data collection initiatives were carried out in the affected areas of Belgium and Germany after the 2021 floods. The resulting datasets include 420 damaged residential buildings in the Vesdre valley in Belgium, 277 in the Ahr valley in Rhineland-Palatinate (Germany) and 332 in North Rhine-Westphalia (Germany). A total of 30 potential damage-influencing variables were harmonized across the regions, providing valuable insights into hazard characteristics, the vulnerability of exposed assets, the coping capacity of inhabitants, and socio-economic factors. Machine learning-based analysis reveals the significant importance of hazard variables, such as water depth and sediment transport, particularly for building damage. In addition to these, exposure (living area) and physical vulnerability factors (building type and wall type) also play a role in determining building damage across the affected regions. For content damage, besides water depth and living area, socio-economic vulnerability (ownership status of the building) and emergency measures were found to be important predictors. These key drivers of building and content damage from flash floods can be utilized to develop more accurate damage models, thereby improving flash flood risk assessments, enhancing risk communication, and supporting better preparedness strategies.
期刊介绍:
Journal of Flood Risk Management provides an international platform for knowledge sharing in all areas related to flood risk. Its explicit aim is to disseminate ideas across the range of disciplines where flood related research is carried out and it provides content ranging from leading edge academic papers to applied content with the practitioner in mind.
Readers and authors come from a wide background and include hydrologists, meteorologists, geographers, geomorphologists, conservationists, civil engineers, social scientists, policy makers, insurers and practitioners. They share an interest in managing the complex interactions between the many skills and disciplines that underpin the management of flood risk across the world.