{"title":"加拿大局部尺度的当前和预测的未来总洪水灾害地图——文献综述","authors":"Slobodan P. Simonovic, Brian Perry","doi":"10.1111/jfr3.70091","DOIUrl":null,"url":null,"abstract":"<p>This review, based on 231 articles, focuses on studies relevant to Canada that assess fluvial, pluvial, and coastal flood hazards at national and broader scales. It evaluates the application of remote sensing and artificial intelligence methods for flood mapping within the Canadian context. The review highlights a growing trend in large-scale flood modeling, with increasing relevance for Canadian flood risk management. Methods for downscaling coarse-resolution flood estimates from physically based models to finer spatial scales are particularly important for Canada's diverse hydrological regions. Global estimates of flood defense standards often rely on socio-economic indicators, but for Canada, physical hazard factors should also be integrated. Advances in LiDAR and radar remote sensing have improved the accuracy of Canadian flood models by providing detailed topographic data. Artificial intelligence techniques show strong potential for predicting flood inundation and enhancing flood hazard mapping across Canadian landscapes.</p>","PeriodicalId":49294,"journal":{"name":"Journal of Flood Risk Management","volume":"18 3","pages":""},"PeriodicalIF":3.0000,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jfr3.70091","citationCount":"0","resultStr":"{\"title\":\"Local Scale Current and Projected Future Total Flood Hazard Mapping for Canada—Literature Review\",\"authors\":\"Slobodan P. Simonovic, Brian Perry\",\"doi\":\"10.1111/jfr3.70091\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>This review, based on 231 articles, focuses on studies relevant to Canada that assess fluvial, pluvial, and coastal flood hazards at national and broader scales. It evaluates the application of remote sensing and artificial intelligence methods for flood mapping within the Canadian context. The review highlights a growing trend in large-scale flood modeling, with increasing relevance for Canadian flood risk management. Methods for downscaling coarse-resolution flood estimates from physically based models to finer spatial scales are particularly important for Canada's diverse hydrological regions. Global estimates of flood defense standards often rely on socio-economic indicators, but for Canada, physical hazard factors should also be integrated. Advances in LiDAR and radar remote sensing have improved the accuracy of Canadian flood models by providing detailed topographic data. Artificial intelligence techniques show strong potential for predicting flood inundation and enhancing flood hazard mapping across Canadian landscapes.</p>\",\"PeriodicalId\":49294,\"journal\":{\"name\":\"Journal of Flood Risk Management\",\"volume\":\"18 3\",\"pages\":\"\"},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2025-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jfr3.70091\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Flood Risk Management\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1111/jfr3.70091\",\"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/jfr3.70091","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}
Local Scale Current and Projected Future Total Flood Hazard Mapping for Canada—Literature Review
This review, based on 231 articles, focuses on studies relevant to Canada that assess fluvial, pluvial, and coastal flood hazards at national and broader scales. It evaluates the application of remote sensing and artificial intelligence methods for flood mapping within the Canadian context. The review highlights a growing trend in large-scale flood modeling, with increasing relevance for Canadian flood risk management. Methods for downscaling coarse-resolution flood estimates from physically based models to finer spatial scales are particularly important for Canada's diverse hydrological regions. Global estimates of flood defense standards often rely on socio-economic indicators, but for Canada, physical hazard factors should also be integrated. Advances in LiDAR and radar remote sensing have improved the accuracy of Canadian flood models by providing detailed topographic data. Artificial intelligence techniques show strong potential for predicting flood inundation and enhancing flood hazard mapping across Canadian landscapes.
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.