{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T18:13:59Z","timestamp":1775585639288,"version":"3.50.1"},"reference-count":69,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T00:00:00Z","timestamp":1667433600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["XDA20030302"],"award-info":[{"award-number":["XDA20030302"]}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["2021YFQ0042"],"award-info":[{"award-number":["2021YFQ0042"]}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["SHZH-IWHR-57"],"award-info":[{"award-number":["SHZH-IWHR-57"]}]},{"name":"Strategic Priority Research Program of the Chinese 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Region","award":["2020YFD1100701"],"award-info":[{"award-number":["2020YFD1100701"]}]},{"name":"Science and Technology Project of Xizang Autonomous Region","award":["XZ201901-GA-07"],"award-info":[{"award-number":["XZ201901-GA-07"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In the context of global climate change, floods have become one of the major natural disasters affecting the safety of human life, economic construction, and sustainable development. Despite significant improvements in flood risk and exposure modeling in some studies, there is still a lack of evidence on the spatiotemporal distribution patterns associated with flood risk across the globe. Meanwhile, numerous studies mostly explore flood risk distribution patterns based on specific spatial scales, ignoring to some extent the fact that flood risk has different distribution patterns on different scales. Here, on the basis of hazard\u2013vulnerability components quantified using game theory (GT), we proposed a framework for analyzing the spatiotemporal distribution patterns of global flood risk and the influencing factors behind them on multiple scales. The results revealed that global flood risk increased during 2005\u20132020, with the percentages of high-risk areas being 4.3%, 4.48%, 4.6%, and 5.02%, respectively. There were 11 global risk hotspots, mainly located in areas with high population concentration, high economic density, abundant precipitation, and low elevation. On the national scale, high-risk countries were mainly concentrated in East Asia, South Asia, Central Europe, and Western Europe. In our experiment, developed countries accounted for the majority of the 20 highest risk countries in the world, with Singapore being the highest risk country and El Salvador having the highest positive risk growth rate (growing by 19.05% during 2015\u20132020). The findings of this study offer much-needed information and reference for academics researching flood risk under climate change.<\/jats:p>","DOI":"10.3390\/rs14215551","type":"journal-article","created":{"date-parts":[[2022,11,4]],"date-time":"2022-11-04T04:00:51Z","timestamp":1667534451000},"page":"5551","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Increasing Global Flood Risk in 2005\u20132020 from a Multi-Scale Perspective"],"prefix":"10.3390","volume":"14","author":[{"given":"Yu","family":"Duan","sequence":"first","affiliation":[{"name":"School of Civil Engineering and Geomatics, Southwest Petroleum University, Chengdu 610500, China"}]},{"given":"Junnan","family":"Xiong","sequence":"additional","affiliation":[{"name":"School of Civil Engineering and Geomatics, Southwest Petroleum University, Chengdu 610500, China"},{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1580-4979","authenticated-orcid":false,"given":"Weiming","family":"Cheng","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Yi","family":"Li","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Nan","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Geographical Sciences, Northeast Normal University, Changchun 130024, China"}]},{"given":"Gaoyun","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Civil Engineering and Geomatics, Southwest Petroleum University, Chengdu 610500, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6000-1836","authenticated-orcid":false,"given":"Jiawei","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Civil Engineering and Geomatics, Southwest Petroleum University, Chengdu 610500, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,3]]},"reference":[{"key":"ref_1","unstructured":"UNDRR (2020). 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