Disaster mapping and assessment of Pakistan's 2022 mega-flood based on multi-source data-driven approach
文献类型：期刊论文
| 作者 | Wang, Juanle4,6,8; Li, Kai1,8; Hao, Lina2,8; Xu, Chen2; Liu, Jingxuan2; Qu, Zheng3; Yan, Xinrong8; Sajjad, Meer Muhammad8; Sun, Yamin5 |
| 刊名 | NATURAL HAZARDS
 |
| 出版日期 | 2023-12-07 |
| 卷号 | N/A |
| 关键词 | Mega-flood Disaster risk reduction Remote sensing monitoring Risk of dam-failure flood Big data |
| DOI | 10.1007/s11069-023-06337-8 |
| 产权排序 | 1 |
| 文献子类 | Article ; Early Access |
| 英文摘要 | Climate change-induced mega-floods have become increasingly frequent worldwide. The rapid mapping and assessment of flood disasters pose urgent challenges for developing countries with poor data facilities or databases. In this study, the characteristics of the 2022 mega-flood in Pakistan were monitored and analyzed based on multi-resources data. The extent of inundation throughout Pakistan and its impact on farmlands, buildings, and roads were mapped using Synthetic Aperture Radar remote sensing data processing technology. The results showed that a 10-m resolution flooding map could be achieved using the Google Earth Engine platform in a timely manner with reasonable precision. A GIS-based bluespot model was used to evaluate the risk of dam-failure floods. The zone risk distribution map of the dam-failure flood was produced with five risk levels, which contribute to the safety of the key infrastructure for flooding control. The potential influencing factors of snow melting in northern Pakistan induced by heat waves and disasters was detected using Earth observations and long-record historical data. The study provides data-driven approach options for monitoring flood hazards over large areas in emergency using multi-available data sources, where in situ monitoring is difficult. This study not only provided direct data products and risk maps for mega-flooding control in Pakistan, but also proposed five aspects of flood prevention and control recommendations for this region and its neighborhood areas to cope with flood disasters effectively under worsening climate change conditions. |
| WOS研究方向 | Geology ; Meteorology & Atmospheric Sciences ; Water Resources |
| 出版者 | SPRINGER |
| WOS记录号 | WOS:001114677700002 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/200976]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 作者单位 | 1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.China Univ Min & Technol Beijing, Sch Earth Sci & Mapping Engn, Beijing 100083, Peoples R China 3.Jiangsu Ocean Univ, Sch Marine Technol & Geomatics, Lianyungang 222005, Peoples R China 4.Shandong Univ Technol, Sch Construct Engn, Zibo 255000, Shandong, Peoples R China 5.Jiangsu Prov Geog Collaborat Innovat Ctr Informat, Nanjing 210023, Peoples R China 6.Inst Geog Sci & Nat Resources Res, Chinese Acad Sci, State Key Lab Resource & Environm Informat Syst, Beijing 100101, Peoples R China 7.Inst Disaster Prevent Sci & Technol, Sch Resources & Environm, Sanhe 065201, Peoples R China 8.China Pakistan Joint Res Ctr Earth Sci, Islamabad 45320, Pakistan |
| 推荐引用方式 GB/T 7714 | Wang, Juanle,Li, Kai,Hao, Lina,et al. Disaster mapping and assessment of Pakistan's 2022 mega-flood based on multi-source data-driven approach[J]. NATURAL HAZARDS,2023,N/A. |
| APA | Wang, Juanle.,Li, Kai.,Hao, Lina.,Xu, Chen.,Liu, Jingxuan.,...&Sun, Yamin.(2023).Disaster mapping and assessment of Pakistan's 2022 mega-flood based on multi-source data-driven approach.NATURAL HAZARDS,N/A. |
| MLA | Wang, Juanle,et al."Disaster mapping and assessment of Pakistan's 2022 mega-flood based on multi-source data-driven approach".NATURAL HAZARDS N/A(2023). |
入库方式： OAI收割
来源：地理科学与资源研究所
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