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Flash flood type identification and simulation based on flash flood behavior indices in China
Zhai, Xiaoyan1; Guo, Liang1; Zhang, Yongyong2
2021-05-18
Source PublicationSCIENCE CHINA-EARTH SCIENCES
ISSN1674-7313
Pages15
Corresponding AuthorZhang, Yongyong()
AbstractFlash floods present significant heterogeneity over both space and time due to diverse topographic, geomorphologic, and hydro-meteorological conditions of catchments. Accurate identification and simulation of typical flash flood types are of great significance for the mitigation of flash flood disasters at national scale. Three flood peak indices and dynamic indices were adopted to characterize the behavioral variability of flash floods. The typical flash flood types and corresponding behavior indices were identified and simulated using statistical analysis (i.e., principal component analysis, dynamic K-means clustering, and analysis of similarity) and hydrological modelling (i.e., HEC and XAJ models). There were 177 flash flood events at hourly scale being selected for case study from eight catchments with various climatic and geographic characteristics. Results showed that all the flash flood events were clustered into three types (named Types 1, 2, and 3). The Type 1 was characterized by low peak flow intensity, early flood peak occurrence time, and thin flood process with short duration. The Type 2 was characterized by low peak flow intensity, late flood peak occurrence time, and flat flood process with long duration. The Type 3 was characterized by high peak flow intensity and late flood peak occurrence time. Flash flood types showed high consistency with their influencing factors (e.g., catchment forest ratio and drainage area, occurrence time and magnitude of maximum storm intensity, and concentration of a storm event). The simulation performances were basically the same for HEC and XAJ models. As for flash flood event simulations, the average relative error varied from 23.25% to 27.98%, from 11.95% to 18.19%, and from 8.30% to 18.25% for Types 1, 2 and 3, respectively. The average Nash-Sutcliffe efficiency coefficient varied from 0.39 to 0.54, from 0.76 to 0.85, and from 0.86 to 0.91, respectively. As for the six flash flood behavior indices simulations, the average relative root-mean-square error (RMSEr) varied from 0.37 to 0.69, from 0.37 to 0.41, and from 0.18 to 0.25 for Types 1, 2, and 3, respectively. The average correlation coefficient (r) varied from 0.52 to 0.68, from 0.78 to 0.85, and from 0.88 to 0.94, respectively. The flood peak indices were the best simulated for Types 2 and 3 with RMSEr varying from 0.18 to 0.28 and r varying from 0.86 to 0.91. The flood dynamic indices were the best simulated for Type 3 with RMSEr varying from 0.19 to 0.21 and r varying from 0.91 to 0.97. The study provided detailed flood information supports for flood management at catchment scale, and also provided new insights into flash flood simulations in small and medium-sized catchments from perspective of flood behavioral processes.
KeywordFlood similarity Flood behavior Statistical analysis HEC and XAJ models Humid and semi-humid catchments
DOI10.1007/s11430-020-9727-1
WOS KeywordCATCHMENT CLASSIFICATION ; RAINFALL THRESHOLD ; RIVER-BASIN ; REDUCTION ; REGION ; RISK
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[41807171] ; National Key Research and Development Program of China[2016YFC0400902] ; China National Flash Flood Disaster Prevention and Control Project[JZ0145B2017]
Funding OrganizationNational Natural Science Foundation of China ; National Key Research and Development Program of China ; China National Flash Flood Disaster Prevention and Control Project
WOS Research AreaGeology
WOS SubjectGeosciences, Multidisciplinary
WOS IDWOS:000652988400001
PublisherSCIENCE PRESS
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/162548
Collection中国科学院地理科学与资源研究所
Corresponding AuthorZhang, Yongyong
Affiliation1.China Inst Water Resources & Hydropower Res, Res Ctr Flood & Drought Disaster Reduct, State Key Lab Simulat & Regulat Water Cycle River, Beijing 100038, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China
Recommended Citation
GB/T 7714
Zhai, Xiaoyan,Guo, Liang,Zhang, Yongyong. Flash flood type identification and simulation based on flash flood behavior indices in China[J]. SCIENCE CHINA-EARTH SCIENCES,2021:15.
APA Zhai, Xiaoyan,Guo, Liang,&Zhang, Yongyong.(2021).Flash flood type identification and simulation based on flash flood behavior indices in China.SCIENCE CHINA-EARTH SCIENCES,15.
MLA Zhai, Xiaoyan,et al."Flash flood type identification and simulation based on flash flood behavior indices in China".SCIENCE CHINA-EARTH SCIENCES (2021):15.
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