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Predicting Flood Event Class Using a Novel Class Membership Function and Hydrological Modeling

文献类型：期刊论文

作者Zhang, Yongyong4; Zhang, Yongqiang4; Zhai, Xiaoyan3; Xia, Jun2,4; Tang, Qiuhong4; Zhao, Tongtiegang1; Wang, Wei4
刊名EARTHS FUTURE
出版日期2024-06-01
卷号12期号:6页码:e2023EF004081
关键词flood event class class membership function hit rate flood regime metrics catchment hydrological model
DOI10.1029/2023EF004081
产权排序1
文献子类Article
英文摘要Predicting flood event classes aids in the comprehensive investigation of flood behavior dynamics and supports flood early warning and emergency plan development. Existing studies have mainly focused on historical flood event classification and the prediction of flood hydrographs or certain metrics (e.g., magnitude and timing) but have not focused on predicting flood event classes. Our study proposes a new approach for predicting flood event classes based on the class membership functions of flood regime metrics and hydrological modeling. The approach is validated using 1446 unimpacted flood events in 68 headstream catchments widely distributed across China. The new approach performs well, with class hit rates of 68.3% +/- 0.4% for all events; 65.8% +/- 0.6%, 56.8% +/- 0.9%, and 69.5% +/- 0.9% for the small, moderate and high spike flood event classes, respectively; and 82.5% +/- 1.2% and 75.4% +/- 1.1% for the moderate and high dumpy flood event classes, respectively. Furthermore, it performs better in the basins of northern China than in those of southern China, particularly for the small spike flood event class in the Songliao and Yellow River Basins, with hit rates of 80.0% +/- 3.2% and 78.8% +/- 3.2%, respectively. Our results indicate that the new approach will help improve the prediction performance of flood events and their corresponding classes, and provide deep insights into the comprehensive dynamic patterns of flood events for early warning and control management. The prediction of flood event classes is more effective and informative than that of individual events for obtaining comprehensive dynamic characteristics of flood events for early warning and development of emergency plans. However, this is challenging owing to massive flood events with remarkable spatiotemporal heterogeneity and unclear class membership functions. We propose a class prediction approach for flood events using the class membership functions of flood behavior metrics based on frequency analysis and catchment hydrological modeling. This approach deeply mines the behavior characteristics of historical flood events with a strong mathematical basis and cooperates with hydrological models to predict flood classes. Over thousand unimpacted flood events in 68 headstream catchments across China were selected to validate the robustness of the prediction scheme. Our results show that the proposed approach well predicts the flood class with class hit rates of 68.3% +/- 0.4% for all events; 65.8% +/- 0.6%, 56.8% +/- 0.9%, and 69.5% +/- 0.9% for the small, moderate and high spike flood event classes, respectively; and 82.5% +/- 1.2% and 75.4% +/- 1.1% for the moderate and high dumpy flood event classes, respectively. Our study has strong implications for scientific flood warning research and flood mitigation management. A prediction approach for flood event classes is developed using class membership functions of flood regime metrics with hydrological model Class membership degree is estimated using the frequency distribution function, which fits well with statistical significance Average class hit rates are 68.3% +/- 0.4% for all the events and predictions of dumpy flood class are better than those of spike flood classes
WOS关键词CLASSIFICATION ; ACCURACY ; IDENTIFICATION ; UNCERTAINTY ; VARIABILITY ; CALIBRATION ; SIMULATION ; FRAMEWORK ; REGIMES ; BASIN
WOS研究方向Environmental Sciences & Ecology ; Geology ; Meteorology & Atmospheric Sciences
WOS记录号WOS:001250341200001
出版者AMER GEOPHYSICAL UNION
源URL[http://ir.igsnrr.ac.cn/handle/311030/205320]  
专题陆地水循环及地表过程院重点实验室_外文论文
通讯作者Zhang, Yongyong; Zhang, Yongqiang
作者单位1.Sun Yat Sen Univ, Southern Marine Sci & Engn Guangdong Lab Zhuhai, Guangzhou, Peoples R China
2.Wuhan Univ, State Key Lab Water Resources & Hydropower Engn Sc, Wuhan, Peoples R China
3.China Inst Water Resources & Hydropower Res, Beijing, Peoples R China
4.Chinese Acad Sci, Key Lab Water Cycle & Related Land Surface Proc, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Yongyong,Zhang, Yongqiang,Zhai, Xiaoyan,et al. Predicting Flood Event Class Using a Novel Class Membership Function and Hydrological Modeling[J]. EARTHS FUTURE,2024,12(6):e2023EF004081.
APA Zhang, Yongyong.,Zhang, Yongqiang.,Zhai, Xiaoyan.,Xia, Jun.,Tang, Qiuhong.,...&Wang, Wei.(2024).Predicting Flood Event Class Using a Novel Class Membership Function and Hydrological Modeling.EARTHS FUTURE,12(6),e2023EF004081.
MLA Zhang, Yongyong,et al."Predicting Flood Event Class Using a Novel Class Membership Function and Hydrological Modeling".EARTHS FUTURE 12.6(2024):e2023EF004081.

入库方式： OAI收割

来源：地理科学与资源研究所

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