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<p>In the context of global climate change, scientifically identifying and selecting typical unfavorable flood events is key to developing effective flood disaster response plans and reducing losses. To enhance the scientific accuracy and representativeness of typical flood event selection, this study proposes a multi-indicator method that integrates an entropy-weighted unfavorable indicator with a two-dimensional return-period classification. Six indicators—peak discharge, flood volume, rising rate, falling rate, skewness coefficient, and proportion of high-pulse duration—were selected to comprehensively reflect differences in flood magnitude, process, and morphology. An integrated unfavorable indicator was constructed using the entropy-weighting method. Additionally, a 4 × 4 classification matrix based on the return periods of peak discharge and flood volume was used to classify the flood events. On this basis, typical unfavorable floods were identified by considering the unfavorable indicators within each flood type. The proposed method was applied to analyze 180 flood events during the flood season (June to October) from 2002 to 2023 at Tongguan Station, and the results were compared with K-means cluster analysis and traditional classification methods. The results show that both the proposed return-period classification method and the K-means cluster analysis passed the significance test. Compared with the K-means cluster, the return-period classification method reduces dependence on input flood characteristics, making it more widely applicable. Furthermore, the classification results in this study exhibited over 94% consistency with those from traditional single-indicator classification. In particular, when significant differences exist between peak discharge and total flood volume, this method effectively identifies flood events with larger unfavorable indicators within the same flood type, demonstrating greater discriminative power than traditional single-indicator methods and better reflecting the inherent patterns of hydrological processes. Therefore, the typical unfavorable flood set constructed in this study provides a more challenging and representative scenario for flood feature analysis, flood-management simulations, risk assessment, and the development of soil- and water-conservation strategies.</p>

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Method for selecting typical floods based on an unfavorable indicator and flood classification

  • 期刊论文

  • 发表日期：

    2025年12月11日

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  • Jiangyu Wang,
  • Zhaohui Jia,
  • Wei Zhang,
  • Xin Li

摘要

In the context of global climate change, scientifically identifying and selecting typical unfavorable flood events is key to developing effective flood disaster response plans and reducing losses. To enhance the scientific accuracy and representativeness of typical flood event selection, this study proposes a multi-indicator method that integrates an entropy-weighted unfavorable indicator with a two-dimensional return-period classification. Six indicators—peak discharge, flood volume, rising rate, falling rate, skewness coefficient, and proportion of high-pulse duration—were selected to comprehensively reflect differences in flood magnitude, process, and morphology. An integrated unfavorable indicator was constructed using the entropy-weighting method. Additionally, a 4 × 4 classification matrix based on the return periods of peak discharge and flood volume was used to classify the flood events. On this basis, typical unfavorable floods were identified by considering the unfavorable indicators within each flood type. The proposed method was applied to analyze 180 flood events during the flood season (June to October) from 2002 to 2023 at Tongguan Station, and the results were compared with K-means cluster analysis and traditional classification methods. The results show that both the proposed return-period classification method and the K-means cluster analysis passed the significance test. Compared with the K-means cluster, the return-period classification method reduces dependence on input flood characteristics, making it more widely applicable. Furthermore, the classification results in this study exhibited over 94% consistency with those from traditional single-indicator classification. In particular, when significant differences exist between peak discharge and total flood volume, this method effectively identifies flood events with larger unfavorable indicators within the same flood type, demonstrating greater discriminative power than traditional single-indicator methods and better reflecting the inherent patterns of hydrological processes. Therefore, the typical unfavorable flood set constructed in this study provides a more challenging and representative scenario for flood feature analysis, flood-management simulations, risk assessment, and the development of soil- and water-conservation strategies.