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Regional Frequency Analysis Based on Precipitation Regionalization Accounting for Temporal Variability and a Nonstationary Index Flood Model

Author

Listed:
  • Qianyu Gao

    (Hohai University)

  • Guofang Li

    (Hohai University)

  • Jin Bao

    (Hohai University)

  • Jian Wang

    (Hohai University)

Abstract

Climate change and human activities have led to nonstationarity in hydrological series. To systematically consider nonstationarity in regional frequency analysis (RFA), the features accounting for temporal variability of data series were developed and a nonstationary index flood model considering the trend and jump mutations was proposed in this study. The features extracted by empirical mode decomposition (EMD) were regarded as attributes to identify homogeneous regions. The fuzzy c-means clustering (FCM) and the combination of the self-organizing feature map and Ward’s agglomerative hierarchical clustering (SOM+Ward) were compared. Then the complete nonstationary RFA was applied to the annual maximum daily precipitation (AMDP) of Jiangxi province, China. The results indicate that the regionalization with the attributes reflecting temporal variability of the data series is more detailed. Moreover, the performance of SOM+Ward is better than FCM. The comparison results of precipitation quantiles, which were estimated by stationary and the proposed nonstationary index model, indicate that ignoring nonstationarity in RFA affects the choice of the best-fit distribution and the determination of index flood. In addition, the complete framework of nonstationary RFA developed in this study can provide more proper information when stations with trend and jump mutations exist in the region.

Suggested Citation

  • Qianyu Gao & Guofang Li & Jin Bao & Jian Wang, 2021. "Regional Frequency Analysis Based on Precipitation Regionalization Accounting for Temporal Variability and a Nonstationary Index Flood Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(13), pages 4435-4456, October.
    • Handle: RePEc:spr:waterr:v:35:y:2021:i:13:d:10.1007_s11269-021-02959-4
    DOI: 10.1007/s11269-021-02959-4
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    References listed on IDEAS

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    1. Yuyin Liang & Shuguang Liu & Yiping Guo & Hong Hua, 2017. "L-Moment-Based Regional Frequency Analysis of Annual Extreme Precipitation and its Uncertainty Analysis," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(12), pages 3899-3919, September.
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    Cited by:

    1. Thiago Victor Medeiros Nascimento & Celso Augusto Guimarães Santos & Camilo Allyson Simões Farias & Richarde Marques Silva, 2022. "Monthly Streamflow Modeling Based on Self-Organizing Maps and Satellite-Estimated Rainfall Data," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(7), pages 2359-2377, May.
    2. Shuhui Guo & Lihua Xiong & Jie Chen & Shenglian Guo & Jun Xia & Ling Zeng & Chong-Yu Xu, 2023. "Nonstationary Regional Flood Frequency Analysis Based on the Bayesian Method," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(2), pages 659-681, January.
    3. Xiaobin Zhang & Ligang Ma & Yihang Zhu & Weidong Lou & Baoliang Xie & Li Sheng & Hao Hu & Kefeng Zheng & Qing Gu, 2022. "Temporal Stability Analysis for the Evaluation of Spatial and Temporal Patterns of Surface Water Quality," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(4), pages 1413-1429, March.

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