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Acquisition of rainfall in ungauged basins: a study of rainfall distribution heterogeneity based on a new method

Author

Listed:
  • Ye Zhao

    (Wuhan University
    Hubei Key Lab of Water System Science for Sponge City Construction)

  • Xiang Zhang

    (Wuhan University
    Hubei Key Lab of Water System Science for Sponge City Construction)

  • Feng Xiong

    (Changjiang Water Resources Commission)

  • Shuying Liu

    (Wuhan University
    Hubei Key Lab of Water System Science for Sponge City Construction)

  • Yao Wang

    (Wuhan University
    Hubei Key Lab of Water System Science for Sponge City Construction)

  • Changmei Liang

    (Hubei Water Resources Research Institute)

Abstract

High-density rainfall data is always desired to capture the heterogeneity of precipitation to accurately describe the components of the hydrological cycle. However, equipping and maintaining a high-density rain gauge network involve high costs, and the existing rain gauges are often unable to meet the density requirements. The objective of this study is to provide a new method to analyze the spatiotemporal variability of the precipitation field and to solve the problem of insufficient site density. To this end, the proper orthogonal decomposition (POD) method is proposed, which can analyze the spatial distribution characteristics of rainfall fields to solve data shortages. To demonstrate the feasibility and advantages of the proposed methodology, four districts and counties (Hongshan District, Jianli County, Sui County, and Xuanen County) in Hubei province in China were selected as case studies. The principal results are as follows. (1) The proposed method is effective in analyzing the spatiotemporal variability of the rainfall field to reconstruct rainfall processes in ungauged basins. (2) Compared with the commonly used Thiessen polygon method, the inverse distance weighting method, and the Kriging method, POD is more accurate and convenient, and the root mean squared error is reduced from 3.22, 1.83, 2.19 to 2.09; the correlation coefficients are improved from 0.60, 0.85, 0.79 to 0.89, respectively. (3) The POD method performs particularly well in simulating the peak value and the peak time and can offer a meaningful reference for analyzing the spatial distribution of rainfall.

Suggested Citation

  • Ye Zhao & Xiang Zhang & Feng Xiong & Shuying Liu & Yao Wang & Changmei Liang, 2022. "Acquisition of rainfall in ungauged basins: a study of rainfall distribution heterogeneity based on a new method," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 114(2), pages 1723-1739, November.
    • Handle: RePEc:spr:nathaz:v:114:y:2022:i:2:d:10.1007_s11069-022-05444-2
    DOI: 10.1007/s11069-022-05444-2
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    References listed on IDEAS

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    1. Carl Eckart & Gale Young, 1936. "The approximation of one matrix by another of lower rank," Psychometrika, Springer;The Psychometric Society, vol. 1(3), pages 211-218, September.
    2. Gulser Meric & Christine Lentz & Wayne Smeltz & Ilhan Meric, 2012. "International Evidence on Market Linkages After the 2008 Stock Market Crash," The International Journal of Business and Finance Research, The Institute for Business and Finance Research, vol. 6(4), pages 45-57.
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