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Assessing the Regional Concept with Sub-Sampling Approach to Identify Probability Distribution for at-Site Hydrological Frequency Analysis

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  • Samiran Das

    () (Nanjing University of Information Science and Technology)

Abstract

The framework of regional analysis allows superior discrimination as well as better identification of the shape of a population distribution in a hydrological frequency analysis. The aim of this study is to incorporate the better of regional concept while performing an at-site frequency analysis. The study proposes a new method in the form of sub-sampling technique with the aid of a regional distribution selection procedure to choose an appropriate probability distribution function for frequency analysis. The technique is evaluated against common distribution selection methods: a widely used goodness-of-fit method in Anderson–Darling (AD) and a popular graphical assessment tool in L-moment ratio diagram (LMRD). The performance is evaluated by applying the technique to gauged annual maximum daily precipitation data series of 24 stations located across China. It is found that the technique accomplished a better performance in discriminating among distributions which or else may not be achievable only by the AD or LMRD test. In general, all results indicate that the proposed technique can be an attractive means in discriminating as well as identifying the best distribution for at-site frequency analysis.

Suggested Citation

  • Samiran Das, 2020. "Assessing the Regional Concept with Sub-Sampling Approach to Identify Probability Distribution for at-Site Hydrological Frequency Analysis," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(2), pages 803-817, January.
  • Handle: RePEc:spr:waterr:v:34:y:2020:i:2:d:10.1007_s11269-019-02475-6
    DOI: 10.1007/s11269-019-02475-6
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    References listed on IDEAS

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    1. Dongliang Wang & Alan D. Hutson, 2013. "Joint confidence region estimation of L-moment ratios with an extension to right censored data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(2), pages 368-379, February.
    2. 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.
    3. Rakesh Kumar & Narendra Goel & Chandranath Chatterjee & Purna Nayak, 2015. "Regional Flood Frequency Analysis using Soft Computing Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(6), pages 1965-1978, April.
    4. Mohit Prakash Mohanty & Mazhuvanchery Avarachen Sherly & Subhankar Karmakar & Subimal Ghosh, 2018. "Regionalized Design Rainfall Estimation: an Appraisal of Inundation Mapping for Flood Management Under Data-Scarce Situations," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(14), pages 4725-4746, November.
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