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Possible Regional Probability Distribution Type of Canadian Annual Streamflow by L-moments

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  • Sheng Yue
  • Chun Wang

Abstract

For effective planning, design, and management of water resources engineering, the probability distribution of annual streamflow is necessary. The method of L-moments is applied to identify the probability distribution type of annual streamflow in different climatic regions of Canada. In the Pacific and southern British Columbia mountains (regions 1 and 2), the generalized extreme value (GEV) distribution fits the observations best with the 3-parameter lognormal (LN3) and log Pearson type III (LP3) as potential candidates. In Yukon and northern British Columbia (region 3), the LN3 distribution corresponds to observations best with the LP3 and P3 as potential candidates. In the northwestern forest (region 5), the LP3 distribution matches observations best with the P3 and GEV as potential candidates. In Arctic Tundra (region 10), the 3-parameter Weibull (W3) is the best one with the LN3 and P3 as potential candidates. The P3 distribution provides a best-fit to observations in the Prairies (region 4), northeastern forest (region 6), great Lakes and St. Lawrence (region 7), Atlantic (region 8), and Mackenzie (region 9) with the LN3, LP3, and GEV as potential candidates. Copyright Kluwer Academic Publishers 2004

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  • Sheng Yue & Chun Wang, 2004. "Possible Regional Probability Distribution Type of Canadian Annual Streamflow by L-moments," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 18(5), pages 425-438, October.
    • Handle: RePEc:spr:waterr:v:18:y:2004:i:5:p:425-438
    DOI: 10.1023/B:WARM.0000049145.37577.87
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    References listed on IDEAS

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    1. Rakesh Kumar & C. Chatterjee & Sanjay Kumar & A. Lohani & R. Singh, 2003. "Development of Regional Flood Frequency Relationships Using L-moments for Middle Ganga Plains Subzone 1(f) of India," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 17(4), pages 243-257, August.
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    Cited by:

    1. Hongxiang Yan & Hamid Moradkhani, 2016. "Toward more robust extreme flood prediction by Bayesian hierarchical and multimodeling," 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. 81(1), pages 203-225, March.
    2. Telesca, Luciano & Lovallo, Michele & Lopez-Moreno, Ignacio & Vicente-Serrano, Sergio, 2012. "Investigation of scaling properties in monthly streamflow and Standardized Streamflow Index (SSI) time series in the Ebro basin (Spain)," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1662-1678.
    3. Zamir Hussain & G. Pasha, 2009. "Regional Flood Frequency Analysis of the Seven Sites of Punjab, Pakistan, Using L-Moments," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(10), pages 1917-1933, August.
    4. Hossein Tabari & Jaefar Nikbakht & P. Hosseinzadeh Talaee, 2013. "Hydrological Drought Assessment in Northwestern Iran Based on Streamflow Drought Index (SDI)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(1), pages 137-151, January.
    5. Neslihan Seckin & Murat Cobaner & Recep Yurtal & Tefaruk Haktanir, 2013. "Comparison of Artificial Neural Network Methods with L-moments for Estimating Flood Flow at Ungauged Sites: the Case of East Mediterranean River Basin, Turkey," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(7), pages 2103-2124, May.
    6. Hongxiang Yan & Hamid Moradkhani, 2016. "Toward more robust extreme flood prediction by Bayesian hierarchical and multimodeling," 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. 81(1), pages 203-225, March.
    7. Su, Ming-Daw & Lin, Chun-Hung & Chang, Ling-Fang & Kang, Jui-Lin & Lin, Mei-Chun, 2009. "A probabilistic approach to rainwater harvesting systems design and evaluation," Resources, Conservation & Recycling, Elsevier, vol. 53(7), pages 393-399.

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