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GAMLSS-based nonstationary modeling of extreme precipitation in Beijing–Tianjin–Hebei region of China

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  • Dong-dong Zhang
  • Deng-hua Yan
  • Yi-Cheng Wang
  • Fan Lu
  • Shao-hua Liu

Abstract

Due to the climate variability and the intensification of human activities, the hydrological time series no longer satisfies the hypothesis of stationarity. In this study, a framework for precipitation frequency analysis is developed based on the Generalized Additive Models for Location, Scale and Shape parameters (GAMLSS), a tool for modeling time series under nonstationary condition. Based on the 12 stations in Beijing–Tianjin–Hebei region of China, two approaches to nonstationary modeling in GAMLSS were applied to the annual maximum daily precipitation records. The results of the first approach, in which the parameters of the selected distributions are modeled as a function of time only, show the presence of clear nonstationarities in the annual maximum daily precipitation. In the second approach, the parameters of the precipitation distributions are modeled as functions of seven climate indices. The results show that the model using the second method captures more adequately the dispersion of precipitation values than the model using the first method. The application of nonstationary analysis shows the differences between the nonstationary quantiles and their stationary equivalents, which suggests the urgent need for nonstationary modeling of extreme precipitation in Beijing–Tianjin–Hebei region of China. Copyright Springer Science+Business Media Dordrecht 2015

Suggested Citation

  • Dong-dong Zhang & Deng-hua Yan & Yi-Cheng Wang & Fan Lu & Shao-hua Liu, 2015. "GAMLSS-based nonstationary modeling of extreme precipitation in Beijing–Tianjin–Hebei region of China," 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. 77(2), pages 1037-1053, June.
    • Handle: RePEc:spr:nathaz:v:77:y:2015:i:2:p:1037-1053
    DOI: 10.1007/s11069-015-1638-5
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    References listed on IDEAS

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    1. Ousmane Seidou & Andrea Ramsay & Ioan Nistor, 2012. "Climate change impacts on extreme floods II: improving flood future peaks simulation using non-stationary frequency analysis," 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. 60(2), pages 715-726, January.
    2. Stasinopoulos, D. Mikis & Rigby, Robert A., 2007. "Generalized Additive Models for Location Scale and Shape (GAMLSS) in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 23(i07).
    3. Bruce A. McCarl & Xavier Villavicencio & Ximing Wu, 2008. "Climate Change and Future Analysis: Is Stationarity Dying?," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 90(5), pages 1241-1247.
    4. J. Rolf Olsen & James H. Lambert & Yacov Y. Haimes, 1998. "Risk of Extreme Events Under Nonstationary Conditions," Risk Analysis, John Wiley & Sons, vol. 18(4), pages 497-510, August.
    5. R. A. Rigby & D. M. Stasinopoulos, 2005. "Generalized additive models for location, scale and shape," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 54(3), pages 507-554, June.
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    Cited by:

    1. L Liu & Z. X. Xu, 2016. "Regionalization of precipitation and the spatiotemporal distribution of extreme precipitation in southwestern China," 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. 80(2), pages 1195-1211, January.
    2. Jenq-Tzong Shiau, 2020. "Effects of Gamma-Distribution Variations on SPI-Based Stationary and Nonstationary Drought Analyses," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(6), pages 2081-2095, April.
    3. Linhan Yang & Jianzhu Li & Aiqing Kang & Shuai Li & Ping Feng, 2020. "The Effect of Nonstationarity in Rainfall on Urban Flooding Based on Coupling SWMM and MIKE21," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(4), pages 1535-1551, March.
    4. Junqiang Yao & Moyan Li & Qing Yang, 2018. "Moisture sources of a torrential rainfall event in the arid region of East Xinjiang, China, based on a Lagrangian model," 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. 92(1), pages 183-195, November.
    5. Sara Hashempour Motlagh Shirazi & Davar Khalili & Shahrokh Zand-Parsa & Amin Shirvani, 2022. "Spatio-temporal variability of extreme precipitation characteristics under different climatic conditions in Fars province, Iran," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(9), pages 11348-11368, September.
    6. Javad Bazrafshan & Somayeh Hejabi, 2018. "A Non-Stationary Reconnaissance Drought Index (NRDI) for Drought Monitoring in a Changing Climate," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(8), pages 2611-2624, June.

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