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Non-Stationary Frequency Analysis of Extreme Water Level: Application of Annual Maximum Series and Peak-over Threshold Approaches

Author

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  • Ali Razmi

    (Shahrood University of Technology)

  • Saeed Golian

    (Shahrood University of Technology)

  • Zahra Zahmatkesh

    (University of Manitoba)

Abstract

A great challenge has been appeared on if the assumption of data stationary for flood frequency analysis is justifiable. Results for frequency analysis (FA) could be substantially different if non-stationarity is incorporated in the data analysis. In this study, extreme water levels (annual maximum and daily instantaneous maximum) in a coastal part of New York City were considered for FA. Annual maximum series (AMS) and peak-over threshold (POT) approaches were applied to build data timeseries. The resulted timeseries were checked for potential trend and stationarity using statistical tests including Man-Kendall, Augmented Dickey–Fuller (ADF) and Kwiatkowski–Phillips–Schmidt–Shin (KPSS). Akaike information criterion (AIC) was utilized to select the most appropriate probability distribution models. Generalized Extreme Value (GEV) distribution and Generalized Pareto Distribution (GPD) were then applied as the probability distribution functions on the selected data based on AMS and POT methods under non-stationary assumption. Two methods of maximum likelihood and penalized maximum likelihood were applied and compared for the estimation of the distributions’ parameters. Results showed that by incorporating non-stationarity in FA, design values of extreme water levels were significantly different from those obtained under the assumption of stationarity. Moreover, in the non-stationary FA, consideration of time-dependency for the distribution parameters resulted in a range of variation for design floods. The findings of this study emphasize on the importance of FA under the assumptions of data stationarity and non-stationarity, and taking into account the worst case flooding scenarios for future planning of the watershed against the probable flood events. There is a need to update models developed for stationary flood risk assessment for more robust and resilient hydrologic predictions. Applying non-stationary FA provides an advanced method to extrapolate return levels up to the desired future time perspectives.

Suggested Citation

  • Ali Razmi & Saeed Golian & Zahra Zahmatkesh, 2017. "Non-Stationary Frequency Analysis of Extreme Water Level: Application of Annual Maximum Series and Peak-over Threshold Approaches," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(7), pages 2065-2083, May.
    • Handle: RePEc:spr:waterr:v:31:y:2017:i:7:d:10.1007_s11269-017-1619-4
    DOI: 10.1007/s11269-017-1619-4
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    References listed on IDEAS

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    Cited by:

    1. Yue Zhang & Ying Wang & Yunxia Zhang & Qingzu Luan & Heping Liu, 2021. "Multi-scenario flash flood hazard assessment based on rainfall–runoff modeling and flood inundation modeling: a case study," 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. 105(1), pages 967-981, January.
    2. Mahkameh Zarekarizi & Vivek Srikrishnan & Klaus Keller, 2020. "Neglecting Uncertainties Biases House-Elevation Decisions to Manage Riverine Flood Risks," Papers 2001.06457, arXiv.org, revised Sep 2020.
    3. Yi-han Tang & Jie-feng Wu & Pei-yi Li & Li-juan Zhang & Xiao-hong Chen & Kai-rong Lin, 2019. "Quantifying Flood Frequency Modification Caused by Multi-Reservoir Regulation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(13), pages 4451-4470, October.
    4. Leandro José Isensee & Adilson Pinheiro & Daniel Henrique Marco Detzel, 2021. "Dam Hydrological Risk and the Design Flood Under Non-stationary Conditions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(5), pages 1499-1512, March.
    5. Wentao Xu & Cong Jiang & Lei Yan & Lingqi Li & Shuonan Liu, 2018. "An Adaptive Metropolis-Hastings Optimization Algorithm of Bayesian Estimation in Non-Stationary Flood Frequency Analysis," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(4), pages 1343-1366, March.
    6. Antonino Cancelliere, 2017. "Non Stationary Analysis of Extreme Events," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(10), pages 3097-3110, August.

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