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Design rainfall estimation: comparison between GEV and LP3 distributions and at-site and regional estimates

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Abstract

Design rainfall, often known as intensity–frequency–duration (IFD) data, is an important input in rainfall runoff modelling exercise. IFD data are derived by fitting a probability distribution to observed rainfall data. Although there are many researches on IFD curves in the literature, there is a lack of systematic comparison among the IFD curves obtained by different distributions and methods. This study compares the latest IFD curves in Australia, published in 2013, as a part of the new Australian rainfall and runoff (ARR) with the at-site IFD curves to examine the expected degree of variation between the at-site and regional IFD data. Ten pluviography stations from eastern New South Wales (NSW) are selected for this study. The IFD curves generated by the two most commonly adopted probability distributions, generalised extreme value (GEV) and log Pearson type 3 (LP3) distributions are also compared. Empirical and polynomial regression methods in smoothing the IFD curves are compared. Based on the three goodness-of-fit tests, it has been found that both GEV and LP3 distributions fit the annual maximum rainfall data (at 1% significance level) for the ten selected stations. The developed IFD curves based on the second-degree polynomial present better fitting than the empirical method. It has been found that the ARR87 and ARR13 IFD curves are generally higher than the at-site IFD curves derived here. The median difference between the at-site and regional ARR-recommended IFD curves is in the range of 13–19%. It is expected that the outcomes of this research will provide better guidance in selecting the correct IFD data for a given application in NSW. The methodology developed here can be adapted to other parts of Australia and other countries.

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Acknowledgements

The authors would like to acknowledge the Australian Bureau of Meteorology for providing the pluviograph data used in this study. The authors would like to acknowledge the comments and suggestions by the reviewers, which have improved the quality of the manuscript.

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  1. School of Computing, Engineering and Mathematics, Western Sydney University, Locked Bag 1797, Penrith, NSW, 2751, Australia

    Evan Hajani & Ataur Rahman

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  1. Evan Hajani
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  2. Ataur Rahman
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Correspondence to Ataur Rahman.

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Hajani, E., Rahman, A. Design rainfall estimation: comparison between GEV and LP3 distributions and at-site and regional estimates. Nat Hazards 93, 67–88 (2018). https://doi.org/10.1007/s11069-018-3289-9

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