Regionalisation of the parameters of the log‐Pearson 3 distribution: a case study for New South Wales,Australia |
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| Authors: | Tom Micevski André Hackelbusch Khaled Haddad George Kuczera Ataur Rahman |
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| Affiliation: | 1. Discipline of Civil, Surveying and Environmental Engineering, University of Newcastle, Callaghan, Australia;2. Faculty of Civil and Environmental Engineering, Ruhr‐University Bochum, Bochum, Germany;3. School of Computing, Engineering and Mathematics, University of Western Sydney, Penrith, Australia |
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| Abstract: | The index flood method is widely used in regional flood frequency analysis (RFFA) but explicitly relies on the identification of ‘acceptable homogeneous regions’. This paper presents an alternative RFFA method, which is particularly useful when ‘acceptably homogeneous regions’ cannot be identified. The new RFFA method is based on the region of influence (ROI) approach where a ‘local region’ can be formed to estimate statistics at the site of interest. The new method is applied here to regionalize the parameters of the log‐Pearson 3 (LP3) flood probability model using Bayesian generalized least squares (GLS) regression. The ROI approach is used to reduce model error arising from the heterogeneity unaccounted for by the predictor variables in the traditional fixed‐region GLS analysis. A case study was undertaken for 55 catchments located in eastern New South Wales, Australia. The selection of predictor variables was guided by minimizing model error. Using an approach similar to stepwise regression, the best model for the LP3 mean was found to use catchment area and 50‐year, 12‐h rainfall intensity as explanatory variables, whereas the models for the LP3 standard deviation and skewness only had a constant term for the derived ROIs. Diagnostics based on leave‐one‐out cross validation show that the regression model assumptions were not inconsistent with the data and, importantly, no genuine outlier sites were identified. Significantly, the ROI GLS approach produced more accurate and consistent results than a fixed‐region GLS model, highlighting the superior ability of the ROI approach to deal with heterogeneity. This method is particularly applicable to regions that show a high degree of regional heterogeneity. Copyright © 2014 John Wiley & Sons, Ltd. |
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| Keywords: | floods regionalisation GLS regression flood frequency analysis |
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