Identification of a preferred statistical distribution for at-site flood frequency analysis in Canada

Abstract

Floods are the most frequent natural disaster in Canada, putting Canadian lives and property at risk. Projected variations in precipitation and temperature are expected to further intensify extreme events, necessitating improved flood planning and water resource management. The Natural Sciences and Engineering Research Council funded FloodNet project is developing a standardized flood estimation manual for Canada; such a nation-wide manual would make flood frequency application more effective, consistent, and reproducible, reducing the need for subjective judgement. This research investigates a preferred at-site flood frequency distribution for Canadian annual peak flow dataset. Four frequently used distributions: Generalized Logistic distribution, Generalized Extreme Value distribution, and Pearson Type III distributions with and without log transformation, are assessed using two robust goodness-of-fit tests (i.e., Modified Anderson-Darling test and L-Moment test) across a wide range of Canadian annual peak flood samples. Goodness-of-fit assessments are analysed using different configurations of flood samples, including annual maximum flow and instantaneous peak flow samples, stationary samples and de-trended non-stationary samples, samples with varied record length, and samples from various geographical regions of Canada. Estimated flood quantiles are compared with estimates of hypothetical true quantiles to assess predictive performance of the considered distribution. Results show the Generalized Extreme Value distribution is better than the other considered distributions because of its acceptance for most station samples and the closeness to the estimates of hypothetical true quantiles. This study provides a basis for recommending the Generalized Extreme Value distribution for at-site flood frequency analysis in Canada.