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Modeling of Extreme Rainfall Processes in a Changing Climate

Author(s): Van-Thanh-Van Nguyen

Linked Author(s): Van-Thanh-Van Nguyen

Keywords: Extreme rainfalls; Downscaling methods; Frequency analyses; Statistical modeling methods; Climate change impact and adaptation

Abstract: Climate change has been recognized as having a profound impact on extreme rainfall process. Hence, there exists an urgent need to assess this impact on the estimation of design rainfalls for improving the design of various urban infrastructures. In current engineering practice, Environment and Climate Change Canada provides the intensity-duration-frequency (IDF) relations for estimating the design rainfalls for approximately 650 stations across the country. Traditionally, these IDF relations were obtained by fitting the two-parameter Gumbel distribution to the annual maximum (AM) rainfall data for each rainfall duration independently using the method of moments (MOM). However, it has been widely known that this Gumbel/MOM traditional approach may not produce accurate extreme rainfall estimates as compared to those given by, for instance, the Generalized Extreme Value (GEV) distribution. Consequently, there are several recently developed design rainfall estimation procedures in Canada. This study presents therefore a comparative study of existing methods to identify the best estimation procedure for Canada. Historical AM data with at least 50 years of record from 39 stations representing diverse climatic conditions across Canada were used. It was found that there are three distinct IDF patterns for different regions of Canada, including convex, linear, and concave patterns. These patterns are linked to the scale-invariance relationships between the statistical moments of observed rainfall amounts over different rainfall durations. Consequently, it was found that the estimation method based on the scale-invariance GEV distribution can provide the best estimates of extreme design rainfalls for Canada in the context of a changing climate.


Year: 2023

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