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A Decision Support Tool for Constructing Rainfall Intensity-Duration-Frequency Relations for Urban Hydraulic Structure Design in a Changing Climate

Author(s): Van-Thanh-Van Nguyen, Myeong-Ho Yeo, Truong-Huy Nguyen

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

Keywords: Design rainfall estimation, hydrologic frequency analysis, downscaling methods, climate change, urban hydraulic structure design.

Abstract: The present study proposed a decision support tool (SDExRAIN) for constructing the rainfall intensity-duration-frequency (IDF) relations at a given site in the context of climate change. More specifically, the SDExRAIN consisted of two components: (i) a spatial statistical downscaling model to describe the linkage between large-scale global climate variables and daily annual maximum rainfalls at a given site; and (ii) a temporal statistical downscaling model to describe the relations between daily and sub-daily annual maximum rainfalls. Consequently, if the linkage between the large-scale climate variability to the historical observations of the extreme rainfall processes at a local site could be established, then the projected change of climate conditions provided by a GCM could be used to predict the resulting impact on the extreme rainfalls at the location of interest. The feasibility and accuracy of this tool was assessed based on the NCEP re-analysis data, the climate simulation outputs from two GCMs (the Canadian GCM3 and the UK HadCM3), and observed daily precipitation data available at two raingages with completely different climatic conditions: Seoul station in South Korea and Dorval Airport station in Canada. Results of this assessment have indicated that it is feasible to use the SDExRAIN for accurately describing the relations between climate predictors provided by GCMs under different climate change scenarios and daily and sub-daily annual maximum rainfalls at a given site. Therefore, the proposed decision support tool can be used for assessing the climate change impact on extreme rainfalls at a given location of interest


Year: 2017

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