Linking climate change to urban storm drainage system design: An innovative approach to modeling of extreme rainfall processes over different spatial and temporal scales

摘要

The main challenge in the assessment of climate change impacts on the estimation of extreme rainfalls (ERs) for urban drainage systems design is how to establish the linkages between the climate projections given by Global Climate Models (GCMs) at global scales and the observed extreme rainfalls at a given local site. Downscaling approaches have been proposed in many previous studies to downscale global-scale GCM daily information to regional-scale daily climate projections. However, these daily downscaled data are still considered too coarse in both spatial and temporal resolutions and hence not suitable for climate change impact studies at a local site or for small urban watersheds. The present study proposes therefore an innovative statistical downscaling (SD) approach for establishing the linkage between daily extreme rainfalls at regional scales and daily and sub-daily extreme rainfalls at a local (point) scale. The feasibility and accuracy of the proposed method were assessed for a case study in Ontario (Canada) using observed ER data from seven raingauges and climate simulation outputs from 21 GCMs that have been downscaled by NASA to a regional 25-km scale for the RCP 4.5 scenario. Results based on various graphical and numerical comparison criteria have indicated the feasibility and accuracy of the proposed SD approach. In addition, a robust assessment of the climate change impacts on the ERs for urban drainage system design was performed using a series of statistical tests in sequence to evaluate the significant changes of rainfalls among different time periods. It was found that significant increases by 8% to 18% in extreme design rainfalls of return periods up to T = 25 years, and insignificant increases by 3% to 8% in the 50-year and 100-year design rainfalls for many locations, except for one location with a significant increase of 18%. The confidence intervals were also computed for these estimated design rainfalls with widths varying from 5% to 22%.