Sensitivity of the Statistical DownScaling Model (SDSM) to reanalysis products.

摘要

To produce accurate daily predictions of future climate variables at the regional scale, the Statistical DownScaling Model (SDSM) identifies relationships between large-scale predictors and local-scale predictands, using a multiple linear regression model. In this study, separate downscaled precipitation and temperature scenarios were generated using the SDSM with the calibrations and validations derived from two different reanalyses for a climate station in southwestern Ontario. From these comparisons, we have identified statistically significant differences between the two downscaled and observed time-series. Furthermore, the separate downscaled scenarios were used as the climatic inputs into the Soil and Water Assessment Tool (SWAT) hydrologic model to statistically identify significant differences between simulated and observed daily discharge from the Fairchild Creek watershed. These comparisons indicated that the choice of the reanalysis used to calibrate the SDSM can significantly impact the downscaled scenarios used for various hydrologic applications over the region evaluated in southwestern Ontario.