A comparison of techniques for statistically downscaling extreme precipitation over the Northeastern United States

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Thursday, 21 January 2010: 9:30 AM
B215 (GWCC)
Lee M. Tryhorn, Cornell University, Ithaca, NY; and A. T. DeGaetano

Increases in the number of extreme precipitation events have already been documented in the observational record. These increases have even occurred in regions where the mean precipitation has decreased or is unchanged. Within the United States, the largest increases in precipitation extremes have occurred in the Northeast. Continued changes in the frequency and/or intensity of extreme precipitation events could have profound consequences for both human society and the natural environment. Given that humans are particularly vulnerable to hydrological extremes such as flooding and drought, accurate estimates of local changes in extreme precipitation are valuable for informing local policy decisions and estimating potential impacts on areas such as health, infrastructure, ecosystems, and agriculture.

This research is aimed at testing different statistical downscaling methods in their ability to reconstruct extremes of daily precipitation and potentially developing a better approach for downscaling extremes. This study takes two commonly used downscaling techniques, the bias correction and spatial disaggregation (BCSD) technique of Wood et al (2002) and the Statistical DownScaling Model (SDSM) Version 4.2, and implements them in innovative ways. First, the BCSD technique was implement with data from the past climate record and from stations in the south (e.g. Raleigh, North Carolina) to construct the current climate. Second, fifteen stations across the northeast were selected and grouped so as to represent five different geographical regions. Statistical models based on these groups were used to downscale rainfall from the United Kingdom Meteorological Office Hadley Centre Climate Model version 3 (HADCM3) using SDSM.

Historical observations from the Northeastern United States were then compared with downscaled rainfall using Generalized Extreme Value (GEV) distributions. Overall, the downscaled data tended to capture the overall trend, yet the most extreme events were underestimated. However, there was a great deal of variation among the locations.

Wood, A. W., E. P. Maurer, A. Kumar, and D. P. Lettenmaier (2002), Long-range experimental hydrologic forecasting for the eastern United States, J. Geophys. Res., 107(D20), 4429, doi:10.1029/2001JD000659.