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Estimates of Twenty-first Century flood risk in the Pacific Northwest based on regional scale climate model simulations
Salathé Jr., E.P., A.F. Hamlet, C.F. Mass, S.-Y. Lee, M. Stumbaugh, and R. Steed. 2014. Estimates of Twenty-First-Century flood risk in the Pacific Northwest based on Regional Climate Model simulations. J. Hydrometeor 15(5): 1881–1899, doi: http://dx.doi.org/10.1175/JHM-D-13-0137.1
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Results from a regional climate model simulation show substantial increases in future flood risk (2040–69) in many Pacific Northwest
river basins in the early fall. Two primary causes are identified: 1) more extreme and earlier storms and 2) warming temperatures that shift precipitation from snow to rain dominance
over regional terrain. The simulations also show a wide range of uncertainty among different basins stemming from localized storm characteristics.
While previous research using statistical downscaling suggests that many areas in the Pacific Northwest are likely to experience substantial increases in flooding in response to global
climate change, these initial estimates do not adequately represent the effects of changes in heavy precipitation. Unlike statistical downscaling techniques applied to global climate model
scenarios, the regional model provides an explicit, physically based simulation of the seasonality, size, location, and intensity of historical and future extreme storms, including atmospheric
This paper presents climate projections from the ECHAM5/Max Planck Institute Ocean Model (MPI-OM) global climate model dynamically downscaled using the Weather Research and Forecasting (WRF)
Model implemented at 12-km resolution for the period 1970–2069. The resulting daily precipitation and temperature data are bias corrected and used as input to a physically based Variable
Infiltration Capacity (VIC) hydrologic model. From the daily time step simulations of streamflow produced by the hydrologic model, probability distributions are fit to the extreme events extracted
from each water year and flood statistics for various return intervals are estimated.