Tuesday, 25 January 2011
Washington State Convention Center
Forcing hydrological models with downscaled climate predictions from global climate models (GCMs) is an important step in climate change impact analysis. Many downscaling methods exist to bridge the gap between the coarse-scale GCM resolution and the fine-scale resolutions required by hydrological models. These results can guide resource managers and policymakers to develop adequately adaptable strategies which encompass the uncertainty in future outcomes of water resources. Here we evaluate the performance of a statistical downscaling methodology, the Bias Correction and Spatial Disaggregation (BCSD) method, a dynamical downscaling method utilizing the Weather Research and Forecasting Model (version 3), and a hybrid statistical-dynamical method in forcing GSFLOW, a calibrated coupled surface and groundwater hydrological model in the Incline Creek watershed in western Nevada using forcing data from NCEP/NCAR Reanalysis and the GCM CCSM3 for the period 1998-2007 and comparing results to observed fields, e.g. streamflow. The proposed hybrid methodology is tested under the hypothesis that combining the computational efficiency and bias correction skill of the BCSD method with the ability of the dynamical method to account for nonstationarity and reduce the uncertainty associated with the spatial aggregation and disaggregation due to an increase of resolution from 1.4 x 1.4 deg. in CCSM3 to 12 and 4 km in the WRF simulations. Consequently, this methodology will result in improved future predictions.
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