Dynamical Downscaling of Winter Precipitation Events to Generate Forcing Data for Hydrologic Models

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Monday, 3 February 2014: 2:15 PM
Room C209 (The Georgia World Congress Center )
Katelyn Anne Watson, Boise State University, Boise, ID; and J. P. McNamara, H. P. Marshall, and A. N. Flores

Distributed hydrologic models require physically consistent and spatiotemporally complete forcing datasets of near surface hydrometeorological states and fluxes. Traditionally this information has been derived from station observations interpolated over the domain of interest. However, in mountainous regions these observations are typically sparse and discontinuous and may be poorly representative of the model domain. Dynamical downscaling of reanalysis data presents an additional method by which to produce forcing datasets for hydrologic models. Previous studies focused primarily on verification of model performance with respect to seasonal snowfall. In this study, we evaluate model results for a suite of near surface hydrometeorological variables over a series of winter precipitation events. We use the Weather Research and Forecasting (WRF) model to downscale reanalysis data for selected winter precipitation events to 1km horizontal resolution over a region of southwest Idaho in order to produce a more spatiotemporally complete and internally consistent dataset of hydrometeorological variables to be used as forcing of hydrologic models and other follow-on applications. We then compare these results to station observations from Dry Creek and Reynolds Creek Experimental Watersheds, SNOTEL stations and output from the National Weather Service's National Operational Hydrologic Remote Sensing Center SNOw Data Assimilation System (SNODAS) to evaluate the forcing datasets derived. We explicitly investigate the dependence on computed errors relative to the way that gridded model output is compared to station observations.