Wednesday, 13 January 2016
New Orleans Ernest N. Morial Convention Center
For most of the western United States, a majority of the available water resources is supplied by melting snowpack in higher elevations. Being able to capture the true state of the snowpack is crucial for spring time water resources planning and decision making. In this study, the NCAR community WRF-Hydro modeling system (Gochis et al., 2013) is ran over the Upper Rio Grande river basin in Colorado. The model was ran at 1km resolution with forcing from the North American Land Data Assimilation System (NLDAS) (Xia et al., 2012). In a simultaneous model run precipitation measured by a (insert radar name) was provided by the National Severe Storms Laboratory (NSSL). This precipitation was used in place of the NLDAS precipitation where available. In the simultaneous model run, additional forcing variables were provided by the National Water Center. These forcings are used operationally as input to the Snow Data Assimilation System (SNODAS). SNODAS gridded snow fields were used for evaluation against snow in WRF-Hydro as SNODAS uses the best available snow measurements for assimilation and analysis. Comparisons against in-situ SNOTEL sites and independent hydro-met stations were made to evaluate various hydrological forcing variables. As another independent observation, snow observations from the NASA Airborne Snow Observatory (Mattmann et al,, 2014) were used to evaluate the operational SNODAS fields and gridded snow from WRF-Hydro. Hydrological responses in the different model configurations were further evaluated using streamflow observations where available. The goal of this study is to diagnose the snow component of WRF-Hydro for improvement in operational water resources forecasting and decision making support. As the WRF-Hydro modeling system becomes operational over the continental US over the next year, identifying weaknesses and strengths in the model's ability to forecast snowpack properties will be crucial for the western US.
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