Approximating input data to a snowmelt model using Weather Research and Forecasting model outputs in lieu of meteorological measurements

Authors: Havens, Scott; Marks, Daniel; FITZGERALD, KATELYN - Boise State University; MASARIK, MATT - Boise State University; FLORES, ALEJANDRO - Boise State University; KORMOS, PATRICK - National Oceanic & Atmospheric Administration (NOAA); Hedrick, Andrew

Submitted to: Journal of Hydrometeorology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/26/2019
Publication Date: 5/8/2019
Citation: Havens, S.C., Marks, D.G., Fitzgerald, K., Masarik, M., Flores, A., Kormos, P., Hedrick, A. 2019. Approximating input data to a snowmelt model using Weather Research and Forecasting model outputs in lieu of meteorological measurements. Journal of Hydrometeorology. 20(5):847-862. https://doi.org/10.1175/JHM-D-18-0146.1.
DOI: https://doi.org/10.1175/JHM-D-18-0146.1

Interpretive Summary: This paper presents one of the first evaluations of using output from the Weather Research and Forecasting (WRF) regional atmospheric model to force the iSnobal energy balance snow model in lieu of meteorological station measurements over a large basin – the 7000 km2 Boise River basin. We present the methods used to downscale the 1 km2 WRF output to the 100m raster used for the snow model, and compare those results to a simulation using only station data. Results indicate that carefully downscaled WRF output adequately represents station data forcing, but has a strong cold bias. Once bias corrected, the WRF-forced snow model produces reliable results. This work indicates that regional atmospheric data can be effectively used to define the distribution of weather forcing data, to force snow and hydrologic models, and should provide reliable forecasts. This is particularly important in the western US where station data are very sparse.

Technical Abstract: Forecasting the timing and magnitude of snowmelt and runoff from mountain basins is critical to managing mountain water resources. Warming temperatures are increasing the rain snow transition elevation and are limiting the forecasting skill of commonly applied statistical methods of relating measured snow water equivalent to streamflow. While physically based methods are available, they require forcing data that accurately represents the spatial and temporal distribution of meteorological variables in complex terrain. Across many mountainous areas, measurements of precipitation and other hydro meteorological variables are limited to a few reference stations and are not adequate to resolve the complex interactions between topography and the atmospheric flow field. In this paper, we evaluate the Weather Research and Forecasting (WRF) model to approximate the inputs required to a physics based snow model, iSnobal, instead of meteorological measurements for the Boise River Basin (BRB) in Idaho, USA. An iSnobal simulation using station data from 41 locations in and around the BRB resulted in an average root mean square error (RMSE) of 5.5 mm compared with SNOTEL measurements. Applying WRF forcings alone was associated with an RMSE of 10.5 mm, while including a simple bias correction to the WRF outputs of temperature and precipitation reduced the RMSE to 6.5 mm. The results highlight the use of WRF outputs for snowmelt modeling, as all required input variables are spatiotemporally complete. This will have important benefits in areas with sparse measurement networks and will aid snowmelt and runoff forecasting in mountainous basins.