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 Weather Service; Hedrick, Andrew

Submitted to: Journal of Hydrometeorology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/26/2019
Publication Date: 5/6/2019
Citation: Havens, S., Marks, D., FitzGerald, K., Masarik, M., Flores, A.N., 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: Forecasting the timing and magnitude of snow melt and runoff from mountain basins is critical to managing mountain water resources. Improved stream flow forecasts can be achieved using physically based models, however the required input data may not be adequate from ground based measurement stations. This paper evaluates using output from the Weather Research and Forecasting (WRF) model as input to a physically based snow model in lieu of ground based measurements. Results show accurate simulation of the snow pack using WRF as input for the large Boise River Basin in Idaho. This shows the potential for using atmospheric models as inputs to physically based models in basins with sparse ground based measurements or over large areas like the western US. With these new techniques, water supply forecasts from physically based models could be generated for any basin, providing improved estimates of the timing and magnitude of runoff to be utilized by water management agencies.

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.