EGU23-10634, updated on 26 Feb 2023
EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Development of an operational snow energy balance model informed by numerical weather prediction and remote sensing for the Western United States

McKenzie Skiles1, Joachim Meyer1, Dillon Ragar1, Patrick Kormos2, and Andrew Hedrick3
McKenzie Skiles et al.
  • 1University of Utah, Salt Lake City, UT, United States of America
  • 2Colorado River Basin Forecast Center, NOAA National Weather Service, Salt Lake City, UT, USA
  • 3Northwest Watershed Research Center, USDA Agricultural Research Service, Boise, ID, USA

The Colorado River, which supplies water to the Western United States (WUS) and Mexico, is fed primarily from snow melting out of the Rocky Mountains. Currently, snowmelt contribution to streamflow is forecast using a calibrated temperature index model (SNOW-17). This approach is simple, and computationally efficient, but loses efficacy when snow conditions are outside the calibration period as temperature index models do not represent all of the physical processes that control accumulation and melt rates. For example, in the southern headwaters of the Colorado River forecasting errors have been related to surface darkening and accelerated melt following episodic dust on snow events. Here, we present an ongoing project to develop and mature a spatially distributed snow energy balance model, informed with numerical weather prediction (NWP) and remote sensing, to support operational decision making. This effort is a collaboration between the University of Utah's Snow Hydrology Research to Operations (Snow HydRO) Laboratory, the USDA-ARS Northwest Watershed Research Center (NWRC), and the Colorado Basin River Forecast Center (CBRFC). The model, iSnobal, is forced with the High Resolution Rapid Refresh (HRRR) NWP and is assessed against in situ observations and snow depth maps from the Airborne Snow Observatory in representative headwater basins. Initial testing of the HRRR-iSnobal combination showed that it can simulate snow accumulation, in terms of both patterns and magnitude, but that snowmelt rates were too slow. This was attributed to inaccurate radiation balance, specifically shortwave radiation due to the traditional treatment of net shortwave, including a 'time since snowfall' albedo decay curve. To account for spatial and temporal variability in snow albedo, daily observations from the spatially and temporally complete MODIS fractional snow products (MODSCAG+MODDRFS) were incorporated to update net solar radiation inputs. The updates were tested in different ways including direct albedo updates, direct decay curve component updates, and basin specific calibration decay curves. Although all remote sensing based update approaches improved snowmelt timing, direct updates had the greatest improvement in years with more intense snow darkening. This presentation will include a summary of current results, updates on incorporation into operational forecasting, and highlight plans for future developments.

How to cite: Skiles, M., Meyer, J., Ragar, D., Kormos, P., and Hedrick, A.: Development of an operational snow energy balance model informed by numerical weather prediction and remote sensing for the Western United States, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-10634,, 2023.