Approximating Input Data to a Snowmelt Model Using Weather Research and Forecasting Model Outputs in Lieu of Meteorological Measurements

Scott Havens, Danny Marks, Katelyn FitzGerald, Matt Masarik, Alejandro N. Flores, Patrick Kormos, Andrew Hedrick

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

Forecasting the timing and magnitude of snowmelt and runoff is critical to managing mountain water resources. Warming temperatures are increasing the rain–snow transition elevation and are limiting the forecasting skill of statistical models relating historical snow water equivalent to streamflow. While physically based methods are available, they require accurate estimations of the spatial and temporal distribution of meteorological variables in complex terrain. Across many mountainous areas, measurements of precipitation and other meteorological variables are limited to a few reference stations and are not adequate to resolve the complex interactions between topography and atmospheric flow. In this paper, we evaluate the ability of the Weather Research and Forecasting (WRF) Model to approximate the inputs required for a physics-based snow model, iSnobal, instead of using meteorological measurements, for the Boise River Basin (BRB) in Idaho, United States. An iSnobal simulation using station data from 40 locations in and around the BRB resulted in an average root-mean-square error (RMSE) of 4.5 mm compared with 12 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 utility of using WRF outputs as input to snowmelt models, 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.

Original languageAmerican English
Pages (from-to)847-862
Number of pages16
JournalJournal of Hydrometeorology
Volume20
Issue number5
DOIs
StatePublished - 1 May 2019

Keywords

  • Coupled models
  • Forcing
  • Hydrometeorology
  • Snow cover
  • Snowpack
  • Watersheds

EGS Disciplines

  • Earth Sciences
  • Geophysics and Seismology

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