2006 PSL Seminars

Characterization of the comparative skill of conceptual and physically-based snow models for streamflow prediction

Kristie J. Franz

Department of Geological and Atmospheric Sciences Iowa State University

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There has been a significant push in the hydrologic community towards advancing hydrologic prediction technology. Efforts to improve streamflow prediction can be focused in any number of ways, including the introduction of more complex, physically-based models into operational forecasting systems. The National Weather Service (NWS) SNOW17, a conceptually-based snow accumulation and ablation model, has been used relatively unchanged for streamflow prediction for several decades. In a series of steps, the potential for improving the accuracy of streamflow predictions through the use of an energy balance snow model is investigated. The study is structured around the current NWS forecasting system, assuming these are the constraints in which an advanced operational snow model will be required to work in the near term. The Snow-Atmosphere-Soil Transfer (SAST) model, which uses the energy-balance method, is evaluated against the SNOW17 for the simulation of seasonal snowpack and the prediction of ensembles of future snow water equivalent. Both snow models are coupled to the NWS rainfall-runoff model and their relative skills at the watershed scale are described. Analyses are conducted for the Mammoth Mountain snow study site, California and the Reynolds Creek Experimental Watershed, Idaho. Significant overall differences between the skill of the two models for modeling snow accumulation and melt were not found for the sites studied. Results show that improvements to streamflow predictions through the use of an energy balance snow model will be minimal, given the current forecast system and available data. The impact of input uncertainties on model results is also highlighted.

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Note: Special 3:00pm time

Wednesday 11 October, 2006
3:00 PM (Refreshments at 2:50 pm)
PSL-South Conference Room (1D403)

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