Operational snow modeling: Addressing the challenges of an energy balance model for National Weather Service forecasts

Abstract

Prediction of snowmelt has become a critical issue in much of the western United States given the increasing demand for water supply, changing snow cover patterns, and the subsequent requirement of optimal reservoir operation. The increasing importance of hydrologic predictions necessitates that traditional forecasting systems be re-evaluated periodically to assure continued evolution of the operational systems given scientific advancements in hydrology. The National Weather Service (NWS) SNOW17, a conceptually based model used for operational prediction of snowmelt, has been relatively unchanged for decades. In this study, the Snow-Atmosphere-Soil Transfer (SAST) model, which employs the energy balance method, is evaluated against the SNOW17 for the simulation of seasonal snowpack (both accumulation and melt) and basin discharge. We investigate model performance over a 13-year period using data from two basins within the Reynolds Creek Experimental Watershed located in southwestern Idaho. Both models are coupled to the NWS runoff model [SACramento Soil Moisture Accounting model (SACSMA)] to simulate basin streamflow. Results indicate that while in many years simulated snowpack and streamflow are similar between the two modeling systems, the SAST more often overestimates SWE during the spring due to a lack of midwinter melt in the model. The SAST also had more rapid spring melt rates than the SNOW17, leading to larger errors in the timing and amount of discharge on average. In general, the simpler SNOW17 performed consistently well, and in several years, better than, the SAST model. Input requirements and related uncertainties, and to a lesser extent calibration, are likely to

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Effect of meteorological forcing and snow model complexity on hydrological simulations in the Sieber catchment (Harz Mountains, Germany)

“…Simple index approaches outperform energy balance approaches in many cases because the latter are more sensitive to the quality of meteorological input data (Zappa et al, 2003). Hence, "inadequate basin-scale hydrologic observations" (Franz et al, 2008) restrict catchmentscale applications of energy balance approaches. However, some studies prove that the energy balance approaches provide better results than temperature-index models, even at the catchment scale (see, e.g., Kuchment and Gelfan, 1996;Fuka et al, 2012).…”

21st century Wisconsin snow projections based on an operational snow model driven by statistically downscaled climate data

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