Submitted to: Journal of Hydrology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/12/2004
Publication Date: 6/1/2004
Citation: Walter, M.T., McCool, D.K., King, L.G., Molnau, M., Boll, J. 2005. Process-based snowmelt modeling: does it require more input data than temperature-index modeling? Journal of Hydrology. 300(1-4): 65-75.
Interpretive Summary: Modeling snow hydrology for cold regions is problematic for many hydro-environmental models. Temperature-index models are commonly used for snowmelt and are routinely justified on the assumption that process-based models require too many parameterization data. To test this assumption, we compared a physical, process-based modeling approach and a calibrated temperature-index model and used only maximum and minimum daily temperature for both models. For the process-based models we estimated the meteorological parameters that are usually not readily available, like atmospheric vapor density, using previously published relationships. We applied the models to four sites across the northern coterminous U.S. The process-based model consistently gave better estimates of snow cover than the temperature-index model, R2 >0.9 and R2 < 0.8, respectively. At one location, Danville, VT, we had enough measured meteorological data to apply the process-based model without having to estimate any parameters and found no significant (' = 0.05) differences between these results and those in which we used the process-based model with only maximum and minimum temperature. These results support the use of process-based rather than calibrated temperature-index snowmelt modeling approaches in hydrological models, especially distributed models where snowmelt differences across the landscape are important.
Technical Abstract: Modeling snow hydrology for cold regions remains a problematic aspect of many hydro-environmental models. Temperature-index methods are commonly used and are routinely justified under the auspices that process-based models require too many input data. To test this claim, we used a physical, process-based model to simulate snowmelt at four locations across the coterminous U.S. using energy-budget components estimated from measured daily maximum and minimum temperature, i.e. using only the same data required for temperature-index models. The results showed good agreement between observed and predicted snow water equivalents, average R2 > 0.9. We duplicated the simulations using a simple temperature-index model best fitted to the data and results were poorer, R2 < 0.8. At one site we applied the process-based model without substantial parameter estimation, and there were no significant (' = 0.05) differences between these results and those obtained using temperature-estimated parameters, despite relatively poorly predicted specific energy budget components (R2 < 0.8). These results encourage the use of process-based snowmelt modeling approaches in hydrological models, especially in distributed hydrological models for which landscape snow distribution may be controlled by spatially distributed components of the environmental energy budget.