Process-based snowmelt modeling: does it require more input data than temperature-index modeling?

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 conterminous US using energy 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 R 2>0.9. We duplicated the simulations using a simple temperature-index model best fitted to the data and results were poorer, R 2<0.8. At one site we applied the process-based model without substantial parameter estimation, and there were no significant ( α=0.05) differences between these results and those obtained using temperature-estimated parameters, despite relatively poorly predicted specific energy budget components ( R 2<0.8). These results encourage the use of mechanistic 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.