ENTERING THE ERA OF DISTRIBUTED SNOW MODELS

Traditionally, snowmelt modelling has been governed by the operational need for runoff forecasts. Parsimony in terms of model complexity and data requirements was a major concern. More recently, the increased i mportance of analyzing environmental problems and extreme conditions h as motivated the development of distributed snow models. Unfortunately , the use of this type of models is limited by a number of factors inc luding a) the extreme heterogeneity of the hydrologic environment, b) the mismatch of stales between observed variables and model state vari ables, c) the large number of model parameters, and d) the observabili ty/testability problem. This paper discusses the implications of these constraints on the use of site and catchment scale concepts, regional isation techniques, and calibration methods. In particular, the point is made that in many cases model parameters are poorly defined or not unique when being optimized on the basis of runoff data. Snow cover de pletion patterns are shown to be vastly superior to runoff data for di scriminating between alternative model assumptions. The patterns are c apable of addressing individual model components representing snow dep osition and albedo while the respective parameters are highly intercor related in terms of catchment runoff. The paper concludes that site sc ale models of snow cover processes are fairly advanced but much is lef t to be done at the catchment scale. Specifically, more emphasis needs to be directed towards measuring and representing spatial variability in catchments as well as on spatially distributed model evaluation.