A conceptual, distributed snow redistribution model

摘要

When applying conceptual hydrological models using a temperature indexapproach for snowmelt to high alpine areas often accumulation of snow duringseveral years can be observed. Some of the reasons why these "snow towers"do not exist in nature are vertical and lateral transport processes. Whilesnow transport models have been developed using grid cell sizes of tens tohundreds of square metres and have been applied in several catchments, nomodel exists using coarser cell sizes of 1 km2, which is acommon resolution for meso- and large-scale hydrologic modelling (hundreds tothousands of square kilometres). In this paper we present an approach thatuses only gravity and snow density as a proxy for the age of the snow coverand land-use information to redistribute snow in alpine basins. The resultsare based on the hydrological modelling of the Austrian Inn Basin in Tyrol,Austria, more specifically the ?tztaler Ache catchment, but the findingshold for other tributaries of the river Inn. This transport model isimplemented in the distributed rainfall–runoff model COSERO (Continuous Semi-distributed Runoff). The results ofboth model concepts with and without consideration of lateral snowredistribution are compared against observed discharge and snow-coveredareas derived from MODIS satellite images. By means of the snowredistribution concept, snow accumulation over several years can be preventedand the snow depletion curve compared with MODIS (Moderate Resolution Imaging Spectroradiometer) data could be improved,too. In a 7-year period the standard model would lead to snowaccumulation of approximately 2900 mm SWE (snow water equivalent) in high elevated regions whereasthe updated version of the model does not show accumulation and does alsopredict discharge with more accuracy leading to a Kling–Gupta efficiency of0.93 instead of 0.9. A further improvement can be shown in the comparison ofMODIS snow cover data and the calculated depletion curve, where theredistribution model increased the efficiency (R2) from 0.70 to 0.78(calibration) and from 0.66 to 0.74 (validation).