Satellite observed SCA and gamma distributed snow in the HBV model

Abstract

Areas having a seasonal snowpack experience a risk of melt-floods. The magnitude of such a flood is highly dependent on the available water stored in the snow reservoir. Therefore it is of vital importance to estimate the reservoir as accurate as possible. The proposed method models the snow reservoir dynamically throughout the winter season, only using precipitation and temperature as input. Every snowfall and melting event, as well as the total accumulated snow reservoir, is spatially distributed according to a gamma distribution. The model has the ability to mimic the observed distributional change from highly right-skewed early in the accumulation season, a more normal distribution near the snow maximum, to an increasingly right-skewed as the melt season progress. This behavior is important for the evolution and producing of snow free areas and hence for the discharge dynamics. Also; a model that can predict a realistic development of bare ground is a prerequisite for utilizing satellite derived snow covered area (SCA), thus making it possible to update the snow reservoir for instance due to faulty model input. In incorporating remotely sensed SCA we have to make sure not to introduce faulty estimations that would propagate to a reduction in the accuracy of the discharge predictions. The work will be presented by means of the HBV-model for two test catchments situated in the mountainous parts of South-Norway. SCA-values for both catchments are derived from AVHRR scenes utilizing the Norwegian-Linear-Reflectance-to-snow-cover (NLR) algorithm.