Abstract
Abstract. The snow cover spatial variability in mountainous terrain changes considerably over the course of a snow season. In this context, fractional snow-covered area (fSCA) is an essential model parameter characterizing how much ground surface in a grid cell is currently covered by snow. We present a seasonal fSCA algorithm using a recent scale-independent fSCA parameterization. For the seasonal implementation, we track snow depth (HS) and snow water equivalent (SWE) and account for several alternating accumulation–ablation phases. Besides tracking HS and SWE, the seasonal fSCA algorithm only requires subgrid terrain parameters from a fine-scale summer digital elevation model. We implemented the new algorithm in a multilayer energy balance snow cover model. To evaluate the spatiotemporal changes in modeled fSCA, we compiled three independent fSCA data sets derived from airborne-acquired fine-scale HS data and from satellite and terrestrial imagery. Overall, modeled daily 1 km fSCA values had normalized root mean square errors of 7 %, 12 % and 21 % for the three data sets, and some seasonal trends were identified. Comparing our algorithm performances to the performances of the CLM5.0 fSCA algorithm implemented in the multilayer snow cover model demonstrated that our full seasonal fSCA algorithm better represented seasonal trends. Overall, the results suggest that our seasonal fSCA algorithm can be applied in other geographic regions by any snow model application.
Highlights
In mountainous terrain, the large spatial variability in the snow cover is driven by the interaction of meteorological variables with the underlying topography
We present the evaluation of our seasonal fractional snow-covered area (fSCA) algorithm in three sections: evaluation with fSCA derived from finescale HS maps near Davos, evaluation with fSCA from timelapse photography in Davos Dorf and evaluation with fSCA from Sentinel-2 snow products over Switzerland
We further present some additional comparisons with Sentinel-2 snow products in the first two sections when Sentinel-2 data were available in the Davos area
Summary
The large spatial variability in the snow cover is driven by the interaction of meteorological variables with the underlying topography. N. Helbig et al.: A seasonal algorithm of the snow-covered area fraction for mountainous terrain models provide spatial snow depth distributions that could be used to derive coarse-scale fSCA, applying such models to larger regions is generally not feasible. Helbig et al.: A seasonal algorithm of the snow-covered area fraction for mountainous terrain models provide spatial snow depth distributions that could be used to derive coarse-scale fSCA, applying such models to larger regions is generally not feasible This is in part due to computational cost, a lack of detailed input data and limitations in model structure or parameters. Modeled fSCA from the CLM5.0 fSCA algorithm was assessed with the measured fSCA data sets and the performances compared to those of our seasonal fSCA algorithm
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