Snow Cover Fraction (SCF) and snow depth obtained using terrestrial photography (2009-2013) in the control area Refugio Poqueira (Sierra Nevada, Spain)

Subgrid variability introduces non-negligible scale effects on the GIS-based representation of snow. This heterogeneity is even more evident in semiarid regions, where the high variability of the climate produces various accumulation melting cycles throughout the year and a large spatial heterogeneity of the snow cover. This variability in a watershed can often be represented by snow depletion curves (DCs). In this study, terrestrial photography (TP) of a cell-sized area (30 x 30 m) was used to define local snow DCs at a Mediterranean site. Snow cover fraction (SCF) and snow depth (h) values obtained with this technique constituted the two datasets used to define DCs. A flexible sigmoid function was selected to parameterize snow behaviour on this subgrid scale. It was then fitted to meet five different snow patterns in the control area: one for the accumulation phase and four for the melting phase in a cycle within the snow season. Each pattern was successfully associated with the snow conditions and previous evolution. The resulting DCs were able to capture certain physical features of the snow, which were used in a decision-tree and included in the point snow model formulated by Herrero et al. (2009). The final performance of this model was tested against field observations recorded over four hydrological years (2009?2013). The calibration and validation of this DC-snow model was found to have a high level of accuracy with global RMSE values of 84.2 mm for the average snow depth and 0.18 m2/m2 for the snow cover fraction in the control area. The use of DCs on the cell scale proposed in this research provided a sound basis for the extension of point snow models to larger areas by means of a gridded distributed calculation.