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Fractional snow-covered area (SCA) is a key parameter in large-scalehydrological, meteorological and regional climate models. Since SCA affectsalbedos and surface energy balance fluxes, it is especially of interest overmountainous terrain where generally a reduced SCA is observed in large gridcells. Temporal and spatial snow distributions are, however, difficult tomeasure over complex topography. We therefore present a parameterization ofSCA based on a new subgrid parameterization for the standard deviation ofsnow depth over complex topography. Highly resolved snow depth data at the peakof winter were used from two distinct climatic regions, in easternSwitzerland and in the Spanish Pyrenees. Topographic scaling parameters arederived assuming Gaussian slope characteristics. We use computationally cheapterrain parameters, namely, the correlation length of subgrid topographicfeatures and the mean squared slope. A scale dependent analysis was performedby randomly aggregating the alpine catchments in domain sizes ranging from 50 m to 3 km. For the larger domain sizes, snow depth was predominantly normallydistributed. Trends between terrain parameters and standard deviation of snowdepth were similar for both climatic regions, allowing one to parameterize thestandard deviation of snow depth based on terrain parameters. To make theparameterization widely applicable, we introduced the mean snow depth as aclimate indicator. Assuming a normal snow distribution and spatiallyhomogeneous melt, snow-cover depletion (SCD) curves were derived for a broad rangeof coefficients of variations. The most accurate closed form fit resembled anexisting fractional SCA parameterization. By including the subgrid parameterization forthe standard deviation of snow depth, we extended the fractional SCA parameterizationfor topographic influences. For all domain sizes we obtained errors lowerthan 10% between measured and parameterized SCA

Fractional snow-covered area parameterization over complex topography