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| Titel |
Fractional snow-covered area parameterization over complex topography |
| VerfasserIn |
N. Helbig, A. van Herwijnen, J. Magnusson, T. Jonas |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1027-5606
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 19, no. 3 ; Nr. 19, no. 3 (2015-03-10), S.1339-1351 |
| Datensatznummer |
250120656
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| Publikation (Nr.) |
 copernicus.org/hess-19-1339-2015.pdf |
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| Zusammenfassung |
| Fractional snow-covered area (SCA) is a key parameter in large-scale
hydrological, meteorological and regional climate models. Since SCA affects
albedos and surface energy balance fluxes, it is especially of interest over
mountainous terrain where generally a reduced SCA is observed in large grid
cells. Temporal and spatial snow distributions are, however, difficult to
measure over complex topography. We therefore present a parameterization of
SCA based on a new subgrid parameterization for the standard deviation of
snow depth over complex topography. Highly resolved snow depth data at the peak
of winter were used from two distinct climatic regions, in eastern
Switzerland and in the Spanish Pyrenees. Topographic scaling parameters are
derived assuming Gaussian slope characteristics. We use computationally cheap
terrain parameters, namely, the correlation length of subgrid topographic
features and the mean squared slope. A scale dependent analysis was performed
by randomly aggregating the alpine catchments in domain sizes ranging from 50 m to 3 km.
For the larger domain sizes, snow depth was predominantly normally
distributed. Trends between terrain parameters and standard deviation of snow
depth were similar for both climatic regions, allowing one to parameterize the
standard deviation of snow depth based on terrain parameters. To make the
parameterization widely applicable, we introduced the mean snow depth as a
climate indicator. Assuming a normal snow distribution and spatially
homogeneous melt, snow-cover depletion (SCD) curves were derived for a broad range
of coefficients of variations. The most accurate closed form fit resembled an
existing fractional SCA parameterization. By including the subgrid parameterization for
the standard deviation of snow depth, we extended the fractional SCA parameterization
for topographic influences. For all domain sizes we obtained errors lower
than 10% between measured and parameterized SCA. |
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