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| Titel |
Statistical modelling of the snow depth distribution in open alpine terrain |
| VerfasserIn |
T. Grünewald, J. Stötter  , J. W. Pomeroy, R. Dadic, I. Moreno Baños, J. Marturià, M. Sproß, C. Hopkinson, P. Burlando, M. Lehning |
| 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 ; 17, no. 8 ; Nr. 17, no. 8 (2013-08-01), S.3005-3021 |
| Datensatznummer |
250018950
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| Publikation (Nr.) |
 copernicus.org/hess-17-3005-2013.pdf |
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| Zusammenfassung |
| The spatial distribution of alpine snow covers is characterised by
large variability. Taking this variability into account is important
for many tasks including hydrology, glaciology, ecology or natural
hazards. Statistical modelling is frequently applied to assess the
spatial variability of the snow cover. For this study, we assembled
seven data sets of high-resolution snow-depth measurements from
different mountain regions around the world. All data were obtained
from airborne laser scanning near the time of maximum seasonal snow
accumulation. Topographic parameters were used to model the snow depth
distribution on the catchment-scale by applying multiple linear
regressions. We found that by averaging out the substantial spatial
heterogeneity at the metre scales, i.e. individual drifts and
aggregating snow accumulation at the landscape or hydrological
response unit scale (cell size 400 m), that 30 to 91% of the snow depth
variability can be explained by models that are calibrated to local
conditions at the single study areas. As all sites were sparsely
vegetated, only a few topographic variables were included as
explanatory variables, including elevation, slope, the deviation of
the aspect from north (northing), and a wind sheltering parameter. In
most cases, elevation, slope and northing are very good predictors of
snow distribution. A comparison of the models showed that importance
of parameters and their coefficients differed among the catchments. A
"global" model, combining all the data from all areas investigated,
could only explain 23% of the variability. It appears that local
statistical models cannot be transferred to different
regions. However, models developed on one peak snow season are good predictors
for other peak snow seasons. |
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