Statistical modelling of the snow depth distribution in open alpine terrain

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dc.contributor.authorGrunewald, T.
dc.contributor.authorStotter, J.
dc.contributor.authorPomeroy, J.W.
dc.contributor.authorDadic, R.
dc.contributor.authorBanos, I.M.
dc.contributor.authorMarturia, J.
dc.contributor.authorSpross, M.
dc.contributor.authorHopkinson, Christopher
dc.contributor.authorBurlando, P.
dc.contributor.authorLehnig, M.
dc.date.accessioned2017-07-12T18:07:19Z
dc.date.available2017-07-12T18:07:19Z
dc.date.issued2013
dc.descriptionSherpa Romeo green journal; open accessen_US
dc.description.abstractThe 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.en_US
dc.description.peer-reviewYesen_US
dc.identifier.citationGrunewald, T., Stotter, J., Pomeroy, J.W., Dadic, R., Banos, I.M., Marturia, J., ... Lehning, M. (2013). Statistical modelling of the snow depth distribution in open alpine terrain. Hydrology and Earth Systems Sciences, 17, 3005-3021. doi:10.5194/hess-17-3005-2013en_US
dc.identifier.urihttps://hdl.handle.net/10133/4860
dc.language.isoen_USen_US
dc.publisherCopernicus Publicationsen_US
dc.publisher.departmentDepartment of Geographyen_US
dc.publisher.facultyArts and Scienceen_US
dc.publisher.institutionWSL Institute for Snow and Avalance Research SLFen_US
dc.publisher.institutionEcole Polytechnique Federale de Lausanneen_US
dc.publisher.institutionUniversity of Innsbrucken_US
dc.publisher.institutionUniversity of Saskatchewanen_US
dc.publisher.institutionInstitute of Environmental Engineeringen_US
dc.publisher.institutionVictoria University of Wellingtonen_US
dc.publisher.institutionInstitut Geologic de Catalunyaen_US
dc.publisher.institutionUniversity of Lethbridgeen_US
dc.subjectStatistical modellingen_US
dc.subjectSnow coveren_US
dc.subjectSnow depthen_US
dc.subjectAlpine terrainen_US
dc.subjectMountain regionsen_US
dc.subject.lcshSnow
dc.subject.lcshMountain hydrology
dc.subject.lcshSnow--Austria--Measurement
dc.subject.lcshSnow--Canadian Rockies (B.C. and Alta.)--Measurement
dc.subject.lcshSnow--Spain--Measurement
dc.subject.lcshSnow--Switzerland--Alps, Swiss--Measurement
dc.subject.lcshSnow--Measurement--Climatic factors
dc.subject.lcshSnow--Research
dc.subject.lcshSnow surveys
dc.titleStatistical modelling of the snow depth distribution in open alpine terrainen_US
dc.typeArticleen_US
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