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| Titel |
Mapping snow depth return levels: smooth spatial modeling versus station interpolation |
| VerfasserIn |
J. Blanchet, 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 ; 14, no. 12 ; Nr. 14, no. 12 (2010-12-13), S.2527-2544 |
| Datensatznummer |
250012529
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| Publikation (Nr.) |
 copernicus.org/hess-14-2527-2010.pdf |
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| Zusammenfassung |
| For adequate risk management in mountainous countries, hazard maps for
extreme snow events are needed. This requires the computation of spatial
estimates of return levels. In this article we use recent developments in
extreme value theory and compare two main approaches for mapping snow depth
return levels from in situ measurements. The first one is based on the
spatial interpolation of pointwise extremal distributions (the so-called
Generalized Extreme Value distribution, GEV henceforth) computed at station
locations. The second one is new and based on the direct estimation of a
spatially smooth GEV distribution with the joint use of all stations. We
compare and validate the different approaches for modeling annual maximum
snow depth measured at 100 sites in Switzerland during winters 1965–1966 to
2007–2008. The results show a better performance of the smooth GEV
distribution fitting, in particular where the station network is sparser.
Smooth return level maps can be computed from the fitted model without any
further interpolation. Their regional variability can be revealed by removing
the altitudinal dependent covariates in the model. We show how return levels
and their regional variability are linked to the main climatological patterns
of Switzerland. |
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