International Snow Science Workshop Proceedings - Montana State University Library

Item: Bivariate spatial modeling of snow depth and snow water equivalent extremes in Austria

• • Title: Bivariate spatial modeling of snow depth and snow water equivalent extremes in Austria

    Proceedings: International Snow Science Workshop Proceedings 2018, Innsbruck, Austria

    Authors:

    • Harald Schellander  [ Zentralanstalt f ür Meteorologie und Geodynamik, Innsbruck, Austria ]
    • Tobias Hell  [ Department of Mathematics, University of Innsbruck, Austria ]

    Date: 2018-10-07

    Abstract: In many applications like hydrological studies focusing on potential runoff, or climate studies investigating water availability, a spatial representation of snow depth (HS) and snow water equivalent (SWE) and their extremes is of great interest. In Austria, although many locations with long-term HS measurements exist, SWE observations are very rare and mostly short. To provide a spatial extreme value model for SWE in Austria, firstly SWE is modeled stationwise from long-term HS observations, employing a newly developed snow layer model, which derives SWE solely from daily HS, without any other meteorological input. Secondly, with a model selection procedure suitable covariates are selected, for modeling the margins of the GEV distribution. Among mean SWE and topographical parameters, a hidden property of the snowpack, namely the time difference between the occurrence dates of the maximum SWE and HS within a winter season is used. Then, different bivariate max-stable processes (H¨usler-Reiss, Extremal-Gaussian, Extremal-t) are fitted to Austrian HS and modeled SWE data. As expected, the bivariate Extremal-t max-stable process remains as the most suitable model, allowing for the estimation of conditional return levels and the use in risk analysis due to its spatial extremal dependence structure. First validation results consolidate the bivariate approach against a smooth model and a univariate Extremal-t max-stable model.

    Language of Article: English

    Presenters:

    Keywords: Bivariate model, spatial extremes, snow depth, snow water equivalent, conditional return level, snow load, max-stable process.

    Page Number(s): 443-446

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