Spatial modeling of extreme snow depth

By Juliette Blanchet, A. C. Davison. 2011

181 citations recorded by the source corpus.

The spatial modeling of extreme snow is important for adequate risk management in Alpine and high altitude countries. A natural approach to such modeling is through the theory of max-stable processes, an infinite-dimensional extension of multivariate extreme value theory. In this paper we describe the application of such processes in modeling the spatial dependence of extreme snow depth in Switzerland, based on data for the winters 1966--2008 at 101 stations. The models we propose rely on a climate transformation that allows us to account for the presence of climate regions and for directional effects, resulting from synoptic weather patterns. Estimation is performed through pairwise likelihood inference and the models are compared using penalized likelihood criteria. The max-stable models provide a much better fit to the joint behavior of the extremes than do independence or full dependence models.

Subfields: Finance, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/226b6fdb428b86460a7ce756a2fc0c64e9a97414

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Citations in the corpus

Non-Stationary Dependence Structures for Spatial Extremes (2014, background): “In an analysis of extreme snow depths, Blanchet and Davison (2011) proposed splitting the region of study into distinct homogeneous climatic zones to which stationary models were fitted separately, and where anisotropy was dealt with simple geometric deformations of the space.”
A Bayesian hierarchical approach to regional frequency analysis (2011, background): “[Blanchet et al., 2009; Blanchet and Lehning, 2010; Blanchet and Davison, 2011] addresses this issue, and proposes practical solutions to derive meaningful distances.”
Generalized Additive Models for Exceedances of High Thresholds With an Application to Return Level Estimation for U.S. Wind Gusts (2019, methodology): “These models often rely on common margins, such as unit Frechét, which may be obtained by probability integral transformation; see Blanchet and Davison (2011), Huser and Davison (2014) and Sang and Gelfand (2010) for examples. Robust marginal models make such margins simpler and more reliable to obt”
Random Fields Simplified: Preserving Marginal Distributions, Correlations, and Intermittency, With Applications From Rainfall to Humidity (2020, background): “…on reproducing extremes, extreme value theory also provides other options such as max‐stable processes and corresponding random fields (e.g., Blanchet & Davison, 2011; Cooley et al., 2006; Davison et al., 2012; Davison & Gholamrezaee, 2012; Robert, 2013; Martin Schlather, 2002; Tyralis &…”
Estimation of Hüsler–Reiss distributions and Brown–Resnick processes (2012, methodology): “We remark that the parameters of the generalized Pareto distributions at different locations can be modelled by a linear model with geographical co-ordinates as explanatory variables (see Blanchet and Davison (2011)). This is especially important when the purpose of the statistical analysis is predi”
Geostatistics of Dependent and Asymptotically Independent Extremes (2013, methodology): “Buishand et al. (2008) take a semiparametric approach to max-stable modeling for rainfall data in a homogeneous region of the Netherlands.”
An M‐estimator of spatial tail dependence (2014, background): “xima of stationary and isotropic Gaussian random elds (Kabluchko et al., 2009); here ˆ>0 and 0 <2. Since isotropy may not be a reasonable assumption for 9 many spatial applications, we follow Blanchet and Davison (2011) and Engelke et al. (2014) and introduce a transformation matrix V dened by”
Space–time modelling of extreme events (2012, background): “Much has been written on pseudolikelihood, quasi-likelihood or composite likelihood; see Varin (2008) and Varin et al. (2011) for general overviews, and Padoan et al. (2010), Blanchet and Davison (2011), Davison et al. (2012) and Davison and Gholamrezaee (2012) for applications to spatial extremes.”
Statistical Modeling of Spatial Extremes (2012, background): “Padoan, Ribatet and Sisson (2010), Blanchet and Davison (2011) and Davison and Gholamrezaee (2012) discuss its application in the context of extremal inference, and its use to fit spatial extremal models based on (21) and (22) has been implemented in the R libraries SpatialExtremes and CompRandFld.”
Mapping extreme snowfalls in the French Alps using max‐stable processes (2013, background): “This again shows the need of using MSP enabling total asymptotic independence for modeling the extreme snowfalls, while extreme snow depth at ground level (much smoother than snowfalls) can be modeled with an asymptotic dependence 2 at large distance [Blanchet and Davison, 2011].”
Threshold modeling of extreme spatial rainfall (2013, methodology): “…models have been proposed for the process Wi xð Þ appearing in equation (6) [see Schlather, 2002; de Haan and Pereira, 2006; Kabluchko et al., 2009; Blanchet and Davison, 2011; Davison et al., 2012; Davison and Gholamrezaee, 2012; Wadsworth and Tawn, 2012; Smith, unpublished manuscript, 1990].”
Statistical Modelling of Spatial Extremes (2012, background): “Padoan et al. (2010), Blanchet and Davison (2011) and Davison and Gholamrezaee (2012) discuss its application in the context of extremal inference, and its use to fit spatial extremal models based on (21) and (22) has been implemented in the R libraries SpatialExtremes and CompRandFld.”
Extremes on river networks (2015, background): “Extremal meteorological data often exhibit anisotropies that can be captured by including a rotation and dilation matrix [Blanchet and Davison (2011), Engelke et al.”