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TitleDévelopement d'un modèle hiérarchique Bayésien appliqué aux épaisseurs de cernes
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LicencePlease note the adoption of the Open Government Licence - Canada supersedes any previous licences.
AuthorBoreux, J -J
SourceUtilisation des archives naturelles pour la reconstitution du passé hydro-climatique; by Bégin, C; Nicault, A; Bégin, Y; Geological Survey of Canada, Open File 8768, 2021 p. 108-115, Open Access logo Open Access
PublisherNatural Resources Canada
Documentopen file
Mediaon-line; digital
RelatedThis publication is contained in Utilisation des archives naturelles pour la reconstitution du passé hydro-climatique
File formatpdf
Subjectsenvironmental geology; hydrogeology; Nature and Environment; Science and Technology; climatology; paleoclimatology; hydrologic environment; dendrochronology; paleoenvironment; statistical analyses; modelling; models; computer simulations; sampling methods; Le projet ARCHIVES; Picea Mariana; DENDRO-AR(1); Methodology; Climate change; Boreal ecosystems; Forests; Trees; Biology; Environmental indicators
Illustrationsflow diagrams; models; time series; plots; schematic representations; location maps; sketch maps
ProgramClimate Change Geoscience Extreme Events
Released2021 06 28
AbstractA basic principle of the dendroclimatology is that the annual tree-rings hide information on past climate. From a statistical viewpoint, all tree rings formed the same year belong to a given perimeter associated with a latent random variable - i.e. an unobservable random variable - integrating all forcings having affected trees of the perimeter during the period they formed their respective ring. This annual latent variable is therefore common of all trees of the investigated area. The succession of these latent variables constitutes temporal series characterizing the handling area and the work of the statistician is to mobilize all the available information to extract a chronicle that may receive a climatic interpretation. The clarity of the output signal varies depending on the tree species, regional factors and the statistical methods used. In the case of black spruce (Picea mariana Mill. BSP), very common in Northern Quebec, we developed a hierarchical Bayesian model named DENDRO-AR which essentially provides a chronological of posterior distributions of the latent variables, each synthesizing the influence of the environment on the growth of trees, including the climatic conditions that prevailed during the period of vegetation. Applying this model to a set of adequately distributed sites over a wide area, the mapping of a particular quantile, e.g. the median, authorizes a spatiotemporal analysis of the common signal.

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