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Bayesian calibration of a carbon balance model PREBAS using data from permanent growth experiments and national forest inventory / Francesco Minunno in Forest ecology and management, vol 440 (15 May 2019)  

Public [article]  inForest ecology and management > vol 440 (15 May 2019) . - pp 208-257 Titre : Bayesian calibration of a carbon balance model PREBAS using data from permanent growth experiments and national forest inventory Type de document : Article/Communication Auteurs : Francesco Minunno, Auteur ; Mikko Peltoniemi, Auteur ; Sanna Härkönen, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 208-257 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] assimilation des données [Termes IGN] Betula pendula [Termes IGN] bilan du carbone [Termes IGN] bois sur pied [Termes IGN] changement climatique [Termes IGN] croissance des arbres [Termes IGN] diamètre à hauteur de poitrine [Termes IGN] estimation bayesienne [Termes IGN] étalonnage de modèle [Termes IGN] Finlande [Termes IGN] gestion forestière [Termes IGN] hauteur à la base du houppier [Termes IGN] hauteur des arbres [Termes IGN] inventaire forestier étranger (données) [Termes IGN] modèle de croissance végétale [Termes IGN] modèle de simulation [Termes IGN] modélisation de la forêt [Termes IGN] Picea abies [Termes IGN] Pinus sylvestris [Vedettes matières IGN] Inventaire forestier Résumé : (auteur) Policy-relevant forest models must be environment and management sensitive and provide unbiased estimates of predicted variables over their intended areas of application. While empirical models derive their structure and parameters from representative data sets, process-based model (PBM) parameters should be evaluated in ranges that have a biological meaning independently of output data. At the same time PBMs should be calibrated against observations in order to obtain unbiased estimates and an understanding of their predictive capability. By means of model data assimilation, we Bayesian calibrated a forest model (PREBAS) using an extensive dataset that covered a wide range of climatic conditions, species composition and management practices. PREBAS was calibrated for three species in Finland: Scots pine (Pinus sylvestris L.), Norway spruce (Picea abies [L.] H. Karst.) and Silver birch (Betula pendula L.). Data assimilation was strongly effective in reducing the uncertainty of PREBAS parameters and predictions. A country-generic calibration showed robust performances in predicting forest variables and the results were consistent with yield tables and national forest statistics. The posterior predictive uncertainty of the model was mainly influenced by the uncertainty of the structural and measurement error. Numéro de notice : A2019-486 Affiliation des auteurs : non IGN Thématique : FORET/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.foreco.2019.02.041 Date de publication en ligne : 20/03/2019 En ligne : https://doi.org/10.1016/j.foreco.2019.02.041 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93666 [article]