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Modeling and propagating inventory-based sampling uncertainty in the large-scale forest demographic model “MARGOT” / Timothée Audinot in Natural Resource Modelling, vol 35 n° 3 (August 2022)  

Public [article]  inNatural Resource Modelling > vol 35 n° 3 (August 2022) . - n° e12352 Titre : Modeling and propagating inventory-based sampling uncertainty in the large-scale forest demographic model “MARGOT” Type de document : Article/Communication Auteurs : Timothée Audinot , Auteur ; Holger Wernsdörfer, Auteur ; Gilles Le Moguédec, Auteur ; Jean-Daniel Bontemps , Auteur Année de publication : 2022 Projets : ModForTrans / Bontemps, Jean-Daniel, ARBRE / AgroParisTech (2007 -) Article en page(s) : n° e12352 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] incertitude des données [Termes IGN] inventaire forestier national (données France) [Termes IGN] modélisation de la forêt [Termes IGN] propagation d'incertitude [Vedettes matières IGN] Inventaire forestier Résumé : (auteur) Models based on national forest inventory (NFI) data intend to project forests under management and policy scenarios. This study aimed at quantifying the influence of NFI sampling uncertainty on parameters and simulations of the demographic model MARGOT. Parameter variance–covariance structure was estimated from bootstrap sampling of NFI field plots. Parameter variances and distributions were further modeled to serve as a plug-in option to any inventory-based initial condition. Forty-year time series of observed forest growing stock were compared with model simulations to balance model uncertainty and bias. Variance models showed high accuracies. The Gamma distribution best fitted the distributions of transition, mortality and felling rates, while the Gaussian distribution best fitted tree recruitment fluxes. Simulation uncertainty amounted to 12% of the model bias at the country scale. Parameter covariance structure increased simulation uncertainty by 5.5% in this 12%. This uncertainty appraisal allows targeting model bias as a modeling priority. Numéro de notice : A2022-576 Affiliation des auteurs : LIF+Ext (2020- ) Autre URL associée : vers HAL Thématique : FORET/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/nrm.12352 Date de publication en ligne : 08/08/2022 En ligne : https://doi.org/10.1111/nrm.12352 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101333 [article]