Modeling and propagating inventory‐based sampling uncertainty in the large‐scale forest demographic model “MARGOT” - Botanique et bio-informatique de l'architecture des plantes et des végétations Accéder directement au contenu
Article Dans Une Revue Natural Resource Modeling Année : 2022

Modeling and propagating inventory‐based sampling uncertainty in the large‐scale forest demographic model “MARGOT”

Résumé

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.
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Origine : Publication financée par une institution

Dates et versions

hal-03903795 , version 1 (16-12-2022)

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Paternité - Pas d'utilisation commerciale - Pas de modification

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Timothée Audinot, Holger H. Wernsdorfer, Gilles Le Moguédec, Jean-Daniel Bontemps. Modeling and propagating inventory‐based sampling uncertainty in the large‐scale forest demographic model “MARGOT”. Natural Resource Modeling, 2022, 35 (4), pp.e12352. ⟨10.1111/nrm.12352⟩. ⟨hal-03903795⟩
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