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

Autor: Timothée Audinot, Holger Wernsdörfer, Gilles Le Moguédec, Jean‐Daniel Bontemps
Přispěvatelé: Laboratoire d'Inventaire Forestier (LIF), École nationale des sciences géographiques (ENSG), Institut National de l'Information Géographique et Forestière [IGN] (IGN)-Université Gustave Eiffel-Institut National de l'Information Géographique et Forestière [IGN] (IGN)-Université Gustave Eiffel, SILVA (SILVA), AgroParisTech-Université de Lorraine (UL)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut National de l'Information Géographique et Forestière [IGN] (IGN)-Université Gustave Eiffel, Botanique et Modélisation de l'Architecture des Plantes et des Végétations (UMR AMAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université de Montpellier (UM), Institut national de l'information géographique et forestière.The Laboratory of Forest Inventory and UMR SILVA are supported by a grant overseen by the French National Research Agency (ANR) as part of the « Investissements d'Avenir » program (ANR-11-LABX-0002-01, Lab of Excellence ARBRE)., ANR-11-LABX-0002,ARBRE,Recherches Avancées sur l'Arbre et les Ecosytèmes Forestiers(2011)
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Natural Resource Modeling
Natural Resource Modeling, 2022, 35 (4), pp.e12352. ⟨10.1111/nrm.12352⟩
ISSN: 0890-8575
1939-7445
Popis: International audience; 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.
Databáze: OpenAIRE
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