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Two large-scale forest scenario modelling approaches for reporting CO(2) removal: a comparison for the Romanian forests

BACKGROUND: Forest carbon models are recognized as suitable tools for the reporting and verification of forest carbon stock and stock change, as well as for evaluating the forest management options to enhance the carbon sink provided by sustainable forestry. However, given their increased complexity...

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Detalles Bibliográficos
Autores principales: Blujdea, Viorel N. B., Sikkema, Richard, Dutca, Ioan, Nabuurs, Gert-Jan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8379742/
https://www.ncbi.nlm.nih.gov/pubmed/34417647
http://dx.doi.org/10.1186/s13021-021-00188-1
Descripción
Sumario:BACKGROUND: Forest carbon models are recognized as suitable tools for the reporting and verification of forest carbon stock and stock change, as well as for evaluating the forest management options to enhance the carbon sink provided by sustainable forestry. However, given their increased complexity and data availability, different models may simulate different estimates. Here, we compare carbon estimates for Romanian forests as simulated by two models (CBM and EFISCEN) that are often used for evaluating the mitigation options given the forest-management choices. RESULTS: The models, calibrated and parameterized with identical or harmonized data, derived from two successive national forest inventories, produced similar estimates of carbon accumulation in tree biomass. According to CBM simulations of carbon stocks in Romanian forests, by 2060, the merchantable standing stock volume will reach an average of 377 m(3) ha(−1), while the carbon stock in tree biomass will reach 76.5 tC ha(−1). The EFISCEN simulations produced estimates that are about 5% and 10%, respectively, lower. In addition, 10% stronger biomass sink was simulated by CBM, whereby the difference reduced over time, amounting to only 3% toward 2060. CONCLUSIONS: This model comparison provided valuable insights on both the conceptual and modelling algorithms, as well as how the quality of the input data may affect calibration and projections of the stock and stock change in the living biomass pool. In our judgement, both models performed well, providing internally consistent results. Therefore, we underline the importance of the input data quality and the need for further data sampling and model improvements, while the preference for one model or the other should be based on the availability and suitability of the required data, on preferred output variables and ease of use. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13021-021-00188-1.