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Modeling and empirical validation of long-term carbon sequestration in forests (France, 1850-2015).

Julia Le NoëSarah MatejAndreas MagerlManan BhanKarl-Heinz ErbSimone Gingrich
Published in: Global change biology (2020)
The development of appropriate tools to quantify long-term carbon (C) budgets following forest transitions, that is, shifts from deforestation to afforestation, and to identify their drivers are key issues for forging sustainable land-based climate-change mitigation strategies. Here, we develop a new modeling approach, CRAFT (CaRbon Accumulation in ForesTs) based on widely available input data to study the C dynamics in French forests at the regional scale from 1850 to 2015. The model is composed of two interconnected modules which integrate biomass stocks and flows (Module 1) with litter and soil organic C (Module 2) and build upon previously established coupled climate-vegetation models. Our model allows to develop a comprehensive understanding of forest C dynamics by systematically depicting the integrated impact of environmental changes and land use. Model outputs were compared to empirical data of C stocks in forest biomass and soils, available for recent decades from inventories, and to a long-term simulation using a bookkeeping model. The CRAFT model reliably simulates the C dynamics during France's forest transition and reproduces C-fluxes and stocks reported in the forest and soil inventories, in contrast to a widely used bookkeeping model which strictly only depicts C-fluxes due to wood extraction. Model results show that like in several other industrialized countries, a sharp increase in forest biomass and SOC stocks resulted from forest area expansion and, especially after 1960, from tree growth resulting in vegetation thickening (on average 7.8 Mt C/year over the whole period). The difference between the bookkeeping model, 0.3 Mt C/year in 1850 and 21 Mt C/year in 2015, can be attributed to environmental and land management changes. The CRAFT model opens new grounds for better quantifying long-term forest C dynamics and investigating the relative effects of land use, land management, and environmental change.
Keyphrases
  • climate change
  • human health
  • magnetic resonance
  • machine learning
  • deep learning