A climate envelope model for the projection of forest carbon storage on
a global scale
- Diego Bengochea Paz,
- Alba Marquez-Torres,
- João Pompeu,
- Olivier Martin-Ducup,
- Ferdinando Villa,
- Carmen Köhler,
- Stefano Balbi
Olivier Martin-Ducup
Ecologie des Forêts Méditerranéennes
Author ProfileAbstract
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Projecting how climate change will reshape global patterns of forest
carbon storage is paramount to investigate the future of the global
carbon cycle. Advances in machine learning and the proliferation of
remote-sensing missions have stimulated the development of statistical
models for the estimation of carbon storage on a global scale. However,
existing models present two major limitations. First, they are not
suitable for forecasting because they are trained on variables that
cannot be projected. Second, they are based on large numbers of
predictors, which hinders the interpretability and explainability of
their results from a biological and ecological perspective. In this
manuscript, we present a machine learning model for the global mapping
of potential Aboveground Biomass Density that overcomes the above
mentioned limitations. On the one hand, our model is based exclusively
on bioclimatic variables and is therefore suitable for forecasting and
interpretable from a biological perspective. On the other hand, we
calibrated our model with a state-of-the-art predictor selection process
to identify the optimal subset of predictors for the maximization of
model accuracy. We found that a minimal model based solely on four
bioclimatic variables performed the best. Furthermore, our model
reproduces field measurements from experimental forest plots equally
well than a state-of-the-art model trained on $190$ predictors. In
conclusion, we provide a simple and computationally cheap model that can
be coupled with climate projections to investigate how climate change
will reshape carbon storage on a global scale.Submission Checks Completed
Assigned to Editor
Reviewer(s) Assigned
09 Jul 2024Review(s) Completed, Editorial Evaluation Pending
13 Jul 2024Reviewer(s) Assigned
10 Sep 2024Editorial Decision: Revise Major
30 Oct 20241st Revision Received
01 Nov 2024Submission Checks Completed
01 Nov 2024Assigned to Editor
01 Nov 2024Review(s) Completed, Editorial Evaluation Pending
01 Nov 2024Reviewer(s) Assigned