Florian Hartig Theoretical Ecology & Ecological Data Science

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Accuracy, realism and general applicability of European forest models

Mats Mahnken, Maxime Cailleret, Alessio Collalti, Carlo Trotta, Corrado Biondo, Ettore D'Andrea, Daniela Dalmonech, Gina Marano, Annikki Mäkelä, Francesco Minunno, Mikko Peltoniemi, Volodymyr Trotsiuk, Daniel Nadal-Sala, Santiago Sabaté, Patrick Vallet, Raphaël Aussenac, David R. Cameron, Friedrich J. Bohn, Rüdiger Grote, Andrey L. D. Augustynczik, Rasoul Yousefpour, Nica Huber, Harald Bugmann, Katarina Merganičová, Jan Merganic, Peter Valent, Petra Lasch-Born, Florian Hartig, Iliusi D. Vega del Valle, Jan Volkholz, Martin Gutsch, Giorgio Matteucci, Jan Krejza, Andreas Ibrom, Henning Meesenburg, Thomas Rötzer, Marieke van der Maaten-Theunissen, Ernst van der Maaten, Christopher P. O. Reyer

Global Change Biology, 28(23), 6921–6943 (2022)
Cite this
@article{mahnken2022accuracy,
  author = {Mats Mahnken and Maxime Cailleret and Alessio Collalti and Carlo Trotta and Corrado Biondo and Ettore D'Andrea and Daniela Dalmonech and Gina Marano and Annikki Mäkelä and Francesco Minunno and Mikko Peltoniemi and Volodymyr Trotsiuk and Daniel Nadal-Sala and Santiago Sabaté and Patrick Vallet and Raphaël Aussenac and David R. Cameron and Friedrich J. Bohn and Rüdiger Grote and Andrey L. D. Augustynczik and Rasoul Yousefpour and Nica Huber and Harald Bugmann and Katarina Merganičová and Jan Merganic and Peter Valent and Petra Lasch-Born and Florian Hartig and Iliusi D. Vega del Valle and Jan Volkholz and Martin Gutsch and Giorgio Matteucci and Jan Krejza and Andreas Ibrom and Henning Meesenburg and Thomas Rötzer and Marieke van der Maaten-Theunissen and Ernst van der Maaten and Christopher P. O. Reyer},
  title = {Accuracy, realism and general applicability of European forest models},
  journal = {Global Change Biology},
  volume = {28},
  number = {23},
  pages = {6921–6943},
  year = {2022},
  doi = {10.1111/gcb.16384},
}

DOI: 10.1111/gcb.16384
Cited by 74 (Google Scholar) · 71 (OpenAlex), as of 07 September 2026

View article (DOI)

Abstract

Forest models are instrumental for understanding and projecting the impact of climate change on forests. A considerable number of forest models have been developed in the last decades, but few systematic and comprehensive model comparisons have been performed in Europe that combine an evaluation of modelled carbon and water fluxes and forest structure. We evaluate 13 widely used, state-of-the-art, stand-scale forest models against field measurements of forest structure and eddy-covariance data of carbon and water fluxes over multiple decades across an environmental gradient at nine typical European forest stands, testing the models’ performance in three dimensions: accuracy of local predictions, realism of environmental responses, and general applicability (proportion of European tree species covered). We find that multiple models are available that excel according to our three dimensions of model performance. For the accuracy of local predictions, variables related to forest structure have lower random and systematic errors than annual carbon and water flux variables, and the multi-model ensemble mean provided overall more realistic daily productivity responses to environmental drivers across all sites than any single individual model. The general applicability of the models is high, as almost all models are currently able to cover Europe’s common tree species. We show that forest models complement each other in their response to environmental drivers and that there are several cases in which individual models outperform the model ensemble, providing a point of reference for future model work aimed at predicting climate impacts and supporting climate mitigation and adaptation measures in forests.

What the paper shows and why it matters (AI-generated)

Thirteen widely used European forest models are run against the same decades of field structure measurements and eddy-covariance flux data across nine sites — not just for prediction accuracy, but for whether each model's internal response to environmental drivers is realistic in the first place. No single model wins outright; a multi-model ensemble mean tracks daily productivity more realistically than most individual models do, giving forest modellers and policymakers an actual evidence base for trusting an ensemble over any one model's untested assumptions. It has since become a reference point for later forest-model intercomparison efforts.