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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. However, 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. We test the models' performance in three dimensions: accuracy of local predictions (agreement of modelled and observed annual data), realism of environmental responses (agreement of modelled and observed responses of daily gross primary productivity to temperature, radiation and vapour pressure deficit) 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. Moreover, 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. Our framework provides a first step to capturing essential differences between forest models that go beyond the most commonly used accuracy of predictions. Overall, this study provides a point of reference for future model work aimed at predicting climate impacts and supporting climate mitigation and adaptation measures in forests., (© 2022 The Authors. Global Change Biology published by John Wiley & Sons Ltd.)

Light use efficiency (LUE or photosynthetically active radiation use efficiency) in production of young spruce stands aboveground biomass was determined at the study sites Rájec (the Drahanská vrchovina Highland) and Bílý Kříž (the Moravian-Silesian Beskids Mountains) in 2014 and 2015. The LUE value obtained for the investigated spruce stands were in the range of 0.45 - 0.65 g DW MJ-1. The different LUE values were determined for highland and mountain spruce stand. The differences were caused by growth and climatic conditions and by the amount of assimilatory apparatus (LAI). [ABSTRACT FROM AUTHOR]

• We evaluated if satellite data allow monitoring radial stem growth (RSG) phenology. • Accuracy depended on tree species, spectral index (SI), and growth stage. • Reasonable accuracies were generally shown for the onset and mid-stages of RSG. • Low accuracies were shown for the cessation of RSG. • Novel SI are needed that are mechanistically linked to RSG phenology. Radial stem growth is a key ecosystem process resulting in long-term carbon sequestration. Despite recognition of its importance to global carbon cycling, high uncertainties remain regarding how radial growth phenology (e.g., the onset, mid, and cessation of radial growth) will be affected by climate change. In this study, we evaluated to what extent high spatially (3 × 3 m) and temporally (up to daily) resolved satellite imagery from PlanetScope can be used to monitor stem growth phenology. For this, we made use of detailed stem growth phenological observations of six common European tree species measured by automated point dendrometers at 14 distinct sites across Switzerland between 2017 and 2021. These growth phenological observations were then linked through multiple regression modeling with metrics extracted from spectral index time series. Our results show that the remote sensing-based models enable monitoring the onset (root mean squared deviation (RMSD) ranges from 5.96 to 27.04 days) and mid-stages of stem growth (RMSD ranges from 10.20 to 36.34 days) with reasonable accuracy as opposed to the cessation of stem growth that showed low accuracy (RMSD ranges from 16.02 to 153.63 days). The accuracy of the remote sensing-based prediction models and their optimal suite of predictors varied across species. The latter has important implications for the remote sensing of stem growth phenology in mixed forests, suggesting that it is important for satellite sensors to resolve individual tree crowns. Overall, our results suggest the need for novel spectral indices that capture the spectral components of mechanistic linkages between stem growth and canopy properties that go beyond the mere detection of leaf phenology. When employing such spectral indices, remote sensing could make it possible to detect not only shifts in leaf phenology caused by climate change but also those in stem growth on a broad spatial scale. [ABSTRACT FROM AUTHOR]