ALBERT

Description: Forests and wood products play a major role in climate change mitigation strategies and the transition from a fossil-based economy to a circular bioeconomy. Accurate estimates of future forest productivity are crucial to predict the carbon sequestration and wood provision potential of forests. Since long, forest managers have used empirical yield tables as a cost-effective and reliable way to predict forest growth. However, recent climate change-induced growth shifts raised doubts about the long-term validity of these yield tables. In this study, we propose a methodology to improve available yield tables of 11 tree species in the Netherlands and Flanders, Belgium. The methodology uses scaling functions derived from climate-sensitive process-based modelling (PBM) that reflect state-of-the-art projections of future growth trends. Combining PBM and stand information from the empirical yield tables for the region of Flanders, we found that for the period 1987–2016 stand productivity has on average increased by 13% compared to 1961–1990. Furthermore, simulations indicate that this positive growth trend is most likely to persist in the coming decades, for all considered species, climate or site conditions. Nonetheless, results showed that local site variability is equally important to consider as the in- or exclusion of the CO2 fertilization effect or different climate projections, when assessing the magnitude of forests' response to climate change. Our projections suggest that incorporating these climate change-related productivity changes lead to a 7% increase in standing stock and a 22% increase in sustainably potentially harvestable woody biomass by 2050. The proposed methodology and resulting estimates of climate-sensitive projections of future woody biomass stocks will facilitate the further incorporation of forests and their products in global and regional strategies for the transition to a climate-smart circular bioeconomy.

Description: 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 vapor 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.

Description: The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) provides a framework for the collation of a set of consistent, multi-sector, multi-scale climate-impact simulations, based on scientifically and politically relevant historical and future scenarios. This framework serves as a basis for robust projections of climate impacts, as well as facilitating model evaluation and improvement, allowing for advanced estimates of the biophysical and socio-economic impacts of climate change at different levels of global warming. It also provides a unique opportunity to consider interactions between climate impacts across sectors. ISIMIP2a is the first simulation round of the second phase of ISIMIP, focusing on historical simulations of climate impacts on agriculture, fisheries, permafrost, biomes, regional and global water and forests. This will serve as a basis for model evaluation and improvement, allowing for improved estimates of the biophysical and socio-economic impacts of climate change at different levels of global warming. This dataset contains ISIMIP2a simulation data from thirteen local forest models: 3D-CMCC FEM (3D-CMCC-FEM LUE, Collalti et al. 2014, 2016), 3D-CMCC-CNR-BGC (3D-CMCC-FEM BGC, Collalti et al. 2019, Collalti et al. 2020), 3PG (Landsberg et al. 2002), 3PGN-BW (Landsberg et al. 1997, Xenakis et al. 2008), 4C (Reyer et al. 2013, Lasch-Born et al. 2020), BASFOR (van Oijen et al. 2014, Cameron et al. 2013), ForClim (Bugmann et al. 2006), FORMIND (Bohn et al. 2014), GOTILWA+ (Nadal-Sala et al. 2017, Keenan et al. 2010, Gracia et al. 2011), Landscape-DNDC (Haas et al. 2012, Grote et al. 2008, 2010, 2011, Holst et al. 2009, Lindauer et al. 2014), PREBAS (Minunno et al. 2016, Valentine et al. 2005), SALEM (Aussenac et al. 2021) and SIBYLA (Fabrika and Ďurský 2006, Hlásny et al. 2014).

Description: Ecology and forestry sciences are using an increasing amount of data to address a wide variety of technical and research questions at the local, continental and global scales. However, one type of data remains rare: fine-grain descriptions of large landscapes. Yet, this type of data could help address the scaling issues in ecology and could prove useful for testing forest management strategies and accurately predicting the dynamics of ecosystem services. Here we present three datasets describing three large European landscapes in France, Poland and Slovenia down to the tree level. Tree diameter, height and species data were generated combining field data, vegetation maps and airborne laser scanning (ALS) data. Together, these landscapes cover more than 100~000~ha and consist of more than 42 million trees of 51 different species. Alongside the data, we provide here a simple method to produce high-resolution descriptions of large landscapes using increasingly available data: inventory and ALS data. We evaluated the overall reliability of our workflow by comparing the stands dominant heights measured by ALS to those calculated from the trees we generated. Overall, the landscapes we generated are in good agreement with the landscapes they aim to reproduce.