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The structure of a forest dictates its function, vulnerability to mortality, and ecosystem services it provides. Many aspects of the environment and management determine forest structures, such as canopy height, stand density, carbon content, etc. Environmental factors, such as climate, limit the extent to which management can maximize structures of a forest. By understanding how climate limits forest structures over large landscapes we can better quantify the potential upper limit that a forest structure can achieve independent of management. Further, by quantifying how climate limits forest structures we can deepen our understanding of the impact climate change has had and will have on our forest resources and services. This type of information goes beyond quantifying how climate will impact the pools and fluxes of a forest, which is typically done for climate change studies over large landscapes. Estimating how climate change will impact structures will allow us to quantify how climate change will impact resources and services unquantifiable by pools and fluxes alone – such as biodiversity, habitat suitability, and market values. We quantified how maximum and minimum temperatures, and precipitation limit 3 forest structures, diameter at breast height, height, and basal area across the European continent. We found that climate zones exist that maximize each forest structure. Further, we estimated how climate change since the 1950’s has influenced the potential structures of European forests by assessing eight individual forests throughout Europe and then Europe as a whole. All three forest structures are limited in different ways depending on their location in Europe. Though some individual forests have seen a benefit from climate change, European forests on average have lost 5.0%, 1.7% and 6.5% of potential forest diameter, height and basal area respectively. Further, the extremes of the climate values in our study, which may support endemic life, have already begun to vanish from the entire continent.

National and international carbon reporting systems require information on forest carbon stocks. This information can be derived from national forest inventory data and remote sensing. Here we present the conceptual challenges in assessing forest resources across Europe by combining MODIS satellite versus terrestrial driven NPP estimates calculated from 13 national forest inventory (NFI) data covering 200.000 sampling plots. The results suggest that MODIS NPP predictions using local daily climate data and addressing stand desnsity effects, provide realistic forest productivity estimates. Ignoring these effects leads to an overestimation in the estimated carbon storage of European forests derived from satellite data.

Optical remote sensing data-based estimates of terrestrial net primary production (NPP) are released by different projects using light use efficiency-type models. Although spatial resolution of the NPP data sets is still too coarse (500–1000 m) for single forest stands, regional monitoring of forest management and growth with 25–100 ha sampling units is feasible if the NPPSAT estimates are sensitive to forest growth differences depending on soil fertility in the area of interest. In this study, NPP estimates for 2,914 mixed forest class pixels (according to the MODIS land cover map) located in Estonia were (1) obtained from three different NPPSAT products, (2) calculated using an empirical soil potential phytoproductivity (SPP) model applied to a 1:10,000 soil map (NPPSPP), and (3) calculated using stem volume increment estimates given in a forest management inventory data base (NPPFIDB). A linear multiple regression model was then used to explore the relationships of NPPSAT with the proportion of coniferous forests, the NPPSPP and distance of the pixels from the Baltic Sea coast – the variables that have been found informative in previous studies. We found a positive moderate correlation (0.57, p < 0.001) between NPPSPP and NPPFIDB. The local or downscaled meteorological data-based NPPSAT estimates were more consistent with the NPPSPP and NPPFIDB, but the correlation with NPPSAT was weak and sometimes even negative. The range of NPP estimates in NPPSAT data sets was much narrower than the range of NPPSPP or NPPFIDB. Errors in land cover maps and in estimates of absorbed photosynthetically active radiation were identified as the main reasons for NPPSAT inconsistencies.

© 2019 The Authors FORMIT-M is a widely applicable, open-access, simple and flexible, climate-sensitive forest management simulator requiring only standard forest inventory data as input. It combines a process-based carbon balance approach with a strong inventory-based empirical component. The model has been linked to the global forest sector model EFI-GTM to secure consistency between timber cutting and demand, although prescribed harvest scenarios can also be used. Here we introduce the structure of the model and demonstrate its use with example simulations until the end of the 21st century in Europe, comparing different management scenarios in different regions under climate change. The model was consistent with country-level statistics of growing stock volumes (R 2 = 0.938) and its projections of climate impact on growth agreed with other studies. The management changes had a greater impact on growing stocks, harvest potential and carbon balance than projected climate change, at least in the absence of increased disturbance rates. ispartof: Environmental Modelling and Software vol:115 pages:128-143 status: published

Forests provide critical ecosystem services that ensure the sustainability of the environment and society. To manage forests on large scales, spatially explicit gridded data that describes the characteristics of these forests over the entire study area are required. There have been multiple efforts to create such data on regional and global scales. This type of gridded spatially explicit data on forest characteristics are typically done by integrating terrestrial forest inventory (NFI) and satellite-based remotely sensed data. Many studies that incorporate remotely sensed data and forest inventory data often directly compare pixels to inventory plots. The standard resolution of 0.0083° is typically used to integrate these two types of data sets. There is an assumption that, when producing gridded data sets incorporating forest inventory data, the finer the resolution the better the information. This assumption may seem intuitive, however at this resolution, in Europe, each 0.0083° cell has on average 1 NFI plot, which results in a sample with 0 degrees of freedom that represents 0.02% of the cell area. In this study, we challenge this assumption and we quantify the optimal resolution with which to compare and combine remotely sensed and NFI data from the largest collated and harmonized NFI data set in Europe including 196,434 plots. We determined that aggregating data with an original resolution of 0.0083° to between 0.0664° and 0.266° (or × 8 to × 32) produces the best agreement between these two forest inventory and remotely sensed data sets, and the lowest standard error in NFI data, and maintains the majority of the local-level spatial heterogeneity.

E-OBS(European Observations) is a gridded climate data set which contains maximum temperature, minimum temperature, and precipitation on a daily time step. The data can be as fine as 0.25° in resolution and extends over the entire European continent and parts of Africa and Asia. However, for studying regional or local climatic effects, a finer resolution would be more appropriate. A continental data set with resolution would allow research that is large in scale and still locally relevant. Until now, a climate data set with high spatial and temporal resolution has not existed for Europe. To fulfil this need, we produced a downscaled version of E-OBS, applying the delta method, which uses WorldClim climate surfaces to obtain a 0.008° (about 1 × 1 km) resolution climate data set on a daily time step covering the European Union. The new downscaled data set includes minimum and maximum temperature and precipitation for the years 1951–2012. It is analysed against weather station data from six countries: Norway, Germany, France, Italy, Austria, and Spain. Our analysis of the downscaled data set shows a reduction in the mean bias error of 3 °C for mean daily minimum temperature and of 4 °C for mean daily maximum temperature. Daily precipitation improved by 0.15 mm on average for all weather stations in the validation. The entire data set is freely and publically available at ftp://palantir.boku.ac.at/Public/ClimateData.

Net primary production (NPP) is an important ecological metric for studying forest ecosystems and their carbon sequestration, for assessing the potential supply of food or timber and quantifying the impacts of climate change on ecosystems. The global MODIS NPP dataset using the MOD17 algorithm provides valuable information for monitoring NPP at 1-km resolution. Since coarse-resolution global climate data are used, the global dataset may contain uncertainties for Europe. We used a 1-km daily gridded European climate data set with the MOD17 algorithm to create the regional NPP dataset MODIS EURO. For evaluation of this new dataset, we compare MODIS EURO with terrestrial driven NPP from analyzing and harmonizing forest inventory data (NFI) from 196,434 plots in 12 European countries as well as the global MODIS NPP dataset for the years 2000 to 2012. Comparing these three NPP datasets, we found that the global MODIS NPP dataset differs from NFI NPP by 26%, while MODIS EURO only differs by 7%. MODIS EURO also agrees with NFI NPP across scales (from continental, regional to country) and gradients (elevation, location, tree age, dominant species, etc.). The agreement is particularly good for elevation, dominant species or tree height. This suggests that using improved climate data allows the MOD17 algorithm to provide realistic NPP estimates for Europe. Local discrepancies between MODIS EURO and NFI NPP can be related to differences in stand density due to forest management and the national carbon estimation methods. With this study, we provide a consistent, temporally continuous and spatially explicit productivity dataset for the years 2000 to 2012 on a 1-km resolution, which can be used to assess climate change impacts on ecosystems or the potential biomass supply of the European forests for an increasing bio-based economy. MODIS EURO data are made freely available at ftp//palantir.boku.ac.at/Public/MODIS_EURO. © 2016 by the authors.

National and international carbon reporting systems require information on carbon stocks of forests. For this purpose, terrestrial assessment systems such as forest inventory data in combination with carbon estimation methods are often used. In this study we analyze and compare terrestrial carbon estimation methods from 12 European countries. The country-specific methods are applied to five European tree species (Fagus sylvatica L.;Quercus robur L.;Betula pendula Roth, Picea abies (L.) Karst.;Pinus sylvestris L.), using a standardized theoretically-generated tree dataset. We avoid any bias due to data collection and/or sample design by using this approach. We are then able to demonstrate the conceptual differences in the resulting carbon estimates with regard to the applied country-specific method. In our study we analyze (i) allometric biomass functions, (ii) biomass expansion factors in combination with volume functions and (iii) a combination of both. The results of the analysis show discrepancies in the resulting estimates for total tree carbon and for single tree compartments across the countries analyzed of up to 140. t. carbon/ha. After grouping the country-specific approaches by European Forest regions, the deviation within the results in each region is smaller but still remains. This indicates that part of the observed differences can be attributed to varying growing conditions and tree properties throughout Europe. However, the large remaining error is caused by differences in the conceptual approach, different tree allometry, the sample material used for developing the biomass estimation models and the definition of the tree compartments. These issues are currently not addressed and require consideration for reliable and consistent carbon estimates throughout Europe. © 2015 Elsevier B.V.

First Online: 07 June 2016; International audience; AbstractKey messageA daily 1-km Pan-European weather dataset can drive the BIOME-BGC model for the estimation of current and future beech gross primary production (GPP). Annual beech GPP is affected primarily by spring temperature and more irregularly by summer water stress.ContextThe spread of beech forests in Europe enhances the importance of modelling and monitoring their growth in view of ongoing climate changes.AimsThe current paper assesses the capability of a biogeochemical model to simulate beech gross primary production (GPP) using a Pan-European 1-km weather dataset.MethodsThe model BIOME-BGC is applied in four European forest ecosystems having different climatic conditions where the eddy covariance technique is used to measure water and carbon fluxes. The experiment is in three main steps. First, the accuracy of BIOME-BGC GPP simulations is assessed through comparison with flux observations. Second, the influence of two major meteorological drivers (spring minimum temperature and growing season dryness) on observed and simulated inter-annual GPP variations is analysed. Lastly, the impacts of two climate change scenarios on beech GPP are evaluated through statistical analyses of the ground data and model simulations.ResultsThe weather dataset can drive BIOME-BGC to simulate most of the beech GPP evolution in all four test areas. Both observed and simulated inter-annual GPP variations are mainly dependent on minimum temperature around the beginning of the growing season, while spring/summer dryness exerts a secondary role. BIOME-BGC can also reasonably predict the impacts of the examined climate change scenarios.ConclusionThe proposed modelling approach is capable of approximately reproducing spatial and temporal beech GPP variations and impacts of expected climate changes in the examined European sites.

The potential effects of climate change on net primary productivity (NPP) of U.S. rangelands were evaluated using estimated climate regimes from the A1B, A2 and B2 global change scenarios imposed on the biogeochemical cycling model, Biome-BGC from 2001 to 2100. Temperature, precipitation, vapor pressure deficit, day length, solar radiation, CO2 enrichment and nitrogen deposition were evaluated as drivers of NPP. Across all three scenarios, rangeland NPP increased by 0.26 % year �1 (7 kg C ha �1 year �1 ) but increases were not apparentuntil after 2030 and significant regional variation in NPP was revealed. The Desert Southwest and Southwest assessment regions exhibited declines in NPP of about 7 % by 2100, while the Northern and Southern Great Plains, Interior West and Eastern Prairies all experienced increases over 25 %. Grasslands dominated by warm season (C4 photosynthetic pathway) species showed the greatest response to temperature while cool season (C3 photo- synthetic pathway) dominated regions responded most strongly to CO2 enrichment. Modeled NPP responses compared favorably with experimental results from CO2 manipulation exper- iments and to NPP estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS). Collectively, these results indicate significant and asymmetric changes in NPP for U.S. rangelands may be expected.