Heatwaves exert disproportionately strong and sometimes irreversible impacts on forest ecosystems. These impacts remain poorly understood at the tree and species level and across large spatial ...scales. Here, we investigate the effects of the record-breaking 2018 European heatwave on tree growth and tree water status using a collection of high-temporal resolution dendrometer data from 21 species across 53 sites. Relative to the two preceding years, annual stem growth was not consistently reduced by the 2018 heatwave but stems experienced twice the temporary shrinkage due to depletion of water reserves. Conifer species were less capable of rehydrating overnight than broadleaves across gradients of soil and atmospheric drought, suggesting less resilience toward transient stress. In particular, Norway spruce and Scots pine experienced extensive stem dehydration. Our high-resolution dendrometer network was suitable to disentangle the effects of a severe heatwave on tree growth and desiccation at large-spatial scales in situ, and provided insights on which species may be more vulnerable to climate extremes.
Abstract
The demand for wood in Europe is expected to increase in the coming decades. However, any theoretical maximum supply will be affected by sustainability constraints, the motivations of forest ...owners and regional factors, such as incentives, species and assortments. However, the influence of these factors on supply is changeable. In this study, we quantify what might be realistically available as additional wood supply from currently existing European forests, based on a combination of results of the forest resource model EFISCEN-Space and a literature review of national supply projections. Wood mobilization scenarios for 10 representative Model Regions in Europe that assume forest owners and managers in the simulated regions will adapt their behaviour to alternative behaviour as recorded from other regions were projected with the EFISCEN-Space model. The realistic additional potential based on the literature review is 90 million m3 yr−1. This potential should be attainable within 10–20 years. However, the simulations in the Model Regions found potentials to be lower in 7 out of 10 cases as compared with the country they are located in. On average, the model regions reached less than half of the potential as compared with the literature review. This suggests that the realistic additional potential at the European scale may well be lower if all mobilization barriers are taken into account in more detail, but also highlights the uncertainty surrounding these estimates. We conclude from the analyses that although there are large differences in potential between regions and the analysis method employed, there are no ‘hotspots’ where a large pool of accessible wood can be quickly mobilized using existing infrastructure for nearby industries. An increase in harvest would therefore only be possible with a large effort that spans the whole chain, from forest owners’ behaviour to capacity building, financial incentives and matching resources to harvesting capacity. The additionally available wood can most likely only be mobilized against higher marginal costs and will thus only become available in times of higher stumpage prices. The largest potential lies in privately owned forests which often have a fragmented ownership but will most likely be able to supply more wood, though mostly from deciduous species. In the long term (more than 20 years), additional wood, compared with the amounts we found for short term, can only be made available through investments in afforestation, forest restoration, improved forest management and more efficient use of raw material and recycled material.
European forests have a long record of management. However, the diversity of the current forest management across nations, tree species and owners, is hardly understood. Often when trying to simulate ...future forest resources under alternative futures, simply the yield table style of harvesting is applied. It is now crucially important to come to grips with actual forest management, now that demand for wood is increasing and the EU Land Use, Land Use Change and Forestry Regulation has been adopted requiring 'continuation of current management practices' as a baseline to set the Forest Reference Level carbon sink.
Based on a large dataset of 714,000 re-measured trees in National Forest inventories from 13 regions, we are now able to analyse actual forest harvesting.
From this large set of repeated tree measurements we can conclude that there is no such thing as yield table harvesting in Europe. We found general trends of increasing harvest probability with higher productivity of the region and the species, but with important deviations related to local conditions like site accessibility, state of the forest resource (like age), specific subsidies, importance of other forest services, and ownership of the forest. As a result, we find a huge diversity in harvest regimes. Over the time period covered in our inventories, the average harvest probability over all regions was 2.4% yr-1 (in number of trees) and the mortality probability was 0.4% yr-1. Our study provides underlying and most actual data that can serve as a basis for quantifying 'continuation of current forest management'. It can be used as a cornerstone for the base period as required for the Forest Reference Level for EU Member States.
•Forest species maps and area proportion estimates were produced for theNetherlands.•New Sentinel-2 harmonic predictors were more accurate than seasonal composites.•NFI-based balanced training ...dataset and reliable accuracy assessment.•New method for coupling and comparing NFI and remote sensing estimates.•Small disagreements between remote sensing and NFI estimates were found.
European forest monitoring is a central topic nowadays due to the critical role that forests can play in combatting climate change. Crucial information on forests is the number of tree species and the area covered by each of them, as they vary concerning growth rates, wood value, value for biodiversity conservation, and susceptibility to disturbances and global warming. The primary source of forest information is national forest inventories (NFIs). However, they are updated too infrequently to accommodate climate change-related analyses, and their estimates are not based on wall-to-wall information. Remotely sensed data offer new opportunities for up-to-date and large-scale forest monitoring and for enhancing NFI estimates. However, despite the huge scientific efforts, it is still challenging to accurately map forest species through satellite imagery analysis.
This study introduces a method for large-scale forest species mapping in the Netherlands using Sentinel-2 (S2) harmonic predictors and demonstrates a scientific procedure for reliably estimating area proportions from remote sensing-based species maps and comparing these estimates with NFI-based estimates.
Compared to more standard predictors, harmonic predictors increased the model performance by 8% in terms of overall accuracy and the kappa coefficient by 9% while reducing omission and commission errors by as much as 18% and 13%, respectively. We estimated the area proportion of forest species for each 10-km cell covering the Netherlands first using NFI data and then using the predicted maps. Although the resulting estimates differ by source data and methods, we found an average deviation between NFI and remote sensing-based area proportion estimates of 9%, with deviations approaching 0% when increasing the number of NFI plots per cell.
The outcomes of this research play an important role in understanding the relative strengths and limitations of remote sensing-based products and NFI data, as well as be a solid basis for forest species area proportion estimation when (i) no field data are available, (ii) more frequently updated estimates are required, or (iii) wall-to-wall and fine resolution spatially explicit estimates are needed.
Ceccherini et al.1 quantify change using map pixel counts, rather than using a statistically rigorous sampling approach that is more appropriate for the estimation of area change7. ...although ...Ceccherini et al.1 considered false positives (incorrect detection of forest loss) in their sample analyses, they did not consider false negatives (undetected forest loss). ...analyses, which address both omission and commission errors, offer accurate and unbiased results of forest change. ...sample reference data tailored to the specific purpose of a given study can be used to discriminate proportions of loss due to natural disturbances within the overall forest loss rates12. ...we are confident that natural disturbances were not correctly excluded. ...information and knowledge are crucial to develop science-based, climate-smart forestry strategies18 to ensure that European forests continue to be an important carbon sink and a key ecosystem service provider in relation to the protection of biodiversity and the development of the bioeconomy. https://doi.org/10.1038/s41586-021-03292-x Received: 3 July 2020 Accepted: 26 January 2021 Published online: 28 April 2021 Check for updates Acknowledgements We thank G. Ceccherini and co-authors for immediately making available all original material, processing codes and results of their study upon request.
Quantification of land surface–atmosphere fluxes of carbon dioxide
(CO2) and their trends and uncertainties is essential for
monitoring progress of the EU27+UK bloc as it strives to meet ambitious
...targets determined by both international agreements and internal regulation.
This study provides a consolidated synthesis of fossil sources (CO2
fossil) and natural (including formally managed ecosystems) sources and
sinks over land (CO2 land) using bottom-up (BU) and top-down (TD)
approaches for the European Union and United Kingdom (EU27+UK), updating
earlier syntheses (Petrescu et al., 2020, 2021). Given the wide scope of
the work and the variety of approaches involved, this study aims to answer
essential questions identified in the previous syntheses and understand the
differences between datasets, particularly for poorly characterized fluxes
from managed and unmanaged ecosystems. The work integrates updated emission
inventory data, process-based model results, data-driven categorical model
results, and inverse modeling estimates, extending the previous period
1990–2018 to the year 2020 to the extent possible. BU and TD products are
compared with the European national greenhouse gas inventory (NGHGI)
reported by parties including the year 2019 under the United Nations
Framework Convention on Climate Change (UNFCCC). The uncertainties of the
EU27+UK NGHGI were evaluated using the standard deviation reported by the
EU member states following the guidelines of the Intergovernmental Panel on
Climate Change (IPCC) and harmonized by gap-filling procedures. Variation in
estimates produced with other methods, such as atmospheric inversion models
(TD) or spatially disaggregated inventory datasets (BU), originate from
within-model uncertainty related to parameterization as well as structural
differences between models. By comparing the NGHGI with other approaches,
key sources of differences between estimates arise primarily in activities.
System boundaries and emission categories create differences in CO2
fossil datasets, while different land use definitions for reporting
emissions from land use, land use change, and forestry (LULUCF) activities
result in differences for CO2 land. The latter has important
consequences for atmospheric inversions, leading to inversions reporting
stronger sinks in vegetation and soils than are reported by the NGHGI. For CO2 fossil emissions, after harmonizing
estimates based on common activities and selecting the most recent year
available for all datasets, the UNFCCC NGHGI for the EU27+UK accounts for
926 ± 13 Tg C yr−1, while eight other BU sources report a mean
value of 948 937,961 Tg C yr−1 (25th, 75th percentiles). The
sole top-down inversion of fossil emissions currently available accounts for
875 Tg C in this same year, a value outside the uncertainty of both the
NGHGI and bottom-up ensemble estimates and for which uncertainty estimates
are not currently available. For the net CO2 land fluxes, during the most recent 5-year period including the NGHGI
estimates, the NGHGI accounted for −91 ± 32 Tg C yr−1, while six
other BU approaches reported a mean sink of −62 -117,-49 Tg C yr−1,
and a 15-member ensemble of dynamic global vegetation models (DGVMs)
reported −69 -152,-5 Tg C yr−1. The 5-year mean of three TD
regional ensembles combined with one non-ensemble inversion of −73 Tg C yr−1 has a slightly smaller spread (0th–100th percentiles of
-135,+45 Tg C yr−1), and it was calculated after removing net
land–atmosphere CO2 fluxes caused by lateral transport of carbon (crop
trade, wood trade, river transport, and net uptake from inland water bodies),
resulting in increased agreement with the NGHGI and bottom-up approaches.
Results at the category level (Forest Land, Cropland, Grassland) generally show good agreement between the NGHGI and category-specific models, but
results for DGVMs are mixed. Overall, for both CO2 fossil and net
CO2 land fluxes, we find that current independent approaches are consistent
with the NGHGI at the scale of the EU27+UK. We conclude that CO2
emissions from fossil sources have decreased over the past 30 years in the
EU27+UK, while land fluxes are relatively stable: positive or negative
trends larger (smaller) than 0.07 (−0.61) Tg C yr−2 can be ruled out
for the NGHGI. In addition, a gap on the order of 1000 Tg C yr−1
between CO2 fossil emissions and net CO2 uptake by the land exists
regardless of the type of approach (NGHGI, TD, BU), falling well outside all
available estimates of uncertainties. However, uncertainties in top-down
approaches to estimate CO2 fossil emissions remain uncharacterized and
are likely substantial, in addition to known uncertainties in top-down
estimates of the land fluxes. The data used to plot the figures are
available at https://doi.org/10.5281/zenodo.8148461 (McGrath et al., 2023).
Quantification of land surface–atmosphere fluxes of carbon dioxide (CO2) and their trends and uncertainties is essential for monitoring progress of the EU27+UK bloc as it strives to meet ambitious ...targets determined by both international agreements and internal regulation. This study provides a consolidated synthesis of fossil sources (CO2 fossil) and natural (including formally managed ecosystems) sources and sinks over land (CO2 land) using bottom-up (BU) and top-down (TD) approaches for the European Union and United Kingdom (EU27+UK), updating earlier syntheses (Petrescu et al., 2020, 2021). Given the wide scope of the work and the variety of approaches involved, this study aims to answer essential questions identified in the previous syntheses and understand the differences between datasets, particularly for poorly characterized fluxes from managed and unmanaged ecosystems. The work integrates updated emission inventory data, process-based model results, data-driven categorical model results, and inverse modeling estimates, extending the previous period 1990–2018 to the year 2020 to the extent possible. BU and TD products are compared with the European national greenhouse gas inventory (NGHGI) reported by parties including the year 2019 under the United Nations Framework Convention on Climate Change (UNFCCC). The uncertainties of the EU27+UK NGHGI were evaluated using the standard deviation reported by the EU member states following the guidelines of the Intergovernmental Panel on Climate Change (IPCC) and harmonized by gap-filling procedures. Variation in estimates produced with other methods, such as atmospheric inversion models (TD) or spatially disaggregated inventory datasets (BU), originate from within-model uncertainty related to parameterization as well as structural differences between models. By comparing the NGHGI with other approaches, key sources of differences between estimates arise primarily in activities. System boundaries and emission categories create differences in CO2 fossil datasets, while different land use definitions for reporting emissions from land use, land use change, and forestry (LULUCF) activities result in differences for CO2 land. The latter has important consequences for atmospheric inversions, leading to inversions reporting stronger sinks in vegetation and soils than are reported by the NGHGI. For CO2 fossil emissions, after harmonizing estimates based on common activities and selecting the most recent year available for all datasets, the UNFCCC NGHGI for the EU27+UK accounts for 926 ± 13 Tg C yr−1, while eight other BU sources report a mean value of 948 937,961 Tg C yr−1 (25th, 75th percentiles). The sole top-down inversion of fossil emissions currently available accounts for 875 Tg C in this same year, a value outside the uncertainty of both the NGHGI and bottom-up ensemble estimates and for which uncertainty estimates are not currently available. For the net CO2 land fluxes, during the most recent 5-year period including the NGHGI estimates, the NGHGI accounted for −91 ± 32 Tg C yr−1, while six other BU approaches reported a mean sink of −62 -117,-49 Tg C yr−1, and a 15-member ensemble of dynamic global vegetation models (DGVMs) reported −69 -152,-5 Tg C yr−1. The 5-year mean of three TD regional ensembles combined with one non-ensemble inversion of −73 Tg C yr−1 has a slightly smaller spread (0th–100th percentiles of -135,+45 Tg C yr−1), and it was calculated after removing net land–atmosphere CO2 fluxes caused by lateral transport of carbon (crop trade, wood trade, river transport, and net uptake from inland water bodies), resulting in increased agreement with the NGHGI and bottom-up approaches. Results at the category level (Forest Land, Cropland, Grassland) generally show good agreement between the NGHGI and category-specific models, but results for DGVMs are mixed. Overall, for both CO2 fossil and net CO2 land fluxes, we find that current independent approaches are consistent with the NGHGI at the scale of the EU27+UK. We conclude that CO2 emissions from fossil sources have decreased over the past 30 years in the EU27+UK, while land fluxes are relatively stable: positive or negative trends larger (smaller) than 0.07 (−0.61) Tg C yr−2 can be ruled out for the NGHGI. In addition, a gap on the order of 1000 Tg C yr−1 between CO2 fossil emissions and net CO2 uptake by the land exists regardless of the type of approach (NGHGI, TD, BU), falling well outside all available estimates of uncertainties. However, uncertainties in top-down approaches to estimate CO2 fossil emissions remain uncharacterized and are likely substantial, in addition to known uncertainties in top-down estimates of the land fluxes. The data used to plot the figures are available at https://doi.org/10.5281/zenodo.8148461 (McGrath et al., 2023).
Quantification of land surface-atmosphere fluxes of carbon dioxide (CO.sub.2) and their trends and uncertainties is essential for monitoring progress of the EU27+UK bloc as it strives to meet ...ambitious targets determined by both international agreements and internal regulation. This study provides a consolidated synthesis of fossil sources (CO.sub.2 fossil) and natural (including formally managed ecosystems) sources and sinks over land (CO.sub.2 land) using bottom-up (BU) and top-down (TD) approaches for the European Union and United Kingdom (EU27+UK), updating earlier syntheses (Petrescu et al., 2020, 2021). Given the wide scope of the work and the variety of approaches involved, this study aims to answer essential questions identified in the previous syntheses and understand the differences between datasets, particularly for poorly characterized fluxes from managed and unmanaged ecosystems. The work integrates updated emission inventory data, process-based model results, data-driven categorical model results, and inverse modeling estimates, extending the previous period 1990-2018 to the year 2020 to the extent possible. BU and TD products are compared with the European national greenhouse gas inventory (NGHGI) reported by parties including the year 2019 under the United Nations Framework Convention on Climate Change (UNFCCC). The uncertainties of the EU27+UK NGHGI were evaluated using the standard deviation reported by the EU member states following the guidelines of the Intergovernmental Panel on Climate Change (IPCC) and harmonized by gap-filling procedures. Variation in estimates produced with other methods, such as atmospheric inversion models (TD) or spatially disaggregated inventory datasets (BU), originate from within-model uncertainty related to parameterization as well as structural differences between models. By comparing the NGHGI with other approaches, key sources of differences between estimates arise primarily in activities. System boundaries and emission categories create differences in CO.sub.2 fossil datasets, while different land use definitions for reporting emissions from land use, land use change, and forestry (LULUCF) activities result in differences for CO.sub.2 land. The latter has important consequences for atmospheric inversions, leading to inversions reporting stronger sinks in vegetation and soils than are reported by the NGHGI.
Quantification of land surface–atmosphere fluxes of carbon dioxide (CO2) and their trends and uncertainties is essential for monitoring progress of the EU27+UK bloc as it strives to meet ambitious ...targets determined by both international agreements and internal regulation. This study provides a consolidated synthesis of fossil sources (CO2 fossil) and natural (including formally managed ecosystems) sources and sinks over land (CO2 land) using bottom-up (BU) and top-down (TD) approaches for the European Union and United Kingdom (EU27+UK), updating earlier syntheses (Petrescu et al., 2020, 2021). Given the wide scope of the work and the variety of approaches involved, this study aims to answer essential questions identified in the previous syntheses and understand the differences between datasets, particularly for poorly characterized fluxes from managed and unmanaged ecosystems. The work integrates updated emission inventory data, process-based model results, data-driven categorical model results, and inverse modeling estimates, extending the previous period 1990–2018 to the year 2020 to the extent possible. BU and TD products are compared with the European national greenhouse gas inventory (NGHGI) reported by parties including the year 2019 under the United Nations Framework Convention on Climate Change (UNFCCC). The uncertainties of the EU27+UK NGHGI were evaluated using the standard deviation reported by the EU member states following the guidelines of the Intergovernmental Panel on Climate Change (IPCC) and harmonized by gap-filling procedures. Variation in estimates produced with other methods, such as atmospheric inversion models (TD) or spatially disaggregated inventory datasets (BU), originate from within-model uncertainty related to parameterization as well as structural differences between models. By comparing the NGHGI with other approaches, key sources of differences between estimates arise primarily in activities. System boundaries and emission categories create differences in CO2 fossil datasets, while different land use definitions for reporting emissions from land use, land use change, and forestry (LULUCF) activities result in differences for CO2 land. The latter has important consequences for atmospheric inversions, leading to inversions reporting stronger sinks in vegetation and soils than are reported by the NGHGI.For CO2 fossil emissions, after harmonizing estimates based on common activities and selecting the most recent year available for all datasets, the UNFCCC NGHGI for the EU27+UK accounts for 926 ± 13 Tg C yr-1, while eight other BU sources report a mean value of 948 937,961 Tg C yr-1 (25th, 75th percentiles). The sole top-down inversion of fossil emissions currently available accounts for 875 Tg C in this same year, a value outside the uncertainty of both the NGHGI and bottom-up ensemble estimates and for which uncertainty estimates are not currently available. For the net CO2 land fluxes, during the most recent 5-year period including the NGHGI estimates, the NGHGI accounted for -91 ± 32 Tg C yr-1, while six other BU approaches reported a mean sink of -62 -117,-49 Tg C yr-1, and a 15-member ensemble of dynamic global vegetation models (DGVMs) reported -69 -152,-5 Tg C yr-1. The 5-year mean of three TD regional ensembles combined with one non-ensemble inversion of -73 Tg C yr-1 has a slightly smaller spread (0th–100th percentiles of -135,+45 Tg C yr-1), and it was calculated after removing net land–atmosphere CO2 fluxes caused by lateral transport of carbon (crop trade, wood trade, river transport, and net uptake from inland water bodies), resulting in increased agreement with the NGHGI and bottom-up approaches. Results at the category level (Forest Land, Cropland, Grassland) generally show good agreement between the NGHGI and category-specific models, but results for DGVMs are mixed. Overall, for both CO2 fossil and net CO2 land fluxes, we find that current independent approaches are consistent with the NGHGI at the scale of the EU27+UK. We conclude that CO2 emissions from fossil sources have decreased over the past 30 years in the EU27+UK, while land fluxes are relatively stable: positive or negative trends larger (smaller) than 0.07 (-0.61) Tg C yr-2 can be ruled out for the NGHGI. In addition, a gap on the order of 1000 Tg C yr-1 between CO2 fossil emissions and net CO2 uptake by the land exists regardless of the type of approach (NGHGI, TD, BU), falling well outside all available estimates of uncertainties. However, uncertainties in top-down approaches to estimate CO2 fossil emissions remain uncharacterized and are likely substantial, in addition to known uncertainties in top-down estimates of the land fluxes. The data used to plot the figures are available at 10.5281/zenodo.8148461 (McGrath et al., 2023).
This paper describes a geo-spatial arable field optimization service (GAOS) and an assessment of users' experiences after three years of experimental operation. The service was developed in close ...cooperation with farmers. It allows farmers to optimize the locations of tracks within their fields, explore different options and download these to commercial Global Navigation Satellite System-guided steering systems. GAOS runs as a web service using standards defined by the Open Geospatial Consortium and is being operated by a group of farmers who received a few hours of training. The objective of optimization is to maximize efficiency by avoiding both inefficient turns and discontinuous swaths. Where applicable, released space is converted into field margins that meet environmental objectives and potentially generate additional income. The system provides dedicated functionality for geometrical operations, such as uploading and splitting of fields, merging and splitting of field edges, and manual editing of reference lines in the headlands. Acknowledged beneficial features include reduced expenditures on time and wasted resources and support for planning spraying paths. Given its complexity, most farmers preferred specialists to operate the system rather than operating it themselves. Future development should aim for simpler operation and full support for interactive coverage planning as well as computational optimization.
•Our spatial planning optimizes the locations of machine tracks and field margins.•It reduces wasting of resources and supports planning of spraying paths.•We developed and tested a prototype web service implementing open standards.•Spatial planning of fields requires both automated and interactive procedures.
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