The net carbon flux from anthropogenic land use and land cover change (fLULCC) comprises about 12% of total anthropogenic CO2 emissions. Its reduction is considered essential in future pathways towards net-zero emissions, necessary to reach the climate mitigation goals of the Paris Agreement. The fLULCC constitutes thus a key component of the global carbon cycle.

Despite its importance, net fLULCC remains highly uncertain in national, regional, and global assessments, mainly because various techniques and definitions for its estimation are used. Techniques comprise modeling approaches, such as semi-empirical bookkeeping models or process-based dynamic global vegetation models that were used in previous large-scale assessments (such as in the IPCC assessment reports or the yearly budgets of the Global Carbon Project). In contrast, countries regularly report their emissions to the United Nations Framework Convention on Climate Change (UNFCCC) based on inventory-based approaches. These data also form the basis for the estimates provided by the Food and Agriculture Organization Corporate Statistical Database (FAOSTAT). These varying approaches often define net fLULCC very differently. For instance, some include all fluxes on managed land while others exclude natural fluxes on managed land, such as those related to climate variability or increasing CO2 concentration. Further, carbon cycle models are inherently uncertain because of the difficulty of simulating complex natural and human processes, while direct Earth observations have difficulties to separate between anthropogenic and natural CO2 fluxes due to their temporal and spatial co-occurrence.

Additionally, the reduction of net fLULCC, needed for pathways towards net-zero emissions, can be achieved through a combination of reducing gross emissions (e.g., by decreasing deforestation) and increasing gross removals (e.g., by fostering afforestation and reforestation). Thus, the quantification of the underlying gross components of net fLULCC, which are currently 2-4 times larger than net fLULCC on global scale, is essential but has gained only little attention in the past. Recent improvements in model resolution now enable to perform this comparison at the country level.Here we compile and analyze country-level net fLULCC as well as its gross components combining data from bookkeeping models, dynamic global vegetation models, and inventory-based approaches.

Modeled and inventory-based estimates generally show a fair agreement when the effects of diverging definitions are accounted for, though estimates are highly differing for some countries. For example, fLULCC estimates from different bookkeeping models are strongly differing in China and the United States of America. Analysis of the gross fluxes reveals that these discrepancies result from strongly differing gross sources in China, and strongly differing gross sinks in the USA, related to varying capabilities of the underlying LULCC forcing data in capturing afforestation and different model comprehensiveness in capturing specific land management practices, such as fire suppression. An additional analysis of the ratio from net to summed gross fLULCC underlines the importance of the gross components for the carbon cycle, albeit spatially heterogeneously. For example, in the USA the net fLULCC represents only about 8% of the gross fluxes while in Indonesia the net flux comprises approximately 50% of the gross fluxes, to name the most extreme ratios of countries with high cumulative emissions. Strikingly, strong discrepancies between bookkeeping estimates and the emission estimates provided by FAOSTAT and UNFCCC are evident for Russia and China. In China, bookkeeping models likely underestimate the increased carbon stock due to large-scale afforestation programs, while FAOSTAT and UNFCCC estimates potentially overestimate the afforestation effects reported from China, which assume high CO2 sequestration in afforested regions despite partly contradictory observational studies.

The newly aggregated data bridges the scales between inventory-based estimates and those from models. By comparing country-wise fLULCC estimates from varying approaches, this study reveals the general suitability of modeling approaches to assess fLULCC even on country-level. This provides the basis for independently validating emissions reported by countries, which is a legal/policy requirement e.g., for the Global Stocktake. Remaining uncertainties highlight the need for systematic evaluation by Earth observation data and their incorporation in fLULCC modeling approaches.

The Forest and Land use Declaration negotiated at the 26th climate Conference of the Parties (COP) in Glasgow, November 2021, confirmed that Tropical Moist Forests (TMFs) are a vital nature-based solution to addressing the climate and ecological emergencies. TMFs are estimated to be a net sink of carbon, storing approximately 0.8 Pg C yr-1 [1]. However, the size of this sink is declining due to human activities such as deforestation and forest degradation through logging and fire, as well as climate variability and change1. Tropical forests are therefore a patchwork of undisturbed, degraded, and secondary forests, creating regionally complex patterns of growth and carbon storage.

While there have been numerous studies exploring and quantifying the recovery rates of secondary forests, quantifying the recovery rate of degraded forests has been largely unexplored on a pan-tropical scale. As many tropical countries are participating in results-based payments frameworks such as REDD+, which includes reducing emissions from degradation and forest recovery, it is essential to be able to quantify the carbon accumulation in recovering degraded forests as well as secondary forests, which collectively, we have termed “Recovering Forests”.
Recent advances in remote sensing products have made it possible to (i) distinguish degraded forests from undisturbed and secondary forests2; and (ii) estimate the carbon sequestration rates within these forests3,4.
Here we use a combination of remote sensing derived products in a space-for-time substitution approach to quantify the carbon accumulation rates in recovering forests. This includes recovering degraded forests and secondary forests in the three major tropical biomes: the Amazon Basin, Island of Borneo and Congo Basin.
Our initial results show growth rates to be the highest in Borneo, in recovering degraded forests5. We attribute these inter-biome/forest variations in growth to differences in disturbance and environmental variables such as water deficit and temperature.
Across the three biomes, we find that the recovering degraded forests have a large carbon sink potential, owing largely to their vast areal extent (10% of forest area). Secondary forests, regrow across a smaller land area (2%) but have faster growth rates (up to 30% faster in the Amazon basin) compared to degraded forest recovery. Additionally, we find that 35% of degraded forest are subject to subsequent deforestation2,5. Ending this cycle of forest degradation-deforestation and protecting all recovering forests wherever possible is key to safeguarding their current and future carbon sink potential.

References:
1. Hubau, W. et al. Asynchronous carbon sink saturation in African and Amazonian tropical forests. Nature 579, 80–87 (2020).
2. Vancutsem, C. et al. Long-term (1990–2019) monitoring of forest cover changes in the humid tropics. Sci. Adv. 7, eabe1603 (2021).
3. Santoro, M. & Cartus, O. ESA Biomass Climate Change Initiative (Biomass_cci): Global datasets of forest above-ground biomass for the years 2010, 2017 and 2018, v2. Centre for Environmental Data Analysis http://dx.doi.org/10.5285/84403d09cef3485883158f4df2989b0c (2021) doi:10.5285/84403d09cef3485883158f4df2989b0c.
4. Heinrich, V. H. A. et al. Large carbon sink potential of secondary forests in the Brazilian Amazon to mitigate climate change. Nat. Commun. 12, 1785 (2021).
5. Heinrich, V. H.A. et al. One quarter of humid tropical forest loss offset by recover. (in Review).

Land-based mitigation plays a significant role in reducing carbon emissions and thus in meeting the goals of the Paris Agreement. However, the attribution of measured carbon fluxes to its sinks and sources and defining the land carbon uptake potential remains highly uncertain. Despite the ever-increasing availability of data, in Europe, there is still no consensus on how much carbon is taken up by the land surface. This is particularly true for Eastern Europe, which is rich in forests with potentially high carbon uptake but poor in ground-based measurement data. This area consists of 13 countries: Belarus, Bulgaria, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Moldova, Poland, Romania, Slovakia, Ukraine, and western Russia (to Ural).

Here, we explore the carbon uptake potential of Eastern Europe in 2010-2019 by comparing multiple methods and datasets, in particular satellite-based L-VOD and XCO2 inversions, bottom-up estimates of biomass carbon (Xu et al., 2021), inventories, a set of Dynamic Global Vegetation Models (TRENDY) and the data-driven Bookkeeping of Land-use Emissions Model (BLUE). By combining and analysing these different datasets, we aim to (1) quantify the Eastern European land-based carbon flux of the last decade, (2) identify the spatial patterns of land carbon sinks and sources and (3) attribute their underlying drivers from land use, land management or climate change.

We find that Eastern Europe accounted for an annual carbon uptake of 0.49 Gt C a⁻¹ in 2010 2019, which is around 75% of the entire carbon uptake of Europe. However, the Eastern European land carbon sink is declining slightly. Datasets differ in the extent of the carbon sink due to various spatial resolutions, methodologies, influencing factors, or included land carbon components. Further, we map and discuss regional hot spots of carbon sinks and sources and their underlying causes from land use change and management (e.g. cropland abandonment, deforestation, forest harvest) and climate/environmental factors (e.g. fire, temperature, precipitation, soil moisture).

This study sheds light on the formerly underexplored terrestrial carbon sink in Eastern Europe. It shows that divergent land use and management dynamics are linked to changes in biomass carbon. Finally, it provides a new data basis for better understanding the underlying causes of biomass carbon fluxes in Eastern Europe, which is and will be essential for mitigating climate change in the future.

The Paris Agreement set the international objective “…to reach global peaking of greenhouse gas emissions as soon as possible … and to undertake rapid reductions thereafter in accordance with best available science...to achieve a balance between anthropogenic emissions by sources and removals by sinks of greenhouse gases in the second half of this century”, in order to keep global warming below two degrees by the end of the century. The Global Stocktake Process that was implemented following the Paris Agreement aims to define clear national targets compatible with this goal and to regularly track progress of individual countries in that direction.

Such effort requires considerable improvement of current scientific capabilities to quantify greenhouse gas (GHG) budgets and their trends at region and up to national scale. It further requires accurate attribution of budgets to natural and anthropogenic processes and the ability to link country-level GHG budgets to global budgets, including their trends, since the global budgets are the ones relevant to evaluate progress towards given temperature targets. Such an effort is currently being undertaken by the GHG community in the Regional Carbon Cycle Assessment and Processes phase-2 project (RECCAP-2), a research initiative coordinated by the Global Carbon Project [1].

Global datasets and models used in Global Carbon Budgets still show large discrepancies at regional scale, especially for small regions [2]. There are multiple reasons for such discrepancies. Among those are (i) large uncertainty in the spatial distribution of fluxes between atmospheric inversions, especially at the scale of small regions; (ii) poor agreement between atmospheric inversions and dynamic global vegetation models (DGVMs) in the sensitivity of carbon fluxes to climate variability and long-term trends; (ii) possible errors in the land-cover datasets used in the global runs; (iv) poor representation of disturbances such as fire. In the ESA-CCI RECCAP2 project, we evaluated in more detail some of these sources of uncertainty, namely the mismatch between atmospheric inversions and DGVMs (Ciais et al., submitted to this session) and the impacts of the land-cover datasets used to force DGVMs on estimated budgets and their variability, including the impact on fire dynamics.

Here, perform a set of simulations designed to quantify the uncertainty in carbon budgets as well as their variability and trends at the scale of the European RECCAP2 region as well as four countries used as case-studies: France, Germany, Italy and the UK. To constrain uncertainties from process representation and parameterization in DGVMs, we use three DGVMs: JULES, OCN and ORCHIDEE-MICT. The simulations were forced with two different land-use datasets: the Land-Use Harmonization v2 dataset used in the Global Carbon Budget 2020 [3] and the HIstoric Land Dynamics Assessment+ (HILDA+) land-use reconstruction [4]. HILDA+ reconstructs annual land use/cover change between 1960-2019 at 1 km spatial resolution, a much finer resolution than the typical scale of the land-use change (LUC) forcings used in global carbon budgets (0.25 degree for LUH2). Furthermore, HILDA+ integrates multiple data streams, from high-resolution remote sensing, to long-term land use reconstructions and statistics. These features are expected to improve estimates of fluxes from land-use and land-cover changes by DGVMs, but can also affect long-term trends and variability in net biospheric fluxes simulated by models.
Our results show that LUC forcing contributes to small differences in the net carbon budgets and their trends at European scale compared to between-model uncertainty, while it contributes to differences comparable to between-model uncertainty for land-use change fluxes (FLUC). The impact of the LUC forcing on carbon budget varies between countries and models, and given the large uncertainty of the reference datasets used to evaluate simulated fluxes (atmospheric inversions for the net budgets and bookkeeping models for FLUC), no clear pattern emerges as to whether one particular forcing leads to considerable improvements. In fire-prone regions such as Italy, the underlying land-use maps have strong influence in simulated fire emissions, but uncertainties are also dominated by between-model differences.




[1] “Global Carbon Project (GCP).” https://www.globalcarbonproject.org/reccap/index.htm (accessed Nov. 26, 2021).
[2] A. Bastos et al., “Sources of uncertainty in regional and global terrestrial CO2‐exchange estimates,” Glob. Biogeochem. Cycles, 2020.
[3] P. Friedlingstein et al., “Global Carbon Budget 2020,” Earth Syst. Sci. Data, vol. 12, no. 4, pp. 3269–3340, Dec. 2020, doi: https://doi.org/10.5194/essd-12-3269-2020.
[4] K. Winkler, R. Fuchs, M. Rounsevell, and M. Herold, “Global land use changes are four times greater than previously estimated,” Nat. Commun., vol. 12, no. 1, p. 2501, May 2021, doi: 10.1038/s41467-021-22702-2.

The Amazon Forest plays an important role in the global carbon cycle due to its large contribution to the land carbon (C) sink. However, historically it has been impacted by land use and land cover changes (LULCC) and forest fires which exacerbated during the interaction of deforestation and extreme droughts events. Studies based on field measurements and top-down estimates (inversions and remote sensing data) show a decreased trend in Amazon land C sink. Still, there are large uncertainties across top-down and bottom-up, i.e. Dynamic Global Vegetation Models (DGVMs), in terms of the spatial-temporal land C sink over Amazon. Improvements on DGVMs have been made in their representation of LULCC processes, however, further improvements and understanding of fire dynamics are needed to better estimate and represent fire emissions in DGVMs. Remote sensing-based estimates of fire emissions, such as those from the Global Fire Emissions Database (GFED4) are based on net fire emissions and assume forest fires area as carbon neutral, i.e. no postfire legacy effects on the C balance of burned forests. However, a recent study shows that net annual emissions peak 4 years after occurrence of forest fires. It is therefore paramount to account for these long-term impacts of forest fires on Amazon C emissions in order to improve the Amazon carbon budget estimates. Spatial-temporal assessments of forest fire emission estimates are possible using a range of satellite products available. In this study, a remote sensing approach is used to estimate these long-term net C emissions by forest fires in the Amazon forests. The burned area product (Fire CCI 5.1.1.) and the static biomass maps from ESA CCI are used to apply the FATE bookkeeping model to estimate the net emissions from forest fires in the Amazon. Finally, we synthetize these results with top-down and bottom-up estimates to deliver a better understand of the current trends of the Amazon natural C sink.

Introduction:

Forested ecosystems provide a significant carbon sink, absorbing roughly 3 billion tons of anthropogenic carbon annually (Canadell & Raupach, 2008). The boreal represents the largest biome in the world, 552 Mha of which is found in Canada, accounting for 28% of the boreal ecosystem globally (Brandt et al., 2013). Understanding the Earth System processes that drive land cover change and vegetation productivity in Canadian boreal ecosystems is therefore critical for accurate assessments of carbon dynamics and accumulation. Although many studies have been undertaken to understand the productivity and carbon cycle in managed forests in southern Canada (Kurz et al., 2009), less is known about carbon dynamics and land cover change transitions in other key ecosystems. In the Canadian boreal, three main sources of uncertainty stand out from the literature: the impact of the warming climate on the northern treeline, carbon estimates in wetland landscapes, and the implications of permafrost thaw. Understanding changes in carbon dynamics and land cover transition in these environments is of paramount concern, yet our carbon balance estimates for these environments are limited due to several key reasons. Lack of accessibility and significant cost are key drivers behind the lack of field studies in the remote environments in question, which has led to a lack of temporally and spatially dense ground based datasets of carbon dynamics (Lees et al., 2019; Srinet et al., 2020).

Current models of ecosystem carbon exchange driven by remote sensing still require input of ground based meteorological measurements and utilize look-up tables based on plant functional type, which limits their utility in remote areas where ground-based observations do not exist (Jones et al., 2017). In addition, there is often a scale mismatch between ground-based observations and remote sensing drivers introducing possible errors or limits when using models to make carbon exchange estimates in highly heterogenous landscapes such as the Canadian boreal. Exclusively remote sensing based methods represent an approach by which we can more directly assess changes in carbon dynamics and land cover transitions without the need for ground-based inputs, and offer a significant opportunity for addressing the sources of uncertainty and improving our predictions of future changes in these heterogenous environments (Lees et al., 2020; Schimel et al., 2015; Sims et al., 2008).

Methods:

In this paper we present some key components of a new analytical model for Canadian terrestrial vegetation carbon productivity mapping and monitoring. We exploit well established links between vegetation greenness and land surface temperature (Sims et al., 2008), and apply these to the data acquired by the European Space Agency (ESA) Sentinel-2 and -3 satellite datasets and compare these to longer time series acquisitions from the National Aeronautics and Space Administration’s (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) data. We also examine the information conveyed by microwave remote sensing data layers, from the ESA Soil Moisture and Ocean Salinity (SMOS) mission in regulating these productivity estimates under changing freeze / thaw conditions using a Hidden Markov Model (HMM) algorithm applied to the SMOS freeze/thaw data product. The SMOS data is utilized to inform on modelling transitions of freeze/thaw, estimates of growing season length, and the impacts of permafrost thaw on boreal vegetation dynamics.

Results:

Using existing land cover information to stratify key wetland and tree line focus sites across the boreal regions of the Yukon, Quebec, and Ontario, we applied our model at a continuous 7-day time-step at 30m spatial resolution from 2016-2020. Seasonal and annual photosynthetic terrestrial carbon sequestration and freeze / thaw dynamics by land cover class were then examined to improve our understanding of land cover transitions and their implications regarding vegetation productivity. We end with a discussion on the future integration of other recently acquired remote sensing datasets which can inform on the influence of soil moisture and other processes on terrestrial carbon sequestration and accumulation in the Canadian boreal.

References:

Brandt, J. P., Flannigan, M. D., Maynard, D. G., Thompson, I. D., & Volney, W. J. A. (2013). An introduction to Canada’s boreal zone: ecosystem processes, health, sustainability, and environmental issues. Environmental Reviews, 21(4), 207–226. https://doi.org/10.1139/er-2013-0040

Canadell, J. G., & Raupach, M. R. (2008). Managing Forests for Climate Change Mitigation . In Science (American Association for the Advancement of Science) (Vol. 320, Issue 5882, pp. 1456–1457). American Association for the Advancement of Science . https://doi.org/10.1126/science.1155458

Jones, L. A., Kimball, J. S., Reichle, R. H., Madani, N., Glassy, J., Ardizzone, J. V, Colliander, A., Cleverly, J., Desai, A. R., Eamus, D., Euskirchen, E. S., Hutley, L., Macfarlane, C., & Scott, R. L. (2017). The SMAP Level 4 Carbon Product for Monitoring Ecosystem Land-Atmosphere CO2 Exchange . In IEEE transactions on geoscience and remote sensing (Vol. 55, Issue 11, pp. 6517–6532). IEEE . https://doi.org/10.1109/TGRS.2017.2729343

Kurz, W. A., Dymond, C. C., White, T. M., Stinson, G., Shaw, C. H., Rampley, G. J., Smyth, C., Simpson, B. N., Neilson, E. T., Trofymow, J. A., Metsaranta, J., & Apps, M. J. (2009). CBM-CFS3: A model of carbon-dynamics in forestry and land-use change implementing IPCC standards . In Ecological modelling (Vol. 220, Issue 4, pp. 480–504). Elsevier B.V . https://doi.org/10.1016/j.ecolmodel.2008.10.018

Lees, K., Khomik, M., Quaife, T., Clark, J., Hill, T., Klein, D., Ritson, J., & Artz, R. (2020). Assessing the reliability of peatland GPP measurements by remote sensing: From plot to landscape scale. In The Science of the total environment (Vol. 766, p. 142613). Elsevier B.V. https://doi.org/10.1016/j.scitotenv.2020.142613

Lees, K., Quaife, T., Artz, R. R. E., Khomik, M., Sottocornola, M., Kiely, G., Hambley, G., Hill, T., Saunders, M., Cowie, N. R., Ritson, J., & Clark, J. M. (2019). A model of gross primary productivity based on satellite data suggests formerly afforested peatlands undergoing restoration regain full photosynthesis capacity after five to ten years. In Journal of environmental management (Vol. 246, pp. 594–604). Elsevier Ltd. https://doi.org/10.1016/j.jenvman.2019.03.040

Schimel, D., Pavlick, R., Fisher, J. B., Asner, G. P., Saatchi, S., Townsend, P., Miller, C., Frankenberg, C., Hibbard, K., Cox, P., & Pacific Northwest National Lab. (PNNL) WA (United States), R. (2015). Observing terrestrial ecosystems and the carbon cycle from space . In Global change biology (Vol. 21, Issue 5, pp. 1762–1776). Blackwell Publishing Ltd . https://doi.org/10.1111/gcb.12822

Sims, D. A., Rahman, A. F., Cordova, V. D., El-Masri, B. Z., Baldocchi, D. D., Bolstad, P. V, Flanagan, L. B., Goldstein, A. H., Hollinger, D. Y., Misson, L., Monson, R. K., Oechel, W. C., Schmid, H. P., Wofsy, S. C., & Xu, L. (2008). A new model of gross primary productivity for North American ecosystems based solely on the enhanced vegetation index and land surface temperature from MODIS . In Remote sensing of environment (Vol. 112, Issue 4, pp. 1633–1646). Elsevier Inc . https://doi.org/10.1016/j.rse.2007.08.004

Srinet, R., Nandy, S., Watham, T., Padalia, H., Patel, N. R., & Chauhan, P. (2020). Spatio-temporal variability of gross primary productivity in moist and dry deciduous plant functional types of Northwest Himalayan foothills of India using temperature-greenness model . In Geocarto international (pp. 1–13). https://doi.org/10.1080/10106049.2020.1801855