This limited observation base gives rise to high uncertainty regarding the future terrestrial carbon sink. Many questions need answer to determine if it will be sustained under future environmental changes, or whether increases in autotrophic respiration or carbon turnover might counteract this negative feedback to climate change. For instance, will accelerated background tree mortality or more frequent and more severe disturbance events (e.g. drought, fire, insect outbreaks) turn vegetation into carbon sources? How will shifts in dynamics of plant mortality, establishment, and growth influence forest composition?
Uncertainties and/or data gaps in large-scale empirical products of vegetation dynamics, carbon fluxes and stocks may be overcome by extensive collections of field data and new satellite retrievals of forest biomass and other vegetation properties. Such novel datasets may be used to evaluate, develop and parametrize global vegetation models and hence to constrain present and future simulations of vegetation dynamics. Where no observations exist, exploratory modelling can investigate realistic responses and identify necessary measurements. We welcome contributions that make use of observational approaches, vegetation models, or model-data integration techniques to advance understanding of the effects of environmental change on vegetation dynamics, tree mortality as well as carbon stocks and fluxes at local, regional or global scales and/or over long periods.
Orals: Mon, 28 Apr | Room N1
Tropical forests are dynamic ecosystems shaped by deforestation, degradation, and recovery processes, with consequences for the carbon cycle. While emissions from deforestation have been well understood and quantified, information on emissions from degradation such as fire, logging, windrow and drought remain relatively poorly quantified, reflecting the complexity of these processes in space and time. Similarly, the carbon recovery potential of degraded forests is understudied compared to secondary forests regrowing after deforestation. Closing these knowledge gaps is crucial to reduce uncertainties in estimates of the tropical carbon budget and for addressing the priorities of international climate policies, which increasingly emphasize the value of protecting and restoring forests, without which we cannot constrain global warming to critical limits.
In recent years, research on carbon emissions and removals in tropical forests has surged, driven in part by advancements in Earth Observation. Here we synthesize these approaches with the aim to bring clarity and advance our understanding on aboveground carbon (AGC) emission and removal factors applicable for tropical moist forests. We contextualise the current studies, highlighting where there are sufficient data estimates to quantify emissions and removals post-disturbance, and where specific kinds of estimates are lacking.
Our synthesis of 66 studies of AGC loss due to disturbance shows emission estimates vary widely across disturbance types: average AGC losses are 3% (range 1–4%) for extreme drought, 27% (range 3–75%) for selective logging, and 52% (range 9–83%) for fire, relative to nearby und previously undisturbed forest. Our analysis underscores the need to account for disturbance severity, frequency and the cumulative effects of interacting disturbances to reduce variability between emissions estimates.
For AGC recovery, our synthesis of 68 studies indicates that degraded forests regained 41–117% of AGC within 20 years relative to undisturbed forests; significantly higher than forests regrowing from deforestation, which regained between 1% and 74% of undisturbed forest AGC. Younger recovering forests (<20 years) exhibit higher absolute regrowth rates, compared to older ones (> 20 years). In the Amazon region, where we have the greatest number of field site and region-specific remote sensing data, we see good agreement between field- and satellite- derived regrowth rate estimates. Remote sensing data therefore has the potential to fill the gaps in our spatial knowledge where field data is limited.
Our results also highlight some of the major gaps that still exist to provide long-lasting and relevant information into the policy and wider carbon budget science domain. Key research needs include: (i) reducing the variability of emission factors within disturbance types by further stratifying according to disturbance severity, frequency and co-occuring disturbances, (ii) addressing the research bias towards the Americas, particularly the Amazon, by expanding studies to areas where there are currently fewer estimates. Finally, we call for a more integrated approach between research focusing on deforestation, degradation and regrowth, recovery and consider these processes as interconnected, co-occurring and influencing each other in space and time.
How to cite: Heinrich, V., Holcomb, A., Besnard, S., Requena Suarez, D., Wheeler, C., Bourgoin, C., Cook-Patton, S., Robinson, N., Gibbs, D., Xu, Y., Chen, N., Silva Junior, C., Aragão, L., and Herold, M.: Too few, too many, or just the right number of estimates? Goldilock’s problem on post-disturbance carbon emissions and removal factors in tropical forests., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11778, https://doi.org/10.5194/egusphere-egu25-11778, 2025.
The European Alps are currently considered among the ecoregions with the highest magnitude of average bark beetle disturbance per year. We present a disturbance regime characterization based on a unique database including more than 50,000 records of ground-based bark beetle disturbance observations in the Eastern Alps for the years 2020 to 2023. The dataset was used to extract precise temporal and spatial information on disturbance events in terms of sizes, distances, intensity and frequency. Disturbance events were modeled as spatial point processes based on scale dependency (landscape-regional) and their deviation from random distributions was assessed. Parameters typically used in forest disturbance models such as clustering degree, intensity slope and probability scale were retrieved. Additionally, above-ground biomass loss was estimated. The disturbance metrics and parameters can help for the correct parameterization of forest disturbance models, and thus supporting our capability of predicting future patterns of beetle dispersal and effects on carbon stocks in the alpine region and beyond.
How to cite: Candotti, A., Carvalhais, N., Wang, S., and Tomelleri, E.: Spatial and temporal dynamics of a bark beetle-induced forest disturbance regime , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1590, https://doi.org/10.5194/egusphere-egu25-1590, 2025.
Forests provide essential ecosystem services, from carbon sequestration and biodiversity conservation to soil protection and socio-economic benefits. Understanding forest regeneration patterns is crucial for predicting future forest composition and ensuring the continued provision of these services. While long-term forest monitoring is well-established in Europe and in particular Switzerland through the National Forest Inventories (NFI) comprehensive analyses of regeneration trends across different forest communities remain limited.
This study analyzes 20 years of regeneration data from the Swiss NFI's presence plots, spanning three inventory periods (NFI3: 2004-2006, NFI4: 2009-2017, NFI5: 2018-ongoing). We examine regeneration patterns across major forest communities, including beech, fir-beech, and fir-spruce forests, focusing on presence data for key tree species in two height categories: 40-130 cm and above 130 cm to 11.9 cm DBH. The presence plot methodology, implemented since NFI3, surveys 200 m² sampling areas, providing standardized data on species occurrence in the regeneration layer.
Our analysis reveals significant temporal trends in species presence across different forest communities, identifying both increasing and decreasing patterns in regeneration success. Preliminary results for beech and fir-beech communities show distinct regeneration patterns: while most conifer species display stable or slightly increasing trends, we observe a notable expansion in deciduous tree presence, particularly beech and maple species. A concerning pattern emerges for European ash, showing a consistent decline across different forest communities. These findings provide crucial insights into the dynamics of Swiss forest regeneration and potential future forest composition.
This comprehensive assessment of regeneration trends across Switzerland's diverse forest ecosystems offers valuable information for forest managers and policymakers, supporting evidence-based decisions in forest management and conservation strategies. The results contribute to our understanding of forest ecosystem resilience and adaptation potential in the face of environmental change.
How to cite: Molteni, U., Abegg, M., Kupferschmid, A. D., Moser, B., Nikolova, P. S., Scherrer, D., and Wohlgemuth, T.: Large-Scale Forest Regeneration Dynamics Over Two Decades in Central Europe: A Representative Analysis , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17002, https://doi.org/10.5194/egusphere-egu25-17002, 2025.
The turnover time of forest biomass carbon is highly dynamic across space and time and is projected to decrease due to the acceleration of land use change and disturbance. However, turnover time may also shift due to changes in within-tree carbon allocation and species composition, processes that are highly unresolved. Using trait datasets and forest surveys, we developed US-wide maps of carbon contained in tree structural pools (leaves, stems, coarse roots, and fine roots), from which we derived forest biomass carbon turnover time. We found that hotter and wetter forests across the US experience lower carbon turnover time, primarily due to differences in tissue longevity and carbon allocation across species. We then tested the extent to which two mechanisms – shifts in carbon allocation and species composition – may affect carbon allocation and turnover time into the future using species distribution modeling and an individual-based tree model that can simulate changes in allocation. Turnover time generally decreased in the future across all methods, but the magnitude of this change, along with its underlying mechanisms, differed greatly depending on model type. This work underscores the need for expanded observations of carbon allocation in field settings on mature trees, and hints at the promise of optimality-based allocation models. Importantly, our results can be used to identify hotspots of carbon sequestration and constrain the sources of uncertainty in future forest carbon turnover time.
How to cite: Kannenberg, S., Anderegg, W., Driscoll, A., Mathias, J., and Wu, C.: The present and future of US forest carbon allocation and turnover time, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2299, https://doi.org/10.5194/egusphere-egu25-2299, 2025.
Understanding carbon cycle dynamics during forest succession is essential for predicting ecosystem responses to environmental change. Vegetation Demographic Models (VDMs), which include detailed demographic processes, offer valuable insights into forest successional dynamics. However, the high complexity of model structure can obscure our understanding of simulated ecosystem carbon dynamics. To address this, we developed a traceability framework to decompose VDM simulations of carbon storage into distinct, traceable components associated with different plant functional types (PFTs). Specifically, the transient carbon storage can be partitioned into three hierarchical layers: (i) carbon storage capacity (Xc) and potential (Xp); (ii) net primary production (NPP), carbon residence time (τN), net carbon pool change (X'), and carbon chasing time (τch); (iii) carbon allocation, transfer, and turnover rates. We applied this framework to a cohort-based VDM, Biome Ecological strategy simulator (BiomeE), and evaluated its utility using field observations of 72 species across three plots spanning 150 years of succession in a subtropical forest. The results showed that early succession exhibited high PFTs diversity, including evergreen broadleaf trees, evergreen broadleaf shrubs, evergreen needleleaf trees, deciduous broadleaf trees, and deciduous broadleaf shrubs, driving rapid increases in Xc and Xp. As succession progressed, deciduous PFTs declined, and evergreen broadleaf trees dominated carbon dynamics, with ecosystem carbon storage reaching approximately 40 kg C m-2 during the mid-succession stage. In the late successional stage, ecosystem carbon storage stabilized at 75 kg C m-2, closely approaching Xc, which is supported by high NPP (1.37 kg C m-2year-1) and long τN (70 years), while Xp and carbon sink strength declined. During succession, evergreen broadleaf trees contributed the most to carbon sequestration, with evergreen broadleaf trees (83.73%) > evergreen needleleaf trees (8.11%) > evergreen broadleaf shrubs (5.24%) > deciduous broadleaf trees (2.39%) > deciduous broadleaf shrubs (0.52%). These findings highlight the critical role of successional shifts in forest structure in shaping carbon dynamics in subtropical regions.
How to cite: Chen, L., Bian, C., Wei, N., Lu, R., Wan, F., Xia, X., Cui, E., Weng, E., Jiang, L., Luo, Y., and Xia, J.: Traceability analysis of forest carbon dynamics with a matrix-represented vegetation demographic model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5508, https://doi.org/10.5194/egusphere-egu25-5508, 2025.
Accurate estimations on forest above-ground biomass (AGB) are essential for improving our ability to simulate vegetation response to climate and assess the future role of forests as carbon sinks. In this context, Dynamic Global Vegetation Models (DGVMs) are the most important and rapidly evolving tool to estimate forest carbon dynamics and its potential trajectories in a warmer future at regional to global scales. However, DGVMs remain rather unprecise in their estimation of biomass components due to the lack of representation of growth processes within the model. Thus, enhancing our forest modelling skills at regional and global scales does not only depend on improving the photosynthetic or ecophysiological module (carbon uptake) but to correctly account for allocation into biomass (carbon storage). In this study, we aim to provide a refined, annually resolved empirical AGB estimates that serves as an accurate benchmark to assess the reliability of DGVMs biomass simulations.
To achieve this, we integrate a network of 230 National Forest Inventory (NFI) plots with tree-ring width data collected from the same locations at the Iberian Peninsula, using both frequentist and Bayesian approaches. The NFI data offer detailed forest structure information at the tree and stand levels, typically recorded at 10-year intervals, while tree-ring data provide a reliable measure of annual tree growth. We retrospectively interpolate annual estimates of diameter at breast height (DBH) in trees from NFI plots based on tree-ring width measurements, the climate drivers of tree growth and stand variables. These estimated DBH values are then used to calculate AGB.
Based on our estimated AGB, we assessed the annual net biomass change (NBC) for the last three decades, which allowed us to infer the impact of interannual climate variability and extreme climate events on forest biomass change. We compared the estimated NBC with Net Primary Production (NPP) outputs from a selected set of DGVMs included in the TRENDY initiative. Our results revealed a general discrepancy between the simulated NPP and the NBC estimates, particularly evident when analyzing the biomass response to extreme climate events. During years marked by extreme summer droughts, such as 1994, 1995, 2003, and 2012, the spatial patterns of NPP anomalies were inconsistent with those observed in the NBC estimates. This discrepancy became even more pronounced during consecutive extreme climate events. During consecutive events, the simulated NPP showed a marked decline during the first year, whereas NBC estimates revealed that drought-induced biomass reduction became more pronounced in the following year due to the legacy effects. These results reflect the source-driven structural deficiency in DGVMs. Incorporating detailed growth dynamics and recovery trajectories into DGVMs is essential for improving their accuracy in a changing climate. In this context, the fusion of tree-ring and NFI data represents a significant advancement, not only for benchmarking DGVMs but also for improving data assimilation procedures.
How to cite: Şahan, E. A., Aguirre Arnáiz, A., Heilman, K. A., Condés, S., Moreno Fernández, D., Alberdi Asensio, I., Cañellas, I., Miranda, J. C., and Dorado-Liñán, I.: Can Tree Rings Help to Refine Vegetation Modelling?: Fused Empirical Data for Benchmarking Forest Biomass Estimates, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13253, https://doi.org/10.5194/egusphere-egu25-13253, 2025.
Plants have a high capacity to both adapt to long term climate condition and to acclimate in response to short-term environmental changes. Superimposed on these individual plant responses, changes in environmental conditions and competition amongst individuals drive shifts in species composition and all these together drive changes in ecosystem function. Historically, vegetation models represented plants as rigid, with little or no capacity to react to change, with a basic representation of biome shift, leading to sometimes unexpected and unrealistic predictions of vegetation shifts under future conditions. Since, models have advanced both in terms of plasticity and vegetation dynamics representations although to some extent on parallel tracks, with little exploration of the interactions between the two. Using the QUINCY land surface model, we explore the implications of representing plant plasticity on both short and long timescales as well as the effect of competitive pressures. We use data from networks of manipulative experiments – DrougthNet and the Nutrient Network – to disentangle the extent to which plastic responses to stressors are general across the globe or adapted to specific conditions. The drought and nutrient manipulation also give us the opportunity to explore concepts around belowground competition for resources, which has been included in models to a far lesser extent than aboveground competition for light. While questions around the effect of increased model complexity remain, increased ecological realism and the inclusion of all relevant processes and their interaction improves our understanding and predictive capability of future vegetation dynamics.
How to cite: Caldararu, S.: From plant plasticity to demography: modelling plant responses to global change across timescales, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16794, https://doi.org/10.5194/egusphere-egu25-16794, 2025.
Terrestrial ecosystems play a major role in the global carbon (C) cycle. However, our ability to quantify where in the world is a net C source or sink, and to what extent this is changing continues to be a critical challenge. Terrestrial ecosystems are responsible for the largest C fluxes in the world dwarfing anthropogenic emissions from fossil fuels. These processes are sensitive to climatic and anthropogenic disturbances on varied scales in time and space. This complex interconnection of internal ecosystem processes and external exchanges, mediated by ecosystem properties, challenges both observation and process-based modelling efforts to understand and quantify ecosystem C exchanges.
The expansion of satellite-based Earth Observation (EO) has provided unprecedented information at global scales on the state and evolution of terrestrial ecosystems. Increasingly, these data are provided with more robust estimates of their uncertainties and their variation in space and time. Process-models of terrestrial ecosystems have advanced with our growing ecological understanding derived from in-situ information. However, while there is great potential for EO to contribute to model calibration and validation, helping diagnose ecological function and improve model predictive skill, at present the connections between EO and process models are weakly developed.
Bayesian model-data fusion (data assimilation) approaches offer a powerful opportunity to integrate EO and process-models by informing the model parameter calibration with a diverse, location-specific array of complementary ecologically relevant observations, fully propagating their uncertainties. In this study, will use the state-of-the-art CARDAMOM Bayesian calibration framework to retrieve parameters for a process-based model of the terrestrial ecosystem (DALEC).
We will present a global (0.5 x 0.5 degree) analysis of the global carbon and water cycles for a 21-year period (2003-2023). CARDAMOM is applied uniquely at each 0.5 degree pixel, retrieving uncertainty bounded estimates of DALEC parameters as a function of information available for that location. From these ‘local’ parameters we estimate the state and dynamics of terrestrial ecosystems with fully realised uncertainties in space and time.
Our analysis will identify where in the world we have confidence in source / sink dynamics and diagnose environmental relationships driving current trajectories, including the large growth in atmospheric CO2 concentrations in 2023. Our preliminary analyses suggest this increase is driven by elevated fire activity, particularly in south west Amazon and Canadian boreal forests, and broad enhancement of heterotrophic respiration driven by warming.
How to cite: Smallman, T. and Williams, M.: Where in the world are we confident in terrestrial carbon balance?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16398, https://doi.org/10.5194/egusphere-egu25-16398, 2025.
Over the past several decades, terrestrial ecosystems have offset a ≈30% of anthropogenic CO2 emissions through increased CO2 uptake. While this carbon enters the biosphere through photosynthesis into biomass, its current distribution across different pools—such as live biomass, dead biomass, and soil and sedimentary organic carbon—remains uncertain. The partitioning of carbon into these pools impacts future terrestrial carbon storage because they have different turnover times and sensitivities to environmental change. By harmonizing a set of global estimates for changes in live woody biomass, we found that while ≈35±14 gigatons of carbon (GtC) have been sequestered on land between 1992-2019, live woody biomass changed by only ≈2±7 GtC. These findings contrasted with results from global vegetation models, which show sustained increases in live biomass. We highlight key processes that are not currently included in models and can account for a large fraction of this discrepancy such as forest degradation or fluxes related to inland waters. We infer that recent gains in terrestrial carbon stocks are sequestered as non-living organic matter in a combination of dead biomass, soils, and other sedimentary deposits. These results suggest that terrestrial carbon accumulated in recent decades might be more persistent than previously appreciated, and that a substantial fraction of it is intimately linked to human activities such as river damming, wood harvest, and waste treatment.
How to cite: Bar-On, Y., Li, X., O'Sullivan, M., Wigneron, J.-P., Sitch, S., Ciais, P., Frankenberg, C., and Fischer, W.: Quantifying the Global and Regional Contribution of Terrestrial Carbon Pools to the Land Sink, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20625, https://doi.org/10.5194/egusphere-egu25-20625, 2025.
The increase in the vegetation carbon uptake, stimulated in the last decades by the elevated CO2 concentration (eCO2), has substantially contributed to the enhancement of terrestrial carbon sink, thus playing a crucial role in mitigating climate change. Changes in the vegetation carbon uptake are affected by eCO2 through two distinct pathways. The first is the direct CO2 effect through the stimulation of the photosynthetic carbon fixation and the increase in water-use efficiency. The second is the indirect CO2 effect through the change in climate and related environmental conditions. Recent studies documented a declining trend in the direct physiological effect of eCO2 on the vegetation carbon sink because of the increasing role of other limiting factors (e.g., nutrients and water availability). Consequently, the indirect effects of eCO2 via associated climate change are expected to become increasingly important in controlling the terrestrial carbon budget. However, the current and future dynamics of such indirect CO2 effects and the underlying ecological mechanisms remain unclear. Here we investigate how the impacts of eCO2-driven climate change on growing-season gross primary production (GPP) have changed globally during the period 1982-2014, using both satellite observations and a suite of CMIP6 Earth system models, and evaluated their evolution until the year 2100 under the high emission scenario SSP5-8.5. We show that the initial positive effect of eCO2-induced climate change on global vegetation carbon uptake has declined significantly during recent decades. In this respect, this indirect effect has shifted to negative in the early 21st century, and is expected to turn firmly negative in the future. Such a decrease in the indirect effect of eCO2 appears more pronounced in northern high latitudes and occurs in combination with a concomitant decrease of the direct physiological effect of eCO2. Together, these changes will likely determine a sharp reduction of the current strong growth benefits induced by climate warming and CO2 fertilization in boreal ecosystems. The progressive weakening of the indirect CO2 effect on vegetation carbon uptake can be partially attributed to the widespread climate drying, except for some non-humid regions where the CO2 and drought-related increase in water-use efficiency potentially relaxes the water limitation to vegetation growth. These results imply that eCO2 may exert a less positive up to negative role on the terrestrial carbon uptake in the near future, ultimately reducing the ecosystems’ capacity to sequester atmospheric CO2. All together these findings contribute to a better understanding of the factors controlling the negative feedback between atmospheric CO2 concentration and the natural terrestrial sink and highlight a worrying decline in its strength that might ultimately lead to an acceleration of climate warming. Consequently, stronger reductions in anthropogenic emissions will be required to meet climate goals.
How to cite: Chen, Z., Forzieri, G., and Cescatti, A.: Transition from positive to negative indirect CO2 effects on the vegetation carbon uptake, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1533, https://doi.org/10.5194/egusphere-egu25-1533, 2025.
Over the past few decades, China has implemented large-scale Forest Ecological Engineering Projects (FEEPs) aimed at restoring and enhancing ecosystem functions. However, global warming has exacerbated the frequency, intensity, and duration of droughts, which may undermine the positive effects of these ecological engineering programs on greening and carbon sequestration. This study utilizes remote sensing indices, namely the Normalized Difference Vegetation Index (NDVI) and Gross Primary Productivity (GPP), to represent vegetation greenness and productivity, respectively. To extract long-term vegetation change trends across eight FEEPs in China, we apply the Ensemble Empirical Mode Decomposition (EEMD) method. Additionally, a multi-scale Standardized Precipitation Evapotranspiration Index (SPEI) is used to assess the sensitivity and response of vegetation greenness and productivity to drought conditions. The results reveal a monotonic increasing trend in both NDVI and GPP across the eight FEEPs, but the rate of increase in GPP in regions such as the Shelterbelt Program for Liaohe River (SPLR), the Afforestation Program for Taihang Mountain (APTM), the Shelterbelt Program for Pearl River (SPPR), and the Coastal Shelterbelt Program (CSP) is significantly lower than that of NDVI. Before 2000, changes in NDVI and GPP followed relatively consistent trajectories. However, a divergence between these two indices became evident after 2000, particularly during the prolonged drought period from 2000 to 2009. The opposite trend between greenness and productivity in humid ecosystems during drought periods mainly caused this trend difference. In humid ecosystems, short-term drought promotes vegetation greening, while long-term drought has no significant impact on greenness. Our findings highlight the complex dynamics of vegetation growth in the context of climate change and underscore the challenges posed by drought in maintaining the effectiveness of afforestation and greening efforts.
How to cite: Zheng, L. and Wu, H.: Drought offsets gross primary productivity benefits from the afforestation initiatives-induced greening in China, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2204, https://doi.org/10.5194/egusphere-egu25-2204, 2025.
Terrestrial ecosystem plays a significant role in global carbon budget. Understanding the variations and controlling mechanisms of ecosystem carbon fluxes is crucial for comprehending carbon cycles and assessing carbon budget. Using observed flux data, this study examined the spatial variation and influencing factors of carbon fluxes across China's terrestrial ecosystems. The results show that typical terrestrial ecosystems in China generally act as carbon sinks. There are clear geographical patterns in carbon fluxes, which tend to decrease linearly with increasing latitude and altitude, while increase linearly with increasing longitude. Carbon fluxes are positively correlated with mean annual temperature, mean annual precipitation, fractional vegetation cover, and leaf area index, while they show negative correlations with mean annual radiation. From 2002 to 2020, China’s terrestrial ecosystem productivity exhibited a slight increasing trend. The structural and functional properties contribute to this trend, with varying regional contributions. In the Northern regions of China, increasing structural properties, such as leaf area index, play a dominant role, while in the Southern and West regions, photosynthetic capacity is more significant. This study enhanced the understanding of the geographical patterns of carbon fluxes in China and provided a theoretical foundation for assessing the regional carbon budgets.
How to cite: Chen, Z., Yu, G., and Zhang, W.: Variation and influencing factors of terrestrial ecosystem carbon fluxes in China, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2437, https://doi.org/10.5194/egusphere-egu25-2437, 2025.
Energy and water are two essential requirements for photosynthesis, and clouds influence both simultaneously by altering radiation and precipitation. Cloud-induced reductions in surface solar radiation and enhancements in precipitation have contrasting effects on photosynthesis. In our study, eddy covariance measurements, satellite observations and dynamic global vegetation models are used to examine the response of photosynthesis to cloud cover on a global scale. The results show that the photosynthesis sensitivity to cloud cover varies across ecosystems, and its global pattern is highly associated with aridity. Specifically, in water-limited dry regions, clouds promote photosynthesis by forming precipitation, while in energy-limited humid regions, clouds inhibit photosynthesis by blocking sunlight. The spatially dependent sensitivity of photosynthesis to cloud cover is further used to estimate projected changes in vegetation productivity driven by clouds.
How to cite: Luo, H. and Quaas, J.: Photosynthesis sensitivity to cloud cover is shaped by aridity on a global scale, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4062, https://doi.org/10.5194/egusphere-egu25-4062, 2025.
Forestry protection constituted a fundamental element of China's forest management policy for the past four decades, which has played a critical role in increasing forest area and biomass stock. Nevertheless, an efficacious forest management policy should balance the dual roles of forests, serving as both carbon sinks and ecosystems, while also satisfying human demands for wood production. As one of the largest developing countries, China exhibited a significant increase in wood consumption from 49.42 million m3 in 1980 to 534.18 million m3 in 2020. However, the forest management policy in China tends to strictly limit domestic wood harvest for production. These forest management policies were formulated 40 years ago according to the situations at that time. Here, we evaluated the continued reliability of these protection-based forest management policies in the context of significant changes in forest structure. Additionally, we proposed a new wood harvest scheme that can satisfy all domestic wood requirements and has no negative impacts on forest carbon sink and soil erosion.
How to cite: li, X. and yuan, W.: It is time to optimize forest management policy for both carbon sinks and wood harvest in China, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7621, https://doi.org/10.5194/egusphere-egu25-7621, 2025.
Land surface-atmosphere interactions are strongly influenced by vegetation, since the latter drives the exchange of energy, water and carbon at canopy level via transpiration and photosynthesis. These biochemical processes are related to both the stomatal response to meteorological variations (linking the canopy to the deepest soil layers), and the allocation of carbon in different parts of the plant such as roots and leaves.
In recent years, the characterization of these processes has gained increasing attention in land surface models (LSMs), which are powerful tools that reproduce the soil-plant-atmosphere continuum and the mutual feedback of its components. Vegetation in LSMs is described either statically -- based on a prescribed vegetation climatology or cover -- or dynamically, that is, evolving in time its characteristics such as leaf area index and vegetation cover fraction, among the others. However, the dynamic simulation of vegetation is often simplified in LSMs with respect to state-of-the-art bio-geophysical and forest models.
In the Noah Multi-Parametrization (Noah-MP, v. 4.0.1) LSM, multiple parametrizations are available for each individual sub-process scheme such as dynamic vegetation, runoff partitioning, groundwater recharge and radiative transfer through the canopy, among others. It is thus important to identify the land cover type, soil and climate characteristics of the specific study site and tailor the parametrization to find the “optimal” combination of sub-process schemes, i.e. the one which best reproduces in-situ observations.
In this study, we evaluate point-scale simulations generated using different parametrizations of dynamic vegetation schemes within Noah-MP, run in offline mode within the NASA’s Land Information System (LIS). We compare the LSM results of gross primary productivity, soil moisture and evapotranspiration over several years between 2000 and 2023 to both ground-based estimates and remote sensing datasets derived from multiple observations and platforms such as MODIS, OCO-2, MSG and FLUXCOM. The study focuses on sites along the Italian peninsula, mostly forests, with croplands and grasslands as well, some of which are equipped with Eddy-covariance stations for carbon and water fluxes measurements and are included in the FLUXNET network.
The sites are all natural, rain-fed ecosystems mostly located in drought-prone, Mediterranean regions. This study is meant to reveal previously neglected uncertainties in dynamic vegetation simulations, especially in dry regions, and to fine-tune the combination of sub-processes schemes in Noah-MP for future data assimilation experiments.
How to cite: Natali, M., Modanesi, S., De Lannoy, G., De Santis, D., Dalmonech, D., Collalti, A., Steele-Dunne, S., and Massari, C.: Evaluating gross primary productivity, soil moisture and evapotranspiration derived from multiple Noah-MP dynamic vegetation schemes and satellite observations across land cover types in the Mediterranean region, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8350, https://doi.org/10.5194/egusphere-egu25-8350, 2025.
Aerosols and clouds are key external factors that significantly influence the light-use efficiency of plants and their primary productivity. Since photosynthesis and transpiration are tightly linked through leaf stomata, the diffuse fertilization effect (DFE) can also impact water-use efficiency (WUE). However, surface measurements have not yet been systematically employed to enhance the ability of global models to capture these effects, particularly across pan-tropical regions. Current global models remain uncalibrated, lacking validation datasets necessary to accurately represent the annual variations in gross primary productivity (GPP) and WUE driven by the DFE. In this study, we assessed the influence of DFE on annual carbon uptake and water dynamics using traditional methods widely applied across diverse forest ecosystems globally. Our analysis utilized long-term micrometeorological data from the FLUXNET dataset in six evergreen broadleaf forest (EBF) ecosystems, located in northern South America (SA), Central Africa (AF), South Asia (AS), and Oceania (OC). Preliminary comparisons were conducted with GPP and ET values simulated using the EMAC/JSBACH numerical systems. Here, we show the optimal physiological thresholds for GPP and WUE under overcast/smoky sky conditions and the typical tipping points linked to irradiance relative (f). Our preliminary results from the FLUXNET dataset indicated GPP and WUE rates as high as 0.4-0.6 g C m-2 h-1 and ET 0.3-0.5 mm h-1 for f between 1.1-0.7 (± 0.39) and solar zenith angles (SZA) ranging 0°–80°. For f ≤ 0.6, GPP and ET decreased rapidly in the studied areas, with a total breakdown of photosynthesis and evapotranspiration reaching around f 0.2 (± 0.36). The EMAC/JSBACH systems satisfactorily reproduced the behavior of the observed variables (aforementioned). However, we found systematic overestimations of temperature and solar radiation compared to FLUXNET measurements (~35%), which also can explain the GPP overestimations. Systematic calibrations in the EMAC/JSBACH are still necessary to achieve more accurate estimates of annual carbon and water losses due to DFE. Potential outcomes and benefits include: (i) improved physical representation and performance of Dynamic Global Vegetation Models (DGVMs) across different Plant Functional Types (PFTs) and landscapes; (ii) identification of the physiological optimum of forests under conditions affected by wildfires or extreme drought periods; and (iii) quantification of annual global water and carbon losses in tropical forests caused by wildfires; enhancements or photodamage caused by exposure to biomass-burning organic aerosol (BBOA), along with improved representation of global carbon cycling.
How to cite: Cirino, G., Martin, A., Vella, R., Rodrigues, S., Palacios, R., Galbraith, D., and Pozzer, A.: Optimal Physiological Thresholds of Pan-Tropical Forests Using Surface Measurements and ECHAM/MESSy Atmospheric Chemistry Numerical Modeling, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8707, https://doi.org/10.5194/egusphere-egu25-8707, 2025.
Soil organic carbon (SOC) dynamics are driven by a complex matrix of factors, including local climate (e.g., temperature, precipitation), soil properties (e.g., mineralogy, clay content, cation-exchange capacity, pH), and land use (e.g., forests, grassland, arable agriculture) and its history. Each parcel of land carries a unique signature within this matrix, resulting in distinct SOC dynamics and varied responses to land-use changes and future climate scenarios.
European political and environmental strategies increasingly emphasize (re) afforestation as a key measure for climate change mitigation. This represents a full-circle transformation for European landscapes, many of which were historically deforested to accommodate grazing and agriculture. With rising reforestation rates, these landscapes are gradually returning to their forested states. However, with this, a critical ecological question arises: can SOC lost during initial forest-to-agriculture transitions be replenished within timeframes required for effective climate mitigation (typically decades)?
This study addresses this question by focusing on irrecoverable carbon stocks—SOC fractions lost during land-use transitions that cannot be restored quickly enough to meet climate targets. Using the terrestrial biosphere model T&C, which incorporates a microbially explicit soil biogeochemistry module to simulate carbon (C), nitrogen (N), and phosphorus (P) dynamics, we investigate SOC recovery potential and how this varies with land management.
Our proof-of-concept approach here presented involves a) validating the T&C model across multiple European sites undergoing forest-to-agriculture and agriculture-to-forest transitions and b) simulating SOC dynamics for a representative European grid cell experiencing these transitions under distinct Shared Socioeconomic Pathway (SSP) climate scenarios.
Such work is imperative to gain critical insights into the persistence of irrecoverable carbon stocks and the feasibility of SOC recovery through European (re)afforestation efforts.
How to cite: Buckley Paules, J., Paschalis, A., and Moustakis, Y.: Can SOC lost during initial forest-to-agriculture transitions be replenished within timeframes required for effective climate mitigation (typically decades)?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9050, https://doi.org/10.5194/egusphere-egu25-9050, 2025.
Eddy covariance measurements are increasingly utilized for assessing the exchange of matter and energy between ecosystems and the atmosphere across various time scales, ranging from hours to years. The flux footprint represents the area observable by flux tower sensors and illustrates how the surface influences the measured flux. Flux footprint models describe both the spatial extent and the specific location of the surface area contributing to the observed turbulent flux. In this study, we utilized a simple two-dimensional parameterization for flux footprint prediction (FFP) developed by Kljun et al. to identify the location of maximum footprint contribution every half hour over six years. These data were then subjected to monthly cluster analysis. Using QGIS, the resulting clusters were overlaid on a base map of the site obtained from the Estonian Land Board, where different compartments have varying growth stages and species composition. The main objective of this research was to integrate forest inventory data with ecosystem exchange and productivity data continuously recorded by the Eddy Covariance measurement tower at Järvselja, Estonia. The dataset obtained from the eddy covariance measurement technique was integrated with forestry inventory data, allowing half-hourly data to be selected and visualized using QGIS software.
How to cite: Thapa Magar, A., Padari, A., and Noe, S. M.: Modelling of forest ecosystem-atmosphere exchange and forest growth patterns with cluster analysis of Eddy covariance flux footprint data from SMEAR Estonia, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9416, https://doi.org/10.5194/egusphere-egu25-9416, 2025.
Global vegetation plays a major role in the Earth carbon budget, storing the largest carbon stock on land. Both direct human activities and natural evolution under a changing climate impact the state of global forests, leading to regional decreases or vegetation growth. Monitoring these variations over long time periods can help better constrain estimates of the land carbon sink, and understand the driving forces of the cyclical and long term variations. This enables a refined understanding of climate effects and policies impact on current and future global vegetation carbon uptake.
Satellite records now span multiple decades, with microwave-based remote sensing providing complementary insights to optical observations. The lowest microwave frequencies are less affected by atmospheric perturbations and enable deeper penetration into the surface cover, with canopy penetration depth increasing with decreasing frequencies. However, achieving multi-decadal records requires the use of multiple instruments over time. These changes in instruments and observation types necessitate careful calibration and harmonization to produce consistent long-term time series of observations. The combination of different observation sources and different frequencies can be used as proxy to monitor geophysical variables variations such as the above ground biomass.
In this work we used a statistical model to combine observations of the Special Sensor Microwave - Imager, Special Sensor Microwave Imager Sounder and the C-band ERS/Advanced Scatterometer and Ku-band QSCAT to estimate above ground biomass on a global scale. These models are applied to create a ~30 years time series of above ground biomass with R2>0.85 and RMSE<40 Mg/ha compared to the reference data from the CCI Biomass map. The retrievals are performed at different timescale highlighting the seasonality of vegetation cover and its impact on the microwave observations. The yearly estimates of AGB enable new insight into the dynamic of vegetation across different regions. The afforestation and deforestation effect can be evaluated across biomes, providing new estimates of the changes in carbon stocks at large scale.
How to cite: Favrichon, S., Santoro, M., Cartus, O., Prigent, C., and Jimenez, C.: Dynamic of above ground biomass variation on a global scale over the last three decades., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9705, https://doi.org/10.5194/egusphere-egu25-9705, 2025.
Understanding the sensitivity of soil carbon cycling to climate change is key to quantifying future carbon cycle feedbacks. Under increased atmospheric CO2, both carbon input to the soil from vegetation and carbon output from the soil due to heterotrophic respiration will increase, and the balance between these will determine the future ability of the land surface to be a sink or source of carbon. The ability of Earth system models (ESMs) to simulate soil carbon and related processes is therefore vital for reliably estimating global carbon budgets required for emission policies. Soil carbon simulation, projections and feedbacks are evaluated in the latest generation of CMIP6 ESMs. Global soil carbon is compared against observational datasets, future changes in global soil carbon stores and fluxes are investigated, and the carbon cycle feedbacks are quantified. The results suggest much of the uncertainty associated with modelled soil carbon stocks can be attributed to the simulation and representation of below ground soil processes in large scale models. These improvements would help reduce the uncertainty in projected carbon release from global soils under increasing levels of global warming.
How to cite: Varney, R., Chadburn, S., Burke, E., Friedlingstein, P., and Cox, P.: Soil carbon simulation, projections and feedbacks in CMIP6 Earth system models, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11268, https://doi.org/10.5194/egusphere-egu25-11268, 2025.
Tree growth and forest development depend to a large degree on climatic conditions. This is mostly because they are determining primary production, respiration losses, water demand and availability. The complex nature interacting climate components, however, represents a challenge if carbon sequestration and forest growth should be evaluated under changing conditions. Therefore, we apply a physiologically-based model (LandscapeDNDC) that has been evaluated on 15 ICOS flux tower sites, to all forested area in Germany in order to investigate the variability of carbon exchange processes in German Forests and their sensitivity to extreme events, specifically drought years. The results indicate that the net carbon sequestration is considerably reduced only in years and at sites with high water deficit (supply-demand) during extended periods of the growing period. The sensitivity to such stress, and thus also the variability between years, however, is different with species. Based on the simulations, we are also discussing the uncertainties related to model applications and the need to account for legacy effects.
How to cite: Moutahir, H., Labenski, P., Imhoff, H., Haas, E., Kiese, R., and Grote, R.: Variability and influences of carbon exchange processes in German Forests, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12652, https://doi.org/10.5194/egusphere-egu25-12652, 2025.
Terrestrial vegetation represents one of the planet’s primary carbon sinks, playing a pivotal role for climate change mitigation. Enhancing carbon storage in natural and managed ecosystems requires a deeper understanding of vegetation dynamics. In this context, Alpine Mountain ecosystems, are dealing with two significant challenges increasing the vulnerability of their carbon sinks: firstly, the atmosphere in the Alps is warming up twice as fast as in other areas of the planet and droughts and heat waves are becoming more frequent; secondly, socio-economic changes have led to partial land abandonment, affecting the composition and distribution of plant species. Specifically, in the Aosta Valley region (Northwest Italian Alps), land-cover and land-use changes (LCLU) are reshaping vegetation dynamics, particularly through the abandonment of mountain pastures below the forest line (~1500 meters asl).
The goal of our research is to investigate how climatic and socio-economic shifts drive woody species encroachment into mountain grasslands, altering carbon sequestration patterns and contributing to ecosystem changes. The activities were carried out at the ‘Integrated Carbon Observation System’ (ICOS) associated site Torgnon (IT-Tor), an abandoned subalpine pasture dominated by Nardus stricta, located in the Aosta Valley region at about 2100 m asl. An area of 15000 square meters was selected in the pasture, which is undergoing recolonization by larches (Larix decidua) and shrubs (specifically Calluna vulgaris, Juniperus communis, Vaccinium myrtillus, V. uliginosum, Rhododendron ferrugineum). Since 2015, periodic surveys (2015, 2018, 2021, and 2024) were conducted to monitor vegetation dynamics. The area was divided into line transects using ropes for sequential monitoring. Employing a GNSS system with 20 cm positional accuracy, we mapped larch tree locations, measured trunk diameters, heights, and crown dimensions, and documented associated shrub growth. Shrubs were independently counted to quantify their spread across the study area.
Continuous measurements of CO2, water fluxes, and meteorological variables are available at the site since 2008. To further evaluate ecosystem fluxes, an additional eddy covariance station was installed in October 2024 in the encroached area.
Results show an increase in the number of larches, most significant in the 2015-2018 period. During that period the number of larches almost doubled. After 2018 growth rates were lower but highlight an ongoing shift from grassland to woody vegetation, that affect carbon and water dynamics. Preliminary flux measurements will be presented, providing first insights into different carbon dynamics in the transition area.
This research underscores the critical role of LCLU changes in shaping present and future global vegetation dynamics and carbon sinks, that need to be considered to improve our understanding and modelling of ecosystem carbon cycle.
How to cite: Ferraris, D., Galvagno, M., Oddi, L., Filippa, G., Cremonese, E., Pogliotti, P., Grosso, F., Morra di Cella, U., Koliopoulous, S., Guarnieri, C., Wohlfahrt, G., Leitinger, G., Migliavacca, M., Hammerle, A., and Papale, D.: Monitoring climate and land-use change impacts on Alpine grassland vegetation dynamics and carbon sinks, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12712, https://doi.org/10.5194/egusphere-egu25-12712, 2025.
The Congo basin forest plays a crucial role in the global carbon cycle, contributing to more than 10% of the global terrestrial sink. This carbon potential appears more stable compared to other tropical forests. Historically, despite its importance for global climate, the Congo basin forest has received much less scientific attention than other tropical forests. Notwithstanding, in recent years the body of data and knowledge has reached a critical level that allows studying the carbon cycle of the Congo basin forest under the present climate.
The main objective of this ongoing research is to quantify the uncertainty related to the carbon cycle in process based models, and to decompose the different aspects contributing to this uncertainty, This will ultimately improve our understanding of the Congo basin carbon cycle within the present climate. We present results from two dynamic vegetation models (ED2 and FATES), which represent structural and functional heterogeneity of forests, over the Congo basin forest. We decompose the uncertainty related to model structure, climate drivers and model parameters. We focus on the data-rich site of Yangambi, located in the central Congo basin in the Democratic Republic of Congo. Both models are initialized with forest inventory plot data and driven with meteorological drivers from GSWP reanalysis. We evaluate the modelled carbon cycle on seasonal and diurnal time-scales against recent measurements from the Congoflux eddy-covariance tower (2020-2024). Preliminary results indicate that both models tend to underestimate observed net ecosystem carbon exchange, especially during the daytime. These results can provide new process-based insights as well as inform on the importance of model structure differences in modelling the carbon cycle over the Congo basin.
How to cite: De Hertog, S., Meunier, F., Longo, M., and Verbeeck, H.: Exploring model uncertainty of the Congo basin rainforest carbon cycle, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12778, https://doi.org/10.5194/egusphere-egu25-12778, 2025.
Forests, which sequester atmospheric CO2 in the form of biomass within long-term reservoirs, are critical to the global land carbon sink. Current land surface models assume a strong coupling between photosynthesis and plant biomass changes, with carbon supply (i.e., carbon assimilation though photosynthesis) has been considered the main driver of plant biomass growth (‘source limitation’). However, the potential for sink limitations to constrain plant biomass growth, where plant biomass change become decoupled from photosynthesis, has been raised and supported by free air CO2 enrichment (FACE) experiment, inventory and tree ring evidence.
In this study, we relied on high spatial resolution satellite-based retrievals of above-ground biomass (AGB) and vegetation primary productivity (GPP), to quantify the extent of decoupling between plant photosynthesis and biomass growth at the ecosystem scales over the past decade in the Northern Hemisphere (from 35ºN to 90ºN). We found that the fraction of decoupled area in non-intact forest is 66 ± 9%, significantly higher than in the intact forest. Extensive decoupling was observed across Europe, Russia and Canada. This spatial pattern was verified using multiple satellite-derived and inventory-derived AGB, and GPP data from P model. To investigate the drivers of decoupling, we built a generalized additive model to predict spatial variations in decoupling fractions within non-intact forests. The model suggests that harvest and logging account for most of the decoupling in Europe, while wildfires in Siberia may promote a recovery of coupling due to rapid vegetation regrowth. More importantly, even in intact forests, 56 ± 13% still exhibited the decoupling signals. In western Russia, this decoupling appears to be driven by droughts, likely due to carbon allocation shifts to support metabolism and critical plant functions, thereby constraining biomass growth. In western Canada, decoupling was found in in old-growth, or dense intact forests, where high decomposition, competition, or mortality may result in stable or declining forest biomass over time. Our analysis provides a geographic overview of regions experienced sink limitations to forest biomass growth, as well as insights into the mechanisms regulating terrestrial carbon sequestration. These findings represent a critical step toward improving process-based models and enhancing predictions of terrestrial carbon dynamics under future climate change scenarios.
How to cite: Luo, Y., Yang, H., Carvalhais, N., Wang, S., and Ciais, P.: Widespread sink limitations on forest biomass growth in the Northern Hemisphere, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14267, https://doi.org/10.5194/egusphere-egu25-14267, 2025.
As one of the most important carbon sinks and carbon pools globally, forest ecosystems play a critical role in absorbing and storing carbon dioxide from the atmosphere. The implementation of eco-engineering has transformed China into one of the hotspots in global greening. However, great differences in natural conditions between subregions, function transformation of forest ecosystem between carbon gain and carbon loss due to rapid land use changes, and uncertainties in the stability and sustainability of forest ecosystem functions resulting from climate change, lead to the large-scale forest carbon sink capacity and future carbon sink potential remaining largely unclear. This lack of clarity is not conducive to the formulation of climate change mitigation strategies in China. Therefore, it’s urgent to undertake the quantification and assessment of forest aboveground biomass carbon with high spatiotemporal resolution. Here, using a machine learning model, six bands of Landsat images, along with 3 indicators derived from the bands by raster calculators at a resolution of 30m × 30m, were used to train the estimate model of aboveground biomass carbon density in combination with the adjusted aboveground biomass / carbon products from 2019. Subsequently, the carbon density from 1985 to 2023 at a 30m × 30m resolution were predicted. The model’s RMSE was 9.03 MgC ha-1 and the R2 of test datasets stabilized around 0.77. We found that forest aboveground biomass carbon stock decreased first and then increased during the period. Despite a decreasing trend in the area of stable forests, the carbon stock increased from 7.50 PgC to 8.05 PgC, at a rate of 0.015 PgC yr-1. The area of secondary forests, however, showed the most rapid regrowth in carbon density during the period, with a rate of 0.46 MgC ha-1 yr-1. Over the past about four decades, carbon loss due to deforestation amounted to approximately 1.49 PgC, while carbon gain from plantation sumed to 4.55 PgC. Spatial and temporal high-resolution data of forest aboveground biomass carbon serve as an invaluable resource for identifying areas with significant carbon stocks and high carbon sink potential, and allows an in-depth understanding of the differences in dynamic patterns over time in China’s forest and provides a scientific reference for optimizing land management.
How to cite: Zhang, X. and Li, W.: Dynamics of aboveground biomass over the past four decades in China’s forests, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15528, https://doi.org/10.5194/egusphere-egu25-15528, 2025.
The consecutive drought events between 2018 and 2020 caused an unprecedented increase in Norway spruce (Picea abies) tree mortality across Germany. Despite the observable forest dieback, process-based vegetation models have difficulty reproducing it. This gap between observed and simulated forest dynamics underscores the pressing need for more advanced modeling approaches to accurately capture drought-induced tree mortality.
In our study, we adopted a data-driven statistical approach to enhance the representation of drought-induced Norway spruce tree mortality in the process-based vegetation model LPJ-GUESS. Using Norway spruce mortality data from the German Crown Condition Survey (Waldzustandserhebung, WZE), as well as climate and weather anomaly data, we developed logistic regression models to predict drought-induced tree mortality, which were then integrated into LPJ-GUESS.
This enhanced modeling framework successfully reproduced the general temporal and spatial patterns of historical Norway spruce mortality rates (1998–2020). Future simulations (2021–2070) under the RCP2.6 and RCP8.5 climate scenarios show periodic increases in Norway spruce mortality, comparable to or even exceeding the high rates observed in 2020. Although the drought-mortality models effectively replicate past dynamics, they diverge in predicting the timing and magnitude of future drought-induced mortality events.
The vegetation model also enabled us to quantify the impacts of mortality on forest productivity. Our projections indicate a drought-driven reduction in aboveground biomass of 18% under RCP2.6 and 36% under RCP8.5 (mean across all simulations). Moreover, we observed a significant decline in potential spruce timber harvests in Germany between 2021 and 2070, with cumulative losses amounting to 310 million Mg of C under RCP2.6 and 447 million Mg of C under RCP8.5. These impacts vary depending on the chosen climate scenario and the statistical mortality model applied.
Our study highlights the severe risk of large-scale future dieback in Norway spruce forests across Germany. However, the prediction of the timing and magnitude of such events remains highly uncertain. Nevertheless, the effects of droughts should be considered in predictive modeling studies, as they could have significant impacts on forest carbon cycling and timber harvests.
How to cite: Anders, T., Hetzer, J., Knapp, N., Forrest, M., Langan, L., Tölle, M. H., Wellbrock, N., and Hickler, T.: Modelling past and future impacts of droughts on tree mortality and carbon storage in Norway spruce stands in Germany, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16393, https://doi.org/10.5194/egusphere-egu25-16393, 2025.
Understanding tree growth in relation to environmental conditions is essential, particularly in the context of climate change,
where rising temperatures, frequent droughts, and disturbances threaten forest health and productivity. This study uses
high-resolution data from four intensively monitored Picea abies stands in Austria (2010-2020), with dendrometers recording
hourly stem increments on 10 trees per site, allowing for detailed analysis of growth responses to environmental changes.For this
purpose we tested different generalized additive mixed models (GAMs) using environmental data collected on site. The best model
consisted of combinations of soil moisture (SM) and soil temperature (ST) data. Furthermore we analysed how the relationships
established differ for three different times during the growing season. We found that high SM consistently had a positive effect on
tree growth, wheras the effect of ST varied depending on the timing.Our findings underscore the importance of monitoring soil
conditions, particularly for species like Picea abies, which are known to react more sensitive to environmental changes due to
their shallow rooting systems and vulnerability to drought.
How to cite: Zolles, A., Vospernik, S., and Schüler, S.: Analysis of the effects of soil parameterson radial stem growth for four sprucestands in Austria, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16869, https://doi.org/10.5194/egusphere-egu25-16869, 2025.
Terrestrial gross primary productivity (GPP) is a linchpin flux in the terrestrial carbon cycle and its simulation is a central component of dynamic global vegetation models (DGVMs). When calculating GPP, DGVMs typically rely on a light use efficiency (LUE) model which relates the amount of absorbed solar radiation to the amount of carbon fixed by photosynthesis. Recent theoretical advances utilising eco-evolutionary optimality (EEO) theory have led to the development of the P model, a parameter-sparse LUE model which has been well-validated at both local and global scales.
Here we implemented the P model into LPJ-GUESS, an established, community-developed DGVM. We compared LPJ-GUESS’s performance with and without the P model to remotely-sensed GPP estimates. The inclusion of the P model reduced the error in the simulated spatial pattern of annual GPP by 17% and markedly improved of the timing of the northern hemisphere spring green up. In order to disentangle the causes of data-model mismatch, we also investigated the GPP errors as a function of the environmental variables such as elevation, and in the case of elevation we found a strong model bias which was similar both with and without the P model.
In addition to the improved model skill, the P model version of LPJ-GUESS uses far fewer parameters (none of which are PFT specific), encapsulates a coherent body of theory reflecting more recent understanding of photosynthetic responses to changing environmental conditions, and has a reduced model run time. Based on this, we conclude that the P model has the potential to improve LPJ-GUESS and other DGVMs.
How to cite: Forrest, M. and Hickler, T.: Improving terrestrial carbon cycle simulations with eco-evolutionary optimality: Including the P model in LPJ-GUESS, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17371, https://doi.org/10.5194/egusphere-egu25-17371, 2025.
Vegetation dynamics across the Tibetan Plateau (TP) are increasingly influenced by climate warming, warranting further investigation. This study integrates multiple data sources, focusing on Normalized Difference Vegetation Index (NDVI) and gross primary productivity (GPP), to analyze their long-term trends during TP’s growing season. We find a noteworthy shift from greening (increased NDVI) to pronounced browning (decreased NDVI) in the third generation NDVI dataset generated by the Global Inventory Modeling and Mapping Studies (GIMMS3g NDVI), evident from 1998 (1982–1998: 0.0006 yr-1, p < 0.1; 1998–2015: −0.0008 yr-1, p < 0.05). This browning trend is corroborated by Moderate-Resolution Imaging Spectroradiometer (MODIS) NDVI (−0.0005 yr-1, p < 0.05) during 2000–2015. In contrast, all GPP products consistently increase during 1982–2015. Browning and increasing GPP trends decouple in the eastern and southern TP, coinciding with terrestrial water storage (TWS) shifting from increasing to decreasing, and rising trends in solar radiation (SR), vapor pressure deficit (VPD), and temperature post-1998. Analysis highlights increasing SR (VPD) dominance and decreased TWS sub-dominance in GIMMS3g (MODIS) NDVI browning. TRENDYv6 multiple model experiments emphasize climate’s primary role, followed by CO2 fertilization, in increasing GPP trends. Furthermore, temperature exerts the most significant promoting effect on GPP enhancement, outweighing adverse effects of soil and atmospheric dryness. Additionally, we reconcile browning and increased GPP by attributing it to environment-induced increased light-use efficiency and highlight subtle plant carbon allocation strategies. This study provides valuable insights into the intricate relationship between changing climate and vegetation dynamics over the TP.
How to cite: Wang, J. and Wang, X.: Increasing gross primary productivity despite vegetation browning over the Tibetan Plateau during 1998−2015, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20579, https://doi.org/10.5194/egusphere-egu25-20579, 2025.
