The EU's climate goals depend primarily on reducing greenhouse gas emissions. However, carbon sequestration by forest ecosystems is an important component in achieving carbon neutrality, but their ability to do so is declining. Between 1990 and 2022, European forests removed about 434 Mt CO2eq yr-1 from the atmosphere, equivalent to about 10% of the EU's total emissions. However, the forest carbon sink has decreased by nearly a third, from an average of -457Mt CO2eq yr-1 between 2010-2014 to -332 Mt CO2eq yr-1 between 2020-2022. To meet the EU's 2050 climate neutrality goal, the forest sector needs to offset 8% of emissions per year, but it is currently only achieving 6% per year. This is a 2% shortfall, equivalent to the emissions of Latvia and Estonia together.
In recent years, significant developments have been made in forest monitoring and modeling and in the understanding of forest ecosystem dynamics. However, scientific and practical challenges still limit the information available for policy decisions. Here, we propose a roadmap for enhanced research and forest management actions for climate adaptation and mitigation from the stand to the continental scale. The aim is to identify forest monitoring and modeling advances needed to inform sustainable policy decisions on forest and land management.
This roadmap includes:
Short-term (< 3 years): Improving monitoring of forest disturbances types and intensity, tree mortality, and biodiversity using satellite data, ground observations, as well as improving the secure access to private forest data.
Medium-term (< 5 years): Understanding how forest management, biodiversity, and climate change affect carbon sinks and forest resilience, in particular the response to climate extremes, and developing long-term projections of the European forest carbon sink (including under worse case scenarios).
Long-term (beyond 5 years): Deepening understanding of how management practices affect deadwood and soil organic carbon to guide policies that integrate these factors into broader forest management and climate adaptation strategies.
We highlight new research results that can contribute to the goals and support the EU's climate objectives, including achieving climate neutrality by 2050, by providing policymakers with robust and reliable information on forest resources and carbon sink.
How to cite: Migliavacca, M. and the Participants JRC expert meeting on forest ecosystems: Supporting EU Climate Goals through Improved Forest Monitoring, Modelling and Management, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7976, https://doi.org/10.5194/egusphere-egu25-7976, 2025.
Climatic drought and changes in precipitation patterns are key features of the ongoing and predicted climatic changes in northern latitudes such as the boreal forest of Norway. Recent droughts highlight on the possible difficult future of spruce forests in southern Norway. To better understand and monitor these forests under a more extreme climate, it is crucial to gain a better understanding of the water relations of spruce trees across forest stands. Sap flow sensors are typically used for directly measuring the water demands for transpiration in individual trees. There are however limitations to their use in examining the hydraulic and physiological responses to extreme water supply variability: i) manufactured high-resolution sensors such as those following the Heat Ratio Method (HRM) or Heat Field Deformation (HFD) are expensive, limiting their deployment to a few trees in a stand, and ii) the sap flow sensors only measure the movement of water within the active sapwood, not accessing other physiological mechanisms and responses (radial growth, water storage) associated with stress response. Point dendrometers have become increasingly used, monitoring sub-daily stem size fluctuations resulting from both seasonal patterns of radial growth increment and the dynamics of plant tissue water balance. Manufactured point dendrometers are much cheaper to buy and easier to install and maintain than manufactured sap flow sensors. They can therefore be much more extensively deployed across forest stands. We aimed to analyse the relationship between sub-daily stem diameter changes and sap flow using point dendrometers and HRM sap flow sensors installed in a Norway spruce forest located 50 km north of Oslo, Norway. We linked these relationships with individual tree physical attributes, meteorology and soil climate over two growing seasons in 2022 and 2023. Our goal was to assess whether a predictive model of sap flow could be built from measured diameter changes, tree properties and climate, to ultimately reduce the uncertainty of stand level transpiration estimation at the daily resolution across entire forest stands.
How to cite: Merlin, M., Lange, H., Zhao, J., Bright, R., Creek, D., and Meissner, H.: Predicting tree-level sap flow from point dendrometer and climate data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10152, https://doi.org/10.5194/egusphere-egu25-10152, 2025.
In the context of global change, desert ecosystems within extremely arid regions, such as the Taklamakan Desert, are confronting severe environmental challenges. This desert is particularly characterized by an annual rainfall of merely 35.1 mm and soils deficient in nutrients, making water and nutrient availability primary limiting factors for vegetation growth. Among the flora in this region, Alhagi sparsifolia, a deep-rooted desert plant, is pivotal for sand stabilization and maintaining the ecological security of oases. This research, conducted through long-term controlled field experiments, delves into the impacts of two critical factors associated with global change—groundwater level and nitrogen deposition—on the survival strategies of A. sparsifolia. The findings indicate that inappropriate groundwater levels, whether excessively deep or shallow, considerably restrict the biomass accumulation in A. sparsifolia. This limitation compels the plant to reallocate biomass among its organs to adapt to environmental stress. Notably, fluctuations in groundwater levels predominantly influence the phosphorus and potassium content within the leaves, while the effects on carbon and nitrogen levels are minimal. The study further reveals that juvenile A. sparsifolia (1-2 years old) respond to groundwater level variations by extensively adjusting their nitrogen, phosphorus, and potassium utilization and recycling strategies. In contrast, older, perennial plants primarily modify their phosphorus and potassium recycling approaches to cope with environmental shifts. Additionally, nitrogen deposition has been found to significantly alter the α-diversity of soil bacteria and the nutrient content of desert plants, underscoring the broader implications of global change. In regions with shallow groundwater, surface nutrients emerge as the most significant environmental factor influencing the nutrient content of A. sparsifolia leaves. Conversely, the impacts of groundwater level, groundwater mineralization, and soil salinity are comparatively minor. These insights highlight the profound effects of global environmental changes on the survival strategies and adaptability of deep-rooted desert plants. This research not only enhances our understanding and predictive capacity regarding the responses of desert plants to global changes in extremely arid regions but also provides a scientific foundation for plant restoration and conservation initiatives in these challenging environments.
How to cite: Zhang, B. and Zeng, F.: Global Change Impacts on Growth Strategies of Deep-Rooted Plants in Hyperarid Deserts, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3394, https://doi.org/10.5194/egusphere-egu25-3394, 2025.
Understanding the link between photosynthesis and carbon allocation to woody biomass remains a critical gap in predicting forest responses to climate change due to the pervasive lack of comprehensive carbon-based data at the whole-stand level. We employed an integrated approach combining micrometeorological techniques (Eddy Covariance, EC), process-based and biogeochemical modelling, tree ring width (TRW), and quantitative wood anatomy to assess changes in carbon fluxes and allocation dynamics over mature stands of black spruce (Picea mariana Mill.) and jack pine (Pinus banksiana Lamb.) from 1999 to 2021 in Canada. We used Gross Primary Production (GPP) from EC to calibrate and validate GPP simulations from the 3D-CMCC-FEM model, incorporating tree ring width (TRW) and wood anatomical traits, such as cell wall area (CWA), as proxies for carbon fixation.
Our findings demonstrated that the forest ecosystem model effectively captured GPP at daily, monthly, and annual scales, strongly correlating with EC-based estimates (P < 0.001). Both stands revealed a strong association between observed and modelled GPP and CWA, highlighting that CWA better reflects carbon assimilation in woody biomass than TRW. Species-specific differences in non-structural carbohydrates (NSCs) dynamics were also evident, as model simulations indicated that Pinus banksiana actively utilized NSCs for growth, while Picea mariana relied on NSCs as a buffer under cold conditions. This multi-proxy approach enhanced our understanding of carbon dynamics and temporal and spatial carbon flux pathways. Our findings provide critical insights into carbon allocation strategies, contributing valuable knowledge for refining climate change models in boreal ecosystems.
How to cite: Puchi, P. F., Dalmonech, D., Castagneri, D., Genovese, G., Brilli, L., and Collalti, A.: Multi-proxy analysis confirms the tight coupling of carbon assimilation and allocation, with divergent NSCs strategies in two boreal forest species, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10284, https://doi.org/10.5194/egusphere-egu25-10284, 2025.
Wood production is an essential component of terrestrial carbon dynamics, but we only have a limited understanding of the environmental cues that trigger wood production to start and stop during the growing season and how these vary among temperate tree species. Moreover, we lack a clear picture of how the seasonal timings of wood production relate to leaf phenology and whole-ecosystem carbon fluxes - severely limiting our ability to estimate woody productivity from remote sensing or eddy covariance flux tower data. To address this knowledge gap, between 2023 and 2024 we used automated dendrometers to take hourly measurements of stem diameter variations across 160 trees representing seven locally-dominant broadleaf and coniferous species in the Wytham Woods 18-ha ForestGEO plot in the UK. We combined these with overlapping flux tower measurements of gross primary production (GPP) and net ecosystem exchange (NEE), NDVI time-series generated from Sentinel-2 to capture canopy phenology and local microclimate data. Using these complementary datasets we found that wood growth started later and ended much earlier than one might estimate from NDVI alone. Moreover, temporal trends in wood production (including the onset, maximum rate and cessation of growth) varied significantly between species - with beech and oak trees growing almost 60 days longer per year than sycamore and ash. This variation in wood phenology across species significantly complicates any attempts to infer wood production from flux tower measurements of GPP and NEE. Our study advances our understanding of the synchronization and mismatches between ecosystem carbon uptake and investment in wood production in temperate forests. We highlight the potential of combining remote sensing, flux tower and high-resolution dendrometer data to improve our ability to track terrestrial carbon cycling at scale and predict its responses to climate change.
How to cite: Krebber, D., Battison, R., Kowalski, K., Malhi, Y., Senf, C., and Jucker, T.: Resolving the links between wood production, leaf phenology and whole-ecosystem carbon fluxes in temperate forests, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12794, https://doi.org/10.5194/egusphere-egu25-12794, 2025.
Tree crown architecture, defined as the 3-D density, distribution and orientation of leaves within a tree crown, strongly influences the processes of photosynthesis, evapotranspiration, and spectral reflectance that help characterize key tree and forest responses to global change. Tree crown economic theory posits that variability in tree functioning can be assessed by a suite of co-varying tree crown architectural traits describing an economic tradeoff of light capture versus water-use efficiency. Using new, consistent measurements from eight broadleaf deciduous forest sites that are part of the USA National Ecological Observatory Network (NEON), we quantified a suite of tree crown architectural traits and assessed whether they were predictive of NIRv, a spectral reflectance index of tree crown functioning. Specifically, we worked with NEON staff to measure: (1) the trait of sunlit mean leaf angle (MLA) from analysis of tower-based profile photographs of tree crowns, (2) the traits of top rugosity (Rt), plant area index (PAI), and accumulative plant area density within the top 50% of the crown (APAD50) from Airborne Observation Platform (AOP) LiDAR data extracted from field-delineated tree crown polygons, and (3) NIRv, the near-infrared reflectance of vegetation, from AOP imaging spectroscopy data. We found several tree crown architectural traits and NIRv to co-vary along a spectrum ranging from “tower” to “dome” crown architectural ideotypes. Optimized for light capture, trees closer to the “dome” ideotype had more horizontally-distributed crowns (lower APAD50) with more horizontal leaves (lower MLA), which was associated with higher NIRv. Conversely, trees closer to the “tower” ideotype had more vertically-distributed crowns with more vertical leaves and lower NIRv. This expected covariation of traits and NIRv was related to species differences, but also to spatial variability within a single species, Liriodendron tulipifera, that occurred in five sites spread across a strong moisture gradient. These data and analyses are consistent with theory and suggest that measurable crown traits can define a branch- and crown-scale economic trade-off that governs how each tree adaptively distributes and orients leaves in their crown as a coordinated strategy affecting tree functioning and their responses to global change.
How to cite: McNeil, B., Fan, Y., and Elmore, A.: Testing Tree Crown Economics with the USA National Ecological Observatory Network, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12922, https://doi.org/10.5194/egusphere-egu25-12922, 2025.
Global warming poses significant threats to ecosystems, primarily due to increasing atmospheric CO2 levels. While literature has enhanced our understanding of carbon and water cycle interactions, a critical question remains: how will plants respond to the changing climate? The present study addresses this gap by investigating the critical soil moisture threshold, which signifies plant water stress. Using the Weather Research and Forecasting model coupled with the Noah-MP land surface model, we conducted three simulations over India, the second highest contributor to global greening, for the period 2004-2018: (1) Varying CO2, (2) Fixed CO2 at 2004 (low CO2), and (3) Fixed CO2 at 2022 (high CO2). We identified the critical soil moisture threshold as the point during drydown where vegetation productivity begins to decline due to decreasing soil moisture and increasing vapor pressure deficit, indicating when plants experience stress. Our findings reveal that critical soil moisture threshold has decreased in response to enhanced water use efficiency by plants under elevated CO2, reflecting variations in plant physiology. Despite this, vegetation productivity has declined under elevated CO2 conditions. This can be attributed to the two-way carbon-climate feedback: while increased atmospheric CO2 enhances plant carbon gain by regulating physiological responses such as altering stomatal conductance, it also acts as a radiative forcing agent, driving temperature increases, altering precipitation patterns, and reducing the effectiveness of ecosystems as carbon sinks. This warming effect, coupled with soil moisture deficit and atmospheric aridity, explains the reduction in vegetation productivity. Our study highlights that although plant physiological alterations in response to elevated CO2 are significant, they are insufficient to counteract the warming and drying impacts. Thus, both feedback mechanisms must be considered when analyzing plant responses to changing climate conditions.
How to cite: Verma, A., Budakoti, S., Anoop, S., and Ghosh, S.: Impact of Elevated CO2 on Plant Water Stress and Vegetation Productivity in India, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-968, https://doi.org/10.5194/egusphere-egu25-968, 2025.
Net radiative forcing (RF) of terrestrial ecosystems is controlled by changes in greenhouse gas fluxes (biogeochemical cycles) and albedo (biophysical properties). Semi-arid savannas, characterized by tree-grass coexistence, are highly sensitive to elevated nitrogen (N) deposition, which can alter biophysical and biogeochemical interactions on the land-atmosphere continuum. This study examines how altered N-to-phosphorus (P) ratios (simulated in a fertilization trial) affect RF at the top of the atmosphere (TOA) and surface temperature (Ts) at both the ecosystem and grass layer scales. We analyzed a long-term dataset (2014–2023) from three co-located eddy-covariance (EC) sites in a Mediterranean savanna in Spain: control (ES_LMa), N-added (ES_LM1 or NT; 16.9 ha), and N+P-added (ES_LM2 or NPT; 21.5 ha). Each site featured two enclosed-path EC systems at heights of 1.6 m and 15 m to capture grass and ecosystem-scale fluxes, respectively. Comparing between fertilized and control sites, we found net RF at TOA was dominated by change of albedo (± 98 %) over net ecosystem exchange (ΔNEE), with NT showing a stronger cooling effect (mean ± SD: -2.37 ± 1.52 Wm-2) than NPT (-2.01 ± 1.82 Wm-2). Interestingly, cooling effect that captured at TOA did not consistently correspond to Ts change (ΔTs) on the surface. At the ecosystem level, NT experienced cooler Ts (ΔTs = -0.41± 0.47 °C), whereas NPT had slightly warmer Ts (i.e., ΔTs = 0.03 ± 0.28 °C). At the grass layer, both fertilization treatments resulted in warming, with higher Ts observed for NPT (ΔTs = 0.80 ± 0.77 °C) than NT (ΔTs = 0.63 ± 0.46 °C). Surface conductance (Gs) patterns also diverged across scales, with NT showing the highest Gs at the ecosystem level, while NPT had the highest Gs at the grass layer. These findings emphasize differences in energy transfer processes across layers and highlight that N addition alone (without P) enhances tree canopy cooling capacity more effectively than combined N+P addition. Conversely, both treatments increased Ts at the grass layer, reshaping eco-physiological interactions in this water- and nutrient-limited ecosystem. Our results underscore the importance of nutrient stoichiometry in regulating biophysical and biogeochemical processes in semi-arid savannas, with implications for ecosystem management and climate modeling.
How to cite: Hanggara, B., El-Madany, T., Carrara, A., Metzger, S., Moreno, G., Gonzalez-Cascon, R., Burchard-Levine, V., Hildebrandt, A., Reichstein, M., and Lee, S.-C.: Impact of altered nutrient balance on radiative forcing of a Mediterranean semi-arid savanna, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6025, https://doi.org/10.5194/egusphere-egu25-6025, 2025.
Nitrogen (N), after water, is considered the key factor limiting net primary production in drylands. However, whether vegetation is N-limited depends on the balance between N supply and biological demand, a relationship that remains unclear in drylands. Here, we established a standardized field survey across 25 countries, including 326 plots, to assess how plant N limitation responds to aridity in global drylands. We found that while N availability decreased with aridity, soil and plant δ¹⁵N—an indicator of the balance between N supply and biological demand—unexpectedly increased in arid regions (aridity > 0.8), suggesting that plants in these regions may not have N-limitation as common views. Variations in soil N forms, functional genes, and fungal data provide further evidence that dryland vegetation has evolved a unique strategy for N uptake and utilization to overcome soil N availability declines. Data support the hypothesis that, with increasing aridity, plants favor the uptake of ammonium, a more toxic but metabolically efficient N source, and reduce their dependence on mycorrhizal associations, relying instead on direct root uptake for more efficient N allocation. Our work also highlights the impact of grazing on the development of this strategy, particularly in grasslands. These results clarify dryland plant N-use patterns and challenge the view that plants become more N-limited with increasing aridity, a perspective that should be considered when evaluating global change and human stress on drylands.
How to cite: Liao, J., Corrochano-Monsalve, M., Fan, K., Biancari, L., Nelson, C., and T. Maestre, F.: Rethinking nitrogen availability in dryland: how arid vegetation overcomes nutrient scarcity, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4936, https://doi.org/10.5194/egusphere-egu25-4936, 2025.
Half a century after Apollo 17's iconic "Blue Marble" photograph, depicting our Earth's life-sustaining hydrosphere, concerns about the future of this hydrosphere have intensified. Climate change is thought to be shifting ecosystems toward drier and more hostile conditions over land, threatening biodiversity and human resilience. Global warming accelerates evaporation, yet precipitation trends remain uncertain, leading to projections of overall desertification and dryland expansion. The IPCC highlights potential catastrophic risks, especially for subhumid ecosystems, and stresses the urgent need for understanding the mechanisms driving this dryland expansion. However, our time series of reliable observations are not sufficiently long to study this slow, creeping process at the global scale with sufficient accuracy. To bridge this knowledge gap, we propose to study the parallels between short-term drought propagation and long-term dryland expansion, hypothesising that the physical mechanisms underlying both are the same.
Specifically, we focus on a critical feedback from drying soils that has proven crucial for drought spatiotemporal propagation: as prolonged dry events decrease land evaporation, both atmospheric humidity and the likelihood of rainfall are further reduced. Simultaneously, drying soils release more sensible heat into the atmosphere, amplifying temperatures, reducing rainfall efficiency and often triggering compound heatwaves. Together, these feedbacks perpetuate drought conditions, reducing rainfall, both locally and downwind, and thus exacerbating droughts' spatial and temporal extent. Using a Lagrangian atmospheric model and four decades of reanalysis data, we confirm that droughts and heatwaves can self-propagate through these land–atmosphere interactions.
Interestingly, this same process may also drive dryland self-expansion over multi-decadal periods. Our findings suggest that nearly half of the 5.2 million km² of humid land that became drylands in the past four decades did so due to dryland self-expansion via land–atmospheric feedbacks. Existing drylands warmed and dried the air flowing towards downwind subhumid regions, decreasing rainfall and increasing potential evaporation there, causing their eventual transition into drylands. These results may help in predicting the broad impacts of dryland expansion, including disruptions to carbon sequestration, nutrient cycling, and land productivity. Identifying self-expansion hotspots enables targeted interventions in land-use and ecosystem management to mitigate dryland growth. Conservation in upwind drylands can slow down this process, while prioritizing vulnerable downwind regions for strategies like restoring vegetation and soil health can preserve their biodiversity and curtail their aridification. Furthermore, our findings highlight the need for improved climate models to predict future ecosystem transitions and emphasize the relevance of land feedbacks to understanding paleoclimatic tipping points.
How to cite: Miralles, D. G., Koppa, A., Keune, J., and Schumacher, D. L.: From Drought Propagation to Dryland Expansion: The Role of Land Feedbacks in Spreading Aridity, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3670, https://doi.org/10.5194/egusphere-egu25-3670, 2025.
Atmospheric deposition of desert dust serves as a significant nutrient source, replenishing soil nutrient stocks and influencing the long-term productivity of infertile terrestrial ecosystems. However, the immediate impact on the vegetation after deposition remains unclear.
We present findings from a pioneering field experiment conducted in a natural Mediterranean ecosystem in Israel which regularly receives desert dust. In this study, we applied dust to three native plant species Salvia fruticosa, Teucrium capitatum and Cistus creticus over the course of three months (the dust period). Our results indicate that, while biomass and new growth organs remain unchanged compared to the control plants, dust application significantly increased the concentrations of Al, Mn, Fe, Ni, and Cu. In some cases, the increase was as high as 100% in the aboveground biomass across all three species, which are considered marginally bioavailable in the local soils due to their high alkalinity. We discovered that the nutrients were taken directly from the plant foliage and not via the roots and show that this unique process was facilitated by the acidic environment of the leaf surface which enables the partial dissolution of nutrients typically unavailable in alkaline soils.
These findings suggest that deposition of dust is an important source of mineral nutrients to plant and can enhance plant nutrition through foliar uptake mechanism, particularly in ecosystems with nutrient-poor soils. The acidic microenvironment on leaf surfaces plays a crucial role in solubilizing dust-borne nutrients, facilitating their uptake. This mechanism may be especially beneficial in regions experiencing frequent dust deposition, contributing to the resilience and productivity of plant communities in such environments.
How to cite: Lokshin, A., Palchan, D., Sternberg, M., and Gross, A.: Desert dust deposition enhances plant nutrition via direct foliar uptake in Mediterranean ecosystems, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14729, https://doi.org/10.5194/egusphere-egu25-14729, 2025.
The relationship between plant primary production and wetland methane (CH4) emission is well established. This relationship is expected because plant production fuels methanogenesis and plants act as conduits for gas exchange between the soil and the atmosphere. Recent global increases in bottom-up measurements of wetland CH4 provide a new opportunity to revisit the hypothesis that plant production and CH4 flux have a positive linear relationship in wetlands.
Using paired CH4 and gross primary productivity (GPP) measurements from 56 wetland sites, we found that CH4 and GPP are weakly related, with the maximum R2 from linear regressions being 0.14 (p= 0.0081). Instead, we found some support for a unimodal relationship (R2= 0.24, p= 0.0016) between GPP and CH4 flux. While flooded sites exhibited strong GPP-CH4 relationships, sites where the mean annual water table depth was below the soil surface showed weak or no GPP-CH4 relationship. This suggests that variable degrees of CH4 oxidation, among other factors, could be weakening the apparent relationship between GPP and CH4.
In this presentation, we will discuss processes that could disrupt the expected positive linear relationship between plant production and CH4 using multi-scale examples from syntheses, lab and field experiments. We will also explore why the GPP-CH4 relationship may not scale across space and time and how this affects the utility of GPP as a CH4 predictor.
How to cite: Malhotra, A., Megonigal, P., Forbrich, I., Määttä, T., Morris, K., Bittencourt Peixoto, R., Wilson, S., Zheng, J., and Bailey, V.: Revisiting the primary production control on wetland methane emission, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12843, https://doi.org/10.5194/egusphere-egu25-12843, 2025.
Peatlands worldwide have been transformed from carbon sinks to carbon sources due to years of intensive agriculture requiring low water tables. In the Netherlands, carbon dioxide (CO2) emissions from drained peatlands mount up to 5.6 Mton annually and, according the Dutch climate agreement, should be reduced by 1 Mton in 2030. It is generally accepted that mitigation measures should include raising the water level, and the exact influence of water table depth has been increasingly studied in recent years. Most studies do this by comparing annual Eddy Covariance (EC) site-specific CO2 budgets to mean annual effective water table depths (WTDe). However, here we apply a different approach: we integrate measurements from 16 EC towers with EC measurements from 141 flights by a low-flying research aircraft, in an interpretable machine learning framework. We make use of the different strengths of tower and airborne data, temporal continuity and spatial heterogeneity, respectively. We apply time frequency wavelet
analysis and a footprint model to relate the measured fluxes to the underlying surface. Using spatio-temporal data, we train and optimize a boosted regression tree (BRT) machine learning algorithm and use Shapley values and various simulations to interpret the model’s outputs. We find that emissions increase with 4.6 tonnes CO2 ha-1 yr-1 for every 10 cm WTDe up to a WTDe of 0.8 meter. For more drained conditions, emissions decrease again, following an optimum-based curve. Furthermore, we find that this effect is stronger in winter than in summer and that it varies between sites. This study shows the added value of using ML with different types of instantaneous data, and holds potential for future applications.
How to cite: Hutjes, R., van der Poel, L., Bataille, L., Kruijt, B., Franssen, W., Jans, W., Biermann, J., Rietman, A., Buzacott, A., and van der Velde, Y.: Groundwater–NEE Relationship in Dutch Peatlands Derived by Machine Learning Using Airborne and Ground-Based Eddy Covariance Data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16164, https://doi.org/10.5194/egusphere-egu25-16164, 2025.
Under the Paris Agreement, countries are encouraged to preserve and enhance existing carbon sinks. Europe, in particular, has committed to achieving climate neutrality—attaining a balance between anthropogenic emissions from sources and removals by sinks—by 2050. Achieving these ambitious goals requires accurate and credible estimation of CO2 fluxes. However, discrepancies between observations and global models hinder the tracking of collective progress towards climate neutrality. Efforts to improve transparency and data comparability are crucial to better align national mitigation strategies with global pathways. In particular, effective climate mitigation policies increasingly depend on local-level actions where detailed data on CO₂ removals from forests and other land uses are traditionally lacking. Addressing the uncertainty in land-sector mitigation potential and enhancing the availability and comparability of data are critical for achieving climate goals by cities and regions. Different models, including process-based and data-driven approaches, exist to estimate land carbon fluxes, but their application and accuracy often vary significantly depending on the scale and quality of input data.
In this study, we tested a data-driven method based on eddy covariance (EC) data to quantify the current role of the regional carbon sink of the Aosta Valley Region (Italy) through the integration of various approaches. Our model relies on FLUXCOM-X framework specifically trained to achieve robust results at the regional scale. An XGBoost model was developed using global hourly meteorological data from sites across the global eddy covariance networks paired with remote sensing data from MODIS. The algorithm was optimized through feature selection analysis and best training subset selection, identifying the ensemble of experimental sites that provided the most accurate predictions while avoiding overfitting. The optimal training subset was obtained via partitioning the full range of sites into subsets based on key characteristics (Plant Functional Type, geographical zone, biogeographical region, elevation). This approach ensured the biophysical comparability of the sites with the target region (Aosta Valley) while maintaining a balance between generalizability and specificity. Model evaluation focused on how the model performed on the local eddy covariance measurements. The resulting model was subsequently upscaled to the regional level. This was achieved using eddy covariance measurements of CO2 fluxes, MODIS NDVI (250 m resolution), daily gridded meteorological data at 100 m resolution, and a land cover map at 250 m resolution. Moreover, the methodology demonstrated potential for replication in other local realities such as regions, providing a flexible framework for assessing local carbon budgets and supporting climate-smart management strategies. Our results were finally compared with independent data from the National Forest Inventory (NFI) available for the target area (Aosta Valley). Discrepancies between methods will be analyzed, considering their strengths, weaknesses, and spatio-temporal variability.
How to cite: Tuzzi, L., Galvagno, M., Filippa, G., and Nelson, J.: Enhancing quantification of local carbon sinks through Eddy Covariance CO2 Flux and Machine Learning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17514, https://doi.org/10.5194/egusphere-egu25-17514, 2025.
Carbon dioxide (CO₂) flux partitioning involves separating net ecosystem exchange (NEE) into its gross primary production (GPP) and ecosystem respiration (RECO) components. Despite 25 years of flux research and abundant data from networks such as FLUXNET [1], the development and validation of new partitioning methods remain hindered by the lack of a standardized benchmark dataset and evaluation protocol. Existing parametric methods, including nighttime (NT) [2] and daytime (DT) [3] approaches, have become integral to FLUXNET data products but face limitations such as dependency on assumptions and variable robustness across biomes and conditions. Emerging machine-learning (ML)-based methods offer flexibility and reduced reliance on assumptions but require rigorous evaluation [4,5,6].
We establish a benchmark dataset and standardized evaluation protocol to address these challenges. The dataset includes synthetic data generated by multiple mechanistic models, allowing comparison against a known ground truth. These models simulate diverse biomes and environmental conditions, including rapid system changes and extreme events. Additionally, the dataset incorporates realistic data gaps and noise scenarios to test method resilience. The evaluation includes multiple performance metrics across different temporal scales. We assess the ability of methods to capture critical meteorological events and ecological transitions.
Our results indicate that for GPP, ML methods outperform parametric methods at half-hourly scales and in capturing daily anomalies, though the extent of improvement depends on the setup of the ML method. Conversely, NT method performs better at representing the monthly diurnal cycle and seasonal trends. For RECO, the NT method yields the most robust overall results but struggles to capture sudden changes in ecosystem dynamics, which ML methods handle more effectively. Across all methods, daily anomalies remain a persistent challenge, highlighting the need for dynamic ML models. Furthermore, we find that NEE data availability below approximately 30% for a site-year reduces the reliability of the current neural network methods, suggesting the need for transfer or meta-learning schemes or improved gap-filling strategies.
This initiative streamlines the development and comparison of partitioning methods, enabling transparent assessment of their strengths and weaknesses.
References:
[1] Baldocchi, Dennis, et al. “FLUXNET: A new tool to study the temporal and spatial variability of ecosystem-scale carbon dioxide, water vapor, and energy flux densities.” Bulletin of the American Meteorological Society 82.11 (2001): 2415-2434.
[2] Reichstein, Markus, et al. "On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm. Global Change Biology 11.9 (2005): 1424-1439. https://doi.org/10.1111/j.1365-2486.2005.001002.x
[3] Lasslop, Gitta, et al. “Separation of net ecosystem exchange into assimilation and respiration using a light response curve approach: critical issues and global evaluation.” Global Change Biology 16.1 (2010): 187–208. https://doi.org/10.1111/j.1365-2486.2009.02041.x
[4] Tramontana, Gianluca, et al. “Partitioning net carbon dioxide fluxes into photosynthesis and respiration using neural networks.” Global change biology 26.9 (2020): 5235-5253. https://doi.org/10.1111/gcb.
[5] Zhan, Weiwei, et al. “Two for one: Partitioning co2 fluxes and understanding the relationship between solar-induced chlorophyll fluorescence and gross primaryp roductivity using machine learning.” Agricultural and Forest Meteorology 321 (2022): 108980. https://doi.org/10.1016/j.agrformet.2022.108980
[6] Kai-Hendrik Cohrs et al. “Causal hybrid modeling with double machine learning—applications in carbon flux modeling.” Machine Learning: Science and Technology 5 (2024): 035021. https://doi.org/10.1088/2632-2153/ad5a60
How to cite: Cohrs, K.-H., Nelson, J., Lee, S.-C., Cuntz, M., Papastefanou, P., Nguyen, N., Zhan, W., Cattry, M., Wutzler, T., Varando, G., Gentine, P., Reichstein, M., and Camps-Valls, G.: A Protocol to Evaluate Carbon Dioxide Flux Partitioning Methods for Eddy Covariance Data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19561, https://doi.org/10.5194/egusphere-egu25-19561, 2025.
The amount of precipitation used by plants is an essential quantity for assessing ecosystem water-use efficiency and forecasting vegetation responses to environmental change in arid and semi-arid regions. The ratio of annual transpiration to annual precipitation (f0) is an important yet neglected parameter, required for the accurate estimation of potential maximum leaf area index, light absorption and gross primary production in water-limited environments. This study estimated transpiration using three methods: the Penman-Monteith equation, Fick’s law, and an energy-balance technique based on flux measurements. Results showed a consistent pattern whereby f0 initially increases with aridity but eventually declines, peaking at around 0.5–0.6 when the aridity index – defined as the ratio of annual (Priestley-Taylor) potential evapotranspiration to P – lies in the range from 2 to 3. This finding establishes a non-linear relationship between water supply and ecosystem water use, and points to a need to incorporate adaptive f₀ values in ecosystem models.
How to cite: Gao, M. and Prentice, I. C.: Shifting dynamics of water use: nonlinear decline of transpiration-to-precipitation ratios with aridity, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7750, https://doi.org/10.5194/egusphere-egu25-7750, 2025.
Photosynthesis is a fundamental ecosystem process coupled with terrestrial cycles of energy, carbon and water. As it is difficult to directly measure photosynthesis by partitioning the exchange of carbon dioxide (CO2) between plants and surrounding air, how photosynthesis responds to a variety of environmental drivers across temporal scales remains unclear. Carbonyl sulfide (COS) fluxes, and Sun-induced Chlorophyll Fluorescence (SIF), have been recently suggested as a promising proxy to infer photosynthesis and track stomatal processes at the ecosystem scale. However, the link between COS fluxes, SIF and CO2 uptake as well as stomatal opening varied with environmental factors across temporal scales remained unstudied given the tight coupling of leaf water and carbon fluxes. We first developed the CoupModel for simultaneous modeling of the COS, CO2, and SIF, and explicitly considered the mesophyll conductance in mediating COS and CO2 diffusion in leaf. By combining the long-term observations of the COS, CO2 fluxes as well as satellited-retrieved SIF from a boreal forest site with CoupModel, we disentangled the impacts of multiple environmental factors on COS, CO2 and SIF. Our results suggested leaf uptake of COS, SIF, gross primary productivity and transpiration show different response to variation in climatic controlling factors. We also demonstrated that the leaf uptake of COS is similar to CO2 on one hand mainly under light and temperature sufficient conditions, e.g., growing-season and daytime. On the other hand, the leaf uptake of COS under the light and temperature limited conditions such as non-growing season and nighttime is existing and different from CO2, as non-negligible uptake of COS occurs while the CO2 uptake is close to zero due to absence of photosynthesis. In summary, our study provides new insights into the controlling factors of COS-CO2-SIF and changes in COS-CO2-SIF relationships across temporal scales. We suggest that more mechanistic study for the ecosystem uptake of COS across multiple time scales is necessary for better utilizing COS to constrain the ecosystem water and carbon fluxes.
How to cite: Zhu, H., Wu, M., and Duan, W.: A unified modeling and understanding of the canopy CO2, COS and SIF processes with a process-based model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17313, https://doi.org/10.5194/egusphere-egu25-17313, 2025.
Land surface models (LSMs) play a pivotal role in Earth System Models by simulating energy, water, and carbon fluxes between the land and the atmosphere. However, existing LSMs face challenges with computational efficiency and the calibration of uncertain parameterizations, particularly for key carbon and water fluxes. To address these limitations, we introduce ClimaLand, a GPU-native LSM designed to integrate machine learning (ML) parameterizations and calibration frameworks with physical models. ClimaLand's modular architecture allows seamless incorporation of data-driven approaches for unresolved processes, such as subgrid-scale hydrology and canopy-atmosphere coupling, for faster iterations and hypothesis testing.
In this study, we focus on calibrating the latent heat flux, or evapotranspiration, a major source of uncertainty in land-atmosphere interactions. Using observational data from flux towers and remote sensing, we demonstrate how ClimaLand employs Ensemble Kalman Processes (EKP) to optimize parameterizations of stomatal conductance and soil moisture evaporation. Calibration approaches reduced bias during extreme events compared to traditional LSMs.
Benchmarking on GPUs highlights ClimaLand’s computational efficiency, enabling rapid uncertainty quantification and parameter ensemble testing. Results showcase the model’s capacity to improve physical realism and predictive accuracy, particularly for water and energy cycles critical to climate risk assessments.
ClimaLand marks a step forward in leveraging modern computational tools and ML to enhance the accuracy and scalability of LSMs. Future developments will extend to optimality-submodels and increased spatial resolution.
How to cite: Braghiere, R. K., Deck, K., Renchon, A. A., Sloan, J., Bozzola, G., Speer, E., Reddy, T., Phan, K., Efrat-Henrici, N., Dunbar, O., Frankenberg, C., and Schneider, T.: ClimaLand: Advancing Land Surface Modeling with Data-Driven Calibration and GPU Acceleration, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20679, https://doi.org/10.5194/egusphere-egu25-20679, 2025.
Plants can access underground water reserves to sustain their activity, releasing moisture into the atmosphere—a critical survival mechanism during drought. Understanding the role of groundwater in regulating photosynthesis is thus key for predicting land-surface processes. However, the impact of groundwater on terrestrial ecosystem productivity remains poorly quantified, particularly when compared to well-known factors like aridity. Here, we use satellite observations of solar-induced fluorescence as a proxy for photosynthesis, together with model estimates of water table depth and aridity, quantified by the moisture index with reanalysis data, to investigate the relationship between groundwater and photosynthesis. Using causality-guided explainable machine learning (Causal Shapley values), we demonstrate that groundwater plays a crucial role in determining spatial patterns of photosynthesis, with varying importance across ecosystem types, and that its effect is comparable to aridity. We show that the relative importance of groundwater accounts for 48 to 101% of the effect attributed to aridity in modulating forest photosynthesis across the contiguous USA. The relative importance of groundwater compared to the aridity remains substantial in savannahs and shrublands (30-58%), grasslands (22-42%), and croplands (15-32%). Our findings highlight the key role of groundwater in driving ecosystem long-term productivity.
How to cite: Giardina, F., Liu, J., Seneviratne, S. I., Stocker, B. D., and Gentine, P.: Strong impact of groundwater on long-term photosynthesis, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12055, https://doi.org/10.5194/egusphere-egu25-12055, 2025.
The exchange of water and carbon between the land-surface and the atmosphere is regulated by meteorological conditions as well as plant physiological processes. Accurate modeling of the coupled system is not only crucial for understanding local feedback loops, but also for global scale carbon and water cycle interactions. Mechanistic modeling approaches, e.g., the Earth system model ICON-ESM with the land component JSBACH4, are mainly applied to study land-atmosphere coupling. However, these models are hampered by relatively rigid and ad-hoc formulations of terrestrial biospheric processes, e.g., semi-empirical parametrizations for stomatal conductance, which often result in non-plausible and biased dynamics.
Here, we develop data-driven, flexible parametrizations controlling terrestrial carbon-water coupling based on eddy-covariance flux measurements (FLUXNET) to be implemented in the JSBACH4 model. Specifically, we introduce a hybrid modeling approach (integration of data-driven and mechanistic modeling), that aims to replace specific empirical parametrizations in JSBACH4’s modules computing coupled photosynthesis (gross primary production, GPP ) and transpiration (Etr) fluxes based on a multi-task feed-forward neural network (FNN) modelling approach pre-trained on observations. First, as a proof-of-concept, we train parametrizations based on original JSBACH4 output to showcase that our approach succeeds in reconstructing the original parametrizations, namely latent dynamic features for stomatal conductance (gs), the maximum carboxylation rate (Vcmax) and maximum electron transport rate (Jmax) that decisively control GPP and Etr . Second, we replace JSBACH4’s original parametrizations by dynamically calling the emulator parameterizations trained on original JSBACH4 output using a Python-FORTRAN bridge. This allows us to understand how small changes can propagate over time and enables us to evaluate the effects of data-driven parameterizations on the results produced by the coupled land-surface model. In the last step, we adopt the approach to infer these parametrizations from FLUXNET observations to construct an observation-informed modelling of water and carbon fluxes within the land model JSBACH4.
Our hybrid approach almost perfectly reproduces the original JSBACH4 parametrizations by emulating the latent variables yielding R 2 values ranging between 0.99-1.0 for GPP and Etr at hourly scale for forest and grassland sites. JSBACH4 equipped with these plugged-in emulations of the parametrizations reveal that the NN parametrizations are capable of reproducing the targets with relatively high accuracy while learning gs , Vcmax and Jmaxwithout prior information. By training the hybrid model on FLUXNET observations and we obtain observations-informed parametrizations to be plugged-in JSBACH4. We find that Hybrid-JSBACH can better capture the variability of GPP and Etr across different ranges of atmospheric and soil dryness in comparison to JSBACH by analyzing the mean hourly residuals for the target variables. While challenges persist in fully integrating carbon and water cycles due to physical constraints in carbon cycle modeling, the Hybrid-JSBACH modeling framework already enables observation-guided coupling of land-atmosphere interactions for the water cycle with key biospheric processes represented by our hybrid observation-informed land-surface model. These developments are key to critically advance our understanding of hydrological processes and linked feedbacks in the climate system, especially in the context of changing climatic conditions.
How to cite: ElGhawi, R., Reimers, C., Schnur, R., Reichstein, M., Körner, M., Carvalhais, N., and Winkler, A. J.: Hybrid-Modeling of Land-Atmosphere Fluxes Using Machine Learning integrated in the ICON-ESM Modeling Framework, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4000, https://doi.org/10.5194/egusphere-egu25-4000, 2025.
Accurate modeling of terrestrial carbon, energy, and water cycles is critical for understanding ecosystem processes and their responses to environmental change. However, a key challenge lies in the parameterization of these complex processes, which vary across scales and ecosystems. This study investigates how hybrid modeling approaches can enhance ecosystem parameter learning and provide deeper insights into terrestrial carbon and water dynamics across Europe. Specifically, we used a hybrid modeling framework that integrates the coupled photosynthesis-evapotranspiration model as a differentiable ecosystem model with a deep neural network to optimize parameter learning. Long-term observations from multiple FLUXNET sites across Europe, including daily evapotranspiration (ET) and gross primary productivity (GPP) data, were used to constrain model parameters in an end-to-end mode. The calibrated model was then used to generate spatial distribution maps of key ecosystem parameters, revealing how they vary under different climatic and ecological conditions.
Results demonstrate that the hybrid model significantly improves simulation accuracy for ET and GPP while capturing parameter variability across European ecosystems. Post-hoc analyses of the embedded neural network quantified the influence of key environmental drivers, such as climate, soil properties, and vegetation, on the learned parameters. These results highlight the value of hybrid modeling for improving understanding of ecosystem processes, providing actionable insights for climate adaptation and ecosystem management in Europe and for improving terrestrial biosphere models.
How to cite: Wang, C., Jiang, S., and Zheng, Y.: Parameter learning and scaling in hybrid ecosystem models for improved understanding of carbon and water dynamics, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6460, https://doi.org/10.5194/egusphere-egu25-6460, 2025.
Maximum light use efficiency (LUEmax) is a key parameter in state-of-the-art global carbon models (GCMs), representing the maximum conversion rate of absorbed photosynthetically active radiation into vegetation biomass under non-stress conditions. Despite its significance, LUEmax is often oversimplified in most GCMs, where its variation is constrained by a limited number of plant functional types (PFTs). This coarse classification overlooks well-documented variability within PFTs and fails to account for adaptation and acclimation processes, introducing substantial uncertainty in carbon cycle estimates.
Recent studies suggest that replacing PFT-based parameterization with spatially explicit LUEmax maps could significantly enhance ecosystem productivity modeling. In this study, we explore the potential of symbolic regression, an emerging machine learning technique based on genetic algorithms for deriving explicit mathematical relationships, alongside Kolmogorov-Arnold Networks (KANs) based on parameterized neural networks, which facilitate interpretable functional discovery, to estimate LUEmax from climatic data and key ecosystem traits.
Using novel plant trait datasets and multiannual flux tower eddy covariance observations combined with MODIS data, we assess the ability of symbolic regression techniques and KANs to derive equations linking LUEmax to ecosystem traits. Our findings demonstrate that these approaches improve the generalization of LUEmax estimation and enhance interpretability, offering significant implications for global-scale environmental modeling and remote sensing applications.
How to cite: Muñoz-Marí, J., Moreno Martínez, Á., Tiavlovsky, E., Hirn, J., and Camps-Valls, G.: Enhancing Light Efficiency Modeling with Symbolic Regression and KANs, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19694, https://doi.org/10.5194/egusphere-egu25-19694, 2025.
The Vegetation Photosynthesis and Respiration Model (VPRM) is a light-use efficiency model used to estimate biogenic CO2 fluxes based on satellite indices, land cover maps, and meteorological data. It models net ecosystem exchange (NEE) with a simple function that uses four adjustable parameters for each vegetation type, fitted using eddy covariance measurements. VPRM is both accurate and computationally efficient, making it a popular choice for calculating CO2 fluxes at high spatial and temporal resolutions, such as in regional inversion studies.
Initially designed for use with MODIS satellite data at a 500-meter resolution, our updated implementation now supports Sentinel-2 data with a much finer 20-meter resolution. This higher resolution improves the accuracy of biospheric flux estimates by (1) better resolving heterogeneous landscapes, such as croplands, and (2) enabling the incorporation of time-dependent flux tower footprints into the parameter fitting procedure. We compared the flux footprint approach to the traditional implementation for Sentinel-2 for Europe. To ensure robust comparisons, we used Monte Carlo Markov Chain (MCMC) sampling to estimate the range of parameter values for both model versions.
How to cite: Kroker, M. J., Glauch, T., Vardag, S. N., Marshall, J., and Butz, A.: Reducing uncertainties in the Vegetation Photosynthesis and Respiration Model (VPRM), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13313, https://doi.org/10.5194/egusphere-egu25-13313, 2025.
Understanding vegetation dynamics is essential for predicting water, carbon, and energy exchanges in terrestrial ecosystems. Despite advances in plant-environment interaction models, challenges remain in accurately representing how key plant traits, such as roots, respond to environmental variability, particularly in arid ecosystems. Current models often rely on fixed mathematical representations, limiting their ability to address complex and dynamic plant-environment interactions. For instance, optimality-based vegetation models, which use long-term carbon profit optimization principles, show promise but are typically still constrained by predefined functional forms.
This work presents a conceptual framework that attempts to integrate machine learning with optimality-based vegetation modeling, aiming to combine the strengths of mechanistic modeling and data-driven approaches. This framework is designed to capture diverse plant-environment processes, such as root development, over various temporal scales. Within this hybrid framework, plants in simulated environments are enabled to dynamically adjust their responses based on optimization objectives. Preliminary simulations with the FLUXNET datasets suggest that the framework has the potential to better predict ecosystem fluxes and improve our understanding of vegetation dynamics under changing conditions.
This study highlights the potential of integrating machine learning with plant physiological processes to address current limitations in modeling plant-environment interactions. The proposed framework could serve as a flexible tool for exploring vegetation dynamics and their implications for ecosystem function.
How to cite: Zhou, J., Jiang, S., Hildebrandt, A., Koirala, S., and Carvalhais, N.: Modeling plant-environment interactions with integrated machine learning and optimality theory, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11348, https://doi.org/10.5194/egusphere-egu25-11348, 2025.
Continuous active chlorophyll fluorescence measurements, e.g. using the MONI- or MICRO-PAM instruments by Walz, are widely used because they provide valuable, real-time information about the photosynthetic activity and stress status of plants by measuring chlorophyll fluorescence. Its ability to provide non-invasive, precise data on photosystem performance makes it an essential tool in both research and practical applications.
However, some derived parameters from these measurements depend on measurements taken from dark-adapted leaves. With continuous measurements, these values are collected during the night. Considering that, in addition to light availability, many other environmental parameters (such as temperature, VPD, etc.) differ significantly from daytime conditions, the question arises whether directly relating parameters measured at night to those measured during the day might lead to errors. One such parameter that is potentially affected is NPQ (non-photochemical quenching), which is calculated from the ratio of the maximum fluorescence of a dark-adapted leaf (Fm) to the maximum fluorescence of a light-adapted leaf (F’m).
We thus present here the results of a laboratory experiment in which we investigated the temperature dependence of Fm in Lantana camara under otherwise constant conditions. We were able to demonstrate that Fm shows a clear dependence on ambient temperature, with Fm increasing as the temperature rises. This implies that, under typical field conditions, where night-time temperatures are lower than daytime temperatures, Fm measured at night would underestimate the actual values of Fm observed under warmer daytime conditions. Ultimately, this leads to an underestimation of NPQ, when calculated from these underestimated Fm values.
In parallel, we investigated whether a typical dark adaptation period of 30 minutes is sufficient to reach a dark-adapted state during daytime conditions. Our results showed that 30 minutes was never enough to ensure adequate dark adaptation in the leaves.
How to cite: Hammerle, A., Pilz, T., de Vries, A., and Wohlfahrt, G.: Temperature-Dependent maximum dark-adapted Fluorescence in Lantana camara: Implications for Accurate NPQ Measurements, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6536, https://doi.org/10.5194/egusphere-egu25-6536, 2025.
This study integrates modern observational data (CRU)(1901-2023) with historical documentary records (REACHES) to reconstruct and analyze long-term drought patterns in the Chinese dynasties since the mid-14th centuries (1368-1911). By focusing on the Standardized Precipitation Index (SPI), we examine spatio-temporal drought trends across multiple timescales. SPI indices for 12-month, 36-month, and 60-month periods (SPI12, SPI36, SPI60) were calculated to capture variability across different temporal scales. Empirical orthogonal function (EOF) analysis was conducted to identify major spatial patterns and analyze temporal series, facilitating the identification of extreme drought periods and sequences of significant anomalies. Wavelet analysis was employed to detect potential periodicities and dominant cycles within the data. Further analysisis underway to assess whether variations in drought patterns might differ when considering evapotranspiration (SPEI). This aspect remains exploratory, offering a potential insight into the broader implications of integrating long-term and additional climatic variables into drought analysis.
How to cite: Chang, C.-H., Lin, K. E., Tseng, W.-L., Lin, C.-W., Huang, H.-C., and Wang, P. K.: Adjusting Standardized Precipitation Index to Analyze Long-Term Drought Patterns from Documentary Records in the Chinese dynasties Since the Mid-14th Century , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11486, https://doi.org/10.5194/egusphere-egu25-11486, 2025.
The movement of water vapor between the soil and the atmosphere plays a crucial role in soil-atmosphere interactions, especially under dry conditions. A previously little-noticed process known as water vapor adsorption in soil occurs when water vapor from the atmosphere is adsorbed in the soil during the night, caused by cooling at the soil surface. This process is based on the fundamental principle that equilibrium vapor pressure decreases in the vicinity of dry soil material, creating conditions under which evaporation turns into condensation. While this phenomenon is well understood at small scales under controlled conditions, its effects on ecosystems at larger scales remain poorly understood due to the lack of continuous, direct measurements.
In this study, we investigate across a worldwide network of eddy covariance measurements under which conditions negative latent heat fluxes (vapor movement towards the soil) are consistent with an established theoretical understanding of soil water vapor adsorption. We find an emerging functional relationship between latent heat flux direction, soil water content, and near-surface relative humidity which facilitates the investigation of adsorption events across the eddy covariance network. Our results confirm that soil water vapor adsorption occurs most frequently in arid areas with sparse vegetation, such as savannahs or dry shrublands. The average duration of soil water vapor adsorption is 4 hours per day in all ecosystems and up to 9 hours per day in some sites. The number of days per year where soil water vapor adsorption was measurable for three hours or longer varied by ecosystem, reaching up to 150 days per year. Our results further suggest that soil texture has a relatively minor influence on the occurrence under field conditions compared to the results of laboratory experiments.
Our analysis confirms recent findings that soil water adsorption can be isolated from eddy covariance measurements. It not only expands our knowledge of the spatial distribution of soil water vapor adsorption in different ecosystems but also facilitates future research to investigate interannual dynamics, management, and extremes. Thus, the study contributes to the understanding of a long-overlooked aspect of soil-atmosphere interaction.
How to cite: Paulus, S. J., Nelson, J. A., Lee, S.-C., Orth, R., Hildebrandt, A., Reichstein, M., and Migliavacca, M.: Assessing the Spatiotemporal Dynamics of Soil Water Vapor Adsorption Using a Global Observation Network, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10779, https://doi.org/10.5194/egusphere-egu25-10779, 2025.
Climate change and climate extremes are severely impacting forest ecosystems, threatening their functioning and diversity. Our ability to accurately monitor forest responses to climatic impacts, however, is limited. This study introduces and discusses the monitoring potential of Internet of Things (IoT) spectral sensors for continuous below-canopy radiation measurements. At the canopy scale, light partitioning into absorbed, reflected and transmitted light is strongly modulated by architectural parameters in addition to leaf level chemistry (canopy pigments and water content). These determine a high spatial, temporal and spectral variability of transmitted light, which requires a large sampling effort to be described at stands and forest scale. The recent availability of spectral sensors connected through IoT technologies is opening new possibilities in the dynamic characterization of forest canopy spectral features. The proposed approach enables the monitoring of structural and physiological traits continuously in time and on larger extents compared to hand-carried instruments. Key applications include validating satellite vegetation products, analyzing light quality variations, investigating tree responses to environmental stresses like drought and heatwaves, exploring the role of light quality in forest renovation, and understanding complex forest ecosystem interactions. We have yet to fully imagine potential applications that could go beyond traditional plant ecology boundaries, ranging from wildlife light preferences to tree insect damage monitoring. By providing continuous, high-resolution data from previously understudied forests, this approach bridges technological innovation with ecological research, potentially revolutionizing our understanding of forest functioning under changing climate conditions.
How to cite: Andreatta, D., Belelli Marchesini, L., Vescovo, L., and Gianelle, D.: Continuous spectral monitoring below forest canopies: an IoT-based approach, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11495, https://doi.org/10.5194/egusphere-egu25-11495, 2025.
Land surface phenology, which describes the seasonal dynamics of vegetated land, plays a crucial role in regulating the seasonality of water and energy exchange between the land and atmosphere. Changes in key phenological metrics, such as the start of season (SOS) and end of season (EOS), have been observed in extratropical ecosystems using satellite data, and are largely attributed to climate change. These changes can have significant impacts on the surface energy balance, affecting the exchange of heat, moisture, and radiation between the land and atmosphere. However, the magnitude and spatial distribution of these impacts are not yet well understood.
This study aims to investigate the sensitivity of surface energy balance variables, including turbulent latent and sensible heat fluxes, evaporative fraction, surface albedo, and surface temperature, to changes in SOS and EOS in the extratropical northern hemisphere during the period 2001-2021. We develop a method to quantify the sensitivity of surface energy balance variables to changes in SOS and EOS, using a linear regression approach to extract the slope of the relationship between the phenological indicators and the surface energy balance variables. Our analysis integrates multiple datasets, including ERA5 reanalysis, MODIS remote sensing estimates, GLEAM and FLUXCOM-X observation-guided data products for the water and energy fluxes, and a land-cover type map, to provide a comprehensive assessment of the impacts of phenological changes on the surface energy balance across different plant functional types (PFT).
Our results show that an earlier SOS is associated with increased turbulent heat fluxes, evaporative fraction, and surface temperature during the time around SOS, while later EOS has similar but less pronounced effects during the time around EOS, with spatial variability and differences among PFTs (for both phenological indicators within and between the different datasets of the surface energy balance variables). These spatial sensitivity patterns are generally consistent across multiple datasets, except for the sensible heat and evaporative fraction sensitivity to SOS, which exhibit considerable variability across datasets. Our analysis by PFT reveals a higher sensitivity of all surface energy balance variable to shifts in SOS in forests, compared to cropland, grassland, and shrubland. Finally, we place our findings on biogeophysical phenology impacts into perspective by comparing them to the effect strength of biogeochemical impacts, providing a comprehensive assessment of the relative importance of these two types of impacts at the land-surface.
How to cite: Hau, O., Winkler, A., Kranz, J., Forkel, M., and Migliavacca, M.: Phenology-Driven Changes in Controls of the Surface Energy Balance Across Different Extratropical Ecosystems , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19775, https://doi.org/10.5194/egusphere-egu25-19775, 2025.
The Tarim River is the longest inland river in China, and its basin represents a typical ecologically fragile area within arid regions, highly susceptible to human activities and climate change. These factors contribute to increased regional desertification and the deterioration of the ecological environment. As the sole community-forming tree species in this basin, a timely understanding of the spatial distribution and growth status of Populus euphratica is essential for maintaining the ecological balance in the desert and ensuring the security of the oasis ecosystem. Currently, there is no spatial distribution map of Populus euphratica in the basin, primarily due to significant variations in stand density and tree branch architecture, along with a lack of high-spatial-resolution data. This study addresses the gap by constructing a comprehensive dataset of single-tree parameters for Populus euphratica through the integration of LiDAR and GF-2 satellite imagery. We developed a deep learning model tailored to different densities and crown architecture of Populus euphratica, enabling accurate quantification of the spatial distribution of these forests in the Tarim River basin. This study provides valuable insights for the inversion of large-scale fine forest structure parameters and serves as a crucial foundation for the management and conservation of forest resources in arid regions.
How to cite: Yang, Q.: Spatial distribution mapping of Populus euphratica in the Tarim River Basin using multi-source remote sensing data and deep learning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14715, https://doi.org/10.5194/egusphere-egu25-14715, 2025.
Arctic regions play a critical role in the global carbon cycle, acting as both a sink and a source of carbon. However, it remains challenging to estimate methane (CH4) and carbon dioxide (CO2) fluxes across Arctic landscapes due to the sparsity of measurements and the complex interactions between environmental factors. Upscaling fluxes from local measurements to broader landscapes is challenging, especially in capturing the variability of land cover types and their unique carbon dynamics. Addressing this heterogeneity is critical to improving flux estimates and reducing uncertainties in Arctic carbon budgets.
Our study domain (~6 km2), the Trail Valley Creek area (Northwest Territories, Canada) illustrates this challenge, featuring a mosaic of upland, shrub, and lichen tundras alongside heterogeneous wetlands, each with distinct moisture regimes and carbon flux contributions. Our study integrates diverse datasets to upscale carbon fluxes with statistical and machine learning models at high spatial resolution (10 m), ensuring that small-scale variations are preserved. We combine chamber measurements of CH₄ and CO₂ fluxes from 39 sites, with different temporal resolutions ranging from high-frequency half-hourly data to a few measurements per day, spanning the entire vegetation season, with soil temperature (from topsoil to 30 cm depth) and soil moisture data (at different depth down to 30 cm depth), remote sensing products such as Sentinel-2 imagery, UAV-derived vegetation height and classifications (1 m resolution), and DEM/DSM (10 cm resolution). Based on these remote sensing products we calculated vegetation and moisture indices (NDVI, NDWI, NDMI, TWI), which provide insight into seasonal variability, and the snow index (NDSI) highlights the timing of snowmelt and its influence on fluxes. This approach allows us to examine both the spatial heterogeneity of fluxes across different land cover types and their temporal dynamics in response to climate-driven changes in soil and vegetation conditions.
How to cite: Ivanova, K., Virkkala, A.-M., and Göckede, M.: High-resolution carbon flux upscaling in Arctic landscapes based on the example of Trail Valley Creek, Canada, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6202, https://doi.org/10.5194/egusphere-egu25-6202, 2025.
