The purpose of this session is to unite scientists investigating the dynamics of tropical ecosystems, employing a range of remote and on-site observational, experimental, modelling, and theoretical approaches. We are particularly interested in studies evidencing/documenting how tropical biomes, at the local or regional scale, respond to human-induced disturbances and climate change. In particular, spatial gradients and temporal scales that mirror global changes. Moreover, we encourage the presentation of innovative interdisciplinary methodologies and techniques that have the potential to reshape existing paradigms, thereby paving the way for exciting new avenues of exploration.
Orals: Mon, 28 Apr | Room 2.23
Large-scale deforestation poses a significant threat to ecosystem stability and climate, leading to increased carbon dioxide emissions, which exacerbates global warming and ecological imbalance. To achieve sustainable forest management, precise large-scale monthly deforestation mapping has become increasingly important. The open access to Sentinel-1 data provides unprecedented opportunities for monthly deforestation mapping. However, previous monthly mapping based on Sentinel-1 and deep learning still needs improvement in accuracy, and the best strategies for large-scale model transfer have not been fully explored. This study proposes a new approach for monthly deforestation mapping based on Sentinel-1 data and an adapted Segment Anything Model (SAM), combined with active learning and transfer learning strategies for large-scale model transfer. The model was tested and evaluated in four different study sites: Rondônia in Brazil, Guangxi in China, California in the USA, and Hainan in China. The results showed the superior performance of our proposed adapted SAM method, with F1 scores ranging from 0.74 to 0.88 and IoU from 0.58 to 0.78. The combined model for the four regions achieved an F1 score of 0.81 and an IoU of 0.68, outperforming the baseline U-net model (combined F1 score of 0.78 and IoU of 0.64). When applied to new sites, the fine tune-based transfer learning significantly improved the model’s spatial generalization capability with the addition of a small number of target domain samples. Moreover, compared with random sampling approach, the active learning technique help reduce the required number of training samples to achieve the same level of accuracy. This study provides a comprehensive workflow for improved monthly deforestation mapping, emphasizing the advantages of combining Sentinel-1 SAR data with advanced models and strategies. Our method offers a reliable and efficient solution for large-scale deforestation monitoring, aiding in the timely detection of deforestation activities and supporting sustainable forest management strategies.
How to cite: Zhao, F.: Monthly mapping of deforestation in the Amazon using Sentinel-1 data and a vision foundation model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19757, https://doi.org/10.5194/egusphere-egu25-19757, 2025.
White sand ecosystems (WSE)- known locally as Campinarana, Campinarana florestada (in Brazil) or Caatinga Amazonica (Venezuela) are typically thin-stemmed, nutrient scarce, low canopy ecosystems located on sandy soils (podzols) distributed across the Amazon basin. It has been previously documented that WSEs can form histosol layers capable of storing significant carbon, but existing studies are limited in geographic scope and quantity of data points. Notably a new study measured up to 2m of peat in Colombian WSEs, but we lack a wider understanding of the distribution and dynamics of peat forming WSEs across Amazonia.
Here we undertake a simple spatial analysis, overlapping a recently published Amazon peat map with previously published WSE distributions. We combine this with recent carbon density data and insights gained from an in-depth study of Colombian white sand peatlands (WSP) by Winton et al. (in review).
We estimate a total white sand peatland area of 78,832 (40,403 – 117,133) km2 across the Amazon basin, corresponding to 26% of all white sand ecosystems. The greatest concentration is in the Rio Negro basin in Brazil. We predict that 39%, 26%, 15% and 6% of Amazon basin WSE forests are underlain by peat in Venezuela, Brazil, Colombia and Peru respectively. We in turn estimate a total carbon stock of 3.86 (0.64–7.48) Pg C in the WSPs of the Amazon basin, comparable to that of the largest known peatland region in the South America- the Pastaza-Maranon Foreland Basin.
We conclude that WSPs are critically understudied ecosystems and represent a fundamental gap in our understanding of the Amazon basin carbon cycle. Crucially, no existing studies appear to be located in the most concentrated areas of peat, with Colombia being the only substantial WSP region to be densely sampled. Our results can inform future research priorities in WSPs.
How to cite: Hastie, A., Fernández, R. H., Honorio Coronado, E. N., and Winton, R. S.: Global importance of Amazonian white-sand peat carbon, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11765, https://doi.org/10.5194/egusphere-egu25-11765, 2025.
The Amazon biome is one of the largest carbon reservoirs, a relevant carbon sink in the world. The large extension and diversity of the Amazon biome hampers the assessment of regional-scale carbon budget based solely on local observations. Land surface models can provide carbon flux estimates, but they require proper calibration to represent the dynamics of the different ecosystems, abiotic conditions and vegetation characteristics in the Amazon Basin. One of the most important land surface model is JULES being increasingly used in tropical forests to estimate carbon fluxes. However, there is a lack of parameterization information that can be applied to the Amazon biome. Thus, this study presents an optimization of JULES main sensitivities parameters for different sites of the Amazon biome. For this attempt, we selected four Eddy-covariance flux towers as a reference based on different regions of the Amazon biome: K34 (Manaus, 2.614S/60.12W); K67 (Santarem, 2.85S/54.97W); RJA (Reserva Jaru, 10.08S/61.93W and ATTO (São Sebastião do Uatumã, 2.15S/59.03W). The variables analyzed to reproduce the carbon dynamics were the Net Ecosystem Exchange (NEE), Gross Primary Production (GPP) and eutrophic respiration (RESP) during one year of analysis. JULES most sensitivities parameters adjusted were related to the Upper-temperature threshold for photosynthesis (tupp_io); Scale factor for dark respiration (fd_io); The maximum ratio of internal to external CO2 (f0_io) and Quantum efficiency (alpha_io). The optimization was made using the Nelder-Mead method and after a leave-one-out cross-validation method was implemented to evaluate the simulation efficiency in each site. Also, the new parametrization in each site was compared with the default version of JULES and with another model Vegetation Photosynthesis and Respiration Model (VPRM). We selected the Wilmott index of agreement (d) and the Root Mean Square Error (RMSE) to analyze simulation efficiency. The Nelder-Mead optimization method reduced the error in GPP simulations in each Tower in comparison to the two models evaluated however the new parametrization of JULES was not able to improve RESP in these sites. However, the optimization procedure presented better results in NEE in each tower evaluated in the Amazon biome being the ATTO tower that demonstrated the most efficient simulations (d =0.60; RMSE = 2.03 g C m-2 day-1) in comparison to the default version (d= 0.52; 3.09 g C m-2 day-1) and VPRM (d = 0.58; 2.29 g C m-2 day-1). In general, results demonstrated that the new parametrization of JULES reduced the error of simulation compared to the last version of JULES for tropical forests and better represented the seasonality compared to the VPRM model.
How to cite: Prudente Junior, A. C., Santos da Silva, F., De Paula Cordeiro, L., Botía, S., Varanda Rizzo, L., Dias Freitas, E., Ambrizzi, T., Artaxo Netto, P. E., and Toledo Machado, L. A.: Optimization of JULES model in different sites of the Amazon biome, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3862, https://doi.org/10.5194/egusphere-egu25-3862, 2025.
The Amazon, with its vast wetlands, is a significant hotspot for greenhouse gas emissions. However, the emissions from aquatic systems remain poorly understood. The Rio Negro is one of the main tributaries of the Amazon river but, to date, there have been few measurements on GHG concentrations and fluxes, and none for the upper Rio Negro region.
We present the first continuous measurements of dissolved CO2, CH4, N2O and CO in the Rio Negro, between the cities of Manaus and São Gabriel de Cachoeira (~1000 km). From a moving research vessel, water was sampled continuously from a depth of 50 cm and passed through a bubble-type-equilibrator. A closed air stream was circulated through the equilibrator, and continuously measured by an in-situ FTIR analyzer. In addition, variables such as pH, air and water temperature, DOC and coliform bacteria were determined.
All measured gases were supersaturated in the water with respect to the atmosphere, indicating an outgoing flux toward the atmosphere. CH4 concentrations showed elevated concentrations in the middle Rio Negro, contrasting with CO2, which peaked in the upper and lower Rio Negro. Both CH4 and CO2 displayed distinct hotspot regions, many of which were centered around human settlements and are therefore likely of anthropogenic origin, as also confirmed by the observed bacterial communities. A few hotspots appeared to be linked to surrounding wetlands, which may release large amounts of CH4 during the rising water phase when reconnected to the main river. N2O showed elevated concentrations in the upper Rio Negro, possibly linked to the extensive white sand forest areas in this part of the catchment. CO concentrations showed a clear diurnal pattern, with highest concentrations coinciding with highest incoming solar radiation.
Based on our measurements, we suggest that anthropogenic influences on remote rivers such as the Rio Negro may be greater than previously assumed, potentially affecting the representativeness of both past and future field measurements.
How to cite: van Asperen, H., Warneke, T., Estefani Batista, C., Souza da Silva, J., Rizzo, L., Klemme, A., Lopes e Oliveira, R., Duvoisin Junior, S., Forsberg, B., and Trumbore, S.: Dissolved greenhouse gases in the Rio Negro (Amazonia, Brazil): the influence of humans versus wetlands, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13543, https://doi.org/10.5194/egusphere-egu25-13543, 2025.
Multidimensional trait relationships are imperative to understanding forest functioning in the face of ongoing environmental changes. Warming and more frequent and severe water stress are expected to adversely affect tropical forests’ large carbon uptake capacity. Therefore, it is, important to evaluate how tropical trees would perform under future climate scenarios. It is challenging to analyse trait-performance relationships solely based on observational data. However, process-based models representing key plant trait trade-offs can be applied to investigate the influence of different plant hydraulic strategies on tropical tree performance.
We used a hydraulics-enabled version of the Lund-Potsdam-Jena General Ecosystem Simulator (LPJ-GUESS) to study how trait relationships influence tropical plant performance on three study sites included in the RwandaTREE (Rwanda tropical elevation gradient) experiment. We parameterised four endemic species based on observational data and ran simulations by varying selected traits within the potential ranges to evaluate how these parameters affect the simulated biomass and woody growth rate.
The results showed a variation in optimum trait values which led to realistic simulated woody growth rates, depending on successional strategies and study sites. This can be attributed to the emerging functional strategies defined by the trait relationships.
Our results highlight that we can evaluate complex trait relationships and trade-offs that cannot feasibly be measured across large scales. This allows us to formulate new hypotheses on which hydraulic and structural trait correlations define plant performance. Increased understanding of drought-related vegetation processes can be used to decrease uncertainty in simulating tropical forest resilience and extreme weather impact on Pan-African carbon stocks.
How to cite: Pongracz, A., Pugh, T. A. M., Olin, S., Eckes-Shephard, A., Uddling, J., Wallin, G., Manzi, O. J. L., Wittemann, M., Nsabimana, D., Zibera, E., Ziegler, C., Manishimwe, A., Papastefanou, P., and Rammig, A.: Linking form to function: Simulating plant hydraulic strategies’ impact on tree drought response in a tropical montane rainforest, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17267, https://doi.org/10.5194/egusphere-egu25-17267, 2025.
Tropical forests, spanning wet and dry forest ecosystems, are pivotal in regulating the global carbon cycle and climate through dynamic exchanges of energy, water, and carbon. These ecosystems influence regional and global climate patterns via biogeochemical feedback mechanisms. However, climate change is altering these processes, with rising temperatures intensifying evaporative demand and affecting photosynthetic activity, as indicated by changes in net ecosystem exchange (NEE). Vegetation and biomass variations further impact microclimates, feeding back into heat and water budgets. Understanding the dynamics and meteorological drivers of carbon and water fluxes is essential for comprehending land surface–atmosphere interactions.
This study compares the climatological and ecological functions of tropical wet and dry forests by examining two contrasting sites in the tropical Andes Mountains of southern Ecuador: the montane dry forest (MDF) in the Laipuna Reserve and the montane rain forest (MRF) in the Reserva Biológica San Francisco. The MDF is characterized by a deciduous forest and exhibits pronounced seasonality, with distinct dry (June–December) and wet (January–May) periods, driven by the inter-hemispheric shift of the Intertropical Convergence Zone (ITCZ). In contrast, the MRF experiences year-round rainfall, sustaining an evergreen lower montane forest type. Eddy-covariance measurements were used to monitor water and carbon fluxes under these contrasting climatic regimes. This comparison provides valuable insights into the differential roles of these ecosystems in regulating the Earth's energy and carbon budgets under changing climatic conditions. The objective of the study is (i) to quantify the magnitude and seasonality of NEE and its partitioned components, gross primary production (GPP), and ecosystem respiration (Reco) And (ii) to identify the meteorological drivers responsible for the variations in carbon exchange within each ecosystem. The results reveal significant variations in NEE in the MDF between wet and dry seasons. During the wet season, the average NEE was -3.9 μmol m⁻² day⁻¹, while in the dry season, it declined substantially to -0.8 μmol m⁻² day⁻¹. In contrast, the MRF demonstrated a consistently higher average NEE of -18 μmol m⁻² day⁻¹. These variations are driven by distinct environmental factors. In the MDF, water availability, regulated primarily by precipitation, is the dominant factor influencing carbon exchange. Conversely, the carbon dynamics in MRF are predominantly governed by energy inputs, with light playing a critical role in driving its NEE.
How to cite: Murkute, C., Sayeed, M., Pucha-Cofrep, F., Raffelsbauer, V., Ahmed, R., Scholz, S., Limberger, O., Carillo-Rojas, G., Fries, A., Bendix, J., and Trachte, K.: Carbon flux dynamics in montane tropical wet and dry forests: A comparative study in Southern Ecuador, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14652, https://doi.org/10.5194/egusphere-egu25-14652, 2025.
In the face of ongoing global crises, such as climate change and biodiversity loss, we urgently need to understand dynamic and complex responses of global forest ecosystems. To do so, we need to develop modeling frameworks that account for multiple temporal and organizational scales, and therefore capture functional adaptations of individuals, species, and ecosystems in response to the environment.
Here we present Plant-FATE (Plant Functional Acclimation and Trait Evolution) an eco-evolutionary vegetation model that embodies functional diversity by representing plant life-history strategies, and adaptations by accounting for short-term physiological acclimation, mid-term demographic shifts, and long-term trait evolution.
Tested with data obtained from an hyperdiverse site in the Amazon Forest, our model predicts a nonlinear response of tropical forests to increasing atmospheric CO2 due to diverse aspects of the growth-mortality tradeoff. At moderately elevated CO2, we found that evolution towards higher wood density increases vegetation C sequestration. By contrast, under highly elevated CO2 levels, a darkening understorey rather triggers lower wood densities, thus reversing gains from the proposed CO2 fertilization effect.
Our results suggest that competition for resources may modulate community-level eco-evolutionary dynamics of forest ecosystems, such that competition-induced changes in wood density may render forests more vulnerable to future climatic extreme events. Our study highlights the importance of accounting for eco-evolutionary dynamics when simulating the functional response of forest ecosystems to projected climate change.
How to cite: Hofhansl, F., Singh, S., Stefaniak, E., Maxwell, T., and Joshi, J.: Simulating plant functional acclimation and trait evolution using an eco-evolutionary vegetation model (PlantFATE), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11302, https://doi.org/10.5194/egusphere-egu25-11302, 2025.
Volatile isoprenoids take part in a wide range of forest-atmosphere processes that scale from plant cell regulation to atmospheric particle formation. Major drivers of plant leaf emissions are light and temperature - i.e., seasonality - and leaf age, suggesting leaf phenological type (i.e., evergreen or brevideciduous) may exert control over emission rates. The Amazon Forest is the greatest and most diverse source of volatile isoprenoid emissions, but the lack of leaf-level studies and the logistical challenges of measuring in such remote and highly bio-diverse sites bring high levels of uncertainty to modeled estimates. Studies indicate that hyperspectral leaf reflectance is an effective tool for estimating leaf morphological, physiological, and chemical traits, being perhaps a promising tool for remotely assessing volatile isoprenoid emissions from vegetation. Considering this, our research aimed at evaluating i) whether leaf phenological type and functional traits are determinants of the presence and magnitude of isoprene emissions and of mono- and sesquiterpene storage, and ii) whether leaf-level hyperspectral reflectance can be used to predict the presence of isoprene emissions and mono- and sesquiterpene storage in central Amazon forest trees. We found that isoprene-emitting evergreen trees were less likely to store monoterpenes and had tougher and less photosynthetically active leaves, while higher isoprene emission rates in brevideciduous trees associated with higher storage of sesquiterpene and phenolic compounds, suggesting that isoprene emissions possibly mediate a mechanical-chemical defense trade-off in evergreen and brevideciduous trees in this forest. Furthermore, we saw that dry leaf hyperspectral reflectance data and fresh leaf reflectance at selected wavelengths (616, 694, and 1155 nm) predicted the presence of isoprene emissions with accuracies of 0.67 and 0.72, respectively. Meanwhile, the presence of terpene storage was well predicted from fresh leaf reflectance data for monoterpene storage (accuracy = 0.65) and sesquiterpene storage (accuracy = 0.67). These results indicate the possibility of using spectral readings from herbarium specimens to assist in the development of more efficient sampling designs targeted at potential isoprene emitters, as well as of using fresh leaf reflectance data to calibrate multi-sensor equipment to remotely detect potential isoprene emitters or orientate sampling efforts in the field toward potential terpene-storing trees. The use of spectral tools for detecting potential volatile isoprenoid emitters and a more functional trait-based, mechanistic representation of emissions can combine to reduce modeling emission uncertainties and contribute to understanding the roles of volatile isoprenoids within forest-atmosphere interactions, atmospheric chemistry, and the carbon cycle.
How to cite: Robin, M., Durgante, F., Mallmann, C. L., Hadlich, H., Römermann, C., Niinemets, Ü., Gershenzon, J., Huang, J., Nelson, B., Taylor, T., de Souza, V., Pinho, D., Falcão, L., Lacerda, C., Duvoisin, S., Schmidt, A., Piedade, M. T. F., Schöngart, J., Wittmann, F., and Gomes Alves, E.: Leaf phenological type, functional traits, and hyperspectral reflectance to predict volatile isoprenoid emissions in central Amazon Forest trees, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20870, https://doi.org/10.5194/egusphere-egu25-20870, 2025.
During the rainy season, black carbon (BC) particles exhibit strong variability in concentration. At the Amazon Tall Tower Observatory (ATTO), located in central Amazonia, elevated BC concentrations have been previously identified as originating from the African continent. However, BC mass concentrations approach zero during certain periods, characterizing pristine episodes. This study aims to identify the primary factors influencing BC concentration in the Amazon. The first analytical approach involved evaluating air mass back trajectories during episodes of high BC concentration (BC > 0.46 µg/m³, with the day of maximum concentration selected from neighboring days) and low BC concentration (BC < 0.08 µg/m³, with the day of minimum concentration chosen). Ensemble back trajectories, analyzed across multiple atmospheric levels, revealed minimal differences between the air trajectories associated with these two contrasting scenarios. The second approach examined accumulated rainfall at ATTO during the three days preceding the selected high- and low-concentration days. The results indicate that precipitation plays a dominant role in modulating BC concentrations. A histogram of precipitation data revealed two distinct patterns: one corresponding to high rainfall during pristine events and another to low or negligible rainfall during more polluted days. Using ERA-5 reanalysis data, this precipitation variability was observed to extend across the Intertropical Convergence Zone (ITCZ) over the Atlantic. Simulations were conducted using the ECHAM/MESSy Atmospheric Chemistry (EMAC) model to investigate this phenomenon further. The simulations demonstrated that rainfall variability influences the transport from Africa to the Amazonas of particles such as BC, dust, and gases, including CO₂ and O₃. Composite analyses of hemispheric synoptic patterns were performed by selecting days with high and low BC concentrations from January–February from 2015 to 2022. These composites revealed that the variability is driven by oscillations in the western hemisphere synoptic patterns linked to the positioning of cold fronts in both hemispheres. This variability has significant implications for transporting vital nutrients to the Amazon rainforest. Understanding the relationship between rainfall, synoptic patterns, and BC transport is crucial, particularly in the context of climate change, which could alter these patterns and profoundly impact the ecological systems of the Amazon basin.
This study was supported by FAPESP 2022/07974-0
How to cite: Toledo Machado, L. A., Gan, M. A., M. Barbosa, H. J., Holanda, B., Pozzer, A., and Pöhlker, C.: Linking ATTO Black Carbon to Rainfall Patterns in the South America-Atlantic Tropical Zone, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1726, https://doi.org/10.5194/egusphere-egu25-1726, 2025.
Tropical rainforests play an important role in the global carbon cycle. They store massive amounts of biomass in their trees and soils, and contribute to climate mitigation by removing carbon from the atmosphere through photosynthesis. In a large-scale free-air CO2 enrichment (FACE) experiment in a highly diverse, old-growth, tropical forest in the Brazilian Amazon, we will assess the ecosystem responses to rising atmospheric CO2 concentrations. The main questions are (1) whether elevated atmospheric CO2 directly and sustainably stimulates photosynthesis (the so-called CO2-fertilization effect) and (2) will reduce stomatal conductance, leading to reduced water loss at leave-level and whether this will result in canopy-scale changes in transpiration and soil water availability, (3) how low nutrient availability (particularly phosphorus) will limit the CO2-fertilization effect, and (4) whether elevated CO2 concentration will alter the functional composition of vegetation. Also the role of biodiversity (through functional traits) and socio-environmental implications of CO2 fertilization will be investigated, with a focus on impacts, adaptations and the science-policy interface. Through integrative modelling activities, the long-term goal of the project is to improve the projections of the Amazon rainforest carbon cycle and regional and global climate under increasing atmospheric CO2 concentrations. We here present the AmazonFACE science plan, give an update on the state of the experiment construction and show baseline measurements and simulation results.
How to cite: Rammig, A. and Lapola, D. and the AmazonFACE Team: The science plan for AmazonFACE, a large-scale Free Air CO2 Enrichment Experiment in the Amazon rainforest, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10816, https://doi.org/10.5194/egusphere-egu25-10816, 2025.
Large areas of the central Amazon basin are characterized by a dense rainforest cover and subtle variation in topography, microclimate and edaphic conditions. This leads to pronounced differences in tree species composition with their specific functional traits. Major forest types in the Central Amazon are seasonally flooded riparian forest along blackwater rivers (igapó), mesic forest in small valleys dissecting the terra firmeplateaus (baixios), extremely nutrient-poor forest on white sands (campinas), and upland terra firme forest on plateaus.
We hypothesize that transpiration patterns and stem growth dynamics of these forest types are significantly different in their response to drought. Therefore, we investigate their reaction and adaptation to reduced soil water availability and atmospheric heat stress during extended droughts.
Information on the water status of trees can be derived from monitoring of hourly radial stem changes using high resolution dendrometers. Such changes are mainly due to two physiological processes: irreversible stem expansion due to cambial growth or reversible variations in stem size driven by call hydration or dehydration. Concurrent measurements of stem xylem sap flow, soil water content and atmospheric conditions allow to determine when and to what extent trees undergo water stress. By observing the daily amplitudes of stem contraction, it is possible to recognize whether and how fast trees recover from drought stress and resume cambial stem growth.
During the first phase of this study, we monitored fifteen trees in a terra firme forest to determine both the effects of water scarcity on the seasonality of stem increment and water uptake and the thresholds for stress caused by heat or soil water shortage. First results indicate significantly reduced transpiration in most trees during the dry season, partly due to leaf shedding, while radial stem growth showed very different dynamics.
How to cite: Horna, V., Dias-Júnior, C. Q., Simonetti, A., Machado Durgante, F., Magnabosco Marra, D., and Trumbore, S.: Drought response of water use and stem growth dynamics of trees in central Amazonia , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20981, https://doi.org/10.5194/egusphere-egu25-20981, 2025.
The increasing severity of droughts and their direct impact on the health of Amazon forest ecosystems underscore the urgent need to understand this phenomenon and to develop tools for large-scale monitoring. Leaf water potential (ψleaf) is a critical indicator of plant water status. However, traditional methods for measuring ψleaf are often logistically challenging and costly. Field spectroscopy offers a more efficient means of assessing plant water status, allowing scaling of information through predictive models that can be combined with imaging spectroscopy techniques from orbital and suborbital sensors. This study collected hyperspectral leaf data from three Amazonian forest environments during the El Niño period in October 2023: White Sand Forest, Flood Forest, and Upland Forest, all located at the Atto site. We collected two species from the forest canopy in each environment, resulting in six species and 43 samples. The reflectance measurements were taken immediately after the ψ measurement using a Scholander pump, around midday, with an ASD spectroradiometer covering the range from 350 nm to 2500 nm. The prediction model was developed using the entire data set by applying an optimized Partial Least Squares (PLS) regression model in Python. This was done after pre-processing the spectral data, which included jump correction functions, a Savitzky-Golay filter, and first derivative analysis. The resulting model showed good performance, with an R² of 0.73 and a mean squared error (MSE) of 0.21, although it still showed moderate generalization ability. The spectral bands that provide the most information about water potential are found in the near-infrared (NIR) range between 780 and 1100 nm, and the shortwave infrared (SWIR) range around 1700 and 2250 nm. These preliminary results support the idea that spectroscopic techniques can effectively indicate plant responses to water stress, which is critical in climate change. Such studies may facilitate more efficient monitoring of water status in Amazonian forest ecosystems. Future research should improve the use of spectroscopy in ecological studies of plant responses to environmental change by expanding sampling to more tree species and considering additional variables that reflect water stress, such as fuel moisture content (FMC), leaf water content (LWC), equivalent water thickness (EWT), and relative water content (RWT).
How to cite: Durgante, F. M., Lorenci Mallman, C., Hadlich, H. L., Vasconcelos, C. D. C., Schöngart, J., Piedade, M. T. F., and Wittmann, F.: Field Spectroscopy for Assessing Midday Leaf Water Potential in Amazonian Forest Environments: Preliminary Results, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20842, https://doi.org/10.5194/egusphere-egu25-20842, 2025.
Secondary succession is one of the major processes in forest habitat restoration across degraded landscapes globally, especially in tropical regions. Hong Kong, situated on the northern fringes of the Asian tropics, has undergone near-complete clearing of its original forests due to human activities in history, and most of its current vegetation was formed by regenerations in recent decades. Understanding the dynamics of vegetation changes over time involves various biotic, abiotic, and anthropogenic factors related to different ecological processes. Remote sensing imagery, with the ability to discern habitat patterns across spatial and temporal scales, provides an effective tool for addressing this requirement. In particular, the Landsat satellite mission has provided continuous earth observation data since 1972 and has been widely used in time-series analyses of habitat transformations.
This study leveraged all available Landsat imagery to examine the coverage of six habitat classes in the forest-regenerating landscape of Hong Kong from 1973 to 2022. A multi-temporal classification workflow was developed, which combined cross-calibration of Landsat sensors, random forest classification, decision-level fusion after classification, and temporal smoothing. An overall accuracy of 90.1% was achieved when assessed using various office- and field-collected data, with accuracy exceeding 86% and 88% when individual classes and mapping periods were considered respectively. Based on the multi-temporal habitat maps produced from the classification workflow, survival analysis was used to examine the time required for successional changes, and correlation analysis was used to associate the transition time with various natural and anthropogenic factors.
The results indicate that (i) a single classification model could be developed using all images acquired by multiple Landsat sensors across years, including the earliest Landsat 1–5 MSS data, which is crucial in extending the temporal baseline and adding a decade of habitat information. (ii) Incorporating more images in the classification model enhanced overall accuracy, with the highest accuracy achieved when all available images were included. Classification performances for earlier years and transitional classes showed higher vulnerability to the reduced proportion of input images. (iii) The natural landscape in Hong Kong gradually transformed from being grassland-dominated in the 1970s to woodland-dominated in the 2010s. Grasslands took a median time of 21 years to become shrublands and another 29 years to become woodlands, but the first quartiles of 7 and 10 years respectively indicate a high spatial variability. Hill fire was the most important factor positively correlated with the transition time (restricting forest succession), while increasing proximity to seed sources and protected area designation produced the highest negative correlations (accelerating the process).
This study demonstrates the value of connecting the Landsat time series with human impacts and management practices to produce spatially explicit ecological insights. The experience of forest regeneration in Hong Kong, formed by both conservation interventions and natural succession, will benefit the increasing interest in forest protection and restoration in the wider tropical region.
How to cite: Kwong, I. H. Y., Lai, D. Y. F., Wong, F. K. K., and Fung, T.: Spatial variations in forest succession rates revealed from multi-temporal habitat maps using Landsat imagery in subtropical Hong Kong, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2667, https://doi.org/10.5194/egusphere-egu25-2667, 2025.
More than 60% of the Amazonian rainforest grows on old and weathered soil with low availability of important rock-derived nutrients like phosphorus (P), and efficient nutrient recycling is the main source of nutrients to maintain forest productivity. Thus, the effect of the elevated CO2 atmospheric concentrations (eCO2) on tree productivity (i.e., fertilization effect) may depend on the capacity of plants to access currently unavailable nutrients or increase nutrient acquisition efficiency. In some Amazon regions, the high root proliferation in the litter layer, where roots intercept newly mineralized nutrients before they are leached into the soil, is an important mechanism. These roots can also influence nutrient mobilization directly by exuding phosphatase enzymes to hydrolyze organic P without releasing carbon or indirectly by exuding labile carbon (i.e., glucose, sucrose) that can be used as energy for the microbial community to increase the decomposition and nutrient release from leaf litter.
In an Open-Top Chamber experiment in a lowland understory forest in the Central Amazon, we investigated how elevated CO2 influences plant-root-microbe interactions during a late-stage (i.e., after one year) leaf litter decomposition. We found that under eCO2 leaf litter mass loss did not change. However, we observed that under eCO2, higher root net production in the leaf litter decreased litter mass loss. This may suggest that increased root exudates under eCO2 influence microbial litter decomposition. Furthermore, we observed a decrease in microbial biomass carbon (C) and an increase in the ratio of enzymes responsible for degrading C, nitrogen (N), and P, normalized by microbial biomass C. This could suggest microbial C and nutrient limitation, which means that the plant root exudates under eCO2 were not benefiting microbial growth, and they needed to invest energy in maintenance and resource acquisition. The lack of change or decrease in mass loss under eCO2, even with a possible microbial C limitation, may be related to the stage of the litter decomposition process and the more recalcitrant C fractions available, or antagonistic interaction between plant and microbial community. Nevertheless, we found a significant decrease in leaf litter P concentration under eCO2 without changing litter decomposition. Still, the decrease in the inorganic microbial P may suggest that C microbial investment did not result in a microbial P mobilization, and probably trees directly took up this available P, indicating that eCO2 intensifies the P competition between plants and microbes.
Our results suggest that under eCO2, trees may change the microbial stoichiometry to increase resource acquisition, and the shift in the competition for P between plants and microbes may be the key factor in controlling plant P mobilization in a late-stage decomposition process. This suggests that plant-microbial interaction may be an important strategy for increasing nutrient availability in scenarios under elevated CO2 atmospheric concentrations, possibly directly impacting the Amazon forest productivity and resilience to climate change.
How to cite: Martins, N., Fuchslueger, L., Lugli, L., Rammig, A., Hartley, I., and Quesada, C. and the AmazonFACE team: Effect of elevated CO2 in a late-stage leaf litter decomposition process in the understory of Amazonian forest: the role of plant root and microbial interaction on nutrient availability , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11396, https://doi.org/10.5194/egusphere-egu25-11396, 2025.
The Amazon rainforest, characterized by its vast biodiversity and diverse vegetation formations, plays a crucial role in global biogeochemical cycles, including the emission and consumption of biogenic volatile organic compounds (BVOCs) and greenhouse gases (GHGs). Soil and litter fluxes have been suggested as important contributors to the overall forest BVOC budget, but these fluxes remain understudied and are therefore poorly understood. Moreover, only a few observations exist from the Amazon rainforest, an ecosystem expected to be the largest BVOC source in the global atmosphere. Even less studied is the influence of the diversity of soils and vegetation types on BVOC and GHG flux (emission and consumption) patterns. This study aimed to assess the fluxes of BVOCs and GHGs, and their potential drivers, in three dominant forest types in Central Amazonia: upland forest (terra firme), ancient river terrace forest (terraço fluvial), and white sand forest (campinaranas). Soil fluxes were determined using flux chambers, from which sampling bags were collected and subsequently analyzed by a PTR-MS (isoprene, monoterpenes, sesquiterpenes) and a Los Gatos Analyzer (CO2, CH4). In addition, soil temperature and moisture were determined, and soil and litter samples were taken to analyze nutrients and microbial biomass. Measurements were conducted in the dry-to-wet transition period, and repeated for the white sand forest in the wet season. In the dry-to-wet season, the highest BVOC fluxes, predominantly emission, were found in the white sand forest, particularly for acetaldehyde and sesquiterpenes. The upland forest exhibited lower fluxes than the white sand forest but stood out for emission and consumption of dimethyl sulfide (DMS) and isoprene. The ancient river terrace forest showed no significant fluxes. Soil moisture and temperature were identified as the primary drivers in the white sand forest, while microbial biomass was the determining factor in the upland forest. In the wet season, fluxes in the white sand forest shifted strongly toward dominance of BVOC consumption and increased methane emissions. Soil phosphorus microbial biomass was identified as a predictor of most BVOC fluxes and CH4 emissions, highlighting the critical role of phosphorus in the wet season. This study offers a significant contribution to the understanding of gas fluxes in Amazonian forest types, emphasizing how nutrients, soil, litter microbial biomass, and seasonality affect BVOC and GHG emissions. These findings provide valuable insights into how environmental changes may impact biogeochemical cycles on the Amazon, providing valuable information for the conservation and management of tropical forests.
How to cite: Pinheiro-Oliveira, D., van Asperen, H., Garcia Caetano, M., Robin, M., Edtbauer, A., Zannoni, N., Byron, J., Williams, J., Oreste Demarchi, L., Piedade, M. T. F., Schöngart, J., Wittmann, F., Duvoisin-Junior, S., Batista, C., Ferreira de Souza, R. A., and Gomes Alves, E.: Soil and Litter BVOC and GHG Fluxes in Central Amazonia: Variability Across Forest Types and Seasons , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20894, https://doi.org/10.5194/egusphere-egu25-20894, 2025.
Methane (CH4) is an important greenhouse gas whose natural sources are still poorly understood, and significant uncertainties in their quantification remain. Tropical upland forests generally present a small CH4 sink with occasional local emission hotspots, making it challenging to determine the net ecosystem flux. This study employs two complementary micro-meteorological methods to estimate methane (CH4) fluxes from a Terra Firme (upland forest) ecosystem in the central Amazon. At the Amazon Tall Tower Observatory (ATTO) field site, CH4 and CO2 concentrations are continuously monitored at five different heights above and below the canopy (79, 53, 38, 24, 4 meters). Additionally, Eddy Covariance measurements of CO2 and H2O fluxes, along with micro-meteorological observations, are conducted at various heights. To estimate ecosystem CH4 fluxes, the Modified Bowen Ratio (MBR) technique was applied. This method uses the concentration gradient and the flux of a trace gas (in this study CO2 and H2O), to calculate the eddy diffusivity (k), which is then used to estimate the flux of another trace gas (in this study CH4). MBR CH4 fluxes were estimated for the period 2014-2021. Additionally, vertical concentration profiles were analyzed to gain further insights into the temporal patterns of CH4 fluxes. The MBR flux estimates and vertical profile analyses revealed clear seasonal patterns. During the wet season, positive concentration gradients indicated net CH4 emissions from the forest, while in the dry season, negative gradients suggested net CH4 uptake. Ecosystem median CH4 fluxes, estimated with the MBR technique, dominantly showed CH4 uptake and ranged between -2 and 2 nmol m-2 s-1. With these results, we aim to highlight the seasonal and interannual patterns of upland forest CH4 fluxes, which are essential for understanding the contribution of tropical upland forests to the Amazon's CH4 budget.
How to cite: Rosalem, L., van Asperen, H., Komiya, S., P. Jones, S., Botía, S., Cunha, F., Trumbore, S., and Quaresma Dias Júnior, C.: Amazon upland forest CH4 fluxes: A small sink or a small source? A case study, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14346, https://doi.org/10.5194/egusphere-egu25-14346, 2025.
The huge pressure on tropical forests due to agricultural expansion threats the capacity of the West African region to sequester atmospheric CO2, a region which is supposed to account for 20% of CO2 emissions of the whole continental tropical belt. Yet, the scarcity of eddy covariance measurements in the tropical humid African region has led to significant challenges in understanding the carbon sequestration potential of forest ecosystems and more broadly the amount of CO2 which will be lost when they are converted into culture. Taking advantage of two nearby eddy covariance sites established in Northern Benin, a mixed crop savannah (Nalohou, lat. 9.74°N, long. 1.60°E) and a clear forest (Bellefoungou, lat. 9.79°N, long.1.72°E), this study compares their net ecosystem exchange (NEE) dynamics and their carbon balance, using data spanning from 2007 to 2017. Driven by the precipitation pattern, the CO2 dynamics display strong seasonality above both ecosystems, with moderate uptakes during extreme precipitation years. We determined the optimal respiration model for both ecosystems, enabling the partitioning of NEE fluxes into total ecosystem respiration (Reco) and gross primary production (GPP). Soil moisture was found to be the main driver of nighttime CO2 emissions, with a sigmoidal model the most appropriate for representing Reco. When using soil moisture as an input in the ecosystem respiration model for partitioning NEE, we found, based on the ten years dataset, an average annual NEE of -512 ± 69 g C m⁻² y⁻1 at the forest site and of -202 ± 53 g C m⁻² y⁻1 at the mixed crop site. Finally, these tropical humid ecosystems were observed to be, during all years analyzed, a net sink of atmospheric CO2, showing that forest CO2 sequestration is 2.5 times the cultivated site one. These results constitute a paramount information for earth system models regarding carbon budget of these typical and understudied African ecosystems.
How to cite: Koukoui, R., Mamadou, O., Houénou, F., Heinesch, B., Bousso, M., and Cohard, J.-M.: Ten years of carbon dioxide fluxes and carbon balance at a mixed cultivated savannah and an open forest in a tropical humid climate in West Africa, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16269, https://doi.org/10.5194/egusphere-egu25-16269, 2025.
Multi-AXis Differential Optical Absorption Spectroscopy (MAX-DOAS) measurements use trace gas absorptions in spectra of scattered sun light recorded under different elevation angles to retrieve vertical profiles of trace gas concentrations and aerosol extinctions in the lower troposphere as well as the corresponding total tropospheric vertical column densities (VCDs). These measurements allow observation of multiple trace gases e.g., formaldehyde (HCHO) and glyoxal (CHOCHO), for the same air mass simultaneously with one instrument. We operate two MAX-DOAS instruments at the Amazon Tall Tower Observatory (ATTO) at altitudes of 80 and 298 m above ground. Besides the full profile retrievals for both instruments, this measurement setup allows the determination of vertical gradients of trace gas and aerosol abundances in the altitude range between both instruments by directly comparing the VCDs and concentrations at instrument altitude. Such small-scale vertical gradients provide important insights into the chemical processing of the different species. Located in a pristine rainforest region in the central Amazon Basin about 150 km north-east of Manaus, the ATTO site offers a unique possibility to study the chemical processing of tropospheric trace gases far away from major anthropogenic emission sources.
Here, we present an overview of these small-scale vertical gradients of formaldehyde and glyoxal abundances at ATTO. We investigate their seasonal variations, the effects of meteorological parameters and compare them to the vertical concentration gradients of isoprene and monoterpenes both being precursor substances of formaldehyde and glyoxal. Also, a comparison to model simulations might yield interesting insights. The main result of our work is that formaldehyde is net formed in the altitude range (around 200 m) between both instruments, while glyoxal is already net degraded in this altitude range. Together with their characteristic profile shapes, these findings indicate different chemical processing (production and degradation) of formaldehyde and glyoxal although both compounds can be produced from isoprene. In particular, glyoxal is likely formed and then photolyzed very rapidly in that height range.
How to cite: Donner, S., Lauster, B., Ziegler, S., Artaxo, P., Beirle, S., Edtbauer, A., Ringsdorf, A., Williams, J., and Wagner, T.: Investigating small-scale vertical concentration gradients of formaldehyde and glyoxal above the canopy at the Amazon Tall Tower Observatory (ATTO) using two MAX-DOAS instruments, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8516, https://doi.org/10.5194/egusphere-egu25-8516, 2025.
Nitrogen oxides (NOx = nitric oxide (NO) and nitrogen dioxide (NO2)) play a crucial role in atmospheric chemistry by influencing the concentrations of ozone (O3) and hydroxyl radicals (HOx = OH and HO2), which are key oxidants in the atmosphere. In pristine ecosystems, these oxidants interact with biogenic volatile organic compounds (BVOCs) like isoprene, leading to the production of oxidized secondary organic compounds. Subsequent reactions with NOx contribute to nitrate formation, which enhances particle growth and cloud condensation nuclei activity. This underscores the significance of NOx even in regions with low atmospheric concentrations.
At the Amazon Tall Tower Observatory (ATTO), located in the central Amazon rainforest, we monitor NO and O3 in a pristine tropical environment. Our study focuses on measurements from a walk-up tower at 40 m, but also a first look into NO and O3 mixing ratios collected at multiple heights ranging from 5 cm to 79 m, covering the vertical profile above and below the canopy (canopy height is approximately 36 m). We analyze diurnal and nocturnal variations at 40 m and seasonal differences between the wet and dry periods to hint at how much NO is coming out of the canopy and its role in this unique ecosystem.
How to cite: Monteiro, C., Tsokankunku, A., and Harder, H.: Nitric oxide (NO) mixing ratio above the canopy in the Amazon rainforest (ATTO site), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20489, https://doi.org/10.5194/egusphere-egu25-20489, 2025.
The Amazon rainforest is a critical component of the global carbon cycle, contributing approximately 16% of the terrestrial ecosystem's gross primary productivity and serving as a significant carbon sink through photosynthesis. The rainforest's ability to store carbon makes it an important sink, helping to mitigate climate change by absorbing carbon dioxide (CO₂) from the atmosphere. However, threats such as deforestation and land-use change can reduce this capacity, highlighting the importance of conserving and restoring the region. According to the Intergovernmental Panel on Climate Change (IPCC), if drastic measures are not taken to reduce greenhouse gas emissions, CO2 levels will continue to rise until 2100. This could have serious consequences for the global climate, including increased temperature, changes in precipitation patterns, and a rising sea level. One of the most concerning potential outcomes is the transition of the Amazon from a carbon sink to a carbon source, further amplifying climate change. Evaluating how the predicted climate change in Amazonas will impact the forest carbon uptake is important to quantify the effect, support adaptation, and reduce vulnerabilities.
The main objective is to predict biogenic CO2 transport in the Amazon region in future land-use and climate scenarios. We will use the Weather Research and Forecasting model with Greenhouse Gases (WRF-GHG) to simulates CO2 transport in the Brazilian Amazon under two contrasting future IPCC scenarios: SSP2-4.5 ("Middle of the Road") and SSP5-8.5 ("Fossil-fueled Development"). These scenarios represent moderate and high emissions pathways, respectively. We will use climate projections from the Coupled Model Intercomparison Project Phase 6 (CMIP-6) and land-use projections from the Land-Use Harmonization 2 (LUH2) dataset for these simulations. These input data will be important to evaluate their effects on CO2 fluxes, concentrations, and transport dynamics. Through simulations under varying deforestation scenarios, we expect to observe substantial changes in CO2 distribution and atmospheric transport patterns across the Amazon.
How to cite: Rojas, N., Botia, S., Glauch, T., Marshall, J., Varanda Rizzo, L., Dias de Freitas, E., and Toledo Machado, L. A.: Forecasting CO2 transport in the Amazon: A WRF-GHG simulation under deforestation and climate change scenarios, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4199, https://doi.org/10.5194/egusphere-egu25-4199, 2025.
