Knowledge of fluxes of water vapor and carbon at the land surface are paramount to our understanding of the Earth system. Large-scale network initiatives such as the Fluxnet allow us to better understand the environmental controls on the evapotranspiration and gross primary productivity. An important aspect of such initiatives is that its large number of sites allow for localized knowledge to be upscaled to a region or even globally. This can be either done by employing physics-based global land models or empirically, via data-driven approaches. Particularly, we have seen a significant increase of data-driven approaches with the use of machine learning techniques more recently. Here, we use a similar structure employed in the FLUXCOM initiative to focus particularly on the role of soil moisture information in predicting evapotranspiration and gross primary productivity at several flux sites encompassing a wide range of hydroclimates and biomes around the globe. Our analyses employ a machine learning method to a predictive model of evapotranspiration and gross primary productivity, while focusing primarily on how changes in the way soil moisture is incorporated into the methodology affects such predictions. First, we evaluate the predictive power of this model when soil moisture is directly estimated via observations against more indirect estimates via bucket-type models. Secondly, we evaluate the role of the spatial resolution of different soil moisture estimates in predicting both fluxes. We do this by using three sets of direct estimates covering distinct spatial footprints co-located at all flux sites: (1) point-scale time-domain reflectometers, (2) field-scale cosmic-ray neutron sensors, and (3) regional-scale satellite remote sensing products. In this talk, we summarize which hydroclimatic regions benefit from having direct estimate of soil moisture for evapotranspiration and gross primary productivity, while also providing some insights on the possible role of spatial scale mismatches between the fluxes and soil moisture.

How to cite: Rosolem, R., Power, D., Rico-Ramirez, M., Gentine, P., McJannet, D., da Rocha, H., Schrön, M., and Rebmann, C.: Using machine learning to quantify multi-scale soil moisture controls on water and carbon fluxes at the land surface, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12444, https://doi.org/10.5194/egusphere-egu23-12444, 2023.

The response of vegetation productivity to water availability provides a key link between the carbon and water cycles. Correctly representing this response in Earth System Models (ESMs) is essential for accurate modelling of the terrestrial carbon cycle and the evolution of the climate system. To investigate how well models capture this relationship at intraseasonal timescales, we use global datasets based on satellite observations to assess the land surface response to intraseasonal precipitation events, and evaluate the performance of CMIP6 ESMs in representing this response in the recent historical period. Whereas models are able to capture the observed surface soil moisture (SSM) response with reasonable agreement, there are large inter-model discrepancies in the response of Gross Primary Productivity (GPP), both in magnitude and timing, even in regions where land cover is similar between models. In particular, ACCESS-ESM and NorESM produce much lower-amplitude GPP responses to rainfall than UKESM and CNRM-ESM. All the models studied are able to represent that the regional amplitude of the GPP response is positively correlated with the amplitude of the SSM response, and negatively correlated with the amplitude of the vapour pressure deficit (VPD) response. All models except NorESM also capture that stronger SSM responses are associated with faster GPP responses. However, the models differ in their sensitivity to these drivers, and can produce very different GPP responses from similar variations in SSM and VPD, particularly in climatologically dry regions. This highlights the need for a better understanding of the uncertainties in the representation of water-vegetation relationships in ESMs, such as the effect of atmospheric vapour pressure deficit on stomatal conductance and the control of soil moisture stress on GPP.

How to cite: L. Harris, B., M. Taylor, C., Quaife, T., and P. Harris, P.: Contrasting responses of vegetation to intraseasonal rainfall in Earth System Models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8013, https://doi.org/10.5194/egusphere-egu23-8013, 2023.

Rain events are becoming less frequent, but stronger in many global locations under a changing climate. These intra-seasonal rainfall features have received less attention than changes in mean temperature and total annual rainfall in their influence on the global carbon cycle. Field rainfall manipulation experiments consistently show non-negligible changes to annual photosynthesis in response to rainfall frequency alterations while holding total annual rainfall constant. However, field and modeling experiments show little consensus on the sign and magnitude of change of annual photosynthesis due to changing storm frequency and magnitude. In this study, we ask: based on satellite observations, how is global photosynthesis changing due to shifts in storm frequency and magnitude? What are the soil-plant-atmosphere drivers of the response?

Using several global satellite-based photosynthesis proxies, we find that the annual photosynthesis response to storm frequency is as high in magnitude and global spatial extent as its response to total annual rainfall. The satellite-based photosynthesis proxies and field tower sites indicate that years with fewer, stronger storms tend to show decreased photosynthesis in humid ecosystems and increased photosynthesis in drylands. The absolute magnitudes of annual photosynthesis trends show 10-20% per century changes due to rainfall frequency trends over nearly half of vegetated surfaces, which is consistent with the magnitude and extent of total annual rainfall trend effects. The contrasting responses observed in humid locations and drylands are shown to be driven by patterns of plant pulse response, soil texture, and mean atmospheric aridity response to rain frequency. Ultimately, our results indicate that intra-seasonal rainfall variability drives global photosynthesis interannual variability similarly to interannual rainfall variability.

How to cite: Feldman, A., Poulter, B., Joiner, J., Asadollahi, M., Biederman, J., Chatterjee, A., Gentine, P., Konings, A., Smith, W., and Wang, L.: Observed Global Photosynthesis Response to Changing Storm Frequency and Magnitude, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3651, https://doi.org/10.5194/egusphere-egu23-3651, 2023.

Drought legacy effects refer to the lasting impacts on the carbon cycle from droughts, being a prime uncertainty in predicting future land carbon sink in a changing climate. While previous studies have been focusing on the drought legacy effects on tree growth using tree ring chronologies, the rapid developments of site and satellite observations over the past decades provide us new opportunities to investigate the effects with improved temporal and spatial coverage. For example, retrievals of canopy structure, photosynthesis, evapotranspiration, and vegetation water content would allow for evaluating the differences in recovery processes in magnitude, timing and duration of the legacy effects. Potential asynchrony and divergence among these multiple legacy indicators result in large uncertainties in understanding the full range of vegetation responses to drought. To address this issue, this study aims to leverage the development of a new generation Earth system model (CliMA) in combination with site and satellite observations to understand the various legacy effects on carbon sink responses from site to regional scales. Through investigating the temporal and spatial patterns of legacy effects, our work will gain a comprehensive understanding of drought related carbon cycle feedback and benefit science-based decision making facing changing climate, especially extreme events. 

How to cite: Yao, Y., Wang, Y., Yin, Y., and Frankenberg, C.: Understanding vegetation drought legacy effects on carbon cycling using observations from multiple platforms, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10949, https://doi.org/10.5194/egusphere-egu23-10949, 2023.

Drought events are projected to become more severe and frequent across many regions in the future, but their impacts will likely differ among ecosystems depending on the capability of ecosystem to maintain functioning during droughts, i.e., the ecosystem resistance. Different plant species have diverse strategies to cope with drought. As a result, responses of different vegetation types have been found to be divergent for similar levels of drought severity. However, it remains unclear whether such divergence is also caused by different drought duration, climatological settings, or co-occurring compound events, etc.

Here, we evaluate vegetation resistance using different proxies for vegetation condition, namely the Vegetation Optical Depth (SMOS L-VOD) data from ESA’s Soil Moisture and Ocean Salinity (SMOS) passive L-band mission and EVI and kNDVI from NASA MODIS. L-VOD has the advantage over more commonly used vegetation indices (such as kNDVI, EVI) in that it provides more information on vegetation structure and biomass and suffers from less saturation over dense forests compared (Wigneron et al., 2020). We apply a linear autoregressive model accounting for drought, temperature and memory effects to characterize ecosystem resistance by their sensitivity to drought duration and temperature anomalies. We analyze how ecosystem resistance varies with land cover across the globe and investigate the modulation effect of forest management and irrigation. Furthermore, estimates of ecosystem resistance obtained from a similar methodology are compared between L-VOD, kNDVI and EVI.

We find that regions with higher forest fraction show stronger ecosystem resistance to extreme droughts than cropland for all three vegetation proxies. L-VOD indicates that primary forests tend to be more resistant to drought events than secondary forests, but this phenomenon cannot be detected in EVI and kNDVI. This is possibly related to their saturation in dense forests. In tropical evergreen deciduous forests, old-growth trees tend to be more resistant to drought than young trees from L-VOD and kNDVI. Irrigation increases the drought resistance of cropland substantially.

These results suggest that ecosystem resistance can be better monitored using L-VOD in dense forests and highlight the role of forest cover, forest management and irrigation in determining ecosystem resistance to droughts.

Wigneron, J.-P., Fan, L., Ciais, P., Bastos, A., Brandt, M., Chave, J., Saatchi, S., Baccini, A., and Fensholt, R.: Tropical forests did not recover from the strong 2015–2016 El Niño event, Science Advances, 6, eaay4603, https://doi.org/10.1126/sciadv.aay4603, 2020.

How to cite: Xiao, C., Zaehle, S., Wigneron, J.-P., Yang, H., Schmullius, C., and Bastos, A.: Land-cover and management modulation of ecosystem resistance to drought stress, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3554, https://doi.org/10.5194/egusphere-egu23-3554, 2023.

Plant water stress due to climate change is posing a threat to various ecosystem services such as carbon sequestration, food and wood production, and climate regulation. To address this issue, methods are needed to assess and monitor plant water stress at various spatial and temporal scales. Passive microwave emission observations from satellites have proven useful in monitoring changes in vegetation water content and assessing plant water stress at a low spatial resolution (> 9 km). In this study, we used vegetation optical depth (VOD) and measurements of hydraulic vulnerability to create a novel model for assessing ecosystem-level water stress. We used L-band VOD and global measurements of xylem water potential at 88% loss of stem hydraulic conductivity (P88) from the TRY database (including 1103 measurements of P88 from 463 species and nine different vegetation biomes) to create a linear regression model between L-band VOD and biome-level P88. We used monthly mean values of L-band VOD and calculated ratios of yearly minimum and maximum VOD (L-VOD_min/max) for each pixel to describe average variability in ecosystem-level water content. The developed L-VOD_min/max metric explained 75% of the variation in P88 at the biome level (R²=0.75) indicating that the novel L-VOD_min/max metric is capable of capturing changes in plant water status. We then used the L-VOD_min/max metric and daily climate data from the ERA5 to see if water stress has increased over time in the world's forests that are more water limited (aridity index below 1.5). For these areas, we found a positive trend in maximum daily vapour pressure deficit, which correlated negatively (p<0.05) with L-VOD_min/max trend for the same time period further confirming that L-VOD_min/max is capable of explaining differences in plant water status. Additionally, we examined the trend in L-VOD_min/max for global forests for the same 2011-2020 period and found a significant negative trend (increasing water stress, p<0.05) for forests in central Africa, southeast Asia, and eastern Australia, and a positive trend (decreasing water stress) for boreal forests in North America and rainforests in Indonesia. Further studies are required to confirm our results suggesting that some of the world's largest carbon sinks are experiencing rapid changes in water stress as a result of climate change.

How to cite: Junttila, S., Descals, A., Filella, I., Peñuelas, J., Brandt, M., Wigneron, J.-P., and Vastaranta, M.: Vegetation optical depth reveals changes in ecosystem-level water stress for global forests, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11564, https://doi.org/10.5194/egusphere-egu23-11564, 2023.

Vegetation is a key driver for carbon uptake from the atmosphere to the land. Yet episodes of plant stress and mortality associated with drought and heat waves due to persistent lack of precipitation have been reported over the last decades and are expected to increase under ongoing climate change. It is presumed that climate-related vegetation stress results in progressively worsening plant health and rising mortality. However, the mechanisms driving such mortality are still up for debate because of the complex interconnections between the processes and the factors. Monitoring plant stress and mortality at the ecosystem level remains challenging to quantify since long-term, tree-individual, reliable observations are uncertain. For this reason, here we adapted a satellite-model approach to work on regional forests, before up scaling the results to the global forest.

In Belgium, the Wallonia region is covered by 30% forests which are the highest among all the three regions. While with the consecutive recent extreme events especially the droughts and heat waves of 2018, 2019, 2020, and 2022 caused water stress and bark beetle attack. According to the 35 years (1985-2022), land use land cover change extracted by LANDSAT 5,7, and 8 satellites, there is no significant change in forest land in Wallonia, Belgium. Meanwhile, in the current years 2021-2022, there is a decrease in the tree canopy with intensive forest management due to tree plant stress. On the other hand, in Wallonia, the forest is distributed in a significant patch of broadleaves, coniferous leaves, and mixed forest. However, we found that consecutive drought events cause water stress on specific plant species like Norway spruce which are in vulnerable states. For example: in a mixed forest when bark beetle or Scolytinae attacked the spruce tree it is more attracted to the other trees and in this consequence tree species –like  birch and oak –are now also in premature death or deteriorating tree health. In this study, we are using a high spatial resolution (25cm) remote sensing images using Artificial Intelligence and machine learning techniques to find out pixel-based individual plant stress or mortality. In addition, the high-resolution tree mortality extracted data will be used to calibrate CARAIB dynamic vegetation model and analyze the impact of extreme events on trees during the recent past and the future (until 2070). In conclusion, from this study, we plan to improve our model regarding the implementation of plant traits and species mortality aspects towards a better prediction of forest tree species' vulnerability to future extreme weather events.

How to cite: Verma, A., Francois, L., Jacquemin, I., Lanssens, B., Hambuckers, A., Ugolotti, A., Tölle, M., and Hallot, E.: Reducing uncertainty in extreme weather vegetation stress modeling using satellite-model approach at high resolution, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15565, https://doi.org/10.5194/egusphere-egu23-15565, 2023.

Water Use Efficiency (WUE) is the variable linking assimilation and storage of carbon in plants with the release of water through transpiration. In this study, we combine multiple datasets including global scale leaf-level gas exchange measurements, tree-ring isotopes, flux-tower observations, and remote sensing products with mechanistic terrestrial biosphere modeling to evaluate whether WUE depends on precipitation or aridity levels and how changes in vapor pressure deficit affect ecosystem scale WUE and intrinsic water use efficiency (IWUE). A constrained range of WUE values across ecosystems and climates are observed with few noticeable exceptions. Observations and model simulations converge towards a weak WUE dependency on precipitation or aridity conditions.

Numerical simulations with a mechanistic model reveal two distinct signatures of VPD on site level WUE and IWUE, with high VPD resulting in increased IWUE, but decreased WUE. Relations with soil moisture are instead more complex and non-monotonic. Multiple data sources in combination with mechanistic modeling offer new insights on WUE variability across spatial and temporal scales and provide reference WUE values for future comparisons.

How to cite: Fatichi, S., Paschalis, A., Bonetti, S., Manoli, G., and Pappas, C.: A review of Water Use Efficiency across space and time, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12507, https://doi.org/10.5194/egusphere-egu23-12507, 2023.

Recent decades have been characterized by increasing temperatures worldwide, resulting in an exponential climb in vapor pressure deficit (VPD). Heat and VPD have been identified as increasingly important drivers of plant functioning in terrestrial biomes and are significant contributors to recent drought-induced tree mortality. Despite this, few studies have isolated the physiological response of plants to high VPD, heat, and soil drought, thus limiting our understanding and ability to predict future impacts on terrestrial ecosystems. I will present diverse experimental approaches to disentangle atmospheric and soil drivers of plant functions across scales. I will further discuss recent findings suggesting that high temperature and VPD can lead to a cascade of impacts, including reduced photosynthesis, foliar overheating, and higher risks of hydraulic failure, independently of soil moisture changes.

How to cite: Grossiord, C.: Disentangling the impact of co-varying changes in soil moisture, vapor pressure deficit, and temperature on plant carbon and water relations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1952, https://doi.org/10.5194/egusphere-egu23-1952, 2023.

Accurate estimation of stomatal regulation is crucial for understanding how plants respond to changing environmental conditions, particularly under climate change. While stomatal optimization models have made significant progress in predicting instantaneous plants' carbon and water exchange, they often do not account for biochemical acclimation to drought over long time scales. In this study, we investigated the impact of incorporating photosynthetic acclimation on the accuracy of six stomatal optimization models in predicting carbon and water exchange in terrestrial C3 plants. By introducing the cost of maintaining a certain level of photosynthetic capacity into the stomatal optimization process, we incorporated photosynthetic acclimation to the previous seven days of environmental conditions. Using experimental data from 37 plant species, we found that accounting for photosynthetic acclimation improved the prediction of carbon assimilation in most of the tested models. Additionally, we found that non-stomatal mechanisms significantly contributed to photosynthesis limitation under drought conditions compared to well-watered conditions in all tested models. The hydraulic impairment functions of the stomatal models were unable to accurately account for drought effects on photosynthesis, indicating the need to consider photosynthetic acclimation to improve estimates of carbon assimilation under drought conditions.

How to cite: Flo, V., Joshi, J., Sabot, M., Sandoval, D., and Prentice, I. C.: Improving stomatal optimization models for accurate prediction of photosynthesis under drought conditions., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2596, https://doi.org/10.5194/egusphere-egu23-2596, 2023.

 The dynamics of the ascent of water from the soil to the leaves of vascular plants determine ecosystem responses to environmental forcing and their recovery from periods of water stress. Recently several models that describe the dynamics of plant hydraulics have been proposed. In this study we introduce four different configurations of a plant hydraulics model in an existing terrestrial biosphere model T&C. The model configurations in increasing order of complexity introduce the basics of the cohesion-tension theory, plant water storage dynamics and long-term damage and repair of the plant's water conducting system. Using the model configurations at six case studies spanning semi-arid to tropical ecosystems we quantify how plant hydraulics can modulate overall ecosystem responses to environmental forcing. As droughts develop, models with plant hydraulics predict a slower onset of plant water stress and can reproduce diurnal patterns of water and carbon fluxes that models that incorporate empirical stomatal conductance only cannot capture. However, when the complex variability of the environmental forcing (i.e., observed hourly meteorological forcing driving the models) is considered, plant hydraulics alone cannot significantly improve model performance. Models that only have simple empirical stomatal conductance models can adequately capture most of the variability of the observed ecosystem responses without explicitly simulating plant hydraulics. Most of the time, the gain from introducing plant hydraulics in ecosystem modelling is limited compared to the possible model improvements from correct representation of other processes such as plant phenology. Nevertheless, during periods of water stress, only models that explicitly simulate plant hydraulics can reproduce observed ecosystem responses to stress and the dynamics of ecosystem recovery. Finally, sensitivity analyses highlight that accurately modelling plant hydraulics relies on good knowledge of plant hydraulics traits, particularly at the leaf level, as stomata are usually the hydraulic bottleneck in the water flow from the soil to the atmosphere.

How to cite: Paschalis, A., Fatichi, S., Sabot, M., and de Kauwe, M.: When do plant hydraulics matter in ecosystem modelling?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7820, https://doi.org/10.5194/egusphere-egu23-7820, 2023.

Terrestrial vegetation is a key component of the Earth system as it mediates the exchange of carbon, water and energy between the land and the atmosphere. Thereby, the vegetation affects the climate through changes in its structure (such as leaf area index, LAI) and its physiology (such as stomatal conductance); However, their relative contributions and respective processes on the land-atmosphere coupling are not yet understood. For instance, increased LAI, referred to as structural changes, promotes transpiration and vegetation productivity, and increases the surface albedo in most cases. In contrast, decreased surface conductance, referred to as physiological changes, could reduce transpiration and productivity. Therefore, the overall feedback of vegetation to climate change via water, carbon and energy exchange will depend on the relative importance of structural and physiological responses. Here we study to what extent dynamic changes in global vegetation structure and physiology modulate land-atmosphere coupling using satellite remote-sensing, data-driven, and earth system modelled vegetation data, as well ashydro-meteorological reanalysis. The land-atmosphere coupling is quantified through the correlation between soil moisture and lagged vapor pressure deficit determined with a moving time window. We employ random forests to quantify vegetation physiology by accounting for functional variability (e.g. GPP and ET) explained by hydro-meteorological data but not by the vegetation structure. Then using an explainable machine learning approach (SHAP), we determine the contributions of vegetation structure and physiology where we find overall larger contributions of structure on regulating land-atmosphere coupling during the growing season. The relative importance of vegetation structure differs across ecosystems, with stronger contributions in dry ecosystems. Furthermore, we analyze the variations of the relevance of vegetation structure over time and in particular during warm and dry periods. The results are partially backed up by using in-situ measurements of physiological traits to interpret the large-scale observed physiological patterns.

How to cite: Li, W., Migliavacca, M., Konings, A. G., Duveiller, G., Reichstein, M., and Orth, R.: Disentangling the influence of vegetation structure and physiology on land-atmosphere coupling, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10417, https://doi.org/10.5194/egusphere-egu23-10417, 2023.

Recent progress in satellite observations has provided unprecedented opportunities to monitor vegetation activity at global scale. However, a major challenge in fully utilizing remotely sensed data to constrain land surface models (LSMs) lies in inconsistencies between simulated and observed quantities. For example, gross primary productivity (GPP) and transpiration (T) that traditional LSMs simulate are not directly measurable from space, although they can be inferred from spaceborne observations using assumptions that are inconsistent with those LSMs. In comparison, canopy reflectance and fluorescence spectra that satellites can detect are not modeled by traditional LSMs. To bridge these quantities, we presented an overview of the next generation land model developed within the Climate Modeling Alliance (CliMA), and simulated global GPP, T, and hyperspectral canopy radiative transfer (RT; 400--2500 nm for reflectance, 640--850 nm for fluorescence) at hourly time step and 1 degree spatially resolution using CliMA Land. CliMA Land predicts vegetation indices and outgoing radiances, including solar-induced chlorophyll fluorescence (SIF), normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and near infrared reflectance of vegetation (NIRv) for any given sun-sensor geometry. The modeled spatial patterns of CliMA Land GPP, T, SIF, NDVI, EVI, and NIRv correlate significantly with existing data-driven products (mean R² = 0.777 for 9 products). CliMA Land would be also useful in high temporal resolution simulations, e.g., providing insights into when GPP, SIF, and NIRv diverge.

How to cite: Wang, Y., Braghiere, R., Bloom, A., and Frankenberg, C.: Modeling global vegetation processes and hyperspectral canopy radiative transfer using CliMA Land, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8747, https://doi.org/10.5194/egusphere-egu23-8747, 2023.

Alpine ecosystems have a high ecological value, high biodiversity, and provide important ecosystem services. However, alpine communities are highly vulnerable to climate changes. Changes in biodiversity and its distribution will affect the goods and services that these ecosystems provide. Also, it can affect climate regulation by altering the exchanges of greenhouse gases (GHG) and the cycles of carbon (C) and nitrogen (N), in feedback processes. Due to their ecological importance and vulnerability, alpine meadows deserve special attention. In this regard, the main objective of the IBERALP project is the analysis of the interactions between components of biodiversity, mainly plant and soil microbial diversity, and their relationship with GHG fluxes; and how these interactions are affected by climate change.

IBERALP is focused on the alpine communities of five Iberian mountain National Parks: Picos de Europa, Ordesa and Monte Perdido, Aigüestortes i Estany de Sant Maurici, Sierra Nevada, and Sierra de Guadarrama. In each National Park, we selected two different altitudes and two different alpine community types based on soil conditions (mesic and xeric). Here we study leaf physiological and fluorescence parameters assimilation, respiration, the quantum yield of photosystem II (PhiPSII), maximum quantum efficiency (Fv`/Fm`) and photochemical quenching (qP) in two representative plant species (a legume (Trifolium repens) and a grass (Nardus strita)) present in each National Park. In addition, we recorded altitude and humidity soil condition using a portable photosyntheic system (Li-cor 6800; Li-Cor Inc.) with an integrated fluorescence chamber head.

Multiple factors affect the ability of plants to assimilate CO₂ and photoprotect themselves from solar radiation excess, so there was no common pattern for all Parks. However, in general, plants at higher altitudes showed a greater photosynthetic and photoprotection capacity against high irradiances compare to those at lower altitudes. Similar behaviour was found in mesic versus xeric communities. Exceptions were found, such as, for example, in Picos de Europa National Park, where the intense fog and grazing (with continuous contribution of N to the soil) modified these patterns of photosynthesis and photoprotection. 

This work was supported by the OAPN through the project PN2021-2820s (IBERALP).

How to cite: Aljazairi, S., Sebastià, M.-T., Agea, D., Sánchez-Cañete, E. P., Kowalski, A., Zamora, R., and Serrano-Ortiz, P.: Variability of the photosynthetic and fluorescence response of high mountain plants to climate change., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14027, https://doi.org/10.5194/egusphere-egu23-14027, 2023.

Peatlands cover only 3 % of emerged lands, but their carbon stock represents about 30 % of the global soil organic carbon. Climate change and local anthropogenic disturbances deeply affect the hydrological functioning of peatlands. This may trigger carbon fluxes to surface waters and the atmosphere, thus leading to a positive feedback for global warming. It is therefore crucial to better estimate carbon fluxes between peatlands and the atmosphere and to delineate their major controlling constraints. To achieve this goal, we studied the functioning of a temperate mid-mountain peatland located in the French Jura Mountains, named the Frasne peatland.

The methane (CH₄) dynamics of the Frasne peatland appear to be constrained by a range of hydrological, physical, biogeochemical, and biotic factors. From a hydrological point of view, the system is fed by local rainwater and injection of carbonated groundwater at the bottom of the peatland, which provides a major input of dissolved inorganic carbon (DIC) to the system. Values of the δ¹³C_DIC were high (even reaching positive values up to 8.1 ‰) compared to the expected values in a limestone and C3 plant-dominated area such as the Jura Mountains, supporting biotic CH₄ production within the peatland. Consistently, high-frequency eddy-covariance monitoring during 2.5 years allowed us to show that the site acted as a source of CH₄ to the atmosphere (23.9 ± 0.6 g C m^-2 year^-1) with interannual, seasonal, and diurnal time scale dynamics. In particular, we found an outstanding diurnal cycle for CH₄ with the highest fluxes at night and lower ones at mid-day. In addition, the mid-day fluxes were negative in spring, highlighting larger oxidative processes than CH₄ production attributed to photosynthesis activity (i.e., soil oxygen penetration and endosymbiotic methanotrophs of Sphagnum). The range of CH₄ emissions was also controlled by the interannual variation in precipitation amounts and by the seasonal temperature variation.

This conceptual production-emission model highlights that water-carbon interactions in the peatland depend on local biotic and abiotic factors but also on hydrological processes at the watershed scale. This also highlights the need to further constrain carbon transfers between the production and the emission zones (i.e., peatland-atmosphere interface and surface water exports). For this purpose, we will soon carry out a field campaign to measure the concentrations and isotopic values of dissolved gases in peat pore water along with an upstream downstream and a vertical gradient.

How to cite: Lhosmot, A., Jacotot, A., Steinmann, M., Gandois, L., Binet, P., Toussaint, M.-L., Gogo, S., Gilbert, D., Moquet, J.-S., Coffinet, S., Boetsch, A., Loup, C., Laggoun-Défarge, F., and Bertrand, G.: Peatlands methane origin and fluxes to the atmosphere: towards an integrative conceptual model of a temperate French peatland, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13093, https://doi.org/10.5194/egusphere-egu23-13093, 2023.

In drying soils, root water uptake is limited by the low soil hydraulic conductance. The magnitude of this conductance drop and its temporal dynamics are function of soil texture, soil water status, root hydraulic architecture, atmospheric demand and canopy conductance.  Under dry climates, in order to survive, plants can adapt their carbon allocation by maximizing their root:shoot surface ratio, thereby decreasing their transpiration surface while increasing their root surface.

Thanks to a simple soil-plant hydraulic model, we show that soil hydraulic conductivity controls the minimum root:shoot surface ratio. A meta-analysis of shoot:root surface ratio is combined with a database of soil hydraulic properties to demonstrate how the minimum root:shoot surface value changes with soil conductivity across soil textural classes for dry biomes. We discuss the mechanisms by which plants can control their carbon allocation in such conditions and investigate the sensitivity of this minimum root:shoot surface ratio to future shifts in evaporative demand.

How to cite: Javaux, M., Cecere, A., Delval, L., Wankmüller, F., and Carminati, A.: Soil hydraulic conductivity defines minimum Root:Shoot surface ratio in moisture-limited environments, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15472, https://doi.org/10.5194/egusphere-egu23-15472, 2023.

High-quality and long-term measurements of water, energy, and carbon fluxes between the land and atmosphere are critical for eco-hydrological monitoring and land surface model (LSM) benchmarking. Eddy Covariance has become the most widely used method for theory development and LSM evaluation. On the other hand, flux tower data as measured (even after site post-processing and gap-filling based on empirical formulation) cannot be used directly for validating LSMs, and most of time, lacking physically-consistent measurement across the soil-plant-atmosphere continuum (SPAC) (e.g., other than surface fluxes, lacking the measurement of soil moisture, soil water potential, leaf water potential, fluorescence, and reflectance). Here we present high-quality and long-term fluxes and corresponding above/below-ground hydrological, physiological, photosynthetic data derived from the STEMMUS-SCOPE model simulations for PLUMBER2 project with 170 FLUXNET sites. Fluxes data from PLUMBER2 and SM data from FLUXNET2015 are used to further validate the effectiveness of the STEMMUS-SCOPE dataset. Results show that the simulated fluxes and SM dataset have reasonable agreements with the in-situ measurements, using the available global input/forcing datasets without any model tunning. This dataset adds to the existing ecosystem flux and SM network to help increase our understanding of ecosystem responses to extreme events.

How to cite: Wang, Y., Zeng, Y., Alidoost, F. (., Song, Z., Yu, D., Tang, E., Han, Q., Bas, R., Serkan, G., van der Tol, C., and Su, Z. (.: STEMMUS-SCOPE for PLUMBER2: Understanding Water-Energy-Carbon Fluxes with a Physically Consistent Dataset Across the Soil-Plant-Atmosphere (SPAC) Continuum, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16633, https://doi.org/10.5194/egusphere-egu23-16633, 2023.