Xylem sap exists in a state some have described as “doubly” metastable[1]: liquid water is transported from root to leaf under negative pressure, and, in some climates, below its freezing point. Sub-100 nm nanobubbles may be injected into the xylem liquid through pit membranes[2], becoming coated with Phospho- and Glycolipids in the process. Their surface properties, and therefore fate within the tree hydraulic system, remain largely unexplored.

In this work, we have used molecular dynamics simulations to produce surface tension – area isotherms of biologically relevant lipid monolayers, as a function of both temperature and negative pressure (i.e. dynamic surface tensions).

We find that glycolipid monolayers resist expansion proportionally to the rate of expansion[3]. Their surface tension increases with the tension applied, stabilising the bubble with respect to embolism. In contrast, a typical phospholipid rapidly condenses into more dense lamellar-like phase, rendering it highly resistant to tensions as high as -3.5 MPa. Mixed monolayers of the two exhibit hybrid behavior, as the glycolipids' larger head group disrupts the more ordered phase of the phospholipid. Finally, it is observed that increasing temperature also increases surface tension, at a given surface area.

[1] Lintunen, A., Hölttä, T., & Kulmala, M. (2013). Anatomical regulation of ice nucleation and cavitation helps trees to survive freezing and drought stress. Scientific Reports, 3, 1–7. https://doi.org/10.1038/srep02031

[2] Schenk, H. J., Steppe, K., & Jansen, S. (2015). Nanobubbles: a new paradigm for air-seeding in xylem. Trends in Plant Science, 20(4), 199–205. https://doi.org/10.1016/j.tplants.2015.01.008

[3] Ingram, S., Salmon, Y., Lintunen, A., Hölttä, T., Vesala, T., & Vehkamäki, H. (2021). Dynamic Surface Tension Enhances the Stability of Nanobubbles in Xylem Sap. Frontiers in Plant Science, 12. https://doi.org/10.3389/fpls.2021.732701

How to cite: Ingram, S., Salmon, Y., Lintunen, A., Hölttä, T., Vesala, T., and Vehkamaki, H.: Dynamic Surface Tensions of Nanobubbles in Plant Xylem: When are they Stable?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2012, https://doi.org/10.5194/egusphere-egu23-2012, 2023.

Plant water-use efficiency (WUE) describes the intimate link between the carbon and water cycles. WUE can be estimated using multiple methodologies concerning different spatial and temporal scales, but empirical evidence has shown that these estimates do not always agree. Two of the most widely used methodologies to estimate WUE are measurements of the ratio of photosynthesis to stomatal conductance to water, using gas-exchange, and analyses of the carbon isotopic composition (δ¹³C) of plant material, most often measured on plant tissues (leaves and woody stems mainly), reflecting the signal of the plant physiological status all along the organ ontogeny. In addition, in tall trees, δ¹³C varies greatly among leaves and thus individual measurements cannot capture whole-tree physiological status. In contrast, analyses of the phloem δ¹³C collected at the base of the trunk should reflect the whole-tree physiological performance. To test this novel approach under contrasting climate change scenarios, we estimated WUE: from measurements of gas-exchange, from δ¹³C of leaf and woody tissues, as well as from δ¹³C of phloem samples collected along the whole plant pathway. We measured gas-exchange and collected samples for analyses of δ¹³C from European beech (Fagus sylvatica) saplings grown under controlled conditions and from adult trees in the field. Saplings were subjected to four climate change scenarios, resulting from a combination of two atmospheric CO₂ levels and two watering regimes. In the field, we measured WUE on adult beech trees during the unusually hot and dry growing season of 2022. Preliminary results show that phloem δ¹³C could serve as a good proxy of whole-plant WUE, provided that the internal leaf conductance is incorporated into the calculations.

How to cite: Gimeno, T. E., Umerez, A., Pérez-López, U., Miranda-Apodaca, J., Porras, J., and López-Castro, G.: Tracking phloem carbon isotopic composition to reconcile water-use efficiency estimates across scales in beech trees, under future climate change scenarios, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13394, https://doi.org/10.5194/egusphere-egu23-13394, 2023.

Improved agricultural practices increasing the water use efficiency (WUE), reducing greenhouse gas emissions (GHG) and/or improving atmospheric C sequestration rates within the soil are crucial for an adaptation and/or mitigation to the global climate crisis. However, processes driving water (H₂O), carbon (C) and GHG fluxes within the soil-plant-atmosphere continuum of agricultural used landscapes are complex and flux dynamics differ substantially in time and space. Hence, to upscale and evaluate the effects/benefits of any new agricultural practice aiming towards improving WUE, soil C sequestration and/or GHG emissions, accurate and precise information on the complex spatio-temporal H2O, C and GHG flux pattern, their drivers and underlying processes are required.

Current approaches to investigate H₂O, C and GHG (CH₄, N²O and CO₂) dynamics as well as underlying processes driving them, are usually laborious and have to choose between high spatial or temporal resolution due to methodological constraints. On the one hand, often used eddy covariance systems are not suitable to account for small scale spatial heterogeneities, despite growing evidence of their importance. On the other hand, chamber systems either lack temporal resolution (manual chambers) or strongly interfere with the measured system (static automatic chambers). Hence, none of these systems enable a proper upscaling and evaluation of effects/benefits of new farming practices for WUE, C sequestration and GHG emissions at especially heterogeneous agricultural landscapes, such as present within inter-alia the also globally widespread hummocky ground moraine landscape of NE-Germany.

In an effort to overcome this, a novel, fully automated robotic field sensor platform was established and combined with an IoT network and remote sensing approaches. Here, an innovative, continuously operating automated robotic field sensor platform is presented. The platform was mounted on fixed tracks, stretching over an experimental field (size) which covers three different, distinct soil types. It carries multiple sensors to measure GHG and water vapor concentrations and isotope signatures of d18O and dD of water vapor. Combined with two chambers which can be accurately positioned in three dimensions at the experimental field below, this system facilitates to detect small-scale spatial heterogeneity and short-term temporal variability of H₂O, C and GHG flux dynamics as well as crop and soil conditions over a range of possible experimental setups. The automated, continuous estimation of d18O and dD of evapotranspiration further provides the basis to partition water fluxes alongside the flux based partitioning of C and GHG fluxes. This particularly promotes to assess not only ecosystem but component specific water use efficiencies. Hence, this platform produces a detailed picture of H2O, C and GHG dynamics across different treatments and crop cycles, with a high-degree of accuracy and reproducibility.

How to cite: Dubbert, M., Vaidya, S., Dahlmann, A., Schmidt, M., Verch, G., Sommer, M., Augustin, J., and Hoffmann, M.: AGROFLUX: An innovative sensor platform to study high-frequency responses in water, carbon and greenhouse gas fluxes in a complex arable landscape, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2321, https://doi.org/10.5194/egusphere-egu23-2321, 2023.

The cropland carbon (C) balance at regional scale still contains high uncertainties not the least due to the problem of up-scaling C fluxes of temporarily and spatially highly divers ecosystems. The C-exchange between the terrestrial ecosystem and the atmosphere constitute the largest and most uncertain flux of the cropland C balance, as opposed to C import from organic (manure) and C export through harvest which are lower and less uncertain.

Combining satellite data with local eddy covariance CO₂-flux data is commonly used to up-scale the C-exchange signal from point to regional scale across global ecosystems. Low spatial resolution products like MODIS limit their applicability and accuracy to larger homogeneous areas involving a high degree of uncertainty rather than detecting and tracing highly dynamic (farm-)field scale CO₂-fluxes from space. We are using eddy-covariance CO₂-flux data of an arable field in conjunction with Sentinel-2 derived vegetation indices (VI) to assess the ability of the satellite data to monitor daily net-ecosystem exchange (NEE), gross-primary productivity (GPP) and ecosystem respiration (Reco) based on a matched footprint. Simple linear regression models are built to test the ability of a range of VIs (NDVI, GNDVI, EVI, EVI2, SAVI, MNDWI, NDWI, SR, S2REP) to monitor and predict CO₂-exchange for croplands. We analyze the correlation between measured CO₂-fluxes and VIs over the course of the growing seasons to assess the suitability and accuracy of the VIs along the phenological year. We present a single site analysis to zoom into short-comings of this approach and how the satellite signal relates to vegetation CO₂-exchange. VIs generally show a high variability in their predictive power. Still, results suggest a similarly high accuracy as mechanistic modelling approaches for suitable VIs, e.g. the cumulative C-exchange (NEE) of winter wheat of one growing season based on NDVI and GNDVI is over- and under-estimated by only 33 (15%) and 41 (18%) g C m^-2 respectively.

How to cite: Gottschalk, P., Kalhori, A., Li, Z., Wille, C., and Sachs, T.: Monitoring daily cropland CO2-exchange at field scale with Sentinel-2 satellite imagery, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11596, https://doi.org/10.5194/egusphere-egu23-11596, 2023.

Tropical forests are responsible for approximately one third of the global terrestrial carbon dioxide uptake. However, the ability of tropical forests to continue to sequester carbon is threatened by climate change. Some species may adapt and become more dominant while less resilient species may not be able to adapt. These changes may ultimately impact overall ecosystem carbon gain. A better understanding of species specific traits related to leaf physiology can potentially help us predict how forest carbon uptake might change in the future. Additionally, to gain a wholistic understanding of forests carbon assimilation and tree stress it is important that we make simultaneous and complementary measurements at different temporal and spatial scales under different climatic conditions. Here we combine leaf-, canopy- and ecosystem- scale measurements to assess plant-environment interactions in the tropical forest of Costa Rica (Tower 2 of La Selva Biological Station). At the leaf scale, we evaluated seasonal and species-specific changes in chlorophyll fluorescence and reflectance-based vegetation indices [the Photochemical Reflectance Index (PRI), the Chlorophyll-Carotenoid Index (CCI) and Normalized Difference Vegetation Index (NDVI)]. We collected sun-exposed leaves from six tree species within the flux tower footprint: Pentaclethra macroloba, Virola koschnyi, Virola sebifera, Goethalsia meiantha, Sacoglottis trichogyna, and Warszewiczia coccinea. Preliminary results for the first sampled years (2022-2023) show that P. macroloba, the dominant species in this forest, has likely the highest photosynthetic capacity due to its higher electron transport rates (ETR), followed by V. koschnyi, the second most dominant. Using different metrics of photosynthetic activity, we found that most species do not show photosynthetic seasonality. However, one species, V. koschnyi, showed decreased reflectance in the visible part of the spectrum during the wetter season (35-45%), indicating increased pigment concentration and, likely, increased photosynthetic activity. G. meiantha had the lowest ETR of all species, as well as the lowest PRI, CCI and NDVI, especially during the drier season, which is coincident with a visually unhealthy colouration during this season. Our results will help improve our understanding of how different species are responding to environmental stress, in particular to increased evaporative demand, ultimately advancing our knowledge of the tropical forest carbon cycle.

How to cite: Rodriguez-Caton, M., Seibt, U., Stutz, J., Parazoo, N., Cooperdock, S., Bigwood, J., Rao, M. P., Pierrat, Z., Wong, C. Y., Dierick, D., Vargas, O., and Magney, T.: Species-specific ecophysiology within a flux tower footprint in an evergreen wet tropical forest in Costa Rica, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13425, https://doi.org/10.5194/egusphere-egu23-13425, 2023.

In Poyda et al. (2019), we determined the carbon balance of six cropland sites in Southwest Germany on the basis of half-hourly net ecosystem exchange (NEE) fluxes measured with the eddy covariance (EC) method over a period of eight years from 2010 to 2017. We came up with the finding, that the sites lost on average about one ton of carbon per hectare and year. This huge loss was surprising, because the sites have been used as croplands already over several decades, and one would expect that the sites should be close to steady-state. In April 2022, we performed a soil organic carbon inventory at one of the six sites, and compared it with carbon data collected in April 2009. The soil inventory data do not give any evidence for a carbon loss in the order of one ton of carbon per hectare and year. Based on the data collected in April 2009, the carbon stock size of the plowing horizon at that time was 39.1 t C ha^-1 yr^-1. The carbon stock size determined in April 2022 was in the same range and amounted 39.9 t C ha^-1 yr^-1 with a standard error of 1.9 t C ha^-1 yr^-1, what means that these data indicate that the carbon budget of the cropland is indeed in or close to steady-state. In Poyda et al. (2019), the NEE flux data were gap-filled with the widely used software tool REddyProc. In REddyProc, the user has the option to apply or not to apply the friction velocity  (u*) filter before gap-filling. In Poyda et al. (2019), the u* filter was applied before gap-filling. We reprocessed the data for the year 2016, the only year with no winter time gaps, because a methanol fuel cell supplied the station with additional power, without applying the u* filter. Without applying the u* filter the cumulated annual NEE was ‑2080 kg C ha^-1. Applying the u* filter increased the NEE by 836 kg C ha^-1 to -1244 kg C ha^-1. We did the same comparison for NEE fluxes measured at the same site over the years 2019, 2020 and 2021, and we got a similar result. The application of the u* filter increased the mean NEE by +730 kg C ha^-1. The cumulated NEE was -2832 kg C ha^-1 without u* filter and -2102 kg C ha^-1 with u* filter. This positive bias is in the range of the EC based derived carbon loss and is able to explain a major part of the suspected positive bias. We recommend all REddyProc users to check their NEE data for this phenomena simply by processing the data once with and once without enabling the u* filter and comparing both results.

How to cite: Ingwersen, J., Poyda, A., Kremer, P., and Streck, T.: The role of REddyProc's friction velocity filter in determining the carbon budget of croplands, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17094, https://doi.org/10.5194/egusphere-egu23-17094, 2023.

Eddy covariance techniques are widely used to measure the net exchange of greenhouse between the surface and the atmosphere, providing high resolution, instantaneous flux measures and long-term observations, which in turn allows more accurate assessments of the ecosystem’s state. However, gaps in eddy covariance time series reduce the statistical efficiency and increase bias estimates, hampering predictions of ecosystem function. Although, several imputation techniques have been proposed to overcome these difficulties, including Marginal Distribution Sampling (MDS), the standard method of FLUXNET, MDS has limitations for filling long gaps (weeks to months). In this study, we combine MDS and machine learning imputation techniques to fill an 18-year time series of carbon fluxes. Our objective was to evaluate whether Random Forest algorithms are able to fill long-gaps and detect seasonality, as well as to identify the best predictors of ecosystem exchange, gross primary productivity, and ecosystem respiration. The eddy covariance raw-data were obtained from an experiment in an upland semi-natural grassland in the Auvergne region of France that has been managed by continuous cattle grazing under low animal stocking rate.  After raw-data processing using EddyPro software, we applied the MDS technique to half-hour data to fill the short-gaps, and then used a Random Forest (RF) algorithm to daily data to fill longer gaps. The time series was split into a training and testing dataset, and all variables describing atmospheric conditions, solar radiation, and energy fluxes were used to predict C fluxes. Random Forest models with high R² and low prediction error increases were used to impute the long-gaps.  The cross-validation between observed and predicted values in the test dataset obtained R² of greater than 0.85 for all carbon flux variables. Our analysis also revealed that the daily carbon flux values could be estimated using the basic meteorological variables, i.e., air temperature, precipitation, atmospheric pression, friction velocity, and wind speed, but also by energy fluxes. Finally, the imputed dataset presented similar seasonality along the years, with the highest C sequestration and respiration in the summer and spring. These results highlight the value of machine learning techniques for producing robust, long-term eddy flux data time series.

How to cite: WINCK, B., BLOOR, J., and KLUMPP, K.: Random forest algorithm for long-gap imputation in Eddy Covariance data: a case study in an upland semi-natural grassland in the Auvergne region, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8031, https://doi.org/10.5194/egusphere-egu23-8031, 2023.

Global change ecology nowadays embraces ever-growing large observational datasets (big-data) and complex mathematical models that track hundreds of ecological processes (big-model). The rapid advancement of the big-data-big-model has reached its bottleneck: high computational requirements prevent further development of models that need to be integrated over long time scales to simulate the distribution of ecosystems carbon and nutrient pools and fluxes. Here we introduce a machine-learning acceleration (MLA) tool to tackle this grand challenge. We focus on the most resource-consuming step in terrestrial biosphere models (TBMs): the equilibration of biogeochemical cycles (spin-up), a prerequisite that can take up to 98% of the computational time. Through three members of the ORCHIDEE TBM family part of the IPSL Earth System Model, including versions that describe the complex interactions between nitrogen, phosphorus and carbon that do not have any analytical solution for the spin-up, we show that MLA reduced the computation demand by 77-80%  for global studies via interpolating the equilibrated state of biogeochemical variables for a subset of model pixels. Despite small biases in the MLA-derived equilibrium, the resulting impact on the predicted regional carbon balance over recent decades is minor. Our tool is agnostic to gridded models (beyond TBMs), compatible with existing spin-up acceleration procedures, and opens the door to a wide variety of future applications, with complex non-linear models benefit most from the computational efficiency.

How to cite: Sun, Y., Goll, D. S., Huang, Y., Ciais, P., Wang, Y.-P., Bastrikov, V., and Wang, Y.: Machine learning for accelerating process-based computation of land biogeochemical cycles, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10692, https://doi.org/10.5194/egusphere-egu23-10692, 2023.

Drought detection is crucial for water resources management and global food security. Drought is typically detected using empirical indices, such as the Palmer Drought Severity Index (PDSI). However, those indices lack objectivity and are therefore suboptimal. Machine learning has been proven to be a powerful tool to define objective and optimal regressions, especially in hydrology. Here, we developed a machine learning (ML) model using Long Short-Term Memory (LSTM), which include memory effects, to predict the Evaporative Fraction (EF), an indicator of water stress at the surface, based on FLUXNET2015 Tier 1 eddy-covariance dataset. Compared to the widely used PDSI, EF is a more direct drought index to indicate water stress conditions. The results show that, firstly, with some routinely available variables, e.g., precipitation, net radiation, air temperature, relative humidity and other static variables like, Plant Functional Type (PFT) and soil property, the model can capture the EF dynamics, especially during the dry season. Secondly, we found there were different LSTM memory lengths across different Plant Functional Types. This indicates different rooting depth and different plant water use strategies that regulate the time scales of droughts. Our results have important implications for future water stress estimation, e.g., drought detection, in order to obtain a more direct and more accurate estimate of water stress.

How to cite: Zhao, W., Winkler, A. J., Reichstein, M., Orth, R., and Gentine, P.: An objective estimate of water stress - going beyond PDSI, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9278, https://doi.org/10.5194/egusphere-egu23-9278, 2023.

Making projections of ecological systems under environmental change is central to many disciplines. Process-based models aim to represent core ecological mechanisms governing ecosystem dynamics, which can then be valuable for projecting change under novel environmental conditions. Yet, as our ecological understanding evolves, updating parameter information can be challenging. In addition, classical statistical approaches to fitting functional relationships often miss the complexity of interacting, non-linear dynamics, which can limit the predictive capacity of models. As such, a growing body of work suggests that the integration of modern machine learning might help to improve the representation of key ecological dynamics within such process-based models.  Here, we present a case study, using machine learning to identify key relationships between relative growth, biomass and mortality compared to classical regression methods. Our results suggest that the inclusion of a deep neural network (DNN) into a “theory-driven” process based global vegetation model can greatly improve model predictions of patch level forest structure and vegetation dynamics. This hybrid approach offers both the benefits of interpretability and physically-realistic structure, combined with the depth of information contained in big datasets and the flexibility of model machine learning.

How to cite: Papastefanou, P., Esquivel-Muelbert, A., Suvanto, S., Olin, S., Crowther, T., Schelhaas, M.-J., and Pugh, T.: Improving ecosystem model development with machine learning: a full hybrid approach, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9948, https://doi.org/10.5194/egusphere-egu23-9948, 2023.

Changes in plant phenology as for example earlier leaf unfolding and delayed autumn senescence can result in variations in the carbon and water cycle. Studies investigating the impact of phenological shifts on biophysical processes such as water availability are still limited. Due to the sensitivity of radar satellite observations to both, structural and dielectric properties of the scattering materials, microwave remote sensing offers the potential to analyse structural (i.e. canopy biomass) and physiological (i.e. water status) dynamics in vegetation.

Here, we aim to derive annual water dynamics of vegetation canopies from the Sentinel-1 C-band radar backscatter signal by removing the influence of vegetation structure on the backscatter seasonality. To decouple the phenology of vegetation structure from the moisture content dynamics, a semi-empirical backscattering model (Water Cloud Model, WCM) is combined with a canopy water balance model. The WCM aims to separate contributions of soil and vegetation to the total backscatter. When introducing physical parameters for vegetation structure like leaf area index (LAI) and and moisture like leaf fresh moisture content (LFMC) to describe the vegetation backscatter, the effect of the seasonal variability of both variables on the radar signal can be assessed. The canopy water balance model estimates interception and changes in the canopy saturation and storage capacity of the vegetation using precipitation and throughfall measurements. Both models are combined to iteratively estimate measures of vegetation moisture. To calibrate the two models, we use measurements of LFMC and of canopy interception for the Tharandt ecosystem site in Germany in 2022, which is part of the ICOS and FLUXNET network. The calibrated model is then used to analyse the individual effects of both vegetation descriptors, LAI and LFMC, by fixing either one and looking at the changes in the seasonality of the S1 signal. The combined use of both models will allow to remove the structural-related changes in the Sentinel-1 radar backscatter to finally retrieve vegetation water dynamics over larger areas.

How to cite: Kranz, J., Forkel, M., Bernhofer, C., Mauder, M., and Queck, R.: Analysing the sensitivity of Sentinel-1 SAR to vegetation water dynamics using a combined model approach, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12640, https://doi.org/10.5194/egusphere-egu23-12640, 2023.

The benefits of the application of weather radar observations for aeroecological research are already well known to the scientific community. The advantages of long-term polarimetric weather radar observations for the detection of bird and insect migration or estimation of their abundances are used by different teams all over the world. In this context, a correct, timely, and meaningful interpretation of polarimetric weather radar observations is an important part of these studies. This interpretation requires a well-developed technique that automates the recognition of separate classes in both spatial and temporal dimensions of the data.

The study presents a novel data-driven technique for identifying different classes in Quasi-Vertical Profiles (QVPs) and in Columnar Vertical Products (CVP) based on observations made by a dual-polarization Doppler weather radar. The top-down optimal clustering is applied to the detection and identification of aeroecological classes in the QVPs and CVPs. We demonstrate its application to the NCAS X-band dual-polarization Doppler weather radar (NXPol) data and the potential of its application to the C-band data of the Met Office radar network. This technique is generally applicable to similar multivariate data from other observational instruments and will improve quantitative observation and monitoring of biodiversity in the UK.

How to cite: Lukach, M.: Potential of AI classification of the weather radar observations for aeroecological research, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16397, https://doi.org/10.5194/egusphere-egu23-16397, 2023.