BG3.17

Understanding the coordination of ecosystem functions across global biomes is still a critical challenge in ecology to better predict biosphere response to environmental changes and for developing indicators of ecosystem multifunctionality. Theories such as the leaf economics spectrum and the global spectrum of plant forms and function showed that several plant and organs traits are coordinated in a few key dimensions representing different ecological strategies However, the main axes of variation of ecosystem-scale functions are still largely unknown.

In this contribution, we first derived a set of ecosystem functions from a dataset of surface gas exchange measurements across major terrestrial biomes. Second, we identify the most important axes of variation of ecosystem functions. Third, we identify the variables that explain the axes of variation. Finally, we analyze the extent to which two state-of-the-art land surface models reproduce the key axes of ecosystem functions.

To do so, we used data of carbon, water, and energy exchange for 203 sites (1484 site-years) from global surface flux datasets. Moreover, we compile site information on canopy-scale measurements of foliar chemistry (Nitrogen concentration), vegetation structural variables (maximum leaf area index, aboveground biomass, and vegetation height), and mean climate data at the sites.

We find evidence that three key axes capture the variability of ecosystem functions (71.8%). The first dimension represents maximum ecosystem productivity, which is explained primarily by vegetation structure, followed by mean climate. The second axis represents the water-use strategies driven by vegetation height and climate. The third dimension reflects the ecosystem carbon-use efficiency; it is controlled by vegetation structure but shows a gradient related to aridity.

The first axis of the spectrum is well captured by ecosystem models, while the second and third axes are poorly reproduced. As a result, the ecosystem functional space that the models can simulate tends to be smaller than the observations'. We assumed that the limited variability of the model output points to the uncertain implementation of plant hydraulics in land surface models. This known key limitation explains the differences between observations and models in the water use strategy axis. Concerning the carbon use strategy axis, one limitation is that models lack flexibility in representing the response of respiration rates and carbon-use efficiency to climate, nutrients, disturbances, and substrate availability (including biomass and stand age, which relate to ecosystem management).

The concept of the key axes of ecosystem functions could be used as an indicator of ecosystem health and multifunctionality, and for the development of land surface models, which might help improve the predictability of the terrestrial carbon and water cycle in response to climate and environmental changes.

How to cite: Migliavacca, M. and the major axes of terrestrial ecosystem functions collaborators: The major axes of terrestrial ecosystem functions derived from ecosystem scale flux observations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2150, https://doi.org/10.5194/egusphere-egu22-2150, 2022.

The impacts of climate extremes on forest ecosystems are still poorly understood but important for predicting carbon and water cycle feedbacks to climate. Despite evidences of the different climatic thresholds of tree carbon source and sink activities, implementations of mechanistic growth components into dynamic global vegetation models (DGVM) remain challenged by lacking observational data into the most important terrestrial biological sink, i.e.; into the cambial zone of tree stems.

In this study, we aim to provide a framework for mechanistic understanding of drought impacts on carbon and water dynamics based on accurate analyses of the physiological processes that indirectly regulate these budgets. We quantified the drought impact and resilience of intra-annual carbon sequestration and water use in four mature Norway spruces from a Swiss subalpine site by comparing high-resolution growth (i.e., xylogenesis and wood anatomy) and physiological (i.e., stable carbon isotope ratios) data from an exceptional dry summer (year 2015) with those from a regular growing season (year 2014).

Our observations described the cascade of impacts from leaf physiology to cambial activity during and after a 41-day period of physiological water deficit. During water deficit, all wood formation processes were strongly reduced diminishing carbon sequestration by 67% despite a 11% increased water-use efficiency. However, with the recovery of the positive hydric state in the stem, we observed a fast recovery of the rates of the different cell formation phases at the expenses of the accumulated assimilates produced during the drought event.

Our results clarify how the interaction between source and sink is modulated via external environmental factors and provide evidence that, under specific circumstances, tree growth can be extremely resilient. These novel findings should provide a framework to improve sink model components in DGVMs and consequently help to bridge understanding of carbon and water fluxes between atmosphere and forest ecosystems. 

How to cite: Fonti, P., Treydte, K., Lehmann, M., Rigling, A., and Martínez-Sancho, E.: Drought impacts on forest carbon sequestration and water use – evidence emerging from quantification of tree-ring formation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10633, https://doi.org/10.5194/egusphere-egu22-10633, 2022.

Sapwood characteristics, such as sapwood area as well as thermal and hydraulic conductivity, are linked to species-specific hydraulic function and resource allocation to water transport tissues (xylem). These characteristics are often unknown and thus a major source of uncertainty in sap flow data processing and transpiration estimates because bulk rather than species-specific values are usually applied. Here, we analyzed the sapwood characteristics of fifteen common tree species in eastern North America from different taxa (i.e., angiosperms and gymnosperms) and xylem porosity groups (i.e., tracheid-bearing, diffuse- or ring-porous species). We quantified their sapwood area changes with stem diameter (allometric scaling) and thermal conductivity. We combined these measurements with species-specific values of wood density and hydraulic conductivity found in literature and assessed the role of wood anatomy in orchestrating their covariation. Angiosperms (ring- and diffuse-porous species), with specialized vessels for water transport, showed steeper relation (scaling) between tree size and sapwood area in comparison to gymnosperms (tracheid-bearing species). Despite the variability in thermal conductivity between species, gymnosperms (angiosperms) were characterized by lower (higher) wood density and higher (lower) sapwood moisture content, resulting in non-significant differences in sapwood thermal conductivity between taxa and xylem porosity groups. Clustering of species sapwood characteristics based on taxa or xylem porosity could facilitate more accurate parameterizations of these attributes. When combined with an increasing number of sap flow observations, these findings can improve tree- and landscape-level transpiration estimates, leading to more robust partitioning of terrestrial water fluxes.

How to cite: Pappas, C., Bélanger, N., Bastien-Beaudet, G., Couture, C., D'Orangeville, L., Duchesne, L., Gennaretti, F., Houle, D., Hurley, A. G., Klesse, S., Lebel Desrosiers, S., Montoro Girona, M., Peters, R. L., Rossi, S., St-Amand, K., and Kneeshaw, D.: Xylem porosity shapes sapwood characteristics and stem water use of temperate and boreal tree species, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7603, https://doi.org/10.5194/egusphere-egu22-7603, 2022.

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 this topic 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 and to separate the soil and farming impact, 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 (150m x 16m) which covers three different, distinct soil types. It carries multiple sensors to measure GHG and water vapour concentrations as well as water vapour isotope signatures of d¹⁸O and dD. 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 WUE. Hence, this platform produces a detailed picture of H₂O, C and GHG dynamics across soil and farming practice combinations and crop rotations, with a high-degree of accuracy and reproducibility.

Keywords: innovative sensor platform, greenhouse gases, H₂O isotopes, Evapotranspiration, wate ruse efficiency

How to cite: Hoffmann, M., Dubbert, M., Vaidya, S., Dahlmann, A., Schmidt, M., Rakowski, P., Bonk, N., Verch, G., Sommer, M., and Augustin, J.: An innovative sensor platform for in-situ studies of dynamics and underlying processes, driving spatio-temporal water, carbon and greenhouse gas flux pattern in a heterogeneous arable landscape, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4451, https://doi.org/10.5194/egusphere-egu22-4451, 2022.

The Amazon River basin contains a substantial amount of carbon stored within terrestrial ecosystems. The unknown fate of this this carbon remains a substantial source of uncertainty in projections of the Earth system. While increasing atmospheric carbon dioxide concentrations could potentially enhance photosynthetic carbon uptake and/or reduce transpiration, increasing vapor pressure deficits have the potential to act with the opposite sign on both of these fluxes. Here, we investigate these competing factors at a process level, using a data assimilation system in which we constrain a parsimonious ecosystem model with observations from river runoff gauging stations, gravimetric water storage anomalies, and solar-induced chlorophyll fluorescence. Our model-data fusion provides us with an observationally consistent reanalysis of 21st-century ecohydrology across 14 Amazon watersheds along side the posterior distribution of key process parameters and emergent ecosystem properties such as water use efficiency (WUE). We find that the response to trends in atmospheric carbon dioxide concentrations and meteorological drivers varies across a hydroclimatic gradient within the Amazon, with implications for how carbon and water cycling could be expected to change subject to future biogeochemical and climatic trends.

How to cite: Levine, P., Bloom, A., Konings, A., Worden, M., Parazoo, N., Braghiere, R., Norton, A., Ma, S., Worden, J., and Reager, J.: Merging multiple observational data streams to constrain carbon uptake and water loss in the Amazon basin, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10625, https://doi.org/10.5194/egusphere-egu22-10625, 2022.

The understanding of the terrestrial carbon budget depends on the development of the terrestrial carbon sink, which is influenced by the forest dynamics under climate change and environmental conditions. In this study, we choose a new approach combining the CARAIB-dynamic vegetation model (DVM) and satellite observed data at a high resolution of 3 km over the Austria-Eisenwurzen region for the year 1985-2020. Using machine learning techniques and remote sensing Landsat satellite data, we extracted the land use and land cover (LULC) over the study region. It is giving the precise estimation of spatial and temporal change of forest dynamic over the year 1985-2020. In addition, the geographical distribution of Eisenwurzen 80 % part is the Northern Alps, 11 % of the area belongs to the Northern Alpine Foothills and 9 % belong to the Central Alps. The objective of this study is to understand the model sensitivities and uncertainties in dynamic conditions which are necessary for a reliable and robust estimation of the terrestrial carbon budget. Here, we will conduct our simulation with CARAIB-DVM in different environments – with and without LULC change, no climate change (de-trending), climate change scenario, constant atmospheric CO_2. Additionally, we are simulating our model over the course of >100 years for analysis of model sensitivity to climatic parameters. From, this study, we explore how the changes in these parameters affect the estimation of the terrestrial carbon sinks. Given that the parameters we are exploring in this analysis are highly uncertain, especially at the regional level and at high resolution, it is important to see how these adjustments affect the estimation of the carbon budget. Hence, with this study, we understand which input parameters are responsible for the uncertainty in the estimation of carbon sequestration. Further, we will calibrate the dynamic vegetation model to minimize uncertainty in the future projection (until 2070). In conclusion, this study allows us to understand the importance of changing land-use, climate, and environment scenarios and to constrain the model with an improved input dataset that reduces the uncertainty in the model evaluation of the regional carbon budget of terrestrial ecosystems.

How to cite: Verma, A., Francois, L., Jacquemin, I., Lanssens, B., Tölle, M., Matej, S., and Egger, C.: Sensitivity analysis of terrestrial carbon budget with changing land use land cover and climate by combining dynamic vegetation model and satellite observed data at high resolution over Austria- Eisenwurzen, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6506, https://doi.org/10.5194/egusphere-egu22-6506, 2022.

Transpiration (E_t) is a key variable in hydrology and climate, yet it remains poorly understood at global scales. In nature, several non-linearly interacting environmental variables, or 'stressors', limit the rates of Et below the demand by the atmosphere. In most process-based formulations of evaporation (E) – e.g., satellite-based algorithms and climate models – only a few of these stressors are considered, and their representation is usually based on limited empirical or experimental studies conducted at local scales. New hybrid approaches offer the opportunity to combine process-based knowledge on E_t and machine learning models in a synergistic manner, and to better characterise the influences of this myriad stressors on E_t.

Using a hybrid approach, we combine in situ and satellite observations of multiple stress variables using deep learning, aiming to construct a new formulation of transpiration stress (S_t) – the ratio by which potential transpiration is reduced to E_t. The data of S_t are assembled from 368 flux towers spread across the globe coming from multiple networks, as well as 90 sapflow-instrumented sites from a recently collected global archive. The covariates used as input features include: plant available water to represent water or drought stress, air temperature to represent heat stress, vapor pressure deficit to account for the effect of atmospheric demand on stomatal conductance, microwave vegetation optical depth to consider the phenological state of vegetation, incoming shortwave radiation as an indicator of light stress, and carbon dioxide which directly and indirectly affects ecosystem transpiration.

We show that our ground-up approach without any prior assumptions compares better than traditional formulations of S_t, both when compared to in situ observations as well as an independent satellite-based stress proxy (SIF/PAR). Embedding the new S_t function within a process-based model of E (the Global Land Evaporation Amsterdam Model, GLEAM) yields a hybrid model of evaporation (GLEAM-Hybrid) which is evaluated in its performance. In this hybrid model, the S_t formulation is bidirectionally coupled to the host model at daily timescales. An extensive validation shows that our hybrid approach (GLEAM-Hybrid) has the potential to outperform traditional process-based formulations (GLEAM) and pure machine learning-based estimates of E (FLUXCOM). Overall, the proposed approach provides a suitable framework to improve the estimation of E in satellite-based algorithms and climate models, and consequently increase our understanding of this crucial variable.

How to cite: Miralles, D. G., Koppa, A., Rains, D., Hulsman, P., and Poyatos, R.: A hybrid model of global land evaporation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5001, https://doi.org/10.5194/egusphere-egu22-5001, 2022.