Displays

Transpiration directly links the water, energy and carbon cycles. It is commonly restricted by soil (through soil moisture) and atmospheric (through vapor pressure deficit, VPD) moisture stresses governed by the movement of water through plants, also known as plant hydraulics. These sources of moisture stress are likely to diverge under climate change, with globally enhanced VPD due to increased air temperatures but more variable and uncertain changes in soil moisture. In most Earth system and land surface models, the ET response to each of the two stresses is evaluated through independent empirical relations, while neglecting plant hydraulics. Comparison of these two models is challenged by the difficulty of ensuring any perceived differences are due to the model structure, not an imperfect parametrization. Here, we use a model-data fusion approach applied to long-term ET records collected at 40 sites across a diverse range of biomes to demonstrate that the widely used empirical approach underestimates ET sensitivity to VPD, but compensates by overestimating the sensitivity to soil moisture stress. The bias originates from the joint control of leaf water potential on plant response to soil moisture and VPD stress. To a lesser degree, it also overestimates from increased sensitivity to VPD under dry (low leaf water potential) conditions in the plant hydraulic model. As a result, a hydraulic model captures ET under high-VPD conditions for wide-ranging soil moisture states better than the empirical approach does. Our findings highlight the central role of plant hydraulics in regulating the increasing importance of atmospheric moisture stress on biosphere-atmosphere interactions under elevated temperatures.

How to cite: Konings, A., Liu, Y., Kumar, M., Feng, X., and Katul, G.: Plant hydraulics accentuates the effects of atmospheric moisture stress on transpiration, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8472, https://doi.org/10.5194/egusphere-egu2020-8472, 2020.

Earth system models predict that atmospheric dryness reduces photosynthesis due to its reductive effect on stomatal conductance. However, while this representation may be appropriate in many environments, in the wet Amazonian tropical rainforest, this is not the case. Using remote sensing data combined with machine learning techniques (k-means clustering and artificial neural networks), we show that in the wettest parts of the Amazon rainforest, gross primary production and evapotranspiration continue to increase alongside atmospheric dryness, i.e. vapor pressure deficit, despite reductions in ecosystem conductance. On the other hand, Earth system models have the opposite photosynthetic response to vapor pressure deficit in the wettest part of the Amazon, overestimating its reductive effect on tropical vegetation photosynthesis and evapotranspiration, leading to an exaggerated carbon source to the atmosphere. As vapor pressure deficit is expected to increase with climate change, our study highlights the importance of reframing how we understand and represent the response of ecosystem photosynthesis to atmospheric dryness in the wettest ecosystems, to accurately quantify the future land carbon sink and atmospheric CO2 growth rate.

How to cite: Green, J. K., Gentine, P., Zhang, Y., Berry, J., and Ciais, P.: Amazon rainforest increases photosynthesis in reponse to atmospheric dryness, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5195, https://doi.org/10.5194/egusphere-egu2020-5195, 2020.

We use a light use efficiency model (LUE)  to describe gross primary productivity (GPP) from 1982 to 2016 using the GIMMS-3g FPAR record and NASA MERRA-2 reanalysis, and explore how GPP trends and anomalies can be explained using annual changes in temperature and hydrology. The GPP model uses optimum LUE (LUE_opt) inferred from the global FLUXNET network and extrapolated using solar induced chlorophyll fluorescence observations. We find that increasing trends in GPP at mid to high latitudes over the 35-year study period are due to reduced cold temperature of plant growth constraints. Our results suggest a persistent and increasing negative carbon-climate feedback at mid to high latitudes. We also find an increasing atmospheric vapor pressure deficit trends over the tropics, which represents an emerging positive climate feedback that results in a negative trend in GPP after the early 2000s. We expect that further warming, increasing water constraints, and disturbance events will significantly reduce global ecosystem productivity.

How to cite: Madani, N., Parazoo, N., Kimball, J., Ballantyne, A., Maneta, M., Saatchi, S., Palmer, P., Liu, Z., and Tagesson, T.: Water Constraint is Limiting Global Gross Primary Productivity, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10458, https://doi.org/10.5194/egusphere-egu2020-10458, 2020.

Given its simplicity, the light use efficiency (LUE) concept is widely used for the estimation of gross primary productivity (GPP) at the ecosystem scale. In the last three decades, many types of LUE models have been developed to explain the dependencies of GPP to different environmental and meteorological conditions across spatial and temporal scales. Despite the simplicity, LUE models are robust against observations from daily to seasonal scales, though entailing challenges in parameter upscaling. The main differences across LUEs resides in the presence/absence of certain meteorological drivers and in the particular formulations of different response functions to diagnose instantaneous light use efficiency (ε*). 

Here, we collected different algorithms for describing the meteorological constraints on ε* from literature and performed a factorial experiment by recombining the different response functions between the different models to assess model performance at site and network level. These models were forced and parameterized by remote sensing data, meteorological data and GPP for 177 eddy covariance flux sites from the FLUXNET2015 and LaThuile using a data assimilation approach. The results show that the two selected optimal LUE models had no significant differences in model performances at site-level at daily, weekly, and monthly scales. The Nash-Sutcliffe Model Efficiency Coefficient (NSE) of 50% sites were larger than 0.726 and 0.725 at daily, 0.788 and 0.783 at weekly, 0.836 and 0.834 at monthly and 0.544 and 0.510 at annual scales. 

Based on the selected models, we further explored the different methods to upscale the optimized parameters: a) site means and medians per plant functional type and climate class, b) random forest regression, using bioclimatic variables and corresponding vegetation index, and c) selection according to the similarity between sites, determined via the NSE in mean seasonal cycle temperature, precipitation, radiation, and vegetation indexes within the same plant functional type. The model efficiencies in cross validation for both models show that using the median parameters per plant functional type had the best performance to upscale parameters from site-level to global-level at daily, weekly and monthly scales. Since the meteorological response functions and the corresponding parameters represent the sensitivity of plant photosynthesis to the meteorological conditions we further explore the relationship between the climate sensitivities and other environmental drivers as well as biophysical plant traits using global retrievals of Sun induced fluorescence. Our results emphasize that novel Earth Observations datasets and transfer learning approaches bridge the LUE formulation tradeoffs between complexity and tractability.

How to cite: Bao, S., Gans, F., Besnard, S., Koirala, S., Moreno, A., Walther, S., Weber, U., Jung, M., Mahecha, M., and Carvalhais, N.: Evaluating Light Use Efficiency Models and Parameter-upscaling Methods, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21053, https://doi.org/10.5194/egusphere-egu2020-21053, 2020.

Terrestrial ecosystems play a key role in climate by dampening the increasing atmospheric CO₂ concentrations primarily caused by anthropogenic fossil fuel emissions. The capability of the land biosphere to act as a carbon sink largely depends on climate conditions, which determine the energy and water availability required by plants to grow. Even though only a small part of the global land area is covered by vegetation, the impact of extreme dry and wet seasons has been shown to largely drive the global interannual variability of gross primary production. The climate in a certain area can be seen as the balance of different heat and moisture fluxes: local surface–atmosphere fluxes from below, entrainment of heat and moisture from aloft, and ‘horizontal’ advection of heat and moisture from upwind regions. The latter provides a mechanism for remote regions to impact gross primary production downwind, and has received less scientific attention. Here, advection is inferred from a bird’s eye perspective, focussing on the five ecoregions with the largest interannual variability in peak productivity around the globe. Employing the atmospheric Lagrangian trajectory model FLEXPART, driven by ERA-Interim reanalysis data, we track the air residing over ecoregions back in time to deduce the origins of heat and moisture that affect ecosystem gross primary production. Utilizing the evaporative source regions supplying water for precipitation to these ecosystems, as well as the analogous source regions of advected heat, we estimate the contribution of advection to gross primary production. Our findings show that source regions of heat and moisture are not congruent: upwind land surfaces typically supply most of the advected heat, whereas upwind oceans tend to provide more moisture. Moreover, low gross primary production in heat-stressed and water-limited ecosystems is often accompanied by enhanced heat and reduced moisture advection from land regions, exacerbated by upwind land–atmosphere feedbacks. These results demonstrate that anomalies in atmospheric advection can cause ecosystem productivity extremes. Particularly in light of ongoing climate change, we emphasize the potentially detrimental effects of upwind areas that may cause long-lasting impacts on the terrestrial carbon budget, thereby further affecting the climate.

How to cite: Schumacher, D. L., Keune, J., and Miralles, D. G.: Moisture and heat advection as drivers of global ecosystem productivity , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11870, https://doi.org/10.5194/egusphere-egu2020-11870, 2020.

Earth’s hydroclimatic variability is increasing, with changes in the frequency of extreme events that may negatively affect forest ecosystems. We examined possible consequences of changing precipitation variability using tree rings in the conterminous U.S. While many growth records showed either little evidence of precipitation limitation or linear relationships to precipitation, growth of some species (particularly those in semiarid regions) responded asymmetrically to precipitation, such that tree growth reductions during dry years were greater than, and not compensated by, increases during wet years. The U.S. Southwest in particular showed both a large increase in precipitation variability coupled with asymmetric responses of growth to precipitation. Simulations suggested roughly a two-fold increase in the probability of large negative growth anomalies across the Southwest resulting solely from 20^th century increases in the variability of cool-season precipitation. Models project continued increases in precipitation variability, portending future growth reductions across semiarid forests of the western U.S.

How to cite: Dannenberg, M., Wise, E., and Smith, W.: Reduced tree growth in the semiarid United States due to asymmetric responses to intensifying precipitation extremes, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20157, https://doi.org/10.5194/egusphere-egu2020-20157, 2020.

Continuous and long-term monitoring of water use are required to reduce uncertainties in modelling forest transpiration. Since drought threatens the vitality and survival of forests worldwide, understanding and modelling responses to drought are of particular interest. Tree species undergo strong selective pressure to develop specialized mechanisms for regulating water-use dynamics during unfavourable climatic conditions. To cope with drought a tree can adjust its “water-use strategy”, by 1) altering the regulation of water release through the leaves to the atmosphere, 2) adjusting the water storage capacitances, or 3) changing the hydraulic conductivity of the xylem, impacting the water flux. There is thus a pressing need to understand the variability of such hydraulic mechanisms, between and within tree species, and quantify how they impact forest transpiration.

We strive to elucidate hydraulic mechanisms in European tree species by combining, for the first time, three hydraulic components (stomatal conductance regulation, storage water capacity and wood anatomical traits) to identify water-use strategies and mechanistically model their effect on water use under increasing drought and warming. We constructed a European monitoring network, integrating ongoing meteorological measurements (e.g., temperature, relative humidity, global radiation and soil moisture) with sap flow (SF) and dendrometer (DM) measurements, as well as wood anatomical properties collected from the same tree individuals. Currently, the network includes 22 sites stretching from Spain till Finland (latitudinal range: 40° - 62° N), with a total of 281 individuals (14 tree species) and hourly-resolution monitoring of SF and DM from ~2011-2018. This large temporal coverage ensures a broad range of dry and wet conditions at each site, while the extensive climatological range of sites promotes the detection of intra-specific variability in hydraulic mechanisms.

Focussing on four common European tree species (Fagus sylvatica, Quercus petraea, Pinus sylvestris and Picea abies), we present initial results from a Swiss temperate forest, where combining SF, DM and wood anatomy allowed us to disentangle species-specific differences in water-use strategies. Building upon these empirical observations, we were able to quantify the impact of these inter-specific differences on water use. Moreover, a mechanistic water transport model was used to assess stem water content, stem water potential (i.e., an indicator for hydraulic vulnerability), and subsequently turgidity within the cambium (i.e., crucial for wood formation) during the summer drought of 2015. Our efforts will advance process-based understanding of drought impacts on water use and could constrain predictions of forest transpiration under changing climatic conditions.

How to cite: Peters, R. L., Poyatos, R., Zweifel, R., Kahmen, A., Grossiord, C., Fonti, P., Gharun, M., Buchmann, N., and Steppe, K.: Define the water-use strategy: A network study on hydraulic mechanisms regulating water use of European tree species during drought. , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11033, https://doi.org/10.5194/egusphere-egu2020-11033, 2020.

Global environmental changes are rapidly altering the functioning and structure of terrestrial ecosystems.Particularly, rising CO₂ atmospheric concentrations have been reported to increase photosynthesis by increasing carbon assimilation and water-use efficiency. This leaf-level CO₂ fertilization effect may lead to an increase in the biomass stock in forest stands. However, previous studies argued that an enhanced tree growth rate is associated with a reduction in the longevity of trees, thus reducing the ability of forest biomass to act as carbon sinks over long timescales. In addition, faster growth may lead to an acceleration of self-thinning whereby tree density in the stand is reduced due to progressive mutual shading as tree crowns expand and a resulting increase in shaded individuals’ mortality. Nevertheless, previous results relied on empirical relationships between tree growth rates and longevity, without considering any positive effects of elevated CO​₂ on individual tree’s carbon balance. Individual-based forest datasets such as tree ring width data and forests inventories have been widely used to monitor long-term changes in forest demography. Yet, the mechanistic underlying these processes remains poorly understood and challenges persist in upscaling from individual measurements to higher level of organization.

Here, we use a vegetation demography model (LM3-PPA) which simulates vegetation dynamics and biogeochemical processes by explicitly scaling from leaf up to ecosystem level by resolving leaf-level physiology, growth, and height-structured competition for light, using the perfect plasticity approximation (PPA). Using this simulation model, we investigate the links between individual trees’ carbon balance under rising CO₂ levels, their longevity under alternative mortality parametrizations, and the implications for forest dynamics and self-thinning rates. Inventory data from long-term forest reserves is used to assess empirical support for these simulated links. Specifically, we test the hypothesis of faster growth-earlier death in order to assess forests’ capacity to store carbon under environmental changes. This provides key mechanistic insights to reveal whether increased CO₂ fertilization on leaf-level photosynthesis positively affects tree’s C balance and thereby reduces the mortality under competition for light in the canopy.

How to cite: Marques, L., Weng, E., and Stocker, B.: Live fast-die young: Scaling CO​2 fertilization effects from leaf to ecosystem levels, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13361, https://doi.org/10.5194/egusphere-egu2020-13361, 2020.

The terrestrial biosphere is a key component of the global carbon cycle and is heavily influenced by climate. This interaction spans a wide range of temporal (from sub-daily to paleoclimatic) and spatial (from local to continental and global) scales and a multitude of bio-physical processes. In part due to this complexity, a comprehensive picture of the physical links and correlations between climate drivers and carbon cycle metrics at different scales remains elusive, framing the scope of this contribution. Here, we specifically explore how precipitation, soil moisture and aggregated climate variability indices relate to the variability of the European terrestrial carbon cycle at sub-daily to interannual scales (i.e. excluding long-term trends). We first discuss broad areas of agreement and disagreement in the literature. For example, while most carbon cycle proxies tend to correlate positively with precipitation, responses to soil moisture and climate indices are more variable. In fact, soil moisture often correlates positively with productivity in water-limited environments, and negatively in light limited ones, or can exhibit nonlinear relations with the carbon cycle proxies. We then conclude by outlining some existing knowledge gaps and by proposing avenues for improving our holistic understanding of the role of climate drivers in modulating the terrestrial carbon cycle.

How to cite: Messori, G., Ruiz-Pérez, G., Manzoni, S., and Vico, G.: Reviewing the role of precipitation and soil moisture in driving the terrestrial carbon cycle variability in Europe: recent advances and known unknowns, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3295, https://doi.org/10.5194/egusphere-egu2020-3295, 2020.

In summer 2018, Northern Europe experienced an extreme summer drought in combination with unusually high temperatures, which had a substantial impact on agricultural yields as well as on forest growth conditions in various ways. An ongoing study, using ICOS and other forest ecosystem stations in the Nordic region, shows that the drought dramatically decreased NEP in the southern Scandinavian and Baltic region, almost nullifying the carbon sinks in some of the forests. However, some of the forests that not were exposed to the most extreme drought actually increased their NEP because of the high evaporative demand. Such severe conditions during a single year could be expected to influence a forest over several following years. Reduced tree storage of carbohydrates leads to a changed carbon allocation pattern in spring that may affect both the woody growth and the resistance to pests. It is thus important to reveal the impact of such climatic events over a longer period.    

This study aims at assessing the carry-over effects of the extreme weather conditions on the carbon fluxes and the forest growth to the year after the event, 2019. The base of the analysis will be eddy covariance data combined with tree ring time series from measurement stations that has been shown to be significantly affected by the drought through reduced carbon fluxes: the spruce forests Hyltemossa and Skogaryd and the mixed forests Norunda, Svartberget, Soontaga and Rumperöd. The eddy covariance and tree ring data will be used to assess the forest ecosystem carbon fluxes and growth recovery in 2019 by comparisons to earlier normal years and extreme events.

How to cite: Linderson, M.-L., Holst, J., Heliasz, M., Klemedtsson, L., Klosterhalfen, A., Krasnova, A., Läänelaid, A., Linderson, H., Martínez García, E., Mölder, M., Peichl, M., Soosaar, K., Tung Chen, T., Vestin, P., Weslien, P., and Lindroth, A.: Boreal forest carbon exchange and growth recovery after the summer 2018 drought, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10559, https://doi.org/10.5194/egusphere-egu2020-10559, 2020.

Terrestrial ecosystems play a major role in the interannual variability of the global carbon budget representing a substantial sink equivalent to about one-third of current anthropogenic CO₂ emissions (Le Quéré et al., 2018). Therefore, it is vital to understand how plants and vegetation respond to the impacts of climate extremes as this impacts the productivity terrestrial ecosystems. The conterminous United States (CONUS) represent a diverse range of climate conditions and ecosystems where productivity and its interannual variability are controlled regionally by rainfall and/or temperature. The responses of ecosystem productivity to wet and dry years have been previously investigated over the CONUS using annual aboveground net primary productivity (ANPP) data from multi-site observations (Knapp and Smith, 2001). From this previous study, a positive asymmetry of ANPP in response to rainfall anomalies was found at individual sites (i.e. an increase of ANPP in wet years was greater than a decline in dry years). Here, we evaluate the asymmetry of ecosystem productivity to rainfall over the entire CONUS from 2010 to 2018 using multiple data streams including: the Global Unified Gauge-Based precipitation data, the GRIDMET surface meteorological data (maximum temperature, minimum temperature, precipitation accumulation, and Palmer Drought Severity Index), the SMOS satellite L-Vegetation optical depth product, CO₂ fluxes (net ecosystem exchange (NEE) & gross primary productivity (GPP)) derived from eddy covariance measurements, MODIS ANPP product, Fluxnet GPP at site scale, and three different GPP products from observation-driven models. We address the following two questions: (1) How does ecosystem productivity across the CONUS respond to rainfall anomalies during the period 2010-2018? (2) Does the evidence for positive asymmetry previously observed using site studies hold true across the entire CONUS? For this, we define an asymmetry index (AI) where positive AI indicate a greater increase of productivity in wet years compared to the decline in dry years, and negative AI indicate a greater decline of productivity in dry years compared to the increases in wet years. We find that the spatial patterns of AI across the CONUS are similar amongst the different products and exhibit more pronounced negative asymmetries over the Great Plains and the west north central region whilst positive asymmetries are observed over the southwestern USA during the 2010-2018 period. While the “shrubland” biome shows a persistent positive asymmetry during the period, the “grasslands” biome appears to have switched from positive (observed by Knapp and Smith, 2001) to negative anomalies during the last decade. The observed asymmetry of the different GPP products is reflected by the negative asymmetry of the precipitation anomalies (skewness of precipitation annual anomalies), which we conclude is the primary driver of negative asymmetry across the US continental surface.

References

Knapp, A.K., Smith, M.D., 2001. Variation Among Biomes in Temporal Dynamics of Aboveground Primary Production. Science (80-. ). 291, 481. https://doi.org/10.1126/science.291.5503.481

Le Quéré, C., Andrew, R.M., Friedlingstein, P., Sitch, S., et al., 2018. Global Carbon Budget 2018. Earth Syst. Sci. Data 10, 2141–2194. https://doi.org/10.5194/essd-10-2141-2018

How to cite: Al-Yaari, A., Wigneron, J.-P., Ciais, P., Knapp, A., Reichstein, M., Ogee, J., Wingate, L., Carvalhais, N., Lei, F., Hufkens, K., Chave, J., Frappart, F., Swenson, J., and Ducharne, D.: Negative asymmetry response of ecosystem productivity to annual rainfall anomalies over the conterminous U.S during the 2010 -2018 period, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3550, https://doi.org/10.5194/egusphere-egu2020-3550, 2020.

The boreal zone is one of the most carbon-dense biomes in the world and is comprised of a highly interconnected mosaic of forest and wetlands which are warming at a rate several times the global average with extreme weather events, such as droughts, becoming increasingly common. At the ecosystem scale, both forests and peatlands are often vulnerable to drought-induced carbon loss, however, the relative resilience of these two ecosystems within the boreal landscape is not well understood. Here we study the effect of the 2018 drought on CO₂ fluxes in two boreal forests and a boreal peatland within <20km radius, i.e. experiencing the same weather conditions. The peatland displayed the strongest response to the drought, with the site becoming a net annual source for CO₂ for the first time in 17 years, with the CO₂ sink slow to recover after the drought broke. In contrast, the response of the forests was mixed, a  spruce/pine forest on glacial till remained unaffected by the drought, whereas a nearby pine forest, situated on drier sandy soil, responded strongly to vapour pressure deficit and declining soil moisture content, decreasing with CO₂ uptake weakening, but still allowing the forest to function as a CO₂ sink. In contrast to the bog, the pine forest CO₂ sink quickly recovered following the end of the drought. We conclude that boreal peatlands are likely to be the most vulnerable component of the boreal landscape to drought and that soil type is likely to play a role in regulating the response of boreal forests.

How to cite: Nilsson, M., Ratcliffe, J., Klosterhalfen, A., Zhao, P., Chi, J., and Peichl, M.: The CO2 balance of a boreal fen is more sensitive to drought than surrounding forests, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18697, https://doi.org/10.5194/egusphere-egu2020-18697, 2020.

Modeling of plant hydraulics is at the forefront of development in vegetation and land-surface models.  Numerical tools that consider water flow within the conductive system of plants, and particularly trees, have been developed and used in studies of hydraulic strategy and consequences of hydraulic behavior for drought tolerance. Several established land-surface models such as ED2, CLM, and E3SM have recently developed “hydro” versions and are ready to extrapolate the consequences of including tree hydraulic behaviors into large scale and global simulations. At the core of any plant hydrodynamic model is the assumption that stomatal conductance is dependent on xylem water potential. Further, “plant hydro” models assume that the effect of soil moisture on stomatal conductance is not direct but cascades through depletion of xylem water content in dry soil conditions.

We use observations of sap flow, soil moisture, and evapotranspiration at a mixed forest in the University of Michigan Biological Station (UMBS) at the footprint of the US-UMd flux tower to characterize the onset and advancement of hydraulic stress and post-stress recovery. We define stress by observing tree-level decrease of stomatal conductance during sunny days as soil-moisture deficit progresses. We use the Penman-Monteith (PM) formulation to calculate stomatal conductance given observed atmospheric forcing: air temperature, humidity, net radiation, soil heat flux, and aerodynamic resistance. Such PM-based approach effectively decouples changes in evapotranspiration due to atmospheric forcing vs. changes due to decreased stomatal conductance. Multiple years of sap-flow measurements in tens of trees of multiple species allow us to identify the species-specific characteristics of the onset of stress, and the hysteretic dynamics of stomatal conductance. The daily hysteresis indicates the severity of stress. Longer-term inter-day hysteresis of the relationship between noon-time stomatal conductance and soil moisture, before and after rain have alleviated the moisture stress, indicates species-specific strategies of hydraulic-stress recovery. Recovery time is related to the degree of stress, and can vary between a nearly reversible state and 1 to 2 days of recovery, to a long recovery of several days. We find large differences between species in the sensitivity to stress and in the strength of coupling between stem water content and stomatal conductance. These are consistent with the hydraulic strategy of the trees along the an/isohydric continuum.    

Identifying the hydraulic characteristics of water stress and direct observations of the coupling between stem water storage, conductance, and transpiration provide key observations with which to tune hydrodynamic models and allow process-based functional-type parameterization of stomatal conductance that accounts for tree hydrodynamics and hydraulic stress recovery.   

How to cite: Bohrer, G., Yazbeck, T., Restrepo Acevedo, A. M., and Matheny, A. M.: Diurnal and inter-day hysteresis of species-specific stomatal conductance from sap-flow measurements illustrates hydraulic-stress responses strategies of trees, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5672, https://doi.org/10.5194/egusphere-egu2020-5672, 2020.

Using IoT technologies represents a novel low cost and efficient tool for studies in many disciplines (plant ecophysiology and hydrology) to unravel the vulnerability of an ecosystem to climatic stress. Taking advantage of IoT, a new multifunctional device, the “TreeTalker”, was developed to monitor in real time physical and biological parameters of single trees as well as some additional ecosystem-related variables. Here, we present performance of the TreeTalker to illustrate mainly the role of stem water content and water transport in tree behavior and function with respect to internal and external forces. TreeTalker is designed based on Granier-type thermal dissipation probe (TDP) and a capacitance sensor to measure stem water content.

In this study, two main experiments are analyzed. In the first experiment, procedures for calibration and use of capacitance sensors are presented. Considering the effect of wood density on frequency data, calibration is performed on different species and diameter harvested stems to convert the sensor-reported values to stem volumetric water content. In the second experiment, application of 20 TreeTalkers with particular emphasis placed on measuring hourly, daily and monthly sap flow and stem water content fluctuations under well-irrigated and deficit-irrigated treatments of Juglans regia L. was conducted on a study site in northeast of Italy.  

The results show that the range of stem water content is highly influenced by environmental factors. Stem water content has a significant portion of the daily tree water uptake. Low water storage occurs in response to drought and less soil water availability, which clarifies the high dependency of trees on stem water content under deficit-irrigated treatments. The diurnal-nocturnal pattern of stem water content and sap flow revealed an inverse relation. Such finding, still under investigation is explained by the important water recharge during the night, likely due to stem volume changes and lateral water distribution rather than by vertical flow rate. 

How to cite: Asgharinia, S., Belelli Marchesini, L., Gianelle, D., and Valentini, R.: Design and Performance Evaluation of Internet of Things (IoT) Based Multifunctional Device for Plant Ecophysiology & Hydrology: Toward Stem Water Content & Sap Flow , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17021, https://doi.org/10.5194/egusphere-egu2020-17021, 2020.

A radiative transfer and process-based model, called Soil-Canopy-Observation of Photosynthesis and Energy fluxes (SCOPE), relates remote sensing signals with plant functioning (i.e., evapotranspiration and photosynthesis). Relying on optical remote sensing data, the SCOPE model estimates evapotranspiration and photosynthesis, but these ecosystem-level fluxes may be significantly overestimated if water availability is the primary limiting factor for vegetation. Remedying this shortcoming, additional information from extra sources is needed. In this study, we propose considering water stress in SCOPE by incorporating soil moisture data in the model, besides using satellite optical reflectance observations. A functional link between soil moisture, soil surface resistance, leaf water potential, and carboxylation capacity is introduced as an extra element in SCOPE, resulting in a soil moisture integrated version of the model, SCOPE-SM. The modified model simulates additional state variables: (i) vapor pressure (e_i), both in the soil pore space and leaf stomata in equilibrium with liquid water potential; (ii) the maximum carboxylation capacity (V_cmax) by a soil moisture dependent stress factor; and (iii) the soil surface resistance (r_ss) through approximation by a soil moisture dependent hydraulic conductivity. The new approach was evaluated at a Fluxnet site (US-Var) with dominant C3 grasses and covering a wet-to-dry episode from January to August 2004. By using the original SCOPE (version 1.61), we simulated half-hourly time steps of plant functioning via locally measured weather data and time series of Landsat (TM and ETM) imagery. Then, SCOPE-SM was similarly applied to simulate plant functioning for three cases using Landsat imagery: (i) with modeled e_i; (ii) with modeled e_i and V_cmax; and (iii) with modeled e_i, V_cmax, and r_ss. The outputs of all four simulations were compared to flux tower plant functioning measurements. The results indicate a significant improvement proceeding from the first to the fourth case in which we used both Landsat optical imagery and soil moisture data through SCOPE-SM. Our results show that the combined use of optical reflectance and soil moisture observations has great potential to capture variations of evapotranspiration and photosynthesis during drought episodes. Further, we found that the information contained in soil moisture observations can describe more variations of measured evapotranspiration compared to the information contained in thermal observations.

How to cite: Bayat, B., van der Tol, C., Yang, P., Montzka, C., Vereecken, H., and Verhoef, W.: Integrating soil moisture in SCOPE model for improving remote sensing of evapotranspiration and photosynthesis under water stress conditions, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5658, https://doi.org/10.5194/egusphere-egu2020-5658, 2020.

The volume of water entering the atmosphere through transpiration is thought to be greater than the flow of all rivers to the oceans. It makes up the majority of evapotranspiration (ET) and significantly contributes to rainfall and therefore also to surface water runoff. However, there is no consensus on how transpiration responds to a changing environment; or even as to whether it is increasing over time. Global transpiration estimates are most commonly made through the partitioning of ET models.  However, in many ET models, the dynamics of vegetation growth and associated impacts on evapotranspiration are overlooked. Therefore, global estimates of transpiration from climate models are poorly constrained, with large uncertainties especially in stomatal conductance.

The ‘P model’ (for Production) is a recently developed, ‘next-generation’ model for Gross Primary Production, GPP. Derived from biochemical process of plants, the P model is built upon the established standard model for photosynthesis – combined with optimality hypotheses for the adaptation and acclimation of key model parameters – to determine GPP. The P model has the potential to provide a coupled global carbon and water model that responds correctly to changing environmental conditions. It requires only elevation, CO2 concentration, incident solar radiation, vapour pressure deficit (VPD) and temperature as inputs, in addition to remotely sensed green vegetation cover (fAPAR). The key idea motivating this research is that by exploiting the coupling of land-atmosphere carbon and water exchanges through stomatal behaviour, it should be possible to develop a near real-time transpiration monitoring system in which fAPAR is a key input. The P-model provides the means to do this. Initial results will be shown for both transpiration and GPP, with validation at >100 eddy-covariance flux-tower sites.

How to cite: Prior, A. and Prentice, I.-C.: Global transpiration modelling: can the optimality hypothesis improve partitioning of ecosystem-scale evapotranspiration?, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6038, https://doi.org/10.5194/egusphere-egu2020-6038, 2020.

How to cite: Strigul, N. and Erickson, A.: Machine-learning emulation of a forest biogeochemistry model for efficient biosphere optimization, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19744, https://doi.org/10.5194/egusphere-egu2020-19744, 2020.

Foliage clumping is known to have a significant effect on the radiative transfer and mass and energy exchanges in forests. It is an important component of canopy structure to consider for the estimation of photosynthesis rates and the interpretation of observed solar induced fluorescence (SIF). Yet, relatively little is known about the drivers of foliage clumping, and few observations of foliage clumping are available at the branch scale. Here, we report on a study using laser light to estimate foliage clumping at the tree branch scale in eight broadleaf species, at different heights above ground, from four sites located in two climatic zones: one water limited, and one light limited. We also integrate our results with published foliage clumping estimates from two sites (one in each climatic zone). We find that foliage arrangement on branches exposed to high solar irradiance tend to be random at the dry sites, but are very clumped at humid sites where competition for light is high. Branches sampled at the top of tall canopies at humid sites showed that foliage clumping increased with tree height, suggesting that higher competition for light results in the production of larger numbers of leaves grouped together which reduces the light interception efficiency on a per leaf area basis. Comparison with landscape clumping values suggests that the spatial availability of a limiting resource is a major driver of foliage clumping in forests.  

How to cite: Beland, M. and Baldocchi, D.: On the relation between tree foliage clumping at the branch scale and light and water limitations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11925, https://doi.org/10.5194/egusphere-egu2020-11925, 2020.

The Moderate Resolution Imaging Radiometer (MODIS) is a primary instrument in the NASA Earth Observing System (EOS) which was designed for monitoring global terrestrial vegetation. MODIS provides global estimates of 8-day mean gross primary productivity (GPP) at 1-km spatial resolution. In this study, the MODIS GPP algorithm using light use efficiency (LUE) approach and the Integrated Carbon-Canadian Land Surface Scheme (IC-CLASS) based on Farquhar photosynthetic model and a sunlit and shaded leaf separation scheme was evaluated against eddy covariance (EC) measured GPP in a variety of ecosystems in Canada. Although GPP simulated by the two models agreed well when they were averaged over Canadian landmass, there were systematic differences between them in spatial distribution patterns. These differences were due to inherent shortcomings of the LUE modeling approach. When a constant maximum LUE value is specified for each biome type, this simplification cannot appropriately deal with the shaded leaf contribution to total canopy GPP. When GPP was simulated by IC-CLASS with the separation of sunlit and shaded leaves, the biases were minimized. Compared with daily and annual GPP derived from EC flux data at 7 Fluxnet Canada sites, IC-CLASS performed better than the MODIS GPP algorithm. The differences between IC-CLASS and MODIS GPP were larger in more clumped canopies (i.e. forests), resulting from the increase in the fraction of shaded leaves. Thus, the LUE models should be improved to consider different LUEs in sunlit and shaded portions of the canopy for their effective and reliable estimation of GPP at regional scale.

How to cite: Chen, B., Arain, A., Chen, J., Wang, S., Mo, G., and Liu, J.: Importance of shaded leaf contribution to the total GPP of high latitude ecosystems: evaluation of MODIS GPP, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2793, https://doi.org/10.5194/egusphere-egu2020-2793, 2020.

Interception is an important hydrological process relating to canopy gas exchange and takes a significant part from precipitation. The real interception process by the needle leaves is worth discussing because their shape may allow interception by both surfaces and thus affects photosynthesis by blocking stomata. Therefore, the aim of this study is to figure out the distribution of interception at needle leaf and its relation with the gas exchange of wet canopy.

We measured ecosystem flux and wetness from a Japanese cypress forest by the advanced water-proof enclosed gas analyzer (LI7200, LI-COR, the USA) and handmade wetness sensors. A SVAT (soil-vegetation-atmosphere transfer) multilayer model with two rainfall interception solutions (free gas exchange with interception only by the adaxial surface and no gas exchange with interception by both surfaces) has been used to figure out the distribution of rainfall interception, snow melting water distribution and photosynthesis process of wet canopy.

The results include precipitation events from 4 years, showing that interception can happen not only on the adaxial surface but also on both surfaces. Meanwhile, when the intensity of rainfall events enhanced, the possibility of interception on both surfaces increased. Hence, such kind of needle leaf can process photosynthesis during the rainfall. Future studies should concentrate on improving the model for snow process and soil respiration. More comparison with other types of forests may also provide worthy results for learning how plants adjust photosynthesis to adapt the climate change.

How to cite: Jiao, L., Sempuku, Y., Chang, T., and Kosugi, Y.: Interception by a temperate coniferous forest and its relationship with wet canopy gas exchange, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12801, https://doi.org/10.5194/egusphere-egu2020-12801, 2020.

Water stress in one of the main limiting factors in agro-systems, causing a reduction in gross primary production (GPP) and by extend, yields. However, it is still unclear to attribute whether the limitations of photosynthesis originate from a strict stomatal control (SOL) or from other non-stomatal limitations (NSOL). In this study, we investigated the effects of drought on potato crop by using eddy covariance data at the Lonzée Terrestrial Observatory during three consecutive cultivation periods (2010, 2014 and 2018). Regardless the years and the timing of the drought appearance, the maximum carboxylation rate V_cmax (one of the NSOL) was reduced with decreasing REW, while the stomatal sensitivity to GPP parameter in the Medlyn et al. model (G₁-SOL) remained constant. We showed that below the REW threshold of 0.55 ± 0.05, the non-consideration of NSOL in the ecosystem CO₂ model led to an overestimation of the modelled GPP, which was about three times higher than its unstressed corresponding value. As a result, decreasing V_cmax while maintaining G₁ constant was sufficient to reproduce GPP and canopy conductance dynamics during drought. At a sub-daily scale, the intrinsic water-use efficiency did not vary during drought, neither its dependence on VPD nor its hourly dynamics. This reinforced the hypothesis of direct and feedback effects of NSOL on canopy conductance and photosynthesis, which was supported by the uniform coupling between carbon and water fluxes. We recommend the implementation of NSOL in ecosystem CO₂ models since non-stomatal factors were responsible for the decrease in potato crop GPP during drought.

How to cite: Beauclaire, Q., Gourlez de la Motte, L., Bernard, H., and Bernard, L.: Proofs of non-stomatal limitations of potato photosynthesis during drought by using in-situ eddy covariance data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5183, https://doi.org/10.5194/egusphere-egu2020-5183, 2020.

Carbon-dioxide (CO₂) fluxes in the soil-plant-atmosphere system contain bidirectional material transport with organic and inorganic sources and sinks, and various pathways. Proportion of irrigated fields in the total area of Hungarian arable lands is low, and incase of a rainfed field water and CO₂ fluxes are only driven by meteorological factors. In this study we focused on maize under different fertilization treatments to see the plot scale variability of CO₂ fluxes and connected parameters.

The site is a multifactorial sowing time-fertilizer-maize variety field experiment near Martonvásár. Two treatment plots were selected for the measurements with contrasting 60 kg N ha^-1 and 180 kg N ha^-1 fertilizer treatments and no other factors were considered in the present study. We performed synchronized observations of (i) CO₂ fluxes: soil respiration (Rs; EGM-5 gas analyser + SRC-1 chamber, PPSystems); leaf scale photosynthesis (A; CIRAS-3 portable photosynthesis system, PPSystems)), (ii) soil temperature and soil water content, (iii) plant parameters: root growth (CI-600, CID-Bioscience), plant height, leaf area index (Accupar LP-80 ceptometer, Li-Cor). Data on the above parameters comprise several spatial replicates to explore spatial heterogeneity in case of a maize field managed in accordance with the typical Hungarian practice. The average applied N amount in the country is around 100-105 kg ha^-1.

Field measurements for CO₂ fluxes and biotic and abiotic drivers were performed six times in the vegetation period to establish relationship among them. Data were analyzed to optimize the labour intensive protocol for this experimental setup. Photosynthesis varied within the vertical canopy as reflected by measurements on five leaves per plant. Soil respiration was more dependent temporally on soil water availability than on temperature.

How to cite: Gelybó, G., Deli, R., Dencső, M., Kósa, B., Mateika, V., Tóth, M., Ujj, E., Árendás, T., Fodor, N., and Bayoumi, H.: Small scale CO2 fluxes in a rainfed maize field under N fertilization, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18170, https://doi.org/10.5194/egusphere-egu2020-18170, 2020.

The response of SOC spatial variability to different soil moisture conditions has not been explored at the global scale in part due to the lack of continuous information of these variables across large areas of the world. Analyzing this relationship could be useful to reduce the current uncertainty around SOC distribution and change. Large scale models and SOC mapping efforts contrast with country specific SOC maps, and large uncertainties on SOC magnitudes and patterns remain across large areas of the world. Our main objective was to explore SOC trends using soil moisture values as prediction factors. Using SOC point data from the World Soil Information Service (WoSIS, n=87002 point data between the years 1991 and 2015) we applied a cross validation-based ensemble learning approach to generate continuous SOC maps in a quinquennial basis (limited to 0-30 cm depth). The cross validated root mean squared error (RMSE) of our ensemble for the period 1991 -1995 varied from 32 to 33 g/kg while the correlation between modeled and observed data varied from r=0.45 to r=0.55. The accuracy of SOC estimates increased for the period 2011-2015 (r=0.75 to r=0.81 and RMSE= 20 to 23 g/kg). However the lower RMSE (16 to 17 gr/kg) was found for the years 2001-2005 (r=52 to r=58). Trend detection analysis applied to SOC predictions reveal areas showing significant (p-value < 0.05) positive trends across ~2.7 million km2 at the global scale ranging from 0.3 to 29 g/kg. Significant negative trends of SOC were found across ~3.6 million km2 at the global scale ranging from -22.2 to -0.3 g/kg. Main SOC losses were found across North America, Europe, central Africa, and Siberia. Our results quantifying the response of soils to changing soil moisture conditions contribute with new insights that are useful for the development of soil carbon monitoring systems.

How to cite: Guevara, M. and Vargas, R.: Global trends of soil organic carbon based on soil moisture and ensemble learning, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19926, https://doi.org/10.5194/egusphere-egu2020-19926, 2020.

Soil heterotrophic respiration has been considered as a key source of CO₂ flux into the atmosphere and thus plays an important role in global warming. Although the relationship between soil heterotrophic respiration and soil water content has been frequently studied both theoretically and experimentally, model development has thus far been empirically based. Empirical models are often limited to the specific condition of their case studies and cannot be used as a general platform for modeling. Moreover, it is difficult to extend the empirical models by theoretically defined affinities to any desired degree of accuracy. As a result, it is of high priority to develop process-based models that are able to describe the mechanisms behind this phenomenon with more deterministic terms.

Here we present a mechanistic, mathematically-driven model that is based on the common geometry of a pore in porous media. Assuming that the aerobic respiration of bacteria requires oxygen as an electron acceptor and dissolved organic carbon (DOC) as a substrate, the CO₂ fluxes are considered a function of the bioavailable fraction of both DOC and oxygen. In this modeling approach, the availability of oxygen is controlled by its penetration into the aquatic phase through the interface between air and water. DOC on the other hand is only available to a section of the soil that is in contact with water. As the water saturation in the pore changes, it dynamically and kinematically impacts these interfaces through which the mass transfer of nutrients occurs, and therefore the CO₂ fluxes are directly controlled by water content. We showcased the model applicability on several case studies and illustrated the model capability in simulating the observed microbial respiration rates versus the soil water contents. Furthermore, we showed the model potential to accept additional physically-motivated parameters in order to explain respiration rates in frozen soils or at different temperatures.

How to cite: Gharasoo, M., Fairbairn, L., Rezanezhad, F., and Van Cappellen, P.: Soil heterotrophic respiration as a function of water content and temperature in a mechanistic pore-scale model, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20943, https://doi.org/10.5194/egusphere-egu2020-20943, 2020.

Four years of growing season eddy covariance measurements of net carbon dioxide (CO₂) and energy fluxes were used to examine the similarities/differences in surface-atmosphere interactions at two dwarf shrub tundra sites within Canada’s Southern Arctic ecozone, separated by approximately 1000 km. Both sites, Trail Valley Creek (TVC) and Daring Lake (DL1), are characterised by similar climate (with some differences in radiation due to latitudinal differences), vegetation composition and structure, and are underlain by continuous permafrost, but differ in their soil characteristics. Total atmospheric heating (the sum of latent and sensible heat fluxes) was similar at the two sites. However, at DL1, where the surface organic layer was thinner and mineral soil coarser in texture, latent heat fluxes were greater, sensible heat fluxes were lower, soils were warmer and the active layer thicker. At TVC, cooler soils likely kept ecosystem respiration relatively low despite similar total growing season productivity. As a result, the 4-year mean net growing season ecosystem CO₂ uptake (May 1 - September 30) was almost twice as large at TVC (64 ± 19 g C m^-2) compared to DL1 (33 ± 11 g C m^-2). These results highlight that soil and thaw characteristics are important to understand variability in surface-atmosphere interactions among tundra ecosystems.

As recent studies have shown, winter fluxes play an important role in the annual CO₂ balance of Arctic tundra ecosystems. However, flux measurements were not available at TVC and DL1 during the cold season. Thus, the process-based ecosystem model CLASSIC (the Canadian Land Surface Scheme including biogeochemical Cycles, formerly CLASS-CTEM) was used to simulate year-round fluxes. In order to represent the Arctic shrub tundra better, shrub and sedge plant functional types were included in CLASSIC and results were evaluated using measurements at DL1. Preliminary results indicate that cold season CO₂ losses are substantial and may exceed the growing season CO₂ uptake at DL1 during 2010-2017. The joint use of observations and models is valuable in order to better constrain the Arctic CO₂ balance.  

How to cite: Meyer, G., Humphreys, E., Melton, J., Lafleur, P., Marsh, P., Detto, M., Helbig, M., Boike, J., Voigt, C., and Sonnentag, O.: The role of soil characteristics on measured and modelled carbon dioxide and energy fluxes for Arctic dwarf shrub tundra sites, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11913, https://doi.org/10.5194/egusphere-egu2020-11913, 2020.

Accurate estimation of the temperature sensitivity of respiration (Q₁₀) is important for understanding terrestrial ecosystem carbon cycle and its response to climate change, especially in the northern high-latitude regions (NHL). The conventional calculation of temperature sensitivity contain seasonal confounding effects on annual temporal scale. The scale-dependent parameter estimation (SCAPE) method which is based on singular spectral analysis could circumvent confounding effects. However, the process of screening a series of high frequency subsignals to identify the best intrinsic Q₁₀ produce large error. In this study, we proposed the SCAPE-M method to improve the approach of screening high frequency subsignals. Three datasets were used to validate the SCAPE-M method in the NHL, namely FLUXNET2015 datasets, MsTMIP multi-model weighted average outputs, and ERA_interim reanalysis data. The main results were as follows: (1) On the site scale, the confounding effects in the forest ecosystems were less than grassland and cropland ecosystems in the NHL. The apparent Q₁₀ derived from conventional approach differed among biomes in the NHL and increased with annual mean temperature. The mean apparent Q₁₀ across 36 FLUXNET sites in the NHL was 2.71 ± 0.77. Contrary to the results of apparent Q₁₀, the intrinsic Q₁₀ across 36 FLUXNET sites in the NHL were independent of annual mean temperature, and were confined to values around 1.54 ± 0.38. (2) On the grid scale, the apparent Q₁₀ increased with annual mean temperature, with high values in the Western Europe and low values in the Mongolian Plateau. There were no significant changes of intrinsic Q₁₀ in the spatial distribution. While the convergence value 1.01 ± 0.15 on the grid scale was smaller than the site scale. The results in this study indicated that the response of carbon cycle to climate warming in the NHL was less pronounced than suggested by most carbon cycle climate models.

How to cite: Wu, D., Liu, S., Xu, Z., Yang, X., Wu, X., Xu, T., and Shi, H.: Diagnosing temperature sensitivity of respiration at multiple spatial scale in the northern high-latitude regions, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7396, https://doi.org/10.5194/egusphere-egu2020-7396, 2020.

Fluvial ecosystems have a huge potential to affect the global carbon budget. In particular, streams and rivers significantly contribute to carbon dioxide emissions. However, CO2 fluxes from streams to the atmosphere exhibit a marked spatial and temporal variability that is difficult to quantify. Spatio-temporal patterns of biogeochemical fluxes are the result of interconnected unsteady hydrological (e.g. discharge, stream’s length and area, air-water gas exchange velocities) and biochemical conditions. Local estimates of carbon dioxide fluxes from a water body require the simultaneous knowledge of gas exchange coefficients and carbon dioxide concentrations. Different methods (e.g. tracer gas addition, oxygen time series, eddy covariance technique, flux chambers) have been recently developed to obtain point or spatially integrated measures of carbon fluxes under different environmental conditions. Here, we present the results of a flume experiment conducted in the Lunzer Rinnen facility in Lunz am See (Austria). The contribution discusses the dependence of the air-water gas exchange velocities on a set of relevant physical flow properties (i.e. slope, water velocity, discharge). The experimental setup is representative of low slope/velocity streams (flume energy dissipation rate less than 0.01). Gas exchange velocities were evaluated interpreting CO2 observations derived from a standard and an ad-hoc designed flexible-foil CO2 chamber under different deployment modes - anchored and drifting. Our data confirms that higher slopes and flow velocity enhance air-water gas exchange velocities; hence, CO2 outgassing rates in rivers. Moreover, the flexible foil chamber developed for this experiment is shown to be a useful tool for the estimate of local CO2 outgassing rates as it reduces the turbulence induced by the standard chamber on the streamflow. Given the flexibility/simplicity of the floating chamber its use can improve the ability to quantify spatio-temporal patterns of CO2 outgassing in streams.

How to cite: Vingiani, F., Durighetto, N., Botter, G., Klaus, M., and Schelker, J.: Optimizing chambers for stream carbon dioxide evasion estimates; results of a controlled flume experiment., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8727, https://doi.org/10.5194/egusphere-egu2020-8727, 2020.

Coastal wetlands play a pivotal role in regulating both carbon (CO₂) and methane (CH₄) concentrations across the globe. The amount of CO₂ and CH₄ stored and released by these ecosystems is becoming more understood, in particular, within each aspect of the ecosystem. However, how the dynamics of the ecosystem affect CO₂ and CH₄ fluxes on a microclimate level is poorly understood, as well as the overall flux of these Greenhouse Gases (GHGs) within temperate, coastal wetlands. Current research primarily focuses on inland wetlands and coastal wetlands in sub-tropical and tropical regions. Thus, this research aims to investigate CO₂ and CH₄ fluxes within coastal, temperate wetlands, and improve the understanding of how environmental dynamics impact the flux of these critically important Greenhouse Gases (GHGs).

To satisfy this aim, the use of the Eddy-Covariance (EC) method was employed. An EC station was installed on the South-West tip of French Island, Victoria, Australia in late February 2018. The collected data demonstrates the challenges with collecting micro-climate data in an ecosystem with ever-changing environmental conditions. The preliminary results indicate how sensitive flux dynamics are within coastal, temperate wetlands, in particular, to factors such as: tidal and seasonal inundation, seasonal vegetation dynamics, and shifting ecological gradients. The data obtained by the EC station provides a preliminary indication of the complexities of accounting for, and understanding, carbon and methane movement through coastal wetlands in general. The full dataset will aid in improving this understanding, specifically for rare, temperate wetland environments, increasing the knowledge base on how flux dynamics of carbon and methane are affected when collected via open-source methods in dynamic environments.

How to cite: Peck, M., Reef, R., Tapper, N., Daly, E., Burgess, L., and Guyot, A.: Using Eddy-Covariance to understand CO2 and CH4 flux dynamics within a temperate, coastal wetland on French Island, Victoria, Australia, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13974, https://doi.org/10.5194/egusphere-egu2020-13974, 2020.

Net Ecosystem Exchange (NEE) is an important factor regarding the impact of land use changes to the global carbon cycle and thus climate change. The Eddy Covariance technique is the most direct way of measuring CO₂ fluxes, however, it provides spatially discontinuous data from a sparse network of stations. Thus, generating high-resolution spatiotemporal products of carbon fluxes remains a major challenge. Machine Learning (ML) techniques are a promising approach to upscale this information to regional and global scales and can thereby help to produce better NEE datasets for earth-system modelling.

Our approach uses statistical relationships between NEE, vegetation indices and meteorological variables to train a Random Forest model with spatial feature selection to predict daily NEE values at 1 km spatial resolution for the Rur-catchment area (ca. 2400 km²) in western Germany. Data from twelve Eddy stations of different land use types of the TERENO Network Eifel/Lower Rhine Valley between 2010 and 2018 were used to train and test the ML model. Factors potentially affecting NEE such as vegetation indices (NDVI, EVI, LAI) extracted from MODIS products, incoming solar radiation from Heliosat (SARAH-2) and additional meteorological variables from COSMO REA6 reanalysis products served as independent variables, which were further evaluated in regard to their relative importance for NEE prediction.

A novel spatial cross-validation scheme has been applied and compared to a conventional random k-fold cross-validation. This is important for the assessment of the model performance regarding spatial predictions beyond the scope of training locations in contrast to mere data reproduction. Results indicate a lower model performance evaluated with spatial cross-validation and that conventional random cross-validation hence leads to an overoptimistic view of the prediction skills. Nonetheless, the ML approach displayed a feasible way to upscale carbon fluxes to a regional scale utilizing different datasets and produced high-resolution NEE-raster for an entire catchment area.

How to cite: Reitz, O., Graf, A., Schmidt, M., Ketzler, G., and Leuchner, M.: A Machine Learning Approach to Upscale Net Ecosystem Exchange to a Regional Scale: Integration of Eddy Covariance, Remote Sensing and Reanalysis Data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-342, https://doi.org/10.5194/egusphere-egu2020-342, 2020.

There are still large uncertainties in the estimates of net ecosystem exchange of CO₂
(NEE) with atmosphere in Asia, particularly in the boreal and eastern part of temperate Asia. To
understand these uncertainties, we assessed the CarbonTracker Asia (CTA2017) estimates of the
spatial and temporal distributions of NEE through a comparison with FLUXCOM and the global
inversion models from the Copernicus Atmospheric Monitoring Service (CAMS), Monitoring
Atmospheric Composition and Climate (MACC), and Jena CarboScope in Asia, as well as
examining the impact of the nesting approach on the optimized NEE flux during the 2001–2013
period. The long‐term mean carbon uptake is reduced in Asia, which is −0.32 ± 0.22 PgC yr^‐1,
whereas –0.58 ± 0.26 PgC yr^‐1 is shown from CT2017 (CarbonTracker global). The domain
aggregated mean carbon uptake from CTA2017 is found to be lower by 23.8%, 44.8%, and 60.5%
than CAMS, MACC, and Jena CarboScope, respectively. For example, both CTA2017 and CT2017
models captured the interannual variability (IAV) of the NEE flux with a different magnitude and
this leads to divergent annual aggregated results. Differences in the estimated interannual
variability of NEE in response to El Niño–Southern Oscillation (ENSO) may result from
differences in the transport model resolutions. These inverse models’ results have a substantial
difference compared to FLUXCOM, which was found to be –5.54 PgC yr^‐1. On the one hand, we
showed that the large NEE discrepancies between both inversion models and FLUXCOM stem
mostly from the tropical forests. On the other hand, CTA2017 exhibits a slightly better correlation
with FLUXCOM over grass/shrub, fields/woods/savanna, and mixed forest than CT2017. The land
cover inconsistency between CTA2017 and FLUXCOM is therefore one driver of the discrepancy in
the NEE estimates. The diurnal averaged NEE flux between CTA2017 and FLUXCOM exhibits
better agreement during the carbon uptake period than the carbon release period. Both CTA2017
and CT2017 revealed that the overall spatial patterns of the carbon sink and source are similar, but
the magnitude varied with seasons and ecosystem types, which is mainly attributed to differences
in the transport model resolutions. Our findings indicate that substantial inconsistencies in the
inversions and FLUXCOM mainly emerge during the carbon uptake period and over tropical
forests. The main problems are underrepresentation of FLUXCOM NEE estimates by limited eddy
covariance flux measurements, the role of CO₂ emissions from land use change not accounted for
by FLUXCOM, sparseness of surface observations of CO₂ concentrations used by the assimilation
systems, and land cover inconsistency. This suggested that further scrutiny on the FLUXCOM and
inverse estimates is most likely required. Such efforts will reduce inconsistencies across various
NEE estimates over Asia, thus mitigating ecosystem‐driven errors that propagate the global
carbon budget. Moreover, this work also recommends further investigation on how the
changes/updates made in CarbonTracker affect the interannual variability of the aggregate and
spatial pattern of NEE flux in response to the ENSO effect over the region of interest.

How to cite: Takele Kenea, S., Labzovskii, L., Goo, T., Li, S., Oh, Y., and Byun, Y.: Comparison of Regional Simulation of Biospheric CO2 Flux from the Updated Version of CarbonTracker Asia with FLUXCOM and Other Inversions over Asia, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4312, https://doi.org/10.5194/egusphere-egu2020-4312, 2020.

Changes in vegetation productivity and species composition have been used as conventional indicators of land degradation and rehabilitation assessments. The two biophysical parameters vary nonlinearly during land change process with various time lags, which provide, as a whole, a useful framework to diagnose degree of land degradation and rehabilitation. In this study, the net primary productivity (NPP) and water use efficiency (WUE), which are the proxies of vegetation productivity and ecophysiological properties related to species composition, were combined to develop an eco-physiological framework to assess the degree of land degradation in the Northeast-Asia dryland regions (NADR) from 1982 to 2012. Results from long-term trends analysis showed early, middle or late degradation stages occurred in northern grassland and central barren or sparsely vegetated regions, respectively, while the rehabilitation prevailed in eastern croplands and forest, southern, and western grassland. In contrast, short-term trend analysis illustrated the recent rehabilitation in mideastern Mongolia and Loess Plateau, which was unseen in long-term trend analysis. The spatial patterns and temporal changes of land degradation and rehabilitation could be explained partly by either or both natural and anthropogenic factors. Longterm drying and warming might induce land degradation in northern and central NADR, respectively, while the recovery projects and wetting conditions after 2000s promoted the land rehabilitation in Loess Plateau and mid-eastern Mongolia. Here, our NPP–WUE framework may contribute further conceptual development and rapid assessments on land degradation and rehabilitation in wide geographic regions.

How to cite: Kang, S. and Kang, W.: Using net primary productivity and water use efficiency for assessing the degree of land degradation and rehabilitation in the Northeast Asia dryland region, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6351, https://doi.org/10.5194/egusphere-egu2020-6351, 2020.