Observations and simulations of the terrestrial carbon and water budget are fundamental to understanding biosphere-atmosphere interactions under a changing climate. Multiple processes determine how mass and energy exchange scale from the level of a leaf, to the whole plant, to the ecosystem level, and to the globe. Empirical studies are subject to the level at which observations are collected, and models imply a choice regarding the scale for which predictions are representative. The underlying ecosystem processes are still relatively poorly described by models. Confronting them at multiple temporal and spatial scales with Earth observation data could benefit simulations of the terrestrial carbon and water budget. Recent research has also revealed systematic differences between observations taken at different levels, e.g., regarding exchange fluxes of carbon and water between the biosphere and the atmosphere. This can add to model-data mismatch and limits process understanding.
This session aims at bridging terrestrial ecosystem observations across multiple temporal and spatial scales and from multiple variables. We particularly invite research with a focus on how we can learn from multiple observations of carbon and water exchange fluxes. We encourage contributions with a focus on process modelling, machine learning, satellite monitoring of ecosystem dynamics or with an empirical focus that aims at learning from parallel measurements, captured at the leaf (e.g. gas exchange), tree (e.g. sap flow and tree growth, dendroecology), and/or ecosystem level (eddy covariance towers, UAVs, aircrafts and satellites).
vPICO presentations: Fri, 30 Apr
Increasing water-use efficiency (WUE), the ratio of carbon gain to water loss, is a key mechanism that enhances carbon uptake by terrestrial vegetation under rising atmospheric CO2 (ca). Existing theory and empirical evidence suggest a proportional increase of WUE in response to rising ca as plants maintain a relatively constant ratio between the leaf internal (ci) and ambient (ca) partial CO2 pressure (ci/ca). This has been hypothesized as the main driver of the strengthening of the terrestrial carbon sink over the recent decades. However, proportionality may not characterize CO2 effects on WUE on longer time-scales and the role of climate in modulating these effects is uncertain. We evaluated the long-term WUE responses to ca and climate from 1901-2012 CE by reconstructing intrinsic WUE (iWUE, the ratio of photosynthesis to stomatal conductance) using carbon isotopes in tree rings across temperate forests in the northeastern USA. We further replicated iWUE reconstructions at eight additional sites for the 1992-2012 period-overlapping with the common period of the longest flux-tower record at Harvard Forest to evaluate the spatial coherence of recent iWUE variation across the region. Finally, we compared tree-ring based and modelled ci/ca over the 1901-2012 period to examine whether temporal patterns of ci/ca reconstructions are consistent with predictions based on the optimality principle of balancing the costs of water loss and carbon gain.
We found that iWUE increased steadily from 1901 to 1975 CE but remained constant thereafter despite continuously rising ca. This finding is consistent with a passive physiological response to ca and coincides with a shift to significantly wetter conditions across the region. Tree physiology was driven by summer moisture at multi-decadal time-scales and did not maintain a constant ci/ca in response to rising ca indicating that a point was reached where rising CO2 had a diminishing effect on tree iWUE. The ci/ca derived from tree-ring d13C and the predicted values based on the optimality theory model had similar median values over the 1901-2012 CE period, though with a modest agreement (R2adj = 0.22, p < 0.001). The reconstructed and predicted ci/ca trends were not statistically different from 0 when estimated over the 1901-2012 CE period; however, isotope-based reconstruction of the ci/ca trendshowed distinct multidecadal variation while the predicted ci/ca remained nearly constant. Our results challenge the mechanism, magnitude, and persistence of CO2’s effect on iWUE with significant implications for projections of terrestrial productivity under a changing climate.
How to cite: Belmecheri, S., Maxwell, R. S., Taylor, A. H., Davis, K. J., Guerrieri, R., Moore, D. J. P., and Rayback, S. A.: Predicted and observed multidecadal variations of tree physiological responses to climate and rising CO2: insights from tree-ring carbon isotopes in temperate forests., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10443, https://doi.org/10.5194/egusphere-egu21-10443, 2021.
Plant photosynthetic physiology is a crucial process reflecting plant growth and productivity. The maximum rate of Rubisco carboxylation (Vc,max) and the maximum rate of electron transport (Jmax) of plant leaves are the main limiting factors of photosynthetic capacity and indispensable parameters in ecosystem mechanism models. Accurate simulation of Vc,max and Jmax is vital to improve the prediction precision of vegetation dynamics under the background of climate changes. However, using traditional CO2 response curves to obtain Vc,max and Jmax was time-consuming (about 30 to 60 minutes for each CO2 response curve) and labor-intensive in the field. The rapid photosynthesis-intercellular CO2 concentration (A-Ci) response technique (RACiR) provided a potential convenient way to obtain A-Ci curve in an open gas exchange system, which would greatly improve the measurement efficiency. Nevertheless, whether the RACiR detecting method verified by limited conifers and deciduous species (especially poplar trees) in previous studies could be generally used for other plant functional groups remains unclear.
Therefore, here we selected Viburnum Odoratissimum as the target and used Li-cor 6800 to test the applicability of the rapid RACiR detecting method on evergreen species. As the changes of CO2 ranges and rates are the most important parameters in the method, we set 10 different change ranges of reference [CO2] (i.e., 400-1500 ppm, 400-200-800 ppm, 420-20-620 ppm, 420-20-820 ppm, 420-20-1020 ppm, 420-20-1220 ppm, 420-20-1520 ppm, 420-20-1820 ppm, 450-50-650 ppm, 650-50-650 ppm) to verify the accuracy of traditional CO2 response curves and RACiR and to explore suitable [CO2] change ranges for evergreen species.
Finally, our results showed that Vc,max and Jmax calculated by 10 rapid A-Ci response curves except Jmax calculated by 650-50-650 ppm [CO2] were not significantly different from those calculated by traditional A-Ci response curves. Moreover, 400-200-800 ppm [CO2] compared with the other [CO2] ranges was suitable for V. Odoratissimum. Our results indicated the advantage of RACiR method for evergreen species and implied that preliminary experiments should be carried out according to specific tree species to determine the most appropriate change range of [CO2] when using RACiR to calculate Vc,max and Jmax.
How to cite: Lin, Q., Zhao, C., Liu, Z., and Tian, D.: A test of the rapid measurement of leaf photosynthesis-intercellular CO2 concentration response curve of an evergreen shrub Viburnum Odoratissimum, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15242, https://doi.org/10.5194/egusphere-egu21-15242, 2021.
Forests have climate change mitigation potential since they sequester carbon. However, their carbon sink strength might depend on management. As a result of the balance between CO2 uptake and emission, forest net ecosystem exchange (NEE) reaches optimal values (maximum sink strength) at young stand ages, followed by a gradual NEE decline over many years. Traditionally, this peak of NEE is believed to be concurrent with the peak of primary production (e.g., gross primary production, GPP); however, in theory, this concurrence may potentially vary depending on tree species, site conditions and the patterns of ecosystem respiration (Reco). In this study, we used eddy-covariance (EC)-based CO2 flux measurements from 8 forest sites that are dominated by Norway spruce (Picea abies L.) and built machine learning models to find the optimal age of ecosystem productivity and that of CO2 sequestration. We found that the net CO2 uptake of Norway spruce forests peaked at ages of 30-40 yrs. Surprisingly, this NEE peak did not overlap with the peak of GPP, which appeared later at ages of 60-90 yrs. The mismatch between NEE and GPP was a result of the Reco increase that lagged behind the GPP increase associated with the tree growth at early age. Moreover, we also found that newly planted Norway spruce stands had a high probability (up to 90%) of being a C source in the first year, while, at an age as young as 5 yrs, they were likely to be a sink already. Further, using common climate change scenarios, our model results suggest that net CO2 uptake of Norway spruce forests will increase under the future climate with young stands in the high latitude areas being more beneficial. Overall, the results suggest that forest management practices should consider NEE and forest productivity separately and harvests should be performed only after the optimal ages of both the CO2 sequestration and productivity to gain full ecological and economic benefits.
How to cite: Zhao, J., Lange, H., and Meissner, H.: Mismatch between the optimal ages for ecosystem productivity and net CO2 sequestration in Norway spruce forests, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4257, https://doi.org/10.5194/egusphere-egu21-4257, 2021.
Current climate change scenarios project altered rainfall frequencies which boosts scientific interest in ecosystems' responses to prolonged dry conditions. Under less rainfall, NRWI may play an increasingly important role, Yet, only sparse data are available to assess the role of non-rainfall water input (NRWI) during times of low water availability across ecoregions. Particularly, soil water vapor adsorption has received little attention at field scale. This term is used for the phase change of water from gas to liquid at highly negative matric potential. Under such conditions, water condensates already at relative humidity < 100%. The process has been broadly studied in laboratories but little is known from field experiments, which rarely cover periods longer than one month. Yet, several studies report soil water uptake from the atmosphere during soil surface cooling and in the early mornings. Lysimeters have played a strong role in quantifying these NRWI. Eddy Covariance (EC) measurements, in contrast, are known for their limited data quality under nighttime conditions when a stable boundary layer hinders the turbulent exchange of mass and energy. Therefore, EC has not been tested yet to trace soil adsorption.
In this contribution we adapt a methodology to derive NRWI from lysimeters data and compare them to EC measurements. We focus mainly on adsorption and evaluate the consistency between adsorption estimated with the lysimeters and negative (downward) latent heat (LE) fluxes from EC. We apply the method to a data set that comprises three years of observations from a semi-arid Spanish tree grass ecosystem.
Our results show that during the dry season the gradient in water vapour established between the atmosphere (more humid) and the soil pores (more dry) leads to adsorption by the soil. The observations from both instruments suggest that during the dry season, nightly transport of humidity from the atmosphere towards the ground is driven by soil vapor adsorption. This process occurs each night typically in the second half, but begins increasingly earlier in the evening the dryer the conditions are. The amount of water adsorbed is not directly comparable between EC and the lysimeter readings. With the latter, we quantified a yearly mean uptake between 8.8 mm and 25 mm per year. With the lysimeters we measure additionally 23.1 mm of water that condenses as dew and fog in winter, when EC is impeded by stable conditions. We further analyze EC LE measurements from different sites to evaluate if adsorption can be detected from EC data collected at different locations.
We conclude that the temporal patterns of adsorption estimates from lysimeters match the nighttime negative LE data from the EC technique, although the absolute numbers are uncertain. This might open interesting perspective to fill the knowledge gap of the role of soil water vapor adsorption from the atmosphere at field scale and open the opportunity to broaden the topic across ecosystem research communities. Our results also highlight a potential shortcoming in the interpretation of EC measurements in the case that negative nighttime values, representing physically plausible adsorption, are neglected.
How to cite: Paulus, S., El-Madany, T. S., Orth, R., Nelson, J. A., Hildebrandt, A., Reichstein, M., Carrara, A., Moreno, G., and Migliavacca, M.: Adsorption of water vapor by soil in semi arid ecosystems: reconciling estimates from Lysimeters and Eddy Covariance, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8947, https://doi.org/10.5194/egusphere-egu21-8947, 2021.
Learning about the gas exchange dynamic (evapotranspiration and photosynthesis) relating to precipitation and leaf wetness is important for understanding the forest hydrological and carbon sink function. Precipitation can lead to the change in meteorological factors, depression on leaf gas exchange as well as increasing CO2 emission from soil. However, few studies fully consider the effect of these changes in ecosystem scale.
This study conducted continuous eddy covariance measurement over a temperate evergreen Japanese cypress forest canopy in the Asian monsoon area from 2016 to 2019. By applying the eddy covariance method with the enclosed path gas analyzer, evapotranspiration and CO2 exchange from the canopy to the atmosphere during and after rainfall and snow are precisely monitored. The chamber method is used to simultaneously measure the soil respiration. Especially, to reveal the mechanism of wet canopy gas exchange mechanism, a SVAT multilayer model with two rainfall interception solution (Model 1: free gas exchange with interception only by the adaxial surface; Model 2: no gas exchange with interception by both surfaces) is applied to figure out the interception distribution over the leaf surface.
The annual average daytime latent heat flux (λE) was 70.92 W m-2, 22.31 W m-2, 139.40 W m-2 for wet canopy, snow-loaded canopy and the first 6 hours after wetness ended; the annual average daytime net ecosystem exchange was -1.9 μmol m-2s-1, -0.42 μmol m-2s-1, -7.43 μmol m-2s-1 for wet canopy, snow-loaded canopy and the first 6 hours after wetness ended. Correspondingly, the annual average daytime soil CO2 flux was 2.53 μmol m-2s-1, 0.26 μmol m-2s-1, 2.93 μmol m-2s-1 when the canopy was wet, snow-loaded and during the first 6 hours after wetness ended. The gas exchange at the first 6 hours after wet is more active than that of wet canopy and snow-loaded canopy despite rainfall increased the CO2 emission from the soil. Both measured data and simulation show the wet canopy can process gas exchange. The simulation showed that both interception situations are possible to happen but Model 1 is more suitable for this temperate forest. Meanwhile, the difference between the two models’ performance is smaller during the rainfall than in the wet period after the rainfall, which means interception distribution had a larger impact on wet canopy gas exchange at the later wet period. Future studies should also concern about the mechanism and effect of gas exchange dynamics relating to different precipitation patterns and water sources for latent heat flux.
How to cite: Jiao, L., Kosugi, Y., Sempuku, Y., Sakabe, A., and Chang, T.-W.: Evapotranspiration and CO2 exchange of wet and snow-loaded canopy in an evergreen temperate coniferous forest, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3765, https://doi.org/10.5194/egusphere-egu21-3765, 2021.
Forests play an important role in climate regulation due to carbon sequestration. However, a deeper understanding of forest carbon flux dynamics are often missing due to a lack of information about forest structure and species composition, especially for non-even-aged and mixed forests. In this study, we combined field inventory data of a mixed deciduous forest in Germany with an individual-based forest gap model to investigate daily carbon fluxes and to examine the role of tree size and species composition for the overall stand productivity. Simulation results show that the forest model is capable to reproduce daily eddy covariance measurements (R2 of 0.73 for gross primary productivity and of 0.65 for ecosystem respiration). The simulation results showed that the forest act as a carbon sink with a net uptake of 3.2 tC ha-1 yr-1 (net ecosystem productivity) and an overall gross primary productivity of 18.2 tC ha-1 yr-1. At the study site, medium sized trees (30-60cm) account for the largest share (66%) of the total productivity. Small (0-30cm) and large trees (>60cm) contribute less with 8.5% and 25.5% respectively. Simulation experiments showed, that species composition showed less effect on forest productivity. Stand productivity therefore is highly depended on vertical stand structure and light climate. Hence, it is important to incorporate small scale information’s about forest stand structure into modelling studies to decrease uncertainties of carbon dynamic predictions. Experiments with such a modelling approach might help to investigate large scale mitigation strategies for climate change that takes local forest stand characteristics into account.
How to cite: Holtmann, A., Huth, A., Pohl, F., Rebmann, C., and Fischer, R.: Carbon sequestration in mixed deciduous forests: The importance of mid-storey trees for forest productivity, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7228, https://doi.org/10.5194/egusphere-egu21-7228, 2021.
The vegetation’s response to climate change is a major source of uncertainty in terrestrial biosphere model (TBM) projections. Constraining carbon cycle feedbacks to climate change requires improving our understanding of both the direct plant physiological responses to global change, as well as the role of legacy effects (e.g. reductions in plant growth, damage to the plant’s hydraulic transport system), that drive multi-timescale feedbacks. In particular, the role of these legacy effects - both the timescale and strength of the memory effect - have been largely overlooked in the development of model hypotheses. This is despite the knowledge that plant responses to climatic drivers occur across multiple time scales (seconds to decades), with the impact of climate extremes (e.g. drought) resonating for many years. Using data from 13 eddy covariance sites, covering two rainfall gradients in Australia, in combination with a hierarchical Bayesian model, we characterised the timescales of influence of antecedent drivers on fluxes of net carbon exchange and evapotranspiration. Using our data assimilation approach we were able to partition the influence of ecological memory into both biological and environmental components. Overall, we found that the importance of ecological memory to antecedent conditions increased as water availability declines. Our results therefore underline the importance of capturing legacy effects in TBMs used to project responses in water limited ecosystems.
How to cite: Page, J., De Kauwe, M., and Abramowitz, G.: Exploring the role of lags and legacies in the productivity of Australian ecosystems, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3684, https://doi.org/10.5194/egusphere-egu21-3684, 2021.
Chlorophyll fluorescence (ChlF) takes place in green leaves of the plants during photosynthesis. It has therefore been proposed that ChlF can be used to track the photosynthetic activity of plants and the current possibility to observe sun-induced chlorophyll fluorescence (SIF) via remote sensing provides an unprecedented tool to monitor terrestrial photosynthesis at global scale. However, the relationship between photosynthesis and ChlF is not linear at all scales and is partly controlled by the non-photochemical quenching - which dissipates excess energy as heat. The relationship between the photochemical and fluorescence yields changes when the photochemical quenching is dominating at low irradiance conditions or at high stress conditions. Interpretation of observed SIF is complicated by its dependence on incoming absorbed radiation, observation geometry and radiative transfer of SIF photons within the canopy. To fully exploit remotely sensed SIF to estimate photosynthesis at ecosystem and global scales, it is important to account for these aspects through modelling that include ecosystem processes.
In this work we have implemented a ChlF model into a state-of-the-art land surface model QUantifying Interactions between terrestrial Nutrient CYcles and the climate system (QUINCY) simulating the terrestrial energy, water and biogeochemical cycles of carbon, nitrogen and phosphorus. The simulation of radiative transfer is highly influential for the simulated SIF signal, but the complex solutions of radiative transfer are computationally too heavy, making them impractical approaches at global scale. Therefore, we have investigated different radiative transfer techniques for the SIF signal of varying complexity at site scale in Niwot Ridge, U.S. The most complex solution is based on the mSCOPE and Fluspects model, that explicitly calculates signal transfer. The intermediate solution is based on a two-stream flux approach and the most simple is using a simple fraction for the escape ratio of SIF. Our aim is to assess which solution is most suitable for simulating the SIF signal at different scales and also test different formulations for modelling of non-photochemical quenching.
How to cite: Thum, T., Pacheco-Labrador, J., Magney, T., Migliavacca, M., Quaife, T., and Zaehle, S.: Using different solutions for radiative transfer of solar-induced fluorescence in a land surface model, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8316, https://doi.org/10.5194/egusphere-egu21-8316, 2021.
Over the past decades, land surface models have evolved into advanced tools which comprise detailed process descriptions and interactions at a broad range of scales. One of the challenges in these models is the accurate simulation of plant phenology. It is a key element at the nexus of the simulated hydrological and carbon cycle, where the leaf area index (LAI) plays a major role in flux partitioning, water balance and gross primary production.
In this study, three well-established models are used to simulate the intrinsically coupled fluxes of water, energy and carbon from terrestrial vegetation. ORCHIDEE, ISBA-CC and the LSA-SAF algorithm each have a different approach to represent plant phenology. Whereas ISBA-CC has a fairly simple biomass allocation scheme to represent the phenological cycle, ORCHIDEE relies on a dedicated phenology module, and LSA-SAF is driven by remote-sensed forcing variables, such as LAI. Simulations were performed for a wide range of hydro-climatic biomes and plant functional types at field scale. The simulated fluxes were validated using eddy-covariance measurements, and the simulated phenology was compared to remote-sensed observations.
These models are tools to extrapolate leaf-level processes to global scale climate predictions. The origin of the parameters controlling phenology-induced variability in these models ranges from plant-scale lab experiments to global-scale calibration. The aim of this study is to investigate the key parameters controlling phenology-induced variability in these models.
How to cite: De Pue, J., Barrios, J. M., Liu, L., Ciais, P., Arboleda, A., Hamdi, R., Balzarolo, M., Janssens, I., Maignan, F., and Gellens-Meulenberghs, F.: Evaluation of key parameters controlling phenology-induced variability of surface fluxes in land surface models, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11445, https://doi.org/10.5194/egusphere-egu21-11445, 2021.
In a boreal region, terrestrial vegetation carbon balance is controlled by vegetation phenology, which steers photosynthesis, respiration, and biomass turnover processes. In the absence of a full mechanistic understanding of environmental processes controlling vegetation phenology (i.e. senescence, dormancy, chilling), empirically-based models are often applied. These models are typically based on greenness proxies (obtained from satellite data) with fixed amplitude thresholds (e.g. of 15%, 50%, 90%) to determine timings of different phenological events. Yet, it is not known how well those percentiles correspond with the timings of events such as the green-up and the senescence. Especially in the boreal region, estimating timings of different phenological events across large spatial scales remains challenging due to the lack of sufficient ground validation data representative of both forest tree canopy and forest understory species compositions, which are both observed by a satellite sensor. From the land surface modeling perspective, there is a need to develop methods to improve the mapping of phenological events for prudent prediction of the land vegetation-atmosphere interactions under different future climates. In this study, we developed a new approach for calibrating boreal forest greenness amplitude thresholds which indicate timings of different phenological transitions in satellite data. The new approach to calibrate satellite-based greenness thresholds was demonstrated using boreal Finland as a case study area (60-70 N°). Using the approach, we computed satellite-based phenological events and compared them to ground reference data on temperature from a network of meteorological stations across Finland. We also investigated the effects of using different phenological events or ground reference temperature data on estimated growing season length. Results showed that while the standard greenness amplitude threshold values corresponded fairly well with the growing season start, the autumn phenology was not well captured by the standard greenness amplitude threshold values, which has direct impact on growing season length. Based on our data, boreal conifer forest senescence (default is 90%) corresponds with the timing of greenness amplitude of ~45%, while boreal conifer forest dormancy (default is 15%) corresponds with the timing of reaching greenness amplitude of ~0%. The approach allows flexible application across spatial scales (i.e. point or grid) and different satellite sensors, and may be combined with any land cover product, and it provides a meaningful linking between surface temperature data and seasonal reflectance measured by satellite sensors.
How to cite: Majasalmi, T. and Rautiainen, M.: A new approach for improving representation of boreal forest phenology in land surface models, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-210, https://doi.org/10.5194/egusphere-egu21-210, 2021.
Accurate monitoring of vegetation stress is required for better modelling and forecasting of primary production, in a world where heatwaves and droughts are expected to become increasingly prevalent. Variability in formaldehyde (HCHO) concentrations in the troposphere is dominated by local emissions of short-lived biogenic (BVOC) and pyrogenic volatile organic compounds. BVOCs are emitted by plants in a rapid protective response to abiotic stress, mediated by the energetic status of leaves (the excess of reducing power when photosynthetic light and dark reactions are decoupled, as occurs when stomata close in response to water stress). Emissions also increase exponentially with leaf temperature. New analytical methods for the detection of spatiotemporally contiguous extremes in remote-sensing data are applied here to satellite-derived atmospheric HCHO columns. BVOC emissions are shown to play a central role in the formation of the largest positive HCHO anomalies. Although vegetation stress can be captured by various remotely sensed quantities, spaceborne HCHO emerges as the most consistent recorder of vegetation responses to the largest climate extremes, especially in forested regions.
How to cite: Morfopoulos, C., Müller, J.-F., Stavrakou, T., Bauwens, M., De Smedt, I., Friedlingstein, P., Prentice, I. C., and Regnier, P.: Vegetation responses to climate extremes recorded by remotely sensed atmospheric formaldehyde, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8331, https://doi.org/10.5194/egusphere-egu21-8331, 2021.
The boreal biome is an important component of the global carbon (C) cycle. However, current estimates of its sink-source strength at regional scales and its responses to climate change rely primarily on models and thus remain uncertain. We investigated the C balance over a north Scandinavian boreal region by integrating observations of land-atmosphere fluxes and atmospheric CO2 concentrations at landscape to regional scales. We also placed a special focus to understand the impact of 2018 drought on the region. Flux estimates can be obtained through various techniques such as in-situ flux measurements, eddy covariance (EC) observations, vegetation modelling and inverse modelling of CO2 observations. These techniques are however typically relevant at very different spatial scales ranging from plot scale to country-scale, which makes it difficult to compare them. The -Svartberget site (SVB), an established ICOS (Integrated Carbon Observation System) station in Northern Sweden offers a unique range of observations, from in-situ flux measurements to EC fluxes and tall-tower concentration measurements. Here we used several vegetation models and an atmospheric transport model to connect the different scales for the period 2016-2018. The land-atmosphere carbon fluxes are from four different vegetation models (VPRM, LPJ-GUESS, ORCHIDEE and SiBCASA) and are used in the LUMIA/FLEXPART atmospheric transport model (Lund University Modular Inversion Algorithm) to generate estimates of atmospheric CO2 concentration. We found that the northern Sweden region remained as a C sink for the study period with models differed in sink strength. It was also noticed that the site SVB can be taken as a representative for the northern Sweden region. All models indicate similar but small reductions in the net CO2 uptake for the drought year 2018 in northern Sweden except LPJ-GUESS that reveal limitations which call for further model improvement. Our work highlights the interest of using combined ecosystem,-atmosphere ICOS sites such as SVB in the Scandinavian region and shows that it is a promising way forward to monitor CO2 fluxes at the regional scale.
How to cite: Sathyanadh, A., Monteil, G., Scholze, M., Klosterhalfen, A., Laudon, H., Wu, Z., Gerbig, C., Schaik, E. V., Bastrikov, V., Nilsson, M. B., and Peichl, M.: Reconciling the carbon balance of northern Scandinavia through the integration of observations and modelling, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1816, https://doi.org/10.5194/egusphere-egu21-1816, 2021.
Rapid warming in northern high latitudes during the past two decades may have profound impacts on the structures and functioning of ecosystems. Understanding how ecosystems respond to climatic change is crucial for the prediction of climate-induced changes in plant phenology and productivity. Here we investigate spatial patterns of polynomial trends in ecosystem productivity for northern (> 30 °N) biomes and their relationships with climatic drivers during 2000–2018. Based on a moderate resolution (0.05°) of satellite data and climate observations, we quantify polynomial trend types and change rates of ecosystem productivities using plant phenology index (PPI), a proxy of gross primary productivity (GPP), and a polynomial trend identification scheme (Polytrend). We find the yearly-integrated PPI (PPIINT) shows a high degree of agreement with an OCO-2-based solar‐induced chlorophyll fluorescence GPP product (GOSIF-GPP) for distinct spatial patterns of trend types of ecosystem productivities. The averaged slope for linear trends of GPP is found positive across all the biomes, among which deciduous broadleaved and evergreen needle-leaved forests show the highest and lowest rates respectively. The evergreen needle-leaved forests, low shrub, and permanent wetland show linear trends in PPIINT over more than 50% of the covered area and permanent wetland also shows a large fraction of the area with the quadratic and cubic trends. Spatial patterns of linear trends for growing season sum of temperature, precipitation, and photosynthetic active radiation have been quantified. Based on the partial correlations between PPIINT and climate drivers, we found that there is a consistent shift of dominant drivers from temperature or radiation to precipitation across all the biomes except the permeant wetland when the trend type of ecosystem productivity changes from linear to non-linear. This may imply precipitation changes in recent years may determine the linear or non-linear responses of ecosystem productivity to climate change. Our results highlight the importance of understanding how changes in climatic drivers may affect the overall responses of ecosystems productivity. Our findings will facilitate the sustainable management of ecosystems accounting for the resilience of ecosystem productivity and phenology to future climate change.
How to cite: Zhang, W., Jin, H., Jamali, S., Duan, Z., Wu, M., Sun, H., and Ran, Y.: Elucidating climatic controls of polynomial trends in interannual variations of northern ecosystem productivities, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12919, https://doi.org/10.5194/egusphere-egu21-12919, 2021.
It is very important to obtain regional crop growth conditions efficiently and accurately in the agricultural field. The data assimilation between crop growth model and remote sensing data is a widely used method for obtaining vegetation growth information. This study aims to present a parallel method based on graphic processing unit (GPU) to improve the efficiency of the assimilation between RS data and crop growth model to estimate rice growth parameters. Remote sensing data, Landsat and HJ-1 images were collected and the World Food Studies (WOFOST) crop growth model which has a strong flexibility was employed. To acquire continuous regional crop parameters in temporal-spatial scale, particle swarm optimization (PSO) data assimilation method was used to combine remote sensing images and WOFOST and this process is accompanied by a parallel method based on the Compute Unified Device Architecture (CUDA) platform of NVIDIA GPU. With these methods, we obtained daily rice growth parameters of Zhuzhou City, Hunan, China and compared the efficiency and precision of parallel method and non-parallel method. Results showed that the parallel program has a remarkable speedup (reaching 240 times) compared with the non-parallel program with a similar accuracy. This study indicated that the parallel implementation based on GPU was successful in improving the efficiency of the assimilation between RS data and the WOFOST model and was conducive to obtaining regional crop growth conditions efficiently and accurately.
How to cite: Zhao, B., Liu, M., Wu, J., Liu, X., Liu, M., and Wu, L.: An efficient and accurate method for obtaining regional scale rice growth conditions based on WOFOST model and satellite images, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10688, https://doi.org/10.5194/egusphere-egu21-10688, 2021.
Biophysical parameters such as the Leaf Area Index (LAI), Fraction Absorbed Photosynthetically Active Radiation (FAPAR) and Canopy Water Content (CWC) are key inputs for ecological, meteorological and agricultural applications and models. Moreover, LAI and FAPAR are considered Essential Climate Variables (ECVs) which are feasible for global climate observation. Within this context, there are two main issues to achieve a reliable biophysical variable retrieval: cloud contamination and oversimplified uncertainties from the operational products. We propose a methodology based on a hybrid method which inverts a radiative transfer model (PROSAIL) with artificial neural networks (ANN) to produce 30m resolution continuous time series of biophysical variables (FAPAR, LAI, FVC, CWC, CCC) over large areas. To obtain gap free input reflectance data, we used a cloud optimized fusion algorithm (HISTARFM) combining MODIS and Landsat information. In addition, HISTARFM provides realistic uncertainty estimates along with the fused reflectances. This valuable information allows us to carry out an exhaustive uncertainty analysis considering the aleatoric uncertainty (data error) that needs to be propagated through the ANN, and the epistemic uncertainty (model error). We validate our biophysical retrieval with operational MODIS and Copernicus products. This study is performed over the contiguous US (CONUS) area with Google Earth Engine (GEE). The proposed retrieval methodology combined with the unprecedented GEE computational power allows to obtain high spatial resolution biophysical products and realistic uncertainty estimates to capture the needed spatial detail and adequately monitor croplands and heterogeneous vegetated landscapes at very broad scales.
How to cite: Martínez-Ferrer, L., Moreno-Martínez, Á., Muñoz-Marí, J., Izquierdo-Verdiguier, E., Campos-Taberner, M., García-Haro, J., Maneta, M., Robinson, N., Clinton, N., Kimball, J., Running, S. W., and Camps-Valls, G.: Epistemic and aleatoric uncertainty maps in high resolution biophysical parameter retrieval , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15196, https://doi.org/10.5194/egusphere-egu21-15196, 2021.
Machine learning (ML) provides a powerful set of tools that can improve the accuracy of distribution models by automatically representing underlying ecological relationships empirically captured from large sets of data. However, more recent methodological advances in ML that have been less frequently applied, e.g. in the field of deep learning, may yield additional potential for Distribution Modeling. In this project, we use two ML algorithms, Random Forest (RF) and multi-layer feed-forward artificial neural networks (ANN), to predict the occurrences of Vegetation Types (VT) across Norway. Accurate predictions may support environmental management or the validation of earth system models. The VT data (derived from the AR18x18 data set; n=31 classes) covers the entire spatial scope in 0.9 ha plots on a systematically sampled 18 km grid (n = 1,081 plots, n = 22,154 observations). It was obtained through a field-based survey by a group of trained experts between 2004 and 2014. We use the cloud-based platform "Google Earth Engine" to generate a set of remotely sensed predictor variables based on SENTINEL-2 satellite imagery (i.e. surface reflectance from 12 spectral bands and six vegetation indices). These are then combined with ancillary environmental rasters used previously to model Norwegian VT distribution, e.g. representing climate, land cover, or geological properties (n=55, before one-hot encoding). Preliminary results suggest that in both modeling approaches, the generated SENTINEL-2 variables, particularly the Normalized Difference Vegetation Index (NDVI), have the highest predictive power as measured by permutation importance. The mean overall accuracy using 5-fold cross-validation shows only minor differences between the two methods (approx. 0.45 for ANN vs. 0.44 for RF; the respective F1-scores are 0.35 for ANN and 0.34 for RF; most frequent class baseline accuracy = 0.136). The modeling challenges we currently face include a class imbalance in the VT data set, reconciling the different spatial resolutions of the environmental predictors, and discrepancies in the timing of data acquisition. The next steps in the project will be to incorporate spatial cross-validation into the workflow and to analyze the differences between the ML methods in detail (e.g. regarding the ability to model rare VTs or differences in variable importance). Moreover, we will evaluate the possibility to include additional satellite data sources. This work is a contribution to the Strategic Research Initiative ‘Land Atmosphere Interaction in Cold Environments’ (LATICE) of the University of Oslo.
How to cite: Keetz, L. T., Bryn, A., Horvath, P., Skarpaas, O., Tallaksen, L. M., and Žliobaitė, I.: Using machine learning to model the distribution of Vegetation Types across Norway, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15211, https://doi.org/10.5194/egusphere-egu21-15211, 2021.
The ALtimetry for BIOMass (ALBIOM) project is an ESA-funded Permanent Open Call Project that aims to retrieve forest biomass using Copernicus Sentinel-3 (S3) altimeter data. The overall goal of ALBIOM is to estimate biomass with sufficient accuracy to be able to increase existing satellite data for biomass retrieval, as well as to improve the global mapping and monitoring of this fundamental variable.
The project core tasks consist of 1) an analysis of the sensitivity of altimetry backscatter data on land parameters; 2) the development and validation of a Sentinel-3 altimeter backscatter simulator, including the effect of both topography and vegetation and 3) the development and validation of a machine-learning biomass estimation algorithm.
Here we present a summary of the results obtained from the project. The sensitivity analysis reveals that both the altimetric waveforms and the corresponding Normalised Radar Cross Sections (NRCSs) can be sensitive to the presence of biomass in the order of 100-400 tons/ha, but they are also influenced by topography and water bodies. Different sensitivities with respect to the different frequencies and resolution modes are observed, highlighting non-linear behaviours of the NRCSs. The use of differential NRCSs, defined as the difference among those calculated over two different bandwidths, was demonstrated to be not necessarily more sensitive to vegetation, as it was instead highlighted by previous studies like [Papa et al., 2003].
The tracking window often appears partly or completely misplaced, when the tracking mode is in open-loop mode prescribing a predetermined range, and its size is often not long enough when collecting data over land, especially over regions with complex topography. The length and correct positioning of the tracking window over land represent therefore critical aspects for a study like ALBIOM.
The modelling work has been focused on the development of a merged model approach to simulate altimeter waveforms over vegetated areas. The merging is obtained via the simultaneous use of the modifiedTor Vergata Scattering Model (TOVSM) [Ferrazzoli and Guerriero, 1995, 1996] to simulate the waveform of a flat surface covered by forest vegetation, and the use of the Soil And Vegetation Reflection Simulator (SAVERS) [Pierdicca et al., 2014], originally conceived for GNSS-Reflectometry, and here adapted to the Altimetry system. The simulator developed within ALBIOM shows promising ability to reproduce the general characteristics of the S3 waveforms. The simulations related to forested surfaces present at least two peaks, due to the top of canopy and to the ground, but the presence of topography may introduce other peaks in the waveforms, making the identification of vegetation and topographic effects challenging.
Initial results on the algorithm development using Artificial Neural Networks (ANN) highlight some promising biomass estimates over specific areas (e.g. Central Africa) but also differences in algorithm performances among different regions. The corrected “ice” backscatter coefficient showed the highest sensitivity to biomass, but its values are often invalid over land, which limits the number of meaningful retrievals. The different altimeter tracking mode of Sentinel-3 over different areas of the globe (i.e., open loop and closed loop) could also be responsible for the differences in results.
How to cite: Clarizia, M. P., Pascual, D., Guerriero, L., De Felice-Proia, G., Vittucci, C., Comite, D., Pierdicca, N., Restano, M., and Benveniste, J.: Recent Results from the ALBIOM Project on Biomass Estimates from Sentinel-3 Altimetry Data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12180, https://doi.org/10.5194/egusphere-egu21-12180, 2021.
Plant roots have less water available when soils have low moisture content and, consequently, limit their root-to-leaf water potential gradient to protect their xylem, which reduces H2O and CO2 exchanges with the atmosphere. In vegetation, hydrological and land-surface models, plant responses to reduced available water in the soil have been implemented in various ways depending on data availability, type of ecosystem, and modelling assumptions. Most models use soil water stress functions – commonly known as beta functions – to reduce transpiration and carbon assimilation, by applying a factor that reflects the soil water availability for plants. These functions usually produce reasonably satisfactory results, but rely on the information on soil properties (e.g. wilting point and field capacity) that are not widely available. On a global level, soil information is mediocre, and data uncertainty is compensated by tuning parameters that rarely represent a physiological process. We propose instead the use of a beta function derived from a mass-balance approach focused on the root zone water capacity. This method quantifies the root zone water storage by calculating the accumulated water deficit based on the balance between water influxes and effluxes, and it does not require land-cover or soil information. We assessed how our approach performs compared to those other soil water stress functions. We used global datasets, including WDFE5 and PMLv2, to extract precipitation and evapotranspiration and compute water deficit. For most vegetation types and climates our approach yielded promising results. Worst results were found for some (semi-)arid sites due to the overestimation of the water deficit. We aim to deliver an approach that can be easily applied on global scales.
How to cite: Nóbrega, R. and Prentice, I. C.: Developing a climate-driven root zone water stress function for different climates and ecosystems, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3480, https://doi.org/10.5194/egusphere-egu21-3480, 2021.
Vegetation indices are the most widely used tool in remote sensing and multispectral imaging applications. This paper introduces a nonlinear generalization of the broad family of vegetation indices based on spectral band differences and ratios. The presented indices exploit all higher-order relations of the involved spectral channels, are easy to derive and use, and give some insight on problem complexity. The framework is illustrated to generalize the widely adopted Normalized Difference Vegetation Index (NDVI). Its nonlinear generalization named, kernel NDVI (kNDVI), largely improves performance over NDVI and the recent NIRv in monitoring key vegetation parameters, showing much higher correlation with independent products, such as the MODIS leaf area index (LAI), flux tower gross primary productivity (GPP), and GOME-2 sun-induced fluorescence. The family of indices constitutes a valuable choice for many applications that require spatially explicit and time-resolved analysis of Earth observation data.
Reference: "A Unified Vegetation Index for Quantifying the Terrestrial Biosphere", Gustau Camps-Valls, Manuel Campos-Taberner, Álvaro Moreno-Martı́nez, Sophia Walther, Grégory Duveiller, Alessandro Cescatti, Miguel Mahecha, Jordi Muñoz-Marı́, Francisco Javier Garcı́a-Haro, Luis Guanter, John Gamon, Martin Jung, Markus Reichstein, Steven W. Running. Science Advances, in press, 2021
How to cite: Camps-Valls, G., Campos-Taberner, M., Moreno-Martinez, A., Walther, S., Duveiller, G., Cescatti, A., Mahecha, M., Muñoz-Marí, J., Garcı́a-Haro, F. J., Guanter, L., Gamon, J., Jung, M., Reichstein, M., and Running, S. W.: Generalization of Vegetation Indices for Monitoring the Terrestrial Biosphere, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14263, https://doi.org/10.5194/egusphere-egu21-14263, 2021.
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