BG3.18 | Present and future global vegetation dynamics and carbon stocks from observations and models
Present and future global vegetation dynamics and carbon stocks from observations and models
Convener: Thomas Pugh | Co-conveners: Ana BastosECSECS, Martin Thurner, Aliénor Lavergne, Matthias Forkel
Orals
| Thu, 27 Apr, 14:00–18:00 (CEST)
 
Room N2
Posters on site
| Attendance Tue, 25 Apr, 16:15–18:00 (CEST)
 
Hall A
Posters virtual
| Attendance Tue, 25 Apr, 16:15–18:00 (CEST)
 
vHall BG
Orals |
Thu, 14:00
Tue, 16:15
Tue, 16:15
The terrestrial vegetation carbon balance is controlled not just by photosynthesis, but by respiration, carbon allocation, turnover (comprising litterfall, background mortality and disturbances) and wider vegetation dynamics. Observed, and likely future, changes in vegetation structure and functioning are the result of interactions of these processes with atmospheric carbon dioxide concentration, nutrient availability, climate and human activities. The quantification and assessment of such changes has proven extremely challenging because of a lack of observations at large scales and over the long time periods required to evaluate trends.

This limited observation base gives rise to high uncertainty as to whether the terrestrial vegetation will continue to act as a carbon sink under future environmental changes, or whether increases in autotrophic respiration or carbon turnover might counteract this negative feedback to climate change. For instance, will accelerated background tree mortality or more frequent and more severe disturbance events (e.g. drought, fire, insect outbreaks) turn vegetation into carbon sources? How will shifts in dynamics of plant mortality, establishment and growth influence forest composition?

Uncertainties and/or data gaps in large-scale empirical products of vegetation dynamics, carbon fluxes and stocks may be overcome by extensive collections of field data and new satellite retrievals of forest biomass and other vegetation properties. Such novel datasets may be used to evaluate, develop and parametrize global vegetation models and hence to constrain present and future simulations of vegetation dynamics. Where no observations exist, exploratory modelling can investigate realistic responses and identify necessary measurements. We welcome contributions that make use of observational approaches, vegetation models, or model-data integration techniques to advance understanding of the effects of environmental change on vegetation dynamics, tree mortality and carbon stocks and fluxes at local, regional or global scales and/or at long time scales.

Orals: Thu, 27 Apr | Room N2

Chairpersons: Thomas Pugh, Ana Bastos
14:00–14:05
14:05–14:25
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EGU23-13377
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ECS
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solicited
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On-site presentation
Michael O'Sullivan, Pierre Friedlingstein, and Stephen Sitch

The global land carbon sink has increased in parallel with anthropogenic CO2 emissions over the last several decades, taking up ~25% of these emissions, and acting as a strong negative feedback to mitigate climate change. However, we have a limited ability to confidently attribute past changes. Here we use the suite of Dynamic Global Vegetation Models (DGVMs) from the Global Carbon Budget to develop a process-attribution framework to identify where models agree and, just as importantly, disagree, and thus guide future modelling efforts. We take a holistic approach to answer the following questions:

What are the 1) external drivers (concurrent rises in atmospheric CO2 and nitrogen deposition, climate, land-use and land-cover change (LULCC)), 2) main regions (tropics, extra tropics), and 3) processes (production vs turnover) primarily responsible for the changes in the net land carbon sink?

We find the observed global net land carbon sink is captured by current land models. However, there is a lack of consensus in the partitioning of the sink between vegetation and soil, where models do not even agree on the direction of change in carbon stocks over the past 60 years. This uncertainty is driven by plant productivity, allocation, and turnover response to atmospheric CO2 (and to a smaller extent to LULCC), and the response of soil to LULCC (and to a lesser extent climate). Overall, differences in turnover explain ~70% of model spread in both vegetation and soil carbon changes.

Using a top-down constraint of net land-atmosphere carbon exchange from atmospheric inversions and remote-sensed products of vegetation functioning, we show that DGVMs underestimate carbon uptake in northern latitudes. A large portion of model error can be explained by the simulated LULCC flux. In tropical lands, models likely overestimate net carbon uptake due to too strong CO2 fertilisation, which can, in part, beexplained by too high modelled forest area and carbon densities.

How to cite: O'Sullivan, M., Friedlingstein, P., and Sitch, S.: Attributing trends in the land carbon cycle using process-based DGVMs and global scale observations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13377, https://doi.org/10.5194/egusphere-egu23-13377, 2023.

14:25–14:35
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EGU23-13746
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ECS
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Highlight
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On-site presentation
Auke van der Woude and Wouter Peters and the 2022 Drought Task Force

Recent years have seen repeated record breaking temperatures and high impact events such as floods, storms, and droughts
across Europe, in line with the expected consequences of its +1.5 oC of warming over the past thirty years. In many metrics
the 2018 summer drought has ranked top of the list for severity and impacts, including in reductions of carbon exchange by
forests. Drought conditions thought unprecedented over the past 500 years trigger strong responses in forest, suffering
from leaf-level vapor-pressure deficits and root-level moisture deficits simultaneously. We report here that in the summer of
2022 close to 30% of the European continent was again under severe or exceptional drought, with temperatures exceeding
those even of 2018 and a similarly large size of area affected (3.4 million km2). Although a stationary blocking atmospheric
pressure pattern over the Atlantic was responsible for both droughts, the 2018 event mostly affected northwestern Europe while
the 2022 drought was centered over France. The more southerly centering exposed more drought resilient semi-arid vegetation
which dampened the peak loss of carbon uptake by forests relative to 2018. Observations and models suggest that vapor
pressure deficits rather than lack of soil moisture played a dominant role in reducing photosynthesis in 2022. Nevertheless we
find a similar cumulative reduction of net ecosystem carbon exchange (~50 TgC less uptake) in 2022, with specifically high
impacts in southern France where widespread summertime carbon release by forests, as well as extensive wildfires (emitting
close to 5 TgC) occurred. Our analysis demonstrates a much improved capacity in our community to rapidly quantify drought
impacts from the atmospheric and ecosystem monitoring network. However, strong impacts on eastern European broadleaf
forests suggested from observed near-infrared reflection by vegetation and simulated by terrestrial carbon cycle models can
not be confirmed currently through in-situ observations, signaling an important gap in our capacity to track carbon exchange in
the European terrestrial biosphere.

How to cite: van der Woude, A. and Peters, W. and the 2022 Drought Task Force: Reduced carbon uptake by European forests during the summer drought of 2022, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13746, https://doi.org/10.5194/egusphere-egu23-13746, 2023.

14:35–14:45
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EGU23-14120
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ECS
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On-site presentation
Annemarie Eckes-Shephard, Arthur Argles, Bogdan Brzeziecki, Peter Cox, Martin G. De Kauwe, Adriane Esquivel Muelbert, Rosie A. Fisher, Jürgen Knauer, Charles D. Koven, Aleksi Lehtonen, Marcos Longo, Sebastiaan Luyssaert, Laura Marqués, Jon Moore, Jessica F. Needham, Stefan Olin, Mikko Peltoniemi, Steven Sitch, Benjamin Stocker, Ensheng Weng, Daniel Zuleta, and Thomas Pugh

Forests in Dynamic Global Vegetation Models (DGVMs) have historically been simulated as area-averaged plant functional types in each gridcell instead of representing communities of trees of different sizes and ages (demography). Just as the behaviour of a tree differs according to its ontogeny, so the behaviour of forests is known to differ depending on their demography. Accurately simulating demography is therefore key in order to address questions on afforestation and management strategies, as well as assessments of resilience of forests to disturbances such as drought and fire or diversity changes after a disturbance. Ultimately, demography determines the overall forest biomass in natural forests and is a key arbiter of growth and mortality rates. DGVMs are now able to simulate size and age structure of the trees in forests. 
However, these models have so far not been benchmarked alongside each other.
We evaluate 6 DGVMs (BiomeE, CABLE-POP, FATES, LPJ-GUESS, JULES-RED, ORCHIDEE) against observations on regrowth dynamics as well as natural forests at boreal, temperate and tropical sites. We examine whether the models capture observed regrowth dynamics after disturbance, well-known stand size structure and well-established processes such as self-thinning. We outline the planned route forward towards a standardised international benchmarking framework for demographic DGVMs.

How to cite: Eckes-Shephard, A., Argles, A., Brzeziecki, B., Cox, P., De Kauwe, M. G., Esquivel Muelbert, A., Fisher, R. A., Knauer, J., Koven, C. D., Lehtonen, A., Longo, M., Luyssaert, S., Marqués, L., Moore, J., Needham, J. F., Olin, S., Peltoniemi, M., Sitch, S., Stocker, B., Weng, E., Zuleta, D., and Pugh, T.: Can we model forest demography globally? Benchmarking of state-of-the-art Demographic DGVMs, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14120, https://doi.org/10.5194/egusphere-egu23-14120, 2023.

14:45–14:55
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EGU23-14276
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On-site presentation
Arthur Argles, Eddy Robertson, Anna Harper, James Morison, Georgios Xenakis, Astley Hastings, Jon Mccalmont, Jon Moore, Ian Bateman, Kate Gannon, Richard Betts, Stephen Bathgate, Justin Thomas, Matthew Heard, and Peter Cox

Afforestation and reforestation are necessary for many countries to meet their Nationally Determined Contributions (NDC) and Net Zero targets (Seddon, N. et al 2019). Many countries estimate carbon sequestered using simple land-based transitions (IPCC, 2006) or have more complicated empirical estimations of carbon sequestered through forestry (Thomson, A. et al. 2020). This often leads to differences between NDC inventory submissions and bio-geophysical land surface modelling (Grassi, G. et al 2022).   The increasing representation of plant/tree demography within the latest Dynamic Global Vegetation Models (DGVMs) (Argles, A. et al 2022) and the availability of higher resolution regional climate datasets, provides new scope to evaluate modelled estimates against national inventories and Net Zero policies. As much afforestation is likely to be in the form of managed forests (Bateman, I.J, et al. 2022), the impact of management needs to be represented within models. We evaluate the JULES-RED model (Argles, A. et al 2020) against measured C stocks and fluxes at Harwood Forest, a Sitka spruce plantation in the UK. Planted in 1973, we will show that the inclusion of thinning in JULES-RED contributes to a more realistic representation of the 2018 carbon stocks and tree size-structure. We estimate a non-linear initial age-effect followed by a linear divergence between fixed and varying CO2 simulations, highlighting the need for better understanding of the effect of CO2 fertilisation in even-age stands and plantations.

How to cite: Argles, A., Robertson, E., Harper, A., Morison, J., Xenakis, G., Hastings, A., Mccalmont, J., Moore, J., Bateman, I., Gannon, K., Betts, R., Bathgate, S., Thomas, J., Heard, M., and Cox, P.: Demography DGVMs, Forest Management, Reforestation, and Afforestation: Evaluations of JULES-RED at a Sitka Spruce Plantation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14276, https://doi.org/10.5194/egusphere-egu23-14276, 2023.

14:55–15:05
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EGU23-5251
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ECS
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On-site presentation
Rui Ma, Yuan Zhang, Philippe Ciais, Jingfeng Xiao, and Shunlin Liang

The carbon sequestration capacity of forest ecosystems is closely related to their stand age. However, land surface models (LSMs) usually omit the impact of stand age and calibrate the carbon fluxes from equilibrated states after the model spin-up followed by a perturbation of biomass only resulting from environmental factors such as CO2 and climate. The mismatch between modelled and real forest stand ages will bring large uncertainties in the simulation of carbon stocks and the projection of future carbon sequestration potential. In this study, we implemented and calibrated age-dependent biomass in the Integrated Biosphere Simulator (IBIS) model using forest age and biomass information at 13 representative forests across the world. Specially, to avoid error compensation in model processes, we developed a stepwise optimization framework that integrates remotely sensed gross primary productivity (GPP), leaf area index (LAI), and age-dependent biomass curves into the IBIS model in three calibration steps. The modified adaptive surrogate modelling optimization (MASM) algorithm was applied in our framework to accelerate the parameterization based on surrogate modelling. Compared with the original model, our improved model leads an average error reduction (AER) in GPP, LAI and biomass by 23.7%, 28.6% and 65.7%, respectively, after each calibration step. The new parameters decreased the mean annual net biomass productivity (NBP) during 2000-2020 by 23.1 and 35.7 gC/m2/year in the mixed forests and deciduous broad-leaved forests, respectively, and increased NBP by 36.1-68.7 gC/m2/year in coniferous forests and evergreen broad-leaved forests. Our work highlights the necessity of considering forest age in LSMs, and provides a new framework for better calibrating LSMs under the constraints of multiple satellite products.

How to cite: Ma, R., Zhang, Y., Ciais, P., Xiao, J., and Liang, S.: Stepwise calibration of age-dependent biomass in the Integrated Biosphere Simulator (IBIS v2.6) model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5251, https://doi.org/10.5194/egusphere-egu23-5251, 2023.

15:05–15:15
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EGU23-11629
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On-site presentation
Bibi S. Naz, Christian Poppe, and Harrie-Jan Hendricks Franssen

Changing environmental conditions impact ecosystem dynamics which have important implications for the land–atmosphere carbon and water exchanges. Land surface models coupled with dynamic vegetation models can be used to understand the impact of changes in terrestrial ecosystems on carbon and water cycles and their interactions with climate. However, process-based vegetation models are highly parameterized and have a large number of uncertain parameters, which lead to uncertainties in the model outputs. Here, we use a dynamic vegetation model, the Functionally Assembled Terrestrial Simulator (FATES) coupled to the Community Land Model (CLM v5) to analyze parameter sensitivities and its effects on forest growth, carbon storage and fluxes. We first calibrate allometry parameters to accurately describe plant functional types, representative of most abundant tree species across Europe (such as Norway spruce and European Beach), at three different European sites. Further, an ensemble of model simulations with perturbed parameters were performed and compared against observations to explore uncertainties in simulated vegetation structure and distributions (forest density, tree basal areas and above ground biomass) and their effects on ecosystem functioning (carbon, water and energy fluxes). Comparison with observation shows that the CLM5-FATES model is able to capture the interannual variability well for water and carbon fluxes (such as ET and GPP), but shows larger uncertainties for simulated forest structure (growth, establishment, and mortality). Future work will focus on parameter optimization to further improve model performance in simulating vegetation growth and composition for different vegetation distributions and climate conditions.

How to cite: Naz, B. S., Poppe, C., and Hendricks Franssen, H.-J.: Parameter sensitivity analysis of vegetation and carbon dynamics using land surface model (CLM5-FATES) at European forest sites., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11629, https://doi.org/10.5194/egusphere-egu23-11629, 2023.

15:15–15:25
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EGU23-11134
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On-site presentation
Belinda Medlyn, Laura Williams, Juergen Knauer, Assaf Inbar, Clare Stephens, Rachael Gallagher, Rachael Nolan, Brendan Choat, Matthias Boer, and Ben Smith

Climate change, driven by rising atmospheric CO2 concentrations, is well under way, and we are already starting to see significant shifts in the function and distribution of vegetation as a result. Dynamic vegetation models, the main platform used to predict the likely magnitude, rate and nature of these shifts, were originally rooted in theories of successional dynamics following disturbance. A key question for these models is how well they can capture vegetation responses to climatic change, which includes both press and pulse disturbances. Here we develop a general framework for representing climate-driven successional dynamics in vegetation models. The framework is illustrated with a series of case studies from Australia of vegetation responses to the major global change drivers of rising CO2, warming, drought and fire. The Australian environment, intrinsically characterized by high climate variability, has experienced increasingly challenging climate extremes in recent years and thus provides an excellent testbed for predictive models.

How to cite: Medlyn, B., Williams, L., Knauer, J., Inbar, A., Stephens, C., Gallagher, R., Nolan, R., Choat, B., Boer, M., and Smith, B.: Climate succession: a framework for predicting vegetation dynamics driven by climate change, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11134, https://doi.org/10.5194/egusphere-egu23-11134, 2023.

15:25–15:35
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EGU23-3362
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On-site presentation
Rüdiger Grote, Daniel Nadal-Sala, Peter Petrík, and Nadine Ruehr

The inclusion of tree hydraulic processes into ecosystem models provides opportunities to better capture instantaneous tree drought responses as well as drought legacy effects. Here we are presenting a simple tree hydrologic approach implemented into a process-oriented ecosystem model that simulates instantaneous tree water potential dynamics based on soil water availability and transpiration demand. Reductions in tree water potential are then calculated into a loss of hydraulic functioning leading to sap wood and leaf area losses. This affects within-tree allocation as tissue becomes damaged, and finally may result in tree death if either hydraulic function is impaired beyond repair or tissues for resource acquisition cannot be sufficiently recovered anymore. This approach further provides potential explanations for various medium- and long-term legacy effects of drought, as well as mortality rates in dependence on environmental conditions.

Here we describe the model and evaluate the approach at a number of different sites over several decades, illustrating the species-specific sensitivity to drought stress and the dependency on precipitation pattern, potential soil water storage, and specific tree physiological traits such as xylem vulnerability. The importance of considering stem water storage and depletion as well as the possibility to link this water pool to micro-dendrometer measurements for evaluation is emphasized. Also, we indicate a possible way to integrate the hydraulic failure hypothesis with the theory of carbon starvation and discuss which process may be dominating under specific environmental conditions.

How to cite: Grote, R., Nadal-Sala, D., Petrík, P., and Ruehr, N.: Simulating forest drought legacy-effects from tree hydraulic damage: An integrated modelling approach, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3362, https://doi.org/10.5194/egusphere-egu23-3362, 2023.

15:35–15:45
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EGU23-11316
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On-site presentation
Tracking 21st century anthropogenic and natural carbon fluxes through model-data integration
(withdrawn)
Selma Bultan, Julia E.M.S. Nabel, Kerstin Hartung, Raphael Ganzenmüller, Xu Liang, Sassan Saatchi, and Julia Pongratz
Coffee break
Chairpersons: Aliénor Lavergne, Matthias Forkel
16:15–16:35
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EGU23-5917
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ECS
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solicited
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Highlight
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On-site presentation
Yan Cheng, Stefan Oehmcke, Martin Brandt, Adrian Das, Lisa Rosenthal, Sassan Saatchi, Fabien Wagner, Wim Verbruggen, Anton Vrieling, Claus Beier, and Stephanie Horion

Tree mortality caused by natural disturbances, such as droughts, insects, and wildfires, is a global issue due to increased frequency and severity of extreme weather. California has been a major hotspot of large-scale tree mortality since 2012-2015 drought. Despite many local studies, there is no confident count of dead trees at the state level. Here we mapped all individual dead trees in California using submeter aerial images and Conventional Neural Network (i.e. EfficientUnet architecture). The model accuracy is about 96% and 83% when comparing to visually interpreted samples from aerial photos and in-situ observations, respectively. In total, we found more than 80 million dead trees from NAIP imagery in 2020, which accounts for 2% of trees reported in 2011. About half of the dead trees belongs to California mixed conifer group. North coast and central and southern Serrie Nevada are the most affected regions. Based on the localization and segmentation of every single dead tree, we retrieved mortality traits (i.e. dead tree density, dead crown size, and classification of old or recent death) and identified hotspots that have emerging mortality and high wildfire fuel load. The mortality traits, along with individual dead tree location at the state scale, provides unprecedented detailed information for forest management and improved carbon accounting, helping to understand dynamics and causes of tree mortality in a changing climate.

How to cite: Cheng, Y., Oehmcke, S., Brandt, M., Das, A., Rosenthal, L., Saatchi, S., Wagner, F., Verbruggen, W., Vrieling, A., Beier, C., and Horion, S.: Mapping and characterising tree mortality in California at individual tree level using deep learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5917, https://doi.org/10.5194/egusphere-egu23-5917, 2023.

16:35–16:45
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EGU23-14228
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On-site presentation
Emilie Joetzjer, Sebastien Lafont, Benjamin Loubet, Gabriel Destouet, Adrien Jacotot, Matthias Cuntz, Philippe Ciais, Zheng Fu, Pedro Herig-Coimbra, Jean-Christophe Domec, and Denis Lousteau

In 2022, Europe experienced a widespread severe summer edaphic drought and heat event, as well as abnormally hot autumn temperatures. By contrasting year 2022 with previous ones and using high-frequency Eddy-covariance and meteorological monitoring from 16 ecosystem ICOS forest stations across Europe, we(i) characterized the impact on carbon uptake and evapotranspiration rates, and (ii) disentangled the effects of soil water deficit from effects of atmospheric dryness on the forest fluxes.

Reduction of observed CO2 uptake and evapotranspiration varied across Europe relative to drought intensity. Scandinavian forests were minimally affected in 2022, unlike the 2018 drought. On the contrary, several sites in southern Europe became a carbon source during the 2022 drought. Specifically, in southern France, some sites experienced a reduction of GPP of up to 70% relative to 2015-2021. Using artificial neural networks to analyze the responses of forests' CO2 uptake to soil water content and atmospheric dryness, showed the very high vapor pressure deficit experienced in 2022 was the major driver of the ecosystem responses in southern France this particular year.  

How to cite: Joetzjer, E., Lafont, S., Loubet, B., Destouet, G., Jacotot, A., Cuntz, M., Ciais, P., Fu, Z., Herig-Coimbra, P., Domec, J.-C., and Lousteau, D.: Responses of European forest fluxes to the 2022 heatwave and drought recorded by ICOS Eddy-covariance stations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14228, https://doi.org/10.5194/egusphere-egu23-14228, 2023.

16:45–16:55
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EGU23-11686
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ECS
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On-site presentation
Klaske van Wijngaarden, Benjamin Smith, Belinda Medlyn, Joshua Larsen, and Thomas Pugh

Rising carbon dioxide (CO2) levels can lead to more carbon sequestration in plant biomass, and forests’ natural ability to store carbonin long-lived woody tissue is of particular interest. However, the extent of CO2-fertilization in trees varies across age, species and the availability of other resources. Woody tissue encompasses more than just the tree’s trunk, and a critical knowledge gap lies in the allocation of carbon to the other woody components like branches and twigs. In addition, the flux of woody carbon from the tree to the forest floor (turnover) is more than events of single tree mortality. These fluxes come in the form of litterfall, breakage of whole branches or complete tree mortality. The goal of this study is to quantify biomass allocation patterns and subsequent turnover rates within the woody carbon pool of two contrasting forest FACE experiments, BIFoR FACE in Staffordshire UK, and EucFACE in Sydney, Australia and answer the following questions: how do these allocation patterns determine the potential for carbon sequestration and how do patterns shift with elevated CO2 concentrations?

Terrestrial laser scanning provided the tools to determine canopy structure on a stand scale, and the use of algorithms on the resulting point cloud trees supplied data on the partitioning of biomass among twigs, branches and stems. These results were then used to test general hypotheses about canopy structure and how it changes with elevated CO2. The fluxes from the different wood components were quantified with monthly observations and collections, litter traps to collect the smallest material and transects to make an inventory of larger compartments like branches. The results of these studies will be combined with the canopy structure partitioning fractions to determine if the allocation patterns vary over time and between two contrasting forest types. It is worthwhile to increase our understanding of the dynamics of all woody components within forests and on a global scale beyond single tree mortality to improve the accuracy of predictive carbon budget models.

How to cite: van Wijngaarden, K., Smith, B., Medlyn, B., Larsen, J., and Pugh, T.: Beyond single tree mortality: understanding biomass allocation and turnover of twigs and branches in forest FACE experiments, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11686, https://doi.org/10.5194/egusphere-egu23-11686, 2023.

16:55–17:05
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EGU23-9939
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ECS
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On-site presentation
Nezha Acil, Joseph Wayman, Susanne Suvanto, Cornelius Senf, Jonathan Sadler, and Thomas Pugh

Storms are natural weather events, varying greatly in frequency and intensity across the world. They are characterised by strong winds that can disturb forests via tree defoliation, breakage and uprooting. Despite close monitoring of forest disturbance occurrences in recent years, we still lack information on the contribution of storms in driving forest dynamics worldwide. Here, we build a machine learning classification model to identify wind-related forest disturbances at the global scale. Forest disturbance patches detected between 2002 and 2014 were associated with multiple covariates as potential indicators of wind damage. These covariates include structural metrics inherent to the disturbances, such as patch size, elongation and spatiotemporal clustering, as well as environmental variables describing topography, weather, and soil conditions. We used these data for 20,000 reference patches (10,000 wind and 10,000 non-wind), widely distributed across forest biomes, to train a random forest classifier. Cross-validation with 20,000 other reference patches over 10 runs showed that the model achieved satisfactory performance scores. It yielded omission errors of ca. 20% and commission errors of ca. 5%, mostly associated with harvest, selective logging and biotic outbreaks.  The most important variable was maximum wind speed, followed by patch temporal clustering. Model deployment at the global scale will provide quantitative insights into storm damage biogeography, regime characteristics and relative contributions to global carbon fluxes and forest dynamics.

How to cite: Acil, N., Wayman, J., Suvanto, S., Senf, C., Sadler, J., and Pugh, T.: Mapping storm damage across the world’s forests, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9939, https://doi.org/10.5194/egusphere-egu23-9939, 2023.

17:05–17:15
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EGU23-12187
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On-site presentation
Christine Wessollek and Matthias Forkel

Knowledge about the state of the vegetation at fire occurrence is essential for estimating fire behavior and fire emissions. The spatial distribution and the temporal dynamics of the biomass in various vegetation components, surface litter and woody debris are important controls on fire spread and emissions. So far, no large-scale product exists that combines all these requirements. Maps of canopy height and above ground biomass (AGB) from satellite retrievals provide information on the regional variability of forest biomass, but they have a limited use for fire-related applications because they do not provide information on different fuel components such as biomass in the canopy, wood, grass or litter. Fire-targeted products like the global fuelbed database and the North American Wildland Fuel Database (NAWFD) combine land cover maps with representative values or statistical distributions of fuel properties such as biomass values for trees, shrubs, grass, woody debris and litter. However, those maps do not provide information on the spatial variability of fuel loads within one vegetation type (fuelbed). In addition, information on the temporal dynamics of the fuels is missing. Temporal dynamics of fuels can be approximated by satellite-derived time series of vegetation indices or biophysical parameters. 

Here, we aim to develop an approach that combines the spatial information from remotely-sensed AGB and canopy height maps, the annual temporal information from land cover maps with the high temporal information from leaf area index (LAI) time series to retrieve information on the spatial variability and temporal dynamics of fuel loads. Therefore, we developed a data-model fusion approach that uses the 10-daily LAI product from Sentinel-3 OLCI and Proba-V and land cover maps as input. We apply the approach to a spatial resolution of 333 x 333 m across different study regions in the Amazon, southern Africa, Siberia and the United States. In a first step, the temporal dynamics in tree height is computed from long-term changes in mean LAI and the fractional tree cover by taking observation of canopy height from GEDI as reference. Since canopy height is closely related to AGB through allometric relations between tree height and biomass, the estimated tree height is then used to estimate stem biomass and consecutively branches and leaf biomass, which is calibrated against maps of AGB. The Biomass and Allometry Database is used to calibrate model parameters that regulate the relationships between canopy height, leaf and woody biomass. Estimated temporal changes in tree height directly translate into changes in stem, branches and leaf biomass and hence result in a carbon turnover (e.g. leaf fall, transfer of woody biomass). Based on a simple decomposition model we then compute the dynamics of surface litter and woody debris. The estimates of litter, fine and coarse woody debris correspond well with databases of in situ observations. Our fuel data-model fusion approach allows estimating spatial patterns and temporal dynamics of vegetation and surface carbon pools for the analysis of carbon turnover and pools and as input into fire behaviour and emission models.

How to cite: Wessollek, C. and Forkel, M.: Estimating biomass compartments and surface fuel loads by integrating various satellite products with a data-model fusion approach, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12187, https://doi.org/10.5194/egusphere-egu23-12187, 2023.

17:15–17:25
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EGU23-1466
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On-site presentation
Mathias Neumann, Christoph Pucher, and Hubert Hasenauer

Deadwood is a prominent part of forest ecosystems. It is important for multiple forest functions, including habitat, water storage, nutrient cycling and carbon storage. Deadwood volume is now routinely measured in many large-scale inventory programs, including the national forest inventory or inventory of nationalparks or nature reserves. Policy changes, increasing climatic stress, more frequent and more intense disturbances and/or abandoning forest management will likely lead to increasing deadwood volumes in the next decades. We will explore here the available knowledge on decay of deadwood in Central Europe, focussing on carbon (C) and nitrogen (N) content and release.

Decay classes can be assessed in the field and provide a potent proxy for deadwood density, pore volume and C:N ratios, based on pilot studies in Eastern Austria. Using C:N as proxy for decomposability suggest that decomposition is non-linear and that advanced decay stages have faster decomposition. Our results highlight that smaller sized deadwood (2-10 cm diameter) can store substantial amounts of C and N. Additional field work, non-destructive methods and modelling can link decay stages with time since death and allow estimating mass and volume loss by decomposing organisms.

For understanding the dynamics between standing and lying deadwood we will need models that are able to predict the disintegration of trees, considering loss of less stable stem parts, like bark or sapwood. We will need continuous deadwood monitoring as well as more complex models to understand the main pathways for deadwood decay, the effects of climate on decomposition and the role of management in deadwood accumulation and dynamics. Complementing available data with new methods and models will allow us to quantify the capacity of managed and unmanaged forests for deadwood, the carbon sequestration in deadwood and its persistance in the future.

References:

Gschwantner T (2019) Totholz-Zunahme ausschließlich positiv? BFW Praxisinformation 20:

Müller-Using S, Bartsch N (2009) Decay dynamic of coarse and fine woody debris of a beech (Fagus sylvatica L.) forest in Central Germany. European Journal of Forest Research 128, 287–296. doi:10.1007/s10342-009-0264-8.

Neumann M, Hasenauer H (2021) Thinning Response and Potential Basal Area — A Case Study in a Mixed Sub‐Humid Low‐Elevation Oak‐Hornbeam Forest. Forests 12:. https://doi.org/10.3390/f12101354

Neumann M, Hasenauer H (in review) A simple concept for estimating deadwood carbon in forests. Carbon Management

Oettel J, Lapin K, Kindermann G, et al (2020) Patterns and drivers of deadwood volume and composition in different forest types of the Austrian natural forest reserves. For Ecol Manage 463:118016. https://doi.org/10.1016/j.foreco.2020.118016

Pietsch SA, Hasenauer H (2006) Evaluating the self-initialization procedure for large-scale ecosystem models. Glob Chang Biol 12:1658–1669. https://doi.org/10.1111/j.1365-2486.2006.01211.x

How to cite: Neumann, M., Pucher, C., and Hasenauer, H.: Decay of deadwood carbon – current knowledge and opportunities for modelling, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1466, https://doi.org/10.5194/egusphere-egu23-1466, 2023.

17:25–17:35
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EGU23-11573
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ECS
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On-site presentation
Adam Pellegrini, Sarah Hobbie, and Peter Reich

Altering management of disturbances in savanna-grasslands, a biome spanning >20 million km2 and under extensive human management, can offset a substantial proportion of anthropogenic carbon emissions. Quantifying the long-term carbon accrual and storage can be challenging because much of the change occurs belowground and it requires an understanding of ecological processes in a diverse set of environmental conditions. Here, we focus on the role of altered fire regimes given ca. 3 million km2 of savanna-grasslands burn annually. Combining repeated measurements of total ecosystem carbon stocks in a 58-yearlong fire-manipulation experiment with a global dataset, we demonstrate that while fire management leads to large changes in carbon sequestration, its magnitude and persistence over time is highly variable. In the experiment, fire exclusion resulted in large increases in carbon in soil organic matter via increased aboveground biomass inputs and faster decomposition. Increased burning frequency decreased carbon but this was partly offset by increased inputs from fine root turnover. However, repeated measurements illustrated both the magnitude of the sequestration within a plot and the differences across treatments changed—and even reversed—through time due to changes in tree inputs following a disease outbreak. The global dataset revealed that in wet sites, carbon sequestered in trees is most important but in drier sites the sequestration in soil organic matter is most important. Within soils, much of the variability in carbon accrual was due to variability in how much woody biomass inputs changed, with drier sites experiencing large changes. Consequently, much of the variability in carbon accrual is due to variability in the amount of woody biomass inputs and decomposition into soils. Because trees can be highly sensitive to changing disturbance regimes in drylands we propose that using fire management to sequester carbon in soils can be highly uncertain and unstable through time.

How to cite: Pellegrini, A., Hobbie, S., and Reich, P.: Nature-based solutions in savanna-grasslands can be both uncertain and unstable, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11573, https://doi.org/10.5194/egusphere-egu23-11573, 2023.

17:35–17:45
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EGU23-13458
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On-site presentation
Wouter Dorigo, Ruxandra Zotta, Robin van der Schalie, Leander Moesinger, and Richard de Jeu

Vegetation optical depth (VOD), derived from space-borne microwave radiometers, is a parameter that quantifies the attenuation of surface microwave emissions by the overlaying vegetation. VOD depends on several factors, such as the water content and density of the vegetation, and the specifications of the satellites and wavelengths used. VOD has been used in various applications such as phenology analysis, drought and biomass monitoring, and the estimation of the likelihood of fire occurrence, leaf moisture, and gross primary productivity. Most of these applications require consistent long-term measurements, while single sensor timeseries are typically too short.  

To bridge this gap, the global, long-term Vegetation Optical Depth Climate Archive (VODCA)[1] combines VOD retrievals from multiple passive microwave sensors spanning from 1987 to 2019, derived through the Land Parameter Retrieval Model (LPRM)[2]. VODCA harmonises these retrievals from various satellites and periods for differences in microwave frequencies, measurement incidence angles, orbit characteristics, radiometric quality, and spatial footprints. VODCA v1 provides separate VOD products in different spectral bands, namely the Ku-band (period 1987–2017), X-band (1997–2018), and C-band (2002–2018). Despite the relatively short time since its publication, VODCA v1 has already been taken up by many researchers and climate reports as an indicator of vegetation condition [3].  

Here, we introduce a new and improved version of the VODCA dataset. VODCA v2 includes a multi-frequency product called VODCA CXKu, obtained by merging the C-, X-, and Ku-band observations. This product, which spans over 30 years of observations (1987-2022), is suitable for canopy dynamics monitoring and, due to the merging process, exhibits less random error than the individual frequency datasets. VODCA v2 also includes an L-band product obtained by merging LPRM-derived VOD from SMOS (Soil Moisture and Ocean Salinity) and SMAP (Soil Moisture Active Passive) missions, covering the period 2010-2022. We explore the properties of the new products in comparison to independent vegetation datasets, and present new insights in ecosystem dynamics facilitated by VODCA. 

[1] Moesinger, L., Dorigo, W., de Jeu, R., van der Schalie, R., Scanlon, T., Teubner, I., and Forkel, M.: The global long-term microwave Vegetation Optical Depth Climate Archive (VODCA), Earth Syst. Sci. Data, 12, 177–196, https://doi.org/10.5194/essd-12-177-2020, 2020. 

[2] Van der Schalie, R., de Jeu, R.A., Kerr, Y.H., Wigneron, J.P., Rodríguez-Fernández, N.J., Al-Yaari, A., Parinussa, R.M., Mecklenburg, S. and Drusch, M., 2017. The merging of radiative transfer based surface soil moisture data from SMOS and AMSR-E. Remote Sensing of Environment189, pp.180-193. 

[3] Dorigo, W. , Moesinger, L., van der Schalie, R., Zotta, R. M., Scanlon, T. and de Jeu, R. A. M. (2021), [State of the Climate in 2020] Long-term monitoring of vegetation state through passive microwave satellites. Bulletin of the American Meteorological Society, 102(8), S110-S112. doi:10.1175/2021BAMSStateoftheClimate.1. 

How to cite: Dorigo, W., Zotta, R., van der Schalie, R., Moesinger, L., and de Jeu, R.: VODCA v2: An updated long-term vegetation optical depth dataset for ecosystem monitoring, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13458, https://doi.org/10.5194/egusphere-egu23-13458, 2023.

17:45–17:55
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EGU23-13184
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ECS
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Highlight
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On-site presentation
Caspar Roebroek, Gregory Duveiller, Sonia Seneviratne, Edouard Davin, and Alessandro Cescatti

Global forests play a key role in the global carbon cycle and are a cornerstone in international policy-making to prevent global warming from exceeding 1.5°C and reach carbon neutrality. In line with recent climate science, the actions taken in the current decade are crucial for obtaining the goals laid out in international agreements. One forest-based strategy with high short-term climate benefits is the return of global forests to their carbon storage potential, by ceasing forest management, but the ecological boundaries of increasing biomass in existing forests remain poorly quantified. Recent studies preferentially focus on the mitigation potential of reforestation, without explicitly accounting for the carbon dynamics in existing forests, thus providing an incomplete evaluation of the possible expansion of the forest carbon stock. Here we integrate satellite remote sensing estimates of current forest biomass with a machine learning framework to show that existing global forests could increase their above-ground biomass by 44.1 PgC at most (an increase of 16% over current levels) if allowed to reach their natural equilibrium state. In total, the maximum carbon storage potential in this hypothetical scenario equates to just about 4 years of global anthropogenic CO2 emissions (at the 2019 rate). This maximum potential would require the complete stop of forest management and harvesting for decades. Therefore, without first strongly reducing CO2 emissions, this strategy holds low climate change mitigation potential. This urges to view storing additional carbon in existing forests as an effective strategy to offset carbon emission from sectors that will be hard to decarbonise, rather than as a tool to compensate all business-as-usual emissions.

How to cite: Roebroek, C., Duveiller, G., Seneviratne, S., Davin, E., and Cescatti, A.: Releasing global forests from management: how much more carbon could be stored?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13184, https://doi.org/10.5194/egusphere-egu23-13184, 2023.

17:55–18:00

Posters on site: Tue, 25 Apr, 16:15–18:00 | Hall A

Chairpersons: Martin Thurner, Aliénor Lavergne
A.261
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EGU23-1032
Bertrand Bonan, Bertrand Decharme, Christine Delire, and Jean-Christophe Calvet

Land Data Assimilation Systems (LDASs) aim to monitor the evolution of land surface variables (LSVs). They were initially designed with a focus on soil moisture and temperature. Since then, the focus has increasingly expanded towards vegetation monitoring with the assimilation of Leaf Area Index (LAI) or other vegetation-related data. LDAS-Monde, the offline land data assimilation system (LDAS) developed by Météo-France’s research centre (CNRM), has been a pioneer in that domain, as it can assimilate LAI while updating directly soil moisture especially in the root-zone area. This approach has also demonstrated several times its ability to improve the simulation of Gross Primary Production (GPP) by the ISBA (Interactions Soil-Biosphere-Atmosphere) land surface model, included in LDAS-Monde.

In this work, the impact of assimilating LAI on GPP and net ecosystem exchange (NEE) is assessed with two versions of the ISBA land surface model: the classical ISBA A-gs involved currently in LDAS-Monde and the more sophisticated ISBA-Carbon Cycle (ISBA-CC) version. ISBA A-gs simulates the assimilation of carbon by photosynthesis following the work of Goudriaan et al. (1985) and Jacobs et al. (1996) while the ecosystem respiration is emulated with a simple Q10 formulation (Rivalland et al., 2005). ISBA-CC uses the same approach for photosynthesis but by modelling all biomass reservoirs (such as roots or wood) can calculate more accurately autotrophic respiration. ISBA-CC also involves a heterotrophic respiration calculated by a soil carbon module. The comparison and the impact assessments are carried out at site levels using the PLUMBER2 datasets and at larger spatial scales using a new version of FLUXCOM GPP and NEE products.

How to cite: Bonan, B., Decharme, B., Delire, C., and Calvet, J.-C.: Impact of LAI assimilation by LDAS-Monde on modelled photosynthesis and respiration in the ISBA land surface model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1032, https://doi.org/10.5194/egusphere-egu23-1032, 2023.

A.262
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EGU23-2758
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ECS
Thanh Le

There are broad impacts of vegetation changes on water cycle, regional climate, carbon budget and ecosystems productivity. Hence, further understanding of the drivers of future vegetation changes are crucial. The El Niño–Southern Oscillation (ENSO) is a main mode of interannual climate variability and is expected to influence vegetation at a global scale. Nevertheless, little is known about the causal impacts of ENSO on future vegetation cover under warming environment and changes in land use. Here, we investigated the links between ENSO and vegetation using leaf area index (LAI) data over the 2015-2100 period from Coupled Modeling Intercomparison Project Phase 6 (CMIP6). Our results show that the vegetated areas influenced by ENSO are projected to increase by 3% and 1% of total land areas in the 21st century of the scenarios SSP2-4.5 and SSP5-8.5, respectively. These results suggest that the impacts of ENSO on global vegetation may increase in the future. While uncertainty remains in several regions for the causal link between ENSO and vegetation changes, this study provides insights on the future impacts of ENSO on global vegetation dynamics.

How to cite: Le, T.: Increased impact of the El Niño–Southern Oscillation on global vegetation under future warming environment, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2758, https://doi.org/10.5194/egusphere-egu23-2758, 2023.

A.263
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EGU23-3016
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ECS
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Yichen Zhang, Shilong Piao, Yan Sun, Brendan M. Rogers, Xiangyi Li, Xu Lian, Zhihua Liu, Anping Chen, and Josep Peñuelas

Climatic warming has greatly increased vegetation productivity in the extratropical Northern Hemisphere since the 1980s, but how long this positive relationship will continue remains unknown. Here we show changes in the effect of warming on Northern Hemisphere summer gross primary productivity for 2001–2100 using Earth system model outputs. The correlation between summer gross primary productivity and temperature decreases in temperate and boreal regions by the late twenty-first century, generally becoming significantly negative before 2070 in regions <60° N, though Arctic gross primary productivity continues to increase with further summer warming. The time when the correlation becomes negative is generally later than the time when summer temperature exceeds the optimal temperature for vegetation productivity, suggesting partial mitigation of the negative vegetation impacts of future warming with photosynthetic thermal acclimation. Our findings indicate that vegetation productivity could be impaired by climate change in the twenty-first century, which could negatively impact the global land carbon sink.

How to cite: Zhang, Y., Piao, S., Sun, Y., Rogers, B. M., Li, X., Lian, X., Liu, Z., Chen, A., and Peñuelas, J.: Future reversal of warming-enhanced vegetation productivity in the Northern Hemisphere, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3016, https://doi.org/10.5194/egusphere-egu23-3016, 2023.

A.264
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EGU23-4189
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ECS
Wenjia Cai and Iain Colin Prentice

Seasonal variations in  atmospheric carbon dioxide (CO2) reflect the responses of terrestrial ecosystems to environmental variations. Accurate estimation of the spatial distribution of global CO2 fluxes would improve our ability to close the global carbon budget and predict the effect of climate change on the terrestrial carbon sink. A large increase in the seasonal cycle amplitude (SCA) of CO2 in northern high latitudes since the 1950s has been observed. However current vegetation models generally fail to reproduce the magnitude of this increase, while the underlying mechanisms are still debated. Using an eco-evolutionary optimality model (the P model) we simulated global gridded atmosphere-ecosystem CO2 exchange from the 1950s onwards and converted the results to atmospheric CO2 concentration variations using the global chemistry-transport model TM5. Our modelled global CO2 flux and derived carbon sink are comparable with that derived from TRENDY models as used in the Global Carbon Project’s annual assessment. The P model could capture the trend of SCA in northern high latitudes, as shown both at remote monitoring stations and in aircraft campaigns. We evaluated the contribution of potential drivers in SCA trends, including atmospheric CO2, climate, land use change and agricultural practices. Our analysis demonstrated that a parameter-sparse model can capture the observed CO2 SCA trend and provide useful insights for carbon cycle dynamics.

How to cite: Cai, W. and Prentice, I. C.: Modelling the trends and drivers of the CO2 seasonal cycle amplitude in northern high latitudes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4189, https://doi.org/10.5194/egusphere-egu23-4189, 2023.

A.265
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EGU23-8073
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ECS
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Highlight
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Qiao-Lin Gu, Phillip Papastephanou, and Anja Rammig

The trend of hotter and drier climate and the increasing intensity and duration of extreme drought events is affecting African tropical rainforests and could induce higher mortality of tropical evergreen trees. The result may be the forest structure shifting towards a more open type. Such a structural shift would lead to smaller carbon stocks in the rainforests and potentially reduced photosynthetic carbon uptake. To understand the relationships between drought and carbon dynamics in the African rainforests, the newly developed version of the dynamic vegetation model LPJ–GUESS including plant hydraulic features was employed to simulate vegetation growth over the past decades. The results showed that the net carbon flux going into the vegetation decreased during the drought events. Especially in the late 1990s, the net carbon flux decreased to the level before 1960 and remained low until ten years after the consecutive extreme drought. This decrease in the net carbon flux was dominated by the decrease in net primary production rather than the instantaneous loss from tree mortality. Simultaneously, the carbon stock in the rainforests continued to grow but the growth decelerated during and after the drought. To conclude, the drought affected the African rainforests primarily by reducing vegetation productivity rather than causing instantaneous mortality. Such long-term effects are of vital importance since they could easily increase the vulnerability of the forests to other disturbances such as wind throw or pathogens, thus provoking the forest mortality further in the future. This implies that future field experiments should draw special attention to the potential long-term or delayed effects of climate change on trees.

How to cite: Gu, Q.-L., Papastephanou, P., and Rammig, A.: Simulating the impacts of drought on the carbon dynamics in African rainforests, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8073, https://doi.org/10.5194/egusphere-egu23-8073, 2023.

A.266
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EGU23-2763
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ECS
Lila Siegfried, Pascal Vittoz, and Eric Verrecchia

The future of wetland forests in temperate regions interests many nature conservationists. Indeed, due to a mosaic of environments influenced by the availability and the dynamics of water, these ecosystems have a specific biological diversity and a potential to store carbon. Sadly, wetland forests are threatened and increasingly rare in Europe, due to human pressure on land, in a context of biodiversity loss and climate change. An important part of these ecosystems is now only semi-wild, and the dynamics of their organic matter is still poorly known. This study focuses on the riparian forests in nature reserves along the lake of Neuchâtel (Switzerland). These forests colonised newly available areas, emerged from the lake after a 3-m drop of the water level at the end of the 19th century.

The aim of the project is to better understand the dynamics of different types of wetland forests along a gradient of water level. Vegetation communities, organic matter and soil were studying in four forest types: wet black alder forests, humid alluvial white alder forests, ash forests and summer dry pine forests. Measured factors include water table fluctuations, soil litter inputs, organic matter decomposition rate, soil respiration, and soil organic matter characteristics, in relation to vegetation type. The first results show a difference in the organic matter pathways between the four habitats. Water dynamics appears to be one of the main drivers in the fate of organic matter. Wet black alder forests have low soil respiration, but a high rate of organic matter decomposition, as contrarily to the drier ash forests. This project will contribute to improve the conservation of these threatened ecosystems.

How to cite: Siegfried, L., Vittoz, P., and Verrecchia, E.: From vegetation to soil: Organic matter dynamics in young temperate riparian forests, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2763, https://doi.org/10.5194/egusphere-egu23-2763, 2023.

A.267
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EGU23-14417
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ECS
Doroteja Bitunjac, Maša Zorana Ostrogović Sever, Katarína Merganičová, and Hrvoje Marjanović

Soil organic carbon (SOC) is the largest terrestrial carbon (C) pool with a vital role in the global C cycle. Considering it is one of five mandatory pools in national greenhouse gas (GHG) inventory reports, it is important to accurately assess SOC stocks and changes. Measuring SOC stock changes is challenging due to costly and destructive soil sampling, the high spatial variability of soil carbon and the slow process of soil C accumulation or loss. In order to reduce the uncertainty of SOC stock changes estimates, repeated national soil inventory is required. In the absence of repeated national inventories, SOC stock changes could be estimated using a modelling approach. The aim of our research is to calibrate and validate the terrestrial ecosystem model Biome-BGCMuSo for the simulation of SOC stock changes in lowland forests as an additional tool for use in national GHG inventory reporting.

In our work, we combine different data sources (chronosequence experiment and eddy-covariance (EC) site) and different data types and frequencies (long-term C stocks and high-frequency C fluxes) of various ecosystem variables (aboveground live wood C (AGC), forest floor C, SOC, Net Ecosystem Exchange (NEE), Gross Primary Productivity (GPP) and Ecosystem Respiration (RECO)). The model calibration was performed using the daily values of main ecosystem C fluxes from the EC tower in the Jastrebarsko pedunculate oak forest and annual data on AGC, forest floor C and SOC from permanent measurement plots in the footprint of the EC tower. For model validation, we used annual data on C stocks in the aboveground live wood biomass, forest floor and mineral soil in the top 30 cm from seven stands of pedunculate oak chronosequence in Jastrebarsko forest. All analyses were performed in R software.  

Measured SOC showed no age trend and high between-stand spatial variability of 19-30%, while for modelled SOC between-stand spatial variability was only 6% and a negative age trend was observed. The calibration using solely daily NEE fluxes resulted in a better overall agreement of model output with observations for this variable, but at the cost of the reduction in intra-seasonal variability. The calibration using aboveground and soil C stocks improved the agreement for these variables but caused greater discrepancies between measured and modelled daily NEE fluxes. The model validation showed a good agreement for C stock change in aboveground live wood biomass and mineral soil for most of the chronosequence stands, but with high disagreement between measured and modelled C stocks in the forest floor in general. Obtained results emphasize the importance of multi-variable calibration and validation to improve model accuracy and robustness across all simulated pools, fluxes and processes.

How to cite: Bitunjac, D., Ostrogović Sever, M. Z., Merganičová, K., and Marjanović, H.: Modelling forest SOC change – calibration and validation challenges, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14417, https://doi.org/10.5194/egusphere-egu23-14417, 2023.

A.268
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EGU23-13177
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ECS
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Guangqin Song, Jing Wang, Michael Liddell, Patricia Morellato, Calvin K.F. Lee, Dedi Yang, Bruna Alberton, Matteo Detto, Xuanlong Ma, Yingyi Zhao, Henry C.H. Yeung, Hongsheng Zhang, Michael Ng, Bruce W. Nelson, Alfredo Huete, and Jin Wu

Tropical leaf phenology signals leaf-on/off status and exhibits strong variability from individual tree crowns to forest ecosystems, which importantly regulates carbon and water fluxes. The availability of daily PlanetScope data with high spatial resolution offers a new chance to monitor phenology variability at both the fine scale and the ecosystem scale across pan-tropics. However, a scalable method for tropical leaf phenology monitoring from PlanetScope with clear biophysical meaning still needs to be developed. To advance tropical leaf phenology monitoring, we developed an index-guided, ecologically constrained autoencoder (IG-ECAE) method to automatically generate a deciduousness metric (percentage of upper tree canopies with leaf-off status within an image pixel) from PlanetScope. The IG-ECAE includes three steps: (1) extracting the initial reflectance spectra of leafy/leafless canopies based on their spectral indices characteristics; (2) training an autoencoder deep learning method with the guidance of derived reflectance spectra and additional ecological constraints to refine the reflectance spectra; and (3) estimating the relative abundance of leafless canopies (or deciduousness) per PlanetScope image pixel with the integration of refined spectra reflectance and linear spectral unmixing method. To test the IG-ECAE method, we compared the PlanetScope-derived deciduousness to the corresponding measures derived from WorldView-2 (n = 9 sites) and local phenocams (n = 9 sites) at 16 tropical forest sites spanning multiple continents and a large precipitation gradient (1470-2819 mm year-1). Our results show that PlanetScope-derived deciduousness agrees: 1) with WorldView-2-derived deciduousness at the patch level (90 m × 90 m) with r2 = 0.89 across all sites; and 2) with phenocam-derived deciduousness to quantify ecosystem-scale seasonality with r2 ranging from 0.62 to 0.96. These results demonstrate that IG-ECAE can accurately characterize the wide variability in deciduousness across scales from pixels to forest ecosystems, and from a single date to the entire annual cycle, indicating the feasibility of tracking the large-scale phenological patterns and responses of tropical forests to climate change with high-resolution satellites.

How to cite: Song, G., Wang, J., Liddell, M., Morellato, P., Lee, C. K. F., Yang, D., Alberton, B., Detto, M., Ma, X., Zhao, Y., Yeung, H. C. H., Zhang, H., Ng, M., Nelson, B. W., Huete, A., and Wu, J.: Tropical leaf phenology characterization by using an ecologically-constrained deep learning model with PlanetScope satellites, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13177, https://doi.org/10.5194/egusphere-egu23-13177, 2023.

A.269
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EGU23-12028
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ECS
Julia Kelly, Stefan H. Doerr, Claudio D'Onofrio, Thomas Holst, Irene Lehner, Anders Lindroth, Cristina Santín, Margarida Soares, and Natascha Kljun

Wildfires have been effectively suppressed in the managed boreal forests of Fennoscandia for over a century. However, recent extremely hot and dry summers have highlighted the vulnerability of these forests to increasing wildfire frequency as a result of climate change. The carbon stored by managed forests plays a key role in many national climate mitigation strategies and more data is needed to assess how forest management shapes the carbon balance of these forests, including those recovering from wildfire. We established two eddy covariance towers in the area burnt by the Ljusdal fire, which affected over 9000 ha during the extreme 2018 wildfire season in Sweden. The two towers measured CO2 fluxes during the first four growing seasons after the fire at two Pinus sylvestris stands with contrasting fire impacts and forest management schemes. At one site, a mature stand had survived low severity fire but was then salvage-logged and reseeded (6 months after the fire), whilst the other site represented a young stand that was killed by high severity fire and replanted with Pinus sylvestris seedlings (2 years after the fire). After the fire, both sites were net CO2 sources at the annual scale. However, the site with dead young trees and replanted seedlings showed a faster recovery towards becoming a CO2 sink, with days of net CO2 uptake during the peak of the growing season three years after the fire. Preliminary results suggest that similar magnitudes of carbon were emitted as CO2 in the first 4 years after the fire compared to the carbon emitted during the fire itself, underlining the importance of monitoring forest CO2 fluxes and the impacts of management decisions during the initial post-fire years.

How to cite: Kelly, J., H. Doerr, S., D'Onofrio, C., Holst, T., Lehner, I., Lindroth, A., Santín, C., Soares, M., and Kljun, N.: The two towers: CO2 fluxes after wildfire in managed Swedish boreal forest stands, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12028, https://doi.org/10.5194/egusphere-egu23-12028, 2023.

A.270
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EGU23-9322
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ECS
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Siyuan Wang, Hui Yang, Sujan Koirala, Matthias Forkel, Markus Reichstein, and Nuno Carvalhais

Different disturbance events lead to varied response patterns in terrestrial biomass, while regulating the terrestrial ecosystems' short- and long-term carbon cycle dynamics. Quantifying the disturbance regimes is essential for understanding and reducing the uncertainty of climate factors affecting vegetation mortality and its responses on the carbon cycle. Based on model-based exercise, we built a machine learning model to predict three disturbance regime parameters, μ (probability scale), α (clustering degree), β (intensity slope) using the spatial pattern of emergent biomass. Here, relying on the model relationships, we utilize Earth observation data of high-resolution biomass, the GlobBiomass with a spatial resolution of 25m, to infer regional disturbance regime statistics.

We first conduct a series of comparison exercises to test whether the current framework is robust for retrieving realistic disturbance regimes, including varying factors controlling: (i) the impacts of disturbance shape setting; (ii) the recovery pattern after perturbation; and (iii) the downsampling process for the biomass simulation. It was found that different model settings mainly lead to the inconsistency of texture features, and the disturbance regime prediction accuracy was maintained with different shape settings,  or even higher after downsampling with a mean of 0.98 for Nash-Sutcliffe model efficiency coefficient (NSE).

Given the robustness in the framework for retrieving disturbance regimes statistics from modelled biomass results we contrasted these spatial patterns with local GlobBiomass patterns across the world. The comparison between model and observations show data aggregation needs that provide information on aspects of scale and spatial resolution required for simulations. Finally, we provide a sparse, but globally distributed, characterization of disturbance regimes based on remote sensing observations and discuss potential climate links, and mechanisms behind, the spatially continuous distribution of disturbance regime.

Given the novelty of the assessment of disturbance regimes with high-resolution biomass data, our study provides opportunities to evaluate and improve the representation of disturbance dynamics in dynamic vegetation and Earth System models.

How to cite: Wang, S., Yang, H., Koirala, S., Forkel, M., Reichstein, M., and Carvalhais, N.: Assessing Global Disturbance Regimes based on High-resolution biomass observations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9322, https://doi.org/10.5194/egusphere-egu23-9322, 2023.

A.271
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EGU23-6679
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ECS
Yahai Zhang, Sujan Koirala, Aizhong Ye, Hui Yang, and Nuno Carvalhais

        Carbon use efficiency (CUE) of vegetation is a property emerging from physiological processes, a key parameter to determine vegetation growth, which ultimately reflects the relative potential of terrestrial ecosystems to store atmospheric carbon in biomass. Large uncertainties were found in global CUE estimated by remote sensing data models or process-based models, especially across plant functional types (PFTs) and regarding seasonal variations. This study explores the specific effects of climate and vegetation state on CUE based on a model-data integration approach by analyzing outputs from a terrestrial ecosystem model driven with local meteorological variables and constrained by in situ observations of vegetation biomass, as well as carbon and water fluxes from eddy covariance measurements. We leverage on a modular model-data-integration framework – SINDBAD – that allows for an integrated model selection, parameterization and evaluation approach based on in situ observations. In addition, various other simulations based on global observations and global modeling approaches, including MODIS, GLASS, Trendy, MsTMIP, and CMIP6, are further explored with the aim of examining spatial and temporal patterns of CUE to better understand the modelled biological and climate controls of CUE.

        The range of global annual CUE values for the 50 models between 2001 and 2010 is 0.3083 to 0.5920. Our study shows that adding constraints on modelled vegetation biomass, in general, brings slight deterioration to the simulation of carbon fluxes but significantly changes the patterns of CUE. In general, biomass constraints decrease the emerging CUE estimates, even in non-forested sites in the Northern Hemisphere. The vegetation state constraints increase 14.42% of sites distributed at 0.2-0.4 median CUE yearly values and decrease 14.92% of sites distributed at 0.4-0.6. Constraints on vegetation carbon stocks result in changes in modelled autotrophic respiration, which change more significantly across sites than gross primary productivity. The information provided by the vegetation state variables results generally in lower wood and slow litter turnover rate, and an increased sensitivity of CUE to moisture and adaptation to temperature. Ultimately, here we provide model-based results for investigating the mechanisms behind the spatial and temporal variability of CUE, potentially contributing to a better quantified CUE variation globally.

How to cite: Zhang, Y., Koirala, S., Ye, A., Yang, H., and Carvalhais, N.: Diagnosing spatial and temporal variations in the response of carbon use efficiency to vegetation states and climate across terrestrial ecosystems, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6679, https://doi.org/10.5194/egusphere-egu23-6679, 2023.

A.272
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EGU23-2436
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ECS
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Rahul Kashyap and Jayanarayanan Kuttippurath

Abstract

In recent decades, vegetation cover and productivity exhibit substantial variability around the globe. Both climate drivers and human induced changes significantly impact vegetation, which is not adequately explored for the Indian region. Here, we use satellite and reanalysis data to unravel the phenomena driving the greening/browning of India. The analysis shows that India is largely greening (62.5%) and marginally browning (14.1%). This greening is predominantly by the contribution of croplands (72.3%), led by Zaid (70.1%), followed by Kharif (59.5%) and Rabi (54.8%) agricultural seasons of India. Among the climate drivers, soil moisture (44%) has the major influence followed by temperature (32%) and precipitation (23%). Greening is predominantly observed in the north west due to positive influence of both increased soil moisture and decreased temperature, termed as the ‘moisture induced greening’. The Indo-Gangetic plain (IGP) is the most extensively irrigated region in the world, which results in greening. Drying due to warming and increased soil heat flux, termed as the ‘warming induced moisture stress’, suppresses Gross Primary Productivity (GPP) (i.e., browning), mainly in the croplands of southern India and eastern IGP. Granger causality test reveals that warming induced moisture stress is taking place at a lag of 1 month in these areas. We also examined the Carbon Use Efficiency (CUE), a metric to define the ability of plants to sequester carbon from atmosphere, of vegetation in different regions. Water availability as soil moisture (32%) and precipitation (26%) has strong positive influence on CUE, establishing its importance in driving vegetation carbon dynamics (VCD) for cropland dominated India. Our analysis shows enhanced productivity (greening) in regions of lower (< 0.3) CUE in western India (moisture induced greening) and IGP (irrigation induced agricultural boom). However, a reduced productivity (browning) is found in the northeast, east (deforestation and extreme events) and south (warming induced moisture stress) India in regions of higher (> 0.6) CUE, which is a concern. Effective management of croplands and conservation of forest resources is the key to achieve sustainable development goals (SDGs). Furthermore, it serves as a tool to counter the challenges of food security, global warming and climate change.

Keywords: Greening; Browning; Vegetation carbon dynamics (VCD); Soil Moisture; Food security; Climate Change

How to cite: Kashyap, R. and Kuttippurath, J.: Unveiling the Mechanisms and Implications of Vegetation Carbon Dynamics for the last two decades in India, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2436, https://doi.org/10.5194/egusphere-egu23-2436, 2023.

A.273
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EGU23-7261
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ECS
Theertha Kariyathan, Ana Bastos, Markus Reichstein, Wouter Peters, and Julia Marshall

Atmospheric CO2 measurements from background sites across the Northern Hemisphere have been used to study the changes in the carbon uptake period (CUP) i.e., when plants are able to grow and assimilate carbon from the atmosphere. Previous studies that use CO2 dry air mole fraction data diagnosed CUP using zero-crossing dates (ZCD, when the detrended seasonal cycle switches from positive to negative sign and vice versa). The CUP can also be estimated using the first derivative of the CO2 seasonal cycle. In previous work we show that applying the first derivative method to an ensemble of fitted CO2 mole fraction curves provides better constraints to the CUP by considering year-to-year uncertainty in estimates across the ensemble members. We call this the ensemble of first derivative method (EFD method).  In addition to curve fitting uncertainty and year-to-year flux variability, atmospheric transport might explain a significant portion of observed CO2 variations at various surface stations, affecting the interpretation of the CUP and similar metrics.

Hence, in this study we examine how atmospheric transport of fluxes, and spatial variations in the start and ending dates of carbon uptake, smooth the signal in atmospheric CO2 and affect the CUP estimates when using remote background observation sites to interpret actual fluxes. We use a synthetic data experiment where idealized NEE fluxes are transported forward (with atmospheric transport model TM3 (Heimann and Körner, 2003) and fixed year meteorology) and the atmospheric concentrations are sampled at the location of the measurement sites. A fixed year from the Jena CarboScope Inversion (Rödenbeck et al., 2003, doi:10.17871/CarboScope-sEXTocNEET_v2022) was used to generate an idealized NEE flux time series with no interannual variability in the CUP at any given pixel. Then, we prescribe changes in the CUP of NEE flux to Northern Hemisphere land pixels with clear seasonal cycles and evaluate the accuracy of the ZCD and EFD methods in capturing this known change from CUP in the surface fluxes, from the resulting CO2 mixing ratio obtained from the forward transport run.

We find that CUP changes estimated by both EFD and ZCD based on CO2 measurements are smaller by a factor of 2-4 than the perturbations applied in NEE space, and that the EFD method is more sensitive to surface CUP changes than the ZCD. This " dampening" factor varies across sites, depending on the mixing of spatially varying NEE signals with differing CUP timing which integrate to a reduced atmospheric expression of CUP. We further analyse the contribution of 1) atmospheric transport by comparing simulation that uses inter annually varying meteorology 2) different TransCom-3 regions to CUP variations by selectively manipulating NEE flux from a region and repeating the experiment.

References:

Heimann, H. and Körner, S. (2003). The global atmospheric tracer model tm3. Technical Reports- Max-Planck-Institut f ̈ur Biogeochemie 5, 5:131.

Rödenbeck, C., Houweling, S., Gloor, M., and Heimann, M. (2003). Co2 flux history 1982–2001 inferred from atmospheric data using a global inversion of atmospheric transport. Atmospheric Chemistry and Physics, 3(6):1919–1964. doi: 10.17871/CarboScope-sEXTocNEET_v2022.

How to cite: Kariyathan, T., Bastos, A., Reichstein, M., Peters, W., and Marshall, J.: Synthetic data experiment to test the accuracy of methods estimating carbon uptake period from atmospheric CO2 time-series, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7261, https://doi.org/10.5194/egusphere-egu23-7261, 2023.

Posters virtual: Tue, 25 Apr, 16:15–18:00 | vHall BG

Chairperson: Thomas Pugh
vBG.4
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EGU23-2315
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ECS
Yuyi Wang and xi chen

Identifying vegetation changes and the associated driving forces provides a valuable reference for developing ecological restoration strategies. However, it remains a challenge to disentangle the impacts of climate, vegetation, and human interference impacts on vegetation changes. In this study, the temporal variations of the Normalized Difference Vegetation Index (NDVI) during 2000 ~ 2015 in space were used to identify the greening (restoration) and browning (degradation) areas in southwest China. The Random Forest (RF) approach was applied to distinguish the main driving forces of the greening and browning areas. Results showed that the RF approach can be effectively used to learn the complex non–linear interactions between vegetation change, local climate, and human interferences. Vegetation greening was prominent in 85.90% of the study area, while 5.59% of the area still experienced significant vegetation degradation. Population pressure was an important factor to alter the sign of long-term vegetation trends. The greening trends are mainly observed in the high elevation areas with low population density (e.g., population density lower than 180 people/km2 and altitudeabove 1000m), which are attributed to both artificial reforestation measures and climate warming. In contrast, the browning trend was concentrated in the low elevation areas with high and temporally intensified population density due to urbanization with a high population density (over 1000 people/km2) and an increased rate (over 20 people/km2 per year). The results of this study strengthen our understanding of the complex convolutions among climate, human activities, and vegetation in southwest China.

How to cite: Wang, Y. and chen, X.: The use of random forest to identify climate and human interference on vegetation NDVI changes in Southwest China, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2315, https://doi.org/10.5194/egusphere-egu23-2315, 2023.

vBG.5
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EGU23-9795
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ECS
Paul Calle-Bermeo, Andrea Urgilez-Clavijo, and David Rivas-Tabares

Agricultural remote sensing provides valuable information on various characteristics of vegetation states and processes that cannot be simultaneously surveyed over large areas of the territory or with high frequency. Grasslands are recognized worldwide as strategic vegetation in ecosystems and especially in southern Ecuador in South America, due to their extension and importance in the configuration of the social fabric. This area presents an interesting area for studying grassland dynamics since there is a complex mosaic of natural, semi-natural and managed grasslands in which ranchers, indigenous and farmers share a bounding and fragmented landscape. This work aims to validate the use of specific remote sensing tools for monitoring grassland dynamics and in the improvement in identifying the general management rules under fragmented landscape features. To do this, indices and metrics of historical series of satellite images are used, this facilitates the development of biomass production evaluation procedures with greater spatiotemporal precision. The vegetation indices coupled with advanced window timing record sensitivity points allow correlating a set of interested plots reducing the uncertainty in similar biogeographic conditions such as soil properties, slope and vegetation management. The preliminary results show that a reduced number of sensitive monitoring points is suitable for stakeholders in monitoring local and regional areas for estimates of grassland impacts in terms of high/low production, drought, and excess rains. Thus, this work supports the advancement towards optimizations in monitoring vegetation dynamics.  Besides, it develops a common methodological framework that can be used as a reference for the monitoring of pastures in mountainous fragmented landscapes and is useful for parameterising/calibrating vegetation and hydrological models.

Acknowledgements
The authors acknowledge the support of Master in Climate Change, Agriculture and Sustainable Rural Development (MACCARD), co-funded by the Erasmus + Programme of the European Union. The authors also acknowledge support from European Union NextGenerationEU and RD 289/2021 and the support of Project No. PGC2018-093854-B-I00 of the Ministerio de Ciencia, Innovación y Universidades de España.

References

  • David Rivas-Tabares, Ana M. Tarquis, Ángel de Miguel, Anne Gobin, Bárbara Willaarts. Enhancing LULC scenarios impact assessment in hydrological dynamics using participatory mapping protocols in semiarid regions. Sci. Total Environ., 803, 149906, 2022. https://doi.org/10.1016/j.scitotenv.2021.149906
  • Rivas-Tabares, A. de Miguel, B. Willarts and A.M. Tarquis. Self-organising map of soil properties in the context of hydrological modeling. Applied Mathematical Modelling, 88,175-189, 2020. https://doi.org/10.1016/j.apm.2020.06.044
  • Rivas-Tabares, D. A., Saa-Requejo, A., Martín-Sotoca, J. J., & Tarquis, A. M. (2021). Multiscaling NDVI Series Analysis of Rainfed Cereal in Central Spain. Remote Sensing13(4), 568.
  • Urgilez‐Clavijo, A., de la Riva, J., Rivas‐Tabares, D. A., & Tarquis, A. M. (2021). Linking deforestation patterns to soil types: A multifractal approach. European Journal of Soil Science72(2), 635-655.

How to cite: Calle-Bermeo, P., Urgilez-Clavijo, A., and Rivas-Tabares, D.: Agricultural remote sensing boosting advances in pasture monitoring: Case of Tarqui river basin, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9795, https://doi.org/10.5194/egusphere-egu23-9795, 2023.

vBG.6
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EGU23-10546
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ECS
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Hayato Abe, Tomonori Kume, and Ayumi Katayama

Recently, an overpopulation of large herbivorous mammals has been observed in the Northern Hemisphere. Their overgrazing can degrade eatable understory vegetation, enhance establishment of unpalatable species, and increase overstory trees mortality. It is still unknown how these vegetation changes affect carbon (C) stock capacity in forest ecosystems. We aimed to evaluate the effects of forest vegetation changes on C stocks due to over 20-years grazing of Japanese sika deer in southern Kyushu, Japan. The study area was divided into less grazed forests (LG) and heavily grazed forests (HG). The HG was further divided into three treatments: forests with no understory vegetation (HG-nu), forests with dominance of unpalatable shrubs (HG-ud), and forests with gap areas created by overstory trees mortality (HG-gap). Four 100-400 m2 survey plots were established for each treatment. We evaluated differences in vegetation structure (e.g., stem density) and C stocks, including overstory trees (height <2 m), understory vegetation, leaf litter, fine woody debris (FWD), and coarse woody debris (CWD) between the LG and HG treatments. We also separated overstory trees into eatable and unpalatable trees for sika deer diet. Stem density in LG, HG-nu, HG-ud, and HG-gap were 2548 ± 1813, 1544 ± 1145, 15619 ± 5326, and 63 ± 75 stems ha-1, respectively. Lower stem density was found in HG-nu compared to that in LG resulting from low density of small-diameter eatable trees (i.e., individuals with the diameter of < 10 cm). Instead, higher stem density was found in HG-ud compared to that in LG resulting from high density of small-diameter unpalatable trees. C stocks of overstory trees in HG-nu (12526.1 ± 5367.0 g C m-2) was comparable to that in LG (10771.4 ± 3351.3 g C m-2). C stocks of overstory trees in HG-ud (5118.2 ± 5656.3 g C m-2) and HG-gap (2028.9 ± 2343.4 g C m-2) were 50% and 81% lower compared to that in LG, respectively. C stocks of overstory trees in these treatments was dominated by few large-diameter trees, and differences in the stem density driven by small-diameter trees of each treatment did not contribute to the difference of C stocks. Understory vegetation C stocks in three HG treatments (i.e., HG-nu, HG-ud, and HG-gap) were >96% lower compared to LG. C stocks of leaf litter and FWD in HGs was also 36-68% lower compared to LG. The understory vegetation biomass was positively correlated with the total amount of leaf litter and FWD, suggesting that understory contributes to litter production and holdings. C stocks of CWD in HG-nu and HG-ud were comparable to LG whereas HG-gap showed 6.8-fold higher C stocks of CWD than that of LG due to the death of large-diameter trees that occurred gap formation. Our results highlight that deer-induced vegetation changes decrease in C stocks due to the mortality of large diameter trees and the loss of understory vegetation, and changes contribution of their components.

How to cite: Abe, H., Kume, T., and Katayama, A.: Carbon stocks in cool temperate forests with different stand structure due to deer overgrazing, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10546, https://doi.org/10.5194/egusphere-egu23-10546, 2023.

vBG.7
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EGU23-6395
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ECS
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K Narender Reddy, Somnath Baidya Roy, Bimal K Bhattacharya, and G Venkateswara Varma

The land surface is an essential component of the Earth's System that interacts with the atmosphere via mass, momentum, and energy exchange. Croplands are one of the most common types of land use. Therefore, a comprehensive understanding of land-atmosphere interactions requires understanding the biogeochemical and biogeophysical processes and interactions in agroecosystems.

Earth System Models (ESMs) can simulate the complex physical, chemical, and biological processes within and between the earth's land, atmosphere, ocean, and other spheres. Croplands have not received adequate attention in ESMs and were previously represented as grasslands. Land components in ESMs, such as the Community Land Model version 5 (CLM5) in the Community Earth System Model (CESM), have recently begun to include specific crops. The addition of crops to land models improved the simulation of energy, carbon, and water fluxes from land. CLM5 can represent a wide range of crops all over the world. However, there are significant errors in crop representation for the Indian region, including cropping areas, cropping season, irrigation, and crop characteristics. CLM5's estimated annual yield of wheat and rice has significant biases compared to UN-FAO estimates due to differences in growing seasons. Furthermore, observational data on the phenology of spring wheat and rice are scarce in the Indian region. As a result, crop growth model simulations in the Indian region suffer from poor calibration and validation.

India is the world's second-largest producer of wheat and rice. Rice and wheat croplands cover more than 70 million ha combined. The current study aims to improve CLM5's representation of spring wheat and rice crops. This is accomplished by incorporating a crop planting window based on observations, wheat and rice cultivated area and irrigated cropland maps from district-level data. To further improve the crop models, we digitized historical crop phenology data and used them for model calibration and validation.

Correcting the spring wheat and rice growing seasons in CLM5 over India has greatly improved crop phenology, yield, and irrigation pattern. As a result, the energy, carbon, and water fluxes are better estimated than the default CLM5 model. If the improved CLM5 is incorporated into the CESM, this can also improve the simulation of atmospheric phenomena.

 

How to cite: Reddy, K. N., Baidya Roy, S., Bhattacharya, B. K., and Varma, G. V.: Improving crop dynamics in the CLM5 land surface model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6395, https://doi.org/10.5194/egusphere-egu23-6395, 2023.

vBG.8
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EGU23-3764
Yue He, Jinzhi Ding, Tsechoe Dorji, Tao Wang, Juan Li, and Pete Smith

Soil heterotrophic respiration (Rh) refers to the flux of CO2 released from soil to atmosphere as a result of organic matter decomposition by soil microbes and fauna. As one of the major fluxes in the global carbon cycle, large uncertainties still exist in the estimation of global Rh, which further limits our current understanding of carbon accumulation in soils. Here, we applied a Random Forest algorithm to create a global dataset of soil Rh, by linking 761 field observations with both abiotic and biotic predictors. We estimated that global Rh was 48.8 ± 0.9 Pg C yr-1 for 1982-2018, which was 16% less than the ensemble mean (58.6 ± 9.9 Pg C yr-1) of 16 terrestrial ecosystem models. By integrating our observational Rh with independent soil carbon stock datasets, we obtained a global mean soil carbon turnover time of 38.3 ± 11 yr. Using observation-based turnover times as a constraint, we found that terrestrial ecosystem models simulated faster carbon turnovers, leading to a 30% (74 Pg C) underestimation of terrestrial ecosystem carbon accumulation for the past century, which was especially pronounced at high latitudes. This underestimation is equivalent to 45% of the total carbon emissions (164 Pg C) caused by global land use change at the same time. Our analyses highlight the need to constrain ecosystem models using observation-based and locally adapted Rh values to obtain reliable projections of the carbon sink capacity of terrestrial ecosystems.

How to cite: He, Y., Ding, J., Dorji, T., Wang, T., Li, J., and Smith, P.: Observation-based global soil heterotrophic respiration indicates underestimated turnover and sequestration of soil carbon by terrestrial ecosystem models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3764, https://doi.org/10.5194/egusphere-egu23-3764, 2023.