Amazon Integrated Carbon Analysis / AMAZONICA

Lead Research Organisation: University of Leeds
Department Name: Sch of Geography


Amazonian tropical forests cover the largest forested area globally, constitute the largest reservoir of above-ground organic carbon and are exceptionally species rich. They are under strong human pressure through logging, forest to pasture conversion and exploitation of natural resources. They face a warming climate and a changing atmospheric environment. These factors have the potential to affect significantly the global atmospheric greenhouse gas burden (CO2, CH4), chemistry and climate. A central diagnostic of the state and changes of the land surface is its net carbon balance but currently we do not even know the sign of this balance. Although estimates of fluxes associated with known contributing processes such as deforestation exist, along with evidence for responses of undisturbed rainforests to a changing environment and substantial inter-annual fluctuations, different estimates vary widely. Thus it is very difficult to determine the overall significance of these independent estimates. The uncertainty of the greenhouse gas balances have also made it difficult to assess the realism of future model simulation predictions of the Amazon, some of them predicting alarming fates for the rainforests. Ultimately, the most stringent constraint on surface fluxes of a compound is its accumulation / depletion in overlying air. A major large-scale constraint on the net balance of the Amazon that would resolve the discrepancy in the various carbon flux estimates is therefore an accurate characterization of the 3D carbon cycle related tropospheric greenhouse gas concentration fields above the entire basin. Spatio-temporal concentration patterns can further be translated into surface flux fields using inverse modelling of atmospheric transport. By incorporating the large amount of existing on-ground data on ecosystem functioning from LBA, the RAINFOR network, and the ongoing TROBIT NERC project / and targeted measurements where knowledge gaps remain - into a coupled land-surface land-ecosystem model, we will develop a properly data-grounded model representation of the system. Further, the model will be tested by comparing its predictions with observed atmospheric concentration patterns. In turn this will permit defensible projections of the future of Amazonian vegetation. Human activity climate interactions and the land river link will also for the first time be included in these simulations. Therefore, we propose a project of 5 year duration based on the following five pillars: 1. To obtain large-scale budgets of greenhouse gases top-down, based on atmospheric concentration data and inverse atmospheric transport modelling. 2. To estimate fluxes associated with individual processes bottom-up, based on existing and new remote sensing information (deforestation and fires), tree-by-tree censuses in undisturbed forests, and river carbon measurements. 3. To use existing, and, where missing, targeted new, on-ground measurements of ecosystem functioning and climate response, in order to constrain land ecosystem and river carbon model representation, which will then be combined in an integrated land carbon cycle model. 4. To couple a fully integrated land carbon cycle model (from 3) into a regional climate model and use it (i) to predict current concentrations, and (ii) to calculate the systems response to a changing climate and human population, given a representative range of scenarios. 5. In a final synthesis step we will analyse and combine top-down (1) and bottom-up estimates (2&3) to develop multiple constraint and mutually consistent carbon fluxes over the four-year measurement period. We expect to obtain much better quantification of a major but currently poorly constrained component of the global carbon cycle, based on a new understanding of the underlying processes and their large-scale effect. The project will also provide much improved predictions of the response of the Amazon to future climate change.


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Anderson LO (2010) Remote sensing detection of droughts in Amazonian forest canopies. in The New phytologist

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Aragão LE (2014) Environmental change and the carbon balance of Amazonian forests. in Biological reviews of the Cambridge Philosophical Society

Description First greenhouse balances for the Amazon
A greenhouse gas monitoring network for the Amazon
Controls / sensitivities of greenhouse gas balances of the Amazon to inter-annual variation of climate in a warming environment and strong human development pressure
We found specifically distinct response to organic Amazon Carbon to anomalously hot and dry years which is of concern given that the Amazon region has been warming rapidly over recent decades. These results have been published in Nature (2009, Gatti et al.). We have furthermore recently been able to reconcile the Amazon methane budget estimated based on our - still ongoing - aircraft based lower troposphere greenhouse gas sampling with flux estimates based on on-ground measurements which have shown that trees function as an important conduit for methane to the atmosphere. This work has been published in Nature (2017, Pangala et al.).
Exploitation Route A basis for tropical South American greenhouse gas observation networks
Sectors Communities and Social Services/Policy,Education,Environment

Description Hard to measure but most likely the results have an impact on policy of greenhouse gas balance reporting.
First Year Of Impact 2014
Sector Education,Environment,Transport
Impact Types Societal

Title Height-diameter input data and R-code to fit and assess height-diameter models, from 'Field methods for sampling tree height for tropical forest biomass estimation' in Methods in Ecology and Evolution 
Description 1. Quantifying the relationship between tree diameter and height is a key component of efforts to estimate biomass and carbon stocks in tropical forests. Although substantial site-to-site variation in height-diameter allometries has been documented, the time consuming nature of measuring all tree heights in an inventory plot means that most studies do not include height, or else use generic pan-tropical or regional allometric equations to estimate height. 2. Using a pan-tropical dataset of 73 plots where at least 150 trees had in-field ground-based height measurements, we examined how the number of trees sampled affects the performance of locally-derived height-diameter allometries, and evaluated the performance of different methods for sampling trees for height measurement. 3. Using cross-validation, we found that allometries constructed with just 20 locally measured values could often predict tree height with lower error than regional or climate-based allometries (mean reduction in prediction error = 0.46 m). The predictive performance of locally-derived allometries improved with sample size, but with diminishing returns in performance gains when more than 40 trees were sampled. Estimates of stand-level biomass produced using local allometries to estimate tree height show no over- or under-estimation bias when compared with estimates using measured heights. We evaluated five strategies to sample trees for height measurement, and found that sampling strategies that included measuring the heights of the ten largest diameter trees in a plot outperformed (in terms of resulting in local height-diameter models with low height prediction error) entirely random or diameter size-class stratified approaches. 4. Our results indicate that even remarkably limited sampling of heights can be used to refine height-diameter allometries. We recommend aiming for a conservative threshold of sampling 50 trees per location for height measurement, and including the ten trees with the largest diameter in this sample. 
Type Of Material Database/Collection of data 
Year Produced 2018 
Provided To Others? Yes  
Description Amazon greenhouse gas measurement network 
Organisation National Institute for Space Research Brazil
Country Brazil 
Sector Public 
PI Contribution PDRA staying at University of Leeds for one year (bolsa ciencia sem fronteiras) (Dr. Luana Basso) Several publications about status and changes of the Amazon forests via analysis of lower-troposphere greenhouse gas measurements and atmospheric transport modelling Have been staying at INPE or several months per year as part of a stipend (Bolsa Ciencia sem fronteiras) guest professorship - the bolsa included research money A proposal to CNPQ for sustaining and expanding the Amazon greenhouse gas monitoring network - which will be resubmitted shortly. It would fund new measurements techniques (COS, d13CO2) as well as support to continue existing measurement sites.
Collaborator Contribution The high precision gas analytics laboratory at INPE led by Luciana Gatti analysis flask air for greenhouse gases and trace substances which permit conclusions the source type of greenhouse gas emissions.
Impact Many publications including sofar one publication in Nature (Gatti et al. 2014).
Start Year 2008
Description Land use change, deforestation Amazonia 
Organisation National Institute for Space Research Brazil
Country Brazil 
Sector Public 
PI Contribution Luiz Aragao, Fabien Wagner, INPE, Sao Jose dos Campos, Brazil
Collaborator Contribution Collaboration on theme listed above. Has led to a joint grant (BIO-RED) with aim to use drones / remote sensing to understand / quantify amongst others deforestation and fire interactions.
Impact Several publications - some currently in preparation.
Start Year 2010