scholarly journals Mapping Amazon Forest Productivity by Fusing GEDI Lidar Waveforms with an Individual-Based Forest Model

2021 ◽  
Vol 13 (22) ◽  
pp. 4540
Author(s):  
Luise Bauer ◽  
Nikolai Knapp ◽  
Rico Fischer
Keyword(s):  
Data Fusion ◽  
Carbon Balance ◽  
Carbon Dynamics ◽  
Forest Productivity ◽  
Canopy Height ◽  
Amazon Rainforest ◽  
Model Data ◽  
Old Growth ◽  
Forest Model ◽  
Global Carbon

The Amazon rainforest plays an important role in the global carbon cycle. However, due to its structural complexity, current estimates of its carbon dynamics are very imprecise. The aim of this study was to determine the forest productivity and carbon balance of the Amazon, particularly considering the role of canopy height complexity. Recent satellite missions have measured canopy height variability in great detail over large areas. Forest models are able to transform these measurements into carbon dynamics. For this purpose, about 110 million lidar waveforms from NASA’s GEDI mission (footprint diameters of ~25 m each) were analyzed over the entire Amazon ecoregion and then integrated into the forest model FORMIND. With this model–data fusion, we found that the total gross primary productivity (GPP) of the Amazon rainforest was 11.4 Pg C a−1 (average: 21.1 Mg C ha−1 a−1) with lowest values in the Arc of Deforestation region. For old-growth forests, the GPP varied between 15 and 45 Mg C ha−1 a−1. At the same time, we found a correlation between the canopy height complexity and GPP of old-growth forests. Forest productivity was found to be higher (between 25 and 45 Mg C ha−1 a−1) when canopy height complexity was low and lower (10–25 Mg C ha−1 a−1) when canopy height complexity was high. Furthermore, the net ecosystem exchange (NEE) of the Amazon rainforest was determined. The total carbon balance of the Amazon ecoregion was found to be −0.1 Pg C a−1, with the highest values in the Amazon Basin between both the Rio Negro and Solimões rivers. This model–data fusion reassessed the carbon uptake of the Amazon rainforest based on the latest canopy structure measurements provided by the GEDI mission in combination with a forest model and found a neutral carbon balance. This knowledge may be critical for the determination of global carbon emission limits to mitigate global warming.

scholarly journals A model-data fusion approach to analyse carbon dynamics in managed grasslands

2020 ◽  
Vol 184 ◽  
pp. 102907
Author(s):  
Vasileios Myrgiotis ◽  
Emanuel Blei ◽  
Rob Clement ◽  
Stephanie K. Jones ◽  
Ben Keane ◽  
...  
Keyword(s):  
Data Fusion ◽  
Carbon Dynamics ◽  
Model Data ◽  
Fusion Approach

scholarly journals Assessing the response of forest productivity to climate extremes in Switzerland using model–data fusion

2020 ◽  
Vol 26 (4) ◽  
pp. 2463-2476 ◽  
Author(s):  
Volodymyr Trotsiuk ◽  
Florian Hartig ◽  
Maxime Cailleret ◽  
Flurin Babst ◽  
David I. Forrester ◽  
...  
Keyword(s):  
Data Fusion ◽  
Climate Extremes ◽  
Forest Productivity ◽  
Model Data

scholarly journals Amazonian Fire Events Disturbed the Global Carbon Cycle: A Study from 2019 Amazon Wildfire Using Google Earth Engine

2020 ◽  
Vol 3 (1) ◽  
pp. 43
Author(s):  
Subhajit Bandopadhyay ◽  
Dany A. Cotrina Sánchez
Keyword(s):  
Carbon Cycle ◽  
Carbon Sink ◽  
Brazilian Amazon ◽  
Global Carbon Cycle ◽  
Forest Productivity ◽  
Google Earth ◽  
Comparative Approach ◽  
Burned Area ◽  
Google Earth Engine ◽  
Global Carbon

An unprecedented number of wildfire events during 2019 throughout the Brazilian Amazon caught global attention, due to their massive extent and the associated loss in the Amazonian forest—an ecosystem on which the whole world depends. Such devastating wildfires in the Amazon has strongly hampered the global carbon cycle and significantly reduced forest productivity. In this study, we have quantified such loss of forest productivity in terms of gross primary productivity (GPP), applying a comparative approach using Google Earth Engine. A total of 12 wildfire spots have been identified based on the fire’s extension over the Brazilian Amazon, and we quantified the loss in productivity between 2018 and 2019. The Moderate Resolution Imaging Spectroradiometer (MODIS) GPP and MODIS burned area satellite imageries, with a revisit time of 8 days and 30 days, respectively, have been used for this study. We have observed that compared to 2018, the number of wildfire events increased during 2019. But such wildfire events did not hamper the natural annual trend of GPP of the Amazonian ecosystem. However, a significant drop in forest productivity in terms of GPP has been observed. Among all 11 observational sites were recorded with GPP loss, ranging from −18.88 gC m−2 yr−1 to −120.11 gC m−2 yr−1, except site number 3. Such drastic loss in GPP indicates that during 2019 fire events, all of these sites acted as carbon sources rather than carbon sink sites, which may hamper the global carbon cycle and terrestrial CO2 fluxes. Therefore, it is assumed that these findings will also fit for the other Amazonian wildfire sites, as well as for the tropical forest ecosystem as a whole. We hope this study will provide a significant contribution to global carbon cycle research, terrestrial ecosystem studies, sustainable forest management, and climate change in contemporary environmental sciences.

scholarly journals The changing global carbon cycle: linking plant-soil carbon dynamics to global consequences

2009 ◽  
Vol 97 (5) ◽  
pp. 840-850 ◽  
Author(s):  
F. Stuart Chapin III ◽  
Jack McFarland ◽  
A. David McGuire ◽  
Eugenie S. Euskirchen ◽  
Roger W. Ruess ◽  
...  
Keyword(s):  
Carbon Cycle ◽  
Soil Carbon ◽  
Carbon Dynamics ◽  
Global Carbon Cycle ◽  
Soil Carbon Dynamics ◽  
Plant Soil ◽  
Global Carbon

scholarly journals Constraining a complex biogeochemical model for CO<sub>2</sub> and N<sub>2</sub>O emission simulations from various land uses by model–data fusion

2017 ◽  
Vol 14 (14) ◽  
pp. 3487-3508 ◽  
Author(s):  
Tobias Houska ◽  
David Kraus ◽  
Ralf Kiese ◽  
Lutz Breuer
Keyword(s):  
Data Fusion ◽  
Arable Land ◽  
Shallow Groundwater ◽  
Scale Model ◽  
Biogeochemical Model ◽  
N2o Emissions ◽  
Model Errors ◽  
Model Data ◽  
Validation Data ◽  
Fusion Concept

Abstract. This study presents the results of a combined measurement and modelling strategy to analyse N2O and CO2 emissions from adjacent arable land, forest and grassland sites in Hesse, Germany. The measured emissions reveal seasonal patterns and management effects, including fertilizer application, tillage, harvest and grazing. The measured annual N2O fluxes are 4.5, 0.4 and 0.1 kg N ha−1 a−1, and the CO2 fluxes are 20.0, 12.2 and 3.0 t C ha−1 a−1 for the arable land, grassland and forest sites, respectively. An innovative model–data fusion concept based on a multicriteria evaluation (soil moisture at different depths, yield, CO2 and N2O emissions) is used to rigorously test the LandscapeDNDC biogeochemical model. The model is run in a Latin-hypercube-based uncertainty analysis framework to constrain model parameter uncertainty and derive behavioural model runs. The results indicate that the model is generally capable of predicting trace gas emissions, as evaluated with RMSE as the objective function. The model shows a reasonable performance in simulating the ecosystem C and N balances. The model–data fusion concept helps to detect remaining model errors, such as missing (e.g. freeze–thaw cycling) or incomplete model processes (e.g. respiration rates after harvest). This concept further elucidates the identification of missing model input sources (e.g. the uptake of N through shallow groundwater on grassland during the vegetation period) and uncertainty in the measured validation data (e.g. forest N2O emissions in winter months). Guidance is provided to improve the model structure and field measurements to further advance landscape-scale model predictions.

scholarly journals Soil carbon debt of 12,000 years of human land use

2017 ◽  
Vol 114 (36) ◽  
pp. 9575-9580 ◽  
Author(s):  
Jonathan Sanderman ◽  
Tomislav Hengl ◽  
Gregory J. Fiske
Keyword(s):  
Land Use ◽  
Carbon Balance ◽  
Mitigation Strategy ◽  
Global Environment ◽  
Climate Mitigation ◽  
Grazing Land ◽  
The Past ◽  
Human Land Use ◽  
History Database ◽  
Global Carbon

Human appropriation of land for agriculture has greatly altered the terrestrial carbon balance, creating a large but uncertain carbon debt in soils. Estimating the size and spatial distribution of soil organic carbon (SOC) loss due to land use and land cover change has been difficult but is a critical step in understanding whether SOC sequestration can be an effective climate mitigation strategy. In this study, a machine learning-based model was fitted using a global compilation of SOC data and the History Database of the Global Environment (HYDE) land use data in combination with climatic, landform and lithology covariates. Model results compared favorably with a global compilation of paired plot studies. Projection of this model onto a world without agriculture indicated a global carbon debt due to agriculture of 133 Pg C for the top 2 m of soil, with the rate of loss increasing dramatically in the past 200 years. The HYDE classes “grazing” and “cropland” contributed nearly equally to the loss of SOC. There were higher percent SOC losses on cropland but since more than twice as much land is grazed, slightly higher total losses were found from grazing land. Important spatial patterns of SOC loss were found: Hotspots of SOC loss coincided with some major cropping regions as well as semiarid grazing regions, while other major agricultural zones showed small losses and even net gains in SOC. This analysis has demonstrated that there are identifiable regions which can be targeted for SOC restoration efforts.

scholarly journals Reviews and syntheses: Systematic Earth observations for use in terrestrial carbon cycle data assimilation systems

2017 ◽  
Author(s):  
Marko Scholze ◽  
Michael Buchwitz ◽  
Wouter Dorigo ◽  
Luis Guanter ◽  
Shaun Quegan
Keyword(s):  
Data Assimilation ◽  
Carbon Cycle ◽  
Global Carbon Cycle ◽  
Full Description ◽  
Model Data ◽  
Terrestrial Carbon ◽  
Carbon Cycle Model ◽  
Terrestrial Carbon Cycle ◽  
Uncertainty Estimates ◽  
Global Carbon

Abstract. The global carbon cycle is an important component of the Earth system and it interacts with the hydrological, energy and nutrient cycles as well as ecosystem dynamics. A better understanding of the global carbon cycle is required for improved projections of climate change including corresponding changes in water and food resources and for the verification 5 of measures to reduce anthropogenic greenhouse gas emissions. An improved understanding of the carbon cycle can be achieved by model-data fusion or data assimilation systems, which integrate observations relevant to the carbon cycle into coupled carbon, water, energy and nutrient models. Hence, the ingredients for such systems are a carbon cycle model, an algorithm for the assimilation, and systematic and 10 well error-characterized observations relevant to the carbon cycle. Relevant observations for assimilation include various in-situ measurements in the atmosphere (e.g. concentrations of CO2 and other gases) and on land (e.g. fluxes of carbon water and energy, carbon stocks) as well as remote sensing observations (e.g. atmospheric composition, vegetation and surface properties).We briefly review the different existing data assimilation techniques and contrast them to model 15 benchmarking and evaluation efforts (which also rely on observations). A common requirement for all assimilation techniques is a full description of the observational data properties. Uncertainty estimates of the observations are as important as the observations themselves because they similarly determine the outcome of such assimilation systems. Hence, this article reviews the requirements of data assimilation systems on observations and provides a non-exhaustive overview of current 20 observations and their uncertainties for use in terrestrial carbon cycle data assimilation. We report on progress since the review of model-data synthesis in terrestrial carbon observations by Raupach et al. (2005) emphasising the rapid advance in relevant space-based observations.

scholarly journals Evaluation of modeled global carbon dynamics: analysis based on global carbon flux and above-ground biomass data

2016 ◽  
Author(s):  
Bao-Lin Xue ◽  
Qinghua Guo ◽  
Tianyu Hu ◽  
Yongcai Wang ◽  
Shengli Tao ◽  
...  
Keyword(s):  
Spatial Patterns ◽  
Carbon Flux ◽  
Large Scale ◽  
Gross Primary Production ◽  
Carbon Dynamics ◽  
Global Scale ◽  
Above Ground Biomass ◽  
Carbon Cycles ◽  
Ground Biomass ◽  
Global Carbon

Abstract. Dynamic global vegetation models are useful tools for the simulation of carbon dynamics on regional and global scales. However, even the most validated models are usually hampered by the poor availability of global biomass data in the model validation, especially on regional/global scales. Here, taking the integrated biosphere simulator model (IBIS) as an example, we evaluated the modeled carbon dynamics, including gross primary production (GPP) and potential above-ground biomass (AGB), on the global scale. The IBIS model was constrained by both in situ GPP and plot-level AGB data collected from the literature. Independent validation showed that IBIS could reproduce GPP and evapotranspiration with acceptable accuracy at site and global levels. On the global scale, the IBIS-simulated total AGB was similar to those obtained in other studies. However, discrepancies were observed between the model-derived and observed spatial patterns of AGB for Amazonian forests. The differences among the AGB spatial patterns were mainly caused by the single-parameter set of the model used. This study showed that different meteorological inputs can also introduce substantial differences in AGB on the global scale. Further analysis showed that this difference is small compared with parameter-induced differences. The conclusions of our research highlight the necessity of considering the heterogeneity of key model physiological parameters in modeling global AGB. The research also shows that to simulate large-scale carbon dynamics, both carbon flux and AGB data are necessary to constrain the model. The main conclusions of our research will help to improve model simulations of global carbon cycles.

scholarly journals High Rates of Ecosytem Metabolism in Five Western Rivers

2011 ◽  
Vol 33 ◽  
pp. 115-118
Author(s):  
Robert Hall ◽  
Jennifer Tank ◽  
Michelle Baker ◽  
Emma Rosi-Marshall ◽  
Michael Grace ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Primary Production ◽  
Carbon Cycling ◽  
Carbon Balance ◽  
Carbon Fixation ◽  
Higher Plants ◽  
Ecosystem Respiration ◽  
Gross Primary Production ◽  
Total Rate ◽  
Global Carbon

Primary production and respiration are core functions of river ecosystems that in part determine the carbon balance. Gross primary production (GPP) is the total rate of carbon fixation by autotrophs such as algae and higher plants and is equivalent to photosynthesis. Ecosystem respiration (ER) measures rate at which organic carbon is mineralized to CO2 by all organisms in an ecosystem. Together these fluxes can indicate the base of the food web to support animal production (Marcarelli et al. 2011), can predict the cycling of other elements (Hall and Tank 2003), and can link ecosystems to global carbon cycling (Cole et al. 2007).

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