scholarly journals Carbon Exchange between the Atmosphere and a Subtropical Evergreen Mountain Forest in Taiwan

2018 ◽  
Vol 2018 ◽  
pp. 1-12
Author(s):  
Falk Maneke-Fiegenbaum ◽  
Otto Klemm ◽  
Yen-Jen Lai ◽  
Chih-Yuan Hung ◽  
Jui-Chu Yu
Keyword(s):  
Eddy Covariance ◽  
Boreal Forests ◽  
East Asian Monsoon ◽  
Ecosystem Respiration ◽  
Cloud Forest ◽  
Net Ecosystem Exchange ◽  
Mountain Forest ◽  
Asian Monsoon Region ◽  
Subtropical Forests ◽  
Flux Corrections

Tropical, temperate, and boreal forests are the subject of various eddy covariance studies, but less is known about the subtropical region. As there are large areas of subtropical forests in the East Asian monsoon region with possibly high carbon uptake, we used three years (2011–2013) of eddy covariance data to estimate the carbon balance of a subtropical mountain forest in Taiwan. Two techniques of flux partitioning are applied to evaluate ecosystem respiration, thoroughly evaluate the validity of the estimated fluxes, and arrive at an estimate of the yearly net ecosystem exchange (NEE). We found that advection is a strong player at our site. Further, when used alone, the nighttime flux correction with the so-called u∗ method (u∗ = friction velocity) cannot avoid underestimating the nighttime respiration. By using a two-technique method employing both nighttime and daytime parameterizations for flux corrections, we arrive at an estimate of the three-year mean NEE of −561 (±standard deviation 114) g·C·m−2·yr−1. The corrected flux estimate represents a rather large uptake of CO2 for this mountain cloud forest, but the value is in good agreement with the few existing comparable estimates for other subtropical forests.

scholarly journals Towards a standardized processing of Net Ecosystem Exchange measured with eddy covariance technique: algorithms and uncertainty estimation

2006 ◽  
Vol 3 (4) ◽  
pp. 571-583 ◽  
Author(s):  
D. Papale ◽  
M. Reichstein ◽  
M. Aubinet ◽  
E. Canfora ◽  
C. Bernhofer ◽  
...  
Keyword(s):  
Eddy Covariance ◽  
Ecosystem Respiration ◽  
Gross Primary Production ◽  
Net Ecosystem Exchange ◽  
Terrestrial Ecosystem ◽  
Climatic Conditions ◽  
High Temporal Resolution ◽  
Eddy Covariance Technique ◽  
Forest Sites ◽  
European Database

Abstract. Eddy covariance technique to measure CO2, water and energy fluxes between biosphere and atmosphere is widely spread and used in various regional networks. Currently more than 250 eddy covariance sites are active around the world measuring carbon exchange at high temporal resolution for different biomes and climatic conditions. In this paper a new standardized set of corrections is introduced and the uncertainties associated with these corrections are assessed for eight different forest sites in Europe with a total of 12 yearly datasets. The uncertainties introduced on the two components GPP (Gross Primary Production) and TER (Terrestrial Ecosystem Respiration) are also discussed and a quantitative analysis presented. Through a factorial analysis we find that generally, uncertainties by different corrections are additive without interactions and that the heuristic u*-correction introduces the largest uncertainty. The results show that a standardized data processing is needed for an effective comparison across biomes and for underpinning inter-annual variability. The methodology presented in this paper has also been integrated in the European database of the eddy covariance measurements.

scholarly journals Drivers of inter-annual variability in Net Ecosystem Exchange in a semi-arid savanna ecosystem, South Africa

2009 ◽  
Vol 6 (2) ◽  
pp. 251-266 ◽  
Author(s):  
S. A. Archibald ◽  
A. Kirton ◽  
M. R. van der Merwe ◽  
R. J. Scholes ◽  
C. A. Williams ◽  
...  
Keyword(s):  
South Africa ◽  
Eddy Covariance ◽  
Photosynthetically Active Radiation ◽  
Ecosystem Respiration ◽  
Net Ecosystem Exchange ◽  
Ann Model ◽  
Rainfall Events ◽  
Active Radiation ◽  
Annual Variability ◽  
Semi Arid

Abstract. Inter-annual variability in primary production and ecosystem respiration was explored using eddy-covariance data at a semi-arid savanna site in the Kruger Park, South Africa. New methods of extrapolating night-time respiration to the entire day and filling gaps in eddy-covariance data in semi-arid systems were developed. Net ecosystem exchange (NEE) in these systems occurs as pulses associated with rainfall events, a pattern not well-represented in current standard gap-filling procedures developed primarily for temperate flux sites. They furthermore do not take into account the decrease in respiration at high soil temperatures. An artificial neural network (ANN) model incorporating these features predicted measured fluxes accurately (MAE 0.42 gC/m2/day), and was able to represent the seasonal patterns of photosynthesis and respiration at the site. The amount of green leaf area (indexed using satellite-derived estimates of fractional interception of photosynthetically active radiation fAPAR), and the timing and magnitude of rainfall events, were the two most important predictors used in the ANN model. These drivers were also identified by multiple linear regressions (MLR), with strong interactive effects. The annual integral of the filled NEE data was found to range from −138 to +155 g C/m2/y over the 5 year eddy covariance measurement period. When applied to a 25 year time series of meteorological data, the ANN model predicts an annual mean NEE of 75(±105) g C/m2/y. The main correlates of this inter-annual variability were found to be variation in the amount of absorbed photosynthetically active radiation (APAR), length of the growing season, and number of days in the year when moisture was available in the soil.

scholarly journals Eddy covariance carbon flux in a scrub in the Mexican highland

2020 ◽  
Author(s):  
Aurelio Guevara-Escobar ◽  
Enrique González-Sosa ◽  
Mónica Cervantes-Jiménez ◽  
Humberto Suzán-Azpiri ◽  
Mónica Elisa Queijeiro-Bolaños ◽  
...  
Keyword(s):  
Primary Production ◽  
Eddy Covariance ◽  
Vegetation Index ◽  
Carbon Sink ◽  
Ecosystem Respiration ◽  
Gross Primary Production ◽  
Net Ecosystem Exchange ◽  
R Package ◽  
Dark Cycle ◽  
Remote Sensors

Abstract. Vegetation fixes C in its biomass through photosynthesis or might release it into the atmosphere through respiration. Measurements of these fluxes would help us understand ecosystem functioning. The eddy covariance technique (EC) is widely used to measure the net ecosystem exchange of C (NEE) which is the balance between gross primary production (GPP) and ecosystem respiration (Reco). Orbital satellites such as MODIS can also provide estimates of GPP. In this study, we measured NEE with the EC in a scrub at Bernal in Mexico, and then partitioned into gross primary production (GPP-EC) and Reco using the recent R package Reddyproc. Measurements of GPP-EC were related to the estimates from the MODIS satellite provided in product MOD17A2H, which contains data of the gross primary productivity (GPP-MODIS). The Bernal site was a carbon sink despite it was an overgrazed site, the average NEE during fifteen months of 2017 and 2018 was −0.78 g C m−2 d−1 and the flux was negative in all measured months. The GPP-MODIS underestimated the ground data when representing the relation with a Theil-Sen regression: GPP-EC = 1.866 + 1.861 GPP-MODIS; an ordinary less squares regression had similar coefficients and the R2 was 0.6. Although cacti (CAM), legume shrubs (C3) and herbs (C3) had a similar vegetation index, the nighttime flux was characterized by positive NEE suggesting that the photosynthetic dark-cycle flux of cacti was lower than Reco. The discrepancy among the GPP flux estimates stresses the need to understand the limitations of EC and remote sensors, while incorporating complementary monitoring and modelling schemes of nighttime Reco, particularly in the presence of species with different photosynthetic cycles.

scholarly journals Influence of spring and autumn phenological transitions on forest ecosystem productivity

2010 ◽  
Vol 365 (1555) ◽  
pp. 3227-3246 ◽  
Author(s):  
Andrew D. Richardson ◽  
T. Andy Black ◽  
Philippe Ciais ◽  
Nicolas Delbart ◽  
Mark A. Friedl ◽  
...  
Keyword(s):  
Climate Change ◽  
Eddy Covariance ◽  
Boreal Forests ◽  
Ecosystem Respiration ◽  
Ecosystem Productivity ◽  
Important Conclusion ◽  
Gross Ecosystem Photosynthesis ◽  
Eddy Covariance Measurements ◽  
Phenological Shifts ◽  
Carry Over

We use eddy covariance measurements of net ecosystem productivity (NEP) from 21 FLUXNET sites (153 site-years of data) to investigate relationships between phenology and productivity (in terms of both NEP and gross ecosystem photosynthesis, GEP) in temperate and boreal forests. Results are used to evaluate the plausibility of four different conceptual models. Phenological indicators were derived from the eddy covariance time series, and from remote sensing and models. We examine spatial patterns (across sites) and temporal patterns (across years); an important conclusion is that it is likely that neither of these accurately represents how productivity will respond to future phenological shifts resulting from ongoing climate change. In spring and autumn, increased GEP resulting from an ‘extra’ day tends to be offset by concurrent, but smaller, increases in ecosystem respiration, and thus the effect on NEP is still positive. Spring productivity anomalies appear to have carry-over effects that translate to productivity anomalies in the following autumn, but it is not clear that these result directly from phenological anomalies. Finally, the productivity of evergreen needleleaf forests is less sensitive to phenology than is productivity of deciduous broadleaf forests. This has implications for how climate change may drive shifts in competition within mixed-species stands.

scholarly journals Incorporating grassland management in ORCHIDEE: model description and evaluation at 11 eddy-covariance sites in Europe

2013 ◽  
Vol 6 (6) ◽  
pp. 2165-2181 ◽  
Author(s):  
J. F. Chang ◽  
N. Viovy ◽  
N. Vuichard ◽  
P. Ciais ◽  
T. Wang ◽  
...  
Keyword(s):  
Eddy Covariance ◽  
Large Scale ◽  
Management Practices ◽  
Ecosystem Respiration ◽  
Net Ecosystem Exchange ◽  
Grassland Management ◽  
Forage Yield ◽  
Model Description ◽  
Co2 Fluxes ◽  
Area Index

Abstract. This study describes how management of grasslands is included in the Organizing Carbon and Hydrology in Dynamic Ecosystems (ORCHIDEE) process-based ecosystem model designed for large-scale applications, and how management affects modeled grassland–atmosphere CO2 fluxes. The new model, ORCHIDEE-GM (grassland management) is enabled with a management module inspired from a grassland model (PaSim, version 5.0), with two grassland management practices being considered, cutting and grazing. The evaluation of the results from ORCHIDEE compared with those of ORCHIDEE-GM at 11 European sites, equipped with eddy covariance and biometric measurements, shows that ORCHIDEE-GM can realistically capture the cut-induced seasonal variation in biometric variables (LAI: leaf area index; AGB: aboveground biomass) and in CO2 fluxes (GPP: gross primary productivity; TER: total ecosystem respiration; and NEE: net ecosystem exchange). However, improvements at grazing sites are only marginal in ORCHIDEE-GM due to the difficulty in accounting for continuous grazing disturbance and its induced complex animal–vegetation interactions. Both NEE and GPP on monthly to annual timescales can be better simulated in ORCHIDEE-GM than in ORCHIDEE without management. For annual CO2 fluxes, the NEE bias and RMSE (root mean square error) in ORCHIDEE-GM are reduced by 53% and 20%, respectively, compared to ORCHIDEE. ORCHIDEE-GM is capable of modeling the net carbon balance (NBP) of managed temperate grasslands (37 ± 30 gC m−2 yr−1 (P < 0.01) over the 11 sites) because the management module contains provisions to simulate the carbon fluxes of forage yield, herbage consumption, animal respiration and methane emissions.

scholarly journals Drivers of interannual variability in Net Ecosystem Exchange in a semi-arid savanna ecosystem, South Africa

2008 ◽  
Vol 5 (4) ◽  
pp. 3221-3266 ◽  
Author(s):  
S. Archibald ◽  
A. Kirton ◽  
M. van der Merwe ◽  
R. J. Scholes ◽  
C. A. Williams ◽  
...  
Keyword(s):  
South Africa ◽  
Eddy Covariance ◽  
Photosynthetically Active Radiation ◽  
Ecosystem Respiration ◽  
Net Ecosystem Exchange ◽  
Ann Model ◽  
Rainfall Events ◽  
Active Radiation ◽  
Annual Variability ◽  
Semi Arid

Abstract. Inter-annual variability in primary production and ecosystem respiration was explored using eddy-covariance data at a semi-arid savanna site in the Kruger Park, South Africa. New methods of extrapolating night-time respiration to the entire day and filling gaps in eddy-covariance data in semi-arid systems were developed. Net ecosystem exchange (NEE) in these systems occurs as pulses associated with rainfall events, a pattern not well-represented in current standard gap-filling procedures developed primarily for temperate flux sites. They furthermore do not take into account the decrease in respiration at high soil temperatures. An artificial neural network (ANN) model incorporating these features predicted measured fluxes accurately (MAE 0.42 g C/m2/day), and was able to represent the seasonal patterns of photosynthesis and respiration at the site. The amount of green leaf area (indexed using satellite-derived estimates of fractional interception of photosynthetically active radiation fAPAR), and the timing and magnitude of rainfall events, were the two most important predictors used in the ANN model. These drivers were also identified by multiple linear models (MLR), with strong interactive effects. The annual integral of the filled NEE data was found to range from −138 to +155 g C/m2/y over the 5 year eddy covariance measurement period. When applied to a 25 year time series of meteorological data, the ANN model predicts an annual mean NEE of 75 (±105) g C/m2/y. The main correlates of this inter-annual variability were found to be variation in the amount of absorbed photosynthetically active radiation (APAR), length of the growing season, and number of days in the year when moisture was available in the soil.

scholarly journals Biosphere-atmosphere exchange of CO<sub>2</sub> in relation to climate: a cross-biome analysis across multiple time scales

2009 ◽  
Vol 6 (10) ◽  
pp. 2297-2312 ◽  
Author(s):  
P. C. Stoy ◽  
A. D. Richardson ◽  
D. D. Baldocchi ◽  
G. G. Katul ◽  
J. Stanovick ◽  
...  
Keyword(s):  
Eddy Covariance ◽  
Time Scales ◽  
Ecosystem Respiration ◽  
Climatic Variability ◽  
Low Frequency ◽  
Net Ecosystem Exchange ◽  
Ecosystem Model ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Terrestrial Carbon

Abstract. The net ecosystem exchange of CO2 (NEE) varies at time scales from seconds to years and longer via the response of its components, gross ecosystem productivity (GEP) and ecosystem respiration (RE), to physical and biological drivers. Quantifying the relationship between flux and climate at multiple time scales is necessary for a comprehensive understanding of the role of climate in the terrestrial carbon cycle. Orthonormal wavelet transformation (OWT) can quantify the strength of the interactions between gappy eddy covariance flux and micrometeorological measurements at multiple frequencies while expressing time series variance in few energetic wavelet coefficients, offering a low-dimensional view of the response of terrestrial carbon flux to climatic variability. The variability of NEE, GEP and RE, and their co-variability with dominant climatic drivers, are explored with nearly one thousand site-years of data from the FLUXNET global dataset consisting of 253 eddy covariance research sites. The NEE and GEP wavelet spectra were similar among plant functional types (PFT) at weekly and shorter time scales, but significant divergence appeared among PFT at the biweekly and longer time scales, at which NEE and GEP were relatively less variable than climate. The RE spectra rarely differed among PFT across time scales as expected. On average, RE spectra had greater low frequency (monthly to interannual) variability than NEE, GEP and climate. CANOAK ecosystem model simulations demonstrate that "multi-annual" spectral peaks in flux may emerge at low (4+ years) time scales. Biological responses to climate and other internal system dynamics, rather than direct ecosystem response to climate, provide the likely explanation for observed multi-annual variability, but data records must be lengthened and measurements of ecosystem state must be made, and made available, to disentangle the mechanisms responsible for low frequency patterns in ecosystem CO2 exchange.

scholarly journals Impacts of active retrogressive thaw slumps on vegetation, soil, and net ecosystem exchange of carbon dioxide in the Canadian High Arctic

2017 ◽  
Vol 3 (2) ◽  
pp. 179-202 ◽  
Author(s):  
Alison E. Cassidy ◽  
Andreas Christen ◽  
Greg H.R. Henry
Keyword(s):  
Eddy Covariance ◽  
Ecosystem Respiration ◽  
Net Ecosystem Exchange ◽  
Growing Season ◽  
High Arctic ◽  
Vegetation Coverage ◽  
Soil Conditions ◽  
Soil Temperatures ◽  
The Impact ◽  
Chamber Measurements

Retrogressive thaw slumps (RTS) are permafrost disturbances common on the Fosheim Peninsula, Ellesmere Island, Canada. During the 2013 growing season, three different RTS were studied to investigate the impact on vegetation composition, soil, and growing season net ecosystem exchange (NEE) of CO2 by comparing to the adjacent undisturbed tundra. Eddy covariance and static chamber measurements were used to determine NEE and ecosystem respiration (Re), respectively. Vegetation cover was significantly lower in all active disturbances, relative to the surrounding tundra, and this affected the overall impact of disturbance on CO2 fluxes. Disturbances were characterized by greater Re compared to surrounding undisturbed tundra. Over the mid-growing season (34 days), eddy covariance NEE measurements indicated that there was greater net CO2 uptake in undisturbed versus disturbed tundra. At one site, the undisturbed tundra was a weak net sink (−0.05 ± 0.02 g C m−2 day−1), while the disturbed tundra acted as a weak net source (+0.07 ± 0.04 g C m−2 day−1). At the other site, the NEE of the undisturbed tundra was −0.20 ± 0.03 g C m−2 day−1 (sink), while the disturbed tundra still sequestered CO2, but less than the undisturbed tundra (NEE = −0.05 ± 0.04 g C m−2 day−1). Two of the RTS exhibited average soil temperatures that were greater compared to the surrounding undisturbed tundra. In one case, the opposite effect was observed. All RTS exhibited elevated soil moisture (+14%) and nutrient availability (specifically nitrogen) relative to the undisturbed tundra. We conclude that RTS, although limited in space, have profound environmental impacts by reducing vegetation coverage, increasing wet soil conditions, and altering NEE during the growing season in the High Arctic.

scholarly journals Model–data fusion across ecosystems: from multisite optimizations to global simulations

2014 ◽  
Vol 7 (6) ◽  
pp. 2581-2597 ◽  
Author(s):  
S. Kuppel ◽  
P. Peylin ◽  
F. Maignan ◽  
F. Chevallier ◽  
G. Kiely ◽  
...  
Keyword(s):  
Land Surface ◽  
Boreal Forests ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Ecosystem Respiration ◽  
Net Ecosystem Exchange ◽  
Co2 Concentration ◽  
Model Data ◽  
Site Specific ◽  
Averaged Model

Abstract. This study uses a variational data assimilation framework to simultaneously constrain a global ecosystem model with eddy covariance measurements of daily net ecosystem exchange (NEE) and latent heat (LE) fluxes from a large number of sites grouped in seven plant functional types (PFTs). It is an attempt to bridge the gap between the numerous site-specific parameter optimization works found in the literature and the generic parameterization used by most land surface models within each PFT. The present multisite approach allows deriving PFT-generic sets of optimized parameters enhancing the agreement between measured and simulated fluxes at most of the sites considered, with performances often comparable to those of the corresponding site-specific optimizations. Besides reducing the PFT-averaged model–data root-mean-square difference (RMSD) and the associated daily output uncertainty, the optimization improves the simulated CO2 balance at tropical and temperate forests sites. The major site-level NEE adjustments at the seasonal scale are reduced amplitude in C3 grasslands and boreal forests, increased seasonality in temperate evergreen forests, and better model–data phasing in temperate deciduous broadleaf forests. Conversely, the poorer performances in tropical evergreen broadleaf forests points to deficiencies regarding the modelling of phenology and soil water stress for this PFT. An evaluation with data-oriented estimates of photosynthesis (GPP – gross primary productivity) and ecosystem respiration (Reco) rates indicates distinctively improved simulations of both gross fluxes. The multisite parameter sets are then tested against CO2 concentrations measured at 53 locations around the globe, showing significant adjustments of the modelled seasonality of atmospheric CO2 concentration, whose relevance seems PFT-dependent, along with an improved interannual variability. Lastly, a global-scale evaluation with remote sensing NDVI (normalized difference vegetation index) measurements indicates an improvement of the simulated seasonal variations of the foliar cover for all considered PFTs.

scholarly journals Machine learning estimates of eddy covariance carbon flux in a scrub in the Mexican highland

2021 ◽  
Vol 18 (2) ◽  
pp. 367-392
Author(s):  
Aurelio Guevara-Escobar ◽  
Enrique González-Sosa ◽  
Mónica Cervantes-Jiménez ◽  
Humberto Suzán-Azpiri ◽  
Mónica Elisa Queijeiro-Bolaños ◽  
...  
Keyword(s):  
Machine Learning ◽  
Eddy Covariance ◽  
Carbon Sink ◽  
Ecosystem Respiration ◽  
Gross Primary Production ◽  
Model Performance ◽  
Net Ecosystem Exchange ◽  
Carbon Dynamics ◽  
Ordinary Least Squares ◽  
Machine Learning Algorithms

Abstract. Arid and semiarid ecosystems contain relatively high species diversity and are subject to intense use, in particular extensive cattle grazing, which has favored the expansion and encroachment of perennial thorny shrubs into the grasslands, thus decreasing the value of the rangeland. However, these environments have been shown to positively impact global carbon dynamics. Machine learning and remote sensing have enhanced our knowledge about carbon dynamics, but they need to be further developed and adapted to particular analysis. We measured the net ecosystem exchange (NEE) of C with the eddy covariance (EC) method and estimated gross primary production (GPP) in a thorny scrub at Bernal in Mexico. We tested the agreement between EC estimates and remotely sensed GPP estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS), and also with two alternative modeling methods: ordinary-least-squares (OLS) regression and ensembles of machine learning algorithms (EMLs). The variables used as predictors were MODIS spectral bands, vegetation indices and products, and gridded environmental variables. The Bernal site was a carbon sink even though it was overgrazed, the average NEE during 15 months of 2017 and 2018 was −0.78 gCm-2d-1, and the flux was negative or neutral during the measured months. The probability of agreement (θs) represented the agreement between observed and estimated values of GPP across the range of measurement. According to the mean value of θs, agreement was higher for the EML (0.6) followed by OLS (0.5) and then MODIS (0.24). This graphic metric was more informative than r2 (0.98, 0.67, 0.58, respectively) to evaluate the model performance. This was particularly true for MODIS because the maximum θs of 4.3 was for measurements of 0.8 gCm-2d-1 and then decreased steadily below 1 θs for measurements above 6.5 gCm-2d-1 for this scrub vegetation. In the case of EML and OLS, the θs was stable across the range of measurement. We used an EML for the Ameriflux site US-SRM, which is similar in vegetation and climate, to predict GPP at Bernal, but θs was low (0.16), indicating the local specificity of this model. Although cacti were an important component of the vegetation, the nighttime flux was characterized by positive NEE, suggesting that the photosynthetic dark-cycle flux of cacti was lower than ecosystem respiration. The discrepancy between MODIS and EC GPP estimates stresses the need to understand the limitations of both methods.

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