scholarly journals Estimation of nighttime ecosystem respiration over a paddy field in China

2010 ◽  
Vol 7 (1) ◽  
pp. 1201-1232 ◽  
Author(s):  
M. S. Hossen ◽  
T. Hiyama ◽  
H. Tanaka
Keyword(s):  
Paddy Field ◽  
Ecosystem Respiration ◽  
Co2 Flux ◽  
Co2 Concentration ◽  
Accurate Estimation ◽  
Concentration Profiles ◽  
Area Index ◽  
Actual Measurement ◽  
Paddy Ecosystem ◽  
Storage Change

Abstract. Accurate estimation of terrestrial ecosystem respiration is crucial for developing regional- to global-scale carbon budget databases. This study evaluated nighttime ecosystem respiration under low turbulence conditions at a paddy field in China during the 2004 growing season. Data from turbulent flux with storage change and alternatively from CO2 concentration profiles measured from the surface to 32 m height were investigated and compared. Conditions were separated into windy and calm using a friction velocity (u∗) threshold. On calm nights, the vertical gradient of CO2 concentration was higher near the canopy level and decreased with height. No differences were detected in terms of quantity and seasonality between the eddy covariance-observed nighttime ecosystem respiration (Re) and the alternatively calculated Re under calm conditions. Nighttime underestimation of paddy ecosystem respiration was low, even under calm conditions. Under stable atmospheric conditions, nighttime "loss" of CO2 flux may result mainly from CO2 being stored in air below the sensor height, and CO2 drainage loss could be small because advection is small. Because the addition of measurement-height storage change is preferable for reducing nighttime underestimation, u∗ filtering and low turbulence data elimination are not required for the paddy ecosystem. Alternatively, under low turbulence conditions, nighttime flux can be calculated from concentration profiles, but actual measurement of the nocturnal boundary layer height is very important. For gap-filling of nighttime CO2 flux data for a paddy ecosystem, development of multiple regression functions based on the crop biomass/leaf area index in association with field water status is preferable to a single regression function using air/soil temperature.

scholarly journals Effects of a windthrow disturbance on the carbon balance of a broadleaf deciduous forest in Hokkaido, Japan

2015 ◽  
Vol 12 (23) ◽  
pp. 6837-6851 ◽  
Author(s):  
K. Yamanoi ◽  
Y. Mizoguchi ◽  
H. Utsugi
Keyword(s):  
Forest Management ◽  
Carbon Balance ◽  
Deciduous Forest ◽  
Carbon Sink ◽  
Ecosystem Respiration ◽  
Gross Primary Production ◽  
Co2 Flux ◽  
White Birch ◽  
Eddy Covariance Method ◽  
Area Index

Abstract. Forests play an important role in the terrestrial carbon balance, with most being in a carbon sequestration stage. The net carbon releases that occur result from forest disturbance, and windthrow is a typical disturbance event affecting the forest carbon balance in eastern Asia. The CO2 flux has been measured using the eddy covariance method in a deciduous broadleaf forest (Japanese white birch, Japanese oak, and castor aralia) in Hokkaido, where incidental damage by the strong Typhoon Songda in 2004 occurred. We also used the biometrical method to demonstrate the CO2 flux within the forest in detail. Damaged trees amounted to 40 % of all trees, and they remained on site where they were not extracted by forest management. Gross primary production (GPP), ecosystem respiration (Re), and net ecosystem production were 1350, 975, and 375 g C m−2 yr−1 before the disturbance and 1262, 1359, and −97 g C m−2 yr−1 2 years after the disturbance, respectively. Before the disturbance, the forest was an evident carbon sink, and it subsequently transformed into a net carbon source. Because of increased light intensity at the forest floor, the leaf area index and biomass of the undergrowth (Sasa kurilensis and S. senanensis) increased by factors of 2.4 and 1.7, respectively, in 3 years subsequent to the disturbance. The photosynthesis of Sasa increased rapidly and contributed to the total GPP after the disturbance. The annual GPP only decreased by 6 % just after the disturbance. On the other hand, the annual Re increased by 39 % mainly because of the decomposition of residual coarse-wood debris. The carbon balance after the disturbance was controlled by the new growth and the decomposition of residues. The forest management, which resulted in the dead trees remaining at the study site, strongly affected the carbon balance over the years. When comparing the carbon uptake efficiency at the study site with that at others, including those with various kinds of disturbances, we emphasized the importance of forest management as well as disturbance type in the carbon balance.

scholarly journals Limitations of Emergent Constraints on Multi-Model Projections: Case Study of Constraining Vegetation Productivity With Observed Greening Sensitivity

2018 ◽  
Author(s):  
Alexander J. Winkler ◽  
Ranga B. Myneni ◽  
Victor Brovkin
Keyword(s):  
Co2 Concentration ◽  
Accurate Estimation ◽  
Spatial And Temporal Variability ◽  
Large Area ◽  
Area Index ◽  
Emergent Constraints ◽  
Data Source ◽  
Fertilization Effects ◽  
Historical Past ◽  
Co2 Forcing

Abstract. Recent research on Emergent Constraints (EC) has delivered promising results. The method utilizes a measurable variable (predictor) from the recent historical past to obtain a constrained estimate of change in a difficult-to-measure variable (predictand) at a potential future CO2 concentration (forcing) from multi-model projections. This procedure critically depends on, first, accurate estimation of the predictor from observations and models, and second, on a robust relationship between inter-model variations in the predictor-predictand space. We investigate issues related to these two themes in this article, using vegetation greening sensitivity to CO2 forcing during the satellite era as a predictor of change in Gross Primary Productivity (GPP) of the Northern High Latitudes region (60° N–90° N, NHL) for a doubling of pre-industrial CO2 concentration in the atmosphere. Greening sensitivity is defined as changes in annual maximum of green leaf area index (LAImax) per unit CO2 forcing realized through its radiative and fertilization effects. We first address the question of how to realistically characterize the greening sensitivity of a large area, the NHL, from pixel-level LAImax data. This requires an investigation into uncertainties in LAImax data source and an evaluation of the spatial and temporal variability in greening sensitivity to forcing in both the data and model simulations. Second, the relationship between greening sensitivity and ΔGPP across the model ensemble depends on a strong coupling among simultaneous changes in GPP and LAImax. This coupling depends in a complex manner on the magnitude (level), time-rate of application (scenarios) and effects (radiative and/or fertilization) of CO2 forcing. We investigate how each one of these three aspects of forcing can impair the EC estimate of the predictand (ΔGPP). Accounting for uncertainties in greening sensitivity and stability of the relation between inter-model variations results in a quantitative estimate of the uncertainty (±0.2 Pg C yr−1) on constrained GPP enhancement (ΔGPP = +3.4 Pg C yr−1) for a doubling of pre-industrial atmospheric CO2 concentration in NHL. This ΔGPP is 60 % larger than the conventionally used average of model projections. The illustrated sources of uncertainty and limitations of the EC method go beyond carbon cycle research and are generally relevant for Earth system sciences.

scholarly journals Observed and modelled ecosystem respiration and gross primary production of a grassland in southwestern France

2010 ◽  
Vol 7 (1) ◽  
pp. 429-462 ◽  
Author(s):  
C. Albergel ◽  
J.-C. Calvet ◽  
A.-L. Gibelin ◽  
S. Lafont ◽  
J.-L. Roujean ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Primary Production ◽  
Root Zone ◽  
Ecosystem Respiration ◽  
Gross Primary Production ◽  
Net Ecosystem Exchange ◽  
Co2 Flux ◽  
Single Equation ◽  
Complex Model ◽  
Area Index

Abstract. In this work, a simple representation of the soil moisture effect on the ecosystem respiration is implemented into the A-gs version of the Interactions between Soil, Biosphere, and Atmosphere (ISBA) model. It results in an improvement of the modelled CO2 flux over a grassland, in southwestern France. The former temperature-only dependent respiration formulation used in ISBA-A-gs is not able to model the limitation of the respiration under dry conditions. In addition to soil moisture and soil temperature, the only parameter required in this formulation is the ecosystem respiration parameter Re25. It can be estimated by the mean of eddy covariance measurements of turbulent nighttime CO2 flux (i.e. ecosystem respiration). The resulting correlation between observed and modelled net ecosystem exchange is r2=0.63 with a bias of −2.18 μmol m−2 s−1. It is shown that when CO2 observations are not available, it is possible to use a more complex model, able to represent the heterotrophic respiration and all the components of the autotrophic respiration, to estimate Re25 with similar results. The modelled ecosystem respiration estimates are provided by the Carbon Cycle (CC) version of ISBA (ISBA-CC). ISBA-CC is a version of ISBA able to simulate all the respiration components whereas ISBA-A-gs uses a single equation for ecosystem respiration. ISBA-A-gs is easier to handle and more convenient than ISBA-CC for practical use in atmospheric or hydrological models. Surface water and energy flux observations as well as gross primary production (GPP) estimates are compared with model outputs. The dependence of GPP to air temperature is investigated. The observed GPP is less sensitive to temperature than the modelled GPP. Finally, the simulations of the ISBA-A-gs model are analysed over a seven year period (2001–2007). Modelled soil moisture and leaf area index (LAI) are confronted with the observed root-zone soil moisture content (m3 m−3), and with LAI estimates derived from surface reflectance measurements.

scholarly journals Constraining a global ecosystem model with multi-site eddy-covariance data

2012 ◽  
Vol 9 (10) ◽  
pp. 3757-3776 ◽  
Author(s):  
S. Kuppel ◽  
P. Peylin ◽  
F. Chevallier ◽  
C. Bacour ◽  
F. Maignan ◽  
...  
Keyword(s):  
Eddy Covariance ◽  
Vegetation Index ◽  
Ecosystem Respiration ◽  
Terrestrial Ecosystem ◽  
Co2 Flux ◽  
Parameter Analysis ◽  
Single Site ◽  
Biogeochemical Model ◽  
Model Parameters ◽  
Area Index

Abstract. Assimilation of in situ and satellite data in mechanistic terrestrial ecosystem models helps to constrain critical model parameters and reduce uncertainties in the simulated energy, water and carbon fluxes. So far the assimilation of eddy covariance measurements from flux-tower sites has been conducted mostly for individual sites ("single-site" optimization). Here we develop a variational data assimilation system to optimize 21 parameters of the ORCHIDEE biogeochemical model, using net CO2 flux (NEE) and latent heat flux (LE) measurements from 12 temperate deciduous broadleaf forest sites. We assess the potential of the model to simulate, with a single set of inverted parameters, the carbon and water fluxes at these 12 sites. We compare the fluxes obtained from this "multi-site" (MS) optimization to those of the prior model, and of the "single-site" (SS) optimizations. The model-data fit analysis shows that the MS approach decreases the daily root-mean-square difference (RMS) to observed data by 22%, which is close to the SS optimizations (25% on average). We also show that the MS approach distinctively improves the simulation of the ecosystem respiration (Reco), and to a lesser extent the gross primary productivity (GPP), although we only assimilated net CO2 flux. A process-oriented parameter analysis indicates that the MS inversion system finds a unique combination of parameters which is not the simple average of the different SS sets of parameters. Finally, in an attempt to validate the optimized model against independent data, we observe that global-scale simulations with MS optimized parameters show an enhanced phase agreement between modeled leaf area index (LAI) and satellite-based observations of normalized difference vegetation index (NDVI).

scholarly journals Investigating the applicability of emergent constraints

2019 ◽  
Vol 10 (3) ◽  
pp. 501-523 ◽  
Author(s):  
Alexander J. Winkler ◽  
Ranga B. Myneni ◽  
Victor Brovkin
Keyword(s):  
Carbon Cycle ◽  
Observational Data ◽  
Temporal Variability ◽  
Co2 Concentration ◽  
Accurate Estimation ◽  
Area Index ◽  
High Latitude Region ◽  
Emergent Constraints ◽  
Data Source ◽  
Co2 Forcing

Abstract. Recent research on emergent constraints (ECs) has delivered promising results in narrowing down uncertainty in climate predictions. The method utilizes a measurable variable (predictor) from the recent historical past to obtain a constrained estimate of change in an entity of interest (predictand) at a potential future CO2 concentration (forcing) from multi-model projections. This procedure first critically depends on an accurate estimation of the predictor from observations and models and second on a robust relationship between inter-model variations in the predictor–predictand space. Here, we investigate issues related to these two themes in a carbon cycle case study using observed vegetation greening sensitivity to CO2 forcing as a predictor of change in photosynthesis (gross primary productivity, GPP) for a doubling of preindustrial CO2 concentration. Greening sensitivity is defined as changes in the annual maximum of green leaf area index (LAImax) per unit CO2 forcing realized through its radiative and fertilization effects. We first address the question of how to realistically characterize the predictor of a large area (e.g., greening sensitivity in the northern high-latitude region) from pixel-level data. This requires an investigation into uncertainties in the observational data source and an evaluation of the spatial and temporal variability in the predictor in both the data and model simulations. Second, the predictor–predictand relationship across the model ensemble depends on a strong coupling between the two variables, i.e., simultaneous changes in GPP and LAImax. This coupling depends in a complex manner on the magnitude (level), time rate of application (scenarios), and effects (radiative and/or fertilization) of CO2 forcing. We investigate how each one of these three aspects of forcing can affect the EC estimate of the predictand (ΔGPP). Our results show that uncertainties in the EC method primarily originate from a lack of predictor comparability between observations and models, the observational data source, and temporal variability of the predictor. The disagreement between models on the mechanistic behavior of the system under intensifying forcing limits the EC applicability. The discussed limitations and sources of uncertainty in the EC method go beyond carbon cycle research and are generally applicable in Earth system sciences.

scholarly journals Observed and modelled ecosystem respiration and gross primary production of a grassland in southwestern France

2010 ◽  
Vol 7 (5) ◽  
pp. 1657-1668 ◽  
Author(s):  
C. Albergel ◽  
J.-C. Calvet ◽  
A.-L. Gibelin ◽  
S. Lafont ◽  
J.-L. Roujean ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Primary Production ◽  
Root Zone ◽  
Ecosystem Respiration ◽  
Gross Primary Production ◽  
Net Ecosystem Exchange ◽  
Co2 Flux ◽  
Single Equation ◽  
Complex Model ◽  
Area Index

Abstract. In this work, the rich dataset acquired at the SMOSREX experimental site is used to enhance the A-gs version of the Interactions between Soil, Biosphere and Atmosphere (ISBA) model. A simple representation of the soil moisture effect on the ecosystem respiration is implemented in the ISBA-A-gs model. It results in an improvement of the modelled CO2 flux over a grassland in southwestern France. The former temperature-only dependent respiration formulation used in ISBA-A-gs is not able to model the limitation of the respiration under dry conditions. In addition to soil moisture and soil temperature, the only parameter required in this formulation is the ecosystem respiration parameter Re25. It can be estimated by means of eddy covariance measurements of turbulent nighttime CO2 flux (i.e. ecosystem respiration). The resulting correlation between observed and modelled net ecosystem exchange is r2=0.63 with a bias of −2.18 μmol m−2 s−1. It is shown that when CO2 observations are not available, it is possible to use a more complex model, able to represent the heterotrophic respiration and all the components of the autotrophic respiration, to estimate Re25 with similar results. The modelled ecosystem respiration estimates are provided by the Carbon Cycle (CC) version of ISBA (ISBA-CC). ISBA-CC is a version of ISBA able to simulate all the respiration components, whereas ISBA-A-gs uses a single equation for ecosystem respiration. ISBA-A-gs is easier to handle and more convenient than ISBA-CC for the practical use in atmospheric or hydrological models. Surface water and energy flux observations, as well as Gross Primary Production (GPP) estimates, are compared with model outputs. The dependence of GPP to air temperature is investigated. The observed GPP is less sensitive to temperature than the modelled GPP. Finally, the simulations of the ISBA-A-gs model are analysed over a seven year period (2001–2007). Modelled soil moisture and Leaf Area Index (LAI) are confronted with the observed surface and root-zone soil moisture content (m3 m−3), and with LAI estimates derived from surface reflectance measurements.

scholarly journals Constraining a global ecosystem model with multi-site eddy-covariance data

2012 ◽  
Vol 9 (3) ◽  
pp. 3317-3380 ◽  
Author(s):  
S. Kuppel ◽  
P. Peylin ◽  
F. Chevallier ◽  
C. Bacour ◽  
F. Maignan ◽  
...  
Keyword(s):  
Eddy Covariance ◽  
Vegetation Index ◽  
Ecosystem Respiration ◽  
Terrestrial Ecosystem ◽  
Co2 Flux ◽  
Parameter Analysis ◽  
Single Site ◽  
Biogeochemical Model ◽  
Model Parameters ◽  
Area Index

Abstract. Assimilation of in situ and satellite data in mechanistic terrestrial ecosystem models helps to constrain critical model parameters and reduce uncertainties in the simulated energy, water and carbon fluxes. So far the assimilation of eddy covariance measurements from flux-tower sites has been conducted mostly for individual sites ("single-site" optimization). Here we develop a variational data assimilation system to optimize 21 parameters of the ORCHIDEE biogeochemical model, using net CO2 flux (NEE) and latent heat flux (LE) measurements from twelve temperate deciduous broadleaf forest sites. We assess the potential of the model to simulate, with a single set of inverted parameters, the carbon and water fluxes at these 12 sites. We compare the fluxes obtained from this "multi-site" (MS) optimization to those of the prior model, and of the "single-site" (SS) optimizations. The model-data fit analysis shows that the MS approach decreases the daily root mean square difference (RMS) to observed data by 22%, which is close to the SS optimizations (25% on average). We also show that the MS approach distinctively improves the simulation of the ecosystem respiration (Reco), and to a lesser extent the gross carbon flux (GPP), although we only assimilated net CO2 flux. A process-oriented parameter analysis indicates that the MS inversion system finds a unique combination of parameters which is not the simple average of the different SS set of parameters. Finally, in an attempt to validate the optimized model against independent data, we observe that global scale simulations with MS optimized parameters show an enhanced phase agreement between modeled leaf area index (LAI) and satellite-based measurements of normalized difference vegetation index (NDVI).

scholarly journals Nondestructive estimation of potato yield using relative variables derived from multi-period LAI and hyperspectral data based on weighted growth stage

Plant Methods ◽  
2020 ◽  
Vol 16 (1) ◽  
Author(s):  
Shanjun Luo ◽  
Yingbin He ◽  
Qian Li ◽  
Weihua Jiao ◽  
Yaqiu Zhu ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Growth Stage ◽  
Estimation Error ◽  
Potato Yield ◽  
Hyperspectral Data ◽  
Single Stage ◽  
Accurate Estimation ◽  
Yield Estimation ◽  
Area Index ◽  
Red Edge

Abstract Background The accurate estimation of potato yield at regional scales is crucial for food security, precision agriculture, and agricultural sustainable development. Methods In this study, we developed a new method using multi-period relative vegetation indices (rVIs) and relative leaf area index (rLAI) data to improve the accuracy of potato yield estimation based on the weighted growth stage. Two experiments of field and greenhouse (water and nitrogen fertilizer experiments) in 2018 were performed to obtain the spectra and LAI data of the whole growth stage of potato. Then the weighted growth stage was determined by three weighting methods (improved analytic hierarchy process method, IAHP; entropy weight method, EW; and optimal combination weighting method, OCW) and the Slogistic model. A comparison of the estimation performance of rVI-based and rLAI-based models with a single and weighted stage was completed. Results The results showed that among the six test rVIs, the relative red edge chlorophyll index (rCIred edge) was the optimal index of the single-stage estimation models with the correlation with potato yield. The most suitable single stage for potato yield estimation was the tuber expansion stage. For weighted growth stage models, the OCW-LAI model was determined as the best one to accurately predict the potato yield with an adjusted R2 value of 0.8333, and the estimation error about 8%. Conclusion This study emphasizes the importance of inconsistent contributions of multi-period or different types of data to the results when they are used together, and the weights need to be considered.

scholarly journals Can land degradation drive differences in the C exchange of two similar semiarid ecosystems?

2018 ◽  
Vol 15 (1) ◽  
pp. 263-278 ◽  
Author(s):  
Ana López-Ballesteros ◽  
Cecilio Oyonarte ◽  
Andrew S. Kowalski ◽  
Penélope Serrano-Ortiz ◽  
Enrique P. Sánchez-Cañete ◽  
...  
Keyword(s):  
Vadose Zone ◽  
Land Degradation ◽  
Ecosystem Respiration ◽  
Gross Primary Production ◽  
Co2 Flux ◽  
C Sequestration ◽  
Co2 Exchange ◽  
Striking Difference ◽  
Soil Variables ◽  
Natural Site

Abstract. Currently, drylands occupy more than one-third of the global terrestrial surface and are recognized as areas vulnerable to land degradation. The concept of land degradation stems from the loss of an ecosystem's biological productivity due to long-term loss of natural vegetation or depletion of soil nutrients. Drylands' key role in the global carbon (C) balance has been recently demonstrated, but the effects of land degradation on C sequestration by these ecosystems still need to be investigated. In the present study, we compared net C and water vapor fluxes, together with satellite, meteorological and vadose zone (CO2, water content and temperature) measurements, between two nearby (∼ 23 km) experimental sites representing “natural” (i.e., site of reference) and “degraded” grazed semiarid grasslands. We utilized data acquired over 6 years from two eddy covariance stations located in southeastern Spain with highly variable precipitation magnitude and distribution. Results show a striking difference in the annual C balances with an average net CO2 exchange of 196 ± 40 (C release) and −23 ± 2 g C m−2 yr−1 (C fixation) for the degraded and natural sites, respectively. At the seasonal scale, differing patterns in net CO2 fluxes were detected over both growing and dry seasons. As expected, during the growing seasons, greater net C uptake over longer periods was observed at the natural site. However, a much greater net C release, probably derived from subterranean ventilation, was measured at the degraded site during drought periods. After subtracting the nonbiological CO2 flux from net CO2 exchange, flux partitioning results point out that, during the 6 years of study, gross primary production, ecosystem respiration and water use efficiency were, on average, 9, 2 and 10 times higher, respectively, at the natural site versus the degraded site. We also tested differences in all monitored meteorological and soil variables and CO2 at 1.50 m belowground was the variable showing the greatest intersite difference, with ∼ 1000 ppm higher at the degraded site. Thus, we believe that subterranean ventilation of this vadose zone CO2, previously observed at both sites, partly drives the differences in C dynamics between them, especially during the dry season. It may be due to enhanced subsoil–atmosphere interconnectivity at the degraded site.

scholarly journals Upside-down fluxes Down Under: CO<sub>2</sub> net sink in winter and net source in summer in a temperate evergreen broadleaf forest

2018 ◽  
Vol 15 (12) ◽  
pp. 3703-3716 ◽  
Author(s):  
Alexandre A. Renchon ◽  
Anne Griebel ◽  
Daniel Metzen ◽  
Christopher A. Williams ◽  
Belinda Medlyn ◽  
...  
Keyword(s):  
Large Scale ◽  
Vegetation Index ◽  
Ecosystem Respiration ◽  
Vapour Pressure Deficit ◽  
Summer Rainfall ◽  
Mean Annual Precipitation ◽  
Surface Conductance ◽  
Mean Annual Temperature ◽  
Eddy Covariance Method ◽  
Area Index

Abstract. Predicting the seasonal dynamics of ecosystem carbon fluxes is challenging in broadleaved evergreen forests because of their moderate climates and subtle changes in canopy phenology. We assessed the climatic and biotic drivers of the seasonality of net ecosystem–atmosphere CO2 exchange (NEE) of a eucalyptus-dominated forest near Sydney, Australia, using the eddy covariance method. The climate is characterised by a mean annual precipitation of 800 mm and a mean annual temperature of 18 ∘C, hot summers and mild winters, with highly variable precipitation. In the 4-year study, the ecosystem was a sink each year (−225 g C m−2 yr−1 on average, with a standard deviation of 108 g C m−2 yr−1); inter-annual variations were not related to meteorological conditions. Daily net C uptake was always detected during the cooler, drier winter months (June through August), while net C loss occurred during the warmer, wetter summer months (December through February). Gross primary productivity (GPP) seasonality was low, despite longer days with higher light intensity in summer, because vapour pressure deficit (D) and air temperature (Ta) restricted surface conductance during summer while winter temperatures were still high enough to support photosynthesis. Maximum GPP during ideal environmental conditions was significantly correlated with remotely sensed enhanced vegetation index (EVI; r2 = 0.46) and with canopy leaf area index (LAI; r2 = 0.29), which increased rapidly after mid-summer rainfall events. Ecosystem respiration (ER) was highest during summer in wet soils and lowest during winter months. ER had larger seasonal amplitude compared to GPP, and therefore drove the seasonal variation of NEE. Because summer carbon uptake may become increasingly limited by atmospheric demand and high temperature, and because ecosystem respiration could be enhanced by rising temperatures, our results suggest the potential for large-scale seasonal shifts in NEE in sclerophyll vegetation under climate change.

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