Gross Primary Production Fluxes Research Articles

Carbon and water vapor exchanges coupling for different irrigated and rainfed conditions on Andean potato agroecosystems

AbstractThe fundamental exchange of water for carbon lays the groundwork for understanding the interplay between carbon and water cycles in terrestrial ecosystems, providing valuable insights into global water and carbon balances and vegetation growth. Inherent water use efficiency (IWUE) was used as a study framework of the diurnal patterns and degree of coupling of carbon and water exchange to investigate the net ecosystem carbon exchange (NEE) responses of three water regime potato cropping systems [full-irrigation (FI), deficit-irrigation (DI), and rainfed (RF)] in Cundinamarca, Colombia. The eddy covariance method was used to determine CO2 and water fluxes, surface resistances, and the omega decoupling factor (Ω). Additionally, leaf area index (LAI), and specific leaf area (SLA) were assessed to determine the canopy influence on carbon and water exchange. The highest carbon sink activity (NEE = -311.96 ± 12.82 g C m−2) at FI, is primarily attributed to a larger canopy with high autotrophic activity and low internal resistance. This supported a highly coupled and synchronized exchange between evapotranspiration (ET) and gross primary production (GPP), as reflected in the highest IWUE (4.7 mg C kPa s−1 kg−1 H2O). In contrast, the lower sink capacity at DI (NEE = − 17.3 ± 4.6 g C m−2) and the net carbon source activity from RF (NEE = 187.21 ± 3.84 g C m−2) were related to a smaller leaf area available for water and carbon exchange, resulting in lower IWUE (2.3 and 1.01 mg C kPa s−1 kg−1 H2O, respectively) and a decoupled and desynchronized gas exchange caused by unbalanced restrictions on ET and GPP fluxes. These results provide new information on carbon–water interactions in potatoes and improve the understanding of carbon sequestration and drought effects on potato sink activity.

Methodological advancement in deriving primary productivity and ecosystem respiration fluxes across different biomes

In this paper, we introduce a methodology that can improve the estimations of Gross Primary Productivity (GPP) and ecosystem Respiration (Reco) processes at a regional scale. This method is based on a satellite data-driven approach which is suitable for regions like India where there exists a serious shortage of ground-based observations of biospheric carbon fluxes (e.g., Eddy Covariance (EC) flux measurements). We relied on the Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance for capturing vegetation dynamics in the Light-Use Efficiency (LUE)-based vegetation model. Further, we utilised recently available satellite-based Solar-Induced Fluorescence (SIF) and other variables such as Soil Moisture (SM) and Soil Temperature (ST) to refine the predictions of GPP and Reco. The methodology involves establishing a relationship between SIF and GPP for different vegetation classes over India. The SIF-GPP relationship established across the biomes was then used to correct the GPP fluxes simulated by the LUE-based model. Similarly, the ecosystem respiration estimations by the model have undergone refinement by incorporating ST and SM information. This innovative method shows remarkable potential to improve biospheric CO2 uptake and release, especially for in situ data-constrained regions like India.• SIF-based information is introduced to a light-use efficiency-based vegetation model.• SIF-GPP relationship is established for major biomes across India.• SM and ST information is incorporated into the Reco simulations in the model.

Dynamic regulation of water potential in Juniperus osteosperma mediates ecosystem carbon fluxes.

Some plants exhibit dynamic hydraulic regulation, in which the strictness of hydraulic regulation (i.e. iso/anisohydry) changes in response to environmental conditions. However, the environmental controls over iso/anisohydry and the implications of flexible hydraulic regulation for plant productivity remain unknown. In Juniperus osteosperma, a drought-resistant dryland conifer, we collected a 5-month growing season time series of in situ, high temporal-resolution plant water potential ( ) and stand gross primary productivity (GPP). We quantified the stringency of hydraulic regulation associated with environmental covariates and evaluated how predawn water potential contributes to empirically predicting carbon uptake. Juniperus osteosperma showed less stringent hydraulic regulation (more anisohydric) after monsoon precipitation pulses, when soil moisture and atmospheric demand were high, and corresponded with GPP pulses. Predawn water potential matched the timing of GPP fluxes and improved estimates of GPP more strongly than soil and/or atmospheric moisture, notably resolving GPP underestimation before vegetation green-up. Flexible hydraulic regulation appears to allow J. osteosperma to prolong soil water extraction and, therefore, the period of high carbon uptake following monsoon precipitation pulses. Water potential and its dynamic regulation may account for why process-based and empirical models commonly underestimate the magnitude and temporal variability of dryland GPP.

Functioning of a canopy-dominated intertidal community during emersion: highly productive but heterotrophic at the annual scale

In order to address lack of data regarding coastal carbon budgets, we estimated the annual metabolic carbon budget of an intertidal rocky reef macroalgal community during emersion. This budget is based on direct in situ measurements during emersion and establishes the seasonal variations of the photosynthetic parameters of such a community. CO2 fluxes were measured hourly to study the response of community gross primary productivity (GPP) to irradiance and the variation of GPP and community respiration (CR) over the emersion period at different times of the year. These were combined together with existing monthly measures of GPP and CR hourly rates to model the variations of these fluxes as a function of irradiance and the tidal cycle throughout an entire calendar year. Daily, monthly and annual values of GPP, CR and net primary productivity (NPP) were calculated with a relatively low sensitivity to any of the parameters used. While GPP fluxes show comparable orders of magnitude to those measured in other systems, higher CR fluxes lead to a heterotrophic system during emersion, both under measured (NPP = −299 gC m−2 year−1) and theoretical irradiances (NPP = −119 gC m−2 year−1). This heterotrophy is directly linked to the light availability, varying according to combined daily, tidal and seasonal cycles, and to temperature at the seasonal scale. Measurements performed in situ at the community scale integrated interactions that are otherwise absent at the individual scale. This gives access to aspects of the functioning that cannot be otherwise identified.

The influence of the hydrometeorological factors on the CO2 fluxes from the oligotrophic bog surface.

The influence of the hydrometeorological factors on the CO2 fluxes from the oligotrophic bog surface.

Dynamics of major environmental disasters involving fire in the Brazilian Pantanal

The 2020 environmental catastrophe in Pantanal has highlighted the fragility of environmental policies and practices for managing and fighting fires in this biome. Therefore, it is essential to know the causes and circumstances that potentiate these fires. This study aimed to: (I) assess the relationship between fire foci and carbon absorption (GPP), precipitation, and carbon dioxide (CO2) flux; (ii) analyze vegetation recovery using the differenced normalized burn ratio (ΔNBR) in Brazilian Pantanal between 2001 and 2022; and (iii) identify priority areas, where the highest intensities of fire foci have occurred, in order to guide public policies in Brazil to maintain local conservation. To this purpose, fire foci were detected using data from the MODIS MOD14/MYD14 algorithm, annual precipitation with CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data), and CO2 flux using the MODIS/MODO9A1 product, and Gross Primary Production (GPP) with the MODIS/MOD17A2 product. The severity of the burned area was also assessed using the ΔNBR index and the risk areas were determined using the averages of these images. During the time series studied, a total of 300,127 fire foci were detected throughout the Pantanal, where 2020 had the highest number of foci and the lowest accumulated precipitation. The years with the highest precipitation were 2014 and 2018. The year 2018 was also the second year with the highest GPP value. The Pettit test showed a trend for 2008 and 2011 as the points of change in the CO2 flux and GPP variables. Principal component analysis clustered fire foci and precipitation on opposite sides, as well as GPP and CO2 flux, while ΔNBR clustered HS, MHS and MLS classes with the years 2020, 2019, 2002 and 2021. There was a high negative correlation between fire foci × rainfall and GPP × CO2 flux. The years with the largest areas of High severity (HS), Moderate-high severity (MHS) and Moderate-low severity (MLS) classes were 2020 and 2019, respectively. The most vulnerable areas for severe fires were the municipalities of Cáceres, Poconé, and Corumbá. The major fire catastrophe in 2020 is correlated with the low precipitation in 2019, the high precipitation in 2018, and the increased GPP, as well government policies unfavorable to the environment.

Continuity of Global MODIS Terrestrial Primary Productivity Estimates in the VIIRS Era Using Model‐Data Fusion

AbstractThe NASA Terra and Aqua satellites have been successfully operating for over two decades, exceeding their original design life. However, the era of NASA's Earth Observing System may be coming to a close as early as 2023. Similarities between the Moderate Resolution Imaging Spectroradiometer (MODIS), aboard Aqua and Terra, and the Visible Infrared Imaging Radiometer Suite (VIIRS) sensors aboard the Suomi NPP, NOAA‐20 and NOAA‐21 satellites enable potential continuity of long‐term earth observational records in the VIIRS era. We conducted a comprehensive calibration and validation of the MODIS MOD17 product, which provided the first global, continuous, weekly estimates of ecosystem gross primary productivity (GPP) and annual estimates of net primary productivity (NPP). Using Bayesian model‐data fusion, we combined 18 years of tower fluxes with prior data on plant traits and hundreds of field measurements of NPP to benchmark MOD17 and to develop the first terrestrial productivity estimates from VIIRS. The updated mean global GPP (NPP) flux from the future MOD17 Collection 7 product and new VNP17 product for 2012–2018 is 127 ± 2.8 Pg C year−1 (58 ± 1.1 Pg C year−1), which compares well with independent top‐down and bottom‐up estimates. MOD17 and VNP17 depict upward productivity trends over recent decades, with 2000–2018 MOD17 GPP (NPP) rising by 0.47 (0.25) Pg C year−2 but slowing to 0.35–0.44 (0.11–0.13) Pg C year−2 over 2012–2021, with a greater reduction in the NPP growth rate. The new VIIRS VNP17 product has the potential to extend these estimates of global, terrestrial primary productivity beyond 2030.

Dynamic relationships between gross primary production and energy partitioning in three different ecosystems based on eddy covariance time series analysis

Ecosystems are responsible for strong feedback processes that affect climate. The mechanisms and consequences of this feedback are uncertain and must be studied to evaluate their influence on global climate change. The main objective of this study is to assess the gross primary production (GPP) dynamics and the energy partitioning patterns in three different European forest ecosystems through time series analysis. The forest types are an Evergreen Needleleaf Forest in Finland (ENF_FI), a Deciduous Broadleaf Forest in Denmark (DBF_DK), and a Mediterranean Savanna Forest in Spain (SAV_SP). Buys-Ballot tables were used to study the intra-annual variability of meteorological data, energy fluxes, and GPP, whereas the autocorrelation function was used to assess the inter-annual dynamics. Finally, the causality of GPP and energy fluxes was studied with Granger causality tests. The autocorrelation function of the GPP, meteorological variables, and energy fluxes revealed that the Mediterranean ecosystem is more irregular and shows lower memory in the long term than in the short term. On the other hand, the Granger causality tests showed that the vegetation feedback to the atmosphere was more noticeable in the ENF_FI and the DBF_DK in the short term, influencing latent and sensible heat fluxes. In conclusion, the impact of the vegetation on the atmosphere influences the energy partitioning in a different way depending on the vegetation type, which makes the study of the vegetation dynamics essential at the local scale to parameterize these processes with more detail and build improved global models.

Scots pine responses to drought investigated with eddy covariance and sap flow methods

Scots pine, as one of the dominant European tree species in the temperate zone, is experiencing intensified water deficits, especially in north-western and central Poland, where it suffers from frequent droughts and generally low precipitation. This work investigates drought impact on forest functioning, by analysing ecosystem transpiration under normal as well as dry conditions. Therefore, eddy covariance (EC) and sap flow measurements (using the thermal heat balance, THB, method) were combined to estimate transpiration (T) in two different-aged Scots pine (Pinus sylvestris) stands in north-western Poland: Mezyk (ME; 26 years old) and Tuczno (TU; 67 years old). Transpiration (T) estimates regarding EC measurements were derived from gross primary productivity (GPP) fluxes and vapour pressure deficit (VPD) dependence, considering their common relationship with stomatal activity. In 2019, the year following severe drought in Poland and Europe in general, total annual transpiration estimated based on sap flow measurements (TSF) was significantly lower than EC-derived transpiration (TEC) at both sites. The total ratio of TSF/TEC for the growing season (March–August) was 0.64 and 0.41 at ME and TU, respectively. We thus speculate that the understory, which was more abundant in TU than in ME, and which could only be observed by the EC system, may be responsible for the observed discrepancies. Bigger differences between TSF and TEC occurred under dry and wet conditions, while both were fairly similar under moderate conditions. The analysis of the relationships between TSF and soil water content (SWC) at depth of 10 cm revealed that there is a thresholds (SWC ~ 3.5%) at which TSF starts to decrease sharply, presumably due to stomatal closure. However, the decrease in GPP fluxes at the same time was less pronounced, indicating the impact of additional non-stomatal factor on water conductivity. We generally conclude that care should be taken if the conclusion of the occurrence of drought stress of some plants is derived from a bulk evapotranspiration flux, as it is commonly done with EC measurements averaging over the whole ecosystem. Our results also support the notion that non-stomatal water losses are an important element during extreme dry conditions, and that these may appear not only when stomata are already closed.

Evaluating gross primary productivity over 9 ChinaFlux sites based on random forest regression models, remote sensing, and eddy covariance data

Accurate modeling of Gross Primary Productivity (GPP) in terrestrial ecosystems is a major challenge in quantifying the carbon cycle. Many light use efficiency (LUE) models have been developed, but the variables and algorithms used for environmental constraints in different models vary importantly. It is still unclear whether the models can be further improved by machine learning methods and the combination of different variables. Here, we have developed a series of RFR-LUE models, which used the random forest regression (RFR) algorithm based on variables of LUE models, to explore the potential of estimating site-level GPP. Based on remote sensing indices, eddy covariance and meteorological data, we applied RFR-LUE models to evaluate the effects of different variables combined on GPP on daily, 8-day, 16-day and monthly scales, respectively. Cross-validation analyses revealed performances of RFR-LUE models varied significantly among sites with R2 of 0.52–0.97. Slopes of the regression relationship between simulated and observed GPP ranged from 0.59 to 0.95. Most models performed better in capturing the temporal changes and magnitude of GPP in mixed forests and evergreen needle-leaf forests than in evergreen broadleaf forests and grasslands. Performances were improved at the longer temporal scale, with the average R2 for four-time resolutions of 0.81, 0.87, 0.88, and 0.90, respectively. Additionally, the importance of the variables showed that temperature and vegetation indices were critical variables for RFR-LUE models, followed by radiation and moisture variables. The importance of moisture variables was higher in non-forests than in forests. A comparison with four GPP products indicated that RFR-LUE model predicted GPP better matcher observed GPP across sites. The study provided an approach to deriving GPP fluxes and evaluating the extent to which variables affect GPP estimation. It may be used for predicting vegetation GPP at the regional scales and for calibration and evaluation of land surface process models.

Simulation of ecosystem fluxes with the SCOPE model: Sensitivity to parametrization and evaluation with flux tower observations

Accurate estimates of carbon, water and energy fluxes between the Earth surface and the atmosphere are crucial for enhancing our understanding of ecosystem–climate interactions. Such estimates can be made by combining remote sensing derived land surface parameters with climate reanalysis data. We analysed to what degree generic (plant functional type (PFT)-independent) satellite-derived vegetation properties and climate reanalysis data can explain land surface fluxes and to what extent the PFT-specific information extends the flux simulations. For this purpose, we used the Soil Canopy Observation, Photochemistry and Energy fluxes (SCOPE) model, which combines radiative transfer in plant leaves and vegetation canopies with photosynthesis and energy balance in a single model representation of the vegetation. We evaluated the performance of SCOPE in simulating fluxes by comparison to 63 eddy covariance sites representing 10 PFTs. We varied the sources of maximum carboxylation capacity (Vcmax25) and BallBerrySlope values (default vs literature), the seasonality of Vcmax25 and the meteorological forcing (locally measured and climate reanalysis). The average performance of daily flux in terms of root-mean-square error (RMSE) was 2.3 ± 0.8 μmol CO2 m−2 s−1 (R2= 0.74 ± 0.12) for gross primary productivity (GPP), 24 ± 8 W m−2 (R2= 0.68 ± 0.16) for latent heat flux (λE) and 50 ± 15 W m−2 (R20.47±0.17) for sensible heat flux (H). The inter-site variability of the annual accumulated GPP flux was captured well with seasonally varying PFT-specific Vcmax25 (R2= 0.74, RMSE = 308 g C m−2 yr−1 and bias = −68 g C m−2 yr−1). The annual accumulated evapotranspiration (ET) was overestimated (R2= 0.31, RMSE = 101 mm yr−1 and bias = 37 mm yr−1), mainly in the ecosystems with subtropical Mediterranean climate, for which the soil resistance to evaporation from porous space (rss) had to be constrained from soil moisture content (SMC) or land surface temperature (LST). Overall, the study demonstrates that SCOPE model can simulate ecosystem flux with high accuracy without site-specific calibration of its parameters.

Uncertainties of gross primary productivity of Chinese grasslands based on multi-source estimation

Gross primary productivity (GPP) is an important parameter in the carbon cycle and climate change studies. The results of GPP fluxes estimated based on multiple models or remote sensing vary widely, but current studies of GPP in Chinese grasslands tend to ignore data uncertainty. In this study, uncertainty analysis of GPP datasets estimated based on terrestrial ecosystem models and remote sensing was conducted using cross-validation, standard error statistics, and ensemble empirical modal decomposition. We found that 1) the fit coefficients R2 of two-by-two cross-validation of GPP datasets mostly exceeded 0.8 at the global scale. 2) GPP from different sources were consistent in portraying the spatial and temporal patterns of GPP in Chinese grasslands. However, due to many differences in model structure, parameterization and driving data, some uncertainties still exist, especially in the parts of dry-cold areas where the standard deviations are relatively large. 3) Uncertainties were higher for future scenarios than for historical periods, and GPP uncertainties were much higher for future high-emissions scenarios than for low- and medium-emissions scenarios. This study highlighted the need for uncertainty analysis when GPP is applied to spatio-temporal analysis, and suggested that when comparing and assessing carbon balance conditions, multiple source data sets should be combined to avoid misleading conclusion due to uncertainty.

Modeling methane dynamics in three wetlands in Northeastern China by using the CLM-Microbe model

ABSTRACT Wetlands account for up to 70% of the natural source of methane (CH4) in terrestrial ecosystems on a global scale. Soil microbes are the ultimate producers and biological consumers of CH4 in wetlands. Therefore, simulating microbial mechanisms of CH4 production and consumptionwould improve the predictability of CH4 flux in wetland ecosystems. In this study, we applied a microbial-explicit model, the CLM-Microbe, to simulate CH4 flux in three major natural wetlands in northeastern China. The CLM-Microbe model was able to capture the seasonal variation of gross primary productivity (GPP), dissolved organic carbon (DOC), and CH4 flux. The CLM-Microbe model explained more than 40% of the variation in GPP and CH4 flux across sites. Marsh wetlands had higher CH4 flux than mountain peatlands. Ebullition dominated the CH4 transport pathway in all three wetlands. The methanogenesis dominates while methanotroph makes a minor contribution to the CH4 flux, making all wetlands a CH4 source. Sensitivity analysis indicated that microbial growth and death rates are the key factors governing CH4 emission and vegetation physiological properties (flnr) and maintenance respiration predominate GPP variation. Explicitly simulating microbial processes allows genomic information to be incorporated, laying a foundation for better predicting CH4 dynamics under the changing environment.

Modeled Surface‐Atmosphere Fluxes From Paired Sites in the Upper Great Lakes Region Using Neural Networks

AbstractThe eddy covariance (EC) method is one of the most widely used approaches to quantify surface‐atmosphere fluxes. However, scaling up from a single EC tower to the landscape remains an open challenge. To address this, we used 63 site years of data to examine simulated annual and growing season sums of carbon fluxes from three paired land‐cover type sites of corn, restored‐prairie, and switchgrass ecosystems. This was also done across the landscape by modeling fluxes using different land‐cover type input data. An artificial neural network (ANN) approach was used to model net ecosystem exchange (NEE), ecosystem respiration (Reco), and gross primary production (GPP) at one paired site using environmental observations from the second site only. With a mean spatial separation of 11 km between paired sites, we were able to model annual sums of NEE, Reco, and GPP with uncertainties of 20%, 22%, and 8%, respectively, relative to observation sums. When considering the growing season only, model uncertainties were 17%, 22%, and 9%, respectively for the three flux terms. We also show that ANN models can estimate sums of Reco and GPP fluxes without needing the constraint of similar land‐cover‐type, with annual uncertainties of 12% and 10%. These results provide new insights to scaling up observations from one EC site beyond the footprint of the EC tower to multiple land‐cover types across the landscape.

Methane fluxes but not respiratory carbon dioxide fluxes altered under Si amendment during drying – rewetting cycles in fen peat mesocosms

Peatlands are globally important sinks and sources for greenhouse gasses. Their carbon (C) balances are dependent on input of biomass, its quality and decomposition. Silicon (Si) is known to influence the C quality, especially of graminoids, and it influences decomposition under oxic conditions. Drying – rewetting events (D/W), which may influence C mineralization and change nutrient availability, have been predicted to increase in frequency. This raises the question if effects of Si on decomposition are modified by such D/W events.To elucidate this question, we conducted a study with mesocosms extracted from a fen peatland.We hypothesized, that (I, II) there would be higher gas and solute concentrations after Si addition compared to the control after D/W. Further we expected (III) that higher respiratory fluxes of CO2 and CH4 would occur after Si addition and D/W and that (IV) there would be a higher gross primary production (GPP) under Si addition due to more viable biomass that is less prone to drought effects. Finally we hypothesized that (V) the chemical composition of Carex rostrata Stokes leaves grown under Si fertilization after a D/W cycle would be less recalcitrant compared to of C. rostrata leaves from the controls. To test these hypotheses, a control and a Si treatment group of mesocosms were subjected to a controlled D/W cycle. We measured soil gas (DIC and CH4), and solute concentrations (P, Si, Fe and TOC) and gas fluxes of CO2 and CH4. Furthermore, we characterized the graminoid biomass for major element contents and by infrared spectroscopy (FTIR) for organic matter quality.Higher CH4 fluxes were observed in the Si fertilized mesocosms. There was no difference between the treatments in the ER CO2 fluxes, whereas the NEE and GPP fluxes indicated higher CO2 uptake in the Si treated mesocosms. Solute concentrations showed no differences, neither for dissolved gasses , nor for solutes. C. rostrata leaves had lower shares of lignin, waxes, fats, and phenols in plants from the Si fertilized mesocosms.We concluded, that during D/W, plants from Si fertilized mesocosms accumulated more Si than plants from the control mesocosms. These plants were more viable, produced potentially more exudates and had more readily decomposable litter than controls, due to their lower share in recalcitrant moieties. This may have stimulated CH4 production and emission of CH4 from the soil into the atmosphere through abundant aerenchyma roots, bypassing oxic, methanotrophic peat layers.

Seasonal Controls of CO2 and CH4 Dynamics in a Temporarily Flooded Subtropical Wetland

AbstractSubtropical and tropical wetlands play a prominent role in the global carbon (C) cycle; yet factors that influence their C fluxes remain uncertain. We collected measurements from a temporarily flooded subtropical wetland over 3 years to investigate environmental drivers impacting CO2 and CH4 fluxes. The wetland was a sink of CO2 (−469 to −380 g C‐CO2 · m−2 · year−1) and a source of CH4 (25.1 to 32.1 g C‐CH4 · m−2 · year−1) to the atmosphere. Dry season CH4 emissions represented 41 to 49% of the annual budget, reflecting the importance of continuous CH4 flux measurements. Gross primary productivity (GPP) increased with temperature and radiation, and the influence of VPD on GPP varied with soil inundation. Higher water tables decreased Reco and increased GPP, and a higher GPP in turn lead to enhanced Reco likely through enhancements of GPP on autotrophic respiration. This suggests that the impact of the water table on Reco depends on the cancelling effects of hydrology and GPP. Emissions of CH4 increased with soil temperature, water table, and GPP until soils were inundated at which point temperature and GPP became the main drivers. Water table and temperature influenced GPP and CH4 fluxes, and increases in GPP directly enhanced CH4 emissions. In addition to impacting C fluxes directly through water table depth, hydrology also determined the hierarchy of the dominance of factors controlling C fluxes and their response. The positive climate forcing of subtropical wetlands may be dictated by plant‐mediated and climate interactions, with hydrological factors playing a major role in determining the greenhouse gas sink or source strength of subtropical wetlands.

Comprehensive assessments of carbon dynamics in an intermittently-irrigated rice paddy

Rice is a major crop that feeds more than half of the global population. Rice paddies play a complex role in the carbon cycle by emitting CH4 to the atmosphere while either sequestering or releasing CO2 to the atmosphere. Here, we present 3.5 years of eddy covariance measurements of CO2 and CH4 fluxes over an intermittently irrigated, single crop rice paddy in South Korea in tandem with carbon stock measurements for leaves, stems, grains, and roots. The rice paddy acted as a slight sink or neutral in CO2 (−47 ± 51 g C m−2y−1; mean ± 95% confidence interval) with 941 ± 130 g C m−2y−1 and 894 ± 108 g C m−2y−1 as the gross primary production (GPP) and ecosystem respiration (Reco), respectively. The mean annual net primary productivity (NPP) was 666 ± 31 g C m−2y−1, which was partitioned to leaves (9%), stems (30%), grains (51%), and roots (10%), for a harvest index of 57%. The paddy emitted 20.6 ± 1.5 g C m−2y−1 in the form of CH4, of which between 82 and 96% was emitted during the cultivation period (~132 days) . The CH4 flux showed a mid-season decline caused by mid-season drainage, which produced aerobic soil conditions that inhibited CH4 emissions. The proportion of estimated ebullitive flux to total CH4 flux ranged from 10 to 17% during the cultivation period. GPP, Reco, and CH4 fluxes varied by 12%, 11%, and 7%, respectively, over the 3-year period. When considering the harvest and the global warming potential of CH4, the paddy shifted from a greenhouse gas sink to a strong source (2,741 ± 468 g CO2 eq m−2y−1). These findings will improve our understanding of carbon budgets and cycles, and help develop greenhouse gas and water management strategies for rice paddies.

Quantifying the Impacts of Land-Use and Climate on Carbon Fluxes Using Satellite Data across Texas, U.S.

Climate change and variability, soil types and soil characteristics, animal and microbial communities, and photosynthetic plants are the major components of the ecosystem that affect carbon sequestration potential of any location. This study used NASA’s Soil Moisture Active Passive (SMAP) Level 4 carbon products, gross primary productivity (GPP), and net ecosystem exchange (NEE) to quantify their spatial and temporal variabilities for selected terrestrial ecosystems across Texas during the 2015–2018 study period. These SMAP carbon products are available at 9 km spatial resolution on a daily basis. The ten selected SMAP grids are located in seven climate zones and dominated by five major land uses (developed, crop, forest, pasture, and shrub). Results showed CO2 emissions and uptake were affected by land-use and climatic conditions across Texas. It was also observed that climatic conditions had more impact on CO2 emissions and uptake than land-use in this state. On average, South Central Plains and East Central Texas Plains ecoregions of East Texas and Western Gulf Coastal Plain ecoregion of Upper Coast climate zones showed higher GPP flux and potential carbon emissions and uptake than other climate zones across the state, whereas shrubland on the Trans Pecos climate zone showed lower GPP flux and carbon emissions/uptake. Comparison of GPP and NEE distribution maps between 2015 and 2018 confirmed substantial changes in carbon emissions and uptake across Texas. These results suggest that SMAP carbon products can be used to study the terrestrial carbon cycle at regional to global scales. Overall, this study helps to understand the impacts of climate, land-use, and ecosystem dynamics on the terrestrial carbon cycle.

Influence of Landscape Heterogeneity and Spatial Resolution in Multi-Temporal In Situ and MODIS NDVI Data Proxies for Seasonal GPP Dynamics

The objective of this paper was to evaluate the use of in situ normalized difference vegetation index (NDVIis) and Moderate Resolution Imaging Spectroradiometer NDVI (NDVIMD) time series data as proxies for ecosystem gross primary productivity (GPP) to improve GPP upscaling. We used GPP flux data from 21 global FLUXNET sites across main global biomes (forest, grassland, and cropland) and derived MODIS NDVI at contrasting spatial resolutions (between 0.5 × 0.5 km and 3.5 × 3.5 km) centered at flux tower location. The goodness of the relationship between NDVIis and NDVIMD varied across biomes, sites, and MODIS spatial resolutions. We found a strong relationship with a low variability across sites and within year variability in deciduous broadleaf forests and a poor correlation in evergreen forests. Best performances were obtained for the highest spatial resolution at 0.5 × 0.5 km). Both NDVIis and NDVIMD elicited roughly three weeks later the starting of the growing season compared to GPP data. Our results confirm that to improve the accuracy of upscaling in situ data of site GPP seasonal responses, in situ radiation measurement biomes should use larger field of view to sense an area, or more sensors should be placed in the flux footprint area to allow optimal match with satellite sensor pixel size.

Partitioning net ecosystem exchange of CO<sub>2</sub> on the pedon scale in the Lena River Delta, Siberia

Abstract. Arctic tundra ecosystems are currently facing amplified rates of climate warming. Since these ecosystems store significant amounts of soil organic carbon, which can be mineralized to carbon dioxide (CO2) and methane (CH4), rising temperatures may cause increasing greenhouse gas fluxes to the atmosphere. To understand how net the ecosystem exchange (NEE) of CO2 will respond to changing climatic and environmental conditions, it is necessary to understand the individual responses of the processes contributing to NEE. Therefore, this study aimed to partition NEE at the soil–plant–atmosphere interface in an arctic tundra ecosystem and to identify the main environmental drivers of these fluxes. NEE was partitioned into gross primary productivity (GPP) and ecosystem respiration (Reco) and further into autotrophic (RA) and heterotrophic respiration (RH). The study examined CO2 flux data collected during the growing season in 2015 using closed-chamber measurements in a polygonal tundra landscape in the Lena River Delta, northeastern Siberia. To capture the influence of soil hydrology on CO2 fluxes, measurements were conducted at a water-saturated polygon center and a well-drained polygon rim. These chamber-measured fluxes were used to model NEE, GPP, Reco, RH, RA, and net primary production (NPP) at the pedon scale (1–10 m) and to determine cumulative growing season fluxes. Here, the response of in situ measured RA and RH fluxes from permafrost-affected soils of the polygonal tundra to hydrological conditions have been examined. Although changes in the water table depth at the polygon center sites did not affect CO2 fluxes from RH, rising water tables were linked to reduced CO2 fluxes from RA. Furthermore, this work found the polygonal tundra in the Lena River Delta to be a net sink for atmospheric CO2 during the growing season. The NEE at the wet, depressed polygon center was more than twice that at the drier polygon rim. These differences between the two sites were caused by higher GPP fluxes due to a higher vascular plant density and lower Reco fluxes due to oxygen limitation under water-saturated conditions at the polygon center in comparison to the rim. Hence, soil hydrological conditions were one of the key drivers for the different CO2 fluxes across this highly heterogeneous tundra landscape.