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Understanding the water and carbon cycles within terrestrial ecosystems is crucial for effective monitoring and management of regional water resources and the ecological environment. However, physical models like the SEB- and LUE-based ones can be complex and demand extensive input data. In our study, we leveraged multiple variables (vegetation growth, surface moisture, radiative energy, and other relative variables) as inputs for various regression algorithms, including Multiple Linear Regression (MLR), Random Forest Regression (RFR), and Backpropagation Neural Network (BPNN), to estimate water (ET) and carbon fluxes (NEE) in the Haihe River Basin, and compared the estimated results with the observations from six eddy covariance flux towers. We aimed to (1) assess the impacts of different input variables on the accuracy of ET and NEE estimations, (2) compare the accuracy of the three regression methods, including three machine learning algorithms and Multiple Linear Regression, and (3) evaluate the performance of ET and NEE estimation models across various regions. The key findings include: (1) Increasing the number of input variables typically improved the accuracy of ET and NEE estimations. (2) RFR proved to be the most accurate for both ET and NEE estimations among the three regression algorithms. Of these, the four types of variables used together with RFR resulted in the best accuracy for ET (R2 of 0.81 and an RMSE of 1.13 mm) and NEE (R2 of 0.83 and an RMSE of 2.83 gC/m2) estimations. (3) Vegetation growth variables (i.e., VIs) are the most important inputs for ET and NEE estimation. (4) The proposed ET and NEE estimation models exhibited some variation in accuracy across different validation sites. Despite these variations, the accuracy levels across all six validation sites remained relatively high. Overall, this study lays the groundwork for an efficient approach to agricultural water resources and ecosystem monitoring and management. [ABSTRACT FROM AUTHOR]

The area of land dedicated to growing maize for bioenergy in the United Kingdom is rapidly expanding. To understand how maize production influences soil carbon (C) dynamics, and whether this is influenced by soil type, we measured net ecosystem exchange (NEE) using the eddy covariance technique over the 2021 growing season. We combined the NEE data with C imports and exports to calculate the net ecosystem productivity (NEP) of two maize crops grown for bioenergy in the United Kingdom, one site on mineral soil and the other on lowland agricultural peat. Maize was similarly productive at both sites—gross primary productivity was 1107 g C m−2 at the site with mineral soil and 1407 g C m−2 at the peat site. However, total ecosystem respiration was considerably higher from the peat site (1198 g C m−2) compared with the mineral soil site (678 g C m−2). After accounting for the removal of C in harvested biomass, both sites were net C sources, but C losses were over two times greater from the peat site (NEP = 290 g C m−2) than the mineral site (NEP = 136 g C m−2). While annual crops may be needed to produce bioenergy in the short term, growing maize for bioenergy in the United Kingdom does not appear to be a viable option for C sequestration over the long term, as it leads to high carbon losses from agroecosystems, especially those on organic soils. Instead, growing perennial bioenergy crops on mineral soils with a low organic C content is a more appropriate option. [ABSTRACT FROM AUTHOR]

Permafrost thaw in northern peatlands causes collapse of permafrost peat plateaus and thermokarst bog development, with potential impacts on atmospheric greenhouse gas exchange. Here, we measured methane and carbon dioxide fluxes over 3 years (including winters) using static chambers along two permafrost thaw transects in northwestern Canada, spanning young (~30 years since thaw), intermediate and mature thermokarst bogs (~200 years since thaw). Young bogs were wetter, warmer and had more hydrophilic vegetation than mature bogs. Methane emissions increased with wetness and soil temperature (40 cm depth) and modelled annual estimates were greatest in the young bog during the warmest year and lowest in the mature bog during the coolest year (21 and 7 g C‐CH4 m−2 year−1, respectively). The dominant control on net ecosystem exchange (NEE) in the mature bog (between +20 and −54 g C‐CO2 m−2 year−1) was soil temperature (5 cm), causing net CO2 loss due to higher ecosystem respiration (ER) in warmer years. In contrast, wetness controlled NEE in the young and intermediate bogs (between +55 and −95 g C‐CO2 m−2 year−1), where years with periodic inundation at the beginning of the growing season caused greater reduction in gross primary productivity than in ER leading to CO2 loss. Winter fluxes (November–April) represented 16% of annual ER and 38% of annual CH4 emissions. Our study found NEE of thermokarst bogs to be close to neutral and rules out large CO2 losses under current conditions. However, high CH4 emissions after thaw caused a positive net radiative forcing effect. While wet conditions favouring high CH4 emissions only persist for the initial young bog period, we showed that continued climate warming with increased ER, and thus, CO2 losses from the mature bog can cause net positive radiative forcing which would last for centuries after permafrost thaw. [ABSTRACT FROM AUTHOR]

Evaluation of the net ecosystem exchange (NEE) of agroecosystems may help to assess regional carbon cycles and ensure carbon neutrality. However, farmland NEE is influenced by climate change, phenological period, and crop management. Therefore, in this study, we investigated the time-lag effects of climate change on the phenology and NEE of winter wheat, as well as the influence of crop management. We found that the impact of climate change on the phenology of winter wheat exhibits a time lag of 1 to 2 months, with notable spatial and temporal heterogeneity. When accounting for time-lag effects, the correlation between climate change and both phenology and NEE significantly strengthens, with the lagged impact on NEE primarily mediated through changes in phenology. Specifically, climate change accelerates the green-up and heading dates but delays the maturity date, while crop management extends the phenological period. Our findings indicated that during the green-up stage, NEE is mainly influenced by temperature changes resulting from climate change. During the heading stage, it is predominantly affected by crop management, and during the maturity stage, it is impacted by both factors, with crop management having a more significant effect. Overall, the accelerated green-up stage and delayed heading and maturity stages contribute to enhanced carbon sequestration. This research provides new insights into carbon exchanges in agricultural ecosystems. [ABSTRACT FROM AUTHOR]

Increased soil nutrient availability, and associated increases in vegetation productivity, could create a negative feedback between Arctic ecosystems and the climate system, thereby reducing the contribution of Arctic ecosystems to future climate change. To predict whether this feedback will develop, it is important to understand the environmental controls over nutrient cycling in High Arctic ecosystems and their impact on carbon cycling processes. Here, we examined the environmental controls over soil nitrogen availability in a High Arctic wet sedge meadow and how abiotic factors and soil nitrogen influenced carbon dioxide exchange processes. The importance of environmental variables was consistent over the 3 years, but the magnitudes of their effect varied depending on climate conditions. Ammonium availability was higher in warmer years and wetter conditions, while drier areas within the wetland had higher nitrate availability. Carbon uptake was driven by soil moisture, active layer depth, and variability between sampling sites and years (R2 = 0.753), while ecosystem respiration was influenced by nitrogen availability, soil temperature, active layer depth, and sampling year (R2 = 0.848). Considered together, the future carbon dioxide source or sink potential of high latitude wetlands will largely depend on climate-induced changes in moisture and subsequent impacts on nutrient availability.

Abstract The area of land dedicated to growing maize for bioenergy in the United Kingdom is rapidly expanding. To understand how maize production influences soil carbon (C) dynamics, and whether this is influenced by soil type, we measured net ecosystem exchange (NEE) using the eddy covariance technique over the 2021 growing season. We combined the NEE data with C imports and exports to calculate the net ecosystem productivity (NEP) of two maize crops grown for bioenergy in the United Kingdom, one site on mineral soil and the other on lowland agricultural peat. Maize was similarly productive at both sites—gross primary productivity was 1107 g C m−2 at the site with mineral soil and 1407 g C m−2 at the peat site. However, total ecosystem respiration was considerably higher from the peat site (1198 g C m−2) compared with the mineral soil site (678 g C m−2). After accounting for the removal of C in harvested biomass, both sites were net C sources, but C losses were over two times greater from the peat site (NEP = 290 g C m−2) than the mineral site (NEP = 136 g C m−2). While annual crops may be needed to produce bioenergy in the short term, growing maize for bioenergy in the United Kingdom does not appear to be a viable option for C sequestration over the long term, as it leads to high carbon losses from agroecosystems, especially those on organic soils. Instead, growing perennial bioenergy crops on mineral soils with a low organic C content is a more appropriate option.

Understanding the water and carbon cycles within terrestrial ecosystems is crucial for effective monitoring and management of regional water resources and the ecological environment. However, physical models like the SEB- and LUE-based ones can be complex and demand extensive input data. In our study, we leveraged multiple variables (vegetation growth, surface moisture, radiative energy, and other relative variables) as inputs for various regression algorithms, including Multiple Linear Regression (MLR), Random Forest Regression (RFR), and Backpropagation Neural Network (BPNN), to estimate water (ET) and carbon fluxes (NEE) in the Haihe River Basin, and compared the estimated results with the observations from six eddy covariance flux towers. We aimed to (1) assess the impacts of different input variables on the accuracy of ET and NEE estimations, (2) compare the accuracy of the three regression methods, including three machine learning algorithms and Multiple Linear Regression, and (3) evaluate the performance of ET and NEE estimation models across various regions. The key findings include: (1) Increasing the number of input variables typically improved the accuracy of ET and NEE estimations. (2) RFR proved to be the most accurate for both ET and NEE estimations among the three regression algorithms. Of these, the four types of variables used together with RFR resulted in the best accuracy for ET (R2 of 0.81 and an RMSE of 1.13 mm) and NEE (R2 of 0.83 and an RMSE of 2.83 gC/m2) estimations. (3) Vegetation growth variables (i.e., VIs) are the most important inputs for ET and NEE estimation. (4) The proposed ET and NEE estimation models exhibited some variation in accuracy across different validation sites. Despite these variations, the accuracy levels across all six validation sites remained relatively high. Overall, this study lays the groundwork for an efficient approach to agricultural water resources and ecosystem monitoring and management.

Improving the accuracy of monitoring cropland CO2 exchange at heterogeneous spatial scales is of high importance to limit spatial and temporal uncertainty of terrestrial carbon (C) dynamic estimates. A combination of field scale eddy covariance EC) CO2 flux data and spatially matched Sentinel-2 derived vegetation indices (VIs) was tested as an approach to upscale agricultural C flux. The ability of different VIs to estimate daily net ecosystem exchange (NEE) and gross primary productivity GPP) based on linear regression models was assessed. Most VIs showed high (>0.9) and statistically significant (p<0.001) correlations with GPP and NEE although some VIs deviated from the seasonal pattern of CO2 exchange. In contrast, correlations between ecosystem respiration (Reco) and VIs were weak and statistically not significant, and no attempt was made to estimate Reco from VIs. Linear regression models explained generally more than 80% and 70% of the variability of NEE and GPP, respectively, with high variability amongst the individual VIs. The performance in estimating daily C fluxes varied amongst VIs depending on C flux component (NEE or GPP) and observation period. RMSE values ranged from 1.35 g C m-2 d-1 using the Green Normilized Difference Vegetation Index (GNDVI) for NEE to 5 g C m-2 d-1 using the Simple Ratio SR) for GPP. This equated to an underestimated net C uptake of only 41 g C m-2 (18%) and an overestimation of gross C uptake of 854 g C m-2 (73%). Differences between measured and estimated C fluxes were mainly explained by the diversion of the C flux and VI signal during winter, when C uptake stayed low while VI values indicated an increased C uptake due to relatively high crop leaf area. Overall, results exhibited similar error margins as mechanistic crop models. Thus, they indicated suitability and developability of the proposed approach to monitor cropland C exchange with satellite derived VIs. [ABSTRACT FROM AUTHOR]

The recurrent extreme El Niño events are commonly linked to reduced vegetation growth and the land carbon sink over many but discrete regions of the Northern Hemisphere (NH). However, we reported here a pervasive and continuous vegetation greening and no weakened land carbon sink in the maturation phase of the 2015/2016 El Niño event over the NH (mainly in the extra-tropics), based on multiple evidences from remote sensing observations, global ecosystem model simulations and atmospheric CO2 inversions. We discovered a significant compensation effect of the enhanced vegetation growth in spring on subsequent summer/autumn vegetation growth that sustained vegetation greening and led to a slight increase in the land carbon sink over the spring and summer of 2015 (average increases of 23.34% and 0.63% in net ecosystem exchange from two independent datasets relative to a 5-years average before the El Niño event, respectively) and spring of 2016 (6.82%), especially in the extra-tropics of the NH, where the water supply during the pre-growing-season (November of the previous year to March of the current year) had a positive anomaly. This seasonal compensation effect was much stronger than that in 1997 and 1998 and significantly alleviated the adverse impacts of the 2015/2016 El Niño event on vegetation growth during its maturation phase. The legacy effect of water supply during the pre-growing-season on subsequent vegetation growth lasted up to approximately six months. Our findings highlight the role of seasonal compensation effects on mediating the land carbon sink in response to episodic extreme El Niño events. [ABSTRACT FROM AUTHOR]

Observations of the annual cycle of atmospheric CO2 in high northern latitudes provide evidence for an increase in terrestrial metabolism in Arctic tundra and boreal forest ecosystems. However, the mechanisms driving these changes are not yet fully understood. One proposed hypothesis is that ecological change from disturbance, such as wildfire, could increase the magnitude and change the phase of net ecosystem exchange through shifts in plant community composition. Yet, little quantitative work has evaluated this potential mechanism at a regional scale. Here we investigate how fire disturbance influences landscape‐level patterns of photosynthesis across western boreal North America. We use Alaska and Canadian large fire databases to identify the perimeters of wildfires, a Landsat‐derived land cover time series to characterize plant functional types (PFTs), and solar‐induced fluorescence (SIF) from the Orbiting Carbon Observatory‐2 (OCO‐2) as a proxy for photosynthesis. We analyze these datasets to characterize post‐fire changes in plant succession and photosynthetic activity using a space‐for‐time approach. We find that increases in herbaceous and sparse vegetation, shrub, and deciduous broadleaf forest PFTs during mid‐succession yield enhancements in SIF by 8–40% during June and July for 2‐ to 59‐year stands relative to pre‐fire controls. From the analysis of post‐fire land cover changes within individual ecoregions and modeling, we identify two mechanisms by which fires contribute to long‐term trends in SIF. First, increases in annual burning are shifting the stand age distribution, leading to increases in the abundance of shrubs and deciduous broadleaf forests that have considerably higher SIF during early‐ and mid‐summer. Second, fire appears to facilitate a long‐term shift from evergreen conifer to broadleaf deciduous forest in the Boreal Plain ecoregion. These findings suggest that increasing fire can contribute substantially to positive trends in seasonal CO2 exchange without a close coupling to long‐term increases in carbon storage. [ABSTRACT FROM AUTHOR]

Permafrost degradation in peatlands is altering vegetation and soil properties and impacting net carbon storage. We studied four adjacent sites in Alaska with varied permafrost regimes, including a black spruce forest on a peat plateau with permafrost, two collapse scar bogs of different ages formed following thermokarst, and a rich fen without permafrost. Measurements included year‐round eddy covariance estimates of net carbon dioxide (CO2), mid‐April to October methane (CH4) emissions, and environmental variables. From 2011 to 2022, annual rainfall was above the historical average, snow water equivalent increased, and snow‐season duration shortened due to later snow return. Seasonally thawed active layer depths also increased. During this period, all ecosystems acted as slight annual sources of CO2 (13–59 g C m−2 year−1) and stronger sources of CH4 (11–14 g CH4 m−2 from ~April to October). The interannual variability of net ecosystem exchange was high, approximately ±100 g C m−2 year−1, or twice what has been previously reported across other boreal sites. Net CO2 release was positively related to increased summer rainfall and winter snow water equivalent and later snow return. Controls over CH4 emissions were related to increased soil moisture and inundation status. The dominant emitter of carbon was the rich fen, which, in addition to being a source of CO2, was also the largest CH4 emitter. These results suggest that the future carbon‐source strength of boreal lowlands in Interior Alaska may be determined by the area occupied by minerotrophic fens, which are expected to become more abundant as permafrost thaw increases hydrologic connectivity. Since our measurements occur within close proximity of each other (≤1 km2), this study also has implications for the spatial scale and data used in benchmarking carbon cycle models and emphasizes the necessity of long‐term measurements to identify carbon cycle process changes in a warming climate. [ABSTRACT FROM AUTHOR]

Increasing temperatures and winter precipitation can influence the carbon (C) exchange rates in arctic ecosystems. Feedbacks can be both positive and negative, but the net effects are unclear and expected to vary strongly across the Arctic. There is a lack of understanding of the combined effects of increased summer warming and winter precipitation on the C balance in these ecosystems. Here we assess the short‐term (1–3 years) and long‐term (5–8 years) effects of increased snow depth (snow fences) (on average + 70 cm) and warming (open top chambers; 1–3°C increase) and the combination in a factorial design on all key components of the daytime carbon dioxide (CO2) fluxes in a wide‐spread heath tundra ecosystem in West Greenland. The warming treatment increased ecosystem respiration (ER) on a short‐ and long‐term basis, while gross ecosystem photosynthesis (GEP) was only increased in the long term. Despite the difference in the timing of responses of ER and GEP to the warming treatment, the net ecosystem exchange (NEE) of CO2 was unaffected in the short term and in the long term. Although the structural equation model (SEM) indicates a direct relationship between seasonal accumulated snow depth and ER and GEP, there were no significant effects of the snow addition treatment on ER or GEP measured over the summer period. The combination of warming and snow addition turned the plots into net daytime CO2 sources during the growing season. Interestingly, despite no significant changes in air temperature during the snow‐free time during the experiment, control plots as well as warming plots revealed significantly higher ER and GEP in the long term compared to the short term. This was in line with the satellite‐derived time‐integrated normalized difference vegetation index of the study area, suggesting that more factors than air temperature are drivers for changes in arctic tundra ecosystems. [ABSTRACT FROM AUTHOR]

In the northern high latitudes, warmer spring temperatures generally lead to earlier leaf onsets, higher vegetation production, and enhanced spring carbon uptake. Yet, whether this positive linkage has diminished under climate change remains debated. Here, we used atmospheric CO2 measurements at Barrow (Alaska) during 1979–2020 to investigate the strength of temperature dependence of spring carbon uptake reflected by two indicators, spring zero‐crossing date (SZC) and CO2 drawdown (SCC). We found a fall and rise in the interannual correlation of temperature with SZC and SCC (RSZC‐T and RSCC‐T), showing a recent reversal of the previously reported weakening trend of RSZC‐T and RSCC‐T. We used a terrestrial biosphere model coupled with an atmospheric transport model to reproduce this fall and rise phenomenon and conducted factorial simulations to explore its potential causes. We found that a strong–weak–strong spatial synchrony of spring temperature anomalies per se has contributed to the fall and rise trend in RSZC‐T and RSCC‐T, despite an overall unbroken temperature control on net ecosystem CO2 fluxes at local scale. Our results provide an alternative explanation for the apparent drop of RSZC‐T and RSCC‐T during the late 1990s and 2000s, and suggest a continued positive linkage between spring carbon uptake and temperature during the past four decades. We thus caution the interpretation of apparent climate sensitivities of carbon cycle retrieved from spatially aggregated signals. [ABSTRACT FROM AUTHOR]

Terrestrial carbon fluxes are crucial to the global carbon cycle. Quantification of terrestrial carbon fluxes over the Tibetan Plateau (TP) has considerable uncertainties due to the unique ecosystem and climate and scarce flux observations. This study evaluated our recent improvement of terrestrial flux parameterization in the weather research and forecasting model coupled with the vegetation photosynthesis and respiration model (WRF-VPRM) in terms of reproducing observed net ecosystem exchange (NEE), gross ecosystem exchange (GEE), and ecosystem respiration (ER) over the TP. The improvement of VPRM relative to the officially released version considers the impact of water stress on terrestrial fluxes, making it superior to the officially released model due to its reductions in bias, root mean square error (RMSE), and ratio of standard deviation (RSD) of NEE to 0.850 μmol·m−2·s−1, 0.315 μmol·m−2·s−1, and 0.001, respectively. The improved VPRM also affects GEE simulation, increasing its RSD to 0.467 and decreasing its bias and RMSE by 1.175 and 0.324 μmol·m−2·s−1, respectively. Furthermore, bias and RMSE for ER were lowered to −0.417 and 0.954 μmol·m−2·s−1, with a corresponding increase in RSD by 0.6. The improved WRF-VPRM simulation indicates that eastward winds drive the transfer of lower CO2 concentrations from the eastern to the central and western TP and the influx of low-concentration CO2 inhibits biospheric CO2 uptake. The use of an improved WRF-VPRM in this study helps to reduce errors, improve our understanding of the role of carbon flux cycle over the TP, and ultimately reduce uncertainty in the carbon flux budget. [ABSTRACT FROM AUTHOR]

The drivers of interannual variability (IAV) of net ecosystem exchange (NEE) in forested wetlands are poorly understood, making it difficult to predict changes in atmospheric fluxes in response to land use and climate change. Similarly, these ecosystems demonstrate dynamic physiological and phenological responses to climate over time yet are typically modeled using static parameters that represent unchanging ecological conditions. Though static first-order ecosystem models are informative, they fundamentally lack the ability to represent dynamic annual changes in ecological processes that may drive IAV of NEE through time. We aimed to improve understanding of how forested wetlands dynamically respond to climate and which key ecological processes may contribute to IAV of NEE. Simultaneously, we aimed to develop tools to evaluate dynamically parameterized process based first-order ecosystem models. To achieve these objectives, long-term ecological data were fused with the Total Ecosystem (TECO) model in three loblolly pine plantations and a bottomland hardwood forest of contrasting stand age in wetland areas of the lower coastal plain of North Carolina. Variance decomposition was used to assess changes in large-scale ecosystem drivers. To investigate individual processes, both static and dynamic data-assimilation were conducted to simulate time–invariant and time–varying ecological response. Anomalies in dynamic ecosystem process response were correlated with NEE anomalies to attribute IAV of NEE to underlying process-based mechanisms that may drive annual changes in NEE across stand age and sites. Assessment of large-scale drivers of IAV of NEE across sites demonstrated that maximum carbon uptake (MCU) dominated IAV of NEE in the mature pine plantation. These large-scale NEE signals were further parsed into ecological processes in the TECO model, where process anomaly correlation showed that slight variations in root maintenance respiration and woody biomass turnover rates may be underlying drivers of IAV of MCU and subsequently NEE. However, in the young pine plantations and bottomland hardwood forest IAV of NEE was not dominated by MCU. In contrast, IAV of NEE in young plantations was influenced most by annual changes in maximum carbon release (MCR) and carbon uptake period (CUP), while IAV of NEE in the bottomland hardwood forest was dominated by CUP. These results demonstrate that dynamic data assimilation (DA), variance decomposition, and process anomaly correlation are investigative and diagnostic tools for process-based models, though maximum GPP was systematically underestimated by models across sites. Despite problems with peak GPP representation, anomaly correlation between ecological processes and IAV of NEE allowed investigation of the specific ecological drivers of annual variability in ecosystem-level carbon exchange. As ecosystems show dynamic physiological and phenological properties through time, it may be important to allow models to have dynamic/time–varying ecological responses, especially if the root causes of IAV of NEE are to be attributed to ecological processes in process-based models. [ABSTRACT FROM AUTHOR]

The response of net ecosystem exchange (NEE) to environmental changes is strongly nonlinear, characterized with threshold behaviors that are not well understood. Here, we investigated the threshold behaviors in the relationship between NEE and surface air temperature based on FLUXNET2015 observations, Community Land Model simulations, and Coupled Model Intercomparison Project Phase 6 (CMIP6) model outputs. An air temperature threshold of 16.4°C, corresponding to the maximum carbon sink (Tsink), was identified for all 205 FLUXNET2015 sites combined. For deciduous broadleaf and mixed forests, wetlands and wheat‐barley croplands, and rice‐maize‐soybean croplands, we identified a carbon‐source threshold (Tsource) of 6.8, 5.0, and 18.0°C, respectively, beyond which the ecosystem becomes less of a carbon source. Five cold climate types mainly encompassing these plant functional types showed a clear carbon‐source Tsource of 12.2°C. Six CMIP6 models project a threshold temperature increase of 1.0–2.8°C by the 2090s, which results primarily from a shift of the optimum temperature for gross primary production. Not accounting for the warming‐induced threshold changes may lead to an estimated time of the average summer air temperature passing Tsink that is earlier by 4.5–6.7 and 6.4–12.2 years at low (15°N–15°S) and high (≥60°S or ≥ 60°N) latitudes, respectively. Plain Language Summary: Net ecosystem exchange (NEE) reflects the ecosystem's role as a carbon source or sink. Previous studies on NEE indicated a temperature threshold for the size of ecosystem carbon sink. It remains unknown whether a similar threshold for carbon source might exist, how the thresholds for both carbon source and sink may vary with climate and ecosystem types, and how they may change with global warming. The present study fills these gaps. Here we extract patterns of temperature thresholds for different climate and ecosystem types, and project a 1.0–2.8°C increase of threshold temperature by the end of the century. This increase has to be properly accounted for when studying the terrestrial carbon‐climate interactions. Key Points: An air temperature threshold of 16.4°C, corresponding to the maximum carbon sink, was identified for all 205 FLUXNET2015 sites combinedDeciduous broadleaf forests, mixed forests, and croplands showed a temperature threshold corresponding to a sudden change of carbon sourceCoupled Model Intercomparison Project Phase 6 models project a 1.0–2.8°C increase of threshold temperature by the end of the century [ABSTRACT FROM AUTHOR]

Despite a rapid development of Nature-Based Solutions (NBS) for carbon removal in recent years, the methods for evaluating NBS still have certain gaps. We propose an approach based on a combination of remote sensing data and meteorological variables to reconstruct the spatiotemporal variation of net ecosystem exchange from eddy-covariance stations. A Lagrangian particle dispersion model was used for upscaling satellite images and flux towers. We trained data-driven models based on kernel methods separately for each selected land-cover class. The results suggest that the proposed approach to quantifying carbon exchange on a medium-to-large scale by blending eddy covariance flux data with moderate resolution satellite and weather data provides a set of key advantages over previously deployed methods: (1) scalability, achieved via the validation design based on a separate set of eddy covariance stations; (2) high spatial and temporal resolution thanks to the use of Landsat imagery; and (3) robust and accurate predictions due to improved data quality control, advanced machine learning techniques, and rigorous validation. The machine learning models yielded high cross-validation results. Stratification that uses separate Fluxnet stations for each fold of validation ensures that the models are accurate across the area covered by the Fluxnet sites. Overall, we present here a globally scaled technology for the land sector based on high resolution remote sensing imagery, meteorological variables, and direct carbon flux measurements of eddy covariance flux stations.

Abstract Using biomass for charcoal production in sub‐Saharan Africa (SSA) may change carbon stock dynamics and lead to irreversible changes in the carbon balance, yet we have little understanding of whether these dynamics vary by biome in this region. Currently, charcoal production contributes up to 7% of yearly deforestation in tropical regions, with carbon emissions corresponding to 71.2 million tonnes of CO2 and 1.3 million tonnes of CH4. With a projected increased demand for charcoal in the coming decades, even low harvest rates may throw the carbon budget off‐balance due to legacy effects. Here, we parameterized the dynamic global vegetation model LPJ‐GUESS for six SSA biomes and examined the effect of charcoal production on net ecosystem exchange (NEE), carbon stock sizes and recovery time for tropical rain forest, montane forest, moist savanna, dry savanna, temperate grassland and semi‐desert. Under historical charcoal regimes, tropical rain forests and montane forests transitioned from net carbon sinks to net sources, that is, mean cumulative NEE from −3.56 ± 2.59 kg C/m2 to 2.46 ± 3.43 kg C/m2 and −2.73 ± 2.80 kg C/m2 to 1.87 ± 4.94 kg C/m2 respectively. Varying charcoal production intensities resulted in tropical rain forests showing at least two times higher carbon losses than the other biomes. Biome recovery time varied by carbon stock, with tropical and montane forests taking about 10 times longer than the fast recovery observed for semi‐desert and temperate grasslands. Our findings show that high biomass biomes are disproportionately affected by biomass harvesting for charcoal, and even low harvesting rates strongly affect vegetation and litter carbon and their contribution to the carbon budget. Therefore, the prolonged biome recoveries imply that current charcoal production practices in SSA are not sustainable, especially in tropical rain forests and montane forests, where we observe longer recovery for vegetation and litter carbon stocks.

This study observed the characteristics and influencing factors of the carbon fluxes of the Momoge salt marsh ecosystem over four years, which behaves as a CO2 sink. The daily, seasonal, and interannual variations in CO2 fluxes in the Momoge salt marshes were observed using the eddy covariance method and were compared with various environmental factors. An overall daily "U"-shaped distribution was observed, with uptake during the day (negative values) and release at night (positive values). Annually, the carbon fluxes in the study area roughly exhibited a "V" shape. The carbon fluxes during the non-growing season predominantly showed positive values, indicating the release of CO2 into the atmosphere. Photosynthetically active radiation was the primary influencing factor affecting the hourly and daytime variations in net ecosystem exchange (NEE) during the growing season, while temperature was the main factor influencing nighttime NEE dynamics. The air temperature, soil temperature, photosynthetically active radiation, precipitation, and water level all had significant impacts on the daily net CO2 exchange. At the monthly scale, larger values of soil temperature, air temperature, photosynthetically active radiation, and aboveground biomass corresponded to a stronger carbon absorption capacity of the ecosystem. Overall, temperature remains the primary factor for carbon fluxes in the Momoge wetlands. [ABSTRACT FROM AUTHOR]

Question: What is the effect of long‐term grazing and no grazing (NG) on carbon dioxide flux and soil carbon storage in Icelandic semi‐natural grasslands. Location: Three farms, with known history of land use, in W Iceland. Methods: On each farm, we located an intensively and an extensively grazed site, which both had been constantly grazed for centuries, and a parallel site with no grazing for over 50 years. We measured net ecosystem exchange (NEE), ecosystem respiration and normalized difference vegetation index on a regular basis over the growing season. Samples were taken from 60 cm deep soil profiles for analysis of soil organic carbon (SOC). Results: The grazed sites showed significantly more negative NEE than the NG sites, indicating more carbon dioxide uptake on the grazed sites compared to the NG sites. The normalized difference vegetation index was also significantly higher on the grazed sites. On all farms, the total SOC content was higher in the grazed sites than in the parallel NG sites. Conclusions: The study indicates that cessation of grazing decreases productivity and carbon dioxide uptake in a semi‐natural grassland in Iceland, as well as SOC content in the soil. Historically, all the NG sites in the study had the same grazing history as the continuously grazed sites until grazing exclusion. The measured lower SOC on the NG sites seems to indicate that, without grazing, SOC is lost with time and/or grazing is needed to maintain SOC in these grasslands. [ABSTRACT FROM AUTHOR]

Net ecosystem exchange (NEE), solar radiation (including photosynthetically active radiation PAR), and meteorological parameters were measured in a subtropical coniferous plantation in China during 2013–2016. Applying the PAR balance principle at a canopy level and analyzing the observation data, an empirical model of respiration (Re, EMRe) considering 3-factor and 2-factor situations was developed and tested for all sky conditions. Generally, the respiration simulations were in reasonable agreement with the observations for the hourly, monthly, and annual sums of respiration. For example, using 3-factor and 2-factor models, the estimated annual sums of daytime and nighttime respiration in 2013–2016 overestimated that which was observed by about 31% and 26%, respectively. Further applications of EMRe and an empirical model of gross primary production (GPP, EMGPP) developed previously at this site, and an empirical model of net ecosystem productivity (NEP, EMNEP) using 3-factor and 2-factor models were obtained (NEP = GPP-Re) and evaluated for all sky conditions. Generally, the simulations of the hourly, monthly, and annual sums of NEP showed reasonable performances. The estimated NEP values overestimated the observations by 22% and 27% for the hourly sums in 2013–2016 when using the 3-factor and 2-factor models, respectively, and 7% and 12% for annual sums in 2013–2015 (2016 data were not used as the CO2 flux measurements had some problems in the 2016 summer). The NEP estimations were evidently improved when more factors (e.g., dark respiration) influencing Re were considered in the daytime respiration compared to those without considering these factors. To simplify the numerous and complicated CO2 processes in the simulations of Re and NEP, the PAR energy method was applied to capture and describe its main processes and energy interactions. The PAR energy method was suitable for studying the energy relationships associated with CO2 processes and developing empirical models for the simulations of GPP, Re, and NEP. These models were useful tools to investigate the multiple interactions and mechanisms between CO2, other atmospheric compositions, and PAR. Thus, the energy method is suggested to be applied to carbon balance. [ABSTRACT FROM AUTHOR]

Land carbon fluxes play a critical role in ecosystems, and acquiring a comprehensive global database of carbon fluxes is essential for understanding the Earth's carbon cycle. The primary methods of obtaining the spatial distribution of land carbon fluxes include utilizing machine learning models based on in situ measurements, estimating through satellite remote sensing, and simulating ecosystem models. Recently, an innovative machine learning product known as the Global Carbon Flux Dataset (GCFD) has been released. In this study, we assessed the reliability of the GCFD by comparing it with existing data products, including two machine learning products (FLUXCOM and NIES (National Institute for Environmental Studies)), two ecosystem model products (TRENDY and EC-LUE (eddy covariance–light use efficiency model)), and one remote sensing product (Global Land Surface Satellite), on both site and global scales. Our findings indicate that, in terms of average absolute difference, the spatial distribution of the GCFD is most similar to the NIES product, albeit with slightly larger discrepancies compared to the other two types of products. When using site observations as the benchmark, gross primary production (GPP), respiration of ecosystem (RECO), and net ecosystem exchange of machine learning products exhibit higher R2 (ranging from 0.57 to 0.85, 0.53–0.79, and 0.31–0.70, respectively) compared to model products and remote sensing products. Furthermore, we analyzed the spatial and temporal distribution characteristics of carbon fluxes in various regions. The results demonstrate an upward trend in both GPP and RECO over the past two decades, while NEE exhibits an opposite trend. This trend is particularly pronounced in tropical regions, where higher GPP is observed in tropical, subtropical, and oceanic climate zones. Additionally, two remote sensing variables that influence changes in carbon fluxes, i.e., fraction absorbed photosynthetically active radiation and leaf area index, exhibit relatively consistent spatial and temporal characteristics. Overall, our study can provide valuable insights into different types of carbon flux products and contribute to understanding the general features of global carbon fluxes. [ABSTRACT FROM AUTHOR]

Dust storms have large impacts on air quality and meteorological elements; however, their relationships with atmospheric greenhouse gases (e.g., CO2) and radiation components remain uncertain. In this study, the co-variation of dust and CO2 concentrations and its possible influencing mechanism are examined using observations at the Shangdianzi (SDZ) regional Global Atmosphere Watch (GAW) station along with simulations of the Vegetation Photosynthesis and Respiration Model coupled with the Weather Research and Forecasting model (WRF-VPRM), during two dust storm events on 15 and 28 March 2021. During these events, hourly CO2 concentrations decreased by 40–50 ppm at SDZ while dust concentrations increased to 1240.6 and 712.4 µg m−3. The elevated dust increased diffusive shortwave irradiance by 50–60% and decreased direct shortwave irradiance by ~60% along with clouds. The dust events were attributed to the passages of two cold front systems over northern China. At SDZ, during the frontal passages, wind speed increased by 3–6 m s−1, and relative humidity decreased by 50–60%. The CO2 variations associated with the frontal systems were captured by the WRF-VPRM despite the overestimated surface CO2 level at SDZ. Biogenic CO2 flux plays an indistinctive role in the large CO2 variation at SDZ, as it is weak during the non-growing season. The cold fronts pushed polluted air southeastward over the North China Plain and replaced it with low-CO2 air from Northwest China, leading to the decline in CO2. These findings demonstrate that mesoscale synoptic conditions significantly affect the regional transport and dispersion of CO2, which can influence the prediction of terrestrial carbon balance on a regional scale. [ABSTRACT FROM AUTHOR]

Meadows are valuable areas that play an important role in the carbon cycle. Depending on several factors, these areas can be carbon sinks or net emitters of carbon dioxide (CO2) into the atmosphere. In the present study, the use of an antitranspirant (AT) with silicon and the groundwater level in a subirrigation system in a three-cut meadow were evaluated on the carbon dioxide exchange balance and the yield of aboveground biomass. The study was carried out in four experimental plots: with high groundwater level (HWL), with a high water level with AT application (HWL_Si), with a lower groundwater level (LWL), and with a lower groundwater level and AT application (LWL_Si). Flux measurements were made using the closed dynamic chamber method. In the drier and colder 2021, the meadow was a net CO2 emitter (mean annual net ecosystem exchange (NEE) of all plots: +247.4 gCO2-C·m−2y−1), whereas in the more wet and warmer 2022, assimilation outweighed emissions (mean annual NEE of all plots: −187.4 gCO2-C·m−2y−1). A positive effect of the silicon antitranspirant application was observed on the reduction of carbon dioxide emissions and the increase of gross primary production (GPP) from the plots with higher groundwater levels. For the area with lower water levels, the positive impact of AT occurred only in the second year of the experiment. The yield of aboveground biomass was higher by 5.4% (in 2021) up to 11.7% (in 2022) at the plot with the higher groundwater level. However, the application of AT with silicon contributed to yield reduction in each cut, regardless of the groundwater level. On an annual basis, AT application with silicon reduced the yield by 11.1–17.8%. [ABSTRACT FROM AUTHOR]

Land-atmosphere carbon exchange is known to be extremely heterogeneous in arctic ice-wedge polygonal tundra regions. In this study, a Kalman filter-based method was developed to estimate the spatio-temporal dynamics of daytime average net ecosystem exchange (NEEday) at 0.5-m resolution over a 550 m by 700 m study site. We integrated multi-scale, multi-type datasets, including normalized difference vegetation indices (NDVIs) obtained from a novel automated mobile sensor system (or tram system) and a greenness index map obtained from airborne imagery. We took advantage of the significant correlations between NDVI and NEEday identified based on flux chamber measurements. The weighted average of the estimated NEEday within the flux-tower footprint agreed with the flux tower data in term of its seasonal dynamics. We then evaluated the spatial variability of the growing season average NEEday, as a function of polygon geomorphic classes; i.e., the combination of polygon types—which are known to present different degradation stages associated with permafrost thaw—and microtopographic features (i.e., troughs, centers and rims). Our study suggests the importance of considering microtopographic features and their spatial coverage in computing spatially aggregated carbon exchange.

Across temperate North America, interannual variability (IAV) in gross primary production (GPP) and net ecosystem exchange (NEE) and their relationship with environmental drivers are poorly understood. Here, we examine IAV in GPP and NEE and their relationship to environmental drivers using two state-of-the-science flux products: NEE constrained by surface and space-based atmospheric CO2 measurements over 2010-2015 and satellite up-scaled GPP from FluxSat over 2001-2017. We show that the arid western half of temperate North America provides a larger contribution to IAV in GPP (104% of east) and NEE (127% of east) than the eastern half, in spite of smaller magnitude of annual mean GPP and NEE. This occurs because anomalies in western ecosystems are temporally coherent across the growing season leading to an amplification of GPP and NEE. In contrast, IAV in GPP and NEE in eastern ecosystems is dominated by seasonal compensation effects, associated with opposite responses to temperature anomalies in spring and summer. Terrestrial biosphere models in the MsTMIP ensemble generally capture these differences between eastern and western temperate North America, although there is considerable spread between models.

The distribution of atmospheric CO2 is not homogenous, primarily due to variations in the CO2 budgets of regional terrestrial ecosystems. To formulate a comprehensive strategy to combat the increasing global CO2 levels and associated warming, it is crucial to consider both the distribution of atmospheric CO2 and the CO2 budgets of ecosystems. This study focused on analyzing the relationship between regional atmospheric CO2 and CO2 budgets in China from 2010 to 2017. Initially, a robust estimation model of net ecosystem CO2 exchange was developed to calculate CO2 budgets using collected emission data. Subsequently, Pearson correlation, redundancy analysis, and geographically weighted regression techniques were employed to examine the link between atmospheric CO2 levels, CO2 budgets, and other meteorological factors at various spatial and temporal scales. The findings from the redundancy analysis and geographically weighted regression indicated that the atmospheric CO2 content of each province could not be solely determined by the regional CO2 budgets. However, a significant and positive correlation between atmospheric CO2 levels and CO2 budgets was observed in non-coastal provinces for a period of six months (R2 ranging from 0.46 to 0.83). Consequently, it is essential to promote a balance between CO2 emissions and the CO2 uptake capacity of regional ecosystems. This balance would minimize positive CO2 budgets and effectively mitigate the increase in atmospheric CO2 levels. [ABSTRACT FROM AUTHOR]

Measurements of net ecosystem exchange (NEE), solar global radiation, photosynthetically active radiation (PAR) and meteorological parameters were carried out on a subtropical conifer plantation in China from 2013 to 2016. These observations were used to develop and evaluate an empirical model of gross primary production (GPP) (EMGPP) with 3-factor and 2-factor models. Using a 3-factor model, the simulated hourly GPP values were consistent with observations with a relative bias of 9.96% and normalized mean square error values of 0.07 mg CO2 m−2 s−1 for the scattering factor S/Q (S and Q are diffuse and global solar radiation) < 0.5 and 15.52% and 0.15 mg CO2 m−2 s−1 for S/Q ≥ 0.5. Validations of the EMGPP for hourly, daily, monthly, and annual GPP values were carried out and showed that both 3-factor and 2-factor EMGPP models can accurately capture diurnal, seasonal and interannual variations in GPP, but most simulated GPP overestimated the observed value. When the scattering factor is not available, the 2-factor EMGPP can be used. The EMGPP using 3-factor and 2-factor models was applied to simulate GPP under all sky conditions from 2013–2016, and the estimated GPP were in reasonable agreement with the measured values and showed systematic overestimations of 31% and 29% for mean hourly GPP and 41% and 29% for annual amounts, respectively. The sensitivity test demonstrated that GPP values were more sensitive to changes in PAR than to changes in water vapor and scattering factor at low S/Q, but were more sensitive to changes in water vapor than to PAR and S/Q at high S/Q. The sensitivity test revealed some mechanisms of GPP and its related processes, including the relationships between GPP and scattering of PAR, GPP and water vapor, which were in good agreement with other observations and model studies. An empirical model based on PAR energy balance can better describe the multiple interactions between GPP and its driving factors (PAR, water vapor, S/Q). The ratio of the emissions of biogenic volatile organic compounds (BVOCs) to net ecosystem exchange clearly varied between forests in different climate zones. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Applied Ecology / Yingyong Shengtai Xuebao is the property of Chinese Journal of Applied Ecology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this research, the magnitude of the net ecosystem exchange (NEE) for both grasslands (GRA) and croplands (CRO) is estimated by different machine learning approaches (MLAs). There are two main goals including prediction/data gap filling of the NEE and developing a new MLA model. The variation of CO2 is affected by soil temperature and meteorological factors, including air temperature, latent heat, and sensible heat considered as inputs. Hourly data of three AmeriFlux sites have been collected for seven years. The normalized smoothed data are applied for modeling. Both artificial neural network (ANN) and genetic algorithm (GA) are the computational MLAs working by deep learning methods. In this study, a new GA-based model named integration of optimization with genetic algorithm and Fourier series (IOGAFS) was proposed for estimation of the NEE. The results show the IOGAFS and ANN methods have acceptable performance with 0.86 and 0.88 determination coefficient for CRO and 0.75 and 0.81 for GRA, respectively. Due to high performance of both methods, they can be used estimation of the NEE in similar ecosystems, mainly where there are no flux towers. [ABSTRACT FROM AUTHOR]

Peatland ecosystems are a highly effective long-term carbon sink. However, the CO2 fluxes could be substantially altered by climate changes and the fate of carbon stored in these ecosystems is still uncertain. Currently, most studies concerning the carbon fluxes in peatlands were performed at high latitude sites, where these ecosystems are more widely distributed compared to temperate regions, where peatlands are less frequent and, in addition to climate pressure, increasingly threatened by human activities. However, the information we have on these peatlands is very scarce. To fill this knowledge gap, we studied CO2 fluxes in an alpine peatland, through light and dark incubations. Using the natural variation in ecological conditions, we identified the main drivers of CO2 fluxes, considering in particular their interactions and covariation. Ecosystem respiration and gross primary production were primarily stimulated by the lowering of the water table and the amount of photosynthetic radiation, respectively, whereas net ecosystem CO2 exchange showed greater variation along the growing season. The influence on CO2 fluxes of the interactions between the drivers investigated, including soil temperature and moisture as well as vegetation type and plant functional diversity, was found to be of pivotal importance. Finally, a substantial part of the variation in CO2 emission and uptake processes was regulated by the joint variation of atmospheric and edaphic factors. To understand and predict the CO2 dynamics of alpine peatlands, it is necessary to consider the interplays among ecological factors, especially in relation to the expected changes in climate and vegetation. [ABSTRACT FROM AUTHOR]

Using biomass for charcoal production in sub‐Saharan Africa (SSA) may change carbon stock dynamics and lead to irreversible changes in the carbon balance, yet we have little understanding of whether these dynamics vary by biome in this region. Currently, charcoal production contributes up to 7% of yearly deforestation in tropical regions, with carbon emissions corresponding to 71.2 million tonnes of CO2 and 1.3 million tonnes of CH4. With a projected increased demand for charcoal in the coming decades, even low harvest rates may throw the carbon budget off‐balance due to legacy effects. Here, we parameterized the dynamic global vegetation model LPJ‐GUESS for six SSA biomes and examined the effect of charcoal production on net ecosystem exchange (NEE), carbon stock sizes and recovery time for tropical rain forest, montane forest, moist savanna, dry savanna, temperate grassland and semi‐desert. Under historical charcoal regimes, tropical rain forests and montane forests transitioned from net carbon sinks to net sources, that is, mean cumulative NEE from −3.56 ± 2.59 kg C/m2 to 2.46 ± 3.43 kg C/m2 and −2.73 ± 2.80 kg C/m2 to 1.87 ± 4.94 kg C/m2 respectively. Varying charcoal production intensities resulted in tropical rain forests showing at least two times higher carbon losses than the other biomes. Biome recovery time varied by carbon stock, with tropical and montane forests taking about 10 times longer than the fast recovery observed for semi‐desert and temperate grasslands. Our findings show that high biomass biomes are disproportionately affected by biomass harvesting for charcoal, and even low harvesting rates strongly affect vegetation and litter carbon and their contribution to the carbon budget. Therefore, the prolonged biome recoveries imply that current charcoal production practices in SSA are not sustainable, especially in tropical rain forests and montane forests, where we observe longer recovery for vegetation and litter carbon stocks. [ABSTRACT FROM AUTHOR]

Global carbon fluxes describe the carbon exchange between land and atmosphere. However, already available global carbon fluxes datasets have not been adjusted by the available site data and deep learning tools. In this work, a global carbon fluxes dataset (named as GCFD) of gross primary productivity (GPP), terrestrial ecosystem respiration (RECO), and net ecosystem exchange (NEE) has been developed via a deep learning based convolutional neural network (CNN) model. The dataset has a spatial resolution of 1 km at three time steps per month from January 1999 to June 2020. Flux measurements were used as a training target while remote sensing of vegetation conditions and meteorological data were used as predictors. The results showed that CNN could outperform other commonly used machine learning methods such as random forest (RF) and artificial neural network (ANN) by leading to satisfactory performance with R2 values of the validation stage as 0.82, 0.72 and 0.62 for GPP, RECO, and NEE modelling, respectively. Thus, CNN trained using reanalysis meteorological data and remote sensing data was chosen to produce the global dataset. GCFD showed higher accuracy and more spatial details than some other global carbon flux datasets with reasonable spatial pattern and temporal variation. GCFD is also in accordance with vegetation conditions detected by remote sensing. Owing to the obtained results, GCFD can be a useful reference for various meteorological and ecological analyses and modelling, especially when high resolution carbon flux maps are required. [ABSTRACT FROM AUTHOR]

The eddy covariance (EC) technique is used to measure the net ecosystem exchange (NEE) of CO2 between ecosystems and the atmosphere, offering a unique opportunity to study ecosystem responses to climate change. NEE is the difference between the total CO2 release due to all respiration processes (RECO), and the gross carbon uptake by photosynthesis (GPP). These two gross CO2 fluxes are derived from EC measurements by applying partitioning methods that rely on physiologically based functional relationships with a limited number of environmental drivers. However, the partitioning methods applied in the global FLUXNET network of EC observations do not account for the multiple co-acting factors that modulate GPP and RECO flux dynamics. To overcome this limitation, we developed a hybrid data-driven approach based on combined neural networks (NNC-part ). NNC-part incorporates process knowledge by introducing a photosynthetic response based on the light-use efficiency (LUE) concept, and uses a comprehensive dataset of soil and micrometeorological variables as fluxes drivers. We applied the method to 36 sites from the FLUXNET2015 dataset and found a high consistency in the results with those derived from other standard partitioning methods for both GPP (R2  > .94) and RECO (R2  > .8). High consistency was also found for (a) the diurnal and seasonal patterns of fluxes and (b) the ecosystem functional responses. NNC-part performed more realistic than the traditional methods for predicting additional patterns of gross CO2 fluxes, such as: (a) the GPP response to VPD, (b) direct effects of air temperature on GPP dynamics, (c) hysteresis in the diel cycle of gross CO2 fluxes, (d) the sensitivity of LUE to the diffuse to direct radiation ratio, and (e) the post rain respiration pulse after a long dry period. In conclusion, NNC-part is a valid data-driven approach to provide GPP and RECO estimates and complementary to the existing partitioning methods.

Understanding the photosynthetic behaviour, radiation use efficiency (RUE) and water use efficiency (WUE) of potatoes in response to diurnal and seasonal fluctuations in weather, is important for optimizing growth and production. The net ecosystem exchange (NEE) of carbon dioxide (CO2), is a parameter used to measure the balance between gross CO2 assimilation (also referred to as gross primary production (GPP)) and ecosystem respiration (R eco). The objectives of this study were (i) to quantify the NEE of potato grown in two contrasting agro-ecologies and partition NEE into R eco and GPP; (ii) to describe seasonal patterns in daily photosynthesis, RUE and WUE in potato; and (iii) to analyse the variability in photosynthesis rate, RUE and WUE as affected by weather and crop growth stage. We measured CO2 and water vapour fluxes using eddy covariance techniques in potato fields in two production regions of South Africa. The winter crop had higher mean RUE and WUE values, whereas the summer crop absorbed more CO2. RUE had a negative relationship with incident radiation. To optimize dry matter production, WUE and RUE, potato crops must be well established and develop a full canopy early during the season. Maintaining the crop for a longer period in the field has yield benefits, although they appear to decline over time due to reduced efficiencies as the crop matures. The RUE observed over the growing period in the present study was high, relative to findings by other field studies, despite relatively warm growing conditions that were likely sub-optimal for potato. [ABSTRACT FROM AUTHOR]

Recent climate predictions for the United Kingdom expect a nationwide shift towards drier and warmer summers, increasing the risk of more frequent and severe drought events. Such shifts in weather patterns impede functioning of global peatlands, especially rare intact blanket bogs abundant in Scotland and representing nearly a quarter of the UK's soil carbon. In this in situ study, carbon dioxide (CO2) fluxes from dominant peatland plant functional types (PFTs) such as Sphagnum spp., graminoids, ericoids and other key cover types (i.e., pools and bare peat) were measured and compared across upland and low‐lying blanket bog margins and centres, immediately before and during a summer drought in 2018, and over the subsequent year. During that period, most sites acted as net sources of CO2 to the atmosphere. Our results showed that net ecosystem exchange (NEE) was limited by water availability during the drought, with ericoid shrubs showing the highest drought resilience, followed by graminoids (which were still limited in GPP in 2019) and Sphagnum mosses. Diverging NEE estimates were observed across centre and margin areas of the blanket bogs, with highest variability across the upland site where signs of active erosion were visible. Overall, our study suggests that estimating growing season carbon fluxes from in situ peatland PFT and cover types can help us better understand global climate change impacts on the dynamics and trajectories of peatland C cycles. [ABSTRACT FROM AUTHOR]

Shrub-dominated ecosystems in the semiarid Horqin Sandy Land are important terrestrial ecosystems, and substantially affect global ecological health and security. However, there have been few studies of climate change’s effects on the carbon fluxes (NEE, net ecosystem exchange; Reco, ecosystem respiration; GPP, gross primary productivity) when these ecosystems are recovering from degradation. We used the eddy covariance technique to determine carbon fluxes and climatic conditions in this ecosystem from 2017 to 2021. The semi-fixed sandy land functioned as a carbon sink in wet years (NEE equaled −14.14 and −126.14 g C m–2 yr–1 in 2019 and 2021, respectively), but was a carbon source in dry years (NEE equaled 48.50 and 51.17 g C m–2 yr–1 in 2017 and 2020, respectively) and a normal year (NEE equaled 74.66 g C m–2 yr–1 in 2018). As expected in these usually water-limited ecosystems, water availability (precipitation and soil water content) were the dominant drivers of NEE, Reco, and GPP, but temperature and photosynthetic photon flux density (PPFD) also played important roles in regulating NEE, Reco and GPP in this recovering semi-fixed sandy ecosystem. With future precipitation and temperature increases, and continuing vegetation restoration, carbon sequestration by this ecosystem is expected to increase. Long-term observations will be necessary to reveal the true source and sink intensities and their response to environmental factors.

Terrestrial carbon fluxes are crucial to the global carbon cycle. Quantification of terrestrial carbon fluxes over the Tibetan Plateau (TP) has considerable uncertainties due to the unique ecosystem and climate and scarce flux observations. This study evaluated our recent improvement of terrestrial flux parameterization in the weather research and forecasting model coupled with the vegetation photosynthesis and respiration model (WRF-VPRM) in terms of reproducing observed net ecosystem exchange (NEE), gross ecosystem exchange (GEE), and ecosystem respiration (ER) over the TP. The improvement of VPRM relative to the officially released version considers the impact of water stress on terrestrial fluxes, making it superior to the officially released model due to its reductions in bias, root mean square error (RMSE), and ratio of standard deviation (RSD) of NEE to 0.850 μmol·m−2·s−1, 0.315 μmol·m−2·s−1, and 0.001, respectively. The improved VPRM also affects GEE simulation, increasing its RSD to 0.467 and decreasing its bias and RMSE by 1.175 and 0.324 μmol·m−2·s−1, respectively. Furthermore, bias and RMSE for ER were lowered to −0.417 and 0.954 μmol·m−2·s−1, with a corresponding increase in RSD by 0.6. The improved WRF-VPRM simulation indicates that eastward winds drive the transfer of lower CO2 concentrations from the eastern to the central and western TP and the influx of low-concentration CO2 inhibits biospheric CO2 uptake. The use of an improved WRF-VPRM in this study helps to reduce errors, improve our understanding of the role of carbon flux cycle over the TP, and ultimately reduce uncertainty in the carbon flux budget.

Peatlands are important nutrient-limited, carbon (C)-accumulating ecosystems where phosphorus (P) is one major limiting nutrient. An increase in P input to peatlands has been observed close to agricultural areas. However, the effects of P input on gross primary productivity (GPP), ecosystem respiration (ER) and net ecosystem exchange (NEE) of peatlands, especially in temperate Asia, have been rarely studied. We selected the P addition plots of a 12-year experiment simulating environmental change in a peatland in northeastern China to evaluate the effect of P addition (5 and 10 kg ha−1 y−1) on carbon dioxide fluxes. Our results showed that the growth of Sphagnum was inhibited but that of vascular plants was facilitated by P addition, resulting in an unchanged GPP. P addition increased ER due to the increased activity potentials of N-acetyl-β-glucosaminidase (NAG) and polyphenol oxidase (POX) that might, in turn, increase the metabolic rate of soil microbes and enhance the decomposition of peat. During the growing season, a mean net CO2 absorption of 0.002 mg m−2 s−1 was observed at high levels of P addition, which was much lower than that of 0.063 mg m−2 s−1 in the controls. The results suggest that long-term P addition will greatly weaken the C sequestration in peatlands by enhancing ER rather than reducing GPP. Our study highlights the importance of the response of vegetation and soil enzyme activities to P addition regarding peatland respiration and C sink function. [ABSTRACT FROM AUTHOR]

Precipitation (PPT) patterns in semiarid grasslands are characterized by infrequent and small PPT events; however, plants and soil microorganisms are adapted to use the unpredictable small pulses of water. Several studies have shown short-term responses of carbon and nitrogen mineralization rates (called the priming effect or the Birch effect) stimulated by wet-dry cycles; however, dynamics, drivers, and the contribution of the “priming effect” to the annual C balance is poorly understood. Thus, we analysed six years of continuous net ecosystem exchange measurements to evaluate the effect of the PPT periodicity, magnitude of individual PPT events on the daily/annual ecosystem C balance (NEE) in a semiarid grassland. We included the period between PPT events, a priori daytime NEE rate and a priori soil moisture content as the main drivers of the priming effect. Ecosystem respiration (ER) responded within few hours following a PPT event whereas it took five-nine days for gross ecosystem exchange (GEE; such as –NEE = GEE + ER) to respond. Precipitation events as low as 0.25 mm increased ER, but cumulative PPT > 40 mm that infiltrated deep into the soil profile stimulated GEE. Overall, ER fluxes following PPT events were related to the change of soil water content at shallow depth and previous soil conditions (e.g., previous NEE rate, previous soil water content) and the size of the stimulus (e.g., PPT event size). Carbon effluxes from priming effect accounted for less than 5% of ecosystem respiration but were significatively high respect to the carbon balance. In the long-term, changes in PPT regimes to more intense and less frequent PPT events, as expected by the climate change effect, could convert the semiarid grassland from a slight C sink to a C source. [ABSTRACT FROM AUTHOR]

The occurrence of hot drought, i.e. low water availability and simultaneous high air temperature, represents a severe threat to ecosystems. Here, we investigated how the 2018 hot drought in Central Europe caused a tipping point in tree and ecosystem functioning in a Scots pine (Pinus sylvestris L.) forest in southwest Germany.Measurements of stress indicators, such as needle water potential, carbon assimilation and volatile organic compound (VOC) emissions, of dominant P. sylvestris trees were deployed to evaluate tree functioning during hot drought. Ecosystem impact and recovery were assessed as ecosystem carbon exchange, normalized difference vegetation index (NDVI) from satellite data and tree mortality data.During summer 2018, needle water potentials of trees dropped to minimum values of −7.5 ± 0.2 MPa, which implied severe hydraulic impairment of P. sylvestris. Likewise, carbon assimilation and VOC emissions strongly declined after mid‐July. Decreasing NDVI values from August 2018 onwards were detected, along with severe defoliation in P. sylvestris, impairing ecosystem carbon flux recovery in 2019, shifting the forest into a year‐round carbon source. A total of 47% of all monitored trees (n = 368) died by September 2020. NDVI recovered to pre‐2018 levels in 2019, likely caused by emerging broadleaved understorey species.The 2018 hot drought had severe negative impacts on P. sylvestris. The co‐occurrence of unfavourable site‐specific conditions with recurrent severe droughts resulted in accelerated mortality. Thus, the 2018 hot drought pushed the P. sylvestris stand towards its tipping point, with a subsequent vegetation shift to a broadleaf‐dominated forest. [ABSTRACT FROM AUTHOR]

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Peatlands drained for agriculture or forestry are susceptible to the rapid release of greenhouse gases (GHGs) through enhanced microbial decomposition and increased frequency of deep peat fires. We present evidence that rewetting drained subtropical wooded peatlands (STWPs) along the southeastern USA coast, primarily pocosin bogs, could prevent significant carbon (C) losses. To quantify GHG emissions and storage from drained and rewetted pocosin we used eddy covariance techniques, the first such estimates that have been applied to this major bog type, on a private drained (PD) site supplemented by static chamber measurements at PD and Pocosin Lakes National Wildlife Refuge. Net ecosystem exchange measurements showed that the loss was 21.2 Mg CO2 ha−1 year−1 (1 Mg = 106 g) in the drained pocosin. Under a rewetted scenario, where the annual mean water table depth (WTD) decreased from 60 to 30 cm, the C loss was projected to fall to 2 Mg CO2 ha−1 year−1, a 94% reduction. If the WTD was 20 cm, the peatlands became a net carbon sink (−3.3 Mg CO2 ha−1 year−1). Hence, net C reductions could reach 24.5 Mg CO2 ha−1 year−1, and when scaled up to the 4000 ha PD site nearly 100,000 Mg CO2 year−1 of creditable C could be amassed. We conservatively estimate among the 0.75 million ha of southeastern STWPs, between 450 and 770 km2 could be rewet, reducing annual GHG emissions by 0.96–1.6 Tg (1 Tg = 1012 g) of CO2, through suppressed microbial decomposition and 1.7–2.8 Tg via fire prevention, respectively. Despite covering <0.01% of US land area, rewetting drained pocosin can potentially provide 2.4% of the annual CO2 nationwide reduction target of 0.18 Pg (1 Pg = 1015 g). Suggesting pocosin restoration can contribute disproportionately to the US goal of achieving net‐zero emission by 2050. [ABSTRACT FROM AUTHOR]

Salt marsh habitats contain some of the highest quantities of soil organic carbon (C) per unit area, but increasing anthropogenic stressors threaten their ability to maintain themselves as large C reservoirs in some regions. We quantify rates of C gas exchange (methane [CH4] and carbon dioxide [CO2]) monthly across a 16‐month period from a low nitrogen "reference" salt marsh on Cape Cod in New England using static chambers. While the summer period is the most dynamic period of marsh C gas exchange, we observed substantial fluxes in the early summer through late fall, highlighting the importance of including shoulder seasons in studies of marsh C exchange. We estimate annual ecosystem respiration between 108 and 252 g C m−2 yr−1, which varied based on temperature and elevation. This flux is lower than in other nearby marshes, which we attribute to the frequently inundated, microtidal nature of the site, resulting in the majority of respired CO2 being exported via lateral, not vertical, fluxes from this marsh. We observed significantly higher temperature sensitivity from the low elevation of the marsh compared to the high marsh. Recent acceleration in the rate of sea level rise is leading to a well‐documented expansion of low marsh into high marsh vegetation zones in this marsh system and others in the region. While rates of C burial are higher in the low marsh compared to the high marsh, the higher temperature sensitivity of respiration in the low marsh may diminish the longevity of marsh C stocks with climate warming. Plain Language Summary: Salt marshes are hot spots of carbon sequestration, storing more carbon per unit area than many other ecosystems. We quantify rates of methane (CH4) emissions and carbon dioxide (CO2) exchange monthly across a 16‐month period from a relatively undisturbed salt marsh in New England and estimate annual rates of ecosystem respiration from the marsh. Ecosystem respiration appears lower at this site compared to other marshes, likely due to frequent inundation and microtidal nature, resulting in large lateral exports of respired carbon with ebbing tides. For the first time, we examine the response functions of ecosystem respiration in a fully saline marsh across the marsh elevation gradient. Per unit temperature increase, we see significantly more CO2 respired from the low marsh compared to the high marsh (i.e., higher temperature sensitivity). Due to accelerated rates of sea level rise, low marsh habitats are increasingly expanding into high marsh zones; our data indicate the large C stocks in low marsh habitats are more vulnerable to respiratory losses as a result of warming, compared to high marsh systems. This work improves our understanding of the temperature sensitivity of respiration in aquatic systems in general, which pales in comparison to knowledge from terrestrial systems. Key Points: Including shoulder seasons is critical in studies of marsh C exchangeEstimates of annual ecosystem respiration are low at this marsh, due to microtidal nature and elevated lateral fluxesThe temperature sensitivity of ecosystem respiration was significantly higher from the low marsh relative to the high marsh [ABSTRACT FROM AUTHOR]