Net Ecosystem CO2 Exchange Research Articles

Interannual variations in grassland carbon fluxes and attribution of influencing factors in Qilian Mountains, China

Clarifying the driving factors of grassland carbon sequestration is essential for understanding its role in the regional carbon balance. However, there is a lack of studies on the upscaling of carbon flux in the Qilian Mountains (QLMs) and the driving factors of its interannual variation (IAV). Based on long-term eddy covariance observations in the QLMs, this study estimated the net ecosystem CO2 exchange (NEE), gross primary productivity (GPP), and ecosystem respiration (ER) of the QLMs grassland using four machine learning methods (random forest regression (RF), extremely randomized tree regression (ETR), support vector regression (SVR), and extreme gradient boosting (XGBoost)) to obtain the optimal estimation model. Subsequently, the spatiotemporal variations of GPP, ER, and NEE in the QLMs grasslands were conducted in a comprehensive analysis. The factors influencing the IAV of carbon flux, the contribution of monthly NEE to NEE IAV, and the contribution of different grassland types of NEE to NEE IAV were explored. Our findings revealed that the accuracy and resolution of the grassland carbon flux estimated by the RF method in this study are higher than those of global products. The grassland exhibited a weak carbon sink from 2000 to 2022, with an average NEE of −26.46 ± 6.80 g Cm−2 yr−1, and it acted as a carbon sink from May to September. The spatial distribution pattern of carbon sequestration was “low in the northwest and high in the southeast”. LAI was the key driving factors of IAV for GPP and ER, while NEE IAV was primarily influenced by precipitation and temperature. Climate and vegetation factors primarily regulated NEE IAV by affecting the GPP and ER of plants, and NEE IAV was primarily driven by GPP. Furthermore, NEE in alpine meadows and alpine steppes dominated the NEE IAV of the entire grassland, and summer NEE contributed the most to the NEE IAV. The results will help us to better understand the carbon cycling mechanism in grassland ecosystems and provide new data support and a theoretical foundation for regional carbon cycling research.

Improving ecosystem respiration estimates for CO2 flux partitioning by discriminating water and temperature controls on above- and below-ground sources

Empirical models for estimating ecosystem respiration (ER) are widely used in CO2 flux partitioning algorithms that partition net ecosystem CO2 exchange (NEE) into gross primary productivity (GPP) and ER due to advantages of simple structures. However, empirical ER models remain limited due to single-source conceptualization that doesn’t discriminate different responses of aboveground respiration (AGR) and belowground respiration (BGR) to environmental factors (i.e., temperature and/or soil moisture). In this study, a dual-source module with only one parameter α was proposed and incorporated into six widely used ER models to enhance model capabilities. Long-term flux measurements of six typical terrestrial ecosystems and soil chamber respiration data at two sites were collected to evaluate models. Results showed that integration of the dual-source module can significantly improve the performance of empirical models in selected ecosystems with mean R2 improvement of 0.10 ± 0.16. The site years with relative increased R2 (ΔR2) larger than 10 % range from 6 % to 79 % amongst different models. Further validation between soil respiration and estimated BGR showed good correlations (r > 0.7) and demonstrated that proposed method can provide robust estimate of above/belowground respiration. Calibrated α varies amongst ecosystem types. Further analysis indicates variation of α is largely influenced by ratio of above/belowground biomass and annual average moisture conditions. Our findings highlight the critical need for partitioning ER models into dual-source for developing CO2 flux partitioning algorithms and support the approach as an effective means to enhance the understanding of global carbon cycles with changing climate.

Carbon dioxide and methane gas exchange following sphagnum moss harvesting in boreal peatland

Understanding the impacts of Sphagnum moss harvesting on peatland carbon (C) balance is crucial due to its potential rise as an anthropogenic land use. We studied eight nutrient-poor peatlands in Finland, harvested between 2015 and 2021, focusing on net ecosystem exchange of CO2 (NEE) and methane (CH4) emissions. The greenhouse gas fluxes were measured to evaluate the sustainability of harvesting practices. Results showed significant variability in Sphagnum regeneration, with wet strip-harvested sites achieving 2–28% re-establishment in 2–8 years, while drier clear-harvested sites saw minimal spontaneous regeneration in 1–6 years. In addition to vegetation succession, GHG emissions were moisture dependent. In wet sites CH4 emissions increased along with time since harvesting and Eriophorum vaginatum (L.) cover, while dry sites exhibited overall lower CH4 fluxes. Younger (1–2 years post-harvest), dry sites were significant CO2 sources due to low photosynthetic activity. Older dry site with sparse ericoid shrub vegetation acted as CO2 sink. Wet sites initially had lower CO2 sink capacity, but this increased as E. vaginatum spread, and reached a plateau when Sphagnum mosses emerged, highlighting the importance of suitable water table levels for efficient CO2 sequestration.

X-BASE: the first terrestrial carbon and water flux products from an extended data-driven scaling framework, FLUXCOM-X

Abstract. Mapping in situ eddy covariance measurements of terrestrial land–atmosphere fluxes to the globe is a key method for diagnosing the Earth system from a data-driven perspective. We describe the first global products (called X-BASE) from a newly implemented upscaling framework, FLUXCOM-X, representing an advancement from the previous generation of FLUXCOM products in terms of flexibility and technical capabilities. The X-BASE products are comprised of estimates of CO2 net ecosystem exchange (NEE), gross primary productivity (GPP), evapotranspiration (ET), and for the first time a novel, fully data-driven global transpiration product (ETT), at high spatial (0.05°) and temporal (hourly) resolution. X-BASE estimates the global NEE at −5.75 ± 0.33 Pg C yr−1 for the period 2001–2020, showing a much higher consistency with independent atmospheric carbon cycle constraints compared to the previous versions of FLUXCOM. The improvement of global NEE was likely only possible thanks to the international effort to increase the precision and consistency of eddy covariance collection and processing pipelines, as well as to the extension of the measurements to more site years resulting in a wider coverage of bioclimatic conditions. However, X-BASE global net ecosystem exchange shows a very low interannual variability, which is common to state-of-the-art data-driven flux products and remains a scientific challenge. With 125 ± 2.1 Pg C yr−1 for the same period, X-BASE GPP is slightly higher than previous FLUXCOM estimates, mostly in temperate and boreal areas. X-BASE evapotranspiration amounts to 74.7×103 ± 0.9×103 km3 globally for the years 2001–2020 but exceeds precipitation in many dry areas, likely indicating overestimation in these regions. On average 57 % of evapotranspiration is estimated to be transpiration, in good agreement with isotope-based approaches, but higher than estimates from many land surface models. Despite considerable improvements to the previous upscaling products, many further opportunities for development exist. Pathways of exploration include methodological choices in the selection and processing of eddy covariance and satellite observations, their ingestion into the framework, and the configuration of machine learning methods. For this, the new FLUXCOM-X framework was specifically designed to have the necessary flexibility to experiment, diagnose, and converge to more accurate global flux estimates.

Dynamics of CO2 fluxes and environmental responses in a Poplar plantation

Forest plantations cover a large percentage of global forest landscapes contributing significantly to carbon sequestration. By using continuous eddy covariance technique, we observed net ecosystem CO2 exchange (NEE), gross primary production (GPP), ecosystem respiration (ER), and meteorological variables from August 2018 to December 2019 in a Poplar plantation. The Poplar plantation ecosystem was a carbon sink overall, with high carbon uptake in growing season and limited uptake/emission in non-growing season. The annual cumulative NEE, GEP, and ER were −763.61, 1542.19, and 778.58 g C m−2 yr−1, respectively. Photosynthetically active radiation (PAR) significantly influenced NEE both at half-hourly and daily scale (P < 0.01 for both), while relative humidity (RH) and vapor pressure deficit (VPD) only significantly affected NEE at half-hourly scale (P < 0.01). The prevailing wind direction throughout 2019 was southeast and it varied between seasons. Southeast wind was the prevailing wind direction in summer and winter, while southwest and northeast wind were the dominant wind direction in spring and autumn, respectively. Our results highlight that polar plantations play an important role in storing carbon, and that understanding meteorological conditions is crucial in investigating ecosystem-atmosphere interactions and their impacts on carbon cycling.

CO2 Footprint of Kiwi Fruits Deduced from Field Measurements and Cultivation Energy Data

The unequivocal understanding of the planetary-global climate change has rendered the apportionment of sources and sinks of greenhouse gases in the terrestrial domain, an urgent priority. In the present study, the micrometeorological method of “dynamic gradient fluxes” coupled with the Monin–Obukhov similarity theory, was utilised for the determination of net ecosystem exchange of carbon dioxide (CO2) from a kiwi plantation. This annual net exchange, in conjunction with the energy and fertiliser equivalent CO2 used, established the CO2 footprint of the produce. For the year 2023, the CO2 Net Ecosystem Exchange (NEE) is −16.20 tonnes per hectare per year (CO2 uptake by the plantation). The cultivation processes used throughout the year consumed +2.96 tonnes per hectare per year, and after deduction of this value from the NEE, the result is in a net CO2 sink for the kiwi plantation of −13.24 tonnes per hectare per year. It is hence obvious that, under these conditions, the kiwi plantations in Greece can be net CO2 sinks. This result is of increasing importance since the country is the fourth largest producer of kiwi globally, with production increasing in later years.

Field assessments on the impact of CO2 concentration fluctuations along with complex-terrain flows on the estimation of the net ecosystem exchange of temperate forests

Abstract. CO2 storage (Fs) is the cumulation or depletion in CO2 amount over a period in an ecosystem. Along with the eddy covariance flux and wind-stream advection of CO2, it is a major term in the net ecosystem CO2 exchange (NEE) equation. The CO2 storage dominates the NEE equation under a stable atmospheric stratification when the equation is used for forest ecosystems over complex terrains. However, estimating Fs remains challenging due to the frequent gusts and random fluctuations in boundary-layer flows that lead to tremendous difficulties in capturing the true trend of CO2 changes for use in storage estimation from eddy covariance along with atmospheric profile techniques. Using measurements from Qingyuan Ker Towers equipped with NEE instrument systems separately covering mixed broad-leaved, oak, and larch forest towers in a mountain watershed, this study investigates gust periods and CO2 fluctuation magnitudes and examines their impact on Fs estimation in relation to the terrain complexity index (TCI). The gusts induce CO2 fluctuations for numerous periods of 1 to 10 min over 2 h. Diurnal, seasonal, and spatial differences (P < 0.01) in the maximum amplitude of CO2 fluctuations (Am) range from 1.6 to 136.7 ppm, and these differences range from 140 to 170 s in a period (Pm) at the same significance level. Am and Pm are significantly correlated to the magnitude of and random error in Fs with diurnal and seasonal differences. These correlations decrease as CO2 averaging time windows become longer. To minimize the uncertainties in Fs, a constant [CO2] averaging time window for the Fs estimates is not ideal. Dynamic averaging time windows and a decision-level fusion model can reduce the potential underestimation of Fs by 29 %–33 % for temperate forests in complex terrain. In our study, the relative contribution of Fs to the 30 min NEE observations ranged from 17 % to 82 % depending on turbulent mixing and the TCI. The study's approach is notable as it incorporates the TCI and utilizes three flux towers for replication, making the findings relevant to similar regions with a single tower.

Satellite-based measurements of temporal and spatial variations in C fluxes of irrigated and rainfed cotton grown in India

The small number of carbon dioxide (CO2) observation networks and the prohibitively high equipment cost restrict the estimation of net ecosystem CO2 exchange (NEE). Satellite-based remote sensing techniques have made it possible to obtain NEE and component carbon (C) fluxes. One-third of the world's cotton area is in India, but the information on NEE is limited. We used the Level 4 Carbon (L4_C) product from the Soil Moisture Active Passive (SMAP) mission to estimate C fluxes based on satellite-derived soil moisture, weather, and vegetation data. For our study (2018-19 to 2020-21), we chose two ecosystems (rainfed central India vs. irrigated northern India). Seasonal variations were observed in C fluxes. Gross primary productivity was the highest during the boll formation phase. This phase was the strongest sink and coincided with the highest CO2 uptake, followed by the flowering and square formation phases. The cotton crop was a C source during the initial vegetative phase and after the boll opening. Overall, the cotton crop was a sink for atmospheric CO2 with an average NEE value of −189.6 g C m−2 under irrigated and −245.6 g C m−2 in rainfed cotton. Higher ecosystem respiration in irrigated cotton resulted in lower C sink strength than rainfed cotton. Our studies indicate that the SMAP L4_C product model estimates can be used to obtain information on C fluxes in real-world situations. Moreover, such satellite-based remote sensing techniques will enable large-scale environmental monitoring with different cropping systems and support policymaking.

Characteristics and regulatory mechanisms of net ecosystem CO2 exchange at the water-air interface in coastal aquaculture ponds

Coastal aquaculture ponds represented a biogeochemical hotspot in the global carbon cycle. However, there was a limited understanding of their dynamics. In this study, the eddy covariance (EC) technique was applied to quantify the net ecosystem CO2 exchange (NEE) over coastal aquaculture ponds in the Liaohe River estuary in northern China during 2020, aiming to investigate and quantify the carbon exchange characteristics of this region. The results showed that (a) a predominant “U” shaped diurnal NEE pattern throughout the year. During the sea cucumber monoculture phase, the ponds exhibited a consistent daytime carbon sink and nighttime carbon source pattern. In contrast, during the shrimp and sea cucumber polyculture phase, the ponds mostly remained in a net carbon sink state. (b) NEE was negatively correlated with photosynthetically active radiation (PAR), air temperature (Tair), and wind speed (WS), while showing a positive correlation with atmospheric pressure (AP). (c) Overall, the entire study area (complex underlying surfaces) functioned as a carbon sink in 2020, with a total net carbon sequestration of 281.533 g C·m−2. This was approximately four times greater than the restored wetlands that naturally formed from decommissioned coastal aquaculture ponds. Adjusting for surface heterogeneity revealed that the complex surfaces led to a 34.28 % underestimation of the aquaculture region's unit area carbon sequestration capacity. This study was crucial for assessing the carbon cycling and sequestration functions of coastal aquaculture pond ecosystems and provided a scientific basis for related ecological restoration projects.

Concurrent nitrogen and phosphorus enrichment increases ecosystem carbon use efficiency in an alpine grassland

Ecosystem carbon use efficiency (CUEe) is a crucial indicator for assessing the capacity of ecosystems to sequester atmospheric carbon (C). However, it remains unclear how nitrogen (N) and phosphorus (P) influence CUEe and how climatic factors regulate CUEe. Here, a four-year manipulative experiment was conducted in a semi-arid alpine grassland ecosystem to explore the response of CUEe to N and P additions with fluctuating interannual precipitation. Our study revealed that the N and P combination synergistically improved CUEe; the single additions of N or P did not affect CUEe. The variation in CUEe was correlated to a larger in net ecosystem CO2 exchange (NEE) relative to gross ecosystem productivity (GEP). Similarly, the combination of N and P significantly increased aboveground biomass, whereas addition alone did not. Under the combined addition of N and P, a significant decrease in species diversity led to decreases in CUEe. Moreover, precipitation indirectly increased CUEe by increasing NEE. Our findings underscore the co-limitation of N and P resources for CUEe in the alpine grassland ecosystem and identify community structure and climate factors as key drivers of CUEe variations following nutrient enrichment.

Seasonal precipitation distribution determines ecosystem CO2 and H2O exchange by regulating spring soil water–salt dynamics in a brackish wetland

Abstract The intensification of the global hydrological cycle is anticipated to increase the variability of precipitation patterns. Brackish wetlands respond to changes in precipitation patterns by regulating the absorption and release of CO2 and H2O to maintain the stability of ecosystem functions. However, there is limited understanding of how the inter‐seasonal precipitation distribution (SPD) affects ecosystem CO2 and H2O exchange compared with annual precipitation totals. Here, we conducted four consecutive years of field experiments in a brackish wetland, manipulating the proportion of precipitation across different seasons while maintaining a constant annual precipitation total. We utilized five inter‐SPD proportions (+73%, +56%, control (CK), −56%, −73%) to examine the effects of SPD on ecosystem CO2 and H2O exchange. Our findings revealed that the annual ecosystem CO2 and H2O fluxes showed a trend of decreasing with the decrease in spring precipitation distribution. Among them, the annual net ecosystem CO2 exchange, evapotranspiration, carbon use efficiency and water use efficiency were shown to be more sensitive to decrease in spring precipitation distribution and increase in summer and autumn precipitation distribution. This negative asymmetric response pattern suggests that annual ecosystem CO2 and H2O exchange is primarily governed by seasonal precipitation variability, with spring soil water–salt dynamics identified as the key driver. Therefore, this association can be explained by the fact that drought of the early growth stage exacerbates soil salinization and inhibits vegetation colonization and growth, thereby greatly impairing the annual CO2–H2O exchange capacity of brackish wetlands. Our results emphasized that the spring extreme precipitation‐induced soil water–salt conditions will greatly influence CO2 and H2O exchange in brackish wetlands in the future. These findings are crucial for improving predictions of the carbon sequestration and water‐holding capacity of brackish wetlands. Read the free Plain Language Summary for this article on the Journal blog.

Representing cropping systems with the MEMS 2 ecosystem model

AbstractCroplands have been the focus of substantial investigation due to their considerable potential for sequestering carbon. Understanding the potential for soil organic carbon (SOC) sequestration and necessary management strategies will be enabled with accurate process‐based models. Accurately representing crop growth and agricultural practices will be critical for realistic SOC modeling. The MEMS 2 model incorporates a current understanding of SOC formation and stabilization, measurable SOC pools, and deep SOC dynamics and is seen as a highly promising tool to inform management intervention for SOC sequestration. Thus far, MEMS 2 has been developed to represent grasslands. In this study, we further developed MEMS 2 to model annual grain crops and common agricultural practices, such as irrigation, fertilization, harvesting, and tillage. Using four Ameriflux sites, we demonstrated an accurate simulation of crop growth and development. Model performance was strong for simulating aboveground biomass (index of agreement [d] range of 0.89–0.98) and green leaf area index (d from 0.90 to 0.96) across corn, soybean, and winter wheat. Good agreement with observations was also achieved for net ecosystem CO2 exchange (d from 0.90 to 0.96), evapotranspiration (d from 0.91 to 0.94), and soil temperature (d of 0.96), while discrepancy with the available soil water content data remain (d from 0.14 to 0.81 at four depths to 100 cm). While we will continue model testing and improvement, MEMS 2 (version 2.14) has now demonstrated its ability to effectively simulate the growth of common grain crops and practices.

Carbon dioxide fluxes over irrigated and rainfed alfalfa in the Southern Great Plains, USA

The annual dynamics of carbon dioxide (CO2) fluxes for irrigated and rainfed alfalfa (Medicago sativa L.) in the Southern Great Plains of the United States of America (USA) under different watering regimes are not yet fully understood. The main objective of this study was to examine the dynamics of eddy covariance (EC) measured CO2 fluxes in relation to various biophysical factors and hay harvests for irrigated and rainfed alfalfa in central Oklahoma, USA. The study also aimed to investigate the relationship between CO2 fluxes and satellite-derived enhanced vegetation index (EVI) at different spatiotemporal scales and to assess the temporal variability in CO2 fluxes and EVI to variable growing conditions and hay harvests. The cumulative hay yields were 7.15 t ha−1 (two harvests in 2019) in the rainfed field and ∼9 t ha−1 (4–5 harvests in 2020 and 2021) in the irrigated field. Having sufficient rainfall during April and May was crucial to achieve economically feasible yields of alfalfa during the first harvest in May. The availability of water strongly regulated the potential for regrowth and carbon uptake of alfalfa following harvesting. The alfalfa fields were near carbon neutral or a small carbon source from January to mid-March and carbon sink after the initiation of vegetative growth in mid-March. The alfalfa fields were strong carbon sinks (cumulative annual net ecosystem CO2 exchange, NEE, up to −578 g C m−2 in irrigated field) on an annual scale. When accounting for the loss of carbon due to the removal of hay from the fields, the carbon balance of the alfalfa fields varied from small carbon sinks to small carbon sources, depending on the amount of hay harvested annually and the growing conditions. In general, the temporal patterns of CO2 fluxes and EVI were similar in relation to growing conditions and hay harvests. However, some discrepancies and time lags were observed due to the coarse spatiotemporal resolution of the EVI products. Thus, it is essential to integrate two or more satellite products with different temporal and spatial resolutions to accurately monitor the frequent and varying sizes of hay harvests and vegetation regrowth after harvesting, and to simulate continuous time-series CO2 fluxes.

Ecosystem level carbon and moisture fluxes from a high biomass fibre producing jute crop (Corchorus olitorius L): An eddy covariance-based analysis

ContextJute (Corchorus olitorius L) is the second-largest natural fibre producing crop in the world and plays a key role in the rural economy of South Asia particularly India and Bangladesh. It has diverse use due to its long length, lusture, biodegradability, high tensile strength, heat resistance and bendability. Jute is also fast-growing high biomass crop and quantification of the carbon / moisture exchanges is need of the hour towards its sustainability. ObjectiveThe study aims at quantitative estimation of the ecosystem level CO2 and H2O exchanges at diurnal, daily, and seasonal scales from jute crop cultivated in the alluvial soil of the Gangetic delta under a tropical hot and moist sub-humid climate during the years 2021–23. MethodsAn eddy covariance flux tower with suite of meteorological sensors were established at the jute growing farm to assess the high frequency ecosystem level exchanges of CO2, H2O and biometeorological parameters. Concurrent crop bio-physical parameters were also measured. The flux data were processed, quality checked, gap-filled and partitioned to construct seamless time-series of half-hourly fluxes for further analysis towards its dynamics, driving parameters, photosynthetic response and derived eco-physiological parameters. ResultsThe half-hourly CO2 fluxes exhibited significant variations across different growth stages of the jute crop, reaching peak values during the fibre development to maturity stages [mean Net Ecosystem CO2 Exchange (NEE) ranging from −20 to −22 µmol m−2 s−1; mean Gross Primary Production (GPP) ranging from 25 to 30 µmol m−2 s−1; and mean Ecosystem Respiration (Reco) ranging from 8 to 10 µmol m−2 s−1]. Seasonal GPP values was found to be high (902–1038 gC m−2) with moderate NEE value of −150 to −223 gC m−2. Seasonal mean Ecosystem Water Use Efficiency (EWUE) was high (2.5–2.7 gC kg−1 H2O) compared to other C3 crop. The daily Crop Coefficient (Kc) peaked at 1–1.2 during 90–110 Days After Sowing (DAS), indicative of high physiological activities. ConclusionDespite relatively short crop duration but high biomass (peak Leaf Area Index, LAI ∼6 m2m−2), the jute crop proved to be a robust gross CO2 sink. Due to the rapid growth, the jute crop allocates a significant portion of GPP as Reco (78–84 %), resulting in a relatively lower seasonal NEE. The high EWUE coupled with a net positive CO2 exchange positioned jute as a potential climate-resilient crop. This study provides valuable insights into the ecosystem exchanges of the jute crop, which in turn contribute to the global carbon and moisture budget.

Upscaling net ecosystem CO2 exchanges in croplands: The application of integrating object-based image analysis and machine learning approaches

Accurately estimating the net ecosystem exchange of CO2 (NEE) in cropland ecosystems is essential for understanding the impacts of agricultural practices and climate conditions. However, significant uncertainties persist in the estimation of regional cropland NEE due to landscape heterogeneity and variations in the efficacy of upscaling models. Here, we applied an integrated approach that combined object-based image analysis (OBIA) techniques with advanced machine learning (ML) approaches to upscale regional cropland NEE. We conducted a thorough evaluation of the upscaling approach across four distinct cropland areas characterized by diverse climate conditions. Our study confirmed that OBIA techniques can efficiently segment cropland objects, thereby enhancing the representation and accuracy of characteristics relevant to cropland features. The sequential least squares programming algorithm, among the three methods used for ML model integration, demonstrated exceptional performance in predicting NEE, with an R2 value exceeding 0.80 across all study areas and peaking at 0.90 in the most successful area. On average, there was an 18 % improvement compared to the poorest-performing ML model and a 6 % enhancement compared to the best-performing ML model. The upscaled regional products exhibited superior performance in characterizing cropland NEE patterns compared to pixel-based products. Additionally, we utilized the SHapley Additive exPlanations (SHAP) to assess driver importance, revealing that phenology and radiation had the greatest influence on prediction accuracy, followed by temperature and soil moisture. This study highlights the potential of integrating OBIA techniques with machine learning approaches for upscaling regional cropland NEE, while concurrently reducing estimation uncertainties.

Capturing the net ecosystem CO2 exchange dynamics of tidal wetlands with high spatiotemporal resolution by integrating process-based and machine learning estimations

Accurate estimation of the net ecosystem CO2 exchange (NEE) at regional scales is of great significance for studying the carbon sink potential of coastal wetland ecosystems and their responses to global climate change. However, current NEE estimation methods are mainly developed for terrestrial ecosystems and are therefore unsuitable for NEE estimation with high spatiotemporal resolution estimation in coastal wetlands subjected to sub-daily tidal flooding. In this study, we proposed a high spatiotemporal resolution NEE estimation method for coastal marsh wetlands that properly considered tidal influence by combining the advantages of process-based modeling and machine learning. This method was verified and applied in the Changjiang estuary and Liaohe estuary marsh wetlands based on eddy covariance and environmental measurements, climate reanalysis data, and satellite images. The proposed method had good performance in the NEE estimation of tidal marsh wetlands, with Phragmites australis, Spartina alterniflora, and Suaeda salsa having coefficients of determination (R2) of 0.850, 0.676, and 0.658, respectively, and root mean square error (RMSE) values of 7.211 μmol m−2 s−1, 8.105 μmol m−2 s−1, and 0.109 μmol m−2 s−1, respectively. By integrating the tide level and salinity, the NEE estimation accuracy for each vegetation type was improved. The total annual NEE values of the Changjiang estuary and Liaohe estuary marsh wetlands in 2022 were estimated to be −0.297 and −0.444 Tg C yr−1, respectively. This study demonstrated that integrating process-based model and machine learning estimation can reliably capture the NEE dynamics of coastal wetlands, providing a useful tool to quantify coastal blue carbon potential with high spatiotemporal resolution at large scales.

Effects of nitrogen and water addition on ecosystem carbon fluxes in a heavily degraded desert steppe

Changes in global precipitation patterns and increased nitrogen (N) deposition have important effects on the dynamics of the carbon (C) cycle in grassland ecosystems. Long-term overgrazing caused heavy degradation of grasslands and more than 60 % of China’s natural grasslands experienced moderate to severe degradation. To elucidate the effects of increasing precipitation and N deposition on C fluxes in a desert steppe ecosystem, experiments with nitrogen and water addition were conducted with four treatments, i.e., control (CK), N addition (N), water addition (W), combined addition of N and water (NW), in a Stipa breviflora desert steppe that had been heavily degraded. The ecosystem CO2 fluxes, including gross ecosystem productivity (GEP), net ecosystem CO2 exchange (NEE), and ecosystem respiration (ER), were determined using the closed chamber method during the growing seasons (May–October) of 2019 and 2020. Our results showed that water and nitrogen addition did not change the patterns of the seasonal dynamics of the ecosystem CO2 fluxes, indicating that water is the main limiting factor of CO2 fluxes in the studied desert steppe ecosystem. Precipitation increase (both W and NW treatments) stimulated all three components of the ecosystem CO2 fluxes, but N addition alone enhanced NEE and GEP while had no significant effects on ER, indicating water-induced increase of GEP was caused by both NEE and ER while N-induced increase of GEP was mainly caused by increases of NEE. Water addition can increase the effectiveness of N addition, but there was little interaction between the two factors. Soil moisture was more significantly correlated than soil temperature with ecosystem CO2 fluxes, and water addition could further enhance the correlation of ecosystem CO2 fluxes with soil temperature and moisture. Overall, ecosystem CO2 fluxes are more sensitive to the precipitation increase than to the N addition. These findings indicate that water supply will play more important role in ecosystem productivity of the desert steppe under the predicted scenarios of precipitation increase and increasing N deposition, which also provides scientific evidence for the restoration of those degraded desert steppe induced by overgrazing can not be achieved simply by increasing soil N availability.

Cultivated Grassland Types Differently Affected Carbon Flux Downstream of the Yellow River

Cultivated grasslands are an important part of grassland ecosystems and have been proven to be major carbon sinks, then playing an important role in the global carbon balance. The effect of cultivated grassland type (Medicago sativa, Triticum aestivum, Secale cereale, and Vicia villosa grasslands) on carbon flux (including net ecosystem CO2 exchange (NEE), ecosystem respiration (ER), and gross ecosystem productivity (GEP)) downstream of the Yellow River was studied via the static chamber technique and a portable photosynthetic system. Bare land was used as a control. The results showed that the four cultivated grassland types were mainly carbon sinks, and bare land was a carbon source. The cultivated grassland types significantly affected carbon flux. The average NEE and GEP of the grassland types were in the following order from high to low: Medicago sativa, Secale cereale, Triticum aestivum, and Vicia villosa grassland. Stepwise regression analysis showed that among all measured environmental factors, soil pH, soil bulk density (BD), soil organic carbon (SOC), and soil microbial carbon (MBC) were the main factors affecting CO2 flux. The combined influence of soil BD, SOC, and pH accounted for 77.6% of the variations in NEE, while soil BD, SOC, and MBC collectively explained 79.8% of changes in ER and 72.9% of the changes in GEP. This finding indicates that Medicago sativa grassland is a cultivated grassland with a high carbon sink level. The changes in carbon flux were dominated by the effects of soil physicochemical properties.

Phenology of Photosynthesis in Winter‐Dormant Temperate and Boreal Forests: Long‐Term Observations From Flux Towers and Quantitative Evaluation of Phenology Models

AbstractWe examined the seasonality of photosynthesis in 46 evergreen needleleaf (evergreen needleleaf forests (ENF)) and deciduous broadleaf (deciduous broadleaf forests (DBF)) forests across North America and Eurasia. We quantified the onset and end (StartGPP and EndGPP) of photosynthesis in spring and autumn based on the response of net ecosystem exchange of CO2 to sunlight. To test the hypothesis that snowmelt is required for photosynthesis to begin, these were compared with end of snowmelt derived from soil temperature. ENF forests achieved 10% of summer photosynthetic capacity ∼3 weeks before end of snowmelt, while DBF forests achieved that capacity ∼4 weeks afterward. DBF forests increased photosynthetic capacity in spring faster (1.95% d−1) than ENF (1.10% d−1), and their active season length (EndGPP–StartGPP) was ∼50 days shorter. We hypothesized that warming has influenced timing of the photosynthesis season. We found minimal evidence for long‐term change in StartGPP, EndGPP, or air temperature, but their interannual anomalies were significantly correlated. Warmer weather was associated with earlier StartGPP (1.3–2.5 days °C−1) or later EndGPP (1.5–1.8 days °C−1, depending on forest type and month). Finally, we tested whether existing phenological models could predict StartGPP and EndGPP. For ENF forests, air temperature‐ and daylength‐based models provided best predictions for StartGPP, while a chilling‐degree‐day model was best for EndGPP. The root mean square errors (RMSE) between predicted and observed StartGPP and EndGPP were 11.7 and 11.3 days, respectively. For DBF forests, temperature‐ and daylength‐based models yielded the best results (RMSE 6.3 and 10.5 days).

Dynamics of carbon and water vapor fluxes in three typical ecosystems of Heihe River Basin, Northwestern China

Understanding the dynamics of carbon and water vapor fluxes in arid inland river basin ecosystems is essential for predicting and assessing the regional carbon-water budget amid climate change. However, studies aiming to unravel the mechanisms driving the variations and coupling process of regional carbon-water budget in a changing environment in arid regions are limited. Here, we used the eddy covariance technique to analyze the relationship between CO2 and H2O fluxes in three typical ecosystems across the upper, middle, and lower reaches of an arid inland river basin in Northwestern China. Our results showed that all ecosystems acted as carbon sinks, with the alpine swamp meadow, cropland, and desert shrubland sequestrating −300.2 ± 0.01, −644.8 ± 2.9, and − 203.7 ± 22.5 g C m−2 yr−1, respectively. Air temperature (Ta) primarily controlled daily gross primary productivity (GPP) and net ecosystem CO2 exchange (NEE) in the irrigated cropland during the growing season, while soil temperature (Ts) and vapor pressure deficit (VPD) regulated these parameters in the alpine swamp meadow and desert shrubland. Additionally, Ta and net radiation (Rn) controlled daily evapotranspiration (ET) in cropland, while Ts and Rn regulated ET at other sites. Consequently, carbon and water vapor fluxes of all three ecosystems tended to be energy-limited during the growing season. The differential responses of carbon and water vapor fluxes in the upper, middle, and lower reaches of these ecosystems to biophysical factors determined their distinct coupling and variations in water use efficiency. Notably, the desert shrub ecosystem in the lower reach of the basin maintained a stable balance between carbon gain and water loss, indicating adaptation to aridity. This study provides valuable insights into the underlying mechanisms behind the changes in carbon and water vapor fluxes and water-use efficiency in arid river basin ecosystems.