Net Ecosystem CO2 Exchange Research Articles

Large inter-annual variation in carbon sink strength of a permanent grassland over 16 years: Impacts of management practices and climate

Permanent grasslands cover one third of the European agricultural area and are known to store large amounts of carbon (C) in their soils. However, long-term assessments of their C sink strength are still scarce. Thus, we investigated the C budget of an intensively managed, permanent grassland in Switzerland over 16 years, compared the results to changes in soil C stocks, and determined the most important drivers of the net ecosystem CO2 exchange (NEE). Combining NEE fluxes with C imports and C exports, we quantified the grassland C budget, i.e., net biome production (NBP). We observed a large inter-annual variation in NBP, with 9 of the 16 years indicating a C sink, and 7 years indicating a C source. On average, the grassland was a small C sink to C neutral, with a NBP of -70±106 g C m−2 yr−1 (mean±95% confidence interval). Mean NEE fluxes were -284±115 g C m−2 yr−1, C exports via harvest 335±73 g C m−2 yr−1, and organic C imports via slurry -121±43 g C m−2 yr−1. Soil C stocks from 0 to 0.7 m did not change significantly (decrease of 27.5 g C m−2 yr−1 over 13 years). Inter-annual variation in NBP was affected by management practices and environmental conditions. In the last five years, NBP was positive (C source), most likely due to decreasing C imports in combination with extreme weather conditions. Our study demonstrated the importance of covering multiple years with different management events when assessing the C sink strength of a site. Maintaining even a small grassland C sink in the future will be challenging and will require continuous organic C imports.

Experimental climate change in desert steppe reveals the importance of C4 plants for ecosystem carbon exchange

Global climate change has greatly altered plant community composition and affected ecosystem carbon exchange of grasslands. However, the influences of long-term increasing temperature and precipitation on the composition of plant community and the ecosystem carbon exchange in degraded desert steppe remain unclear. After seven years of simulated climate warming (ambient temperature, W0, 2 ℃ and 4 ℃ increase in temperature, W1 and W2) and increasing precipitation (plant growing season precipitation, P0, 25% and 50% increase in precipitation, P1 and P2), we recorded the composition of plant community, the moisture and temperature of soil and calculated net ecosystem CO2 exchange (NEE). Results showed that 1) long-term warming and increasing precipitation improved the cover and height of C4 plants in the plant community, while decreasing the cover and density of C3 plants. 2) Climate warming and increasing precipitation significantly increased NEE. Compared with W0P0, the mean NEE in the plant growing season increased by 115% in W2P2. 3) Redundancy analysis showed that soil moisture content and the height and density of C4 plants significantly explained ecosystem carbon exchange (P < 0.01), while the effect of C3 plants on ecosystem carbon exchange was insignificant (P > 0.01). Therefore, our research revealed that warming and increasing precipitation positively affected ecosystem carbon exchange in degraded desert steppe through changes in plant community composition, and C4 plants will play a vital role in ecosystem carbon exchange in desert steppe under climate change.

Alternate intercropping of cotton and peanut increases productivity by increasing canopy photosynthesis and nutrient uptake under the influence of rhizobacteria

ContextAlternate intercropping of cotton and peanut is an emerging cropping practice that involves the intercropping of two crops in a wide strip, with annual switching of planting positions. The productivity achieved through alternate intercropping, which combines intercropping and rotation, surpasses that of either mode used alone. However, the underlying mechanisms responsible for the observed increase in crop productivity remain elusive. We put forth the hypothesis that alternate intercropping enhances productivity by promoting canopy photosynthesis and nutrient uptake, facilitated by the influence of rhizosphere bacteria. MethodsA two-year field experiment was conducted employing a randomized block design to investigate the effects of monoculture, traditional intercropping, and alternate intercropping on yield, yield components, canopy photosynthesis, 13C photoassimilate partitioning, uptake of major nutrients, and diversity of rhizosphere bacterial communities. ResultsAlternate intercropping increased biological by 9.3% and seed cotton yield by 20.0% compared to monocultures without sacrificing peanut yields. In comparison to traditional intercropping, alternate intercropping also increased seed cotton yield by 3.9%, peanut pod yield by 6.7%, and total productivity by 10.6%. The cotton harvest index in alternate intercropping was similar to that in traditional intercropping but 9.9% higher than in monoculture. However, the peanut harvest index did not differ between alternate intercropping, traditional intercropping, and monoculture. Net ecosystem CO2 exchange (NEE) under alternate intercropping was higher in cotton than that under traditional intercropping and monoculture, and 13C partitioning to reproductive organs in cotton was higher in alternate intercropping than in monoculture, although there was no difference between alternate and traditional intercropping. In peanut, NEE was higher under alternate intercropping than under traditional intercropping at all stages. At peak pod setting, 13C partitioning to reproductive organs was higher under both alternate intercropping and monoculture than under traditional intercropping. Alternate intercropping resulted in a 7.2% increase in cotton nitrogen uptake compared to traditional intercropping, and a 8.8% increase in nitrogen uptake, 10.9% increase in phosphorus uptake, and 8.5% increase in potassium uptake compared to monoculture cotton. Peanut uptake of nitrogen, phosphorus, and potassium was greater in alternate intercropping than in traditional intercropping and was approximately the same as that in monoculture. Alternate intercropping increased the relative abundances of plant growth promoting rhizobacteria (PGPR). Significant correlations were detected between NEE and biological yield, 13C partitioning and harvest index, as well as the relative abundance of PGPR and uptake of major nutrients. ConclusionThe enhanced crop productivity observed in alternate intercropping can be attributed to improved shoot-root coordination, as evidenced by the increased canopy photosynthesis and nutrient uptake associated with altered rhizosphere bacterial communities.

A dataset of water, heat, and carbon fluxes over the winter wheat-summer maize croplands in Luancheng during 2013–2017

The winter wheat-summer maize double-cropping agro-ecosystem holds the largest planting area in the North China Plain. The carbon and water flux observation data of the croplands are crucial to understanding regional carbon and water cycles and their regulation mechanism. Located in the piedmont Plain of Taihang Mountain, Luancheng Agro-ecosystem Experimental Station (Luancheng Station) is the typical groundwater irrigation district. We have been using the eddy covariance technology to carry out the observation work of the winter-summer maize croplands since October 2007. This is the dataset of water, heat, and carbon fluxes over the winter wheat-summer maize croplands in Luancheng from October 2013 to September 2017 (covering four complete growing seasons). The dataset includes latent heat flux (or evapotranspiration), sensible heat flux, net ecosystem exchange of CO2, solar radiation, photosynthetically active radiation, air temperature/humidity, precipitation, soil temperature and volumetric soil water content at the half-hourly, daily, crop-season, and crop-year scales. The network observation, quality control and storage process of this dataset strictly abide by ChinaFLUX data management technology system to ensure data reliability. This dataset can provide solid data support for deeply understanding the processes of water, heat and carbon exchange, and improving the management of water resource to relieve the overdraft of groundwater in North China.

A dataset of the observations of carbon, water and heat fluxes over an alpine shrubland in Haibei (2011–2020)

Alpine shrubland is one of the important vegetation types on the Qinghai-Tibet Plateau, which mainly lies in the shady or semi-shady slope of snowpack mountains or the high-altitude alluvium and diluvium on plains. It plays a crucial role in carbon sequestration, water conservation and climate regulation. Since 2002, Haibei National Field Research Station for Alpine Grassland (Haibei Station) has been using eddy covariance techniques to continuously observe the carbon, water and heat exchange between an alpine Potentilla fruticosa shrubland ecosystem and the atmosphere and has accumulated nearly 20-year data. On the basis of the previous publication of relevant data from 2003 to 2010, the carbon, we further released water and heat fluxes of the alpine shrubland and supplementary meteorological data from 2011 to 2020. This dataset consists of the subsets of meteorological factors, covering air temperature, air relative humidity, water vapor pressure, wind speed, wind direction, ambient pressure, total solar radiation, net radiation, photosynthetically active radiation, precipitation, soil temperature, and soil moisture, as well as net ecosystem CO2 exchange, ecosystem respiration, gross ecosystem CO2 exchange, latent heat flux, and sensible heat flux. The temporal resolutions of the dataset include half-hourly, daily, monthly, and yearly scales. This dataset can not only be used to scientifically evaluate the environmental drivers and evolution trends of the ecological functions of carbon, water and heat in alpine shrub ecosystems, but also provide ground data support for parameter validation and optimization of remote sensing-based ecological process models.

A dataset of the observations of carbon, water and heat fluxes over an alpine meadow in Haibei (2015–2020)

Covering an area of 5.04×105 km2 on the Qinghai-Tibetan Plateau, Alpine meadow is essential to the plateau ecological barrier function and regional sustainable development. Since May 2014, Haibei National Field Research Station for Alpine Grassland (Haibei Station hereafter) has been accumulating amounts of valuable data by employing eddy covariance techniques to continuously measure carbon and water cycles and energy exchanges between an alpine Graminoid-Kobresia meadow ecosystem and the atmosphere. In the following of data processing such as outlier removal and flux data gaps filled by boosted regression tree model, Haibei Station plans to publish a dataset of the continuous observations of carbon, water, and heat fluxes of the alpine meadow from 2015 to 2010. This dataset consists of the subsets of carbon, water, and heat fluxes data (i.e. net ecosystem CO2 exchange, ecosystem CO2 respiration, gross ecosystem CO2 exchange, latent heat flux, and sensible heat flux) and the subsets of routine meteorological data (i.e. air temperature, air relative humidity, total solar radiation, net radiation, photosynthetically active radiation, precipitation, soil temperature, volumetric soil moisture content). The temporal resolutions of the dataset are half-hourly, daily, monthly, and yearly scales. This dataset can be used to validate the parameters of processes-based ecological models of carbon and water cycles and to evaluate the spatiotemporal patterns and evolution trends in ecological functions of carbon sequestration and water-holding capacity in alpine meadow ecosystems.

Regional Atmospheric CO2 Response to Ecosystem CO2 Budgets in China

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.

Two-level eddy covariance measurements reduce bias in land-atmosphere exchange estimates over a heterogeneous boreal forest landscape

Estimates of land-atmosphere exchanges of carbon, energy, water vapor, and other greenhouse gases based on the eddy covariance (EC) technique rely on the fundamental assumption that the flux footprint area is homogeneous. We investigated the impact of source area heterogeneity on flux estimates in single-level EC measurements over a managed boreal forest landscape. For this purpose, we compared single-level measurements with those from a two-level approach consisting of concurrent EC measurements at 60 and 85 m above the ground. This two-level set-up provided a unique opportunity to obtain nearly congruent diel footprint areas by combining data from the higher and lower levels during day- and nighttime, respectively.We found that the variation in the averaged footprint area between day- and nighttime was reduced by up to 89% in the two-level approach compared to the single-level data at the higher level (85 m). Considering spring, summer, and fall months, the resulting relative potential bias in flux observations due to landscape heterogeneity was highest at short time steps (≤ daily) ranging between 35% and 325% for half-hourly data. During winter months, when stable atmospheric regimes prevailed during day and night, the footprints within the diel course nearly overlapped also at a given single level and hence no improvement of flux estimates was found. The absolute cumulated sums for the study period (excluding winter months) of gross primary production, ecosystem respiration, latent heat, and sensible heat flux were underestimated by about 28%, 52%, 5%, and 3%, respectively, whereas that of net ecosystem CO2 exchange was overestimated by about 109% in the single-level approach. Overall this study suggests that footprint heterogeneity may introduce considerable bias in single-level flux estimates — particularly at short time scales — with large implications for model-data fusion studies, site comparisons, and up- or downscaling of land-atmosphere exchange processes.

Snow–vegetation–atmosphere interactions in alpine tundra

Abstract. The interannual variability of snow cover in alpine areas is increasing, which may affect the tightly coupled cycles of carbon and water through snow–vegetation–atmosphere interactions across a range of spatio-temporal scales. To explore the role of snow cover for the land–atmosphere exchange of CO2 and water vapor in alpine tundra ecosystems, we combined 3 years (2019–2021) of continuous eddy covariance flux measurements of the net ecosystem exchange of CO2 (NEE) and evapotranspiration (ET) from the Finse site in alpine Norway (1210 m a.s.l.) with a ground-based ecosystem-type classification and satellite imagery from Sentinel-2, Landsat 8, and MODIS. While the snow conditions in 2019 and 2021 can be described as site typical, 2020 features an extreme snow accumulation associated with a strong negative phase of the Scandinavian pattern of the synoptic atmospheric circulation during spring. This extreme snow accumulation caused a 1-month delay in melt-out date, which falls in the 92nd percentile in the distribution of yearly melt-out dates in the period 2001–2021. The melt-out dates follow a consistent fine-scale spatial relationship with ecosystem types across years. Mountain and lichen heathlands melt out more heterogeneously than fens and flood plains, while late snowbeds melt out up to 1 month later than the other ecosystem types. While the summertime average normalized difference vegetation index (NDVI) was reduced considerably during the extreme-snow year 2020, it reached the same maximum as in the other years for all but one of the ecosystem types (late snowbeds), indicating that the delayed onset of vegetation growth is compensated to the same maximum productivity. Eddy covariance estimates of NEE and ET are gap-filled separately for two wind sectors using a random forest regression model to account for complex and nonlinear ecohydrological interactions. While the two wind sectors differ markedly in vegetation composition and flux magnitudes, their flux response is controlled by the same drivers as estimated by the predictor importance of the random forest model, as well as by the high correlation of flux magnitudes (correlation coefficient r=0.92 for NEE and r=0.89 for ET) between both areas. The 1-month delay of the start of the snow-free season in 2020 reduced the total annual ET by 50 % compared to 2019 and 2021 and reduced the growing-season carbon assimilation to turn the ecosystem from a moderate annual carbon sink (−31 to −6 gC m−2 yr−1) to a source (34 to 20 gC m−2 yr−1). These results underpin the strong dependence of ecosystem structure and functioning on snow dynamics, whose anomalies can result in important ecological extreme events for alpine ecosystems.

Carbon budgets and environmental controls in alpine ecosystems on the Qinghai-Tibet Plateau

Rapid warming in alpine regions exerts important effects on carbon cycling in alpine ecosystem, which are sensitive to environmental changes. So far, little is known about the spatial and temporal variation in carbon budgets and the main influencing factors over different ecosystems. Here, we examined the monthly and annual gross primary production (GPP), net ecosystem CO2 exchange (NEE) and ecosystem respiration (ER) during 2004–2017 in four types of ecosystems (i.e., alpine meadow, steppe, forest and cropland) on the Tibetan Plateau. We explored the relationships between carbon fluxes and environmental factors. The results show that forest, cropland and alpine meadow ecosystems acted as carbon sinks, with NEE values ranging from −21.25 ± 3.54 to −308.75 ± 21.65 g C m-2a-1, while alpine steppe and overmature forest ecosystems serve as carbon sources (mean annual NEE: 23.12 ± 15.88 g C m-2a-1). The temperature sensitivity values (Q10) of ER in the forest (9.39) and alpine steppe (7.47) ecosystems were greater than those in the alpine meadow ecosystems (Q10 = 4.20), indicating that the carbon emissions in the forest and alpine steppe ecosystems were more sensitive to warming. Multiple linear regression analysis indicated that the carbon fluxes (GPP, NEE, ER) of alpine steppe and alpine meadow in the permafrost regions were more sensitive to water forcing (precipitation, soil water content), while in the forest and cropland ecosystems temperature forcing (air and soil temperature) were strong predictors of all the carbon flux indices. Our results showed differential responses of carbon budgets among ecosystems, which could be considered in the future modeling of carbon cycle in alpine regions.

On the variability of the leaf relative uptake rate of carbonyl sulfide compared to carbon dioxide: Insights from a paired field study with two soybean varieties

Carbonyl sulfide (COS) has been proposed as a promising tracer for the estimation of the gross primary productivity (GPP) from ecosystem to global scale in recent years. Despite substantial work at spatial scales from leaf to regions, the uncertainty of COS-based GPP estimates are poorly known compared to widely used GPP estimates derived from the net ecosystem CO2 exchange. One key uncertainty in this context is the leaf relative uptake (LRU) of the COS with respect to the GPP, which must be known a priori. To investigate the influence of environmental factors, like drought, on the variability of the LRU, we conducted an experiment using ecosystem flux measurements of COS, CO2 and H2O from two eddy covariance towers above a soybean field, growing a commercial cultivar and a chlorophyll deficient mutant variety, in two separate plots. Our findings suggest that the LRU does not only differ between plant varieties due to differences in the ratio of the internal to ambient CO2 mole fraction and the internal resistance to COS, but also changes in response to drought. We also found the internal resistance to COS uptake to be a significant factor in controlling the total COS flux for both varieties, but more so for the commercial cultivar. Our study indicates that species-specific differences in the LRU need to be investigated further, and that environmental stress might complicate the usage of COS as a tracer for predicting GPP at ecosystem and global scale.

Hydrometeorological controls on net carbon dioxide exchange over a temperate desert steppe in Inner Mongolia, China

Understanding the effect of environmental factors on the net ecosystem CO2 exchange (NEE) and the response of NEE to rainfall events is of great significance for an accurate understanding of the carbon cycle for desert steppe ecosystems. Based on the long-term (2011–2018) eddy covariance flux data of a temperate desert steppe in Inner Mongolia, China, this study used path analysis to analyze the combined impact of the environmental factors on NEE. The results showed that during the growing season, vapor pressure deficit (VPD) and soil water content (SWC) was the most prominent environmental factor for the daytime NEE and nighttime NEE, respectively. NEE responds differently to individual environmental factors among multi-year climatic conditions. The size of rainfall event has significant impacts on NEE, it can effectively promote the CO2 uptake of the desert steppe ecosystem when rainfall event size is greater than 5 mm, and the NEE response increased with the rainfall event size. Moreover, NEE peaked approximately 1–3 days after a 5–10 mm rainfall event, while the rainfall event size &amp;gt;10 mm, it would take 3–5 days for NEE to reach a peak value; and yet, small rainfall events (&amp;lt; 5 mm) slightly increased CO2 emissions. During the growing season, carbon uptake increased with monthly rainfall, except in May. Our results are important for understanding the carbon cycle and its control mechanisms in the temperate desert steppe of Inner Mongolia.

Interactions and Covariation of Ecological Drivers Control CO2 Fluxes in an Alpine Peatland

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.

Variations in CO2 and CH4 Exchange in Response to Multiple Biophysical Factors from a Mangrove Wetland Park in Southeastern China

Mangrove ecosystems can be both significant sources and sinks of greenhouse gases. The restoration of mangrove forests is increasingly used as a natural climate solution tool to mitigate climate change. However, the estimates of carbon exchanges remain unclear, especially from restored mangroves. In this study, we observed the temporal variations in carbon dioxide (CO2) and methane (CH4) fluxes and their biophysical controls for 4 years, based on a closed-path eddy covariance (EC) system. The measurements were conducted in a mangrove wetland park with 14-year-old restored mangroves surrounded by open waters in Guangdong Province, China. The EC measurements showed that the mangrove ecosystem acted as a CO2 source with a net CO2 ecosystem exchange (NEE) of 305 g C m−2 from January 2019 to May 2020 by the 5-m tower measurement. After the tower was adjusted to 10 m, the mangrove showed a CO2 sink with an NEE of −345 g C m−2 from June 2020 to December 2022. The change in tower height influenced the interpretation of interannual trends on NEE. There were no significant interannual trends in the gross primary productivity (GPP) and the ecosystem respiration (Re) values. The change from CO2 source to sink may be attributed to the decrease in land surface proportion by the tower replacement, which reduces the proportion of the mangrove canopy respiration and, therefore, captures lower CO2 fluxes from open waters. The restored mangroves indicated strong CH4 sources of 23.2–26.3 g C m−2 a−1. According to the random forest analysis, the land surface proportion, radiation, and relative humidity were the three most important predictors of NEE, while the CH4 flux was most sensitive to air temperature. Compared to the natural and long-term restored mangroves, this 14-year-old restored mangrove had not yet achieved a maximum carbon sequestration capability. Our study highlights the need for the careful design of long-term observations from restored mangroves and proposes future needs in the context of carbon neutrality.

NDVI changes in the Arctic: Functional significance in the moist acidic tundra of Northern Alaska.

The Normalized Difference Vegetation Index (NDVI), derived from reflected visible and infrared radiation, has been critical to understanding change across the Arctic, but relatively few ground truthing efforts have directly linked NDVI to structural and functional properties of Arctic tundra ecosystems. To improve the interpretation of changing NDVI within moist acidic tundra (MAT), a common Arctic ecosystem, we coupled measurements of NDVI, vegetation structure, and CO2 flux in seventy MAT plots, chosen to represent the full range of typical MAT vegetation conditions, over two growing seasons. Light-saturated photosynthesis, ecosystem respiration, and net ecosystem CO2 exchange were well predicted by NDVI, but not by vertically-projected leaf area, our nondestructive proxy for leaf area index (LAI). Further, our data indicate that NDVI in this ecosystem is driven primarily by the biochemical properties of the canopy leaves of the dominant plant functional types, rather than purely the amount of leaf area; NDVI was more strongly correlated with top cover and repeated cover of deciduous shrubs than other plant functional types, a finding supported by our data from separate "monotypic" plots. In these pure stands of a plant functional type, deciduous shrubs exhibited higher NDVI than any other plant functional type. Likewise, leaves from the two most common deciduous shrubs, Betula nana and Salix pulchra, exhibited higher leaf-level NDVI than those from the codominant graminoid, Eriophorum vaginatum. Our findings suggest that recent increases in NDVI in MAT in the North American Arctic are largely driven by expanding deciduous shrub canopies, with substantial implications for MAT ecosystem function, especially net carbon uptake.

Examining the Stand Level CO2 Fluxes of Spring Forest Geophytes

Spring forest ephemerals often create homogeneous patches in the understory; however, our knowledge about their stand level characteristics is deficient. Our aims were to examine, parallel to their phenology, the stand level Net Ecosystem CO2 Exchange (NEE) and evapotranspiration (ET) fluxes as well as the dependence of NEE on leaf area (LA), air temperature (Tair) and light (PPFD) in three spring forest geophytes that are widespread in Europe. Furthermore, we compared the leaf and stand level net photosynthesis. The methods used included open chamber measurements with an infrared gas analyser in permanent plots on a weekly basis. The results showed that the stand levels of all three species proved to be carbon sinks from the beginning of the vegetation period until the end of it or until the last phase of fruit formation. The largest amount of carbon sink was observed at the peak of blooming. A positive linear correlation was measured between NEE and PPFD as well as between NEE and LA, while a negative linear regression was measured between NEE and Tair. The remarkable carbon uptake capacity indicates the non-negligible role of geophyte vegetation in the carbon flux of temperate forests. In addition, the research provided new proof about the role of stand level operation, stability and regulation.

Thermal acclimation of plant photosynthesis and autotrophic respiration in a northern peatland

Peatlands contain one-third of global soil carbon (C), but the responses of peatland ecosystems to long-term warming are not well understood. Here, we pursue an emergent understanding of warming effects on ecosystem C fluxes at peatlands by constraining a process-oriented model, the terrestrial ECOsystem model, with observational data from a long-term warming experiment at the Spruce and Peatland Responses Under Changing Environments site. Model-based assessments show that ecosystem-level photosynthesis and autotrophic respiration exhibited significant thermal acclimation, with temperature sensitivities being linearly decreased with warming. Using the thermal-acclimated parameter values, simulated gross primary production, net primary production, and plant autotrophic respiration (R a), were all lower than those simulated with non-thermal acclimated parameter values. In contrast, ecosystem respiration simulated with thermal acclimated parameter values was higher than that simulated with non-thermal acclimated parameter values. Net ecosystem CO2 exchange was much higher after constraining model parameters with observational data from the warming treatments, releasing C at a rate of 28.3 g C m−2 yr−1 °C−1. Our data-model integration study suggests that peatlands are likely to release more C than previously estimated. Earth system models may overestimate C uptake by peatlands under warming if physiological thermal acclimation of plants is not incorporated. Thus, it is critical to consider the long-term physiological thermal acclimation of plants in the models to better predict global C dynamics under future climate and their feedback to climate change.

Carbon dioxide exchanges in an alpine tundra ecosystem (Gran Paradiso National Park, Italy): A comparison of results from different measurement and modelling approaches

Mountain ecosystems are particularly sensitive to the effects of climate change and require a detailed understanding of how their components respond to the varying environmental conditions. However, the knowledge of crucial biogeochemical variables, such as carbon dioxide (CO2) fluxes in the Critical Zone of high-altitude grassland and Alpine tundra, is still patchy. In this framework, two methods are typically adopted to measure CO2 exchanges between the atmosphere and the underlying ecosystem: eddy covariance and accumulation chambers. Potential drawbacks affect both approaches, also depending on the scale at which studies are performed. On the one hand, eddy covariance method provides large spatial coverage, despite requiring specific statistical assumptions that are not always valid in remote environments. In addition, the eddy covariance method allows measuring only the CO2 net ecosystem exchange (NEE), while the separation of CO2 emission (ecosystem respiration, ER) and uptake (gross primary production, GPP), needed for understanding ecosystem functioning and its possible future changes, requires partitioning procedures based on regressive models. On the other hand, accumulation chambers allow for direct measurements of both NEE and ER but are usually limited to sampling small areas and the chambers themselves could potentially disturb plant-soil dynamics. The present study investigates such issues by combining three complementary methods (i.e., eddy covariance, portable and automated accumulation chambers) to measure summer CO2 fluxes at the soil-vegetation-atmosphere interface in a heavily instrumented high-altitude Alpine tundra site. While eddy covariance and portable chamber measurements provide consistent estimates of the NEE, a significant discrepancy between the results of the different methodologies was found for daytime ER. In particular, the eddy covariance daytime and nighttime partitioning methods failed in representing ER at the ecosystem scale. The discrepancy between modelled and measured ER suggests that different processes are involved during day and night, which are not fully represented in models. These aspects would require further explorations to achieve site-specific assessments of daytime ER.

Seasonal precipitation regulates magnitude and direction of the effect of nitrogen addition on net ecosystem CO2 exchange in saline-alkaline grassland of northern China

Increased nitrogen (N) deposition and altered precipitation regimes have profound effects on carbon (C) flux in semi-arid grasslands. However, the interactive effects between N enrichment and precipitation alterations (both increasing and decreasing) on ecosystem CO2 fluxes and ecosystem resource use efficiency (water use efficiency (WUE) and carbon use efficiency (CUE)) remain unclear, particularly in saline-alkaline grasslands. A four-year (2018–2021) field manipulation experiment was conducted to investigate N enrichment and precipitation alterations (decreased and increased by 50 % of ambient precipitation) and their interactions on ecosystem CO2 fluxes (gross- ecosystem productivity (GEP), ecosystem respiration (ER), and net ecosystem CO2 exchange (NEE)), as well as their underlying regulatory mechanisms under severe salinity stress in northern China. Our results showed that N addition and precipitation alteration alone did not significantly affect the GEP, ER and NEE. While the interaction of N addition and increased precipitation over the four years significantly improved the mean GEP and NEE by 24.9 % and 15.9 %, respectively. The interactive effects of N addition and increased precipitation treatment significantly stimulated the mean value of WUE by 39.1 % compared with control, but had no significant effects on CUE over the four years. Based on the four-year experiment, the magnitude and direction of the effects of N addition on the NEE were related to seasonal precipitation. Nitrogen addition increased the NEE under increased precipitation and decreased it during extreme drought. Soil salinization (pH and base cations) could directly or indirectly affect GEP and NEE via plants productivity, plant communities, as well as ecosystem resource use efficiency (WUE and CUE) based on structural equation model. Our results address lacking investigations of ecosystem C flux in saline-alkaline grasslands, and highlight that precipitation regulates the magnitude and direction of N addition on NEE in saline-alkaline grasslands.

Gap Filling Method and Estimation of Net Ecosystem CO2 Exchange in Alpine Wetland of Qinghai–Tibet Plateau

The net ecosystem CO2 exchange (NEE) and water and energy fluxes at the alpine ecosystem level were obtained through the eddy covariance technique in an alpine wetland of the Longbao Region, Qinghai–Tibet Plateau. Our research used the NEE as the research object combined with meteorological factors. The NEE prediction model was constructed using Reddyproc and machine learning. Moreover, the effects of the data and features on the models and the selection of the model parameters were discussed. The results revealed the following information: (1) After removing the NEE outliers according to the friction wind speed thresholds of the different seasons, the NEE interpolation accuracy (R2) reached 0.65. Additionally, the NEE data dispersion decreased after removing the outliers, and the data quality improved effectively. (2) The decision coefficients (R2) of the eight kinds of combined machine learning algorithm models varied from 0.22 to 0.62, and the root mean square error (RMSE) ranged from 2.10 to 2.99 μmol s−1 m−2. Additionally, the multilayer perceptron (MLP) model had the best stability and the best interpolation effect. (3) There was a seasonal difference between the estimated values of Reddyproc and the estimated values of MLP. The monthly mean values of January, February, March, and October were lower than the monthly mean values of the latter, while the monthly mean values from April to September were higher than the monthly mean values of the latter, indicating that the prediction of the machine learning algorithm tends towards the carbon source in the cold season (nongrowing season) and tends towards the carbon sink in the warm season (growing season). (4) Reddyproc detected the outliers through the relationship between the night NEE and frictional wind speed, which made it possible to accurately estimate the nighttime flux under the condition of determining the threshold of the night frictional wind speed, thus obtaining a better NEE estimate with fewer input parameters. Before the training and prediction of the MLP model, the NEE was detected for the time series outliers, and the prediction accuracy was significantly improved, indicating that the elimination of the time series outliers is essential for NEE model training and further indicating that the understanding of the potential mechanism of the NEE is of great significance for the prediction model.