Songhua River basin, encompassing numerous vital ecological protection areas and water conservation zones, is crucial in facilitating sustainable economic development in Northeast China. The basin exhibits a complex array of land use types, resulting in a diverse ecosystem. The dynamic monitoring of watershed-scale land use/land cover changes (LULCC) and carbon stocks estimation of the terrestrial ecosystem can provide suggestions for optimizing land utilization, enhancing terrestrial ecosystem carbon storage, and achieving the objective of "dual carbon". Based on the Landsat 5 TM and Landsat 8 OLI images from 1986 to 2022, this study employed random forest to obtain ten land use/land cover maps of the Songhua River Basin with high accuracy and conducted dy-namic monitoring of LULCC and its ecosystem carbon storage using an Integrated Valuation of Ecosystem Ser-vices and Trade-offs model, Mann-Kendall tests and Theil-Sen Median trend analysis. Results showed that farm-land has the largest area in the basin, followed by forest land, grasslands, unused lands, water, construction land, sparse forest land, and shrub land. Among them, farmland, forest, and grassland are the dominant land use types in the study area. During 1986-2022 period, the farmland expanded 11462.68 km2 while forest land decreased 18567.21 km2; the construction land experienced the largest change rate of 5.3% with an increased area of 3505.82 km2; the change rate of the sparse forest is 4.7%, ranking the second after construction land, but having minimal impact on the overall basin due to limited area changes. The change rate of unused land was 4.5%, with an increased area of 5385.43 km2. There was evident spatial heterogeneity in the distribution of the terrestrial ecosystem carbon stocks within the Songhua basin, with high carbon stocks predominantly found in the Dax-ingan and Xiaoxingan as well as the Changbai Mountains. The median carbon stock values were observed in Hinggan League, Songnen Plain, and Sanjiang Plain. In contrast, the areas with low carbon values were observed in Daqing and Baicheng. Over the 36 years, there was an overall decline in carbon storage within the basin, pri-marily concentrated in the regions initially characterized by high carbon stock values. However, the area with increased carbon stock is scatterly located in the basin. Notably, three recovery instances of ecosystem carbon stock occurred in 1994, 2002, and 2018 within the Songhua River Basin, all related to the changes in forest land. Based on ensuring no reduction of current forest land, it is recommended to expand forestland and continue im-plementing forestry projects to effectively prevent further depletion of terrestrial ecosystem carbon storage in the Songhua River basin.

Aims Carbon sequestration in terrestrial ecosystems is one of the important ways to slow down the rise of atmospheric CO₂ concentration. Therefore, understanding the natural vegetation ecosystems carbon storage (ECS) in response of future climate change is critical for making of regional land management policies.
Methods In this study, the sensitive parameters of LPJ-GUESS model are calibrated based on genetic algorithm. Using the downscale climate data-driven model, combined with Mann Kendall test, Sen’s slope estimation and partial correlation analysis, the temporal and spatial patterns, trend change characteristics and climate dominant factors of China’s ECS from 2001 to 2100 are analyzed.
Important findings The Nash-Sutcliffe efficiency coefficient and Pearson correlation coefficient of the calibrated LPJ-GUESS model in simulating ECS are 0.751 and 0.901, respectively, indicating that the LPJ-GUESS model can simulate China’s ECS well. During 2001-2020, China’s ECS decreased from southeast to northwest, with a total amount of 156.06 Pg. Vegetation, litter and soil carbon storage accounted for 34.2%, 1.9% and 63.8% of total ECS, respectively. The ECS in 2081-2100 shows similar spatial pattern with that in historical periods. The total amount of ECS at the end of this century are expected to increase by 0.51-11.16 Pg. The growth rates of China’s ECS was 8.5 g·m^-2·a^-1 and 3.7-21.0 g·m^-2·a^-1 during 2001-2020 and 2021-2100, respectively. During 2021-2100, significant increases of ECS are observed in southeast China, Nei Mongol Plateau, Qingzang Plateau (37-44 g·m^-2·a^-1), while obvious decreases (45-72 g·m^-2·a^-1, in the southern Yunnan-Guizhou Plateau, hilly areas in Guangxi and Guangdong. In northwest China, temperature is the dominant factor affecting ECS. The influences of precipitation on ECS are strengthened from the southeast to northwest. In high latitude and high-altitude areas, radiation is the dominant factor of ECS. CO₂ plays the most important role on ECS across about 47.9%-56.1% of China’s area.

The alteration of terrestrial carbon cycling under climate warming is regulated by soil organic carbon (SOC) dynamics. Previous studies have developed multiple warming methods, mainly including laboratory incubation experiment, field in-situ manipulative experiment, and temperature gradient sampling, to investigate the responses and mechanisms of SOC dynamics to climate warming. However, due to the methodological limitations, the studies on the effect of warming on SOC dynamics cannot lead to consistent conclusions. SOC dynamics mainly include two processes: carbon input and carbon decomposition, and are also regulated by carbon persistence. The changes of carbon input, carbon decomposition, and carbon persistence together determine the response of SOC dynamics to warming. Previous studies showed that both carbon input and decomposition may positively respond to warming, which is related to the enhanced activities of plants and soil microbes. However, some studies pointed out that warming-induced alterations of soil physical and chemical properties (e.g., the decrease of soil water content) and biological processes (e.g., microbial community thermal adaptation) may affect the responses of carbon input and decomposition to warming. Moreover, inconsistent responses may arise when focusing on the SOC responses to warming in top (0-30 cm) or deep (>30 cm) soils due to the limitations of environmental factors on carbon input and decomposition in deep soils, as well as the different persistence of SOC in deep soils compared to top soils. Future research should focus on developing new warming methods, increasing research on deep soils and climate-sensitive ecosystems, introducing new technologies to study the source, structure, and protection of soil organic matter, paying attention to the response of plant-soil animal-soil microbe system to warming and its regulation on SOC dynamics, to improve uncertainties in carbon cycle models and more accurately predict the feedback of the global carbon cycle to climate warming.

Aims Nei Mongol is an important ecological security barrier in northern China, and the study of changes in its vegetation productivity is of great significance to the ecological security of the northern region.

Methods Based on multi-source remote sensing data such as Eddy Covariance-Light Use Efficiency Gross Primary Productivity (EC-LUE GPP) in Nei Mongol from 1982 to 2017, this paper uses trend analysis and correlation analysis to analyze the temporal and spatial variation characteristics of vegetation gross primary production (GPP) in Nei Mongol and its correlation with air temperature, precipitation and soil moisture. On this basis, multiple linear regression and residual analysis methods were used to decompose and quantify GPP under the influence of climate changes and human activities, divide different time periods to carry out its impact on vegetation GPP, and explore the impact of different vegetation types on the driving factors response.

Important findings (1) Three meteorological elements showed good correlation with vegetation GPP, among which precipitation and soil moisture had higher correlations with GPP. (2) During the period 1982-1990, vegetation GPP showed an insignificant increasing trend with large fluctuations and the remaining three time periods (1991-2000, 2001-2010, 2011-2017) showed an insignificant downward trend. The areas with an overall downward trend accounted for 55% of the total area, and the other 45% showed a significant upward trend. (3) Except for the period from 2001 to 2010, climate changes played a decisive role in vegetation restoration in the other three time periods (1982-1990, 1991-2000, 2011-2017), explaining 20%, 16% and 13% of vegetation restoration, respectively. Human activities dominated vegetation degradation areas, explaining 13%, 19% and 20% of vegetation degradation, respectively. The research results can provide scientific reference for the implementation of ecological environmental protection and management policies and green and sustainable development in Nei Mongol.

There are substantial carbon exchange fluxes among soil, vegetation and atmosphere in the terrestrial ecosystems, which are highly relevant to global climate changes. Mycorrhizal fungi can form symbiotic associations with most terrestrial plants, linking the above- and below-ground ecosystems through mineral nutrient-carbon exchange; thus, mycorrhizal fungi play crucial roles in terrestrial carbon cycling. This review summarized the involvements of mycorrhizal fungi in the terrestrial carbon cycling processes, including the carbon input, and formation, stabilization, and decomposition of soil organic matter. Studies have demonstrated that mycorrhizal fungi markedly influence the terrestrial carbon input processes by alleviating plant nutrient deficiencies, improving plant stress resistance, influencing plant photosynthesis, and regulating plant diversity-productivity relationships, subsequently sustaining or improving primary productivity of terrestrial vegetation. A considerable proportion of photosynthetic carbon is channeled directly into the soil matrix via the fungal mycelial network, where it is partly converted into microbial-derived organic carbon, further changes the composition of soil organic carbon, and be stabilized through association with minerals and/or forming soil aggregates. Mycorrhizal fungi can affect the decomposition and transformation of soil organic matter mainly through two mechanisms: the rhizosphere priming effects and/or hyphosphere biogeochemical processes. These mechanisms involve the secretion of specific extracellular enzymes, shaping hyphosphere microbial communities, induction of chemical oxidation, and competition for limited resources (e.g., nutrients and water) with free-living saprotrophs. Considering the sensitivity of mycorrhizal fungi to environmental and climate changes, we also discuss the impact of global change factors on soil carbon cycling mediated by mycorrhizal fungi. Finally, we proposed future research directions, emphasizing a need for in-depth studies on the role of mycorrhizal fungi in terrestrial carbon cycling and their environmental dependence based on network experiments in typical ecosystems. Quantitative studies should be strengthened to integrate mycorrhizal fungi into ecosystem carbon cycling models, and mycorrhizal technologies should be developed and practiced in ecological restoration and agriculture to facilitate terrestrial carbon sequestration for achieving the national carbon neutrality goals and combating climate changes.

Aims Both the carbon cycle and the function of grassland ecosystem as a carbon sink are impacted by the rising nitrogen deposition. Active organic carbon content is an important measure that can reveal changes in soil carbon pool. For a thorough understanding of carbon cycling and the creation of sensible ecosystem management strategies, it is essential to investigate the impacts of nitrogen addition on the active organic carbon fractions of grassland soils.

Methods Five different nitrogen addition treatments were set up in a temperate typical steppe of Nei Mongol. Soil organic carbon fractions content, soil physical and chemical properties, aggregate stability, microbial activities and extracellular enzyme activities were measured. Pearson correlation and structural equation model (SEM) were used to examine the relationships.

Important findings Nitrogen addition reduced the contents of dissolved organic carbon (DOC), microbial biomass carbon (MBC), and easily oxidizable organic carbon (EOC). The contents of DOC, MBC, and EOC all decreased with the increases of soil depth. The treatment of 5 g·m^-2·a^-1 nitrogen addition significantly promoted the decomposition of active organic carbon fractions. The effect of nitrogen addition on soil active organic carbon fractions content was regulated by biotic (microbial biomass, extracellular enzyme activity, etc.) and abiotic (soil physical and chemical properties, aggregate stability, etc.) factors. Nitrogen addition reduced soil density, increased mean mass diameter and the proportion of large aggregates, increased the contact between organic matter and substrate, promoted the decomposition of active organic carbon, and reduced the contents of DOC and EOC. Nitrogen addition inhibited the activities of polyphenol oxidase and peroxidase, reduced the decomposition of difficult-to-decompose organic matter and the contents of EOC and MBC. Nitrogen addition increased the activities of β-glucosidase and cellulose hydrolase, promoted the utilization of DOC by microorganisms, and reduced the content of DOC. Our results indicated that nitrogen addition treatment can affect the decomposition process of active organic carbon by changing soil physicochemical properties and the secretion of extracellular enzymes from microorganisms, promoting the release of carbon in grassland soils. This provided a theoretical basis for further exploration of grassland soil carbon dynamics under nutrient addition in the future.

Aims How nitrogen (N) addition impacts the emission of greenhouse gases (GHGs) is now becoming a hot issue in the study of global change. We aim to delineate the effects of N addition on the emission of major greenhouse gases (CO₂, CH₄and N₂O).

Methods In order to achieve this goal, the flux of the three major GHGs was measured using static chamber gas chromatography during the growing seasons (May through September) of 2020 and 2021 in a meadow steppe of Hulun Buir in Nei Mongol. The experiment was conducted by applying NH₄NO₃ to simulate the atmospheric N deposition, which involved six N addition levels (i.e., 0, 2, 5, 10, 20, 50 g·m^-2·a^-1) and two grassland utilization levels (i.e., mown and unmown).

Important findings The results showed that the response of the three GHGs to N addition showed clear nonlinear patterns, but there was a remarkable difference in the patterns among the three GHGs. The emission of CO₂ was increased with increasing N addition but saturated at around 10 g·m^-2·a^-1. The uptake of CH₄ was promoted with increasing N addition when N addition was low (0-5 g·m^-2·a^-1), but this promotion effect was diminished with further increase in N addition (5-10 g·m^-2·a^-1), and the uptake of CH₄ was inhibited when N addition reached 50 g·m^-2·a^-1. The emission of N₂O increased significantly with the increase of N addition rates, but the response patterns and amplitude showed remarkable difference between the two years. With the data in the two years pooled, the CO₂ flux had a significant positive correlation with precipitation and nitrate nitrogen (NO- 3-N) content, and a significant negative correlation with pH; CH₄ absorption flux was significantly positively correlated with precipitation and ammonium nitrogen (NH+ 4-N) content, while negatively correlated with pH; N₂O flux was significantly positively correlated with soil temperature and NH+ 4-N content, while significantly negatively correlated with NO- 3-N content. Our findings demonstrated that the response of the three GHGs to increasing atmospheric N deposition was largely nonlinear, and the response patterns were remarkably different among the three GHGs. These findings may be of great importance for controlling N fertilizer use, selecting appropriate grassland use, and evaluating grassland ecosystem warming potential under increasing atmospheric N deposition.

Arid and semi-arid ecosystem areas, which constitute an important component of the global land surface, act to regulate the long-term trend and interannual variations in global carbon and water cycles. Previous studies on the mechanisms underlying ecosystem carbon and water cycling and the development of relevant data products focus primarily on forest, grassland, and cropland ecosystems, with few research attention given to semi-arid shrublands. This research gap hinders the evaluation and projection of ecosystem functions at the regional scale. Since 2011, we used the eddy covariance technique to make continuous in situ measurements of carbon, water and heat fluxes in a shrubland ecosystem at Yanchi Research Station, the Mau Us Sandy Land. Data processing steps mainly included data collection, post-processing of raw data, quality control, gap-filling and carbon flux partitioning. We produced flux and micro-meteorological datasets at half-hourly, daily, monthly, and annual temporal resolutions for the years 2012-2016, and analyzed the overall quality of the datasets in terms of the proportion of valid data and the energy balance closure of flux measurements. Results showed: (1) After quality control, the proportion of valid data for half-hour net ecosystem CO₂ exchange (NEE), latent heat flux (LE), and sensible heat flux (Hs) was 56.23%-62.19%, 79.40%-94.12%, and 77.56%-91.27%, respectively. (2) Annual and monthly energy balance ratio ranged 0.78-0.83 and 0.59-1.19, respectively. (3) The energy balance closure estimated using the “ordinary least squares” regression method showed that the interannual and seasonal variations in the slope of regression curves varied with a range of 0.73-0.79 at interannual scale and 0.73-0.92 at seasonal scale, respectively. These results indicate that our datasets have a high proportion of valid data and a reasonable energy balance closure, and thus can be used in studies related to ecosystem processes and functions at varing spatio-temporal scales.

Aims This study aims to explore a high-precision interpolation method of evapotranspiration based on machine learning to construct high-quality data set of actual evapotranspiration.

Methods Taking the typical alpine marsh wetland on the Qingzang Plateau as the observation station to study evapotranspiration, combined with meteorological factors (net radiation, air temperature, soil heat flux, wind speed, relative humidity, soil volumetric water content), we established a prediction model to construct an actual evapotranspiration data set with a high-precision interpolation method based on combining five methods including multiple linear regression (MLR), decision tree (CART), random forest (RF), support vector regression (SVR) and multi-layer perceptron (MLP).

Important findings 1) The correlation between evapotranspiration and net radiation was the largest in the study area, and soil heat flux was the key factor affecting the evapotranspiration process. 2) The determination coefficients are from 0.58 to 0.83 among five machine learning algorithm models with seven combinations, and the root mean square error ranges from 0.038 to 0.089 mm·30 min^-1. 3) The random forest regression model has the highest determination coefficient, the best model stability and the best interpolation. 4) Interpolated evapotranspiration data had the same diurnal variation trend with net radiation, soil heat flux and ari temperature, but the opposite diurnal variation trend with wind speed and relative humidity. Daily evapotranspiration is mainly concentrated in the growing season, with the daily maximum (8.77 mm) on July 9 and the daily minimum (0.21 mm) on January 30.

Soil respiration is mainly composed of the CO₂ released from atmosphere-soil interface and change of CO₂ stored in the soil. Understanding the production and migration of CO₂ in the soil is essential for measuring the carbon cycle in terrestrial ecosystems. The flux gradient method calculates soil CO₂ flux by measuring the diffusion-driven CO₂ concentration gradient and diffusion coefficient. The flux of soil CO₂ and its stable carbon isotopes composition (δ¹³C) at different depths can be calculated based on Fickʼs law. The amount of CO₂ released from soil and the amount of CO₂ stored in different soil layers can thus be measured. The underground soil CO₂ (¹³CO₂ and ¹²CO₂) concentration is mainly controlled by pore tortuosity, the depth of root distribution, microbial activity and total soil CO₂ production. The underground CO₂ transmission process is mainly controlled by the CO₂ concentrations, porosity and water content at different depths of the soil. These physical, chemical and biological features of the soil are key factors affecting the application of the soil flux gradient method, and directly determine the precision and accuracy of soil CO₂ and its δ¹³C flux calculation. The gradient method is a useful complement to the chamber method, which can clarify the process of production and migration of soil CO₂ at different depths and thus the impacts on the release and storage of soil CO₂, elucidating the contribution of soils at different depths to CO₂ release and uncovering the underlying environmental and physical mechanisms.

Aims Under the background of climate warming, the contradiction between forest and water in semi-arid areas is becoming increasingly prominent. Understanding the changes in energy flux and evapotranspiration (ET) of the plantation ecosystems in this area can provide a reference for future selection of afforestation tree species.

Methods In this paper, the water and heat fluxes of Pinus tabuliformis and Pinus sylvestris var. mongolica plantations in semi-arid area of western Liaoning were observed continuously for one year (October 2019 to October 2020) using the eddy covariance method. The length of growing season (April 11 to October 10) was determined using the critical temperature combined with the variation characteristics of normalized differential vegetation index (NDVI). The seasonal dynamics of latent heat flux (LE), sensible heat flux (H), net radiation (R_n), soil heat flux (G) and ET were analyzed. The effects of air temperature (T_a), R_n, vapor pressure deficit (VPD), soil water content (SWC), and NDVI on ET were discussed by applying regression analysis and path analysis.

Important findings The R_n, G and H of P. tabuliformis and P. sylvestrisvar. mongolica plantations showed single-peak seasonal variation trends, and the seasonal dynamics of LE fluctuated more sharply. During the whole year, energy consumption was dominated by H, followed by LE, and G consumed less energy. The average values of Bowen ratio during the growing season were 1.82 and 2.23, respectively, smaller than annual average values (3.43 and 4.44). The ET during the growing season was 302.79 and 247.54 mm, accounting for 82.89% and 84.20% of the annual ET, respectively. The annual ET was 365.29 and 293.99 mm accounting for 87.81% and 72.23% of precipitation in the same period. Priestley-Taylor coefficient (α) and decoupling factor (Ω) were used to analyze the effects of SWC and canopy conductance (g_c) on ET. The annual mean values ​​of α was 0.30 and 0.24, respectively, and the Ω values was 0.12 and 0.07, respectively. During the whole year, SWC was the dominant factor affecting the ET of two plantations, followed by R_n. Under non-water-stressed conditions, R_n had a greater impact on ET.The combined effects of T_a and VPD on ET were small, which were mostly indirect effects. NDVI and g_c were important biological factors affecting the ET of the two plantations, especially during the growing season. This study shows that P. tabuliformis and P. sylvestrisvar. mongolica plantations in the semi-arid area of western Liaoning Province adopt a conservative water consumption strategy to maintain water balance of the ecosystem, and these species are suitable afforestation tree species in this area.

Aims Arid and semi-arid regions are typical ecologically fragile areas, and they also have an important impact on global warming. Those regions are considered to be important CH₄ sinks since most soils are under aerobic conditions. Studies have found that along with the increase of CH₄ uptake velocity, the rate of CO₂ emissions also has increased. This study was carried out to examine whether there is an offset phenomenon and under what environmental conditions it occurs.

Methods Based on the integration of soil greenhouse gas fluxes and relevant environmental data in arid and semi-arid regions of China, correlations between soil CO₂ and soil CH₄ fluxes, on seasonal and daily scales, were analyzed.

Important findings The results showed that there were three levels of soil CO₂ and soil CH₄ flux, i.e., synergy (positively correlated), offset (negatively correlated), and random (not correlated). Among which, the proportion of random relationships was the highest, on seasonal and daily scales 83% and 54%, respectively. Compared to water content and vegetation conditions, air temperature correlated with the correlations between the two fluxes more strongly, showing a quadratic relationship (the absolute values of correlation coefficients between fluxes decreased with increasing temperature). On a seasonal scale, the mean air temperature during the sampling period determined the correlations between the fluxes with an accuracy of 92%, and the air temperature threshold of flux coupling-decoupling was 12.5 °C. On the daily scale, the diurnal air temperature difference determined fluxes relationships with an accuracy of 79% and the temperature threshold of flux coupling-decoupling was 15.2 °C. In addition, when the soil was in the state of absorbing CH₄ on a daily scale, the relationship between soil CH₄fluxand soil CO₂flux was positive in more cases. This phenomenon was difficult to explain by temperature alone. We speculate that a one-way coupling relationship between soil respiration and CH₄ oxidation formed through O₂ competition, that is, soil respiration would inhibit the CH₄ oxidation by consuming O₂, resulting in an increase in soil CO₂ emissions and a decrease in CH₄ absorption. The study suggests that coupling-decoupling of soil CO₂ and CH₄ fluxes might be driven by a mechanism of temperature regulation linked with oxygen competition regulation. Climate warming may cause decoupling of the two fluxes across space and time and increase the complexity of carbon cycles, thereby increasing the uncertainty of regional carbon flux estimations.

Aims Water use efficiency (WUE) is a crucial parameter reflecting the coupling of carbon and water cycles in terrestrial ecosystems. Qingzang Plateau (QP) is the ecological barrier of China and its accommodated ecosystem is extremely sensitive to global change. Revealing the ecosystem WUE pattern and the driving forces is critical for improving our understanding on the process and mechanism of carbon and water cycles in the alpine ecosystem of the QP, which are the basis for vegetation conservation and restoration.

Methods Using the Global Land Surface Satellite (GLASS) data, meteorological data and vegetation type data, the spatio-temporal changes of WUE and their responses to temperature, precipitation, solar radiation, vapor pressure deficit (VPD), CO₂ concentration and leaf area index (LAI) during 1982-2018 over the QP were analyzed in this study. The trend magnitude and the influencing factors on WUE were further compared among vegetation types.

Important findings (1) The WUE decreases gradually from southeast to northwest on the QP, with an overall annual mean value of 1.64 g C·kg^-1. Evident differences in WUE are observed among vegetation types, with the highest value in forest and the lowest value in alpine desert. In addition, the WUE in alpine meadow is higher than that in alpine steppe. (2) The QP is prevailed by an increasing trend in WUE. Significantly increasing trends are observed in all vegetation types except for forest and cultural vegetation. Meanwhile, the variation of WUE is dominated by ecosystem gross primary productivity over 77.84% of the study area. (3) The WUE variation is mainly regulated by LAI and CO₂ concentration on the QP, and these two factors both cause positive effects on WUE. Increasing VPD inhibits WUE in alpine steppe, alpine vegetation, cultural vegetation and alpine desert.

Aims We aimed to explore the response of net ecosystem productivity (NEP) and carbon use efficiency (CUE) to asymmetric daytime vs. nighttime warming in Artemisia ordosica shrublands, and to examine the sensitivity of carbon balance components to daytime vs. nighttime warming.

Methods The BIOME-BGC model was parameterized and validated against eddy covariance measurements of ecosystem carbon fluxes, and used for simulating the impacts of different warming scenarios on NEP and CUE and their components, including gross primary productivity (GPP), net primary productivity (NPP), ecosystem respiration (Re), autotrophic respiration (AR), heterotrophic respiration (HR), maintenance respiration (MR), and growth respiration (GR). Two warming scenarios were simulated: (1) asymmetric warming according to the historical trends from 1954 to 2020 (i.e. daytime warming 1.2 °C, nighttime warming 1.8 °C); (2) daytime or nighttime warming separately with different temperature increase treatments (2, 4, 6 °C).

Important findings (1) Modeled GPP on the daily and annual scales, Re on the daily timescale and NEP on the annual scale showed good agreement with the observed values (coefficient of determination (R²): 0.72-0.88; Nash-Sutcliffe efficiency coefficient (NS): 0.72-0.79). Modeled Re on the annual timescale and NEP on the daily timescale showed weak agreement with observed values (R²: 0.57 and 0.26; NS 0.46 and 0.12, respectively). (2) All warming scenarios promoted GPP, NPP, Re and all respiration components. GPP, Re, AR, and MR were more sensitive to daytime than to nighttime warming, while NPP, HR, GR were more sensitive to nighttime than daytime warming. (3) Greater increases in Re (about 13%) and AR (about 16%) than that in GPP (about 10%) under all warming scenarios, leading to the decreases in NEP and CUE. In addition, both NEP and CUE were more sensitive to daytime than nighttime warming. (4) NEP and CUE decreased by about 68% and 5% under the historical trend of asymmetric daytime vs. nighttime warming treatment. Greater response of NEP and CUE to the daytime warming than nighttime warming. Our results highlight the negative impacts of climatic warming on carbon sink of the semiarid shrublands, and justify the efforts to mitigate climate change are vital for dryland ecosystems.

Aims Ecosystem apparent quantum yield (α) and maximum photosynthetic rate (P_max) are important parameters reflecting the photosynthetic characteristics of ecosystem, and also important physiological parameters in ecosystem model simulation and remote sensing inversion. The objectives of this study were to: (1) analyze the characteristics and spatial-temporal variations of the light response parameters of ecosystems in arid and semi-arid areas; and (2) reveal the key factors affecting the photosynthetic parameters and its underlying mechanisms in arid and semi-arid areas, so as to provide a scientific basis for the study of ecosystem photosynthesis and response to climate change on a regional scale.

Methods The observational fluxes and synchronous meteorological data of 9 stations in arid and semi-arid area were integrated from ChinaFLUX. The non-rectangular hyperbolic equations were used to fit the light response parameters and which influencing factors were identified by linear regression, multiple stepwise regression and path analysis.

Important findings There were obvious spatial and temporal variations in ecosystem photosynthetic parameters in arid and semi-arid areas. The photosynthetic parameters increased gradually from desert, desert grassland, typical grassland to meadow grassland. Precipitation was the dominant environmental factor of the spatial variations of photosynthetic parameters, and it also affected the spatial variation of leaf area index, both of them jointly determine the spatial variation of photosynthetic parameters. α and P_max have an obvious increasing trend with the increase of precipitation, and there was a significant negative correlation between temperature and α, but the effect of radiation on the spatial variation of photosynthetic parameters was not significant. In the growing season, P_max and α increased first and then decreased, but the monthly variability and peak time of different vegetation types were different, the photosynthetic parameters of meadow grassland had the greatest monthly variability. The monthly dynamics of α was mainly controlled by temperature and radiation, while P_max was regulated by temperature and radiation in desert and desert grassland, and by soil water content in typical grassland and meadow grassland. Ecosystem α were 0.000 47-0.002 12 mg·μmol^-1, and P_max were 0.11-0.78 mg·m^-2·s^-1 in arid and semi-arid area, which were at a low level compared with other grassland ecosystems. High temperature and low soil water supply were likely the main factors restricting the photosynthetic parameters in arid and semi-arid areas.

Aims Eddy covariance (EC) systems are widely used for measuring the fluxes of carbon, water, and energy, as well as meteorological factors. As one important reference of independently evaluating scalar flux by EC technique, energy balance closure is widely used for evaluating data quality of carbon, water, and energy fluxes.

Methods Using the data of energy fluxes and meteorological variables retrieved from 56 site-year, the energy balance closure of six sites across three ecosystems (i.e. desert steppe, typical steppe, and meadow steppe) was analyzed by two widely used methods: linear regression from the ordinary least squares (OLS) and the energy balance ratio (EBR). The overall evaluation of energy balance closure, the seasonal and interannual variations and the related influencing factors were investigated.

Important findings The results show that: 1) the multiple-year EBR and OLS slope over the six sites had a mean value of 0.89 ± 0.11 and 0.96 ± 0.04, respectively, which are better than the results of the FLUXNET and ChinaFLUX. 2) There were significant differences over different sites and grassland types, with EBR of desert steppe (1.01 ± 0.09) and typical steppe (0.90 ± 0.11) both higher than meadow steppe (0.83 ± 0.05). There were seasonal variations of EBR over the six studied sites, and with better and stable results in growing season than non-growing season. The air temperature (T_a), vapor pressure deficit (VPD), soil moisture (SWC), and Albedo regulated the seasonal variation of EBR, with the low T_a and high Albedo remarkably reducing EBR during the non-growing season. 3) There were significant interannual variations of EBR across different sites and grassland types. The latent heat fraction (the ratio of latent heat flux to net radiation, LE/R_n), mean annual air temperature (MAT) and growing season Albedo significantly influenced interannual variation of EBR. The LE/R_n showed the strongest impact and explained 44% of the interannual variation of EBR. The significantly increasing in leaf area index (LAI) strongly regulated the upward of the available energy (net radiation minus ground heat flux, R_n- G₀), which contributes to the significant downward of EBR during observed years. It should be noted that EBR and OLS slope should be combined to better evaluate the energy balance closure. In conclusion, this study help improve our understanding of the potential linkage between energy balance closure and environmental factors, evaluate the quality of scalar flux estimates from EC technique, as well as improve the data processing protocol of flux data in the semi-arid and arid grassland region.

Aims The continuous observation datasets of water, heat, and carbon fluxes measured by the eddy covariance technique are important basis for accurate assessment of regional carbon sequestration and water-holding capacity. However, the rate of gaps in flux datasets is high and common due to various reasons, and different gap-filling methods increase the uncertainties of the related studies. The aim of this study is to introduce and test the applicability of boosted regression trees model (BRT), one of the up-to-date machine learning algorithms, for the gap- filling to flux datasets.

Methods Based on the published valid dataset of water, heat and CO₂ flux, and main environmental factors, including air temperature, atmospheric water vapor pressure, wind speed, solar shortwave radiation, topsoil temperature, and topsoil water content of an alpine Potentilla fruticosa scrubland on the northeastern Qingzang Plateau from 2003 to 2005, the BRT were trained to fill flux data gaps and the results were compared to those corresponding data serials provided by Chinese Flux Observation and Research Network (ChinaFLUX).

Important findings The results showed that the BRT performed well for a large amount of samples (N > 10 000) and the regression slopes of observation data against predicted value were between 1.01 and 1.05 with R² > 0.80. The BRT revealed that the daytime 30-min CO₂ flux (net ecosystem CO₂ exchange, NEE) in the growing season (i.e., May to October) was mainly controlled by solar shortwave radiation and atmospheric vapor pressure, whose relative contributions to NEE variability were up to 74.7%. The topsoil temperature was the determinant for NEE at night during the growing season and the whole day during the non-growing season, and its relative contribution was 68.5%. The 30-min sensible heat flux (H) and latent heat flux (LE) were both linearly related to solar radiation, and their relative contributions were above 58.6%. 30-min flux data gap amount filled by the BRT was significantly less than those by ChinaFLUX. Except for daily net ecosystem CO₂ exchange (p = 0.14), daily gross ecosystem CO₂ exchange (GEE), ecosystem respiration (RES), H, and LE of the BRT were significantly less than those of ChinaFLUX by 17.5%, 21.0%, 2.7%, and 2.2%, respectively. However, there was a reasonable consistency between the daily fluxes of 2003-2005 interpolated by the BRT and by ChinaFLUX due to the small magnitude difference (the regression slopes of the two data series were between 0.95 and 1.17). Except for monthly GEE and RES, monthly NEE, H, and LE of the BRT had no significant difference between the BRT and ChinaFLUX (p > 0.09). Compared with the ChinaFLUX gap-filling method, BRT can simulate the nonlinear relationships between fluxes and environmental factors without complicated mathematical expressions and quantify the relative contribution of environmental factors to the flux data gaps, which is a feasible technique for the integrated analysis of flux data.

Aims Diffuse radiation is one of the important factors affecting forest carbon uptake. However, the response of gross primary productivity (GPP) of planted forest ecosystems to diffuse radiation in China is still unclear. We explored the effects of diffuse radiation on GPP at 6 plantation ecosystems in eastern China during the growing season.

Methods Based on carbon flux data and meteorological data during the growing season of 2019-2020, we estimated the diffuse radiation and identified the direct and diffuse conditions. The important light response parameters of plantation ecosystems were obtained by the rectangular hyperbolic curve. Meanwhile, we quantified the variations of GPP responding to diffuse and direct radiation. The contribution of light and environmental factors to the diurnal variation of GPP was analyzed by partial correlation method.

Important findings Diffuse radiation can effectively promote canopy photosynthesis. The values of light response parameter canopy quantum efficiency (α) and photosynthesis at photosynthetically active radiation of 1 000 µmol·m^-2·s^-1 (P₁₀₀₀)increased by 47%-150% and 2%-65%, respectively. Compared with direct sky conditions, GPP increased by 0.86%-1.70% in response to 1 μmol·m^-2·s^-1 enhancement of photosynthetically active radiation (PAR) under diffuse sky conditions, which was affected by forest type and vegetation phenology. In diffuse skies, the increment of the variation of GPP under increasing per unit PAR (0.86%-1.00%) at Pinus sylvestrisvar. mongolica and P. tabuliformissites with lower normalized difference vegetation index (NDVI) value was significantly lower than other plantation sites (1.04%-1.70%), and there was a significant positive correlation between NDVI and P₁₀₀₀. Under low light level, PAR controlled the averaged gross primary productivity (GPP_a), but diffuse fraction (DF) mainly regulated GPP_ain middle and high light level. The photosynthesis corresponding to diffuse radiation under moderate light was roughly equal to photosynthesis corresponding to total radiation under high light. Under middle light conditions, the GPP_a value in medium and high DF (≥0.5) at Cunninghamia lanceolata, Populusspp., Quercus variabilis and Larix gmeliniiwas about 27%-50% higher than under low DF condition (<0.5), and the GPP_a value at high DF was about 2% more than under low DF conditions at Pinus sylvestrisvar. mongolica and P. tabuliformis sites. Under diffuse radiation conditions, diffuse photosynthetically active radiation (PAR_dif) explained 16%-45% of the variation of GPP. Air temperature (T_a) and vapor pressure deficit (VPD) explained 10%-19% of the variation of GPP at Cunninghamia lanceolata, Quercus variabilis and Larix gmelinii sites. Under diffuse radiation conditions, the P₁₀₀₀ will be the highest when T_a is 15-25 °C and VPD is 0-1 kPa.

Aims This study aimed to examine the practicability of eddy covariance method in a conifer-broadleaf mixed forest ecosystem in Jinyun Mountain of Chongqing, China, and to analyze the dynamics of water vapor flux in this forest ecosystem. Meanwhile, the main environmental factors that influence water vapor flux was also discussed. Our results may provide a case for such study in forest water vapor budget.

Methods The eddy covariance method was used to continuously observe the vapor fluxes and meteorological factors from September 2019 to August 2020 in a conifer-broadleaf mixed forest. The original data of water vapor flux was corrected and interpolated by Eddy Pro software. We used these data to analyze the energy closure and variation of water vapor fluxes, and as well as environmental factors.

Important findings (1) The energy closure rate in our study forest is 0.77. The direction of the high contribution area of flux footprints in such forest is similar to the annual main wind direction (northeast), indicating that the method of vorticity related technology is practicable and reliable in this kind of forest. (2) In our study forest, the annual water vapor flux is over zero, and the monthly average daily variation is -0.001-6.623 mmol·m^-2·s^-1, suggesting that this forest is a source of water vapor in study area. There is a single peak trends for monthly average daily variation and seasonal variation of water vapor fluxes. By contrast, the average value of water vapor fluxes is the highest (4.620 mmol·m^-2·s^-1) in summer with strong fluctuations, and the lowest (2.077 mmol·m^-2·s^-1) in winter with weak fluctuations. (3) The total annual evapotranspiration (792.40 mm) in this forest accounts for 53.12% of the total precipitation (1 489.18 mm), and the summer evapotranspiration (325.53 mm) and precipitation (680.52 mm) are the highest, accounting for the annual evapotranspiration and precipitation respectively 41% and 46%. Compared with other ecosystem sites, we found that total annual evapotranspiration was less in our study forest than in wetland, but more than in farmland and grassland. (4) The water vapor flux was positively correlated with net radiation, air temperature and vapor pressure deficit. Such correlations (R²) were the highest in summer, and reached to 0.85, 0.53 and 0.60, respectively. Conversely, the water vapor flux was negatively correlated with wind speed, and the R² equal to 0.61 in the summer. It seems likely that net radiation and air temperature are the main drivers in water circulating at our study forest.

Aims The generalized complementary principle of evapotranspiration is one of the important methods to estimate evapotranspiration when the observed data are scarce. In implementing this method, an accurate estimation of parameter α_e is critical. The temporal and spatial variation of α_e and the applicability of different methods for calculating α_e were investigated at eight flux stations under different climatic conditions and ecosystem types in China.
Methods Firstly, the annual and monthly values of α_e were calibrated based on the measured data. The spatiotemporal variability of α_e was investigated and the influence of α_e with different temporal scales on the calculation accuracy of the generalized complementarity principle model were compared. Considering that α_e can not be calibrated without measured evapotranspiration data, the applicability of two statistical models of annual α_e values based on aridity index (AI)(Liu method and Brutsaert method) were evaluated to determine whether α_e can be determined using AI. Finally, the error sources of each calculation method were analyzed.
Important findings α_e value varies with season, and the monthly variations of α_e differ among different flux stations. In terms of spatial variation, the annual values of α_e at humid sites were larger than those at arid sites. The α_e calculated by Liu method and Brutsaert method were close to the calibrated values. In applying the generalized complementary principle model, high simulation accuracy can be achieved by using the calibrated annual α_e, and the accuracy can be further improved by using the monthly α_e. Two AI-based methods also achieved accurate simulation results, which further confirmed the potential of predicting α_e based on AI in the absence of observed data. The generalized complementary principle model can simulate the annual variation trend of evapotranspiration when using annual α_e, but the estimated value were biased in some months. The evapotranspiration calculated by Liu method and Brutsaert method were underestimated in summer months of the drought sites, which may be caused by the fact that the AI was overestimated in summer months when rainfall was concentrated. The results further demonstrate the potential of the generalized complementary principle in estimating evapotranspiration in a wide range of natural environments.

Aims The responses of water and heat fluxes of temperate forest ecosystems to environmental factors are hypothesized to vary with time scales. This study aimed to examine the variations of water and heat fluxes in a typical natural deciduous broad-leaved forest ecosystem in Songshan and their response mechanisms to environmental factors at different time scales.
Methods The eddy-covariance (EC) method was used to continuously monitor the evapotranspiration (ET), sensible heat (H), latent heat (LE), soil heat flux (G), vapor pressure deficit (VPD), air temperature (T_a), photosynthetically active radiation (PAR), normalized difference vegetation index (NDVI), and soil water content at a depth of 10 cm (VWC) in a typical deciduous broad-leaved forest ecosystem in Songshan, Beijing in 2019. The wavelet analysis was used to examine the regulation mechanism of biotic and abiotic factors on energy distribution and water vapor exchange at different time scales.
Important findings The mean annual Bowen ratio (β) was 1.53 in 2019. ET had obvious seasonal dynamics, increasing gradually from day 100, peaking in July, and decreasing to the lowest level on day 300. The maximum daily evapotranspiration was 5.01 mm·d^-1, the cumulative annual evapotranspiration was 476.2 mm, and the cumulative annual rainfall was 503.3 mm. The main influencing factors of the water and heat fluxes varied with time scales, being mostly controlled by VPD at the daily scale, and by PAR at the seasonal scale. At the diurnal scale, water and heat fluxes lagged 3.36 h behind VPD. At the seasonal scale, water and heat fluxes lagged 8 days behind PAR. At the seasonal scale, PAR had an indirect impact on ET through its effects on VPD and a direct impact on β. The results indicate the time-delay relationships between water and heat fluxes and environmental factors at different time scales, which provides support for selecting the optimal input parameters of the models for quantitatively forecasting ecosystem processes at different time scales in northern temperate deciduous broad-leaved forests.

Aims Wetlands are important sources of atmospheric methane (CH₄), but there are few reports on the CH₄ emissions of subtropical and subalpine wetlands. In particular, the accurate estimation of CH₄ emissions from plots with different types of plant cover and the controlling environmental factors are not clear. The objective of this study is to study characteristics and influencing factors of CH₄ emission fluxes from a Sphagnum bog with different plant cover types in a subalpine area, southwest of Hubei Province.
Methods A Sphagnum bog in the subalpine area in southwest of Hubei Province was selected, and CH₄emission fluxes were measured in the Sphagnum bog with three types of plant cover using closed static chamber and gas chromatography method from November 2018 to October 2019. Simultaneously, air temperature, soil temperature of 5 cm depth, and groundwater level were recorded.
Important findings (1) Under sunlight, the CH₄-C fluxes from the bare land plot (B), the Sphagnum paluster plot (S), and the Polytrichum commune plot (P) varied throughout the year within the following ranges: 0.012- 1.372, 0.022-1.474 and 0.027-3.385 mg·m^-2·h^-1, respectively; under shading treatment, the variation range of CH₄-C flux throughout the year from B, S and P plots were 0.012-1.372, 0.009-1.839 and 0.017-2.484 mg·m^-2·h^-1, respectively, indicating CH₄emission sources for all types. At the same time, for all plant cover types, CH₄ emissions under sunlight conditions were slightly higher than those under dark conditions, but the difference was not significant. (2) For all plant cover types, CH₄ emissions showed obvious seasonal variations, with the order of summer > autumn > spring > winter; and summer emissions were significantly greater than emissions in other seasons, accounting for about 57% to 84% of the annual cumulative flux. This study found that the flux of CH₄ emission was highly related to air temperature and soil temperature of 5 cm depth showing exponential relationships, which indicated that temperature is the main environment factor affecting the temporal variations of CH₄ emission from the Sphagnum bog. (3) Plant cover types significantly affected CH₄ emissions from theSphagnum bog. The annual average and cumulative CH₄ emissions from the three cover types were in the order of: P > S > B. P plot showed significantly higher emission than B plot. This study found a significant correlation between vegetation types and CH₄ emissions, indicating that plant cover type is the main influencing factor of the spatial variations of CH₄ emissions from the Sphagnum bog. (4) CH₄ emissions were not significantly related to the groundwater levels. This study further enriched the mechanisms of CH₄ emission in the Sphagnum bog and provided basic data for regional carbon cycling.

Aims Bowen ratio (β) is an important parameter in land-surface processes. It affects the energy exchange between the surface and the atmosphere. This paper used integrated analyses to investigate the spatial variability and influencing factors of β.
Methods We collected the published literature on the measurement of surface energy balance by the Eddy Covariance (EC) method carried out in different ecosystem types in China, constructed the database of β and meteorological environment factors and analyzed the difference of β among ecosystems, the spatial variation characteristics of β and its influencing factors.
Important findings (1) The variation of β follows a lognormal distribution. The average β in all ecosystems was 0.95 ± 0.64, the coefficient of variation of β was 67%, the skewness was 1.58, and the kurtosis was 3.07. The shrub ecosystem has the highest mean value (1.26) and the wetland ecosystem has the lowest (0.49). (2) β is significantly different among ecosystems: β of shrub ecosystems is significantly higher than those in grassland, forest and wetland ecosystems, and β of croplands is between grassland ecosystems and forest with wetland ecosystems. (3) β increases with increasing latitude and does not change with longitude and altitude. For every 1° increase in latitude,β increases by 0.038. (4) β decreases with increase in mean annual precipitation (MAP), mean annual temperature (MAT), net radiation (R_n), precipitation of the studied year (PPT), mean temperature of the studied year (T_a), and leaf area index (LAI). (5) There are significant differences in the response of β to biotic and abiotic factors in different ecosystems: β of grassland, forest and shrub ecosystems are sensitive to changes in biotic and abiotic factors, while β of croplands and wetland ecosystems have no correlations with biotic and abiotic factors. (6) MAPand R_n are the direct factors influencing β. MAT affects βindirectly by affecting MAP, R_n and LAI. LAI affects β indirectly by affectingR_n. Our results indicate significant effect of the interaction between vegetation types and climatic factors on β. The most important factor affecting energy distribution is precipitation, and the regulation of leaf area on energy distribution is not significant.

Carbon (C) and water cycles are the most critical processes in terrestrial ecosystems, which links the materials and energy flows through the pedosphere-biosphere-atmosphere integration. Most attention has been paid to the responses of C and water and their feedbacks to global climate change. Flux observation is the basic pathway to quantify the rate of material and energy exchange across soil-plant-atmosphere continuum. As an only technique can directly measure the carbon, water and energy fluxes between vegetation and atmosphere, eddy covariance (EC) technique has been considered as a standard method for flux observation internationally. With broad applications of EC technique on global C and water cycles, long-term flux observations provide scientific data on assessing ecosystem C sequestration capability, water and energy balance, and ecosystem feedback to climate change; optimizing and validating models on regional and global scales; and understanding responses of ecosystem functions to extreme events. Based on long-term flux observation in individual site, scientists have described the seasonal and inter-annual dynamics, and quantified the baseline rates of ecosystem carbon and water fluxes across different climate and vegetation types. With the development of regional and global flux networks, researchers further understood the spatial patterns of ecosystem carbon and water fluxes and their climatic control mechanisms at regional and global scales. This paper briefly introduces the basic principles, hypothesis and instrument system composition, summarizes the major applications of EC observation on C and water fluxes in terrestrial ecosystems, and finally discusses future directions of EC observation network.

Aims Our objective was to determine the spatial variation of the temperature sensitivity of soil respiration (Q₁₀) and it’s controlling factors in forest ecosystems across China.
Methods Based on published papers, the field measurement data of soil respiration were collected to build the dataset of annual Q₁₀ in forest ecosystems across China. Further, the spatial variation and the drivers of Q₁₀ in different forest types were analyzed.
Important findings The results showed that 1) Q₁₀ ranges from 1.09 to 6.24, with a mean value (± standard error) of 2.37 (± 0.04) and no significant difference among different forest types; 2) When all forest types were considered, Q₁₀ increased with increasing latitude, altitude, soil organic carbon content (SOC) and soil total nitrogen content (TN), but decreased with increasing longitude, mean annual temperature (MAT) and mean annual precipitation (MAP). Climate (MAT, MAP) and soil (SOC, TN) factors together explained 32.8% variations in Q₁₀. MAT and SOC were considered as the primary factors driving the spatial variation of Q₁₀. 3) Q₁₀ of different forest types responded differently to climate and soil factors. Q₁₀ decreased with the increase of MAP in the deciduous needleleaf forest (DNF), while Q₁₀ showed no significant correlation with MAP in other forest types. Q₁₀ increased with the increase of TN in evergreen broadleaved forest (EBF), deciduous broadleaved forest (DBF), evergreen needleleaf forest (ENF), and the sensitivity of Q₁₀ to TN was the highest in EBF and the lowest in ENF. Although Q₁₀ showed concentrated distribution trend, more attention should be paid to the large range of variation in future C budget studies. The primary driving factors and the response to environmental factors of Q₁₀ varied among forest types. Under the scenario of future climate change, Q₁₀ may vary divergently among different forest types. Therefore, the divergent responses of key parameters of carbon cycle in different forest types to climate change should also be considered in future carbon-climate models.

The exchange flux of greenhouse gases, such as carbon (CO₂, CH₄), nitrogen (N₂O) and water vapour (H₂O), is the core of material cycle in the ecosystem and the bond of interaction among geosphere, biosphere and atmosphere. The development of stable isotope infrared spectroscopy and mass spectrometry technology and methods makes it possible to measure carbon stable isotopic composition (δ ¹³C) and oxygen stable isotopic composition (δ ¹⁸O)(CO₂), δ ¹³C (CH₄), nitrogen stable isotope composition (δ ¹⁵N) and δ ¹⁸O (N₂O), hydrogen stable isotopic composition (δD) and δ ¹⁸O (H₂O), which realizes the observation of greenhouse gas and its isotope flux at the soil, plant and ecosystem scales in combined with chamber-based technology and methods for flux measurement. Taking the chamber-based technology and methods for CO₂ and its δ ¹³C flux measurement as an example, this review which summarizes the basic principle and classification of the flux measurement system, expounds the theory requirements and assumptions of system design, summarizes the application advance and problems of chamber-based technology and methods for flux measurement in soil, plants (leaf, stem, and root) and ecosystem scales from the field to indoor, and prospects the importance of precision and accuracy of gas analysis and measurement data and the representativeness of measurement data in chamber-based flux measurement.

Flux-gradient method and eddy covariance technique are classical micrometeorological methods, which observe fluxes of mass and energy. Flux-gradient method can effectively measure the greenhouse gas and isotope fluxes between ecosystem (or soil) and atmosphere although gas analyzer with high measuring frequency was not available or the fetch was small. Flux-gradient method can be viewed as an ancillary measurement and useful complement of eddy covariance technique. This paper reviewed from the following aspects: the fundamental theory, concepts and assumptions of flux-gradient method; the methods measuring the gradient of greenhouse gases and the theory on turbulent diffusion coefficients; the applications of this method in measuring greenhouse gas fluxes, especially on isotope fluxes, over various ecosystems including forest, cropland, grassland, wetland and water bodies. Finally, the considerations and suggestions were provided regarding the measurement on concentration gradients of greenhouse gases and isotopes, and the calculation of turbulent diffusion coefficients.

Aims The agro-pastoral ecotone is considered as fragile ecosystems which are strongly affected by agriculture and animal husbandry. The saline-alkali grassland is a unique grassland type in the agro-pastoral ecotone. A large amount of fertilizers are used to increase productivity in this area, which also promotes the emission of reactive nitrogen (N) gases and leads to the changes in soil carbon and N cycles. Mowing is a primary management practice in the agro-pastoral grassland in northern China. In order to explore the impact of N addition and mowing on carbon dynamic in this saline-alkali grassland located in the agro-pastoral ecotone, we determined the response of soil respiration to N addition and mowing.
Methods This study area is located in Youyu County, an agro-pastoral grassland ecosystem in northern China. The field experiment was set up in May, 2017. The treatments included: control (without mowing and mowing), addition of urea, addition of slow release urea, addition of urea + mowing, addition of slow release urea + mowing. Each treatment included 6 replicates. Therefore, there were totally 36 plots in this experiment. Soil respiration rate, soil temperature, soil moisture content, microbial biomass, inorganic N content, above-ground and below-ground biomass were measured under different treatments, and the cumulative carbon emissions and CO₂ fluxes were calculated.
Important findings Our results showed that: (1) Short-term (2017-2018) N addition significantly increased soil respiration rates and soil cumulative carbon emissions. Meanwhile, soil respiration rates and cumulative carbon emissions were significantly higher under urea treatment than those under slow release urea addition. (2) Mowing significantly reduced soil respiration rates and cumulative carbon emissions. (3) The interaction of short-term N addition and mowing had no significant effect on soil respiration rate. Therefore, short-term N addition can promote soil carbon release from the saline-alkali grassland in the agro-pastoral ecotone of northern China. Mowing can reduce soil respiration and decrease cumulative of carbon emissions. This may be because that mowing reduced the input of litter and further reduced soil substrate for microbes, which led to a decrease in soil microbial activity. However, long-term effect of N addition and mowing on soil carbon dynamics in saline-alkaline grasslands in the agro-pastoral ecotone still needs to be further explored.

Nutrient additions such as nitrogen and phosphorus are important strategies to improve the productivity of the grassland ecosystem. However, their effect on soil nitrous oxide (N₂O) emissions remains unclear.

A field study was conducted in an alpine grassland located in the north slope of Kunlun Mountains in Southern Xinjiang. Four treatments included nitrogen addition alone (N), phosphorus addition alone (P), mixture of nitrogen and phosphorus additions (N + P) and an unfertilized control (CK). Gas samples were collected and analyzed using the static chamber chromatography methodology during the 2017 growing season. Treatment effects on the characteristics of N₂O emissions from grassland soil were thoroughly investigated. Pearson correlation analysis was used to identify and quantify the influence of environmental variables on soil N₂O emissions.

The results showed that N and (N + P) treatments induced N₂O flux peaks after three weeks of fertilizer addition, with the maximum daily N₂O flux rates of 42.3 and 15.4 g N·hm ^-2·d ^-1, respectively. The N treatment significantly increased growing season cumulative N₂O emissions by 1.8 to 3.2 times compared to P treatment, (N + P) treatment and CK, and there were no significant differences between the three treatments. Pearson correlation analysis showed that daily N₂O flux rate was correlated negatively with soil microbial biomass carbon, and positively with soil pH and dissolved organic carbon. There was no significant correlation between daily N₂O flux rate and other environmental variables. These results suggest that simultaneous addition of nitrogen and phosphorus nutrients can significantly reduce soil N₂O emission compared to N treatment for the alpine grassland in this region.

Aims The emergence and application of ecosystem process models have provided useful tools for studying carbon and water balances of terrestrial ecosystems at large spatiotemporal scales, but the accuracy of model simulations is affected by the parameterization of key variables among many factors. Sensitivity analysis is commonly used to screen the critical parameters that have predominant influences on model simulations. The objective of this study was to identify the critical ecophysiological parameters in Biome-BGC model in simulating annual net primary productivity (NPP) and evapotranspiration (ET) of broadleaved-Korean pine forests in Northeast China.

Methods We simulated carbon and water fluxes of broadleaved-Korean pine forests with the Biome-BGC (version 4.2) at a daily time step based on site- and species-specific parameters. Daily meteorological data for the period 1958-2015 was obtained from the China Meteorological Administration. Initialization parameters such as geographical position, soil depth, and soil texture of the site were obtained from field measurements. Among the 43 ecophysiological parameters represented in the model, 30 were derived either from field measurements or from published data for the study sites in literature, and the default values were used for 13 of the parameters. The modeled forest NPP was compared with the tree-ring width index to test the model’s ability to simulate the inter-annual variations in forest productivity. The modeled NPP and ET were also compared with existing remote sensing products for the period 2000-2014 for validation purpose. Sensitivity analysis was conducted using a variance-based sensitivity analysis method—Extended Fourier Amplitude Sensitivity Test (EFAST) to acquire the first order and total order sensitivity index of the parameters.

Important findings Our locally parameterized Biome-BGC model well simulated the carbon and water fluxes of the broadleaved-Korean pine forests. The uncertainty of simulated NPP is higher for Korean pine trees than for broad-leaved trees, while that of ET was small for both tree types. Both NPP and ET of broad-leaved trees were generally less sensitive to ecophysiological parameters than Korean pine. Leaf carbon to nitrogen ratio, fine root carbon to nitrogen ratio, specific leaf area (SLA), and water interception coef?cient were among the highly sensitive parameters affecting the modeled NPP; while fine root carbon to new leaf carbon allocation, new stem carbon to new leaf carbon allocation and SLA were the highly sensitive parameters influencing ET. In addition, fraction of leaf N in Rubisco, leaf and fine root turnover, ratio of all sided to projected leaf area are also critical parameters affecting the output of Biome-BGC simulations. The degree of sensitivity of the critical parameters varied with species and sites, highlighting the need to adopt local parametrization of Biome-BGC model in simulating regional forest carbon and water fluxes. For other non-sensitive parameters, model default value can be readily used.

Aims Nitrous oxide (N₂O) is one of the most important greenhouse gases, which contributes a lot to global warming. However, considerable variations are observed in the responses of soil N₂O emissions to experimental warming, and the underlying microbial processes remain unknown.

Methods A warming experiment based on open-top chambers (OTCs) was set up in a typical alpine steppe on the Qinghai-Xizang Plateau. The static chamber combined gas chromatography method was applied to investigate soil N₂O flux under control and warming treatments during the growing seasons in 2014 and 2015. Gene abundances of ammonia-oxidizing archaea (AOA) and ammonia-oxidizing bacteria (AOB) were quantified using quantitative real-time PCR.

Important findings Our results showed that the warming treatments increased soil temperature by 1.7 and 1.6 °C and decreased volumetric water content by 2.5% and 3.3% respectively during the growing season (May to October) in 2014 and 2015. However, there were no significant differences in other soil properties. Our results also revealed that, the magnitude of soil N₂O emissions exhibited substantial variations between the two experimental years, which were 3.23 and 1.47 μg·m ^-2·h ^-1in 2014 and 2015, respectively, but no significant difference in N₂O fluxes was observed between control and warming treatments. AOA and AOB abundances are 15.2 × 10 ⁷and 10.0 × 10 ⁵copies·g ^-1 in 2014, and 5.0 × 10 ⁷and 4.7 × 10 ⁵copies·g ^-1in 2015, with no significant differences between control and warming treatments during the experimental period. Furthermore, warming-induced changes in N₂O emissions had no significant relationship with the changes in soil temperature, but showed a significant positive correlation with the changes in soil moisture at seasonal scale. Overall, these results demonstrate that soil moisture regulates the responses of N₂O emissions to experimental warming, highlighting the necessity to consider the warming-induced drying effect when estimating the magnitude of N₂O emissions under future climate warming.

Aims Alpine shrub-meadows and steppe-meadows are the two dominant vegetation types on the Qinghai-Xizang Plateau, and plays an important role in regional carbon cycling. However, little is known about the temporal-spatial patterns and drivers of CO₂ fluxes in these two ecosystem types.

Methods Based on five years of consecutive eddy covariance measurements (2004-2008) in an eastern alpine shrub-meadow at Haibei and a hinterland alpine steppe-meadow at Damxung, we investigated the seasonal and annual variation of net ecosystem productivity (NEP) and its components, i.e. gross primary productivity (GPP) and ecosystem respiration (R_e).

Important findings The CO₂ fluxes (NEP, GPP and R_e) were larger in the shrub-meadow than in the steppe-meadow during the study period. The shrub-meadow functioned as a carbon sink through the five years, with the mean annual NEP of 70 g C·m ^-2·a ^-1. However, the steppe-meadow acted as a carbon neutral, with mean annual NEP of -5 g C·m ^-2·a ^-1. The CO₂ fluxes of steppe-meadow exhibited large variability due to the inter-annual and seasonal variations in precipitation, ranging from a carbon sink (54 g C·m ^-2·a ^-1) in 2008 to a carbon source (-88 g C·m ^-2·a ^-1) in 2006. The differences in carbon budget between the two alpine ecosystems were firstly attributed to the discrepancy of normalized difference vegetation index (NDVI) because NDVI was the direct factor regulating the seasonal and inter-annual NEP. Secondly, the shrub-meadow had higher carbon use efficiency (CUE), which was substantially determined by annual precipitation (PPT) and NDVI. Our results also indicated that the environmental drivers of CO₂ fluxes were also different between these two alpine ecosystems. The structure equation model analyses showed that air temperature (T_a) determined the seasonal variations of CO₂ fluxes in the shrub-meadow, with NEP and GPP being positively correlated with T_a. By contrast, the seasonal CO₂ fluxes in the steppe-meadow were primarily co-regulated by soil water content (SWC) and T_a, and increased with the increase of SWC and T_a. In addition, the changes of R_e during the growing season in two ecosystems were directly affected by GPP and soil temperature at 5 cm depth (T_s), while R_e during non-growing season were determined by T_s. These results demonstrate that the synergy of soil water and temperature played crucial roles in determining NEP and GPP of the two alpine meadows on the Qinghai-Xizang Plateau.

Aims Recent studies have shown that artificial addition of biochar is an effective way to mitigate atmospheric carbon dioxide concentrations. However, it is still unclear how biochar addition influences soil respiration in Phyllostachys edulis forests of subtropical China. Our objectives were to examine the effects of biochar addition on the dynamics of soil respiration, soil temperature, soil moisture, and the cumulative soil carbon emission, and to determine the relationships of soil respiration with soil temperature and moisture.
Methods We conducted a two-year biochar addition experiment in a subtropical P. edulis forest from 2014.05 to 2016.04. The study site is located in the Miaoshanwu Nature Reserve in Fuyang district of Hangzhou, Zhejiang Province, in southern China. The biochar addition treatments included: control (CK, no biochar addition), low rate of biochar addition (LB, 5 t·hm^-2), medium rate of biochar addition (MB, 10 t·hm^-2), and high rate of biochar addition (HB, 20 t·hm^-2). Soil respiration was measured by using a LI-8100 soil CO₂ efflux system.
Important findings Soil respiration was significantly reduced by biochar addition, and exhibited an apparent seasonal pattern, with the maximum occurring in June or July (except LB in one of the replicated stand) and the minimum in January or February. There were significant differences in soil respiration between the CK and the treatments. Annual mean soil respiration rate in the CK, LB, MB and HB were 3.32, 2.66, 3.04 and 3.24 μmol·m^-2·s^-1, respectively. Compared with CK, soil respiration rate was 2.33%-54.72% lower in the LB, 1.28%-44.21% lower in the MB, and 0.09%-39.22% lower in the HB. The soil moisture content was increased by 0.97%-75.58% in LB, 0.87%-48.18% in MB, and 0.68%-74.73% in HB, respectively, compared with CK. Soil respiration exhibited a significant exponential relationship with soil temperature and a significant linear relationship with combination of soil temperature and moisture at the depth of 5 cm; no significant relationship was found between soil respiration and soil moisture alone. The temperature sensitivity (Q₁₀) value was reduced in LB and HB. Annual accumulative soil carbon emission in the LB, MB and HB was reduced by 7.98%-35.09%, 1.48%-20.63%, and -4.71%-7.68%, respectively. Biochar addition significantly reduced soil carbon emission and soil temperature sensitivity, highlighting its role in mitigating climate change.

Aims Seasonal snow cover is one of the most important factors that control winter soil respiration in the cold biomes. The warming-induced decreases in snowpack could affect winter soil respiration of subalpine forests. The aim of this study was to explore the effects of snow removal on winter soil respiration in a Picea asperata forest.Methods A snow removal experiment was conducted in a P. asperata forest stand in western Sichuan during the winter of 2015/2016. The snow removal treatment was implemented using wooden roof method. Soil temperatures, snow depth and soil respiration rate were simultaneously measured in plots of snow removal and controls during the experimental period.Important findings Compared to the control, snow removal increased the fluctuations of soil temperatures. The average daily temperature of the soil surface and that at 5 cm depth were 1.12 °C and 0.34 °C lower, respectively, and the numbers of freeze-thaw cycles of the soil surface and that at 5 cm depth were increased by 39 and 12, respectively, in plots of snow removal than in the controls. The average rate of winter soil respiration and CO₂ efflux were 0.52 μmol·m^-2·s^-1 and 88.44 g·m^-2, respectively. On average, snow removal reduced soil respiration rate by 21.02% and CO₂ efflux by 25.99%, respectively. More importantly, the snow effect mainly occurred in the early winter. The winter soil respiration rate had a significant exponential relationship with soil temperature. However, snow removal significantly reduced temperature sensitivity of the winter soil respiration. Our results suggest that seasonal snow reduction associated with climate change could inhibit winter soil respiration in the subalpine forests of western Sichuan, with significant implications for the carbon dynamics of the subalpine forests.

Aims Our objectives were to investigate: 1) How does litter affect the ecosystem carbon fluxes in mature and degraded community ecosystems? and 2) What are the effects of litter on the ecosystem carbon fluxes of the two ecosystems?

Methods The study was carried out at Baiyinxile Ranch experiment site, which is located in the semiarid agriculture-pasture transition region in southeastern Nei Mongol, China. The treatments were litter removal (50% and 100%) in mature community and litter addition (50% and 100%) in degraded community. We measured net ecosystem CO₂ exchange (NEE) by the chamber method during the growing season of 2013 and 2014.

Important findings Our results showed that there were significant seasonal changes of NEE in both mature and degraded community. After the consecutive treatments for two years, in mature community, the 50% litter removal significantly increased NEE and the 100% litter removal significantly reduced the NEE, while litter removal had no significant effect on the ecosystem gross primary productivity (GEP) and ecosystem respiration (ER). In the degraded community, litter addition significantly increased NEE and GEP and had no effect on ER. Meanwhile, neither litter removal nor litter addition had significant effect on the total ecosystem respiration (ER). In both communities, the correlation between GEP and soil temperature at 10 cm was significantly positive (p < 0.05). However, the changes of GEP and NEE under litter treatments was contrary to the changes of soil temperature, and consistent with the changes of soil moisture content at 10 cm depth. We concluded that the mechanism underlying the effects of litter removal and addition on the carbon flux of ecosystem was mainly attributed to soil moisture and above ground biomass.

Aims The project was to analyze the carbon stock, seasonal dynamics of carbon flux and the responses of net ecosystem CO₂ exchange (NEE) to various environmental factors of Zoysia japonica warm tussock ecosystem in Shandong Province.

Methods We used field sampling and fixed-point observation-static chamber method (LI-840 infrared analyzer).

Important findings (1) The average carbon density (carbon stock per area) of Z. japonica warm tussock ecosystem in Yaoxiang small watershed was about 2.74 Mg C·hm^-2 and the order of carbon density was as follows: soil carbon (89%) > vegetation carbon (9%) > litter carbon (2%), the total amount of carbon stock of warm tussock in Shandong Province was about 15.88 Tg C. (2) The NEE seasonal dynamics of Z. japonica warm tussock ecosystem was low in summer but high in winter. This ecosystem functioned as carbon source (i.e., CO₂ emissions) during the non-growing seasons (October to March of next year), but acted as carbon sink (net absorption of CO₂) during the growing seasons (April to September). The average carbon sequestration rate during the peak months was -2.58- -4.46 μmol CO₂·m^-2·s^-1. The annual average NEE of small watershed warm tussock was respectively -0.43 and -0.31 μmol CO₂·m^-2·s^-1 in the year of 2012 and 2013, indicating this ecosystem exhibited carbon sink effect. (3) The photosynthetic active radiation (PAR), atmospheric temperature (T_a), vapor pressure deficit (VPD) and the temperature and water content of 10 cm soil depth were the major factors regulating NEE dynamics in Z. japonica warm tussock ecosystem, but drivers of NEE dynamics in different months were different and had the interaction effects between factors. Principal component analysis indicated that the seasonal dynamics of NEE was mainly controlled by the temperature, moisture and light intensity.

Aims Stem CO₂ efflux (E_s) is an important component of annual carbon budget in forest ecosystems, but how biotic and environmental factors regulate seasonal and inter-specific variations in E_s is poorly understood. The objectives of this study were: (1) to compare seasonal dynamics in E_s for four temperate coniferous tree species in northeastern China, including Korean pine (Pinus koraiensis), Korean spruce (Picea koraiensis), Mongolian pine (Pinus sylvestris var. mongolica), and Dahurian larch (Larix gmelinii); and (2) to explore factors driving the inter-specific variability in E_s during the growing and non-growing seasons.
Methods Ten to twelve trees for each tree species were sampled for E_s and stem temperature at 1 cm depth beneath the bark (T_s) measurements in situ with an infrared gas analyzer (LI-6400 IRGA) and a digital thermometer, respectively, from July to October 2013 and March to July 2014. The daily stem circumference increment (S_i), sapwood nitrogen concentration ([N]), and related environmental factors were monitored simultaneously.
Important findings The temporal variation in E_s for the four tree species overall followed the changes in T_s throughout the study period, with the maxima occurring in the summer months (late May to early July) characterized by higher temperature and more rapid stem growth and the minima in spring (late March to April) or autumn (October) having lower temperature. T_s accounted for 42%-91% and 56%-89% of variations in E_s during the growing (May to September) and non-growing (other months) seasons, respectively. Furthermore, apart from T_s, we also found significant regression relationships between E_s and S_i, relative air humidity and [N] during the growing season, but their forms and correlation coefficients were species-dependent. These results indicated that T_s was the dominant environmental factor affecting seasonal variations in E_s, but the magnitude of the effect varied with tree species and growth rhythm. Mean E_s for each of the four tree species was significantly higher in the growing season than in the non-growing season, whereas within the season there were also significant differences in mean E_s among the tree species (all p < 0.05). The temperature sensitivity of E_s (Q₁₀ value) did not differ significantly among the tree species during the growing season, ranging from 1.64 for Dahurian larch to 2.09 for Mongolian pine, but did differ during the non-growing season which varied from 1.80 for Korean pine to 3.14 for Dahurian larch. Moreover, Korean spruce, Mongolian pine and Dahurian larch had significantly greater Q₁₀ values in the non-growing season than in the growing season (p < 0.05). These findings suggested that the differences of the response of E_s to temperature change for different tree species were mainly from the non-growing season. Because the seasonality and inter-specific variability in E_s for these temperate coniferous tree species were primarily controlled by multiple factors such as temperature, we conclude that using a single annual temperature response curve to estimate the annual E_s may lead to more uncertainty.

Aims Soil respiration of the lands covered by biocrusts is an important component in the carbon cycle of arid, semi-arid and dry-subhumid ecosystems (drylands hereafter), and one of the key processes in the carbon cycle of drylands. However, the responses of the rate of soil respiration with biocrusts to water and temperature are uncertain in the investigations of the effects of experimental warming and precipitation patterns on CO₂ fluxes in biocrust dominated ecosystems. The objectives of this study were to investigate the relationships of carbon release from the biocrust-soil systems with water and temperature in drylands. Methods Intact soil columns with two types of biocrusts, including moss and algae-lichen crusts, were collected in a natural vegetation area in the southeastern fringe of the Tengger Desert. Open top chambers were used to simulate climate warming, and the soil respiration rate was measured under warming and non-warming treatments using an automated soil respiration system (LI-8150). Important findings Over the whole observational period (from April 2016 to July 2016), soil respiration rates varied from -0.16 to 4.69 μmol·m^-2·s^-1 for the moss crust-covered soils and from -0.21 to 5.72 μmol·m^-2·s^-1 for the algae-lichen crust-covered soils, respectively, under different rainfall events (the precipitations between 0.3-30.0 mm). The mean soil respiration rate of the moss crust-covered soils is 1.09 μmol·m^-2·s^-1, which is higher than that of the algae-lichen crust-covered soils of 0.94 μmol·m^-2·s^-1. The soil respiration rate of the two types of biocrust-covered soils showed different dynamics and spatial heterogeneities with rainfall events, and were positively correlated with precipitation. The mean soil respiration rate of the biocrust-covered soils without warming was 1.24 μmol·m^-2·s^-1, significantly higher than that with warming treatments of 0.79 μmol·m^-2·s^-1 (p < 0.05). By increasing the evaporation of soil moisture, the simulated warming impeded soil respiration. In most cases, soil temperature and soil respiration rate displayed a similar single-peak curve during the diel cycle. Our results show an approximately two hours’ lag between soil temperature at 5 cm depth and the soil respiration rate of the biocrust-covered soils during the diel cycle.

Aims Desert soils play an important role in the exchange of major greenhouse gas (GHG) between atmosphere and soil. However, many uncertainties existed in understanding of desert soil role, especially in efflux evaluation under a changing environment. Methods We conducted plot-based field study in center of the Gurbantünggüt Desert, Xinjiang, and applied six rates of simulated nitrogen (N) deposition on the plots, i.e. 0 (N0), 0.5 (N0.5), 1.0 (N1), 3.0 (N3), 6.0 (N6) and 24.0 (N24) g·m^-2·a^-1. The exchange rates of N₂O, CH₄ and CO₂ during two growing seasons were measured for two years after N applications. Important findings The average efflux of two growing seasons from control plots (N0) were 4.8 μg·m^-2·h^-1, -30.5 μg·m^-2·h^-1 and 46.7 mg·m^-2·h^-1 for N₂O, CH₄ and CO_2, respectively. The effluxes varied significantly among seasons. N0, N0.5 and N1 showed similar exchange of N₂O in spring and summer, which was relatively higher than in autumn, while the rates of N₂O in N6 and N24 were controled by time points of N applications. The uptake of CH₄ was relatively higher in both spring and summer, and lower in autumn. Emission of CO₂ changed minor from spring to summer, and greatly decreased in autumn in the first measured year. In the second year, the emission patterns were changed by rates of N added. N additions generally stimulated the emission of N₂O, while the effects varied in different seasons and years. In addition, no obvious trends were found in the emission factor of N₂O. The uptake of CH₄ was not significantly affected by N additions. N additions did not change CO₂ emissions in the first year, while high N significantly reduced the CO₂ emissions in spring and summer of the second year, without affected in autumn. Structure equation model analysis on the factors suggested that N₂O, CH₄ and CO₂ were dominantly affected by the N application rates, soil temperature or moisture and plant density, respectively. Over the growing seasons, both the net efflux and the global warming potential caused by N additions were small.