Search Results (12)

In eutrophic shallow lakes, dissolved oxygen (DO) exhibits significant temporal variations, regulated by the combined effects of photosynthesis and water temperature (WT). High-frequency monitoring enables a detailed capture of DO diel cycles, providing a more comprehensive understanding of the dynamic changes within lake ecosystems. This study involved high-frequency (10 min intervals) in situ monitoring of DO over a three-year period (2020–2022) in the littoral zone of Taihu Lake, China. Random forest regression analysis identified WT, photosynthetically active radiation (PAR), and relative humidity (RH) as the three most influential variables governing DO dynamics. The relative importance of these factors varied seasonally (0.117–0.392), with PAR dominating in summer (0.383), whereas WT had the highest importance in other seasons (0.312–0.392). Cusum analysis further revealed that the DO-WT relationship changed from a dome-shaped pattern in spring, autumn, and winter to a bowl-shaped pattern in summer, indicating that thermal stratification intensified oxygen gradients. In addition, the majority of DO recovery occurred in the late afternoon during summer, suggesting that severe oxygen consumption delayed the daytime accumulation of DO. Our findings emphasize the critical roles of photosynthesis, respiration, and abiotic factors in shaping DO dynamics. This research enhances our understanding of DO fluctuations in eutrophic shallow lakes and provides valuable insights for ecosystem management, supporting the development of effective strategies to prevent and mitigate hypoxia. Full article

Understanding the sensitivity of ecosystem respiration (ER) to increasing temperature is crucial to predict how the terrestrial carbon sink responds to a warming climate. The temperature sensitivity of ER may vary on a diurnal basis but is poorly understood due to the paucity of observational sites documenting real ER during daytime at a global scale. Here, we used an improved flux partitioning approach to estimate the apparent temperature sensitivity of ER during the daytime (E_0,day) and nighttime (E_0,night) derived from multiyear observations of 189 FLUXNET sites. Our results demonstrated that E_0,night is significantly higher than E_0,day across all biomes, with significant seasonal variations in the day–night discrepancy in the temperature sensitivity of ER (ΔE₀ = E_0,night/E_0,day) except for evergreen broadleaf forest and savannas. Such seasonal variations in ΔE₀ mainly result from the effect of temperature and the seasonal amplitude of NDVI. We predict that future warming will decrease ΔE₀ due to the reduced E_0,night by the end of the century in most regions. Moreover, we further find that disregarding the ΔE₀ leads to an overestimation of annual ER by 10~80% globally. Thus, our study highlights that the divergent temperature dependencies between day- and nighttime ER should be incorporated into Earth system models to improve predictions of carbon–climate change feedback under future warming scenarios. Full article

As the climate warms, the thickening of the active layer of permafrost has led to permafrost melting and surface collapse, forming thermokarst landforms. These changes significantly impact regional vegetation, soil physicochemical properties, and hydrological processes, thereby exacerbating regional carbon cycling. This study analyzed the relationship between soil respiration rate (Rs), soil temperature (T), and volumetric water content (VWC) in the thermokarst depression zone of the headwater wetlands of Qinghai Lake, revealing their influence on these soil parameters. Results showed a significant positive correlation between soil temperature and Rs (p < 0.001), and a significant negative correlation between VWC and Rs (p < 0.001). The inhibitory effect of VWC on Rs in the thermokarst depression zone was stronger than under natural conditions (p < 0.05). Single-factor models indicated that the temperature-driven model had higher explanatory power for Rs variation in both the thermokarst depression zone (R² = 0.509) and under natural conditions (R² = 0.414), while the humidity-driven model had lower explanatory power. Dual-factor models further improved explanatory power, slightly more so in the thermokarst depression zone. This indicates that temperature and humidity jointly drive Rs. Additionally, during the daytime, temperature had a more significant impact on Rs under natural conditions, while increased VWC inhibited Rs. At night, the positive correlation between Rs and temperature in the thermokarst depression zone increased significantly. The temperature sensitivity (Q₁₀) values of Rs were 3.32 and 1.80 for the thermokarst depression zone and natural conditions, respectively, indicating higher sensitivity to temperature changes at night in the thermokarst depression zone. This study highlights the complexity of soil respiration responses to temperature and humidity in the thermokarst depression zone of Qinghai Lake’s headwater wetlands, contributing to understanding carbon cycling in wetland ecosystems and predicting wetland carbon emissions under climate change. Full article

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

Climate warming has profoundly influenced community structure and ecosystem functions in the terrestrial biosphere. However, how asymmetric rising temperatures between daytime and nighttime affect soil microbial communities that predominantly regulate soil carbon (C) release remains unclear. As part of a decade-long warming manipulation experiment in a semi-arid grassland, we aimed to examine the effects of short- and long-term asymmetrically diurnal warming on soil microbial composition. Neither daytime nor nighttime warming affected soil microbial composition in the short term, whereas long-term daytime warming instead of nighttime warming decreased fungal abundance by 6.28% (p < 0.05) and the ratio of fungi to bacteria by 6.76% (p < 0.01), which could be caused by the elevated soil temperature, reduced soil moisture, and increased grass cover. In addition, soil respiration enhanced with the decreasing fungi-to-bacteria ratio, but was not correlated with microbial biomass C during the 10 years, indicating that microbial composition may be more important than biomass in modulating soil respiration. These observations highlight the crucial role of soil microbial composition in regulating grassland C release under long-term climate warming, which facilitates an accurate assessment of climate-C feedback in the terrestrial biosphere. Full article

Ecosystem respiration (Reco) represents a major component of the global carbon cycle. An accurate estimation of Reco dynamics is necessary for a better understanding of ecosystem–climate interactions and the impact of climate extremes on ecosystems. This paper proposes a new data-driven method for the estimation of the nonlinear dynamics of Reco using the method of dynamic mode decomposition with control input (DMDc). The method is validated on the half-hourly Fluxnet 2015 data. The model is first trained on the night-time net ecosystem exchange data. The day-time Reco values are then predicted using the obtained model with future values of a control input such as air temperature and soil water content. To deal with unobserved drivers of Reco other than the user control input, the method uses time-delay embedding of the history of Reco and the control input. Results indicate that, on the one hand, the prediction accuracy of Reco dynamics using DMDc is comparable to state-of-the-art deep learning-based methods, yet it has the advantages of being a simple and almost hyper-parameter-free method with a low computational load. On the other hand, the study of the impact of different control inputs on Reco dynamics showed that for most of the studied Fluxnet sites, air temperature is a better long-term predictor of Reco, while using soil water content as control input produced better short-term prediction accuracy. Full article

Forest carbon sequestration offset protocols have been employed for more than 20 years with limited success in slowing deforestation and increasing forest carbon trading volume. Direct measurement of forest carbon flux improves quantification for trading but has not been applied to forest carbon research projects with more than 600 site installations worldwide. In this study, we apply carbon accounting methods, scaling hours to decades to 28-years of scientific CO₂ eddy covariance data for the Harvard Forest (US-Ha1), located in central Massachusetts, USA and establishing commercial carbon trading protocols and applications for similar sites. We illustrate and explain transactions of high-frequency direct measurement for CO₂ net ecosystem exchange (NEE, gC m⁻² year⁻¹) that track and monetize ecosystem carbon dynamics in contrast to approaches that rely on forest mensuration and growth models. NEE, based on eddy covariance methodology, quantifies loss of CO₂ by ecosystem respiration accounted for as an unavoidable debit to net carbon sequestration. Retrospective analysis of the US-Ha1 NEE times series including carbon pricing, interval analysis, and ton-year exit accounting and revenue scenarios inform entrepreneur, investor, and landowner forest carbon commercialization strategies. CO₂ efflux accounts for ~45% of the US-Ha1 NEE, an error of ~466% if excluded; however, the decades-old coupled human and natural system remains a financially viable net carbon sink. We introduce isoflux NEE for t¹³C¹⁶O₂ and t¹²C¹⁸O¹⁶O to directly partition and quantify daytime ecosystem respiration and photosynthesis, creating new soil carbon commerce applications and derivative products in contrast to undifferentiated bulk soil carbon pool approaches. Eddy covariance NEE methods harmonize and standardize carbon commerce across diverse forest applications including, a New England, USA regional eddy covariance network, the Paris Agreement, and related climate mitigation platforms. Full article

Temperate forests and grasslands carry key ecosystem functions and provide essential services. Remote-sensing derived greenness has been widely used to assess the response of ecosystem function to climate and land-cover changes. Although reforestation and grassland restoration have been proposed to enhance the regional greenness in Northern China, the independent contribution of climate without the interference of land-cover change at meso and large scales has rarely been explored. To separate the impacts of climate change on vegetation greenness from those of land-cover/use change, we identified large patches of forests and grasslands in Northern China without land-cover/use changes in 2001–2015 and derived their greenness using MODIS enhanced vegetation index (EVI). We found that most deciduous-broadleaved forest patches showed greening, and the significant slope of the annual mean and maximum EVI are 3.97 ± 0.062 × 10⁻³ and 4.8 ± 0.116 × 10⁻³ yr⁻¹, respectively. On the contrary, grassland patches showed great spatial heterogeneity and only those in the east showed greening. The partial correlation analysis between EVI and climate showed that the greening of grassland patches is primarily supported by the increased growing-season precipitation with mean significant coefficient of 0.72 ± 0.01. While wet-year (0.57 ± 0.01) and nongrowing-season precipitation (0.68 ± 0.01) significantly benefit greening of deciduous-broadleaved forests, the altered temperature seasonality modulates their greening spatial-heterogeneously. The increased growing-season minimum temperature might lengthen the growing season and contribute to the greening for the temperature-limited north as shown by positive partial correlation coefficient of 0.66 ± 0.01, but might elevate respiration and reduce greening of the forests in the south as shown by negative coefficient of −0.70 ± 0.01. Daytime warming in growing season is found to favor the drought-tolerant oak dominated forest in the south due to enhanced photosynthesis, but may not favor the forests dominated by less-drought-tolerant birch in the north due to potential water stress. Therefore, grassland greening was essentially promoted by the growing-season precipitation, however, in addition to being driven by precipitation, greening of deciduous forests was regulated spatial-heterogeneously by asymmetrical diurnal and seasonal warming which could be attributed to species composition. Full article

Soil respiration (R_s) is seldom analyzed using remotely sensed data because satellite technology has difficulty monitoring various respiratory processes in the soil. We investigated the potential of remote sensing data products to estimate R_s, including land surface temperature (LST) and spectral vegetation indices from the Moderate Resolution Imaging Spectroradiometer (MODIS), using a nine-year (2007–2015) field measurement dataset of R_s and soil temperature (T_s) at five forest sites at the eastern Loess Plateau, China. The results indicate that soil temperature is the primary factor influencing the seasonal variation of R_s at the five sites. The accuracy of the model based on the observed data is not significantly different from the model based on MODIS-derived nighttime LST values. There was a significant difference with the model based on MODIS-derived daytime LST values. Therefore, nighttime LST was the optimum LST for estimation of R_s. The normalized difference vegetation index (NDVI) consistently exhibited a stronger correlation with R_s when compared to the green edge chlorophyll index and enhanced vegetation index. Further analysis showed that adding the NDVI into the model considering only T_s or nighttime LST could significantly improve the simulation accuracy of R_s. The models depending on nighttime LST and NDVI showed comparable accuracy with the models based on the in situ T_s and NDVI. These results suggest that models based entirely on remote sensing data from MODIS have the potential to estimate R_s at the cold temperate coniferous forest sites. The performance of the model in other vegetation types or regions has also been proved. Our conclusions further confirmed that it is feasible for large-scale estimates of R_s by means of MODIS data in temperate coniferous forest ecosystems. Full article

Methane (CH₄) oxidation is an important process for regulating CH₄ emissions from peatlands as it oxidizes CH₄ to carbon dioxide (CO₂). Our current knowledge about its temporal dynamics and contribution to ecosystem CO₂ fluxes is, however, limited due to methodological constraints. Here, we present the first results from a novel method for quantifying in-situ CH₄ oxidation at high temporal resolution. Using an automated chamber system, we measured the isotopic signature of heterotrophic respiration (CO₂ emissions from vegetation-free plots) at a boreal mire in northern Sweden. Based on these data we calculated CH₄ oxidation rates using a two-source isotope mixing model. During the measurement campaign, 74% of potential CH₄ fluxes from vegetation-free plots were oxidized to CO₂, and CH₄ oxidation contributed 20 ± 2.5% to heterotrophic respiration corresponding to 10 ± 0.5% of ecosystem respiration. Furthermore, the contribution of CH₄ oxidation to heterotrophic respiration showed a distinct diurnal cycle being negligible during nighttime while contributing up to 35 ± 3.0% during the daytime. Our results show that CH₄ oxidation may represent an important component of the peatland ecosystem respiration and highlight the value of our method for measuring in-situ CH₄ oxidation to better understand carbon dynamics in peatlands. Full article

The net ecosystem productivity (NEP) of drainage basins plays an important role in maintaining the carbon balance of those ecosystems. In this study, the modified CASA (Carnegie Ames Stanford Approach) model and a soil microbial respiration model were used to estimate net primary productivity (NPP) and NEP of the Yellow River Basin’s (YRB) vegetation in the terrestrial ecosystem (excluding rivers, floodplain lakes and other freshwater ecosystems) from 1982 to 2015. After analyzing the spatiotemporal variations in the NEP using slope analysis, the coefficient of variation, and the Hurst exponent, precipitation was identified as the main factor limiting vegetation growth in the YRB. Hence, precipitation was treated as the control variable and a second-order partial correlation method was used to determine the correlation between diurnal asymmetric warming and the YRB’s NEP. The results indicate that: (i) diurnal asymmetric warming occurred in the YRB from 1982 to 2015, with nighttime warming (T_min) being 1.50 times that of daytime warming (T_max). There is a significant correlation between variations in NPP and diurnal warming; (ii) the YRB’s NEP are characterized by upward fluctuations in terms of temporal variations, large differences between the various vegetation types, high values in the western and southeastern regions but low values in the northern region in terms of spatial distribution, overall relative stability in the YRB’s vegetation cover, and changes in the same direction being more dominant than those in the opposite direction (although the former is not sustained); and (iii) positive correlations between the NEP and nighttime and daytime warming are approximately 48.37% and 67.51% for the YRB, respectively, with variations in nighttime temperatures having more extensive impacts on vegetation cover. Full article

Net ecosystem exchange (NEE) of carbon dioxide (CO₂) was measured in a cool temperate peatland in northwestern Turkey on a continuous basis using eddy covariance (EC) sensors and multiple (non-)linear regression-M(N)LR-models. Our results showed that hourly NEE varied between −1.26 and 1.06 mg CO₂ m⁻² s⁻¹, with a mean value of 0.11 mg CO₂ m⁻² s⁻¹. Nighttime ecosystem respiration (R_E) was on average measured as 0.23 ± 0.09 mg CO₂ m⁻² s⁻¹. Two best-fit M(N)LR models estimated daytime R_E as 0.64 ± 0.31 and 0.24 ± 0.05 mg CO₂ m⁻² s⁻¹. Total R_E as the sum of nighttime and daytime R_E ranged from 0.47 to 0.87 mg CO₂ m⁻² s⁻¹, thus yielding estimates of gross primary productivity (GPP) at −0.35 ± 0.18 and −0.74 ± 0.43 mg CO₂ m⁻² s⁻¹. Use of EC sensors and M(N)LR models is one of the most direct ways to quantify turbulent CO₂ exchanges among the soil, vegetation and atmosphere within the atmospheric boundary layer, as well as source and sink behaviors of ecosystems. Full article