Search Results (168)

Ecosystems modulate Earth’s climate through the exchange of carbon and water fluxes. However, long-term trends in these terrestrial fluxes remain unclear due to the lack of continuous measurements on the global scale. This study combined flux data from 197 eddy covariance sites with satellite-retrieved solar-induced chlorophyll fluorescence (SIF) to investigate spatiotemporal variations in gross primary productivity (GPP), evapotranspiration (ET), and their coupling via water use efficiency (WUE) from 2001 to 2020. We developed six global GPP and ET products at 0.05° spatial and 8-day temporal resolution, using two machine learning models and three SIF products, which integrate vegetation physiological parameters with data-driven approaches. These datasets provided mean estimates of 128 ± 2.3 Pg C yr⁻¹ for GPP, 522 ± 58.2 mm yr⁻¹ for ET, and 1.8 ± 0.21 g C kg⁻¹ H₂O yr⁻¹ for WUE, with upward trends of 0.22 ± 0.04 Pg C yr⁻² in GPP, 0.64 ± 0.14 mm yr⁻² in ET, and 0.0019 ± 0.0005 g C kg⁻¹ H₂O yr⁻² in WUE over the past two decades. These high-resolution datasets are valuable for exploring terrestrial carbon and water responses to climate change, as well as for benchmarking terrestrial biosphere models. Full article

Soil viruses, ubiquitous and abundant biological entities that are integral to microbial communities, exert pivotal impacts on ecosystem functionality, particularly within carbon (C) and nitrogen (N) cycles, through intricate interactions with bacteria, archaea, fungi, and other microbial taxa. While their contributions to soil ecosystem dynamics are increasingly elucidated, the specific roles of soil viruses in karst forest soil remain largely underexplored. Karst ecosystems (covering 15% of the global terrestrial surface) are characterized by unique geological formations, thin and patchy soil layers, high pH and Ca²⁺, and rapid hydrological dynamics, collectively fostering unique environmental conditions that may shape viral ecology and modulate C and N cycling. This perspective synthesizes existing knowledge of soil viral functions with the distinctive characteristics of karst forest soil, proposing potential mechanisms by which soil viruses could influence C and N cycling in such fragile ecosystems. Soil viruses regulate C and N cycles both directly and indirectly via their interactions with microbial hosts, mainly including shaping the microbial community structure, mediating horizontal gene transfer and microbial metabolism, increasing C and N availability and alleviating nutrient limitations, promoting C and N sequestration, and mitigating climate change. This work aims to bridge soil viral ecology and karst biogeochemical cycles, providing insights into sustainable forest stewardship and climate resilience. We delineate critical knowledge gaps and propose future perspectives, advocating for targeted metagenomic and long-term experimental studies into viral diversity, virus–host-environment interactions, and temporal dynamics. Specifically, we advocate the following research priorities to advance our understanding of soil viruses in karst forest ecosystems in future studies: (I) soil viral diversity, abundance, and activity: characterizing the diversity, abundance, and activity of soil viruses in karst forests using metagenomics and complementary molecular approaches; (II) virus–host interactions: investigating the dynamics between the viruses and key microbial taxa involved in C and N cycling; (III) biogeochemical impacts: quantifying the contributions of viral lysis and horizontal gene transfer to C and N fluxes within karst forest soil; and (IV) modeling the viral impacts on C and N cycles: developing integrative models that incorporate soil virus-mediated processes into existing karst forest soil biogeochemical frameworks at different temporal and spatial scales. Such efforts are essential to validate the hypothesized viral roles and underlying mechanisms, offering a foundation for nature-based solutions to facilitate C and N cycling and support ecological restoration in vulnerable karst regions amid global climate change. Full article

Poa alpigena Lindm is a dominant forage grass in the temperate grasslands of the Qinghai Lake Basin, commonly used for grassland restoration. Soil microorganisms are crucial in material cycling within terrestrial ecosystems. This study aimed to investigate the effects of P. alpigena on the microbial community composition and structure in rhizosphere and non-rhizosphere soils in the Qingbaya grassland area. Using high-throughput sequencing, we identified microbial gene pools and compared microbial diversity. Metagenomic analysis showed that non-rhizosphere soil contained 35.42–36.64% known microbial sequences, with bacteria making up 79.25% of the microbiota. Alpha diversity analysis indicated significantly higher microbial richness and diversity in non-rhizosphere soil, influenced by electrical conductivity, total carbon, and total nitrogen content. LEfSe analysis revealed that Alphaproteobacteria and Betaproteobacteria were major differential taxa in rhizosphere and non-rhizosphere soils, respectively. Key metabolic pathways in rhizosphere microorganisms were related to AMPK signaling, secondary metabolite biosynthesis, and starch metabolism, while non-rhizosphere microorganisms were involved in aromatic compound degradation, purine metabolism, and microbial metabolism in diverse environments. The enrichment of microbial taxa and functional pathways related to methane oxidation in rhizosphere soil suggests a potential role of P. alpigena in shaping microbial processes linked to greenhouse gas regulation, although direct evidence of methane flux changes was not assessed. Similarly, the presence of aromatic compound degradation pathways in non-rhizosphere soil indicates microbial potential for processing such compounds, but no direct measurements of specific contaminants were performed. Full article

Accurately understanding the changes in global light-response parameters (i.e., maximum light use efficiency, LUEmax) is essential for improving the simulation of terrestrial ecosystem’s photosynthetic carbon cycling under climate change, but a comprehensive understanding and assessments are still lacking. In this study, LUEmax was quantified using data from 23 global flux stations, and the change patterns in LUEmax across various vegetation types and climate zones were analyzed. The extent of significant increases or decreases in LUEmax during different phenological stages of vegetation growth was evaluated using trend analysis methods. The contribution rates of environmental factors were determined using the Geodetector method. The results show that the LUEmax values of the same vegetation type varied across different climate types. More variable climates (e.g., polar and alpine climates) are associated with more significant fluctuations in LUEmax. Conversely, more stable climates (e.g., temperate climates) tend to show more consistent LUEmax values. Within the same climate type, evergreen needleleaf forests (ENF) and deciduous broadleaf forests (DBF) generally exhibited higher LUEmax values in temperate and continental climates, whereas the LUEmax values of wetlands (WET) were relatively high in polar and alpine climates. The mechanisms driving variations in LUEmax across different vegetation types exhibited significant disparities under diverse environmental conditions. For ENF and DBF, LUEmax is predominantly influenced by temperature and radiation. In contrast, the LUEmax of GRA, WET, and croplands is more closely associated with vegetation indices and temperature factors. The findings of this study play an important role in advancing the theoretical development of gross primary productivity (GPP) models and enhancing the accuracy of carbon sequestration simulations in terrestrial ecosystems. Full article

Gross primary productivity (GPP) quantifies the rate at which plants convert atmospheric carbon dioxide into organic matter through photosynthesis, playing a vital role in the terrestrial carbon cycle. Machine learning (ML) techniques excel in handling spatiotemporally complex data, facilitating accurate spatial-scale inversion of forest GPP by integrating limited ground flux measurements with Remote Sensing (RS) observations. Enhancing ML algorithm performance for precise GPP estimation is a key research focus. This study introduces the Random Grid Search Algorithm (RGSA) for hyperparameters tuning to improve Random Forest (RF) and eXtreme Gradient Boosting (XGB) models across four major forest regions in China. Model optimization progressed through three stages: the Unoptimized (UO) XGB model achieved $R^2$ = 0.77 and $RMSE$ = 1.42 g Cm⁻² d⁻¹; the Hyperparameter Optimized (HO) XGB model using RGSA improved performance by 5.19% in $R^2$ (0.81) and reduced $RMSE$ by 9.15% (1.29 g Cm⁻² d⁻¹); the Hyperparameter and Variable Combination Optimized (HVCO) XGB model with selected variables (LAI, Temp, NR, VPD, and NDVI) further enhanced $R^2$ to 0.83 and decreased $RMSE$ to 1.23 g Cm⁻² d⁻¹. The optimized GPP estimates exhibited high spatial consistency with existing high-quality products like GOSIF GPP, GLASS GPP, and FLUXCOM GPP, validating the model’s reliability and effectiveness. This research provides crucial insights for improving GPP estimation accuracy and optimizing ML methodologies for forest ecosystems in China. Full article

Organic matter (OM) is the primary carrier for the generation and occurrence of shale oil and gas. The combination of sequence stratigraphy and elemental geochemistry plays a crucial role in the study of organic matter enrichment mechanisms in marine shale, but it is rarely applied to terrestrial lacustrine basins. As a product of the last large-scale lake transgression in the Sichuan Basin, the Early Jurassic Lianggaoshan Formation (LGS Fm.) developed multiple organic-rich shale intervals, which is a good example for studying the OM enrichment in lacustrine basins. Based on a high-resolution sequence stratigraphic framework, the evolutionary process of terrestrial debris input, redox conditions, and paleo-productivity during the sedimentary period of the Lianggaoshan Formation lacustrine shale at different stages of lake-level variations has been revealed. The main controlling factors for OM enrichment and the establishment of their enrichment patterns have been determined. Sequence stratigraphy studies have shown that there are three third-order lake transgression-lake regression (T-R) cycles in the LGS Formation. The total organic carbon content (TOC) is higher in the TST cycle, especially in the T-R3 cycle, and lower in the RST cycle. There are differences in the redox conditions, paleo-productivity, terrestrial detrital transport, and OM accumulation under the influence of lacustrine shale deposition in different system tracts. The results indicate that changes in lake level have a significant impact on the reducibility of bottom water and paleo-productivity of surface seawater, but have a relatively small impact on the input of terrestrial debris. In the TST cycle, the reducibility of bottom water gradually increases, and the paleo-productivity gradually increases, while in the RST cycle, the opposite is true. Within the TST cycle, the OM accumulation is mainly influenced by paleo-productivity and redox condition of bottom water, with moderate input of terrestrial debris playing a positive role. In the RST cycle, the redox condition of bottom water is the main inducing factor for OM enrichment, followed by paleo-productivity, while terrestrial input flux plays a diluting role, which is generally not conducive to OM accumulation. Full article

The carbon sink over land plays a key role in the mitigation of climate change by removing carbon dioxide (CO₂) from the atmosphere. Accurately assessing the land sink capacity across regions should contribute to better future climate projections and help guide the mitigation of global emissions towards the Paris Agreement. This study estimates terrestrial CO₂ fluxes over India using a high-resolution global inverse model that assimilates surface observations from the global observation network and the Indian subcontinent, airborne sampling from Brazil, and data from the Greenhouse gas Observing SATellite (GOSAT) satellite. The inverse model optimizes terrestrial biosphere fluxes and ocean-atmosphere CO₂ exchanges independently, and it obtains CO₂ fluxes over large land and ocean regions that are comparable to a multi-model estimate from a previous model intercomparison study. The sensitivity of optimized fluxes to the weights of the GOSAT satellite data and regional surface station data in the inverse calculations is also examined. It was found that the carbon sink over the South Asian region is reduced when the weight of the GOSAT data is reduced along with a stricter data filtering. Over India, our result shows a carbon sink of 0.040 ± 0.133 PgC yr⁻¹ using both GOSAT and global surface data, while the sink increases to 0.147 ± 0.094 PgC yr⁻¹ by adding data from the Indian subcontinent. This demonstrates that surface observations from the Indian subcontinent provide a significant additional constraint on the flux estimates, suggesting an increased sink over the region. Thus, this study highlights the importance of Indian sub-continental measurements in estimating the terrestrial CO₂ fluxes over India. Additionally, the findings suggest that obtaining robust estimates solely using the GOSAT satellite data could be challenging since the GOSAT satellite data yield significantly varies over seasons, particularly with increased rain and cloud frequency. Full article

Satellite XCO₂ retrievals have been widely used in estimating fossil fuel carbon (FFC) emissions at point and urban scales. However, at the regional scale, it remains a significant challenge. Furthermore, current global and regional atmospheric inversions often overlook the uncertainties associated with FFC emissions. To meet the needs of the global carbon stocktake, we developed an inversion method based on Bayesian statistical theory and OCO-2 satellite XCO₂ observations to optimize FFC emissions alongside terrestrial ecosystem carbon fluxes (NEE). The methodology’s core is to distinguish the contributions of NEE and FFC to the observed concentrations using their different spatial distributions. We designed an observing system simulation experiment to invert the 2016 FFC emissions. The results showed that posterior FFC emissions were significantly optimized during the non-growing seasons in the regions with high emissions, with the optimization effect diminishing as emissions shrank. Average FFC emissions uncertainty reductions are in the range of 13–82% in the non-growing season for the eight largest emitting regions globally. By assuming the same uncertainty reduction for FFC emissions in both the growing and non-growing seasons, we can optimize annual emissions for high-emission areas. We believe this study provides a new idea for the inversion of FFC emissions at the regional scale, which is important for achieving the goal of carbon neutrality. Full article

Net ecosystem productivity (NEP) is an extremely important flux for terrestrial ecosystems, indicating the value of net ecosystem exchange (NEE) between terrestrial ecosystems and the atmosphere, excluding carbon fluxes from disturbances. Leveraging flux network NEE annual measurements, this study focuses on upscaling the tower-based NEP to a global 250 m resolution dataset with flux site distribution considerations. Firstly, the data augmentation method was presented to address issues related to the uneven spatial distribution of flux sites. Secondly, a random forest model was developed for NEP estimation using the optimized tower-based NEP and remotely sensed and meteorological gridded sample sets, giving an R² value of 0.73 and an RMSE value of 149.83 gC m⁻² yr⁻¹. Finally, a global NEP product at a 250 m resolution was generated (2001–2022, average 13.79 PgC yr⁻¹) and evaluated. In summary, we present a solution to the overestimation of global NEP by data-driven methods, producing a long-time-series, high-resolution NEP dataset that is more comparable to atmospheric inversion results. This dataset enhances comparability with atmospheric inversion results, thereby boosting our confidence in conducting a consistency analysis of terrestrial carbon sinks across different methods within the framework. Full article

The continental margin is a major repository for organic carbon; however, anthropogenic alterations to global sediment and particulate terrestrial organic carbon (TerrOC) fluxes have reduced delivery by rivers and offshore burial in recent decades. Despite the absence of mainstem damming, land use change in the Ayeyarwady and Thanlwin River catchments in Myanmar has accelerated over the last 50 years. As a result, deforestation and landscape erosion have likely altered fluvial fluxes to the Northern Andaman Sea shelf; however, the magnitude and preservation of geochemical signals associated with development are unknown. Utilizing elemental and bulk stable and radioisotope analysis, this study investigates spatial and temporal trends in sediment sources and TerrOC concentrations to identify the potential impacts of recent (<100 years) offshore development. While our results demonstrate an along-shelf trend in provenance and TerrOC concentrations, temporal (downcore) trends are not observed. We attribute this observation to frequent, large-scale seabed resuspension and suggest that extensive mixing on the inner shelf creates a low-pass filter that effectively attenuates such signatures. This is in contrast to other large Asian deltas, where signals of human landscape disturbance are clearly preserved offshore. We predict that planned mainstem damming in Myanmar will result in larger alterations in sediment and TerrOC supply that may become apparent offshore in the near future. Full article

The spatiotemporal variability of precipitation profoundly influences terrestrial carbon fluxes, driving shifts between carbon source and sink dynamics through gross primary productivity (GPP) and ecosystem respiration (ER). As a result, the sensitivities of GPP and ER to precipitation (S_GPP and S_ER), along with their differential responses, are pivotal for understanding ecosystem reactions to precipitation changes and predicting future ecosystem functions. However, comprehensive evaluations of the spatiotemporal variability and differences in S_GPP and S_ER remain notably scarce. In this study, we utilized eddy covariance flux data to investigate the spatial patterns, temporal dynamics, and differences in S_GPP and S_ER. Spatially, S_GPP and S_ER were generally strongly correlated. Among different ecosystems, the correlation between S_GPP and S_ER was lowest in mixed forest and highest in broadleaf and needleleaf forest. Within the same ecosystem, S_GPP and S_ER exhibited considerable variation but showed no significant differences. In contrast, they differed significantly across ecosystems, with pronounced variability in their magnitudes. For example, shrubland exhibited the highest values for S_GPP, whereas needleleaf forest showed the highest values for S_ER. Temporally, S_ER demonstrated more pronounced changes than S_GPP. Different ecosystems displayed distinct trends: shrubland exhibited an upward trend for both metrics, while grassland showed a downward trend in both S_GPP and S_ER. Forest, on the other hand, maintained stable S_GPP but displayed a downward trend in S_ER. Additionally, S_GPP and S_ER exhibited a notable non-linear response to changes in the aridity index (AI), with both showing a rapid decline followed by stabilization. However, S_ER demonstrated a wider adaptive range to precipitation changes. Generally, this research enhances our understanding of the spatiotemporal variations in ecosystem carbon fluxes under changing precipitation patterns. Full article

A good quantitative knowledge of regional sources and sinks of atmospheric carbon dioxide (CO₂) is essential for understanding the global carbon cycle. It is also a key prerequisite for elaborating cost-effective national strategies to achieve the goals of the Paris Agreement. However, available estimates of CO₂ fluxes for many regions of the world remain uncertain, despite significant recent progress in the remote sensing of terrestrial vegetation and atmospheric CO₂. In this study, we investigate the feasibility of inferring reliable regional estimates of the net ecosystem exchange (NEE) using column-averaged dry-air mole fractions of CO₂ (XCO₂) retrieved from Orbiting Carbon Observatory-2 (OCO-2) observations as constraints on parameters of the widely used Vegetation Photosynthesis and Respiration model (VPRM), which predicts ecosystem fluxes based on vegetation indices derived from multispectral satellite imagery. We developed a regional-scale inverse modeling system that applies a Bayesian variational optimization algorithm to optimize parameters of VPRM coupled to the CHIMERE chemistry transport model and which involves a preliminary transformation of the input XCO₂ data that reduces the impact of the CHIMERE boundary conditions on inversion results. We investigated the potential of our inversion system by applying it to a European region (that includes, in particular, the EU countries and the UK) for the warm season (May–September) of 2021. The inversion of the OCO-2 observations resulted in a major (more than threefold) reduction of the prior uncertainty in the regional NEE estimate. The posterior NEE estimate agrees with independent estimates provided by the CarbonTracker Europe High-Resolution (CTE-HR) system and the ensemble of the v10 OCO-2 model intercomparison (MIP) global inversions. We also found that the inversion improves the agreement of our simulations of XCO₂ with retrievals from the Total Carbon Column Observing Network (TCCON). Our sensitivity test experiments using synthetic XCO₂ data indicate that the posterior NEE estimate would remain reliable even if the actual regional CO₂ fluxes drastically differed from their prior values. Furthermore, the posterior NEE estimate is found to be robust to strong biases and random uncertainties in the CHIMERE boundary conditions. Overall, this study suggests that our approach offers a reliable and relatively simple way to derive robust estimates of CO₂ ecosystem fluxes from satellite XCO₂ observations while enhancing the applicability of VPRM in regions where eddy covariance measurements of CO₂ fluxes are scarce. Full article

Basic inventory is required for proper understanding and utilization of Earth’s natural resources, especially with increasing soil degradation and species loss. Soil carbon is newly refined at >30,000 Gt C (gigatonnes C), ten times above prior totals. Soil organic carbon (SOC) is up to 24,000 Gt C, plus plant stocks at ~2400 Gt C, both above- and below-ground, hold >99% of Earth’s biomass. On a topographic surface area of 25 Gha with mean 21 m depth, Soil has more organic carbon than all trees, seas, fossil fuels, or the Atmosphere combined. Soils are both the greatest biotic carbon store and the most active CO₂ source. Values are raised considerably. Disparity is due to lack of full soil depth survey, neglect of terrain, and other omissions. Herein, totals for mineral soils, Permafrost, and Peat (of all forms and ages), are determined to full depth (easily doubling shallow values), then raised for terrain that is ignored in all terrestrial models (doubling most values again), plus SOC in recalcitrant glomalin (+25%) and friable saprock (+26%). Additional factors include soil inorganic carbon (SIC some of biotic origin), aquatic sediments (SeOC), and dissolved fractions (DIC/DOC). Soil biota (e.g., forests, fungi, bacteria, and earthworms) are similarly upgraded. Primary productivity is confirmed at >220 Gt C/yr on land supported by Barrow’s “bounce” flux, C/O isotopes, glomalin, and Rubisco. Priority issues of species extinction, humic topsoil loss, and atmospheric CO₂ are remedied by SOC restoration and biomass recycling via (vermi-)compost for 100% organic husbandry under Permaculture principals, based upon the Scientific observation of Nature. Full article

Forest carbon exchange is affected by various environmental variables, among which photosynthetically active radiation, temperature, saturated water vapor pressure deficit, and soil moisture content dominate. The global atmospheric temperature has risen significantly in recent decades, and the saturated water vapor pressure deficit has also increased, which has had a widespread and lasting impact on terrestrial carbon sinks. Here, using flux data from Mazongling in Jinzhai County from July 2020 to June 2023, the relationship between saturated water vapor pressure deficit and forest carbon flux was investigated on the basis of carbon flux changes in the forest ecosystem in response to environmental factors. Results revealed that vapor pressure deficit (VPD) and net ecosystem productivity (NEP) exhibited a quadratic relationship at the daily and monthly scales. When the VPD was greater than 1.2 kPa at the monthly scale, the NEP of the fir forest ecosystem decreased with increasing VPD. At the daily scale, the impact of the VPD on NEP was studied by month and season. The results revealed that the threshold value at which the VPD affected NEP differed across different months and seasons. Therefore, the VPD is an important factor in forest ecosystems and should be considered in the assessment of ecosystem carbon sinks. It also has far-reaching significance in the carbon cycle and ecological sustainable development. Full article

In terrestrial ecosystems, the quantification of carbon absorption is primarily represented by the gross primary productivity (GPP), which signifies the initial substances and energy acquired by the ecosystem. The GPP also serves as the foundation for the carbon cycle within the entire terrestrial ecosystem. The Biome-BGC model is a widely used biogeochemical process model for simulating the stocks and fluxes of water, carbon, and nitrogen between ecosystems and the atmosphere. However, it is the abundance of eco-physiological parameters that lead to challenges in calibrating the model. The parameter optimization method of coupling the differential evolution algorithm (DE) with the Biome-BGC model was used to calibrate and validate the eco-physiological parameters of the seven typical vegetation types in the Yellow River Basin (YRB). And then we used the calibrated parameters to simulate the GPP by way of grid-based simulation. Finally, we conducted model adaptability testing and spatiotemporal analysis of GPP variations in the YRB. The results of the validation (R², RMSE) were: temperate grasses (0.94, 24.33 g C m⁻²), alpine meadows (0.94, 18.13 g C m⁻²), shrubs (0.94, 29.20 g C m⁻²), evergreen needle leaf forests (0.96, 27.88 g C m⁻²), deciduous broad leaf forests (0.94, 32.09 g C m⁻²), one crop a year (0.96, 16.19 g C m⁻²), and two crops a year (0.90, 38.15 g C m⁻²). After adaptability testing, the average R² value between the simulated GPP values and the GPP product values in the YRB was 0.85, and the average RMSE value was as low as 50.92 g C m⁻². Overall, the model exhibited strong simulation accuracy. Therefore, after calibrating the model with the DE algorithm, the Biome-BGC model could effectively adapt to the ecologically complex YRB. Moreover, it was able to accurately estimate the GPP, which establishes a foundation for analyzing the spatiotemporal trends of the GPP in the YRB. This study provides a reference for optimizing Biome-BGC model parameters and simulating diverse vegetation types on a large scale. Full article