Carbon Cycling In Terrestrial Ecosystems Research Articles

General reversal of N-decomposition relationship during long-term decomposition in boreal and temperate forests

Decomposition of dead organic matter is fundamental to carbon (C) and nutrient cycling in terrestrial ecosystems, influencing C fluxes from the biosphere to the atmosphere. Theory predicts and evidence strongly supports that the availability of nitrogen (N) limits litter decomposition. Positive relationships between substrate N concentrations and decomposition have been embedded into ecosystem models. This decomposition paradigm, however, relies on data mostly from short-term studies analyzing controls on early-stage decomposition. We present evidence from three independent long-term decomposition investigations demonstrating that the positive N-decomposition relationship is reversed and becomes negative during later stages of decomposition. First, in a 10-y decomposition experiment across 62 woody species in a temperate forest, leaf litter with higher N concentrations exhibited faster initial decomposition rates but ended up a larger recalcitrant fraction decomposing at a near-zero rate. Second, in a 5-y N-enrichment experiment of two tree species, leaves with experimentally enriched N concentrations had faster decomposition initial rates but ultimately accumulated large slowly decomposing fractions. Measures of amino sugars on harvested litter in two experiments indicated that greater accumulation of microbial residues in N-rich substrates likely contributed to larger slowly decomposing fractions. Finally, a database of 437 measurements from 120 species in 45 boreal and temperate forest sites confirmed that higher N concentrations were associated with a larger slowly decomposing fraction. These results challenge the current treatment of interactions between N and decomposition in many ecosystems and Earth system models and suggest that even the best-supported short-term controls of biogeochemical processes might not predict long-term controls.

A spectral mixture analysis based framework for estimating and charactering water use efficiency in heterogeneous drylands

Climate change and irrational land utilization have significantly disrupted the function and structure of the dryland ecosystems, leading to tremendously potential effects on the carbon and water cycles of the global terrestrial ecosystems. Ecosystem-scale water use efficiency (WUE) is a crucial metric that defines the ratio of gross primary productivity (GPP) to evapotranspiration (ET), revealing the coupling relationship between such two ecological processes and playing a significant role in water resource management decision-making. However, quantifying WUE in drylands remains a challenge because of their heterogeneous land surface challenging to characterize model parameters using traditional remote sensing techniques. Here, we developed a process-based framework to estimate GPP and ET, as well as WUE based on physical endmember fractions from spectral mixture analysis (SMA) model. And such framework is used to quantify the spatial and temporal evolution of WUE by optimizing the light use efficiency and assisting in the construction of a GV-Cws model for the separation of transpiration and evaporation processes during computation by utilizing the physically fractional vegetation and soil obtained from SMA in the Hexi Corridor of China, a typical dryland ecosystem. The results demonstrate that our framework can accurately estimate GPP and ET in the Hexi Corridor, as indicated by favorable performance in R2 (0.669, 0.705), RMSE (0.383,0.885) and NSE (0.534, 0.690) respectively. Such results surpass the accuracy of both two Moderate Resolution Imaging Spectroradiometer (MODIS) products and Penman-Monteith-Leuning Evapotranspiration V2 (PML_V2)’s ET product. These advantages highlight that our SMA-based framework can provide highly accurate and distinct characterization of surface carbon and water interaction processes. Meanwhile, the WUE of the Hexi Corridor ecosystem exhibited a positive trend from 2001 to 2017 (0.016 gC·m−2·mm−1·a-1), characterized by simultaneous increases in GPP and ET.

Modeling the Ecosystem Water Use Efficiency of Croplands Using Machine Learning and MODIS Data

The ecosystem water use efficiency (WUE), which is the ratio of carbon gain (gross primary productivity, GPP) to water consumption (evapotranspiration, ET), is widely regarded as a crucial link in coupling the carbon and water cycles of terrestrial ecosystems on a global scale. The estimation of cropland WUE is considerably important for improving agricultural management, simulating the carbon–water cycle processes of croplands, and enhancing crop yields. Currently, the estimate of WUE primary relies on the modeled GPP and ET data. However, differences in model structures and parameter uncertainties cause remarkable differences in the accuracy of the estimated WUE among different models. The eddy covariance technology enables the continuous observation and research of carbon and water fluxes at a site scale, ensuring simulation accuracy. However, the technology is constrained by its limited spatial coverage. In this study, we integrated data from 20 global cropland sites and built 7 machine learning models based on remote sensing to directly estimate cropland WUE, thereby expanding the spatial scope of WUE simulations, omitting the simulation process for obtaining GPP and ET, and ensuring a certain degree of precision. Results show that, compared with other machine learning algorithms, the ensemble learning model has the strongest ability to reproduce cropland WUE, with a determination coefficient of 0.90 and a root mean square error of 1.54–1.75 . These results indicate that a combination of machine learning methods and remote sensing can reasonably simulate the WUE of agricultural ecosystems.

Impacts of Compound Hot–Dry Events on Vegetation Productivity over Northern East Asia

Climate extremes, such as heatwaves and droughts, significantly impact terrestrial ecosystems. This study investigates the influence of compound hot–dry (CHD) events on vegetation productivity in northern East Asia. Four of the most widespread CHD events occurring during the summer from 2003 to 2019 were selected as the focus of this research. We first verified the performance of the Community Land Model version 5 (CLM5) in the region and then conducted factor-controlled experiments using CLM5 to assess the effects of different climate factors on gross primary productivity (GPP) changes during CHD events. Our results show that vegetation productivity exhibits greater sensitivity to CHD events within the transitional climatic zone (TCZ) than in other affected areas. In grassland areas within the TCZ, precipitation deficit is the primary factor leading to the decrease in GPP (explaining 56%–90% of GPP anomalies), while high temperatures serve as a secondary detrimental factor (explaining 13%–32% of GPP anomalies). In high-latitude forests outside the TCZ, high temperature has a more significant impact on suppressing GPP, while the decrease in soil moisture has a synchronously negligible impact on GPP. There are differences in the effects of high solar radiation on grasslands and woodlands during CHD events. It was observed that high radiation benefits trees by increasing the maximum carboxylation rate (Vcmax) and maximum electron transport rate (Jmax), as well as enhancing photosynthesis, but has a negligible impact on grasses. Furthermore, this study highlights the potential for compound events to impact vegetation productivity more than expected from individual events due to confounding nonlinear effects between meteorological factors. More than 10% of the negative anomalies in GPP during two CHD events in 2017 and 2010 were attributed to these nonlinear effects. These research findings are significant for understanding ecosystem responses to climate extremes and their influence on carbon cycling in terrestrial ecosystems. They can also contribute to more precisely evaluating and predicting carbon dynamics in these regions.

Global photosynthetic capacity jointly determined by enzyme kinetics and eco-evo-environmental drivers

Accurate understanding of global photosynthetic capacity (i.e. maximum RuBisCO carboxylation rate, Vc, max) variability is critical for improved simulations of terrestrial ecosystem photosynthesis metabolisms and carbon cycles with climate change, but a holistic understanding and assessment remains lacking. Here we hypothesized that Vc, max was dictated by both factors of temperature-associated enzyme kinetics (capturing instantaneous ecophysiological responses) and the amount of activated RuBisCO (indexed by Vc, max standardized at 25 ℃, Vc, max25), and compiled a comprehensive global dataset (n = 7339 observations from 428 sites) for hypothesis testing. The photosynthesis data were derived from leaf gas exchange measurements using portable gas exchange systems. We found that a semi-empirical statistical model considering both factors explained 78% of global Vc, max variability, followed by 55% explained by enzyme kinetics alone. This statistical model outperformed the current theoretical optimality model for predicting global Vc, max variability (67%), primarily due to its poor characterization on global Vc, max25 variability (3%). Further, we demonstrated that, in addition to climatic variables, belowground resource constraint on photosynthetic machinery built-up that directly structures the biogeography of Vc, max25 was a key missing mechanism for improving the theoretical modelling of global Vc, max variability. These findings improve the mechanistic understanding of global Vc, max variability and provide an important basis to benchmark process-based models of terrestrial photosynthesis and carbon cycling under climate change.

Distribution Characteristics of Carbon Density in Plant–Soil System of Temperate Steppe and Temperate Desert in the Longzhong Loess Plateau

Grassland, as a key component of the carbon cycle in terrestrial ecosystems, is vital in confronting global climate change. Characterising the carbon density of grassland ecosystems in the Longzhong Loess Plateau is important for accurately assessing the contribution of grasslands to global climate change and achieving the goal of “peak carbon” and “carbon neutral”. In this study, the Longzhong Loess Plateau was used as the research object to explore changes in the plant–soil system carbon density in two grassland types by analysing the aboveground vegetation biomass carbon density, belowground vegetation biomass carbon density, 0–100 cm soil carbon density, and ecosystem carbon density of temperate steppe and temperate desert. The results showed that the vegetation biomass (standing and living, litter, and belowground biomass), soil, and ecosystem carbon densities of the temperate steppe were significantly higher than those of the temperate desert (p < 0.05). Their carbon densities were 700.51, 7612.95, and 8313.45 g·m−2, respectively. The vertical distribution of belowground biomass and soil carbon density in the temperate steppe was significantly higher than that in the temperate desert. The overall trend of belowground biomass carbon density in the temperate steppe and temperate desert showed a gradual decrease, whereas soil carbon density showed a steady increase. More than 91% and 96% of the carbon was stored in soil in the temperate steppe and temperate desert, respectively, and the belowground biomass carbon stock accounted for more than 84% of the total biomass carbon pools in both temperate steppe and temperate desert. Temperate steppe has a significant effect in improving the carbon stock of grassland ecosystems, so ecological protection and restoration of grassland should be strengthened in the future to enhance the capacity of grassland to sequester carbon and increase sinks.

Global spectra of plant litter carbon, nitrogen and phosphorus concentrations and returning amounts

Abstract Litter decomposition is a key ecological process that determines carbon (C) and nutrient cycling in terrestrial ecosystems. The initial concentrations of C and nutrients in litter play a critical role in this process, yet the global patterns of litter initial concentrations of C, nitrogen (N) and phosphorus (P) are poorly understood. We employed machine learning with a global database to quantitatively assess the global patterns and drivers of leaf litter initial C, N and P concentrations, as well as their returning amounts (i.e. amounts returned to soils). The medians of litter C, N and P concentrations were 46.7, 1.1, and 0.1%, respectively, and the medians of litter C, N and P returning amounts were 1.436, 0.038 and 0.004 Mg ha−1 year−1, respectively. Soil and climate emerged as the key predictors of leaf litter C, N and P concentrations. Predicted global maps showed that leaf litter N and P concentrations decreased with latitude, while C concentration exhibited an opposite pattern. Additionally, the returning amounts of leaf litter C, N and P all declined from the equator to the poles in both hemispheres. Synthesis: Our results provide a quantitative assessment of the global concentrations and returning amounts of leaf litter C, N and P, which showed new light on the role of leaf litter in global C and nutrients cycling.

The strengthened impact of water availability at interannual and decadal time scales on vegetation GPP

AbstractWater availability (WA) is a key factor influencing the carbon cycle of terrestrial ecosystems under climate warming, but its effects on gross primary production (EWA‐GPP) at multiple time scales are poorly understood. We used ensemble empirical mode decomposition (EEMD) and partial correlation analysis to assess the WA‐GPP relationship (RWA‐GPP) at different time scales, and geographically weighted regression (GWR) to analyze their temporal dynamics from 1982 to 2018 with multiple GPP datasets, including near‐infrared radiance of vegetation GPP, FLUXCOM GPP, and eddy covariance–light‐use efficiency GPP. We found that the 3‐ and 7‐year time scales dominated global WA variability (61.18% and 11.95%), followed by the 17‐ and 40‐year time scales (7.28% and 8.23%). The long‐term trend also influenced 10.83% of the regions, mainly in humid areas. We found consistent spatiotemporal patterns of the EWA‐GPP and RWA‐GPP with different source products: In high‐latitude regions, RWA‐GPP changed from negative to positive as the time scale increased, while the opposite occurred in mid‐low latitudes. Forests had weak RWA‐GPP at all time scales, shrublands showed negative RWA‐GPP at long time scales, and grassland (GL) showed a positive RWA‐GPP at short time scales. Globally, the EWA‐GPP, whether positive or negative, enhanced significantly at 3‐, 7‐, and 17‐year time scales. For arid and humid zones, the semi‐arid and sub‐humid zones experienced a faster increase in the positive EWA‐GPP, whereas the humid zones experienced a faster increase in the negative EWA‐GPP. At the ecosystem types, the positive EWA‐GPP at a 3‐year time scale increased faster in GL, deciduous broadleaf forest, and savanna (SA), whereas the negative EWA‐GPP at other time scales increased faster in evergreen needleleaf forest, woody savannas, and SA. Our study reveals the complex and dynamic EWA‐GPP at multiple time scales, which provides a new perspective for understanding the responses of terrestrial ecosystems to climate change.

Involvements of mycorrhizal fungi in terrestrial ecosystem carbon cycling

Involvements of mycorrhizal fungi in terrestrial ecosystem carbon cycling

Large Divergence of Satellite Monitoring of Diffuse Radiation Effect on Ecosystem Water‐Use Efficiency

AbstractWater‐use efficiency (WUE), defined as the ratio of gross primary productivity (GPP) and evapotranspiration (ET), is a key indicator of the coupling of carbon and water cycles in terrestrial ecosystems. Diffuse radiation significantly affects WUE as diffuse radiation has stronger fertilization effects on GPP than ET. Remote sensing data‐driven (RS) models were widely used to estimate WUE. However, the performance of RS models in simulating diffuse radiation effect on WUE is poorly understood. We used GPP and ET products from MODIS, PML, BESS, and GLASS to explore the response of WUE to diffuse radiation across different vegetation types. We found that there was a large divergence among the RS models in estimating the response of WUE to FDP. PML model performed better than other RS models in simulating diffuse radiation effect on WUE. This research has significant implications for improving model simulations and understanding global carbon and water cycles.

Effects of corn stalks returning on soil microbial carbon use efficiency and corn yield in semi-arid cropland

Soil microbial carbon use efficiency (CUE) is a key parameter controlling the short-term carbon (C) cycle in terrestrial ecosystems. The effect of urea application (156 kg N ha-1) and corn stalks returning (9.0 tons ha-1) on soil microbial CUE and corn yield in semi-arid cropland was studied using the 18O-labeled water approach during a one-year experiment. In semi-arid cropland, applying urea reduced soil microbial CUE by 44%, while the soil microbial CUE was increased significantly by 34% after returning corn stalks to the field. The application of urea increased the total nitrogen content of soil by 23%, and corn stalks returning further increased nitrate nitrogen (NO3–-N) by 45%, dissolved organic carbon (DOC) by 53%, and dissolved organic nitrogen (DON) by 122%. Compared with no fertilization, urea application increased the corn height by 4% and the corn yield by 21%. Corn stalks returning combined with urea reduced the corn stalks by 37% compared with no fertilizer. There was no significant difference in corn yield between corn stalks returning combined with urea and single urea application. Therefore, corn stalks returning combined with urea may be an effective agronomic measure to increase soil carbon sequestration, improve soil fertility, maintain corn yield, restore soil fertility, and improve production capacity.

Contribution of multi-objective land use optimization to carbon neutrality: A case study of Northwest China

Land use and land cover (LULC) patterns fundamentally influence the carbon cycle of terrestrial ecosystems, and rational optimization of LULC patterns can contribute to achieving carbon neutrality goals. This study constructs a multi-objective LULC optimization coupled model by integrating the objective of carbon neutrality into the LULC optimization process, with Northwest China serving as the study case. This model integrated the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model with the Intergovernmental Panel on Climate Change (IPCC) inventory methodology to assess the spatiotemporal variations in carbon storage and emissions in Northwest China between 2000 and 2020, which aimed to elucidate the mechanisms through which changes in LULC affect carbon neutrality. Additionally, through the amalgamation of the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and the Patch-Generating Land Use Simulation (PLUS) model, optimized the quantitative structure and spatial layout of four LULC scenarios for Northwest China in 2060: natural development (ND), low carbon emissions (CE), high carbon storage (CS), and carbon neutrality (CN), projected the evolving pathways of carbon storage and emissions within these scenarios while examining the values of different LULC scenarios and their respective contributions to carbon neutrality. The study's findings underscore that the substantial expansion of built-up land development areas and farmland from 2000 to 2020 has resulted in notable carbon storage and emissions increases. Moreover, changes in carbon emissions and carbon storage show significant spatial consistency with changes in LULC patterns. In contrast to the ND scenario, the LULC patterns within the CE, CS, and CN scenarios exhibit higher LULC values and contribute more substantially to carbon neutrality. Remarkably, among these scenarios, the CN scenario stands out for achieving the maximum contribution to carbon neutrality and LULC value, thereby establishing it as the preeminent LULC pattern for the Northwest China region. As the study concludes, it recommended that future developmental strategies in Northwest China prioritize curtailing the expansion of built-up areas while bolstering the conservation of vital ecological zones such as forests and grasslands. This study addresses the inherent tension between regional economic progress and environmental preservation, offering a foundation of scientific insights for effectively managing LULC patterns and attaining carbon neutrality goals.

The global biogeography of tree leaf form and habit

Understanding what controls global leaf type variation in trees is crucial for comprehending their role in terrestrial ecosystems, including carbon, water and nutrient dynamics. Yet our understanding of the factors influencing forest leaf types remains incomplete, leaving us uncertain about the global proportions of needle-leaved, broadleaved, evergreen and deciduous trees. To address these gaps, we conducted a global, ground-sourced assessment of forest leaf-type variation by integrating forest inventory data with comprehensive leaf form (broadleaf vs needle-leaf) and habit (evergreen vs deciduous) records. We found that global variation in leaf habit is primarily driven by isothermality and soil characteristics, while leaf form is predominantly driven by temperature. Given these relationships, we estimate that 38% of global tree individuals are needle-leaved evergreen, 29% are broadleaved evergreen, 27% are broadleaved deciduous and 5% are needle-leaved deciduous. The aboveground biomass distribution among these tree types is approximately 21% (126.4 Gt), 54% (335.7 Gt), 22% (136.2 Gt) and 3% (18.7 Gt), respectively. We further project that, depending on future emissions pathways, 17–34% of forested areas will experience climate conditions by the end of the century that currently support a different forest type, highlighting the intensification of climatic stress on existing forests. By quantifying the distribution of tree leaf types and their corresponding biomass, and identifying regions where climate change will exert greatest pressure on current leaf types, our results can help improve predictions of future terrestrial ecosystem functioning and carbon cycling.

Successional action of Bacteroidota and Firmicutes in decomposing straw polymers in a paddy soil

BackgroundDecomposition of plant biomass is vital for carbon cycling in terrestrial ecosystems. In waterlogged soils including paddy fields and natural wetlands, plant biomass degradation generates the largest natural source of global methane emission. However, the intricate process of plant biomass degradation by diverse soil microorganisms remains poorly characterized. Here we report a chemical and metagenomic investigation into the mechanism of straw decomposition in a paddy soil.ResultsThe chemical analysis of 16-day soil microcosm incubation revealed that straw decomposition could be divided into two stages based on the dynamics of methane, short chain fatty acids, dissolved organic carbon and monosaccharides. Metagenomic analysis revealed that the relative abundance of glucoside hydrolase (GH) encoding genes for cellulose decomposition increased rapidly during the initial stage (3–7 days), while genes involved in hemicellulose decomposition increased in the later stage (7–16 days). The increase of cellulose GH genes in initial stage was derived mainly from Firmicutes while Bacteroidota contributed mostly to the later stage increase of hemicellulose GH genes. Flagella assembly genes were prevalent in Firmicutes but scarce in Bacteroidota. Wood–Ljungdahl pathway (WLP) was present in Firmicutes but not detected in Bacteroidota. Overall, Bacteroidota contained the largest proportion of total GHs and the highest number of carbohydrate active enzymes gene clusters in our paddy soil metagenomes. The strong capacity of the Bacteroidota phylum to degrade straw polymers was specifically attributed to Bacteroidales and Chitinophagales orders, the latter has not been previously recognized.ConclusionsThis study revealed a collaborating sequential contribution of microbial taxa and functional genes in the decomposition of straw residues in a paddy soil. Firmicutes with the property of mobility, WLP and cellulose decomposition could be mostly involved in the initial breakdown of straw polymers, while Bacteroidota became abundant and possibly responsible for the decomposition of hemicellulosic polymers during the later stage.

Synergistic changes in precipitation and soil water use efficiency and their driving mechanisms of terrestrial ecosystems in China

Soil water use efficiency (SWUE) and precipitation use efficiency (PUE) are paramount indicators in examining the coupling mechanism of carbon and water cycles in terrestrial vegetation ecosystems. However, the mechanisms influencing variations in PUE and SWUE warrant further investigation due to existing research gaps. The gross primary productivity (GPP) and net primary productivity (NPP) in China were simulated using the TL-LUE model and improved CASA model, respectively. PUE and SWUE were then calculated in conjunction with precipitation and soil water data. A further investigation was conducted on the joint evolutionary patterns of PUE and SWUE. The impact of factors such as climate change, vegetation variation, carbon dioxide emission, and human activities on PUE and SWUE was separately identified using partial derivatives and elasticity coefficients. The results suggest an insignificant downward in PUE and a significant upward trend in SWUE in China from the period of 2000–2018. Notably, drought mitigation in the arid zone led to a steeper decrease in PUE compared to humid, sub-humid and semi-arid zones. Drought mitigation resulted in a decrease of SWUE in arid zones, yet an increase in humid, sub-humid and semi-arid zones. The correlations between vegetation change and precipitation and PUE or SWUE, respectively, were predominantly influenced by changes in drought conditions. NPP and precipitation collectively dominated the alterations in PUE, contributing 41.55% and −58.45%, respectively. However, GPP exerted a more substantial influence on SWUE compared to precipitation, with notable contributions of 93.93% and −6.07%, respectively. The contributions to PUE from climate variation, human activities, vegetation change and carbon dioxide were −57%, 30%, 12% and −1%, respectively. Simultaneously, the contributions to SWUE from these same factors appeared as 20%, 63%, 16% and 1%, respectively. This study intends to augment the comprehension of the influence exerted by global climate change and human activities on the correlation between carbon and water in terrestrial vegetation ecosystems.

Uncertain Spatial Pattern of Future Land Use and Land Cover Change and Its Impacts on Terrestrial Carbon Cycle Over the Arctic–Boreal Region of North America

AbstractLand use and land cover change (LULCC) represents a key process of human‐Earth system interaction and has profound impacts on terrestrial ecosystem carbon cycling. As a key input for ecosystem models, future gridded LULCC data is typically spatially downscaled from regional LULCC projections by integrated assessment models, such as the Global Change Analysis Model (GCAM). The uncertainty associated with the different spatial downscaling methods and its impacts on the subsequent model projections have been historically ignored and rarely examined. This study investigated this problem using two representative spatial downscaling methods and focused on their impacts on the carbon cycle over the Arctic‐Boreal Vulnerability Experiment (ABoVE) domain, where extensive LULCC is expected. Specifically, we used the Future Land Use Simulation model (FLUS) and the Demeter model to generate 0.25° gridded LULCC data (i.e., LULCCFLUS and LULCCDemeter, respectively) with the same input of regional LULCC projections from GCAM, under both the low (i.e., SSP126) and high (i.e., SSP585) greenhouse gas emission scenarios. The two sets of downscaled LULCC were used to drive the Community Land Model version 5 and prognostically simulate the terrestrial carbon cycle dynamics over the 21st century. The results suggest large spatial‐temporal differences between LULCCFLUS and LULCCDemeter, and the spatial distributions of the needleleaf evergreen boreal tree, broadleaf deciduous boreal tree, broadleaf deciduous boreal shrub, and C3 arctic grass are particularly different under both SSP126 and SSP585. Additionally, the spatiotemporal differences are larger under SSP126 than SSP585, due to more intensive LULCC under SSP126 than SSP585 from GCAM projection. The differences in LULCC further lead to large discrepancies in the spatial patterns of projected gross primary productivity, ecosystem respiration, and net ecosystem exchange, which represent more than 79% of the contributions of future LULCC in 2100. Additionally, the difference in carbon flux under SSP126 is generally larger than those under SSP585. This study highlights the importance of considering the uncertainties induced by the spatial downscaling process in future LULCC projections and carbon cycle simulations.

Exploring the contribution of environmental factors to evapotranspiration dynamics in the Three-River-Source region, China

Evapotranspiration (ET) exerts substantial effects on terrestrial ecosystem carbon and water cycles. A deep understanding of the spatial and temporal variability in ET and its dominant controls has crucial implications for future ecological management, especially for the ecologically-fragile Three-River-Source region (TRSR) in China. The study examined the performance of three ET products including Global Land Evaporation Amsterdam Model (GLEAM), Famine Early Warning Systems Network Land Data Assimilation System (FLDAS) and Global Land Surface Satellite (GLASS) based on eddy covariance-based flux observations. On this basis, ET dynamics in the TRSR from 1982 to 2018 were analyzed at two time scales using the GLASS ET product. Spatially, the pattern in ET showed a gradual upward trend from northwest to southeast. Meanwhile, ET in the majority of regions significantly enhanced, with multiannual mean ET and summer ET (June to August) increasing by 0.609 mm/yr and 0.765 mm/yr, respectively. Especially, ET in summer dominated the interannual dynamics of ET in the TRSR. The individual contributions of five environmental factors including Leaf Area Index (LAI), solar radiation (Rs), air temperature (Ta), precipitation (PRE) and vapour pressure deficit (VPD), to the variability in ET were isolated using ridge regression. Significant differences appeared when explaining ET over different time scales. Rs (33.05 %) and LAI (25.29 %) were the dominant control variables in shaping the interannual ET, with VPD contributing the least by 7.19 %. However, the long-term dynamics in summer ET were mainly controlled by LAI (35.73 %) and PRE (31.19 %), and Ta exerted the least effect by 5.73 %. All analyses will strengthen our understanding of the relationships between climate, vegetation and ET in the TRSR area, China.

The Effects of Precipitation Event Characteristics and Afforestation on the Greening in Arid Grasslands, China

Global greening and its relationship with climate change remain the hot topics in recent years, and are of critical importance for understanding the interactions between the terrestrial ecosystem carbon cycle and the climate system. China, especially north China, has contributed a lot to global greening during the past few decades. As a water-limited ecosystem, human activities, not precipitation amount, were thought as the main contributor to the greening of north China. Considering the importance of precipitation event characteristics (PEC) in the altered precipitation regimes, we integrated long-term normalized difference vegetation index (NDVI) and meteorological datasets to reveal the role of precipitation regimes, especially PECs, on vegetation growth across temperate grasslands in north China. Accompanied with a significantly decreased growing season precipitation (GSP), NDVI increased significantly in the largest area of the temperate grasslands during 1982–2015, i.e., greening. We found that 28.44% of the area was explained by PECs, including more heavy or extreme precipitation events, alleviated extreme drought, and fewer light events, while only 0.92% of the area was associated with GSP. NDVI did not always increase over the 30 years and there was a decrease during 1996–2005. Taking afforestation projects in desertified lands into account, we found that precipitation, mainly PECs, explained more the increase and decline of NDVI during 1982–1995 and 1996–2005, respectively, while an equivalent explanatory power of precipitation and afforestation projects to the increase in NDVI after 2005. Our study indicates a possible higher productivity under future precipitation regime scenario (e.g., fewer but larger precipitation events) or intensive afforestation activity, implying more carbon sequestration or livestock production of temperate steppe in the future.

Estimating Forest Aboveground Biomass Combining Pléiades Satellite Imagery and Field Inventory Data in the Peak–Cluster Karst Region of Southwestern China

The mountainous region of southwest China has the largest karst geomorphology in China and in the world. Quantifying the forest aboveground biomass in this karst region is of great significance for the investigation of carbon storage and carbon cycling in terrestrial ecosystems. In this study, the actual measured aboveground biomass was calculated based on the allometric functions of 106 quadrats from 2012 to 2015. A backpropagation artificial neural network (BPANN) inversion model was constructed by combining very high-resolution satellite imagery, field inventory data, and land use/land cover data to estimate the forest aboveground biomass in the Banzhai watershed, a typical peak–cluster karst basin in southern Guizhou Province. We used 70% of the actual measured aboveground biomass for training the BPANN model, 20% for accuracy verification, and 10% to prevent overtraining. The results show that the absolute root mean square error of the BPANN model was 11.80 t/ha, which accounted for 9.92% of the mean value of aboveground biomass. Based on the BPANN inversion model, the average value of the forests’ aboveground biomass was 135.63 t/ha. The results showed that our study presented a quick, easy, and relatively high-precision method for estimating forest aboveground biomass in the Banzhai watershed. This indicates that the Pléiades image-based BPANN model displayed satisfactory results for estimating the forests’ aboveground biomass in a typical peak–cluster karst basin. This method can be applied to the estimation of forest AGB in the karst mountainous areas of southwest China.

Divergent Decomposition Patterns of Leaf Litter and Fine Roots from an Urban Forest in Mid-Subtropical China

Litter decomposition plays a pivotal role in carbon (C) and nutrient cycling in terrestrial ecosystems. However, little is known about the litter decomposition processes and nutrient dynamics in urban green space. In this study, the decomposition and nutrient dynamics of leaf litter and fine roots from Cinnamomum officinarum Nee ex Wall. and Elaeocarpus decipiens Hemsl. were studied in an urban forest in subtropical China. The results showed that the leaf litter mass loss, and nitrogen (N) and phosphorus (P) mineralization of E. decipiens were faster than that of C. officinarum in the first 180 days, but in the whole decomposition period, the leaf litter decomposition constant of C. officinarum was higher than that of E. decipiens. There was no difference in fine root decomposition constant and P mineralization, although the fine root N immobilization was higher relative to C. officinarum during the 90th to 270th days. Additionally, both the leaf litter mass loss, decomposition rate, and nutrient mineralization were faster than fine roots for these two tree species. The soil microbial biomass showed positive effects on leaf litter decomposition and negative effects on fine root decomposition. The correlation analysis indicated that initial litter quality, soil physicochemical properties, and microbial activity mainly affected early-stage litter decomposition and nutrient mineralization. Also, the leaf litter production and N and P storages of E. decipiens were higher than that of C. officinarum, suggesting faster decomposition rate and nutrient return for E. decipiens leaf litter. Consequently, we propose that tree species with fast nutrient return, such as E. decipiens, could be introduced to urban green space with pervious surfaces in respect of the nutrient balance. This work improves the understanding of litter decomposition and nutrient cycling and promotes the management for urban green space.