Ecosystem Gross Primary Production Research Articles

Disproportionate contributions of land cover and changes to ecosystem functions in Kazakhstan and Mongolia

Land use and land cover change (LULCC) have profoundly altered land surface properties and ecosystem functions, including carbon and water production. While mapping these changes from local to global scales has become more achievable due to advancements in earth observations and remote sensing, linking land cover changes to ecosystem functions remains challenging, especially at regional scale. Our study attempts to fill this gap by employing a computationally efficient method and two types of widely used high-resolution satellite images. We first investigated the contribution of landscape composition to ecosystem function by examining how land cover and proportion affected gross primary production (GPP) and evapotranspiration (ET) at six macro-landscapes in Mongolia and Kazakhstan. We hypothesized that both ecosystem and landscape GPP and ET are disproportionate to their composition and, therefore, changes in land cover will have asymmetrical influences on landscape functions. We leveraged a computational-friendly linear downscaling approach to align the coarse spatial resolution of MODIS (500 m) with a fine-grain and localized land cover map developed from Landsat (30 m) for six provinces in countries where intensive LULCC occurred in recent decades. By establishing two metrics—function to composition ratio (F/C) and function to changes in composition change (ΔF/ΔC)—we tested our hypothesis and evaluated the impact of land cover change on ecosystem functions within and among the landscapes. Our results show three major themes. (1) The five land cover types have signature downscaled ET and GPP that appears to vary between the two countries as well as within each country. (2) F/C of ET and GPP of forests is statistically greater than 1 (i.e., over-contributing), whereas F/C of grasslands and croplands is close to or slightly less than 1 (i.e., under-contribution). F/C of barrens is clearly lower than 1 but greater than zero. Specifically, a unit of forest generates 1.085 unit of ET and 1.123 unit of GPP, a unit of grassland generates 0.993 unit of ET and GPP, and a unit of cropland produces 0.987 unit of ET and 0.983 unit of GPP. The divergent F/C values among the land cover classes support the hypothesis that landscape function is disproportionate to its composition. (3) ΔET/ΔC and ΔGPP/ΔC of forests and croplands showed negative values, while grasslands and barrens showed positive values, indicating that converting a unit of forest to other land cover leads to a decrease in ET and GPP, while converting units of grassland or barren to other land cover classes will result in increased ET and GPP. This linear downscaling approach for calculating F/C and ΔF/ΔC is labor-saving and cost-effective for rapid assessment on the impact of land use land cover change on ecosystem functions.

GPP of a Chinese Savanna Ecosystem during Different Phenological Phases Simulated from Harmonized Landsat and Sentinel-2 Data

Savannas are widespread biomes with highly valued ecosystem services. To successfully manage savannas in the future, it is critical to better understand the long-term dynamics of their productivity and phenology. However, accurate large-scale gross primary productivity (GPP) estimation remains challenging because of the high spatial and seasonal variations in savanna GPP. China’s savanna ecosystems constitute only a small part of the world’s savanna ecosystems and are ecologically fragile. However, studies on GPP and phenological changes, while closely related to climate change, remain scarce. Therefore, we simulated savanna ecosystem GPP via a satellite-based vegetation photosynthesis model (VPM) with fine-resolution harmonized Landsat and Sentinel-2 (HLS) imagery and derived savanna phenophases from phenocam images. From 2015 to 2018, we compared the GPP from HLS VPM (GPPHLS-VPM) simulations and that from Moderate-Resolution Imaging Spectroradiometer (MODIS) VPM simulations (GPPMODIS-VPM) with GPP estimates from an eddy covariance (EC) flux tower (GPPEC) in Yuanjiang, China. Moreover, the consistency of the savanna ecosystem GPP was validated for a conventional MODIS product (MOD17A2). This study clearly revealed the potential of the HLS VPM for estimating savanna GPP. Compared with the MODIS VPM, the HLS VPM yielded more accurate GPP estimates with lower root-mean-square errors (RMSEs) and slopes closer to 1:1. Specifically, the annual RMSE values for the HLS VPM were 1.54 (2015), 2.65 (2016), 2.64 (2017), and 1.80 (2018), whereas those for the MODIS VPM were 3.04, 3.10, 2.62, and 2.49, respectively. The HLS VPM slopes were 1.12, 1.80, 1.65, and 1.27, indicating better agreement with the EC data than the MODIS VPM slopes of 2.04, 2.51, 2.14, and 1.54, respectively. Moreover, HLS VPM suitably indicated GPP dynamics during all phenophases, especially during the autumn green-down period. As the first study that simulates GPP involving HLS VPM and compares satellite-based and EC flux observations of the GPP in Chinese savanna ecosystems, our study enables better exploration of the Chinese savanna ecosystem GPP during different phenophases and more effective savanna management and conservation worldwide.

Data-constrained modeling of terrestrial gross primary production over the Tibetan Plateau for 2003–2019

The gross primary productivity (GPP) of terrestrial ecosystem is the largest carbon flux between atmosphere and land surface. However, accurately simulating ecosystem GPP remains a great challenge for most land surface models (LSMs) due to the biased leaf area index (LAI) simulated by the models. In this study, we use remotely sensed LAI dataset for the period 2003–2019 to drive the Farquhar model and simulate the spatial-temporal changes of GPP for the alpine ecosystem over Tibetan Plateau. The annual GPP over the Tibetan Plateau is estimated to be 540.8 ± 27.3 Tg C yr−1, which is consistent with the benchmark datasets derived from flux tower measurements and remote sensing estimations. The GPP for the past two decades has been increasing at a rate of 2.6 Tg C yr−2. The canopy greening featured by increasing LAI contributed to 27.2 % of the increasing trend in GPP, while the direct impacts from warming and wetting climate contributed to 65.2 % of GPP changes. The contribution from the direct impact of elevated atmosphere CO2 concentration is only 6.7 %. In this study we show that constraining Farquhar model with remotely sensed LAI datasets could provide reliable simulation of GPP. Given the great challenge in modeling LAI and the considerable overestimation of LAI in current LSMs, our results highlight the importance of improving LAI modeling in LSMs and the necessity of constraining LAI when tuning the parameters for photosynthesis module.

Disaggregation of canopy photosynthesis among tree species in a mixed broadleaf forest.

Carbon dioxide sequestration from the atmosphere is commonly assessed using the eddy covariance method. Its net flux signal can be decomposed into gross primary production and ecosystem respiration components, but these have seldom been tested against independent methods. In addition, eddy covariance lacks the ability to partition carbon sequestration among individual trees or species within mixed forests. Therefore, we compared gross primary production from eddy covariance versus an independent method based on sap flow and water-use efficiency, as measured by the tissue heat balance method and δ13C of phloem contents, respectively. The latter measurements were conducted on individual trees throughout a growing season in a mixed broadleaf forest dominated by three tree species, namely English oak, narrow-leaved ash and common hornbeam (Quercus robur L., Fraxinus angustifolia Vahl, and Carpinus betulus L., respectively). In this context, we applied an alternative ecophysiological method aimed at verifying the accuracy of a state-of-the-art eddy covariance system while also offering a solution to the partitioning problem. We observed strong agreement in the ecosystem gross primary production estimates (R2=0.56; P<0.0001), with correlation being especially high and nearly on the 1:1 line in the period before the end of July (R2=0.85; P<0.0001). After this period, the estimates of gross primary production began to diverge. Possible reasons for the divergence are discussed, focusing especially on phenology and the limitation of the isotopic data. English oak showed the highest per-tree daily photosynthetic rates among tree species, but the smaller, more abundant common hornbeam contributed most to the stand-level summation, especially early in the spring. These findings provide a rigorous test of the methods and the species-level photosynthesis offers avenues for enhancing forest management aimed at carbon sequestration.

Effects of drought on ecosystem evapotranspiration and gross primary productivity in the Haihe River Basin

Evapotranspiration is a crucial component of the hydrological cycle and Gross Primary Productivity (GPP) is the largest flux in the global carbon cycle. However, both evapotranspiration and GPP are significantly affected by climate disasters, especially extreme drought events. Therefore, in this study, MODIS product data from 2002 to 2021 was used to analyze the spatiotemporal variations of actual evapotranspiration (ET), potential evapotranspiration (PET) and GPP within the Haihe River Basin. Then extreme drought events were identified by using EDDI drought index. A statistical analysis was performed to assess the impact of extreme drought on ecosystem evapotranspiration and GPP. The results showed that: (1) the annual evapotranspiration in the Haihe River Basin fluctuated greatly, ET showed a general increasing trend which was higher in the eastern region and lower in the central and western regions, PET showed an overall decreasing trend which was higher in the central and western regions and lower in the eastern region; (2) the annual GPP in the Haihe River Basin from 2002 to 2021 was 646.88 g C/m2/a and the increase rate was 14.75 g C/m2/a, which exhibited a decreasing trend from the northern to the southern and central-western regions; (3) under extreme drought conditions, ET decreased compared with the mean value, while PET increased, the Zhangweihe and the Tuhaimajiahe sub-basins were the most affected, with ET decreasing by 16.36% and PET increasing by 7.00% compared to the reference year. Additionally, extreme drought led to a decrease in GPP, with a reduction ranging from 7% to 18%.

Spatiotemporal variation and response of gross primary productivity to climate factors in forests in Qiannan state from 2000 to 2020

Accurate estimation of terrestrial gross primary productivity (GPP) is essential for quantifying the carbon exchange between the atmosphere and biosphere. Light use efficiency (LUE) models are widely used to estimate GPP at different spatial scales. However, difficulties in properly determining the maximum LUE (LUEmax) and downregulation of LUEmax into actual LUE result in uncertainties in the LUE-estimated GPP. The recently developed P model, a new LUE model, captures the adaptability of vegetation to the environment and simplifies parameterization. Site-level studies have proven the superior performance of the P model over LUE models. As a representative karst region with significant changes in forest cover in Southwest China, Qiannan is useful for exploring the spatiotemporal variation in forest GPP and its response to climate change for formulating forest management policies to address climate changes, e.g., global warming. Based on remote sensing and meteorological data, this study estimated the forest ecosystem GPP in Qiannan from 2000–2020 via the P model. This study explored the spatiotemporal changes in GPP in the study region over the past 20 years, used the Hurst index to predict future development trends from a time series perspective, and used partial correlation analysis to analyse the spatiotemporal GPP changes over the past 20 years in response to three factors: temperature, precipitation, and vapor pressure deficit (VPD). Our results showed that (1) the total amount of GPP and average GPP in Qiannan over the past 21 years (2000–2020) were 1.9 × 104 ± 2.0 × 103 MgC ha−1 year−1 and 1238.9 ± 107.9 gC m−2 year−1, respectively. The forest GPP generally increased at a rate of 6.1 gC m−2 year−1 from 2000 to 2020 in Qiannan, and this increase mainly occurred in the nongrowing season. (2) From 2000 to 2020, the forest GPP in Qiannan was higher in the southeast and lower in the northwest, indicating significant spatial heterogeneity. In the future, more than 70% of regional forest GPP will experience a weak increase in nonsustainability. (3) In Qiannan, forest GPP was positively correlated with both temperature and precipitation, with partial correlation coefficients of 0.10 and 0.11, respectively. However, the positive response of GPP to precipitation was approximately 70.47%, while that to temperature was 64.05%. Precipitation had a stronger restrictive effect on GPP than did temperature in this region, and GPP exhibited a negative correlation with VPD. The results showed that an increase in VPD inhibits GPP to some extent. Under rapid global change, the P model GPP provides new GPP data for global ecology studies, and the comparison of various stress factors allows for improvement of the GPP model in the future. The results of this study will aid in understanding the dynamic processes of terrestrial carbon. These findings are helpful for estimating and predicting the carbon budget of forest ecosystems in karst regions, clarifying the regional carbon absorption capacity, clarifying the main factors limiting vegetation growth in these regions, promoting sustainable regional forestry development and serving the “dual carbon goal.” This work has important guiding significance for policy formulation to mitigate climate change.

The impact of drought time scales and characteristics on gross primary productivity in China from 2001 to 2020

ABSTRACT Drought events have occurred frequently in the past and have had profound effects on Gross Primary Productivity (GPP). However, the role of different drought event characteristics in the response of GPP to drought is not well understood. To address this issue, we investigated the spatiotemporal response patterns of Chinese terrestrial ecosystem GPP to different time scales of meteorological droughts, and explored the difference of GPP response to drought events under various drought characteristics. Our research revealed that: Different time scales of meteorological droughts significantly impacted the GPP of 17.3%–25.6% area of China. Among them, the impact range of 1-month and 12-month meteorological drought was the smallest and largest respectively. GPP along the 400 mm isohyet line was more susceptible to drought. Second, grasslands and shrubs are more susceptible to drought than forested areas. Among the characteristics of drought events, drought severity had the greatest impact on GPP changes, while drought frequency had the least impact. An increase in drought frequency intensified the impact of 1-month meteorological drought on GPP. Increased drought duration and severity weakened the impact of 3-month meteorological drought on GPP and intensified the impact of 1-month and 12-month meteorological droughts on GPP. Our study reveals the impact of different time scales drought and drought characteristics on plant productivity, and provides more possibilities for understanding the effect of drought on plant productivity.

Vegetation canopy structure mediates the response of gross primary production to environmental drivers across multiple temporal scales

Gross primary production (GPP) is a critical component of the global carbon cycle and plays a significant role in the terrestrial carbon budget. The impact of environmental factors on GPP can occur through both direct (by influencing photosynthetic efficiency) and indirect (through the modulation of vegetation structure) pathways, but the extent to which these mechanisms contribute has been seldom quantified. In this study, we used structural equation modeling and observations from the FLUXNET network to investigate the direct and indirect effects of environmental factors on terrestrial ecosystem GPP at multiple temporal scales. We found that canopy structure, represented by leaf area index (LAI), is a crucial intermediate factor in the GPP response to environmental drivers. Environmental factors affect GPP indirectly by altering canopy structure, and the relative proportion of indirect effects decreased with increasing LAI. The study also identified different effects of environmental factors on GPP across time scales. At the half-hourly time scale, radiation was the primary driver of GPP. In contrast, the influences of temperature and vapor pressure deficit took on greater prominence at longer time scales. About half of the total effect of temperature on GPP was indirect through the regulation of canopy structure, and the indirect effect increased with increasing time scale (GPPNT-based models: 0.135 (half-hourly) vs. 0.171 (daily) vs. 0.189 (weekly) vs. 0.217 (monthly); GPPDT-based models: 0.139 vs. 0.170 vs. 0.187 vs. 0.215; all values were reported in gC m−2 d−1 °C−1, P < 0.001); while the indirect effect of radiation on GPP was comparatively lower, accounting for less than a quarter of the total effect. Furthermore, we observed a direct, negative-to-positive impact of precipitation on GPP across timescales. These findings provide crucial information on the interplay between environmental factors and LAI on GPP and enable a deeper understanding of the driving mechanisms of GPP.

Modeling China's terrestrial ecosystem gross primary productivity with BEPS model: Parameter sensitivity analysis and model calibration

Terrestrial ecosystems are the largest sink for carbon, and their ecosystem gross primary productivity (GPP) regulates variations in atmospheric carbon dioxide (CO2) concentrations. Current process-based ecosystem models used for estimating GPP are subject to large uncertainties due to poorly constrained parameter values. In this study, we implemented a global sensitivity analysis (GSA) on parameters in the Boreal Ecosystem Productivity Simulator (BEPS) considering the parameters’ second-order impacts. We also applied the generalized likelihood estimation (GLUE) method, which is flexible for a multi-parameter calibration, to optimize the GPP simulation by BEPS for 10 sites covering 7 plant functional types (PFT) over China. Our optimized results significantly reduced the uncertainty of the simulated GPP over all the sites by 17 % to 82 % and showed that the GPP is sensitive to not only the photosynthesis-related parameters but also the parameters related to the soil water uptake as well as to the energy balance. The optimized GPP across South China showed that the mix forest, shrub, and grass have a higher GPP and are more controlled by the soil water availability. This study showed that the GLUE method together with the GSA scheme could constrain the ecosystem model well when simulating GPP across multiple ecosystems and provide a reasonable estimate of the spatial and temporal distribution of the ecosystem GPP over China. We call for more observations from more sites, as well as data on plant traits, to be collected in China in order to better constrain ecosystem carbon cycle modeling and understand its response to climate change.

Divergent impacts of VPD and SWC on ecosystem carbon-water coupling under different dryness conditions

Ecosystem water use efficiency (WUE) is an indicator of carbon-water interactions and is defined as the ratio of gross primary productivity (GPP) to evapotranspiration (ET). However, it is currently unclear how WUE responds to atmospheric and soil drought events in terrestrial ecosystems with different dryness conditions. Additionally, the contributions of GPP and ET to the WUE response remain poorly understood. Based on measurements from 26 flux tower sites distributed worldwide, the binning method and random forest model were employed to separate the sensitivities of daily ecosystem WUE, GPP, and ET to vapor pressure deficit (VPD) and soil water content (SWC) under different dryness conditions (dryness index = potential evapotranspiration/precipitation, DI). Results showed that the sensitivity of WUE to VPD was negative at humid sites (DI < 1), while the sensitivity of WUE to SWC was positive at arid sites (DI > 2). Furthermore, the contribution of GPP to VPD-induced WUE variability was 63 % at humid sites, and the contribution of ET to SWC-induced WUE variability was 68 % when SWC was less than the 60th percentile at arid sites. Consequently, one increasing VPD-induced decrease in GPP was generally linked to a decrease in WUE at humid sites, and one drying soil moisture-caused decrease in ET was linked to a WUE increase under low SWC conditions at arid sites. Finally, VPD had a stronger effect on WUE than SWC when VPD was less than the 90th percentile or SWC was greater than the 50th percentile. Our findings underscore the importance of considering ecosystem dryness when investigating the impacts of VPD and SWC on ecosystem carbon-water coupling.

Stability of the ecosystem gross primary productivity increasing in Chinese forestry ecological engineering area

Gross Primary Production (GPP) is an important component of global carbon cycle. In the context of ecosystem construction, ecosystem GPP is significantly increasing, however, the stability of the increasing remains unclear. In this study, the nonlinearity of GPP increasing and its stability were studied with Ensemble Empirical Mode Decomposition method in Chinese forestry ecological engineering (FEE) areas during 1982–2018. The results showed that (1) although linearly increasing trend of GPP occupied 50.83% of the area, it implied different nonlinear trends as monotonic increasing, decreasing to increasing, and increasing to decreasing, accounting for 25.30%, 9.03% and 7.76%, respectively. Meanwhile, for insignificantly linear changes of GPP (41.19%), among them, 5.99% and 5.41% showed trend reversal from increasing to decreasing and from decreasing to increasing, respectively. (2) the trends in all FEE areas were dominated by monotonic increasing, especially for shelterbelt programs of Middle Yellow River (YR). Meanwhile, the trend from decreasing to increasing also has high area percentage (15.75%), especially for YR and coast shelterbelt program (CS), indicating early degradation and later recovery. However, there are large areas (>10%) of the trend shifts from increasing to decreasing in most FEE areas, especially in the shelterbelt programs of Huaihe River and Taihu Lake (HT), Taihang Mountain(TH) and upper and middle reaches of Yangtze River (YZ), indicating high risk of future degradation. (3) Although GPP changes are mainly dominated by interannual variations except YR, contribution of trends to GPP changes are high in HT and TH; For monotonic increasing and decreasing to increasing, the trend contributions to whole GPP changes were > 33%, especially in YR and TH, indicating high stability of ecosystem improvement. For monotonic decreasing, increasing to decreasing and insignificant changes, the contributions were low, indicating low stability but high recovery potential or degradation risk;(4) Both agriculture reclamation and ecological engineering not only favored GPP increasing, but also enhanced its stability. Our results highlight the importance of the nonlinearity of trend and its stability, which provides scientific implications for evaluating the effectiveness of ecological engineering.

Soil moisture content-based analysis of terrestrial ecosystems in China: Water use efficiency of vegetation systems

Water is a prerequisite for the formation of earth-biochemical-ecological systems. Differences in the spatial and temporal distribution of water resources are important factors in the formation of differences in the distribution of vegetation in terrestrial ecosystems and are key to the differences in vegetation productivity. Vegetation water use efficiency is calculated by the total amount of fixed biogenic carbon per unit mass of water consumed in photosynthesis and can be used to assess the intensity and capacity of an ecosystem to use water biomass. Based on remote satellite sensing data, this study proposes a new water use efficiency assessment model. The model was validated using flux site data, and we analyzed the relative contribution of climate factors to soil moisture use efficiency using a first-difference method. The results revealed the following: (1) The soil moisture use efficiencies (SUE) of remote sensing data inversions that were evaluated using flux site data based on correlation coefficients and Nash coefficients showed high reliability, and only the NMG (Inner Mongolia) station showed low correlation among the nine sites. (2) Among the nine agricultural sub-regions in China, only the SUE of the Qinghai-Tibet Plateau region showed a decreasing trend (−1.08 g C/m−2 kg H2O yr), while all other regions showed an increasing trend. (3) The highest vegetation soil moisture use efficiency (1.83 g C/m−2 kg H2O) was found in ferralisols, while the lowest vegetation SUE (0.17 g C/m−2 kg H2O) was found in arid soils. The SUE of different vegetation types showed the characteristics of forest > scrub > cultivated vegetation > wetland > grassland. (4) The relative contribution of gross primary productivity (GPP) to the change in SUE was 37.53%, while the relative contribution of soil moisture content to the change in SUE was −26.71%. Among the five climatic factors, temperature was the most dominant factor affecting the change in SUE, followed by precipitation, net radiation, leaf area index, and potential evapotranspiration. Revealing the relationship between terrestrial ecosystem GPP, soil moisture content, and their responses to climate factors is a prerequisite for understanding the adaptation strategies of regional terrestrial ecosystems to global climate change, which can help to inform decision-making for the sustainable development of ecosystems.

Evaluating the Losses and Recovery of GPP in the Subtropical Mangrove Forest Directly Attacked by Tropical Cyclone: Case Study in Hainan Island

The gross primary production (GPP) of the mangrove ecosystem determines the upper limit of the scale of its “blue carbon” sink. Tropical cyclones (TCs) are among the most important extreme events that threaten the subtropical mangrove ecosystem and have a serious impact on mangrove ecosystem GPP. However, there are somewhat insufficient scientific findings on regional-scale mangrove ecosystem GPP responding to large-scale weather events such as TCs. Therefore, we selected the subtropical Hainan Island mangrove ecosystem, where more than two TCs pass through per year, as the research area; selected direct-attack TCs as the research object; and took the mangrove vegetation photosynthesis light-use efficiency model established based on the eddy covariance observation data as the tool to evaluate the loss and recovery of mangrove ecosystem GPP after TCs attacked at a regional scale. We found that the TC impacted the mangrove ecosystem GPP through the photosynthetic area and rate, and the recovery of the rate occurred prior to the recovery of the area; the loss of mangrove ecosystem GPP is inversely proportional to the distance to the center of the TC and the distance to the coastline; and the canopy height, diameter at breast height, and aspect where the tree stands significantly influence the response of the mangrove ecosystem GPP to TCs. However, the response varies for different mangrove community compositions, soil conditions, and planting densities as well as different frequencies and intensities of TCs, and they should be analyzed in detail. This study is expected to provide technical and data support for the protection of blue carbon in a subtropical island mangrove ecosystem in response to extreme events and post-disaster recovery.

A critical review of methods, principles and progress for estimating the gross primary productivity of terrestrial ecosystems

The gross primary productivity (GPP) of terrestrial ecosystems reflects the total amount of organic carbon assimilated by vegetation through photosynthesis per given unit of time and area, which represents the largest carbon flux in carbon budget and plays a fundamental part in the carbon cycle. However, challenges such as determining how to select appropriate methods to improve GPP estimation accuracy at the regional/global scale remain. Therefore, it is of great importance to comprehensively review the research progress on the methods for estimating the GPP of terrestrial ecosystems and to summarize their flaws, merits and application fields. In this study, we reviewed studies of GPP estimation at different spatiotemporal scales, and systematically reviewed the principles, formulas, representative methods (Ground observations, Model simulations, SIF based GPP, and NIRv based GPP) at different scales and models (Statistical/Ecological process/Machine learning/Light use efficiency models), as well as the advantages and limitations of each research method/models. A comprehensive comparison of GPP research methods was performed. We expect that this work will provide some straightforward references for researchers to further understand and to choose appropriate models for assessing forest ecosystem GPP according to the research objectives and area. Thus, critical and effective GPP estimation methods can be established for the terrestrial carbon cycle, carbon neutralization accounting and local carbon emission reduction policy formulation and implementation.

Atmospheric dryness thresholds of grassland productivity decline in China

Atmospheric dryness events are bound to have a broad and profound impact on the functions and structures of grassland ecosystems. Current research has confirmed that atmospheric dryness is a key moisture constraint that inhibits grassland productivity, yet the risk threshold for atmospheric dryness to initiate ecosystem productivity loss has not been explored. Based on this, we used four terrestrial ecosystem models to simulate gross primary productivity (GPP) data, analyzed the role of vapor pressure deficit (VPD) in regulating interannual variability in Chinese grasslands by focusing on the dependence structure of VPD and GPP, and then constructed a bivariate linkage function to calculate the conditional probability of ecosystem GPP loss under atmospheric dryness, and further analyzed the risk threshold of ecosystem GPP loss triggered by atmospheric dryness. The main results are as follows: we found that (1) the observed and modeled VPD of Chinese grasslands increases rapidly in both historical and future periods. VPD has a strongly negative regulation on ecosystem GPP, and atmospheric dryness is an important moisture constraint that causes deficit and even death to ecosystem GPP. (2) The probability of the enhanced atmospheric dryness that induced GPP decline in Chinese grasslands in the future period increases significantly. (3) When the VPD is higher than 40.07 and 27.65 percentile of the past and future time series, respectively, the risk threshold of slight ecosystem GPP loss can be easily initiated by atmospheric dryness. (4) When the VPD is higher than 82.57 and 65.09 percentile, respectively, the threshold of moderate ecosystem GPP loss can be exceeded by the benchmark probability. (5) The risk threshold of severe ecosystem GPP loss is not initiated by atmospheric dryness in the historical period, and the threshold of severe ecosystem GPP loss can be initiated when the future VPD is higher than 91.92 percentile. In total, a slight atmospheric dryness event is required to initiate a slight ecosystem GPP loss threshold, and a stronger atmospheric dryness event is required to initiate a severe ecosystem GPP loss. Our study enhances the understandings of past and future atmospheric dryness on grassland ecosystems, and strongly suggests that more attention be invested in improving next-generation models of vegetation dynamics processes with respect to the response of mechanisms of ecosystem to atmospheric dryness.

Light use efficiency models incorporating diffuse radiation impacts for simulating terrestrial ecosystem gross primary productivity: A global comparison

Estimating dynamic changes in gross primary productivity (GPP) of terrestrial ecosystems has always been challenging. Indeed, light use efficiency (LUE) models are extensively employed to capture GPP dynamics. Improved big-leaf LUE models by introducing a cloudiness scalar and two-leaf LUE models by dividing the canopy into shaded and sunlit leaves are two improved families for addressing the widely-recognized effects of diffuse radiation on terrestrial ecosystem GPP. However, the global performance in simulating GPP dynamics between such models has not been evaluated comprehensively. Here, we assess and compare the global performance of the ten LUE models considering diffuse radiation impacts (CFLUX, DIFFUSE, CI-LUE, CCW, Wang's Model, CI-EF, TL-LUE, TL-LUEn, DTEC, and RTL-LUE) at 102 flux sites in a standardized framework. Results indicate that LUE models considering diffuse radiation impacts can explain 46.7–63.6% of daily GPP variability obtained by the eddy covariance technique. Seven of the ten models show a better performance across all ecosystems than MOD17, despite the relatively poor performance of three (i.e., DIFFUSE, Wang's Model, and DTEC). The seven improved models exhibiting a similar explanation of GPPEC perform poorly in evergreen broadleaf forests and croplands but well in deciduous broadleaf forests. Although two-leaf models with higher leaf area index (LAI) sensitivity and better model structure have greater potential to describe diffuse radiation fertilization (DRF), their performance in capturing GPP dynamics is limited by the uncertainties of remote sensing data. This study indicates the importance of accurate LAI for two-leaf models and emphasizes the necessity of incorporating diffuse radiation impacts into the global GPP estimates in the future.

Reduced Net CO2 Uptake During Dry Summers in a Boreal Shield Peatland

AbstractPeatlands are globally important long‐term sinks of atmospheric carbon dioxide (CO2). However, there is concern that climate change‐mediated drying will reduce gross primary productivity (GPP) and increase ecosystem respiration (ER) making peatlands vulnerable to a weaker carbon sink function and potential net carbon loss. While large and deep peatlands are usually resilient to moderate summer drying, CO2 exchange in shallow Boreal Shield peatlands is likely more sensitive to drying given the reduced groundwater connectivity and water storage potential. To better understand the carbon cycling responses of Boreal Shield peatlands to meteorological conditions, we examined ecohydrological controls on CO2 fluxes using the eddy covariance technique at a shallow peatland during the summer season for 5 years, from 2016–2020. We found lower GPP in dry summer years. Mean summer water table depth (WTD) was found to be significantly correlated with summer total net ecosystem CO2 exchange (R2 = 0.78; p value = 0.046) and GPP (R2 = 0.83; p value = 0.03), where wet summers with a WT close to the peat surface sequestered more than twice the amount of CO2 than dry summers. Our findings suggest that shallow Boreal Shield peatland GPP may be sensitive to climate‐mediated drying as they may switch to a net CO2 source in the summer season when WTDs exceed a critical ecohydrological threshold for a prolonged period of time.

2020-2050年CO2施肥效应促进全球陆地生态系统碳吸收

PDF HTML阅读 XML下载 导出引用 引用提醒 2020-2050年CO2施肥效应促进全球陆地生态系统碳吸收 DOI: 10.5846/stxb202107051782 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 福建省自然科学基金面上项目(2021J01502);福建省农业科学院自由探索科技创新项目(ZYTS202233);福建省农业高质量发展协同创新工程(XTCXGC2021015);福建省智慧农业科技创新团队 (CXTD2021013-1) CO2 fertilization promoting the carbon uptake of global terrestrial ecosystems in 2020-2050 Author: Affiliation: Fund Project: the Natural Science Foundation of Fujian Province (2021J01502)，Fujian Academy of Agricultural Sciences Free Exploration Science and Technology Innovation Project (ZYTS202233); Collaborative Innovation Project of High-Quality Development of Agriculture in Fujian Province (XTCXGC2021015), and Fujian Intelligent Agricultural Science and Technology Innovation Team (CXTD2021013-1) 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:CO2施肥效应是全球变绿的主要原因,随着大气中CO2浓度的持续增加,预估未来气候变化条件下,CO2施肥效应对陆地生态系统的影响对减缓全球气候变化具有重大意义。基于未来气候情景数据和Farquhar模型,并结合生态过程模型BEPS(Boreal Ecosystem Productivity Simulator),定量化研究2020-2050年CO2施肥效应对全球叶面积指数(LAI)和总初级生产力(GPP)的影响。研究结果显示2020-2050年,在RCP2.6、RCP4.5和RCP8.5气候情景下,CO2施肥效应导致的LAI年际变化趋势分别为0.002、0.003和0.005 m-2m-2a-1;三个气候情景下CO2施肥效应对LAI的影响为CO2每增加0.1%,LAI平均增加约8.1%-9.2%,由此导致GPP对应增加7.9%-14.6%;由CO2施肥效应导致的全球LAI的增加对未来GPP年际变化趋势的贡献分别为66.7%、48.7%和57.1%。表明在未来气候变化情景下,LAI的增加仍然主要受CO2施肥效应的影响,CO2施肥效应导致的LAI的增加将显著促进全球陆地生态系统碳吸收。 Abstract:CO2 fertilization is the main reason for global greening. With the persistent increase in CO2 concentration, it is critically significant to evaluate the effect of CO2 fertilization on the global terrestrial ecosystem. Gross primary productivity (GPP) quantifies the photosynthetic uptake of carbon by terrestrial ecosystems, and it is the basis of the global carbon cycle. Leaf area index (LAI) is a vegetation structural parameter that modulates the interaction between the land surface and the atmosphere and therefore is used in many terrestrial biosphere models. LAI is a critical parameter for leaf-to-canopy upscaling in the terrestrial biosphere models at the regional and global scales. Therefore, a reliable estimation of CO2 fertilization on LAI is crucial for understanding and predicting the terrestrial carbon cycle under future climate change. Due to the complexity and spatiotemporal difference in the global terrestrial ecosystems, there are still large uncertainties in simulating global LAI based on phenology and carbon dynamic allocation in Earth system models. Thus, in this study, we used the future scenario climate data, combined with the Farquhar and Boreal Ecosystem Productivity Simulator (BEPS) model to investigate the effect of CO2 fertilization on the global LAI and GPP during 2020-2050. The results showed that for RCP2.6, RCP4.5, and RCP8.5 scenarios, the CO2 fertilization led to the global LAI interannual trends were 0.002, 0.003, and 0.005 m-2m-2a-1, respectively. The LAI increased 8.1%-9.2%, resulting in a corresponding increase in GPP of 7.9%-14.6% per 0.1% CO2 concentration. The contributions of LAI to the global terrestrial ecosystem GPP were 66.7%, 48.7%, and 57.1%, respectively. It shows that CO2 fertilization is still the primary reason for increasing LAI under future climate scenarios. The increase of LAI caused by CO2 fertilization will significantly promote the carbon uptake of global terrestrial ecosystems. 参考文献 相似文献 引证文献

Comparing Different Light Use Efficiency Models to Estimate the Gross Primary Productivity of a Cork Oak Plantation in Northern China

Light use efficiency (LUE) models have been widely used to estimate terrestrial gross primary production (GPP). However, the estimation of GPP still has large uncertainties owing to an insufficient understanding of the complex relationship between water availability and photosynthesis. The plant water stress index (PWSI), which is based on canopy temperature, is very sensitive to the plant stomatal opening and has been regarded as a good indicator for monitoring plant water status at the regional scale. In this study, we selected a cork oak plantation in northern China with an obvious seasonal drought as the research object. Using the ground-observed data, we evaluated the applicability of the LUE models with typical water stress scalars (MOD17, MODTEM, EC-LUE, ECM-LUE, SM-LUE, GLO-PEM, and Wang) in a GPP simulation of the cork oak plantation and explored whether the model’s accuracy can be improved by applying PWSI to modify the above models. The results showed that among the seven LUE models, the water stress scalar had a greater impact on the model’s performance than the temperature stress scalar. On sunny days, the daily GPP simulated by the seven LUE models was poorly matched with the measured GPP, and all models explained only 23–52% of the GPP variation in the cork oak plantation. The modified LUE models can significantly improve the prediction accuracy of the GPP and explain 49–65% of the variation in the daily GPP. On cloudy days, the performance of the modified LUE models did not improve, and the evaporative fraction was more suitable for defining the water stress scalar in the LUE models. The ECM-LUE and the modified GLO-PEM based on PWSI had optimal model structures for simulating the GPP of the cork oak plantation under cloudy and sunny days, respectively. This study provides a reference for the accurate prediction of GPP in terrestrial ecosystems in the future.

Compound drought constrains gross primary productivity in Chinese grasslands

Water constraints disturb and damage the growth and development of grassland vegetation mainly through both atmospheric and soil pathways. In the background of rapid climate change in the future, the impacts of water constraints on grasslands are expected to further deepen. However, current studies lack reports exploring the frequency, intensity, and area of land-atmospheric compound drought on carbon indicators in grassland ecosystems. In this study, we analyze the response of China grasslands to dual terrestrial-atmospheric water constraint events using ISIMIP gross primary productivity (GPP) data to reveal the carbon cycle-climate feedback relationships over the Chinese grassland. We found that the occurrence probability of compound drought events (i.e. land-atmospheric water constraint) was 3–4 times higher than that of random drought events, and the frequency, intensity, and affected area of compound droughts were significantly higher than that of single droughts. Compound droughts caused a decline of up to 20.27% in GPP of grassland ecosystems in China, while the decline of single atmospheric drought or soil drought was only 12.34% and 14.32%. Which is because vapor pressure deficit and soil moisture are a set of strongly coupled bivariate variables, and the continued strengthening of the land-atmospheric feedback causes a higher probability of occurrence of compound drought events and an increased impact on ecosystem GPP.