Boreal Ecosystem Productivity Simulator Research Articles

Asynchrony of the seasonal dynamics of gross primary production and ecosystem respiration

Abstract The phenological cycles of terrestrial ecosystems have shifted with the changing climate, and the altered timings of biogeochemical fluxes may also exert feedback on the climate system. As regulators of land carbon balance, relative shifts in photosynthetic and respiratory phenology under climate change are of great importance. However, the relative seasonal dynamics of these individual processes and their sensitivity to climate factors as well as the implications for carbon cycling are not well understood. In this study, we examined the relationship in the seasonality of gross primary production (GPP) and ecosystem respiration (RE) as well as their temperature sensitivities and the implications for carbon uptake with around 1500 site-years’ of data from FLUXNET 2015 and Boreal Ecosystem Productivity Simulator (BEPS) at 212 sites. The results showed that RE started earlier in the spring and ended later in the autumn than GPP over most biomes. Furthermore, the flux phenology metrics responded differently to temperature: GPP phenology was more sensitive to changes during the spring temperature than RE phenology, and less sensitive to autumn temperature than RE. We found large BEPS-observation discrepancies in seasonality metrics and their apparent temperature sensitivity. The site-based BEPS projections did not capture the observed seasonal metrics and temperature sensitivities in either GPP or RE seasonality metrics. Improved understanding of the asynchrony of GPP and RE as well as different sensitivity of environmental factors are of great significance for reliable future carbon balance projections.

Evaluation of Leaf‐To‐Canopy Upscaling Approaches for Simulating Canopy Carbonyl Sulfide Uptake and Gross Primary Productivity

AbstractCarbonyl sulfide (COS) measurements provide an important mechanism for quantifying the terrestrial carbon and water cycles. Terrestrial photosynthesis can be inferred from vegetative uptake of COS due to the shared diffusion pathway of COS and CO2 into the chloroplasts. However, the efficacy of different leaf‐to‐canopy upscaling approaches for estimating plant COS uptake and photosynthesis at the canopy scale remains uncertain. In this study, the daytime canopy COS uptake rate (Fcosc) and gross primary productivity (GPP) were simulated using big leaf (BL), two big leaf (TBL) and two leaf (TL) upscaling approaches in the Boreal Ecosystem Productivity Simulator terrestrial biosphere model for two forest sites. We found that TL and TBL performed better than BL in estimating GPP as evaluated by the eddy covariance derived GPP at the studied sites. BL overestimated GPP and canopy stomatal conductance to COS (Gs_cos), resulting from the overestimation of canopy intercepted solar radiation. The positive biases of GPP by TBL could be attributed to the positive biases in GPP of shaded leaves by TBL. The results in this study demonstrate the improved accuracy in using TL for estimating GPP at the canopy scale. Improved leaf‐to‐canopy upscaling approach in terrestrial biosphere models will improve the accuracy of canopy COS uptake and GPP estimation on regional to global scales.

A fine digital soil mapping by integrating remote sensing-based process model and deep learning method in Northeast China

Accurate soil type maps provide an important basis for agricultural decision-making and land degradation control. In soil classification studies, the various environmental covariates are often selected based on the soil-forming framework. Since the mapping area and available observation data are limited, meteorological and vegetation cover factors have not been fully developed, and their role in soil classification needs to be evaluated. In addition, whether deep learning has out-performance in soil classification remains to be tested. The aim of this paper is to evaluate the accuracy of deep learning modelling techniques in classifying soil type using different combinations of input variables, and evaluate the importance of soil-forming variables in soil type classification. Therefore, we collected commonly used environmental covariates in Northeast China (NEC), including multiple meteorological factors and adopted a satellite-based biophysical model (Boreal Ecosystem Productivity Simulator, BEPS) to enrich vegetation cover factors. Next, four modeling strategies were developed: the soil-forming factors of soil and relief were considered as traditional environmental covariates (T), as well as combined with meteorologic variables (T + C), vegetation cover variables (T + V) and all available environmental covariates (T + C + V). Then, the effectiveness of different modeling strategies for soil classification was explored with convolutional neural network (CNN) model and multi-layer random forest (MRF) model based on soil separability. Finally, a 30 m resolution soil type map was established. The results demonstrated that both MRF and CNN can achieve high accuracy soil classification, while the CNN model performs better. The descending order of classification accuracy based on different modeling strategies of the CNN model is shown as T + C + V: 91.08%, T + V: 88.84%, T + C: 86.82%, and T: 83.96%. Meanwhile, the separability of different soil-forming factors for soil classification is soil properties, vegetation cover, temporal variation, meteorologic and relief in descending order. For Castanozems and Brown soils, MRF has higher classification accuracy, while CNN has better performance in Meadow soils and Fluvo-aquic soils. The methodology proposed in this paper aims to achieve high accuracy soil classification, provide an approach to understand the importance of soil-forming factors for the region, as well as for different soil types, and provide references for facilitating the interpretation of misclassified areas. Our results are accurate in the core areas, and therefore, this work facilitates researchers to be able to focus more on areas where different soil types intersect, thus significantly improves efficiency and saves resources, and promises to be a useful tool for future soil surveys.

CMLR: A Mechanistic Global GPP Dataset Derived from TROPOMIS SIF Observations

Solar-induced chlorophyll fluorescence (SIF) has shown promise in estimating gross primary production (GPP); however, there is a lack of global GPP datasets directly utilizing SIF with models possessing clear expression of the biophysical and biological processes in photosynthesis. This study introduces a new global 0.05° SIF-based GPP dataset (CMLR GPP, based on Canopy-scale Mechanistic Light Reaction model) using TROPOMI observations. A modified mechanistic light response model was employed at the canopy scale to generate this dataset. The canopy q L (opened fraction of photosynthesis II reaction centers), required by the CMLR model, was parameterized using a random forest model. The CMLR GPP estimates showed a strong correlation with tower-based GPP ( R 2 = 0.72) in the validation dataset, and it showed comparable performance with other global datasets such as Boreal Ecosystem Productivity Simulator (BEPS) GPP, FluxSat GPP, and GOSIF (global, OCO-2-based SIF product) GPP at a global scale. The high accuracy of CMLR GPP was consistent across various normalized difference vegetation index, vapor pressure deficit, and temperature conditions, as well as different plant functional types and most months of the year. In conclusion, CMLR GPP is a novel global GPP dataset based on mechanistic frameworks, whose availability is expected to contribute to future research in ecological and geobiological regions.

Spatiotemporal patterns of net primary productivity of subtropical forests in China and its response to drought

Net primary productivity (NPP) is an important indicator used to evaluate the carbon sequestration capacity of forest ecosystems. Subtropical forest ecosystems play an indispensable role in maintaining the global carbon balance, while frequently occurring drought events in recent years have seriously damaged their productivity. However, the spatiotemporal patterns of NPP, as well as its response to drought, remain uncertain. In this study, the multiscale drought characteristics in subtropical China during 1981–2015 were analyzed based on the standardized precipitation evapotranspiration index. Then, simulated and analyzed the spatiotemporal NPP of subtropical forests by the boreal ecosystem productivity simulator model. Finally, the response of NPP to drought was analyzed based on multiple statistical indices. The results show that most regions in subtropical China experienced mild and moderate drought during 1981–2015. In particular, the extent of drought severity has shown a noticeable increasing trend after 2000. The forest NPP ranged from 622.64 to 1323.82 gC·m−2·a−1, with an overall increase rate of 16.15 gC·m−2·a−1; in particular, the contribution of the western forest NPP became increasingly important. Drought stress has limited the growth of subtropical forest NPP in China, with summer and wet season time scales of drought having the greatest impact on forest NPP anomalies, followed by autumn time scales. The limitation is mostly because the drought duration continually increased, leading to differences in the impact of drought on forest NPP before and after 2000, with declines of 59.55 % and 82.45 %, respectively, mainly concentrated in southwestern regions, such as Yunnan, Guangxi, and Sichuan provinces. This study quantitatively analyzed the impact of drought on subtropical forest NPP, and provides scientific basis for subtropical forest response and adaptation to climate change.

Quantitative Assessment of Factors Influencing the Spatiotemporal Variation in Carbon Dioxide Fluxes Simulated by Multi-Source Remote Sensing Data in Tropical Vegetation

Vegetation plays a vital role in the global carbon cycle, a function of particular significance in regulating carbon dioxide fluxes within tropical ecosystems. Therefore, it is crucial to enhance the precision of carbon dioxide flux estimates for tropical vegetation and to explore the determinants influencing carbon sequestration. In this study, Landsat series images and Sentinel-2 Multispectral Instrument satellite data were used to invert vegetation biophysical parameters, thereby improving the timeliness and resolution of state variables from the boreal ecosystem productivity simulator (BEPS). The BEPS model at a 30 m resolution was developed to accurately capture tropical vegetation carbon dioxide fluxes across Hainan Island (HN) over the preceding two decades. The impacts of climate variations and anthropogenic activities on the carbon dioxide fluxes of tropical vegetation were further quantified using quantile regression models and a land-use transfer matrix. Results indicate significant increases in both net primary productivity (NPP) and net ecosystem productivity (NEP) in HN during the period 2000–2020, by 5.81 and 4.29 g C/m2 year, respectively. Spatial trends in vegetation carbon dioxide fluxes exhibited a consistent decline from inland regions to coastal zones. Anthropogenic activities were the dominant factor in the reduced stability of coastal NPP, while the post-2005 vegetation restoration promoted the southward expansion of high NPP (>1200 g C/m2) in the central part of HN. NPP in this tropical island was more sensitive to temperature than to precipitation, with a 1 °C temperature increase resulting in 4.1 g C/m2 reduction in dry-season NPP compared to wet-season NPP. Upgrades of cropland quality and grassland restoration have improved NPP yields, and land use transfers have resulted in a 0.301 Tg C net increase in NPP. This study provides new insight into the improvement of the carbon dioxide flux model at a finer scale for tropical vegetation and highlights ecological construction as an adaptation strategy to enhance the carbon sinks of tropical vegetation under negative climate change conditions.

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.

Aboveground Biomass Dynamics of a Coastal Wetland Ecosystem Driven by Land Use/Land Cover Transformation

Accurately estimating aboveground biomass (AGB) is essential for assessing the ecological functions of coastal wetlands, and AGB of coastal wetlands is affected by Land use/land cover (LULC) types of conversion. To address this issue, in the current study, we used the Boreal Ecosystem Productivity Simulator (BEPS) model to simulate the AGB of the Yellow River Delta during 2000–2015. Based on the LULC types transform, we analyzed the spatiotemporal dynamics of the AGB simulation results and their relationship with the human-nature driving process. At the same time, combined with the actual situation of LULC transformation in the Yellow River Delta, a new driving process (Replace) is introduced. The results show that from 2000 to 2015, 755 km2 of natural wetlands in the Yellow River Delta were converted into constructed wetlands, and AGB increased by 386,121 Mg. Both single and multiple driving processes contributed to the decrease in AGB, with 72.6% of the increase in AGB associated with single artificial (such as Restore) or natural (such as Accretion) driving processes and 27.4% of the increase in AGB associated with multiple driving processes. Naturally driven processes bring much more AGB gain than loss, and human-driven processes bring the largest AGB gain. LULC conversion brought on by anthropogenic and natural driving processes has a large impact on AGB in coastal wetlands, and exploring this impact has a significant role in planning coastal wetland land use and protecting blue carbon ecosystems.

Evaluation of the impacts of ozone on the vegetation productivity of woodland and grassland ecosystems in China

In the context of global warming, with the increasing tropospheric ozone concentration and the obvious trend of aridity, terrestrial ecosystems are experiencing multiple pressures. However, there are still many uncertainties in modeling the effects of ozone on ecosystem productivity. In this study, an ozone uptake module was coupled to the BEPS (Boreal Ecosystem Productivity Simulator) model, and the new version, the BEPS-O3 model, was used to simulate the impacts of ozone on the gross primary productivity (GPP) of woodland and grassland ecosystems in China. Observational data from four sites were used to verify and improve the models, namely, DXG (grassland), HBG (shrub), DHS (evergreen broad-leaved) and CBS (coniferous forest). The regional results show that the total GPP of forest, grassland and shrub in China was 3.62 Pg C, 0.75 Pg C 0.33 Pg C in 2016, respectively. The GPP loss of forests, grasslands and shrubs caused by ozone was 2.8% and 1.1% and 1.5% of the GPP in woodland and grassland ecosystems, respectively (excluding farmland). Subtropical regions experienced the worst GPP losses, although ozone concentrations were not high. However, higher stomatal conductance leads to higher ozone absorption. By comparing the parameters of previous studies, we conclude that the ozone uptake function was better than the ozone exposure function in modeling. Furthermore, the selection of the ozone uptake function will have a significant impact on the final simulation results, especially for grassland.

Modelling the crop yield gap with a remote sensing-based process model: A case study of winter wheat in the North China Plain

Understanding the spatial distribution of the crop yield gap (YG) is essential for improving crop yields. Recent studies have typically focused on the site scale, which may lead to considerable uncertainties when scaled to the regional scale. To mitigate this issue, this study used a process-based and remote sensing driven crop yield model for winter wheat (PRYM-Wheat), which was derived from the boreal ecosystem productivity simulator (BEPS), to simulate the YG of winter wheat in the North China Plain from 2015 to 2019. Yield validation based on statistical yield data revealed good performance of the PRYM-Wheat Model in simulating winter wheat actual yield (Ya). The distribution of Ya across the North China Plain showed great heterogeneity, decreasing from southeast to northwest. The remote sensing-estimated results show that the average YG of the study area was 6 400.6 kg ha−1. The YG of Jiangsu Province was the largest, at 7 307.4 kg ha−1, while the YG of Anhui Province was the smallest, at 5 842.1 kg ha−1. An analysis of the responses of YG to environmental factors showed no obvious correlation between YG and precipitation, but there was a weak negative correlation between YG and accumulated temperature. In addition, the YG was positively correlated with elevation. In general, studying the specific features of the YG can provide directions for increasing crop yields in the future.

Response of Global Terrestrial Carbon Fluxes to Drought from 1981 to 2016

Precipitation plays a dominant role in regulating terrestrial carbon fluxes. In concert with global warming, aridity has been increasing during recent decades in most parts of the world. How global terrestrial carbon fluxes respond to this change, however, is still unclear. Using a remote-sensing-driven, process-based model, the Boreal Ecosystem Productivity Simulator (BEPS), this study investigated the responses of global terrestrial carbon fluxes to meteorological drought, which were characterized by the standardized precipitation evapotranspiration index (SPEI). The results showed that the response of terrestrial carbon fluxes to drought exhibited distinguishable spatial heterogeneity. In most regions, terrestrial carbon fluxes responded strongly to drought. With an increase in annual water balance (annual precipitation minus annual potential evapotranspiration), the response of carbon fluxes to drought became weaker. The lagged time of terrestrial carbon fluxes responding to drought decreased with the increasing strength of carbon fluxes in response to drought. The sensitivity of terrestrial carbon fluxes to drought also showed noticeable spatial heterogeneity. With an increase in annual water balance, the sensitivity first increased and then decreased. Terrestrial carbon fluxes exhibited the highest sensitivity to drought in semi-arid areas.

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

Simulating canopy carbonyl sulfide uptake of two forest stands through an improved ecosystem model and parameter optimization using an ensemble Kalman filter

The uptake of carbonyl sulfide (COS) by plant canopy closely correlated with photosynthetic CO2 uptake as they share the same diffusion pathway. Process-based ecosystem models have been used to simulate the CO2, H2O and COS fluxes between terrestrial ecosystems and atmosphere. Some model parameters often vary seasonally and interannually. In this study, the hourly Boreal Ecosystem Productivity Simulator (BEPS) was first improved to simulate the canopy COS uptake rate. Then, an ensemble Kalman filter was designed to optimize the key parameters of the hourly BEPS model, considering the errors in the input, parameters and measurements. The optimized parameters include leaf maximum carboxylation rate at 25 °C at top of canopy (Vcmax250) and a calibration term (α) used to scale the apparent conductance for COS uptake from the intercellular airspace (gcos) to Vcmax. The fluxes of gross primary productivity (GPP), latent heat (LE) and COS uptake by canopy (Fcos_veg) measured using eddy covariance method at a temperate deciduous forest during 2012–2013 and a boreal conifer forest during 2013–2017 were used for data assimilation. Parameters were optimized daily and presented as 10-day averages. It was found that the parameters showed considerable seasonally and interannually variations. With parameter optimization using EnKF, the improved hourly BEPS model could explain 86%, 68% and 55% of variations in measured daily GPP, LE and Fcos_veg at US-Ha1 site during 2012 and 2013 and captured 90%, 74% and 69% of variations in the measured GPP, LE and Fcos_veg at FI-Hyy site during 2013–2017, respectively. The simulated daily Fcos_veg with the parameters optimized using EnKF could capture the day-to-day variations of Fcos_veg at the two studied sites and performed better than the simulations with calibrated parameter values. Further efforts were required to investigate the impacts of model structure and different parameter optimization methods on the optimized parameter values.

Study of spatialtemporal changes in Chinese forest eco-space and optimization strategies for enhancing carbon sequestration capacity through ecological spatial network theory

The conservation of forest ecosystems and the enhancement of carbon sequestration capacity play a crucial role in maintaining ecological balance and human development. However, with excessive deforestation, the flow of energy and information within the ecosystem has changed, which in turn has led to changes in the topological properties and carbon sequestration capacity of forest ecosystems. In order to better investigate the nature and carbon sequestration capacity of forest ecological space in mainland China during 2000–2018, we constructed a time-series Chinese forest ecological spatial network based on complex network theory and graph theory, combined with the modified minimal cumulative resistance model (MCR). By combining the net primary productivity (NPP) values obtained from the Boreal Ecosystem Productivity Simulator (BEPS) model of existing scholars, we further explored the relationship between topology and carbon sequestration capacity within forest ecosystems, and proposed strategies and suggestions for optimization. The results show that forest ecological sources and ecological corridors showed an increasing trend and resistance values decreased year by year during 2000–2018, especially in the western region, indicating that ecological restoration projects in western China have achieved certain effects. However, the stability of forest ecosystems has been decreasing year by year, and the forest carbon sequestration capacity in western China is also decreasing. Through correlation analysis, we found that carbon sequestration capacity showed highly significant positive correlation with closeness centrality, harmonic closeness centrality, clustering, and eigen centrality, and carbon sequestration capacity showed highly significant negative correlation with betweeness centrality. Through Principal Components Analysis (PCA), we suggest that consolidating small patches in the northeast, reducing the number of redundant ecological corridors, adding stepping stone patches to shorten the length of ecological corridors, and increasing ecological corridors in non-northeast areas are conducive to enhancing plant carbon sequestration capacity. This study provides theoretical support and ecological engineering recommendations for China to achieve its strategic goals of carbon neutrality and carbon peaking.

Detection of Carbon Use Efficiency Extremes and Analysis of Their Forming Climatic Conditions on a Global Scale Using a Remote Sensing-Based Model

Carbon use efficiency (CUE) represents the proficiency of plants in transforming carbon dioxide (CO2) into carbon stock in terrestrial ecosystems. CUE extremes represent ecosystems’ extreme proficiency in carbon transformation. Studying CUE extremes and their forming climate conditions is critical for enhancing ecosystem carbon storage. However, the study of CUE extremes and their forming climate conditions on the global scale is still lacking. In this study, we used the results from the daily Boreal Ecosystem Productivity Simulator (BEPS) model to detect the positive and negative CUE extremes and analyze their forming climatic conditions on a global scale. We found grasslands have the largest potential in changing global CUE, with the contribution being approximately 32.4% to positive extremes and 30.2% to negative extremes. Spring in the Northern Hemisphere (MAM) contributed the most (30.5%) to positive CUE extremes, and summer (JJA) contributed the most (29.7%) to negative CUE extremes. The probabilities of gross primary production (GPP) extremes resulted in CUE extremes (>25.0%) being larger than autotrophic respiration (Ra), indicating CUE extremes were mainly controlled by GPP rather than Ra extremes. Positive temperature anomalies (0~1.0 °C) often accompanied negative CUE extreme events, and positive CUE extreme events attended negative temperature anomalies (−1.0~0 °C). Moreover, positive (0~20.0 mm) and negative precipitation (−20.0~0 mm) anomalies often accompanied positive and negative CUE extremes, respectively. These results suggest that cooler and wetter climate conditions could be beneficial to enhance carbon absorptions of terrestrial ecosystems. The study provides new knowledge on proficiency in carbon transformation by terrestrial ecosystems.

Seasonal Variations in Leaf Maximum Photosynthetic Capacity and Its Dependence on Climate Factors Across Global FLUXNET Sites

AbstractThe maximum carboxylation rate (Vcmax) is an important parameter affecting the photosynthesis rate of plant leaves. In terrestrial ecosystem models (TEMs), Vcmax at 25°C (Vm25°) is often assigned as constants according to plant functional types (PFTs), while its variations with leaf temperature and nutrient contents are described using empirical functions. However, Vm25° could itself vary seasonally due to changes in the leaf physiological state that cannot be described by the empirical functions, potentially causing large uncertainties in simulated water and carbon fluxes. So far, the seasonal variation in Vm25° has not been systematically studied. Here, we generated a Vm25° data set of eight main biomes from 2000 to 2020 using eddy covariance (EC) measurements at globally distributed 176 sites. The boreal ecosystem productivity simulator was combined with a light response curve model (BEPS‐LRC) to invert Vm25° from EC data. We investigated seasonal variations of Vm25° and analyzed how different environmental and physiological factors, such as physiological (leaf chlorophyll content, LCC and Rubisco or RuBP) and climatic environment factors, including air temperature (Ta), solar shortwave radiation, CO2 concentration (CO2), and soil water content (SWC), influence this parameter. Vm25° values derived from flux data using BEPS‐LRC correlate well with Vm25° of the reference data set (R2 = 0.74, slope = 0.77, and root mean square error = 24.45 μmol m−2 s−1, p < 0.001). Leaf Vm25° has strong seasonal variations in all PFTs except for in evergreen broadleaf forests, but its seasonal variation patterns differ greatly among eight biomes. Air temperature (Ta) is the most important determinant of Vm25°, followed by SWC. The interactive effects of Ta and SWC on Vm25° vary among different biomes. The seasonal variation of Vm25° was strongly dependent on LCC. After the correction of temperature effect, the contribution of LCC to the seasonal variation of Vm25° averaged 26% among eight biomes. These findings provide useful information for better parameterization of Vm25° in TEMs.

Effects of Increasing C4-Crop Cover and Stomatal Conductance on Evapotranspiration: Simulations for a Lake Erie Watershed

Accurate quantification of evapotranspiration (ET) is crucial for surface water resources and best agricultural management practices in watersheds. The aim of this study was to better understand ET changes caused by the rapid expansion of C4 (corn) cover and rapid changes in stomatal conductance, which may be amplified in the future due to environmental and human-contributing factors, such as climate change and agricultural practices. Linking the enlargement of agricultural land with the physiological properties of crops, such as photosynthetic adaptations and stomatal conductance, is necessary to explore the magnitude of these impacts. This study examined the effects of increased C4 (corn) crop cover and stomatal conductance on evapotranspiration (ET) rates in the Lower Maumee River Watershed, Ohio, USA, during the 2018 growing season. Simulation results using a modified-for-crops version of the Boreal Ecosystem Productivity Simulator (BEPS) showed that a hypothetical increase of corn cover by as much as 100% would not significantly impact the watershed ET rate, with a 5.05% overall increase in ET in July and a 3.96% increase in August. Changes in the stomatal conductance of crops, however, impacted ET more. The results showed a significant increase in the ET rate (up to 24.04% for corn and 5.10% for soybean) for the modeling scenario that integrated high stomatal conductance, which agreed with the thermal-based ECOSTRESS ET product derived over the study area (+/−0.9 mm day−1) for the same period. We suggest that the alteration of the crop stomata mechanism, caused largely by rapid climate change and intensive farming practices, should be carefully quantified, and its impact on hydrology at the ecosystem level further explored.

Spatiotemporal Evolution of the Carbon Fluxes from Bamboo Forests and their Response to Climate Change Based on a BEPS Model in China

Carbon flux is the main basis for judging the carbon source/sink of forest ecosystems. Bamboo forests have gained much attention because of their high carbon sequestration capacity. In this study, we used a boreal ecosystem productivity simulator (BEPS) model to simulate the gross primary productivity (GPP) and net primary productivity (NPP) of bamboo forests in China during 2001–2018, and then explored the spatiotemporal evolution of the carbon fluxes and their response to climatic factors. The results showed that: (1) The simulated and observed GPP values exhibited a good correlation with the determination coefficient (R2), root mean square error (RMSE), and absolute bias (aBIAS) of 0.58, 1.43 g C m−2 day−1, and 1.21 g C m−2 day−1, respectively. (2) During 2001–2018, GPP and NPP showed fluctuating increasing trends with growth rates of 5.20 g C m−2 yr−1 and 3.88 g C m−2 yr−1, respectively. The spatial distribution characteristics of GPP and NPP were stronger in the south and east than in the north and west. Additionally, the trend slope results showed that GPP and NPP mainly increased, and approximately 30% of the area showed a significant increasing trend. (3) Our study showed that more than half of the area exhibited the fact that the influence of the average annual precipitation had positive effects on GPP and NPP, while the average annual minimum and maximum temperatures had negative effects on GPP and NPP. On a monthly scale, our study also demonstrated that the influence of precipitation on GPP and NPP was higher than that of the influence of temperature on them.

Evaluation and Validation of the Net Primary Productivity of the Zoigê Wetland Based on Grazing Coupled Remote Sensing Process Model

Zoigê wetland is located in the southeastern edge of the Qinghai–Tibet Plateau, in recent years, the wetland local area grassland productivity decline, land sanding, and other serious degradation phenomenon. Using remote sensing and hydrology data combined with the basic principles of vegetation ecology, we applied the Carnegie–Ames–Stanford Approach (CASA) model and Boreal Ecosystem Productivity Simulator (BEPS) model to invert the net primary productivity (NPP) of the Zoigê wetland ecosystem and then identified the deficiencies of the two models. In this article, we tested the factors that influence the light use efficiency of vegetation and considered the effects of livestock grazing on grassland ecosystems and wetland moisture content on vegetation growth. A new grazing coupled remote sensing process (GCRSP) model suitable for the productivity of the Zoigê wetland was proposed. Field survey data were combined with the CASA model, BEPS model, and Moderate Resolution Imaging Spectroradiometer (MODIS) NPP product, and the accuracy of the proposed GCRSP model was analyzed at regional level. The results show that after fitting the field measurements to the GCRSP model, the fitted variance between the model and the measured data is 0.84, which is larger than the variance between the other two methods and the measured data. In addition, the variance of the linear fit between the GCRSP model and the MODIS NPP product was 0.11, which was relatively small, indicating that the regional-scale GCRSP model was relatively accurate for the Zoigê wetland NPP. Therefore, this model was suitable for the inversion of regional-scale NPP. The results indicated that the average grassland NPP of the Zoigê wetland was 487.57 gC/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , and larger NPP values were distributed near the rivers and wetland and smaller NPP values were distributed in the mountain glacier and desertified land areas. These results were consistent with the regional characteristics. This article provided technical and theoretical support for the desertification management of the Zoigê grassland and a new method for studying vegetation NPP, and the findings could help improve the accuracy of regional-scale vegetation NPP estimates.

Impact of different ERA reanalysis data on GPP simulation

The performance of the terrestrial biosphere models (TBMs) is limited by the accuracy of climate forcing data. As the reanalysis products of the European Centre for Medium-Range Weather Forecasts (ECMWF), ERA-Interim and ERA5 data are widely used in the simulation of terrestrial carbon budgets and reveal their responses to climate change. However, the discrepancy between simulated carbon budgets driven by ERA-Interim and ERA5 on a global scale has not been evaluated. In this study, driven by ERA-Interim and ERA5, we conducted two simulations by a TBM, BEPS (Boreal Ecosystem Productivity Simulator), to investigate the differences of simulated Gross primary productivity (GPP) in temporal trends and spatial patterns and to identify the differences in climate factors resulted in the spreads of simulated GPPs. We found that by 2015, the relatively stable difference of simulated GPP by ERA-Interim and ERA5 was about 3.55 Pg C yr−1. Since 2016, the differences of simulated GPP increased gradually and peaked in the last year of our simulation in 2018 at 13.16 Pg C yr−1. This significant difference in GPP was due to the changes of GPP in the Amazon Basin, Congo Basin and South Asia, where tropical forests and tropical savannahs & grasslands were widely distributed. In these regions, the GPP of ERA5 in total was at least 3.0 Pg C yr−1 lower than that of ERA-Interim after 2016. The difference of GPP in such regions was the main reason why ERA5 and ERA-Interim GPP showed different interannual variability. And less precipitation and higher temperature of ERA5 in tropical regions mainly results in the reduction of GPP compared with the results driven by ERA-Interim. Our results highlight challenges in using ERA5 and ERA-Interim to evaluate responses of ecosystem to climate change and provide implications for reducing the uncertainty of climate forcing data in simulating terrestrial carbon cycle.