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Wang C, Chen J, Xiong L, Tong S, Xu CY. Trigger thresholds and their dynamics of vegetation production loss under different atmospheric and soil drought conditions. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 950:175116. [PMID: 39084387 DOI: 10.1016/j.scitotenv.2024.175116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Revised: 06/23/2024] [Accepted: 07/27/2024] [Indexed: 08/02/2024]
Abstract
Many evidences have shown that both atmospheric and soil droughts can constrain vegetation growth and further threaten its ability to sequester carbon. However, the trigger thresholds of vegetation production loss under different atmospheric and soil drought conditions are still unknown. In this study, we proposed a Copula and Bayesian equations-based framework to investigate trigger thresholds of various vegetation production losses under different atmospheric and soil drought conditions. The trigger thresholds dynamics and their possible causes were also investigated. To achieve this goal, we first simulated the gross primary production, soil moisture, and vapor pressure deficit over China during 1961-2018 using an individual-based, spatially explicit dynamic global vegetation model. The main drivers of the dynamic change in trigger thresholds were then explored by Random Forest model. We found that soil drought caused greater stress on gross primary production loss than atmospheric drought, with a larger impact area and higher probability of damage. In terms of spatial distribution, the risk probability of gross primary production loss was higher in eastern China than in western China, and the drought trigger threshold was also smaller in eastern China. In addition, the trigger thresholds for atmospheric and soil drought in most regions exhibited a decreasing trend from 1961 to 2018, while the CO2 fertilization enhanced the drought tolerance of vegetation. The reduction in CO2 fertilization effect slowed down the downward trend of trigger threshold for soil drought, while the increase in temperature exacerbated the downward trend of trigger threshold for atmospheric drought. This study highlighted the larger effect of soil drought on vegetation production loss than atmospheric drought and implied that climate change can modulate the trigger threshold of vegetation production losses under drought conditions. These findings provide scientific guidance for managing the increasing risk of drought on vegetation and optimizing watershed water allocation.
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Affiliation(s)
- Chengyun Wang
- State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, Hubei 430072, PR China
| | - Jie Chen
- State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, Hubei 430072, PR China.
| | - Lihua Xiong
- State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, Hubei 430072, PR China
| | - Shanlin Tong
- State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, Hubei 430072, PR China
| | - Chong-Yu Xu
- State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, Hubei 430072, PR China; Department of Geosciences, University of Oslo, Oslo N-0316, Norway
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2
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Ji Y, Zeng S, Liu X, Xia J. Mutual inhibition effects of elevated CO 2 and climate change on global forest GPP. ENVIRONMENTAL RESEARCH 2024; 252:119145. [PMID: 38754610 DOI: 10.1016/j.envres.2024.119145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Revised: 05/12/2024] [Accepted: 05/13/2024] [Indexed: 05/18/2024]
Abstract
The impact of CO2 fertilization on enhancing global forest gross primary productivity (GPP) is acknowledged, but its interaction with climate factors-air temperature (Tem), precipitation (Pre), vapor pressure deficit (VPD), and radiation (Rad)-remains unclear. In this study, global forest GPP trends from 1982 to 2018 were examined using BEPS, NIRv, FLUXCOM, and revised EC-LUE datasets, with interannual trends of 5.618 (p < 0.01), 5.831 (p < 0.01), 0.227, and 6.566 g C m-2 yr-1 (p < 0.01), respectively. Elevated CO2 was identified as the primary driver of GPP trends, with the dominant area ranging from 51.11% to 90.37% across different GPP datasets. In the NIRv and revised EC-LUE datasets, the positive impact of CO2 on GPP showed a decrease of 0.222 g C m-2 yr-1, while the negative impact of Rad increased by 0.007 g C m-2 yr-1. An inhibitory relationship was found between the actual effects of elevated CO2 and climate change on GPP in most forest types. At lower latitudes, Tem primarily constrained CO2 fertilization, while at higher latitudes, VPD emerged as the key limiting factor. This was mainly attributed to the potential trade-off or competition between elevated CO2 and climate change in influencing GPP, with strategic resource allocation varying across different forest ecosystems. This study highlights the significant inhibitory effects of elevated CO2 and climate change on global forest GPP, providing insights into the dynamic responses of forest ecosystems to changing environments.
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Affiliation(s)
- Yongyue Ji
- Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, 400714, China; Chongqing School, University of Chinese Academy of Sciences, Chongqing, 400714, China
| | - Sidong Zeng
- Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, 400714, China; Chongqing School, University of Chinese Academy of Sciences, Chongqing, 400714, China.
| | - Xin Liu
- Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, 400714, China; Chongqing School, University of Chinese Academy of Sciences, Chongqing, 400714, China
| | - Jun Xia
- State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, 430072, China
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Xiao Y, Chen T, Chen X, Yang Y, Wang S, Zhou S. CMIP6 ESMs overestimate greening and the photosynthesis trends in Dryland East Asia. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 937:173432. [PMID: 38797402 DOI: 10.1016/j.scitotenv.2024.173432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/25/2024] [Revised: 04/05/2024] [Accepted: 05/20/2024] [Indexed: 05/29/2024]
Abstract
The Dryland East Asia (DEA) is one of the largest inland arid regions, and vegetation is very sensitive to climate change. The complex environment in DEA with defects of modeling construction make it difficult to simulate and predict changes in vegetation structure and productivity. Here, we use the emergent constraint (EC) method to constrain the future interannual leaf area index (LAI) and gross primary productivity (GPP) trends in DEA, under four scenarios of the latest Sixth Coupled Model Intercomparison Project (CMIP6) model ensemble. LAI and GPP increase in all scenarios in the near term (2015-2050), with continued growth in SSP370 and SSP585 and stasis in SSP126 and SSP245 in the far term (2051-2100). However, after building effective EC relationships, the constrained increasing trends of LAI (GPP) are reduced by 43.5 %-53.9 % (30.5 %-50.0 %) compared with the uncertainties of the original ensemble, which are reduced by 10.0 %-45.7 % (4.6 %-34.3 %). We also extend the EC in moving windows and grid cells, further strengthening the robustness of the constraints, especially by illustrating spatial sources of these emergent relationships. Overestimations of LAI and GPP trends suggest that current CMIP6 models may be insufficient to capture the complex relationships between climate change and vegetation dynamics in DEA; however, these models can be adjusted based on established emergent relationships.
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Affiliation(s)
- Yinmiao Xiao
- School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing, China
| | - Tiexi Chen
- School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing, China; Qinghai Provincial Key Laboratory of Plateau Climate Change and Corresponding Ecological and Environmental Effects, Qinghai Institute of Technology, Xining, China; School of Geographical Sciences, Qinghai Normal University, Xining, China.
| | - Xin Chen
- School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing, China
| | - Yang Yang
- Qinghai Provincial Key Laboratory of Plateau Climate Change and Corresponding Ecological and Environmental Effects, Qinghai Institute of Technology, Xining, China; School of Geographical Sciences, Qinghai Normal University, Xining, China
| | - Shengzhen Wang
- Qinghai Provincial Key Laboratory of Plateau Climate Change and Corresponding Ecological and Environmental Effects, Qinghai Institute of Technology, Xining, China; School of Geographical Sciences, Qinghai Normal University, Xining, China
| | - Shengjie Zhou
- School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing, China
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Hussain N, Gonsamo A, Wang S, Arain MA. Assessment of spongy moth infestation impacts on forest productivity and carbon loss using the Sentinel-2 satellite remote sensing and eddy covariance flux data. ECOLOGICAL PROCESSES 2024; 13:37. [PMID: 38756370 PMCID: PMC11093731 DOI: 10.1186/s13717-024-00520-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/10/2024] [Accepted: 04/25/2024] [Indexed: 05/18/2024]
Abstract
Background Deciduous forests in eastern North America experienced a widespread and intense spongy moth (Lymantria dispar) infestation in 2021. This study quantified the impact of this spongy moth infestation on carbon (C) cycle in forests across the Great Lakes region in Canada, utilizing high-resolution (10 × 10 m2) Sentinel-2 satellite remote sensing images and eddy covariance (EC) flux data. Study results showed a significant reduction in leaf area index (LAI) and gross primary productivity (GPP) values in deciduous and mixed forests in the region in 2021. Results Remote sensing derived, growing season mean LAI values of deciduous (mixed) forests were 3.66 (3.18), 2.74 (2.64), and 3.53 (2.94) m2 m-2 in 2020, 2021 and 2022, respectively, indicating about 24 (14)% reduction in LAI, as compared to pre- and post-infestation years. Similarly, growing season GPP values in deciduous (mixed) forests were 1338 (1208), 868 (932), and 1367 (1175) g C m-2, respectively in 2020, 2021 and 2022, showing about 35 (22)% reduction in GPP in 2021 as compared to pre- and post-infestation years. This infestation induced reduction in GPP of deciduous and mixed forests, when upscaled to whole study area (178,000 km2), resulted in 21.1 (21.4) Mt of C loss as compared to 2020 (2022), respectively. It shows the large scale of C losses caused by this infestation in Canadian Great Lakes region. Conclusions The methods developed in this study offer valuable tools to assess and quantify natural disturbance impacts on the regional C balance of forest ecosystems by integrating field observations, high-resolution remote sensing data and models. Study results will also help in developing sustainable forest management practices to achieve net-zero C emission goals through nature-based climate change solutions.
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Affiliation(s)
- Nur Hussain
- School of Earth, Environment and Society and McMaster Centre for Climate Change, McMaster University, Hamilton, ON L8S 4K1 Canada
| | - Alemu Gonsamo
- School of Earth, Environment and Society and McMaster Centre for Climate Change, McMaster University, Hamilton, ON L8S 4K1 Canada
| | - Shusen Wang
- Canada Centre for Remote Sensing, Natural Resources Canada, 1280 Main Street West, Ottawa, ON Canada
| | - M. Altaf Arain
- School of Earth, Environment and Society and McMaster Centre for Climate Change, McMaster University, Hamilton, ON L8S 4K1 Canada
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Wang B, Smith B, Waters C, Feng P, Liu DL. Modelling changes in vegetation productivity and carbon balance under future climate scenarios in southeastern Australia. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 924:171748. [PMID: 38494011 DOI: 10.1016/j.scitotenv.2024.171748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 03/10/2024] [Accepted: 03/14/2024] [Indexed: 03/19/2024]
Abstract
Australia, characterized by extensive and heterogeneous terrestrial ecosystems, plays a critical role in the global carbon cycle and in efforts to mitigate climate change. Prior research has quantified vegetation productivity and carbon balance within the Australian context over preceding decades. Nonetheless, the responses of vegetation and carbon dynamics to the evolving phenomena of climate change and escalating concentrations of atmospheric carbon dioxide remain ambiguous within the Australian landscape. Here, we used LPJ-GUESS model to assess the impacts of climate change on Gross Primary Productivity (GPP) and Net Biome Productivity (NBP) of carbon for the state of New South Wales (NSW) in southeastern Australia. LPJ-GUESS simulations were driven by an ensemble of 27 global climate models under different emission scenarios. We investigated the change of GPP for different vegetation types and whether NSW ecosystems will be a net sink or source of carbon under climate change. We found that LPJ-GUESS successfully simulated GPP for the period 2003-2021, demonstrating a comparative performance with GPP derived from upscaled eddy covariance fluxes (R2 = 0.58, nRMSE = 14.2 %). The simulated NBP showed a larger interannual variation compared with flux data and other inversion products but could capture the timing of rainfall-driven carbon sink and source variations in 2015-2020. GPP would increase by 10.3-19.5 % under a medium emission scenario and 19.7-46.8 % under a high emission scenario. The mean probability of NSW acting as a carbon sink in the future showed a small decrease with a large uncertainty with >8 of the 27 climate models indicating an increased potential for carbon sink. These findings emphasize the significance of emission scenarios in shaping future carbon dynamics but also highlight considerable uncertainties stemming from different climate projections. Our study represents a baseline for understanding natural ecosystem dynamics and their key role in governing land carbon uptake and storage in Australia.
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Affiliation(s)
- Bin Wang
- NSW Department of Primary Industries, Wagga Wagga Agricultural Institute, Wagga Wagga, NSW 2650, Australia; Gulbali Institute for Agriculture, Water and Environment, Charles Sturt University, Wagga Wagga, NSW 2678, Australia.
| | - Benjamin Smith
- Hawkesbury Institute for the Environment, Western Sydney University, Richmond, NSW, Australia; University of Lund, Department of Physical Geography and Ecosystem Science, 223 62 Lund, Sweden
| | - Cathy Waters
- GreenCollar, The Rocks, Sydney, NSW 2000, Australia; Formerly NSW Department of Primary Industries, Dubbo, NSW 2830, Australia
| | - Puyu Feng
- College of Land Science and Technology, China Agricultural University, Beijing 100193, China
| | - De Li Liu
- NSW Department of Primary Industries, Wagga Wagga Agricultural Institute, Wagga Wagga, NSW 2650, Australia; Gulbali Institute for Agriculture, Water and Environment, Charles Sturt University, Wagga Wagga, NSW 2678, Australia; Climate Change Research Centre and ARC Centre of Excellence for Climate Extremes, University of New South Wales, Sydney, NSW 2052, Australia
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6
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Zuo X, Wang H. Impact of aerosol concentration changes on carbon sequestration potential of rice in a temperate monsoon climate zone during the COVID-19: a case study on the Sanjiang Plain, China. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:29610-29630. [PMID: 38580873 DOI: 10.1007/s11356-024-33149-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/24/2023] [Accepted: 03/26/2024] [Indexed: 04/07/2024]
Abstract
The emission reduction of atmospheric pollutants during the COVID-19 caused the change in aerosol concentration. However, there is a lack of research on the impact of changes in aerosol concentration on carbon sequestration potential. To reveal the impact mechanism of aerosols on rice carbon sequestration, the spatial differentiation characteristics of aerosol optical depth (AOD), gross primary productivity (GPP), net primary productivity (NPP), leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FPAR), and meteorological factors were compared in the Sanjiang Plain. Pearson correlation analysis and geographic detector were used to analyze the main driving factors affecting the spatial heterogeneity of GPP and NPP. The study showed that the spatial distribution pattern of AOD in the rice-growing area during the epidemic was gradually decreasing from northeast to southwest with an overall decrease of 29.76%. Under the synergistic effect of multiple driving factors, both GPP and NPP increased by more than 5.0%, and the carbon sequestration capacity was improved. LAI and FPAR were the main driving factors for the spatial differentiation of rice GPP and NPP during the epidemic, followed by potential evapotranspiration and AOD. All interaction detection results showed a double-factor enhancement, which indicated that the effects of atmospheric environmental changes on rice primary productivity were the synergistic effect result of multiple factors, and AOD was the key factor that indirectly affected rice primary productivity. The synergistic effects between aerosol-radiation-meteorological factor-rice primary productivity in a typical temperate monsoon climate zone suitable for rice growth were studied, and the effects of changes in aerosol concentration on carbon sequestration potential were analyzed. The study can provide important references for the assessment of carbon sequestration potential in this climate zone.
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Affiliation(s)
- Xiaokang Zuo
- Heilongjiang Province Key Laboratory of Geographical Environment Monitoring and Spatial Information Service in Cold Regions/School of Geographical Sciences, Harbin Normal University, Harbin, 150025, China
| | - Hanxi Wang
- Heilongjiang Province Key Laboratory of Geographical Environment Monitoring and Spatial Information Service in Cold Regions/School of Geographical Sciences, Harbin Normal University, Harbin, 150025, China.
- Heilongjiang Province Collaborative Innovation Center of Cold Region Ecological Safety, Harbin, 150025, China.
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7
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Lu Q, Liu H, Wei L, Zhong Y, Zhou Z. Global prediction of gross primary productivity under future climate change. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 912:169239. [PMID: 38072275 DOI: 10.1016/j.scitotenv.2023.169239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 12/06/2023] [Accepted: 12/07/2023] [Indexed: 12/17/2023]
Abstract
The ecosystem gross primary productivity (GPP) is crucial to land-atmosphere carbon exchanges, and changes in global GPP as well as its influencing factors have been well studied in recent years. However, identifying the spatio-temporal variations of global GPP under future climate changes is still a challenging issue. This study aims to develop data-driven approach for predicting the global GPP as well as its monthly and annual variations up to the year 2100 under changing climate. Specifically, Catboost was employed to examine the potential relationship between the GPP and environmental factors, with climate variables, CO2 concentration and terrain attributes being selected as environmental factors. The predicted monthly and annual GPP from Coupled Model Intercomparison Project phase 6 (CMIP6) under future SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 scenarios were analyzed. The results indicate that the global GPP is predicted to increase under the future climate change in the 21st century. The annual GPP is expected to be 115.122 Pg C, 116.537 Pg C, 117.626 Pg C, and 120.097 Pg C in 2100 under four future scenarios, and the predicted monthly GPP shows seasonal difference. Meanwhile, GPP tends to increase in the northern mid-high latitude regions and decrease in the equatorial regions. For the climate zones form Köppen-Geiger classification, the arid, cold, and polar zones present increased GPP, while GPP in the tropical zone will decrease in the future. Moreover, the high importance of climate variables in GPP prediction illustrates that the future climate change is the main driver of the global GPP dynamics. This study provides a basis for predicting how global GPP responds to future climate change in the coming decades, which contribute to understanding the interactions between vegetation and climate.
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Affiliation(s)
- Qikai Lu
- Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China; Hubei Key Laboratory of Regional Development and Environmental Response, Hubei University, Wuhan 430062, China; Key Laboratory of Digital Mapping and Land Information Application, Ministry of Natural Resources, Wuhan University, Wuhan 430079, China; Key Laboratory of Natural Resources Monitoring and Supervision in Southern Hilly Region, Ministry of Natural Resources, Second Surveying and Mapping Institute of Hunan Province, Changsha 410118, China
| | - Hui Liu
- Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China
| | - Lifei Wei
- Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China; Hubei Key Laboratory of Regional Development and Environmental Response, Hubei University, Wuhan 430062, China.
| | - Yanfei Zhong
- State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
| | - Zheng Zhou
- Changjiang Basin Ecology and Environment Monitoring and Scientific Research Center, Changjiang Basin Ecology and Environment Administration, Ministry of Ecology and Environment, Wuhan 430010, China
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Sun Z, An Y, Kong J, Zhao J, Cui W, Nie T, Zhang T, Liu W, Wu L. Exploring the spatio-temporal patterns of global mangrove gross primary production and quantifying the factors affecting its estimation, 1996-2020. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 908:168262. [PMID: 37918724 DOI: 10.1016/j.scitotenv.2023.168262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 10/17/2023] [Accepted: 10/30/2023] [Indexed: 11/04/2023]
Abstract
Mangrove ecosystems, as an important component of "Blue Carbon", play a curial role on global carbon cycling; however, the lack of the global estimates of mangrove ecosystem gross primary production (GPP) and the underlying environmental controls on its estimation remain a gap in knowledge. In this study, we utilized global mangrove eddy covariance data and applied Gaussian Process Regression (GPR) to estimate GPP for global mangrove ecosystems, aiming to elucidate the factors influencing these estimates. The optimal GPR achieved favorable estimation performance through cross-validation (R2 = 0.90, RMSE = 0.92 gC/m2/day, WI = 0.86). Over the study period, the globally annual averaged GPP was 2054.53 ± 38.51 gC/m2/yr (comparable to that of evergreen broadleaf forests and exceeds the GPP of most other plant function types), amounting to a total of 304.82 ± 7.71TgC/yr, hotspots exceeding 3000 gC/m2/yr observed near the equator. The analysis revealed a decline in global mangrove GPP during 1996-2020 of -0.89 TgC/yr. Human activities (changes in mangrove cover area) played a relatively consistent role in contributing to this decrease. Conversely, variations in external environmental conditions showed distinct inter-annual differences in their impact. The spatio-temporal distribution patterns of mangrove ecosystems GPP (e.g., the bimodal annual pattern, latitudinal gradients, etc.) demonstrated the regulatory influence of external environmental conditions on GPP estimates. The model ensemble attribution analysis indicated that the fraction of absorbed photosynthetically active radiation exerted the dominant control on GPP estimations, while temperature, salinity, and humidity acted as secondary constraints. The findings of this study provide valuable insights for monitoring, modeling, and managing mangrove ecosystems GPP; and underscore the critical role of mangroves in global carbon sequestration. By quantifying the influences of environmental factors, we enhance our understanding of mangrove carbon cycling estimates, thereby helping sustain of these disproportionately productive ecosystems.
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Affiliation(s)
- Zhongyi Sun
- School of Ecology and Environment, Hainan University, Haikou 570208, China; Key Laboratory of Agro-Forestry Environmental Processes and Ecological Regulation, Hainan University, Haikou 570228, China
| | - Yinghe An
- School of Ecology and Environment, Hainan University, Haikou 570208, China
| | - Jiayan Kong
- School of Ecology and Environment, Hainan University, Haikou 570208, China
| | - Junfu Zhao
- Hainan Provincial Ecological and Environmental Monitoring Centre, Haikou 571126, China
| | - Wei Cui
- Development Research Center, National Forestry and Grassland Administration, Beijing 100714, China
| | - Tangzhe Nie
- School of Water Conservancy and Electric Power, Heilongjiang University, Harbin 150080, China
| | - Tianyou Zhang
- College of Grassland Agriculture, Northwest A&F University, Xianyang 712100, China
| | - Wenjie Liu
- School of Ecology and Environment, Hainan University, Haikou 570208, China; Key Laboratory of Agro-Forestry Environmental Processes and Ecological Regulation, Hainan University, Haikou 570228, China
| | - Lan Wu
- School of Ecology and Environment, Hainan University, Haikou 570208, China.
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Wu C, Ju Y, Yang S, Zhang Z, Chen Y. Reconstructing annual XCO 2 at a 1 km×1 km spatial resolution across China from 2012 to 2019 based on a spatial CatBoost method. ENVIRONMENTAL RESEARCH 2023; 236:116866. [PMID: 37567384 DOI: 10.1016/j.envres.2023.116866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 08/06/2023] [Accepted: 08/08/2023] [Indexed: 08/13/2023]
Abstract
Long-time-series, high-resolution datasets of the column-averaged dry-air mole fraction of carbon dioxide (XCO2) have great practical importance for mitigating the greenhouse effect, assessing carbon emissions and implementing a low-carbon cycle. However, the mainstream XCO2 datasets obtained from satellite observations have coarse spatial resolutions and are inadequate for supporting research applications with different precision requirements. Here, we developed a new spatial machine learning model by fusing spatial information with CatBoost, called SCatBoost, to fill the above gap based on existing global land-mapped 1° XCO2 data (GLM-XCO2). The 1-km-spatial-resolution dataset containing XCO2 values in China from 2012 to 2019 reconstructed by SCatBoost has stronger and more stable predictive power (confirmed with a cross-validation (R2 = 0.88 and RSME = 0.20 ppm)) than other traditional models. According to the estimated dataset, the overall national XCO2 showed an increasing trend, with the annual mean concentration rising from 392.65 ppm to 410.36 ppm. In addition, the spatial distribution of XCO2 concentrations in China reflects significantly higher concentrations in the eastern coastal areas than in the western inland areas. The contributions of this study can be summarized as follows: (1) It proposes SCatBoost, integrating the advantages of machine learning methods and spatial characteristics with a high prediction accuracy; (2) It presents a dataset of fine-scale and high resolution XCO2 over China from 2012 to 2019 by the model of SCatBoost; (3) Based on the generated data, we identify the spatiotemporal trends of XCO2 in the scale of nation and city agglomeration. These long-term and high resolution XCO2 data help understand the spatiotemporal variations in XCO2, thereby improving policy decisions and planning about carbon reduction.
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Affiliation(s)
- Chao Wu
- School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China; Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China
| | - Yuechuang Ju
- School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China
| | - Shuo Yang
- School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China
| | - Zhenwei Zhang
- School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, No.219, NingLiu Road, Nanjing, China
| | - Yixiang Chen
- School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China; Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China.
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10
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Brissette LEG, Wong CYS, McHugh DP, Au J, Orcutt EL, Klein MC, Magney TS. Tracking canopy chlorophyll fluorescence with a low-cost light emitting diode platform. AOB PLANTS 2023; 15:plad069. [PMID: 37937046 PMCID: PMC10626922 DOI: 10.1093/aobpla/plad069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 10/12/2023] [Indexed: 11/09/2023]
Abstract
Chlorophyll fluorescence measured at the leaf scale through pulse amplitude modulation (PAM) has provided valuable insight into photosynthesis. At the canopy- and satellite-scale, solar-induced fluorescence (SIF) provides a method to estimate the photosynthetic activity of plants across spatiotemporal scales. However, retrieving SIF signal remotely requires instruments with high spectral resolution, making it difficult and often expensive to measure canopy-level steady-state chlorophyll fluorescence under natural sunlight. Considering this, we built a novel low-cost photodiode system that retrieves far-red chlorophyll fluorescence emission induced by a blue light emitting diode (LED) light source, for 2 h at night, above the canopy. Our objective was to determine if an active remote sensing-based night-time photodiode method could track changes in canopy-scale LED-induced chlorophyll fluorescence (LEDIF) during an imposed drought on a broadleaf evergreen shrub, Polygala myrtifolia. Far-red LEDIF (720-740 nm) was retrieved using low-cost photodiodes (LEDIFphotodiode) and validated against measurements from a hyperspectral spectroradiometer (LEDIFhyperspectral). To link the LEDIF signal with physiological drought response, we tracked stomatal conductance (gsw) using a porometer, two leaf-level vegetation indices-photochemical reflectance index and normalized difference vegetation index-to represent xanthophyll and chlorophyll pigment dynamics, respectively, and a PAM fluorimeter to measure photochemical and non-photochemical dynamics. Our results demonstrate a similar performance between the photodiode and hyperspectral retrievals of LEDIF (R2 = 0.77). Furthermore, LEDIFphotodiode closely tracked drought responses associated with a decrease in photochemical quenching (R2 = 0.69), Fv/Fm (R2 = 0.59) and leaf-level photochemical reflectance index (R2 = 0.59). Therefore, the low-cost LEDIFphotodiode approach has the potential to be a meaningful indicator of photosynthetic activity at spatial scales greater than an individual leaf and over time.
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Affiliation(s)
- Logan E G Brissette
- Department of Plant Sciences, University of California, Davis, Davis, CA 95616, USA
| | - Christopher Y S Wong
- Department of Plant Sciences, University of California, Davis, Davis, CA 95616, USA
| | - Devin P McHugh
- Department of Plant Sciences, University of California, Davis, Davis, CA 95616, USA
| | - Jessie Au
- Department of Plant Sciences, University of California, Davis, Davis, CA 95616, USA
| | - Erica L Orcutt
- Department of Plant Sciences, University of California, Davis, Davis, CA 95616, USA
- Department of Geography, California State University, Sacramento, Sacramento, CA 95819, USA
| | - Marie C Klein
- Department of Plant Sciences, University of California, Davis, Davis, CA 95616, USA
| | - Troy S Magney
- Department of Plant Sciences, University of California, Davis, Davis, CA 95616, USA
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11
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Panwar A, Migliavacca M, Nelson JA, Cortés J, Bastos A, Forkel M, Winkler AJ. Methodological challenges and new perspectives of shifting vegetation phenology in eddy covariance data. Sci Rep 2023; 13:13885. [PMID: 37620417 PMCID: PMC10449856 DOI: 10.1038/s41598-023-41048-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Accepted: 08/18/2023] [Indexed: 08/26/2023] Open
Abstract
While numerous studies report shifts in vegetation phenology, in this regard eddy covariance (EC) data, despite its continuous high-frequency observations, still requires further exploration. Furthermore, there is no general consensus on optimal methodologies for data smoothing and extracting phenological transition dates (PTDs). Here, we revisit existing methodologies and present new prospects to investigate phenological changes in gross primary productivity (GPP) from EC measurements. First, we present a smoothing technique of GPP time series through the derivative of its smoothed annual cumulative sum. Second, we calculate PTDs and their trends from a commonly used threshold method that identifies days with a fixed percentage of the annual maximum GPP. A systematic analysis is performed for various thresholds ranging from 0.1 to 0.7. Lastly, we examine the relation of PTDs trends to trends in GPP across the years on a weekly basis. Results from 47 EC sites with long time series (> 10 years) show that advancing trends in start of season (SOS) are strongest at lower thresholds but for the end of season (EOS) at higher thresholds. Moreover, the trends are variable at different thresholds for individual vegetation types and individual sites, outlining reasonable concerns on using a single threshold value. Relationship of trends in PTDs and weekly GPP reveal association of advanced SOS and delayed EOS to increase in immediate primary productivity, but not to the trends in overall seasonal productivity. Drawing on these analyses, we emphasise on abstaining from subjective choices and investigating relationship of PTDs trend to finer temporal trends of GPP. Our study examines existing methodological challenges and presents approaches that optimize the use of EC data in identifying vegetation phenological changes and their relation to carbon uptake.
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Affiliation(s)
- Annu Panwar
- Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Hans-Knöll-Straße 10, 07745, Jena, Germany.
| | - Mirco Migliavacca
- European Commission, Joint Research Centre (JRC), Ispra, Lombardia, Italy
| | - Jacob A Nelson
- Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Hans-Knöll-Straße 10, 07745, Jena, Germany
| | - José Cortés
- Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Hans-Knöll-Straße 10, 07745, Jena, Germany
| | - Ana Bastos
- Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Hans-Knöll-Straße 10, 07745, Jena, Germany
| | - Matthias Forkel
- TUD Dresden University of Technology, Faculty of Environmental Sciences, Dresden, Germany
| | - Alexander J Winkler
- Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Hans-Knöll-Straße 10, 07745, Jena, Germany
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12
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Srinet R, Nandy S, Patel N, Padalia H, Watham T, Singh SK, Chauhan P. Simulation of forest carbon fluxes by integrating remote sensing data into biome-BGC model. Ecol Modell 2023. [DOI: 10.1016/j.ecolmodel.2022.110185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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13
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Zhang Z, Li X, Ju W, Zhou Y, Cheng X. Improved estimation of global gross primary productivity during 1981-2020 using the optimized P model. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 838:156172. [PMID: 35618136 DOI: 10.1016/j.scitotenv.2022.156172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 05/12/2022] [Accepted: 05/19/2022] [Indexed: 06/15/2023]
Abstract
Accurate estimation of terrestrial gross primary productivity (GPP) is essential for quantifying the net 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 proper determination of maximum LUE (LUEmax) and downregulation of LUEmax into actual LUE result in uncertainties in GPP estimated by LUE models. The recently developed P model, as a LUE-like model, captures the deep mechanism of photosynthesis and simplifies parameterization. Site level studies have proved the outperformance of P model over LUE models. However, the global application of the P model is still lacking. Thus, the effectiveness of 5 water stress factors integrated into the P model was compared. The optimal P model was used to generate a new long-term (1981-2020) global monthly GPP dataset at a spatial resolution of 0.1° × 0.1°, called PGPP. Validation at globally distributed 109 FLUXNET sites indicated that PGPP is better than three widely-used GPP products. R2 between PGPP and observed GPP equals to 0.75, the corresponding root mean squared error (RMSE) and mean absolute error (MAE) equal to 1.77 g C m-2 d-1 and 1.28 g C m-2 d-1. During the period from 1981 to 2020, PGPP significantly increased in 69.02% of global vegetated regions (p < 0.05). Overall, PGPP provides a new GPP product choice for global ecology studies and the comparison of various water stress factors provides a new idea for the improvement of GPP model in the future.
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Affiliation(s)
- Zhenyu Zhang
- International Institute of Earth System Science, Nanjing University, Nanjing 210023, China; School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China; State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Hangzhou 311300, Zhejiang, China; Jiangsu Center for Collaborative Innovation in Geographic Information Resource Development and Application, Nanjing, Jiangsu 210023, China
| | - Xiaoyu Li
- State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Hangzhou 311300, Zhejiang, China
| | - Weimin Ju
- International Institute of Earth System Science, Nanjing University, Nanjing 210023, China; Jiangsu Center for Collaborative Innovation in Geographic Information Resource Development and Application, Nanjing, Jiangsu 210023, China.
| | - Yanlian Zhou
- School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China; Jiangsu Center for Collaborative Innovation in Geographic Information Resource Development and Application, Nanjing, Jiangsu 210023, China
| | - Xianfu Cheng
- Key Laboratory of Earth Surface Processes and Regional Response in the Yangtze-Huaihe River Basin, Anhui Province, Wuhu 241003, China
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14
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A Study on the Vulnerability of the Gross Primary Production of Rubber Plantations to Regional Short-Term Flash Drought over Hainan Island. FORESTS 2022. [DOI: 10.3390/f13060893] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Rapidly developing droughts, including flash droughts, have occurred frequently in recent years, causing significant damage to agroforestry ecosystems, and they are expected to increase in the future due to global warming. The artificial forest area in China is the largest in the world, and its carbon budget is crucial to the global carbon sink. As the most prominent plantation plant in the tropics, the rubber (Hevea brasiliensis (Willd. ex A. Juss.) Muell. Arg.) ecosystem not only has important economic significance, but also has the potential to be a major natural carbon sink in hot areas. Frequent drought events have a significant impact on rubber ecosystem productivity, yet there have been few reports on the vulnerability of rubber productivity to drought. The objective of this study is to evaluate the vulnerability of rubber ecosystem gross primary production (GPP) to short-term flash drought (STFD) in Hainan Island, utilizing the localized EC-LUE model (eddy covariance–light use efficiency) validated by flux tower observations as the research tool to conduct the scenario simulations which defined by standard relative humidity index (SRHI), in a total of 96 scenarios (timing × intensity). The results show that, in terms of time, the rubber ecosystem in Hainan Island has the highest vulnerability to STFD during the early rainy season and the lowest at the end of the rainy season. From the dry season to the rainy season, the impact of STFD gradually extends to the northeast. Spatially, the vulnerability of the northern island is higher than that of the southern island and that of the western part is higher than that of eastern Hainan Island. With the increase in STFD intensity, the spatial distribution center of the vulnerability of rubber ecosystem GPP in Hainan Island gradually moves southward. The spatiotemporal pattern of the vulnerability of the rubber ecosystem GPP to STFD over Hainan Island plotted by this study is expected to provide decision makers with more accurate information on the prevention and control of drought disaster risk in rubber ecosystems.
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15
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Improving global gross primary productivity estimation by fusing multi-source data products. Heliyon 2022; 8:e09153. [PMID: 35345404 PMCID: PMC8956891 DOI: 10.1016/j.heliyon.2022.e09153] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 02/27/2022] [Accepted: 03/16/2022] [Indexed: 11/21/2022] Open
Abstract
A reliable estimate of the gross primary productivity (GPP) of terrestrial vegetation is essential for both making decisions to address global climate change and understanding the global carbon balance. The lack of consistency in global terrestrial GPP estimates across various products leads to great uncertainty. In this study, we improve the quantification of global gross primary productivity by integrating multiple source GPP products without using any prior knowledge through the Bayesian-based Three-Cornered Hat (BTCH) method to generate a new weighted GPP data set. The fusion results demonstrate the superiority of weighted GPP, which greatly reduces the random error of individual datasets and fully takes advantage of the characteristics of multi-source data products. The weighted dataset can largely reproduce the interannual variation of regional GPP. Overall, the merging scheme based on the BTCH method can effectively generate a new GPP dataset that integrates information from multiple products and provides new ideas for GPP estimation on a global scale.
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16
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Zheng Y, Takeuchi W. Estimating mangrove forest gross primary production by quantifying environmental stressors in the coastal area. Sci Rep 2022; 12:2238. [PMID: 35140321 PMCID: PMC8828879 DOI: 10.1038/s41598-022-06231-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 01/18/2022] [Indexed: 11/10/2022] Open
Abstract
Mangrove ecosystems play an important role in global carbon budget, however, the quantitative relationships between environmental drivers and productivity in these forests remain poorly understood. This study presented a remote sensing (RS)-based productivity model to estimate the light use efficiency (LUE) and gross primary production (GPP) of mangrove forests in China. Firstly, LUE model considered the effects of tidal inundation and therefore involved sea surface temperature (SST) and salinity as environmental scalars. Secondly, the downscaling effect of photosynthetic active radiation (PAR) on the mangrove LUE was quantified according to different PAR values. Thirdly, the maximum LUE varied with temperature and was therefore determined based on the response of daytime net ecosystem exchange and PAR at different temperatures. Lastly, GPP was estimated by combining the LUE model with the fraction of absorbed photosynthetically active radiation from Sentinel-2 images. The results showed that the LUE model developed for mangrove forests has higher overall accuracy (RMSE = 0.0051, R2 = 0.64) than the terrestrial model (RMSE = 0.0220, R2 = 0.24). The main environmental stressor for the photosynthesis of mangrove forests in China was PAR. The estimated GPP was, in general, in agreement with the in-situ measurement from the two carbon flux towers. Compared to the MODIS GPP product, the derived GPP had higher accuracy, with RMSE improving from 39.09 to 19.05 g C/m2/8 days in 2012, and from 33.76 to 19.51 g C/m2/8 days in 2015. The spatiotemporal distributions of the mangrove GPP revealed that GPP was most strongly controlled by environmental conditions, especially temperature and PAR, as well as the distribution of mangroves. These results demonstrate the potential of the RS-based productivity model for scaling up GPP in mangrove forests, a key to explore the carbon cycle of mangrove ecosystems at national and global scales.
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Affiliation(s)
- Yuhan Zheng
- Institute of Industrial Science, The University of Tokyo, Tokyo, 1538505, Japan.
| | - Wataru Takeuchi
- Institute of Industrial Science, The University of Tokyo, Tokyo, 1538505, Japan
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17
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Keenan TF, Luo X, De Kauwe MG, Medlyn BE, Prentice IC, Stocker BD, Smith NG, Terrer C, Wang H, Zhang Y, Zhou S. A constraint on historic growth in global photosynthesis due to increasing CO 2. Nature 2021; 600:253-258. [PMID: 34880429 DOI: 10.1038/s41586-021-04096-9] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Accepted: 10/05/2021] [Indexed: 11/09/2022]
Abstract
The global terrestrial carbon sink is increasing1-3, offsetting roughly a third of anthropogenic CO2 released into the atmosphere each decade1, and thus serving to slow4 the growth of atmospheric CO2. It has been suggested that a CO2-induced long-term increase in global photosynthesis, a process known as CO2 fertilization, is responsible for a large proportion of the current terrestrial carbon sink4-7. The estimated magnitude of the historic increase in photosynthesis as result of increasing atmospheric CO2 concentrations, however, differs by an order of magnitude between long-term proxies and terrestrial biosphere models7-13. Here we quantify the historic effect of CO2 on global photosynthesis by identifying an emergent constraint14-16 that combines terrestrial biosphere models with global carbon budget estimates. Our analysis suggests that CO2 fertilization increased global annual photosynthesis by 11.85 ± 1.4%, or 13.98 ± 1.63 petagrams carbon (mean ± 95% confidence interval) between 1981 and 2020. Our results help resolve conflicting estimates of the historic sensitivity of global photosynthesis to CO2, and highlight the large impact anthropogenic emissions have had on ecosystems worldwide.
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Affiliation(s)
- T F Keenan
- Department of Environmental Science, Policy and Management, UC Berkeley, Berkeley, CA, USA. .,Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
| | - X Luo
- Department of Environmental Science, Policy and Management, UC Berkeley, Berkeley, CA, USA.,Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.,Department of Geography, National University of, Singapore, Singapore
| | - M G De Kauwe
- ARC Centre of Excellence for Climate Extremes, Sydney, New South Wales, Australia.,Climate Change Research Centre, University of New South Wales, Sydney, New South Wales, Australia.,School of Biological Sciences, University of Bristol, Bristol, UK
| | - B E Medlyn
- Hawkesbury Institute for the Environment, Western Sydney University, Penrith, New South Wales, Australia
| | - I C Prentice
- Department of Life Sciences, Imperial College London, Ascot, UK.,Department of Biological Sciences, Macquarie University, North Ryde, New South Wales, Australia.,Department of Earth System Science, Tsinghua University, Haidian, Beijing, China
| | - B D Stocker
- Department of Environmental Systems Science, ETH, Zurich, Switzerland.,Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland
| | - N G Smith
- Department of Biological Sciences, Texas Tech University, Lubbock, TX, USA
| | - C Terrer
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA, USA.,Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Boston, MA, USA
| | - H Wang
- Department of Earth System Science, Tsinghua University, Haidian, Beijing, China
| | - Y Zhang
- Department of Environmental Science, Policy and Management, UC Berkeley, Berkeley, CA, USA.,Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.,Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, China
| | - S Zhou
- Department of Environmental Science, Policy and Management, UC Berkeley, Berkeley, CA, USA.,Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.,Lamont-Doherty Earth Observatory of Columbia University, Palisades, NY, USA.,Earth Institute, Columbia University, New York, NY, USA.,Department of Earth and Environmental Engineering, Columbia University, New York, NY, USA.,State Key Laboratory of Earth Surface Processes and Resources Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing, China
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18
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Sang Y, Huang L, Wang X, Keenan TF, Wang C, He Y. Comment on "Recent global decline of CO 2 fertilization effects on vegetation photosynthesis". Science 2021; 373:eabg4420. [PMID: 34554773 DOI: 10.1126/science.abg4420] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
[Figure: see text].
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Affiliation(s)
- Yuxing Sang
- Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Ling Huang
- Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Xuhui Wang
- Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Trevor F Keenan
- Department of Environmental Science, Policy and Management, University of California, Berkeley, CA 94720, USA.,Earth and Environmental Science Area, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Chenzhi Wang
- Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Yue He
- Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
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19
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Zhang Y, Ye A. Would the obtainable gross primary productivity (GPP) products stand up? A critical assessment of 45 global GPP products. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 783:146965. [PMID: 33866164 DOI: 10.1016/j.scitotenv.2021.146965] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 04/01/2021] [Accepted: 04/01/2021] [Indexed: 06/12/2023]
Abstract
Gross primary productivity (GPP) is a vital variable of the global carbon cycle, but the quantification of global GPP is subject to significant uncertainty due to the lack of direct observations at a global scale. Here, we evaluated and compared 45 GPP products in terms of their applicability to different vegetation types at various spatiotemporal scales. The results show that 44 GPP products and obsGPP (Model Tree Ensemble GPP derived from observations and named obsGPP) have similar global patterns with correlation coefficients greater than 0.8 except for NGT, where GOSIF, RS, and BESS are prominent. GPP products have the greatest variation in Suriname, with a mean 75th and 25th percentile difference value of 0.4748 (normalized), and we recommend RS, SDGVM and LPJ-wsl as they provide GPP estimates close to the average GPP. In terms of seasonal estimations, considerable disagreement occurs among the GPP products in winter, with a range from 118.76 to 314.95 gC/m2/season, among which JULES has the closest GPP value to the average GPP estimation. For studies concerning vegetation types preference is given to the LUE average GPP. The 45 GPP products are more consistent on grasslands but, have obvious differences for savannas. All GPP products have their own specific spatiotemporal scales, such as global or national scales or different seasons and different vegetation types (forest, grasslands, etc.). This study provides guidelines for selecting GPP products.
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Affiliation(s)
- Yahai Zhang
- State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
| | - Aizhong Ye
- State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China.
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20
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Comparison of Machine Learning Methods to Up-Scale Gross Primary Production. REMOTE SENSING 2021. [DOI: 10.3390/rs13132448] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Eddy covariance observation is an applicable way to obtain accurate and continuous carbon flux at flux tower sites, while remote sensing technology could estimate carbon exchange and carbon storage at regional and global scales effectively. However, it is still challenging to up-scale the field-observed carbon flux to a regional scale, due to the heterogeneity and the unstable air conditions at the land surface. In this paper, gross primary production (GPP) from ground eddy covariance systems were up-scaled to a regional scale by using five machine learning methods (Cubist regression tree, random forest, support vector machine, artificial neural network, and deep belief network). Then, the up-scaled GPP were validated using GPP at flux tower sites, weighted GPP in the footprint, and MODIS GPP products. At last, the sensitivity of the input data (normalized difference vegetation index, fractional vegetation cover, shortwave radiation, relative humidity and air temperature) to the precision of up-scaled GPP was analyzed, and the uncertainty of the machine learning methods was discussed. The results of this paper indicated that machine learning methods had a great potential in up-scaling GPP at flux tower sites. The validation of up-scaled GPP, using five machine learning methods, demonstrated that up-scaled GPP using random forest obtained the highest accuracy.
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21
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A Modeling Application for GHG Fluxes Estimates in Betel Nuts Plantations in Taiwan. Processes (Basel) 2021. [DOI: 10.3390/pr9050895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Perennial woody crops could have a positive impact on carbon balance, absorbing carbon during growing season and storing it for several years, whereas annual crops do not have this particular effect. Usually, techniques for GHG (greenhouse gases) flux measurements have limited spatial representativeness, with some difficulties to extend leaf measurements to field scale. Models, especially if supported by remote sensing data, allow for upscaling the monitoring of these fluxes. The aim of this work was to evaluate the carbon fluxes (gross primary production, GPP; net ecosystem production, NEP) of the betel nut cultivars in Taiwan by a vegetation photosynthesis model (VPM). The model permitted estimating seasonal dynamics of GPP in a moist tropical evergreen forest. These plantations are very common in Taiwan and their role could be significant in environmental and development policies even though, until now, the consumption of the fruit of this tree is at the center of controversy due to their use and effects on the population. To obtain estimates of carbon fluxes on a large area that would appreciate its spatial variability, a model based on physiological processes was used. This model incorporated a series of procedures and monthly mean meteorological data, light use efficiency, and satellite enhanced vegetation index (EVI) were used as inputs. An additional purpose of this work was to compare the carbon uptake of different cultivars in Taiwan and Italy. Using a different model, always based on light use efficiency, a similar project was carried on Italian vineyards, with other climate conditions and different agricultural practices.
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22
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Walker AP, De Kauwe MG, Bastos A, Belmecheri S, Georgiou K, Keeling RF, McMahon SM, Medlyn BE, Moore DJP, Norby RJ, Zaehle S, Anderson-Teixeira KJ, Battipaglia G, Brienen RJW, Cabugao KG, Cailleret M, Campbell E, Canadell JG, Ciais P, Craig ME, Ellsworth DS, Farquhar GD, Fatichi S, Fisher JB, Frank DC, Graven H, Gu L, Haverd V, Heilman K, Heimann M, Hungate BA, Iversen CM, Joos F, Jiang M, Keenan TF, Knauer J, Körner C, Leshyk VO, Leuzinger S, Liu Y, MacBean N, Malhi Y, McVicar TR, Penuelas J, Pongratz J, Powell AS, Riutta T, Sabot MEB, Schleucher J, Sitch S, Smith WK, Sulman B, Taylor B, Terrer C, Torn MS, Treseder KK, Trugman AT, Trumbore SE, van Mantgem PJ, Voelker SL, Whelan ME, Zuidema PA. Integrating the evidence for a terrestrial carbon sink caused by increasing atmospheric CO 2. THE NEW PHYTOLOGIST 2021; 229:2413-2445. [PMID: 32789857 DOI: 10.1111/nph.16866] [Citation(s) in RCA: 130] [Impact Index Per Article: 43.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Accepted: 07/06/2020] [Indexed: 05/22/2023]
Abstract
Atmospheric carbon dioxide concentration ([CO2 ]) is increasing, which increases leaf-scale photosynthesis and intrinsic water-use efficiency. These direct responses have the potential to increase plant growth, vegetation biomass, and soil organic matter; transferring carbon from the atmosphere into terrestrial ecosystems (a carbon sink). A substantial global terrestrial carbon sink would slow the rate of [CO2 ] increase and thus climate change. However, ecosystem CO2 responses are complex or confounded by concurrent changes in multiple agents of global change and evidence for a [CO2 ]-driven terrestrial carbon sink can appear contradictory. Here we synthesize theory and broad, multidisciplinary evidence for the effects of increasing [CO2 ] (iCO2 ) on the global terrestrial carbon sink. Evidence suggests a substantial increase in global photosynthesis since pre-industrial times. Established theory, supported by experiments, indicates that iCO2 is likely responsible for about half of the increase. Global carbon budgeting, atmospheric data, and forest inventories indicate a historical carbon sink, and these apparent iCO2 responses are high in comparison to experiments and predictions from theory. Plant mortality and soil carbon iCO2 responses are highly uncertain. In conclusion, a range of evidence supports a positive terrestrial carbon sink in response to iCO2 , albeit with uncertain magnitude and strong suggestion of a role for additional agents of global change.
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Affiliation(s)
- Anthony P Walker
- Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Martin G De Kauwe
- ARC Centre of Excellence for Climate Extremes, University of New South Wales, Sydney, NSW, 2052, Australia
- Climate Change Research Centre, University of New South Wales, Sydney, NSW, 2052, Australia
- Evolution and Ecology Research Centre, University of New South Wales, Sydney, NSW, 2052, Australia
| | - Ana Bastos
- Ludwig Maximilians University of Munich, Luisenstr. 37, Munich, 80333, Germany
| | - Soumaya Belmecheri
- Laboratory of Tree Ring Research, University of Arizona, 1215 E Lowell St, Tucson, AZ, 85721, USA
| | - Katerina Georgiou
- Department of Earth System Science, Stanford University, Stanford, CA, 94305, USA
| | - Ralph F Keeling
- Scripps Institution of Oceanography, UC San Diego, La Jolla, CA, 92093, USA
| | - Sean M McMahon
- Smithsonian Environmental Research Center, Edgewater, MD, 21037, USA
| | - Belinda E Medlyn
- Hawkesbury Institute for the Environment, Western Sydney University, Locked Bag 1797, Penrith, NSW, 2751, Australia
| | - David J P Moore
- School of Natural Resources and the Environment, 1064 East Lowell Street, Tucson, AZ, 85721, USA
| | - Richard J Norby
- Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Sönke Zaehle
- Max Planck Institute for Biogeochemistry, Hans-Knöll-Str. 10, Jena, 07745, Germany
| | - Kristina J Anderson-Teixeira
- Conservation Ecology Center, Smithsonian Conservation Biology Institute, MRC 5535, Front Royal, VA, 22630, USA
- Center for Tropical Forest Science-Forest Global Earth Observatory, Smithsonian Tropical Research Institute, Panama City, Panama
| | - Giovanna Battipaglia
- Department of Environmental, Biological and Pharmaceutical Sciences and Technologies, Università della Campania, Caserta, 81100, Italy
| | | | - Kristine G Cabugao
- Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Maxime Cailleret
- INRAE, UMR RECOVER, Aix-Marseille Université, 3275 route de Cézanne, Aix-en-Provence Cedex 5, 13182, France
- Swiss Federal Institute for Forest Snow and Landscape Research (WSL), Zürcherstrasse 111, 8903 Birmensdorf, Switzerland
| | - Elliott Campbell
- Department of Geography, University of California Santa Barbara, Santa Barbara, CA, 93106, USA
| | - Josep G Canadell
- CSIRO Oceans and Atmosphere, GPO Box 1700, Canberra, ACT, 2601, Australia
| | - Philippe Ciais
- Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, F-91191, France
| | - Matthew E Craig
- Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - David S Ellsworth
- Hawkesbury Institute for the Environment, Western Sydney University, Locked Bag 1797, Penrith, NSW, 2751, Australia
| | - Graham D Farquhar
- Plant Sciences, Research School of Biology, The Australian National University, Canberra, ACT, 2601, Australia
| | - Simone Fatichi
- Department of Civil and Environmental Engineering, National University of Singapore, 1 Engineering Drive 2, Singapore, 117576, Singapore
- Institute of Environmental Engineering, ETH Zurich, Stefano-Franscini Platz 5, Zurich, 8093, Switzerland
| | - Joshua B Fisher
- Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Dr., Pasadena, CA, 91109, USA
| | - David C Frank
- Laboratory of Tree Ring Research, University of Arizona, 1215 E Lowell St, Tucson, AZ, 85721, USA
| | - Heather Graven
- Department of Physics, Imperial College London, South Kensington Campus, London, SW7 2AZ, UK
| | - Lianhong Gu
- Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Vanessa Haverd
- CSIRO Oceans and Atmosphere, GPO Box 1700, Canberra, ACT, 2601, Australia
| | - Kelly Heilman
- Laboratory of Tree Ring Research, University of Arizona, 1215 E Lowell St, Tucson, AZ, 85721, USA
| | - Martin Heimann
- Max Planck Institute for Biogeochemistry, Hans-Knöll-Str. 10, Jena, 07745, Germany
| | - Bruce A Hungate
- Center for Ecosystem Science and Society, Northern Arizona University, Flagstaff, AZ, 86011, USA
| | - Colleen M Iversen
- Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Fortunat Joos
- Climate and Environmental Physics, Physics Institute and Oeschger Centre for Climate Change Research, University of Bern, Sidlerstr. 5, Bern, CH-3012, Switzerland
| | - Mingkai Jiang
- Hawkesbury Institute for the Environment, Western Sydney University, Locked Bag 1797, Penrith, NSW, 2751, Australia
| | - Trevor F Keenan
- Department of Environmental Science, Policy and Management, UC Berkeley, Berkeley, CA, 94720, USA
- Earth and Environmental Sciences Area, Lawrence Berkeley National Lab., Berkeley, CA, 94720, USA
| | - Jürgen Knauer
- CSIRO Oceans and Atmosphere, GPO Box 1700, Canberra, ACT, 2601, Australia
| | - Christian Körner
- Department of Environmental Sciences, Botany, University of Basel, Basel, 4056, Switzerland
| | - Victor O Leshyk
- Center for Ecosystem Science and Society, Northern Arizona University, Flagstaff, AZ, 86011, USA
| | - Sebastian Leuzinger
- School of Science, Auckland University of Technology, Auckland, 1142, New Zealand
| | - Yao Liu
- Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Natasha MacBean
- Department of Geography, Indiana University, Bloomington, IN, 47405, USA
| | - Yadvinder Malhi
- School of Geography and the Environment, University of Oxford, Oxford, OX1 3QY, UK
| | - Tim R McVicar
- CSIRO Land and Water, GPO Box 1700, Canberra, ACT, 2601, Australia
- Australian Research Council Centre of Excellence for Climate Extremes, 142 Mills Rd, Australian National University, Canberra, ACT, 2601, Australia
| | - Josep Penuelas
- CSIC, Global Ecology CREAF-CSIC-UAB, Bellaterra, Barcelona, Catalonia, 08193, Spain
- CREAF, Cerdanyola del Vallès, Barcelona, Catalonia, 08193, Spain
| | - Julia Pongratz
- Ludwig Maximilians University of Munich, Luisenstr. 37, Munich, 80333, Germany
- Max Planck Institute for Meteorology, Bundesstr. 53, 20146 Hamburg, Germany
| | - A Shafer Powell
- Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Terhi Riutta
- School of Geography and the Environment, University of Oxford, Oxford, OX1 3QY, UK
| | - Manon E B Sabot
- ARC Centre of Excellence for Climate Extremes, University of New South Wales, Sydney, NSW, 2052, Australia
- Climate Change Research Centre, University of New South Wales, Sydney, NSW, 2052, Australia
- Evolution and Ecology Research Centre, University of New South Wales, Sydney, NSW, 2052, Australia
| | - Juergen Schleucher
- Department of Medical Biochemistry & Biophysics, Umeå University, Umea, 901 87, Sweden
| | - Stephen Sitch
- College of Life and Environmental Sciences, University of Exeter, Exeter, Laver Building, EX4 4QF, UK
| | - William K Smith
- School of Natural Resources and the Environment, 1064 East Lowell Street, Tucson, AZ, 85721, USA
| | - Benjamin Sulman
- Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Benton Taylor
- Smithsonian Environmental Research Center, Edgewater, MD, 21037, USA
| | - César Terrer
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA, 94550, USA
| | - Margaret S Torn
- Earth and Environmental Sciences Area, Lawrence Berkeley National Lab., Berkeley, CA, 94720, USA
| | - Kathleen K Treseder
- Department of Ecology and Evolutionary Biology, University of California Irvine, Irvine, CA, 92697, USA
| | - Anna T Trugman
- Department of Geography, 1832 Ellison Hall, Santa Barbara, CA, 93016, USA
| | - Susan E Trumbore
- Max Planck Institute for Biogeochemistry, Hans-Knöll-Str. 10, Jena, 07745, Germany
| | | | - Steve L Voelker
- Department of Environmental and Forest Biology, State University of New York College of Environmental Science and Forestry, Syracuse, NY, 13210, USA
| | - Mary E Whelan
- Department of Environmental Sciences, Rutgers University, 14 College Farm Road, New Brunswick, NJ, 08901, USA
| | - Pieter A Zuidema
- Forest Ecology and Forest Management group, Wageningen University, PO Box 47, Wageningen, 6700 AA, the Netherlands
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Tighten the Bolts and Nuts on GPP Estimations from Sites to the Globe: An Assessment of Remote Sensing Based LUE Models and Supporting Data Fields. REMOTE SENSING 2021. [DOI: 10.3390/rs13020168] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Gross primary production (GPP) determines the amounts of carbon and energy that enter terrestrial ecosystems. However, the tremendous uncertainty of the GPP still hinders the reliability of GPP estimates and therefore understanding of the global carbon cycle. In this study, using observations from global eddy covariance (EC) flux towers, we appraised the performance of 24 widely used GPP models and the quality of major spatial data layers that drive the models. Results show that global GPP products generated by the 24 models varied greatly in means (from 92.7 to 178.9 Pg C yr−1) and trends (from −0.25 to 0.84 Pg C yr−1). Model structure differences (i.e., light use efficiency models, machine learning models, and process-based biophysical models) are an important aspect contributing to the large uncertainty. In addition, various biases in currently available spatial datasets have found (e.g., only 57% of the observed variation in photosynthetically active radiation at the flux tower locations was explained by the spatial dataset), which not only affect GPP simulation but more importantly hinder the simulation and understanding of the earth system. Moving forward, research into the efficacy of model structures and precision of input data may be more important for global GPP estimation.
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24
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Li Y, Yuan L, Cao HB, Tang CD, Wang XY, Tian B, Dou ST, Zhang LQ, Shen J. A dynamic biomass model of emergent aquatic vegetation under different water levels and salinity. Ecol Modell 2021. [DOI: 10.1016/j.ecolmodel.2020.109398] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
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25
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Ji Y, Zhou G, Wang S, Wang L. Prominent vegetation greening and its correlation with climatic variables in northern China. ENVIRONMENTAL MONITORING AND ASSESSMENT 2020; 192:636. [PMID: 32918617 DOI: 10.1007/s10661-020-08593-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/13/2020] [Accepted: 09/03/2020] [Indexed: 06/11/2023]
Abstract
Global vegetation has been reported to be turning greener, especially in China and India. The Yellow River Basin is one of the most prominent greening areas in China. While some studies have attributed vegetation greening to large-scale ecological restoration efforts, our study focuses on the role of climate change in vegetation greening. We selected a time series of annual vegetation net primary productivity (NPP) and vegetation coverage from satellite data to quantify the vegetation greening trend. Annual temperature and precipitation were selected to examine the climate trend from 2000 to 2019. The results showed that the Yellow River Basin experienced a rapid increase in temperature and precipitation during this period. Annual temperature increased with an average speed of 0.905 °C per decade, approximately 4.5 times larger than that of global warming. Annual precipitation increased by 82.8%, with an average speed of 9.17 mm per year. There was widespread vegetation greening in the Yellow River Basin during 2000-2019. This was demonstrated by an increase in vegetation NPP and vegetation coverage in the Yellow River Basin. The increase of annual NPP and coverage from 2000 to 1019 was 26.6% and 30.8%, respectively. Even while considering the effects of conservation and restoration efforts, the rapid increases in temperature and precipitation allowed vegetation to flourish, as evidenced by significant positive correlations between climate variables and vegetation variables. Therefore, climate change played an important positive role in vegetation greening, rather than an undesirable disturbance.
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Affiliation(s)
- Yuhe Ji
- State Key Laboratory of Severe Weather (LASW), Chinese Academy of Meteorological Science, Beijing, 100081, China
- School of Earth Science and Technology, Zhengzhou University, Zhengzhou, 450001, Henan, China
| | - GuangSheng Zhou
- State Key Laboratory of Severe Weather (LASW), Chinese Academy of Meteorological Science, Beijing, 100081, China.
- School of Earth Science and Technology, Zhengzhou University, Zhengzhou, 450001, Henan, China.
| | - Shudong Wang
- State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, 100101, China
| | - Lixia Wang
- Ministry of Ecology and Environment Center for Satellite Application on Ecology and Environment, Beijing, 100094, China
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26
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Empirical vs. light-use efficiency modelling for estimating carbon fluxes in a mid-succession ecosystem developed on abandoned karst grassland. PLoS One 2020; 15:e0237351. [PMID: 32764813 PMCID: PMC7413509 DOI: 10.1371/journal.pone.0237351] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Accepted: 07/23/2020] [Indexed: 11/19/2022] Open
Abstract
Karst systems represent an important carbon sink worldwide. However, several phenomena such as the CO2 degassing and the exchange of cave air return a considerable amount of CO2 to the atmosphere. It is therefore of paramount importance to understand the contribution of the ecosystem to the carbon budget of karst areas. In this study conducted in a mid-succession ecosystem developed on abandoned karst grassland, two types of model were assessed, estimating the gross primary production (GPP) or the net ecosystem exchange (NEE) based on seven years of eddy covariance data (2013-2019): (1) a quadratic vegetation index-based empirical model with five alternative vegetation indices as proxies of GPP and NEE, and (2) the vegetation photosynthesis model (VPM) which is a light use efficiency model to estimate only GPP. The Enhanced Vegetation Index (EVI) was the best proxy for NEE whereas SAVI performed very similarly to EVI in the case of GPP in the empirical model setting. The empirical model performed better than the VPM model which tended to underestimate GPP. Therefore, for this ecosystem, we suggest the use of the empirical model provided that the quadratic relationship observed persists. However, the VPM model would be a good alternative under a changing climate, as it is rooted in the understanding of the photosynthesis process, if the scalars it involves could be improved to better estimate GPP.
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27
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Evaluating the Performance of Sentinel-3A OLCI Land Products for Gross Primary Productivity Estimation Using AmeriFlux Data. REMOTE SENSING 2020. [DOI: 10.3390/rs12121927] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Accurate and reliable estimation of gross primary productivity (GPP) is of great significance in monitoring global carbon cycles. The fraction of absorbed photosynthetically active radiation (FAPAR) and vegetation index products of the Moderate Resolution Imaging Spectroradiometer (MODIS) are currently the most widely used data in evaluating GPP. The launch of the Ocean and Land Colour Instrument (OLCI) onboard the Sentinel-3 satellite provides the FAPAR and the OLCI Terrestrial Chlorophyll Index (OTCI) products with higher temporal resolution and smoother spatial distribution than MODIS, having the potential to monitor terrain GPP. OTCI is one of the red-edge indices and is particularly sensitive to canopy chlorophyll content related to GPP. The purpose of the study is to evaluate the performance of OLCI FAPAR and OTCI for the estimation of GPP across seven biomes in 2017–2018. To this end, OLCI FAPAR and OTCI products in combination with insitu meteorological data were first integrated into the MODIS GPP algorithm and in three OTCI-driven models to simulate GPP. The modeled GPP (GPPOLCI-FAPAR and GPPOTCI) were then compared with flux tower GPP (GPPEC) for each site. Furthermore, the GPPOLCI-FAPAR and GPP derived from the MODIS FAPAR (GPPMODIS-FAPAR) were compared. Results showed that the performance of GPPOLCI-FAPAR was varied in different sites, with the highest R2 of 0.76 and lowest R2 of 0.45. The OTCI-driven models that include APAR data exhibited a significant relationship with GPPEC for all sites, and models using only OTCI provided the most varied performance, with the relationship between GPPOTCI and GPPEC from strong to nonsignificant. Moreover, GPPOLCI-FAPAR (R2 = 0.55) performed better than GPPMODIS-FAPAR (R2 = 0.44) across all biomes. These results demonstrate the potential of OLCI FAPAR and OTCI products in GPP estimation, and they also provide the basis for their combination with the soon-to-launch Fluorescence Explorer satellite and their integration with the Sentinel-3 land surface temperature product into light use models for GPP monitoring at regional and global scales.
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