Collaborative Research: MRA: Constraining the continental-scale terrestrial carbon cycle using NEON data

合作研究:MRA:使用 NEON 数据约束大陆尺度的陆地碳循环

基本信息

  • 批准号:
    2017884
  • 负责人:
  • 金额:
    $ 44.87万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2020
  • 资助国家:
    美国
  • 起止时间:
    2020-09-01 至 2022-10-31
  • 项目状态:
    已结题

项目摘要

Plant life in U.S. forests consume large volumes of carbon dioxide, playing an important role in regulating the concentrations of this important gas in the atmosphere. There is an urgent need for scientists to reduce uncertainties in future projections of atmospheric carbon dioxide concentrations and to more confidently assess whether plants in forests will continue to offset carbon dioxide emissions to the atmosphere from the burning of fossil fuels. The NSF-funded National Ecological Observatory Network (NEON) is a continental-scale ecological observation facility that collects and provides quality-controlled data from 81 field sites across the United States that characterize and quantify how our nation's ecosystems are changing. NEON's measurements of plant photosynthesis and respiration are providing the data needed for computer projections of continental scale patterns, year-to-year variations, and trends in the global carbon cycle. This project will develop more refined continental-scale carbon profiles by using a novel approach to improve complex computer models and delivering a widely-sought application of NEON data. The investigators will broaden impacts of this project by providing training to early-career scientists, broadening diversity, sharing products with the research community, and enhancing research and education infrastructure. The results are anticipated to be of high value for integrated assessments of global change and will be useful for federal, state and local agencies, and land managers who are making decisions on managing U.S. natural resources.The investigators will constrain the continental-scale terrestrial carbon cycle by integrating observations from NEON, satellite data, data-driven methods, and data-model integration techniques. The objectives are to: (1) use NEON data to develop continental-scale flux products that are need to help realize NSF's goals for NEON; (2) evaluate information content of NEON data sets for reducing model uncertainty; (3) assimilate multiple NEON data sets into complex land models to quantify the U.S. land carbon sink and its uncertainty; (4) understand the continental-scale carbon dynamics and underlying regulatory mechanisms. First, the investigators will utilize NEON data to develop an hourly, gridded Gross Primary Production(GPP) product with uncertainty estimates based on satellite observations and meteorological data. Second, they will use NEON data along with solar-induced chlorophyll fluorescence (SIF) data from satellites to develop gridded complimentary GPP products with uncertainty estimates. Third, they will utilize multiple heterogeneous datasets from NEON and the gridded GPP products for quantifying the US land carbon sink potential and assess the effectiveness of NEON data to constrain parameter estimation and model prediction. Fourth, they will develop a multiple model ensemble to understand constraints of flux- vs. pool-based data on structure uncertainty in model prediction. Fifth, they will apply data assimilation techniques to quantify parameter uncertainty and assess constraints of multiple vs. single NEON data sets on parameter estimation with three different models. Finally, they will assess the US land carbon sink, its uncertainty, and regulatory mechanisms with NEON data-based gridded flux products, NEON data-trained models, and the traceability framework. This project will ultimately provide feedback towards improvement of models and NEON observations via uncertainty analysis.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
美国森林中的植物消耗大量的二氧化碳,在调节这种重要气体在大气中的浓度方面发挥着重要作用。科学家迫切需要减少未来大气二氧化碳浓度预测中的不确定性,并更有信心地评估森林植物是否会继续抵消燃烧化石燃料向大气排放的二氧化碳。NSF资助的国家生态观测网络(NEON)是一个大陆规模的生态观测设施,它收集并提供来自全美81个野外站点的质量控制数据,这些数据表征并量化了我们国家的生态系统正在如何变化。霓虹灯对植物光合作用和呼吸作用的测量为计算机预测大陆尺度模式、年际变化和全球碳循环趋势提供了所需的数据。该项目将使用一种新的方法来改进复杂的计算机模型,并提供广泛寻求的霓虹灯数据应用,从而开发出更精细的大陆尺度碳剖面。研究人员将通过为职业生涯早期的科学家提供培训、扩大多样性、与研究界共享产品以及加强研究和教育基础设施来扩大该项目的影响。研究结果预计将对全球变化的综合评估具有很高的价值,并将对联邦、州和地方机构以及土地管理者做出管理美国自然资源的决策有用。调查人员将通过整合霓虹灯、卫星数据、数据驱动的方法和数据模型集成技术来限制大陆尺度的陆地碳循环。其目标是:(1)利用霓虹灯数据开发大陆尺度的通量产品,以帮助实现NSF的霓虹灯目标;(2)评估霓虹灯数据集的信息含量,以减少模式的不确定性;(3)将多个霓虹灯数据集同化到复杂的陆地模式中,以量化美国陆地碳汇及其不确定性;(4)了解大陆尺度的碳动态和潜在的调控机制。首先,研究人员将利用霓虹灯数据开发一种每小时网格化的初级生产总值(GPP)产品,该产品基于卫星观测和气象数据进行不确定性估计。其次,他们将使用霓虹灯数据和来自卫星的太阳诱导的叶绿素荧光(SIF)数据来开发具有不确定性估计的网格免费GPP产品。第三,他们将利用来自NEON和网格GPP产品的多个不同类型的数据集来量化美国陆地的碳汇潜力,并评估NEON数据的有效性,以约束参数估计和模型预测。第四,他们将开发一个多模型集合,以了解基于通量和基于池的数据对模型预测中结构不确定性的限制。第五,他们将应用数据同化技术来量化参数的不确定性,并使用三种不同的模型评估多个与单个霓虹灯数据集对参数估计的限制。最后,他们将使用基于霓虹灯数据的网格化通量产品、霓虹灯数据训练模型和可追溯性框架来评估美国陆地碳汇、其不确定性和监管机制。这个项目最终将通过不确定性分析为模型和霓虹灯观测的改进提供反馈。这个奖项反映了NSF的法定使命,并通过使用基金会的智力优势和更广泛的影响审查标准进行评估,被认为值得支持。

项目成果

期刊论文数量(5)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Microbe-iron interactions control lignin decomposition in soil
  • DOI:
    10.1016/j.soilbio.2022.108803
  • 发表时间:
    2022-08
  • 期刊:
  • 影响因子:
    9.7
  • 作者:
    Cuijuan Liao;Wenjuan Huang;Jon Wells;Ruiying Zhao;K. Allen;E. Hou;Xin Huang;Han Qiu;F. Ta
  • 通讯作者:
    Cuijuan Liao;Wenjuan Huang;Jon Wells;Ruiying Zhao;K. Allen;E. Hou;Xin Huang;Han Qiu;F. Ta
Matrix Approach to Land Carbon Cycle Modeling
  • DOI:
    10.1029/2022ms003008
  • 发表时间:
    2022-06
  • 期刊:
  • 影响因子:
    6.8
  • 作者:
    Yiqi Luo;Yuanyuan Huang;Carlos A. Sierra;J. Xia;Anders Ahlström;Yizhao Chen;O. Hararuk;Enqing Hou;Lifen Jiang;Cuijuan Liao;Xingjie Lu;Zheng Shi;Benjamin Smith;F. Tao;Ying-Ping Wang
  • 通讯作者:
    Yiqi Luo;Yuanyuan Huang;Carlos A. Sierra;J. Xia;Anders Ahlström;Yizhao Chen;O. Hararuk;Enqing Hou;Lifen Jiang;Cuijuan Liao;Xingjie Lu;Zheng Shi;Benjamin Smith;F. Tao;Ying-Ping Wang
Across‐model spread and shrinking in predicting peatland carbon dynamics under global change
  • DOI:
    10.1111/gcb.16643
  • 发表时间:
    2023-02
  • 期刊:
  • 影响因子:
    11.6
  • 作者:
    E. Hou;Shuang Ma;Yuanyuan Huang;Yu Zhou;Hyung-Sub Kim;E. López-Blanco;Lifen Jiang;J. Xia;F. Tao;Christopher Williams;M. Williams;D. Ricciuto;P. Hanson;Yiqi Luo
  • 通讯作者:
    E. Hou;Shuang Ma;Yuanyuan Huang;Yu Zhou;Hyung-Sub Kim;E. López-Blanco;Lifen Jiang;J. Xia;F. Tao;Christopher Williams;M. Williams;D. Ricciuto;P. Hanson;Yiqi Luo
Matrix Approach to Accelerate Spin‐Up of CLM5
加速 CLM5 旋转的矩阵方法
  • DOI:
    10.1029/2023ms003625
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    6.8
  • 作者:
    Liao, Cuijuan;Lu, Xingjie;Huang, Yuanyuan;Tao, Feng;Lawrence, David M.;Koven, Charles D.;Oleson, Keith W.;Wieder, William R.;Kluzek, Erik;Huang, Xiaomeng
  • 通讯作者:
    Huang, Xiaomeng
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Yiqi Luo其他文献

Winter warming in Alaska accelerates lignin decomposition contributed by Proteobacteria
阿拉斯加冬季变暖加速了变形菌引起的木质素分解
  • DOI:
    10.1186/s40168-020-00838-5
  • 发表时间:
    2020-06
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Xuanyu Tao;Jiajie Feng;Yunfeng Yang;Gangsheng Wang;Renmao Tian;Fenliang Fan;Daliang Ning;Colin T. Bates;Lauren Hale;Mengting Yuan;Liyou Wu;Qun Gao;Jiesi Lei;Edward A. G. Schuur;Julian Yu;Rosvel Bracho;Yiqi Luo;Konstantinos T. Konstantinidis;Eric R. Johnst
  • 通讯作者:
    Eric R. Johnst
Alterations in soil bacterial community in relation to Spartina alterniflora Loisel. invasion chronosequence in the eastern Chinese coastal wetlands
与互花米草相关的土壤细菌群落变化。
  • DOI:
    10.1016/j.apsoil.2018.11.009
  • 发表时间:
    2019-03
  • 期刊:
  • 影响因子:
    4.8
  • 作者:
    Wen Yang;Nasreen Jeelani;Zhihong Zhu;Yiqi Luo;Xiaoli Cheng;Shuqing An
  • 通讯作者:
    Shuqing An
Optimized Separation of Isoquinoline Alkaloids in Thalictrum Herbal Medicine by Microemulsion Electrokinetic Chromatography
微乳电动色谱法优化唐松草中异喹啉生物碱的分离
Forecasting Responses of a Northern Peatland Carbon Cycle to Elevated CO2 and a Gradient of Experimental Warming
预测北部泥炭地碳循环对二氧化碳升高和实验变暖梯度的响应
  • DOI:
    10.1002/2017jg004040
  • 发表时间:
    2018-03
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Jiang Jiang;Yuanyuan Huang;Shuang Ma;Mark Stacy;Zheng Shi;Daniel M. Ricciuto;Paul J. Hanson;Yiqi Luo
  • 通讯作者:
    Yiqi Luo
Temporal and Spatial Variations in Soil Respiration
土壤呼吸的时空变化
  • DOI:
  • 发表时间:
    2006
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Yiqi Luo;Xuhui Zhou
  • 通讯作者:
    Xuhui Zhou

Yiqi Luo的其他文献

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{{ truncateString('Yiqi Luo', 18)}}的其他基金

Collaborative Research: MRA: Constraining the continental-scale terrestrial carbon cycle using NEON data
合作研究:MRA:使用 NEON 数据约束大陆尺度的陆地碳循环
  • 批准号:
    2242034
  • 财政年份:
    2022
  • 资助金额:
    $ 44.87万
  • 项目类别:
    Standard Grant
Training courses on the matrix approach to modeling land carbon and nitrogen cycles; 2018-2021: Flagstaff, AZ
关于模拟土地碳和氮循环的矩阵方法的培训课程;
  • 批准号:
    1838972
  • 财政年份:
    2018
  • 资助金额:
    $ 44.87万
  • 项目类别:
    Standard Grant
Collaborative Research: Grassland Sensitivity to Climate Change at Local to Regional Scales: Assessing the Role of Ecosystem Attributes vs. Environmental Context
合作研究:地方到区域尺度上草地对气候变化的敏感性:评估生态系统属性与环境背景的作用
  • 批准号:
    1807529
  • 财政年份:
    2017
  • 资助金额:
    $ 44.87万
  • 项目类别:
    Continuing Grant
EAGER: Collaborative Research: Environmental Variability at Dryland Ecotones
EAGER:合作研究:旱地生态交错带的环境变化
  • 批准号:
    1748135
  • 财政年份:
    2017
  • 资助金额:
    $ 44.87万
  • 项目类别:
    Standard Grant
Collaborative Research: Grassland Sensitivity to Climate Change at Local to Regional Scales: Assessing the Role of Ecosystem Attributes vs. Environmental Context
合作研究:地方到区域尺度上草地对气候变化的敏感性:评估生态系统属性与环境背景的作用
  • 批准号:
    1137293
  • 财政年份:
    2012
  • 资助金额:
    $ 44.87万
  • 项目类别:
    Continuing Grant
RCN: Forecasts Of Resource and Environmental Changes: data Assimilation Science and Technology (FORECAST)
RCN:资源和环境变化预测:数据同化科学与技术(FORECAST)
  • 批准号:
    0840964
  • 财政年份:
    2009
  • 资助金额:
    $ 44.87万
  • 项目类别:
    Continuing Grant
DOE-NSF Workshop: Coordinated Approaches to Address Long-Term Issues in Global Change Experiments, to be held on August 13-14, 2009, at the Hyatt in Bethesda, MD
DOE-NSF 研讨会:解决全球变化实验中长期问题的协调方法,将于 2009 年 8 月 13 日至 14 日在马里兰州贝塞斯达凯悦酒店举行
  • 批准号:
    0938795
  • 财政年份:
    2009
  • 资助金额:
    $ 44.87万
  • 项目类别:
    Standard Grant
Development of a Data Assimilation Capability Towards Ecological Forecasting in a Data-Rich Era
数据丰富时代生态预测的数据同化能力发展
  • 批准号:
    0850290
  • 财政年份:
    2009
  • 资助金额:
    $ 44.87万
  • 项目类别:
    Standard Grant
LTREB: Effects of Warming and Clipping on Coupling of Carbon and Water Cycles in a Tallgrass Prairie
LTREB:变暖和削剪对高草草原碳和水循环耦合的影响
  • 批准号:
    0743778
  • 财政年份:
    2008
  • 资助金额:
    $ 44.87万
  • 项目类别:
    Continuing Grant
Workshop: Data-Model Assimilation in Ecology: Techniques and Applications, July 2007
研讨会:生态学中的数据模型同化:技术与应用,2007 年 7 月
  • 批准号:
    0714142
  • 财政年份:
    2007
  • 资助金额:
    $ 44.87万
  • 项目类别:
    Standard Grant

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合作研究:MRA:大陆尺度土壤有机质组成的功能模型
  • 批准号:
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