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CAREER: Observation-Driven Mapping of the Linkages between the Terrestrial Water, Energy and Carbon Cycles

CAREER: Observation-Driven Mapping of the Linkages between the Terrestrial Water, Energy and Carbon Cycles
职业:观测驱动的陆地水、能源和碳循环之间联系的绘图
批准号:
1944457
负责人:
Leila Farhadi
金额:
$49.97万
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
2020
资助国家:
美国
项目状态:
未结题
起止时间:
2020-05-01 至 2025-04-30

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中文摘要
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英文摘要
The exchange of water, energy, and carbon between the land, biosphere and atmosphere play a key role in the Earth’s climate. The terrestrial or land component of water, energy and carbon cycles are strongly linked and operate in a harmonized manner. For example, an increase in atmospheric carbon dioxide modifies the amount of vegetation biomass, thus altering ecosystem photosynthesis and transpiration (hence, heat exchange) rates. Land Surface Models used in hydrologic, ecological and climate models require accurate representation of the links between terrestrial cycles. The lack of direct observation of key variables that can quantify these linkages result in uncertain projections from these models. Using satellite information on land surface state measurements of soil moisture, soil temperature and vegetation index, this project applies a novel observation-driven approach to diagnose and map the linkages at regional scale. Mapping the linkages across different seasons, ecological and environmental conditions advances understanding of how the terrestrial water, energy and carbon cycles are linked and operate in the real world. The educational goal of this CAREER proposal is to enhance environmental knowledge and promote scientific discovery on the part of graduate, undergraduate and 6-12th precollege students in the Washington DC area. This education effort includes engaging minority students from local schools in STEM fields with a focus on climate and land-atmosphere interaction. This research addresses the current knowledge gap in understanding the spatio-temporal variations of land-atmosphere interactions/couplings. The research plan includes (i) developing a state of the art framework based on a variational methodology to diagnose and map the linkages between the terrestrial water, energy and carbon cycles across a range of temporal and spatial scales from the implicit information contained in land surface state observations; (ii) testing the methodology at point scale (field site, synthetic simulations) and at large scale (Continental US) (iii) and expanding our fundamental understanding of the coupling between the terrestrial cycles by mapping and studying the linkages across different biomes, echo hydrological regions, seasons and environmental conditions. The observation-driven form of the linkages between the cycles can be used to guide improvements in the predictive capabilities of Land Surface/Earth System models and hence improve simulation of regional land surface fluxes, climate and climate projections. The research method and the low order inverse modeling techniques developed in this proposal is of interest to a broad community, including physicists, applied mathematicians, meteorologists, climate scientists and hydrologists. The educational plan leverages the GLOBE (Global Learning and Observations to Benefit the Environment) framework to engage 6-12 students from the DC metro area in STEM by engaging them in activities that will enhance their understanding of land atmosphere interaction. We will ensure long-term sustainability of this program by developing a network of participating teachers, online resources, and program assessment and evaluation tools.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.
期刊论文(2)
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科研奖励(0)
会议论文
Estimation of Root Zone Soil Moisture Profile by Reduced-Order Variational Data Assimilation Using Near Surface Soil Moisture Observations
使用近地表土壤湿度观测通过降阶变分数据同化估计根区土壤湿度剖面
DOI: 10.1109/jstars.2022.3147166
发表时间: 2022
期刊: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
影响因子: 5.5
作者: [Heidary, Parisa, Farhadi, Leila, Altaf, Muhammad Umer]
通讯作者: Altaf, Muhammad Umer
A Reduced-Adjoint Variational Data Assimilation for Estimating Soil Moisture Profile from Surface Soil Moisture Observations
从表层土壤湿度观测估计土壤湿度剖面的减少伴随变分数据同化
DOI: 10.1109/igarss47720.2021.9554864
发表时间: 2021
期刊: International Geoscience and Remote Sensing Symposium
影响因子: --
作者: [Heidary, Parisa, Farhadi, Leila, Altaf, Muhammad Umer]
通讯作者: Altaf, Muhammad Umer
国内基金
海外基金
Graphon mean field games with partial observation and application to failure detection in distributed systems
  • 批准号:
  • 项目类别:
    省市级项目
  • 资助金额:
    --
  • 批准年份:
    2025
  • 负责人:
    MATHIEULOUROCHLAURIERE
  • 依托单位: