Collaborative Research: MSA: Upscaling soil organic carbon measurements at the continental scale: Evaluating emergent ecosystem properties using multivariate quantitative methods
合作研究:MSA:扩大大陆尺度土壤有机碳测量:使用多元定量方法评估新兴生态系统特性
基本信息
- 批准号:2106137
- 负责人:
- 金额:$ 25.26万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2021
- 资助国家:美国
- 起止时间:2021-08-01 至 2024-07-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Increase in atmospheric carbon dioxide (CO2) is a major cause of global climate change. One of the most effective nature-based solutions to this challenge lies right under our feet - the soil. Globally, soil contains more carbon than in the Earth's atmosphere and vegetation combined. National and international initiatives are in place to increase soil organic carbon (SOC) content and storage capacity to combat climate change. The multifaceted benefits of SOC storage can also ensure food and nutritional security for the Earth's human population and help meet many of the United Nations Sustainable Development goals. However, it is not clear how long soil can provide these ecosystem services to our global community. This is partly because SOC data available from various sources and predictions based on computer models don’t agree with each other. This project aims to provide a robust estimate of SOC for the conterminous United States (CONUS), which can help identify potential reasons for inconsistency across different models and ultimately facilitate policy-makers in making informed decisions about climate change. It will also offer research training opportunities for students as well as workshops and training courses for teachers. For the U.S., there is a unique opportunity to use spatial clustering approaches to reduce uncertainties in SOC dynamics and constrain models at the continental scale by upscaling site-based measurements across the National Ecological Observatory Network (NEON). Emergent ecosystem properties will be evaluated by using multivariate quantitative methods to extrapolate or interpolate point-scale SOC measurements from a spatial constellation of NEON terrestrial sites to CONUS. Data collected across NEON terrestrial sites will be coupled with an array of multivariate geographic clustering algorithms (k-means clustering, ensemble clustering) and machine-learning (convolutional neural network, artificial neural network) approaches. These quantitative analyses will also enable uncertainty quantification of spatial representativeness of SOC and help identify potential future relocatable (or mobile) sites for additional ground-truth measurements of variables related to terrestrial C cycle processes. Existing NEON biogeochemistry, microbial, hydrology, sensor, and remote sensing data products will be leveraged to produce quantitative SOC regional maps for CONUS using similar combinations of climatic, ecological, environmental, geochemical, and microbial variables. The algorithms developed with NEON data will be validated with other point-scale data like SoDaH (SOils DAta Harmonization database) and ISNC (International Soil Carbon Network). The spatial mismatch of derived representativeness-based SOC regional maps for CONUS will be evaluated with existing gridded databases: SoilGrids, Harmonized World Soil Database (HWSD), Northern Circumpolar Soil Carbon Database (NCSCD), and gridded U.S. Soil Survey Geographic Database (gSSURGO).EON-based SOC regional maps for CONUS will also be integrated with downscaled historical SOC predictions from participating models of the Coupled Model Intercomparison Project Phase 6 (CMIP6). The robust (and scalable) estimate of SOC for CONUS will enable the diagnosis of terrestrial C cycle processes using historical CMIP6 model runs. Broader impacts will involve training opportunities at the undergraduate and graduate levels, and workhops and training courses to teach data analysis workflow methods.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.
大气中二氧化碳(CO2)的增加是全球气候变化的主要原因。应对这一挑战的最有效的自然解决方案之一就在我们的脚下-土壤。在全球范围内,土壤中的碳含量比地球大气和植被中的碳含量总和还要多。国家和国际倡议已经到位,以增加土壤有机碳含量和储存能力,以应对气候变化。SOC存储的多方面好处还可以确保地球人口的粮食和营养安全,并有助于实现许多联合国可持续发展目标。然而,目前尚不清楚土壤能为我们的全球社会提供这些生态系统服务多久。这部分是因为从各种来源获得的SOC数据和基于计算机模型的预测相互不一致。该项目旨在为美国大陆(CONUS)提供一个可靠的SOC估计,这可以帮助确定不同模型之间不一致的潜在原因,并最终帮助政策制定者做出明智的气候变化决策。它还将为学生提供研究培训机会,并为教师提供讲习班和培训课程。 对美国来说,有一个独特的机会,使用空间聚类的方法,以减少SOC动态的不确定性和约束模型在大陆尺度上,通过升级的网站为基础的测量整个国家生态观测网络(氖)。将使用多变量定量方法外推或内插从氖陆地站点空间星座到CONUS的点尺度SOC测量值,评估紧急生态系统特性。在氖地面站点收集的数据将与一系列多变量地理聚类算法(k-均值聚类、集成聚类)和机器学习(卷积神经网络、人工神经网络)方法相结合。这些定量分析还将使SOC的空间代表性的不确定性量化,并帮助确定潜在的未来可重新定位(或移动的)网站的额外地面实况测量有关的陆地碳循环过程的变量。现有的氖地球化学、微生物、水文、传感器和遥感数据产品将被用来使用气候、生态、环境、地球化学和微生物变量的类似组合为美国大陆制作定量SOC区域图。使用氖数据开发的算法将使用SoDaH(土壤数据协调数据库)和ISNC(国际土壤碳网络)等其他点尺度数据进行验证。将使用现有网格数据库评价CONUS基于代表性的SOC区域地图的空间不匹配:SoilGrids、协调世界土壤数据库(HWSD)、北方环极土壤碳数据库(NCSCD)、和网格化的美国土壤调查地理数据库(gSSURGO)。美国本土的基于SOC的区域地图也将与来自耦合模型的参与模型的降尺度历史SOC预测相结合相互比较项目第六阶段。稳健的(和可扩展的)估计SOC的CONUS将能够诊断陆地碳循环过程使用历史CMIP 6模型运行。更广泛的影响将包括本科生和研究生水平的培训机会,以及教授数据分析工作流程方法的工作站和培训课程。该奖项反映了NSF的法定使命,并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。
项目成果
期刊论文数量(3)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Utilizing Novel Field and Data Exploration Methods to Explore Hot Moments in High-Frequency Soil Nitrous Oxide Emissions Data: Opportunities and Challenges
- DOI:10.3389/ffgc.2022.674348
- 发表时间:2022-05
- 期刊:
- 影响因子:0
- 作者:C. O’Connell;T. Anthony;M. Mayes;T. Perez;D. Sihi;W. Silver
- 通讯作者:C. O’Connell;T. Anthony;M. Mayes;T. Perez;D. Sihi;W. Silver
Upscaling soil organic carbon measurements at the continental scale using multivariate clustering analysis and machine learning
使用多元聚类分析和机器学习在大陆尺度上升级土壤有机碳测量
- DOI:10.5281/zenodo.8057232
- 发表时间:2023
- 期刊:
- 影响因子:0
- 作者:wang, zhuonan;Kumar, Jitendra;R., Samantha Weintraub-Leff;Todd-Brown, Katherine;Mishra, Umakant;Sihi, Debjani
- 通讯作者:Sihi, Debjani
Standardized Data to Improve Understanding and Modeling of Soil Nitrogen at Continental Scale
- DOI:10.1029/2022ef003224
- 发表时间:2023-05
- 期刊:
- 影响因子:0
- 作者:S. Weintraub‐Leff;S. Hall;M. Craig;D. Sihi;Zhuonan Wang;S. Hart
- 通讯作者:S. Weintraub‐Leff;S. Hall;M. Craig;D. Sihi;Zhuonan Wang;S. Hart
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Debjani Sihi其他文献
Debjani Sihi的其他文献
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{{ truncateString('Debjani Sihi', 18)}}的其他基金
Collaborative Research: Understanding biophysical drivers of the CH4 source sink transition in Northern Forests
合作研究:了解北部森林 CH4 源汇转变的生物物理驱动因素
- 批准号:
2208659 - 财政年份:2022
- 资助金额:
$ 25.26万 - 项目类别:
Standard Grant
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Research on Quantum Field Theory without a Lagrangian Description
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Cell Research
- 批准号:31224802
- 批准年份:2012
- 资助金额:24.0 万元
- 项目类别:专项基金项目
Cell Research
- 批准号:31024804
- 批准年份:2010
- 资助金额:24.0 万元
- 项目类别:专项基金项目
Cell Research (细胞研究)
- 批准号:30824808
- 批准年份:2008
- 资助金额:24.0 万元
- 项目类别:专项基金项目
Research on the Rapid Growth Mechanism of KDP Crystal
- 批准号:10774081
- 批准年份:2007
- 资助金额:45.0 万元
- 项目类别:面上项目
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