Dynamic monitoring, reporting and verification for implementing negative emission strategies in managed ecosystems (RETINA)

在受管理的生态系统中实施负排放策略的动态监测、报告和验证(RETINA)

基本信息

  • 批准号:
    NE/V003232/1
  • 负责人:
  • 金额:
    $ 7.43万
  • 依托单位:
  • 依托单位国家:
    英国
  • 项目类别:
    Research Grant
  • 财政年份:
    2020
  • 资助国家:
    英国
  • 起止时间:
    2020 至 无数据
  • 项目状态:
    已结题

项目摘要

Carbon sequestration in soil is one of the most promising biological negative emission (BNE) technologies to mitigate climate change. Soil carbon sequestration relies on the adoption of best management practices to increase the amount of carbon stored in soil. An advantage of soil carbon sequestration in agriculture is that carbon stocks are most depleted in cropland systems, so there is great potential to capture atmospheric carbon without land use conversion and competition for land resources. The successful implementation of land based negative emission technologies will require continuous monitoring, reporting and verification of soil storage changes and greenhouse gas (GHG) emissions to estimate net carbon sequestration in soils. Currently, a lack of cost effective, robust, consistent, transparent and accurate methods limits large-scale implementation of these technologies. Monitoring, reporting and verification of carbon sequestration and GHG emissions from soils could be achieved by combining information from novel cost-effective technological developments in field-based sensors, remote sensing, and/or smartphone apps and integration of models on cloud platforms to confirm management practice effectiveness. The process of detecting and inferring soil carbon changes and GHG emissions is extremely data intensive. In order to understand the variability in soil carbon and GHG emissions there is a need to combine information from diverse sensor networks in different environments and to accurately model soil carbon changes and GHG emissions from various management practices. Here we propose a cloud-based platform that combines new development in sensor-based technologies with cloud-based model simulations to overcome major obstacles for implementing a monitoring, reporting and verification (MRV) system for land based negative emission technologies. To operationalize the MRV system, we will collect and process sensor information from the field, land scape level sensors and national scale (Satellite data) and harmonize data feeds to cloud-based models. This setup allows near time simulations on carbon changes and GHG emissions on the cloud without the need for individual user inputs. This project offers the quality data and confidence required for visualising a future, rising to the demands of a net zero carbon UK by 2050. This project will undertake transdisciplinary research to harness recent advances in digital technology combined with novel approaches in stakeholder engagement to make a step change in delivering integrated management options, co-produced with stakeholders, which can help to mitigate climate change. There are several groups who will benefit from the outcomes of this research. We identify various stakeholders and interested groups; UK Farmers will benefit from a freely available mobile-App to help plan various management options to increase/maintain soil organic matter in soil while also accounting for GHG emissions from soil. For policy makers, web-based decision support tool developed in this project will forecast regional estimates of net soil carbon sequestration and GHG emissions. This project could help in designing strategies to monitor and improve environmental quality and reduce GHG emissions from managed ecosystems to meet net zero Britain by 2050. We anticipate wide interest from academia in the GHG budgets and various environmental data sources this project will generate. Keywords: Climate change, soils, carbon sequestration, Greenhouse gas emissions, cloud-based modelling
土壤固碳是减缓气候变化最有前途的生物负排放技术之一。土壤固碳依赖于采用最佳管理做法,以增加土壤中碳的储存量。农业土壤固碳的一个优点是,碳储存在农田系统中消耗最多,因此在不改变土地用途和不竞争土地资源的情况下,有很大的潜力捕获大气中的碳。成功实施陆基负排放技术将需要持续监测、报告和核实土壤储存变化和温室气体排放,以估计土壤中的净碳固存。目前,缺乏成本效益高、稳健、一致、透明和准确的方法,限制了这些技术的大规模实施。通过结合实地传感器、遥感和/或智能手机应用程序中成本效益高的新技术发展信息,以及在云平台上整合模型,可以实现对土壤固碳和温室气体排放的监测、报告和核实,以确认管理实践的有效性。检测和推断土壤碳变化和温室气体排放的过程是非常数据密集的。为了了解土壤碳和温室气体排放的变化,有必要联合收割机的信息,从不同的传感器网络在不同的环境中,并准确地模拟土壤碳的变化和温室气体排放的各种管理措施。在这里,我们提出了一个基于云的平台,结合了基于传感器的技术与基于云的模型模拟的新发展,以克服实施陆基负排放技术的监测,报告和验证(MRV)系统的主要障碍。为了运行MRV系统,我们将收集和处理来自实地的传感器信息,景观级传感器和国家尺度(卫星数据),并将数据馈送到基于云的模型。这种设置允许在云端对碳变化和温室气体排放进行近时间模拟,而无需个人用户输入。该项目提供了可视化未来所需的高质量数据和信心,到2050年达到净零碳英国的要求。该项目将进行跨学科研究,利用数字技术的最新进展,结合利益相关者参与的新方法,在提供与利益相关者共同制作的综合管理选项方面做出重大改变,这有助于减缓气候变化。有几个群体将从这项研究的成果中受益。我们确定了各种利益相关者和感兴趣的团体;英国农民将受益于免费提供的移动应用程序,以帮助规划各种管理方案,以增加/维持土壤中的土壤有机质,同时也考虑到土壤的温室气体排放。对于政策制定者来说,本项目开发的基于网络的决策支持工具将预测土壤净固碳和温室气体排放的区域估计数。该项目可以帮助制定战略,以监测和改善环境质量,减少管理生态系统的温室气体排放,到2050年实现英国的净零排放。我们预计学术界对该项目将产生的温室气体预算和各种环境数据源的广泛兴趣。关键词:气候变化,土壤,碳固存,温室气体排放,基于云的建模

项目成果

期刊论文数量(4)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
How Modelers Model: the Overlooked Social and Human Dimensions in Model Intercomparison Studies.
  • DOI:
    10.1021/acs.est.2c02023
  • 发表时间:
    2022-09-20
  • 期刊:
  • 影响因子:
    11.4
  • 作者:
    Albanito, Fabrizio;McBey, David;Harrison, Matthew;Smith, Pete;Ehrhardt, Fiona;Bhatia, Arti;Bellocchi, Gianni;Brilli, Lorenzo;Carozzi, Marco;Christie, Karen;Doltra, Jordi;Dorich, Christopher;Doro, Luca;Grace, Peter;Grant, Brian;Leonard, Joel;Liebig, Mark;Ludemann, Cameron;Martin, Raphael;Meier, Elizabeth;Meyer, Rachelle;Migliorati, Massimiliano De Antoni;Myrgiotis, Vasileios;Recous, Sylvie;Sandor, Renata;Snow, Val;Soussana, Jean-Francois;Smith, Ward N.;Fitton, Nuala
  • 通讯作者:
    Fitton, Nuala
Agroecological Management and Increased Grain Legume Area Needed to Meet Nitrogen Reduction Targets for Greenhouse Gas Emissions
  • DOI:
    10.3390/nitrogen3030035
  • 发表时间:
    2022-09-01
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Squire,Geoffrey R.;Young,Mark W.;Hawes,Cathy
  • 通讯作者:
    Hawes,Cathy
What makes an operational farm soil carbon code? Insights from a global comparison of existing soil carbon codes using a structured analytical framework
  • DOI:
    10.1080/17583004.2022.2135459
  • 发表时间:
    2022-01-02
  • 期刊:
  • 影响因子:
    3.1
  • 作者:
    Black,Helaina I. J.;Reed,Mark S.;Ziv,Guy
  • 通讯作者:
    Ziv,Guy
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David Cameron其他文献

Members of the Joint Working Group on Refinement
细化联合工作组成员
  • DOI:
  • 发表时间:
    2001
  • 期刊:
  • 影响因子:
    0
  • 作者:
    P. Hawkins;D. Morton;David Cameron;I. Cuthill;R. Francis;R. Freire;A. Gosler;Susan D. Healy;A. Hudson;I. Inglis;A. Jones;J. Kirkwood;M. Lawton;P. Monaghan;C. Sherwin;P. Townsend
  • 通讯作者:
    P. Townsend
Flood frequency estimation under climate change (with uncertainty).
气候变化下的洪水频率估计(具有不确定性)。
  • DOI:
  • 发表时间:
    2000
  • 期刊:
  • 影响因子:
    0
  • 作者:
    David Cameron;K. Beven;P. Naden
  • 通讯作者:
    P. Naden
Big Data in Exploration and Production: Silicon Snake-Oil, Magic Bullet, or Useful Tool?
  • DOI:
    10.2118/167837-ms
  • 发表时间:
    2014-04
  • 期刊:
  • 影响因子:
    0
  • 作者:
    David Cameron
  • 通讯作者:
    David Cameron
Slow component of [Vdot ]O2 kinetics: the effect of training status, fibre type, UCP3 mRNA and citrate synthase activity
[Vdot ]O2 动力学的慢速成分:训练状态、纤维类型、UCP3 mRNA 和柠檬酸合酶活性的影响
  • DOI:
  • 发表时间:
    2002
  • 期刊:
  • 影响因子:
    4.9
  • 作者:
    Aaron P. Russell;Glenn D. Wadley;Rodney J. Snow;J. Giacobino;P. Muzzin;Andrew P. Garnham;David Cameron
  • 通讯作者:
    David Cameron
Development of a Publicly Available Database of Randomized Controlled Trials for Posttraumatic Stress Disorder: The PTSD-Repository
  • DOI:
    10.1016/j.apmr.2020.09.032
  • 发表时间:
    2020-11-01
  • 期刊:
  • 影响因子:
  • 作者:
    Maya O'Neil;David Cameron;Sonya Norman;Jessica Hamblen
  • 通讯作者:
    Jessica Hamblen

David Cameron的其他文献

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{{ truncateString('David Cameron', 18)}}的其他基金

PRAFOR: Probabilistic drought Risk Analysis for FORested landscapes
PRAFOR:森林景观概率干旱风险分析
  • 批准号:
    NE/T009861/1
  • 财政年份:
    2020
  • 资助金额:
    $ 7.43万
  • 项目类别:
    Research Grant
Modelling uncertainty for decision making on ammonia mitigation with trees in the landscape (MUDMAT).
利用景观中的树木对氨氮减排决策的不确定性进行建模 (MUDMAT)。
  • 批准号:
    NE/T004185/1
  • 财政年份:
    2019
  • 资助金额:
    $ 7.43万
  • 项目类别:
    Research Grant
Modelling uncertainty for decision making on ammonia mitigation with trees in the landscape (MUDMAT).
利用景观中的树木对氨氮减排决策的不确定性进行建模 (MUDMAT)。
  • 批准号:
    NE/T004185/2
  • 财政年份:
    2019
  • 资助金额:
    $ 7.43万
  • 项目类别:
    Research Grant
A Model of Cellular Pattern Formation in the Growing Retina
视网膜生长中细胞图案形成的模型
  • 批准号:
    0351250
  • 财政年份:
    2004
  • 资助金额:
    $ 7.43万
  • 项目类别:
    Continuing Grant

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Dynamic monitoring, reporting and verification for implementing negative emission strategies in managed ecosystems (RETINA)
在受管理的生态系统中实施负排放策略的动态监测、报告和验证(RETINA)
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Dynamic monitoring, reporting and verification for implementing negative emission strategies in managed ecosystems (RETINA)
在受管理的生态系统中实施负排放策略的动态监测、报告和验证(RETINA)
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心理健康评估和肿瘤学动态转诊 (MHADRO)
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Dynamic Imaging of Retinal Microglia
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