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Nature-based solutions for permafrost stabilization and permafrost carbon fixation

Nature-based solutions for permafrost stabilization and permafrost carbon fixation
基于自然的永久冻土稳定和永久冻土碳固定解决方案
批准号:
576419-2022
负责人:
Maghoul, PoonehP
金额:
$6.24万
依托单位国家:
加拿大
项目类别:
Alliance Grants
财政年份:
2022
资助国家:
加拿大
项目状态:
已结题
起止时间:
2022-01-01 至 2023-12-31

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中文摘要
翻译
气候变暖对(亚北极)地区建筑环境的不利影响是史无前例的,而且正在加速。加拿大北部已建成的建筑物(如公路、铁路、大坝和管道)正在以比以往更快的速度下沉到融化的地面上。将需要数十亿美元来适应和修复正在受到永久冻土消失影响的现有基础设施。此外,永冻层也是人类历史上陆地碳汇的最大来源。如果只考虑渐进和自上而下的融化,如果不采取积极的限制政策或气候变化适应战略,到2100年,永久冻土的累计排放量可能会达到1500亿吨碳。如果我们想要将全球变暖控制在2摄氏度以下,这一数字几乎是世界剩余碳预算的一半,这是国际气候协议设定的上限。在这个项目的过程中,我们将在项目1中首次开发一种尖端的基于自然的纳米增强生物胶结协议,用于地基改良和多年冻土稳定。我们将开发方法、实验室和大规模协议和模型,并将技术转让给合作组织者,以促进在实践中采用。在项目2中,我们将首次开发一个数字孪生平台,通过开发一个基于物理的、由多模式AI/ML平台馈送的多尺度模拟器来预测未来植被水平、微生物活动和永久冻土碳反馈。这项工作将使用项目1中开发的方法,并将应用从过去的经验中获得的知识和与合作伙伴一起规划的实地试验。这将为利益攸关方提供气候政策的循证决策工具。在该项目期间,将对5名总部主任进行培训。
英文摘要
The adverse effects of climate warming on the built environment in (sub)arctic regions are unprecedented andaccelerating. The built structures (such as roads, railways, dams, and pipelines) in Canada's North are sinkinginto the thawing ground faster than before. Billions of dollars will be required to adapt and repair the existinginfrastructure that is being affected by the disappearance of permafrost. In addition, permafrost is also thegreatest source of terrestrial carbon sink in human history. By considering only gradual and top-down thaw,cumulative permafrost emissions could reach 150 billion tons of carbon by the year 2100 without aggressiveclimate policies or climate change adaptation strategies. This amount is nearly half the world's remainingcarbon budget if we want to keep warming under 2 C, which is the limit set in the international climateagreements. Over the course of this project, we will develop for the first time a cutting-edge nature-based nano-enhanced bio-cementation protocol for ground improvement and permafrost stabilization in Project 1. We will develop methodologies, laboratory and large-scale protocols, and models and will transfer technology to the partner organizers to promote adoption in practice. In Project 2, we will develop for the first time a Digital Twin platform by developing a physics-based, multiscale simulator fed by a multi-modalities AI/ML platform to predict the level of vegetation, microbial activity, and permafrost carbon feedback in the future. This work will use the methods developed in Project 1 and will apply the knowledge gained from past experiences and field trials being planned with the partners. This will provide stakeholders with an evidence-based decision-making tool for climate policy. During this project, 5 HQPs will be trained.
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