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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前所未有的,而且正在加速。加拿大北部的建筑结构(如公路、铁路、水坝和管道)比以前更快地沉入融化的地面。将需要数十亿美元来适应和修复受永久冻土消失影响的现有基础设施。此外,永久冻土也是人类历史上最大的陆地碳汇来源。如果只考虑渐进和自上而下的融化,到2100年,如果没有积极的气候政策或气候变化适应战略,永久冻土的累积排放量可能达到1500亿吨碳。如果我们想把全球变暖控制在2摄氏度以内,这个数字几乎是全球剩余碳预算的一半。2摄氏度是国际气候协议规定的上限。在这个项目的过程中,我们将首次在项目1中开发一种基于自然的尖端纳米增强生物胶结技术,用于地面改善和永久冻土稳定。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多尺度模拟器,由多模态AI/ML平台提供支持,以预测未来植被、微生物活动和永久冻土碳反馈的水平。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和与合作伙伴一起计划的现场试验中获得的知识。这将为利益攸关方提供一个以证据为基础的气候政策决策工具。项目期间将培训5名hqp。
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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