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Collaborative Research: Navigating Disturbance Regimes in the New Arctic

合作研究：驾驭新北极的干扰制度

基本信息

批准号：
1927772
负责人：
Melissa Chipman
金额：
$ 34.87万
依托单位：
Syracuse University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-03-01 至 2025-02-28
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1927772&HistoricalAwards=false
关键词：
Collaborative Research Navigating Disturbance Regimes

项目摘要

The Arctic has experienced unprecedented warmth over the past several decades. These areas have also experienced increased disturbances due to wildfires, permafrost degradation, and shrub expansion. Evidence suggests dynamic interactions and feedbacks exist among Arctic disturbance regimes. However, the interdependence of these disturbances makes quantifying their impact challenging. Overcoming this challenge is the first step in improving our capacity to predict future disturbance regimes in the face of climate change. To achieve this goal, researchers will evaluate the vulnerability of Arctic tundra regions in northern Alaska to disturbances over decadal to centennial-time scales. Successful completion of this work will facilitate our ability to predict impacts to local indigenous communities, regional Arctic infrastructure, and global carbon and energy dynamics. Outreach to 20 indigenous Arctic communities will communicate results and develop knowledge to assist these communities adapt to and mitigate impacts.The objective of this research is to investigate decadal to centennial time-scale interactions between climate, disturbances, and the physical environment across the Alaskan tundra. This will be achieved by examining permafrost degradation, tall shrub expansion, and potential biophysical and biogeochemical feedbacks during disturbances induced by climate change. Historical observations of permafrost degradation and tall shrub expansion will be used to project future landscape evolution. Spatial heterogeneity will be represented by spatially disaggregating the landscape into key state factors that are subdivided into terrain categories and disturbance types. Sophisticated machine learning algorithms will be used to assess decadal patterns of land cover change using high resolution aerial and satellite imagery. These will be used to determine the primary drivers of change to landscape evolution. Paleoecological reconstructions (2,000+ years) of climate, wildfire, permafrost degradation, and shrub expansion will be derived from lake sediment cores and linked with satellite observations to capture patterns of land-cover change spanning decadal to centennial timescales. Together, these results will provide an independent test of the historical drivers of landscape evolution under a range of disturbance regimes. The biogeophysical implications of landscape evolution will be estimated using surveys of vegetation and soil properties, linked with empirical land-cover change models. This project will reconcile several fundamental knowledge gaps that currently limit global climate change projections. In addition, results will help Arctic communities anticipate and adapt to environmental change.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.

在过去的几十年里，北极经历了前所未有的温暖。由于野火、永久冻土退化和灌木扩张，这些地区也经历了越来越多的干扰。有证据表明，北极扰动机制之间存在动态的相互作用和反馈。然而，这些干扰的相互依赖性使得量化它们的影响具有挑战性。克服这一挑战是提高我们在气候变化面前预测未来扰动机制的能力的第一步。为了实现这一目标，研究人员将评估阿拉斯加北部北极苔原地区在十年到百年时间尺度上对干扰的脆弱性。这项工作的成功完成将有助于我们预测对当地土著社区、北极区域基础设施以及全球碳和能源动态的影响。与20个北极土著社区的联系将交流结果并发展知识，以帮助这些社区适应和减轻影响。本研究的目的是调查阿拉斯加苔原地区气候、干扰和自然环境之间的十年至百年时间尺度的相互作用。这将通过研究永久冻土退化、高大灌木扩张以及气候变化引起的扰动期间潜在的生物物理和生物地球化学反馈来实现。冻土退化和高大灌木扩张的历史观测将用于预测未来的景观演变。空间异质性表现为在空间上将景观分解为关键状态因子，并将其细分为地形类别和干扰类型。复杂的机器学习算法将用于利用高分辨率航空和卫星图像评估土地覆盖变化的年代际模式。这些将用于确定景观演变变化的主要驱动因素。气候、野火、永久冻土退化和灌木扩张的古生态重建（2000多年）将来自湖泊沉积物岩心，并与卫星观测相结合，以捕获跨越十年至百年时间尺度的土地覆盖变化模式。总之，这些结果将为一系列干扰制度下景观演变的历史驱动因素提供一个独立的测试。将利用植被和土壤性质调查，结合经验土地覆盖变化模型，估计景观演变的生物地球物理意义。该项目将弥补目前限制全球气候变化预测的几个基本知识空白。此外，研究结果将有助于北极社区预测和适应环境变化。该奖项反映了美国国家科学基金会的法定使命，并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，被认为值得支持。