NNA Track 1: Collaborative Research: Maritime transportation in a changing Arctic: Navigating climate and sea ice uncertainties
NNA 第 1 轨道:合作研究:不断变化的北极的海上运输:应对气候和海冰的不确定性
基本信息
- 批准号:1928112
- 负责人:
- 金额:$ 78万
- 依托单位:
- 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:2020-01-01 至 2024-12-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Navigating the New Arctic (NNA) is one of NSF's 10 Big Ideas. NNA projects address convergence scientific challenges in the rapidly changing Arctic. The Arctic research is needed to inform the economy, security and resilience of the Nation, the larger region and the globe. NNA empowers new research partnerships from local to international scales, diversifies the next generation of Arctic researchers, and integrates the co-production of knowledge. This award fulfills part of that aim. Recent changes in climate have resulted in less sea ice in the Arctic ocean. This development has opened new Arctic travel routes and increased vessel traffic along existing shipping routes. Increased shipping requires a reliable Arctic maritime navigation system for safety and efficiency. However, navigation risk management in the Arctic poses many challenges compared to general maritime transportation due to significant uncertainties in climate, weather, and sea ice conditions. This project addresses these uncertainties by developing a comprehensive approach to forecast future climate and sea ice conditions in the Arctic. The results are being used to evaluate the resulting risk of Arctic maritime navigation. The outcome of this research supports a safe, reliable, and resilient navigation system for shippers seeking efficient transit routes through the Arctic. Others will benefit as well, including emergency responders, local communities, Arctic researchers, fishing and pleasure craft, and other stakeholders. This project is developing and applying a risk-based framework integrating an analysis of environmental conditions, an assessment of navigation risk, and an evaluation of the consequences of incidents. Climate and sea ice models are analyzed to probabilistically assess environmental conditions and navigability of Arctic routes. Modeled sea ice thickness is used as a tracer for navigability of shipping routes. Climate model output is analyzed to better understand the mean state and variability spatially along routes and over time. The likelihood of an incident is being evaluated using Bayesian predictive models to incorporate uncertainty with historical data and updated information from climate and sea ice models. The predictive models are integrated with economic interdependency models to evaluate consequences of Arctic navigation incidents. The resulting probabilistic economic models are a function of the likelihood of an incident occurring and evaluate direct and indirect economic impact at the local and global scales. The outcome of the risk and economic assessment provides feedback on the most significant risk factors. Additional climate and sea ice model runs will be used to further evaluate variables to refine risk models. A stochastic simulation will iterate through the integrated risk analysis approach over a range of possible scenarios of climate models and sea ice conditions to evaluate the range of likelihood and economic impact of incidents.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.
新北极航行(NNA)是NSF的十大创意之一。NNA项目解决快速变化的北极地区的趋同科学挑战。北极研究需要为国家、更大地区和地球仪的经济、安全和复原力提供信息。NNA授权从地方到国际规模的新研究伙伴关系,使下一代北极研究人员多样化,并整合知识的共同生产。这个奖项实现了这个目标的一部分。最近的气候变化导致北冰洋的海冰减少。这一发展开辟了新的北极旅游路线,并增加了沿着现有航线的船只交通。增加航运需要可靠的北极海上导航系统,以确保安全和效率。然而,由于气候、天气和海冰条件的重大不确定性,与一般海上运输相比,北极地区的航行风险管理面临许多挑战。该项目通过制定预测北极未来气候和海冰状况的综合方法来解决这些不确定性。研究结果被用于评估北极海上航行的风险。这项研究的结果为寻求北极高效过境路线的托运人提供了一个安全、可靠和有弹性的导航系统。其他人也将受益,包括应急人员、当地社区、北极研究人员、渔船和游艇以及其他利益相关者。该项目正在制定和应用一个基于风险的框架,其中包括环境条件分析、航行风险评估和事故后果评价。气候和海冰模式进行了分析,以概率评估北极航线的环境条件和通航能力。模拟的海冰厚度被用作航运路线可航行性的示踪剂。气候模式输出进行分析,以更好地了解平均状态和变化空间沿着路线和随着时间的推移。目前正在使用贝叶斯预测模型评估发生事故的可能性,以便将不确定性与历史数据以及来自气候和海冰模型的最新信息结合起来。将预测模型与经济相互依赖模型相结合,以评估北极航行事件的后果。由此产生的概率经济模型是事件发生的可能性的函数,并在当地和全球范围内评估直接和间接的经济影响。风险和经济评估的结果提供了关于最重要风险因素的反馈。将使用更多的气候和海冰模型运行来进一步评估变量,以完善风险模型。一个随机模拟将通过综合风险分析方法,在一系列可能的气候模型和海冰条件下,评估事件的可能性和经济影响的范围。该奖项反映了NSF的法定使命,并已被认为是值得支持的,通过评估使用基金会的知识价值和更广泛的影响审查标准。
项目成果
期刊论文数量(1)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
A data-driven risk assessment of Arctic maritime incidents: Using machine learning to predict incident types and identify risk factors
- DOI:10.1016/j.ress.2023.109779
- 发表时间:2023-11
- 期刊:
- 影响因子:0
- 作者:Rajesh Kandel;H. Baroud
- 通讯作者:Rajesh Kandel;H. Baroud
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Hiba Baroud其他文献
The convergence of AI, IoT, and big data for advancing flood analytics research
人工智能、物联网和大数据的融合促进洪水分析研究
- DOI:
- 发表时间:
2022 - 期刊:
- 影响因子:2.9
- 作者:
Samrat Chatterjee;Hiba Baroud;S. Samadi - 通讯作者:
S. Samadi
Variance-based sensitivity analysis of climate variability impact on crop yield using machine learning: A case study in Jordan
基于方差的气候变化对作物产量影响的敏感性分析(使用机器学习方法):约旦的案例研究
- DOI:
10.1016/j.agwat.2025.109409 - 发表时间:
2025-05-31 - 期刊:
- 影响因子:6.500
- 作者:
Yingqiang Xu;Abeer Albalawneh;Maysoon Al-Zoubi;Hiba Baroud - 通讯作者:
Hiba Baroud
Cyber-Physical technologies in freight operations and sustainability: A case study of smart GPS technology in trucking
- DOI:
10.1016/j.scs.2020.102017 - 发表时间:
2020-04-01 - 期刊:
- 影响因子:
- 作者:
Amirhassan Kermanshah;Hiba Baroud;Mark Abkowitz - 通讯作者:
Mark Abkowitz
Hiba Baroud的其他文献
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{{ truncateString('Hiba Baroud', 18)}}的其他基金
CAREER: Policy-Infrastructure-Community Interdependencies: The Next Frontiers in Dynamic Networks
职业:政策-基础设施-社区相互依赖关系:动态网络的下一个前沿
- 批准号:
1944559 - 财政年份:2020
- 资助金额:
$ 78万 - 项目类别:
Standard Grant
I-Corps: Assessing the Challenges of Energy Systems and Evaluating the Suitability of Mobile Energy Storage Transmission
I-Corps:评估能源系统的挑战并评估移动储能传输的适用性
- 批准号:
1829321 - 财政年份:2018
- 资助金额:
$ 78万 - 项目类别:
Standard Grant
Collaborative Research: Bayesian Methods for the Data-Driven Recovery of Networks: Measuring Impact and Building Resilience in Infrastructures and Communities
合作研究:用于数据驱动的网络恢复的贝叶斯方法:衡量基础设施和社区的影响并建立弹性
- 批准号:
1635717 - 财政年份:2016
- 资助金额:
$ 78万 - 项目类别:
Standard Grant
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