CAREER: The Role of Groundwater Storage in Earth System Dynamics; Research to Improve Understanding of Current Hydrologic Regimes and Future Climate Response

职业:地下水储存在地球系统动力学中的作用;

基本信息

  • 批准号:
    1945195
  • 负责人:
  • 金额:
    $ 49.53万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Continuing Grant
  • 财政年份:
    2020
  • 资助国家:
    美国
  • 起止时间:
    2020-05-15 至 2025-04-30
  • 项目状态:
    未结题

项目摘要

Groundwater is a critical water resource across the US and world-wide. Its natural interactions with surface water help stabilize our rivers and ecosystems. Despite its central role in the water balance, groundwater is hard to see, leading to much uncertainty on its availability. As a result, it is simply characterized in global models, and often misunderstood outside the hydrologic community. Though the need for better accounting of groundwater storage in global models has been widely acknowledged, there are still critical gaps in our understanding of how groundwater storage functions across large spatial scales and over long time periods. This uncertainty must be addressed to rigorously evaluate new modeling approaches and improve the hydrologic projections we make with global models. This project explores the role of groundwater storage in large scale models and develops tools to validate groundwater behavior in large coupled systems. The project will further develop ways to improve effective communication about groundwater to the public. We will use our modeling platforms as a resource to engage K-12 students in computer science with locally relevant water issues. The proposed research will quantify the role of large-scale storage dynamics in water and carbon cycles, which is an identified weakness of our current earth systems projections. More specifically, this project seeks to understand how storage dynamics control hydrologic regimes, and what role storage changes could play in future hydrologic regime shifts. Research will leverage the first fully integrated groundwater surface water model of the Continental US to develop direct comparisons with other large-scale modeling approaches and simplified groundwater representations over large spatial extents at varying spatial resolutions. This will provide unique insights on the impact of process representation and spatial resolution on global water and carbon projections not possible with previous approaches. The key outcomes of this research will (1) characterize the most sensitive hydrogeologic settings for simplification of groundwater approximations and spatial resolution, (2) map spatiotemporal ‘groundwater regimes’ across the US and analyze spatial controls, (3) analyze existing global model storage benchmarks, (4) quantify relationship between groundwater configuration and response to climate change, and (5) identify regions that are the most vulnerable to groundwater storage changes. In parallel with these technical outcomes this project will improve public understanding and engagement around groundwater sustainability. Broadcast meteorologists already regularly communicate hydrologic information with the general public. This project will provide training and broadcast resources on groundwater systems to this group, reaching a much larger and more diverse audience than would be possible through direct public outreach. Undergraduates at the UA will help develop broadcast materials through existing science communication courses, thus also providing exposure to hydrology for students outside of hydrology. Finally, the simulation platforms used here will be leveraged as a tool to engage high school students in local watershed issues while learning computer science and coding skills.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.
地下水是整个美国和全球的关键水资源。它与地表水的自然相互作用有助于稳定我们的河流和生态系统。尽管在水平平衡中的核心作用,但很难看到地下水,导致其可用性不确定性。结果,它只是在全球模型中进行的,并且在水文社区之外经常被遗忘。尽管已经广泛认可了在全球模型中更好地计算地下水存储的需求,但是我们对大型空间尺度和长期范围内地下水存储功能的理解仍然存在关键的差距。必须解决这种不确定性,以严格评估新的建模方法并改善我们使用全球模型进行的水文项目。该项目探讨了地下水在大型模型中的作用,并开发了在大型耦合系统中验证地下水行为的工具。该项目将进一步开发改善与公众有关地下水的有效沟通的方法。我们将使用我们的建模平台作为一种资源,使K-12学生与本地相关的水问题参与计算机科学。拟议的研究将量化大规模存储动力学在水和碳周期中的作用,这是我们当前的地球系统项目的弱点。更具体地说,该项目旨在了解存储动力学如何控制水文制度,以及存储的角色变化在未来的水文制度转移中可能会发挥哪些作用。研究将利用美国大陆的第一个完全集成的地下水地表水模型,以与其他大型建模方法进行直接比较,并在不同的空间分辨率下进行大型空间范围,并简化地下水表示。这将提供有关过程表示和空间分辨率对以前的方法无法实现全球水和碳项目的影响的独特见解。这项研究的主要结果(1)将(1)描述最敏感的水文地质设置,以简化地下水近似值和空间分辨率,((2)地图,(2)在整个美国的时空“地下水制度”,并分析空间控制,(3)现有的全球模型存储基准,(4)量化的关系,(4)量化了(4)量化的关系(4)(4)(4)量化的关系(4),(4)量化了(4)量化的关系,(4)量化了(4)量化的关系(4)(4)(4)(4)量化的关系(4)(4)量化的关系(4)量化的关系(4)容易发生地下水存储的变化。与这些技术成果同时,该项目将提高公众对地下水可持续性的理解和参与。广播气象学家已经定期与公众传达水文信息。该项目将向该小组提供地下水系统上的培训和广播资源,吸引比通过直接公开宣传的更多受众群体。 UA的本科生将通过现有的科学通信课程来帮助开发广播材料,从而为水文学以外的学生提供对水文学的影响。最后,此处使用的仿真平台将被用作吸引高中生在当地分水岭问题的工具,同时学习计算机科学和编码技巧。该奖项反映了NSF的法定任务,并被认为是通过基金会的智力优点和更广泛的影响来通过评估来获得支持的支持。

项目成果

期刊论文数量(2)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Toward a Multi‐Representational Approach to Prediction and Understanding, in Support of Discovery in Hydrology
  • DOI:
    10.1029/2021wr031548
  • 发表时间:
    2022-12
  • 期刊:
  • 影响因子:
    5.4
  • 作者:
    Luis De la Fuente;H. Gupta;L. Condon
  • 通讯作者:
    Luis De la Fuente;H. Gupta;L. Condon
Global Groundwater Modeling and Monitoring: Opportunities and Challenges
  • DOI:
    10.1029/2020wr029500
  • 发表时间:
    2021-12-01
  • 期刊:
  • 影响因子:
    5.4
  • 作者:
    Condon, Laura E.;Kollet, Stefan;Abesser, Corinna
  • 通讯作者:
    Abesser, Corinna
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Laura Condon其他文献

British Journal of General Practice Introducing new genetic testing with case finding for familial hypercholesterolaemia in primary care: qualitative study of patient and health professional experience
英国全科医学杂志在初级保健中引入新的基因检测和家族性高胆固醇血症病例发现:患者和健康专业经验的定性研究
  • DOI:
  • 发表时间:
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Academic Fellow;Laura Condon
  • 通讯作者:
    Laura Condon

Laura Condon的其他文献

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{{ truncateString('Laura Condon', 18)}}的其他基金

Track D: Hidden Water and Extreme Events: HydroGEN, A Physically Rigorous Machine Learning Platform for Hydrologic Scenario Generation
轨道 D:隐藏的水和极端事件:HydroGEN,一个用于水文情景生成的物理严格的机器学习平台
  • 批准号:
    2134892
  • 财政年份:
    2021
  • 资助金额:
    $ 49.53万
  • 项目类别:
    Cooperative Agreement
NSF Convergence Accelerator - Track D: Hidden Water and Hydrologic Extremes: A Groundwater Data Platform for Machine Learning and Water Management
NSF 融合加速器 - 轨道 D:隐藏水和水文极端情况:用于机器学习和水管理的地下水数据平台
  • 批准号:
    2040542
  • 财政年份:
    2020
  • 资助金额:
    $ 49.53万
  • 项目类别:
    Standard Grant
Collaborative Research: Sustainability in the Food-Energy-Water nexus; integrated hydrologic modeling of tradeoffs between food and hydropower in large scale Chinese and US basins
合作研究:食品-能源-水关系的可持续性;
  • 批准号:
    1855912
  • 财政年份:
    2018
  • 资助金额:
    $ 49.53万
  • 项目类别:
    Standard Grant
Collaborative Research: Framework: Software: NSCI : Computational and data innovation implementing a national community hydrologic modeling framework for scientific discovery
合作研究:框架:软件:NSCI:计算和数据创新实施国家社区水文建模框架以促进科学发现
  • 批准号:
    1835794
  • 财政年份:
    2018
  • 资助金额:
    $ 49.53万
  • 项目类别:
    Standard Grant
Collaborative Research: Sustainability in the Food-Energy-Water nexus; integrated hydrologic modeling of tradeoffs between food and hydropower in large scale Chinese and US basins
合作研究:食品-能源-水关系的可持续性;
  • 批准号:
    1805094
  • 财政年份:
    2018
  • 资助金额:
    $ 49.53万
  • 项目类别:
    Standard Grant

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