Collaborative Research: Revealing the hidden groundwater storage dynamics of the Great Lakes Basin by synthesizing geodesy, hydrologic modeling, and remote sensing

合作研究：通过综合大地测量学、水文建模和遥感，揭示五大湖盆地隐藏的地下水储量动态

• 批准号: 2217994
• 负责人: Zhong Lu
• 金额: $ 28.93万
• 依托单位: Southern Methodist University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2217994&HistoricalAwards=false
• 关键词: Collaborative; Research; Revealing; hidden; groundwater; Collaborative; Research; Revealing; hidden; groundwater

Understanding the complex and interwoven effects of changing climate and land use on hydrology is necessary to manage water resources. The Great Lakes Basin (GLB), the world’s largest surface freshwater resource, has experienced dramatic shifts in lake levels from record lows to record highs over the last decade. Despite over a century of lake level measurements, we do not fully understand what drives such fluctuations in Great Lakes levels. One reason is that the basin’s extensive groundwater resources are poorly monitored, and often ignored in models. Here, we propose to develop a hydrologic model that more accurately simulates both surface and groundwater flows, driven by and validated using data from both ground GPS instruments and satellites. This model will provide an unprecedented view of how groundwater, the “sixth Great Lake”, affects lake levels across the GLB. We propose a fusion of hydrologic modeling with GPS, InSAR, satellite imagery, and GRACE data to more accurately simulate surface and subsurface flows in the GLB. This will be the first such integration of geodesy (deformation and gravity change), measurements of groundwater-coupled surface water extent from remote sensing, and integrated hydrologic modeling. Through data assimilation, we will produce both historical and nowcast groundwater flow and storage reanalysis products to reveal the spatio-temporal distribution of groundwater across the basin, and relate this to fluctuating lake levels. This will provide reliable information about surface and groundwater storage, lake levels, and flow rates that are needed for water resource management.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.

了解气候和土地利用对水文学的复杂而交织的影响是管理水资源的必要条件。大湖盆地（GLB）是世界上最大的地表淡水资源，在过去十年中，湖泊水平从创纪录的低点到创纪录的高点经历了巨大的变化。尽管有一个多世纪的湖泊水平测量，但我们并不完全了解是什么驱动了大湖水平的这种波动。原因之一是盆地的广泛地下水资源受到监控，并且在模型中经常被忽略。在这里，我们建议开发一种水文模型，该模型更准确地模拟了地表和地下水流，该模型是由地面GPS仪器和卫星的数据驱动和验证的。该模型将提供前所未有的观点，即“第六大湖”地下水如何影响GLB的湖泊水平。我们建议将水文建模与GPS，InsAR，卫星图像和GRACE数据融合，以更准确地模拟GLB中的表面和地下流。这将是第一个地球偶联地下水偶联地表水位的测量值（变形和重力变化）的首次整合，以及遥感的整合水文建模。通过数据同化，我们将同时生产历史和现在的地下水流量和存储重新分析产品，以揭示整个盆地地下水的时空分布，并将其与波动的湖泊水平相关。这将提供有关水资源管理所需的地面和地下水储存，湖泊水平和流量的可靠信息。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响来审查标准，被认为是珍贵的支持。