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: 2218028
• 负责人: Jeffrey Freymueller
• 金额: $ 95.73万
• 依托单位: Michigan State 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=2218028&HistoricalAwards=false
• 关键词: 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.

了解气候变化和土地利用对水文的复杂和相互交织的影响对于管理水资源是必要的。五大湖流域是世界上最大的地表淡水资源，在过去十年中，湖泊水位从历史最低点急剧上升到历史最高点。尽管进行了超过世纪的湖泊水位测量，但我们并不完全了解是什么导致了五大湖水位的波动。原因之一是，该盆地广泛的地下水资源监测不力，而且在模型中经常被忽视。在这里，我们建议开发一个水文模型，更准确地模拟地表和地下水流，驱动和验证使用地面GPS仪器和卫星的数据。这个模型将提供一个前所未有的视图，地下水，“第六大湖”，如何影响整个GLB的湖泊水位。我们提出了一个融合的水文建模与GPS，干涉合成孔径雷达，卫星图像，和GRACE数据，更准确地模拟地表和地下流动的GLB。这将是第一次将大地测量学（变形和重力变化）、从遥感测量地下水-地表水耦合范围以及综合水文建模结合起来。通过数据同化，我们将产生历史和临近的地下水流量和存储再分析产品，以揭示整个流域的地下水的时空分布，并将其与波动的湖泊水位。这将为水资源管理提供有关地表水和地下水储存、湖泊水位和流速的可靠信息。该奖项反映了NSF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。