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: 2218194
• 负责人: Yuning Fu
• 金额: $ 24.84万
• 依托单位: Bowling Green 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=2218194&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且相互交织的影响对于管理水资源至关重要。五大湖盆地（GLB）是世界上最大的地表淡水资源，其湖泊水位经历了从历史低点到历史高点的巨大变化。尽管对湖泊水位进行了一个多世纪的测量，但我们仍不完全了解导致五大湖水位波动的原因之一是该盆地的大量地下水资源监测不力，并且在模型中经常被忽视。发展一个更准确地模拟地表水和地下水流的水文模型，由地面 GPS 仪器和卫星的数据驱动并进行验证。该模型将为地下水（“第六大湖”）如何影响整个湖泊水位提供前所未有的视角。 GLB。我们提出将水文建模与 GPS、InSAR、卫星图像和 GRACE 数据相融合，以更准确地模拟 GLB 中的地表和地下水流，这将是首次将大地测量学（变形和重力变化）集成在一起。通过遥感测量地下水耦合地表水范围，并通过数据同化，我们将产生历史和即时地下水流量和储存再分析产品，以揭示整个流​​域地下水的时空分布，并将其联系起来。这将提供有关水资源管理所需的地表水和地下水储存、湖泊水位和流量的可靠信息。该奖项授予 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准。