Improving representation of groundwater in foundational Great Lakes hydrologic and hydrodynamic models and data sets

Groundwater plays a critical role in the water balance, however the groundwater component of the hydrologic cycle is frequently overlooked at basin scales because it is difficult to observe and quantify.  We address this problem through a novel framework that combines existing hydrological models and data sets with groundwater flux estimates across Earth's largest system of lakes; the Laurentian Great Lakes.  Aside from serving as a template for combining surface and ground water data and models, the Laurentian Great Lakes recently transitioned from a period characterized by water scarcity (water levels on the lakes were persistently below average from 1998 through 2013) to extreme water abundance (all-time high water levels were set in 2017 and 2019).  Throughout this transition, we know of no comprehensive data record or modeling system that explicitly linked changes in observed land surface hydrology with critical subsurface groundwater processes.  In addition to utilizing our novel framework for combining existing surface and ground water data sets and model simulations, we will also demonstrate the impact of ground water representation in existing lake physics models that serve as the basis for understanding lake evaporation, circulation patterns, and pollutant fate and transport dynamics.

Principal Investigators:
Andrew D. Gronewold (University of Michigan)
Sandra M. Eberts (USGS - Water Mission Area)
Juliane Mai (Univresity of Waterloo)
Howard W. Reeves (USGS - Upper Midwest Water Science Center)


The Great Lakes from space.