Improving the Representation of Groundwater in Foundational Great Lakes Hydrologic and Hydrodynamic Models and Datasets
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 datasets with groundwater flux estimates across Earth’s largest system of lakes, the Laurentian Great Lakes. Beyond serving as a template for combining surface water and groundwater data and models, the Great Lakes are a compelling test case: they recently transitioned from a period of water scarcity (water levels 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 links changes in observed land surface hydrology with critical subsurface groundwater processes. In addition to using our framework to combine existing surface water and groundwater datasets and model simulations, we will demonstrate the impact of groundwater representation in existing lake physics models that serve as the basis for understanding lake evaporation, circulation patterns, and pollutant fate and transport dynamics.
- Investigated the importance of groundwater representation in existing lake physics models with support from the U.S. Geological Survey
- Modeled integrated groundwater and surface-water flow with GSFLOW, coupling the Precipitation-Runoff Modeling System (PRMS) land-surface model with the MODFLOW groundwater flow model
- Used the FloPy Python package to post-process model results and examine differences in predicted baseflow and overall streamflow