Helio Guerra Neto and Jeff Freymueller from Michigan State University, East Lansing, USA, will present
“GIA in the Great Lakes Basin: A Geodetic Perspective on Vertical Motion and Hydrological Loading”
Vertical crustal motion in the Great Lakes Basin, North America, results from a combination of ongoing Glacial Isostatic Adjustment (GIA) and environmental and hydrological loadings. The hinge line for present-day vertical motion from GIA passes through the region, and the last 15 years have featured significant rise and then fall of the water levels in the Great Lakes. The region is also densely instrumented with continuous GNSS sites, due to extensive networks operated by US states. We are involved in a project with the goal of constraining groundwater changes in the region, and to make those measurements we need to remove the GIA signal accurately. In this presentation, we will discuss recent advances in our project, using GNSS time series to quantify vertical land motion across the basin, emphasizing the challenges of distinguishing long-term uplift signals (GIA) from the elastic responses to local mass variations.
We identify three distinct periods with very different patterns of local mass variations. Before 2012, the water levels in the Great Lakes were stable, and GRACE data suggest minimal total water changes across the region; this time interval should thus feature motions entirely due to GIA. From 2012-2019, water levels in the Lakes rose to record highs, and since 2020 the water levels have been falling at a similar rate. We thus examine the velocities in map view and along profiles roughly normal to the ice front.
We have compared the predictions of a large number of GIA models (mainly variations in the Earth models) with the pre-2012 observations, which should be due entirely to GIA. We find that none of the GIA models adequately predict the observed data, and models fail systematically: they all predict the hinge line to be located too far south. When viewed along a profile normal to the ice front, our data look very much like a GIA model curve. We thus experimented with an empirical correction in which we shift the GIA model predictions laterally and identify a suite of models that predict profiles with the same shape as the data. The model predictions need to be shifted northward to northeastward, which would likely mean that an ice model with less ice near the southern ice front in the Great Lakes region would fit the data better.
