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  • Presentation | G31B: Measurements and Modeling of the Earth's Response to Surface Mass Variability and Human-Hydrosphere Interactions I Poster
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  • G31B-0287: Attributing vertical land motion in the Western U.S.: Comparing SOPAC and JPL GNSS-derived displacements against GFZ loading models and GRACE
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  • Board 0287‚ Hall EFG (Poster Hall)
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Author(s):
Roland Hohensinn, GFZ Helmholtz Centre for Geosciences (First Author, Presenting Author)
Yehuda Bock, Scripps Institution of Oceanography, University of California San Diego
Lavoisiane Ferreira, Scripps Institution of Oceanography, University of California San Diego
Laura Jensen, GFZ Helmholtz Centre for Geosciences
Robert Dill, GFZ Helmholtz Centre for Geosciences
Angelyn Moore, Jet Propulsion Laboratory, California Institute of Technology
Donald Argus, Jet Propulsion Laboratory, California Institute of Technology
Zhen Liu, Jet Propulsion Laboratory, California Institute of Technology
Hilary Martens, Department of Geosciences, University of Montana


Vertical land motion (VLM) in the Western U.S. results from a combination of tectonic activity, elastic surface deformation due to environmental loading, and other longer-term processes. This study compares GNSS-derived VLM time series from SOPAC and JPL with model-based surface mass load estimates from GFZ (NTAL, NTOL, HYDL) and GRACE. Our goal is to understand how much of the observed vertical motion can be explained by physical models of loading, and how this varies across secular, seasonal, and transient timescales.


We find that current hydrological loading models may lead to misinterpretation of broad-scale subsidence in the Southwest. Long-term VLM trends depend strongly on both the GNSS data source and the glacial isostatic adjustment (GIA) correction applied. Notably, inter-annual VLM signals in both datasets correlate with major drought and wetting events. A statistical method known as common-mode error (CME) correction consistently reduces spatially correlated signals more effectively than the tested physical models.


While atmospheric and oceanic loading explain short-term signals well, hydrology dominates longer timescales but remains poorly constrained. We assess ongoing improvements in hydrological modeling using LISFLOOD and evaluate GIA-related uncertainties. Our findings suggest that hydrological variability likely drives much of the unexplained VLM, particularly on inter-annual scales.




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