Modeling Surface Mass Load Displacements in the Western US

The surface of the Earth is under constant stress from a variety of mass loads. Surface mass loads, such as oceans, atmosphere, glaciers, seasonal snowpack, and ground water reservoirs, exert forces on the surface of the Earth, causing elastic crustal deformation. Surface mass loads migrate across the Earth’s surface on a range of time scales from daily to several thousand years. Horizontal and vertical displacement responses of the Earth can be recorded using Global Positioning System (GPS) receivers. Modeling and removing surface-mass loading signals, which are present in all GPS time series, can reduce the variance in these time series. My research project focuses on using the python-based software program LoadDef to accurately compute displacement responses of the Earth’s surface to surface mass loads. The modeled mass load responses are compared to the observed GPS displacement responses measured by the Plate Boundary Observatory (PBO), and then removed to determine the relative contributions of each loading source at each station in the PBO network throughout the Western US. These contributions are mapped and colored based on value contribution. Currently, we have already shown that atmospheric mass loading (ATML) contributes a large portion to GPS time series in the western US. Contributions vary spatially with distance from the ocean, with over 25% RMS reduction for stations 1000 km inland from the coast versus about 12% contribution within 100 km of the coast. We are collaborating with NASA’s Jet Propulsion Laboratory to better constrain snow and water storage in the western US from GPS using our daily estimates of ATML. ATML models can be used to correct GPS time series for atmospheric loading effects. GPS data is also important in understanding plate motions at subduction zones. Subduction zones are capable of causing some of the most destructive earthquakes on Earth. By improving the ability to characterize loading deformation in GPS time series, we can improve the ability to monitor tectonic deformation