GPS Vertical Velocities across the Rio Grande Rift and Southern Rocky Mountains Abstract


  • We analyze over 5 years of continuous GPS measurements from 69 GPS sites across a large swath of the western United States centered on the Rio Grande Rift (RGR), Southern Rocky Mountains, and the adjoining eastern Colorado Plateau (CP) and western Great Plains (GP). Surface velocities for the GPS sites are computed by JPL’s GIPSY/OASIS II software in the NA12 North America-fixed reference frame that is a 300-station IGS08 North America- fixed reference frame developed by the University of Nevada. We examine the GPS height displacement time series to assess vertical velocities. An attempt is made to estimate the surface water loading correction due to hydrology from Gravity Recovery and Climate Experiment (GRACE) vertical displacement measurements – also computed in the NA12 reference frame. This GRACE-derived vertical displacement time series is subtracted from the GPS-observed vertical displacement time series to remove both the seasonal and linear trend of water loading effects and to reduce the uncertainty in the estimation of GPS vertical linear trend. A post-glacial rebound model is taken into account for both GPS vertical time series and GRACE gravity time series. After excluding the sites where local tectonic movements are causing instability (e.g., P029, P031, and SC01) and also the sites whose seasonal variations differ significantly (e.g., P027, P038, and P039), with the neighboring sites, the resultant GPS vertical displacement rate shows predominantly negative rates of -0.8 mm/yr to -0.1 mm/yr over the northern RGR and smaller yet positive rates of 0.1 mm/yr to 0.5 mm/yr over the southern RGR. The interpretation of these rates as to the subsidence/uplift of GPS sites solely due to tectonic movement are examined further because of the error sources in the GPS and GRACE measurements and the accuracy of the NA12 reference frame. For the verification of GRACE-derived vertical displacements, the vertical displacements computed from Land Data Assimilation System (LDAS) as well as GSFC Terrestrial Water Storage (TWS) model are studied.


publication date

  • 2014

presented at event