Common mode signals and vertical velocities in the great Alpine area from GNSS data

Abstract

Abstract. We study time series of vertical ground displacements from continuous GNSS stations to investigate the spatial and temporal contribution of different geophysical processes to the time-varying displacements that are superimposed on vertical linear trends across the European Alps. We apply a multivariate statistics-based blind source separation algorithm to both GNSS displacement time series and to ground displacements associated with atmospheric and hydrological loading processes, as obtained from global reanalysis models. This allows us to associate each retrieved geodetic vertical deformation signal with a corresponding forcing process. Atmospheric loading is the most important one, reaching amplitudes larger than 2 cm. Besides atmospheric loading, seasonal displacements with amplitudes of about 1 cm are associated with temperature-related processes and with hydrological loading. We find that both temperature and hydrological loading cause peculiar spatial features of GNSS ground displacements. For example, temperature-related seasonal displacements show different behaviour at sites in the plains and in the mountains. Atmospheric and hydrological loading, besides the first-order spatially uniform feature, are associated also with NS and EW displacement gradients. We filter out signals associated with non-tectonic deformation from the raw time series to study their impact on both the estimated noise and linear rates in the vertical direction. While the impact on rates appears rather limited, given also the long-time span of the time-series considered in this work, the uncertainties estimated from filtered time-series assuming a power law + white noise model are significantly reduced, with an important increase in white noise contributions to the total noise budget. Finally, we present the filtered velocity field and show how vertical ground velocities are positively correlated with topographic features of the Alps.