Vertical displacement time series from GNSS stations in the great Alpine area

Pintori, Francesco; Serpelloni, Enrico; Gualandi, Adriano

doi:10.1594/pangaea.938422

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“…Precipitation data are available from https://disc.gsfc.nasa.gov/datasets/GPM_3IMERGDF_06/ summary (NASA GES DISC, 2020). Temperature data are available from https://www.ecad.eu/download/ensembles/download.php (E-OBS, 2020), and IGb14 GPS time series are available at https://doi.org/10.1594/PANGAEA.938422 (Pintori et al, 2021b).…”

Section: Discussionmentioning

confidence: 99%

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

2022

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Abstract. We study the time series of vertical ground displacements from continuous global navigation satellite system (GNSS) stations located in the European Alps. Our goal is to improve the accuracy and precision of vertical ground velocities and spatial gradients across an actively deforming orogen, investigating the spatial and temporal features of the displacements caused by non-tectonic geophysical processes. We apply a multivariate statistics-based blind source separation algorithm to both GNSS displacement time series and ground displacements modeled from atmospheric and hydrological loading, as obtained from global reanalysis models. This allows us to show that the retrieved geodetic vertical deformation signals are influenced by environment-related processes and to identify their spatial patterns. Atmospheric loading is the most important process, reaching amplitudes larger than 2 cm, but hydrological loading is also important, with amplitudes of about 1 cm, causing the peculiar spatial features of GNSS ground displacements: while the displacements caused by atmospheric and hydrological loading are apparently spatially uniform, our statistical analysis shows the presence of N–S and E–W displacement gradients. We filter out signals associated with non-tectonic deformation from the GNSS time series to study their impact on both the estimated noise and linear rates in the vertical direction. Taking into account the long time span of the time series considered in this work, while the impact of filtering on rates appears rather limited, the uncertainties estimated from filtered time series assuming a power law plus 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 velocity spatial gradients are positively correlated with topographic features of the Alps.

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Section: Discussionmentioning

confidence: 99%