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Article Dans Une Revue Remote Sensing Année : 2021

Analysis of GNSS Displacements in Europe and Their Comparison with Hydrological Loading Models

Alvaro Santamaría-Gómez
Jean-Paul Boy
Félix Perosanz
Sylvain Loyer
  • Fonction : Auteur

Résumé

Thanks to the increasing number of permanent GNSS stations in Europe and their long records, we computed position solutions for more than 1000 stations over the last two decades using the REPRO3 orbit and clock products from the IGS CNES-CLS (GRGS) Analysis Center. The velocities, which are mainly due to tectonics and glacial isostatic adjustment (GIA), and the annual solar cycle have been estimated using weighted least squares. The interannual variations have been accounted for in the stochastic model or in the deterministic model. We demonstrated that the velocity and annual cycle, in addition to their uncertainties, depend on the estimation method we used and that the estimation of GPS draconitic oscillations minimises biases in the estimation of annual solar cycle displacements. The annual solar cycle extracted from GPS has been compared with that from loading estimates of several hydrological models. If the annual amplitudes between GPS and hydrological models match, the phases of the loading models were typically in advance of about 1 month compared to GPS. Predictions of displacements modelled from GRACE observations did not show this phase shift. We also found important discrepancies at the interannual frequency band between GNSS, loading estimates derived from GRACE, and hydrological models using principal component analysis (PCA) decomposition. These discrepancies revealed that GNSS position variations in the interannual band cannot be systematically interpreted as a geophysical signal and should instead be interpreted in terms of autocorrelated noise.
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hal-03466945 , version 1 (09-12-2021)

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Alexandre Michel, Alvaro Santamaría-Gómez, Jean-Paul Boy, Félix Perosanz, Sylvain Loyer. Analysis of GNSS Displacements in Europe and Their Comparison with Hydrological Loading Models. Remote Sensing, 2021, 13 (22), pp.4523. ⟨10.3390/rs13224523⟩. ⟨hal-03466945⟩
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