## 2 GNSS Stations and Data Processing
### Methodology Employed  
A station log has been created for each station, documenting the maintenance activities carried out at the various sites as well as the instrumentation changes over the years. Data processing was performed with PRIDE-PPPAR (Precise Point Positioning with Ambiguity Resolution) software (3.1.2) [28], which was used to obtain the daily time series. This program allows us to obtain coordinate files, starting from the RINEX files of the observations with 30 s sampling intervals every 24 h. The observation period for every station is shown in Table 1. Daily position files are converted into an annual series of positions, in which we obtain the displacements that have occurred since the beginning of our time series. The data were processed using the PPP (Precise Point Positioning) technique [29]. The final combined-solution products (ephemeris, clock files, and Earth Rotation Parameters) used correspond to those provided by the IGS Central Office and the IGS Reference Framework Coordinator for the 3 rd IGS reprocessing campaign land frame (repro3) [30], which form the IGS contribution to the ITRF2020 [31]. An ionosphere-free combination was used along with the application of High Order Ionosphere corrections. For the ZTD estimation, the Saastamoinen model [32] was used, and for the troposphere mapping performed every hour, the Global Mapping Function was selected [33]. The tidal model was calculated using [34], which refers to the FES2014 tidal model [35]. The LAMBDA (Least-squares Ambiguity Decorrelation Adjustment) method [36] has been used for the resolution of ambiguities. SARI (February 2025) [37] is a software that allows users to visualize different formats of discrete time series data and also process and analyze the series individually, fit unidimensional models, and, finally, save the results. Coordinated time series of the stations were uploaded directly from the servers of the different GNSS data web servers (EUREF, IGS...) [38; 39; 40] if they were available, although they had also been processed by PRIDE-PPPAR (3.1.2) for a verification of the results. In our case, we entered a file in PBO format (ASCII file with.pos extension) which removes outliers with a residual threshold; also we could manually remove them. We were able to upload complementary information for the purpose of the GNSS signal analysis, and it was used to incorporate the site logs of the stations and to introduce the predefined plate motion model which it refers to [41]. For model fitting, the least-squares (LS) option was used [42]. From among the options, the linear trend was introduced. Discontinuities were detected in the series by defining the reference epochs of each one, or by using an external file. The periodic signals are modeled using sine and cosine terms, users also can manually define additional periods if needed (found by filtering out the annual periodic signals observed in the time series, as well as other significant periodic components that may be present). This correction had a reference epoch that is the average of the available measurement data, where the phase values are estimated accordingly. For velocity verification with SARI (February 2025), the Median Interannual Difference Adjusted for Skewness (MIDAS) algorithm [43] was used. SARI models stochastic noise in GNSS time series using Gaussian processes and optimizes the covariance matrix through Maximum Likelihood Estimation (MLE). We used a combination of white noise and random walk, adjusting their parameters and selecting the best model based on log-likelihood. It also estimates the impact of noise on trend uncertainty and offers an option to separate noise components using a Wiener filter. To improve efficiency, we validated the fit by comparing the power spectrum of the residuals with the estimated noise model. SARI was used to resolve the different offsets identified; other effects, as indicated in other studies such as [44; 45], have not been considered.