The frequency of extreme hydrological events (droughts and floods) has increased significantly due to global warming (e.g.,

Seasonal variations in the vertical Global Positioning System (GPS) time series are mainly caused by environmental loading, e.g., hydrological loading (HYDL), atmospheric loading (ATML), and nontidal oceanic loading (NTOL), which can be synthesized based on models developed by various institutions. A comprehensive comparison among these models is essential to extract reliable vertical deformation data, especially on a regional scale. In this study, we selected 4 HYDL, 5 ATML, 2 NTOL, and their 40 combined products to investigate their effects on seasonal variations in vertical GPS time series at 27 GPS stations in Yunnan, southwest China. These products were provided by the German Research Center for Geosciences (GFZ), School and Observatory of Earth Sciences (EOST), and International Mass Loading Service (IMLS). Furthermore, we used the Cross Wavelet Transform (XWT) method to analyze the relative phase relationship between the GPS and the environmental loading time series. Our result showed that the largest average Root-Mean-Square (RMS) reduction value was 1.32 mm after removing the deformation associated with 4 HYDL from the vertical GPS time series, whereas the RMS reductions after 5 ATML and 2 NTOL model corrections were negative at most stations in Yunnan. The average RMS reduction value of the optimal combination of environmental loading products was 1.24 mm, which was worse than the HYDL (IMLS_GEOSFPIT)-only correction, indicating that HYDL was the main factor responding for seasonal variations at most stations in Yunnan. The XWT result showed that HYDL also explained the annual variations reasonably. Our finding implies that HYDL (IMLS_GEOSFPIT) contributes the most to the environmental loading in Yunnan, and that the ATML and NTOL models used in this paper cannot be effective to correct seasonal variations.

Optimizing the noise model for global navigation satellite system (GNSS) vertical time series is vital to obtain reliable uplift (or subsidence) deformation velocity fields and assess the associated uncertainties. In this study, by thoroughly considering the effects of hydrological loading (HYDL) that dominates the seasonal fluctuations and common mode error (CME), we analyzed the optimal noise characteristics of GNSS vertical time series at 39 stations spanning from January 2011 to August 2019 in the Chuandian region, southeast of the Qinghai–Tibet Plateau. Our results showed that the optimal noise models without HYDL correction were white noise plus flicker noise (WN + FN), white noise plus power law noise (WN + PL), and white noise plus Gauss–Markov noise (WN + GGM), which accounted for 87%, 10%, and 3% of GNSS stations, respectively. By contrast, the optimal noise models at all stations were WN + FN and WN + PL after correction by different HYDLs. The correlation between CME and HYDL provided by the School and Observatory of Earth Sciences (EOST), namely EOST_HYDL, was 0.63~0.8 and the value of RMS reduction was 18.9~40.3% after removing EOST_HYDL time series from the CME, with a mean value of 31.8%, there is a good correlation and consistency between CME and EOST_HYDL. The absolute value of vertical velocity and its uncertainty with and without EOST_HYDL correction varied from 0.11 to 0.55 mm/a and 0 to 0.23 mm/a, respectively, implying that the effect of HYDL should not be neglected when performing optimal noise model analysis for GNSS vertical time series in the Chuandian region.

Accurate noise model identification for GNSS time series is crucial for obtaining a reliable GNSS velocity field and its uncertainty for various studies in geodynamics and geodesy. Here, by comprehensively considering time span and missing data effect on the noise model of GNSS time series, we used four combined noise models to analyze the duration of the time series (ranging from 2 to 24 years) and the data gap (between 2% and 30%) effects on noise model selection and velocity estimation at 72 GNSS stations spanning from 1992 to 2022 in global region together with simulated data. Our results show that the selected noise model have better convergence when GNSS time series is getting longer. With longer time series, the GNSS velocity uncertainty estimation with different data gaps is more homogenous to a certain order of magnitude. When the GNSS time series length is less than 8 years, it shows that the flicker noise and random walk noise and white noise (FNRWWN), flicker noise and white noise (FNWN), and power law noise and white noise (PLWN) models are wrongly estimated as a Gauss–Markov and white noise (GGMWN) model, which can affect the accuracy of GNSS velocity estimated from GNSS time series. When the GNSS time series length is more than 12 years, the RW noise components are most likely to be detected. As the duration increases, the impact of RW on velocity uncertainty decreases. Finally, we show that the selection of the stochastic noise model and velocity estimation are reliable for a time series with a minimum duration of 12 years.

In view of the fact that there is no unified understanding of the GNSS horizontal velocity field in the Beijing Plain and the serious land subsidence in this area, we collected GNSS data from 2011 to 2021 and Sentinel 1A data from 2017 to 2021 and conducted high-precision GNSS data processing and PS-InSAR verification in order to determine the reason for the differences in the GNSS horizontal velocity field in the Beijing Plain. The results show that, under the stable Eurasian framework, the horizontal velocity of GNSS stations in the Beijing Plain is significantly inconsistent. The velocity of all GNSS stations ranged from −1.32 to 10.41 mm/yr in the E component and from −8.83 to 3.00 mm/yr in the N component. From 2011 to 2021, there was significant uneven land subsidence in the Beijing Plain, and the maximum land subsidence rate from 2017 to 2021 reached 107 mm/yr. In analyzing the observation data of the GNSS and InSAR, we conclude that the land subsidence in the Beijing Plain will indeed affect the GNSS horizontal velocity field in the subsidence area. Under the EURA_I08 reference framework, the horizontal deformation field in the Beijing Plain is mainly caused by the tectonic activity-derived overall SEE-direction movement, accompanied by the velocity field anomaly caused by local land subsidence.