Comparisons of GRACE and GLDAS derived hydrological loading and the impacts on the GPS time series in Europe

Abstract

The surface displacement caused by hydrological loading makes an important contribution to the non-linear crustal movement observed at the International Global Navigation Satellite System Service (IGS) stations. In this paper, the amplitude, correlation, and root mean square (RMS) of the vertical displacement time series signals of 47 IGS stations are used to analyze which data of Gravity Recovery and Climate Experiment (GRACE) or Global Land Data Assimilation System (GLDAS) can better reflect the hydrological load effect in Europe. The results show that in Europe, the hydrological load effect calculated based on GRACE data is more accurate than that of GLDAS, which has not been reported before. Then, the relationship between the GPS height and GRACE load deformation in terms of annually-oscillating signals, correlation, and phase is analyzed by using singular spectrum analysis, the Pearson correlation coefficient, and wavelet coherence (WTC). It was found that GPS and GRACE agree at some stations (e.g., BOR1 and ZIMM), while they differ significantly in amplitude and phase at other stations (e.g., KIRU and NOT1), indicating that not all GRACE-derived displacements of IGS stations can clearly explain their nonlinear motion. The correlation coefficients between GPS and GRACE are higher than 0.7 at 85 % of stations. Amongst them, the values are obviously greater than 0.8 (e.g., ZIMM and LAMA) around inland areas and high mountains, and even less than 0.6 (e.g., ANKR and KIRU) along the coast of the Mediterranean ocean, which more precisely shows that the hydrological load effect has obvious spatial and regional characteristics compared with previous studies. In addition, the relative phase of the WTC solution is basically consistent under non-detrend and detrend, which shows that the relative phase difference of each station is only related to the nonlinear movement and not to the linear trend caused by the tectonic deformation. Finally, we study the influence of GRACE hydrological load on the RMS of GPS height, which is reduced by 24.60 % on average, and the reduction rate distribution of the RMS is in good agreement with the spatial distribution of the correlation coefficient.

An Innovative Slepian Approach to Invert GRACE KBRR for Localized Hydrological Information at the Sub-Basin Scale

“…The KBRR inversion using SF for both cases of the Congo and Nile basins, yield results that are very comparable with those of the GLDAS model for frequency (temporal variations) and latitudinal zonation (spatial variations). In terms of intensity, a relatively constant and clear bias is observed like many authors have observed before (in South America [37], Africa [38], Poland [39], and at global scale [40]) and it corresponds, in our case, to a factor~2. So, in order to be able to compare the estimated SF maps, we have chosen to divide the outputs of the GLDAS simulation by 2, at least to ease the graphical representation using the same color scale (Figure 3).…”

Modeling of Residual GNSS Station Motions through Meteorological Data in a Machine Learning Approach

“…The comparison to the state of the art in literature is not directly possible, as every study uses a different set of stations and loading models. Similar studies, which also focus on the area of Europe, are Bian [5], achieving an average RMS reduction of 16-25%, and Springer et al [28], obtaining 20-30% with NTAL and NTOL and adding another 7% with HYDL.…”

“…In the southern hemisphere, most of their test stations were located closer to coastal regions, and the assumption was that the missing improvement can be traced back to mis-modeled tropospheric zenith delay. Recent studies look into the comparison of different environmental surface loading models [5][6][7][8], the comparison of geophysical models and information drawn from the Gravity Recovery and Climate Experiment (GRACE) [9], and the assimilation of both approaches [10,11]. Up to today, there is no standardized procedure of modeling and reducing the station position by environmental surface loadings [12].…”

Identifying the sensitivity of GPS to non-tidal loadings at various time resolutions: examining vertical displacements from continental Eurasia

“…Non-tidal oceanic loading appears to be significant only for the coastal regions and islands, while nontidal atmospheric loading seems to dominate over Eastern Europe and Asia (Brondeel and Willems 2003;Williams and Penna 2011). Williams and Penna (2011) and Bian et al (2020) showed that removing loading-predicted displacements from the Global Positioning System (GPS) displacement time series leads to an improvement in their standard deviations. The standard deviations improve even more when the same loading effects are applied at the observation level (Dach et al 2011;Männel et al 2019).…”