Characterizing the Seasonal Hydrological Loading Over the Asian Continent Using GPS, GRACE, and Hydrological Model

Xiang, Yang; Yue, Jianping; Cong, Kanglin; Xing, Yin; Cai, Dongjian

doi:10.1007/s00024-019-02251-y

Cited by 8 publications

(4 citation statements)

References 36 publications

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“…For the European region, some scholars have not combined GPS, GRACE and the hydrological model, and the load data sources used are different (e.g., Van Dam et al, 2007;Ma, 2017). Xiang et al (2019) used GPS, LSDM and GRACE data to study the seasonal crustal deformation in the Asian continent, and found that the hydrological load effect of LSDM is better than that of GRACE, and can effectively improve GPS observations. Although some previous studies have been carried out, there are obvious differences in hydrological load effects in different regions, and the selection of hydrological models will be different.…”

Section: Introductionmentioning

confidence: 99%

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

Bian¹

2020

Acta Geodynamica Et Geomaterialia

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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.

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Section: Introductionmentioning

confidence: 99%

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

Bian¹

2020

Acta Geodynamica Et Geomaterialia

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“…When using SSA and MSSA to model GNSS position time series, a key issue is the selection of lag-window size. Some studies suggest that the 2-year window may be more suitable for the extraction of seasonal signals, and can obtain higher spectral resolution (Ghil et al, 2002;Rangelova et al, 2012;Li et al, 2017a;Xiang et al, 2019). Thus, the lag-window size is chosen as 2 years in order to extract and model the signal more accurately.…”

Section: Comparison Of Mssa and Ssamentioning

confidence: 99%

Monte Carlo MSSA to modeling GNSS position time series and its implication for crustal deformation monitoring

Xiang¹

2022

Acta Geodynamica Et Geomaterialia

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In this paper, a scheme of signal extraction and modeling for GNSS position time series based on Monte Carlo Multi-channel Singular Spectrum Analysis (MC-MSSA) is introduced, which can effectively consider the spatial correlation of different directions by processing the different components of position time series at the same time. Meanwhile, the Monte Carlo significance test is utilized to distinguish the signal from the colored noise. By comparing with Singular Spectrum Analysis (SSA), it can be confirmed that MSSA has better signal extraction and modeling performance by taking into account the correlation of different channels. Then, taking the LHAZ station as an example, MC-MSSA is utilized to simultaneously model the three components of GNSS position time series, and the trend and periodic signals are respectively identified by Kendall nonparametric test and W-correlation correlation analysis. The result denotes that MC-MSSA can effectively model the tectonic and non-tectonic signals of GNSS position time series, and the modeled signals can more intuitively reflect the dynamic movement of the station. Finally, based on the MC-MSSA-modeled tectonic signal, we characterize the crustal deformations around the eastern Tibetan Plateau, mainly including the crustal movement and strain rate change. The results suggest that the pushing movement of the Tibetan Plateau from the Indian plate is blocked by the South China block, and the crustal movement rate is obviously decreased and appears a right-handed movement trend. Meanwhile, the junction of the Tibetan Plateau and South China block has accumulated a certain amount of stress, and the tectonic activity at the junction is relatively strong and still belongs to the dangerous zone of seismic activity.

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“…The surface water, atmosphere and non-tidal ocean loads lead to the load-deformation, and especially contribute to the vertical crustal non-tectonic movement [41]. The load-deformation can be estimated using the GRACE spherical harmonic (SH) coefficients based on the theory of elastic load-deformation [42] as follows:…”

Section: ) Grace Data and Inversion Modelmentioning

confidence: 99%

Feature Extraction Algorithm Using a Correlation Coefficient Combined With the VMD and Its Application to the GPS and GRACE

et al. 2021

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Characterizing the Seasonal Hydrological Loading Over the Asian Continent Using GPS, GRACE, and Hydrological Model

Cited by 8 publications

References 36 publications

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

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

Monte Carlo MSSA to modeling GNSS position time series and its implication for crustal deformation monitoring

Feature Extraction Algorithm Using a Correlation Coefficient Combined With the VMD and Its Application to the GPS and GRACE

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