Detecting hydrologic deformation using GRACE and GPS

Tregoning, Paul; Watson, CS; Ramillien, Guillaume; McQueen, Herbert; Zhang, Jianjun

doi:10.1029/2009gl038718

Cited by 183 publications

(154 citation statements)

References 23 publications

(43 reference statements)

Supporting

Mentioning

142

Contrasting

Unclassified

Order By: Relevance

“…The linear trends caused by tectonic process were removed from the time series [8,11,18]. To further analyze the correlation between the three time series, the mean, trend, annual and semiannual signals were simultaneously fitted using Equation (2) [39].…”

Section: Resultsmentioning

confidence: 99%

“…However, the seasonal vertical deformation, caused by environmental mass redistribution, can be observed through some other geodetic techniques, such as the Gravity Recovery Recovery and Climate Experiment (GRACE) and surface loading models (SLMs) [7][8][9][10][11]. The crustal vertical deformation obtained by GRACE can be used to exclude the non-tectonic impact caused by land water storage in the GPS results.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Characterizing the Seasonal Crustal Motion in Tianshan Area Using GPS, GRACE and Surface Loading Models

Zhao

Bao

et al. 2017

Remote Sensing

View full text Add to dashboard Cite

Abstract:Complex tectonic and non-tectonic movements exist in the Tianshan area. However, we have not acquired good knowledge of such movements yet. In this study, we combine Global Positioning System (GPS), the Gravity Recovery and Climate Experiment (GRACE) and Surface Loading Models (SLMs) data to study the seasonal vertical crustal displacements in the Tianshan area. The results show that all three datasets exhibit significant annual variations at all 26 local GPS stations. Correlation coefficients higher than 0.8 between the GRACE and GPS data were observed at 85% of the stations, and it became 92% when comparing GPS and SLMs. The Weighted Root Mean Squares (WRMS) reductions were 41% and 47% after removing the annual displacements of GRACE and SLMs from the GPS time series, respectively. The consistency between the GPS and SLMs data was higher than that between the GPS and GRACE data, which is mainly due to the dominant position of atmospheric loading in the study area. For the abnormal station XJYN (43 • N, 81 • E), the GPS time series showed an abnormal uplift from early 2013 to early 2015, but this not shown in the GRACE and SLMs results. We attribute this discrepancy to groundwater variations, which are not resolvable by GRACE and SLMs for small-scale regions.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Characterizing the Seasonal Crustal Motion in Tianshan Area Using GPS, GRACE and Surface Loading Models

Zhao

Bao

et al. 2017

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…In all these cases, one normally estimates a secular motion or velocity together with seasonal signals. These annual and semi-annual signals have amplitudes of few millimeters and are partially caused by atmospheric and hydrological loadings (van Dam et al 2001;Tregoning et al 2009;Bogusz and Figurski 2014), thermal expansion of ground and monuments (Yan et al 2009;Hill et al 2009), varying tropospheric delay (Munekane et al 2008) or multi-path variations (King et al 2008). Although the seasonal signals are generally modeled as having constant amplitudes and phase-lags with the parameters estimated using Weighted Least Squares (WLS), their values might vary slightly from year to year because their geophysical causes are not constant.…”

Section: Introductionmentioning

confidence: 99%

Detecting time-varying seasonal signal in GPS position time series with different noise levels

2017

View full text Add to dashboard Cite

The coordinate time series determined with the Global Positioning System (GPS) contain annual and semi-annual periods that are routinely modeled by two periodic signals with constant amplitude and phase-lag. However, the amplitude and phaselag of the seasonal signals vary slightly over time. Various methods have been proposed to model these variations such as Wavelet Decomposition (WD), writing the amplitude of the seasonal signal as a Chebyshev polynomial that is a function of time (CP), Singular Spectrum Analysis (SSA), and using a Kalman Filter (KF). Using synthetic time series, we investigate the ability of each method to capture the time-varying seasonal signal in time series with different noise levels. We demonstrate that the precision by which the varying seasonal signal can be estimated depends on the ratio of the variations in the seasonal signal to the noise level. For most GPS time series, this ratio is between 0.05 and 0.1. Within this range, the WD and CP have the most trouble in separating the seasonal signal from the noise. The most precise estimates of the variations are given by the SSA and KF methods. For real GPS data, SSA and KF can model 49-84 and 77-90% of the variance of the true varying seasonal signal, respectively.

show abstract

“…Previous research has confirmed the strong correlation between Continental Water Storage (CWS) induced vertical surface displacement and the global positioning system (GPS) height time series (van Dam et al 2001(van Dam et al , 2007Tregoning et al 2009;Fritsche et al 2012). This environmentally driven displacement adds noise to the GPS data being used for geodynamic investigations, such as postglacial rebound, sea level rise, etc.…”

Section: Introductionmentioning

confidence: 93%

The Phase 2 North America Land Data Assimilation System (NLDAS-2) Products for Modeling Water Storage Displacements for Plate Boundary Observatory GPS Stations

Dam

2015

Refag 2014

View full text Add to dashboard Cite

To remove continental water storage (CWS) signals from the GPS data, CWS mass models are needed to obtain predicted surface displacements. We compared weekly GPS height time series with five CWS models: (1) the monthly and (2) three-hourly Global Land Data Assimilation System (GLDAS); (3) the monthly and (4) one-hourly ModernEra Retrospective Analysis for Research and Applications (MERRA); (5) the six-hourly National Centers for Environmental Prediction-Department of Energy (NCEP-DOE) global reanalysis products (NCEP-R-2). We find that of the 344 selected global IGS stations, more than 77% of stations have their weighted root mean square (WRMS) reduced in the weekly GPS height by using both the GLDAS and MERRA CWS products to model the surface displacement, and the best improvement concentrate mainly in North America and Eurasia. We find that the one-hourly MERRA-Land dataset is the most appropriate product for modeling weekly vertical surface displacement caused by CWS variations. The threehourly GLDAS data ranks the second, while the GLDAS and MERRA monthly products rank the third. The higher spatial resolution MERRA product improves the performance of the CWS model in reducing the scatter of the GPS height by about 2-6% compared with the GLDAS. Under the same spatial resolution, the higher temporal resolution could also improve the performance by almost the same magnitude. We also confirm that removing the ATML and NTOL effects from the weekly GPS height would remarkably improve the performance of CWS model in correcting the GPS height by at least 10%, especially for coastal and island stations. Since the GLDAS product has a much greater latency than the MERRA product, MERRA would be a better choice to model surface displacements from CWS. Finally, we find that the NCEP-R-2 data is not sufficiently precise to be used for this application. Further work is still required to determine the reason.

show abstract

Detecting hydrologic deformation using GRACE and GPS

Cited by 183 publications

References 23 publications

Characterizing the Seasonal Crustal Motion in Tianshan Area Using GPS, GRACE and Surface Loading Models

Characterizing the Seasonal Crustal Motion in Tianshan Area Using GPS, GRACE and Surface Loading Models

Detecting time-varying seasonal signal in GPS position time series with different noise levels

The Phase 2 North America Land Data Assimilation System (NLDAS-2) Products for Modeling Water Storage Displacements for Plate Boundary Observatory GPS Stations

Contact Info

Product

Resources

About