Skip to main content
SpringerLink
Log in

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

  • Published:
Pure and Applied Geophysics Aims and scope Submit manuscript

Abstract

Based on combined data of the Global Positioning System (GPS), Global Land Data Assimilation System (GLDAS), and Gravity Recovery and Climate Experiment (GRACE), the seasonal hydrological loading over the Asian continent is characterized in this study. The hydrological loading effects over the Asian continent display strong latitude dependence. The significant hydrological loading effects appear at the GPS stations situated in the coastal areas, some regions near large rivers and lakes, and high-latitude areas in Russia, as evidenced by the fact that a large root mean square (RMS) and high percentage of the variance related to the annual signal modeled by singular spectrum analysis (SSA) for each measurement are cumulated at the stations located in these regions. In contrast, the hydrological loading effects are not pronounced in mid-latitude areas of the Asian continent (e.g., Central Asia, northern and plateau regions of China), which is due to the high topographical variability and scarce water resources in these regions. Then, the cross wavelet transform (XWT) is used to quantify the consistency between different data sets. For the data sets of GPS/GLDAS, the XWT-based semblance for 64% of the stations reaches above 0.8, while it reaches above 0.8 for 48% for the data sets of GPS/GRACE, indicating that the data sets of GPS/GLDAS present better consistency. In addition, we also discuss the effects of hydrological loading on GPS observations from the RMS value, noise characteristic, and velocity uncertainty. After applying the hydrological loading correction, the RMS values of almost all GPS observations are reduced with different amplitudes, implying that the hydrological loading correction can reduce the RMS values of most GPS observations in the Asian continent. Meanwhile, the variations of noise and velocity uncertainty suggest that hydrological loading has changed the noise characteristic of almost all GPS observations, and thus lead to the overestimation of velocity uncertainty.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  • Altamimi, Z., Rebischung, P., Me´tivier, L., & Collilieux, X. (2016). ITRF2014: A new release of the international terrestrial reference frame modelling nonlinear station motions. Journal of Geophysical Research Solid Earth,121(8), 6109–6131.

    Article  Google Scholar 

  • Blewitt, G., Lavalle, D., Clarke, P., & Nurutdinov, K. (2001). A new global mode of Earth deformation: seasonal cycle detected. Science,294(5550), 2342–2345.

    Article  Google Scholar 

  • Bock, Y., Kedar, S., Moore, A. W., Fang, P., et al. (2016). Twenty-two years of combined gps products for geophysical applications and a decade of seismogeodesy. International Association of Geodesy Symposia (pp. 1–6). Berlin: Springer.

    Google Scholar 

  • Bos, M., Fernandes, R., Williams, S., & Bastos, L. (2013). Fast error analysis of continuous GNSS observations with missing data. Journal of Geodesy,87, 351–360.

    Article  Google Scholar 

  • Bos, M. S., Williams, S. D. P., Araújo, I. B., & Bastos, L. (2014). The effect of temporal correlated noise on the sea level rate and acceleration uncertainty. Geophysical Journal International,196(3), 1423–1430.

    Article  Google Scholar 

  • Broomhead, D., & King, G. P. (1986). Extracting qualitative dynamics from experi-mental data. Physica D Nonlinear Phenomena,20, 217–236.

    Article  Google Scholar 

  • Carrere, L., & Lyard, F. (2003). Modeling the barotropic response of the global ocean to atmospheric wind and pressure forcingcomparisons with observations. Geophysical Research Letters,30, 1275.

    Article  Google Scholar 

  • Chen, Q., Dam, T. V., Sneeuw, N., Collilieux, X., Weigelt, M., & Rebischung, P. (2013). Singular spectrum analysis for modeling seasonal signals from GPS time series. Journal of Geodynamics,72(12), 25–35.

    Article  Google Scholar 

  • Farrell, W. E. (1972). Deformation of the earth by surface loads. Reviews of Geophysics,10(3), 761–797.

    Article  Google Scholar 

  • Fritsche, M., Doll, P., & Dietrich, R. (2012). Global-scale validation of model-based load deformation of the earth’s crust from continental watermass and atmospheric pressure variations using GPS. Journal of Geodynamics,59–60, 133–142.

    Article  Google Scholar 

  • Fu, Y., Freymueller, J. T., & Jensen, T. (2012). Seasonal hydrological loading in southern Alaska observed by GPS and GRACE. Geophysical Research Letters. https://doi.org/10.1029/2012GL052453.

    Article  Google Scholar 

  • Geoffrey, B., & David, L. (2002). Effect of annual signals on geodetic velocity. Journal of Geophysical Research Solid Earth,107, 9–11. (ETG-1-ETG 9-11).

    Google Scholar 

  • Grinsted, A., Moore, J. C., & Jevrejeva, S. (2004). Application of the cross wavelet transform and wavelet coherence to geophysical time series. Nonlinear Processes in Geophysics,11(5/6), 561–566.

    Article  Google Scholar 

  • Gu, Y., Yuan, L., Fan, D., You, W., & Su, Y. (2016). Seasonal crustal vertical deformation induced by environmental mass loading in mainland china derived from GPS, GRACE and surface loading models. Advances in Space Research,59(1), 88–102.

    Article  Google Scholar 

  • Jiang, W., Li, Z., Dam, T. V., & Ding, W. (2013). Comparative analysis of different environmental loading methods and their impacts on the GPS height time series. Journal of Geodesy,87(7), 687–703.

    Article  Google Scholar 

  • Klos, A., Bos, M. S., & Bogusz, J. (2018). Detecting time-varying seasonal signal in gps position time series with different noise levels. GPS Solutions,22(1), 21.

    Article  Google Scholar 

  • Langbein, J. (2004). Noise in two-color electronic distance meter measurements revisited. Journal of Geophysical Research Solid Earth,109, B04406.

    Google Scholar 

  • Li, W., Dam, T. V., Li, Z., & Shen, Y. (2016). Annual variation detected by GPS, GRACE and loading models. Studia Geophysica et Geodaetica,60(4), 1–14.

    Article  Google Scholar 

  • Li, Z., Yue, J. P., Li, W., Lu, D. K., et al. (2017a). Investigating mass loading contributes of annual GPS observations for the Eurasian plate. Journal of Geodynamics,111, 43–49.

    Article  Google Scholar 

  • Li, Z., Yue, J. P., Li, W., Lu, D. K., Li, X., et al. (2017b). A comparison of hydrological deformation using GPS and global hydrological model for the Eurasian plate. Advances in Space Research,60, 587–596.

    Article  Google Scholar 

  • Liu, R., Li, J., Fok, H. S., Shum, C. K., & Li, Z. (2014). Earth surface deformation in the north china plain detected by joint analysis of grace and GPS data. Sensors,14(10), 19861.

    Article  Google Scholar 

  • Loomis, B. D., & Luthcke, S. B. (2016). Mass evolution of mediterranean, black, red, and caspian seas from grace and altimetry: accuracy assessment and solution calibration. Journal of Geodesy,91(2), 1–12.

    Google Scholar 

  • Luthcke, S. B., Sabaka, T. J., Loomis, B. D., Arendt, A. A., Mccarthy, J. J., & Camp, J. (2013). Antarctica, greenland and gulf of alaska land-ice evolution from an iterated grace global mascon solution. Journal of Glaciology,59(216), 613–631.

    Article  Google Scholar 

  • Nikolaidis, R. (2002). Observation of Geodetic and Seismic Deformation with the Global Positioning System Ph.D. Thesis. University of California, San Diego.

  • Plaut, G., & Vautard, R. (1994). Spells of low-frequency oscillations and weather regimesin the northern hemisphere. Journal of the atmospheric sciences,51, 210–236.

    Article  Google Scholar 

  • Ray, J., Griffiths, J., Collilieux, X., & Rebischung, P. (2013). Sub-seasonal GNSS positioning errors. Geophysical Research Letters,40, 5854–5860.

    Article  Google Scholar 

  • Rodell, M., Houser, P. R., Jambor, U., et al. (2004). The global land data assimilation system. Bulletin of the American Meteorological Society,85, 381–394.

    Article  Google Scholar 

  • Tesmer, V., Steigenberger, P., van Dam, T., et al. (2011). Vertical deformations from homogeneously processed GRACE and global GPS long-term series. Journal of Geodesy,85(5), 291–310.

    Article  Google Scholar 

  • Torrence, C., & Compo, G. P. (1998). A practical guide to wavelet analysis. Bulletin of the American Meteorological society,79, 61–78.

    Article  Google Scholar 

  • Tregoning, P., Watson, C., Ramillien, G., et al. (2009). Detecting hydrological deformation using GRACE and GPS. Geophysical Research Letters. https://doi.org/10.1029/2009GL038718.

    Article  Google Scholar 

  • van Dam, T., Collilieux, X., Rebischung, P., Ray, J., Altamimi, Z. (2011). Quantifying load model errors by comparison to a global GPS time series solution, in AGU Fall Meeting Abstracts, G53B-0905.

  • van Dam, T., Wahr, J., & Lavalle’e, D. (2007). A comparison of annual vertical crustal displacements from GPS and gravity recovery and climate experiment (GRACE) over Europe. Journal of Geophysical Research,112, B03404.

    Google Scholar 

  • Vautard, R., & Ghil, M. (1989). Singular spectrum analysis in nonlinear dynamics, with applications to paleoclimatic time series. Physica D Nonlinear Phenomena,35, 395–424.

    Article  Google Scholar 

  • Vautard, R., Yiou, P., & Ghil, M. (1992). Singular-spectrum analysis: A toolkit forshort, noisy chaotic signals. Physica D Nonlinear Phenomena,58, 95–126.

    Article  Google Scholar 

  • Wu, Y., Zhao, Q., Zhang, B., & Wu, W. (2017). Characterizing the seasonal crustal motion in Tianshan area using GPS, GRACE and surface loading models. Remote Sensing,9(12), 1303.

    Article  Google Scholar 

  • Wunsch, C., Heimbach, P., Ponte, R. M., Fukumori, I., & ECCOGODAE Consortium Members. (2009). Global general circulation of the ocean estimated by the ECCO-consortium. Oceanography,22, 88–103.

    Article  Google Scholar 

  • Xiang, Y., Yue, J. P., Tang, K., Li, Z., et al. (2018). Joint analysis of seasonal oscillations derived from GPS observations and hydrological loading for mainland China. Advances in Space Research. https://doi.org/10.1016/j.asr.2018.08.028.

    Article  Google Scholar 

  • Xu, C. (2016). Investigating mass loading contributors of seasonal oscillations in GPS observations using wavelet analysis. Pure and Applied Geophysics,173(8), 2767–2775.

    Article  Google Scholar 

  • Xu, C. (2017a). Evaluating mass loading products by comparison to GPS array daily solutions. Geophysical Journal International,208(1), 24–35.

    Article  Google Scholar 

  • Xu, C. (2017b). Detecting periodic oscillations in astronomy data: revisiting wavelet analysis with coloured and white noise. Monthly Notices of the Royal Astronomical Society,466(4), 3827–3833.

    Article  Google Scholar 

  • Xu, C., & Yue, D. (2015). Monte carlo SSA to detect time-variable seasonal oscillations from GPS-derived site position time series. Tectonophysics,665, 118–126.

    Article  Google Scholar 

  • Zhang, B., Liu, L., Khan, S. A., Dam, T., Zhang, E., & Yao, Y. (2017). Transient variations in glacial mass near Upernavik Isstrøm (west Greenland) detected by the combined use of GPS and GRACE data. Journal of Geophysical Research Solid Earth,122(12), 10626–10642.

    Article  Google Scholar 

Download references

Acknowledgements

We are very grateful to EOST/IPGS loading service for providing mass loading data, and Scripps Orbit and Permanent Array Center (SOPAC) for providing GPS daily position time series. This study is support by the Fundamental Research Funds for the Central Universities (2019B60614), Postgraduate Research & Practice Innovation Program of Jiangsu Province (SJKY19_0514), and National Key R&D Program of China (no. 2018YFC1508603).

Author information

Authors and Affiliations

  1. School of Earth Sciences and Engineering, Hohai University, Nanjing, 211100, China

    Yunfei Xiang, Jianping Yue & Yin Xing

  2. College of Information Science and Engineering, Shandong Agricultural University, Taian, 271018, China

    Kanglin Cong

  3. Suzhou Industrial Park Surveying, Mapping and Geoinformation Co., Ltd., Suzhou, 215027, China

    Dongjian Cai

Authors
  1. Yunfei Xiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jianping Yue
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kanglin Cong
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yin Xing
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Dongjian Cai
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yunfei Xiang.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xiang, Y., Yue, J., Cong, K. et al. Characterizing the Seasonal Hydrological Loading Over the Asian Continent Using GPS, GRACE, and Hydrological Model. Pure Appl. Geophys. 176, 5051–5068 (2019). https://doi.org/10.1007/s00024-019-02251-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00024-019-02251-y

Keywords

Search

Navigation