Impacts of Local Effects and Surface Loads on the Common Mode Error Filtering in Continuous GPS Measurements in the Northwest of Yunnan Province, China

While seasonal hydrological mass loading, derived from Gravity Recovery and Climate Experiment (GRACE) measurements, shows coherent spatial patterns and is an important source for the common mode error (CME) in continuous global positioning system (cGPS) measurements in Yunnan, it is a challenge to quantify local effects and detailed changes in daily GPS measurements by using GRACE data due to its low time and spatial resolutions. In this study, we computed and compared two groups of CMEs for nine cGPS sites in the northwest Yunnan province; rCMEs were computed with the residual cGPS time series having high inter-station correlations, while oCMEs were computed with all the GPS time series. The rCMEs-filtered time series had smaller variances and larger root mean square (RMS) reductions than those that were oCMEs-filtered, and when the stations local effects were not removed, spurious transient-like signals occurred. Compared with hydrological mass loading (HYDL), its combination with non-tidal atmosphere pressure and ocean mass reached a better agreement with the CME in the vertical component, with the Nash–Sutcliffe efficiency (NSE) increasing from 0.28 to 0.55 and the RMS reduction increasing from 15.19% to 33.4%, respectively. Our results suggest that it is necessary to evaluate the inter-station correlation and remove the possible noisy stations before conducting CME filtering, and that one should carefully choose surface loading models to correct the raw cGPS time series if CME filtering is not conducted.

[1]  Weiwei Li,et al.  The Consideration of Formal Errors in Spatiotemporal Filtering Using Principal Component Analysis for Regional GNSS Position Time Series , 2018, Remote. Sens..

[2]  Nico Sneeuw,et al.  Annual variations of monsoon and drought detected by GPS: A case study in Yunnan, China , 2017, Scientific Reports.

[3]  Xianghong Hua,et al.  Review of current GPS methodologies for producing accurate time series and their error sources , 2017 .

[4]  G. Blewitt,et al.  A New Global Mode of Earth Deformation: Seasonal Cycle Detected , 2001, Science.

[5]  Peng Yuan,et al.  Effects of Spatiotemporal Filtering on the Periodic Signals and Noise in the GPS Position Time Series of the Crustal Movement Observation Network of China , 2018, Remote. Sens..

[6]  Yehuda Bock,et al.  Spatiotemporal filtering using principal component analysis and Karhunen-Loeve expansion approaches for regional GPS network analysis , 2006 .

[7]  Y. Bock,et al.  Anatomy of apparent seasonal variations from GPS‐derived site position time series , 2001 .

[8]  Yunfeng Tian,et al.  Extracting the regional common‐mode component of GPS station position time series from dense continuous network , 2016 .

[9]  Yehuda Bock,et al.  Southern California permanent GPS geodetic array: Spatial filtering of daily positions for estimating coseismic and postseismic displacements induced by the 1992 Landers earthquake , 1997 .

[10]  Qi Wang,et al.  Noise analysis of continuous GPS coordinate time series for CMONOC , 2012 .

[11]  Wu Chen,et al.  Crustal vertical deformation response to different spatial scales of GRACE and GCMs surface loading , 2016 .

[12]  C. Demets,et al.  Crustal velocity field of Mexico from continuous GPS measurements, 1993 to June 2001: Implications for the neotectonics of Mexico , 2003 .

[13]  Duncan Carr Agnew,et al.  Ongoing drought-induced uplift in the western United States , 2014, Science.

[14]  Jeffrey T. Freymueller,et al.  Vertical crustal movement around the southeastern Tibetan Plateau constrained by GPS and GRACE data , 2016 .

[15]  R. Nikolaidis.  Observation of geodetic and seismic deformation with the Global Positioning System , 2002 .

[16]  P. E. O'connell,et al.  River flow forecasting through conceptual models part III - The Ray catchment at Grendon Underwood , 1970 .

[17]  R. Dill,et al.  Numerical simulations of global‐scale high‐resolution hydrological crustal deformations , 2013 .

[18]  Thomas A. Herring,et al.  Transient signal detection using GPS measurements: Transient inflation at Akutan volcano, Alaska, during early 2008 , 2011 .

[19]  D. Alsdorf,et al.  Seasonal fluctuations in the mass of the Amazon River system and Earth's elastic response , 2005 .

[20]  Hua Chen,et al.  Extracting Common Mode Errors of Regional GNSS Position Time Series in the Presence of Missing Data by Variational Bayesian Principal Component Analysis , 2020, Sensors.

[21]  S. Bettadpur,et al.  Modeling Earth deformation from monsoonal flooding in Bangladesh using hydrographic, GPS, and Gravity Recovery and Climate Experiment (GRACE) data , 2010 .

[22]  A. Moore,et al.  Sustained Water Loss in California's Mountain Ranges During Severe Drought From 2012 to 2015 Inferred From GPS , 2017 .

[23]  Michael Bevis,et al.  Trajectory models and reference frames for crustal motion geodesy , 2014, Journal of Geodesy.

[24]  J. Nash,et al.  River flow forecasting through conceptual models part I — A discussion of principles☆ , 1970 .

[25]  Xavier Collilieux,et al.  ITRF2008 plate motion model , 2011 .

[26]  Xinyu Xu,et al.  Common Mode Component and Its Potential Effect on GPS-Inferred Three-Dimensional Crustal Deformations in the Eastern Tibetan Plateau , 2019, Remote. Sens..

[27]  Markus Rothacher,et al.  Processing aspects related to permanent GPS arrays , 2000 .

[28]  Linguo Yuan,et al.  Seasonal crustal vertical deformation induced by environmental mass loading in mainland China derived from GPS, GRACE and surface loading models , 2017 .

[29]  Yuanxi Yang,et al.  Spatiotemporal filtering for regional GPS network in China using independent component analysis , 2017, Journal of Geodesy.

[30]  Jeffrey T. Freymueller,et al.  Seasonal and long-term vertical deformation in the Nepal Himalaya constrained by GPS and GRACE measurements , 2012 .