Seasonal fluctuations in the mass of the Amazon River system and Earth's elastic response

Bevis, Michael; Alsdorf, Douglas; Kendrick, Eric; Fortes, Luiz Paulo Souto; Forsberg, Bruce R.; Smalley, Robert; Becker, J. M.

doi:10.1029/2005gl023491

Cited by 161 publications

(122 citation statements)

References 17 publications

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“…Because a great many GNET stations are located in the near-field of these load changes, the earth's response will be sensitive to the elastic structure of the upper and lower crust (19,20), which is much more variable than deeper earth structure and is not well known in Greenland. Recently installed seismic networks will soon help in this regard.…”

Section: Discussionmentioning

confidence: 99%

Bedrock displacements in Greenland manifest ice mass variations, climate cycles and climate change

Bevis

Wahr

Khan³

et al. 2012

Proc. Natl. Acad. Sci. U.S.A.

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127

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The Greenland GPS Network (GNET) uses the Global Positioning System (GPS) to measure the displacement of bedrock exposed near the margins of the Greenland ice sheet. The entire network is uplifting in response to past and present-day changes in ice mass. Crustal displacement is largely accounted for by an annual oscillation superimposed on a sustained trend. The oscillation is driven by earth's elastic response to seasonal variations in ice mass and air mass (i.e., atmospheric pressure). Observed vertical velocities are higher and often much higher than predicted rates of postglacial rebound (PGR), implying that uplift is usually dominated by the solid earth's instantaneous elastic response to contemporary losses in ice mass rather than PGR. Superimposed on longer-term trends, an anomalous 'pulse' of uplift accumulated at many GNET stations during an approximate six-month period in 2010. This anomalous uplift is spatially correlated with the 2010 melting day anomaly.climate change | climate cycles | elasticity | crustal motion geodesy

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Section: Discussionmentioning

confidence: 99%

Bedrock displacements in Greenland manifest ice mass variations, climate cycles and climate change

Bevis

Wahr

Khan³

et al. 2012

Proc. Natl. Acad. Sci. U.S.A.

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127

125

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“…Annual displacement cycles at POVE are driven by seasonal variations in the mass of water residing in the Central Amazon Basin (e.g. Bevis et al 2005). The vertical cycle has a peak to peak amplitude close to 40 mm, about an order of magnitude larger than the horizontal cycles.…”

Section: The Evolution Of Station Trajectory Modelsmentioning

confidence: 99%

Trajectory models and reference frames for crustal motion geodesy

Bevis

Brown

2014

J Geod

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We sketch the evolution of station trajectory models used in crustal motion geodesy over the last several decades, and describe some recent generalizations of these models that allow geodesists and geophysicists to parameterize accelerating patterns of displacement in general, and postseismic transient deformation in particular. Modern trajectory models are composed of three sub-models that represent secular trends, annual oscillations, and instantaneous jumps in coordinate time series. Traditionally the trend model invoked constant station velocity. This can be generalized by assuming that position is a polynomial function of time. The trajectory model can also be augmented as needed, by including one or more logarithmic transients in order to account for typical multi-year patterns of postseismic transient motion. Many geodetic and geophysical research groups are using general classes of trajectory model to characterize their crustal displacement time series, but few if any of them are using these trajectory models to define and realize the terrestrial reference frames (RFs) in which their time series are expressed. We describe a global GPS reanalysis program in which we use two general classes of trajectory model, tuned on a station by station basis. We define the network trajectory model as the set of station trajectory models encompassing every station in the network. We use the network trajectory model from the each global analysis to assign prior position estimates for the next round of GPS data processing. We allow our daily orbital solutions to relax so as to maintain their consistency with the network polyhedron. After several iterations we produce GPS time series expressed in a RF similar to, but not identical with ITRF2008.M. Bevis (B) · A. Brown Division of Geodetic Sciences, School of Earth Sciences, Ohio State University, Columbus, OH, USA e-mail: mbevis@osu.edu We find that each iteration produces an improvement in the daily repeatability of our global time series and in the predictive power of our trajectory models.

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“…Site-position time series recorded by continuous GPS arrays have revealed the vertical displacement variations resulted from trend or seasonal distribution of mass in a region or global changes, e.g. a change of continental water (Bevis et al, 2005;van Dam et al, 2007;Wahr et al, 2013), ice (Sauber et al, 2000;Khan et al, 2010;Nielsen et al, 2013), snow (Heki, 2001;Grapenthin et al, 2006), ocean Wahr et al, 2014), and atmospheric mass (van Dam et al, 1994;Boehm et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Detecting seasonal and long-term vertical displacement in the North China Plain using GRACE and GPS

Wang

Chen

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. In total, 29 continuous Global Positioning System (GPS) time series data together with data from Gravity Recovery and Climate Experiment (GRACE) are analysed to determine the seasonal displacements of surface loadings in the North China Plain (NCP). Results show significant seasonal variations and a strong correlation between GPS and GRACE results in the vertical displacement component; the average correlation and weighted root-meansquares (WRMS) reduction between GPS and GRACE are 75.6 and 28.9 % respectively, when atmospheric and nontidal ocean effects were removed, but the annual peak-topeak amplitude of GPS (1.2-6.3 mm) is greater than the data (1.0-2.2 mm) derived from GRACE. We also calculate the trend rate as well as the seasonal signal caused by the mass load change from GRACE data; the rate of GRACEderived terrestrial water storage (TWS) loss (after multiplying by the scaling factor) in the NCP was 3.39 cm yr −1 (equivalent to 12.42 km 3 yr −1 ) from 2003 to 2009. For a 10-year time span (2003 to 2012), the rate loss of TWS was 2.57 cm yr −1 (equivalent to 9.41 km 3 yr −1 ), which is consistent with the groundwater storage (GWS) depletion rate (the rate losses of GWS were 2.49 and 2.72 cm yr −1 during 2003-2009 and 2003-2012 respectively) estimated from GRACE-derived results after removing simulated soil moisture (SM) data from the Global Land Data Assimilation System (GLDAS)/Noah model. We also found that GRACEderived GWS changes are in disagreement with the groundwater level changes from observations of shallow aquifers from 2003 to 2009, especially between 2010 and 2013. Although the shallow groundwater can be recharged from the annual climate-driven rainfall, the important facts indicate that GWS depletion is more serious in deep aquifers. The GRACE-derived result shows an overall uplift in the whole region at the 0.37-0.95 mm yr −1 level from 2004 to 2009, but the rate of change direction is inconsistent in different GPS stations at the −0.40-0.51 mm yr −1 level from 2010 to 2013. Then we removed the vertical rates, which are induced by TWS from GPS-derived data, to obtain the corrected vertical velocities caused by tectonic movement and human activities. The results show that there are uplift areas and subsidence areas in NCP. Almost the whole central and eastern region of NCP suffers serious ground subsidence caused by the anthropogenic-induced groundwater exploitation in the deep confined aquifers. In addition, the slight ground uplifts in the western region of NCP are mainly controlled by tectonic movement (e.g. Moho uplifting or mantle upwelling).

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Seasonal fluctuations in the mass of the Amazon River system and Earth's elastic response

Cited by 161 publications

References 17 publications

Bedrock displacements in Greenland manifest ice mass variations, climate cycles and climate change

Bedrock displacements in Greenland manifest ice mass variations, climate cycles and climate change

Trajectory models and reference frames for crustal motion geodesy

Detecting seasonal and long-term vertical displacement in the North China Plain using GRACE and GPS

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