Seasonal hydrological loading in southern Alaska observed by GPS and GRACE

Abstract

We compare vertical seasonal loading deformation observed by continuous GPS stations in southern Alaska and modeled vertical displacements due to seasonal hydrological loading inferred from GRACE. Seasonal displacements are significant, and GPS‐observed and GRACE‐modeled seasonal displacements are highly correlated. We define a measure called the WRMS Reduction Ratio to measure the fraction of the position variations at seasonal periods removed by correcting the GPS time series using a seasonal model based on GRACE. The median WRMS Reduction Ratio is 0.82 and the mean is 0.73 ± 0.26, with a value of 1.0 indicating perfect agreement of GPS and GRACE. The effects of atmosphere and non‐tidal ocean loading are important; we add the AOD1B de‐aliasing model to the GRACE solutions because the displacements due to these loads are present in the GPS data, and this improves the correlations between these two geodetic measurements. We find weak correlations for some stations located in areas where the magnitude of the load changes over a short distance, due to GRACE's limited spatial resolution. GRACE models can correct seasonal displacements for campaign GPS measurements as well.

Earth Surface Deformation in the North China Plain Detected by Joint Analysis of GRACE and GPS Data

“…Davis et al [10] found high correlation of annual hydrologic variations between GRACE- and GPS-derived vertical surface displacements from their respective residual height time series in the Amazon Basin. Consistent seasonal displacements between GPS and GRACE have been demonstrated in West Africa [11], the Nepal Himalayas [12] and southern Alaska [13]. Perhaps due to technical errors in GPS data processing, poorer agreement between GRACE and GPS has also been reported over Europe [14] and over Central America [15].…”

Analysis of systematic differences from GPS-measured and GRACE-modeled deformation in Central Valley, California

“…Figure 7 is a conceptualization of this process. Several studies have shown that GRACE gravity observations can model the earth's annual displacement (Davis et al, 2004;van Dam et al, 2007;Tregoning et al, 2009;Tesmer et al, 2011;Fu et al, 2012aFu et al, , 2012bNahmani et al, 2012). But, for GRACE, a discrepancy exists when comparing with GPS solutions for groundwater induced surface motions.…”

“…Later, Davis et al (2004) suggested that annual displacement over the Amazon River Basin measured by GPS and modeled by GRACE (Gravity Recovery and Climate Experiment) data show a high agreement that proved the feasibility of deriving crustal seasonal variations from GRACE. Recent studies have focused on the combination of GPS, GRACE and loading models to infer earth's annual deformation (Dong et al, 2006;van Dam et al, 2007;Tregoning et al, 2009;Tesmer et al, 2011;Fu et al, 2012aFu et al, , 2012bNahmani et al, 2012;Demir et al, 2014).…”

Improving InSAR geodesy using Global Atmospheric Models

“…Long-wavelength deformation signals, such as those expected along a subduction zone for instance [e.g., Béjar-Pizarro et al, 2013], can trade off with inaccurate satellite orbits, oceanic tidal load signals [DiCaprio and Simons, 2008], hydrological load signals [Fu et al, 2012], and long-wavelength variations in atmospheric stratification. Therefore, orbital parameters, which mimic long-wavelength phase variations, are often estimated during the inversion for tectonic parameters (i.e., slip rate, slip distributions,…), introducing more variability in the inversion process.…”

“…As shown by DiCaprio and Simons [2008], oceanic tidal load signals can be modeled and removed, while models are currently being developed to predict the influence of seasonal hydrological load on continents [e.g., Fu et al, 2012]. As a consequence, by using external data, such as GPS [e.g., Tong et al, 2013, Béjar-Pizarro et al, 2013, to constrain the residual orbital errors, or as the quality of estimated orbits should drastically increase with the future SAR missions, our method will allow one to decipher between long-wavelength atmospheric signals and long-wavelength deformation signals.…”