Analysis of seasonal loading-induced displacements from GPS and GRACE

Abstract

Mass transport within the Earth system over time (e.g., hydrological circulation) induces the mass redistribution on the surface. The temporal variation of mass load on the surface consequently leads to elastic deformation of Earth’s surface (van Dam et al., 2001; Ilk et al., 2005; De Linage et al., 2007). The surface deformation could be derived from GRACE through time-variable gravity field and also be observed by IGS stations in GPS 3D coordinates. The surface deformations derived from GRACE are spatially smoothed with about 350 km resolution. However, the deformations of IGS stations observed by GPS are discrete point measurements on the globe. Therefore, a validation of the consistency between the deformations from GRACE and GPS is necessary to be done, which would benefit the further research on mass transport and climate change. In this study, using the data from GFZ, the deformations from GRACE are theoretically calculated in vertical and horizontal directions (Wahr et al., 1998; Kusche and Schrama, 2005). To investigate the disagreement between GPS and GRACE, a number of IGS stations in three regions are selected (i.e., Tibetan plateau, Danube basin and Great Lakes area) with period of 8 years (2003 – 2011). For a proper comparison, the spatial and temporal reference of GRACE and GPS need to be unified. For validation, the correlation coefficient, the Nash-Sutcliffe efficiency, and WRMS reduction are estimated. After comparisons of deformation time series, almost all the stations in those regions show good consistency between GRACE and GPS in vertical component. There is distinct disagreement in horizontal component, probably due to the weak loading signals and strong local effects. Thus, several representative stations in those regions would be discussed and analysed in detail. Furthermore, to detect an optimal filter for GRACE, 40 IGS stations in Europe are involved to evaluate the filter performance. As a result, 52.5% stations filtered by the stochastic filter (i.e., Wiener filter) show better results, which indicates the optimal choice.