ALBERT

Beschreibung: To assess climate change effects, it is important to understand how the water cycle interacts with other Earth system processes. Redistributions of environmental masses - atmosphere, ocean, continental hydrology - induce detectable variations in the Earth's gravity field and crustal deformations, known as loading effects. The GNSS (Global Navigation Satellite System) and GRACE (Gravity Recovery And Climate Experiment) and GRACE Follow-On space gravity missions allow monitoring water mass transfers giving long time series (〉 20 years) highly complementary in terms of both spatial and temporal resolutions. South America, with its large hydrological basins, has the strongest seasonal hydrological signal in the world. The hydrological loading signal exhibits different spectral contributions resulting from the superposition of different phenomena acting at different scales. It contains several markers of climate change including changes in precipitation, water storage and extreme events. However, climate markers are not directly accessible in the signals observed by space geodesy. Reliable extraction of the hydrological part therefore requires the use of efficient signal processing method to separate the different contributions of mass redistribution and to compare the observations with the deformations predicted by geodynamical models. We apply an innovative multivariate analysis method combining MSSA (Multi-Channel Singular Spectrum Analysis) and MICA (Multidimensional Independent Component Analysis) to permanent GNSS sites in South America. We demonstrate how better inferring hydrological loading signal contributes to a better understanding of the water cycle, enhancing global inference of the mass transport at the Earth’s surface and gives significant insights on climate change driven signals.