ALBERT

Description: The Svalbard archipelago is strongly impacted by past and present-day ice melting. This area offers the benefit that several in situ and space datasets are available at different spatial and temporal resolutions. We perform a multi-technique intercomparison for a better understanding of the different processes and extract common climate-related signals, allowing climate change signature analysis. Space geodetic techniques provide time series of daily crustal deformation and monthly gravity field variations over several years at local and regional scales. Seasonal signals included in these time series are mainly caused by the (visco)elastic response of variable mass load due to ice and snow accumulation. GNSS positioning and GRACE equivalent water height time series are compared to geophysical models based on mass redistributions and snow models. For this, we applied specific data analysis methods to accurately separate the different sources and reveal climate change signature from seasonal signals. In addition, ground gravimetry, field datasets, sediment analysis, Sentinel observations, aerial photography, and laser telescan are used to characterize the evolution of the area. These datasets are used to produce maps representing the marine sedimentary facies and glacial environments of different glaciers. We estimate the footprints of glacier retreat and ice thickness loss by monitoring the evolution of the coastline and follow the hydrological network for different glaciers (Kronebreen and Lovenbreen) and fjords to show the contraction of the glacier's drainage during melting. The joint analysis of the maps and space geodesy time series gives the estimation of crustal uplift due to glacier melt.

Description: 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.