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: The Amazon River estuary is the largest in the world in longitudinal extent and export of both freshwater and sediment to the ocean. Although previous studies have revealed the spatiotemporal tidal variability of the estuary, its hydrodynamics is still poorly understood. Here we evaluate the seasonal and interannual variability of the tide in the Amazon estuary and show how it is affected by the hydrological regime of the Amazon River using a high-resolution 2D hydrodynamic model. The tide was well represented with an average complex error of 16 cm in the low water season and 23 cm in the flood season. The semi-diurnal tide is highly variable at seasonal timescales, and the seasonality of the discharge affects the tidal amplitude, the geographic extent of tidal influence, the tidal wave celerity, and the tidal flow reversal. The tidal influence on water level remains detectable up to 800 km inland during the low water season while during the flood season it extends from the ocean to only 500 km. On the other hand, the upstream limit of the domain where the tide induces a periodic flow reversal is shifted by 170 km along the year due to the seasonality of the discharge. At interannual scale, anomalous hydrological discharges affect the tidal amplitude by up to 30% in the central reach of the estuary. Our findings open unprecedented opportunities to understand biogeochemical and geomorphological processes, help navigation, and assess flooding hazards.

Description: PPP-RTK (Precise Point Positioning - Real Time Kinematic) is consistently emerging as an alternative to provide centimeter level accuracy for real-time GNSS (Global Navigation Satellite Systems) positioning. This method is based on the Space State Representation (SSR) concept, which requires that errors affecting GNSS observables be either corrected or estimated in GNSS data processing. The parameters estimation is usually ensured by the application of the S-system theory. It has been shown that introducing atmospheric SSR corrections as a priori information to PPP-RTK users can significantly improve their results. PPP-RTK is especially useful in regions where only GNSS active reference stations with a sparse spatial distribution are available to users, such as South America. This region faces several challenges in GNSS positioning performance due to ionospheric disturbances, so providing high-quality ionospheric delay SSR corrections to PPP-RTK is crucial for enhancing GNSS applications there. Most PPP-RTK studies focus on geographic regions with low ionospheric activity. This contribution assesses an alternative for generating and applying SSR ionospheric corrections for simulated PPP-RTK users in regions affected by high levels of ionospheric activity. The proposed approach involves using adaptive constraints for ionospheric SSR corrections and ensuring consistency with the recommended International GNSS Service (IGS) standards for broadcasting SSR corrections to users. Preliminary results show that this approach can reduce the time required for PPP-RTK to achieve 10 centimeters horizontal accuracy by approximately 42%.

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.