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The Impact of Surface Loading on GNSS Stations in Africa

Authors
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Usifoh,  Saturday
1.1 Space Geodetic Techniques, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

Le,  Nhung
External Organizations;

Männe,  Benjamin
External Organizations;

Sakic,  Pierre
External Organizations;

Dodo,  Joseph
External Organizations;

/persons/resource/schuh

Schuh,  H.
1.1 Space Geodetic Techniques, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

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Citation

Usifoh, S., Le, N., Männe, B., Sakic, P., Dodo, J., Schuh, H. (2024 online): The Impact of Surface Loading on GNSS Stations in Africa. - Pure and Applied Geophysics.
https://doi.org/10.1007/s00024-024-03480-6


Cite as: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5025629
Abstract
The movement of the Earth's surface mass, including the atmosphere and oceans, as well as hydrology and glacier melting, causes the redistribution of surface loads, deformation of the solid Earth, and fluctuations in the gravity field. Global Navigation Satellite Systems (GNSS) provide useful information about the movement of the Earth's surface mass. The impact of surface loading deformation over 145 GNSS sites in Africa was investigated using vertical height time series analysis. The study investigates and quantifies the impact of surface loading on the GNSS coordinates utilizing GNSS Precise Point Positioning (PPP) approach. The German Research Center for Geosciences (GFZ) EPOS.P8 software was used to process and analyze eleven years of GPS data from all the stations, as well as dedicated hydrological and atmospheric loading correction models given by the Earth System Modeling group at Deutsches GeoForschungsZentrum (ESMGFZ). The results of the hydrological loading corrections arising from the surface-deformation were analysed to determine the extent of station improvements. The results revealed about 40% of the stations showed improvement with an average Root Mean Square Error (RMSE) residual of 7.3 mm before the application of the hydrological loading corrections and 7.1 mm Root Mean Square Error (RMSE) after the application of the hydrological loading corrections. Similarly, the atmospheric loading corrections gave an improvement of about 57%. Furthermore, the amplitude values decreased from 4.1–8.1 mm to 3.5–6.2 mm after atmospheric loading corrections. This finding presupposes that applying loading corrections to the derived time series reduces amplitude in some African regions.