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Prospects for Imaging Terrestrial Water Storage in South America Using Daily GPS Observations

Authors

Ferreira,  Vagner
External Organizations;

Ndehedehe,  Christopher
External Organizations;

Montecino,  Henry
External Organizations;

Yong,  Bin
External Organizations;

/persons/resource/pyuan

Yuan,  Peng       
0 Pre-GFZ, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

Abdalla,  Ahmed
External Organizations;

Mohammed,  Abubakar
External Organizations;

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Citation

Ferreira, V., Ndehedehe, C., Montecino, H., Yong, B., Yuan, P., Abdalla, A., Mohammed, A. (2019): Prospects for Imaging Terrestrial Water Storage in South America Using Daily GPS Observations. - Remote Sensing, 11, 6, 679.
https://doi.org/10.3390/rs11060679


Cite as: https://gfzpublic.gfz.de/pubman/item/item_5022702
Abstract
Few studies have used crustal displacements sensed by the Global Positioning System (GPS) to assess the terrestrial water storage (TWS), which causes loadings. Furthermore, no study has investigated the feasibility of using GPS to image TWS over South America (SA), which contains the world’s driest (Atacama Desert) and wettest (Amazon Basin) regions. This work presents a resolution analysis of an inversion of GPS data over SA. Firstly, synthetic experiments were used to verify the spatial resolutions of GPS-imaged TWS and examine the resolving accuracies of the inversion based on checkerboard tests and closed-loop simulations using “TWS” from the Noah-driven Global Land Data Assimilation System (GLDAS-Noah). Secondly, observed radial displacements were used to image daily TWS. The inverted results of TWS at a resolution of 300 km present negligible errors, as shown by synthetic experiments involving 397 GPS stations across SA. However, as a result of missing daily observations, the actual daily number of available stations varied from 60–353, and only 6% of the daily GPS-imaged TWS agree with GLDAS-Noah TWS, which indicates a root-mean-squared error (RMSE) of less than 100 kg/m2. Nevertheless, the inversion shows agreement that is better than 0.50 and 61.58 kg/m2
in terms of the correlation coefficient (Pearson) and RMSE, respectively, albeit at each GPS site.