Geopotential-number estimation based on regional and global geoid models towards vertical datum modernization in India
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Ana Isela Vidal-Vega
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Karan Nayak
Abstract
This study focuses on the analysis of the dataset currently available over the Indian subcontinent for the calculation of geopotential numbers and orthometric heights, mainly the performance of regional and global geoid models across diverse topographic conditions. Using Global Navigation Satellite System (GNSS) observations from 17 Asia Pacific Regional Geodetic Project (APRGP) stations distributed across plains, plateaus, coastal areas, and complex mountainous regions, we compared six geoid models: three global (EIGEN-6C4, xGM2019e_2159, SGG-UGM-2) and three regionals (IndGG-SH2021, IndGG-CUT2021, and IndGG-LSMSA2021). Interpolated gravity values, geoidal heights, and geodetic coordinates were utilized to derive geopotential numbers, which were further analyzed to estimate orthometric heights and quantify the discrepancies obtained using currently available data. Our results indicate that recent global geoid models, particularly XGM2019e_2159 and SGG-UGM-2, have improved and have consistency with respect to regional geoid models, mainly with IndGG-SH2021 in terms of accuracy and reliability. Stations located in moderate terrain zones such as Bangalore, Delhi, and Hyderabad showed minimal variations (<±15 cm) in orthometric heights, However, significant discrepancies were observed in mountainous regions like Kashmir, where orthometric height differences exceeded ±25 cm, especially with the IndGG-CUT2021 model. Based on these findings, it is strongly recommended to use gravimetric geoid models derived from satellite missions to analyze the strength of regional models that allow to observation of the trend of the results in each model to verify its consistency. Furthermore, obtain absolute gravity data for India’s vertical datum modernization.
Acknowledgments
The authors thank the International Center for Global Earth Models (ICGM) for geoidal height and gravimetric data. This work was carried out with the support (CVU: 915661) of the Secretariat of Science, Humanities, Technology and Innovation (SECIHTI).
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: All other authors state no conflict of interest.
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Research funding: This work was carried out with the support (CVU: 915661) of the Secretariat of Science, Humanities, Technology and Innovation (SECIHTI).
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Data availability: The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.
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