Abstract
In recent years, the potential of Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) for monitoring sea-level variations has been explored extensively. However, most studies have typically selected coastal sites with optimal GNSS-IR observation conditions, often limiting verification to one or two stations and overlooking other viable sites. This study introduces a site-evaluation strategy named facilitated interferometric reflectometry evaluation (FIRE), which utilizes two constructed indices—normalized available observation time and normalized sampling deficiency—to assess the suitability of GNSS sites for sea-level retrieval. Implemented in Hong Kong, China, the strategy was applied to all 19 sites within the local reference GNSS network. In addition to the two sites previously used, we identified five additional sites conducive to tidal GNSS-IR, demonstrating precisions between 0.07 m and 0.37 m and correlations ranging from 0.986 to 0.711. With denser GNSS-IR observations, we were able to map sea-level variations during three historic typhoon storm surges in the region more precisely: 2017 Typhoon Hato, 2018 Typhoon Mangkhut, and 2023 Typhoon Saola. Typhoon Saola presented a longer duration of six days and a slightly lower sea-level peak between 2.88 m and 3.13 m. The deployment of additional tidal GNSS-IR sites revealed variations in sea levels during typhoon storm surges, with differences influenced by coastline topography of up to 1.24 m observed during Typhoon Mangkhut. These sites offer a valuable supplement to traditional tide measurements, filling gaps in historical records. The FIRE strategy demonstrates the untapped potential of existing GNSS networks globally for sea-level monitoring and can be employed to unlock further observational opportunities.
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Data availability
The GNSS data and related information in Hong Kong can be found at https://www.landsd.gov.hk/. Precise orbit products are at ftp://ftp.gfz-potsdam.de/pub/GNSS/products/mgex/. The TPXO9-atlas model is obtained through https://www.tpxo.net/home. Tide gauge data are at https://www.ioc-sealevelmonitoring.org.
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Acknowledgements
We express our gratitude to Professor Kristine Larson for developing the Python software package gnssrefl (https://github.com/kristinemlarson/gnssrefl) and MATLAB code for the Fresnel zones demonstration (https://geodesy.noaa.gov/gps-toolbox/GNSS-IR.htm), also for providing guidance on GNSS-IR studies. Oregon State University contributed to the TPXO9-atlas model. GNSS data and tide gauge records from the Hong Kong Special Administration Region Lands Department and Hong Kong Observatory were essential to this research. The plotting of geographic maps utilized the Generic Mapping Tools (Wessel et al. 2013). GFZRNX from GFZ was used for the data pre-processing (Nischan 2016). We would also like to express our appreciation to the two anonymous reviewers whose insightful comments significantly enhanced the quality of this paper.
Funding
This research was funded by the National Natural Science Foundation of China (Grant 42074024) and Guangdong Provincial Key Laboratory of Geophysical High-resolution Imaging Technology (Grant No. 2022B1212010002).
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KC conceived the application of the GNSS-IR technique to Typhoon Saola of 2023. HC analyzed the data and verified the results. HC and KC wrote and discussed the manuscript.
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Chai, H., Chen, K. Facilitated interferometric reflectometry evaluation and its application in monitoring three typhoon storm surges in Hong Kong with multi-GNSS constellation. GPS Solut 28, 99 (2024). https://doi.org/10.1007/s10291-024-01642-6
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DOI: https://doi.org/10.1007/s10291-024-01642-6