Advances in estimating Sea Level Rise: A review of tide gauge, satellite altimetry and spatial data science approaches

• Main Authors: Adebisi, N., Balogun, A.-L., Min, T.H., Tella, A.
• Format: Article
• Published: Elsevier Ltd 2021
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103654057&doi=10.1016%2fj.ocecoaman.2021.105632&partnerID=40&md5=985d2d652fee8f2cf8c8c08cc12bf93b; http://eprints.utp.edu.my/30331/
• Institution: Universiti Teknologi Petronas

Significant developments have been made in the observation systems and techniques of estimating sea level towards meeting the standard accuracy requirement of Global Climate Observation Systems (GCOS). This study undertakes a systematic review of the current advances in estimating sea level change in the context of the 4th industrial revolution. Trends in the use of main observation systems such as tide gauges, satellite altimetry, and ancillary systems such as GNSS and Autonomous Surface Vehicles were explored. Crucially, we examined the contribution of dedicated waveform retracking strategies, advanced corrections and radar technology such as Ka-band altimetry of SARAL/Altika and SAR mode innovations to the progress in coastal altimetry. Further, we show the role of emerging spatial data science concepts and processing workflows in sea level study. Findings suggest that in-situ sea level observation through tide gauges remains the best approach for long-term coastal sea level study despite its limitations while satellite altimetry is suitable for contemporary global and regional scales. Detailed understating of global, regional and local mean sea level change will require an augmentation of tide gauge, satellite altimetry and other ancillary remote sensing and in situ systems. Densification of tide gauges and co-located GNSS networks at sparsely covered regions and improvement in precision of satellite altimetry data for coastal use are also essential for a fully integrated sea level observation system. From the analysis of over 30 trend models that span exploratory, parametric, non-parametric, stochastic and advanced classes in the literature, we conclude that the best model is the one with good statistical foundation and similar assumption with the sea level pattern. © 2021 Elsevier Ltd