Application of the multi adaptive regression splines to integrate sea level data from altimetry and tide gauges for monitoring extreme sea level events

Gharineiat, Zahra and Deng, Xioali (2015) Application of the multi adaptive regression splines to integrate sea level data from altimetry and tide gauges for monitoring extreme sea level events. Marine Geodesy, 38 (3). pp. 261-276. ISSN 0149-0419

Abstract

This paper determines sea level fields with nonlinear components along the northern coast of Australia using a state-of-the-art approach of the Multi-Adaptive Regression Splines (MARS).The 20 years of data from multi-missions of satellite altimetry (e.g. Topex, Jason-1 and Jason-2)and 14 tide gauges are combined to provide a consistent view of sea levels. The MARS is chosen because it is capable of dividing measured sea levels into distinct time intervals where different linear relationships can be identified, and of weighting individual tide gauge according to the importance of their contributions to predicted sea levels. In the study area, the mean R-squared (R2) of 0.62 and Root Mean Squared (RMS) error of 6.73 cm are obtained from modelling sea levels by MARS. The comparison of the MARS with the multiple-regression shows an improved sea level prediction, as MARS can explain 62% of sea level variance while multiple-regression only accounts for 44% of variance. The predicted sea levels during six tropical cyclones are validated against sea level observations at three independent tide-gauge sites. The comparison results show a strong correlation (~99%) between modelled and observed sea levels, suggesting that the MARS can be used for efficiently monitoring sea level extremes.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Files associated with this item cannot be displayed due to copyright restrictions.
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Civil Engineering and Surveying
Date Deposited: 21 Jun 2016 02:31
Last Modified: 10 Aug 2016 01:33
Uncontrolled Keywords: satellite radar altimetry; tropical cyclone; coastal sea level; multiple regression; multi adaptive regression spline
Fields of Research : 05 Environmental Sciences > 0599 Other Environmental Sciences > 059999 Environmental Sciences not elsewhere classified
04 Earth Sciences > 0405 Oceanography > 040503 Physical Oceanography
04 Earth Sciences > 0405 Oceanography > 040599 Oceanography not elsewhere classified
Socio-Economic Objective: D Environment > 96 Environment > 9607 Environmental Policy, Legislation and Standards > 960701 Coastal and Marine Management Policy
D Environment > 96 Environment > 9699 Other Environment > 969902 Marine Oceanic Processes (excl. Climate Related)
D Environment > 96 Environment > 9602 Atmosphere and Weather > 960203 Weather
Identification Number or DOI: 10.1080/01490419.2015.1036183
URI: http://eprints.usq.edu.au/id/eprint/29071

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