- Title
- Coastal altimetry for sea level changes in Northern Australian coastal oceans
- Creator
- Gharineiat, Zahra
- Relation
- University of Newcastle Research Higher Degree Thesis
- Resource Type
- thesis
- Date
- 2017
- Description
- Research Doctorate - Doctor of Philosophy (PhD)
- Description
- Sea level rise is undoubtedly one of the most threatening consequences of climate change. The impact of sea level rise will be strongly felt in northern Australian coastal regions, where a rapid rising of sea levels is causing an increase in the frequency and severity of storm surge events. This dissertation investigates two main objectives: (1) the short-term local sea level variability associated with tropical cyclones, and (2) the long-term regional sea level variability and trends for the northern coasts of Australia. This study uses 21 years of sea level observations from multiple satellite altimetry missions (e.g., TOPEX/Poseidon, Jason-1, and Jason-2) and 14 tide gauges. First, it focuses on the analysis of the Non-Tidal Sea Level component of Sea Level Anomalies (SLAs), which theoretically only contains the storm surge level, and is constructed by removing the mean sea surface and ocean tides from sea level observations. The SLAs are analysed using the Power Spectral Density method to explore the tidal features in the study area. This concludes that the pointwise response method provides better ocean tidal corrections than the global tidal models, which were available at the time of this study, due to the complexity of the study area. Then, a multivariate regression (MR) model is used to predict sea level variations using both altimetry and tide gauge data. The modelled solution provides sea level predictions at the times of interest, which can be used to monitor extreme sea level events. To overcome the drawback that the MR model cannot model the non-linear variations, a new method has been developed to investigate non-linear components of sea level through the rebuilding the model using a state-of-the-art Multivariate Adaptive Regression Spline (MARS). The comparison results show that MARS can, in general, explain 62% of sea level variance while MR only accounts for 45% of the variance, suggesting an improved sea level prediction from MARS. Comparison results also indicate that the cyclone-induced surge peaks predicted by the MARS model agree well with those observed at independent validating tide gauges. Finally, to achieve the second objective, sea level observations from both datasets are used to characterise sea level trends and interannual variability over the study region. The results show that the interannual sea level fingerprint in the northern Australian coastline is closely related to El Niño Southern Oscillation (ENSO) and Madden-Julian Oscillation (MJO) events. The rate of sea level rise (6.3 ± 0.4 mm/yr) estimated from tide gauges is slightly higher than (6.1 ± 0.3 mm/yr) from altimetry in the period of 1993-2013, which varies with the length of the time interval. This study also provides a novel framework for examining the significance of sea level trends by applying the non-parametric Mann-Kendall test, which is of significance in interpreting sea level trends. Recommendations for further research are to improve the altimetry sea level measurement close to the coastline using re-tracking techniques and to investigate the potential capability of monitoring coastal sea level from new satellite altimetry data, such as Jason-CS, Jason-3, CryoSat and Saral/AltiKa. An improved understanding of sea level rise and storm surges will be helpful in evaluating the coastal flooding scenario in the high flooding risk regions.
- Subject
- sea level; satellite altimetry; Australian coastal oceans; tide gauge; tropical cyclone; storm surge; coastal altimetry
- Identifier
- http://hdl.handle.net/1959.13/1342416
- Identifier
- uon:28962
- Rights
- Copyright 2017 Zahra Gharineiat
- Language
- eng
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| Thumbnail | File | Description | Size | Format | |||
|---|---|---|---|---|---|---|---|
| View Details Download | ATTACHMENT01 | Thesis | 9 MB | Adobe Acrobat PDF | View Details Download | ||
| View Details Download | ATTACHMENT02 | Abstract | 447 KB | Adobe Acrobat PDF | View Details Download |