全球海水面上升衍生出許多嚴重的問題，例如危及居住於沿海低窪地區人們的生命財產安全，因此瞭解及監測沿岸海水面變化成為一重要的課題。本研究使用全球導航衛星系統(Global Navigation Satellite System, GNSS)之訊噪比(Signal-to-Noise Ratio, SNR)資料，透過Lomb Scargle Periodogram (LSP)法、調和分析輔助LSP法以及最小二乘平差反演法分別推求臺灣高雄港、蘇澳港、中央大學臨海工作站(TaiCOAST)及瑞典Onsala Space Observatory (OSO)海水面高度，推求之海水面變化會進一步與鄰近或共站潮位站之海水面變化比較並進行精度評估。結果顯示，利用高雄港和蘇港港之GNSS SNR資料所推求的海水面變化與傳統驗潮站觀測有良好的一致性，其差值之標準偏差介於7.1 -11.1公分，相關係數為0.94-0.97，而兩GNSS驗潮站皆可提供超過80%的有效海水面高度觀測量。利用高雄港2006-2011期間之地表垂直變動速率及相對海水面變化趨勢可求得絕對海水面變化趨勢為 mm/yr，與衛星測高所求得趨勢 mm/yr相吻合。然而，由於高雄港數據所涵蓋時間較短(5年)，導致所求之最佳估值與不確定性幾乎為同一量級，故此比較成果較不顯著。相較之下，中央大學臨海工作站僅能提供40%的有效海水面觀測量，與驗潮站觀測量差異之標準偏差高達1.12公尺，相關係數僅0.13。精度差的原因為該地區在退潮時，所接收之反射訊號大多來自潮間帶，導致該站監測海水面變化能力欠佳。瑞典之GNSS驗潮站利用LSP及最小二乘平差法皆可提供90%以上的有效海水面觀測量。其中，利用最小二乘平差反演法所推求之海水面變化精度可提升約2公分，相關係數亦從0.91增加至0.97。綜合以上結果，臺灣地區除了中央大學臨海工作站外，高雄港和蘇澳港之GNSS測站皆展現監測沿岸海水面變化之潛力。

Global sea level rise (SLR) has caused many kinds of disasters, damaging the lives and properties of numerous human beings, especially in low-lying coastal regions. Therefore, understanding and monitoring coastal sea level variations are of great importance for human society. This research used Global Navigation Satellite System (GNSS) signal-to-noise ratio (SNR) data from the GNSS stations located in Taiwan (Kaohsiung, Suao and TaiCOAST) and Sweden (Onsala Space Observatory, OSO) to compute sea level heights (SLH) by using three different methods, including Lomb Scargle Periodogram (LSP) aided with tidal harmonic analysis, LSP-only and inverse modeling (IM). The GNSS-derived sea level variations are compared with those from co-located or nearby traditional tide gauges. In Taiwan, the GNSS-derived sea level variations in Kaohsiung and Suao show good agreement with those from tide gauges with the standard deviations (STDs) of differences ranging from 7.1 - 11.1 cm and the correlation coefficients of 0.94-0.97. In addition, more than 80 % of SLH can be successfully achieved during the SNR available periods. Besides, the absolute sea level trend in Kaohsiung during 2006-2011 calculated by combining the vertical motion and the relative sea level from GNSS, is mm/yr, which agrees with that derived from satellite altimetry of mm/yr. However, this comparison is not robust because the uncertainty is almost the same level with the estimate, resulting from the short time coverage of data. In contrast, merely 40 % of SLH can be successfully provided by TaiCOAST and the STD of differences between GNSS-derived and tide gauge sea level changes is 1.12 m with a correlation coefficient of 0.13. TaiCOAST has poor performance for monitoring sea level changes since the GNSS signals may be reflected from intertidal zone when sea level ebbs. On the other hand, the GNSS-based tide gauge in Sweden can offer over 90 % of SLH by both LSP and IM methods. The STD of differences between sea level changes derived from GNSS SNR by IM and the tide gauge decreases about 2 cm compared with that by LSP and the correlation coefficient increases from 0.91 to 0.97. From the bottom line, the GNSS stations in Taiwan except for TaiCOAST demonstrate the potential of serving as GNSS-based tide gauges to measure sea level changes like a specially designed one (e.g. OSO) does.

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