利用Kolmogorov-Zurbenko时间序列分析方法在气候变化背景下预测沿海水位
Predicting Coastal Water Levels in the Context of Climate Change Using Kolmogorov-Zurbenko Time Series Analysis Methods
AI总结：
本研究利用Kolmogorov-Zurbenko时间序列分析方法分解弗吉尼亚礁水位变化，预测长期趋势并估算最高水位，为沿海洪水预测和政策制定提供依据。
摘要：
鉴于气候变化导致近期海洋水位上升，本研究将沿海水位变化分解为其基本组成部分，以预测特定沿海地点的最高水位。研究聚焦于美国佛罗里达州迈阿密海岸附近的弗吉尼亚礁。从国家数据浮标中心获取了1994年1月28日至2023年12月31日期间的每小时平均较低低水位（MLLW）数据。在时间维度上，使用Kolmogorov-Zurbenko滤波器提取长期趋势、年度和每日潮汐以及更高频率的谐波；在频谱维度上，使用经DiRienzo-Zurbenko算法平滑的Kolmogorov-Zurbenko周期图来确认已知的潮汐频率和周期。线性模型预测，从1994年1月至2050年12月，水位长期趋势将上升2.02英尺，而二次模型预测同期将上升5.91英尺。此外，年度潮汐、每日潮汐和更高频率谐波的组合波峰可使水位增加高达2.16英尺，从而得出下限为4.18英尺、上限为8.09英尺的组合总量。这些发现为在恶劣天气事件期间更准确地预测沿海洪水奠定了基础，并为涉及住宅社区、企业和野生动物栖息地的政策选择提供了推动力。此外，利用Kolmogorov-Zurbenko分析方法研究全球沿海站点，可以更全面地描绘气候变化对全球沿海水域的影响。
英文摘要：
Given recent increases in ocean water levels brought on by climate change, this investigation decomposed changes in coastal water levels into its fundamental components to predict maximum water levels for a given coastal location. The study focused on Virginia Key, Florida, in the United States, located near the coast of Miami. Hourly mean lower low water (MLLW) levels were obtained from the National Data Buoy Center from January 28, 1994, through December 31, 2023. In the temporal dimension, Kolmogorov-Zurbenko filters were used to extract long-term trends, annual and daily tides, and higher frequency harmonics, while in the spectral dimension, Kolmogorov-Zurbenko periodograms with DiRienzo-Zurbenko algorithm smoothing were used to confirm known tidal frequencies and periods. A linear model predicted that the long-term trend in water level will rise 2.02 feet from January 1994 to December 2050, while a quadratic model predicted a rise of 5.91 during the same period. In addition, the combined crests of annual tides, daily tides, and higher frequency harmonics increase water levels up to 2.16 feet, yielding a combined total of 4.18 feet as a lower bound and a combined total of 8.09 feet as an upper bound. These findings provide a foundation for more accurate prediction of coastal flooding during severe weather events and provide an impetus for policy choices with respect to residential communities, businesses, and wildlife habitats. Further, using Kolmogorov-Zurbenko analytic methods to study coastal sites throughout the world could draw a more comprehensive picture of the impact climate change is having on coastal waters globally.
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