Predicting Coastal Water Levels in the Context of Climate Change Using Kolmogorov-Zurbenko Time Series Analysis Methods

TL;DR

This study applies Kolmogorov-Zurbenko time series analysis to decompose and predict changes in coastal water levels due to climate change, focusing on Virginia Key, Florida, with implications for flood prediction and policy.

Contribution

It introduces a novel application of Kolmogorov-Zurbenko filters and periodograms for detailed decomposition and prediction of coastal water levels influenced by climate change.

Findings

Long-term water level trend predicted to rise up to 5.91 feet by 2050.

Combined tidal and harmonic effects could increase water levels by up to 8.09 feet.

Method provides a foundation for improved coastal flood prediction and policy planning.

Abstract

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.