RTide: automating the tidal response method

Journal article

Nonstationary tidal processes, such as tidal rivers and storm surge, present challenges for analysis and prediction because their inherent nonstationarity encumbers the use of standard tidal analysis tools like harmonic analysis. Moreover, specialized approaches impose problem-specific functional forms and rely on auxiliary data, limiting their applicability across different nonstationary tidal processes. Although Munk and Cartwright's tidal response method avoids these assumptions, its lack of automation has hindered broader application. Here, we develop a nonparametric, automated response-based analysis procedure. Our approach embeds a class of neural networks capable of representing any arbitrary Volterra series—the mathematical basis of the response method—within the classic framework. Our model facilitates the inclusion of meteorological and other non-tidal forcing. By explicitly accounting for nonstationarity, our method yields improved astronomical tidal estimates. We further devise a strategy to extract physical insights from the learned model, demonstrating its utility in studying the interaction and modulation of astronomical tides by external forcing. By taking a nonparametric approach, our framework enables the investigation of phenomena that heretofore could not be accounted for straightforwardly, as illustrated by several case studies on tide–surge interaction, riverine tides, and storm surge. These applications, and more, can be replicated with just three lines of code using the open-source Python package, RTide.

Authors: Monahan, T; Tang, T; Roberts, S; Adcock, T

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Wiley
• Journal: Journal of Geophysical Research: Machine Learning and Computation
• Volume: 2
• Issue: 2
• Article number: e2024JH000525
• Publication date: 2025-05-23
• Acceptance date: 2025-04-25
• DOI: 10.1029/2024jh000525
• EISSN: 2993-5210
• Language: English
• Keywords: SciML; deep learning; tides; storm surge; estuaries; forecasting