Response-based prediction of tidal currents

Journal article

This study evaluates the response method for predicting tidal currents. We introduce a coupled response model which explicitly accounts for interactions between velocity components. By leveraging non-parametric and data-driven weight estimation, the approach demonstrates superior predictive accuracy compared to classical harmonic analysis (HA), particularly for fast-moving and non-linear tidal currents. Using ADCP data from the world’s largest deployment of tidal stream turbines, the coupled model achieves superior accuracy with fewer than 30 days of input measurements compared to HA using over 180 days of data. This performance advantage increases with the complexity of the tidal currents, offering more modest reductions in absolute error of 9.6% on average across 40 active NOAA current stations. The framework offers several opportunities for future work in understanding the role of non-tidal forcing and sediment transport and has significant economic implications for tidal energy site development. The proposed approach is implemented and freely available in the open-source RTide Python package. Critically, the non-parametric approach reduces the need for expertise when applying the response method to study higher-order nonlinear processes.

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

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Wiley
• Journal: Journal of Geophysical Research: Oceans
• Volume: 130
• Issue: 12
• Article number: e2025JC022758
• Publication date: 2025-12-23
• Acceptance date: 2025-12-08
• DOI: 10.1029/2025jc022758
• EISSN: 2169-9291
• ISSN: 2169-9275
• Language: English
• Keywords: tidal currents; scientific machine learning; tidal energy