First continuous year-long wave overtopping study could power smarter coastal planning

An image of overtopping wave, taken as part of the data collection. Image: University of Plymouth

A new study provides valuable information which could help to develop improved coastal hazard predictions

By combining new observations with national monitoring networks for waves, tides, and wind conditions, scientists from the National Oceanography Centre (NOC) and University of Plymouth have built the first continuous dataset of overtopping events, where a wave exceeds sea defences such as sea walls.

Combining these data with existing forecasts provided an AI-ready resource to develop new predictive tools to support coastal decision-making.

With more than a third of the UK’s road and railway networks in flood risk areas, improving forecasting and understanding the causes of these events is critical to support improved flood protection and safety protocols.

The new research, ‘Spatial and Temporal Variation in Wave Overtopping Across a Coastal Structure Based on One Year of Field Observations’, has been published in the Journal of Marine Science and Engineering. It highlights the conditions driving the most frequent overtopping differ from those pushing water further inland, complicating hazard communication for multi-use coastal infrastructure.

Key findings include:

Storms have a low contribution (less than 2%) to the number of tides associated with wave overtopping each year

Prevailing wind direction may not be the direction associated with most overtopping events

Overtopping likelihood varies over high tide, demonstrating site-specific complexities and the need for understanding local wave dynamics before decisions are made for infrastructure protection

Professor Jennifer Brown, a coastal scientist at NOC and lead author, said: “The successful year-long deployment of our wave overtopping equipment at Dawlish was a great achievement. The high-energy environment and passing of Storm Barra tested the robustness of the monitoring equipment’s design, which we will continue to advance.

“Our aim is to collect data that can support those working in flood hazard management and help train Artificial Intelligence (AI) to alert of overtopping hazards.”

Read the full research here: https://www.mdpi.com/2077-1312/13/11/2194