SPLASH project

Digital approaches to predict wave hazards

New Haven during Storm Eunice. Huge wave hits cars on coast

Project Context

The SPLASH project is being led by Dr Nieves Valiente and colleagues Dr Christopher Stokes and Dr Timothy Poate at the University of Plymouth, working alongside Dr Jenny Brown (project co-lead), Dr Paul Bell and Clive Neil from the National Oceanographic Centre (NOC). SPLASH is one of the five projects receiving funding delivered by the Natural Environment Research Council (NERC), in partnership with the Met Office, as part of the Twinning Capability for the Natural Environment (TWINE) programme.

Advancing current understanding on wave-related coastal hazards

With sea level rise accelerating and weather extremes becoming increasingly stronger, tools to help climate adaptation of coastal communities are of paramount importance. SPLASH will provide an overtopping tool that will act as forecast model directly helping coastal communities mitigate effects of this coastal hazard, and ultimately, guiding new climate adaptation strategies.

Aim

The principal aim of SPLASH is to 'build a deployable coastal overtopping warning tool (SPLASH) with the vision of transforming weather and climate research and services through transformative technologies.'

SPLASH tries to tackle one of the main hazards that coastal communities are currently suffering, wave overtopping (high priority).

The project will improve the current understanding of wave overtopping hazard (generates new knowledge) using cutting edge techniques (i.e., AI and EO application in coastal areas)

The project will provide tools that can be applied widely (impacts world-leading research)

The final digital model will help mitigate overtopping hazards, which will ultimately have a direct impact on coastal communities and their economy. Other identified contributions are contributing to data feed, end users, and machine learning and earth observations application expertise.

Aerial view of damage on Dawlish coastline

The project

Artificial Intelligence (AI) algorithms will be trained to predict spatial variability in wave overtopping hazard and ultimately, risk to life, property, and infrastructure. The project aim will be met by completing objectives covered by two work packages (WPs):

Develop a digital twin of wave overtopping using machine learning in overtopping observations, model data of wind, waves and water level and beach profile geometry (WP1)
Study of the influence of metocean interactions on regular overtopping asymmetries using wave fields derived from EO data (WP2); (3) provide coastal overtopping projections to assess future changes in hazard frequency (WP1); and (4) compile a report / peer review paper documenting the project and lessons learned (both WPs). WP1 objectives will be conducted by University of Plymouth (UP) whereas WP2 will be led by the National Oceanography Centre (NOC).