SPLASH: digital approaches to predict wave hazards

Lead Research Organisation: Plymouth University
Department Name: Sch of Biological and Marine Sciences


With sea level rise accelerating and coastal populations increasing, the requirement of accurate tools to predict natural hazards and mitigate damages to infrastructure, property and human life is ever more urgent. Our project sits in the frame of assessing impacts of changing environmental conditions, particularly extremes, on the state of the natural world, affected by both natural variability and impact of human activity. Coastal flooding is normally caused by wave overtopping that occurs when water is discharged by waves over a coastal structure such as a breakwater. There are multiple methods to forecast coastal overtopping, and most of them demonstrate a lack of precision and large dependency to local processes. Statistical analysis of Earth Observations (EO) will provide a method to assess wave fields to better understand how processes (winds, tides, coastal sheltering, swell and wind waves) interact across a coastal area to change the coastal wave hazard through, for example, (depth and current) refraction, wind shadowing and bimodal wave contributions. From March 2021-2022 monitoring of the wave overtopping at Dawlish and Penzance provided in-situ hazard alerts, indicating when overtopping starts and stops, along with a measure of the severity (WireWall data). Such observations can be used alongside national monitoring networks of waves, water levels and Earth Observations (EO) data to develop an environmental digital twin pilot, and ultimately, improve operational hazard management and increase UK resilience to natural hazards. The principal aim of this proposal is to build a deployable coastal overtopping warning tool (SPLASH) with the vision of transforming weather and climate research and services through transformative technologies. The main outcomes of the proposal will be (1) a method to analyse coastal wave fields from EO to determine regular asymmetries in hazards conditions, (2) a digital twin of wave overtopping in which machine learning has been applied to produce a warning tool using model predictions of wind, waves and water level, and (3) coastal overtopping projections to assess future changes in hazard frequency. The proposed project will use: (i) overtopping Dawlish/Penzance WireWall data (observations) to train and validate machine learning algorithms based on model predictions (wind, waves and water levels); (ii) camera images for calibration and validation; and (iii) satellite images to study variability in wave field indicators. Met Office reanalysis and analysis model data will be obtained from freely available data portals (e.g., Copernicus Marine Service) and through the UK Marine and Climate Advisory Service. Furthermore, case studies will be used as a demo and the approach will be tested in other wave hazard hotspot locations along the UK coastline where there are CCTV cameras or webcams (e.g., Chesil, Teignmouth). Reliable warning tools such as SPLASH provide essential information to those coastal communities that are currently experiencing wave related hazards. The combined application of SPLASH as a forecasting and a projection tool will facilitate coastal practitioners' decision making, helping mitigate the effects of climate change in already vulnerable locations.


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