DescriptionCo-authored with Ian Shennan, Richard Hardy, and Laura Turnbull-Lloyd
Coastal numerical models (CNMs) are data driven tools often used to predict shoreline change for guiding coastal management. Too much detail incorporated into CNMs can result in computational intensity and numerical instability, while too little data can under-represent the coastal environment and affect the accuracy and precision of shoreline change predictions. For many small island states of the Caribbean and elsewhere, limited availability of scientific data present challenges for the design and implementation of effective coastal management strategies. This paper aims to determine the minimal data needed to effectively represent coastal processes in modelling shoreline change. In order to simulate changes in coastal morphology, CNMs require a good representation of coastal processes, namely currents, tides, waves, and wind. An inadequate representation of these processes can compromise a model prediction of shoreline change. We consider two locations, both managed sandy shorelines, in New York and Southern California. We use coastal relief and processes data from NOAA to develop multiple two-dimensional coupled wave, flow, and sediment transport models, each referenced to mean high water with varying coastal processes time-series data resolution. These provide the basis, at both sites, to simulate shoreline change using the MIKE 21 Coupled Model. We calibrate and verify the models against observations of currents and shoreline morphology. We quantify the impacts of time-series data resolution on predicted sediment transport rates and bed level changes. From these, we identify the minimal coastal processes data needed to effectively model shoreline change, refine CNMs and improve their applicability to support coastal management in data-poor countries.
|Period||18 Sept 2018|
|Event title||DHI Symposium|