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Enhanced coastal shoreline modelling using an Ensemble Kalman Filter to include non-stationarity in future wave climates
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  • Raimundo Ibaceta,
  • Kristen D. Splinter,
  • Mitchell Harley,
  • Ian Turner
Raimundo Ibaceta
Water Research Laboratory, UNSW Sydney, Water Research Laboratory, UNSW Sydney

Corresponding Author:[email protected]

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Kristen D. Splinter
UNSW Australia, Water Research Laboratory, UNSW Sydney
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Mitchell Harley
University of New South Wales, Water Research Laboratory, UNSW Sydney
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Ian Turner
University of New South Wales, Water Research Laboratory, UNSW Sydney
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A novel approach to improve seasonal to interannual sandy shoreline predictions is presented, whereby model free parameters can vary in time, adjusting to potential non-stationarity in the underlying model forcing. This is achieved by adopting a suitable data assimilation technique (Dual State-Parameter Ensemble Kalman Filter) within the established shoreline evolution model, ShoreFor. The method is first tested and evaluated using synthetic scenarios, specifically designed to emulate a broad range of natural sandy shoreline behavior. This approach is then applied to a real-world shoreline dataset, revealing that time-varying model free parameters are linked through physical processes to changing characteristics of the wave forcing. Greater accuracy of shoreline predictions is achieved, compared to existing stationary modelling approaches. It is anticipated that the wider application of this method can improve our understanding and prediction of future beach erosion patterns and trends in a changing wave climate.
28 Nov 2020Published in Geophysical Research Letters volume 47 issue 22. 10.1029/2020GL090724