How to Cite

Ibaceta, R., Splinter, K., Harley, M., & Turner, I. (2020). ENHANCED SHORELINE MODELLING USING DATA ASSIMILATION TO INCLUDE NON-STATIONARITY IN WAVE CLIMATES . Coastal Engineering Proceedings, (36v), sediment.12. https://doi.org/10.9753/icce.v36v.sediment.12


Coastal zone planning requires tools to predict shoreline response to changes in waves, water levels and sediment supply at time scales ranging from daily to decades. Despite the complexity of the underlying processes driving coastal change, the emergence of a range of semi-empirical models is proving to be increasingly successful at predicting shoreline response at seasonal to interannual timescales (e.g. Davidson et al., 2013). Recent improvements include the addition of processes such as longshore sediment transport and shoreline recession by SLR (e.g. Vitousek et al., 2017). But notably, in all these model formulations to-date, free-parameters are assumed to be time-invariant, relying on calibration over relatively short periods to measured shorelines and wave climate. Adopting a time-invariant set of model free-parameters ignores the bias introduced by the training dataset and any likely future changes in beach state and forcing conditions. The alternative approach presented here allows for time-varying model parameters, with the potential to improve model predictability due to non-stationarity in the underlying forcing. This work makes significant advances on previous shoreline modelling efforts by considering model parameters as potential time-varying quantities, as is common in the field of hydrology (e.g. Pathiraja et al., 2016). This is achieved by adopting a suitable data assimilation technique (Dual State-Parameter Ensemble Kalman Filter, EnKF) within the established shoreline evolution model, ShoreFor (Splinter et al., 2014). The method is first tested and evaluated using synthetic scenarios, specifically designed to emulate a broad range of natural sandy shoreline behavior.

Recorded Presentation from the vICCE (YouTube Link): https://youtu.be/doAvC89vs4s


D’Anna, M., Idier, D., Castelle, B., Le Cozannet, G., Rohmer, J., & Robinet, A. (2020). Impact of model free parameters and sea‐level rise uncertainties on 20‐years shoreline hindcast: the case of Truc Vert beach (SW France). Earth Surface Processes and Landforms. https://doi.org/10.1002/esp.4854

Montaño, J., Coco, G., Antolínez, J. A. A., Beuzen, T., Bryan, K. R., Cagigal, L., et al. (2020). Blind testing of shoreline evolution models, 1–10. https://doi.org/10.1038/s41598-020-59018-y

Morim, J., Hemer, M., Wang, X. L., Cartwright, N., Trenham, C., Semedo, A., et al. (2019). Robustness and uncertainties in global multivariate wind-wave climate projections. Nature Climate Change, 9(9), 711–718. https://doi.org/10.1038/s41558-019-0542-5

Pathiraja, S., Marshall, L., Sharma, A., & Moradkhani, H. (2016b). Hydrologic modeling in dynamic catchments: A data assimilation approach. Water Resources Research, 52(5), 3350–3372. https://doi.org/10.1002/2015WR017192

Splinter, K. D., Turner, I. L., Davidson, M. A., Barnard, P., Castelle, B., & Oltman-Shay, J. (2014). A generalized equilibrium model for predicting daily to inter-annual shoreline response. Journal of Geophysical Research: Earth Surface, 119, 1936–1958. https://doi.org/10.1002/2014JF003106

Splinter, K. D., Turner, I. L., Reinhardt, M., & Ruessink, G. (2017). Rapid adjustment of shoreline behavior to changing seasonality of storms: observations and modelling at an open-coast beach. Earth Surface Processes and Landforms, 42(8), 1186–1194. https://doi.org/10.1002/esp.4088

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