How to Cite
Vitousek, S., Cagigal, L., Montano, J., Rueda, A., Mendez, F., Coco, G., & Barnard, P. (2020). LONG-TERM, ENSEMBLE, DATA-ASSIMILATED SHORELINE CHANGE MODELING. Coastal Engineering Proceedings, (36v), sediment.25. https://doi.org/10.9753/icce.v36v.sediment.25
Abstract
We present an ensemble Kalman filter shoreline change model to predict long-term coastal evolution due to waves, sea-level rise, and other natural and anthropogenic processes responsible for sediment transport. The model utilizes ensemble simulations to improve both reliability (via data assimilation) and uncertainty quantification. Coastal change projections exhibit significant differences when simulated with and without ensemble wave conditions. Many long-term coastal change projections rely on a single realization of the future wave climate, often derived from atmospheric conditions simulated by a global climate model. Yet, the single realization approach does not account for the stochastic nature of future wave conditions across a variety of temporal scales (e.g., daily, weekly, seasonally, and interannually). Here, by applying ensemble time series of wave forcing conditions, we demonstrate a sizable increase in model uncertainty compared with the unrealistic case of model projections based on a single realization (e.g., a single time series) of wave forcing.Recorded Presentation from the vICCE (YouTube Link): https://youtu.be/V-VwC-cIiQ0
References
Yates, Guza, & O'reilly (2009). Equilibrium shoreline response: Observations and modeling. Journal of Geophysical Research: Oceans, 114(C9). Engineering, ELSEVIER, vol. 10, pp. 32-39.
Evensen (1994). Sequential data assimilation with a nonlinear quasi‐geostrophic model using Monte Carlo methods to forecast error statistics. Journal of Geophysical Research: Oceans, 99(C5), 10143-10162.
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