The Application of Ensemble Wave Forcing to Quantify Uncertainty of Shoreline Change Predictions. Issue 7 (15th July 2021) - Vitousek, Sean; Cagigal, Laura; Montaño, Jennifer; Rueda, Ana; Mendez, Fernando; Coco, Giovanni; Barnard, Patrick L.
Abstract: Reliable predictions and accompanying uncertainty estimates of coastal evolution on decadal to centennial time scales are increasingly sought. So far, most coastal change projections rely on a single, deterministic realization of the unknown future wave climate, often derived from a global climate model. Yet, deterministic projections do not account for the stochastic nature of future wave conditions across a variety of temporal scales (e.g., daily, weekly, seasonally, and interannually). Here, we present an ensemble Kalman filter shoreline change model to predict coastal erosion and uncertainty due to waves at a variety of time scales. We compare shoreline change projections, simulated with and without ensemble wave forcing conditions by applying ensemble wave time series produced by a computationally efficient statistical downscaling method. We demonstrate a sizable (site‐dependent) increase in model uncertainty compared with the unrealistic case of model projections based on a single, deterministic realization (e.g., a single time series) of the wave forcing. We support model‐derived uncertainty estimates with a novel mathematical analysis of ensembles of idealized process models. Here, the developed ensemble modeling approach is applied to a well‐monitored beach in Tairua, New Zealand. However, the model and uncertainty quantification techniques derived here are generally applicable to a variety of coastal settings around the world. Key Points: We assess uncertainty of ensemble Kalman filter data‐assimilated shoreline change predictions using the spread of model trajectories We demonstrate an increase in modeled shoreline uncertainty due to ensemble wave conditions compared with single realization simulations Uncertainty in shoreline predictions can be quantified using a novel, straightforward relationship, derived analytically