Erika E. Lentz Jennifer L. Miselis Ilgar Safak Owen T. Brenner Kathleen Wilson 2019 <p><span>The upper beach, between the astronomical high tide and the dune-toe, supports habitat and recreation along many beaches, making predictions of upper beach change valuable to coastal managers and the public. We developed and tested a Bayesian network (BN) to predict the cross-shore position of an upper beach elevation contour (Z</span>_l<span>D) following 1&nbsp;month to 1-year intervals at Fire Island, New York. We combine hydrodynamic data with series of island-wide topographic data and spatially limited cross-shore profiles. First, we predicted beach configuration of Z</span>_l<span>D positions at high spatial resolution (50&nbsp;m) over intervals spanning 2005–2014. Compared to untrained model predictions, in which all six outcomes are equally likely (prior likelihood = 0.16), our prediction metrics (skill = 0.52; log likelihood ratio = 0.14; accuracy = 0.56) indicate the BN confidently predicts upper beach dynamics. Next, the BN forecasted three intervals of beach recovery following Hurricane Sandy. Results suggest the pre-Sandy training data is sufficiently robust to require only periodic updates to beach slope observations to maintain confidence for forecasts. Finally, we varied input data, using observations collected at a range of temporal (1–12&nbsp;months) and spatial (50&nbsp;m to &gt; 1&nbsp;km) resolutions to evaluate model skill. This experiment shows that data collection techniques with different spatial and temporal frequencies can be used to inform a single modeling framework and can provide insight to BN training requirements. Overall, results indicate that BNs and inputs can be developed for broad coastal change assessment or tailored to a set of predictive requirements, making this methodology applicable to a variety of coastal prediction scenarios.</span></p> application/pdf 10.1007/s12237-018-0444-1 en Springer A Bayesian approach to predict sub-annual beach change and recovery article