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A Bayesian approach to predict sub-annual beach change and recovery

August 27, 2018

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 (ZlD) following 1 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 ZlD positions at high spatial resolution (50 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 months) and spatial (50 m to > 1 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.

Publication Year 2019
Title A Bayesian approach to predict sub-annual beach change and recovery
DOI 10.1007/s12237-018-0444-1
Authors Kathleen Wilson, Erika Lentz, Jennifer L. Miselis, Ilgar Safak, Owen T. Brenner
Publication Type Article
Publication Subtype Journal Article
Series Title Estuaries and Coasts
Index ID 70201364
Record Source USGS Publications Warehouse
USGS Organization St. Petersburg Coastal and Marine Science Center