A Bayesian Approach to Predict Sub-Annual Beach Change and Recovery
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.
KeywordsBayesian network Coastal geomorphology Barrier island Hurricane Sandy Beach recovery
The authors are grateful to our partners at the National Park Service: Fire Island National Seashore. We thank USGS’s Joseph Long, Cheryl Hapke, and Rachel Henderson for their help through thoughtful discussions. Rachel Henderson extracted morphologic features from many of the datasets. We thank Benjamin Gutierrez, Nathaniel Plant, and two anonymous reviewers for helpful comments and critiques. This work was supported by the National Park Service National Resource Preservation Program. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.
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