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Fire Island Coastal Change

Morphologic Change - Predicting Future Change

The modeling approach, a Bayesian network, uses statistical methods to estimate probabilities of predicted outcomes based on existing observations. Bayesian networks are easily updated with new data or observations making this approach ideal for use in coastal systems that experience rapid and frequent changes.

A suite of Bayesian networks (Figure 1) was developed to A) generate scenarios of total water level, B) forecast storm impacts and C) predict magnitudes of beach recovery. The Bayesian networks incorporate topographic, bathymetric, and shoreline data available from the historical and post Hurricane Sandy research programs at Fire Island. The Bayesian networks generate predictions in the form of probability of coastal change.

Linking storm-response and recovery models creates an iterative prediction feedback; the magnitude of coastal change predicted during storm or fair-weather conditions can be used as the beach condition of the next prediction step of the model (Figure 1B; 1C).


Schematic of three linked hydrodynamic and geomorphic Bayesian Networks.
Figure 1. Schematic of three linked hydrodynamic and geomorphic Bayesian Networks. A. is the hydrodynamic network that links off-shore wave conditions with nearshore wave height and period, based on model simulations. Tides and beach slope are combined with nearshore wave conditions to predict total water level. B is the schematic of the storm-induced coastal change model. C is the framework to predict beach recovery during fair-weather conditions. In the geomorphic models, physical drivers influence the initial conditions of the system and long term system behavior. Together, physical controls and geomorphic conditions force the magnitude of beach change from a storm or recovery interval. [larger version]

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