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Using a Bayesian network to predict barrier island geomorphologic characteristics

Journal of Geophysical Research

By:
, , , and
https://doi.org/10.1002/2015JF003671

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Abstract

Quantifying geomorphic variability of coastal environments is important for understanding and describing the vulnerability of coastal topography, infrastructure, and ecosystems to future storms and sea level rise. Here we use a Bayesian network (BN) to test the importance of multiple interactions between barrier island geomorphic variables. This approach models complex interactions and handles uncertainty, which is intrinsic to future sea level rise, storminess, or anthropogenic processes (e.g., beach nourishment and other forms of coastal management). The BN was developed and tested at Assateague Island, Maryland/Virginia, USA, a barrier island with sufficient geomorphic and temporal variability to evaluate our approach. We tested the ability to predict dune height, beach width, and beach height variables using inputs that included longer-term, larger-scale, or external variables (historical shoreline change rates, distances to inlets, barrier width, mean barrier elevation, and anthropogenic modification). Data sets from three different years spanning nearly a decade sampled substantial temporal variability and serve as a proxy for analysis of future conditions. We show that distinct geomorphic conditions are associated with different long-term shoreline change rates and that the most skillful predictions of dune height, beach width, and beach height depend on including multiple input variables simultaneously. The predictive relationships are robust to variations in the amount of input data and to variations in model complexity. The resulting model can be used to evaluate scenarios related to coastal management plans and/or future scenarios where shoreline change rates may differ from those observed historically.

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Additional publication details

Publication type:
Article
Publication Subtype:
Journal Article
Title:
Using a Bayesian network to predict barrier island geomorphologic characteristics
Series title:
Journal of Geophysical Research
DOI:
10.1002/2015JF003671
Volume:
120
Issue:
12
Year Published:
2015
Language:
English
Publisher:
Wiley
Contributing office(s):
Woods Hole Coastal and Marine Science Center
Description:
24 p.
First page:
2452
Last page:
2475
Country:
United States
State:
Maryland and Virginia
Other Geospatial:
Assateague Island
Online Only (Y/N):
N
Additional Online Files (Y/N):
N