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OPEN-FILE REPORT 00-179 Very low Low Moderate High Very high 1 2 3 4 5 Ranking of coastal vulnerability index VARIABLE Coastal Slope (%) >0.115 0.115 − 0.055 0.055 − 0.035 0.035 −0.022 < 0.022 Geomorphology Rocky, cliffed coasts Fiords Fiards Medium cliffs Indented coasts Low cliffs Glacial drift Salt marsh Coral reefs Alluvial plains Cobble beaches Estuary Lagoon Mangrove Barrier beaches Sand Beaches Mud flats Deltas Relative sea-level change (mm/yr) < 1.8 1.8 − 2.5 2.5 − 3.0 3.0 − 3.4 > 3.4 Shoreline erosion/ accretion (m/yr) >2.0 1.0 −2.0-1.0 − +1.0 <-2.0-1.1−-2.0 Stable Erosion Accretion Mean tide range (m) Mean wave height (m) <0.55 0.55 − 0.85 0.85 − 1.05 1.05 −1.25 >1.25

[1] Sea level rise during the 21st century will have a wide range of effects on coastal environments, human development, and infrastructure in coastal areas. The broad range of complex factors influencing coastal systems contributes to large uncertainties in predicting long-term sea level rise impacts. Here we explore and demonstrate the capabilities of a Bayesian network (BN) to predict long-term shoreline change associated with sea level rise and make quantitative assessments of prediction uncertainty. A BN is used to define relationships between driving forces, geologic constraints, and coastal response for the U.S. Atlantic coast that include observations of local rates of relative sea level rise, wave height, tide range, geomorphic classification, coastal slope, and shoreline change rate. The BN is used to make probabilistic predictions of shoreline retreat in response to different future sea level rise rates. Results demonstrate that the probability of shoreline retreat increases with higher rates of sea level rise. Where more specific information is included, the probability of shoreline change increases in a number of cases, indicating more confident predictions. A hindcast evaluation of the BN indicates that the network correctly predicts 71% of the cases. Evaluation of the results using Brier skill and log likelihood ratio scores indicates that the network provides shoreline change predictions that are better than the prior probability. Shoreline change outcomes indicating stability (−1 < rate < 1 m/yr) or erosion (rate < −1 m/yr) tend to occur for two sets of input scenarios. Stable shoreline change rates occur mainly for low rates of relative sea level rise and occur in low-vulnerability geomorphic settings. Rates indicating erosion result for cases where the rate of relative sea level rise is high and moderate-to-high vulnerability geomorphic settings occur. In contrast, accretion (rate > 1 m/yr) was not well predicted. We find that BNs can assimilate important factors contributing to coastal change in response to sea level rise and can make quantitative, probabilistic predictions that can be applied to coastal management decisions.Citation: Gutierrez, B. T., N. G. Plant, and E. R. Thieler (2011), A Bayesian network to predict coastal vulnerability to sea level rise,

2 Sea-level rise (SLR) poses a range of threats to natural and built environments 1,2 , making assessments of SLR-induced hazards essential for informed decision-making 3 . We develop a probabilistic model that evaluates the likelihood that an area will inundate (flood) or dynamically respond (adapt) to SLR. The broad-area applicability of the approach is demonstrated by producing 30x30 m resolution predictions for more than 38,000 km 2 of diverse coastal landscape in the northeastern United States (U.S.). Probabilistic SLR projections, coastal elevation, and vertical land movement are used to estimate likely future inundation levels. Then, conditioned on future inundation levels and the current land-cover type, we evaluate the likelihood of dynamic response vs. inundation. We find that nearly 70% of this coastal landscape has some capacity to respond dynamically to SLR, and we show that inundation models over-predict land likely to submerge.This approach is well-suited to guiding coastal resource management decisions that weigh future SLR impacts and uncertainty against ecological targets and economic constraints. As an alternative, we developed a data-driven coastal response (CR) model that considers both inundation and dynamic response using a range of SLR scenarios and datasets describing elevation and vertical land movement. We integrate these elements with land-cover information to assess CR likelihoods in the form of a dynamic probability, DP = 1-Prob. (inundate), using a Bayesian network Maine through Virginia, and includes a region with a wide range of coastal development, infrastructure, and environments found globally; including uplands, barrier beaches, spits, islands, mainland beaches, cliffs, rocky headlands, estuaries, and wetlands. The study area is defined by the -10 and +10 m elevation contours and mapped as a 30 m grid.To predict CR likelihoods (Figure 2), we first compute an adjusted land elevation with respect to projected sea levels:where AE represents the adjusted elevation with respect to a future sea level; E denotes the initial land elevation; SL is a projected sea level in the 2020s, 2030s, 2050s, or 2080s; and VLM gives the current rate of vertical land movement due to glacial isostatic adjustment, tectonics, and other non-climatic effects such as groundwater withdrawal and sediment compaction 15 . Sources of uncertainty in AE predictions include SLR projections, elevation data accuracy, vertical datum adjustments, and the interpolation of VLM rates from point data; these geospatially-explicit input uncertainties are propagated through the model to produce a probability mass function P(AE) for every grid cell (Figure 2c,d). Once generated, AEs are related through evaluation of their dynamic response potential with generalized landcover information and used to produce a CR likelihood (Figures 1, 2).Discretized AE predictions provide an estimated submergence level comparable to many existing inundation models 3, 16 (Figure 2). However, our predictions include several notable improvement...