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Nathaniel G.
Plant
Nathaniel G.
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ArticlePredicted sea-level rise-driven biogeomorphological changes on Fire Island, New York: implications for people and plovers(American Geophysical Union, 2022-03-29) Zeigler, Sara L. ; Gutierrez, Benjamin T. ; Lentz, Erika E. ; Plant, Nathaniel G. ; Sturdivant, Emily ; Doran, Kara S.Forecasting biogeomorphological conditions for barrier islands is critical for informing sea-level rise (SLR) planning, including management of coastal development and ecosystems. We combined five probabilistic models to predict SLR-driven changes and their implications on Fire Island, New York, by 2050. We predicted barrier island biogeomorphological conditions, dynamic landcover response, piping plover (Charadrius melodus) habitat availability, and probability of storm overwash under three scenarios of shoreline change (SLC) and compared results to observed 2014/2015 conditions. Scenarios assumed increasing rates of mean SLC from 0 to 4.71 m erosion per year. We observed uncertainty in several morphological predictions (e.g., beach width, dune height), suggesting decreasing confidence that Fire Island will evolve in response to SLR as it has in the past. Where most likely conditions could be determined, models predicted that Fire Island would become flatter, narrower, and more overwash-prone with increasing rates of SLC. Beach ecosystems were predicted to respond dynamically to SLR and migrate with the shoreline, while marshes lost the most area of any landcover type compared to 2014/2015 conditions. Such morphological changes may lead to increased flooding or breaching with coastal storms. However—although modest declines in piping plover habitat were observed with SLC—the dynamic response of beaches, flatter topography, and increased likelihood of overwash suggest storms could promote suitable conditions for nesting piping plovers above what our geomorphology models predict. Therefore, Fire Island may offer a conservation opportunity for coastal species that rely on early successional beach environments if natural overwash processes are encouraged.
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ArticleCoupling centennial-scale shoreline change to sea-level rise and coastal morphology in the Gulf of Mexico using a Bayesian network(John WIley & Sons, 2016-05-02) Plant, Nathaniel G. ; Thieler, E. Robert ; Passeri, DavinaPredictions of coastal evolution driven by episodic and persistent processes associated with storms and relative sea-level rise (SLR) are required to test our understanding, evaluate our predictive capability, and to provide guidance for coastal management decisions. Previous work demonstrated that the spatial variability of long-term shoreline change can be predicted using observed SLR rates, tide range, wave height, coastal slope, and a characterization of the geomorphic setting. The shoreline is not sufficient to indicate which processes are important in causing shoreline change, such as overwash that depends on coastal dune elevations. Predicting dune height is intrinsically important to assess future storm vulnerability. Here, we enhance shoreline-change predictions by including dune height as a variable in a statistical modeling approach. Dune height can also be used as an input variable, but it does not improve the shoreline-change prediction skill. Dune-height input does help to reduce prediction uncertainty. That is, by including dune height, the prediction is more precise but not more accurate. Comparing hindcast evaluations, better predictive skill was found when predicting dune height (0.8) compared with shoreline change (0.6). The skill depends on the level of detail of the model and we identify an optimized model that has high skill and minimal overfitting. The predictive model can be implemented with a range of forecast scenarios, and we illustrate the impacts of a higher future sea-level. This scenario shows that the shoreline change becomes increasingly erosional and more uncertain. Predicted dune heights are lower and the dune height uncertainty decreases.
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ArticleA Bayesian network approach to predicting nest presence of the federally-threatened piping plover (Charadrius melodus) using barrier island features(Elsevier, 2014-01-31) Gieder, Katherina D. ; Karpanty, Sarah M. ; Fraser, James D. ; Catlin, Daniel H. ; Gutierrez, Benjamin T. ; Plant, Nathaniel G. ; Turecek, Aaron M. ; Thieler, E. RobertSea-level rise and human development pose significant threats to shorebirds, particularly for species that utilize barrier island habitat. The piping plover (Charadrius melodus) is a federally-listed shorebird that nests on barrier islands and rapidly responds to changes in its physical environment, making it an excellent species with which to model how shorebird species may respond to habitat change related to sea-level rise and human development. The uncertainty and complexity in predicting sea-level rise, the responses of barrier island habitats to sea-level rise, and the responses of species to sea-level rise and human development necessitate a modeling approach that can link species to the physical habitat features that will be altered by changes in sea level and human development. We used a Bayesian network framework to develop a model that links piping plover nest presence to the physical features of their nesting habitat on a barrier island that is impacted by sea-level rise and human development, using three years of data (1999, 2002, and 2008) from Assateague Island National Seashore in Maryland. Our model performance results showed that we were able to successfully predict nest presence given a wide range of physical conditions within the model's dataset. We found that model predictions were more successful when the ranges of physical conditions included in model development were varied rather than when those physical conditions were narrow. We also found that all model predictions had fewer false negatives (nests predicted to be absent when they were actually present in the dataset) than false positives (nests predicted to be present when they were actually absent in the dataset), indicating that our model correctly predicted nest presence better than nest absence. These results indicated that our approach of using a Bayesian network to link specific physical features to nest presence will be useful for modeling impacts of sea-level rise or human-related habitat change on barrier islands. We recommend that potential users of this method utilize multiple years of data that represent a wide range of physical conditions in model development, because the model performed less well when constructed using a narrow range of physical conditions. Further, given that there will always be some uncertainty in predictions of future physical habitat conditions related to sea-level rise and/or human development, predictive models will perform best when developed using multiple, varied years of data input.
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ArticlePredictions of barrier island berm evolution in a time-varying storm climatology(John Wiley & Sons, 2014-02-19) Plant, Nathaniel G. ; Flocks, James ; Stockdon, Hilary F. ; Long, Joseph W. ; Guy, Kristy ; Thompson, David M. ; Cormier, Jamie M. ; Smith, Christopher G. ; Miselis, Jennifer L. ; Dalyander, P. SoupyLow-lying barrier islands are ubiquitous features of the world's coastlines, and the processes responsible for their formation, maintenance, and destruction are related to the evolution of smaller, superimposed features including sand dunes, beach berms, and sandbars. The barrier island and its superimposed features interact with oceanographic forces (e.g., overwash) and exchange sediment with each other and other parts of the barrier island system. These interactions are modulated by changes in storminess. An opportunity to study these interactions resulted from the placement and subsequent evolution of a 2 m high sand berm constructed along the northern Chandeleur Islands, LA. We show that observed berm length evolution is well predicted by a model that was fit to the observations by estimating two parameters describing the rate of berm length change. The model evaluates the probability and duration of berm overwash to predict episodic berm erosion. A constant berm length change rate is also predicted that persists even when there is no overwash. The analysis is extended to a 16 year time series that includes both intraannual and interannual variability of overwash events. This analysis predicts that as many as 10 or as few as 1 day of overwash conditions would be expected each year. And an increase in berm elevation from 2 m to 3.5 m above mean sea level would reduce the expected frequency of overwash events from 4 to just 0.5 event-days per year. This approach can be applied to understanding barrier island and berm evolution at other locations using past and future storm climatologies.
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ArticleEffects of sea-level rise on barrier island groundwater system dynamics – ecohydrological implications(John Wiley & Sons, 2014-11-12) Masterson, John P. ; Fienen, Michael N. ; Thieler, E. Robert ; Gesch, Dean B. ; Gutierrez, Benjamin T. ; Plant, Nathaniel G.We used a numerical model to investigate how a barrier island groundwater system responds to increases of up to 60 cm in sea level. We found that a sea-level rise of 20 cm leads to substantial changes in the depth of the water table and the extent and depth of saltwater intrusion, which are key determinants in the establishment, distribution and succession of vegetation assemblages and habitat suitability in barrier islands ecosystems. In our simulations, increases in water-table height in areas with a shallow depth to water (or thin vadose zone) resulted in extensive groundwater inundation of land surface and a thinning of the underlying freshwater lens. We demonstrated the interdependence of the groundwater response to island morphology by evaluating changes at three sites. This interdependence can have a profound effect on ecosystem composition in these fragile coastal landscapes under long-term changing climatic conditions.
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ArticleA Bayesian network to predict coastal vulnerability to sea level rise(American Geophysical Union, 2011-04-22) Gutierrez, Benjamin T. ; Plant, Nathaniel G. ; Thieler, E. RobertSea 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.
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ArticleUsing a Bayesian network to understand the importance of coastal storms and undeveloped landscapes for the creation and maintenance of early successional habitat(Public Library of Science, 2019-07-25) Zeigler, Sara L. ; Gutierrez, Benjamin T. ; Sturdivant, Emily ; Catlin, Daniel H. ; Fraser, James D. ; Hecht, Anne ; Karpanty, Sarah M. ; Plant, Nathaniel G. ; Thieler, E. RobertCoastal storms have consequences for human lives and infrastructure but also create important early successional habitats for myriad species. For example, storm-induced overwash creates nesting habitat for shorebirds like piping plovers (Charadrius melodus). We examined how piping plover habitat extent and location changed on barrier islands in New York, New Jersey, and Virginia after Hurricane Sandy made landfall following the 2012 breeding season. We modeled nesting habitat using a nest presence/absence dataset that included characterizations of coastal morphology and vegetation. Using a Bayesian network, we predicted nesting habitat for each study site for the years 2010/2011, 2012, and 2014/2015 based on remotely sensed spatial datasets (e.g., lidar, orthophotos). We found that Hurricane Sandy increased piping plover habitat by 9 to 300% at 4 of 5 study sites but that one site saw a decrease in habitat by 27%. The amount, location, and longevity of new habitat appeared to be influenced by the level of human development at each site. At three of the five sites, the amount of habitat created and the time new habitat persisted were inversely related to the amount of development. Furthermore, the proportion of new habitat created in high-quality overwash was inversely related to the level of development on study areas, from 17% of all new habitat in overwash at one of the most densely developed sites to 80% of all new habitat at an undeveloped site. We also show that piping plovers exploited new habitat after the storm, with 14–57% of all nests located in newly created habitat in the 2013 breeding season. Our results quantify the importance of storms in creating and maintaining coastal habitats for beach-nesting species like piping plovers, and these results suggest a negative correlation between human development and beneficial ecological impacts of these natural disturbances.
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PreprintEvaluation of dynamic coastal response to sea-level rise modifies inundation likelihood( 2016-02) Lentz, Erika E. ; Thieler, E. Robert ; Plant, Nathaniel G. ; Stippa, Sawyer R. ; Horton, Radley M. ; Gesch, Dean B.Sea-level rise (SLR) poses a range of threats to natural and built environments1, 2, making assessments of SLR-induced hazards essential for informed decision-making3. 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 km2 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.
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ArticleRelationships between regional coastal land cover distributions and elevation reveal data uncertainty in a sea-level rise impacts model(European Geosciences Union, 2019-05-15) Lentz, Erika E. ; Plant, Nathaniel G. ; Thieler, E. RobertUnderstanding land loss or resilience in response to sea-level rise (SLR) requires spatially extensive and continuous datasets to capture landscape variability. We investigate the sensitivity and skill of a model that predicts dynamic response likelihood to SLR across the northeastern US by exploring several data inputs and outcomes. Using elevation and land cover datasets, we determine where data error is likely, quantify its effect on predictions, and evaluate its influence on prediction confidence. Results show data error is concentrated in low-lying areas with little impact on prediction skill, as the inherent correlation between the datasets can be exploited to reduce data uncertainty using Bayesian inference. This suggests the approach may be extended to regions with limited data availability and/or poor quality. Furthermore, we verify that model sensitivity in these first-order landscape change assessments is well-matched to larger coastal process uncertainties, for which process-based models are important complements to further reduce uncertainty.
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ArticleDecoupling processes and scales of shoreline morphodynamics(Elsevier, 2016-08-24) Hapke, Cheryl J. ; Plant, Nathaniel G. ; Henderson, Rachel E. ; Schwab, William C. ; Nelson, Timothy R.Behavior of coastal systems on time scales ranging from single storm events to years and decades is controlled by both small-scale sediment transport processes and large-scale geologic, oceanographic, and morphologic processes. Improved understanding of coastal behavior at multiple time scales is required for refining models that predict potential erosion hazards and for coastal management planning and decision-making. Here we investigate the primary controls on shoreline response along a geologically-variable barrier island on time scales resolving extreme storms and decadal variations over a period of nearly one century. An empirical orthogonal function analysis is applied to a time series of shoreline positions at Fire Island, NY to identify patterns of shoreline variance along the length of the island. We establish that there are separable patterns of shoreline behavior that represent response to oceanographic forcing as well as patterns that are not explained by this forcing. The dominant shoreline behavior occurs over large length scales in the form of alternating episodes of shoreline retreat and advance, presumably in response to storms cycles. Two secondary responses include long-term response that is correlated to known geologic variations of the island and the other reflects geomorphic patterns with medium length scale. Our study also includes the response to Hurricane Sandy and a period of post-storm recovery. It was expected that the impacts from Hurricane Sandy would disrupt long-term trends and spatial patterns. We found that the response to Sandy at Fire Island is not notable or distinguishable from several other large storms of the prior decade.
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ArticleBridging groundwater models and decision support with a Bayesian network(John Wiley & Sons, 2013-10-09) Fienen, Michael N. ; Masterson, John P. ; Plant, Nathaniel G. ; Gutierrez, Benjamin T. ; Thieler, E. RobertResource managers need to make decisions to plan for future environmental conditions, particularly sea level rise, in the face of substantial uncertainty. Many interacting processes factor in to the decisions they face. Advances in process models and the quantification of uncertainty have made models a valuable tool for this purpose. Long-simulation runtimes and, often, numerical instability make linking process models impractical in many cases. A method for emulating the important connections between model input and forecasts, while propagating uncertainty, has the potential to provide a bridge between complicated numerical process models and the efficiency and stability needed for decision making. We explore this using a Bayesian network (BN) to emulate a groundwater flow model. We expand on previous approaches to validating a BN by calculating forecasting skill using cross validation of a groundwater model of Assateague Island in Virginia and Maryland, USA. This BN emulation was shown to capture the important groundwater-flow characteristics and uncertainty of the groundwater system because of its connection to island morphology and sea level. Forecast power metrics associated with the validation of multiple alternative BN designs guided the selection of an optimal level of BN complexity. Assateague island is an ideal test case for exploring a forecasting tool based on current conditions because the unique hydrogeomorphological variability of the island includes a range of settings indicative of past, current, and future conditions. The resulting BN is a valuable tool for exploring the response of groundwater conditions to sea level rise in decision support.
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ArticleInundation of a barrier island (Chandeleur Islands, Louisiana, USA) during a hurricane : observed water-level gradients and modeled seaward sand transport(John Wiley & Sons, 2014-07-15) Sherwood, Christopher R. ; Long, Joseph W. ; Dickhudt, Patrick J. ; Dalyander, P. Soupy ; Thompson, David M. ; Plant, Nathaniel G.Large geomorphic changes to barrier islands may occur during inundation, when storm surge exceeds island elevation. Inundation occurs episodically and under energetic conditions that make quantitative observations difficult. We measured water levels on both sides of a barrier island in the northern Chandeleur Islands during inundation by Hurricane Isaac. Wind patterns caused the water levels to slope from the bay side to the ocean side for much of the storm. Modeled geomorphic changes during the storm were very sensitive to the cross-island slopes imposed by water-level boundary conditions. Simulations with equal or landward sloping water levels produced the characteristic barrier island storm response of overwash deposits or displaced berms with smoother final topography. Simulations using the observed seaward sloping water levels produced cross-barrier channels and deposits of sand on the ocean side, consistent with poststorm observations. This sensitivity indicates that accurate water-level boundary conditions must be applied on both sides of a barrier to correctly represent the geomorphic response to inundation events. More broadly, the consequence of seaward transport is that it alters the relationship between storm intensity and volume of landward transport. Sand transported to the ocean side may move downdrift, or aid poststorm recovery by moving onto the beach face or closing recent breaches, but it does not contribute to island transgression or appear as an overwash deposit in the back-barrier stratigraphic record. The high vulnerability of the Chandeleur Islands allowed us to observe processes that are infrequent but may be important at other barrier islands.
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ArticlePiping plovers demonstrate regional differences in nesting habitat selection patterns along the U. S. Atlantic coast(Ecological Society of America, 2021-03-11) Zeigler, Sara L. ; Gutierrez, Benjamin T. ; Hecht, Anne ; Plant, Nathaniel G. ; Sturdivant, EmilyHabitat studies that encompass a large portion of a species’ geographic distribution can explain characteristics that are either consistent or variable, further informing inference from more localized studies and improving management successes throughout the range. We identified landscape characteristics at Piping Plover nests at 21 sites distributed from Massachusetts to North Carolina and compared habitat selection patterns among the three designated U.S. recovery units (New England, New York–New Jersey, and Southern). Geomorphic setting, substrate type, and vegetation type and density were determined in situ at 928 Piping Plover nests (hereafter, used resource units) and 641 random points (available resource units). Elevation, beach width, Euclidean distance to ocean shoreline, and least-cost path distance to low-energy shorelines with moist substrates (commonly used as foraging habitat) were associated with used and available resource units using remotely sensed spatial data. We evaluated multivariate differences in habitat selection patterns by comparing recovery unit-specific Bayesian networks. We then further explored individual variables that drove disparities among Bayesian networks using resource selection ratios for categorical variables and Welch’s unequal variances t-tests for continuous variables. We found that relationships among variables and their connections to habitat selection were similar among recovery units, as seen in commonalities in Bayesian network structures. Furthermore, nesting Piping Plovers consistently selected mixed sand and shell, gravel, or cobble substrates as well as areas with sparse or no vegetation, irrespective of recovery unit. However, we observed significant differences among recovery units in the elevations, distances to ocean, and distances to low-energy shorelines of used resource units. Birds also exhibited increased selectivity for overwash habitats and for areas with access to low-energy shorelines along a latitudinal gradient from north to south. These results have important implications for conservation and management, including assessment of shoreline stabilization and habitat restoration planning as well as forecasting effects of climate change.
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ArticleSmartphone technologies and Bayesian networks to assess shorebird habitat selection(John Wiley & Sons, 2017-09-28) Zeigler, Sara L. ; Thieler, E. Robert ; Gutierrez, Benjamin T. ; Plant, Nathaniel G. ; Hines, Megan K. ; Fraser, James D. ; Catlin, Daniel H. ; Karpanty, Sarah M.Understanding patterns of habitat selection across a species’ geographic distribution can be critical for adequately managing populations and planning for habitat loss and related threats. However, studies of habitat selection can be time consuming and expensive over broad spatial scales, and a lack of standardized monitoring targets or methods can impede the generalization of site-based studies. Our objective was to collaborate with natural resource managers to define available nesting habitat for piping plovers (Charadrius melodus) throughout their U.S. Atlantic coast distribution from Maine to North Carolina, with a goal of providing science that could inform habitat management in response to sea-level rise. We characterized a data collection and analysis approach as being effective if it provided low-cost collection of standardized habitat-selection data across the species’ breeding range within 1–2 nesting seasons and accurate nesting location predictions. In the method developed, >30 managers and conservation practitioners from government agencies and private organizations used a smartphone application, “iPlover,” to collect data on landcover characteristics at piping plover nest locations and random points on 83 beaches and barrier islands in 2014 and 2015. We analyzed these data with a Bayesian network that predicted the probability a specific combination of landcover variables would be associated with a nesting site. Although we focused on a shorebird, our approach can be modified for other taxa. Results showed that the Bayesian network performed well in predicting habitat availability and confirmed predicted habitat preferences across the Atlantic coast breeding range of the piping plover. We used the Bayesian network to map areas with a high probability of containing nesting habitat on the Rockaway Peninsula in New York, USA, as an example application. Our approach facilitated the collation of evidence-based information on habitat selection from many locations and sources, which can be used in management and decision-making applications. © 2017 The Authors. Wildlife Society Bulletin published by Wiley Periodicals, Inc. on behalf of The Wildlife Society.
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ArticleUsing a Bayesian network to predict barrier island geomorphologic characteristics(John Wiley & Sons, 2015-12-04) Gutierrez, Benjamin T. ; Plant, Nathaniel G. ; Thieler, E. Robert ; Turecek, Aaron M.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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ArticleIntegrating Bayesian Networks to forecast sea‐level rise impacts on Barrier Island characteristics and habitat availability(American Geophysical Union, 2022-10-14) Gutierrez, Benjamin T. ; Zeigler, Sara L. ; Lentz, Erika ; Sturdivant, Emily J. ; Plant, Nathaniel G.Evaluation of sea‐level rise (SLR) impacts on coastal landforms and habitats is a persistent need for informing coastal planning and management, including policy decisions, particularly those that balance human interests and habitat protection throughout the coastal zone. Bayesian networks (BNs) are used to model barrier island change under different SLR scenarios that are relevant to management and policy decisions. BNs utilized here include a shoreline change model and two models of barrier island biogeomorphological evolution at different scales (50 and 5 m). These BNs were then linked to another BN to predict habitat availability for piping plovers (Charadrius melodus), a threatened shorebird reliant on beach habitats. We evaluated the performance of the two linked geomorphology BNs and further examined error rates by generating hindcasts of barrier island geomorphology and habitat availability for 2014 conditions. Geomorphology hindcasts revealed that model error declined with a greater number of known inputs, with error rates reaching 55% when multiple outputs were hindcast simultaneously. We also found that, although error in predictions of piping plover nest presence/absence increased when outputs from the geomorphology BNs were used as inputs in the piping plover habitat BN, the maximum error rate for piping plover habitat suitability in the fully‐linked BNs was only 30%. Our findings suggest this approach may be useful for guiding scenario‐based evaluations where known inputs can be used to constrain variables that produce higher uncertainty for morphological predictions. Overall, the approach demonstrates a way to assimilate data and model structures with uncertainty to produce forecasts to inform coastal planning and management.
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ArticlePredicting coastal cliff erosion using a Bayesian probabilistic model(Elsevier B.V., 2010-10-10) Hapke, Cheryl J. ; Plant, Nathaniel G.Regional coastal cliff retreat is difficult to model due to the episodic nature of failures and the along-shore variability of retreat events. There is a growing demand, however, for predictive models that can be used to forecast areas vulnerable to coastal erosion hazards. Increasingly, probabilistic models are being employed that require data sets of high temporal density to define the joint probability density function that relates forcing variables (e.g. wave conditions) and initial conditions (e.g. cliff geometry) to erosion events. In this study we use a multi-parameter Bayesian network to investigate correlations between key variables that control and influence variations in cliff retreat processes. The network uses Bayesian statistical methods to estimate event probabilities using existing observations. Within this framework, we forecast the spatial distribution of cliff retreat along two stretches of cliffed coast in Southern California. The input parameters are the height and slope of the cliff, a descriptor of material strength based on the dominant cliff-forming lithology, and the long-term cliff erosion rate that represents prior behavior. The model is forced using predicted wave impact hours. Results demonstrate that the Bayesian approach is well-suited to the forward modeling of coastal cliff retreat, with the correct outcomes forecast in 70–90% of the modeled transects. The model also performs well in identifying specific locations of high cliff erosion, thus providing a foundation for hazard mapping. This approach can be employed to predict cliff erosion at time-scales ranging from storm events to the impacts of sea-level rise at the century-scale.