Thieler E. Robert

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Thieler

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UAS-SfM for coastal research : geomorphic feature extraction and land cover classification from high-resolution elevation and optical imagery

(MDPI AG, 2017-10-03) Sturdivant, Emily ; Lentz, Erika E. ; Thieler, E. Robert ; Farris, Amy S. ; Weber, Kathryn M. ; Remsen, David P. ; Miner, Simon ; Henderson, Rachel E.

The vulnerability of coastal systems to hazards such as storms and sea-level rise is typically characterized using a combination of ground and manned airborne systems that have limited spatial or temporal scales. Structure-from-motion (SfM) photogrammetry applied to imagery acquired by unmanned aerial systems (UAS) offers a rapid and inexpensive means to produce high-resolution topographic and visual reflectance datasets that rival existing lidar and imagery standards. Here, we use SfM to produce an elevation point cloud, an orthomosaic, and a digital elevation model (DEM) from data collected by UAS at a beach and wetland site in Massachusetts, USA. We apply existing methods to (a) determine the position of shorelines and foredunes using a feature extraction routine developed for lidar point clouds and (b) map land cover from the rasterized surfaces using a supervised classification routine. In both analyses, we experimentally vary the input datasets to understand the benefits and limitations of UAS-SfM for coastal vulnerability assessment. We find that (a) geomorphic features are extracted from the SfM point cloud with near-continuous coverage and sub-meter precision, better than was possible from a recent lidar dataset covering the same area; and (b) land cover classification is greatly improved by including topographic data with visual reflectance, but changes to resolution (when <50 cm) have little influence on the classification accuracy.
Coupling 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, Davina

Predictions 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.
A 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. Robert

Sea-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.
Effects 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.
Transient coastal landscapes : rising sea level threatens salt marshes

( 2018-05) Valiela, Ivan ; Lloret, Javier ; Bowyer, Tynan ; Miner, Simon ; Remsen, David P. ; Elmstrom, Elizabeth ; Cogswell, Charlotte ; Thieler, E. Robert

Salt marshes are important coastal environments that provide key ecological services. As sea level rise has accelerated globally, concerns about the ability of salt marshes to survive submergence are increasing. Previous estimates of likely survival of salt marshes were based on ratios of sea level rise to marsh platform accretion. Here we took advantage of an unusual, long-term (1979-2015), spatially detailed comparison of changes in a representative New England salt marsh to provide an empirical estimate of habitat losses based on actual measurements. We show prominent changes in habitat mosaic within the marsh, consistent and coincident with increased submergence and coastal erosion. Model results suggest that at current rates of sea level rise, marsh platform accretion, habitat loss, and with the limitation of the widespread “coastal squeeze”, the entire ecosystem might disappear by the beginning of the next century, a fate that might be likely for many salt marshes elsewhere.
Geologic framework of the northern North Carolina, USA inner continental shelf and its influence on coastal evolution

(Elsevier, 2013-11-20) Thieler, E. Robert ; Foster, David S. ; Himmelstoss, Emily A. ; Mallinson, David J.

The inner continental shelf off the northern Outer Banks of North Carolina was mapped using sidescan sonar, interferometric swath bathymetry, and high-resolution chirp and boomer subbottom profiling systems. We use this information to describe the shallow stratigraphy, reinterpret formation mechanisms of some shoal features, evaluate local relative sea-levels during the Late Pleistocene, and provide new constraints, via recent bedform evolution, on regional sediment transport patterns. The study area is approximately 290 km long by 11 km wide, extending from False Cape, Virginia to Cape Lookout, North Carolina, in water depths ranging from 6 to 34 m. Late Pleistocene sedimentary units comprise the shallow geologic framework of this region and determine both the morphology of the inner shelf and the distribution of sediment sources and sinks. We identify Pleistocene sedimentary units beneath Diamond Shoals that may have provided a geologic template for the location of modern Cape Hatteras and earlier paleo-capes during the Late Pleistocene. These units indicate shallow marine deposition 15–25 m below present sea-level. The uppermost Pleistocene unit may have been deposited as recently as Marine Isotope Stage 3, although some apparent ages for this timing may be suspect. Paleofluvial valleys incised during the Last Glacial Maximum traverse the inner shelf throughout the study area and dissect the Late Pleistocene units. Sediments deposited in the valleys record the Holocene transgression and provide insight into the evolutionary history of the barrier-estuary system in this region. The relationship between these valleys and adjacent shoal complexes suggests that the paleo-Roanoke River did not form the Albemarle Shelf Valley complex as previously proposed; a major fluvial system is absent and thus makes the formation of this feature enigmatic. Major shoal features in the study area show mobility at decadal to centennial timescales, including nearly a kilometer of shoal migration over the past 134 yr. Sorted bedforms occupy ~ 1000 km2 of seafloor in Raleigh Bay, and indicate regional sediment transport patterns between Capes Hatteras and Lookout that help explain long-term sediment accumulation and morphologic development. Portions of the inner continental shelf with relatively high sediment abundance are characterized by shoals and shoreface-attached ridges, and where sediment is less abundant, the seafloor is dominated by sorted bedforms. These relationships are also observed in other passive margin settings, suggesting a continuum of shelf morphology that may have broad application for interpreting inner shelf sedimentation patterns.
A Bayesian network to predict coastal vulnerability to sea level rise

(American Geophysical Union, 2011-04-22) Gutierrez, Benjamin T. ; Plant, Nathaniel G. ; Thieler, E. Robert

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.
Sorted bed forms as self-organized patterns : 2. Complex forcing scenarios

(American Geophysical Union, 2007-08-14) Coco, Giovanni ; Murray, A. Brad ; Green, Malcolm O. ; Thieler, E. Robert ; Hume, T. M.

We employ a numerical model to study the development of sorted bed forms under a variety of hydrodynamic and sedimentary conditions. Results indicate that increased variability in wave height decreases the growth rate of the features and can potentially give rise to complicated, a priori unpredictable, behavior. This happens because the system responds to a change in wave characteristics by attempting to self-organize into a patterned seabed of different geometry and spacing. The new wavelength might not have enough time to emerge before a new change in wave characteristics occurs, leading to less regular seabed configurations. The new seabed configuration is also highly dependent on the preexisting morphology, which further limits the possibility of predicting future behavior. For the same reasons, variability in the mean current magnitude and direction slows down the growth of features and causes patterns to develop that differ from classical sorted bed forms. Spatial variability in grain size distribution and different types of net sediment aggradation/degradation can also result in the development of sorted bed forms characterized by a less regular shape. Numerical simulations qualitatively agree with observed geometry (spacing and height) of sorted bed forms. Also in agreement with observations is that at shallower depths, sorted bed forms are more likely to be affected by changes in the forcing conditions, which might also explain why, in shallow waters, sorted bed forms are described as ephemeral features. Finally, simulations indicate that the different sorted bed form shapes and patterns observed in the field might not necessarily be related to diverse physical mechanisms. Instead, variations in sorted bed form characteristics may result from variations in local hydrodynamic and/or sedimentary conditions.
Using 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. Robert

Coastal 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.
Evaluation 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.
Relationships 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. Robert

Understanding 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.
Bridging 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. Robert

Resource 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.
Smartphone 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.
Smartphone-based distributed data collection enables rapid assessment of shorebird habitat suitability

(Public Library of Science, 2016-11-09) Thieler, E. Robert ; Zeigler, Sara L. ; Winslow, Luke A. ; Hines, Megan K. ; Read, Jordan S. ; Walker, Jordan I.

Understanding and managing dynamic coastal landscapes for beach-dependent species requires biological and geological data across the range of relevant environments and habitats. It is difficult to acquire such information; data often have limited focus due to resource constraints, are collected by non-specialists, or lack observational uniformity. We developed an open-source smartphone application called iPlover that addresses these difficulties in collecting biogeomorphic information at piping plover (Charadrius melodus) nest sites on coastal beaches. This paper describes iPlover development and evaluates data quality and utility following two years of collection (n = 1799 data points over 1500 km of coast between Maine and North Carolina, USA). We found strong agreement between field user and expert assessments and high model skill when data were used for habitat suitability prediction. Methods used here to develop and deploy a distributed data collection system have broad applicability to interdisciplinary environmental monitoring and modeling.
Using 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.
A catastrophic meltwater flood event and the formation of the Hudson Shelf Valley

(Elsevier B.V., 2007-01-04) Thieler, E. Robert ; Butman, Bradford ; Schwab, William C. ; Allison, Mead A. ; Driscoll, Neal W. ; Donnelly, Jeffrey P. ; Uchupi, Elazar

The Hudson Shelf Valley (HSV) is the largest physiographic feature on the U.S. mid-Atlantic continental shelf. The 150-km long valley is the submerged extension of the ancestral Hudson River Valley that connects to the Hudson Canyon. Unlike other incised valleys on the mid-Atlantic shelf, it has not been infilled with sediment during the Holocene. Analyses of multibeam bathymetry, acoustic backscatter intensity, and high-resolution seismic reflection profiles reveal morphologic and stratigraphic evidence for a catastrophic meltwater flood event that formed the modern HSV. The valley and its distal deposits record a discrete flood event that carved 15-m high banks, formed a 120-km2 field of 3- to 6-m high bedforms, and deposited a subaqueous delta on the outer shelf. The HSV is inferred to have been carved initially by precipitation and meltwater runoff during the advance of the Laurentide Ice Sheet, and later by the drainage of early proglacial lakes through stable spillways. A flood resulting from the failure of the terminal moraine dam at the Narrows between Staten Island and Long Island, New York, allowed glacial lakes in the Hudson and Ontario basins to drain across the continental shelf. Water level changes in the Hudson River basin associated with the catastrophic drainage of glacial lakes Iroquois, Vermont, and Albany around 11,450 14C year BP (~ 13,350 cal BP) may have precipitated dam failure at the Narrows. This 3200 km3 discharge of freshwater entered the North Atlantic proximal to the Gulf Stream and may have affected thermohaline circulation at the onset of the Intra-Allerød Cold Period. Based on bedform characteristics and fluvial morphology in the HSV, the maximum freshwater flux during the flood event is estimated to be ~ 0.46 Sv for a duration of ~ 80 days.
Importance of coastal change variables in determining vulnerability to sea- and lake-level change

(Coastal Education and Research Foundation, 2010-01) Pendleton, Elizabeth A. ; Thieler, E. Robert ; Williams, S. Jeffress

In 2001, the U.S. Geological Survey began conducting scientific assessments of coastal vulnerability to potential future sea- and lake-level changes in 22 National Park Service sea- and lakeshore units. Coastal park units chosen for the assessment included a variety of geological and physical settings along the U.S. Atlantic, Pacific, Gulf of Mexico, Gulf of Alaska, Caribbean, and Great Lakes shorelines. This research is motivated by the need to understand and anticipate coastal changes caused by accelerating sea-level rise, as well as lake-level changes caused by climate change, over the next century. The goal of these assessments is to provide information that can be used to make long-term (decade to century) management decisions. Here we analyze the results of coastal vulnerability assessments for several coastal national park units. Index-based assessments quantify the likelihood that physical changes may occur based on analysis of the following variables: tidal range, ice cover, wave height, coastal slope, historical shoreline change rate, geomorphology, and historical rate of relative sea- or lake-level change. This approach seeks to combine a coastal system's susceptibility to change with its natural ability to adapt to changing environmental conditions, and it provides a measure of the system's potential vulnerability to the effects of sea- or lake-level change. Assessments for 22 park units are combined to evaluate relationships among the variables used to derive the index. Results indicate that Atlantic and Gulf of Mexico parks have the highest vulnerability rankings relative to other park regions. A principal component analysis reveals that 99% of the index variability can be explained by four variables: geomorphology, regional coastal slope, water-level change rate, and mean significant wave height. Tidal range, ice cover, and historical shoreline change are not as important when the index is evaluated at large spatial scales (thousands of kilometers).

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