Relationships between regional coastal land cover distributions and elevation reveal data uncertainty in a sea-level rise impacts model

© The Author(s), 2019. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Lentz, E. E., Plant, N. G., & Thieler, E. R. Relationships between regional coastal land cover distributions and elevation reveal data uncertainty in a sea-level rise impacts model. Earth Surface Dynamics, 7(2), (2019):429-438, doi:10.5194/esurf-7-429-2019.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.This research was funded by the U.S. Geological Survey Coastal and Marine Geology Program. We thank P. Soupy Dalyander for early reviews and discussion of this paper. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the US Government

Predicted sea-level rise-driven biogeomorphological changes on Fire Island, New York: implications for people and plovers

© The Author(s), 2022. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Zeigler, S. L., Gutierrez, B. T., Lentz, E. E., Plant, N. G., Sturdivant, E. J., & Doran, K. S. Predicted sea-level rise-driven biogeomorphological changes on Fire Island, New York: implications for people and plovers. Earth’s Future, 10(4), (2022): e2021EF002436, https://doi.org/10.1029/2021EF002436.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.Funding for this work was provided by the U.S. Geological Survey's Coastal and Marine Hazards and Resources Program, with supplemental funding through the Disaster Relief Act

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

© The Author(s), 2017. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Remote Sensing 9 (2017): 1020, doi:10.3390/rs9101020.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.This project was funded by the U.S. Geological Survey (USGS) Coastal and Marine Geology Program and the Department of the Interior Northeast Climate Science Center

Evaluating Coastal Landscape Response to Sea-Level Rise in the Northeastern United States - Approach and Methods

The U.S. Geological Survey is examining effects of future sea-level rise on the coastal landscape from Maine to Virginia by producing spatially explicit, probabilistic predictions using sea-level projections, vertical land movement rates (due to isostacy), elevation data, and land-cover data. Sea-level-rise scenarios used as model inputs are generated by using multiple sources of information, including Coupled Model Intercomparison Project Phase 5 models following representative concentration pathways 4.5 and 8.5 in the Intergovernmental Panel on Climate Change Fifth Assessment Report. A Bayesian network is used to develop a predictive coastal response model that integrates the sea-level, elevation, and land-cover data with assigned probabilities that account for interactions with coastal geomorphology as well as the corresponding ecological and societal systems it supports. The effects of sea-level rise are presented as (1) level of landscape submergence and (2) coastal response type characterized as either static (that is, inundation) or dynamic (that is, landform or landscape change). Results are produced at a spatial scale of 30 meters for four decades (the 2020s, 2030s, 2050s, and 2080s). The probabilistic predictions can be applied to landscape management decisions based on sea-level-rise effects as well as on assessments of the prediction uncertainty and need for improved data or fundamental understanding. This report describes the methods used to produce predictions, including information on input datasets; the modeling approach; model outputs; data-quality-control procedures; and information on how to access the data and metadata online

Geologic Framework Influences on the Geomorphology of an Anthropogenically Modified Barrier Island: Assessment of Dune/Beach Changes at Fire Island, New York

Antecedent geology plays a crucial role in determining the inner-shelf, nearshore, and onshore geomorphology observed in coastal systems. However, the influence of the geologic framework on a system is difficult to extract when evaluating responses to changes due to storms and anthropogenic modifications, and few studies have quantified the potential for these influences in dune/beach environments. This study evaluates topographic change to the dune/beach system at Fire Island, New York over a ten year period (1998-2008) at two sites representing eastern and western reaches of the island where morphology has been shown to vary. The sites are situated along swaths of coast eroding differentially and where the inner shelf geologic framework differs substantially. Fewer large storms occurred in the first half of the study period, compared with the later part of the study period which includes several severe and prolonged extratropical storms. Additionally, a major beach replenishment project was conducted at one of the study sites. Topographic data from LiDAR and RTK GPS surveys are used to construct high-resolution 3D surfaces, which are used to determine volumetric change and to extract 2D alongshore features and profiles for analysis. The study sites help to further characterize morphologic differences between eastern and western reaches of the island. The western site displays higher sand volumes, lower dunes, and a lower gradient profile slope when compared with the eastern site. In addition to these fundamental morphologic differences, the two sites also differ significantly in their response to coastal storms and in the fact that their replenishment histories are different. The replenished areas show reduced vulnerability to storms through minimal volume loss and shoreline accretion that should be considered when evaluating the response of replenished areas to episodic events. We propose that site-specific differences evident throughout the study period can be linked to alongshore variations in the framework geology of the system. Anthropogenic modifications may have intensified differences already inherent in the system

The Development of a Probabilistic Approach to Forecast Coastal Change

This study demonstrates the applicability of a Bayesian probabilistic model as an effective tool in predicting post-storm beach changes along sandy coastlines. Volume change and net shoreline movement are modeled for two study sites at Fire Island, New York in response to two extratropical storms in 2007 and 2009. Both study areas include modified areas adjacent to unmodified areas in morphologically different segments of coast. Predicted outcomes are evaluated against observed changes to test model accuracy and uncertainty along 163 cross-shore transects. Results show strong agreement in the cross validation of predictions vs. observations, with 70-82% accuracies reported. Although no consistent spatial pattern in inaccurate predictions could be determined, the highest prediction uncertainties appeared in locations that had been recently replenished. Further testing and model refinement are needed; however, these initial results show that Bayesian networks have the potential to serve as important decision-support tools in forecasting coastal change

A Review of Sediment Budget Estimations at Fire Island National Seashore, New York

This report presents a review of the existing body of scientific literature, as it pertains to issues relevant to coastal sediment budgets, both at Fire Island National Seashore (FIIS) and in general, in order to provide the National Park Service (NPS) with the information they need to best manage park resources at FIIS. The review outlines the state of knowledge on processes of sediment transport, and addresses the relationship of the nearshore with the shoreface and beach. Few studies of sediment budget processes exist for Fire Island and the south shore of Long Island, and as such, this review includes the current knowledge base of studies conducted worldwide

A Review of Sediment Budget Estimations at Fire Island National Seashore, New York

This report presents a review of the existing body of scientific literature, as it pertains to issues relevant to coastal sediment budgets, both at Fire Island National Seashore (FIIS) and in general, in order to provide the National Park Service (NPS) with the information they need to best manage park resources at FIIS. The review outlines the state of knowledge on processes of sediment transport, and addresses the relationship of the nearshore with the shoreface and beach. Few studies of sediment budget processes exist for Fire Island and the south shore of Long Island, and as such, this review includes the current knowledge base of studies conducted worldwide

Application of Bayesian Networks to Hindcast Barrier Island Morphodynamics

Prediction of coastal vulnerability is of increasing concern to policy makers, coastal managers and other stakeholders. Coastal regions and barrier islands along the Atlantic and Gulf coasts are subject to frequent, large storms, whose waves and storm surge can dramatically alter beach morphology, threaten infrastructure, and impact local economies. Given that precise forecasts of regional hazards are challenging, because of the complex interactions between processes on many scales, a range of probable geomorphic change in response to storm conditions is often more helpful than deterministic predictions. Site-specific probabilistic models of coastal change are reliable because they are formulated with observations so that local factors, of potentially high influence, are inherent in the model. The development and use of predictive tools such as Bayesian Networks in response to future storms has the potential to better inform management decisions and hazard preparation in coastal communities. We present several Bayesian Networks designed to hindcast distinct morphologic changes attributable to the Nor\u27Ida storm of 2009, at Fire Island, New York. Model predictions are informed with historical system behavior, initial morphologic conditions, and a parameterized treatment of wave climate. We refine a preliminary Bayesian Network by 1) increasing model experience through additional observations, 2) including anthropogenic modification history, and 3) replacing parameterized wave impact values with maximum run-up elevation. Further, we develop and train a pair of generalized models with an additional dataset encompassing a different storm event, which expands the observations beyond our hindcast objective. We compare the skill of the generalized models against the Nor\u27Ida specific model formulation, balancing the reduced skill with an expectation of increased transferability. Results of Nor\u27Ida hindcasts ranged in skill from 0.37 to 0.51 and accuracy of 65.0 to 81.9%

Evaluation of Dynamic Coastal Response to Sea-level Rise Modifies Inundation Likelihood

Sea-level rise (SLR) poses a range of threats to natural and built environments, making assessments of SLR-induced hazards essential for informed decision making. 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 30x30m resolution predictions for more than 38,000 sq km of diverse coastal landscape in the northeastern United States. 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 versus 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