Geology topics

Nathaniel G. Plant

Publications and source records attributed to Nathaniel G. Plant.

At least 19 recordsLinked to original sources

USGS2020

Development and application of an empirical dune growth model for evaluating barrier island recovery from storms

Coastal zone managers require models that predict barrier island change on decadal time scales to estimate coastal vulnerability, and plan habitat restoration and coastal protection projects. To meet these needs, methods must be available for predicting dune recovery as well as dune erosion. In the present study, an empirical dune growth model (EDGR) was developed to predict the evolution of the primary foredune of a barrier island. Within EDGR, an island is represented as a sum of Gaussian shape functions representing dunes, berms, and the underlying island form. The model evolves the foredune based on estimated terminal dune height and location inputs. EDGR was assessed against observed dune evolution along the western end of Dauphin Island, Alabama over the 10 years following Hurricane Katrina (2005). The root mean square error with EDGR (ranging from 0.18 to 0.74 m over the model domain) was reduced compared to an alternate no-change model (0.69–0.96 m). Hindcasting with EDGR also supports the study of dune evolution processes. At Dauphin Island, results suggest that a low-lying portion of the site was dominated by overwash for ~5 years after Katrina, before approaching their terminal height and becoming growth-limited after 2010. EDGR’s computational efficiency allows dune evolution to be rapidly predicted and enables ensemble predictions to constrain the uncertainty that may result if terminal dune characteristics are unknown. In addition, EDGR can be coupled with an external model for estimating dune erosion and/or the long-term evolution of other subaerial features to allow decadal-scale prediction of barrier island evolution.

Alabama↗

USGS2020

Probabilistic patterns of inundation and biogeomorphic changes due to sea-level rise along the northeastern U.S. Atlantic coast

Context Coastal landscapes evolve in response to sea-level rise (SLR) through a variety of geologic processes and ecological feedbacks. When the SLR rate surpasses the rate at which these processes build elevation and drive lateral migration, inundation is likely. Objectives To examine the role of land cover diversity and composition in landscape response to SLR across the northeastern United States. Methods Using an existing probabilistic framework, we quantify the probability of inundation, a measure of vulnerability, under different SLR scenarios on the coastal landscape. Resistant areas—wherein a dynamic response is anticipated—are defined as unlikely (p < 0.33) to inundate. Results are assessed regionally for different land cover types and at 26 sites representing varying levels of land cover diversity. Results Modeling results suggest that by the 2050s, 44% of low-lying, habitable land in the region is unlikely to inundate, further declining to 36% by the 2080s. In addition to a decrease in SLR resistance with time, these results show an increasing uncertainty that the coastal landscape will continue to evolve in response to SLR as it has in the past. We also find that resistance to SLR is correlated with land cover composition, wherein sites containing land cover types adaptable to SLR impacts show greater potential to undergo biogeomorphic state shifts rather than inundating with time. Conclusions Our findings support other studies that have highlighted the importance of ecological composition and diversity in stabilizing the physical landscape and suggest that flexible planning strategies, such as adaptive management, are particularly well suited for SLR preparation in diverse coastal settings.

Connecticut, Maine, Maryland, Massachusetts, New H↗

USGS2020

The roles of storminess and sea level rise in decadal barrier island evolution

Models of alongshore sediment transport during quiescent conditions, storm‐driven barrier island morphology, and poststorm dune recovery are integrated to assess decadal barrier island evolution under scenarios of increased sea levels and variability in storminess (intensity and frequency). Model results indicate barrier island response regimes of keeping pace, narrowing, flattening, deflation (narrowing and flattening), and aggradation. Under lower storminess scenarios, more areas of the island experienced narrowing due to collision. Under higher storminess scenarios, more areas experienced flattening due to overwash and inundation. Both increased sea levels and increased storminess resulted in breaching when the majority of the island was not keeping pace and deflation was the dominant regime due to increased overtopping. Under the highest storminess scenario, the island was unable to recover elevation after storms and drowned in just 10 years.

Alabama↗

USGS2020

Development of a modeling framework for predicting decadal barrier island evolution

Predicting the decadal evolution of barrier island systems is important for coastal managers who propose restoration or preservation alternatives aimed at increasing the resiliency of the island and its associated habitats or communities. Existing numerical models for simulating morphologic changes typically include either long-term (for example, longshore transport under quiescent conditions) or short-term (for example, storm-driven waves) processes, with limited capacity to predict the decadal time-scale that is often most relevant in coastal planning. As part of the Alabama Barrier Island Restoration Assessment, a methodology was developed to predict barrier island evolution on decadal time scales. The developed modeling scheme uses multiple models including (1) Delft3D; (2) the empirical dune growth model (EDGR); and (3) XBeach that run sequentially to simulate evolution of barrier island geomorphology. The model framework was developed and applied to hindcast the evolution of Dauphin Island, Alabama, between 2004 and 2015, and was assessed using lidar data over the same period.

Alabama↗

USGS2020

Application of decadal modeling approach to forecast barrier island evolution, Dauphin Island, Alabama

Forecasting barrier island evolution provides coastal managers and stakeholders the ability to assess the resiliency of these important coastal environments that are home to both established communities and existing natural habitats. This study uses an established coupled model framework to assess how Dauphin Island, Alabama, responds to various storm and sea-level change scenarios, along with a suite of restoration measures, over the course of a decade. The coupled model framework uses validated models for long-term alongshore sediment transport (Delft 3D), short-term storm induced impacts (XBeach), as well as dune building and recovery (empirical dune growth model). This model framework was simulated with the various storm and sea-level change scenarios on a non-restored Dauphin Island, then a subset of the storm and sea-level change scenarios were applied to a suite of seven different restoration measures to determine how they would influence the morphologic evolution over a decadal period. Topographic and bathymetric changes captured in post-simulation digital elevation models were then passed on to partners for various simulations to determine the effects on habitat evolution and water quality as it relates to oyster reef and submerged aquatic vegetation.

Alabama↗

USGS2020

Blind testing of shoreline evolution models

Beaches around the world continuously adjust to daily and seasonal changes in wave and tide conditions, which are themselves changing over longer time-scales. Different approaches to predict multi-year shoreline evolution have been implemented; however, robust and reliable predictions of shoreline evolution are still problematic even in short-term scenarios (shorter than decadal). Here we show results of a modelling competition, where 19 numerical models (a mix of established shoreline models and machine learning techniques) were tested using data collected for Tairua beach, New Zealand with 18 years of daily averaged alongshore shoreline position and beach rotation (orientation) data obtained from a camera system. In general, traditional shoreline models and machine learning techniques were able to reproduce shoreline changes during the calibration period (1999–2014) for normal conditions but some of the model struggled to predict extreme and fast oscillations. During the forecast period (unseen data, 2014–2017), both approaches showed a decrease in models’ capability to predict the shoreline position. This was more evident for some of the machine learning algorithms. A model ensemble performed better than individual models and enables assessment of uncertainties in model architecture. Research-coordinated approaches (e.g., modelling competitions) can fuel advances in predictive capabilities and provide a forum for the discussion about the advantages/disadvantages of available models.

Scientific Reports↗

USGS2019

A pragmatic approach for comparing species distribution models to increasing confidence in managing piping plover habitat

Conservation management often requires decision-making without perfect knowledge of the at-risk species or ecosystem. Species distribution models (SDMs) are useful but largely under-utilized due to model uncertainty. We provide a case study that utilizes an ensemble modeling approach of two independently derived SDMs to explicitly address common modeling impediments and to directly inform conservation decision-making for piping plovers in a heavily populated mid-Atlantic (USA) coastal zone. We summarized previously published Bayesian network and maximum entropy modeling approaches to highlight similarities and differences in model structure, and we compared the relative importance of predictors used. Despite marked differences in analytical approach, the relative importance of factors driving nest-site selection was consistent. Comparison of raw suitability scores revealed high dissimilarity between modeling approaches, but models demonstrated considerable agreement when comparing a binary (suitable/unsuitable) measure of suitability. Instances of model consensus (i.e., overlapping areas of predicted piping plover nesting habitat between models) provide a stronger ‘signal’ in model results, reducing uncertainty related to biases or errors associated with either model. We tested model accuracy using a common dataset of plover nests initiated within the focal areas between 2013 and 2015, and we examined congruency in model outputs. Nearly 90% of all nests occurred in areas predicted suitable by at least one model, and at least 33% of the total nests were predicted in areas suitable by both. Because models predominantly agreed on what drives piping plover nest-site selection, areas predicted suitable by a single model should not be discounted. This case study demonstrates how models can effectively inform conservation planning by explicitly identifying the management objective, presenting robust evidence to allow managers to evaluate outcomes of alternative management decisions, and clearly communicating results that address real-world conservation problems. The results presented here can greatly increase the piping plover management community’s ability to prioritize candidate sites for future protection, manage existing nesting habitat appropriately, and make a compelling case for conservation actions against competing land use objectives.

New Jersey, New York↗

USGS2019

Predicting surf zone injuries along the Delaware coast using a Bayesian network

Personnel at Beebe Healthcare in Lewes, Delaware, collected surf zone injury (SZI) data for eight summer seasons from 2010 through 2017. Data include, but are not limited to, time of injury, gender, age, and activity. More than 2000 SZI events, including 196 spinal injuries and 6 fatalities, occurred at the five most populated beaches along the 25 miles of Atlantic-fronting coast. SZI are predominantly wave related incidents associated with wading (50.1%), body surfing (18.4%), and body boarding (13.3%). The episodic nature of SZI indicate the importance of linking the environmental conditions and human behavior in the surf zone to predict days with high injury rates. Higher order statistics are necessary to effectively consider all associated factors related to SZI. Two Bayesian networks (BN) were constructed to model SZI and predict changes in injury rate (proportion of injuries to bathers) and injury likelihood (probability of at least one injury occurrence) on an hourly basis. The models incorporate environmental data collected by weather stations, wave gauges, and researcher personnel on the beach. The models include prior (e.g., historic) information to infer relationships between provided parameters. Sensitivity analysis determined the most influential parameters related to injury rates were significant wave height, foreshore slope, and water temperature. Exposure parameters (e.g., air temperature) influenced the number of people in the water, resulting in strong correlation between injury likelihood and the related meteorological conditions (variance reduction > 0.4%). Log likelihood ratio (LLR) scores indicate the network predicts SZI likelihood during any specified hour with more skill than prior predictions with the best performing model improving prediction 69.1% of the time (LLR = 69.1%). An alternative BN predicting injury rate performed worse with the prior probability model out predicting the injury rate network (positive LLR = 36.7%). Issues persist with predicting SZI that have an LLR ≪ -1 (< 5% of 2017 injuries) and occur in conditions different than when most other SZI occur. Better understanding of SZI will improve awareness techniques to both educate beachgoers and assist beach patrol decision making during high risk conditions.

Delaware↗

USGS2019

Using a Bayesian network to understand the importance of coastal storms and undeveloped landscapes for the creation and maintenance of early successional habitat

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.

New Jersey, New York, Virginia↗

USGS2019

Combining numerical and statistical models to predict storm-induced dune erosion

Dune erosion is an important aspect to consider when assessing coastal flood risk, as dune elevation loss makes the protected areas more susceptible to flooding. However, most advanced dune erosion numerical models are computationally expensive, which hinders their application in early-warning systems. Based on a combination of probabilistic and process-based numerical modeling, we develop an efficient statistical tool to predict dune erosion during storms. The analysis focuses on Dauphin Island, AL in the northern Gulf of Mexico, where we combine synthetic sea storms with a calibrated and validated XBeach model to develop and test a range of different surrogate models for their ability to predict barrier-island geometric parameters under storm conditions. Surrogate models are developed by combining the oceanographic forcing from 100 optimally sampled sea-storm events covering the entire multivariate parameter space (used as XBeach input) and associated changes in the dune system (XBeach output). We test four surrogate models using a k-fold approach for validation. All models perform well in predicting changes in dune elevation, barrier-island area, and width, but are less accurate in predicting alterations in the cross-shore locations of dune morphological features. Multivariate adaptive regression splines are identified as the best surrogate model based on their fast development and good performance, attaining a modified Mielke index of 0.81 for dune crest height. As demonstrated at Dauphin Island, our approach shows potential to be used in an operational framework to predict dune response (in particular crest elevation change) when water level and wave forecasts are available.

Alabama↗

USGS2019

Surrogate model development for coastal dune erosion under storm conditions

Early coastal dune erosion predictions are essential to avoid potential flood consequences but most dune erosion numerical models are computationally expensive, hence their application in Early Warning Systems is limited. Here, based on a combination of optimally sampled synthetic sea storms with a calibrated and validated XBeach model, we develop a surrogate model capable of producing fast and accurate dune erosion predictions under storm conditions when water level and wave forecasts are available. The analysis is performed on Dauphin Island, AL, where we train Multiple Linear Regression Models with oceanographic forcing from the selected sea storms (i.e., XBeach input) and predicted changes in the dune system (i.e., XBeach output). Surrogate model performance is assessed with a rigorous k-fold cross validation. Although changes in the location of dune features are not well predicted, the model attains good performance when predicting changes in dune elevation, barrier-island width and volume.

Alabama↗

USGS2019

Forecasts of coastal change hazards

Model predictions of severe storm impacts provide coastal residents, emergency managers, and partner organizations valuable predictive information for planning and response to extreme storm events. The foundation of this work is a USGS-developed numerical model to forecast storm-induced coastal water levels and expected coastal change, including dune erosion, overwash, and inundation. The model is operated in three modes: generalized scenarios, real-time storms, and an operational forecast, with each mode requiring slightly different water level inputs. To evaluate and improve the accuracy of the models, we collect data on water levels and coastal change. In particular, observations before, after, and during storm conditions are used to test the different model applications. Forecast validation for Hurricanes Matthew (2016) and Irma (2017) illustrate three cases with demonstrated forecast skill and three cases with poor skill, and reveal elements of the modeling and/or testing approach which require improvement.

Conference Paper↗

USGS2019

iCoast – Did the Coast Change?: Storm-impact model verification using citizen scientists

The USGS provides model predictions of severe storm impacts prior to landfall based on pre-storm morphology and predicted total water levels, including waves and surge. Presented in near real time on the USGS Coastal Change Hazard Portal, they provide coastal residents, scientists, and emergency managers valuable coastal response information. iCoast – Did the Coast Change?, an online tool for comparing pre- and post-storm photography, allows volunteer citizen scientist help researchers classify storm impacts through aerial photography. Using spatially matched pre- and post-storm image pairs from Hurricane Sandy, iCoast users identified the coastal processes seen in each image. These classifications were compared to the predictive model probabilities just prior to Sandy’s landfall. User classifications agreed with the model predictions with a high degree of confidence for dune erosion and overwash. However, for inundation, user classifications, limited to observations of island breaching, were less confident and should be used with caution

Conference Paper↗

USGS2019

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

Understanding land loss or resilience in response to sea-level rise (SLR) requires spatially extensive and continuous datasets to capture landscape variability. We investigate sensitivity and skill of a model that predicts dynamic response likelihood to SLR across the northeastern U.S. 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.

Earth Surface Dynamics↗

USGS2019

A review of machine learning applications to coastal sediment transport and morphodynamics

A range of computer science methods under the heading of machine learning (ML) enables the extraction of insight and quantitative relationships from multidimensional datasets. Here, we review some common ML methods and their application to studies of coastal morphodynamics and sediment transport. We examine aspects of ‘what’ and ‘why’ ML methods contribute, such as ‘what’ science problems ML tools have been used to address, ‘what’ was learned when using ML, and ‘why’ authors used ML methods. We find a variety of research questions have been addressed, ranging from small-scale predictions of sediment transport to larger-scale sand bar morphodynamics and coastal overwash on a developed island. We find various reasons justify the use of ML, including maximize predictability, emulation of model components, smooth and continuous nonlinear regression through data, and explicit inclusion of uncertainty. Overall the expanding use of ML has allowed for an expanding set of questions to be addressed. After reviewing the studies we outline a set of ‘best practices’ for coastal researchers using machine learning methods. Finally we suggest possible areas for future research, including the use of novel machine learning techniques and exploring ‘open data’ that is becoming increasingly available.

Earth-Science Reviews↗

USGS2018

Field observations of alongshore runup variability under dissipative conditions in presence of a shoreline sandwave

Video measurements of runup were collected at low tide along several profiles covering an alongshore distance of 500 m. The morphology displayed a complex shape with a shoreline sandwave in the lower beach face of about 250 m long mirrored in the inner sandbar. Wave conditions were stationary and moderate (offshore height of 2 m and peak period of nearly 13 s) but yet dissipative. Runup energy was dominated by infragravity frequencies. Alongshore variations in runup (by a factor up to 3) observed both in the incident and infragravity bands were much higher than reported previously (e.g., Guedes et al., 2012, https://doi.org/10.1016/j.csr.2012.08.022 ; Ruggiero et al., 2004, https://doi.org/10.1029/2003JC002160 ) while the alongshore variations in other environmental parameters (e.g., foreshore beach slope) appear to be much lower. Our data suggest that the beach morphology in the inner surf zone plays a crucial role by inducing rapid and significant modification in the incident wave pattern and the alongshore coherence length scales were consistent with the typical alongshore length scale of the morphology.

Journal of Geophysical Research C: Oceans↗

USGS2018

Science for a risky world—A U.S. Geological Survey plan for risk research and applications

Executive Summary Natural hazards—including earthquakes, tsunamis, volcanic eruptions, landslides, hurricanes, droughts, floods, wildfires, geomagnetic storms, and pandemics—can wreak havoc on human communities, the economy, and natural resources for years following an initial event. Hazards can claim lives and cause billions of dollars in damage to homes and infrastructure as well as lost or compromised economic activity and threats to national security. They also can have adverse environmental, social, economic, and health effects that extend well beyond the immediate area, sometimes with global implications. Changes in population growth, climate, and urbanization may exacerbate hazard impacts. Because of the potential severity of a single hazard event, reducing risk—the potential loss of societally important assets caused by these hazards—is a high priority for everyone, including policy makers, community members, emergency managers, resource managers, utility operators, business owners, and planners. These stakeholders demand usable, user-centric information to support decisions for planning a resilient future and for responding to and recovering from unanticipated events in more adaptable and cost-effective ways. Meeting this demand requires maximizing the use of environmental observations; hazards science; and research on communications, social stressors, and human behavior to deliver risk information in forms that are accessible by decision makers and the public alike. To achieve this, scientists and stakeholders must collaborate to match community needs with actionable insights, research, products, and tools, using advances in technology to improve information discovery and delivery.

Circular↗

USGS2018

Dynamic modeling of barrier island response to hurricane storm surge under future sea level rise

Sea level rise (SLR) has the potential to exacerbate the impacts of extreme storm events on the coastal landscape. This study examines the coupled interactions of SLR on storm-driven hydrodynamics and barrier island morphology. A numerical model is used to simulate the hydrodynamic and morphodynamic impacts of two Gulf of Mexico hurricanes under present-day and future sea levels. SLR increased surge heights and caused overwash to occur at more locations and for longer durations. During surge recession, water level gradients resulted in seaward sediment transport. The duration of the seaward-directed water level gradients was altered under SLR; longer durations caused more seaward-directed cross-barrier transport and a larger net loss in the subaerial island volume due to increased sand deposition in the nearshore. Determining how SLR and the method of SLR implementation (static or dynamic) modulate storm-driven morphologic change is important for understanding and managing longer-term coastal evolution.

Dauphin Island↗