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After decades of study and significant data collection of time-varying swash on sandy beaches, there is no single deterministic prediction scheme for wave runup that eliminates prediction error – even bespoke, locally tuned predictors present scatter when compared to observations. Scatter in runup prediction is meaningful and can be used to create probabilistic predictions of runup for a given wave climate and beach slope. This contribution demonstrates this using a data-driven Gaussian process predictor; a probabilistic machine-learning technique. The runup predictor is developed using 1 year of hourly wave runup data (8328 observations) collected by a fixed lidar at Narrabeen Beach, Sydney, Australia. The Gaussian process predictor accurately predicts hourly wave runup elevation when tested on unseen data with a root-mean-squared error of 0.18 m and bias of 0.02 m. The uncertainty estimates output from the probabilistic GP predictor are then used practically in a deterministic numerical model of coastal dune erosion, which relies on a parameterization of wave runup, to generate ensemble predictions. When applied to a dataset of dune erosion caused by a storm event that impacted Narrabeen Beach in 2011, the ensemble approach reproduced ∼85 % of the observed variability in dune erosion along the 3.5 km beach and provided clear uncertainty estimates around these predictions. This work demonstrates how data-driven methods can be used with traditional deterministic models to develop ensemble predictions that provide more information and greater forecasting skill when compared to a single model using a deterministic parameterization – an idea that could be applied more generally to other numerical models of geomorphic systems.

We performed a series of laboratory experiments to study the growth and development of wave-generated ripples under constant and varying conditions. There are two aspects of our study. First, under time varying wave conditions, we measure the rate of ripple adjustment and the equilibrium mean ripple wavelength before and after the change in forcing. Our experiments with increasing wave forcing (i.e., increasing wave amplitude or wave period) lead to faster ripple growth and larger final wavelengths. Decreasing wave forcing leads to shorter ripple wavelengths and hysteresis. Existing predictors provide a reasonable description of both the final equilibrium spacing and of the spacing evolution after wave conditions are changed. The presence of defects (crestline irregularities) impacts the final equilibrium ripple wavelength. Second, we investigate the role of initial perturbation on ripple growth and geometry. Larger initial perturbations in the bed lead to faster growth of ripples and larger initial ripple wavelength. Our results suggest that irregularities at the pattern scale, such as defects and initial bed perturbations, are likely to impact ripple evolution and can be a control factor on hysteresis.

Coastal foredunes provide the first line of defense against rising sea levels and storm surge and for this reason there is increasing interest in understanding and modeling foredune formation and post-storm recovery. However, there is limited observational data available to provide empirical guidance for the development of model parameterizations. To provide guidance for improved representation of dune grass growth in models, we conducted a two-year multi-species transplant experiment on Hog Island, VA, U.S.A. and measured the dependence of plant growth on elevation and distance from the shoreline, as well as the relationship between plant growth and sand accumulation. We tracked total leaf growth (length) and aboveground leaf length and found that Ammophila breviligulata (American beachgrass) and Uniola paniculata (sea oats) grew more than Spartina patens (saltmeadow cordgrass) by a factor of 15% (though not statistically significant) and 45%, respectively. Our results also suggest a range of basal/frontal area ratios (an important model parameter) from 0.5-1 and a strong correlation between transplant growth and total sand deposition for all species at the scale of two years, but not over shorter temporal scales. Distance from the shoreline and elevation had no effect on transplant growth rate but did have an effect on survival. Based on transplant survival, the seaward limit of vegetation at the end of the experiment was approximately 30 m from the MHWL and at an elevation of 1.43 m, corresponding to inundation less than 7.5% of the time according to total water level calculations. Results from this experiment provide evidence for the dune-building capacity of all three species, suggesting S. patens is not a maintainer species, as previously thought, but rather a moderate dune builder even though its growth is less stimulated by sand deposition than A. breviligulata and U. paniculata.

Deliberate, real‐time human interventions into geomorphic processes are a phenomenon that no off‐the‐shelf numerical model of morphodynamics is built to capture. We suggest that active, responsive human processes that affect sediment transport during major storm events be included in evolving efforts to model geomorphic change.

We use Genetic Programming (GP), a type of Machine Learning (ML) approach, to predict the total and infragravity swash excursion using previously published datasets that have been used extensively in swash prediction studies. Three previously published works with a range of new conditions are added to this dataset to extend the range of measured swash conditions. Using this newly compiled dataset we demonstrate that a ML approach can reduce the prediction errors compared to well-established parameterizations and therefore it contributes to the error in coastal hazards assessment (e.g. coastal inundation). Predictors obtained using GP can also be physically sound and replicate the functionality and dependencies of previous published formulas. Overall, we show that ML techniques are capable of both improving predictability (compared to classical regression approaches) and providing physical insight into coastal processes.

The Geological Society of America Bulletin was an early home for quantitative geomorphology research. Although geomorphology papers are not uniformly the highest cited papers in the Bulletin, many show ‘delayed recognition’—they garner only few citations directly after publication, before suddenly being widely and numerously cited (sometimes decades after publication). I focus here on (1) algorithmically detecting cases of delayed recognition in geomorphology literature from the Bulletin and (2) providing insight into why delayed recognition occurred for these papers.

Beach nourishment, a method for mitigating coastal storm damage or chronic erosion by deliberately replacing sand on an eroded beach, has been the leading form of coastal protection in the United States for four decades. However, investment in hazard protection can have the unintended consequence of encouraging development in places especially vulnerable to damage. In a comprehensive, parcel‐scale analysis of all shorefront single‐family homes in the state of Florida, we find that houses in nourishing zones are significantly larger and more numerous than in non‐nourishing zones. The predominance of larger homes in nourishing zones suggests a positive feedback between nourishment and development that is compounding coastal risk in zones already characterized by high vulnerability.

In Earth-surface science, numerical models are used for a range of purposes, from making quantitatively accurate predictions for practical or scientific purposes ('simulation' models) to testing hypotheses about the essential causes of poorly understood phenomena ('exploratory' models). We argue in this contribution that whereas established methods for uncertainty quantification (UQ) are appropriate (and crucial) for simulation models, their application to exploratory models are less straightforward, and in some contexts not relevant. Because most models fall between the end members of simulation and exploratory models, examining the model contexts under which UQ is most and least appropriate is needed. Challenges to applying state-of-the-art UQ to Earth-surface science models center on quantifying 'model-form' uncertainty-the uncertainty in model predictions related to model imperfections. These challenges include: 1) the difficulty in deterministically comparing model predictions to observations when positive feedbacks and associated autogenic dynamics (a.k.a. 'free' morphodynamics) determine system behavior over the timescales of interest (a difficulty which could be mitigated in a UQ approach involving statistical comparisons); 2) the lack of available data sets at sufficiently large space and/or time scales; 3) the inability to disentangle uncertainties arising from model parameter values and model form in some cases; and 4) the inappropriateness of model 'validation' in the UQ sense for models toward the exploratory end member of the modeling spectrum. In some Earth-science modeling contexts, uncertainty quantification (UQ) faces challenges.The ability to evaluate model-form uncertainty can be limited.Limitations arise when autogenic dynamics dominate.Limitations arise when scales of interest exceed those for which observations exist.Uncertainties from model parameter values and model form can be entangled.

On sandy coastlines, foredunes provide protection from coastal storms, potentially sheltering low areas—including human habitat—from elevated water level and wave erosion. In this contribution we develop and explore a one-dimensional model for coastal dune height based on an impulsive differential equation. In the model, coastal foredunes continuously grow in a logistic manner as the result of a biophysical feedback and they are destroyed by recurrent storm events that are discrete in time. Modeled dunes can be in one of two states: a high “resistant-dune” state or a low “overwash-flat” state. The number of stable states (equilibrium dune heights) depends on the value of two parameters, the nondimensional storm frequency (the ratio of storm frequency to the intrinsic growth rate of dunes) and nondimensional storm magnitude (the ratio of total water level during storms to the maximum theoretical dune height). Three regions of phase space exist (1) when nondimensional storm frequency is small, a single high resistant-dune attracting state exists; (2) when both the nondimensional storm frequency and magnitude are large, there is a single overwash-flat attracting state; (3) within a defined region of phase space model dunes exhibit bistable behavior—both the resistant-dune and the low overwash-flat states are stable. Comparisons to observational studies suggest that there is evidence for each state to exist independently, the coexistence of both states (i.e., segments of barrier islands consisting of overwash-flats and segments of islands having large dunes that resist erosion by storms), as well as transitions between states.

Accelerated sea level rise and the potential for an increase in frequency of the most intense hurricanes due to climate change threaten the vitality and habitability of barrier islands by lowering their relative elevation and altering frequency of overwash. High-density development may further increase island vulnerability by restricting delivery of overwash to the subaerial island. We analyzed pre-Hurricane Sandy and post-Hurricane Sandy (2012) lidar surveys of the New Jersey coast to assess human influence on barrier overwash, comparing natural environments to two developed environments (commercial and residential) using shore-perpendicular topographic profiles. The volumes of overwash delivered to residential and commercial environments are reduced by 40% and 90%, respectively, of that delivered to natural environments. We use this analysis and an exploratory barrier island evolution model to assess long-term impacts of anthropogenic structures. Simulations suggest that natural barrier islands may persist under a range of likely future sea level rise scenarios (7–13 mm/yr), whereas developed barrier islands will have a long-term tendency toward drowning.

We use a machine learning approach to seek an accurate, physically sound predictor, to estimate the mean velocity for open-channel flow when submerged arrays of rigid cylinders (model vegetation) are present. A genetic programming routine is used to find a robust relationship between relevant properties of the model vegetation and flow parameters. We use published data from laboratory experiments covering a broad range of conditions to obtain an equation that matches the performance of other predictors from recent literature in terms of accuracy, while showing a less complex structure. We also investigate how different criteria for data selection, as well as the size of the data set used to train the algorithm, influences the accuracy of the resulting predictors. Our results show that a proper use of Machine-Learning techniques does not only provide empirical correlations, but can yield physically sound models as representative of the physical processes involved. We provide a clear, thorough example of the application of GP, its advantages and shortcomings, to encourage the use of data-driven techniques as part of the data analysis process, and to address common misconceptions of machine learning as simple correlation techniques or physically senseless statistical analysis.

We use a machine learning approach based on genetic programming to predict noncohesive particle settling velocity. The genetic programming routine is coupled to a novel selection algorithm that determines training data from a collected database of published experiments (985 measurements). While varying the training data set size and retaining an invariant validation set we perform multiple iterations of genetic programming to determine the least data needed to train the algorithm. This method retains a maximum quantity of data for testing against published predictors. The machine learning predictor for settling velocity performs better than two common predictors in the literature and indicates that particle settling velocity is a nonlinear function of all the provided independent variables: nominal diameter of the settling particle, kinematic viscosity of the fluid, and submerged specific gravity of the particle.

We have developed an exploratory model of plan view, millennial-scale headland and bay evolution on rocky coastlines. Cross-shore coastline relief, or amplitude, arises from alongshore differences in sea cliff lithology, where durable, erosion-resistant rocks protrude seaward as headlands and weaker rocks retreat landward as bays. The model is built around two concurrent negative feedbacks that control headland amplitude: (1) wave energy convergence and divergence at headlands and bays, respectively, that increases in intensity as cross-shore amplitude grows and (2) the combined processes of beach sediment production by sea cliff erosion, distribution of sediment to bays by waves, and beach accumulation that buffers sea cliffs from wave attack and limits further sea cliff retreat. Paired with the coastline relief model is a numerical wave transformation model that explores how wave energy is distributed along an embayed coastline. The two models are linked through genetic programming, a machine learning technique that parses wave model results into a tractable input for the coastline model. Using a pool of 4800 wave model simulations, genetic programming yields a function that relates breaking wave power density to cross-shore headland amplitude, offshore wave height, approach angle, and period. The goal of the coastline model is to make simple, but fundamental, scaling arguments on how different variables (such as sea cliff height and composition) affect the equilibrium cross-shore relief of headland and bays. The model's generality highlights the key feedbacks involved in coastline evolution and allows its equations (and model behaviors) to be easily modified by future users.

We integrate published data sets of field and laboratory experiments of wave ripples and use genetic programming, a machine learning paradigm, in an attempt to develop a universal equilibrium predictor for ripple wavelength, height, and steepness. We train our genetic programming algorithm with data selected using a maximum dissimilarity selection routine. Thanks to this selection algorithm; we use less data to train the genetic programming software, allowing more data to be used as testing (i.e., to compare our predictor vs. common prediction schemes). Our resulting predictor is smooth and physically meaningful, different from other machine learning derived results. Furthermore our predictor incorporates wave orbital ripples that were previously excluded from empirical prediction schemes, notably ripples in coarse sediment and long wavelength, low height ripples (‘hummocks’). This new predictor shows ripple length to be a weakly nonlinear function of both bottom orbital excursion and grain size. Ripple height and steepness are both nonlinear functions of grain size and predicted ripple length (i.e., bottom orbital excursion and grain size). We test this new prediction scheme against common (and recent) predictors and the new predictors yield a lower normalized root mean squared error using the testing data. This study further demonstrates the applicability of machine learning techniques to successfully develop well performing predictors if data sets are large in size, extensive in scope, multidimensional, and nonlinear.

Podcasting is a recent creation combining old and new technologies allowing rapid, inexpensive delivery of media content (primarily audio) to the end user, both via the desktop environment and personal media players. The authors' group (the Society of Critical Care Medicine) saw the educational and communication potential for the podcasting concept, and have successfully designed and implemented the first podcast of a national medical society. As of this writing, there are an average of (mean +/- SD) 664 +/- 290 total downloads per podcast, and their podcast feed has been hit over 68,000 times in its first seven months. In this manuscript, the authors provide documentation of their successful endeavor, as well as a structured framework for other organizations to create similar products.

Neck masses, frequently encountered by physicians, comprise a vast range of diagnoses, with malignancy being the greatest concern. Calcifying fibrous pseudotumor (CFP) is a rare lesion with unknown pathogenesis, characterized pathologically by a predominance of abundant hyalinized collagenous tissue with focal lymphoplasmacytic infiltrate and psammomatous or dystrophic calcifications. We present the case of a 29-year-old woman who presented with a 4-cm left neck mass, accompanied by constitutional symptoms of vague weakness and lethargy. After the lesion failed to respond to a course of antibiotic therapy, fine-needle aspiration was performed, the pathology of which was indeterminate. The concern was that the lesion was a lymphoproliferative disorder–further workup was performed. CT of the chest, abdomen, and pelvis revealed no evidence of adenopathy or neoplasms. Subsequently, an incisional biopsy was performed, suggesting a diagnosis of CFP. Magnetic resonance imaging with contrast, performed to delineate the anatomy, revealed the lesion in the left neck, deep to the left clavicle, that extended superiorly into the supraclavicular fossa. Complete surgical removal of the lesion was successfully performed, with immunophenotyping confirming the initial diagnosis of CFP. We present a case report of cervical CFP, discuss the approach to neck masses, and review the recent literature on this rare, benign entity.

Although gallstone disease is highly prevalent, cholelithiasis causing gallstone ileus is uncommon. Consideration has been given for nonoperative strategies to resolve obstruction due to the significant age and comorbidities afflicting this population. A 94-year-old man presented with a 5-day history of abdominal distension and tenderness. CT scan revealed multiple large gallstones within the gallbladder, pneumobilia, and two ectopic gallstones (antrum of the stomach and distal ileum). The patient was taken to the operating room where an enterolithotomy and gastrotomy was performed with removal of gallstones and subsequent relief of obstruction. During the postoperative course, the patient developed symptoms of gastric outlet obstruction and underwent gastrointestinal endoscopy for diagnosis and treatment. Two large gallstones, present in the duodenum, were retracted into the stomach using a Roth net but could not be retrieved beyond the upper esophageal sphincter. A holmium: yttrium-aluminum-garnet (Holmium: YAG) laser was used for fragmentation of the stones, with subsequent successful removal. This is the first documented successful use of the holmium: YAG laser for the treatment of recurrent gallstone ileus. Physicians should remember that in a small but important subgroup of patients, endoscopy accompanied by laser lithotripsy may prove beneficial.