Delaware Bay Research Articles

Determining Undersampled Coastal Tidal Harmonics Using Regularized Least Squares

AbstractSatellite altimetry, which measures water level with global coverage and high resolution, provides an unprecedented opportunity for a wide and refined understanding of the changing tides in the coastal area. But its sampling frequency is too low to satisfy the Nyquist frequency requirement and too few data points per year are available to recognize a sufficient number of tidal constituents to capture the trend of tidal changes on a yearly basis. To address these issues, a novel regularized least‐square approach is developed to relax the sampling interval limit to the range of 9–11 days, reaching the revisit time of the existing satellites. In this method, the prior information of the regional tidal amplitudes is used to support a least square analysis to obtain the amplitudes of the tidal constituents for water elevation time series of different lengths and time intervals. Synthetic data experiments performed in Delaware Bay and Galveston Bay showed that the proposed method can determine the tidal amplitudes with high accuracy and the sampling interval can be extended to the application level of major altimetry satellites. The proposed algorithm was further validated using the data of the altimetry mission, Jason‐3, to show its applicability to irregular and noisy data. The new method could help identify the changing tides with sea‐level rise and anthropogenic activities in coastal areas, informing coastal flooding risk assessment and ecosystem health analysis.

Reducing vertical bias and error in tidal marsh digital elevation models with machine learning and LiDAR derivatives

The vertical bias and uncertainty of LiDAR-derived elevation data in tidal marshes is a major challenge for researchers in these critical ecosystems. Small positive bias errors in land surface elevation can lead to large underestimates of coastal inundation. Previous studies have shown that the bias and uncertainty of elevation data can be reduced using a variety of statistical techniques. However, many studies cover a relatively small extent, cover a large extent at coarse resolution, or rely on local data products that may not be widely available or are of unknown quality. This study aimed to develop and compare bias reduction approaches for digital elevation models (DEMs) using LiDAR derivatives in tidal marshes along Delaware Bay, Delaware, USA, a region that experiences high rates of relative sea-level rise and frequent coastal flooding. In this study, several statistical and machine-learning approaches were evaluated based on their ability to reduce vertical DEM error using field GPS survey data and LiDAR and terrain derivatives as inputs. The evaluated approaches for reducing DEM error included ensembles of multiple linear regressions, kernel-K Nearest Neighbors, gradient boosted regression trees, random forests, and deep neural networks (DNNs), which were compared based on statistical performance metrics. An ensemble of deep neural networks performed best at removing vertical DEM bias and reducing DEM uncertainty, though other approaches also performed well. GPS survey data indicated a mean absolute error, mean bias, and root mean square error in GPS surveys of 11.9 cm, 10.9, and 14.8 cm, respectively, in the original DEM. These error metrics were reduced to 3.5 cm, −0.2 cm, and 5.1 cm in the DNN ensemble-corrected DEM. The DNN ensemble was then applied to all tidal marshes throughout the study area. This corrected DEM clearly showed how interpretations of marsh platform inundation based on uncorrected DEM surfaces could be misguided. The approach used in this study only requires LiDAR-derived products and field surveys, so it may be applied readily in other regions.

Metal Levels in Delaware Bay Horseshoe Crab Eggs from the Surface Reflect Metals in Egg Clutches Laid beneath the Sand.

Understanding variations in metal levels in biota geographically and under different environmental conditions is essential to determining risk to organisms themselves and to their predators. It is often difficult to determine food chain relationships because predators may eat several different prey types. Horseshoe crab (Limulus polyphemus) eggs form the basis for a complex food web in Delaware Bay, New Jersey, USA. Female horseshoe crabs lay thumb-sized clutches of eggs, several cm below the surface, and often dislodge previously laid eggs that are brought to the surface by wave action, where they are accessible and critical food for migrant shorebirds. This paper compares metal and metalloid (chromium [Cr], cadmium [Cd], lead [Pb], mercury [Hg], arsenic [As] and selenium [Se]) concentrations in horseshoe crab eggs collected on the surface with concentrations in eggs from clutches excavated from below the sand surface, as well as examining metals in eggs from different parts of the Bay. The eggs were all collected in May 2019, corresponding to the presence of the four main species of shorebirds migrating through Delaware Bay. These migrating birds eat almost entirely horseshoe crab eggs during their stopover in Delaware Bay, and there are differences in the levels of metals in blood of different shorebirds. These differences could be due to whether they have access to egg clutches below sand (ruddy turnstones, Arenaria interpres) or only to eggs on the surface (the threatened red knot [Calidris canutus rufa] and other species of shorebirds). Correlations between metals in clutches were also examined. Except for As and Cd, there were no significant differences between the metals in crab egg clutches and eggs on the surface that shorebirds, gulls, and other predators eat. There were significant locational differences in metal levels in horseshoe crab eggs (except for Pb), with most metals being highest in the sites on the lower portion of Delaware Bay. Most metals in crab eggs have declined since studies were conducted in the mid-1990s but were similar to levels in horseshoe crab eggs in 2012. The data continue to provide important monitoring and assessment information for a keystone species in an ecosystem that supports many species, including threatened and declining shorebird species during spring migration.

Shark teeth zinc isotope values document intrapopulation foraging differences related to ontogeny and sex

Trophic ecology and resource use are challenging to discern in migratory marine species, including sharks. However, effective management and conservation strategies depend on understanding these life history details. Here we investigate whether dental enameloid zinc isotope (δ66Znen) values can be used to infer intrapopulation differences in foraging ecology by comparing δ66Znen with same-tooth collagen carbon and nitrogen (δ13Ccoll, δ15Ncoll) values from critically endangered sand tiger sharks (Carcharias taurus) from Delaware Bay (USA). We document ontogeny and sex-related isotopic differences indicating distinct diet and habitat use at the time of tooth formation. Adult females have the most distinct isotopic niche, likely feeding on higher trophic level prey in a distinct habitat. This multi-proxy approach characterises an animal’s isotopic niche in greater detail than traditional isotope analysis alone and shows that δ66Znen analysis can highlight intrapopulation dietary variability thereby informing conservation management and, due to good δ66Znen fossil tooth preservation, palaeoecological reconstructions.

Spring migration patterns of red knots in the Southeast United States disentangled using automated telemetry

Red Knots use the Southeast United States as a stopover during north and southbound migration and during the winter. We examined northbound red knot migration routes and timing using an automated telemetry network. Our primary goal was to evaluate the relative use of an Atlantic migratory route through Delaware Bay versus an inland route through the Great Lakes en route to Arctic breeding grounds and to identify areas of apparent stopovers. Secondarily, we explored the association of red knot routes and ground speeds with prevailing atmospheric conditions. Most Red Knots migrating north from the Southeast United States skipped or likely skipped Delaware Bay (73%) while 27% of the knots stopped in Delaware Bay for at least 1 day. A few knots used an Atlantic Coast strategy that did not include Delaware Bay, relying instead on the areas around Chesapeake Bay or New York Bay for stopovers. Nearly 80% of migratory trajectories were associated with tailwinds at departure. Most knots tracked in our study traveled north through the eastern Great Lake Basin, without stopping, thus making the Southeast United States the last terminal stopover for some knots before reaching boreal or Arctic stopover sites.

Modeling surface wave dynamics in upper Delaware Bay with living shorelines

Living shorelines gain increasing attention because they stabilize shorelines and reduce erosion. This study leverages physics-based models and bagged regression tree (BRT) machine learning algorithm to simulate wave dynamics at a living shoreline composed of constructed oyster reefs (CORs) in upper Delaware Bay. The physics-based models consist of coupled Delft3D-FLOW and SWAN in four-level nested domains. The model accuracy converges with increasing mesh resolution. The simulated wave-induced current circulation substantiates the effectiveness of CORs in trapping sediments. The simulated yearly-averaged wave power correlates qualitatively with historical shoreline retreat rates. BRT is adopted to improve the model accuracy, identify key processes responsible for simulation errors in wave height (Hs) and wave period (Tp), and quantify their importance. In the CORs sheltered area, BRT reveals that simulation errors of wind seas mainly arise from wind forcing, wave breaking and wave triad interactions. Wave breaking is seven times more important than wind forcing for simulating Hs, while wind forcing and triad interactions are of equal importance for simulating Tp. Simulation errors of swells mostly stem from bottom friction and offshore wave boundary conditions. Results from this study can help the assessment and adaptive management of CORs-based living shoreline restoration projects under climate change.

Fall migration, oceanic movement, and site residency patterns of eastern red bats (Lasiurus borealis) on the mid-Atlantic Coast

Along the mid-Atlantic coast of the United States, eastern red bats (Lasiurus borealis) are present during fall mating and migration, though little is currently known about most aspects of bat migration. To reveal migration patterns, and understand drivers of over-water flight, we captured and radio-tagged 115 eastern red bats using novel technology, and subsequently tracked and described their movements throughout the region. We compared over-water flight movements to randomly generated patterns using a use-availability framework, and subsequently used a generalized linear mixed effects model to assess the relationship of over-water flight to atmospheric variables. We used hidden Markov models to assess daily activity patterns and site residency. Most bats with long-distance movements traveled in a southwesterly direction, however path vectors were often oriented interior toward the continental landmass rather than along the coastline. We observed that some bats transited wide sections of the Chesapeake and Delaware bays, confirming their ability to travel across large water bodies. This over-water flight typically occurred in the early hours of the night and during favorable flying conditions. If flight over large water bodies is a proxy for over-ocean flight, then collision risk at offshore wind turbines – a major source of migratory bat fatalities – may be linked nightly to warm temperatures that occur early in the fall season. Risk, then, may be somewhat predictable and manageable with mitigation options linking wind-energy operation to weather conditions and seasonality.

Jamie L.H. Goodall, Pirates & Privateers from Long Island Sound to Delaware Bay by Michael Toth

Jamie L.H. Goodall, Pirates & Privateers from Long Island Sound to Delaware Bay by Michael Toth

Consequences of Salinity Change, Salinity History, and Shell Morphology on Early Growth of Juvenile Oysters

Estuaries provide valuable habitat for the eastern oyster (Crassostrea virginica). Although salinity at a given location fluctuates regularly with tides, upbay and downbay salinity differences span a broad estuarine salinity gradient. Higher salinity habitats downbay support faster oyster growth, whereas lower salinities upbay act as a refuge from predation and disease but slows growth. Two experiments were performed to investigate the effect of salinity, postsettlement salinity changes, and shell morphology on juvenile oyster growth. One experiment used wild oyster spat collected from three distinct Delaware Bay salinity zones that were then transplanted into various salinity conditions in the laboratory, where growth was monitored. Transplanting into low salinity led to decreased growth compared with transplanting to higher salinity, and growth of oyster spat was overall highest for spat from the lowest salinity source. Growth did not differ among shell morphologies. A second experiment used hatchery reared larvae set in one of four different salinity conditions. Those spat were maintained in settlement salinities 22, 16, 10, and 6 for 2–3 wk postsettlement, then measured before fully factorial transfer into new salinity conditions with measurement 3 wk later. Lower final salinity treatments were associated with lower growth, lower initial salinity treatments were associated with faster final treatment growth, and final growth depended on the interaction between initial and final salinity. Therefore, in addition to the effects of acute salinity changes on growth, early postsettlement hyposalinity stress can generate compensatory juvenile oyster growth. As increased freshwater events due to climate change are expected in the Delaware Bay and regionally in the Northeast, these results indicate that nonlinear early life stress responses are important to quantify to better understand oyster stock resilience and plan management.

Fitness of Wild and Selected Eastern Oyster (Crassostrea virginica) Larvae under Different Conditions

The eastern oyster, Crassostrea virginica, has been an important fishery species in the United States for hundreds of years. The emergence of population devastating diseases such as MSX and Dermo was met with the development of disease-resistant lines. Although selectively bred oysters have shown improved survival and growth under diseases in the field, it is unknown if long-term selection has altered larval fitness. In this study, the larval fitness of a selected line (DBX) was studied and compared with that of the Delaware Bay wild (Wild) population under hatchery and natural conditions with the latter using untreated seawater, 50% feeding, and declining salinity. No difference in growth was observed between DBX and Wild larvae under either condition, but DBX exhibited a higher survival under hatchery conditions (P = 0.090), whereas the Wild performed equally well under both conditions. Under natural conditions, the DBX larvae had higher survival during the first week when the salinity was high, but lower survival later under low salinity. The DBX larvae had a much higher setting success and a preference for oyster shells as substrate compared with the Wild larvae. Results of this study indicate that selective breeding has unintentionally altered larval fitness of DBX and led to adaptation to hatchery and high salinity conditions with greater setting success, and the Wild larvae have wider adaptability to different conditions. These findings suggest that careful evaluation is needed to fully assess the recruitment potential of selected stocks used in aquaculture and restoration.

A numerical investigation of the mechanisms controlling salt intrusion in the Delaware Bay estuary

A numerical investigation of the mechanisms controlling salt intrusion in the Delaware Bay estuary

Understanding the dynamic response of Durafet-based sensors: A case study from the Murderkill Estuary-Delaware Bay system (Delaware, USA)

The use of Durafet-based sensors has proliferated in recent years, but their performance in estuarine waters (salinity < 20) where rapid changes in temperature and salinity are frequently observed requires further scrutiny. Here, the responses of the Honeywell Durafet and its internal (pHINT) and external (pHEXT) reference electrodes integrated into a SeapHOx sensor at the confluence of the Murderkill Estuary and Delaware Bay (Delaware, USA) were assessed over extensive ranges of temperature (1.34–32.27°C), salinity (1.17–29.82), and rates of temperature (dT/dt; −1.46 to +1.53°C (0.5 h)−1) and salinity (dSalt/dt; −3.55 to +11.09 (0.5 h)−1) change. Empirical analyses indicated dynamic errors in the temperature and salinity responses of the internal and external reference electrodes, respectively, driven by tidal mixing were introduced into our pH time-series. These dynamic errors drove large anomalies between pHINT and pHEXT (denoted ΔpHINT−EXT) that reached >±0.8 pH in winter when the lowest temperatures and maximum tidal salinity variability occurred and >±0.15 pH in summer when the highest temperatures and minimum tidal salinity variability occurred. The ΔpHINT−EXT anomalies demonstrated a clear linear relationship with dSalt/dt thereby making dSalt/dt the strongest limiting factor of reference electrode response in our application. A dynamic sensor response correction for the external reference electrode (solid-state chloiride ion-selective electrode, Cl-ISE) was also developed and applied in the voltage domain. This correction reduced winter and summer ΔpHINT−EXT anomaly ranges by >40% and 68.7%, respectively. Summer anomalies were notably reduced to <±0.04 pH across all measurements. Further, this correction also removed the first-order salinity dependence of these anomalies. Consequently, dynamic errors in reference electrode response cannot be ignored and must be considered in future experimental designs. Further work to better understand the dynamic temperature and salinity responses of both reference electrodes is underway. Ultimately, we hope this work will stimulate further discussion around the role and treatment of large ΔpHINT−EXT anomalies as a part of future data quality control and data reporting as well as the dynamic errors in reference electrode response that drive them in the context of Sensor Best Practices.

Interacting Effects of Watershed and Coastal Processes on the Evolution of Compound Flooding During Hurricane Irene

AbstractIn low‐lying estuarine regions, compound flooding (CF) is caused by the co‐occurrence of extreme precipitation, river flooding and storm surge. In recent decades, there has been a rise in the frequency and intensity of pluvial‐coastal CF events in different parts of the U.S. due to the increased frequency of intense precipitation and storm surge events. However, in estuarine and deltaic regions, the CF characteristics depend mainly on the storm tide and river flow interaction. Understanding how the fluvial‐coastal CF may respond to changes to watershed and estuarine characteristics is essential for future CF hazard prediction. This study examined two critical processes: (a) the interplay between antecedent soil moisture conditions and peak river flow, and (b) how the impact of sea level rise (SLR) on storm surge and river flood distribution alters the CF in complex estuaries. As the study area, we selected the Delaware Bay and River, a shallow and convergent estuary in the US Mid‐Atlantic region—where flood hazards during a CF can become more significant than the surge and river flood processes occurring in isolation. For the focal event for the study, we selected Hurricane Irene (2011) because it reportedly produced the most extreme CF over the past two decades in the same region. Ultimately, our results illustrated that the potential changes to the catchment and bay characteristics from the global temperature increase and SLR could significantly modulate the fluvial‐coastal CF variability. The potential increase in global temperature and rainfall intensity might not always exacerbate the CF.

Functional and Compositional Changes in the Fecal Microbiome of a Shorebird during Migratory Stopover.

Shorebirds migrate long distances twice annually, which requires intense physiological and morphological adaptations, including the ability to rapidly gain weight via fat deposition at stopover locations. The role of the microbiome in weight gain in avian hosts is unresolved, but there is substantial evidence to support the hypothesis that the microbiome is involved with host weight from mammalian microbiome literature. Here, we collected 100 fecal samples of Ruddy Turnstones to investigate microbiome composition and function during stopover weight gain in Delaware Bay, USA. Using 16S rRNA sequencing on 90 of these samples and metatranscriptomic sequencing on 22, we show that taxonomic composition of the microbiome shifts during weight gain, as do functional aspects of the metatranscriptome. We identified 10 genes that are associated with weight class, and polyunsaturated fatty acid biosynthesis in the microbiota is significantly increasing as birds gain weight. Our results support that the microbiome is a dynamic feature of host biology that interacts with both the host and the environment and may be involved in the rapid weight gain of shorebirds. IMPORTANCE Many animals migrate long distances annually, and these journeys require intense physiological and morphological adaptations. One such adaptation in shorebirds is the ability to rapidly gain weight at stopover locations in the middle of their migrations. The role of the microbiome in weight gain in birds is unresolved but is likely to play a role. Here, we collected 100 fecal samples from Ruddy Turnstones to investigate microbiome composition (who is there) and function (what they are doing) during stopover weight gain in Delaware Bay, USA. Using multiple molecular methods, we show that both taxonomic composition and function of the microbiome shifts during weight gain. We identified 10 genes that are associated with weight class, and polyunsaturated fatty acid biosynthesis in the microbiota is significantly increasing as birds gain weight. Our results support that the microbiome is a dynamic feature of host biology that interacts with both the host and the environment and may be involved in the rapid weight gain of shorebirds.

Estimating recruitment rate and population dynamics at a migratory stopover site using an integrated population model

AbstractConsideration of the full annual cycle population dynamics can provide useful insight for conservation efforts, but collecting data needed to estimate demographic parameters is often logistically difficult. For species that breed in remote areas, monitoring is often conducted during migratory stopover or at nonbreeding sites, and the recruitment rate of new breeding adults can be difficult to estimate directly. Here, we present an integrated population model that uses mark‐resight and count data to estimate survival probability, population growth rate, and recruitment rate for an Arctic‐breeding shorebird of conservation concern, the red knot (Calidris canutus rufa), from data collected during spring stopover in Delaware Bay, USA, from 2005 to 2018. At this site, red knots feed primarily on the eggs of spawning horseshoe crabs (Limulus polyphemus), a legally harvested species. We used this model to estimate the relationship between horseshoe crab abundance and red knot demographics, which informed a recent revision to the framework used to establish horseshoe crab harvest regulations. Our analysis indicates that the red knot population was most likely stable from 2005 to 2018 (average λ = 1.03, 95% credible interval [CRI]: 0.961, 1.15) despite low recruitment rates (average ρ = 0.088, 95% CRI: 0.012, 0.18). Adult survival probability was positively associated with horseshoe crab abundance in the same year (β = 0.35, 95% CRI: 0.09, 0.63), but we found no effect of horseshoe crab abundance two years previously on recruitment of new adults (β = −0.08, 95% CRI: −0.41, 0.38). Our approach demonstrates the utility of integrated population models for understanding population dynamics, even when data are only available from migratory stopover monitoring.

Storm Surge Modeling as an Application of Local Time‐Stepping in MPAS‐Ocean

AbstractThis paper presents the first practical application of local time‐stepping (LTS) schemes in the Model for Prediction Across Scales‐Ocean (MPAS‐O). We use LTS schemes in a single‐layer, global ocean model that predicts the storm surge around the eastern coast of the United States during Hurricane Sandy. The variable‐resolution meshes used are of unprecedentedly high resolution in MPAS‐O, containing cells as small as 125 m wide in Delaware Bay. It is shown that a particular, third‐order LTS scheme (LTS3) produces sea‐surface height solutions that are of comparable quality to solutions produced by the classical four‐stage, fourth‐order Runge‐Kutta method (RK4) with a uniform time step on the same meshes. Furthermore, LTS3 is up to 35% faster in the best cases considered, where the number of cells using the coarse time‐step relative to those using the fine time‐step is as low as 1:1. This shows that LTS schemes are viable for use in MPAS‐O with the added benefit of substantially less computational cost. The results of these performance experiments inform us of the requirements for efficient mesh design and configuration of LTS regions for LTS schemes. In particular, we see that for LTS to be efficient on a given mesh, it is important to have enough cells using the coarse time‐step relative to those using the fine time‐step, typically at least 1:5 to see an increase in performance.

Redescription of Photuris pensylvanica (DeGeer) (Coleoptera: Lampyridae) with the Designation of a Neotype

The firefly species Photuris pensylvanica (DeGeer) has been the source of much taxonomic confusion. The species name P. pensylvanica (also P. pennsylvanica) has been applied erroneously for decades and as a result, entomological collections and publications are replete with misidentifications. In the absence of type specimens, Herbert S. Barber applied the name P. pensylvanica to the diminutive Mid-Atlantic tidal marsh-inhabiting “dot-dash” flashing species in 1951. To stabilize the genus Photuris Dejean, we made a concerted effort to locate the original type series, including the holotype, but were unsuccessful. As such, we briefly summarize the history of the taxon, accept Barber's designation, provide the first modern description of the species, designate a neotype from the proper type locality of Wilmington, Delaware, USA, and review its confirmed distribution. Thus far, the name-bearing taxon is recognized only from the freshwater tidal lowlands of the Chesapeake Bay and Delaware Bay estuaries. The species should be looked for in freshwater tidal marshes both north and south of these estuaries.

Impact of sea-level rise on cultural resources in the Delaware Bay region, USA

The Delaware Bay coastal zone includes at least 1580 cultural heritage resource sites; 700 of them could be damaged or lost by the year 2100. Future projections of sea-level rise (SLR) and storm surges identified cultural resources at high risk for inundation by the years 2030, 2050, 2080, and 2100. The projections are aimed to be high-end estimates preparing for long-term planning and “worse-case” scenarios. Sites located along the riverbanks are facing the highest threats from SLR. Coastal sites will be more frequently impacted by storms as surge levels increase with SLR. Storm-surge simulations predicted a non-linear increase in surge levels, reaching greater heights along the New Jersey side of the Bay compared to the Delaware side. Coastal sites without a protective dune system, or a dune less than 2 m in elevation, should be considered at immediate risk from coastal flooding. Shoreline erosion, and more frequent storms combined with SLR will increase vulnerability of coastal sites in the future. All endangered sites cannot be protected, therefore the results of the study should be of particular interest to historic preservation planners, cultural resources managers, Indigenous community leaders, and scholars to prioritize resources, develop the most effective preservation strategies, and plans for reconnaissance, survey, or data recovery from threatened sites and landscapes.

Organic Signatures of Surfactants and Organic Molecules in Surface Microlayer and Subsurface Water of Delaware Bay

Organic Signatures of Surfactants and Organic Molecules in Surface Microlayer and Subsurface Water of Delaware Bay

Compound effects of rain, storm surge, and river discharge on coastal flooding during Hurricane Irene and Tropical Storm Lee (2011) in the Mid-Atlantic region: coupled atmosphere-wave-ocean model simulation and observations

Coastal flooding from landfalling tropical cyclones (TCs) is a major hazard with increasing severity in a warming climate and rising seas. It is difficult to predict because of highly complex compound effects of TC induced heavy rainfall, storm surge, and river discharge. This can be further exacerbated by sequential TCs such as Hurricane Irene and Tropical Storm Lee in late August to mid-September 2011, which caused major coastal flooding in the Mid-Atlantic region. This study focuses on better understanding and improving prediction of the compound effects of rain, storm surge, and river discharge using a high-resolution coupled atmosphere-wave-ocean model, namely the Unified Wave INterface–Coupled Model (UWIN-CM) and observations from NDBC buoys, NOAA tide gauges, and USGS estuary sites. UWIN-CM effectively captured the storm track and intensity, surface winds, surface waves, and ocean surface evolution associated with the two storms, compared with the observations. Compound effects of wind, rain, storm surge, and river-stream discharge on coastal flooding are investigated. The storm surge from Hurricane Irene was observed along the coasts Maryland, New Jersey, and New York, Delaware Bay, the lower reaches of the Delaware River, and in lower Chesapeake Bay. Strong onshore wind pushes water upstream, which has the highest compound effects on coastal flooding. Heavy rain and river-stream discharge into the coastal zone contributes mainly to locations upstream away from the open bay water. A new, indirect machine learning method of estimating the spatial extent of coastal flooding using simulated coastal sea surface height is shown.