Geology topics

Kara S. Doran

Publications and source records attributed to Kara S. Doran.

At least 19 recordsLinked to original sources

USGS2026

Effects of storm surge exceedance value on overwash and inundation forecasts

Hurricane induced sediment transport and flooding can cause catastrophic damage to coastal communities. Forecasts test our understanding of the drivers of sediment transport and flooding, as well as quantify potential storm effects on coastlines. Real-time availability of forecasts allows emergency response and management decisions to be made with additional information. This study analyzed the skill of probabilistic forecasts of overwash and inundation along the Louisiana coastline from back-to-back Hurricanes Laura and Delta in 2020. To test skill, forecasts were compared with observations of mean water level relative to beach elevations and evidence of overwash and inundation identified in post-storm aerial imagery. We found that probabilistic forecasts accurately identified areas where overwash and inundation were likely to occur. In some cases, forecasts also produced a conservative estimation of overwash and inundation. For the first time, we explored the role of exceedance value in probabilistic forecasts of overwash and inundation and found that choice of exceedance value can influence forecasts. Additionally, we found that waves contributed up to 27% of the mean water level and up to 63% of the extreme water level during Hurricanes Laura and Delta.

Louisiana, Texas↗

USGS2026

Decadal-scale characteristics of natural and anthropogenic dune morphology along North Carolina Barrier Islands (SE Atlantic Coast)

Dunes offer protection to coastal communities from storms, yet quantitative research characterizing human influences on dune systems across decades and 10–100 km spatial scales constitutes a known data gap. This study analyzed a multidecadal archive of dune crest elevations and positions to map mesoscale spatiotemporal trends in foredune morphology across the barrier island coastline of North Carolina, SE Atlantic coast. Results are summarized within four categories of coastline representing a range of coastal management intensities, integrating beach width and dune lateral accommodation space to assess the comparative influence of differing intensities of anthropogenic intervention on dune morphology. These categories account for the presence of structures, roads, and alongshore connectivity to anthropogenic sediment sources (beach nourishment). These categories show distinct dune and beach system characteristics. Dunes are largest in the areas with the longest legacy of coastal management. As categories decrease in development intensity, dune and beach system characteristics generally depart from the large, static dunes and narrow beaches consistent with hold-the-line management. Where small or transgressive dunes exist on heavily developed coasts, they coincide with relatively strong hydrodynamic forcing or sediment-limiting geology. There is evidence of indirect coastal management influence on undeveloped barrier islands with alongshore connectivity to developed barriers. While this analysis was conducted at the mesoscale, the results provide researchers with the framework to make insights at smaller scales than those demonstrated in this study.

North Carolina↗

USGS2025

Comparisons of shoreline positions from satellite-derived and traditional field- and remote-sensing techniques

Satellite-derived shorelines (SDS) have the potential to help researchers answer critical coastal science questions and support work to predict coastal change by filling in the spatial and temporal gaps present in current field-based and remote-sensing data collection methods. The U.S. Geological Survey conducted comparison analyses of traditionally sourced shorelines and SDS in diverse coastal landscapes to determine how SDS could be used in ongoing and future work across varied coastal environments and provided some initial findings that could be used for implementation. Using CoastSeg, a browser-based program for SDS detection and mapping, SDS for the period 1984–2023 for multiple locations across the United States were compared to shoreline positions from traditionally sourced shoreline data. In this report, the authors present these comparisons alongside lessons learned and challenges encountered when building SDS workflows in different coastal locations. Results show that individual SDS have larger uncertainty and yet produced similar linear trends to sparser, traditionally sourced shoreline data; because SDS methods provide orders of magnitude more data than traditional shoreline-detection methods, they can be used to evaluate shoreline behaviors. Refining average scalar slopes used in tidal corrections did not result in substantial decreases in uncertainty. Using lessons from this work to outline needs for regional implementation, initial setup time would be considerable, being on the order of weeks. However, once complete, shoreline detections and analyses are fast (on the order of minutes to hours) and achievable using a desktop computer.

Alaska, Florida, Massachusetts, Washington↗

USGS2025

Surface ice effects on water levels, wave dynamics and wave runup

Surface ice-induced changes in hydrodynamics may have significant implications on coastal hazards such as erosion and flooding. The objective of this study was to investigate how Lake Superior's water levels (WLs) and wave dynamics, that is, hydrodynamics, as well as the wave power and wave runup are influenced by the formation of surface ice. This study utilized a coupled circulation and spectral wave model, ADvanced CIRCulation Model (ADCIRC) + Simulating WAves Nearshore (SWAN). In this study, we considered six different ice-on, incorporating either ice thickness or concentration, and one hypothetical ice-free, ignoring any form of ice forcing, scenarios. The impact of surface ice on lake's WLs was found to be fairly insignificant. In contrast, surface ice-induced modifications in wave spectra resulted in smaller significant wave heights but longer peak wave periods. These observations were attributed to less energetic and narrower wave spectra of ice-on models compared to that of the ice-free model. The power loss caused by surface ice in a month was found to have enough capacity to meet the electrical demands of 55–100 households for an entire year in the United States. According to wave runup comparisons of ice-on and ice-free models at different regions, the effect of surface ice on wave runup could potentially exacerbate the adverse effect of coastal flooding by increasing the wave runup more than 20 cm. Such a geographic variation in the wave runup was observed to be influenced by the distance traveled by the waves within the ice field and duration of ice-wave interaction.

Lake Superior↗

USGS2025

Automated generation of an urban synthetic elevation checkpoint network across the North Carolina coastline, USA

Lidar and structure from motion-derived digital elevation and surface models have widespread application. Consideration of a topographic model's vertical root mean squared error (RMSEz) and systematic directional bias is important for many of these applications, particularly landscape change detection and measurement. Due to logistic, resource, and time constraints, wide area remotely sensed topographic surveys are not always accompanied by an in situ checkpoint network for validating and characterizing survey error. Here we describe and test a method for automatically generating synthetic elevation checkpoints in bulk across hundreds of kilometers using a publicly available lidar-derived DEM time-series, road vector network, and landcover classification map. Our method produced 6000–10,000 synthetic checkpoints across the developed barrier island coastline of North Carolina. These checkpoints characterized vertical error metrics in a statistically similar way as in situ checkpoints when assessing the vertical accuracy of a contemporary lidar-derived DEM and produced RMSEz metrics an average of 0.018 m from the RMSEz of historical lidar DEMs published with tested accuracy metrics. This new method has the potential to A) lower the cost and time required to validate new remotely sensed topographic surveys by reducing or eliminating the field work associated with in situ checkpoint surveys, B) provide a means of retroactively assessing the absolute vertical accuracy and systematic bias of historical topographic datasets that were not published with tested accuracy metrics, and C) generate reference networks to assess and correct spatially variable patterns of vertical bias in topographic datasets.

North Carolina↗

USGS2024

Skill assessment of a total water level and coastal change forecast during the landfall of a hurricane

The Total Water Level and Coastal Change Forecast (TWL&CC Forecast) provides coastal communities with 6-day notice of potential elevated water levels and coastal change (i.e., dune erosion, overwash, or inundation) on sandy beaches that threatens safety, infrastructure, or resources. This continuously operating model provides hourly information for select regions along U.S. Gulf of Mexico and Atlantic Ocean coastlines. The objective of this work is to assess the skill of forecasts during a period of elevated water levels along the coasts of North Carolina (NC) and South Carolina, USA caused by Hurricane Isaias in August 2020, using a combination of observations and model hindcasts. Water levels and waves were observed throughout the storm at three locations near Wrightsville Beach, NC, which provided information to assess forecast skill; a wave buoy offshore, a tide gage at a local pier, and a pressure sensor deployed at the pier. In addition to observations, the non-hydrostatic phase-resolving model SWASH (Simulating WAves till SHore) was forced with hourly wave energy spectra derived from a coupled Delft3D-SWAN simulation during the peak of Isaias, to complement observations by computing nearshore wave height and wave-induced setup and runup at the shoreline. During the storm peak, SWASH-simulated water levels at the sensor position were comparable to those at the maximum landward extent (bias = −0.05 m; gain = 0.26; r 2 = 0.99), suggesting that observations at the USGS sensor location were a useful proxy for total water level (TWL; sum of tide, surge and wave runup) at the shoreline that are predicted by the TWL&CC Forecast. The TWL forecast at Wrightsville Beach was consistent with observations from the USGS sensor (bias = −0.38 m and −0.74 m, scatter index = 0.22 and 0.28 for the two forecast model grids considered, respectively; weighted regression considering model uncertainty explained 95 percent of variability in observed TWL). Observed TWL was within the confidence interval of the TWL&CC Forecast for the 5 h at the storm peak. Forecast mean water levels (MWL; sum of tide, surge and wave setup) and tide gage observations were also consistent (bias = 0.07 m and 0.02 m for the forecast model grids; scatter index = 0.46; r 2 = 0.80). Forecast MWL at the storm peak was within 0.06 m of the observed MWL from the tide gage for both sites. In the region where Isaias made landfall, eight additional pressure sensors were compared to the peak TWL forecast (bias = 0.14 m; scatter index = 0.18). Forecast TWL explained 90 percent of observed variability in TWL when considering uncertainty of the forecast with a weighted regression. The results demonstrate that wave-driven water levels contributed a significant portion of the forecast TWL during Isaias (52 percent during the three peak hours of the storm), and that TWL were represented using the forecast model. Mean absolute error of the coastal change forecast and observed overwash is 0.4 and 0.14 for the two forecast model grids considered. The skill demonstrated by this computationally efficient method indicates that the forecasting system can provide fast and reliable predictions of TWL across hundreds of km of coastline at sub-km resolution, days to hours in advance of when storms threaten coastal regions.

Coastal Engineering↗

USGS2023

Operational forecasts of wave-driven water levels and coastal hazards for US Gulf and Atlantic coasts

Predictions of total water levels, the elevation of combined tides, surge, and wave runup at the shoreline, are necessary to provide guidance on potential coastal erosion and flooding. Despite the importance of early warning systems for these hazards, existing real-time meteorological and oceanographic forecast systems at regional and national scales, until now, have lacked estimates of runup necessary to predict wave-driven overwash and erosion. To address this need, we present an approach that includes wave runup in an operational, national-scale modeling system. Using this system, we quantify the contribution of waves to potential dune erosion events along 4,700 km of U.S. Atlantic and Gulf of Mexico sandy coastlines for a one-year period. Dune erosion events were predicted to occur at over 80% of coastal locations, where waves dominated shoreline total water levels, representing 73% of the signal. This shows that models that neglect the wave component underestimate the hazard. This new, national-scale operational modeling system provides communities with timely, local-scale (0.5 km resolution) coastal hazard warnings for all wave conditions, allowing for rapid decision-making related to safety and emergency management. The modeling system also enables continued research into wave-driven processes at a broad range of coastal areas.

Atlantic Ocean, Gulf of Mexico↗

USGS2023

Toward a total water level forecast of the Great Lakes

The combined effect of storm surge and large waves is the main driving mechanism that erodes beaches, inundates low-lying areas, leading to millions of dollars in property damage, loss of natural resources, and lives. The U.S. Geological Survey (USGS) aims to expand the real-time total water level (TWL) forecast provided in the Operational Total Water Level and Coastal Change Forecasts (TWL&CC) to the Great Lakes short- (0 – 36 hours) to medium-term (3 – 5 days) coastal-hazard forecasts to inform planners and emergency responders. This study assesses the skill of forecast water levels and wave characteristics required as input to the TWL forecasts. It finds that, while skill generally decreases as forecast period increases, these data are suitable as input to the TWL forecast system. As the TWL predictions depend on the water level and wave forecasts, validation against field observations allow evaluation of their suitability for the Great Lakes. These results have implications on the prediction of water levels and their potential impacts on coastal resiliency in North America.

Great Lakes↗

USGS2022

Characterizing storm-induced coastal change hazards along the United States West Coast

Traditional methods to assess the probability of storm-induced erosion and flooding from extreme water levels have limited use along the U.S. West Coast where swell dominates erosion and storm surge is limited. This effort presents methodology to assess the probability of erosion and flooding for the U.S. West Coast from extreme total water levels (TWLs), but the approach is applicable to coastal settings worldwide. TWLs were derived from 61 years of wave and water level data at shore-perpendicular transects every 100-m along open coast shorelines. At each location, wave data from the Global Ocean Waves model were downscaled to the nearshore and used to empirically calculate wave run-up. Tides were simulated using the Oregon State University’s tidal data inversion model and non-tidal residuals were calculated from sea-surface temperature and pressure anomalies. Wave run-up was combined with still water levels to generate hourly TWL estimates and extreme TWLs for multiple return periods. Extremes were compared to onshore morphology to determine erosion hazards and define the probability of collision, overwash, and inundation.

California, Oregon, Washington↗

USGS2022

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

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.

New York↗

USGS2021

Distinguishing between regression model fits to global mean sea level reconstructions

Global mean sea level (GMSL) has been rising since the last century, posing a serious challenge for the coastal areas. A variety of regression models have been utilized for determining GMSL rise over the past one hundred years, resulting in a large spread of sea level rise rates and multidecadal variations. In this study, we develop a new nonparametric noise model that is data-dependent and considers overfitting due to regression. The noise model is used to determine whether one regression model has significantly better skill than others over the period 1900–2010. The choices of background noise and GMSL reconstruction influence whether two sea level models can be statistically distinguished. With our new nonparametric noise spectra, the differences of model skills in explaining sea level variance are significant only in 34% of model comparisons. However, stepwise trends with three inflection points are significantly more skillful than the linear, quadratic, or exponential trend for most GMSL reconstructions, suggesting the importance of multidecadal variability of sea level rise in the twentieth century. Nevertheless, stepwise trend models cannot be distinguished from models with a long-term harmonic oscillation, indicating that the shape of multidecadal variability is not conclusive. The multidecadal variability is also significant in the steric and barystatic sea level contributions and is related to both natural and anthropogenic forcings. GMSL predictions based on regression fits in the twentieth century underestimate the sea level rise rate over the period 2011–2020 because the sea level acceleration in the recent decade (2011–2020) is not well represented.

Journal of Geophysical Research- Oceans↗

USGS2019

Hurricane Matthew: Predictions, observations, and an analysis of coastal change

Hurricane Matthew, the strongest Atlantic hurricane of the 2016 hurricane season, made land-fall south of McClellanville, S.C., around 1500 Coordinated Universal Time (UTC) on October 8, 2016. Hurricane Matthew affected the States of Florida, Georgia, South Carolina, and North Carolina along the U.S. Atlantic coastline. Numerous barrier islands were breached, and the erosion of beaches and dunes occurred along most of the South Atlantic coast. The U.S. Geological Survey (USGS) fore-casted potential coastal-change effects—including dune erosion and overwash that can threaten coastal resources and infrastructure—to assist with pre-storm management decisions. Following the storm, oblique aerial photography was collected, and lidar topographic survey missions were flown. These two datasets were used to document the changes that resulted from the storm and to validate coastal change forecasts. Comparisons of pre- and post-storm photographs were used to characterize the nature, extent, and spatial variability of hurricane-induced coastal changes. Analyses of pre- and post-storm lidar eleva-tions were used to quantify magnitudes of change in shoreline positions, dune elevations, and beach volumes. Erosion was observed along the coast from Florida to North Carolina; however, the coastal response exhibited extensive spatial variability, as would be expected over such a large region.

Florida, Georgia, South Carolina, North Carolina↗

USGS2019

Toward a national coastal hazard forecast of total water levels

Storm surge and large waves combine to erode beaches, cause marsh and coral decay, and inundate low-elevation areas, resulting in hazards to coastal communities and loss of natural resources. The USGS, in collaboration with NOAA, is developing a real-time system to provide ∼ 6-day forecasts of total water levels (TWLs) combining tides, storm surge, and wave runup. TWL is compared with dune elevations along sandy coastlines to obtain regional flood and coastal hazard forecasts. The predictions are available online (https://coastal.er.usgs.gov/hurricanes/research/twlviewer/) and provide intuitive representations of regional coastal risk. Current efforts include 1) assessing skill by comparing forecasts with observations along sandy coastlines and 2) developing methods more appropriate for non-sandy coastlines (e.g., rocky, reef, marsh, cliff, and built shorelines). The vision of this work is to provide a tool for short- to medium-term coastal hazard forecasts to inform planners and emergency responders along nearly the entire US coastline.

Conference Paper↗

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↗

USGS2018

Comparing methods used by the U.S. Geological Survey Coastal and Marine Geology Program for deriving shoreline position from lidar data

The U.S. Geological Survey Coastal and Marine Geology Program uses three methods to derive a datum-based, mean high water shoreline on open-ocean coasts from light detection and ranging (lidar) elevation surveys. This work compared the shorelines produced by the three methods for two different surveys: one survey with simple beach morphology, and one survey with complex beach morphology. For the survey with simple beach morphology, the three methods gave very similar results. The mean differences were less than 0.1 meter, and the root mean square differences were all less than 1.0 meter. For the survey of a beach with complex morphology, the quality control used in the Profile method and Smoothed Contour/Manual Hybrid method produced cleaner shorelines than the Grid method. Only the Profile method can extrapolate if there is no data around mean high water. The Grid and Profile methods produce a point by point estimate of uncertainty which is needed for some applications. Only the Contour method can be easily transferred to external users.

Open-File Report↗

USGS2017

Examples of storm impacts on barrier islands

This chapter focuses on the morphologic variability of barrier islands and on the differences in storm response. It describes different types of barrier island response to individual storms, as well as the integrated response of barrier islands to many storms. The chapter considers case study on the Chandeleur Island chain, where a decadal time series of island elevation measurements have documented a wide range of barrier island responses to storms and long-term processes that are representative of barrier island behaviour at many other locations. These islands are low elevation, extremely vulnerable to storms and exhibit a diversity of storm responses. Additionally, this location experiences a moderately high rate of relative sea-level rise, increasing its vulnerability to the combined impacts of storms and long-term erosional processes. Understanding how natural processes, including storm impacts and intervening recovery periods interact with man-made restoration processes is also broadly relevant to understand the natural and human response to future storms.

Book chapter↗

USGS2016

Detecting sea-level hazards: Simple regression-based methods for calculating the acceleration of sea level

This report documents the development of statistical tools used to quantify the hazard presented by the response of sea-level elevation to natural or anthropogenic changes in climate and ocean circulation. A hazard is a physical process (or processes) that, when combined with vulnerability (or susceptibility to the hazard), results in risk. This study presents the development and comparison of new and existing sea-level analysis methods, exploration of the strengths and weaknesses of the methods using synthetic time series, and when appropriate, synthesis of the application of the method to observed sea-level time series. These reports are intended to enhance material presented in peer-reviewed journal articles where it is not always possible to provide the level of detail that might be necessary to fully support or recreate published results. The purpose of this report is to document and compare three simple methodologies that have previously been used to provide estimates with associated errors of the acceleration of sea-level elevation. These techniques have been used by coastal scientists and planners in assessing coastal risk over a wide range of spatial and temporal scales. Because relative sea-level (SL) elevation time series contain energetic fluctuations at many time scales, extracting what can be relatively small rate and acceleration signals (along with estimates of the error) from much larger “noise” has proven to be both difficult and controversial. Acceleration is a preferred measure of SL response to recent changes in the Earth’s climate because over time scales of 100 years or less slow vertical land motions (such as glacial isostatic adjustment) contribute only to the linear signal and not to acceleration, thus reducing the complexity of the analysis. Hence acceleration is useful if the goal of a study is to characterize and quantify the hazard associated with the changing relative elevation of water with respect to land on decadal time scales. Although in some cases it may be necessary to determine the cause of relative sea level rise, as a first step, it is important to accurately estimate the magnitude of the threat. Most researchers agree that global sea level (GSL) rose persistently through much of the 20th century at about 1.5–2.0 millimeters per year (mm/yr). There is far less agreement about whether the rate of sea-level rise (SLR) is increasing (that is, an acceleration in SL). Recent studies, and most of their predecessors, use tide gage data to quantify SL acceleration, A SL (t). In the current study, three techniques were used to calculate acceleration from tide gage data, and of those examined, it was determined that the two techniques based on sliding a regression window through the time series are more robust compared to the technique that fits a single quadratic form to the entire time series, particularly if there is temporal variation in the magnitude of the acceleration. The single-fit quadratic regression method has been the most commonly used technique in determining acceleration in tide gage data. The inability of the single-fit method to account for time-varying acceleration may explain some of the inconsistent findings between investigators. Properly quantifying A SL (t) from field measurements is of particular importance in evaluating numerical models of past, present, and future SLR resulting from anticipated climate change.

Open-File Report↗

USGS2015

National assessment of nor’easter-induced coastal erosion hazards: mid- and northeast Atlantic coast

Beaches serve as a natural buffer between the ocean and inland communities, ecosystems, and natural resources. However, these dynamic environments move and change in response to winds, waves, and currents. During extreme storms, changes to beaches can be great, and the results are sometimes catastrophic. Lives may be lost, communities destroyed, and millions of dollars spent on rebuilding. During storms, large waves may erode beaches, and high storm surge may shift the erosive force of the waves higher on the beach. In some cases, the combined effects of waves and surge may cause overwash (when waves and surge overtop the dune, transporting sediment inland) or flooding. Buildings and infrastructure on or near a dune can be undermined during wave attack and subsequent erosion. A number of strong northeast storms—storms with winds tending to blow from the northeast direction—referred to as nor’easters, have hit the mid- and northeast Atlantic coast of the United States in recent years (February 2013 and January 2015). Waves from these storms caused severe erosion, flooding, and undermining of roads in many areas along the northeast Atlantic coast. Waves overtopping a dune can transport water and sand inland, covering roads and blocking evacuation routes or impeding emergency relief. If storm surge inundates barrier island dunes, currents flowing across the island can create a breach, or a new inlet, completely severing evacuation routes. Extreme coastal changes caused by hurricanes or nor’easters may increase the vulnerability of communities both during a storm and to future storms. For example, when sand dunes are substantially eroded, inland structures are exposed to storm surge and waves. On barrier islands, absent or low dunes allow water to flow inland across the island.

Delaware, Maine, Maryland, Massachusetts, New Hamp↗