Geology topics

Justin J. Birchler

Publications and source records attributed to Justin J. Birchler.

11 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↗

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↗

USGS2024

Wave runup and total water level observations from time series imagery at several sites with varying nearshore morphologies

Coastal imaging systems have been developed to measure wave runup and total water level (TWL) at the shoreline, which is a key metric for assessing coastal flooding and erosion. However, extracting quantitative measurements from coastal images has typically been done through the laborious task of hand-digitization of wave runup timestacks. Timestacks are images created by sampling a cross-shore array of pixels from an image through time as waves propagate towards and run up a beach. We utilize over 7000 hand-digitized timestacks from six diverse locations to train and validate machine learning models to automate the process of TWL extraction. Using these data, we evaluate two deep learning model architectures for the task of runup detection. One is based on a fully convolutional architecture trained from scratch, and the other is a transformer-based architecture trained using transfer learning. The deep learning models provide a probability of each pixel being either wet or dry. When contoured at the 50% level (equal chance of being wet or dry), the deep learning models more accurately identified TWL maxima than minima at all sites. This resulted in accurate predictions of 2% exceedance runup, but under predictions of significant swash and over predictions of wave setup. Improved agreement with the complete TWL time series was obtained through post-processing by utilizing the wet/dry probability of each pixel to weight the contouring toward lower dryness probabilities for runup minima (maxima agreed well with observations without tuning). Overall, a transformer-based model using transfer learning provided the best agreement with wave runup statistics, including a) the 2% exceedance runup, b) significant swash, and c) wave setup at the shoreline. For a random subset of images, the model was found to be within the uncertainty range of hand-digitization. The relative success of the transfer learning model suggests that fine-tuning a large model has advantages compared to training a smaller model from scratch. Models provide per-pixel probabilistic estimates in less than 10 s per timestack on a single computational unit, versus the more than 5 min required for hand-digitization. The model is therefore well-suited for near real-time applications, allowing for the development of early warning systems for difficult to forecast events. Real-time wave runup and total water level observations can also be incorporated into coastal hazards forecasts for data assimilation and continual model validation and improvement.

Coastal Engineering↗

USGS2024

Developing a decision tree model to forecast runup and assess uncertainty in empirical formulations

The coastal zone is a dynamic region that can change rapidly and significantly with respect to the morphology of the beach and incoming wave conditions. Runup forecasts may be improved by adapting a dynamic approach that allows for different runup models to be implemented in response to changes in beach state. Accurately forecasting wave runup is critical to characterize exposure to coastal hazards and provide an early warning against potential erosion and inundation. Here, we developed a decision tree model to produce a weighted ensemble of existing runup models to predict 1.25 years of runup at Duck, North Carolina, USA. We then applied the calibrated decision tree model to reproduce observed runup during the DUNEX experiment in Pea Island, North Carolina, USA. We found that the decision tree approach yielded a prediction that was comparable or greater in accuracy (i.e. higher r2, lower RMSE) than the individual runup models. We also interrogated the decision tree predictions to determine how the individual models perform relative to each other and why certain models perform better than others under the same observed wave and beach conditions. We found that the decision tree approach drew on the processes represented in the individual models in the ensemble to produce a forecast that is accurate and explainable without relying on prior knowledge of the study site(s) or requiring manual adjustments beyond the initial model training.

North Carolina↗

USGS2020

Data-driven, multi-model workflow suggests strong influence from hurricanes on the generation of turbidity currents in the Gulf of Mexico

Turbidity currents deliver sediment rapidly from the continental shelf to the slope and beyond; and can be triggered by processes such as shelf resuspension during oceanic storms; mass failure of slope deposits due to sediment- and wave-pressure loadings; and localized events that grow into sustained currents via self-amplifying ignition. Because these operate over multiple spatial and temporal scales, ranging from the eddy-scale to continental-scale; coupled numerical models that represent the full transport pathway have proved elusive though individual models have been developed to describe each of these processes. Toward a more holistic tool, a numerical workflow was developed to address pathways for sediment routing from terrestrial and coastal sources, across the continental shelf and ultimately down continental slope canyons of the northern Gulf of Mexico, where offshore infrastructure is susceptible to damage by turbidity currents. Workflow components included: 1) a calibrated simulator for fluvial discharge (Water Balance Model - Sediment; WBMsed ); 2) domain grids for seabed sediment textures ( dbSEABED ); bathymetry, and channelization; 3) a simulator for ocean dynamics and resuspension (the Regional Ocean Modeling System; ROMS ); 4) A simulator ( HurriSlip ) of seafloor failure and flow ignition; and 5) A Reynolds-averaged Navier–Stokes ( RANS ) turbidity current model ( TURBINS ). Model simulations explored physical oceanic conditions that might generate turbidity currents, and allowed the workflow to be tested for a year that included two hurricanes. Results showed that extreme storms were especially effective at delivering sediment from coastal source areas to the deep sea, at timescales that ranged from individual wave events (~hours), to the settling lag of fine sediment (~days).

Alabama, Florida, Louisiana, Mississippi, Texas↗

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↗

USGS2018

Sediment transport model including short-lived radioisotopes: Model description and idealized test cases

Geochronologies derived from sediment cores in coastal locations are often used to infer event bed characteristics such as deposit thicknesses and accumulation rates. Such studies commonly use naturally occurring, short-lived radioisotopes, such as Beryllium-7 ( 7 Be) and Thorium-234 ( 234 Th), to study depositional and post-depositional processes. These radioisotope activities, however, are not generally represented in sediment transport models that characterize coastal flood and storm deposition with grain size patterns and deposit thicknesses. We modified the Community Sediment Transport Modeling System (CSTMS) to account for reactive tracers and used this capability to represent the behavior of these short-lived radioisotopes on the sediment bed. This paper describes the model and presents results from a set of idealized, one-dimensional (vertical) test cases. The model configuration represented fluvial deposition followed by periods of episodic storm resuspension. Sensitivity tests explored the influence on seabed radioisotope profiles by the intensities of bioturbation and wave resuspension and the thickness of fluvial deposits. The intensity of biodiffusion affected the persistence of fluvial event beds as evidenced by 7 Be. Both resuspension and biodiffusion increased the modeled seabed inventory of 234 Th. A thick fluvial deposit increased the seabed inventory of 7 Be and 234 Th but mixing over time greatly reduced the difference in inventory of 234 Th in fluvial deposits of different thicknesses.

Journal of Marine Science and Engineering↗

USGS2018

Numerical model of geochronological tracers for deposition and reworking applied to the Mississippi subaqueous delta

Measurements of naturally occurring, short-lived radioisotopes from sediment cores on the Mississippi subaqueous delta have been used to infer event bed characteristics such as depositional thicknesses and accumulation rates. Specifically, the presence of Beryllium-7 ( 7 Be) indicates recent riverine-derived terrestrial sediment deposition; while Thorium-234 ( 234 Th) provides evidence of recent suspension in marine waters. Sediment transport models typically represent coastal flood and storm deposition via estimated grain size patterns and deposit thicknesses, however, and do not directly calculate radioisotope activities and profiles, which leads to a disconnect between the numerical model and field observations. Here, observed radioisotopic profiles from the Mississippi subaqueous delta cores were directly related to a numerical model that represented resuspension and deposition using a new approach to account for the behavior of short-lived radioisotopes. Appropriate selection of parameters such as the biodiffusion coefficient, sediment accumulation rate, and radioisotopic source terms enabled a good match between the modeled and observed cores. Comparisons of modelled profiles with geochronological analytical models that estimate accumulation rate and flood layer thickness revealed potential avenues for refining these tools, and highlight the importance of constraining the biodiffusion coefficient.

Gulf of Mexico, Mississippi River Delta↗

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↗

USGS2015

Enhancing evaluation of post-storm morphologic response using aerial orthoimagery from Hurricane Sandy

Improved identification of morphological responses to storms is necessary for developing and maintaining predictive models of coastal change. Morphological responses to Hurricane Sandy were measured using lidar and orthophotos taken before and after the storm. Changes to dune features measured from lidar were compared to the occurrence of overwash deposits measured using orthophotos. Thresholds on morphologic change (e.g. overwash volume and dune height change) were defined to optimize agreement between the classification of lidar and orthophoto-derived dune erosion and overwash. A linear regression showed that overwash volume can be calculated from orthophoto-derived overwash extent.

Fire Island and New Jersey shore↗

USGS2014

National assessment of hurricane-induced coastal erosion hazards: 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 sand inland) or flooding. Buildings and infrastructure on or near a dune can be undermined during wave attack and subsequent erosion. Hurricanes Irene and Sandy made landfall along the Eastern United States in August 2011 and October 2012, respectively. Although these hurricanes made landfall south and west of the northeast Atlantic coast, waves from these storms caused severe erosion and undermining, destroying roads in some areas of the coast (for example Westport, Massachusetts). 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 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, potentially increasing storm surge in the back bay, on the sound-side of the barrier, and on the mainland.

Connecticut, Maine, Massachusetts, New Hampshire, ↗