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Geology topics

K. Todd Holland

Publications and source records attributed to K. Todd Holland.

6 recordsLinked to original sources
USGS2013

cBathy: A robust algorithm for estimating nearshore bathymetry

A three-part algorithm is described and tested to provide robust bathymetry maps based solely on long time series observations of surface wave motions. The first phase consists of frequency-dependent characterization of the wave field in which dominant frequencies are estimated by Fourier transform while corresponding wave numbers are derived from spatial gradients in cross-spectral phase over analysis tiles that can be small, allowing high-spatial resolution. Coherent spatial structures at each frequency are extracted by frequency-dependent empirical orthogonal function (EOF). In phase two, depths are found that best fit weighted sets of frequency-wave number pairs. These are subsequently smoothed in time in phase 3 using a Kalman filter that fills gaps in coverage and objectively averages new estimates of variable quality with prior estimates. Objective confidence intervals are returned. Tests at Duck, NC, using 16 surveys collected over 2 years showed a bias and root-mean-square (RMS) error of 0.19 and 0.51 m, respectively but were largest near the offshore limits of analysis (roughly 500 m from the camera) and near the steep shoreline where analysis tiles mix information from waves, swash and static dry sand. Performance was excellent for small waves but degraded somewhat with increasing wave height. Sand bars and their small-scale alongshore variability were well resolved. A single ground truth survey from a dissipative, low-sloping beach (Agate Beach, OR) showed similar errors over a region that extended several kilometers from the camera and reached depths of 14 m. Vector wave number estimates can also be incorporated into data assimilation models of nearshore dynamics.

North Carolina↗
USGS2012

Field observations of swash zone flow patterns and 3D morphodynamics

Rapid video measurements of foreshore morphology and velocity were collected at Duck, NC in 1997 to investigate sediment transport processes in the swash zone. Estimates of foreshore evolution over a roughly 30 m cross-shore by 80 m alongshore study area were determined using a stereogrammetric technique. During the passage of a small storm (offshore wave heights increased from 1.4 to 2.5 m), the foreshore eroded nearly 40 cm in less than 4 hours. Dense, horizontal surface velocities were measured over a sub-region (roughly 30 m by 40 m) of the study area using a new particle image velocimetry technique. This technique was able to quantify velocities across the bore front approaching 5 m s –1 as well as the rapid velocities in the very shallow backwash flows. The velocity and foreshore topography measurements were used to test a three-dimensional energetics-based sediment transport model. Even though these data represent the most extensive and highly resolved swash measurements to date, the results showed that while the model could predict some of the qualitative trends in the observed foreshore change, it was a poor predictor of the observed magnitudes of foreshore change. Model — data comparisons differed by roughly an order of magnitude with observed foreshore changes on the order of 10's of centimeters and model predictions on the order of meters. This poor comparison suggests that future models of swash-zone sediment transport may require the inclusion of other physical processes such as bore turbulence, fluid accelerations and skewness, infiltration/exfiltration, water depth variations, and variable friction factors (to name a few).

North Carolina↗
USGS2012

Use of vertical temperature gradients for prediction of tidal flat sediment characteristics

Sediment characteristics largely govern tidal flat morphologic evolution; however, conventional methods of investigating spatial variability in lithology on tidal flats are difficult to employ in these highly dynamic regions. In response, a series of laboratory experiments was designed to investigate the use of temperature diffusion toward sediment characterization. A vertical thermistor array was used to quantify temperature gradients in simulated tidal flat sediments of varying compositions. Thermal conductivity estimates derived from these arrays were similar to measurements from a standard heated needle probe, which substantiates the thermistor methodology. While the thermal diffusivities of dry homogeneous sediments were similar, diffusivities for saturated homogeneous sediments ranged approximately one order of magnitude. The thermal diffusivity of saturated sand was five times the thermal diffusivity of saturated kaolin and more than eight times the thermal diffusivity of saturated bentonite. This suggests that vertical temperature gradients can be used for distinguishing homogeneous saturated sands from homogeneous saturated clays and perhaps even between homogeneous saturated clay types. However, experiments with more realistic tidal flat mixtures were less discriminating. Relationships between thermal diffusivity and percent fines for saturated mixtures varied depending upon clay composition, indicating that clay hydration and/or water content controls thermal gradients. Furthermore, existing models for the bulk conductivity of sediment mixtures were improved only through the use of calibrated estimates of homogeneous end-member conductivity and water content values. Our findings suggest that remotely sensed observations of water content and thermal diffusivity could only be used to qualitatively estimate tidal flat sediment characteristics.

Journal of Geophysical Research C: Oceans↗
USGS2011

Prediction and assimilation of surf-zone processes using a Bayesian network: Part I: Forward models

Prediction of coastal processes, including waves, currents, and sediment transport, can be obtained from a variety of detailed geophysical-process models with many simulations showing significant skill. This capability supports a wide range of research and applied efforts that can benefit from accurate numerical predictions. However, the predictions are only as accurate as the data used to drive the models and, given the large temporal and spatial variability of the surf zone, inaccuracies in data are unavoidable such that useful predictions require corresponding estimates of uncertainty. We demonstrate how a Bayesian-network model can be used to provide accurate predictions of wave-height evolution in the surf zone given very sparse and/or inaccurate boundary-condition data. The approach is based on a formal treatment of a data-assimilation problem that takes advantage of significant reduction of the dimensionality of the model system. We demonstrate that predictions of a detailed geophysical model of the wave evolution are reproduced accurately using a Bayesian approach. In this surf-zone application, forward prediction skill was 83%, and uncertainties in the model inputs were accurately transferred to uncertainty in output variables. We also demonstrate that if modeling uncertainties were not conveyed to the Bayesian network (i.e., perfect data or model were assumed), then overly optimistic prediction uncertainties were computed. More consistent predictions and uncertainties were obtained by including model-parameter errors as a source of input uncertainty. Improved predictions (skill of 90%) were achieved because the Bayesian network simultaneously estimated optimal parameters while predicting wave heights.

Coastal Engineering↗
USGS2011

Prediction and assimilation of surf-zone processes using a Bayesian network: Part II: Inverse models

A Bayesian network model has been developed to simulate a relatively simple problem of wave propagation in the surf zone (detailed in Part I). Here, we demonstrate that this Bayesian model can provide both inverse modeling and data-assimilation solutions for predicting offshore wave heights and depth estimates given limited wave-height and depth information from an onshore location. The inverse method is extended to allow data assimilation using observational inputs that are not compatible with deterministic solutions of the problem. These inputs include sand bar positions (instead of bathymetry) and estimates of the intensity of wave breaking (instead of wave-height observations). Our results indicate that wave breaking information is essential to reduce prediction errors. In many practical situations, this information could be provided from a shore-based observer or from remote-sensing systems. We show that various combinations of the assimilated inputs significantly reduce the uncertainty in the estimates of water depths and wave heights in the model domain. Application of the Bayesian network model to new field data demonstrated significant predictive skill (R 2 = 0.7) for the inverse estimate of a month-long time series of offshore wave heights. The Bayesian inverse results include uncertainty estimates that were shown to be most accurate when given uncertainty in the inputs (e.g., depth and tuning parameters). Furthermore, the inverse modeling was extended to directly estimate tuning parameters associated with the underlying wave-process model. The inverse estimates of the model parameters not only showed an offshore wave height dependence consistent with results of previous studies but the uncertainty estimates of the tuning parameters also explain previously reported variations in the model parameters.

Coastal Engineering↗