Neil Frazer - Academia.edu (original) (raw)

Papers by Neil Frazer

Http Dx Doi Org 10 2112 Jcoastres D 11 00114 1, Jan 5, 2012

Page 1. www.cerf-jcr.org Vulnerability Assessment of Hawai&#x27;i&#x27;s Cultural... more Page 1. www.cerf-jcr.org Vulnerability Assessment of Hawai&#x27;i&#x27;s Cultural Assets Attributable to Erosion Using Shoreline Trend Analysis Techniques Haunani H. Kane, Charles H. Fletcher, Bradley M. Romine, Tiffany R. Anderson, Neil L. Frazer, and Matthew M. Barbee ...

Following the detection of 'silent' or slow earthquakes (SE) using strainmeters, continuo... more Following the detection of 'silent' or slow earthquakes (SE) using strainmeters, continuous GPS (CGPS) observations have led to the discovery of SE afterslip events after conventional subduction zone earthquakes in Japan, a SE in Cascadia unassociated with a conventional earthquake, and recognition that Cascadia SEs are periodic and accompanied by seismic tremor. The first SE detected in an intraplate setting

Coastal Sediments '07, 2007

Ocean Seismo-Acoustics, 1986

It is important that coastal erosion studies produce valid erosion rates to aid in the develop- m... more It is important that coastal erosion studies produce valid erosion rates to aid in the develop- ment of public policy. Currently, the Single-Transect (ST) method is the most common shore- line change model, which calculates a rate at each shore-normal transect without regard to influences of data from adjacent transects along a beach. Improving on ST, we have devel- oped the PX (Polynomial in alongshore X) and PXT (Polynomial in X and Time) calculation methods, which model all shoreline positions on a beach simultaneously using polynomial techniques. PXT lets the rate change with time (acceleration) as well as alongshore. This is an important advance, as beaches may not erode or accrete at a constant (linear) rate. These methods are an improvement on previous methods as they produce more meaningful, i.e., statistically significant rates. In companion with the work presented by Ayesha Genz on the PX and PXT rate methods, we present results from the Southeast Oahu Shoreline Study.

Natural Hazards, 2015

Chronic erosion in Hawaii causes beach loss, damages homes and infrastructure, and endangers crit... more Chronic erosion in Hawaii causes beach loss, damages homes and infrastructure, and endangers critical habitat. These problems will likely worsen with increased sea level rise (SLR). We forecast future coastal change by combining historical shoreline trends with projected accelerations in SLR (IPCC RCP8.5) using the Davidson-Arnott profile model. The resulting erosion hazard zones are overlain on aerial photos and other GIS layers to provide a tool for identifying assets exposed to future coastal erosion. We estimate rates and distances of shoreline change for ten study sites across the Hawaiian Islands. Excluding one beach (Kailua) historically dominated by accretion, approximately 92 and 96 % of the shorelines studied are projected to retreat by 2050 and 2100, respectively. Most projections (*80 %) range between 1–24 m of landward movement by 2050 (relative to 2005) and 4–60 m by 2100, except at Kailua which is projected to begin receding around 2050. Compared to projections based only on historical extrapolation, those that include accelerated SLR have an average 5.4 ± 0.4 m (±standard deviation of the average) of additional shoreline recession by 2050 and 18.7 ± 1.5 m of additional recession by 2100. Due to increasing SLR, the average shoreline recession by 2050 is nearly twice the historical extrapolation, and by 2100 it is nearly 2.5 times the historical extrapolation. Our approach accounts for accretion and long-term sediment processes (based on historical trends) in projecting future shoreline position. However, it does not incorporate potential future changes in nearshore hydrodynamics associated with accelerated SLR.

Solutions to Coastal Disasters 2008, 2008

Journal of Coastal Research, 2014

ABSTRACT Traditional long-term (decadal) and large-scale (hundreds of kilometers) shoreline chang... more ABSTRACT Traditional long-term (decadal) and large-scale (hundreds of kilometers) shoreline change modeling techniques, known as single transect, or ST, often overfit the data because they calculate shoreline statistics at closely spaced intervals along the shore. To reduce overfitting, recent work has used spatial basis functions such as polynomials, B splines, and principal components. Here, we explore an alternative to such basis functions by using regularization to reduce the dimension of the ST model space. In our regularized-ST method, traditional ST is an end member of a continuous spectrum of models. We use an evidence information criterion (EIC = -2 times the log of the prior predictive distribution) to select the optimal value of the regularization parameter, instead of the usual L-curve method, because the EIC can also be used to evaluate basis function models yet does not require counting model parameters. To test the method, we apply it to historical shoreline data from Kailua, Hawaii, comparing the results with those from B splines (basis functions) and traditional ST. As expected, the regularized-ST and B-spline models both give shoreline change rates that vary more smoothly alongshore than the rates from ST. The regularized-ST model, along with the B-spline model, also shows significantly better predictive capability over the traditional ST model from a fivefold cross-validation. The regularized-ST model is more straightforward to implement than splines and may be attractive to users because of its continuous connection with the familiar ST method.

Coral Reefs of the USA, 2008

Coral Reefs, 2003

We used high-resolution, airborne, digital, multi-spectral imagery to map bathymetry and the perc... more We used high-resolution, airborne, digital, multi-spectral imagery to map bathymetry and the percent of living coral in the nearshore marine environment of Kailua Bay, Oahu, Hawai'i. Three spectral bands, with centers at 488, 551, and 577 nm (each with a full-width half maximum of 10 nm), were selected for good water transmission and good coral/sand/algae discrimination. However, the third band

Science, 1990

Knowledge of the critical temperature, T(*), the temperature at which a phase change occurs, grea... more Knowledge of the critical temperature, T(*), the temperature at which a phase change occurs, greatly improves the efficiency of simulated annealing when used for optimization or inversion. A numerical method of accurately determining T(*) in a relatively short computation time has been developed. This method is used to recover the seismic soundspeed profile from wavefield data, a problem in which cycle skipping causes many local minima of the energy function and the averaging of the medium by finite length waves results in many states with similar energies. Computations indicate that it is cost-effective to spend about 80 percent of the computing budget looking for T(*) instead of annealing, and that in the course of finding T(*) many states with energies near the global minimum will also be found. The a posteriori probability distribution of the solution has been constructed from trial solutions generated at T(*).

Proceedings of the Royal Society B: Biological Sciences, 2012

Host density thresholds are a fundamental component of the population dynamics of pathogens, but ... more Host density thresholds are a fundamental component of the population dynamics of pathogens, but empirical evidence and estimates are lacking. We studied host density thresholds in the dynamics of ectoparasitic sea lice (Lepeophtheirus salmonis) on salmon farms. Empirical examples include a 1994 epidemic in Atlantic Canada and a 2001 epidemic in Pacific Canada. A mathematical model suggests dynamics of lice are governed by a stable endemic equilibrium until the critical host density threshold drops owing to environmental change, or is exceeded by stocking, causing epidemics that require rapid harvest or treatment. Sensitivity analysis of the critical threshold suggests variation in dependence on biotic parameters and high sensitivity to temperature and salinity. We provide a method for estimating the critical threshold from parasite abundances at subcritical host densities and estimate the critical threshold and transmission coefficient for the two epidemics. Host density thresholds may be a fundamental component of disease dynamics in coastal seas where salmon farming occurs.