Toward Parsimony in Shoreline Change Prediction (II): Applying Basis Function Methods to Real and Synthetic Data

Genz, Ayesha S.; Frazer, L. Neil; Fletcher, Charles H.

doi:10.2112/06-0757.1

Cited by 15 publications

(15 citation statements)

References 4 publications

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“…In addition, a single polynomial model correctly assumes that the shoreline data from adjacent transects is related (e.g., dependent). Frazer et al (2009) andGenz et al (2009) have developed polynomial shoreline change rate calculation methods that include the alongshore variation of shoreline change rates in their models. These methods build polynomial models in the alongshore direction using linear combinations of mathematical basis functions.…”

Section: Polynomial Methodsmentioning

confidence: 99%

“…Recent work by Frazer et al (2009) andGenz et al (2009) identifies a number of shortcomings with the ST method. ST tends to overfit the data by using more mathematical parameters than necessary.…”

Section: Single Transectmentioning

confidence: 99%

“…To further this goal, Frazer et al (2009) and Genz et al (2009) have developed polynomial methods for calculating shoreline change rates. The new methods may calculate rates that are constant in time or rates that vary with time (acceleration, both increasing and decreasing).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Historical Shoreline Change, Southeast Oahu, Hawaii; Applying Polynomial Models to Calculate Shoreline Change Rates

Romine

Fletcher

Frazer

et al. 2009

Journal of Coastal Research

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ROMINE, B.M.; FLETCHER, C.H.; FRAZER, L.N.; GENZ, A.S.; BARBEE, M.M., and LIM, S.-C.; 2009. Historical shoreline change, southeast Oahu, Hawaii; applying polynomial models to calculate shoreline change rates. Journal of Coastal Research, 25(6), 1236-1253. West Palm Beach (Florida), ISSN 0749-0208.Here we present shoreline change rates for the beaches of southeast Oahu, Hawaii, calculated using recently developed polynomial methods to assist coastal managers in planning for erosion hazards and to provide an example for interpreting results from these new rate calculation methods. The polynomial methods use data from all transects (shoreline measurement locations) on a beach to calculate a rate at any one location along the beach. These methods utilize a polynomial to model alongshore variation in the rates. Models that are linear in time best characterize the trend of the entire time series of historical shorelines. Models that include acceleration (both increasing and decreasing) in their rates provide additional information about shoreline trends and indicate how rates vary with time. The ability to detect accelerating shoreline change is an important advance because beaches may not erode or accrete in a constant (linear) manner. Because they use all the data from a beach, polynomial models calculate rates with reduced uncertainty compared with the previously used single-transect method. An information criterion, a type of model optimization equation, identifies the best shoreline change model for a beach. Polynomial models that use eigenvectors as their basis functions are most often identified as the best shoreline change models.

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Section: Polynomial Methodsmentioning

confidence: 99%

Section: Single Transectmentioning

confidence: 99%

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Historical Shoreline Change, Southeast Oahu, Hawaii; Applying Polynomial Models to Calculate Shoreline Change Rates

Romine

Fletcher

Frazer

et al. 2009

Journal of Coastal Research

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“…We address the dependency issue as in Frazer et al [2009a] and Genz et al [2009] by the use of alongshore basis functions: We subtract the pre-storm survey temporally nearest to the storm, y (0) , from all the columns of Y to obtain a matrix Z, find its singular value decomposition…”

Section: Assateague Islandmentioning

confidence: 99%

Transient and persistent shoreline change from a storm

Anderson

Frazer

Fletcher

2010

Geophysical Research Letters

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[1] There is disagreement as to whether shoreline position eventually recovers from large storms. In an earlier paper we showed that statistical modeling of historical shoreline data was improved by including large storms in the model via a transient storm function. Here we show that, at shorter timescales of months to years, modeling of the shoreline at Assateague Island, MD is improved by a storm model with both transient and persistent components. We find that the shoreline recovers from the storm rapidly, almost within a year, but that the recovery is only partial, despite anthropogenic reconstruction of a pre-existing berm. The long-term trend of a shoreline (whether erosive, accretive, or stationary) can thus be regarded as the cumulative persistent component of successive storms, although most long-term data sets are too temporally sparse to make such a parameterization more useful than a steady longterm rate. Citation: Anderson, T. R., L. N. Frazer, and C. H.Fletcher (2010), Transient and persistent shoreline change from a storm, Geophys. Res. Lett., 37, L08401,

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“…In a similarly technical vein, diverse studies seek to refine tried and tested approaches to the analysis of time-variation in shoreline position (e.g. Pendleton et al, 2010;Ruggiero and List, 2009) as well as to advance the statistical basis of shoreline change analysis (exemplified by the innovative use of information criteria to ensure parsimony in shoreline change modelling; Frazer et al, 2009;Genz et al, 2009) and the use of observed changes to validate predictive models (e.g. Davidson et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Coastal geomorphology

French

Burningham

2011

Progress in Physical Geography: Earth and Environment

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This progress report presents a selective review of developments in coastal geomorphological research, and their relation to trends in geomorphology as a whole. The continuing advance of environmental monitoring technology is evidenced by the number of papers showcasing new instruments and the data sets that they can generate, especially in relation to the analysis of coastal change. As ever, some areas of research rise in prominence while others fade away, a pattern that probably owes more to the current vogue for journal special issues than any real focusing or coordination of research effort. Rocky coasts and fetch-limited shorelines feature strongly in the literature for 2009 and 2010, while a more diverse set of studies attack the problem of disaggregating temporal variability in sediment fluxes and morphology into specific process controls. Quantitative sediment budgets continue to underpin analyses of coastal change, especially those that attempt to relate erosion with human activities. Of particular interest are studies that attempt to achieve more rigorous closure of budgets through explicit treatment of onshore-offshore fluxes, and analyses that address the interplay between anthropogenic and natural forcing. More generally, there are signs that a new age of discovery is being facilitated by the worldwide coverage of aerial and satellite imagery provided by portals such as Google Earth. This has the potential to enrich the geographical context of geomorphological research, and to contribute also to classificatory and empirical studies.

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Toward Parsimony in Shoreline Change Prediction (II): Applying Basis Function Methods to Real and Synthetic Data

Cited by 15 publications

References 4 publications

Historical Shoreline Change, Southeast Oahu, Hawaii; Applying Polynomial Models to Calculate Shoreline Change Rates

Historical Shoreline Change, Southeast Oahu, Hawaii; Applying Polynomial Models to Calculate Shoreline Change Rates

Transient and persistent shoreline change from a storm

Coastal geomorphology

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