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

Abstract

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.

Erosion and Flooding—Threats to Coastal Infrastructure in the Arctic: A Case Study from Herschel Island, Yukon Territory, Canada

“…DSAS is capable of producing various shoreline movement statistics while accounting for uncertainty in the analysis to provide statistical robustness (Thieler et al 2009). Sources of shoreline uncertainty may include pixel, digitization, rectification, seasonal, and tidal fluctuation errors (Romine et al 2009). This study addressed pixel, digitizing, and rectification errors (see Table 2).…”

Assessment of shoreline changes along the Lithuanian Baltic Sea coast during the period 1947–2010

“…Shorelines of different years were derived from collected historical topographic maps and orthorectified aerial photo mosaics in a digital environment. The orthorectification and mosaicking was performed using ArcGis 9.3 software considering root mean square (RMS) positional error which is based on misfit of the orthorectification model to a master orthorectified image (Romine et al 2009). The processed images of 2010 served as master images for the georeferencing of older aerial photos and topographic maps.…”

“…Mapping the historical shorelines introduce additional uncertainties. Three positional and four measurement errors were recently described (Fletcher et al 2003(Fletcher et al , 2012Genz et al 2007;Romine et al 2009;Hapke et al 2010) for the historical shoreline positions digitized from aerial photographs and topographic maps. The impact of tides on the shoreline location is negligible in this part of the Baltic Sea where the tidal range is a few cm (Leppäranta, Myrberg 2009).…”

“…Its position is one of the most common indicators of environmental change representing the historical rearrangement of beaches (Hapke et al 2010). The methodology used to measure shoreline change is frequently based on delineation of historical shoreline positions from aerial photos, orthophotos, topographic maps and estimation of erosion rates (Smith, Zarillo 1990;Crowell et al 1993;Fletcher et al 2003;Romine et al 2009;Kortekaas et al 2010;Kartau et al 2011). Latest methods for determination shoreline alteration using LIDAR (light detection and ranging) and satellite data incorporate more comprehensive assessments (Maiti, Bhattacharya 2009;Kumar et al 2010;Hapke et al 2009Hapke et al , 2010).…”

“…Latest methods for determination shoreline alteration using LIDAR (light detection and ranging) and satellite data incorporate more comprehensive assessments (Maiti, Bhattacharya 2009;Kumar et al 2010;Hapke et al 2009Hapke et al , 2010). Short-term shoreline dynamics is typically monitored using beach profiling techniques (Masselink, Pattiaratchi 2001;Žilinskas, Jarmalavičius 2003, Anthony et al 2006, whereas statistical approaches usually integrate analysis of both short and long-term shoreline change (Maiti, Bhattacharya 2009;Romine et al 2009). …”