Tiffany R. Anderson

Vulnerability Assessment of Hawai'i's Cultural Assets Attributable to Erosion Using Shoreline Trend Analysis Techniques

Abstract KANE, H.H.; FLETCHER, C.H.; ROMINE, B.M.; ANDERSON, T.R.; FRAZER, N.L., and BARBEE, M.M., 2012. Vulnerability assessment of Hawai′i′s cultural assets attributable to erosion using shoreline trend analysis techniques. Hawai‘i’s beaches are a focal point of modern lifestyle as well as cultural tradition. Yet coastal erosion threatens areas that have served as burial grounds, home sites, and other forms of cultural significance. To improve understanding of the convergence of erosion patterns and cultural uses, we mapped shoreline changes from Kawela Bay to Kahuku Point on the capital island of O‘ahu. Shoreline change rates are calculated from historical photographs using the single-transect (ST) and eigenbeaches (EX) method to define the 50- and 100-year erosion hazard zones. To ensure that shoreline change rates reflect long-term trends, we include uncertainties attributable to natural shoreline fluctuations and mapping errors. A hazard zone overlay was compared to cultural data provided by the Hawaii State Historic Preservation Division (SHPD) and the Office of Hawaiian Affairs (OHA) to identify threats to cultural features. Cultural features identified in the study include iwi kupuna (burials), Hawaiian artifacts, and Punaulua (a freshwater spring). Our analysis indicates that, except for Punaulua, all cultural features identified are vulnerable to coastal erosion at historical rates. The data produced in this study may be used as a proactive management tool to rank the vulnerability to threatened cultural features, as well as to develop protocols to appropriately manage cultural assets.

Improving Adaptation Planning for Future Sea-Level Rise Understanding Uncertainty and Risks Using a Probability-Based Shoreline Model

Sea-level rise (SLR) presents risks to communities and ecosystems because of hazards like coastal erosion. In order to adapt, planners and the public seek estimates of shoreline change with high confidence and accuracy. The complexity of shorelines produces considerable uncertainty in the timing, location and magnitude of change. We present and discuss a probabilistic shoreline model for SLR planning. Using the coast of Maui as an illustrative case, we compare this model to a common deterministic model. We discuss the advantages of a probability-based model for SLR adaptation, including for prioritizing actions, phasing, visualizing risk and uncertainty, and improving adaptive management.

Long-Term Shoreline Change at Kailua, Hawaii, Using Regularized Single Transect

ABSTRACT Anderson, T.R.; Frazer, L.N., and Fletcher, C.H., 2015. Long-term shoreline change at Kailua, Hawaii, using regularized single transect. 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.

Correction to “Modeling storms improves estimates of long-term shoreline change”

[1] In the paper ‘‘Modeling storms improves estimates of long-term shoreline change’’ by Frazer et al. (Geophysical Research Letters, 36, L20404, doi:10.1029/2009GL040061, 2009), the standard errors of the rate parameters in column 2 of Table 2 were incorrect. The corrected Table 2 is given below. No figures are affected. The last two sentences in the abstract should be ‘‘Data from Cotton Patch Hill, DE, yield a long-term shoreline loss rate of 0.49 ± 0.13 m/yr, about 16% less than published estimates. A minimum loss rate of 0.34 ± 0.07 m/yr is given by a model containing the 1929, 1962 and 1992 storms.’’ [2] With similar corrections to standard errors, statements made in section 8 should be ‘‘At Cotton Patch Hill, DE, the minimum long-term rate of shoreline loss is 0.34 ± 0.07 m/yr (from a model with all three storms). The model-averaged rate, 0.49 ± 0.13 m/yr, is about 16% lower than earlier estimates.’’ [3] In Table E1 of the auxiliary material the standard errors of the storm amplitudes are now corrected, and the third to the last sentence in section 8 should be ‘‘The sudden shoreline loss associated with the 1929, 1962 and 1992 storms was 19.4 ± 11.7 m, 94.8 ± 16.4 m and 9.6 ± 10.1 m, respectively.’’

Modeling multiple sea level rise stresses reveals up to twice the land at risk compared to strictly passive flooding methods

Planning community resilience to sea level rise (SLR) requires information about where, when, and how SLR hazards will impact the coastal zone. We augment passive flood mapping (the so-called “bathtub” approach) by simulating physical processes posing recurrent threats to coastal infrastructure, communities, and ecosystems in Hawai‘i (including tidally-forced direct marine and groundwater flooding, seasonal wave inundation, and chronic coastal erosion). We find that the “bathtub” approach, alone, ignores 35–54 percent of the total land area exposed to one or more of these hazards, depending on location and SLR scenario. We conclude that modeling dynamic processes, including waves and erosion, is essential to robust SLR vulnerability assessment. Results also indicate that as sea level rises, coastal lands are exposed to higher flood depths and water velocities. The prevalence of low-lying coastal plains leads to a rapid increase in land exposure to hazards when sea level exceeds a critical elevation of ~0.3 or 0.6 m, depending on location. At ~1 m of SLR, land that is roughly seven times the total modern beach area is exposed to one or more hazards. Projected increases in extent, magnitude, and rate of persistent SLR impacts suggest an urgency to engage in long-term planning immediately.