Accuracy of shoreline forecasting using sparse data Farris, Amy S. Long, Joseph W. Himmelstoss, Emily A. 2024-02-16T19:32:35Z 2024-02-16T19:32:35Z 2023-04-28
dc.description © The Author(s), 2023. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Farris, A. S., Long, J. W., & Himmelstoss, E. A. Accuracy of shoreline forecasting using sparse data. Ocean & Coastal Management, 239, (2023): 106621,
dc.description.abstract Sandy beaches are important resources providing recreation, tourism, habitat, and coastal protection. They evolve over various time scales due to local winds, waves, storms, and changes in sea level. A common method used to monitor change in sandy beaches is to measure the movement of the shoreline over time. Typically, the rate of change is estimated by fitting a linear regression through a time series of shoreline positions. To best manage the valuable resources within a coastal environment, accurate forecasts of shoreline position are needed. A simple way to estimate future shoreline position is to extrapolate a linear regression into the future, this method is often used to establish management guidelines like construction setback lines. A more recently developed shoreline forecasting technique utilizes the Kalman filter to assimilate shoreline data and modify the linear regression. This paper calculates the uncertainty and accuracy of both the extrapolated linear regression and Kalman filter forecasting methods for 10- and 20-year hindcasts using data collected at five diverse study areas. These data are inherently sparse (8–10 measurements per location, collected over 150 years) and are representative of the observed historical data available for the continental United States for this timeframe. Both methods produced similar results and had regionally averaged forecast accuracies of 5–16 m. We determined that the inaccuracy of the forecasts is largely due to the effects of shorter time scale variability. This variability is roughly proportional to the standard error of the linear regression, which is a useful measure of forecast uncertainty.•Extrapolated linear regression (ELR) can provide suitable shoreline forecasts.•A new method using the Kalman filter has forecast accuracy similar to the ELR.•Standard error of the linear regression is useful to estimate forecast uncertainty.•The SE of the linear regression can be used to test usefulness of old shorelines.
dc.description.sponsorship Financial support provided by U.S. Geological Survey Coastal and Marine Hazards and Resources Program in collaboration with the Massachusetts Office of Coastal Zone Management.
dc.identifier.citation Farris, A. S., Long, J. W., & Himmelstoss, E. A. (2023). Accuracy of shoreline forecasting using sparse data. Ocean & Coastal Management, 239, 106621.
dc.identifier.doi 10.1016/j.ocecoaman.2023.106621
dc.publisher Elsevier
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International *
dc.rights.uri *
dc.subject Shoreline
dc.subject Coastal change
dc.subject Erosion
dc.subject DSAS
dc.subject Kalman filter
dc.subject Shoreline forecasting
dc.subject ELR forecast accuracy
dc.subject East coast of the United States
dc.title Accuracy of shoreline forecasting using sparse data
dspace.entity.type Publication
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