Long Joseph W.

No Thumbnail Available
Last Name
First Name
Joseph W.

Search Results

Now showing 1 - 3 of 3
  • Article
    Predictions of barrier island berm evolution in a time-varying storm climatology
    (John Wiley & Sons, 2014-02-19) Plant, Nathaniel G. ; Flocks, James ; Stockdon, Hilary F. ; Long, Joseph W. ; Guy, Kristy ; Thompson, David M. ; Cormier, Jamie M. ; Smith, Christopher G. ; Miselis, Jennifer L. ; Dalyander, P. Soupy
    Low-lying barrier islands are ubiquitous features of the world's coastlines, and the processes responsible for their formation, maintenance, and destruction are related to the evolution of smaller, superimposed features including sand dunes, beach berms, and sandbars. The barrier island and its superimposed features interact with oceanographic forces (e.g., overwash) and exchange sediment with each other and other parts of the barrier island system. These interactions are modulated by changes in storminess. An opportunity to study these interactions resulted from the placement and subsequent evolution of a 2 m high sand berm constructed along the northern Chandeleur Islands, LA. We show that observed berm length evolution is well predicted by a model that was fit to the observations by estimating two parameters describing the rate of berm length change. The model evaluates the probability and duration of berm overwash to predict episodic berm erosion. A constant berm length change rate is also predicted that persists even when there is no overwash. The analysis is extended to a 16 year time series that includes both intraannual and interannual variability of overwash events. This analysis predicts that as many as 10 or as few as 1 day of overwash conditions would be expected each year. And an increase in berm elevation from 2 m to 3.5 m above mean sea level would reduce the expected frequency of overwash events from 4 to just 0.5 event-days per year. This approach can be applied to understanding barrier island and berm evolution at other locations using past and future storm climatologies.
  • Article
    Inundation of a barrier island (Chandeleur Islands, Louisiana, USA) during a hurricane : observed water-level gradients and modeled seaward sand transport
    (John Wiley & Sons, 2014-07-15) Sherwood, Christopher R. ; Long, Joseph W. ; Dickhudt, Patrick J. ; Dalyander, P. Soupy ; Thompson, David M. ; Plant, Nathaniel G.
    Large geomorphic changes to barrier islands may occur during inundation, when storm surge exceeds island elevation. Inundation occurs episodically and under energetic conditions that make quantitative observations difficult. We measured water levels on both sides of a barrier island in the northern Chandeleur Islands during inundation by Hurricane Isaac. Wind patterns caused the water levels to slope from the bay side to the ocean side for much of the storm. Modeled geomorphic changes during the storm were very sensitive to the cross-island slopes imposed by water-level boundary conditions. Simulations with equal or landward sloping water levels produced the characteristic barrier island storm response of overwash deposits or displaced berms with smoother final topography. Simulations using the observed seaward sloping water levels produced cross-barrier channels and deposits of sand on the ocean side, consistent with poststorm observations. This sensitivity indicates that accurate water-level boundary conditions must be applied on both sides of a barrier to correctly represent the geomorphic response to inundation events. More broadly, the consequence of seaward transport is that it alters the relationship between storm intensity and volume of landward transport. Sand transported to the ocean side may move downdrift, or aid poststorm recovery by moving onto the beach face or closing recent breaches, but it does not contribute to island transgression or appear as an overwash deposit in the back-barrier stratigraphic record. The high vulnerability of the Chandeleur Islands allowed us to observe processes that are infrequent but may be important at other barrier islands.
  • Publication
    Accuracy of shoreline forecasting using sparse data
    (Elsevier, 2023-04-28) Farris, Amy S. ; Long, Joseph W. ; Himmelstoss, Emily A.
    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.