Marsh shoreline erosion and supporting data in areas affected by the Deepwater Horizon oil spill
Funded By:
Gulf of Mexico Research Initiative
Funding Cycle:
RFP-VI
Research Group:
Meta-analytic synthesis of long-term wetland impacts, recovery, and resilience following the Deepwater Horizon oil spill
Scott Zengel
Research Planning, Inc.
szengel@researchplanning.com
Deepwater Horizon, oiled marsh, manual cleanup treatment, mechanical cleanup treatment, marsh erosion, vegetation planting, restoration, wave power, aerial imagery, soil shear strength
Abstract:
Data on erosion rates in northern Barataria Bay at sites that were oiled during the Deepwater Horizon oil spill and subjected to different cleanup methods and restoration planting. Datasets include erosion rates measured in the field, erosion rates calculated from shorelines extracted from aerial imagery, modeled wave power, and soil shear strength.
Suggested Citation:
Zengel, Scott, Zachary Nixon, Jennifer Weaver, Nicolle Rutherford, Brittany M. Bernik, and Jacqueline Michel. 2022. Marsh shoreline erosion and supporting data in areas affected by the Deepwater Horizon oil spill. Distributed by: GRIIDC, Harte Research Institute, Texas A&M University–Corpus Christi. doi:10.7266/VF524143
Purpose:
Data were obtained from field observations and analysis of imagery to assess erosion rates in areas affected by the Deepwater Horizon oil spill.
Data Parameters and Units:
Erosion rate (meters per year), Plot ID, Plot Type (treatment classification for this study), Oiled (oiled or unoiled plots), n (number of transects that intersect the plot and were used to generate this estimate), SD (standard deviation associated with the estimate), SE (standard error of the estimate), WE WM (weighted mean wave power density in watts (w)/m), WE MAX (maximum wave power density in watts (w)/m), SHST KPA (shear strength in kiloPascals), Time period (period of which erosion rates were calculated), Wave power (watts per meter), Soil shear strength (kilopascals) MECH. TREATMENT = Mechanical treatment; same as TREATED – MECHANICAL in the DSAS dataset MAN. TREATMENT = Manual treatment; same as TREATED – MANUAL in the DSAS dataset
Methods:
Field data were collected using high resolution differentially-corrected GPS. Remotely sensed data was collected and processed from digital aerial photographs using GIS and the USGS Digital Shoreline Analysis System (DSAS). Wave power data was modeled using fetch, wind direction, and wind speed data. Wind speed and direction data were compiled from publicly available NOAA weather data. Soil shear strength was measured using a shear vane attached to a direct reading torque gauge. To estimate wave power at a transect representing each plot location over each time period of interest, we used a probabilistic method for conducting steady-state empirical shallow-water wave modeling wherein 240 different climatological scenarios representing different wind speeds and directions with different occurrence frequencies were used to parameterize empirical shallow-water wave models. Fetch was computed separately for each time period of interest. Land and water data were constructed for each time period by reclassifying the land-loss data generated by Couvillion et al. to create different 30 m land-water grids at the following time steps: 1956, 2004, 2006, 2010, 2013, and 2016. At each plot location, a single transect was generated, running through the centroid of that plot, and having as its azimuth the average of all the DSAS transects that also intersected that plot. For each plot centroid transect, a single location was generated at 50 m from the shoreline position for each of the following years that approximately correspond to the time steps used for the land-water grids: 1956, 2004, 2005, 2010, 2012, and 2018. A location 50 m offshore the shoreline corresponding to each time step was used to compute fetch representing a location approximately far enough from the marsh scarp to realistically represent incident wind waves and where water depth is asymptotically approaching equilibrium depth. At each transect offshore location, fetch was computed in 15° increments using the effective fetch methodology with 9 cosine-weighted radials in 3 increments about the primary angle. Fetch was computed using the waver package for the R statistical computing language. A maximum fetch of 30 kilometers (km) was assumed in any direction. Fetch for each time period was then computed as the average of fetch calculated at each bracketing time step. We then compiled all available (1986 to 2021) hourly wind data from the BURL1 station from the NOAA National Data Buoy Center (NDBC) and converted them to standard wind speeds (m-s-1) at 10 m using the correction described in Mariotti et al. For each time period, we then computed wind summary statistics within 24 directional bins at 15° increments, and 10 velocity bins with breakpoints at 0, 2, 4, 6, 8, 10, 15, 20, 25, 30, and 35 m-s-1, yielding a total of 240 individual wind speed and direction scenarios. For each transect offshore location, time period, fetch direction, and wind speed and direction scenario we computed wave power statistics, generally following the methods outlined in Allison et al. (2017).
Provenance and Historical References:
Allison, Paul D, Richard Williams, and Enrique Moral-Benito. 2017. Maximum likelihood for cross-lagged panel models with fixed effects. Socius, 3, pp. 1-17. DOI: 10.1177/2378023117710578 Couvillion, Brady R., Holly Beck, Donald Schoolmaster, and Michelle Fischer. 2017. Land area change in coastal Louisiana (1932 to 2016). Scientific Investigations Map. DOI: 10.3133/sim3381 Mariotti Annarita, Cory Baggett, Elizabeth A. Barnes, Emily Becker, Amy Butler, Dan C. Collins, Paul A. Dirmeyer, Laura Ferranti, Nathaniel C. Johnson, Jeanine Jones, Ben P. Kirtman, Andrea L. Land, Andrea Molod, Matthew Newman, Andrew W. Robertson, Siegfried Schubert, Duane E. Waliser, and John Albers. 2020. Windows of opportunity for skillful forecasts subseasonal to seasonal and beyond. Bulletin of the American Meteorological Society, 101 (5), pp. E608-E625.
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