6. Prediction of Coastal Response to Extreme Storms Using High-Resolution Modeling and Data Assimilation

Much of the Nation’s coastlines are eroding due to the confounding effects of persistent relative sea level rise and infrequent severe storms. There is a need to make quantitative predictions of both the rapid coastal response to large storms and the gradual response to more mild conditions. Presently, there are observational data sets that capture both rapid response as well as the underlying gradual response. These data can be used to guide quantitative forecasts of coastal topographic and bathymetric change using detailed numerical models. Optimal forecasts of dune, shoreline, and beach evolution will require some form of data assimilation in order to provide accurate predictions and meaningful estimates of prediction uncertainty.

The objectives of this effort are to:

• evaluate the predictive skill of state-of-the-art numerical models,
• quantify prediction uncertainty due to both model and input errors and their integration over time scales ranging from several days to several decades,
• design a data assimilation scheme that takes advantage of the skillful components of model predictions and high-resolution bathymetry/topography data, and
• generate meaningful forecasts of coastal topography and bathymetry, including uncertainty estimates.

We envision that the research under this Mendenhall postdoctoral research will bring together huge investments in both modeling and observational technology. The successful candidate will leverage these technologies with new methodology that will have scientific and applied impact. Specifically, the investigation of model errors will isolate those processes that can benefit most from further fundamental scientific investment. The forecast products, if they include accurate predictions and accurate estimates of uncertainty, will support coastal management decisions that are based on risk analysis. Finally, the target prediction time scales span a wide range, allowing the successful candidate to take advantage of a variety of observational technologies, which might include real-time sampling over short scales or long-term, large-scale sampling (e.g., LIDAR observations).  With these resources, the candidate will be able to pursue research topics leading to improved understanding of coastal hydrodynamics, sediment transport, and/or morphologic processes.

Proposed Duty Station: St. Petersburg, FL

Areas of Ph.D.: Geology, oceanography, mathematics, engineering, and/or statistics

Qualifications: Applicants must meet one of the following qualifications: Research Geologist, Research Oceanographer, Research Mathematician, Research Statistician,
Research Civil Engineer/Civil Engineer

(This type of research is performed by those who have backgrounds for the occupations stated above. However, other titles may be applicable depending on the applicant's background, education, and research proposal. The final classification of the position will be made by the Human Resources specialist.)

Research Advisor(s): Nathaniel Plant, 727-803-8747 (x-3072), nplant@usgs.gov; Abby Sallenger, 727-803-8747 (x-3015), asallenger@usgs.gov; Peter Howd, 727-803-8747 (x-3019), phowd@usgs.gov; Hilary Stockdon, 727-803-8747 (x-3074), hstockdon@usgs.gov

Human Resources Office contact: Brian Arnold-Renicker, (703) 648-7468, brenicke@usgs.gov

Summary of Opportunities