Description of Research Opportunity
Coastal marsh environments exist at the intersection of human and ecological communities. They provide economic output through commercial fisheries, tourism and recreation, and offer protection to coastal communities during storm events. Rising seas have the potential to affect coastal systems with increased inundation, increased shoreline erosion and wetland loss; the effects are dynamic and interrelated. Salt marshes may struggle to keep pace with sea level rise (SLR) and will rely on sediment accumulation and the availability of uplands for migration to avoid drowning. Ongoing partnerships with coastal managers have revealed that information regarding future marsh extents and the potential for migration under SLR is needed to understand impacts to critical species, designate future land use and develop restoration strategies to mitigate marsh loss. Holistic assessments that transition away from simplistic “bathtub” approaches and evaluate the coupled interactions between hydrodynamics and biological feedbacks can provide a better understanding of the longevity of coastal ecosystems. There are several well-known biological models that project future marsh vulnerability to SLR including (but not limited to) the Marsh Equilibrium Model (MEM [Morris et al., 2002]), WARMER (Swanson et al., 2014; Buffington et al., 2021) and SLAMM. These models can be coupled with hydrodynamic models to incorporate dynamic feedbacks between water levels and marsh ecology (e.g., Hydro-MEM [Alizad et al., 2016]), Additionally, integrative metrics derived from remote sensing such as the sediment-based Unvegetated to Vegetated Marsh Ratio (UVVR) can inform estimates of marsh lifespan (Ganju et al., 2022). However, no single model or metric meets all end user needs, particularly at the national scale. Outputs from these approaches may vary due to differences in governing equations, neglecting important dynamic processes and simplifications to inherent biological processes. Stakeholders have expressed concerns about transparency in marsh model processes and uncertainty in the outputs including which model they should use, what is the utility of different output types for different management decisions and how certain are the predictions? (Martin et al., 2022). Thus, the USGS and its partners have identified a need for nationally consistent predictions of marsh evolution to inform the protection of coastal communities, economies and their natural resources. This requires robust predictions with efficient computational costs, applicability to a variety of coastlines and quantifications of model uncertainty.
This research opportunity will focus on developing new methods for integrating and evaluating results from different marsh models to assess multiple future scenarios of marsh evolution under SLR. Over the past decade, USGS has partnered with other federal agencies and academics to develop local to regional scale models and metrics that project salt marsh vulnerability to SLR along the U.S. Atlantic, Gulf, and Pacific coasts. The USGS and its partners have a variety of relevant observational data sets including in situ and remote sensing records of marsh evolution. This robust resource of observational data and existing models and their outputs at specific estuaries can be leveraged for quantitative comparisons of marsh extents and vulnerability, as well as hindcast assessments. Techniques such as surrogate models, machine learning methods and multi-model ensembles for future projections may be explored to address limitations and uncertainty in the individual models.
Interested applicants are strongly encouraged to contact the Research Advisor(s) early in the application process to discuss project ideas.
References:
Alizad, K., S. C. Hagen, J. T. Morris, P. Bacopoulos, M. V. Bilskie, J. F. Weishampel and S. C. Medeiros (2016). "A coupled two-dimensional hydrodynamic-marsh model with biological feedback." Ecological Modelling 327.https://doi.org/10.1016/j.ecolmodel.2016.01.013.
Buffington, K.J., Janousek C.N., Dugger B.D., Callaway J.C., Sloane E., Schile-Beers L., and Thorne K.M. 2021. Incorporation of uncertainty to improve projections of tidal wetland elevation and carbon accumulation with sea-level rise. PLOS One 16(10): e0256707.
Buffington, K.J., MacKenzie, R.A., Carr, J., Apwong, M., Krauss, K.W., Thorne, K.M. 2021. Mangrove species’ response to sea-level rise across Pohnpei, Federated States of Micronesia. U.S. Geological Survey Open-File Report 2021-1002.
Ganju, N.K. and Defne, Z. 2022. “Lifespan of marsh units in Assateague Island National Seashore and Chincoteague Bay, Maryland and Virginia”. U.S. Geological Survey Data Release. doi: 10.5066/P9WSYCAN
Martin, S. Cameron, C., Collini, R., Woodward, N., Buckel, C., Meckley, T., Spiegler, S., Alizad, K., Bienn, H., Clough, J., Esposito, C., Medeiros, S., Morris, J., Schoell, M. (2022), Marsh Model Retrospective Workshop. April 11-12, 2022. https://placeslr.org/wp-content/uploads/2022/08/Marsh-Model-Retrospective-Workshop-Final-1.pdf
Morris, J. T., P. V. Sundareshwar, C. T. Nietch, B. Kjerfve, and D. R. Cahoon (2002), Responses of coastal wetlands to rising sea level, Ecology, 83(10), 2869– 2877, doi:10.1890/0012-9658(2002)083[2869:rocwtr]2.0.co;2.
Swanson, K.M., Drexler, J.Z., Schoellhamer, D.H., Thorne, K.M., Casazza, M.L., Overton, C.T., Callaway, J.C., Takekawa, J.Y., 2014. Wetland Accretion Rate Model of Ecosystem Resilience (WARMER) and Its Application to Habitat Sustainability for Endangered Species in the San Francisco Estuary. Estuaries Coasts 37, 476–492. https://doi.org/10.1007/s12237-013-9694-0
Proposed Duty Station(s)
St Petersburg, Florida
Woods Hole, Massachusetts
Santa Cruz, California
Areas of PhD
Oceanography, ecology, biology, coastal engineering or related fields (candidates holding a Ph.D. in other disciplines, but with extensive knowledge and skills relevant to the Research Opportunity may be considered).
Qualifications
Applicants must meet one of the following qualifications: Research Oceanographer, Research Physical Scientist, Research Ecologist, Research Geographer, Research Biologist.
(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.)
