19-12. Dynamic likelihood forecasts for coastal wetland landscapes

Sea-level rise (SLR) is a primary challenge facing coastal communities, requiring scientific data and models that inform planning tools that provide actionable information. Indeed, societal adaptation to sea-level rise and preservation of coastal wetland habitat highlight the need for decision support that provides forecasts and associated uncertainty to evaluate risk. Tidal wetlands provide a suite of ecosystem services, including coastal protection, long-term carbon storage, and habitat provision for both threatened and economically important species. Under historical rates of SLR, salt marshes have persisted through accretion rates that were similar to the rate of relative SLR (Gonneea et al. 2019), while maintaining an intertidal position as a result of upslope migration and erosion. Sustaining tidal wetlands, particularly high marsh habitat, is a major societal challenge during the 21st century due to insufficient accretion and platform maintenance in many locations (Ganju et al. 2017), diminished resilience behind dikes and related structures (Kroeger et al. 2017), and widespread and increasing human-built barriers to transgression (Schuerch et al. 2018). This research opportunity seeks innovative data-model-tool integration, evaluation and synthesis to improve coastal response predictions in habitats where current model integration is not well constrained by anthropogenic and environmental drivers. 

The USGS employs a variety of data and tool-based approaches, including a probabilistic modeling approach using Bayesian belief networks (BNs), to forecast landscape responses to sea-level rise.  BNs simultaneously pose a hypothesis based on theory, assumptions, and uncertainty, and then evaluate and update the hypothesis with new data. Predictions are developed through an informed framework of ecosystem and morphologic response and explicitly include uncertainty estimates that are necessary for strategic hazard evaluation in the context of resource management and planning.  BNs have been used to predict the likelihood of change under various sea-level rise scenarios to assess future hazards in coastal environments, including: forecasts of beach and dune morphology changes, shoreline change and dynamic coastal response to sea-level rise (e.g. Lentz et al. 2016).  The extension of this approach to wetland systems is currently restricted to first-order, broad scale (regional) predictions, and with limited evaluation against historical data and trends. As a consequence, the application of forecasts to explore various management scenarios in altered and impacted wetlands, where natural hydrology and biogeophysical feedbacks are interrupted, is problematic without additional constraints, model parameterization, and assessment.  

We seek a postdoctoral fellow to develop or improve models and evaluation frameworks that can robustly forecast coastal eco-morphologic landscape response to sea-level rise. A successful proposal will develop new approaches to data-model integration that are critical for coastal wetland habitats in the context of sea-level rise and management decisions. The postdoctoral fellow will be able to leverage extensive ongoing research programs in coastal wetland environments that serve as a backdrop for model development and data-model integration. A key goal will be to produce a framework that is flexible and agile, allowing for broad application, particularly for environments that are currently not well-captured or detailed at the site-level within current regional approaches (e,g., managed wetlands in Lentz et al., 2016). One significant potential outcome of the research opportunity is improvement in management application for coastal decision-makers tasked with managing coastal landscapes, particularly wetlands, and predicting resiliency benefits under future sea-level rise scenarios.  

Potential research foci related to this opportunity include: 

• Using a Bayesian Network approach, build new models, refine established frameworks, and rigorously evaluate forecasts of coastal landscape change  

• Explore new methods for data integration into models of coastal wetland geomorphology and response, including both natural and altered wetland environments, leveraging existing data products and observations as well as contributing to those in development. 

• Develop scalable future response metrics for coastal environments and test their suitability to support regional to national assessments and strategic planning 

Interested applicants are strongly encouraged to contact the Research Advisor(s) early in the application process to discuss project ideas. 

References:  

Ganju, N.K., Defne, Z., Kirwan, M.L., Fagherazzi, S., D’Alpaos, A., Carniello, L., 2017. Spatially integrative metrics reveal hidden vulnerability of microtidal salt marshes. Nature Communications 8, 14156. https://doi.org/10.1038/ncomms14156 

Gonneea, M.E., C.M. Maio, K.D. Kroeger, A.D. Hawkes, J. Mora, R. Sullivan, S. Madsen, R.M. Buzard, N. Cahill, J.P. Donnelly. 2019. Salt marsh ecosystem restructuring enhances elevation resilience and carbon storage during accelerating sea-level rise. Estuarine and Coastal Shelf Science. 217, 56-68, https://doi.org/10.1016/j.ecss.2018.11.003. 

Kroeger, KD, Crooks, S, Moseman-Valtierra, S, Tang, J. 2017. Restoring tides to reduce methane emissions as a new and potent Blue Carbon intervention. Scientific Reports 7, Article number: 11914, doi:10.1038/s41598-017-12138-4 

Lentz, E.E., Thieler, E.R., Plant, N.P, Stippa, S.R., Horton, R., and Gesch, D.B., 2016, Evaluation of dynamic coastal response to sea-level rise modifies inundation likelihood, Nature Climate Change, 6, p. 1-6. doi:10.1038/nclimate295.  

Schuerch, M., Spencer, T., Temmerman, S., Kirwan, M.L., Wolff, C., Lincke, D., McOwen, C.J., Pickering, M.D., Reef, R., Vafeidis, A.T., Hinkel, J., Nicholls, R.J., Brown, S., 2018. Future response of global coastal wetlands to sea-level rise. Nature 561, 231–234. https://doi.org/10.1038/s41586-018-0476-5 

Proposed Duty Station: Woods Hole, MA 

Areas of PhD: Environmental science, chemistry, oceanography, geology, ecology, geography, 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 Chemist, Research Geologist, Research Physical Scientist, Research Ecologist, Research Oceanographer, Research Geographer 

(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.) 

Human Resources Office Contact: Kimberly Sales, 703-648-7478, ksales@usgs.gov 

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