Session Lead: Jared D. Smith

Session Co-lead: Daniel E. Kaufman

Session format: Oral presentations

Abstract:

Management of the Chesapeake Bay is a multi-objective problem that relies on a wide variety of data types (biogeochemical, ecological, physical, socio-economic) and models that describe their interactions. How can these sources of information be used to their fullest? For example, the various sources of uncertainty in numerical and statistical models do not represent simply a lack of knowledge; rather, they are valuable sources of information regarding the likelihood of possible system states, given the available data. Efforts to not only minimize uncertainties, but also to quantify and interpret the information they provide can help address both research and management questions. Moreover, efforts to expand the capabilities of models and data (e.g. through improved process representations, data assimilation, or optimization) as well as to distill their results into useful insights (e.g. data visualization) are essential to making the most of the extensive scientific resources in the Chesapeake Bay research community.

In this session, we aim to bring together field, lab, and computational researchers, engineers, and project managers to discuss 1) how advances in modeling and analytics are being or can be used to inform and communicate improved policies for Chesapeake Bay resource management, and 2) data and modeling gaps that need to be addressed for better management decisions to be discovered.

We welcome contributions that describe advances in watershed and Bay modeling, and the assimilation, analysis, and use of various data types in modeling exercises. Particular topics of interest include (but are not limited to):

  • The impacts of model structure, parameter, or scenario uncertainty on model predictions (e.g., via a Bayesian framework),
  • Optimization, visualization, and decision support tools for Bay management
  • Coupling of numerical and statistical modeling approaches, or coupling of biogeochemical, ecological, physical, or socio-economic processes
  • Data assimilation and skill assessments
  • Matching the spatial scale and complexity of the model to the available data, etc.