CSDMS 2020: Linking Ecosphere and Geosphere

21st Century Science For 21st Century Environmental Decision Making: The Challenges And Opportunities Of Near-Term Iterative Environmental Forecasting

Michael Dietze

Boston University, United States

Society is facing unprecedented environmental challenges that have pushed us into a world dominated by transients and variability. Informed decision making in this era, at scales from the individual to the globe, requires explicit predictions on management-relevant timescales, based on the best available information, and considering a wide range of uncertainties. As a research community, we are not yet meeting this need. In this talk I will introduce the Ecological Forecasting Initiative (EFI), an international grass-roots research consortium aimed at building a community of practice. I will discuss EFI’s cross-cutting efforts to tackle community-wide bottlenecks in cyberinfrastructure, community standards, methods and tools, education, diversity, knowledge transfer, decision support, and our theoretical understanding of predictability. I will highlight examples of near real-time iterative ecological forecasts across a wide range of terrestrial and aquatic systems, as well as work done by my own group developing PEcAn (a terrestrial ecosystem model-data informatics and forecasting system) and our recent efforts to generalize these approaches to other forecasts. Finally, I will also introduce EFI’s ecological forecasting competition, which relies on a wide range of continually-updated NEON (National Ecological Observatory Network) data.

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Of interest for:
  • Terrestrial Working Group
  • Coastal Working Group
  • Marine Working Group
  • Education and Knowledge Transfer (EKT) Working Group
  • Cyberinformatics and Numerics Working Group
  • Hydrology Focus Research Group
  • Chesapeake Focus Research Group
  • Critical Zone Focus Research Group
  • Human Dimensions Focus Research Group
  • Ecosystem Dynamics Focus Research Group
  • Coastal Vulnerability Initiative
  • Artificial Intelligence & Machine Learning Initiative