CSDMS 2024: Coastlines, Critical Zones and Cascading Hazards: Modeling Dynamic Interfaces from Deep Time to Human Time

Vegetation as ecogeomorphic features: incorporating vegetation into Earth Surface Models

Todd Swannack

USACE, United States
Candice D. Piercy USACE United States

Vegetation is a critical ecogeomorphic agent within landscapes and is instrumental to many physical, biochemical, and ecological processes that can vary across spatial and temporal scales (e.g., erosion, sediment deposition, primary productivity, nutrient cycling, etc.). Modeling vegetation dynamics can be challenging, not only because of these scale-dependent variations, but also because of the breadth of existing approaches. The purpose of this clinic is to provide a technical overview for incorporating or developing vegetation models for earth surface dynamics modeling questions. The instructors will introduce vegetation processes commonly modeled, existing types of vegetation models, and how to choose an appropriate level of complexity for your system. Attendees will gain hands-on experience with existing vegetation components within and outside the Landlab system. These models will include the Cellular Automaton Tree Grass Simulator (CATGraSS), a mechanistic, photosynthesis-driven generalized vegetation model as well as how to incorporate vegetation models from Netlogo into Landlab. While active developers in the Landlab community will find this clinic useful, advanced programming experience is not needed.

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Of interest for:
  • Marine Working Group
  • Terrestrial Working Group
  • Coastal 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
  • Geodynamics Focus Research Group
  • Ecosystem Dynamics Focus Research Group
  • Coastal Vulnerability Initiative
  • Continental Margin Initiative
  • Artificial Intelligence & Machine Learning Initiative
  • Modeling Platform Interoperability Initiative
  • River Network Modeling Initiative