Forecasting in the Yellowstone Ecosystem: Development of Remotely-Sensed Estimates of Vegetation 3-D Structure, and Disturbance Dynamics for Initializing, Constraining and Testing Terrestrial Biosphere Models
Bob
Crabtree, Yellowstone Ecological Research Center, crabtree@yellowstoneresearch.org
(Presenting)
Paul
Moorcroft, Harvard University, paul_moorcroft@harvard.edu
Sassan
Saatchi, CALTECH/Jet Propulsion Laboratory, saatchi@congo.jpl.nasa.gov
Shengli
Huang, Yellowstone Ecological Research Center, huang@yellowstoneresearch.org
Christopher
Potter, NASA Ames Research Center, cpotter@mail.arc.nasa.gov
Jennifer
Sheldon, Yellowstone Ecological Research Center, sheldon@yellowstoneresearch.org
Kerry
Halligan, Yellowstone Ecological Research Center, halligan@yellowstoneresearch.org
Ecosystems worldwide face an uncertain future given recent and predicted trends in climate change. Biosphere models that are realistic, mechanistic, and accurate are thus needed to link with atmospheric models (GCMs) to provide future predictions of carbon, water and energy flux and community structure and composition. In order to achieve these goals, we have, and are developing, estimates of 3D vegetation structure and disturbance based on NASA data and data products for ingestion into biosphere models such the Ecosystem Demography (ED) and Carnegie-Ames-Stanford Approach (CASA) models. We demonstrate this for a variety of disturbance types and also develop functional cover types (FCTs), a hybrid of community composition, stand biomass, stand age, and land cover classes necessary for initializing, constraining, and testing biosphere models in forested communities. We are also developing techniques to map disturbances such as fire and pathogen mortality. We also evaluate estimates for fire fuels, soil moisture and NPP as products for ingestion into, and testing of, biosphere models. Initial model simulation outputs of the Yellowstone Ecosystem are provided. These products in turn can be used as inputs to other models such as RRSC (risk-reward spatial capacity) models used to predict changes in native and invasive species distributions and abundance.