1. Ecosystem Demography model version 2 (ED2)
Model structure
Inputs and outputs
Gap, big leaf, hybrid?
Applications
PEcAn framework for running ED2 and assimilating data

2. ED2: Overview Terrestrial biosphere model Mechanistic
Size- and age-structured vegetation dynamics coupled with ecophysiology and biogeochemistry models
Key sub-models:
Leaf physiology (C3 from Farquhar, Bar-Berry, et al.)
C allocation to leaves, fine roots, sapwood, stored leaf pool (from LEAF, Walko et al.)
Active biomass (GPP – allocated pools + storage pool - respiration)
Phenology (MODIS + equations)
Soil biogeochemistry model (CENTURY)

3. ED2: Structure Calculated within each tile: Water (W),
Internal energy (H)
Carbon (C)
Based on:
Plant functional type (determines physiology)
Size/age (determines access to water and light)
Kim et al. 2012:
Seasonal carbon dynamics and water fluxes in an Amazon rainforest
Medvigy et al. 2009:
Mechanistic scaling of ecosystem function and dynamics in space and time

4. ED2: Inputs & Outputs Key Inputs: Daily met data
Plant functional type parameters
Stand inventory data
Soil type
Land use
Key Outputs:
Ecosystem structure:
Growth, mortality, AGB, etc.
Ecosystem fluxes
NPP, NEP, ET, etc.)
Respiration/photosynthetic activity
Hydrologic data
Kim et al. 2012

5. ED2: Optimization Harvard Forest (optimized) Quebec (far from Harvard)
Howland (near Harvard)
Medvigy et al. 2009:
Mechanistic scaling of ecosystem function and dynamics in space and time

6. ED2: Scaling heterogeneity
Observed
“Big-leaf” estimate
ED2 estimate
“ED2 captures subgrid scale
biotic heterogeneity using a system of size- and age-structured partial differential equations that closely approximate the ensemble mean behavior of an individual-based stochastic gap model”
Nov Oct. 1996
Medvigy et al. 2009: JGR Biogeosciences

7. ED2: Predicting ecosystem response to climate change
ED2 big leaf
ED2 structured
Kim et al. 2012:
Seasonal carbon dynamics and water fluxes in an Amazon rainforest

8. ED2: Predicting and optimizing agricultural yields
Dietz et al. 2013: Ecological Applications

9. ED2: Predicting post-disturbance carbon balance
Frasson et al. 2015: JGR Biogeosciences

10. ED2: Testing the potential of new tools

11. Integrating data and models
Continuous data, lab research
Field measurements
Models and modelers

12. PEcAn (Predictive Ecosystem Analyzer)
Model-Data feedback:
Uses Bayesian statistics to estimate parameters
Quantifies contribution of each parameter to overall model variance
Help to decide what variables are important to measure and assimilate
LeBauer et al Facilitating feedbacks between field measurements and ecosystem models

13. PEcAn: Informing parameter estimates with data
Model-Data feedback:
Uses Bayesian statistics to estimate parameters
Quantifies contribution of each parameter to overall model variance
Help to decide what variables are important to measure and assimilate

14. PEcAn: Constraining model parameters
Coefficient of variation (CV): uncertainty associated with each parameter
Elasticity: normalized sensitivity of modeled AGB to each parameter
Percentage of total variance explained by each parameter

15. PEcAn: Comparing model parameters

16. PEcAn: Interface

17. PEcAn: Interface

18. PEcAn: Interface

19. Summary ED2 simulates an individual canopy-gap model at large scales
Extensively validated in forest and ag. systems
Can be applied broadly to test hypotheses, make predictions, etc.
PEcAn runs ED2 and other models
Provides a framework to assimilate data and refine parameters
Enables broad community to use terrestrial biosphere (and other) models