The document discusses Pecan Project's uncertainty analysis for LTER ASM 2018. It analyzes parameter uncertainty and sensitivity, and outputs uncertainty through trait meta-analysis, iterative Bayesian calibration, and variance decomposition. Key findings include growth respiration and root water uptake being highly sensitive, and stomatal conductance variability differing between conifer and hardwood species and latitudes. The analysis aims to characterize, propagate, analyze, and reduce uncertainties.

1. PEcAn UNCERTAINTY
ANALYSIS
LTER ASM
2018
@PEcAnProject
@mcdietze

2. Parameter
Uncertainty
Sensitivit
yOutput
Uncertainty
Trait meta-analysis (AUTOMATED)
Iterative Bayesian calibration
(WEB)
(AUTOMATED
)
(AUTOMATED
)

3. 3

4. Variance Decomposition
*can easily be extended to
driver and IC uncertainties
*
Prior
Posterior
Parameter
Uncertainty
Sensitivit
y
Output
Uncertainty

7. • Growth Respiration:
sensitivity
• Root water uptake:
data deficient
• Stomatal conductance:
• Conif: sensitivity
increases North -> South
• Hardwood: var in data
• Quantum Efficiency:
data deficient at high lat.
Dietze et al 2014 JGR-

8. Characterize
Propagate
Analyze
Reduce

9. http://pecanproject.org
https://github.com/PecanProject
NSF ABI #1458021, DIBBS #1261582, EF #1318164, EF
#1241894, EF#1638577, EF #1702996, NASA 13-TE13-0060,
16-CMS16-0007, DOD SERDB, Amazon AWS, Google GSoC