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Assessing the Influence of Observational Data Error on SiB 2 Model Parameter Uncertainty. 9 th LBA-ECO Science Team Meeting. Luis A. Bastidas 1 , E. Rosero 1 , S. Pande 1 , W.J. Shuttleworth 2 1 Civil and Environmental Engineering and Utah Water Research Laboratory, Logan, Utah

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Presentation Transcript
slide1
Assessing the Influence of Observational Data Error on SiB 2 Model Parameter Uncertainty

9th LBA-ECO Science Team Meeting

Luis A. Bastidas1,

E. Rosero1, S. Pande1, W.J. Shuttleworth2

1Civil and Environmental Engineering and

Utah Water Research Laboratory, Logan, Utah

2Hydrology and Water Resources,

SAHRA – NSF Science and Technology Center

University of Arizona, Tucson, Arizona

modeling
Model Qualification

Analysis

Conceptual

Model

Reality

Model Validation

Code Verification

Simulation

Programming

Model

Construction

Model Code

Model

Model Calibration

Modeling

Modified from Refsgaard, 2001

components of a model
Xo

Inputs

Outputs

State Variables

X

I

O

Model Structure

Model Structure

Xt = F ( Xt-1, , It-1 )

Ot = G ( Xt, , It )

Initial States

Parameters

Components of a Model
pareto optimality
Pareto Optimality

Parameter Space

Criterion Space

f2

β

f1

Criterion f2

Parameter 2

δ

γ

α

α

γ

δ

β

Parameter 1

Criterion f1

sensitivity analysis sib 2 @ santarem km 83
Sensitivity Analysis. SiB 2 @ Santarem Km 83

MOGSA Algorithm

Bastidas et al., JGR,1999

Multi Objective Generalized

Sensitivity Analysis

The further away from

the center the more

sensitive the parameter

Pre-logging

Post-logging

performance measures
Performance Measures

E

H

CO2

E

H

CO2

R

NSE

BIAS

RMSE

daily average1
Daily Average

E

H

CO2

0

24

0

24

0

24

beware
Beware ….

FOOL OUTSIDE

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