Assessing the Influence of Observational Data Error on SiB 2 Model Parameter Uncertainty
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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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9 th LBA-ECO Science Team Meeting

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9 th lba eco science team meeting

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


Pareto set of parameters

Pareto Set of Parameters


Observed vs computed pre logging

Observed vs Computed. Pre-logging


Dry typical periods

Dry Typical Periods


Wet typical periods

Wet Typical Periods


Daily average

Daily Average

E

H

CO2


Performance measures

Performance Measures

E

H

CO2

E

H

CO2

R

NSE

BIAS

RMSE


Objectives for radiation and precipitation errors

Objectives for Radiation and Precipitation Errors

Heteroscedastic Error Added


Parameter distributions

Parameter Distributions


Parameter distributions1

Parameter Distributions

0

1


Daily average1

Daily Average

E

H

CO2

0

24

0

24

0

24


Beware

Beware ….

FOOL OUTSIDE

“A fool with a tool is still a fool”


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