9 th lba-eco science team meeting assessing the influence of observational data error on sib 2 model...

9th LBA-ECO Science Team Meeting

Assessing the Influence of Observational Data Error on SiB 2

Model Parameter Uncertainty

Assessing the Influence of Observational Data Error on SiB 2

Model Parameter Uncertainty

Luis A. Bastidas1,E. Rosero1, S. Pande1, W.J. Shuttleworth2

1Civil and Environmental Engineering andUtah Water Research Laboratory, Logan, Utah

2Hydrology and Water Resources,SAHRA – NSF Science and Technology Center

University of Arizona, Tucson, Arizona

Luis.Bastidas @ usu.edu 2/16

Modeling

Modified from Refsgaard, 2001

ModelModel

ConstructionConstruction

RealityReality ConceptualConceptualModelModel

ModelModel Model CodeModel Code

AnalysisAnalysis

Sim

ula

tio

nS

imu

lati

on

Pro

gram

min

gP

rog

ramm

ing

Model Calibration

Mo

del

Val

idat

ion

Model Qualification

Co

de V

erification


Components of a Model

I

Inputs

XState Variables

Model Structure

Xt = F ( Xt-1, , It-1 )

Ot = G ( Xt, , It )

Model Structure

O

Outputs

Parameters

Xo

Initial States


Pareto Optimality

Parameter 1

Par

amet

er

2

Criterion f1

Cri

teri

on

f 2

α

β

γ

γ

δ

δ

α

β

Parameter Space Criterion Space

f1

f2


Sensitivity Analysis. SiB 2 @ Santarem Km 83

Pre-loggingPost-logging

MOGSA AlgorithmBastidas et al., JGR,1999

Multi Objective GeneralizedSensitivity Analysis

The further away fromthe center the more sensitive the parameter


Pareto Set of Parameters


Observed vs Computed. Pre-logging


Dry Typical Periods


Wet Typical Periods


Daily Average

E H CO2


Performance Measures

E H CO2 E H CO2

R

RMSE

NSE

BIAS


Objectives for Radiation and Precipitation ErrorsHeteroscedastic Error Added


Parameter Distributions


Parameter Distributions

0 1


Daily Average

E H CO2

0 24 0 24 0 24


Beware ….

“A fool with a tool is still a fool”

9 th lba-eco science team meeting assessing the influence of observational data error on sib 2 model...

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