A fully multiple-criteria implementation of the Sobol' method for parameter sensitivity analysis

Rafael Rosolem, Hoshin Vijai Gupta, W. James Shuttleworth, Xubin Zeng, Luis Gustavo Gonçalves De Gonçalves

Research output: Contribution to journalArticle

58 Citations (Scopus)

Abstract

We present a novel rank-based fully multiple-criteria implementation of the Sobol variance-based sensitivity analysis approach that implements an objective strategy to evaluate parameter sensitivity when model evaluation involves several metrics of performance. The method is superior to single-criterion approaches while avoiding the subjectivity observed in "pseudo" multiple-criteria methods. Further, it contributes to our understanding of structural characteristics of a model and simplifies parameter estimation by identifying insensitive parameters that can be fixed to default values during model calibration studies. We illustrate the approach by applying it to the problem of identifying the most influential parameters in the Simple Biosphere 3 (SiB3) model using a network of flux towers in Brazil. We find 27-31 (out of 42) parameters to be influential, most (∼78%) of which are primarily associated with physiology, soil, and carbon properties, and that uncertainties in the physiological properties of the model contribute most to total model uncertainty in regard to energy and carbon fluxes. We also find that the second most important model component contributing to the total output uncertainty varies according to the flux analyzed; whereas morphological properties play an important role in sensible heat flux, soil properties are important for latent heat flux, and carbon properties (mainly associated with the soil respiration submodel) are important for carbon flux (as expected). These distinct sensitivities emphasize the need to account for the multioutput nature of land surface models during sensitivity analysis and parameter estimation. Applied to other similar models, our approach can help to establish which soil-plant-atmosphere processes matter most in land surface models of Amazonia and thereby aid in the design of field campaigns to characterize and measure the associated parameters. The approach can also be used with other sensitivity analysis procedures that compute at least two model performance metrics.

Original languageEnglish (US)
Article numberD07103
JournalJournal of Geophysical Research: Space Physics
Volume117
Issue number7
DOIs
StatePublished - 2012

Fingerprint

sensitivity analysis
Sensitivity analysis
soils
Carbon
Fluxes
Soils
carbon
carbon flux
Parameter estimation
Heat flux
heat flux
land surface
method
parameter
biosphere
physiology
sensitivity
Latent heat
Physiology
respiration

ASJC Scopus subject areas

  • Atmospheric Science
  • Geophysics
  • Earth and Planetary Sciences (miscellaneous)
  • Space and Planetary Science

Cite this

A fully multiple-criteria implementation of the Sobol' method for parameter sensitivity analysis. / Rosolem, Rafael; Gupta, Hoshin Vijai; Shuttleworth, W. James; Zeng, Xubin; De Gonçalves, Luis Gustavo Gonçalves.

In: Journal of Geophysical Research: Space Physics, Vol. 117, No. 7, D07103, 2012.

Research output: Contribution to journalArticle

@article{aee68976e7ab4c688f521adcb6732a00,
title = "A fully multiple-criteria implementation of the Sobol' method for parameter sensitivity analysis",
abstract = "We present a novel rank-based fully multiple-criteria implementation of the Sobol variance-based sensitivity analysis approach that implements an objective strategy to evaluate parameter sensitivity when model evaluation involves several metrics of performance. The method is superior to single-criterion approaches while avoiding the subjectivity observed in {"}pseudo{"} multiple-criteria methods. Further, it contributes to our understanding of structural characteristics of a model and simplifies parameter estimation by identifying insensitive parameters that can be fixed to default values during model calibration studies. We illustrate the approach by applying it to the problem of identifying the most influential parameters in the Simple Biosphere 3 (SiB3) model using a network of flux towers in Brazil. We find 27-31 (out of 42) parameters to be influential, most (∼78{\%}) of which are primarily associated with physiology, soil, and carbon properties, and that uncertainties in the physiological properties of the model contribute most to total model uncertainty in regard to energy and carbon fluxes. We also find that the second most important model component contributing to the total output uncertainty varies according to the flux analyzed; whereas morphological properties play an important role in sensible heat flux, soil properties are important for latent heat flux, and carbon properties (mainly associated with the soil respiration submodel) are important for carbon flux (as expected). These distinct sensitivities emphasize the need to account for the multioutput nature of land surface models during sensitivity analysis and parameter estimation. Applied to other similar models, our approach can help to establish which soil-plant-atmosphere processes matter most in land surface models of Amazonia and thereby aid in the design of field campaigns to characterize and measure the associated parameters. The approach can also be used with other sensitivity analysis procedures that compute at least two model performance metrics.",
author = "Rafael Rosolem and Gupta, {Hoshin Vijai} and Shuttleworth, {W. James} and Xubin Zeng and {De Gon{\cc}alves}, {Luis Gustavo Gon{\cc}alves}",
year = "2012",
doi = "10.1029/2011JD016355",
language = "English (US)",
volume = "117",
journal = "Journal of Geophysical Research: Space Physics",
issn = "2169-9380",
publisher = "Wiley-Blackwell",
number = "7",

}

TY - JOUR

T1 - A fully multiple-criteria implementation of the Sobol' method for parameter sensitivity analysis

AU - Rosolem, Rafael

AU - Gupta, Hoshin Vijai

AU - Shuttleworth, W. James

AU - Zeng, Xubin

AU - De Gonçalves, Luis Gustavo Gonçalves

PY - 2012

Y1 - 2012

N2 - We present a novel rank-based fully multiple-criteria implementation of the Sobol variance-based sensitivity analysis approach that implements an objective strategy to evaluate parameter sensitivity when model evaluation involves several metrics of performance. The method is superior to single-criterion approaches while avoiding the subjectivity observed in "pseudo" multiple-criteria methods. Further, it contributes to our understanding of structural characteristics of a model and simplifies parameter estimation by identifying insensitive parameters that can be fixed to default values during model calibration studies. We illustrate the approach by applying it to the problem of identifying the most influential parameters in the Simple Biosphere 3 (SiB3) model using a network of flux towers in Brazil. We find 27-31 (out of 42) parameters to be influential, most (∼78%) of which are primarily associated with physiology, soil, and carbon properties, and that uncertainties in the physiological properties of the model contribute most to total model uncertainty in regard to energy and carbon fluxes. We also find that the second most important model component contributing to the total output uncertainty varies according to the flux analyzed; whereas morphological properties play an important role in sensible heat flux, soil properties are important for latent heat flux, and carbon properties (mainly associated with the soil respiration submodel) are important for carbon flux (as expected). These distinct sensitivities emphasize the need to account for the multioutput nature of land surface models during sensitivity analysis and parameter estimation. Applied to other similar models, our approach can help to establish which soil-plant-atmosphere processes matter most in land surface models of Amazonia and thereby aid in the design of field campaigns to characterize and measure the associated parameters. The approach can also be used with other sensitivity analysis procedures that compute at least two model performance metrics.

AB - We present a novel rank-based fully multiple-criteria implementation of the Sobol variance-based sensitivity analysis approach that implements an objective strategy to evaluate parameter sensitivity when model evaluation involves several metrics of performance. The method is superior to single-criterion approaches while avoiding the subjectivity observed in "pseudo" multiple-criteria methods. Further, it contributes to our understanding of structural characteristics of a model and simplifies parameter estimation by identifying insensitive parameters that can be fixed to default values during model calibration studies. We illustrate the approach by applying it to the problem of identifying the most influential parameters in the Simple Biosphere 3 (SiB3) model using a network of flux towers in Brazil. We find 27-31 (out of 42) parameters to be influential, most (∼78%) of which are primarily associated with physiology, soil, and carbon properties, and that uncertainties in the physiological properties of the model contribute most to total model uncertainty in regard to energy and carbon fluxes. We also find that the second most important model component contributing to the total output uncertainty varies according to the flux analyzed; whereas morphological properties play an important role in sensible heat flux, soil properties are important for latent heat flux, and carbon properties (mainly associated with the soil respiration submodel) are important for carbon flux (as expected). These distinct sensitivities emphasize the need to account for the multioutput nature of land surface models during sensitivity analysis and parameter estimation. Applied to other similar models, our approach can help to establish which soil-plant-atmosphere processes matter most in land surface models of Amazonia and thereby aid in the design of field campaigns to characterize and measure the associated parameters. The approach can also be used with other sensitivity analysis procedures that compute at least two model performance metrics.

UR - http://www.scopus.com/inward/record.url?scp=84859704416&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84859704416&partnerID=8YFLogxK

U2 - 10.1029/2011JD016355

DO - 10.1029/2011JD016355

M3 - Article

VL - 117

JO - Journal of Geophysical Research: Space Physics

JF - Journal of Geophysical Research: Space Physics

SN - 2169-9380

IS - 7

M1 - D07103

ER -