Optimal Parameter and Uncertainty Estimation of a Land Surface Model: Sensitivity to Parameter Ranges and Model Complexities

Youlong XIA; Zong-Liang YANG; Paul L. STOFFA; Mrinal K. SEN

doi:10.1007/BF02930878

Impact Factor: 6.5

Volume 22 Issue 1

Jan. 2005

Article Contents

Article > ADVANCES IN ATMOSPHERIC SCIENCES > 2005 > 22(1): 142-157

Optimal Parameter and Uncertainty Estimation of a Land Surface Model: Sensitivity to Parameter Ranges and Model Complexities

1.
Institute for Geophysics, The John A. and Katherine G. Jackson School of Geosciences,University of Texas at Austin, 4412 Spicewood Spring Road, Austin, TX 78759-8500, USA,Department of Geological Sciences, The John A. and Katherine G. Jackson School of Geosciences, University of Texas at Austin, USA,Institute for Geophysics, The John A. and Katherine G. Jackson School of Geosciences,University of Texas at Austin, 4412 Spicewood Spring Road, Austin, TX 78759-8500, USA,Institute for Geophysics, The John A. and Katherine G. Jackson School of Geosciences,University of Texas at Austin, 4412 Spicewood Spring Road, Austin, TX 78759-8500, USA

doi: 10.1007/BF02930878

Abstract
References
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Abstract:
Most previous land-surface model calibration studies have defined global ranges for their parameters to search for optimal parameter sets. Little work has been conducted to study the impacts of realistic versus global ranges as well as model complexities on the calibration and uncertainty estimates. The primary purpose of this paper is to investigate these impacts by employing Bayesian Stochastic Inversion (BSI)to the Chameleon Surface Model (CHASM). The CHASM was designed to explore the general aspects of land-surface energy balance representation within a common modeling framework that can be run from a simple energy balance formulation to a complex mosaic type structure. The BSI is an uncertainty estimation technique based on Bayes theorem, importance sampling, and very fast simulated annealing.The model forcing data and surface flux data were collected at seven sites representing a wide range of climate and vegetation conditions. For each site, four experiments were performed with simple and complex CHASM formulations as well as realistic and global parameter ranges. Twenty eight experiments were conducted and 50 000 parameter sets were used for each run. The results show that the use of global and realistic ranges gives similar simulations for both modes for most sites, but the global ranges tend to produce some unreasonable optimal parameter values. Comparison of simple and complex modes shows that the simple mode has more parameters with unreasonable optimal values. Use of parameter ranges and model complexities have significant impacts on frequency distribution of parameters, marginal posterior probability density functions, and estimates of uncertainty of simulated sensible and latent heat fluxes.Comparison between model complexity and parameter ranges shows that the former has more significant impacts on parameter and uncertainty estimations.
- optimal parameters,
- uncertainty estimation,
- CHASM model,
- bayesian stochastic inversion,parameter ranges,
- model complexities
References

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Manuscript History

Manuscript received: 10 January 2005

Manuscript revised: 10 January 2005

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

Optimal Parameter and Uncertainty Estimation of a Land Surface Model: Sensitivity to Parameter Ranges and Model Complexities

1. Institute for Geophysics, The John A. and Katherine G. Jackson School of Geosciences,University of Texas at Austin, 4412 Spicewood Spring Road, Austin, TX 78759-8500, USA,Department of Geological Sciences, The John A. and Katherine G. Jackson School of Geosciences, University of Texas at Austin, USA,Institute for Geophysics, The John A. and Katherine G. Jackson School of Geosciences,University of Texas at Austin, 4412 Spicewood Spring Road, Austin, TX 78759-8500, USA,Institute for Geophysics, The John A. and Katherine G. Jackson School of Geosciences,University of Texas at Austin, 4412 Spicewood Spring Road, Austin, TX 78759-8500, USA

Manuscript received: 2005-01-10
Manuscript revised: 2005-01-10

Abstract: Most previous land-surface model calibration studies have defined global ranges for their parameters to search for optimal parameter sets. Little work has been conducted to study the impacts of realistic versus global ranges as well as model complexities on the calibration and uncertainty estimates. The primary purpose of this paper is to investigate these impacts by employing Bayesian Stochastic Inversion (BSI)to the Chameleon Surface Model (CHASM). The CHASM was designed to explore the general aspects of land-surface energy balance representation within a common modeling framework that can be run from a simple energy balance formulation to a complex mosaic type structure. The BSI is an uncertainty estimation technique based on Bayes theorem, importance sampling, and very fast simulated annealing.The model forcing data and surface flux data were collected at seven sites representing a wide range of climate and vegetation conditions. For each site, four experiments were performed with simple and complex CHASM formulations as well as realistic and global parameter ranges. Twenty eight experiments were conducted and 50 000 parameter sets were used for each run. The results show that the use of global and realistic ranges gives similar simulations for both modes for most sites, but the global ranges tend to produce some unreasonable optimal parameter values. Comparison of simple and complex modes shows that the simple mode has more parameters with unreasonable optimal values. Use of parameter ranges and model complexities have significant impacts on frequency distribution of parameters, marginal posterior probability density functions, and estimates of uncertainty of simulated sensible and latent heat fluxes.Comparison between model complexity and parameter ranges shows that the former has more significant impacts on parameter and uncertainty estimations.

Keywords: