NDLI: Optimal parameter and uncertainty estimation of a land surface model: Sensitivity to parameter ranges and model complexities

Content Provider	Springer Nature Link
Author	Xia, Youlong Yang, Zong Liang Stoffa, Paul L. Sen, Mrinal K.
Copyright Year	2005
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.
Starting Page	142
Ending Page	157
Page Count	16
File Format	PDF
ISSN	02561530
Journal	Advances in Atmospheric Sciences
Volume Number	22
Issue Number	1
e-ISSN	18619533
Language	English
Publisher	Science Press
Publisher Date	2005-01-01
Publisher Place	Beijing
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	optimal parameters uncertainty estimation CHASM model bayesian stochastic inversion parameter ranges model complexities Meteorology/Climatology Geophysics/Geodesy
Content Type	Text
Resource Type	Article
Subject	Atmospheric Science

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