skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Quantification of structural uncertainty in a land surface model.


Abstract not provided.

 [1];  [1]; ;
  1. (PNNL)
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
OSTI Identifier:
Report Number(s):
DOE Contract Number:
Resource Type:
Resource Relation:
Conference: Proposed for presentation at the SIAM Conference on Computational Science and Engineering held March 14-18, 2015 in Salt Lake City, UT.
Country of Publication:
United States

Citation Formats

Hou, Zhangshuan, Huang, Maoyi, Ray, Jaideep, and Swiler, Laura Painton. Quantification of structural uncertainty in a land surface model.. United States: N. p., 2015. Web.
Hou, Zhangshuan, Huang, Maoyi, Ray, Jaideep, & Swiler, Laura Painton. Quantification of structural uncertainty in a land surface model.. United States.
Hou, Zhangshuan, Huang, Maoyi, Ray, Jaideep, and Swiler, Laura Painton. 2015. "Quantification of structural uncertainty in a land surface model.". United States. doi:.
title = {Quantification of structural uncertainty in a land surface model.},
author = {Hou, Zhangshuan and Huang, Maoyi and Ray, Jaideep and Swiler, Laura Painton},
abstractNote = {Abstract not provided.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = 2015,
month = 3

Other availability
Please see Document Availability for additional information on obtaining the full-text document. Library patrons may search WorldCat to identify libraries that hold this conference proceeding.

Save / Share:
  • Uncertainties in hydrologic parameters could have significant impacts on the simulated water and energy fluxes and land surface states, which will in turn affect atmospheric processes and the carbon cycle. Quantifying such uncertainties is an important step toward better understanding and quantification of uncertainty of integrated earth system models. In this paper, we introduce an uncertainty quantification (UQ) framework to analyze sensitivity of simulated surface fluxes to selected hydrologic parameters in the Community Land Model (CLM4) through forward modeling. Thirteen flux tower footprints spanning a wide range of climate and site conditions were selected to perform sensitivity analyses by perturbingmore » the parameters identified. In the UQ framework, prior information about the parameters was used to quantify the input uncertainty using the Minimum-Relative-Entropy approach. The quasi-Monte Carlo approach was applied to generate samples of parameters on the basis of the prior pdfs. Simulations corresponding to sampled parameter sets were used to generate response curves and response surfaces and statistical tests were used to rank the significance of the parameters for output responses including latent (LH) and sensible heat (SH) fluxes. Overall, the CLM4 simulated LH and SH show the largest sensitivity to subsurface runoff generation parameters. However, study sites with deep root vegetation are also affected by surface runoff parameters, while sites with shallow root zones are also sensitive to the vadose zone soil water parameters. Generally, sites with finer soil texture and shallower rooting systems tend to have larger sensitivity of outputs to the parameters. Our results suggest the necessity of and possible ways for parameter inversion/calibration using available measurements of latent/sensible heat fluxes to obtain the optimal parameter set for CLM4. This study also provided guidance on reduction of parameter set dimensionality and parameter calibration framework design for CLM4 and other land surface models under different hydrologic and climatic regimes.« less
  • Abstract not provided.