Quasi-Monte Carlo integration on the grid for sensitivity studies

Atanassov, Emanouil; Karaivanova, Aneta; Gurov, Todor; Ivanovska, Sofiya; Durchova, Mariya K.; Dimitrov, Dimitar

doi:10.1007/s12145-010-0069-9

Cited by 7 publications

(1 citation statement)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…QMC requires a choice regarding input of a low‐discrepancy sequence (the discrepancy of a sequence is low if the proportion of points in the sequence falling into an arbitrary set is close to the proportion measure of the set). We thereby select the Sobol sequences [ Sobol , 1967], which are widely acknowledged to perform well for problems with greater than six dimensions, and avoid degradation effects observed in many other low‐discrepancy sequences [ Atanassov et al , 2010; Sobol and Shukhman , 2007; Wang and Sloan , 2008].…”

Section: Methodology and Site Informationmentioning

confidence: 99%

Sensitivity of surface flux simulations to hydrologic parameters based on an uncertainty quantification framework applied to the Community Land Model

et al. 2012

View full text Add to dashboard Cite

[1] 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 perturbing 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.Citation: Hou, Z., M. Huang, L. R. Leung, G. Lin, and D. M. Ricciuto (2012), Sensitivity of surface flux simulations to hydrologic parameters based on an uncertainty quantification framework applied to the Community Land Model, J. Geophys.

show abstract