A fractional factorial probabilistic collocation method for uncertainty propagation of hydrologic model parameters in a reduced dimensional space

Shuo Wang, G. H. Huang, W. Huang, Y. R. Fan, Z. Li

Research output: Journal article publicationJournal articleAcademic researchpeer-review

21 Citations (Scopus)


In this study, a fractional factorial probabilistic collocation method is proposed to reveal statistical significance of hydrologic model parameters and their multi-level interactions affecting model outputs, facilitating uncertainty propagation in a reduced dimensional space. The proposed methodology is applied to the Xiangxi River watershed in China to demonstrate its validity and applicability, as well as its capability of revealing complex and dynamic parameter interactions. A set of reduced polynomial chaos expansions (PCEs) only with statistically significant terms can be obtained based on the results of factorial analysis of variance (ANOVA), achieving a reduction of uncertainty in hydrologic predictions. The predictive performance of reduced PCEs is verified by comparing against standard PCEs and the Monte Carlo with Latin hypercube sampling (MC-LHS) method in terms of reliability, sharpness, and Nash-Sutcliffe efficiency (NSE). Results reveal that the reduced PCEs are able to capture hydrologic behaviors of the Xiangxi River watershed, and they are efficient functional representations for propagating uncertainties in hydrologic predictions.
Original languageEnglish
Pages (from-to)1129-1146
Number of pages18
JournalJournal of Hydrology
Publication statusPublished - 1 Oct 2015
Externally publishedYes


  • Hydrologic model parameters
  • Interaction
  • Multi-level factorial design
  • Polynomial chaos expansion
  • Probabilistic collocation
  • Uncertainty propagation

ASJC Scopus subject areas

  • Water Science and Technology

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