Toward improved identifiability of hydrologic model parameters: The information content of experimental data

Jasper A. Vrugt, Willem Bouten, Hoshin V. Gupta, Soroosh Sorooshian

Research output: Contribution to journalArticle

119 Scopus citations

Abstract

We have developed a sequential optimization methodology, entitled the parameter identification method based on the localization of information (PIMLI) that increases information retrieval from the data by inferring the location and type of measurements that are most informative for the model parameters. The PIMLI approach merges the strengths of the generalized sensitivity analysis (GSA) method [Spear and Hornberger, 1980], the Bayesian recursive estimation (BARE) algorithm [Thiemann et al., 2001], and the Metropolis algorithm [Metropolis et al., 1953]. Three case studies with increasing complexity are used to illustrate the usefulness and applicability of the PIMLI methodology. The first two case studies consider the identification of soil hydraulic parameters using soil water retention data and a transient multistep outflow experiment (MSO), whereas the third study involves the calibration of a conceptual rainfall-runoff model.

Original languageEnglish (US)
Pages (from-to)48-1-48-13
JournalWater Resources Research
Volume38
Issue number12
DOIs
StatePublished - Dec 1 2002

Keywords

  • Identification most informative measurements
  • Parameter estimation
  • Rainfall-runoff modeling
  • Soil hydraulic properties

ASJC Scopus subject areas

  • Water Science and Technology

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