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Toward improved identifiability of hydrologic model parameters: The information content of experimental data
Author(s) -
Vrugt Jasper A.,
Bouten Willem,
Gupta Hoshin V.,
Sorooshian Soroosh
Publication year - 2002
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/2001wr001118
Subject(s) - identifiability , identification (biology) , sensitivity (control systems) , calibration , computer science , hydrological modelling , bayesian probability , data mining , surface runoff , information criteria , estimation theory , algorithm , environmental science , mathematics , statistics , machine learning , artificial intelligence , engineering , geology , model selection , ecology , botany , climatology , electronic engineering , biology
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.