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The future of distributed models: model calibration and uncertainty prediction.

Research output: Contribution to journalJournal article

Published

Journal publication date1992
JournalHydrological Processes
Journal number3
Volume6
Number of pages20
Pages279-298
Original languageEnglish

Abstract

This paper describes a methodology for calibration and uncertainty estimation of distributed models based on generalized likelihood measures. The GLUE procedure works with multiple sets of parameter values and allows that, within the limitations of a given model structure and errors in boundary conditions and field observations, different sets of values may be equally likely as simulators of a catchment. Procedures for incorporating different types of observations into the calibration; Bayesian updating of likelihood values and evaluating the value of additional observations to the calibration process are described. The procedure is computationally intensive but has been implemented on a local parallel processing computer.