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

Research output: Contribution to journalJournal article

<mark>Journal publication date</mark>1992
<mark>Journal</mark>Hydrological Processes
Issue number3
Number of pages20
Pages (from-to)279-298
<mark>Original language</mark>English


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.