The future of distributed models: Model calibration and uncertainty prediction

Keith Beven; Andrew Binley

doi:10.1002/hyp.3360060305

journal article Jul 01, 1992

The future of distributed models: Model calibration and uncertainty prediction

Keith Beven

Andrew Binley

Hydrological Processes Vol. 6 No. 3 pp. 279-298 · Wiley

View at Publisher Save 10.1002/hyp.3360060305

Abstract

AbstractThis 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. the methodology is illustrated by an application of the Institute of Hydrology Distributed Model to data from the Gwy experimental catchment at Plynlimon, mid‐Wales.

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Details

Published: Jul 01, 1992
Vol/Issue: 6(3)
Pages: 279-298
License: View

Authors

Cite This Article

Keith Beven, Andrew Binley (1992). The future of distributed models: Model calibration and uncertainty prediction. Hydrological Processes, 6(3), 279-298. https://doi.org/10.1002/hyp.3360060305

The future of distributed models: Model calibration and uncertainty prediction

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