Abstract
The development of automated (computer-based) calibration methods has focused mainly on the selection of a single-objective measure of the distance between the model-simulated output and the data and the selection of an automatic optimization algorithm to search for the parameter values which minimize that distance. However, practical experience with model calibration suggests that no single-objective function is adequate to measure the ways in which the model fails to match the important characteristics of the observed data. Given that some of the latest hydrologic models simulate several of the watershed output fluxes (e.g. water, energy, chemical constituents, etc.), there is a need for effective and efficient multi-objective calibration procedures capable of exploiting all of the useful information about the physical system contained in the measurement data time series. The MOCOM-UA algorithm, an effective and efficient methodology for solving the multiple-objective global optimization problem, is presented in this paper. The method is an extension of the successful SCE-UA single-objective global optimization algorithm. The features and capabilities of MOCOM-UA are illustrated by means of a simple hydrologic model calibration study.
Highlights
Introduction and scopeTo calibrate a hydrologic model, the hydrologist must specify values for its "parameters" in such a way that the model's behavior closely matches that of the real system it represents
Practical experience with model calibration suggests that any single-objective function, no matter how carefully chosen, may not adequately measure the ways in which the model fails to match the important characteristics of the observed data
Many of the latest hydrologic models simulate several of the watershed output fluxes for which measurement data are available, and all these data must be properly utilized to ensure proper model calibration (e.g. Beven and Kirkby, 1979; De Grosbois et al, 1988; Kuczera, 1982 Kuczera, 1983; Woolhiser et al, 1990; Yan and Haan, 1991a, b)
Summary
To calibrate a hydrologic model, the hydrologist must specify values for its "parameters" in such a way that the model's behavior closely matches that of the real system it represents. Watershed hydrochemical models may simulate several physical and chemical properties of the hydrograph, in addition to the rate of flow (Wolford and Bales, 1996), while land-surface hydrology models designed for coupling with General Circulation Models typically simulate several energy and water fluxes and state variables including latent heat, sensible heat, temperature, runoff, and soil moisture at various depths (Dickinson et al, 1993). Because many of these models employ distributed representations of the watershed, the state variables and output fluxes may be simulated and measured at numerous locations. An effective and efficient methodology for solving the multiple-objective global optimization problem is presented, and its features and capabilities are demonstrated by means of a simple example
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