Abstract. Several contributions to the hydrological literature have brought into question the continued usefulness of the classical paradigm for hydrologic model calibration. With the growing popularity of sophisticated "physically based" watershed models (e.g., landsurface hydrology and hydrochemical models) the complexity of the calibration problem has been multiplied many fold. We disagree with the seemingly widespread conviction that the model calibration problem will simply disappear with the availability of more and better field measurements. This paper suggests that the emergence of a new and more powerful model calibration paradigm must include recognition of the inherent multiobjective nature of the problem and must explicitly recognize the role of model error. The results of our preliminary studies are presented. Through an illustrative case study we show that the multiobjective approach is not only practical and relatively simple to implement but can also provide useful information about the limitations of a model.

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The development of automated (computer-based) calibration methods has focused mainly on the selection of a singleobjective 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 multiobjective 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 multipleobjective 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.

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This paper presents a new approach to streamflow forecasting, based on a Markov chain model for estimating the probabilities that the one‐step ahead streamflow forecast will be within specified flow ranges. With the new approach, flood forecasting is possible by focusing on a preselected range of streamflows. In addition, the approach introduces a multiobjective (two‐criterion) function for the assessment of model performance. The two criteria are (1) the probability of issuing a false alarm and (2) the probability of failing to predict a flood event. The goal is to minimize both criteria simultaneously. Three versions of the model are presented: a first‐order Markov chain model, a second‐order Markov chain model, and a first‐order Markov chain with rainfall as exogenous input model. These models compared favorably to time series models, using data from two watersheds (a semiarid watershed and a temperate watershed), when evaluated in terms of the multiobjective performance criterion.