The expanding use and increased complexity of hydrological runoff models has given rise to a concern about overparameterization and risks for compensating errors. One proposed way out is the calibration and validation against additional observations, such as snow, soil moisture, groundwater or water quality. A general problem, however, when calibrating the model against more than one variable is the strategy for parameter estimation. The most straightforward method is to calibrate the model components sequentially. Recent results show that in this way the model may be locked up in a parameter setting, which is good enough for one variable but excludes proper simulation of other variables. This is particularly the case for water quality modelling, where a small compromise in terms of runoff simulation may lead to dramatically better simulations of water quality. This calls for an integrated model calibration procedure with a criterion that integrates more aspects on model performance than just river runoff. The use of multi-variable parameter estimation and internal control of the HBV hydrological model is discussed and highlighted by two case studies. The first example is from a forested basin in northern Sweden and the second one is from an agricultural basin in the south of the country. A new calibration strategy, which is integrated rather than sequential, is proposed and tested. It is concluded that comparison of model results with more measurements than only runoff can lead to increased confidence in the physical relevance of the model, and that the new calibration strategy can be useful for further model development. Copyright (C) 2002 John Wiley Sons, Ltd.
