The Quest for an Improved Dialog Between Modeler and Experimentalist

Multi -criteria calibration of runoff models using additional data, such as groundwater levels or soil moisture, has been proposed as a way to constrain parameter values and to ensure the realistic simulation of internal varables. Nevertheless, in many cases the availabilty of such ' hard data' is limited. We argue that experimentalists working in a catchment often have much more knowledge of catchment behavior than is currently used for model calibration and testing. Whle potentially highly useful, this information is difficult to use directly as exact numbers in the calibration process. We present a framework whereby these 'soft' data from the experimentalist are made useful though fuzzy measures of model-simulation and parameter-value acceptabilty. The use of soft data is an approach to formalize the exchange of information and calibration measures between experimentalst and modeler. Ths dialog may also greatly augment the traditional and few 'hard' data measures available. We ilustrate the value of 'soft data' with the application of a thee-box conceptual model for the Maimai catchment in New Zealand. The model was calibrated against hard data (runoff and groundwater-levels) as well as a number of criteria derived from the soft data (e. , percent new water, reservoir volume). Whle very good fits were obtained when calibrating against runoff only (model effciency = 0.93), parameter sets obtained in this way showed, in general, poor internal consistency. Inclusion of soft-data criteria in the model calibration process resulted in lower model-effciency values (around 0.84 when including all criteria) but led to better overall performance, as interpreted by the experimentalist' s view of catchment runoff dynamcs.

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