Automatic calibration of a whole-of-basin water accounting model using a comprehensive learning particle swarm optimiser

Abstract We present a two-step framework for calibrating complex, many-parameter hydrological models at basin-scale. The framework first calibrates parameters for each catchment/sub-basin sequentially and then fine-tunes parameters as needed. We implemented a comprehensive learning particle swarm optimiser (CLPSO) as the calibrator and applied the two-step CLPSO tool in calibrating parameters of a water accounting model for the Murray-Darling Basin, Australia. The visual and quantitative results indicated that our tool produced satisfactory calibration and prediction outcomes for the model’s intended purpose. The comparison experiments demonstrated that the calibration framework and the CLPSO were competent in calibrating large-scale hydrological models. This framework can guarantee spatial coherence, balance objective trade-offs among all catchments, and calibrate many parameters at a low computational cost. By providing better parameter estimates in complex whole-of-basin hydrological models, our calibration tool has the potential to increase the development and application of these models, and thereby improve the management of large river basins.