Shafii, MahyarTolson, Bryan A.2017-07-312017-07-312015-05-31http://dx.doi.org/10.1002/2014WR016520http://hdl.handle.net/10012/12101© American Geophysical Union: Shafii, M., & Tolson, B. A. (2015). Optimizing hydrological consistency by incorporating hydrological signatures into model calibration objectives. Water Resources Research, 51(5), 3796–3814. https://doi.org/10.1002/2014WR016520The simulated outcome of a calibrated hydrologic model should be hydrologically consistent with the measured response data. Hydrologic modelers typically calibrate models to optimize residual-based goodness-of-fit measures, e.g., the Nash-Sutcliffe efficiency measure, and then evaluate the obtained results with respect to hydrological signatures, e.g., the flow duration curve indices. The literature indicates that the consideration of a large number of hydrologic signatures has not been addressed in a full multiobjective optimization context. This research develops a model calibration methodology to achieve hydrological consistency using goodness-of-fit measures, many hydrological signatures, as well as a level of acceptability for each signature. The proposed framework relies on a scoring method that transforms any hydrological signature to a calibration objective. These scores are used to develop the hydrological consistency metric, which is maximized to obtain hydrologically consistent parameter sets during calibration. This consistency metric is implemented in different signature-based calibration formulations that adapt the sampling according to hydrologic signature values. These formulations are compared with the traditional formulations found in the literature for seven case studies. The results reveal that Pareto dominance-based multiobjective optimization yields the highest level of consistency among all formulations. Furthermore, it is found that the choice of optimization algorithms does not affect the findings of this research.EnSelf-Organizing MapsChain Monte-CarloMultiobjective CalibrationEvolutionary OptimizationCatchment ClassificationParameter-EstimationGlobal OptimizationUngauged BasinsNash ValuesUncertaintyArticle