Uncertainty-based automatic calibration of HEC-HMS model using sequential uncertainty fitting approach

• Published in: Journal of hydroinformatics Vol. 14; no. 2; pp. 286 - 309
• Main Authors: Mousavi, S. Jamshid, Abbaspour, K. C., Kamali, B., Amini, M., Yang, H.
• Format: Journal Article
• Language: English
• Published: London IWA Publishing 01.01.2012
• Subjects: Basins; Calibration; Computer simulation; Hydrologic models; Hydrology; Identification; Modelling; Optimization; Parameter estimation; Parameter identification; Parameters; Routing; Uncertainty; Uncertainty analysis; Iran
• ISSN: 1464-7141; 1465-1734
• DOI: 10.2166/hydro.2011.071

This study presents the application of an uncertainty-based technique for automatic calibration of the well-known Hydrologic Engineering Center-Hydrologic Modelling System (HEC-HMS) model. Sequential uncertainty fitting (SUFI2) approach has been used in calibration of the HEC-HMS model built for Tamar basin located in north of Iran. The basin was divided into seven sub-basins and three routing reaches with 24 parameters to be estimated. From the four events, three were used for calibration and one for verification. Each event was initially calibrated separately. As there was no unique parameter set identified, all events were then calibrated jointly. Based on the scenarios of separately and jointly calibrated events, different candidate parameter sets were inputted to the model verification stage where recalibration of initial abstraction parameters commenced. Some of the candidate parameter sets with no physically meaningful parameter values were withdrawn after recalibration. Then new ranges of parameters were identified based on minimum and maximum values of the remaining parameter sets. The new parameter ranges were used in an uncertainty analysis using SUFI2 technique resulting in much narrower parameter intervals that can simulate both verification and calibration events satisfactorily in a probabilistic sense. Results show that the SUFI2 technique linked to HEC-HMS as a simulation–optimization model can provide a basis for performing uncertainty-based automatic calibration of event-based hydrologic models.