Print Email Facebook Twitter Impact of dataset size on the signature-based calibration of a hydrological model Title Impact of dataset size on the signature-based calibration of a hydrological model Author Mohammed, Safa A. (United Arab Emirates University; IHE Delft Institute for Water Education) Solomatine, D.P. (TU Delft Water Resources; IHE Delft Institute for Water Education; Russian Academy of Sciences) Hrachowitz, M. (TU Delft Water Resources) Hamouda, Mohamed A. (United Arab Emirates University) Date 2021 Abstract Many calibrated hydrological models are inconsistent with the behavioral functions of catchments and do not fully represent the catchments’ underlying processes despite their seemingly adequate performance, if measured by traditional statistical error metrics. Using such metrics for calibration is hindered if only short-term data are available. This study investigated the influence of varying lengths of streamflow observation records on model calibration and evaluated the usefulness of a signature-based calibration approach in conceptual rainfall-runoff model calibration. Scenarios of continuous short-period observations were used to emulate poorly gauged catchments. Two approaches were employed to calibrate the HBV model for the Brue catchment in the UK. The first approach used single-objective optimization to maximize Nash-Sutcliffe efficiency (NSE) as a goodness-of-fit measure. The second approach involved multiobjective optimization based on maximizing the scores of 11 signature indices, as well as maximizing NSE. In addition, a diagnostic model evaluation approach was used to evaluate both model performance and behavioral consistency. The results showed that the HBV model was successfully calibrated using short-term datasets with a lower limit of approximately four months of data (10% FRD model). One formulation of the multiobjective signature-based optimization approach yielded the highest performance and hydrological consistency among all parameterization algorithms. The diagnostic model evaluation enabled the selection of consistent models reflecting catchment behavior and allowed an accurate detection of deficiencies in other models. It can be argued that signature-based calibration can be employed for building adequate models even in data-poor situations. Subject Brue catchmentDataset sizeDiagnostic evaluation approachHBV modelHydrological signaturesLumped model calibrationMultiobjective optimizationPoorly gauged catchments To reference this document use: http://resolver.tudelft.nl/uuid:bc3fb5f3-711b-4802-94a1-a395f93335e4 DOI https://doi.org/10.3390/w13070970 ISSN 2073-4441 Source Water, 13 (7), 1-25 Part of collection Institutional Repository Document type journal article Rights © 2021 Safa A. Mohammed, D.P. Solomatine, M. Hrachowitz, Mohamed A. Hamouda Files PDF water_13_00970_v4.pdf 7.5 MB Close viewer /islandora/object/uuid:bc3fb5f3-711b-4802-94a1-a395f93335e4/datastream/OBJ/view