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Open AccessArticle

Impact of Dataset Size on the Signature-Based Calibration of a Hydrological Model

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Department of Civil and Environmental Engineering, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
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IHE-Delft, Institute for Water Education, P.O. Box 3015, 2601 DA Delft, The Netherlands
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Water Resources Section, Faculty of Civil Engineering and Applied Geosciences, Delft University of Technology, P.O. Box 5048, 2628 CN Delft, The Netherlands
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Water Problems Institute of RAS (Russian Academy of Sciences), 119991 Moscow, Russia
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National Water and Energy Center, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
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Author to whom correspondence should be addressed.
Academic Editor: Xing Fang
Water 2021, 13(7), 970; https://doi.org/10.3390/w13070970
Received: 8 March 2021 / Revised: 22 March 2021 / Accepted: 29 March 2021 / Published: 31 March 2021
(This article belongs to the Section Hydrology and Hydrogeology)
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. View Full-Text
Keywords: HBV model; hydrological signatures; multiobjective optimization; diagnostic evaluation approach; dataset size; lumped model calibration; poorly gauged catchments; Brue catchment HBV model; hydrological signatures; multiobjective optimization; diagnostic evaluation approach; dataset size; lumped model calibration; poorly gauged catchments; Brue catchment
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MDPI and ACS Style

Mohammed, S.A.; Solomatine, D.P.; Hrachowitz, M.; Hamouda, M.A. Impact of Dataset Size on the Signature-Based Calibration of a Hydrological Model. Water 2021, 13, 970. https://doi.org/10.3390/w13070970

AMA Style

Mohammed SA, Solomatine DP, Hrachowitz M, Hamouda MA. Impact of Dataset Size on the Signature-Based Calibration of a Hydrological Model. Water. 2021; 13(7):970. https://doi.org/10.3390/w13070970

Chicago/Turabian Style

Mohammed, Safa A.; Solomatine, Dimitri P.; Hrachowitz, Markus; Hamouda, Mohamed A. 2021. "Impact of Dataset Size on the Signature-Based Calibration of a Hydrological Model" Water 13, no. 7: 970. https://doi.org/10.3390/w13070970

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