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

Many calibrated hydrological models are inconsistent with the behavioral functions of catchments and do not fully represent catchments’ underlying processes despite their seemingly adequate performance, if measured by traditional statistical error metrics. Using such metrics for calibration is hindered only short-term data available. This study investigated influence varying lengths streamflow observation records on model evaluated usefulness a signature-based approach in conceptual rainfall-runoff calibration. Scenarios continuous short-period observations were used to emulate poorly gauged catchments. Two approaches employed calibrate HBV Brue catchment UK. The first single-objective optimization maximize Nash–Sutcliffe efficiency (NSE) as goodness-of-fit measure. second involved multiobjective based maximizing scores 11 signature indices, well NSE. In addition, diagnostic evaluation was evaluate both performance consistency. results showed that successfully using datasets lower limit approximately four months (10% FRD model). One formulation yielded highest consistency among all parameterization algorithms. enabled selection consistent reflecting behavior allowed an accurate detection deficiencies other models. It can be argued building even data-poor situations.