Abstract. Hydrological analysis utilizing a hydrological model requires a parameter calibration process, which is largely influenced by the length of calibration data period and prevailing hydrological conditions. This study aimed to quantify these uncertainties in future runoff projection and hydrological drought based on future climate data and the calibration data of the hydrological model. Future climate data were sourced from three Shared Socioeconomic Pathway (SSP) scenarios (SSP2-4.5, SSP3-7.0, and SSP5-8.5) of 20 general circulation models (GCMs). The Soil and Water Assessment Tool (SWAT) was employed as the hydrological model, and hydrological conditions were determined using the Streamflow Drought Index (SDI), with calibration data lengths ranging from 1 to 20 years considered. Subsequently, the uncertainty was quantified using Analysis of Variance (ANOVA). After calibrating SWAT parameters, the validation performance was found to be influenced by the hydrological conditions of the calibration data. Hydrological model parameters calibrated using a dry period simulated runoff with 11.4 % higher performance in dry conditions and 6.1 % higher performance in normal conditions, while hydrological model parameters calibrated using a wet period simulated runoff with 5.1 % higher performance in wet conditions. While the ANOVA results confirmed that GCMs are the dominant source of total uncertainty, the uncertainty contribution from the hydrological model calibration in estimating future runoff was analyzed to be 3.9 %–9.8 %, particularly significant in the low runoff period. The uncertainty contribution in future hydrological drought analysis resulting from the calibration of hydrological model parameters was analyzed to be 2.7 % on average, which is lower than that of future runoff projection.
Kim et al. (Thu,) studied this question.