Sensitivity and uncertainty-based evaluation and simulation of MIKE SHE model in Guishui River Basin, Beijing, China

Abstract

In the process of building a hydrological model, some basin feature parameters are expressed inaccurately. It is an important way to construct models and estimate the uncertainty parameters for evaluating the uncertainty of the overall output. In this paper, an uncertainty-based study was calibrated and evaluated the comprehensive distributed model MIKE SHE to hydrological data in the Guishui River Basin, Beijing of China. The generalised likelihood uncertainty estimation (GLUE) method was used to quantify uncertainties originating from the use of discharge observations and the presence of equifinal solutions. Monte Carlo sampling is randomly generated to 10,000 parameter sets during GLUE calibration. MIKE SHE parameter sets are identified and 5% and 95% uncertainty bounds for monthly streamflow are calculated. The behavioural values of nine individual parameters for MIKE SHE were explored against the likelihood measure values. The results show that more than 50% observations in calibration period fell within the corresponding uncertainty bounds, suggesting a similar level of model performance. The simulation results are corresponded better with the measured flow, but still need to be improved for higher accuracy. There are some relative sensitive and insensitive parameters in the result of uncertainty analysis.