Developing an integrated PCE-ANOVA-RF method for uncertainty quantification of hydrological model – The Amu Darya River Basin in Central Asia

Uncertainty quantification of hydrological model parameters plays an important role in the streamflow prediction of river basins. It is still a challenge to evaluate the effect of parameter uncertainty on hydrological models in association with moderate computation and high accuracy. In this study, a PCE-ANOVA-RF (abbreviated as PAR) method is developed to analyze the individual and interactive effects of multiple uncertain parameters in the Soil and Water Assessment Tool (SWAT) model as well as generate probabilistic forecasts of daily streamflow. The proposed PAR method can not only unveil the effect of parameter uncertainty and save computation time without reducing the simulation accuracy, but also expand PCE’s ability to predict future streamflow processes. The Amu Darya River Basin in Central Asia is introduced to verify the feasibility and applicability of PAR. The results disclose that (i) compared with the Sequence Uncertainty Fitting ver-2 (SUFI-2) algorithm, the simulation accuracy of PAR is acceptable (e.g., NSE = 0.85 in calibration period and 0.81 in validation period); (ii) in terms of variance analysis and streamflow simulation, PAR is more efficient and can largely reduce the number of model runs (about 88%); (iii) the parameters related to snow melting process have significant effects on both average flow and peak flow; besides, the interaction among these parameters are more obvious than that of other sensitive parameters.