Development of a Sparse Polynomial Chaos Expansions Method for Parameter Uncertainty Analysis

Abstract

Abstract Incorporating uncertainty assessment into hydrological simulation is of vital significance for providing valuable information for conserving and restoring the ecology environment in arid and semiarid regions. In this study, a sparse polynomial chaos expansions method was developed to quantify hydrological model parameter uncertainties on model performance in Kaidu river basin, China. A four dimension two order polynomial chaos expansions model was built and the effect of four parameters were quantified based on the coefficients of the polynomial chaos expansions model. Results indicated that precipitation in summer has more significant influence on model output than that in other seasons. High Sobol sensitivity indices values (0.22 in spring, 0.17 in summer, 0.21 in autumn and 0.29) for the interaction of precipitation and maximum capacity for fast store demonstrate that they are the major factors affecting runoff generation. These results can help reveal the flow processes and provide valuable information for water resources management.