A PCM-based stochastic hydrological model for uncertainty quantification in watershed systems

摘要: In this study, an uncertainty quantification framework is proposed for hydrologic models based on probabilistic collocation method (PCM). The PCM first uses polynomial chaos expansion (PCE) to approximate the hydrological outputs in terms of a set standard Gaussian random variables, and then estimates unknown coefficients PCE through method. conceptual model, Hymod, used demonstrate applicability quantifying uncertainties predictions. Two parameters Hymod are considered as uniformly distributed certain intervals. Two-dimensional 2-order two-dimensional 3-order PCEs applied quantify Hymod’s results indicate that, both 2- can well reflect streamflow means variances consistent with those obtained by Monte Carlo (MC) simulation However, detailed distributions at selected periods, histograms more accurate than generated PCE, when compared MC