Stochastic Residual-Error Analysis for Estimating Hydrologic Model Predictive Uncertainty

Abstract

A hybrid time series-nonparametric sampling approach, referred to herein as semiparametric, is presented for the estimation of model predictive uncertainty. The methodology is a two-step procedure whereby a distributed hydrologic model is first calibrated, then followed by brute force application of time series analysis with nonparametric random generation to synthesize serially correlated model residual errors. The methodology is applied to estimate uncertainties in simulated streamflows and related flow attributes upstream from the mouth of a rapidly urbanizing watershed. Two procedures for the estimation of model output uncertainty are compared: the Gaussian-based