Quantifying predictive uncertainty of streamflow forecasts based on a Bayesian joint probability model

Abstract

Uncertainty is inherent in streamflow forecasts and is an important determinant of the utility of forecasts for water resources management. However, predictions by deterministic models provide only single values without uncertainty attached. This study presents a method for using a Bayesian joint probability (BJP) model to post-process deterministic streamflow forecasts by quantifying predictive uncertainty. The BJP model is comprised of a log-sin. h transformation that normalises hydrological data, and a bi-variate Gaussian distribution that characterises the dependence relationship. The parameters of the transformation and the distribution are estimated through Bayesian inference with a Mo..