Multisite probabilistic forecasting of seasonal flows for streams with zero value occurrences

Abstract

Skillful and reliable forecasts of seasonal streamflows are highly valuable to water management. In a previous study, we developed a Bayesian joint probability (BJP) modeling approach for seasonal forecasting of streamflows at multiple sites. The approach has been adopted by the Australian Bureau of Meteorology for seasonal streamflow forecasting in Australia. This study extends the applicability of the BJP modeling approach to streams with zero flow occurrences. The aim is to produce forecasts for these streams in the form of probabilities of zero seasonal flows and probability distributions of above zero seasonal flows. We turn a difficult mathematical problem of mixed discrete‐continuous multivariate probability distribution modeling into one of continuous multivariate probability distribution modeling by treating zero flow occurrences as censored data. This paper presents the mathematical formulation and implementation of the modeling approach, methods for forecast verification, and results of a test application to the Burdekin river catchment in northern Queensland, Australia.