Annual Runoff Forecasting through Bayesian Causality

This contribution is focused on forecasting ability of Bayesian causality (BC) on annual runoff series. For that, the time series was synthetized through a Bayesian net, in which the probability propagation over the time was performed. The BC analytical ability identified the hidden logic structure of the hydrological records that describes its overall behavior. This allowed us to quantify the runoff, through a novel dependence matrix, through two fractions, one conditional on time (temporally conditioned runoff) and one not (temporally nonconditioned runoff). This conditionality allowed the development of two predictive models for each fraction, analyzing their reliability under a double probabilistic and metrological approach.