PROBABILISTIC FORECASTS USING BAYESIAN NETWORKS CALIBRATED WITH DETERMINISTIC RAINFALL-RUNOFF MODELS

Abstract

A flood forecasting approach based on the combination of Bayesian networks and physically-based deterministic models is presented. Bayesian networks are data-driven models where the joint probability distribution of a set of related variables is inferred from observations. Their application to flood forecasting is limited because basins with long data sets for calibration or validation of this type of models are relatively scarce. To solve this problem, the data set for the calibration and validation is obtained through Monte-Carlo simulation, combining a stochastic rainfall generator and a deterministic rainfall-runoff model. The approach has been tested successfully in the Spanish Mediterranean region.