A probabilistic model to support reservoir operation decisions during flash floods

Abstract

A probabilistic model to assist decision makers in selecting the best reservoir operation strategy during flash floods is presented, based on Bayesian networks calibrated with the results of a rainfall—runoff model coupled with a reservoir operation model. During real-time operation, rainfall recorded in the basin is used to make probabilistic predictions of inflow discharge into the reservoir with a rainfall—runoff Bayesian network. The reservoir Bayesian network takes these probabilistic discharge values as input data and gives the probabilistic outflow discharge and water level at future time steps for the different operation strategies considered. From these probabilistic results, the best strategy for the operation of the floodgate can be selected in terms of the probability of maximum discharge downstream of the reservoir and risk of damage to the dam. Two data sets of 4000 inflow hydrographs were obtained through Monte Carlo simulation with a rainfall—runoff model and a reservoir management model. The Bayesian networks learned from the first data set and were validated with the second one. The methodology was tested successfully for one reservoir located in the south of Spain with observed data recorded during a recent flood event, checking its usefulness as a decision-making tool in real-time reservoir management.