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Flood forecasting describes the rainfall-runoff transformation using simplified representations These representations are based on either empirical descriptions or on equations of classical mechanics of the involved physical processes The performances of the existing flood predictions are affected by several sources of uncertainties coming not only from the approximations involved but also from imperfect knowledge of input data initial conditions of the river basin and model parameters Quantifying these uncertainties enables the decision maker to better interpret the predictions and constitute a valuable decision-making tool for flood risk management Uncertainty analysis on existing rainfall-runoff models are often performed using Monte Carlo MC - simulations The implementation of this type of techniques requires a large number of simulations and consequently a potentially important calculation time Therefore quantifying uncertainties of real-time hydrological models is challenging In this project we develop a methodology for flood prediction based on Bayesian networks BNs BNs are directed acyclic graphs where the nodes correspond to the variables characterizing the modelled system and the arcs represent the probabilistic dependencies between these variables The presented methodology suggests to build the RBs from the main hydrological factors controlling the flood generation using both the available observations of the system response and the deterministic equations describing the processes involved It is thus designed to take into account the time variability of different involved variables The conditional probability tables parameters can be specified using observed data existing hydrological models or expert opinion Thanks to their inference algorithms BN are able to rapidly propagate through the graph different sources of uncertainty in order to estimate their effect on the model output e g riverflow Several case studies are tested The first case study is the Salat river basin located in the south-west of France where a BN is used to simulate the discharge at a given station from the streamflow observations at 3 hydrometric stations located upstream The model showed good performances estimating the discharge at the outlet Used in a reverse way the model showed also satisfactory results when characterising the discharges at an upstream station by propagating back discharge observations of some downstream stations The second case study is the Sagelva basin located in Norway where a BN is used to simulate the accumulation of snow water equivalent SWE given available weather data observations The performances of the model are affected by the learning dataset used to train the BN parameters In the absence of relevant observation data for learning a methodology for learning the BN-parameters from deterministic models is proposed and tested The resulted BN can be used to perform uncertainty analysis without any MC-simulations to be performed in real-time From these case studies it appears that BNs are a relevant decisionsupport tool for flood risk management

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Stochastic modelling of flood phenomena based on the combination of mechanist and systemic approaches

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