Dynamic real-time prediction of flood inundation probabilities
نویسندگان
چکیده
The Bayesian Generalised Likelihood Uncertainty Estimation (GLUE) methodology, previously used in rainfall-runoff modelling, is applied to the distributed problem of predicting the space and time varying probabilities of inundation of all points on a flood plain. Probability estimates are based on conditioning predictions of Monte Carlo realizations of a distributed quasi-twodimensional flood routing model using known levels at sites along the reach. The methodology can be applied in the flood forecasting context for which the iV-step ahead inundation probability estimates can be updated in real time using telemetered information on water levels. It is also shown that it is possible to condition the Nstep ahead forecasts in real time using the (uncertain) on-line predictions of the downstream water levels at the end of the reach obtained from an adaptive transfer function model calibrated on reach scale inflow and outflow data. Prévision dynamique de la probabilité de l'inondation en temps réel Résumé La méthodologie bayésienne GLUE (Generalised Likelihood Uncertainty Estimation), précédemment utilisée dans la modélisation pluie-débit, est ici appliquée à la prévision distribuée, dans le temps et l'espace, des probabilités d'inondation en chaque point d'une plaine inondable. L'estimation distributee de la probabilité est basée sur un conditionnement des simulations de type Monte Carlo d'un modèle de propagation bi-dimensionnel. Le conditionnement utilise les mesures disponibles du niveau de l'eau à quelques stations situées le long du tronçon de rivière. La méthodologie peut être utilisée pour la prévision en temps réal, à échéance de iVpas de temps. Dans ce cas, les estimations de probabilité peuvent être révisées en utilisant des télémesures. On montre aussi qu'il est possible de conditionner les prévisions distribuées en temps réel en utilisant les prévisions (incertaines) du niveau de l'eau à la sortie du tronçon qui sont obtenues à l'aide d'un modèle adaptif de type fonction de transfert, initialement calé sur les données d'entrée et de sortie du tronçon de rivière.
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