Uncertainty propagation in a cascade modelling approach to flood mapping

Abstract

Abstract. The purpose of this investigation is to study the propagation of meteorological uncertainty within a cascade modelling approach to flood mapping. The methodology is comprised of a Numerical Weather Prediction Model (NWP), a distributed rainfall–runoff model and a standard 2-D hydrodynamic model. The cascade of models is used to reproduce an extreme flood event that took place in the Southeast of Mexico, during November 2009. The event is selected as high quality field data (e.g. rain gauges; discharge) and satellite imagery are available. Uncertainty in the meteorological model (Weather Research and Forecasting model) is evaluated through the use of a multi-physics ensemble technique, which considers twelve parameterization schemes to determine a given precipitation. The resulting precipitation fields are used as input in a distributed hydrological model, enabling the determination of different hydrographs associated to this event. Lastly, by means of a standard 2-D hydrodynamic model, hydrographs are used as forcing conditions to study the propagation of the meteorological uncertainty to an estimated flooded area. Results show the utility of the selected modelling approach to investigate error propagation within a cascade of models. Moreover, the error associated to the determination of the runoff, is showed to be lower than that obtained in the precipitation estimation suggesting that uncertainty do not necessarily increase within a model cascade.