Ensemble Prediction of Flood Maps Under Uncertain Conditions

Abstract:

Hydro-meteorological hazards can have cascading effects and far-reaching implications on water security, with socio-economic and environmental consequences. Worldwide the magnitude of recent floods highlight the necessity to generate a better understanding on their causes and associated risk. An improved flood risk strategy should incorporate the communication of uncertain research results to decision-makers. Therefore, it is of paramount importance to generate a robust framework that enables its quantification. The purpose of this study is to investigate 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 2D hydrodynamic model. The cascade of models is used to reproduce an extreme flood event that took place in Southern Mexico, during September 2013. The event is selected as high quality field data (e.g. LiDAR; rain gauges) 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 parameterisation 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 2D hydrodynamic model, resulting 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.