THE UNCERTAINTY CASCADE IN FLOOD FORECASTING

A methodology for propagating and constraining the uncertainty inherent in real-time flood forecasting is presented and demonstrated on an application to the River Severn, UK. The flood forecasting system is based on a cascade of rainfall-runoff and flood routing models, developed using stochastic transfer functions with state dependent parameterisations to allow for nonlinearity. The nonlinearities require a Monte Carlo sampling approach to propagation of uncertainty. Model updating and uncertainty constraint as new water level data become available is based on a Kalman filtering approach. The methodology is being implemented into the UK National Flood Forecasting System.

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