Advances in real-time flood forecasting.
This paper discusses the modelling of rainfall-flow (rainfall-run-off) and flow-routeing processes in river systems within the context of real-time flood forecasting. It is argued that deterministic, reductionist (or 'bottom-up') models are inappropriate for real-time forecasting because of the inherent uncertainty that characterizes river-catchment dynamics and the problems of model over-parametrization. The advantages of alternative, efficiently parametrized data-based mechanistic models, identified and estimated using statistical methods, are discussed. It is shown that such models are in an ideal form for incorporation in a real-time, adaptive forecasting system based on recursive state-space estimation (an adaptive version of the stochastic Kalman filter algorithm). An illustrative example, based on the analysis of a limited set of hourly rainfall-flow data from the River Hodder in northwest England, demonstrates the utility of this methodology in difficult circumstances and illustrates the advantages of incorporating real-time state and parameter adaption.
Young
Peter C
PC
Centre for Research on Environmental Systems and Statistics, University of Lancaster, Lancaster LA1 4YQ, UK.
D000465:Algorithms; D003198:Computer Simulation; D004190:Disasters; D004739:England; D005544:Forecasting / Q000379:methods; D005618:Fresh Water; D015233:Models, Statistical; D011891:Rain; D018570:Risk Assessment / Q000379:methods / Q000639:trends