Advances in real–time flood forecasting

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Abstract
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.