Real-time flow forecasting.

The Chapter discusses the modelling of rainfall-flow (rainfall-runoff) and flow routing 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 parameterized Data-Based Mechanistic (DBM) 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 data assimilation and 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 daily data from the ephemeral Canning River in SW Australia, demonstrates the utility of this methodology and illustrates the advantages of incorporating real-time state and parameter adaption.