DATA-BASED MECHANISTIC MODELLING AND RIVER FLOW FORECASTING

The paper briefly reviews the topic of rainfall-flow modelling and the inductive, Data-Based Mechanistic (DBM) approach to modelling stochastic, dynamic systems. It then uses DBM modelling methods to investigate the nonlinear relationship between daily rainfall and flow in the Leaf River, Mississippi, USA. Initially, recursive State-Dependent Parameter (SDP) estimation is used to identify, in non-parametric (graphical) terms, the location and nature of the 'effective rainfall' nonlinearity. Parameterization of this nonlinearity and optimization of a constrained version of the resulting model allow for its interpretation in a hydrologically meaningful State-Dependent Parameter Transfer Function (SDTF) form. Finally, the model its used as the basis for the design of a realtime flow forecasting using an optimized SDP Kalman Filter (SDPKF) forecasting engine that includes a model of the heteroscedastic measurement noise.