This paper describes data assimilation (DA) and adaptive forecasting techniques for flood forecasting and their application to forecasting water levels at various locations along a 120 km reach of the river Severn, United Kingdom. The methodology exploits the top-down, data-based mechanistic (DBM) approach to the modeling of environmental processes, concentrating on the identification and estimation of those “dominant modes” of dynamic behavior that are most important for flood prediction. In particular, hydrological processes active in the catchment are modeled using the state-dependent parameter (SDP) method of estimating a nonlinear, effective rainfall transformation together with a linear stochastic transfer function (STF) method for characterizing both the effective rainfall–river level behavior and the river level routing processes. The complete model consists of these lumped parameter, linear and nonlinear stochastic, dynamic elements connected in a quasi-distributed manner that represents the physical structure of the catchment. The adaptive forecasting system then utilizes a state-space form of the complete catchment model, including allowance for heteroscedasticity in the errors, as the basis for data assimilation and forecasting using a Kalman filter forecasting engine. Here the predicted model states (water levels) and adaptive parameters are updated recursively in response to input data received in real time from sensors in the catchment. Direct water level forecasting is considered, rather than flow, because this removes the need to transform the level measurement through the rating curve and tends to decrease the forecasting errors.