Uncertainty propagation in a sequential model for flood forecasting

The aim of this paper is the estimation of uncertainty in an online data assimilation model applied to a sequential, multiple-step-ahead flood forecasting system. The main aim of the forecasting system under consideration is the derivation of real-time forecasts of the water levels with maximum possible lead-time. This is achieved through a two-level, sequential data assimilation procedure. In order to extend the maximum lead-time, we incorporate the forecasts obtained from the earlier stages of the forecasting system, both rainfall-water level and water level routing processes. The updating of the gain each of the subsystems introduces non-linearity into the system performance. The Generalized Likelihood Uncertainty Estimation (GLUE) technique is used to estimate the uncertainty of model predictions in the decomposed online forecasting system.