Abstract. This study investigates the suitability of the asynchronous ensemble Kalman filter (AEnKF) and a partitioned updating scheme for hydrological forecasting. The AEnKF requires forward integration of the model for the analysis and enables assimilation of current and past observations simultaneously at a single analysis step. The results of discharge assimilation into a grid-based hydrological model (using a soil moisture error model) for the Upper Ourthe catchment in the Belgian Ardennes show that including past predictions and observations in the data assimilation method improves the model forecasts. Additionally, we show that elimination of the strongly non-linear relation between the soil moisture storage and assimilated discharge observations from the model update becomes beneficial for improved operational forecasting, which is evaluated using several validation measures.
For the Netherlands, accurate water level forecasting in the coastal region is crucial, since large areas of the land lie below sea level. During storm surges, detailed and timely water level forecasts provided by an operational storm surge forecasting system are necessary to support, for example, the decision to close the movable storm surge barriers in the Eastern Scheldt and the Rotterdam Waterway. In the past years, a new generation operational tide-surge model (Dutch Continental Shelf Model version 6) has been developed covering the northwest European continental shelf. In a previous study, a large effort has been put in representing relevant physical phenomena in this process model as well as reducing parameter uncertainty over a wide area. While this has resulted in very accurate water level representation (root-meansquare error (RMSE) ∼7-8 cm), during severe storm surges, the errors in the meteorological model forcing are generally non-negligible and can cause forecast errors of several decimetres. By integrating operationally available observational data in the forecast model by means of real-time data assimilation, the errors in the meteorological forcing are prevented from propagating to the hydrodynamic tide-surge model forecasts. This paper discusses the development of a computationally efficient steady-state Kalman filter to enhance the predictive quality for the shorter lead times by improving the system state at the start of the forecast. Besides evaluating the model quality against shelf-wide tide gauge observations for a year-long hindcast simulation, the predictive value of the Kalman filter is determined by comparing the forecast quality for various lead time intervals against the model without a steady-state Kalman filter. This shows that, even though the process model has a water level representation that is substantially better than that of other comparable operational models of this scale, substantial improvements in predictive quality in the first few hours are possible in an actual operational setting.
For operational water management in lowlands and polders (for instance, in the Netherlands), lowland hydrological models are used for flow prediction, often as an input for a real‐time control system to steer water with pumps and weirs to keep water levels within acceptable bounds. Therefore, proper initialization of these models is essential. The ensemble Kalman filter (EnKF) has been widely used due to its relative simplicity and robustness, while the unscented Kalman filter (UKF) has received little attention in the operational context. Here, we test both UKF and EnKF using a lowland lumped hydrological model. The results of a reforecast experiment in an operational context using an hourly time step show that when using nine ensemble members, both filters can improve the accuracy of the forecast by updating the state of a lumped hydrological model (Wageningen Lowland Runoff Simulator, WALRUS) based on the observed discharge, while UKF has achieved better performance than EnKF. Additionally, we show that an increase in the ensemble members does not necessarily mean a significant increase in performance. WALRUS model with either UKF or EnKF could be considered for hydrological forecasting for supporting water management of polders and lowlands, with UKF being the computationally leaner option.
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