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High quality on-line flow forecasts are useful for real-time operation of urban drainage systems and wastewater treatment plants. This requires computationally efficient models, which are continuously updated with observed data to provide good initial conditions for the forecasts. This paper presents a way of updating conceptual rainfall-runoff models using Maximum a Posteriori estimation to determine the most likely parameter constellation at the current point in time. This is done by combining information from prior parameter distributions and the model goodness of fit over a predefined period of time that precedes the forecast. The method is illustrated for an urban catchment, where flow forecasts of 0-4 h are generated by applying a lumped linear reservoir model with three cascading reservoirs. Radar rainfall observations are used as input to the model. The effects of different prior standard deviations and lengths of the auto-calibration period on the resulting flow forecast performance are evaluated. We were able to demonstrate that, if properly tuned, the method leads to a significant increase in forecasting performance compared to a model without continuous auto-calibration. Delayed responses and erratic behaviour in the parameter variations are, however, observed and the choice of prior distributions and length of auto-calibration period is not straightforward. [ABSTRACT FROM AUTHOR]

This article demonstrates the incorporation of stochastic grey-box models for urban runoff forecasting into a full-scale, system-wide control setup where setpoints are dynamically optimized considering forecast uncertainty and sensitivity of overflow locations in order to reduce combined sewer overflow risk. The stochastic control framework and the performance of the runoff forecasting models are tested in a case study in Copenhagen (76 km 2 with 6 sub-catchments and 7 control points) using 2-h radar rainfall forecasts and inlet flows to control points computed from a variety of noisy/oscillating in-sewer measurements. Radar rainfall forecasts as model inputs yield considerably lower runoff forecast skills than “perfect” gauge-based rainfall observations (ex-post hindcasting). Nevertheless, the stochastic grey-box models clearly outperform benchmark forecast models based on exponential smoothing. Simulations demonstrate notable improvements of the control efficiency when considering forecast information and additionally when considering forecast uncertainty, compared with optimization based on current basin fillings only. [ABSTRACT FROM AUTHOR]

A case study for integrated real-time control (RTC) of an urban drainage system in the city of Wilhelmshaven (Germany) is explained. The fuzzy based RTC strategy combines control of the sewer system and inflow to the waste water treatment plant. The main objective in controlling the sewer system is to reduce the number of overflows and the volume at a combined sewer overflow (CSO), located close to a bathing beach. Based on online measurements, the operation mode of two pumping stations is modified. This approach allows the safe activation of free storage volume in the sewer system without constructive measures. To avoid critical situations in the treatment process, the inflow to the treatment plant is automatically reduced to a defined value if high inflows to the treatment plant occur in combination with unfavorable conditions on the secondary clarifiers during rainfall events. The integrated RTC system has been operational for approximately one year. [ABSTRACT FROM PUBLISHER]