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An innovative and generalised approach to the integrated real time control of urban drainage systems is presented. The Dynamic Overflow Risk Assessment (DORA) strategy aims to minimise the expected Combined Sewer Overflow (CSO) risk by considering (i) the water volume presently stored in the drainage network, (ii) the expected runoff volume (calculated by radar-based nowcast models) and -most important -(iii) the estimated uncertainty of the runoff forecasts. The inclusion of uncertainty allows for a more confident use of Real Time Control (RTC). Overflow risk is calculated by a flexible function which allows for the prioritisation of the discharge points according to their sensitivity and intended use. DORA was tested on a hypothetical example inspired by the main catchment in the city of Aarhus (Denmark). An analysis of DORA's performance over a range of events with different return periods, using a simple conceptual model, is presented. Compared to a traditional local control approach, DORA contributed to reduce CSO volumes from the most sensitive points while reducing total CSO volumes discharged from the catchment. Additionally, the results show that the inclusion of forecasts and their uncertainty contributed to further improving the performance of drainage systems. The results of this paper will contribute to the wider usage of global RTC methods in the management of urban drainage networks.

In this synthesis, we assess present research and anticipate future development needs in modeling water quality in watersheds. We first discuss areas of potential improvement in the representation of freshwater systems pertaining to water quality, including representation of environmental interfaces, in-stream water quality and process interactions, soil health and land management, and (peri-)urban areas. In addition, we provide insights into the contemporary challenges in the practices of watershed water quality modeling, including quality control of monitoring data, model parameterization and calibration, uncertainty management, scale mismatches, and provisioning of modeling tools. Finally, we make three recommendations to provide a path forward for improving watershed water quality modeling science, infrastructure, and practices. These include building stronger collaborations between experimentalists and modelers, bridging gaps between modelers and stakeholders, and cultivating and applying procedural knowledge to better govern and support water quality modeling processes within organizations.

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