D7.4 describes the innovation and Intellectual Property Rights (IPR) management procedures within DWC. It introduces the concepts of Intellectual Property (IP), the types of protection rights as well as the IPR rules in the project. It summarizes the key procedures introduced in the Grant Agreement and Consortium Agreement documents. Finally, it explains the role of the innovation and IPR manager and the detailed activities that will be carried out to foster innovation and secure the protection of our key results. Compared to the previous versions, the IPR repository has been updated.

Urban wastewater management and the associated modelling has become indispensable today. Reliable calibration is essential for these models, and water level data is used as a standard. However, data collection can be limited due to high sensor costs and harsh conditions in the sewer. A novel solution is collecting data using low-cost temperature sensors, placing one in the stream, the other at the crest of the weir. In the case of dry weather, the sensor measures the air phase, whereas, in the case of Combined Sewer Overflow (CSO), the discharged storm and wastewater is measured. Autocalibration was performed using OSTRICH for a SWMM model in Berlin, with water level and fictional temperature data, and various number of measuring sites. Results showed that calibration using temperature data was as good as using water level data, with promising outcomes achieved by using one measuring site, offering a cost-effective alternative for water utilities.

The management of urban wastewater systems and the associated modelling of these systems has become indispensable in today's world. In order for these models to represent reality as accurately as possible, a reliable calibration is essential. Water level data is used as a standard, but due to expensive sensors and harsh conditions in the sewer, data can only be collected at a few key points of the system. One novel solution, that has experienced an upswing in recent years, is collecting data using low-cost temperature sensors. Two sensors are needed; one is placed in the stream; the other is placed at the crest of the weir. In the case of dry weather, the sensor measures the air phase, whereas, in the case of Combined Sewer Overflow (CSO), the discharged storm and wastewater is measured. The start and end of a CSO event can be determined via the merging of measured temperature values in both points of the overflow structure. Due to this method, the duration of CSO events in a sewer system can be detected.

In this work, the potential benefits of this novel method for model calibration are assessed. Therefore, autocalibration runs with water level data and fictional temperature data were carried out via OSTRICH for a SWMM model located in Berlin. Furthermore, calibration runs with a different number of measuring sites were performed, to evaluate the amount of necessary measuring sites for a reliable calibration. In order to be able to compare the different approaches, a calibration period of 19 events was first required for the respective datatype. Next, a validation period which consisted of 18 events was carried out and evaluated by the R² of three water level measuring sites for both approaches to ensure comparability. It was revealed that the calibration with duration data based on temperature sensors was able to achieve results as good as the conventional approach using water level data. Due to low spatial distribution of the measuring sites in the model, it could not be finally answered if more measuring sites would yield to even better results. However, already with one measuring site, promising calibration outcomes could be achieved and thus, offers an alternative for water utilities and practitioners.

An innovative tool for modeling the specific flood volume was presented that can be applied to assess the need for stormwater network modernization as well as for advanced flood risk assessment. Field measurements for a catchment area in Kielce, Poland, were used to apply the model and demonstrate its usefulness. This model extends the capability of recently developed statistical and machine learning hydrodynamic models developed from multiple runs of the US Environmental Protection Agency (EPA) Storm Water Management Model (SWMM). The extensions enable the inclusion of (1) the characteristics of the catchment and its stormwater network, calibrated model parameters expressing catchment retention, and the capacity of the sewer system; (2) extended sensitivity analysis; and (3) risk analysis. Sensitivity coefficients of calibrated model parameters include correction coefficients for percentage area, flow path, depth of storage, and impervious area; Manning roughness coefficients for impervious areas; and Manning roughness coefficients for sewer channels. Sensitivity coefficients were determined with respect to rainfall intensity and characteristics of the catchment and stormwater network. Extended sensitivity analysis enabled an evaluation of the variability in the specific flood volume and sensitivity coefficients within a catchment, in order to identify the most vulnerable areas threatened by flooding. Thus, the model can be used to identify areas particularly susceptible to stormwater network failure and the sections of the network where corrective action should be taken to reduce the probability of system failure. The simulator developed to determine the specific flood volume represents an alternative approach to the SWMM that, unlike current approaches, can be calibrated with limited topological data availability; therefore, the aforementioned simulator incurs a lower cost due to the lower number and lower specificity of data required.

In DWC, different digital solutions will be tested and assessed regarding their potential to improve the performance and return on investment of water infrastructures. The present report (D2.4) describes the individual solutions with their technical specifications, their addressed challenges and their added value in the form of fact sheets. The document aims to help cities and water utilities in finding appropriate solutions for their operational, environmental or public health deficits

D5.4 gives a summary on the drivers, barriers and requirements for transferability and replicability of the 15 digital solutions developed within DWC. In particular, it assesses key operational (e.g. local challenges and management practices, etc.), organizational (e.g. local ICT governance and system interoperability) and technical conditions (e.g. type of network, precipitation pattern, etc.) for a successful implementation of the solution. The results can be used as a checklist for water utilities to assess the potential for local deployment of each digital solution.