The WaterML-UD project aims to make monitoring data from urban drainage research projects more FAIR (Findable, Accessible, Interoperable, and Reusable). Currently, large amounts of data are collected from different systems, which can be difficult to integrate. The project addresses this challenge by aligning urban drainage data with established standards, making it more useful and interoperable. Instead of creating a new standard, the project team first assessed existing standards like SWE, O&M, and WaterML to see how well they fit the project's data model. They also shifted from traditional OGC Service Models like SOS to SensorThings API (STA), which allows for more flexible data sharing. The project team then structured the data into an STA format, making sure it was properly transformed, cleaned, and enriched with metadata to improve interoperability. To improve the interoperability of Urban Drainage data, the project team reviewed information models like Swiss VSA-DSS, ArcHydro, and WISKI, as well as data from scientific experiments. The team found that OGC standards like SensorML, Sensor Observation Service (SOS), Observations & Measurements (O&M), and WaterML 2.0 cover many UDS data needs. The UWO data was successfully mapped to STA, ensuring compatibility with the previous SOS-based model. Recognizing that semantic consistency is essential before defining usability metrics, the project focused on ontologies to provide a common framework for structuring all urban drainage data types, which would ensure consistent and automated data discovery, integration, and reuse. We identified key frameworks for monitoring sensor networks, including INSPIRE, SSN/SOSA, OGC SWE, and field specific like the Dutch GWSW Ontology. The GWSW urban water standard is a promising UDS framework that we have translated into English, aligning its terms with European wastewater engineering standards. The project's concepts were tested in the urban drainage community to develop standards and validate data workflows for interoperability and reusability. By applying FAIR metadata, datasets were structured for automated reuse in modelling, calibration, and analysis.