Stormwater management systems are under increasing pressure from challenges such as urbanisation and climate change, and better understanding and management is essential to reduce flood risk, protect water quality and ensure the resilience of urban infrastructure. Traditionally, there has been a strong reliance on physics-based hydrological and hydraulic models; however, model-based approaches are subject to limitations and can be challenging to implement in many scenarios. Consequently, there is now growing interest in the use of data-driven methods to complement or augment traditional modelling approaches. However, the scarcity of openly available, integrated hydrometric and sewer system monitoring data poses a significant barrier to the development, validation and implementation of 'smarter' stormwater management systems, and continues to limit progress in data-driven approaches. To address this gap, this paper provides detailed, high density hydrometric and sewer level data collected from 497 new gauges across 78 km<sup>2</sup> urban catchment in the United Kingdom, including from rainfall gauges, fluvial water level gauges, groundwater monitoring boreholes, road gullies and in-sewer level sensors. This data is supplemented by information on the sewer system design, fluvial network and catchment topology, and scripts for accessing, processing and visualising the data. It is intended that publishing this large-scale, high resolution urban hydrometric and sewer level dataset will support development of improved methods for understanding urban stormwater systems, advance data-driven modelling approaches, and enable the next generation of smart stormwater management strategies. Several potential research opportunities are discussed in detail, including understanding spatio-temporal variability in urban hydrological processes and responses; improving sensor network design; machine learning and artificial intelligence applications; characterising groundwater-sewer interactions; improving data-driven asset-management and decision making; and application in benchmarking and open science applications more broadly.</p> <p>The dataset repository is available at https://doi.org/10.5281/zenodo.20699634 (Sweetapple et al., 2026a) and example scripts at https://doi.org/10.5281/zenodo.21390823 (Sweetapple et al., 2026b).