The significant increase in computing power over the past 70 years has progressively enabled the use of extensive datasets for hydrological modelling. The colossal scale of these datasets, i.e., over one million timesteps per station for a 30-year record at 15-min resolution, makes implementing effective quality control (QC) particularly challenging. In this study, we present a national-scale, open-source quality-control framework tailored for the UK’s 15-minute river flow dataset, UK-Flow15, which is described in Part 1 of this paper series. The framework combines manual visual inspection of anomalies with automated detection of statistical artefacts, incorporating both established and novel procedures. In particular, we introduce methods to evaluate high-flow events by comparing them with rainfall records and flow observations from neighbouring catchments. Application of the framework within a UK dataset reveals that while many stations maintain generally reliable records, over 20 % exhibit visually identifiable issues such as truncations, discontinuities, or missing data. Automated checks indicate that most (78 %) stations contain at least isolated segments of suspicious behaviour. Our high-flow event validation procedures confirm most peak flows, but also flag a small proportion of events as potentially spurious due to a lack of consistency with nearby flow (10.5 %) or rainfall (14.5 %) support. We further demonstrate that data quality has a measurable impact on hydrological modelling, with catchments containing flagged anomalies producing the least reliable simulations in terms of NSE and High-Flow Bias. By making flagged data and metadata openly accessible, the framework enables users to make informed decisions about data suitability. This work highlights the critical importance of rigorous QC in sub-daily hydrology and provides a scalable tool to support the development of more reliable, high-resolution hydrological data.