Unlocking value from hyper-local watercourse monitoring

Background Hyper-local, dense sensor networks can transform monitoring and understanding of small catchments, providing real-time hydrological insights without having to rely on traditional hydraulic models. This paper explores findings from two UK pilots – ResilienTogether (Letchworth Garden City) and Blue Heart (Eastbourne and Southern Wealden).Methods In the ResilienTogether case study, we examine the downstream water‑level response of an actively controlled reservoir gate under two scenarios (i.e. gates-open and gates-closed). In the Blue Heart case study, we determine how tidal interactions influence water levels in a large pond adjacent to the town’s sea defences. The time series was segmented into 277 peak-to-trough events, and metrics were computed to establish normal vs abnormal conditions.Results In the ResilienTogether case study, Results show that in the gate-closed scenario, peak transmission decreased and duration of the main recession increased, indicating that the gate-closed scenario attenuated the downstream water-level peak as expected. In the Blue Heart case study, statistical approaches were deployed to identify four incident candidates (i.e. abnormal conditions), each exhibiting elevated early retention and high-end residuals.Conclusion Overall, these analyses show how event-based metrics can quantify the impact of operational rules (e.g. ResilienTogether gates) and identify abnormal drainage behaviour without the need for a hydraulic model.