From Rain to Drain: Field-scale monitoring of sustainable drainage systems (SuDS)

We present evidence from long-term field-scale test environments in the United Kingdom, drawing on work from SuDSlab at the University of Hull and the Defra-funded Doncaster, Immingham and Grimsby Surface Water Resilience Project (DIG). Together, these initiatives employ a ‘Rain to Drain’ approach that tracks water from rainfall, through soils and sustainable drainage systems (SuDS), into drainage networks at catchment scale. Rain gardens, swales, ponds, permeable surfacing, retrofit downpipe interventions, and combined sewers have been monitored for up to four years. More than 2,000 internet-connected discrete sensors record meteorological, hydrological, and hydraulic variables continuously at five-minute intervals with live, real-time data acquisition.

High-resolution monitoring reveals several behaviours that are not apparent from design calculations or short deployment studies. Soil moisture profiles measured to depths of 0.6 m show that infiltration and storage capacity vary substantially with depth and season, with near-surface horizons responding within minutes of rainfall, while deeper layers may respond only during prolonged or intense events. Some systems operate primarily as infiltration features during drier periods, but transition to storage and attenuation dominated behaviour during wetter months. Event-based monitoring of retrofit planters and rain gardens shows delays in peak outflow of 10 to 60 minutes, with reductions in peak discharge commonly between 30 and 60% at asset scale. Downstream sewer measurements indicate that, under certain conditions, these effects can translate into longer response times and reduced short duration peaks at network scale. Monitoring also highlights important mismatches between assumed and actual system behaviour, including differences of tens of percent in contributing areas and inflow volumes between nominally similar assets.

Our work shows that long-term, high-frequency monitoring fundamentally improves understanding of how SuDS function in practice. By capturing seasonal variability, event-scale responses, and links between assets and receiving networks, monitoring provides evidence that can be used to refine design assumptions, support model validation, and diagnose underperformance. Sustained monitoring is essential not only to demonstrate that SuDS work, but to understand when they work, why performance varies, and how future schemes can be designed and managed more effectively.