Professor Virginia Stovin

Department of Civil and Structural Engineering

Professor of Green Infrastructure for Stormwater Management

Headshot of Virginia
Profile picture of Headshot of Virginia
v.stovin@sheffield.ac.uk
+44 114 222 5051

Full contact details

Professor Virginia Stovin
Department of Civil and Structural Engineering
Room MezC4
Sir Frederick Mappin Building (Broad Lane Building)
Mappin Street
Sheffield
S1 3JD
Profile

My research into urban drainage aims to understand how we can use natural components (soils and plants) to control storm runoff from urban areas, meaning we can work to reduce flood risk and improve water quality.

Professor Virginia Stovin


Virginia studied her first degree in Geography at the University of Manchester and joined the Department as a lecturer in 1995.

Her research focuses on Urban Stormwater Management and Sustainable Drainage Systems (SuDS); how we can develop engineered drainage systems using natural components such as soil and plants to manage storm flows generated by urban constructs such as buildings, pavements and car parks.

Virginia looks at the technical performance of vegetated SuDS (particularly green roofs and ponds) and Green Infrastructure, aiming to understand the processes that control the quantity and quality of urban runoff in order to develop fit-for-purpose models of those processes and generate novel strategies to enable storm water to be managed more effectively and sustainably.

The work embraces fundamental science (hydrology and hydrodynamics), and has strong practical relevance. 

Research interests

Virginia is an internationally-acknowledged authority on the hydrological performance of vegetated stormwater infrastructure such as green roofs and rain gardens (bioretention cells). The work is informed by data collected from full-scale installations in the field, as well as intensively instrumented test beds and complementary laboratory trials.

This work supports the development and validation of modelling tools that are used by drainage engineers to reduce flood risk.

Virginia also uses 3D Computational Fluid Dynamics (CFD) to understand the flow patterns of water in storm water ponds and conventional sewer system components such as manholes and Combined Sewer Overflows (CSOs). By understanding and modelling how vegetation impacts on water movement in ponds, better predictions can be made of pollutant transport and removal, and the ability of ponds to improve water quality.

This has a positive impact on the ecological status of rivers, and helps to ensure that the UK’s waterways meet the Water Framework Directive.

Virginia has also published work on retrofitting of Sustainable Drainage Systems (SuDS), metrics for stormwater management and ET (evapotranspiration) rates associated with urban green infrastructure.

Publications

Journal articles

Research group

Water - Environmental Fluid Mechanics

Water - SuDS (Sustainable drainage systems) & Urban Drainage

Grants

View all research projects

Past Grants

Grand Challenge for Water

The EPSRC-funded Grand Challenge Centre for Water, comprising 6 UK universities and 100+ industrial partners working in collaboration to develop the sustainable water solutions of the future and to accelerate innovation uptake across the water sector.


Urban Green DaMS

Urban Green Design and Monitoring of Sustainable Drainage Systems (Urban Green DaMS) is a collaborative research project between the University of Sheffield and Newcastle University, led by Prof. Virginia Stovin and Prof. Richard Dawson respectively.

PhD opportunities

Probabilistic Performance Specifications for SuDS/Green Infrastructure
This project will utilise commercial and in-house hydrological/hydraulic modelling tools to develop robust probabilistic performance specifications for SuDS (Sustainable Drainage Systems). We will utilise continuous long time-series rainfall, incorporating best estimates of climate change scenarios, to capture the hydrological functioning of these systems under both extreme and routine rainfall inputs.

Use of Machine Learning (AI) for Rainfall Disaggregation
This proposal follows on from our EPSRC-funded 'Urban Green DaMS' project. DaMS refers to 'Design and Modelling of Sustainable Drainage Systems (SuDS)'. One of the outcomes from this project was confirmation that design work should be underpinned by the use of continuous simulation modelling (to properly capture how these nature-based solutions (e.g. bioretention cells) wet and dry in response to weather patterns and plant growth cycles. To do this, we (i.e. academics and practitioners in the UK) need appropriate continuous rainfall time-series at high temporal resolution (e.g. 5 min time-steps). UKCP18 provides us with an excellent resource in terms of future rainfall time-series, but these are only available with hourly time-steps. We need a tool that disaggregates these projected future rainfall time-series from hourly to 5-minute time-steps. There are several different ways of doing this, but we would like to try AI, which is often well-suited to 'pattern-matching' type problems. The NIMROD radar data provides access to high quality historic rainfall data at high temporal resolution, which can be used to train the AI.

Sustainable growing media for vegetated SuDS
Vegetated SuDS, e.g. bioretention cells, manage rainfall inputs and/or stormwater inflows through a range of hydrological processes including interception and evapotranspiration (ET) by plants, infiltration at the surface, percolation through the growing media and exfiltration into the native subsoil. There are strong drivers to utilise locally-recycled growing media within these SuDS, but there are risks & uncertainties in so doing. For example: how accurate are the materials data sheets associated with each delivery of the media, and how much variability is typical/acceptable?; how do different materials affect the performance of the blended mixture overall, both at the time of installation and as the media evolves/ages over time?; how do media characteristics impact on the risk of clogging at the surface, and how can this be mitigated? The aim of this PhD is to undertake detailed laboratory studies to answer all or some of the above questions, focusing on currently-available recycled materials. Use will be made of infiltration columns to assess hydraulic conductivity and laboratory visualisation techniques utilising fluorescent tracer particles.

Beyond Drainage: The Impact of SuDS on Urban Microclimates
Urban stormwater management increasingly makes use of SuDS (Sustainable Drainage Systems), which often incorporate open water and/or vegetation. Evapotranspiration from SuDS is expected to have a beneficial (cooling) impact on the urban microclimate, and may also have benefits for the indoor climate in adjacent buildings. The project will use a range of modelling tools (and possibly some experimental work) to quantify these effects.

Inflow Capture Efficiency for SuDS inlets
This project will focus on the development of robust CFD-modelling procedures to enable better design and analysis of inlets used to direct road runoff into SuDS (Sustainable Drainage Systems) devices such as bioretention cells. Use will be made of new and/or existing field or laboratory data sets to validate the CFD work.

Computational Fluid Dynamics (CFD) Applications in Urban Drainage
CFD modelling tools enable engineers to visualise 3D flow patterns within complex structures and to represent the movement of sediments and/or dissolved materials within the flows. This approach has provided insights into, for example, sediment deposition within combined sewage storage chambers, gross solids separation in combined sewer overflows and the passage of intermittently-discharged solutes through pipes and manholes. There are a number of ways in which this work might be further developed, including the exploration of links between residence time distributions and energy losses or the development of robust time-dependent modelling methodologies. In all cases use will be made of either new or existing field or laboratory data sets to validate the CFD work.