Rain gardens—planted, soil-filled basins designed to capture stormwater—are being put through floods and droughts in a series of controlled lab experiments and real-world field tests in Edinburgh to find out which plants and soils keep them working when the weather turns extreme. This matters because urban green infrastructure is often designed for one stressor at a time, but cities face both flash floods and prolonged dry spells. No one has systematically measured how rain garden soil, water, and plant systems behave under both extremes, or how to choose the right vegetation for resilience. The project fills that gap by combining environmental sensors, X-ray CT scanning of root-soil interactions, and computer modelling. If successful, the research will produce open-source mathematical models that predict rain garden performance under uncertain future climates. City planners and engineers could use those models to select plant species and soil mixes tailored to local conditions, making urban drainage systems more reliable without costly over-engineering. The work is applied, not fundamental—it directly targets design guidance for the green infrastructure that quietly manages stormwater in cities across the UK.
View original technical description
Rapid growth of urban areas has highlighted the importance of integrating high-quality green infrastructure into urban planning to create sustainable, resilient and liveable cities. Rain gardens, Nature-based Solutions (NbS) and Sustainable Drainage Systems (SuDS) are soil and planting areas designed to capture runoff from impermeable surfaces and slowly release it into drainage systems or water courses. These devices assist in handling multiple urban stressors (e.g., floods and droughts) while offering ecological, amenity and public health benefits to cities. This PhD project aims to address the effectiveness of GI systems, particularly vegetated rain gardens and their functioning under multiple urban stressors, such as floods and droughts. This project will involve extensive real-time environmental monitoring (laboratory- and field-based) coupled with advanced modelling techniques, to understand soil-water-plant dynamics in rain gardens situated within the City of Edinburgh (facilitated by the Royal Botanic Gardens Edinburgh; RBGE) and will provide the student with multidisciplinary skills and expertise in the fields of environmental monitoring, modelling and nature-based solutions. The project will analyse environmental interactions and quantify the impact of different stressors on core hydrological, ecological, and soil processes at different scales. Firstly, laboratory-scale, monocultured mesocosm pilot testbeds will be used to understand plant-soil-water interactions in closed, controlled conditions to assess environmental responses to different environmental stressors. Running a series of sequential laboratory studies within climate-controlled conditions will allow strategic multivariant assessments of individual system variables (i.e., hydroclimatic, vegetation choice/density and soil-specific parameters). This will allow us to determine the impact and extent of multiple sensors at the individual plant-scale. At the field-scale, we will commission real-world, monitored environmental networks at the RBGE. Here, we will be commission large-scale monitoring to quantify rain garden soil-water-plant interactions during field conditions. X-Ray Computed Tomography (XRT) will be used to supplement long-term monitoring to visualise soil-water-root interactions under different stressors within the laboratory- and field-experiments. XRT will help us visualise 3D/4D structural differences to identify changes in the performance of rain garden systems during floods or droughts. These laboratory- and field-based measurements will be used to develop, parameterise and validate mathematical models for soil-water-root interactions at the scale of a single plant and at the field-scale. Open-source finite element software (e.g. Fenics) will be used for numerical simulations of the model equations. This will allow testing of a wide range of scenarios to make predictions on rain garden performance under uncertain climate scenarios. Additionally, the physically-based models will be leveraged to forecast robust predictions of rain garden performance over time and space and under climate uncertainties. This will allow predictive upscaling and the quantification of site-specific performance to climate-related stressors. Further, this will inform plant and soil selection for vegetated bioretention systems. This project offers insights on the design, implementation, and management of green infrastructure systems in urban areas to enhance resilience and sustainability in response to increasing environmental challenges.
Plain English summaries and category classifications on this site are generated by AI and may not perfectly reflect the original research.
Is something wrong? Let us know