Quantifying the role of land cover in ‘slowing the flow’ for flood risk reduction in a changing climate

Year
2025
Primary Supervisor
Steph Bond <S.G.Bond@leeds.ac.uk>
Institution
University of Leeds (School of Geography)
Academic Supervisors
Prof Joseph Holden <J.Holden@leeds.ac.uk> (University of Leeds, School of Geography)
Project Partners
National Trust

Background

Flooding accounted for 44% of disasters between 2000-2019, affecting 1.6 billion people and costing $651Bn. Storms are increasing in frequency and magnitude with climate change leading to enhanced flooding and associated risks. In response, there has been growing interest in Nature Based Solutions (NBS) to reduce flood risk through catchment-scale management of water storage and flow pathways. However, the extent to which land cover management can realistically mitigate flood risk in a changing climate is uncertain. We require practical methods and novel data science to quantify and reduce uncertainty in the effectiveness of land cover management in ‘slowing the flow’. We also require improved understanding of how climate uncertainty, combined with field-based methods influence hydrological model uncertainty. This PhD could combine field and laboratory work with hydrological modelling, data science and climate science to directly inform policy and planning for flood risk reduction.

PhD opportunity

Hydrological models can estimate catchment-scale flood risk associated with land cover management. Climate change will influence flood risk via increased storm event frequency and duration, but there is uncertainty that needs to be addressed in how this translates to overland flow (OLF) occurrence. Climate change will also influence vegetation composition and growth-decay cycles which interact with OLF, but there is inherent uncertainty in these drivers and interactions. Due to a lack of suitable data covering temperate landscapes, there is high uncertainty in the extent to which surface roughness (drag provided by vegetation) influences OLF velocity and associated flood risk, under both current and future climate scenarios.
In this PhD, you will have flexibility to design your project. Some potential avenues:
1) Use multicriteria and multidata approaches (Panchanthan et al 2024 – doi.org/10.1016/j.earscirev.2024.104956) to investigate the uncertainty in OLF velocity with differing applied climate-driven ‘storm’ conditions in previously uncharacterised land covers.
2) Use hydrological modelling and Generalized likelihood uncertainty estimation (GLUE) to examine catchment-scale flood risk under multiple climate scenarios, accounting for storm and vegetation variability and uncertainty source tracing.
3) Develop and compare methods for quantifying vegetative surface roughness to reduce uncertainty.
4) Examine how field data and different levels of model complexity can support reductions in climate-driven flood risk uncertainty.
You will have access to world-leading facilities such as the Sorby Environmental Fluid Dynamics Laboratory. The project may involve use of the new NERC Floods and Droughts Research Infrastructure (field and data analytics tools) and project support from the National Trust. You will have opportunities to engage with stakeholders to influence NBS inclusion in policy and practice, and directly contribute to land cover management plans at multiple scales.

Other information

River Basin Processes and Management research cluster:
https://environment.leeds.ac.uk/geography-research-river-basin-processes-management