Flood risk and resilience research areas

Brunel University London's flood risk and resilience research focuses on five strategic research areas.

Flood event prediction, management, and response

We develop assessment tools and models to enable decision-support along the whole flood event chain; providing improved prediction of flood extents; uncertainty analysis and statistical hydrology; data science; identifying critical decision points at multiple scales that reflect the transdisciplinary nature of flooding and flood impacts; development of technological and engineering solutions to enhance climate proofing and structural, economic, and generation, visualisation, and communication of decision-support results to support the development of flood resilient communities.

Critical infrastructure and sector inter-/dependencies

Technical, engineering (green, grey, and blue), and social solutions to flood risk impacts for sustainable critical infrastructure; evaluations of inherent interdependencies of each sector; the role and co-benefits of nature-based solutions (e.g., SuDS; NFM); policy-facing work to provide direct evidence for climate change risk assessment processes as well as disaster risk reduction efforts.

Hazard vulnerabilities

Mitigation, adaptation, and recovery to extreme flood events and subsequent impacts designed to grasp the complexities, root-causes, and anthropogenic drivers of disaster scenarios; social resilience; vulnerability analysis of assets, systems of assets and networks for diverse flood scenarios and climate exacerbations; community involvement – citizen science, data availability and communication; multi-disciplinary society-policy interactions in humanitarian supply chains, survivor-influenced and driven redevelopment, climate-driven migration, social and environmental justice and adaptive capacity.

Flooding,water quality and health

Maintaining and restoring water security in pre- and post-flood environments; fluid dynamics, treatment, storage, supply, and purification to restore water security to vulnerable populations as effectively as possible during and post-flood event; increased understanding of the flood-drought continuum and the role of land and water management in reducing extreme water vulnerabilities.

Big Data, deep/machine learning and AI applications in flood risk & resilience

Big data analytics for temporal-spatial variations for future meteorological extremes under climate change; open and digital data for infrastructure resilience assessments; advanced deep/machine learning techniques to assess and model complex relationships within flood-prone areas under consideration of different climate change conditions, including infrastructure damage characterisation; multivariate statistical analysis to reveal interdependence among meteorological and flood hazards.