PhD Studentship: Exploring Effective Strategies of Communicating Flood Forecasting Using a CHANS Modelling Framework

Rationale

Surface water flooding - also referred to as pluvial flooding - is caused by intense, highly localised convective rainfall creating excessive runoff that cannot drain away quickly enough.

According to a recent Defra report [1], it is the UK’s most widespread form of flooding, with 3.2m properties at risk in England alone. Recent events, e.g., the July 2021 floods in London, demonstrate inadequate preparedness for such events [2]. Several recent UK government reports highlight an urgent need for surface water flood risk mitigation and management so owners of at-risk homes and businesses can better protect their property (e.g., [1]).

Under the National Surface Water Management Action Plan [1], the Environment Agency (EA), Met Office and Flood Forecasting Centre are committed to exploring improved surface water flood forecasting. The challenge of effective communication of forecasts and warnings was further emphasised at the symposium, and users specifically pointed out flood forecasting and warning is ‘30% technology and 70% communication’. This project will deliver inter-disciplinary research to address this important challenge.

Methodology

The aim is to apply a newly developed Coupled Human And Natural Systems (CHANS) model [3] to simulate and understand the interactive human behaviours and social dynamics before and during a surface water flood event induced by intense rainfall. This will be related to different scenarios of flood forecasting and warning provision.

Subsequently, we will design and carry out systematic numerical experiments to explore effective strategies of communicating flood forecasting and warning. The adopted CHANS modelling framework consists of a distributed agent-based model (ABM) to represent the human systems and a hydrodynamic model (the High-Performance Integrated hydrodynamic Modelling System (HiPIMS)) to predict the flooding dynamics in a natural system. The CHANS model is implemented on high-performance multiple graphics processing units to support large-scale high-resolution simulations. In the ABM, agents can be flexibly defined and used to represent individuals, households and related organisations to depict the interactive social dynamics interrupted by flooding or other driving factors. Data from different sources, e.g. UK national census, social media, literature, will be processed to understand and describe human and organisational behaviours.

Participatory Action Research methodologies will be deployed to unlock a deeper understanding of different groups and types of agents and their interactions in order to construct the coupled human and natural system in the case study site (jointly decided with the partners). Scenarios will be co-developed and simulated to understand the human response to flood forecasting and warnings and explore effective communication strategies that maximize their impact on flood forecasting and warning effectiveness.

This project is part of the NERC funded Flood-CDT studentship competition. For more information, please visit the Flood-CDT website.

Please see this PhD project’s dedicated webpage for more information via the above ‘Apply’ button.

Additional Funding Information

Studentship type – UKRI through Flood-CDT. The studentship is for 3.5 years and provides a tax-free stipend of £18,622 per annum plus tuition fees at the UK rate. Excellent International candidates are eligible for a full international fee waiver however due to UKRI funding rules, no more than 30% of the studentships funded by this grant can be awarded to International candidates.

For details on what documents you will be required to submit for this, please see the dedicated project page, available via the Apply button.

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