Modelling compound (combined fluvial, pluvial) and coastal flood inundation on large spatial scales

Dr Ivan Haigh, Niall Quinn (FATHOM)
Rationale: 

Floods are the most dangerous and costly natural hazard. From 1980 to 2013, floods accounted for more than $1 trillion in losses and resulted in at least 220,000 fatalities globally. More than 50% of these deaths and a large proportion of the economic losses occurred in densely populated low-lying coastal regions. Globally, coastal areas are home to 600 million people and constitute strategic economic centres.

Flooding in coastal areas potentially arises from four main sources: (1) storm surges plus tides; (2) local or remotely (swell) generated waves; (3) river discharge (fluvial); and (4) direct surface runoff (pluvial). The latter two sources mainly arise from heavy precipitation (but can also arise from snow melt). The adverse consequences of a flood can be disproportionately large when these different sources occur concurrently, or in close succession, a phenomenon that is known as ‘compound flooding’. However, most existing risk assessments and numerical modelling studies do not account for this possibility, in that they only consider the main drivers of flooding separately. 

Methodology: 

This research aims to improve representation of compound flooding in numerical flood inundation modelling over a UK national or larger (Europe or Global) scale.
The first objective will be to build on recent studies characterizing compound flooding within observed national datasets (fluvial, pluvial, coastal). This will determine where compound events occur (and where they don’t) and identify which combinations of source-variables are most relevant. Issues surrounding how to best define compound flooding across entire countries, particularly where observed datasets are not available, and methods for addressing sampling uncertainties inherent in relatively short observed datasets will be also be considered.

The second objective will be to develop a method for accurately representing compound flood risk over a large (e.g. national) spatial area. The numerical inundation modelling will be undertaken with a widely used, computationally efficient, two-dimensional hydrodynamic model (LISFLOOD-FP).

The third objective is to quantify the extent to which compound effects exacerbate impacts to coastal communities. While it is known that compound flood events lead to greater damages (than events arising from a single source), no study has quantified precisely: (i) how much flood risk is under-estimated if compound effects are ignored, and; (ii) which combinations of variables most exacerbate the impacts.

Location: 
University of Southampton
Training: 

The INSPIRE DTP programme provides comprehensive personal and professional development training alongside extensive opportunities for students to expand their multi-disciplinary outlook through interactions with a wide network of academic, research and industrial/policy partners. The student will be registered at the University of Southampton and hosted at the School of Ocean and Earth Science with placement opportunities at FATHOM in Bristol. Specific training will include numerical modelling and big data analysis in Python or MATLAB.

Eligibility & Funding Details: 

Please see https://inspire-dtp.ac.uk/how-apply for details.

Background Reading: 

Bates, P.D., et al. (2020) Combined modelling of US fluvial, pluvial and coastal flood hazard under current and future climates. In review Water resources research.

Couasnon, A., Eilander, D., Muis, S., Veldkamp, T.I.D., Haigh, I.D, Wahl, T., Winsemius, H., Ward, P.D., 2020. Measuring compound flood potential from discharge and storm surge extremes at the global scale and its implications for flood hazard. Natural Hazards and Earth System Sciences, https://doi.org/10.5194/nhess-2019-205.

Hendry, A., Haigh, I.D.., Nicholls, R.J., Winter, H., Neal, R., Wahl, T., Joly-Laugel, A., and Darby, S.E., 2019. Assessing the characteristics and drivers of compound flooding events around the UK coast. Hydrol. Earth Syst. Sci., 23, 3117–3139.