Dr Georges Kesserwani
School of Mechanical, Aerospace and Civil Engineering
Senior Lecturer in Water Engineering
+44 114 222 5746
Full contact details
School of Mechanical, Aerospace and Civil Engineering
Room F117, Broad Lane Building
Sir Frederick Mappin Building (Broad Lane Building)
Mappin Street
91直播
S1 3JD
- Profile
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My research bridges advances in numerical and data analysis techniques with the aim of transforming flood mapping and forecasting to become more automated and flexible for end use, so that we can better mitigate against hazardous events.
Dr Georges Kesserwani
Welcome and thank you for reading! Overview of research developments and outcomes can be found in this .
Dr Kesserwani completed his PhD in fluid mechanics at the University of Strasbourg (2005-2008). He went on to a three-year research post in flood modelling at Newcastle University (2008鈥2011), and joined the Department in 2011. He was awarded a DAAD Visiting Fellowship to RWTH Aachen (2013鈥2014) and has been an EPSRC Fellow, holding a prestigious Early Career Fellowship (2018鈥2024).
His research has aimed to forge new ties between the disciplines of water engineering, flood risk modelling, mathematics, and software science; with a vision to deliver new thinking and methods that help improve our capability to predict and prepare against flooding processes in an ever-changing climate.
Dr Kesserwani frequently reviews papers for the top scientific journals; he received the 鈥2024 Best Reviewer Award鈥 by the ASCE鈥檚 Journal of Hydraulic Engineering. He organised the international events of the 鈥淎dvances in numerical modelling of hydrodynamics鈥 (2015), 鈥淔lood modelling and forecasting challenges in industry鈥 (2021), and the 鈥淎dvances in flood modelling and forecasting鈥 (2024) workshops.
He is fond of reproducible research and open-sourcing activities and is dedicated to teaching students the science of hydraulics, hydrology and flooding as well as the the art of computer modelling
- Research interests
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Dr Kesserwani has been interested in developing advanced solutions to improve our ability to predict and coexist with flooding. This has included hard modelling solutions for fast and detailed hazard analysis, from combining the multiwavelets filtering with the Galerkin鈥檚 polynomial smoothening of terrain data to optimise grid generation and speedup operational flood simulations (). It also included soft agent-based modelling solutions such as to plan evacuation dynamics of people during a flood (), or pre-flood protection measures ().
Dr Kesserwani has also addressed flood-related modelling problems of practical interest that include the following aspects:
- Efficient uncertainty quantification methods ()
- Interaction between sewer and floodplain flows ()
- Simplistic modelling of wakes and eddies in the flow ()
- Solute mixing and sediment transport in channels including vegetation
- Publications
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Journal articles
- . Journal of Hydraulic Research, 61(5), 631-650.
- . Water Resources Research, 59(7).
- . Journal of Hydraulic Engineering, 149(5).
- . Natural Hazards and Earth System Sciences, 21(10), 3175-3198.
- . Journal of Flood Risk Management, 14(2).
- . Journal of Hydrology, 594.
- . Advances in Water Resources, 137.
- . Journal of Hydraulic Engineering, 146(3).
- . Advances in Water Resources, 129, 31-55.
- . Computer Methods in Applied Mechanics and Engineering, 342, 710-741.
- . Water Resources Research, 54(9), 6408-6422.
- . Journal of Hydrology, 552, 421-432.
- . Journal of Hydraulic Engineering, 136(9), 662-668.
- . Advances in Water Resources, 31(2), 287-297.
- . Geoscientific Model Development, 16(9), 2391-2413.
- . Journal of Hydraulic Research, 61(5), 631-650.
- Grants
- Professional activities and memberships
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- 2024 Best Reviewer award by ASCE鈥檚 J. Hydraul. Engrg.
- Member of the EPSRC Peer Review College
- Fellow of the UK Higher Education Academy
- Member of the Carnegie Trust Research assessors
- Guest editor in Applied Mathematical Modelling journal (2016)
- Invited speaker to many UK, EU and international institutions
- Contribution to a public-domain toolkit for flood risk assessment
- Review of journal paper and grant application submissions
- External PhD examiner for University of Zaragoza (2015), University of Sussex (2017) and University of L鈥橝quila (2018)
- PhD opportunities
Dr Kesserwani is dedicated for the training of PhD researchers in on subjects related to the modelling of water resources. If you are interested in one of the below-listed projects, or wish to self-propose a project, please get in touch at the above email.
High-performance computing of flooding at regional to continental scales
To advance an open-source flood modelling framework with hybrid high-performance computing and include river channel drivers in the context of operational and large scale modelling of floods.
Practical hydrodynamic modelling of solute mixing characteristics
Supported by laboratory datasets obtained by our unique experimental facilities, the project will explore an efficient combination of methods for reproducing solute mixing past vegetation and assess their feasibility for use to support national flood risk management.
Optimised modelling of temporary pre-flood defences
This project aims at addressing the challenges in translating the behaviour of emergency first responders to floods in a computational tool: to assess the more suited type of temporary flood defences that can be effectively deployed and the extent to which the flood risk is reduced?
Modelling human escape to urban flood emergencies
Data from observation is increasingly emerging on how people behave in/to flood in small and populated areas such as metro stations. This project will use such empirical data to augment a flood-people simulator for producing a framework that can suggest the safest evacuation routes in flooding emergencies.
Physics-informed AI for flood forecasting with data assimilation
Physics-based AI is ideal to speedup flood forecasting by integrating probabilistic Gaussian or Bayesian emulators. Case studies are emerging that include observation data from sensors for which data assimilation can be used to update the flood forecasting.