Dr Xilin Xia BEng, MEng, PhD, FHEA

• Fellow of the Higher Education Academy, 2020
• PhD in Water Resources, Newcastle University, 2017
• MEng in Road and Railway Engineering, Wuhan University, 2012
• BEng in Civil Engineering, Wuhan University, 2010

Dr Xia obtained his BEng (Civil Engineering) and MEng (Road and Railway Engineering) from Wuhan University, China in 2010 and 2012.

In 2017, he was awarded his PhD by Newcastle University for his work on numerical modelling of rainfall-related hazards including flash flooding and landslides. Following his PhD study, he worked on flood forecasting and flood risk assessment in the same university as a research associate of a major NERC-funded programme – Flooding from Intense Rainfall.

In 2018, he took his first lectureship at Loughborough University, where he worked on developing an impact-based flooding forecasting system for India in partnership with the Met Office, UKCEH, HSE and Indian Ministry of Earth Sciences. He has also helped creating new ‘uplifts’ that can be applied to design storm events to represent climate change effects on storms and recommendations on the updates of existing methods and tools used to tackle surface water flooding. He is a keen and leading advocate of applying the lasted GPU-based high-performance computing technologies in flood modelling, which is becoming the mainstream approach in industry. He has also devoted his time to develop and maintain the open-source code ‘hipims’, which has now been recognised as a leading open-source flooding modelling software. His work is underpinning high-profile applications such as the National Digital Twin Programme’s Climate Resilience Demonstrator (CReDO).

In May 2022, Dr Xia took his current position at the University of Birmingham to work on climate and weather resilience of infrastructure.

Dr Xia teaches the following modules at the University of Birmingham:

- Water Engineering and Management

Dr Xia is interested in supervising postgraduate research projects in the broad area of modelling climate and weather-related risks, such as:

• Numerical simulation of rainfall-related hazards, such as flooding, landslides, and debris flows.
• Computation methods for fluid dynamics and solid-fluid interactions.
• High-performance computing techniques for environmental modelling.
• Applications of machine learning for water engineering problems.
• Big-data analytics for hazard risk management and resilience.
• Modelling impacts of extreme weather events on infrastructure system.

He welcomes inquiries from prospective researchers about project ideas and opportunities.

Mitigating and adapting to climate change has become one of the most important societal challenges today. We are seeing increasingly more extreme weather such as heavy rainfall events in the globe including the UK. Such events may cause hazards such as flooding, landslides, debris and failure of earthworks and structures. Our work has been focusing on modelling these hazards to manage the risk and increase resilience, which is very important for users such as infrastructure operators and (re)insurance firms.

Robust computational methods for water-related natural hazards

Robust computational methods are underpinning applications such as early warning and risk analytics, which are essential for climate resilience. We are developing accurate, fast, and stable computational methods for flood inundation, landslide runout, fluvial sediment transport and debris flows. A key challenge we are addressing is numerically modelling interacting physical processes across different scales. We have been working a range of methods such as finite volume method, smoothed particle hydrodynamics and material point method.

High-performance computing for environmental modelling

Modern parallel computing based on GPUs creates a paradigm shift for scientific computing. We have been pioneering the application of GPU-based parallel computing for environmental modelling, such as flood inundation modelling. Our work has enabled very large-scale (city and major river catchment) and very high-resolution (meters) simulations.

Applications of machine learning for water engineering

Machine learning shows great potential to solve some long-standing problems, such as bridging the gap of data needs and emulating complex systems. We are building machine-learning based surrogate models of sophisticated process-based models to enable stochastic risk analytics. We are also applying deep learning to downscale remote sensing data for hazard risk management in data-scarce regions.

Risk-based flood forecasting

It is important to know ‘what does flood do’ rather than just ‘where is the flood’. This is what risk-based flood forecasting is about. We are coupling our flood inundation models with broad-scale hydrological models and receptor data (population, infrastructure, agriculture, etc.) to build risk-based flood forecasting system over large geographical areas such as India.

Current and recent research projects

 

• 2022 – 2023, Building a Flood Hazard Impact Model for India (FHIM-India) Phase 3, Newton Fund through UK Met Office, PI (UoB), £19563, P109479
• 2021 – 2022, ENACT: Evaluating the feasibility and efficacy of integrated catchment-scale Nature-based solutions for Climate Change adaptaTion in India, NERC, Co-I £90000
• 2021 – 2022, Building a Flood Hazard Impact Model for India (FHIM-India) Phase 2, Newton Fund through UK Met Office, PI (LU), £18196, P109479
• 2020 – 2022, Beyond the networked city: building innovative delivery systems for water, sanitation and energy in urban Africa, ESRC, Co-I £355330, ES/T007656/1
• 2019 – 2021, Building a Flood Hazard Impact Model for India (FHIM-India), Newton Fund through UK Met Office, PI (LU) £185000, P106860
• 2018 – 2019, Newton Fund Research Link Workshop Grant: Hydro-geohazards and Resilient Urban Growth, British Council, Co-I £24000, 2018-RLWK10-10625
• 2019 – 2020, FUTURE-DRAINAGE: Ensemble climate change rainfall estimates for sustainable drainage, NERC, Co-I £110000, NE/S016678/1
• 2019 – 2021, River basins as ‘living laboratories’ for achieving sustainable development goals across national and sub-national scales, NERC, Co-I £160000, NE/S012427/1

Software

We develop and maintain the following software for modelling rainfall-related hazards:

HiPIMS

HiPIMS standards for High-Performance Integrated hydrodynamic Modelling System. It uses state-of-art numerical schemes (Godunov-type finite volume) to solve the 2D shallow water equations for flood simulations. To support high-resolution flood simulations, HiPIMS is implemented on multiple GPUs (Graphics Processing Unit) using CUDA/C++ languages to achieve high-performance computing.

PyPIMS

Pypims provides a user-friendly Python-based interface for users to prepare the inputs, run the hipims model and visualise the outputs.

• 2022, Special Session Organiser, The 39th IAHR World Congress
• 2022 – present, Reviewer Editor, Frontiers in Water
• 2020, Editor, Special issue on shallow water flow modelling in Advances in Water Resources
• 2018, Convenor, The 13th International Hydroinformatics Conference (HIC)
• 2017 – present, Editor, Geoenvironmental Disaster
• 2017, Session Chair, The 37th IAHR World Congress
• 2017, Session Chair, The 15th International Symposium on Geo-disaster Reduction
• 2016 – present, Invited Reviewer,
• Journal of Hydraulic Research’, ‘Advances in Water Resources’, ‘Journal of Hydrology’, ‘ICE – Water Management’, ‘Water Science and Engineering’, ‘Journal of Hydroinformatics’, ‘Journal of Hydrodynamics’, ‘Computational Methods and Applications in Mechanics and Engineering’, ‘Science of Total Environment’, ‘Quarterly Journal of Engineering Geology’, ‘Water Resources Research’, ‘Journal of Geophysical Research: Earth Surface’, ‘Ocean Engineering’, ‘Computers and Geosciences’, ‘Natural Hazards’.
• 2015 – present, Invited seminar speaker, Hohai University, Sun Yat-Sen University, Wuhan University, Technical University of Berlin, University of Edinburgh.