- High-resolution modelling of the dynamics of flooding and associated processes interacting with natural flood management measures
- Digital twins for natural flood management to support its design, implementation and operation
- Next generation of modelling techniques that could step change the current practice
Flooding, which may be fluvial, pluvial, coastal or groundwater related, or caused by a combination of these processes, is the most damaging natural hazard in the UK and across the globe (Dadson et al. 2017). The sustainability of traditional hard engineered solutions has become questionable, and providing adequate protection is rapidly becoming unacceptably expensive (Wingfield et al. 2019). To improve resilience and reduce the vulnerability of our communities, a recent trend is to manage flood risk at source through Catchment-Based Flood Management (CBFM), which emphasises working with – rather than against – natural processes, i.e. Natural Flood Management (NFM); defined as altering, restoring or using a landscape’s features to reduce flood risk (House of Parliament 2011). Beyond mitigating flood risk, a successful NFM based CBFM scheme may also have many co-benefits, i.e. reduce erosion, improve soil and water quality, provide carbon storage and increase biodiversity (Dadson et al. 2017). Attempts have been made to implement NFM-based CBFM across the UK, such as Belford, Pickering and Holnicote (Somerset). However, most current approaches involve designing and installing catchment runoff attenuation features (RAFs; Figure 1) by trial and error or informed by simplified modelling tools (Burgess-Gamble et al. 2018). The former illustrates the lack of understanding; the latter are not capable of representing detailed dynamic flooding processes and often do not take into account other relevant processes, e.g. sediment, pollution transport, associated dynamic feedback loops, and therefore do not reliably inform the design and implementation of NFM schemes for multiple benefits and maximised performance. Progress has been made to inform NFM through modelling (e.g. Nicholson et al. 2019), however, a new generation of models and tools are still urgently needed to create solid scientific evidence, facilitate implementation and elucidate the practice in a rapidly changing environment.
This exciting PhD project therefore aims to develop a flexible computing environment to integrate a high-performance multi-process modelling system with data streams from multiple sources to facilitate design and implementation of effective NFM schemes for CBFM. This will essentially deliver an innovative NFM digital twin to replicate the relevant natural and built systems and related interactive processes.
The proposed NFM digital twins will be based on the High-Performance Integrated hydrodynamic Modelling System (HiPIMS) developed at Loughborough University (Xia et al. 2019), which simulates detailed dynamics of rainfall-induced flooding and associated processes (e.g. sediment and pollutant transport). HiPIMS will be further developed to include approaches to represent RAFs of different types and sizes. A flexible computing environment will be developed to allow HiPIMS to: 1) receive and efficiently pre-process (near/real-time) data streams, providing inputs and boundary conditions to drive simulations and observations to validate the model and improve prediction; 2) visualise model outputs for different purposes; and 3) allow instantaneous user intervention to modify model settings/RAFs to test different design options in real time. This essentially creates an NFM digital twin that can run in real time to monitor and assess the performance of the catchment and RAFs during a storm event.
Training and Skills
This project requires a student with an aptitude for data analytics, computer-based process modelling and a good understanding of hydrological processes and flood risk management. For further development of key skills, the student will be able to benefit from in-house courses or training activities at Arup, and NERC Advanced Training Short Courses in topics such as numerical modelling and forecasting in flood risk management and understanding uncertainty in environmental modelling.
Year 1: The existing modelling tools, data and the current practice in NFM research and implementation will be systematically reviewed, commencing with research and analysis work by the EA (Burgess-Gamble et al. 2018). Working closely with relevant staff from Arup and other stakeholders (e.g. EA), the problems and challenges to inform model developments will be identified and a suitable case study site will be selected.
Year 2: HiPIMS will be set up in the case study site to simulate rainfall-runoff, flooding and other associated processes including hydro-geomorphology, pollutant transport and water quality, and tested by reproducing past events. This will enable identification of catchment features that contribute to increased flood risk, excessive erosion and degradation of water quality. HiPIMS will be further improved by developing extra components to model NFM features of different types and sizes. Further simulations will be undertaken to assess the performance of existing NFM schemes and identify limitations of current practice.
Year 3: A flexible computing environment will be developed to integrate HiPIMS with data streams from different sources to create a NFM digital twin of the case study site, which can run in real time to monitor and assess the performance of the catchment and RAFs during a storm event and also perform scenario simulations to test different design and implementation options to achieve the overall goals of NFM for multi-beneficial CBFM. The digital twin will be used to design an improved NFM scheme in the case study site to overcome the identified limitations, which will be tested and visualised using historical records and real-time events.
Due to the highly innovative nature of this PhD project, at least 3 high-quality peer-reviewed journal papers will be prepared for publication, coving topics e.g. in high-performance modelling NFM, scientific evidence of NFM for flood risk reduction and co-benefits informed by high-resolution hydrodynamic modelling, and NFM digital twins and application.
Partners and collaboration (including CASE)
This collaborative studentship will bring together Loughborough University’s strong flood modelling expertise and experience and Arup’s abundant field experience and data to develop the next generation of modelling for NFM to improve resilience to flooding and climate change, addressing an urgent societal challenge facing the UK. The student will spend time in Arup and work closely with Dr Alex Nicholson to gain first-hand engineering experience in NFM. Through Dr Nicholson, the student will also have opportunities to interact closely with relevant staff from the Environment Agency (EA).
For further information, please contact Prof Qiuhua Liang (Q.Liang@lboro.ac.uk), Dr Jiaheng Zhao (J.Zhao@lboro.ac.uk) or Dr Xilin Xia (X.Xia2@lboro.ac.uk). For enquiries about the application process, please contact Berkeley Young email@example.com, School of Civil and Building Engineering, Loughborough University. Please quote CENTA2 when completing the application form: http://www.lboro.ac.uk/study/apply/research/