Data driven hydrological modelling for railway water management

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When railway drainage infrastructure fails, resulting flooding can cause delay and risk to life for passengers, and penalty costs for Network Rail (NR), but can also cause other severe failures such as land slips, as seen in the recent serious derailment event in North-East Scotland.Recent collaboration between NR and The University of Sheffield (UoS) has begun to shed light on the performance and degradation of drainage assets, and the resulting impacts on parent assets. However, this understanding is contingent on accurate and reliable knowledge of the volume of water entering the railway system from the surrounding catchment.Current hydrological models operate at large scale to predict broad areas of flooding, and are not developed for accurate modelling of flow paths at scales relevant to individual railway assets. Digital terrain models are typically limited in resolution and can omit smaller scale drainage features such as roadside ditches that can substantially alter a small catchment. Models also suffer from deep uncertainty in factors such as infiltration rate, storage capacities and flow routing. This project will use a data-driven approach to address the uncertainty in water transport processes from rainfall to railway, enabling more accurate prediction of arrival flow volumes at the individual railway asset level.The project will be supervised by Dr Nichols and Professor Tait in collaboration with partners from Network Rail. Network Rail will provide industrial focus and enable the successful applicant to work at the interface between industry and academia, with substantial time spent both at the university and the company