PhD in Mathematics

Climate change is driving more frequent and intense extreme rainfall events, creating serious challenges for flood risk management across the UK. Current rainfall datasets are not fit for purpose: radar estimates can be inaccurate, and rain gauge networks, while reliable, are too sparse to capture highly localised storms. Reliable, high-resolution rainfall data is urgently needed to improve flood prediction, climate adaptation, and infrastructure resilience. This project will tackle that challenge by developing a new blended UK precipitation dataset at high temporal (15-minute) and spatial resolution. The student will integrate data from multiple sources—including tipping bucket rain gauges, weather radar, and satellites—using robust coding and data science techniques. A comprehensive quality control framework will be built to identify and correct errors, apply bias adjustments, and assess data quality. State-of-the-art multisource blending methods will then be applied (e.g. kriging, probabilistic merging, machine learning) to combine datasets and preserve extremes. Uncertainty will be quantified explicitly, with outputs designed to inform both researchers and practitioners. The resulting dataset, open-access and user-focused, will directly support advanced flood risk modelling, hydrological predictions, and adaptation planning.