Physically based (PB) hydrological models are essential for flood and water resources management under changing climate and land use patterns. With hydrological extremes on the rise, advanced PB models are receiving increasing attention in research and industry as key tools for adaptation planning. At Newcastle we have developed the fully integrated surface-subsurface PB hydrological model SHETRAN over several decades, applied it to a range of water quantity and quality problems, and recently introduced a national modelling capability to simulate any catchment across the UK.
The main limitation in advanced PB models is appropriate representation of the subsurface (hydro-) geological structure and its hydraulic properties. Data to characterise the hydrostratigraphy is often lacking or in difficult-to-use sources and formats. However, there is a lot of information (increasingly available in the public domain) from borehole logs, geological cross-sections, aquifer properties manuals, regional groundwater model calibration exercises, industry and research reports and papers. This information now needs to be brought together to make an improved hydrogeological dataset for the UK that can be used by various PB hydrological/groundwater and land surface models.
In this project you will trial machine learning as tools for scraping data from borehole logs, geological cross-sections, the Aquifer Properties Manual and various reports/papers. By combining this information with the British Geological Survey (BGS) 3D solid geology model and parameterisations from regional groundwater models, you will develop a state-of-the-art 3D hydrogeological dataset for the UK representing the structure and hydraulic properties of both solid geology and superficial deposits. This will build on recent work at Newcastle on rapid hydrogeological modelling. You will build the dataset into the national SHETRAN model, which will be used as a benchmark for the performance of cutting-edge model developments in the Hydro-JULES land surface model and in groundwater modelling at the BGS. You will also use the improved SHETRAN setup to conduct the latest hydrological projections using UKCP18 climate scenarios. The project will provide you with skills in data handling/processing, python, machine learning, hydrogeological mapping, physically based hydrological and groundwater modelling and climate impact assessment. You will work closely with BGS throughout the project.
Suitable for candidates with numerate degrees and a background in any combination of geology, hydrogeology, geoscience, maths, physics or computational sciences.
This project is part of the ONE Planet DTP. Find out more here: View Website