Loughborough University is a top-ten rated university in England for research intensity (REF2014) and an outstanding 66% of the work of Loughborough’s academic staff who were eligible to be submitted to the REF was judged as ‘world-leading’ or ‘internationally excellent’, compared to a national average figure of 43%. In choosing Loughborough for your research, you’ll work alongside academics who are leaders in their field. You will benefit from comprehensive support and guidance from our Doctoral College, including tailored careers advice, to help you succeed in your research and future career. Industry-standard streamflow forecasts use state-of-the art weather forecasts to drive hydrological models and predict hydrological extremes (floods and droughts) weeks to months before they occur. However, these traditional approaches are often computationally demanding and have limited skill, which hinders their uptake by end users. Now, with the advent of advanced statistical forecasting techniques alongside a range of Big Data sources (such as Earth Observation from satellite archives), it is possible to start exploring ‘intelligent’ approaches to forecast hydrological extremes. This project will evaluate the viability of skilful long-range statistical forecasting based on a range of Global Big Data sources, including climate forecasts, teleconnection indices, and Earth Observation data. The research will involve accessing climate hindcasts (e.g. precipitation, temperature, sea surface temperature, atmospheric moisture) from a range of modelling centres (e.g. ECMWF, NOAA, and NASA), Earth Observation data from satellite archives (e.g. SMOS, Landsat, or Sentinel), and streamflow time series for catchments in different physical and climatic environments around the world. Seasonal streamflow extremes will be forecast using a range of statistical models in R, including machine learning models. The skill of the statistical forecasts will be compared with that of industry-standard forecasts. This research will require a degree of quantitative expertise, strong analytical skills, understanding of time series analysis, and the ability to code in R or Python. The student will acquire state-of-the-art skills and knowledge of forecasting methodologies, data science and ‘Big Data’ analysis techniques.