Justin Sheffield is Professor of Hydrology and Remote Sensing at the University of Southampton in the UK. He spent 16 years at Princeton University in the US before returning to the UK in 2016. Prof. Sheffield’s research is centred on large-scale hydrology and its interactions with climate variability and change. He has published extensively on hydrological extremes, climate change, and hydrological processes from catchment to global scale, and on the application of fundamental research to natural hazards impacts reduction, and water and food security particularly in developing regions, including monitoring and prediction systems.
He has received a number of accolades including the Prince Sultan Bin Abdulaziz International Prize for Water in 2014 for research work on drought monitoring and prediction, and the Plinius Medal of the European Geosciences Union in 2013 for outstanding multi-disciplinary research and applications in hydrological hazards. Most recently he was named as the 2019 Robert E. Horton Lecturer in Hydrology by the American Meteorological Society for advancing hydrologically coherent analyses of drought across time and space scales, and for pioneering the development of integrated drought monitoring tools for food-insecure countries.
Work History
- 2016-present: Professor of Hydrology and Remote Sensing, Geography and Environment, University of Southampton
- 2000-2016: Research Scholar, in Department of Civil and Environmental Engineering at Princeton University, USA.
- 1992-2000: Senior Research Associate, in Department of Civil Engineering at University of Newcastle, UK.
Research Overview
Justin’s research is centred on large-scale hydrology and its interactions with climate variability and change. He has published extensively on hydrological extremes, climate change, and hydrological processes from catchment to global scale, and on the application of fundamental research to natural hazards impacts reduction, and water and food security particularly in developing regions, including monitoring and prediction systems.