Ben Evans

Research Area

I build understanding of environmental systems from a data-driven perspective. I primarily work on monitoring and predicting iceberg populations and dynamics in the Antarctic and Arctic. Icebergs represent critical components at the interface between ice sheets and the oceans. They control spatiotemporal patterns of freshwater and nutrient inputs, thereby influencing ocean circulations and primary productivity. Ultimately, therefore, they have effects on global climate through physical and biological mechanisms that are currently poorly understood and represented in predictive models.

I use large-scale Earth observation datasets and develop machine learning methods for identifying and tracking icebergs. I use these observations to understand controls on iceberg calving, movement, melt and fragmentation with a view to building predictive models to better constrain freshwater and nutrient distribution estimates and mitigate hazards to shipping. Through collaborations I use our improved understanding of iceberg fragmentation to improve numerical ice fracture and calving models. This mechanistic modelling, along with satellite-derived parameters relating to ice fracture such as crevassing or surface meltwater, and oceanographic data will then help to predict the timescales and locations of climate impacts from the stage where ice is still within the ice sheet to when it finally melts out at sea.

Project Interests

I am particularly interested in developing projects coupling Earth observation, in-situ data and simulation methods to investigate controls on calving from the ice sheet and the extent to which these are also applicable to predicting how, when and where icebergs break up once calved. There is plenty of scope to explore improvements on our existing machine learning and numerical methods, develop novel data assimilation and hybrid modelling approaches and implement multimodal datastreams, ultimately moving towards fully data-driven predictive models of iceberg (and sea ice) dynamics.