Vincent VerjansPhD student, Associate Lecturer
My field of research is glaciology. I am currently a PhD student at Lancaster University, supervised by Amber Leeson. My research project focuses on the firn densification process. I have implemented numerical schemes of liquid water percolation in firn densification models relying on physically based theories of infiltration in porous media. This has required linking theories and modelling strategies from hydrology, snow science and glaciology. I collaborate closely with the University of Washington and I have implemented different liquid water schemes in the Community Firn Model that has been developed there.
I also aim at improving the calibration process of firn densification models, which are empirical models tuned to observations. I develop a Bayesian analysis methodology in order to have a better knowledge of the sensitivity of firn models to their parameter values, to better constrain the uncertainty about these parameters and to assess the sensitivity of the densification process to climatic variables and boundary conditions. Ultimately, a better understanding of the firn densification process would help us to constrain the quantification of the mass balance of ice sheets, to correct altimetry measurements of their surface elevation and to interpret paleoclimate signals from ice cores with more precision.
Throughout my research, I have developed a particular interest in numerical modelling and in statistical methods for Earth and environmental sciences (e.g. for sensitivity and uncertainty analysis). I strongly believe that these are essential tools in our understanding, representation and prediction of complex geosciences-related processes.
2014-2017: Master in Environmental sciences at Université Libre de Bruxelles, Master thesis: Sensitivity of the Greenland ice sheet to climatic forcing and model resolution, supervised by F. Pattyn and H. Goelzer
2017 - : PhD at Lancaster University, Firn densification Modelling, supervised by Amber Leeson and Keith Beven