Multivariate distributional modelling of low, moderate, and large intensities for hydrological applications

In fields such as hydrology and climatology, modelling the entire distribution of positive  data (e.g. precipitation) is essential, as stakeholders require insights into the full range of possible values, from low recordings (droughts)  to large extremes (heavy rainfall). Traditional approaches often segment the distribution into separate regions, which introduces subjectivity (threshold selection) and brings inferential complexity. This is especially true when dealing with multivariate data.
In this talk, I will present a few recent proposals to deal such issues. Different inference schemes will be proposed and tested on simulated data. Concerning the application, we will focus on hydrological problems. In particular the modelling of aggregated rainfall times series in France will be shown, as well as the analysis of  multivariate flood levels for a UK river. 

This talk is based on joint works with Pierre Ailliot, Noura Alotaibi, Carlo Gaetan, Raphael Huser and Matthew Sainsbury-Dale.