Quantifying uncertainty in hydrological systems: A Bayesian point of view
Convener: Ashish Sharma | Co-Conveners: Ilaria Prosdocimi, Dmitri Kavetski, Lucy Marshall, Mojtaba Sadegh, Alberto Viglione

Past decades have seen a flurry of activity in Bayesian applications in hydrology. These applications include those to quantify model and parameter uncertainty, develop alternatives to reduce structural uncertainty through sensible averaging procedures, to new alternatives of defining uncertainty by relaxing the framework for specifying model likelihoods. Additionally, hydrologists are starting to adopt data assimilation as a new way to both reduce predictive uncertainty, and also to assess where assumed model structures may not be fully adequate. This session invites contributions involving new and innovative ways of using Bayesian methods for the type of hydrological problems mentioned above, as well as other emerging problems that such techniques have been put for use in.