Water quality, mass balance of glacier measurements, snow and water extremes request very pragmatical approach to model extremal events.
In particular, global climate change is expected to increase both the frequency and intensity of climate extremes, such as severe drought, heat waves and periods of heavy rainfall, and there is an urgent need to understand their ecological consequences (see e.g. Intergovernmental Panel on Climate Change, IPCC).
In particular, robustness, distributive sensitivity and choice of appropriate statistical learning mechanism will be discussed.
I will introduce novel approaches to understand and model extremal events in moderate size and possibly contaminated samples. I acknowledge support of FONDECYT Regular N 1151441 and LIT-2016-1-SEE-023.