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The students will: LO1. understand the relevance of uncertainty quantification for practical hydrological problems,LO2. be able to apply uncertainty quantification methods to hydrological problems, LO3. be able to critically analyze the results of the applied methods, LO4. be able to effectively work in an interdisciplinary team, LO5. be able to effectively present and communicate the results to an interdisciplinary audience.

The lecture with integrated exercises is offered as MOOC. A series of lectures (recorded and always available on Moodle) will introduce uncertainty quantification methods and their application in hydrology. This part will cover about 30 h of on line lecture material including practical work (e.g., how to develop phython scripts step by step and providing them on line). Topics covered are: - Introduction about uncertainties in hydrology - Rating curves and their uncertainties - Uncertainties in surface water - groundwater interaction - Uncertanties in lumped hydrological models - Sensitivity analysis of hydrological models - Introduction about uncertainty quantification methods - Basics of python programming - Markov Chain Monte Carlo - Bayesian inversion for linear and non linear models - Active subspace method - Presenting uncertainties to an interdisciplinary audience - Presenting the results on an interdisciplinary project to an interdisciplinary audience

Lineare Algebra (MA0004) Einführung in die Wahrscheinlichkeitstheorie und Statistik (MA0009) Einführung in die Programmierung (MA0010) Grundmodul Hydrologie Hydrologische statistik