816355 Uncertainties in hydrological and ecosystem modelling
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- Lecture and exercise
- Semester hours
- Lecturer (assistant)
- Schulz, Karsten , Lücking, Sophie
- Offered in
- Wintersemester 2021/22
- Languages of instruction
The course will cover the following topics:
Introduction into hydrological and ecosystem modelling concepts; the quality of observation - parameter and input data; mathematical concepts for describing uncertainties, methods to propagate uncertainties (error propapgation, stochastic simulation, monte carlo methods); model calibration, measures of goodness-of-fit, and optimization methods; parameter and prediction uncertainty; Introduction into Bayesaian uncertainty estimation „Generalized Likelihood Uncertainty Estimation (GLUE)“; Sensitivity analysis.
- Previous knowledge expected
It is expected that participants have some background in statistics, calculus and environmental systems. Starting in week 3, it is expected that students have developed via self-learning some introductory knowledge in R- or Python-programming.
The course is designed for students at the M.Sc. or PhD level with interest in ecosystem modelling.
- Objective (expected results of study and acquired competences)
After having finished the course, students should have
- an understooding of the theoretical background of the methods and topics covered in the course and listed above;
- the ability to transfer these methods to other systems and problem areas; this will include evaluating the model complexity needed given the available objectives and data/information available;
- the ability to critically assess the quality of data and final model predictions.
You can find more details like the schedule or information about exams on the course-page in BOKUonline.