816355 Uncertainties in hydrological and ecosystem modelling

Lecture and exercise
Semester hours
Lecturer (assistant)
Schulz, Karsten
Offered in
Wintersemester 2023/24
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.