Uncertainty Propagation in Spatial Environmental Modelling

To be announced

Scope

Due to various causes errors can propagate through environmental models. Although users may be aware ofi t,they rarely pay attention to this problem. However, the accuracy of the data may be insufficient for the intended use, causing inaccurate model results, wrong conclusions and poor decisions. The purpose of this course is to familiarize participants with statistical methods to analyse uncertainty propagation in spatial modelling, such that they can apply these methods to their own models and data. Both attribute and positional errors are considered. Attention is also given to the effects of spatial auto- and cross-correlations on the results of an uncertainty propagation analysis and on methods to determine the relative contribution of individual sources of uncertainty to the accuracy of the final result. Quantification of model parameter uncertainty is covered using Bayesian calibration techniques. The methodology is illustrated with real-world examples. Computer practicals make use of the R language for statistical computing.
This course:

• focuses on uncertainty propagation in spatial models, while SUADM concentrates on uncertainty analysis of dynamic models;
• uses basic to intermediate statistical approaches and graphical tools to analyse uncertainty and uncertainty propagation, while SUADM uses more advanced statistical approaches;
• dedicates one full day to the use of geostatistics for quantification of spatial uncertainty, while SUADM draws specific attention to stochastic sensitivity analysis.

Programme

• Day 1, morning: Course overview; lectures and exercises probabilistic modelling of uncertainty; lecture and exercises Taylor series method.
• Day 1, afternoon: Computer practical Taylor series method.
• Day 1, evening: Group dinner in Wageningen town.
• Day 2, morning: Lecture and exercises Monte Carlo method and quantification of uncertainty source contributions.
• Day 2, afternoon: Computer practical Monte Carlo method and analysis of uncertainty source contributions.
• Day 3, morning: Lecture and exercises geostatistics for spatial uncertainty quantification.
• Day 3, afternoon: Computer practical geostatistics.
• Day 4, morning: Lecture and exercises Bayesian calibration for quantification of model structural and model parameter uncertainty.
• Day 4, afternoon: Computer practical Bayesian calibration.
• Day 5, morning: Lecture, exercises and computer practical positional uncertainty propagation.
• Day 5, afternoon: Guest lecture; course evaluation; uncertainty game; drinks and snacks.

General information

Fees ¹

Generally, the following fees apply for this course, but note that the actual fees may be somewhat different for the next edition of this course.

¹ The course fee includes a reader, coffee/tea, and lunches.

More information

Dr. Claudius van de Vijver (PE&RC)
Phone: +31 (0) 317 485116
Email: claudius.vandevijver@wur.nl

Dr. Lennart Suselbeek (PE&RC)
Phone: +31 (0) 317 485426
Email: lennart.suselbeek@wur.nl

Registration

At this moment, this course is not scheduled yet. However, if you register your interest in this activity below, we will inform you as soon as the course is scheduled and registration of participation is opened.