Research School for Socio-Economic and
Natural Sciences of the Environment
Research School for Socio-Economic and
Natural Sciences of the Environment

Uncertainty propagation in spatial environmental modelling

Date: 07 December 2020 - 11 December 2020
Location: Wageningen/Online

Due to various causes errors can propagate through environmental models. Although users may be aware of it, 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 familiarise 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 Statistical Uncertainty Analysis of Dynamic Models (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.

Course FrequencyOnce every two years
Prior knowledgeIntermediate knowledge of statistics, geo-information science and spatial modelling. Familiarity with the R programming language is preferred but not required.
Intended credits1.5 ECTS
Course organisationThe C.T. de Wit Graduate School for Production Ecology and Resource Conservation (PE&RC)
More informationCourse website