2015 - Uncertainty Propagation in Spatial Environmental Modelling


PhD Course ‘Uncertainty Propagation in Spatial Environmental Modelling’

10-13 November 2015, Copenhagen, Denmark

Teachers: Gerard Heuvelink and Sytze de Bruin, Wageningen University, The Netherlands


Input data for spatial environmental models may have been measured in the field or laboratory, derived from remotely sensed imagery or obtained from expert elicitation. Data are also often digitised, interpolated, classified or generalised prior to submission to a model. In all these cases errors are introduced. Although users may be aware that errors propagate through their models, they rarely pay attention to this problem. However, when the accuracy of the data is insufficient for the intended use then this may result in 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 modelling, such that they can apply these methods to their own data and models. The emphasis is on Monte Carlo simulation methods. Attention is also given to geostatistics for quantification of spatial interpolation errors and on the effects of spatial auto- and cross-correlations on the results of an uncertainty propagation analysis. The course also addresses methods to determine the relative contribution of individual sources of uncertainty to the uncertainty of the final result. Quantification of model parameter uncertainty is covered using Bayesian calibration techniques.

The methodology is illustrated with real-world examples on heavy metal pollution of the soil, on the flooding of a river area in the Netherlands after a dike break, and on calculation of soil hydrological parameters using a combination of linear regression and kriging. The uncertainty analysis of the examples is largely carried out by the course participants themselves in computer practicals. For this, use is made of the R language for statistical computing.

After completing this course, participants will have a clear understanding of how uncertainties in spatial information can be represented statistically using probability distributions, how uncertainties propagate through spatial analyses, and how to apply uncertainty propagation techniques in their own work.

The target group for the course are PhD students with an interest in uncertainty assessment of environmental data and models. In addition, researchers and professionals with the same interests may also benefit from following the course.

Background of participants
Participants are expected to have an intermediate understanding of statistics and basic understanding of environmental science and geo-information science. Familiarity with the R programming language is preferred but not required.

Study material
Relevant literature, lecture materials and computer practical exercises and answers will all be made available digitally to the participants.

Download program with bios of the teachers.

Enrolled Ph.D. students have first priority. Master students and other students will be considered if the course is not filled with Ph.D. students. Registrations from non-Ph.D.-students will be considered after the cut off date mentioned below.

If you come from outside the Copenhagen area:
Please postpone your purchase of air tickets and booking of hotel room until you have received a confirmation from us that the course will actually be held. We will inform applicants from abroad around October 19th. We will have to cancel the course if very few people sign up.

Travel and accommodation
The course is free to attend for Ph.D. students and other students.

Participants are expected to cover their own travel, food, and accommodation expenses. Coffee and tea will be served during the course.

Hotel suggestions:

Budget: Hotel CabInn Scandinavia.

Mid-range: Hotel Østerport.

Participants (6th November):

  1. Gerard Heuvelink, Wageningen University
  2. Sytze de Bruin, Wageningen University
  3. Lennart Ehlers, University of Copenhagen
  4. Trine Henriksen, DTU
  5. Michael Tso, Lancaster University
  6. Jeppe Malmros, University of Copenhagen
  7. Feng Tian, University of Copenhagen
  8. Xiaoye Tong, University of Copenhagen
  9. Johannes Keller, RWTH Aachen/Forschungszentrum Jülich
  10. Dorina Walther, Forschungszentrum Jülich & University of Bonn
  11. Giorgia Faraca, DTU
  12. Donghua Zhang, University of Copenhagen
  13. Kasia Sawicka, Wageningen University
  14. Toke Emil Panduro, University of Copenhagen
  15. Ida Karlsson, GEUS
  16. Hieu Mai, University of Stuttgart
  17. Mehrdis Danapour, University of Copenhagen
  18. Mojtaba Karami, University of Copenhagen
  19. Gorka Mendiguren Gonzalez, GEUS

Registrations from non-PhD students will be considered around October 19th.


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