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.
Target group: The course is aimed at PhD candidates and other academics working with spatial models who want to know how errors in inputs propagate to model outputs.
Course duration: 5 days
Contact: PE&RC Office: office.pe@wur.nl
Registration of interest: You can register your interest HERE (note: this is not an official registration).
