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Uncertainty Analysis and Statistical Validation of Spatial Environmental Models (UA&SV)

https://www.pe-rc.nl/postgraduate-courses/Uncertainty-Analysis-Statistical-Validation

Input data for spatial environmental models may have been measured in the field or laboratory, spatially interpolated, derived from remotely sensed imagery or obtained from expert elicitation. 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, 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 environmental modelling, such that they can apply these methods to their own models and data. It also teaches methods that quantify the contribution of individual uncertainty sources and statistical validation methods to assess the accuracy of spatial model outputs with independently sampled data. 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 differs from the “Statistical Uncertainty Analysis of Dynamic Models” (SUADM) course, in that it:
• 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 a full day to statistical validation of outputs of spatial models using spatial sampling theory, while SUADM draws specific attention to stochastic sensitivity analysis.