Preface -- Introduction and necessary distinctions -- Step 1: Search -- Step 2: Quantify -- Step 3: Propagate -- Step 4: Estimate uncertainty -- Step 5: Rank uncertainties -- Step 6: Present the analysis and interpret its results -- Practical execution of the analysis -- Uncertainty analysis when separation of uncertainties is required -- Practical examples -- References -- Subject index.

This book is a practical guide to the uncertainty analysis of computer model applications. Used in many areas, such as engineering, ecology and economics, computer models are subject to various uncertainties at the level of model formulations, parameter values and input data. Naturally, it would be advantageous to know the combined effect of these uncertainties on the model results as well as whether the state of knowledge should be improved in order to reduce the uncertainty of the results most effectively. The book supports decision-makers, model developers and users in their argumentation for an uncertainty analysis and assists them in the interpretation of the analysis results.