The guidance provided in this online book can be used to frame consistent and rigorous experimental designs for better understanding the consequences and insights from modeling choices. The progression of sections in this text provides a thorough introduction to the concepts and definitions of diagnostic model evaluation, sensitivity analysis, and uncertainty characterization (UC). In addition, it comprehensively discusses how specific modeling objectives and applications should guide the selection of appropriate techniques; broadly, these can include model diagnostics, in-depth analysis of the behavior of the abstracted system, and projections under conditions of deep uncertainty. This text also contains a detailed presentation of the main sensitivity analysis methods and a discussion of their features and limitations. Readers are provided with an overview of computer tools and platforms that have been developed and could be considered in performing UC. The appendices include an overview of uncertainty quantification methods, a terminology glossary of the key concepts, as well as Jupyter notebooks providing hands-on training with example test cases and scripts to showcase various UC-related capabilities. 

View online at https://uc-ebook.org/.