MultiSector Dynamics (MSD) research explores the dynamics and co-evolutionary pathways of human and Earth systems and the emerging interdependent sectors of energy, water, agriculture, and transportation among others. The interactions between these sectors are central to MSD science, as they capture how processes and feedbacks across Earth, environmental, infrastructure, and societal systems shape transitions, socioeconomic risks, and the provision of services. By definition, MSD research requires deep integration across diverse scientific disciplines, ranging from the natural to the social sciences and engineering. All these disciplines apply a variety of numerical simulation models to study and understand their underlying systems of focus. The utility of these models hinges on the fidelity with which they represent the real systems and their ability to produce novel insights about systems and their interactions. The coupled human-natural systems typically represented are shaped by a multitude of interdependent human and natural processes which, when modeled, translate to highly complex, non-linear, interacting behaviors. This ever-increasing complexity massively expands the uncertainty space of a model and can be found in model inputs, processes and parameters. This is further amplified when several models are combined to answer multisectoral questions, as additional uncertainty regarding coupling relationships and interactions is introduced.

The ambition of MSD research therefore relies on the development and application of tools to address model uncertainty and should ideally promote rigorous quantitative mappings of its effects on scientific insights. Unfortunately, there is currently no singular guide on how to best confront the computational and conceptual challenges of multi-model and transdisciplinary workflows necessary for MSD science. This work presents a recently published and continually updated online eBook written to specifically fill this gap. The book overviews methods and concepts of uncertainty characterization, sensitivity analysis, exploratory modeling, scenario discovery, and uncertainty quantification. The book is also accompanied by a GitHub repository containing application examples and exercises.