The food-energy-water nexus (FEWN) has been receiving increasing interest in the open literature as a framework to address the widening gap between natural resource availability and demand, towards more sustainable and cost-competitive solutions. The FEWN aims at holistically integrating the three interconnected subsystems of food, energy and water, into a single representative network. However, such an integration poses formidable challenges due to the complexity and multi-scale nature of the three subsystems and their respective interconnections. Additionally, the significant input data uncertainty and variability, such as energy prices and demands, or the evaluation of emerging technologies, contribute to the system’s inherent complexity. In this work, we revisit the FEWN problem in an attempt to elucidate and address in a systematic way issues related to its multi-scale complexity, uncertainty and variability. In particular, we provide a classification of the sources of data and technology uncertainty from historic data, forecasting and process parameters, and propose ways to quantify their impact on the integrated system analysis. To effectively tame the FEWN’s multi-scale complexity, we distinguish between the introduced error of approximation and optimization of employed surrogate models. In turn, it is possible to characterize their impact on optimal FEWN decision-making based on the quantification of the introduced errors at all levels. Thus, we present strategies to systematically characterize FEWN process systems modeling and optimization. Ultimately, this facilitates translating obtained solutions into actionable knowledge by quantifying the level of confidence one can have in the derived process model and optimal results.