Accounting for hydro-meteorological uncertainty in water resources systems analysis (WRSA) is fundamental to robust system design and operations. As water resources systems become more stressed due to factors like complex human population demands and climate change, the need for faithful representation of this uncertainty is increasingly more salient. Going forward, the challenge of adapting these systems to new hydro-meteorological regimes further underscores the importance of attempting to understand and model emergent properties of this uncertainty. Moreover, the continued evolution of water resources management and planning strategies make legacy methods of hydro-meteorological uncertainty characterization inadequate. In this study, we develop novel methodologies to address these emerging requirements for uncertainty modeling brought about both by new adaptation strategies (e.g. forecast informed operations) and the need to address anthropogenic non-stationarity in hydro-meteorological errors. We first develop a modeling approach to produce synthetic forecasts, which are emulations of hindcasts produced by computationally demanding meteorological and hydrological forecast models. This computational demand and short period of availability (~1980 to present) severely limit the utility of the native hindcasts for robust system analysis and design. Synthetic forecasts can be generated anywhere observations exist with manageable computational effort allowing for a much richer characterization of forecast uncertainty. We extend this effort to hydrologic ensemble forecasts that underpin current efforts to implement forecast informed reservoir operations (FIRO) in the western U.S. Through operational testing with the latest FIRO operations model, we show that these synthetic forecasts both faithfully replicate operational behaviors of the original hindcasts and elucidate system vulnerabilities. Finally, we address emergent properties of hydro-meteorological uncertainty through an idealized ‘model-as-truth’ experimental design that shows the effect of climate shifts on hydrologic uncertainty. We then develop a hybrid machine learning-statistical approach that can capture these shifts in uncertainty through model state relationships and propagate it into new simulations through a stochastic watershed model (SWM) architecture. Overall, the methodological advances forwarded in this work provide a rich suite of hydro-meteorological uncertainty modeling tools to address fundamental challenges in the critically important sphere of water resources systems adaptation.