The framework presents a method to quantify #uncertainty propagation in #dynamic #scenarios, focusing on discrete #stochastic processes over a limited time span. These dynamic uncertainty sets encompass various uncertainties like distributional ambiguity, utilizing tools like the Wasserstein distance and $f$‑divergences. Dynamic robust #risk #measures, defined as maximum #risks within uncertainty sets, exhibit properties like convexity and coherence based on uncertainty set conditions. $f$‑divergence‑derived sets yield strong time‑consistency, while Wasserstein distance leads to a new non‑normalized time‑consistency. Recursive representations of one‑step conditional robust risk measures underlie strong or non‑normalized time‑consistency.