Shallow water models are successfully used for simulating geophysical flows like river floods, tsunamis, sediment transport, or debris flows. Depth-averaged models are in general attractive due to their low computational cost. However, information on the vertical velocity is required a priori, typically by assuming a parametrized profile using a time-independent function, e.g., a constant. A systematic generalization of depth-averaged flow models is given by the shallow moment method. This method retains transient information about the vertical flow profile by using a finite Legendre expansion for said profile with time and space-dependent coefficients. The shallow moment approach allows to include vertical information and generates a hierarchy of equations that in the limit, recover the reference system before depth-averaging. Even a low number of basis functions significantly increases the model’s capability to resolve vertical information and, whenever this is relevant for the modeling task at stake, significantly improves the predictive power compared to classical shallow water systems. In the paper, we show a two-dimensional simulation result based on the shallow moment system to predict processes unobservable in classical shallow flow, namely secondary flow and the redistribution of velocity profiles, which are of relevance in curved channelized flow as experimentally demonstrated by Steffler.