Continuous radar surface velocity records require purpose-specific conditioning because the temporal information available for processing differs between real-time monitoring and retrospective analysis. Short-period fluctuations and spikes can obscure stage-related flow responses under both settings. We evaluated a stepwise quality control framework that separates these two processing roles using 10 min records from six monitoring sites in South Korea. Causal preprocessing combined Huber-weighted recursive least squares, fuzzy correction, and a trailing Hampel filter to generate a provisional series. Retrospective processing applied a centered Hampel filter followed by criterion-based zero-phase moving average smoothing. Causal preprocessing reduced the standard deviation of successive velocity increments by 14.1–53.4%, with a further 1.4–7.8% reduction observed after centered filtering. A three-point moving-average window was selected at all sites, retaining 98.4–99.9% of the peak velocity and a velocity sum ratio of 1.000. Three sites satisfied all the selection criteria, two satisfied the shape retention criteria, and one was retained under a flagged fallback because the increment variance and slope criteria were not met. Postprocessed index-velocity-method-derived hydrographs showed a lower RMSE and higher R² when compared to the operational stage–discharge benchmark at all sites, while signed biases varied by site. The proposed framework provides a traceable pathway from observation availability and data status to shape assessment and downstream discharge evaluation.