In this study, the performance and applicability of non-contact discharge estimation methods were evaluated using surface velocity time series obtained from a Doppler radar-based multi-line surface velocimetry system (PAVOs). The focus was on improving cross-sectional representativeness beyond conventional single-line observations, and complementing contact-based discharge measurements during high-stage and peak-flow periods in the flood season. The quality of the surface velocity data was improved by optimizing operational parameters and removing outliers using a Hampel filter. Discharge was estimated using two methods: (i) the mid-section method, which reflects multi-line surface velocity distributions, converts surface velocities to mean velocities, and integrates discharge over subsections, and (ii) the index-velocity method, which combines stage-area and index velocity-mean velocity relationships. The estimated discharges were compared with the reference discharge (Qref) and their performance was assessed using the Nash–Sutcliffe efficiency (NSE), correlation coefficient (r), mean absolute error (MAE), percent bias (PBIAS), and peak discharge error (peak_err). The results showed that both methods generally reproduced the reference discharge variations under medium- to high-stage conditions, although their applicability differed depending on the stage conditions. These findings suggest that multi-line non-contact surface velocity measurements can support peak flood discharge estimation and continuous discharge monitoring by complementing the limitations of conventional single-line observations and more effectively reflecting cross-sectional velocity variability.

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.