A Robust Inversion Method for Cross-Sectional River Surface Flow Velocity With Zonal Adaptive Multiangle Pairing Using UHF Streamflow Radar

This paper proposes a Zonal Adaptive Multi-Angle Pairing Robust Inversion (ZAMPRI) method to address the challenge of limited accuracy in retrieving cross-sectional surface flow velocity using Ultra-High Frequency (UHF) streamflow radar in complex riverine environments. Building upon the conventional Teague method, ZAMPRI incorporates longitudinal cross-section partitioning, adaptive estimation of the main flow direction, robust pre-screening based on the Median Absolute Deviation (MAD), and a multi-angle pairing-weighted fusion mechanism, which enables stable retrieval of cross-sectional surface flow velocity under complex conditions such as flow direction deviation, boundary interference, and data gaps. Simulation experiments demonstrate that under noisy, data-gapped conditions, ZAMPRI significantly reduces the MAE and RMSE of the retrieved cross-sectional surface flow velocity compared to the traditional Teague method, thereby exhibiting enhanced robustness. Multi-scale validation (single survey and long-term time series) using field data from the Yangtze River further shows that the cross-sectional surface flow velocity retrieved by ZAMPRI achieves a correlation coefficient exceeding 0.9 with measurements from a horizontal acoustic doppler current profiler, demonstrating stable reflection of true cross-sectional flow velocity variations under diverse hydrological and noise conditions. This study provides a highly accurate, generalizable new approach for estimating river cross-sectional flow velocity and discharge using monostatic UHF streamflow radar.