Accurate measurement of river hydrological characteristics is critical for assessing the impacts of flooding caused by meteorological and geomorphological factors. Flow velocity are key indicators in hydrological monitoring. Traditional measurement approaches, such as continuous-wave Doppler radar and pulsed radar systems, are typically mounted on bridges or fixed supports and offer only single-point measurements. These methods often suffer from limited detection range, low accuracy, and poor resistance to environmental interference. To address these limitations, this study proposes a three-dimensional flow detection framework based on multi-input multi-output (MIMO) radar sensors. By leveraging the high reliability and interference resistance of MIMO radar, along with a Space-Velocity-Time (SVT) algorithm that incorporates spatiotemporal information (two-dimensional surface velocity and time), the proposed method enables robust 3D river flow monitoring. In this study, comparative experiments were conducted on four rivers in China with different flow conditions, geomorphic features and weather environments. Results demonstrate that the proposed method achieves a measurement error of less than 5 % compared to acoustic Doppler current profilers (ADCP) and other conventional mechanical approaches, while also offering improved safety and real-time performance. Moreover, an adaptive flow correction algorithm is presented, which uses three optimized prediction models to compute the correction factor and reduces the mean streamflow measurement error to 0.79 % after correction, providing an effective solution for river gauging, flood control and flood resilience.
Zhang et al. (2025) studied this question.