实现水文测站在线流量高精度计算对于水文监测、水旱灾害防御、水资源管理等具有重要研究意义。采用水文测站监测的上下游水位作为边界条件,构建水文测站附近河段一维水动力模型,使用卡尔曼滤波技术根据水文测站实测流量对所建模型糙率参数进行自动率定,利用BP神经网络对水位糙率关系进行学习训练,实现水文测站流量在线计算;以兰溪水文站为例,基于该方法的兰溪测站在线洪峰流量计算精度高于目前使用的指标流速法。研究成果具有良好的参考意义和推广应用价值。

[Objective] Hydrological stations play a crucial role in monitoring hydrological regime changes. Achieving online flow calculation at hydrological stations and improving flow calculation accuracy are of great research significance for hydrological monitoring, flood and drought disaster prevention, and water resource management. [Methods] By monitoring the upstream and downstream water levels of hydrological stations as boundary conditions, a one-dimensional hydrodynamic model of the river reach near the hydrological station was constructed. The Kalman filter technique was employed to automatically calibrate the model’s roughness parameters based on measured flow data from the station. Using the upstream and downstream water levels as inputs and the automatically calibrated roughness data as outputs, a BP neural network was constructed to fit the complex relationship between water levels and model roughness. During online flow calculation, the roughness values were corrected using the real-time upstream and downstream water levels and the water level-roughness neural network relationship to improve flow calculation accuracy. By correcting the roughness based on real-time upstream and downstream water levels and using the constructed one-dimensional hydrodynamic model for simulation calculation, online flow calculation at hydrological stations was achieved. [Results] Taking Lanxi Hydrological Station as an example, the accuracy of online peak flow calculation at Lanxi Station using the proposed method was higher than that of the currently used index velocity method. For three major flood events selected, the flood flow calculation accuracy at Lanxi Station using the proposed method was higher than that of the index velocity method currently used at Lanxi Station. The reason was that the index velocity method, when establishing the relationship between index velocity and cross-sectional average velocity, used only boat-measured flow data to calibrate the relationship, which could lead to significant errors and consequently larger errors in peak flow simulation. In contrast, this study constructed a one-dimensional hydrodynamic model, used measured flow data to automatically calibrate the model roughness parameters, corrected roughness based on real-time upstream and downstream water levels to perform online flow calculation with the model, and utilized more real-time water level information than the index velocity method for model calibration and assimilation, thus achieving higher peak flow calculation accuracy. [Conclusion] This study achieves online flow calculation at hydrological stations and improves flow calculation accuracy by utilizing upstream and downstream water levels. The applicability of the method is verified using Lanxi Hydrological Station as an example, demonstrating significantly improved flow calculation accuracy compared to the index velocity method, particularly during major floods with high water levels. The method proposed in this paper is suitable for online flow calculation at hydrological stations located in upstream or midstream river reaches with relatively stable riverbed cross-sections where sediment erosion and deposition effects can be neglected. Considering the relatively low cost of water level gauges, the method demonstrates good application prospects and promotion value.