基于相似性理论，提出一种基于相似过程动态识别的径流预报新方法，以提升实时径流预报的精度和适应性。通过构建降雨-径流时空特征指标体系，采用滑动窗口多时间尺度相似性搜索算法，从历史降雨径流事件中动态识别与当前过程最相似的典型事件，对其未来径流过程进行加权组合，生成综合相似径流预报，并通过动态展延将预报过程与当前实测流量衔接校正。以赣江上游峡山水文站以上流域的历史日降雨径流数据为例，采用逐年留一交叉验证评估模型性能。结果表明：滑动窗口多时间尺度相似性搜索策略可以充分利用邻近时段的关键特征。在无预见期降雨预报信息的情况下，1–2天预见期的径流预报具有较高预报精度和可靠性，能够准确捕捉径流过程的洪峰流量及峰现时间；3天预见期时预报精度有所下降。同时，降雨空间分布等特征指标的引入有效提升了预报准确性。方法的参数率定难度较低，对特征权重变化不敏感，表现出良好的稳健性。本方法可为短期径流预报和洪水预警提供有效的新思路。

A new runoff forecasting method based on dynamic identification of similar processes is proposed, based on similarity theory, aiming to improve the accuracy and adaptability of real-time runoff forecasting. By constructing a spatiotemporal feature index system for rainfall-runoff processes and applying a multi-timescale similarity search algorithm with sliding windows, the method dynamically identifies historical events most similar to the ongoing process. The future runoff trajectories of these identified events are then combined through weighted averaging to generate a composite forecast, which is further corrected and extended by dynamically aligning it with the current observed discharge. Using historical daily rainfall and runoff data from the basin upstream of Xiashan Hydrological Station in the upper Ganjiang River as a case study, a leave-one-out cross-validation approach is employed to evaluate model performance. The results show that the multi-timescale similarity search algorithm with sliding windows can fully utilize the key features of adjacent time periods. Even in the absence of rainfall forecasts, the method yields high prediction accuracy and reliability for 1-2 day lead times, effectively capturing both peak discharge and peak timing of the runoff process. Although forecast accuracy decreases for a 3-day lead time, the inclusion of rainfall spatial distribution indicators significantly enhances the predictive performance. Moreover, the method requires relatively low effort for parameter calibration, is insensitive to variations in feature weighting, and exhibits robustness. This approach offers an effective and innovative solution for short-term runoff forecasting and flood warning.