Real-time peak flow prediction under heavy precipitation is critically important for flood emergency evacuation planning and management. In the case of emergency evacuation, every second matters as a slightly longer lead time could save more lives and reduce the associated social, economic, and health impacts. Here, we present a model (named SIGMA) based on the principle of signal matching to facilitate real-time peak flow prediction at sub-hourly scales (e.g., minutes to seconds). The SIGMA model divides the target watershed into small zones and the heavy precipitation falling into each zone is collected into a small water tank. As the water tank moves downstream and arrives in the watershed outlet, it will discharge the collected precipitation and generate a small single-pulse streamflow signal. By combining all small signals coming from all zones within the watershed, we will be able to generate a synthesized peak flow signal. The proposed model is applied to simulate the peak flow events observed in a real-world watershed to verify its effectiveness in real-time flood prediction. The results suggest that the presented model can reasonably predict three key aspects of a peak flow event, including the peak flow rate, the arrival time of peak flow, and the duration of the peak flow event. The proposed model is demonstrated to be effective in real-time flood prediction and can be used to support flood emergency evacuation planning and management.

强降水条件下的实时洪峰流量预测对于洪水应急疏散规划和管理至关重要。在紧急疏散的情况下，每一秒都很重要，因为稍长的准备时间可以挽救更多的生命并减少相关的社会、经济和健康影响。在这里，我们提出了一个基于信号匹配原理的模型（名为 SIGMA），以促进亚小时尺度（例如分钟到秒）的实时峰值流量预测。SIGMA模型将目标流域划分为小区域，落入每个区域的强降水被收集到一个小水箱中。当水箱向下游移动并到达流域出口时，它将排出收集到的降水并产生小的单脉冲水流信号。通过组合来自流域内所有区域的所有小信号，我们将能够生成合成的峰值流量信号。该模型用于模拟在现实世界流域中观察到的峰值流量事件，以验证其在实时洪水预测中的有效性。结果表明，该模型可以合理预测高峰流量事件的三个​​关键方面，包括高峰流量、高峰流量到达时间和高峰流量事件的持续时间。该模型被证明在实时洪水预测方面是有效的，可用于支持洪水应急疏散规划和管理。