针对我国平原区小城镇面临严重内涝灾害威胁的问题，结合平原区小城镇的排涝体系特点，探索一种简便 的平原区小城镇排涝体系评估和优化方法。基于暴雨洪水管理模型（storm?water?management?model,?SWMM) 建立 平原区典型小城镇排涝模拟模型，并采用历史洪涝数据对模型进行率定和验证。在此基础上，通过对典型重现期 暴雨下的内涝模拟，评估典型城镇排涝能力。根据管渠超载时间、节点积水时间和积水量，找出排涝体系薄弱环 节，提出有针对性的排涝体系优化方案，并对优化效果进行评估。结果表明：建立的排涝模拟模型基本可靠，模拟 结果与实际情况基本相符；典型小城镇排涝体系薄弱点主要为主干管渠排涝压力大，蓄水池未得到有效利用；据 此提出的优化方案效果明显，在 20 年一遇暴雨情况下，可以减少典型小城镇 90% 的积水时间和 67% 的积水量， 显著提高城镇排涝能力。

The specific requirement of the major historical task of building a new socialist countryside is constructing beautiful countryside. However, frequent waterlogging disasters restrict the economic and social development of the small villages and towns in the plain areas in China. Therefore, it is particularly necessary to strengthen the flood control and drainage capacity of the small villages and towns in rural areas. The drainage system inside and around the village is an important measure for flood control and waterlogging removal, which needs to be scientifically evaluated and optimized. However, recently, there are few studies on the drainage system in rural plain areas. The stormwater management model is an open-source model developed by the U.S. Environmental Protection Agency. It has been applied to storm runoff and drainage simulation by researchers for a long time.A typical plain village is chosen as the research object to explore a scientific and convenient method to evaluate and optimize the drainage system of small villages in the plain area. First, the basic situation, drainage system, and historical waterlogging disasters of the village are investigated , and remote sensing images of the region are referenced. This survey considers various situations such as the siltation of peripheral rivers and pipes and canals. 30-year rainfall observation data of the surrounding area is collected. Frequency analysis method is used to calculate the maximum 72-hour rainfall with different frequencies and predict the rainstorm time through the distribution table of rainfall patterns in the basin. A stormwater management model suitable for this village is established based on the investigated data. Most parameters in the stormwater management model are measured to avoid calibration. However, the roughness parameters and the impervious area ratio in the model are very sensitive and need to be calibrated accurately to ensure the accuracy of the model simulation. The historical waterlogging disaster data are used to calibrate and verify the model. This model is used to simulate and evaluate the village waterlogging in different rainstorm return periods. According to the simulation results, some targeted optimization measures are proposed, and the optimization effect is also analyzed.The results show that the village has been affected by the once-in-five-year rainfall. Its maximum ponding time is up to nine hours and the maximum volume is nearly 6 000 cubic meters. Through the analysis, it is revealed that the main drainage channel is overloaded and the detention tank has not been effectively utilized. Given the above problems, some optimization measures, such as digging deep trunk and drainage channels with high drainage pressure and increasing the connectivity of the detention tank are put forward. After optimization, the disaster situation of the village is effectively alleviated. The maximum ponding time is reduced to 0.2 hours and the maximum ponding volume is reduced to 417 cubic meters. Under different rainfall conditions, the optimized ponding time and ponding volume of the village have been effectively improved.Furthermore, taking the typical villages in the plain area of Shandong Province as an example, this study the village waterlogging simulation model is established by SWMM model. The drainage capacity of the village is evaluated, and a practical and effective optimization scheme is put forward. Thus, some conclusions can be put forward. The waterlogging situation of this village can be better reflected by the waterlogging model based on the stormwater management model. There are many weak points in the drainage system of villages, and the ability to prevent waterlogging is low. However, through targeted optimization measures, its ability to prevent waterlogging can be effectively improved.