Manning’s roughness coefficient(n) is considered a key parameter in a one-dimensional (1D) hydrodynamic model. However, it is highly variable and time- and site- dependent. Further, identifying proper n values is not easy, especially in plain looped river network areas. Therefore, a more systematic approach is needed. This study proposes a coupled optimization-simulation model to systematically estimate the spatial distribution of n values. The particle swarm optimization (PSO) algorithm and InfoWorks Integrated Catchment Modelling (ICM) software were integrated to solve the objective function and hydraulic process, respectively. Crisscrossing rivers were partitioned into river reaches that were each assumed to have a uniform and constant Manning’s roughness coefficient according to their network topology and cross-section variation. In addition, a sensitivity analysis was implemented to determine the weights of measured data from different gauging stations, and a large difference in the spatial distribution of the sensitivity index illustrated the importance of identifying the weights of multiple stations. Then, a systematic approach was applied to estimate the n values in the Changshu Grand Polder Area (CGPA), which is crisscrossed by 150 rivers, under the water diversion stage. The calculation statistics and efficiency indicated that the proposed method performs well for model calibration.

曼宁粗糙度系数（n）被认为是一维（1D）流体动力学模型的关键参数。但是，它是高度可变的，并且与时间和地点有关。此外，要确定合适的n值并不容易，尤其是在平原环状河网地区。因此，需要一种更系统的方法。这项研究提出了一个耦合的优化模拟模型来系统地估计n的空间分布价值观。集成了粒子群优化（PSO）算法和InfoWorks集成流域建模（ICM）软件，分别解决了目标函数和水力过程。纵横交错的河流被划分为河段，根据河网的网络拓扑结构和横截面变化，每条河段的曼宁粗糙度系数均一致且恒定。此外，还进行了灵敏度分析，以确定来自不同测量站的测量数据的权重，并且灵敏度指标的空间分布差异很大，说明了识别多个站的权重的重要性。然后，采用系统化的方法来估计ñ分水阶段，常熟大Pol田地区（CGPA）的水位值（CGPA）遍布150条河流。计算统计和效率表明，该方法在模型校正中表现良好。