We present large-eddy simulations (LES) of riverine flow in a study reach in the Sacramento River, California. The riverbed bathymetry was surveyed in high-resolution using a multibeam echosounder to construct the computational model of the study area, while the topographies were defined using aerial photographs taken by an Unmanned Aircraft System (UAS). In a series of field campaigns, we measured the flow field of the river river across multiple transects throughout the field site using an acoustic Doppler current profiler (ADCP) and estimated using large-scale particle velocimetry of the videos taken during the operation UAS. We used the measured data of the river flow field to evaluate the accuracy of the LES-computed hydrodynamics. The propagation of uncertainties in the LES results due to the variations in the riverbed’s effective roughness height and the river’s inflow discharge was studied and showed that both parameters redistributed the flow distribution laterally and vertically in the velocity profile. For the uncertainty quantification (UQ) analyses, the polynomial chaos expansion (PCE) method was used to develop a surrogate model, which was randomly sampled sufficiently by the Monte Carlo Sampling (MCS) method to generate confidence intervals for the LES-computed velocity field. Also, Sobol indices derived from the PCE coefficients were calculated to help understand the relative influence of different input parameters on the global uncertainty of the results. The UQ analysis showed that uncertainties of LES results in the shallow near bank regions of the river were mainly related to the roughness, while the variation of inflow discharge leads to uncertainty in the LES results throughout the river, indiscriminately.

我们在加利福尼亚州萨克拉门托河的一项研究中展示了河流流动的大涡模拟 (LES)。使用多波束回声测深仪以高分辨率测量河床水深，以构建研究区域的计算模型，而地形则使用无人机系统（UAS）拍摄的航空照片定义。在一系列现场活动中，我们使用声学多普勒电流剖面仪 (ADCP) 测量了整个现场多个样带的河流流场，并使用 UAS 操作期间拍摄的视频的大规模粒子速度测量法进行估计。我们使用河流流场的测量数据来评估 LES 计算的流体动力学的准确性。研究了由于河床有效粗糙度高度和河流流入流量的变化而导致的 LES 结果中的不确定性传播，结果表明这两个参数在速度剖面中横向和纵向重新分布了流量分布。对于不确定性量化 (UQ) 分析，使用多项式混沌扩展 (PCE) 方法开发代理模型，该模型通过蒙特卡洛采样 (MCS) 方法进行充分随机采样，以生成 LES 计算的速度场的置信区间. 此外，计算了从 PCE 系数导出的 Sobol 指数，以帮助了解不同输入参数对结果的全局不确定性的相对影响。