  We present large-eddy simulations (LESs) 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 using the 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 effective roughness height of the riverbed and the inflow discharge of the
river was studied using 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.
