Analysis of the Uncertainty in Estimates of Manning’s Roughness Coefficient and Bed Slope Using GLUE and DREAM

River discharge data are critical to elaborating on engineering projects and water resources management. Discharge data must be precise and collected with good temporal resolution. To elaborate on a more accurate database, this paper aims to quantify the uncertainty generated while applying Bayesian inference through the GLUE and DREAM methods. Both methods were used to estimate hydraulic parameters and compare between them with Manning’s equation. Throughout the statistical analysis, the uncertainties in the application of the models are used to determine the parameters of Manning’s roughness coefficient and bed slope. The validation was made via a comparison of the calculated maximum and minimum discharges, and the observed flow available at HidroWeb. In conclusion, both methods estimated the hydraulic parameters well, but a higher relative deviation was seen in the intervals with smaller calculated discharges; DREAM appears to be more accurate than GLUE, once the relative deviation in GLUE became greater.

Calibration method for Manning's roughness coefficient for a river flume model

This paper takes the Tarim River as an example to study the selection of Manning's roughness coefficient (n) in numerical simulation and presents a new method for calibrating Manning's roughness coefficient of a flume model. The measured topographic data and hydraulic data obtained from the flume experiments are taken as the initial boundary conditions in flow simulation, and n value for a flume model of the Qiman reach of Tarim River is calibrated by using a CCHE2D model. The consistency between the simulated water surface and the measured water surface with different n value is compared by using the error analysis method. Manning's n value for a flume model which meets the minimum error requirements is determined. The relative error between n value obtained by the empirical method and n value obtained by the numerical simulation method is analyzed. The result shows that the calibration method of n value for a flume model by using the CCHE2D model and error analysis presented in this paper is feasible and reliable.

Effect of the Number of Leaves in Submerged Aquatic Plants on Stream Flow Dynamics

The main purpose of this study is to investigate the effects of aquatic plants with no leaves (L0), 4 leaves (L4), 8 leaves (L8), and 12 leaves (L12) on the mean streamwise velocity, turbulence structure, and Manning’s roughness coefficient. The results show that the resistance of submerged aquatic plants to flow velocity is discontinuous between the lower aquatic plant layer and the upper free water layer. This leads to the difference of flow velocity between the upper and lower layers. An increase of the number of leaves leads to an increase in the flow velocity gradient in the upper non-vegetation area and a decrease in the flow velocity in the lower vegetation area. In addition, aquatic plants induce a momentum exchange near the top of the plant and increase the Reynold’s stress and turbulent kinetic energy. However, because of the inhibition of leaf area on the momentum exchange, the Reynold’s stress and turbulent kinetic energy increase first and then decrease with the increase in the number of leaves. Quadrant analysis shows that ejection and sweep play a dominant role in momentum exchange. Aquatic plants can also increase the Reynold’s stress by increasing the ejection and sweep. The Manning’s roughness coefficient increases with the increasing number of leaves.

Manning’s roughness coefficient for the Doce River

ABSTRACT The Manning’s roughness coefficient is used for various hydraulic modeling. However, the decision on what value to adopt is a complex task, especially when dealing with natural water courses due to the various factors that affect this coefficient. For this reason, most of the studies carried out on the subject adopt a local approach, such as this proposal for the Doce River. Due to the regional importance of this river in Brazil, the objective of this article was to estimate the roughness coefficient of Manning along the river, in order to aid in hydraulic simulations, as well as to discuss the uncertainties and variations associated with this value. For this purpose, information on flow rates and water depths were collected at river flow stations along the river. With this information, the coefficients were calculated using the Manning equation, using the software Canal, and their space-time variations were observed. In addition, it was observed that the uncertainties in flow and depth measurements affect the value of the Manning coefficient in the case studied.

Evaluation of Manning’s n Roughness Coefficient in Arid Environments by Using SAR Backscatter

The prediction of arid region flash floods (magnitude and frequency) is essential to ensure the safety of human life and infrastructures and is commonly based on hydrological models. Traditionally, catchment characteristics are extracted using point-based measurements. A considerable improvement of point-based observations is offered by remote sensing technologies, which enables the determination of continuous spatial hydrological parameters and variables, such as surface roughness, which significantly influence runoff velocity and depth. Hydrological models commonly express the surface roughness using Manning’s roughness coefficient (n) as a key variable. The objectives were thus to determine surface roughness by exploiting a new high spatial resolution spaceborne synthetic aperture radar (SAR) technology and to examine the correlation between radar backscatter and Manning’s roughness coefficient in an arid environment. A very strong correlation (R2 = 0.97) was found between the constellation of small satellites for Mediterranean basin observation (COSMO)-SkyMed SAR backscatter and surface roughness. The results of this research demonstrate the feasibility of using an X-band spaceborne sensor with high spatial resolution for the evaluation of surface roughness in flat arid environments. The innovative method proposed to evaluate Manning’s n roughness coefficient in arid environments with sparse vegetation cover using radar backscatter may lead to improvements in the performance of hydrological models.