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

Abstract

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.

Hydrodynamic Modeling for Flow and Velocity Estimation from an Arduino Ultrasonic Sensor

“…Flow conditions change rapidly, affecting the relationship between the water level and discharge [9,10]. Another limitation is the inaccuracy of extrapolating the rating curve to values above the unmeasured values, as the rating curve is based on a limited set of data, which may not represent all the possible flow conditions [11][12][13]. The described behaviors are caused by the hysteresis effect caused by the storage condition in the channel, and they are a phenomenon related to subcritical flow and backwater effect [13].…”