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...

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Bibliographic Details
Published in:Water (Basel) 2020-11, Vol.12 (11), p.3270
Main Authors: Reis, Guilherme da Cruz dos, Pereira, Tatiane Souza Rodrigues, Faria, Geovanne Silva, Formiga, Klebber Teodomiro Martins
Format: Article
Language:English
Subjects:
Bayesian analysis
Calibration
Deviation
Estimates
Floods
Geometry
Hydraulics
Hydrologic data
Mathematical models
Optimization algorithms
Parameter estimation
Project management
Resource management
River discharge
River flow
Roughness coefficient
Statistical analysis
Statistical methods
Temporal resolution
Uncertainty analysis
Water discharge
Water management
Water resources
Water resources management
Water shortages
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Summary: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.
ISSN:2073-4441
2073-4441
DOI:10.3390/w12113270