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.

中文翻译：

---

使用GLUE和DREAM分析曼宁粗糙度系数和床坡度估计的不确定性

河流流量数据对于详细阐述工程项目和水资源管理至关重要。排放数据必须精确并以良好的时间分辨率收集。为了阐述更准确的数据库，本文旨在通过 GLUE 和 DREAM 方法量化应用贝叶斯推理时产生的不确定性。两种方法都用于估计水力参数，并将它们与曼宁方程进行比较。在整个统计分析过程中，利用模型应用中的不确定性来确定曼宁粗糙度系数和河床坡度的参数。通过比较计算的最大和最小流量以及 HidroWeb 上可用的观察流量来进行验证。总之，两种方法都很好地估计了水力参数，但在计算流量较小的区间内，相对偏差较大；一旦 GLUE 的相对偏差变得更大，DREAM 似乎比 GLUE 更准确。