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.

Understanding cyanobacterial dominance in tropical reservoirs is crucial for water management. This study investigated water quality dynamics in the João Leite Reservoir, Brazilian Cerrado, using 30 months of monitoring data from five sites. Physical, chemical, and biological parameters, including fluorometric chlorophyll-a and phycocyanin, were analysed alongside the Trophic State Index (TSI) using multivariate statistics (PCA, CCA). Results showed temporal variations exceeded spatial differences. Cyanobacteria were dominant despite generally low nutrient levels and an oligotrophic TSI classification. Principal Component Analysis identified temperature as strongly associated with cyanobacterial density. However, Canonical Correspondence Analysis and correlastions revealed limited direct statistical influence of measured physicochemical parameters, including nutrients, on cyanobacterial abundance. Findings suggest that in this warm, tropical system, high temperatures combined with stable hydrodynamics due to long hydraulic retention (>180 days) likely facilitate cyanobacterial success, overriding direct nutrient limitation.

Understanding cyanobacterial dominance in tropical reservoirs is crucial for water management. This study examined the dynamics of water quality in the João Leite Reservoir, situated in the Brazilian Cerrado, utilising 30 months of monitoring data from five sites. Physical, chemical, and biological parameters, including fluorometric chlorophyll-a, using multivariate statistics (Cluster Analysis, Principal Component Analysis, PCA; Canonical Correlation Analysis, CCA), were analysed alongside the Trophic State Index (TSI). Results showed temporal variations exceeded spatial differences. Cyanobacteria were dominant despite generally low nutrient levels and an oligotrophic TSI classification. Principal Component Analysis revealed that temperature is strongly associated with cyanobacterial density. However, Canonical Correspondence Analysis and correlations revealed limited direct statistical influence of measured physicochemical parameters, including nutrients, on cyanobacterial abundance. Findings suggest that in this warm, tropical system, high temperatures combined with stable hydrodynamics, resulting from long hydraulic retention times (>180 days), likely facilitate cyanobacterial success, overriding direct nutrient limitation.

Reservoir sedimentation remains a critical challenge for the long-term performance of large hydropower systems in tropical regions. This study investigates sedimentation patterns and storage capacity loss in the Itumbiara Reservoir, central Brazil, using an integrated hydrogeophysical and sedimentological approach. High-resolution multibeam bathymetry and sub-bottom profiler data were combined with granulometric analyses of 34 sediment samples to map the pre- and post-impoundment stratigraphy, quantify sediment thickness, and estimate sedimentation rates along the main tributary axes. Results show that post-dam deposits form a discontinuous fine-grained layer, with thickness generally ranging from 0 to 1.33 m and concentrating in deeper zones near the dam and along the former fluvial thalwegs. The Corumbá River arm exhibits the greatest sediment accumulation and the highest normalized silting rate, whereas upstream segments of the Paranaíba and Araguari rivers display substantially lower infilling. Despite localized hotspots of deposition, the reservoir has lost less than 2% of its original storage capacity over 44 years of operation, indicating low sediment yields from cratonic source areas and high trap efficiency with slow morphological adjustment. The integrated dataset reveals that resuspension during water level drawdown and subsequent redeposition in deep, low-energy areas play a key role in redistributing fine sediment within the basin. These findings demonstrate the value of combining acoustic imaging and grain-size data to support sediment management and lifetime assessments of large tropical reservoirs.

Effective water resource management and risk mitigation in urban environments require an accurate understanding of river flow dynamics. Traditionally, river discharge estimation relies on the rating curve method, which, despite being simple and low-cost, presents significant limitations in complex channels or under transient flow regimes. Aiming for greater accuracy in discharge estimates, this study employed a one-dimensional hydraulic model based on the Standard Step Method for gradually varied flows. High-quality in situ data, including detailed bathymetric surveys and water level measurements, were used for model calibration and validation. Discharge uncertainty was quantified through the estimation of hydraulic parameters (S₀ and n), comparing the Bayesian approaches GLUE and DREAM. The final uncertainty ranges in discharge estimates (Relative Error) using the DREAM method varied between 6.75% and 7.34%, while the GLUE method presented ranges between 7.31% and 8.64%. The DREAM method demonstrated superior performance, including faster processing speed. It was also observed that model accuracy tends to improve with decreasing water surface elevation, indicating the need for longer data collection intervals during extreme events.