Flow is a crucial variable in water resources, although its determination is challenging. Rating curves are standard but have conceptual limitations, leading to significantly high uncertainties. Hydrodynamic models offer a more precise alternative, but they necessitate continuous measurements of velocities, which are complex and expensive to obtain. Thus, this article aimed to validate a hydrodynamic model that estimates flows and velocities in transient conditions based on water levels measured using a low-cost ultrasonic sensor. The results indicated that these estimates can be reliable if (1) hydrodynamic models are used to represent the flow, (2) the channel bed slope is well represented in the geometric data, and (3) Manning’s coefficients are accurately estimated during calibration. The calculated flow and velocity showed a maximum variation of 40% for the same water level compared to estimates using the rating curve. The model exhibited higher sensitivity in terms of the flow when varying the channel bed slope, highlighting the importance of topographic surveys for the estimates. The validity of the implemented model was assessed with experimental data, indicating precision and reliability for practical applications in natural channels.

Stage-based discharge estimation rests on assumptions stated in words and implemented in code, and the gap between the two is not usually measured. This paper sets out a numerical-experiment framework that prices those assumptions against a reference in which discharge is known, and applies it to a one-dimensional model of a 3.5 km urban reach. Steady, gradually varied flow costs 0.08 to 6.79 % in mean absolute discharge error over 45 flood waves and falls 7.3-fold as the rise time grows from 1 to 16 h. The reading of a three-band roughness description costs more than the parameters. Five readings all supportable from the same archived material spread the rating curve by a median of 142 % at unchanged parameters, and a reading calibrated against data from another fits the calibration range to within 1 % and errs by 11.7 % above it.

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.

Hydropower reservoirs in the Brazilian Cerrado combine dendritic morphology, regulated water levels, and seasonal stratification, and their forebays and tributary arms are rarely covered by fixed monitoring stations. We used the João Leite Reservoir, a dendritic Cerrado impoundment of the scale of regional small hydropower plants, as a retrospective case to test whether Sentinel-2 MSI imagery could support chlorophyll-a and cyanobacterial monitoring and to define the evidence a hydropower operator needs before adopting a satellite layer. The record comprised 64 surface observations at five stations over 14 monthly periods but only 11 unique Sentinel-2A acquisitions. We retrieved Level-2A products, extracted median reflectance from 60 m × 60 m footprints, and evaluated six prespecified indices under leave-one-scene-out and leave-one-station-out validation. In 29 matchups from seven scenes, the NDCI leave-one-scene-out $R^2$ was $-0.122$ for chlorophyll-a and $-0.240$ for cyanobacterial abundance; RMSE was 1.80 $\mathsf{\mu }$g L⁻¹ and $1.74\times {10}^5$ cells mL⁻¹. Scene-level atmospheric residuals of the Level-2A product exceeded the red-edge signal expected across the observed 0.7–6.5 $\mathsf{\mu }$g L⁻¹ range, and the MSI lacked the 620 nm phycocyanin band, so no prespecified index achieved positive out-of-scene skill. Removing the shoreline station or applying the Brazilian regulatory cyanobacterial triggers, which were exceeded in every strict matchup, did not change the result. The record is therefore unfit for quantitative mapping, and event-screening performance remains unestablished. The transferable contribution is a five-gate evidence protocol, a four-state fitness framework, and a sensor-by-variable observability assessment for hydropower reservoir monitoring.