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Recent advances in instruments are transforming our capabilities to better understand, monitor, and model river systems. The present paper illustrates such capabilities by providing new insights into unsteady flows captured with a Horizontal Acoustic Current Profiler (HADCP) integrated at an operational index-velocity gaging station. The illustrations demonstrate that the high-resolution stage and velocity measurements directly acquired during flood wave propagation reveal the intricate interplay among flow variables that are essential for better supporting judicious decision making for river management, flooding, sediment transport, and stream ecology. The paper confirms that the index-velocity method better captures the unsteady flow dynamics in comparison with the stage-discharge monitoring approach. At a time when the intensity and frequency of floods is continuously increasing, a better understanding of the critical features of flood waves during extreme events and the possibility of capturing more accurately their dynamics in real time is of special socio-economic significance.

To understand the intricate runoff-sediment relationship in the middle Yellow River basin (MYRB), the Toudaoguai, Longmen, Tongguan and Huayuankou sites in the MYRB were selected to analyze the deterministic equilibrium and uncertainty relations of runoff-sediment based on 55-year hydrological data at multi-time scales. The Johansen test and wavelet neural network were used to verify the cointegration relationship among hydrological series. Runoff-sediment uncertain statistical relations and dynamics in the MYRB were also analyzed based on rating curves and hysteresis loops. The results showed that the logarithmic sequences of sediment load (SL), runoff and suspended sediment concentration (SSC) conformed to a linear cointegration relationship at the Toudaoguai station or in spring, winter or under small flow at other stations, but a nonlinear cointegration relationship was observed in other cases at other stations. Regarding runoff-sediment uncertain relationships, the rating curves, and hysteresis loops differed in stations (Toudaoguai and the other stations), as well as discharge (threshold: 1000 m3/s), season (ice-flood and rainy season) and saturation of flow at flood and monthly scales. At the annual scale, phased and unsynchronized characteristics of runoff and sediment load were evident with a decreasing trend. This study on the runoff-sediment relationship can rationally provide a theoretical basis for the management and development of the Yellow River and other similar rivers with sufficient sediment, especially for areas with serious soil erosion.

The Sahelian regions are affected by an increasing number of catastrophic floods in recent years as a consequence of climate and land use/land cover changes. River flow data is key to understanding river behavior and develop flood mitigation and prevention strategies. The present study provides a revision and an update of the existing discharge dataset of the Sirba River with the aim of enhancing the reliability of these data. The revision also includes the recalibration of the Garbey Kourou rating curves. The analysis of the revised discharge time series strengthens the previous findings, evidencing a positive trend in flood frequency and intensity over the entire analyzed period of 1956–2018. This positive trend is more pronounced for the last 40 years due to a significant underestimation of the rating curves used. A relevant finding is a new changepoint in the time series, detected for 2008, which represents the beginning of the period in which the highest flood magnitudes were registered. The effect of land use/land cover changes and climate changes on the water resource is depicted using flow duration curves. This research produces a revised and more reliable discharge time series that will be a new starting point for future hydrological analyses.

© 2015 by the authors. This pilot study explores the potential of using low-resolution (0.2 points/m2) airborne laser scanning (ALS)-derived elevation data to model stream rating curves. Rating curves, which allow the functional translation of stream water depth into discharge, making them integral to water resource monitoring efforts, were modeled using a physics-based approach that captures basic geometric measurements to establish flow resistance due to implicit channel roughness. We tested synthetically thinned high-resolution (more than 2 points/m2) ALS data as a proxy for low-resolution data at a point density equivalent to that obtained within most national-scale ALS strategies. Our results show that the errors incurred due to the effect of low-resolution versus high-resolution ALS data were less than those due to flow measurement and empirical rating curve fitting uncertainties. As such, although there likely are scale and technical limitations to consider, it is theoretically possible to generate rating curves in a river network from ALS data of the resolution anticipated within national-scale ALS schemes (at least for rivers with relatively simple geometries). This is promising, since generating rating curves from ALS scans would greatly enhance our ability to monitor streamflow by simplifying the overall effort required.

Discharge hydrograph estimation during floods, in rivers with torrential regime, is often based on the use of rating curves extrapolated from very low stage-discharge measurements. To get a more reliable estimation, a reverse flow routing problem is solved using water level data measured in two gauged stations several kilometers from each other. Validation of the previous analysis carried out on the flood event of February 2016 at the Europa Bridge and Castiglione Scalo sections of the Crati River (Cosenza, Italy) is based on the use of 'soft' discharge measurement data and the comparison of the water level data computed in the downstream gauged section by three different hydraulic models with the 'hard' available water level measures. Results confirm that the 1D diffusive model provides more reliable results than the 1D complete one and no significant improvement is gained by the use of a more computationally demanding 2D model.