When completed, the Renaissance Dam on the Blue Nile River will be the largest hydropower dam in Africa. The consequent reduction of sediment fluxes to the lower Blue Nile by the Grand Ethiopian Renaissance Dam (GERD) is expected to impact agricultural production, river channel morphology, and downstream reservoirs’ management. Due to its vicinity, Roseires Dam is expected to be the most affected. This study assesses sedimentation in the GERD and its impact on Roseires sedimentation. A novel approach was used, combining flow velocities from a 2D hydrodynamic model (Delft3D) with empirical trap efficiency (TE) formulas by Churchill and Tan. The operational rules of both dams were considered in the TE calculation. Results were verified using Roseires bathymetric surveys. The Blue Nile sediment load was estimated using historical discharge and sediment concentration data (1981–2022) and a data-driven model (ANN) to fill in missing sediment records. Churchill and Tan formulas showed different responses to reservoir capacity and inflow variations, with long-term TE estimates of 92 % (Tan) and 97 % (Churchill). GERD's annual storage loss was calculated at 0.28 % (189 Mm³/year). Roseires's annual storage loss was estimated at 0.26 % before the GERD and is projected to drop to 0.01 % after GERD completion.

The quantification of pebble shape has been of interest to geomorphologists for decades. Several authors developed parameters to describe pebble shapes from their images. The extraction of this information from images involves two steps: the segmentation of pebble contours and the application of a computational geometry algorithm to estimate shape parameters. When images are taken in the field, unavoidable shadows might hinder the possibility of using automatic segmentation methods. This paper introduces a new method for automatic segmentation of pebbles that improves segmentation accuracy in the presence of shadows. The method is based on the Canny edge detection algorithm which uses a double thresholding process to provide a classification of the strength of the detected edges. The proposed method applies this algorithm with an ensemble of thresholding values, estimating, for each pixel, the probability of being an edge. The resulting pebble contours were analysed using two computational geometry algorithms to obtain shape parameters. The algorithm was calibrated on a sample of five pebbles and then validated on a sample of 1696 pebbles. Its accuracy has been estimated by comparing the resulting shape parameters with those obtained using reference software, which was used as ground truth (GT). The proposed segmentation method was capable of accurately segmenting around 91% of the sample with a relative error for roundness of −1.7% and −0.4%; for elongation of −0.2% and −0.3% and for circularity of 0.2% and 0.1%, when shape parameters were computed using the algorithms of Zheng or Roussillon, respectively. The method could therefore be used to segment images of pebbles collected in the field with low contrast and shadowing, providing comparable accuracy with ‘manual’ segmentation, while removing operator bias.

The increase in economic activities, population and rural electrification has significantly increased the energy demand in most of the developing nations. This demand has to be supplied from various sources, preferably renewable, among which hydropower is expected to be one of the major contributors. Though developed nations have already harnessed most of their hydropower potential, developing nations are still struggling in project identification and capacity assessment, mainly due to lack of data and difficulties of access. We present and test an assessment framework, developed for data scarce regions, to identify optimal location and installed capacity of multiple run-of-river hydropower projects within a river basin. The developed framework consists of two components: the first component is a hydrological model for flow duration curves, the second component is a so-called hydropower model. Flow duration curves are obtained using an existing probabilistic hydrological model which derives the probability distribution of streamflow as a function of few topographic and climatic parameters. A novel optimization procedure is developed, where viable hydropower projects are identified minimizing their specific cost, which depends mainly on discharge, head and length of conduit system. We tested the assessment framework in the West Rapti basin (Nepal). The application showed that the total potential of this basin maybe achieved with 79 different projects with capacity ranging from 1 to 17 MW. The framework was developed using open languages and software and can therefore be freely used after request to the corresponding author.