Vertical slot fishways are hydraulic structures which allow the upstream migration of fish through obstructions in rivers. The velocity, water depth, and turbulence fields are of great importance in order to allow the fish swimming through the fishway, and therefore must be considered for design purposes. The aim of this paper is to assess the possibility of using a two-dimensional shallow water model coupled with a suitable turbulence model to compute the flow pattern and turbulence field in vertical slot fishways. Three depth-averaged turbulence models of different complexity are used in the numerical simulations: a mixing length model, a k−ε model, and an algebraic stress model. The numerical results for the velocity, water depth, turbulent kinetic energy, and Reynolds stresses are compared with comprehensive experimental data for three different discharges covering the usual working conditions of vertical slot fishways. The agreement between experimental and numerical data is very satisfactory. The re...

This paper presents the results of a modeling study of the hypothetical dam break of Chipembe dam in Mozambique. The modeling approach is based on the software Iber, a freely available dam break and two-dimensional finite volume shallow water model. The shuttle radar topography mission (SRTM) online digital elevation model (DEM) is used as main source of topographic data. Two different DEMs are considered as input for the hydraulic model: a DEM based on the original SRTM data and a hydrologically-conditioned DEM. A sensitivity analysis on the Manning roughness coefficient is performed. The results demonstrate the relevant impact of the DEM used on the predicted flood wave propagation, and a lower influence of the roughness value. The low cost modeling approach proposed in this paper can be an attractive option for modeling exceptional flood caused by dam break, when limited data and resources are available, as in the presented case. The resulting flood-inundation and hazard maps will enable the Regional Water Management Administration of Mozambique (ARA) to develop early warning systems.

Several port authorities are involved in the R+D+i projects for developing port management decision-making tools. We recorded the movements of 46 ships in the Outer Port of Punta Langosteira (A Coruña, Spain) from 2015 until 2020. Using this data, we created neural networks and gradient boosting models that predict the six degrees of freedom of a moored vessel from ocean-meteorological data and ship characteristics. The best models achieve, for the surge, sway, heave, roll, pitch and yaw movements, a 0.99, 0.99, 0.95, 0.99, 0.98 and 0.98 R² in training and have a 0.10 m, 0.11 m, 0.09 m, 0.9°, 0.11° and 0.15° RMSE in testing, all below 10% of the corresponding movement range. Using these models with forecast data for the weather conditions and sea state and the ship characteristics and berthing location, we can predict the ship movements several days in advance. These results are good enough to reliably compare the models’ predictions with the limiting motion criteria for safe working conditions of ship (un) loading operations, helping us decide the best location for operation and when to stop operations more precisely, thus minimizing the economic impact of cargo ships unable to operate.