The purpose of the study is to reveal changes of the hydrodynamics and sediment transport induced into the river channel by the operation of small hydropower plants (SHPs). A 36-km reach of the upper Prahova River in Romania has been chosen, along which 10 run-of-river developments are built. A 1D modeling software was used to compute hydrodynamic parameters over the selected reach. Two discharge scenarios were considered into the channel: (1) mean annual flow (for the natural regime, without SHPs), and (2) the minimum discharge (worst case scenario of SHP operation for the environment). Results of the second scenario show that ecologically relevant hydraulic parameters in the channel are approaching the thresholds of endangering the fish habitat. On average, depth and shear stress drop by 50%, mean velocity by 40%, and flow area by 75%. Comparison between computed nondimensional shear stress and its corresponding critical value indicates that minimum discharge can only transport finer particles than average from the bed surface, thus leading to potential filling of fish spawning areas with sand.

Different scenarios of an arch-dam breach and their impact on the time-space evolution of flood waves are analysed using numerical modelling. As the accidents involving this type of dam are among the most catastrophic ones, the 108 m in height Paltinu arch-dam, Romania, was chosen as a case study due to its problems in the past. Three dam breach magnitudes and two inflow hydrographs for the worst-case scenario of Normal Operating Pool elevation in the reservoir were chosen as variable parameters, to assess their influence on the dam break wave characteristics and downstream flooded areas. The flood was routed along the 18 km reach of the Doftana River down to the confluence with the Prahova River. A 2D numerical model was set up with the help of HEC-RAS software, which was also used to analyse the resulting hazard maps under a GIS environment. Comparison of inundation boundary, maximum depths and velocities, as well as the arrival time at control sections allow for conclusions to be drawn. These predictive results of shape, magnitude, and time to peak of the flood waves are essential for flood risk management to obtain the risk maps, estimated damage costs, and possible affected areas.

Water bodies can offer local microclimates that have the potential to attenuate the effects of urban heat islands by reducing local temperature. This capability is shaded when the river is channelized. In such cases, the river temperature rises during hot periods, leading to negative impacts on the water quality. The main aim of this paper is to develop a local simple model to predict the temperature of the Dâmbovița River at its exit from Bucharest City, the capital of Romania. The location is chosen based on the historical critical impacts, in terms of extreme heatwaves that took place during hot summers, as well as future possible risks due to climate change. The water temperature prediction model is based on an artificial neural network that uses the Levenberg–Marquardt algorithm, due to its stability and rapid convergence capabilities. The model forecasts, with an accuracy of ±1 °C, the water temperature in an ungauged, downstream location, as a function of measured air and upstream water temperatures. The proposed model represents a first attempt to provide water managers in Bucharest City with a useful tool that will allow them to take timely measures to counteract the unwanted effects that can be generated by high water temperatures.

Flood hazard studies for dam break cases are of utmost importance for understanding potential risks and minimizing the impact of such accidents. Siriu Dam, which has a clay core, is ranked as the third highest embankment dam in Romania. A fully dynamic 2D hydraulic numerical model was developed using HEC-RAS software to simulate the routing of the flood waves formed by breaching this dam. Four different failure scenarios were considered: two for overtopping and two for piping. The breach parameters were chosen based on the dam characteristics in accordance with appropriate empirical relationships. The flood hazard was quantified and analyzed in terms of depths, velocities, depth x velocity values, and flooded areas. The results provide useful information concerning flood risk mitigation, such as the dam break wave routing, peak discharges, arrival time, travel velocity, and inundation boundary. The influence of the scenario and site characteristics (topography, river morphology, and constructions) on the results was analyzed. Depths and velocities over 10 m and 15 m/s, respectively, were obtained close to the dam, while those in Buzău City (90 km away) were under 1 m and 2 m/s, respectively. The city was flooded 7–8.5 h after the breach (depending on the scenario), and over 15 to 50% of its total area was affected. Moreover, the flood hazard parameters were compared for the different scenarios, providing the practical details necessary to develop flood risk management plans and the associated response measures for the inhabited areas. This is the first numerical study to simulate the impact of a potential break accident that can occur for this dam.

The study analyses the qualitative influence of different parameters on Dambovita River water temperature along the 17.5km length reach of concrete canal passing through Bucharest City, Romania. Air temperature has been increasing during the investigated last 4 decades with detrimental consequences on the river ecosystem. The simplified physical processes taken into account, equations and numerical methods used to develop a deterministic water temperature model are explained. Different scenarios of possible extreme summer heatwaves with influence on river water temperature are defined analysing the available data. A sensitivity analysis is performed for meteorological parameters such as: air temperature, humidity, short wave solar radiation, cloudiness and wind speed to determine their influence on river temperature.

The objective of this paper is to propose an artificial neural network (ANN) model to forecast the Danube River temperature at Chiciu–Călărași, Romania, bordered by Romanian and Bulgarian ecological sites, and situated upstream of the Cernavoda nuclear power plant. Given the temperature increase trend, the potential of thermal pollution is rising, impacting aquatic and terrestrial ecosystems. The available data covered a period of eight years, between 2008 and 2015. Using as input data actual air and water temperatures, and discharge, as well as air temperature data provided by weather forecasts, the ANN model predicts the Danube water temperature one week in advance with a root mean square deviation (RMSE) of 0.954 °C for training and 0.803 °C for testing. The ANN uses the Levenberg–Marquardt feedforward backpropagation algorithm. This feature is useful for the irrigation systems and for the power plants in the area that use river water for different purposes. The results are encouraging for developing similar studies in other locations and extending the ANN model to include more parameters that can have a significant influence on water temperature.