The Bui Dam, the second-largest hydropower dam in Ghana, plays a significant role in the sustainable energy mix of the country. It is managed by the Bui Power Authority (BPA) and has a capacity of hydro-clean generation of 404MW, contributing to 17% of the country’s total electricity generation. However, decision-making at the dam lacks the use of predictive models and meteorological measurements. This can lead, in the case of perceived flooding risks and high dam water levels, to valuable water being spilled and endangering the downstream areas. Balancing the tradeoff between energy production and safety can be effectively achieved by implementing predictive models that anticipate peak flows in advance.

Since its commissioning in 2013, the Bui Dam has experienced two instances of emergency spillage, resulting in significant financial losses, property destruction, and displacement of downstream communities. Currently, the reservoir management decision-making process uses two discharge stations upstream, with one of them yielding some unreliable outcomes for high flows. Therefore, it is crucial to prioritize the analysis and updating of rating curves to ensure accurate forecasting.

This research aims to address these limitations by recalibrating the rating curve using the reservoir balance in a conservative manner, i.e. leaning on the safe side to avoid overestimation. Additionally, a conceptual, semi-distributed model was developed simulating high flows, specifically focusing on the years 2019 and 2022 when spillage events occurred. Five different hydrological conceptual models, with three different structures: single, serial, and parallel structures, were tested. The serial model yielded the best results. Then the Black Volta Basin was divided into five sub-catchments, and each sub-catchment was lumped. In the absence of discharge data for the upstream sub-catchments, remote sensing data from GRACE and satellite altimetry (3 virtual stations with data from 2016 to 2022) were used to impose restrictions on the feasible model parameter sets, thereby improving accuracy.

The final model output was calibrated using discharge data obtained from the recalibrated rating curve, along with satellite altimetry data. In the calibrated benchmark case, the model effectively reproduced daily river flows, demonstrating an optimum Nash-Sutcliffe efficiency (NSE) of 0.85 for the period of 2018 to 2022. Subsequently, the model underwent extensive testing under various conditions, including an independent time period without recalibration, different precipitation input sources, transitioning from actual evapotranspiration (AET) to potential evapotranspiration (PET) input, and a change in the testing discharge location. Throughout these testing phases, the model consistently produced favorable results, with NSE values ranging from 0.74 to 0.86.

Furthermore, the model was tested for its progressive predictive capability in simulating the unexpected peak inflows that led to the spillage event in 2019, utilizing iv only precipitation data from the TAHMO precipitation stations, which are openly accessible with near-live timing. The model successfully predicted the occurrence of the large peak inflow, on October 22nd, which ultimately caused the spillage. The model anticipated the occurrence of the ”unexpected” second peak, to some extent, as early as October 12th, providing an 11-day predicting window.

Overall, this research enhances the understanding of the Bui Dam system by implementing a recalibrated rating curve and developing a conceptual model that incorporates remote sensing data. The results demonstrate the model’s capability to simulate past events accurately and predict future inflow patterns, thereby providing valuable insights for effective dam management and spillage prevention.

One significant discovery regarding the character of the Black Volta River at the Bui Dam is the limitation of the prediction period to a strict maximum of two weeks. While the model proves effective within this time-frame, it is advisable for future research to consider incorporating weather predictions to extend this window further. Doing so would enhance the model’s forecasting capabilities and provide even more valuable information for dam operators and decision-makers.

Flash floods are a damaging and recurring problem in Cebu city, Philippines. Very little data is known about the intensities and precipitation amounts and the resulting river discharges. This research project firstly aims to gather as much data as possible on precipitation and river discharges that could cause the floods, it focuses on a small catchment in the city called the Mahiga catchment. Data is gathered by installing three tipping buckets and two trail cameras. The cameras were able to calculate the river discharges using an innovative open-source program called OpenRiverCam. Thanks to this program a hydrograph can be
made of the river for each precipitation event. The used cameras were trail cameras of the Brand Bushnell. During this project it was concluded that, due to their unreliability, using trail cameras with OpenRiverCam is really not recommended. Security cameras with a Raspberry Pi are more suited. Due to bad luck with the weather and faulty material only three different hydrographs could be made during our time abroad (10 weeks). These hydrographs however remained useful for the second part of this research project. The second part consists of modelling the discharge of the Mahiga catchment to different
precipitation amounts using HEC-RAS. HEC-RAS is a computer program meaning Hydrologic Engineering Center’s River Analysis System. The model has been calibrated using the gathered precipitation data from the tipping buckets and the discharge results from OpenRiverCam. Graphs have been made about discharges and accumulated volumes and rating curves. The accuracy of the model is reasonable but should be improved using more discharge events. What stood out was the high infiltration rate and the fast response time of the Mahiga catchment. In section three, the results from the HEC-RAS model are used to understand the impact gabion dams make on reducing the peak flow in the Mahiga creek.
The third part summarises the effectiveness of the gabion dams in preventing flash floods. Unfortunately there is no ’real’ flash flood event captured by the tipping buckets, so three precipitation events are used based on analog measurements of a tipping bucket nearby the catchment. The gabion dams are tested on a maximum precipitation intensity of 35 mm/h, 30 mm/h and 25 mm/h with a total amount of 40 mm. Higher amounts of total precipitation
are realistic, but have a larger time duration and are not considered flash floods anymore. The volume that gabion dams can retain is too little for these large amounts of precipitation and are therefore not in the scope of this report. The results show that with at least five gabion dams, the peak flow reduces for all above mentioned precipitation intensities, but for the 35 mm/h it is getting less effective. The model also showed that the effectiveness is very dependent on the volume that can be retained by the dams. Maintenance of the gabion dams is therefore of crucial importance especially with the large amount of sediments and
debris in the creek.