Water is essential for life on earth and is vital for numerous sectors of our society. Pressures arising from climate change, growing populations, and the shift towards clean energy accentuate the importance of effective water management. To make decisions about water resource allocation, hydraulic modeling studies have been undertaken on large African rivers. These studies employ global terrain models, utilize remotely sensed water surface elevation data, and often involve estimating river channel bathymetry. Typically, these models also require an estimation of the river channel bathymetry. However, observed bathymetry data is seldom available and crucial for the hydraulic model performance. It has resulted in a key challenge of modelling large rivers in this data-sparse context.
This research aims to model a medium to large sized river with wide floodplains in three dimensions by integrating discharge data and a highly accurate bathymetry. The primary objective is to quantify the friction coefficient and establish a reliable rating-curve for the river system. By utilizing these key components, the study seeks to provide a comprehensive understanding of the hydraulic behavior of the river, contributing to improved water flow predictions and management strategies. The bathymetry data is acquired through two different methods. The dry bathymetry is obtained using an UAV (DJI Phantom 4) and photogrammetry (WebODM). The wet bathymetry data is collected using both, sonar with the Deeper Chirp+ and spatial referencing with the RTK-GNSS from ArduSimple. These methods are cost-effective and require minimal manpower, making them practical options for acquiring accurate bathymetric information. The discharge data is acquired using the open-source software, OpenRiverCam. OpenRiverCam uses Large Scale Particle Image Velocimetry (LSPIV) to determine the surface velocities and combines the results with the bathymetry data to calculate discharges, providing an efficient solution for assessing river flow characteristics. LSPIV has the advantage that it is a non-intrusive method of measuring the flow velocity and does not require physical probes or instruments in the water. The bathymetry data and discharge data are integrated into the Delft3D FM Suite to assess the accuracy of the measurements and estimate the friction coefficient in both the river and the floodplain. This modeling approach enables a comprehensive analysis of the hydraulic characteristics of a medium to large sized river and supports the evaluation of flow resistance in the study area.
The data acquisition took place at three study sites close to the Bui Dam, in the Black Volta Region, Ghana. The Bui Dam is the second largest hydro-power dam in Ghana managed by the Bui Power Authority (BPA). The Bui Bridge and Bamboi Bridge study sites are positioned downstream of the Bui Dam, allowing for accurate quantification of the discharge and the bathymetry measurements. The third study site, Chache, is positioned upstream of the dam, where daily water level measurements are taken. BPA has observed that the rating curve at this location is outdated. Therefore, efforts are made to update the rating curve and quantify the friction coefficient at this site in both the river and the floodplain.
This research has made significant progress in developing a three-dimensional discharge model and rating curve for medium to large rivers using advanced data collection methods and integration techniques. The study successfully combined photogrammetry and sonar measurements to effectively determine the bathymetry of the river, overcoming challenges related to high water velocities and dense vegetation. The LSPIV technique and OpenRiverCam were utilized to integrate surface velocities and discharge measurements, leading to a more comprehensive understanding of river dynamics. However, limitations were encountered in assessing the accuracy of the model at the Bamboi Bridge site due to the LSPIV results. This highlights the importance of obtaining more comprehensive data and observations to enhance the model’s accuracy. The comparison of rating curves at the Chache site resulted in positive results. Although, further verification during the wet period is required through velocity and discharge measurements to determine the accuracy. Overall, this research contributes to a better understanding of river behavior and provides valuable insights for water flow prediction in an efficient, cost-effective manner with minimal intensive manpower, ensuring a non-intrusive approach.

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.