3D River Discharge Modelling using UAV photogrammetry

Abstract

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.