Flood Wave Monitoring using LSPIV

Abstract

To develop warning systems for flood events, create precipitation-runoff relationships, validate runoff models, or to understand the behaviour of rivers, understanding of the amount of water flowing through rivers is needed. The low-cost and novel gauging method using Large-Scale Particle Image Velocimetry (LSPIV) could complement discharge measurements at locations and stages where the possibilities of using traditional gauging methods are limited. This report investigates the feasibility of using LSPIV to quantify river discharges in an equatorial urban stream with fast response time. LSPIV uses videos to extract surface flow velocities by tracing movements of seeds on the water's surface. Combined with the local bathymetry and water level, an estimation of the river's discharge can be made. This study consists of two sets of experiments. The first set of experiments were performed at the Dommel regarding processing software, image preparation, seeding densities, and point of views and discussed by assessing their accuracy relative to benchmark measurements -- using the mean error and root mean squared error -- and the method's precision – using the relative standard deviation. The second set of experiments were performed along the Chuo Kikuu, Dar es Salaam, Tanzania. A flood wave was monitored through the capture of 73 5 second videos. These videos were turned into separate frames and corrected for lens distortion and perspective distortion. Thereafter the frames were gray scaled and gamma correction was applied. After the LSPIV process additional filtering removed unrealistic low flow velocities, and through substitution missing velocities were replaced with flow velocities based on the vertical logarithmic progression relationship between the surface flow velocities and water depth. The surface flow velocities found using this method match optical observations. Discharges were estimated using the empirical depth-average coefficient and local bathymetry. Results showed that the post-processing reduces the uncertainty bandwidth with 37% and increases the mean flow velocities with 96%. The found discharges were compared with precipitation measurements observed at a nearby TAHMO meteorological station. The total volumetric precipitation was determined by estimating the contributing catchment using a digital elevation map and the locations of man-made drainage systems. When comparing the volumetric precipitation with the flood wave, a runoff coefficient of 53% [35-68] is found. This coefficient falls within the ranges found in literature, but is probably an underestimation of the true runoff due to an overestimation of the catchment area and underestimation of the discharges. This study shows that the LSPIV method is feasible for continuously monitoring flood waves in an urban environment. Especially during peak flows LSPIV proves to be valuable, as observations using conventional gauging methods are labour intensive, unsafe, or not executable. Because of the possibility to monitor streams from a distance -- which ensures access to power and safety against vandalism -- there is a possibility to observe complete flood waves at regular intervals without the need for direct contact with the water. For Dar es Salaam, this method opens doors for continuous and secure stream monitoring, at low costs and with local devices.