Using River Geometry for Rating Curve Computation

This thesis study aims to provide a solution for the shortcomings concerning conventional rating curve computation. Conventional rating curve computation struggles with the issues of being data-intensive, inaccurate in high-flow regimes and susceptibility to changes in cross-sectional geometry. In the view of the researchers, an answer can be found in a more physically-based rating curve that allows for substantiation through a detailed representation of the river’s geometry. For a detailed reconstruction of the geometry, we see a big potential in the use of state of the art remote sensing technologies. Apart from (1) increasing accuracy by removing the need for extrapolation to high-flow regimes, the physically-based rating curve would (2) be less susceptible to changes in geometrical changes due to scouring and sediment-deposition, (3) be more easily to update if geometrical changes do occur, and (4) would require less data and is therefore likely to be cheaper to establish. The physically-based rating curve is based on Manning’s formula for steady uniform flow. The research included two months of fieldwork in the north-western province of Mozambique, Tete. Here, as a guest of HCB in Songo, the researcher collaborated with local parties HCB and ARAZambeze to perform measurements on a set of tributaries to the Zambezi river. A small commercial drone was used and aerial photographs were converted into Digital Elevation Models (DEMs). Eight rivers were visited in total, of which two the data was of insufficient quality and quantity to be used in further analysis. Three factors seem to be of main influence to accuracy of the DEM: size of the measurement area, coverage of the area by Ground Control Points (GCP)s and presence of vegetation. Absolute errors in the DEMs, i.e. the average error of GCPs per river, ranged from 0.16 m to 2.57 m in the horizontal plane and from 0.01 m to 0.30 m in the vertical direction. It can be concluded that a high resolution elevation model can be computed showing very little relative error in both the horizontal plane (max 1.90%) and the vertical plane (max 3.65%). A lack of flowing water in the observed rivers gave rise to the need for an alternative way of quantifying slope (i). A solution was found by using the increase of the conveyance (C or AR / ) in downstream direction (Y) for a constant water level (i = 0). By enforcing a slope on the water surface, / can be brought down to zero, which in theory corresponds to the situation for uniform-flow. Through this process, the theoretical ’local’ slope corresponding to uniform flow in the measured reach can be established. A comparative measure was formed by deriving the ’regional’ slope from Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTGDEM) by looking at the elevation profile of a reach of 20 km around the measuring location. A factor two difference showed that the slope is likely to be influenced by the water level (i.e. local slope will dominate low flows and regional slope will dominate high flows) and stipulates the importance of (1) calibration or real time measurements of i and (2) the need to validate proposed method for slope calculation. Taking into account parameter uncertainty, it can be concluded that the physically-based rating curve shows sufficient similarity with that of ARA-Zambeze’s to assume the validity of the method for physically-based rating curve computation. Theory of error propagation is used to quantify the relative contribution of the uncertainties in the parameter values. The error in Q can be seen to lie between 67% and 30% where the roughness coefficient (n) is estimated based on literature, but this decreases to values between 50% and 13% when n is calibrated. An important observation is that the uncertainty of h has a very big influence for low water levels (42% at h = 1.6 m) and becomes significantly lower at higher water levels (4.2% at h = 4.6 m). This analysis shows that, in exception for very low-flows, accuracy of the physically based rating-curve will benefit most from decreasing uncertainty in n. For low flows, the major contribution to the error comes from uncertainty in h . Another important conclusion is that in contrast to conventional rating curves, the physically based rating curve shows an increasing accuracy for increasing water levels due to the decreasing influence of the error in h . This resolves the biggest flaw in conventional rating curve computation, the decreasing predictive capability for high flows due to extrapolation of low flow data. The biggest overall limiting factor to accuracy is the need to calibrate n which requires at least one relevant flow measurement under the assumption of uniform roughness as is done in this study. When