Rivers have long since exceeded their natural purpose of discharging excess water, by becoming subject to many practical applications demanded by present day society [61]. In order to comply withthis variety of needs and demands, the necessity for proper water management arises, which in turn requires data and knowledge of hydrological parameters like water levels, water quality and river dis-charge [50]. This research focuses on the hydrological data demand and specifically on the measurement of riverdischarge. Discharge is generally estimated with intrusive measurement methods [64], this means that the measurement device is in physical contact with the water which can be difficult in strong current or high discharges and even dangerous during floods . Furthermore, in remote and low-resource settings, collecting discharge data is compromised by accessibility problems and difficulties maintaining and acquiring monitoring equipment. When numerous measurements are performed, it is common practice to establish a stage-discharge relationship [66] to facilitate discharge determination, i.e. by shifting to stage measurements. However, due to the empirical character of the method and the sporadic occurrence of high discharges, the relationship can contain considerable uncertainties for these higher discharges [67]. The aim of this research is to provide a sustainable and low-cost data collection and processing method in order to establish a rating curve based on a three dimensional hydraulic modelling approach. One of the main processing methods is Large-Scale Particle Image Velocimetry (LSPIV). LSPIV is a computer based technique that computes flow velocities at the river surface based on video images. Hence, with the development of such a model a more physically based stage-discharge relationship can be determined based on non-intrusive measurements, meaning that measurements can be taken during safe (low flow) conditions in a restricted amount of time. Furthermore, due to the sole use of relatively simple methods and the limited amount of observations needed, this method is particularly suitable for remote and low resource settings. The study is based on data collected during a two month field trip at the Luangwa river in Zambia. The dataset consists of point clouds collected with the aid of photogrammetry, sonar and RTK GPS which are used to create a bathymetric chart, videos recorded with a drone for the computation of the surface flow velocities, surface flow velocities measured with a current meter for LSPIV validation and discharges measured with an ADCP. The bathymetric chart is used as bed level for the three dimensional discharge model created with Delf3D D-Flow FM which is calibrated with the surface flowvelocities (LSPIV) and ADCP discharge measurements. The discharge model represents approximately 9.2 kilometres of the Luangwa river in length and can reach a maximum width of about 390 metres. The model is calibrated at a discharge of 191 m3/s by minimising the difference between measured and simulated values of ten surface flow velocities and five water levels. This resulted eventually in a Manning friction coefficient of푛= 0.014 s/m1/3. The calibrated model resembles the actual river in location, depth, width and surface flow velocity. The LSPIV velocities are approached to a mean average deviation of 0.07 m/s (1.1 m/s average) and the water level deviates 0.06 m at the research area (1.3 m average). The model is used to establish a stage-discharge relationship which is subsequently compared to two existing relationships, one based on a similar approach using a 1D model and one based on stage-discharge data measured at a conventional gauging station. The three stage-discharge relationships are in the same order of magnitude although the geometry of the river at all sites is likely to be different. Since a stage-discharge relationship is heavily dependent on the geometry [66] this comparison is only a rough indication of the accuracy. Ideally, the discharge, water level, and surface flow velocity should be measured for different discharges and compared (using the model) to the established relationship. The stage-discharge relationship could, if needed, be adjusted based on the new measurements.

This report contains the findings of a multidisciplinary project in Mozambique which ran from mid-November 2018 to mid-January 2019. This study is part of the IWACA-TECH project, which is an abbreviation for “Improved Water efficiency Control based on remote sensing TECHnologies. The goal is to increase water efficiency and crop yield without increasing the consumptive use of water, using remote sensing and Model Predictive Control (MPC). The structural water scarcity in the region points out the relevance of the IWACATECH project and with that this study. The research, carried out by students of Delft University of Technology, is of importance for both the company Tongaat Hulett and all inhabitants who are direct or indirect dependent on the water of the Incomati river. The study area on the plantation in Xinavane copes not only with inefficient irrigation water use but also suffers a sugarcane yield decline in recent years ??. Therefore, the overall aim of this report is to improve water efficiency and crop yield within Tongaat Hulett. To achieve this from a multidisciplinary perspective several research questions have been formulated. Although they all contribute to the overall aim, they do so from different angles and in varying degrees. Therefore, to increase the readability of the report, the research questions have been divided into three sections: (1) Irrigation Practices, (2) Field Assessment on Yield Variability and (3) Decision Support System. Fieldwork was conducted over a five week period in order to gather data. Groundwater levels were measured, water quality of irrigation, ground- and precipitation water samples was analysed and soil moisture content was measured. This led to further research of soil types and quality. Soil profiles along the edges of both fields were made revealing a shallow aquifer in the bad-performing field. Irrigation water quality seems to form no hazard, but ground water quality analyses revealed significantly high electrical conductivity levels and a high sodium adsorption ratio in areas without growth. These findings, combined with an analysis of the digital elevation map and socio-technical data revealed that evaporation of irrigation water seems to be a large contributor to the impaired crop growth. When excess water cannot run off, puddles are formed. When these puddles evaporate, salts can be taken up by the soil once the thickened irrigation water infiltrates. This process is strengthened by the clay soil layer and the shallow aquifer, which prevent water from infiltrating deeper into the ground. Results concerning remote sensing prove the relationship between soil moisture content and precipitation for meteo-station XNA-20. Combining spatial and temporal variability of soil moisture content with remote sensing can play an essential role in managing irrigation practices. For the decision support system, and in specific the controller part of the system, measurements have been done. It can be concluded that storage area and delay times can be considered insignificant, and that canals do not have to be modelled. This makes the controller significantly easier and thus more time and effort can be spend on other aspects of the controller.