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.

Small to medium-sized man-made freshwater reservoir are a reliable source for drinking water supply, hydropower generation and irrigation purposes worldwide. However, water volumes in these reservoirs can be significantly affected by prolonged droughts, resulting in severe impacts on society (Kozacek, 2014; Mahr, 2018). To mitigate the impact of such events it is crucial for decision makers to know when the available water resources are lacking. Although many reservoirs are closely monitored, this data is not always readily available. Inadequate information sharing, inaccessibility, and a lack of tools to predict future reservoir storages contribute to this problem.

Remote sensing has the potential to address this problem. The Global Water Watch is a platform that and provides earth-observed surface area dynamics that can be used to monitor small to medium-sized reservoirs worldwide and detect trends in water availability. While this method serves as a valuable indicator of water availability, it falls short in providing decision-makers with the necessary absolute volume time series and volume predictions. Currently, no platform exists beyond in-situ measurements to meet this essential need.

This thesis presents a novel method for retrieving near real-time volume time series in small to medium-sized man-made reservoirs worldwide using remotely sensed open data. The method utilises the MERIT-Hydro digital elevation model, HydroMT and stream flow methods by Eilander et al. (2023), and literature by Messager et al. (2016) to reconstruct reservoir bathymetry. This novel approach in reconstructing reservoir bathymetry enables the conversion of available reservoir area time series into volume time series. These were employed in autoregressive and multi-linear regression models to predict water availability up to six months in advance. The models incorporate ERA5 precipitation data by Hersbach's (2020) and the Standardised Precipitation and Evaporation Index (SPEI) by Beguería et al. (2021) to improve the accuracy of the volume predictions.

When comparing the novel method to the method proposed by Messager et al. (2016), the novel method yielded more accurate reservoir volume estimations. The method successfully obtained bathymetries and accurate volume estimations when validating using 2 reservoirs in Zambia and 48 in India, demonstrating the potential of this novel approach. However, some reservoirs with complex shapes faced initial delineation challenges, resulting in inaccurate volume predictions. These issues could be resolved by manually delineating the area for bathymetry reconstruction. Moreover, regression models were applied to case study reservoirs in Eswatini and Lesotho, demonstrating reasonable predictive capabilities with the Heidke Skill Scores ranging from 0.77 to 1 for up to 2 months ahead. However, precise prediction of extreme decreases in reservoir levels requires a physically based approach that incorporates the volumetric time series provided by this novel method. The study emphasises the necessity of considering the volume time series’ memory to predict water availability and provides a valuable foundation for volume time series analysis using remotely sensed data.

Weather generators (WGs) based on temporal resampling algorithms are not able to generate new extremes with the same or a higher temporal resolution as the historical data series of the thus far
observed precipitation. This is problematic for catchments with a time of concentration shorter than the resolution of the used data and conflicts with the increasing occurrence of more local, short duration extremes not yet observed.

In this thesis, it was researched if spatial permutation of precipitation could provide a solution to these problems by introducing historical events from related locations into the area of interest. The generated precipitation series were expected to have a larger variety of precipitation events compared to the historical data, thereby representing the current changing weather patterns better and being more suitable for small basins. This is beneficial for insurance companies, governments and aid organizations which rely on long term precipitation series to generate event catalogues and risk predictions.

To develop, improve and widen the knowledge about the effects of spatial permutation, four different questions were formulated for a case study on spatial permutation in the Rhine basin. A literature study showed that precipitation regimes in Europe can be defined based on spatial and temporal variability, precipitation amounts and the influence of controlling factors like atmospheric circulations, topography and climate change. This information was used to built three permutation models. The first model shifted historical precipitation fields over fixed distances and directions. In the second model this fixed approach was replaced by semi-random vectors including spatial and temporal correlation. The last
model used a vector approach with vectors conditioned with historical wind data. The effect of each model on the Generalized Extreme Value (GEV) distributions, the cumulative distribution functions (cdfs) and the main characteristics of precipitation for different basins and aggregation times was determined. The July 2021 Meuse flood was used as example to show the working method of each model visually and to better understand the effect of each permutation model on individual extreme events.

The results showed that spatial permutation did influence precipitation patterns, characteristics and statistics. Strongest changes in extreme precipitation events were seen for small basins and short aggregation times. The permutation direction and distance were important determinants for the outcome of each model. Precipitation permutation with semi-random vector fields was shown to be a promising method which allowed for the inclusion of both spatial and temporal correlation. However, the model had a high sensitivity to the initial and boundary conditions. Wind based vector fields were able to replicate the most important historical precipitation characteristics while at the same time generating new extremes. Yet, a clear trade off was visible between similarity of the historically observed and modelled precipitation characteristics and the number of new extremes introduced.

With the knowledge obtained, it can be concluded that spatial permutation is a promising method to generate more divers precipitation time series for the Rhine basin. Both semi-random and wind-based vector permutations can already be used to generate new precipitation series as long as the initial and boundary conditions are chosen carefully. To improve the results, a fusion of spatial and temporal relations and a physically wind-based vector generation method is advised. In addition, possibilities are seen in a combination of the currently used temporal resampling algorithms and a spatial permutation approach. The outcomes of these suggestions are not known yet. Nevertheless, it is expected that a better understanding of spatial permutation, on top of the results presented in this thesis, can advance the current methodologies used to generate long term precipitation series. Therefore, it is hoped that this research provides the incentives to explore spatial permutation of precipitation patterns in more detail and in such, contributes to a more accurate risk profile for the livelihoods of people worldwide.

Humans have always populated in the vicinity of river systems, where thesupply of water, nourishment and transportation is obtained from the river.However, inundation is a re-occurring problem and impact of floods are ex-pected to increase due to climate change. Accurate flood forecasting andearly warning is critical for disaster risk management. Tackling the problemof forecasting, in data scarce environments, has become increasingly impor-tant due to the changing climate. Remotely sensed river monitoring can bean effective, systematic and time-efficient technique to monitor and forecastextreme floods. Conventional flood forecasting systems require extensivedata inputs and software to model floods. Moreover, most models rely ondischarge data, which is not always available and is less accurate in a over-bank flow situations. There is a need for an alternative method which de-tects riverine inundation, using open-source data and software. This thesisaims to research the use of passive microwave radiometry for the detection,classification and forecasting of inundation.Brightness temperatures are extracted from the passive microwave radiom-etry and are converted in a discharge estimator: the C/M-ratio. Surfacewater has a low emission, thus let the C/M-ratio increase as the surfacewater percentage in the pixel increases. Sharp increases are observed forover-bank flow conditions. The research combines the identification of in-undation with a probability analysis via a quantile regressional fit. Floodforecasts can be obtained from an upstream catchment area. In the mostideal situation with a delay of2,5hours. This allows for probabilistic earlywarning decision making, with a lead time up to14days. (location specific)Strong Spearmans correlation coefficients between the discharge and C/M-ratio are found (>0.883). Allowing the model to forecast floods as gaugeddischarge records do. The model used has a comparable skill to the localGloFas forecast. This research investigated the impact the remote sensedtechnology could have on the flood forecast, response and warning system.An added model to an Early Action Protocol has the ability to lower uncer-tainty within decision making and enlarges the intervention window. Theadvice is to use such a model in combination with other forecasting modelssuch as GloFas.The challenge using this technology is the integration of hydrological com-plexity. The method allows for automated, global-covered creation of gridbased flood forecasts, independent to cloud coverage. Creating low spatialresolution flood forecasts combined with a probability bound in hours aftersatellite detection. The method has a high potential for data scarce flood-prone river basins around the world. The future for this technology lies inthe global daily availability of the data. With satellite sensors improving,spatial resolution is expected to increase. Allowing for even better floodforecasting ability.

With climate change increasing its mark on all aspects of the hydrological cycle, societies all over the world living in flood-prone areas are increasingly exposed to flood hazards. In many parts of the world, especially in less developed areas, societies lack knowledge and data to predict future flood events. By predicting a future flood event, an organization creates a time frame in which it can implement a mitigating action that reduces the financial damage inflicted. In recent years, development in new measuring techniques has significantly lowered the cost of collecting data and information on different aspects of the hydrological cycle. These developments enable organizations in regions restrained of knowledge and data to establish methods to analyze aspects of the hydrological cycle and thereby predicting the probability of a flood hazard several hours or days in advance. This thesis explores various possibilities of designing and implementing an \ac{EWS} for the \ac{BRT} in Dar es Salaam. The EWS design is based on the forecasting requirements, investigated with the BRT-system. Several operational forecasting methods are available. The EWS designed in this thesis makes use of rainfall data obtained from rainfall stations located in the Dar es Salaam region, installed and managed by the \ac{TAHMO}. This forecasting data is chosen because it provides the needed lead-time with the lowest margin of error. This forecasting data is processed and analyzed by the designed EWS and subsequently produces a probability level on a flood event. It thereby provides an advice on if the BRT-system should implement a mitigating action based on the principle of pursuing an optimal economic outcome. The designed EWS produces the flood probability in real-time, updated every hour with a lead time of one hour. This time frame enables the BRT-system to implement a mitigating action, thereby reducing the inflicted cost.

The probability level of a flood event is determined by training the EWS with historic flood and rainfall data. In addition, the implementation of both a hydrological and relational model in the EWS was tested. The results show that the hydrological model is the better option. The results also show that the implementation of an EWS ensures a decrease in financial damage endured by the BRT-system. The produced outcome of the EWS was validated by a 'leave one out' method. This validation was done by consecutively leaving one flood event out of the historical data frame and analyzing the variability of the resulting outcome. Finally, the designed EWS is best implemented in the BRT-system alongside the EWS-systems currently in place.

River data on discharge and characteristics is essential for water management and water supply, as well as for flood prediction and flood control (Pan, Wang, and Xi 2016). In practice, many watersheds are ungauged due to high costs, inaccessibility and even due to political instability (Pan, Wang, and Xi 2016). For this reason, measuring remotely without the need of being physically present, for instance by remote sensing satellites, can be interesting for many applications. The large amount of satellite data can result in the ability to extend short observation series into larger series with satellite missions.
Discharge is one of the conditions in a river, which is relevant to have data on during regular periods but in particular during or after extreme events. This thesis focussed on an approach, by using remote sensing, to obtain data that can be used for further research to determine discharge. River-width is one of the current variables researched to be used as a substitute for river stage data. River stage is currently used to obtain estimations for river discharge via earlier obtained river stage-discharge relations, which can be transformed into river width-discharge relations.
The objective of this thesis was to develop a method to obtain sub-pixel accurate river-width estimations by remote sensing. The objective to estimate river-widths on sub-pixel base originates from the need of river-width estimations with higher accuracy than the freely available optical satellite resolutions of 10 to 20 metres. The study contains the improvement of the current water classification methods by including analyses for discriminating band combinations, to construct site-specific indices. This was noticed to be needed, due to the conventional indices, like the NDWI, performing differently with the presence of certain land types.
By having multiple indices based on uncorrelated satellite bands transformed into probability bands, it is possible to combine indices, via Bayes theorem. Based on the site-specific indices and index combinations, the aim is to develop relations between spectral information and water fractions of pixels that could lead to a more detailed river-width estimation by including sub-pixel information.
The resulting method was able to show discriminating abilities in satellite bands and band combinations, specifically for an area of interest, other than the conventional NDWI and MNDWI. With the use of river edge information, the spectral bands could be transformed into spatial water probability bands, indicating a probability for the present pixels to be water. The probability indices and index combinations showed to reduce a large part of the occurring misclassifications. With the use of ROC curves, to assess the classification performance of the indices and combination of indices, variation in misclassification of certain land types between days were observed for certain indices.
The probability bands, which are based on the river’s edge value distribution, also seemed to be useful, especially for the pan-sharpened MNDWI and the Bayes 0-3 indices, to obtain the needed water fraction relations for sub-pixel base estimations. A comparison in river-width estimation of a conventional automated water classification method; Otsu’s thresholding method and a Supervised training map classification method were made against the use of probability indices with sub-pixel water fraction relationships. It was found that the use of sub-pixel information resulted in a significant improvement of the accuracy for river-width estimations. For the first and second fieldwork day, the average river-width deviations of the pan-sharpened MNDWI and Bayes0-3 decreased, respectively, from 16 and 9 metres, to under 5 and 7 metre deviation by including the found water fraction relationships.

Heavy rainfall, combined with expanding (unplanned) urban settlements in flood prone areas, expose Dar es Salaam (Tanzania) to the risks of flooding. The urbanisation is so rapid in many areas that it is not beneficial to carry out expensive surveys which are quickly out of date. The work carried out by community-mapping project Dar Ramani Huria (Swahili for "Open map") aims to make a detailed map of Dar es Salaam, to enable the hydrologic models to approach the real situation more closely. However, the surveying methods used until recently are not sufficiently accurate. However, an alternative emerges in the form of community members using a low-cost, dual-frequency global navigation satellite system (GNSS) receiver during surveys. However, before this receiver can be implemented a detailed research has to be done. In this thesis the horizontal and vertical performance of the U-blox ZED-F9P receiver in Delft (the Netherlands) and Dar es Salaam is studied. The research is divided into two parts: performance and case study. For the performance study a series of post-processed kinematic (PPK) experiments were conducted in Delft and Dar es Salaam. The experiments have been designed in order to provide a variety of location, antenna-performance, baseline length, software package and movability. In addition, two re-initialisation experiments were conducted to measure how fast the interrupted GNSS signal is regained by the receiver. The case study focused on the desirability and feasibility, mainly focussing on accuracy, of implementation in the project of Dar Ramani Huria. Structured and unstructured interviews with employees of the Humanitarian OpenStreetMap Team (HOT) Tanzania were held to find out the requirements of implementation. The positioning performance of the receiver varies between the different experiments. The conclusions regarding the positioning performance are based on the scatter plots in the horizontal plane and the positioning over time for the three separate directional components; East, North and Up. The values for the horizontal performance (RMS East, RMS North) and for the vertical performance (RMS Up) of the fix solutions insofar as they fall inside the 95% confidence ellipse are decisive. Only the relevant experiments, namely those who can map a larger area with a single reference station are taken into consideration. The horizontal positioning performance ranges from 1.13 till 16.83 However the latter, high value is from the 9 baseline Dar es Salaam experiment with a very low percentage of fixed solutions. If we disregard the experiments with low percentage of fixed solutions then the horizontal positioning performance ranges van 1.13 till 9.42. The vertical positioning performance shows less accuracy ranging from 3.56 till 14.75. If we compare this performance with the requirements for Dar Ramani Huria’s project, even the strictest of 2cm, the performance is more than adequate according to the "few cm accuracy" requirement. The experiment with the high-end antenna shows with values 2.44mm (RMS East) and 3.42mm (RMS North) the best horizontal and with the value 3.75$mm(RMS Up) the best vertical performance. Another factor influencing the performance is the location, in particular the aspect of atmospheric delay that varies between Dar es Salaam and Delft. This research thesis concludes that the implementation of the receiver in Dar Ramani Huria's project is well possible and that the performance of the receiver is adequate. This conclusion is confirmed by what is actually occurring in the field: HOT Tanzania and Dar Ramani Huria already started using the GNSS receiver and carrying out surveys with this receiver.

Direct measurement of river discharge is difficult, time consuming and costly. Therefore a rating curve is often used to estimate the river discharge. Limited measurements under extreme conditions result in extrapolation of the rating curve for high flow conditions. This induces uncertainties and errors in the stage-discharge relation. Recently there has been a gradual shift to more physically based rating curves, where the geometry of a river is included and no extrapolation is needed. This seems to be a promising shift to improve traditional river rating. However, the challenge now is to accurately determine the parameters bed roughness and hydraulic slope. The aim of this research is to develop and evaluate a method to better estimate the hydraulic parameters bed roughness and hydraulic slope. To do so a case study has been carried out in the Luangwa river catchment in Zambia.