Floods are among the most common and devastating natural hazards worldwide. A key challenge is to select and implement efficient measures to reduce the flood risk, within limitations of budget, time, space, and societal acceptance. Interactions between joining rivers in flood plains and cities lead to several challenges in flood risk studies. Typical ‘critical’ events of such river systems may differ as small (large) rivers are affected by short duration-high intensity (long duration large volume) rainfall events. The challenges are 1) to define design flood conditions (design events) for systems on which the flood resistance measures can be based, e.g. dimensions of measures like levees or reservoirs, and 2) to understand and determine the risk and consequences that a flood can cause. A multivariate analysis is needed to account for the interactions and statistical dependencies (Bender et al., 2016). Copula functions have been applied in different fields as a multivariate method. Nevertheless, its application in risk analysis for river confluences still needs further evaluation.
In this thesis, the objective is to develop a methodology to determine design flood events that account for the different statistical dependencies and interactions between joining rivers, and that balances the required simulation time with the required accuracy of the results. For this objective, we evaluated the different statistical dependencies and interactions of joining rivers according to the catchment characteristics for each model domain: meteorological, hydrological, and hydraulic. First, identifying how the extreme precipitation events (meteorological) of neighbouring catchments are correlated, and evaluating the differences whit correlations of extreme river discharge events (hydrological). Second, we performed hydraulic simulations at a confluence to evaluate the hydraulic interactions of the joining rivers and the flood impacts, from which a response function was obtained. Subsequently, we evaluated the flood risk by implementing an approach to sample combinations of discharges of joining rivers, and determining the flood impacts from the response function. The developed approach consists of three main steps: 1) the selection of the extremes sets at the confluence, 2) the estimation of the copula parameters and 3) Monte Carlo simulations where the discharges of joining rivers are sampled from their respective marginal probability distributions, and the flooded area is calculated by using the response function. The dependence between the two joining rivers is taken into consideration by using a copula (Gaussian, Gumbel, or Clayton) to construct the joint distribution of the confluence from the marginal distributions.

Due to climate change, temperatures are rising worldwide resulting in sea level rise, higher evaporation and possible changes in precipitation patterns. These effects of climate change influence salt intrusion, particularly by affecting the freshwater supply. In The Gambia also a planned hydropower dam and possibly future irrigation development will affect the freshwater supply. The combined effects of climate change, the hydropower dam and irrigation development on salt intrusion are unknown. A river south of The Gambia, the Casamance, turned strongly hypersaline during the Sahelian droughts, which strongly affected the rivers discharge in the early 80s. The freshwater supply in the Casamance after these droughts was insufficient to flush away the salt and the estuary never restored. This led to the decay of the entire ecosystem that existed in and around the Casamance. The Gambia river is of major importance to the country, the agricultural sector relies on the freshwater it supplies for growing crops, the fishing industry relies on sufficient numbers of fish to catch and the forestry industry makes use of the mangroves growing around the river. All these industries combined account for ±75% of the working force in The Gambia. If the Gambia estuary turns strongly hypersaline such as the Casamance, this would have disastrous results. This thesis aims to clarify the effect that climate change, the hydropower dam and irrigation development have on salt intrusion in the Gambia river. A 1D model, SALNST, is used to model salt concentrations over the river length up to the year 2100. The effects of climate change, the hydropower dam and irrigation development are represented by four model parameters: climate projections, sea level rise, dam operations and irrigation development. Multiple parameter values, ranging from mild to strong conditions or impact are implemented, creating a set of 27 scenarios. These scenarios treat the parameters independently to distinguish the individual parameters effect on salt intrusion. It can be concluded that The Gambia is prone to climate change when considering salt intrusion. Due to the importance of the river, monitoring sea level rise and the development of climate change will be essential for planning and taking counter measures. Understanding the effects that climate change can have regarding salt intrusion can also be used in negotiations to reserve some freedom in the hydropower dam operations. In addition, solutions that limit the increase in evaporative surface for strong sea level rise (e.g., dikes along a part of the Gambia river) prevent extreme salt intrusion lengths and hyper salinity. The hydropower dam causes a reduced range of salt intrusion lengths, this creates easier to predict salt concentrations which is beneficial for aquaculture. Furthermore, the reduced maximum salt intrusion is also beneficial for agriculture that makes use of irrigation.

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.

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.

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.

Our climate is changing. In the Netherlands, higher rainfall intensities and longer periods of drought are expected in the coming years. Urban environments have to be adapted in order to maintain (or improve) the living standards that we have set for today. Models of the urban drainage system play a vital role in the identification of flood-prone areas, participation of stakeholders and the design of effective measures. This research focuses on the modelling of the urban drainage system to assess pluvial flooding. A method is selected to compare the main modelling concepts and differences between the most often used modelling concepts are assessed.

Phenomena like urbanization and climate change will increase flooding problems, making it clear that more research and investments are needed in sewer systems to improve urban flood prevention. This research was done to determine factors influencing the sediment load to gully pots. It was assumed that in the measurement period, the retaining efficiency of the gully pots remains unchanged. Sediment levels in gully pots for a period of 3 months after cleaning were measured in the fall/winter period, in the cities of Rotterdam and The Hague. A probabilistic approach was used, consisting of a deterministic and random part. For the deterministic part, a generalized linear model is constructed to determine the influence of chosen parameters from the surrounding environment and the gully pot itself. For the random part, a normal distribution is used. The factors which were deemed to be relevant (p>0.05) are the tree factor (p=2,39E-06), inlet structure (p=2,05E-05), sand trap volume (p=9,52E-03) and road surface (p=1,28E-02). Parameters deemed not to be relevant are connected surface area (p=4,39E-01) and traffic intensity (p=3,33E-01). In further research, rainfall intensities should be included as well.