This report details the development of a Flood Early Warning System (FEWS) for the Tana Basin in Kenya, executed by a multidisciplinary team from the Delft University of Technology. Recognizing the Tana Basin’s vulnerability to flood risks, exacerbated by climatic variability, limited funds, and limited available data, the project proposes a model that combines computationally efficient hydrological and hydrodynamic modelling with robust stakeholder collaboration. The study area comprises the entire Tana Basin, with a specific focus on the flood-prone area near Garissa used for validation. The FEWS developed incorporates local and scientifi-cally derived knowledge to forecast floods, aiming to aid the transition from a technologically intermediate to a technologically advanced FEWS. Through an iterative process of model selection, validation, and stakeholder feedback, the system attempts to integrate the GR4J hydrological model in SuperflexPy and combines this with the Super Fast INundation of CoastS (SFINCS) model. Data sources include global remote sensing datasets like FABDEM & CHIRPS. Furthermore, it uses the water level gauge data provided by the Water Resource Authority of Kenya, as well as TAHMO weather station data.

The report concludes by reflecting on the modelling techniques for both the hydrological and hydrodynamic models and provides recommendations for the further development of a FEWS in the Tana Basin in Kenya. The implementation of the hydrological model was not able to propagate external flows through the network, making it poorly suited for use in the Tana Basin. The hydrodynamic model works decently well in flood conditions but overpredicts flooding during regular flow conditions. Recommendations on stakeholder engagements and data-sharing practices to foster a resilient flood management system in the Tana Basin include more comprehensive Memoranda of Understanding (MoU) and stricter adherence to the Disaster Risk Management Framework of the United Nations.

This report details the development of a Flood Early Warning System (FEWS) for the Tana Basin in Kenya, executed by a multidisciplinary team from the Delft University of Technology. Recognizing the Tana Basin’s vulnerability to flood risks, exacerbated by climatic variability, limited funds, and limited available data, the project proposes a model that combines computationally efficient hydrological and hydrodynamic modelling with robust stakeholder collaboration. The study area comprises the entire Tana Basin, with a specific focus on the flood-prone area near Garissa used for validation. The FEWS developed incorporates local and scientifi-cally derived knowledge to forecast floods, aiming to aid the transition from a technologically intermediate to a technologically advanced FEWS. Through an iterative process of model selection, validation, and stakeholder feedback, the system attempts to integrate the GR4J hydrological model in SuperflexPy and combines this with the Super Fast INundation of CoastS (SFINCS) model. Data sources include global remote sensing datasets like FABDEM & CHIRPS. Furthermore, it uses the water level gauge data provided by the Water Resource Authority of Kenya, as well as TAHMO weather station data.

The report concludes by reflecting on the modelling techniques for both the hydrological and hydrodynamic models and provides recommendations for the further development of a FEWS in the Tana Basin in Kenya. The implementation of the hydrological model was not able to propagate external flows through the network, making it poorly suited for use in the Tana Basin. The hydrodynamic model works decently well in flood conditions but overpredicts flooding during regular flow conditions. Recommendations on stakeholder engagements and data-sharing practices to foster a resilient flood management system in the Tana Basin include more comprehensive Memoranda of Understanding (MoU) and stricter adherence to the Disaster Risk Management Framework of the United Nations.

This report details the development of a Flood Early Warning System (FEWS) for the Tana Basin in Kenya, executed by a multidisciplinary team from the Delft University of Technology. Recognizing the Tana Basin’s vulnerability to flood risks, exacerbated by climatic variability, limited funds, and limited available data, the project proposes a model that combines computationally efficient hydrological and hydrodynamic modelling with robust stakeholder collaboration. The study area comprises the entire Tana Basin, with a specific focus on the flood-prone area near Garissa used for validation. The FEWS developed incorporates local and scientifi-cally derived knowledge to forecast floods, aiming to aid the transition from a technologically intermediate to a technologically advanced FEWS. Through an iterative process of model selection, validation, and stakeholder feedback, the system attempts to integrate the GR4J hydrological model in SuperflexPy and combines this with the Super Fast INundation of CoastS (SFINCS) model. Data sources include global remote sensing datasets like FABDEM & CHIRPS. Furthermore, it uses the water level gauge data provided by the Water Resource Authority of Kenya, as well as TAHMO weather station data.

The report concludes by reflecting on the modelling techniques for both the hydrological and hydrodynamic models and provides recommendations for the further development of a FEWS in the Tana Basin in Kenya. The implementation of the hydrological model was not able to propagate external flows through the network, making it poorly suited for use in the Tana Basin. The hydrodynamic model works decently well in flood conditions but overpredicts flooding during regular flow conditions. Recommendations on stakeholder engagements and data-sharing practices to foster a resilient flood management system in the Tana Basin include more comprehensive Memoranda of Understanding (MoU) and stricter adherence to the Disaster Risk Management Framework of the United Nations.

This report details the development of a Flood Early Warning System (FEWS) for the Tana Basin in Kenya, executed by a multidisciplinary team from the Delft University of Technology. Recognizing the Tana Basin’s vulnerability to flood risks, exacerbated by climatic variability, limited funds, and limited available data, the project proposes a model that combines computationally efficient hydrological and hydrodynamic modelling with robust stakeholder collaboration. The study area comprises the entire Tana Basin, with a specific focus on the flood-prone area near Garissa used for validation. The FEWS developed incorporates local and scientifi-cally derived knowledge to forecast floods, aiming to aid the transition from a technologically intermediate to a technologically advanced FEWS. Through an iterative process of model selection, validation, and stakeholder feedback, the system attempts to integrate the GR4J hydrological model in SuperflexPy and combines this with the Super Fast INundation of CoastS (SFINCS) model. Data sources include global remote sensing datasets like FABDEM & CHIRPS. Furthermore, it uses the water level gauge data provided by the Water Resource Authority of Kenya, as well as TAHMO weather station data.

The report concludes by reflecting on the modelling techniques for both the hydrological and hydrodynamic models and provides recommendations for the further development of a FEWS in the Tana Basin in Kenya. The implementation of the hydrological model was not able to propagate external flows through the network, making it poorly suited for use in the Tana Basin. The hydrodynamic model works decently well in flood conditions but overpredicts flooding during regular flow conditions. Recommendations on stakeholder engagements and data-sharing practices to foster a resilient flood management system in the Tana Basin include more comprehensive Memoranda of Understanding (MoU) and stricter adherence to the Disaster Risk Management Framework of the United Nations.

The Netherlands is a country that is being threatened by water, both from the rivers and from the sea. The Dutch have built dikes to keep their lands from inundation. To ensure the strength and stability of these dikes, they are being assessed on the basis of several failure mechanisms. One of these failure mechanisms is Backward Erosion Piping, or piping for short. In piping, the current underneath a dike is strong enough to take soil particles with it. Tests on piping in tidal subsoil were conducted in the summer of 2021, where a pipe was found to have grown at greater depth than expected The occurrence of this deeper piping has rarely been seen before, let alone described. This lack of knowledge poses a potential safety risk, as it may underestimate the vulnerability of certain subsoil configurations. Therefore, the objective of this thesis is to develop a comprehensive understanding of deeper piping and identify the key parameters influencing its formation. To achieve this objective, a definition of deeper piping and its differentiation from conventional piping is established. Sub-mechanisms governing deeper piping are examined by analysing the forces responsible for grain movement and the forces that maintain grain stability. A Finite Element Model of the subsoil is constructed to quantify the driving forces within the subsoil, which, when combined with resisting forces, enables the determination of whether deeper piping can occur in a given subsoil configuration. To investigate the factors contributing to deeper piping, a series of simulations are conducted using this Finite Element Model. By varying the parameter values while keeping other factors constant, the influence of each parameter on the occurrence of deeper piping was examined. The analysis revealed that several key parameters significantly affect deeper piping formation, including cohesion force (P), cohesion anisotropy (?P ), permeability and thickness of the top layer (X0 and 70, respectively), permeability of underlying layer (X1), permeability anisotropy (?X ) and representative grain diameter Q_R]. Also, it was found that the entrance configuration plays a large role in deeper pipe formation. These findings provide valuable insights into the mechanisms underlying deeper piping and enhance our ability to identify subsoil configurations that are prone to this phenomenon. These findings enhance the identification of subsoil configurations prone to deeper piping, thereby improving risk assessment and mitigation strategies associated with this failure mechanism.

The applicability of the Sellmeijer design rule to Limburg dike sections near Well, Hout-Blerick, Buggenum, and Thorn was investigated in this thesis. This was accomplished by building a finite element numerical model, in COMSOL Multiphysics, to assess the piping in the hydrogeological systems of the research locations. The proposed model is composed by combining several groundwater and piping model principles, schematizing dike cross-sections, and calibrating the model. The schematization choices that define the model geometry of the average pipe cross-section are critical. It was decided to apply a fracture flow pipe cross-section and the piping assessment was performed iteratively for multiple pipe height values, because the true pipe height is unknown. The pipe height is expressed as a function of the number of grains and the representative grain size.

According to a deterministic assessment, which was performed analytically using multiple versions of the Sellmeijer design rule and numerically using the proposed FEM model, piping does not occur at the research locations Well, Hout-Blerick and Thorn. Piping is theoretically possible at Buggenum, but only with a very small pipe height, making it appear very unlikely. The assessment also revealed that the critical head determined by using revised Sellmeijer design rule (2021) is conservative for the examined dike sections in Limburg. Furthermore, it is discovered that the original Sellmeijer design rule with the new geometry factor (1988/2011) produces critical heads that are similar to the results of the revised Sellmeijer design rule (2021) multiplied by 1.8, which is the current design rule used to assess piping in Limburg.

The Sellmeijer design rule (1988/2011) was stochastically evaluated to determine how the design rule changed when calibrated to Limburg parameter values. 1000 unique randomly generated dike cross-sections were evaluated on piping using the proposed FEM numerical models. The unique dike cross-sections are made up of a random combination of model parameters that were sampled from a uniform distribution using Latin Hypercube Sampling (Olsson et al., 2003). The randomly generated dike cross-sections that demonstrate piping for a realistic pipe height, were used to re-calibrate the Sellmeijer scale factor. Using a linear regression, it was demonstrated that the scale factor corresponding to the broadened application range (including Limburg parameter values), of the Sellmeijer design rule, is 1.56 times greater than the original scale factor. This means that the original Sellmeijer design rule with the new geometry factor (1988/2011) and the additional factor of 1.56, can be applied to (Limburg) dike sections, for which the model parameters fall within the newly set parameter application ranges (e.g. 100 µm ≤ d70 ≤ 900 µm).

Indonesia faces the challenge of attaining renewable energy goals and reducing carbon emissions by 29% by 2030. Despite a renewable energy goal of 23% of the national energy mix by 2025, only 14% of electricity production is projected to be generated from renewable sources by 2023. Accelerated deployment of renewable energy solutions is required to achieve these goals.

Indonesia has abundant renewable energy sources, such as hydropower, solar, and wind. Yet, a challenge arises from the difference between the electricity demand and supply patterns of these sources, which do not match throughout the day. Fluctuating energy supply patterns and variable energy demand necessitate using efficient Energy Storage Systems (ESS) to bridge the gap.

With its extensive coastline, Indonesia can potentially explore single reservoir Seawater Pumped Hydro Storage (SPHS), a variant of Pumped Hydro Energy Storage (PHES), as an alternative to solve these challenges. Similar to an enormous rechargeable battery, the reservoir in an SPHS system functions as an energy storage system. The system works by pumping up the seawater to the reservoir to store surplus energy during periods of ample supply and discharging it to generate electricity through a hydropower plant during periods of high electricity demand. This research aims to identify the ideal locations for SPHS systems in coastal areas of Indonesia. A Python GIS algorithm was developed to automate the selection process. The identified SPHS sites are then evaluated economically to ascertain their viability. The study concludes by comparing the carbon reduction potential of these systems to Indonesia's carbon emission reduction goals.

The research reveals 609 potential SPHS sites across Indonesia, with a total peak power that can be regenerated of technically potential sites of 29 Gigawatt-peak (GWp). Among these, 297 locations are deemed economically feasible, contributing a potential peak power that can be regenerated of 15 GWp. The peak electricity demand in Indonesia is approximately 44 GW, typically occurring at 8 p.m. The technical potential of SPHS promises an 11% reduction in carbon emissions from the energy sector, while the economically feasible sites could achieve a 6% reduction in carbon emissions projected in 2030.

In many places in Uganda, people do not have a connection to the drinking water supply system and there is a lack of treated water supply, meaning that people only have access to water a certain part of the day. As a result many people rely on springs, handpumps, rivers or lakes, of which the quality cannot be monitored or controlled.

During this multi-disciplinary project, we worked together with the National Water & Sewerage Corporation (NWSC) and the Ministry of Water and Environment (MWE) to research the possibilities of extending the water supply system of two project areas, Bugiri District and Hoima City. The current water supply in both areas use groundwater as a source and the possibilities for the extension also consider using surface water besides groundwater.

The different alternatives for the extension of the water supply in Hoima City and Bugiri District are evaluated using a multi-criteria analysis (MCA), consisting of a financial analysis, a performance analysis and a risk analysis. By evaluating the different options using an MCA, the decision-making process can become less complicated.

The MCA-tool that is set up in this research can be used by engineers to study different areas in Uganda and make it easier to compare different options for the extension of a drinking water supply system in an early design stage. The tool is for the two project areas as examples, after which it is also tested during a case study with engineers from both NWSC and MWE. Useful feedback came out of this session which will be used to finalize the tool and elaborate on it.

To design the different alternatives for the project areas and to get insight into the drinking water supply of Uganda, Hoima and Bugiri are visited at the beginning of the project.

For both project areas, it is recommended to improve the operational performance of the already existing groundwater supply system as a short-term (5 years) solution. The long-term (25 years) solutions consider groundwater options as well as surface water options, using for example Lake Victoria, Lake Albert and River Nile as water sources.

The frequent occurrence of catastrophic tailings dam failures underscores the urgent need to improve safety practices and minimise associated risks. This study introduces an advanced tool to evaluate the total Probability of Failure (PoF) of existing tailings dams systematically and effectively, supporting the rational prioritisation of mitigation efforts and minimise risk within the 'As Low As Reasonably Practicable' (ALARP) principle.

The tool utilises a semi-quantitative approach, combining observation frequency and expert judgment. A baseline PoF for each dam construction method and failure category is established using a database of 450 tailings dam incidents and accidents developed in this study. This baseline PoF is subsequently modified based on site-specific factors influencing failure prevalence. A total of 255 key contributing factors are identified based on a fault tree analysis, the Global Industry Standard on Tailings Management (GISTM), and experts in the field. The factors encompass site conditions, design elements, and the level of practise, which address all major credible failure modes and mechanisms. The factors are linked to the failure categories they influence and assigned relative weights through the analytical hierarchy processing method for each dam construction method and failure category. Subsequently, the weights are multiplied by modifiers to account for the effects of site-specific conditions (favourable: 0.2, neutral: 1, adverse: 5, and unknown: 2). Within the tool, users can choose fulfilment conditions for each factor from drop-down menus and the selected inputs are connected to the modifiers. The adjusted weights are multiplied by the baseline PoF of each failure category, given the dam construction method. The summation of these products yields the total PoF of the investigated dam.

The results provide preliminary insight into factors significantly affecting the total PoF for the dam under investigation, aiding in evaluating whether the PoF reduction justifies costs. It facilitates preliminary, rational prioritisation of mitigation measures in accordance with the ALARP principle, contributing to ongoing efforts to improve tailings dam safety.

To validate the tool’s capabilities, two case studies with available data are analysed: the Aznalcóllar failure to examine the ability to identify high-risk factors and a recently improved dam to evaluate if the mitigation efforts are adequately reflected. The studies demonstrate the tool’s potential but also reveal uncertainties, inaccuracies, and limitations. These stem from discrepancies in the baseline PoF, weightings, modifiers, and unaccounted factors. Therefore, caution is warranted in the tool’s utilisation. Recommendations include various improvements and further verification and validation across a broader range of case studies. Value can be added by incorporating additional components and adapting the tool for new dams.

The production and consumption of electricity need to be balanced at all times. Due to the ever-growing shift towards renewable energy generation, this poses an increasingly difficult challenge. Currently, supply is regulated to maintain balance. However, there is potential to improve reliability and save costs by shifting the balancing to the demand side, known as demand response. The flexibility of water systems can play a role in this, thereby benefiting from cheaper price fluctuations and reducing operating costs.

This research investigates the IJmuiden pumping station, which drains water from the Noordzeekanaal-Amsterdam-Rijnkanaal system into The North Sea. The primary focus of the control of this system is ensuring safe water levels as it runs through areas of high economic value. The flexibility of the range of safe water levels allows costs to be minimized by selecting favourable moments to consume electricity. This simultaneously contributes to the stability of the electrical grid. This research explores the potential for a Reinforcement Learning controller for such an optimization problem, as there are some drawbacks to the Model Predictive Control methods that are currently widely used. The research objective is formulated as follows:

To optimize the control of the IJmuiden pumping station using Reinforcement Learning while complying with local water level restrictions and compare it to the state-of-the-art Model Predictive Control methods in terms of constraint violation, energy costs, and computational speed.

The Reinforcement Learning controller will use a deep Q-learning algorithm that chooses the most cost efficient control in IJmuiden while respecting the water level restrictions. To do so, the model makes decisions based on electricity prices and details about the state of the water system for the current time step as well as a forecast of 48 hours ahead. This data is provided as an input to the model.

The inputs of the model consist of historical data, meaning that the associated uncertainties are not included. The water system that the model can interact with is represented by a linear reservoir model. Therefore, the water system is influenced dynamically by the actions taken by the model. The possible actions are determined by the state of the water system.

The trained model was tested on 2 years of unseen data (data that was not used during training). Using the same test data, control plans were generated using Model Predictive Control. The Reinforcement Learning model was very successful in ensuring safe water levels. However, this did result in approximately 50\% higher energy costs. The use of the gate was close to optimal but the pumping was not clearly correlated with favourable prices and power consumption. The trained model was robust, with consistently accurate results with regards to respecting the water level constraints.

The most significant difference with the Model Predictive Control was the computation time. The Reinforcement Learning model was able to create a control plan approximately 300 times faster. This opens doors for further development of the model and increased complexity. A more accurate model of the water system can be used to take into account temporal and spatial effects and individually representing the six pumps in IJmuiden.

There are still many steps before such a model can be used for operational control, but the method has potential for such an application. Many aspects of the model can be improved as well as making adjustments to increase the usability for control operators.

Anticipatory action requires models that can accurately and reliably predict the impact of natural hazards. However, impact forecasts are often underestimated when consecutive hazards are not considered. In the Bicol region in the Philippines, typhoons trigger 90% of landslides, causing a lot of fatalities and damage to infrastructure and agriculture. The lack of information on past landslide events has hampered the construction of landslide forecasting models. Currently, a machine learning (ML) impact-based forecasting (IBF) model for typhoons is operational in the Philippines. The model was developed by 510, an initiative of the Netherlands Red Cross. The model predicts impact due to the high wind speeds associated with typhoons and includes the possible impact due to landslides only via a static landslide susceptibility map. Hence, this study focused on extending the 510 typhoon model via hybrid modeling into a multi-hazard forecasting model for both typhoons and landslides to improve the forecast by considering impact from typhoon-induced landslides. The implementation of the hybrid multi-hazard impact-based forecasting model was tested on two typhoon events in the Bicol region.

A hydrometeorological landslide IBF model was successfully created, even with the limited data on landslide occurrences and rainfall available. The newly established regional event duration threshold for Bicol was applied on the case study events with an increased impact boundary of 300 km compared to the typhoon impact boundary of 100 km. The results of the hybrid multi-hazard model showed an improved impact forecast -compared to the model considering solely static input of landslides, which underestimated impact- in both location extent of the impact forecast and in accuracy: the True Positives doubled, whereas the False Negatives reduced by half. The separate landslide IBF model as an extension of the existing ML typhoon model provided additional benefits as these models can be decoupled to optimize the performance and reliability of both. This study resulted in the prototype of an impact-based multi-hazard model for typhoons and landslides for the Philippines and demonstrated the importance of considering impact from consecutive hazards.

The Netherlands is a country prone to flooding. Recent assessments led to the insight that protection levels of many flood defences should be increased. Integrating reinforcement measures is a difficult task as many dikes are situated in densely populated areas. Conventional reinforcement measures include berm construction or the implementation of sheet pile walls. The first can become very expensive in case houses are situated close to a dike, the latter is rather expensive and irreversibly changes dike composition. Geotechnical failure modes piping and slope instability are most important failure modes for Dutch river dikes. In this thesis a case study is carried out on such a dike that is disqualified for those failure modes. The dike is situated in an urban area with limited space available for reinforcement works. It is studied whether pore pressure measurements behind the dike can be used to improve the reliability estimate for piping. Subsequently it is analyzed whether implementing pressure relief wells can be used to increase dike reliability for both considered failure modes. For the case study an advanced modelling framework was used consisting of groundwater modelling software and a random field generator. The case study was divided into two parts. The first part consisted of a dike section of 100 m based on a dike section at Wijk bij Duurstede. The second part consisted of the same dike section, only now extrapolated over a length of 2 km for which variations in soil conditions become more important. First, an analysis was conducted to define the optimal amount of pore pressure sensors behind the dike. It was found that for a dike section of 100 m a total of four sensors could be used to perform reliability updates, for a dike section of 2000 m it was found that a total of six sensors could be used. For the 100 m section piping failure probability improved from 5.21E-3 per year to 1.62E-4 per year. For the 2000 m section piping failure probability improved from 5.21E-3 per year to 1.89E-4 per year. Pressure relief well implementation behind the dike was considered as a measure to increase dike reliability for both failure modes. The system was designed based on a target reliability level for slope instability. The same modelling framework was applied. An analysis was conducted and it was shown that for the 100 m section a well spacing of 50 m would sufficiently increase dike reliability for slope instability. For the 2000 m section a well spacing of 45 m was found. For the 100 m section failure probability for slope instability increased from 8.81E-5 per year to 1.22E-6 per year, failure probability for piping increased from 1.62E-4 per year to 2.03E-7 per year. For the 2000 m section failure probability for slope instability increased from 8.81E-5 per year to 1.30E-6 per year, failure probability for piping increased from 1.89E-4 per year to 1.02E-7 per year. It was shown that for both trajectories target reliability levels for all failure modes were met. For this case study it was shown that pressure relief wells provide a good design alternative for dike reinforcement in urban areas. A life cycle cost analysis was applied and it was shown that implementation of relief wells is economically attractive compared to traditional design alternatives berms and sheet pile walls. For the first relocation of houses forms an important cost driver, for the latter initial construction cost are high. Furthermore it was shown that implementation of pore pressure monitoring prior to the design of a relief well system yields a positive value of information.

Large areas of the world are protected by flood defence systems. A common part of flood defence systems is a levee with grass cover, with the primary function to protect hinterland against floods. Waveovertopping might lead to erosion of the grass cover, followed by erosion of the inner slope and potentially levee breaching. The wave overtopping simulator (WOS) was developed for full-scale and insitu testing of the erosional resistance of levees against wave overtopping. The first experiment with this simulator was carried out in 2007 and more followed. However, the data gathered during these overtopping experiments has always been considered on individual experiment scale or in small subsets, exception being the Cumulative Overload method. The experiments proved to be difficult to reproduce, seemingly identical experiments gave different results. The objective of this thesis is to combine WOS data and multiple grass erosion models to make a prediction for the failure of the grass cover. Key features that determine the prediction of failure are the location and moment of failure. Therefore, these are included in the main research question, which is identified as: How can grass erosion models and WOS experiment data be combined in a new method to generate a prediction of the moment and location of the grass cover failure due to wave overtopping? A literature study on grass erosion and grass erosion provides the basis for modelling grass erosion, a review of combination techniques gives insight in combining results for a prediction. A selection of models is discussed and used for setting up a prediction method. These include three flow-based models which are semi-realistic simplified representations and time-independent over a single overtopping event: Cumulative Overload Method, Analytical Grass Erosion Model and Dean Stream Power. These models are, where required, adjusted to model grass cover failure and to comply with identical hydrodynamic input. A fourth model, the Wave Impact Approach, is an impact-based approach. Each model is calibrated on each WOS experiment, creating a set of calibrated models, which function as the set of predictors. The number of predictors equals the number of models times the number of experiments. During calibration, the moment of failure has been traced back using the control list of the WOS, and vice-versa, resulting in a moment during the experiment at which the grass cover failed. Detailed review of all factual reports on WOS experiments and the nature of the grass erosion models highlighted the need for a locationdependent resistance parameter to determine the location of failure. Therefore, calibration was based on a location-depended resistance parameter; critical flow velocity for the flow-based models and critical pressure for the impact based model. This set of predictors has been used to create a set of predictions for each of the five validation experiments, input being the geometry, loading and initial condition of the slope. The initial condition given by a registration of anomalies to the average grass cover. Failure is determined by vote and averaging. After validation, none of the final predictions proved to be fully correct. Meaning that none of the final predictions correctly indicated the location and moment of the first grass cover failure. Despite this, in four of the five validations at least one correct failure location was indicated when considering the specific anomalies. The main shortcoming is concluded to be the prediction of number of waves until grass cover failure. Based on this research and given a certain average grass cover quality, it is concluded that the resistance against grass erosion by wave overtopping is described by resistance against erosion of anomalies. For at least 22 of 28 sections in the dataset, grass cover failure occurred at an anomaly of the average grass cover. No grass cover failures occurred related to the average grass quality. Especially mole activity showed a significant 46% decrease in the averaged calibrated critical flow velocity with relative to that for the average grass cover. This leads to the conclusion that design and safety assessments should include conditions other than the average grass quality. For future research a method is recommended that divides mole activity into classes, based on certain characteristics. Each class distinguishable by unique combination of properties and assigned a resistance against erosion. For design, the probability of occurrence of each class must be determined and combined with the corresponding influence on the resistance against erosion. For assessment, an inventory or representative sample of animal activity must be available to asses if the occurrences of animal activity are within the design requirements.

Hydrokinetic energy is an environmentally friendly source of electricity and it has the advantage to be more predictable compared to other renewables. According to IRENA (2020), the capacity of converting hydrokinetic energy into electricity is expected to increase in the coming decades as many concepts are in the pipeline. One of these hydrokinetic energy concepts is the patented Van Rompay Turbine. The Van Rompay Turbine consists of a duct, an air chamber and a paddlewheel. The duct, which is based on a Venturi working principle, consists of a converging inlet, a throat and a diverging outlet. Above the throat, an air chamber is present to provide an air environment to reduce resistance for the paddlewheel. The goal of this research was to experimentally and numerically test a prototype (L:4.05m x W:1.17m x H:1.17m) of the ducted system.Three inclination angles: 21°, 26° & 31°, for the converging inflow and diverging outflow segment were tested, referred to as the small, medium and large ramp size, respectively, to determine which size represents the lowest energy losses in the system. The small in -and outflow ramp represents the lowest energy losses compared to the medium and large ramp. Subsequently, on the small inflow ramp, five designs were tested, referred to as the straight, curved and the high, mid & low frequency waved designs; to again determine which design represents the lowest energy losses. Roughed waved wall designs are streamwise wavy walls with each a different wavelength and amplitude. The small ramp with a high, mid & low frequency waved design results in the lowest energy losses in comparison to the straight and curved designs for inlet velocities between 0 m/s and 0.4 m/s. For inlet velocities between 0.4 m/s and 0.5 m/s, the small ramp with a curved design represents the lowest energy losses in comparison to the straight and high, mid & low frequency waved designs.The data points acquired during the experiments were scattered due to external factors increasing the uncertainty of the measurements. Besides this, the experimental investigations were tested over an inlet velocity between 0 m/s and 0.5 m/s, limiting the data analysis. For this reason, several CFD simulations were carried out to acquire data with no influence of external factors and to investigate the system for higher velocities. The CFD simulations were executed for the five different inflow ramp designs with an in -and outflow inclination angle of 21°. The CFD model solves the Reynolds-averaged Navier–Stokes (RANS) equations with a k-epsilon turbulence closure model. Based on these CFD simulations, the best performing design for the inflow ramp in terms of the lowest energy losses for an inlet flow velocity between 0 m/s and 0.75 m/s is the high frequency waved design. Regarding velocities between 0.75 m/s and 3 m/s, the curved design represents thelowest energy losses compared to the straight and high, mid & low frequency waved designs. After scaling up the ducted system in CFD simulations with a factor 2 and 10, the flow velocity reduces as there are more energy losses in the system. However, as larger systems have a marginal lower specific surface of contact between the water and the duct, an increase in available flow power could be found. The extent to which the ducted system can be scaled up depends on the space restrictions at the specific location. Besides, the minimal required inflow area of the free water flow should be met. It can therefore be concluded that the potentially available energy for generation also depends the location where the Van Rompay will be deployed.Experiments need to be performed to study the hydrodynamic impact and the hydrodynamic efficiency of the paddlewheel to determine the power performance output and thus its potential value for the hydrokinetic energy market. It is recommended for future research to test the prototype at higher velocities and to test the prototype in a laboratory facility as these offer the benefits of controlled testing.

Geogrids are commonly used for reinforcement of soils in construction of railway and road embankments and bridge abutments. A relatively new application of geogrids is the anchorage of sheet pile walls, in which one or more layers of geogrid are attached to the sheet pile wall. This type of anchorage is particularly suitable for projects with sufficient space behind the sheet pile wall. This anchorage system has some mayor advantages in comparison to conventional sheet pile wall anchors as it is cost-effective and requires less steel. Moreover, prestressing is possible during construction and pile foundations can be installed through the geogrids. An analytical design calculation method for this type of anchorage is not yet available in the Dutch design guideline for the design of sheet pile walls (CUR166). The demand for short anchorage systems because of the commonly limited space behind sheet pile walls, shifts the focus of the design to the minimum required length of the geogrid-anchorage.
This report makes a first step into the formulation of a design-calculation for the mobilised anchor resistance as a function of the length of the geogrid(s) at ultimate limit state. By means of a 1g small-scale physical model and a 2D finite element model (PLAXIS) of the small-scale physical model, the global failure mechanism and soil-geogrid interaction have been analysed. In a series of these experiments, an 18 cm long geogrid anchor was connected to a 30 cm high sheet pile wall and installed in sand in a small transparent test box and loaded with a strip footing load. In addition, experiments were conducted while varying several features, among which the number and length of the geogrid anchors and the location of the slip footing load.

Flooding currently affects 100 million people on average per year globally, and the number is increasing. The performance of dikes under extreme conditions is crucial to societal flood protection. By deploying measurement instruments in and around the embankment, more accurate insights into the actual status of the dike can be achieved that can be used to prevent the failure of a dike.
Despite growing awareness of the potential of dike monitoring techniques and effort put into the development and testing of instruments, implementation of instrumental dike monitoring into regular practice remains scarce. The objective of this thesis is therefore to shed light on the factors that influence the implementation of innovation in dike monitoring practice by: (1) assessing and analysing the barriers and drivers for implementation of innovation in dike monitoring, by studying a case study of novel optic fibre pressure sensors and trying to influence this practice by (2) designing a communication oriented intervention for the development phase that enhances the implementation process.