Climate change increases the likelihood of extreme rainfall events, while ongoing urbanization
leads to greater surface imperviousness. Together, these trends result in more frequent and
severe urban flooding. Sustainable Urban Drainage Systems (SUDS) are implemented to
enhance the resilience of urban drainage infrastructure and mitigate urban flooding. Among the
available modelling approaches, coupling a one-dimensional (1D) sewer system model with a
two-dimensional (2D) surface model (1D2D) is considered the most accurate method to assess
urban flooding. However, the practical application of 1D2D models in the design of SUDS is
limited by their high computational requirements. In comparison, a 1D urban drainage model
demands significantly less computational power, allowing for many more simulation iterations
to be completed within the same timeframe.
This study investigates the trade-offs between 1D and 1D2D models in the design of SUDS for
flood prevention. It proposes a heuristic approach that integrates both a 1D and a 1D2D model
(method 1). This approach aims to leverage the speed of the 1D model and the accuracy of the
1D2D model to optimize SUDS design. The methodology was applied to an urban drainage
model of Bloemendaal, the Netherlands. To evaluate the efficiency of the proposed method, its
results were compared to those obtained using a second approach that relies solely on the
1D2D model (method 2).
Method 1 was more effective than method 2 in reducing the number of flooded buildings.
Specifically, method 1 achieved the greatest reduction in areas affected by higher flood levels
(>0.30 m), while method 2 was more effective at decreasing the area exposed to lower flood
levels (>0.10 m).
Method 1 may assist decision makers in selecting and implementing SUDS more effectively for
flood prevention, ultimately leading to more resilient urban drainage systems. Future research
could expand method 1 to incorporate additional benefits of SUDS, enabling a multi-objective
design approach.

The implementation of Sustainable Urban Drainage Systems (SUDS) is increasingly adopted to address stormwater management challenges in urban environments. Among these, bioswales – vegetated channels with bioretention media – are designed to mitigate urban flooding while improving runoff quality through physical and chemical pollutant removal. Despite their widespread application in the Netherlands, especially as climate adaptation measures, limited research has assessed their water treatment performance, particularly with respect to nitrate removal. Existing studies are either insufficiently monitored at full scale or confined to controlled lab settings that fail to represent field heterogeneity. Additionally, municipal bioswale monitoring remains limited due to the high resource requirements of standard sensing equipment.

This study introduced MeSUDa (Managed experimental Sustainable Urban Drainage area), an open-air lab developed to support research in bioswale development and monitoring. The setup was equipped with both low-cost and industry standard sensors, evaluated using a set of performance indicators to assess their suitability for municipal implementation. Tracer testing was conducted to characterise MeSUDa’s hydrological behaviour, and a 1D advection-dispersion model was developed to investigate nitrate removal via woodchip-enhanced denitrification.

Results indicated that low-cost sensors may be appropriate for short-term monitoring but lack the reliability required for long-term municipal use. Hydrological testing revealed excessively slow infiltration and extended residence times, reducing MeSUDa’s representativeness of actual bioswale conditions; however, design modifications could address these limitations. The nitrate removal model projected up to 42% removal, with further improvements possible by optimising woodchip mass and placement.

Overall, MeSUDa proved to be a flexible and adaptable research platform for bioswale development and monitoring. The study provides a foundation for continued investigation into enhanced denitrification and offers design and monitoring recommendations for future research.

This thesis presents a comprehensive investigation into the optimization of Slow Sand Filter (SSF) performance through data driven modeling, with a focus on bacterial (E. coli, Coliform) and viral (Enterovirus, Adenovirus, Bacteriophage MS2) removal efficiencies. Combining an extensive literature review, exploratory data analysis (EDA), and machine learning modeling using XGBoost, this research provides new insights into the complex interactions between design parameters and SSF performance, ultimately contributing to the development of more efficient and sustainable SSF configurations. The study began with a systematic literature review of 135 experimental studies, identifying key design parameters and operational conditions of SSFs, including Filtration Rate (FR), Hydraulic Retention Time (HRT), Effective Grain Size (D10), Total Bed Height (T.B.H.), and Schmutzdecke characteristics. The literature review was supplemented with Exploratory Data Analysis (EDA) techniques such as scatterplots and correlation matrices to uncover trends and potential relationships between design parameters and removal efficiencies for bacteria and viruses. While the scatterplots and correlation matrices revealed limited linear relationships, they highlighted the complexity of the interactions, indicating the need for advanced modeling techniques capable of capturing nonlinear patterns. To address these complexities, XGBoost, a powerful machine learning model known for its ability to handle nonlinear relationships and parameter interactions, was developed and applied to the dataset. The model’s predictive capabilities were evaluated using performance metrics such as R², Mean Squared Error (MSE), and cross-validation results. Additionally, feature importance analysis was conducted using the XGBoost model, which provided insights into which design parameters and operational conditions had the most significant influence on removal efficiencies. A key contribution of this research was the development of interactive tools, such as feature contribution sliders and interactive heatmaps, which enabled dynamic exploration of how changes in design parameters affect removal efficiencies. These tools facilitated the simulation of design scenarios, offering an intuitive understanding of parameter impacts and guiding the formulation of new design paradigms. The model outputs were used to propose optimized parameter ranges that improve bacterial and viral removal while maintaining operational efficiency. The thesis further explored the robustness and reliability of the XGBoost models, discussing the results from controllable and uncontrollable design parameters and operational conditions and comparing the model’s predictive performance for bacterial and viral removal. In conclusion, this research highlights the effectiveness of XGBoost modeling for both predicting and explaining SSF performance. The insights gained provide a foundation for developing more efficient SSF design paradigms, enabling targeted adjustments to parameters based on model predictions. Additionally, the interactive tools created in this study offer practical resources for engineers to simulate different design configurations and optimize SSF performance. Overall, this thesis emphasizes the potential of machine learning to revolutionize the design and operation of SSFs, providing a foundation for future advancements in sustainable and effective water treatment technologies.

Southern Zimbabwe is an illustrative example where (increasing) water scarcity can lead to food- and financial insecurity and hinder socio-economic development. Sand rivers are a nature-based alternative to reservoirs, hosting shallow aquifers in their sandy beds with potential for decentralized and clean water abstraction. However, their extent and storage potential remain poorly understood, limiting awareness and investment. Riparian vegetation, which depends on sand rivers and remains green during the dry season, could serve as a valuable proxy for water storage.

This study uses a remote sensing approach to estimate and map the capacity of sand rivers to store water across three sub-catchments of the Limpopo River Basin (Shashani, Mzingwane, and Shashe), with two methods. Firstly, the sand river and riparian zones were delineated. The minimum storage capacity was estimated and mapped by summing WaPOR v3 evaporation data over the dry season. Secondly, 35 depth measurements were combined with spatial analyses to empirically predict depth and geometric storage capacity for the whole study area.

The results showed that there is a significant water storage potential in the channels. The two estimates can be combined for an unconsumed water availability, showing a significant sustainable potential (totaling 83 x10^6 m3 potentially irrigating 8300ha). Dry season evaporation was consistently exceeded by geometric storage at medium to large evaporation rates, suggesting that these rivers have unconsumed storage potential. The maps suggest that most potential water storage is located in the main river stems (the largest 10% hold 45-55% of water). Most tributares seemed to evaporate all water. Using water for irrigation would directly compete with vegetation. However, some local hotspots were still observed at smaller rivers.

Although significant uncertainties remain and field validation is still needed, the models show promise for wide-scale planning and development. The findings highlight that sand rivers, with rough estimates of their decentralized, cost-effective, and sustainable water storage, can be mapped remotely with minimal effort or field data. This could open up possibilities to offer support to farmers in semi-arid regions and pave the way for farmer-led irrigation initiatives.

Hydrological modelling is an essential aspect of water resources management and has many applications, particularly water quality monitoring and the pollutant transmission. Recent research found that hydrology also plays a crucial role in plastic transport from land to the ocean, making it vital to understand the impact of spatially distributed precipitation on the hydrological processes. This leads to the question of how a hydrological model can help waste management and prevent plastic waste in the ocean. However, developing a suitable hydrological model with limited data availability, complex topography, and human interaction is a challenge. This research uses the Rio Las Vacas catchment as a case study and focuses on testing the applicability of the wflow hydrological model and assessing the impact of heterogeneous precipitation on the model result.

The research analysed the ground data available in Rio Las Vacas to verify and validate its quality. This study uses precipitation gauges, ERA5, and CHIRPS to assess the most suitable input for modelling rainfall-runoff in the Rio Las Vacas catchment. The catchment's hydrology was analysed and a conceptual model of the catchment was proposed. Using the wflow model, the hydrological response was modelled using the three rainfall products and the effect of uncertainty and spatially distributed rainfall was assessed.

The result shows that the Rio Las Vacas catchment suffers from a shortage of hydrometeorological information which is also of spurious quality. Only three rain gauges were available, and their analysis revealed high error variance and independence between stations. Hydrological characterisation analysis revealed significant human-induced changes. A calibrated wflow model effectively simulated the catchment’s hydrological response, demonstrating that global parameterisation was sufficient as a starting point for the model in this region. The soil water parameters are one of the most significant parameters in generating runoff for the model. The impact of spatially distributed precipitation is prominent in the model. This is one of the reasons for ERA5 to perform poorly in simulating the streamflow in the catchment. Based on the quantitative validation, the best modelling results came from using rain gauge data interpolated with the IDW method, followed by CHIRPS. However, the qualitative assessment shows CHIRPS is more reliable among the datasets.

In conclusion, this study shows the difficulties of modelling a data-scarce basin in the mountainous region. However, extensive data verification and validation, in combination with hydrological characterisation, helps to improve the model result. It also shows the precipitation inputs were crucial for a distributed model. The use of precipitation input with finer resolution could improve the model significantly. The study shows that the spatio-temporal distribution of the precipitation did not only affect the streamflow at the measurement point, but also the spatio-temporal runoff generated in the basin at pixel level. This could propagate to the water quality or plastic transport study. Among the datasets tested, CHIRPS emerged as the most reliable and suitable for hydrological modelling in this basin, both quantitatively and qualitatively.

The increasing demand for stream restoration projects in the Netherlands, driven by legislation, prompted interest in more integrated approaches. TAUW (Technische Adviesbureau van Unie Waterschappen) and WSAM (Waterboard Aa and Maas) collaborated on stream restoration measures for the Lage Raam stream in the Raamvallei, initially using a 1-dimensional model. However, questions arose regarding the suitability of a 2D model for this project.

This research explored the applicability and potential of a 2D hydrological model made in Tygron to provide new insights and outputs for this stream restoration project, including inundation maps, water level fluctuations, and evaluating designed restoration measures. Simultaneously, the study assessed Tygron's applicability for large water systems in the Netherlands by evaluating its underlying settings and parameters.

In the first part of this research, the study demarcated stream restoration measures for the Lage Raam, focusing on redesigning the stream to enhance nature-friendly banks. The Tygron water module was introduced, emphasizing critical simulation setup adjustments such as the rainfall overlay and simulation settings investigated in the initial testcase study. The settings investigated in the testcase were: 'Water level to shorelines', 'Waterline reconstruction', 'Angle stabilizers for partly flooded cells', 'Manning value', 'Grid cell size', and 'Grid/stream placement'.

Results from part 1 indicated that among the six settings tested, only three significantly influenced water level simulations in channels. Variations in Manning values demonstrated a pronounced effect on water height accuracy, with lower values correlating with better simulation outcomes in the testcase. The influence of Manning values was more pronounced in narrower streams, where shallower water depth worsened inaccuracies in the model's backwater effect. Notably, Grid cell size and Grid/stream placement were crucial for achieving accurate outcomes. The optimal grid cell size was found to be 1 by 1 meter or of higher resolution. Additionally, aligning streams parallel to grid cells generally improved results, although the influence of grid placement diminished with increased grid cell count per channel.

The second part introduced the study area, the 'Raamvallei', for case studies 2 and 3, outlining designs for cross sections with swamp areas as restoration measures. Case study 2 validated the Tygron model using measured data from the Raamvallei obtained from WSAM and rain events, testing its suitability and model setup for water systems. Case study 3 implemented TAUW's restoration design to evaluate Tygron's effectiveness of these measures.

The results in part 2 showed that evaluation in a larger watershed scenario (Raamvallei) underscored the model's robustness when configured for extensive water systems. Grid cell size sensitivity analysis highlighted the optimal range (1m x 1m or smaller), lower resolutions causing water loss in the Lage Raam water system, underscoring the resolution’s impact on modeling outcomes. Achieving accurate connectivity between primary, secondary, and tertiary waterways was crucial, requiring iterative adjustments including culvert generation and hydraulic structure calibration. The third case study highlighted challenges in data retrieval and storage due to Tygron's limitations in exporting detailed simulation data over time. However, it also demonstrated Tygron's capability in simulating level fluctuations and flow rates, despite challenges in data analysis.

In conclusion, Tygron was capable of using the explicit Saint-Venant scheme to calculate 2D shallow water equations where it accurately simulated a complex large water system in the Netherlands. Additionally, it could be used for projects such as the Lage Raam to provide insights into stream restoration designs. However, for a model to be successfully used and have results that could be easily understood, some settings were important to look at and some changes in data collection were needed. Future research should encompass diverse test cases to validate Tygron's performance across various scenarios and compare it with other 2D hydrological models for broader applicability insights.

Based on the study's findings, several recommendations were proposed to enhance Tygron's utility in hydrological modeling. These included exploring new data storage approaches to handle extensive datasets more efficiently, optimizing the use of limit areas to simplify model complexity without compromising simulation accuracy, and improving connectivity tools like the culvert generator for seamless integration with external data sources.

Many cities in Vietnam are located at the coast and are exposed to heavy weather events that yearly causes casualties and economic losses. At the same time the country is developing rapidly and thus more space is allocated to accommodate urban growth. Studies have shown that there are cases in Vietnam were residential expansions have led to an increase in risk of pluvial flooding. This type of flooding is caused by insufficient capacity of the urban drainage system as a result of heavy rainfall.

Keeping in mind the exposure of flooding in Vietnam and the potential of SUDS to provide flood risk reduction, it is desirable to set general design guidelines with respect to urban drainage. On top of that, 1D/2D hydrodynamic models have proven to be useful when mapping flood risk at different scales. Therefore, this study aims to show how 1D/2D hydrodynamic models can be put into practice when designing drainage infrastructure and implementing flood adaptation measures. For this research, hydrodynamic modelling tool Delft 3D-FM (D-HYDRO) was implemented in the drainage design of Hà Nam island in Quang Ninh province.

This study provides predictions of pluvial flooding for the future development scenario of Hà Nam island. Based on the 2023 masterplan, all projected land use and terrain changes have been incorporated in the 1D/2D hydrodynamic model. Hà Nam island currently discharges it’s stormwater by means of sluice gates that can only be opened during low tide. Based on sea level rise predictions, it is estimated that the probability that Hà Nam receives rainfall during high tide will increase the coming decades. Therefore the urban drainage design considers extreme rainfall events during which the area can only be drained by means of pumping stations. Based on the modelling study, it is concluded that the existing residents of Hà Nam will face an increased risk of pluvial flooding in the future development scenario. This study proposes the implementation of two types of SUDS to increase local stormwater storage, namely vegetated swales and park overflow zones. It was found that these do result in flood risk reduction, but still leave current residents of Hà Nam vulnerable to flooding during extreme rain events. This study recommends making modifications to the 2023 masterplan, emphasizing the safety and wellbeing of Hà Nam residents.

River networks are vital ecosystems that face increasing threats from societal pressures and global environmental changes. Effective biomonitoring of these systems is crucial for maintaining water quality and biodiversity by guiding policy and management actions. Over the past two decades, environmental DNA (eDNA) has emerged as a promising tool, offering advantages over traditional biomonitoring methods. However, the downstream transport and decay of genetic material in river networks poses a challenge for the spatial interpretation of eDNA samples. This study follows from work of Carraro et al. (2021) and aimed to find the optimal sampling density to determine taxon distributions at the catchment scale based on eDNA sampling. This was done by means of a virtual experiment, where a large number of sampling campaigns were simulated over multiple synthetic taxon distributions and river networks. This enabled the development of generalized sampling principles irrespective of system-specific confounding factors. Results showed that taking at least one sample per 10 km2 of catchment area provides a reliable estimate for taxon abundance across the catchment. Taking more samples only marginally improved the accuracy of the predictions when it concerns taxa that are commonly found in different parts of the catchment. In case of finding rare taxa, a minimum of two samples per 10 km2 is necessary to ensure that the taxon is correctly located. This study also underscores the limited capability of eDNA in capturing fine- scale spatial patterns, suggesting its complementary role alongside traditional surveys. In summary, this study provides a generalized sampling guideline for eDNA biomonitoring as a tool for broad-scale environmental assessments and biodiversity conservation.

The expected effects of climate change on increased and more frequent rainfall events ask for more innovative solutions to manage urban stormwater. Sustainable Urban Drainage Systems (SuDS) offer an eco-friendly method to disconnect stormwater from the sewer system. The Municipality of Rotterdam, the Netherlands, has integrated multiple SuDS into its drainage network, including bioswales. Bioswales are vegetated areas that slow down, collect, and filter (storm) runoff. However, uncertainty exists regarding their performance under different conditions. This thesis aims to answer the following research question: How do bioswales perform under various conditions, as evaluated by a hydrological groundwater model?

The bioswale groundwater model used in this thesis, developed by Deltares, utilizes the Unsaturated Zone Flow (UZF) package of MODFLOW to simulate the hydrological response of bioswales. The model was calibrated and validated using existing monitoring data, and a one-at-a-time sensitivity analysis was performed to identify the most influential factors affecting bioswale performance. The case-study bioswale was tested under design storms reflecting current and 2050 summer and winter conditions, as well as prolonged wet winter rainfall. Two design scenarios were proposed to improve bioswale performance.

The case-study calibration results showed that the model could realistically simulate water levels and discharges. However, the existence of preferential flow in the unsaturated zone, not accounted for by the UZF package, led to a time-lag in modelled drain discharge. The sensitivity analysis indicated that infiltration parameters strongly influence emptying time, peak discharge, and time-lag in the model. The performance assessment showed that the case-study bioswale met the emptying time criterion, but the peak discharge limit was exceeded during summer events. Simulating prolonged wet winter rainfall showed that consecutive rainfall events could be more critical regarding winter bioswale performance, compared to a single winter design storm. The bioswale design improvements demonstrated that relocating the drain from the centre to the side of the bioswale, thereby increasing the distance water needs to travel, significantly reduced peak discharge, though at the cost of longer emptying times. Widening the bioswale increased storage volume; therefore, the connected paved surface area could be increased, but the effect of adding additional drains on bioswale performance was limited.

To increase the understanding of bioswale performance, further empirical research on vegetation, macropores, and preferential flow is recommended, along with improvements to the modelling of these processes. In terms of model application, the bioswale groundwater model, with some adjustments, can be applied to other SuDS types that might be less sensitive to the natural influences of vegetation change and macropores. Combining the modelling of individual bioswales and SuDS, as done in this study, with urban-scale modelling could significantly improve Rotterdam’s climate resilience.

A personal weather station (PWS) is a set of weather measuring instruments that is owned and operated by an individual, club, association, or business. Even tho these stations might be less advanced as the professional stations, the amount of amateur stations that are active is much higher. With these PWS networks in full effect and still growing, the potential to use PWS data for weather forecasting or research purposes has gained popularity in the scientific field for its potential to ”fill data gaps”. However, since the stations are managed by “amateurs”, the “usefulness” and “value” of these stations are under questions. In this thesis, we tried to solve this by proposing a new way of assessing the “Value” of an amateur weather station in a citizen network. We utilized bots to extract all information about stations in the Netherlands and for the first time did a national analysis of PWS networks. Significant progress has been made in developing a framework to assess the value of the Dutch PWS network. The research process involved overcoming numerous obstacles, which have provided valuable insights and lessons for the future development of a more refined PWS network assessment system.

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).

Ceramic ultrafiltration is a promising technology for water reclamation, but issues with membrane fouling and the removal of organic micro-pollutants have limited its development. Recent research has shown that catalysis-based ultrafiltration has the potential to address these problems. In this study, catalysis-modified (CuFe2O4 and Pd) ultrafiltration was investigated for fouling mitigation and organic micro-pollutant removal.

The membranes were successfully synthesized and it was found that using Fenton oxidation (a combination of CuFe2O4 membrane with H2O2 backwash) had a 93.9% higher cleaning efficiency than applying demi-water. Besides, it was found that the backwash flux and backwash time were crucial in determining the cleaning efficiency of Fenton oxidation, with a smaller backwash flux or longer backwash time resulting in better permeability recovery.

However, the application of Fenton oxidation was also found to be limited by the instability of the CuFe2O4 catalyst in acidic environment, and the problem of catalyst leaching led to a decrease in cleaning efficiency. Additionally, it was discovered that using PMS oxidation was more effective for OMPs removal compared to Fenton oxidation, with a removal efficiency of up to
90%. Overall, this work demonstrated the potential of catalyst-coated ultrafiltration for water treatment and highlighted the benefits of combining membrane filtration with advanced oxidation processes.

The hydropower development in the Mekong Basin affects the silt and clay concentrations in the river which has ecological as well as socio-economic effects on the Mekong Delta and its inhabitants. Therefore, the first objective of this research was to obtain a better understanding of the effect of hydropower development on the silt and clay load in the Mekong Delta. Subsequently, the objective was to determine possible sediment management strategies to mitigate the impacts on the silt and clay load in the Mekong Delta.

A conceptual model was developed and applied to analyse these impacts. By means of a sediment balance model with distributed sediment yield and individual trapping efficiencies of dams the annual sediment load has been determined.

This research has shown that the annual silt and clay load downstream has decreased from 140 Megaton ± 40 [Mt] before hydropower development to 26 ± 9 Mt in 2020. Including future hydropower dam projects, this could further decrease to 4-8 ± 3-5 Mt in 2040. Approximately 4% of this sediment load will deposit in the Mekong Delta and therefore it is predicted that the clay and silt deposition will be close to zero in the future.

By 2040, approximately 75% of the total trapped sediment load is trapped in mainstream dams even though they account for 16% of the total dams. The majority (50%) will get trapped in Chinese dams that account for 9% of the total number of dams. This means that almost all sediment coming from China is trapped and will not reach the Mekong Delta. Around 80-85% of the future sediment load that will reach the delta has its source in the 3S region.

This reduction has implications for the sustainability of ecosystems and the productivity of the Mekong Delta. Clay and silt are nutrient rich alluvial sediments that support the ecosystems in the floodplains and delta. These nutrients are crucial for the soil fertility vital for rice cultivation and for various biological processes for fish. Today, nearly 80% of the 20 million people living in the Mekong Delta depend on the river system for their livelihood. However, the rice cultivation is expected to decrease significantly and by 2040, the loss of fisheries could cost close to $23 billion.

Thus, the computed reduction displayed that sediment management strategies are inevitable to maintain the productivity of the Mekong Delta in the future. Therefore, two sediment management strategies were reviewed in this research: cancelling planned dams and reservoir sluicing. Reservoir sluicing is passing incoming sediment-laden water through the reservoir by discharging high flows to enable sediment to move past the dam without depositing.

The analysis of these two strategies showed that the maximum sediment load increase is 1-3 Mt as a result of cancelling planned dams and 2-3 Mt due to reservoir sluicing. This means a very small increase for the deposition in the Mekong Delta (4% deposits). Therefore, the sediment load increase was considered unsatisfactory, suggesting that these strategies are not providing the solution for the Mekong Basin.

The results indicate that effective sediment management strategies are urgently needed and that more research is needed into the effects of other strategies on the silt and clay load in the Mekong Delta.

A monitoring case study of the hydrologic performance of the Aquaflow infiltrating road foundation. The results show a good performance in peak delay of 4 - 8 hours, volume retention of 50% to 97% and peak reduction of 15% to 76% for varying rainfall events and initial soil conditions. The results are promising for future implementation to obtain more sustainable urban drainage systems in Rotterdam.

The wastewater system in the Netherlands is ageing, which increases the probability of failure of the systems as time progresses. A vital failure mechanism is leakages, which occur in leaky connections, joints, or at cracks and holes along the pipe. Eventually, leakages result in infiltration of groundwater into the sewer pipe or exfiltration of sewage into the groundwater system, depending on the location, condition, and system type of the sewer. When the sewer pipe is located below the groundwater table, infiltration of groundwater will occur, and when the sewer pipe is located above the groundwater table, exfiltration of wastewater will most likely occur. Both can have severe consequences for the sewer system, e.g. collapsing pipes, reduction of the groundwater quality, silting up the sewer, and high hydraulic influent load. To prevent these failures, maintenance of the urban drainage system is essential, also referred to as ‘asset management’. It is crucial to find out whether there is a leak in the sewer pipe, where the infiltration or exfiltration occurs, and what the amount of leakage is.

There are different methods to detect leaks, such as camera inspection, pressure tests, and distributed temperature sensing. However, these different techniques each have their specific limitations. The Focussed Electro Leak Location (FELL) system, which is the main focus of this report, has been developed to detect and locate leakages by using electric voltage between an electrode in a sewer pipe and an electrode in the surrounding ground. The electrode in the sewer pipe, also called the probe, consists of three parts. The outer electrodes serve as a guard and also to send electric voltages, like the middle electrode, but they are mainly there to block the area behind it so the signal does not escape. This probe is pulled through the sewer. As soon as an increase in current is visible, this means that the resistance in the sewer pipe wall decreases, and there is a risk of leakage. There is some noise in the signal, but generally, when there is a leak, a more prominent peak in current is present. This peak can say something about the length of the leak, it could say something about the size of the leak, and perhaps it could say something about the leakage rate. There are doubts about whether the last is possible.

Explorative experiments with a PVC pipe above the surface, under ideal conditions, have shown that a clear signal can be obtained caused by leaking water using the FELL prototype 2.0 equipment at hand. The results show that the settings of the FELL prototype 2.0 have a major impact on signal size and noise. However, based on the performed field experiments with a concrete pipe below the surface, it can be concluded that also passing trains, moisture of the soil surrounding the grounding pin, and the position of the FELL prototype 2.0 in the sewer pipe have a strong influence on the resulting leak detection signal of the FELL prototype 2.0. The signal-to-noise ratio was ~1. Environmental factors influence the results in such a way, to an order of magnitude of even greater than 0.1 mA, that it is impossible to see where and whether there is a leakage in the concrete sewer pipe below the surface. To make the signal visible in practice, the FELL prototype 2.0 has to be redesigned, so that environmental influences have limited and known influences on the overall measurements. Also, the vertical movement of the FELL prototype 2.0 during the pulling of the system through the concrete sewer pipe has to be avoided as much as possible, as any change in the measuring geometry clearly affects the obtained signal. This research showed that the FELL prototype 2.0 should best be positioned precisely in the centre of the pipe. In short, the measurement principle theoretically sounds promising under ideal conditions, but the technical implementation of the FELL prototype 2.0 is very fragile, as shown in this study. Theoretically, it is possible to detect leakages, but in a practical sense, many improvements still need to be made to make the implementation robust. It can be confirmed that the performance of the FELL prototype 2.0 was not sufficient yet to tell if this device is able to detect, locate and quantify leaks under simulated field conditions. Both the design and practical condition first need to be optimised.

Humans have a long history of intervening in natural river systems. These human interventions affect how water and sediment interact with the bed and banks of the river. The interaction between water and sediment is called morphodynamics and is an important component of the natural ecosystem of the river and its surroundings. This research aims to investigate if human interventions have disturbed the natural morphodynamics in the Karnali fluvial fan and if this can be the cause of grassland degradation in the adjacent floodplains of the Bardiya national park. To do so, the following research question is answered: What is the relation between human interventions and the morphodynamics of the Karnali fluvial fan?

To do so, a method is developed where a time-series analysis of the morphodynamics is performed, based on discharge and optical remote sensing data. The flow regime is investigated by computing the temporal evolution of discharge, the flow duration curve, the base flow index, and the water distribution at the bifurcation in the upstream part of the fan. Morphodynamic parameters that can be described using remote sensing data concern the planform and the land cover of the river channel-belt. The planform dynamics are described by looking at the evolution of the sinuosity and braiding, and the land cover dynamics are represented by the evolution of the river's channel, sediment bar, and grassland areas. The most important human interventions are identified, after which the relation between the morphodynamics and the human interventions is assessed.

No evidence was found that human interventions have significantly influenced the evolution of the morphodynamics of the Karnali fluvial fan. Only on a small scale, changes or peculiarities in morphodynamic parameters can be attributed to human interventions. In this case study, the construction of embankments reduced lateral erosion, and the Kailashpuri dam influenced upstream sedimentation and erosion processes. A shift of the main stream from the eastern to western river branch in 2009 most likely caused a change in river-floodplain interaction in the branch closest to the Bardiya national park. The quantities of sediment and water extraction were found to be negligible in comparison to the natural water and sediment fluxes.

This research is the first step in quantifying the morphodynamic behaviour of the Karnali fluvial fan and indicates which are the most important drivers of morphodynamic changes. Besides this contribution, the study also includes the development of a methodology that could improve remote sensing-based morphodynamics studies.

The Catterline Bay, located in North East Scotland, is prone to erosion processes. Using Terrestrial Laser Scanning (TLS) two point clouds were obtained in 2019 and 2022 from this area of ca. 0.2 km². Here, a methodology was proposed and tested to assess how erosion processes can be monitored from high resolution point clouds in small study areas. Filtering of ground points from the point clouds was done using a full 3D approach. The local dimensionality on multiple scales was used to generate features for the classification, for which Linear Discriminant Analysis was used. The algorithm was able to precisely separate non ground points from ground points with a precision on test data sets of 94.7% on ground points and 94.3% on non-ground points.

The ground points provided a basis for the Digital Terrain Model (DTM) raster. Several geomorphological quantities could be derived from this DTM. Next to these quantities, also information about vegetation height and 3D change detection using the Multi-scale Model to Model Cloud Comparison (M3C2) was extracted.

From a combination of the Terrain Ruggedness Index (TRI), change detection using the Multiscale Model to Model Cloud Comparison (M3C2) technique and photos, erosion zones were derived. Also stable, non-erosion, zones were identified. Various statistics from the geomorphological quantities were analysed for both zone types. Erosion zones have clear edges of high TRI values, indicating the scarps of landslides. The erosion zones also contain groups of negative M3C2 distances, indicating depletion zones. Not only the TRI and M3C2 distances have significantly different statistics for erosion and non-erosion zones, also the slope is steeper in these zones and the Topographic Wetness Index is smaller. The mean slope for erosion zones is 39.3° compared to 30.9° for non-erosion zones. To capture the behaviour of the TRI in the defined zones, p_TRI was introduced which gives a rate of how many cells have a high TRI value in a defined zone. The p_TRI, the rate of TRI values above 0.06 m, is more than 4 times larger for erosion zones (0.17) than for non-erosion zones (0.04).

Changes in vegetation height could be linked to locations of implemented Nature Based Solutions (NBS). The results were in agreement with changes in NDVI, which were calculated from optical satellite imagery.

In conclusion, the new methodology has great potential to identify erosion zones in complex sloped terrain and monitor changes in vegetation.

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.