With increasing focus on global clean water goals and stricter surface water quality legislation within the European Union, new strategies are needed to improve the performance of urban drainage systems. Wastewater treatment plants play a key role in nutrient removal for better surface water quality. However, the highly variable inflow from combined sewer systems--driven by both diurnal wastewater patterns and rainfall--can hinder optimal wastewater treatment plant operation, often resulting, amongst others, in elevated nitrogen and phosphorus levels in the wastewater treatment plant's effluent.

This study proposes a real-time control strategy that stabilises dry weather flow by actively using in-sewer storage, thereby minimising daily inflow fluctuations to the wastewater treatment plant. The method was applied to the Eindhoven wastewater treatment plant catchment, focusing on its two largest contributing catchments, Eindhoven and Riool Zuid. A simplified Storm Water Management Model model, based on an existing integral system model, was combined with a PySWMM-based control script and an empirical water quality model to estimate pollutant concentrations. Control decisions were based on current and forecasted rainfall, using both perfect (historic) and ensemble-based forecast data, determining four possible operational states: dry, transition, wet, and lingering wet weather.

This research demonstrates that dry weather flow control can improve daily wastewater treatment plant and surface water effluent peak concentrations without additional negative effects on the urban drainage system itself--for improvement in averages of effluent or surface water, additional treatment plant operational adjustments are required. The study also showcased the potential of real forecasts, with inherent uncertainty, in quantitative real-time control decision making. Lastly, the study underlines the potential of integrated, forecast-based control strategies to better align urban drainage and treatment plant operation, encouraging further development of a feedback-based optimisation framework.

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.

Rapid urbanization and climate change pose increasing flood risks to cities in Sub-Saharan Africa (SSA). This study developed and tested a method to assess how Nature-Based Solutions (NBS) implemented across peri-urban areas can reduce downstream urban flooding, using the city of Nakuru in Kenya as a case study. Coupling the Wflow hydrological model with the SWMM hydraulic model shows that external runoff is a major contributor to urban flooding in Nakuru, accounting for approximately \(70\%\) of the total flood volume for a \(T=5\) year event, in all four simulation scenarios.
Three types of NBS were modeled: reforestation, terracing, and reservoirs. Among these, reservoirs proved most effective, reducing the flood volume for a \(T=5\) event by \(17\%\). The cumulative NBS reduced the flood volume by \(19\%\), indicating a limited impact of reforestation and terracing. This limited impact is partly due to their small-scale implementation and simplified modeling of their effects. The study revealed that a catchment-scale approach is necessary to fully utilize the potential of NBS for urban flood mitigation. To have a tangible impact on urban flooding, NBS implementation should scale to the size of the upstream catchment areas, not to the size of administrative boundaries.
In conclusion, NBS hold potential for sustainable urban flood management in SSA. Their success depends on catchment-wide integration in flood risk management, improved data and modeling practices, and continued research on their context-specific effectiveness and model implementation.

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.

The Spangen neighborhood in Rotterdam West, the Netherlands, is highly vulnerable to pluvial flooding due to its low elevation, numerous impervious surfaces, and proximity to the Delfshavense Schie canal and a surrounding feeder canal. These factors contribute to high local surface runoff and strain on the local combined sewer system (CSS), leading to combined sewer overflows (CSOs) and local street fooding (“water op straat”). This study investigates the potential of integrating blue-green infrastructure (BGI) together with real-time control (RTC) to mitigate flood risk by reducing both the volume and frequency of CSOs and swale overflow events. A hydrological model, built utilizing 20 years of meteorological data with a 10-minute resolution from November 01, 2004 to October 01, 2024, simulated the hydrological effects of three progressively integrated bioswale configurations, a baseline scenario, and further modeled the effectiveness of RTC as valves outfitted at the base of the swale overflow tructure in preemptively releasing forecasted runoff over a 12-hour lead time. Results demonstrated that while the baseline scenario revealed 582.5 mm of CSO overflow over 188 events during the study period, when implemented, bioswales alone reduced net flow to the receiving water body by up to 95%, and RTC-enabled bioswales, in the most intensive configuration, could fully, pre-emptively remove stored swale volume to completely eliminate uncontrolled overflow. The 25% interception fraction performed best for Configuration 1, and 50% fraction performed best for Configurations 2 and 3, which all minimized the net flow out to the receiving water body, most efficiently integrating the existing CSS and proposed bioswales. Beyond mitigating flood risks, BGI offer co-benefits such as enhanced ecological connectivity, improved water quality, and urban livability, serving as a tool to provide Gemeente Rotterdam with a new possibility to achieve other sustainability goals, such as increasing green space access. This research demonstrates that the integration of RTC with BGI can serve as a scalable model for climate-resilient urban water management in Rotterdam, and worldwide.

This thesis investigates the potential of Nature-based Solutions (NbS) to address urban water management challenges in Paramaribo, Suriname. The city, characterized by intense rainfall and inadequate drainage infrastructure, frequently faces significant flooding, highlighting the urgent need for sustainable and resilient solutions. The research focuses on the applicability of NbS as an alternative to traditional grey infrastructure, particularly in the context of rapidly urbanizing tropical environments, where such solutions are still relatively unexplored.

Paramaribo faces a range of urban water management challenges, including high surface runoff and socio-economic impacts from frequent flooding. These issues are exacerbated by rapid urbanization, leading to extensive soil sealing that reduces the natural capacity for water infiltration. Against this backdrop, the thesis explores the potential of NbS as a promising but underutilized solution, particularly in tropical settings where existing literature on this topic is sparse. The study emphasizes that small-scale investigations, such as this one, can be critical in addressing the gap between theoretical research and practical implementation.

The literature review discusses various types of NbS, such as green roofs, bioswales, and permeable pavements, highlighting their potential benefits, including runoff reduction, water quality improvement, and biodiversity enhancement. However, in the context of Paramaribo, these co-benefits work differently.

The research focuses on a study area around Wakapasi in Paramaribo, using it as a small-scale case study to explore broader urban challenges. The selection of a smaller study area allows for detailed investigation, offering valuable insights into the effective implementation of NbS in similar contexts. The study acknowledges that while NbS holds promise, the challenges of ensuring their effectiveness in tropical environments must be critically examined.

Methodologically, the study employs a Multi-Criteria Analysis (MCA) to evaluate different NbS options based on effectiveness, cost, and social acceptability. Semi-structured interviews with local stakeholders and think-tank discussions emphasize the importance of local knowledge and institutional support, advocating for a bottom-up, participative approach to climate adaptation. The full-scale bioswale tests at AdeKUS provide empirical data, but the results are presented with critical nuance. While the bioswale tests did not show an emptying time of less than 24 hours, which is typically desirable to avoid standing water, the tests did not attract mosquitoes or pests. Additionally, the potential for preferential flow due to vegetation growth is noted, which could significantly increase infiltration rates over time, potentially by tenfold, once native vegetation fully establishes.

The results offer a mixed assessment of the potential for NbS in Paramaribo. On one hand, the bioswale tests demonstrate tangible measures that can be integrated into urban landscapes, providing proof of efficacy within a clearly defined context. The study also provides a clear description of the conditions under which NbS designs should be implemented based on the context of the region, offering practical guidance for future projects. However, the research also highlights the limitations and challenges, particularly in achieving rapid infiltration rates in combination with heavy rainfall and the uncertainties associated with long-term performance.

The study concludes by reflecting on the broader implications for urban water management in Paramaribo and similar tropical environments. The findings suggest that NbS could play a significant role in enhancing urban resilience, but their success depends on careful design, robust stakeholder engagement, and adaptive management strategies and the potential effects need to be researched further. The thesis emphasizes the potential for a bottom-up implementation strategy, where local involvement and incremental, evidence-based approaches guide the deployment of NbS. However, it also underscores the importance of remaining critical about the efficacy of these solutions, as the success of NbS is not guaranteed and must be rigorously tested and adapted to local conditions.

Urban stormwater management is put under pressure by increasing urbanisation and climate change leading to more frequent urban flooding. Such flooding can introduce ecological and public health issues by releasing contaminated water into the environment. Upgrading traditional grey infrastructure and implementing blue-green infrastructure (BGI) can be limited in their applicability for flood mitigation making Real-Time Control (RTC) an increasingly popular alternative due to its cost-effectiveness and potential performance benefits.
This thesis investigated the accuracy of rainfall forecasts and examined how forecast-informed RTC procedures could reduce flooding frequency and overflow depth while minimising negative side effects. The rainfall forecast accuracy was assessed based on three key properties: rainfall depth, forecast horizon, and mean forecast intensity. Using the insights from this assessment, three forecast-informed RTC procedures were designed to enhance the flood mitigation performance of a reactive RTC.
With perfect forecast data, flooding was completely prevented for two of the procedures, which used the longest but distinct horizons. The procedure using the shortest horizon was not able to prevent every flooding event due to the restriction in pre-emptive water release ahead of the forecasted rainfall events. The forecast accuracy analysis showed that the accuracy declined with increasing rainfall depth and lengthening horizon. Furthermore, the results indicated a shift from underestimation to overestimation of the rainfall depth as the mean forecast intensity increased. Applying the real forecast data, all designed forecast-informed procedures demonstrated reduction in total overflow depth of up to 70%, with the reduction linked to pump operation. However, the flood mitigation performance did not align with the expected results based on forecast accuracy, indicating that the procedures’ logic and implementation played a significant role in determining their effectiveness.
The findings highlight the trade-offs inherent in using forecast-informed RTC procedures, particularly the balance between the uncertainties of longer forecast horizons and the need for sufficient lead time to take preventive action. By addressing these challenges, this thesis provides practical insights to inform the design and implementation of advanced RTC systems, marking a critical step toward more sustainable and resilient urban water management.

Urban wastewater systems can impact the urban ecology by untreated wastewater discharges through combined sewer overflow (CSO) events, or by partial treatment of the wastewater at the wastewater treatment plant (WWTP). CSO events can cause oxygen depletion, eutrophication, and the discharge of pathogens. The partial treatment of the wastewater causes an increased concentration of ammonium in the WWTP effluent, which can lead to toxic ammonium levels in the receiving river water. These problems can be (partially) mitigated by optimizing the existing infrastructure. Optimization of the available storage will help handle the increased pressure on the urban drainage system (UDS) and the stricter environmental regulations simultaneously. A method to control the dynamic performance of the combined sewer system is Real-Time Control (RTC). A RTC strategy controls the combined sewer system dynamically based on real-time information about the system state. This research aims to develop a RTC strategy that decreases the negative ecological impact of the combined sewer system on the river by optimizing the available in-sewer volume. By doing so, the objectives to reduce the total amount of spilled CSO volume and to decrease the ammonium peaks towards the WWTP should be met. This research is applied to the case study of Geldrop-Mierlo, this is a municipality located in the UDS of Eindhoven. The trade-off between those two objectives was explored in the Wastewater Process simulator WEST. Rainfall events with a maximum intensity of 3.1 mm/hr and higher or rainfall events with maximum intensity < 3 mm/hr and total rainfall depth of > 4.8 mm, were found to be more likely to cause DO dips. The objective function which is used in the optimization process is dependent on the forecasted rainfall and the trade-off described above. The UDS is modeled in a full-hydrodynamic (FH) model and a simplified conceptual model. The conceptual model is made to reduce the computation time. The catchment of the FH model is split up into 3 different catchments, and each is modeled as a reservoir in the conceptual model. The characteristics of each reservoir are dependent on the characteristics of these catchments in the FH model. The characteristics that are included are storage curve, outflow dynamics, and CSO dynamics. Both models are calibrated and validated. The UDS is controlled with the Model Predictive Control (MPC) methodology using a Genetic Algorithm (GA) to find the optimal solution to minimize the negative ecological impact of the UDS on the river. Both the FH model and simplified model are used in the MPC optimization. Based on the analysis of the case study, the optimization results show that the impact of the MPC procedure on the receiving river is not significant. The reasons for this are location specific, but the main findings are that 1) the hydraulic constraints of the catchments restrict the MPC procedure from working, 2) although the calibration results of the conceptual model indicated accurate results, this does not guarantee that the model is also accurate enough to use in the MPC procedure.