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.

 Rising global temperatures, combined with increasingly dense urban environments, raise concerns regarding outdoor thermal comfort and indoor liveability. As cities become warmer, both energy demand and health risks increase, urging the need for effective climate adaptation strategies. This thesis investigates how different green interventions affect outdoor and indoor temperatures in the Netherlands, and how these measures influence building cooling loads during heatwaves. Green interventions used in this study are trees, green roofs, green walls, shrubs, and grass. Within 10 m of the buildings, green is implemented to represent realistic opportunities for greening in private gardens and immediate surroundings. As a result, the simulated cooling effects reflect local-scale interventions rather than district-wide greening strategies. The Dutch heatwave from 23–27 August 2019 served as a reference event to assess three representative Dutch building typologies: low-rise, mid-rise, and high-rise.

A one-way coupled approach between an urban climate model (UCM) and a building energy model (BEM) is applied. For both the UCM and BEM, different model options were considered and compared. The selected models were then further evaluated against findings from literature to assess their suitability and accuracy. The outdoor microclimate was simulated with the ENVI-met model, which provides detailed outputs of air temperature, wind speed, and Physiological Equivalent Temperature (PET) at pedestrian height (1.5 m). Indoor thermal conditions and cooling energy demand were then evaluated using the building energy model IES-VE, with the microclimatic outputs from ENVI-met serving as boundary conditions. This coupling provides a more realistic estimation of the benefits of green infrastructure compared to building energy modelling that relies on standardised weather data.

The results show that the coupled ENVI-met–IES-VE framework successfully reproduced outdoor and indoor temperature reductions within the range reported in the literature, though typically at the lower end. This is expected, as the relatively small amount of greenery applied was intentionally chosen to isolate and quantify the local effect of greening around buildings. Among all interventions, trees produced the strongest cooling effect due to their shading capacity and evapotranspiration, with the largest impact observed when trees were combined with other types of greenery. Across all typologies, reductions of up to 1.17 °C in average outdoor air temperature, 3.92 °C in local maximum air temperature, and 13.45 °C in maximum PET were observed. Indoors, green interventions reduced peak air temperatures by up to 1.20 °C and cooling loads by as much as 32%. The relative effectiveness differed across building typologies: low-rise dwellings benefited most due to their closer interaction with the modified microclimate, whereas high-rise buildings showed more limited improvements.

This thesis provides a coupled UCM–BEM framework applicable to multiple Dutch building typologies, offering new insights into the role of vegetation in a temperate maritime climate. The modelling approach can be used to test different greening scenarios across building typologies. Future research should validate this framework with on-site measurements to further improve accuracy. Furthermore, combinations with shading devices and adaptive occupant behaviour should be explored in future research. The insights gained can guide municipalities, urban planners, and property owners in designing greener, more comfortable, and energy-efficient urban environments.

Accurate measurements of global solar radiation are essential for applications ranging from photovoltaic (PV) yield assessment to grid operation and agricultural management. However, direct measurements with pyranometers are sparse and costly, motivating alternative approaches based on widely available PV power production data. This thesis investigates how accurately global radiation can be estimated using machine learning models trained on PV power output data, supplemented with weather reanalysis data.

Two tree-based models, Random Forest and Gradient Boosting, were applied to PV data from two sites in the Netherlands combined with globally available weather reanalysis data, using on-site pyranometer measurements as the ground truth for training and validation. Both single-location models, trained and validated on the same site, and cross-location models, trained on one site and applied to another, were evaluated. For comparison, a linear regression baseline model was also tested.

The single-location models achieved strong performance, with R2 ≈ 0.97, mean absolute errors (MAE) of 10–15 [W/m²], and near-zero bias at a 15-minute resolution, substantially outperforming established reanalysis products such as ERA5. Cross-location models retained reasonable accuracy (R² ≈ 0.94, MAE 15–30 [W/m²]), though with increased bias. Feature importance analysis highlighted the dominant influence of PV power output and the clear-sky index for photovoltaics (K_PV), while reanalysis variables contributed little. The top-of-atmosphere radiation proved to be a consistently useful predictor.

These results demonstrate that PV power data can be transformed into accurate, high-frequency radiation estimates, offering a cost-effective complement to pyranometer measurements. While site-specific calibration currently limits full generalisation, the findings point toward the feasibility of scalable models based on transferable features such as KP V . Expanding datasets across more climates and system configurations, and exploring advanced model architectures, are promising next steps towards generalised PV-based radiation models that could be applied without requiring on-site pyranometer measurements for training.

This research addresses the issue of rising soil temperatures, driven by climate change. As the Drinking Water Distribution System (DWDS) is located in the sub-surface, the drinking water temperature in the distribution mains attains this rising soil temperature. Because the temperature of drinking water is an important determinant of water quality, Dutch law mandates that drinking water temperature may not exceed 25 °C. This study assesses several measures, including their quantitative impact on soil temperature reduction, and consequently on drinking water temperature reduction. These measures center on modifying the ground coverage, as this measure can be implemented without excavation and disrupting existing infrastructure in the sub-surface. This includes different types of ground coverage (e.g. changing concrete tiles, which are the existing top layer type, to vegetation), and creating temporary shade or applying white paint to the existing coverage type. The effect of (re)locating pipes to a deeper level or in the shade are also considered. The effect at the depth of a distribution main is analysed, which is shallow (0.7 m) in Rotterdam, and 1 m depth in general in the Netherlands.
Measurements confirm the significant influence of the ground coverage, mainly impacting the soil temperature in vertical (in-depth) direction. A detailed analysis reveals the quantitative effectiveness of the considered measures, indicating substantial cooling potential by modifying the ground coverage. Replacing concrete tiles with vegetation (in sandy soil) results in the most cooling effect of all analysed measures. White paint on concrete tiles emerges as particularly effective in reducing the soil temperature at the current depth in Rotterdam (0.7 m), and is considered an easily executable measure to prevent exceedances of the 25 °C threshold, mainly in case of urgency. In general, modifying the top layer as measure is considered feasible. This is because it avoids the need for excavation, in contrast to the alternative measures of relocation to a deeper level or in the shade. Additionally, the study quantifies soil temperature through simulations. A comparative analysis between simulations conducted by the soil temperature model and measurements facilitates an examination of the anticipated progression and the predicted effect of measures by the model, allowing for the formulation of recommendations to enhance the model.
As the results indicate that modifications to the ground coverage can significantly contribute to cooling the soil, this can mitigate potential threats to drinking water quality. The study advocates an integrated approach to decision-making, which includes collaboration between municipalities, drinking water companies, and research institutes. This cooperative effort is essential for identifying and implementing the most suitable and effective measures to safeguard high-quality drinking water, both now and in the future.

Personal weather station (PWS) networks have the potential to supply precipitation data at high spatial and temporal resolution for urban hydrological modeling. Past research has shown promising results on the quality of PWS data, for example from Netatmo gauges, but studies on other PWS brands are limited. This thesis assesses the quality of precipitation measurements from the Alecto WS-5500 personal weather station. During a controlled experimental setup in an urban environment, the Alecto was found to overestimate rainfall due to incomplete emptying of the tipping bucket. Correcting this mechanical error by a 10 percent reduction factor lowered the relative bias to 0.00 or 0.06, when comparing the station to official KNMI gauge or KNMI gauge-adjusted radar, respectively. Correlations were high between stations with non-faulty setups, but at the 5 minute resolution, correlations were substantially lowered by sampling errors caused during the data transfer to PWS data platforms. A quality control method from de Vos et al. (2019) was adapted and applied to data from a citizen science project in Delft, the Netherlands, which had a 12-month period of measurements for 20 stations, and a 3-month period of measurements from 40 stations. The filtering of faulty zero measurements was improved by applying the filter on hourly accumulations, and the bias correction was stabilized. The variation over individual PWSs, however, remained high due to setup differences. The complex installation process for citizens and issues with software and data accessibility are limiting factors and warrant further research to improve the usability of PWS data for urban hydrological applications.