Access to clean and safe drinking water is essential for public health and sustainable development. Drinking water treatment plants (DWTPs) ensure water quality, but fluctuating raw water characteristics, particularly turbidity, challenge efficient coagulation and dosing control. Traditional strategies like jar tests and feed-backward control are limited by delayed results, making timely adjustments difficult. Short-term predictive tools based on machine learning (ML) offer a solution by forecasting water quality variations and enabling proactive control. This study develops several different ML models for short-term turbidity prediction at the Lekkanaal DWTP, addressing three questions: (1) which parameters influence turbidity, (2) which feature combinations yield optimal predictions, and (3) how far in advance turbidity can be reliably forecasted.

Historical water quality and hydrological data were collected from Waternet, KNMI, and Rijkswaterstaat, followed by preprocessing for reliable inputs. Candidate features were selected using Spearman correlation and Self-Organizing Maps (SOMs). Three regression models—AutoRegressive Integrated Moving Average (ARIMA), Random Forest (RF), and Long Short-Term Memory (LSTM)—were trained for different horizons, and feature importance analyzed using greedy selection and visualization tools. An RF classifier evaluated the feasibility of predicting peak turbidity events.

Results showed turbidity was driven by hydrological and physicochemical factors. Upstream discharge and turbidity strongly correlated with local measurements, highlighting the Lek River as a primary contributor, while EC and temperature showed negative correlations, reflecting dilution and seasonal sediment mobilization. SOMs confirmed high turbidity coincides with northward flows from the Lek River into the Amsterdam-Rhine Canal.

Feature analysis indicated univariate models using recent sensor\_turbidity outperformed multivariate models; additional features introduced noise. The last three hours of turbidity dominated predictions across ARIMA, RF, and LSTM.

All models provided reliable short-term forecasts, with RF outperforming ARIMA and LSTM for 3- and 6-hour horizons. Extreme peaks were systematically underestimated, and RF classification detected fewer than 16\% of peak events. Short-term forecasts up to six hours are feasible, but high-magnitude events remain challenging, emphasizing the need for enhanced monitoring and tailored strategies. ... Read more

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. ... Read more

Accurate water-level monitoring is vital for effective river management, flood prevention, and environmental conservation efforts. The increasing frequency and severity of flooding events, driven by climate change, highlight the critical need for reliable, robust, and sustainable water-level measurement systems. Such systems are particularly necessary in remote and resource-constrained settings where conventional infrastructure is lacking or insufficient.

Traditional water-level measurement methods, including radar, ultrasonic, pressure sensors, and conventional imaging systems, encounter significant limitations. These include high acquisition and maintenance costs, susceptibility to environmental damage, vandalism risks due to conspicuous placement, and dependence on stable, continuous power sources. Consequently, there remains an unmet demand for affordable, resilient, and autonomous monitoring devices capable of functioning reliably in challenging and off-grid environments.

To address this gap, this study presents the development and validation of an innovative water-level monitoring prototype as a prove of concept. The proposed system integrates a low-cost Raspberry Pi-based imaging sensor equipped with infrared illumination to enable accurate measurements both during the day and at night. An onboard processing unit autonomously captures and analyses images in real-time using one of two distinct image-processing algorithms: the mean-difference method and the Kolmogorov–Smirnov (KS) test method. These algorithms quantify water levels by detecting pixel-intensity contrasts, thus eliminating the need for direct physical contact with the water body.

The prototype underwent field validation in a natural river environment, demonstrating robust and consistent performance. The system achieved an accuracy within ±4.6 cm at a 95% confidence interval. Notably, it exhibited stable performance under varying environmental conditions, with moderate bias differences between daytime and nighttime scenarios, and minimal sensitivity to precipitation effects. The mean-difference algorithm demonstrated superior precision, while the KS-test method offered enhanced robustness against environmental variability.

This research underscores the practical feasibility and significant potential of low-cost, autonomous camera-based systems for sustainable water-level monitoring. Such solutions can substantially improve disaster preparedness and environmental management, especially in remote or economically disadvantaged regions lacking traditional monitoring infrastructure. Future research directions include integrating renewable energy solutions such as solar power to enhance operational autonomy, exploring advanced image-processing algorithms for increased accuracy, and conducting extended validations across a broader range of hydrological and environmental scenarios. ... Read more

Over the past centuries, the Dutch rivers have been extensively engineered with the construction of hydraulic training works. A changing climate and increasing demands of river functionalities require innovations to the system to ensure its resilience. Exploring potential applications of Xstream elements might contribute to the development of sustainable river management strategies. The 33 cm high concrete three-dimensional cross is the little version of the Xbloc, a widely used breakwater element for coastal protection. Random arrangement of Xstream elements creates 60% porous spaces and the ability to build slopes at a 45∘ angle. Alterations to the riverbed morphology induced in the near-field of homogeneous Xstream hydraulic structures are assessed in the present study. A physical scale model featuring a movable bed of lightweight sediment was employed to simulate a river section. Minimising any compromises on Froude scaling, this material was used to properly scale the Shields parameter, striving dynamic similarity between the model and reality. Bed load transport processes are reasonably well represented by this material, providing well matched equilibrium scour depths and good qualitative comparisons of the influence of structural variations on these processes. Multiple setups differing in material and geometry were tested in a 12 m long and 2.6 m wide sediment recirculating flume. The primary comparisons of this study include:
• the flexible groyne head in the model against field data;
• two abutments with a 1:2.5 slope, varying in material between stone and Xstream elements;
• two abutments made of Xstream elements, differing in slope between 1:1 and 1:2.5.
Water levels were gauged and flow velocities were measured with acoustic Doppler velocimeters during the experiments. High-resolution bed elevation data was collected by means of a laser scanner. Generally, results of the model show an overestimation of bedform dimensions. This is attributed to the concessions necessitated by the limitations of the test facilities due to the low length scale factor. The effect of contraction was exaggerated in the model as the structure width to flow width ratio was 3 times greater and the influence of any upstream river training works was neglected. The flexible groyne head blocked 43% of the cross sectional flow area in the model. Furthermore, due to the laminar flow behaviour, the water encountered increased resistance within the structure. The deepest scour is found along the leading edge of the structures. This can be explained by the intricate flow patterns created by the primary vortex entering the flow acceleration. Inspired by the longitudinal training wall as built in the river Waal, a less porous stone abutment was constructed to compare the effect of porosity and roughness of Xstream elements on local sediment displacement under high water conditions. The increase in water level in front of the Xstream abutment was half that of the stone abutment and streamwise velocities in the main flow were 5% lower. These findings indicate better dissipation of energy by the Xstream abutment. Nevertheless, the relative turbulence level was 17% higher close to the Xstream abutment due to the higher roughness, resulting in equivalent peak velocities in both experiments. Along the Xstream abutment, however, the scour depth was twice as large. Taking into account the live-bed conditions, where sediment is supplied from upstream causing the scour depth to fluctuate around its equilibrium, a significant difference of at least 20% remained. The principle of a falling apron, a mechanism where individual units at the toe of a structure tumble down, covering one slope of the scour hole and thereby increasing the roughness, could be the reason for this. The erratic shaped Xstream elements enhance complex turbulence patterns very locally. Due to the absence of a supporting filter construction or a bed protection layer, the Xstream abutment was undermined, leading to individual elements decaying at the base of the structure. The interlocking ability of Xstream seems to be stronger when the structure is built with a steeper slope. This insight can be taken into account in the design of Xstream hydraulic structures. The substantial discrepancy in density between the Xstream model units and the angular polystyrene particles must be considered as the geotechnical properties may differ from the real world. The present study has shown the kinetic energy absorbing capacity of Xstream. Turbulence levels increase due to higher roughness. Exposing a uniform structure of Xstream elements to high water conditions might lead to instability which alters both positive and negative effects. Future research must give valuable insight into how the design of Xstream structures could look like for a durable implementation in the Dutch river system. ... Read more

Protecting forests from agricultural expansion and wildfires while the world population is growing and the climate is warming remains one of the biggest challenges humanity currently faces. While global modelling and regional observation based studies have found significant effects from deforestation on precipitation, leading mostly to drying precipitation trends and shorting rainy seasons, this study represents the first global estimate of first order deforestation effects on precipitation. Using a recently developed precipitationshed database and actual deforestation data, a new measure is developed to quantify potential deforestation impact per grid cell which in turn is related to annual precipitation trends as well as seasonal differences in tropical regions. In seven regions analysed, a majority of subregions suggested a relationship between deforestation impact and a relative drying precipitation trend in the 2001-2018 study period compared to the long term average. While these results provide further evidence of deforestation contributing to a downwind drying precipitation trend across different continents and climate regions, five other regions studied showed no significant relation or suggest relative wetting related to deforestation impact. One of this regions is the South America Tropical (SAT) region, the region most well-known for its widespread and intense Amazonian deforestation. The two regions downwind of the SAT region however are highly impacted by SAT deforestation and experience most relative drying in the areas impacted most impacted by deforestation, suggesting strong teleconnecting effects. In the seasonal analysis, only two out of four tropical regions studied show more subregions linking deforestation impact to relative drying in the first wet month compared to the wettest month. While these results provide new insights into the global influence deforestation can have on moisture availability, more research needs to be done into the indirect and feedback effects related to deforestation. Additionally, a more robust way of including other factors influencing precipitation trends like large scale oscillations could further enhance the understanding of this important issue. ... Read more

Hydrological models are used for all kinds of water management applications. Detailed hydrological simulations are needed to solve the hydrological problems of the 21st century, especially in developing countries. However, sufficient hydrological and meteorological data is often not available. The use of remote sensing (RS) datasets may offer a solution to this problem. RS datasets can perfectly be applied in distributed conceptual hydrological models. In this study, several RS datasets are applied in the calibration of a distributed conceptual hydrological model, and the influence of this approach on the overall model performance is assessed. The RS data applied in this study include terrestrial water storage anomaly (TWSA) data, normalized difference vegetation index (NDVI) data, and soil moisture (SM) data. Also the input and forcing data for the hydrological model consists of datasets based on satellite observations. This data is used in a wflow hbv model, which is applied to the Volta basin in Western Africa as a case-study. In this study, not only the effect of including RS data in the calibration of a distributed hydrological model on streamflow is assessed, but also the effect on a set of internal components of the system, directly related to the datasets used for calibration. These internal stocks and fluxes are the TWSA, the actual evapotranspiration (AET) and the amount of soil moisture in the unsaturated zone. Together with streamflow, the assessment of these stocks and fluxes make up the overall model performance of the system. The effect on the overall model performance is examined using different scenarios, in which different combinations of datasets are used for calibration. Not the absolute values, but the spatiotemporal patterns of the remote sensing datasets are used for model assessment. This is done using the spatial pattern efficiency metric (ESP ). Model optimization was done using the Dynamically Dimensioned Search (DDS) algorithm. The results show that the hydrological model developed for this case-study is already able to simulate streamflow and the temporal patterns of the RS datasets quite well, when it is calibrated on streamflow only. However, the spatial pattern representation of the RS datasets was found to be inadequate and the differences in streamflow simulation performance for the different subcatchments is large. When SM or TWSA data was added to the calibration procedure, the temporal and spatial pattern representations only changed minimally, which is attributed to limited model complexity and flexibility. However, generally a trade-off effect was observed in which the spatial and temporal pattern representation improved, but the streamflow performance decreased. This effect was stronger for the addition of the SM dataset to the calibration than for the addition of TWSA dataset. Although there is definitely a strong connection between NDVI and AET, the physical relation between the two variables was found to be too weak to be used for hydrological model calibration, even when only the spatial and temporal pattern information was used. The overall model performance did improve most in the calibration catchments in the scenario in which Q, SM and TWSA data were combined in the calibration procedure, but the differences with the baseline scenario were only small. For the streamflow performance however, the differences between the scenarios are quite significant. It was shown that calibrating a hydrological model on the spatial and temporal patterns of RS data only (non-Q calibration) can accurately represent the temporal pattern of streamflow observations, but not the magnitude of the flow values. It is recommended to repeat this study using a more complex and more flexible model setup, which allows the model to use the freedom it is given to better represent the spatial patterns observed with RS. ... Read more

Global hydrological models (GHMs) have become an increasingly valuable tool in a range of global impact studies related to water resources. However, glacier parameterization is often overly simplistic or non-existent in GHMs. The representation of glacier dynamics and evolution, including related products such as glacier runoff, can be improved by relying on dedicated global glacier models (GGMs). In this study we test the hypothesis that coupling a GGM to a GHM can lead to increased GHM predictive skills and decreased GHM uncertainty through better glacier parameterization. To this end, the GGM GloGEM is coupled with the GHM PCR-GLOBWB 2 within the eWaterCycle II framework. For the years 2001-2012, the coupled model is evaluated against the uncoupled benchmark in 25 large (>50.000 km2) glacierized basins. Across all basins, the coupled model produces higher runoff throughout the melt season. In July and August, it ranges between 100.07% and 352% of the mean monthly benchmark runoff in lowly and highly glaciated basins respectively. The difference can primarily be explained by the inability of PCR-GLOBWB 2 to simulate snow redistribution and glacier retreat, causing an underestimation of glacier runoff. The coupled model better reproduces basin runoff observations primarily in highly glaciated basins, i.e. where the coupling has the most impact. This study underlines the importance of glacier representation in GHMs and demonstrates the potential of coupling a GHM with a GGM for better glacier representation and runoff predictions in glaciated basins. ... Read more

The purpose of this MSc thesis is to do an experimental study into the spatial and temporal variation of rocking armour units. Armour units on a breakwater slope under wave loading can sometimes start to move back and forth, this phenomenon is known as rocking. Rocking can lead to significant impacts between armour units, which can result in breakage. This is especially important for single layer armour units, like the Xbloc. The development of the smart Xbloc makes it possible to measure accelerations and angular velocity with a stand alone sensors at a sampling frequency of around 100 Hz. The current literature does not provide the spatial distribution of the number of impacts and the impact velocities due to rocking. Furthermore, only limited knowledge is available on the distribution in time. The research aim of this thesis is: Determining the spatial and temporal distribution of the number of moving armour units, the number of impacts and the impact velocity of rocking armour units. To achieve this aim a physical scale model was set up and model tests have been performed with 10 smart Xbloc units to measure rocking. The collected data, analysis and results provide a unique look into the behaviour of single layer armour units. The results can be used to validate rocking models and provide valuable statistical information on the number of impacts and the impact velocities.

... Read more