Because most of the area in Westland is taken up by greenhouses, water hardly infiltrates into the ground which may result in flooding. Furthermore, rain showers are getting heavier, more variable and harder to predict due to climate change (Beersma et al., 2019). The space to find solutions for mitigating flooding is limited by the greenhouse area.

The waterboard of Delfland and the greenhouse horticulturists have found a small-scale solution that is called Rainlevelr. The solution focuses on releasing reservoir water before a predicted heavy rain event such that the reservoirs can capture precipitation during the actual event. The greenhouse horticulturists participate voluntarily in Rainlevelr. While both the horticulturists and the water board want to mitigate flooding, the greenhouse horticulturists also want to keep as much water as possible in their reservoirs for irrigation. When predicted rainfall differs from the actual rainfall, the resulting suboptimal strategy for releasing water from the reservoir can result in flooding the polder or wasting reservoir water. This raises the question of how to define an algorithm to assess trade-offs between the interests of different stakeholders.

A numerical model could predict polder water levels and reservoir water levels based on a weather forecast. A controller could then be added to the model to optimize the amount of water that should be released. The goal of this thesis is to design a Model Predictive Control (MPC) strategy for a polder system in Delfland by optimizing the valve setting of the Rainlevelr pipes in the reservoirs. With the proposed strategy the risks can be reduced, and possible trade-offs can be identified. Polder sections fail when the water level exceeds a threshold water level and reservoirs fail when their water level falls short.

The Python model POKKA was developed in order to study the effects of the valve settings on the water levels of both the reservoirs and the polder sections. The Kralingerpolder was used in a case study.

A MPC simulation showed that the controller was able to reduce the number of polder section threshold exceedances at the expense of reservoir threshold exceedances. However, the controller was not able to prevent flooding of the polder during the heavy rain event at the beginning of November 2023. The controller selected an optimal sequence of hourly valve settings out of a set of 100 realizations. Increasing this value to 1000 only leads to a small difference in predicted water levels whereas the computation time increased with a factor 10. A parallel computation over a period of a year took about 2 hours for 100 realizations. For this reason the remaining calculations were performed with 100 realizations. The controller implements the trade-offs by setting weights on the deviations of the polder water levels and the reservoir water levels from their reference level. A simulation showed that fewer polder sections fail if the weights favor of the polder sections. The controller computes the optimal valve settings based on weather predictions and its performance decreases with decreasing accuracy of the weather forecast. Its performance was improved by introducing a feedback loop where measured water levels were used to correct the model predictions. Increasing the Rainlevelr capacity has a large effect on the Model Predictive Control performance. The simulation showed that the controller came very close to completely preventing a level exceedance of the polder during the heavy rain event at the beginning of November if all reservoirs in the Kralingerpolder were part of the Rainlevelr initiative.

This thesis examines the role of the concepts of “Build Back Better”, “Disaster Cycle”, and “Sustainability” in water management and the possibility of the interactions between these concepts when designing water management infrastructure. Especially in cases where the disaster cycle is relatively short: 10 to 15 years between disasters.
The case study is on the island of Grand Bahama. The island of Grand Bahama depends on the freshwater groundwater lens for its drinking water. Flooding during hurricanes introduces saline water into that groundwater lens. It is therefore highly probable that the current drinking water production method will not be able to meet the demand in the future. An alternative drinking water supply system should therefore be examined.
To investigate the interactions between the concepts, they are put into a conceptual design process, which uses the methodology “RenewIsland”, and used to set up some preliminary designs. Three Alternatives were made with the methodology “RenewIsland”. These Alternative designs were then subjected to a Multi-Criteria Analysis (MCA).

This thesis substantiates from the process that the interaction between the concepts is possible and that the inclusion of Build Back Better in the Water management decisions gives more possibilities to make balanced choices.
A blueprint has been made for a method to analyze swiftly if an alternative is compatible with the different cycles and phases of a location regarding a disaster and normal circumstances. This thesis also states that the study has to be extended with more research to make it generally applicable.

Rapid discharge of excess water has always been the core of Dutch water management. Due to climate change, a change in strategy is required. A future-proof drainage strategy consists of three steps: 1) retention, 2) storage, and 3) controlled removal. This thesis comprises the design and evaluation of an algorithm for groundwater-based Model Predictive Control (MPC) of water table control systems in polder areas. The objective is to maximize retention of precipitation, thereby contributing to the first component of the new drainage strategy. The control algorithm is developed for water table control on a single plot surrounded by a ditch, and three variants of water table control systems are considered: 1) water table control without drains, 2) water table control with submerged drains, controlled by ditch water level, and 3) water table control with submerged drains, controlled by sump water level. As a first approach, a relatively high, fixed setpoint is applied to maximize retention of precipitation. The controller adjusts the crest level of a weir that manages the ditch or sump water level. The algorithm performance is assessed through model simulations for two case study areas with distinct geohydrological response times within the management area of Hoogheemraadschap van Rijnland: 1) Polder Het Langeveld, a sandy polder located in the Flower Bulb Region, and 2) Polder Vierambacht, a clayey polder located in the Green Heart. Groundwater-based MPC anticipates precipitation events. Since the forecast horizon for which accurate precipitation forecasts are available is restricted, the feasibility of groundwater-based MPC is dependent on the response time of the groundwater system. Systems with a fast groundwater response are not suitable for water retention, while proper groundwater table control is possible. Control of systems with long system response times is hard, but these systems are applicable for water storage. A trade-off between controllability and water storage should thus be made. For the applied weir settings and objective function, a system response time lower than 10 days is required for proper control. Direct control of groundwater levels could induce a certain groundwater storage or water retention, depending on the applied setpoint.

Small islands that support urban communities provide a unique opportunity to study the urban water cycle, its energy needs, and possible links to renewable energy. The aim of this paper is to explore to what extent an island’s urban water cycle and the renewable electricity production system required to satisfy the urban water cycle’s demand can become sustainable using Caye Chapel (Belize) as a study case. For this research, the water-energy system is the urban water cycle and the renewable electricity production for the urban water cycle. Twelve alternatives were proposed for the water-energy system. The different alternatives are divided among those that consider the reuse of wastewater, rainwater harvesting, and the use of wind turbines, PV panels, or both for the renewable electricity production. Then, those alternatives were optimized to produce the minimum water demand shortage, minimum amount of treated water that is not reused, and renewable electricity shortage. Later, the optimized alternatives are evaluated using multi-criteria decision analysis (MCDA). It was observed that the alternatives that only consider one renewable source for the electricity generation and do not consider the reuse of wastewater, are outperformed by the alternatives that consider more than one renewable source and reuse the treated wastewater.

Climate change is incompatible with the assumption of stationarity. This has lead to a sharp increase in the detection and study of nonstationarity in hydro-meteorological processes. Most hydro-meteorological processes are still analyzed by studying time series of observations. From the perspective of statistical characteristics, a stationary time series does not show significant changes. On the contrary, a nonstationarity time series often shows a slowly increasing/decreasing trend or a sudden change. A sudden change or a change point is a time point that a time series shows a great change in its statistical characteristics, for instance in themean or the standard deviation. For stationary cases, hydrologists have a large number of statistical tools to analyse these time series. These tools can not only help hydrologists to gain a deep insight into time series, but they can also analyse the corresponding uncertainty. For nonstationary cases, the detection of changes has drawn the majority of attention, however, the uncertainty associated with the detection has still been rarely studied. Therefore, this PhD research aims at bridging the gap between nonstationarity detection and the uncertainty of detection. To be more specific the main scope is rooted in analysing the uncertainty associated with detecting a change point in hydro-meteorological time series. When it comes to representing uncertainties, a traditional choice is using a confidence interval with a certain confidence level. In this research instead, the uncertainty is represented by confidence curves because they are capable of capturing more information by including all confidence intervals at all confidence levels and they visualize uncertainty in a curve. To verify the general applicability of a confidence curve in representing uncertainties, both a discrete parameter and a continuous parameter are considered in this research. The location of a change point is considered as a discrete parameter, and the dependence parameter in copula models will be considered as a continuous one. Additionally, in order to simplify the construction of a confidence curve, several new approaches have been presented in this research. Based on results and findings, confidence curves have been proven to be more informative and theoretically they can represent uncertainties of all types of parameter of interest. With a confidence curve, hydrologists can easily read the uncertainty of the detected change point and this would also provide decision-makers a better insight into the nonstationarity of a time series of a hydro-meteorological observations.

The application of optimal control structures for water irrigation systems (WISs) can be enabled by applying wireless event-triggered control (ETC). The term WIS, is used to describe open-water channels, that are mainly used to supply water to farmers all around the world. The water levels in these channels need to be controlled, but because of the large scale of WISs, it is very expensive to create centralized control structures when using wired connections between individual sensors, actuators and a centralized controller. Previously, WISs were typically controlled using individual decentralized (non-communicating) controllers. Applying wireless technologies enables communication between (smart) sensors, actuators and a centralized controller without the expense of installing and maintaining cables over lengths of kilometers. To create such a wireless infrastructure, a network needs to be designed, consisting of multiple nodes that are able to communicate with each other over wireless. Each sensor and actuator will be connected to (or integrated in) a node, just like the centralized controller needs to be connected to a node. To minimize the costs related to creating such an infrastructure, the nodes should have their own power source in order to prevent that a maintenance worker has to change the batteries of the nodes periodically. The nodes could be powered using a solar panel, or by using energy harvesting, which could be done by using a turbine to extract energy from the flow in a water channel. When using such energy sources, it is important to minimize the power consumption of the nodes. Most of the consumed power is used in communication when transmitting information. By applying ETC, the amount of communication between the individual parts of the control system is minimized, while still retaining good closed-loop system control using a centralized controller. In this research, techniques on wireless control, ETC and WIS control are combined and the application of an event-triggered centralized controller is presented using simulations, as well as the achievable reduction in communication compared to regular periodic control. Furthermore, a cyber-physical lab setup is designed and built which makes it possible to test these techniques in the Delft Center for Systems and Control (DCSC) lab.

This thesis investigates the potential of a large pumping station in IJmuiden, the Nether-lands, for participating in Demand Response. Due to climate change, renewable energy is onthe rise. The intermittency of energy, together with its unpredictable supply, are a big hurdlefor the energy transition. Two methods are promising solutions to this problem; large scaleenergy storage and demand response. Since large scale energy storage is not yet economi-cally feasible, demand response has an important role to play in the early days of the energytransition.Using energy when it is generated requires a data-stream from the generation facilities onproduction, which is not (yet) widely available. The market price, however, is an indicationof the scarcity of energy, since it is based on the ratio between supply and demand. Besidesthat, there is a correlation between a low energy price and sustainable energy productionsince marginal costs of sustainable energy production are lower than fossil energy produc-tion. This makes using sustainable energy cheaper that fossil energy, and gives DemandResponse a business case.In this thesis, a Model Predictive Control is created that uses energy market data to minimizeenergy costs. Multiple energy markets are analyzed with respect for their suitability for thepumping station in IJmuiden to act on them. The day ahead market is called the APX inthe Netherlands, and this is where energy is bought and sold the day before consumption.The intraday market, also called the flexibility market, is where energy can be bought andsold up to 5 minutes before consumption. A strategy combining these two markets will beevaluated. This is done by using a predicted day ahead price, generated by a SARIMA model,to create a plan. This plan will then be followed, but deviations from the plan are allowedagainst intraday market price.Due to imperfections of the market (mismatch between supply and demand), imbalances areoccurring. These imbalances result in frequency deviations of the grid, and voltage devia-tions. Tenner, the Dutch TSO (Transmission system operator), is responsible for minimizingthese imbalances. In order to minimize the imbalance, TenneT gives a real-time indication ofthe imbalance on the grid, and positive contributions are rewarded while negative contribu-tions are punished. This is done through the use of the imbalance price; a price per volumeof imbalance caused or solved. The imbalance price is based on the aFRR market, wherebids can be done on possible activation. Since the imbalance market is a fast-acting market,it is not suitable for a large pumping station like IJmuiden. However, the aFRR market willbe analyzed in this thesis.The effects of expected future development, like sea level rise and energy market changes,will be analyzed and simulated as well. A higher sea level would result in more pumping, andless discharging under gravity. Which causes the the pump schedule to become less flexible.The results show that it is possible to apply demand response to a pumping station, and theintraday market makes it possible for the MPC to adjust its energy use during the day.The aFRR market analysis shows a lot of potential for the pumping station, possibly makingup for all energy costs made through the spot markets.The conclusion of this thesis is that Rijkswaterstaat can possibly save energy costs on pump-ing, based on the fixed energy price, provided by Rijkswaterstaat, they pay now. Based ona reference scenario where the MPC only minimizes energy use, and a fixed ENDEX energyprice, the proposed MPC makes about 10% less costs in the German market scenario. TheDutch market scenario does not show cost savings. In the Netherlands there is not muchcorrelation between low energy prices and renewable energy yet, since renewable energy isnot a big part of the energy mix in the Netherlands. This correlation is expected to becomemore present when the Dutch energy mix becomes more sustainable. This is expected toresult in lower CO2emission through the energy use of the pumping station. However, moreresearch is needed to confirm this.

Calibration of groundwater models can be done manually by comparing the measured and computed groundwater heads or automatically using algorithms which do the work for you. Modules for this purpose are included in or linked externally to traditional groundwater modelling software and used. One such module PEST (Parameter ESTimation) uses the Levenburg Marquardt algorithm which is a combination of Gauss Newton and Gradient Descent methods to find the minima of a function. However, PEST is capable of finding only a local minimum for the objective function. A possibility exists that there is a global minimum that better fits our function and could give even better results for the optimization problem in, in our case, parameter estimation in groundwater models. A performance assessment has been done with a Genetic algorithm on a groundwater modelling problem in this study.