Leakage is the main source of water loss in water distribution networks (WDNs). Therefore, leak detection and localization technology is a major concern for water utilities to save water and meet the ever-growing water demand. This study presents two methodologies for leak localization in District Metered Areas (DMAs): (1) a model-based method using the hydraulic model, flow and pressure measurements, as well as leak flow; (2) a data-driven method that relies on graph-based interpolation. The performance of the model-based method is proved to be negatively affected by model errors and limited sensors. To solve these two problems, on the one hand, flows and residuals between observed and model-simulated data in the non-leak situation are used to develop a residual model to calculate offset values for model output correction. On the other hand, graph-based interpolation is introduced to create ‘virtual’ sensor measurements in the presence of a limited number of sensors. The data-driven method proposed in this work uses graph-based interpolation to estimate the head signals at the nodes without sensors and subsequently create pressure maps. Leak localization is achieved by comparing pressure maps in the non-leak and leak situations. In this process, this methodology does not require a well-calibrated model and leak flow information. Both two methodologies are tested on fire hydrant leak tests in DMAs in the United Kingdom. Results obtained by using the model-based method illustrate the positive impact of model output correction on localization results and the performance of this method under different conditions such as different times of the leak and different sizes of leak flow. The data-driven method performs fairly well in DMAs with a higher spatial density of sensors. Furthermore, the results of both two methods are compared to demonstrate the suitability of the methods in different cases.

The cities of the Global South need to become more flood resilient in light of climate change and rapid urbanisation. In this thesis, a land-use modelling case study for the Mumbai Metropolitan Region is executed on the interaction of flood risk and land use, through which future flood resilience was assessed for several urban planning strategies. Concluded was that the urban environment will become less resilient in case no governmental intervention occurs, and urban planning can contribute to the resilience of the urban environment. Furthermore, the effectiveness of urban planning was largely dependent on the strictness with which these policies were implemented and regulated, but largely independent of the perceived climate future.

The Akosombo dam in Ghana's Lower Volta River Basin provides essential economic benefits through hydropower generation, flood protection, and irrigation opportunities. However, since its construction was completed in 1965, the livelihood of the riverine communities drastically changed. The seasonal peak and low flows in the river, which provided environmental services for many downstream households, are now replaced with a stable river flow throughout the year to favour hydropower production. A study on dam reoperation is not only of interest to look into the possibilities of restoring healthy ecosystems but also to look at how current water demands in the basin can be sustained. Climate change and increased water demands for irrigation are expected to strain the water resources in the Lower Volta. Reoperation is also complicated by the absence of water treaties with neighbouring countries. This can result in lower flows from upstream riparian states due to their increasing water needs. In this study, we aim to understand the vulnerabilities of the Akosombo system while finding robust policies that can cope with challenging climate and demographic conditions. This study highlights the challenges in balancing irrigation, environmental, and hydropower goals while also identifying system vulnerabilities and opportunities for robust operations to meet the Lower Volta River water demands under future challenging conditions. This is done by reevaluating previously found optimal release policies under uncertainty. From this study, it can be concluded that the uncertainties included in the analysis will alter system functioning. Not only will floods occur with the onset of climate change, but energy production and irrigation potential will also become lower if upstream states start to use more water. The most critical abstractions are those of Togo and Côte d'Ivoire. A benefit of this is that these abstractions also provide some protection against floods. It is therefore wise to create cooperation projects with these countries to safeguard the current benefits of the Akosombo and Kpong dam and create new benefits for all parties.

More and more people are living in cities. As these cities grow, the benefits of living in them are increasingly unequally distributed. One factor that can turn the tide of glooming urban inequality is equitable access to opportunities. Designing policies for equitable access requires taking into account several components that impact accessibility: (i) land-use, (ii) transportation, (iii) individual needs and opportunities, and (iv) temporal constraints. In this research, the interaction between a novel mode of public transport, microtransit, and the first three components of accessibility is investigated. The accessibility implications of microtransit in the case study area: the Southern suburbs of the Minneapolis-St. Paul metropolitan, are assessed. It is concluded that microtransit manages to reach vulnerable rider groups and interacts with the land-use environment by pairing residential areas with commercial centers. Microtransit also interacts with other modes of transit by adding both extra demand and lowering the demand for ride-hailing and personal car use predominantly. Microtransit rarely seems to replace public transit or walking alternatives. Overall, it is found that microtransit does significantly increase accessibility levels in the case study area and especially impacts groups with low car ownership. Lastly, the benefits of microtransit are more fairly distributed than those of traditional transit in the studied area. This research demonstrates the positive access equity impact of microtransit and suggests further research into choice-riders, the application of microtransit in urban (core) and rural environments, and the interplay between public transit and microtransit, as well as policy interventions for policy-makers and transit providers.

Over recent decades there have been advances in the research behind adaptive policy approaches. More recently, emerging qualitative approaches for managing deep uncertainty has drawn the attention of planners in the water industry, particularly in water utilities. Despite interest from both the water industry and the research community to see these novel approaches applied, there have been limited applications and no published guidance to support the operationalisation of these approaches in water utilities. This thesis seeks to bridge this gap by answering the question: “What are the factors influencing the adoption of adaptive policy approaches by water utilities?” To answer this question, a design science approach was used to understand current barriers and enablers to the adoption of such methods in water utilities, and to design a framework to support adoption of adaptive approaches. This work was conducted through a grounded theory analysis of interviews of relevant water utility practitioners in Australia and the Netherlands and members of the decision making under deep uncertainty and adaptive planning research community internationally. The outcome of this study is a maturity framework of barriers and enablers to the uptake of adaptive approaches in water utilities, designed to support utilities and researchers in evaluating their level of adoption and to identify strategies for increasing the implementation of adaptive approaches where appropriate.

Urbanization makes cities more vulnerable in the face of climate change risks. Understanding urban growth under climate change can help planners optimize land allocation and development strategies that are resilient to the impacts of climate change. However, the complexity and uncertainty of climate change hinder the urban growth projections and make it hard to form local climate adaptation plans. Therefore, this research aims to develop a methodology to incorporate climate uncertainties into land-use models to explore plausible futures.

The literature review results indicate the lack of a methodology to quantitatively link climate change effects with land-use models and to systematically explore the full parameter space of the climate uncertainties. Hence, our main research question becomes “How can an integrated land-use modelling methodology be developed to help systematically explore the impacts of climate uncertainties on urban growth?” This research question is answered by integrating Metronamica, a cellular-automata-based land-use modelling framework with Exploratory Modelling. The Metropolitan Region of Amsterdam (MRA) is selected as the case city to demonstrate the proposed methodology, and the research scope focuses on flooding, a typical and important climate impact. Specifically, we include the flooding probability maps into the “suitability” section under Metronamica, based on the principle that the higher the flooding risk of an area, the lower the suitability value. These flooding suitability values are deemed as uncertain in our research and they are not given fixed values but certain uncertainty ranges.

The flooding factors and their defined uncertainty ranges are added to a model established for the case city. Then this model is connected with the Exploratory Modelling Analysis (EMA) workbench and generates 2000 experiments by the random samplings and combinations of the uncertainties. In the result analysis step, we use clustering algorithms to select 34 representative maps, followed by the comparisons between them and the base map where no flooding factor is included. The 34 representative maps show some land-use change characteristics because of the introduction of the flooding suitability variables and the uncertainties. These characteristics of the projected land-use outcomes are extracted and the reasons behind these observations are explored. Finally, the land-use changes from 2015 to 2050 with the flooding risk considerations are summarized. Based on the summarized changes, we formulate some policy implications for urban planning under climate uncertainties.

This research presents a complete workflow of dealing with climate uncertainties in land-use analysis, including the selection of methods, the model conceptualization to include climate variables, the model implementation to set up a Metronamica model for the case city, and the connection between the Metronamica and the Exploratory modelling techniques. In the last step, we carry out result analysis and interpretation, finding out the land-use changes caused by the inclusion of flooding into urban dynamics. This integrated framework could be further applied and improved in the intersections of climate change and urban development, to provide insights into climate adaptation and urban climate resilience.

Land-use change models are often used to explore future land-use. Currently, most land-use change models run a small number of predetermined scenarios. To better address the multidimensional nature of uncertainty about the future, previous studies have argued for covering a wider range of the uncertainty space than is possible with existing scenario approaches. One way to approach this, is by using exploratory modelling. Instead of running the model on a small number of pre-defined scenarios, one uses sampling techniques over a plausible range of the uncertainty space to generate large-scale simulation experiments. With respect to land-use change models, this results in a large number of future land-use maps. To better make sense of the resulting maps, the next step necessitates the identification of plausible distinctive land-use patterns through clustering algorithms. Previous studies regarding quantification of land-use maps (dis)similarity focus only on comparing maps on a one-to-one basis or in small numbers for validation and calibration purposes. In this study the use of different map of similarity metrics on the resulting clusters of land-use patterns is systematically investigated. Specifically, the implications of using various cell-by-cell similarity metrics and landscape structure similarity metrics to cluster the resulting land-use maps are tested. The Land Use Scanner model is used for this purpose. It was found that the choice of (dis)similarity metric plays a significant role in the formed clusters of maps. If exploratory modelling is to be applied to land-use change models, it is thus important that great care is taken in the selection of the proper clustering algorithm.

It is vital for adequate management, and operation of water distribution systems (WDS) to have reliable short-term water demand forecasts. Conventional time-series models present limitations when dealing with non-linear changes in water demand. Thus, it is proposed to employ deep learning algorithms to offer a more reliable forecast. Three models are used, two 1-dimensional convolutional neural networks (1D-CNN), (a simple CNN, and a dilated causal CNN), and a recurrent neural network, particularly a long short-term memory (LSTM) based model. The performance of the models is tested on seven real-life water distribution systems in Italy with different uses and number of users. Also, a comparison with benchmark algorithms based on time-window techniques and pattern-based models is made. Additionally, the use of meteorological variables such as rainfall occurrence, temperature, and relative humidity is intended to test whether there is a positive effect on the forecast. Furthermore, a global model is built taking several years of data for training to test whether this bigger model increases generalization and improves accuracy in comparison to the individual cases. In addition, transfer learning is employed to predict individual cases and a WDS in the Netherlands. Lastly, a bigger global model is built and trained with 14 years of data to improve the performance of transfer learning on the Dutch WDS. To begin with, it was seen that 1D-CNNs outperformed the LSTM-based model, and the benchmark algorithms using data of the water demand, and a binary index indicating whether it is a weekday or a weekend day for six of the seven case studies. For the remaining case study, the results indicated that there is less than 1% in error between the best benchmark model and the proposed 1D-CNN algorithms. Moreover, the addition of meteorological variables showed to improve the calibration performance of the models but worsened the predictions on unseen data. It was observed that a simple 1D-CNN overfits when adding these extra variables due to its lack of regularization. Also, the global model showed to improve in accuracy compared to the individual models. The use of transfer learning (TL) did not indicate to improve the performance of one of the case studies, nonetheless, TL showed that by only using 75% of the data for training, the model offers a good generalization on the case with sudden changes in demands by the rapid increase of users due to seasonal touristic activities. For the Dutch WDS, TL performed similarly to the individual model, there errors ranged between 15% and 16% using different quantities of data for training. In addition, when having no data for training, the pre-trained model displayed showed lower than using 25% of data for training for the Italian cases. Lastly, the bigger global model performed in the same way as the smaller global model on the Dutch WDS. Also, when having no data for training, the model performed better.

Cities and regions around the world are experiencing an increase in extreme weatherrelated disruptions as a result of climate change. These disruptions are testing the integrity of energy systems including electricity grids. An emerging concept to assess the vulnerability of systems to disruptions is the concept of resilience. Resilience is used to assess the entire cycle of a disruptive event or specific phases such as the before, during, and after the event. In this thesis, a set of criteria to assess the resilience of the electricity grid is defined. Then the electricity grid sectors of Harris County, Texas are assessed by applying the developed and operationalized resilience criteria. This is done to understand the current resilience conditions of electricity grid sectors. The criteria are applied for assessment purposes by using the MultiAttribute Value Theory (MAVT) method of MultiCriteria Decision Analysis (MCDA). The results of this method are displayed using a series of maps. Results from the electricity grid sectors’ assessment provide an understanding of the resilience of a city’s electricity grid sectors, highlighting which low scoring sectors need more attention. In turn, having an overview of the resilience of electricity grid sectors can be used to plan or improve infrastructure including the integration of local renewable energy generation.

Due to climate change, extreme phenomena like droughts are going to be intensified. Even though droughts in agriculture have been studied, regarding urban environment their consequences are rather unexplored. Cities are susceptible to droughts and the estimation of their vulnerability is the first step for their protection. The objective of this thesis is to determine vulnerability of cities to groundwater and soil moisture droughts. Since drought is a complex phenomenon which is difficult to define, its analysis is not straightforward especially in cities. For that reason, an urban drought categorization framework is created. In the current research, two of the four drought categories were studied (groundwater and soil moisture droughts). A case study for the vulnerability calculation was chosen for Leiden, the Netherlands. Before determining its vulnerability, different techniques to identify drought exposure characteristics were investigated. More specifically, regarding deficit and duration, the following methods were used: (i) fixed, (ii) variable, (iii) moving window method, and (iv) median groundwater level as threshold for the case of groundwater droughts. Spatial analysis was performed to estimate the areal extent of droughts whereas frequency distribution analysis is assessed of the minima of monthly blocks. A similar approach was applied for soil moisture droughts. Soil moisture is modeled via the lumped Urban Water Balance Model. Vulnerability was estimated as the aggregation of exposure and sensitivity (physical and social). For both components of vulnerability, their indicators were normalized. Out of the four drought characteristics (deficit, duration, spatial extent, and frequency), only the two first were included as exposure indicators for the vulnerability estimation. The indicators' weights were computed using the Analytical Hierarchy Process (AHP). Vulnerability estimation was applied for both groundwater and soil moisture droughts separately. One of the main results is that variable threshold performance is higher than fixed threshold and moving window for deficit and duration estimation regarding groundwater droughts. However, a combination of fixed and variable threshold can provide a profound insight into drought exposure. That applies to soil moisture droughts too. To increase performance of deficit and duration identification, pooling can be applied. An inter-event time of around 10 days for the case of variable threshold and 30th percentile is suggested regarding groundwater droughts. The analysis on soil moisture droughts was not sufficient to draw conclusions regarding inter-event time. Vulnerability follows sensitivity patterns - rather than exposure pattern - for both groundwater and soil moisture droughts at both fine and coarse space resolution. Besides, out of all indicators used, those which contribute to vulnerability variation the most were determined for both studied types of droughts. Those indicators are: `land use' and `percentage of households belonging to the lowest 40% income nationwide' for groundwater droughts whereas for soil moisture ones, it is `green areas'. Another result is that the differences in vulnerability vary marginally using different techniques to identify drought events for both studied types of droughts. Therefore, even applying a sophisticated technique to identify drought events, will not lead to significant improvement regarding vulnerability estimation. All aforementioned conclusions regarding vulnerability are highly uncertain since drought experts assigned different indicators' weights; the convergence of opinions was low. Based on the proposed methodology, water managers would be able to determine vulnerability of cities to droughts and policy makers would be able to protect the regions which are highly vulnerable. Consequently, the adverse impacts of droughts on cities could be mitigated, reducing residents' hardship.

The use of scenario planning has a long history in decision-making and public policy (Bryant and Lempert, 2010). Traditional scenario planning, as, for example, used by the Shell Scenarios group, provides tools to communicate and characterize uncertainty,allowing decision-makers to anticipate the future and create more robust strategies (Bradfield et al., 2005).

However, the classical qualitative approach, where scenario narratives are developed, has some severe limitations. While this approach provides results which are readily communicated to decision-makers, it often overlooks truly unexpected (but plausible) scenarios (Kwakkel and Cunningham, 2016). Besides, they are not readily implemented for problems where the structure is also disputed (Bryant and Lempert, 2010).

Scenario discovery, a quantitative, brute-force approach to scenario development,developed by Bryant et al. (2010) aims to address these limitations. This approach has been successfully applied to numerous grand challenges, among others climate change (Kwadijk et al., 2010), sustainable water management (Haasnoot et al., 2011), and global natural resource management (Kwakkel et al., 2013).

The original approach to quantitative scenario discovery relies on three consecutive steps: sampling, labeling and searching for subspaces leading to the regions of interest.The sample is typically collected using Latin Hypercube Sampling (Kwakkel, 2017). For uniform, independent sampling, the chance that a random sample falls within the region of interest decreases quadratically with increasing size of the bounds (Vrugt, 2016). Therefore, if the prior distribution cannot be estimated, or the problem at hand demands a wide range of possible values, and the problem has a high number of dimensions, these sampling techniques require an unreasonably high number of samples to adequately delineate the region(s) of interest.

Therefore, this thesis proposes using dependent sampling, which concentrates the sampling on the regions of interest, rather than attempting to represent the entire input space. After convergence, the resulting sample approximates the true posterior distribution. By performing a Kolmogorov-Smirnov test for uniformity, the uncertain factors leading to the behavior of interest can be derived. The relevant ranges of parameter values can be recognized and communicated to decision-makers.

Urban areas are seeing influx of population and therefore are experiencing increasing stress on the systems currently in place. In traffic networks, a larger population means that their are inevitably a short term increase in mobility demands. To accommodate this, governments and and the private sector are proposing new solutions for mobility. One such focus is the introduction of new transport modes. A common classification for these modes is Mobility-as-a-Service (MaaS), which includes modes such as carsharing, shared last-mile transportation like bicycles, and ridesharing. MaaS also overlaps with other new modes such as autonomous and electric vehicles. Modeling these types of modes and their subsequent interactions with each other and traditional modes requires more complex modeling techniques than have been traditionally used in transport modeling.

The purpose of this thesis is to propose a method for using agent-based modeling and simulation (ABMS) to assess traffic network resilience, as ABMS has the dynamic qualities that match well with the time variant nature resilience.. This gap is especially prevalent when considering novel modes of transport, such as ridesharing. Data from the metropolitan region of Rotterdam and The Hague (Metropoolregio Rotterdam Den Haag or MRDH) is used for a case study on the corridor between Rotterdam and The Hague. The goal is to prove feasibility of a method for assessing resilience in traffic networks using ABMS.

A resilience framework for urban mobility is proposed that uses six categories to characterize resilience: (1) reflective, redundant, flexible, resourceful, inclusive, and integrated. Using this framework as a guide, three metrics are proposed for measuring resilience using agent-based simulation: origin-destination (OD) travel time, link travel time, and link volume. These metrics are used to compare scenarios that either include a disturbance in the network or do not, which in this case occurs for a 30 minute period on the A4 roadway between Rotterdam and The Hague. While the OD travel time metric is limited in its usability, the link travel time metric makes apparent the the recovery time to achieve normal operating conditions after a disturbance. In the presented case study, scenarios that included ridesharing had worse recovery time then the car only scenario, as well as a higher maximum travel time across the disturbed link. The link volume metric contextualizes these results, showing that while the overall volume throughout the simulation is lower for the ridesharing cases, the volume during the disturbance across the disrupted portion of the A4 roadway is higher. The higher volume shows why the travel time is higher with the presence of ridesharing in the disturbance scenario. These results are subject to the limitations of the model, though, which include dynamic routing that may not avoid the disturbed portion of the network when the disturbance occurs.