One of the key challenges of the twenty-first century is the decarbonisation of energy systems amid a rapidly growing demand for energy. One of the key processes to achieve this objective is the electrification of these energy systems, coupled with the decarbonisation of the power system. This ambition has led to increasing investments in variable renewable energy deployment, mostly in the form of solar and wind energy. While their capital costs have plummeted in recent years, these technologies are inherently subject to a high degree of intermittency due to their dependence on meteorological conditions. Therefore, energy storage is widely recognised as an indispensable enabler of the energy transition. The interdependencies between generators, storage facilities and other network components make long-term energy planning a highly complex task. Energy system optimisation models are commonly employed to tackle this complexity. However, these are typically used in a deterministic manner, hereby failing to represent the significant uncertainty associated with a range of important policy, technical and economic exogenous factors. Also, this approach fails to reveal any non-linear dynamics in these models, such as regime shifts and tipping points. Responding to the growing emphasis in the literature on the need for global sensitivity analysis in long-term energy planning, this paper proposes a Bayesian methodology for the exploration and probabilistic characterisation of scenarios based on the output of an investment and dispatch co-optimisation model.

To illustrate the applicability of the proposed methodology in long-term energy planning, the exploration of an optimal spatial deployment strategy of closed-loop pumped hydro energy storage in Kenya by 2050 is used as a case study. For this purpose, the paper introduces PyPSA-KE, an investment and dispatch co-optimisation model for Kenya based on the PyPSA-Earth framework, calibrated using context-specific data. While the absolute capacities to deploy are highly dependent on the input parameters, the model indicates that PHS offers great potential to contribute to Kenya's energy transition and growth, supported by batteries for short-term storage.

The Gaussian copula-based Bayesian Network provides a computationally efficient and accurate surrogate for scenario inference in a context of epistemic uncertainty. It also captures the intrinsic structural sensitivity of the IDOM it is based on. This enables the derivation of probabilistic risk metrics that are essential for robust, risk-aware investment optimisation. The proposed Bayesian methodology is readily transferable to other ESOM-based problems. ... Read more

The growing energy demand, along with more greenhouse gas emissions, highlights the need for renewable alternatives to fossil fuels. One promising option for space heating is aquathermal energy from surface water (TEO), especially in areas with a significant amount of surface water. However, TEO systems generally require substantial spatial integration, which poses challenges in dense urban areas such as the historic city centre of Amsterdam. In particular, limited (sub)surface space and existing infrastructure complicate implementation. Quay walls may offer a potential solution for the spatial integration of TEO, but the conditions under which this would be technically, spatially, and economically viable remain unclear.

This research investigates the feasibility of utilising TEO in historic city centres, with a case study in Amsterdam. It focuses on the potential for integration into quay walls. A linked modelling approach was used to assess the spatial requirements, CO₂ reduction, and investment costs under various heating demand scenarios and system base loads. Heating demand was varied based on different retrofitting levels, and first-order TEO system designs were developed accordingly.

The results of the study indicate that a higher base load to be covered by the renewable source (TEO), combined with less retrofitted buildings results in significantly higher spatial requirements. This could potentially complicate quay wall integration. However in most scenarios, TEO systems could be integrated into the quay walls.

The CO2 reduction was assessed for all scenarios. For increased base loads covered by TEO systems and high retrofitting levels of the buildings, the CO2 reduction was the highest. This would outperform the all-electric scenario.

Financially, the lower retrofitting levels would result in slightly lower investment costs per dwelling. The effect of a higher base load delivered by TEO systems was found to be relatively minor. Although the TEO systems require a significantly higher financial investment, the national costs could be lower. This should be studied further in future research. ... Read more

Climate change is making flood prediction and management increasingly important. In 2020, South Korea suffered severe flood damage in areas downstream of multipurpose reservoirs, primarily due to unexpected heavy rainfalls occurring in quick succession. All multipurpose reservoirs in Korea are managed by K-water, which follows an operating procedure that analyzes scenarios using real-time observation data and weather forecasts. Although there are existing operating rules, these rules are often based on common-sense practices that can be bypassed in critical situations, allowing decision-makers to adjust reservoir operations flexibly based on Flood Water Level (FWL), Flood season Restricted Water Level (FRWL), and Normal High Water Level (NHWL). This method works well when flood predictions are accurate or when experienced decision-makers are present. However, accurate rainfall prediction remains a challenge. Thus, systematic and model-based approaches are necessary to help determine water release amounts, especially when rainfall forecasts have high uncertainty.

Real-time reservoir flood control has traditionally relied on simulation models, but time constraints often prevent the review of extensive scenarios, potentially missing optimal and explicitly risk-aware options. While optimization approaches offer alternatives, practical implementation faces several challenges. First, operational objectives are rarely specified clearly in legal/operational guidelines, and when expressed as nonlinear formulas, the problem becomes computationally intractable. Second, operators' preferences regarding the relative importance of objectives change with flood conditions. Multi-objective optimization approaches that generate Pareto fronts could help visualize the trade-offs between competing objectives, but generating these Pareto sets at each time step requires optimizing the parameters that capture these dynamic preferences and system constraints. To address these challenges, a Model Predictive Control (MPC) framework incorporating practical objectives often overlooked in theoretical studies, such as minimizing the magnitude and frequency of changes in outflow schedules, is presented. We integrate a model-based learning concept for dynamic optimization of weights and parameters, which converts the originally intractable multi-objective nonlinear optimization problem into parameterized linear MPC problems.

However, system nonlinearity combined with these dynamic preferences results in optimization problems that are still computationally expensive and impractical during rapidly evolving flood events. Due to the computational challenges of real-time operation of Parameterized Dynamic Model Predictive Control (PD-MPC), we propose two data-driven approaches: (1) an explicit MPC using deep neural networks to directly determine optimal outflow schedules, and (2) a switched MPC that combines data-driven models that produce optimal weights based on hydrological conditions with linear MPC. Both methods leverage offline learning from the PD-MPC framework to dramatically reduce computation time from approximately 10 minutes to less than one second, enabling prompt decision-making during rapidly evolving flood events. The explicit MPC demonstrates reliable performance for conditions similar to its training data, while the switched MPC maintains robustness across diverse scenarios due to its receding time horizon optimization process.

Although the above approaches work well as deterministic control tools, more advanced stochastic optimization-based MPC can offer ways to explicitly handle uncertainty. Uncertainty management with stochastic control requires the generation of a sufficiently large number of representative scenarios for inflows. However, traditionally used scenario generation models struggle to capture temporal dependencies or generate scenarios without requiring explicit probability distributions. From this perspective, a Bayesian Neural Network (BNN) model can successfully capture temporal dependencies in inflow time series with high accuracy for short prediction horizons, without requiring explicit probability distributions of variables to be known in advance. Although we can generate a large number of required scenarios with a BNN to assure we sufficiently represent the uncertainty in inflow, it would make stochastic optimization difficult as computational time scales nonlinearly with the number of scenarios. We therefore need scenario reduction approaches that preserve representativeness while reducing the number of scenarios significantly. However, existing scenario reduction approaches themselves lack appropriate distance measures optimally suited for hydrological applications. Therefore, this thesis investigates four distance measures with corresponding reduction algorithms, which are widely used in references: Manhattan with the K-median, Euclidean with the K-mean, Wasserstein with a one-step forward selection, and energy distances with a one-step forward selection algorithm. While the energy distance best preserves statistical characteristics of the original scenario set, the Euclidean distances have significantly lower computational costs. Additionally, the Manhattan and Euclidean distances retain extreme scenarios, which is crucial for flood control, in terms of a tailored performance measure that represents the size of the envelope of a scenario set (using l1-norm), ensuring the reduced sets retain the range of maximum and minimum flows of the original scenarios.

Finally, this thesis combines these advanced scenario reduction methods to define a risk-constrained MPC problem using Conditional Value-at-Risk (CVaR) to reflect changes in operator risk-averseness by changing a confidence level. Traditional chance constraints in stochastic MPC only consider exceedance probability, while CVaR, which quantifies the expectation of exceedance, remains underutilized in reservoir flood control despite its proven value in risk minimization. By incorporating CVaR as soft constraints, operators can specify risk thresholds that reflect practical considerations rather than relying solely on physical limits that are rarely activated during typical flood events. A stochastic MPC with CVaR outperforms a deterministic counterpart in terms of reflecting the operator's risk-averseness and robustness to inflow uncertainty. Moreover, scenario reduction based on the Euclidean distance is more effective than energy distance-based reduction for real-time flood control applications, considering both closed-loop performance and computational efficiency.
Each proposed framework is validated through numerical experiments for the Geum River and Daecheong Reservoir in South Korea. Intractability in a nonlinear multi-objective optimization problem due to dynamic preferences can be effectively addressed by a PD-MPC framework and its explicit and switched extension based on data-driven models. In addition, stochastic MPC with CVaR incorporating scenario generation and reduction proves beneficial for managing hydrological uncertainty and operators' risk-averseness. We believe the proposed approaches offer sufficient flexibility to accommodate region-specific constraints and objectives, suggesting their potential utility for addressing water resource management challenges in diverse geographical contexts. We anticipate this research will contribute to expanding the application possibilities of real-time optimal reservoir flood control and lay the foundation for practical implementation. The presented methodology is not intended to replace manual operation but rather to provide tools for reducing operator stress in critical situations and ultimately enhancing decision-making capabilities. ... Read more

Ghana runs the risk of becoming water insecure. As population growth increases, so does water demand, and climate change increases uncertainties in supply. There are furthermore many inequalities at play that significantly affect how Ghanaians access water. It has been established that good water governance can alleviate both water security and water equity issues. However, many solutions of good water governance revolve around centralized governance, while the Ghana government has reformed to a decentralized system in the 1990s. Local government is now responsible for water governance, but local government often lacks the capacity to manage access to water effectively. A good governance approach that could circumvent these issues is participatory governance, an approach that focusses on the inclusion of community members in decision-making processes.

Little research has been performed on the links between participatory governance and access to water. To fill this gap, this thesis has selected two case-studies. As peri-urban areas are often overlooked in research into access to water, two peri-urban areas that have undergone the implementation of a participatory governance approach were selected. The first is the peri-urban town of Dodowa, where a Transition Management approach was implemented focused on transitions in sustainable groundwater management and the inclusion of community members. The second is the peri-urban municipality of Ejisu-Juaben where the Community Ownership and Management approach was implemented.

The objective of this research is thus to uncover how participatory water governance influences access to water in the cases of Dodowa and Ejisu-Juaben, in Ghana. To achieve this, levels of access are measured along five measures: quality, accessibility, availability, affordability, and equity of access. Next to that, governance approaches are analysed and levels of participation of community members are determined. Two communities were selected in each peri-urban area where community representatives were interviewed, and interview surveys were performed with community members. The results from these interviews and surveys were supplemented with literature research.

Most significantly, the results show the complexity of the relation between governance and access to water. Access to water in and of itself is a complicated topic and so is governance. Different complex governance modes, successful and less successful participatory approaches, and complex configurations of access to water came to the forefront. The results furthermore showed that not only governance but also the presence of natural resources can be a determinant for good access to water. Next to that, for these cases intervention of government or non-government agencies played a large role in determining the success of the participatory approaches. One of the underlying reasons for this was the lack of financial resources available to communities and community representatives to provide access to water for their community.

Considering the complexity of the relation between access to water and water governance, this research recommends for future research to include more cases for comparison as the results from cases in this research on their own cannot be extended to other cases. It is also recommended to research specific context factors that may influence the link between governance and access, and to further research why governance approaches fail. Lastly it is recommended to do more extensive research into the development of a ladder of access that takes the use of multiple sources into account. ... Read more

This report details the development of a Flood Early Warning System (FEWS) for the Tana Basin in Kenya, executed by a multidisciplinary team from the Delft University of Technology. Recognizing the Tana Basin’s vulnerability to flood risks, exacerbated by climatic variability, limited funds, and limited available data, the project proposes a model that combines computationally efficient hydrological and hydrodynamic modelling with robust stakeholder collaboration. The study area comprises the entire Tana Basin, with a specific focus on the flood-prone area near Garissa used for validation. The FEWS developed incorporates local and scientifi-cally derived knowledge to forecast floods, aiming to aid the transition from a technologically intermediate to a technologically advanced FEWS. Through an iterative process of model selection, validation, and stakeholder feedback, the system attempts to integrate the GR4J hydrological model in SuperflexPy and combines this with the Super Fast INundation of CoastS (SFINCS) model. Data sources include global remote sensing datasets like FABDEM & CHIRPS. Furthermore, it uses the water level gauge data provided by the Water Resource Authority of Kenya, as well as TAHMO weather station data.

The report concludes by reflecting on the modelling techniques for both the hydrological and hydrodynamic models and provides recommendations for the further development of a FEWS in the Tana Basin in Kenya. The implementation of the hydrological model was not able to propagate external flows through the network, making it poorly suited for use in the Tana Basin. The hydrodynamic model works decently well in flood conditions but overpredicts flooding during regular flow conditions. Recommendations on stakeholder engagements and data-sharing practices to foster a resilient flood management system in the Tana Basin include more comprehensive Memoranda of Understanding (MoU) and stricter adherence to the Disaster Risk Management Framework of the United Nations.

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Due to quick population growth and urbanisation in Kumasi, Ghana, groundwater depletion is accelerating, and land cover changes reduce the rate of natural infiltration. A promising measure to combat rapid aquifer depletion is implementing Managed Aquifer Recharge (MAR), by rooftop rainwater harvesting and pumping this into wells. The objective of this paper is to delineate the (qualitative) impact of precipitation through Managed Aquifer Recharge on the groundwater level, by analyzing groundwater level changes of sites with and without MAR around Kumasi. To achieve this, multiple groundwater level and flow models have been constructed over different time periods with varying temporal resolutions to show the short- and long-term effect of precipitation on the groundwater level on sites with and without MAR. A rapid increase of groundwater level is observed during rain events, followed by a decelerating curve of infiltration towards areas with lower elevations. This dissipation is much faster in areas with high hydraulic conductivity (hours) than with low hydraulic conductivity (weeks). The groundwater level is recharged by MAR less in the dry season than in the wet seasons. MAR has a highly positive influence on the groundwater recharge. It will be most crucial to implement MAR in high elevations, where the overburden has low hydraulic conductivity, as natural recharge is limited here. The lack of soil and hydraulic head data limited the reliability of the models. Therefore, it is recommended to extend the database in these and additional research areas, aiming to differentiate the effect of MAR and the natural infiltration on the hydraulic head level. ... Read more

The Eastern Nile Basin is a highly populated region dealing with inadequate energy systems and water scarcity issues. Moreover, its population is expected to increase in the following decades, leading to even higher electricity and water demands.
In order to sustainably meet this demands and guarantee access to electricity and water for all, new technologies and careful energy planning can play an important role.
In this context, floating solar power is a relatively new technology with promising advantages, such as the synergies between solar and hydropower resources, the exploitation of already existing infrastructures, and the reduction of evaporation rates and land use. These become even more relevant if seen in the context of the Eastern Nile Basin countries, where the need for efficient energy sources and solutions to the water scarcity issues are vital.

In this work, the role of floating solar power in the sustainable fulfillment of the increasing energy demand of the region is explored. The novelty of this study consist in the introduction of floating solar power in a long term regional energy system cost-optimization model (OSeMOSYS-TEMBA) at a single plant resolution. To do so, the single hydropower plants are also explicitly modelled, allowing both the spatial disaggregation of floating solar power plants and the connectivity between the countries via the Nile river. The regional approach is further enhanced by the presence of electricity trade links between countries, which connect the energy systems of the single countries directly.
Finally, the role of floating solar power on the energy system's footprints is evaluated in terms of CO2 emissions, land use and water savings. To this extent, a new methodology for land use accounting and pricing is proposed, and findings from previous studies are brought together to assess the evaporation reduction rates caused by the floating solar power plants.
This extended modelling framework is then used to analyse different scenarios, exploring hydrological regimes under different climate change projections and policy decisions such as the introduction of taxes for carbon emissions and land use change.

The results show that floating solar photovoltaics are a cost-optimal technology since early stages in the modelling horizon, and their full assumed potential is developed under every scenario. Their role in satisfying the energy demand of the whole region reaches 3\% of the generation mix in the reference scenario, but it increases to 4.3\% with the introduction of taxes on carbon emissions and land use. Moreover, the introduction of such policies cause an anticipation of floating solar power's capacity expansion. On the other hand, the tested climate change projections do not affect the results relevantly.
The sensitivity analyses, however, prove that the obtained results are very sensitive to the assumptions behind capacity constraints and costs of these technologies, which need more dedicated research.
As far as the energy system's footprints are concerned, the results show that the implementation of floating solar power can help reduce the total emissions and land use slightly, and cause evaporation reduction rates up to 376 million m\textsuperscript{3}/y (approximately 2\% of the total evaporation from hydropower reservoirs).
The optimal locations to invest in this technology are identified to be the largest hydropower plants in the system (Lake Nasser, the Grand Ethiopian Renaissance Dam and Merowe reservoir), but the reason of this choice relies in the very large size of these plants, which emerge for highest
FPV capacity deployment and water evaporation savings at the large scales considered.
Future research is still needed to reduce the uncertainty behind the key parameters (costs, capacity constraints), improve the representation of hydropower production, improve the evaporation assessments and investigate the effects of implementing floating solar power at smaller spatial and temporal scales. ... Read more

This thesis explores risk-aware operational decision-making methods to support the integration of Renewable Energy Sources (RES) into the energy system by enhancing energy flexibility under operational uncertainty. Amidst the urgent global shift towards RES to combat climate change, this work identifies and addresses the challenges posed by the intermittent and uncertain nature of renewable energies, such as wind and solar power, to grid stability and energy reliability.... ... Read more

This PhD thesis advances resource recovery from wastewater by focusing on two key technologies: Purple Phototrophic Bacteria (PPB) raceway reactors and anaerobic digesters (ADs). To address challenges such as process variability, monitoring limitations, and operational inefficiencies, this research employs three complementary approaches: mechanistic modeling, hybrid system identification, and adaptive predictive control. Mechanistic models offer detailed insights into microbial interactions and process dynamics; hybrid system identification develops low-order models for practical data reconciliation and forecasting; and adaptive model predictive control dynamically optimizes operations to enhance performance. Key contributions include a novel mechanistic model for PPB cultivation in raceway reactors, a temperature-dependent extension for the anaerobic digestion model no.1, a hybrid system identification method to approximate complex mechanistic models, and an adaptive hierarchical process-oriented MPC framework for PPB reactors and ADs to manage variable operations and unknown disturbances. These approaches advance wastewater resource recovery in their intended perspectives, providing efficient solutions for both case studies. ... Read more

The Bui Dam, the second-largest hydropower dam in Ghana, plays a significant role in the sustainable energy mix of the country. It is managed by the Bui Power Authority (BPA) and has a capacity of hydro-clean generation of 404MW, contributing to 17% of the country’s total electricity generation. However, decision-making at the dam lacks the use of predictive models and meteorological measurements. This can lead, in the case of perceived flooding risks and high dam water levels, to valuable water being spilled and endangering the downstream areas. Balancing the tradeoff between energy production and safety can be effectively achieved by implementing predictive models that anticipate peak flows in advance.

Since its commissioning in 2013, the Bui Dam has experienced two instances of emergency spillage, resulting in significant financial losses, property destruction, and displacement of downstream communities. Currently, the reservoir management decision-making process uses two discharge stations upstream, with one of them yielding some unreliable outcomes for high flows. Therefore, it is crucial to prioritize the analysis and updating of rating curves to ensure accurate forecasting.

This research aims to address these limitations by recalibrating the rating curve using the reservoir balance in a conservative manner, i.e. leaning on the safe side to avoid overestimation. Additionally, a conceptual, semi-distributed model was developed simulating high flows, specifically focusing on the years 2019 and 2022 when spillage events occurred. Five different hydrological conceptual models, with three different structures: single, serial, and parallel structures, were tested. The serial model yielded the best results. Then the Black Volta Basin was divided into five sub-catchments, and each sub-catchment was lumped. In the absence of discharge data for the upstream sub-catchments, remote sensing data from GRACE and satellite altimetry (3 virtual stations with data from 2016 to 2022) were used to impose restrictions on the feasible model parameter sets, thereby improving accuracy.

The final model output was calibrated using discharge data obtained from the recalibrated rating curve, along with satellite altimetry data. In the calibrated benchmark case, the model effectively reproduced daily river flows, demonstrating an optimum Nash-Sutcliffe efficiency (NSE) of 0.85 for the period of 2018 to 2022. Subsequently, the model underwent extensive testing under various conditions, including an independent time period without recalibration, different precipitation input sources, transitioning from actual evapotranspiration (AET) to potential evapotranspiration (PET) input, and a change in the testing discharge location. Throughout these testing phases, the model consistently produced favorable results, with NSE values ranging from 0.74 to 0.86.

Furthermore, the model was tested for its progressive predictive capability in simulating the unexpected peak inflows that led to the spillage event in 2019, utilizing iv only precipitation data from the TAHMO precipitation stations, which are openly accessible with near-live timing. The model successfully predicted the occurrence of the large peak inflow, on October 22nd, which ultimately caused the spillage. The model anticipated the occurrence of the ”unexpected” second peak, to some extent, as early as October 12th, providing an 11-day predicting window.

Overall, this research enhances the understanding of the Bui Dam system by implementing a recalibrated rating curve and developing a conceptual model that incorporates remote sensing data. The results demonstrate the model’s capability to simulate past events accurately and predict future inflow patterns, thereby providing valuable insights for effective dam management and spillage prevention.

One significant discovery regarding the character of the Black Volta River at the Bui Dam is the limitation of the prediction period to a strict maximum of two weeks. While the model proves effective within this time-frame, it is advisable for future research to consider incorporating weather predictions to extend this window further. Doing so would enhance the model’s forecasting capabilities and provide even more valuable information for dam operators and decision-makers. ... Read more

The integrated management of river catchments is a challenge to be addressed by many water authorities, where some of them even today do not yet incorporate the aquifers’ status in decision-making. This situation has led to the depletion of many aquifers, deeply affecting the drinking water supply, agriculture, and industries. This challenge is combined with the difficulty of decision-making under highly uncertain long-term weather forecasts.
This thesis research proposes a new water management methodology for the Elqui River basin in Chile by using an optimization model aligned with the water authorities’ main objectives and additionally incorporating the aquifer criteria. The optimization model is validated by comparing the results obtained over the 2010–2020 period with the water management practices employed during the same period.
Furthermore, an analysis of the performance of the model using different moving window lengths is executed by the implementation of a Receding Horizon Control (RHC) methodology, evaluating how well the solution is by comparing it with the historical simulation over the same period. The latter is done by looking at the performance of the key optimization goals and using a RMSE and R2 analysis.
Finally, a weather generator was used to randomly generate weather data, based on the 30-year period between 1990 and 2020. The random weather conditions are incorporated in a hydrological model to translate weather data into water volume into the reservoir. Making use of the optimization model, the RHC methodology, and the weather generator, the proposed methodology is tested, enabling the simulation of the decision-making processes. The results are again compared with the water management practices employed over the simulation period.
The research concludes that the proposed methodology brings significant benefits to the aquifers’ status, with neglectable impact on the Desmarque values. Receding horizon (RH) length plays a crucial role, with a balance between achieving optimal results and avoiding computational delays, recommending a RH length of 360 days for best results. The stochastic weather generator effectively replaces unpredictable forecast data, yielding comparable results to real future weather conditions, with temperature and accumulated snowpack playing important roles.
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Throughout sub-Saharan Africa, agricultural activities in urban and peri-urban areas are threatened by urban sprawl. These activities are important not just for the livelihoods of those who take part in it, but also for the food security of the city. Historically, in Kumasi 40% of the crops in the city are sourced from urban and peri-urban agriculture. However, Kumasi is also a city that is growing rapidly both in population and in area: since the 1980’s population has more than quadrupled and the area has increased more than tenfold. Because of this, there is a need to find ways to maintain urban and peri-urban agriculture under the threat of urban sprawl and the climate change in the future and also work well in the Kumasi context.
We used the agro-hydrological model AquaCrop informed by 4.5 years of local weather data, soil data and crop data to model the effectiveness of adaptations in maintaining food security for three neighbourhoods in Kumasi. Local farmer management practices that were determined by a survey of 150 Kumasi farmers made a distinction in management practices between adaptations and crops. This model gave yields and irrigation water uses for five crop groups that are commonly cited as crucial for food security. Combined with population growth predictions and land use and land cover analysis this allowed us to make a statement about how well adaptations can meet current and future demand of crops and how much space is needed to meet demand. This model was supplemented with a survey of vendors and farmers to investigate barriers against- and preferences for adaptations.
Backyard gardening and sack gardening turned out to be the best options to maintain peri-urban agriculture. These adaptations use the least space, for the highest yields. From the survey of farmers and vendors, no strong objections were found against the implementation of these adaptations. Over 80% of surveyed farmers and vendors felt that backyard gardening was a good adaptation to maintain peri-urban agriculture in Kumasi. Backyard gardening and sack gardening are optimally suited for growing vegetables and legumes, which are the easily perishable crops and thus benefit from a short supply line, which many vendors cite as solutions for spoilage.
It is possible to ensure self-sufficiency for these crops with 5-9% of total land in the Feyiase neighbourhood, 11-20% in Ejisu and with 14-22% in the Kwadaso neighbourhood. It is therefore recommended to target at least a majority self sufficiency, by reserving a >50% fraction of this land for backyards and sack gardening spaces. At the same time, any available marginal lands should be allocated for the growth of cereals and tubers, to allow for the production of these subsistence crops, until the production of the remaining demand is fully met by rural import. With these adaptations and recommendations, it is possible for peri-urban agriculture in Kumasi to maintain its important role throughout the coming decades.
In order to determine if there are any deviations from these conclusions, future research should focus on including empirical crop data tailored to AquaCrop or use a model that is better suited to represent the chaotic nature of (peri-) urban agriculture. While the model results do not differ significantly from the crop yields as found in literature, a model informed by local crop data can be an even better representation of the situation in Kumasi. Furthermore, a study into the long-term effects of adaptation on nutrition can reinforce our conclusions on food security.
Finally, there is an opportunity to develop more high-tech agricultural methods like greenhouses and aquaponics. There is also an opportunity to incorporate wastewater reuse schemes, following examples from other countries, such a urine reuse, or low-tech treatment with sludge harvest. For this, it is recommended to set up educational programs and pilots. ... Read more

This report presents the findings of a comprehensive analysis of the literature on impact evaluations of foreign aid funded interventions in the water sector of rural Sub-Saharan Africa (SSA). The study was conducted using a dataset extracted from the International Initiative for Impact Evaluation (3ie) Development Evidence Portal (DEP), which is one of the largest repositories for evidence-based research in Low and Middle Income Countries (LMICs). The extracted publications underwent systematic screening based on predetermined criteria. Studies that pertained to interventions including infrastructure development and water resource management were included, while papers focusing solely on education, behavioral change, awareness campaigns, or the exclusive provision of soap were excluded. After classification, it was found that interventions related to Water, Sanitation and Hygiene (WASH) dominated the dataset, so studies related to agricultural productivity were excluded. The final dataset consisted of 30 papers. These publications were classified and analyzed using bibliographic and network analyses to explore the structure of the research community. The content of the papers was then analyzed and situated within external literature to validate conclusions, contextualize findings, and identify gaps and areas for further research.

The dataset consisted solely of impact evaluations of randomized controlled trials (RCTs) related to WASH interventions. The trials were spread across nine countries, with the majority conducted in Kenya, and their duration varied from eight weeks to 29 months. Most interventions focused on low-cost household water treatment technologies, with chlorination being the most common. Regarding outcomes, most trials investigated health-related outcomes for children under five years, with diarrhea prevalence being the most frequently studied outcome. The results showed significant heterogeneity among trial findings, especially for diarrhea prevalence, suggesting that intervention effectiveness may depend on contextual factors that are not yet fully understood.

The research community within the dataset showed disparities in the distribution of trials across SSA, with most studies conducted by organizations from the Global North and published in journals targeting a Global North audience. The studies used similar approaches and focused on highly recurrent outcomes, suggesting a potential lack of diversity in the knowledge base. The dataset emphasized low-cost interventions suitable for large-scale implementation and showed a preference for RCTs due to their ability to establish causal relationships, minimize bias, and produce generalizable findings. However, the RCTs included design limitations that compromised these strengths, including limited blinding and reliance on subjective outcome indicators. Furthermore, reporting on field challenges and logistical information was insufficient, limiting generalizability.

The study highlights the need for greater diversity in approaches and knowledge, stronger local representation, and increased innovation in research on WASH interventions in SSA. To address these issues, several recommendations are made. First, greater representation of organizations from the Global South and increased collaboration with the Global North are needed, as such partnerships were associated with high-impact research. Second, standardized reporting guidelines and data-sharing protocols should be developed to improve transparency and reproducibility of impact evaluations. Third, greater diversity in targeted outcomes and intervention approaches is recommended, along with overall improvements in research quality.

Overall, this study provides insights into the state of impact evaluations in LMICs, highlighting both progress and remaining challenges. By identifying gaps and offering recommendations, it contributes to improving the quality and impact of future research in development contexts. ... Read more

In many places in Uganda, people do not have a connection to the drinking water supply system and there is a lack of treated water supply, meaning that people only have access to water a certain part of the day. As a result many people rely on springs, handpumps, rivers or lakes, of which the quality cannot be monitored or controlled.

During this multi-disciplinary project, we worked together with the National Water & Sewerage Corporation (NWSC) and the Ministry of Water and Environment (MWE) to research the possibilities of extending the water supply system of two project areas, Bugiri District and Hoima City. The current water supply in both areas use groundwater as a source and the possibilities for the extension also consider using surface water besides groundwater.

The different alternatives for the extension of the water supply in Hoima City and Bugiri District are evaluated using a multi-criteria analysis (MCA), consisting of a financial analysis, a performance analysis and a risk analysis. By evaluating the different options using an MCA, the decision-making process can become less complicated.

The MCA-tool that is set up in this research can be used by engineers to study different areas in Uganda and make it easier to compare different options for the extension of a drinking water supply system in an early design stage. The tool is for the two project areas as examples, after which it is also tested during a case study with engineers from both NWSC and MWE. Useful feedback came out of this session which will be used to finalize the tool and elaborate on it.

To design the different alternatives for the project areas and to get insight into the drinking water supply of Uganda, Hoima and Bugiri are visited at the beginning of the project.

For both project areas, it is recommended to improve the operational performance of the already existing groundwater supply system as a short-term (5 years) solution. The long-term (25 years) solutions consider groundwater options as well as surface water options, using for example Lake Victoria, Lake Albert and River Nile as water sources.


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Ethiopia’s energy demand is expected to increase sevenfold in the coming 30 years, resulting in increased variable renewable electricity (VRE) production by solar PV and wind. Energy storage acts as a buffer that mitigates the effects of over- or under-capacity in production by VRE. With 97% of global bulk energy storage, pumped hydro storage is the most widely used and mature energy storage method. With its long operational life, high round-trip efficiency (80%) and stable cost trajectory, it is a beneficial option for many energy systems. However, drawbacks of pumped storage include heavy technical, site-specific restrictions, long construction times and high initial capital investment requirements.

This study investigates if Ethiopia’s energy pathways benefit from adding pumped storage, where to build it, and if storage increases system resilience. The long-term energy planning tool OSeMOSYS is used, which allows for detailed investigation into system dynamics whilst parallelly minimising costs. OSeMOSYS enables the investigation into Ethiopia by looking at an extensive host of techno-economic
specifications and supply and demand dynamics from the electrification of transport and integration of variable renewables to residential cooking demands.

This report discusses thirteen scenarios which are separated into three main categories: Base Case (3), Emission Penalty (EMI) (6) and Varying Wind Capacity and Seasonality (WND) (6). The base case introduces pumped storage to the energy pathways. The EMI scenario characterises three pathways for carbon pricing. In the WND scenario, wind power’s capacity factor and seasonality are altered to
investigate the potential effects of using more accurate local data or prioritising some supply zones on the energy system configuration. Additionally, the most favourable locations for solar PV and wind are combined with potential PHS locations to find optimal sites for storage construction.

The results of the research show that pumped hydro storage is adopted into the energy system in all scenarios, following both a diurnal and seasonal (dis)charge pattern. Variable renewable integration increases by an average of 10% from the addition of storage (78 GWh). The emission penalty increases the electrification of residential cooking demand and boosts VRE penetration but does not integrate
storage integration further than the base case due to reaching the upper limit of the storage capacity set in the planning experiments. Lastly, the changes in capacity factor and seasonality have a marginal effect on the energy pathways.

Pumped hydro storage increases the energy system’s resilience to climate-driven seasonal uncertainties and prices due to fossil fuel and carbon price uncertainties by making it less dependent on fossil fuels, decreasing vulnerability for potential emission penalties and seasonal capacity fluctuations. The introduction of PHS does not increase overall system costs, making it a prime candidate for large-scale energy storage in Ethiopia, combined with the stable levelised cost of storage and high maturity ... Read more

This thesis investigates the social-environmental-technological transformations associated with the transition towards low-carbon and renewable urban heating in Amsterdam. Using urban heating infrastructures as the unit of analysis, the research addresses three themes: water use, committed emissions, and energy justice. The first theme examines water use through a multi-scale energy and water model, showing that water withdrawal for Aquifer Thermal Energy Storage (ATES) systems could reach levels comparable to current national water use for cooling in electricity production. The study highlights the importance of multi-scale assessment of water use, including virtual water embedded in fuels, to prevent future water stress. The second theme focuses on committed emissions, defined as cumulative carbon emissions over a planning period. By integrating a bottom-up heat demand model with a mixed-integer nonlinear optimisation framework, scenarios are evaluated to minimise emissions between 2030 and 2050. Results indicate that ambitious building insulation and electricity decarbonisation measures increase uptake of low-temperature heating systems, reducing emissions and limiting reliance on high-temperature fossil-based systems. Risks of carbon lock-ins due to insufficient heat density in low-temperature networks are also identified. The third theme addresses energy justice through ethnographic research on collective heating initiatives, revealing that these initiatives both contest and shape the transition, enhancing decision-making liberties, ownership, and responsibilities within communities. Collectively, the studies demonstrate how multi-scale, multidisciplinary approaches combining technical modelling with social science methods provide novel insights into urban sustainability transitions, informing the design and governance of future low-carbon heating systems. ... Read more

The United Nations (UN) Sustainable Development Goal (SDG) \#6 reads that by 2030 universal and equitable access to safe and affordable drinking water is achieved for all In order to achieve this goal, proper and complete monitoring, capturing all the facets of safe water access, is essential. In this thesis it is argued that the current monitoring techniques come with limitations and subsequently solutions are presented to come to a more complete picture of water access.

Monitoring safe water access happens primarily through household health surveys. These surveys are often incomplete, not covering entire nations, focus on only the primary water source and are often spatially aggregated for privacy reasons. Besides, health surveys almost never include questions on consumed water volumes while that is an important indicator for proper hygiene (WELL, 1998), and something that, at the same time, should be in balance with the natural available water resources. Next to this survey based monitoring, there is the Water Point Data Exchange (WPDx) that monitors safe access by providing a platform at which the exact location and type of water access points (such as boreholes, springs, etc.) are registered. This does give more insight into the presence and usage of a variety of sources, but also the WPDx is often incomplete: not covering entire nations.

In this thesis we present a dual methodology that gap-fills the incompleteness of the WPDx database through modeling and in parallel, researches the complex local dynamics of water access, the variety of water sources used by households and the relationships between access and water consumption by means of a household survey.

By improving a machine learning biological species modeling technique (called MaxEnt), successful predictions on the number of presences of eight different water access types across Uganda were made, also into areas that have little presence in the WPDx data. It was found that population density, precipitation, elevation, poverty and groundwater storage are important indicators for the (non)presence of water access points.

Next to modeling, a survey campaign was executed in Bushenyi-Ishaka municipality, a mid-sized town in the South West of Uganda comprising a mixture of both urban and rural areas. This was done in collaboration with Makerere University (Kampala). The survey results showed that water consumption increases with education and wealth, but also with higher number of water point presences predicted by the model. It was also found that households in Bushenyi make use of an average of two different water sources on a regular basis and often express preference for sources off premises compared to on premises (piped) for both cost and perceived quality reasons.

Lastly, modi operandi were suggested for the results to improve water access such as prioritising areas with poor(est) water access and investing in rainwater harvesting, infrastructure and education. ... Read more

Urban water management is faced with upcoming challenges due to climate change, the deterioration of existing infrastructures and the depletion of water sources. Climate change brings about more intense rainfall events and longer periods of drought. On top of that, the deterioration of the piped network urges for investments in the existing system, to ensure that the provision of water services can be maintained. Furthermore, the depletion of water sources is raising awareness regarding the value of freshwater which gives rise to the re­use of water flows that have up to now often been considered as nuisance rather than viable resource. Examples of these flows are the wastewater and the stormwater flow.

The existing centralised infrastructure consists of three reliable systems. The first system supplies high­quality water, the second drains out stormwater and the third one discharges wastewater. These three systems operate separately from each other and follow a linear approach to
water management. In recent years, circular water management has become more prevalent. Instead of following a linear approach in the three separate systems, the re­use of water flows as viable sources are applied more often. New, often local, solutions can be designed to effectively complement existing systems in maintaining the high provision of water services that societies have consistently been using over the past decade. This circular approach can ensure that current water requirement levels can be met sustainably by the improved urban water systems.

By dealing with the upcoming challenges of high­intensity rainfall and long periods of drought– which both have a high spatial variability – local solutions are able to support the centralised infrastructure. This is done by both mitigating the pluvial flood risk, as well as by providing a high ­quality water source. An arguably ideal solution which addresses both the pluvial flood risk and also provides a high­quality water source, is the Bluebloqs system. The Bluebloqs system can help mitigate pluvial flood risk by the attenuation of flow, the result of implementing an attenuation tank in the stormwater drainage system. Also, the Bluebloqs system is able to filter and store this stormwater to provide a high­quality water source during water­scarce seasons.

Whether the Bluebloqs system is a viable solution which addresses both the mitigation of the pluvial flood risk as well as the provision of water challenge, is investigated in this research. The Bluebloqs system is a circular water solution that makes use of an attenuation tank, a bio­filter and an aquifer storage and recovery (ASR) system. The attenuation tank is physically connected to the drainage system and it consequently decreases the risk of surcharged pipes in the drainage network. From the attenuation tank, the water is pumped towards the bio­filter, where pollutants from the water are removed and the water is filtered to such an extent that it can be infiltrated into the aquifer. In the aquifer, the water is stored to overcome seasonal variations in water availability. The Bluebloqs system has the objective to supply water in the dry season, even though its source is stormwater, which enters the system during the wet season. This research has analysed the performance of the Bluebloqs system for different dimensions and operations.

Within this thesis, a framework has been built that presents the performance of the Bluebloqssystem. This framework consists of three groups; the interactions with centralised infrastructure, the water quality indicators and the impact of the Bluebloqs system on its environment. These three have their own distinct performance indicators, which characterise the effectiveness of the Bluebloqs system in providing a specific water service. The group of the framework dealing with the interactions with the centralised infrastructure uses performance indicators for the volume of water lost in overflow events, and volume of water supplied through the Bluebloqs system as high­ quality water source to the end­user. The indicators for the other groups characterise the Bluebloqs system differently. Each group within the framework projects the performance of the Bluebloqs system for other interest groups. For example, the indicators regarding the impact of the Bluebloqs system on its environment are of interest to municipalities thinking about implementing the system.

The behaviour of the Bluebloqs system has been modelled. This model presents the physical processes taking place within the Bluebloqs system. The output of the model are the performance indicators of the framework. By running the model under different input parameters, the performance of the modelled Bluebloqs system is tested on the three groups of the framework.

The outcome of testing the model on the framework has shown that the Bluebloqs system can be improved. One of the suggested improvements is to work with feed cycles for the bio­filter. These feed cycles consist of the periodic saturation of the bio­filter. Once the bio­filter is saturated, the flow from the attenuation tank towards the bio­filter is interrupted, to let the water gradually filter through the bio­filter.
By applying feed cycles, the bio­filter is better capable of removing pollutants from the water. However, periodically interrupting the water flow from the attenuation tank towards the bio­filter negatively impacts the effective storage capacity of the attenuation tank. When having more feed cycles in a day, these interruptions last for a shorter period of time. Depending on the desired performance of the system, which is based on the performance indicators, these feed cycles should be aligned with the capacity of the attenuation tank and the discharge of the pump for the flow between the attenuation tank and the bio­filter.
Fitting the feed cycles to the seasonality of rainfall patterns can further increase the performance of the system in mitigating flood risks. Applying predictive control when overflow events occur is an additional control option that minimises the environmental impact of the system. Finally, the performance of the Bluebloqs system can be presented based on all the performance indicators included in the framework, and the model can be used to adjust the system dimensioning and operations to present the consequences of adjustments to the desired performance of the Bluebloqs system.

The frameworks’ performance indicators can be used to understand the Bluebloqs ideal configuration to deliver a specific desired performance. The desired performance of the system determines the dimensions and operations of the system. Co­designing the Bluebloqs system is thus crucial to its delivered performance.

In conclusion, the framework and model can be used to present the Bluebloqs system for different scenarios. The framework can generate a comprehensive overview of what can be expected of the Bluebloqs system when implementing it in a specific project site, in comparison to other solutions that may be considered, such as green roofs or storage tanks. Also, its use as circular solution being complementary to existing urban water infrastructure can be presented by the framework and model output. This will help in the transition of urban water systems in dealing with the upcoming challenges related to climate change, the deterioration of the piped infrastructure and the depletion of water sources. ... Read more

Purple non-sulphur bacteria (PNSB) are phototrophic bacteria currently under study in the wastewater treatment sector due to their performant nutrient and resource recovery. Until recently, most of researches have focused on closed anaerobic photobioreactors resulting in high selectivity of PNSB and appealing hydrogen, microbial proteins, and carotenoids productivity. Unfortunately, these researches also showed that the implementation of this technology is hampered by its relatively high costs. As is the case for microalgae technology, raceway reactors could possibly overcome this problem thanks to their low investment and maintenance costs. However, a survey of the scientific literature shows that only few researches have investigated the application of PNSB technology with open raceway reactors and that the limited available mechanistic models do not consider the specific conditions which characterize these reactors. Therefore, this study aims to construct a mechanistic mathematical model which includes the mixotrophic metabolism of PNSB and could be used to predict the nutrient removal and recovery and PNSB relative abundance in the raceway reactor. The model was structured mainly considering the photoorganoheterotrophic and respiring chemoorganoheterotrophic growth of PNSB competing with standard (an)aerobic-respiring and fermenting chemoorganoheterophic bacteria under semi-aerobic conditions. The model was tuned with seven batch and sequencing batch reactor (SBR) laboratory experiments. It simulates the reactor performance (COD removal rate 480-780 mgCOD/L/d, yield 0.40– 0.65 mgCODx/mgCODs) and the relative PNSB abundance (10-60%) under different operational conditions (light, dissolved oxygen, surface area) with a relative error around ± 20%. This research proposes an one-at-the-time sensitivity analysis, analysing the impact of those variables (TSS, SRT, COD, light, and biotic competition) which could play an important role in real-case scenarios. From this analysis, it emerged that influent suspended solids (TSS>250 mgTSS/L), hourly variations of the natural light cycle intensity and drops of the available soluble substrate (COD<1000mgCOD/L) could strongly disturb the abundance of PNSB in the system (from 48% to 10%). An extension of the sludge retention time, from 2 to 5 days, was observed to favour the relative abundance of PNSB (from 48% to 60%) and to increase the TSS productivity (from 235 to 400 mgTSS/L/d). The model was intended as a first attempt to simulate the nutrient removal and the PNSB dynamic in a raceway reactor. It has the flexibility to study the impact of the crucial parameters evidenced from the literature review (light, oxygen, carbon source). However, several implementations will be needed in the future, mainly focusing on anoxic chemoheterotrophic growth and hydrolysis. ... Read more