This opinion paper reflects on the current challenges facing urban drainage systems (UDS) research, along with solutions for fostering sustainable development. Over the course of a year-long project involving 92 participants aged 24-38, including PhD candidates, post-doctoral researchers, and early-career academics, we identified critical challenges and opportunities for the sustainable development of UDS. Our exploration highlights four key challenges: limited public visibility leading to resource constraints, insufficient collaboration across subfields, issues with data scarcity and data sharing, and geographical specificities. We emphasise the importance of raising public and political awareness regarding UDS's vital role in climate adaptation and urban resilience, advocating for blue-green infrastructure and open data practices. Additionally, we address systemic academic barriers that hinder innovative research. We call for a shift away from metrics that prioritise quantity over quality. We recommend establishing stable career pathways that empower early-career researchers. This paper aims to catalyse a broader community dialogue about the future of UDS research, uniting voices from various career stages. By presenting actionable recommendations, we aim to inspire fundamental changes in research conduct, evaluation, and sustainability, ensuring the field of UDS is prepared to meet pressing urban water management challenges worldwide.

Large Multimodal Models are emerging general AI models capable of processing and analyzing diverse data streams, including text, imagery, and sequential data. This paper explores the possibility of exploiting multimodality to develop more interpretable AI-based predictive tools for the water sector, with a first application for sewer defect detection from CCTV imagery. To this aim, we test the zero-shot generalization performance of three generalist large language-vision models for binary sewer defect detection on a subset of the SewerML dataset. We compared the LMMs against a state-of-the-art unimodal Deep Learning approach which has been trained and validated on >1 million SewerML images. Unsurprisingly, the chosen benchmark showcases the best performances, with an overall F1 Score of 0.80. Nonetheless, OpenAI GPT4-V demonstrates relatively good performances with an overall F1 Score of 0.61, displaying equal or better results than the benchmark for some defect classes. Furthermore, GPT4-V often provides text descriptions aligned with the provided prediction, accurately describing the rationale behind a certain decision. Similarly, GPT4-V displays interesting emerging behaviors for trustworthiness, such as refusing to classify images that are too blurred or unclear. Despite the significantly lower performance from the open-source models CogVLM and LLaVA, some preliminary successes suggest good potential for enhancement through fine-tuning, agentic workflows, or retrieval-augmented generation.

The paper proposes a novel methodology to locate and quantify entrapped air pockets created during pipe-filling events often found in intermittent water supply systems. Different filling conditions were tested in an experimental pipe with a high point. Measurements were taken and video recordings were carried out to assess air pocket volumes for different air release conditions at the downstream end of the pipe. The stochastic nature of air pocket creation resulted in varying air volumes. A new numerical model capable of simulating the air pocket creation, dragging and entrainment has been proposed. The new model, AirSWMM, was implemented as an extension of the Stormwater Management Model (SWMM) with stochasticity of air pocket formation reproduced by simulations with different air entrainment rates. The obtained numerical results show that the proposed model, even though based on a single-phase one-dimensional flow, can accurately locate and approximately quantify the entrapped air pocket volumes.

Model Predictive Control (MPC) of Urban Drainage Systems (UDS) has been established as a cost-effective method to reduce pollution. However, the operation of large UDS (containing over 20 actuators) can only be optimized by oversimplifying the UDS dynamics, potentially leading to a decrease in performance and reduction in users' trust, thus inhibiting widespread implementation of MPC procedures. A Heuristic And Predictive Policy (HAPPy) was set up, relying on the dynamic selection of the actuators with the highest impact on the UDS functioning and optimizing those in real-time. The remaining actuators follow a pre-set heuristic procedure. The HAPPy procedure was applied to two separate UDS in Rotterdam with the control objective being the minimization of overflow volume in each of the two cases. Results obtained show that the level of impact of the actuators on the UDS functioning changes during an event and can be predicted using a Random Forest algorithm. These predictions can be used to provide near-global optimal actuator settings resulting in the performance of the HAPPy procedure that is comparable to a full-MPC control and outperforming heuristic control procedures. The number of actuators selected to obtain near-global optimal settings depends on the UDS and rainfall characteristics showing an asymptotic real-time control (RTC) performance as the number of actuators increases. The HAPPy procedure showed different RTC dynamics for medium and large rainfall events, with the former showing a higher level of controllability than the latter. For medium events, a relatively small number of actuators suffices to achieve the potential performance improvement.

Urban areas are constantly developing and thereby affect the local water cycle. Real-time control (RTC) strategies are used to operate urban drainage systems optimally during these transitions. This paper aims to develop a methodology to study the impacts of common gradual changes occurring in the urban environment (densification of the urban area and implementation of blue-green infrastructure), forming cumulative transitions, on the functioning of real-time optimization procedures. A new generic methodology, relying on a comprehensive evaluation strategy based on three indicators assessing the continued optimal performance of RTC was proposed. This methodology was applied to two urban drainage catchments in Eindhoven and Rotterdam using both probabilistic and projected transitional paths. Based on the results obtained it can be noted that the performances of the RTC procedures were not strongly affected by the modeled transitions although the relative performance compared to the maximum performance potential decreases significantly with the large-scale implementation of blue-green infrastructure, indicating that the revision of RTC procedures could improve the sewer system functioning further. The relative performance loss associated with the modeled transitions was higher for model predictive control compared to heuristic RTC procedures for one case study and vice versa for the other. Continuous re-evaluation of the RTC strategy is, therefore, an important but overlooked part of the implementation of RTC procedures.

The management of an Urban Drainage System (UDS) is a complex task, as it requires extensive knowledge about precipitation regime, hydrological features of the catchment, hydraulic characteristics of the drainage network, and information about the water use by the served inhabitants. Complex semi-distributed hydrological and physically based hydraulic models are nowadays available to summarise such information and run simulations. However, in many cases, the uncertainty of the available hydrological information hampers the use of complex models. Hence, simple models with few parameters and small computational effort may be preferable, especially for UDS management problems requiring the execution of many simulations. This paper proposes a convenient approach to define effective lumped Simplified Models (SMs) of UDSs, the parameters of which can be estimated directly from cartographic information. For several case studies of UDS with different morphological and topological characteristics, SMs were built, capable of reproducing the hydrographs provided by available semi-distributed Detailed Models (DMs), assumed as benchmark in absence of measured hydrographs. To this aim, the SWMM simulation software was used, and the SM lumped parameters were calibrated by maximising the goodness of fit between the hydrograph of the DM and of the SM. The results show that SMs satisfactorily predict the hydrographs for all the case studies, and that robust relationships between the calibrated parameters and morphological and topological characteristics of the UDS can be established. This suggests that SMs can be used by decision makers for preliminary design, planning studies and management problems of UDSs, as their parameters can be soundly estimated from cartographic information. An example of application of SMs to Combined Sewer Overflow prediction is also presented.

Model Predictive Control (MPC) of combined sewer systems can reduce environmental degradation caused by uncontrolled overflows. However, practical uncertainties are often neglected when assessing the potential of MPC strategies. This paper aims to understand the risks associated with using a non-perfect internal MPC-model, real precipitation forecast, and realistic dynamic system capacity fluctuations. An MPC with the objective to reduce the total combined sewer overflow (CSO) volume was implemented in the case study of Eindhoven in the Netherlands where highly sensitive waterways receive the sewer overflows. Two types of risks were identified: relative system performance loss and operative deterioration. The former entails a practical decrease in efficacy of controlling CSO spills compared to the theoretical situation, whereas the latter describes the aggravation of environmental pollution compared to a static form of system operation. The results obtained demonstrate that precipitation forecast uncertainty is associated with a small relative system performance loss. Opposite to this, significant performance loss was observed as a consequence of uncertainties in the internal MPC model and the actual sewer system capacity available. The latter caused additional combined sewer overflows compared to a statically optimised control for smaller precipitation events.

Urban Drainage Systems can cause ecological and public health issues by releasing untreated contaminated water into the environment. Real-time control (RTC), augmented with rainfall nowcast, can effectively reduce these pollution loads. This research aims to identify key dynamics in the nowcast accuracies and relate those to the performance of nowcast-informed rule-based (RB)-RTC procedures. The developed procedures are tested in the case study of Rotterdam, the Netherlands. Using perfect nowcast data, all developed procedures showed a reduction in combined sewer overflow volumes of up to 14.6%. Considering real nowcast data, it showed a strong ability to predict if no more rain was expected, whilst performing poorly in quantifying rainfall depths. No relation was found in the nowcast accuracy and the consistency of the predicted rainfall using a moving horizon. Using the real nowcast data, all procedures, with the exception of the one predicting the end of the rainfall event, showed a significant risk of operative deterioration (performing worse than the baseline RB-RTC), linked to the relative performance of the nowcast algorithm. Understanding the strengths of a nowcast algorithm can ensure the reliability of the RB-RTC procedure and can negate the need for detailed modelling studies by inferring risks from nowcast data.

Real Time Control (RTC) is widely accepted as a cost-effective way to operate urban drainage systems (UDS) effectively. However, what factors influence RTC efficacy and how this might change in the long term remains largely unknown. This paper reviews the literature to understand what these factors likely are, and how they can be assessed in the future. Despite decades of research, inconsistent definitions of the performance of RTC are used, hindering an objective and quantitative examination of the benefits and drawbacks of different control strategies with regard to their performance and robustness. Furthermore, a discussion on the changes occurring and projected to occur to UDS reveals that the potential impact of these changes on the functioning of RTC systems can be significant and should be considered in the design stage of the RTC strategy. Understanding this 'best-before' characteristic of an RTC strategy is the key step to ensure long term optimal functioning of the UDS. Additionally, unexplored potential for RTC systems might exist in the transitions, rehabilitation and construction of drainage systems. The research gaps highlighted here could guide the way for further development of RTC strategies, and enabling more optimal, long term implementation of RTC for urban drainage systems.