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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.

Achieving Urban Flood Resilience (UFR) is essential for modern societies, requiring the implementation of effective practices in different countries to mitigate hydrological events. Green Water Systems (GWSs) emerge as a promising alternative to achieve UFR, but they are still poorly explored and present varied definitions. This article aims to define GWSs within the framework of sustainable practices and propose a regulation that promotes UFR. Through a systematic review of existing definitions and an analysis of international regulations on sustainable urban drainage systems (SuDSs), this study uncovers the varied perceptions and applications of GWSs and their role in Blue–Green Infrastructure (BGI). Furthermore, the research puts forth a standardized definition of GWSs and emphasizes the implementation of SuDSs in Peru. This approach aims to address the existing knowledge gap and contribute to the advancement of sustainable urban infrastructure.

Stormwater runoff from urban catchments is affected by the changing climate and rapid urban development. Intensity of rainstorms is expected to increase in Northern Europe, and sealing off surfaces reduces natural stormwater management. Both trends increase stormwater peak runoff volume that urban stormwater systems (UDS) have to tackle. Pipeline systems have typically limited capacity, therefore measures must be foreseen to reduce runoff from new developed areas to existing UDS in order to avoid surcharge. There are several solutions available to tackle this challenge, e.g. low impact development (LID), best management practices (BMP) or stormwater real time control measures (RTC). In our study, a new concept of a smart in-line storage system is developed and evaluated on the background of traditional in-line and off-line detention solutions. The system is operated by real time controlled actuators with an ability to predict rainfall dynamics. This solution does not need an advanced and expensive centralised control system; it is easy to implement and install. The concept has been successfully tested in a 12.5 ha urban development area in Tallinn, the Estonian capital. Our analysis results show a significant potential and economic feasibility in the reduction of peak flow from dense urban areas with limited free construction space.

The performance of urban drainage systems is typically examined using hydrological and hydrodynamic models where rainfall input is uniformly distributed, i.e., derived from a single or very few rain gauges. When models are fed with a single uniformly distributed rainfall realization, the response of the urban drainage system to the rainfall variability remains unexplored. The goal of this study was to understand how climate variability and spatial rainfall variability, jointly or individually considered, affect the response of a calibrated hydrodynamic urban drainage model. A stochastic spatially distributed rainfall generator (STREAP – Space-Time Realizations of Areal Precipitation) was used to simulate many realizations of rainfall for a 30-year period, accounting for both climate variability and spatial rainfall variability. The generated rainfall ensemble was used as input into a calibrated hydrodynamic model (EPA SWMM – the US EPA's Storm Water Management Model) to simulate surface runoff and channel flow in a small urban catchment in the city of Lucerne, Switzerland. The variability of peak flows in response to rainfall of different return periods was evaluated at three different locations in the urban drainage network and partitioned among its sources. The main contribution to the total flow variability was found to originate from the natural climate variability (on average over 74 %). In addition, the relative contribution of the spatial rainfall variability to the total flow variability was found to increase with longer return periods. This suggests that while the use of spatially distributed rainfall data can supply valuable information for sewer network design (typically based on rainfall with return periods from 5 to 15 years), there is a more pronounced relevance when conducting flood risk assessments for larger return periods. The results show the importance of using multiple distributed rainfall realizations in urban hydrology studies to capture the total flow variability in the response of the urban drainage systems to heavy rainfall events.

Sustainable urban drainage systems (SuDS) are decentralized stormwater management practices that mimic natural drainage processes. The hydrological processes of SuDS are often modeled using process-based models. However, it can require considerable effort to set up these models. This study thus proposes a machine learning (ML) method to directly learn the statistical correlations between the hydrological responses of SuDS and the forcing variables at sub-hourly timescales from observation data. The proposed methods are applied to two SuDS catchments with different sizes, SuDS practice types, and data availabilities in the USA for discharge prediction. The resulting models have high prediction accuracies (Nash–Sutcliffe efficiency, NSE, >0.70). ML explanation methods are then employed to derive the basis of each ML prediction, based on which the hydrological processes being modeled are then inferred. The physical realism of the inferred hydrological processes is then compared to that would be expected based on the domain-specific knowledge of the system being modeled. The inferred processes of some models, however, are found to be physically implausible. For instance, negative contributions of rainfall to runoff have been identified in some models. This study further empirically shows that an ML model's ability to provide accurate predictions can be uncorrelated with its ability to offer plausible explanations to the physical processes being modeled. Finally, this study provides a high-level overview of the practices of inferring physical processes from the ML modeling results and shows both conceptually and empirically that large uncertainty exists in every step of the inference processes. In summary, this study shows that ML methods are a useful tool for predicting the hydrological responses of SuDS catchments, and the hydrological processes inferred from modeling results should be interpreted cautiously due to the existence of large uncertainty in the inference processes.

Changes in precipitation characteristics directly affect society through their impacts on drought and floods, hydro-dams, and urban drainage systems. Global warming increases the water holding capacity of the atmosphere and thus the risk of heavy precipitation. Here, daily precipitation records from over 700 Chinese stations from1956 to 2005 are analyzed. The results show a significant shift from light to heavy precipitation over eastern China. An optimal fingerprinting analysis of simulations from 11 climate models driven by different combinations of historical anthropogenic (greenhouse gases, aerosols, land use, and ozone) and natural (volcanic and solar) forcings indicates that anthropogenic forcing on climate, including increases in greenhouse gases (GHGs), has had a detectable contribution to the observed shift toward heavy precipitation. Some evidence is found that anthropogenic aerosols (AAs) partially offset the effect of the GHG forcing, resulting in a weaker shift toward heavy precipitation in simulations that include the AA forcing than in simulations with only the GHG forcing. In addition to the thermodynamic mechanism, strengthened water vapor transport from the adjacent oceans and by midlatitude westerlies, resulting mainly from GHG-induced warming, also favors heavy precipitation over eastern China. Further GHG-induced warming is predicted to lead to an increasing shift toward heavy precipitation, leading to increased urban flooding and posing a significant challenge for mega-cities in China in the coming decades. Future reductions in AA emissions resulting from air pollution controls could exacerbate this tendency toward heavier precipitation.

Narrow-Band-Internet of Things (NB-IoT) is a promising wireless communication technology for harsh signal propagation conditions, including underground connectivity. The purpose of this study is to evaluate the real-life signal quality of an NB-IoT cellular sensor network for the monitoring and control of urban drainage systems. The implementation of the control systems toward smart infrastructures increase the need for low-cost and low-energy-consumption underground sensing devices. A measurement campaign was conducted in Rakvere, Estonia, to analyze the NB-IoT network's coverage and underground signal quality. The results were used to analyze the flood risk reduction potential in the pilot catchment, considering the quality of NB-IoT cellular network coverage. The signal measurements showed that in the case of plastic manholes, the best cellular transmitter placement is 0.5 m below the manhole cover, and at the ground level NB-RSRP reference signal strength of −104 dBm or better must be ensured to have a link budget with optimal energy consumption for communication. The results of flood risk reduction showed that excluding control locations with insufficient signal strength increased the number of major flooding nodes by 3.9% and mean flooding duration by 4.2% which is a relatively small change and characterizes the flexibility of the developed control system.

In the last few years, some regions of the Mediterranean area have witnessed a progressive increase in extreme events, such as urban and flash floods, as a response to the increasingly frequent and severe extreme rainfall events, which are often exacerbated by the ever-growing urbanization. In such a context, the urban drainage systems may not be sufficient to convey the rainwater, thus increasing the risk deriving from the occurrence of such events. This study focuses on a particularly intense urban flood that occurred in Palermo (Italy) on 15 July 2020; it represents a typical pluvial flood due to extreme rainfall on a complex urban area that many cities have experienced in recent years, especially in the Mediterranean region. A conceptual hydrological model and a 2D hydraulic model, particularly suitable for simulations in a very complex urban context, have been used to simulate the event. Results have been qualitatively validated by means of crowdsourced information and satellite images. The experience of Palermo, which has highlighted the urgent need for a shift in the way stormwater in urban settlements is managed, can be assumed to be a paradigm for modeling pluvial floods in complex urban areas under extreme rainfall conditions. Although the approaches and the related policies cannot be identical for all cities, the modeling framework used here to assess the impacts of the event under study and some conclusive remarks could be easily transferred to other, different urban contexts.

The tidal jacking effect is a crucial factor exacerbating waterlogging in coastal cities, but its mechanism is complex and difficult to quantify. In this study, a comprehensive framework is established to explore how tidal jacking exacerbates waterlogging. The framework includes three components: hydrodynamic simulations of urban waterlogging combing rainfall and tide levels, analysis of the drainage system to reveal how tidal jacking impedes water flow and exacerbates waterlogging, and quantification of changes in flooded buildings to assess the impact of waterlogging hazards. Taking the Liede River Basin in Guangzhou, China, as a case study, the results show that tide levels intensify waterlogging through a series of cascading processes: jacking of drainage outfalls, impeded pipeline drainage, pipe overflow, and eventually surface waterlogging. When the drainage system encounters tidal jacking, the number and duration of jacked outfalls increase, extending the duration of full pipes. This leads to a 9%–43% increase in pipe overflow and a 4%–27% expansion of the waterlogging area. River overflow exceeds pipe overflow under tidal jacking. Tidal jacking changes the proportion of areas with different waterlogging risk levels, concentrating higher risk downstream. Tidal jacking also causes differential increase in losses among different building types. This study provides essential insights into how tide level exacerbates waterlogging and offers crucial evidence for mitigating waterlogging hazards. Plain Language Summary Urban waterlogging refers to the accumulation of water caused by excessive rainfall that exceeds the drainage capacity of urban drainage systems. This issue affects many cities globally, resulting in substantial social and economic losses while posing significant threats to public safety. In coastal cities, beyond the impact of rainfall, tides can further exacerbate waterlogging. Existing studies largely focus on predicting tide level variations or analyzing waterlogging outcomes but rarely explore how tide levels intensify urban waterlogging. To bridge this research gap, we propose a novel framework. This framework quantifies the physical processes by which tide levels exacerbate waterlogging through a series of hydraulic indicators. Furthermore, it assesses the impact of tide levels on flooded building losses. The framework is adaptable and can be broadly applied to waterlogging studies in other coastal cities. Key Points A new framework considering the hydraulic characteristics of the drainage system is developed to reveal how tide exacerbates waterlogging Multi‐scenario analyses of tide levels and rainfall reveal that tidal jacking notably alters the spatial distribution of waterlogging Under the tidal jacking effect, differences in loss increments are driven by the number of flooded buildings rather than building types

Recent studies have demonstrated the effectiveness of low impact development (LID) in preventing urban flooding in urban catchments. Majority of the past research focuses on the overall effects of LID on urban flood reduction in various configurations. However, how urban drainage system (UDS) performance changes at spatial scale under LID effectiveness within urban catchment is rarely explored. This study evaluates performance of UDS under different spatial placement strategies of LID to understand how urban flood dynamics of drainage system changes at catchment and local-scales. A practical UDS in China was chosen as a case study and divided into three sections (upstream, center, and downstream), with a combination of four LID practices installed on one of these sections or the entire catchment under six different rainfall scenarios and five different setting scales. An evaluation of individual LID practices demonstrated bioretention cell takes first place, followed by rain garden and green roof, and permeable pavement ranked at last place based on their overall performances. Results also confirmed the significant impact of the placement location of LID on UDS performance. Uniform placement strategy proves to be the best among four strategies because of the maximum potential for flood mitigation and improvement of UDS performance. Other investigated spatial placement strategies have approximately similar performances but are relatively poorer compared to the uniform strategy. Furthermore, the placement of LID facilities nearer to the flooded locations maximizes the benefits in terms of flood reduction and also reduces probability of transferring hydraulic load to other parts of UDS.