Urban Drainage Networks Research Articles

Urban drainage network simulation using EPA SWMM for SuDS measures planning: a case study in Brazil

This paper investigates the implementation of sustainable drainage systems (SuDS) measures in a small urban catchment as runoff peak flow and total runoff volume control. The study area was calibrated for the pre-occupation scenario and, subsequently, a post-occupation scenario was proposed. Three structural measures were then tested: infiltration trench, rainwater harvesting, and a detention basin. The modelling was performed using the EPA SWMM. The simulation results showed that there was an increase of more than 115% of the total runoff volume in the post-occupation scenario. The results confirm that these structural measures can contribute to the reduction of peak flows in developing areas. Combinations of measures were more efficient in controlling runoff than isolated measures. To avoid using end-of-pipe solutions, a combination of infiltration trenches and lot scale rainwater tanks measures should be considered. The methodology can be used to test various combinations of structural measures.

Interaction between hydrodynamic and morphodynamic processes at the confluence of pipe and channel

Confluences between a pipeline and a channel are commonly found in urban drainage networks. The mechanisms of sediment transport and the coherent structure within the confluence zone remain ambiguous. The hydro-morpho-sedimentary processes at pipe and channel confluences, which are influenced by discharge ratios, are investigated using a laboratory-scale confluence. The pipe current compresses and diverts downstream upon entering the main channel, creating a scour hole that carries sediment downstream. Coarser sediment deposits first, forming the slope of the downstream boundary of the scour hole (D50 = 60.31 μm) and the inner bar (D50 = 62.67 μm). Finer sediments are then deposited in the outer bar (D50 = 53.12 μm), resulting in a bed morphology consisting of the scour hole, outer and inner bars, and corridor. The penetration of the pipe current causes redistribution of the main channel flow, creating two shear layers. The height of the bar is directly related to the intensity of turbulence. Within the range of discharge ratios from 0.06 to 0.09, the shear layers and bars are displaced toward the outer bank by a distance of approximately one pipe diameter (0.05 m). Turbulence in this area is not isotropic, with power-law relations for streamwise and lateral velocity having an exponent of approximately −5/3. Conversely, the separation zone deviates from this pattern. Overall, this study highlights the significant influence of the bed morphology in modifying the flow structure compared to channel confluences and jets into crossflow, which are important in drainage engineering designs and fluvial studies.

Enhancing real-time urban drainage network modeling through Crossformer algorithm and online continual learning

This study presents an innovative approach to real-time modeling of urban drainage networks, leveraging a highly accurate coupled one- and two-dimensional hydrodynamic model to generate a training dataset for node water levels. By employing global states inferred from monitoring points as model inputs, this study overcomes the limitations imposed by the scarcity of monitoring data and the challenge of capturing all node levels. The Crossformer algorithm, which simultaneously accounts for correlation at both temporal and feature scales, is applied to enhance the precision of simultaneous water level predictions across the network. Comparative analysis of different prediction patterns reveals that extending predictions based on a high-accuracy infrastructure offers more benefits than direct modifications to the algorithm's structure. In addition, this paper pioneered the application of online continuous learning concepts to update the prediction model in real time, achieving a balanced integration of measured and simulated data. Consequently, this paper establishes a complete monitoring-predicting-updating real-time simulation system for urban drainage networks.

Identifying urban prone areas to flash floods: The case of Santa Cruz de Tenerife

Floods are the natural hazard that causes the largest annual losses in the world Urban expansion and population growth have made cities the most hazardous areas, mainly due to poor planning, occupation of the drainage network and soil sealing. Santa Cruz de Tenerife is one of the many cities worldwide threatened by this phenomenon. Its typically Mediterranean rainfall pattern, characterized by extreme precipitation events in short periods of time, together with its orography of steep ravines, short length and width, as well as its disorderly growth, make it a space prone to the occurrence of flash floods. In addition, the increase in torrential rainfall as a consequence of climate change and the tendency towards greater irregularity in precipitation is considerably intensifying the problem. This paper studies the characteristics of these episodes and the black spots inventoried in its General Management Plan (PGO for its initials in Spanish). On the other hand, flood modeling is carried out based on the rainfall characterization, whose maximum flows, in total, range between 500 and 1600 m3/s. Finally, a methodology that allows integrating both analyses to obtain a detailed hazard map is proposed as an alternative to the more traditional flood hazard analyses. A design storm of 288 mm is applied and the data is validated against the largest rainfall event on record, March 31, 2002. It has been shown that in an urban drainage network, the main watercourses and those that have disappeared due to urbanization represent areas susceptible to flooding and are the sectors that should be emphasized during the implementation of risk reduction measures. Finally, emphasis is placed on the need to integrate future climate projections of precipitation to better define the maximum flood flows.

Urban Drainage Network Structure Optimization Using Emperor Penguin Optimization Algorithm

Urban drainage systems are an essential component of urban infrastructure that plays a critical role in managing stormwater and preventing flooding in urban areas. With the changing climate and urbanization, the challenges faced by urban drainage systems are becoming increasingly complex. Additionally, aging infrastructure further exacerbates the problem, creating anurban sewage line that must be made more resilient. Redundancy is just a fundamental characteristic of such a robust urban drainage network. Redundancy in urban drainage systems can help to ensure that the system continues to function during extreme weather events or emergencies, reducing the likelihood of flooding and damage. However, the exact locations where redundancy should be increased and its contribution to resilience are not well understood. In recent years, several studies have focused on developing frameworks for optimising urban drainage structures which account pipeline redundancy.One similar research presented a paradigm for constructing the ideal network layout for urban drainage infrastructure, which considers pipeline redundancy under consideration. The original architecture and structure of the urban drainage network was developed using emperor penguin optimizer algorithms and graph theory in the research. Complicated system modelling was done to find extra water pathways or redundancy which might well be implemented to boost resistance. The suggested approach has been utilised to the test region in Dongying City, Shandong Province, China, and its findings revealed even under rainfall above the design specification, the entire overflow capacity of such urban drainage network including pipeline redundancy significantly decreased about 20-30%, compared to the network without pipeline redundancies. The interest in creating optimization algorithms had also increased recently that can be used to design and manage urban infrastructure systems. One such algorithm is the Emperor Penguin Optimization (EPO) algorithm. EPO was a recently developed swarm-based optimization An algorithm which simulates Emperor penguins behaviour in their search for food in Antarctica. The algorithm has shown promising results in solving complex optimization problems in different fields, including engineering, computer science, and management. The EPO algorithm's key features include an emperor search strategy, local search, and randomization, enabling that to efficiently and successfully examine the search process. The algorithm's emperor penguin search strategy enables it to dynamically adjust the search parameters based on the problem's characteristics and progress. The local search feature allows it to escape local optima and explore the search space further. Finally, the randomization feature adds stochasticity to the search process, helping to ensure that the algorithm can avoid getting stuck in a sub-optimal solution. In this article, we aim to explore the potential of EPO as a tool for optimizing Urban drainage solutions which take into account pipeline redundancy. We will start by reviewing the existing Literature upon that optimization in urban drainage facilities, particularly the application of particle swarm optimization, genetic algorithms, and ant colony enhancement. The EPO algorithm will next be described in full including its working principle, key features, and the steps involved in applying it to an optimization problem. Finally, we will present a case study that applies the EPO technique was developed to optimise the network model of such an urban drainage infrastructure taking into account pipeline redundancies, and compare the results with other optimization algorithms. Through this, we aim to demonstrate the potential of EPO as a powerful tool for designing and managing urban infrastructure systems, particularly for enhancing the resilience of urban drainage systems. The study will provide valuable insights into the optimal design of urban drainage technologies which take into account pipeline redundancy, helping policymakers and urban planners make informed decisions about improvingthe adaptability of urban drainage networks. The findings can contribute to the development of sustainable and resilient urban infrastructure systems, which are essential for ensuring the well-being and prosperity of urban residents.

Clarifying urban flood response characteristics and improving interpretable flood prediction with sparse data considering the coupling effect of rainfall and drainage pipeline siltation

With climate warming and accelerated urbanisation, severe urban flooding has become a common problem worldwide. Frequent extreme rainfall events and the siltation of drainage pipes further increase the burden on urban drainage networks. However, existing studies have not fully considered the effects of rainfall and pipeline siltation on the response characteristics of flooding when constructing numerical models of urban flooding simulations. To solve this problem, a surface-subsurface coupling model was constructed by combining the Saint-Venant equation, Manning equation, a one-dimensional pipeline model (SWMM), and a two-dimensional surface overflow model (LISFLOOD-FP). Then, the SWMM model considering pipeline siltation and the two-dimensional surface overflow model (LISFLOOD-FP) were coupled with the flow exchange governing equation, and the urban flooding response characteristics considering the coupling effect of “rainfall and drainage pipeline siltation” were analysed. To enhance the solvability of waterlogging prediction, an intelligent prediction model of urban flooding based on Bayes-CNN-BLSTM was established by combining a convolutional neural network (CNN), bidirectional long short-term memory neural network (BLSTM), Bayesian optimisation (Bayes), and an interpretable loss function error correction method. The actual rainfall events and flooding processes recorded by the monitoring equipment at Huizhou University were used to calibrate and verify the model. The results show that in the Rainfall 1 and Rainfall 2 scenarios, the overload rates of the pipelines in the current siltation scenario were 60.06 % and 68.37 %, respectively, and the proportions of overflow nodes were 24.87 % and 25.89 %, respectively. When the drainage network was initially put into operation, the overload rates of the pipeline were 36.67 % and 41.16 %, and the overflow nodes accounted for 3.05 % and 4.06 %, respectively. The inundated area and volume of urban flooding increased when the combined siltation coefficient (CSC) was 0.2; therefore, two desilting schemes were determined. Under Rainfall 1, Rainfall 2, and the four rainfall recurrence periods, the Bayes-CNN-BLSTM model had clear advantages in terms of accuracy, reliability, and robustness.

Exploring the network structure of coupled green-grey infrastructure to enhance urban pluvial flood resilience: A scenario-based approach focusing on ‘centralized’ and ‘decentralized’ structures

Urban pluvial floods pose a significant risk to cities, occurring when precipitation exceeds the carrying capacity of the urban drainage network. Coupled green-grey infrastructure has emerged as a sustainable solution for mitigating urban pluvial floods. This study aims to explore best practices in the network configuration of urban drainage systems coupled with low-impact development (LID) to enhance flow distribution and stormwater infiltration. To do so, we focused on two competing key concepts in network analysis: (1) Centralization and (2) Decentralization. We integrated a one-dimensional stormwater model with a rapid flood spreading model to assess the flood mitigation performance of various centralized and decentralized network configurations in the Gangnam region of Seoul, South Korea. To further assess the combined effects of green and grey infrastructure, we compared the performance of each drainage network configuration with and without identical mixed LID practices. Here we show that the centralized drainage network scenario performed best in reducing flood volume by 40.3%, the decentralized drainage network scenario performed best in shortening flood duration by 47.8%, and the LID practices scenario performed best in mitigating peak flooding rates by 4.2%, each as independent scenarios. When all three scenarios were coupled together, flood volume could be reduced by 73.5%, flood duration by 54.7%, and peak flooding rates by 19.8% in the study area. This exploratory study underscores the potential of network analysis in urban flood research, particularly the effectiveness of loosely-connected network topology. Our findings contribute to the development of best practices for coupled green-grey infrastructure, facilitating sustainable stormwater management and urban flood resilience.

Predesign of an Urban Rainfall Drainage Network with Genetic Algorithms

The pre-design of an urban storm drainage network is proposed with an optimization method consisting of two parts: in one part, the connection of the manholes is proposed so that there is little water flow in the pipes; in the other part, it is based on a genetic algorithm to find the lowest cost of acquisition of the pipes and of the excavation necessary for their installation, complying with several hydraulic and constructive restrictions recommended in construction manuals of networks of this type. The described procedure is made from the linking of the pipes that make up the network (network layout) trying to obtain the smallest sum of the lengths. It is considered that the conduits are of circular section, where their diameters only take commercial values. The slope of each pipe in the network is calculated so that the hydraulic depth of the water flow is 80% of the pipe diameter, in order to approximate the flow conduction capacity of the liquid within it. Finally, its application to a real case is demonstrated, and the hydraulic performance of the pre-designed network is evaluated by simulating the non-permanent free surface flow in the network using the EPA-SWMM 5.1.015 model, in order to ensure that the hydraulic restrictions and assumptions made for permanent flow are met.

Graph neural network-based surrogate modelling for real-time hydraulic prediction of urban drainage networks

Physics-based models are computationally time-consuming and infeasible for real-time scenarios of urban drainage networks, and a surrogate model is needed to accelerate the online predictive modelling. Fully-connected neural networks (NNs) are potential surrogate models, but may suffer from low interpretability and efficiency in fitting complex targets. Owing to the state-of-the-art modelling power of graph neural networks (GNNs) and their match with urban drainage networks in the graph structure, this work proposes a GNN-based surrogate of the flow routing model for the hydraulic prediction problem of drainage networks, which regards recent hydraulic states as initial conditions, and future runoff and control policy as boundary conditions. To incorporate hydraulic constraints and physical relationships into drainage modelling, physics-guided mechanisms are designed on top of the surrogate model to restrict the prediction variables with flow balance and flooding occurrence constraints. According to case results in a stormwater network, the GNN-based model is more cost-effective with better hydraulic prediction accuracy than the NN-based model after equal training epochs, and the designed mechanisms further limit prediction errors with interpretable domain knowledge. As the model structure adheres to the flow routing mechanisms and hydraulic constraints in urban drainage networks, it provides an interpretable and effective solution for data-driven surrogate modelling. Simultaneously, the surrogate model accelerates the predictive modelling of urban drainage networks for real-time use compared with the physics-based model.

Flow dynamics and turbulent coherent structures around sediment reduction plates of a sewer system

In the management of urban drainage networks, great interest has been generated in the removal of sediments from sewer systems. The unsteady three-dimensional (3D) flow and turbulent coherent structures surrounding sediment reduction plates in a sewer system are investigated by means of the detached-eddy simulation (DES). Particular emphasis is given to detailing the instantaneous velocity and vorticity fields within the grooves, along with an examination of the three-dimensional, long-term, average flow structure at a Reynolds number of approximately 105. Velocity vectors demonstrate continuous flapping of the flow on the groove wall, periodically interacting with ejections of positive and negative vorticity originating from the grooves. The interaction between the three-dimensional groove flow and the shear flow leads to the downstream transport of patches of positive and negative vorticity, which significantly influence sediment transport. The high-velocity shear flows and strong vortices generated in undulating topography, as identified by the Q-criteria, are the key factors contributing to the efficient sediment reduction capabilities of the sediment reduction plates. The sediment reduction plates with partially enclosed structures exhibit low sedimentation rates in grooves on the plate, a broader acceleration region, and a lesser impact on the flow capacity. The results improve the understanding of the hydrodynamics and turbulent coherent structures surrounding the sediment reduction plates while elucidating the driving factors behind the enhancement of sediment scouring and suspension capacities. These results indicate that the redesign of the plates as partially enclosed structures contributes to further improving their sediment reduction performance.

Data-driven urban configuration optimization: An XGBoost-based approach for mitigating flood susceptibility and enhancing economic contribution

The indiscriminate evolution of urban configurations aggravates flood vulnerabilities, threatening sustainable urban expansion. Present methodologies fall short in supplying urban planners with flood mitigative strategies centered on urban configuration facets. Leveraging the power of the XGBoost algorithm, this study posits an advanced optimization schema, adroitly balancing the dual objectives of mitigating urban flooding and enhancing economic growth, with minimal disruption to established urban layouts. Shenzhen serves as the investigative ground, where the model displays exceptional accuracy, resilience, and interpretability in predicting Pluvial Flooding Susceptibility (PFS) and Economic Contribution (EC). Model interpretation divulges the profound influence of three-dimensional urban configuration elements, primarily the Building Congestion Degree, on PFS and EC. Pareto solution exploration for multi-objective optimization unveils the ideal urban configuration interval. To minimize PFS while maximizing EC, the research suggests pertinent measures: augmenting vegetation density, regulating the impervious coverage ratio within 50–70%, limiting two- and three-dimensional building density thresholds, and moderately escalating urban drainage network density. Additionally, it encourages a comprehensive appreciation of function-oriented land usage and intrinsic site topographical characteristics to reconcile varied urban development goals during planning. By fusing data-derived insights with multi-objective optimization, this research anticipates influencing urban planning models, thus enhancing decision-making related to urban configuration and fostering flood-resilient, sustainable, and economically prosperous urban habitats.

Categorization of precipitation for predicting combined sewer overflows. Application to the City of Montréal

Combined sewer systems are widespread in America and Europe. They often face limitations in transport or treatment capacity, especially during heavy rain events or thaw periods, resulting in combined sewer overflows (CSOs). Predictive modeling for CSOs is essential in a risk management context, and some studies have presented methods to categorize precipitations based on their potential to generate overflows. However, the precipitation classification is usually based on a few characteristics, and its predictive power is limited. The objective of this study is to present a simple yet powerful method to categorize precipitation for predicting CSO occurrences. A prediction model, based on an optimized classification tree, is proposed to predict CSO occurrences as a function of publicly accessible precipitation data. We fit the model on 9 overflow outlets in Montréal city from 2013 to 2019 and use this model to predict CSOs in 2020. The results showed a very good predictive power of overflows, with a prediction rate of 89%, a sensitivity rate of 83%, and a specificity rate of 91%. The method is also more accurate than the 5-category classification currently used by the City of Montréal. The proposed method could be easily applied to another region where CSO data are available, providing a simple and rigorous method for predicting CSOs across urban drainage networks containing many overflow outlets.

Intelligent optimal layout of drainage pipe network monitoring points based on information entropy theory

The rapid expansion of urban drainage pipe networks, driven by economic development, poses significant challenges for efficient monitoring and management. The complexity and scale of these networks make it difficult to effectively monitor and manage the discharge of urban domestic sewage, rainwater, and industrial effluents, leading to illegal discharges, leakage, environmental pollution, and economic losses. Efficient management relies on a rational layout of drainage pipe network monitoring points. However, existing research on optimal monitoring point layout is limited, primarily relying on manual analysis and fuzzy clustering methods, which are prone to human bias and ineffective monitoring data. To address these limitations, this study proposes a coupled model approach for the automatic optimization of monitoring point placement in drainage pipe networks. The proposed model integrates the information entropy index, Bayesian reasoning, the Monte Carlo method, and the stormwater management model (SWMM) to optimize monitoring point placement objectively and measurably. The information entropy algorithm is utilized to quantify the uncertainty and complexity of the drainage pipe network, facilitating the identification of optimal monitoring point locations. Bayesian reasoning is employed to update probabilities based on observed data, while the Monte Carlo method generates probabilistic distributions for uncertain parameters. The SWMM is utilized to simulate stormwater runoff and pollutant transport within the drainage pipe network. Results indicate that (1) the relative mean error of the parameter inversion simulation results of the pollution source tracking model is linearly fitted with the information entropy. The calculation shows that there is a good positive linear correlation between them, which verifies the feasibility of the information entropy algorithm in the field of monitoring node optimization; (2) the information entropy algorithm can be well applied to the optimal layout of a single monitoring node and multiple monitoring nodes, and it can correspond well to the inversion results of the tracking model parameters; (3) the constructed monitoring point optimization model can well realize the optimal layout of monitoring points of a drainage pipe network. Finally, the pollution source tracking model is used to verify the effectiveness of the optimal layout of monitoring points, and the whole process has less human participation and a high degree of automation. The automated monitoring point optimization layout model proposed in this study has been successfully applied in practical cases, significantly improving the efficiency of urban drainage network monitoring and reducing the degree of manual participation, which has important practical significance for improving the level of urban water environment management.

Intelligent Diagnosis of Urban Underground Drainage Network: From Detection to Evaluation

During the process of urban development, there is large-scale laying of underground pipeline networks and coordinated operation of both new and old networks. The underground concrete drainage pipes have become a focus of operation and maintenance due to their strong concealment and serious corrosion. The current manual inspections for subterranean concrete drainage pipelines involve high workloads and risks, which makes meeting the diagnostic needs of intricate urban pipeline networks challenging. Through advanced information technology, it has reached a consensus to intelligently perceive, accurately identify, and precise prediction of the condition of urban subterranean drainage networks. The development process of detection and evaluation methods for underground concrete drainage pipe networks is the focus of this study. The study discusses common algorithms for classifying, locating, and quantifying pipeline defects by combining the principles of deep learning with typical application examples. The intelligent progression of information collection methods, image processing techniques, damage prediction models, and pipeline diagnostic systems is systematically elaborated upon. Lastly, prospects for future research of intelligent pipeline diagnosis are provided.

Assessing urban drainage pressure and impacts of future climate change based on shared socioeconomic pathways

The increasing frequency of urban flood disasters presents a significant obstacle to urban sustainability. Urban flood management aims to reduce the flood occurrences, currently addressed through urban drainage systems. Previous studies have demonstrated future precipitation extremes will pose larger pressure on urban drainage network, but when and where the pressure will reach a dangerous level have never been assessed in any city of China. This study establishes the initial framework for identifying critical decades and hot spots of urban drainage pressure changes due to future climate change, through a case study conducted in southern China (Haining city). Urban drainage pressure was assessed by a combination of the urban drainage model known as the Storm Water Management Model (SWMM) and pipe statistics. Using climate projections from the latest phase of Coupled Model Intercomparison Project (CMIP6) under four typical SSP-RCP (shared socioeconomic pathway-representative concentration pathway) scenarios, we project the changes in urban drainage pressure by 21st century, and identify the key decades and high risk areas with the occurrence of dangerous pressure levels. The results indicate an overall upward trend in urban drainage pressure for Haining city, with over 97% of the flooding nodes projected to firstly reach the dangerous level by the 2030 s. Comparisons of the patterns under different SSP-RCP scenarios, suggest that a higher forcing pathway would expedite the deterioration of urban drainage pressure, particularly in urban areas with lower DEM and high building intensity. This has broad implications for better informing disaster management and policy-making in similar cities, especially those with inadequate drainage capacities.

Experimental study of rainwater grate blocking and submergence of outfall on drainage network capacity

Accurately evaluating the performance of urban underground drainage network and its influencing factors is a challenging problem, as this process is affected by many complex factors. In this study, based on an overland flow experiment considering drainage process of pipe network, a series of physical model experiments were conducted to investigate the influences of different surface slopes, rainwater grate blockage and the submergence of outfall on the performance of the drainage pipe network system. The hydrographs of surface runoff and pipe network flow were recorded in collection tanks by precise digital pressure sensors to provide comprehensive information about the characteristics of drainage performance in the pipe network. Through a series of experimental data collection and analysis, the following conclusions are drawn from this study: (1) The longitudinal slope of the road decreases the pipe drainage capacity by 1.68%–8.94%, and this reduction effect is more significant with the increase of slope. (2) The blockage of rainwater grate at different locations has different impacts on the road drainage system, the downstream rainwater grate blockage has the most obvious impact on the performance of the drainage system, which reduces the drainage capacity by 22.59%–25.38%. (3) Different submergence degrees of rainwater outlet have different impacts on the drainage system. Under different slopes, the drainage capacity of the pipe network decreases by 1.88%–23.46% with the increase of the submergence degree of the outfall. These experimental results are helpful in understanding the working conditions of urban road drainage system and the influencing factors of the system's drainage capacity, and also provide measured data for verification of relevant numerical models and coefficient calibration.

Dissemination of Resistance Integrons and Genes Coding for Blse and Cabapenemases in the Urban Drainage Network in Cote d’Ivoire

Dissemination of Resistance Integrons and Genes Coding for Blse and Cabapenemases in the Urban Drainage Network in Cote d’Ivoire

A parsimonious approach to predict regions affected by sewer-borne contaminants in urban aquifers

Leaky urban drainage networks (UDNs) exfiltrating wastewater can contaminate aquifers. Detailed knowledge on spatiotemporal distributions of water-dissolved, sewer-borne contaminants in groundwater is essential to protect urban aquifers and to optimize monitoring systems. We evaluated the effect of UDN layouts on the spreading of sewer-borne contaminants in groundwater using a parsimonious approach. Due to the UDN’s long-term leakage behavior and the existence of non-degradable sewer-borne contaminants (equivalent to a conservative and constant contaminant source), we employed a concept of horizontal line sources to mimic the UDN layout. This does not require the consideration of bio-degradation processes or temporal delay and effectively bypasses the vadose zone, thus reducing computational requirements associated with a full simulation of leakages. We used a set of synthetic leakage scenarios which were generated using fractals and are based on a real-world UDN layout. We investigated the effects of typical leakage rates, varying groundwater flow directions, and UDN’s layouts on the shape of the contaminant plume, disregarding the resulted concentration. Leakage rates showed minimal effects on the total covered plume area, whereas 89% of the variance of the plume’s geometry is explained by both the UDN’s layout (e.g., length and level of complexity) and groundwater flow direction. We demonstrated the potential of applying this approach to identify possible locations of groundwater observation wells using a real UDN layout. This straightforward and parsimonious method can serve as an initial step to strategically identify optimal monitoring systems locations within urban aquifers, and to improve sewer asset management at city scale.

Assessment of the jacking effect of high tide on compound flooding in a coastal city under sea level rise based on water tracer modeling

High tide is one of the main drivers of compound flooding in coastal cities. The capacity of the coastal urban drainage network is reduced due to the jacking effect of the high tide, which will be further amplified by future sea level rise (SLR). Although most studies have evaluated compound flooding in coastal cities under SLR, there is still a lack of studies analyzing the jacking effect of high tide. In this paper, the jacking effect of high tide under SLR on compound flooding is quantified based on the water tracer method using Haikou City as an example. The water tracer method tracks water flow paths by realizing the coupling of water flow and tracer. The future sea level of Haikou City in 2050 is predicted using the MGF model and the SLR projection results of IPCC AR6. An urban flood model coupled with the tracer method is constructed to simulate the compound flooding of coastal cities under baseline and SLR scenarios. The results show that the extent and depth of compound flooding increases under SLR and the high tide has a more pronounced jacking effect on the drainage network and flooding. More than 13% of the drainage network and more than 40% of the flooded inundation areas in the study area experience significant jacking effect as a result of the high tide under SLR, and more than 80% of the areas experience jacking effect for more than 4 h. In addition, the effectiveness of pumping stations and storage facilities in regulating the jacking effect of high tide levels is assessed. For reducing the jacking effect of SLR on the drainage network, pump stations are considerably more useful than storage facilities. This study provides a theoretical basis for understanding the jacking effect of high-tide level under SLR and formulating measures to regulate the jacking effect.

A digital twin model for contaminant fate and transport in urban and natural drainage networks with online state estimation

Increased pollutant loads caused by urbanization and climate change have led to widespread impairment of surface water systems. To better manage these threats, water managers are seeking digital twins that combine online models with sensor data to respond to water quality hazards in real-time. This study introduces Pipedream-WQ, a new model for contaminant transport in drainage networks that combines a novel implicit solver for the unsteady advection–reaction–diffusion (ARD) equation with an efficient data assimilation scheme based on Kalman Filtering. We show that this solver reliably reproduces analytical solutions to the ARD equation in steady conditions, and accurately captures unsteady contaminant transport behavior in a complex drainage network. Furthermore, we show that online sensor data assimilation enables better estimation of contaminant concentrations at ungauged locations compared to a model-only approach. This model will enable improved pollutant tracking and source identification, and active water quality management through real-time control of hydraulic infrastructure.