Reliable, Resilient and Sustainable Urban Drainage Systems: An Analysis of Robustness under Deep Uncertainty

Enhancing the resilience of urban drainage system using deep reinforcement learning

Real-time control (RTC) is an effective method used in urban drainage systems (UDS) for reducing flooding and combined sewer overflows. Recently, RTC based on Deep Reinforcement Learning (DRL) has been proven to have various advantages compared to traditional RTC methods. However, the existing DRL methods solely focus on reducing the total amount of CSO discharge and flooding, ignoring the UDS resilience. Here, we develop new DRL models trained by two new reward functions to enhance the resilience of UDS. These models are tested on a UDS in eastern China, and found to enhance UDS resilience and, simultaneously, reduce the total amount of flooding and CSO discharges. Their performance is influenced by the rainfalls and the DRL types. Specifically, different rainfalls lead to different resilience performance curves and DRL model generalization. The value-based DRL model trained with the duration-weighted reward achieves the best performance in the case study.

Integrating nature-based solutions for compound flood risk mitigation in China: A case study of Shanghai

Coastal cities are increasingly vulnerable to escalating flood risks due to climate change and socio-economic development, highlighting the urgent need for effective risk mitigation strategies. This study develops a comprehensive modeling framework to assess the effectiveness of hybrid green and grey strategies in reducing future flood risks in Shanghai, a coastal mega-city in China. The framework comprehensively integrates flood risk analysis, adaptation scenarios design, and cost-benefit analysis. The results indicate that without additional adaptation, the Expected Annual Damage (EAD) could reach US$ 0.5 billion/year, representing 0.09 % of Shanghai's 2020 GDP. Under the RCP2.6 scenario, the EAD is projected to increase by US$ 0.62-0.77 billion/year from 2030 to 2100 without timely interventions. The analysis demonstrates that integrating grey and green infrastructure is both effective and economically viable, yielding a positive benefit-cost ratio. The most promising strategy integrates storage tanks, drainage enhancement, deep tunnels, and dry floodproofing, yielding greater economic benefits than individual measures. This study provides valuable insights for long-term flood adaptation planning, particularly in deltaic regions facing climate uncertainty.

Evaluating the effectiveness of environmental sustainability indicators in optimizing green-grey infrastructure for sustainable stormwater management

Green-grey infrastructure is recommended as an innovative stormwater management strategy in response to urban flooding and climate change. Currently, the indicators used to optimize sustainable green-grey infrastructure and evaluate its stormwater management performance have been limited and based on self-defined criteria. In this study, we developed a comprehensive environmental sustainability indicator that integrates reliability, resilience, vulnerability, and hydrological sustainability as one of the objectives for optimizing green-grey infrastructure layout. The new indicator fully considered both system-level and component-level failures. Graph-theoretic algorithm coupled with NSGA-Ⅱ was applied to support the layout design and optimization. Additionally, the hydro-hydraulic performance of representative optimized layouts under extreme storms and climate change scenarios was re-evaluated to compare the effectiveness of various self-defined environmental sustainability indicators. The results demonstrated that under the same budget conditions, layouts optimized using the environmental sustainability indicator proposed in this study demonstrated superior performance, primarily reflected in less flood severity, flood duration, and conduit surcharge. Furthermore, it was effective and necessary to comprehensively consider the system-level overload consequences, the component-level failure-recovery process, and the extent of restoration to the natural hydrological state in the green-grey optimization process. This framework aims to address the stormwater management challenges posed by short-term extreme storms and long-term climate changes, while balancing sustainable economic and natural hydrological states.

The role of hyetograph shape and designer subjectivity in the design of an urban drainage system

Even though it has been established that a hyetograph's shape affects the results of hydrological simulations, common engineering practice does not always account for this fact. Instead, a single design storm is often considered sufficient for designing a urban drainage system. This study examines the impact that this design paradigm, combined with the uncertainty introduced by subjective choices made during the design process, has on the robustness of a designed system. To do so, we evaluated a set of individual designs created by engineering students using the same Chicago hyetograph as a design storm. We then created ensembles of hyetographs with the same precipitation volume and duration as the Chicago hyetograph and evaluated the designs' hydrological responses. The results showed that designs, which performed equally well for the initial design storm, triggered varying responses for the storms in the ensembles and, consequently, showed different levels of robustness, hinting at a need to adapt the current design approach.

Smart roofs featuring predictive control: An upgrade for mitigating precipitation extreme-induced pluvial floods

In the face of escalating urban pluvial floods exacerbated by climate change, conventional roof systems fall short of effectively managing precipitation extremes. This paper introduces a smart predictive solution: the Smart Internal Drainage Roof (SIDR) system, which leverages forecasted data to enhance the mitigation of pluvial floods in Central Business District (CBD) areas. Unlike traditional approaches, SIDRs utilize a synergistic combination of Rule-based Control (RBC) and Model Predictive Control (MPC) algorithms, tailored to optimize the operational efficiency of both grey and green roofs. Within the examined 1.3 km2 area in Beijing, China, SIDRs, covering 11% of the site, decreased total flooded areas by 30%-50% and eliminated 60%-100% of high-risk zones during three actual events. Moreover, SIDRs streamlined outflow processes without extending discharge time and reduced flood duration at a high-risk underpass by more than half. The SIDR's distinct features, including a high control resolution of 5 min, integration with existing waterproofs, and advanced 2D dynamic runoff visualization, position it as a scalable and cost-efficient upgrade in urban flood resilience strategies.

Multi-objective optimization of the spatial layout of green infrastructures with cost-effectiveness analysis under climate change scenarios

Green infrastructure (GI) plays a significant role in alleviating urban flooding risk caused by urbanization and climate change. Due to space and financial limitations, the successful implementation of GI relies heavily on its layout design, and there is an increasing trend in using multi-objective optimization to support decision-making in GI planning. However, little is known about the hydrological effects of synchronously optimizing the size, location, and connection of GI under climate change. This study proposed a framework to optimize the size, location, and connection of typical GI facilities under climate change by combining the modified non-dominated sorting genetic algorithm-II (NSGA-II) and storm water management model (SWMM). The results showed that optimizing the size, location, and connection of GI facilities significantly increases the maximum reduction rate of runoff and peak flow by 13.4 %-24.5 % and 3.3 %-18 %, respectively, compared to optimizing only the size and location of GI. In the optimized results, most of the runoff from building roofs flew toward green space. Permeable pavement accounted for the highest average proportion of GI implementation area in optimal layouts, accounting for 29.8 %-54.2 % of road area. The average cost-effectiveness (C/E) values decreased from 16 %/105 Yuan under the historical period scenario to 14.3 %/105 Yuan and 14 %/105 Yuan under the two shared socioeconomic pathways (SSPs), SSP2-4.5 and SSP5-8.5, respectively. These results can help in understanding the optimization layout and cost-effectiveness of GI under climate change, and the proposed framework can enhance the adaptability of cities to climate change by providing specific cost-effective GI layout design.

Integrated modeling of urban mobility, flood inundation, and sewer hydrodynamics processes to support resilience assessment of urban drainage systems

With the increasing frequency of extreme weather events and a deepening understanding of disasters, resilience has received widespread attention in urban drainage systems. The studies on the resilience assessment of urban drainage systems are mostly indirect assessments that did not simulate human behavior affected by rainfall or semi-quantitative assessments that did not build simulation models, but few research characterizes the processes between people and infrastructure to assess resilience directly. Our study developed a dynamic model that integrates urban mobility, flood inundation, and sewer hydrodynamics processes. The model can simulate the impact of rainfall on people's mobility behavior and the full process including runoff generation, runoff entering pipes, node overflow, flood migration, urban mobility, and residential water usage. Then, we assessed the resilience of the urban drainage system under rainfall events from the perspectives of property loss and urban mobility. The study found that the average percentage increase in commuting time under different return periods of rainfall ranged from 6.4 to 203.9%. Calculating the annual expectation of property loss and traffic obstruction, the study found that the annual expectation loss in urban mobility is 9.1% of the annual expectation of property loss if the rainfall is near the morning commuting peak.

Model parameter estimation with imprecise information

Model parameter estimation is a well-known inverse problem, as long as single-value point data are available as observations of system performance measurement. However, classical statistical methods, such as the minimization of an objective function or maximum likelihood, are no longer straightforward, when measurements are imprecise in nature. Typical examples of the latter include censored data and binary information. Here, we explore Approximate Bayesian Computation as a simple method to perform model parameter estimation with such imprecise information. We demonstrate the method for the example of a plain rainfall-runoff model and illustrate the advantages and shortcomings. Last, we outline the value of Shapley values to determine which type of observation contributes to the parameter estimation and which are of minor importance.

Deciphering carbon emissions in urban sewer networks: Bridging urban sewer networks with city-wide environmental dynamics

As urbanization accelerates, understanding and managing carbon emissions from urban sewer networks have become crucial for sustainable urban water cycles. This review examines the factors influencing greenhouse gas (GHG) emissions within urban sewage systems, analyzing the complex effects between water quality, hydrodynamics, and sewer infrastructure on GHG production and emission processes. It reveals significant spatiotemporal heterogeneity in GHG emissions, particularly under long-term scenarios where flow rates and temperatures exhibit strong impacts and correlations. Given the presence of fugitive and dissolved potential GHGs, standardized monitoring and accounting methods are deemed essential. Advanced modeling techniques emerge as crucial tools for large-scale carbon emission prediction and management. The review identifies that traditional definitions and computational frameworks for carbon emission boundaries fail to fully consider the inherent heterogeneity of sewers and the dynamic changes and impacts of multi-source pollution within the sewer system during the urban water cycle. This includes irregular fugitive emissions, the influence of stormwater systems, climate change, geographical features, sewer design, and the impacts of food waste and antibiotics. Key strategies for emission management are discussed, focusing on the need for careful consideration of approaches that might inadvertently increase global emissions, such as ventilation, chemical treatments, and water management practices. The review advocates for an overarching strategy that encompasses a holistic view of carbon emissions, stressing the importance of refined emission boundary definitions, novel accounting practices, and comprehensive management schemes in line with the water treatment sector's move towards carbon neutrality. It champions the adoption of interdisciplinary, technologically advanced solutions to mitigate pollution and reduce carbon emissions, emphasizing the importance of integrating cross-scale issues and other environmentally friendly measures in future research directions.

Metamodeling-based reliability analysis framework for activated sludge processes

The reliability of activated sludge processes will be adversely affected by alterations in wastewater production and pollutant loading foreseen due to population growth, urbanization, and climate change, as well as the tendency to amend environmental regulations to mandate stricter effluent quality standards to alleviate water pollution. Until now, there was no framework capable of effectively managing these multifaceted challenges in reliability analysis. Previous attempts conducted a low number of simulations leading to insufficient statistical significance to properly validate reliability quantification. A metamodeling-based reliability analysis framework for the activated sludge process is introduced to cope with alterations in wastewater production and pollutant loading, assesses the reliability under different effluent regulations, and leverages metamodels to conduct extensive simulation work, to estimate the reliability. All metamodels produced high-resolution results, enabling reliability estimation after 100 000 simulations. The framework effectively assessed the annual failure rates of various activated sludge facility designs under four regulations, demonstrating the impact of stricter effluent quality standards. Integrating metamodels for reliability analysis greatly lowers computational costs, making the framework a time and resource-efficient choice for quick decision-making in facility design.

Exploring optimal deep tunnel sewer systems to enhance urban pluvial flood resilience in the gangnam region, South Korea

Urban pluvial flooding is becoming a global concern, exacerbated by urbanization and climate change, especially in rapidly developing areas where existing sewer systems lag behind growth. In order to minimize a system's functional failures during extreme rainfalls, localized engineering solutions are required for urban areas chronically suffering from pluvial floods. This study critically evaluates the Deep Tunnel Sewer System (DTSS) as a robust grey infrastructure solution for enhancing urban flood resilience, with a case study in the Gangnam region of Seoul, South Korea. To do so, we integrated a one-dimensional sewer model with a rapid flood spreading model to identify optimal routes and conduit diameters for the DTSS, focusing on four flood-related metrics: the total flood volume, the flood duration, the peak flooding rate, and the number of flooded nodes. Results indicate that, had the DTSS been in place, it could have reduced historical flood volumes over the last decade by 50.1-99.3%, depending on the DTSS route. Regarding the conduit diameter, an 8 m diameter was found to be optimal for minimizing all flood-related metrics. Our research also developed the Intensity-Duration-Frequency (IDF) surfaces in three dimensions, providing a correlation between simulated flood-related metrics and design rainfall characteristics to distinguish the effect of DTSS on flood risk reduction. Our findings demonstrate how highly engineered solutions can enhance urban flood resilience, but they may still face challenges during extreme heavy rainfalls with a 80-year frequency or above. This study contributes to rational decision-making and emergency management in the face of increasing urban pluvial flood risks.

The effect of green infrastructure on resilience performance in combined sewer systems under climate change

Climate change is currently reshaping precipitation patterns, intensifying extremes, and altering runoff dynamics. Particularly susceptible to these impacts are combined sewer systems (CSS), which convey both stormwater and wastewater and can lead to combined sewer overflow (CSO) discharges during heavy rainfall. Green infrastructure (GI) can help mitigate these discharges and enhance system resilience under historical conditions; however, the quantification of its effect on resilience in a future climate remains unknown in the literature. This study employs a modified Global Resilience Analysis (GRA) framework for continuous simulation to quantify the impact of climate change on CSS resilience, particularly CSOs. The study assesses the efficacy of GI interventions (green roofs, permeable pavements, and bioretention cells) under diverse future rainfall scenarios based on EURO-CORDEX regional climate models (2085-2099) and three Representative Concentration Pathways (2.6, 4.5, 8.5 W/m2). The findings underscore a general decline in resilience indices across the future rainfall scenarios considered. Notably, the total yearly CSO discharge volume increases by a range of 145 % to 256 % in response to different rainfall scenarios. While GI proves effective in increasing resilience, it falls short of offsetting the impacts of climate change. Among the GI options assessed, green roofs routed to pervious areas exhibit the highest adaptive capacity, ranging from 9 % to 22 % at a system level, followed by permeable pavements with an adaptation capacity between 7 and 13 %. By linking the effects of future rainfall scenarios on CSO performance, this study contributes to understanding GI's potential as a strategic tool for enhancing urban resilience.

A resilience-based robustness evaluation framework for sustainable urban flood management under uncertainty

Urban drainage systems (UDSs) may experience failure encountering uncertain future conditions. These uncertainties arise from internal and external threats such as sedimentation, blockage, and climate change. In this paper, a new resilience-based framework is proposed to assess the robustness of urban flood management strategies under some distinct future scenarios. The robustness values of flood management strategies are evaluated by considering reliability, resiliency, and socio-ecological resilience criteria. The socio-ecologic resilience criteria are proposed considering the seven principles of building resilience proposed by Biggs et al. (2012). The evidential reasoning (ER) approach and the regret theory are utilized to calculate the total robustness of the flood management strategies. In this framework, the non-dominated sorting genetic algorithms III (NSGA-III) optimization model and the storm water management model (SWMM) simulation model are linked and run to quantify the criteria. The novelty of this paper lies in presenting a new framework to increase the sustainability and resilience of cities against floods considering the deep uncertainties in the main economic, social, and hydrological factors. This methodology provides policies for redesigning and sustainable operation of urban infrastructures to deal with floods. To evaluate the applicability and efficiency of the framework, it is applied to the East drainage catchment of the Tehran metropolitan area in Iran. The results show that real-time operation of existing flood detention reservoirs, along with implementing five new relief tunnels with a construction cost of 37.1 million dollars, is the most robust non-dominated strategy for flood management in the study area. Comparing the results of the proposed framework with those of a traditional framework shows that it can increase the robustness value by about 40% with the same implementation cost.

Global resilience analysis of combined sewer systems under continuous hydrologic simulation

Managing and reducing combined sewer overflow (CSO) discharges is crucial for enhancing the resilience of combined sewer systems (CSS). However, the absence of a standardised resilience analysis approach poses challenges in developing effective discharge reduction strategies. To address this, our study presents a top-down method that expands the existing Global Resilience Analysis to quantify resilience performance in CSS. This approach establishes a link between threats (e.g., rainfall) and impacts (e.g., CSOs) through continuous and long-term simulation, accommodating various rainfall patterns, including extreme events. We assess CSO discharge impacts from a resilience perspective by introducing eight new metrics. We conducted a case study in Fehraltorf, Switzerland, analysing the performance of three green infrastructure (GI) types (bioretention cells, green roofs, and permeable pavements) over 38 years. The results demonstrated that GI enhanced all resilience indices, with variations observed in individual CSO performance metrics and their system locations. Notably, in Fehraltorf, green roofs emerged as the most effective GI type for improving resilience, while the downstream outfall displayed the highest resilience enhancement. Overall, our proposed method enables a shift from event-based to continuous simulation analysis, providing a standardised approach for resilience assessment. This approach informs the development of strategies for CSO discharge reduction and the enhancement of CSS resilience.

Green infrastructure optimization considering spatial functional zoning in urban stormwater management

Green infrastructure (GI) is used as an alternative and complement to traditional urban drainage system for mitigating urban stormwater issues mainly caused by climate change and urbanization. The combination of hydrological model and optimization algorithm can automatically find the optimal solution under multiple objectives. Given the multi-functional characteristics of GI, choosing the optimization objectives of GI are critical for multiple stakeholders. This study proposes a GI optimization method considering spatial functional zoning. Based on the basic conditions, the study area is divided into the flood risk control zone (FRCZ) and the total runoff control zone (TRCZ). The integrated model coupling hydrological model and optimization algorithm is applied to obtain the Pareto fronts and corresponding non-dominated solutions. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is used to support the decision-making process. The optimal solution obtained for the FRCZ achieves a flood risk reduction rate of 60.49% with an average life cycle cost per year of 0.20 × 108 Chinese Yuan (CNY); The optimal solution obtained for the TRCZ achieves a total runoff reduction rate of 22.83% with an average life cycle cost per year of 0.17 × 108 CNY. This study provides a reference for stakeholders in GI planning and design.

Assisting decision-makers select multi-dimensionally efficient infrastructure designs - Application to urban drainage systems

Multi-objective design approaches can help identify future infrastructure system designs that appropriately balance different engineering, environmental, and other societal goals. Planners benefit from assessing the trade-offs implied by the best-performing infrastructure system solutions. However, a large number of possible efficient system designs, obtained when using multi-objective optimization, can be overwhelming to interpret. This study attempts to aid decision-making in multi-criteria infrastructure system design by reducing the complexity of the identified set of efficient infrastructure designs, i.e., the Pareto-front. A soft clustering algorithm is applied, which identifies similarities between solutions, partitions the front accordingly, and selects a set of representative solutions while preserving the multi-dimensional structure of the solutions on the efficiency frontier. Three post-optimization decision-making metrics are introduced to help quantify the overall performance of the Pareto-optimal designs to further summarize design process outputs for decision-makers. We apply the method to an illustrious urban drainage network case study. Results show how the approach can simplify Pareto-fronts with thousands of solutions into sets of highlighted designs that aid interpreting the trade-offs implied by the best-performing simulated systems.

Optimal sensor placement for the routine monitoring of urban drainage systems: A re-clustering method

The construction of an efficient monitoring network is critical for the effective and safe management of urban drainage systems. This study developed a re-clustering methodology that incorporates additional perspectives beyond node similarity to improve the traditional clustering process for optimal sensor placement. Instead of targeting event-specific water quality or hydraulic monitoring, the method integrates the water hydraulic and quality characteristics of nodes in response to the demand for routine monitoring. The implementation of this method first applies model simulation to generate the attribute datasets required for clustering analysis, and then re-clusters the initial clustering result according to the constructed re-clustering potential indices. And the information theory-based evaluation metrics were introduced to quantitatively assess the sensor deployment scheme obtained by amalgamating the two clustering results. Two networks with different drainage systems and sizes were chosen as case studies to illustrate the application of the framework. The results demonstrate that the clustering process enables to expand the information contained in the monitoring network, and that the re-clustering strategy can generate more comprehensive and practical solutions upon this basis.

Effect of microbial network complexity and stability on nitrogen and sulfur pollutant removal during sediment remediation in rivers affected by combined sewer overflows

Discovering the complexity and improving the stability of microbial networks in urban rivers affected by combined sewer overflows (CSOs) is essential for restoring the ecological functions of urban rivers, especially to improve their ability to resist CSO impacts. In this study, the effects of sediment remediation on the complexity and stability of microbial networks was investigated. The results revealed that the restored microbial community structure using different approaches in the river sediments differed significantly, and random matrix theory showed that sediment remediation significantly affected microbial networks and topological properties; the average path distance, average clustering coefficient, connectedness, and other network topological properties positively correlated with remediation time and weakened the small-world characteristics of the original microbial networks. Compared with other sediment remediation methods, regulating low dissolved oxygen (DO) shifts the microbial network module hubs from Actinobacteria and Bacteroidetes to Chloroflexi and Proteobacteria. This decreases the positive association of networks by 17%-18%, which intensifies the competitiveness among microorganisms, further weakening the influence and transmission of external pressure across the entire microbial network. Compared with that of the original sediment, the vulnerability of the restored network was reduced by more than 36%, while the compositional stability was improved by more than 12%, with reduced fluctuation in natural connectivity. This microbial network succession substantially increased the number of key enzyme-producing genes involved in nitrogen and sulfur metabolism, enhancing nitrification, denitrification, and assimilatory sulfate reduction, thereby increasing the removal rates of ammonia, nitrate, and acid volatile sulfide by 43.42%, 250.68% and 2.66%, respectively. This study comprehensively analyzed the succession patterns of microbial networks in urban rivers affected by CSOs before and after sediment remediation, which may provide a reference for reducing the impact of CSO pollution on urban rivers in the subsequent stages.

Construction and application of sponge city resilience evaluation system: a case study in Xi'an, China

Urban vulnerability is evident when highly complex flood risks overlap with diverse cities, and it is important to enhance the resilience of cities to flood shocks. In this study, a sponge city resilience assessment system is established considering engineering, environmental and social indicators, and the grey relational analysis method (GRA) is used to quantify sponge city resilience. At the same time, a multi-objective optimization model is established based on the three dimensions of water ecological environment, drainage safety, and waterlogging safety. The optimal configuration of grey-green infrastructure is weighed by combining the ideal point method, aiming to ensure that cities effectively reduce flood risk through the optimal configuration scheme. Taking the Xiaozhai area in Xi'an as the study area, the evaluation results show that the grey relational degree (GRD) of the resilience indexes of the original scheme is between 0.390 and 0.661 under the seven different return periods, while the optimization scheme ranges from 0.648 to 0.765, with the best sponge city resilience at a return period of 2a. Compared with the original scheme, the optimized sponge city resilience level increases from level II to nearly level I in the low return period and from level IV to level II in the high return period, indicating that city's ability to cope with waterlogging and pollution is enhanced significantly. Besides, the main factor affecting the sponge city resilience is the runoff control rate, followed by pollutant load reduction rate, which can provide a methodological framework for the assessment and improvement of sponge city resilience.

Operational reliability of urban drainage systems under uncertainties

Water quality risks from overflows have attracted significant research attention, and the reliability of urban drainage systems (UDS) is in urgent need of assessment and improvement. The overflow volume and concentration of critical pollutants are generally used as assessment indicators, which is quite time consuming and cumbersome especially under continuous rainfall. Simplifying the water quality risk assessment indicators for the UDS reliability is intractable. For this purpose, this study proposes the detention tank emptying time as a new reliability evaluation indicator, which greatly reduces the calculation burden by converting water quality risk into hydraulic risk. On this basis, the effects of rainfall, dry weather flow (DWF), actuators and their interactions on reliability are quantified by massive scenarios. It shows that the DWF affects the emptying process via weekly and daily seasonality and its interaction with rainfall is mainly responsible for unreliability. Further, the engineering facility linkage controlled by the actuator to cope with the interaction is the key. Particularly, the Prophet algorithm is innovatively applied to mine the patterns and generate the DWF series for the challenge of sparse DWF data. In conclusion, the indicator proposed expands the connotation of UDS reliability assessment, prompting a small investment in replacing actuators with better controllability and greatly improving reliability. It guides the engineering planning and enhancement from a new perspective of whole-chain optimization from the global to the detailed level.

An integrated AHP-PROMETHEE II ranking method to evaluate the resilience of sewer networks considering urban flood and ground collapse risks

The aim of this study is to present criteria to evaluate the resilience of sewer networks related to ground collapse and urban flooding likely to occur in a specific region and then to determine the ranks of the sewer networks resilience of the selected regions to show the applicability of the analytic hierarchy process (AHP) and the Preference Ranking Organization Method for Enriching Evaluations (PROMETHEE II) method. Fourteen evaluation criteria representing resistance, reliability, redundancy, and response and recovery are presented and their weights are estimated by the AHP by asking questionnaires to 10 sewer experts, leading to the result that the sub-criteria of reliability showed the highest importance, followed by the length ratio of good pipelines (under resistance) and adequacy of the flow capacity of the bypass pipelines (under redundancy). Four separate small blocks of drainage areas (total area of 3.57 km²; sewer length of 50.6 km) in Seoul are chosen for the case study. Using appropriate preference functions and thresholds for each evaluation criterion for PROMETHEE II application yields the resilience rankings of four blocks as Block III > Block IV > Block I > Block II. A sensitivity analysis was also carried out by changing the weights.

Optimization and trade-off framework for coupled green-grey infrastructure considering environmental performance

Implementing runoff control infrastructure has been regarded as an efficacious measure in stormwater management. The issue of its cost-effectiveness is a primary concern for decision makers since it is an exorbitant investment. However, most of existed studies only concentrated on the cost-effectiveness optimization of runoff control infrastructure, especially green infrastructure, between hydrological and economic aspects, and therefore, the potential layout scenarios with high extra environmental benefits could be neglected in the traditional two-dimensional frameworks. In this study, a novel carbon dioxide equivalent-based index was quantified to represent the extra environmental benefits of runoff control infrastructure besides stormwater management and was further integrated into the assessment framework. The effectiveness of green and grey infrastructure was comprehensively evaluated and traded off between hydrological, environmental and economic aspects. The results demonstrated that grey infrastructure is a better measure than green infrastructure when only hydrological (HF index) and economic (CI index) performances were considered. Nevertheless, the environmental performance (EROI index) of green infrastructure prevails over grey infrastructure, and when optimizing green and grey infrastructure simultaneously in the three-dimensional framework considering environmental effectiveness, green infrastructure is comparable with grey infrastructure. Furthermore, an appropriate composition of coupled green-grey infrastructure is requisite, which could achieve an optimal trade-off between hydrological and environmental effectiveness. The sources of environmental benefits were also identified and analyzed from three representative preference scenarios. The findings of the study could serve as a trade-off basis between green and grey infrastructure, as well as between EROI and HF.

Multi-stage planning of LID-GREI urban drainage systems in response to land-use changes

Long-term planning of urban drainage systems aimed at maintaining the sustainability of urban hydrology remains challenging. In this study, an innovative multi-stage planning framework involving two adaptation pathways for optimizing hybrid low impact development and grey infrastructure (LID-GREI) layouts in opposing chronological orders was explored. The Forward Planning and Backward Planning are adaptation pathways to increase LID in chronological order based on the initial development stage of an urban built-up area and reduce LID in reverse chronological order based on the final development stage, respectively. Two resilience indicators, which considered potential risk scenarios of extreme storms and pipeline failures, were used to evaluate the performance of optimized layouts when land-use changed and evolved over time. Compared these two pathways, Forward Planning made the optimized layouts more economical and resilient in most risk scenarios when land-use changed, while the layouts optimized by Backward Planning showed higher resilience only in the initial stage. Furthermore, a decentralized scheme in Forward Planning was chosen as the optimal solution when taking costs, reliability, resilience, and land-use changes into an overall consideration. Nevertheless, this kind of reverse optimization order offers a novel exploration in planning pathways for discovering the alternative optimization schemes. More comprehensive solutions can be provided to decision-makers. The findings will shed a light on the exploration of optimized layouts in terms of spatial configuration and resilience performance in response to land-use changes. This framework can be used to support long-term investment and planning in urban drainage systems for sustainable stormwater management.

Designing coupled LID-GREI urban drainage systems: Resilience assessment and decision-making framework

As flooding risks rise in urban areas, research suggests combining low impact development (LID) and grey infrastructure (GREI) in urban drainage systems. Several frameworks have been proposed to plan such coupled systems, but there is not a comprehensive framework to assess their resilience under diverse failure scenarios and sources of uncertainty. This study proposes a framework which considers both technological and operational resilience. Technological resilience has to do with the performance of the system under extreme loads. Operational resilience has to do with the performance and long-term efficiency of the system after structural damage or degradation, using appropriate probability distributions to quantify the likelihood of failures. The proposed framework is based on an optimization and multi-criteria decision-making platform. It improves on previous research, which lacked consideration of uncertainty in resilience over the life span. We also apply the proposed framework to a real-world test case, and find that in a high-density urban area, a coupled system is more cost-effective than GREI alone. Furthermore, decentralized systems with greater flexibility show significantly better technological and operational resilience. The proposed framework can better support decision-making for planning robust and cost-effective urban drainage systems, particularly in highly urbanized areas.

Battle of centralized and decentralized urban stormwater networks: From redundancy perspective

Recent research underpinned the effectiveness of topological decentralization for urban stormwater networks (USNs) during the planning stage in terms of both capital savings and resilience enhancement. However, how centralized and decentralized USNs' structures with various degrees of redundancy (i.e., redundant water flow pathways) project resilience under functional and structural failure remains an unresolved issue. In this work, we present a systemic and generic framework to investigate the impact of adding redundant flow paths on resilience based on three strategies for optimal centralized versus decentralized USNs. Furthermore, a tailored graph-theory based measure (i.e., eigenvector centrality) is proposed to introduce redundant paths to the critical locations of USNs. The proposed framework is then applied to a real large-scale case study. The results confirm the critical role of layout decentralization under both functional (e.g., extreme precipitation events), and structural failure (e.g., pipe collapse). Moreover, the findings indicate that the implementation of redundant paths could increase resilience performance by up to 8% under functional failure without changing the network's major structural characteristics (i.e., sewer diameters, lengths, and storage capacity), only by leveraging the effective flow redistribution. The scheme proposed in this study can be a fruitful initiative for further improving the USNs' resilience during both planning and rehabilitation stages.

Urban stormwater management for sustainable and resilient measures and practices: a review

Stormwater drainage in urban areas has become a challenge due to the rapid and random growth of urban areas, removal of vegetation, reduction in the effectiveness of drainage infrastructure, and climate change. Sustainable Urban Drainage Systems (SUDS), Low Impact Development (LID), Best Management Practices (BMP), Water Sensitive Urban Design (WSUD) and the Sponge City Programme (SCP) are various aspects for urban stormwater management in a few parts of the world. Urban hydrology plays a vital role in the urban stormwater management system. However, optimal results can only be possible when the combined effect of climate change, land use patterns, reuse, treatment, ecology, and societal aspects are considered. There is a need to provide sustainable and resilient urban drainage systems to manage stormwater more efficiently. The present review has thoroughly discussed various features related to urban stormwater management, highlighted key drivers, identified knowledge gaps in each of the measures and/or practices, recommended future research needs of urban stormwater management to become sustainable and resilient. Integrated modelling approaches considering various key drivers including reuse and real time governance enables stormwater management to be sustainable and resilient in urban environments.

Evaluation of sewer network resilience index under the perspective of ground collapse prevention

Generally, when evaluating the resilience of infrastructure, the four properties of resilience (robustness, rapidity, resources, and redundancy, 4Rs) are widely considered. However, there is little research on the resilience assessment of sewer networks. Therefore, to establish a framework to evaluate sewer network resilience under the perspective of urban ground collapse prevention, this study considers the 13 second-level detailed indicators corresponding to the four first-level indicators (4Rs) based on literature reviews and experts' opinions. An analytic hierarchy process (AHP) is used to obtain relative weights of each indicator and a weighted sum method (WSM) is used to evaluate the sewer network resilience index (SRI). The evaluation system is applied to eight small blocks of selected drainage areas in Seoul, South Korea, and the SRI of eight small blocks are computed. This study could help the sewer management department to make decisions and manage sewer network assets that enhance the resilience of the sewer networks.

A Bibliometric and Visual Analysis of Global Urban Resilience Research in 2011–2020: Development and Hotspots

Urban resilience (UR), which promotes the implementation of resilient cities, has received widespread attention. The purpose of this study is to visualize the knowledge background, research status, and knowledge structure of relevant literatures by using a Citespace based scientometrics survey. The results show that UR is an increasingly popular topic, with 2629 articles published during the study period. (1) The most prolific publications and journals involved in the flourishment of UR research were identified by co-citation. The United States was the most productive contributor, with numerous publications and active institutions. Journal of Cleaner Production, Sustainability, International Journal of Disaster Risk Reduction were the three most cited journals. (2) Co-occurrence analysis was employed to determine the highly productive keywords, and subject categories in the UR domain, including “environmental science & ecology”, “environmental sciences, “science & technology”, “environmental studies”, “green & sustainable science & technology”, and “water resources”. (3) The diversity of highly cited authors in different countries and regions confirmed the evolution of UR studies. (4) Furthermore, the classification of UR knowledge was performed in the form of clusters and knowledge structure to achieve ten distinct sub-domains (e.g., Urban floods and stormwater management, Urban ecosystem services, Urban landscapes, and Trauma). This study provides an overview of UR research and research topics so that future researchers can identify their research topics and partners.

Exploring the Spatial Impact of Green Infrastructure on Urban Drainage Resilience

This paper explores the spatial impact of green infrastructure (GI) location on the resilience of urban drainage systems by the application of exploratory spatial data analysis (ESDA). A framework that integrates resilience assessment, location sensitivity analysis and ESDA is presented and applied to an urban catchment in the United Kingdom. Three types of GI, namely a bioretention cell, permeable pavement, and green roof, are evaluated separately and simultaneously. Resilience is assessed using stress-strain tests, which measure the system performance based on the magnitude and duration of sewer flooding and combined sewer overflows. Based on the results of a location sensitivity analysis, ESDA is applied to determine if there is spatial autocorrelation, spatial clusters, and spatial outliers. Results show a stronger spatial dependency using sewer flooding indicators. Different GI measures present differences in spatial autocorrelation and spatial cluster results, highlighting the differences in their underlying mechanisms. The finding of conflicting spatial clusters indicates that there are trade-offs in the placement of GI in certain locations. The proposed framework can be used as a tool for GI spatial planning, helping in the development of a systematic approach for resilience-performance orientated GI design and planning.

A Comparative Study of the Robustness and Resilience of Retail Areas in Seoul, Korea before and after the COVID-19 Outbreak, Using Big Data

This study aimed to assess the robustness and resilience of retail areas in Seoul, based on the changes in sales before and after the COVID-19 outbreak. The spatial range and temporal scope of the study were set as district- and community-level retail areas in Seoul, from January 2019 to August 2020, to consider the effect of the COVID-19 outbreak. The data used in this study comprised sales information from the retail sector, namely Shinhan Card sales data for domestic and foreigners by business type in Seoul, provided by Seoul Big Data Campus. We classified the retail areas based on the change in sales before and after the COVID-19 outbreak, using time series clustering. The results of this study showed that time series clustering based on the change in sales can be used to classify retail areas. The similarities and differences were confirmed by comparing the functional and structural characteristics of the district- and community-level retail areas by cluster and by retail area type. Furthermore, we derived knowledge on the decline and recovery of retail areas before and after a national crisis such as the emergence of a COVID-19 wave, which can provide significant information for sustainable retail area management and regional economic development.

Towards urban resilience through Sustainable Drainage Systems: A multi-objective optimisation problem

The necessity of incorporating a resilience-informed approach into urban planning and its decision-making is felt now more than any time previously, particularly in low and middle income countries. In order to achieve a successful transition to sustainable, resilient and cost-effective cities, there is a growing attention given to more effective integration of nature-based solutions, such as Sustainable Drainage Systems (SuDS), with other urban components. The experience of SuDS integration with urban planning, in developed cities, has proven to be an effective strategy with a wide range of advantages and lower costs. The effective design and implementation of SuDS requires a multi-objective approach by which all four pillars of SuDS design (i.e., water quality, water quantity, amenity and biodiversity) are considered in connection to other urban, social, and economic aspects and constraints. This study develops a resilience-driven multi-objective optimisation model aiming to provide a Pareto-front of optimised solutions for effective incorporation of SuDS into (peri)urban planning, applied to a case study in Brazil. This model adopts the SuDS's two pillars of water quality and water quantity as the optimisation objectives with its level of spatial distribution as decision variables. Also, an improved quality of life index (iQoL) is developed to re-evaluate the optimal engineering solutions to encompass the amenity and biodiversity pillars of SuDS. Rain barrels, green roofs, bio-retention tanks, vegetation grass swales and permeable pavements are the suitable SuDS options identified in this study. The findings show that the most resilient solutions are costly but this does not guarantee higher iQoL values. Bio-retention tanks and grass swales play effective roles in promotion of water quality resilience but this comes with considerable increase in costs. Permeable pavements and green roofs are effective strategies when flood resilience is a priority. Rain barrel is a preferred solution due to the dominance of residential areas in the study area and the lower cost of this option.

Approaches to Multi-Objective Optimization and Assessment of Green Infrastructure and Their Multi-Functional Effectiveness: A Review

Green infrastructure (GI) is a contemporary area of research worldwide, with the implementation of the findings alleviating issues globally. As a supplement and alternative to gray infrastructure, GI has multiple integrated benefits. Multi-objective GI optimization seeks to provide maximum integrated benefits. The purpose of this review is to highlight the integrated multifunctional effectiveness of GI and to summarize its multi-objective optimization methodology. Here, the multifunctional effectiveness of GI in hydrology, energy, climate, environment, ecology, and humanities as well as their interrelationships are summarized. Then, the main components of GI multi-objective optimization including the spatial scale application, optimization objectives, decision variables, optimization methods and optimization procedure as well as their relationships and mathematical representation are examined. However, certain challenges still exist. There is no consensus on how to measure and optimize the integrated multi-functional effectiveness of GI. Future research directions such as enhancing integrated multi-objective assessment and optimization, improving life cycle analysis and life cycle cost, integrating benefits of GI based on future uncertainties and developing integrated green–gray infrastructure are discussed. This is vital for improving its integrated multifunctional effectiveness and the final decision-making of stakeholders.

The Role of Sewer Network Structure on the Occurrence and Magnitude of Combined Sewer Overflows (CSOs)

Combined sewer overflows (CSOs) prevent surges in sewer networks by releasing untreated wastewater into nearby water bodies during intense storm events. CSOs can have acute and detrimental impacts on the environment and thus need to be managed. Although several gray, green and hybrid CSO mitigation measures have been studied, the influence of network structure on CSO occurrence is not yet systematically evaluated. This study focuses on evaluating how the variation of urban drainage network structure affects the frequency and magnitude of CSO events. As a study case, a sewer subnetwork in Dresden, Germany, where 11 CSOs are present, was selected. Scenarios corresponding to the structures with the lowest and with the highest number of possible connected pipes, are developed and evaluated using long-term hydrodynamic simulation. Results indicate that more meshed structures are associated to a decrease on the occurrence and magnitude of CSO. Event frequency reductions vary between 0% and 68%, while reduction of annual mean volumes and annual mean loads ranged between 0% and 87% and 0% and 92%. These rates were mainly related to the additional sewer storage capacity provided in the more meshed scenarios, following a sigmoidal behavior. However, increasing network connections causes investment costs, therefore optimization strategies for selecting intervention areas are needed. Furthermore, the present approach of reducing CSO frequency may provide a new gray solution that can be integrated in the development of hybrid mitigation strategies for the CSO management.

Strategic planning of the integrated urban wastewater system using adaptation pathways

Emerging threats such as climate change and urbanisation pose an unprecedented challenge to the integrated management of urban wastewater systems, which are expected to function in a reliable, resilient and sustainable manner regardless of future conditions. Traditional long term planning is rather limited in developing no-regret strategies that avoid maladaptive lock-ins in the near term and allow for flexibility in the long term. In this study, a novel adaptation pathways approach for urban wastewater management is developed in order to explore the compliance and adaptability potential of intervention strategies in a long term operational period, accounting for different future scenarios and multiple performance objectives in terms of reliability, resilience and sustainability. This multi-criteria multi-scenario approach implements a regret-based method to assess the relative performance of two types of adaptation strategies: (I) standalone strategies (i.e. green or grey strategies only); and (II) hybrid strategies (i.e. combined green and grey strategies). A number of adaptation thresholds (i.e. the points at which the current strategy can no longer meet defined objectives) are defined to identify compliant domains (i.e. periods of time in a future scenario when the performance of a strategy can meet the targets). The results obtained from a case study illustrate the trade-off between adapting to short term pressures and addressing long term challenges. Green strategies show the highest performance in simultaneously meeting near and long term needs, while grey strategies are found less adaptable to changing circumstances. In contrast, hybrid strategies are effective in delivering both short term compliance and long term adaptability. It is also shown that the proposed adaption pathways method can contribute to the identification of adaptation strategies that are developed as future conditions unfold, allowing for more flexibility and avoiding long term commitment to strategies that may cause maladaptation. This provides insights into the near term and long term planning of ensuring the reliability, resilience and sustainability of integrated urban drainage systems.

Assessing Redundancy in Stormwater Structures Under Hydraulic Design

As environmental change is happening at an unprecedented pace, a reliable and proper urban drainage design is required to alleviate the negative effects of unexpected extreme rainfall events occurring due to the natural and anthropogenic variations such as climate change and urbanization. Since structure/configuration of a stormwater network plays an imperative role in the design and hydraulic behavior of the system, the goal of this paper is to elaborate upon the significance of possessing redundancy (e.g., alternative flow paths as in loops) under simultaneous hydraulic design in stormwater pipe networks. In this work, an innovative approach based on complex network properties is introduced to systematically and successively reduce the number of loops and, therefore, the level of redundancy, from a given grid-like (street) network. A methodology based on hydrodynamic modelling is utilized to find the optimal design costs for all created structures while satisfying a number of hydraulic design constraints. As a general implication, when structures are subject to extreme precipitation events, the overall capability of looped configurations for discharging runoff more efficiently is higher compared to more branched ones. The reason is due to prevailing (additional) storage volume in the system and existing more alternative water flow paths in looped structures, as opposed to the branched ones in which only unique pathways for discharging peak runoff exist. However, the question arises where to best introduce extra paths in the network? By systematically addressing this question with complex network analysis, the influence of downstream loops was identified to be more significant than that of upstream loops. Findings, additionally, indicated that possessing loop and introducing extra capacity without determining appropriate additional pipes positions in the system (flow direction) can even exacerbate the efficiency of water discharge. Considering a reasonable and cost-effective budget, it would, therefore, be worthwhile to install loop-tree-integrated stormwater collection systems with additional pipes at specific locations, especially downstream, to boost the hydraulic reliability and minimize the damage imposed by the surface flooding upon the metropolitan area.

Meshness of sewer networks and its implications for flooding occurrence

Understanding the factors that affect the occurrence of failures in urban drainage networks (UDNs) is a key concept for developing strategies to improve the reliability of such systems. Although a lot of research has been done in this field, the relationship between UDN structure (i.e. layout) and its functional failures is still unclear. In this context, the present study focuses first on determining which are the most common sewer layout topologies, based on a data set of 118 UDNs, and then on analyzing the relationship between these and the occurrence of node flooding using eight subnetworks of the sewer system of Dresden, Germany, as a study case. A method to 'quantify' the topology of a UDN in terms of similarity to a branched or meshed system, referred to as Meshness, is introduced. Results indicate, on the one hand, that most networks have branched or predominantly branched topologies. On the other hand, node flooding events in networks with higher Meshness values are less likely to occur, and have shorter durations and smaller volumes than in predominantly branched systems. Predominantly meshed systems are identified then as more reliable in terms of flooded nodes and flooding volumes.

Optimal adaptation pathway for sustainable low impact development planning under deep uncertainty of climate change: A greedy strategy

Robustness and cost effectiveness are major concerns for sustainable stormwater management under deep uncertainty of climate change. Given that many traditional static planning strategies are not working with unpredictable future conditions, the possibility of system failure, and the lock-in effects, the Adaptation Pathway (AP) approach was adopted for dynamically robust and cost-effective planning in this paper. In order to increase optimization accuracy of multi-staged planning, a continuous definition of the AP optimization problem was raised by improving the simplified versions in existing studies. A case study in Suzhou, a provincial pilot Sponge City in China undergoing increasing annual rainfall and severe water environment deterioration, was included by integrating Long-Term Hydrologic Impact Assessment-Low Impact Development model with optimization methods, aiming to persistently control the non-point source total phosphorus loading below an acceptable amount in the following unforeseen 20 years via multi-staged low-impact development (LID) construction. A novel optimization method developed by the authors in a companion paper, namely marginal-cost-based greedy strategy (MCGS), was successfully applied to efficiently solve the continuous version of the AP optimization problem. The popular genetic algorithm (GA) was used as a contrast. A weather generator was elaborated based on four Representative Concentration Pathway scenarios and 17 spatial downscaled general circulation models to simulate the unforeseen future annual rainfalls that helped with evaluating cost effectiveness of each prospective LID plan. Results showed that the adaptation pathways optimized by MCGS could save the whole life net present cost of an LID plan by 1%-60% compared with those optimized by GA, and the computational efficiency of MCGS was over 13 times faster than GA.

Unified and rapid assessment of climate resilience of urban drainage system by means of resilience profile graphs for synthetic and real (persistent) rains

Urban drainage system (UDS) researchers have applied the concept of resilience for minimizing the magnitude and duration of urban flooding in response to climate change. Currently, the relationship between conventional design and resilience analysis still remains unknown, while persistent rain has not been included in resilience assessment. The present study proposes new metrics by means of resilience profile graph for UDS stressed by synthetic short-duration storms and real persistent rains. The graph unifies the concepts of reliability, robustness, resilience and failure, as well as design standards for sewer surcharging, sewer flooding and property flooding, which are linked into curves to show a complete performance under climate stress scenarios. The obtained results show that resilience profile curves for short-duration storms are well fitted by power functions with coefficient of determination 98.13%-99.9%. Chicago hyetograph was used as critical input hyetograph where the error range was -0.34%-6.83% compared with actual hyetograph. Resilience profile graphs for persistent rains reveal that resilience assessment based on short-duration storms underestimates the effect of persistent rains, and it can be obtained by using segmental and reference reliability metrics to reduce working time from weeks to hours. For the rain of the same intensity, resilience to persistent rain was 18.4-33.1% lower than for single rains. Threat of persistent rain doesn't fall under the rains of high intensity but under large rainfall in total (which exceeds 25% of local annual rainfall), while re-planning water landscape as retarding basin reduces the impact of persistent rains to 5.8-11.8%.

Recent Advances in Adaptive Catchment Management and Reservoir Operation

This editorial introduces the latest research advances in the special issue on catchment management and reservoir operations. River catchments and reservoirs play a central role in water security, community wellbeing and social-economic prosperity, but their operators and managers are under increasing pressures to meet the challenges from population growth, economic activities and changing climates in many parts of the world. This challenge is tackled from various aspects in the 27 papers included in this special issue. A synthesis of these papers is provided, focusing on four themes: reservoir dynamics and impacts, optimal reservoir operation, climate change impacts, and integrated modelling and management. The contributions are discussed in the broader context of the field and future research directions are identified to achieve sustainable and resilient catchment management.