Realising smarter stormwater management: A review of the barriers and a roadmap for real world application.

Effective management of stormwater systems is necessary for protection of both the built and natural environments. However, stormwater management is facing multiple, growing challenges, including climate change, ageing infrastructure, population growth, urbanisation, environmental concerns, regulatory and institutional changes and public awareness. While the potential of 'smart', internet-of-things enabled stormwater management systems to address these challenges is increasingly being recognised, with considerable evidence in literature for the benefits of more data-driven approaches, implementation to date remains low. This paper, therefore, provides a comprehensive review of the potential barriers to adoption of smarter stormwater management practices that require addressing, and provides a roadmap for real world application. Barriers related to all elements of stormwater management, from the asset sensing to the data analytics and online optimisation, are identified. Technical challenges discussed include the availability and reliability of technologies, technological and physical limitations, decision making, uncertainty and security. Technical barriers are rapidly reducing and there is increasing evidence in the academic literature of the efficacy of smart technologies. However, socio-economic barriers remain a significant challenge, and issues such as trust and lack of confidence, resistance to change, expense, and lack of knowledge and guidance are reviewed. A 'smart stormwater management wheel' that provides a flexible and iterative approach for implementing smart functionality is also presented. Whilst acting as a roadmap, this aims to facilitate a structured methodology for overcoming barriers and benchmarking progress, and may be used to explore trade-offs and relationships between differing levels of implementation for each of the constituent technologies in a smart stormwater system.

Dynamic population normalisation in wastewater-based epidemiology for improved understanding of the SARS-CoV-2 prevalence: a multi-site study.

Wastewater-based epidemiology (WBE) is a valuable tool for monitoring the circulation of COVID-19. However, while variations in population size are recognised as major sources of uncertainty, wastewater SARS-CoV-2 measurements are not routinely population-normalised. This paper aims to determine whether dynamic population normalisation significantly alters SARS-CoV-2 dynamics observed through wastewater monitoring, and whether it is beneficial or necessary to provide an understanding of COVID-19 epidemiology. Data from 394 sites in England are used, and normalisation is implemented based on ammoniacal nitrogen and orthophosphate concentrations. Raw and normalised wastewater SARS-CoV-2 metrics are evaluated at the site and spatially aggregated levels are compared against indicators of prevalence based on the Coronavirus Infection Survey and Test and Trace polymerase chain reaction test results. Normalisation is shown, on average, to have a limited impact on overall temporal trends. However, significant variability in the degree to which it affects local-level trends is observed. This is not evident from previous WBE studies focused on single sites and, critically, demonstrates that while the impact of normalisation on SARS-CoV-2 trends is small on average, this may not always be the case. When averaged across many sites, normalisation strengthens the correlation between wastewater SARS-CoV-2 data and prevalence indicators; however, confidence in the improvement is low.

A new framework for distributed storage tanks placement based on a resilience characteristic metric and reduced modelling.

In recent years, urban flooding has been a frequent occurrence, and seriously threatens the safety of lives and properties. Rational placement of distributed storage tanks is one of the effective ways to solve urban flooding, addressing stormwater management and rainwater reuse. However, existing optimization methods (such as genetic algorithm (GA) and other evolutionary algorithms) for determining the placement of storage tanks typically have a high computational burden; as such, they can be very time-consuming, and are not conducive to energy saving, carbon reduction and work efficiency improvements. In this study, a new approach and framework based on a resilience characteristic metric (RCM) and reduced modelling requirements are proposed. In this framework, the resilience characteristic metric, which is based on the linear superposition principle of system resilience metadata, is introduced, and a small number of simulations based on a coupling of MATLAB with SWMM are used to obtain the final placement scheme of storage tanks. The framework is demonstrated and verified with two cases in Beijing and Chizhou, China, and compared with a GA. The GA requires 2000 simulations for two cases (considering the placement of 2 and 6 tanks respectively), while the proposed method needs 44 simulations for the Beijing case and 89 simulations for the Chizhou case. The results show that the proposed approach is feasible and effective, and cannot only obtain a relative better placement scheme, but also considerably reduce computational time and energy consumption. It significantly improves the efficiency of determining the placement scheme of storage tanks. This method provides a new approach for the determining better storage tank placement schemes, and is useful for informing device placement in sustainable drainage systems.

General resilience: Conceptual formulation and quantitative assessment for intervention development in the urban wastewater system.

General resilience addresses the resilience of a water system to any threat including unknowns, in contrast to specified resilience to individual identified threats. However, quantification of general resilience is challenging and previous assessments have typically been qualitative or based on system properties that are assumed to be indicative of resilient performance. Here we present a General Resilience Assessment Methodology (GRAM), which uses a middle-state based approach to decompose general resilience into contributing components to provide a quantitative and performance-based resilience assessment. GRAM enables the accounting of the effects of any threat if all modes of system failure are identifiable. It is applied to an integrated urban wastewater system where five interventions are explored. The results obtained show that whilst substantial improvements in specified resilience are achieved, increasing the general resilience of the system is challenging. However, general resilience analysis enables identification of system failure modes to which level of service is least resilient and highlights key opportunities for intervention development. GRAM is beneficial as it can inform the development of interventions to increase the resilience of a system to unknowns such as unforeseeable natural hazards in a quantifiable manner.

Building knowledge of university campus population dynamics to enhance near-to-source sewage surveillance for SARS-CoV-2 detection.

Wastewater surveillance has been widely implemented for monitoring of SARS-CoV-2 during the global COVID-19 pandemic, and near-to-source monitoring is of particular interest for outbreak management in discrete populations. However, variation in population size poses a challenge to the triggering of public health interventions using wastewater SARS-CoV-2 concentrations. This is especially important for near-to-source sites that are subject to significant daily variability in upstream populations. Focusing on a university campus in England, this study investigates methods to account for variation in upstream populations at a site with highly transient footfall and provides a better understanding of the impact of variable populations on the SARS-CoV-2 trends provided by wastewater-based epidemiology. The potential for complementary data to help direct response activities within the near-to-source population is also explored, and potential concerns arising due to the presence of heavily diluted samples during wet weather are addressed. Using wastewater biomarkers, it is demonstrated that population normalisation can reveal significant differences between days where SARS-CoV-2 concentrations are very similar. Confidence in the trends identified is strongest when samples are collected during dry weather periods; however, wet weather samples can still provide valuable information. It is also shown that building-level occupancy estimates based on complementary data aid identification of potential sources of SARS-CoV-2 and can enable targeted actions to be taken to identify and manage potential sources of pathogen transmission in localised communities.

Understanding and managing uncertainty and variability for wastewater monitoring beyond the pandemic: Lessons learned from the United Kingdom national COVID-19 surveillance programmes.

The COVID-19 pandemic has put unprecedented pressure on public health resources around the world. From adversity, opportunities have arisen to measure the state and dynamics of human disease at a scale not seen before. In the United Kingdom, the evidence that wastewater could be used to monitor the SARS-CoV-2 virus prompted the development of National wastewater surveillance programmes. The scale and pace of this work has proven to be unique in monitoring of virus dynamics at a national level, demonstrating the importance of wastewater-based epidemiology (WBE) for public health protection. Beyond COVID-19, it can provide additional value for monitoring and informing on a range of biological and chemical markers of human health. A discussion of measurement uncertainty associated with surveillance of wastewater, focusing on lessons-learned from the UK programmes monitoring COVID-19 is presented, showing that sources of uncertainty impacting measurement quality and interpretation of data for public health decision-making, are varied and complex. While some factors remain poorly understood, we present approaches taken by the UK programmes to manage and mitigate the more tractable sources of uncertainty. This work provides a platform to integrate uncertainty management into WBE activities as part of global One Health initiatives beyond the pandemic.

Assessing flood resilience of urban drainage system based on a 'do-nothing' benchmark.

To deal with the threat of urban flooding, it is necessary to assess the flood resilience of urban drainage systems at the planning and design stage. This study proposes a system resilience assessment methodology based on a 'do-nothing' benchmark. In this new benchmark, the number of flooded nodes used in computation of mean flood duration in the system is that observed under a 'do-nothing' scenario (i.e. with no intervention), irrespective of the scenario under evaluation. This methodology is demonstrated using a case study in Chizhou city, China, a simple stormwater drainage network with seven subcatchments. Schemes of interventions (with distributed storage tanks) that aim to mitigate flooding are then produced by zero-one integer programming and schemes sampling. The results show that the proposed method can compute the mean flood duration and system resilience reasonably and helps identify effective intervention schemes. Compared with traditional methods, this resilience assessment method based on a 'do-nothing' scenario can correctly indicate the change in trend of system resilience provided by different schemes, and aids understanding of different interventions to improve system resilience to urban flooding. This study also provides a new way to test different interventions and to explore which provide the greatest improvement in system resilience.

Combination and placement of sustainable drainage system devices based on zero-one integer programming and schemes sampling.

The combination and placement of sustainable drainage systems (SuDS) devices is important for system design, but differing site characteristics and device properties can make this a challenging task. Opinion-based and optimization-based approaches have the disadvantages of subjectivity and excessive computational burden respectively. This paper presents a new framework for SuDS device combination and placement in system design. It integrates zero-one integer programming, random sampling, scheme filtering and cost-effectiveness analysis. The effectiveness of the framework is tested with a SuDS device placement design case in Chizhou city, Anhui province, China. The proposed framework will help to objectively choose the best SuDS device combination and placement scheme for cost-effective implementation.

Exploring wastewater system performance under future threats: Does enhancing resilience increase sustainability?

Sustainability and resilience are both key considerations in the design and operation of wastewater systems. However, there is currently a lack of understanding of the relationship between these two goals and of the effects of increasing resilience on sustainability. This paper, therefore, presents a framework for analysis of the effects of resilience-enhancing interventions on sustainability, and applies this to an urban wastewater system. Given that sustainability addresses the long term, the framework includes a novel sustainability assessment approach which captures a continuum of potential future conditions and enables identification of tipping points where applicable. This method allows a wide range of potential futures to be captured whilst removing the need to develop scenarios or future projections. While it may be possible to develop interventions that are beneficial in terms of their effects on both resilience and sustainability, the results obtained from the case study demonstrate that implementing measures designed to increase resilience of an integrated urban wastewater system does not guarantee a universal improvement in sustainability. Therefore, when proposing measures to increase resilience, the potential effects on sustainability should be considered also. It is also shown that the extent of any negative effects on system sustainability can vary significantly depending on future conditions, with the case study intervention (increasing pump capacity) achieving the highest degree of sustainability if rainfall depths or imperviousness in the catchments reduce. However, trade-offs between sustainability indicators are present irrespective of future conditions. Furthermore, while an intervention that enhances resilience may be considered sustainable with respect to specific indicators under current conditions, tipping points exist and it will cease to be sustainable if future threat magnitudes exceed these.

Design and operation of urban wastewater systems considering reliability, risk and resilience.

Reliability, risk and resilience are strongly related concepts and have been widely utilised in the context of water infrastructure performance analysis. However, there are many ways in which each measure can be formulated (depending on the reliability of what, risk to what from what, and resilience of what to what) and the relationships will differ depending on the formulations used. This research has developed a framework to explore the ways in which reliability, risk and resilience may be formulated, identifying possible components and knowledge required for calculation of each and formalising the conceptual relationships between specified and general resilience. This utilises the Safe & SuRe framework, which shows how threats to a water system can result in consequences for society, the economy and the environment, to enable the formulations to be derived in a logical manner and to ensure consistency in any comparisons. The framework is used to investigate the relationship between levels of reliability, risk and resilience provided by multiple operational control and design strategies for an urban wastewater system case study. The results highlight that, although reliability, risk and resilience values may exhibit correlations, designing for just one is insufficient: reliability, risk and resilience are complementary rather than interchangeable measures and one cannot be used as a substitute for another. Furthermore, it is shown that commonly used formulations address only a small fraction of the possibilities and a more comprehensive assessment of a system's response to threats is necessary to provide a comprehensive understanding of risk and resilience.

Topological attributes of network resilience: A study in water distribution systems.

Resilience has been increasingly pursued in the management of water distribution systems (WDSs) such that a system can adapt to and rapidly recover from potential failures in face of a deep uncertain and unpredictable future. Topology has been assumed to have a great impact on resilience of WDSs, and is the basis of many studies on assessing and building resilience. However, this fundamental assumption has not been justified and requires investigation. To address this, a novel framework for mapping between resilience performance and network topological attributes is proposed. It is applied to WDSs here but can be adaptable to other network systems. In the framework, resilience is comprehensively assessed using stress-strain tests which measure system performance on six metrics corresponding to system resistance, absorption and restoration capacities. Six key topological attributes of WDSs (connectivity, efficiency, centrality, diversity, robustness and modularity) are studied by mathematical abstraction of WDSs as graphs and measured by eight statistical metrics in graph theory. The interplay between resilience and topological attributes is revealed by the correlations between their corresponding metrics, based on 85 WDSs with different sizes and topological features. Further, network variants from a single WDS are generated to uncover the value of topological attribute metrics in guiding the extension/rehabilitation design of WDSs towards resilience. Results show that only certain aspects of resilience performance, i.e. spatial and temporal scales of failure impacts, are strongly influenced by some (not all) topological attributes, i.e. network connectivity, efficiency, modularity and centrality. Metrics for describing the topological attributes of WDSs need to be carefully selected; for example, clustering coefficient is found to be weakly correlated with resilience performance compared to other metrics of network connectivity (due to the grid-like structures of WDSs). Topological attribute metrics alone are not sufficient to guide the design of resilient WDSs and key details such as the location of water sources also need to be considered.

Attribute-based intervention development for increasing resilience of urban drainage systems.

Resilience building commonly focuses on attributes such as redundancy. Whilst this may be effective in some cases, provision of specific attributes does not guarantee resilient performance and research is required to determine the suitability of such approaches. This study uses 250 combined sewer system virtual case studies to explore the effects of two attribute-based interventions (increasing distributed storage and reducing imperviousness) on performance-based resilience measures. These are found to provide improvement in performance under system failure in the majority of case studies, but it is also shown that attribute-based intervention development can result in reduced resilience.

Optimization of storage tank locations in an urban stormwater drainage system using a two-stage approach.

Storage is important for flood mitigation and non-point source pollution control. However, to seek a cost-effective design scheme for storage tanks is very complex. This paper presents a two-stage optimization framework to find an optimal scheme for storage tanks using storm water management model (SWMM). The objectives are to minimize flooding, total suspended solids (TSS) load and storage cost. The framework includes two modules: (i) the analytical module, which evaluates and ranks the flooding nodes with the analytic hierarchy process (AHP) using two indicators (flood depth and flood duration), and then obtains the preliminary scheme by calculating two efficiency indicators (flood reduction efficiency and TSS reduction efficiency); (ii) the iteration module, which obtains an optimal scheme using a generalized pattern search (GPS) method based on the preliminary scheme generated by the analytical module. The proposed approach was applied to a catchment in CZ city, China, to test its capability in choosing design alternatives. Different rainfall scenarios are considered to test its robustness. The results demonstrate that the optimal framework is feasible, and the optimization is fast based on the preliminary scheme. The optimized scheme is better than the preliminary scheme for reducing runoff and pollutant loads under a given storage cost. The multi-objective optimization framework presented in this paper may be useful in finding the best scheme of storage tanks or low impact development (LID) controls.

A framework to support decision making in the selection of sustainable drainage system design alternatives.

This paper presents a new framework for decision making in sustainable drainage system (SuDS) scheme design. It integrates resilience, hydraulic performance, pollution control, rainwater usage, energy analysis, greenhouse gas (GHG) emissions and costs, and has 12 indicators. The multi-criteria analysis methods of entropy weight and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) were selected to support SuDS scheme selection. The effectiveness of the framework is demonstrated with a SuDS case in China. Indicators used include flood volume, flood duration, a hydraulic performance indicator, cost and resilience. Resilience is an important design consideration, and it supports scheme selection in the case study. The proposed framework will help a decision maker to choose an appropriate design scheme for implementation without subjectivity.

Reliable, resilient and sustainable water management: the Safe & SuRe approach.

Global threats such as climate change, population growth, and rapid urbanization pose a huge future challenge to water management, and, to ensure the ongoing reliability, resilience and sustainability of service provision, a paradigm shift is required. This paper presents an overarching framework that supports the development of strategies for reliable provision of services while explicitly addressing the need for greater resilience to emerging threats, leading to more sustainable solutions. The framework logically relates global threats, the water system (in its broadest sense), impacts on system performance, and social, economic, and environmental consequences. It identifies multiple opportunities for intervention, illustrating how mitigation, adaptation, coping, and learning each address different elements of the framework. This provides greater clarity to decision makers and will enable better informed choices to be made. The framework facilitates four types of analysis and evaluation to support the development of reliable, resilient, and sustainable solutions: "top-down," "bottom-up," "middle based," and "circular" and provides a clear, visual representation of how/when each may be used. In particular, the potential benefits of a middle-based analysis, which focuses on system failure modes and their impacts and enables the effects of unknown threats to be accounted for, are highlighted. The disparate themes of reliability, resilience and sustainability are also logically integrated and their relationships explored in terms of properties and performance. Although these latter two terms are often conflated in resilience and sustainability metrics, the argument is made in this work that the performance of a reliable, resilient, or sustainable system must be distinguished from the properties that enable this performance to be achieved.

Global resilience analysis of water distribution systems.

Evaluating and enhancing resilience in water infrastructure is a crucial step towards more sustainable urban water management. As a prerequisite to enhancing resilience, a detailed understanding is required of the inherent resilience of the underlying system. Differing from traditional risk analysis, here we propose a global resilience analysis (GRA) approach that shifts the objective from analysing multiple and unknown threats to analysing the more identifiable and measurable system responses to extreme conditions, i.e. potential failure modes. GRA aims to evaluate a system's resilience to a possible failure mode regardless of the causal threat(s) (known or unknown, external or internal). The method is applied to test the resilience of four water distribution systems (WDSs) with various features to three typical failure modes (pipe failure, excess demand, and substance intrusion). The study reveals GRA provides an overview of a water system's resilience to various failure modes. For each failure mode, it identifies the range of corresponding failure impacts and reveals extreme scenarios (e.g. the complete loss of water supply with only 5% pipe failure, or still meeting 80% of demand despite over 70% of pipes failing). GRA also reveals that increased resilience to one failure mode may decrease resilience to another and increasing system capacity may delay the system's recovery in some situations. It is also shown that selecting an appropriate level of detail for hydraulic models is of great importance in resilience analysis. The method can be used as a comprehensive diagnostic framework to evaluate a range of interventions for improving system resilience in future studies.