Cascade vulnerability for risk analysis of water infrastructure

Graph method for critical pipe analysis of branched and looped drainage networks

Enhancing resilience of drainage networks is a crucial practice to protect both humans and nature. One way to enhance resilience is to identify critical parts of drainage networks for targeted management and maintenance strategies. While hydrodynamic modelling approaches for identification are computationally intensive, in this study, a novel method based on complex network analysis is used to determine the most critical pipes in a benchmark and a real network of an Alpine municipality. For evaluation, the results of the proposed graph method are compared with hydrodynamic simulations in terms of accuracy and computational time. Results show that the proposed method is very accurate (R² = 0.98) for branched benchmark network while the accuracy reduces slightly for the more complex real network (R² = 0.96). Furthermore, the accuracy of the proposed method decreases with increasing loop degree and when the system is pressured with higher return period rainfall. Although the outcomes of the proposed method show slight differences to hydrodynamic modelling, it is still very useful because the computational time and data required are much less than a hydrodynamic model.

Dual graph characteristics of water distribution networks-how optimal are design solutions?

Urban water infrastructures are an essential part of urban areas. For their construction and maintenance, major investments are required to ensure an efficient and reliable function. Vital parts of the urban water infrastructures are water distribution networks (WDNs), which transport water from the production (sources) to the spatially distributed consumers (sinks). To minimize the costs and at the same time maximize the resilience of such a system, multi-objective optimization procedures (e.g., meta-heuristic searches) are performed. Assessing the hydraulic behavior of WDNs in such an optimization procedure is no trivial task and is computationally demanding. Further, deciding how close to optimal design solutions the current solutions are, is difficult to assess and often results in an unnecessary extent of experiment. To tackle these challenges, an answer to the questions is sought: when is an optimization stage achieved from which no further improvements can be expected, and how can that be assessed? It was found that graph characteristics based on complex network theory (number of dual graph elements) converge towards a certain threshold with increasing number of generations. Furthermore, a novel method based on network topology and the demand distribution in WDNs, specifically based on changes in 'demand edge betweenness centrality', for identifying that threshold is developed and successfully tested. With the proposed novel approach, it is feasible, prior to the optimization, to determine characteristics that optimal design solutions should fulfill, and thereafter, test them during the optimization process. Therewith, numerous simulation runs of meta-heuristic search engines can be avoided.

Visual Cascade Analytics of Large-Scale Spatiotemporal Data

Many spatiotemporal events can be viewed as contagions. These events implicitly propagate across space and time by following cascading patterns, expanding their influence, and generating event cascades that involve multiple locations. Analyzing such cascading processes presents valuable implications in various urban applications, such as traffic planning and pollution diagnostics. Motivated by the limited capability of the existing approaches in mining and interpreting cascading patterns, we propose a visual analytics system called VisCas. VisCas combines an inference model with interactive visualizations and empowers analysts to infer and interpret the latent cascading patterns in the spatiotemporal context. To develop VisCas, we address three major challenges 1) generalized pattern inference; 2) implicit influence visualization; and 3) multifaceted cascade analysis. For the first challenge, we adapt the state-of-the-art cascading network inference technique to general urban scenarios, where cascading patterns can be reliably inferred from large-scale spatiotemporal data. For the second and third challenges, we assemble a set of effective visualizations to support location navigation, influence inspection, and cascading exploration, and facilitate the in-depth cascade analysis. We design a novel influence view based on a three-fold optimization strategy for analyzing the implicit influences of the inferred patterns. We demonstrate the capability and effectiveness of VisCas with two case studies conducted on real-world traffic congestion and air pollution datasets with domain experts.

A Simplified Sanitary Sewer System Generator for Exploratory Modelling at City-Scale

Future climatic, demographic, technological, urban and socio-economic challenges call for more flexible and sustainable wastewater infrastructure systems. Exploratory modelling can help to investigate the consequences of these developments on the infrastructure. In order to explore large numbers of adaptation strategies, we need to re-balance the degree of realism of sewer network and ability to reflect key performance characteristics against the model's parsimony and computational efficiency. We present a spatially explicit algorithm for creating sanitary sewer networks that realistically represent key characteristics of a real system. Basic topographic, demographic and urban characteristics are abstracted into a squared grid of 'Blocks' which are the foundation for the sewer network's topology delineation. We compare three different pipe dimensioning approaches and found a good balance between detail and computational efficiency. With a basic hydraulic performance assessment, we demonstrate that we attain a computationally efficient and high-fidelity wastewater sewer network with adequate hydraulic performance. A spatial resolution of 250 m Block size in combination with a sequential Pipe-by-Pipe (PBP) design algorithm provides a sound trade-off between computational time and fidelity of relevant structural and hydraulic properties for exploratory modelling. We can generate a simplified sewer network (both topology and hydraulic design) in 18 s using PBP, versus 36 min using a highly detailed model or 1 s using a highly abstract model. Moreover, this simplification can cut up to 1/10th to 1/50th the computational time for the hydraulic simulations depending on the routing method implemented. We anticipate our model to be a starting point for sophisticated exploratory modelling into possible infrastructure adaptation measures of topological and loading changes of sewer systems for long-term planning.

Modular interdependency analysis for water distribution systems

Complexity in water distribution systems (WDSs) poses a challenge for analysis and management of the systems. To reduce the complexity, the recent development of complex network science provides a system decomposition technique that converts a complex WDS with a large number of components into a simple system with a set of interconnected modules. Each module is a subsystem with stronger internal connections than external connections. Thus far, the topological features of the modular structure in WDS have been extensively studied but not the behavioural features, e.g. the hydraulic interdependencies among modules. Therefore, this paper aims to quantitatively measure and graphically visualize the module interdependency in WDSs, which helps understanding the behavioural complexity of WDSs and thus various WDS analyses, such as pipe maintenance, model calibration, rehabilitation, and District Metered Areas planning. Specifically, this study first identifies the WDS's modular structure then measures how changes in the state of one module (i.e. any single pipe failure or perturbed demand within each module) affect the state of another module. Modular interdependencies are summarized in an interdependency matrix and visualized by the digraph. Four real-world systems are analysed, and three of them shows low interdependencies among most of the modules and there are only a few critical modules whose status changes will substantially affect a number of other modules. Hence, highly interconnected topologies may not result in strong and complex module interdependency, which is a fact that simplifies several WDS analysis for practical applications as discussed in this paper.

Emergency preparedness after COVID-19: A review of policy statements in the U.S. water sector

Although COVID-19 has impacted water and wastewater utilities in new and profound ways, they must still provide their vital services despite the disruptions. The pandemic brings into focus their need for proactive emergency preparedness. In the United States, professional associations have long advocated in this area and have already developed considerable policy guidance and resources to help water and wastewater utilities prepare for and respond to emergencies. In the midst of the crisis, several U.S. policies are reviewed here. Utilities should reflect on their COVID-19 experience, learn from it, and apply their newfound perspective to strengthen future emergency preparedness.

Insecure Security: Emergency Water Supply and Minimum Standards in Countries with a High Supply Reliability

Drinking water supply is at the core of both, humanitarian action in times of crisis, as well as national policies for regular and emergency supply. In countries with a continuous water supply, the population mostly relies ingenuously on the permanent availability of tap water due to high supply standards. In case of a disruption in the drinking water infrastructure, minimum supply standards become important for emergency management during disasters. However, wider recognition of this issue is still lacking, particularly in countries facing comparably fewer disruptions. Several international agencies provide guideline values for minimum water provision standards in case of a disaster. Acknowledging that these minimum standards were developed for humanitarian assistance, it remains to be analyzed whether these standards apply to disaster management in countries with high supply standards. Based on a comprehensive literature review of scientific publications and humanitarian guidelines, as well as policies from selected countries, current processes, contents, and shortcomings of emergency water supply planning are assessed. To close the identified gaps, this paper flags potential improvements for emergency water supply planning and identifies future fields of research.

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.

Drinking water turbidity and emergency department visits for gastrointestinal illness in New York City, 2002-2009

Background

Studies have examined whether there is a relationship between drinking water turbidity and gastrointestinal (GI) illness indicators, and results have varied possibly due to differences in methods and study settings.

Objectives

As part of a water security improvement project we conducted a retrospective analysis of the relationship between drinking water turbidity and GI illness in New York City (NYC) based on emergency department chief complaint syndromic data that are available in near-real-time.

Methods

We used a Poisson time-series model to estimate the relationship of turbidity measured at distribution system and source water sites to diarrhea emergency department (ED) visits in NYC during 2002-2009. The analysis assessed age groups and was stratified by season and adjusted for sub-seasonal temporal trends, year-to-year variation, ambient temperature, day-of-week, and holidays.

Results

Seasonal variation unrelated to turbidity dominated (~90% deviance) the variation of daily diarrhea ED visits, with an additional 0.4% deviance explained with turbidity. Small yet significant multi-day lagged associations were found between NYC turbidity and diarrhea ED visits in the spring only, with approximately 5% excess risk per inter-quartile-range of NYC turbidity peaking at a 6 day lag. This association was strongest among those aged 0-4 years and was explained by the variation in source water turbidity.

Conclusions

Integrated analysis of turbidity and syndromic surveillance data, as part of overall drinking water surveillance, may be useful for enhanced situational awareness of possible risk factors that can contribute to GI illness. Elucidating the causes of turbidity-GI illness associations including seasonal and regional variations would be necessary to further inform surveillance needs.