Water distribution networks (WDNs) are critical infrastructures prone to vulnerabilities which lead to failures. Identifying vulnerable components, especially multiple pipe failure combinations, is crucial for effective management and ensuring high reliability. Hydraulic simulations are commonly used for analysing the criticality of WDN, but are time-consuming and highly data-reliant, limiting the number of testable combinations. To address these limitations and constraints, a graph-based method is proposed to quantify the impact magnitude of multiple pipe failure scenarios on performance, enabling the identification of critical combinations. The proposed graph-based approach utilizes structural and topological characteristics of WDNs as well as spatial demand distribution to replicate hydraulic behaviour. The accuracy of the approach is assessed by testing it on three case studies with various pipe failure combinations, and the results are compared with hydraulic analyses. The results demonstrate a strong correlation (Spearman coefficient > 0.75) between graph-based ranking and state-of-the-art hydraulic-based ranking. Additionally, the method exhibits a significant computational gain factor of greater than 30 compared with the hydraulic-based method, rendering it valuable for actively exploring a wide range of critical pipe failure combinations and devising countermeasures. Furthermore, a hybrid-based method that integrates both the graph and hydraulic-based methods is proposed for enhanced accuracy and robust assessments.

Panic buying during crises, like the initial hoarding of toilet paper amid COVID-19 restrictions, is mainly driven by social and emotional factors influenced by risk perception and social media content. Similarly, unreasonable drinking water hoarding, i.e., simultaneous withdrawal of water by a large number of customers, can influence the performance of water supply systems (WSS). Decreasing performance (e.g., more friction losses and therefore a smaller outflow rate at faucets) can cause a negative feedback loop that might trigger further water hoarding behaviours. This research explores the socio-technical implications of water hoarding in crisis situations within a WSS. An analysis of an Alpine WSS in Austria reveals that up to 40% of households, with a filling rate of 0.1 L/s (bathtub filling), can hoard without performance drop and negative feedback. Beyond this threshold, impacts become noticeable, leading to inadequate water supply for some households, causing disruptions and negative feedback loops. This emphasizes the need for information campaigns to counter false information, preventing emotional triggers. In conclusion, the research highlights the interplay between technical and social factors in crisis water demand management, stressing the importance of informed interventions to mitigate hoarding behaviour and maintain efficient WSS operation.

Continuous drinking water supplied by water distribution networks (WDNs) is essential for social well-being and economic development. WDNs are often divided by isolation valves into segments containing various elements, including nodes, pipes, tanks, and pumps. In the event of an element failure (e.g., pipe failure) within a segment, that segment can be isolated to facilitate repair. Effective failure management requires identifying critical valves and segments to minimize the number of affected users in such an event. Traditional hydraulic-based criticality analysis requires an hydraulic model to assess the criticality of isolation valves and segements, which can be time-consuming particularly for complex systems. Placing new valves also often requires data-intensive and time-consuming optimization methods thatare typically impractical for small and medium-sized WDN operators. To address these challenges, this study introduces a graph-based method to assess and improve WDN resilience by evaluating the criticality of valves and segmentsand the placement of new valves. The approach first evaluates the criticality of isolation valves based on their impact on network performance. Then, it reduces segment criticality by strategically adding new valves to minimize unmet water demand during isolation events. To achieve this, the mathematical graph of a WDN is constructed based on GIS data where valves are considered as nodes and segments as weighted edges. Subsequently, graph-based segment failure magnitudes are calculated, and eigenvector centrality is used to rank valves based on their influence, considering the importance of connected valves. Then critical segments are identified, and the Louvain-based community detection technique is used to determine the optimal placement of additional isolation valves. The method iteratively reassesses critical values to progressively reduce the criticality of both: segments and valves. The method was applied to a benchmark case study and a real WDN in an Alpine municipality in Austria. Results show a strong correlation (>0.9 Spearman) with hydraulic-based approaches. The developed approach effectively identified the most critical segments and valve, reducing the segment criticality by at least 40% and the number of critical valves to one fourth. These findings highlight the efficiency of community detection in valve placement and its potential to reduce both segment and valve criticality. This graph-based methodology is particularly beneficial for small-to-medium-scale WDNs lacking resources for hydraulic models.Funding: The project “RESIST” is funded by the Austrian security research programme KIRAS of the Federal Ministry of Finance (BMF).

Water distribution networks (WDNs) are susceptible to vulnerabilities that necessitate proactive management to ensure efficient incident management. When dealing with a sequence of failures triggered, for example, by hydraulic redistributions under failure conditions, the computational burden often becomes the limiting factor for exploring scenarios. Therefore, this study proposes a hybrid method combining a graph-based approach for prescreening critical pipes with a hydraulic assessment to rapidly identify critical nodes. Tested on an Austrian WDN, this approach effectively pinpoints primary failures (pipe failures) while reducing computational time. By integrating the hydraulic approach, the method successfully identified the most critical elements due to the failures. This method empowers operators to mitigate the impact of potential failures and enhance disaster robustness.

Critical infrastructures such as water, power, telecommunications, and transportation systems are an important part of human life in an urban environment, which are vulnerable to disasters, failures, asset forfeiture, and sabotage. The focus of this work is on water distribution networks (WDNs) including different hydraulic elements such as pipes, tanks, valves, and reservoirs to transport drinking water from central treatment plants or sources to consumers. Thereby, efficient identification of system parts in a WDN that are vulnerable to failures is important for efficient management and to provide high reliability. Therefore, often hydraulic simulations are applied, which are computationally intensive and impractical for large networks and multiple pipe failures.In this study, a graph theory-based analysis is used to identify the critical elements (pipes) in a WDN based on "demand edge betweenness centrality (EBCQ)". Therefore, the connectivity of the network based on spatial layout is modelled using a mathematical graph-based approach that represents the topology of the WDN and incorporates hydraulic factors to imitate hydraulic behavior. The mathematical graph consists of #N (vertices) connected by a set of #E (edges) corresponding to nodes and pipes respectively. The ratio of length and diameter is used as edge weight to determine the shortest path, and adds the demands of the nodes to obtain EBCQ values for all edges in that path. In case of pipe failures, the corresponding edge is removed, and the EBCQ of the new graph is calculated and compared with the maximum possible flow to find overloaded and affected edges. Thereby, single and multiple pipe failures are investigated with this method.The method is applied to a benchmark case study and to a real network of an Alpine municipality in Austria. Furthermore, the results of the graph-based method are compared with the results from hydraulic modelling in terms of accuracy and computational time. The first results show only slight differences in the results of the graph-based method compared to those from hydraulic modeling. Further, the graph-based method is able to identify the same order of critical pipes with low deviation compared to those from the hydraulic model. Additionally, the computational time and data requirements, in the calculation of pipe criticality by graph-based approach is significantly less compared to the hydraulic modelling method. This method is useful in disaster or contamination scenarios where many scenarios or combinations are required.Funding: The project “RESIST” is funded by the Austrian security research programme KIRAS of the Federal Ministry of Finance (BMF).

Water distribution networks (WDNs) contain isolation valves that divide the network into segments. In the event of a failure within a segment, it must be isolated from the network by closing all surrounding isolation valves for facility repair. If one of these isolation valves is faulty, connected segments have to be isolated, increasing the number of affected nodes and demand not supplied. Therefore, identifying critical valves whose failure significantly impacts network performance, is of great importance to reduce the impact of failures. To address this, this work introduces (1) a graph-based analyses using eigenvector centrality to identify critical isolation valves within the WDNs, and (ii) a strategic method for adding new valves to high-demand segments using the Louvain community detection method. In contrast to existing approaches, the proposed methodology relies solely on graph-based approaches, eliminating the need for hydraulic simulations or models. The approach is tested on two case studies, demonstrating a high correlation between the identified critical valves and results from a state-of-the-art hydraulic-based method while being computationally more efficient. The findings of this research can assist WDN operators in implementing targeted rehabilitation programs such as prioritizing maintenance and replacement of critical valves.