Supplementary Material (Data): Do Resilience Metrics of Water Distribution Systems Really Assess Resilience? A Critical Review

This dataset contains the data for the paper "Do Resilience Metrics of Water Distribution Systems Really Assess Resilience? A Critical Review". It contains the table with the categoriation of WDS resilience metrics and a table with literature search protocol

Supplementary Material (Code): Do Resilience Metrics of Water Distribution Systems Really Assess Resilience? A Critical Review

This repository contains Jupyter notebooks that use the original datasets used to generate the results presented in the paper "Do Resilience Metrics of Water Distribution Systems Really Assess Resilience? A Critical Review". The notebooks recreate the whole analysis and all diagrams presented in the Revie

Do Resilience Metrics of Water Distribution Systems Really Assess Resilience? A Critical Review

Having become vital to satisfying basic human needs, water distribution systems (WDSs) are considered critical infrastructure. They are vulnerable to critical events such as extreme weather, natural and man-made disasters, armed conflicts etc. To account for critical events in the context of design and operation of WDSs, the concept of resilience is frequently mentioned. How resilience of WDSs can be assessed using resilience metrics has been the subject of research of many publications. The aim of this paper is to inspect the alignment between a general understanding of resilience in WDSs and the metrics used for resilience assessment. A novel framework for categorising resilience metrics for WDSs is presented. A literature review of resilience metrics for WDSs is performed and the results are analysed using the framework designed. The results show that resilience metrics do not really assess resilience of the systems, but rather only specific functions and properties of systems which can make them resilient

Do resilience metrics of water distribution systems really assess resilience? A critical review

Having become vital to satisfying basic human needs, water distribution systems (WDSs) are considered critical infrastructure. They are vulnerable to critical events such as extreme weather, natural and man-made disasters, armed conflicts etc. To account for critical events during design and operation of WDSs, the concept of resilience is frequently mentioned. How resilience of WDSs can be assessed using resilience metrics has been the subject of research of many publications. The aim of this paper is to inspect the alignment between a general understanding of resilience in WDSs and the metrics used for their resilience assessment. A novel framework for categorising resilience metrics for WDSs is presented. A literature review of resilience metrics for WDSs is performed and the results are analysed using the developed framework. The results show that the existing resilience metrics are not able to capture resilience in its complexityresilience metrics do not really assess resilience of the WDSs as a whole, but rather focus only on specific functions and properties which can make the WDSs resilient

A Method for Modeling Urban Water Infrastructures Combining Geo-Referenced Data

Water distribution networks are the backbone of any municipal water supply. Their task is to supply the population regardless of the respective demand. High resilience of these infrastructures is of great importance and has brought these infrastructures into the focus of science and politics. At the same time, the data collected is highly sensitive and often openly unavailable. Therefore, researchers have to rely on models that represent the topology of these infrastructures. In this work, a model is developed that allows the topology of an urban water infrastructure to be mapped using the example of Cologne, Germany by combining freely available data. On the one hand, spatial data on land use (local climate zones) are used to disaggregate the water demand within the city under consideration. On the other hand, the parallelism of water and urban transportation infrastructures is used to identify the topology of a network by applying optimization methods. These networks can be analyzed to identify vulnerable areas within urban structures.ISSN:2073-444

A Method for Modeling Urban Water Infrastructures Combining Geo-Referenced Data

Water distribution networks are the backbone of any municipal water supply. Their task is to supply the population regardless of the respective demand. High resilience of these infrastructures is of great importance and has brought these infrastructures into the focus of science and politics. At the same time, the data collected is highly sensitive and often openly unavailable. Therefore, researchers have to rely on models that represent the topology of these infrastructures. In this work, a model is developed that allows the topology of an urban water infrastructure to be mapped using the example of Cologne, Germany by combining freely available data. On the one hand, spatial data on land use (local climate zones) are used to disaggregate the water demand within the city under consideration. On the other hand, the parallelism of water and urban transportation infrastructures is used to identify the topology of a network by applying optimization methods. These networks can be analyzed to identify vulnerable areas within urban structures

A Method for Modeling Urban Water Infrastructures Combining Geo-Referenced Data

Water distribution networks are the backbone of any municipal water supply. Their task is to supply the population regardless of the respective demand. High resilience of these infrastructures is of great importance and has brought these infrastructures into the focus of science and politics. At the same time, the data collected is highly sensitive and often openly unavailable. Therefore, researchers have to rely on models that represent the topology of these infrastructures. In this work, a model is developed that allows the topology of an urban water infrastructure to be mapped using the example of Cologne, Germany by combining freely available data. On the one hand, spatial data on land use (local climate zones) are used to disaggregate the water demand within the city under consideration. On the other hand, the parallelism of water and urban transportation infrastructures is used to identify the topology of a network by applying optimization methods. These networks can be analyzed to identify vulnerable areas within urban structures