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Schematic diagram of floods and demonstration of the effects of floods, random damage, and localized damage on a road system. a River and road network without flooding. b River and road network when rainfall-induced flooding inundates some road segments. c Sectional view of the boxed region in a. d Sectional view of the boxed region in b. The rainfall forms runoff on the ground. A large amount of runoff converges into a flood and inundates some road segments. The flood follows the river channel and damages infrastructure (e.g., roads) in a river basin. The flood-induced failures have a distinct trajectory. Schematic demonstrations of (e) random damage and (g) localized damage in a sketched network respectively. f 2D and h 3D views of flood disturbance in the sketched network
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The adverse effect of climate change continues to expand, and the risks of flooding are increasing. Despite advances in network science and risk analysis, we lack a systematic mathematical framework for road network percolation under the disturbance of flooding. The difficulty is rooted in the unique three-dimensional nature of a flood, where altit...
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... flood, as a special and realistic type of disturbance, threatens the robustness of infrastructure systems 12,43,44 . As shown in Fig. 1, floods are locally destructive and the situation is similar to localized damage from this perspective. However, a flood can also affect the entire network owing to its wide spatial dispersion through rivers and the altitude of roads with a three-dimensional (3D) network structure, which is similar to the case of random damage. For ...
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... collected the maximum observed flooding from Dartmouth Flood Observatory data sets 52 . After inputting the flood information to the actual US road network, we use our failure model to identify road intersections that have directly and indirectly failed (as shown in Supplementary Fig. 1). We collected information of road closures reported by TranStar and flooded streets (road segments) reported by public media 53 in Houston and refer to these road closures and flooded streets as reported failures. ...
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... the unimportant nodes become the last straw that breaks the camels back and how these nodes combine with damage patterns to trigger abrupt systematic malfunctions call for more research in the broad field of disaster risk science. Comparing the affected population between China and the US (see Supplementary Fig. 10 and Supplementary Note 2 for details), we find that more highly populated counties are affected by floods more in China than in the US; counties with an extremely high population density ( [95,100] percentile) are more likely to be directly affected by floods in China; and more people are indirectly affected by floods in China. All these findings indicate that flood mitigation will be more challenging in China than in the US. ...
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... shown in Fig. 1, we use this sketch network (jN j ¼ 20) to compare the failures resulting from floods with those resulting from random damage and localized damage on the network. The nodes are removed as a result of any type of disturbance; i.e., the red nodes in Fig. 1e-g. We refer to these removed nodes as direct failures D. When one flood event ...
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... shown in Fig. 1, we use this sketch network (jN j ¼ 20) to compare the failures resulting from floods with those resulting from random damage and localized damage on the network. The nodes are removed as a result of any type of disturbance; i.e., the red nodes in Fig. 1e-g. We refer to these removed nodes as direct failures D. When one flood event occurs, we acquire the inundated nodes (direct failures) in the road network, remove them, and its size is jDj ¼ 5 (Fig. 1e). When comparing the effect of this flood event with the effects of random damage and localized damage, we set the fraction of direct ...
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... from random damage and localized damage on the network. The nodes are removed as a result of any type of disturbance; i.e., the red nodes in Fig. 1e-g. We refer to these removed nodes as direct failures D. When one flood event occurs, we acquire the inundated nodes (direct failures) in the road network, remove them, and its size is jDj ¼ 5 (Fig. 1e). When comparing the effect of this flood event with the effects of random damage and localized damage, we set the fraction of direct failures in total nodes, 1 À p ¼ 1 4 , for all three types of disturbance in this example. After removing these nodes (direct failures), some nodes are disconnected from the giant connected component of ...
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... nodes (direct failures), some nodes are disconnected from the giant connected component of the road system (i.e., the majority of the road system). This means the vehicles on these nodes cannot reach the majority of nodes in a network. These nodes are referred to as indirect failures and the set of these nodes is denoted I . In the example of Fig. 1, we find the fractions of indirect failures among all nodes are 11 20 , 1 2 , and 2 5 , and the network breaks into three distinct connected components (C), two distinct connected components (C & P), and only one connected component (P) for random damage, floods, and localized damage respectively. The joint set of direct and indirect ...
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... flood, as a special and realistic type of disturbance, threatens the robustness of infrastructure systems 12,43,44 . As shown in Fig. 1, floods are locally destructive and the situation is similar to localized damage from this perspective. However, a flood can also affect the entire network owing to its wide spatial dispersion through rivers and the altitude of roads with a three-dimensional (3D) network structure, which is similar to the case of random damage. For ...
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... collected the maximum observed flooding from Dartmouth Flood Observatory data sets 52 . After inputting the flood information to the actual US road network, we use our failure model to identify road intersections that have directly and indirectly failed (as shown in Supplementary Fig. 1). We collected information of road closures reported by TranStar and flooded streets (road segments) reported by public media 53 in Houston and refer to these road closures and flooded streets as reported failures. ...
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... the unimportant nodes become the last straw that breaks the camels back and how these nodes combine with damage patterns to trigger abrupt systematic malfunctions call for more research in the broad field of disaster risk science. Comparing the affected population between China and the US (see Supplementary Fig. 10 and Supplementary Note 2 for details), we find that more highly populated counties are affected by floods more in China than in the US; counties with an extremely high population density ( [95,100] percentile) are more likely to be directly affected by floods in China; and more people are indirectly affected by floods in China. All these findings indicate that flood mitigation will be more challenging in China than in the US. ...
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... shown in Fig. 1, we use this sketch network (jN j ¼ 20) to compare the failures resulting from floods with those resulting from random damage and localized damage on the network. The nodes are removed as a result of any type of disturbance; i.e., the red nodes in Fig. 1e-g. We refer to these removed nodes as direct failures D. When one flood event ...
Context 12
... shown in Fig. 1, we use this sketch network (jN j ¼ 20) to compare the failures resulting from floods with those resulting from random damage and localized damage on the network. The nodes are removed as a result of any type of disturbance; i.e., the red nodes in Fig. 1e-g. We refer to these removed nodes as direct failures D. When one flood event occurs, we acquire the inundated nodes (direct failures) in the road network, remove them, and its size is jDj ¼ 5 (Fig. 1e). When comparing the effect of this flood event with the effects of random damage and localized damage, we set the fraction of direct ...
Context 13
... from random damage and localized damage on the network. The nodes are removed as a result of any type of disturbance; i.e., the red nodes in Fig. 1e-g. We refer to these removed nodes as direct failures D. When one flood event occurs, we acquire the inundated nodes (direct failures) in the road network, remove them, and its size is jDj ¼ 5 (Fig. 1e). When comparing the effect of this flood event with the effects of random damage and localized damage, we set the fraction of direct failures in total nodes, 1 À p ¼ 1 4 , for all three types of disturbance in this example. After removing these nodes (direct failures), some nodes are disconnected from the giant connected component of ...
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... nodes (direct failures), some nodes are disconnected from the giant connected component of the road system (i.e., the majority of the road system). This means the vehicles on these nodes cannot reach the majority of nodes in a network. These nodes are referred to as indirect failures and the set of these nodes is denoted I . In the example of Fig. 1, we find the fractions of indirect failures among all nodes are 11 20 , 1 2 , and 2 5 , and the network breaks into three distinct connected components (C), two distinct connected components (C & P), and only one connected component (P) for random damage, floods, and localized damage respectively. The joint set of direct and indirect ...
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Citations
... Here, a first extreme storm induced high antecedent soil moisture for a subsequent storm that caused extreme runoff and then extensive landslides and flooding. Strong non-linearities are also observed in the impacts, for instance, when local flooding triggers large-scale abrupt failures of infrastructure networks (Wang et al., 2019). Randomness is another factor contributing to the complexity of the system, in particular, when the probability distribution of the flood magnitudes at the upper end decays more slowly than exponentially, a behaviour referred to as "heavy tailed distribution". ...
Extremely large floods that far exceed previously observed records are often considered virtually “impossible”, yet they are an ever-present threat similar to the sword suspended over the head of Damocles in the classical Greek anecdote. Neglecting such floods may lead to emergency situations where society is unprepared and to disastrous consequences. Four reasons why extremely large floods are often considered next to impossible are explored here, including physical (e.g. climate change), psychological, socio-economic and combined reasons. It is argued that the risk associated with an “impossible” flood may often be larger than expected and that a bottom-up approach should be adopted that starts from the people affected and explores possibilities of risk management, giving high priority to social in addition to economic risks. Suggestions are given for managing this risk of a flood considered impossible by addressing the diverse causes of the presumed impossibility.
... In their study, [12] and [13] opined that population growth, land use, and the need for housing have promoted continued watershed alterations via the development of more impermeable surfaces, construction on flood plains and land reclamation from swampy areas within the metropolis have become flood risk accelerators together with the challenge of rising sea levels, more incessant and aggressive rainfalls and the overall decadence and in some cases obsolescence in drainage infrastructure in many communities. Inquiries conducted by [14], [15], [16] and [17] revealed that floods can cause local accumulation and phase transition disruptions, resulting in widespread malfunctioning of road networks at crucial locations. Tackling this challenge effectively requires the development of integrated mathematical and network science-based frameworks to enhance the resilience of critical infrastructure networks against flooding. ...
The integration of remote technology into the construction industry has advanced the emergence of smart, self-learning infrastructure across all levels of land transportation design and development. However, energy harvesting capabilities for smart road ventilation purposes have not yet been fully researched as the world gravitates towards energy sustainability. This study thus presents a mathematical investigation of the self-draining design potentials of road pavements, leveraging on energy absorption and conversion to accelerate conductive and convective ventilation of these pavements during wet conditions, as a means of ensuring the skid resistance and safety of such pavements are not compromised. Applying numerical methods with the aid of commercial software code MATLAB®, the classical physics of fluid-surface interaction was used to model accelerated drainage of microflood off paved surfaces through methodical modification of the thickness of the boundary layer associated with the flow regime over the flat road surface. Results indicate significant influences of energy exchange, thermal, and viscous diffusivity on flow acceleration. Alterations in parameters such as the slip factor, thermally induced Brownian motion or phoretic characteristics of the fluid particles at the boundary induce accelerated flow at the surface of the pavement. The findings contribute to the advancement of smart infrastructure by offering practical insights into the potential benefits of integrating energy-harvesting technologies into road pavement systems. By optimizing flow acceleration mechanisms, smart pavements can play a crucial role in improving road safety and sustainability in urban environments.
... To achieve SDG 11.5 by 2030 in lowland floodplain areas, flood impact assessments at different scales have attracted the attention of domestic and foreign researchers. For instance, Hirabayashi et al. (2013), Vousdoukas et al. (2018), and Wang et al. (2019) published studies on the change in flood risk due to climatic alterations, socioeconomic networks, and road networks, respectively, at the global, European, and Chinese scales [13][14][15]. Lin et al. (2020) explored multi-scenario flood risk assessment in the Guangzhou, China, estuary area using the future land use (FLUS) model [16]. ...
... To achieve SDG 11.5 by 2030 in lowland floodplain areas, flood impact assessments at different scales have attracted the attention of domestic and foreign researchers. For instance, Hirabayashi et al. (2013), Vousdoukas et al. (2018), and Wang et al. (2019) published studies on the change in flood risk due to climatic alterations, socioeconomic networks, and road networks, respectively, at the global, European, and Chinese scales [13][14][15]. Lin et al. (2020) explored multi-scenario flood risk assessment in the Guangzhou, China, estuary area using the future land use (FLUS) model [16]. Further research has focused on the formulation of policies via the creation of disaster prevention and mitigation procedures [17][18][19]. ...
Floods are common and inevitable natural disasters. Achieve Sustainable Development Goal (SDG) 11.5 is a critical challenge for coastal cities, especially those in deltaic lowlands such as in the case of Guangzhou, China. Regarding the spatial planning and design of such urban regions, it is crucial to study the impacts of flooding in compact or decentralized spatial development pathways. This reinforces the understanding of the relationship between strategic decisions for spatial planning and flood mitigation. However, the lack of a computer model to assess spatial evolution paths is a significant limitation. The non-dominated Sorting Genetic Algorithm II (NSGA-II) explores the possibility of a compact built-up land layout in 2030. The results showed that, concerning the 2030 decentralized scenario, the 2030 compact scenario presents a large increase in the integrated fitness function value from 0.618 to 0.771 (the increase is equivalent to 0.153 or about 24.75%). In addition, different development scenarios were constructed by setting different target weights. Compared to the decentralized scenario results, the fitness function values of the optimization results of each scenario showed better results at different levels. They could also serve as a reference for other similar coastal areas to achieve SDG 11.5 by 2030.
... This could also promote the population's willingness to commute by public transportation during snowstorms, reducing land use while also reducing greenhouse gas emissions, and further achieving low-carbon travel [47]. In addition, most of the previous studies focused on cities with well-developed integrated public transport networks, especially those with well-developed subway networks, and provided little help to the design and planning of integrated public transport networks in developing cities [1,48]. The second contribution of this study was to select a northern Chinese city, Shenyang, as a case study, in which the integrated public transportation network is building. ...
The development of integrated public transportation networks has received widespread attention in recent years. Especially in global northern cities, improving the substitution of subways for buses could meet population travel demand during snowstorms, which minimizes the impact of snowstorms on the public transportation network. Furthermore, the development of rail transit is conducive to the intensive and efficient use of land resources. Therefore, in this study, we selected a northern Chinese city, Shenyang, as a case study. For obtaining the population travel demand, we collected the actual population flow data in the morning and evening peaks during snowstorms. The network analysis was used to identify the loopholes and key stations in the subway and bus networks, respectively. A coupling model was built to measure the coupling value of each station in the subway and bus networks, according to its population travel demand and supply capacity, which was further used to measure the substitution of subways for buses in the morning and evening peaks during snowstorms. The results indicate that some subway stations were in a coupling state, while their surrounding bus stations were in a decoupling state. These subway stations could replace the bus stations to reduce the impact and damage of snowstorms on public transportation network. However, some subway stations and the surrounding bus stations were all in a decoupling state, which were under great pressure to meet the population commuting demand during snowstorms. This study can provide insight into optimizing public transportation network planning and design in many northern regions and help to coordinate land and transportation utilization.
... Critical infrastructure like transportation systems, power grids, etc., are generally interdependent in cities. Within interdependent networks, failure of one element or node may result in failure of multiple nodes that are interconnected (Saidi et al., 2018;Wang et al., 2019). In light of climate change, cities are to expected to face an increase in devastating floods in the coming years, which are in turn compounded by increases in population size, urbanization, and impermeable surfaces (Pregnolato et al., 2017a). ...
Transportation networks, which are vital to society’s function, are vulnerable to extreme events like floods. Under the influence of climate change, the intensity and the frequency of these food events are increasing globally at an alarming rate. These events result in a series of flood impacts and associated cascading effects, which reduce the overall resilience of the road network. Flood risk assessment methods, which are applied to analyze these impacts are almost always associated with hybrid methods/models, which improvise the overall assessment methodology and impact quantification. The present article is a short review of the hybrid models like GIS-based models, cellular automata, traffic simulation, and input–output models, which are the most applied in the flood assessment methods. Of these models, GIS-based models have wide range of applications and depict the impacts in spatial scale. The Calgary floods of 2013 is used to display the spatial impact and flood prone/risk areas.
... This approach requires extensive data, which might not be readily available. Some researchers [48] use simulated flood data to study the effects of flooding on road networks. This method alone is not very effective due to the lack of consideration for network robustness [25]. ...
As climate change influences flood frequency, transportation damage and disruptions will become more common. Given the network’s expanse and cost of construction, communities’ mitigation efforts should be informed by analyses that span normal conditions and disaster management phases. This paper analyzes road segment criticality in normal, flood response, and recovery phases in Anderson County, South Carolina, considering impacts on emergency services, healthcare, industry, education, recreation, and transit. A 100-year event provides context for analyzing flood impacts to the time-based shortest paths, determined using ArcGIS Pro 3.1.3. Local and secondary roads were especially affected, with rerouting concentrating around the Anderson City area. Blocked road sections identified potentially vulnerable roads, and normalized betweenness centrality metrics identified community dependence on road segments for daily and emergency operations. While the quantity and dispersion of parks and grocery stores mitigated rerouting distance, other purposes faced challenges from impassable routes. The analysis revealed the southeastern and southern regions as most impacted across purposes, suggesting targeted mitigation. I-85, State Routes 28 and 81, and Federal Routes 29, 76, and 178 were the most critical roads before, during, and after the flood. This study highlights commonalities in road criticality across phases to support resilient transportation planning and sustainability.
... Roadway network robustness refers to the network's ability to access various destinations despite disruptions such as flood inundation (Snelder, van Zuylen and Immers, 2012). Percolation theory is often used to analyse network robustness under link and node disruption, with network robustness measured by topological connectivity based on the size of the largest connected component (Wang et al., 2019). While various studies have employed percolation theory to examine roadway robustness, these approaches do not fully capture the impact of local disruptions on the network during a flooding event (Loreti et al., 2022). ...
... The challenges for integrating context information with road segments involve (i) no explicit local community representation as the boundary for road networks since local impacts of nodes on networks often rely on explicit topology information (Hillier and Hanson, 1984); (ii) no uniform framework to integrate road networks with various levels of local impact under flooding event, which require inundation map information (Wang et al., 2019). Corresponding to the above challenges, this module is designed to collect related datasets to support further calculation. ...
... It is noteworthy that smaller components in the network. However, they may consist of just a few nodes and can also reflect the type of network interruption that can quickly reduce urban connectivity (Wang et al., 2019). Ignoring these small, connected components may exacerbate emergency management difficulties due to limited access. ...
Floods affect an average of 21 million people worldwide each year, and their frequency is expected to increase due to climate warming, population growth, and rapid urbanization. Previous research on the robustness of transportation networks during floods has mainly used percolation theory. However, the component size of disrupted networks cannot capture the entire network's information and, more importantly, does not reflect the local reality. To address this issue, this study introduces a novel approach to context-based bounded centrality to extract the local impact of disruption. In particular, we propose embedding travel behaviour into the road network to calculate bounded centrality and develop new measures characterizing connected component size during flooding. Our analysis can identify critical road segments during floods by comparing the decreasing trend and dispersibility of component size on road networks. To demonstrate the feasibility of these approaches, a case study of the London transportation infrastructure that integrates road networks with relevant urban contexts is presented in this paper. We find that this approach is beneficial for practical risk management, helping decision-makers allocate resources effectively in space and time.
... This was the case for the Kamp flood in 2002, when a moderate increase in rainfall led to a disproportionate and sudden increase in river flow, which was unexpected based on the information available at the time. Strong non-linearities are also observed in the impacts, for instance, when local flooding triggers large-scale abrupt failures of infrastructure networks (Wang et al., 2019). ...
Extremely large floods, or mega-floods, are often considered virtually ‘impossible’, yet are an ever-present threat similar to the sword suspended over the head of Damocles in the classical Greek anecdote. Neglecting such floods may lead to emergency situations where society is unprepared, and to disastrous consequences. Four reasons why extremely large floods are often considered next to impossible are explored here, including physical (e.g. climate change), psychological, socio-economic and combined reasons. It is argued that the risk associated with an ‘impossible’ flood may often be larger than expected, and that a bottom-up approach should be adopted that starts from the people affected and explores possibilities of risk management, giving high priority to social in addition to economic risks. Suggestions are given for managing this risk of a flood considered impossible by addressing the diverse causes of the presumed impossibility.
... Floods from a network science perspective were studied in [5,43,[49][50][51][52][53][54]. A working version of this paper was first published in [55]. ...
Simple Summary
The framework for assessing the loss of access to critical infrastructure is presented, taking into account surface elevation and different types of critical facilities. Two types of analysis were performed—ex post to quantify the immediate damage and ex ante to provide information for precautionary measures. The framework provides detailed information on the population’s loss of access to each type of facility; we propose the use of network statistics to quantify and explain the consequences of flooding. To account for the possibility of accessing buildings from different sides, the notion of a multiedge facility is introduced. The simulations with randomly generated flood events, using a probability model estimated from observations, were performed as part of the ex ante assessment.
Abstract
This work presents a framework for assessing the socio-physical disruption of critical infrastructure accessibility using the example of Greater Jakarta, a metropolitan area of the Indonesian city. The first pillar of the framework is damage quantification based on the real flood event in 2020. Within this pillar, the system network statistics before and shortly after the flood were compared. The results showed that the flood impeded access to facilities, distorted transport connectivity, and increased system vulnerability. Poverty was found to be negatively associated with surface elevation, suggesting that urbanization of flood-prone areas has occurred. The second pillar was a flood simulation. Our simulations identified the locations and clusters that are more vulnerable to the loss of access during floods, and the entire framework can be applied to other cities and urban areas globally and adapted to account for different disasters that physically affect urban infrastructure. This work demonstrated the feasibility of damage quantification and vulnerability assessment relying solely on open and publicly available data and tools. The framework, which uses satellite data on the occurrence of floods made available by space agencies in a timely manner, will allow for rapid ex post investigation of the socio-physical consequences of disasters. It will save resources, as the analysis can be performed by a single person, as opposed to expensive and time-consuming ground surveys. Ex ante vulnerability assessment based on simulations will help communities, urban planners, and emergency personnel better prepare for future shocks.
... Users are free to opt-out of location sharing at any time. By analyzing the aggregated mobility patterns of more than 500,000 anonymous users (representing 12.5% of the population of the Puget Sound region under analysis), Wang et al. (2019) determined that smartphone-derived GPS data, as compared to cellular network and in-vehicle GPS data, benefits from a superior combination of large-scale, high-accuracy, precision, and observational frequency (Wang et al., 2019). Beyond validating scale and accuracy, the research (Wang et al., 2019) found that the data source is highly demographically representative. ...
... Users are free to opt-out of location sharing at any time. By analyzing the aggregated mobility patterns of more than 500,000 anonymous users (representing 12.5% of the population of the Puget Sound region under analysis), Wang et al. (2019) determined that smartphone-derived GPS data, as compared to cellular network and in-vehicle GPS data, benefits from a superior combination of large-scale, high-accuracy, precision, and observational frequency (Wang et al., 2019). Beyond validating scale and accuracy, the research (Wang et al., 2019) found that the data source is highly demographically representative. ...
... By analyzing the aggregated mobility patterns of more than 500,000 anonymous users (representing 12.5% of the population of the Puget Sound region under analysis), Wang et al. (2019) determined that smartphone-derived GPS data, as compared to cellular network and in-vehicle GPS data, benefits from a superior combination of large-scale, high-accuracy, precision, and observational frequency (Wang et al., 2019). Beyond validating scale and accuracy, the research (Wang et al., 2019) found that the data source is highly demographically representative. The data has a wide set of attributes, including anonymized device ID, point-of-interest ID, latitude, longitude, and dwell time of visitation. ...
This study uses mobility data in the context of 2017 Hurricane Harvey in Harris County to examine the impact of flooding on access to dialysis centers. We examined access dimensions using static and dynamic metrics. The static metric is the shortest distance from census block groups to the closest centers. Dynamic metrics are: 1) redundancy (daily unique number of centers visited), 2) frequency (daily number of visits to dialysis centers), and 3) proximity (visits weighted by distance to dialysis centers). The results show that: the extent of dependence of regions on dialysis centers varies; flooding significantly reduces access redundancy and frequency of dialysis centers; regions with a greater minority percentage and lower household income were likely to experience extensive disruptions; high-income regions more quickly revert to pre-disaster levels; larger centers located in non-flooded areas are critical to absorbing the unmet demand from disrupted facilities.
