Analysis of Flood Vulnerability and Transit Availability with a Changing Climate in Harris County, Texas

Pulcinella, Joshua A.; Winguth, Arne; Allen, Diane Jones; Gangadhar, Niveditha Dasa

doi:10.1177/0361198119839346

Cited by 10 publications

(4 citation statements)

References 14 publications

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“…The likelihood of such events has been greatly increased by the dual processes of climate change and urbanization (Emanuel 2017 ; Zhang et al 2018 ). Such events cause massive transit disruptions in Harris County, creating challenges for evacuation and emergency response in this highly car dependent region (Pulcinella et al 2019 ). Flooding is also particularly concerning for low-income households—15.9% of the Harris County population was in poverty in 2019 (U.S. Census Bureau 2019a )—which are disproportionately located within floodplains and more exposed to storm surges (Pulcinella et al 2019 ).…”

Section: Methodsmentioning

confidence: 99%

“…Such events cause massive transit disruptions in Harris County, creating challenges for evacuation and emergency response in this highly car dependent region (Pulcinella et al 2019 ). Flooding is also particularly concerning for low-income households—15.9% of the Harris County population was in poverty in 2019 (U.S. Census Bureau 2019a )—which are disproportionately located within floodplains and more exposed to storm surges (Pulcinella et al 2019 ). These disparities intersect with patterns of racial segregation in the highly racially and ethnically diverse county, which the American Community Survey (ACS) reports is 28.5% white alone (not Hispanic or Latino), 18.5% Black or African American, 7% Asian, and 43.7% Hispanic or Latino (U.S. Census Bureau 2019b ).…”

Section: Methodsmentioning

confidence: 99%

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Assessing changes in food pantry access after extreme events

et al. 2022

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Food pantries play a growing role in supporting households facing or at risk of food insecurity in the United States. They also support emergency response and recovery following disasters and extreme weather events. Although food pantries are often placed in close proximity to communities with the highest rates of poverty and risk of food insecurity, access to these facilities can be disrupted during and after extreme events. Decreased access to food pantries following disasters can be particularly problematic as the need for these services is also likely to grow after such events. Despite the vast body of research on food retail access and food environments, relatively little research has utilized spatial analysis to understand food pantry access, particularly after extreme events. Using Geographic Information Systems (GIS), we characterize changes in access to food pantries following flooding events in Harris County, Texas—a highly populated and flood prone region with high levels of food insecurity and poverty. Specifically, our study models disruptions in road networks due to flooding and assesses the impacts of these disruptions on proximity to food pantries. The results reveal that much of Harris County sees only small increases in travel distance to food pantries due to extreme flooding, but some areas may be unable to access food pantries at all. This research highlights the potential and some of the limits of private food assistance networks to support emergency response efforts.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Assessing changes in food pantry access after extreme events

et al. 2022

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“…Numerous studies have quantified resource access inequities under normal conditions and without the influence of specific climate change or other environmental related events (Liu et al, 2014;Akhavan et al, 2019;Barbosa et al, 2021). While the term food desert has undergone intense scrutiny in recent years due to its simplistic nature of equating access to only geographic distance (Donald, 2013;Ghosh-Dastidar et al, 2017;Widener, 2018), resource accessibility remains highly variable from city to city and resource to resource (Akhavan et al, 2019;Pulcinella et al, 2019;Barbosa et al, 2021). In the context of emergency response, it does not matter what causes the inequitable disruption just as long as emergency managers are able to identify it and respond accordingly.…”

Section: Background Inequitable Access To Critical Resourcesmentioning

confidence: 99%

A network-based analysis of critical resource accessibility during floods

Preisser,

Passalacqua,

Bixler

et al. 2023

Front. Water

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Numerous government and non-governmental agencies are increasing their efforts to better quantify the disproportionate effects of climate risk on vulnerable populations with the goal of creating more resilient communities. Sociodemographic based indices have been the primary source of vulnerability information the past few decades. However, using these indices fails to capture other facets of vulnerability, such as the ability to access critical resources (e.g., grocery stores, hospitals, pharmacies, etc.). Furthermore, methods to estimate resource accessibility as storms occur (i.e., in near-real time) are not readily available to local stakeholders. We address this gap by creating a model built on strictly open-source data to solve the user equilibrium traffic assignment problem to calculate how an individual's access to critical resources changes during and immediately after a flood event. Redundancy, reliability, and recoverability metrics at the household and network scales reveal the inequitable distribution of the flood's impact. In our case-study for Austin, Texas we found that the most vulnerable households are the least resilient to the impacts of floods and experience the most volatile shifts in metric values. Concurrently, the least vulnerable quarter of the population often carries the smallest burdens. We show that small and moderate inequalities become large inequities when accounting for more vulnerable communities' lower ability to cope with the loss of accessibility, with the most vulnerable quarter of the population carrying four times as much of the burden as the least vulnerable quarter. The near-real time and open-source model we developed can benefit emergency planning stakeholders by helping identify households that require specific resources during and immediately after hazard events.

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“…Multiple studies have explored the spatial-temporal properties of floods in urban networks, including impact evaluation of environmental stress [6][7][8] and cascading effects in road networks 9,10 . In particular, empirical studies adopting remote sensors 11 , hydraulic data 12 , or satellite images 13 have attempted to capture the properties of urban flooding.…”

Section: Introductionmentioning

confidence: 99%

A Network Percolation-based Contagion Model of Flood Propagation and Recession in Urban Road Networks

Fan,

Jiang,

Mostafavi

2020

Preprint

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In this study, we propose a contagion model as a simple and powerful mathematical approach for predicting the spatial spread and temporal evolution of the onset and recession of flood waters in urban road networks. A network of urban roads resilient to flooding events is essential for provision of public services and for emergency response. The spread of floodwaters in urban networks is a complex spatial-temporal phenomenon. This study presents a mathematical contagion model to describe the spatial-temporal spread and recession process of flood waters in urban road networks. The evolution of floods within networks can be captured based on three macroscopic characteristics-flood propagation rate (β ), flood incubation rate (α), and recovery rate (µ)-in a system of ordinary differential equations analogous to the Susceptible-Exposed-Infected-Recovered (SEIR) model. We integrated the flood contagion model with the network percolation process in which the probability of flooding of a road segment depends on the degree to which the nearby road segments are flooded. The application of the proposed model was verified using high-resolution historical data of road flooding in Harris County during Hurricane Harvey in 2017. The results show that the model can monitor and predict the fraction of flooded roads over time. Additionally, the proposed model can achieve 90% precision and recall for the spatial spread of the flooded roads at the majority of tested time intervals. The findings suggest that the proposed mathematical contagion model offers great potential to support emergency managers, public officials, citizens, first responders, and other decision makers for flood forecast in road networks.

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Analysis of Flood Vulnerability and Transit Availability with a Changing Climate in Harris County, Texas

Cited by 10 publications

References 14 publications

Assessing changes in food pantry access after extreme events

Assessing changes in food pantry access after extreme events

A network-based analysis of critical resource accessibility during floods

A Network Percolation-based Contagion Model of Flood Propagation and Recession in Urban Road Networks

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