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Disparities of population exposed to flood hazards in the United States


Abstract and Figures

This study integrates publicly available datasets to provide a county-based assessment of socio-economic disparities of population exposure to flood hazards in the United States. Statistical methods were applied to reveal the national trends and local deviations from the trends. Results show that approximately 21.8 million (6.9% of) U.S. population are living in flood zones in 2015. The flood exposure varies greatly across the space. Communities near water bodies are more responsive to potential flood hazards by avoiding residence in flood zones than inland communities. At the national scale, economically disadvantaged population are more likely to reside in flood zones than outside. At the national scale, economically disadvantaged population tend to reside in flood zones in inland areas, while coastal flood zones are more occupied by wealthier and old people. These findings point to an alarming situation of inland communities where people are generally less responsive to flood hazards and people in flood zones have a lower economic condition. Using “hot spot” analysis, local clusters of disadvantaged population groups with high flood exposure were identified. Overall, this study provides important baseline information for policy-making of different levels of administration and pinpoints local deviations where diversified and targeted strategies are needed to mitigate flood risk in communities with different socio-economic characteristics. The valuable insights offered in this study advance understanding about the dynamic interactions between flood exposure and human factors.
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Observing Community Resilience from Space: Using
Nighttime Lights to Model Economic Disturbance and
Recovery Pattern in Natural Disaster
YiQianga, Qingxu Huangb, Jinwen Xua
aDepartment of Geography and Environment, University of Hawaii Manoa, USA
bCenter for Human-Environment System Sustainability (CHESS), State Key Laboratory of Earth
Surface Processes and Resource Ecology (ESPRE), Beijing Normal University, China
Published in Sustainable Cities and Society in February 2020.
1 Introduction
Due to climate change and rapid population growth, human society is faced with increasing threats
from natural disaster that can cause significant socio-economic consequences. Coastal
communities around the world are particularly vulnerable to natural disasters including both large-
scale rapid-moving disturbances such as hurricane and storm surges (Tebaldi et al., 2012), and the
slow-moving processes such as coastal erosion, sea level rise (Nicholls et al., 1999) and reduction
of ecosystem services (Spalding et al., 2014). According to the data from U.S. Census Bureau
(2011), 39% of the total population in the United States are living in counties directly on the
shoreline and the population density in coastal counties is more than four times the average density
of the whole United States. Since 2005 when Hurricane Katrina and Rita caused catastrophic
damage in Central Gulf Coast, much attention has been paid the resilience and long-term
sustainability of coastal communities. Empirical observations suggest that, under the same strength
of disaster, different communities endured different levels of disturbance and presented different
recovery patterns in socio-economic (Finch et al., 2010; Fussell et al., 2010), health (Burton, 2006;
Sastry and VanLandingham, 2009), and psychological conditions (Adeola, 2009). These observed
disparities can be attribute to various resilience of the communities.
Resilience describes the ability of an individual or a system to adapt to and recover from external
shocks or stresses (Adger, 2000). Although substantial knowledge has been gained on ecological
resilience (Perz et al., 2013) and engineering resilience (Yodo and Wang, 2016), there is yet a
consensus on how to measure resilience of human communities due to their complexity. In general,
quantitative assessment of community resilience is challenged by two issues. First, the definition
of community resilience various in different domains, which will be discussed in Section 2.1.
Moreover, resilience is often used interchangeably with other relevant concepts such as
vulnerability and adaptive capacity. The various definitions and conceptual frameworks of
community resilience influence how researchers select indicators and models to measure resilience
(Cutter et al., 2008; Lam et al., 2016; Sherrieb et al., 2010). The definition disagreement hampers
the development of standard metrics to measure resilience. Second, there is lack of empirical data
and approaches to quantify community resilience. Most of the existing assessments are based on
an index approach, which integrates a set of presumed indicators into a composite score to measure
resilience (Cutter et al., 2010; Hung et al., 2016; Sempier et al., 2010; Sherrieb et al., 2010). The
model specification, indicator selection and weighting are based on prior knowledge or expert
opinions. Although these indices provide general guidance for predicting community resilience,
their accuracies have not been validated against empirical observations in disasters (Bakkensen et
al., 2017; Beccari, 2016).
Empirical data about human activities and states are difficult to obtain in a disaster condition when
many social systems fail to function. Traditional data sources for resilience assessment (e.g.
surveys and census data) have limitations in various aspects, which will be elaborated in the next
section. Recently, remote sensing imageries become popular instruments to monitor human
dynamics on the earth surface such as urban growth (Shahtahmassebi et al., 2016), land cover
change (Joshi et al., 2016) and socio-economic conditions (Kuffer et al., 2016). Among the various
remote sensing products, night-time light (NTL) remote sensing has unique ability to capture
fluctuations of human activities, which can provide empirical data for resilience assessment. This
study introduces a quantitative framework to assess community resilience using the DMSP-OLS
NTL annual composite images as the data source. Specifically, stable lights in the time series of
DMSP-OLS annual images are used as a proxy to model recovery patterns of economic activity in
Hurricane Katrina in 2005. Spatial and statistical analyses are conducted to explore the
geographical disparities of the recovery patterns and their relationships with selected resilience
indicators. Specific questions answered in the case study are: 1) which communities appeared to
be more or less resilient in the disaster; 2) how the observed resilience levels are associated with
the environmental and socio-economic conditions? The introduced framework aims to fill the
critical gap of empirical data and assessment methods for community resilience. The analysis
results from the case study increase our understanding about community resilience and provide
actionable information to predict and prompt community resilience.
The rest of the article is organized as follows. Section 2 briefly reviews the related work about the
definitions, conceptual frameworks and assessment methods of community resilience. Section 3
introduces the data sources, assessment framework of community resilience based on NTL data
and statistical analyses. Section 4 presents the analysis results in the case study of Hurricane
Katrina, followed by the discussions in Section 5 and conclusions in Section 6.
2 Related Work
2.1 Definition and Conceptual Framework
The concept of resilience was first introduced by Holling (1973), who views resilience as the
ability of an ecological system to absorb change in the face of extreme perturbation and yet
continue to persist. Later, Timmerman (1981) applied the concept of resilience to social systems
and defined resilience as the measure of a system’s capacity to absorb and recover from disastrous
events. The resilience of a social system is also known as community resilience. Extending
Timmerman’s definition, Cutter et al., (2008) further elaborated that community resilience
includes both the inherent conditions of a system to absorb impacts and cope with an event and
post-event, adaptive processes that facilitate the ability of the social system to re-organize, change,
and learn in response to a threat. Norris et al., (2008) considered resilience as a process linking
communities’ capacities in response to the disturbance. In the field of engineering and
infrastructure systems, resilience describes the ability of resisting and absorbing disturbances and
the ability of adapting to disruptions, and returning to normal functionalities (Faturechi and Miller-
Hooks, 2015). Extensive reviews about the definitions of resilience and the related terms can be
found in (Cutter et al., 2008; Lam et al., 2016; Liao, 2012; Peacock, 2010). Despite the various
definitions in the literature, community resilience is often associated with two abilities: 1) the
ability to absorb/resist/withstand disturbance, and 2) the ability to respond/recover/restore the
acceptable level of functioning and structure.
In addition to the qualitative descriptions, a number of theoretical frameworks have been
developed to quantify community resilience. For instance, Cutter et al. (2010, 2014) define that
resilience consists of six components including social, economic, infrastructural, institutional,
community, and environmental, which is used as a guidance to select indicators for resilience
indices. Lam et al. (2016) measure resilience from the relationships among exposure, damage and
recovery. Additionally, resilience can be conceptualized as a dynamic process. The recovery
trajectory (also known as recovery curve) describes the continuous change of a functional capacity
of a system affected by a disturbance (e.g. natural disaster). The functional capacity could be the
social and economic capacity of a human community (White et al., 2015), biomass or population
of an ecological community (Qiang and Xu, 2019; Vercelloni et al., 2019), or the functionality or
serviceability of an infrastructure system (Koliou et al., 2018). The Curve A, B and C in Figure 1
illustrate common scenarios of resilience from high to low, where the functional capacity suddenly
declines after a disturbance, gradually recovers afterwards, and finally restores to the pre-disaster
condition (e.g. Trajectory B) or a new equilibrium (Trajectory A and C). Given the same strength
of disaster, the variation of the recovery trajectory is indicative of community resilience. The
maximum deviation from the pre-disaster condition (i.e. maximum disturbance) reflects the ability
of a system to absorb/resist/withstand disturbance from the disaster. The recovery speed or time
indicates the ability to respond/recover/restore the functional capacity.
Figure 1: Recovery trajectory of a system due to an external disturbance. Curve A, B and C represent three
possible scenarios of resilience from high to low. (Modified from White et al., 2015)
2.2 Resilience AssessmentIndex Approaches
Previous work of resilience assessment is mostly based on an index approach, which aggregates a
number of socio-economic and environmental indicators into an overall resilience index. The
Baseline Resilience Index for Communities (BRIC) developed by Cutter et al., (2010) is one of
the first and most cited resilience index. Analogous to the previous work of the Social Vulnerability
Index (SoVI) (Cutter et al., 2003), BRIC is aggregated from 36 indicators of the baseline
characteristics of community resilience. The indicators are rescaled into [0,1] and then aggregated
in five categories including social, economic, institutional, infrastructure, and community. The
overall BRIC index is the summation of the aggregated indices in the five categories. Analogously,
the Community Disaster Resilience Index (CDRI) by (Peacock, 2010) categorize resilience
indicators into a 4×4 matrix with capital domains (i.e. the social, economic, physical, and human
capital) and disaster phases (i.e. the mitigation, preparedness, response and recovery phase). The
selected indicators are aggregated in the 16 categories in the matrix, which are then aggregated
into the CDRI index. Other work on resilience indices include (Foster, 2012; Hung et al., 2016;
Sherrieb et al., 2010). Although these indices provide general predictions of resilience by
integrating prior knowledge and expert opinions, they do not inform specific disaster outcomes.
For instance, it is unclear whether a high resilience index implies low economic loss, low casualty
and injury, or fast economic recovery. The unspecified outcomes diminish the value of the indices
for specific decision-making. Moreover, most of the resilience indices have not been calibrated or
validated against empirical observations. Bakkensen et al., (2017) validated the BRIC and CDRI
with observed losses, fatalities, and disaster declarations, and found low or even contradictory
correlations between the indices and disaster outcomes.
2.3 Resilience Assessment Empirical Approaches
In addition to the index approach, efforts have been made to assess community resilience using
empirical observations in disasters. For instance, Lam et al., (2009) and LeSage et al., (2011)
conducted a series of telephone and street surveys to continuously monitor business reopening in
New Orleans after Hurricane Katrina. The time series of open businesses resemble the recovery
trajectory illustrated in Figure 1, which declines sharply after Katrina and gradually recovers. The
various recovery patterns in different communities were associated with environmental and socio-
economic variables to explain why some communities restored businesses more quickly than
others. However, the surveys were costly, labor-intensive and time consuming, which is not widely
applicable. Later, Lam et al., (2015, 2016) developed the Resilience Inference Model (RIM) which
uses the number of disasters, damage and population growth as proxies to measure community
resilience. Based on aggregated data in a 10-year period, the RIM model assumes that a resilient
community can resist damage and maintain high population growth while endured a high number
of disasters. Recently, with the advent of the Big Data era, data crowdsourcing and social media
platforms provide new opportunities to observe individuals’ activities and narratives at finer spatial
and temporal resolutions. For instance, Zou et al., (2017) used the frequency and sentiment of geo-
tagged Twitter messages (tweets) to monitor dynamic conditions of communities in Hurricane
Sandy. The recovery trajectories of communities can be reflected from time series of ratios and
average sentiment of tweets during the disaster. Timeliness, low cost and scalability are the main
advantages of social media data. However, the Big Data approaches are criticized for the biased
user profile (Zou et al., 2018) and low data quality (much noise and misinformation) (Li et al.,
2.4 Remote Sensing for Disaster Management
Remotely sensed imageries have been widely applied in disaster risk mapping (Bates, 2004; Hong
et al., 2007) and damage assessment (Cooner et al., 2016; Dong and Shan, 2013; Vetrivel et al.,
2018). However, most remote sensing products are not very useful for resilience assessment due
to their insensitivity to decline of human activity. For instance, it is difficult to detect a ‘ghost town’
or a damaged city from Landsat images. As an alternative, nighttime light (NTL) remote sensing
images have been proved an effective means to observe the dynamics (both increase and decline)
of population (Zhuo et al., 2009), urbanization (Xie and Weng, 2017) and economic activities (Li
et al., 2013). A comprehensive review of the applications of NTL data can be found in (Huang et
al., 2014). For disaster management, the NTL data have been applied to identify damage
(GILLESPIE et al., 2014; Kohiyama et al., 2004), power outage (Hultquist et al., 2015; Zhao et
al., 2018), and analyze the change of human activities (Li et al., 2018) and urbanization (Huang et
al., 2019) affected by disasters. Despite these applications, the utility of NTL data in modeling
community resilience has not been fully exploited in a theoretical framework. Extending the
conceptual framework of recovery trajectory, this study introduces a quantitative approach to
model resilience using DMSP/OLS NTL images as the data source.
3 Method
3.1 Inter-Calibration of NTL Images
The Stable Lights images collected by the Defense Meteorological Satellite Program Operational
Line Scanner (DMSP/OLS) were used for this study. The images are cloud-free composites created
using all the available archived DMSP-OLS smooth resolution data from the year 1992 to 2013.
The DMSP/OLS images include 34 annual composites at a 30 arc second resolution collected by
six different satellites (F10, F12, F14, F15, F16, and F18). Due to the absence of inter-satellite
calibration and onboard calibration, the digital numbers (DN) in the DMSP-OLS images cannot
be converted to exact radiance. To analyze continuous recovery trajectories over time, the DMSP-
OLS images need to be calibrated to make the images in different years and satellites comparable.
A widely applied inter-calibration procedure was proposed by (Elvidge et al., 2009), which uses a
quadratic polynomial regression to adjust the DNs against a reference image (see Equation 1).
Equation 1
The inter-calibration process follows the same procedure as introduced in (Elvidge et al., 2009).
By reviewing the data, it was found that the image F121999 has the highest average DN in the
United States. Due to the saturation of the DMSP/OLS in bright areas (e.g. city centers), the
F121999 was used as the reference image and all other images were calibrated to match the DNs
in F121999. Los Angeles was chosen as the reference site, as it has been a mature metropolis where
the light change is negligible (Hsu et al., 2015). Due to the uneven distribution of DNs in the
images, a random sampling will lead to overfit near the two extremes of DNs (i.e. 1 and 63) where
pixels are concentrated. To ensure the regression equations equally fit the entire value range, a
stratified sample of lit pixels (200 pixels in each DN value) were extracted in the reference site for
the calibration. The 2nd order regression equation was calibrated for each image with the reference
to F121999. The coefficients of the regression equations are in Table 1. After the calibration,
images in the same year were averaged into one image, leading to a time series of annual images
from 1992 to 2013.
Table 1: Coefficients of the quadratic polynomial regression for inter-calibration.
3.2 Estimation of Gross Domestic Product
The inter-calibrated DMSP-OLS images were clipped in the affected area in Hurricane Katrina,
which include 179 counties declared as disaster areas by the Federal Emergency Management
Agency (FEMA). Hurricane Katrina made the first landfall in Florida on August 25th, 2005 and
the second in Louisiana on August 29th, 2005. The disaster areas include the entire Louisiana (64
parishes) and Mississippi (82 counties), 22 counties in Alabama, and 11 counties in Florida (Figure
2). Zonal operation was applied to aggregate the DNs within individual counties. After logarithm
transformation, the sum of DNs and number of lit pixels can well predict (= 0.92) the gross
domestic product (GDP) in the 179 counties in a linear model (Equation 2). The goodness of fit
(i.e. ) is highest for GDP compared to other economic indicators (e.g. personal income, number
of employees and business establishments). As county-level GPD data in the U.S. is only available
in 2012-2015 (BEA, 2018), the GDP data and DMSP-OLS images in 2012 and 2013 (the
overlapping years of the two data sources) were used to derive the regression equation. All the
GDP data are adjusted to the value of current U.S. dollar. Finally, the GDP of the counties from
1992 to 2013 was estimated using Equation 3.
log (
) = 3.375 log(
)2.548 log(
Equation 2
Equation 3
Figure 2: The track of Hurricane Katrina and counties that are presidentially declared as disaster area.
3.3 Measurement Framework
The estimated GDP was analyzed in the framework in Figure 3 to assess the resilience of the
counties. The GDP of each county is rescaled into [0, 1] to be comparable. Two regression lines
were derived for the normalized GDP from 1992 to 2004 and from 2005 to 2013 to represent the
pre- and post-Katrina economic trajectory respectively. Although various models are available for
GDP projection, the linear model is still considered a valid benchmark to project GDP growth in
the U.S. (Ferrara et al., 2015; Marcellino, 2008), especially in the recent decades when the
volatility of US GDP growth tend to decline (Burren and Neusser, 2010). Despite the slight decline
(-1.8%) in 2009 due to the financial crisis, the annual GDP in the U.S. from 1990 to 2018 generally
follows a linear trend (World Bank, 2019). The extension of the pre-Katrina trajectory (thin dashed
line in Figure 3) represents the business-as-usual condition as if the pre-Katrina economic growth
Three metrics were calculated for each county. First, the difference between the projected GDP in
the business-as-usual trajectory ( (2005)) and the actual 2005 GDP in the post-Katrina
trajectory ( (2005)) was calculated to represent instant economic disturbance caused by
Katrina (Equation 4). This metric (denoted as D for simplicity) measures the ability of a system to
absorb/resist/withstand disturbance in a disaster. Second, the difference between the slopes of the
post-disaster trajectory () and the business-as-usual trajectory () was calculated to indicate the
recovering rate () of GDP after Katrina (Equation 5). A high (positive) would implies a
strong ability to respond/recover/restore the GDP growth to catch up with the business-as-usual
trajectory. Third, the accumulated difference between the post-disaster GDP and the business-as-
usual GDP from 2005 to 2013 was calculated using Equation 6. This metrics measures the
accumulated economic loss (L) due to the deviation of GDP growth from the business-as-usual
trajectory, which is illustrated as the grey area in Figure 3. L represents the combined effect of D
and RR. A high L can be interpreted as low resilience, which typically consists of a high instant
disturbance (D) and slow recovery (Rr). A low L means the opposite. L differentiates the
intermediate situations such as high D and high RR or low D and low RR. L is calculated from 2005-
2013 after Katrina when the DMSP-OLS images are available.
= (()()
Figure 3: Framework of GDP trajectory in Hurricane Katrina.
3.4 Regression Analysis
Regression analyses were applied to examine the associations of the three resilience metrics (D,
RR and L) with environmental and socio-economic variables. The selected variables fall into four
categories, including impact intensity, environmental, socio-economic, and industrial structure.
Max wind gust speed and accumulated rainfall represent the destructive power of a hurricane
(NOAA, 2006). Elevation, proximity to coast and ratio of urban in flood zones indicate the
exposure to Hurricane-induced storm surge and flooding (Dasgupta et al., 2011; Jonkman et al.,
2009). The socio-economic category includes 10 commonly used indicators in community
resilience measurements (Cai et al., 2018; Cutter et al., 2014). Ratios of establishments in different
scales and industrial sectors represent the industry structure, which potentially influences
economic recovery (Martin and Sunley, 2015). Most of the variables were acquired from datasets
released before 2005 to represent pre-disaster conditions. Variables that are not reported in
counties were preprocessed and averaged into counties (Table 2). Instead of composing a
comprehensive index for resilience, the objective of the analysis is examining the relationships
between the GDP recovery trajectories and the hypothetical resilience indicators.
Two types of regression analysis were carried out in this study. First, univariate regression was
applied to examine the relationships between D, RR and L and each individual variable. Second,
multivariate regression was utilized to relate the three metrics with all the variables and variables
in the four categories. R2 of the multivariate regression indicates the proportion of variance
explained by the different categories of variables. Adjusted R2 were compared to evaluate the
prediction power of the different categories for the recovery metrics. The regression analyses aim
to explore the underlying factors that influence resilience and proportion of variance of resilience
can be explained by the variables. All variables were re-scaled into z-scores for the regression
analyses. Thus the b coefficient of the regression represents the direction and standard deviations
of the relationship. The regression analyses were conducted in the linear model (lm) function in R.
Table 2: Description of variables used in the regression analyses.
Data source
Max gust
Recorded maximum gust speed from
Aug. 23 to 30, 2005
Knobb et al. (2005)
Kriging interpolation,
Zonal operation
Total rainfall from Aug. 23 to 30,
National Weather
Re-sampling, Zonal
Mean elevation
Mean elevation
U.S. Geological
Zonal operation
Distance to Coast
Mean distance to coast line
Euclidean distance, Zonal
% of urban in
flood zone
Percent of developed land in flood
FEMA flood map,
National Land Cover
Method in (Qiang, 2019)
% White
Percent of population in one race:
U.S. decennial census
% Black
Percent of population in one race:
Black or African American
U.S. decennial census
% Asian
Percent of population in one race:
U.S. decennial census
% Hispanic &
Percent of Hispanic or Latino
U.S. decennial census
% children
Percent of population under 18 year s
U.S. decennial census
% elderly adult
Percent of population above 65
years old
U.S. decennial census
% owner
occupied homes
Percent of owner occupied housing
U.S. decennial census
% bachelor
Percent of population (> 25 years
old) with 4 or more years of college
or bachelor's degree or higher
U.S. decennial census
% poverty
Percent of population whose income
is below poverty level
U.S. decennial census
Per cap. income
Per capita income
U.S. decennial census
Industrial structure
% small
Percent of establishments with < 20
County business
patterns (2004)
% large
Percent of establishments with >500
County business
patterns (2004)
% Agriculture
Percent of establishments in
agriculture, forestry, fishing and
County business
patterns (2004)
% Mining
Percent of establishments in mining,
quarrying, and oil and gas extraction
County business
patterns (2004)
% Manufacture
Percent of establishments in
County business
patterns (2004)
4 Result
4.1 Overall Economic Impact
Figure 4 demonstrates the change of NTL brightness (DN value) from 2004 to 2005 near the
landfall location of Katrina, where a decline of NTL brightness can be observed in New Orleans
and the surrounding coastal cities. The annual GDP estimates from the NTL data (see Figure 5)
reveal that Hurricane Katrina has fundamentally altered the economic growth in the declared
disaster area. In contrast to the steady growth in the Southeast Region (despite the slight drop in
2009 due to the financial crisis), the GDP in the disaster area declined sharply in 2005 and grew
at a slower rate in the following years. The Southeast Region is delineated by Bureau of Economic
Analysis (BEA) (2015) for releasing economic statistics (see Figure 2). According to the
estimation, the GDP in 2005 in the entire disaster area declined 24.2% compared with the business-
as-usual condition if the pre-Katrina growth trend persists. The accumulated loss of GDP from
2005 to 2013 (gray area in in Figure 5) is about 2.2 billion current dollar and the GDP is unlikely
to restore to the business-as-usual trajectory in the following years. Note, this loss is estimated
only from the declined GDP in the FEMA declared disaster area, which does not include other
indirect losses in a broader area. Note, due to the instability of the DMSP-OLS images, the
estimated GDP fluctuates from year to year. The estimation of GDP in a specific year may not be
accurate. However, the general trend indicates the strong economic impact of the disaster.
Figure 4: Changes of NTL brightness (DN value) in the 2004 and 2005 DMSP-OLS annual composite
Figure 5: Annual estimated GDP in current dollars in the affected area (refer to the left axis) and the
Southeast Region (refer to the right axis) (Data source: Bureau of Economic Analysis).
4.2 Spatial Variation of Resilience
The GDP recovery trajectory varies in counties. As illustrated in Figure 6, the estimated GDP in
Orleans Parish (blue) and St. Bernard Parish (red) had a substantive decline in 2005 when Katrina
stroke. Both parishes are in the metropolitan area of New Orleans near the landfall location of
Katrina. The post-Katrina GDP in St. Bernard Parish shows a faster recovery rate than Orleans
Parish after Katrina. In contrast, St. Tammany Parish (green) in the north shore of Lake
Pontchartrain had little economic impact and maintained a steady GDP growth after Katrina,
despite its proximity to the hurricane track.
Figure 6: Estimated GDP time series of Orleans Parish (blue), St. Bernard Parish (red) and St. Tammany
Parish (green).
As shown in Figure 7 (a), Hurricane Katrina caused large instant disturbance () in counties near
the two landfall locations in Florida and Louisiana. Note, the high in southwest Louisiana
(around Lake Charles Parish) is possibly due to Hurricane Rita, a Category 3 hurricane landed near
the Louisiana-Texas border a month after Katrina. Figure 7 (b) shows that Louisiana counties have
a higher recovery rate (Rr) of GDP after Katrina, except Jefferson and Orleans Parish near the
center of New Orleans which were struggling to recover after Katrina. High accumulated GDP
loss (L) are distributed in coastal cities, including Gulfport (MS), Mobile (AL), Panama City and
Miami (FL). The inland counties generally have lower accumulated loss.
Figure 7: Spatial variation of (a) instant disturbance (), (b) recovering rate (Rr) and (c) accumulated GDP
loss (). Red indicates high instant disturbance (D), slower recovery (Rr) and high accumulated loss (L).
4.3 Regression Analysis
Various relationships were found between the three metrics (D, Rr, L) and selected variables (Table
Instant disturbance (D) has the strongest relationship with the percent of Asian people (highest R2)
among the 20 selected variables. The positive coefficient b indicates that counties with a higher
ratio of Asian people have endured greater GDP disturbance in Katrina. This result confirms the
findings in Vu et al. (2009) that 78% of Vietnamese (the largest Asian group in Louisiana and
Mississippi) left their home during the hurricane and gradually returns afterwards. D is also highly
correlated with the physical impacts and environmental conditions. Specifically, counties with
high gust speed, high accumulated rainfall, low elevation, high ratio of urban in flood zone, or
close to the coast line tend to have a high D. This reflects the fact that New Orleans, which has
low elevation and high ratio of urban flood exposure, was serious damaged by flood inundation
due to levee breech. Additionally, counties with a higher percentage of agricultural industry have
a lower D.
High recovery rates (Rr) are associated with high percentages of energy industry (e.g. mining,
quarrying, and oil and gas extraction), implying that the energy industry bounced back more
quickly after the disturbance. Moreover, low elevation, proximity to coast, high ratios of children
and elderly people tend to impede the GDP recovery.
Accumulated economic loss (L) is negatively correlated (highest R2) with ratio of owner-occupied
housing units, indicating communities with more home-owner residents managed to prevent the
overall economic loss. Communities with a higher ratio of Asian, Hispanic and Latino, children
and elderly adults have suffered more accumulated loss. High L is also found in counties with a
high urban exposure to flood zone. Additionally, counties reliant on agricultural and mining
industries tend to have a lower long term loss.
Table 3: Univariate regression analysis of instant disturbance (D), recovery (Rr) and economic loss (L) with
environmental and socio-economic variables. Bold font indicates statistical significance (p<0.01). Detailed
statistics of the regression analysis (e.g. confidence intervals, standard errors of residuals, and residual
distributions) can be found in the supplementary material.
Instant disturbance (D)
Recovery rate (Rr)
Economic loss (L)
p value
p value
p value
Max gust
Mean elevation
Distance to Coast
% of urban in flood
% White
% Black
% Asian
% Hispanic & Latino
% children
% elderly adult
% owner occupied
% bachelor degree
% poverty
Per cap. income
Industrial structure
% small businesses
% large businesses
% Agriculture
% Mining
% Manufacture
R2 of the multivariate regression indicates that the selected variables can only explain 39.9%, 29.3%
and 27.4% variance of D, Rr, and L respectively (Table 4), which implies that other variables
should be considered to fully explain the variance of recovery pattern. Due to the different numbers
of variables in the four categories, adjusted R2 was used to compare the variance explained by the
different categories. In general, socio-economic variables can best predict (highest adjusted R2)
instant disturbance (D), followed by environmental condition and industrial structure. Industrial
structure is the best indicator of recovery rate (Rr), followed by environmental and socio-economic
variables. Socio-economic variables can best predict the accumulated economic loss (L). It is
worth-noting that the disaster impacts explain the least variance in all the three metrics, which
implies that the intrinsic community capacities (e.g. socio-economic conditions) are more decisive
to economic recovery than the physical disaster impacts.
Table 4: Adjusted R2 of the multivariate regressions between the resilience metrics (D, Rr, and L) and
variables in different categories.
Variable category
Instant disturbance (D)
Recovery rate (Rr)
Economic loss (L)
Adj. R
Adj. R
Adj. R
All variables
Impact intensity
Industrial structure
Despite the extensive discussions in the literature, quantitative assessment of community resilience
is still a challenge due to the lack of empirical data continuously collected in disasters. In the U.S,
census data are released decennially and county-level GDP data is only available in limited years,
not to mention the developing world. Other data collection methods such as field surveys and
interviews are costly and time consuming. As an alternative, NTL remote sensing is an efficient
means to observe human activities (such as population, GDP, and energy consumption) from space.
Most importantly, the NTL images have the unique ability to detect declines of human activities,
which is not easy for other types of remote sensing imageries. The continuous scan of NTL images
can capture the disturbance and recovery pattern of human activity during natural disasters at low
cost and in a timely manner. The introduced framework can increase our understanding about
community resilience and help to improve resilience prediction models in terms of variable
selection and weighting.
In addition to Hurricane Katrina, the introduced assessment framework based on NTL data is
applicable to other natural disasters (e.g. tsunami and earthquake) that cause economic disturbance.
The framework is particularly useful in situations where official socio-economic data are not
available at the desired spatio-temporal resolution. Not limited to the DMSP/OLS images, the
introduced framework can use other data sources as input. For instance, the Visible Infrared
Imaging Radiometer Suite (VIIRS) images, the new generation NTL remote sensing products since
2011, provide many new features (e.g. higher spatial and radiometric resolution) that are useful
for measuring community resilience in disasters with a smaller impact area and shorter period.
As demonstrated in this study, the GDP time series estimated from the NTL data can capture the
various recovery trajectories at the county level. The analysis results uncover the strong economic
impact of Katrina in the affected region, where the GDP has given up its rapid growth and shifted
to a different trajectory since 2005. This finding confirms the tremendous and long-lasting impact
of Katrina on population migration (Fussell et al., 2014, 2010), declined urban growth (Qiang and
Lam, 2016) and economic activity (Baade et al., 2007; Petterson et al., 2006). According to the
recent estimates of the U.S. Census (2019), until 2017 the population and housing price in New
Orleans has not yet recovered to the pre-Katrina level. The various GDP recovery patterns reveal
geographical disparities of community resilience related to the local environmental and socio-
economic conditions.
The recovery pattern of a social system is dependent on both intensity of disaster impacts and
resilience of the system. The analysis results (see Table 4) indicates the physical impact variables
(including max gust speed and accumulated rainfall) can only explain limited variance (R2 = 0.065)
in the recovery pattern. Instead, the inherent conditions of communities (including environmental,
socio-economic and industrial conditions) play a central role in shaping the recovery pattern. The
univariate regression with individual variables provides empirical evidence about the underlying
factors that influence the recovery. The results may also inform specific plan to prompt resilience
at different phases of a disaster. For instance, the strong correlation between instant disturbance
(D) and the environmental conditions (e.g. elevation, proximity to coast and % of urban in flood
zone) suggests that reducing exposure is the most effective way to reduce the direct impact caused
by disasters. In the recovery phase, communities with some demographic and socio-economic
characteristics may have difficulty to bounce back, pinpointing areas where special assistance or
policy levers s be applied. Compared with the traditional resilience indices without specific outputs,
these analysis results can better provide more actionable information to support decision-making
at different phases of a disaster.
Despite the merits demonstrated in this study, the introduced approach can be improved in the
following aspects. First, despite the good model fit at the county level, the DMSP-OLS images
cannot perfectly predict economic activity due to their inherent limitations (e.g. low spatial
resolution, lack of onboard and inter-satellite calibration, and limited dynamic range). In future
studies, the assessment results needs to be validated against additional datasets such as other NTL
products (e.g. VIIRS images) or socio-economic data continuously collected during the disaster.
Second, other factors that influence economic recovery should be taken into account in future
studies. For instance, economic cycles (e.g. great recessions) can slow down business recovery
after a disaster. In the introduced approach, linear models were used to generalize the economic
growth in the pre- and post-Katrina periods, where the yearly fluctuations (e.g. the financial crisis
in 2009) are averaged in the trend lines. Ideally, the “business-as-usual” trajectories should be
projected with more sophisticated models to eliminate the effect of economic cycles. Third, the 20
selected variables can only explain ~40% variation of the measured metrics, which reveal the
complexity of community resilience. The prediction power of the model can be improved by
including more variables and using more sophisticated model specifications (e.g. non-linear
models). Robust model for community resilience prediction can be developed by accumulating
empirical evidence in more disaster events.
This study introduces a quantitative framework for resilience assessment using DMSP-OLS
Nighttime Lights images. The framework was applied to model the recovery patterns of economic
activity in the affected area in Hurricane Katrina 2005. The analyses show the great economic
disturbance caused by Katrina and the slow recovery in the entire affected area. The county-level
analyses indicate strong spatial variation of the recovery pattern. Statistical analyses were carried
out to explore the underlying factors that influence the recovery patterns. This study demonstrates
the utility of NTL images in monitoring human dynamics in natural disasters, which filled the
critical gap of empirical data and assessment methods for resilience research. Based on the
framework of recovery trajectory, resilience is modelled as a dynamic process. Compared with the
traditional resilience indices, the modeling results can provide more specific and actionable
measures resilience promotion in diverse communities and in different phases of a disaster. This
study re-visits Hurricane Katrina using DMSP-OLS NTL images. However, the assessment
approach is applicable for other disaster events using other NTL products (e.g. VIIRS DNB
images). The results increase our understanding about the complexity of community resilience and
provide support for decision-makers to develop resilient and sustainable communities.
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... Disproportionate flooding, less resilient infrastructure, and more limited resources for preparation and recovery among people of color can contribute to health disparities in the wake of extreme storms. Recent national-level flood risk assessments have found that economically disadvantaged populations are more likely to live in counties within flood zones and that greater flood risk is associated with higher poverty and unemployment rates [105,106]. Studies in Texas and New York have found that flooding was significantly greater in neighborhoods with a higher proportion of non-White and socioeconomically deprived residents during Hurricanes Harvey and Sandy [107,108]. ...
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Purpose of Review Climate change is causing warming over most parts of the USA and more extreme weather events. The health impacts of these changes are not experienced equally. We synthesize the recent evidence that climatic changes linked to global warming are having a disparate impact on the health of people of color, including children. Recent Findings Multiple studies of heat, extreme cold, hurricanes, flooding, and wildfires find evidence that people of color, including Black, Latinx, Native American, Pacific Islander, and Asian communities are at higher risk of climate-related health impacts than Whites, although this is not always the case. Studies of adults have found evidence of racial disparities related to climatic changes with respect to mortality, respiratory and cardiovascular disease, mental health, and heat-related illness. Children are particularly vulnerable to the health impacts of climate change, and infants and children of color have experienced adverse perinatal outcomes, occupational heat stress, and increases in emergency department visits associated with extreme weather. Summary The evidence strongly suggests climate change is an environmental injustice that is likely to exacerbate existing racial disparities across a broad range of health outcomes.
... Individuals that fall within this category make such choices for their residence because of the low property prices of homes within flood zones. Looking it at it from a local level, coastal flood zones are more occupied by higher income groups whereas inland flood zones are occupied by the poorer population, signifying a worrying situation where inland communities are less responsive to flood hazards as a result of their lower economic condition (Qiang, 2019). Some other studies on environmental justice have shown that households of lower socioeconomic status experience a higher impact from flooding than those from a higher socioeconomic status. ...
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Onion Creek is a neighborhood south of downtown Austin that falls within the base floodplains where Base elevations are provided. As a result, it is a high-intensity flood zone. Demographically speaking, the region is occupied by residents of which the majority live above the poverty line and have an average Household Income of over $90,000. The aim of this study is to i) identify and map out relatively low to high flood risk areas of the neighborhood and, ii) understand the social, economic, political and/or cultural factors that influence the residents’ decisions to stay in the neighborhood. Flood vulnerability levels will be analyzed and mapped based on the analysis of a Digital Elevation Model (DEM) data and Stream data of the study area. A survey was distributed to residents, analysis were conducted to understand their general background, awareness levels, flood mitigation efforts in the area, and experiences/reflection about flooding. Results from the study show that the residents’ decision to continue residing within the area is based off the knowledge their homes are not located within the floodplain, and a sense of familiarity and community that they feel within the neighborhood.
... EJ principles refer to fair treatment and equal protection under environmental laws, regulations, and policies for all people and communities irrespective of race, colour, ethnic origin, or income (Bullard, 1994;Mohai et al., 2009). Central questions of EJ research include whether disadvantaged communities experience disproportionate exposure to environmental hazards, where those communities are located, how risks manifests, and what can be done to reduce the risks (Buzzelli, 2008;Maantay and Maroko, 2009;Qiang, 2019;Sayers et al., 2018). EJ research in the context of flood hazards heavily relies on analyzing, assessing, and addressing disproportionate exposure of racial or ethnic minorities, people of low socioeconomic status, and other socially vulnerable groups either to pre-flood risks (e. g., residences in 100-year flood zones) or to an actual flood event (Collins et al., 2019a). ...
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This study explores flood-related environmental injustices by deconstructing racial, ethnic, and sociodemographic disparities and spatial heterogeneity in the areal extent of fluvial, pluvial, and coastal flooding across Canada. The study integrates JBA Risk Management's 100-year Canada Flood Map with the 2016 national census-based socioeconomic data to investigate whether traditionally recognized vulnerable groups and communities are exposed inequitably to inland (e.g., fluvial and pluvial) and coastal flood hazards. Social vulnerability was represented by neighbourhood-level socioeconomic deprivation, including economic insecurity and instability indices. Statistical analyses include bivariate correlation and a series of non-spatial and spatial regression techniques, including ordinary least squares, binary logistic regression, and simultaneous autoregressive models. The study emphasizes the quest for the most appropriate methodological framework to analyze flood-related socioeconomic inequities in Canada. Strong evidence of spatial effects has motivated the study to test for the spatial heterogeneity of covariates by employing geographically weighted regression (GWR) on continuous outcome variables (e.g., percent of residential properties in a census tract exposed to flood hazards) and geographically weighted logistic regression on dichotomous outcome variables (e.g., a census tract in or out of flood hazard zone). GWR results show that the direction and statistical significance of relationships between inland flood exposure and all explanatory variables under consideration are spatially non-stationary. We find certain vulnerable groups, such as females, lone-parent households, Indigenous peoples, South Asians, the elderly, other visible minorities, and economically insecure residents, are at a higher risk of flooding in Canadian neighbourhoods. Spatial and social disparities in flood exposure have critical policy implications for effective emergency management and disaster risk reduction. The study findings are a foundation for a more detailed investigation of the disproportionate impacts of flood risk in Canada.
Flood has always been a devastating hazard for social and economic assets and activities. Especially, low-land areas such as coastal regions can be more vulnerable to inundations. The combination of different natural hazards observed at the same time is definitely worsening the situation in the affected regions. The goal of this study is to conduct a distinctive combined hazards analysis considering flood hazards with the contribution of potential earthquake-triggered tsunamis that might be observed through the Fethiye coastline and city center. For this purpose, tsunami hazard curves are generated based on Monte Carlo Simulations. Comprehensive stochastic hazard analyses are performed considering aleatory variability of earthquake-triggered tsunamis and epistemic uncertainty of flood having 10 year return period. Numerical simulations are conducted to combine the potential tsunamis and flood events that are able to adversely affect the selected region. The results of this study show that the blockage of stream outlets due to tsunami waves drastically increases the inundated areas and worsens the condition for the selected region.
The benefit allocation of fair and justice is an inevitable guarantee for the long-term operation of the joint prevention and control of air pollution (JPCAP). The ignorance of interest demands of various governance subjects in the existing benefit allocation mechanism results in the widespread “free rider” behavior in the joint control and unsatisfactory effects of JPCAP. Given this, it is imperative to build a reasonable benefit allocation model. The innovation of this paper is proposing a benefit allocation model of JPCAP to achieve the symmetry between control costs and benefits based on environmental justice. The control objectives and total benefits of JPCAP are calculated through the adjustment of optimal removal rates. The interest demands of various control subjects and benefit compensation scheme are clarified by adopting an improved Shapley method, which comprehensively considers factors affecting environmental justice. An empirical analysis is conducted on SO2 governance in Beijing-Tianjin-Hebei (BTH) and its surrounding areas. The results show that the benefit allocation model based on environmental justice can not only accurately evaluate the benefits of joint control, but also effectively achieve the symmetry between control costs and benefits. This study provides a scientific and reasonable theoretical basis for the benefit allocation of SO2 control and can be extended to the researches and practices of other air pollutants control.
The increased frequency and intensity of flooding and related disasters result from changing climatic conditions and other socio-economic factors. As flooding can be highly destructive and negatively impact human lives, this study attempts to estimate the population, capital stock and disparities in exposure to flooding hazards in Nigeria using GIS and Statistical methodologies. First, the study assessed the spatial distribution of the population and capital stock exposed to flood by utilising population and socio-economic datasets. Then, the distribution of the vulnerable groups affected is estimated by superimposing the population and socio-economic datasets onto the flood hazard maps. The results show that approximately 24.7 million (8.3%) of Nigeria’s Population were exposed to floods in 2015. Most exposed groups were primarily in urban areas irrespective of the income class. Additionally, the clusters of communities within the high-risk flood hazard zones had significantly increased, evident in the number of residents exposed to flood within the 15 years (2000-2015) growing exponentially. These findings further highlight a disturbing state of localities where people are generally less responsive to climate change and natural hazards. Overall, this study provides essential information for disaster risk management and policy formation at different levels of administration and identifies areas where varied and informal strategies are needed to mitigate flood risk and climate change in regions with diverse socio-economic conditions. In addition, this study provides empirical proof of the socio-economic disparities associated with flood exposure in Nigeria and presents valuable insights into the underlying factors.
The management of flood risk poses significant challenges for communities across the United States. At the forefront of managing flood risk in the U.S. are local floodplain administrators (FPAs). Despite the importance of their role in addressing flood hazards at the local level, little is known about those who serve in this capacity. Using survey data gathered from local floodplain administrators in Arkansas, Louisiana, New Mexico, Oklahoma, and Texas, this study provides an overview of floodplain administrators in Federal Emergency Management Agency (FEMA) Region 6. Specifically, this research examines perceived strengths and challenges of floodplain administrators in their ability to carry out their roles and responsibilities, and explores how perceived strengths and challenges in ability vary based on location, experience, and previous floodplain management training. This study provides new insights into the strengths and challenges individuals experience in this role and analyses suggest that significant differences exist in the perceived abilities of floodplain managers based on whether they work in an urban and rural setting, their years of experience, and previous training. Based on the findings, this study offers recommendations about training needs and strategies that would benefit current floodplain administrators as they enhance resilience to floods in their communities.
Crowdsourced Personal Weather Stations (PWSs) adoption has been growing rapidly and provides the potential to fill in hyper-local rainfall observation gaps. However, current adoption patterns exhibit spatial biases that must be understood when using the data for modeling and decision-making. Here, we first examine the PWS rainfall spatial representation at HUC-12 watersheds in twelve metropolitan areas in the U.S. Furthermore, by modeling the PWS adoption using socio-economic and flood-related data at census tract level, the results suggest current adoption patterns exhibit spatial biases toward wealthier neighborhoods and flood-prone regions. The findings provide insights to inform how policies could be made to distribute resources to improve the rainfall data collection efforts in PWS-underrepresented regions. As crowdsourced data are increasingly used for decision-making by policymakers, efforts to close the gap in current non-uniform PWS spatial adoption will allow crowdsourced rainfall data to be better positioned to support decision-makers in their flood resilience efforts.
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A non-stationary increasing streamflow trend has been observed on the Mississippi River and other major river basins around the world. The current study analyzed the non-stationary streamflow effects (NSFEs) on flood management in backwater areas adjacent to the Atchafalaya Basin floodway in Louisiana, USA. A continuous simulation hydrology model coupled with a quasi-two-dimensional hydrodynamic model of the basin floodway and surrounding regions was used to develop over 180 simulation scenarios by superimposing local flood events (early summer 2014 and late summer 2016) against 90-years' worth of daily Atchafalaya River streamflow hydrographs. The NSFE on the Atchafalaya River induced substantial reductions in the performance of major flood regulating structures with seasonal effects based on the annual flood cycle. Capacity reductions at the structures were demonstrated to trigger a cascade of effects in ostensibly protected backwater areas including amplification of erosion potential near the levee and within tidal passes during early summer floods. Increases in mean and peak flood levels on the order of 15-20 cm during local storm events were shown to extend as far as 20 km away from the floodway protection levee during both early and late summer local flooding scenarios. Low-lying areas closest to the levee were adversely affected during both the high (early summer) and low flow (late summer) periods of the annual discharge cycle. The approach and findings of this study are relevant for risk management in river basins around the world affected by NSFEs.
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Extreme precipitation events are intensifying due to a warming climate, which, in some cases, is leading to increases in flooding. Detection of flood extent is essential for flood disaster response, management, and prevention. However, it is challenging to delineate inundated areas through most publicly available optical and short‐wavelength radar data, as neither can “see” through dense forest canopies. In 2018, Hurricane Florence produced heavy rainfall and subsequent record‐setting riverine flooding in North Carolina, USA. NASA/JPL collected daily high‐resolution full‐polarized L‐band Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) data between September 18th and 23rd. Here, we use UAVSAR data to construct a flood inundation detection framework through a combination of polarimetric decomposition methods and a Random Forest classifier. Validation of the established models with compiled ground references shows that the incorporation of linear polarizations with polarimetric decomposition and terrain variables significantly enhances the accuracy of inundation classification, and the Kappa statistic increases to 91.4% from 64.3% with linear polarizations alone. We show that floods receded faster near the upper reaches of the Neuse, Cape Fear, and Lumbee Rivers. Meanwhile, along the flat terrain close to the lower reaches of the Cape Fear River, the flood wave traveled downstream during the observation period, resulting in the flood extent expanding 16.1% during the observation period. In addition to revealing flood inundation changes spatially, flood maps such as those produced here have great potential for assessing flood damages, supporting disaster relief, and assisting hydrodynamic modeling to achieve flood‐resilience goals.
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Climate change and natural hazards pose great threats to road transport systems which are ‘lifelines’ of human society. However, there is generally a lack of empirical data and approaches for assessing resilience of road networks in real hazard events. This study introduces an empirical approach to evaluate road network resilience using crowdsourced traffic data in Google Maps. Based on the conceptualization of resilience and the Hansen accessibility index, resilience of road network is measured from accumulated accessibility reduction over time during a hazard. The utility of this approach is demonstrated in a case study of the Cleveland metropolitan area (Ohio) in Winter Storm Harper. The results reveal strong spatial variations of the disturbance and recovery rate of road network performance during the hazard. The major findings of the case study are: (1) longer distance travels have higher increasing ratios of travel time during the hazard; (2) communities with low accessibility at the normal condition have lower road network resilience; (3) spatial clusters of low resilience are identified, including communities with low socio-economic capacities. The introduced approach provides ground-truth validation for existing quantitative models and supports disaster management and transportation planning to reduce hazard impacts on road network.
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Hurricanes, as one of the most devastating natural hazards, have posed a great threat to people in coastal areas. A better understanding of the spatiotemporal dynamics of human settlement in hurricane-prone areas largely benefits sustainable development. This study uses the nighttime light (NTL) data from the Defense Meteorological Satellite Program's Operational Linescan System (DMSP/OLS) to examine human settlement development in areas with different levels of hurricane proneness from 1992 to 2013. The DMSP/OLS NTL data from six satellites were intercalibrated and desaturated with the Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) optical imagery to derive the Vegetation Adjusted NTL Urban Index (VANUI), a popular index that quantifies human settlement intensity. The derived VANUI time series was examined with the Mann–Kendall test and Theil–Sen test to identify significant spatiotemporal trends. To link the VANUI product to hurricane impacts, four hurricane-prone zones were extracted to represent different levels of hurricane proneness. Aside from geographic division, a wind-speed-weighted track density function was developed and applied to historical storm tracks which originated in the North Atlantic Basin to better categorize the four levels of hurricane proneness. Spatiotemporal patterns of human settlement in the four zones were finally analyzed. The results clearly exhibit a north–south and inland–coastal discrepancy of human settlement dynamics. This study also reveals that both the zonal extent and zonal increase rate of human settlement positively correlate with hurricane proneness levels. The intensified human settlement in high hurricane-exposure zones deserves further attention for coastal resilience.
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Preserving coral reef resilience is a major challenge in the Anthropocene, yet recent studies demonstrate failures of reef recovery from disturbance, globally. The wide and vigorous outer-reef system of French Polynesia presents a rare opportunity to assess ecosystem resilience to disturbances at a large-scale equivalent to the size of Europe. In this purpose, we analysed long-term data on coral community dynamics and combine the mixed-effects regression framework with a set of functional response models to evaluate coral recovery trajectories. Analyses of 14 years data across 17 reefs allowed estimating impacts of a cyclone, bleaching event and crown-of-thorns starfish outbreak, which generated divergence and asynchrony in coral community trajectory. We evaluated reef resilience by quantifying levels of exposure, degrees of vulnerability, and descriptors of recovery of coral communities in the face of disturbances. Our results show an outstanding rate of coral recovery, with a systematic return to the pre-disturbance state within only 5 to 10 years. Differences in the impacts of disturbances among reefs and in the levels of vulnerability of coral taxa to these events resulted in diverse recovery patterns. The consistent recovery of coral communities, and convergence toward pre-disturbance community structures, reveals that the processes that regulate ecosystem recovery still prevail in French Polynesia.
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Whereas monthly and annual nighttime light (NTL) composite datasets are being increasingly used to estimate socioeconomic status, use of the National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) Day/Night Band (DNB) daily data has been limited for detecting and assessing the impact of short-term disastrous events. This study explores the application of daily NPP-VIIRS DNB data in assessing the impact of three types of natural disasters: earthquakes, floods, and storms. Daily DNB images one month prior to and 10 days after a disastrous event were collected and a Percent of Normal Light (PNL) image was produced as the ratio of the mean DNB radiance of the pre- and post-disaster images. Areas with a PNL value lower than one were considered as being affected by the event. The results were compared with the damaged proxy map and the flood proxy map generated using synthetic aperture radar data as well as the reported power outage rates. Our analyses show that overall NPP-VIIRS DNB daily data are useful for detecting damages and power outages caused by earthquake, storm, and flood events. Cloud coverage was identified as a major limitation in using the DNB daily data; rescue activities, traffic, and socioeconomic status of the areas also affect the use of DNB daily data in assessing the impact of natural disasters. Our findings offer new insight into the use of the daily DNB data and provide a practical guide for researchers and practitioners who may consider using such data in different situations or regions.
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Time series monitoring of earthquake-stricken areas is significant in evaluating post-disaster reconstruction and recovery. The time series of nighttime light (NTL) data collected by the defense meteorological satellite program-operational linescan system (DMSP/OLS) sensors provides a unique and valuable resource to study changes in human activity (HA) because of the long period of available data. In this paper, the DMSP/OLS NTL images’ digital number (DN) is used as a proxy for the intensity of HA since there is a high correlation between them. The purpose of this study is to develop a methodology to analyze the changes of intensity and distribution of HA in different areas affected by a 2008 earthquake in Wenchuan, China. In order to compare the trends of HA before and after the earthquake, the DMSP/OLS NTL images from 2003 to 2013 were processed and analyzed. However, their analysis capability is greatly limited owing to a lack of in-flight calibration. To improve the continuity and comparability of DMSP/OLS NTL images, this study developed an automatic intercalibration method to systematically correct NTL data. The results reveal that: (1) compared with the HA before the earthquake, the reconstruction and recovery of the Wenchuan earthquake have led to a significant increase of HA in earthquake-stricken areas within three years after the earthquake; (2) the fluctuation of HA in a severely-affected area is greater than that in a less-affected area; (3) recovery efforts increase development in the most affected areas to levels that exceeded the rates in similar areas which experienced less damage; and (4) areas alongside roads and close to reconstruction projects exhibited increased development in regions with otherwise low human activity.
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Past attempts to estimate rainfall-driven flood risk across the US either have incomplete coverage, coarse resolution or use overly simplified models of the flooding process. In this paper, we use a new 30 m resolution model of the entire conterminous US with a 2D representation of flood physics to produce estimates of flood hazard, which match to within 90% accuracy the skill of local models built with detailed data. These flood depths are combined with exposure datasets of commensurate resolution to calculate current and future flood risk. Our data show that the total US population exposed to serious flooding is 2.6–3.1 times higher than previous estimates, and that nearly 41 million Americans live within the 1% annual exceedance probability floodplain (compared to only 13 million when calculated using FEMA flood maps). We find that population and GDP growth alone are expected to lead to significant future increases in exposure, and this change may be exacerbated in the future by climate change.
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Community resilience has been addressed across multiple disciplines including environmental sciences, engineering, sociology, psychology, and economics. Interest in community resilience gained momentum following several key natural and human-caused hazards in the United States and worldwide. To date, a comprehensive community resilience model that encompasses the performance of all the physical and socio-economic components from immediate impact through the recovery phase of a natural disaster has not been available. This paper summarizes a literature review of previous community resilience studies with a focus on natural hazards, which includes primarily models of individual infrastructure systems, their interdependencies, and community economic and social systems. A series of national and international initiatives aimed at community resilience are also summarized in this study. This paper suggests extensions of existing modeling methodologies aimed at developing an improved, integrated understanding of resilience that can be used by policy-makers in preparation for future events.
Social media such as Twitter is increasingly being used as an effective platform to observe human behaviors in disastrous events. However, uneven social media use among different groups of population in different regions could lead to biased consequences and affect disaster resilience. This paper studies the Twitter use during 2017 Hurricane Harvey in 76 counties in Texas and Louisiana. We seek to answer a fundamental question: did social-geographical disparities of Twitter use exist during the three phases of emergency management (preparedness, response, recovery)? We employed a Twitter data mining framework to process the data and calculate two indexes: Ratio and Sentiment. Regression analyses between the Ratio indexes and the social-geographical characteristics of the counties at the three phrases reveal significant social and geographical disparities in Twitter use during Hurricane Harvey. Communities with higher disaster-related Twitter use in Harvey generally were communities having better social and geographical conditions. These results of Twitter use patterns can be used to compare with future similar studies to see whether the Twitter use disparities have increased or decreased. Future research is also needed to examine the effects of Twitter use disparities on disaster resilience and to test whether Twitter use can predict community resilience.
Economic theory suggests that, other things being equal, properties located within a floodplain should suffer a price discount. A survey of the existing evidence nonetheless reveals that this price discount lies anywhere between − 75.5% to a + 61.0% price premium. In this paper we summarise and explore the wide variation in the results to obtain ‘best’ estimates with which to guide policy. Results from our meta-analysis comprising 37 published works and 364 point estimates indicate marked differences between studies according to when and where they were conducted. For coastal regions the results show that properties located in the floodplain command higher prices; this finding is however likely to be caused by a high correlation between omitted coastal amenities and flood risk. There is moreover, evidence that publication bias affects the coastal flooding literature. Results from meta-regression analyses intended to uncover sources of heterogeneity confirm that controlling for time elapsed since the most recent flood is especially important. For inland flooding the price discount associated with location in the 100-year floodplain is − 4.6%. Although other estimates are defensible, we suggest this figure be used as a rule of thumb to determine the benefits of flood relief projects to households.
Studies on how variables of community resilience to natural hazards interact as a system that affects the final resilience (i.e., their dynamical linkages) have rarely been conducted. Bayesian network (BN), which represents the interdependencies among variables in a graph while expressing the uncertainty in the form of probability distributions, offers an effective way to investigate the interactions among different resilience components and addresses the natural–human system as a whole. This article employs a BN to study the interdependencies of ten resilience variables and population change in the Lower Mississippi River Basin (LMRB) at the census block group scale. A genetic algorithm was used to identify an optimal BN where population change, a cumulative resilience indicator, was the target variable. The genetic algorithm yielded an optimized BN model with a cross-validation accuracy of 67 percent over a period of 906 generations. Six variables were found to have direct impacts on population change, including level of threat from coastal hazards, hazard damage, distance to coastline, employment rate, percentage of housing units built before 1970, and percentage of households with a female householder. The remaining four variables were indirect variables, including percentage agriculture land, percentage flood zone area, percentage owner-occupied house units, and population density. Each variable has a conditional probability table so that its impacts on the probability of population change can be evaluated as it propagates through the network. These probabilities could be used for scenario modeling to help inform policies to reduce vulnerability and enhance disaster resilience.