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Analysis of Flood-Vulnerable Areas for Disaster Planning Considering Demographic Changes in South Korea


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Regional demographic changes are important regional characteristics that need to be considered for the establishment of disaster prevention policies against climate change worldwide. In this study, we propose urban disaster prevention plans based on the classification and characterization of flood vulnerable areas reflecting demographic changes. Data on the property damage, casualties, and flooded area between 2009 and 2018 in 229 municipalities in South Korea were collected and analyzed, and 74 flood vulnerable areas were selected. The demographic change in the selected areas from 2000 to 2018 was examined through comparative analyses of the population size, rate of population change, and population change proportion by age group and gender. Flood vulnerable areas were categorized into three types through K-mean cluster analysis. Based on the analysis results, a strategic plan was proposed to provide information necessary for establishing regional flood-countermeasure policies.
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Sustainability 2020, 12, 4727; doi:10.3390/su12114727
Analysis of Flood-Vulnerable Areas for Disaster
Planning Considering Demographic Changes in
South Korea
Hye-Kyoung Lee, Young-Hoon Bae, Jong-Yeong Son and Won-Hwa Hong *
School of Architectural, Civil, Environmental and Energy Engineering, Kyungpook National University,
Daegu 41566, Korea; (H.-K.L.); (Y.-H.B.); (J.-Y.S.)
* Correspondence:; Tel.: +82-53-950-5597
Received: 15 April 2020; Accepted: 4 June 2020; Published: 9 June 2020
Abstract: Regional demographic changes are important regional characteristics that need to be
considered for the establishment of disaster prevention policies against climate change worldwide.
In this study, we propose urban disaster prevention plans based on the classification and
characterization of flood vulnerable areas reflecting demographic changes. Data on the property
damage, casualties, and flooded area between 2009 and 2018 in 229 municipalities in South Korea
were collected and analyzed, and 74 flood vulnerable areas were selected. The demographic change
in the selected areas from 2000 to 2018 was examined through comparative analyses of the
population size, rate of population change, and population change proportion by age group and
gender. Flood vulnerable areas were categorized into three types through K-mean cluster analysis.
Based on the analysis results, a strategic plan was proposed to provide information necessary for
establishing regional flood-countermeasure policies.
Keywords: urban disaster prevention plan; flood vulnerability; climate change; demographic
change; cluster analysis
1. Introduction
Floods are one of the most dangerous and destructive natural hazards that can cause human loss
and economic damages [1–3]. Climate change is expected to increase the frequency of flooding and
the extent of damage caused by it [4–6].
To minimize the damage caused by natural hazards, policies, treaties, and conventions,
including the Nations Framework Convention on Climate Change (1992), Kyoto Protocol (1997),
Hyogo Framework (2005), Sendai Framework (2015) and Paris Agreement (2015), have been
established. There is increasing awareness of the important role of policies that consider regional
characteristics on the mitigation of damages, adaptation to climate change, and sustainable
development in response to climate change [7–11].
Although there are several studies on regional flood risk management globally, policies,
practices, and approaches relevant to and effective in some countries may not be applicable to the
rest. Indeed, differences in governance structures and processes, topography, weather patterns, and
vulnerabilities will lead to difficulties in developing effective community flood risk management
strategies [12,13].
Various approaches for establishing a flood disaster prevention plan considering regional
characteristics have been proposed, including the development of flood vulnerability assessment
indicators and their application for the selection of vulnerable regions.
Sustainability 2020, 12, 4727 2 of 17
Cardona et al. [14] calculated the local disaster index using flood damage indicators, such as the
number of casualties and victims and the monetary value of the flood damage. Kubal et al. [15]
proposed three types of area vulnerability indicators: Social indicators, e.g., transport, housing, and
commerce; economic indicators, e.g., schools, hospitals, children, and elderly people; and ecological
indicators, e.g., forest, biodiversity, and potential pollution. They used these indicators to evaluate
the flood vulnerability of a city in Germany by a multicriteria approach. Balica et al. [16] proposed
the flood vulnerability index (FVI) with four types of vulnerability indicators: Social, economic,
environmental, and physical. Planners and policymakers can use the FVI as a tool to prioritize flood
risk hotspots. Balica et al. [17] developed the coastal city FVI using hydro-geological, socio-
economical, and politico-administration indicators and applied it to nine coastal cities around the
world to evaluate the most vulnerable ones.
In South Korea, various studies on flood damage mitigation have been carried out. The potential
flood damage indicator was proposed to evaluate the flood potential by evaluating the possibility of
flooding and the flood control capability considering potential damage factors, such as the
population, types of property, urbanization rate, and infrastructures, and risk factors, such as flood
damage extent, rainfall probability, river improvement rate, and flood control capacity [18]. Regional
safety assessment techniques have been proposed to identify the risk of disaster related to natural
hazards in specific regions [19]. The flood damage index was developed considering the main causes
of flood damage subdivided into natural, social, policy, and facility factors. This index considers 11
causes representing each factor [20]. In areas were divided into risk and vulnerability groups through
the excess flood vulnerability index (EFVI) [21].
The results of the flood vulnerability analyses depend on the type, characteristics, and duration
of analysis of the selected evaluation indicators, as well as the region under investigation [22–24].
Several studies on flood vulnerability analyses considered flood damage indicators (e.g., casualties
and amount of damage), socio-environmental indicators (e.g., urbanization rate, traffic, and
residential ratio), regional indicators (coastal area, urban area, and rural area), and population
indicators (e.g., the number of people living in the disaster-prone areas, such as coastal areas and
areas with a history of multiple floods, disaster-vulnerable population, such as children and the
elderly, and the population affected by the disaster).
Demographic change is one of the most important topics discussed globally, along with the issue
of climate change [25,26]. Demographic change is an important criterion for policymaking because it
cannot be reversed in a short time, and it is necessary to analyze its characteristics to use them for
policymaking in a pre-emptive manner [27,28]. In its Fifth Assessment Report, the Intergovernmental
Panel on Climate Change (IPCC, 2015) highlighted demographic characteristics such as age, gender,
income, and health status as parameters to shape the resilience capacities of urban areas [29].
South Korea is the country with the fastest decrease in the birth rate and the fastest growth in an
aging population in the world [30,31]. Therefore, research has been conducted in various fields on
problems and prevention based on demographic changes, and many related national policies have
been established. When establishing a disaster prevention policy that prevents human losses and
property damage during disasters, it is essential to reflect demographic changes. However, studies
discussing and investigating demographic change are scarce in the flood disaster prevention plan. In
this study, we propose a strategy for establishing flood disaster preventive urban policies by
classifying flood vulnerable areas based on flood characteristics and demographic changes in each
region in South Korea.
2. Materials and Methods
2.1. Study Area: Spatial Division and Regional Characteristics of Floods
South Korea is characterized by its meteorological features: Rainfall during the rainy season
between June and September—when air masses from the northern Pacific and tropical cyclones head
north—accounts for 55% of the annual precipitation [32]. In addition, more than two-thirds of the
entire land consists of mountainous terrain with weathered rocks, and most of the heavy rainfall is
Sustainability 2020, 12, 4727 3 of 17
concentrated in the west and south river basin because of the tilted landform (high altitudes in the
east and low altitudes in the west). Therefore, streams occur frequently in the western region. Further,
coastal floods occur repeatedly every year because the three sides of the peninsula are in contact with
the sea [33]. However, the recent industrialization and urbanization have resulted in a larger
population concentration in metropolitan city areas and increased land use. This has led to an
increase in impervious surfaces. Therefore, the frequency and scale of damage caused by urban floods
are increasing.
South Korea is divided into 17 administrative regions: Three special cities, six metropolitan
cities, and eight provinces (Table 1). There are a total of 69 autonomous districts in nine special cities
and metropolitan cities, 78 autonomous cities and 82 counties in nine special cities and metropolitan
cities, and eight provinces, making up a total of 229 municipalities, hereinafter referred to as city
(si)/county (gun)/district (gu). In this study, we analyzed the flood damage characteristics and the
demographic change in the 229 municipalities of si/gun/gu in South Korea.
Table 1. Seventeen administrative regions of South Korea.
Regions Area (km2) Si
1 Seoul-si 605.24 - - 25
2 Busan-si 769.94 - 1 15
3 Deagu-si 883.52 - 1
4 Incheon-si 1063.27 - 2 8
5 Gwanju-si 501.18 - - 5
6 Deajeon-si 539.53 - - 5
7 Ulsan-si 1061.54 - 1 4
8 Sejong-si 464.91 1 - -
9 Gyeonggi-do 10,187.79 28
3 -
10 Gangwon-do 16,827.91 7 11 -
11 Chungcheongbuk-do 7407.85 3 8 -
12 Chungcheongnam-do 8229.20 8 7 -
13 Jeollabuk-do 8069.07 6 8 -
15 Gyeongsangbuk-do 19,032.87 10
13 -
16 Gyeongsangnam-do 10,540.12 8 10 -
17 Jeju-si 1850.16 2 - -
100,377.68 78
82 69
2.2. Methods
The purpose of this study is to provide the basis and implications for the establishment of urban
disaster preventive policies in response to climate change. To this end, we considered two aspects
that were not considered in previous studies. First, in relation to the establishment of a flood disaster
prevention policy considering regional characteristics, we conducted regional flood disaster
vulnerability assessments of the 229 municipalities in South Korea. In this study, the term “regional
flood disaster vulnerability” represents the degree to which flood disaster will occur again based on
past flood damage. Second, we categorized the flood vulnerable areas in South Korea by considering
the demographic change indicators. In this study, the “Demographic change” implies any change in
the population, for example, a change in population size, the rate of population change, and the
population proportion change by age groups and gender. The research flow chart applying the two
aspects considered is explained below (Figure 1).
Sustainability 2020, 12, 4727 4 of 17
Figure 1. Flow chart of the procedure followed in this study.
First, we analyzed the three types of flood damage characteristics, i.e., the property damage,
casualties, and flooded area between 2009 and 2018. We rated the extent of flood damage among the
229 municipalities of si/gun/gu and selected the 30 most flooded areas for each category as the flood
vulnerable areas. Second, for the selected flood vulnerable areas, we compared the population size,
rate of population change, and population proportion change between 2000 and 2018 and analyzed
them by age groups and gender. Third, to generalize the demographic changes in the flood
vulnerable areas for comparison, we performed typing through cluster analysis.
In addition, through a comparison of the characteristics by flood vulnerable area type derived
from cluster analysis and spatial distribution analysis, this study aims to provide the information
necessary to establish flood countermeasure policies for each type of region in the future.
2.3. Flood Vulnerable Area Selection Data
Past flood damage data have been widely used for the selection of flood vulnerable areas
because they reflect the characteristics and status of the flood damage in each region [3436]. In
particular, flood damage data comprehensively reflect socio-economic and topographical factors. In
this study, property damage (one million South Korean won), casualties (persons), and flooded area
(km2) data were selected as the flood vulnerable area characterization variables.
The South Korean Ministry of the Interior and Safety publishes major statistics on natural hazard
damage and recovery status in the annual disasters related to the natural hazard report in Statistics
Korea [37]. Statistical data on annual disaster related to the natural hazard report provide statistics
for each natural hazard period by facility (public or private facility) and cause (e.g., typhoon, heavy
rain, heavy snow, storm, and earthquake). In terms of spatial distribution, they provide the statistics
for each major city and province in the 17 administrative regions up to the level of gun/gu. However,
the statistics by province are provided according to each natural cause (e.g., heavy rain, typhoon,
heavy snow, strong wind, and earthquake), while the statistics in the unit of gun/gu provide only the
total of the natural hazard and no classification according to the causes.
In this study, statistical damage data from the flooding of 229 municipalities are extracted by
comparing the total number of the natural hazards in the 229 municipalities with the flood damage
in the 17 administrative regions and the natural hazard for each period.
The damage status derived for comparative analysis by si/gun/gu was divided into the total area
by si/gun/gu to determine the amount of damage per km2, and through comparison, the 30 most
damaged regions in each category, i.e., property damage (one million South Korean won/km2),
casualties (persons/km2), and flooded area (km2/km2) were selected as the flood vulnerable areas.
2.4. Demographic Change Analysis Data
Characteristics, such as the population size, distribution, and structure, provide the basic data
for policymaking, planning, research, and evaluation. Statistics Korea periodically conducts a
Sustainability 2020, 12, 4727 5 of 17
population census for all persons in the country. Statistical data for the population census are
prepared up to the eup-myeon-dong unit (the sub-district unit of si/gun/gu) and published every five
years. However, from 2015, the survey method changed to a registered census method using national
administrative data from the total number of surveys, and the data are now published every year [38].
For the analysis of the demographic change in flood vulnerable areas, the total population,
population by age, and gender between 2000 and 2018 of the 229 municipalities were extracted from
Statistics Korea's population census data. Unlike most demographic analysis studies and the data
provided by the National Statistical Office, which categorizes demographic age groups into youth
(0–14 years old), working-age (15–64 years old), and aged (+65 years old) [27,39,40], this study further
divided the youth group and aged group because they are more vulnerable to the flood damage. In
total, five age groups were categorized: Infants (0–9 years old), school-age (10–19 years old), working-
age (20–64 years old), aged (65–74 years old), and super-aged (+75). Then, a comparative analysis by
si/gun/gu was performed on the population size, rate of population change, population proportion,
and proportion change by age group and gender in the last 18 years.
2.5. Cluster Analysis
Cluster analysis is a statistical analysis whereby data values are converted into distances based
on the similarity between variables, and nearby variables are classified into clusters. This method can
be broadly divided into hierarchical clustering and non-hierarchical clustering [41,42]. For the
analysis by typing of flood vulnerable areas corresponding to demographic change, this study
utilized the K-mean method, which is a non-hierarchical method.
We selected the flood damage characteristics as indicators, i.e., the property damage (million
South Korean won/km2), casualties (person/km2), and flooded area (km2/km2), and the demographic
change characteristics, i.e., the population by age group and gender (population in 2018), population
change (population in 2018–population in 2000), rate of population change ((population in 2018
population in 2000)/population in 2018 × 100), proportion (population by age group or gender/total
population in 2018 × 100) and proportion change (proportion in 2018–proportion in 2000) (Table 2).
Table 2. Variables considered for the cluster analysis.
Variable Calculation Method
Vulnerable area characteristics
Property damage (million South Korean won/km2) Flood property damage between
2009 and 2018
Casualties (person/km2) Flood casualties 2009 and 2018
Flooded areas (km2/km2) Flooded areas between 2009 and 2018
Demographic characteristics
Population (person) 0–9
years old Male
Population in 2018
Population change (person) 10–19
years old Female
Population in 2018–Population in 2000
Rate of population change (%) 20–64
years old
(Population in 2018Population in
2000)/Population in 2018 × 100
Population proportion (%) 65–74
years old
Population by age group or
gender/population in 2018 × 100
Rate of population proportion
change (%)
years old
Population proportion in 2018–Population
proportion in 2000
Sustainability 2020, 12, 4727 6 of 17
3. Results
3.1. Flood Vulnerable Area Selection
3.1.1. Selection and Characteristics of Vulnerable Areas by Major Administrative Region
Prior to the selection of the detailed flood vulnerable areas among the 229 municipalities of
si/gun/gu, based on the annual disaster report of the Ministry of the Interior and Safety, the flood
damage of 17 administrative regions nationwide in the last 10 years was recalculated as the ratio of
the area, and the results were compared and analyzed (Figure 2).
Figure 2. Comparison of flood damage for the 17 administrative regions (2009 to 2018).
As shown in Figure 2, Busan-si was the most damaged with the amount of damage reaching
5077 million won/km2 (36.45%), followed by Seoul-si with 2085 million won/km2 (14.97%), and
Gyeonggi-do with 1767 million won/km2 (12.68%). The casualties were 4632 persons/km2 (63.12%),
1061 persons/km2 (14.46%), and 801 persons/km2 (10.91%) in Seoul-si, Incheon-si, and Gyeonggi-do,
respectively. Seoul-si, Jeollabuk-do, and Busan-si had flooded areas of 23.68 km2/km2 (77.60%), 0.38
km2/km2 (11.07%), and 0.14 km2/km2 (4.13%), respectively. Overall, the flood damage in the western
provinces was greater than that in the eastern provinces because of the topographical features of the
tilted landform (higher altitudes in the east and low altitudes in the west) of South Korea.
In addition, more than half of the property damages, casualties, and flooded areas were
observed in two to three administrative regions. A large variation in flood damage was obtained in
different regions. These results indicate that flood disaster prevention plans need to be established
such that the most damaged regions are prioritized in terms of the implementation of plans. Further,
the most damaged areas need to be analyzed with more precise units such as si/gun/gu.
3.1.2. Selection and Characteristics of Vulnerable Areas (si/gun/gu)
Considering the last 10 years, the regions were ranked according to the property, casualty, and
flood area damages caused by floods in 229 municipalities of si/gun/gu, and the 30 most damaged
towns and districts for each category were selected as flood vulnerable areas (Figure 3 and Table 3).
Sustainability 2020, 12, 4727 7 of 17
Figure 3. Spatial analysis of flood vulnerable regions (a) property damage, (b) casualty losses, (c)
flooded areas, (d) overlap.
Table 3. Characteristics of regions most vulnerable to floods.; Regions in the two categories of
property and casualty(yellow), property and flood area(red), casualty and flood area(green).regions
in the three categories of property, casualty, and flood area(gray).
Property Damage Casualties Flooded Areas
% Municipalities
m2 % Municipalities
m2 %
Total 7337.55 100.0
0 Total 3.46 100.0
Sub-Total : Top 30
Sub-Total : Top 30
6440.45 87.77
Sub-Total: Top 30 3.46 99.94
Busan Suyeong 843.49
6.06 Seoul Yangcheon
658.19 8.97
Seoul Gangseo 2.683 77.59
Busan Seo 763.30
5.48 Seoul Dongjak 497.73 6.78
uk Jeonju 0.368 10.36
Busan Yeongdo 702.68
5.04 Seoul Gwanak 496.70 6.77
am Seocheon 0.080 2.31
Busan Saha 555.82
3.99 Incheo
n Michuhol 441.16 6.01
Busan Yeonje 0.076 2.20
Busan Nam 438.00
3.14 Seoul Gangdong
364.00 4.96
Busan Haeundea 0.066 1.91
Seoul Seocho 436.77
3.14 Seoul Guro 352.38 4.80
m Sacheon 0.028 0.82
Busan Yeonje 332.96
2.39 Seoul Geumcheo
n 339.93 4.63
0.028 0.81
Busan Gijang 316.96
2.28 Seoul Gwangjin 314.18 4.28
Jeollanam Suncheon 0.024 0.68
Seoul Yangcheo
n 299.54
2.15 Incheo
n Bupyeong 313.88 4.28
Gyeonggi Hwaseong 0.017 0.50
Busan Buk 235.70
1.69 Seoul Seocho 267.71 3.65
uk Gimje 0.017 0.49
Jeollanam Wando 228.65
1.64 Seoul Gangseo 240.85 3.28
m Miryang 0.011 0.31
Seoul Gwanak 228.59
1.64 Gyeon
ggi Bucheon 239.78 3.27
am Taean 0.010 0.29
Busan Dongnae
178.56 2.43
Gwanju Gwangsan 0.009 0.29
Busan Haeundea
1.63 Busan Dongnae 167.68 2.29
k Pohang 0.007 0.20
1.53 Seoul Gangnam 162.22 2.21
uk Iksan 0.007 0.19
Jeollanam Mokpo 191.51
1.37 Seoul
145.02 1.98
0.006 0.18
Busan Geumjeon
g 176.15
1.26 Seoul Songpa 119.21 1.62
m Jinju 0.006 0.18
Gwanju Nam 149.98
1.08 Seoul Mapo 115.68 1.58
am Buyeo 0.005 0.16
Seoul Seodaem
un 145.63
1.05 Busan Yeonje 112.49 1.53
Jeollanam Naju 0.005 0.14
Ulsan Buk 138.71
1.00 Seoul Eunpyeong
106.54 1.45
Jeollanam Boseong 0.002 0.06
Sustainability 2020, 12, 4727 8 of 17
0.99 Incheo
n Namdong 102.09 1.39
am Yesan 0.002 0.06
Gyeonggi Gwangju
0.90 Busan Nam 89.21 1.22
Incheon Jung 0.002 0.06
Gyeonggi Yangju 119.23
0.86 Busan Dong 87.41 1.19
Jeollanam Gurye 0.002 0.05
Seoul Dongjak 114.73
0.82 Incheo
n Gyeyang 87.37 1.19
Incheon Bupyeong 0.001 0.04
Ulsan Jung 114.17
0.82 Seoul
82.94 1.13
uk Imsil 0.001 0.02
Gyeonggi Uiwang 111.31
0.80 Seoul Seodaemun
80.57 1.10
Incheon Ongjin 0.001 0.02
g 99.70
0.72 Seoul Gangbuk 72.96 0.99
Deagu Dalseong 0.001 0.01
Jeollanam Yeosu 98.03
0.70 Gyeon
ggi Anyang 70.73 0.96
Busan Gangseo 0.000 0.01
Seoul Songpa 97.71
0.70 Busan Yeongdo 68.80 0.94
am Hongseong 0.000 0.01
Jeollanam Shinan 94.65
0.68 Incheo
n Seo 64.47 0.88
Seoul Seocho 0.000 0.01
Property damage to the 30 most damaged si/gun/gu accounted for 57.18% of the total damage.
Busan-si Suyeong-gu was the most damaged, accounting for 6.06% of the total damage. Among the
administrative regions analyzed, 12 gu/gun in Busan-si, 6 gu in Seoul-si, 5 si in Gyeonggi-do, and 4
si/gun in Jeollanam-do were the 30 most damaged. Among the administrative regions not categorized
into flood vulnerable areas in the analysis, Gwangju-si Nam-gu, Ulsan-si Buk-gu, Ulsan-si Jung-gu,
and Gyeongsangnam-do Tongyeong-si were among the 30 most damaged.
The 30 most damaged municipalities accounted for 87.77% of the total casualties. Yangcheon-gu
in Seoul-si had the largest number of casualties, accounting for 8.97% of the total. The si/gun/gu
distribution showed that these 30 areas were in Seoul-si (17 gu), Incheon-si (5 gu/gun), Gyeonggi-do
(3 cities), and Busan-si (4 districts).
Gangseo-gu in Seoul-si had the largest damaged area, accounting for 77.59% of the total
damaged area. The flooded areas of the 30 most damaged municipalities accounted for 99.94% of the
total damage. This result indicates that all the regions with flooded areas were ranked in the top 30.
With the analysis of the overlap between the 30 most damaged regions in the three categories of
property, casualty, and flood area, two si/gun/gu (i.e., Seocho-gu in Seoul-si and Yeonje-gu in Busan-
si) overlapped across all three categories of damage, whereas 12 si/gun/gu (i.e., Seodaemun-gu,
Yangcheon-gu, Gangseo-gu, Dongja-gu, Gwanak-gu and Songpa-gu in Seoul-si; Yeongdo-gu,
Dongrae-gu, Nam-gu and Haeundae-gu in Busan-si; Bupyeong-gu in Incheon-si; and
Gwangmyeong-si in Gyeonggi-do) overlapped across two categories. Considering these, 74
si/gun/gu were selected as the final flood vulnerable areas.
3.2. Demographic Change Analysis
3.2.1. Demographic Change Across South Korea
As of 2018, Korea's total population was 51,630,000, which is an increase of 12% (5,644,000) from
that in 2000.). However, the population decreased in 136 out of 229 municipalities (59.39%) and
increased in only 93 regions (40.61%) (Figure 4). This indicates that, like flood damage, population
change has large variations among regions, and this must be considered in disaster preventive urban
Sustainability 2020, 12, 4727 9 of 17
Figure 4. Spatial analysis of Korea's population change (2000–2018). (a) 2018 population, (b) 2000
population, (c) population change (2018-2000).
Table 4a shows the demographic change for all age groups in South Korea between 2000 and
2018. The super-aged (+75) and infants (0–9) were the age group with the highest increase (3.96%)
and decrease (−6.01%), respectively. The proportion of infants, school-age (10–19), and working-age
(20–64) decreased, while that of aged (65–74) and super-aged (+75) increased. Similar to the results of
many previous studies, the above findings show that South Korea is the country with the fastest
decrease in the birth rate and the fastest aging population in the world. In addition, the low birth rate
and fast population aging are expected to accelerate the demographic change in the long run, and the
most flood vulnerable group is also changing from the younger groups to the older ones. Based on
several analyses, this change is expected to be more severe in the future.
Examining the demographic change by age group and by gender, the proportion of women in
the super-aged group (+75) showed the highest increase of 3.97% in 2018, which represents an
increase of 2.38% compared to the 1.59% increase in 2000. The proportion of men in the infants (0–9)
group showed the largest decrease to 4.26% in 2018, which represents a decrease of 3.30% over to the
7.55% increase in 2000. These results indicated that the proportion of women in the flood
vulnerability group increased.
3.2.2. Demographic Change in Flood Vulnerable Areas
The 74 flood vulnerable areas selected in Section 3.1 account for 17.18% (17,247 km2) of the total
land area in Korea and include 40.29% (20,799 thousand people) of the total population in 2018.
Therefore, most of the vulnerable areas are densely populated.
Table 4b also shows the demographic change for all age groups in flood vulnerable areas
between 2000 and 2018. Similar to the overall pattern in South Korea, the proportion of aged (65–74)
and super-aged (+75) people increased, and the proportion of infants (0–9) and school-age (10–19)
people decreased in flood vulnerable areas, indicating a low birth rate and fast population aging. The
demographic change according to gender in flood vulnerable areas also showed the largest increase
in the proportion of women in the super-aged (+75) groups (+2.26%) and the largest decrease in the
proportion of men in the infants (0–9) groups (−3.03%), similar to the overall pattern in South Korea.
Furthermore, the population decreased in 42 regions and increased in 32 regions. The demographic
changes by region according to age group are as follows. For infants (0–9) and school-age children
(10–19), the population decreased in 67 and 63 regions, respectively, and increased in 7 and 11
regions, respectively, which indicates many regions witnessed a decrease in the population of these
age groups. For the working (20–64) and aged (65–74) groups, the population increased in 43 and 63
regions, respectively, and decreased in 31 and 11 regions, respectively. This indicates that many
regions demonstrated an increase in the population of these age groups. The super-aged (+75) group
demonstrated a population increase in all 74 vulnerable areas.
Sustainability 2020, 12, 4727 10 of 17
Table 4. Demographic changes between 2000 and 2018 in South Korea and in flood vulnerable areas in South Korea.
(a) South Korea (b) Flood Vulnerable Areas in South Korea
Proportion (%) Population
Proportion (%)
2000 2018 2018–2000 2000 2018 2018–2000 2000 2018 2018–2000 2000 2018 2018–2000
Sustainability 2020, 12, 4727 11 of 17
3.3. Characteristics of Flood Vulnerable Area Type Reflecting Demographic Change
3.3.1. Type Classification through Cluster Analysis
For the analysis of flood vulnerable area type reflecting demographic changes, a K-mean cluster
analysis was conducted by selecting flood vulnerability and demographic change characteristics as
variables. Unlike hierarchical cluster analyses, the K-mean cluster analysis requires the number of
clusters to be specified in advance. To select the appropriate number of clusters, the number of
clusters was varied, and the number of cases for each cluster was determined through ten iterations
(Table 5). In this process, it was confirmed that each case (18, 25, 30, 1) showed relatively uniform
distribution when classified into four clusters, and, excluding one region with exceptional
characteristics, 73 si/gun/gu were categorized into three types.
Table 5. Comparison of the number of cases by clusters.
Cluster Number of Cases in Each Cluster
1 2 3 4 5 6
2 30 44
3 35 38 1
4 18 25 30 1
5 11 12 21 29 1
6 18 2 18 21 22 1
3.3.2. Analysis of Characteristics by Type
The characteristics of three types of flood vulnerable areas derived through cluster analysis were
examined by comparing the mean values of the flood vulnerable area characteristic variables and
demographic change characteristic variables (Table 6).
Table 6. Characteristics of clusters and comparison of the variables’ mean values.
Variable Cluster 1 Cluster 2 Cluster 3
Property damage 94.50 million won/
112.43 million
173.53 million
Casualties 241.09 person/km2 10.24 person/km2 71.83 person/km2
Flooded area 0.17 km2/km2 0.01 km2/km2 0.00 km2/km2
Male Female Male Female Male Female
253,366 259,782 40,145 39,388 145,500 147,723
Infants 20,284 19,209 2998 2847 12,420 11,775
School-age 24,313 22,593 3454 3166 14,590 13,706
Working-age 178,617 179,774 26,298 22,947 100,926 99,145
Aged 19,866 21,822 4305 4849 11,280 12,717
Super-aged 10,285 16,385 3088 5578 6283 10,380
Population change
4273 13,851
2088 10,025 13,011
Working-age 14,109 17,266 693
1974 12,670 11,683
Aged 12,584 11,532 1,250 402 6643 6139
Super-aged 7803 10,680 1953 3230 4773 6879
Sustainability 2020, 12, 4727 12 of 17
Rate of population change
Working-age +6.71% +8.63%
18.70% +11.29% +11.24%
Aged +63.06% +52.87% +25.03% +2.80% +59.51% +48.62%
Super-aged +75.79% +64.71% +62.93% +57.80% +76.28% +66.91%
Population proportion 49.37% 50.63% 50.48% 49.52% 49.62% 50.38%
Infants 3.95% 3.74% 3.77% 3.58% 4.24% 4.02%
School-age 4.74% 4.40% 4.34% 3.98% 4.98% 4.67%
Working-age 34.81% 35.03% 33.07% 28.85% 34.42% 33.81%
Aged 3.87% 4.25% 5.41% 6.10% 3.85% 4.34%
Super-aged 2.00% 3.19% 3.88% 7.01% 2.14% 3.54%
Rate of population
proportion change
Working-age +1.58% +2.21% +1.88%
1.50% +1.75% +1.44%
Aged +2.40% +2.17% +1.69% +0.68% +2.13% +1.90%
Super-aged +1.50% +2.04% +2.50% +4.15% +1.58% +2.24%
Regions in Cluster 1 (Type 1) have the smallest property damage due to floods but the largest
casualties and flooded areas, and includes 18 si/gun/gu, with Yangcheon-gu, Seoul-si, which has the
largest number of casualties, and Dongjak-gu, Gwanak-gu in Seoul-si, Haeundae-gu in Busan-si,
Anyang-si in Gyeonggi-do, and Pohang-si in Gyeongsangbuk-do(Table 7). In addition, Type 1
regions have the largest mean population and relatively large population growth (smaller than that
of Type 3 regions). Although the male population in Type 1 regions increased by 4273 thousand
people on average, the female population increased by 13,851 thousand people, indicating a
significant increase in the female population. The decrease in the infants (0–9) and school-age (10–19)
population and the increase in the working-age (20–64), aged (65–74), super-aged (+75) population is
characteristic of the pattern of South Korea, i.e., low birth rate and fast population aging.
Type 2 regions cover 25 si/gun/gu, including Seo-gu and Dong-gu in Busan-si, Jung-gu and
Ongjin-gun in Incheon-si, Dongducheon-si, and Uiwang-si in Gyeonggi-do, Buyeo-gun and
Seocheon-si in Chungcheongnam-do Kim Jae-gun, Insil-gun in Chungcheongbuk-do, Naju-si, and
Gurye-gun in Jeollanam-do and Tongyeong-si and Sacheon-si in Gyeongsangnam-do(Table 7).
Excluding four gu in Busan-si and two gu/gun in Incheon-si, the majority of the Type 2 regions are
regional small- or medium-sized cities with the smallest population sizes and are the only ones where
the populations decreased. In the Type 2 regions, the population decreased across a wide range of
ages from infants (0–9) to working-age (20–64), and the rate of decrease was greater than that in Type
1 and three regions. The working-age (20–64) female population (
18.70%) and the infants (0–9) male
population (
101.40%) decreased sharply. The proportion of infants and school-age people was the
smallest, and that of aged and super-aged was the highest, which had the greatest contribution to the
low birth rate and fast population aging. In addition, the casualties due to floods were the fewest
among the three types, and the property damages and flooded areas were the second largest, which
is attributed to the larger average land area of Type 2 flooded areas.
Type 3 regions have the largest property damage owing to floods and include 30 si/gun/gu.
Among these, Suyeong-gu in Busan-si, Gwangjin-gu, and Dongdaemun-gu in Seoul-si, Dalseong-
gun in Daegu-si, Gyeyang-gu in Incheon-si, Nam-gu in Gwangju-si, and Jung-gu in Ulsan-si have the
greatest property damage(Table 7). The population size in Type 3 regions is smaller than that in Type
1 regions, but the population growth is the largest. The number of men and women increased
Sustainability 2020, 12, 4727 13 of 17
uniformly, and the growth rate of working-age (20–64) (male: 11.29%, female: 11.24%) is large. In
addition, the reduction rate of infants (0–9) is the smallest, and the population proportion is the
Table 7. Regional classification by type.
(number) Characteristics Regions (Si/Do/Gun/Gu)
Large number of
large flooded areas;
low population
Seoul: Eunpyeong, Yangcheon, Gangseo, Guro, Yeongdeungpo, Dongjak,
Gwanak, Gangnam, Songpa, and Gangdong; Busan: Haeundea; Incheon:
Michuhol, Namdong, and Bupyeong; Gyeonggi: Anyang and Bucheon;
Chungcheongbuk: Jeonju; Gyeongsangbuk: Pohang
Small and medium
cities; population
low birth rate; fast
population aging
Busan: Seo, Dong, Yeongdo, and Gangseo; Incheon: Jung and Ongjin;
Gyeonggi: Dongducheon and Uiwang; Chungcheongnam: Buyeo, Seocheon,
Cheongyang, Hongseong, Yesan, and Taean; Chungcheongbuk: Gimje and
Imsil; Jeollanam: Naju, Gurye, Boseong, Wando, and Shinan;
Gyeongsangnam: Tongyeong, Sacheon, Miryang, and Changnyeong
Large property
high population
high proportion of
young people
Seoul: Gwangjin, Dongdaemun, Gangbuk, Seodaemun, Mapo, Geumcheon,
and Seocho; Busan: Dongnae, Nam, Buk, Saha, Geumjeong, Yeonje,
Suyeong, and Gijang; Deagu: Dalseong;, Incheon: Gyeyang and Seo;
Gwanju: Nam and Gwangsan; Ulsan: Jung and Buk; Gyeonggi:
Gwangmyeong, Gwangju, and Yangju; Chungcheongbuk: Iksan; Jeollanam:
Mokpo, Yeosu, and Suncheon; Gyeongsangnam: Jinju
3.4. Recommendations
Flood disaster risk reduction management is a continuous process that involves identifying
issues, defining objectives, assessing risks, appraising strategies, implementation, monitoring, and
review [43]. Flood damage and demographic characteristics are constantly changing, and there is a
need to monitor the changing trends and re-evaluate flood vulnerable areas. Further, the effectiveness
of the proposed strategy for each region needs to be monitored based on type, and the goals and
strategies need to be adjusted according to the monitoring results. Within this monitoring system,
evaluating flood vulnerable areas is considered a cyclical process involving the design and evaluation
of alternative strategies. The proposed monitoring system is illustrated in Figure 5 and explained
1. Data monitoring: Identify trends of change through flood damage and population census data
2. Flood vulnerable area monitoring: The flood vulnerable areas are re-selected and re-categorized
according to the changing trend.
3. Strategic planning monitoring: The regional type is divided into “existing” and “new” to
monitor the strategic plan by region type.
4. Strategic effectiveness monitoring: The strategy is adjusted by monitoring the effectiveness of
the strategy in a region, or by reviewing the application of other strategies in other regions where
they worked well.
Sustainability 2020, 12, 4727 14 of 17
Figure 5. Monitoring flood risk prevention strategies.
4. Discussions and Conclusions
In this study, we designed a methodology for categorizing flood-vulnerable areas through cross
and cluster analyses of flood damage and demographic changes. The methodology was applied to
229 local governments in South Korea to derive three types of flood-vulnerable areas.
Type 1 regions comprise of 18 municipalities (si/gun/gu) with metropolitan cities having a large
average population (4865 people/km2). The total population has increased since 2000. In particular,
the female population in these areas has increased significantly compared to the male population,
and floods have caused substantial human casualties.
Type 2 areas typically include small- and medium-sized regional cities with a small population
over a large area (209 people/km2). Korea has 25 Type 2 municipalities (si/gun/gu). The flood damage
in these regions is not as extensive as that in other areas, however, they are the only regions
characterized by population decrease, low birth rate, and aging population.
Type 3 areas include 30 Korean municipalities (si/gun/gu) with a population size (1716
people/km2) larger than the overall average population size of Korea (514 people/km2), but smaller
than the metropolitan Type 1. This is a new city type with large population growth and the largest
increase in the youth population.
The vulnerable areas of categorized floods should be handled at the community level through
various strategies, such as installing structures that increase height (e.g., levees and sandbags [44,45]),
acquiring open spaces and conserving wetlands [46–49], land use planning for further development
and densification regulation in flood-prone areas [50], flood-proofing buildings [51], insurance
programs and tax incentives for flood risk [52–54], flood risk response strategy communication (e.g.,
flood information, early disaster warning, risk mapping and distribution, and evacuation training
[50,55]), involvement of local community members in the flood planning and recovery processes
[49,50], minimization of the constraints of the disaster vulnerable population [49,56] and mitigation
efforts for flood risk factors through continuous monitoring of the local community [49,50]. The
strategy presented in this paper can be additionally considered when establishing flood prevention
measures on the local community level. Therefore, the proposed strategy cannot encompass all
elements of community flood disaster prevention planning. A flood risk prevention strategy
appropriate to the area type can be developed through additional research. Moreover, the suitability
of the proposed strategy should be verified through continuous monitoring.
This study differs from previous studies in that it categorized flood-vulnerable areas into three
types considering the demographic change factors and improved the accuracy of the regional
differences by subdividing Korea into 229 municipalities and performing a detailed analysis.
However, as suggested in Subsection 3.4, flood damage and demographic factors are constantly
Sustainability 2020, 12, 4727 15 of 17
changing variables. Therefore, flood-vulnerable areas must be re-evaluated through monitoring and
continuous improvement and update of response measures and planning. In addition, in terms of
demographic change, the quality of research is expected to be further improved if various disaster
vulnerable group variables, such as foreigners, the disabled, and the poor, can be additionally
considered and if long-term demographic data are supplemented.
Author Contributions: Conceptualization, H.-K.L.; Methodology, H.-K.L.; Writing—original draft, H.-K.L.;
Writing—review and editing, Y.-H.B. and J.-Y.S.; Visualization, Y.-H.B. and J.-Y.S.; Supervision, W.-H.H. All
authors have read and agreed to the published version of the manuscript. All authors have read and agreed to
the published version of the manuscript.
Funding: This research was funded by NATIONAL RESEARCH FOUNDATION OF KOREA (NRF), funded by
the government of Korea (MSIT), grant number 2019R1A2C3002219.
Conflicts of Interest: The authors declare no conflict of interest.
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... The eastern part of the peninsula is characterized by mountainous terrain and small streams while the western and southern parts are rather flat and low in comparison (YOON et al. 2010;JUNG et al. 2012, CHOI et al. 2017. Two thirds of the country consist of elevated landscapes (LEE, H.-K. et al. 2020, CHOI et al. 2017). Korea's annual average temperature was 13.2°C and the annual precipitation 1.237,4mm during the time period from 1912 to 2017 (SOUTH KOREAN NATIONAL INSTITUTE OF METEOROLOGICAL SCIENCE, 2018). ...
... Between mid-July and mid-August some of the heaviest rain events take place with amounts ranging from 220mm to 322mm on a daily basis in recent years (PARK and LEE 2020). While summer in Korea is mostly humid, it has a monsoon season from June to September, locally referred to as "jang ma chul", when air masses and tropical cyclones originating from the Northern Pacific move north towards Korea (CHAE et al. 2005;LEE, H.-K. et al. 2020;PARK and LEE 2020 Natural and man-made disasters on local and global level have frequently hit Korea in past and present (HA 2015). The country has five large river basins and most of its major cities are located at their outlets (JUNG et al. 2012). ...
... While the capital (Seoul) consisted of 7.8% impervious surfaces in 1962, the number grew to 47.7% in 2010 due to industrialization and urbanization. This has had a direct impact on the surface runoff which grew from 10.6% to 51.9% (LEE, H.-K. et al. 2020;SHAFIQUE and KIM 2018). During summer monsoon or typhoon events urban floods are often caused in the downstream where urban areas are located (CHOI et al. 2017). ...
South Korea (hereinafter: Korea) is a country on the Korean Peninsula in East Asia. Its eastern part is characterized by mountainous terrain and small streams while the western and southern parts have flat landscapes in comparison. The annual average precipitation was at 1.237,4mm in the time period from 1912 to 2017. 70% of the annual precipitation takes place during the monsoon season in summer. The most severe precipitation occurs between mid-July and mid-August. This can vary between 220mm and 322mm daily due to Korea’s location on the edge of the Northern Pacific Ocean, which frequently brings tropical cyclones towards the Korean Peninsula. Typhoons are characterized by strong winds and high waves as well as heavy rainfall, which frequently leads to flooding. These floods affect the general public of Korea regularly. Between 1995 and 2006, an average of ten natural disasters happened annually, primarily typhoons and floods. This bachelor thesis analyzes the impacts of pluvial flooding on three dimensions: ecological, social and economic. In addition to that, the discussion covers future developments, like climate change, and a critical examination of the Korean disaster management. The results show that the impacts vary a lot. The ecological impacts affect sedimentation along flood plains, disturb fish populations and cause changes in seed banks of plants and their physico-chemical characteristics. The social impacts range from the interference of infrastructure over growing health concerns up to fatalities. Especially vulnerable are elderly people and individuals from low-income households. Furthermore, traumata can be triggered by flood events, e.g. when individuals have experienced a severe loss. The mental health impacts can be of short- but also long-term duration. Considering the economy, there are losses up to billions of US dollars in the most severe events. On average, the economic loss through heavy rain, which is responsible for more than 63% of disaster damage in Korea, exceeds 120 million US dollar per year. While the impacts of climate change have already been observed with the increase of precipitation of 0.11mm annually, the projection of climate models shows further increase of the maximum precipitation of up to 13.3% until the year 2100. This will also impact local pluvial flood events. Because of the topographical differences within the country regional differences have to be expected. Therefore, local adaptation strategies need to be developed. Last but not least, the disaster management needs to be restructured to ensure that the right measures will be taken when a flood event occurs.
... Politics-oriented management is still central to the field of natural disaster management in Korea. From the outset of modern history, politics has been inscribed into all aspects of natural disaster management, such as in the recruitment of personnel and the allocation of funding, among others (Lee et al. 2020;Pritchard 2012). Thus, politics has shaped the ways of natural disaster management in Korea, in particular by pushing for linkages between diverse natural disasters and their specific management. ...
This research has examined the reality of Korean natural disaster management by considering four analytical factors, namely, the characteristics of natural disasters, government policy, business strategy, and volunteer activity, towards drawing lessons for the international community. Qualitative content analysis has been used to test the following hypothesis: “If Korean natural disaster management is not the best but only satisfactory, then there will be implications for the international community.” The success and failure of Korean natural disaster management has been compared from the international perspective. By accepting the hypothesis, this research maintains that the Korean field of natural disaster management has always been surrounded by certain barriers, according to the bounded rationality of human beings.
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The number of shamans, as a category of disaster management stakeholders, has significantly increased in Korea. However, the role of shamans in mitigating the psychological impact of disasters has not been adequately studied. This research explores how to improve the role of sha-manism in the field of Korean disaster management toward the ultimate goal of mitigating the psychological impact of disasters. Descriptive content analysis is used to systematically compare the secularism approach with the psychological impact mitigation approach by considering professional shamans, community leaders, educators and researchers, and disaster victims. The most significant finding is that Korea needs to supplement its current secularism approach with the psychological impact mitigation approach. Asian nations could benefit from insights on the significance of behavioral change, cultural competency, neo-shamanism, and multiple networks. The value of this study lies in its more rigorous investigation of Korean shamanism in relation to disaster management compared with previous works.
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Floods have always been associated with widespread devastation and destruction since the emergence of human civilization. Assessment of vulnerability is the primary objective of flood hazard management. This study makes a credible attempt to present a coherent review on the approaches and methodologies used for assessing flood vulnerability to improve our understanding of flood vulnerability in multiperspectives. A systematic review with content analysis and bibliometric analysis using Bibliometrix an R package were employed to analyze 193 selected documents published during 2010–2021 on Scopus and Web of Science. The bibliometric analysis results indicate the annual scientific publications, country scientific production, most relevant sources/authors of publications, and the foundation documents of publications in studies period. Besides, the content analysis provides the trend of methods used, main data sources of three major approaches in flood vulnerability assessment which are indicator-based approach, modeling approach and approach which based on damage survey; and analysis of key objectives and main findings of some most cited publications in each approach. The research states that the indicator-based approach is the most popular and provides a better understanding of overall flood vulnerability assessment in each area rather than other approaches. The results of this study is a comprehensive guide of materials and resources for undertaking flood vulnerability assessment.
In this study, our objective was to assess coastal vulnerability to inundation due to global mean sea level rise in response to climate change to help prepare for future scenarios and typhoon-induced surges. To accomplish this, the coastal vulnerability index (CVI) was defined as a function of indicators representing elements of exposure, sensitivity, and adaptive capacity. Using these indicators, we analyzed the effects of sea level rise on coastal regions and their socio-economic levels. The relationship between exposure and adaptive capacity, as obtained from statistical data, was defined as the status assessment (SA). The effect of global mean sea level rise was obtained by adopting scenarios provided by the Intergovernmental Panel on Climate Change (IPCC) for 2030, 2050, and 2080. SA and CVI were calculated in 55 coastal regions consisting of metropolitan areas, cities, and rural areas. This study showed that the effects of climate change-driven sea level rise would increase CVI as the most dominant element. In rural areas, lower adaptive capacity scores would also lead to higher CVIs. Comparing future CVIs to the present, the most noticeable increases were predicted for rural areas. These estimations of CVI in response to climate change, alongside comparisons made with respect to time, region, and indicators, may help researchers in designing effective countermeasures for coastal management.
Floods are major social and environmental concerns in many urban areas. We investigated how changes in land cover, sociodemographic conditions, and meteorological factors affect flood damage in districts of South Korea. Using historical maps and spatial analysis, we showed that flood damages increased in the areas where rapid urbanization happened without coordinated urban planning. High flood damage areas are not spatially randomly distributed, and the hotspots of high damage areas are concentrated in population centers that underwent rapid development after 1975. Additionally, human modifications of natural channels further exacerbated flood risks during the development stage and subsequent periods. Total annual precipitation is positively related to the flood damage at a higher spatial unit. This study underscores the importance of understanding the historical–geographical conditions, and how humans either increased or reduced the flood damage through social and infrastructure interventions. Findings of this study have implications for resilient flood management for regions that are currently facing the dual challenges of land densification and climate change-induced heavy precipitation.
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There are frequent floods in Lebak Regency, especially in the central city, i.e., Rangkasbitung and its surroundings, which is detrimental to local society. In the last five years, there has been 43 times the flood disaster. The problem of this flooding has not been entirely resolved, although there are indications of an increase in frequency, duration, and distribution in the study area. This study discusses the vulnerability of the area to flooding based on social, economic, and physical characteristics. K-Means Clustering is used to analyze the level of vulnerability for each village from 39 villages in Rangkasbitung, Cibadak, and Kalanganyar Districts. The results showed that the vulnerability level to flooding is dominated by a moderate level spread in the center of the study area. In contrast, the high level of vulnerability spread in the eastern and western parts of the study area. These results can be used as a basis for determining the flood risk areas in future studies.
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South Korea’s fertility rate declined rapidly beginning in the late 1970s. By 2005, it had fallen to an all-time low of 1.08 per woman. This shift has contributed to a significant and continuing increase in the ratio of elderly to working-age population. In 2006 and 2010, the Korean government instituted policy plans intended to support child-rearing in the context of a modern industrialized society. However, increases in fertility rates during the years that followed were slight and inconsistent. In 2016, the government presented its Third Initiative for Low Fertility and Aging Society, but this program has been criticized, especially for focusing, like the earlier plans, too narrowly on financial subsidies rather than addressing culture and infrastructure as well. The present study examines these policy responses and compares them to the case of Japan, where a substantial reversal in the recent low fertility trend appears to have followed targeted, broad-based policies from both the central and regional governments, as well as voluntary programs by employers. The study recommends that Korea, like Japan, should not only engage multiple stakeholders but should broaden the scope of policy responses to account for the research-supported interrelationship among factors associated with education, culture, and economics.
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Flood risk is increasing worldwide and there is a growing need to better understand the co-benefits of investments in disaster resilience. Utilizing a multinational community flood resilience dataset, this paper takes a systems approach to understanding community-level flood resilience. Using a cluster analysis and bivariate correlation methods, we develop a typology of community flood resilience capacity based on community characteristics and five capitals (human, financial, natural, physical, and social). Our results reinforce the importance of context-specific policymaking and give recommendations of four distinct clusters to investigate the relationship between flood resilience and prevailing development conditions. We especially find that communities with higher interactions between their capital capacities tend to have higher flood resilience levels. Additionally, there are indications that stronger interactions between community capacities can help to induce multiple co-benefits when investing in disaster resilience. Our results also have important policy implications on the individual community level. For example, based on our results, we suggest that communities with lower flood resilience capacities and interactions can best build resilience on leveraging their relatively higher human capital capacities to strengthen the financial and social capitals. Negative effects might happen for urban communities when co-benefits of natural and physical capital are not fully integrated. The highest flood resilience capacity is found in communities with a well-balanced household income distribution which is likely a contributing factor to the importance of financial capital for this cluster. Our results emphasize the importance of an integrative approach to management when implementing systematic flood disaster resilience metrics and development measures.
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This study aims to examine how the aging population of each region has changed from 2009 to 2018, and how age-friendly the current neighborhood environments are in those areas in Daegu, Korea. The 139 administrative units are used as spatial units to identify aging regions, while 100 m × 100 m grid cells are employed as spatial units to capture the environmental variables of the neighborhood comprehensively. To analyze Daegu’s aging regions, emerging hotspot analysis was performed, demonstrating the spatio-temporal patterns of the elderly population. ANOVA analysis and a case study with field surveys were used to examine the age-friendly environmental conditions in aging regions. Findings of this study showed that Daegu’s aging regions were increasing rapidly and spreading from the city center over time. In addition, it was found that the neighborhood environmental conditions of the aging regions were very poor in terms of accessibility, safety, and pleasurability. Significant differences were also found in the levels of age-friendliness of the neighborhood environments, depending on whether they are urban or suburban. The results herein support public policy proposals relevant to urban planning, environmental design, and aging policies.
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Nowadays, extreme weather and atmospheric conditions are becoming more frequent and more intense. It seems obvious that together with climate change, the vulnerability of the public and of individual regions to the risks of various types of natural hazards also increases. This would increase the importance of organization concerning potential measures to protect against these extraordinary events, and to prepare for reducing their ramifications. One such initiative is the creation of an early warning system for inhabitants of a given area of a country, to help guard against the extraordinary threat associated with a natural disaster; especially floods. The creation of such a system is aimed at increasing public safety and limiting losses caused by the occurrence of natural, technological, and synergistic hazards. Particular emphasis during the construction of a current system is placed on supporting flood risk management, which is aimed at increasing the safety of citizens and reducing losses caused by the occurrence of flooding in Poland. This would be possible by the identification of areas threatened by flooding throughout the country, and then limiting economic expansion in these areas. Ultimately, the project aims to consolidate information regarding hazardous events and gather them in a professional Information Technology (IT) system, using an integrated database and a modern module for disseminating information to end users. The system is to provide access to this information for both the administration and the individual citizen. This article presents the potential of a so called “IT System for the Country’s Protection Against Extreme Hazards,” which is currently being developed in Poland, with particular emphasis on reducing the risks related to natural disasters and minimizing the problems of crisis management in Poland. This article is also aimed at opening discussions and creating a basis for the exchange of information from countries implementing similar solutions, especially neighboring countries, with which joint action could be undertaken.
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This study systematically reviews the diverse body of research on community flood risk management in the USA to identify knowledge gaps and develop innovative and practical lessons to aid flood management decision-makers in their efforts to reduce flood losses. The authors discovered and reviewed 60 studies that met the selection criteria (e.g., study is written in English, is empirical, focuses on flood risk management at the community level in the USA, etc.). Upon reviewing the major findings from each study, the authors identified seven practical lessons that, if implemented, could not only help flood management decision-makers better understand communities’ flood risks, but could also reduce the impacts of flood disasters and improve communities’ resilience to future flood disasters. These seven lessons include: (1) recognizing that acquiring open space and conserving wetlands are some of the most effective approaches to reducing flood losses; (2) recognizing that, depending on a community’s flood risks, different development patterns are more effective at reducing flood losses; (3) considering the costs and benefits of participating in FEMA’s Community Rating System program; (4) engaging community members in the flood planning and recovery processes; (5) considering socially vulnerable populations in flood risk management programs; (6) relying on a variety of floodplain management tools to delineate flood risk; and (7) ensuring that flood mitigation plans are fully implemented and continually revised.
Natural hazards and disasters distress populations and inflict damage on the built environment, but existing studies yielded mixed results regarding their lasting demographic implications. I leverage variation across three decades of block group exposure to an exogenous and acute natural hazard—severe tornadoes—to focus conceptually on social vulnerability and to empirically assess local net demographic change. Using matching techniques and a difference-in-difference estimator, I find that severe tornadoes result in no net change in local population size but lead to compositional changes, whereby affected neighborhoods become more White and socioeconomically advantaged. Moderation models show that the effects are exacerbated for wealthier communities and that a federal disaster declaration does not mitigate the effects. I interpret the empirical findings as evidence of a displacement process by which economically disadvantaged residents are forcibly mobile, and economically advantaged and White locals rebuild rather than relocate. To make sense of demographic change after natural hazards, I advance an unequal replacement of social vulnerability framework that considers hazard attributes, geographic scale, and impacted local context. I conclude that the natural environment is consequential for the sociospatial organization of communities and that a disaster declaration has little impact on mitigating this driver of neighborhood inequality.
This paper uses a continuous-time overlapping-generations model with endogenous growth and pollution accumulation over time to study the link between longevity and global warming. It is seen that increasing longevity accelerates climate change in a business-as-usual scenario without climate policy. If a binding emission target is set exogenously and implemented via a cap-and-trade system, the price of emission permits is increasing in longevity. Longevity has no effect on the optimal solution of the climate problem if perfect intergenerational transfers are feasible. If these transfers are absent, the impact of longevity is ambiguous.
Purpose As climate change shocks and stresses increasingly affect urban areas in developing countries, resilience is imperative for the purposes of preparation, recovery and adaptation. This study aims to investigate demographic characteristics and social networks that influence the household capacity to prepare, recover and adapt when faced with prolonged droughts or erratic rainfall events in Mbale municipality in Eastern Uganda. Design/methodology/approach A cross-sectional research design was used to elicit subjective opinions. Previous studies indicate the importance of subjective approaches for measuring social resilience but their use has not been well explored in the context of quantifying urban resilience to climate change shocks and stresses. This study uses 389 structured household interviews to capture demographic characteristics, social networks and resilience capacities. Descriptive and inferential statistics were used for analysis. Findings The ability of low-income households to meet their daily expenditure needs, household size, and networks with relatives and non government organizations (NGOs) were significant determinants of preparedness, recovery and adaptation to prolonged droughts or erratic rainfall events. Practical implications The results imply that policymakers and practitioners have an important role vis-à-vis encouraging activities that boost the ability of households to meet their daily expenditure needs, promoting small household size and reinforcing social networks that enhance household resilience. Originality/value Even the low-income households are substantially more likely to prepare for and recover from prolonged droughts or erratic rainfall events if they can meet their daily expenditure needs. This finding is noteworthy because the poorest in society are generally the most vulnerable to hazards.