Tsuda and Tebakari ﻿Progress in Earth and Planetary Science(2023) 10:36
https://doi.org/10.1186/s40645-023-00569-9

RESEARCH ARTICLE

Progress in Earth andPlanetary Science

Open Access

A macroscopic analysis of the demographic
impacts of flood inundation in Thailand (2005–
2019)
Hinako Tsuda1*   and Taichi Tebakari1

Abstract
In Thailand, floods are occurring more frequently due to climate change, and recent economic development
and population growth may have altered the way in which people interact with floods, including migration to otherregions. In this study, we analyzed the relationship between flooding and population distribution across Thailand
from 2005 to 2019 to improve measures for minimizing flood damage. We used population distribution point datafrom 2009 to 2019 produced by Oak Ridge National Laboratory to analyze trends in population movement and distribution, by examining whether population sizes were greater than, less than, or equal to estimated values in regions
throughout Thailand. The results suggest that floods in 2011 and 2017 caused temporary migration to areas that were
not inundated or to the metropolitan Bangkok area. Flood responses changed after the 2011 floods, which have been
described as the worst flood in Thai history. Next, we examined the relationship between the number of regionswith lower than estimated population and flood data for the previous year including precipitation, inundated area,and deaths caused by flooding. Inundation area had a significant impact on population decline, with correlation
coefficients of 0.426 and 0.501 for the north and northeast, respectively. The number of deaths caused by flooding
in a given year also led to a population decline in the following year. However, precipitation did not exhibit the same
trend. Therefore, population demographics after floods have shown regional characteristics in recent years, with Thai
people shifting from a flood-tolerance lifestyle to a flood-avoidance lifestyle, mainly in local urban areas and the metropolitan Bangkok area.
Keywords Demographics, Flood, Inundation, Thailand
1 Introduction
Floods, and especially large-scale floods, are frequentin Thailand, resulting in considerable property damage, injury, and loss of life. However, as rivers are an
important source of water, including for agriculture,
local populations have chosen to remain in areas at riskof flooding by developing measures to cope with floods*Correspondence:
Hinako Tsuda
a18.84hf@g.chuo-u.ac.jp
1
Civil, Human and Environmental Science and Engineering Course,
Graduate School of Science and Engineering, Chuo University, 1‑13‑27
Kasuga, Bunkyo‑Ku, Tokyo, Japan(Phanthuwongpakdee 2016). Traditional flood mitigation measures include the selection of micro elevations,channel excavation, and construction of stilt houses andearthen mounds (Iwaki 2013), all of which recognize thecharacteristics of floods in Thailand, where many rivers have gentle gradients, water levels rise slowly during
flooding, and inundation is not rampant. These characteristics stand in contrast to those of flooding in Japan,
for example, where many rivers flow swiftly and heavy
rains are frequent, such that the intervals between inundations are short.
Rapid population growth in Thailand since the beginning of the twentieth century has led to the expansion and greater density of urban areas and increased© The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of thislicence, visit http://​creat​iveco​mmons.​org/​licen​ses/​by/4.​0/.

Tsuda and Tebakari ﻿Progress in Earth and Planetary Science(2023) 10:36

socioeconomic development. Extensive road construction, the widening of existing roads, and an increasingly
Westernized lifestyle in Thailand have been accompanied by declines in the number of stilt houses andearthen mounds in rural areas (Iwaki 2013). Instead,the government has constructed levees and causeways,
reflecting a shift from an emphasis on relatively smallscale control measures that allow local populations tolive with floods to larger-scale measures. However, thelatter is far from sufficient, as construction has not kept
pace with faster-than-anticipated growth and effectiveplanning has been hindered by the country’s politicalturmoil. The Thai government is aware of the disadvantages of these urbanization trends and has recently
introduced green infrastructure including water supply
in regions requiring flood control (Climate Bonds Initiative 2022). Nong Bon Lake and the Nong Bon Water
Sports Center in southeastern Bangkok are an artificial
lake and park constructed to prevent autumn floods;
similar green infrastructure is already in place in urban
areas. In the Chao Phraya Basin, Loc et al. (2020) foundthat 73.7% of the total flood volume during the 2011
flood was caused by precipitation and 25.3% by river
runoff. Park et al. (2021) reported that the Bhumibol
Dam and Sirikit Dam, located in the upper part of thebasin, are the main resources for sediment absorption.
Therefore, the changes in demographics and the government’s approach to flood control may lead to significant changes in the way in which Thai people deal withfloods and their choice of where to live. Through theanalysis of global examples of disasters such as nuclear
accidents, forest fires, and heat waves, Karácsonyi and
Taylor (2021) concluded that seven factors explain thecomplex and diverse links between populations anddisasters: population impacts, vulnerability, mass relocation, spatial and regional approaches, climate change,
urbanization, and response methods.
Bangladesh provides an example of a country-specificrelationship between population dynamics and disaster.
In Bangladesh, floods cause short-term, but few longterm, population movements, in contrast to agricultural
crop failures (Gray and Mueller 2012). An econometrics analysis detected no significant long-term impact of
three types of natural disaster (earthquakes, tornadoes,
and hurricanes) on population density increases in the
US counties (Wang 2019). Mizutani (1989) studied thefactors driving population declines in disaster areas inJapan, including quantitative relationships, and the population recovery process. Inoue (2021) compared the
population structure and future population estimates inan area of expected inundation in Mabi-Cho, Kurashiki
City, Okayama Prefecture, Japan, with nationwide data,
finding a significant decrease in population density inPage 2 of 13

the expected inundation area relative to non-inundation
areas nationwide.
The tracking of macro-population dynamics after disasters has been facilitated by the use of cell phone SIMs
and location-based services (Bengtsson et al. 2011; Yabeet al. 2020). Observations of the movement trajectories
of cell phone users before and after five large-scale disasters in the USA, Puerto Rico, and Japan showed that
population recovery patterns can be approximated by acommon negative exponential function (Yabe et al. 2020).
However, in a review of 83 research papers, Thompson
et al. (2017) found that far fewer studies have examined
the relationship between floods and evacuation thanhave addressed the relationship between hurricanes andcyclones and evacuation. The one paper in their sample that focused on Thailand examined the relationshipbetween tsunamis and evacuation.
The 2011 floods in Thailand were the worst in the
country’s history, with damage occurring in 61 provinces. A recent study of floods and demographics in Thailand involved comparison of the results of a nationwide
analysis of household evacuation responses in 2011 withthose of a partial analysis focusing on Ayutthaya Province(Bhula-or et al. 2020). Household evacuation decisions
were shown to depend on a complex and diverse set offactors; specifically, vulnerable groups, such as womenand elderly individuals, but also individuals with more
assets and more stable employment, were less likely toevacuate, whereas households with reduced opportunity
were more likely to evacuate to avoid loss and death. In a
study of the relationship between floods and demographics on a regional scale (Tahira and Kawasaki 2015), rural
poor families living in flood-prone areas were shown tohave remained for generations, not because of any advantage conferred by staying, but rather because of the difficulty of moving due to economic factors and the highratio of elderly individuals in this population.
Analyses of the relationship between flooding andpopulation dynamics must take into account the demographic characteristics of areas at risk of future large
floods to enable the development of disaster prevention and evacuation plans. However, few such studies
have been conducted using time series data throughout
Thailand.
Kiguchi et al. (2021) analyzed the effects of climatechange and adaptation measures in Thailand and foundthat most climate models showed an increasing trend in
the national average precipitation in the future (2080–
2099) compared with the baseline period (1980–1999).
Given the increases in urbanization and densification in
the country, heavy rainfall events and subsequent largescale floods will likely cause more damage and affect alarger proportion of the population. Komori et al. (2013)

Tsuda and Tebakari ﻿Progress in Earth and Planetary Science(2023) 10:36

reported that the heavy rainfall that caused the 2011
flood corresponded roughly to a 1/100-year probabilityof occurrence, and we found no evidence that climatechange is implicated in the large-scale floods described inthis study. Based on these projections, further studies areneeded to determine whether the Thai approach of “living with flooding” will continue to be possible or desirable, and of whether the lifestyle and mobility patterns ofThai people are changing to avoid floods.
Thus, the objective of this study is to examine therecent (2005–2019) relationship between flood andpopulation dynamics in Thailand, including regional
characteristics and the impacts of recent lifestyle andsocioeconomic changes. Therefore, this study is the firstto clarify the relationship between inundation and population dynamics using recent time series data throughout
Thailand, and its findings will contribute to the development of flood protection measures and urban policy
under a changing climate.

2 Data and methods
A flowchart of the overall study design is presented inFig. 1.
2.1 Data

The data are summarized in Table 1. For the inundation area, polygon data for the period 2005–2019 wereobtained from the Thailand Flood Monitoring System,
provided by the Geo-Informatics and Space Technology Development Agency (GISTDA 2005–2019). These
data are based on ALOS/PALSAR and THEOS-2 satellite images, with areas inundated at least once during theyear considered to be inundated. However, according to
Kawasaki et al. (2012), this definition likely overestimatesinundation areas, as it leads to the inclusion of paddy
fields and wetlands.

 =1×
Pop
t
3



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registration system 2005–2019). However, when the data
were used to calculate the change in Thailand’s overall
population from 2005 to 2019, the population was shownto decline from 2010 to 2011. Thus, instead of using the
population data as obtained, we used the rate of change
for each data point, converted from raster data, for the
analysis.
Model grid precipitation data from the JRA-55 Reanalysis conducted by the Japan Meteorological Agency,
covering the period of 1958–2021 (Kobayashi et al. 2015),
were used. These data output 0–3 h (00–03, 06–09,
12–15, and 18–21 UTC; mm/day) and 3–6 h (03–06,
09–12, 15–18, and 21–24 UTC; mm/day) forecast averages with a spatial resolution of 0.5625°. Daily andmonthly precipitation amounts, calculated by integratingthese data, were used for the analysis.
2.2 Methods

The population distribution data were used to analyze
trends in population movement and distribution, by
examining whether the population size at each point
throughout Thailand was greater than, less than, or thesame as the estimated value. If the actual population of
an inundated area in a given year was lower than the estimated population in the following year, then we inferredthat people in that area moved to another area because ofinundation. If the actual population in an urban area waslarger than the estimated population, then we inferredthat the urban population became more concentrated
than predicted. Specifically, for each population data
point from 2009 to 2019, the value estimated based on anatural population increase or decrease was determined,
and the difference from the actual population value (Dt )was calculated as shown in Eqs. (1) and (2).

Popt−3 −Popt−4−Popt−3−Popt−2+ Popt−2+ Popt−1
Popt−4Popt−3Popt−2

The LandScan Global population data from Oak Ridge
National Laboratory were used (e.g., Bright et al. 2006).
The data are annual, and for this study covered theperiod 2005–2019. This global population distribution
dataset has a resolution of 30 arc seconds, thus covering the potential activity space of people throughout the
day and night rather than merely at residential locations.
Population data for the Thailand region were extracted.
According to data from the Office of Registration Administration, Department of Provincial Administration, thepopulation of Thailand increased steadily from 2005 to
2019, with no decrease during any year (Official statistics ,Dt = Popt − Popt(1)

× Popt−1 + Popt−1,(2)

 is thewhere Popt is the actual population value, Poptestimated value due to a natural population increase or
decrease, and t is the year for which the difference is calculated. This calculation requires population data fromthe previous 4 years for estimation of the population ina certain year. As the distribution data required to estimate the population for the years 2005–2008 were notavailable, t ranged from 2009 to 2019. In Eq. (2), Popt is
the population calculated from the change in the natural population averaged from year 4 to year 3, year 3 toyear 2, and year 2 to year 1. Averaging the change in the
population over the previous 3 years, rather than over

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Collecting data (2005–2019)
Population
data

Inundationdata

Calculation ofprojected
population

Calculation ofinundation
area

Number ofdeaths byflood

Calculation of daily
and monthly
precipitation amounts

Calculation of theratio ( ) of deviationof actual population
data from estimatedvalueCollecting flood information(2005–2019) to understandrecent flood damage

Understanding recent
trends in population
movement anddistribution
throughout Thailand

Calculation of thenumber of points
which actual
population is smaller
than estimated value
region by region

Understanding
impact of the2011 flood

Understanding
recentpopulation
concentration in
urban area

Understanding recent
trends in population
movement region byregionPrecipitationdata

Calculation of the
precipitation ratio(max/average)
Collecting precipitation
information (2005–2019)
to understand resent
precipitation trends andcharacteristics by region

Causal analysis of population
decline and each factor(inundation area, number ofdeaths and precipitation)
Understanding which factors
influence population decline

Understanding demographics after floods and Thai people’s response toward floods
Trend of population change, including regional characteristics
Impact of the great 2011 floods
Population concentration in cities
Factors of population change

Contributing to the development of flood protection measures and urban policy
under a changing climate
Fig. 1 Flowchart of this study. The overall framework of this study, including input data and process, and objectives of each process

Tsuda and Tebakari ﻿Progress in Earth and Planetary Science(2023) 10:36

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Table 1 Data information
Data

Data name

Organization

PeriodFormat (resolution)

Inundation area data

Thailand Flood Monitoring System

GISTDA

2005–2019

Polygon data

Population data

LandScan Global

Oak Ridge National Laboratory

2005–2019

Raster data (30 arc seconds)

Precipitation data

The JRA-55 Reanalysis

Japan Meteorological Agency

1958–2021

Raster data (0.5625°)

Deaths by flood

Thailand Country Report

ADRC

2005–2019

PDF file

We used inundated area data, population data, precipitation data, and number of deaths by flood for analysis. Data information which includes the source for eachtype of the data, period, their formats, and resolution

only 1 year, minimized the effects of irregular population
changes, such as temporary rapid population increases ordecreases. Equation (3) presents Pt , defined as the ratioof the deviation of the actual population data from theestimated value.
Dt
× 100(%).
Pt = 

(3)

Popt

Table 2 shows yearly flood information for 2005–2019:
the inundated area, the most severely affected areas, thenumber of deaths caused by flooding, and precipitation
information. The total inundated area for the years 2005–2019 was calculated using ArcGIS Pro and the inundated
area data. Data on the main areas affected by floods eachyear and the total annual number of deaths due to flooding were based on qualitative information from the Asian
Disaster Reduction Center (ADRC 2005–2019).
The average daily and monthly precipitation for theyears 1958–2021 and the annual maximum daily andmonthly precipitation for each year between 2005 and2019 were calculated for each data point converted
from raster data (Fig. 2a, b). The location of the annual
maximum daily and monthly precipitation and theratio of the annual maximum daily and monthly precipitation to the average daily and monthly precipitation are shown in Fig. 2c, d, and the ratio of the annual
maximum daily and monthly precipitation to the average daily and monthly precipitation is also shownin Table 2. Monthly precipitation was calculated forquantitative evaluation of the magnitude of rainfall
related to flooding on monthly bases and for the identification of rainfall characteristics for each region andyear. The trends in the distributions of average daily
and monthly precipitation in Thailand from 1958 to
2021 were similar, but those for the distributions of
the ratio of the annual maximum to the average daily(monthly) precipitation from 2005 to 2019 differed.
The distribution of the daily precipitation ratio was

Table 2 Flood information for each year from 2005 to 2019
Year

Flood area (­ km2)

Main affected area

Total deaths by flood(ADRC)

Precipitation ratio (max/average)
Daily

Monthly

2005

9881.8South, North

75

22.07

2.92

2006

28,804.3

North, Northeast, Central

446

12.24

2.38

2007

11,507.6

South, North and Central

36

24.25

2.92

2008

14,407.2

South, North and Northeast

113

16.21

2.58

2009

7952.9

South

53

12.36

3.58

2010

34,260.6

Northeast, North

266

32.15

7.26
2.85

2011

49,762.0

Central and Northeast, South

1026

29.40

2012

8479.9South

About 10

21.67

4.14

2013

18,571.3

North and Northeast, South

About 140

15.38

2.44

2014

4493.9South

About 20

13.21

2.87

2015

925.9

North

0

27.02

3.45

2016

7458.0

South

99

22.75

2.76

2017

27,777.4

South, North and Central

44

17.40

2.66

2018

3975.3

North and Central, South

8

14.73

4.28

2019

8171.2

Northeast, South

5

20.52

2.74

Flood information includes the inundated area, the most severely affected areas, the number of deaths caused by flooding, and precipitation information which are
the precipitation ratio of the annual maximum daily and monthly precipitation to the average daily and monthly precipitation for the years 1958–2021

Tsuda and Tebakari ﻿Progress in Earth and Planetary Science(2023) 10:36

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Fig. 2 Maps of precipitation in Thailand shown in Table 2. a The average daily precipitation for the years 1958–2019 [mm], b The average monthly
precipitation for the years 1958–2019 [mm], c The location of the annual maximum daily precipitation, and the ratio of the annual maximum daily
precipitation to the average daily precipitation for each year between 2005 and 2019, d The location of the annual maximum monthly precipitation,and the ratio of the annual maximum monthly precipitation to the average monthly precipitation for each year between 2005 and 2019

concentrated mainly in the northeastern inland andsouthern coastal areas, whereas the distribution of
the monthly precipitation ratio showed that rainfall in2010 was highest in the southern region and was concentrated in the northeast, north, and east. Overall,daily and monthly precipitation tended to be higher in
the northeast, east, and south.
Using this flood information, we analyzed the relationship between the inundation area, number ofdeaths, precipitation, and population distribution. We
predicted that larger values of inundation area, deaths,
and precipitation would have a greater impact on population decline in the following year. However, it is also
possible to compare the size of these factors by comparing their causal relationships.
We also analyzed the relationship between inundation and population on a regional basis, i.e., northern,
northeastern, central, eastern, and southern Thailand
and the metropolitan Bangkok area (Fig. 3). Our analysis of the relationship between flood information andpopulation distribution for each region allowed us toclarify regional characteristics of population movement. In the metropolitan Bangkok area, which has
a rapidly growing population, floods may not have asignificant impact on population movement, and population growth and in-migration may exceed any population decline caused by flooding. Figure 4 shows the
topographical features of Thailand from the MERITDEM (Yamazaki et al. 2017) and the flood frequency
during the 16-year period from 2005 to 2020, as calculated by the Thailand Flood Monitoring System. These
results were compared with the calculated distribution of Pt to determine the impact of inundation on the

population distribution and the trend of population
migration in recent years.

3 Results
According to the ratio of inundated area to the regionalarea, the largest flooded areas were recorded in the 2011
flood throughout Thailand and in the northern andsouthern regions inundated in 2017, followed by the 2006
flood in the metropolitan Bangkok and eastern regions,and the 2010 flood in the northeastern and metropolitan
Bangkok regions (Fig. 3).
Figure 5 shows the distribution of Pt values for 2009–2019. The blue points show that the actual population
was smaller than estimated based on natural population
changes, and the red points show that the actual population was larger than estimated, with darker colors indicating larger changes. The range of −5 < Pt ≤ 5 (%) was
the cutoff for a small difference between the actual andestimated populations (Fig. 5). Points within this range
are white. For 2009 and 2010, areas with mixes of blue
and red points are present; the largest number of white
points is present for 2011. In 2012, the actual populationdiffered from the estimated value in many areas acrossthe country, indicating that the 2011 floods, the worst
in Thailand’s history, had a significant impact on the
population distribution. In particular, many points withPt ≥ 90(%) fall in the vicinity of Bangkok, and for the
Mun and Chi river basins many points indicate that theactual population was smaller than the estimated value.
For 2013, the opposite distribution was often observed.
For example, a concentration of points representing
Pt ≤ −5 (the actual population is smaller than the estimated value) is present near Bangkok for 2013, although

Tsuda and Tebakari ﻿Progress in Earth and Planetary Science(2023) 10:36

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Fig. 3 Map of regions and watersheds in Thailand. The map of six regions and 22 watersheds in Thailand, and the change over timeof the inundated area in each region as a percentage of the total area of the region from 2005 to 2019a concentration of points representing Pt > 5 (the actual
population is larger than the estimated value) is present
for 2012. Conversely, in the Mun and Chi river basins,a concentration of points representing Pt ≤ −5 is present for 2012 and a concentration of points representing
Pt > 5 is present for 2013. The trend for 2014 and 2015
was the same as that for 2013, whereas areas of Pt ≤ −5
points and Pt > 5 points were mixed for 2016 and 2017.
For 2018, Pt > 5 points were concentrated in some partsof the eastern region, in the metropolitan Bangkok area,
and in the southern region, with Pt ≤ −5 areas concentrated elsewhere. For 2019, points that were Pt ≤ −5 in2018, such as those in the north, northeast, and south,
tended to be Pt > 5 points.
Figure 6 shows the changes over time in points withPt ≤ −5 and Pt > 5 as a percentage of the total numberof points in each region (north, northeast, central, metropolitan Bangkok area, east, south, and all of Thailand).
The two graphs in Fig. 6 show opposite trends except in
2011, when large numbers of points were in the range of−5 < Pt ≤ 5 (%) (white points in Fig. 5).
Comparison of the Pt results with the previous year’s
flood data revealed the previous-year factors that
affected the population distribution in the following year
and whether they differed by region. Figure 7 shows therelationship between the area inundated in the previous

year and the population decline in the following year ineach region, analyzed by the relationship between theratio of the number of points with Pt ≤ −5 (%) in eachregion to the total number of points in that region andthe ratio of the inundated area in the previous year to theregional area. For example, In the metropolitan Bangkok
area and Eastern area, significant inundation damage wasoccurred in the 2011 floods, but the impact on population decline was small, a trend different from otherregions. The correlation coefficients and the p value forthe northern and northeastern regions were r = 0.426,
p = 0.191 and r = 0.501, p = 0.055, respectively, so thesevalues indicated a statistically significant trend in thenortheastern region.
Figure 8 shows the relationship between the number ofpoints with Pt ≤ −5 as a percentage of the total numberof points for all of Thailand and the number of deaths byflooding in the previous year. The latter was determinedusing data from the ADRC. The correlation for the effectof the number of deaths by flooding in the previous year
on population decline in the following year was positive(r = 0.503, p = 0.114), although it was largely attributable
to the results obtained for 2012.
Figure 9 shows the relationship between the ratio of
the annual maximum daily and monthly precipitation in
the previous year to the average daily precipitation from

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Fig. 4 Maps of elevation and flood frequency in Thailand. a Physiographic (elevation) map of Thailand from MERIT DEM, b Flood frequency map
to visualize the concentration of inundation from 2005 to 2020, calculated by the Thailand Flood Monitoring System

1958 to 2021 and the ratio of the number of points withPt ≤ −5 to the number of points in each region, including the location with annual maximum daily or monthly
precipitation. A slightly positive correlation was obtained
for the daily precipitation, whereas a negative correlation (r = − 0.395, p = 0.229) was obtained for monthly
precipitation.

4 Discussion
4.1 Trend of population change

The difference in the distribution of Pt (Fig. 5) and theincrease in the percentage of points with Pt ≤ −5 in2012 relative to 2013 only in the metropolitan Bangkok
and eastern regions (Fig. 6) suggest that the 2011 floods
caused people to move to the metropolitan Bangkok areain 2012, but to return to the areas they had left in 2013.
In previous studies, we examined whether the population decline in the inundated area from 2011 to 2012 wasdue not only to repeated inundations, but specifically to
the 2011 floods (Tsuda et al. 2022a,b). The ratio of thenumber of points inundated only in 2011 with D2012< 0 to

the number of points inundated only in 2011 was 87.9%
for the whole of Thailand, indicating that the 2011 inundations alone had a significant effect on people’s mobility.
This study showed that this migration was temporary, asaffected people returned to their previous residences in2013.
Migration into the metropolitan Bangkok area after
the 2011 floods may have been driven by the government’s flood control policy. Water overflowing the Chao
Phraya River would normally have flowed to the westernand southern parts of Bangkok, which are at lower elevations than the eastern part. However, the floodgates wereclosed, and large sandbags were piled up to prevent thewater from entering the center of the city, such that flooding was confined to northern Bangkok (Tamada 2013).
This government response may have caused people tomove into the King’s Dike area, based on the assumption that it would be protected during flooding. Thesefindings indicate that the 2011 floods affected the population distribution, but that most migration was shortterm. Similar changes in the population distribution wereTsuda and Tebakari ﻿Progress in Earth and Planetary Science(2023) 10:36

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Fig. 5 Maps of the distribution of Pt values for 2009–2019. Points indicating that the actual population was smaller (larger) than estimated basedon natural population changes are blue (red), with darker colors indicating larger increases (declines). The range of −5 < Pt ≤ 5 (%) was the cutoff
for a small difference between the actual and estimated populations

Fig. 6 Percentage of points with Pt ≤ −5 (%) and Pt > 5 (%) shown in Fig. 5. The change over time in points with Pt ≤ −5 as a percentageof the total number of points in each region: north, northeast, central, Bangkok metropolitan area, east, south, and all of Thailand (a). The changeover time in points with Pt > 5 as a percentage of the total number of points in each region (b)observed in 2018 and 2019 and reflect the consequencesof the flood damage that occurred in 2017, especially insouthern and northern Thailand. However, the changes
were less remarkable than those occurring in 2012 and2013.
Our findings suggest that in 2014 and 2015, as in 2013,
the population decline in the metropolitan Bangkok
area and migration to other regions such as northeastern Thailand were mainly influenced by political factors
such as anti-government demonstrations and the military
coup that occurred in 2013–2014. In particular, the coup

led to the implementation of martial law and triggered
the current military regime in Thailand, which affectedmigration.
4.2 Impact of the great 2011 floods

The percentage of points with Pt ≤ −5 (%) (Fig. 6)
decreased from 2014–2015 to 2015–2016 in all regions.
This reduction can be attributed to the occurrence ofless inundation damage in 2014 and 2015, such that theincreases in the population distribution in 2015 and2016 corresponded to areas where the actual population

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Fig. 7 Relationship between the area inundated in the previous year and the population decline. This is indicated by the relationshipbetween the ratio of the number of points with Pt ≤ −5 (%) in each region to the total number of points in that region and the ratioof the inundated area in the previous year to the regional area

Fig. 8 Relationship between the number of deaths by flooding and population decline. This is indicated by the relationship between the numberof points with Pt ≤ −5 as a percentage of the total number of points for all of Thailand and the number of deaths by flooding in the previous year,
and the right panel is scatterplot for excluding the case t = 2012

was slightly larger than or almost equal to the estimatedpopulation. However, in a year following a year with relatively extensive inundation damage, such as 2011 or 2017,
the percentage of points with Pt ≤ −5 tended to increase.
The exception was in 2010, when the total inundation
area was 34,260.6 ­km2, the second largest after 2011, andapproximately 250 flood-related deaths occurred. The
absence of a significant impact on the population distribution can be explained by the fact that the large 2010
flood was one of many floods that occurred in Thailandthat year and thus did not cause people to move. However, the 2011 floods may have changed people’s attitudes
toward evacuation and residential mobility, such that therelatively large floods in 2013 and 2017 may have affected
the population distribution. People who had previously
felt that the benefits of staying in place and living withfloods outweighed the risk may have decided to move tourban and metropolitan areas protected from flooding,or to areas where inundation is rare.
4.3 Population concentration in cities

A closer examination of Fig. 5 shows that regions with
red points are clustered in the center of the region withblue points throughout the country, which indicate people tend to concentrate in cities from suburbs. This suggests increased population concentration in urban areas

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Fig. 9 Relationship between daily and monthly precipitation in the previous year and population decline. This is indicated by the relationshipbetween the ratio of the annual maximum daily and monthly precipitation in the previous year to the average daily precipitation from 1958 to 2021
and the ratio of the number of points with Pt ≤ −5 to the number of points in each region, including the location with annual maximum daily
or monthly precipitationand depopulation of the surrounding areas. When cities are affected by floods, the dense urban population is
at high risk for injury or death and property loss, which
increases public demand for faster implementation offlood countermeasures.
The objective of this study was to determine whether
the traditional lifestyle of living with floods remains suitable for the current Thai population, or whether theirlifestyle and post-flood migration patterns have alreadychanged with recent socioeconomic development, indicating a shift to a flood-avoidance lifestyle. As indicatedin Sect. 4.2, it was clear that post-flood movement has
become more active since the 2011 flood, and the population has become more concentrated in urban areas. We
inferred that Thai people are shifting from a flood-tolerance lifestyle to a flood-avoidance lifestyle, with higher
concentrations of people in urban areas where stronger
flood mitigation measures are in place.
4.4 Factors of population change

Figures 7, 8 and 9 show the factors that have induced
population distribution changes in each region and in
Thailand as a whole. In Fig. 7, the correlation between thepercentage of points with Pt ≤ −5 and the percentage ofthe area inundated in the previous year versus the total
area of the region was negative only for the metropolitan
Bangkok area. Thus, in the metropolitan Bangkok area,
where economic development and population growth
are rapid, the inundation is not a key factor in population
movement, and the speed of population growth is greaterthan that of population loss due to inundation.
The number of deaths by flooding in the previous year
was associated with a population decline in the following

year for all of Thailand (Fig. 8). However, the impactof the 2011 floods on population movements was significantly large, while the number of deaths from theprevious year’s floods had a relatively small impact on
population decline in the following year. These findings
suggest that the total amount of damage that is outside
the scope of this study and the aforementioned area
inundated may have a greater impact than the number ofdeaths on the population decline after floods in Thailand.
The effects of daily and monthly precipitation in theprevious year on population decline in the following year
were also analyzed (Fig. 9). The daily precipitation did
not significantly affect the population distribution in thefollowing year, and no regional characteristic could be
identified. The number of points representing a smallerthan-predicted population increased with the ratio of theannual maximum monthly precipitation to the averagemonthly precipitation, possibly due to the concentrationof maximum monthly precipitation points in the northeastern, eastern, and southern regions, where people areaccustomed to long-lasting rainfall and thus unlikely to
migrate because of it.

5 Conclusions
This study was conducted to examine the relationshipbetween flooding and population distribution acrossThailand. Our aim was to contribute to the planningof adaptation measures to minimize the flood damage
caused by climate change. The results suggest that the2011 and 2017 floods, which inundated 9.5% and 5.3%of the total area of Thailand, respectively, caused thetemporary migration of people to areas that were notinundated or to the metropolitan Bangkok area. They

Tsuda and Tebakari ﻿Progress in Earth and Planetary Science(2023) 10:36

also suggest that people’s response to floods changed
after the 2011 floods.
The correlation between the number of points indicating a smaller-than-estimated population and thearea inundated in the previous year in each region was
positive especially for the northeastern area (r = 0.501,
p = 0.055). The slightly different trend in the metropolitan Bangkok area compared to the other regions can be
attributed to the vertical structure of Bangkok’s urban
fabric. According to this relationship, a larger area
inundated in the previous year has a greater impact on
population decline. Similarly, the positive correlation
between the number of points indicating a smallerthan-predicted population and the number of deathsby flooding in the previous year in all of Thailand(r = 0.503) indicates that the number of deaths by flooding in the previous year led to a population decline in
the following year. However, an analysis of the effects
of precipitation in the previous year on the population
decline in the following year showed a slightly positive
correlation for daily precipitation and a negative correlation (r = − 0.395) for monthly precipitation. Thus,
precipitation in the previous year did not account forthe population decline in the following year.
In recent years, population concentration in urban
areas has increased vulnerability to flooding in Thailand. These factors suggest that the Thai people areshifting from a lifestyle that included living in harmony
with floods to an avoidance of inundation, mostly inurban areas, where flood control measures are more
stringent.
Finally, changes in population distribution cannot be
attributed solely to flooding, but also reflect the effects ofdroughts and government policies. Additional researchis needed to separate flood-related factors from suchother factors. Therefore, it is necessary to collect dataon drought and government policies to separate population movements caused by floods from those caused byother factors. However, it is difficult to obtain detailed
data on these factors throughout Thailand. To confirm
our results, we are conducting a survey of flood awareness among Thai people, including questions about flood
experiences and movement after floods. As more detailed
population data, including location data and detailed
demographic data, become available, it will be easier tocharacterize migration patterns in Thailand.
Abbreviations
ADRC	Asian Disaster Reduction Center
ALOS	Advanced Land Observing Satellite
GIS	Geographic Information System
GISTDA	Geo-Informatics and Space Technology Development Agency
JRA-55	Japanese 55-year Reanalysis
PALSAR	Phased Array Type L-band Synthetic Aperture Radar

Page 12 of 13SIM	Subscriber Identity Mmodule
THEOS	Thailand Earth Observation Satellite
Acknowledgements
This research was partially supported by the JST/JICA SATREPS program andthe SICORP e-ASIA JST.
Author contributions
HT processed the data and analyzed the simulation results with TT; both
authors read and approved the final manuscript.
Funding
This study was supported by the Japan Science and Technology Agency(JST)/Japan International Cooperation Agency (JICA) Science and Technology Research Partnership for Sustainable Development (SATREPS) program(PI: Prof. Taikan OKI) and the Strategic International Collaborative Research
Program East Asia Joint Research Program (SICORP e-ASIA JRP; PI: Prof. Taikan
OKI) (Grant nos. 21338544, 15543675).
Availability of data and materials
Data on inundation area were obtained from the Thailand Monitoring Systemprovided by the GISTDA (https://​flood.​gistda.​or.​th/). LandScan Global population distribution data were obtained from Oak Ridge National Laboratory(https://​lands​can.​ornl.​gov/). Statistic population data were obtained from thestatistics registration system of the Registration Technology Development
and Management Division Office, Department of Provincial Administration(https://​stat.​bora.​dopa.​go.​th/​stat/​statn​ew/​statM​enu/​newSt​at/​home.​php). JRA55 reanalysis precipitation data were downloaded from the National Center
for Atmospheric Research (NCAR, https://​rda.​ucar.​edu/​datas​ets/​ds628.0/).
JRA-55 reanalysis data are available for download to those with a user ID andpassword obtained through registration with NCAR (https://​rda.​ucar.​edu/​
index.​html?​hash=​data_​user&​action=​regis​ter). Elevation data were downloaded from the MERIT DEM provided by the Yamazaki Laboratory, Universityof Tokyo (http://​hydro.​iis.u-​tokyo.​ac.​jp/​~yamad​ai/​MERIT_​DEM/).

Declarations
Competing interests
The authors declare that they have no competing interests to report.
Received: 5 August 2022 Accepted: 5 July 2023

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