Social Design Engineering Series

SDES-2014-11

Six or four seasons? An evidence for seasonal change in
Bangladesh

Moinul Islam
Graduate School of Environmental Studies, Tohoku University

Koji Kotani
Kochi University of Technology
Research Center for Social Design Engineering, Kochi University of Technology

21st October, 2014

School of Economics and Management
Research Center for Social Design Engineering
Kochi University of Technology
KUT-EMS working papers are preliminary research documents published by the School of Economics and Management jointly with the ResearchCenter for Social Design Engineering at Kochi University of Technology. To facilitate prompt distribution, they have not been formally reviewed
and edited. They are circulated in order to stimulate discussion and critical comment and may be revised. The views and interpretations expressedin these papers are those of the author(s). It is expected that most working papers will be published in some other form.

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Six or four seasons? An evidence for seasonal change in
Bangladesh

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Moinul Islam∗

Koji Kotani†

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September 18, 2014

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Abstract
Bangladesh is reported to suffer from climatic changes, and many local people begin to
wonder that six seasons in Bangladeshi annual calendar transition to four seasons where thetraditional one (Bangla calendar) is considered to have consisted of the six seasons. We collected observations of key climate variables (1953-2010) from the weather station located in
Dhaka, and conducted face-to-face surveys with 1,011 respondents and seven experts to elicit
their current perception about whether six seasons are becoming four seasons. To scientificallyconfirm this, we apply nonparametric statistical methods to the key climate variables and test
whether any pair of two neighboring seasons in Bangla calendar is converging into one. The
statistical analysis shows “convergence” for specific two pairs of two neighboring seasons,meaning that the annual calendar now consists of four seasons, not six. Approximately 65% of
respondents believe that annual calendar transitions to four seasons from six seasons. Overall,people’s perception and the statistical analysis are consistent each other. The effect of global
climatic changes now becomes significant to the extent that local people correctly perceive
some fundamental seasonal changes of annual calendar and it is really ongoing on the basis of
our statistical analysis.

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Key Words: Climatic change; seasonal change; perception∗

Graduate School of Environmental Studies, Tohoku University, Japan
Professor, School of Economics and Management, Kochi University of Technology, 185 Miyanokuchi,
Tosayamada-cho, Kami-shi, Kochi, 782-8502, Japan (e-mail: kojikotani757@gmail.com).
†

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Contents

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1

Introduction

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Study area and data collection
2.1 Study area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2 Meteorological data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Methodology and data analysis
3.1 Seasonal change from six to four seasons . . . . . . . . . . . . . . . . . . . . . .

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Results and discussion
4.1 Seasonal change from six to four seasons . . . . . . . . . . . . . . . . . . . . . .
4.1.1 Rainy season vs. pre-autumn season . . . . . . . . . . . . . . . . . . . . .
4.1.2 Summer season vs. rainy season . . . . . . . . . . . . . . . . . . . . . . .

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Conclusion

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Bibliography

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List of Figures

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List of Tables

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1

Introduction

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Bangladesh is one of the most disaster-prone countries in the world because of its geographical39

setting (Brouwer et al., 2007). Bangladesh is part of the Bengal Basin, one of the largest geo-40

synclinal countries in the world. It lies in the northeastern part of South Asia, between latitudes 20◦

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340 N and 26◦ 380 N and longitudes 88◦ 010 E and 92◦ 410 E and has a gross area of approximately

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147,570 km2 . Approximately 80% of the country’s land is the floodplains of three large rivers, the43Ganges, the Brahmaputra and the Meghna. Only 10% of Bangladesh is 1 m above the mean sea

44level (MSL) and one-third is under tidal influence.

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Bangladesh is likely to be affected by more intense and frequent flood events in the foresee-

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able future due to potential climate changes and the associated MSL rise (Schiermeier, 2011a,b).

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This is an issue of great concern, because the location and geography of Bangladesh makes it both

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particularly susceptible to such effects of climatic changes on agriculture and other industries, and49

extremely difficult to protect. To make matters worse, many Bangladeshi people gradually recog-

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nize some potential change of seasons in annual calendar. That is, annual calendar in Bangladesh

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transitions from six seasons to four seasons. Despite the importance of this fundamental climate

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issue, no studies have examined the possible seasonal change and the corresponding people’s per-53

ceptions in relation to historical climate data. Thus, this paper seeks to address these issues.

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We conducted a questionnaire survey of 1,011 respondents and seven experts to elicit their per-55

ceptions about whether Bangladeshi annual calendar consists of four or six seasons, and obtained56

corresponding climate data from three meteorological stations located in the same area. Using57

the climate data, we apply nonparametric statistical tests to scientifically identify whether any pair58

of two neighboring seasons in Bangla calendar is converging into one seasons, and compare the59statistical result with people’s perceptions. With this approach, our research addresses a following

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question: “Is Bangladesh subject to four seasons or six seasons in an annual calendar, and what are61

people’s perceptions of this possible seasonal change?”

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2

Study area and data collection

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2.1

Study area

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The Meghna Basin area of Bangladesh was selected as a study area because it is vulnerable65

to climatic changes and frequent flooding. Within the Meghna Basin area in central Bangladesh,66

the administrative Upazilas—Narsingdi Sadar and Raipura were chosen. The two Upazilas are

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characterized by different production potentials. Figure 1 is a map of the research area. Raipura has

68relatively higher agricultural potential, whereas Narsingdi Sadar has lower agricultural but higher

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industrial potential. The household is a unit of analysis, because it is the decision-making unit in70

livelihood processes, with the senior and earning male person household member as the decision

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maker. The survey was conducted in 2011 and 2012. The climatic conditions in Raipura and72

Narsingdi Sadar have relatively uniform temperatures, high humidity, and heavy rainfall. Heavy

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rain usually occurs from June to September. The average annual temperature ranges from 13◦ C to74

35◦ C. The rivers in the Upazilas are Meghna (the most important), Old Brahmaputra, Arial Khan75

and Kakan. Because Raipura Upazila and Narsingdi Sadar Upazila are plain lands, the Meghna

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floods, especially in the rainy seasons.
[Figure 1 about here.]

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2.2

Meteorological data

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Daily weather data were collected from the Bangladesh Meteorological Department. The data

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includes daily rainfall, daily average temperature, daily maximum temperature and daily minimum

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temperature. To identify a change from six to four seasons in the annual calendar, we analyzed

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Dhaka station’s data from the last 57 years because only this station has data covering more than83

50 years in Bangladesh and because it is closest to the study area (38.4 km). Finally, figure 2

84summarizes the data collection procedure consisting of a primary field survey, a household survey,

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an expert interview and the collection of meteorological data.

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[Figure 2 about here.]

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Methodology and data analysis

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3.1

Seasonal change from six to four seasons89

The usage and popularity of the Bangla calendar in Bangladesh are partly due to its adaptation90

to the unique seasonal patterns of the region. Bangladesh has a climate that has been considered91

to be divided into six seasons, including the rainy season and the dry season in addition to spring,

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summer, fall and winter. In our survey, a large share of respondents think that the timing of seasonal

93changes has become unpredictable. In addition, local people and experts wonder that the six-season

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country is losing its seasonal variation and is changing to a land of four seasons or less, although95

no previous works present supporting evidence for this. Table 1 presents the Bangla local calendar.

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We use this calendar to test whether people’s perceptions of seasonal changes are in line with

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actual climate data obtained from the Bangladesh Meteorological Department. More specifically,98

our analysis was conducted by utilizing the climate data taken from the Dhaka meteorological99

station and analyzing four key climate variables: average daily maximum temperature, average

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daily minimum temperature, average daily mean temperature and average daily rainfall.

101[Table 1 about here.]102

We analyzed all possible pairs of two consecutive seasons in the Bangla calendar to identify

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whether the two seasons are merging into a single season. First, we began with a simple graphical104

analysis to observe the temporal trend of climate variables over the years of 1953 to 2010 in each105

season. Next, we applied non-parametric Mann-Whitney tests by dividing the sample of a climate

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variable in each season into two subsamples. Each subsample represents data from 1953 to 1984 as

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a “old period” subsample of the season or data from 1985 to 2010 as the “recent period” subsample.

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Note that this separation was determined by the experts’ opinions in the survey.

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Mann-Whitney tests can be used to compare the subsamples of a climate variable in the same
period (old or recent period). The hypotheses can be posed as follows:• H0 : The two “old” (“recent”) subsamples of a climate variable over the two consecutive
seasons follow an identical distribution.
• HA : The two “old” (“recent”) subsamples of a climate variable over the two consecutive
seasons follow different distributions.

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Utilizing old and recent subsamples of a climate variable, i.e., average temperature, in the two

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consecutive seasons, the Mann-Whitney test should be able to statistically conclude whether a pair117

of two neighboring seasons within the six-season calendar are converging. More concretely, when118

two consecutive seasons do not merge, the Mann-Whitney test should reject the null hypothesis for119both old and recent subsamples of the climate variable over the two seasons. For instance, the two120

old subsamples of average daily temperatures over the summer and rainy seasons should reject the121

null hypothesis, so do the two recent subsamples over the summer and rainy seasons.

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When two seasons are converging, the null hypothesis should be rejected for the old subsam-

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ples, but not for the recent subsamples. This means that Bangladesh was subject to six seasons

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within a year, implying that the null hypothesis must be rejected using old subsamples. At the same

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time, if our “merging” or “four-season calendar” hypothesis is true, the null hypothesis should not126

be rejected using recent subsamples, implying that the recent subsamples of climate data over the127

two seasons do not differ. For example, the two old subsamples of rainfall over the summer and128

rainy seasons should reject the null, but the two recent subsamples of the rainfall over the summer

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and rainy seasons should not reject the null. In that case, we interpret that these two seasons were130

different, but they have been converging in the recent years.

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Results and discussion

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4.1

Seasonal change from six to four seasons

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This study examines whether six seasons become four seasons in the Bangla annual calendar.

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To test this hypothesis, we analyzed all possible pairs of neighboring seasons to identify whether

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a climate variable in the two neighboring seasons is becoming indistinguishable or at least less136

distinguishable over time. The set of key variables in the analysis of seasonal changes consists

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of temperature and rainfall in Dhaka where the richest climate data are available. In the following

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subsection, we present the two pairs of consecutive seasons that support our “merging” hypothesis.

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Note that analysis of the other pairs rejected the “merging” hypothesis, and thus we omit the140

presentation of the “rejected” results.

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4.1.1

Rainy season vs. pre-autumn season

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The rainy and pre-autumn seasons are consecutive Bengali seasons that have been considered143

distinct and that have been believed to have significant, but different impacts on agriculture and144

daily life (table 1). This conclusion had been supported by individual experiences and meteoro-

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logical data. However, we hypothesize that in recent years, these seasons have been becoming

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indistinguishable or more similar each other.

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To analyze whether the two seasons are becoming more similar, we focus on average daily min-149

imum, maximum and mean temperatures and rainfall for the rainy and pre-autumn seasons (figure

1503). Climate variability in these two seasons has significance for domestic crops and everyday life.

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Subfigures 3(a), 3(b) and 3(c) are the time series plots of the average daily minimum, maximum

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and mean temperatures for the rainy and pre-autumn seasons from 1953 to 2010, respectively.

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They show an increasing temporal trend, and the coefficients of the trend lines in each subfigure154

are greater for pre-autumn season than for the rainy season. The pre-autumn temperatures were6

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lower than those in the rainy season, but the two seasons are converging over time. The trend lines

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for the pre-autumn season cross those in the rainy season in all three subfigures 3(a), 3(b) and 3(c).

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Regarding rainfall, figure 3(d) plots the daily average rainfalls in the rainy and autumn seasons

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from 1953 to 2010. This figure shows that the temporal trend in the rainy season is constant,159

whereas it is increasing in the pre-autumn season. Consequently, the trend lines for the two seasons

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cross (see figure 3(d)). The single crossover suggests that the daily average rainfalls in the rainy

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and pre-autumn seasons are converging. The Mann-Whitney tests for the rainy vs. pre-autumn

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seasons examine the null hypothesis of “merging” that the two subsamples (the rainy vs. pre-163

autumn seasons) from the old period (1953-1984) follow an identical distribution (or the same data

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generating process) for each climate variable. The same test is applied using the two subsamples

165(the rainy vs. pre-autumn seasons) from the recent period (1985-2010), too. Table 2(a) summarizes

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the test results and suggests that climate variables in the rainy and pre-autumn seasons differ in old

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subsamples, but do not differ in recent subsamples, supporting our hypothesis that the rainy and168

pre-autumn seasons are converging.
[Table 2 about here.]

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4.1.2

Summer season vs. rainy season

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The summer and rainy seasosn are consecutive Bengali seasons that have been considered172

distinct (table 1). These two seasons are hypothesized to be converging based on the opinions of173

experts and local people. Following the same procedure before, we analyze each climate variable

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for the two seasons. Figure 4 consists of four subfigures with time-series plots of climate variables

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for the two seasons. Each subfigure shows that climate variables of the two seasons are becoming176

closer over time. In particular, subfigures 4(a), 4(c) and 4(d) are consistent with this trend for the177

minimum, mean temperature and rainfall, respectively. The two trend lines (summer vs. rainy)

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for each climate variable cross except the maximum temperature of subfigure 4(b). In general,179

however, the two trend lines for the rainy and summer seasons can be said to become closer over

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time.
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[Figure 4 about here.]

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Based on the observations summarized in figure 4, it is likely that the rainy and summer sea-

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sons are converging. To confirm this observation, we ran Mann-Whitney tests for the four climate

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variables. Subtable 2(b) presents the result, suggesting that for old subsamples, minimum tem-

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perature, maximum temperature and rainfall differ, while mean temperature does not. For recent

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subsamples, only maximum temperature significantly differ between the two seasons, while mini-

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mum, mean temperatures and rainfall do not differ. In summary, this result supports our hypothesis

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that the rainy and summer seasons are converging; three climate variables are different in old sub-189

samples, but the only one climate variable is different in recent subsamples.

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The results presented in this subsection for this seasonal change is quite consistent with the per-191

ceptions of local people. According to the household survey from the study area, 660 respondents

192(660/1,011, 65%) perceived the change from six to four seasons. In contrast, 351 respondents did

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not perceive any such change. Furthermore, seven experts asserted that this change is occurring.

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Overall, the statistical analysis, people’s perceptions and experts’ opinions are consistent in this195

regard.

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5Conclusion

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This paper examined whether annual Bangladeshi calendar transitions to four seasons from six

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seasons by looking at the basic daily climate variables of temperature and rainfall. Surprisingly,

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we find that two pairs of two consecutive seasons in Bangla calendar are merging or at least indis-

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tinguishable for the recent periods. This evidence for the seasonal change must be seriously taken,

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because they fundamentally affect agriculture and daily life of Bangladeshi people where the cur-202

rent practices of economic production activities have been adapted to six season assumptions. We

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believe that no previous papers identify an evidence for the seasonal change in a single country and204

this Bangladeshi evidence shall be a starting point to examine whether any other possible seasonal205

change may occur in other parts of the world. We would like to note that climatic changes now
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become significant to the extent that local people realize the seasonal change, consistently with the207time series climate data.

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6

Bibliography

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Brouwer, R., Akter, S., Brander, L., and Haque, E. (2007). Socioeconomic vulnerability andadaptation to environmental risk: A case study of climate change and flooding in Bangladesh.
Risk analysis, 27(2):313–326.

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Schiermeier, Q. (2011a). Extreme measures. Nature, 477:148–149.

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Schiermeier, Q. (2011b). Increased flood risk linked to global warming. Nature, 470(7334):316.

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List of Figures
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A map of the study area. The left map depicts the positions of 34 ground-baseweather stations located in Bangladesh with each station marked by a circle on themap. The right map shows the position of Narsingdi Sadar and Raipura Upazilas
in Narsingdi District, where we conducted surveys . . . . . . . . . . . . . . . . .
The entire procedure of data collection . . . . . . . . . . . . . . . . . . . . . . . .
Rainy season vs. pre-autumn season with respect to average daily maximum, minimum and mean temperatures and average daily rainfall . . . . . . . . . . . . . . .
Rainy season vs. summer season with respect to average daily maximum, minimum and mean temperatures and average daily rainfall . . . . . . . . . . . . . . .

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Figure 1: A map of the study area. The left map depicts the positions of 34 ground-base weather
stations located in Bangladesh with each station marked by a circle on the map. The right map
shows the position of Narsingdi Sadar and Raipura Upazilas in Narsingdi District, where we conducted surveys

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Figure 2: The entire procedure of data collection

Procedure

Data

Pilot field survey(15 households)

Analysis with the data
1. Time trend analysis for climate
variables
2. Compare the time trend withpeople's perception
3. WTP regression

Field survey(Questionnaireinterviews for 1011
households)

Households' characteristics, perception toclimate and WTP for flood protection

Expert survey(Interviews for 7
experts)

Expert opinions about climatic change
and flood

Data collection
from Bangladesh
meteorologicaldepartment

Daily weather and climate data related totemperature and rainfall

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Average max temperature (℃)

Average min temperature (℃)

27.5
y = 0.0097x + 25.906(Rainy)

26.5
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25.5
y = 0.0208x + 25.326(Pre-autumn)

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Rainy

32.5

y = 0.0269x + 30.874(Rainy)32
31.5
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y = 0.036x + 30.467
Pre-autumn

30.5
30
Rainy Pre-autumn
29.5(b) Average daily maximum temperature in the rainy
and pre-autumn seasons from 1953 to 2010(a) Average daily minimum temperature in the rainy
and pre-autumn seasons from 1953 to 201030

Average rainfall (mm)Average temperature (℃)

33Pre-autumn

24.5

29.5

33.5

y = 0.0189x + 27.928(Rainy)29
28.5
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y = 0.0225x + 27.702(Pre-autumn)

27.5

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y = 0.0075x + 11.69(Rainy)Rainy Pre-autumn

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Rainy Pre-autumn

26.5

y = 0.0723x + 8.3735(Pre-autumn)

4(c) Average daily mean temperature in the rainy andpre-autumn seasons from 1953 to 2010(d) Average daily rainfall in the rainy and pre-autumn
seasons from 1953 to 2010

Figure 3: Rainy season vs. pre-autumn season with respect to average daily maximum, minimum
and mean temperatures and average daily rainfall

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Average max temperature (℃)

Average min temperature (℃)

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y = 0.0097x + 25.906(Rainy)2625

y = 0.039x + 24.462(Summer)

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Rainy

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y = 0.008x + 32.699(Summer)

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y = 0.036x + 30.467(Rainy)Rainy Summer

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Summer23(a) Average daily minimum temperature in rainy andsummer seasons from 1953 to 2010(b) Average daily maximum temperature in rainy andsummer seasons from 1953 to 2010

Average temperature (℃)

Average rainfall (mm)3030

y = 0.0159x + 28.104(Summer)29

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y = 0.0075x + 11.69(Rainy)

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y = 0.0189x + 27.928(Rainy)Rainy Summer

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Rainy

y = 0.0946x + 7.5832(Summer)
Summer2(c) Average daily mean temperature in rainy and summer seasons from 1953 to 2010(d) Average daily rainfall in rainy and summer seasons
from 1953 to 2010

Figure 4: Rainy season vs. summer season with respect to average daily maximum, minimum andmean temperatures and average daily rainfall

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List of Tables
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2

Bangla calendar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
Mann-Whitney test to compare the two seasons for each climate variable in bothold and recent periods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

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Table 1: Bangla calendar
Bangla season
SummerRainy season
Pre-autumn
Late-autumn
WinterSpringBangla calendar

Gregorian calendar

Days

Baishakh
Jaishtha
Ashar
ShrabanBhadraAshwin
Karttik
Agrahayan
Paush
Magh
FalgunChaitra14 April - 14 May
15 May - 14 June
15 June - 15 July
16 July - 15 August
16 August - 15 September
16 September - 15 October
16 October - 14 November
15 November -14 December
15 December - 13 January
14 January - 12 February
13 February - 13 March
14 March - 13 April

31
31
31
31
31
30
30
30
30
30
30∗
30

∗ It becomes 31 in leap year.

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Table 2: Mann-Whitney test to compare the two seasons for each climate variable in both old andrecent periods(a) Rainy season vs. Pre-autumn season

Subsample

Min temp

Max temp

Mean temp

RainfallOld
Recent

4.726∗∗∗
0.126

2.256∗∗
0.34

1.772∗
0.31

3.223∗
0.941(b) Rainy season vs. Summer season

Subsample

Min temp

Max temp

Mean temp

RainfallOld
Recent5.948∗∗∗−0.708−6.432∗∗−4.104∗∗∗−0.121−0.8053.357∗
0.437

Note: ∗Significant at the 10% level, ∗∗Significant at the 5% level, ∗ ∗ ∗Significant at the 1% level.

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