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 Research
Center 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 expressed
in these papers are those of the author(s). It is expected that most working papers will be published in some other form.
1
2
Six or four seasons? An evidence for seasonal change in
Bangladesh
3
Moinul Islam∗
Koji Kotani†
4
September 18, 2014
5
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 the
traditional 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 scientifically
confirm 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.
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
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).
†
1
22
Contents
23
1
Introduction
2
24
2
Study area and data collection
2.1 Study area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2 Meteorological data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3
3
3
3
Methodology and data analysis
3.1 Seasonal change from six to four seasons . . . . . . . . . . . . . . . . . . . . . .
4
4
4
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 . . . . . . . . . . . . . . . . . . . . . . .
6
6
6
7
33
5
Conclusion
8
34
6
Bibliography
35
List of Figures
11
36
List of Tables
16
25
26
27
28
29
30
31
32
10
1
37
1
Introduction
38
Bangladesh is one of the most disaster-prone countries in the world because of its geographical
39
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◦
41
340 N and 26◦ 380 N and longitudes 88◦ 010 E and 92◦ 410 E and has a gross area of approximately
42
147,570 km2 . Approximately 80% of the country’s land is the floodplains of three large rivers, the
43
Ganges, the Brahmaputra and the Meghna. Only 10% of Bangladesh is 1 m above the mean sea
44
level (MSL) and one-third is under tidal influence.
45
Bangladesh is likely to be affected by more intense and frequent flood events in the foresee-
46
able future due to potential climate changes and the associated MSL rise (Schiermeier, 2011a,b).
47
This is an issue of great concern, because the location and geography of Bangladesh makes it both
48
particularly susceptible to such effects of climatic changes on agriculture and other industries, and
49
extremely difficult to protect. To make matters worse, many Bangladeshi people gradually recog-
50
nize some potential change of seasons in annual calendar. That is, annual calendar in Bangladesh
51
transitions from six seasons to four seasons. Despite the importance of this fundamental climate
52
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.
54
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 obtained
56
corresponding climate data from three meteorological stations located in the same area. Using
57
the climate data, we apply nonparametric statistical tests to scientifically identify whether any pair
58
of two neighboring seasons in Bangla calendar is converging into one seasons, and compare the
59
statistical result with people’s perceptions. With this approach, our research addresses a following
60
question: “Is Bangladesh subject to four seasons or six seasons in an annual calendar, and what are
61
people’s perceptions of this possible seasonal change?”
2
62
2
Study area and data collection
63
2.1
Study area
64
The Meghna Basin area of Bangladesh was selected as a study area because it is vulnerable
65
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
67
characterized by different production potentials. Figure 1 is a map of the research area. Raipura has
68
relatively higher agricultural potential, whereas Narsingdi Sadar has lower agricultural but higher
69
industrial potential. The household is a unit of analysis, because it is the decision-making unit in
70
livelihood processes, with the senior and earning male person household member as the decision
71
maker. The survey was conducted in 2011 and 2012. The climatic conditions in Raipura and
72
Narsingdi Sadar have relatively uniform temperatures, high humidity, and heavy rainfall. Heavy
73
rain usually occurs from June to September. The average annual temperature ranges from 13◦ C to
74
35◦ C. The rivers in the Upazilas are Meghna (the most important), Old Brahmaputra, Arial Khan
75
and Kakan. Because Raipura Upazila and Narsingdi Sadar Upazila are plain lands, the Meghna
76
floods, especially in the rainy seasons.
[Figure 1 about here.]
77
78
2.2
Meteorological data
79
Daily weather data were collected from the Bangladesh Meteorological Department. The data
80
includes daily rainfall, daily average temperature, daily maximum temperature and daily minimum
81
temperature. To identify a change from six to four seasons in the annual calendar, we analyzed
82
Dhaka station’s data from the last 57 years because only this station has data covering more than
83
50 years in Bangladesh and because it is closest to the study area (38.4 km). Finally, figure 2
84
summarizes the data collection procedure consisting of a primary field survey, a household survey,
85
an expert interview and the collection of meteorological data.
3
[Figure 2 about here.]
86
87
3
Methodology and data analysis
88
3.1
Seasonal change from six to four seasons
89
The usage and popularity of the Bangla calendar in Bangladesh are partly due to its adaptation
90
to the unique seasonal patterns of the region. Bangladesh has a climate that has been considered
91
to be divided into six seasons, including the rainy season and the dry season in addition to spring,
92
summer, fall and winter. In our survey, a large share of respondents think that the timing of seasonal
93
changes has become unpredictable. In addition, local people and experts wonder that the six-season
94
country is losing its seasonal variation and is changing to a land of four seasons or less, although
95
no previous works present supporting evidence for this. Table 1 presents the Bangla local calendar.
96
We use this calendar to test whether people’s perceptions of seasonal changes are in line with
97
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 meteorological
99
station and analyzing four key climate variables: average daily maximum temperature, average
100
101
daily minimum temperature, average daily mean temperature and average daily rainfall.
[Table 1 about here.]
102
We analyzed all possible pairs of two consecutive seasons in the Bangla calendar to identify
103
whether the two seasons are merging into a single season. First, we began with a simple graphical
104
analysis to observe the temporal trend of climate variables over the years of 1953 to 2010 in each
105
season. Next, we applied non-parametric Mann-Whitney tests by dividing the sample of a climate
106
variable in each season into two subsamples. Each subsample represents data from 1953 to 1984 as
107
a “old period” subsample of the season or data from 1985 to 2010 as the “recent period” subsample.
108
Note that this separation was determined by the experts’ opinions in the survey.
4
109
110
111
112
113
114
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.
115
Utilizing old and recent subsamples of a climate variable, i.e., average temperature, in the two
116
consecutive seasons, the Mann-Whitney test should be able to statistically conclude whether a pair
117
of two neighboring seasons within the six-season calendar are converging. More concretely, when
118
two consecutive seasons do not merge, the Mann-Whitney test should reject the null hypothesis for
119
both old and recent subsamples of the climate variable over the two seasons. For instance, the two
120
old subsamples of average daily temperatures over the summer and rainy seasons should reject the
121
null hypothesis, so do the two recent subsamples over the summer and rainy seasons.
122
When two seasons are converging, the null hypothesis should be rejected for the old subsam-
123
ples, but not for the recent subsamples. This means that Bangladesh was subject to six seasons
124
within a year, implying that the null hypothesis must be rejected using old subsamples. At the same
125
time, if our “merging” or “four-season calendar” hypothesis is true, the null hypothesis should not
126
be rejected using recent subsamples, implying that the recent subsamples of climate data over the
127
two seasons do not differ. For example, the two old subsamples of rainfall over the summer and
128
rainy seasons should reject the null, but the two recent subsamples of the rainfall over the summer
129
and rainy seasons should not reject the null. In that case, we interpret that these two seasons were
130
different, but they have been converging in the recent years.
5
131
4
Results and discussion
132
4.1
Seasonal change from six to four seasons
133
This study examines whether six seasons become four seasons in the Bangla annual calendar.
134
To test this hypothesis, we analyzed all possible pairs of neighboring seasons to identify whether
135
a climate variable in the two neighboring seasons is becoming indistinguishable or at least less
136
distinguishable over time. The set of key variables in the analysis of seasonal changes consists
137
of temperature and rainfall in Dhaka where the richest climate data are available. In the following
138
subsection, we present the two pairs of consecutive seasons that support our “merging” hypothesis.
139
Note that analysis of the other pairs rejected the “merging” hypothesis, and thus we omit the
140
presentation of the “rejected” results.
141
4.1.1
Rainy season vs. pre-autumn season
142
The rainy and pre-autumn seasons are consecutive Bengali seasons that have been considered
143
distinct and that have been believed to have significant, but different impacts on agriculture and
144
daily life (table 1). This conclusion had been supported by individual experiences and meteoro-
145
logical data. However, we hypothesize that in recent years, these seasons have been becoming
146
indistinguishable or more similar each other.
147
[Figure 3 about here.]
148
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
150
3). Climate variability in these two seasons has significance for domestic crops and everyday life.
151
Subfigures 3(a), 3(b) and 3(c) are the time series plots of the average daily minimum, maximum
152
and mean temperatures for the rainy and pre-autumn seasons from 1953 to 2010, respectively.
153
They show an increasing temporal trend, and the coefficients of the trend lines in each subfigure
154
are greater for pre-autumn season than for the rainy season. The pre-autumn temperatures were
6
155
lower than those in the rainy season, but the two seasons are converging over time. The trend lines
156
for the pre-autumn season cross those in the rainy season in all three subfigures 3(a), 3(b) and 3(c).
157
Regarding rainfall, figure 3(d) plots the daily average rainfalls in the rainy and autumn seasons
158
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
160
cross (see figure 3(d)). The single crossover suggests that the daily average rainfalls in the rainy
161
and pre-autumn seasons are converging. The Mann-Whitney tests for the rainy vs. pre-autumn
162
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
164
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
166
the test results and suggests that climate variables in the rainy and pre-autumn seasons differ in old
167
subsamples, but do not differ in recent subsamples, supporting our hypothesis that the rainy and
168
pre-autumn seasons are converging.
[Table 2 about here.]
169
170
4.1.2
Summer season vs. rainy season
171
The summer and rainy seasosn are consecutive Bengali seasons that have been considered
172
distinct (table 1). These two seasons are hypothesized to be converging based on the opinions of
173
experts and local people. Following the same procedure before, we analyze each climate variable
174
for the two seasons. Figure 4 consists of four subfigures with time-series plots of climate variables
175
for the two seasons. Each subfigure shows that climate variables of the two seasons are becoming
176
closer over time. In particular, subfigures 4(a), 4(c) and 4(d) are consistent with this trend for the
177
minimum, mean temperature and rainfall, respectively. The two trend lines (summer vs. rainy)
178
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
180
time.
7
[Figure 4 about here.]
181
182
Based on the observations summarized in figure 4, it is likely that the rainy and summer sea-
183
sons are converging. To confirm this observation, we ran Mann-Whitney tests for the four climate
184
variables. Subtable 2(b) presents the result, suggesting that for old subsamples, minimum tem-
185
perature, maximum temperature and rainfall differ, while mean temperature does not. For recent
186
subsamples, only maximum temperature significantly differ between the two seasons, while mini-
187
mum, mean temperatures and rainfall do not differ. In summary, this result supports our hypothesis
188
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.
190
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
193
not perceive any such change. Furthermore, seven experts asserted that this change is occurring.
194
Overall, the statistical analysis, people’s perceptions and experts’ opinions are consistent in this
195
regard.
196
5
Conclusion
197
This paper examined whether annual Bangladeshi calendar transitions to four seasons from six
198
seasons by looking at the basic daily climate variables of temperature and rainfall. Surprisingly,
199
we find that two pairs of two consecutive seasons in Bangla calendar are merging or at least indis-
200
tinguishable for the recent periods. This evidence for the seasonal change must be seriously taken,
201
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
203
believe that no previous papers identify an evidence for the seasonal change in a single country and
204
this Bangladeshi evidence shall be a starting point to examine whether any other possible seasonal
205
change may occur in other parts of the world. We would like to note that climatic changes now
8
206
become significant to the extent that local people realize the seasonal change, consistently with the
207
time series climate data.
9
208
6
Bibliography
211
Brouwer, R., Akter, S., Brander, L., and Haque, E. (2007). Socioeconomic vulnerability and
adaptation to environmental risk: A case study of climate change and flooding in Bangladesh.
Risk analysis, 27(2):313–326.
212
Schiermeier, Q. (2011a). Extreme measures. Nature, 477:148–149.
213
Schiermeier, Q. (2011b). Increased flood risk linked to global warming. Nature, 470(7334):316.
209
210
10
214
215
List of Figures
1
216
217
218
219
220
2
3
221
222
223
4
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 . . . . . . . . . . . . . . . .
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 . . . . . . . . . . . . . .
11
. 12
. 13
. 14
. 15
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
224
225
226
227
12
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 with
people's perception
3. WTP regression
Field survey
(Questionnaire
interviews for 1011
households)
Households' characteristics, perception to
climate and WTP for flood protection
Expert survey
(Interviews for 7
experts)
Expert opinions about climatic change
and flood
Data collection
from Bangladesh
meteorological
department
Daily weather and climate data related to
temperature and rainfall
13
27
Average max temperature (℃)
Average min temperature (℃)
27.5
y = 0.0097x + 25.906
(Rainy)
26.5
26
25.5
y = 0.0208x + 25.326
(Pre-autumn)
25
Rainy
32.5
y = 0.0269x + 30.874
(Rainy)
32
31.5
31
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 2010
30
Average rainfall (mm)
Average temperature (℃)
33
Pre-autumn
24.5
29.5
33.5
y = 0.0189x + 27.928
(Rainy)
29
28.5
28
y = 0.0225x + 27.702
(Pre-autumn)
27.5
18
y = 0.0075x + 11.69
(Rainy)
Rainy Pre-autumn
16
14
12
10
8
27
6
Rainy Pre-autumn
26.5
y = 0.0723x + 8.3735
(Pre-autumn)
4
(c) Average daily mean temperature in the rainy and
pre-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
14
Average max temperature (℃)
Average min temperature (℃)
27
y = 0.0097x + 25.906
(Rainy)
26
25
y = 0.039x + 24.462
(Summer)
24
Rainy
35
y = 0.008x + 32.699
(Summer)
34
33
32
y = 0.036x + 30.467
(Rainy)
Rainy Summer
31
Summer
23
(a) Average daily minimum temperature in rainy and
summer seasons from 1953 to 2010
(b) Average daily maximum temperature in rainy and
summer seasons from 1953 to 2010
Average temperature (℃)
Average rainfall (mm)
30
30
y = 0.0159x + 28.104
(Summer)
29
18
y = 0.0075x + 11.69
(Rainy)
16
14
12
10
8
28
27
6
y = 0.0189x + 27.928
(Rainy)
Rainy Summer
4
Rainy
y = 0.0946x + 7.5832
(Summer)
Summer
2
(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 and
mean temperatures and average daily rainfall
15
228
229
230
231
List of Tables
1
2
Bangla calendar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
Mann-Whitney test to compare the two seasons for each climate variable in both
old and recent periods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
16
Table 1: Bangla calendar
Bangla season
Summer
Rainy season
Pre-autumn
Late-autumn
Winter
Spring
Bangla calendar
Gregorian calendar
Days
Baishakh
Jaishtha
Ashar
Shraban
Bhadra
Ashwin
Karttik
Agrahayan
Paush
Magh
Falgun
Chaitra
14 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.
232
233
Table 2: Mann-Whitney test to compare the two seasons for each climate variable in both old and
recent periods
(a) Rainy season vs. Pre-autumn season
Subsample
Min temp
Max temp
Mean temp
Rainfall
Old
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
Rainfall
Old
Recent
5.948∗∗∗
−0.708
−6.432∗∗
−4.104∗∗∗
−0.121
−0.805
3.357∗
0.437
Note: ∗Significant at the 10% level, ∗∗Significant at the 5% level, ∗ ∗ ∗Significant at the 1% level.
17