Social Design Engineering Series SDES-2014-11

Six or four seasons? An evidence for seasonal change in

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

2 Bangladesh

3 Moinul Islam∗ Koji Kotani†

4 18, 2014

5 Abstract

6 Bangladesh is reported to suffer from climatic changes, and many local people begin to 7 wonder that six seasons in Bangladeshi annual transition to four seasons where the 8 traditional one (Bangla calendar) is considered to have consisted of the six seasons. We col- 9 lected observations of key variables (1953-2010) from the weather station located in 10 , and conducted face-to-face surveys with 1,011 respondents and seven experts to elicit 11 their current perception about whether six seasons are becoming four seasons. To scientifically 12 confirm this, we apply nonparametric statistical methods to the key climate variables and test 13 whether any pair of two neighboring seasons in Bangla calendar is converging into one. The 14 statistical analysis shows “convergence” for specific two pairs of two neighboring seasons, 15 meaning that the annual calendar now consists of four seasons, not six. Approximately 65% of 16 respondents believe that annual calendar transitions to four seasons from six seasons. Overall, 17 people’s perception and the statistical analysis are consistent each other. The effect of global 18 climatic changes now becomes significant to the extent that local people correctly perceive 19 some fundamental seasonal changes of annual calendar and it is really ongoing on the basis of 20 our statistical analysis.

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: [email protected]).

1 22 Contents

23 1 Introduction 2

24 2 Study area and data collection 3 25 2.1 Study area ...... 3 26 2.2 Meteorological data ...... 3

27 3 Methodology and data analysis 4 28 3.1 Seasonal change from six to four seasons ...... 4

29 4 Results and discussion 6 30 4.1 Seasonal change from six to four seasons ...... 6 31 4.1.1 Rainy season vs. pre-autumn season ...... 6 32 4.1.2 season vs. rainy season ...... 7

33 5 Conclusion 8

34 6 Bibliography 10

35 List of Figures 11

36 List of Tables 16

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 Basin, one of the largest geo-

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

0 ◦ 0 ◦ 0 ◦ 0 41 34 N and 26 38 N and longitudes 88 01 E and 92 41 E and has a gross area of approximately

2 42 147,570 km . Approximately 80% of the country’s land is the floodplains of three large , the

43 , 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 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 — 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 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 and are lands, the Meghna

76 floods, especially in the rainy seasons.

77 [Figure 1 about here.]

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 86 [Figure 2 about here.]

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 . 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 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

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 Mann-Whitney tests can be used to compare the subsamples of a climate variable in the same

110 period (old or recent period). The hypotheses can be posed as follows:

111 • H0: The two “old” (“recent”) subsamples of a climate variable over the two consecutive

112 seasons follow an identical distribution.

113 • HA: The two “old” (“recent”) subsamples of a climate variable over the two consecutive

114 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.

169 [Table 2 about here.]

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.

7 181 [Figure 4 about here.]

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

209 Brouwer, R., Akter, S., Brander, L., and Haque, E. (2007). Socioeconomic vulnerability and 210 adaptation to environmental risk: A case study of and flooding in Bangladesh. 211 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.

10 214 List of Figures

215 1 A map of the study area. The left map depicts the positions of 34 ground-base 216 weather stations located in Bangladesh with each station marked by a circle on the 217 map. The right map shows the position of Narsingdi Sadar and Raipura Upazilas 218 in , where we conducted surveys ...... 12 219 2 The entire procedure of data collection ...... 13 220 3 Rainy season vs. pre-autumn season with respect to average daily maximum, min- 221 imum and mean temperatures and average daily rainfall ...... 14 222 4 Rainy season vs. summer season with respect to average daily maximum, mini- 223 mum and mean temperatures and average daily rainfall ...... 15

11 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 con- ducted surveys

224

225

226

227

12 Figure 2: The entire procedure of data collection

Procedure Data

Analysis with the data 1. Time trend analysis for climate Pilot field survey variables (15 households) 2. Compare the time trend with people's perception 3. WTP regression

Field survey (Questionnaire Households' characteristics, perception to interviews for 1011 climate and WTP for protection households)

Expert survey (Interviews for 7 Expert opinions about climatic change experts) and flood

Data collection from Bangladesh Daily weather and climate data related to meteorological temperature and rainfall department

13 ) ) 27.5 33.5 ℃ ℃ 33 27 y = 0.0097x + 25.906 y = 0.0269x + 30.874 (Rainy) 32.5 (Rainy) 26.5 32 26 31.5 31 25.5 30.5 y = 0.036x + 30.467

Average min temperature ( min temperature Average y = 0.0208x + 25.326 Average max temperature ( max temperature Average Pre-autumn 25 (Pre-autumn) 30 Rainy Pre-autumn Rainy Pre-autumn 24.5 29.5 (a) Average daily minimum temperature in the rainy (b) Average daily maximum temperature in the rainy and pre-autumn seasons from 1953 to 2010 and pre-autumn seasons from 1953 to 2010

) 30 y = 0.0075x + 11.69 Rainy Pre-autumn ℃ 18 (Rainy) 29.5 y = 0.0189x + 27.928 (Rainy) 16 29 14 28.5 12 28 Average rainfall (mm) Average 10 Average temperature ( temperature Average 27.5 y = 0.0225x + 27.702 (Pre-autumn) 8 27 6 y = 0.0723x + 8.3735 Rainy Pre-autumn (Pre-autumn) 26.5 4 (c) Average daily mean temperature in the rainy and (d) Average daily rainfall in the rainy and pre-autumn pre-autumn seasons from 1953 to 2010 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 ) ) 35 y = 0.0097x + 25.906 y = 0.008x + 32.699 ℃ ℃ 27 (Rainy) (Summer) 34

26 33

25 y = 0.039x + 24.462 32 (Summer) 24 Average min temperature ( min temperature Average Average max temperature ( max temperature Average 31 y = 0.036x + 30.467 (Rainy) Rainy Summer Rainy Summer 23 30 (a) Average daily minimum temperature in rainy and (b) Average daily maximum temperature in rainy and summer seasons from 1953 to 2010 summer seasons from 1953 to 2010

) y = 0.0075x + 11.69 ℃ 18 y = 0.0159x + 28.104 30 (Rainy) (Summer) 16 14

29 12 10 Average rainfall (mm) Average 8 Average temperature ( temperature Average 28 y = 0.0189x + 27.928 6 y = 0.0946x + 7.5832 (Summer) (Rainy) 4 Rainy Summer Rainy Summer 27 2 (c) Average daily mean temperature in rainy and sum- (d) Average daily rainfall in rainy and summer seasons mer seasons from 1953 to 2010 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 List of Tables

229 1 Bangla calendar ...... 17 230 2 Mann-Whitney test to compare the two seasons for each climate variable in both 231 old and recent periods ...... 17

16 Table 1: Bangla calendar

Bangla season Bangla calendar Days Baishakh 14 April - 14 May 31 Summer Jaishtha 15 May - 14 June 31 Ashar 15 June - 15 July 31 Rainy season Shraban 16 July - 15 August 31 Bhadra 16 August - 15 September 31 Pre-autumn Ashwin 16 September - 15 October 30 Karttik 16 October - 14 30 Late-autumn Agrahayan 15 November -14 30 Paush 15 December - 13 January 30 Winter Magh 14 January - 12 30 Falgun 13 February - 13 30∗ Spring Chaitra 14 March - 13 April 30

∗ It becomes 31 in .

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 4.726∗∗∗ 2.256∗∗ 1.772∗ 3.223∗ Recent 0.126 0.34 0.31 0.941

(b) Rainy season vs. Summer season

Subsample Min temp Max temp Mean temp Rainfall Old 5.948∗∗∗ −6.432∗∗ −0.121 3.357∗ Recent −0.708 −4.104∗∗∗ −0.805 0.437

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

17