impact of climate change on heavy rainfall in bangladesh
TRANSCRIPT
Impact of Climate Change on Heavy Rainfall in Bangladesh
FINAL REPORT
R01 / 2014
A.K.M. Saiful Islam
Sonia Binte Murshed Md. Shah Alam Khan
Mohammad Alfi Hasan
October 2014
Institute of Water and Flood Management (IWFM)
Bangladesh University of Engineering and Technology (BUET)
Impact of Climate Change on Rainfall Intensity in Bangladesh
FINAL REPORT
R01 / 2014
Research Team
A.K.M. Saiful Islam Sonia Binte Murshed Md. Shah Alam Khan
Mohammad Alfi Hasan
October 2014
Institute of Water and Flood Management (IWFM)
Bangladesh University of Engineering and Technology (BUET)
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TABLE OF CONTENTS
Page No.
TABLE OF CONTENTS .......................................................................................................................... i
LIST OF TABLES ..................................................................................................................................iii
LIST FIGURES ...................................................................................................................................... iv
ABBREVIATIONS AND ACRONYMS ............................................................................................... vi
ACKNOWLEDGEMENT ..................................................................................................................... vii
EXECUTIVE SUMMARY ..................................................................................................................viii
CHAPTER ONE INTRODUCTION ...................................................................................................... 1
1.1 Background ........................................................................................................................................ 1
1.2 Rational of the study .......................................................................................................................... 2
1.3 Objective of the Study ....................................................................................................................... 2
1.4 Limitations of the Study ..................................................................................................................... 2
CHAPTER TWO LITERATURE REVIEW .......................................................................................... 3
2.1 Climate of Bangladesh ....................................................................................................................... 3
2.2 Rainfall of Bangladesh ....................................................................................................................... 3
2.3 Climate Change .................................................................................................................................. 8
2.4 Present rainfall trend ........................................................................................................................ 10
2.5 Trend Detection and Future Assessment ......................................................................................... 11
CHAPTER THREE METHODOLOGY .............................................................................................. 14
3.1 Data Collection and Processing ....................................................................................................... 14
3.2 Seasonal Trend and Spatial Distribution .......................................................................................... 15
3.3 Indices Calculation ........................................................................................................................... 16
3.4 Future Prediction .............................................................................................................................. 20
3.5 Relationship of precipitation with climatic variables....................................................................... 22
CHAPTER FOUR OBSERVED CHANGES OF EXTREME RAINFALL ....................................... 23
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4.1 Seasonal Rainfall patterns and trends .............................................................................................. 23
4.2 Spatial distribution of rainfall in Bangladesh .................................................................................. 25
4.3 Comparing present and future trend of high intensity rainfall ......................................................... 28
4.4 Relationship between climatic variables and rainfall characteristics ............................................... 30
4.5 Variations of Rainfall ....................................................................................................................... 37
4.6 Relationship between Precipitation and Return Periods .................................................................. 39
4.7 Rainfall indices ................................................................................................................................ 42
CHAPTER FIVE CLIMATE INDUCED CHANGES OF RAINFALL EXTREMES OVER
BANGLADESH .................................................................................................................................... 48
5.1 Introduction ...................................................................................................................................... 48
5.2 Extreme Indices. ............................................................................................................................. 48
5.3 Results and Discussions ................................................................................................................... 49
CHAPTER SIX CONCLUSION AND RECOMMENDATION ......................................................... 53
REFERENCES ...................................................................................................................................... 55
Appendix A Hydrological region wise variation in seasonal rainfall pattern .................................. 59
A.1 Hydrological region wise variation in rainfall pattern for Pre Monsoon season ............................. 60
A.2 Hydrological region wise variation in rainfall pattern for Monsoon Season .................................. 65
A.3 Hydrological region wise variation in rainfall pattern for Post Monsoon season ........................... 70
A.4 Hydrological region wise variation in rainfall pattern for winter season ........................................ 74
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LIST OF TABLES
Page No.
Table 3.1: Precipitation Indices ............................................................................................................ 17
Table 3.2: Temperature Indices ............................................................................................................ 17
Table 3.3: The list of 34 BMD stations with their geographical coordinates. ...................................... 20
Table 4.1: Season wise Rainfall trend in Bangladesh. .......................................................................... 24
Table 4.2: Decadal average rainfalls for 29 BMD stations in Bangladesh ........................................... 25
Table 4.3: Trends in SDII for individual stations in Bangladesh (1961-2010). .................................... 28
Table: 4.4: Trends of SDII for different hydrologic region .................................................................. 29
Table 4.5: Trend of probability of SDII ................................................................................................ 30
Table 4.6: Proportions of stations showing trend of temperature and precipitation indicators. ............ 31
Table 4.7: Annual variability of rainfalls and rainy days ...................................................................... 38
Table 4.8. Annual Precipitations, Probabilities and Return Period for Fifty years (1961-2010) for
Bangladesh .......................................................................................................................... 40
Table 4.9: Trends of precipitation indices for individual stations in Bangladesh (1961-2010) ............ 43
Table 4.10. Trend of precipitation indices with respect to hydrological region. .................................. 47
Table 5.1: List of extreme climate indices used in the study ................................................................ 49
Table 5.2: Mean and standard deviations of precipitation for present and three future time slices. ..... 50
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LIST FIGURES
Page No.
Figure 3.1: Climatic Regions of Bangladesh .......................................................................................... 5
Figure 3.2: Spatial distribution of the monthly rainfall (mm) over Bangladesh [Source: Kripalani et al.
(1996)] .................................................................................................................................. 7
Figure 3.3: Hydrological region of Bangladesh with rainfall stations of BMD. ................................... 19
Figure 3.4: PRECIS domain over south Asia. ...................................................................................... 21
Figure 4.1: Decadal spatial distribution of rainfall in Bangladesh for 1961-1970 (top left), 1971-1980
(top right), 1981-1990 (middle left), 1991-2000 (middle right) and 2001-2010 (Bottom). 26
Figure 4.2: five years moving average for SDII concerning eight hydrological regions ...................... 29
Figure 4.3: PDFs of SDII (mm/rainy day) for present and three future time slices. ............................. 30
Figure 4.4: Proportions of stations showing specific trends in extreme weather indicators in
Bangladesh. ......................................................................................................................... 32
Figure 4.5: Relationship between temperature and rainfalls. ................................................................ 33
Figure 4.6: Relationship between humidity and rainfalls. .................................................................... 34
Figure 4.7: Relationship between sea level pressure and rainfalls. ....................................................... 34
Figure 4.8: Relationship between sunshine hours and rainfalls. ........................................................... 35
Figure 4.9: Relationship between wind speed and rainfalls. ................................................................. 36
Figure 4.10: Probability plots of rainfall where plotting the logs of rainfall (mm) on arithmetic scale
and the return periods (years) and the probability of occurrence (%), on probability scales.
............................................................................................................................................ 41
Figure 4.11: Five years of moving average for CDD............................................................................ 44
Figure 4.12: Five years of moving average for CWD. .......................................................................... 44
Figure 4.13: Five years of moving average for PRCPTOT. ................................................................. 45
Figure 4.14: Five years of moving average for R95. ............................................................................ 45
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Figure 4.15: Five years of moving average for R99. ............................................................................ 46
Figure 4.16: Five years of moving average for R100. .......................................................................... 46
Figure 5.1: Spatial pattern of changes of one day maximum precipitation (RX1) over Bangladesh
during premonsoon, monsoon and post monsoon seasons for 2050s from the baseline year
1980s, respectively (from left). ........................................................................................... 50
Figure 5.2: Spatial pattern of changes of one day maximum precipitation (RX1) over Bangladesh
during pre-monsoon, monsoon and post monsoon seasons for 2080s from the baseline year
1980s, respectively (from left). ........................................................................................... 50
Figure 5.3: Spatial distribution of changes of days when precipitation is more than 20 mm over
Bangladesh for future time slices of 2020s, 2050s and 2080s from baseline year 1980s,
respectively (from left). ...................................................................................................... 51
Figure 5.4: Probability distribution functions (PDFs) of daily intensity (mm/rainy days), Five days
rainfall (mm), number of days when rainfall > 20mm, and consecutive wet days over
Bangladesh. ......................................................................................................................... 51
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ABBREVIATIONS AND ACRONYMS
BMD Bangladesh Meteorological Department
BUET Bangladesh University of Engineering and Technology
BWDB Bangladesh Water Development Board
CDD Consecutive Dry Days
CWD Consecutive Wet Days
EDA Exploratory Data Analysis
GIS Geographic Information System
IPCC Intergovernmental Panel on Climate Change
IWFM Institute of Water and Flood Management
LGED Local Government Engineering Department
NGO Non Government Organization
PRCPTOT Total Annual Precipitation
PRECIS Providing REgional Climates for Impacts Studies
SDII Simple Daily Intensity Index
SRES Special Report on Emission Scenarios
SAARC South Asian Association for Regional Cooperation
SMRC SAARC Meteorological Research Centre
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ACKNOWLEDGEMENT
The authors wish to express their sincere thanks to the Research and Academic Committee
(RAC) of the Institute of Water and Flood Management (IWFM) of Bangladesh University of
Engineering and Technology (BUET) for taking initiatives to conduct this study. We
gratefully acknowledge the funds provided by BUET to conduct this study. We also pay our
sincere gratitude to Dr. Md. Munsur Rahman, Professor and Dr. G.M. Trekul Islam, Professor
and Director of the institute for their continuous support in completing the research study
successfully.
We offer our special thanks to Hadley Center, Met Office, UK for providing lateral boundary
condition GCM data and PRECIS software to carry out the regional climate modeling. We
would also like to thank Mr. Abdul Mannan of Bangladesh Meteorological Department for
providing valuable suggestions on rainfall data processing. This study has been funded by
Bangladesh University of Engineering and Technology (BUET).
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EXECUTIVE SUMMARY
Rainfall plays an important role in the agro-economy of Bangladesh, located in the tropical
zone. Its climate is characterized by large variations in seasonal rainfall with moderately
warm temperatures and high humidity. Due to its geographic location and dense population,
Bangladesh has been identified as one of the most vulnerable countries to climate change.
This research draws attention to the fact that high-intensity rainfall has become more frequent
in the recent years, which is evident from the events like 341mm of rainfall in 8 hours in
2004 and 333mm of rainfall in 2009 in Dhaka, and 408mm of rainfall in 2007 in Chittagong.
These rainfall events indicate a change in rainfall characteristics in Bangladesh. This study
conducted a detailed analysis of the effects of climate change on rainfall pattern, magnitude,
frequency, and intensity to investigate the hydro-climatic patterns.
The investigation has been carried out using daily records of six important climatic variables,
namely, precipitation, temperature, humidity, sea level pressure, sun shine hour and wind
speed, observed at 29 ground based stations of Bangladesh Meteorological Department
(BMD) distributed over the country during the time period 1961-2010. The information from
each station have been studied and analyzed, while grouping the stations in one of the eight
hydrological (planning) regions of Bangladesh (NWMP, 2001). These regions are: North East
(NE), North Central (NC), North West (NW), South East (SE), South Central (SC), South
West (SW), Eastern Hill (EH) and River and Estuary (RE). Five-year moving average, a
finite impulse response filter, is used to analyze and compute the trends in precipitation to
smooth out short-term fluctuations and highlight longer-term trends or cycles. Altogether 11
and 14 climate indices for the precipitation and temperature, respectively, at different
thresholds have been calculated. These indices greatly facilitate assessment of the changes in
precipitation and temperature patterns, intensities, frequency and extremes. Annual and
seasonal trends of precipitation indices and their spatial distributions are analyzed. A
software RClimDex 2.14, has been used for processing data and calculating indices. In
addition, decadal changes in annual rainfalls are also determined. Regional climate model
PRECIS is used to predict various climatic parameters such as temperature and rainfall over
Bangladesh. The data of the Special Report on Emission Scenarios (SRES) A1B, which is a
moderate emission scenario (a balance across all sources), have been used to generate the
PRECIS model. Results of PRECIS simulation for 2020s (2011-2040), 2050s (2041-2070)
and 2080s (2071-2100) are used in this study.
Based on the analysis of observed data, this study has identified that the highest increasing
precipitation trend is seen in the EH region. Rainfall is increasing at 8.49mm/year during
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monsoon and 5.12mm/year during the pre-monsoon season in the EH region. Hilly
topography of this region along with elevation ranging between 600 and 900m above mean
sea level contributes to the heavy rainfall. Although rainfall is increasing in Bangladesh, in
general, interestingly, the NE region exhibits a considerably different scenario. A remarkable
increase in the pre-monsoon season (5.624mm/year) with decreasing trends in other three
seasons (-0.6994 mm/year in the monsoon, -0.246 mm/year in the post-monsoon and -0.0906
mm/year in the winter seasons) indicate a shifting of the rainy season. A spatial increase of
moderate rainfall in major parts of Bangladesh is also noticeable. At the same time, five
consecutive decadal annual average rainfall amounts indicate an increasing trend in rainfall
intensity in Bangladesh.
Simple Daily Intensity Index (SDII) is used to analyze variations in daily precipitation
intensity over Bangladesh and to evaluate the variations in observed data for each
hydrological region along with a comparison of present rainfall intensity with that of the
future. When the trends at individual stations are considered, 18 stations out of 27 exhibit
negative trends. Among those, five stations show significant negative trends. The
probabilities of SDII with respect to four time spans (i.e., 1970s, 2020s, 2050s and 2080s) are
analyzed. Such findings show a rapidly increasing trend of present SDII (1971-2000) from
8.0 to 9.5 mm/day. However, SDII higher than 9.5 mm/day shows a decreasing trend. On the
other hand, the probabilities of SDII for future time spans do not vary much although that for
a future time span from 2040 to 2070 shows marginally increasing trend (0.005 mm/year with
an R2value of 0.91). SDII higher than 9.5 mm/day exhibits a decreasing trend. It is
anticipated that there will not be much variation in the probability of SDII in future.
Although most of the stations show positive and negative trends for both temperature and
precipitation indicators, a good number of stations show significant changes in the postive
direction. It indicates that the trend in temperature along with precipitation is increasing.
Temperature and rainfall has positive correlation. Humidity is also positively correlated with
precipitation. Excess humid condition (87%) prevails in the monsoon season (June-
September) and then followed by the post monsoon season (October-November). Humidity is
the least (70%) during the pre-monsoon season (March-May), which coincides with summer
in Bangladesh, followed by the dry/winter season (December-February). An inverse
relationship between sea level pressure and rainfall has been found in this study. The highest
sea level pressure (1015 mbar) exists in the dry period and the lowest pressure (1000 m bar)
prevails during the monsoon, especially in July when the highest rainfalls usually occur in the
country. A fluctuating condition of sunshine duration with higher values during May to
August and the lowest in October are also seen in the observed records of the past 50 years
(1961-2010). In these records, wind speed has a positive correlation with rainfall. Relatively
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low wind speed prevails in the dry season and then a sharp rise to 2.2 to 4.5 knots occurs in
the pre-monsoon, which remains high (4.5-3.5 knots) in the monsoon. It decreases again in
the post monsoon.
An approximately equal proportion of increasing and decreasing trends of precipitation
indices is found. Since precipitation is a highly variable climatic parameter, a very small
portion of rainfall indices is found to be significant. Consecutive Dry Days (CDD) shows the
highest significant increasing trend. Although 87.5% BMD stations exhibit increasing trends
in CDD, only 25% of trends are significant. It is followed by the Simple Daily Intensity Index
(SDII) with a significant negative trend. Analysis of rainfall greater than 10mm, 20mm,
100mm (R10, R20, R100) and the yearly total precipitation amount (PRCPTOT) reveal very
few significant trends. On the other hand, analyses of the monthly maximum one day
precipitation (RX1) and the monthly maximum five days precipitation (RX5) exhibit non-
significant increasing trends at 65% and 75% BMD stations, respectively.
In case of regionally averaged trends, almost all the precipitation indices show positive
trends. The total amount of annual precipitation (PRCPTOT) is increasing for all the eight
regions along with increasing trends in consecutive dry days (CDD). It is prominent in the
EH region with the highest increasing trend of 6.12 mm/year of PRCPTOT and 0.157
day/year of CDD. This indicates that a higher amount of rainfall will occur within a shorter
period of time. Annual total precipitation greater than the 95th percentile (R95) also exhibits
an increasing trend except in the NE hydrological region. Rainfall greater than 100 mm
(R100) is also decreasing in the NE region. Although the trend in PRCPTOT is increasing,
this trend (0.1576 mm/year) is relatively less significant than others in this particular region.
CDD is also found to be increasing. Therefore it is predicted that a longer drier condition will
prevail in the NE region, where the highest rainfall occurs at present. The SW region shows
the highest significant change in precipitation indices whereas the RE region exhibits the
least significant variation in precipitation indices. It is revealed from this study that short
duration high intensity rainfall is increasing in Bangladesh, which is a direct consequence of
the changing climate.
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CHAPTER ONE
INTRODUCTION
1.1 Background
Bangladesh’s unique geographic location, with the Indian Ocean to the south, the Himalayas
to the North and the prevailing monsoons, has made it one of the wettest countries of the
world. While the mean annual rainfall over the country is about 2320 mm, there are places
with a mean annual rainfall of 6000mm or more (Hossain et al., 1987). A long duration of
heavy rainfall associated with “norwester” thunder storms is very common in Bangladesh
(Hossain et al., 1987, Rafiuddin et al., 2009). In September 2004, 341mm rainfall occurred in
8 hours in Dhaka which led to severe urban flooding (Ahmed, 2008). Serious urban floods
also took place in Dhaka city due to 333mm rainfall on 28 July, 2009 (Uddin, 2009). On that
day around 290mm rainfall occurred in (a record) six hours. On 11 June, 2007 around 408
mm rainfall was measured in Chittagong, which resulted in urban flooding and landslide
killing at least 124 people (Uddin, 2009).
According to the fourth assessment report of IPCC the mean temperature of the earth has
been increasing at a rate of 0.74 degree centigrade per century (IPCC, 2007). It is also found
that climate change has profound impacts on the pattern of rainfall intensity and its variability
(Wasimi, 2009). Global Climate Models show that global warming will increase the intensity
of extreme precipitation events (Allan and Soden, 2008). Regional projections also reveal that
climate changes would strengthen monsoon circulation, increase surface temperature, and
increase the magnitude and frequency of extreme rainfall events. Regional climate models
predict a large increase in annual precipitation although the more recent PRECIS run show
that the dry season is becoming drier and water deficit is increasing due to population growth
(Fung et al., 2006). Therefore, climate change will certainly bring an additional stress to
rainfall pattern.
The pattern of rainfall will change due to global warming although the exact amount of this
change is not yet evaluated. This change will affect fresh water supplies that have already
been strressed by the rising population and increased per capita consumption. This change
will also cause the extreme events to be more erratic, which will pause higher degree of
difficulty in estimating extreme rainfall events since there will no longer be a homogeneous
series of values which can be extrapolated statistically (Linarce, 1992).
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1.2 Rational of the study
Rainfall variability, shifts and trends largely impact the economic, social and biophysical
conditions of a country (Gallant et al, 2007). Changes in the mean rainfall have direct effects
on agriculture, fisheries, ecosystem and hydrological condition. Hence, it is essential to know
the changes of rainfall pattern and intensity to study the impacts of climate change. While the
present characteristics can be analyzed using the historical observed data, future changes in
rainfall characteristics can be studied using the data of regional climate model. This study
conducted a detailed investigation to establish a link between climatic variables and rainfall
characteristics considering the impact of climate change.
1.3 Objective of the Study
The overall objective of this research project was to gather information on the effect of
climate change on rainfall pattern and intensity.
The specific objectives were-
1. to assess the rainfall trend and pattern in the pre monsoon, monsoon and post
monsoon seasons,
2. to identify geographical/ spatial distribution of rainfall in Bangladesh,
3. to assess the present trend of high intensity rainfall and compare it with the predicted
future trend, and
4. to determine the relationship between climatic variables and rainfall characteristics.
1.4 Limitations of the Study
High quality observed meteorological data set are very important for this kind of study.
However, consistent, long term records of meteorological data were very difficult to obtain
for this study. There are only 36 BMD stations, only 29 of which could be considered for this
study. Although Bangladesh Water Development Board (BWDB) has more rainfall stations
than BMD for collecting rainfall data, due to poor data quality rainfall data only from BMD
are considered. Regional climate model experiments are conducted at a grid size of 50 km
due to the lack of computational facility (high speed super computer). Hence, this study has
to conduct with the 50km x 50km PRECIS output. This study uses only one regional climate
model, namely PRECIS and one climate change scenario of SRES A1B. Multi-model
ensemble scenarios would have captured the uncertainties of projections better than one
model and single scenario.
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CHAPTER TWO
LITERATURE REVIEW
2.1 Climate of Bangladesh
Geographical location and physical settings govern the climate of any country. Bangladesh
extends from 20°34'N to 26°38'N latitude and from 88°01'E to 92°41'E longitude, surrounded
by the Assam Hills in the east, the Meghalaya Plateau in the north, the lofty Himalayas lying
farther to the north. To its south lies the Bay of Bengal, and to the west lie the plain land of
west Bengal and the vast tract of the Gangetic Plain. It is located in the tropical monsoon
region and its climate is characterized by high temperature, heavy rainfall, often excessive
humidity, and fairly marked seasonal variations. The most striking feature of its climate is the
reversal of the wind circulation between summer and winter, which is an integral part of the
circulation system of the South Asian subcontinent. From the climatic point of view, three
distinct seasons can be recognized in Bangladesh - the cool dry season from November
through February, the pre-monsoon hot season from March through May, and the rainy
monsoon season which lasts from June through October (Banglapedia, 2006).
2.2 Rainfall of Bangladesh
The single most dominant element of the climate of Bangladesh is the rainfall. Because of the
country's location in the tropical monsoon region, the amount of rainfall is very high. During
the early part of the pre-monsoon season, a narrow zone of air mass discontinuity lies across
the country that extends from the southwestern part to the northeastern part. This narrow zone
of discontinuity lies between the hot dry air coming from the upper Gangetic plain and the
warm moist air coming from the Bay of Bengal. As this season progresses, this discontinuity
weakens and retreats toward northwest and finally disappears by the end of the season,
making room for the onset of the summer monsoon. The rainy season, which coincides with
the summer monsoon, is characterized by southerly or southwesterly winds, very high
humidity, heavy rainfall, and long consecutive days of rainfall which are separated by short
spells of dry days. Rainfall in this season is caused by the tropical depressions that enter the
country from the Bay of Bengal (Banglapedia, 2006).
However, there is a distinct seasonal pattern in the annual cycle of rainfall, which is much
more pronounced than the annual cycle of temperature. The winter season is very dry, and
accounts for only 2%-4% of the total annual rainfall. Rainfall during this season varies from
less than 2 cm in the west and south to slightly over 4 cm in the northeast. The amount is
4
slightly enhanced in the northeastern part due to the additional uplifting of moist air provided
by the Meghalaya Plateau. As the winter season progresses into the pre-monsoon hot season,
rainfall increases due to intense surface heat and the influx of moisture from the Bay of
Bengal. Rainfall during this season accounts for 10%-25% of the total annual rainfall which
is caused by the thunderstorms or Nor’wester (locally called Kalbaishakhi). The amount of
rainfall in this season varies from about 20 cm in the west central part to slightly over 80 cm
in the northeast. The additional uplifting (by the Meghalaya Plateau) of the moist air causes
higher amount of rainfall in the northeast. Rainfall during the rainy season is caused by the
tropical depressions that enter the country from the Bay of Bengal. These account for 70% of
the annual total in the eastern part, 80% in the southwest, and slightly over 85% in the
northwestern part of Bangladesh. The amount of rainfall in this season varies from 100 cm in
the west central part to over 200 cm in the south and northeast. Average rainy days during the
season vary from 60 in the west-central part to 95 days in the southeastern and over 100 days
in the northeastern part. Geographic distribution of annual rainfall shows a variation from 150
cm in the west-central part of the country to more than 400 cm in the northeastern and
southeastern parts. The maximum amount of rainfall has been recorded in the northern part of
Sylhet district and in the southeastern part of the country (Cox's Bazar and Bandarban
districts) (Banglapedia, 2006).
5
Figure 3.1: Climatic Regions of Bangladesh
Kripalani et al. (1996) discussed on Monthly rainfall patterns of Bangladesh to understand the
interannual variability of the summer monsoon rainfall. Figure 3.2 shows the spatial
distribution of rainfall (in mm) over Bangladesh for all the 12 months. Monthly rainfall may
be described by considering four climatological periods. The rainfall distribution patterns for
each month are similar and in general the isohytes display a gradient from east to west. The
details of spatial distribution of rainfall as per Kripalani are given below-
(i) March-May. During March some areas, in particular the north-east, receive moderate
rainfall (70-100 mm), although in most of Bangladesh, the rainfall is still below 50 mm. By
April the eastern half of the country receives over 100 mm of rain and the north-eastern part
receives over 300 mm. In May the whole country receives well over 170 mm with a
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maximum over the north-east region (more than 500 mm). On an average this season
contributes 19 per cent of the annual rainfall.
(ii) June-August. During this period the south-west monsoon is at its peak. During June the
whole country receives over 300 mm of rain with a maximum over the north-east and south-
east part of the country. The rainfall distribution patterns for July and August are similar to
June. During this period rainfall is especially heavy in the Chittagong region because it is
exposed to the full force of the south-west monsoon and Cox's Bazar receives more than 700,
900, and 700 mm of rain during June, July, and August respectively. These three months
together contribute about 57 per cent of the annual rainfall.
(iii) September-October. These are the months of the withdrawal of the south-west monsoon.
Although the rainfall pattern remains similar as the pattern during the peak of the monsoon,
the rainfall over the eastern parts of the country has become half that during the peak of the
south-west monsoon. These two months contribute about 20 per cent of the annual rainfall.
(iv) November-February. This is the season of the north-east monsoon and Bangladesh is
practically dry during this period. In November the whole of the country receives well below
50 mm of rain, except the Chittagong region. During December and January the rainfall is
around 10 mm over the entire country. During February the rainfall is between 20 mm and 30
mm. These four months contribute about 4 per cent of the annual rainfall.
7
Figure 3.2: Spatial distribution of the monthly rainfall (mm) over Bangladesh [Source:
Kripalani et al. (1996)]
Although the mean annual rainfall is about 2320mm, it varies from 1527mm in the west to
4197mm in the northeast. As previously mentioned, the additional uplifting effect of the
neighboring Meghalaya Plateu contributes much to the higher rainfall in the northeast part of
Bangladesh (Hossain et al., 1987, Shahid, 2011, Banglapedia, 2006). Some recent erratic
rainfall events like 341mm rainfall occurred in 8 hours (September, 2004, in Dhaka), 333mm
rainfall (on 28th July, 2009, in Dhaka) of which 290mm rainfall occurred in a record six hour,
around 408 mm (on the 11th June, 2007, in Chittagong) lead very serious sufferings and
economic losses to general people (Ahmed, 2008, Uddin, 2009). These were caused by heavy
rainfall events that occurred within a very short period leading to record-breaking monthly-
to-seasonal rainfall totals. The question was raised as to whether such rainfall events may be
related to human-induced climate change.
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2.3 Climate Change
Climate change can be labeled as the most significant challenge faced by global population.
(Nikolova, 2007). Any climatic change in Bangladesh will, of course, be a part of worldwide
climatic changes. It is generally claimed that the temperature of the earth has been increasing
since the beginning of the 20th century. This phenomenon, called Global warming, is
attributed to the increase in atmospheric carbon dioxide (CO2) due to the burning of fossil
fuel. However, not all scientists subscribe to the global warming hypothesis (Banglapedia,
2006).
In the advent of global warming, there are increased concerns regarding extreme weather
events. As elsewhere across the globe, South Asian countries have been observing an
increase in occurrence of extreme climate events in recent decades. Researchers have found
evidences of increasing extreme weather events such as heat waves, cold waves, floods,
droughts, tornados and severe cyclones over the past few decades. The IPCC projected
changes in frequency, intensity and duration of extreme events as consequences of increasing
atmospheric accumulation of greenhouse gases (SMRC, 2009). Variations of climatic
variables both in mean and extreme values along with shape of their statistical distribution are
some important characteristics of climate change (Santos, 2011).
According to the fourth assessment report of IPCC the mean temperature of the earth has
been increasing at a rate of 0.74 degree centigrade per century (IPCC, 2007). It is also found
that climate change has profound impact on the pattern of rainfall intensity and its variability
(Wasimi, 2009). Global Climate Models showed that global warming will increase the
intensity of extreme precipitation events (Allan and Soden, 2008). Regional projections also
revealed that climate changes would strengthen monsoon circulation, increase in surface
temperature, and increase the magnitude and frequency of extreme rainfall events.
Over the past 100 years, the broad region encompassing Bangladesh has warmed by about
0.5°C. The warming trend is consistent with that of the northern hemisphere as a whole. As
with the observed global warming, it is yet not possible to say unequivocally that the
warming in Bangladesh region has been due to greenhouse gases. There has been no
discernible trend in average rainfall, although rainfall variability appears to have increased in
recent decades (Ahmad et al., 1994).
9
In the future, Bangladesh may get warmer and wetter. For the IPCC (1990) “Business as
usual” emissions scenario, Bangladesh is projected to be 0.5 to 2°C warmer than today by the
year 2030, based on a range of global climate model results. Rainfall is more difficult to
predict. However climate models generally agree that regional monsoon rainfall should
increase in warmer world. The best estimate is a 10 to 15 percent increase in average
monsoon rainfall by the year 2030, although the uncertainties are very large. Little can be
said specifically regarding future changes in the frequency and intensity of cyclones in the
Bay of Bengal (Ahmad et al., 1994).
10
2.4 Present rainfall trend
SMRC’s study (2009) on “Understanding the rainfall climatology detection of extreme
weather events in the SAARC region” shows that the trends of consecutive wet days (CWD)
and consecutive dry days (CDD), averaged for 1961-1990 is decreasing at a rate of 0.103 and
0.365 days /year respectively. Warm spell duration indicator (WSDI) is increasing at a rate
about 0.334 days / year compared to slow decreasing rate of 0.098 days per year of Cold
Spell Duration Indicator (CSDI). These indicate that Bangladesh is more vulnerable due to
warm spell duration at least six consecutive days when maximum temperature> 90th
percentile.
A report on “Characterizing Long-term Changes of Bangladesh Climate in Context of
Agriculture and Irrigation” by Climate Change Cell of DOE (2011) revealed that trend of
rainfall is increasing during summer and winter for the entire country, while is decreasing
during monsoon. Singh and Sontakke (2002) also found a decreasing trend (statistically
insignificant) in monsoon rainfall over central and eastern Indo-Gangetic plain. But, these
findings are slightly different with the findings of Mondal and Wasimi (2004) who have
analyzed the monsoon rainfall data of the Ganges basin within Bangladesh and Rahman et
al.(1997) who have analyzed the monsoon rainfall data at 12 stations of Bangladesh and
found no conclusive evidence of any changing pattern of monsoon rainfall. The trend of
temperature in general, both maximum and minimum is increasing except in the winter
season. The average sunshine duration in Bangladesh is declining at an alarming rate which
results in decreasing crop evapotranspiration although temperatures have rising trends.
Institute of Water and Flood Management of Bangladesh University of Engineering and
Technology conducted a study on spatial and temporal distribution of four climatic variables.
The researchers found an increasing trend of rainfall throughout the year except the months
of June and August of the Monsoon season. Some regional variations in the monthly rainfalls
along with increasing trend in the inter-annual variabilities in rainfalls for most months are
noticed. In addition, the numbers of days with high rainfalls also show increasing trend.
Interestingly, this study reveals that the annual rainfall at country level is essentially free of
any significant change and trend (IWFM, 2012However, different types of results on the
significance of annual rainfall are found by Shahid (2011). He observed a significant
increment in annual and pre-monsoon rainfall. An increasing trend in heavy precipitation
days and decreasing trend in consecutive dry days are also seen in his study. Moreover,
significant variations in most of the extreme rainfall indices are observed in North West
Bangladesh.
11
2.5 Trend Detection and Future Assessment
Detection of changes in long time series of hydrological data is an important and difficult
issue, of increasing interest (Kundzewicz, Z. W. 2004). Systematic observations of
meteorological and hydrological information are a precondition to estimate and forecast
hazard risks and vulnerabilities. For Bangladesh, this is critical, as both climate variability
and change are strongly evidenced. Weather patterns, seasonal variations are becoming
increasingly erratic, hence uncertainty becoming the order of the day (CCC, 2009).
Distribution-free testing methods, particularly the re-sampling methods is recommended to
use for the change detection of hydrological data, which are often strongly skewed (non
normal), seasonal and serially correlated (Kundzewicz, 2004). Climate indices are also a very
useful technique to detect and monitor climate change. A set of indices are developed by the
expert team on climate change detection, monitoring and indices, supported by WMO (World
Meteorological Organization), Commission for Climatology (CCI) and the Climate
Variability and Predictability Project (CLIVAR) (Santos, 2011). Climate indices are used to
present the changes in a uniform way that is internationally accepted. Among them,
precipitation indices are very useful to assess the changes of precipitation patterns, intensities
and extremes. Trends of extreme precipitation indices are becoming key concern to scientists
due to global warming and climate change (Sensoy, et al. 2008, Insaf, et al. 2012).SAARC
Meteorological Research Centre analyzed a good number of indices for the rainfall
parameters at different thresholds, [e.g.R10mm (number of heavy precipitation days when
precipitation ≥10mm), R20mm (number of heavy precipitation days when precipitation
≥20mm), R95p (very wet days when rain rate >95th Percentile), R99p (extremely wet days
when rain rate >99th Percentile), RX1 day (monthly maximum 1 day precipitation,), RX5 day
(monthly maximum consecutive 5 day precipitation), CDD (consecutive dry days when rain
rate <1mm), CWD (Consecutive wet days when rain rate >1mm) and PRCPTOT (annual
total wet day precipitation when rain rate >1mm) ] to obtain the trend of extreme rainfall
events in SAARC region (Islam and Uyeda,2009).
The overall climate response to increasing atmospheric concentrations of greenhouse gases
may prove much simpler and more predictable than the chaos of short-term weather.
Quantifying the diversity of possible responses is essential for any objective, probability-
based climate forecast, and this task will require a new generation of climate modelling
experiments, systematically exploring the range of model behavior that is consistent with
observations. It will be substantially harder to quantify the range of possible changes in the
hydrologic cycle than in global-mean temperature, both because the observations are less
complete and because the physical constraints are weaker (Allen and Ingram, 2002).
12
Various climate models are used to predict and analyse the future rainfall in Bangladesh. It is
believed that rainfall forecasting is difficult and also a challenging task for anyone because
rainfall data are multi-dimensional and nonlinear (Banik et al., 2008).
May (2004) used ECHAM4 atmospheric general circulation model (GCM) at a high
horizontal resolution of T106 and rainfall data from the ECMWF re-analysis (ERA, 1958–
2001) for future rainfall investigation of Indian summer monsoon. ERA reveals serious
deficiencies in its representation of the variability and extremes of daily rainfall during the
Indian summer monsoon.
A sequence of empirical models and the MIKE11-GIS hydrodynamic model are used by
Mirza et al., (2003) to assess possible changes in the magnitude, extent and depth of floods of
the Ganges, Brahmaputra and Meghna (GBM) rivers in Bangladesh. Climate change
scenarios were constructed from the results of four General Circulation Models (GCMs) -
CSIRO9, UKTR, GFDL and LLNL, which demonstrate a range of uncertainties. The
precipitation and discharge data were examined with respect to their adequacy of empirical
modelling. Statistical tests show that the precipitation observations in all meteorological sub-
divisions are normally distributed.
A regional climate model named Providing REgional Climates for Impacts Studies (PRECIS)
adapted in generating rainfall scenarios for the SAARC (Islam, 2009) Regional climate
models predict a large increase in annual precipitation although the more recent PRECIS run
showed that the dry season is becoming drier and water deficit is increasing due to the
population growth (Fung et al., 2006). Therefore, climate change will certainly bring an
additional stress to rainfall pattern. SMRC’s study (Report No.30, 2008) on the analysis of
rainfall and temperature in the SAARC region indicated that PRECIS simulated rainfall and
temperature are not directly useful in application purposes. Without calibration with ground
truth data, model outputs are very risky in providing long term rainfall prediction. However
after performing calibration acceptable result is obtained in estimating rainfall and
temperature which are almost similar to the observed values. PRECIS generated rainfall over
Bangladesh is calibrated with the observed data at 27 location over the country. Calibration
of PRECIS simulated rainfall for Bangladesh was carried out by Islam (2008) and Islam et
al., (2008). Initially, PRECIS underestimated large amount of rainfall during April to
November. With the help of slopes and constants, the PRECIS simulated and calibrated
rainfalls in Bangladesh are much closed to the observed data. It is mentioned that large
mismatch in rainfall amounts obtained from model and observation during April to
November are not seen in the calibrated amount. Without calibration, PRECIS can calculate
13
only about 54.85% (3.73mm/d) of the observed rainfall (6.81 mm/d). However after
calibration, PRECIS estimated rainfall is about 100.00% (6.81 mm/d) of the observed data.
This is the advantage of using calibration tables in utilizing PRECIS outputs for application
purposes up to the local scale. (Islam et.al, 2008).
14
CHAPTER THREE
METHODOLOGY
3.1 Data Collection and Processing
The investigation has been carried out using daily records of six important climatic variables,
i.e., precipitation, temperature, humidity, sea level pressure, sun shine hour and wind speed,
observed at 29 ground based stations of Bangladesh Meteorological Department (BMD)
distributed over the country during the time period 1961-2010.Although Bangladesh
Meteorological Department (BMD) has thirty seven ground based stations, but only data of
thirty five (35) stations are available. At initial stage, quality of rainfall and temperature data
are checked by verifying the following criteria (Peralta-Hernandez et al., 2009; Shahid,
2011)-
1. Non-existence of dates
2. Negative daily precipitation
3. Maximum Temperature<Minimum temperature
4. Daily winter rainfall>100mm
5. Consecutive dry days>10 in Monsoon
6. Weather stations>35% missing data
7. Stations with gaps three or more years in between series
If any of the above mentioned point from i to v is true for any dataset, it is identified as
erroneous data. Stations fulfilling the criteria of vi or vii or both are rejected. So, six BMD
stations (Chittagong (Patenga), Chuadanga, Kutubdia, Mongla, Sayedpur, Tangail) are
discarded after following the preceding conditions considering data period from 1961 to
2010. To assess the rainfall pattern and trend of whole Bangladesh, data of twenty nine (29)
stations are considered for this study. R-based program, RHtest, developed at the
Meteorological Service of Canada, is used to detect non-homogeneities in the daily data
series. This software uses a two phase regression model to check the multiple step-change
points that could exist in a time series (Wang, 2003).
15
3.2 Seasonal Trend and Spatial Distribution
To assess the seasonal rainfall trend and pattern daily rainfall data are arranged in to four
climatic seasons, i.e. pre-monsoon, monsoon, post monsoon and winter seasons. Generally,
for this tropical country, the calendar months March-April-May are considered as pre
monsoon season. June-July-August-September and October-November are considered as
monsoon and post monsoon seasons respectively. Winter/dry season is consists of December,
January and February. Five-year moving average, a type of finite impulse response filter, is
used to analyze and compute the trends of precipitation records to smooth out short-term
fluctuations and highlight longer-term trends or cycles (Gallant et al., 2007). The information
of each station have been studied and analyzed on the basis of eight hydrological planning
regions of Bangladesh classified by Water Resources Planning Organization, Bangladesh
(NWMP, 2001). Regions for planning purposes are: North East(NE), North Central(NC),
North West(NW), South East (SE), South Central (SC), South West (SW), Eastern Hill (EH)
and River and Estuary (RE).
Categorizing stations into regional groupings assists in understanding the spatial patterns of
precipitation variations. Additionally, spatial distribution per decade , starting from 1961-
1970, and then 1971-1980, 1981-1990, 1991-2000 and finally 2001-2010 have been plotted
to view decadal change in rainfall distribution.
16
3.3 Indices Calculation
A total of 11 and 14 climate indices for the precipitation and temperature parameters,
respectively, at different thresholds have been calculated. Indices greatly facilitate to assess
the changes in precipitation and temperature patterns, intensities, frequency and extremes.
Annual and seasonal trends of precipitation indices and their spatial distributions are
analyzed. The software RClimDex 2.14 has been used for processing data and calculating
indices. Negative daily precipitation and maximum temperature less than minimum
temperature can easily be solved with this RClimDex software. In addition to that, outliers of
data can be simply identified in terms of standard deviations from the long term daily mean.
The value of standard deviation is chosen as 3.5 to follow other similar category of research
works (New et al, 2006). In this process, erroneous data are replaced by missing value (-
99.9). After the quality control step, precipitation and temperature indices are computed.
Linear regressions to assess trends of these extreme indicators for each station are calculated.
RClimDex program is used to perform a bootstrapping procedure to provide cross-validation
of these values (Zhang and Yang, 2004). A total number of 11 precipitation and 14
temperature indices were calculated and subsequent analyses were done. The following table
3.1 and table 3.2 describe the resulted precipitation and temperature indices respectively.
17
Table 3.1: Precipitation Indices
Table 3.2: Temperature Indices
ID Indicator name Definitions Units
su25 Summer Days Annual Count when TX (daily maximum) > 25◦C Days
tr20 Tropical nights Annual Count when TN (daily minimum) > 20◦C Days
gsl Growing Season
Length
Annual (1st Jan to 31st Dec) count between first span of at least six days with
TG>5◦C and first span after July 1 of 6 days with TG <5◦C Days
txx Max Tmax Monthly maximum value of daily maximum temperature ◦C
tnx Max T min Monthly maximum value of daily minimum temperature ◦C
tx90p Warm days Percentage of days when tx>90th Percentile Days
tn90p Warm nights Percentage of days when tn>90th Percentile Days
wsdi Warm spell duration
indicator
Annual count of days with at least six consecutive days when tx>90th
percentile Days
dtr Diurnal temperature
range Monthly mean difference between tx and tn mm
txn Min Tmax Monthly minimum value of daily maximum temperature ◦C
tnn Min Tmin Monthly minimum value of daily minimum temperature ◦C
tx10p Cold days Percentage of days when tx<10th Percentile Days
tn10p Cold Nights Percentage of days when tn<10th Percentile Days
csdi Cold spell duration
indicator
Annual count of days with at least six consecutive days when tn<10th
percentile Days
ID
Indicator name Definitions Units
RX1day Max 1-day precipitation amount Monthly maximum 1-day precipitation mm
Rx5day Max 5-day precipitation amount Monthly maximum consecutive 5-day
precipitation mm
SDII Simple daily intensity index
Annual total precipitation divided by the number
of wet days (defined as PRCP>=1.0mm) in the
year
mm/d
R10 Number of heavy precipitation days Annual count of days when PRCP>=10mm Days
R20 Number of very heavy precipitation
days Annual count of days when PRCP>=20mm Days
Rnn Number of days above nn mm Annual count of days when PRCP>=nn mm, nn is
user defined threshold Days
CDD Consecutive dry days Maximum number of consecutive days with
RR<1mm Days
CWD Consecutive wet days Maximum number of consecutive days with
RR>=1mm Days
R95p Very wet days Annual total PRCP when RR>95th percentile mm
R99p Extremely wet days Annual total PRCP when RR>99th percentile mm
PRCPTOT Annual total wet-day precipitation Annual total PRCP in wet days (RR>=1mm) mm
18
The computed trends of indices are used non parametric Kendall’s tau based slope estimator.
This method is not suitable to assume distribution of data but is robust to deal with outliers. A
trend is considered to be significant if P value is less than 0.05. The resulted precipitation
indices from twenty nine (29) BMD stations are then divided in to eight hydrological regions.
This course of action is done by computing regionally averaged anomaly series (New et al.,
2006) as follows (Eqn. 3.1)-
xr,t = ∑ (nt𝑖=1 x i,t –�̅�i) / nt (3.1)
Where,
xr,t = regionally averaged index at year t;
x i,t = index for station i at year t ,
�̅�I = index mean at station i over the period 1961-2010
nt= number of stations with data in year t
The regionally averaged series are expressed the index units.
Thana level shape files of Bangladesh and latitude, longitude of BMD stations are used in
Arc Map to produce the Bangladesh map indicating the locations of BMD stations as shown
in Figure 3.3 and the geographical coordinates of the 29 BMD stations are shown in Table
3.3. After checking the quality of data, Chuadanga, Kutubdia, Mongla, Sayedpur and Teknaf
stations are discarded.
19
Figure 3.3: Hydrological region of Bangladesh with rainfall stations of BMD.
20
Table 3.3: The list of 34 BMD stations with their geographical coordinates.
Station
Longit
ude
Latitud
e
Altitud
e
Station
ID Station
Longit
ude
Latitud
e
Altitud
e
Station
ID
Barisal 90.37 22.72 2.1 11704 Madaripur 90.18 23.17 7 11513
Bhola 90.65 22.68 4.3 11706 Maijdeecourt 91.1 22.87 4.9 11809
Bogra 89.37 24.85 17.9 10408 Mongla 89.6 22.47 1.8 41958
Chandpur 90.7 23.23 4.9 11316 Mymensing 90.42 24.73 18 10609
Chittagong 91.82 22.35 33.2 11921 Patuakhali 90.33 22.33 1.5 12103
Chuadanga 88.82 23.65 11.6 41926 Rajshahi 88.7 24.37 19.5 10320
Comilla 91.18 23.43 9 11313 Rangamati 92.15 22.63 68.9 12007
CoxsBazar 91.97 21.45 2.1 11927 Rangpur 89.27 25.73 32.6 10208
Dhaka 90.38 23.78 6.5 11111 Sandwip 91.43 22.48 2 11916
Dinajpur 88.68 25.65 37.6 10120 Satkhira 89.08 22.72 4 11610
Faridpur 89.85 23.93 8.1 11505 Sayedpur 88.92 25.75 39.6 41858
Feni 91.42 23.03 6.4 11805 Sitakunda 91.7 22.63 7.3 11912
Hatiya 91.1 22.45 2.4 11814 Srimongal 91.73 24.3 22 10724
Ishurdi 89.03 24.15 12.9 10910 Sylhet 91.88 24.9 33.5 10705
Jessore 89.33 23.2 6 11407 Tangail 89.93 24.25 10.2 41909
Khepupara 90.23 21.98 1.8 12110 Teknaf 92.3 20.87 5 11929
Khulna 89.53 22.78 2.1 11604
Kutubdia 91.85 21.82 2.7 11925
3.4 Future Prediction
PRECIS (Providing Regional Climate for Impact Studies) developed by the Hadley Centre of
the UK Meteorological Office is used in this study. PRECIS was developed to generate high-
resolution climate change information for as many regions of the world as possible. RCMs
are full climate models and physically based. The PRECIS RCM is based on the atmospheric
component of the HadCM3 climate model (Gordon et al., 2000). In this study, PRECIS
model domain for South Asia has been set up to determine climate change impact over
Bangladesh with a horizontal resolution of 50×50 km. This domain approximately stretched
over latitudes 3.5 -36.2 N and longitudes 65.8-103.3 E and has 88×88 grid points (see Figure
1). This domain allows full development of internal meso-scale circulation and regional
forcing at the regional level. The SRES A1B scenario of IPCC was used to derive the lateral
boundary conditions of the simulation using three dimensional ocean-atmospheric coupled
model (HadCM3Q) to generate diagnostic variables over the simulated domains over the
Indian sub continent which includes Bangladesh.
21
Figure 3.4: PRECIS domain over south Asia.
Climate model PRECIS is used to predict various climatic parameters such as temperature
and rainfall over Bangladesh. The data of the Special Report on Emission Scenarios (SRES)
A1B, which is moderate emission scenario (a balance across all sources), have been used to
generate the PRECIS model. Results of PRECIS simulation for 2020s (2011-2040), 2050s
(2041-2070) and 2080s (2071-2100) are used in this study.
22
3.5 Relationship of precipitation with climatic variables
Return period is a very common method in hydrology to show probability of an event
(UriasUrias et al., 2007). Change in return period of precipitation events is also an important
tracking method of climate change. Hazen plotting position is used to determine the
relationship between precipitation and return period. The application of the Hazen method
consisted in determining the statistical distribution of the annual precipitation for required
duration by calculating the yearly precipitation, probabilities and return periods (Urias et al.,
2007). The average daily rainfall data (computed in section 3.5) are also used in this section.
Initially, normality of the sample distribution is checked by statistical descriptive analysis.
Next, annual precipitation values are arranged in ascending order and ranks of each value are
assigned.
Probability of occurrence of rainfall event are fitted with log-normal distributions. The return
period are determined by following equations (Eqn. 3.2) –
Probability (P) = 100/Period of Return (R) (3.2)
Where, P = Probability of occurrence (%) and R = Period of return
The resultant probabilities and return periods versus annual precipitation amounts are plotted
on log normal probability graph paper. A regression line is drawn through the plotted points
by using least square method. Thus a relationship between precipitation and return period has
been deduced.
23
CHAPTER FOUR
OBSERVED CHANGES OF EXTREME RAINFALL
4.1 Seasonal Rainfall patterns and trends
A tropical monsoon climate prevails in Bangladesh. It is characterized by large variations in
seasonal rainfall with moderately warm temperatures and high humidity. Monsoon is the
prime season of rainfalls in Bangladesh. It is the outcome from the contrasts between low and
high air pressure areas that result from differential heating of land and water (Wikipedia,
2012). There are four climatic seasons in Bangladesh. Pre-monsoon season characterized by
hot weather consist of March, April and May. Monsoon season, when almost 80% of rainfall
occurs starts from June and end it by September. October and November are termed as Post
Monsoon and December, January and February represents dry winter season. Cyclones and
Northwester thunderstorms in pre and post monsoon also contributes a lot in the rainfall of
Bangladesh. One of the objectives of this study is to reveal the seasonal variation of rainfall.
The overall trend of five years moving average shows increasing trend of rainfall in
Bangladesh. Table 4.1 shows the summary of trends for five years moving average with
respect to the hydrological region.
24
Table 4.1: Season wise Rainfall trend in Bangladesh.
Hydrological Region Pre Monsoon Season Monsoon Season
Y R2 Y R2
North West y = 1.8986x - 3480.9 R² = 0.1338 y = 4.2578x - 7165 R² = 0.2151
North East y = 5.6243x - 10328 R² = 0.2925 y = -0.6994x + 3432.6 R² = 0.0052
North Central y = 1.3683x - 2267.2 R² = 0.0469 y = 3.2861x - 5189.4 R² = 0.1544
South West y = 3.2506x - 5987.1 R² = 0.2231 y = 7.052x - 12799 R² = 0.4596
South East y = 2.1305x - 3745.5 R² = 0.0896 y = -2.2481x + 6071 R² = 0.0247
South Central y = 2.0299x - 3626.9 R² = 0.0937 y = 5.8759x - 9923.1 R² = 0.1453
River and Estuary y = 3.8645x - 7220.9 R² = 0.2165 y = 1.2798x - 386.48 R² = 0.0037
Eastern Hilly y = 5.1241x - 9732.8 R² = 0.6117 y = 8.4946x - 14476 R² = 0.2733
Hydrological Region
Post Monsoon Season Winter Season
Y R2 Y R2
North West y = 1.9921x - 3808.6 R² = 0.3116 y = 0.1038x - 177.77 R² = 0.0114
North East y = -0.246x + 712.65 R² = 0.0049 y = -0.0906x + 230.59 R² = 0.0043
North Central y = 1.4022x - 2581 R² = 0.2237 y = 0.2631x - 486.35 R² = 0.0516
South West y = 1.3742x - 2591.3 R² = 0.2398 y = 0.6784x - 1286.8 R² = 0.0844
South East y = -0.1554x + 531.4 R² = 0.0013 y = 0.1286x - 218.67 R² = 0.0115
South Central y = 1.1892x - 2110.8 R² = 0.0634 y = 0.1037x - 167.71 R² = 0.0058
River and Estuary y = 1.8088x - 3315.1 R² = 0.0697 y = -0.081x + 199.73 R² = 0.0052
Eastern Hilly y = 1.3328x - 2374.8 R² = 0.0854 y = 0.3174x - 599 R² = 0.0698
The highest increasing trend can be seen in Eastern Hilly region. Rainfall increases at
8.49mm/year for monsoon and 5.12mm/year for pre monsoon season in Eastern Hilly region.
Hilly topography of this region with elevation ranges between 600 and 900m above mean sea
level, contributes a lot in rainfall. Post monsoon and winter season for North East region
tends to be drier than present condition as rainfall trend is negative (-0.246 mm per year for
post monsoon and -0.0906 mm per year for winter season). Similar decreasing trends with
lesser magnitude are also seen in South East region for post monsoon (-0.1554 mm per year)
and in River and Estuary region for winter season (-0.081 mm per year). Interestingly, North
East hydrological region exhibits a totally different scenario. A remarkable increase in Pre
Monsoonal Season (5.624mm per year) with decreasing trends for other three seasons
(0.6994 mm per year for Monsoon, -0.246 mm per year for Post Monsoon and -0.0906 mm
per year for Winter) gives an indication of shifting of rainy season. Hydrological region wise
variations in rainfall pattern for each season (pre-monsoon, monsoon, post-monsoon and dry
season) are shown in appendix A.
25
4.2 Spatial distribution of rainfall in Bangladesh
This study also tries to identify the decadal variations of average rainfalls in Bangladesh.
Table 4.2 represents decadal average rainfalls for 29 BMD stations.
Table 4.2: Decadal average rainfalls for 29 BMD stations in Bangladesh
BMD Station Longitude Latitude
1961-
1970
1971-
1980
1981-
1990
1991-
2000
2001-
2010
Barisal 90.37 22.72 1964.20 2056.10 2188.00 2059.20 2069.49
Bhola 90.65 22.68 2088.80 2558.22 2410.40 2182.20 2234.83
Bogra 89.37 24.85 1496.60 1765.11 1873.60 1819.90 1687.51
Chandpur 90.7 23.23 1909.33 1612.25 2586.10 1982.90 1957.61
Chittagong
(Patenga) 91.82 22.35 2718.70 2640.90 2960.30 2984.30 2617.88
Comilla 91.18 23.43 2400.33 1873.22 2052.30 2178.70 2061.40
CoxsBazar 91.97 21.45 4023.80 3126.22 3687.60 3778.80 3854.37
Dhaka 90.38 23.78 1967.80 2079.67 2203.80 2087.70 2086.94
Dinajpur 88.68 25.65 1726.60 2115.90 1989.10 2035.32
Faridpur 89.85 23.93 1636.30 1872.50 2020.40 1833.10 1697.79
Feni 91.42 23.03 2490.57 3116.90 3131.70 2730.93
Hatiya 91.1 22.45 2837.60 3172.00 2739.56 3028.60 3273.07
Ishurdi 89.03 24.15 1470.86 1918.63 1614.60 1521.20 1400.46
Jessore 89.33 23.2 1965.70 1825.33 2494.60 2240.50 2435.27
Khepupara 90.23 21.98 2539.57 2489.50 2945.90 2832.11
Khulna 89.53 22.78 1509.11 1943.67 1854.40 1698.00 1873.66
Madaripur 90.18 23.17 3002.00 2437.13 3486.10 3000.80 2767.62
Maijdeecourt 91.1 22.87 1987.00 2119.30 2010.40 2133.10
Mymensing 90.42 24.73 1986.80 1939.71 2472.10 2302.40 2279.26
Patuakhali 90.33 22.33 2224.00 2758.30 2653.30 2594.04
Rajshahi 88.7 24.37 1482.60 1627.22 1547.30 1496.10 1374.91
Rangamati 92.15 22.63 2605.13 2392.90 2418.50 2756.20 2499.68
Rangpur 89.27 25.73 1826.67 1876.78 2423.20 2155.80 2350.01
Sandwip 91.43 22.48 3103.60 3677.33 3381.70 3348.40 3982.03
Satkhira 89.08 22.72 1642.57 1559.22 1766.20 1748.30 1763.48
Sitakunda 91.7 22.63 2414.33 3374.70 3136.30 3130.48
Srimongal 91.73 24.3 2378.56 2091.22 2326.56 2253.60 2490.54
Sylhet 91.88 24.9 3931.40 3783.44 4509.30 4033.10 3863.23
Teknaf 92.3 20.87 2530.25 3865.90 4481.60 4240.36
Spatial distribution per decade , starting from 1961-1970, and then 1971-1980, 1981-1990,
1991-2000 and finally 2001-2010 have been plotted to view decadal change in rainfall
distribution. Figure 4.1 shows five decadal rainfall distributions in Bangladesh.
26
Figure 4.1: Decadal spatial distribution of rainfall in Bangladesh for 1961-1970 (top left),
1971-1980 (top right), 1981-1990 (middle left), 1991-2000 (middle right) and 2001-2010
(Bottom).
The first decade (1961-1970) of this sequence of analysis showed that very high rainfall
prevailed in the Sylhet of North East hydrological region and northern side (nearby locations
of Cox’sbazar ) of Eastern Hilly region. A small portion of area surrounded the Madaripur
27
BMD station also exhibited very high average decadal rainfall. Srimongal of Northeast
region, South Central region and Coast and Estuary region showed moderate to high rainfall
whereas the entire west side along with a major portion of North Central region exhibited low
rainfall. Again in the next decade (1971-1980), extend of very low rainfall decreased in the
west side, moderate to high rainfall increased in the middle to eastern side of Bangladesh. A
major portion of North East, Eastern Hilly region, Coast and Estuary region exhibited very
high average decadal rainfalls. The area of very low rainfall had been reduced further in the
later decade (1981-1990). A significant spatial increase of moderate rainfall was noticed in
this decade. Again, a slight increase in areal extent of low rainfall from the west to east was
observed in the next 1991-2000 decade. The entire area of Eastern Hilly region and far north
East region exhibited very high rainfall. The last decade (2001-2010) was relatively wetter
than the previous one (1991-2000). The low rainfall prevailed only in Rajshahi, Ishurdi,
Bogura and Faridpur. A noticeable spatial increase of moderate rainfall in major part of
Bangladesh was exposed. Five consecutive decal annual average rainfalls also revealed the
fact that Rajshahi and its nearby locations are the drier part whereas North East and Eastern
Hlly regions are the wetter part of Bangladesh. The decadal change in annual rainfall also
indicates Bangladesh is heading towards more intense rainfalls.
28
4.3 Comparing present and future trend of high intensity rainfall
Another aim of this study is to uncover variations in daily precipitation intensity over
Bangladesh and to evaluate the observed variations with respect to hydrological region along
with a comparison of present rainfall intensity with that of future. We use Simple Daily
Intensity Index (SDII) for these purposes.
As precipitation is a highly variable climate parameter, a very small portion of rainfall indices
is found to be significant. Same thing is also applicable for SDII. If the trend of individual
station is considered, 18 stations out of 27 exhibits negative trends. Among them five
individual stations show significant negative trends. Table 4.3 represents the trends of SDII
for individual BMD stations.
Table 4.3: Trends in SDII for individual stations in Bangladesh (1961-2010).
Hydrologic Region Stations SDII Hydrologic Region Stations SDII
North East Sreemongal -0.041
South Central
Barisal -0.032
Sylhet -0.043 Khepupara 0.008
North West
Bogura 0.011 Madaripur -0.113
Dinajpur -0.01 Patuakhali -0.185
Ishardi -0.022
River and Estuary
Bhola -0.044
Rajshahi -0.098 Hatia 0.035
Rangpur 0.047 Sandwip -0.189
North Central Dhaka 0.024
Eastern Hilly Region
Chittagong 0.05
Mymensingh -0.001 Cox'sbazar -0.074
South East
Chandpur -0.143 Rangamati -0.007
Comilla -0.154 Sitakundo 0.035
Feni -0.007 Teknaf 0.224
Maijdicourt -0.146
South West
Faridpur -0.054
Jessore 0.049
Note: Bold shaded values represent significant trend as their corresponding P values are less than 0.05. .
On the other hand, if SDII are considered with respect to eight hydrological regions, more or
less positive trends are found. Figure 4.2 represents five years moving average for SDII
concerning eight hydrological regions and Table 4.4 shows the respective trends. The least-
squares fitting process throws out a value - R-squared - which is the square of the residuals of
the data after the fit. Most of these R squared values (except North East and River and
Estuary regions) for hydrological region wise SDII are close to 1.0 which indicates a better fit
of coefficient of determination.
29
Figure 4.2: five years moving average for SDII concerning eight hydrological regions
Table: 4.4: Trends of SDII for different hydrologic region
Hydrological Region Trends of SDII
y R2
North East Region 0.0013x - 2.5715 0.0115
North West Region 0.0279x - 55.442 0.7743
North Central Region 0.0166x - 32.973 0.696
South East Region 0.0191x - 37.88 0.5012
South West Region 0.0222x - 44.111 0.8565
South Central Region 0.0406x - 80.586 0.7592
Eastern Hilly Region 0.0555x - 110.27 0.7766
River and Estuary Region 0.0064x - 12.558 0.0614
Again Figure 4.3 presents the probability of SDII with respect to four time spans.
Present time span considering the data from 1971 to 2000. Future data predicted with
the help of PRECIS model presents three time span, e.g., from 2010 to 2040, 2040 to
2070 and 2070 to 2100.
-1.5
-1
-0.5
0
0.5
1
1.5
1950 1960 1970 1980 1990 2000 2010 2020
SD
II (
mm
/da
y)
Year
Hydrological Regionwise 5 years Moving Average for SDII
5 years moving average (NE) 5 years moving average (NW)
5 years moving average (NC) 5 years moving average (SE)
5 years moving average (SW) 5 years moving average (SC)
5 years moving average (EH) 5 years moving average (RE)
30
Figure 4.3: PDFs of SDII (mm/rainy day) for present and three future time slices.
Table 4.5: Trend of probability of SDII
Time span Trend of probability for SDII
y R2
1971-2000 -0.0856x + 1.1187 0.5941
2010-2040 -0.0247x + 0.487 0.9181
2040-2070 0.005x + 0.1916 0.9181
2070-2100 -0.0075x + 0.2904 0.9181
The above chart shows a rapid increasing probability trend of present SDII (1971-2000) for
the value of 8.0 to 9.5 mm per days. But the value of SDII higher than 9.5 mm per day shows
decreasing trend. On the other hand, the probabilities of SDII for future time span do not vary
much although future time span from 2040 to 2070 shows marginal increasing trend
(0.005mm per years with a R2 of 0.91). SDII values higher than 9.5 mm/ day exhibits
decreasing trend. In future there will be not much variation in the probability of SDII.
4.4 Relationship between climatic variables and rainfall characteristics
The factors that govern the climate are called climatic variables. The most important factors
among them are precipitation, atmospheric pressure, wind, humidity, and temperature. This
study tries to find linkage between different climatic variables. Assessments of trend for 14
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
8 9 10 11 12 13 14
Pro
bab
ility
SDII( mm/rainy day)
1971 to 2000 2010 to 2040 2040 to 2070 2070 to 2100
31
temperature and 11 precipitation indicators have been done to find a correlation between
temperature and precipitation. Table 4.6 provides proportion of stations with positive and
negative trends accompanying their statistical significant changes and Figure 4.4 depicts
these findings. Trend values are considered significant when their corresponding P values are
less than 0.05.
Table 4.6: Proportions of stations showing trend of temperature and precipitation indicators.
Temperature Indicators Positive
Trend
Positive
Significant
Trend
Negative Trend Negative
Significant Trend
War
m W
eath
er
su25 89.66 51.72 -10.34
tr20 82.76 41.38 -17.24 -3.45
gsl 93.10 13.79 -6.90
txx 55.17 31.03 -44.83 -13.79
tnx 72.41 13.79 -27.59 -3.45
tx90p 17.24 13.79 -82.76
tn90p 17.24 17.24 -82.76
wsdi 17.24 17.24 -82.76
dtr 58.62 20.69 -41.38 -20.69
Co
ld W
eath
er txn 20.69 3.45 -79.31 -31.03
tnn 55.17 31.03 -44.83 -20.69
tx10p 0.00 -100.00 -10.34
tn10p 0.00 -100.00 -10.34
csdi 6.90 3.45 -93.10
Precipitation Indicators Positive
Trend
Positive
Significant
Trend
Negative Trend Negative
Significant Trend
Wet
Wea
ther
RX1 day 51.72413793 -48.27586207
RX5 Day 62.06896552 10.34482759 -37.93103448
SDII 31.03448276 -68.96551724 -20.68965517
R10mm 65.51724138 10.34482759 -34.48275862
R20mm 55.17241379 6.896551724 -44.82758621
R100mm 31.03448276 6.896551724 -68.96551724 -3.448275862
CWD 51.72413793 3.448275862 -48.27586207
R95P 41.37931034 -58.62068966 -3.448275862
R99P 51.72413793 3.448275862 -48.27586207
PRCPTOT 55.17241379 10.34482759 -44.82758621
Dry
Wea
ther
CDD 86.20689655 24.13793103 -13.79310345
32
Figure 4.4: Proportions of stations showing specific trends in extreme weather indicators in
Bangladesh.
Although most of the stations show positive and negative trends for both indicators but a
good number of stations illustrate the significant changes in postive directions. It indicates the
trend of temperature alongwith precipitation is increasing. Again, 50 years data of 29 BMD
stations on precipitation, temperature ,humidity, sea level pressure and wind speed are also
analyzed to view the relationship of precipitation with other climatic parameters. For this
particular analysis, average of 29 BMD stations has been taken in to consideration as a
representation of whole Bangladesh.
Pre
cip
itat
ion
an
d T
emp
erat
ure
Ind
icat
ors
NegativeSignificant Trend
Negative Trend
Positive SignificantTrend
Positive Trend
% of stations with Negative Trends
% of stations with PositiveTrends
33
As a tropical country, there is not much variation in temperature for Bangladesh.50 years data
(1961-2010) shows It varies generally from 19°C in winter to 29°C in Summer. Figure 4.5
shows temperature remains high from April to October at the time when rainfalls is also
high. Temperature falls from late October and remain cold till February. At that time,
precipitation is also very low , almost negligible. So it can be said that temperature and
rainfall has positive correlation. If one increases, the other one also increses and vise versa.
Figure 4.5: Relationship between temperature and rainfalls.
Bangladesh is very humid country and the range varies from 70% to 87%. Humidity is also
positively correlated with precipitation. Excess humid condition (87%) prevails in Monsoon
and then followed by post monsoon season. Pre Monsoon when Summer of Bangladesh
coincides has the least humidity followed by dry/winter season. Figure 4.6 depicts the above
mention fact.
0
5
10
15
20
25
30
35
0
100
200
300
400
500
600
700
800
900
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Tem
pe
ratu
re ◦
C
Rai
nfa
ll in
mm
Month
Average Rainfalls Average Temp
34
Figure 4.6: Relationship between humidity and rainfalls.
Figure 4.7 shows an inverse relationship with sea level pressure and rainfall. Highest sea
level pressure exists in dry period and lowest pressure prevails in the monsoon season,
especially in the month of July when usually highest rainfalls occurs.
Figure 4.7: Relationship between sea level pressure and rainfalls.
Actually, there is hardly any relationship of rainfall with Sunshine hours. Figure 4.8
illustrates a fluctuating condition of sunshine hour with higher in May, June, July and August
and lowest in October.
0
10
20
30
40
50
60
70
80
90
100
0
100
200
300
400
500
600
700
800
900
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Hu
mid
ity
in p
erc
en
tage
Rai
nfa
ll in
mm
Month
Average Rainfalls Average Humidity
990
995
1000
1005
1010
1015
1020
0
100
200
300
400
500
600
700
800
900
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Sea
Leve
l Pre
ssu
re m
bar
Rai
nfa
ll in
mm
Month
Average Rainfalls Average Sea Level Pressure
35
Figure 4.8: Relationship between sunshine hours and rainfalls.
Wind speed has also positive correlation with rainfalls. Low wind speed prevails in the dry
season and then a sharp rise from 2.2 to 4.5 knots in the pre monsoon and almost high (4.5-
3.5 knots) in Monsoon. It decreases again in the post monsoon season. Figure 4.9 shows an
annual relationship between wind speed and rainfall based on a 50 years data (1961-2010).
0
1
2
3
4
5
6
7
8
9
0
100
200
300
400
500
600
700
800
900
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Sun
sh
ine
ho
ur
Rai
nfa
ll in
mm
Month
Average Rainfalls Average Sunshine Hour
36
Figure 4.9: Relationship between wind speed and rainfalls.
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
0
100
200
300
400
500
600
700
800
900
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Win
d S
pe
ed
m/s
Rai
nfa
ll in
mm
Month
Average Rainfalls Average Wind Speed
37
4.5 Variations of Rainfall
Coefficients of variation for 50 years (1961-2010) rainfall data are analyzed to determine
annual variability in Bangladesh. Table 4.7 shows the range of coefficient of variation for
annual average rainfalls and it varies from 27.21% to 14.57%. Both these stations are situated
in North Central hydrological region of Bangladesh and it implies that highest variation of
rainfall occurs in this region. Again, the annual variability for rain days varies from 19.93%
(Sandwip) to 8.73% (Sylhet). Average coefficient variation for annual rainfall is 20.86 and
for number of annual rainy days are 13.76 for overall Bangladesh.
38
Table 4.7: Annual variability of rainfalls and rainy days
Hydrologic
Region Stations
Annual Average
Rainfall
Standard
Deviation
CV of
Rainfall
Annual
Average
Raindays
Standard
Deviation
CV of
Raindays
North East Sreemongal 2310.82 498.77 21.58 125.28 21.44 17.11
Sylhet 4029.01 677.46 16.81 157.35 13.73 8.73
North West
Bogura 1711.96 395.02 23.07 104.24 12.75 12.23
Dinajpur 1966.73 473.16 24.06 94.73 14.68 15.50
Ishardi 1577.95 405.33 25.69 99.64 13.12 13.17
Rajshahi 1492.49 318.00 21.31 95.38 12.86 13.48
Rangpur 2167.19 510.37 23.55 106.91 11.44 10.70
North Central
Dhaka 2085.29 379.83 18.21 120.76 11.45 9.48
Faridpur 1812.02 372.21 20.54 108.06 15.81 14.63
Mymensingh 2212.42 525.93 23.77 115.47 20.99 18.18
South East
Comilla 2112.21 452.42 21.42 107.77 17.19 15.95
Feni 2951.81 655.45 22.21 115.58 15.22 13.17
Maijdicourt 2078.45 420.80 20.25 114.79 13.49 11.75
South West
Khulna 1779.15 380.84 21.41 105.84 21.00 19.84
Satkhira 1718.73 279.19 16.24 107.51 16.77 15.60
Jessore 2199.77 586.56 26.66 109.84 13.55 12.34
South Central
Barisal 2065.32 368.74 17.85 117.08 13.81 11.79
Chandpur 2079.28 565.72 27.21 107.35 18.59 17.31
Khepupara 2714.92 395.52 14.57 119.89 16.09 13.42
Madaripur 3011.30 609.63 20.24 115.89 20.67 17.84
Patuakhali 2650.89 483.47 18.24 121.56 19.37 15.93
River and
Estuary
Bhola 2312.33 487.69 21.09 117.86 16.15 13.70
Hatia 3079.32 627.02 20.36 120.16 13.98 11.63
Sandwip 3506.78 712.96 20.33 110.00 21.92 19.93
Eastern Hilly
Region
Chittagong 2798.90 535.17 19.12 114.28 13.99 12.24
Cox'sbazar 3765.12 579.14 15.38 124.60 13.68 10.98
Rangamati 2529.08 523.67 20.71 127.88 16.22 12.69
Sitakundo 3220.56 672.71 20.89 120.06 12.89 10.73
Teknaf 3999.99 888.39 22.21 123.62 11.13 9.00
39
4.6 Relationship between Precipitation and Return Periods
Hazen Plotting position method is applied to determine the relationship between precipitation
and return periods. The statistical descriptive results show an approximately normal
distribution of annual precipitation. The arithmetic mean value of annual precipitation data
2443.162 mm and median value is 2458.099mm. 68% yearly data are above 2300 mm. So the
mode value is also near the mean and median. Which suggests the distribution of the data is
normal.
Next, the Hazen method is used to determine return period, probability of occurrence in terms
of annual precipitation values. First, the annual precipitation values are arranged in ascending
order and assign a rank for each value. Afterwards, probabilities and return periods are
determined using equation no. Table 4.8 shows annual precipitations, probabilities and return
period of fifty years (1961-2010) for Bangladesh.
40
Table 4.8. Annual Precipitations, Probabilities and Return Period for Fifty years (1961-2010)
for Bangladesh
Rank
Year
Annual
Precipitation (mm)
Annual
Precipitation (cm)
Probability, P
Return
Period, T
1 1983 2962 296 1 100.0
2 1991 2887 289 3 33.3
3 1984 2872 287 5 20.0
4 1988 2864 286 7 14.3
5 2004 2835 284 9 11.1
6 2007 2826 283 11 9.1
7 1998 2806 281 13 7.7
8 2002 2785 278 15 6.7
9 1993 2772 277 17 5.9
10 1987 2769 277 19 5.3
11 1974 2754 275 21 4.8
12 1977 2731 273 23 4.3
13 1999 2726 273 25 4.0
14 1990 2700 270 27 3.7
15 2000 2675 267 29 3.4
16 2001 2609 261 31 3.2
17 1981 2605 260 33 3.0
18 1971 2578 258 35 2.9
19 2005 2573 257 37 2.7
20 1986 2548 255 39 2.6
21 1973 2522 252 41 2.4
22 1978 2495 250 43 2.3
23 1976 2481 248 45 2.2
24 1970 2479 248 47 2.1
25 1995 2460 246 49 2.0
26 2008 2456 246 51 2.0
27 1964 2403 240 53 1.9
28 1965 2396 240 55 1.8
29 1982 2386 239 57 1.8
30 1997 2384 238 59 1.7
31 1996 2366 237 61 1.6
32 1969 2357 236 63 1.6
33 2003 2338 234 65 1.5
34 1968 2305 230 67 1.5
35 1985 2295 229 69 1.4
36 1963 2248 225 71 1.4
37 2006 2222 222 73 1.4
38 2009 2215 222 75 1.3
39 1967 2213 221 77 1.3
40 1989 2168 217 79 1.3
41
Rank
Year
Annual
Precipitation (mm)
Annual
Precipitation (cm)
Probability, P
Return
Period, T
41 1980 2164 216 81 1.2
42 1966 2136 214 83 1.2
43 1975 2112 211 85 1.2
44 2010 2062 206 87 1.1
45 1961 2038 204 89 1.1
46 1994 2020 202 91 1.1
47 1979 1986 199 93 1.1
48 1962 1947 195 95 1.1
49 1992 1891 189 97 1.0
50 1972 1735 173 99 1.0
Resultant annual rainfall, probabilities and return period values are plotted on log probability
graph paper. Log annual precipitation values are plotted in log scale and probabilities and
return periods in probability scale. Figure 4.10 shows the graphical relationship of these three
variables.
Figure 4.10: Probability plots of rainfall where plotting the logs of rainfall (mm) on
arithmetic scale and the return periods (years) and the probability of occurrence (%), on
probability scales.
42
4.7 Rainfall indices
An approximately equal proportion of increasing and decreasing trends of precipitation
indices is found for this tropical country, Bangladesh. As precipitation is a highly variable
climate parameter, a very small portion of rainfall indices is found to be significant. The
Table 4.9 depicts the trends of precipitation indices for individual stations in Bangladesh for a
period from 1961 to 2010. Consecutive Dry Days (CDD) shows the highest significant
increasing trend. Although, 87.5% BMD stations exhibit increasing trend for CDD but only
25% of them are significant. It is followed by Simple Daily Intensity Index (SDII) with a
significant negative trend. Afterwards, Rainfall greater than 10mm, 20mm, 100mm (R10,
R20, R100) and yearly total precipitation amount (PRCPTOT) reveal few significant trends.
On the other hand, monthly maximum one day precipitation (RX1) and monthly maximum 5
day precipitation (RX5) exhibit a non-significant increasing trend at 65% and 75% BMD
stations respectively.
43
Table 4.9: Trends of precipitation indices for individual stations in Bangladesh (1961-2010)
Note: Bold shaded values represent significant trends.
Hydrologic
Region Stations
RX1
day
RX5
Day SDII R10mm R20mm R100mm CDD CWD R95P R99P PRCPTOT
North East Sreemongal
-
0.396
-
0.738
-
0.041 -0.018 -0.026 -0.003 0.336 0.073 -1.042 1.878 -0.258
Sylhet
-
0.394
-
0.447
-
0.043 -0.084 -0.034 -0.013 0.583 -0.07 -4.868 -2.422 -5.36
North
West
Bogura
-
0.091 0.07 0.011 0.15 0.077 0.004 0.701
-
0.042 -0.033 -1.328 3.856
Dinajpur 0.744 1.066 -0.01 0.151 0.048 0.044 0.471 0.104 5.82 3.543 9.687
Ishardi
-
0.773
-
0.566
-
0.022 0.043 -0.012 -0.016 0.273 0.004 -2.898 -2.03 -2.459
Rajshahi 0.214 0.536
-
0.098 -0.077 -0.066 -0.015 0.459
-
0.008 -3.086 -0.75 -3.696
Rangpur 0.87 1.69 0.047 0.175 0.144 -0.006 0.372
-
0.041 0.963 2.84 5.914
Sayedpur 1.149 2.507
-
0.164 -0.247 -0.281 -0.085 3.253 0.002 0 0 -19.859
North
Central
Dhaka 0.013 0.406 0.024 0.044 0.02 -0.02 0.599
-
0.028 -1.727 0.483 1.605
Mymensingh 0.775 1.106
-
0.001 0.011 0.06 -0.01 0.494 0.057 1.667 1.806 5.177
Tangail 6.159 6.447 0.048 -0.165 -0.088 -0.046 3.072 -0.14 -2.653 0.228 -1.841
Faridpur
-
0.568
-
0.028
-
0.054 -0.004 -0.033 0.002 0.428
-
0.001 -0.996 -1.7 -2.392
South East
Comilla
-
0.527
-
0.256
-
0.154 -0.022 -0.069 -0.025 0.526 0.032 -4.132 -4.438 -6.203
Feni
-
0.836 0.26
-
0.007 -0.231 -0.209 -0.019 1.438
-
0.128 -4.982 0.155 -9.211
Maijdicourt 0.704 0.694
-
0.146 -0.005 -0.041 -0.031 0.315 0.13 -3.728 1.609 -3.15
South
West
Chuadanga 3.703 6.148 0.055 -0.306 -0.127 0.029 0.617
-
0.097 4.187 5.889 -2.297
Jessore
-
0.057 0.683 0.049 0.177 0.115 0.024 0.454
-
0.031 3.904 2.163 8.109
Mongla 0.127 1.923 0.125 0.214 0.155 0.024 3.199 0.465 0 0 4.81
South
Central
Chandpur
-
1.419
-
1.731
-
0.143 0.028 -0.113 -0.082 0.062 0.082
-
11.493 -4.356 -9.478
Barisal
-
0.551 -0.57
-
0.032 0.038 0.021 -0.003 -0.05
-
0.013 -2.678 -1.585 -0.382
Khepupara 1.08 5.495 0.008 0.343 0.207 0.034 0.537 0.021 5.42 3.316 12.987
Madaripur 0.345 1.494
-
0.113 -0.36 -0.25 -0.006 1.172 0.046 -2.732 -0.622 -14.39
Patuakhali 0.015 2.355
-
0.185 -0.11 -0.07 -0.067 0.975 0.089 -6.839 -4.634 -8.687
River and
Estuary
Bhola 4.779 5.357
-
0.044 -0.279 -0.183 -0.005
-
0.237
-
0.144 1.965 2.133 -7.096
Hatia 0.979 2.261 0.035 0.121 0.115 0.015 0.656 0.012 4.818 8.308 8.746
Sandwip 1.179 4.644
-
0.189 0.143 -0.014 0.002 0.092 0.073 6.612 9.949 7.349
Eastern
Hilly
Region
Chittagong 0.49 1.232 0.05 0.074 0.058 -0.018 0.383
-
0.029 -2.828 -1.725 1.69
Cox'sbazar 0.589
-
0.179
-
0.074 0.105 0.01 -0.053
-
0.251
-
0.027 -6.201 -4.335 -2.529
Kutubdia 2.967 5.051 0.105 0.525 0.482 0.048 0.408 0.082 5.216 4.053 23.774
Rangamati 1.183 1.245
-
0.007 0.023 0.034 -0.002
-
0.088
-
0.002 3.142 2.566 3.424
Sitakundo
-
0.135 3.313 0.035 0.141 0.199 -0.002 0.01 0.132 0.329 -0.995 8.135
Teknaf 3.222 5.965 0.224 0.391 0.449 0.082 0.312
-
0.029 15.138 10.141 32.636
44
The 31 BMD stations are grouped in to eight hydrological regions depending on their
geographical coordinates. Figure 4.11 -4.16 illustrate regionally averaged precipitation
indices and Table 4.10 presents the summary of their trends.
Figure 4.11: Five years of moving average for CDD.
Figure 4.12: Five years of moving average for CWD.
-6
-4
-2
0
2
4
6
8
1961 1971 1981 1991 2001 2011
Day
s
Years
Hydrological Regionwise 5 years moving average for CDD
5 year MA (NE) 5 year MA (NW) 5 year MA (NC) 5 year MA (SE)
5 year MA (SW) 5 year MA (SC) 5 year MA (EH) 5 years MA (RE)
-1.5
-1
-0.5
0
0.5
1
1.5
1961 1971 1981 1991 2001 2011
Day
s
Year
Hydrological regionwise 5 years moving average for CWD
5 years MA (NE) 5 years MA (NW) 5 years MA (NC) 5 years MA (SE)
5 years MA (SW) 5 years MA (SC) 5 years MA (EH) 5 years MA (RE)
45
Figure 4.13: Five years of moving average for PRCPTOT.
Figure 4.14: Five years of moving average for R95.
-200
-150
-100
-50
0
50
100
150
200
1961 1966 1971 1976 1981 1986 1991 1996 2001 2006 2011
Rai
nfa
ll i
n m
m
Year
Hydrlogical regionwise 5 years moving average for PRCPTOT
5 years MA (NE) 5 years MA (NW)
5 years MA (NC) 5 years MA (SE)
5 years MA (SW) 5 years MA (SC)
5 years MA (EH) 5 years MA (RE)
-50
-40
-30
-20
-10
0
10
20
30
40
50
1950 1960 1970 1980 1990 2000 2010 2020
Rai
nfa
ll i
n m
m
Year
Hydrlogical regionwise 5 years moving average for R95
5 years MA (NE) 5 years MA (NW) 5 years MA (NC)
5 years MA (SE) 5 years MA (SW) 5 years MA (SC)
5 years MA (EH) 5 years MA (RE)
46
Figure 4.15: Five years of moving average for R99.
Figure 4.16: Five years of moving average for R100.
-20
-15
-10
-5
0
5
10
15
20
25
1950 1960 1970 1980 1990 2000 2010 2020
Rai
nfa
ll i
n m
m
Year
Hydrological regionwise 5 years moving avergage for R99
5 years MA (NE) 5 years MA (NW) 5 years MA (NC) 5 years MA (SE)
5 years MA (SW) 5 years MA (SC) 5 years MA (EH) 5 years MA (RE)
-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
1950 1960 1970 1980 1990 2000 2010 2020
Rai
nfa
ll i
n m
m
Year
Hydrological regionwise 5 years moving avergage for R100
5 years moving average (NE) 5 years moving average (NW)5 years moving average (NC) 5 years moving average (SE)5 years moving average (SW) 5 years moving average (SC)
47
Table 4.10. Trend of precipitation indices with respect to hydrological region.
Region Trend of CDD
Y R2
NE 0.0214x - 42.61 0.17
NW 0.1406x - 279.41 0.65
NC 0.0877x - 174.37 0.74
SE 0.0952x - 189.1 0.84
SW 0.1435x - 285.06 0.90
SC 0.1168x - 231.93 0.79
EH 0.1575x - 312.97 0.72
RE 0.0243x - 48.151 0.12
Region Trend of CWD
Y R2
NE 0.0015x - 2.9984 0.01
NW 0.0141x - 28.091 0.60
NC 0.0098x - 19.552 0.43
SE 0.0155x - 30.747 0.60
SW 0.025x - 49.593 0.87
SC 0.0245x - 48.626 0.62
EH 0.0311x - 61.761 0.58
RE 0.0042x - 8.3879 0.04
Region Trend of PRCPTOT
Y R2
NE 0.1576x - 313.48 0.01
NW 2.8438x - 5648.5 0.66
NC 1.6817x - 3341.1 0.56
SE 2.4093x - 4786 0.50
SW 3.7256x - 7399.1 0.85
SC 3.1271x - 6209.4 0.55
EH 6.1201x - 12158 0.72
RE 0.6884x - 1363 0.05
Region Trend of R95
Y R2
NE -0.0422x + 83.765 0.01
NW 0.4822x - 957.46 0.33
NC 0.4236x - 841.64 0.47
SE 0.4359x - 865.77 0.26
SW 0.7616x - 1512.5 0.88
SC 0.597x - 1185.3 0.30
EH 1.6777x - 3333.2 0.67
RE 0.3095x - 613.74 0.10
Region Trend of R99
Y R2
NE 0.0096x - 19.196 0.002
NW 0.224x - 444.93 0.393
NC 0.1833x - 364.27 0.323
SE 0.0944x - 187.59 0.095
SW 0.3304x - 656.2 0.808
SC 0.1086x - 215.64 0.065
EH 0.5675x - 1127.3 0.688
RE 0.2575x - 511.1 0.167
Region Trend of R100
Y R2
NE -6E-05x + 0.1234 0.001
NW 0.0036x - 7.0609 0.514
NC 0.0007x - 1.4174 0.103
SE 0.0026x - 5.1807 0.273
SW 0.0039x - 7.7837 0.899
SC 0.002x - 4.0687 0.141
EH 0.0104x - 20.758 0.626
RE 0.0005x - 1.0501 0.009
The precipitation indices are also analyzed over eight precipitation hydrological region for
better water management practices. In case of regionally averaged trends, almost all the
precipitation indices show positive trend. Table 4.10 represents the regional averaged trends
of precipitation indices for the eight hydrological regions. The total amount of annual
precipitation (PRCPTOT) is increasing in the entire eight regions along with the increasing
trend of consecutive dry days (CDD). It is prominent in Eastern Hilly (EH) region with the
highest increasing trend of 6.12 mm per year of PRCPTOT and 0.157 days per year of CDD.
It reveals that higher amount of rainfall will occur within a short period of time. Annual total
precipitation greater than 95th percentile (R95) also exhibit increasing trend except for the
North East (NE) hydrological region. Again, Rainfall greater than 100 mm (R100) is also
decreasing for NE. Although the trend of PRCPTOT is increasing but the amount of
increasing trend (0.1576 mm pr year) is comparatively less significant than others for this
particular region. CDD is also increasing. So it might be predicted that a longer drier
condition will prevail in North East region, where the highest rainfall occurs at present. South
West (SW) region shows the highest significant change in precipitation indices whereas River
and Estuary (R&E) region indicates least significant variation for precipitation indices.
48
CHAPTER FIVE
CLIMATE INDUCED CHANGES OF RAINFALL EXTREMES
OVER BANGLADESH
5.1 Introduction
Bangladesh is well known for its natural disasters such as cyclone, storm surges, floods,
droughts and river erosions. Precipitation is the major meteorological variable which plays a
significant role in the hydrological cycles as well as these extreme climatic events. Under the
greenhouse warming condition, extreme daily precipitation will be increasing despite the
decrease of mean precipitation. According to Wasimi, climate change has profound impact on
rainfall intensity and variability [1]. Global climate models showed that global warming will
increase the intensity of extreme precipitation events [2]. Alexander et al. [3] has shown that
observed trends of extremes in precipitation is increasing globally and consequently the
heavy precipitation indices are increasing. A recent study shows that extreme rainfall events
over Central India during the summer monsoon period, 1951–2002 has significantly rising in
the frequency and magnitude of extreme rain events (Revadekar et al., 2011) has found that
increasing trends of frequency and intensity of heavy precipitation events over India using
regional climate model at the end of 21st century. Considering the results of the above studies,
this paper investigated changes of extreme precipitation events using the future climate
change projections over Bangladesh.
Bangladesh is located between 20034’N and 26033’N latitudes and 88001’E and 92041’E
longitudes. Bangladesh is bounded by India in the west, north and east, Mayanmar in the
south-east, and the Bay of Bengal in the south. Bangladesh is a flood plain delta of the three
major rivers: the Ganges, the Brahmaputra and the Meghna which meet inside Bangladesh
before discharging to the Bay of Bengal through a single outfall. Most of Bangladesh consists
of extremely low and flat land with elevation ranges between 1 and 5 meters. Coastal areas in
the southern parts of the country are prone to cyclonic and storm surge hazards. Drought has
been found in the northwest parts of the country. Every year roughly 25% of the area has
been normally flooded from the spills of three major rivers during the monsoon season. Flash
floods are normally occurred in the premonsoon (MAM) seasons in the northeast parts of the
country. Changes of precipitation patterns will change the intensity and frequency of these
natural hazards and extreme events which can cause major catastrophes.
5.2 Extreme Indices.
The joint Expert Team (ET) on Climate Change Detection and Indices (ETCCDI) has
recognized a suite of 27 core climate change indices which derived from daily precipitation
and temperature data using user-friendly software package “RClimdex” (available at
49
http://cccma.seos.uvic.ca/ETCCDMI/). From that list, eight extreme precipiation related
indices are used to analysis extreme rainfalls and which are shown in Table 5.1.
Table 5.1: List of extreme climate indices used in the study
Index Description Definition
R20mm Frequencies in days Number of days with precipitation > 20mm
R99 p Frequencies in mm Extremely wet days due to heavy precipitation event
exceeding 95% R99 p Frequencies in mm Very wet days due to heavy precipitation event
exceeding 99% RX1day Intensity in mm One-day maximum precipitation
RX5day Intensity in mm Five-day maximum precipitation
CDD Longest spell in
days Consecutive dry days when precipitation < 1mm
CWD Longest spell in
days Consecutive wet days when precipitation > 1mm
SDII Daily intensity Simple Daily Intensity index in mm/rainy days
5.3 Results and Discussions
PRECIS simulation was made for one baseline period 1980s (1961-90) and three future so called
time-slices for 2020s (2011-2040), 2050s (2041-2070) and 2080s (2071–2100) corresponding to
IPCC-SRES A1B emission scenarios. Table 1 gives the seasonal rainfall statistics for the four time
slices. During the winter season (December to February), mean precipitation will be slightly
decreased for 2020s and then again increased for 2050s and 2080s time slices. Pre-monsoon
(March to May) precipitation also follows same trends as winter precipitation. However, man
monsoon (June to September) and post monsoon (October to November) precipitation will
constantly increase in all three future time slices. Variability of the monsoon precipitation will be
much higher in future than other seasons of the year. At the end of 21st century, mean monsoon
precipitation will be increased about 23% from the present condition (1980s) and variability will be
increased about 70% (212mm).
The spatial patterns of changes of seasonal one day maximum precipitation, RX1 as simulated by
PRECIS for the future time slices of 2050s and 2080s from the baseline period are shown in Figure
2 and Figure 3, respectively. During premonsoon season, precipitation will increase in the northern
parts of the country than the central and south. However, during monsoon and post monsoon
seasons, there will be mixed pattern of changes of seasonal one day maximum precipitation for
2050s. However, changes of one day maximum precipitation will be observed all over the country
during monsoon season for 2080s. During the post monsoon season for 2080s, increase of one day
maximum precipitation will be found in the northern parts and Haor areas of the country.
Spatial patterns of changes of days when precipitation is more than 20 mm over Bangladesh for
three future time slices are shown in Figure 3. Frequency of heavy precipitation (>20mm) shows
increasing trends in future time slices in the northern parts of the country. However, these
increasing trends will be observed during the monsoon season. Days of heavy precipitation will be
increasing more for 2080s than for 2050s and 2020s. Heavy precipitation will be more frequent in
the greater Rangpur areas and Haor areas of Bangladesh.
50
Table 5.2: Mean and standard deviations of precipitation for present and three future time slices.
Mean Precipitation (mm) Standard deviations of precipitation (mm)
DJF MAM JJAS ON Annual DJF MAM JJAS ON Annual
1980s 51 276 918 91 1337 35 114 131 50 141
2020s 44 229 962 112 1347 28 107 159 51 223
2050s 84 288 1012 98 1481 70 130 149 48 257
2080s 67 279 1130 125 1602 42 144 222 65 289
Figure 5.1: Spatial pattern of changes of one day maximum precipitation (RX1) over
Bangladesh during premonsoon, monsoon and post monsoon seasons for 2050s from the
baseline year 1980s, respectively (from left).
Figure 5.2: Spatial pattern of changes of one day maximum precipitation (RX1) over
Bangladesh during pre-monsoon, monsoon and post monsoon seasons for 2080s from the
baseline year 1980s, respectively (from left).
51
Figure 5.3: Spatial distribution of changes of days when precipitation is more than 20 mm
over Bangladesh for future time slices of 2020s, 2050s and 2080s from baseline year 1980s,
respectively (from left).
Figure 5.4: Probability distribution functions (PDFs) of daily intensity (mm/rainy days), Five
days rainfall (mm), number of days when rainfall > 20mm, and consecutive wet days over
Bangladesh.
Probability distribution functions (PDFs) are calculated for indices of precipitation extremes
for baseline, and three future time slices. Figure 5.4 shows the PDFs for (1) daily intensity
(SDII, mm/rainy days); (2) five-day maximum precipitation (RX5, day, mm); (3) count of
days when rainfall exceeds 20mm (R20mm, days) and (4) maximum spell of continuous wet
days (CWD, days) for baseline and three future time slices, respectively.
52
Probabilities of the intensity of precipitation, consecutive 5 day precipitation and heavy
precipitation show positive trends of precipitation extremes for all three future time slices.
Higher changes are found in the 2080s than 2050s and 2020s. On the other hand, probabilities
of consecutive wet days will be reduced in future. The reduction of the probabilities of CWDs
represents than the length of monsoon will be shorter but intensified.
Changes of intensity, duration and frequency of the precipitation extremes are examined
through a number of widely used indicators. Using results from regional climate models,
future changes of extreme climate event has been quantified which would have profound
impacts on human society, natural resources, and ecosystem. It has been found in general, that
intensity and frequency of extreme events will be increasing. Monsoon precipitation exhibits
increasing trends which is an indication towards the wetter climate, with notable increases in
summer monsoon precipitation extremes
53
CHAPTER SIX
CONCLUSION AND RECOMMENDATION
Bangladesh, an agro economy based country is largely depends on the natural precipitation.
Variations of climatic variables both in mean and extreme values along with shape of their
statistical distribution may be detrimental to its economic condition. This study conducted a
detailed exploration to gather information on the effect of climate change on rainfall pattern,
magnitude, frequency, and intensity with a target to reveal its potentially important hydro-
climatic patterns.
This study has identified that the highest increasing precipitation trend can be seen in the EH
region. Rainfall increases at 8.49mm/year for monsoon and 5.12mm/year for the pre-monsoon
season in EH region. Hilly topography of this region along with elevation ranging between
600 and 900m above mean sea level contributes to the heavy rainfall. Although overall
rainfall is increasing in Bangladesh but interestingly, the NE hydrological region exhibits a
considerably different scenario. A remarkable increase in the pre-monsoon season
(5.624mm/year) with decreasing trends for other three seasons (-0.6994 mm/year for the
monsoon, -0.246 mm/year for the post-monsoon and -0.0906 mm/year for the winter seasons)
gives an indication of shifting of the rainy season. A noticeable spatial increase of moderate
rainfall in major parts of Bangladesh is exposed. Five consecutive decadal annual average
rainfalls also revealed the fact that Bangladesh is heading towards more intense rainfalls.
Humidity is also positively correlated with precipitation. Excess humid condition (87%)
prevails in monsoon and then followed by post monsoon season. Pre-monsoon season, which
coincides with summer in Bangladesh, has the least humidity (70%), followed by dry/winter
season. An inverse relationship between sea level pressure and rainfall has been found in this
study. The highest sea level pressure (1015 mbar) exists in dry period and the lowest pressure
(1000 m bar) prevails in the monsoon season, especially in the month of July when usually
the highest rainfalls occurs in the country. A fluctuating condition of sunshine hour with
higher values during May to August and the lowest in October are also seen based on the past
50 years (1961-2010) records. Wind speed also has a positive correlation with rainfall. Low
wind speed prevails in the dry season and then a sharp rise occurs from 2.2 to 4.5 knots in the
pre-monsoon and remains high (4.5-3.5 knots) in the monsoon. It decreases again in the post
monsoon season.
An approximately equal proportion of increasing and decreasing trends of precipitation
indices is found. As precipitation is a highly variable climatic parameter, a very small portion
of rainfall indices is found to be significant. Consecutive Dry Days (CDD) shows the highest
significant increasing trend. Although, 87.5% BMD stations exhibit increasing trend for CDD
but only 25% of them are significant. It is followed by the Simple Daily Intensity Index
(SDII) with a significant negative trend. Afterwards, rainfall greater than 10mm, 20mm,
100mm (R10, R20, R100) and the yearly total precipitation amount (PRCPTOT) reveal few
significant trends. On the other hand, the monthly maximum one day precipitation (RX1) and
the monthly maximum five days precipitation (RX5) exhibit a non-significant increasing
trend at 65% and 75% BMD stations, respectively.
In case of regionally averaged trends, almost all the precipitation indices show positive trends.
The total amount of annual precipitation (PRCPTOT) is increasing for the entire eight regions
along with the increasing trend of the consecutive dry days (CDD). It is prominent in the EH
54
region with the highest increasing trend of 6.12 mm/year of PRCPTOT and 0.157 day/year of
CDD. It indicates that higher amount of rainfall will occur within a shorter period of time.
Annual total precipitation greater than the 95th percentile (R95) also exhibits an increasing
trend except in the NE hydrological region. Again, rainfall greater than 100 mm (R100) is
also decreasing for the NE region. Although the trend of PRCPTOT is increasing, the
increasing trend (0.1576 mm/year) is relatively less significant than others in this particular
region. CDD is also found to be increasing. So, it may be predicted that a longer drier
condition will prevail in the NE region, where the highest rainfall occurs at present. The SW
region shows the highest significant change in precipitation indices whereas the RE region
exhibits the least significant variation in precipitation indices. It is revealed from this study
that short duration high intensity rainfall is increasing in Bangladesh, which is a profound
impact of the changing climate.
Finer resolution of future rainfall data is recommended for further analysis. Although this
study only considers BMD stations but BWDB stations are encouraged for further evaluation.
The more the number of stations will considered, the more clearly the spatial and temporal
variations can be detected.
55
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59
Appendix A
Hydrological region wise variation in seasonal rainfall pattern
60
A.1 Hydrological region wise variation in rainfall pattern for Pre Monsoon season
y = 1.8986x - 3480.9
R² = 0.1338
0
100
200
300
400
500
600
700
800
1950 1960 1970 1980 1990 2000 2010 2020
Rai
nfa
ll i
n m
m
Years
Rainfall Pattern at North West Region for Pre Monsoon Season
Bogra (5 yrs MA) Dinajpur (5 yrs MA)
Ishurdi (5 yrs MA) Rajshahi (5 yrs MA)
Rangpur (5 yrs MA) Mean (5yrs MA)
Linear (Mean (5yrs MA))
y = 1.3683x - 2267.2
R² = 0.0469
0
100
200
300
400
500
600
700
800
1950 1960 1970 1980 1990 2000 2010 2020
Rai
nfa
ll i
n m
m
Year
Rainfall Pattern at North Central for Pre Monsoon Season
Dhaka (5yrs MA) Faridpur (5yrs MA)
Mymensing (5yrs MA) Mean (5 yrs MA)
Linear (Mean (5 yrs MA))
61
y = 5.6243x - 10328
R² = 0.2925
0
200
400
600
800
1000
1200
1400
1600
1950 1960 1970 1980 1990 2000 2010 2020
Rai
nfa
ll i
n m
m
Year
Rainfall Pattern at North East Region for Pre Monsoon Season
Srimongal (5 yrs MA) Sylhet (5 yrs MA)
Mean (5yrs MA) Linear (Mean (5yrs MA))
y = 3.2506x - 5987.1
R² = 0.2231
0
200
400
600
800
1000
1200
1400
1950 1960 1970 1980 1990 2000 2010 2020
Rai
nfa
ll i
n m
m
Years
Rainfall Pattern at South West Region for Pre Monsoon Season
Jessore (5yrs MA) Khulna (5yrs MA)
Satkhira (5yrs MA) Mean (5 yrs MA)
Linear (Mean (5 yrs MA))
62
y = 2.0299x - 3626.9
R² = 0.0937
0
200
400
600
800
1000
1200
1950 1960 1970 1980 1990 2000 2010 2020
Rai
nfa
ll i
n m
m
Years
Rainfall Pattern at South Central Region for Pre Monsoon Season
Barisal (5yrs MA) Chandpur (5yrs MA)
Khepupara (5yrs MA) Madaripur (5yrs MA)
Patuakhali (5yrs MA) Mean (5 yrs MA)
Linear (Mean (5 yrs MA))
63
y = 2.1305x - 3745.5
R² = 0.0896
0
100
200
300
400
500
600
700
800
900
1960 1970 1980 1990 2000 2010
Rai
nfa
ll i
n m
m
Years
Rainfall Pattern at South East Region for Pre Monsoon Season
Comilla (5 yrs MA) Feni ( (5 yrs MA)
Maijdeecourt (5 yrs MA) Mean (5 yrs MA)
Linear (Mean (5 yrs MA))
y = 3.8645x - 7220.9
R² = 0.2165
0
200
400
600
800
1000
1950 1960 1970 1980 1990 2000 2010 2020
Rai
nfa
ll i
n m
m
Year
Rainfall Pattern at River and Estuary Region for Pre Monsoon
Season
Bhola (5yrs MA) Hatiya(5 yrs MA)
Sandwip (5 yrs MA) Mean (5 yrs MA)
Linear (Mean (5 yrs MA))
64
y = 5.1241x - 9732.8
R² = 0.6117
0
100
200
300
400
500
600
700
800
1950 1960 1970 1980 1990 2000 2010 2020
Rai
nfa
ll i
n m
m
Year
Rainfall Pattern at Eastern Hilly Region for Pre Monsoon Season
Chittagong (5yrs MA) CoxsBazar (5yrs MA)
Rangamati (5yrs MA) Sitakunda(5yrs MA)
Teknaf (5yrs MA) Mean (5 yrs MA)
Linear (Mean (5 yrs MA))
65
A.2 Hydrological region wise variation in rainfall pattern for Monsoon Season
y = 4.2578x - 7165
R² = 0.2151
0
500
1000
1500
2000
2500
1950 1960 1970 1980 1990 2000 2010 2020
Rai
nfa
ll i
n m
m
Years
Rainfall Pattern at North West Hydrological Region for Monsoon
Season
Bogra (5 yrs MA) Dinajpur (5 yrs MA)
Ishurdi (5 yrs MA) Rajshahi (5 yrs MA)
Rangpur (5 yrs MA) Mean (5yrs MA)
Linear (Mean (5yrs MA))
y = 3.2861x - 5189.4
R² = 0.1544
0
200
400
600
800
1000
1200
1400
1600
1800
2000
1950 1960 1970 1980 1990 2000 2010 2020
Rai
nfa
ll i
n m
m
Years
Rainfall Pattern at North Central for Monsoon Season
Dhaka (5yrs MA) Faridpur (5yrs MA)
Mymensing (5yrs MA) Mean (5 yrs MA)
Linear (Mean (5 yrs MA))
66
y = -0.6994x + 3432.6
R² = 0.0052
0
500
1000
1500
2000
2500
3000
3500
4000
1950 1960 1970 1980 1990 2000 2010 2020
Rai
nfa
ll i
n m
m
Year
Rainfall Pattren at North East Region for Monsoon Season
Srimongal (5 yrs MA) Sylhet (5 yrs MA)
Mean (5yrs MA) Linear (Mean (5yrs MA))
67
Mean Trend:
y = 7.052x - 12799
R² = 0.4596
0
200
400
600
800
1000
1200
1400
1600
1800
2000
1950 1960 1970 1980 1990 2000 2010 2020
Rai
nfa
ll i
n m
m
Years
Rainfall Pattern at South West Region for Monsoon Season
Jessore (5yrs MA) Khulna (5yrs MA)
Satkhira (5yrs MA) Mean (5 yrs MA)
Linear (Mean (5 yrs MA))
y = 5.8759x - 9923.1
R² = 0.1453
0
500
1000
1500
2000
2500
3000
3500
1950 1960 1970 1980 1990 2000 2010 2020
Ra
infa
ll i
n m
m
Years
Rainfall Pattern at South Central Region for Monsoon Season
Barisal (5yrs MA) Chandpur (5yrs MA)
Khepupara (5yrs MA) Madaripur (5yrs MA)
Patuakhali (5yrs MA) Mean (5 yrs MA)
Linear (Mean (5 yrs MA))
68
y = -2.2481x + 6071
R² = 0.0247
0
500
1000
1500
2000
2500
3000
1960 1970 1980 1990 2000 2010
Rai
nfa
ll i
n m
m
Year
Rainfall Pattern at South East Region for Monsoon Season
Comilla (5 yrs MA) Feni ( (5 yrs MA)
Maijdeecourt (5 yrs MA) Mean (5 yrs MA)
Linear (Mean (5 yrs MA))
y = 1.2798x - 386.48
R² = 0.0037
0
500
1000
1500
2000
2500
3000
3500
4000
1950 1960 1970 1980 1990 2000 2010 2020
Rai
nfa
ll i
n m
m
Year
Rainfall Pattern at River and Estuary Region for Monsoon Season
Bhola (5yrs MA) Hatiya(5 yrs MA)
Sandwip (5 yrs MA) Mean (5 yrs MA)
Linear (Mean (5 yrs MA))
69
Mean Trend:
y = 8.4946x - 14476
R² = 0.27330
500
1000
1500
2000
2500
3000
3500
4000
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010
Rai
nfa
ll i
n m
m
Year
Rainfall Pattern at Eastern Hilly Region for Monsoon Season
Chittagong (5yrs MA) CoxsBazar (5yrs MA) Rangamati (5yrs MA)
Sitakunda(5yrs MA) Teknaf (5yrs MA) Mean (5yrs MA)
Linear (Mean (5yrs MA))
70
A.3 Hydrological region wise variation in rainfall pattern for Post Monsoon season
y = 1.9921x - 3808.6
R² = 0.3116
0
50
100
150
200
250
300
350
400
450
1950 1960 1970 1980 1990 2000 2010 2020
Rai
nfa
ll i
n m
m
Year
Rainfall Pattern at North West Region for Post Monsoon Season
Bogra (5 yrs MA) Dinajpur (5 yrs MA)
Ishurdi (5 yrs MA) Rajshahi (5 yrs MA)
Rangpur (5 yrs MA) Mean (5yrs MA)
Linear (Mean (5yrs MA))
y = 1.4022x - 2581
R² = 0.2237
0
50
100
150
200
250
300
350
400
1950 1960 1970 1980 1990 2000 2010 2020
Rai
nfa
ll i
n m
m
Years
Rainfal Pattern at North Central for Post Monsoon Season
Dhaka (5yrs MA) Faridpur (5yrs MA)
Mymensing (5yrs MA) Mean (5 yrs MA)
Linear (Mean (5 yrs MA))
71
y = -0.246x + 712.65
R² = 0.0049
0
50
100
150
200
250
300
350
400
450
500
1950 1960 1970 1980 1990 2000 2010 2020
Rai
nfa
ll i
n m
m
Years
Rainfall Pattern at North East Region for Post Monsson Season
Srimongal (5 yrs MA) Sylhet (5 yrs MA)
Mean (5yrs MA) Linear (Mean (5yrs MA))
y = 1.3742x - 2591.3
R² = 0.2398
0
50
100
150
200
250
300
350
1950 1960 1970 1980 1990 2000 2010 2020
Rai
nfa
ll i
n m
m
Years
Rainfall Pattern at South West Region for Post Monsoon Season
Jessore (5yrs MA) Khulna (5yrs MA)
Satkhira (5yrs MA) Mean (5 yrs MA)
Linear (Mean (5 yrs MA))
72
y = 1.1892x - 2110.8
R² = 0.0634
0
100
200
300
400
500
600
1950 1960 1970 1980 1990 2000 2010 2020
Rai
nfa
ll i
n m
m
Year
Rainfall Pattern at South Central Region for Post Monsoon Season
Barisal (5yrs MA) Chandpur (5yrs MA)
Khepupara (5yrs MA) Madaripur (5yrs MA)
Patuakhali (5yrs MA) Mean (5 yrs MA)
Linear (Mean (5 yrs MA))
y = -0.1554x + 531.4
R² = 0.0013
0
50
100
150
200
250
300
350
400
450
500
1960 1970 1980 1990 2000 2010
Rai
nfa
ll i
n m
m
Years
Rainfall Pattern at South East Region for Post Monsoon Season
Comilla (5 yrs MA) Feni ( (5 yrs MA)
Maijdeecourt (5 yrs MA) Mean (5 yrs MA)
Linear (Mean (5 yrs MA))
73
Mean Trend:
y = 1.8088x - 3315.1
R² = 0.0697
0
100
200
300
400
500
600
700
1950 1960 1970 1980 1990 2000 2010 2020
Rai
nfa
ll i
n m
m
Year
Rainfall Pattern at River and Estuary Region for Post Monsoon
Season
Bhola (5yrs MA) Hatiya(5 yrs MA)
Sandwip (5 yrs MA) Mean (5 yrs MA)
Linear (Mean (5 yrs MA))
y = 1.3328x - 2374.8
R² = 0.0854
0
100
200
300
400
500
600
700
1960 1970 1980 1990 2000 2010
Rai
nfa
ll i
n m
m
Years
Rainfall Pattern at Eastern Hilly Region for Post Monsoon Season
Chittagong (5yrs MA) CoxsBazar (5yrs MA) Rangamati (5yrs MA)
Sitakunda(5yrs MA) Teknaf (5yrs MA) Mean (5 yrs MA)
Linear (Mean (5 yrs MA))
74
A.4 Hydrological region wise variation in rainfall pattern for winter season
y = 0.1038x - 177.77
R² = 0.0114
0
20
40
60
80
100
120
140
1950 1960 1970 1980 1990 2000 2010 2020
Rai
nfa
ll i
n m
m
Year
Rainfall pattern at North West Region for Winter Season
Bogra (5 yrs MA) Dinajpur(5yrsMA) Ishurdi( 5 yrs MA)
Rajshahi(5yrs MA) Rangpur(5 yrs MA) Mean (5yrs MA)
Linear (Mean (5yrs MA))
y = 0.2631x - 486.35
R² = 0.0516
0
10
20
30
40
50
60
70
80
90
100
1950 1960 1970 1980 1990 2000 2010 2020
Rai
nfa
ll i
n m
m
Years
Rainfall Pattern at North Central Region for Winter Season
Dhaka (5yrs MA) Faridpur (5yrs MA)
Mymensing (5yrs MA) Mean (5 yrs MA)
Linear (Mean (5 yrs MA))
75
y = -0.0906x + 230.59
R² = 0.0043
0
20
40
60
80
100
120
1950 1960 1970 1980 1990 2000 2010 2020
Rai
nfa
ll i
n m
m
Years
Rainfall Pattern at North East Region for Winter Season
Srimongal (5 yrs MA) Sylhet (5 yrs MA)
Mean (5yrs MA) Linear (Mean (5yrs MA))
y = -0.081x + 199.73
R² = 0.0052
0
20
40
60
80
100
120
140
1960 1970 1980 1990 2000 2010
Rai
nfa
ll i
n m
m
Years
Rainfall Pattern at River and Estuary Region for Winter Season
Bhola (5yrs MA) Hatiya(5 yrs MA)
Sandwip (5 yrs MA) Mean (5 yrs MA)
Linear (Mean (5 yrs MA))
76
y = 0.3174x - 599
R² = 0.0698
0
10
20
30
40
50
60
70
80
90
100
1960 1970 1980 1990 2000 2010
Rai
nfa
ll i
n m
m
Year
Rainfall Pattern at Eastern Hilly Region for Winter Season
Chittagong (5yrs MA) CoxsBazar (5yrs MA)Rangamati (5yrs MA) Sitakunda(5yrs MA)Teknaf (5yrs MA) Mean (5yrs MA)Linear (Mean (5yrs MA))