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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) 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 i 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 ii 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 iii 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 iv 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 v 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 GIS IPCC Exploratory Data Analysis Geographic Information System 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 Simple Daily Intensity Index Special Report on Emission Scenarios South Asian Association for Regional Cooperation SAARC Meteorological Research Centre SDII SRES SAARC SMRC vi 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). vii 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 viii 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 (JuneSeptember) 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 ix 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 nonsignificant 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. x 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). 1 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 were1. 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. 2 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 3 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). 4 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 5 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 southeast 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. 6 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 monthlyto-seasonal rainfall totals. The question was raised as to whether such rainfall events may be related to human-induced climate change. 7 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). 8 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). 9 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. 10 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, probabilitybased 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). 11 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 subdivisions 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 12 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). 13 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. 5. 6. 7. Daily winter rainfall>100mm Consecutive dry days>10 in Monsoon Weather stations>35% missing data 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). 14 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 19611970, and then 1971-1980, 1981-1990, 1991-2000 and finally 2001-2010 have been plotted to view decadal change in rainfall distribution. 15 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. 16 Table 3.1: Precipitation Indices ID Definitions Indicator name Units RX1day Max 1-day precipitation amount Monthly maximum 1-day precipitation mm Rx5day Max 5-day precipitation amount SDII Simple daily intensity index 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 CDD Consecutive dry days CWD Consecutive wet 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 Monthly maximum consecutive 5-day precipitation Annual total precipitation divided by the number of wet days (defined as PRCP>=1.0mm) in the year mm mm/d Annual count of days when PRCP>=nn mm, nn is user defined threshold Maximum number of consecutive days with RR<1mm Maximum number of consecutive days with RR>=1mm Days Days Days Table 3.2: Temperature Indices ID Indicator name su25 tr20 Summer Days Tropical nights Growing Season Length Max Tmax Max T min Warm days Warm nights Warm spell duration indicator Diurnal temperature range Min Tmax Min Tmin Cold days Cold Nights Cold spell duration indicator gsl txx tnx tx90p tn90p wsdi dtr txn tnn tx10p tn10p csdi Definitions Units Annual Count when TX (daily maximum) > 25◦C Annual Count when TN (daily minimum) > 20◦C 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 Monthly maximum value of daily maximum temperature Monthly maximum value of daily minimum temperature Percentage of days when tx>90th Percentile Percentage of days when tn>90th Percentile Annual count of days with at least six consecutive days when tx>90th percentile Days Days Monthly mean difference between tx and tn mm Monthly minimum value of daily maximum temperature Monthly minimum value of daily minimum temperature Percentage of days when tx<10th Percentile Percentage of days when tn<10th Percentile Annual count of days with at least six consecutive days when tn<10th percentile ◦C ◦C Days Days Days ◦C ◦C Days Days Days Days 17 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 = ∑nt 𝑖=1( x i,t –𝑥̅ i) / nt (3.1) Where, xr,t = regionally averaged index at year t; x i,t = index for station i at year t , 𝑥̅ 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. 18 Figure 3.3: Hydrological region of Bangladesh with rainfall stations of BMD. 19 Table 3.3: The list of 34 BMD stations with their geographical coordinates. Station Longit ude Latitud e Altitud e Station ID Longit ude Latitud e Barisal 90.37 22.72 2.1 11704 Madaripur Bhola 90.65 22.68 4.3 11706 Maijdeecourt 90.18 23.17 7 11513 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 Station Altitud e Station ID 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 highresolution 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. 20 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. 21 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. 22 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. 23 Table 4.1: Season wise Rainfall trend in Bangladesh. Hydrological Region North West North East North Central South West South East South Central River and Estuary Eastern Hilly Hydrological Region North West North East North Central South West South East South Central River and Estuary Eastern Hilly Pre Monsoon Season Y R2 Monsoon Season Y R2 y = 1.8986x - 3480.9 R² = 0.1338 y = 4.2578x - 7165 R² = 0.2151 y = 5.6243x - 10328 R² = 0.2925 y = -0.6994x + 3432.6 R² = 0.0052 y = 1.3683x - 2267.2 R² = 0.0469 y = 3.2861x - 5189.4 R² = 0.1544 y = 3.2506x - 5987.1 R² = 0.2231 y = 7.052x - 12799 R² = 0.4596 y = 2.1305x - 3745.5 R² = 0.0896 y = -2.2481x + 6071 R² = 0.0247 y = 2.0299x - 3626.9 R² = 0.0937 y = 5.8759x - 9923.1 R² = 0.1453 y = 3.8645x - 7220.9 R² = 0.2165 y = 1.2798x - 386.48 R² = 0.0037 y = 5.1241x - 9732.8 R² = 0.6117 y = 8.4946x - 14476 R² = 0.2733 Post Monsoon Season Y R2 Winter Season Y R2 y = 1.9921x - 3808.6 R² = 0.3116 y = 0.1038x - 177.77 R² = 0.0114 y = -0.246x + 712.65 R² = 0.0049 y = -0.0906x + 230.59 R² = 0.0043 y = 1.4022x - 2581 R² = 0.2237 y = 0.2631x - 486.35 R² = 0.0516 y = 1.3742x - 2591.3 R² = 0.2398 y = 0.6784x - 1286.8 R² = 0.0844 y = -0.1554x + 531.4 R² = 0.0013 y = 0.1286x - 218.67 R² = 0.0115 y = 1.1892x - 2110.8 R² = 0.0634 y = 0.1037x - 167.71 R² = 0.0058 y = 1.8088x - 3315.1 R² = 0.0697 y = -0.081x + 199.73 R² = 0.0052 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. 24 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 Barisal Bhola Bogra Chandpur Chittagong (Patenga) Comilla CoxsBazar Dhaka Dinajpur Faridpur Feni Hatiya Ishurdi Jessore Khepupara Khulna Madaripur Maijdeecourt Mymensing Patuakhali Rajshahi Rangamati Rangpur Sandwip Satkhira Sitakunda Srimongal Sylhet Teknaf Longitude Latitude 90.37 22.72 90.65 22.68 89.37 24.85 90.7 23.23 91.82 91.18 91.97 90.38 88.68 89.85 91.42 91.1 89.03 89.33 90.23 89.53 90.18 91.1 90.42 90.33 88.7 92.15 89.27 91.43 89.08 91.7 91.73 91.88 92.3 22.35 23.43 21.45 23.78 25.65 23.93 23.03 22.45 24.15 23.2 21.98 22.78 23.17 22.87 24.73 22.33 24.37 22.63 25.73 22.48 22.72 22.63 24.3 24.9 20.87 19611970 1964.20 2088.80 1496.60 1909.33 19711980 2056.10 2558.22 1765.11 1612.25 19811990 2188.00 2410.40 1873.60 2586.10 19912000 2059.20 2182.20 1819.90 1982.90 20012010 2069.49 2234.83 1687.51 1957.61 2718.70 2400.33 4023.80 1967.80 1726.60 1636.30 2640.90 1873.22 3126.22 2079.67 2960.30 2052.30 3687.60 2203.80 2115.90 2020.40 3116.90 2739.56 1614.60 2494.60 2489.50 1854.40 3486.10 2119.30 2472.10 2758.30 1547.30 2418.50 2423.20 3381.70 1766.20 3374.70 2326.56 4509.30 3865.90 2984.30 2178.70 3778.80 2087.70 1989.10 1833.10 3131.70 3028.60 1521.20 2240.50 2945.90 1698.00 3000.80 2010.40 2302.40 2653.30 1496.10 2756.20 2155.80 3348.40 1748.30 3136.30 2253.60 4033.10 4481.60 2617.88 2061.40 3854.37 2086.94 2035.32 1697.79 2730.93 3273.07 1400.46 2435.27 2832.11 1873.66 2767.62 2133.10 2279.26 2594.04 1374.91 2499.68 2350.01 3982.03 1763.48 3130.48 2490.54 3863.23 4240.36 2837.60 1470.86 1965.70 1509.11 3002.00 1986.80 1482.60 2605.13 1826.67 3103.60 1642.57 2378.56 3931.40 1872.50 2490.57 3172.00 1918.63 1825.33 2539.57 1943.67 2437.13 1987.00 1939.71 2224.00 1627.22 2392.90 1876.78 3677.33 1559.22 2414.33 2091.22 3783.44 2530.25 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. 25 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 26 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. 27 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 North East North West North Central South East South West Stations SDII Sreemongal -0.041 Sylhet -0.043 Bogura Dinajpur Ishardi Hydrologic Region Stations SDII Barisal -0.032 Khepupara 0.008 0.011 Madaripur -0.113 -0.01 Patuakhali -0.185 Bhola -0.044 South Central -0.022 River and Estuary Rajshahi -0.098 Rangpur 0.047 Sandwip Dhaka 0.024 Chittagong 0.05 Mymensingh -0.001 Cox'sbazar -0.074 Chandpur -0.143 Rangamati -0.007 Comilla -0.154 Sitakundo 0.035 Feni -0.007 Teknaf 0.224 Maijdicourt -0.146 Faridpur -0.054 Jessore 0.049 Eastern Hilly Region Hatia 0.035 -0.189 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 leastsquares 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. 28 Hydrological Regionwise 5 years Moving Average for SDII 1.5 SDII (mm/day) 1 0.5 0 -0.5 -1 -1.5 1950 1960 1970 1980 1990 2000 2010 2020 Year 5 years moving average (NE) 5 years moving average (NC) 5 years moving average (SW) 5 years moving average (EH) 5 years moving average (NW) 5 years moving average (SE) 5 years moving average (SC) 5 years moving average (RE) Figure 4.2: five years moving average for SDII concerning eight hydrological regions Table: 4.4: Trends of SDII for different hydrologic region Trends of SDII Hydrological Region 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. 29 1971 to 2000 2010 to 2040 2040 to 2070 2070 to 2100 0.5 0.45 0.4 Probability 0.35 0.3 0.25 0.2 0.15 0.1 0.05 0 8 9 10 11 12 13 14 SDII( mm/rainy day) 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 1971-2000 2010-2040 2040-2070 2070-2100 Trend of probability for SDII y R2 -0.0856x + 1.1187 0.5941 -0.0247x + 0.487 0.9181 0.005x + 0.1916 0.9181 -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 30 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. Cold Weather Warm Weather Temperature Indicators su25 tr20 gsl txx tnx tx90p tn90p wsdi dtr txn tnn tx10p tn10p csdi Dry Weather Wet Weather Precipitation Indicators Positive Trend 89.66 82.76 93.10 55.17 72.41 17.24 17.24 17.24 58.62 20.69 55.17 0.00 0.00 6.90 Positive Trend RX1 day RX5 Day SDII R10mm R20mm R100mm CWD R95P R99P PRCPTOT 51.72413793 62.06896552 31.03448276 65.51724138 55.17241379 31.03448276 51.72413793 41.37931034 51.72413793 55.17241379 CDD 86.20689655 Positive Significant Trend 51.72 41.38 13.79 31.03 13.79 13.79 17.24 17.24 20.69 3.45 31.03 3.45 Positive Significant Trend Negative Trend -10.34 -17.24 -6.90 -44.83 -27.59 -82.76 -82.76 -82.76 -41.38 -79.31 -44.83 -100.00 -100.00 -93.10 Negative Trend 3.448275862 10.34482759 -48.27586207 -37.93103448 -68.96551724 -34.48275862 -44.82758621 -68.96551724 -48.27586207 -58.62068966 -48.27586207 -44.82758621 24.13793103 -13.79310345 10.34482759 10.34482759 6.896551724 6.896551724 3.448275862 Negative Significant Trend -3.45 -13.79 -3.45 -20.69 -31.03 -20.69 -10.34 -10.34 Negative Significant Trend -20.68965517 -3.448275862 -3.448275862 31 Precipitation and Temperature Indicators Negative Significant Trend Negative Trend Positive Significant Trend Positive Trend % of stations with Negative % of stations with Positive Trends Trends 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. 32 900 35 800 30 Rainfall in mm 700 25 600 500 20 400 15 300 10 200 Temperature ◦C 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. 5 100 0 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Month Average Rainfalls Average Temp 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. 33 100 90 80 70 60 50 40 30 20 10 0 800 Rainfall in mm 700 600 500 400 300 200 100 0 Humidity in percentage 900 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Month Average Rainfalls Average Humidity Figure 4.6: Relationship between humidity and rainfalls. 900 800 700 600 500 400 300 200 100 0 1020 1015 1010 1005 1000 995 Sea Level Pressure mbar Rainfall in mm 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. 990 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Month Average Rainfalls Average Sea Level Pressure 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. 34 9 800 8 700 7 600 6 500 5 400 4 300 3 200 2 100 1 0 Sun shine hour Rainfall in mm 900 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Month Average Rainfalls Average Sunshine Hour 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 (4.53.5 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). 35 5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 800 Rainfall in mm 700 600 500 400 300 200 100 0 Wind Speed m/s 900 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Month Average Rainfalls Average Wind Speed Figure 4.9: Relationship between wind speed and rainfalls. 36 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. 37 Table 4.7: Annual variability of rainfalls and rainy days Annual Average Rainfall Standard Deviation CV of Rainfall Annual Average Raindays Standard Deviation CV of Raindays 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 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 Hydrologic Region Stations North East North West North Central South East South West South Central River and Estuary Eastern Hilly Region 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 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 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 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 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 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 38 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. 39 Table 4.8. Annual Precipitations, Probabilities and Return Period for Fifty years (1961-2010) for Bangladesh Rank Year Annual Annual Probability, P Return Precipitation (mm) Precipitation (cm) 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 40 Rank Year 41 42 43 44 45 46 47 48 49 50 1980 1966 1975 2010 1961 1994 1979 1962 1992 1972 Annual Precipitation (mm) 2164 2136 2112 2062 2038 2020 1986 1947 1891 1735 Annual Precipitation (cm) 216 214 211 206 204 202 199 195 189 173 Probability, P 81 83 85 87 89 91 93 95 97 99 Return Period, T 1.2 1.2 1.2 1.1 1.1 1.1 1.1 1.1 1.0 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. 41 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. 42 Table 4.9: Trends of precipitation indices for individual stations in Bangladesh (1961-2010) Hydrologic Region North East RX1 day RX5 Day 0.738 0.447 Ishardi 0.396 0.394 0.091 0.744 0.773 Rajshahi 0.214 0.536 Rangpur 0.87 1.69 Sayedpur 1.149 2.507 Dhaka 0.013 0.406 Mymensingh Tangail 1.106 6.447 0.028 0.256 Feni 0.775 6.159 0.568 0.527 0.836 Maijdicourt 0.704 0.694 0.024 0.001 0.048 0.054 0.154 0.007 0.146 Chuadanga 6.148 0.683 1.923 1.731 Barisal Khepupara 3.703 0.057 0.127 1.419 0.551 1.08 Madaripur 0.345 1.494 Patuakhali 0.015 2.355 Bhola Hatia 4.779 0.979 5.357 2.261 Sandwip 1.179 4.644 Chittagong 0.49 Cox'sbazar Kutubdia 0.589 2.967 1.232 0.179 5.051 Rangamati Sitakundo 1.183 0.135 Teknaf 3.222 Stations Sreemongal Sylhet Bogura Dinajpur North West North Central Faridpur Comilla South East South West Jessore Mongla Chandpur South Central River and Estuary Eastern Hilly Region SDII 0.041 0.043 R10mm R20mm R100mm CDD CWD R95P -0.018 -0.026 -0.003 0.336 0.073 -1.042 1.878 -0.258 -0.084 -0.034 -0.013 0.583 -4.868 -2.422 -5.36 0.011 -0.01 0.022 0.098 0.15 0.151 0.077 0.048 0.004 0.044 0.701 0.471 -0.07 0.042 0.104 -0.033 5.82 -1.328 3.543 3.856 9.687 0.043 -0.012 -0.016 0.273 -2.898 -2.03 -2.459 -0.077 -0.066 -0.015 0.459 -3.086 -0.75 -3.696 0.175 0.144 -0.006 0.372 0.004 0.008 0.041 0.963 2.84 5.914 -0.247 -0.281 -0.085 3.253 0 0 -19.859 0.044 0.02 -0.02 0.599 0.002 0.028 -1.727 0.483 1.605 0.011 -0.165 0.06 -0.088 -0.01 -0.046 0.494 3.072 1.667 -2.653 1.806 0.228 5.177 -1.841 -0.004 -0.033 0.002 0.428 0.057 -0.14 0.001 -0.996 -1.7 -2.392 -0.022 -0.069 -0.025 0.526 -4.132 -4.438 -6.203 -0.231 -0.209 -0.019 1.438 0.032 0.128 -4.982 0.155 -9.211 -0.005 -0.041 -0.031 0.315 -3.728 1.609 -3.15 0.055 -0.306 -0.127 0.029 0.617 4.187 5.889 -2.297 0.049 0.125 0.143 0.032 0.008 0.113 0.185 0.044 0.035 0.189 0.177 0.214 0.115 0.155 0.024 0.024 0.454 3.199 0.13 0.097 0.031 0.465 2.163 0 8.109 4.81 0.028 -0.113 -0.082 0.062 -4.356 -9.478 0.038 0.343 0.021 0.207 -0.003 0.034 -0.05 0.537 0.082 0.013 0.021 3.904 0 11.493 -2.678 5.42 -1.585 3.316 -0.382 12.987 -0.36 -0.25 -0.006 1.172 0.046 -2.732 -0.622 -14.39 -0.11 -0.07 -0.067 -4.634 -8.687 -0.183 0.115 -0.005 0.015 0.089 0.144 0.012 -6.839 -0.279 0.121 0.975 0.237 0.656 1.965 4.818 2.133 8.308 -7.096 8.746 0.143 -0.014 0.002 0.092 6.612 9.949 7.349 0.074 0.058 -0.018 -1.725 1.69 0.105 0.525 0.01 0.482 -0.053 0.048 -6.201 5.216 -4.335 4.053 -2.529 23.774 0.023 0.034 -0.002 0.383 0.251 0.408 0.088 -2.828 1.245 0.05 0.074 0.105 0.007 0.073 0.029 0.027 0.082 0.002 3.142 2.566 3.424 3.313 0.035 0.141 0.199 -0.002 0.01 0.329 -0.995 8.135 5.965 0.224 0.391 0.449 0.082 0.312 15.138 10.141 32.636 0.07 1.066 0.566 0.26 -0.57 5.495 0.047 0.164 0.132 0.029 R99P PRCPTOT Note: Bold shaded values represent significant trends. 43 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. Hydrological Regionwise 5 years moving average for CDD 8 6 Days 4 2 0 -2 -4 -6 1961 1971 1981 1991 2001 2011 Years 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) Figure 4.11: Five years of moving average for CDD. Hydrological regionwise 5 years moving average for CWD 1.5 1 Days 0.5 0 -0.5 -1 -1.5 1961 1971 1981 1991 2001 2011 Year 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) Figure 4.12: Five years of moving average for CWD. 44 Hydrlogical regionwise 5 years moving average for PRCPTOT 200 150 Rainfall in mm 100 50 0 -50 -100 -150 -200 1961 1966 1971 1976 1981 1986 Year 1991 5 years MA (NE) 5 years MA (NC) 5 years MA (SW) 5 years MA (EH) 1996 2001 2006 2011 5 years MA (NW) 5 years MA (SE) 5 years MA (SC) 5 years MA (RE) Figure 4.13: Five years of moving average for PRCPTOT. Rainfall in mm Hydrlogical regionwise 5 years moving average for R95 50 40 30 20 10 0 -10 -20 -30 -40 -50 1950 1960 1970 1980 1990 2000 2010 2020 Year 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) Figure 4.14: Five years of moving average for R95. 45 Hydrological regionwise 5 years moving avergage for R99 25 20 Rainfall in mm 15 10 5 0 -5 -10 -15 -20 1950 1960 1970 1980 1990 2000 2010 2020 Year 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) Figure 4.15: Five years of moving average for R99. Hydrological regionwise 5 years moving avergage for R100 0.4 0.3 Rainfall in mm 0.2 0.1 0 -0.1 -0.2 -0.3 -0.4 1950 1960 1970 1980 1990 2000 2010 2020 Year 5 years moving average (NE) 5 years moving average (NC) 5 years moving average (SW) 5 years moving average (NW) 5 years moving average (SE) 5 years moving average (SC) Figure 4.16: Five years of moving average for R100. 46 Table 4.10. Trend of precipitation indices with respect to hydrological region. Region NE NW NC SE SW SC EH RE Region NE NW NC SE SW SC EH RE Trend of CDD 2 Y 0.0214x - 42.61 0.1406x - 279.41 0.0877x - 174.37 0.0952x - 189.1 0.1435x - 285.06 0.1168x - 231.93 0.1575x - 312.97 0.0243x - 48.151 Trend of R95 R 0.17 0.65 0.74 0.84 0.90 0.79 0.72 0.12 Y -0.0422x + 83.765 0.4822x - 957.46 0.4236x - 841.64 0.4359x - 865.77 0.7616x - 1512.5 0.597x - 1185.3 1.6777x - 3333.2 0.3095x - 613.74 R2 0.01 0.33 0.47 0.26 0.88 0.30 0.67 0.10 Region NE NW NC SE SW SC EH RE Region NE NW NC SE SW SC EH RE Trend of CWD 2 Y R 0.0015x - 2.9984 0.01 0.0141x - 28.091 0.60 0.0098x - 19.552 0.43 0.0155x - 30.747 0.60 0.025x - 49.593 0.87 0.0245x - 48.626 0.62 0.0311x - 61.761 0.58 0.0042x - 8.3879 0.04 Trend of R99 Y 0.0096x - 19.196 0.224x - 444.93 0.1833x - 364.27 0.0944x - 187.59 0.3304x - 656.2 0.1086x - 215.64 0.5675x - 1127.3 0.2575x - 511.1 R2 0.002 0.393 0.323 0.095 0.808 0.065 0.688 0.167 Region NE NW NC SE SW SC EH RE Region NE NW NC SE SW SC EH RE Trend of PRCPTOT Y 0.1576x - 313.48 2.8438x - 5648.5 1.6817x - 3341.1 2.4093x - 4786 3.7256x - 7399.1 3.1271x - 6209.4 6.1201x - 12158 0.6884x - 1363 Trend of R100 Y -6E-05x + 0.1234 0.0036x - 7.0609 0.0007x - 1.4174 0.0026x - 5.1807 0.0039x - 7.7837 0.002x - 4.0687 0.0104x - 20.758 0.0005x - 1.0501 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. 47 R2 0.01 0.66 0.56 0.50 0.85 0.55 0.72 0.05 R2 0.001 0.514 0.103 0.273 0.899 0.141 0.626 0.009 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 48 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 R20mm R99 p R99 p RX1day RX5day CDD CWD SDII Description Frequencies in days Frequencies in mm Frequencies in mm Intensity in mm Intensity in mm Longest spell in days Longest spell in days intensity Daily Definition Number of days with precipitation > 20mm Extremely wet days due to heavy precipitation event exceeding 95%due to heavy precipitation event Very wet days exceedingmaximum 99% One-day precipitation Five-day maximum precipitation Consecutive dry days when precipitation < 1mm Consecutive wet days when precipitation > 1mm 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. 49 Table 5.2: Mean and standard deviations of precipitation for present and three future time slices. 1980s 2020s 2050s 2080s Mean Precipitation (mm) DJF MAM JJAS 51 276 918 44 229 962 84 288 1012 67 279 1130 ON 91 112 98 125 Annual 1337 1347 1481 1602 Standard deviations of precipitation (mm) DJF MAM JJAS ON Annual 35 114 131 50 141 28 107 159 51 223 70 130 149 48 257 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). 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). 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. 51 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 52 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 hydroclimatic 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 53 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. 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(2004), RClimDex (1.0) User Manual, Climate Research Branch Environment Canada, Downsview, Ontario, Canada 58 Appendix A Hydrological region wise variation in seasonal rainfall pattern 59 A.1 Hydrological region wise variation in rainfall pattern for Pre Monsoon season Rainfall Pattern at North West Region for Pre Monsoon Season 800 Rainfall in mm 700 y = 1.8986x - 3480.9 R² = 0.1338 600 500 400 300 200 100 0 1950 1960 1970 1980 1990 2000 2010 2020 Years Bogra (5 yrs MA) Ishurdi (5 yrs MA) Rangpur (5 yrs MA) Linear (Mean (5yrs MA)) Dinajpur (5 yrs MA) Rajshahi (5 yrs MA) Mean (5yrs MA) Rainfall Pattern at North Central for Pre Monsoon Season 800 y = 1.3683x - 2267.2 R² = 0.0469 700 Rainfall in mm 600 500 400 300 200 100 0 1950 1960 1970 1980 Dhaka (5yrs MA) Mymensing (5yrs MA) 1990 Year 2000 2010 2020 Faridpur (5yrs MA) Mean (5 yrs MA) Linear (Mean (5 yrs MA)) 60 Rainfall Pattern at North East Region for Pre Monsoon Season 1600 y = 5.6243x - 10328 R² = 0.2925 1400 Rainfall in mm 1200 1000 800 600 400 200 0 1950 1960 1970 1980 1990 2000 2010 2020 Year Srimongal (5 yrs MA) Sylhet (5 yrs MA) Mean (5yrs MA) Linear (Mean (5yrs MA)) Rainfall Pattern at South West Region for Pre Monsoon Season 1400 y = 3.2506x - 5987.1 R² = 0.2231 Rainfall in mm 1200 1000 800 600 400 200 0 1950 1960 1970 1980 1990 2000 2010 2020 Years Jessore (5yrs MA) Khulna (5yrs MA) Satkhira (5yrs MA) Mean (5 yrs MA) Linear (Mean (5 yrs MA)) 61 Rainfall Pattern at South Central Region for Pre Monsoon Season 1200 Rainfall in mm 1000 y = 2.0299x - 3626.9 R² = 0.0937 800 600 400 200 0 1950 1960 1970 1980 1990 2000 2010 2020 Years Barisal (5yrs MA) Khepupara (5yrs MA) Patuakhali (5yrs MA) Linear (Mean (5 yrs MA)) Chandpur (5yrs MA) Madaripur (5yrs MA) Mean (5 yrs MA) 62 Rainfall in mm Rainfall Pattern at South East Region for Pre Monsoon Season 900 800 700 600 500 400 300 200 100 0 1960 y = 2.1305x - 3745.5 R² = 0.0896 1970 1980 1990 2000 2010 Years Comilla (5 yrs MA) Feni ( (5 yrs MA) Maijdeecourt (5 yrs MA) Mean (5 yrs MA) Linear (Mean (5 yrs MA)) Rainfall Pattern at River and Estuary Region for Pre Monsoon Season 1000 y = 3.8645x - 7220.9 R² = 0.2165 Rainfall in mm 800 600 400 200 0 1950 1960 1970 1980 1990 2000 2010 2020 Year Bhola (5yrs MA) Hatiya(5 yrs MA) Sandwip (5 yrs MA) Mean (5 yrs MA) Linear (Mean (5 yrs MA)) 63 Rainfall Pattern at Eastern Hilly Region for Pre Monsoon Season 800 y = 5.1241x - 9732.8 R² = 0.6117 Rainfall in mm 700 600 500 400 300 200 100 0 1950 1960 1970 1980 1990 2000 2010 2020 Year Chittagong (5yrs MA) Rangamati (5yrs MA) Teknaf (5yrs MA) Linear (Mean (5 yrs MA)) CoxsBazar (5yrs MA) Sitakunda(5yrs MA) Mean (5 yrs MA) 64 A.2 Hydrological region wise variation in rainfall pattern for Monsoon Season Rainfall Pattern at North West Hydrological Region for Monsoon Season 2500 y = 4.2578x - 7165 R² = 0.2151 Rainfall in mm 2000 1500 1000 500 0 1950 1960 1970 1980 Bogra (5 yrs MA) Ishurdi (5 yrs MA) Rangpur (5 yrs MA) Linear (Mean (5yrs MA)) 1990 Years 2000 2010 2020 Dinajpur (5 yrs MA) Rajshahi (5 yrs MA) Mean (5yrs MA) Rainfall in mm Rainfall Pattern at North Central for Monsoon Season 2000 1800 1600 1400 1200 1000 800 600 400 200 0 1950 y = 3.2861x - 5189.4 R² = 0.1544 1960 1970 1980 1990 2000 2010 2020 Years Dhaka (5yrs MA) Faridpur (5yrs MA) Mymensing (5yrs MA) Mean (5 yrs MA) Linear (Mean (5 yrs MA)) 65 Rainfall Pattren at North East Region for Monsoon Season 4000 y = -0.6994x + 3432.6 R² = 0.0052 3500 Rainfall in mm 3000 2500 2000 1500 1000 500 0 1950 1960 1970 1980 1990 2000 2010 2020 Year Srimongal (5 yrs MA) Sylhet (5 yrs MA) Mean (5yrs MA) Linear (Mean (5yrs MA)) 66 Rainfall Pattern at South West Region for Monsoon Season 2000 Mean Trend: y = 7.052x - 12799 R² = 0.4596 1800 Rainfall in mm 1600 1400 1200 1000 800 600 400 200 0 1950 1960 1970 1980 1990 2000 2010 2020 Years Jessore (5yrs MA) Khulna (5yrs MA) Satkhira (5yrs MA) Mean (5 yrs MA) Linear (Mean (5 yrs MA)) Rainfall Pattern at South Central Region for Monsoon Season 3500 y = 5.8759x - 9923.1 R² = 0.1453 Rainfall in mm 3000 2500 2000 1500 1000 500 0 1950 1960 1970 1980 1990 2000 2010 2020 Years Barisal (5yrs MA) Chandpur (5yrs MA) Khepupara (5yrs MA) Madaripur (5yrs MA) Patuakhali (5yrs MA) Mean (5 yrs MA) Linear (Mean (5 yrs MA)) 67 Rainfall Pattern at South East Region for Monsoon Season 3000 y = -2.2481x + 6071 R² = 0.0247 Rainfall in mm 2500 2000 1500 1000 500 0 1960 1970 1980 1990 2000 2010 Year Comilla (5 yrs MA) Feni ( (5 yrs MA) Maijdeecourt (5 yrs MA) Mean (5 yrs MA) Linear (Mean (5 yrs MA)) Rainfall Pattern at River and Estuary Region for Monsoon Season 4000 Rainfall in mm 3500 y = 1.2798x - 386.48 R² = 0.0037 3000 2500 2000 1500 1000 500 0 1950 1960 1970 1980 1990 2000 2010 2020 Year Bhola (5yrs MA) Hatiya(5 yrs MA) Sandwip (5 yrs MA) Mean (5 yrs MA) Linear (Mean (5 yrs MA)) 68 Rainfall Pattern at Eastern Hilly Region for Monsoon Season 4000 3500 Rainfall in mm 3000 2500 2000 1500 1000 Mean Trend: y = 8.4946x - 14476 R² = 0.2733 500 0 1960 1965 1970 1975 1980 Chittagong (5yrs MA) 1985 1990 Year CoxsBazar (5yrs MA) Sitakunda(5yrs MA) Teknaf (5yrs MA) 1995 2000 2005 2010 Rangamati (5yrs MA) Mean (5yrs MA) Linear (Mean (5yrs MA)) 69 A.3 Hydrological region wise variation in rainfall pattern for Post Monsoon season Rainfall in mm Rainfall Pattern at North West Region for Post Monsoon Season 450 400 350 300 250 200 150 100 50 0 1950 y = 1.9921x - 3808.6 R² = 0.3116 1960 1970 1980 1990 2000 2010 2020 Year 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)) Rainfal Pattern at North Central for Post Monsoon Season 400 Rainfall in mm 350 y = 1.4022x - 2581 R² = 0.2237 300 250 200 150 100 50 0 1950 1960 1970 1980 1990 2000 2010 2020 Years Dhaka (5yrs MA) Faridpur (5yrs MA) Mymensing (5yrs MA) Mean (5 yrs MA) Linear (Mean (5 yrs MA)) 70 Rainfall Pattern at North East Region for Post Monsson Season 500 450 Rainfall in mm 400 y = -0.246x + 712.65 R² = 0.0049 350 300 250 200 150 100 50 0 1950 1960 1970 1980 1990 2000 2010 2020 Years Srimongal (5 yrs MA) Sylhet (5 yrs MA) Mean (5yrs MA) Linear (Mean (5yrs MA)) Rainfall Pattern at South West Region for Post Monsoon Season 350 Rainfall in mm 300 y = 1.3742x - 2591.3 R² = 0.2398 250 200 150 100 50 0 1950 1960 1970 1980 1990 2000 2010 2020 Years Jessore (5yrs MA) Khulna (5yrs MA) Satkhira (5yrs MA) Mean (5 yrs MA) Linear (Mean (5 yrs MA)) 71 Rainfall Pattern at South Central Region for Post Monsoon Season 600 Rainfall in mm 500 y = 1.1892x - 2110.8 R² = 0.0634 400 300 200 100 0 1950 1960 1970 1980 1990 2000 2010 2020 Year Barisal (5yrs MA) Chandpur (5yrs MA) Khepupara (5yrs MA) Madaripur (5yrs MA) Patuakhali (5yrs MA) Mean (5 yrs MA) Linear (Mean (5 yrs MA)) Rainfall in mm Rainfall Pattern at South East Region for Post Monsoon Season 500 450 400 350 300 250 200 150 100 50 0 1960 y = -0.1554x + 531.4 R² = 0.0013 1970 1980 1990 2000 2010 Years Comilla (5 yrs MA) Feni ( (5 yrs MA) Maijdeecourt (5 yrs MA) Mean (5 yrs MA) Linear (Mean (5 yrs MA)) 72 Rainfall Pattern at River and Estuary Region for Post Monsoon Season 700 Mean Trend: y = 1.8088x - 3315.1 R² = 0.0697 Rainfall in mm 600 500 400 300 200 100 0 1950 1960 1970 1980 1990 2000 2010 2020 Year Bhola (5yrs MA) Hatiya(5 yrs MA) Sandwip (5 yrs MA) Mean (5 yrs MA) Linear (Mean (5 yrs MA)) Rainfall Pattern at Eastern Hilly Region for Post Monsoon Season 700 Rainfall in mm 600 y = 1.3328x - 2374.8 R² = 0.0854 500 400 300 200 100 0 1960 1970 1980 1990 2000 2010 Years Chittagong (5yrs MA) CoxsBazar (5yrs MA) Rangamati (5yrs MA) Sitakunda(5yrs MA) Teknaf (5yrs MA) Mean (5 yrs MA) Linear (Mean (5 yrs MA)) 73 A.4 Hydrological region wise variation in rainfall pattern for winter season Rainfall pattern at North West Region for Winter Season 140 Rainfall in mm 120 100 y = 0.1038x - 177.77 R² = 0.0114 80 60 40 20 0 1950 1960 1970 1980 1990 2000 2010 2020 Year Bogra (5 yrs MA) Dinajpur(5yrsMA) Ishurdi( 5 yrs MA) Rajshahi(5yrs MA) Rangpur(5 yrs MA) Mean (5yrs MA) Linear (Mean (5yrs MA)) Rainfall in mm Rainfall Pattern at North Central Region for Winter Season 100 90 80 70 60 50 40 30 20 10 0 1950 y = 0.2631x - 486.35 R² = 0.0516 1960 1970 1980 1990 2000 2010 2020 Years Dhaka (5yrs MA) Faridpur (5yrs MA) Mymensing (5yrs MA) Mean (5 yrs MA) Linear (Mean (5 yrs MA)) 74 Rainfall Pattern at North East Region for Winter Season 120 y = -0.0906x + 230.59 R² = 0.0043 Rainfall in mm 100 80 60 40 20 0 1950 1960 1970 1980 1990 2000 2010 2020 Years Srimongal (5 yrs MA) Sylhet (5 yrs MA) Mean (5yrs MA) Linear (Mean (5yrs MA)) Rainfall Pattern at River and Estuary Region for Winter Season 140 Rainfall in mm 120 y = -0.081x + 199.73 R² = 0.0052 100 80 60 40 20 0 1960 1970 1980 1990 2000 2010 Years Bhola (5yrs MA) Hatiya(5 yrs MA) Sandwip (5 yrs MA) Mean (5 yrs MA) Linear (Mean (5 yrs MA)) 75 Rainfall Pattern at Eastern Hilly Region for Winter Season 100 y = 0.3174x - 599 R² = 0.0698 90 Rainfall in mm 80 70 60 50 40 30 20 10 0 1960 1970 1980 1990 2000 2010 Year Chittagong (5yrs MA) Rangamati (5yrs MA) Teknaf (5yrs MA) Linear (Mean (5yrs MA)) CoxsBazar (5yrs MA) Sitakunda(5yrs MA) Mean (5yrs MA) 76
