INTERNATIONAL CLIMATE CHANGE SYMPOSIUM-2015: Impacts of Extreme Atmospheric Events on Geo-surface in a Changing Climate Environmental Technology Section, Industrial technology Institute (ITI) 14th 15th May 2015 at Hotel Galadari, Colombo, Sri Lanka Analysis of the Extreme Rainfall Indices over Bangladesh A.K.M. Saiful Islam Professor Institute of Water and Flood Management Bangladesh University of Engineering and Technology (BUET)
Outline Investigate trends, patterns, intensity, magnitude and frequency of Extreme Rainfall events of Bangladesh. Changes of future rainfall extreme through rainfall extreme indicators using regional climate model data. Possible changes of climate extremes for the various hydrological regions of Bangladesh.
Introduction Changes of the rainfall extremes has profound impact on the economy, livelihood and ecosystems of Bangladesh. The 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 extreme rainfall characteristics in Bangladesh. This study conducted a detailed analysis of the changes of the trends of the heavy rainfall, its pattern, magnitude, frequency, and intensity.
Climate of Bangladesh 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.
Hydrological Regions of Bangladesh Eight hydrological planning regions of Bangladesh classified by Water Resources Planning Organization to facilitate water management of the country. 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). NW NC SE NE Results obtained from this study has presented for the stations in one of the eight hydrological (planning) regions of Bangladesh as per NWMP, 2001. SW SC RE EH
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Rainfall in mm Sea Level Pressure mbar Rainfall in mm Sun shine hour Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Rainfall in mm Temperature C Rainfall in mm Humidity in percentage Rainfall in mm Wind Speed m/s Relationship with Rainfall 900 800 700 600 500 400 300 200 100 0 35 30 25 20 15 10 5 0 900 800 700 600 500 400 300 200 100 0 100 90 80 70 60 50 40 30 20 10 0 900 800 700 600 500 400 300 200 100 0 5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 Month Average Rainfalls Average Temp Month Average Rainfalls Average Humidity Month Average Rainfalls Average Wind Speed 900 800 700 600 500 400 300 200 100 0 1020 1015 1010 1005 1000 995 990 900 800 700 600 500 400 300 200 100 0 9 8 7 6 5 4 3 2 1 0 Month Average Rainfalls Average Sea Level Pressure Month Average Rainfalls Average Sunshine Hour
Data Quality and consistency check of meteorological data 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)- Non-existence of dates Negative daily precipitation 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
Selection of Meteorological stations Stations that are not able to fulfilling the criteria of quality check 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. The investigation has been carried out using daily records of rainfall from 29 ground based stations of Bangladesh Meteorological Department (BMD) distributed over the country during the time period 1961-2010.
Homogeneity Test 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). Non-homogeneous data sets are eliminated based on the above test from the trend analysis.
SDII (mm/day) Trend Analysis To smooth out short-term fluctuations and highlight longerterm trends or cycles, five-year moving average, a type of finite impulse response filter, is used to analyze and compute the trends of precipitation records (Gallant et al., 2007). 1.5 1 0.5 0-0.5 Hydrological Regionwise 5 years Moving Average for SDII The computed trends of indices are used non parametric Kendall s tau based slope estimator known as Theil-Sen Slope estimator or Sen s slope estimator. It is more accurate for skewed distribution (e.g. rainfall) than simple linear regression. -1-1.5 1950 1960 1970 1980 1990 2000 2010 2020 Year 5 years moving average (NE) 5 years moving average (NW) 5 years moving average (NC) 5 years moving average (SE) 5 years moving average (SW) 5 years moving average (SC)
Trends of Rainfall (mm/year) Zones North West North East North Central South West South East South Central River and Estuary Eastern Hilly Pre monsoon Monsoon Post Monsoon Winter 1.89 4.26 1.99 0.10 5.62-0.69-0.25-0.09 1.36 3.29 1.40 0.26 3.25 7.05 1.37 0.68 2.13-2.25-0.16 0.13 2.03 5.88 1.19 0.10 3.86 1.28 1.81-0.08 5.12 8.49 1.33 0.32
Decadal Changes of Annual Rainfall 1970-1980 1991-2000 2001-2010
Extreme Climate using Indices A total of 11 indices for the precipitation 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.
Precipitation related Climate Indices Index Description Definition R99p Frequencies in mm Very wet days due to heavy rainfall event exceeding 99% R95p Frequencies in mm Extremely wet days due to heavy rainfall event exceeding 95% PRCPTOT Total rainfall Annual total wet day when rain rate >1mm SDII Daily Intensity index Annual total rainfall divided by the number of wet days (mm/day) CDD Longest spell in days Consecutive dry days when rainfall < 1mm CWD Longest spell in days Consecutive wet days when rainfall > 1mm RX1day Intensity in mm One-day maximum rainfall RX5day Intensity in mm Five-day maximum rainfall RX10 Frequency in days No of days when rainfall > 10mm RX20 Frequency in days No of days when rainfall > 20mm RX 100 Frequency in days No of days when rainfall > 100mm
North East, Central and West Regions Hydrologic Region Stations RX1 day RX5 Day SDII R10mm R20mm R100mm CDD CWD R95P R99P PRCPTOT North East Sreemongal -0.396-0.738-0.041-0.018-0.026-0.003 0.336 0.073-1.042 1.878-0.258 Sylhet -0.394-0.447-0.043-0.084-0.034-0.013 0.583-0.07-4.868-2.422-5.36 Bogura -0.091 0.07 0.011 0.15 0.077 0.004 0.701-0.042-0.033-1.328 3.856 Dinajpur 0.744 1.066-0.01 0.151 0.048 0.044 0.471 0.104 5.82 3.543 9.687 North West Ishardi -0.773-0.566-0.022 0.043-0.012-0.016 0.273 0.004-2.898-2.03-2.459 Rajshahi 0.214 0.536-0.098-0.077-0.066-0.015 0.459-0.008-3.086-0.75-3.696 Rangpur 0.87 1.69 0.047 0.175 0.144-0.006 0.372-0.041 0.963 2.84 5.914 Sayedpur 1.149 2.507-0.164-0.247-0.281-0.085 3.253 0.002 0 0-19.859 Dhaka 0.013 0.406 0.024 0.044 0.02-0.02 0.599-0.028-1.727 0.483 1.605 North Central Mymensingh 0.775 1.106-0.001 0.011 0.06-0.01 0.494 0.057 1.667 1.806 5.177 Tangail 6.159 6.447 0.048-0.165-0.088-0.046 3.072-0.14-2.653 0.228-1.841 Faridpur -0.568-0.028-0.054-0.004-0.033 0.002 0.428-0.001-0.996-1.7-2.392 Trends that are significant as per Mann-Kendall test
South East, Central and West Regions Hydrolo gic Region Stations RX1 day RX5 Day SDII R10mm R20mm R100mm CDD CWD R95P R99P South East Comilla -0.527-0.256-0.154-0.022-0.069-0.025 0.526 0.032-4.132-4.438-6.203 Feni -0.836 0.26-0.007-0.231-0.209-0.019 1.438-0.128-4.982 0.155-9.211 Maijdicourt 0.704 0.694-0.146-0.005-0.041-0.031 0.315 0.13-3.728 1.609-3.15 South West Chuadanga 3.703 6.148 0.055-0.306-0.127 0.029 0.617-0.097 4.187 5.889-2.297 Jessore -0.057 0.683 0.049 0.177 0.115 0.024 0.454-0.031 3.904 2.163 8.109 Mongla 0.127 1.923 0.125 0.214 0.155 0.024 3.199 0.465 0 0 4.81 PRCPTO T South Central Chandpur -1.419-1.731-0.143 0.028-0.113-0.082 0.062 0.082-11.493-4.356-9.478 Barisal -0.551-0.57-0.032 0.038 0.021-0.003-0.05-0.013-2.678-1.585-0.382 Khepupara 1.08 5.495 0.008 0.343 0.207 0.034 0.537 0.021 5.42 3.316 12.987 Madaripur 0.345 1.494-0.113-0.36-0.25-0.006 1.172 0.046-2.732-0.622-14.39 Patuakhali 0.015 2.355-0.185-0.11-0.07-0.067 0.975 0.089-6.839-4.634-8.687 Trends that are significant as per Mann-Kendall test
Estuary and Hilly Regions Hydrolog ic Region Stations RX1 day RX5 Day SDII R10mm R20mm R100mm CDD CWD R95P R99P PRCPTO T River and Bhola 4.779 5.357-0.044-0.279-0.183-0.005-0.237-0.144 1.965 2.133-7.096 Estuary Hatia 0.979 2.261 0.035 0.121 0.115 0.015 0.656 0.012 4.818 8.308 8.746 Sandwip 1.179 4.644-0.189 0.143-0.014 0.002 0.092 0.073 6.612 9.949 7.349 Eastern Hilly Region Chittagong 0.49 1.232 0.05 0.074 0.058-0.018 0.383-0.029-2.828-1.725 1.69 Cox'sbazar 0.589-0.179-0.074 0.105 0.01-0.053-0.251-0.027-6.201-4.335-2.529 Kutubdia 2.967 5.051 0.105 0.525 0.482 0.048 0.408 0.082 5.216 4.053 23.774 Rangamati 1.183 1.245-0.007 0.023 0.034-0.002-0.088-0.002 3.142 2.566 3.424 Sitakundo -0.135 3.313 0.035 0.141 0.199-0.002 0.01 0.132 0.329-0.995 8.135 Teknaf 3.222 5.965 0.224 0.391 0.449 0.082 0.312-0.029 15.138 10.141 32.636 Trends that are significant as per Mann-Kendall test
Sreemongal Sylhet Bogura Dinajpur Ishardi Rajshahi Rangpur Sayedpur Dhaka Mymensingh Tangail Faridpur Comilla Feni Maijdicourt Chuadanga Jessore Mongla Chandpur Barisal Khepupara Madaripur Patuakhali Bhola Hatia Sandwip Chittagong Cox'sbazar Kutubdia Rangamati Sitakundo Teknaf Sreemongal Sylhet Bogura Dinajpur Ishardi Rajshahi Rangpur Sayedpur Dhaka Mymensingh Tangail Faridpur Comilla Feni Maijdicourt Chuadanga Jessore Mongla Chandpur Barisal Khepupara Madaripur Patuakhali Bhola Hatia Sandwip Chittagong Cox'sbazar Kutubdia Rangamati Sitakundo Teknaf CDD and CWD (dry and wet spells) 3.5 3 CDD 2.5 2 1.5 1 0.5 0-0.5 0.5 0.4 CWD 0.3 0.2 0.1 0-0.1-0.2
Sreemongal Sreemongal Sylhet Sylhet Trends of Maximum 1-day and 5-day precipitation (Intensity) Bogura Bogura Dinajpur Dinajpur Ishardi Ishardi Rajshahi Rajshahi Rangpur Rangpur Sayedpur Sayedpur Dhaka Dhaka Mymensingh Mymensingh Tangail Tangail Faridpur Faridpur Comilla Comilla Feni Feni Maijdicourt Maijdicourt Chuadanga Chuadanga Jessore Jessore Mongla Mongla Chandpur Chandpur Barisal Barisal Khepupara Khepupara Madaripur Madaripur Patuakhali Patuakhali Bhola Bhola Hatia Hatia Sandwip Sandwip Chittagong Chittagong Cox'sbazar Cox'sbazar Kutubdia Kutubdia Rangamati Rangamati Sitakundo Sitakundo Teknaf Teknaf 7 6 5 4 3 2 1 0-1 -2 RX1 7 6 RX5 5 4 3 2 1 0-1 -2-3
Sreemongal Sylhet Bogura Dinajpur Ishardi Rajshahi Rangpur Sayedpur Dhaka Mymensingh Tangail Faridpur Comilla Feni Maijdicourt Chuadanga Jessore Mongla Chandpur Barisal Khepupara Madaripur Patuakhali Bhola Hatia Sandwip Chittagong Cox'sbazar Kutubdia Rangamati Sitakundo Teknaf Sreemongal Sylhet Bogura Dinajpur Ishardi Rajshahi Rangpur Sayedpur Dhaka Mymensingh Tangail Faridpur Comilla Feni Maijdicourt Chuadanga Jessore Mongla Chandpur Barisal Khepupara Madaripur Patuakhali Bhola Hatia Sandwip Chittagong Cox'sbazar Kutubdia Rangamati Sitakundo Teknaf SDII and PRCPTOT (Magnitude) 40 30 PRCPTOT 20 10 0-10 -20-30 0.25 0.2 0.15 0.1 0.05 0-0.05-0.1-0.15-0.2-0.25 SDII
Sreemongal Sylhet Bogura Dinajpur Ishardi Rajshahi Rangpur Sayedpur Dhaka Mymensingh Tangail Faridpur Comilla Feni Maijdicourt Chuadanga Jessore Mongla Chandpur Barisal Khepupara Madaripur Patuakhali Bhola Hatia Sandwip Chittagong Cox'sbazar Kutubdia Rangamati Sitakundo Teknaf Sreemongal Sylhet Bogura Dinajpur Ishardi Rajshahi Rangpur Sayedpur Dhaka Mymensingh Tangail Faridpur Comilla Feni Maijdicourt Chuadanga Jessore Mongla Chandpur Barisal Khepupara Madaripur Patuakhali Bhola Hatia Sandwip Chittagong Cox'sbazar Kutubdia Rangamati Sitakundo Teknaf R95 and R99 (Frequency) 20 15 R95 10 5 0-5 -10 12 10-15 R99 8 6 4 2 0-2 -4-6
Regional Climate Change Scenarios GCM provides climate change predictions in a coarser resolution (>100km) which often fail to capture the sub-grid scale processes such as cloud formation occurs within 10km. Regional climate change modeling is dynamically downscaled using the same physical model but for a limited areas. PRECIS regional climate model has been used to dynamically downscaled to generate climate change information at a spatial resolution of 25km.
Future Climate Change Scenarios using Multi-member simulations of PRECIS UK Met office Hadley Center s Regional Climate Model is used for downscaling GCM data at 25km resolution. The Quantifying Uncertainty in Model Predictions (QUMP)ensembles of 17 members of A1B scenarios are used to provide probabilistic predictions of future climate over the GBM basins. RCM domain with 25km resolution
17 Member QUMP ensembles Comparing the Global mean temperature change ( 0 C) of the 17 member ensemble of HadCM3 with AR4 GCMs (Collins et al., 2011) Atmospheric CO 2 concentrations (ppm) for the SRES A1B emissions scenario along with the four RCP scenarios (Rogelj et al., 2012)
Predictions of future changes using Regional Climate Model results Decadal changes in annual rainfalls in the future 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.
Probability Distribution Functions of SDII, CDD, CWD, RX5 SDII RX5 R20 CWD
Changes of one day maximum precipitation, RX1 for 2050s and 2080s from baseline for the premonsoon, monsoon and post monsoon seasons Pre-monsoon Monsoon Post-monsoon 2050s Pre-monsoon Monsoon Post-monsoon 2080s
Future changes of the Number of Days above Rainfall 20mm (RX20) 2020s 2050s 2080s
Conclusions Monthly maximum one day precipitation (RX1) and the monthly maximum five days precipitation (RX5) exhibit non-significant increasing trends at 65% and 75% BMD stations, respectively. 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.
Conclusions Present Day climate Annual total precipitation greater than the 95 th 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.
Conclusions Present day climate Simple Daily Intensity Index (SDII) is used to analyze variations in daily precipitation intensity over Bangladesh. When the trends at individual stations are considered, 18 stations out of 27 exhibit negative trends. However, SDII higher than 9.5 mm/day shows a decreasing trend. 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.
Conclusions Future Changes 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. 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.
Future Works We are now working on using CORDEX South Asia data from Regional Climate Models derived by the CMIP5 GCMS. Conducting regional rainfall analysis using the BWDB data. Comparing monsoon rainfall trends with large scale atmospheric process using ERA Interim data.
Thank you Questions? More information can be found at my Hydrology and Climate (HCL) Research Group at IWFM, BUET http://teacher.buet.ac.bd/akmsaifulislam/group/index.html
Additional slides