By Ahmad Hasan Nury Advisor: Prof. Dr. rer. nat. Manfred Koch, Kassel University

1. Investigation and Prediction of Time Series of Temperature and Rainfall Variation in North-Eastern Region of Bangladesh By Ahmad Hasan Nury Advisor: Prof. Dr. rer. nat. Manfred Koch, Kassel University Dr. Md. Jahir Bin Alam, SUST, Bangladesh Department of Geohydraulics and Engineering Hydrology, University of Kassel

2. Introduction: • Climate change is one of the biggest environmental threats to food production, water availability, forest biodiversity and livelihoods. It is widely believed that developing countries such as Bangladesh will be impacted more severely than developed countries (e.g. UK and USA). • According to F.HoVerhoog (1987) to estimate the impact of climatic change on the morphology of river basins, firstly estimation of the impact of climate change on precipitation and temperature variation analysis are necessary. • In this study, it is considered the region for the analysis where huge amount of rainfall occur in every year, which is Sylhet and Moulvibazar district. Every year there is great loss in rice production in this zone due to flood.

3. Scope of the study: • Time series graph • Statistical analysis: • Mean • Standard deviation • Mann-Kendall trend test • Rainfall frequency analysis • Short term prediction: • ARIMA (p d q) model

4. Study Area: • Sylhet division, the northeastern administrative division of Bangladesh, located at 24053’ latitude and 91052’ longitudes. • The climate of Sylhet division is tropical monsoon with a predominantly hot and humid summer and a relatively cold winter. • Change in global climate rise of sea level may alter the productions in this area. Figure: Study Area

5. Data Collection: Table: Monthly Rainfall data (Source: Bangladesh Water Development Board) Table: Monthly Temperature data (Source : Bangladesh Meteorological Department)

6. Data Analysis: Figure 1: Variation of monthly maximum temperature of sylhet. Figure 2: Variation of monthly minimum temperature of sylhet. Figure 4: Variation of monthly minimum temperature of Moulvibazar. Figure 3: Variation of monthly maximum temperature of Moulvibazar.

7. Figure 6: Variation of monthly Rainfall of sylhet station CL228. Figure 5: Variation of monthly Rainfall of sylhet station CL129. Figure 8: Variation of monthly Rainfall of Moulvibazar station CL126. Figure 7: Variation of monthly Rainfall of Moulvibazar station CL104. Figure 9: Variation of monthly Rainfall of Moulvibazar station CL229.

8. Mean and Standard deviation of monthly maximum and minimum temperature: Table 2: Mean temperature of monthly maximum temperature of sylhet station with their standard deviation for last 35 years (from 1977 to 2011) Table 3: Mean temperature of monthly minimum temperature of sylhet station with their standard deviation for last 35 years (from 1977 to 2011)

9. Table 4: Mean temperature of monthly maximum temperature of Moulvibazar station with their standard deviation for last 35 years (from 1977 to 2011) Table 5: Mean temperature of monthly minimum temperature of Moulvibazar station with their standard deviation for last 35 years (from 1977 to 2011)

10. Mean and Standard deviation of monthly rainfall: Table 6: Mean rainfall of every month of Sylhet station CL129 with their standard deviation for last 30 years (from 1981 to 2010) Table 7: Mean rainfall of every month of Sylhet station CL228 with their standard deviation for last 30 years (from 1981 to 2010)

11. Table 8: Mean rainfall of every month of Moulvibazar station CL104with their standard deviation for last 30 years (from 1981 to 2010) Table 9: Mean rainfall of every month of Moulvibazar station CL126 with their standard deviation for last 30 years (from 1981 to 2010) Table 10: Mean rainfall of every month of Moulvibazar station CL229with their standard deviation for last 30 years (from 1981 to 2010)

12. Mann-Kendall trend test: • H0: There is no trend in the series • Ha: There is a trend in the series • It can be used for nonnormal data such as seasonal data Table 11: MK Statistics and their corresponding p-Value at 5 % significance level for Temperature Table 12: MK Statistics and their corresponding p-Value at 5 % significance level for Rainfall

13. Annual maximum Rainfall frequency: Figure 10: Rainfall frequency curve for Sylhet CL129 Figure 11: Rainfall frequency curve for Sylhet CL228

14. Figure 12: Rainfall frequency curve for Moulvibazar CL104 Figure 13: Rainfall frequency curve for Moulvibazar CL126 Figure 14: Rainfall frequency curve for Moulvibazar CL229

15. •p order of autoregressive terms •d order of integrated term (non-seasonal differences; linear, quadratic, etc.) •q order of moving average (forecast errors) ARIMA (p,d,q) model: • If seasonality in time series than ARIMA will be (p,d,q)(P,D,Q)12 Figure 15: Autocorrelation plot of time series of monthly maximum temperature of Sylhet station. Figure 16: Partial autocorrelation plot of time series of monthly maximum temperature of Sylhet station. The order of d and D have been selected as 1

16. Figure 17: Autocorrelation plot of differenced ( d and D = 1) time series of monthly maximum temperature of Sylhet station. Figure 18: Partial autocorrelation plot of differenced ( d and D = 1) time series of monthly maximum temperature of Sylhet station. • The order of p and P have been selected as 1 • The order of q and Q have been selected as 1

17. Figure: ACF and PACF plot of residuals for the monthly maximum temperature of Sylhet Station.

18. The selected ARIMA model of Monthly maximum temperature of Sylhet station is (1 1 1)(1 1 1)12 Table 13: Parameter estimation for ARIMA (1 1 1) (1 1 1)12 model of monthly maximum temperature of sylhet • Data of temperature station for the period from 1977 to 2009 has been used for the calibration and from 2010 to 2011 has been used for the verification of the prediction. • Data of rainfall station for the period from 1981 to 2008 has been used for the calibration and from 2009 to 2010 has been used for the verification of the prediction.

19. PBIAS = 0.18% PBIAS = - 0.33% Figure 19: Comparison graph of Observed vs. Predicted values of the Monthly Maximum Temperature data of Sylhet Station. Figure 20: Comparison graph of Observed vs. Predicted values of the Monthly Minimum Temperature data of Sylhet Station. PBIAS = 0.43% PBIAS = - 0.53% Figure 21: Comparison graph of Observed vs. Predicted values of the Monthly Maximum Temperature data of Moulvibazar Station. Figure 22: Comparison graph of Observed vs. Predicted values of the Monthly Minimum Temperature data of Moulvibazar Station.

20. PBIAS = - 1.07% PBIAS = 4.5% Figure 23: Comparison graph of Observed vs. Predicted values of the Rainfall data of Sylhet Station CL129. Figure 24: Comparison graph of Observed vs. Predicted values of the Rainfall data of Sylhet Station CL228. PBIAS = - 3.62% PBIAS = - 6.56% Figure 25: Comparison graph of Observed vs. Predicted values of the Rainfall data of Moulvibazar Station CL104. Figure 26: Comparison graph of Observed vs. Predicted values of the Rainfall data of Moulvibazar Station CL126.

21. PBIAS = - 1.89% Figure 27: Comparison graph of Observed vs. Predicted values of the Rainfall data of Moulvibazar Station CL229.

22. Conclusion: • The upward trend line and positive Mann-Kendall test statistics of time series of temperature (for the period between 1977 and 2011) indicates that both the monthly maximum and minimum temperature is increasing with time. • The downward trend line and negative Mann-Kendall test statisticsof time series of rainfall (for the period between 1981 and 2010) indicates that monthly rainfall is decreasing with time except Sylhet station CL228 (almost upward trend line). • Mean temperature with their standard deviation indicates the variation of temperature in year to year is not very high. • Mean rainfall with their standard deviation indicates the variation of rainfall in year to year is high. • A best fitted curve has been drawn between return period and annual maximum rainfall for each rainfall station to see the frequency of it. • The fitness of ARIMA model for the temperature and rainfall is well. The temperature and rainfall time series fitted to ARIMA model for the selected stations can be used for estimating missing temperature and rainfall values, forecasting and investigating short term temperature and rainfall change.

23. References: • F.HoVerhoog, the Influence of Climate Change and Climatic Variability on the Hydrologic Regime and Water Resources (Proceedings of the Vancouver Symposium, August 1987) IAHSPubl.no.168, 1987 • Ministry of Environment and Forests, Government of the People's Republic of Bangladesh(2008), Bangladesh Climate Change Strategy and Action Plan • CEGIS & ADB (2009), Inception Report on Field Based Research on the Impacts of Climate Change on Bangladesh Rivers • IWM (2005), Hydrological Impact Study of Tipaimukh Dam Project of India on Bangladesh • UNFCC (2007), Climate Change: Impacts, Vulnerabilities and Adaptation In Developing Countries. • Sarwar G. M. (2005), Impacts of Sea Level Rise on the Coastal Zone of Bangladesh, M.Sc. Thesis, Lund University International Masters Program in Environmental Science, Lund University, Sweden. • Subramanya K, (2005), Engineering Hydrology, Tata McGraw-Hill Publishing Company Limited, New Delhi, India.

24. Thank You