Develop statistical model to predict extreme precipitation through


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Predict Sri Lanka Extreme Precipitation through El Nino Southern Oscillation

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Develop statistical model to predict extreme precipitation through

  1. 1. Predict Sri Lanka ExtremePrecipitation through El Nino Southern Oscillation R.M.S.P. Ratnayake PGIS/SC/M.Sc./ APS/10/20 MSc in Applied Statistics Post Graduate Institute of Science/ University of Peradeniya
  2. 2. Over view• Introduction• Motivation and Background• Problem• Objectives• Hypothesis• Methodology• Organization• Time Frame
  3. 3. Introduction• Sri Lanka economy mainly depend on Agriculture Industry.• Sri Lankan Agriculture mainly depend on two monsoons.• Therefore extreme precipitation changes the natural agriculture cycle.• Expose to Disaster and Hazard potentials.
  4. 4. Problem• Extreme Precipitation requires extra effort beyond basic Statistical Analysis.• There is no proper model to predict Extreme Precipitation.• Heavy Precipitation is a result of multiple courses.• Sri Lanka climate data are spatially coherent.• Analysis required longer period precipitation data
  5. 5. Motivation and Background Case Study : Early 2011 rainfallNo of Affected Families 268544No of Affected People 990471No of Reported Deaths 18No of Injuries 24No of Missing People 3No of Fully Damaged Houses 4216No of Partially Damaged Houses 22186 Department of Metrology : Sri alnka
  6. 6. Objectives• Identify Relationship between Extreme Precipitation and ENSO.• Develop a model to relate Extreme Precipitation and ENSO.• Validate defined model with recent data.
  7. 7. Hypothesis• Null hypothesis that “There is a significant relationship between extreme precipitation and ENSO behaviour.”• Against the alternative hypothesis that “There is no significant relationship between extreme precipitation and ENSO behaviour. ”
  8. 8. Others Work• 2009 – Comparative analysis of indices of extreme rainfall events: variations and trends from Mexico• 2008 - Predictability of Sri Lankan rainfall based on ENSO• 1998 – ENSO influence on Intraseasonal Extreme Rainfall and Temperature Frequency in the Contiguous United State: Implications for Long Range Predictability• 2011 – Research on the Relationship of ENSO and the Frequency of Extreme Precipitation Events in China
  9. 9. Methodology : Overview• Data Collection• Defining Threshold value• Analysis – Distribution of Data – Identifying Extreme Percentile – Spatial Distribution of Extreme Precipitation – Correlation Analysis – Time Series Analysis
  10. 10. Methodology : Data Collection• Quarterly Cumulative Rainfall data• At least 50 years• 11 out of 21 Stations• Treating missing rainfall data : By Multiplying each year value by multiplying N/(N-m)• NINO 3.4 – monthly data from 1951 to 2002
  11. 11. Methodology : Threshold value• Gamma Distribution is used.• Rainfall above 95% percentile.• Separately calculated to Individual Stations and All Island.
  12. 12. Methodology : Analysis• Distribution of Data – Histogram – Normality Test
  13. 13. Methodology : Analysis• Correlation Analysis between ENSO and Seasons January - March April - June July - September October - December
  14. 14. Methodology : Analysis• Correlation Analysis between ENSO and Different Stations and All Island Anuradhapura Mannar Batticoloa Nuwara Eliya Colombo Puttalam Hambanthota Ratmalana Kankasanthure Trincomalee Katunayake
  15. 15. Expected Results End of the Research• In JFM/ AMJ/ JAS/ OND Extreme Precipitation days in Anuradhapura/ Batticoloa/ Colombo/ Hambanthota/ Kankasanthure/ Katunayake/ Mannar/ Nuwara Eliya/ Puttalam/ Ratmalana/ Trincomalee/ All Island are significantly More or Less Frequent in El Nino than La Nino
  16. 16. Statistical Software• R• Excel
  17. 17. Organization• Irrigation Department• Department of Meteorology of Sri Lanka• Foundation of Environment and Climate Technology• Institute of Post Graduate Studies – University of Peradeniya.
  18. 18. Time Line Require Data Data Study Analyzing Developing Testing Report Presentation ment Gathering Arranging Existing Model and preparation Analysis Approaches ValidatingWeek1Week2Week3Week4Week5Week6Week7Week8Week9Week10Week11Week12
  19. 19. Acknowledgement• Dr. Lareef Zubair at Foundation of Environment and Climate Technologies, Dhigana.• Eng. R.M.W. Ratnayake at Director (Water Resources) Ministry of Irrigation and Water Resource Management.• Post Graduate Institute of Science University of Peradeniya
  20. 20. Thanking you Weather is a great metaphor for life -sometimes its good, sometimes its bad, andtheres nothing much you can do about it butcarry an umbrella. ~Terri Guillemets