This document outlines a proposed study to predict extreme precipitation in Sri Lanka through analysis of the relationship with El Niño Southern Oscillation (ENSO). The objectives are to identify the relationship between extreme precipitation and ENSO, develop a predictive model, and validate it with recent data. The methodology will include collecting rainfall and ENSO data, defining a threshold for extreme precipitation, distribution and correlation analysis between stations and seasons, and time series analysis. The results are expected to show whether extreme precipitation days are more or less frequent during El Niño versus La Niña phases. The study will be conducted over 12 months with the support of Sri Lankan government agencies and universities.

1. Predict Sri Lanka Extreme
Precipitation 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. Over view
• Introduction
• Motivation and Background
• Problem
• Objectives
• Hypothesis
• Methodology
• Organization
• Time Frame

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. 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. Motivation and Background
Case Study : Early 2011 rainfall
No of Affected Families 268544
No of Affected People 990471
No of Reported Deaths 18
No of Injuries 24
No of Missing People 3
No of Fully Damaged Houses 4216
No of Partially Damaged Houses 22186
Department of Metrology : Sri alnka

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. 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. 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. 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. 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. Methodology : Threshold value
• Gamma Distribution is used.
• Rainfall above 95% percentile.
• Separately calculated to Individual Stations
and All Island.

12. Methodology : Analysis
• Distribution of Data
– Histogram
– Normality Test

13. Methodology : Analysis
• Correlation Analysis between ENSO and Seasons
January - March
April - June
July - September
October - December

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. 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. Statistical Software
• R
• Excel

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. Time Line
Require Data Data Study Analyzing Developing Testing Report Presentation
ment Gathering Arranging Existing Model and preparation
Analysis Approaches Validating
Week1
Week2
Week3
Week4
Week5
Week6
Week7
Week8
Week9
Week10
Week11
Week12

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. Thanking you
Weather is a great metaphor for life -
sometimes it's good, sometimes it's bad, and
there's nothing much you can do about it but
carry an umbrella.
~Terri Guillemets