Develop statistical model to predict extreme precipitation through
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Transcript of Develop statistical model to predict extreme precipitation through
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Predict Sri Lanka Extreme Precipitation through El Nino Southern Oscillation
R.M.S.P. RatnayakePGIS/SC/M.Sc./ APS/10/20
MSc in Applied StatisticsPost Graduate Institute of Science/
University of Peradeniya
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Over view
• Introduction• Motivation and Background• Problem• Objectives• Hypothesis • Methodology• Organization• Time Frame
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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.
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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
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Motivation and Background
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
Case Study : Early 2011 rainfall
Department of Metrology : Sri alnka
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Objectives
• Identify Relationship between Extreme Precipitation and ENSO.
• Develop a model to relate Extreme Precipitation and ENSO.
• Validate defined model with recent data.
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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. ”
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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
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Methodology : Overview
• Data Collection• Defining Threshold value• Analysis– Distribution of Data– Identifying Extreme Percentile– Spatial Distribution of Extreme Precipitation– Correlation Analysis– Time Series Analysis
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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
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Methodology : Threshold value
• Gamma Distribution is used.• Rainfall above 95% percentile.• Separately calculated to Individual Stations
and All Island.
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Methodology : Analysis
• Distribution of Data– Histogram– Normality Test
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Methodology : Analysis
• Correlation Analysis between ENSO and Seasons
January - March
April - June
July - September
October - December
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Methodology : Analysis
• Correlation Analysis between ENSO and Different Stations and All Island
Anuradhapura Mannar
Batticoloa Nuwara Eliya
Colombo Puttalam
Hambanthota Ratmalana
Kankasanthure Trincomalee
Katunayake
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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
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Statistical Software
• R• Excel
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Organization
• Irrigation Department • Department of Meteorology of Sri Lanka• Foundation of Environment and Climate
Technology• Institute of Post Graduate Studies – University
of Peradeniya.
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Time LineRequirement Analysis
Data Gathering
Data Arranging
Study Existing Approaches
Analyzing DevelopingModel
Testing and Validating
Report preparation
Presentation
Week1
Week2
Week3
Week4
Week5
Week6
Week7
Week8
Week9
Week10
Week11
Week12
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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
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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