Even though standard precipitation index (SPI) is not a drought predicting tool using downscaled results it was possible to forecast SPI. Forecasted SPI can use for two purposes. Firstly, in order to implement better drought relief payment policy. Secondly, to better water resource planning in order to reduce agriculture risk. Statistical downscaling procedure was developed over Sri Lanka for drought risk. The system was based on Climate Information tool Kit 2.0 (CLIK) by Asia-Pacific Economic Corporation (APEC) Climate Center (APCC). As the predictor region El Niño Southern Oscillation (ENSO) region was considered because recent droughts are more influenced by ENSO compared to traditional South West monsoons (SWM) and North East monsoon (NEM) for the island. Seasonal drought study was carried out considering 3 months intervals for 4 seasons January to March (JFM), April to June (AMJ), July to September (JAS) and October to November (OND). In order to identify optimal SPI scales, models, variables and NINO regions for each season a preliminary study was carried out using a sample stations and based on the results detailed study was carried out for 34 stations covering central and southern parts of the island. Heidke skill score was considered as the categorical verification measure. In order to improve the magnitude of forecasted SPI 2 variance inflation techniques were employed. In CLIK multi model ensemble (MME) predictions were tested considering 3 deterministic models: simple composition method (SCM), multiple regression method (MRG) and synthetic multi model super ensemble method (SSE). However due to low resolution regression based downscaling was considered. Interestingly central high hills showed a very high correlation with ENSO during OND. For highly skilled stations drought characteristics such as trends, onset, duration, frequency and severity can be calculate. Our results highlight that CLIK is skillful over Sri Lanka. Specially in identifying best downscaling characteristics over a station.

1. Downscaling Global Climate Model
Outputs to Fine Scales over Sri Lanka
for Assessing Drought Impacts
Sanjaya Ratnayake
Foundation for Environment Climate and Technology
Sri Lanka

2. Overview
• Introduction
• Problem Definition
• Aims and Objectives
• My approach
• Data and Study Area
• Methodology
• Results
• My Prediction
• Conclusion
• Acknowledgement

3. Introduction
• Due to the geographical distribution the island
is affected with different kinds of natural
disasters.
• The most frequent natural hazards that affect
the island are drought, floods, landslides,
cyclones and coastal erosion.
• Among all natural disasters, droughts occur
most frequently, have the longest duration,
and cover the largest area.

4. Problem Definition
• So far there is no proper drought index over
Sri Lanka.
• So far there is no proper drought
characteristics predicting mechanism.
• So far drought relief payments made in
districts across the country by the government
over a 40 years period used as a proxy for
drought risk.

5. Aims and Objectives
• Asses the APCC CLIK tool skill over Sri Lanka
• Observer station correlations with NINO
regions
• Identify most appropriate downscale
parameters for individual station for different
seasons
• Develop drought index over Sri Lanka
• Predict Seasonal Droughts

6. My Approach
• Calculate SPI over Sri Lanka
• Obtain downscale results using SPI
• Observe skill of forecasted downscale SPI
results
• Amplitude the results using variance inflation
methods
• Predict SPI using most appropriate method.

7. Data and Study Area
• 132 station monthly precipitation data
covering entire island.
• 26 stations with missing and corrupted data.
• SPI 30 years (24 years here)
• CLIK most models support : 1984 to 2008
• Finally, 34 stations covering southern and
central parts of the Island.

8. (a) (c)
(b)

9. Methodology
• Standard Precipitation Index (SPI)
• Climate Information tool Kit 2.0
• Preliminary Study
• Detailed Downscaling
• Variance Inflation
• Heidke Skill Score

10. Meteorological drought index
• Standard Precipitation Index requires only
precipitation as input (McKee, et al., 1993)
• SPI scale 1, 3, 6 and 12
– Appropriate for short term drought studies
– Appropriate for seasonal drought studies
– 3 months droughts have a drastic impact on the
agriculture
– Highly successful with drought data

11. SPI Classification
SPI Drought classification
>2.0 Extreme wet
1.5 to 1.99 Very wet
1.0 to 1.49 Moderately wet
-0.99 to 0.99 Near Normal
-1.0 to -1.49 Moderately Dry
-1.5 to -1.99 Severe Dry
<-2.0 Extremely Dry
SPI Drought category
0 to -0.99 Mild drought
-1.0 to -1.49 Moderately drought
-1.5 to -1.99 Severe drought
<-2.0 Extremely drought

12. Climate Information tool Kit 2.0 (CLIK)
• By APEC Climate Center (APCC)
• Statistical Downscaling
• Linear Regression
• 16 GCM models
• 10 predictor variables
• 3 months seasonal predictions
• Screening Process
– First Screening Process (FSC) : Hopeful stations
– Second Screening Process (SCP) : Good stations

13. Preliminary Study
Predictor
• Training Period: 1984-2008
• Variables: SLP, SST, U850
• Models: NCEP, NASA, MSC_CANCM4, HMC, CWB, IRIF,
GDAPS_F, COLA, MSC_CANCM3, MGO, POAMA, PNU and BCC
• Region : NINO1+2, NINO3, NONO4, NINO3.4

14. Preliminary Study
Predictand
• Season: JFM, AMJ, JAS, OND
• Variables: Precipitation
• Statistical Downscaling: Linear Regression
• Significant Level: 5%
• Minimum pattern score: 0.3

15. Detailed Downscaling Study
Season JFM AMJ JAS OND
Predictor Model NCEP NCEP NCEP MGO
Predictor Region NINO3 NINO3 NINO3.4 NINO3
Predictor Variable SLP SLP SST U850
No of Stations 34
Predictand Variable SPI
Training Period 1984-2008
Statistical Downscaling Linear Regression
Significant Level 5%
Mini. Pattern score 0.3

16. Variance Inflation

17. Heidke Skill Score
Forecast HSS 1: Perfect forecast
Observed
Yes No HSS 0: No skill
Yes A B HSS < 0: Chance
No C D forecast is better
• For Mild, Moderate, Severe and Extreme
droughts HSS was calculated separately.
• JFM and OND seasons were considered
separately.

18. Results
• Preliminary Study Results
• Downscale results
• HSS Results comparison for
– Downscale results
– VIF1 Results
– VIF2 Results

19. Preliminary Study Results

20. Preliminary Study Results
• OND showed the best correlations fallowed by
JFM
• NINO3 showed higher correlation
• JFM : NCEP model with SLP variable
• OND : MGO models with U850 variable
• Low results for AMJ and JAS

21. Detailed Downscaling results
Season JFM AMJ JAS OND
Only FSP 3 - 2 8
SCP 15 1 7 18
Highest Correlation 0.52 0.26 0.59 0.7
Corr > 0.6 - - - 3
Corr > 0.3 9 - 5 13

23. JFM AMJ
JAS OND
- - - - - - -

25. Variance Inflation
• Mild drought
– Almost Downscale and VIF2 similar results
– VIF2 slight improvement in OND
• Moderate drought : Good improvement for
VIF (specially in OND)
2
• Severe Droughts : Good improvement from
VIF1
• Extreme Droughts : Longer period data are
required

26. Mild Drought
Downscale VIF1 VIF2
JFM
Downscale VIF1 VIF2
OND

27. Moderate Drought
JFM Downscale VIF1 VIF2
OND Downscale VIF1 VIF2

28. My Prediction for 2012 OND

29. Conclusion
• Overall CLIK is skillful over Sri Lanka
• Variance Inflation methods are important
when drought magnitude increases
• OND was high skill season
• Central High hills showed higher correlation
• NINO3 showed good correlation over all
seasons except AMJ

30. How to use my study results
• Even though SPI is not a drought predicting
tool using downscaled SPI it was possible to
forecast drought in Sri Lanka.
• Predicted drought characteristics for better
water resource planning in order to reduce
agriculture risk
• Use my drought index to proper drought
payment relief payment

31. Acknowledgement
• Funders
– Asia-Pacific Economic Corporation (APEC) Climate Center (APCC)
• Supervisors
– Dr. Soo-Jin Sohn, Team Leader(Climate Prediction Team), APCC
– Dr. Lareef Zubair, Principal Scientist, Foundation for Environment Climate and Technology, Sri Lanka
• Data Providers
– Precipitation data : Ministry of Irrigation and Water Resources Management, Sri Lanka
– GCM model data providers
• Individuals
– Mr. Wimal Ratnayake (Irri Mini) : Station details
– Ms. Hye-Jin Park (APCC) : CLIK
– Dr. Prasanna Venkatraman (APCC) : ENSO
– Ms. Ruvindee Rupasinghe (Uni Peradeniya) : English and Report corrections
– Ms. Sooyang Joo (APCC) : Operation support
– Mr. Dede Tarmana (The Indonesian Agency for Meteorology Climatology and Geophysics, Indonesia) : Arc GIS
• Free Software
– SPI_tool : The National Drought Mitigation Center
– Arc Portable : ESRI
– Goole earth : Google Inc
– Goole Fusion : : Google Inc
– CLIK : APCC
– Eviews : HIS Inc.

32. Flashback
• Introduction
• Problem Definition
• Aims and Objectives
• My approach
• Data and Study Area
• Methodology
• Results
• Forecast
• Conclusion
• Acknowledgement

33. Thank you