1. Kreshna GOPAL C. Prakash KHEDUN Anoop SOHUN
Modeling the Influence of Large-Scale Circulation Patterns on Precipitation in Mauritius
Kreshna GOPAL C. Prakash KHEDUN Anoop SOHUN

2. Our Background Scinova Team Members
Innovation through science, research, and networking
Team Members
C. Prakash Khedun (PI)
PhD in Water Management & Hydrological Science, Texas A&M University
Hydrologic Extremes (Floods and Drought) and Water Conservation
Kreshna Gopal
PhD in Computer Science, Texas A&M University
Computational Intelligence, Bioinformatics, Statistics
Anoop Sohun
BSc in Computer Science, University of Mauritius
Software Engineering

3. Water Problems in Mauritius
Mauritius is water stressed1
Forecast supply for 2020 is 970 m3/person/day
Water scarce category2
Recent major droughts
– MUR 2 Billion loss in GDP
– Loss in GDP, social unrest, etc.
Precipitation is influenced by large-scale circulation patterns
(e.g. El Niño, Indian Ocean Dipole)
Climate change will influence these phenomena
1 UNDP definition: Less than 1700 m3/person/day
2 Less than 1000 m3/person/day

4. Large-scale Circulation Patterns Water Levels in Reservoirs
Hypothesis
Large-scale Circulation Patterns
ENSO
IOD
Hydro-meteorological Parameters
Precipitation
Temperature
Evaporation
Wind
Streamflow, etc.
Water Levels in Reservoirs
Consumption
Losses, etc.

5. Our Approach Hydroinformatics Computational Intelligence
Hydrology, atmospheric science, computer science, statistics, and machine learning
Computational Intelligence
Artificial Neural Network

6. OVERVIEW OF DATA

7. Data Precipitation for Vacoas station Niño 3.4
1961 – 1990 (National Climate Data Center)
1991 – 2012 (Mauritius Meteorological Services)
Niño 3.4
Source: International Research Institute
Southern Oscillation Index (SOI)
Source: National Center for Atmospheric Research
Indian Ocean Dipole (IOD)
Source: Japan Agency for Marine-Earth Science and Technology

8. Precipitation at Vacoas
Seasonal patterns
Mostly below average precipitation spells

9. LARGE-SCALE CIRCULATION PATTERNS

10. El Niño Southern Oscillation (ENSO)
Normal Conditions
El Niño Conditions
[Madl, 2000]

11. ENSO El Niño Southern Oscillation (ENSO)
Most dominant large-scale circulation pattern
Affects the hydrological cycle around the globe
Recurrence pattern of 3 – 6 years
Normally lasts for around a year
ENSO: coupled ocean-atmosphere phenomenon
Sea surface temperature anomaly
Niño Indices (Niño 3, 4, 3.4, etc.)
Sea-level pressure difference
Southern Oscillation Index (SOI)

12. ENSO: Niño 3.4 & SOI Mexico Australia
(Source: National Oceanic and Atmospheric Administration)

13. Indian Ocean Dipole (IOD)
(Source: Australian Bureau of Meteorology)

14. Correlation Between ENSO/IOD with Precipitation
Precipitation, Niño 3.4, SOI, and IOD data were divided into two series
Winter: May to October
Summer: November to April
Averaged over each season
Pearson correlation

15. Correlation Between ENSO and IOD with Precipitation
Index
Period
Winter
Summer
Niño 3.4
0.237 (0.208)
0.031 (0.875)
0.065 (0.797)
0.296 (0.219)
0.161 (0.276)
0.075 (0.610)
SOI
−0.329 (0.076)
−0.015 (0.941)
0.042 (0.858)
−0.348 (0.113)
−0.184 (0.198)
−0.118 (0.411)
IOD
0.375 (0.041)
0.108 (0.572)
0.210 (0.388)
0.135 (0.482)
0.309 (0.031)
0.124 (0.394)

16. PREDICTION OF PRECIPITATION USING NEURAL NETWORKS

17. Artificial Neural Network
Inspired by structure of biological neural networks in the brain
Predict output based on inputs
Training phase
Multi-Layer Perceptron
Three layers: input, hidden, and output

18. Artificial Neural Network
Inputs
ENSO (Niño 3.4 and SOI)
IOD
Output
Precipitation
Winter
Summer
Monthly

19. Accuracy of Average Winter Precipitation using SOI
Average accuracy: 83%

20. Prediction of Yearly and Seasonal Average Precipitation

21. Prediction of Monthly Precipitation

22. Conclusion Mauritius is a water stressed country
Mauritius cycles through short and long-duration droughts
Precipitation prediction is crucial for short, medium, and long-term water resources management
Precipitation is influenced by large-scale circulation patterns
ENSO and IOD
Winter precipitation can predicted from ENSO and IOD
Not the case for summer

23. Conclusion Prediction with ANN ANN outperforms regression and ARIMA
83% accuracy in predicting winter precipitation using SOI
ANN outperforms regression and ARIMA
ANN captures non-linear relationships
More accurate prediction for winter precipitation
Both regression and ANN

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