Practices of Downscaling Methods for Water Resources Management in Sri Lanka
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This document discusses methods for downscaling water resources management predictions in Sri Lanka. It examines using statistical downscaling with seasonal climate models (SCM), spatial synoptic classification (SSE), and multi-model regression (MRG) to predict rainfall and temperature in the OND season. SCM achieved a 44% success rate for both rainfall and temperature predictions. Dynamic downscaling was also explored using RegCM4 software for the South-West monsoon season, which achieved results consistent with past data. Further long-term simulations with RegCM3 and more station data are recommended to improve predictions.
Practices of Downscaling Methods for Water Resources Management in Sri Lanka
- 1. Practices of Downscaling Methods for Water Resources Management in Sri Lanka Sewwandhi Chandrasekara Sanjaya Rathnayake
- 2. Introduction • Sri Lanka owns different types of water resources .Click>> • Historically Sri Lanka had great hydraulic civilization • Presently water resources management is essential to Sri Lanka
- 3. – 103 river basins (4560 km) (Ministry of Forestry and Environment, 1999) – More than 20 major wetlands – Minor and major irrigation systems – Groundwater resources – Coastal and marine resources. Click>> • Sri Lanka – 2400 m3 of available per capita water resources – 2000 mm of average annual RF (Ariyabandu, 2008) click>>
- 4. Click>>
- 5. Historically • Great ancient hydraulic civilization in Sri Lanka – Water used for • Irrigation • Domestic requirements • Urban recreations • Small scale industries – However • Water was managed properly – Sustainably – Participatory
- 7. But….. • Temporal and spatial water scarcity • Pollution • Agrarian demand to sectoral demand. Click>> – Industrial demand – Environmental demand – Electricity demand – Recreational demand Sustainable water resources management is essential… Click>>
- 8. Click>>
- 9. Why prediction is important?
- 10. Threats for the water management
- 11. Statistical Downscaling… • Downscaling for – Rainfall OND • Second inter-monsoon period • 16% of quantity • Important to land preparations for “Maha” season (64% of paddy cultivation) – Temperature OND • Average 270C
- 12. Using SCM…. 0.2 mm wetter than the normal
- 13. 44% of success rate
- 14. Using SSE… 0.2 –0.4 mm wetter than the normal
- 15. 44% of success rate
- 16. Using MRG…. 0.2 –0.8 mm wetter than the normal
- 18. Using SSE…. 0.2 0C warmer than normal
- 20. Using MRG… 0.2 0C warmer than normal
- 22. Using SCM… 0.2 0C warmer than normal
- 23. 55% success rate
- 24. Conclusions • For OND season SSE & SCM can used to predict rainfall at 44% success rate • For OND season SCM can used to predict temperature at 44% success rate Unable to predict rainfall for the next season, because the stations what we selected were failed at screening
- 25. Using Dynamic Downscaling • Software : RegCM 4 • Predicted Region : Sri Lanka • Latitude : 5 N – 10 N • Longitude : 75 E – 85 E • Duration : 1998 June
- 26. Westerly Wind
- 27. Southerly Wind
- 29. Air Temperature
- 31. Water Vapor
- 32. Cloud Water
- 33. Surface Pressure
- 36. Total Soil Water in mm H2O
- 38. Conclusion • RegCM 4.0 is very usable to predict South-west monsoon to Sri lanka • Because past experiences showed the same results observed from the RegCM 4.0 for the same monsoon season
- 39. Further Work • Simulate long term predictions via RegCM3 • Upload more applicable Sri Lankan station precipitation data for CLIK on-line application
- 40. Acknowledgement • Dr. Lareef Zubair and staff at Foundation of Environment and Climate Technologies, Digana. Sri Lanka. • Organizing Committee and Lecturers APEC Climate Center. South Korea. • Colleagues who participated with us.
- 41. Thanking you! Sewwandhi Chandrasekara APEC Climate Center Sanjaya Ratnayake South Korea (Sri Lanka)