Research Inst. for Humanity and Nature chikyu.ac.jp (also at IIS, University of Tokyo)

1. Water Resources Application Project (WRAP) Research Inst. for Humanity and Nature http://www.chikyu.ac.jp (also at IIS, Universityof Tokyo) Taikan Oki

2. Made by RID (the most critical problem) Sirikit Bhumipol Almost dry up !!! 8 Sub River Basin 6 =Ping 7 =Wang 8 =Yom 9 =Nan 10 =Main Chao Phraya 11 =Sakakrang 12 =Pasak 13 =Thachin Drought Data Source: RID, 2001; PAL and Panya ,1999. 1. Water Resources and Water Problems in the Study Area Study Area Water-related Problems • Flooding • Drought • Water Pollution • Excessive Groundwater Extraction Criteria: the most critical problem • Recovery Cost • Duration of problem • Frequency of occurrence • Future perspective

3. Water Situation in Drought 1992-1994 Data Source: EGAT (for whole year) Planning Problem Possible Solution: Reliable Long-term Hydroclimatic Prediction

4. SOI = (Standardized Tahiti - Standardized Darwin) / MSD Bureau of Meteorology (BOM) Australia, AU Upper Ping River Basin Sea Surface Temperature (SST) Thai Meteorological Department (TMD), TH Royal Irrigation Department (RID), TH Global Energy and Water Cycle Experiment (GEWEX), Asian Monsoon Experiment - Tropics (GAME-T) The British Atmospheric Data Centre (BADC), UK -GISST_2.3B Dataset (1 degree x 1 degree) 2.DATA AND METHODOLOGY USED IN HYDROCLIMATIC PREDICTION Data Used(Monthly 1960-2000) • Rainfall (RF) and Streamflow • Southern Oscillation Index (SOI) • Sea Surface Temperature (SST)

5. Understand the physical mechanisms Physically Based Model Climate Model (GCM, AGCM, etc.) Linear Regression Model Generalized Additive Model Artificial Neural Networks, etc. More Accurate Predicted Result (for real application) Statistically Based Model • Use limited data • Computational skill in complex problem • No assumption needed as other statistical models • Updating parameters process Artificial Neural Network (ANN) Model Used for Long-term Hydroclimatic Prediction MODEL PURPOSE TYPE By Manusthiparom (2003)

6. Influence of ENSO On rainfall and streamflow 1.1 El Niño/La Niña composites 1.2 Categorical contingency analysis 1 Prediction process 2 2.1 Prediction by ANN modeling 3.1 Prediction using additional predictors 3.2 Input Sensitivity Analysis 3.3 Spatial rainfall prediction Improvement and extension 3 Potential use of prediction for improved WRM system 4 4.1 Irrigation Water Demand Forecasting Methodology Used for Hydroclimate Prediction By Manusthiparom (2003)

7. Feed-Forward Direct Multi-step Network Physical considerations, Correlation analysis SST&RF, SOI& RF, RF& RF Determination of Input Nodes 1 Determination of No.of Hidden Layers and Hidden Nodes Trial and error process Hidden layer=1 No. of hidden node=5-10 2 Adaptive process with changing initial weighting parameters Good pattern=97 % (target error=15%) Training Process (Weighting factors) 3 3. LONG-TERM RAINFALL PREDICTION BY ANN MODELING APPROACH Difficulties in Using ANN Modeling

8. Training Testing Rainfall Anomaly Prediction 12 months ahead SSTs: 3 areas Case 1: Train (1962-1979, 18 yrs), Test (1980-1999, 20 yrs) Case 2: Train (1962-1989, 28 yrs), Test (1990-1999, 10 yrs) Case 3: Train (1962-1994, 33 yrs), Test (1995-1999, 5yrs)

9. Target Error=15% Large Error Drought Bad Pattern Case 3: Train (1962-1994, 33 yrs), Test (1995-1999, 5 yrs) Smaller Error Target Error=15% Bad Pattern SSTs: 3 areas Rainfall Prediction 12 months ahead Case 2: Train (1962-1989, 28 yrs), Test (1990-1999, 10 yrs)

10. 1997 1998 1999 Spatial Rainfall Prediction Rainfall Anomaly in August 1997-1999 One Month Ahead Observation • Software: Surfer 8 • Total: 16 stations • Gridding method: Kriging • Variogram model: Linear • Slope =1.0, Aniso= 1,0 • Kriging type: point • Drift type: None • No search: Use all data (16) Prediction By Manusthiparom (2003)

11. Date: 2 November 2002 Project: Krasieo Operation and Maintenance Project, Royal Irrigation Department Location: Suphanburi, Thailand Water Manager Irrigation Eng. Learning the existing system of WRM Period: 4-15 November 2002 Tutor: Mr. Sombat Sontisri (Irrigation Eng.) Chief: Mr. Pongsak Arunwichitsakul Water Allocation Group Office of Hydrology & Water Management Royal Irrigation Department (RID), Thailand 5. POTENTIAL USE OF PREDICTION IN IMPROVING WATER RESOURCES MANAGEMENT SYSTEM Meeting and Interview Water Users • Water Users (Agriculture) • Drought is the most serious problem • They want to know how much water will be available for them in next growing season Learning Existing System of WRM from Water Manager • Water Manager (RID) • They want to know how much water will be available in next season • They want to improve the existing system if it is easy to understand and easy to do in practice)

12. Rainfall Anomaly Value Absolute Value Irrigation Water Demand (IWD) Using predicted rainfall is worse Using predicted rainfall is better 12-month ahead forecasted IWD Drought:1993 Mae Ngat Irrigation Project, Chiang Mai Forecasted Irrigation Water Demand Using long-term mean: 120.61 mcm Using observation: 150.25 mcm Using prediction: 147.61 mcm By Manusthiparom (2003)

13. IWD Water Situation in Drought 1992-1994 Water scarcity situation in 1994 should have been improved. 5,953 6,202 4,373 Potential Use of Forecasted IWD to improve Planning System Drought:1993 Forecasted Irrigation Water Demand Using long-term mean: 120.61 mcm Using observation: 150.25 mcm Using prediction: 147.61 mcm Adjustment of Irrigation Area for 120.61 mcm Based on long-term mean: 30,000 rai (120.61/120.61*30,000) Based on observation: 24,081 rai (120.61/150.25*30,000) Based on prediction: 24,502 rai (120.61/147.61*30,000) 1 rai =1,600 m2 1 km2 = 625 rai By Manusthiparom (2003)

14. Summary • ANN can predict monthly rainfall a year ahead with fairly good accuracy based on SST, SOI, and preceding rainfall. • Seasonal prediction of rainfall will substantially contribute for better water resources/reservoir operations. • GAME-T Database is there: http://game-t.nrct.go.th/GAME-T/ • New research opportunities under GAME-Tropics/Phase II and WRAP for everybody!

15. Thank you!