1. http://hydro.iis.u-tokyo.ac.jp/GAME-T/ http://game-t.nrct.go.th/GAME-T/ Research Inst. for Humanity and Nature http://www.chikyu.ac.jp (also at IIS, University of Tokyo) Taikan Oki Water Resources Application Project (WRAP)

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

3. http://hydro.iis.u-tokyo.ac.jp/GAME-T/ http://game-t.nrct.go.th/GAME-T/ Water Situation in Drought 1992-1994 Data Source: EGAT (for whole year) Planning Problem Planning Problem Possible Solution: Reliable Long-term Hydroclimatic Prediction

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

5. http://hydro.iis.u-tokyo.ac.jp/GAME-T/ http://game-t.nrct.go.th/GAME-T/ Model Used for Long-term Hydroclimatic Prediction PURPOSE TYPE MODEL Physically Based Model Understand the physical mechanisms Climate Model (GCM, AGCM, etc.) Statistically Based Model More Accurate Predicted Result (for real application) Linear Regression Model Generalized Additive Model Artificial Neural Networks, etc. Artificial Neural Network (ANN) Use limited data Computational skill in complex problem No assumption needed as other statistical models Updating parameters process Manusthiparom (2003) By Manusthiparom (2003)

6. http://hydro.iis.u-tokyo.ac.jp/GAME-T/ http://game-t.nrct.go.th/GAME-T/ Methodology Used for Hydroclimate Prediction Influence of ENSO On rainfall and streamflow 1.1 El Niño/La Niña composites 1.2 Categorical contingency analysis1 Prediction process 2.1 Prediction by ANN modeling 2 Improvement and extension 3.1 Prediction using additional predictors 3.2 Input Sensitivity Analysis 3.3 Spatial rainfall prediction 3 Potential use of prediction for improved WRM system 4.1 Irrigation Water Demand Forecasting 4 Manusthiparom (2003) By Manusthiparom (2003)

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

8. http://hydro.iis.u-tokyo.ac.jp/GAME-T/ http://game-t.nrct.go.th/GAME-T/ Rainfall Anomaly Prediction 12 months ahead Testing Training Case 1 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, 5 yrs) SSTs: 3 areas

9. http://hydro.iis.u-tokyo.ac.jp/GAME-T/ http://game-t.nrct.go.th/GAME-T/ Case 3: Train (1962-1994, 33 yrs), Test (1995-1999, 5 yrs) Target Error=15% Case 2: Train (1962-1989, 28 yrs), Test (1990-1999, 10 yrs) Large Error Drought Target Error=15% Smaller Error Bad Pattern Rainfall Prediction 12 months ahead SSTs: 3 areas

10. http://hydro.iis.u-tokyo.ac.jp/GAME-T/ http://game-t.nrct.go.th/GAME-T/ Spatial Rainfall Prediction Rainfall Anomaly in August 1997-1999 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 One Month Ahead 199719981999 Manusthiparom (2003) By Manusthiparom (2003)

11. http://hydro.iis.u-tokyo.ac.jp/GAME-T/ http://game-t.nrct.go.th/GAME-T/ Date: 2 November 2002 Project: Krasieo Operation and Maintenance Project, Royal Irrigation Department Location: Suphanburi, Thailand 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 Water Manager Irrigation Eng. Learning Existing System of WRM from Water Manager Meeting and Interview Water Users Water Users (Agriculture) Drought is the most serious problemDrought is the most serious problem They want to know how much water will be available for them in next growing seasonThey want to know how much water will be available for them in next growing season Water Manager (RID) They want to know how much water will be available in next seasonThey 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) They want to improve the existing system if it is easy to understand and easy to do in practice) 5. POTENTIAL USE OF PREDICTION IN IMPROVING WATER RESOURCES MANAGEMENT SYSTEM

12. http://hydro.iis.u-tokyo.ac.jp/GAME-T/ http://game-t.nrct.go.th/GAME-T/ Rainfall Irrigation Water Demand (IWD) Anomaly Value Absolute Value Drought:1993 Forecasted Irrigation Water Demand Using long-term mean:120.61 mcm Using observation:150.25 mcm Using prediction:147.61 mcm Mae Ngat Irrigation Project, Chiang Mai Using predicted rainfall is better Using predicted rainfall is worse 12-month ahead forecasted IWD Manusthiparom (2003) By Manusthiparom (2003)

13. http://hydro.iis.u-tokyo.ac.jp/GAME-T/ http://game-t.nrct.go.th/GAME-T/ Water Situation in Drought 1992-1994 Forecasted Irrigation Water Demand Using long-term mean:120.61 mcm Using observation:150.25 mcm Using prediction:147.61 mcm IWD 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 m 2 1 km 2 = 625 rai Drought: 1993 Potential Use of Forecasted IWD to improve Planning System Water scarcity situation in 1994 should have been improved. 5,953 6,202 4,373 Manusthiparom (2003) By Manusthiparom (2003)

14. http://hydro.iis.u-tokyo.ac.jp/GAME-T/ http://game-t.nrct.go.th/GAME-T/ 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. http://hydro.iis.u-tokyo.ac.jp/GAME-T/ http://game-t.nrct.go.th/GAME-T/ Thank you!