1. GENERATING STREAMFLOW FORECASTS FOR THE SOUTHEASTERN/SOUTHERN BRAZILIAN HYDROPOWER PRODUCTION USING EUROBRISA´S INTEGRATED RAINFALL CLIMATE FORECASTS Alexandre K. Guetter University of Parana Brasil EUROBRISA Final Workshop, Barcelona, 13-16/Dec/2010

2. PROBLEM DESCRIPTION Streamflow Monthly Forecasting For 3-month Forecasting Horizon At 68 specific basins (average basin scale ~ 40.000 km 2 )

3. Brazilian Energy Supply: Market Size: EU$100 billion/yr Hydropower: 75% Thermal: 15% Nuclear: 2% Import: 8% Brazil Different hydrologic regimes grouped on a continental scale Installed Capacity Southeastern/Midwest: 63% Southern: 17% Northeastern: 14% Northern: 6% Assumptions About Seasonal Precipitation Forecasting Skill High: Southern, Northern, Northeastern Low: Southeastern

4. % of Brazil´s Energy Production Southeastern (Parana): 63% EU$50 billion/yr market share Southern (Uruguai): 17% EU$13 billion/yr market share STUDY AREA: PARANA BASIN + URUGUAI BASIN

5. HYDROPOWER ENERGY: PHYSICAL CONCEPTS Q = FLOW THROUGH TURBINES (m 3 /s) H = HEAD (m) DAM AND INTAKE RESERVOIR WATER LEVEL POWER GRID CONNECTION TURBINE GENERATOR CONDUIT POWERHOUSE DOWNSTREAM WATER LEVEL DRAFT TUBE IF WE COULD PREDICT STREAMFLOW => THEN WE CAN PREDICT THE ENERGY PRODUCTION FOR EACH POWERPLANT

6. HYDROPOWER SYSTEM COMPLEXITY Interconnected Hydropower System Cascade – Centered in the Southeast 51 reservoirs 17 throughflow Equivalent Reservoir Concept

7. Current operational energy programming tools: – Equal probability streamflow scenarios based on synthetic time series generation for each basin preserving spatial correlation through the statistics for 68 locations We propose: – Monthly streamflow forecasting using EUROBRISA´s integrated rainfall forecasting as input data for a basin calibrated rainfall-runoff-routing model + raingauge data for basin mean-areal precipitation

8. OBJECTIVE Check whether the state of the science for seasonal climate forecasting is already useful for hydropower optimization programming

9. ACTIVITIES Hydrologic model calibration for 8 large basins within the Southern/Southeastern areas; Calibration of the hydrologic model updating parameters (we neglected updating); 1987-2001 EUROBRISA´s rainfall hindcast as input data for the hydrologic model; Evaluation of Indices for Streamflow Forecasting Accuracy ;

10. 1987-2001 EUROBRISA´s HINDCAST Four Climate Dynamic Models – System 3 (ECMWF) – GloSea (UKMO) – Méteo-France – CPTEC One Empirical Model (CPTEC) Integrated product – 5 Model Bayesian Combination

11. STUDY AREA: Southern/Southeasten Brazil 2.000.000 km 2

12. Study Area 5 large basins in Southern/Southeastern Brazil 6 reservoirs sampling each one of the large basins + 2 on the Parana River Furnas (Grande River) Emborcação (Paranaiba River) Foz do Areia (Iguacu River) Ilha Solteira (Parana River) Itaipu (Parana River Itá (Uruguai River)

13. Study Area

14. Study Area - Southeastern

15. Study Area - Southern

16. Data Raingauge at selected locations (monthly), instead of GPCP gridded rainfall; Naturalized streamflow series (monthly); Potential Evapotranspiration; EUROBRISA´s integrated rainfall forecasting for 1987-2001;

17. Data Joint Consistency Analysis Monthly surface water balance Input data should be stationary Soil-water variability estimates

18. 3R Hydrologic Model

19. RESULTS – Southeastern Brazil FURNAS

20. Naturalized Streamflow Annual Cycle Furnas – wet: October-April ; dry: May-September

21. FURNAS – Interannual Variability

22. Observed Monthly Streamflow Variability

23. Intercomparison between observed-forecasted basin mean- areal precipitation (1987 – 2001) ERRORFurnas Average (mm/mês)0,0 Standard Deviation (mm/mês)33,6 Correlation Coefficient0,92

24. PercentilRaingaugeForecasting (%) 0.115.519.626.5 0.3356.7570.5 97.2110.713.9 0.67158.2161.72.2 0.9245.5237-3.5

25. Data Joint Consistency Analysis Soil Water Intercomparison

26. Hydrologic Model Calibration

27. Soil Water Variability FluxoObservado (mm mes -1 ) Simulado (mm mes -1 ) Chuva1412 Evaporação Potencial1049 Evaporação Real857 Vazão539542 Escoamento Base234 Escoamento Superficial308 Recarga do Aqüífero12 Ave Error <1% Obs Vs. Forecasted Streamflow: ρ=0.93

28. 1-Month Streamflow Forecasting

29. StatisticQ prev (1 month)Q prev (2 months)Q prev (3 months) Average Observed Streamflow 45 Standard Dev Obs Streamflow 29 Average Forecasted Streamflow 424345 Standard Deviation Obs Streamflow 192024 Ave (Pred-Obs)-2.11.6 Sdev (Pred-Obs)19.319.420.0 Correlation (Pred-Obs) 0.76 0.74 * Dados de vazão e desvio padrão em mm/mês Streamflow Forecasting Statistics

30. 2-mon forecasting 3-mon forecasting

31. Intercomparison perfect rainfall forecasting

32. Statistics – Perfect rainfall forecasting StatisticQ prev (1 month)Q prev (2 months)Q prev (3 months) Average Observed Streamflow 45 Standard Dev Obs Streamflow 27 Average Forecasted Streamflow 424345 Standard Deviation Obs Streamflow 192024 Ave (Pred-Obs)-2.5-1.31.4 Sdev (Pred-Obs)14.215.716.6 Correlation (Pred-Obs) 0.860.810.80

33. Correlation Conditioned on Oct-Apr Streamflow for the 80% of the empirical distribution => 60% hit rate

34. CONCLUSIONS: For Southeastern Brazil (which is generally regarded as having low predictability)

35. Streamflow forecasting was surprisingly accurate with regard to hydrograph phase and intensity; Streamflow forecasting identified whether the rainy season started at the expected month; Streamflow forecasting identified both wet and dry spells;

36. IN REGARD TO MODELLING: Raingauge rainfall (local data) should be used for model calibration and both simulation of past events and climatology for rainfall forecasting; Basin model calibration is necessary to achieve streamflow forecasting accuracy required for hydropower programming;

37. WHAT WE HAVE ALREADY ACHIEVED: Seasonal forecasting is very useful for the Southeasten Region (60% of Brazilian hydropower generation) – strong annual cycle; Seasonal forecasting is somewhat useful for the Southern Region (15% of Brazilian hydropower generation) – almost uniform annual cycle

38. WHAT WE PLAN TO ACHIEVE IN 2011 Analysis for the Northeastern Region (14% of Brazilian hydropower generation) – strong annual cycle; Analysis for the Northern Region (6% of Brazilian hydropower generation) – stong annual cycle; Develop an aggregate energy model (Method of Natural Energy) to estimate economic value;