GENERATING STREAMFLOW FORECASTS FOR THE SOUTHEASTERN/SOUTHERN

BRAZILIAN HYDROPOWER PRODUCTION USING EUROBRISA´S INTEGRATED

RAINFALL CLIMATE FORECASTS

Alexandre K. GuetterUniversity of Parana Brasil

EUROBRISA Final Workshop, Barcelona, 13-16/Dec/2010

PROBLEM DESCRIPTIONStreamflow Monthly ForecastingFor 3-month Forecasting Horizon

At 68 specific basins (average basin scale ~ 40.000 km2)

• Brazilian Energy Supply:Market Size: EU$100 billion/yr

Hydropower: 75%

Thermal: 15%

Nuclear: 2%

Import: 8%Brazil

NO- North

NE-Northeasten

SE – Southeast/ Midwest

SU - Southern

• 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

% 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

HYDROPOWER ENERGY: PHYSICAL CONCEPTS

Q = FLOW THROUGH

TURBINES (m3/s)

H = HEAD (m)DAM AND

INTAKE

RESERVOIR WATER LEVEL

POWER GRID CONNECTION

TURBINE

GENERATOR

CONDUIT

POWERHOUSE

DOWNSTREAM

WATER LEVEL

DRAFT TUBE

3

* [KWh]

time [h]

Q = flow [m /s] large variation

H = head [m] assumed constant

ENERGY t gQH

t

IF WE COULD PREDICT STREAMFLOW => THEN WE CAN PREDICT THE ENERGY PRODUCTION FOR EACH POWERPLANT

HYDROPOWER SYSTEM COMPLEXITY

Interconnected Hydropower System

Cascade – Centered in the Southeast51 reservoirs17 throughflowEquivalent Reservoir Concept

• 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

OBJECTIVE

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

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 ;

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

STUDY AREA: Southern/Southeasten Brazil

2.000.000 km2

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)

Study Area

Study Area - Southeastern

Study Area - Southern

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;

tQPtW

PEWt

W

PEW

iSii

ii

i

)1(

21

21

001

Data Joint Consistency Analysis• Monthly surface water balance

• Input data should be stationary• Soil-water variability estimates

3R Hydrologic Model

RESULTS – Southeastern Brazil FURNAS

Naturalized Streamflow Annual CycleFurnas – wet: October-April ; dry: May-September

FURNAS – Interannual Variability

Observed Monthly Streamflow Variability

• Intercomparison between observed-forecasted basin mean-areal precipitation (1987 – 2001)

ERROR Furnas

Average (mm/mês) 0,0Standard Deviation (mm/mês) 33,6

Correlation Coefficient 0,92

Percentil Raingauge Forecasting ∆ (%)0.1 15.5 19.6 26.5

0.33 56.7 57 0.50.5 97.2 110.7 13.9

0.67 158.2 161.7 2.20.9 245.5 237 -3.5

Data Joint Consistency Analysis• Soil Water Intercomparison

Hydrologic Model Calibration

Hydrologic Model Calibration

Soil Water Variability

Fluxo Observado (mm mes-1)

Simulado (mm mes-1)

Chuva 1412

Evaporação Potencial 1049

Evaporação Real 857

Vazão 539 542

Escoamento Base 234

Escoamento Superficial 308

Recarga do Aqüífero 12

Ave Error <1%

Obs Vs. Forecasted Streamflow: ρ=0.93

1-Month Streamflow Forecasting

Statistic Qprev (1 month) Qprev (2 months) Qprev (3 months)

Average Observed Streamflow

45

Standard DevObs Streamflow

29

Average ForecastedStreamflow

42 43 45

Standard DeviationObs Streamflow

19 20 24

Ave (Pred-Obs) -2.1 -1.0 1.6

Sdev (Pred-Obs) 19.3 19.4 20.0

Correlation(Pred-Obs)

0.76 0.76 0.74

* Dados de vazão e desvio padrão em mm/mês

Streamflow Forecasting Statistics

2-mon forecasting

3-mon forecasting

Intercomparison perfect rainfall forecasting

Statistics – Perfect rainfall forecasting

Statistic Qprev (1 month) Qprev (2 months) Qprev (3 months)

Average Observed Streamflow

45

Standard DevObs Streamflow

27

Average ForecastedStreamflow

42 43 45

Standard DeviationObs Streamflow

19 20 24

Ave (Pred-Obs) -2.5 -1.3 1.4

Sdev (Pred-Obs) 14.2 15.7 16.6

Correlation(Pred-Obs)

0.86 0.81 0.80

• Correlation Conditioned on Oct-Apr Streamflow for the <20% and >80% of the empirical distribution => 60% hit rate

CONCLUSIONS:For Southeastern Brazil

(which is generally regarded as having low predictability)

• 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;

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;

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

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;