Department of Civil and Environmental Engineering Universidade Federal do Paraná Curitiba, Paraná

Department of Civil and Environmental EngineeringUniversidade Federal do Paraná

Curitiba, ParanáBrasil

Alexandre Guetter

Streamflow Prediction Validation with the Application of Large Basin Rainfall-Runoff

Model

Second EUROBRISA Workshop - Dartmoor, Devon, UK - 21-24 July 2009Second EUROBRISA Workshop - Dartmoor, Devon, UK - 21-24 July 2009


Overview of the Brazilian Hydropower System

• Total annual power production: 445 TWh

• Annual rate of increase: 6%

• State-run companies: 90%

• Demand:

Hydropower (>30 MW): 72.6%

Hydropower (<30MW): 1.7%

Thermal: 14.7%

Nuclear: 2.5%

Import: 8.5%

Brazil

World


Overview of the Brazilian Hydropower System

• Electricity Demand per Sector:

Industrial: 47%

Residential: 22%

Services: 14%

Other: 17%

• Hourly Demand Curve

Peak at 6 pm (residential)

Base: hydro+thermal

Peak: hydro


Power production Integrated System

NO- North

NE-Northeasten

SE – Southeast/ Midwest

SU - Southern

• Different regional climates grouped on a continental scale

• Installed Capacity

Southeastern/Midwest: 63%

Southern: 17%

Northeastern: 14%

Northern: 6%

• Seasonal Precipitation Forecasting Skill

Adequate: SU-NE-NO

Low: SE


Hydropower Integrated System

SE63%

SU17%

NE14%

NO6%

Capacidade instaladaCapacidade instalada

SE68%

SU6%

NE21%

NO5%

Energia armazenada máximaEnergia armazenada máxima

Installed Capacity

Energy Storage

• Well-defined rainy season

Southeastern/Midwest: SON-DJF

Northeastern: FMAM (largest storage)

• Quasi-uniform rainfall

Southern (smallest storage)

• What do we want to predict:

Southeastern: in August if SON will have little rainfall – energy production

Southeastern: in November if DJF will be very rainy – flood control

Northeastern: in January if FMAM will have little rainfall


The size of our problem:

NO- North

NE-Northeasten

SE – Southeast/ Midwest

SU - Southern

• About 60 large hydropower stations: require seasonal streamflow prediction


Pilot Study: Streamflow Prediction Validation• 20 prediction points in 11 large basins

• 9 basins with 2 points: the most upstream/downstream hydroplants


Pilot Study: Activities

• Naturalized streamflow analysis (1931-2007)

• Diagnostic study of the association between high/low flows and SST anomalies (what is the climate model skill for such forcings?)

• Data collection: raingauge mean-areal rainfall (variable period)

• Large-scale rainfall-runoff model calibration

• Streamflow assimilation: hydrologic model state updating

• Validation study: use monthly precipitation forecast as input data for the state-updated rainfall-runoff model (1981-2005)


Naturalized Streamflow Analysis – Iguaçu River

IGUAÇU - FOZ DO AREIA - ANNUAL CYCLE8

48

11

08

98

1

70

0

12

80

13

34

13

34

11

26

15

03

13

95

10

48

83

9

26

8

30

3

31

3

24

1

18

0 24

0

22

3

17

6 23

8 33

7

27

9

21

5

0

200

400

600

800

1000

1200

1400

1600

1800

2000

JAN

FE

B

MA

R

AP

R

MA

I

JUN

JUL

AU

G

SE

P

OC

T

NO

V

DE

C

MÊS

ST

RE

AM

FL

OW

(m

3 /s)

AVERAGE

85%

15%


Naturalized Streamflow Analysis – Iguaçu River

IGUAÇU - FOZ DO AREIA - ANNUAL CYCLE

84

8

11

08

98

1

70

0

12

80

13

34

13

34

11

26

15

03

13

95

10

48

83

9

26

8

30

3

31

3

24

1

18

0 24

0

22

3

17

6 23

8 33

7

27

9

21

5

0

200

400

600

800

1000

1200

1400

1600

1800

2000

JAN

FE

B

MA

R

AP

R

MA

I

JUN

JUL

AU

G

SE

P

OC

T

NO

V

DE

C

MÊSS

TR

EA

MF

LO

W (

m3 /s

)

AVERAGE

85%

15%

0

5

6

7 7

6 6

3

6

3

1 1

0 0 0

2

1 1 1

2

0

1

0 0 0 0 0 0 0 0 0

1

0 0 00

2

4

6

8

10

12

14

16

100

200

300

400

500

600

700

800

900

1000

1100

1200

1300

1400

1500

1600

1700

1800

1900

2000

2100

2200

2300

2400

2500

2600

2700

2800

2900

3000

3100

3200

3300

3400

3500

STREAMFLOW (m3/s)

NO

. OF

CA

SE

S (

TO

T=

60

)


High Flows and SST anomalies – Iguaçu River

High Flows Low Flows

ENSO

SouthernAtlantic Run Updated

Hydrologic Model

Ensemble StreamflowForecast

Select Precipitation Data from Historic Record Associated with

Forecast Climate Category

DynamicModelForecast

Streamflow Forecasting Using Either Resampled or Predicted Rainfall

Second EUROBRISA Workshop - Dartmoor, Devon, UK - 21-24 July 2009

Rainfall – Runoff Model


Rainfall-Runoff Model With State-Updating


Rainfall-Runoff Model With State-Updating

Hydrologic Model State Updating

αu e αp : fatores de ponderação, a calibrar

U : covariance matrix of input errors

W : covariance errors of model parameters

M(t) : input data sensitivity matrix

N(t) : model parameter sensitivity matrix

)t(NW)t(N)t(MU)t(MQ Tp

Tu


Streamflow Prediction Using Resampled Rainfall

IGUAÇU - FOZ DO AREIA - PREVISÃO: MAR/200540

5

274

206 26

8 397

569

388

404 44

9

851

772

622

542

498

716

0

200

400

600

800

1000

1200

1400

1600

1800

DE

C(i

-1)

JAN

(i)

FE

B(i

)

MA

R(i

)

AP

R(i

)

MA

I(i)

JUN

(i)

JUL

(i)

AU

G(i

)

SE

P(i

)

OC

T(i

)

NO

V(i

)

DE

C(i

)

JAN

(i+

1)

FE

B(i

+1

)

MÊS

VA

ZÃ

O IN

CR

EM

EN

TA

L (

m3/s

)

CLIMATOLOGY

85%

15%

AVE-ENSEMBLE

MONITORAMENTO PREVISÃO

0

100000

200000

300000

400000

500000

600000

700000

800000

oct

nov

dec

jan

feb

mar apr

may jun jul

aug

sep

Str

eam

flo

w (

cfs)

* This results are not for the Iguaçu BasinHighest Simulation (1981-2005)

Lowest Simulation (1981-2005)

ForecastEnsemble

Streamflow Prediction Using Climate Model RainfallStreamflow Prediction Using Resampled Rainfall

Second EUROBRISA Workshop - Dartmoor, Devon, UK - 21-24 July 2009


Conclusions and Recommendations

• The diagnostic study of the relationship between high/low flows and SST anomalies was accomplished in year-1;

• The collection of raingauge precipitation data and the rainfall-runoff model calibration was accomplished in year-2;

• The streamflow prediction validation using seasonal rainfall prediction has not been completed yet;

• The sensitivity analysis of energy production to rainfall prediction skill is a task for year-3;

• Selection of period and basin for joint papers.

Department of Civil and Environmental Engineering Universidade Federal do Paraná Curitiba, Paraná

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