U.S. Department of the Interior

U.S. Geological Survey

Open-File Report 2014–1206

Low-Head Hydropower Assessment 

of the Brazilian State of São Paulo

Tietê River 

near Ibitinga, 

São Paulo, 

Brazil
Cover. Portion of a Landsat 8 image showing Tietê River near Ibitinga, São Paulo, Brazil. Image acquired September 2, 2014.
Low-Head Hydropower Assessment of the 

Brazilian State of São Paulo

By Guleid A. Artan, William M. Cushing, Melissa L. Mathis, and Larry L. Tieszen

Open-File Report 2014–1206

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

SALLY JEWELL, Secretary

U.S. Geological Survey

Suzette M. Kimball, Acting Director

U.S. Geological Survey, Reston, Virginia: 2014

For more information on the USGS—the Federal source for science about the Earth, its natural and living 

resources, natural hazards, and the environment, visit http://www.usgs.gov or call 1–888–ASK–USGS.

For an overview of USGS information products, including maps, imagery, and publications, 

visit http://www.usgs.gov/pubprod

To order this and other USGS information products, visit http://store.usgs.gov

Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the 

U.S. Government.

Although this information product, for the most part, is in the public domain, it also may contain copyrighted materials 

as noted in the text. Permission to reproduce copyrighted items must be secured from the copyright owner.

Suggested citation:

Artan, G.A., Cushing, W.M., Mathis, M.L., and Tieszen, L.L., 2014, Low-head hydropower assessment 

of the Brazilian State of São Paulo: U.S. Geological Survey Open-File Report 2014–1206, 15 p., 

http://dx.doi.org/10.3133/ofr20141206.

ISSN 2331-1258 (online)
iii

Contents

Abstract...........................................................................................................................................................1

Introduction.....................................................................................................................................................1

Assessment Area..................................................................................................................................1

Digital Elevation Model........................................................................................................................2

River Network Datasets.......................................................................................................................2

Regionalized Stream Discharge Characteristics ............................................................................2

Streamflow and Rainfall Data.............................................................................................................2

DEM Data Conditioning.................................................................................................................................3

Stream Conditioning.............................................................................................................................3

Stream Segmentation and Drop Estimations ...................................................................................5

Synthetic Streams Contributing Area Compared to Agência Nacional de Águas

Streamgages Contributing Area ...........................................................................................5

Assessment of the Streamflow Estimates.................................................................................................5

Mean Annual Flow................................................................................................................................6

Flow Duration Curves...........................................................................................................................6

Hydropower Assessment Results.............................................................................................................10

Hydropower Potential by Basin........................................................................................................11

Segment Hydropower Potential .......................................................................................................11

Available Hydropower Potential.......................................................................................................12

Developed and Reaming Hydropower ............................................................................................14

Web Mapping Service And Contour Level Extraction...........................................................................14

Summary........................................................................................................................................................15

References Cited..........................................................................................................................................15

Figures

1. Map showing the extent of the study area of the State of São Paulo, shown 

with the map of Brazil ..................................................................................................................2

2. Map showing the hydrologic units within the State of São Paulo .......................................3

3. Maps showing locations of the Agência Nacional de Águas gages that were 

used to estimate the distribution of rainfall and to validate estimated streamflow..........4

4. Maps showing stream networks from the Agência Nacional de Águas dataset

overlaid on synthetic streams created from the SRTMDEM.................................................5

5. Scatterplot with the 1:1 line of upstream area recorded in the Agência Nacional 

de Águas database and contributing area calculated from the digital elevation 

model for the same locations, and bar chart of percent disagreement between 

the two areas.................................................................................................................................6

6. Maps showing part of the study area showing mean annual precipitation fields,

and mean annual precipitation for the same areas when weighted by 

upstream area ...............................................................................................................................7

7. Graphs showing evaluation of the annual average streamflows estimated with the

hydrologic regionalization model compared to observed long-term annual mean

streamflows ...................................................................................................................................7
iv

8. Graphs showing comparison of Flow Duration Curves (FDCs) calculated from

log-Pearson distribution fit of observed annual mean flows with FDCs estimated

from the regionalized hydrologic equation of watersheds in the State of São Paulo.......8

9. Graphs showing observed flows at gages with long-term data at 40 percent, 

60 percent, and 70 percent exceedance probability plotted with flows estimated 

with the regionalized hydrologic equations and estimated with the modified 

regionalized equations.................................................................................................................9

10. Map showing major streams in São Paulo where the hydropower assessment 

was completed............................................................................................................................10

11. Graph showing three classes of low head/low power technologies ................................11

12. Pie diagram showing power category distribution for the potential annual mean

hydropower of all the streams that are entirely in the State of São Paulo.......................12

13. Map showing total annual mean hydropower potential of the 21 basins within the

State of São Paulo ......................................................................................................................12

14. Graph showing total low head/low power potential summarized by basin......................13

15. Map showing distribution of available power potential (annual mean power) of 

São Paulo energy resources among three hydropower classes .......................................13

16. The user interface of the Web mapping site that was developed for extraction

of contour lines from the DEM, and an example of an extracted contour line

product .........................................................................................................................................14

Table

1. Hydropower classes ..................................................................................................................11
v

Conversion Factors

SI to Inch/Pound

Multiply By To obtain

Length

millimeter (mm) 0.03937 inch (in.)

meter (m) 3.281 foot (ft)

kilometer (km) 0.6214 mile (mi)

Area

square meter (m2) 0.0002471 acre

square kilometer (km2) 247.1 acre

square meter (m2) 10.76 square foot (ft2)

square kilometer (km2) 0.3861 square mile (mi2)

Flow rate

cubic meter per second (m3/s) 35.31 cubic foot per second (ft3/s)

cubic meter per second per square 

kilometer [(m3/s)/km2]

91.49 cubic foot per second per square 

mile [(ft3/s)/mi2]

Elevation, as used in this report, refers to distance above the vertical datum.

Vertical and horizontal coordinate information is referenced to the World Geodetic System 

1984 (WGS 84).

NOTE TO USGS USERS: Use of hectare (ha) as an alternative name for square hectometer 

(hm2) is restricted to the measurement of small land or water areas. Use of liter (L) as a special 

name for cubic decimeter (dm3) is restricted to the measurement of liquids and gases. No prefix 

other than milli should be used with liter. Metric ton (t) as a name for megagram (Mg) should be 

restricted to commercial usage, and no prefixes should be used with it.

Low-Head Hydropower Assessment of the Brazilian State 

of São Paulo

By Guleid A. Artan,1 William M. Cushing,2 Melissa L. Mathis,3 and Larry L. Tieszen2

1

InuTeq ASRC, Contractor to the U.S. Geological Survey; work performed 

under contract G13PC00028.

2

U.S. Geological Survey.

3

SGT, Inc., Contractor to the U.S. Geological Survey; work performed 

under contract G10PC00044.

Abstract

This study produced a comprehensive estimate of the 

magnitude of hydropower potential available in the streams 

that drain watersheds entirely within the State of São Paulo, 

Brazil. Because a large part of the contributing area is outside of São Paulo, the main stem of the Paraná River was 

excluded from the assessment. Potential head drops were 

calculated from the Digital Terrain Elevation Data,which has 

a 1-arc-second resolution (approximately 30-meter resolution at the equator). For the conditioning and validation of 

synthetic stream channels derived from the Digital Elevation 

Model datasets, hydrography data (in digital format) supplied by the São Paulo State Department of Energy and the 

Agência Nacional de Águas were used. Within the study area 

there were 1,424 rain gages and 123 streamgages with longterm data records. To estimate average yearly streamflow, a 

hydrologic regionalization system that divides the State into 

21 homogeneous basins was used. Stream segments, upstream 

areas, and mean annual rainfall were estimated using geographic information systems techniques. The accuracy of the 

flows estimated with the regionalization models was validated. 

Overall, simulated streamflows were significantly correlated 

with the observed flows but with a consistent underestimation 

bias. When the annual mean flows from the regionalization 

models were adjusted upward by 10 percent, average streamflow estimation bias was reduced from -13 percent to -4 percent. The sum of all the validated stream reach mean annual 

hydropower potentials in the 21 basins is 7,000 megawatts 

(MW). Hydropower potential is mainly concentrated near 

the Serra do Mar mountain range and along the Tietê River. 

The power potential along the Tietê River is mainly at sites 

with medium and high potentials, sites where hydropower has 

already been harnessed. In addition to the annual mean hydropower estimates, potential hydropower estimates with flow 

rates with exceedance probabilities of 40 percent, 60 percent, 

and 90 percent were made.

Introduction

This report describes methods and processes used to 

produce a hydropower assessment for the Brazilian State of 

São Paulo by the U.S. Geological Survey (USGS). The work 

was done on behalf of the Corporación Andina de Fomento 

(CAF) – Development Bank of Latin America. The assessment 

emphasizes low-head hydropower potential. Typically turbines 

that operate on streams with drops in elevation that are less 

than 20 meters (m) are classified as low-head hydropower. 

Low-head hydropower plants usually do not require damming 

of the streams; therefore, they have fewer negative environmental impacts than high-head hydropower plants and they 

offer the opportunity to provide off-grid power in remote areas 

where the installation of power grids often is prohibitively 

expensive. Assessment of hydropower potential requires calculating the drops in elevation and estimating the potentially 

available streamflow with associated exceedance probability 

levels for all stream segments that are within the study area.

In the subsequent sections of this report, we introduce 

the data and methods used to produce a hydropower potential assessment for the Brazilian State of São Paulo. The 

objective of the work was to build datasets that can be used 

for detailed feasibility studies on the development of small 

hydropower plants; these studies will be done by a Brazilian 

consulting firm.

Assessment Area

The hydropower assessment was completed for all the 

streams located entirely within the State of São Paulo (fig. 1). 

We excluded streams that the State of São Paulo shares with 

other Brazilian states; for example, no assessment was carried 

out for the main stem of the Paraná River.
2 Low-Head Hydropower Assessment of the Brazilian State of São Paulo

Digital Elevation Model

Potential head drops and stream connectivity were calculated using a Digital Elevation Model (DEM) from the Shuttle 

Radar Topography Mission (SRTM) Digital Terrain Elevation 

Data (DTED2), which has a 1-arc-second resolution (approximately 30-m at the equator) (Farr and others, 2007). The data 

are from the National Aeronautics and Space Administration 

(NASA) and the National Geospatial-Intelligence Agency 

(NGA), and are archived by the USGS Earth Resources 

Observation and Science (EROS) Center. The USGS and NGA 

have an agreement to improve the accuracy of the DEM and 

to archive the data. Even though the SRTM DEM is restricted 

from public access, this agreement allows the USGS to process the full resolution 1-arc-second DEM data and to create 

derivative products for most of South and Central Americas.

River Network Datasets

The São Paulo State Department of Energy and the 

Agência Nacional de Águas (ANA) supplied the USGS with 

hydrography data (in digital format). This data layer was used 

for the conditioning and validation of synthetic stream channels derived from the DEM datasets. The ANA hydrography 

data layer was digitized from 411 topographic sheets at a 

1:50,000 scale in a horizontal datum South American Datum 

1969 (SAD-69) and Lambert Conformal Conic projection. 

To have more confidence the synthetic streams derived from 

the DEM, which is the basis for the hydropower assessment, the ANA hydrography layer was used to validate the 

drainage network of the synthetic streams and to eliminate 

erroneous streams.

Regionalized Stream Discharge Characteristics

Streamflow data used to assess hydropower potential 

were produced using the hydrologic regionalization method 

of São Paulo State (Liazi and others, 1988). The hydrologic 

regionalization divides the State into 21 homogeneous regions 

(fig. 2). Estimating flow rates using the São Paulo State 

regionalization method on the synthetic streams required two 

inputs: the spatial extent and the mean annual rainfall of the 

upstream contributing area to the location of interest. The 

hydrologic regionalization models for estimating the mean 

annual flow rate (Liazi and others, 1988) are of the form

Q=(a+b*P)*Area/1,000 (1)

where 

Q is the mean annual flow (m3/sec), 

a and b are model parameters that are basin specific, 

P is the mean annual precipitation of the 

contributing watershed area above the 

segment end in millimeters (mm), and

Area is the contributing area to the stream segment 

where the computation is done (km2).

Streamflow and Rainfall Data

Stream and rain gage data used for this study are from 

the ANA hydrological information system Website Sistema 

de Informações Hidrológicas-HidroWeb (http://hidroweb.ana.

gov.br). Data used were from 1,424 rain gages with at least 

10 years of data records and 123 streamgages with at least 

28 years of data records. The rain gage stations were well 

distributed throughout the study area and sufficient to characterize annual rainfall (fig. 3A). The streamgages were more 

Figure 1. The extent of the study area

(shaded black) is the State of São Paulo, 

shown with the map of Brazil.

0 500 1,000 KILOMETERS

0 500 1,000 MILES

São Paulo

N Paraná River

BRAZIL

Pacific Ocean

Atlantic Ocean
DEM Data Conditioning 3

densely distributed in the southeastern section and had minimal coverage in western portions of São Paulo (see fig. 3B).

The spatial mean annual rainfall for the study area was 

estimated from the ANA rainfall database using an inverse 

distance weighted interpolation method. The most recent 

streamgage data are from the early 1990s.

DEM Data Conditioning

Potential head drops and stream connectivity were 

calculated from the DTED2 DEM dataset. For some areas, 

especially areas with low relief, synthetic streams derived 

from DEM data could diverge from the real conditions hydrographically present on the ground. Hydrological conditioning 

is the process of modifying elevation pixels from the DEM to 

improve stream channel modeling that more closely represents 

ground conditions.

Stream Conditioning

For areas having moderate to high relief and a welldeveloped drainage network, the watershed data derived from 

the DEM generally worked well. In areas with low relief, the 

synthetic hydrography derived from the first DEM processing deviated from the ANA-supplied hydrographic network. 

In cases where the synthetic streams deviated substantially 

from the ANA hydrography layer, a conditioning process was 

applied to better align synthetic streams to the topographic 

hydrographic data. Figure 4 shows an overlay of synthetic 

streams from the DEM and the hydrography dataset from 

ANA in part of the study area. In general, there is good spatial 

agreement between the two datasets, excluding the fact that 

the synthetic stream reaches were shorter than the ANA 

hydrography reaches in the upstream areas. The shortness of 

the synthetic streams was a result of setting the stream cells’ 

generation threshold higher to avoid the inclusion of “false” 

synthetic stream reaches. False synthetic stream reaches would 

have inflated the power potential estimates.

Five DEM conditioning steps were made: (1) areas in the 

DEM that needed modifications were identified by comparing synthetic streams with the ANA-supplied hydrography 

data, (2) the stream segments that intersect those areas were 

selected, (3) DEM pixels underlying areas of the selected 

stream segments were set to zero, (4) synthetic stream networks were created from the DEM with the modified pixels, 

and (5) using the synthetic stream networks from the modified 

DEM and the original DEM, all elevation pixels underlying the network were reduced by 20 m and then hydrologic 

fill processing was applied. This relative depth conditioning 

technique allows the enforcement of natural stream channels with minimal alteration of relative elevation measurements. Because all pixels intersecting the stream network 

Figure 2. The hydrologic units within the State of São Paulo.

N

0 100 200 KILOMETERS

0 100 200 MILES

HENRY BORDEN DAM

Atlantic

Ocean
4 Low-Head Hydropower Assessment of the Brazilian State of São Paulo

Figure 3. Locations of the Agência Nacional de Águas gages that were used to estimate the distribution of rainfall and to 

validate estimated streamflow: A, rain gages, and B, streamgages.

N

Atlantic

Ocean

0 100 200 KILOMETERS

0 100 200 MILES

Streamgage network

EXPLANATION

N

Atlantic

Ocean

0 100 200 KILOMETERS

0 100 200 MILES

Rain gage network

EXPLANATION

A

B
Assessment of the Streamflow Estimates 5

were modified, their elevation relative to one another does not 

change, which allows for consistent elevation drop calculations on the stream network.

Stream Segmentation and Drop Estimations

Elevation drop is a fundamental component in estimating low-head hydropower potential. The drop is calculated at 

intervals of 1-km segments along the synthetic streams presented in the Stream Conditioning section. Stream segmenting 

starts at the mouth or at a confluence of other streams with the 

stream of interest; some segments can be less than 1 km, but 

this only happens at the end of a stream or downstream from a 

confluence. This process continues upstream in every synthetic 

stream until the points have been equally distributed along the 

stream. During this segmentation process, a unique identification is given to every stream segment, which is used as a 

key for all the attributes of the segments. The upstream and 

downstream points of the stream segments created in the first 

step are used to extract the upstream and downstream elevation values from the DEM. Elevation drops were calculated by 

taking the difference between the upstream and downstream 

elevation measurements of the segments. Calculated elevation 

drops assigned to the stream segments are part of the hydropower assessment database attribute table.

Synthetic Streams Contributing Area Compared 

to Agência Nacional de Águas Streamgages 

Contributing Area

The synthetic stream network created from the DEM 

was visually compared with ANA-generated hydrographic 

data and quantitatively compared with watershed areas in the 

ANA streamgage database. There was good spatial agreement 

between upstream areas recorded in the ANA database and 

those estimated from the DEM data. The comparison indicated 

that there were no large discrepancies in drainage area from 

the two sources (fig. 5A); however, although the agreement 

was within 2 percent on average, there were a few small basins 

where the discrepancy between the two area estimates was 

greater than 20 percent (fig. 5B).

Assessment of the Streamflow 

Estimates

Streamflows were estimated with hydrologic regionalization models. Estimated streamflow was validated by comparisom with average flows calculated from gages with long-term 

data records. The flow validation was extended to the streamflows estimated from the developed Flow Duration Curves. 

Figure 4. Stream networks from the Agência Nacional de Águas dataset overlaid on synthetic streams created 

from the SRTMDEM.

Synthetic stream

Cartographic stream

EXPLANATION

0 100 200 KILOMETERS

0 100 200 MILES

N

0 2 4 KILOMETERS

0 2 4 MILES
6 Low-Head Hydropower Assessment of the Brazilian State of São Paulo

Mean Annual Flow

The equations for the hydrologic regionalization of Liazi 

and others (1988), described in the “Streamflow and Rainfall 

Data” section, were applied at every stream segment. Stream 

segments, upstream areas, and mean annual rainfall were 

estimated using geographic information systems (GIS) techniques. Pixel-to-pixel flow direction and flow accumulation 

calculated from the 30-m DEM provided the framework for 

the calculation of the upstream mean rainfall for every pixel 

on the stream segments.

The continuous parameterization technique described 

by Harvey and Eash (1996) was applied to calculate the 

upstream area-weighted mean annual rainfall of every pixel 

on the streams. This parameterization technique uses the same 

rationale as the flow accumulation algorithm, but instead of 

treating every pixel as one unit, each pixel is weighted by the 

rainfall value calculated for the pixel with inverse distance 

weighted interpolation. Figures 6A–B show the differences 

between the local mean annual rainfall and area-weighted 

mean annual rainfall values for a neighborhood of several 

pixels in the study area.

The accuracy of the flows estimated with the regionalization method for São Paulo was validated by comparing 

the estimated flows to the ANA streamgage-observed flows 

from sites with long-term data records (more than 28 years). 

The ANA streamgage database contains 119 gages, of which 

34 gages had long-term data records. Average annual flows 

and flows with exceedance probabilities of 40 percent, 60 percent, and 90 percent estimated with the regionalization method 

were compared to observed streamflow with corresponding 

exceedance probabilities. Figure 7A shows the comparison 

of the mean streamflow estimates from the regionalization 

equations with the flows from the ANA database. Overall, 

simulated streamflows were significantly correlated with the 

observed flows but with a consistent underestimation bias 

(fig. 7A). When the annual mean flows from the regionalization models (Liazi and others, 1988) were adjusted upward by 

10 percent, average streamflow estimation bias reduced from 

-13 percent to -4 percent (fig. 7B).

Flow Duration Curves

In addition to the average yearly streamflows used to 

estimate annual potential hydropower, several values of the 

streamflow Flow Duration Curves (FDCs) were calculated. 

The FDCs are usually shown as a plot of a percentage of time 

that streamflow is likely to equal or exceed a specified value 

of interest. With the given FDC dataset, users should be able 

to calculate reliability levels for various hydropower potential 

levels. For example, the FDC can be used to show the percentage of time river flows can be expected to exceed a design 

flow of some specified value, or to show the discharge of the 

stream that occurs or is exceeded some percentage of the time 

(for example, 90 percent of the time).

The FDCs of the segments were predicted using equations from the São Paulo regionalization method (Liazi and 

others, 1988). Predicted FDCs were validated by comparing 

those estimated with a log-Pearson Type III distribution fit to 

ANA streamgage datasets. The log-Pearson distribution was 

fit to only those streamgages with at least 28-year records. For 

the hydropower assessment, the shapes of the FDCs in lowflow regions are of most interest. The shapes of the curves in 

the high-flow regions indicate the type of likely flood regime, 

whereas the shapes of the low-flow regions characterize the 

ability of the basin to sustain low-flow regimes during dry 

seasons. The median flow is the discharge that is exceeded 

50 percent of the time. The basic time unit of flow used in 

Figure 5. Scatterplot with the 1:1 line of upstream area recorded in the Agência Nacional de Águas database and contributing area 

calculated from the digital elevation model for the same locations; and B, bar chart of percent disagreement between the two areas.

A

Agência Nacional de Águas (ANA) area, in square kilometers 

Digital elevation model (DEM), in square kilometers 

60,000

60,000

40,000

20,000

0

80,000

0 20,000 40,000

B

Gage site

Area difference, in percent

20

-10

-20

-30

30

80,000 5

R2 = 0.9997

0

10

0 10 15 20 25 30 35 40
Assessment of the Streamflow Estimates 7

Figure 6. Part of the study area showing A, mean annual precipitation fields, and B, mean annual precipitation for the same 

areas when weighted by upstream area.

Figure 7. Evaluation of the annual average streamflows estimated with the hydrologic regionalization model compared to 

observed long-term annual mean streamflows: A, streamflows estimated with original equations, and B, streamflows estimated 

with improved regression equations derived from available stream and rain gage data of the study area.

preparing the FDC affects the appearance of the FDC. When 

mean flows for a 1-year period are used instead of daily values, as in this case, the averaging flattens the resulting FDC.

Figures 8A–O depict the FDC estimates from the regionalization method at 15 selected sites from the ANA streamflow 

database. The FDCs from the observed streamflow data were 

from the annual average flows fitted to a log-Pearson distribution. Overall, the two FDC estimates agree for most parts of 

the curves, but there is constant underestimated bias in the 

FDCs estimated from the regionalization method for the parts 

of the FDC that represent the low-flow regime conditions. The 

regionalized hydrologic equations underestimation bias of the 

low-flow regimes is more apparent in figures 9A–F.

Included in the estimated flows database delivered with 

the final product is the value of the 7-day minimum flow 

expected in the stream segments. The estimates of 7-day 

minimum flows could be used to estimate flows that should 

be left in the streams for ecological protection and subtracted 

from the discharge that goes into the calculation of the hydropower. We did not validate the 7-day minimum flow estimates 

because of a lack of observed minimum flow data, but from 

a literature review (Rezende and others, 2010) we concluded 

that the minimum flow estimates seem to have poor predictive skills. The most recent studies of the observed streamflow 

in the ANA database were from the early 1990s; hence, there 

could be new water abstraction developments that are not 

reflected in either of the observed or estimated flow data.

A B

A

Simulated flows, in cubic meters per second 

300

200

100

0

400

B

0 100 200 300 400

Observed flows, in cubic meters per second

0 100 200 300 400

R2 = 0.98

Bias = -4 percent

R2 = 0.98

Bias = -13 percent

300

200

100

0

400
8 Low-Head Hydropower Assessment of the Brazilian State of São Paulo Discharge, in cubic meters per second 

30

B. Site 61895000

20

25

20

15

10

5

0

80

60

40

20

120

0

100

100

0

80

60

40

20

140

A. Site 58030000 C. Site 62496000

G. Site 58105000 H. Site 58110000 I. Site 62496000

J. Site 61782000 K. Site 61817000 L. Site 62410000

M. Site 58158000 N. Site 61830000 O. Site 62707000

0

120

100

80

60

40

20

140

D. Site 62540000 E. Site 62600000 F. Site 62675100

160

180

300

250

200

150

100

50

0

Percent time flow was exceeded 

0 40 60 80 100 0 20 40 60 80 100 0 20 40 60 80 100

Observed

EXPLANATION

Simulated

Figure 8. Comparison of Flow Duration Curves (FDCs) calculated from log-Pearson distribution fit of observed annual mean flows with 

FDCs estimated from the regionalized hydrologic equation of watersheds in the State of São Paulo (Liazi and others, 1988).
Assessment of the Streamflow Estimates 9

Figure 9. Observed flows at gages with long-term data at 40 percent, 60 percent, and 70 percent exceedance probability 

plotted with flows estimated with the regionalized hydrologic equations on the right (A, C, E ), and estimated with the modified 

regionalized equations on the left (B, D, F ). Simulated flows, in cubic meters per second 

600

400

200

0

600

400

200

0

Observed flows, in cubic meters per second 

100

200

300

400

500

0 600

100

200

300

400

500

0 600

0 200 400 600 0 200 400 600

0 200 400 600 0 200 400 600

600

300

100

0

400

500

200

Original regressions Improved new regressions

A. B.

C. D.

E. F.

40-percent exceedance probability 40-percent exceedance probability

60-percent exceedance probability 60-percent exceedance probability

70-percent exceedance probability 70-percent exceedance probability
10 Low-Head Hydropower Assessment of the Brazilian State of São Paulo

Hydropower Assessment Results

The study produced a comprehensive estimate of the 

magnitude of hydropower potential available in the streams 

that drain watersheds entirely in the State of São Paulo. Watershed assessment locations are shown in figure 10. Streams 

in São Paulo but with sizeable contributing areas outside of 

the borders of the State of São Paulo were excluded from the 

analysis and are shown in light blue in figure 10. The hydropower potential of each stream reach was calculated using the 

hydraulic head and annual mean flow rates at the inlet of the 

reach estimated using the regionalization model (Liazi and 

others, 1988). The hydraulic head associated with each stream 

reach was obtained using the elevation data in the DEM dataset. The hydropower for the river segments was then estimated 

as

P=g*H*Q*10−3 (2)

where 

P is hydropower potential [megawatt], 

H is hydraulic head [m] of the stream segment,

Q is the flow rate of the water in the segment 

[m3/s], and 

g is gravitational acceleration of 9.81 m/s2. 

For potential hydropower, turbine efficiency was assumed 

to be 100 percent and no hydraulic head loss was considered 

in the calculation; therefore, the resulting hydropower estimates are for gross potential.

In addition to the annual mean hydropower estimates, 

potential hydropower estimates with mean annual flows 

estimated using an improved set of regression equations, and 

with flow rates with exceedance probabilities of 40 percent, 

60 percent, and 90 percent, are given with the hydropower 

assessment datasets delivered to CAF.

The results of the hydropower assessment are presented 

with an emphasis on five power classes shown in table 1. The 

sum of the first four classes is equal to the total power and 

represents classification by power class, and the low head/

low power class is a classification by power technology (see 

fig. 11). The final product of the hydropower assessment is a 

GIS streams vector layer segmented every 1 km with attributes 

containing estimated flows (m3/s), head drop (m), and power 

potentials (Megawatts (MW)) at the four above-described levels of the probability. Figure 12 presents a summary of results 

for average power potential of the four power categories for 

the streams considered in the analysis.

The accuracy of the power potential estimates is dependent on the accuracy of the individual stream reach power 

potentials that were summed to produce total values. The 

Figure 10. Major streams in São Paulo where the hydropower assessment was completed (shown in dark blue).

0 100 200 KILOMETERS

0 100 200 MILES

Base from U.S. National Park Service

N Atlantic

Ocean
Hydropower Assessment Results 11

calculated reach flow rates had a standard error of 13 percent. The DEM data, for a random discrete location in South 

America, had an absolute height error of 6.2 m (Farr, 2007; 

Rodríguez, 2006), but the analyses indicate that the uncertainty in the difference between two elevations in near proximity (hydraulic head drop) is much better than the elevation 

uncertainty for an individual location. Because of the direct 

relation between power potential and flow rate, the standard 

error of the reach power potential would be at least 13 percent. 

The uncertainty of the calculated hydraulic head values further increases the uncertainty of the power potential values. 

However, if the errors are uniformly distributed, the accuracy 

of a total value produced by summing a large number of reach 

power potentials will be better than the accuracy associated 

with the individual values that were summed.

Hydropower Potential by Basin

Potential hydropower was aggregated by basin for the 

State of São Paulo (fig. 2). The total power potential for 

each of the four basic power classes was calculated by adding power potential totals for each power category within 

each basin. A GIS layer containing the basin boundaries was 

intersected with a hydropower assessment dataset of stream 

reaches. Figure 13 shows total annual mean power potentials 

of the 21 hydrologic regions stacked by power categories. The 

sum of all the validated stream reach mean annual hydropower 

potentials in the 21 basins is 7,000 MW. Hydropower potential 

is mainly concentrated near the Serra do Mar mountain range 

and along the Tietê River (fig. 10). The power potential along 

the Tietê River is mainly at sites with medium and high potentials, sites where hydropower has already been harnessed.

Segment Hydropower Potential

Stream segments with power potentials less than 1 MW 

and more than 100 kilowatts (kW) with hydraulic heads less 

than 9 m were summed to provide an estimate of total low 

head/low power potential at watersheds and regional levels 

(Hall and others, 2004). Low head/low power potential is 

presented as separate categories in figure 14 because to exploit 

low head/low power potential requires the deployment of 

unconventional systems or microhydro technology that could 

increase the cost of developing hydropower potential. Basins 

G and P (fig. 2) had the highest potential for low head/low 

power potential among the 21 basins (fig. 14).

Table 1. Hydropower classes.

[<, less than; >, greater than]

Hydropower class Power class

Micro < 100 Kilowatt.

Mini 100 to 1,000 Kilowatt.

Small 1 to 50 Megawatt.

Medium and large > 50 Megawatt.

Low head/low power < 1 Megawatt and < 9.1 meter drop.

Figure 11. Three classes 

of low head/low power 

technologies (from Hall and 

others, 2004).

100 Kilowatt

1 Megawatt

EXPLANATION

Hydraulic head, in meters

Flow rate, in cubic meters per second

0 10 20 30 50 60

6

4

2

0

8

12

10

14

40

Conventional

turbines

Unconventional systems

Microhydro (ultra-low head and kinetic energy turbines)
12 Low-Head Hydropower Assessment of the Brazilian State of São Paulo

Available Hydropower Potential

The amount of potential available hydropower is equal 

to the potential hydropower minus the hydropower already 

developed. For every stream, we calculated developed 

hydropower from the ANA hydroelectric database coverage. 

Figure 15 summarizes the undeveloped hydropower potential 

within the watersheds in the State of São Paulo, subdivided 

into three categories. In the undeveloped streams, there is no 

site within the medium and large power category. A few sites 

along the major rivers (Tietê and do Peixe) have small elevation drops and large enough flows to possibly achieve lowhead hydropower within the medium power categories.

We determined that the hydropower capacity within the 

study area as provided by the ANA hydroelectric database 

overestimated the amount of developed potential power for 

some sites. Some of the developed plant capacities in the ANA 

hydroelectric database could not be justified on the natural 

average annual streamflow rates alone. Power capacities of 

some plants were greater than the average power that could be 

warranted using average flow rates and the hydraulic head of 

the plant’s location (for example, Henry Borden Dam, fig. 2). 

To produce an estimate of developed power potential 

that is comparable to the potential power estimates, which 

are based on annual mean flow rates, it will be necessary to 

estimate the average energy generated by each hydroelectric 

plant, a task that is beyond the scope of this study. A drawback 

of using actual power generated is that efficiency of power 

generation is not included in the numbers estimated with such 

a method.

Figure 12. Power category distribution for the 

potential annual mean hydropower of all the 

streams that are entirely in the State of São Paulo.

Total annual mean hydropower potential by basin—

Power megawatts (basin code): 180 (P)

Hydropower category

EXPLANATION

0 100 200 KILOMETERS

0 100 200 MILES

Micro (less than 100 kilowatts)

Mini (100 to 1,000 kilowatts)

Small (1 to 50 megawatts)

Medium (greater than 50 megawatts)

Total hydropower: 7,000 megawatts

N

39 (U)

180 (P)

479 (O) 215 (N)

785 (T)

64 (S)

54 (R)

60 (Q)

88 (L)

526 (M)

65 (K)

862 (H)

123 (I)

584 (D)

78 (A)

401 (F)

86 (E)

1,278 (B) 98 (C)

180 (P)

392 (J)

Figure 13. Total annual mean hydropower potential of the 21 basins within the State of São Paulo.
Hydropower Assessment Results 13

Available power potential (annual mean power) by basin—

Power megawatts (basin code): 133 (P)

Hydropower category

EXPLANATION

0 100 200 KILOMETERS

0 100 200 MILES

Micro (less than 100 kilowatts)

Mini (100 to 1,000 kilowatts)

Small (1 to 50 megawatts

Total available hydropower: 3,700 megawatts

N

39 (U)

133 (P)

196 (N) 210 (O)

785 (T)

64 (S) 54 (R)

58 (Q)

84 (L)

89 (M)

60 (K)

633 (H)

116 (I)

580 (D) 78 (A)

86 (E) 126 (F)

374 (B)

98 (C)

133 (P)

Figure 15. Distribution of available power potential (annual mean power) of São Paulo energy resources among three 

hydropower classes.

100

80

60

40

20

0

120

Basin

C AE F K B T U Q O I R S D J H L M N P G

Low head, in megawatts

Figure 14. Total low head/low power potential summarized by basin (see fig. 2 for basin locations).
14 Low-Head Hydropower Assessment of the Brazilian State of São Paulo

Developed and Reaming Hydropower

To determine the final potential hydropower that can be 

developed, areas where hydropower cannot be developed (for 

example, parks, wildlife refuges) need to be identified and 

excluded. At the time of this report, maps of areas excluded 

from hydropower development in São Paulo were not available. If available, an excluded area data layer could be intersected with the potential hydropower data layer to identify 

stream reaches that should be excluded from consideration as 

potential sources of hydropower. In the final product, stream 

reaches would be coded as either excluded or not excluded 

from hydropower development.

Web Mapping Service And Contour 

Level Extraction

In addition to the GIS streams layer, the data are available in a Web mapping service for collaborating agencies to 

view and extract information. This service provides access 

to an interactive map of São Paulo that allows users to select 

a stream segment and identify its estimated power potential 

and related attributes (fig. 16). In addition to presenting the 

information as a Web map, the data also are distributed as 

an Esri Map Service (http://www.geosur.info/map-viewer/

hydropower).

CAF and the consulting firm IX Estudos e Projetos 

needed access to the 1-arc-second (30-m) resolution DEM of 

the region for a follow-up detailed hydropower assessment for 

the high potential areas identified in this study. The restricted 

nature of the SRTM data presented a unique challenge to the 

feasibility phase of the project. With permission from NGA, 

the USGS developed a Web geoprocessing service that provides a method for collaborating agencies to extract contours 

from the DEM. The extraction is done through a Web mapping site on the Esri Geoprocessing Server (http://tps.geosur.

info/arcgis/rest/services/Models/GeoSUR_HydroDerivatives/

GPServer). The contour extraction is limited to a 20-km2 area 

surrounding the selected stream. The contour intervals are set 

to 2 m and range between the minimum elevations at the prospective dam location and a height specified by the user. After 

running the Web mapping tool, the contour lines are delivered 

as a downloadable Esri shapefile product (fig. 16).

Figure 16. The user interface of the Web mapping site (A) that was developed for extraction of contour lines from the DEM, and an 

example of an extracted contour line product (B).

A B
References Cited 15

Summary

A comprehensive assessment of the hydropower potential 

available in all the watersheds in the Brazilian State of São 

Paulo was completed. Potential head drops were calculated 

from a digital elevation model with a 1-arc-second resolution 

(approximately 30-meter resolution at equator). The hydrometeorological data (rainfall and discharge) used come from 

a dense network of gages. Streamflow was estimated using 

21 hydrologic regionalization functions. The potential hydropower of all streams was estimated as 7,000 megawatts. The 

potential hydropower is mostly concentrated near the Serra do 

Mar mountain range and along the Tietê River, areas where 

most hydropower has already been harnessed. To estimate 

the potential for low head/low power, stream segments 

with power potentials less than 1 megawatt and more than 

100 kilowatts with hydraulic heads less than 9 meters were 

aggregated. Basins G and P showed the highest potential for 

low head/low power.

References Cited

Farr, T.G., Rosen, P.A., Caro, E., Crippen, R., Duren, R., 

Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., Roth, 

L., Seal, D., Shaffer, S., Shimada, J., Umland, J., Werner, 

M., Oskin, M., Burbank, D., and Alsdorf, D., 2007, The 

Shuttle Radar Topography Mission: Reviews of Geophysics, 

v. 45, no. 2, p. 21–22.

Hall, D.G., Cherry, S.J., Reeves, K.S., Lee, R.D., Carroll, 

G.R., Sommers, G.L., and Verdin, K.L., 2004, Water energy 

resources of the United States with emphasis on low head/

low power resources, Idaho Falls, Idaho: U.S. Department 

of Energy, Energy Efficiency and Renewable Energy, Wind 

and Hydropower Technologies, Idaho National Engineering 

and Environmental Laboratory.

Harvey, C., and Eash, D., 1996, Description of BASINSOFT, 

a computer program to quantify drainage basin characteristics: Proceedings of the Sixteenth Annual ESRI User 

Conference,Palm Springs, California, accessed December 2010 at http://proceedings.esri.com/library/userconf/

proc96/to100/pap072/p72.htm.

Liazi, A., Conejo, J.L., Palos, J.C.F., and Cintra, P.S., 1988, 

Regionalização Hidrológica no estado de São Paulo, 

Revista Águas e Energia Elétrica DAEE, v. 5, no. 14, 

p. 4–10.

Rezende J.H., Pires, J.S.R., and Mendiondo, E.M., 2010, 

Hydrologic pulses and remaining natural vegetation in Jaú 

and Jacaré-Pepira watersheds: Brazilian Archives of Biology and Technology, v. 53, no. 5, p. 1127–1136.

Rodríguez, E., Morris, C.S, and Belz, J.E., 2006, A global 

assessment of the SRTM performance: Photogrammetric 

Engineering & Remote Sensing, v. 72, no. 3, p. 249–260.
Publishing support provided by: 

Rolla Publishing Service Center

For more information concerning this publication, contact: 

U.S. Geological Survey Earth Resources Observation and 

Science (EROS) Center 

47914 252nd Street 

Sioux Falls, South Dakota 57198 

(605) 594-6151

Or visit the EROS Center Web site at: 

http://eros.usgs.gov/

Artan and others—Low-Head Hydropower Assessment of the Brazilian State of São Paulo—Open-File Report 2014–1206

ISSN 2331-1258 (online)

http://dx.doi.org/10.3133/ofr20141206