Water Resour Manage (2012) 26:873–882
DOI 10.1007/s11269-011-9941-8
Estimation of Small Reservoir Storage Capacities
with Remote Sensing in the Brazilian Savannah Region
Lineu N. Rodrigues & Edson E. Sano &
Tammo S. Steenhuis & Denílson P. Passo
Received: 22 September 2010 / Accepted: 26 October 2011 /
Published online: 11 November 2011
# Springer Science+Business Media B.V. 2011
Abstract Small reservoirs play an important role in supporting the local economy in the
savannah areas of Brazil and are primarily used for the provision of water for irrigation and
watering livestock. Hundreds of small reservoirs have been built in the last few decades in
the Preto River Basin, but efficient water management and sound planning are hindered by
inadequate knowledge of the number, storage capacity and spatial distribution of reservoirs
in the basin. The main reason for the lack of this information is that current methodologies
for quantifying the physical parameters of reservoirs are laborious, time consuming and
costly. To address this lack of data, a simple method to estimate reservoir storage volumes
based on remotely sensed reservoir surface area measured with LANDSAT was developed.
The method was validated with a subset of reservoirs in the Preto River Basin for which
surface areas, shapes and depths were determined with ground-based survey measurements.
The agreement between measured and the remotely sensed reservoir volumes was
satisfactory, indicating that remotely-sensed images can be used for improved management
of water in the Brazilian Savannah region. With the newly developed methods we found
that the Preto River Basin’s 147 small reservoirs can store 19×106 m3 of water at full
capacity.
Keywords Water resources . Dams . Bathymetry . Storage volumes
1 Introduction
People living in dry environments with highly variable rainfall frequently experience
droughts and floods. Reservoirs capture surface runoff during the rainy season that can be
released when rainfall is lacking. Small reservoirs, therefore, are important for overcoming
L. N. Rodrigues (*) : E. E. Sano : D. P. Passo
Embrapa Cerrados, BR 020, Km 18, Caixa Postal 08223, 73010-970 Planaltina, DF, Brazil
e-mail: lineu@cpac.embrapa.br
T. S. Steenhuis
Department of Biological and Environmental Engineering, Cornell University, Ithaca, NY, USA
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L.N. Rodrigues et al.
minor droughts. While management information is available for large reservoirs, it is
invariably lacking for the small reservoirs in the world (Liebe et al. 2009). In this paper we
are interested in findings ways to improve the management with small reservoirs. They
have dam heights of generally less than 15 m.
Arrays of small reservoirs can potentially have a large impact on the hydrology and the
ecology of the surrounding environment. Information about reservoir locations and storage
volumes is critical for decision-making processes regarding planning and management of
water resources. While there are many methods for locating reservoirs, few methods are
available for estimating storage capacity. Current techniques include direct methods
(reservoir surveys using boats) and indirect methods such as the use of topographical maps
and satellite data. When great precision is not crucial, a quick survey can be made by
measuring throwback, maximum depth and maximum width of reservoirs (Sawunyama et
al. 2006). Meigh (1995) used the indirect method, based on topographical maps, to estimate
surface areas of the small farm reservoirs in Botswana and developed a power relationship
between reservoir capacity and surface area measured from topographical maps.
Remotely sensed data can make very precise measurements (Kääb et al. 2005). With this
technique water boundaries can be delineated over large areas (Frazier and Page 2000;
Kuleli 2010). Magome et al. (2003) comment that is desirable to develop technology to
monitor reservoir storage using satellite remote sensing techniques, even though volume of
water storage cannot be determined directly by the technology. Several studies have been
published on mapping or delineating water boundaries using Landsat TM imaginary
(Carvalho et al. 2009; Hui et al. 2008; Rodrigues et al. 2007; Liebe et al. 2005; Frazier and
Page 2000). Reis and Yilmaz (2008) monitored water levels effectively with LandSat TM
images.
Mialhe et al. (2008) mapped and quantified water stocks in small irrigation reservoirs in
India from Landsat images. Magome et al. (2003) used satellite observations and digitized
topographic data to monitor seasonal and inter-annual variation of water storage in Volta
Lake reservoirs (Akosombo dam) in Ghana. Liebe et al. (2005) surveyed 61 small
reservoirs in the Upper East Region of Ghana and derived an expression relating reservoir
areas (measured with remote sensing techniques) to storage volumes. Liebe’s method was
simple, cost effective and had a high precision (NS=0.975). Using the same technique,
Sawunyama et al. (2006) surveyed 12 small reservoirs in the Mzingwane catchment in
Limpopo River Basin and found a good correlation (R2 =0.95) between surface area and
storage volume as well. Liebe et al. (2009) recently showed another important application
of the technique using small reservoirs as runoff gauges. They estimated runoff volume by
change in reservoir areas satellite imagery. Remote sensing techniques are more cost
effective for determining small reservoir storage than maintaining level recording devices.
Despite the large number of poorly-defined reservoirs in drought prone areas in South
America and particularly Brazil, the indirect satellite imagery methods developed in Africa
have not yet been employed in South America. The main reason is that valley shapes are
different in Africa than Brazil because of the underlying geology is different. As describe
by Liebe (2002) the cross sections of inland valleys in West Africa mainly are rectilinear
types, while in the savannahs area of Brazil they are mainly concave. In addition, reservoirs
in Africa dry up during the dry season, while reservoirs in many parts of Brazil retain water
throughout the year. Consequently the surface area/volume relationship developed in Africa
cannot be used in Brazil.
Our objective was, therefore, to test and adapt the current methods for estimating
reservoir volumes based on surface area measurements for conditions in the Preto River
Basin in the Brazilian savannah. Small reservoirs in Preto Basin are crucial for supplying
Estimation of Small Reservoir Storage Capacities
875
water for livestock and for irrigation of crops during droughts. These reservoirs were
constructed by various government and private agencies with little or no coordination
among them. After construction either individual farmers or farmers’ association are
responsible for maintenance but maintenance is irregularly at best.
2 Methodology
2.1 Study Area
The 10,500 km2 Preto River Basin drainage area is located in the central portion of Brazil,
on the western side of the Middle portion of the São Francisco Basin. The Basin traverses
two states, Minas Gerais and Goiás, as well as the Federal District, and encompasses 10
municipalities (6 in Minas Gerais, 4 in Goiás). The Preto River is a main tributary of the
Paracatu River, one of the headwater tributaries to the São Francisco River. About 80% of
the agricultural production from the Federal District comes from the Preto River Basin.
The basin has a tropical wet and dry climate, with a long dry season lasting from May to
September, and rainy season that usually starts around October and ends in April. The
average annual rainfall is around 1,200 mm, of which 85% occurs during the rainy season.
The length of the dry season contributes to various problems with forest fires, water
shortages and conflicts, and insecure food production.
The geological environment of the Basin consists basically of low-grade metamorphic rocks.
Over the Precambrian rocks is a lateritic layer varying in thickness from centimeters up to 30 m.
Latosols dominate the existing plateaus, while laterite crusts and immature soils are dominant in
the transition zones between plateaus and river valleys (Mendonça et al. 1994).
Viable agriculture is only possible with irrigation, which depends on the water stored in
the small reservoirs. The shape and volume of reservoirs depend on the shape of the stream
channel beds and valleys in which they are constructed. The small reservoirs in the Preto
River basin are generally shallow, because of the gently undulating terrain.
2.2 Reservoir Inventory with Satellite Imagery
The inventory and classification of reservoirs was conducted by means of remote sensing
using three Landsat ETM images taken in 2005 (Rodrigues et al. 2007). In the basin, 252
small dams were identified with reservoir surface areas varying between 1 and 413 ha. In
this classification good user accuracy was achieved, with just one misclassification (that of
a lagoon listed as a reservoir) detected in the 51% of reservoirs visited. The satellite and
ground based surface areas were compared and used as a second quality indicator.
2.3 Field Work and Data Collection
A questionnaire-based and semi-structured interview was carried out with farming households
prior to each reservoir survey, with questions focused on reservoir characteristics such as
existence of technical information (area, depth, maps, etc.), maintenance, age, purpose, etc.
The initial database consisted of 252 small reservoirs, but only those with surface areas
between 1 and 50 ha were considered. There were a total of 147 small reservoirs in this size
range. For informational purposes, the reservoirs were split into three categories as used by
Liebe et al. (2005) (Fig. 1): Category 1, with lake surface areas between 1 and 3 ha (68
reservoirs), Category 2 with areas between 3 and 10 ha (51 reservoirs), and Category 3 10
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L.N. Rodrigues et al.
Fig. 1 Box 1 is showing the São Francisco River Basin in the context of Brazil. Box 2 is showing the Preto
River Basin in the context of the São Francisco. In Box 3 the Preto River Basin was blown up to show
reservoirs identified in satellite imagery (LandSat) and distribution of the surveyed reservoirs by surface area
category (Category 1: 1–3 ha; Category 2: 3–10 ha; and Category 3: 10–50 ha)
to 50 ha (28 reservoirs). To have an extensive sample set and to ensure sufficient coverage
for finding surface area/volume relationships, 42 out of the 147 (28%) of the 147 small
reservoirs were evaluated. This represented 29% of the reservoirs in Categories 1 and 2, and
25% of Category 3. Maximum reservoir depths and surface areas varied from 1.80 m to
10.25 m (average=5.0 m) and 1.1 ha to 35 ha (average=7.1 m), respectively.
The selected reservoirs were visited and measured during three fieldwork periods that
were conducted from March to April and October to November of 2006 and from March to
May of 2007. The shape and size of the reservoir surface area were determined by walking
around each reservoir with a handheld GPS and taking large numbers of points along the
shoreline. Following that step, bathymetric maps were compiled.
To estimate the volume, water depths were measured using a plummet from a boat and
GPS coordinates determined at many locations inside the reservoir. In taking the
measurements the emphasis was on finding the deepest point that was usually located
near the dam and not according to a predefined grid. Care was taken to achieve good
coverage of points well spread over the reservoir while focusing on the areas closest to the
dam wall where the reservoir is usually the deepest. In order to assure good quality data and
whether more readings should be takes, the depth and GPS data were downloaded to a
laptop and analyzed before leaving the site.
A box plot representing the relationship between reservoir surface areas and number of
measurements taken in each reservoir per category is presented in Fig. 2a. It can be noted
that in average it was taken 24 measurements/ha, in category 1, nine in category 2 and 9.4
in three. The reservoirs in category one have a much higher variation in the measurements
that was taken, this is due to the fact that they were used to better understand the number
and distribution of measures that was necessary to have a good volume estimation.
In Fig. 2b is presented the measured surface areas and the maximum water depth of the
reservoirs evaluated. It can be seen that there is no relationship between the maximum areas
and depths. For instance, the reservoir number three, with area of 20.50 ha and maximum
water depth of 3 m, and the reservoir 34 with area of 1.60 ha and water depth of 8.50 m.
60
(a)
50
40
30
20
10
0
1
2
Categories
3
877
Maximum surface area (ha) and depth (m)
Number of depth measurements per ha
Estimation of Small Reservoir Storage Capacities
40
(b)
Maximum surface area
Maximum water depth
30
20
10
0
0
10
20
30
40
ID
Fig. 2 Variation of the number of depth measurements taken per hectare in each category (a) and measured
surface areas and the maximum water depth of the reservoirs evaluated (b)
2.4 Reservoir Modeling and Storage Capacity (Volume) Estimation
Satellite measurements of 75 reservoir surface areas were well correlated with field
measured areas (R2 of 0.92). Deviations in surface area measurements between the two
methods were the result of both the coarse image spatial resolution (30 m) and the presence
of herbaceous water plant cover in the shallow tail parts of the reservoirs. Liebe et al.
(2005) observed a similar problem in the reservoirs in Ghana for Landsat imagery in which
perimeter cells have mixed spectral signatures.
Surfer™ (Golden Software) was used to calculate the surveyed reservoir volumes by
creating a 3D-Model for each reservoir (Fig. 3). Using the kriging interpolation method, the
point data was interpolated over a 1-meter regular grid. The water level at the shoreline was
Fig. 3 Picture and 3D model of a small reservoir in the Savannah Region. Maximum water depth=4.05 m
and surface area=1.4 ha. Ninety one depth measurements were taken to create the 3D model
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set as the 0-level by incorporating the outline of the reservoir as a break line polygon, so
each point along the shoreline was interpreted as a 0 value in the interpolation process.
2.5 Volume-Area Relationship and Total Small Reservoir Storage Capacity
The volume-area relationship was determined by a linear regression of the log of both the
maximum surface area and storage capacity. The goodness of fit of the resulting power
relationship was then determined with the standard R2 and the model efficiency measure of
Nash and Suttcliffe (1970). With this relationship the total storage capacities of all small
reservoirs in the Preto River Basin were then calculated using surface area estimated from
Landsat imaginary.
3 Results
3.1 Questionnaires and Semi-Structured Interviews
Of the 47 reservoir sites visited, 42 interviews were conducted. In some cases the field team
was not allowed to take measurements and farmers were unwilling to answer questions.
Questionnaire responses were analyzed and synthesized as follows: i) 17% of the small
reservoirs were at least 30 years old and 7% were built during the last 5 years; ii) both
private and public reservoirs were constructed independently, with little or no coordination
among the implementing organizations; iii) the majority were built without taking into
account that reservoirs are hydrologically connected vi) a significant number of reservoirs
were functioning sub optimally and/or falling into disrepair; v) reservoir maintenance is
inadequate, and dam failure could cause serious damage to downstream communities; vi)
the lack of vegetation in the reservoir boundaries contributed to erosion and increases the
sedimentation rate, with reduction of reservoir storage capacity causing economic problems
to the users; vii) in 73% of the reservoirs visited, there was no technical information about
their construction; viii) 38% of the reservoirs were used for irrigation. Center pivot was the
main irrigation system; and ix) 57% of the farmers did not manage irrigation water use.
Based on both the survey results above and how agriculture developed in the region, it
becomes obvious that the reservoirs were built during the time that the irrigated area was
rapidly expanding. Currently some reservoirs are not used because during dry spells they do
not contain sufficient water for irrigation due to leakage as a consequence of poor
construction and improper maintenance. In the future, new reservoirs have to be built and
the ones that are available better utilized. The remotely sensed imageries of the temporal
reservoir storages will allow better and more coordinated reservoirs planning and avoid
shortage of water for irrigation and resulting socioeconomic impacts.
3.2 Remotely Sensed Surface Area-Volume Relationship
Area/volume relationships are first presented for each state because it is important for water
agencies to have this information that can be directly applied in their respective states. A
generalized area/volume relationship is also presented for the overall Brazilian Savannah
region. The volume area relationships are as follows:
&
State of Minas Gerais—Nine small reservoirs were evaluated, Category 1: 3, Category
2: 4, and Category 3: 2. Water depths in the measured reservoirs varied from 1.8 to
Estimation of Small Reservoir Storage Capacities
879
8.1 m and the remotely sensed surface areas 1 to 46.5 ha. The equation that relates
capacity (V) to surface area (A) for all nine reservoirs is:
V ¼ 2:01 A 2:3»104 ;
2
&
3
where the area is in m and the volume in m .
Federal District—Nineteen small reservoirs were evaluated (Category 1: 14, Category
2: 4, Category 3: 1). Water depths varied from 1.8 to 9.2 m and the remotely- sensed
surface area were 1.1 to 19.3 ha. The equation that relates capacity, V (m3), to area A
(m2) for the 19 reservoirs is
V ¼ 1:23 A1:20
&
ð1Þ
R2 ¼ 0:98
ð2Þ
R2 ¼ 0:76
State of Goiás—Fourteen reservoirs were evaluated (Category 1: 2, Category 2: 7, and
Category 3: 5). Water depths varied from 1.8 m to 10.3 m and the remotely-sensed
surface areas were 1.6 ha to 34.9 ha. The area/volume equation is:
V ¼ 2:66 A 7:4»104
ð3Þ
R2 ¼ 0:82
When we pool the 42 observations for the whole basin, we find that the area volume
relationship is
ð4Þ
R2 ¼ 0:83
V ¼ 0:45 A1:11
Fig. 4 General storage capacity
and remotely sensed area relationships considering all surveyed
small reservoirs in the Preto River
Basin
Storage capacity (m3)*10,000
Despite the variety of reservoir shapes, the derived equation for all reservoirs,
independent of size, for the whole Preto River Basin fitted the observed data quite well
(Fig. 4), with a Nash and Suttcliffe (1970) efficiency of 82%. Since the sides of these
reservoirs have gentle slopes, minor changes in depth result in relatively large surface area
changes. When comparing the area volume relationships for the Preto River basin in Eq. 4
with those obtained for basins in Africa by Liebe et al. (2005) for Upper East Region of
Ghana and Sawunyama et al. (2006) for Mzingwane catchment, we note that the shapes are
different. Liebe et al. (2005) and Sawunyama et al. (2006) fitted the data with an
exponential coefficient of 1.43 and 1.33, respectively, while the coefficient obtained to
Preto Basin is 1.11.
Thus the relationship in Brazil is closer to a straight line while those from West Africa
are more curved. In fact in the relationships of the states of Goiás and Minas Gerais showed
120,0
100,0
V = 0,45 A1,11
R² = 0,83
80,0
60,0
40,0
20,0
0,0
0,0
5,0
10,0
15,0
20,0
25,0
30,0
Surface area (m2) x 10,000
35,0
40,0
880
L.N. Rodrigues et al.
a linear trend. The linearity is a direct consequence of the nearly uniform slopes towards the
river in a relatively flat area where the reservoirs are located. If the equation to calculate
storage capacity developed by Liebe et al. 2005 for Africa, was used in the Preto River
Basin in Brazil, the equation would have underestimated the volume for the reservoirs with
surface areas smaller than 25 ha. Our results suggest that the volume-area equations are
region-specific and as expected, vary with the valley shape and geology of the area.
3.3 Water Storage Capacity of Small Reservoirs in the Preto River Basin and in its States
The equation stored volume-area derived for the basin was used to calculate the total water
stored in small reservoirs in the Preto River basin and its states (Fig. 5). The portion of the
Federal District falling within the Preto River Basin has on the average one reservoir per
30 km2, with a total capacity of 2.6×106 m3, or 14% of the total water stored in the basin.
Minas Gerais state has on the average one reservoir in each 90 km2 and a total storage of
10.8×106 m3, which represents 57% of the total water stored in the basin. Goiás state has
one reservoir per 70 km2, and a total storage of 5.4×106 m3, which represents 29% of the
total water stored in the basin.
4 Conclusions
Information about reservoir capacities is essential for management of small reservoirs,
hydrological modeling studies and for catchment dynamics assessment. Reservoirs are not
typically regarded as part of the hydrological system by farmers or by local agencies, but
can both impact the hydrology and can be the cause of water conflicts because they take
water from the available stream flow. Remote sensing was found to be a suitable means to
detect small reservoirs and accurately measure their surface areas in the Preto River Basin.
20x106
800x103
3
DF
MG
GO
Preto River Basin total stored volume (m )
3
States total stored volume (m )
1x106
28.7%
15x106
600x103
10x106
400x103
57.3%
5x106
200x103
14.0%
0
0
0
DF
20
40
60
MG
80
100
120
140
GO
Basin
160
Fig. 5 Water stored in small reservoirs in the states of the Preto River basin (Federal District—DF; Minas
Gerais—MG; Goiás—GO) and total water stored in the basin for the year of 2005
Estimation of Small Reservoir Storage Capacities
881
The majority of small reservoirs, for which detailed information has been lacking, can now
be included in water management planning for the catchment without the need to carry out
extensive field surveys.
The general relationship between measured reservoir volumes and their remotely sensed
surface areas showed good agreement. The R2 =83% gives, in some extent, confidence in
its use, especially for reservoirs with no other available information. Combining this
relationship with periodic satellite-based reservoir area measurements will allow hydrologists and planners to have a clear picture of water resource system in the Preto River Basin,
especially in ungauged subbasins.
At full capacity, the water that the Preto River Basin’s 147 small reservoirs can capture is
likely small, but these act as a set of well-distributed and easily accessible water source
systems that have multiple uses, reducing the population’s vulnerability and improving their
livelihoods.
The applicability of the derived relationships to other catchments should be examined in
future, as should hydrological modeling to investigate the impacts of small reservoirs on
water resources available in the basin.
Acknowledgments The presented research was carried out as part of the Small Reservoir Project. We
gratefully acknowledge financial support of Advisory Service on Agricultural Research for Development
(BEAF) through the Challenge Program on Water and Food.
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