Water Footprint - Universidade Federal de Santa Catarina

The Water Footprint of Hydroelectricity in Santa Catarina State Southern Brazil
F. Fischmann
&
Pedro L. B. Chaffe*
Laboratory of Hydrology – LabHidro (www.labhidro.ufsc.br)
Department of Sanitary and Environmental Engineering
Universidade Federal de Santa Catarina - Brazil
3. Results and Discussion
Large hydropower plants are Brazil’s main source of electricity,
comprising approximately 70% of the installed capacity.
Santa Catarina State presents a similar pattern, where hydropower
corresponds to 70.39% of the total installed capacity of 4,572.105 MW.
6.0E+06
25.00
5.0E+06
20.00
4.0E+06
15.00
3.0E+06
10.00
2.0E+06
5.00
1.0E+06
0.00
0.0E+00
1
3
5
7
9
11
13
15
17
19
21
23
25
27
29
31
33
35
37
39
41
43
45
47
49
51
53
55
57
59
61
63
65
67
69
71
73
75
77
79
81
83
85
87
89
91
93
95
97
99
101
103
105
107
Due to the significant disparities in size and layout between hydroelectric
facilities, there has been considerable debate over the method to be
adopted in order to normalize their associated water consumption.
30.00
Month
Water Footprint
Water Footprint (m 3 GJ-1 )
Mekonnen and Hoekstra (2012) proposed the Water Footprint concept
as a general approach to standardize the allocation of water resources to
human activities.
AET
Power Generation (GJ)
30
30
25
25
20
15
10
R² = 0.2893
5
Our objective was to estimate the water footprint of hydroelectricity
generation in the Santa Catarina State, Brazil.
20
15
10
R² = 0.6929
5
0
0
0
1000000
2000000
3000000
4000000
5000000
6000000
0
500000
1000000
AET (m 3 )
1500000
2000000
2500000
Power Generation (GJ)
Figure 2. Water Footprint estimation for the case of Campos Novos Power Plant. Monthly
variation of estimated Areal Evaporation, Power Generation and Water Footprint (top panel).
Relation of Water Footprint to Areal Evaporation and to Power Generation (bottom panel).
The state of Santa Catarina is located within the geographic coordinates
25°57’41” S to 29°23’55” S and 48°19’37” W to 53°50’00” W, and
comprehends an area of approximately 95,346 km2. According to the
Köppen classification, there are two predominant climate types in the
state: Cfa and Cfb. The average annual rainfall is 1500 mm.
Foz do Chapecó
Foz do Chapecó
Salto Pilão
Monthly Min
Salto Pilão
Palmeiras
Monthly Max
Palmeiras
Annual Average
Quebra Queixo
Monthly energy output data series were available for nine hydropower
stations according to the period each one was commissioned. Each
power plant was allocated a weather time series from the nearest station,
as illustrated in Figure 1.
Quebra Queixo
Itá
Itá
Machadinho
Machadinho
Garibaldi
Garibaldi
Campos Novos
Campos Novos
Barra Grande
Barra Grande
0.00
10.00
20.00
30.00
Water footprint
40.00
50.00
60.00
0.0
50.0
100.0
(m 3 /GJ)
150.0
Water Footprint (m3 GJ -1 )
200.0
250.0
300.0
Mean water footprint (m 3 /GJ)
Figure 3. Water Footprint of the major hydropower plants in Santa Catarina State.
25.00
10000
20.00
1000
Mean Water Footprint (m3/GJ)
2. Methods
Estimated Areal Evaporation (m3)
Monthly Power Generation (GJ)
Water Footprint (m3/GJ)
Campos Novos Power Station
Water Footprint ( (m 3 GJ-1 )
1. Introduction
15.00
y = -1.38x + 16.41
R² = 0.31
10.00
5.00
100
10
1
0.1
0.00
0
2
4
6
Reservoir area (km 2 )
8
10
0.01
0.0
50.0
100.0
Reservoir area
150.0
200.0
250.0
(km2)
Figure 4. Water footprint relation to reservoir area when considering only Santa Catarina State
major reservoirs (left figure) and when considering major reservoirs of the entire Southern
Brazilian Region (right figure).
4. Conclusions
Figure 1. Southern Brazil, location of different Hydropower plants and Meteorological Staitions is
indicated.
Water Footprint Estimation for Hydroelectricity
We adopted the water footprint formula for hydroelectricity proposed by
Mekonnen and Hoekstra (2012) :
Significant variation in monthly evaporation and power output values –
and, hence, of the water footprint – provides support for adopting a
monthly time step for such estimates.
Despite the considerably large range of mean water footprint estimates
between facilities, such values were generally low in comparison to those
reported for power plants in other locations, and particularly in other
regions of Brazil.
This study was limited by lack of data availability regarding reservoir
properties and electricity generation of smaller facilities..
Aknowledgements
where WF is the water footprint (m3 GJ-1), WE is the volume of
evaporated water (m3 month-1) and EG is the electricity generated (GJ
month-1).
Data made available by ONS, ANEEL, Epagri Simepar and IAPAR is
gratefully acknowledged. We are grateful for the organizing committee
support.
References
The Complementary Relationship and the WREVAP algorithm
To estimate evaporation from reservoirs we used the WREVAP
(McMahon et al, 2013a,b) which implements CRLE algorithm (Morton,
1986). Input data are site characteristics (latitude, elevation and average
annual rainfall) and monthly time series of air temperature, humidity and
daily sunshine hours). Average depth and salinity are also required.
McMahon, T. A. et al. (2013a), Estimating actual, potential, reference crop and pan evaporation using
standard meteorological data: a pragmatic synthesis, Hydrol. Earth Syst. Sci., 17 (4), 1331-1363.
McMahon,
T.
A.
et
al.
(2013b).
Morton
WREVAP
Fortran
code.
http://people.eng.unimelb.edu.au/mpeel/morton.html
Mekonnen, M. M. and Hoekstra, A. Y. (2012), The blue water footprint of electricity from hydropower,
Hydrology and earth system sciences, 16 179-187.
Morton, F. I. (1986), Practical Estimates of Lake Evaporation, Journal of Climate and Applied
Meteorology, 25 (3), 371-387.