Revista Facultad de Ingeniería, Universidad de Antioquia, No.110, pp. 110-119, Jan-Mar 2024
Estimation of methane emissions from
reservoirs for hydroelectric generation in Costa
Rica
Estimaciones de las emisiones de metano de embalses para la generación hidroeléctrica en
Costa Rica
Rhonmer Orlando Pérez-Cedeño1*, Rodrigo Ramírez-Pisco2, Carmen Luisa Vásquez-Stanescu1,
Leonardo Suárez-Matarrita3,4, Mercedes Gaitán-Angulo5, Melva Gómez-Caicedo6
1Universidad Nacional Experimental Politécnica Antonio José de Sucre. Avenida Corpahuaico entre Avenida La Salle y Av.
Rotaria, C. P. 3001, Barquisimeto, Venezuela.
2Universitat Carlemany. Avenida Verge de Canòlich, 47 AD 600 Sant Julià de Lòria. Principat d’Andorra. Andorra, Andorra.
3Universidad Técnica Nacional. Villa Bonita de Alajuela, Apartado Postal 1902-4050. Alajuela, Costa Rica.
4Universidad de Costa Rica. Sede "Rodrigo Facio Brenes" Montes de Oca. C. P. 11501-2060. San José, Costa Rica.
5Fundación Universidad Konrad Lorenz. Carrera 9 Bis # 62 – 43. C. P. 110231. Bogotá, Colombia.
6Fundación Universidad Los Libertadores. Carrera 16 # 63a - 68. C. P. 110231. Bogotá, Colombia.
CITE THIS ARTICLE AS:
R. O. Pérez-Cedeño, R.
Ramírez-Pisco, C. L.
Vásquez-Stanescu, L.
Suárez-Matarrita, M.
Gaitán-Angulo and M.
Gómez-Caicedo. ”Estimation
of methane emissions from
reservoirs for hydroelectric
generation in Costa Rica”,
Revista Facultad de Ingeniería
Universidad de Antioquia, no.
110, pp. 110-119, Jan-Mar,
2024. [Online]. Available:
https:
//www.doi.org/10.17533/
udea.redin.20230522
ARTICLE INFO:
Received: September 13, 2022
Accepted: May 17, 2023
Available online: May 17, 2023
KEYWORDS:
Methane emissions;
hydroelectric dam; emission
factor; Costa Rica
Emisiones de metano;
embalse hidroeléctrico; factor
de emisión; Costa Rica
ABSTRACT: Greenhouse gas emissions are related to non-renewable sources. For this
reason, the methodological guide for the estimation of methane and carbon dioxide
emissions in flooded lands was published in 2006 by the Intergovernmental Panel on
Climate Change. Since 2016, several studies have been carried out in temperate and
tropical zones reservoirs. Costa Rica is a Central American country known for its large
hydroelectric resources and its highly renewable electricity generation matrix. This
work represents the first study for 11 of 24 hydroelectric plants managed by the Costa
Rican Electricity Institute. Methane emissions, energy density and emission factors for
electricity generation are determined. Furthermore, a static mathematical model is
used to determine these factors with little input data. It is estimated that the greatest
contribution to methane emissions corresponds to the Arenal reservoir, which has the
largest surface area and the lowest energy density.
RESUMEN: Las emisiones de gases de efecto invernadero están relacionadas con fuentes
no renovables; sin embargo, la guía metodológica para la estimación de las emisiones de
metano y dióxido de carbono en terrenos inundados fue publicada en el año 2006 por el
Grupo Intergubernamental de Expertos sobre el Cambio Climático. Diez años después,
se han realizado varios estudios sobre la estimación de gas metano en yacimientos
ubicados en zonas templadas y tropicales. Costa Rica es un país centroamericano
conocido por sus grandes recursos hidroeléctricos y su matriz de generación eléctrica
altamente renovable. Este trabajo es el primer estudio, para 11 de los 24 embalses
hidroeléctricos gestionados por el Instituto Costarricense de Electricidad, donde se
determinan las emisiones de metano, la densidad energética y los factores de emisión
para la generación de electricidad. Se utiliza un modelo matemático estático para
determinar estos factores con escasos datos de entrada, estimando que el mayor
aporte en emisiones de metano corresponde al embalse del Arenal, que es el de mayor
superficie y el de menor densidad energética.
1. Introduction
Since the development of countries and the production
of electricity are closely related, hydropower is just one
110
* Corresponding author: Rhonmer Orlando Pérez-Cedeño
E-mail: rhonmerperez@gmail.com
ISSN 0120-6230
e-ISSN 2422-2844
DOI: 10.17533/udea.redin.20230522
110
Estimation of methane emissions from
reservoirs for hydroelectric generation in Costa
Rica
Estimaciones de las emisiones de metano de embalses para la generación hidroeléctrica en
Costa Rica
Rhonmer Orlando Pérez-Cedeño1*, Rodrigo Ramírez-Pisco2, Carmen Luisa Vásquez-Stanescu1,
Leonardo Suárez-Matarrita3,4, Mercedes Gaitán-Angulo5, Melva Gómez-Caicedo6
1Universidad Nacional Experimental Politécnica Antonio José de Sucre. Avenida Corpahuaico entre Avenida La Salle y Av.
Rotaria, C. P. 3001, Barquisimeto, Venezuela.
2Universitat Carlemany. Avenida Verge de Canòlich, 47 AD 600 Sant Julià de Lòria. Principat d’Andorra. Andorra, Andorra.
3Universidad Técnica Nacional. Villa Bonita de Alajuela, Apartado Postal 1902-4050. Alajuela, Costa Rica.
4Universidad de Costa Rica. Sede "Rodrigo Facio Brenes" Montes de Oca. C. P. 11501-2060. San José, Costa Rica.
5Fundación Universidad Konrad Lorenz. Carrera 9 Bis # 62 – 43. C. P. 110231. Bogotá, Colombia.
6Fundación Universidad Los Libertadores. Carrera 16 # 63a - 68. C. P. 110231. Bogotá, Colombia.
CITE THIS ARTICLE AS:
R. O. Pérez-Cedeño, R.
Ramírez-Pisco, C. L.
Vásquez-Stanescu, L.
Suárez-Matarrita, M.
Gaitán-Angulo and M.
Gómez-Caicedo. ”Estimation
of methane emissions from
reservoirs for hydroelectric
generation in Costa Rica”,
Revista Facultad de Ingeniería
Universidad de Antioquia, no.
110, pp. 110-119, Jan-Mar,
2024. [Online]. Available:
https:
//www.doi.org/10.17533/
udea.redin.20230522
ARTICLE INFO:
Received: September 13, 2022
Accepted: May 17, 2023
Available online: May 17, 2023
KEYWORDS:
Methane emissions;
hydroelectric dam; emission
factor; Costa Rica
Emisiones de metano;
embalse hidroeléctrico; factor
de emisión; Costa Rica
ABSTRACT: Greenhouse gas emissions are related to non-renewable sources. For this
reason, the methodological guide for the estimation of methane and carbon dioxide
emissions in flooded lands was published in 2006 by the Intergovernmental Panel on
Climate Change. Since 2016, several studies have been carried out in temperate and
tropical zones reservoirs. Costa Rica is a Central American country known for its large
hydroelectric resources and its highly renewable electricity generation matrix. This
work represents the first study for 11 of 24 hydroelectric plants managed by the Costa
Rican Electricity Institute. Methane emissions, energy density and emission factors for
electricity generation are determined. Furthermore, a static mathematical model is
used to determine these factors with little input data. It is estimated that the greatest
contribution to methane emissions corresponds to the Arenal reservoir, which has the
largest surface area and the lowest energy density.
RESUMEN: Las emisiones de gases de efecto invernadero están relacionadas con fuentes
no renovables; sin embargo, la guía metodológica para la estimación de las emisiones de
metano y dióxido de carbono en terrenos inundados fue publicada en el año 2006 por el
Grupo Intergubernamental de Expertos sobre el Cambio Climático. Diez años después,
se han realizado varios estudios sobre la estimación de gas metano en yacimientos
ubicados en zonas templadas y tropicales. Costa Rica es un país centroamericano
conocido por sus grandes recursos hidroeléctricos y su matriz de generación eléctrica
altamente renovable. Este trabajo es el primer estudio, para 11 de los 24 embalses
hidroeléctricos gestionados por el Instituto Costarricense de Electricidad, donde se
determinan las emisiones de metano, la densidad energética y los factores de emisión
para la generación de electricidad. Se utiliza un modelo matemático estático para
determinar estos factores con escasos datos de entrada, estimando que el mayor
aporte en emisiones de metano corresponde al embalse del Arenal, que es el de mayor
superficie y el de menor densidad energética.
1. Introduction
Since the development of countries and the production
of electricity are closely related, hydropower is just one
110
* Corresponding author: Rhonmer Orlando Pérez-Cedeño
E-mail: rhonmerperez@gmail.com
ISSN 0120-6230
e-ISSN 2422-2844
DOI: 10.17533/udea.redin.20230522
110
R. O. Pérez-Cedeño et al., Revista Facultad de Ingeniería, Universidad de Antioquia, No. 110, pp. 110-119, 2024
of many different types of technologies that have been
developed to meet this need [1]. Among the regions of the
world, Latin America has developed enough potential for
this type of generation. According to Jang et al. [2, 3], this
has made it possible to offer the service to more people
at the lowest possible price. Policies for new facilities or
expansion of existing ones have been halted due to the
vulnerability of water reservoirs, electricity production,
and other infrastructure to climate change and other
extreme weather events [4, 5].
One of the problems facing civilization is climate change
caused by greenhouse gases (GHG) from anthropogenic
and natural sources [6]. The Paris Agreement aims to end
global poverty, reduce emissions everywhere, and limit
the increase in average global temperature to 1.5 °C by
2030 [7, 8].
The energy industry will emit more than 33,884.1 million
tons of CO2-eq into the environment in 2021, according
to the most recent statistical data from British Petroleum
(bp) [9]. South America’s contribution to this total is only
4.7% (1,586.9 million tons of CO2-eq ), partly because
most of its generation depends on hydropower. By way of
illustration, the generation for the same year is 40.26 and
6.55 EJ (input-equivalent), or 16.3%, globally and in South
America, respectively [9].
Global environmental trends and regulations aim to
reduce GHG emissions by standardizing procedures
and continuously improving ecosystem conservation.
Therefore, renewable energy sources should replace fossil
fuel technology [10]. Hydroelectric power production
increased by more than 60% worldwide between 1995
and 2010, according to data from the United Nations
Educational, Scientific, and Cultural Organization
(UNESCO), notably benefiting countries in Latin America,
Africa, and Asia [11].
For a time, it was thought that new hydroelectric dams
did not emit GHG [12]. Enriquez and Cremona [13] affirm
that state the main contribution of water reservoirs to
climate change is through the emissions of CO2 and
methane (CH4) to the earth’s atmosphere, through the
water surface [14–16], and that also increases its carbon
footprint [17]. This is true even though they favor the
accumulation of carbon dioxide (CO2) in their sediments
and do not participate. In addition to lakes, marshes, open
inland water bodies, karst waterfalls, and other natural
sources, these emissions may also come from reservoirs
used for hydropower [18–20], reservoirs used for other
purposes [21, 22], and other hydropower [19].
The Intergovernmental Panel on Climate Change (IPCC)
has shown that 22% of total CH4 emissions are attributed
to water storage in reservoirs and lakes, due to flooded
land [11]. Annual CH4 emissions are only 3% by weight
of those associated with CO2 (0.56 GtCH4/year versus
14.5 GtCO2/year). However, CH4 has a radiative forcing
approximately 120 times greater than CO2 immediately
after its emission [23]. In this framework, CH4 emissions
from reservoirs could represent 12% of global emissions
of this gas [24].
Aspects such as the morphology of the reservoir, its
use, the physicochemical characteristics of the water, the
external contributions of carbon by runoff and organic
matter carried by the rivers that feed it and, finally,
the point of the life cycle at which it is located must be
taken into account in order to quantify the contribution
of emissions to climate change [15, 16, 25]. There are
significant temporal variations with respect to tropical
zones [26–28], such as in Costa Rica [29] and Colombia
[30], or temperate zones [19, 22]. Their own evaporation
processes, accelerated by climate change, also give them
spatial properties [5, 20]. In warmer latitudes, emissions
are higher, which adds a negative argument regarding the
use of hydropower in these regions [11]. The IPCC offers
methodological guidance; there are several approaches
that use direct measurement to obtain their estimate
[31, 32].
It should be noted that the methodology provided by
the IPCC is not complete enough to provide accurate
measurements of CH4 emissions, as these depend on
variables such as depth, age of the reservoir, as well as
climate and previous use, and pre-flood use; however, it is
applicable for estimates where direct measurements are
not taken [31].
Due to the regulations implemented to achieve carbon
neutrality in the country’s energy and transportation
sectors, Costa Rica, a natural resource-rich nation in Latin
America, has an energy matrix focused on renewable
sources [33]. More than 65% of the country’s electricity
is produced by hydroelectricity, with an installed capacity
of 2,343 MW [34]. This indicates that their reservoirs
contribute to GHG emissions from electricity generation.
The objective of this work is to estimate CH4 emissions
from the reservoirs of 11 hydroelectric generation plants
in the country managed by the Costa Rican Electricity
Institute (ICE).
This article includes a section on CH4 emissions from
land flooded by hydroelectric reservoirs, such as diffusive,
bubble, or gas emissions, and outgassing emissions.
Subsequently, a section is dedicated to the CO2 emissions
that were produced in the first ten years of the formation
of the reservoir. In addition, the 11 reservoirs in Costa
Rica are included with their years of operation.
111
of many different types of technologies that have been
developed to meet this need [1]. Among the regions of the
world, Latin America has developed enough potential for
this type of generation. According to Jang et al. [2, 3], this
has made it possible to offer the service to more people
at the lowest possible price. Policies for new facilities or
expansion of existing ones have been halted due to the
vulnerability of water reservoirs, electricity production,
and other infrastructure to climate change and other
extreme weather events [4, 5].
One of the problems facing civilization is climate change
caused by greenhouse gases (GHG) from anthropogenic
and natural sources [6]. The Paris Agreement aims to end
global poverty, reduce emissions everywhere, and limit
the increase in average global temperature to 1.5 °C by
2030 [7, 8].
The energy industry will emit more than 33,884.1 million
tons of CO2-eq into the environment in 2021, according
to the most recent statistical data from British Petroleum
(bp) [9]. South America’s contribution to this total is only
4.7% (1,586.9 million tons of CO2-eq ), partly because
most of its generation depends on hydropower. By way of
illustration, the generation for the same year is 40.26 and
6.55 EJ (input-equivalent), or 16.3%, globally and in South
America, respectively [9].
Global environmental trends and regulations aim to
reduce GHG emissions by standardizing procedures
and continuously improving ecosystem conservation.
Therefore, renewable energy sources should replace fossil
fuel technology [10]. Hydroelectric power production
increased by more than 60% worldwide between 1995
and 2010, according to data from the United Nations
Educational, Scientific, and Cultural Organization
(UNESCO), notably benefiting countries in Latin America,
Africa, and Asia [11].
For a time, it was thought that new hydroelectric dams
did not emit GHG [12]. Enriquez and Cremona [13] affirm
that state the main contribution of water reservoirs to
climate change is through the emissions of CO2 and
methane (CH4) to the earth’s atmosphere, through the
water surface [14–16], and that also increases its carbon
footprint [17]. This is true even though they favor the
accumulation of carbon dioxide (CO2) in their sediments
and do not participate. In addition to lakes, marshes, open
inland water bodies, karst waterfalls, and other natural
sources, these emissions may also come from reservoirs
used for hydropower [18–20], reservoirs used for other
purposes [21, 22], and other hydropower [19].
The Intergovernmental Panel on Climate Change (IPCC)
has shown that 22% of total CH4 emissions are attributed
to water storage in reservoirs and lakes, due to flooded
land [11]. Annual CH4 emissions are only 3% by weight
of those associated with CO2 (0.56 GtCH4/year versus
14.5 GtCO2/year). However, CH4 has a radiative forcing
approximately 120 times greater than CO2 immediately
after its emission [23]. In this framework, CH4 emissions
from reservoirs could represent 12% of global emissions
of this gas [24].
Aspects such as the morphology of the reservoir, its
use, the physicochemical characteristics of the water, the
external contributions of carbon by runoff and organic
matter carried by the rivers that feed it and, finally,
the point of the life cycle at which it is located must be
taken into account in order to quantify the contribution
of emissions to climate change [15, 16, 25]. There are
significant temporal variations with respect to tropical
zones [26–28], such as in Costa Rica [29] and Colombia
[30], or temperate zones [19, 22]. Their own evaporation
processes, accelerated by climate change, also give them
spatial properties [5, 20]. In warmer latitudes, emissions
are higher, which adds a negative argument regarding the
use of hydropower in these regions [11]. The IPCC offers
methodological guidance; there are several approaches
that use direct measurement to obtain their estimate
[31, 32].
It should be noted that the methodology provided by
the IPCC is not complete enough to provide accurate
measurements of CH4 emissions, as these depend on
variables such as depth, age of the reservoir, as well as
climate and previous use, and pre-flood use; however, it is
applicable for estimates where direct measurements are
not taken [31].
Due to the regulations implemented to achieve carbon
neutrality in the country’s energy and transportation
sectors, Costa Rica, a natural resource-rich nation in Latin
America, has an energy matrix focused on renewable
sources [33]. More than 65% of the country’s electricity
is produced by hydroelectricity, with an installed capacity
of 2,343 MW [34]. This indicates that their reservoirs
contribute to GHG emissions from electricity generation.
The objective of this work is to estimate CH4 emissions
from the reservoirs of 11 hydroelectric generation plants
in the country managed by the Costa Rican Electricity
Institute (ICE).
This article includes a section on CH4 emissions from
land flooded by hydroelectric reservoirs, such as diffusive,
bubble, or gas emissions, and outgassing emissions.
Subsequently, a section is dedicated to the CO2 emissions
that were produced in the first ten years of the formation
of the reservoir. In addition, the 11 reservoirs in Costa
Rica are included with their years of operation.
111
R. O. Pérez-Cedeño et al., Revista Facultad de Ingeniería, Universidad de Antioquia, No. 110, pp. 110-119, 2024
Figure 1 Thermal stratification of a deep lake [40, 41]
2. Development
2.1 CH4 emissions
The construction of reservoirs and hydroelectric dams
because of the flooding of large areas of land is one of
the human consequences. In the reservoir construction
stage, large amounts of organic matter present in the
soil after flooding lead to its decomposition, forming and
releasing CO2 and CH4 [15, 25]. In the deepest part of
the reservoirs (hypolimnion), the water layer has little
oxygen, and only under these conditions, does CH4 have
the particularity of oxidizing and turning into CO2 when it
reaches the surface [35]- In reservoirs located in tropical
areas, with little water, they do not allow CH4 bubbles
to oxidize and therefore tend to contribute high GHG
emissions [36–39].
In tropical latitudes, a thermal stratification of the water
is formed, depending on its density and temperature,
which are classified as epilimnion (superficial layer),
metalimnion (intermediate layer) and, finally, hypolimnion
(bottom of the reservoir). In this stratification, which
causes extreme temperature changes between the
layers, there is no mixing between the epilimnion and the
hypolimnion without the presence of an external factor
such as wind or temperature variations. These variations
force the mixture to generate a variation in the stability of
the water column, causing effects on the dynamics of the
oxygen present and the formation and emission of GHG in
hydroelectric reservoirs [25]. Figure 1 shows the vertical
temperature profile of the reservoirs.
Some of the variables that affect the decomposition
of organic matter in a reservoir are age, temperature,
depth, geographic location, water residence time, shape
and volume, and the amount and type of flooded vegetation
[35]. The age of the reservoir and the type and amount
of vegetation, which have been shown to increase in the
period immediately after reservoir creation, have the
greatest impact on CH4 emissions [42].
According to the IPCC [31], CH4 emissions in flooded land
can be caused by:
• Diffusive emissions originated by molecular
propagation through the air-water interface.
• Emissions of bubbles or gases from the sediment
through the water column in the form of bubbles.
This is a major source of CH4 emissions, especially
in temperate and tropical regions. However,
country-specific factors are required to estimate
these emissions.
• Outgassing emissions because of a sudden change
in hydrostatic pressure as well as large air/water
exchange surface. This occurs when water flows
through any outlet or a turbine.
Based on the methodology established by the IPCC [31],
each of the emissions reflects a tier. Levels 1, 2, and 3
reflect diffusive, bubble, or gas emissions and finally refer
to the complete approach that includes the previous two (2)
plus outgassing, respectively. However, Level 2, bubble,
or gas emissions, is applicable in temperate zones, where
there are ice and ice-free seasons. Finally, the choice of
the estimation method depends on the availability of data
and emission factors in each country.
2.2 CO2 emissions
The GHG fluxes are significantly affected by the time
elapsed since a significant area of land was inundated. The
rate of change of post-flood emissions may depend on the
location of the reservoir; however, it appears to vary over
a ten (10) year period [43]. Evidence suggests that CO2
emissions during the first ten (10) years are the result of
the decomposition of organic matter previously existing
in the land before the flood. Beyond this period, CO2
emissions are sustained by the transfer of organic matter
between reservoir inlets and floods. For this reason, only
the first ten (10) years after the flood are considered to
estimate emissions [43]. After a flood and any cleanup,
CO2 emissions from land converted to flooded land can
occur through the same pathways as CH4 emissions, i.e.,
112
Figure 1 Thermal stratification of a deep lake [40, 41]
2. Development
2.1 CH4 emissions
The construction of reservoirs and hydroelectric dams
because of the flooding of large areas of land is one of
the human consequences. In the reservoir construction
stage, large amounts of organic matter present in the
soil after flooding lead to its decomposition, forming and
releasing CO2 and CH4 [15, 25]. In the deepest part of
the reservoirs (hypolimnion), the water layer has little
oxygen, and only under these conditions, does CH4 have
the particularity of oxidizing and turning into CO2 when it
reaches the surface [35]- In reservoirs located in tropical
areas, with little water, they do not allow CH4 bubbles
to oxidize and therefore tend to contribute high GHG
emissions [36–39].
In tropical latitudes, a thermal stratification of the water
is formed, depending on its density and temperature,
which are classified as epilimnion (superficial layer),
metalimnion (intermediate layer) and, finally, hypolimnion
(bottom of the reservoir). In this stratification, which
causes extreme temperature changes between the
layers, there is no mixing between the epilimnion and the
hypolimnion without the presence of an external factor
such as wind or temperature variations. These variations
force the mixture to generate a variation in the stability of
the water column, causing effects on the dynamics of the
oxygen present and the formation and emission of GHG in
hydroelectric reservoirs [25]. Figure 1 shows the vertical
temperature profile of the reservoirs.
Some of the variables that affect the decomposition
of organic matter in a reservoir are age, temperature,
depth, geographic location, water residence time, shape
and volume, and the amount and type of flooded vegetation
[35]. The age of the reservoir and the type and amount
of vegetation, which have been shown to increase in the
period immediately after reservoir creation, have the
greatest impact on CH4 emissions [42].
According to the IPCC [31], CH4 emissions in flooded land
can be caused by:
• Diffusive emissions originated by molecular
propagation through the air-water interface.
• Emissions of bubbles or gases from the sediment
through the water column in the form of bubbles.
This is a major source of CH4 emissions, especially
in temperate and tropical regions. However,
country-specific factors are required to estimate
these emissions.
• Outgassing emissions because of a sudden change
in hydrostatic pressure as well as large air/water
exchange surface. This occurs when water flows
through any outlet or a turbine.
Based on the methodology established by the IPCC [31],
each of the emissions reflects a tier. Levels 1, 2, and 3
reflect diffusive, bubble, or gas emissions and finally refer
to the complete approach that includes the previous two (2)
plus outgassing, respectively. However, Level 2, bubble,
or gas emissions, is applicable in temperate zones, where
there are ice and ice-free seasons. Finally, the choice of
the estimation method depends on the availability of data
and emission factors in each country.
2.2 CO2 emissions
The GHG fluxes are significantly affected by the time
elapsed since a significant area of land was inundated. The
rate of change of post-flood emissions may depend on the
location of the reservoir; however, it appears to vary over
a ten (10) year period [43]. Evidence suggests that CO2
emissions during the first ten (10) years are the result of
the decomposition of organic matter previously existing
in the land before the flood. Beyond this period, CO2
emissions are sustained by the transfer of organic matter
between reservoir inlets and floods. For this reason, only
the first ten (10) years after the flood are considered to
estimate emissions [43]. After a flood and any cleanup,
CO2 emissions from land converted to flooded land can
occur through the same pathways as CH4 emissions, i.e.,
112
R. O. Pérez-Cedeño et al., Revista Facultad de Ingeniería, Universidad de Antioquia, No. 110, pp. 110-119, 2024
diffusive, bubbling, and outgassing emissions.
2.3 Reservoirs in Costa Rica
In 2011, the installed hydroelectric generation capacity
in Central America was estimated at around 5,000 MW,
of which Costa Rica represented approximately 1,644 MW
[44]. According to [34], Costa Rica has a diverse energy
matrix with different types of generation (Table 1), where
more than 85% comes from renewable sources. Gross
electricity generation for the year 2019 was 11,312.85 GWh,
of which 99.15% corresponds to energy generated with
renewable sources and only 0.85% with thermoelectric
energy. Hydroelectric generation is the one with the
highest proportion, with 69.18% of the total, showing the
relevance of this type of source for the country. The general
information of the reservoirs under study belonging to the
National Electric System (SEN) is shown in Table 2, and the
geographic location of the reservoirs under study can be
detailed in Figure 2.
Table 1 Generation in Costa Rica (2020) [45]
Generation Installed % of
Source capacity (MW) total
Hydroelectric 2,331,291 65.91
Thermoelectric 474,112 13.40
Geothermal 261,860 7.40
Bagasse 71,000 2.01
Wind 393,515 11.13
Solar 5,400 0.15
Total 3,537,178 100.00
2.4 Energy generated in hydroelectric
reservoirs
The reservoirs under study have a total power of 1,412
MW, which represents 60.27% of the total power plants
of the ICE group. This represents 65.70% of the total
installed capacity and 2,343 MW of installed hydroelectric
generation (public and private), which is combined with
other generation sources [28]. Table 3 shows the electric
power generated by these reservoirs. This information is
presented by the National Energy Control Center (CENCE)
and will be used to estimate the Emission Factor of
Electricity Generation (EFEG) resulting from the use of
hydroelectric sources.
3. Methodology
3.1 CH4 emissions
According to the Kyoto Protocol [56], a project with a
power density lower than 5MW/km2 is not considered
Table 2 General information on hydroelectric reservoirs under
study (2020) based on [47–52]
Dam Year of Installed Surface
or start of Capacity area
Reservoir operation (MW) ((ha)
Arenal 1979 157.4 8,780.00
Reventazón 2010-2016 305.5 700.00
Cachí 1966 160.0 324.00
Angostura 1992-2000 172.2 256.00
Pirris 2011 134.0 114.00
Dam Year of Installed Surface
or start of Capacity area
Reservoir operation (MW) ((ha)
Sandillal 1992 32.0 71.00
Peñas Blancas 2000-2002 37.7 23.00
Garita I 1953-1958 37.4 7.60
Río Macho 1978 120.0 6.00
Cari Blanco 2003-2007 82.0 0.48
Table 3 Electricity generated by the hydroelectric power plants
under study based on [34, 53–55]
Dam or Reservoir Energy (MWh)
2016 2017 2018 2019
Arenal 763,624.01 642,770.30 765,709.25 725,805.50
Reventazón 748,660.39 979,820.81 635,080.16 845,631.50
Cachí 513,258.72 392,740.57 370,599.97 414,901.23
Angostura 674,339.90 654,222.44 553,716.32 601,565.06
Pirris 419,332.82 548,628.39 336,855.73 372,906.01
Sandillal 143,381.83 118,696.05 144,171.64 137,053.89
Peñas Blancas 152,169.90 153,468.27 157,367.74 136,991.84
Garita 1 y 2 182,125.86 175,771.58 180,678.27 159,634.81
Río Macho 192,937.09 498,425.13 536,535.71 418,563.15
Cari Blanco 226,838.40 254,722.37 261,866.47 181,702.20
emission-free. According to the IPCC methodological
guidelines [31], Equation (1) is used to estimate the
diffusive emissions of CH4 in (1).
CH4E = P ∗ E (CH4)dif ∗ At ∗ 10−6 (1)
Where:
• CH4 E: total CH4 emissions from floodplains
(GgCH4 year −1)
• P: Ice-free period expressed in days per year −1 (365
days, for our case study).
• E (CH4)dif : Daily average of diffusive emissions (kg
of CH4ha−1 day −1)
• At: average total surface of the flooded surface (ha).
Table 4 shows the average daily value of diffusive
emissions, data taken from [31].
According to the IPCC, hydroelectric reservoirs are
classified with a climate called very humid tropical [31].
This is based on the geographic location of the reservoirs
(latitude and longitude) and the Köppen-Geiger global
climate classification [12]. To make comparisons of the
emissions of the different GHG, the conversion factors to
113
diffusive, bubbling, and outgassing emissions.
2.3 Reservoirs in Costa Rica
In 2011, the installed hydroelectric generation capacity
in Central America was estimated at around 5,000 MW,
of which Costa Rica represented approximately 1,644 MW
[44]. According to [34], Costa Rica has a diverse energy
matrix with different types of generation (Table 1), where
more than 85% comes from renewable sources. Gross
electricity generation for the year 2019 was 11,312.85 GWh,
of which 99.15% corresponds to energy generated with
renewable sources and only 0.85% with thermoelectric
energy. Hydroelectric generation is the one with the
highest proportion, with 69.18% of the total, showing the
relevance of this type of source for the country. The general
information of the reservoirs under study belonging to the
National Electric System (SEN) is shown in Table 2, and the
geographic location of the reservoirs under study can be
detailed in Figure 2.
Table 1 Generation in Costa Rica (2020) [45]
Generation Installed % of
Source capacity (MW) total
Hydroelectric 2,331,291 65.91
Thermoelectric 474,112 13.40
Geothermal 261,860 7.40
Bagasse 71,000 2.01
Wind 393,515 11.13
Solar 5,400 0.15
Total 3,537,178 100.00
2.4 Energy generated in hydroelectric
reservoirs
The reservoirs under study have a total power of 1,412
MW, which represents 60.27% of the total power plants
of the ICE group. This represents 65.70% of the total
installed capacity and 2,343 MW of installed hydroelectric
generation (public and private), which is combined with
other generation sources [28]. Table 3 shows the electric
power generated by these reservoirs. This information is
presented by the National Energy Control Center (CENCE)
and will be used to estimate the Emission Factor of
Electricity Generation (EFEG) resulting from the use of
hydroelectric sources.
3. Methodology
3.1 CH4 emissions
According to the Kyoto Protocol [56], a project with a
power density lower than 5MW/km2 is not considered
Table 2 General information on hydroelectric reservoirs under
study (2020) based on [47–52]
Dam Year of Installed Surface
or start of Capacity area
Reservoir operation (MW) ((ha)
Arenal 1979 157.4 8,780.00
Reventazón 2010-2016 305.5 700.00
Cachí 1966 160.0 324.00
Angostura 1992-2000 172.2 256.00
Pirris 2011 134.0 114.00
Dam Year of Installed Surface
or start of Capacity area
Reservoir operation (MW) ((ha)
Sandillal 1992 32.0 71.00
Peñas Blancas 2000-2002 37.7 23.00
Garita I 1953-1958 37.4 7.60
Río Macho 1978 120.0 6.00
Cari Blanco 2003-2007 82.0 0.48
Table 3 Electricity generated by the hydroelectric power plants
under study based on [34, 53–55]
Dam or Reservoir Energy (MWh)
2016 2017 2018 2019
Arenal 763,624.01 642,770.30 765,709.25 725,805.50
Reventazón 748,660.39 979,820.81 635,080.16 845,631.50
Cachí 513,258.72 392,740.57 370,599.97 414,901.23
Angostura 674,339.90 654,222.44 553,716.32 601,565.06
Pirris 419,332.82 548,628.39 336,855.73 372,906.01
Sandillal 143,381.83 118,696.05 144,171.64 137,053.89
Peñas Blancas 152,169.90 153,468.27 157,367.74 136,991.84
Garita 1 y 2 182,125.86 175,771.58 180,678.27 159,634.81
Río Macho 192,937.09 498,425.13 536,535.71 418,563.15
Cari Blanco 226,838.40 254,722.37 261,866.47 181,702.20
emission-free. According to the IPCC methodological
guidelines [31], Equation (1) is used to estimate the
diffusive emissions of CH4 in (1).
CH4E = P ∗ E (CH4)dif ∗ At ∗ 10−6 (1)
Where:
• CH4 E: total CH4 emissions from floodplains
(GgCH4 year −1)
• P: Ice-free period expressed in days per year −1 (365
days, for our case study).
• E (CH4)dif : Daily average of diffusive emissions (kg
of CH4ha−1 day −1)
• At: average total surface of the flooded surface (ha).
Table 4 shows the average daily value of diffusive
emissions, data taken from [31].
According to the IPCC, hydroelectric reservoirs are
classified with a climate called very humid tropical [31].
This is based on the geographic location of the reservoirs
(latitude and longitude) and the Köppen-Geiger global
climate classification [12]. To make comparisons of the
emissions of the different GHG, the conversion factors to
113
R. O. Pérez-Cedeño et al., Revista Facultad de Ingeniería, Universidad de Antioquia, No. 110, pp. 110-119, 2024
Figure 2 Geographical location of the Costa Rican hydroelectric reservoirs under study [46]
Table 4 CH4 emissions from flooded lands [31]
Weather
Diffusive Emissions(kgCH4ha−1day−1)
Median Minimal Maximum
Polar/very humid boreal 0.086 0.011 0.30
Cool temperate, humid 0.061 0.001 0.20
Warm temperate, humid 0.150 -0.050 1.10
Warm temperate, dry 0.044 0.032 0.09
Tropical, very humid 0.630 0.067 1.30
Tropical, dry 0.295 0.070 1.10
CO2 equivalent (CO2-eq) are used [11]. For this purpose,
the Global Warming Potential (GWP) with a value of 21 for
CH4 is used [12]. Equation (2) shows the calculation of
CO2-eq emissions.
CO2−eq of CH4 = Molecular weight of CH4 ∗ GW PCH4
(2)
3.2 CO2 emissions
According to IPCC methodological guidelines [43], the
estimate of CO2 diffusive emissions is given by Equation
(3).
CO2E = P ∗ E (CO2)dif ∗ At ∗ fA ∗ 10−6 (3)
Where:
• CO2 E: total CO2 emissions from flooded land
(GgCO2 year −1)
• P: Ice-free period given in days per year −1 (365 days,
for our case study).
• E (CO2)dif : Daily average of diffusive emissions (kg
of CO2ha−1 day −1)
• At: average total area of the flooded surface (ha)
• fA: fraction of the total reservoir area that has been
flooded in the last ten years (a value of 100% = 1 is
used in our case study).
Table 5 shows the daily average value of diffusive emissions
for CO2.
Table 5 CO2 emissions from flooded lands [43]
Weather
Diffusive Emissions(kgCO2ha−1day−1)
Median Minimal Maximum
Polar/very humid boreal 11.8 0.8 34.5
Cool temperate, humid 15.2 4.5 86.3
Warm temperate, humid 8.1 -10.3 57.5
Warm temperate, dry 5.2 -12.0 31.0
Tropical, very humid 44.9 11.5 90.9
Tropical, dry 39.1 11.7 58.7
4. Results
4.1 Emissions
Table 6 shows the CH4, CO2 and CO2-eq emissions per
year for each reservoir. It should be mentioned that due
to their age, the Reventazón and Pirris reservoirs, and the
IPCC’s recommendations [43], emissions are predicted to
be added to GHG emissions until 2020 and 2021 for each
reservoir, respectively. After this year, only CH4 emissions
are considered for each reservoir. Table 6 shows the CH4
emissions per year, where the highest contribution is given
by the Arenal reservoir with 2.0190 Gg.
4.2 Energy density
Table 7 shows the energy density of hydropower plants
using data of Table 1. Additionally, this table shows that
the highest density corresponds to Cari Blanco, with more
than 17,000 MW/km2.
114
Figure 2 Geographical location of the Costa Rican hydroelectric reservoirs under study [46]
Table 4 CH4 emissions from flooded lands [31]
Weather
Diffusive Emissions(kgCH4ha−1day−1)
Median Minimal Maximum
Polar/very humid boreal 0.086 0.011 0.30
Cool temperate, humid 0.061 0.001 0.20
Warm temperate, humid 0.150 -0.050 1.10
Warm temperate, dry 0.044 0.032 0.09
Tropical, very humid 0.630 0.067 1.30
Tropical, dry 0.295 0.070 1.10
CO2 equivalent (CO2-eq) are used [11]. For this purpose,
the Global Warming Potential (GWP) with a value of 21 for
CH4 is used [12]. Equation (2) shows the calculation of
CO2-eq emissions.
CO2−eq of CH4 = Molecular weight of CH4 ∗ GW PCH4
(2)
3.2 CO2 emissions
According to IPCC methodological guidelines [43], the
estimate of CO2 diffusive emissions is given by Equation
(3).
CO2E = P ∗ E (CO2)dif ∗ At ∗ fA ∗ 10−6 (3)
Where:
• CO2 E: total CO2 emissions from flooded land
(GgCO2 year −1)
• P: Ice-free period given in days per year −1 (365 days,
for our case study).
• E (CO2)dif : Daily average of diffusive emissions (kg
of CO2ha−1 day −1)
• At: average total area of the flooded surface (ha)
• fA: fraction of the total reservoir area that has been
flooded in the last ten years (a value of 100% = 1 is
used in our case study).
Table 5 shows the daily average value of diffusive emissions
for CO2.
Table 5 CO2 emissions from flooded lands [43]
Weather
Diffusive Emissions(kgCO2ha−1day−1)
Median Minimal Maximum
Polar/very humid boreal 11.8 0.8 34.5
Cool temperate, humid 15.2 4.5 86.3
Warm temperate, humid 8.1 -10.3 57.5
Warm temperate, dry 5.2 -12.0 31.0
Tropical, very humid 44.9 11.5 90.9
Tropical, dry 39.1 11.7 58.7
4. Results
4.1 Emissions
Table 6 shows the CH4, CO2 and CO2-eq emissions per
year for each reservoir. It should be mentioned that due
to their age, the Reventazón and Pirris reservoirs, and the
IPCC’s recommendations [43], emissions are predicted to
be added to GHG emissions until 2020 and 2021 for each
reservoir, respectively. After this year, only CH4 emissions
are considered for each reservoir. Table 6 shows the CH4
emissions per year, where the highest contribution is given
by the Arenal reservoir with 2.0190 Gg.
4.2 Energy density
Table 7 shows the energy density of hydropower plants
using data of Table 1. Additionally, this table shows that
the highest density corresponds to Cari Blanco, with more
than 17,000 MW/km2.
114
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Figure 3 Mathematical model of the relationship between energy density and EFEG
Table 6 Emissions of CH4, CO2 and CO2 − eq for the
reservoirs under study
Dam or Reservoir GgCH4/year GgCO2/year tCO2-eq/year
Arenal 2.0190 - 42,398.18
Reventazón 0.1610 11.4720 14,852.22
Cachí 0.0745 - 1,564.58
Angostura 0.0589 - 1,236.21
Pirris 0.0262 1.8683 2,418.79
Sandillal 0.0163 - 342.86
Peñas Blancas 0.0053 - 111.07
Garita I 0.0017 - 36.70
Río Macho 0.0014 - 28.97
Cari Blanco 0.0001 - 2.32
Table 7 Energy density of the reservoirs studied
Dam or Energy Density
Reservoir (MW/km2)
Arenal 1.79
Reventazón 43.64
Cachí 49.38
Angostura 67.27
Pirris 117.54
Sandillal 45.03
Peñas Blancas 164.09
Garita I 491.58
Macho River 2,000.00
Cari Blanco 17,083.33
4.3 Emission Factor of Electricity Generation
Based on the CO2-eq emissions for each reservoir,
extracted from Table 6, and the data shown in Table 3
regarding the energy generated per year, the EFEG for the
reservoirs under study are determined as shown in Table
8.
4.4 Mathematical model
Maybe a non-dynamic mathematical model, that was
created by looking at each reservoir’s EFEG for the
year 2019 and comparing it with its energy density is
applied to create an equation for recalculating the GHG
Table 8 EFEG for the period 2016 - 2019 for the reservoirs
under study
Dam or Reservoir Emission Factor (EFEG) =
(tCO2-eq/MWh) * 10−3
2016 2017 2018 2019
Arenal 55.52 65.96 55.37 58.42
Reventazón 19.84 15.16 23.39 17.56
Cachí 3.50 3.98 4.22 3.77
Angostura 1.83 1.89 2.23 2.05
Pirris 5.77 4.41 7.18 6.49
Sandillal 2.39 2.89 2.38 2.50
Peñas Blancas 0.73 0.72 0.71 0.81
Garita I 0.20 0.21 0.20 0.23
Río Macho 0.15 0.06 0.05 0.07
Cari Blanco 0.01 0.01 0.01 0.01
emission estimations. This section is based on the IHA
[57] indicating that run-of-river hydroelectric plants
or with a power density of not less than 5 W/m2 will
not necessarily have to carry out their GHG emissions
assessment based on their life cycle. In addition, [58]
determines the mathematical model using the EFEG and
the Power Density of the reservoirs for Colombia.
Due to the large scale between the energy density
data and the EFEG, the Neperian logarithm tool is applied
to make the data easier to handle. A scatter plot is also
used to plot these transformed values. Equation 4 shown
in Figure 3 is derived from the trend line, together with the
coefficient of determination (R2 =0.9132), which indicates
a correlation (R) of the data of 95.56%.
y = −0.9877x + 5.2133 (4)
Where:
x: Neperian logarithm of the energy density of the selected
reservoir.
y: Neperian logarithm of EFEG.
It is important to note that the coefficient of determination
R2 expresses the proportion of the total variation of the
values of the variable and that are caused or explained by a
115
Figure 3 Mathematical model of the relationship between energy density and EFEG
Table 6 Emissions of CH4, CO2 and CO2 − eq for the
reservoirs under study
Dam or Reservoir GgCH4/year GgCO2/year tCO2-eq/year
Arenal 2.0190 - 42,398.18
Reventazón 0.1610 11.4720 14,852.22
Cachí 0.0745 - 1,564.58
Angostura 0.0589 - 1,236.21
Pirris 0.0262 1.8683 2,418.79
Sandillal 0.0163 - 342.86
Peñas Blancas 0.0053 - 111.07
Garita I 0.0017 - 36.70
Río Macho 0.0014 - 28.97
Cari Blanco 0.0001 - 2.32
Table 7 Energy density of the reservoirs studied
Dam or Energy Density
Reservoir (MW/km2)
Arenal 1.79
Reventazón 43.64
Cachí 49.38
Angostura 67.27
Pirris 117.54
Sandillal 45.03
Peñas Blancas 164.09
Garita I 491.58
Macho River 2,000.00
Cari Blanco 17,083.33
4.3 Emission Factor of Electricity Generation
Based on the CO2-eq emissions for each reservoir,
extracted from Table 6, and the data shown in Table 3
regarding the energy generated per year, the EFEG for the
reservoirs under study are determined as shown in Table
8.
4.4 Mathematical model
Maybe a non-dynamic mathematical model, that was
created by looking at each reservoir’s EFEG for the
year 2019 and comparing it with its energy density is
applied to create an equation for recalculating the GHG
Table 8 EFEG for the period 2016 - 2019 for the reservoirs
under study
Dam or Reservoir Emission Factor (EFEG) =
(tCO2-eq/MWh) * 10−3
2016 2017 2018 2019
Arenal 55.52 65.96 55.37 58.42
Reventazón 19.84 15.16 23.39 17.56
Cachí 3.50 3.98 4.22 3.77
Angostura 1.83 1.89 2.23 2.05
Pirris 5.77 4.41 7.18 6.49
Sandillal 2.39 2.89 2.38 2.50
Peñas Blancas 0.73 0.72 0.71 0.81
Garita I 0.20 0.21 0.20 0.23
Río Macho 0.15 0.06 0.05 0.07
Cari Blanco 0.01 0.01 0.01 0.01
emission estimations. This section is based on the IHA
[57] indicating that run-of-river hydroelectric plants
or with a power density of not less than 5 W/m2 will
not necessarily have to carry out their GHG emissions
assessment based on their life cycle. In addition, [58]
determines the mathematical model using the EFEG and
the Power Density of the reservoirs for Colombia.
Due to the large scale between the energy density
data and the EFEG, the Neperian logarithm tool is applied
to make the data easier to handle. A scatter plot is also
used to plot these transformed values. Equation 4 shown
in Figure 3 is derived from the trend line, together with the
coefficient of determination (R2 =0.9132), which indicates
a correlation (R) of the data of 95.56%.
y = −0.9877x + 5.2133 (4)
Where:
x: Neperian logarithm of the energy density of the selected
reservoir.
y: Neperian logarithm of EFEG.
It is important to note that the coefficient of determination
R2 expresses the proportion of the total variation of the
values of the variable and that are caused or explained by a
115
R. O. Pérez-Cedeño et al., Revista Facultad de Ingeniería, Universidad de Antioquia, No. 110, pp. 110-119, 2024
Table 9 EFEG for the period 2016 - 2019 for the reservoirs
under study
Reservoir
EFEG estimated
(tCO2-eq/MWh) * 10−3
Mathematical model IPCC Methodology
Arenal 103.208 58.415
Reventazón 4.409 17.563
Cachí 3.903 3.771
Angostura 2.876 2.055
Pirris 1.657 6.486
Sandillal 4.275 2.502
Peñas Blancas 1.192 0.811
Garita I 0.403 0.230
Río Macho 0.101 0.069
Cari Blanco 0.012 0.013
Median of data 2.267 2.055
Table 10 EFEG for the period 2016-2019 for
Costa Rica
EFEG= (tCO2-eq/MWh) * 10−3
2016 2017 2018 2019 2019*
89.49 95.29 95.74 91.92 122.04
*With the mathematical model
linear relationship with the values of the random variable
x [59]. For this section, the correlation coefficient (R)
shows a close relationship between the random variables
considered, such as energy density (variable x) and EFEG
(variable y).
Table 9 shows the new EFEG values obtained using
Equation 4, where there is proximity in the values with
certain exceptions. Table 10 shows the EFEG for the
period 2016-2019 obtained from the IPCC methodology
and additional for the year 2019 as part of the use of the
mathematical model. This emission accounting model
used in this work differs from the methodology used by
the National Meteorological Institute (IMN) of Costa Rica
for the annual calculation of emission factors, which only
considers emissions from non-renewable energy [60].
5. Conclusions
Regarding the estimated CH4 emissions of the reservoirs
under study, a high contribution of 85.39% of the total
corresponds to the Arenal dam, with more than 2.0 Gg
of this GHG per year, having the lowest energy density
(less than 2.0 MW/km2). The Cari Blanco dam has more
than 17,000 MW/km2 with the lowest CH4 emissions. This
shows an inverse relationship between GHG emissions
and energy density.
Applying the static mathematical model proposed as
a new way of computing the EFEG, a good approximation of
the results obtained can be seen, with certain exceptions
due to the nature of the model. Comparing the medians
of the data sets, it was not determined a high disparity,
thus taking the model obtained as a valid approximation
method with a smaller amount of input data.
All of Costa Rica’s EFEG for the years 2016 to 2019 provided
by the reservoirs under consideration, accounting for more
than 55% of the nation’s installed hydroelectric capacity,
is less than 100 tCO2-eq/MWh*10-3.
6. Declaration of competing interest
We declare that we have no significant competing interests,
including financial or non-financial, professional, or
personal interests interfering with the full and objective
presentation of the work described in this manuscript.
7. knowledgements
The authors of this article would like to thank the Programa
Ciencia y Tecnología para el Desarrollo de Iberoamérica
(CYTED), since it was developed within the framework
of the Red de Transporte y Movilidad Urbana Sostenible
project (RITMUS, 718RT0566)
8. Funding
The author(s) received no financial support for the
research, authorship, and/or publication of this article.
9. Author contributions
Conceptualization: R.O. Pérez, R. Ramírez, L. Suárez and
C. Vásquez; Methodology: R. O. Pérez and L. Suárez;
Validation: R. O. Pérez and L. Suárez; Formal analysis:
R.O. Pérez, R. Ramírez and C. Vásquez; Investigation:
R. O. Pérez and C. Vásquez; Data curation: R. O.
Pérez, R. Ramírez and C. Vásquez; Writing—original draft
preparation and visualization: R. O. Pérez, C. Vásquez, M.
Gaitán and M. Gómez. All authors have read and agreed to
the published version of the manuscript.
10. Data availability statement
The authors confirm that the data supporting the findings
of this study are available within the article [and/or] its
supplementary materials.
11. Complements
116
Table 9 EFEG for the period 2016 - 2019 for the reservoirs
under study
Reservoir
EFEG estimated
(tCO2-eq/MWh) * 10−3
Mathematical model IPCC Methodology
Arenal 103.208 58.415
Reventazón 4.409 17.563
Cachí 3.903 3.771
Angostura 2.876 2.055
Pirris 1.657 6.486
Sandillal 4.275 2.502
Peñas Blancas 1.192 0.811
Garita I 0.403 0.230
Río Macho 0.101 0.069
Cari Blanco 0.012 0.013
Median of data 2.267 2.055
Table 10 EFEG for the period 2016-2019 for
Costa Rica
EFEG= (tCO2-eq/MWh) * 10−3
2016 2017 2018 2019 2019*
89.49 95.29 95.74 91.92 122.04
*With the mathematical model
linear relationship with the values of the random variable
x [59]. For this section, the correlation coefficient (R)
shows a close relationship between the random variables
considered, such as energy density (variable x) and EFEG
(variable y).
Table 9 shows the new EFEG values obtained using
Equation 4, where there is proximity in the values with
certain exceptions. Table 10 shows the EFEG for the
period 2016-2019 obtained from the IPCC methodology
and additional for the year 2019 as part of the use of the
mathematical model. This emission accounting model
used in this work differs from the methodology used by
the National Meteorological Institute (IMN) of Costa Rica
for the annual calculation of emission factors, which only
considers emissions from non-renewable energy [60].
5. Conclusions
Regarding the estimated CH4 emissions of the reservoirs
under study, a high contribution of 85.39% of the total
corresponds to the Arenal dam, with more than 2.0 Gg
of this GHG per year, having the lowest energy density
(less than 2.0 MW/km2). The Cari Blanco dam has more
than 17,000 MW/km2 with the lowest CH4 emissions. This
shows an inverse relationship between GHG emissions
and energy density.
Applying the static mathematical model proposed as
a new way of computing the EFEG, a good approximation of
the results obtained can be seen, with certain exceptions
due to the nature of the model. Comparing the medians
of the data sets, it was not determined a high disparity,
thus taking the model obtained as a valid approximation
method with a smaller amount of input data.
All of Costa Rica’s EFEG for the years 2016 to 2019 provided
by the reservoirs under consideration, accounting for more
than 55% of the nation’s installed hydroelectric capacity,
is less than 100 tCO2-eq/MWh*10-3.
6. Declaration of competing interest
We declare that we have no significant competing interests,
including financial or non-financial, professional, or
personal interests interfering with the full and objective
presentation of the work described in this manuscript.
7. knowledgements
The authors of this article would like to thank the Programa
Ciencia y Tecnología para el Desarrollo de Iberoamérica
(CYTED), since it was developed within the framework
of the Red de Transporte y Movilidad Urbana Sostenible
project (RITMUS, 718RT0566)
8. Funding
The author(s) received no financial support for the
research, authorship, and/or publication of this article.
9. Author contributions
Conceptualization: R.O. Pérez, R. Ramírez, L. Suárez and
C. Vásquez; Methodology: R. O. Pérez and L. Suárez;
Validation: R. O. Pérez and L. Suárez; Formal analysis:
R.O. Pérez, R. Ramírez and C. Vásquez; Investigation:
R. O. Pérez and C. Vásquez; Data curation: R. O.
Pérez, R. Ramírez and C. Vásquez; Writing—original draft
preparation and visualization: R. O. Pérez, C. Vásquez, M.
Gaitán and M. Gómez. All authors have read and agreed to
the published version of the manuscript.
10. Data availability statement
The authors confirm that the data supporting the findings
of this study are available within the article [and/or] its
supplementary materials.
11. Complements
116
R. O. Pérez-Cedeño et al., Revista Facultad de Ingeniería, Universidad de Antioquia, No. 110, pp. 110-119, 2024
Table 11 Complementary information on hydroelectric reservoirs under study (2020) based on [47–52]
Reservoir River Location
State/City Coordinates
Arenal River and streams of the Arenal Guanacaste 10°32’00”N 84°56’00”O
Lagoon basin
Reventazón Reventazón Limón 10°04’33”N 83°34’25”O
Cachí Reventazón Cartago 9°49’38”N 83°49’00”O
Angostura Reventazón, Tuis and Turrialba Cartago 9°52’00”N 83°40’00”O
Pirrís Middle basin of the Pirrís river San José 9°38’35”N 84°05’54”O
Sandillal Sandillal reservoir and Guanacaste 10°27’42”N 85°05’54”O
Santa Rosa River
Peñas Blancas Peñas Blancas Alajuela 10°23’4”N 84°34’45”O
Garita I Grande de Alajuela, Poás, Alajuela 9°59’21”N 84°20’26”O
Ciruelas and Alajuela
Río Macho Río Macho, Tapantí, Porras Blanco, Cartago 9°46’31”N 83°50’28”O
Badilla, Humo, Villegas and Pejibaye.
Cari Blanco Cari Blanco Alajuela 10°14’33”N 84°9’50”O
References
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Available: https://doi.org/10.5402/2012/730631
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L. Gangwoong, and et al., “Preliminary analysis of the development
of the carbon tracker system in latin america and the caribbean,”
Atmósfera, vol. 27, no. 1, Jul. 15, 2014. [Online]. Available:
https://doi.org/10.1016/S0187-6236(14)71101-4
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of environmental tax and renewable energy in co2 emissions in
latin america and caribbean countries: Evidence from method of
moments quantile regression,” Environmental Challenges, vol. 6,
Nov. 30, 2022. [Online]. Available: https://doi.org/10.1016/j.envc.
2021.100412
[4] E. V. Sperling, “Hydropower in brazil: Overview of positive and
negative environmental aspects,” Energy Procedia, vol. 18, Jun. 09,
2012. [Online]. Available: https://doi.org/10.1016/j.egypro.2012.05.
023
[5] L. N. Guo, C. She, D. B. Kong, S. L. Yan, Y. P. Xu, and et
al., “Prediction of the effects of climate change on hydroelectric
generation, electricity demand, and emissions of greenhouse gases
under climatic scenarios and optimized ann model,” Energy Reports,
vol. 7, Aug. 19, 2021. [Online]. Available: https://doi.org/10.1016/j.
egyr.2021.08.134
[6] E. F. Moran, M. C. Lopez, N. Moore, and D. W. Hyndman, “Sustainable
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2018. [Online]. Available: https://doi.org/10.1073/pnas.1809426115
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V. Leite, “Methane emissions and energy density of reservoirs
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Ibero-American Conference on Smart Cities, Costa rica, 2020, pp.
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Giorgio, A. Harby, and et al., “A new modelling framework to
assess biogenic ghg emissions from reservoirs: The g-res tool,”
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[Online]. Available: https://doi.org/10.1016/j.envsoft.2021.105117
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temporal variability in surface water co2 and ch4 concentrations in
a newly created complex of boreal hydroelectric reservoirs,” Science
of The Total Environment, vol. 815, Apr. 01, 2022. [Online]. Available:
https://doi.org/10.1016/j.scitotenv.2021.152459
[17] A. Levasseur, S. Mercier-Blais, Y. T. Prairie, A. Tremblay, and
C. Turpin, “Improving the accuracy of electricity carbon footprint:
Estimation of hydroelectric reservoir greenhouse gas emissions,”
Renewable and Sustainable Energy Reviews, vol. 136, Sep. 26, 2020.
[Online]. Available: https://doi.org/10.1016/j.rser.2020.110433
[18] E. Calamita, A. Siviglia, G. M. Gettel, and B. Wehrli, “Unaccounted
co2 leaks downstream of a large tropical hydroelectric reservoir,”
PNAS, vol. 118, no. 25, Jun. 14, 2021. [Online]. Available: https:
//doi.org/10.1073/pnas.2026004118
[19] L. Yang, X. Li, W. Yan, P. Ma, and J. Wang, “Ch4 concentrations
and emissions from three rivers in the chaohu lake watershed
in southeast china,” Journal of Integrative Agriculture, vol. 11,
no. 4, May. 12, 2011. [Online]. Available: https://doi.org/10.1016/
S2095-3119(12)60054-9
[20] I. de Vicente, “Biogeochemistry of mediterranean wetlands: A
review about the effects of water-level fluctuations on phosphorus
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25, 2021. [Online]. Available: https://doi.org/10.3390/w13111510
[21] T. Nirupada-Chanu, S. Kumar-Nag, S. Kumar-Koushlesh,
M. Shaya-Devi, and B. Kumar-Das, “Greenhouse gas emission
117
Table 11 Complementary information on hydroelectric reservoirs under study (2020) based on [47–52]
Reservoir River Location
State/City Coordinates
Arenal River and streams of the Arenal Guanacaste 10°32’00”N 84°56’00”O
Lagoon basin
Reventazón Reventazón Limón 10°04’33”N 83°34’25”O
Cachí Reventazón Cartago 9°49’38”N 83°49’00”O
Angostura Reventazón, Tuis and Turrialba Cartago 9°52’00”N 83°40’00”O
Pirrís Middle basin of the Pirrís river San José 9°38’35”N 84°05’54”O
Sandillal Sandillal reservoir and Guanacaste 10°27’42”N 85°05’54”O
Santa Rosa River
Peñas Blancas Peñas Blancas Alajuela 10°23’4”N 84°34’45”O
Garita I Grande de Alajuela, Poás, Alajuela 9°59’21”N 84°20’26”O
Ciruelas and Alajuela
Río Macho Río Macho, Tapantí, Porras Blanco, Cartago 9°46’31”N 83°50’28”O
Badilla, Humo, Villegas and Pejibaye.
Cari Blanco Cari Blanco Alajuela 10°14’33”N 84°9’50”O
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Available: https://doi.org/10.5402/2012/730631
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L. Gangwoong, and et al., “Preliminary analysis of the development
of the carbon tracker system in latin america and the caribbean,”
Atmósfera, vol. 27, no. 1, Jul. 15, 2014. [Online]. Available:
https://doi.org/10.1016/S0187-6236(14)71101-4
[3] Y. Wolde-Rufael and E. Mulat-Weldemeskel, “The moderating role
of environmental tax and renewable energy in co2 emissions in
latin america and caribbean countries: Evidence from method of
moments quantile regression,” Environmental Challenges, vol. 6,
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2021.100412
[4] E. V. Sperling, “Hydropower in brazil: Overview of positive and
negative environmental aspects,” Energy Procedia, vol. 18, Jun. 09,
2012. [Online]. Available: https://doi.org/10.1016/j.egypro.2012.05.
023
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al., “Prediction of the effects of climate change on hydroelectric
generation, electricity demand, and emissions of greenhouse gases
under climatic scenarios and optimized ann model,” Energy Reports,
vol. 7, Aug. 19, 2021. [Online]. Available: https://doi.org/10.1016/j.
egyr.2021.08.134
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public transport systems on urban mobility and the environment,”
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Available: https://doi.org/10.13140/RG.2.2.15226.64965
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https://doi.org/10.1016/j.scitotenv.2021.152459
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C. Turpin, “Improving the accuracy of electricity carbon footprint:
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[Online]. Available: https://doi.org/10.1016/j.rser.2020.110433
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