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Assessment of risk of GHG emissions from Tehri
hydropower reservoir, India
Amit Kumar
Mahendra PAL Sharma
Indian Institute of Technology Roorkee
Indian Institute of Technology Roorkee
Available from: Amit Kumar
Retrieved on: 24 November 2015
Human and Ecological Risk Assessment: An International
ISSN: 1080-7039 (Print) 1549-7860 (Online) Journal homepage: http://www.tandfonline.com/loi/bher20
Assessment of risk of GHG emissions from Tehri
hydropower reservoir, India
Amit Kumar & M. P. Sharma
To cite this article: Amit Kumar & M. P. Sharma (2015): Assessment of risk of GHG emissions
from Tehri hydropower reservoir, India, Human and Ecological Risk Assessment: An
International Journal, DOI: 10.1080/10807039.2015.1055708
To link to this article: http://dx.doi.org/10.1080/10807039.2015.1055708
Accepted online: 15 Jun 2015.Published
online: 15 Jun 2015.
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Date: 12 October 2015, At: 11:11
2015, VOL. 0, NO. 0, 1 15
Assessment of risk of GHG emissions from Tehri hydropower
reservoir, India
Amit Kumar and M. P. Sharma
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Alternate Hydro Energy Centre, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India
The hydropower reservoirs, considered as a green source of energy,
are now found to emit significant quantities of greenhouse gas
(GHG) to the atmosphere. This article attempts to predict the
vulnerability of Tehri reservoir, India to GHG emissions using the
GHG risk assessment tool (GRAT). The GRAT is verified with
experimental GHG fluxes. The annual mean CO2 fluxes from
diffusion, bubbling, and degassing were 425.93 § 122.50, 4.81 §
1.33, and 7.01 § 2.77 mg m¡2d¡1, whereas CH4 fluxes were 23.11 §
7.08, 4.79 § 1.08, and 7.41 § 4.50 mg m¡2d¡1, respectively, during
2011 12. The model found that Tehri reservoir emitted higher CO2
and CH4 (i.e., 790 mg m¡2d¡1 and 64 mg m¡2d¡1, respectively) in
2011, which came within vulnerability range causing more climate
change impact. By the year 2015, it would scale down to medium
risks necessitating no further assessment of GHG. Significant
difference between predicted and experimental GHG emission are
assessed, which may be due to insufficient data, spatial and
temporal variations, decomposition of flooded biomass, limitation
of GRAT model, and inadequate methodology. The study reveals
that GHG emission from Tehri reservoir is less than predicted by the
Received 20 February 2015
Revised manuscript
accepted 25 May 2015
greenhouse gas (GHG);
vulnerability; emissions; risk;
The economic development and the urbanization are vulnerable to climate changes like
urban heat-island effect, high outdoor and indoor air pollution, high population density,
and poor sanitation (Diarmid and Carlos 2012). The climate change caused by increasing
greenhouse gases (GHGs) has lead to rise in global average temperature from 3.7 to 4.8 C
by the year 2100 (IPCC 2014). The increasing GHGs levels and associated climate change
will have both positive and negative impact. On the positive side, due to increase in temperature and increased concentrations of CO2, the productivity of crops (in the region
where moisture is not a constraint) will boost up (Mendelssohn et al. 1994). Senapati
et al. (2013) observed that the higher level of CO2 will stimulate photosynthesis in certain
plants (30 100%). On the negative side, climate change will bring in temperature,
CONTACT Amit Kumar
[email protected]
Alternate Hydro Energy Centre, Indian Institute of Technology
Roorkee, Roorkee, Uttarakhand 24766, India
Color versions of one or more of the figures in the article can be found online at www.tandfonline.com/bher.
© 2015 Taylor & Francis Group, LLC
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precipitation, and heavy rainfall, thereby resulting in natural calamities like drought,
flooding, storms, sea-level rise, and other effects like environmental health risks and the
overall impact on agriculture by way of increased proportion of solar radiation and prevalence of pests. In short and summarized form, climate change will have adverse impact on
agriculture, hydropower, forest management, and biodiversity. The risk assessment of vulnerability based on “state of the art” modeling simulations can predict the long-term fate
of GHG emissions from reservoirs/lakes/rivers/wetland and assess potential for, and
impact of, emissions in both the short and long term. Risk is a function of the values of
threat, consequence, and vulnerability. Risk studies can also assist the development of
monitoring programs for injection sites.
Vulnerability is the assessment of the threats from potential hazards to the population and
allows one to take suitable measures to reduce the consequences. The vulnerability due to
GHG emissions calls the policy-makers to predict the magnitude to country/region and enables authorities to take the corrective measures timely to minimize the consequences (Cutter
1996, 2003; Cutter et al. 2003; Fussel and Klein 2006). Countries that are exposed to high
GHG emissions can support actions/take corrective measures to reduce the effect/consequences while other countries/regions with low impact do not require any corrective actions. In
the recent times, the assessment of the vulnerability due to GHG emissions is becoming the
focus of current research. Several models were used to assess the vulnerability like ModVege
model to grassland ecosystems (Romain et al. 2012), WatBal hydrological water balance
model (David and Yates 1996) to runoffs, Dynamic interactive vulnerability assessment
(DIVA) model (Marcel et al. 1998) to sea-level rise and pasture simulation model to dry matter production and fluxes of C, N, and so on. The GHG emissions from global inland waters
are reported as 0.65 Pg of C (CO2 eq) yr¡1 as CH4 (Bastviken et al. 2011) and 1.2 2.1 Pg C
yr¡1 as CO2 (Raymond et al. 2013). The GHG emissions from global inland water constitute
around 4% of the total as compared to emissions from other sources (Barros et al. 2011). It
may be a serious concern as agricultural productivity, crop pattern; hydrological cycle, and so
on will be affected due to emission of GHG. However, global estimates are constrained by
paucity of data and poor coverage of Asia, in particular.
In recent years, it is reported that GHG emissions from artificial reservoirs located in
tropical/sub tropical regions are one of the serious concerns. When organic matter (accumulated at the bottom of the reservoir) gets degraded by aerobic and anaerobic process,
there is an excess release of GHG into the atmosphere. The increase in GHGs emissions is
also due to nutrient loading, enhanced bacterial activity, and decomposition of labile
organic carbon (Kumar and Sharma 2012, 2014b). The magnitude of emissions for both
reservoirs and natural aquatic systems depend on physico-chemical characteristics of the
water body and the incoming carbon from the watershed. A small amount of GHGs is
released from the reservoir through the bubbling, degassing, but a significant amount is
released through diffusion from water surface as well as when the water is passed through
the turbines and spillways (Fearnside 2006). Kumar and Sharma (2012, 2014a) developed
correlations between GHG emissions, water quality, and reservoirs characteristics the
impact of which were not significant. High uncertainty in the GHGs are reported due to
the lack of data from geographical regions, spatial and temporal variability of reservoirs
(Barros et al. 2011; Joel 2012; Tremblay et al. 2010; Li and Lu 2012), its surface area,
decomposition of flooded biomass (Hiroki 2005), inconsistent methodologies (Tremblay
et al. 2010; IPCC 2006), and labile organic carbon (Kumar and Sharma 2014b). The
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GHGs of 141.6 Tg CO2 yr¡1 and 9.1 Tg CH4 yr¡1 is released by India’s inland waters (Li
and Bush 2015). But, Panneer et al. (2014) reported that this is 2.1 times greater than the
land carbon sink of India. He has also worked on the coordinated flux measurements of
CH4 and CO2 in multiple lakes, ponds, rivers, open wells, reservoirs, springs, and canals
in India and found that the total CH4 flux (bubbling and diffusion) from all the 45 systems
ranged from 0.01 to 52.1 mmol m¡2d¡1. Moreover, CO2 fluxes ranged from 28.2 to
262.4 mmol m¡2d¡1. To improve the current estimates of GHGs on a national scale,
efforts are needed to measure the flux data at the dams.
The present paper reports the assessment of the vulnerability of the Tehri hydropower
reservoir located in the Uttarakhand state of India using GHG risk assessment tool
(GRAT) based on experimental and predicted gross CO2 and CH4 emissions data. The
model gives the output in the form of high, medium and low vulnerability to gross GHG
emissions. This will gives an idea to the environmentalist or policy-maker to make a suitable mitigation plan if the reservoir is highly vulnerable to GHGs.
Material and methods
Study area
Tehri reservoir (Figure 1) is a multipurpose rock and earth-fill embankment on the Bhagirathi River near Tehri in Uttarakhand, India, is the fifth deepest reservoir in the world.
Its catchment area is about 7511 Km2 with dam height of 260.5 m from deepest foundation and 239.5 m from river bed. The dam construction was completed in the year 2006
with total generation capacity of 2400 MW. The storage volume of dam is 4.0 Km3 and
surface area of 52 Km2. The maximum reservoir area observed during full reservoir level
(830 m) was 42 km2 while 18 km2 areas were observed at minimum water level (740 m).
The area has mean maximum temperature of 35.5 C (May July) and the mean minimum
temperature of 4.6 C (Dec Feb) (Bagchi and Singh 2011). The annual rainfalls in Tehri
Garhwal district is variable and ranges from 956 2449 mm, while the average numbers
of rainy days (having daily rainfall 2.5 mm) are 61.5 days (Bagchi and Singh 2011). The
submergence zones lie between 30 200 30 410 N and 78 150 78 400 E alone an altitudinal range from 569 to 830 m msl (Figure 1).
About GHG risk assessment tool (GRAT)
GRAT (Beta version) was developed by UNESCO/IHA in 2012 to estimate the vulnerability of
freshwater reservoirs to GHG emissions (UNESCO/IHA 2012). UNESCO/IHA developed this
tool, which does not evaluate the net GHG emissions but can assess the vulnerability of a reservoir based on gross GHG emissions in short period, when site-specific data are not available. It
can only predict gross diffusive fluxes of CH4 and CO2 and can indicate the need of assessing
net GHG emissions. Consequently, the predicted total fluxes do not include some pathways,
such as CH4 bubbling and downstream degassing. Predicted gross emissions are including
emissions from unrelated anthropogenic sources and emissions in the area before reservoir
impoundment. GRAT can also be used for the life cycle assessment of GHG and their vulnerability as low, medium, and high over a period of 100 years. It thus corresponds to average emission rate over the 100 year integration period. The GRAT model is limited to only gross GHG
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Figure 1. GHG sampling points at Tehri reservoir.
diffusion fluxes. A simple decision-tree model is used to analyze GHG emissions from freshwater reservoirs. The approach to risk assessment of the vulnerability of a freshwater reservoir is
presented as a three-step process in Figure 2, shows that if reservoirs are found to have low or
medium vulnerability to gross GHG emissions, there is no need to further assess the GHG
fluxes. But if, the reservoir is highly vulnerable to gross emissions, assessment of net emission
becomes indispensible, thereby necessitating estimation of pre- and post-impoundment of
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Figure 2. Risk assessment of the vulnerability of a freshwater reservoir.
GHG emissions in the reservoirs. On the basis of net GHG emissions, behavior of reservoir as
carbon sink/carbon source and its magnitude of the GHG risk can be known.
Prediction of GHG fluxes from GRAT
Prediction of CO2 and CH4 fluxes (each one or both) are based on the input data calculated from mean annual temperature, rainfall, runoff, and age of the reservoir. Annual
precipitation and temperature data are computed from daily data of study year 2011.
Mean annual runoffs were calculated using monthly runoff data of study year 2011. The
input parameters are useful for GRAT model as given in Table 1.
Table 1. Input parameters for GRAT model (UNESCO/IHA 2012).
S. No.
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Age of reservoir
Input Data
Input for estimation
of CO2 and CH4 flux
Mean annual air temp
Mean annual runoff
( C)
Mean annual
Input for estimation
of CO2 flux
Input for estimation
of CH4 flux
Source of data
NASA , Agro climatology
NASA, Agro climatology
Nawani (2006)
NASA (1983)
Fekete et al.
NASA (1983)
National Aeronautics and Space Administration (NASA), Global Runoff Data Center (GRDC), Tehri Hydro Development
Corporation (THDC).
GRAT model can predict gross GHG emission for the given reservoir age and integrates over a defined period (100 years). Further assessment of net GHG emissions depends
on the out of the model results and is required to primarily ascertain the adverse impact
on human population, reduction in agriculture production, melting of ice (if reservoirs
are located in high altitude region), flooding in nearby area, rise in sea level, and so on.
The results of predicted gross, CO2 and CH4 fluxes generated by this model are shown
graphically in Figures 3 and 4.
Concept of vulnerability assessment
Figure 5 shows the linkages of GHG to climate change vulnerability, concept of adaptation
and mitigation, ecosystem stability, exposure, and impacts on climate change. The concept
is based on the assumptions that GHGs are the primary factors influencing the climate
and so GHG emissions into the atmosphere become the key drivers of the climate change.
Figure 5 shows that mitigation can reduce the sources or enhance the sinks of GHG
(Fussel and Klein 2006), whereas the adaptation reduces the negative and inevitable effects
of climate change. This can be done, if adequate resources are available.
Vulnerability of reservoir due to GHG
Higher is the capacity of a reservoir to emit GHG, the higher would be its vulnerability,
accordingly assessment of net GHG emissions may be required. As per UNESCO/IHA
(2012) report, the low/medium vulnerability of a reservoir is an indication of low carbon
and nutrients availability in the catchment and does not require the assessment of net
Figure 3. Predicted CO2 fluxes from Tehri hydropower reservoir.
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Figure 4. Predicted CH4 fluxes from Tehri hydropower reservoir.
GHG emissions. According to the magnitude of vulnerability based on gross GHG fluxes,
GHG vulnerability of a reservoir can be predicted. These ranges of fluxes are applicable to
hydropower reservoirs and lakes only. The GHG risk assessment of hydropower reservoirs is assessed using GHG risk assessment tool (Beta version).
Measurement of GHG fluxes
During the four field campaigns, the diffusive fluxes of CH4 and CO2 across the water air
interface were measured using floating chambers at all stations at pre-monsoon (June
2011), post-monsoon (Sep 2011), winter (Jan 2012) and summer season (April 2012). The
diffusion flux, ebullitions (bubbling emissions), and degassing were measured at eight
Figure 5. Key concept of vulnerability to climate change. Arrows represent the feedbacks of mitigation
and adaptation strategies onto climate change impacts.
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sampling locations (Figure 1) with different depths in entire surface area (52 Km2) of the
reservoir to calculate the flux rates. The bubbling fluxes were measured using submerged
funnel technique (Tremblay et al. 2005). Six sampling locations were selected on the basis
of water depth in the Tehri Reservoir and two sampling station immediately 50 100 m
below the outlet from the powerhouse. This implies that the degassing flux (in the small
area of water below the outlet) is applied to the same area as the bubbling and diffusion
fluxes (which are implicitly for the reservoir surface as a whole).
Floating chamber measurements
The floating chambers are rectangular boxes (0.20 m wide and long with 0.50 m high; volume D 21.6 L). The floating chambers were covered with a reflective surface to limit the
warming of inside air during measurements. Within 45 minutes, four air samples were
collected with a syringe from the chambers (duplicates) at 15 min interval. Air samples
for CH4 were collected in 10 ml glass vials that contained 6M NaCl solution capped with
high density butyl stoppers and aluminium seals, whereas air samples for CO2 were collected in vials flushed with N2. All samples were analyzed within 48 hours by Gas Chromatography (GC). GHG fluxes were calculated from the slope of the linear regression of
gas concentration in the chamber versus time (Abril et al. 2005; Guerin et al. 2006; Yang
et al. 2008). The fluxes correlation coefficient (R2) of the linear regression comes higher
than 0.80 (R2: 0.95).
Ebullition/bubbling of GHG
CH4 produced through anaerobic degradation in sediments leads to the bubbling emissions. Temperature and hydrostatic pressure affects the bubbling rate in the reservoirs.
Bubbles come as bursts and not as a steady flow, but contribute to the total amount of
methane released (Eugster et al. 2011; Delsontro et al. 2011) in reservoirs. Gas transport
can also be mediated by macrophytes, aquatic plants, and so on (Kumar et al. 2011). CH4
bubbles in the reservoir were measured using funnels as per the procedure adopted by
Tremblay et al. (2005).
Several sets of 5 10 funnels were positioned at the water surface, and attached at a distance of 1 m from each other. The sets of funnels were placed above particular water
depths, ranging from 20 to 50 m. The funnels remained on site for 24 or 48 hours. After
this period, the captured gas sample was collected from the funnel and stored in 10-ml
glass vials that contained 6M NaCl solution capped with high density butyl stoppers and
aluminum seals. The collected gas samples were taken to the laboratory for analysis using
Gas chromatography
Analysis of GHG concentrations were performed by GC (SRI 8610C, Torrance, CA, USA)
equipped using a flame ionization detector (FID) with a methanizer for CH4 and CO2. A
1 ml of air from flux sample vials was injected. Simultaneous integration of peaks was
made using the peak simple 3.54 software. Gas standards (400, 1000, and 1010 ppmv for
CO2; 2, 10, 100, 1000 ppmv for CH4) were injected after every 10 samples of analysis to
calibrate the GC. The detection and quantification limits are 0.2 and 0.6 ppm respectively
for CO2 and 0.1 and 0.3 ppm for CH4. The laboratory analysis shows an accuracy of 5% &
4% for CO2 and CH4, respectively, whereas repeatability found to be 4% & 3%.
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Downstream emissions
Water in a hydroelectric plant is often drawn from some depth in the reservoir, where the
pressure is higher and the temperature is lower than normal pressure and temperature.
Water leaving the turbine becomes super-saturated with gases. One part of the CH4 is
released directly when the water is passed through the turbines while another part is
released from supersaturated water through diffusion or bubbling some distance from the
dam (Guerin et al. 2006; Kemenes et al. 2007). Downstream emissions (degassing and diffusive fluxes) are observed below reservoir outlets and their influence may range from a
few tens of meters up to 50 km downstream in the river (Abril et al. 2005). Degassing
downstream of a dam and spillway can be estimated by the difference between the gas
concentration upstream and downstream of the hydroelectric plant multiplied by the outlet discharge. The results of the CO2 and CH4 fluxes released by diffusion, bubbling, and
degassing pathways at different sampling points are graphically presented in
Figures (6 10), which shows that the diffusive fluxes constitute 90 95% of the total emissions from the reservoir followed by bubbling and degassing.
Results and discussion
The GRAT result indicated that higher vulnerability to gross GHG emissions indicates the
need of assessing net GHG emissions to the reservoir. The model yielded 67% confidence
level (root mean square error: 0.36); that is, gross CO2 fluxes was between 343 1816 mg
m¡2d¡1 whereas the gross CH4 fluxes between 18 226 mg m¡2d¡1 during 2011 (Figures 3
and 4). During the study period (2011), the predicted gross CO2 fluxes are found as
790 mg m¡2d¡1, which reduced to 375 mg m¡2d¡1 over a period of average 100 years.
But in the case of gross CH4 fluxes, the predicted emission was found as 64 mg m¡2d¡1,
which further reduced to 44 mg m¡2d¡1 over the same period (Figures 3 and 4).
Figures 3 and 4 show that Tehri reservoir presently emits lot of CO2 and CH4 (790 and
64 mg m¡2d¡1) thereby make it necessary to measure the net emissions. Although the reservoir impoundment period (six years) is less than 100 years, which means that after 100 years
there will be no need to assess Net GHG (Table 2). The figures also show that, at the time of
reservoir impoundment (year 2006), higher CH4 and CO2 fluxes were found as 77 and
1187 mg m¡2d¡1, respectively. It is also predicted that the CH4 and CO2 fluxes were 44 and
339 mg m¡2d¡1, respectively, in year 2006 and it will keep on decreasing rapidly till the year
2030 and thereafter, will reduce slowly over a period of 100 year (year 2105). But, it can
Figure 6. CO2 diffusion fluxes of Tehri hydroelectric reservoir at all sampling locations during 2011 12.
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Figure 7. CO2 bubbling fluxes of Tehri hydroelectric reservoir at all sampling locations during
2011 12.
increase or maintain current emission flux due to the other environmental factor such as carbon load, temperature, flooding, and further climate change effect. Barros et al. (2011) verified the results of GRAT model that Carbon emissions are negatively correlated to reservoir
age and latitude, with the highest emission rates from the tropical region as compare to temperate and keep on decreasing over the period of 100 year.
The CH4 and CO2 monitoring was conducted at 10 different sampling stations during various seasons (pre-monsoon, post-monsoon, winter, and summer) in 2011 12. The study
provided an observation on diffusion, bubbling, and degassing fluxes to calculate gross CO2
and CH4 fluxes. The annual mean CO2 fluxes (mean § standard deviation) from diffusion,
bubbling and degassing pathways were found as 425.93 § 122.50, 4.81 § 1.33, and 7.01 §
2.77 mg m¡2d¡1 during 2011 12, respectively, whereas CH4 fluxes are found as 23.11 §
7.08, 4.79 § 1.08, and 7.41 § 4.50 mg m¡2d¡1, respectively (Figures 6 10). It also shows
that during pre-monsoon, the diffusion fluxes of CO2 were more (658.75 mg m¡2d¡1) than
the winter (114.37 mg m¡2d¡1) due to the temperature difference, whereas diffusion fluxes
of CH4 were found maximum (50.49 mg m¡2d¡1) in post-monsoon compared to winter
(9.99 mg m¡2d¡1) due to thermal stratification. The maximum diffusion fluxes of CO2 in
pre-monsoon are also due to the evenly distributed and decreased monsoonal precipitation
from mid-October onward and appropriate temperature that provided an optimum environment for soil respiration as reported by Li et al. (2012). This allowed the rain water to infiltrate and flush out soil carbon to the river and ultimately reach to the reservoir, and therefore
resulted in the crest level of diffusion flux (CO2) in pre-monsoon. Henceforth, little rainfall
and lowest temperature in December through January limited the export of soil carbon to rivers, leading to very low diffusion flux of CO2 in winter.
Figure 8. CH4 diffusion fluxes of Tehri hydroelectric reservoir at all sampling locations during 2011 12.
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Figure 9. CH4 bubbling fluxes of Tehri hydroelectric reservoir at all sampling locations during 2011 12.
Bubbling flux for CO2 is found to be maximum in post-monsoon period (average
10.79 mg m¡2d¡1) and minimum in summer (average 0.88 mg m¡2d¡1), whereas CH4
flux shows that summer is on the higher side with an average of 6.54 mg m¡2d¡1 and winter is on the lower side (i.e., 1.85 mg m¡2d¡1) (Figures 7 and 9).
Degassing fluxes were noted to be minimum in winter (average 4.05 mg m¡2d¡1) and
maximum in pre-monsoon (average 9.5 mg m¡2d¡1) for CO2 (Figure 10). Similarly, CH4
fluxes were also found to be minimum in winter (average 6.05 mg m¡2d¡1) and maximum in pre-monsoon (average 10.65 mg m¡2d¡1) as shown in Figure 10. Degassing
fluxes (CO2 and CH4) are minimum in winter due to low temperature and maximum during pre-monsoon as the temperature is at its peak. The emissions (CO2 and CH4) are
higher near Koti colony and minimum at Zero point due to high pressure difference and
higher reservoir depth (Depth at Koti colony D 30 57 m and at Zero point D 25 38 m).
The operation of power stations also affects the CO2 and CH4 emissions from downstream rivers below dams.
The depth of reservoir has found as 205 239 m at nearer to water intake and 35 52 m
at outlet. This range varies from pre-monsoon to post-monsoon. The Tehri reservoir has a
big water column (>190 m) making stratification in terms of reducing temperature and
dissolved oxygen (DO) through depth. Oxygen stratification is undesirable because of
anoxic conditions in the hypolimnion limit habitat availability that can impact water
Figure 10. Degassing flux of Tehri hydroelectric reservoir at two sampling locations during 2011 12.
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quality throughout the reservoir and downstream indirectly affecting the GHG emissions.
Hypolimnion water, rich in dissolved CO2 and CH4, is discharged into the surface of
downstream rivers by turbines and spillways as a result more GHGs are emitted in the
surface waters. These GHGs diffuse into the atmosphere faster because of the enhanced
gas concentration gradient (DC) and strong disturbance in the downstream rivers.
GRAT and field sampling results were compared in Table 2. It shows that the predicted
CO2 flux was found to be 85% more than the experimental while the predicted CH4 flux
was 177%. This high uncertainty is due to lack of sufficient emissions data. Moreover, current estimates suffered from data limitation on reservoirs particularly GHG emission from
drawdown zone and reservoir downstream, are recognized to be significant carbon emitters (Lima et al. 2008). An experimental result of CO2 and CH4 fluxes indicates medium
vulnerability, but the predicted gross GHG fluxes are on higher side. Therefore, no assessment is required over the period of next 100 years. As per the GRAT model, assessment
of net GHG emissions is required on account of high vulnerability in the year 2011, after
that it will keep on diminishing over a period of 100 years. Therefore, on the basis of present study, it has been found that to maintain the reservoirs at medium/low GHG risk a
dredging operation may be necessary in the river before its confluence to reservoir where
the organic matter is going to aerobic and anaerobic degradation resulting into GHG
emissions to the atmosphere. A huge amount of GHG has been emitted after thermal
stratification. Methods to prevent stratification include hypolimnetic discharges, air bubbling/injection to generate water movement and mechanical pumping between the hypolimnion to either generate water movement, or to aerate hypolimnietic water by passing
through baffle systems (Raune et al. 1986). Mechanical pumping can also be used to avoid
oxygen stratification without disrupting temperature stratification by lifting hypolimnetic
water to the surface where gases such as CH4, hydrogen sulfide (H2S), and CO2 are dispersed and then water is returned to the hypolimnion without substantial increase in temperature (McQueen and Lean 1983). Aeration of the hypolimnion through injection of
oxygen has been reported to be more cost effective than through lift systems (Mauldin
et al. 1988). Aeration of hypolimnion through bubbling and injection of oxygen can be
treated in destratification of Tehri reservoir, which will reduce the GHG significantly.
The gross GHG emissions predicted by the GRAT model have indicated that Tehri reservoir has emitted a significant amount of GHG (790 mg m¡2d¡1 and 64 mg m¡2d¡1) in
the year 2011 and is reducing over a period of next 100 years. Results of the model in
2011 show that the Tehri reservoir had a high GHG risk, thereby, making the net GHG
Table 2. Comparison of predicted and observed gross GHG flux from Tehri reservoir during 2011 12.
Diffusion CO2 flux (mg m¡2d¡1)
High vulnerability
Medium vulnerability
Need of assessment No need of assessment
Diffusion CH4 flux (mg m¡2d¡1)
Error (%)
Medium vulnerability
Medium vulnerability
No need of assessment No need of assessment
Error (%)
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assessment mandatory, but the risk will be in medium range by year 2015 and so no GHG
assessment would be required. The experimental annual mean CO2 fluxes from diffusion,
bubbling and degassing pathways are found as 425.93 § 122.50, 4.81 § 1.33, and 7.01 §
2.77 mg m¡2d¡1, whereas CH4 fluxes are found as 23.11 § 7.08, 4.79 § 1.08, and 7.41 §
4.50 mg m¡2d¡1, respectively, during 2011 12. High uncertainty in experimental and
predicted gross GHG fluxes are found due to lack of sufficient data, limitation of GRAT
model, rate of degradation of organic matter in the reservoirs, and lack of appropriate
methods for the determination of GHGs. The model can be used to assess the risk of large
numbers of hydropower reservoirs/lakes in the country and help the decision-makers to
take appropriate mitigation measures when the GHG vulnerability is high. It would also
help one to identify the hydropower reservoirs that are safer from a GHG emissions point
of view due to their much less/negligible contribution to global emissions. For the future
development of risk assessment methodology, demonstration projects will undoubted be
a significant source of information that can be drawn on to help develop confidence. To
make the use of the GRAT model more meaningful, it is recommended to collect more
and more temporal and spatial GHG emissions variations in reservoirs.
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