See discussions, stats, and author profiles for this publication at: http://www.researchgate.net/publication/282777562 Assessment of risk of GHG emissions from Tehri hydropower reservoir, India DATASET · OCTOBER 2015 READS 20 2 AUTHORS: Amit Kumar Mahendra PAL Sharma Indian Institute of Technology Roorkee Indian Institute of Technology Roorkee 20 PUBLICATIONS 9 CITATIONS 117 PUBLICATIONS 1,732 CITATIONS SEE PROFILE SEE PROFILE Available from: Amit Kumar Retrieved on: 24 November 2015 Human and Ecological Risk Assessment: An International Journal 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. Submit your article to this journal Article views: 41 View related articles View Crossmark data Citing articles: 1 View citing articles Full Terms & Conditions of access and use can be found at http://www.tandfonline.com/action/journalInformation?journalCode=bher20 Download by: [Indian Institute of Technology Roorkee] Date: 12 October 2015, At: 11:11 HUMAN AND ECOLOGICAL RISK ASSESSMENT 2015, VOL. 0, NO. 0, 1 15 http://dx.doi.org/10.1080/10807039.2015.1055708 Assessment of risk of GHG emissions from Tehri hydropower reservoir, India Amit Kumar and M. P. Sharma Downloaded by [Indian Institute of Technology Roorkee] at 11:11 12 October 2015 Alternate Hydro Energy Centre, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India ABSTRACT ARTICLE HISTORY 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 GRAT. Received 20 February 2015 Revised manuscript accepted 25 May 2015 KEYWORDS greenhouse gas (GHG); vulnerability; emissions; risk; GRAT Introduction 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 amit.agl09@gmail.com 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 Downloaded by [Indian Institute of Technology Roorkee] at 11:11 12 October 2015 2 A. KUMAR AND M. P. SHARMA 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 Downloaded by [Indian Institute of Technology Roorkee] at 11:11 12 October 2015 HUMAN AND ECOLOGICAL RISK ASSESSMENT 3 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 A. KUMAR AND M. P. SHARMA Downloaded by [Indian Institute of Technology Roorkee] at 11:11 12 October 2015 4 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 Downloaded by [Indian Institute of Technology Roorkee] at 11:11 12 October 2015 HUMAN AND ECOLOGICAL RISK ASSESSMENT 5 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. 6 A. KUMAR AND M. P. SHARMA Table 1. Input parameters for GRAT model (UNESCO/IHA 2012). S. No. 1. Downloaded by [Indian Institute of Technology Roorkee] at 11:11 12 October 2015 Parameters Age of reservoir Unit Input Data Remarks (years) 6 Input for estimation of CO2 and CH4 flux 2. 3. Mean annual air temp Mean annual runoff ( C) (mm) 18.9 650 4. Mean annual precipitation (mm) 1157 Input for estimation of CO2 flux Input for estimation of CH4 flux Source of data THDC NASA , Agro climatology UNH/GRDC NASA, Agro climatology Reference Nawani (2006) NASA (1983) Fekete et al. (2002) 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. HUMAN AND ECOLOGICAL RISK ASSESSMENT 7 Downloaded by [Indian Institute of Technology Roorkee] at 11:11 12 October 2015 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. 8 A. KUMAR AND M. P. SHARMA Downloaded by [Indian Institute of Technology Roorkee] at 11:11 12 October 2015 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 GC. 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%. Downloaded by [Indian Institute of Technology Roorkee] at 11:11 12 October 2015 HUMAN AND ECOLOGICAL RISK ASSESSMENT 9 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. 10 A. KUMAR AND M. P. SHARMA Downloaded by [Indian Institute of Technology Roorkee] at 11:11 12 October 2015 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. Downloaded by [Indian Institute of Technology Roorkee] at 11:11 12 October 2015 HUMAN AND ECOLOGICAL RISK ASSESSMENT 11 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. Downloaded by [Indian Institute of Technology Roorkee] at 11:11 12 October 2015 12 A. KUMAR AND M. P. SHARMA 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. Conclusions 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) Predicted Experimental 790 425.93 High vulnerability Medium vulnerability Need of assessment No need of assessment Diffusion CH4 flux (mg m¡2d¡1) Error (%) 85 — — Predicted Experimental 64 23.11 Medium vulnerability Medium vulnerability No need of assessment No need of assessment Error (%) 177 — — Downloaded by [Indian Institute of Technology Roorkee] at 11:11 12 October 2015 HUMAN AND ECOLOGICAL RISK ASSESSMENT 13 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. References Abril G, Guerin F, Richard S, et al. 2005. Carbon dioxide and methane emissions and the carbon budget of a 10 year old tropical reservoir (Petit Saut, French Guiana). Glo Biogeo Cycle 19:1 16 Bagchi D and Singh RD. 2011. Ground water brochure district Tehri Garhwal, Uttarakhand. Central Ground Water Board, India Barros N, Cole JJ, Tranvik LJ, et al. 2011. Carbon emission from hydroelectric reservoirs linked to reservoir age and latitude. Nat Geosci 4:593 6 Bastviken D, Tranvik LJ, Downing JA, et al. 2011. Freshwater methane emissions offset the continental carbon sink. Sci 331:50 Cutter SL. 1996. Vulnerability to environmental hazards. Progress in Human Geogra 20:529 39 Cutter SL. 2003. The vulnerability of science and the science of vulnerability. Annals of the Asso Ameri Geogra 93:1 12 Cutter SL, Boruff BJ, and Shirley WL. 2003. Social vulnerability to environmental hazards. Social Sci Quart 84:242 61 David N and Yates WB. 1996. An integrated water balance model for climate impact assessment of river basin runoff. J Wat Res Devel 12(2):121 140 Delsontro T, Kunz MJ, Kempter T, et al. 2011. Spatial heterogeneity of methane ebullition in a large tropical reservoir. Environ Sci Technol 45:9866 Diarmid CL and Carlos C. 2012. Vulnerability and adaptation assessment. Protecting health from climate change, pp 1 59. Available at http://www.who.int/globalchange/publications/Final_ Climate_Change.pdf Eugster W, Delsontro T, and Sobek S. 2011. Eddy covariance flux measurements confirm extreme CH4 emissions from a Swiss hydropower reservoir and resolve their short-term variability. Biogeosci 8:2815 31 Fearnside PM. 2006. Greenhouse gas emissions from hydroelectric dams. Clim Change 75:103 9 Fekete BM, Voroosmarty CJ, and Grabs W. 2002. High-resolution fields of global runoff combining observed river discharge and simulated water balances. Glo Bioge Cycle 16(3), doi: 10.1029/ 1999GB001254 Fussel HM and Klein RJT. 2006. Climate change vulnerability assessments: An evolution of conceptual thinking. Climatic Change 75:301 29 Downloaded by [Indian Institute of Technology Roorkee] at 11:11 12 October 2015 14 A. KUMAR AND M. P. SHARMA Guerin F, Abril G, Richard S, et al. 2006. Methane and carbon dioxide emissions from tropical reservoirs: Significance of downstream rivers. Geophys Res Lett 33:L21407 Hiroki H. 2005. Life cycle GHG emission analysis of power generation systems: Japanese case. Energy 30:2042 56 IPCC. 2006. Climate change: The scientific basis. Contribution of Working Group I to the Third Assessment Report of the IPCC. New York: Cambridge University Press IPCC. 2014. Summary for policymakers In: Climate Change 2014: Impacts, Adaptation and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, UK Joel AG. 2012. Challenges and solutions for assessing the impact of freshwater reservoirs on natural GHG emissions. Ecohydro Hydrobiol 12:115 22 Kemenes A, Forsberg BR, and Melack JM. 2007. Methane release below a tropical hydroelectric dam. Geophys Res Lett 34:L12809 Kumar A, Schei T, Ahenkora A, et al. 2011. Hydropower. In: Edenhofer, O, Pichs-Madruga R, Sokona Y, et al. (Eds), IPCC Special Report on Renewable Energy Sources and Climate Change Mitigation. Cambridge University Press: Cambridge, UK and New York, NY, USA Kumar A and Sharma MP. 2012. Greenhouse gas emissions from hydropower reservoirs. Hydro Nepal: J Water, Energy and Enviro 11:37 42 Kumar A and Sharma MP. 2014a. Impact of water quality on GHG emissions from hydropower reservoir. J Mater Environ Sci 5(1):95 100 Kumar A and Sharma MP. 2014b. Review of methodology for estimation of labile organic carbon in reservoirs and lakes for GHG emission. J Mater Environ Sci 5(3):653 60 Li S and Bush TR. 2015. Revision of methane and carbon dioxide emissions from inland waters in India. Glo Cha Bio 21:6 8 Li SY and Lu XX. 2012. Uncertainties of carbon emission from hydroelectric reservoirs. Nat Haz 62:1343 5 Li SY, Lu XX, He M, et al. 2012. Daily CO2 partial pressure and CO2 out gassing in the upper Yangtze River basin: A case study of the Longchuan River, China. J Hydro 466 467:141 50 Lima IBT, Ramos FM, Bambace LAW, et al. 2008. Methane emissions from large dams as renewable energy resources: A developing nation perspective. Mitig Adapt Strat Global Change Biol 13:193 206 Marcel R, Anton G, Marc R, et al. 1998. A pasture simulation model for dry matter production, and fluxes of carbon, nitrogen, water and energy. Ecolo Mode 105:141 83 Mauldin G, Miller R, Gallagher J, et al. 1988. Injecting an oxygen fix. Civil Eng 54(6):54 6 McQueen DJ and Lean DRS. 1983. Hypolimnetic aeration and dissolved gas concentrations: Enclosure experiments. Wat Res 17:1781 90 Mendelssohn R, Nordhaus WD, and Shaw D. 1994. The impact of climate change on agriculture: A Ricardian analysis. Amer Eco Rev 84(4):753 71 NASA. 1983. POWER, Climatology Resource for Agroclimatology. Available at http://power.larc. nasa.gov/cgibin/cgiwrap/solar/agro.cgi?emailDagroclim@larc.nasa.gov Nawani PC. 2006. Tehri Dam Project. Available at http://en.wikipedia.org/wiki/Tehri_Dam Panneer SB, Natchimuthu S, Arunachalam L, et al. 2014. Methane and carbon dioxide emissions from inland waters in India—Implications for large scale greenhouse gas balances. Global Change Bio 20(11):3397 3407 Raymond PA, Hartmann J, Lauerwald R, et al. 2013. Global carbon dioxide emissions from inland waters. Nat 503:355 59 Romain L, Raphael M, Bruno B, et al. 2012. Ecosystem Climate Change Vulnerability Assessment Framework. International Environmental Modelling and Software Society (IEMSS) 2012 International Congress on Environmental Modelling and Software Managing Resources of a Limited Planet, Sixth Biennial Meeting, Leipzig, Germany. Available at http://www.iemss.org/society/ index.php/iemss-2012-proceedings Downloaded by [Indian Institute of Technology Roorkee] at 11:11 12 October 2015 HUMAN AND ECOLOGICAL RISK ASSESSMENT 15 Ruane RJ, Bohac CE, Seawell WM, et al. 1986. Improving the Downstream Environment by Reservoir Release Modification. In: Hall GE, and Van Den Avyle MJ (Eds), Reservoir Fisheries Management: Strategies for the 80’s, American Fisheries Society, Bethesda, Maryland, USA, p. 99 Senapati MR, Behera B, and Mishra SR. 2013. Impact of climate change on Indian agriculture & its mitigating priorities. Ame J Enviro Prote 1(4): 109 11 Tremblay A, Bastien J, Bonneville MC, et al. 2010. Net Greenhouse Gas Emissions at Eastmain 1 Reservoir, Quebec, Canada; World Energy Congress, Montreal, pp 1 19 Tremblay A, Varfalvy L, Roehm C, et al. 2005. Greenhouse Gas Emissions—Fluxes and Processes Hydroelectric Reservoirs and Natural Environments. Springer, Berlin, Heidelberg, New York UNESCO/IHA. 2012. GHG Risk Assessment Tool (Beta Version). Available at http://www.hydro power.org/ghg/tool Yang H, Xing Y, Xie P, et al. 2008. Carbon source/sink function of a subtropical, eutrophic lake determined from an overall mass balance and a gas exchange and carbon burial balance. Envir Pollu 151:559 68
