The net GHG emissions of the China Three Gorges Reservoir: I. Pre-impoundment GHG inventories and carbon balance

Journal of Cleaner Production 256 (2020) 120635

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The net GHG emissions of the China Three Gorges Reservoir: I. Preimpoundment GHG inventories and carbon balance Zhe Li a, *, Zhiyu Sun b, **, Yongbo Chen b, Chong Li b, Zhenhua Pan c, Atle Harby d, Pingyu Lv e, Dan Chen c, Jinsong Guo f a

CAS Key Lab of Reservoir Environment, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, No. 266 Fangzheng Avenue, Shuitu Hi-tech Industrial Park, Beibei, Chongqing, 400714, China China Three Gorges Corporation, No. 1 South Yuyuantan Road, Haidian District, Beijing, 100038, China c Danish Hydraulic Institute (DHI), Fourth Floor, Building A, No. 181, Guyi Road, Xuhui District, Shanghai, 200235, China d SINTEF Energy Research, P.O. Box 4761 Torgarden, 7465, Trondheim, Norway e Water-Environment Monitoring Center for the Upper Reach of Changjiang, Changjiang Water Resource Commission, Chongqing, No. 410, Haier Road, Jiangbei District, Chongqing, 400021, China f School of Environment and Ecology, Chongqing University, No. 83, Shabeijie Street, Campus B, Chongqing University, Shapingba District, Chongqing, 400044, China b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 11 November 2019 Received in revised form 10 February 2020 Accepted 16 February 2020 Available online 22 February 2020

One of the paramount questions related to environmental and climate change impacts from hydropower and reservoirs, is how to quantify the greenhouse gas (GHG) emissions of dam construction and reservoir creation, mostly in terms of reservoir net GHG emissions. Net emissions are described as the emissions after impoundment subtracting the emissions before the reservoir was built (pre-impoundment). The evaluation of pre-impoundment GHG emissions is essential to answer the above questions, yet there are few related case studies. Herein, we proposed a conceptual framework to evaluate the pre-impoundment GHG emissions of China’s Three Gorges Reservoir (TGR). Reservoir flooded areas prior to impoundment were divided into two categories: 1) flooded land, where pre-impoundment CO2 and CH4 fluxes from different historical land uses were estimated following tier 1 methodology of IPCC national inventories, and 2) river surface, where pre-impoundment CO2 fluxes were estimated by a calibrated two dimensional modified biogeochemical model with an air-water gas transfer module. An empirical regression model between measured air-water CO2 and CH4 fluxes in unflooded river reaches was used to estimate pre-impoundment river surface CH4 flux. The pre-impoundment GHG emissions of the TGR were 5.1
105 tCO2eq$yr1, with 95% confidence intervals of 4.7e6.1
105 tCO2eq$yr1. Approximately 46% of the pre-impoundment GHG emissions were from flooded land, while the rest 54% were from river surfaces. Mass balance indicated that approximately 72% of the downstream riverine C export was from the upstream river basin of the Yangtze River. Pre-impoundment C emissions were only ~6.58% of the total riverine C export downstream. Most of the C in the system was mainly from the upstream river basin of the Yangtze River; thus, an increase in anthropogenic loads of C and nutrients in the Three Gorges Reservoir Area did not result in an apparent increase in pre-impoundment river surface GHG emissions. © 2020 Elsevier Ltd. All rights reserved.

Handling Editor: Jing Meng Keywords: Reservoir net GHG emissions Modeling framework Inventories System boundary Carbon budget Mass balance

1. Introduction Dam construction and reservoir creation significantly change

* Corresponding author. ** Corresponding author. E-mail addresses: [email protected] (Z. Li), [email protected] (Z. Sun). https://doi.org/10.1016/j.jclepro.2020.120635 0959-6526/© 2020 Elsevier Ltd. All rights reserved.

land use and alter hydrological regimes in river basins. They have been providing conspicuous services for sustaining human wellbeings, e.g., water supply, flood control, hydroelectricity production, irrigation, navigation, aquaculture, recreational activities and tourism. However, their potential greenhouse gas (GHG) emissions have received worldwide concern over the past two decades (Almeida et al., 2019; Barros et al., 2011; Deemer et al., 2016; Fearnside, 2016; Hertwich, 2013; Ocko and Hamburg, 2019). Carbon

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footprint is a measure of the exclusive total amount of carbon dioxide equivalents (CO2eq) emissions that is directly and indirectly caused by an activity or is accumulated over the life cycle of a product (Wiedmann and Minx, 2008). From life cycle perspective (Li et al., 2019), carbon footprints of a dam or reservoir project consist of two parts in general: 1) carbon emissions directly caused by engineering activities, e.g. construction work, dam operation, routine maintenance, dam demolition, etc.; and 2) associated nonengineering aspects of carbon emissions, i.e. those emissions due to land-use changes such as flooding and reservoir operation. While carbon footprints from direct engineering activities are relatively explicitly quantifiable (Li et al., 2017a, 2019), the carbon emissions due to flooding and reservoir operation are still difficult or uncertain to quantify. GHG emissions from reservoir surface have been accepted to be not zero since 1990s (Oud, 1993; Rudd et al., 1993). It showed that river basin background, i.e., meteorology, hydro-morphology and biogeochemistry, reservoir age, types of organic deposits in flooded land and the allochthonous input from river basin were among the important factors that regulate reservoir GHG emissions (Barros et al., 2011; Hertwich, 2013). The most recent estimation stated that the global gross reservoir GHG emissions were 0.8 PgCO2eq/yr, with a possible range between 0.5 PgCO2eq/yr and 1.2 PgCO2eq/yr (Deemer et al., 2016). Nevertheless, the estimates of gross reservoir GHG emissions could not be applied to quantify carbon footprints due to reservoir formation. The net changes of GHG emissions are defined as the differences between the post-impoundment GHG balances, excluding GHG emissions from unrelated anthropogenic sources (UAS), and the pre-impoundment balances of GHG emissions and removals (Alm et al., 2015; Kumar et al., 2011). To our best knowledge, there are few studies on evaluation of reservoir net GHG emissions under the conceptual framework at present. Knowledge gaps still exist in the following aspects: 1) lack of information of pre-impoundment GHG balances; 2) well-developed methodology in evaluation of UAS; 3) a clearer definition of the spatio-temporal boundary. Pre-impoundment GHG balances of a reservoir are the status of GHG emissions and removals prior to construction of the dam and flooding of the reservoir. Information of this “baseline” can be gathered and synthesized from GHG fluxes from the existing river system and the different land cover and land uses in the reservoir flooded area (Alm et al., 2015). For planned new reservoirs, wellorganized field sampling campaigns and investigations in flooded land and river surface prior to impoundment are the best practices, such as Teodoru et al. (2012) and dos Santos et al. (2019). However, for existing reservoirs, GHG balances in both terrestrial and aquatic ecosystems prior to impoundment are not easy to assess. This was primarily due to the limited or inaccessible information of land cover and land use in flooded area, as well as limited or no data for the natural riverine GHG emissions and carbon transport. When no other data or information exists, the International Hydropower Association (IHA) G-res Tool developed a proxy assuming that the land cover in the immediate vicinity of the current reservoir approximates the pre-impoundment landscape. The method evaluates the land cover of a buffer zone around the reservoir measuring 25% of the equivalent spherical diameter (Prairie et al., 2017). In deep river valley impounded reservoir, whose vicinity significantly deviates from flooded low land area, the applicability of the “buffer zone” method is still questionable. In addition, anthropogenic activities in flooded land, e.g. towns, industrial and rural area, may also significantly contribute to riverine GHG emissions prior to impoundment via point and non-point sources. Hypothetically, changes in anthropogenic loads, i.e. organic carbon and nutrients, may cause variations of riverine GHG emissions and carbon budget prior to impoundment. These aspects are important in evaluation

of reservoir net GHG emissions. Unfortunately, there are still lack of methods and data at full reservoir scale, particularly for large rivervalley dammed reservoir. The Three Gorges Reservoir (TGR) is China’s largest reservoir in volume and installed capacity for hydropower production. Field surveys of GHG emissions in the TGR have indicated that the gross emission of CO2 and CH4 in the reservoir is comparable to those from other temperate reservoirs, but significantly less than tropical ones (Chen et al., 2011; Yang et al., 2013; Zhao et al., 2013). Nevertheless, the status of GHG emissions prior to its impoundment were not reported due to the lack of historical information, e.g., land-use history, anthropogenic carbon loads, etc. Ambiguous information misled scientists and policy makers in assessing the human GHG footprint of the China’s largest hydro-project. For example, Chen et al. (2009) reported intensive GHG emissions from a newly created reservoir drawdown. Without any information regarding pre-impoundment land use history (probably rice paddies according to follow-up survey and satellite image by authors), Chen et al. (2009) used only gross emissions post-impoundment and improperly extrapolated their results to estimate gross GHG emissions of the TGR, which caused the worldwide concern on the GHG effects of China’s hydropower (Qiu, 2009). Understanding preimpoundment GHG emissions of the TGR is the primary step to quantify the human GHG footprint of the hydro-project. However, the construction of the Three Gorges Project began in 1997, and the impoundment of the TGR initiated in 2003. Up to date, there isn’t any information reporting the GHG emissions either from flooded land or river surface prior to reservoir impoundment, making it impossible to synthesize and estimate the pre-impoundment GHG emissions of the TGR in full scale. The overall objective of the study is to evaluate the net GHG emissions of the TGR at present status, which were divided and organized into two papers. It followed the conceptual framework proposed by International Energy Agency Technology Collaboration Platform on Hydropower (IEA Hydro), Annex XII technical guidelines (Alm et al., 2015). For pre-impoundment status, an inventory-based approach is proposed to assess GHG emissions from terrestrial and aquatic ecosystems in flooded area of the reservoir. We believe this approach could be further applied to existing river valley dammed reservoir with little information prior to impoundment. For postimpoundment status, we gathered and synthesized data from three one-year monthly field sampling campaigns from 2010 to 2017 (Li et al., 2020). Contributions of unrelated anthropogenic sources of carbon and nutrients, i.e. UAS, were assessed based on a modified biogeochemical model in both pre-impoundment and postimpoundment status. We believe our study were among the first to evaluate GHG inventories and carbon balances in full-scale of a large river-valley dammed reservoir before and after reservoir impoundment. As the first part of the study, this paper highlights the following contributions: 1) pre-impoundment GHG emissions of the TGR; 2) the contribution of pre-impoundment UAS. The preimpoundment carbon budget in this paper will contribute to follow-up study on analysis of the net change of carbon before and after reservoir impoundment. 2. Method 2.1. Site description, system boundary The Three Gorges Reservoir (TGR) is a typical river valley dammed reservoir. Impoundment of the reservoir involved 632 km2 of flooded land, including the downtown areas of two cities, i.e., Wanzhou and Fuling, 11 county-level downtown areas, 227 smaller towns, and 1680 villages (Supporting information S1 for detail). The TGR covers the main stem of the Yangtze River, approx. 700 km and

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a number of the tributaries. Among these impacted tributaries, over 150 have river basin areas larger than 100 km2. Approximately 14 of them have river basin areas larger than 1200 km2. The two largest tributaries of the Yangtze River entering the TGR are the Jialing River (JL in Fig. 1, approx. 159,800 km2) and the Wu River (WJ in Fig. 1, approx. 115,747 km2). Because most of the Jialing and Wu Rivers are not flooded by the impoundment of TGR, we select one cross section from each river, i.e., Beibei (BB) in the Jialing River and Wulong (WL) in the Wu River, as the background cross section indicating the upstream reservoir boundary of the TGR (Fig. 1). Together with the background cross section of the Yangtze River, i.e., Zhutuo (ZT) and the dam site (TGD), the four cross sections form the river basin area of TGR (in short as “TGRA”), approx 70 584 km2 as the spatial boundaries of the study (Fig. 1). Within the spatial boundary, pre-impoundment status of the landscape was divided into two compartments, i.e., flooded land and river reaches. Pre-impoundment GHG emissions was the sum of the GHG emissions from flooded land and river surface. Flooded land comprised of different land use types as described in section 2.2. River reaches included the 732 km main stem of the Yangtze River, i.e., from ZT to TGD. Both CO2 and CH4 emissions from river surface were considered in the study. Potential emissions of N2O during the life cycle of hydro-projects were not included according to IPCC national inventories (IPCC, 2006). Anthropogenic activities in the TGRA were assumed to cause point and nonpoint pollution loads into river reaches, which were considered to be part of preimpoundment GHG emissions. Approved by state council in 1993, the Three Gorges Project was officially launched in 1994. Started from 1994 and continued

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to 2003, large campaigns of pre-impoundment clearance and migration of local citizens caused substantial land use change in the flooded land (Li et al., 2017b). In 2003, the TGR started a threestage impoundment continuing to 2010 when the TGR reached its designated water level for normal operation. The timeline of the project is shown in supporting information S1. Temporal boundary in this study was defined between the year of 1994 and 2003. The data of land use in flooded land before pre-impoundment clearance were based on the pre-project survey in 1994 (Supporting Information Tables 2e1). Comparably, the first governmental level full-scale field survey of anthropogenic loads of carbon and nutrients in the TGR were carried out in 1997 and 1998 (Huang et al., 2006). Governmental research on hydrodynamic and water quality model was also carried out for the year of 1998 (Huang et al., 2006). Complexities of the giant hydro-project and inconsistency in collected data sources caused great challenges of our method development. While there were no alternatives to tackle the inconsistency of data sources, in the present study, we were forced to use the land use data in 1993 to estimate GHG emissions in flooded land, and the modeling results in the reference year of 1998 to estimate river surface GHG emissions. Potential uncertainties and possible solutions are discussed in section 4.1. 2.2. Pre-impoundment GHG emissions in flooded land Land use in the flooded land prior to impoundment included croplands, floodplains, forests for both fruits and specific economic values, fishponds, fuelwood piles, cities and towns in 1994

Fig. 1. Spatial boundaries of the present study. Red solid lines are the river basin boundary of the Three Gorges Reservoir Area (TGRA), whereas the black solid and dashed lines indicate the county-level administrative regions List of related administrative districts in the TGRA are shown in the embedded table. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

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(Supporting Information Tables 2e1). Complete representation of land use types in the flooded land was compiled according to IPCC National GHG Inventories (IPCC, 2006). Specifically, floodplains and fishponds were categorized as wetlands. Fuelwood piles were considered to be other land (Fig. 2). Spatial heterogeneity within each land use was assumed to be negligible due to limitations of pre-impoundment information. To assure the applicability of the IPCC Tier 1 methodology, it was assumed that there were no significant land-use changes for over two decades before the baseline year. This was reasonable due to the following facts: social-economic development in the flooded land was limited at a relatively low growth rate for decades mainly by the state government who planned the construction of the Three Gorges Project since 1980s. Large campaigns of urbanization in flooded land were also evidently limited by the geographic and geomorphic conditions in the deep valley area. Therefore, conversions between different land uses were not accounted according to the IPCC Tier 1 methodology (IPCC, 2006). Annual CO2 emissions from different land uses were evaluated by changes in carbon stocks. According to the Tier 1 method in IPCC’s framework (IPCC, 2006), it was further assumed that no carbon stock changes in dead organic matter occurred in all land use types. Changes in the carbon stock of biomass, i.e., both above ground and below ground in both forestland and cropland were evaluated in the study. Soil organic carbon was not considered in forestland but was evaluated in cropland (Supporting information S2 for details). For wetlands, it was assumed that floodplains in the flooded area were natural and unmanaged wetlands that were beyond the scope of IPCC national inventories. CO2 emissions flux in fishponds were estimated to be 422.16 ± 62.52 mgCO2,m2,d1, assuming that the surface area of most fishponds was less than 0.001 km2 according to Holgerson and Raymond (2016). No changes in carbon stocks were assumed in settlements or other land. The estimation of CH4 fluxes involves an emission rate from a source directly to the atmosphere (IPCC, 2006). There have been very few studies on CH4 emissions in the flooded land before impoundment. The CH4 emission rates from different land uses were estimated from collected publications (Table 1). The selection of publications was determined by the similarities of region and land-use situation (Supporting information S2.4 for detail). 2.3. Modeling pre-impoundment river surface emissions 2.3.1. Anthropogenic sources of carbon and nutrients Data on pre-impoundment anthropogenic sources of carbon and nutrients were collected in the year of 1998. Point sources

Fig. 2. Relative abundance of different land uses in flooded land complied according to the IPCC consistent representation of lands (IPCC, 2006). Cropland included agroforestry, e.g., citrus orchard and other types of fruits, rice paddies and other types of cropland, e.g., vegetable field in IPCC consistent representation of lands (IPCC, 2006).

came from industrial sources and urban sewage. Non-point sources included distributed sources from rural residents, urban runoff, agriculture runoff, livestock and mobilizing sources from navigation. Chemical Oxygen Demand (COD), ammonia (NHþ 4 -N), total nitrogen (TN), and total phosphorus (TP) were the pollution parameters applied to evaluate the anthropogenic sources. Nonpoint source pollution were estimated based on a preestablished SWAT nonpoint source model of the TGRA (Wang and Li, 2015; Wu et al., 2012). Validated loss-coefficients of each type of pollution load were selected and assigned in the model to estimate the pollution loads finally entering the Yangtze River. A synthesis of the anthropogenic sources of carbon and nutrients was discussed in the supporting information S3.

2.3.2. River surface GHG emissions modeling A two-dimensional hydrodynamic model of the study river reaches was established by the MIKE 21 Hydrodynamic (HD) module based on a flexible mesh (FM) approach (DHI, 2017a). The spatial domain was discretized using a cell centered finite volume method by subdivision of the continuum into nonoverlapping element/cells. An unstructured grid composed of quadrilateral elements was applied in the main stem of the Yangtze River and its tributaries. A total of 55 360 (3460
16) grid elements were in the domain of the Yangtze River main stem. A total of 30 432 grid elements were in the 14 largest tributaries of the Yangtze River. Supporting information S4 show detailed information on the modeling approach. The process-based CO2 emission model was based on the MIKE ECO Lab Water Quality Template (DHI, 2017b) but revised and updated specifically for modeling CO2 emissions (Fig. 3, and supporting information S4 for detail). Historical data of total alkalinity (TA, in terms of CaCO3 mg/L) with auxiliary information on water pH, temperature, pressure and phosphorus were used to estimate partial pressure of CO2. The estimation was based on the CO2Sys Program (Excel Version 2.1, https://www.nodc.noaa.gov/ocads/ oceans/CO2SYS/co2rprt.html). The salinity was assumed to be zero in freshwaters and the constants, i.e., K1 and K2 in freshwater were selected according to Millero et al. (1979). The partial pressure of CO2 was expressed with the unit of mg/L for the purpose of mass balance calculation within the model. Air-water CO2 flux was estimated by the thin boundary layer method briefly shown in Fig. 4 (Goldenfum, 2010). The following assumptions were within the ecological model: 1) The modeled system was assumed to be lotic, i.e., well-mixed fast running river. Thermal stratification was assumed to be unimportant in the model system. 2) The river system was assumed to be heterotrophic. The food web was not considered in the model system. Photosynthesis by primary producers was assumed to be the only controlling process that contributed to the carbon sink in the model system. Carbon sources included decomposition of allochthonous and autochthonous organic matter and respiration by microorganisms (including primary producers). All these carbon sources were parameterized in the model system. Secondary production, i.e., the increase in biomass of heterotrophic microorganisms due to decomposition and degradation of organic matter was not considered. 3) It was assumed that the pH did not significantly vary in any of the model runs, which meant that the carbonate balance in the water phase was assumed to be steady. CO2 decomposed from phytoplankton carbon (PC) and detritus carbon (DC) would finally emit into the atmosphere from the river surface.

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Table 1 Selected CH4 emission factors from different land uses in the reservoir flooded land. No.

Land use

Estimated CH4 emission rates (mgCH4$m2$d1)

Possible range (mgCH4$m2$d1)

References

1. 2.

Forestland Cropland-rice paddies

2.856 36.40

28.83~-0.38 14.78e92.80

3. 4. 5. 6. 7. 8.

Cropland-agroforestry Cropland-other types of cropland Wetlands-fishponds Wetlands-floodplain Settlements Other land

7.05 4.55 17.2 43.51 N/A 0

16.57e5.50 12.42e8.36 4.48e36.48 6.47e163.20 N/A 0

(Tang et al., 2006; Wei et al., 2008) IPCC (2006) Chen et al. (2013) Lu (2009) Lu (2009) Holgerson and Raymond (2016) (Chen et al., 2009, 2011; Li et al., 2016)

Fig. 3. Framework of the conceptual model, state variable and processes in river surface CO2 modeling. The section above the dash lines in the figure shows the water quality template in MIKE Ecolab (DHI, 2017b). However, concerning our data availability, the biogeochemical processes regarding benthic vegetation and zooplankton shown in gray color are not modeled. The bottom part below the dash lines shows the modeled biogeochemical processes of C, N and P respectively. TBL is the abbreviation of the thin boundary layer model (Goldenfum, 2010). PC, PN and PP are the phytoplankton C, N and P respectively. DC, DN and DP indicate detritus C, N and P respectively. SedC, SedN and SedP indicate C, N and P in sediments respectively. Inorganic P is assumed to be the soluble reactive phosphorus (SRP). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 4. A brief schematic showing the calculation steps of air-water CO2 flux based on the thin boundary layer method. F CO2 is the air-water CO2 flux. kCO2 was the gas exchange coefficient. Sc is the Schmidt number for CO2, depending on water temperature t. k600 is a dependent parameter on wind speed at 10 m above the water surface U 10 . x is an adjustable parameter depending on the wind speed on site. pðCO2 Þwater and pðCO2 Þair are CO2 partial pressure in water phase and air phase respectively. In water phase, it depends on the chemical equilibrium that could be estimated by HCO 3 and CO2 3 based on the equilibrium constants K1 and K2. Kh is the gas solubility of CO2 depending on water temperature (Weiss, 1974). In air phase, i.e. pðCO2 Þair , the atmospheric mean CO2 partial pressure was set as 380 ppm. Since the unit of F CO2 is expressed as mmolCO2,m2,d1, (pðCO2 Þwater - pðCO2 Þair Þ should be multiplied by gas solubility of CO2 to obtain the correct results. Detail description was in Goldenfum (2010).

2.3.3. Calibration and validation The hydrodynamic model was calibrated by hydrological and water quality data in 1998. Relative errors of water level elevation between measured data and modeled results were less than 1%, while those of the river surface were generally less than 10% (Fig. 5). Cuntan (CT) and Wanxian (WX) (see Fig. 1 for CT and WX location) were the two cross-sections where water quality data in 1998 were available for calibration. Model calibration results of water quality parameters, as well as CO2 and air-water CO2 flux, are shown in Fig. 6. The model results of DO and nutrients, e.g., ammonia and inorganic phosphorus, had the best fit with the measured historical data. The relative errors of DO, ammonia and inorganic phosphorus were 9%, 18%, and 12%, respectively. The relative error of the model results in the CO2 concentration and air-water CO2 flux were relatively higher among all the state variables, i.e., CO2 and CO2 flux. 31% and 35%, respectively. While variations of the model results significantly fitted with the monthly variations of water quality parameters in both CT and WX, relative errors of state variables were within an acceptable range, i.e., 9%e35%; the model was validated and applicable.

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Fig. 5. Results of parameter calibration of the hydrodynamic model based on MIKE 21HD. A) shows the water surface elevation in the system between modeled and measured results. B) shows the cumulative surface area under different flow levels estimated by the hydrodynamic model. C) is the comparison between modeled and measured water surface in model calibration. D) is the water surface of both main stem and tributaries estimated by the hydrodynamic model.

Fig. 6. Model calibration results of the water quality model based on modified MIKE Eco Lab. The left column lists a series of sub-figures showing the temporal variations of  modeled and measured results of major variables in the model, i.e. dissolved oxygen (DO), ammonia (NHþ 4 ), nitrate (NO3 ), Inorganic phosphorus (IP), CO2 concentration and CO2 flux. The right column are three scatter-dots showing distribution between modeled and measured results of CO2 concentration, CO2 flux and NHþ 4.

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2.3.4. River surface CH4 emission River surface CH4 emission was not of the same order as CO2 emissions and was assumed to be a small part in the model system. However, concerning the global warming potentials, preimpoundment CH4 emission from the river surface was evaluated by an empirical approach. The Yangtze River is a CH4 source. It was found that river surface CH4 flux showed a significant positive correlation with CO2 flux (Supporting Information S5). Multiple linear regression was proposed to model CH4 emission in the main stem and tributaries of the Yangtze River in the study domain with other affiliated water quality parameters. The proposed empirical model for CH4 emission in the main stem of the Yangtze River was as follows:

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3. Results 3.1. River surface GHG emissions Modeling results of the river surface CO2 and CH4 emissions were synthesized in Fig. 7. In 1998, the estimated annual average river surface pCO2 at the main stem was 795.1 matm, with 95% confidence intervals between 784.9 matm and 805.2 matm. The river surface pCO2 concentration ranged between 340.4 matm and 1943.4 matm. The annual average air-water CO2 flux and CH4 flux at the main stem in 1998 was 8.34 ± 4.55 mmol,m2,d1 (range between 0.55 mmol,m2,d1 and 24.94 mmol,m2,d1) and 0.13 ± 0.06 mmol,m2,d1 (range between 0.01 mmol,m2,d1

lnðCH4 Þ ¼ 0:676,lnðCO2 Þ  0:431,DO  0:358,DTN þ 4:021
102 $temp  4:161
105 $flow þ   1:648 n ¼ 54; Multiple R2 ¼ 0:4842; p ¼ 2:622
105 < 0:05; Range of residual : 1:2422  1:3889

(1)

For tributaries, the multiple linear regression model for river surface methane emission was as follows:

lnðCH4 Þ ¼ 0:2451,lnðCO2 Þ þ 0:09424,DO þ 9:702
105 $temp þ 0:06466,DOC þ 0:5718,DTN 2:061  n ¼ 48; Multiple R2 ¼ 0:5421; p ¼ 2:451
104 < 0:05; Range of residual : 0:9076  0:5753

In both Eq. (1) and Eq. (2), the unit of CH4 flux (denoted as “CH4” in the equations) and CO2 flux (denoted as “CO2” in the equations) were mg∙m2∙d1. The units of dissolved oxygen (DO) and dissolved total nitrogen (DTN) were mg∙L1. The unit of water temperature (denoted as “temp” in the equation) was  C. The units of flow and water level at ZT were m3∙s1 and m, respectively.

2.4. Uncertainty analysis In general, uncertainty analysis in both models, i.e. flooded land and river surface was followed the conceptual framework by Beck (1987). Estimation of pre-impoundment GHG emission in flooded land were based on a set of linear functions. Their potential uncertainty was mainly from the selection of parameters within a possible range shown in supporting information section 2.3 and 2.4. Selected parameters were assumed to be normally distributed. Monte Carlo permutations, e.g. 1000 runs, were applied to select these parameters and to obtain the possible modelling results. Similar treatment processes were discussed in Li et al. (2017b). For water quality modeling, uncertainty analysis were performed following the methodology discussed in Omlin et al. (2001). In brief, detritus C (DC) mineralization, phytoplankton growth and nitrification were ranked the three most sensitive processes in the model. Parameters were randomly selected with the built-in ranges before Monte Carlo simulations. Model outputs were synthesized with estimates in flooded land to obtain the preimpoundment GHG emissions of the TGR.

(2)

and 0.37 mmol,m2,d1), respectively. The summer flood season (JuneeAugust), particularly in July, the main stem of the Yangtze River had the maximum emission rates of both CO2 and CH4. In the winter season (NovembereMarch), the riverine surface CO2 and CH4 fluxes reduced to the lowest levels of the whole year (Fig. 7A, B and 7C). The longitudinal gradients of CO2 and CH4 fluxes along the main stem showed several distinctive rivers reaches with peak levels of CO2 and CH4 emissions in summer, which corresponded well with the large cities located along the Yangtze River, i.e., Chongqing downtown (~180 km downstream of ZT), and Wanzhou (~390 km). It seemed that tributaries were more homogene compared to the significant spatial and temporal variations in the main stem (Fig. 7G, H, 7I and 7J). The annual average CO2 and CH4 fluxes in the tributaries were 7.76 ± 1.58 mmol,m2,d1 and 0.25 ± 0.07 mmol,m2,d1, respectively. The flux of CH4 in the Yangtze tributaries was significantly higher than that in the main stem, whereas the flux of CO2 was lower. The pre-impoundment river surface total emissions of CO2 and CH4 in 1998 were estimated to be 69393.5 tCO2/d and 515.6 tCH4/d, respectively. The main stem contributed approximately 81% of the CO2 emissions and 67% of the CH4 emissions.

3.2. Synthesis of pre-impoundment GHG inventories Pre-impoundment GHG inventories of the TGR are summarized in Table 2. The terrestrial ecosystem of the reservoir flooded land exhibited in general as carbon sink prior to reservoir impoundment. The annual carbon sink in terms of CO2 equivalent was 1 450 170.4 tCO2eq,yr1 with 95% confidence intervals between 1

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Fig. 7. A synthetical modeling results of river surface GHG emissions. A), B) and C) are the modeled spatial and temporal variations of CH4 flux, CO2 flux and pCO2 (converted from CO2 concentration). D) shows the temporal variation of river flow at ZT in 1998. E) and F) shows the corresponding modeled temporal variations of CO2 and CH4 fluxes in main stem of the Yangtze. G) and H) are the temporal variations of CO2 and CH4 fluxes in tributaries in the model system. I) and J) show the results of modeled CO2 and CH4 fluxes in tributaries. Full names and locations of these tributaries are illustrated in Fig. 1. Abbreviations of these tributaries are applied here. K) and L) are the temporal variations of total CO2 and CH4 emissions in main stem, tributary and the full river system in the model system.

Table 2 Summary of pre-impoundment GHG inventories of the TGR. No. Land use

Area (km2)

Annual CO2 emissions evaluated by carbon stocks Annual estimate of CH4 change (tC$yr1) emissions (tC$yr1)

Total emissions in CO2 equivalents (tCO2eq$yr1)

1 2 3. 4.

31.97 80.35 72.14 87.11

2412.2 ± 114.8 4017.5 ± 3750.4 22870.7 ± 8909.1 62370.8 ± 1184.7

25.0 ± 1.7 803.4 ± 11.0 139.7 ± 2.7 113.9 ± 3.1

9777.5 ± 485.7 44725.4 ± 14162.6 89073.5 ± 32769.2 224440.2 ± 4459.9

3.06 38.65 296.18 22.53 520.52 1152.51

12859.4 ± 1995.8 N/A N/A 0.0 69393.5 ± 13184.8 123358.3

14.2 ± 0.2 458.1 ± 11.7 N/A 0.0 515.6 ± 154.7 1512.7

47682.6 ± 7324.2 17101.5 ± 436.0 N/A 0.0 273690.1 ± 68422.5 508788.8

Forestland (FL) Cropland-rice paddies (CRP) Cropland-agroforestry (CAF) Cropland-other types of cropland (COT) 5. Wetlands-fishponds (WFP) 6. Wetlands-floodplain (WFD) 7. Settlements (SE) 8. Other land (OL) 9 River surface (RF) Subtotal

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Fig. 8. Comparison of pre-impoundment GHG balances among different land uses. A) and C) were the subtotal of CH4 emission and weighted CH4 flux from different land uses respectively. B) and D) were the subtotal of CO2 emission and weighted CO2 flux correspondingly. E) showed the emissions of the two types of GHGs in different land uses. F) showed the summary of areal weighted average and total emissions in terms of CO2 equivalents in flooded land and river surface.

Fig. 9. Pre-impoundment river surface CO2 and CH4 emissions under different pollution loads. A) and B) are the estimates of CO2 and CH4 emissions in main stem, tributary and full river system in our model under different levels of anthropogenic loads. C) and D) are the temporal variations of the corresponding CO2 and CH4 emissions in our model under different levels of anthropogenic loads.

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306 893.6 tCO2eq,yr1 and -1 576 915.3 tCO2eq,yr1. Based on weighted average, the flooded land in the TGR contributed approximately 6286.6 mgCO2eq∙m2∙d1. Forestland and cropland (approx. 43% of flooded land), contributed the most to the carbon sink, noted that this was based on the assumptions that no changes in carbon stocks were considered for settlement and other land. River surface GHG emissions in 1998 were estimated to be 273 690.1 ± 68 422.5 tCO2eq$yr1. Weighted area averaged GHG emissions from the river surface (RF in Fig. 8) and other types of cropland (COT in Fig. 8) were among the highest evaluated total emissions in CO2 equivalents. In summary, pre-impoundment GHG balances in the TGR were estimated to be 5.1
105 tCO2eq$yr1, with 95% confidence intervals of 4.6e6.1
105 tCO2eq$yr1. Approximately 46% of the pre-impoundment GHG emissions were from flooded land, with approximately 54% of these from river surfaces. 3.3. Contribution of anthropogenic sources of carbon and nutrients from the TGRA To evaluate the potential contribution of anthropogonic pollution loads to river surface GHG emissions, two additional scenarios were modeled, i.e., zero pollution loads in the TGRA (the zero scenario) and pollution loads in the year 2015 (the 2015 scenario), were applied. The scenario of zero pollution loads means that there was no anthropogenic sources of carbon and nutrients coming from the TGRA. The year 2015 was the reference year of postimpoundment reservoir GHG modeling primarily due to the availability of datasets from our field sampling campaign. Details will be discussed in our follow-up paper (Li et al., 2020). In both scenarios, the hydrodynamic model was the same as in 1998. Compared to 1998, the pollution loads of Chemical Oxygen Demand (COD), ammonia (NH3), total Nitrogen (TotN) and total Phosphorus (TotP) in 2015 increased by 17.6%, 91.5%, 136.4% and 164.3%, respectively. There was a significant difference (t-test, p < 0.05) in the CO2 and CH4 emissions between the zero scenario and the year 1998. Apparently, the increase in anthropogenic loads in the TGRA at the levels in 1998 contributed 9.9% of the increase in river surface CO2 emissions, and this ratio in river surface CH4 emissions was 38.3%. Both increases occurred mainly in tributaries and were more apparent in dry seasons, i.e., from November to May (Fig. 9 C and B). Comparatively, the increase in anthropogenic loads from the zero scenario to the 1998 scenario did not show an evident increase in both CO2 and CH4 emissions at the main stem in general. River

surface CO2 emissions in the 2015 scenario were not evidently higher than those in the 1998 scenario (t-test, p < 0.05), although COD in 2015 increased approximately 17%, and concentration of ammonia increased approximately 95% in 2015. Surprisingly, main stem CH4 emissions in the 2015 scenario were 36.1% less than those in the 1998 scenario. In contrast, tributary CH4 emissions in the 2015 scenario were 36.8% higher than those in the 1998 scenario. This caused 12.2% reduction of CH4 emissions in the 2015 scenario. After double checking the modeling processes and raw data, we attributed the above unexpected results to the different empirical models of CH4 in the main stem and the tributaries. Although both CO2 and CH4 were positively and significantly correlated from our field measurements (supporting information S2) and well retained in the model results, the increase in DOC and DTN in tributary would cause the increase of CH4 emission as the DOC and DTN positively contributed to the estimation of CH4 emission. However, in the main stem, contributions of the term DTN were negative and may cause the reverse prediction results of CH4 emission in our empirical model. 3.4. Comparison with post-impoundment measurements Impoundment of the TGR changes the hydro-morphological condition between upstream and downstream of the dam, redirecting the pathways of GHG emissions. The newly created landscapes, e.g., reservoir drawdown wetlands and downstream river reaches, significantly altered the patterns of air-water GHG interactions. Here, we used a series of one-year field sampling data after reservoir impoundment, from June 2010 to May 2011, of water surface GHG emissions. Part of the dataset has been published by Zhao et al. (2013). Full scale post-impoundment GHG synthesis and inventories will be discussed in Li et al. (2020). Post-impoundment CH4 fluxes from the reservoir surface (PostRS in Fig. 10) did not show a significant increase (t-test p > 0.05) compared to pre-impoundment river surface CH4 fluxes in our results. CH4 emissions from downstream river surfaces after the dam were ~20% higher compared with our pre-impoundment estimations. In the main stem, the gradual increase in air-water CO2 fluxes from pre-impoundment, post-impoundment reservoir surface (Post-RS) and downstream river surface after the dam (Post-DR) were evident. In tributaries, post-impoundment reservoir surface CH4 and CO2 emissions were significantly increased. Variances in the datasets also increased significantly. In particular, the growth of phytoplankton in tributaries caused an apparent CO2 sink, i.e., negative CO2 flux. The gross emission in 2010 estimated by Zhao et al. (2013) was 1.43
106 tCO2eq$yr1. Using these data, the net emissions of the TGR were 9.21
105 tCO2eq$yr1, which was ~10.9 gCO2eq$kWh1 based on the corresponding annual hydroelectricity production in 2010 (84.369 TW h, data obtained from China Three Gorges Corporation). 4. Discussion 4.1. Methodology development and limitations

Fig. 10. Comparison of air-water surface flux before and after impoundment of the TGR. Pre is the pre-impoundment river surface flux estimated in the preset study. PostRS is the GHG fluxes from the reservoir subface after impoundment of the TGR in 2010e2011. Post-DR is the GHG fluxes from the reservoir downstream reaches after the dam. Data is discussed in Li et al. (2020).

From a theoretical perspective, the fundamentals of reservoir net GHG emissions include the change of biogeochemical cycling of carbon due to impoundment. Before reservoir impoundment, the landscape that will be inundated by a reservoir is, a mosaic of different ecological sub-units: forests, wetlands, agricultural areas, settlements, lakes, streams and naturally the main river. Each of these sub-units may have a particular behavior with regard to land use intensity and GHG balances (Prairie et al., 2017). Prairie et al. (2018) outlined a methodology to account for GHG emissions directly and indirectly due to reservoir creation. Both CO2 and CH4

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balances prior to reservoir impoundment should be considered and evaluated. Officially launched in 1994, the Three Gorges hydro-project experienced substantial land use change in flooded land caused by pre-impoundment clearance and large campaigns of migration of local citizens (Li et al., 2017b). During the following decade, i.e. from 1994 to 2003, pre-impoundment clearance removed about 881602.2 ± 5946.7 tC out of the system, mainly from forests and agroforestry (Li et al., 2017b). In this case, estimated subtotal of preimpoundment CO2 emissions evaluated by carbon stocks change would increase from 123358.3 tC$yr1 in Table 1to207414.6 tC$yr1 when considering the impact of pre-impoundment clearance. From life cycle perspective, the pre-impoundment clearance was in the preparation phase of the hydro-project (Li et al., 2019). Life cycle GHG footprint of the project need to consider the status of GHG balance before the start of the project. At this point, we tend to use the term of “pre-project” GHG balance in the year of 1994 as the result of our study, although we provided two estimates here. Intensive anthropogenic activities in the Yangtze River basin persisted for thousands of years. Despite the dam construction and reservoir creation, anthropogenic activities that potentially impact pre-impoundment GHG emissions in general included 1) hydrological regulation by upstream dams that resulted in a change of inflow and input of sediments and 2) organic carbon and nutrients from point and nonpoint sources. The meaningful aspect of the spatial boundary in our research was the division of pre-impoundment anthropogenic activities into two distinctive sections, i.e., anthropogenic activities in the TGRA and those from the upstream river basin, i.e., river basin upstream of ZT, BB, and WL. Regardless of the reservoir operation, pre-impoundment anthropogenic activities in the TGRA were evaluated in the form of the point and nonpoint pollution loads, whereas the impact of all types of anthropogenic activities were inductively summarized as the river inflow and macronutrients (C, N and P) at ZT, BB and WL. All the “external” factors, i.e., land-use changes in the upstream river basin, changes in hydrology and upstream pollution loads, were not easy to fully access and clearly quantify in such a large dam with a 1 million km2 upstream river basin. This spatial boundary assumption and treatment was technically applied and reasonably modeled. Pre-impoundment air-water CO2 and CH4 fluxes from river surface was technically assumed to be dependent of anthropogenic sources of carbon and nutrients and estimated according to a water quality-based semi-empirical model for the year 1998 and 2015. This treatment allowed us to compare contribution of anthropogenic sources of carbon and nutrients in the TGRA emissions and estimate carbon budget within the spatial boundary. However,

Fig. 11. Pre-impoundment water, C, N and P mass balances in 1998. Zuotuo (ZT), Beibei (BB) and Wulong (WL) are the three major inputs in our model system, while export was at the dam site. Within the model system, inputs of water, C, N and P from tributaries and non-point sources are summarized according to our mode assumptions. C emissions are the subtotal of CO2 and CH4 in terms of C in both river surface and flooded land.

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inconsistency of data sources caused potential uncertainties as. our evaluation of GHG balance in flooded land was performed for the baseline year was 1994. The Yangtze river basin, particularly in mid and downstream part, was hit by historical floods in 1998 (https:// en.wikipedia.org/wiki/1998_Yangtze_River_Floods). These created biased estimation of river surface CO2 and CH4 emissions compared to the baseline year of. 1994. To solve the problem of internal inconsistency, we first proposed the “steady state assumption” discussed in section 2.3. In addition, different levels of anthropogenic sources of carbon and nutrients were discussed to estimate the possible range river surface CO2 and CH4 fluxes based on modeling. Nevertheless, this problem may be commonly existing in the evaluation of reservoir GHG emissions in both regional and river basin scale. A further study on error propagation and uncertainties is needed. By defining the above spatial boundary, the proposed concept of “pre-impoundment GHG emissions” is in fact not in a theoretical “steady state” but varies temporally depending on several factors under different levels of anthropogenic activities in both TGRA and the upstream river basin. This would be better expressed as virtual “baseline” GHG emissions. The virtual “baseline” status hypothesizes that there would be no dam but still remain similar intensities of anthropogenic activities in both the TGR and upstream river basin presumably. However, within the limits of our study and knowledge, we could not further estimate the virtual “baseline” GHG emissions. The long-term river inflow and input of macronutrients from ZT, BB and CT, including cues of anthropogenic activities from the upstream, within a range, less than 15%, compared to the year 1998 (unpublished data series started from 1980 provided by Water-Environment Monitoring Center for the upper reach of Yangtze River). In light of this, we could speculate that the virtual “baseline” GHG emissions would fall within a possible range of 3.6e6.9
105 tCO2eq$yr1 (1st and 3rd quantiles). 4.2. Mass balance of water, carbon and nutrients Water, C, N and P mass balance in 1998 were shown in Fig. 11. In 1998, 1.70
106 tC was input from the upstream river basin. sum of ZT, BB and WL. Tributary C inputs and anthropogenic loads in the TGRA were 1.44
105 tC. Pre-impoundment C emissions from the river surface were only 3.68% of the total C export, i.e., The sum of TGD export and C emissions from flooded land were 2.90% of total C export. C was not strictly conservative in the mass balance of the TGRA. The difference between the total input of C and export was 5.73
104 tC, ~3.11% of the total input. While the water balance was not conservative, we inferred that groundwater input was the potential contributor of C balance in the TGR. Although C emissions from the river surface and the flooded land did not largely share in the C mass balance of the TGRA, total pre-impoundment C emissions, from both the river surface and the flooded land, were comparably 86.6% of the C input from tributaries and anthropogenic loads in the TGRA. The stoichiometry of macronutrients, i.e., the ratios of C:N:P in ZT, BB and WL were 107.1:16.1:1, 109.4:16.9:1, and 81.4:19.2:1, respectively. However, the stoichiometry of macronutrients of the TGRA inputs was 45.3:8.6:1 in the tributaries and nonpoint sources and 53.0:9.6:1 from point sources. Compared with the stoichiometry at the export of the TGD, i.e., 95.3:15.0:1, the relative scarcity of carbon compared to N and P in point and nonpoint pollution loads not only explained the effects of GHG emissions caused by the three different scenarios of anthropogenic loads in the TGRA but also ss further inference that accumulation of N and P after reservoir impoundment from anthropogenic loads may cause apparent eutrophication, which ultimately changes the biogeochemistry cycles of carbon and post-impoundment GHG emissions.

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4.3. Implications and future work The global carbon cycle has benefited greatly in the past decade from the development of the conceptual framework of the “freshwater pipe” (Cole, 2007; Maranger et al., 2018; Tranvik et al., 2009). Reservoirs in the “freshwater pipe model” were regarded as carbon sinks that stored a majority of allochthonous C, while some of them emitted it into the atmosphere through different pathways. However, the change in the C cycle due to reservoir creation and operation is far more complex on the river basin scale. Organic carbon (OC) transport through the passive “freshwater pipes” that settle in the estuary or downstream floodplains may finally produce CO2 and CH4 that is released to the atmosphere (Abril and Borges, 2019; Moore et al., 2018; Rosentreter et al., 2018). It was estimated that 0.15 PgC (range 0.06e0.25) of OC is buried in inland waters, of which ~40% was stored in reservoirs (Mendonca et al., 2017). GHG emissions in reservoirs play an active role as reactors of CH4 and CO2 production, being a spatial displacement of GHG emissions from estuaries, or downstream floodplains to inland waters, i.e., reservoirs. The concept of reservoir net GHG emissions initially proposed by IPCC was based on the recognition that there would be a specific quantifiable system that could elucidate the potential and existing changes in regional GHG emissions and removals due to reservoir creation and operation. Similar to the fundamental physiology in IPCC’s work, quantification of reservoir net GHG emissions is a dialectical but not metaphysical approach. Inclusion of everything in the fate and transport of carbon, regardless of the existing spatial and temporal boundaries, could cause empiricism or nihilism, which would not help in understanding the issue based on state-ofthe-art knowledge. The function of scientific research is to advance human understanding, turning the gray box into a white box. However, to the best of our knowledge, there have been few studies working specifically on pre-impoundment GHG emissions, although IEA published technical guidelines in 2015 (Alm et al., 2015; Teodoru et al., 2012). The lack of pre-impoundment information limited our work in the following aspects, which require further research and case studies: 1) The time scale of air-gas transfer, which is a typical physical process, differs due to variations of CO2 in the water column, which is mainly controlled by biological and chemical processes. In our model, we simply applied the thin-boundary layermodel to link variations of CO2 in the water column and its air-water transfer. The scientific applicability with respect to the difference in time scale requires further discussion and modeling research (Ulseth et al., 2019). 2) From the perspective of “baseline” GHG emissions as discussed above, a virtual but reasonable estimation of social and economic development in reservoir flooded land, as well as the derivative GHG emissions, is promising but challenging and is beyond the scope of our research. 5. Conclusion An inventory-based approach was proposed to assess GHG emissions from terrestrial and aquatic ecosystems in flooded area of the TGR. We concluded that the area flooded by the reservoir was a carbon source. Pre-impoundment GHG emissions of the reservoir were estimated with 95% confidence intervals of 4.6e6.1
105 tCO2eq$yr1.46% of the pre-impoundment GHG emissions were from flooded land, 54% of which were from river surfaces.72% of the system riverine C exports downstream were from the upstream river basin of the Yangtze River. Pre-impoundment C emissions were ~6.58% of total system riverine C export downstream. While

most of the C in the system was mainly from the upstream river basin of the Yangtze River, the increase in anthropogenic sources of carbon and nutrients in the Three Gorges Reservoir Area did not result in an apparent increase in pre-impoundment river surface GHG emissions. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. CRediT authorship contribution statement Zhe Li: Conceptualization, Writing - original draft, Methodology, Funding acquisition. Zhiyu Sun: Conceptualization, Funding acquisition. Yongbo Chen: Project administration, Methodology, Data curation. Chong Li: Project administration, Supervision. Zhenhua Pan: Software, Investigation, Validation. Atle Harby: Writing - review & editing. Pingyu Lv: Resources, Data curation. Dan Chen: Software, Investigation. Jinsong Guo: Resources, Validation. Acknowledgements The research was jointly funded by the National Natural Science Foundation of China (Project No. 51861125204, and 51679226) and the China Three Gorges Corporation (Key research projects on the Greenhouse Gas Emissions of the Three Gorges Reservoir). Dr. Zhe Li is also supported by the “Light of West China” Program funded by the Chinese Academy of Sciences. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.jclepro.2020.120635. References Abril, G., Borges, A.V., 2019. Ideas and perspectives: carbon leaks from flooded land: do we need to replumb the inland water active pipe? Biogeosciences 16 (3), 769e784. Alm, J., Nielsen, N., Damazio, J.M., Harby, A., Chanudet, V., Li, Z., Tremblay, A., 2015. Guidelines for quantitative analysis of net GHG emissions from reservoirs volume 2 modeling. In: IEA-Hydro (Ed.), IEA-hydro Annex XII: Hydropower and Environment, p. 69. Almeida, R.M., Shi, Q., Gomes-Selman, J.M., Wu, X., Xue, Y., Angarita, H., Barros, N., Forsberg, B.R., García-Villacorta, R., Hamilton, S.K., Melack, J.M., Montoya, M., Perez, G., Sethi, S.A., Gomes, C.P., Flecker, A.S., 2019. Reducing greenhouse gas emissions of Amazon hydropower with strategic dam planning. Nat. Commun. 10 (1), 4281. Barros, N., Cole, J.J., Tranvik, L.J., Prairie, Y.T., Bastviken, D., Huszar, V.L.M., del Giorgio, P., Roland, F., 2011. Carbon emission from hydroelectric reservoirs linked to reservoir age and latitude. Nat. Geosci. 4 (9), 593e596. Beck, M.B., 1987. Water quality modeling: a review of the analysis of uncertainty. Water Resour. Res. 23 (8), 1393e1442. Chen, H., Wu, Y., Yuan, X., Gao, Y., Wu, N., Zhu, D., 2009. Methane emissions from newly created marshes in the drawdown area of the Three Gorges Reservoir. J. Geophys. Res. Atmos. 114. Chen, H., Yuan, X., Chen, Z., Wu, Y., Liu, X., Zhu, D., Wu, N., Zhu, Q.a., Peng, C., Li, W., 2011. Methane emissions from the surface of the three Gorges reservoir. J. Geophys. Res. Atmos. 116. Chen, H., Zhu, Q.a., Peng, C., Wu, N., Wang, Y., Fang, X., Jiang, H., Xiang, W., Chang, J., Deng, X., Yu, G., 2013. Methane emissions from rice paddies natural wetlands, lakes in China: synthesis new estimate. Global Change Biol. 19 (1), 19e32. Cole, J.J., 2007. Plumbing the global carbon cycle: integrating inland waters into the terrestrial carbon budget. Ecosystems 10, 172e185. Deemer, B.R., Harrison, J.A., Li, S., Beaulieu, J.J., Delsontro, T., Barros, N., BezerraNeto, J.F., Powers, S.M., dos Santos, M.A., Vonk, J.A., 2016. Greenhouse gas emissions from reservoir water surfaces: a new global synthesis. Bioscience 66 (11), 949e964. DHI, 2017a. MIKE 21 Flow Model Hydrodynamic Module User Guide, MIKE 21 Documentation. Danish Hydraulic Institute (DHI), Denmark, p. 144.

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