Greenhouse gas emissions from riparian zone cropland in a tributary bay of the Three Gorges Reservoir, China

General description of farm land and experimental design

The study area was located at the Wuyang Bay (31^∘11′20″N, 108^∘27′40″E) in the Yetang Creek of the Pengxi river basin in the TGR (Fig. 2A). The total area is ca.2.44 × 10⁵m², with a mean annual air temperature of 18.2 °C (Chen et al., 2014). According to hydrologic records, the water level of this area rises to 175m at the end of October (the reservoir charging at peak level) and drops to 155 m at early April as the reservoir discharges. Thus, agricultural activities in this area are carried out between April to August. In this study, an area from 165 m to 180 m during the crop growing season was chosen to analyze GHG emissions.

According to field surveys in 2015, a total of 25 households with a population of 106 were recorded, and the main income source is agricultural production in Wuyang Bay. The soil type of this area is yellow soil (soil taxonomy), and the basic physic-chemical properties of experimental soil are shown in Table 1. Main crops in the study area are corn in dry lands and rice in paddy fields, and some vegetables at an elevation of 165 m (Figs. 2B and 2C). Some abandoned croplands were not used for several years, and have gradually become grass fields. Fertilizers were manually applied immediately after corn planting, and additional fertilizers were applied to paddy fields in March. Amounts of the fertilizers were about 1,500 kg hm⁻² a⁻¹ of urea and 1,500 kg hm⁻² a⁻¹ of compound fertilizer in corn fields, and about 1,050 to 1,500 kg hm⁻² a⁻¹ of urea and 1,050 kg hm⁻² a⁻¹ of compound fertilizer in paddy fields. Manure was applied in vegetable fields. After crop harvest, most rice straw was cut and moved to outside fields and corn straw was left in the field.

Field sampling and measurements

Based on variations in the water levels and different land-use types, the experimental sites were chosen and classified into three groups: (1) different elevations (165–180 m) of croplands; (2) different land use types of dry lands, paddy fields and grass fields; (3) diurnal variations in dry lands and paddy fields.

Soil surface gas samples were collected almost monthly using the static-chamber method and a modified gas chromatograph method from April through September in 2015 and 2016. The closed static chamber (φ30.5 cm) (Fig. 3) was an opaque PVC material that was mounted on a PVC base frame in each plot, which had been inserted into the soil to a 10 cm depth. To make the covers gastight, water was poured into the grooves of the frame before each gas sampling. Each sampling site in every elevation was represented by one static chamber in dry land and one in paddy field in 2015. Three replicate static chambers were set up in dry lands, paddy fields and grass fields separately at the elevation of 170 m in 2016 for land use analyses. In each plot, GHG flux were made at the row position between plants in croplands, and aboveground vegetation in each plot of grasslands was cut off before gas sampling. Sampling time was selected from 9 a.m. to 11 a.m., when transient emissions represent the mean emission of the day (Li et al., 2016). Gas samples were collected from each chamber at 0, 10, 20 and 30 min after the chamber was deployed using a polypropylene medical syringe through a triple valve.

Samplings to assess diurnal variations of GHG emissions were conducted in May, July and September. May and July represent the growing season and September represents the harvest season. One sampling site in dry land and paddy field at 170 m was chosen to measure the diurnal GHG emissions. Gas samples were collected every 2 h from 8:00 of the first day to 6:00 of the subsequent day in Beijing standard time.

The concentration of N₂O, CH₄ and CO₂ in gas samples were determined by gas chromatography (Agilent 7890B). CO₂ andCH₄ were detected with a hydrogen flame ionization detector (FID). N₂O was detected with a Porapak Q packed column (80/100 mesh) and electron-capture detector (ECD) with a 63Ni. The temperature of the detector was 300 °C. Fluxes of N₂O, CH₄ and CO₂ were calculated as by the algorithm follows (Flessa, Dörsch & Beese, 1995): (1)${\displaystyle F=\rho \mathrm{\ast }\frac{V}{A}\mathrm{\ast }\frac{P}{P_0}\mathrm{\ast }\frac{273}{273+T}\mathrm{\ast }\frac{dc}{dt}}$

Where ρ(kg m⁻³) is the concentration of GHG in standard atmospheric condition; V(m³) is the air volume in the chamber; A (m²) is the soil surface area in the chamber; P (Pa) is atmospheric pressure; T (^∘C) is the air temperature in the chamber; dc/dt is concentration changes of GHGs in chamber per unit time (ppm h⁻¹). F is the flux of GHGs (mg m⁻² h⁻¹).

Total daily flux (TDF) was calculated using the following formula (Xu et al., 2017): (2)${\displaystyle TDF=\left(\sum _{\mathrm{i}=1}^{\mathrm{n}=12}{F}_i\right)\times 2}$

Where i is the measuring time and F_imeans the flux at different times of the day.

Cumulative emission of GHG (CE) was calculated as follows: (3)${\displaystyle CE=\sum _{i=1}^n\left(\frac{F_i+F_{i+1}}{2}\right)\mathrm{\ast }\left(t_{i+1}-t_i\right)\mathrm{\ast }24∕100}$

Where CE is the cumulative fluxes of GHG (kg hm⁻¹); F is the flux of GHG; i is the number of sampling events; t is the day of the sampling events; n is the total number of sampling events. Cumulative fluxes of GHG in paddy fields and dry lands were calculated from April to September in 2015 and 2016.

During each gas sampling event, soil volumetric water content (VWC) and soil temperature (ST) (10 cm depth) were measured within a radius of 30 cm from the sampling chamber using a portable moisture meter (HH2) and thermometer in soil. Soil gravimetric water content (GWC), nitrate nitrogen (NO₃⁻-N), ammonia nitrogen (NH₄⁺-N), total carbon (TC), total nitrogen (TN) and dissolved organic carbon (DOC) were measured for the topsoil (0–15 cm) within a radius of 30 cm from the sampling chamber. Soil samples were taken to the lab and stored at 4 °C for the physicochemical analysis. NO₃⁻-N and NH₄⁺-N were measured by a flow injection analyzer (FIA star 5000); TC and TN were measured with an elemental analyzer (VarioEL cube). PH was measured after shaking with water for 30 min (Ye et al., 2016), soil organic matter was measured by LOI550 (Zhou et al., 2014), soil GWC was determined using the drying method (105 °C for 12 h), and bulk density was determined in the gravimetric method (Avnimelech et al., 2001). Dissolved organic carbon was measured through colorimetric analysis (Yang et al., 2016).

Statistical analysis

The Global Warming Potential (GWP) was developed to reflect the global warming impacts of different gases. To reflect the total GHG emissions, the CH₄ and N₂O fluxes were converted to a CO₂-equivalent with unit mg-CO₂m⁻² h⁻¹(CE_N2O and CE_CH4, respectively), using a GWP of 28 for CH₄ and 265 for N₂O over 100 years (IPCC, 2013). (4)${\displaystyle {\mathrm{GWP}}_{\text{soil export}}=C{E}_{C{O}_2}+C{E}_{C{H}_4}\times 28+C{E}_{N_{2}O}\times 265}$

Where GWP_soilexport is the Global Warming Potential of soil export (kg (CO₂) hm⁻²); CE is accumulative emission flux of GHG.

All datasets were processed with Origin 20.0 software for figure construction. One-Way ANOVA was implemented in the software of SPASS 22.0 to compare the significant differences in GHG fluxes and soil environmental parameters from different elevations and land-use sites. To ensure that residuals from different statistical analyses were normally distributed and homogeneous, variables were log transformed. As an alternative, Kruskal-Wallis H test was used to deal with those with non-normal distributions (CH₄ fluxes and N₂O fluxes in different land uses). A value of P < 0.05 was used to infer statistical significance.

The type of ordination method appropriate for GHG emissions across a gradient length was determined through detrended correspondence analysis (DCA) (Borcard, Legendre & Drapeau, 1992). Because gradient length was smaller than 4 in this study, redundancy analysis (RDA) was chosen to analyze the relationships between GHG metrics and environmental parameters. All explanatory variables and GHG metrics were standardized with the function: (y_i − y_min)∕(y_max − y_min). RDA was performed using the R package “vegan”, and validated by Monte Carlo methods with 999 permutations (Shan et al., 2019).

GHG fluxes from different elevations during the growing season

During the discharging period, CH₄ fluxes from dry lands at four elevations ranged from −0.48 to 2.44 mg m⁻² h⁻¹ (Fig. 4A). CH₄ fluxes from paddy fields at three elevations in 2015 ranged from −0.87 to 10.13 mg m⁻² h⁻¹ between April to September (Fig. 4B).

No significant differences in CH₄ and CO₂ emissions from different elevations were found (Table 2). However, CO₂ emissions in dry lands and paddy fields varied in different elevations during growing season (Figs. 4C and 4D). Mean CO₂ fluxes from dry lands were estimated as 358.75 ± 139.78 mg m⁻² h⁻¹ (mean ± standard error) (180 m), 231.95 ± 189.22 mg m⁻² h⁻¹ (175 m), 342.91 ± 196.22 mg m⁻² h⁻¹ (170 m) and 328.31 ± 100.73 mg m⁻² h⁻¹ (165 m). Meanwhile, mean CO₂ fluxes from paddy fields were measured as 161.36 ± 272.79 mg m⁻² h⁻¹ (175 m), 183.68 ± 293.36 mg m⁻² h⁻¹ (170 m) and 311.85 ± 539.82 mg m⁻² h⁻¹ (165 m). A decreasing trend was showed along with the rising elevation in paddy fields.

N₂O fluxes varied from −39.92 to 1,594.14 µg m⁻² h⁻¹, and the highest N₂O flux was from dry lands at the elevation of 165 m in June (Fig. 4E). The mean N₂O fluxes from dry lands at an elevation of 165 m (627.04 ± 721.16 µg m⁻² h⁻¹) were significantly higher (F = 4.809, P = 0.008) than those at an elevation of 180 m (44.73 ± 71.51 µg m⁻² h⁻¹), 175 m (59.82 ± 148.26 µg m⁻² h⁻¹) and 170 m (22.67 ± 15.56 µg m⁻² h⁻¹) (Table 2). Mean N₂O fluxes at different elevations in paddy fields ranged from 2.88 to 20.03 µg m⁻² h⁻¹, but the means were associated with very high variability (Fig. 4F). Therefore, there might be visible trends in the overall means, but they exhibited a very weak or no statistical difference in N₂O flux at different sampling elevations. Concretely, the mean of N₂O flux at an elevation of 175 m (20.03 ± 22.41 µg m⁻² h⁻¹) in paddy fields tended to be higher than those values at 170 m (9.52 ± 25.77 µg m⁻² h⁻¹) and 165 m (2.88 ± 16.45 µg m⁻² h⁻¹).

GHG flux from different land use types

From April to September in 2016, the mean CH₄ fluxes from paddy fields (0.35 ± 0.72 mg m⁻² h⁻¹) tended to be higher than those from the dry lands (0.036 ±  0.09 mg m⁻² h⁻¹) and grasslands (0.08 ±  0.20 mg m⁻² h⁻¹) (Table 3, Fig. 5A). However, there was no significant difference in CH₄ fluxes of these three land uses. Monthly CH₄ fluxes varied greatly among the sampling period, and the CH₄ flux exhibited lowest level in August 17th. In paddy fields, the maximum CH₄ flux was observed in July, and it was the source of methane throughout the sampling time.

CO₂ fluxes were positive in dry lands and grasslands, but negative in paddy fields in the early growth stage (Fig. 5B). In the summer, CO₂ fluxes in paddy fields increased to positive levels as the temperature rose. On August 17th, CO₂ emissions in the three land use types increased. The mean CO₂ fluxes from grasslands (395.43 ± 283.32 mg m⁻² h⁻¹) were significantly higher (F = 9.958, P <0.001) than those values from the dry lands (266.91 ± 180.79 mg m⁻² h⁻¹) and paddy fields (151.63 ±  213.55 mg m⁻² h⁻¹) (Table 3).

N₂O fluxes were positive in these three land use types from April to September in 2016 (Fig. 5C). With respect to N₂O emission in different land use types, there was significant difference (H = 19.556, P = 0.005) between paddy fields and dry lands. Due to high variability in measured values, no significant difference was found between grass fields and paddy fields in N₂O flux. However, the overall means might exhibit a distinct trend. For instance, the mean N₂O fluxes from paddy fields (22.36 ± 34.89 µg m⁻² h⁻¹) was approximately half of those values from dry lands (50.61 ± 40.71 µg m⁻² h⁻¹) and grasslands (50.42  ± 75.18 µg m⁻² h⁻¹) (Table 3).

Diurnal emissions of GHG emissions during the growing season

Table 4 exhibited the diurnal emissions of GHGs during different seasons in cropland of the riparian zone. It was shown that TDF of CO₂ in paddy field in September was much higher than that in other seasons, while TDF of CH₄ and N₂O flux in paddy field in September were much lower than that in other seasons. According to the lines of diurnal variations of GHG emissions in Fig. 6 and GHGs flux in Table 4, GHG flux from 9:00 to 11:00 basically can represented the mean emission of the day.

Diurnal changes in CO₂ and N₂O fluxes from dry lands and paddy fields were observed during the growing season (Fig. 6). CO₂ andN₂O fluxes were low at night and high during the day in dry lands, and the same trajectory was observed in daily air temperature. The daily emission peaks of CO₂ and N₂O fluxes were all at 2–4 pm, and the amplitude (the difference between the highest and lowest values within a day) of diurnal variations of the CO₂ andN₂O fluxes were various in different season. Diurnal variations in CH₄ emissions were not obvious both in dry lands and paddy fields in different seasons. Moreover, no obvious fluctuating regularity of the N₂O diurnal emission could be observed during the observation period in paddy fields.

Mean CO₂ diurnal emission values in dry lands amounted to 404.11 ± 114.68 mg m⁻² h⁻¹ in September in 2015, 475.92.11 ± 209.88 mg m⁻² h⁻¹ in May, 146.18 ± 45.51 mg m⁻² h⁻¹ in July and 291.98 ± 255.33 mg m⁻² h⁻¹ in September in 2016(Table 4). Mean CO₂ diurnal emission values in paddy fields amounted to 237.78 ± 340.43 mg m⁻² h⁻¹ in September in 2015, 2.35 ± 107.78 mg m⁻² h⁻¹ in May, 50.12 ± 46.35 mg m⁻² h⁻¹ in July and 284.66 ± 316.18 mg m⁻² h⁻¹ in September in 2016. In August 2016, CO₂ flux increased, the amplitude of diurnal variation was also quite high, and the peak emission were above 1,000 mg m⁻² h⁻¹ in dry lands and paddy fields.

Mean N₂O diurnal emission values in dry lands amounted to 120.71 ± 29.73 µg m⁻² h⁻¹ in September 2016, which was higher than in other sampling occasions. However, there were no differences in the amplitude of diurnal variation in different seasons. Mean N₂O diurnal emission values in paddy fields amounted to −3.67 ±16.72 in September 2015 and 2.24 ± 2.05 µg m⁻² h⁻¹ in September 2016 which was lower than that at other sampling times, and the amplitude of diurnal variation in September was also much lower than other seasons.

Cumulative emission of GHG in different land use types

Mean CH₄ fluxes of the riparian zone from 2015 to 2016 in this study was 0.47 ± 1.53 mg m⁻² h⁻¹(Figs. 4 and 5). Cumulative emission fluxes of CH₄ in paddy fields were much higher than those in dry lands and grass fields (Table 5). Whereas, cumulative emission fluxes of CO₂ and N₂O in dry lands and grass fields were higher than those in paddy fields. GWP was 8,045.46 kg ha⁻¹ in 2015 and 113,392.06 kg ha⁻¹ in 2016 in paddy fields, while 14,426.07 kg ha⁻¹ in 2015 and 12340.58 kg ha⁻¹ in 2016 in dry lands during the growing season. GWP in grasslands (18,322.66 kg hm⁻¹) in 2016 was higher than that in cropland, which is beneficial to the high flux of CO₂ (17,700.26 kg hm⁻¹). CO₂ emission from the sampling site contributed the most to GWP, CH₄ emission contributed the second most, and N₂O emission contributed the least. In the whole riparian zone of the study area, CO₂ emissions represented the greatest proportion of GWP (>70.0%).

Influencing factors of GHG emissions

Air temperatures through sampling time ranged from 22.0  °C to 40.0 °C. Soil temperature at 165m of paddy fields was significantly higher (F = 6.113, P = 0.003) than other elevations. The mean content of NO₃⁻-N in soil at 165m (57.66 mg kg⁻¹) in dry lands was significantly (F = 4.809, P = 0.008) higher than that of other elevations. The mean TC (F = 5.585, P = 0.004) and TN (F = 50.293, P = 0.00) at 180 m (unflooded area) were significantly lower than others, while the TC and TN at 170 m for paddy fields were lower than others. It is clearly shown that significant variations in soil VWC exhibited an increasing trend with the increase of elevations in riparian zone (Table 2).

Mean soil temperatures in riparian zone for dry lands, paddy fields and grass fields were 24.93 °C, 26.44  °C and 22.29 °C through sampling time, respectively. Soil MWC in dry lands (30.38%) was significantly (F = 9.892, P <0.001) lower than that in paddy fields (49.89%) and grass fields (39.87%), while the content of NO₃⁻-N in dry lands (6.02 mg kg-1) was higher and NH₄ ⁺-N (4.86 mg kg-1) was lower than that in the other two land uses. TC, TN and DOC in dry lands were lower than those in paddy fields and grasslands (Table 3).

The redundancy analysis (Fig. 7) explored several potential impact factors that may regulate three types of GHG emissions based on the data obtained from all sites in the study area. Strengths of the correlations between environmental factors and the first two axes were summarized in Table 6. The first two axes accounted for 37.30% of GHGs variation in all data (2015–2016), 45.6% in dataset from paddy fields, 53.96% in dataset from dry lands and 26.18% in dataset from grass fields. The RDA ordinations showed that the flux of CO₂ positively correlated with ST, but negatively correlated with soil GWC in all data (Fig. 7A). Soil temperature and soil GWC were positively (r = 0.994) and negatively (r =  − 0.998) correlated with the first axis, respectively. The flux of N₂O positively related to AT (r = 0.544) and soil NO₃⁻-N (r = 0.906), which were positively correlated with the second axis. Soil NO₃⁻-N (r = 0.999) and AT (r = 0.962) were positively correlated with the first axis, and soil GWC was positively (r = 0.952) correlated with the second axis in dry lands (Fig. 7B). With respect to the paddy fields, soil temperature (r = 0.999) and air temperature (r = 1.0) were positively correlated with the first axis. While soil GWC (r = 0.999) and DOC (r = 0.999) negatively correlated with the first axis (Fig. 7C). Increase of CO₂ fluxes was stimulated by increase of soil T and air T, but with the decrease of soil GWC and DOC. In grass fields, CO₂ fluxes was negatively correlated with soil VWC which was positively correlated with the first axis (r =  − 0.896) and second axis (r =  − 0.444) (Fig. 7D).

The contribution of cultivation to GHG emissions

This study shows that soil of the bay riparian zone acts as a weak CH₄ and N₂O source, but a strong CO₂ source during the growing season.

Mean CH₄ fluxes of the riparian zone from 2015 to 2016 in this study (0.47 ± 1.53 mg m⁻² h⁻¹) were similar to values reported for other wetland catchments (0.0∼2.22 mg m⁻² h⁻¹) (Badiou et al., 2011; Finocchiaro, Tangen & Gleason, 2014; Yang et al., 2014). The production of CH₄ in various eco-systems is closely related to the anaerobic environment, which is beneficial to the methanogen metabolic activities (Zhou et al., 2014; Zhang et al., 2018). Soil water content, which affects the methane consuming capacity of methane-oxidizing bacteria by changing the transmission rate and the soil environmental condition (Koh, Ochs & Yu, 2009), probably was a reason of the significant difference in the CH₄ fluxes among the paddy fields, dry lands and grasslands.

In recent years, the riparian zone is believed be an important source of N₂O emissions, because it might be the key area of nitrogen transformation in the whole biogeochemical system (Groffman, Gold & Addy, 2000; Wang et al., 2006). Incomplete denitrification under anaerobic conditions was considered to be the major reason for the production of N₂O (Wilcock and Sorrell, 2008; Zhou et al., 2014). Since N₂O eventually produces N₂ in anaerobic conditions, N₂O emissions from paddy filed were much lower than those from dry lands and grasslands. N₂O emission is limited when soil humidity completely saturates to values >60% in paddy fields (Davidson et al., 2000; Finocchiaro, Tangen & Gleason, 2014) and nitrifiers convert ammonium into nitrates to promote the N₂O flux in an aerobic soil environment after the wet soil has dried (Kaiser & Heinemeyer, 1996; Yang et al., 2017). Therefore, N₂O fluxes in paddy fields showed a double-peak pattern before planting and after crop harvest in 2016 (Fig. 5). The N₂O emission in grasslands without fertilizer might be due to surface runoff or sufficient substrate from high mineralization rates under the dry-wet alternating actions in the riparian zone (Muller et al., 2004).

Cumulative fluxes of GHGs (Table 5) showed that CO₂ emission dominated the GHG fluxes when the CH₄ and N₂O fluxes were calculated as CO₂ equivalents during the growing season, which is comparable to the results archived in Audet et al. (2013) and Zhou et al. (2017). Litter from soil surface, root residues and exudates in splits are the main source of soil organic matter in terrestrial ecosystems (Gleixner, 2005; Gonzalez-Meler et al., 2018). The high organic matter in riparian soil along with water level fluctuation created slow decomposition of external organic matter and microorganisms (Schimel et al., 2011), and insoluble organic carbon transferred to inorganic carbon through mineralization resulting in the increase of CO₂ emissions (Andrews & Schlesinger, 2001). Soil TC and DOC in croplands were lower than in grass fields (Table 3), probably due to the intensive interference of anthropogenic activities in cropland which may loosen the soil, increase the soil permeability, and hence lead to the easy run-off of soil DOC (Wang et al., 2005). These may also be the reasons why grass fields that grow herbaceous plants without tillage have the greatest emissions of CO₂, and have the highest levels of GHG fluxes.

The influence of water level fluctuation and tillage on GHG emission

Although no significant variations in CH₄ emission were found at different elevations, the mean CH₄ fluxes at 180 m (−0.036 mg m⁻² h⁻¹) (unflooded area) were lower than at other elevations (flooded area), and soil TC and TN were also lower than that at the riparian zone (Table 2), which were similar to results from Tangen, Finocchiaro & Gleason (2015). However, an increasing trend did not appear as the elevation decreased in the riparian zone, which was different from the research on grasslands in TGR by Chai et al. (2017). The variations in the CH₄ emission in different land uses correlated with the changes in the soil water content (Fig. 7), but the CH₄ emission in dry lands at different elevations did not show the same decreasing trend with soil water content in riparian zone (Table 2). That means different inundation duration varied the soil water content in dry lands, but did not lead to the variations of GHG emissions, and the variations in different seasons may correlate with various characteristics, such as soil temperature and human interference specially, as time proceeds.

N₂O fluxes in the research area showed great spatial and temporal variability. The highest N₂O flux from dry lands was from the elevation of 165 m (May to June), which reached 1,594.14 µg m⁻² h⁻¹ in June. N₂O fluxes in dry lands showed a one-peak pattern after a flooding period. The flux of N₂O positively related to soil NO₃⁻-N (Fig. 7). In paddy fields and dry lands, N₂O fluxes were positively related to air temperature and negatively related to soil TC. Application of farm manure provided the substrate for soil microbial growth, and promotes nitrification and denitrification reaction (Bouwman, 1996; Zheng et al., 2004). The major reason for the highest observed flux of N₂O might be tillage and fertilization in dry lands at 165 m for vegetable planting in May.

Fluxes of CO₂ in this study showed an insignificant increasing trend along the elevations, which was different with the study in Miyun reservoir (Yang, 2016) and Fuling TGR (Xu et al., 2017). Crop harvest exposes soil at the surface. In addition, water–table position and temperature, which influences the development of aerobic and microbial activities, would influence the potential of GHGs emmission (Glatzel, Basiliko & Moore, 2004; Waddington, Rotenberg & Warren, 2001). The research site went through a draught period from 10th to the 24th of August, 2016. After crop harvest, soil VWC, GWC, ST and NH₄⁺-N decreased, and NO₃⁻-N increased on August 17th, 2016 (Figs. S1 and S2). Therefore, crop harvest combined with continual dry and hot weather in August increased the CO₂ emission especially in paddy fields.

Differences of GHG fluxes between the bay riparian zone and other zones

GHG fluxes measured in this typical tributary bay riparian zone of the TGR were within the ranges of previous research across different sites (Table 7). Because of the higher soil moisture content, CH₄ flux in paddy fields was higher than that in grass fields and dry lands in the riparian zone, but lower than that in paddy fields in Hubei and SiChuan (Lin et al., 2000; Han et al., 2005). Based on the data in Table 7 (Shiau et al., 2016; Yu et al., 2010), N₂O fluxes in grass fields in riparian zone in this research were higher than in other natural wetlands due to high soil NO₃⁻-N and soil redox conditions, which favored mineralization in periodic water level fluctuation (Badiou et al., 2011; Shiau et al., 2016). In addition, the application of nitrogen fertilizer would increase the denitrification rate and denitrification product ratio (N₂O/N₂) (Xing, 1998). So N₂O fluxes in cropland system were higher than that in wetland without tillaging. Discrepancies in N₂O and CO₂fluxes in different croplands are likely related to the different amount of fertilizer, as well as geographic location, weather, and timing and intensity of sampling (Stelzer et al., 2011).

GHG fluxes from this study were in line with values reported for other typical tributary bay riparian zones in TGR. CH₄ emission fluxes in the natural wetland (e.g., marsh wetland) and man-made wetland (e.g., paddy fields) were higher than that from the different types of dry lands. GHG emissions in reservoirs are mainly released at the initial periods after the impoundment due to the abrupt release of nutrient substances in the flooded lands, the elevation of microbial activities, and the decomposition of unstable carbon matters, such as soils, litters, and leaves (Tremblay et al., 2005). As it is in the early development of TGR riparian zone, there may be in a decreasing trend in GHG emissions in future.