Gradual riparian wetland drying is increasingly sensitive to
global warming and contributes to climate change. Riparian wetlands play a
significant role in regulating carbon and nitrogen cycles. In this study, we
analyzed the emissions of carbon dioxide (CO2), methane (CH4), and
nitrous oxide (N2O) from riparian wetlands in the Xilin River basin to
understand the role of these ecosystems in greenhouse gas (GHG) emissions.
Moreover, the impact of the catchment hydrology and soil property variations
on GHG emissions over time and space was evaluated. Our results demonstrate
that riparian wetlands emit larger amounts of CO2 (335–2790 mgm-2h-1 in the wet season and 72–387 mgm-2h-1 in the dry season) than CH4
and N2O to the atmosphere due to high plant and soil respiration. The
results also reveal clear seasonal variations and spatial patterns along the
transects in the longitudinal direction. N2O emissions showed a
spatiotemporal pattern similar to that of CO2 emissions. Near-stream
sites were the only sources of CH4 emissions, while the other sites
served as sinks for these emissions. Soil moisture content and soil
temperature were the essential factors controlling GHG emissions, and
abundant aboveground biomass promoted the CO2, CH4, and N2O
emissions. Moreover, compared to different types of grasslands, riparian
wetlands were the potential hotspots of GHG emissions in the Inner Mongolian
region. Degradation of downstream wetlands has reduced the soil carbon pool
by approximately 60 %, decreased CO2 emissions by approximately
35 %, and converted the wetland from a CH4 and N2O source to a
sink. Our study showed that anthropogenic activities have extensively
changed the hydrological characteristics of the riparian wetlands and might
accelerate carbon loss, which could further affect GHG emissions.
Introduction
With the increasing rate of global warming, the change in the concentrations
of greenhouse gases (GHGs) in the atmosphere is a source of concern in the
scientific community (Cao et al., 2005). According to the World
Meteorological Organization (WMO, 2018), the concentrations of carbon
dioxide (CO2), methane (CH4), and nitrous oxide (N2O) in the
atmosphere have increased by 146 %, 257 %, and 122 %, respectively,
since 1750. Despite their lower atmospheric concentrations, CH4 and
N2O absorb infrared radiation approximately 28 and 265 times more
effectively at centennial timescales than CO2 (IPCC, 2013),
respectively. On a global scale, CO2, CH4, and N2O
together are responsible for 87 % of the GHG effect (Ferrón et al.,
2007).
Wetlands are unique ecosystems that serve as transition zones between
terrestrial and aquatic ecosystems. They play an important role in the
global carbon cycle (Beger et al., 2010; Naiman and Decamps, 1997). Wetlands
are sensitive to hydrological changes, particularly in the context of global
climate change (Cheng and Huang, 2016). Moreover, wetland hydrology is
affected by local anthropogenic activities, such as the construction of
reservoirs, resulting in gradual drying. Although wetlands cover only
4 %–6 % of the terrestrial land surface, they contain approximately
12 %–24 % of global terrestrial soil organic carbon (SOC), thus acting as
carbon sinks. Moreover, they release CO2, CH4, and N2O
into the atmosphere and serve as carbon sources (Lv et al., 2013). During
plant photosynthesis, the amount of carbon accumulated is generally higher
than the amount of CO2 consumed (plant respiration, animal respiration,
and microbial decomposition) in the wetland; thus, the net effect of the
wetland is that of a carbon sink. Wetlands are increasingly recognized as an
essential part of nature, given their simultaneous functions as carbon
sources and sinks. Excessive rainfall causes an expansion in wetland area
and a sharp increase in soil moisture content, thus enhancing respiration,
methanogenesis, nitrification, and denitrification rates (Mitsch et al.,
2009). On the other hand, reduced precipitation or severe droughts decrease
water levels, causing the wetlands to dry up. The accumulated carbon is
released back into the atmosphere through oxidation. Due to the increasing
impact of climate change and human activity, drying of wetlands has been
widely observed in recent years (Liu et al., 2006); more than half of global
wetlands have disappeared since 1900 (Mitsch and Gosselink, 2007), and this
tendency is expected to continue in the future. The loss of wetlands may
directly shift the soil environment from anoxic to oxic conditions, while
modifying the CO2 and CH4 source and sink functions of wetland
ecological systems (Waddington and Roulet, 2000; Zona et al., 2009).
The Xilin River basin in China is characterized by a marked spatial gradient
in soil moisture content. It is a unique natural laboratory that may be used
to explore the close relationships between the spatiotemporal variations in
hydrology and riparian biogeochemistry. Wetlands around the Xilin River play
an irreplaceable role with regard to local climate control, water
conservation, the carbon and nitrogen cycles, and husbandry (Gou et al.,
2015; Kou, 2018). Moreover, the Xilin River region is subjected to seasonal
alterations in precipitation and temperature regimes. Construction of the
Xilin River reservoir has resulted in highly negative consequences, such as
the drying of downstream wetlands, thereby affecting riparian hydrology and
microbial activity in riparian soils. GHG emissions in riparian wetlands
vary immensely. Therefore, understanding the interactions between the GHG
emissions and hydrological changes in the Xilin River riparian wetlands has
become increasingly important. Moreover, it is necessary to estimate the
changes in GHG emissions as a result of wetland degradation at local and
global scales.
In this work, GHG emissions from riparian wetlands and adjacent hillslope
grasslands of the Xilin River basin were investigated. GHG emissions, soil
temperature, and soil moisture content were measured in the dry and wet
seasons. The main objectives of this study were to (1) investigate the
temporal and spatial variations in CO2, CH4, and N2O
emissions from the wetlands in the riparian zone and examine the main
factors affecting the GHG emissions; (2) compare the GHG emissions from the
riparian wetlands with those from different types of grasslands; and (3)
evaluate the impact of wetland degradation in the study area on GHG
emissions.
Materials and methodsStudy site
The Xilin River is situated in the southeastern part of the Inner Mongolia
Autonomous Region in China (43∘26′–44∘39′ N, 115∘00′–117∘30′ E). It is a typical inland river of the
Inner Mongolia grasslands. The river basin area is 10 542 km2, total
length is 268.1 km, and average altitude is 988.5 m. According to the
meteorological data
provided by the Xilinhot Meteorological Station (Xi et
al., 2017; Tong et al., 2004), the long-term annual mean air temperature is
1.7 ∘C, and the maximum and minimum monthly means are
20.8 ∘C in July and -19.8∘C in January, respectively.
The average annual precipitation was 278.9 mm for the period of 1968–2015.
Precipitation is distributed unevenly among the seasons, with 87.41 % of
the total precipitation occurring between May and September.
Soil types in the Xilin River basin are predominantly chernozems (86.4 %),
showing a significant zonal distribution as light chestnut soil,
dark chestnut soil, and chernozems from the northwest to southeast. Soil
types in this basin also present a vertical distribution with elevation.
Soluble chernozems and carbonate chernozems are primarily observed at
altitudes above 1350 m, with a relatively fertile and deep soil layer.
Dark chestnut soil, boggy soil, and dark meadow with high humus content are
distributed between the altitudes of 1150 and 1350 m. Meanwhile, light
chestnut soil, saline meadow soil, and meadow solonchak with low soil humus,
a thin soil layer, and coarse soil texture are distributed between the
altitudes of 902 and 1150 m above sea level (Xi et al., 2017).
Field measurements and laboratory analyses
In this study, five representative transects were selected as the primary
measurement sites in the entire Xilin River. Each transect cuts through the
riparian wetlands near the river and the hillslope grasslands further away
(Fig. 1).
(a) Location of the Xilin River basin and distribution of five
riparian–hillslope transects (T1–T5). (b) Elevation details of each
transect in the Xilin River basin.
The layout of the sampling points of each transect is shown in Fig. 2. Each
sampling point, from T1–T5, was extended from either side of the river to
the grassland on the slopes by using five to seven sampling points for each transect,
resulting in 24 points in total. The sampling sites on the left and right
banks were defined as L1–L3 and R1–R4 from the riparian wetlands to the
hillslope grasslands. As transect T3 was located on a much wider flood
plain, none of its sampling points were located on the hillslope grassland.
The last transect (T5) was located downstream in the dry lake and contained
seven sampling points. They were defined as S1–S7, where S1, S2, and S7
were located along the lakeshore (the lakeside zone), and S3–S6 were
located in the dry lake bed (S3 and S4 in the mudbank, S5 in
saline–alkali soil, and S6 in sand–gravel geology). Moreover,
characterizations for the T1, T2, and T3 transects were located along the
continuous river flow, and the T4 and T5 transects were located along the
intermittent river flow.
Distributions of sampling points in transects T1–T5 (the images are the
authors' own).
The CO2, CH4, and N2O emissions from each site were measured
in August (wet season) and October (dry season) in 2018 using a static dark
chamber and the gas chromatography method. The static chambers were made of
a cube-shaped polyvinyl chloride (PVC) pipe (dimensions: 0.4m×0.2m×0.2m). A battery-driven fan was installed horizontally
inside the top wall of the chamber to ensure proper air mixing during
measurements. To minimize heating from solar radiation, white adiabatic
aluminum foil was used to cover the entire aboveground portion of the
chamber. During measurements, the chambers were driven into the soil to
ensure airtightness and connected with a differential gas analyzer (Li-7000
CO2/H2O analyzer, LI-COR, USA) to measure the changes in the soil
CO2 concentration. The air in the chamber was sampled using a 60 mL
syringe at 0, 7, 14, 21, and 28 min. The gas samples were stored in a
reservoir bag and taken to the laboratory for CH4 and N2O
measurements using gas chromatography (GC-2030, Japan). The measurements
were scheduled for 09:00–11:00 or 15:00–17:00 UTC+8.
Soil temperature (ST) was measured at depths of 0–10 and 10–20 cm with
a geothermometer (DTM-461, Hengshui, China). Plant samples were collected in
a static chamber and oven-dried in the laboratory to obtain aboveground
biomass (BIO). A 100 cm3 ring cutter was used to collect surface soil
samples at each site, which were placed in aluminum boxes and immediately
brought back to the laboratory to measure soil mass moisture content (SMC)
and soil bulk density (ρb) using national standard methods
(NATESC, 2006). Topsoil samples were collected, sealed in plastic bags, and
brought back to the laboratory to measure soil pH, electrical conductivity
(EC), total soil organic carbon (TOC) content, and soil C:N ratio.
Physical and chemical properties (mean ± SD) of soils at
various sites within each transect.
TransectZoneSample numberSMC10-VSMC20-VSoil C:NTOC (gkg-1)BIO (g)ρbpHEC (µscm-1)SSM (%)T1Riparian1212.16±7.5512.88±12.0512.46±0.9130.16±6.5414.67±5.441.28±0.077.25±0.62154.71±23.7047.77±7.04Hillslope62.72±0.915.05±3.0911.41±0.0910.77±4.726.70±1.481.45±0.037.22±0.4082.02±16.3731.02±1.32T2Riparian1226.75±19.5212.19±7.8211.70±1.1419.96±5.7124.76±9.651.23±0.058.95±0.45303.88±102.1651.21±6.49Hillslope95.85±4.823.03±1.439.77±0.8814.87±11.216.10±3.191.38±0.138.10±0.55162.97±128.1835.09±6.75T3Riparian1228.04±22.9514.53±8.9815.80±4.1622.40±9.696.37±2.951.35±0.199.50±0.671233.20±829.8347.56±11.65L33116.37±56.91113.36±23.1716.8±0.5836.1±1.84107.75±16.940.592±0.028.5±0.17403±57.21>100T4Riparian125.42±3.344.07±4.3112.52±2.069.96±1.2511.97±4.501.30±0.088.84±0.22461.72±314.2744.08±7.07Hillslope63.35±2.064.27±1.949.97±0.509.65±1.057.84±2.481.30±0.098.23±0.14118.5±8.2539.43±5.55T5Dry lake bed1217.47±15.0814.49±13.2863.74±12.9331.41±6.555.48±2.351.16±0.109.88±0.187320.87±4300.0358.47±7.16Lakeshore92.64±1.482.82±1.2715.92±4.716.35±1.1601.33±0.099.41±0.7281.82±162.7337.52±5.34
Soil particle composition of soils at various sites within each
transect.
TransectZoneSoil particle composition Clay %Silt %Sand(<0.002 mm)(0.02–0.002 mm)(2.0–0.02 mm)T1Riparian2.52.794.8Hillslope9.66.185.3T2Riparian5.55.890.7Hillslope10.88.680.6T3Riparian4.11.194.8T4Riparian11.41.587.1Hillslope12.75.981.4T5Lakeshore5.12.192.8Dry lake bed46.14.849.1Calculation of GHG emissions
The CO2, CH4, and N2O emissions were calculated using Eq. (1)
(Qin et al., 2016):
F=VA×dcdt×ρ=H×dcdt×MV×273.15273.15+t,
where F denotes the flux of CO2, CH4, and N2O emissions
(mgm-2h-1); H is the height of the static
chamber (0.18 m); M is the relative molecular weight (44 for CO2 and
N2O, and 16 for CH4); V is the volume of gas in the standard state
(22.4 Lmol-1); dc/dt is the rate of change of the gas
concentration (10-6h-1); and T is the temperature in
the black chamber (∘C).
The annual cumulative emissions were calculated using Eq. (2) (Whiting and
Chanton, 2001):
M=∑Fi+1+F12×(ti+1-ti)×24,
where M denotes the total cumulative emission amounts of CO2, CH4,
or N2O (kghm2); F is the emission flux of CO2,
CH4, or N2O; i is the sampling frequency; and
ti+1-ti represents the interval between two adjacent measurement
dates.
In this study, a 100-year timescale was selected to calculate the global warming
potential (GWP) of soil CH4 and N2O emissions (Whiting and
Chanton, 2001):
GWP=1×[CO2]+25×[CH4]+298×[N2O],
where 25 and 298 are the GWP multiples of CH4 and N2O relative to
CO2 on a 100-year timescale, respectively.
Statistical analysis
All statistical analyses were performed using SPSS for Windows version 18.0
(SPSS Inc., Chicago, IL, USA). Statistical significance was set at P<0.05. Pearson correlation analysis was conducted to estimate the
relationships between GHG fluxes and environmental variables. A Wilcoxon
test was used to determine the differences in the GHG fluxes between the two
seasons.
ResultsSpatiotemporal patterns of SMC for each transect
The temporal and spatial variations in SMC10 occurred in the following
order: wet season > dry season and riparian wetlands > hillslope
grasslands (Fig. 3a, c, and e). Similar variations were observed in
SMC20 (Fig. 3b, d, and f). The average SMC10 and SMC20 in the continuous river
transects in the riparian zones (SMC10 values were 37.44 % in the wet
season and 19.40 % in the dry season, while SMC20 values were 25.96 % in
the wet season and 17.39 % in the dry season) were higher than those in
the hillslope grasslands (SMC10 values were 9.12 % in the wet season and
4.15 % in the dry season; SMC20 values were 6.51 % in the wet season and
5.96 % in the dry season). During the study period, both SMC10 and
SMC20 changed as the distance from the river increased, and the highest
value was observed at the near-stream sites (L1 and R1). SMC10 fluctuations
were low in the intermittent transect compared with those in the upstream
transects, with mean values being 11.79 % in the wet season and 3.72 %
in the dry season in the riparian areas. The mean SMC10 in the hillslopes
was 6.58 % in the wet season and 2.86 % in the dry season. SMC20 showed
similar fluctuation; it was 7.22 % in the wet season and 2.98 % in the
dry season in the riparian areas and 7.56 % in the wet season and 4.4 %
in the dry season in the hillslopes. In transect T5, average SMC10 and SMC20
at the center of the lake (SMC10 values were 29.00 % in the wet season and
13.36 % in the dry season; SMC20 values were 29.30 % in the wet season
and 9.69 % in the dry season) were higher than those along the lakeshore
(SMC10 values were 4.90 % in the wet season and 3.13 % in the dry
season; SMC20 values were 3.34 % in the wet season and 5.22 % in the dry
season).
Soil mass moisture contents (SMCs) at soil depths of 0–10 cm (SMC10)
and 10–20 cm (SMC20) for transects T1–T5 in the wet and dry seasons. Error
bars represent the SD about the mean.
Spatiotemporal patterns of ST in each transect
Spatiotemporal differences in ST during the entire observation period are
displayed in Fig. 4. ST variations in the wet season (mean was 27.4 ∘C) were noticeably higher than those in the dry season (mean was 8.97 ∘C). Moreover, ST at riparian sites (mean, 26.0 ∘C in
the wet season and 8.41 ∘C in the dry season) was slightly lower
than that at the hillslope grasslands (mean, 30.9 ∘C in the wet
season and 10.3 ∘C in the dry season) for the 0–10 cm soil
depth, with the exception of transect T5. Similar results were observed for
the 10–20 cm soil depth.
Soil temperatures (STs) at soil depths of 0–10 cm (ST10) and 10–20 cm (ST20) for transects T1–T5 in the wet and dry seasons. Error bars
represent the SD about the mean.
Spatiotemporal patterns of GHG emissions in each transect
Figure 5 shows the spatiotemporal variations in GHG emissions in the wet and
dry seasons in each transect. CO2 emissions in each transect were
higher in the wet season than in the dry season. The average emissions in
the riparian wetland transects T1–T4 (1582.09±679.34mgm-2h-1 in the wet season and 163.24±84.98mgm-2h-1 in the dry season) were higher
than the transects in the hillslope grasslands (1071.54±225.39mgm-2h-1 in the wet season and 77.68±25.32mgm-2h-1 in the dry season).
High CO2 fluxes occurred in the riparian zones, while lower CO2
fluxes were observed in the hillslope grasslands in continuous river
transects (T1, T2, and T3). Transect T4 exhibited lower CO2 emission in
the riparian wetlands near the channel than at sites away from the channel.
CO2 emissions in transect T5 in the wet and dry seasons decreased from
the lakeshore to the lake center.
Spatiotemporal patterns of CO2 (first column), CH4 (second column), and N2O (third column) emission (F) for each
transect. Data are shown for the wet season (orange) and the dry season
(blue). Error bars depict standard deviation.
CH4 emissions at the continuous river flow transects (T1, T2, and T3)
varied between the wet and dry seasons, except for those at T4
(characterized by intermittent river flow) and T5 (the dry lake). In the wet
season, the near-stream sites (L1 and R1) in T1, T2, and T3 were
characterized as high-CH4 sources (average, 3.74±3.81mgm-2h-1), but the sites located away from
the river gradually turned into CH4 sinks. Moreover, all the sites in
transects T4 and T5 were sinks. CH4 emissions (mean value: 0.2±0.45mgm-2h-1) at the wetland sites were
always lower in the dry season than those in the wet season. However, the
sites on the hillslope grasslands served as CH4 sinks (mean
value: -0.05±0.03mgm-2h-1). In
transect T5, CH4 emissions showed the opposite trend; a CH4 sink
was observed in the wet season, but it was transformed into a CH4
source in the dry season.
Similar to the CO2 and CH4 emissions, N2O emissions showed a
distinct spatiotemporal pattern in all the transects. N2O emissions in
the wet season were higher than those in the dry season. These emissions
were higher in the riparian wetlands than in the hillslope grasslands.
Moreover, almost all sites with continuous river flow were N2O sources,
while more than half of the sites with intermittent river flow were sinks.
Table 3 shows that CO2 fluxes were significantly correlated between the
wet and dry seasons, while CH4 and N2O fluxes were not correlated
between the two seasons.
Significant correlations between GHGs fluxes and two seasons (n=31).
GHG fluxFCO2 in the wet season –FCH4 in the wet season –FN2O in the wet season –FCO2 in the dry seasonFCH4 in the dry seasonFN2O in the dry seasonSignificant correlations (P)0.0000.1330.290
Note: P<0.05 denotes significant correlation and P>0.05 denotes no significant correlation.
Spatiotemporal patterns of GHG emission in upstream and downstream areas
Figure 6 shows the detailed spatial and seasonal patterns of GHG emission in
the wet and dry seasons in the longitudinal direction from the upstream (T1,
T2, and T3) to the downstream areas (T4 and T5). The CO2, CH4, and
N2O emissions were calculated using the average values of the
respective emissions in the wetlands and hillslope grasslands in each
transect.
Spatiotemporal patterns of CO2 (first line), CH4 (second line), and N2O (third line) emissions (F) in the upstream (T1,
T2, and T3) and downstream areas (T4 and T5). Bars represent the mean values
for each transect, and error bars show the standard errors.
CO2 emissions at the riparian wetlands (Fig. 6a) in the wet season
decreased from 2444.69±228.58mgm-2h-1 in the upstream area to 665.08±347.57mgm-2h-1 downstream, and the corresponding values for
the dry season were 238.12±48.20 and 94.14±7.67mgm-2h-1,
respectively. However, in the hillslope grasslands (Fig. 6b), CO2
emissions exhibited no significant seasonality between the upstream and
downstream areas, with mean values being 1103.40±190.44mgm-2h-1 in the wet season and 79.18±24.52mgm-2h-1 in the dry season.
In addition, the CO2 emissions in transect T5 were low for both months,
with averages of 162.83±149.15mgm-2h-1 and 63.26±12.40mgm-2h-1
in the wet and dry seasons, respectively. The upstream riparian zones
exhibited higher CO2 emissions (894.32±868.47mgm-2h-1) than their downstream counterparts (621.14±704.10mgm-2h-1). Mean CO2
emissions showed no significant differences in the grasslands, averaging
524.16±450.10mgm-2h-1 upstream
and 508.06±534.77mgm-2h-1 downstream.
CH4 emissions showed a marked spatial pattern in the riparian zones
from upstream to downstream (Fig. 6c). The transects with continuous river
flow were CH4 sources in the wet and dry seasons, with average
emissions of 1.42±3.41
and 0.27±0.49mgm-2h-1,
respectively; while those with intermittent river flow served as CH4
sinks, with the corresponding means of -0.21±0.45 and -0.02±0.05mgm-2h-1, respectively. Moreover, the hillslope
grassland sites in all transects were CH4 sinks (Fig. 6d).
N2O emissions in riparian wetlands (Fig. 7e) showed spatial patterns
similar to those of CH4 emissions. In the wet season, the transects
with continuous river flow served as N2O sources, with a mean
emission of 0.031±0.031mgm-2h-1;
meanwhile, transects with intermittent river flow acted as N2O sinks with an average emission of -0.037±0.05mgm-2h-1. In the dry season, N2O emissions occurred
as weak sources in the longitudinal transects, exhibiting an average
emission of 0.002±0.007mgm-2h-1.
However, the N2O emission in the hillslope grasslands did not show any
spatial patterns (Fig. 7f).
Correlation between aboveground biomass (BIO) and GHG emission (F).
DiscussionMain factors influencing GHG emissionsEffects of SMC on GHG emissions
SMC constitutes one of the main factors affecting GHG emission in wetlands.
In this study, transects T1–T4 were characterized by a marked spatial SMC
gradient (i.e., a gradual decrease in SMC10 and SMC20 from the riparian
wetlands to the hillslope grasslands and from the upstream to downstream
regions; Fig. 3). The CO2, CH4, and N2O emissions showed a
similar trend. Table 4 shows that SMC10 is positively correlated with CO2
emission (P<0.05) and that SMC10 and SMC20 are significantly
positively correlated with CH4 emission (P<0.01) and with
N2O emission (P<0.05 and P<0.01, respectively). These
results indicate the influence of wetland SMC on GHG emission.
Typically, the optimal SMC associated with CO2 emission in the riparian
wetlands ranges from 40 % to 60 % (Sjögersten et al., 2006), creating
better soil aeration, and improving soil microorganism activity and
respiration in plant roots, thereby promoting CO2 emission. Excessive
SMC reduces soil gas transfer due to the formation of an anaerobic
environment in the soil, and microbial activity is lowered, favoring the
accumulation of organic matter (Hui, 2014). The SMC of the hillslope
grasslands was found to be less than 10 %. Low soil moisture inhibits the
growth of vegetation, with few vegetation residues and litters. Meanwhile,
low soil moisture is not conducive to the survival of soil microorganisms,
leading to lower CO2 emission from the hillslope grasslands than from
the riparian zones (Moldrup et al., 2000; Hui, 2014). Similar results were
obtained in our study. The change in CO2 emission in transect T5 was
contrary to the changes in SMC10 and SMC20, likely because the optimal range
of soil C:N is between 10–12 (Pierzynski et al., 1994), but the value in the
dry lake bed of T5 is higher than 60. The high soil C:N resulted in nitrogen
limitation in the process of decomposition of organic matter by
microorganisms. Further, other sediment properties (like Soil pH
>9.5) for this transect were not conducive to the survival of
microorganisms (Table 1), and the increase in SMC did not increase the
respiration activity of the microorganisms.
The highest CH4 emissions were observed at the near-stream sites (i.e.,
L1 and R1) in T1, T2, and T3, with average SMC of 30.29 %, while the SMC
at the other sites, which were either weak sources or sinks, averaged at
14.57 %. These results indicate that a higher SMC is favorable for
CH4 emissions. This may be because a higher SMC accompanies soil in a
reduced state, which is beneficial for CH4 production and inhibits
CH4 oxidation. A similar result was reported by Xu et al. (2008). They
conducted experiments analyzing CH4 emissions from a variety of paddy
soils in China and showed that CH4 production rates increased with
the increase in SMC at the same incubation temperature. Meng et al. (2011)
also reported that water depth was the main factor affecting CH4
emissions from wetlands. When the water level dropped below the soil
surface, the decomposition of organic matter accelerated, and CH4
emission decreased. If the oxide layer is large, the soil is transformed
into a CH4 sink (Meng et al., 2011).
The N2O fluxes showed a clear spatial pattern associated with the
changes in SMC. The moisture content of wetland soils directly affects the
aeration status of the soil. Besides, the aeration status affects the
partial pressure of oxygen, which has an important impact on
nitrifying and denitrifying bacterial activity and ultimately affects soil
N2O emissions (Zhang et al., 2005). Table 4 shows that N2O
emission is significantly positively correlated with SMC10 and SMC20 (P<0.01). Generally, when SMC is below the saturated water content,
the microorganisms are in an aerobic environment, and N2O mainly comes
from the nitrification reaction. N2O emission increases with increase
in SMC (Niu et al., 2017; Yu et al., 2006). In our study, the sampling sites
with higher SMC (riparian zones and some hillslope grassland zones in the
upstream transects) have higher N2O emissions. When SMC increases to
the saturated water content or is in a flooded state, the system is an
anaerobic environment, and the nitrous oxide reductase activity is higher
due to excessively high SMC, which is conducive to denitrification and
eventually produces N2 (Niu et al., 2017; Yu et al., 2006), such as
at site L1 in transect T3 in this study. Rückauf et al. (2004) showed that
denitrification was the main process under flooded soil conditions in
wetland soils and that the release of N2 exceeds that of N2O.
These findings are consistent with those of Liu et al. (2003), who showed
that SMC is an essential factor affecting N2O emission.
Nitrification:
Denitrification:
The enzymes involved in the formula include ammonia monooxygenase (AMO),
hydroxylamine oxidase (HAO), nitrite REDOX enzyme (HAO), nitrate reductase
(Nar), nitrite reductase (Nir), nitric oxide reductase (Nor), and nitrous
oxide reductase (Nos).
Effects of ST on GHG emissions
ST was another important factor affecting CO2 emission in this study;
it was found to be significantly correlated with CO2 emission (P<0.01) (Table 4). The activity of soil microorganisms increases
with rising soil temperature, leading to increased respiration and
consequently higher CO2 emission (Heilman et al., 1999). Previous
studies have reported that ST partially controls seasonal CO2 emission
patterns (Inubushi et al., 2003). Concurrently, CO2 emissions in the
wet season were significantly higher than those in the dry season in this
study.
CH4 emissions showed a clear seasonal pattern, likely because high
summer temperatures improve the activity of both CH4-producing and
CH4-oxidizing bacteria (Ding et al., 2010). However, as Table 4 indicates, the
correlation between CH4 emission and temperature was not significant in
this study, likely because SMC was a more critical factor than temperature
in our study region given its very dry climate. SMC showed a positive
correlation with GHG emissions. In addition, SMC affected ST to a certain
extent, while the interactions between SMC and ST had a mutual influence on
CH4 emission. During the study period, the near-stream sites (L1 and
R1) maintained a super-wet state on the ground surface for a long time,
which was beneficial for the production of CH4. However, the wetlands
maintained a state without water accumulation on the soil surface in August,
which was conducive to the oxidative absorption of CH4. SMC thus masked
the effect of ST on CH4 emissions.
Previous studies have indicated that temperature is an important factor
affecting N2O emission (Sun et al., 2011) through primary
mechanisms impacting the nitrifying and denitrifying bacteria in the soil.
As Table 4 shows, the correlations between N2O emission and ST10 and
ST20 were poor (P>0.05). This can be attributed to the wide
suitable temperature range for nitrification–denitrification and weak
sensitivity to temperature. Malhi and Mcgill (1982) found that the optimum
temperature for nitrification was
20 ∘C and that it inhibits
entirely at 30 ∘C. However, Brady (1999) believed that the suitable
temperature range for nitrification is 25–35 ∘C and
that nitrification inhibits below 5 ∘C or above 50 ∘C. This
shows that the temperature requirements of nitrifying microorganisms in
wetland soils are possibly different in different temperature belts. The
suitable temperature range was the performance of the long-term adaptability
of nitrifying microorganisms. Meanwhile, several studies have revealed that
denitrification can be carried out in a wide temperature range
(5–70 ∘C) and that it is positively related to
temperature (Fan, 1995). However, the process is inhibited when the
temperature is too high or too low. The average ST in the wet season was
27.4 ∘C, conducive to the growth of denitrifying microorganisms,
while that in the dry season was 8.97 ∘C, and the microbial activity
was generally low (Sun et al., 2011). Furthermore, ST fluctuations
were low in both the wet and dry seasons. Therefore, the effect of ST on
N2O emission may have been masked by other factors, such as moisture
content.
Effects of BIO and soil organic matter content on GHG emissions
CO2 and CH4 emissions were higher in the riparian wetlands than in
the grasslands, mainly because of the greater vegetation cover in the
former. Typically, CO2 emissions in the riparian wetlands originate
from plants and microorganisms, with plant respiration accounting for a
large proportion in the growing season. Previous studies have shown that
plant respiration accounts for 35 %–90 % of the total respiration in the
wetland ecosystem (Johnson-Randall and Foote, 2005). The good soil
physicochemical properties and high soil TOC content of the riparian
wetlands improve both the activity of soil microorganisms and plant root
respiration. As Table 4 shows, BIO is significantly correlated with CO2
(P<0.05) and CH4 (P<0.01) emissions. These results
are indicated by the significant linear positive correlation between the
respiration rate and plant biomass (Lu et al., 2007). Higher plant biomass
storage can achieve more carbon accumulation during photosynthesis and
higher exudate release by the roots. This, in turn, promotes the
accumulation of soil organic matter. An increased amount of organic matter
stimulates the growth and reproduction of soil microorganisms, ultimately
promoting CO2 and CH4 emission. Moreover, plants act as gas
channels for CH4 transmission, and a larger amount of biomass promotes
CH4 emission, given the increased number of channels. In transect T3,
the high CO2 emission observed at site L3 can be attributed to the
relatively high levels of SMC, BIO, and soil nutrients, which stimulate
microbial respiration rates.
BIO had a weak correlation with N2O emission (Table 4), which indicates
that plants increase N2O production and emission, although this may not
be the most critical factor. Previous studies have reported mechanisms
wherein the plants are able to absorb the N2O produced in the soil
through the root system before releasing it into the atmosphere.
Additionally, the root exudates of plants can enhance the activity of
nitrifying and denitrifying bacteria in the soil, ultimately promoting the
production of N2O. Finally, oxygen stress caused by plant respiration
can regulate the production and consumption of N2O in the soil,
eventually affecting the conversion of nitrogen in the soil (Koops et al.,
1996; Azam et al., 2005).
Site L3 in transect T3 was covered by tall reeds, and its BIO was much
higher than that of any of the other sites; thus, the data for this site
were excluded from the correlation analysis.
Soil C:N ratio refers to the ratio of the concentration of biodegradable
carbonaceous organic matter to nitrogenous matter in the soil, and it forms
a soil matrix with TOC. TOC decomposition provides energy for microbial
activity, while the C:N ratio affects the decomposition of organic matter by
soil microorganisms (Gholz et al., 2000). The correlation results (Fig. 8)
indicate that TOC had a weak positive correlation with CO2 emission (P>0.05), but the soil C:N ratio had a significant negative
correlation with CO2 emission (P<0.05), indicating that
nitrogen has a limiting effect on soil respiration by affecting microbial
metabolism. Liu (2019) have reported that N addition promotes
CO2 emission from wetlands soil, and the effect of organic N input was
significantly higher than that of inorganic N input. Organic carbon acts as
a carbon source for the growth of plants and microorganisms, which boosts
their respiration. Moreover, TOC has a significant correlation with N2O
emissions (P<0.05). Most heterotrophic microorganisms use soil
organic matter as carbon and electron donors (Morley and Baggs,
2010). Soil carbon sources have an important influence on microbial
activity. Nitrifying or denitrifying microorganisms need organic matter to
act as the carbon source during the assimilation of NH3 or
NO3-. High content of organic matter in the soil can promote the
concentration of heterotrophic nitrifying bacteria, consume dissolved oxygen
in the medium, and cause the soil to become more anaerobic, thereby slowing
down autotrophic growth nitrifying bacteria. This reduces the nitrification
rate, ultimately promoting N2O release. Enwall et al. (2005) studied
the effect of long-term fertilization on soil denitrification microbial
action intensity. They found that the soil with long-term organic fertilizer
application has a significant increase in organic matter content and
consequently a significant increase in denitrification activity. Typically,
low soil C:N ratios are favorable for the decomposition of microorganisms,
the most suitable range being between 10 and 12 (Pierzynski et al., 2005).
As Table 4 shows, N2O emission was significantly related to the soil
C:N ratios (P<0.05), which means that denitrifying bacteria could
use their endogenous carbon source for denitrification when the external
carbon source was insufficient. Moreover, incomplete denitrification leads
to the accumulation of NO2–N, which is conducive to N2O release.
Meanwhile, due to the weak competitive ability of Nos to electrons, a low
soil C:N ratio inhibits the synthesis of Nos, which is also a reason for
N2O release. In this study, all sites in transects T1–T4 exhibited
similar soil C:N ratios in the optimum range (Table 1), which is favorable
for microbial decomposition. However, the soil C:N ratios in transect T5
were higher than those in the other transects, especially in the dry lake
bed. Therefore, transect T5 showed severe mineralization and a low microbial
decomposition rate.
Correlations between soil organic carbon (TOC) content and GHG
emission (F).
Correlations between CO2, CH4, and N2O emissions and
impact factors (n=62).
Note: 1. The analysis method used in the table is Pearson correlation
analysis, and the numbers represent Pearson correlation coefficients.
2. a and b denote significant and highly significant correlations (P<0.05 and P<0.01), respectively.
3. ST – soil temperature, SMC – soil moisture content, ρb – soil
bulk density, soil C:N – soil carbon–nitrogen ratio, pH – soil pH, EC – soil
electrical conductivity, and BIO – aboveground biomass.
Riparian wetlands as hotspots of GHG emissions
The results of this study emphasized that the rate of CO2 emission in
the riparian wetlands was higher than that in the hillslope grasslands,
owing to a variety of factors. ST is an important factor affecting GHG
emission. Mclain and Martens (2006) showed that seasonal fluctuations
in ST and SMC in semi-arid regions have important effects on CO2,
CH4, and N2O emissions in riparian soils. Poblador et al. (2017)
studied the GHG emission in forest riparian zones and suggested that the
difference in the CO2 and N2O emissions in these zones is caused
by the spatial gradient of the regional SMC. In this study, the upstream
riparian wetlands were characterized by higher TOC content, lower soil C:N
ratio, and more abundant BIO than those in the hillslope grasslands
(Table 1). These soil conditions benefited the soil microbial activity,
ultimately enhancing respiration as well as CO2 emissions. However, the
CO2 emission in the downstream areas was nearly identical to that in
the grasslands, likely because the wetlands gradually evolved into
grasslands after their degradation. N2O emission showed spatial
patterns similar to that of CO2 emission, likely because the CO2
concentration was closely related to the nitrification and denitrification
processes. High CO2 concentrations can promote the carbon and nitrogen
cycles in soil (Azam et al., 2005), increasing belowground C allocation,
which is associated with increased root biomass, root turnover, and root
exudation. Elevated pCO2 in plants provides the energy for
denitrification in the presence of high available N, and there is increased
O2 consumption under elevated pCO2 (Baggs et al., 2003). Moreover,
soil respiration increases during soil denitrification (Liu et al., 2010;
Christensen et al., 1990). In this study, a weak correlation was observed
between the CO2 and CH4 emissions in the riparian zones (r=0.228), but CO2 emission was significantly correlated with N2O
emission (r=0.322, P<0.05). The soil became anaerobic in the
riparian areas as the SMC increased, and this was conducive to the survival
of CH4-producing bacteria and to denitrification reactions, eventually
leading to an increase in CH4 and N2O emissions. Jacinthe et al.
(2015) reported that inundated grassland-dominated riparian wetlands were
CH4 sinks (-1.08±0.22kgCH4-Cha-1yr-1), and Lu et al. (2015) also indicated that
grasslands were CH4 sinks. In our study, a marked water gradient across
the transects led to the transformation of the soil from anaerobic to
aerobic soil, which changed the wetland to either a CH4 source or
sink. Therefore, during the transition from the riparian wetlands to the
hillslope grasslands, CH4 sources only appeared in the near-stream
sites, while sinks appeared at other sites.
Further, we compared the GHG emissions in the riparian wetlands and the
hillslope grasslands around the Xilin River basin with those in various
types of grasslands (meadow grassland, typical grassland, and desert
grassland) in the Xilingol League in Inner Mongolia (Table 5). CO2
emission in the wet season decreased in the following order: upstream
riparian wetlands > downstream riparian wetlands > hillslope
grasslands > meadow grassland > typical
grassland > desert grassland. Moreover, the upper riparian
wetlands acted as sources of CH4 emission, while the downstream
transects and grasslands served as CH4 sinks. Similarly, except in the
downstream transects, N2O emissions occurred as weak sources in
different types of grasslands and upstream riparian wetlands. The GHG
emissions showed similar spatial patterns in October. Although these
estimates were made only in the growing season in August and the non-growing
season in October, our results suggest that the riparian wetlands are the
potential hotspots of GHG emission. Thus, it is important to study GHG
emission to obtain a comprehensive picture of the role of the riparian
wetlands in climate change.
GHG emission fluxes of riparian wetlands and grasslands.
Sample plot GHG emissions in August GHG emissions in October Reference(mgm-2h-1) (mgm-2h-1) CO2CH4N2OCO2CH4N2OWetlands of upstream transects (T1, T2, and T3)n=131606.28±697.781.417±3.410.031±0.03182.35±88.260.272±0.490.002±0.005This studyWetlands of downstream transects (T4 and T5)n=71144.15±666.50-0.215±0.45-0.037±0.0598.13±15.11-0.015±0.050.001±0.01Hillslope grasslands of all transectsn=71071.54±225.39-0.300±0.400.003±0.0377.68±25.32-0.048±0.03-0.002±0.005Meadow grassland 166.39±45.89-0.038±0.0090.002±0.001–––Guo et al. (2017)Typical grassland 240.32±87.56-0.042±0.0250.037±0.034–––Desert grassland 107.59±54.10-0.036±0.0150.003±0.001–––Typical grassland 520.25±59.07-0.102±0.0120.007±0.00188.34±9.84-0.099±0.0030.005±0.001Zhang (2019)Typical grassland 232.42±18.90-0.090±0.0050.004±0.001–––Chao (2019)Typical grassland 265.23±31.43-0.185±0.0180.005±0.001189.41±28.96-0.092±0.0120.004±0.001Meadow grassland 553.85-0.1630.00347.73-0.0190.011Geng (2004)Typical grassland 308.60-0.1050.00270.25-0.0290.007
We roughly estimated the annual cumulative emission amounts of CO2,
CH4, and N2O from the riparian wetlands and hillslope grasslands
around the Xilin River basin and further calculated their global warming
potential. As Table 6 indicates, annual cumulative emissions of CO2 and
CH4 decreased in the following order: upstream riparian wetlands
> downstream riparian wetlands > hillslope grasslands. N2O decreased in the following order: upstream riparian wetlands
> hillslope grasslands > downstream riparian wetlands. In this
study, we used the static dark-box method to measure CO2 emissions,
which does not consider the absorption and fixation of CO2 by plant
photosynthesis. Therefore, the total annual cumulative CO2 emissions
are high. This result clearly showed the more significant impact of CO2
emission than that of CH4 and N2O emissions on global warming. The
GWP depends on the cumulative emissions of the GHGs. The GWPs, shown in
Table 6, were in the following order: upstream riparian wetlands (13 474.91 kghm-2)
> downstream riparian wetlands (8974.12 kghm-2)
> hillslope grasslands (8351.24 kghm-2).
Therefore, both the riparian wetlands and the grasslands are the “sources”
of GHGs on a 100-year timescale. The source strength of the wetlands is
higher than that of the grasslands, further indicating that the riparian
wetlands are hotspots of GHG emission.
Cumulative annual emission flux and global warming potential of GHGs
in riparian wetlands and grasslands.
Sample plotCO2/kghm-2CH4/kghm-2N2O/kghm-2GWP/CO2kghm2Wetlands of upstream transects (T1, T2, and T3)13092.80±5378.1612.36±26.400.25±0.2313474.91±5828.68Wetlands of downstream transects (T4 and T5)9093.47±4831.82-1.68±3.23-0.26±0.408974.12±4912.75Hillslope grasslands of all transects8412.26±1614.26-2.55±3.120.01±0.208351.24±1648.22Effects of riparian wetland degradation on GHG emissions
The hydrology and soil properties showed evident differences between
transects because the downstream zone was dry all year due to the presence
of the Xilinhot Dam (Fig. 1). The dam caused the degradation of the riparian
wetlands, resulting in reduced GHG emission. The average CO2 emission
amounted to 1663 mgm-2h-1 in the upstream
transects (T1, T2, and T3) at the riparian wetlands, while the downstream
transects (T4 and T5) recorded an average emission of 1084 mgm-2h-1, 35 % lower than that in the upstream
transects. The N2O emission from the riparian wetlands was lower in the
downstream transects.
Wetland degradation first resulted in the continuous reduction of SMC, which
led to the deepening of the wetland's aerobic layer thickness. Besides, SMC
may affect ST and thus transport the CH4 emissions from a source to a
sink by affecting methanogen activity (Yan et al., 2018). Second, the
reduction of SMC impeded physiological activities of aboveground plants and
inhabited related enzyme activities in the respiration process. Meanwhile,
various enzyme reactions of underground microorganisms under water stress
influence and reduced CO2 emissions (Zhang et al., 2017). Finally,
after wetland degradation, long-term drought led to an extremely low SMC,
which is not conducive to the growth of nitrifying and denitrifying bacteria
and causes the transport of N2O emissions from source to sink (Zhu et
al., 2013). As Table 1 shows, soil TOC content in the upstream transects
(average: 25.1 gkg-1) was higher than that in the
downstream transects (average: 8.41 gkg-1). The relatively
low SMC and the aerobic environment were conducive to the mineralization and
decomposition of the TOC. The degradation of plants in the wetlands led to
the gradual reduction of BIO. Ultimately, the plant carbon source input of
the degraded wetlands decreased, and the bare land temperature increased due
to the reduced plant shelter. This accelerated the decomposition of TOC,
leading to its decrease. This result indicates that wetland degradation
caused the soil carbon pool's loss and weakened the wetland carbon
source–sink function. These results are in agreement with those of Xia et al. (2017).
The degraded wetlands also caused soil desertification and salinization,
leading to a decline in the physical protection afforded by organic carbon
and a reduction in soil aggregates. Thus, the preservative effect provided
by organic carbon declined. The TOC content and SMC in the dry lake bed in
transect T5 were relatively high; however, the GHG emission was very low
along this transect because soil pH values increased after the degradation
of the lake soil, exceeding the optimum range required for microorganism
activity. The soil C:N ratio was very high, resulting in severe
mineralization and a low microbial decomposition rate, thus affecting the
GHG emissions.
Conclusions
The riparian wetlands in the Xilin River basin constitute a dynamic
ecosystem. The present spatial and temporal transfers in the studied
biogeochemical processes were attributed to the changes in SMC, ST, and soil
substrate availability. Our simultaneous analysis of CO2, CH4, and
N2O emissions from the riparian wetlands and the hillslope grasslands
in the Xilin River basin revealed that the majority of the GHG emissions
occurred in the form of CO2. Moreover, our results clearly illustrate a
marked seasonality and spatial pattern of GHG emissions along the transects
and in the longitudinal direction (i.e., upstream and downstream). SMC and
ST were two critical factors controlling the GHG emissions. Moreover, the
abundant BIO promoted the CO2, CH4, and N2O emissions.
The riparian wetlands are potential hotspots of GHG emissions in the Inner
Mongolian region. However, the degradation of these wetlands has transformed
the area from a source to a sink for CH4 and N2O emissions and
reduced CO2 emissions, which has severely affected the wetland carbon
cycle processes. Our results show that though the riparian wetlands have
high CO2 emissions, the wetlands are CO2 sinks due to the
photosynthesis of plants. Overall, our study suggests that anthropogenic
activities have significantly changed the hydrological characteristics of
the studied area and that this can accelerate carbon loss from the riparian
wetlands and further influence GHG emissions in the future.
Data availability
All relevant data are included as graphics and tables in the paper. All raw data will be made available on request.
Author contributions
XinL, XixL, and RY designed the research framework and wrote
the manuscript. XXL and RHY supervised the study. XinL, HX, ZQ, ZC, and ZZ carried out the field
experiments and laboratory analyses. ZZ drew the GIS mapping in this
paper. TL proofread a previous version of the manuscript. HS contributed much to
the revised version of our manuscript.
Competing interests
The authors declare that they have no conflict of interest.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
This study was funded by the National Key Research and Development Program
of China (grant no. 2016YFC0500508), Major Science and Technology Projects
of Inner Mongolia Autonomous Region (grant nos. 2020ZD0009 and ZDZX2018054),
National Natural Science Foundation of China (grant no. 51869014), Key
Scientific and Technological Project of Inner Mongolia (grant no.
2019GG019), and Open Project Program of the Ministry of Education Key
Laboratory of Ecology and Resources Use of the Mongolian Plateau (grant no.
KF2020006). We thank Wiley Editing Services
(http://wileyeditingservices.com, last access: 24 June 2021) for its linguistic assistance during the
preparation of a previous version of this paper.
Financial support
This research has been supported by the National Key Research and Development Program of China (grant no. 2016YFC0500508), the National Natural Science Foundation of China (grant no. 51869014), the Science and Technology Major Project on Lakes of Inner Mongolia (grant nos. 2020ZD0009 and ZDZX2018054), Key
Scientific and Technological Project of Inner Mongolia (grant no.
2019GG019), and Open Project Program of the Ministry of Education Key
Laboratory of Ecology and Resources Use of the Mongolian Plateau (grant no.
KF2020006).
Review statement
This paper was edited by Anja Rammig and reviewed by two anonymous referees.
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