- Email: [email protected]
Net heterotrophy and low carbon dioxide emissions from biological processes in the Yellow River Estuary, China
Journal Pre-proof Net heterotrophy and low carbon dioxide emissions from biological processes in the Yellow River Estuary, China Xiaomei Shen, Meirong Su, Tao Sun, Sihao Lv, Zhi Dang, Zhifeng Yang PII:
S0043-1354(19)31234-5
DOI:
https://doi.org/10.1016/j.watres.2019.115457
Reference:
WR 115457
To appear in:
Water Research
Received Date: 4 September 2019 Revised Date:
26 December 2019
Accepted Date: 30 December 2019
Please cite this article as: Shen, X., Su, M., Sun, T., Lv, S., Dang, Z., Yang, Z., Net heterotrophy and low carbon dioxide emissions from biological processes in the Yellow River Estuary, China, Water Research (2020), doi: https://doi.org/10.1016/j.watres.2019.115457. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.
Discharge events and seasonality jointly affect estuarine CO2 flux Solar radiation
O2
CO2
Artificial water and
Wind
CO2
sediment regulation Waves Freshwater
Suspended matters
Light attenuation
Assimilation
Phytoplankton photosynthesis
Nutrients Organic matters
Turbulent
DO
Respiration of phytoplankton, animals and bacteria
Mixing
Emissions
DIC
°C Winter
Benthos
Convection
Suspended sediment
Summer
Thermocline Deposition
Estuarine continental shelf
The near-shore coast
1
Net Heterotrophy and Low Carbon Dioxide Emissions from Biological
2
Processes in the Yellow River Estuary, China Xiaomei Shen a, b, Meirong Su a*, Tao Sunc, Sihao Lv a, Zhi Dang d, Zhifeng Yang b, c
3
a
4
Research Center for Eco-environmental Engineering, Dongguan University of Technology, Dongguan, Guangdong, 523808, China
5 b
6
Institute of environmental & ecological engineering, Guangdong University of Technology, Guangzhou, Guangdong, 510006, China
7 c
8
State Key Laboratory of Water Environment Simulation & School of Environment, Beijing Normal University, Beijing, 100875, China
9 d
10
School of Environment and Energy, South China University of Technology, Guangzhou, Guangdong, 510640, China
11
12
13
*
Corresponding author: Meirong Su ([email protected])
14
1
15
Abstract
16
Although estimates of total CO2 emissions from global estuaries are gradually decreasing, current
17
numbers are based on limited data and the impacts of anthropogenic and seasonal disturbances
18
have not been studied extensively. Our study estimates annual and seasonal CO2 fluxes in
19
China’s Yellow River Estuary (YRE) which incorporated spatiotemporal variations and the
20
effects of water and sediment regulation (WSR). Aquatic metabolism was estimated using
21
Odum’s open water dissolved oxygen methods and used to represent the production and
22
assimilation of CO2. Net ecosystem production (NEP) was used to represent the CO2 flux from
23
biological activities and estimate the major CO2 emitters in the YRE.
24
According to our measurements, the annual CO2 release was 6.14 ± 33.63 mol C m−2 yr−1 from
25
2009–2013 and the annual CO2 efflux from the 1521.3 km2 of estuarine surface area was 0.11 ±
26
0.61 Tg C yr−1 in the YRE. High CO2 emissions in autumn were balanced by high CO2
27
sequestration in summer, leading to a lower than expected annual net CO2 efflux. The system is
28
an atmospheric CO2 source in spring and winter, near neutral in early summer, a large sink in
29
late summer after WSR, and finally a large atmospheric CO2 source in autumn. Discharge events
30
and seasonality jointly affect estuarine CO2 flux. High CO2 sequestration in summer is due
31
mainly to a combination of high water temperature, chlorophyll a levels, dissolved inorganic
2
32
carbon, and solar radiation and low turbidity, discharge, and chemical oxygen demand (COD)
33
after WSR. WSR supports the high gross primary productivity rate which exceeds the increase in
34
ecosystem respiration.
35
Although the YRE, as a whole, is a source of atmospheric CO2, the amount of CO2 released is
36
lower than the average estuarine value of mid-latitude regions. Our findings therefore suggest
37
that global CO2 release from estuarine systems is overestimated if spatiotemporal variations and
38
the effects of anthropogenic disturbance are excluded. The NEP method is effective for
39
estimating the CO2 flux, especially in estuaries where CO2 variation is mainly due to biological
40
processes.
41
42
Keywords: Net ecosystem production, Biological processes, Carbon dioxide flux, Seasonal
43
Variability, Anthropogenic disturbance, Yellow River Estuary
44
3
45
1 Introduction
46
Estuaries and other near-shore ecosystems are considered net heterotrophic environments,
47
acting as a source of CO2 to the atmosphere and balancing the global net CO2 uptake by
48
continental shelves (Cai, 2011; Laruelle et al., 2010). The total CO2 emissions from European
49
estuaries is 0.03–0.06 Gt C yr −1 (1 Gt =1015 g, Frankignoulle et al., 1998), equivalent to 5-10%
50
of the total anthropogenic CO2 emissions from Western Europe in 1995. However, the role of
51
estuaries as an atmospheric CO2 source or sink is still a matter of debate. Estimates on the total
52
CO2 emissions from global estuaries are also gradually decreasing from 0.34 Gt C yr −1 (Borges
53
et al., 2005), 0.27 ± 0.23 Gt C yr−1 (Laruelle et al., 2010), 0.25 ± 0.25 Gt C yr−1 (Cai, 2011), to
54
0.10 Gt C yr−1 (Chen et al., 2013).
55
The disparity is due to the following. Firstly, the current global estuarine CO2 flux
56
estimate is based on a limited dataset. Specifically, the CO2 flux estimates for particular Asian
57
estuaries (Guo et al., 2009; Sarma et al., 2011; Shim et al., 2007; Wang et al., 2005; Zhai & Dai
58
2009) are under or overestimated due to limited spatiotemporal data (Laruelle et al., 2010). For
59
example, the Yellow River is the second longest river in China and sixth longest in the world;
60
however, CO2 flux data for the Yellow River estuary (YRE) are still lacking. Secondly, highly
61
heterotrophic estuaries are likely overrepresented in the global estimate, and recent studies have
4
62
shown low air-sea CO2 exchange rates for high-emission estuarine environments (Harley et al.,
63
2015; Mørk et al., 2016). In addition, estuarine organic carbon metabolism is significantly more
64
affected by nearby anthropogenic disturbance levels and climate change than any other aspect
65
(Smith and Hollibaugh, 1993; Regnier et al., 2013). Therefore, to accurately estimate the CO2
66
flux and the role of estuaries as carbon sources or sinks, both spatiotemporal variations and the
67
effects of anthropogenic disturbance should be considered.
68
Estuaries are typically subject to natural hydrological changes and intense anthropogenic
69
disturbances that are reflected in the elevated loading of detrital organic matter, high respiration
70
rates, and the production of dissolved CO2. Time-series observations from Indian estuaries
71
identified high CO2 emissions (52.6 mol C m−2 yr−1 in Godavari estuary) because of high
72
bacterial respiration due to high quantities of organic matter associated with high monsoonal
73
river discharges (Sarma et al., 2011, 2012). Simultaneously, high levels of inorganic matter and
74
nutrients were loaded due to anthropogenic disturbance, which may support photosynthesis, and
75
the consumption of dissolved CO2. For example, dissolved inorganic carbon (DIC)
76
concentrations increased when water and sediment regulation measures were in place, but
77
decreased during natural flood periods in the YRE (Liu et al., 2014). The former enhances the
78
photosynthesis rate, but the latter does not.
5
79
Aquatic metabolism, represented by the gross primary productivity (GPP), ecosystem
80
respiration (ER), and net ecosystem production (NEP) of the water column, provides a useful
81
composite indicator of aquatic ecosystem function (Cole et al., 2000; Gu et al., 2010; Shen et al.,
82
2015). NEP (analogous to net ecosystem metabolism, NEM) is the difference between organic
83
matter production and respiration, and provides an estimate of the role of oceanic organic
84
metabolism in the carbon budget (Muller-Karger et al., 2005; Smith & Hollibaugh, 1993; Turner
85
et al., 2013). An ecosystem is identified as "net autotrophic" (NEP > 0) if it produces more
86
organic matter than it consumes and acts as an atmospheric CO2 sink. Alternatively, if organic
87
consumption exceeds production (NEP < 0), the system is identified as "net heterotrophic" and
88
acts as a source of atmospheric CO2. Heterotrophy or autotrophy has a significant influence on
89
dissolved CO2 level and emissions (Sarma et al., 2011). Studies suggest that about 10% of the
90
CO2 outgassing from estuaries is sustained by the input from upstream freshwaters and 90% by
91
local net heterotrophy (Borges & Abril, 2011). For these estuaries, as the variability of CO2 flux
92
is predominantly controlled by changes in carbon concentrations from biological activities, the
93
NEP reflects CO2 fluxes generated from biological reactions, as well as major CO2 fluxes in the
94
estuary.
95
The CO2 emissions from intertidal zones and coastal marshes have been assessed in
96
Yellow River delta wetlands (Han et al., 2013; Sun et al. 2018). However, there was insufficient 6
97
water-air CO2 partial pressure data (pCO2) in the YRE; thus, annual CO2 fluxes are yet to be
98
determined. Here, we estimated the aquatic metabolism of the YRE and NEP was used to
99
represent the CO2 flux from biological processes. Our objective is to clarify the effect of spatial
100
and temporal variability on the role of the YRE as an atmospheric CO2 source or sink, especially
101
under the double influence of seasonal factors and anthropogenic disturbance (artificial water
102
and sediment regulation). In addition, we also want to clarify if the effects of water and sediment
103
regulation on CO2 flux were more significant than that of the seasonal factors.
104
2 Sampling and Methods
105
2.1 Study region and environmental monitoring
106
The YRE is in a temperate climate zone in North China (117°31'–119°30' E, 36°55′–
107
38°30′ N) with an arid and semiarid climate. Its sediment load as measured at Lijin hydrometric
108
station, about 100 km from the Gulf of Bohai, was 1.1 × 109 t yr−1 between the 1950s and 1970s.
109
Annual water discharge prior to 1970 was consistently greater than 25 km3 yr−1 and greater than
110
90 km3 yr−1 for several years in the early 1960s. However, the sediment load and discharge
111
decreased significantly due to intensive irrigation and damming from the 1950s to 2011 (Yang et
112
al., 1998; Liu et al., 2014). The flow was not high enough to transport sediment to the ocean and
113
the water column in the estuary contains high sediment concentrations, resulting in a “perched
7
114
river” downstream. Since 2002, the Yellow River Conservancy Commission (YRCC) has
115
implemented an annual WSR scheme to prevent heavy sedimentation of the riverbed by
116
releasing water from several dams in the middle and upper reaches of the river. The enforced
117
discharge events usually begin at the end of June and typically last for more than ten days; two or
118
three events can last a few dozens of days. The frequency and timing of WSR events depend on
119
dam storage, river flow, bottom sediment, and seasonal precipitation. Thus, annual WSR events
120
provide the YRE with a significant amount of nutrients, organic carbon, and high turbidity from
121
river runoff (Bai et al., 2012; Shen et al., 2015).
122
The YRE experiences a warm-temperate continental monsoon climate with distinct
123
seasons, including high precipitation and warm temperatures in the summer months. The inner
124
estuary has a shallow water depth of 1.3-5.5 m. We monitored the water chemistry at four
125
sampling sites in the YRE from 2009 to 2013, including May, June, August, and September in
126
2009, April, June, and September in 2010, April, June, August, and October in 2011, April and
127
May in 2012, and June and December in 2013 (Figure 1). Salinity, pH, water temperature,
128
dissolved oxygen (DO), DO saturation percentage (DO%), and chlorophyll a concentrations (Chl
129
a) were measured at 15-min intervals over one or three days using a Hydrolab series DS5X
130
multi-parameter water quality data sonde (Hydrolab DS5X, Hach, Loveland, CO, U.S.). The data
8
131
sonde sensors were placed at depth of 1 m, and the "Hydrolab" equipment was mostly hanged in
132
the middle of the river section by an anchor, except at site D. Daily wind velocity (m s−1) and
133
daily solar radiation (W m−2) were recorded at 15 min intervals using a WatchDog 2000 series
134
weather station (Spectrum Technologies, Inc. Aurora, U.S.) located 10 km from the estuarine
135
channel (Figure 1). Daily river discharge data (m3 s−1) at the Lijin hydrometric station was
136
collected from the YRCC (2013). Nutrient concentrations and chemical oxygen demand (COD)
137
were measured three times per day using prefabricated reagents (Hach, Loveland, CO, U.S.).
138
Turbidity (±2%) was also measured three times per day using a turbidimeter (2100P, Hach,
139
Loveland, CO, U.S.).
140
141
<<<<<<<<<<<<<<<<<<<<<<
S0043-1354(19)31234-5
DOI:
https://doi.org/10.1016/j.watres.2019.115457
Reference:
WR 115457
To appear in:
Water Research
Received Date: 4 September 2019 Revised Date:
26 December 2019
Accepted Date: 30 December 2019
Please cite this article as: Shen, X., Su, M., Sun, T., Lv, S., Dang, Z., Yang, Z., Net heterotrophy and low carbon dioxide emissions from biological processes in the Yellow River Estuary, China, Water Research (2020), doi: https://doi.org/10.1016/j.watres.2019.115457. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.
Discharge events and seasonality jointly affect estuarine CO2 flux Solar radiation
O2
CO2
Artificial water and
Wind
CO2
sediment regulation Waves Freshwater
Suspended matters
Light attenuation
Assimilation
Phytoplankton photosynthesis
Nutrients Organic matters
Turbulent
DO
Respiration of phytoplankton, animals and bacteria
Mixing
Emissions
DIC
°C Winter
Benthos
Convection
Suspended sediment
Summer
Thermocline Deposition
Estuarine continental shelf
The near-shore coast
1
Net Heterotrophy and Low Carbon Dioxide Emissions from Biological
2
Processes in the Yellow River Estuary, China Xiaomei Shen a, b, Meirong Su a*, Tao Sunc, Sihao Lv a, Zhi Dang d, Zhifeng Yang b, c
3
a
4
Research Center for Eco-environmental Engineering, Dongguan University of Technology, Dongguan, Guangdong, 523808, China
5 b
6
Institute of environmental & ecological engineering, Guangdong University of Technology, Guangzhou, Guangdong, 510006, China
7 c
8
State Key Laboratory of Water Environment Simulation & School of Environment, Beijing Normal University, Beijing, 100875, China
9 d
10
School of Environment and Energy, South China University of Technology, Guangzhou, Guangdong, 510640, China
11
12
13
*
Corresponding author: Meirong Su ([email protected])
14
1
15
Abstract
16
Although estimates of total CO2 emissions from global estuaries are gradually decreasing, current
17
numbers are based on limited data and the impacts of anthropogenic and seasonal disturbances
18
have not been studied extensively. Our study estimates annual and seasonal CO2 fluxes in
19
China’s Yellow River Estuary (YRE) which incorporated spatiotemporal variations and the
20
effects of water and sediment regulation (WSR). Aquatic metabolism was estimated using
21
Odum’s open water dissolved oxygen methods and used to represent the production and
22
assimilation of CO2. Net ecosystem production (NEP) was used to represent the CO2 flux from
23
biological activities and estimate the major CO2 emitters in the YRE.
24
According to our measurements, the annual CO2 release was 6.14 ± 33.63 mol C m−2 yr−1 from
25
2009–2013 and the annual CO2 efflux from the 1521.3 km2 of estuarine surface area was 0.11 ±
26
0.61 Tg C yr−1 in the YRE. High CO2 emissions in autumn were balanced by high CO2
27
sequestration in summer, leading to a lower than expected annual net CO2 efflux. The system is
28
an atmospheric CO2 source in spring and winter, near neutral in early summer, a large sink in
29
late summer after WSR, and finally a large atmospheric CO2 source in autumn. Discharge events
30
and seasonality jointly affect estuarine CO2 flux. High CO2 sequestration in summer is due
31
mainly to a combination of high water temperature, chlorophyll a levels, dissolved inorganic
2
32
carbon, and solar radiation and low turbidity, discharge, and chemical oxygen demand (COD)
33
after WSR. WSR supports the high gross primary productivity rate which exceeds the increase in
34
ecosystem respiration.
35
Although the YRE, as a whole, is a source of atmospheric CO2, the amount of CO2 released is
36
lower than the average estuarine value of mid-latitude regions. Our findings therefore suggest
37
that global CO2 release from estuarine systems is overestimated if spatiotemporal variations and
38
the effects of anthropogenic disturbance are excluded. The NEP method is effective for
39
estimating the CO2 flux, especially in estuaries where CO2 variation is mainly due to biological
40
processes.
41
42
Keywords: Net ecosystem production, Biological processes, Carbon dioxide flux, Seasonal
43
Variability, Anthropogenic disturbance, Yellow River Estuary
44
3
45
1 Introduction
46
Estuaries and other near-shore ecosystems are considered net heterotrophic environments,
47
acting as a source of CO2 to the atmosphere and balancing the global net CO2 uptake by
48
continental shelves (Cai, 2011; Laruelle et al., 2010). The total CO2 emissions from European
49
estuaries is 0.03–0.06 Gt C yr −1 (1 Gt =1015 g, Frankignoulle et al., 1998), equivalent to 5-10%
50
of the total anthropogenic CO2 emissions from Western Europe in 1995. However, the role of
51
estuaries as an atmospheric CO2 source or sink is still a matter of debate. Estimates on the total
52
CO2 emissions from global estuaries are also gradually decreasing from 0.34 Gt C yr −1 (Borges
53
et al., 2005), 0.27 ± 0.23 Gt C yr−1 (Laruelle et al., 2010), 0.25 ± 0.25 Gt C yr−1 (Cai, 2011), to
54
0.10 Gt C yr−1 (Chen et al., 2013).
55
The disparity is due to the following. Firstly, the current global estuarine CO2 flux
56
estimate is based on a limited dataset. Specifically, the CO2 flux estimates for particular Asian
57
estuaries (Guo et al., 2009; Sarma et al., 2011; Shim et al., 2007; Wang et al., 2005; Zhai & Dai
58
2009) are under or overestimated due to limited spatiotemporal data (Laruelle et al., 2010). For
59
example, the Yellow River is the second longest river in China and sixth longest in the world;
60
however, CO2 flux data for the Yellow River estuary (YRE) are still lacking. Secondly, highly
61
heterotrophic estuaries are likely overrepresented in the global estimate, and recent studies have
4
62
shown low air-sea CO2 exchange rates for high-emission estuarine environments (Harley et al.,
63
2015; Mørk et al., 2016). In addition, estuarine organic carbon metabolism is significantly more
64
affected by nearby anthropogenic disturbance levels and climate change than any other aspect
65
(Smith and Hollibaugh, 1993; Regnier et al., 2013). Therefore, to accurately estimate the CO2
66
flux and the role of estuaries as carbon sources or sinks, both spatiotemporal variations and the
67
effects of anthropogenic disturbance should be considered.
68
Estuaries are typically subject to natural hydrological changes and intense anthropogenic
69
disturbances that are reflected in the elevated loading of detrital organic matter, high respiration
70
rates, and the production of dissolved CO2. Time-series observations from Indian estuaries
71
identified high CO2 emissions (52.6 mol C m−2 yr−1 in Godavari estuary) because of high
72
bacterial respiration due to high quantities of organic matter associated with high monsoonal
73
river discharges (Sarma et al., 2011, 2012). Simultaneously, high levels of inorganic matter and
74
nutrients were loaded due to anthropogenic disturbance, which may support photosynthesis, and
75
the consumption of dissolved CO2. For example, dissolved inorganic carbon (DIC)
76
concentrations increased when water and sediment regulation measures were in place, but
77
decreased during natural flood periods in the YRE (Liu et al., 2014). The former enhances the
78
photosynthesis rate, but the latter does not.
5
79
Aquatic metabolism, represented by the gross primary productivity (GPP), ecosystem
80
respiration (ER), and net ecosystem production (NEP) of the water column, provides a useful
81
composite indicator of aquatic ecosystem function (Cole et al., 2000; Gu et al., 2010; Shen et al.,
82
2015). NEP (analogous to net ecosystem metabolism, NEM) is the difference between organic
83
matter production and respiration, and provides an estimate of the role of oceanic organic
84
metabolism in the carbon budget (Muller-Karger et al., 2005; Smith & Hollibaugh, 1993; Turner
85
et al., 2013). An ecosystem is identified as "net autotrophic" (NEP > 0) if it produces more
86
organic matter than it consumes and acts as an atmospheric CO2 sink. Alternatively, if organic
87
consumption exceeds production (NEP < 0), the system is identified as "net heterotrophic" and
88
acts as a source of atmospheric CO2. Heterotrophy or autotrophy has a significant influence on
89
dissolved CO2 level and emissions (Sarma et al., 2011). Studies suggest that about 10% of the
90
CO2 outgassing from estuaries is sustained by the input from upstream freshwaters and 90% by
91
local net heterotrophy (Borges & Abril, 2011). For these estuaries, as the variability of CO2 flux
92
is predominantly controlled by changes in carbon concentrations from biological activities, the
93
NEP reflects CO2 fluxes generated from biological reactions, as well as major CO2 fluxes in the
94
estuary.
95
The CO2 emissions from intertidal zones and coastal marshes have been assessed in
96
Yellow River delta wetlands (Han et al., 2013; Sun et al. 2018). However, there was insufficient 6
97
water-air CO2 partial pressure data (pCO2) in the YRE; thus, annual CO2 fluxes are yet to be
98
determined. Here, we estimated the aquatic metabolism of the YRE and NEP was used to
99
represent the CO2 flux from biological processes. Our objective is to clarify the effect of spatial
100
and temporal variability on the role of the YRE as an atmospheric CO2 source or sink, especially
101
under the double influence of seasonal factors and anthropogenic disturbance (artificial water
102
and sediment regulation). In addition, we also want to clarify if the effects of water and sediment
103
regulation on CO2 flux were more significant than that of the seasonal factors.
104
2 Sampling and Methods
105
2.1 Study region and environmental monitoring
106
The YRE is in a temperate climate zone in North China (117°31'–119°30' E, 36°55′–
107
38°30′ N) with an arid and semiarid climate. Its sediment load as measured at Lijin hydrometric
108
station, about 100 km from the Gulf of Bohai, was 1.1 × 109 t yr−1 between the 1950s and 1970s.
109
Annual water discharge prior to 1970 was consistently greater than 25 km3 yr−1 and greater than
110
90 km3 yr−1 for several years in the early 1960s. However, the sediment load and discharge
111
decreased significantly due to intensive irrigation and damming from the 1950s to 2011 (Yang et
112
al., 1998; Liu et al., 2014). The flow was not high enough to transport sediment to the ocean and
113
the water column in the estuary contains high sediment concentrations, resulting in a “perched
7
114
river” downstream. Since 2002, the Yellow River Conservancy Commission (YRCC) has
115
implemented an annual WSR scheme to prevent heavy sedimentation of the riverbed by
116
releasing water from several dams in the middle and upper reaches of the river. The enforced
117
discharge events usually begin at the end of June and typically last for more than ten days; two or
118
three events can last a few dozens of days. The frequency and timing of WSR events depend on
119
dam storage, river flow, bottom sediment, and seasonal precipitation. Thus, annual WSR events
120
provide the YRE with a significant amount of nutrients, organic carbon, and high turbidity from
121
river runoff (Bai et al., 2012; Shen et al., 2015).
122
The YRE experiences a warm-temperate continental monsoon climate with distinct
123
seasons, including high precipitation and warm temperatures in the summer months. The inner
124
estuary has a shallow water depth of 1.3-5.5 m. We monitored the water chemistry at four
125
sampling sites in the YRE from 2009 to 2013, including May, June, August, and September in
126
2009, April, June, and September in 2010, April, June, August, and October in 2011, April and
127
May in 2012, and June and December in 2013 (Figure 1). Salinity, pH, water temperature,
128
dissolved oxygen (DO), DO saturation percentage (DO%), and chlorophyll a concentrations (Chl
129
a) were measured at 15-min intervals over one or three days using a Hydrolab series DS5X
130
multi-parameter water quality data sonde (Hydrolab DS5X, Hach, Loveland, CO, U.S.). The data
8
131
sonde sensors were placed at depth of 1 m, and the "Hydrolab" equipment was mostly hanged in
132
the middle of the river section by an anchor, except at site D. Daily wind velocity (m s−1) and
133
daily solar radiation (W m−2) were recorded at 15 min intervals using a WatchDog 2000 series
134
weather station (Spectrum Technologies, Inc. Aurora, U.S.) located 10 km from the estuarine
135
channel (Figure 1). Daily river discharge data (m3 s−1) at the Lijin hydrometric station was
136
collected from the YRCC (2013). Nutrient concentrations and chemical oxygen demand (COD)
137
were measured three times per day using prefabricated reagents (Hach, Loveland, CO, U.S.).
138
Turbidity (±2%) was also measured three times per day using a turbidimeter (2100P, Hach,
139
Loveland, CO, U.S.).
140
141
<<<<<<<<<<<<<<<<<<<<<<
>>>>>>>>>>>>>>>>>>>>>>
2.2 Calculations of metabolism and CO2 flux
142
We applied Odum’s open water dissolved oxygen method to estimate daily dissolved
143
oxygen flux (g O2 m-2 d-1) caused by biological activities in aquatic ecosystems. The specific
144
calculation methods can be found in Beck et al. (2016) (see also Shen et al., 2015):
145
NEP(dt) = [(Ct − Ct−1)/dt] · Z – KD
(1)
146
where t (s) is time and dt is the time interval between measurements; Ct (mg L−1) is the
147
concentration of O2 measured at time t; Z is the monitoring water depth (1 m in our study). KD is 9
148
the rate of oxygen exchange across the air-water interface; K (g O2 m−2s−1) is the re-aeration
149
coefficient; D is the oxygen deficit (D = 1 − (St + St−1)/200), which represents the difference
150
between the measured oxygen concentration and the concentration for water that is fully
151
saturated with oxygen; St is the DO saturation (%) measured at time t. K was determined based
152
on water temperature (Tw) and wind velocity as proposed by Antonopoulos and Gianniou (2003)
153
as follows:
154
K = 0.2v · Tcf · exp (Tw − 20)
(2)
155
where v (m s−1) is the wind velocity 10 m above the water surface and Tcf = 1.024, a temperature
156
correction factor. The monitoring time interval was 15 min; thus, we divided K by 4 to obtain a
157
15 min rate. The 15 min wind and water temperature-diffusion-corrected rates of dissolved
158
oxygen change were then summed over a 24 h period to calculate daily NEP (g O2 m−2d−1 or mg
159
O2 L−1d−1). We present ER value as positive; NEP that occurs at night was multiplied by −1 to
160
give a night respiration rate (ERn), where solar radiation at night was defined as < 2 W m−2. ERn
161
divided by hours of night equals the hourly respiration rate (g O2 m−2h−1), and ER (g O2 m−2d−1)
162
equals the hourly respiration rate multiplied by 24. Thus, GPP = NEP + ER. This assumes the
163
hourly respiration rate is the same during the day and night, a claim that is sometimes challenged
164
but is currently consistent with most standard methods (Marcarelli et al., 2010). Some
10
165
metabolism data in the oxygen unit (g O2 m–2d–1) has been published (Tang et al, 2015; Shen et al,
166
2015; 2018).
167
We assume that photosynthesis produces 1 mole of O2 for every mole of CO2 consumed
168
in the estuarine ecosystem; similarly, we assume respiration releases 1 mole of CO2 for every
169
mole of O2 consumed. The metabolic oxygen unit (g O2 m–2d–1) was converted to carbon units
170
(mmol CO2 m–2d–1) by assuming 1 g O2 m−2d−1 is equal to 1/32 mol CO2 m−2d−1 (1 g O2 m–2d–1 =
171
1000/32 mmol CO2 m−2d−1). NEP estimates also represent the estuarine CO2 flux caused by
172
biological activities in aquatic ecosystems, and it can allow us to determine estuary's role as a
173
sink or a source of atmospheric CO2. Positive (Negative) NEP can result in sequestration
174
(emission) of CO2 and a sharp decrease (increase) in DIC.
175
To calculate the CO2 flux, the estuarine surface area must be accurately estimated
176
(Borges, 2005). Here, we divided the estuarine area into two regions: the inner estuary area and
177
the outer estuary area. Firstly, the inner estuary included the tidal watercourse and the coastline
178
area (Figure 1, white area). The tidal watercourse area is 20 km2 with 20 km annual average
179
length and 1 km width of the tidal river section. The coastline area is 30 km2 with a width of 0.2
180
km and a length of 150 km, and water depth of this area is less than 2 m. Thus, the inner estuary
181
area is 50 km2. For the outer estuary area, waters within 13 m depth limit were considered to be
11
182
the outer boundary according to the bathymetric chart of Bohai Bay. Because the water depth
183
range is less than 13 m in most areas of the YRE due to sediment transportation from the middle
184
reaches of the loess plateau, and the deepest water depth is ~20 m in the southern Bohai Bay
185
(Peng et al., 2015; Sun et al., 2015; Zhang et al., 2011). In order to distinguish the outer
186
boundary of other estuaries which flow into Bohai, such as Luan River estuary and Xiaoqing
187
River estuary (Xiao et al., 2012), we set the "near-shore coastal waters" as the big boundary (in
188
Figure 1, bay blue area, 119°0’-119°30’ E, 37°30’-38.0° N). We used ArcMap 10.2 to estimate
189
outer estuary area. CO2 flux from biological processes was calculated using estuarine carbon flux
190
per unit and estimated estuarine surface area.
191
2.3 Statistical analysis
192
The average seasonal and monthly metabolism was computed by combining all samples
193
obtained during 2009-2013 and aggregating and averaging the calculated fluxes according to the
194
sampling month. We applied repeated-measures analysis of variance (ANOVA) at a confidence
195
level of 0.05 to test the differences in environmental factors and metabolism between different
196
months, seasons and sites using SPSS 22. To explain the variation in metabolism, we performed
197
principal component analysis (PCA) (for environmental factors), redundancy analysis (RDA)
198
and variation partitioning analyses (VPA) using Canoco 5 (TerBraak & Smilauer, 2012).
12
199
3 Results
200
3.1 Variations in environmental parameters
201
There were eleven short-term, high-flow pulses from 2009 to 2013 in the YRE
202
(maximum discharge for each flow pulse > 2500 m3s−1, Figure 1), and the maximum discharge
203
reached 4350 m3s−1 on July 31, 2013 (Figure 2).
<<<<<<<<<<<<<<<<<<<<<<
2.2 Calculations of metabolism and CO2 flux
142
We applied Odum’s open water dissolved oxygen method to estimate daily dissolved
143
oxygen flux (g O2 m-2 d-1) caused by biological activities in aquatic ecosystems. The specific
144
calculation methods can be found in Beck et al. (2016) (see also Shen et al., 2015):
145
NEP(dt) = [(Ct − Ct−1)/dt] · Z – KD
(1)
146
where t (s) is time and dt is the time interval between measurements; Ct (mg L−1) is the
147
concentration of O2 measured at time t; Z is the monitoring water depth (1 m in our study). KD is 9
148
the rate of oxygen exchange across the air-water interface; K (g O2 m−2s−1) is the re-aeration
149
coefficient; D is the oxygen deficit (D = 1 − (St + St−1)/200), which represents the difference
150
between the measured oxygen concentration and the concentration for water that is fully
151
saturated with oxygen; St is the DO saturation (%) measured at time t. K was determined based
152
on water temperature (Tw) and wind velocity as proposed by Antonopoulos and Gianniou (2003)
153
as follows:
154
K = 0.2v · Tcf · exp (Tw − 20)
(2)
155
where v (m s−1) is the wind velocity 10 m above the water surface and Tcf = 1.024, a temperature
156
correction factor. The monitoring time interval was 15 min; thus, we divided K by 4 to obtain a
157
15 min rate. The 15 min wind and water temperature-diffusion-corrected rates of dissolved
158
oxygen change were then summed over a 24 h period to calculate daily NEP (g O2 m−2d−1 or mg
159
O2 L−1d−1). We present ER value as positive; NEP that occurs at night was multiplied by −1 to
160
give a night respiration rate (ERn), where solar radiation at night was defined as < 2 W m−2. ERn
161
divided by hours of night equals the hourly respiration rate (g O2 m−2h−1), and ER (g O2 m−2d−1)
162
equals the hourly respiration rate multiplied by 24. Thus, GPP = NEP + ER. This assumes the
163
hourly respiration rate is the same during the day and night, a claim that is sometimes challenged
164
but is currently consistent with most standard methods (Marcarelli et al., 2010). Some
10
165
metabolism data in the oxygen unit (g O2 m–2d–1) has been published (Tang et al, 2015; Shen et al,
166
2015; 2018).
167
We assume that photosynthesis produces 1 mole of O2 for every mole of CO2 consumed
168
in the estuarine ecosystem; similarly, we assume respiration releases 1 mole of CO2 for every
169
mole of O2 consumed. The metabolic oxygen unit (g O2 m–2d–1) was converted to carbon units
170
(mmol CO2 m–2d–1) by assuming 1 g O2 m−2d−1 is equal to 1/32 mol CO2 m−2d−1 (1 g O2 m–2d–1 =
171
1000/32 mmol CO2 m−2d−1). NEP estimates also represent the estuarine CO2 flux caused by
172
biological activities in aquatic ecosystems, and it can allow us to determine estuary's role as a
173
sink or a source of atmospheric CO2. Positive (Negative) NEP can result in sequestration
174
(emission) of CO2 and a sharp decrease (increase) in DIC.
175
To calculate the CO2 flux, the estuarine surface area must be accurately estimated
176
(Borges, 2005). Here, we divided the estuarine area into two regions: the inner estuary area and
177
the outer estuary area. Firstly, the inner estuary included the tidal watercourse and the coastline
178
area (Figure 1, white area). The tidal watercourse area is 20 km2 with 20 km annual average
179
length and 1 km width of the tidal river section. The coastline area is 30 km2 with a width of 0.2
180
km and a length of 150 km, and water depth of this area is less than 2 m. Thus, the inner estuary
181
area is 50 km2. For the outer estuary area, waters within 13 m depth limit were considered to be
11
182
the outer boundary according to the bathymetric chart of Bohai Bay. Because the water depth
183
range is less than 13 m in most areas of the YRE due to sediment transportation from the middle
184
reaches of the loess plateau, and the deepest water depth is ~20 m in the southern Bohai Bay
185
(Peng et al., 2015; Sun et al., 2015; Zhang et al., 2011). In order to distinguish the outer
186
boundary of other estuaries which flow into Bohai, such as Luan River estuary and Xiaoqing
187
River estuary (Xiao et al., 2012), we set the "near-shore coastal waters" as the big boundary (in
188
Figure 1, bay blue area, 119°0’-119°30’ E, 37°30’-38.0° N). We used ArcMap 10.2 to estimate
189
outer estuary area. CO2 flux from biological processes was calculated using estuarine carbon flux
190
per unit and estimated estuarine surface area.
191
2.3 Statistical analysis
192
The average seasonal and monthly metabolism was computed by combining all samples
193
obtained during 2009-2013 and aggregating and averaging the calculated fluxes according to the
194
sampling month. We applied repeated-measures analysis of variance (ANOVA) at a confidence
195
level of 0.05 to test the differences in environmental factors and metabolism between different
196
months, seasons and sites using SPSS 22. To explain the variation in metabolism, we performed
197
principal component analysis (PCA) (for environmental factors), redundancy analysis (RDA)
198
and variation partitioning analyses (VPA) using Canoco 5 (TerBraak & Smilauer, 2012).
12
199
3 Results
200
3.1 Variations in environmental parameters
201
There were eleven short-term, high-flow pulses from 2009 to 2013 in the YRE
202
(maximum discharge for each flow pulse > 2500 m3s−1, Figure 1), and the maximum discharge
203
reached 4350 m3s−1 on July 31, 2013 (Figure 2).
<<<<<<<<<<<<<<<<<<<<<<
>>>>>>>>>>>>>>>>>>>>>>
204
205
Mean daily discharge varied from 53.40 m3s−1 to 1870 m3s−1 (Table 1). The monitored
206
daily discharge showed significant monthly variations with the highest daily discharge in
207
October (Figure 3a, F= 33.51, P = 0.0001). The monthly variation in turbidity (from 8.18 to 1000
208
NTU, F= 31.64, P = 0.001) and chemical oxygen demand (COD, from 2 to 84 mg L-1, F= 24.42,
209
P = 0.000) showed obvious synchronicity with discharge. Both turbidity and COD peaked in
210
October (Figure 3b, c). The variation in water temperatures (from 2.78 to 28.20 °C, F= 122.66, P
211
= 0.000) and daily mean Chl a (from 0.60 to 41.20 µg L-1, F= 17.48, P = 0.001) showed obvious
212
synchronicity (Table 1, Figure 3d, e). Water temperature and Chl a were the highest in summer.
213
The daily mean solar radiation ranged from 48.72 to 347.16 W m−2 (Figure 3f, F= 3.33, P =
214
0.016). Mean daily dissolved oxygen saturation ranged from 66.02% to 149%, maximum values 1
The values of F and P were the results of repeated measures of ANOVA on the monthly variations in the environment factors.
13
215
of DO occurred in August (Table 1, Figure 3g, F= 9.21, P = 0.003). Mean daily pH ranged from
216
7.86 to 10.14, and mean daily salinity ranged from 0.29 to 33.15. Dissolved inorganic nitrogen
217
content (DIN) ranged from 0.16 to 5.04 mg L−1, and dissolved inorganic phosphorus content
218
(DIP, analogous to "Soluble Reactive Phosphorus, SRP"), ranged from 0.02 to 0.75 mg L−1. The
219
last four factors (pH, salinity, and nutrients) did not show significant monthly variations (Figure
220
3h, i).
221
<<<<<<<<<<<<<<<<<<<<<<< Table 1>>>>>>>>>>>>>>>>>>>>>>>
222
<<<<<<<<<<<<<<<<<<<<<<
204
205
Mean daily discharge varied from 53.40 m3s−1 to 1870 m3s−1 (Table 1). The monitored
206
daily discharge showed significant monthly variations with the highest daily discharge in
207
October (Figure 3a, F= 33.51, P = 0.0001). The monthly variation in turbidity (from 8.18 to 1000
208
NTU, F= 31.64, P = 0.001) and chemical oxygen demand (COD, from 2 to 84 mg L-1, F= 24.42,
209
P = 0.000) showed obvious synchronicity with discharge. Both turbidity and COD peaked in
210
October (Figure 3b, c). The variation in water temperatures (from 2.78 to 28.20 °C, F= 122.66, P
211
= 0.000) and daily mean Chl a (from 0.60 to 41.20 µg L-1, F= 17.48, P = 0.001) showed obvious
212
synchronicity (Table 1, Figure 3d, e). Water temperature and Chl a were the highest in summer.
213
The daily mean solar radiation ranged from 48.72 to 347.16 W m−2 (Figure 3f, F= 3.33, P =
214
0.016). Mean daily dissolved oxygen saturation ranged from 66.02% to 149%, maximum values 1
The values of F and P were the results of repeated measures of ANOVA on the monthly variations in the environment factors.
13
215
of DO occurred in August (Table 1, Figure 3g, F= 9.21, P = 0.003). Mean daily pH ranged from
216
7.86 to 10.14, and mean daily salinity ranged from 0.29 to 33.15. Dissolved inorganic nitrogen
217
content (DIN) ranged from 0.16 to 5.04 mg L−1, and dissolved inorganic phosphorus content
218
(DIP, analogous to "Soluble Reactive Phosphorus, SRP"), ranged from 0.02 to 0.75 mg L−1. The
219
last four factors (pH, salinity, and nutrients) did not show significant monthly variations (Figure
220
3h, i).
221
<<<<<<<<<<<<<<<<<<<<<<< Table 1>>>>>>>>>>>>>>>>>>>>>>>
222
<<<<<<<<<<<<<<<<<<<<<<
>>>>>>>>>>>>>>>>>>>>>>
223
PCA was used to identify and correlate important environmental variables. Principal
224
components 1 and 2 (PC 1 and 2) accounted for 70.62% and 14.86% of the variance,
225
respectively. Combined, PC 1 and 2 explain 85.48% of the total variance (Figure 4a). PC 1 is
226
correlated with variables that are dependent on the discharge pulse, such as high turbidity, high
227
COD, high nutrients, and low salinity. Maximum turbidity (>1000 NTU) and COD (> 80 mg L−1)
228
both occurred in October 2011, when there was higher daily discharge (> 1500 m3 s-1) and lower
229
salinity. PC 2 is correlated with seasonally changing environmental factors, such as high solar
230
radiation, and high Chl a concentrations.
14
231
The environmental conditions at each site varied depending on the distance from the
232
discharge source. Coastal site D is characterized by higher salinity, lower turbidity, lower COD,
233
and lower nutrients relative to the other 3 sites (Figure 4b). The highest mean salinities were
234
observed at site D because it is flushed by tidal action; the remaining three sites are almost
235
entirely filled with freshwater during peak discharge periods (mean salinity = 0.45 ‰). Both
236
maximum and minimum values of DO occurred at site D.
237
238
<<<<<<<<<<<<<<<<<<<<<<
223
PCA was used to identify and correlate important environmental variables. Principal
224
components 1 and 2 (PC 1 and 2) accounted for 70.62% and 14.86% of the variance,
225
respectively. Combined, PC 1 and 2 explain 85.48% of the total variance (Figure 4a). PC 1 is
226
correlated with variables that are dependent on the discharge pulse, such as high turbidity, high
227
COD, high nutrients, and low salinity. Maximum turbidity (>1000 NTU) and COD (> 80 mg L−1)
228
both occurred in October 2011, when there was higher daily discharge (> 1500 m3 s-1) and lower
229
salinity. PC 2 is correlated with seasonally changing environmental factors, such as high solar
230
radiation, and high Chl a concentrations.
14
231
The environmental conditions at each site varied depending on the distance from the
232
discharge source. Coastal site D is characterized by higher salinity, lower turbidity, lower COD,
233
and lower nutrients relative to the other 3 sites (Figure 4b). The highest mean salinities were
234
observed at site D because it is flushed by tidal action; the remaining three sites are almost
235
entirely filled with freshwater during peak discharge periods (mean salinity = 0.45 ‰). Both
236
maximum and minimum values of DO occurred at site D.
237
238
<<<<<<<<<<<<<<<<<<<<<<
>>>>>>>>>>>>>>>>>>>>>>
3.2 Variations in metabolism and CO2 flux from biological processes
239
NEP was highly variable during the sampling period, fluctuating between -181 mmol C
240
m−2 d−1 and 227.91 mmol C m−2 d−1 and averaging -16.83 ± 92.14 mmol C m−2 d−1 (Table 2, and
241
Figure 5). The highly variable NEP indicates that CO2 sourcing or sinking from biological
242
activities is also highly variable in the YRE. The observed average negative NEP value of the
243
estuary indicates the dominance of respiration over primary production (Table 2); therefore,
244
biological activities are a net source of atmospheric CO2 in the estuary. The mean value of CO2
245
emitted from biological activities alone is 6.14 ± 33.63 mol C m−2 yr−1 (73.73 ± 403.57 g C m−2
246
yr−1). NEP of the estuary was highest in summer (52.03 ± 91.96 mmol C m−2 d−1), and lowest in
247
autumn (-114.07 ± 60.93 mmol C m−2 d−1) (Figure 5a, Table 2), inferring CO2 influx from the 15
248
atmosphere via net photosynthesis in summer, and estuarine CO2 efflux to the atmosphere via net
249
respiration in autumn. Estuarine CO2 degassing also occurred during spring (-29.97 ± 33 mmol C
250
m−2 d−1) and winter (-58.23 ± 5.16 mmol C m−2 d−1) according to our NEP values.
251
<<<<<<<<<<<<<<<<<<<<<<>>>>>>>>>>>>>>>>>>>>>>
3.2 Variations in metabolism and CO2 flux from biological processes
239
NEP was highly variable during the sampling period, fluctuating between -181 mmol C
240
m−2 d−1 and 227.91 mmol C m−2 d−1 and averaging -16.83 ± 92.14 mmol C m−2 d−1 (Table 2, and
241
Figure 5). The highly variable NEP indicates that CO2 sourcing or sinking from biological
242
activities is also highly variable in the YRE. The observed average negative NEP value of the
243
estuary indicates the dominance of respiration over primary production (Table 2); therefore,
244
biological activities are a net source of atmospheric CO2 in the estuary. The mean value of CO2
245
emitted from biological activities alone is 6.14 ± 33.63 mol C m−2 yr−1 (73.73 ± 403.57 g C m−2
246
yr−1). NEP of the estuary was highest in summer (52.03 ± 91.96 mmol C m−2 d−1), and lowest in
247
autumn (-114.07 ± 60.93 mmol C m−2 d−1) (Figure 5a, Table 2), inferring CO2 influx from the 15
248
atmosphere via net photosynthesis in summer, and estuarine CO2 efflux to the atmosphere via net
249
respiration in autumn. Estuarine CO2 degassing also occurred during spring (-29.97 ± 33 mmol C
250
m−2 d−1) and winter (-58.23 ± 5.16 mmol C m−2 d−1) according to our NEP values.
251
<<<<<<<<<<<<<<<<<<<<<<