Coupled carbon and silicon cycling and characteristics in a subtropical watershed, China

Ecological Indicators 110 (2020) 105897

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Coupled carbon and silicon cycling and characteristics in a subtropical watershed, China

T

Yao Lua,b, Yang Gaoa,b,c, , Ning Hana,b, Yunxian Yana,b,d, Zixia Xiea,b,e, Junjie Jiaa,b, Zhuo Haoa,b ⁎

a

Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China c CAS Center for Excellence in Tibetan Plateau Earth Sciences, Beijing 100101, China d Center for Environmental Remediation, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China e Key Laboratory of Resource Utilization and Environmental Remediation, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China b

ARTICLE INFO

ABSTRACT

Keywords: Silicon cycling Carbon cycling Silicate weathering Coupling cycle Subtropical Watershed

Silicon (Si) and carbon (C) can couple at different timescales. Phytolith carbon fixation and silicate weathering are two key processes of this type of coupling. Silicate rock weathering sequesters CO2 and thus generates a net C sink, which is one way that C is removed from ecosystems. This study selected the Xiangxi River watershed as the research object, which is characteristic of typical watersheds found in subtropical regions, and continuously monitored for Si and C transfers in this basin. We were able to ascertain the Si and C coupled cycling relationship in this basin by analyzing the influence of environmental changes on these two elements. The main findings were as follows: (1) The atmospheric chemical CO2 sequestration rate in this subtropical basin was 14.2 kg hm−2 yr−1, wherein carbonates sequestered atmospheric CO2 at a rate of 5.4 kg hm−2 yr−1 and silicate rock sequestered atmospheric CO2 at a rate of 8.8 kg hm−2 yr−1. (2) Both rainfall and vegetation type had significant effects on Si and C migration. Different characteristics of canopy interception of each tree species had obvious dissociation effects on dissolved organic carbon (DOC), while the absorption or dissociation effect on dissolved silicate (DSi) differed among the various tree species. Basin soil was enriched with a large amount of organic C during the dry season, which was subsequently exported during the rainy season. (3) The Si input into the watershed was less than the Si output (the net accumulation of Si was −27 kg hm−2 yr−1), and the C input was greater than the C output (the net accumulation of C was + 2785 kg hm−2 yr−1). This study determined that the Xiangxi River watershed was an Si “source” and a C “sink”. The results of this study can provide a reference for further study of the coupling relationship between Si and C, and provide support for the study of ecosystem material balance.

1. Introduction Carbon (C) and silicon (Si) belong to the same family in the periodic table of elements, and they have many similar properties. C is an element that supports life. During ecosystem cycling processes, C can promote the migration of other material elements while self-circulating. (Richter et al., 1999; Falkowski et al., 2000; Luyssaert et al., 2008; Street-Perrott and Barker, 2008; Janssens and Luyssaert, 2009; Smith and Fang, 2010). Si is an element that supports the inorganic environment. The abundance of Si in the Earth’s crust (28.8%) is second only to oxygen (O) (Wedepohl, 1995), and the biogeochemical cycle of Si plays a key role in surface material circulation (Alexandre et al., 1997). The coupling between C and Si can partly reflect the coupling

between organic and inorganic environments. During multiple terrestrial biogeochemical processes, Si can couple with C at different timescales (Zhang et al., 2015). Key processes associated with Si and C coupling cycles include: silicate weathering, secondary aluminosilicate accumulation, and the formation and dissolution of phytoliths (Song et al., 2012). Dissolved silicate (DSi) originally derives from surface silicate weathering processes. The C sequestration flux of the global silicate mineral chemical crystallization rate is 8.67 × l012 mol yr−1 (Gallardet et al., 1999). Therefore, Si plays a vital role in regulating atmospheric carbon dioxide (CO2) (Berner et al., 1983; Tréguer and Pondaven, 2000; Street-Perrott and Barker, 2008; Li et al., 2011; Li et al., 2006). The weathering rate of silicate is not only affected by rock lithology,

⁎ Corresponding author at: Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China. E-mail address: [email protected] (Y. Gao).

https://doi.org/10.1016/j.ecolind.2019.105897 Received 9 July 2019; Received in revised form 26 October 2019; Accepted 31 October 2019 1470-160X/ © 2019 Elsevier Ltd. All rights reserved.

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climatic conditions (i.e., environmental factors such as rainfall, temperature, and pH), and biological activity, but it is also affected by atmospheric CO2 concentrations (Lucas et al., 1993; Berner, 1995; Lucas, 2001; Knoll and James, 1987). Therefore, silicate weathering can absorb excessive CO2 in the atmosphere, thus alleviating the greenhouse effect (Sommer et al., 2006; Li et al., 2002; Wang et al., 2008). China's subtropical regions are characterized by suitable hydrothermal conditions, abundant vegetation types and high forest productivity, which play an important role as global terrestrial ecosystem C sinks (An et al., 2012). However, China’s subtropical watersheds are currently facing issues that may counteract this benefit, such as air pollution and acid deposition. These issues may in fact alter patterns of Si and C cycling in rivers and lakes, which will lead to a series of progressively worsening environmental issues, such as decline of soil fertility and decrease in species diversity and primary productivity. Presently, research on Si and C coupling mechanisms in China and abroad remains limited. In this study, we attempt to obtain the source and sink states of Si and C in the watershed and the strength of silicate weathering, which is one of the processes of carbon-silicon coupling. To achieve this goal, we measured the basic flow, rainwater inputs, soil Si and C content, pH, as well as other indicators, to determine Si and C deposition flux and Si and C output in this watershed in 2015 and 2016. Results were then compared to determine whether Si and organic C content in the Xiangxi River watershed was in a state of loss or gain. In addition, we discussed the effects of environmental conditions such as rainfall, acid deposition and vegetation types on the migration and conversion of Si and C. Finally, we calculated the strength of silicate weathering in this small watershed. The results of this study can provide a reference for further study of the coupling relationship between Si and c, and provide support for the study of ecosystem material balance.

2. Methods 2.1. Site description The experimental research site is the Xiangxi River watershed (Fig. 1) where the Qianyanzhou Ecology Station is located (115°04′13″ E, 26°44′48″ N) under the authority of the Chinese Academy of Sciences in Taihe County, Jiangxi Province, China. The total area of the Xiangxi River watershed is approximately 97.38 hm2, with an average elevation of 110.8 m. The annual average temperature is 17.9 °C. The basin is hot and humid in summer and warm and dry in winter, which is characteristic of a typical subtropical monsoon climate. Average annual precipitation is 1542 mm, and the rainy season is mostly concentrated between March and August. Rainfall that occurs from April to June accounts for approximately half of the yearly rainfall in the watershed. Temperatures are low, and it is rainy from July to August (Xu et al., 2014; Hao et al., 2017a,b). The vegetation in the study area belongs to an artificial mid-subtropical evergreen broad-leaved forest type. The forest was planted in 1985. Through natural competition among species and cooperation with the government’s “near-natural management”, the forest land has been able to regenerate naturally and the forest ecosystem is stable and has high species richness. There are five different land-use types that are characteristic of this small watershed, including forestland, agricultural land, grassland, pit and pond, and residential land. Forestland is mainly composed of slash pine, Masson’s pine, and coniferous and broad-leaved mixed forests, while agricultural land is mainly composed of paddy fields and orange orchards. Water from the Xiangxi River flows into the Jiazhu River, which is the secondary tributary of the Ganjiang River basin (Fig. 1) (Gao et al., 2016; Gao et al., 2017; Hao et al., 2017a,b; Han et al., 2018). The main soil types are red soil, paddy soil, tidal soil and meadow soil (Gong, 1999; Gao et al., 2014). Soil-

Fig. 1. Geographical location and sampling point distribution in the study area.

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forming parent materials are mostly red sandstone, glutenite or mudstone, and river alluvium. The basic physical and chemical properties of soil are shown in Table S1.

F=

where F represents sedimentation flux (kg hm ); p represents rainfall (mm) for each field of rain; and c represents the concentration of each ion in rain (mg L-1). ③ Atmospheric CO2 sequestration and the sequestration rate (derived from the weathering reaction equation):

Considering the spatial distribution, soil use types, sampling convenience and uniformity of sampling points, eight sampling points were established (Fig. 1), where point A was established at the exit of the upstream fish pond, points B and C were established at the outer edge of forestland, and points D, E, and F were established in the intermediate zone between the farmland and the orange orchard in the middle reaches of the river. Additionally, points G and H were established downstream in the orange orchard. The trapping triangle was established at sampling point G where a portable water sampler (ISCO 6710) was installed to monitor and record changes in water levels in real time. The following samples were collected separately in the study area: ① Conventional water samples: samples were collected at the eight established sampling points twice a month (where manual sampling was conducted on the 10th and 20th day of each month). ② Rainwater samples: rainwater was collected manually during rain events using a rain gauge installed on the roof of a structure in the test area. ③ Runoff water samples: after rain events, the ISCO 6710 water sampler automatically collected rainfall runoff water samples with the rain trigger module set to 5 mm; however, when rainfall exceeded 5 mm, the automatic sampling device was programmed to collect surface runoff. Furthermore, when the rain trigger module was running, it was set to automatically collect 200 mL of runoff samples every 30 min until 24 samples were collected. ④ Penetrating rain samples: Three rain gauges were installed in each tree plot for purposes of replication. The distances between each rain gauge were equal. ⑤ Soil solution samples: These samples were collected using the negative pressure method (suction cup method) (Brandi-Dohrn et al., 1996; Grossmann and Udluft, 2016) for each land-use type (mode). The collected water samples were dispensed into polyethylene plastic bottles (100 mL), and samples were taken to the laboratory for analysis immediately after collection.

(CO2) =

Fs =

0

2

1

y=

3.1. Characteristics of variation in silicon and carbon deposition in the watershed Rainfall in the study area is unevenly distributed throughout the year, but mainly occurs between March and August (Fig. 2a). We collected a total of 69 samples from rainfall events in 2015 and a total of 56 rainfall events in 2016. DSi concentrations in rainwater ranged between 0.11 and 0.54 mg L-1, DOC concentrations in rainwater were ranged between 0.86 and 6.77 mg L-1, which varied greatly and exhibited the opposite trend to rainfall. DSi concentrations was significantly correlated at a 0.05 significance level (Pearson correlation coefficients = 0.496, P < 0.05), and DOC concentrations was weakly correlated with rainfall (Pearson correlation coefficients were 0.368). DOC concentrations were lower during the rainy season compared to the dry season, and this was probably because increased rainfall promotes ion dilution. The trend in the monthly sedimentation flux of DSi and DOC was consistent with the trend in monthly average concentrations (Fig. 2b). The range in monthly deposition flux of DSi and DOC was 0.01–1.21 kg hm−2 and 0.26–43.51 kg hm−2, respectively. Moreover, the average deposition flux of DSi and DOC was significantly affected by rainfall. Among these, the average DSi and DOC sediment flux was highest in January 2015 when rainfall was low, while the average DSi and DOC sediment flux was higher in May, November, and December 2015 when rainfall was high.

0

(1)

n 1

ct t× qi (t)dt

t i=1

q + qi + 1 ci + ci + 1 × i 2 2

(6)

3. Results

qi + qi + 1

i=1

Cs × pi 100

where Fs is the soil leaching flux (kg hm ); Cs is the concentration of DSi and DOC (mg L-1) in the soil solution of each land-use type; and pi is the runoff depth of interflow during one month (mm). ⑤ Statistics and mapping: In this study, principal component analysis and cluster analysis were conducted in R 3.4.3 (packages used: vegan and plyr), and correlation analysis and regression analysis were conducted in SPSS 21, using Origin 2018 mapping.

① Calculation of surface runoff and C and Si output loads (Lu et al., 2019; Hao et al., 2017a,b; Xu et al., 2009):

t

(5)

−2

2.4. Data analysis

n 1

1 ZQ(X + 0.5Y) A

where (CO2) is atmospheric CO2 sequestration; (CO2) is the atmospheric CO2 sequestration rate. Z is the HCO3– average concentration (mg L-1) in river water; Q is the annual average runoff (m3). X and Y are the relative contribution rates of silicate and carbonate rock chemical weathering to HCO3– in river water (X + Y = 100%). In this paper, X and Y were calculated by principal component analysis in Section 4.3. A is the area of the watershed (hm2). ④Monthly average soil leaching flux (Chen et al., 2014):

In the laboratory, pH was analysed using a water quality tester (6PFCE model, Myron L Company, the United States of America). DSi, K+, Na+, Ca2+, Mg2+, Cl- and SO42- concentrations were analysed using an inductively coupled plasma optical spectrometer (ICP-OES). Dissolved organic carbon (DOC) was analysed using a total organic carbon (TOC) analyzer (Elementar Analysensysteme GmbH, Germany) (Fig. S1).

qi (t)dt

(4)

(CO2) = ZQ(X + 0.5Y)

2.3. Sample analysis

1

(3) −2

2.2. Sample collection

x=

p×c 100

(2)

3.2. Changes in silicon and carbon concentrations on the vegetation-soil interface

where y is the discharge load (g) of DSi or DOC; ct is the concentration of the DSi or DOC in runoff at time t (mg L-1); qt is the flow at time t (m3 s−1); ci is the concentration of the DSi or DOC in the ith monitoring of samples; qi is the is the flow rate for the ith monitoring (m3 s−1); x is runoff (m3); ti is the time interval(s) between i and i + 1. ② Calculation of Si and C deposition (Hao et al., 2017a,b):

The canopy had a retention and redistribution effect on atmospheric rainfall. This process not only changed the concentration of different elements in rainfall, but also caused the deposition flux of rainfall to 3

Ecological Indicators 110 (2020) 105897

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n . b. r. r. y n l . g p. t . v . c. n . b. r. r. y n l . g p. Ja Fe Ma Ap Ma J u Ju A u Se Oc N o De Ja Fe Ma Ap Ma J u Ju A u Se 2016 2015

Monthly average flux(kg hm-2)

a

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Monthly average concentration(mg L-1)

20

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n. b. r. r. y n l. g p. t. v. c. n. b. r. r. y n l. g p. Ja Fe Ma Ap Ma Ju Ju Au Se Oc No De Ja Fe Ma Ap Ma Ju Ju Au Se 2016 2015

0

Fig. 2. Monthly rainfall from 2015 to 2016 and the monthly average (a) DSi and DOC concentration and (b) DSi and DOC sedimentation flux in rainwater.

indicating that rainfall has the effect of promoting Si and C cycles. Furthermore, pH values and DSi and DOC concentrations in soil solutions of the different vegetation types in the study area differed (Fig. 3). DSi concentrations varied from 10.97 to 17.71 mg L-1, with the highest concentration measured in the orange orchard and the lowest concentration measured in Slash pine II. DOC concentrations varied from 3.42 to 4.41 mg L-1, with the highest concentration measured in Masson’s pine and the lowest concentration measured in Slash pine II. Generally, the soil solution in the study area is alkaline, with a pH range of 7.77–8.01. There were significant differences between DSi and DOC concentration flux in the throughfall of different vegetation types (P < 0.05) as there were significant differences between DSi and DOC concentrations for leaching flux in the underlying soil (P < 0.05).

Table 1 Percentage increase of atmospheric rainfall deposition flux under different canopy interception types. Time

Tree species

DSi

DOC

Rainy season

Slash pine I Slash pine II Masson’s pine Mixed forest Orange orchard Slash pine I Slash pine II Masson’s pine Mixed forest Orange orchard

253.92 37.31 244.31 165.52 182.76 1.43 −30.20 39.75 73.42 544.83

199.09 168.72 122.18 137.37 138.44 195.73 119.25 130.81 104.11 130.90

Dry season

3.3. Characteristics of silicon and carbon export

change. When surface vegetation types differed, the percentage increase in deposition flux also differed (Table 1). The canopy of different tree species significantly increased DOC and DSi concentrations in throughfall during both the rainy and dry seasons. Only during the dry season did the canopy of Slash pine II (slash pine at a higher forest density) significantly reduce the DSi concentration in rainfall. During the rainy season, DSi concentrations in throughfall increased more than DOC concentrations, while during the dry season, DOC concentrations in throughfall increased more. Concentrations of DSi and DOC in throughfall during the rainy season increased more than the dry season,

This study derived DSi and DOC river concentrations from the average value taken from the eight sampling points, and trends in concentration change and runoff are shown in Fig. 4. In 2015, the total runoff output was 4.72 × 105 m3, of which the minimum runoff was 71.9 m3 in February, and the maximum runoff was 1.11 × 105 m3 in March. In 2016, the total runoff output was 4.55 × 105 m3, of which the minimum runoff was 8.12 × 103 in August, and the maximum runoff was 1.24 × 105 m3 in April. In 2015, the DSi concentration ranged from 4.39 mg L-1 (April) to 9.18 mg L-1 (July), while the DOC

Fig. 3. DSi and DOC concentrations and pH levels in soil solutions under different land-use types Note: error bars represent standard deviation (SD). 4

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DOC

DSi

Runoff

120000 100000

16

80000 12 60000 8

Runoff(m3)

concentration(mg·L-1)

20

40000

4

20000 0

Ja n Fe . b M . ar A . pr M . ay Ju n Ju l. A ug Se p O . ct N . ov D . ec . Ja n. Fe b M . ar A . pr M . ay Ju n Ju l. A ug Se p O . ct N . ov D . ec .

0

2015

2016

Fig. 4. Trends in DSi and DOC concentrations in river water and runoff variation.

concentration ranged from 3.41 mg L-1 (April) to 14.52 mg L-1 (November). In 2016, the DSi concentration ranged from 4.94 mg L-1 (August) to 8.80 mg L-1 (November), while the DOC concentration ranged from 2.70 mg L-1 (December) to 10.73 mg L-1 (January). Xiangxi is a closed small watershed. No smaller river flows into the Xiangxi watershed. Therefore, the input of Si and C from the tributaries of Xiangxi River was not considered.

interception and soil leaching processes (Miao et al., 2016, 2019), which could have resulted from the selective absorption of elements between different tree species. Based on vegetation interception and soil leaching, the maximum distance of vegetation types in cluster analysis was conducted (Fig. S3). If vegetation interception and soil leaching were considered together, they could be clustered into two types: Slash pine I and Slash pine II as well as other vegetation types. If only vegetation interception was considered, Slash pine II was a category on its own, and other vegetation types were classified as one type (Fig. S3a). If only soil leaching was considered, Slash pine I, Slash pine II, and burnt area were considered one type, and other vegetation types were collectively considered one type. In summary, when considering vegetation interception processes in the Xiangxi River watershed, vegetation types were classified into “type I” (Slash pine I and Slash pine II) and “type II” (orange orchard, Masson’s pine, and mixed forest) (Fig. S3b). When considering soil leaching processes, they are classified into three categories, namely, “type I”, “type II”, and “burnt ground” (Fig. S3c). According to such classification, we generated Si and C cycle diagrams of the Xiangxi River watershed for both the rainy season and the dry season (Fig. 8). Plant roots absorb Dsi from soil solution and fill in plant cells to form phytolith (Wilding et al., 1967; Carter et al., 2009; Parr and Sullivan, 2010; Piperno et al., 1988; Ma et al., 2003). Among all vegetation types, rainfall had a significant effect on DSi and DOC migration at vegetation-soil interface (P < 0.05). It is noteworthy that soil leaching flux of DOC in the rainy season was not significantly different from that in the dry season, but that of Dsi was significantly lower than that in the dry season (P < 0.05). The possible reason was that the dry season was also the slow growth period of vegetation. At this time, the plant growth rate of Phytolith was slow and the demand for Dsi was small, which led to more silicon loss through leaching. It was inferred that plants might also fix more silicon during the period of rapid carbon sequestration (the rainy season).

4. Discussion 4.1. Influencing factors on silicon and organic carbon migration In 2015, we collected 69 rainwater samples with pH levels between 2.48 and 7.62. In 2016, we collected 56 rainwater samples with pH levels between 3.78 and 8.17 (Fig. 5a). Seasonal variation in precipitation acidity was not significant. In general, acidity was weak in summer and stronger in winter. For the study period (2015–2016), the pH value interval was 0.5 (Fig. 5b), where the highest pH in rainwater was 6.5–7.0, which converts to 20.41% and 53.57%, respectively. We found little correlation between pH and DSi deposition; however, we found a significant negative correlation between pH and DOC sedimentation (P = 0.057) (Fig. S2). This result indicated that DOC deposition flux increased but DSi deposition flux did not change significantly under acidic depositional conditions. DOC concentrations in soil solutions are derived from biodegradation, throughfall, or leaching from the litter layer and root exudates (Bolan et al., 2011). Three processes constituted the export of DOC and DSi in soil solutions: mineralization, gaseous conversion to the atmosphere, and loss to rivers through leaching processes (Kalbitz et al., 2000). When DSi produced by weathering of silicate minerals enters the soil solution, part of it is absorbed by terrestrial plants and transformed into BSi, part of it is precipitated in the form of secondary silicate minerals, and the rest migrates into rivers with runoff (Zhang et al., 2015). Rainfall had a significant impact on the migration of Si and C in the basin (Kong et al., 2015). There were significant differences between DSi and DOC concentrations in depositional flux, runoff output flux, vegetation interception flux, and soil leaching flux throughout the different rainfall periods (i.e., the rainy season and dry season) (P < 0.05). Vegetation types had a significant impact on vegetation

4.2. The strength of silicate weathering The DOC concentration flux in rivers was mainly controlled by runoff depth, watershed slope, and soil organic C storage (Tao, 1997).

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a

pH

8 6 4 2

Jan.

percentage(%)

50

Feb.

Mar.

Apr.

May

Jun

Jul.

Aug

Sep.

Oct.

Nov.

b

40

Dec.

2015 2016

30 20 10 5 8.

8. 0-

8. 0 5-

5

7.

7.

7. 0-

7. 0 5-

5

6.

6. 0-

6.

6. 0 5-

5.

0-

5. 5

0

5.

5. 5-

4.

4. 5 0-

4.

4. 0 5-

3.

3. 0 5-

2.

2-

2. 5

0

Fig. 5. Monthly changes in pH and the interval distribution of precipitation.

Changes in DOC concentrations fluctuated, and the peak concentration was measured in winter (Fig. 4). This could have resulted after the end of seasonal farming practices when there was an increase in residual rice straw and surface litter. Such organic C was released by microbial decomposition and entered into water bodies through soil leaching. The main source of DSi in rivers was surface silicate mineral weathering. Silicate mineral weathering is affected by factors such as temperature, precipitation, and physical ablation (Bluth and Kump, 1994; White and Blum, 1995). DSi concentrations also fluctuated but were higher in the rainy season than in the dry season (Fig. 4). This was mainly because an increase in rainfall accelerates the weathering rates of rock, and

weathering products form rapidly and migrate under the force of rainfall, which subsequently promotes rock weathering (Xiong et al., 1999; Wang et al., 2007). In Fig. 6, we provide a water chemistry triangulation analysis of the small-sized Xiangxi River watershed, which can reflect the chemical composition of river water. The Ca2+ concentration in river water was relatively high, while Mg2+, Na+, and K+ concentrations were relatively low (Fig. 6a). In addition, DSi concentrations in river water were high (Fig. 6b). The chemical composition of river water in this small watershed was jointly controlled by the weathering of both silicate rock and carbonate rock.

Fig. 6. Ternary plots illustrating the ratios of silicon and other anions and cations in river water.

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Fig. 7. Factor loading for principal component analysis.

The DSi concentration in river water was significantly correlated with SO42- and Cl- (Fig. S4a), correlated well with Na+ (P = 0.05) and Ca2++Mg2+ (P = 0.08), and had no correlation with K+ (Fig. S4b). This was related to the balance of reversible reactions. For example, DSi was negatively correlated with Na+, namely, the greater the DSi concentration, the greater that equilibrium would shift in the subsequent reaction (2NaAlSi3O8 + 2CO2 + 11H2O—4H4SiO4 + 2HCO3–+4Na++Al2Si2(OH)4), resulting in a decrease in Na+ concentrations. DSi and Ca2+ showed a negative correlation, namely, greater the DSi concentration, the less that equilibrium would shift in the subsequent reaction (CaAl2Si2O8 + 2CO2 + 3H2O—Al2Si2O5(OH)4 + 2HCO3–+Ca2+). This indicated that the reaction of silicate occupied a certain proportion of the overall reaction that took place in river water. Ions in aqueous river solutions mainly derived from weathering, which included silicate rock weathering, carbonate rock weathering, and evaporite dissolution. In order to illustrate the proportion of silicate weathering in these roles, principal component analysis was conducted on the concentrations of various ionic river components. Three principal components were extracted with a cumulative contribution rate of 85.5% (Table S2). The main component “1″ had the greatest correlation with SO42-, Cl-, and K+, and was the product of evaporative rock dissolution (KCl—K++Cl-; K2SO4—2K++SO42-); therefore, it represented evaporite dissolution. The main component “2” had the greatest correlation with Na+ and Mg2++Ca2+, and was the weathering product of carbonate rock (CaCO3 + CO2 + H2O—Ca2++2HCO3–; NaCO3 + CO2 + H2O— Na++2HCO3–); therefore, it represented carbonate rock weathering. The main component “3” had the greatest correlation with CO32–+HCO3– and DSi, and was the product of silicate rock weathering (2NaAlSi3O8 + 2CO2 + 11H2O—Al2Si2(OH)4 + 4H4SiO4 + 2HCO3–+4Na+; CaAl2Si2O8 + 2CO2 + 3H2O—Al2Si2O5(OH)4 + 2HCO3–+Ca2+); therefore, it represented silicate rock weathering (Fig. 7). 37.2% of the basin’s water chemistry derived from the first principal component (representing the dissolution of evaporating salt rock), 26.7% derived from the second principal component (representing carbonate rock weathering), and 21.6% derived from the third principal component (representing silicate rock weathering). Based on this, we calculated that the relative contribution rates of silicate and carbonate rock chemical weathering to HCO3– in river water (X = 44.7%; Y = 55.3%).

interception, soil leaching, and river output), and a circulation diagram was generated to illustrate this process (Fig. 8). The vegetation interception process showed that both type I and type II increased DOC concentrations in rainwater, but type I increased DOC concentrations comparatively more. Type II increased DSi concentrations in rainwater during both the rainy and dry seasons, but type I increased DSi concentrations during the rainy season and decreased DSi concentrations during the dry season. From the perspective of the soil leaching process, there was little difference in DSi and DOC flux between the rainy season and the dry season, while flux of the vegetation interception process during the dry season was much greater than during the rainy season, indicating that the soil was enriched with a large amount of organic carbon during the dry season. Comparing the deposition process and the river output process showed that the Si input into the watershed was less than the Si output, and the organic C (deposition) input was greater than the organic carbon output (soil leaching and runoff output) during both the rainy and dry seasons. This indicated that Si was in a state of loss, organic C was in a cumulative state, and the Xiangxi River watershed was a “source” of Si and a “sink” of organic C. In 2015 and 2016, the CO2 consumption rates of chemical weathering of rocks were 14.4 and 13.9 kg hm−2 yr−1, respectively (Table 2). If we wanted to know the source-sink state of C in Xiangxi watershed ecosystem, we needed to consider all input and output processes of organic carbon and inorganic carbon of the watershed ecosystem (Fig. 9). The net ecosystem exchange (NEE) in the Xiangxi River watershed was −2710 kg yr−1 hm−2 (Liu et al., 2006). The net inorganic C input in deposition was 25 kg yr−1 hm−2 (calculated by rainwater monitoring data in 2017–2019). The net carbon input in this watershed is 2843 kg yr−1 hm−2, which is much larger than the net carbon output (58 kg yr−1 hm−2). Xiangxi River watershed is not only a “sink” of organic C, but also a “sink” of C. 5. Conclusions The atmospheric chemical CO2 sequestration rate in this subtropical basin was 14.0 kg hm−2 yr−1, wherein carbonates sequestered atmospheric CO2 at a rate of 5.4 kg hm−2 yr−1 and silicate rock sequestered atmospheric CO2 at a rate of 8.6 kg hm−2 yr−1. Both rainfall and the vegetation type had a significant effect on Si and C migration in this watershed. DOC and DSi concentrations in the throughfall of each tree species increased significantly. Furthermore, a large amount of organic C was stored during the dry season and was output during the rainy season. The Si input into the basin was less the Si output, and the C input was greater than the C output. Therefore, this watershed was a

4.3. The source and sink states of Si and C The migration process of silicon and rganic carbon in the Xiangxi River watershed was divided into four parts (deposition, vegetation

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Fig. 8. DSi and DOC cycling during the rainy and dry seasons in the Xiangxi River watershed. Table 2 Atmospheric CO2 sequestration during chemical weathering in the Xiangxi River watershed. Year

2015 2016 Average

Runoff (m3 yr−1)

4.72 × 105 4.55 × 105 4.63 × 105

CO2 sequestration (kg yr−1)

CO2 sequestration rate (kg yr−1 hm−2)

Carbonate rock

Silicate rock

Total sequestration

536.7 517.4 527.1

867.7 836.4 852.1

1404.4 1353.8 1379.1

8

14.4 13.9 14.2

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Fig. 9. Input and output fluxes of carbon in ecosystem of Xiangxi watershed.

“source” of Si and the “sink” of C. Based on the results from our study, it is important to comprehensively investigate the mechanisms related to Si cycling processes in the Xiangxi River watershed in future research, explore the whereabouts of these “disappearing” C sinks, and to further ascertain the impact of climate change on the Si–C cycle in this basin.

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