Abstract

Soil water plays a crucial role in biogeochemical processes within karst ecosystems. However, geochemical variations of soil waters under different land covers and the related karst critical zone processes are still unclear. In this study, five land covers, including grassland, dry land, shrub land, reforestation land, and bamboo land in the Qingmuguan karst area of Chongqing Municipality, Southwest (SW) China were investigated in order to better understand the spatio-temporal variations of soil water geochemistry and its controlling mechanisms. The hydrochemistry of soil water and stable carbon isotopic compositions of dissolved inorganic carbon (δ13CDIC) in soil water were analyzed by a semi-monthly sampling strategy. The results show that there is remarkable spatio-temporal variation in the hydrochemistry and δ13CDIC of soil waters under different land covers in the studied area. Soil waters collected from shrub, dry, and afforestation lands have higher total dissolved solids (TDS), Ca2+, and HCO3 concentrations and heavier δ13CDIC, which is probably associated with the stronger carbonate dissolution caused by higher soil CO2 and carbonate content in soils under these land covers. However, lower TDS, Ca2+, and HCO3 concentrations as well as δ13CDIC values but higher SO42− concentrations are found in soil waters collected from bamboo land and grassland. The reason is that higher gypsum dissolution or oxidation of sulfide minerals and less soil CO2 input occurs in soils under these two land covers. Under the shrub, dry, and afforestation lands, higher concentrations of Ca2+ and HCO3 in soil waters occur in rainy seasons than in dry seasons, which are probably linked to higher CO2 input due to stronger microbial activities and root respiration in the wet summer seasons. In addition, seasonal variations of NO3 concentrations in soil waters from the dry land are observed, and much higher NO3 concentration occurs in the rainy seasons than that in the dry seasons, which suggest that the agricultural fertilization may lead to high NO3 in soil water. On the vertical soil profile, except for the bamboo land, soil waters under different land covers commonly show an increasing trend of main ion concentrations with the increase of depth. This vertical variation of hydrochemistry and δ13CDIC values in soil waters is primarily controlled by the intensity of carbonate dissolution related to carbonate content in soils and soil CO2 production. The soil waters under different land covers have great variations in δ13CDIC values which ranged from −20.68‰ to −6.90‰. Also, the [HCO3]/([Ca2+] + [Mg2+]), [NO3]/[HCO3], and [SO42−]/([Ca2+] + [Mg2+]) molar ratios in soil waters show a large amplitude of variation. This suggested that carbonic acids could not be a unique dissolving agent and sulfuric/nitric acids may play a role in the weathering of carbonate in the Qingmuguan karst area.

INTRODUCTION

Karst aquifer systems are becoming increasingly important as groundwater resources, covering approximately 20–25% of the world's demand (Ford & Williams 2007). In a karst aquifer, soil water plays a complex and vital role in the transformation of precipitation to groundwater. There are a large number of conduits, fissures and high matrix porosity, and sinkholes, which further facilitate the rapid transport of surface water to groundwater and decrease water storage capacity (White 1988; Yuan 2007). Although Southwest (SW) China's karst regions have a subtropical monsoon climate with a mean annual precipitation ranging from 1,000 mm to 2,000 mm (Wang et al. 2017), the karst habitat is deficient in soil water for vegetation growth because of the thin soil layer and relatively high hydraulic conductivity (Jiang et al. 2014; Chen et al. 2017). Thus, soil water is an important limiting factor for crop growth in SW China's karst regions (Jiang et al. 2014).

Soil water can dissolve organic and inorganic solutes of soil into underground aquifers. In the karst environment, soil CO2 and dissolved organic acids can drive the chemical dissolution of carbonate rocks (Yang et al. 2012). Therefore, soil water has a great influence on the geochemical characteristics of karst groundwater (Yuan 2007). In recent decades, with the increase in population and the rapid development of economy in the karst areas of SW China, arbitrary discharge of wastewater, unreasonable disposal of solid wastes, and abuse of agricultural pesticides and fertilizers have caused severe soil and karst aquifer pollution (Liu et al. 2006; Gutiérrez et al. 2014; Kong et al. 2018). The soil water chemistry can provide useful information regarding the spatial and temporal distribution of nutrients, minerals, magnitude, and the movement rates of pollutants into groundwater (e.g., Schulz 2004; Schwarzenbach et al. 2006; Futter et al. 2009; Stehle & Schulz 2015). Consequently, an in-depth understanding of soil water chemistry in karst regions will help us to manage shallow groundwater resources and deepen our knowledge of water-related hydrogeochemical processes.

The hydrochemical behavior of karstic aquifers is very sensitive to the environmental changes (e.g., Ford & Williams 2007; Zhang et al. 2011; Hartmann et al. 2014; Xiao et al. 2015). Springs and underground rivers in the karst system respond rapidly to the changes in climatic and hydrologic conditions, such as extremely intensive to precipitation events (e.g., Liu et al. 2007; Chen et al. 2012; Yang et al. 2012; Lan et al. 2016; Luo et al. 2016; Kadić et al. 2017). Farmer & Williams (2001) revealed that chloroform concentrations in a karst spring can vary as much as 60-fold in a rainstorm cycle. Previous studies have reported that hydrochemical behavior in karst spring exhibits marked annual, seasonal, diurnal, and storm-scale variations (Liu et al. 2007). Recently, there is a growing interest in investigating dissolved inorganic carbon (DIC) concentrations, δ13CDIC, as well as karstification related with hydrothermal activities, under different land covers because of its essential contributions of carbon sink to global carbon cycle (Liu et al. 2011; Zhao et al. 2015). Because different sources of DIC have different isotopic compositions, δ13CDIC is a direct reflection of the karstification processes. Changes of physicochemical parameters in springs and underground rivers have been frequently reported in many karst regions. However, studies on the soil water quality are very limited because of constraints on soil water sampling and measurement (Xiao & Wang 2009). Although many studies have been carried out to identify the soil water movement parameters such as flow path, mean residence time recharge, and runoff generation, few data sets addressed the geochemical responses to the environmental changes (Ozyurt et al. 2014; Chen et al. 2017). A series of hydrogeochemical processes accompanied by the water–rock–gas–organism interaction will lead to the hydrochemical variations of groundwater (Liu & Dreybrodt 2015). Therefore, research on the effect of different land covers and climate processes on the wet–dry-seasonal and spatial geochemical variations in soil water is important.

In this study, soil water under different vegetation types, including grass, dry, shrub, reforestation, and bamboo lands in Qingmuguan karst catchment (QKC) were collected semi-monthly from July 2010 to July 2011. This study has two main objectives: (1) to examine wet–dry-seasonal and spatial variations of major ion concentrations, DIC concentrations and δ13CDIC; and (2) to investigate the influences of lithology, soil CO2, and other controlling factors/mechanisms including human activities on soil water chemistry.

STUDY SITE DESCRIPTION

The study was conducted at the Qingmuguan Observation and Research Station for Karst Dynamics in the Southwest University in the Beibei district of northwest Chongqing, SW China (29°40′40″–29°47′00″N, 106°16′48″–106°20′10″E) (Figure 1(a)). This research area was selected as a hydrogeological field supported by the IGCP513 ‘Global Study of Karst Aquifers and Water Resources’ project.

Figure 1

Maps of (a) location, (b) topographic, and (c) schematic hydrogeology and land use of the study area, QKC in Chongqing city, SW China. QKC is a typical karst valley that extends towards the northeast–southwest. Red points correspond to the sampled points. The numbers correspond to those in Table 1. Please refer to the online version of this paper to see this figure in color: http://dx.doi.10.2166/nh.2019.061.

Figure 1

Maps of (a) location, (b) topographic, and (c) schematic hydrogeology and land use of the study area, QKC in Chongqing city, SW China. QKC is a typical karst valley that extends towards the northeast–southwest. Red points correspond to the sampled points. The numbers correspond to those in Table 1. Please refer to the online version of this paper to see this figure in color: http://dx.doi.10.2166/nh.2019.061.

This region belongs to a humid subtropical monsoon climate. The annual mean temperature and precipitation were 16.5 °C and 1,250 mm during 2010–2016, respectively, as recorded at the Qingmuguan meteorological station (Figure 2). The precipitation from May to October accounts for 77–84% of the precipitation for the entire year. The discontinuous soils have been developed from limestone and contain significant amounts of rock fragments. The soil depth in the depression varies from 20 to 190 cm. On most parts of the hillslope, there is a thin soil cover and exposed bedrock. The vegetation consists of grassland, subtropical forest (shrub), and crops (rice and vegetables). Land uses in the catchment are cultivated lands in depressions, shrub, or grass on the hillslope. The proportion of forest land, paddy fields, and dry fields accounts for 64%, 6%, and 28%, respectively. In addition, 70% of the agricultural area in this catchment lies within the Ganjiacao depression, at approximately 0.6 km2. The fertilizer for rice and vegetables is mainly carbamide and farmyard manure. The season for fertilizer application is from May to early September.

Figure 2

Series of precipitation, air temperature in QKC from 2010 to 2016.

Figure 2

Series of precipitation, air temperature in QKC from 2010 to 2016.

The experimental site is a karst valley area covering 13.4 km2, 12.7 km in length with a northeast–southwest structural trend (Figure 1(b)). The QKC's elevation ranges from 310 to 705 m. The north part of QKC has a generally higher elevation than the south part. The exposed strata are grey and black limestones and yellow sandstone. On the two sides of the catchment is Upper Triassic Xujiahe formation (T3xj), a feldspar-quartz sandstone, siltstone, mudstone, and shale rock or coal, with a thickness greater than 700 m (Figure 1(c)). The outcropping strata in the middle part of the anticline include Lower Triassic Jialingjiang formation (T1j) a grey, thick, massive limestone, dolomite, dolomite limestone, and brecciaous limestone, with a thickness greater than 600 m; the Middle Triassic Leikoupo formation (T2l) argillaceous limestone, gypsum, with a thickness less than 80 m (Yang et al. 2010). The carbonate rocks of Jialingjiang formation and Leikoupo formation constitute the regional karst aquifer. The sand shale of Xujiahe formation is the relative water-resisting layer, and the mud shale of Feixianguan formation of Lower Triassic is not exposed is the aquifer floor (Yang et al. 2013). Due to the widely distributed limestones, a series of large or small depressions, caves, sinkholes, and funnels are distributed in the catchment. There is an underground river with a length of approximately 7.4 km, flowing from northeast to southwest (Figure 1(c)). In the upper stream, the surface water of Ganjiacao depression mainly recharges the underground river through the Yankou sinkhole. In the midstream, the underground river is also recharged from the other sinkholes and karst fissures. Downstream, the Jiangjia spring serves as the only outlet of the underground river (Figure 1(c)). There is a single conduit between Yankou sinkhole and Jiangjia spring tested by a high-resolution tracer experiment (Yang et al. 2010). The discharge of Jiangjia spring was perennial but varied greatly (0.5–3 m3·s−1) in response to monsoonal precipitation distribution. The population in the catchment is approximately 6,000 and most drink water from Jiangjia spring.

MATERIAL AND METHODS

Sampling

To estimate the influence of different land covers on soil water, five sampling sites, including grassland (Q1), dry land (Q2), shrub land (Q3), reforestation land (Q4), and bamboo land (Q5), were chosen in QKC. The water sampling sites are illustrated in Figure 3 and Table 1. Soil waters were sampled at three different depths from 30, 60, and 90 cm in five sampling sites and sequentially collected semi-monthly. Soil water was sampled by using ceramic suction tension lysimeters (product model: 1900; Soil Moisture Co., Ltd, USA), which consisted of suction lysimeters, a porous ceramic cup, a santoprene stopper, a sampling bottle, tubes, and a pressure monitor (Huygens et al. 2008). An extraction kit was connected to the device. Percolating water in the sampler was drained by using an assorted syringe 3–5 days before sampling. The samples were compressed to negative pressure, and its neoprene tubes were bent and clamped to ensure that the vacuum was maintained in the sampler.

Table 1

The details of soil profiles under different land covers

Vegetation type Location site Soil parent material Dominant plant Soil depth Soil texture Human activities 
Dry land 29°44′54″N, 106°18′48″E Limestone Corn 0.5–1.5 m Upper for loam, lower for the clay Chemical fertilizer and manure 
Grassland 29°44′3″N, 106°18′37″E Limestone Herbaceous plant 1–2 m Clay – 
Shrub land 29°43′41″N, 106°18′23″E Limestone Shrubbery 0.5–1 m Upper for loam, lower for the clay – 
Afforestation land 29°43′32″N, 106°18′13″E Limestone Eucommia ulmoides 0.5–1 m Upper for loam, lower for the clay Returning farmland to forest has 4–5 years 
Bamboo land 29°43′18″N, 106°18′2″E Limestone Bamboo 1–2 m Upper for loam, lower for the clay – 
Vegetation type Location site Soil parent material Dominant plant Soil depth Soil texture Human activities 
Dry land 29°44′54″N, 106°18′48″E Limestone Corn 0.5–1.5 m Upper for loam, lower for the clay Chemical fertilizer and manure 
Grassland 29°44′3″N, 106°18′37″E Limestone Herbaceous plant 1–2 m Clay – 
Shrub land 29°43′41″N, 106°18′23″E Limestone Shrubbery 0.5–1 m Upper for loam, lower for the clay – 
Afforestation land 29°43′32″N, 106°18′13″E Limestone Eucommia ulmoides 0.5–1 m Upper for loam, lower for the clay Returning farmland to forest has 4–5 years 
Bamboo land 29°43′18″N, 106°18′2″E Limestone Bamboo 1–2 m Upper for loam, lower for the clay – 
Figure 3

Schematic diagram of geological section under different land covers. The main lithology is limestone and dolomite, which are intercalated with gypsum and coal strata. Carbonate is a prominent component of the soils.

Figure 3

Schematic diagram of geological section under different land covers. The main lithology is limestone and dolomite, which are intercalated with gypsum and coal strata. Carbonate is a prominent component of the soils.

Field investigations included on-site measurement of pH, electric conductivity (EC), and concentrations of HCO3 and Ca2+. Conductivity and pH were determined by a portable water quality analyzer (Hach, USA), with precisions of 1 μS/cm and 0.01, respectively. The concentrations of HCO3 and Ca2+ were evaluated by using a calcium assay kit (Aquamerck, Germany), with precisions of 6 and 2 mg/L, respectively. A calibrated CDU440 CO2 meter manufactured by Industrial Scientific, USA, was used to monitor the soil CO2 concentration under different land covers. The measurement range is 0–60,000 ppmv, and the testing accuracy is 10 ppmv.

Chemical analysis

The water samples for hydrochemical analysis were stored in clean polyvinyl fluoride bottles and sealed with wax. All of the water samples were preserved at 4 °C until analysis, which was conducted within 10 days of sampling. In the laboratory, concentrations of Na+, K+, and Mg2+ were measured by using inductively coupled plasma optical emission spectrometry (ICP–OES Optima 2100 DV, with a lower limit of 0.01 mg/L). NO3, SO42−, and Cl concentrations were determined through ion chromatography (IC, with a lower limit of 0.01 mg/L). The precision of the IC and ICP–OES analysis was within ±5% for major elements.

Samples for δ13CDIC were collected by using polyethylene bottles with 0.45 μm cellulose–acetate filter paper and then were added 0.2 mL saturated HgCl2 solution to prevent biological activity. Analysis of δ13CDIC was conducted by using a modified method of Atekwana & Krishnamurthy (1998). About 10 mL of water sample was injected into tubes that were pre-filled with 1 mL of 85% phosphoric acid. The CO2 was extracted and purified after cryogenic removal of H2O using a liquid nitrogen–ethanol trap. Finally, the CO2 was cryogenically transferred into a tube for isotope measurement. The δ13C measurement has an overall precision of 0.1‰. A number of duplicate samples were measured and the results show that the differences were less than the range of measurement accuracy.

Soil moisture was measured via an alcohol combustion method. Soil carbonate content was determined through a HCl titration analysis. pH electrometric titration and the densimeter method were applied to perform pH determination and physical examination, respectively. Then, 10 g of fresh soil sample was extracted at a 1:5 (w/v) soil–to–solution ratio with 2 M KCl by shaking for 1 h. After the sample was centrifuged and filtered, the NO3–N concentration was determined via the phenolate disulphonic acid method. Analyses were conducted in the Laboratory of Isotopic Geochemistry, Southwest University, China.

RESULTS

Hydrochemical characteristics of soil water under different land covers

Table 2 shows the minimum, maximum, and mean values of the measured pH, EC, major cations and anions, as well as the calculated total dissolved solids (TDS) values in the soil waters and precipitation at each site. The pH of precipitation in the study area ranged from 4.21 to 5.53, with a mean value of 4.58 (n = 8). The EC (6781 μS/cm) and TDS (23.13–46.79 mg/L) of precipitation were relatively low. The HCO3 and SO42− concentrations in precipitation varied from 6.10 to 18.60 mg/L and from 9.96 to 15.85 mg/L, respectively.

Table 2

Physical parameters and chemical compositions of soil water and precipitation within the QKC

Sample ID Vegetation type Index pH Ec K+ Na+ Ca2+ Mg2+  HCO3  NO3  SO42− Cl TDS δ13CDIC 
(μs/cm) mg/L (‰) 
Q1 Grassland (n = 52) Maximum 7.03 206.00 1.63 1.47 25.85 3.91 24.40 12.53 80.70 8.85 134.61 − 14.50 
Minimum 4.98 92.00 0.09 0.44 5.33 0.82 6.10 0.20 8.48 0.41 18.62 − 20.68 
Mean 5.86 132.10 0.53 0.90 20.49 2.37 14.52 4.24 41.27 1.99 74.81 − 17.21 
30 cm 5.82 119.00 0.53 0.75 15.39 1.88 13.60 4.35 38.34 1.45 65.14 − 17.00 
60 cm
90 cm 
5.84
5.90 
118.50
157.45 
0.42
0.69 
0.81
1.16 
14.03
20.05 
2.27
3.04 
14.82
15.14 
3.05
5.32 
43.64
58.70 
1.33
1.40 
69.91
92.61 
− 17.11
−17.80 
Wet season (n = 26)
Dry season (n = 26) 
5.59
6.02 
125.80
147.09 
0.54
0.55 
0.87
0.92 
13.97
19.49 
2.04
2.69 
16.58
12.93 
2.83
5.63 
42.89
50.28 
1.84
1.31 
70.44
81.71 
− 15.21
−20.13 
Q2 Dry land (n = 56) Maximum 8.10 739.00 3.19 3.29 154.70 10.97 244.00 212.53 92.05 6.54 392.74 − 7.73 
Minimum 6.88 361.00 0.89 1.22 46.71 3.38 61.00 15.65 22.26 1.95 106.91 − 15.56 
Mean 7.37 567.08 1.78 2.21 100.13 7.96 154.63 96.68 69.14 3.63 262.17 − 12.30 
30 cm 7.42 477.60 1.40 1.83 85.56 6.42 108.01 78.09 58.06 3.21 210.49 − 12.05 
60 cm 7.46 559.61 1.32 2.10 104.17 7.74 146.81 101.49 58.76 3.53 251.03 − 12.31 
90 cm 7.22 654.00 2.56 2.70 127.47 9.63 206.49 107.33 74.59 4.08 324.28 − 12.30 
Wet season (n = 28)
Dry season (n = 28) 
7.24
7.60 
623.23
509.07 
2.06
1.54 
2.41
1.94 
113.97
95.95 
8.05
7.08 
154.66
142.41 
132.07
64.66 
70.09
58.90 
4.40
3.15 
278.31
239.78 
− 9.75
−14.62 
Q3 Shrub land (n = 50) Maximum 8.16 743.00 1.19 1.74 145.30 4.52 289.75 94.79 95.19 14.59 407.41 − 6.90 
Minimum 7.05 386.00 0.06 0.35 75.29 0.64 152.50 10.56 19.28 2.30 174.17 − 14.78 
Mean 7.41 548.30 0.37 0.76 113.41 3.26 220.50 42.70 60.16 6.34 294.55 − 11.89 
30 cm 7.35 474.14 0.39 0.70 95.16 2.72 189.30 39.11 46.59 6.80 247.01 − 11.83 
60 cm 7.44 566.00 0.34 0.74 118.41 3.34 235.96 34.40 68.26 6.38 315.45 − 12.11 
90 cm 7.45 592.00 0.36 0.83 125.19 3.67 233.49 53.17 64.19 5.88 316.87 − 11.98 
Wet season (n = 27) 7.36 559.81 0.41 0.88 116.94 4.08 235.14 35.28 64.98 5.21 310.08 − 8.72 
Dry season (n = 23) 7.56 563.67 0.30 0.62 113.78 3.09 201.46 51.92 54.99 7.22 280.73 − 13.64 
Q4 Afforestation land (n = 32) Maximum 7.46 467.00 0.66 1.86 84.00 12.34 178.00 41.67 76.87 5.34 270.07 − 8.38 
Minimum 6.97 349.00 0.36 1.39 44.89 8.69 70.14 3.06 45.73 2.37 138.50 − 13.49 
Mean 7.21 397.33 0.54 1.64 57.96 10.69 127.11 23.01 63.95 3.39 201.73 − 11.65 
30 cm 7.14 385.11 0.46 1.48 50.55 9.87 100.3 18.13 59.56 3.8 175.87 − 11.12 
60 cm
90 cm 
7.23
7.27 
395.56
411.33 
0.57
0.59 
1.72
1.71 
58.45
64.87 
10.92
11.28 
138.7
142.36 
20.83
30.058 
61.68
70.62 
3.12
3.19 
205.81
223.44 
− 11.34
−11.23 
Wet season (n = 16)
Dry season (n = 15) 
7.16
7.28 
414.00
376.50 
0.55
0.52 
1.63
1.65 
62.34
52.49 
11.21
10.03 
140.24
110.68 
17.40
30.03 
63.93
63.97 
3.29
3.52 
213.08
187.52 
− 9.92
−13.13 
Q5 Bamboo land (n = 35) Maximum 6.64 307.00 0.91 1.36 48.27 3.97 30.50 43.38 83.00 22.18 174.94 − 14.55 
Minimum 5.73 164.00 0.26 0.53 19.41 1.75 12.20 2.18 24.09 1.65 53.79 − 19.21 
Mean 6.13 208.50 0.56 0.94 30.21 2.62 16.53 10.47 61.56 6.39 110.55 − 16.56 
30 cm 6.03 236.12 0.65 0.81 34.93 2.73 16.81 15.75 73.46 10.66 131.64 − 16.56 
60 cm
90 cm 
6.13
6.22 
196.31
196.33 
0.59
0.50 
0.88
1.10 
27.80
29.47 
2.60
2.70 
15.32
17.28 
10.35
6.18 
59.91
44.33 
4.31
3.86 
103.75
90.60 
− 16.64 
− 16.30 
Wet season (n = 18)
Dry season (n = 17) 
5.96
6.30 
218.39
206.21 
0.62
0.51 
0.99
0.91 
30.05
30.37 
2.57
2.67 
16.45
16.61 
11.14
9.55 
63.75
59.36 
6.62
6.06 
112.83
108.18 
− 15.65
−19.21 
R1 Precipitation (n = 8) Maximum 5.53 81.00 1.55 0.35 14.00 0.59 18.60 6.31 15.85 5.15 46.79 − 
Minimum 4.21 67.00 0.68 0.16 6.12 0.13 6.10 4.07 9.96 3.03 23.13 − 
Mean 4.58 72.38 1.10 0.27 11.14 0.54 13.00 5.10 13.07 4.28 36.90 − 
Wet season (n = 4) 4.75 75.33 1.18 0.24 10.16 0.30 12.20 5.30 13.85 4.43 36.25 − 
Dry season (n = 4) 4.48 70.60 1.06 0.28 10.13 0.36 13.48 4.97 12.60 4.19 35.37 − 
Sample ID Vegetation type Index pH Ec K+ Na+ Ca2+ Mg2+  HCO3  NO3  SO42− Cl TDS δ13CDIC 
(μs/cm) mg/L (‰) 
Q1 Grassland (n = 52) Maximum 7.03 206.00 1.63 1.47 25.85 3.91 24.40 12.53 80.70 8.85 134.61 − 14.50 
Minimum 4.98 92.00 0.09 0.44 5.33 0.82 6.10 0.20 8.48 0.41 18.62 − 20.68 
Mean 5.86 132.10 0.53 0.90 20.49 2.37 14.52 4.24 41.27 1.99 74.81 − 17.21 
30 cm 5.82 119.00 0.53 0.75 15.39 1.88 13.60 4.35 38.34 1.45 65.14 − 17.00 
60 cm
90 cm 
5.84
5.90 
118.50
157.45 
0.42
0.69 
0.81
1.16 
14.03
20.05 
2.27
3.04 
14.82
15.14 
3.05
5.32 
43.64
58.70 
1.33
1.40 
69.91
92.61 
− 17.11
−17.80 
Wet season (n = 26)
Dry season (n = 26) 
5.59
6.02 
125.80
147.09 
0.54
0.55 
0.87
0.92 
13.97
19.49 
2.04
2.69 
16.58
12.93 
2.83
5.63 
42.89
50.28 
1.84
1.31 
70.44
81.71 
− 15.21
−20.13 
Q2 Dry land (n = 56) Maximum 8.10 739.00 3.19 3.29 154.70 10.97 244.00 212.53 92.05 6.54 392.74 − 7.73 
Minimum 6.88 361.00 0.89 1.22 46.71 3.38 61.00 15.65 22.26 1.95 106.91 − 15.56 
Mean 7.37 567.08 1.78 2.21 100.13 7.96 154.63 96.68 69.14 3.63 262.17 − 12.30 
30 cm 7.42 477.60 1.40 1.83 85.56 6.42 108.01 78.09 58.06 3.21 210.49 − 12.05 
60 cm 7.46 559.61 1.32 2.10 104.17 7.74 146.81 101.49 58.76 3.53 251.03 − 12.31 
90 cm 7.22 654.00 2.56 2.70 127.47 9.63 206.49 107.33 74.59 4.08 324.28 − 12.30 
Wet season (n = 28)
Dry season (n = 28) 
7.24
7.60 
623.23
509.07 
2.06
1.54 
2.41
1.94 
113.97
95.95 
8.05
7.08 
154.66
142.41 
132.07
64.66 
70.09
58.90 
4.40
3.15 
278.31
239.78 
− 9.75
−14.62 
Q3 Shrub land (n = 50) Maximum 8.16 743.00 1.19 1.74 145.30 4.52 289.75 94.79 95.19 14.59 407.41 − 6.90 
Minimum 7.05 386.00 0.06 0.35 75.29 0.64 152.50 10.56 19.28 2.30 174.17 − 14.78 
Mean 7.41 548.30 0.37 0.76 113.41 3.26 220.50 42.70 60.16 6.34 294.55 − 11.89 
30 cm 7.35 474.14 0.39 0.70 95.16 2.72 189.30 39.11 46.59 6.80 247.01 − 11.83 
60 cm 7.44 566.00 0.34 0.74 118.41 3.34 235.96 34.40 68.26 6.38 315.45 − 12.11 
90 cm 7.45 592.00 0.36 0.83 125.19 3.67 233.49 53.17 64.19 5.88 316.87 − 11.98 
Wet season (n = 27) 7.36 559.81 0.41 0.88 116.94 4.08 235.14 35.28 64.98 5.21 310.08 − 8.72 
Dry season (n = 23) 7.56 563.67 0.30 0.62 113.78 3.09 201.46 51.92 54.99 7.22 280.73 − 13.64 
Q4 Afforestation land (n = 32) Maximum 7.46 467.00 0.66 1.86 84.00 12.34 178.00 41.67 76.87 5.34 270.07 − 8.38 
Minimum 6.97 349.00 0.36 1.39 44.89 8.69 70.14 3.06 45.73 2.37 138.50 − 13.49 
Mean 7.21 397.33 0.54 1.64 57.96 10.69 127.11 23.01 63.95 3.39 201.73 − 11.65 
30 cm 7.14 385.11 0.46 1.48 50.55 9.87 100.3 18.13 59.56 3.8 175.87 − 11.12 
60 cm
90 cm 
7.23
7.27 
395.56
411.33 
0.57
0.59 
1.72
1.71 
58.45
64.87 
10.92
11.28 
138.7
142.36 
20.83
30.058 
61.68
70.62 
3.12
3.19 
205.81
223.44 
− 11.34
−11.23 
Wet season (n = 16)
Dry season (n = 15) 
7.16
7.28 
414.00
376.50 
0.55
0.52 
1.63
1.65 
62.34
52.49 
11.21
10.03 
140.24
110.68 
17.40
30.03 
63.93
63.97 
3.29
3.52 
213.08
187.52 
− 9.92
−13.13 
Q5 Bamboo land (n = 35) Maximum 6.64 307.00 0.91 1.36 48.27 3.97 30.50 43.38 83.00 22.18 174.94 − 14.55 
Minimum 5.73 164.00 0.26 0.53 19.41 1.75 12.20 2.18 24.09 1.65 53.79 − 19.21 
Mean 6.13 208.50 0.56 0.94 30.21 2.62 16.53 10.47 61.56 6.39 110.55 − 16.56 
30 cm 6.03 236.12 0.65 0.81 34.93 2.73 16.81 15.75 73.46 10.66 131.64 − 16.56 
60 cm
90 cm 
6.13
6.22 
196.31
196.33 
0.59
0.50 
0.88
1.10 
27.80
29.47 
2.60
2.70 
15.32
17.28 
10.35
6.18 
59.91
44.33 
4.31
3.86 
103.75
90.60 
− 16.64 
− 16.30 
Wet season (n = 18)
Dry season (n = 17) 
5.96
6.30 
218.39
206.21 
0.62
0.51 
0.99
0.91 
30.05
30.37 
2.57
2.67 
16.45
16.61 
11.14
9.55 
63.75
59.36 
6.62
6.06 
112.83
108.18 
− 15.65
−19.21 
R1 Precipitation (n = 8) Maximum 5.53 81.00 1.55 0.35 14.00 0.59 18.60 6.31 15.85 5.15 46.79 − 
Minimum 4.21 67.00 0.68 0.16 6.12 0.13 6.10 4.07 9.96 3.03 23.13 − 
Mean 4.58 72.38 1.10 0.27 11.14 0.54 13.00 5.10 13.07 4.28 36.90 − 
Wet season (n = 4) 4.75 75.33 1.18 0.24 10.16 0.30 12.20 5.30 13.85 4.43 36.25 − 
Dry season (n = 4) 4.48 70.60 1.06 0.28 10.13 0.36 13.48 4.97 12.60 4.19 35.37 − 

The wet season is from May to October and the dry season is from November to next April.

Soil water pH ranged from 4.98 to 8.16. Most of the soil water samples were neutral to slightly alkaline, and their pH levels are higher than those of rainwater samples. For all soil water samples, HCO3 was the dominant anion, and Ca2+ was the dominant cation. There is a remarkable variation of HCO3, Ca2+, and TDS in soil waters under different land covers. The TDS of soil water samples vary from 18.62 mg/L (grassland) to 407.41 mg/L (shrub land), with a mean value of 188.76 mg/L for soil water, which was about five times higher than the mean value of 36.90 mg/L for precipitation. The mean concentrations of HCO3, Ca2+, and TDS were ranked as follows: shrub land > dry land > afforestation land > bamboo land > grassland. The concentrations of K+, Na+, and Cl in soil water were relatively lower than those of Mg2+ and Ca2+. The average NO3 concentrations in soil water from grass, bamboo, afforestation, shrub, and dry lands were 4.24, 10.47, 23.01, 42.71, and 96.68 mg/L, respectively. The [Ca2+]/[Mg2+] molar ratio of soil water was more variable, with a range of 2.73–24.98, and a mean value of 9.02. The [Cl]/[Na+] molar ratios of most soil water samples were >1. The [Cl]/[Na+] molar ratios of the soil water samples in shrub land yielded mean values (5.41).

The major ion concentrations of soil water and precipitation were shown as a hexa diagram in Figure 4. The soil water in this study area could be divided into three types. The concentrations of Ca2+ and HCO3 were high in soil water from dry land and shrub land under the Ca–HCO3 type. The average SO42− concentrations in soil water from grassland and bamboo land were 41.27 and 61.56 mg/L, respectively. These concentrations were significantly higher than those in the other soil water samples. These samples belonged to the SO4–Ca type. HCO3 and (HCO3+ SO42−) accounted for 61% and 93% of the TZ on average in afforestation land and precipitation samples, respectively. Thus, these samples belonged to the HCO3.SO4–Ca type.

Figure 4

Hexa-diagrams of soil water and precipitation. The solid and dotted line indicates the mean main ions of soil water for the wet season and dry season.

Figure 4

Hexa-diagrams of soil water and precipitation. The solid and dotted line indicates the mean main ions of soil water for the wet season and dry season.

Seasonal variation of physicochemical parameters in soil water

The pH of all soil water samples is slightly lower in the wet season (6.66) than in the dry season (6.95) in the study areas. The TDS in the soil water samples from all land types except grassland (Q1) was higher in the wet season than in the dry season. A detailed wet–dry-seasonal fluctuations for major ions are shown in Figure 5. The concentrations of Na+, K+, Ca2+, Mg2+, HCO3, and Cl in the soil water samples collected from bamboo land and grassland exhibited minor seasonal changes. The concentrations of these ions vary by <8%. This indicated that the chemical characteristics in soil water collected from bamboo land were stable in different seasons. By contrast, pronounced seasonal variations were observed in Ca2+, HCO3, NO3, and SO42− in soil water collected from dry land. The average concentrations of Ca2+, HCO3, NO3, and SO42− in the rainy season collected from dry lands were 18.78%, 8.60%, 104.25%, and 18.99% higher than those in the dry season, respectively. The NO3 concentrations in soil water samples collected from dry land in May, June, and September were 146.8, 157.4, and 187.8 mg/L, respectively. Monitoring time of these values was consistent with the time of precipitation peak and agricultural fertilization season. In contrast to the changes in NO3 in the soil water samples from dry land, remarkably high NO3 concentrations were detected in others types of soil water samples in the dry season, but low NO3 concentrations were observed in the wet season. The concentrations of Na+, K+, Mg2+, and Cl in the soil water samples from shrub land and afforestation land showed a slight seasonal variation, that is, their concentrations were slightly higher in the wet season than in the dry season. However, the concentrations of Ca2+ and HCO3 in the samples from shrub land and afforestation land exhibited remarkable seasonal variation.

Figure 5

Wet- and dry-seasonal variations of ion concentrations in soil water under different land covers. The red and blue bars indicate the mean main ions of soil water for the wet season and dry season, respectively. Please refer to the online version of this paper to see this figure in color: http://dx.doi.10.2166/nh.2019.061.

Figure 5

Wet- and dry-seasonal variations of ion concentrations in soil water under different land covers. The red and blue bars indicate the mean main ions of soil water for the wet season and dry season, respectively. Please refer to the online version of this paper to see this figure in color: http://dx.doi.10.2166/nh.2019.061.

Vertical variations of physicochemical parameters in soil water

The pH values of soil water from grass, shrub, reforestation, and bamboo lands all showed an increasing trend with the increase in depth (Table 2). By contrast, pH values in dry land samples showed a negligible change with an increase between soil profiles from 30 to 90 cm. In general, except for bamboo lands, the TDS of soil waters increased among the soil profiles. The concentration of mains ions in soil waters from grassland exhibited minor fluctuations within soil profiles (Figure 6). However, the concentrations of Na+, Ca2+, Mg2+, HCO3, NO3, and SO42− in the soil water from dry land were notably lower at a depth of 30 cm than at depths of 60 and 90 cm. The concentrations of Ca2+, HCO3, NO3, and SO42− in the soil water from afforestation land showed a gradual increase as depth increased. Other ions slightly changed with depth. The concentrations of Ca2+, HCO3, and SO42− in the soil water from shrub land varied from 95.16 to 118.41 mg/L, from 189.30 to 235.96 mg/L, and from 46.59 to 64.19 mg/L, respectively, at depths of 30 and 60 cm. The concentrations of these ions were also lower than 125.19, 235.96, and 68.26 mg/L, respectively, at depths of 90 cm. The concentrations of Na+, K+, and NO3 in shrub land samples slightly varied with depth. The Cl concentrations decreased as the soil depth increased. For other vegetation types, the concentrations of Ca2+, Mg2+, K+, NO3, SO42−, and Cl in the soil water of bamboo land decreased as soil depth increased. Therefore, the ions accumulated in the topsoil.

Figure 6

Vertical variations of ion concentrations in soil water under different land covers. The box plots show the median, the quartiles Q1 and Q3 (color box), the upper and lower whiskers (horizontal bars outside of the box) as well as extreme outliers beyond the whiskers. Whiskers mark those values which are minimum (hollow circle) and maximum (black circle) unless these values exceed 1.5 times the inter-quartile range (distance between Q1 and Q3). Please refer to the online version of this paper to see this figure in color: http://dx.doi.10.2166/nh.2019.061.

Figure 6

Vertical variations of ion concentrations in soil water under different land covers. The box plots show the median, the quartiles Q1 and Q3 (color box), the upper and lower whiskers (horizontal bars outside of the box) as well as extreme outliers beyond the whiskers. Whiskers mark those values which are minimum (hollow circle) and maximum (black circle) unless these values exceed 1.5 times the inter-quartile range (distance between Q1 and Q3). Please refer to the online version of this paper to see this figure in color: http://dx.doi.10.2166/nh.2019.061.

Variations of DIC and δ13CDIC in soil water

At pH between 7 and 9, HCO3 was the dominant constituent of DIC in most karst water (Jiang 2013). Thus, under the observed pH conditions, concentrations of DIC were expressed as HCO3 in this study. The δ13CDIC in soil waters were listed in Table 2. Soil water derived from grassland and bamboo land had low DIC concentrations and light δ13CDIC values, with a mean value of 14.52 mg/L and −17.21‰, 16.53 mg/L and −16.56‰, respectively. Soil water derived from the dry, shrub, and afforestation lands had higher DIC concentrations and heavier δ13DIC values with the averages of 154.63 mg/L and −12.30‰, 220.50 mg/L and −11.89‰, 127.11 mg/L and −11.65‰, respectively. The δ13CDIC values of soil waters exhibited minor fluctuations within soil profiles (Figure 7). The median of δ13CDIC in soil waters collected from shrub, reforestation, and dry lands decreased as the soil depth increased.

Figure 7

Variations of δ13CDIC in soil water with different depths. The box plots (see also embedded sketch higher right) show the median (horizontal line within the color box), the quartiles Q1 and Q3 (color box), the upper and lower whiskers (horizontal bars outside of the box) as well as extreme outliers beyond the whiskers. Whiskers mark those values which are minimum (hollow circle) and maximum (black circle) unless these values exceed 1.5 times the inter-quartile range (distance between Q1 and Q3). Please refer to the online version of this paper to see this figure in color: http://dx.doi.10.2166/nh.2019.061.

Figure 7

Variations of δ13CDIC in soil water with different depths. The box plots (see also embedded sketch higher right) show the median (horizontal line within the color box), the quartiles Q1 and Q3 (color box), the upper and lower whiskers (horizontal bars outside of the box) as well as extreme outliers beyond the whiskers. Whiskers mark those values which are minimum (hollow circle) and maximum (black circle) unless these values exceed 1.5 times the inter-quartile range (distance between Q1 and Q3). Please refer to the online version of this paper to see this figure in color: http://dx.doi.10.2166/nh.2019.061.

The isotope of dissolved inorganic carbon (δ13CDIC) in soil waters varied seasonally for different types of soil waters (Figure 8). The mean δ13C values of the DIC for grass, dry, shrub, afforestation, and bamboo land samples were −20.13‰, −14.62‰, −13.64‰, −13.13‰, and −19.21‰ during the dry season, and −15.21‰, −9.75‰, −8.72‰, −9.92‰, and −15.65‰ during the wet season, respectively (Figure 8). Generally, these δ13CDIC values of soil water were 4–5‰, which were higher in the wet season than those in the dry season.

Figure 8

Seasonal variation of δ13CDIC in soil water under different land covers.

Figure 8

Seasonal variation of δ13CDIC in soil water under different land covers.

Soil carbonate content

The parent material of this study area is carbonate rock. The carbonate contents of the soil samples were summarized in Table 3. The carbonate concentrations in soils under different land covers are high, with a range of 4.57 to 31.93 g/kg (Table 3). The mean carbonate concentration in soils was ranked in the following order: shrub land > dry land > afforestation land > bamboo land > grassland. Shrub land yields the highest carbonate content of 31.93 g/kg at 30–60 cm. By contrast, grassland exhibits the lowest carbonate content of 4.57 g/kg at 0–30 cm. The carbonate content is higher in the deeper layer (30–60 cm) than in the upper layer (0–30 cm) of all land covers except bamboo land.

Table 3

Carbonate content of the soil samples under different land covers (g/kg)

Soil depth Grassland Dry land Shrub land Afforestation land Bamboo land 
0–30 cm 4.57 11.6 12.31 10.44 7.62 
30–60 cm 5.02 13.93 31.93 11.66 4.89 
Soil depth Grassland Dry land Shrub land Afforestation land Bamboo land 
0–30 cm 4.57 11.6 12.31 10.44 7.62 
30–60 cm 5.02 13.93 31.93 11.66 4.89 

Soil samples were collected at 10 cm intervals in the soil profile under different land covers.

Soil pCO2

Figure 9 illustrates that there is a remarkable spatio-temporal variation of the soil CO2 concentrations with different land covers. In general, the soil CO2 concentrations were much higher than the CO2 concentrations near the surface of the atmosphere. At soil depths of 0–80 cm from February to August, the CO2 concentration changed from 1,000 to 27,100 ppmv, 1,700 to 42,300 ppmv, and 2,200 to 29,700 ppmv for grass, shrub, and bamboo lands, respectively. In this study area, seasonal pattern was represented by a yearly cycle of change, with remarkable changes in summer but slight variations in winter. In addition, the highest CO2 concentration was detected in July for both grassland (with 27,100 ppmv at 60–80 cm) and bamboo land (with 29,700 ppmv at 60–80 cm). However, the highest CO2 concentration was recorded in August for shrub land (with 42,300 ppmv at 60–80 cm) with a time lag of a month. The concentrations of CO2 in the soil environment observed at various depths were increased with an increasing soil depth.

Figure 9

The variations of soil CO2 concentration at 0–80 cm depth with different vegetation types. The soil CO2 concentration also showed remarkable temporal variations, with higher in summer and lower in winter. The data of soil CO2 concentration are obtained from Zhai (2011).

Figure 9

The variations of soil CO2 concentration at 0–80 cm depth with different vegetation types. The soil CO2 concentration also showed remarkable temporal variations, with higher in summer and lower in winter. The data of soil CO2 concentration are obtained from Zhai (2011).

DISCUSSION

Effect of subsurface mineralogy on the hydrochemistry of soil water

The soil water hydrochemistry and δ13CDIC showed remarkable differences with the different land covers. It might be due to the manifold factors such as soil pCO2, carbonate dissolution, precipitation, water residence time, and human activities. Previous studies demonstrated that soil water hydrochemistry was widely controlled by the interaction of water with soil minerals and host rock (Yuan 2002). In this region, the main lithology is limestone and dolomite, which are intercalated with gypsum. Under the control of parent material, carbonate is a prominent component of the soils (Table 3), which is consistent with results presented by Li et al. (2015). In the soil environment, carbonate minerals react with carbonic acid to produce bicarbonate (HCO3). As shown in Table 2 and Figure 4, Ca2+ and HCO3 are the dominant ions in the soil water samples. The correlation between Ca2+ and HCO3 in soil water was positive, with a coefficient of 0.85 (N = 225, ρ < 0.01). The carbonate content of soil was also positively correlated (R2 = 0.809, ρ< 0.01) with the Ca2+ concentration in soil water (Table 4). Therefore, Ca2+ and HCO3 in soil water mainly originated from carbonate dissolution in the study area. The carbonate contents in soils from shrub, dry, and afforestation lands were obviously higher than those in soils from bamboo and grass areas (Table 4). Consisting of the carbonate contents data, the Ca2+ and HCO3 in soil water collected from shrub, dry, and afforestation lands were apparently higher than those in soil water from bamboo and grass areas. Meanwhile, on the soil profile, the carbonate contents in soils and the concentrations of Ca2+ and HCO3 in soil waters collected from shrub, dry, afforestation, and grass lands increased with an increasing depth; however, the carbonate content in soils and the concentrations of Ca2+ and HCO3 in soil waters collected from bamboo land decreased with an increasing depth (Figure 6). The results indicated that the spatio-temporal variability of Ca2+ and HCO3 in soil waters was determined by carbonate weathering, which was driven by soil CO2 and carbonate content in soils under different land covers.

Table 4

Correlation among moisture content, pH, clay content, carbonate content, and NO3–N of soil and NO3, K+, Na+, Ca2+, Mg2+ in soil water

 Moisture content pH Clay content Carbonate content NO3–N  NO3 K+ Na+ Ca2+ Mg2+ 
Moisture content 1.000          
pH 0.520 1.000         
Clay content 0.799b 0.421b 1.000        
Carbonate content 0.160 0.902b 0.564b 1.000       
NO3–N −0.279 0.447b −0.08 0.571b 1.000      
NO3 −0.475 0.313 −0.178 0.415 0.845a 1.000     
K+ −0.609a −0.113 −0.653a −0.26 0.572a 0.825b 1.000    
Na+ −0.806b 0.022 −0.643a 0.010 0.457 0.734b 0.855b 1.000   
Ca2+ −0.186 0.763b 0.364 0.809b 0.805b 0.819b 0.455 0.413 1.000  
Mg2+ −0.811b 0.312 −0.37 0.175 0.182 0.428 0.422 0.798a 0.334 1.000 
 Moisture content pH Clay content Carbonate content NO3–N  NO3 K+ Na+ Ca2+ Mg2+ 
Moisture content 1.000          
pH 0.520 1.000         
Clay content 0.799b 0.421b 1.000        
Carbonate content 0.160 0.902b 0.564b 1.000       
NO3–N −0.279 0.447b −0.08 0.571b 1.000      
NO3 −0.475 0.313 −0.178 0.415 0.845a 1.000     
K+ −0.609a −0.113 −0.653a −0.26 0.572a 0.825b 1.000    
Na+ −0.806b 0.022 −0.643a 0.010 0.457 0.734b 0.855b 1.000   
Ca2+ −0.186 0.763b 0.364 0.809b 0.805b 0.819b 0.455 0.413 1.000  
Mg2+ −0.811b 0.312 −0.37 0.175 0.182 0.428 0.422 0.798a 0.334 1.000 

aCorrelation is significant at the 0.05 level (1-tailed).

bCorrelation is significant at the 0.01 level (2-tailed).

The concentrations of Na+ and K+ in the soil water samples exhibited minor spatial changes. The content of clay particles in the soil profile was negatively correlated with the concentration of K+ and Na+ in the soil water, with a coefficient of −0.653 (ρ < 0.01), −0.643 (ρ < 0.01), respectively (Table 4). Clay particles can strongly absorb K+ and Na+ in the soil (Li et al. 2010). Therefore, the more positive ions were absorbed by soil, the less is dissolved in the soil water.

On the two sides of the catchment is the Upper Triassic Xujiahe formation (T3xj), which consists of thin coal strata (Yang et al. 2010). The Middle Triassic Leikoupo formation (T2l) interbedded gypsum strata. As stated earlier (Table 2), the concentrations of SO42− in soil water collected from grass and bamboo lands were significantly higher than those in soil water collected from shrub, dry, and afforestation lands despite being under the same climatic conditions. The primary cause of the hydrochemical differences may be subsurface mineralogy in the soil. SO42− in soil water can be derived from the dissolution of gypsum and the oxidation of sulfide minerals (Lang et al. 2006). In addition to subsurface mineralogy origin, the precipitation was significantly enriched in SO42− (average of 13.07 mg/L). Precipitation acts as the water source of soil water. Therefore, the concentrations of SO42− in soil water partly originated from precipitation. In summary, the dissolution of gypsum and acid rain provides abundant Ca2+ and SO42− ions for soil water from grass and bamboo lands; hence, these samples belonged to the SO4–Ca type.

Effect of land covers on the hydrochemistry of soil water

As stated above (Figure 9), there were remarkable differences in measured soil CO2 with the different vegetation types. In general, soil CO2 inputs controlled by biological activity, available soil moisture and precipitation, and dissolution of carbonate (Zhao et al. 2015). Shrub land had a higher carbonate content and a strong bioactivity, due chiefly to the root respiration and microbial activity (Hanson et al. 2000). Root respiration is enhanced, leading to an increase in soil CO2 production. Therefore, the soil CO2 concentrations in shrub land were more significant than those in grass and bamboo lands. In karst systems, a small part of the soil CO2 diffuses to the atmosphere, whereas most parts are dissolved in the infiltration water (Jassal et al. 2004). Soil CO2 dissolves in soil water to form carbonic acid, which is a significant chemical driving force for carbonate dissolution (Jiang et al. 2014). A higher soil pCO2 of the shrub land led to a higher carbonate dissolution. Therefore, high contents of TDS, Ca2+, and HCO3 in soil water in the shrub land corresponded to a high CO2 dissolution in water and a high carbonate content in soil, in which more limestone and dolomite are dissolved. The bamboo land has the lowest carbonate content results in lower soil CO2 concentration. Furthermore, the bamboo land located in sloping land, with a thin soil layer, and then soil CO2 in the bamboo land can readily degas. Thus, there was a low soil CO2 concentration gradient in bamboo lands. With a low soil CO2 concentration gradient in bamboo lands, a relatively weak vertical hydrochemical variation is observed in the soil water.

The CO2 concentration also showed remarkable temporal variations, with a higher level in summer and a lower level in winter. The catchment experiences a subtropical monsoon humid climate with a higher temperature and abundant precipitation in summer (June to August). In summer, root respiration in the soil is strong, with the lush growth of plants; hence, larger amounts of CO2 are generated (Atkin et al. 2000). By contrast, soil CO2 generation decreased as most weeds wither and die, and a low soil temperature was detected in winter. Therefore, temperature and humidity are the most important factors that determine the bioactivity in soils and make a significant contribution to soil CO2 generation. Similarity, Ouyang & Zheng (2000) reported that precipitation determines the soil water content available for biological respiration and the air-filled pore spaces available for CO2 flux. Yang et al. (2012) revealed that pCO2 (partial pressure of dissolved CO2 in water) in karst water changed synchronously with soil CO2.

The similarities of insignificant seasonal variations between soil pCO2 and the concentrations of Ca2+ and HCO3 in soil waters indicated that the soil water hydrochemistry was mainly determined by carbonate weathering, which is driven by soil pCO2, the latter varying with both precipitation and temperature. In summer rainy seasons, more corrosive CO2 water solution is generated as the soil CO2 concentration increases; as a consequence, erosion strongly occurs and high TDS and HCO3 levels can be detected in soil water. This variation shows that the soil CO2 stimulates the seasonal hydrochemical variations in karst areas. Liu & Zhao (2000) demonstrated similar seasonal hydrochemical variations of the epikarst springs in Guilin Karst Experimental Site. They found that Ca2+, HCO3, and pCO2 in water showed almost a simultaneous significant change as soil CO2 partial pressure varies remarkably within a year. Yuan (2002) revealed that the soil CO2 concentrations varied from 800 to 7,400 ppmv at 0–20 cm depths from February to June. The HCO3 concentrations changed from 201.3 to 286.7 mg/L. The soil CO2 produced by plant root respiration in soil affects the diurnal hydrochemical change in the karst springs (Zhao et al. 2010; Yang et al. 2012). These differences indicate that climatic factors, particularly precipitation and temperature, and vegetation control the production, migration, and conversion of CO2 in the soil–aquifer system and the intensity of karst processes (Zhang 2011). Hence, changes in temperature, precipitation, and vegetation can effectively regulate the soil CO2 changes and result in wet–dry-seasonal and spatial variations of geochemistry in soil waters.

Influence of anthropogenic pollution on the geochemistry of soil water

Compared with those collected from other land use covers, soil water samples collected from dry land were relatively high in Na+, K+, NO3, SO42−, and Cl contents. Especially, the average NO3 concentration in soil water from dry land was 96.68 mg/L, which far exceeded WHO's maximum contaminant level (10 mg/L) (Table 2). In a pollution-free area, natural nitrate concentrations in groundwater are very low (Debernardi et al. 2008). However, many karst aquifers are characterized by elevated NO3 concentrations from anthropogenic pollution (Musgrove et al. 2016; Xiao et al. 2016). In the investigated area, some crops, such as corn, potato, and capsicum dry, were planted in dry lands. Fertilizers containing 225 kg of urea, 750 kg of ammonium bicarbonate per hm2, and a certain proportion of potassium dihydrogen phosphate were applied to the dry land from May to June (Yang et al. 2010). Excessive N fertilization in the dry land has resulted in severe nitrate pollution. NO3–N of soil is easily dissolved in water, which leads to the significant positive correlation (R2 = 0.845) between the concentration of NO3 in the soil water and the content of NO3–N of soil through long-term eluviation (Table 4). Therefore, the higher NO3 contents in soil water under dry land may be introduced by intense farming activities, such as synthetic chemical fertilizers. This is supported by the fact that the NO3δ15N values of soil water samples collected from dry land ranged from −3.74‰ to 5.56‰ (Wang et al. 2009). Normally, the NO3δ15N values of chemical fertilizers ranged from −4‰ to ±4‰, while the NO3δ15N values of mineralized soil organic nitrogen ranged from 2‰ to 9‰ (Heaton 1986). Previously published studies report that many sources of nitrate contaminants include human and animal excreta and bio-combustion, the nitrification of organic N and NH4+, and agricultural fertilizers (Heaton 1986; Galloway et al. 2008).

Usually, the time for agricultural fertilization is from May to September in this study area. Thus, evident seasonal variations in NO3 were observed in soil waters from dry land with a higher NO3 in the rainy season, and vice versa during the dry seasons. The soil waters collected from dry land had significantly higher NO3 contents in the upper layer (0–30 cm) than in the deeper layer (30–60 cm). This is mainly due to the fact that surface soil is more susceptible to nitrate pollution.

Geochemistry of soil water and its related karst process

Conventionally, the DIC in water generally has three sources, including atmospheric CO2, CO2 from organic respiration, and carbonate dissolution (Aucour et al. 1999). As infiltrating waters dissolve soil CO2, some of the dissolved CO2 is hydrated and dissociated into HCO3 (bicarbonate) and CO32− (carbonate), and the distribution of DIC is set by pH (Clark & Fritz 1997). The contribution of atmospheric CO2 is minor because of the high partial pressure of soil CO2 and low pH values (lower than 5.6) of rainwater. Thus, DIC in soil water studied here may have two primary sources, soil CO2, and weathering of carbonate minerals.

δ13CDIC as a natural tracer in karst systems can help to differentiate material originated. δ13CCO2 depends on both the source material (C3 or C4 plants) and the rate of CO2 diffusion. The C3 photosynthetic pathway dominates in most terrestrial ecosystems and produces organic matter with δ13C values that ranged from −30‰ to −24‰ (Vogel 1993). C4 plant-derived lipids exhibit δ13C values ranged from −20‰ to ±2‰ (Chikaraishi & Naraoka 2006). As suggested by Cerling et al. (1991), molecular diffusion of CO2 in the soil can result in an isotopic enrichment of +4.4‰. Thus, it can be assumed that the δ13C of soil CO2 for biogenic carbon is approximately −23 ± 3‰ (Li et al. 2008). Marine carbonate has a relatively narrow range of C isotope ratios, with δ13C values close to 0‰ by definition (Telmer & Veizer 1999; Clark & Fritz 1997). The δ13CDIC related to the dissolution of carbonate by carbonic acid should be around −12‰ (Jiang 2013). The soil waters displayed remarkable variations in δ13CDIC values in the range of −20.68‰ to −6.90‰ under different land covers in the study area (Table 2). This suggested that the δ13CDIC of soil waters under different land covers was controlled by different geochemical processes. The soil water samples collected from shrub, afforestaion, and dry lands had δ13CDIC values approaching −12‰, suggesting that DIC in soil waters mainly originated carbonate dissolution by carbonic acid. The δ13CDIC values in soil waters collected from grass and bamboo lands were lower than −12‰. This is mainly due to the δ13CDIC values which were determined by the mixing ratio of soil CO2 and carbonate bedrock, and the ratio of soil CO2 was higher than carbonate bedrock.

In a closed system, carbonate weathering generally by carbonic/sulfuric acid could lead to water chemistry characterized by [HCO3]/([Ca2+] + [Mg2+]) molar ratios of 2 and 1, and [SO42−]/([Ca2+] + [Mg2+]) molar ratios of 1 and 0.5, respectively. The following equations can express these processes.

1. Weathering of carbonate bedrock by acids (e.g., carbonic, sulfuric acids, and hydrogen nitrate). 
formula
(1)
 
formula
(2)
 
formula
(3)
 
formula
(4)

The [HCO3]/([Ca2+] + [Mg2+]), [NO3]/[HCO3] and [SO42−]/([Ca2+] + [Mg2+]) molar ratios in soil waters have large amplitude of variation (Figure 10). The δ13C values of DIC display a positive relationship with the [HCO3]/([Ca2+] + [Mg2+]) ratio and a negative relationship with [NO3]/[HCO3] and [SO42−]/([Ca2+] + [Mg2+]) and [SO42−]/[HCO3] ratios, with a coefficient of 0.58 (ρ < 0.01), –0.06, –0.68 (ρ < 0.001), and –0.66 (ρ < 0.001), respectively (Figure 10). This suggested that carbonic acid could not be a unique dissolving agent and sulfuric/nitric acids played an important role in the carbonate dissolution in the investigated area (Jiang 2013). Specifically, the soil waters derived from grass and bamboo lands had lower [HCO3]/([Ca2+] + [Mg2+]) molar ratio values and had higher [SO42−]/([Ca2+] + [Mg2+]) and [SO42−]/([Ca2+] + [Mg2+]) molar ratio values (Figure 10(b)). This suggested that carbonate dissolution by sulfuric acid may be insignificant in the grass and bamboo lands (Lang et al. 2006). It was demonstrated in previous studies that sulfuric acid from oxidation of pyrite and acid rain might be implicated in chemical weathering of carbonate rocks in SW China (Li et al. 2008; Jiang et al. 2018).

Figure 10

Relationships between isotopic compositions of DIC and elemental ratios (in mol/mol) in the soil water samples. The δ13CDIC and elemental ratios in soil waters collected from grass land and bamboo land are significantly different from those collected from dry land, shrub land, and afforestation land. The arrow indicates the soil water hydrochemistry and δ13CDIC tends to be affected by agricultural fertilization.

Figure 10

Relationships between isotopic compositions of DIC and elemental ratios (in mol/mol) in the soil water samples. The δ13CDIC and elemental ratios in soil waters collected from grass land and bamboo land are significantly different from those collected from dry land, shrub land, and afforestation land. The arrow indicates the soil water hydrochemistry and δ13CDIC tends to be affected by agricultural fertilization.

The soil waters collected from dry land had high NO3 concentrations (∼132.07 mg/L) and high [NO3]/[HCO3] ratios (Figure 10(b)). There is no distinct correlation between [NO3]/[HCO3] ratios and δ13CDIC values. In the investigated area, nitrate–nitrogen in the soil waters is mainly derived from the fertilizer mixture and soil organic nitrogen. The oxidation of organic fertilizer and NH4+ fertilizers can produce acid (Equations (3) and (4)). The nitrification of NH4+ fertilizers can enhance soil degradation and contribute to carbonate dissolution (Semhi et al. 2000). This suggests that weathering of carbonate rocks by nitric acid may be insignificant in a dry land.

As mentioned above, carbonate dissolved by carbonic acid, sulphuric acid, and nitric acids has made important contributions to the changes in hydrochemistry and δ13CDIC in soil waters. A conceptual model with geochemical variations and its related controlling mechanisms in soil waters under different land covers is shown in Figure 11. It highlights that soil water hydrochemistry and δ13CDIC are widely controlled by both land covers and carbonate lithology. The weathering of carbonate bedrock controlled by mixed acids in this study area. The various mechanisms can be described by the following Equations (5) and (6).

Figure 11

Conceptual model of the geochemical variations and its related controlling mechanisms in soil waters under different land covers. The figure highlights that soil water hydrochemistry and δ13CDIC are widely controlled by both land covers and carbonate lithology.

Figure 11

Conceptual model of the geochemical variations and its related controlling mechanisms in soil waters under different land covers. The figure highlights that soil water hydrochemistry and δ13CDIC are widely controlled by both land covers and carbonate lithology.

2. Weathering of carbonate bedrock by mixed acids. 
formula
(5)
 
formula
(6)
According to Equations (5) and (6), the relationship between chemical weathering rates and fluxes of CO2 consumed is not straightforward because there are sources of acidity other than CO2, potentially including sulfuric and nitric acids (Spence & Telmer 2005). Summary, the seasonal variation of soil water hydrochemistry and δ13CDIC may be controlled by at least four factors: (1) carbonate bedrock dissolution controlled by lithology; (2) soil CO2 input related to vegetation cover and land use; (3) climatic conditions; (4) human activity. Soil waters collected from shrub, dry, and afforestation lands have heavier δ13CDIC, which is primarily controlled by the intensity of carbonate dissolution related to carbonate content in soils and soil CO2 production. The seasonal variations of δ13CDIC in soil waters are probably associated with the climatic change.

CONCLUSIONS

Semi-monthly hydrochemical data and δ13CDIC in karst soil water samples were obtained to reveal the controlling mechanisms on geochemistry and δ13CDIC under different land covers, namely grass, shrub, dry, afforestation, and bamboo lands, in Qingmuguan, a small karst catchment in Chongqing Province, SW China. Results show that there is a remarkable spatio-temporal variation of hydrochemistry and δ13CDIC in soil waters under different land covers. The concentrations of TDS, Ca2+, and HCO3 in soil waters showed significant variation under different land covers. The average concentrations of TDS, Ca2+, and HCO3 in soil water ranked as shrub > dry > reforestation bamboo > grass lands. SO42− was dominant in soil water collected from grass and bamboo lands. Hence, the soil water belonged to SO4–Ca type. The concentrations of Ca2+ and HCO3 in the samples exhibit remarkable seasonal variation, with a higher level in summer and a lower level in winter from dry, shrub, and afforestation lands. However, the concentrations of Na+, K+, Ca2+, Mg2+, HCO3, and Cl in the soil water samples from bamboo and grass lands exhibited minor seasonal changes. There are clear seasonal variations in NO3 where observed in the soil water samples from dry land, with a higher NO3 level in the rainy season, and vice versa during the dry seasons. Except for bamboo lands, main ions in soil waters accumulated in deep soil layers. The soil waters displayed remarkable variations in δ13CDIC values in the range of −20.68‰ to −6.90‰, and the δ13CDIC values were 4–5‰ lighter in the wet season than in the dry season.

DIC increased due to the increase of soil CO2 resulting from stronger microbial activities and root respiration in summer and wet seasons. The seasonal variation of soil water hydrochemistry and δ13CDIC mainly controlled by carbonate dissolution related to carbonate content in soils, soil CO2 input related to vegetation cover and climatic conditions and human activity, etc., and according to the range of δ13CDIC and the relationship between δ13CDIC and elemental ratios, carbonate dissolution by carbonic acid and other strong acids, such as sulphuric acid and nitric acids.

ACKNOWLEDGEMENTS

This research was supported by the Open Project Program of Chongqing Key Laboratory of Karst Environment (Grant No. Cqk 201702), the National Natural Science Foundation of China (Grant No. 51709127), the Natural Science Foundation of Guangdong Province, China (Grant No. 2017A030310172).

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