An isotope study (δ18O and δD) of water movements on the Loess Plateau of China in arid and semiarid climates

Ecological Engineering 93 (2016) 226–233

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An isotope study (␦18 O and ␦D) of water movements on the Loess Plateau of China in arid and semiarid climates Hao Wan a,c,∗ , Weiguo Liu a,b a b c

State Key Laboratory of Loess and Quaternary, Institute of Earth Environment, Chinese Academy of Sciences, Xi’an 710061, China School of Human Settlement and Civil Engineering, Xi’an Jiaotong University, Xi’an 710049, China University of Chinese Academy of Sciences, Beijing 100049, China

a r t i c l e

i n f o

Article history: Received 27 May 2015 Received in revised form 25 December 2015 Accepted 12 May 2016 Keywords: The loess plateau of China Isotopic tracer ı18O Soil water content

a b s t r a c t Precipitation infiltration and evaporation are the main controlling factors on soil water content (SWC) in the Chinese Loess Plateau (CLP). However, the temporal and spatial variations of soil water in the CLP are still unclear. Here, we investigate the stable isotope compositions (␦18 O and ␦D) of soil water for five different vegetation cover types in the central CLP, to trace the dynamics and movement mechanisms of soil water. Our results show that the depth of precipitation infiltration is approximately 120 cm in five different vegetation cover types under natural rainfall conditions throughout the year. The rapid 18 O-enrichment of shallow (<30 cm depth) soil water, which is observed in all profiles, indicates that the evaporation effect mainly occurs in the shallow layer. The ␦18 O isotope dynamic pattern between 30 and 120 cm depth is probably controlled by the precipitation infiltration characteristics at a mean annual precipitation of 572.4 mm. In contrast, deep (>120 cm depth) soil water is in a steady state in our study period, which suggest that the residence times of this water can be several months or more. Although the vegetation cover types can affect the profile dynamics of ␦18 O, we find that variations in seasonal precipitation are the key factor that influences the profile dynamics of ␦18 O, which is attributed to the large differences in the climate parameters and the frequency of rainfall. We suggest that ␦18 O is more sensitive in tracing the precipitation infiltration depth and recharge mechanisms of soil water than the soil water content. Further observation over a much longer time scale and an combination of both the oxygen and hydrogen isotope compositions of soil water in the CLP would provide more insight into role of isotopic techniques in tracing the soil water cycle. © 2016 Elsevier B.V. All rights reserved.

1. Introduction Water is the most essential resource for ecological and biological survival and is a strategic resource for socio-economics. Soil water plays a critical role in both crop growth and vegetation restoration in semiarid environments (Fu et al., 2003). Through its utilization of soil water, vegetation has an important impact on the shallow soil water system (Jackson, 2000). Shallow soil water serves as an important reservoir in the hydrological cycle (Gat, 1996) and is a primary water source for most plants (Ehleringer et al., 2000) and a solvent for numerous nutrients (Gazis and Feng, 2004). In the arid and semiarid regions of the Chinese Loess Plateau (CLP), where water can be scarce, water resource management requires

∗ Corresponding author at: State Key Laboratory of Loess and Quaternary, Institute of Earth Environment, Chinese Academy of Sciences, Xi’an 710061, China. E-mail address: [email protected] (H. Wan). http://dx.doi.org/10.1016/j.ecoleng.2016.05.039 0925-8574/© 2016 Elsevier B.V. All rights reserved.

a balance of water requirements for ecological conservation and economic development. Therefore, investigating the transformation from precipitation to soil water in different seasons, and their relationship with vegetation cover types is vital to understanding the water cycle and the recharge mechanisms of soil water in the CLP. The ␦18 O and ␦D of soil water have been used to investigate infiltration, evaporation, mixing processes and to quantitatively estimate groundwater recharge and evaporation rates (Zimmermann et al., 1967; Barnes and Allison, 1988; Mathieu and Bariac,1996; Hsieh,1998; Cheng and Liu, 2014; Liu, 2011a,b; Liu et al., 2015). Zimmermann et al. (1967) reported the effect of soil water evaporation on the isotopic composition, which has been broadly used to determine the infiltration profile and evaporation rate in arid and semi-arid regions (Abu-awwad, 2001; AraguasAraguas et al., 1995; Mellander and Bishop, 2004; Schachtschneider and February, 2007). The variations in ␦18 O and ␦D in soil water have been determined in many later studies. Stable isotopes of

H. Wan, W. Liu / Ecological Engineering 93 (2016) 226–233

227

Fig. 1. Map of the sampling sites (red rectangle). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

water can be used to characterize the mixing dynamics of subsurface water (Carreon-Diazconti et al., 2003), the extent of soil water recharge (Girard et al., 1997; Cane and Clark, 1998), and the flow mechanisms in the soil (Asano et al., 2002; Gazis and Feng, 2004). In addition, such isotope data can be used to measure different water residence times (Rosen et al., 1999; Landon et al., 2000; Asano et al., 2002). In general, rainwater enters the soil via diffusive, piston, and preferential flows, diffusive recharge usually occurs in unsaturated soil. Under piston flow, water from recent precipitation events forces older residual soil water downward and often mixes with the old soil water (Asano et al., 2002; Gazis and Feng, 2004). Piston flow can be documented based on the stable isotope characteristics of soil water when precipitation events have different temporal and meteorological sources. Preferential flows may exist in saturated and unsaturated conditions through macropores caused by cracks, decayed plant roots, earthworm burrows, rocks, and other forces (Gazis and Feng, 2004; Dusek et al., 2012). However, the use of ␦18 O and ␦D isotope compositions to trace soil water movement and the infiltration characteristics in a soil profile has rarely been applied to the central CLP under several typical vegetation cover types as a function of time. The study areas in our research has exhibit climatic, soil, vegetation and hydrological characteristics that are representative of the CLP, which makes them ideal subjects to study the interaction between the hydrological dynamics and landscape characteristics in this region. In this study, we selected five typical vegetation cover types and determined their SWC and the stable isotopes in the soil water to investigate the possible sensitivity of ␦18 O to vegetation cover types and climate parameters. The specific goals of this study are (1) to examine the sensitivity of the soil water ␦18 O of different landscape characteristics in temporal and spatial and (2) to understand the infiltration characteristics and water movements in the CLP in arid and semiarid climates. 2. Study area This study was carried out on the CLP, where is covered largely by alternating loess–paleosol layers ranging from 30 to 80 m in

thickness. The samples were collected from the Laohequ Basin (Fig. 1), which is located in the Bin County, Shaanxi Province, China. The Laohequ Basin is situated in the tableland-gully region of the central CLP in the middle reaches of the Yellow River. The tableand-gully landscape is one of the main topographic-ecological units on the CLP and consists of two geomorphic subunits: complete tableland and dissected tableland. The experimental site is fairly flat and is covered by five vegetation types (Table 1) with an average altitude of 1170 m above the sea level. The climate of the Laohequ Basin belongs to the continental monsoon type. The annual precipitation in 2013 and 2014 was 417.2 and 572.4 mm, respectively, with approximately 67% and 50% of the total rainfall between July and September in 2013 and 2014, respectively. The average annual temperature is 11.8 ◦ C, and the mean monthly temperature for July and January is 23.4 ◦ C and −2.1 ◦ C, respectively. 3. Materials and methods 3.1. Sampling methods Field investigation and sampling were performed in May, August and November 2014. Six soil corn samples were taken by a soil borer at 10 cm depth intervals at depths of 0–100 cm and at 20 cm depth intervals at depths of 100–200 cm at every study site. Meanwhile, topsoil samples collected. Then, the soil was packed into sealed bags for laboratory analysis. Precipitation samples were collected in 2014 at the experimental station using sealed glass bottles. The samples were stored in a freezer to prevent evaporation. 3.2. Gravimetric water content The soil water was extracted by vacuum distillation. The gravimetric water content of each soil sample was measured by weighing the frozen soil sample, heating the sample for 30 min at 100 ◦ C, and weighing the dry soil. SWC(%) = (Mw /Ms ) × 100

(1)

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Table 1 Site description. Land-use pattern

Altitude (m)

Location

Soil type

Samples number

Sample depth (cm)

Plant age

Main Vegetation

Coverage (%)

Apple orchard

1236.39

Dark loessial soil

96

200

>20

Malus pumila Mill

65–75

Corn field

1239.34

Dark loessial soil

96

200

>20

Zea mays

70–85

Alfalfa field

1242.73

Dark loessial soil

96

200

>5

Medikago sativa

85–90

Grass land

1034.02

108◦ 9 32.198(E) 35◦ 14 24.422(N) 108◦ 9 53.171(E) 35◦ 14 22.452(N) 108◦ 9 55.282(E) 35◦ 14 18.796(N) 108◦ 9 12.936(E) 35◦ 13 51.724(N)

Dark loessial soil

96

200

>30

Tree land

1098.43

108◦ 9 32.708(E) 35◦ 13 52.673(N)

Dark loessial soil

96

200

>30

Artemisia leucophylla, 80–90 Artemisia capillaries, Eragrostis pilosa, Artemisia sacrorum 75–80 Robinia pseudoacacia Spiraea wilsonii

where SWC is the soil water content, Mw is the extracted water and Ms is dry soil. 3.3. Stable isotopic composition measurements The soil water was extracted in the laboratory using the vacuum extraction method. The stable isotopic composition (␦18 O and ␦D) of liquid water was determined using a Liquid Water Isotope Analyzer Picarro L2130-i wavelength scanned cavity ringdown spectroscopy (CRDS) isotopic water analyser (Picarro, Inc., Sunnyvale, CA, USA) at the State Key Laboratory of Loess and Quaternary, Institute of Earth Environment. Each sample was injected six times, but the first two injections were discarded to eliminate memory effects. The average composition for injections 3–6 was used for the isotope ratio calculation. The accuracy of the measurements was ±1 (‰) for ␦D and ±0.1(‰) for ␦18 O. The results are expressed as ␦ values relative to the Vienna Standard Mean Ocean Water (VSMOW ) in per mil (‰):



ısample (‰) =

Rsample − Rvsmow Rvsmow



× 1000

Fig. 2. Precipitation ␦18 O values in monthly variation.

(2)

where ␦ sample is the deviation of the isotope ratio of a sample relative to that of the VSMOW , Rsample is the ratio of 18 O to 16 O atoms (or D to H atoms) in the sample, and Rvsmow is the ratio of18 O to 16 O atoms (or D to H atoms) in the VSMOW. 3.4. Source partitioning using stable isotopes When n isotope systems are used to determine the proportional contributions of n + 1 sources to a mixture, standard linear mixing models can be used to mathematically solve for the unique combination of source proportions that conserves mass balance for all n isotopes (Philips, 2001). For example, with one isotope system and two sources, the following system of mass balance equations can be solved to determine the proportions (fA , fB ) of source isotopic signatures (␦A , ␦B ) that coincide with the observed signature for the mixture (␦M ):

and underground biomass, respectively (Liu, 2011a,b). Rootlet biomass samples of herbaceous plants were taken by a soil borer (inner diameter is 90 mm) at 10 cm intervals at depths of 0–100 cm. Soil samples were collected by stratified sampling, and each horizon was mixed in a bag. Subsequently, the soil was soaked and then rinsed in a 0.25 mm sieve under running water to obtain plant roots. The roots were weighted after being air-dried and then kept at 65 ◦ C to calculate the biomass.

3.6. Statistical analysis All statistical analyses were performed using Origin 8 and SPSS 19.0. One-way ANOVA followed by the Tukey’s HSD test (P < 0.05) was used to compare the effect of seasonal changes and vegetation differences on the soil water content at the studied sites.

␦M = f A ␦A + f B ␦B 1 = fA + fB

(3)

4. Results 4.1. Isotopic composition in precipitation

3.5. Biomass investigation LnW 1 = 2.2235LnDn − 1.9634(R2 = 0.92)

(4)

LnW 2 = 2.0885LnDn − 3.4782(R2 = 0.92)

(5)

W Ti = W 1 + W 2

(6)

Eqs. (4)–(6) were used to calculate the tree biomass. D is the DBH (diameter at breast height), and W1 and W2 are the aboveground

In total, thirty five precipitation samples were collected from May to December in 2014. The ␦18 O values ranged from −17.1‰ to 0.1‰, with an average value of −8.5‰. The seasonal variation in ␦18 O in the precipitation is shown in Fig. 2. The isotopic values were lower in September, whereas they were generally higher from May to August. Meanwhile, lower ␦18 O values were observed during heavy and continuous (e.g., September 10–18, 2014) rainfall events.

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Fig. 3. Temporal variations of SWC in five vegetation cover types. The average SWC value is minimum in August, and has the obvious water consumption layer corresponding to the roots distribution in profile (Table 2). Picture showed that soil water seasonal dynamics in five vegetation cover types can be divided into three different stages which were loss stage (from May to August), increasing stage (from August to November) and relatively stable stage (from November to next May).

Fig. 4. Spatial variations of SWC in five vegetation cover types. The SWC is minimum in shallow soil layer of our study sites, except treeland. However, the soil water in treeland is lowest in deep soil layer through the study period. The result showed that Apple orchard and Corn field has the highest water retention ability in the five vegetation cover types, for the highest SWC throughout the study period.

4.2. Soil water variations in five vegetation cover types Figs. 3 and 4 show the changes in the mean SWC for the five vegetation cover types. In May, the SWC followed the trend corn field (20.33%) > apple orchard (19.8%) > grassland (16.5%) > alfalfa field (14.03%) > treeland (13.61%). In August, the SWC followed the trend apple orchard (17.66%) > corn field (15.14%) > alfalfa field (10.62%) > grassland (10.01%) > treeland (9.46%). In November, the SWC followed the trend apple orchard (19.41%) > corn field (19.32%) > treeland (15.96%) > alfalfa field (15.09%) > grassland (14.32%). The SWC in the treeland decreased with the soil depth (May: R2 = 0.69; August: R2 = 0.39; November: R2 = 0.7), thus, the maximum values occurred in the surface layers. The SWC at a depth of 40–100 cm decreased in the summertime, which corresponds to the vegetation growing period, with a minimum at 70 cm depth. 4.3. Biomass investigation Table 2 lists the root distributions in the five vegetation cover types. In the upper 40 cm, the mean rootlet biomass of Malus pumila accounted for 77.4% of the total biomass, and Zea mays accounted for 84% of the soil profile. The root distributions of Medikago sativa and grass are well- proportioned except for the minimum values at 80–100 cm. A soil layer with concentrated roots was found at 40–50 cm for Malus pumila and Robinia pseudoacacia, which corresponds to the water loss peak point in the profile (Fig. 3). 4.4. Isotopic composition in soil water Figs. 5 and 6 show the ␦18 O characteristics of soil water in the different profiles in May, August, and November. The average values in May were −6.28‰ in the apple orchard, −3.27‰ in the corn field, −4.33‰ in the alfalfa field, −4‰ in the grassland and −4.5‰ in

the treeland. The mean value of ␦18 O was approximately −10‰ at a depth of 100–200 cm, and the values for the deeper soil layers were relatively stable. In August, the ␦18 O values of soil water ranged from −10.5‰ to 0.52‰, with an average value of −7.94‰. The peak negative value appeared at 10 cm depth in the apple orchard, corn field, and treeland and at 20 cm depth in the alfalfa field and grassland, which indicates that rainfall events occurred on August 8 and 12, 2014 (30.8 mm). The mean value of ␦18 O was approximately −10‰ at a depth of 100–200 cm compared to the profile isotopic characteristics in May. In November, the ␦18 O values of the soil water ranged from −13.2‰ to 2.9‰, with an average value of −9.39‰, which was lower than that in May and November. Double peaks with positive values occurred in the profile with opposite signals: one in the upper soil (30 cm depth) and the other in the deep soil layer (approximately 80–90 cm depth).

5. Discussion 5.1. Evaporation effect in shallow layer soil water Precipitation is the only source for the soil water in the CLP region, and evaporation is a major factor for the enrichment of isotopes in shallow layers. According to Fig. 6, the ␦18 O values exhibit different variations with each season for the five vegetation cover types. In the apple orchard, the isotope profile for May showed a remarkable decrease in the shallow soil layers until a depth of 70 cm, ranging from −1.12‰ in the surface layer to a relatively stable value of −10.76‰. These phenomena were also observed for the other four vegetation types (Fig. 7). According to the isotope profile for May and August, the ␦18 O variation mainly occurred in the upper 70 cm layer for all the vegetation cover types, because of evaporation and infiltration. The negative peak in August indicates that dry conditions were responsible for the strong evaporation of rainfall and limited recharge

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Table 2 Biomass investigation in five vegetation types. Depth (cm)

Apple orchard (kg tree−1 )

Corn field (kg m−2 )

Alfalfa field (kg m−2 )

Grassland (kg m−2 )

Treeland (kg tree−1 )

Aboveground biomass 0–10 10–20 20–30 30–40 40–50 50–60 60–70 70–80 80–90 90–100 Underground biomass

53.24 1.52 2.66 6.25 32.11 4.21 3.56 2.54 1.23 0.9 0 54.98

2.21 0.12 0.32 0.35 0.09 0.01 0.01 0.01 0 0 0 0.91

0.95 0.06 0.12 0.14 0.21 0.12 0.11 0.09 0.08 0.04 0.02 0.99

0.81 0.15 0.24 0.13 0.1 0.09 0.06 0.04 0.03 0.01 0 0.85

88.65 2.11 3.26 5.22 6.98 20.63 10.23 9.32 8.32 9.28 5.36 80.71

Fig. 5. Soil water ␦18 O variations in profile of different vegetation cover types. ␦18 O value is enriched near the soil surface and decreases exponentially with depth in May. The decreasing trend of ␦18 O was interrupted by the precipitation in early days in August and November, and another peak values was found in November in 80–90 cm depth.

Fig. 6. Seasonal variation of soil water ␦18 O in profile of different vegetation cover types. The ␦18 O in deep soil (>120 cm) is stable in our study, and the seasonal variation were found in the shallow soil layer. The peak value in 20–30 cm depth is close to the “new water” (close to continuous rain in September with average ␦18 O of −12.7‰) and another one is close to the “old water” in 80–90 cm depth in November.

of the SWC. In addition to evaporation, the isotope composition in the soil water was also influenced by precipitation, which means that the isotopic profiles in the soil water did not decrease smoothly as predicted by Zimmermann et al. (1967). However, the overall trend still exists, with isotopes enriched in the topsoil compared to deeper layers (Figs. 5 and 6). The soil water ␦18 O values decreased toward the bottom of the profile because the ␦18 O values of incoming rain were always more negative than that of the soil water. During dry periods, water was preferentially lost from the surface layers, which created SWC profiles that increased and ␦18 O profiles that decreased toward the bottom of the soil profile (Hsieh, 1998). In our study, the ␦18 O profile pattern above 70 cm depth in May is the same as that in Zimmermann et al. (1967), who concluded that oxygen isotopes were enriched near the surface and decreased exponentially with depth. In May, the linear relationships between ␦18 O and depth (0–70 cm) were as follows: apple orchard: y = −6.42x − 8.9, (r = 0.946, p < 0.01); corn field: y = −5.38x + 14.13, (r = 0.925, p = 0.001); alfalfa field:

y = −5.58x + 7.58, (r = 0.916, p = 0.001); grassland: y = −6.37x + 5.75, (r = 0.942, p < 0.01); and treeland: y = −8.88x − 9.06, (r = 0.931, p = 0.001). The vegetation cover type significantly affected the profile dynamics of ␦18 O above 70 cm in May, probably because of field management (weeding & fertilization) and the vegetation coverage. The average values are −6.28‰ in the apple orchard, −3.27‰ in the corn field, −4.33‰ in the alfalfa field, −4‰ in the grassland and −4.5‰ in the treeland. An ANOVA analysis of the ␦18 O in the top 70 cm of the soil water shows that the ␦18 O in the soil water under the apple orchard was significantly lighter than that the under the corn field, alfalfa field, grassland and treeland (p = 0.005, 0.015, 0.01, 0.04). Because the five soil profiles received recharge from same precipitation, the ␦18 O difference in the soil water must have resulted from different evaporation intensity. Hence, the evaporation of soil water under the apple orchard was weaker than that under the corn field, alfalfa field, grassland and treeland, wheres the evaporation of soil water under the four veg-

H. Wan, W. Liu / Ecological Engineering 93 (2016) 226–233

Apple Orchard

0 -20

, ‰)

-5

0

May:y = 6.0508x - 16.036 R² = 0.970 5

-40 -60 -80

-100 -120 -140

-15

5

Aug.: y = 5.2859x - 21.66 R² = 0.9507

-10

δ18 O(VMSOW -5

0 -20 -60

5.0

δD (VMSOW , ‰)

δD (VMSOW , ‰)

5

0 -20 -40 -60 -80

-100

-100

May: y = 5.9025 x - 18 .70 7 R² = 0.96

Aug.:y = 4.3714x - 30.141 R² = 0.8297

-120 Nov.:y = 6.015x - 21.419 -140 R² = 0.9481

Nov.: y = 5.2598x - 27.545 R² = 0.8605

Treeland

Aug.: y = 5.7247x - 17.12 8 R² = 0.929 8 Nov.: y = 5.7166x - 24 .56 6 R² = 0.885

δ18O(VMSOW , ‰) -10 -5 0

δ18O(VMSOW

-60

-12 0

-15 20

Aug.:y = 5.5468x - 19.828 R² = 0.9241

-80 -120

-40

-14 0

10

May: y = 5.6409x - 19.945 R² = 0.953 4

Grassland

-10 0

5

-40

-140

δ18O(VMSOW

-80

, ‰)

20

Nov.: y = 6.6552x - 12.29 R² = 0.9274

, ‰) -10 .0 -5.0 0.0 -15.0 20 May: y = 6.2357x - 15.66 5 0 R² = 0.968 6 -20

0

Alfalfa field

δD(VMSOW , ‰)

δD (VMSOW , ‰)

20

-10

Corn field

δD(VMSOW , ‰)

-15

δ18 O(VMSOW

231

, ‰) -10 .0 -5.0 0.0 -15 .0 20 0 May: y = 6.9061 x - 3.935 2 R² = 0.9717 -20

5.0

-40 -60 -80

Aug.: y = 6.7416x - 7.6067 R² = 0.96 5

-10 0 -12 0 -14 0

Nov: y = 5.8742 x - 22 .99 3 R² = 0.899 6

Fig. 7. The soil water isotopic composition of different vegetation cover types. The black line is global meteoric water line (GMWL) of Carig (1961) is shown for reference. The cross plotting D and O isotope record with different slope shows the evidence of evaporative enrichment of 18 O in seasonal variation. (blue, red and green line represents may, august and November isotope profile respectively). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

etation cover types did not significantly differ, except between the corn field and alfalfa field (p = 0.042). This difference in the energy distribution of the soil and ground temperatures under different vegetation cover led to a different degree of soil water evaporation. In the present study, we found that the spatial and temporal changes in water isotopes (Figs. 5 and 6) were caused by evaporation, which could enrich the remaining rainwater in heavy isotopes (Acheampong and Hess, 2000; Gazis and Feng, 2004; Tang and Feng, 2001; Tian et al., 2002). This temporal trend in the ␦18 O profile of the water in the soils from the five vegetation cover types might have also been affected by enriched water that was pushed in by piston flow from upper soil layers (Kortelainen and Karhu, 2004; Tang and Feng, 2001; Tian et al., 2002). 5.2. Tracing soil water movements and infiltration mechanisms based on ı18 isotopic characteristics Fig. 6 shows that the profiles for November were diverse for the 0–60 cm depth, and the ␦18 O was similarly affected by rainfall in September, which suggests that new rainfall replaced old water, with the most negative value at 20 cm depth being −12.6‰ and that at 90 cm depth being −6.6‰. The value at the 80–90 cm depth corresponds with the ␦18 O values at 20 cm depth in August. The isotope profile and precipitation isotope values in November suggest that piston flow occurred across all five vegetation cover types in this research. The above phenomenon was observed for all the vegetation cover types except for the corn field. The ␦18 O value of −9.29‰ was relatively stable below a depth of 120 cm, which suggests that ordinary rainfall events and even continuous rain with 162.4 mm precipitation rarely affected the soil water in the deeper layers. No double peak values were observed in the isotope profile for November, most likely because of the high water requirements of corn. We conclude from this observation that plant transportation did not affect isotope fractionation but was a potential factor for changes in the isotope profile. An examination of the soil water

isotope profiles and a comparison of these profiles with the isotopic composition of precipitation prior to sampling can be used to unravel the mechanisms of water movement through shallow and deep soil layers and to determine the residence time of soil water at different depths (Gazis and Feng, 2004). The isotope profiles of the alfalfa and treeland in November were found to exhibit abruptly lower values at depths of 80 cm and 60 cm, respectively, which indicates that preferential flow occurred in the alfalfa and treeland in November only, may be due to the cracks and fissures in loess provide paths for preferential flow (Xue, 1995). We found that piston flow was dominant in all the vegetation cover types. This result disagrees with the assertion that “in unsaturated soil columns, preferential flow was dominant when the water content was low with little or no precipitation” (Padilla et al., 1999). This difference may be related to the soil type or vegetation types. In conclusion, piston flow and preferential flow coexist during rainfall infiltration and are greatly influenced by land use types (Mathieu and Bariac, 1996; Cheng and Liu, 2014). However, preferential flow was observed in the alfalfa fields and treeland, and piston flow was detected in all the vegetation cover types after high-intensity rainfall events. The infiltration depths were 10 cm in the apple orchard, corn field, and treeland and 20 cm in the alfalfa field and grassland under similar rainfall intensity (21.6 mm on August 8, 2014), which indicates that canopy interception can effectively enhance evapotranspiration and reduce the recharge content (McCole and Stern, 2007). Continuous rainfall with a total precipitation amount of 187.8 mm and a high relative humidity of 97.4% from August 30 to September 28 suggest weak evaporation conditions. Thus, a considerable amount of rainwater entered the soil, and evapotranspiration had only a small impact on the soil water recharge. As shown in Fig. 6 (Nov., graph), the isotopic curve has two peak values in all the profiles except for the corn field, which exhibits a lighter peak in the shallow layer with a new rain profile and a heavier peak at a depth of approximately 80–90 cm with an old rainfall profile. The results provide new data for understanding the infiltra-

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tion rates of piston flow. In conclusion, the isotope characteristics in the soil profile were mainly influenced by the infiltration depth and the vegetation coverage, and precipitation rarely recharged the deep soil (>120 cm) at the CLP. 5.3. Tracing the soil water mixing effect in the infiltration process based on ı18 isotopic characteristics The difference in ␦18 O values between precipitation and soil water cannot account for evaporation when considering the climate conditions in our study areas, which exhibited lower temperatures (15 ◦ C) and higher RH (97%) in autumn. Meanwhile, the most depleted ␦18 O value in the corn field was −11.25‰, very close to the −11.9‰ value in the precipitation, which further illustrates that precipitation evaporation could have been negligible in the enrichment event during November (Fig. 5). Therefore, the difference may be explained by the mixing of rainfall with antecedent soil water. The ␦18 O profile in November (Fig. 6) may be seen as a mixture of precipitation in autumn (“new water”) and antecedent soil water in summer (“old water”). The time series of soil water ␦18 O values and water content can be used to determine the fraction of water that is lost through evaporation and transpiration (Hsieh, 1998). During transpiration, the SWC changes, but the soil water ␦18 O remains constant. In contrast, evaporation changes both the soil water content and soil water ␦18 O. The isotopic mass balance is calculate by Eq. (3): ␦mix = fold ␦old + fnew ␦new , with 1 = fold + fnew . The mixing effect was occurred along all profiles. Gazis and Feng (2004) compared the ␦18 O of the precipitation and soil water from profiles at six sites near Hanover, USA, and found that shallow soil water after precipitation (44 mm) contained greater than 20% old water. In our study, the average “fnew ” value reached 64%, 83%, 74%, 68% and 66% in the five vegetation cover types above 70 cm depth after continuous precipitation (187.8 mm). The average soil water values in the apple orchard, corn field, alfalfa field, grassland and treeland under 120 cm throughout the year were −9.29‰, −9.5‰, −9.08‰, −9.9‰, −9.9‰ and −9.8‰, respectively. The data shows that the ␦18 O in the soil water was in a steady state during our study period under a depth of 120 cm, which suggests that the precipitation infiltration depth was approximately 120 cm in the five different vegetation cover types under natural rainfall conditions throughout the year and that the residence times of this water can be several months or more. Mixing with “old water” could account for the isotopic enrichment of the precipitation in profile as well as the evaporation effect of it, which suggests that ␦18 O is useful in tracing the soil water mixing during the infiltration process and the residence time of deeper soil water in the CLP. 5.4. Tracing soil water movement by cross plotting ıD and ı18 O Through its utilization of soil water, vegetation has an important impact on shallow soil water systems (Jackson, 2000), and the vegetation-soil-roots water pathway is a major component of subsurface hydrological systems (Mendel et al., 2003). Moreover, the depth and distribution of plant roots define the area from which plants can potentially absorb soil water (Zencich et al., 2002). Generally, the stable isotope ratios of hydrogen and oxygen in water samples can identify water sources and water dynamics in watersheds. (Mortathi et al., 1997; Ruck and Mayer, 2007). In our study, comparing the regression line for soil water with the GMWL, specifically, ␦D = 8(␦18 O) + 10 (Carig, 1961), shows that the evaporation effect varied for different vegetation coverage types and different physiological stages. Stronger evaporation occurred in the corn field and alfalfa field during the early growing season, which could be indicated

by lower slope values of 5.64 and 6.23 from lower precipitation (<40 mm) and uncovered land. On the other hand, the slope value (6.91) was higher in treeland with thick litter and dense canopies. Plant transpiration and evaporation became stronger during the middle of the growing season. The soil water content decreased along the root distribution in the profiles in the corn field and treeland, but this phenomenon was not present in the apple orchard. This observation requires further work in future studies. The plant root distribution has a close relationship with the soil water, and the water uptake layer is often related to the root distribution zone (Shan et al., 2003). The treeland and corn field, which contain Robinia pseudoacacia and Zae mays as the dominant plants have a high root biomass at 40–100 cm and 0–40 cm depth, respectively, which agree with the water loss in the profiles. Yu et al. (1996) observed a similar result in a previous study. During the late growing season, the regression line for the soil water in the apple orchard, corn field, alfalfa field and grassland were expressed as y = 6.66x − 12.29 (R2 = 0.93), y = 5.26x − 27.55 (R2 = 0.86), y = 6.02x − 21.42 (R2 = 0.95), and y = 5.72x − 24.56 (R2 = 0.96), respectively, which indicates weak evaporation. The higher slope values and soil water suggest that a compensation effect occurred during the late growing season. However, a similar phenomenon was not observed in the treeland, probably because the thick litter and dense canopies decreased the precipitation infiltration rate, and held water on the surface layer. This hypothesis can be supported by the soil water content in November, which reached 32.48%. 6. Conclusions The dynamics of soil water movement in different vegetation cover types were studied for a small drainage area based on stable isotopes. The strong evaporation in the central CLP may be the dominant factor that explains for the 18 O-enrichment in the shallow layer, whereas, the precipitation infiltration characteristics in different seasons are the key factor that controls the profile dynamics of ␦18 O in the deeper soil. The ␦18 O of the soil water was in a steady state (>120 cm) during our study period, which suggest that the residence times of this water can be several months or more. The lighter isotope composition in the isotope fronts that were observed after continuous rainfall suggests infiltration that was accompanied by considerable mixing with stationary soil water. The ␦18 O isotope composition profile characteristics indicate that piston flow and preferential flow coexist in the process of rainfall infiltration and that piston flow is the main recharge pattern, whereas preferential flow occurs both accidentally and randomly. In conclusion, we suggest that ␦18 O is more sensitive in tracing the precipitation infiltration depth and recharge mechanisms than the SWC. The results provide reliable reference information for similar hydrological studies in the field-based hydrological studies.

Reference Abu-awwad, A.M., 2001. Influence of different water quantities and qualities on lemon trees and soil salt distribution at the Jordan Valley. Agric. Water Manage. 52, 53–71. Acheampong, S.Y., Hess, J.W., 2000. Origin of the shallow groundwater system in the southern Voltaian Sedimentary Basin of Ghana: an isotopic approach. J. Hydrogen 233, 37–53. Araguas-Araguas, L., Rozanski, K., Gonfiantini, R., Louvat, D., 1995. Isotope effects accompanying vacuum extraction of soil water for stable analyses. J. Hydrol. 168 (1–4), 159–171. Asano, Y., Uchida, T., Ohte, N., 2002. Residence times and flow paths of water in steep unchannelled catchments, Tanakam, Japan. J. Hydrol. 261, 173–192. Barnes, C.J., Allison, G.B., 1988. Tracing of water movement in the unsaturated zone using stable isotopes of hydrogen and oxygen. J. Hydrogen 100, 143–176. Cane, G., Clark, I.D., 1998. Tracing ground water recharge in an agricultural watershed with isotopes. Ground Water 37 (1), 133–139.

H. Wan, W. Liu / Ecological Engineering 93 (2016) 226–233 Carig, H., 1961. Standard for reporting concentrations of deuterium and oxygen-18 in natural water. Science 133, 1833. Carreon-Diazconti, C., Nelson, S.T., Mayo, A.L., Tingey, D.G., Smith, M., 2003. A mixed groundwater system at Midway, UT: discriminating superimposed local and regional discharge. J. Hydrogen 273, 119–138. Cheng, L., Liu, W., 2014. Study of water movement and ground water recharge for the Loess Tableland using environment tracers. Am. Soc. Agric. Biol. Eng. 57 (1), 23–30. Dusek, J., Vogel, T., Sanda, M., 2012. Hillslope hydrograph analysis using synthetic and natural oxygen-18 signatures. J. Hydrogen 475, 415–427. Ehleringer, J.R., Roden, J., Dawson, T.E., 2000. Assessing ecosystem-level water relations through stable isotope ratio analysis. In: Methods in Ecosystem Science. Springer, New York, pp. 181–198. Fu, B.J., Wang, J., Chen, L., 2003. The effects of land use on soil moisture variation in the Danangou catchment of the Loess Plateau. China Catena 54, 197–213. Gat, J.R., 1996. Oxygen and hydrogen isotopes in the hydrological cycle. Annu. Rev. Earth Planet. Sci. 24, 225–262. Gazis, C., Feng, X., 2004. A stable isotope study of soil water: evidence for mixing and preferential flow paths. Geoderma 119, 97–111. Girard, Hillaire-Marcel, C., Oga, M.S., 1997. Determining the recharge mode of Sahelian aquifers using water isotopes. J. Hydrology 197, 189–202. Hsieh, J.C.C., 1998. Oxygen isotopc composition of soil water: quantifying evaporation and transpiration. Geoderma 82, 269–293. Jackson, R.B., 2000. Root distributions and carbon and water fluxes. GCTE News 15, 10–15. Kortelainen, N.M., Karhu, J.A., 2004. Regional and seasonal trends in the oxygen and hydrogen isotope ratios of Finnish groundwaters: a key for mean annual precipitation. J. Hydrol. 285, 143–157. Landon, M.K., Delin, G.N., Komor, S.C., Regan, C.P., 2000. Relation of pathways and transit times of recharge water to nitrate concentrations using stable isotopes. Ground Water 38 (3), 381–395. Liu, Y., Liu, F., Dorland, E., 2015. Variations of soil water isotopes and effective contribution times of precipitation and throughfall to alpine soil water, in Wolong Nature Reserve, China. Catena 126, 201–208. Liu, Y.H., 2011a. Biomass and its allocation of the main vegetation types in liupan mountains. For. Res. 24 (4), 443–452. Liu, Y.H., 2011b. Analyzing relationships among water uptake patterns, rootlet biomass distribution and soil water content profile in a subalpine shrubland using water isotopes. Eur. J. Soil Biol. 47, 380–386. Mathieu, R., Bariac, T., 1996. An isotopic study (2 H and 18 O) of water movements in clayey soils under a semiarid climate. Water Resour. Res. 32 (4), 779–789. McCole, Amy A., Stern, Libby A., 2007. Seasonal water use patterns of Juniperus ashei on the Edwards Plateau, Texas, based on stable isotopes in water. J. Hydrogen 342, 238–248.

233

Mellander, P.E., Bishop, K., 2004. The influence of soil temperature on transpiration: a plot scale manipulation in a young Scots pine stand. For. Ecol. Manage. 195, 15–28. Mendel, M., Hergarten, S., Neugebauer, H.J., 2003. Water uptake by plant roots-a multi-scale apporch. In: Neugebauer, H.J., simmer, C. (Eds.), Dynamics of Multi-scale Earth System, Lecture Notes in Earth Sciences, 97. Springer, Heidelberg, pp. 215–232. Mortathi, J., Moraes, J.M., Rodrigues, J.C., Victoria, R.L., Martinelli, L.A., 1997. Hydrograph separation of the Amazon River using 18 O as an isotopic tracer. Sci. Agric. 54 (3), 167–173. Padilla, I.Y., Yeh, T.C.J., Conklin, M.H., 1999. The effect of water content on solute transport in unsaturated porous media. Water Resour. Res. 35, 3303–3313. Philips, D.L., 2001. Mixing models in analyses of diet using multiple stable isotope: a critique. Oecologia 127, 166–170. Rosen, M.R., Bright, J., Carran, P., Stewart, M.K., Reeves, R., 1999. Estimating rainfall recharge and soil water residence times in Pukekohe, New Zealand, by combining geophysical, chemical, and isotopic methods. Ground Water 37 (6), 836–844. Ruck, L., Mayer, B., 2007. Isotope hydrology of the Oldman river basin, southern Alberta, Canada. Hydrol. Process 21, 3301–3315. Schachtschneider, K., February, E., 2007. Unravelling the mystery of tree water uptake along a Namibian ephemeral river–Which tree gets what and from where? S. Afr. J. Bot. 73, 310–311. Shan, C.J., Liang, Z.S., Hao, W.F., 2003. Review on growth of locust and soil water in Loess Plateau. Acta Bot. Borea. Sin. 23 (8), 1341–1346 (in Chinese with English abstract). Tang, K.L., Feng, X.H., 2001. The effect of soil hydrology on the oxygen and hydrogen isotopic compositions of plants’ source water. Earth Planet. Sci. Lett. 185, 355–367. Tian, L.D., Yao, T.D., Tsujimura, M., Sun, W.Z., 2002. Stable isotope in soil water in the middle of Tibet plateau. Acta Pedol. Sin. 39 (3), 289–295 (in Chinese with English abstract). Xue, G.L., 1995. The mechanism of recharge and occurrence of the groundwater in loess areas. Hydrogeol. Eng. Geol. 22 (1), 39–40 (in Chinese with English abstract). Yu, X., Zhang, J., Zhu, J., 1996. Analysis and evaluation of soil water conditions of protective forest ecosystem in Loess area. Sci. Silvae Sin. 32 (4), 289–297 (in Chinese with English abstract). Zencich, S.J., Froend, R., Turner, H., et al., 2002. influence of groundwater depth on the seasonal sources of water accessed by Banksia tree species on a shallow, sandy coastal aquifer. Oecologia 131, 8–19. Zimmermann, U., Munnich, K.O., Roether, W., Kreutz, W., Schubach, K., Siegel, O., 1967. Tracers determine movement of soil moisture and evapotranspiration. Science 152 (3720).