J Soils Sediments (2018) 18:791–803 DOI 10.1007/s11368-017-1815-0

SOILS, SEC 2 • GLOBAL CHANGE, ENVIRON RISK ASSESS, SUSTAINABLE LAND USE • RESEARCH ARTICLE

Root-induced changes to soil water retention in permafrost regions of the Qinghai-Tibet Plateau, China Zeyong Gao 1,2,3 & Fujun Niu 1 & Yibo Wang 3 & Zhanju Lin 3 & Jing Luo 3 & Minghao Liu 3

Received: 30 March 2017 / Accepted: 15 August 2017 / Published online: 29 August 2017 # Springer-Verlag GmbH Germany 2017

Abstract Purpose Soil water retention plays a crucial role in regulating soil moisture dynamics, water circulation, plant growth, contaminant transport, and permafrost stability, and it is an issue of concern in water-limited ecosystems. However, our understanding of the relationship between plant roots and soil water retention is still relatively poor in the alpine grasslands of permafrost regions. To addresses this, our study evaluated the effect of plants on the soil water retention in permafrost regions of the Qinghai-Tibet Plateau. Materials and methods Three alpine grassland sites were identified and characterized as alpine wet meadow (AWM), alpine meadow (AM), and alpine steppe (AS). Root biomass, soil water retention, and soil physico-chemical properties were examined in the top 0–50 cm of active layer in the three experimental sites in the hinterland of the Qinghai-Tibet Plateau (QTP). Pedotransfer functions (PTFs) and Retention Curve program (RETC) were employed to illustrate how the plant roots affect soil water retention.

Results and discussion Approximately 80, 65, and 60% of root biomass was distributed in the top 0–20 cm in the AWM, AM, and AS soil, respectively. Soil water retention was enhanced with the presence of plant roots; thereinto, the highest values of field capacity were found in AWM soil: on average, about 0.45 cm3 cm−3. Field capacity of AWM soil was almost twice as high as that of AM soil, and triple higher than that of AS soil. Correlation and regression analysis showed that root-induced changes to soil water retention were caused by altering the soil organic matter and soil structure. In addition, we evaluated the Retention Curve (RETC) program’s performance and found that the program underestimated soil water retention if the effects of plant roots were not considered. Conclusions A lack of alpine plants is associated with a decline in soil physical conditions and soil water retention in permafrost regions, and the function of plant roots should be considered when predicting hydrological processes. Keywords Alpine grassland . Permafrost region . Qinghai-Tibet Plateau . Root . Soil water retention

Responsible editor: Yongping Wei * Fujun Niu [email protected]

1

State Key Laboratory of Frozen Soil Engineering, Northwest Institute of Eco-Environment and Resources, CAS, 320 Donggang West Road, Lanzhou, Gansu Province 730000, People’s Republic of China

2

University of Chinese Academy of Sciences, 19 Yuquan Road, Beijing 100049, People’s Republic of China

3

College of Earth and Environment Sciences, Lanzhou University, 222 Tianshui South Road, Lanzhou, Gansu Province 730000, People’s Republic of China

1 Introduction Soil hydrological properties are key factors that control water flow and solution transport in soil (Zeng et al. 2013), and they influence ecosystem responses to changes in precipitation and climate warming by partitioning rainwater among runoff, evaporation, and transpiration components (Bens et al. 2007; Weng and Luo 2008). As one of the vital soil hydrological properties, soil water retention is commonly described as the constructive relationship between matric potential and soil water content. Knowledge of soil water retention is indispensable for solving many water and soil management problems

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related to environmental, ecological, and agricultural issues, especially in water-limited regions (García-Fayos et al. 2000; Rawls et al. 2003; Wang et al. 2013; Wu et al. 2014a, b). Moreover, soil water retention is dependent on numerous factors, including soil properties (e.g., organic matter content, particle size distribution, and soil structure) (Rawls et al. 2003; Yang et al. 2014), vegetation (Archer et al. 2002; Wang et al. 2008, 2013; Leung et al. 2015b), additives (Lei and Zhang 2013; Andrenelli et al. 2016; Wong et al. 2017), human and animal activities (Mahe et al. 2005; Smagin and Prusak 2008; Liu et al. 2013), and wildfires (Ebel 2012). Because these factors vary in time and space (Strudley et al. 2008; Zhou et al. 2008), soil water retention does not change in the same pattern, even to a limited extent. Additionally, direct measures of soil water retention in different matrices are time-consuming and costly. In light of these difficulties, some pedotransfer functions (PTFs) have been developed to predict soil water retention based on organic matter and soil texture data (Saxton and Rawls 2006; Ghanbarian-Alavijeh et al. 2010). Because PTFs are generally built for different purposes, it is necessary to evaluate their practicality in a variety of ways. Soil and vegetation are two important interrelated components of grasslands, and they interact and cannot be separated from each other (Yi et al. 2012; Wang et al. 2017b). Plant roots link the soil and the vegetation and have great significance in community succession, soil property, and hydrology. Some recent studies have shown that plant roots are a crucial factor in soil hydrological properties. On the one hand, as root growth, rhizodeposition, and repeated dry-wet cycles change soil texture, soil structure, soil mineralogy, etc., plant roots affect soil hydrological properties (Archer et al. 2002; Augé et al. 2001; Scholl et al. 2014), which in turn exert a major influence on catchment hydrology. On the other hand, soil organic matter is composed largely of major metabolites of plant roots, and it is one of the earth’s largest reservoirs of actively cycled carbon, playing a vital role in various ecosystem functions, and containing carbohydrates, proteins, lipids, phenol-aromatics, protein-derived and cyclic nitrogenous compounds, and some still unknown compounds (Paul 2016). Soil organic matter plays a leading role in controlling soil hydrological properties by changing soil structural parameters and soil adsorptive parameters (Saxton and Rawls 2006; Yang et al. 2014), especially in coarse-textured soils (Rawls et al. 2003). Soil organic matter also influences soil structure through its positive effect on soil aggregate formation and stability (Huntington 2007). Soil organic matter changes soil water adsorptive properties by modifying the availability of adsorption sites of clay minerals (Cristensen 1996). In addition, some studies have also shown plant roots can increase air entry values and the size of the hysteresis loop, and so induce higher suction and enhance soil water retention (Leung et al. 2015a, b). Although numerous studies have been conducted, there is a dearth of information available in the literature about

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the relationship between alpine plant roots and soil properties, especially with respect to soil water retention in the QinghaiTibet Plateau (QTP). The QTP is often called Bthe third pole of the Earth^ due to its high elevation and cold climate (Qiu 2008). Alpine grassland covers more than 60% of the total area of the plateau, and this grassland can be divided into three main subcategories based on different soil and plant species types: alpine wet meadow, alpine meadow, and alpine steppe. In QTP, permafrost is important to maintain the existing ecosystems, but in recent years a trend toward permafrost degradation has been increasing due to the warming climate (Wu and Zhang 2008; Gao et al. 2010, 2015; Wu et al. 2015). As a result of the thaw of permafrost, alpine grassland is gradually changing. However, scientists hold different views on how permafrost degradation will change alpine ecosystems. Wang et al. (2006) believed that the vegetation coverage and biomass would undergo a significant reduction with the increase of the active layer. Chen et al. (2014) speculated that a warm-wet climate and less human activity might benefit alpine grassland growth in permafrost regions, similar to what has been found in continental Antarctica (Guglielmin et al. 2014). Contrary to the above findings, a study based on MODIS Aqua products found that alpine grassland would respond differently to permafrost degradation in different types of permafrost areas (Yi et al. 2011, 2014). There is thus still a possibility of unexpected changes in alpine ecosystems in the future. It is well known that energy and soil water are important limiting factors affecting the growth of alpine vegetation under climate warming in permafrost regions (Yi et al. 2011). Thus, the qualitative and quantitative study of the relationship between vegetation and soil properties, especially soil water retention, is critical to predict changes in the alpine ecosystems in the future. Root-induced changes to soil hydrological properties have been widely reported (Archer et al. 2002; Bengough 2012; Leung et al. 2015b). However, there has been little information on how soil water retention responds to alpine plant roots in the permafrost areas of the QTP. Consequently, the aims of this study were: (1) to characterize and compare the distribution of root biomass in different alpine ecosystems; (2) to examine the relations among soil water retention, plant roots, and soil physico-chemical properties; and (3) to evaluate the parameters of the van Genuchten (VG) model in different alpine ecosystems. The results from this study would identify reliable parameters for models that can better predict the hydrological processes on the QTP.

2 Materials and methods 2.1 Experimental sites The field test sites were located in the Beiluhe basin in the Hoh Xil National Nature Reserve, Qinghai Province,

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China (Fig. 1). The elevation of the area is almost 4600 m a.s.l. The area has an arid climate with a mean air temperature of −3.8 to −5.0 °C and a mean annual precipitation of nearly 370 mm (data collected from 2003 to 2006 at the Belihe Meteorological Station). It should be noted that about 90% of the precipitation falls between June and September in the form of rainfall, and the other 10% occurs from midSeptember to mid-June as snow; the growing season is from May to October. Additionally, the Beiluhe basin is underlain by continuous permafrost 50–80 m thick, with a high percentage of ground ice and typical periglacial phenomena such as thaw slumping and thermokarst depressions (Luo et al. 2015). The soil types of the alpine wet meadow, alpine meadow and alpine steppe are Gleysols, Chernozems, and Arenosols in the WRB soil classification system, respectively (IUSS Working Group WRB 2015). In the study area, non-degraded alpine wet meadow, alpine meadow, and alpine steppe are the dominant vegetation types. We therefore chose these three kinds of ecosystems as the research subjects. The environmental characteristics of the study sites are shown in Table 1. 2.2 Experimental design and sampling methods During August of 2015, a randomized block design with three replications was set up for biomass and soil sampling in each of the three vegetation types: the alpine wet meadow, alpine meadow, and alpine steppe. The three land cover type sites were located within 200 m and soil environment had not been affected

Fig. 1 Location of study sites

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by human activities. The soil pits were excavated with shovel and spade. Soil samples were taken at 0–10, 10–20, 20–30, 30–40, and 40–50 cm depths in 25 cm × 25 cm quadrates from exposed soil profile. The soil blocks were then washed (separately) with water, and the live roots were separated according to their color, consistency, and attached fine roots. The root samples were then transported to the laboratory to determine plant biomass. In each soil profile, undisturbed soil samples were taken using a cutting ring sampler of 100 cm3 volume (5 cm in height) at 10-cm intervals from 0 to 60 cm soil, and the cutting ring sampler was inserted in a vertical direction at each soil depth, and kept intact in the sampler, then sealed the samples and transported to laboratory until analysis. These samples were used to determine soil bulk density and soil water release curves. At each depth interval, 1-kg soil sample was taken for determination of particle size distribution and soil organic matter content. 2.3 Laboratory analysis In the laboratory, water release curves were measured for each sample using the centrifugation method (Xing et al. 2017). To test soil saturated water content, the samples were soaked in distilled water for 24 h and then weighed. Then, a HITACHI CR21G centrifuge (Hitachi Koki Co., Ltd. Japan) was employed to measure soil water release curves and soil volumetric water content at suctions of −1, −10, −30, −50, −80, −100, −300, −500 and −700 kPa. The centrifugal direction was consistent with the soil sampling direction. In addition,

794 Table 1

J Soils Sediments (2018) 18:791–803 Environmental characteristics of the study sites

Parameters

Alpine wet meadow

Alpine meadow

Alpine steppe

Latitude

34° 49′ 36.0″ N

34° 49′ 33.2″ N

34° 49′ 37.6″ N

Longitude Altitude/m

92° 55′ 19.3″ E 4642 93 ± 4 36.2 ± 2.4 8.2 ± 2.7

92° 55′ 18.4″ E 4646 87 ± 3 24.1 ± 2.9 9.5 ± 1.9

92° 55′ 15.6″ E 4651 56 ± 5 12.8 ± 2.0 11.0 ± 3.5

2.1 Kobresia tibetica

2.4 Kobresia capillifolia,

2.7 Carex moorcroftii

Kobresia humilis Kobresia capillifolia

Kobresia pygmaea, Lobularia maritima,

Festuca ovina L. Pedicularis cyathophylla,

Poa annua L.

Carex atrata,

Potentilla fruticosa L.

Aster flaccidus Bge.

Saussurea arenaria

Polygonum viviparum L.

Vegetation cover/%a Mean soil moisture/%b Mean soil temperature/°Cb Active layer thickness/m Dominant species

Values are mean ± standard error a

The vegetation coverage was measured using multi-spectral camera (ADC, Tetracam Inc., Chatsworth, CA, USA)

b

Soil moisture and soil temperature were measured in the 0–50 cm with Moisture Meter (HH2, Delta-T Devices Ltd., Burwell, Cambs, UK), and they were mean values in 0–50 cm

the soil samples were oven dried at approximately 40 °C and the standard loss-on-ignition (LOI) method was employed to determine soil organic matter (SOM) content (Pansu and Gautheyrou 2006). The undisturbed soil samples were oven dried at 105 °C to determine soil bulk density, and the extracted plant roots were oven dried at 65 °C for 72 h to determine root biomass. The samples were then air-dried and sieved using a 2mm sieve, and a Malvern Master Sizer 2000 laser grain-size analyzer (Malvern Instruments, Malvern, UK) was employed to measure soil particle size. We used the following particle size classes: 2000 um < sand > 50 um, 50 um < silt > 2 um, and clay < 2 um. The results of soil physical-chemical properties were presented in Table 2. In addition, the soil profiles were divided into three categories according to the length of plant roots: upper root zone (URZ) in 0–20 cm, middle root zone (MRZ) in 20–40 cm, and lower root zone (LRZ) in 40–50 cm. To calculate the soil water retention curves, the soil bulk density and soil particle size data were input to the Retention Curve program (RETC) that was developed by the US Salinity Laboratory. The program uses the statistical pore-size distribution models of Mualem and Burdine to obtain a predictive equation for the unsaturated hydraulic conductivity function in terms of soil water retention parameters. In this study, we adopted the Mualem model. The expressions of van Genuchten (1980) are given by Eq. (1): θðψÞ ¼ θr þ

θs −θr m ½1 þ ðαjψjÞn 

ð1Þ

where θ(Ψ) is the volumetric water content (cm3 cm−3), θs is the saturated volumetric water content (cm3 cm−3), θr is the residual volumetric water content (cm3 cm−3), ψ is the value of

the matric potential (kPa), and α is an empirical parameter related to the inverse of the air entry suction (kPa−1); in m = 1–1/n, m and n are dimensionless curve-shape parameters. We used the measured data to fit the van Genuchten (VG) model, which considered the effect of plant roots on soil water release curves. Then, we compared the differences between the fitted curves and the calculated curves from the RETC program. In the samples of soil with plant, θs was measured from laboratory, and θr was estimated by using the RETC (Retention Curve) program to fit measured soil water retention data and the parameters of α, n, and m were determined from the measured data. In the samples of soil without plant, the parameters of van Gennuchten (1980) was estimated by using the RETC program, which only required the data of soil particle Table 2 Characteristics of the soil physical-chemical properties in study sites Ecosystem type

Soil depth (cm)

Sand (%)

Silt (%)

Clay (%)

SOM (%)

BD (g cm−3)

AWM AWM AWM AM AM AM AS AS AS

0–20 20–40 40–50 0–20 20–40 40–50 0–20 20–40 40–50

67.65 62.93 60.01 79.99 84.51 83.32 89.43 92.30 94.05

28.14 32.59 33.83 16.89 12.96 14.55 8.54 6.13 4.27

4.21 4.48 6.17 3.11 2.54 3.13 2.03 1.58 1.68

9.71 11.48 11.76 6.60 7.00 7.38 5.44 5.67 5.84

0.79 0.96 1.09 0.98 1.24 1.33 1.31 1.32 1.42

AWM alpine wet meadow, AM alpine meadow, AS alpine steppe, SOM soil organic matter, BD bulk density

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size and soil bulk density. In this study, the soil water content at 0 kPa matric potential was used to evaluate the soil saturated water content, the soil water content at −10 kPa matric potential was used to evaluate the field capacity (Richards and Weaver 1944), and the soil water content at −700 kPa matric potential was used to evaluate the soil water retention in stable phase.

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respectively. In the MRZ, the proportions of root biomass were 29.5% (AM), 26.8% (AS), and the distinctly lower 18.4% (AWM). Similarly, root biomass in the LRZ in AS occupied a greater proportion of total root biomass than in AWM and AM. These results indicate that the distribution of root biomass was significantly different among AWM, AM, and AS over the whole soil profile.

2.4 Statistical analysis Pearson correlation analysis was employed to identify the relationships between soil water retention, root biomass, and soil physico-chemical properties. Stepwise regression was then conducted to identify the key predictive variables for soil water retention. All the statistics were calculated in SPSS 22.0 (SPSS Inc., Chicago, IL, USA).

3 Results 3.1 Root biomass Total root biomass differed significantly among alpine grassland ecosystems, and was highest in AWM sites and lowest in AS sites (Fig. 2). The vertical distribution of root biomass was distinctive in AWM, AM, and AS ecosystems, with values of 11,037, 1633, and 583 g m−2 in the 0- to 10-cm layer, respectively. With increasing of soil depth, root biomass decreased exponentially at the study sites: in the lower root zone; the root biomass was 1209 g m−2 (AWM), 128 g m−2 (AM), and 184 g m−2 (AS). It should be noted that the root biomass of AS was higher than AM in the lower root zone, which may be related to differences in the dominant species. The contributions of the root portions to total root biomass differed markedly (Fig. 3). The vertical stratified portion of root biomass distributed in the URZ constituted approximately 80, 67, and 61% of root biomass in the AWM, AM, and AS,

Fig. 2 Vertical distribution of root biomass in different alpine ecosystems

3.2 Soil water retention The soil water release curves revealed the relationship between soil suction and soil water content, which indicated the variations of soil water retention. The results showed that soil water content was significantly different in ecosystems and root zones (Fig. 4a). Overall, the soil water content was higher in the AWM sites, followed by the AM sites and the AS sites, in the range of 0 ~ − 700 kPa matric potential. With increasing of matric potential, soil water content first decreased sharply (in the 0 ~ − 30 kPa phase), then decreased moderately (in the −30 ~ − 100 kPa phase), and finally the soil water release curves became nearly flat (in the −100 ~ − 700 phase) (Fig. 4b, c, d). In the AWM sites (Fig. 4b), the soil water content values were 62.6% (URZ), 63.8% (MRZ), and 57.6% (LRZ) under saturated conditions. In the range of 0 ~ − 700 kPa matric potential, the URZ held more water, as the rate of soil water content fell from saturation to − 700 kPa more quickly in the MRZ and LRZ than in the upper root zones. The soil water content values were 22.3% (URZ), 18.3% (MRZ), and 17.3% (LRZ) in − 700 kPa matric potential. In the AM sites (Fig. 4c), the URZ held more water than the MRZ and LRZ in the range of 0 ~ − 700 kPa; the differences in soil water retention between the MRZ and LRZ were nearly indistinguishable. The

Fig. 3 Vertically stratified proportion of below-ground biomass. a Alpine wet meadow. b Alpine meadow. c Alpine steppe

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3.3 Relationships between root biomass, soil physico-chemical properties, and soil water retention There are some soil properties controlling soil water retention, such as soil texture, soil organic matter content, soil structure and soil mineralogy, etc. Thus, it is necessary to evaluate the relationship between root biomass, soil physico-chemical properties, and soil water retention. As shown in Table 3, soil physico-chemical properties and root biomass were significantly correlated

Fig. 4 Variation in soil water content at 0 ~ − 700 kPa matric potentials. a Changes in soil water content in different ecosystems. b Changes in soil water content along with soil matric potential in alpine wet meadow sites.

Saturated water content/ (cm3 cm-3 )

0.8 0.7

a

AWM

AS

0.5 0.4 0.3 0.2 0.1 0

0.8 0.7

Field capacity/ (cm3 cm-3 )

AM

0.6

URZ

b

MRZ

AWM

LRZ

AM

AS

0.6 0.5 0.4 0.3 0.2 0.1 0 URZ 0.8

Stabilized water content/ (cm3 cm-3)

soil water content values were 12.1% (URZ), 8.0% (MRZ), and 7.4% (LRZ) in − 700 kPa. In the AS sites (Fig. 4d), the URZ could hold more soil water in the range of 0 ~ − 700 kPa, and the soil water retention of the MRZ and LRZ were not distinguished obviously in the range of 0 ~ − 700 kPa. The soil water content values were 7.1% (URZ), 5.6% (MRZ), and 5.1% (LRZ) in − 700 kPa. In addition, the differences of soil saturated water content, field capacity, and soil water content in the stabilization stage was compared in different ecosystems and root zones (Fig. 5). The results showed that AWM soil hold higher saturated water content than AM and AS soils, and the difference of saturated water contents were not obvious in AM and AS soils (Fig. 5a). The highest values of field capacity were found in AWM soil: on average, about 0.45 cm3 cm−3. The field capacity of AWM soil was almost twice as high as that of AM soil, and triple as that of AS soil. And also, soil field capacity in URZ was obviously higher than those in MRZ and LRZ (Fig. 5b). In addition, the highest values of soil water content at − 700 kPa matric potential were also found in AWM soil: on average, about 0.19 cm3 cm−3. And the water content of AWM soil was almost twice as high as that of AM soil, and triple as that of AS soil (Fig. 5c). In brief, the variations of soil water retention were generally consistent with the changes of SOM.

J Soils Sediments (2018) 18:791–803

0.7

c

MRZ

AWM

LRZ

AM

AS

0.6 0.5 0.4 0.3 0.2 0.1 0.0 URZ

MRZ

LRZ

Fig. 5 Variations of soil water content in different ecosystems and locations. a Saturated water content. b Field capacity. c Stabilized water content. AWM alpine wet meadow, AM alpine meadow, AS alpine steppe, URZ upper root zone, MRZ middle root zone, LRZ lower root zone

c Changes in soil water content along with soil matric potential in alpine meadow sites. d Changes in soil water content along with soil matric potential in alpine steppe sites

J Soils Sediments (2018) 18:791–803 Table 3

RB

797

Pearson correlations between soil water retention capacity, root biomass, and soil physico-chemical properties RB

BD

SOM

1

Sandy

Silt

* ** **

**

*

BD SOM

−0.81 0.40

1 −0.67

* 1

**

* **

Sandy Silt

−0.37 0.40

0.64 −0.67

−0.90 0.92

1 −0.94

** 1

0.15 0.62 0.60 0.62 0.66 0.67 0.77

−0.36 −0.70 −0.71 −0.87 −0.88 −0.90 −0.94

0.72 0.76 0.79 0.93 0.91 0.91 0.86

−0.93 −0.59 −0.67 −0.91 −0.85 −0.84 −0.78

Clay θ0kPa θ-1kPa θ-10kPa θ-50kPa θ-100kPa θ-700kPa

0.90 0.6 0.69 0.93 0.87 0.86 0.81

Clay

1 0.33 0.44 0.72 0.63 0.60 0.52

θ0kPa

θ-1kPa

θ-10kPa

* *

* *

** **

* ** 1 0.83 0.83 0.83 0.85

1 0.98 0.78 0.80 0.81 0.84

θ-50kPa

θ-100kPa

θ-700kPa

*

*

*

** **

** **

** **

** **

** **

* **

* **

* * ** 1 0.99 0.99 0.97

* ** ** 1 0.99 0.98

** ** ** ** 1 0.99

** ** ** ** ** 1

θ0kPa, θ−1 kPa, θ−10kPa, θ−50kPa, θ−100kPa, and θ−700kPa represent soil water content (cm3 cm−3 ) at 0, −1, −10, −50, −100, and −700 kPa, respectively RB root biomass (g m−2 ), BD bulk density (g cm−3 ), SOM soil organic matter (%) *P < 0.05; **P < 0.01

with soil water retention under different matric potential. Root biomass, soil organic matter, and silt content exhibited a significant positive correlation with soil water retention, while soil bulk density and sand content exhibited a significant negative correlation with soil water retention. Clay concentration was not significantly correlated with soil water retention except at − 10 kPa matric potential. In addition, soil water content values under different matric potential levels were significantly inter-correlated (p < 0.05). Similarly, root biomass was significantly positively correlated with soil organic matter (p < 0.05) and negatively correlated with soil bulk density (p < 0.01). In order to eliminate the influence of overlapping factors on soil water retention, the method of forward stepwise regression Table 4

was employed to illustrate how the plant roots effect on soil water retention (Table 4). At saturated and near-saturated conditions, soil organic matter was the dominant factor for predicting soil water retention (p < 0.05). When the matric potential became lower (< − 10 kPa), soil bulk density stepped into the PTFs, companied with soil organic matter and silt content, and significantly improved the relations. In addition, at some matric potentials, such as −50, −100, and −700 kPa, there were two or three PTFs, and the more selective the predictors, the higher the determination coefficient (R2). The results showed that soil bulk density and soil organic matter were the two most important factors to explain the variation of soil water content under different matric potentials. In addition, the relationship between soil bulk density and soil organic matter can be described by following

PTFs for soil water retention

Matric potential (kPa)

Model

PTFs

R-squareda

0 −1 −10

1 1 1

θ = 0.017 × SOM + 0.414* θ = 0.021 × SOM + 0.368* θ = 0.007 × Silt − 0.249 × BD + 0.467***

0.58 0.62 0.97

−50

1 2 1 2 1 2 3

θ = 0.034 × SOM − 0.083** θ = 0.022 × SOM − 0.213 × BD + 0.262*** θ = 0.03 × SOM − 0.076** θ = 0.018 × SOM − 0.199 × BD + 0.246*** θ = − 0.281 × BD + 0.44** θ = 0.011 × SOM − 0.197 × BD + 0.259*** θ = 3.295 × 10−6 × RB + 0.012 × SOM − 0.133 × BD + 0.163***

0.84 0.97 0.82 0.97 0.88 0.97 0.99

−100 −700

PTFs pedotransfer functions, θ soil water content (v/v), SOM soil organic matter (%), Silt silt content (%), BD bulk density (g cm−3 ), RB root biomass (g m−2 ) *P < 0.05; **P < 0.01; ***P < 0.001 a

R-squared is multiple correlation coefficient, which is calculated by comparing measured data to the simulated data

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Soil water retention exhibited obvious differences between soils with and without roots (Fig. 6). In the same soil type, soil with roots exhibited a stronger soil water retention than

soil without roots at the AWM, AM, and AS sites (Fig.6). The results indicate that soil water retention in alpine ecosystems is often underestimated by the RETC procedure if plant roots are not considered. It is therefore necessary to re-evaluate the parameters of the van Genuchten (1980) model to fit the soil water retention curves and simulate water movement better. We used the test data to fit the VG model given the values of the parameters (Fig. 6, Table 5). In the VG model, soil with roots enhanced the values of θs and α, and reduced the values of n, m compared with soil without roots, the values of θs were not showed an obvious variation when soil with plant or not. In alpine wet meadow sites, the mean values of θs, θr, α, n, and m were 0.614 cm3 cm−3, 0.042 cm3 cm−3, 0.350 kPa−1, 1.262, and 0.207, respectively. In alpine meadow, the mean values of θs, θr, α, n, and m were 0.509 cm3 cm−3, 0.040 cm3 cm−3, 0.459 kPa−1, 1.418, and 0.293, respectively. And in alpine steppe sites, the mean values of θs, θr, α, n, and m were 0.525 cm3 cm−3, 0.045 cm3 cm−3, 0.489 kPa−1, 1.727, and 0.418, respectively.

Fig. 6 Comparison of measured data and models fit using the van Genuchten (1980) approach. a, b, and c represent the differences in the upper root zone, middle root zone, and lower root zone of alpine wet meadow sites; d, e, and f represent the differences in the upper root zone, middle root zone, and lower root zone of alpine meadow sites; g, h, and i

represent the differences in the upper root zone, middle root zone, and lower root zone of alpine steppe sites. Gray circles represent measured data, the red solid line represents the fitted curves from the measured data, and the blue solid line represents the simulated curves from the RETC program

regression equation (Eq. (2): BD ¼ −0:059  SOM þ 1:63ðp < 0:05; n ¼ 105Þ

ð2Þ

where BD is soil bulk density (g cm−3), and SOM is soil organic matter (%). This relationship is not limited to the QTP, but holds true everywhere. This equation here again demonstrates the fact that SOM is the controlling factor on soil bulk density as elsewhere in the world (Michaelson et al. 2013). The value of this equation is for pedotransfer in the environment sittings of mid-central QTP.

3.4 The effect of root biomass on the predictions of the VG model

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As shown in Table 5, the fitted VG model can explain over 98% of the variation in soil water content for AWM, AM, and AS soil. It is therefore reasonable to believe that the modified parameters of the VG model can successfully simulate the changes in soil water retention in alpine ecosystems. We also compared the differences in the soil water retention curves between the soil with and without roots (Fig. 7). The results showed that the differences were just the reverse of the distribution of root biomass except that in lower root zone of alpine wet meadow (Fig. 7a). In general, the differences were greater in alpine wet meadow, followed by alpine meadow, and lowest in alpine steppe. The values of Δθ could reach almost 0.12 cm3 cm−3 in the upper root zone of alpine wet meadow. On the contrary, the values of Δθ were very low in the alpine steppe and they could be ignored. Those results indicated that the RETC program can simulate soil water retention well when plant roots concentration is lower.

4 Discussion 4.1 Response of root biomass to soil structure Alpine grassland in the QTP exhibited the shallower root distribution, with approximately 85% of total root biomass Table 5

Fig. 7 The difference in soil water content between the soil without plant model and soil with plant model in the 0 ~ − 700-kPa matric potential. Δθ was estimated by the difference between soil with plant and without plant. AWM alpine wet meadow, AM alpine meadow, AS alpine meadow

occurring in the top 30 cm of the soil profile. Those results lie in the range of previous studies in the QTP (Yang et al. 2009), and they may be due to physical barriers inhibiting root growth in cold regions, such as soil temperature, soil moisture, nutrient supply, and permafrost (Yang et al. 2009, 2013; Wang et al. 2016). Although the alpine plants have shallower root distribution, they are very important in maintaining the stability of the permafrost and hydrological processes (Runyan and

The parameters of the VG model

Position

Model

θs (cm3 cm−3)

θr (cm3 cm−3)

α (kPa−1)

n

m

R-squared

AWM-URZ

Without plant

0.557

0.043

0.224

1.379

0.275

–

AWM-MRZ

With plant Without plant With plant

0.626 0.487 0.638

0.041 0.039 0.040

0.411 0.180 0.430

1.224 1.420 1.275

0.183 0.296 0.216

0.986 – 0.992

Without plant With plant Without plant With plant Without plant With plant Without plant With plant Without plant With plant Without plant With plant Without plant With plant

0.450 0.576 0.522 0.518 0.453 0.495 0.425 0.512 0.439 0.568 0.438 0.486 0.410 0.521

0.040 0.045 0.040 0.038 0.041 0.041 0.041 0.042 0.046 0.044 0.048 0.048 0.042 0.044

0.165 0.207 0.456 0.471 0.458 0.492 0.428 0.414 0.418 0.471 0.396 0.593 0.347 0.401

1.447 1.286 1.463 1.326 1.818 1.470 1.879 1.458 2.324 1.624 2.704 1.666 3.265 1.892

0.309 0.222 0.316 0.246 0.450 0.320 0.468 0.314 0.570 0.384 0.630 0.400 0.694 0.474

– 0.998 – 0.992 – 0.995 – 0.999 – 0.991 – 0.997 – 0.998

AWM-LRZ AM-URZ AM-MRZ AM-LRZ AS-URZ AS-MRZ AS-LRZ

θs saturated volumetric water content (cm3 cm−3 ); θr residual volumetric water content (cm3 cm−3 ); α inverse of the matric suction at the inflection point of the water retention curve (kPa−1 ); in m = 1–1/n, m and n are dimensionless curve-shape parameters; AWM alpine wet meadow; AM alpine meadow; AS alpine steppe; URZ upper root zone; MRZ middle root zone; LRZ lower root zone; R-squared multiple correlation coefficient, which is calculated by comparing measured soil water retention data to the van Genuchten (1980) model fit

800

D’Odorico 2012; Wang et al. 2012, 2014). As a controlling factor of soil hydrological properties, soil structure is strongly affected by plant roots, which induce changes to soil conditions in the following ways. Firstly, the presence of thin roots in shallow layers blocks the channels of soil porosity, and impedes deep-water percolation (Archer et al. 2002; Wang et al. 2014), while enhancing the strength of soil freeze-thaw cycles under conditions of sufficient water supply. This decreases the void ratio of loose soils and homogenizes the soil structure (Qi et al. 2006). Moreover, there is a close relationship between plant roots and soil aggregate stability (Fig. 8). On the one hand, a significant increase in soil aggregate stability can be observed with the growth of plant roots (Vergani and Graf 2015). On the other hand, the plant roots can protect the finer particles (silt and clay) from wind erosion (Fig. 8). As a result, the alpine wet meadow was less sandy and had a higher content of silt and clay than alpine meadow and alpine steppe (Table 2). Secondly, soil porosity frequently varies with the growth, development, and decay of the plant roots. Studies have shown that the number of macropores and mesopores are often higher in sandy soil than in silty or clayey soils, but their connectivity is greatly reduced (Bens et al. 2007). Thus, the poor connectivity of macropores and mesopores works against water retention capacity in sandy soil. However, soil macropores and mesopores are quickly changed to micropores through the development of plant roots (Bengough 2012) (Fig. 8). Moreover, total porosity is increased by the presence of plant roots (Fig. 8). This can be obtained from Table 5, which shows the saturated soil water content was higher when plant roots were considered. These results were consistent with the study of Yuge et al. (2012), who concluded that soil porosity was formed by crop roots. Scanlan and Hinz (2010) also developed a model to predict the effect of roots on soil hydraulic properties, based on the assumption that the geometry of roots within pore space can be represented by

Fig. 8 A hypothetical packing pattern of particle in the same soil. a without root biomass; b with root biomass

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concentric cylinders. Root-induced changes to soil water retention mainly depend on pore space, and the size of the effect depends upon soil texture, root decay, and the connectivity of root-modified pores. In addition, the extent of the effect of plant roots on soil structure and soil water retention is different, and depends on plant species. In alpine wet meadow and alpine meadow, the dominant species are Kobresia tibetica, Kobresia capillifolia, and Kobresia pygmaea, which have abundant fine roots. These plants form thick matting that affects the soil structure (Archer et al. 2002), thereby enhancing water retention within the soil. The dominant species in alpine steppe, however, is Carex moorcroftii. Generally, C. moorcroftii exhibits a limited density of individuals, with mainly coarse roots. It is difficult to play a role in the formation of soil structures. Besides, the wide spread of macropores in sandy soil facilitates water movement into the deeper soil layer. As a result, the soil is relatively loose and its capacity for soil water retention is more limited. The effects of plant roots coupled with soil structure on soil water retention are summarized in Fig. 8. 4.2 Effect of soil organic matter on soil water retention Our research suggests that root-induced changes to soil water retention may alter soil organic matter and soil bulk density (Table 4). These results are similar to those of Ilek et al. (2017) in forest soils, where the index of decomposition of organic matter had a strong correlation with soil water storage capacity. Moreover, Rossi and Nimmo (1994) documented that capillary retention mechanisms were dominant at high and medium water content levels and adsorption was dominant at low water content levels. In this study, it seems that the capillary helps to increase soil water contents at near saturation to around −300 kPa, but humified soil organic matter plays an important role at higher suction, and clay dominates soil water retention at even higher metric potential up to −1500 kPa, as the commonly accepted Bwilting point.^ Thus, soil water

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retention was underestimated if the plant roots were not considered. As shown in Fig. 7, the differences of soil water retention were small between soil with plant and soil without plant in the alpine steppes. The result suggests that the effects of alpine steppe plants on soil capillary, soil organic matter, and soil clay content are limited comparing with alpine wet meadow and alpine meadow. In the past decade, the upper active layer of QTP have showed an increasing trend in soil organic matter content, which probably resulted from alpine vegetation growth (Ding et al. 2017; Wang et al. 2017a). Thus, soil water retention may be enhanced in the upper active layer of the QTP. 4.3 Limiting factors of the investigation The present research has shown that plant roots induce changes to soil water retention by altering soil organic matter and soil structure. It is well known that soil water retention exhibits great spatial and temporal variability, and our work was conducted at only a few sample sites in a specific region, and only investigated non-degraded alpine grassland. The results were limited to evaluating the dynamics of soil hydrological processes at large scales. Meanwhile, plant roots underwent sprouting, growth, and decomposition from May to October. We only investigated soil water retention during a certain period. Therefore, it is necessary to go further into the question of spatial-temporal variability of soil water retention caused by plant roots in the QTP. We expect the present research can provide accurate parameters to develop hydrological models and estimate changes in soil properties under the background of permafrost degradation superimposed on vegetation succession in the QTP.

5 Conclusions Our work examined plot-scale differences in soil water retention caused by plant roots in the hinterland of the Qinghai-Tibet Plateau. We concluded that alpine plant roots are mainly present in the top soil, and the relative proportions showed that approximately 80, 65, and 60% of the root biomass is distributed in the 0- to 20-cm layer in alpine wet meadow, alpine meadow, and alpine steppe, respectively. In alpine wet meadow, the root biomass varied from 11,037 to 1209 g m−2 between 0 and 50 cm, and these values are almost 10-fold higher than in alpine meadow and fivefold higher than in alpine steppe. In addition, soil water retention varied significantly according to soil depth and ecosystem type: the values were highest in alpine wet meadow, intermediate in alpine meadow, and lowest at alpine steppe. Thereinto, the highest values of field capacity were found in AWM soil: on average, about 0.45 cm3 cm−3. And field capacity of AWM soil was almost twice as high as that of AM soil, and triple as that of AS soil. The results of the Pearson correlation and

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stepwise regression showed that root-induced changes to soil water retention altered soil organic matter and soil bulk density and so changed soil suction. If the functions of plant roots are not considered, the widely used RETC program underestimates soil water retention, and we offered reasonable parameters for a van Genuchten model based on measured data in undisturbed alpine ecosystems. This work will thus contribute to understanding the relationship between alpine soil and vegetation against the background of climate change, especially in permafrost regions. Acknowledgements This work was supported by the National Natural Scientific of China (No. 41601069, 41571065) and the Open Foundations of the State Key Laboratory of Frozen Soil Engineering (No. SKLFSE201501). We thank the editor Zhihong Xu and two anonymous reviewers for their constructive comments, which led to significant improvements in the work.

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