Received: 29 October 2023

Accepted: 6 March 2024

DOI: 10.1002/vzj2.20334
Vadose Zone Journal

ORIGINAL ARTICLE
S p e c i a l S e c t i o n : Tr i b u t e t o R i e n v a n G e n u c h t e n , R e c i p i e n t o f t h e 2 0 2 3 Wo l f P r i z e f o r A g r i c u l t u r e

Unsaturated hydraulic property measurements of subtropicalanthropogenic (purple) soils in China
Luwen Zhuang1
Hao Chen1
Ping Yan2
Xingmei Liang1
Wenceslau G. Teixera3
Martinus Th. van Genuchten4,5
Kairong Lin1
1 Center for Water Resources and Environment, and Guangdong Key Laboratory of Marine Civil Engineering, School of Civil Engineering, Sun Yat-sen

University, Guangzhou, China
2 Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangdong Academy of Sciences,

Guangzhou, China
3 Soil Physics, Embrapa Soils, Rio de Janeiro, Brazil
4 Department of Earth Sciences, Utrecht University, Utrecht, The Netherlands
5 Department of Nuclear Engineering, Federal University of Rio de Janeiro, Rio De Janeiro, Brazil

Correspondence
Kairong Lin, Center for Water Resources
and Environment, and Guangdong Key
Laboratory of Marine Civil Engineering,
School of Civil Engineering, Sun Yat-sen
University, Guangzhou 510275, China.
Email: linkr@mail.sysu.edu.cn
Assigned to Associate Editor Céline Pallud.
Funding information
National Key Research and Development
Program of China, Grant/Award Number:
2022YFC3002903; National Natural
Science Foundation of China, Grant/Award
Numbers: 42007165, 42377057

Abstract
Many anthropogenic soils, often referred to as red bed or purple soils, are distributedin various areas of southern China. Purple soils typically are highly weathered andoften lead to natural and engineering hazards because of their relatively poor water
retention properties. Knowledge of the unsaturated soil hydraulic properties of purple
soils is crucial for their optimal management and various assessment studies. In thiswork, the hydraulic properties of purple soils from southern China were measured
in the laboratory over the full moisture range using a combination of evaporation(HYPROP) and psychrometer (WP4C) approaches. Measured data were analyzed
in terms of four different unimodal and bimodal soil hydraulic models. The measurements and analyses showed that bimodality was not overly significant for most
samples. The good fit of the Peters–Durner–Iden models furthermore suggested
that corner and film flows were important under relative dry conditions. Existingsoil pedotransfer functions were found to provide a fairly close match for the slopeof water retention curves with the exception of near saturated water contents andthe saturated conductivity. To the best of our knowledge, this is the first time that
unsaturated hydraulic data of purple soils are provided over the full moisture range.

1
Abbreviations: BiVG, bimodal van Genuchten; BiVGPDI, bimodal van
Genuchten-Peters–Durner–Iden; VG, van Genuchten; VGPDI, vanGenuchten-Peters–Durner–Iden.

INTRODUCTION

Purple soils in the Soil Genetic Classification of China arelithologic soils developed from purple sedimentary rocks byweathering and erosion (Zhong et al., 2019). They are mostlyThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided
the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
© 2024 The Authors. Vadose Zone Journal published by Wiley Periodicals LLC on behalf of Soil Science Society of America.
Vadose Zone J. 2024;e20334.
https://doi.org/10.1002/vzj2.20334

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1 of 9ZHUANG ET AL.

Vadose Zone Journalclassified as Regosols in the Food and Agriculture Organization taxonomy system (FAO, 1988) and as Entisols in
the United States Department of Agriculture taxonomy system (He, 2003; Wei et al., 2006; Soil Survey Staff, 2022).
Geologically, purple sedimentary rocks originated from “redbeds,” which refers to red-colored sedimentary strata, including sandstone, mudstone, and shale, formed during different
geological periods (Meng et al., 2023; L. Yan et al., 2019).
For this reason, purple soils are also known as red bed soils(Turner, 1978), although the term “red bed” is used only minimally in the US soils and hydrology literature. Red beds are
spread all over the world, especially in tropical and subtropicalareas (X. C. Zhang et al., 2017; Zhou et al., 2019), including the humid areas of southern China (L. B. Yan & Milica,2019). Purple soils, owing to their high fertility, hold significant importance for agriculture (Wei et al., 2006). However,
due to their special structure and low water retention properties, natural or engineering problems such as landslides,
unstable rock formations, and droughts often occur in red bed
soil areas (Sun & Kong, 2011; Y. Wang et al., 2017; L. Yanet al., 2016; P. Yan et al., 2021; Z. T. Zhang et al., 2020).
Knowledge of the unsaturated hydraulic properties of purple
soils is important for understanding and predicting these hazards (Iverson, 2000; H. L. Wang et al., 2015; Wu et al., 2018;
Zhao et al., 2015). However, there are a limited number ofunsaturated hydraulic data of purple soils available, while nosystematic analyses of unsaturated hydraulic properties haveever been reported. The lack of reliable unsaturated hydraulicdata significantly restricts accurate hazard predictions related
to purple soils.
Typically, the constitutive soil hydraulic properties include
the soil water retention function relating the water content (θ)
with the pressure head (h) and the hydraulic conductivity (K)as a function of θ or h. Many laboratory methods have beendeveloped over the years to determine the hydraulic properties of soils (Dane & Hopmans, 2002; Novák & Hlaváčikova,2019). Among these, column outflow and evaporation methods have been used widely to simultaneously obtain pressure
head and unsaturated conductivity data (e.g., Hopmans et al.,
2002; Kool et al., 1985; Wendroth et al., 1993; Zhuang
et al., 2017). Early versions of the evaporation method usually
involved measurements of the evaporation rate and the pressure head at multiple depths of the sample (Šimůnek et al.,
1998; Wendroth et al., 1993; and references therein). A new
popular semiautomated evaporation setup is the HYPROP
system of the METER Group, which was designed to continuously collect tensiometer readings at two depths from theevaporating surface as well as corresponding sample weights
during the entire evaporation process (Peters et al., 2015;
Schindler et al., 2010). The HYPROP system has been used
recently to obtain unsaturated hydraulic data of a range of soils
and rocks (e.g., Coelho et al., 2017; Lipovetsky et al., 2020;
Schindler, 2014; Shokrana & Ghane, 2020).

Core Ideas∙ Hydraulic properties of Chinese subtropical purple soils (Regosols and Entisols) were measured
experimentally.
∙ The data were analyzed in terms of unimodal andbimodal unsaturated soil hydraulic functions.
∙ Bimodal porosity was not found to be overly
significant for most samples.
∙ Pedotransfer functions closely matched the slope of
the water retention curves, except near saturation.

As noted above, a comprehensive and precise understanding of unsaturated hydraulic properties is essential for many
evaluations related to purple soils. However, to the best of ourknowledge, there are few experimental unsaturated hydraulicdata in current literature covering the full moisture range. The
aim of this study is to offer a systematic experimental analysisof the unsaturated hydraulic properties of purple soils. For thispurpose, we collected six soil samples from Nanxiong Basin,a typical red bed area in southern China (P. Yan et al., 2021).
The hydraulic properties were measured over the full moisture range by combining HYPROP evaporation data obtainedat relatively wet and intermediate moisture contents with psychrometer dew point (WP4C) measurements at relatively dry
conditions. The data were analyzed in terms of unimodal andbimodal unsaturated soil hydraulic models and also comparedalso predictions obtained using pedotransfer functions.

2
2.1

MATERIALS AND METHODS
Materials

The purple soils were collected from Nanxiong Basin(Figure 1), located in Shaoguan City, northern Guangdong
Province, China, between coordinates of 114˚ 29’ 50’’ to 114˚
33’ 30’’ E and 25˚ 13’ 23’’ to 25˚ 16’ 57’’ N. Nanxiong Basinis a typical red bed area in southern China.
The soil samples, denoted as PS1 through PS6, werecollected in situ from the surface horizons of six sites in
Nanxiong Basin. Microscope images (Sigma 300) of thesamples are presented in Figure 2. The purple soil particles typically exhibited a relatively rounded morphology, withrough and uneven surfaces. The mineral composition (X’Pert
PRO MPD, Nalytical) of a typical purple soil is shown in
Table 1. Table 2 provides an overview of selected properties of the soil samples, which were determined mostly after
the HYPROP measurements. Soil organic matter was measured by oxidation with potassium dichromate (K2 Cr2 O7 ).

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2 of 9F I G U R E 1 Map of Nanxiong Basin in Shaoguan City, Northern
Guangdong Province, China.

Vadose Zone Journal

3 of 9quently, we derived water content and hydraulic conductivity
directly or inversely from the sample weight and the pressure heads (Peters et al., 2015). Following the experiments,
the soil samples were oven-dried to obtain corrected dryweights.
The HYPROP system was used to obtain hydraulic data
exclusively in the relatively wet range (i.e., for pF < 3, where
pF is defined as log10 |h| with h representing the pressure headin cm). This limitation stems from the bubble entry propertiesof the tensiometers. The WP4C dew point method (METER)
was subsequently used to obtain water retention data in therelatively dry region. WP4C psychrometers measure the sumof matric and osmotic (negligible) potentials during equilibration of liquid-phase water and vapor-phase water in asealed container (Bittelli & Flury, 2009; Solone et al., 2012).
The dimensions of the WP4C sample container were 1 cm
in height and 4 cm in diameter. When packing the saturated
soil samples into the container, we ensured that the porosities
matched those used for the HYPROP system. The upper surface of the initially saturated WP4C samples was exposed toevaporation, thus creating primary drying conditions similar
to those of the HYPROP measurements. Pressure heads of thesamples were measured every 15 min, while water contents
were determined gravimetrically until negligible changes inthe sample weights were observed. The WP4C measurements
were deemed reliable within the approximate range of pF= 4.0 (−104 cm pressure head) to pF = 6.5 (about −3.1 ×
104 cm pressure head) (METER, 2021). All experiments wereconducted under constant temperature and humidity conditions, employing degassed deionized water to saturate the soil
samples.

Saturated hydraulic conductivities were determined using theconstant-head method (Reynolds et al., 2002), yielding values
within the range of 24–82 cm/day.

2.3

2.2

The van Genuchten (VG) model (van Genuchten, 1980) andseveral other VG type models were used for analyses of thedata. The standard VG soil hydraulic property expressions are
given as follows:

Experimental procedures

We determined hydraulic properties of the soil samples using
a combination of the evaporation (HYPROP) and the psychrometry (WP4C) methods. For the HYPROP measurementsin the intermediate and relatively wet region, we used thestandard setup as described in the operational manual by
METER (2015). The collected samples were first saturated
overnight, ensuring the soil surface gleamed with water toconfirm their complete saturation. The saturated samples subsequently were mounted directly onto the sensor unit. Two
tensiometers of different lengths were inserted from below
into the 5-cm soil columns. While the upper surface of thesoil samples was exposed to air for evaporation, the entire
setup (including sensor unit and soil sample) was positioned
on a mass balance to record evaporation rate data. Subse-

Soil hydraulic models

𝑆𝑒 (ℎ) =𝜃 (ℎ) − 𝜃𝑟1=
𝜃𝑠 − 𝜃𝑟[1 + |𝛼ℎ|𝑛 ]𝑚

[
( )
(
)𝑚 ]2
𝐾 𝑆𝑒 = 𝐾𝑠 𝑆𝑒 𝑙 1 − 1 − 𝑆𝑒 1∕𝑚

(1)

(2)

where θ is the volumetric water content, θs and θr denote saturated and residual water contents, respectively, Se is effective
water saturation, h is the soil water pressure head, K is thehydraulic conductivity (here given as a function of Se ), Ks is
the saturated hydraulic conductivity, and α, n, m (m = 1 −1/n), and l are quasi-empirical shape parameters.

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ZHUANG ET AL.

Vadose Zone Journal

FIGURE 2
TA B L E 1

Microscope images of the purple soil samples.

Mineral composition of a typical purple soil.

QuartzK-feldspar

AlbitePyriteIllite/montmorillonite

IlliteKaolinite

Chlorite

78.4

0.50

3.00

1.30

4.75

5.16

5.65

1.24

Note: Total mineralogical composition in mass %.

TA B L E 2

Soil physical properties of the six purple soils used in this study.
PS1

PS2

PS3

0.45

0.57

0.52

PS5

PS6

0.59

0.60

22.24 ± 2.35

Soil organic matter (g/kg)
Mean particle diameter, d50 (mm)

PS4
0.55

Uniformity coefficient, Cu (d60 /d10 )

5.27

3.78

4.13

4.19

3.79

4.25

Saturated hydraulic conductivity, Ks (cm/day)

82.0

24.0

82.0

75.0

55.0

53.0

Porosity (-)

0.51

0.44

0.47

0.48

0.45

0.47

Bulk density (g/cm3 )

1.30

1.48

1.40

1.38

1.46

1.40

Sand (%)

76.7

75.7

72.8

71.4

80.9

78.8

Silt (%)

10.0

5.5

15.5

16.8

1.0

4.3

Clay (%)

13.3

18.8

11.7

11.8

18.1

16.9

The above VG functions represent a unimodal-type
hydraulic model that mainly considers capillary flow. To
capture the possible bimodal nature of the purple soils, dualporosity VG type models were also employed. For the bimodal
functions, we used the water retention formulation initially

proposed by Durner (1994) and later extended by Priesack andDurner (2006) to the hydraulic conductivity:
𝑆𝑒 (ℎ) =𝑤1
𝑤2
𝜃 (ℎ) − 𝜃𝑟=[
]𝑚1 + []𝑚𝑛
𝜃𝑠 − 𝜃𝑟1
1 + |𝛼1 ℎ|
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4 of 9

Vadose Zone Journal

[
[
(
(
(
)𝑙 {
)𝑚 ]
)𝑚 ]}2
𝑤1 𝛼1 1 − 1 − 𝑆𝑒 1∕𝑚1 1 + 𝑤2 𝛼2 1 − 1 − 𝑆𝑒 1∕𝑚2 2
𝑤 1 𝑆 1 + 𝑤2 𝑆 2( )𝐾 𝑆𝑒 = 𝐾𝑠()2
𝑤1 𝛼1 + 𝑤2 𝛼2where wi (i = 1, 2) represents weight factors attributed to themacropore and micropore regions, respectively (with w1 + w2
= 1), and αi , ni , and mi (i = 1, 2) are shape parameters for thebimodal model, with mi = 1 − 1/ni .
To account for film, corner, and vapor flow at the lower
water content levels, we extended the unimodal and bimodal
VG models to a broader pressure head range by incorporating
the van Genuchten-Peters–Durner–Iden (VGPDI) formulation (Iden & Durner, 2008; Peters & Durner, 2008; Peters
et al., 2015). In the VGPDI model, an additional fitting
parameter, pFdry , is to be optimized based on the experimental measurements. Optimizations were conducted using
the HYPROP-FIT software (METER, 2015; Pertassek et al.,2015). Several statistical parameters, notably the root meansquare error (RMSE) and the Akaike information criterion(AICc), were used to evaluate the models. Their respective
equations are given by:√√ 𝑛𝑡√∑ 1 ()2
𝑦𝑐𝑖 − 𝑦𝑖
RMSE = √𝑛𝑖=1 𝑡

AICc =[]
2𝑘(𝑘 + 1)
2𝑘 − 2𝐿+𝑛𝑡
𝑛𝑡 − 𝑘 − 1

(5)

(6)

where nt is the number of measurements; yi and yi c are themeasured and fitted values, respectively; k denotes the number of fitting parameters in the model, and L is the logarithmic
likelihood. The Akaike information criterion with a correction (AICc) is typically negative, with larger absolute values
indicating a superior fit of the model.

3

RESULTS AND DISCUSSION

Figure 3 shows the observed HYPROP and WP4C water
retention data of the six purple soil samples. The HYPROP
measurements covered water contents up to pF = 3, while
the WP4C measurements contributed data at the drier range.
The measured data were fitted using four different models: the original VG model, the bimodal van Genuchten(BiVG) model, the VGPDI model, and the bimodal van
Genuchten-Peters–Durner–Iden (BiVGPDI) model. Values ofthe saturated water content were held constant as individually

5 of 9(4)

measured data for all optimizations. The resulting parametervalues and statistical analyses are presented in the Supporting
Information.
The measured and fitted retention curves for PS1 areshown in Figure 3a. Except for some minor oscillations in
the water retention data near saturation, the observed retention data were quite uniform, with no discernible evidenceof showing bimodality. The optimized water retention curves
yielded nearly identical results within the wet moisture range,
while showing only minor differences in the relatively dry
range, particularly when using the VG and BiVG models.
The VGPDI and BiVGPDI models exhibited significant overlap and good agreement with the water retention data. The
resulting fitting parameters and accuracy analysis are includedin Table S1. The parameter RMSEθ in the table is the root
mean square error values for water content and AICc is the
Akaike information criterion. As anticipated, the BiVGPDI
model, with its greater number of fitting parameters, predicted
the observations best among the four models. However, it isnoteworthy that the VGPDI model, despite its fewer fitting
parameters, also described the data very well.
The measurements for sample PS2 did not exhibit distinct bimodal patterns (see Figure 3b). Regarding the water
retention optimizations, the VG function showed some discrepancies with the measurements, while the BiVG results
displayed a slightly more bimodal shape than what appears
to be evident in the data. The VGPDI and BiVGPDI models both closely matched the measurements. Based on theaccuracy analysis in Table S2, the BiVGPDI model again
showed the best fit with the data, with the VGPDI model
also performing well, albeit with minimally larger deviations
with the data. One caution is needed about the BiVGPDI fit.
This function showed a bimodal shape between HYPROP
and WP4C data, where few data were present. It is likely
caused by considerable uncertainty in the macropore sideof the bimodal functional description. This suggests that
overparameterization may be an issue in this case.
The water retention data of PS3 exhibited a more distinct bimodal pattern (see Figure 3c). Regarding the fittings,
discrepancies are apparent between the VG model and themeasured data. The BiVG model described the water retention data much better. The VGPDI and BiVGPDI models bothshowed notable enhanced agreement with the measure data
although they did not fully capture the bimodal characteristic(see Table S3).

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Vadose Zone Journal

ZHUANG ET AL.

F I G U R E 3 Measured and fitted water retention curves of purple soils using various hydraulic models. BiVG, bimodal van Genuchten;
BiVGPDI, bimodal van Genuchten-Peters–Durner–Iden; VG, van Genuchten; VGPDI, van Genuchten-Peters–Durner–Iden.

The measured data for the purple soil PS4 were quite uniform (Figure 3d). The optimized water retention curves of thefour models exhibited close agreement with each other as wellas with the data. This is also reflected by the values of RMSEθ
in Table S4. For soil PS5, the water retention data and fitted
curves did not display any discernible bimodal characteristics,
except for some drastic oscillations in the water retention datanear saturation (see Figure 3e). All of the models describedthe data well, except perhaps the standard VG formulation,
which showed some deviations in the very dry moisture range.
As expected, the VGPDI model performed better in that partof the retention curve. Detailed information of the fitted andstatistical parameter values for PS5 are provided in Table S5.
Regarding purple soil sample RS6 (Figure 3f), the water
retention data exhibited only a very minimal nonuniform
trend. Within the wet moisture range, the VG model describedthe data very well, but encountered limitations in the dry
moisture range. The BiVG model gave better agreement withthe data, which had a remarkable bimodal nature. Nevertheless, the results obtained using the VGPDI and BiVGPDI
models were nearly indistinguishable, both offering commendable predictive accuracy for the data. The statisticalanalysis and the fitting parameter values are listed in Table
S6.
Of all six samples, only sample PS3 exhibited a distinct
bimodal trend in the observed water retention data over thefull moisture range. In terms of model fittings, the BiVGPDI
model was capable of reproducing all of the observed data
very well. This could be attributed to having more fitting
parameters compared to the other models. Nevertheless, it isimportant to note that for many of the purple soil samples,
the results obtained using the BiVGPDI model exhibited

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6 of 9

TA B L E 3

Experiments (Exp.) and pedotransfer function (PTF) predicted hydraulic parameters of the six purple soils.
PS1

PS2

Parameter (unit)

Exp.

PTF

α (1/cm)

0.141

n (-)

1.20

1.56

θr (-)

0.00

0.06

0.030

PS3

PS4

PS5

PS6

Exp.

PTF

Exp.

PTF

Exp.

PTF

Exp.

PTF

Exp.

PTF

0.025

0.026

0.246

0.031

0.124

0.030

0.030

0.025

0.044

0.026

1.17

1.51

1.18

1.53

1.29

1.51

1.23

1.64

1.21

1.61

0.00

0.06

0.00

0.05

0.00

0.05

0.00

0.07

0.00

0.07

θs (-)

0.51

0.45

0.44

0.41

0.47

0.42

0.48

0.42

0.45

0.42

0.47

0.43

Ks (cm/day)

82.0

123.0

24.0

53.7

82.0

79.3

75.0

78.3

55.0

83.5

53.0

92.1

considerable overlap with those generated by the VGPDI
model. This overlap suggests that the bimodal nature of
the purple soils may not be of significance. Furthermore,
the good agreement of the models with the PDI formations(VGPDI and BiVGPDI models) implies that corner and film
flow mechanisms are dominant, especially under relative
dry conditions. The rough and uneven surfaces of purple
soil grains have the potential to significantly enhance water
retention (Di Raimo et al., 2022).
The results were further compared with predicted VG
parameters values using soil pedotransfer functions. We used
for this purpose the Rosetta light pedotransfer functions(Schaap & Leij, 1998a, 1998b; Schaap et al., 1998) asembedded with the RETC and HYDRUS software packages(Šimůnek et al., 2016). Rosetta predictions were based on themeasured fractions of sand, silt, and clay, and the bulk density.
The predicted hydraulic parameters are shown in Table 3. The
calculated α values exhibited slight variations around 0.030
1/cm. These values closely matched the fitted values based on
the measurements only for three soil samples, with the exception of soils PS1, PS3, and PS4. The predicted n values wereslightly larger than the fitted values, resulting in relatively
steeper retention curves. The predicted hydraulic conductivities were fairly close to the measured values only for PS3 andPS4. The Rosetta pedotransfer functions primarily estimatessoil hydraulic properties based on sand, clay, and silt fractions.
They do not consider information regarding macropores andpore connectivity, which are mostly evident near the air entry
pressure (related to the α value in the VG model) and in thesaturated conductivity. Therefore, it is reasonable to expect
less accurate predictions of α and Ks . In general, however,
the hydraulic parameter values estimated with the pedotransfer model closely matched the slope of water retention curvesof the purple soils, with the exception of water contents near
the air entry value and the saturated conductivity.

4

7 of 9Vadose Zone Journal

SUMMARY AND CONCLUSIONSIn this study, we measured the hydraulic properties of six purple soils covering the full moisture range. Observed water
retention data were fitted using unimodal and BiVG typemodels. Bimodal behavior was not found to be extensive formost of the soil samples. The BiVGPDI model with its more
numerous fitting parameters yielded the best agreement withthe observations. Nevertheless, the results obtained using the
BiVGPDI model were very similar to those produced by the
VGPDI model for most of the soil samples. The good agreement of the PDI models suggests that film and corner flow
may be significant under dry moisture conditions. Predictions
with the Rosetta pedotransfer model were found to reasonably
match the slope of water retention curves, although discrepancies were observed for the saturated conductivity and the αparameter.
This work has provided comprehensive analyses of theunsaturated hydraulic properties of purple soils over the entire
moisture range. The obtained hydraulic parameter values
could be used for agriculture and engineering management
applications, including risk analyses. It is important to notethat our study involved a limited number of purple soil samples. Investigations for a more diverse set of purple soils
from around the world are clearly needed. Additionally, thedevelopment of more appropriate pedotransfer models is
imperative, particularly for enhancing predictions of the saturated conductivity and air entry (or α−1 ) values specific topurple soils.
AU T H O R C O N T R I B U T I O N S
Luwen Zhuang: Funding acquisition; investigation; methodology; writing—original draft. Hao Chen: Investigation;
writing—original draft. Ping Yan: Resources. Xingmei
Liang: Investigation. Wenceslau G. Teixeira: Methodology.
Martinus Th. van Genuchten: Methodology; writing—review and editing. Kairong Lin: Funding acquisition;
project administration; writing—review and editing.
AC K N OW L E D G M E N T S
The authors acknowledge financial support by Projects of the
National Key R&D Program of China (2022YFC3002903)
and the National Natural Science Foundation of China (Grant
Nos. 42377057 and 42007165).
ORCID
Luwen Zhuang

https://orcid.org/0000-0002-6300-9999

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Wenceslau G. Teixera https://orcid.org/0000-0002-20106078
Martinus Th. van Genuchten https://orcid.org/0000-00031654-8858
Kairong Lin https://orcid.org/0000-0001-6533-3357
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S U P P O RT I N G I N F O R M AT I O N
Additional supporting information can be found online in the
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How to cite this article: Zhuang, L., Chen, H., Yan,
P., Liang, X., Teixera, W. G., van Genuchten, M. Th.,
& Lin, K. (2024). Unsaturated hydraulic property
measurements of subtropical anthropogenic (purple)
soils in China. Vadose Zone Journal, e20334.
https://doi.org/10.1002/vzj2.20334

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