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Estimating soil water retention for wide ranges of pressure head and bulk density based on a fractional bulk density concept

  • Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, China

Abstract and Figures

Soil water retention determines plant water availability and contaminant transport processes in the subsurface environment. However, it is usually difficult to measure soil water retention characteristics. in this study, an analytical model based on a fractional bulk density (fBD) concept was presented for estimating soil water retention curves. the concept allows partitioning of soil pore space according to the relative contribution of certain size fractions of particles to the change in total pore space. the input parameters of the model are particle size distribution (pSD), bulk density, and residual water content at water pressure head of 15,000 cm. The model was tested on 30 sets of water retention data obtained from various types of soils that cover wide ranges of soil texture from clay to sand and soil bulk density from 0.33 g/cm 3 to 1.65 g/cm 3. Results showed that the FBD model was effective for all soil textures and bulk densities. the estimation was more sensitive to the changes in soil bulk density and residual water content than pSD parameters. the proposed model provides an easy way to evaluate the impacts of soil bulk density on water conservation in soils that are manipulated by mechanical operation.
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SCIENTIFIC REPORTS | (2020) 10:16666 | 
Estimating soil water retention
for wide ranges of pressure
head and bulk density based
on a fractional bulk density concept
Huihui Sun1, Jaehoon Lee1, Xijuan Chen2 & Jie Zhuang1,3*
Soil water retention determines plant water availability and contaminant transport processes in the
subsurface environment. However, it is usually dicult to measure soil water retention characteristics.
In this study, an analytical model based on a fractional bulk density (FBD) concept was presented for
estimating soil water retention curves. The concept allows partitioning of soil pore space according
to the relative contribution of certain size fractions of particles to the change in total pore space. The
input parameters of the model are particle size distribution (PSD), bulk density, and residual water
content at water pressure head of 15,000 cm. The model was tested on 30 sets of water retention
data obtained from various types of soils that cover wide ranges of soil texture from clay to sand and
soil bulk density from 0.33 g/cm3 to 1.65 g/cm3. Results showed that the FBD model was eective
for all soil textures and bulk densities. The estimation was more sensitive to the changes in soil bulk
density and residual water content than PSD parameters. The proposed model provides an easy way
to evaluate the impacts of soil bulk density on water conservation in soils that are manipulated by
mechanical operation.
Modeling of water ow and chemical movement in unsaturated soils has been emphasized by soil scientists and
hydrologists for dierent purposes, such as evaluations of root water uptake, soil erosion, and groundwater pol-
lution risk. However, high variability and complexity of soil texture in natural eld make direct measurements
of soil hydraulic properties costly and time-consuming. It is desirable to utilize readily available information,
such as soil texture and bulk density, to estimate soil hydraulic properties13. is kind of approach benets the
development of computationally ecient methods for evaluating soil hydraulic heterogeneity in watershed or
agricultural eld while ensuring the economic feasibility of eld investigation eorts within acceptable accuracy.
To date, many modeling eorts have been made to relate soil texture (expressed as particle size distribution), soil
structural properties, bulk density, and/or organic matter content to soil water retention47. Soil water retention
was estimated using multiple regression, neural network analyses, and other methods814. However, the applicabil-
ity and accuracy of the models are more or less unsatisfactory. Several prediction models were derived on global
soil hydraulic datasets, such as applying the Miller-Miller scaling approach to the soil dataset of SoilGrids1km
to provide a global consistent soil hydraulic parameterization15, but some of them possess a high correlation to
particular soil types and thereby may not be suitable for other soils1618.
An important advancement in using soil particle size distribution to derive a soil water retention character-
istic was the development of a physical empirical model by Arya and Paris19,20, Later, Haverkamp and Parlange21
proposed a similar model by combining physical hypotheses with empirical representations and tested the model
on sandy soil. Tyler and Wheatcra22 interpreted the empirical scaling parameter α in the Arya and Paris model
as being equivalent to the fractal dimension of a tortuous fractal pore system. However, Arya etal.20 argued that
the fractal scaling was limited in estimating water retention characteristics in the complex soil matrix. In the
optimized model of Arya etal.20, three methods were proposed for calculating the scaling parameter α, but the
calculation still involved empirical component to some extent, making the model sometimes relatively dicult
             
USA. Key Laboratory of Pollution Ecology and Environmental Engineering, Institute of Applied Ecology, Chinese
      Center for Environmental Biotechnology, The University of
 *email:
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for broad application. e physical basis of the model of Arya and Paris19 or Arya etal.20 is weakened by the
assumption that the void ratio of bulk sample is equivalent to the void ratio of individual particle size class.
To improve the mathematical description of physical relations between soil particles and soil pores, we assume
that dierent fractions of soil particles may make dierent contributions to the total porosity or volumetric
water content in the bulk soils and that soil pore volume and associated bulk density are specic for particle size
fractions. is line of thinking might help derive a better physical model for mathematical estimation of soil
water characteristics. erefore, the objective of this work was to apply a fractional bulk density (FBD) concept
to the development of a soil water retention model that is eective for all soil textures and a wide range of soil
bulk density.
Estimation accuracy. Model estimation of water retention characteristics for some soils is presented in
Fig.1. e results indicate that the new procedure was in good agreement with the measured data for most of
the soil textures except for sand in the range of water pressure head from 15cm to 15,000cm, which covers the
entire range of available water content. Table1 shows comparisons of the coecient of determination (R2), root
mean square error (RMSE), and t value of Student’s t distribution between the FBD model and the curve tting
using the Campbell model23, which was extended from the similar media concept24. e Campbell model is
expressed as
where ψe is air-entry water potential, θs is saturated volumetric water content, and q can be obtained using
In the equation, Dgi is the diameter of the ith particle-size fractions, and Mi is the cumulative mass percent-
age of the Dgi particles.
RMSE values were computed from soil water contents measured and estimated as described in the section
of methods. Table1 shows that the mean value of RMSE of the FBD model was 0.032 cm3/cm3 while that of the
Campbell model was 0.024 cm3/cm3. is result was acceptable because the Campbell model used the meas-
ured data to t ψe. e R2 values also supported the acceptability of the FBD model compared to the Campbell
model. According to the t values, the FBD model results had no signicant dierence and systematic bias from
the measurements for 25 out of the 30 soils. Figure2 shows an overall comparison between the water contents
ln Dgi
Miln Dgi
Figure1. Water retention characteristics measured (circle) and estimated (line) using the fractional bulk
density (FBD) model for eight dierent soil textures.
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Table 1. Statistical comparison of soil water contents estimated by the fractional bulk density (FBD) model
and tted by the Campbell model23. t is the value of Students t distribution, and the critical values of t0.05 for 04,
08, 09, 10, 15, 18 and 30 degrees of freedom are 2.776, 2.306, 2.262, 2.228, 2.131, 2.101 and 2.042, respectively;
R2 is determination coecient; RMSE is root mean square errors (cm3/cm3); n is the number of measured pairs
of water content and pressure head.
Soil No.
nFBD model Campbell model FBD model Campbell model FBD model Campbell model
01 1.600 −0.508 0.662 0.507 0.097 0.117 16
02 −1.316 −0.288 0.939 0.888 0.039 0.040 9
03 −1.924 −0.213 0.982 0.958 0.011 0.014 5
04 −0.123 −0.308 0.968 0.932 0.012 0.015 5
05 −0.191 0.115 0.962 0.900 0.059 0.060 10
06 −1.374 −0.331 0.951 0.922 0.068 0.039 5
07 −1.948 −0.123 0.908 0.922 0.027 0.018 5
08 −2.411 0.125 0.985 0.942 0.020 0.031 9
09 0.532 −0.270 0.954 0.923 0.017 0.024 5
10 1.345 0.179 0.980 0.998 0.042 0.003 9
11 0.144 0.164 0.977 0.995 0.028 0.007 9
12 0.266 0.070 0.995 0.964 0.020 0.020 9
13 10.840 −0.819 0.938 0.899 0.029 0.010 6
14 3.572 3.885 0.951 0.956 0.023 0.024 28
15 −0.883 −0.128 0.932 0.980 0.028 0.013 10
16 −0.209 −0.163 0.876 0.877 0.062 0.057 16
17 −0.794 −0.056 0.908 0.958 0.027 0.014 5
18 −0.441 −0.227 0.957 0.967 0.019 0.014 10
19 −0.256 −0.065 0.891 0.936 0.027 0.017 5
20 1.153 −0.253 0.892 0.920 0.032 0.023 10
21 1.932 −0.156 0.908 0.928 0.030 0.020 10
22 −5.494 −0.218 0.945 0.881 0.035 0.024 20
23 −3.341 −0.067 0.922 0.966 0.056 0.015 5
24 −1.421 −0.061 0.939 0.971 0.023 0.012 5
25 −1.558 −0.077 0.938 0.970 0.023 0.013 5
26 1.707 −0.075 0.953 0.969 0.022 0.010 10
27 −0.108 −0.052 0.916 0.963 0.020 0.013 5
28 −0.346 −0.113 0.958 0.956 0.014 0.014 9
29 −0.026 0.016 0.968 0.969 0.014 0.012 9
30 −1.823 −0.032 0.952 0.965 0.021 0.013 5
Mean 0.375 −0.009 0.934 0.931 0.032 0.024
Figure2. Comparison of measured and estimated volumetric water content using the fractional bulk density
(FBD) model for 30 soils with ranges of soil texture from clay to sand and bulk density from 0.33 to 1.65g/cm3.
e circle represents measured values, and the line denotes a 1:1 agreement.
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measured and estimated by the FBD model for the 30 soils. e values coalesced to the 1:1 line with the RMSE
being 0.041 cm3/cm3. is RMSE value was larger than the average in Table1. e discrepancy was due that
dierent methods were used for averaging the RMSE values for individual soils and all soils. Mayr and Jarvis25
presented pedotransfer functions to estimate soil water retention parameters of the Brooks–Corey model. e
resulting mean RMSE value was 0.043 cm3/cm3 for the dependent dataset and 0.048 cm3/cm3 for the independent
dataset. Tomasella etal.26 derived a pedotransfer function to predict the water retention parameters of the van
Genuchten equation. e mean RMSE values ranged from 0.038 cm3/cm3 to 0.058 cm3/cm3. Our model compared
favorably with these pedotransfer functions in terms of mean RMSE values. It could thus be concluded that the
FBD model behaved overall well, except for Acolian sandy soil (Soil #01). For sandy soil, the relatively poor
capture of the rapid change of water content was attributed to the limitation of applicability of capillary law (i.e.,
Young–Laplace equation) to sandy media and existence of macropores that might reduce the pore continuity8.
e continuity of soil pores was the dominant factor that aected the performance of our proposed model.
e FBD model also had relatively larger estimation errors for soils originated from ash parent materials (e.g.,
Soil #05, 14, 16, and 22) than for other soils (Table1). is was due likely to the oversimplication of soil particle
size distribution as a sigmoid curve, whereas the particle arrangement of soils developed from ash parent materi-
als was actually very complex (i.e., non-sigmoid). e less accurate prediction for sandy soils relative to the other
soil textures suggested that the sigmoid-shape assumption of particle size distribution might be arbitrary, despite
it was well applied to the particle systems of other soil textures. We infer that the sigmoid-type distribution was
more applicable to the soils with a broader range of particle sizes, which demonstrated a lognormal distribution
of particle fractions27,28. Soil aggregates with hierarchical pore structure have dual-porosity system. Dual-porosity
assumes that porous medium consists of two interacting regions, one associated with the macropore or fracture
system and the other comprising micropores inside soil material. Bimodal pore size distributions are frequently
observed in dual-porosity soil29. e water retention estimated with the FBD model for a wide range of water
pressure head (15–15,000cm) should thus be a sum of the eects of macropores and micropores30. e sigmoid-
type distribution should be more suitable for hierarchical soil aggregates than for less structured soils, such as
sandy soil whose pore system was simply dominated by primary particles. erefore, the FBD model might not
perform very well against the soils if their particle sizes have a narrow range.
Sensitivity analysis of model parameters. We performed a sensitivity analysis to identify input param-
eters that most strongly aected the model behavior and to determine the required precision of the key param-
eters. e parameters included in the sensitivity test were saturated water content (θs), residual water content
(θr), rate coecient (λ) of Logistic-type model for particle size distribution, and particle size distribution index
(ε). e value of each parameter was assumed to increase or decrease by 20% of its actual value since its measure-
ment error could be up to 20% according to our experience in eld survey. By taking Soil #22 as an example, the
test was implemented to monitor the change in the estimated soil water content caused by changing the value of
one parameter at a time while others remained constant. e sensitivity analysis not only showed the inuence
patterns of the parameters on the model behavior but also ranked the parameters in terms of the magnitude of
inuences. Figure3 shows that θs and θr had similarly large impacts on the model estimation. In comparison,
λ and ε played less roles in dening the model performance, but their accuracy was still very important for the
estimation accuracy. e sensitivity analysis provided insights into the behavior of the FBD model (Eq.20) and
Figure3. A sensitivity analysis on the parameters of the fractional bulk density (FBD) model (Eq.20). e
analysis was based on a sandy clay soil (Andisols, Soil #22 in Table2). θs, θr, ε, and λ refer to volumetric saturated
water content, volumetric residual water content at a pressure head of 15,000cm water, particle size distribution
index, and rate coecient in Eq.(15) for particle size distribution, respectively.
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supported the notion that parameter values may have physical meanings no matter in whatever ways the related
parameters are structured into a model.
Particle size distribution forms a common descriptor of natural soils. It has been used routinely as one of the
inputs to estimate some of soil physical properties, for example, water retention characteristic3133, bulk density34,
and hydraulic conductivity3537. In this study, two parameters, rate coecient (λ in Eq.15) of the Logistic-type
model for particle size distribution and particle size distribution index (ε in Eq.13), were employed to translate
particle size distribution to soil water retention characteristic. However, two parameterization issues should be
mentioned for broadening model applicability. One is the estimation of λ in the case that the upper size limit of
the particle size distribution is 1,000μm for some soils while it is 2,000μm for other soils. In order to perform
a consistent comparison among all soils, the particle size distribution with the upper limit of 2,000μm was
normalized to that with the upper limit of 1,000μm using a normalization formula,
where Mi and Mi are measured and normalized percentage content of particles with sizes smaller than or equal
to the ith particle size, respectively. M1,000 denotes the mass percentage of particles with a diameter smaller than
or equal to 1,000μm. e other issue is pertinent to the calculation of ε. It involved three particle sizes (D10, D40,
and D60) below which the mass percentage of particles is 10%, 40%, and 60%, respectively. It is easy to identify
D60 but sometimes relatively dicult to nd D10 and D40. In some soils, the mass of particles with sizes smaller
than or equal to the measured lower limit size (e.g., 1μm or 2μm) was larger than 10%. In this case, an expo-
nential equation, which was obtained by tting the relation between the cumulative mass percentage and the
corresponding particle sizes, was used to extrapolate for estimating D10. To minimize the deviations arising from
the extrapolation, we used 50μm as the upper size limit of the particle size distribution.
ere is no doubt that particle assembling and resulting pore characteristics play important roles in regulating
physical, chemical, and biological functions of soils at various scales. e FBD model was generally based on the
assumption that the sizes of soil particles and the density of their packing are the primary determinants of the
pore size and pore volume. is, however, may not be the case under some conditions. Aggregation of primary
particles into secondary and tertiary particles, root channels, and microcracks would account for a fraction of
the pore volume with pore sizes not determined by the size distribution of primary particles. e abundance of
such pores considerably determines the extent of deviation of prediction. erefore, it is important to incorporate
information of soil structure into soil hydraulic modeling if possible38. Soil structure is a non-negligible factor
for accurate estimation of soil hydraulic properties using pedotransfer functions39,40. But this work is dicult to
initiate because soil structure information (e.g., soil aggregate size distribution) is mostly unavailable compared
to soil basic properties (e.g., particle size distribution, organic matter content, and bulk density). Insuciency
of identication of soil structure indices precludes the inclusion of soil structure characteristics into soil water
retention modeling. Relevant eorts have been made in some large-scale models that consider soil structure.
For instance, Fatichi etal.41 proposed to assess the impact of soil structure on global climate using an Ocean-
Land–Atmosphere Model (OLAM). Although the model in this study does not explicitly include a structural
component, in the FDB model we assume that soil bulk density could indirectly bring the inuence of soil
structure into the estimation of soil water retention.
Soil water retention characteristics were estimated using the FBD model from particle size distribution, bulk
density, and measured residual water content. e starting point was the similarity of curve shapes between
cumulative particle size distribution and soil water retention characteristics. Similarly, Arya and Paris19 and
Haverkamp and Parlange21 used a simple equation to derive a set of soil water content according to the mass
fraction of soil particles, and then a series of expressions were employed to regulate soil water pressure head to
pair with measured soil water content. e FBD model adopted an opposite approach. A set of water pressure
head from 15cm to 15,000cm were derived using a simple expression as Eq.(19), and then soil water contents
were estimated with Eq.(8) to match the derived water pressure head. Eventually, an analytical model (Eq.20)
was obtained. In the FBD model, the water retention function included a residual water content in relation to
the maximum water pressure head (15,000cm) and the parameter (b) of soil pore size distribution. Similarly, the
residual water content was considered in the van Genuchten model42 or Brooks and Corey model43. However,
Campbell23 described soil water retention curve by assuming there was no residual water content. An advantage
of the Campbell equation is its excellent tting capability. us, we evaluated the performance of the FBD model
by comparing it to the Campbell model in this study.
e selection of a Logistic-type equation for the model formulation was mainly due to the consideration that
particle size distribution and pore size distribution in most soils were approximately lognormal27,4446. e logistic
growth equation generated a curve that tended towards an exponential form at low values and a power form at
high values, with a power index smaller than 1. is characteristic implicitly included the consideration that the
drainage of water in small pores at large suction was usually expected to be more impaired than the release of
water from large pores at small suctions47,48.
An analytical model, which is based on a fractional bulk density concept, was presented for estimating soil water
retention for the entire range of water pressure head that determines water availability. e proposed model was
tested using 30 sets of soil water retention data measured for various textures of soils that had a wide range of
soil bulk density from 0.33g/cm3 to 1.65g/cm3. Results showed that the proposed model could convert readily
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available soil physical properties into soil retention curves in very good agreement with the measurements, and
the model was applicable to soils with limited data of soil particle size distribution at small loss of estimation
accuracy in the middle portion of water retention curves of sandy soils. Sensitivity analyses revealed that satu-
rated and residual water contents were two parameters of high sensitivity for accurate estimation of the water
retention curves. e agreement between the estimated and measured results supported the concept underly-
ing the FBD model. e modeling followed a process of conceptual partitioning of pore space according to the
relative contribution of certain sizes of particles to the change in pore space. In addition, the model assumed a
sigmoid curve of water retention characteristic for most soils. However, these assumptions need further veri-
cation by considering the physical reality of soils and potential improvements and extensions. Compared to
subsurface soils, larger deviations should be expected for surface soil materials where aggregation, cracking, and
root eects may be pronounced. Further tests of the model application to other soils (e.g., Vertisols, Aridisols,
and salt aected soils) and evaluation of the eects of water hysteresis, soil aggregation, and swelling-shrinkage
behaviors might reveal the weaknesses of the FBD model and help identify additional variables needed for model
Material and methods
Fractional bulk density concept. e rst assumption is that soil particles with dierent sizes contribute
to dierent porosities and water holding capacities in bulk soil. Based on a non-similar media concept (NSMC)
dened by Miyazaki49, soil bulk density (ρb) is dened as
where M is the mass of a given soil, V is the volume of bulk soil, ρs is soil particle density, and S and d are char-
acteristic lengths of solid phase and pore space, respectively. e parameter τ is a shape factor of the solid phase,
dened as the ratio of the substantial volume of solid phase to the volume S3. e value of τ is 1.0 for a cube and
π/6 for a sphere. As pointed out by Miyazaki49, these characteristic lengths are not directly measurable but are
representative lengths in the sense of the characteristic length in a similar media concept (SMC). Following the
approach of NSMC represented by Eq.(4), we conceptually dened the volume of bulk soil as
where mi and ρbi are the solid mass and equivalent bulk density of the ith size fraction of soil particles, respec-
tively. In this study, diameters of the rst particle fraction and the last one were assumed to be 1µm and 1000µm,
respectively8. is equation suggests that dierent particle size fractions are associated with dierent equivalent
bulk densities due to dierent contributions of particle arrangement to soil pore space. As a result, the particles
with the same size fraction could have dierent equivalent bulk densities in soils with dierent textures or aer
the soil particles are rearranged (e.g., compaction). Figure4 provides a diagrammatic representation of such
fractional bulk density concept for the variation of soil pore volume with soil particle assemblage.
Calculation of volumetric water content. For a specic soil, Eq.(5) means
where Vpi(≤ Di) denotes the volume of the pores with diameter Di generated by soil particles with diametes ≤ Dgi
in unit volume of soil. Mi is the cumulative mass percentage of the Dgi particles. Since the pore volume has
the maximum value for a given bulk soil and the cumulative distribution of pore volume could be generally
hypothesized as a sigmoid curve for most of the natural soils44,45, we formulated Eq.(6) using a lognormal
Logistic equation,
where Vpmax is the maximum cumulative volume of pores pertinent to the particles smaller than or equal to the
maximum diameter (Dgmax) in unit volume of soil. In fact, here Vpmax is equal to the total porosity (φT) of soil. Vpi
(≤ Dgi) is the volume of the pores produced by Dgi particles in unit volume of soil, and bi is a varying parameter
of increase in cumulative pore volume with an increment of Dgi. By assuming a complete saturation of soil pore
space, Eq.(7) changes into
where θs is saturated volumetric water content calculated with
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In the above equations, ρbis measured soil bulk density, and ρs is soil particle density (2.65g/cm3). e empiri-
cal parameter κ in Eqs. (7) and (8) is dened as
where θr is measured residual water content. In this study, θr is set as the volumetric water content at water pres-
sure head of 15,000cm. e empirical parameter bi is dened as
with ε, a particle size distribution index, calculated with
where D10, D40, and D60 represent the particle diameters below which the cumulative mass percentages of soil
particles are 10%, 40%, and 60%, respectively.
e parameter ωi is coecient for soil particles of the ith size fraction, with a range of value between θr/θs
and 1.0. By incorporating soil physical properties, ωi can be estimated with
where g is regulation coecient (1.0–1.2). We set it to be 1.2 in this study. λ is the ratio coecient of particle size
distribution tted using the lognormal Logistic model,
Figure4. Diagrammatic representation of the fractional bulk density (FBD) model. V and ρb are the volume of
bulk soil and the bulk density of whole soil, respectively. mi, and ρbi refer to the solid mass and equivalent bulk
density associated with the ith particle-size fractions, respectively.
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SCIENTIFIC REPORTS | (2020) 10:16666 | 
where MT represent the total mass percentage of all sizes of soil particles, and η is a tting parameter. We set
MT = 101 in Eq.(15) for best t of the particle size distribution. In this study, this continuous function was gener-
ated from the discrete data pairs of Dgi and Mi at cutting particle diameters of 1,000, 750, 500, 400, 350, 300, 250,
200, 150, 100, 50, 30, 15, 7.5, 5, 3, 2, and 1μm. Considering the dierence in the upper limits of particle sizes
associated with existing datasets of Dgi and Mi, the particle size distribution with the upper limit of 2,000μm
for the Acolian sandy soil and volcanic ash soils in Table2 was normalized to the case with the upper limit of
1,000μm using Eq.(3).
Calculation of water pressure head. To estimate the capillary tube or pore diameter (Di in µm), which
was composed of particles with the size of Dgi (µm), Arya and Paris19 developed an expression
where α is the empirical scaling parameter varying between 1.35 and 1.40 in their original model19, but was
thought to vary with soil particle size in the optimized model of Arya etal.20. In Tyler and Wheatcras model22 α
is the fractal dimension of the pore. e parameter e is the void rate of entire soil and assumed unchanging with
particle size. However, according to Eqs. (5) and (6), e in Eq.(16) should vary with particle size and be replaced by
ei, which depends on soil particle sizes. ni is the number of particles in the ith size fraction with a particle diameter
Table 2. Physical properties of soils used in the study. ρb is bulk density (g/cm3); θr is residual water content
(cm3/cm3) at 15,000cm water pressure head; ε is particle size distribution index. eSoil water retention
data of uvo-aquic soil, red earth, humid-thermo ferralitic, purplish soil, meadow soil, and yellow earth were
measured with pressure membrane apparatus51,52. e soil water retention data of black soil, chernozem soil,
cinnamon soil, brown earth, and albic soil were obtained using the suction and pressure plate method50. e
soil water retention data of volcanic ash soil and Acolian sandy soil were measured using the suction and
pressure plate method5355.
No Soil USDA soil taxonomy Texture
Particle percentage
ρbθrεSource < 2μm < 20μm
01 Acolian sandy soil Entisols Sand 0.11 0.53 1.65 0.024 1.37 53
02 Meadow soil Inceptisols Sandy loam 6.04 35.20 1.38 0.039 1.38 51
03 Fluvo-aquic soil Inceptisols Sandy loam 9.51 38.01 1.33 0.055 1.82 52
04 Fluvo-aquic soil Inceptisols Sandy loam 10.20 33.20 1.27 0.051 1.87 52
05 Volcanic ash soil Andisols Sandy loam 10.22 35.00 0.33 0.199 3.09 55
06 Fluvo-aquic soil Inceptisols Sandy loam 13.55 45.60 1.27 0.062 1.75 52
07 Fluvo-aquic soil Inceptisols Loam 10.76 42.40 1.32 0.088 1.65 52
08 Meadow soil Inceptisols Loam 13.27 44.37 1.28 0.054 1.74 51
09 Fluvo-aquic soil Inceptisols Loam 13.40 47.88 1.32 0.059 2.48 52
10 Purplish soil Inceptisols Loam 16.32 48.04 1.30 0.092 1.58 51
11 Yellow earth Inceptisols Silt clay loam 27.35 73.87 1.29 0.108 1.61 51
12 Meadow soil Inceptisols Clay loam 22.09 47.32 1.29 0.082 1.95 51
13 Fluvo-aquic soil Inceptisols Clay loam 28.86 58.39 1.28 0.159 2.21 52
14 Volcanic ash soil Andisols Clay loam 28.01 65.00 0.80 0.370 1.73 53
15 Chernozem soil Mollisols Sandy clay 30.14 48.56 1.24 0.148 4.57 50
16 Volcanic ash soil Andisols Sandy clay 34.56 45.60 0.70 0.263 1.57 54
17 Fluvo-aquic soil Inceptisols Sandy clay 36.22 76.05 1.29 0.185 2.15 52
18 Brown earth Alsols Sandy clay 36.77 54.36 1.29 0.142 3.85 50
19 Fluvo-aquic soil Inceptisols Sandy clay 40.02 73.30 1.28 0.195 2.31 52
20 Cinnamon soil Alsols Sandy clay 40.12 59.37 1.19 0.138 3.74 50
21 Black soil Mollisols Sandy clay 42.18 59.34 1.15 0.186 3.44 50
22 Volcanic ash soil Andisols Sandy clay 45.37 63.28 0.82 0.385 3.14 53
23 Fluvo-aquic soil Inceptisols Silty clay 34.20 73.98 1.31 0.148 2.10 52
24 Fluvo-aquic soil Inceptisols Silty clay 33.31 78.73 1.30 0.161 2.12 52
25 Fluvo-aquic soil Inceptisols Silty clay 33.56 79.44 1.35 0.169 2.17 52
26 Albic soil Spodosols Clay 52.76 77.60 1.16 0.230 1.66 50
27 Fluvo-aquic soil Inceptisols Clay 56.05 89.82 1.25 0.283 2.76 52
28 Red earth Ultisols Clay 58.88 79.26 1.22 0.195 1.03 51
29 Humid-thermo ferralitic Oxisols Clay 72.57 85.60 1.15 0.225 1.05 51
30 Fluvo-aquic soil Inceptisols Clay 68.81 98.02 1.08 0.303 2.04 52
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(Dgi in μm), assuming that the particles are spherical and that the entire pore volume formed by assemblage of
the particles in this class is represented by a single cylindrical pore. e equation for calculating ni is given as19
where mi is the mass of particles in the ith size fraction of particles. Assuming that soil water has a zero contact
angle and a surface tension of 0.075N/m at 25°C, the minimum diameter of soil pore (Dmin) was taken to be
0.2µm in this study, which is equivalent to the water pressure head of 15,000cm according to Young–Laplace
equation. We set this minimum pore size to correspond the minimum particle size (Dgmin = 1.0µm). e FBD
model might thus not apply well to porous media with pores smaller than 0.2μm. As a result, Eq.(16) can be
simplied into the following equation.
e equivalent capillary pressure (ψi in cm) corresponding to the ith particle size fraction can be calculated
In Eq.(19), the maximum water pressure head (ψr = 15,000cm) corresponds to θr and Dgmin (1μm). e
minimum water pressure head (ψ0 = 15cm) corresponds to θs and Dgmax (1,000μm). ese assumptions were
arbitrary and might not be appropriate for some soil types. But these values were used in the study because they
approximated the practical range of measurements well.
The resulting model of soil water retention. Equations8 and 19 formulate a FBD-based model for
estimation of soil water retention curve. To simplify the computation, we incorporated the two equations into
the following analytical form,
with the parameter b obtained using
In Eq.(21), a water pressure head of 15,000.1cm is employed to consecutively predict the soil water content
until the water pressure head of 15,000cm.
Soil dataset. Evaluation of the applicability of the proposed modeling procedure required datasets that
included soil bulk density, residual water content, and soil particle size distribution covering three particle
diameters (D10, D40, and D60) below which the cumulative mass fractions of particles were 10%, 40%, and 60%,
respectively. In addition, measured water content and water pressure head were required for the actual reten-
tion curve in order to compare with the result of the FBD model. In this study, the soil water retention data of
30 dierent soils, measured by Yu etal.50, Chen and Wang51, Zhang and Miao52, Liu and Amemiya53, Hayano
etal.54, and Yabashi etal.55 were used for model verication (Table2). e data covered soils in China (such as
black soil, chernozem soil, cinnamon soil, brown earth, uvo-aquic soil, albic soil, red earth, humid-thermo
ferralitic, purplish soil, meadow soil, and yellow earth) and soils in Japan (such as volcanic ash soil and acolian
sandy soil). e USDA soil taxonomy of these soils was provided in Table2. e 30 soils ranged in texture from
clay to sand and in bulk density from 0.33g/cm3 to 1.65g/cm3, which covered a much wider range of soil bulk
density than many of the existing models or pedotransfer functions5659. Particle size fractions (Dgi) were chosen
as the upper limit of the diameters between successive sieve sizes. For the data set in which particle density was
not determined, 2.65g/cm3 was used.
Statistical parameters for model verication
Four statistical properties, R2, RMSE, mean residual error (ME), and t value were calculated to determine the
accuracy of the FBD model. e R2 values were computed at the same value of ψ, with the values of θ measured
and estimated by the FBD model (Eq.20). RMSE and ME were obtained, respectively, by
est θmea)2
(θest θmea
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SCIENTIFIC REPORTS | (2020) 10:16666 | 
where θmea was measured soil water content, θest was soil water content estimated with the FBD model, and n was
the number of measured pairs of water content and pressure head. With the assumption of normal distribution
and independence of dierences between the water contents measured and estimated by the FBD model, t was
calculated with
when calculated |t| was larger than t0.05 (the critical value of the Students t distribution for P = 0.05 and n−1
degrees of freedom), the dierences between the measured and estimated water contents were statistically sig-
nicant. If t < 0, soil water contents were underestimated and vice versa. us, t was a measure for the systematic
bias in the estimation. Values of t close to zero indicated that the measured and estimated soil water contents
were not dierent systematically from each other or, equivalently, that there was no consistent bias. Values of t
that diered greatly from zero indicated the presence of systematic bias. RMSE was a measure for the scatter of
the data points around the 1:1 line. Low RMSE values indicated less scatter. Low RMSE values also implied low
ME. Regarding the result that t was low while RMSE was high, it could be explained that negative and positive
deviations distributed more evenly on the two sides of 1:1 line.
Received: 29 April 2020; Accepted: 22 September 2020
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is work was initially supported by Japan Society for the Promotion of Science (JSPS) [Grant number P97470]
and later by the AgResearch Program of the University of Tennessee, Knoxville, USA.
Author contributions
Authors H.S. and J.Z. designed and tested the model. H.S. prepared the original dra. J.Z., X.C. and J.L. reviewed
and edited the manuscript. J.Z. supervised the research. All authors participated in improving the manuscript.
Competing interests
e authors declare no competing interests.
Additional information
Correspondence and requests for materials should be addressed to J.Z.
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... Saturation θ s is highly affected by the ratio of pores and fine particles. Positive correlations always exhibited with silt% and clay% were due to increased pore ratio and, in contrast, a negative correlation for ρ and sand% [52][53][54]. As shown in Fig 6, θ s ranges from 0.28 to 0.377 cm 3 .cm ...
... −3 . A highly positive correlation of θ s with the percentage of clay and silt is observed, which agrees with many other studies [52,54,55]. θ s is generally low in oasis soils due to the dominant content of sand combined with rare clay [36,42]. ...
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This study aims to produce digital maps showing the physical and hydraulic soil properties of the Al-Ahsa Oasis in Saudi Arabia by employing the capabilities of the GIS technique. These maps can display the pattern distribution of different physical and hydraulic properties of soil accurately and accessibly. Recently developed local pedotransfer function (PTF) models were applied to the basic soil data of earlier research covering 566 points. An analysis was conducted using a spatial interpolation technique of the GIS program. Maps of spatial patterns described essential soil physical and hydraulic properties such as sand%, silt%, clay%, bulk density ( ρ ), saturation (θ s ), field capacity (FC), wilting point (WP), and soil water characteristic curve (SWCC) fitting parameters b , c , d . Sand dominates most of the study area, particularly in the northeast near Hufof. This may be attributed to the deposition of drifting sand and dune movement. Silt and clay increased in other locations. Bulk density ρ was positively increased with sand and negatively with silt and CaCO 3 content. Soil hydraulic properties (θ, FC, WP, and SWCC fitting parameters b , c , d ) were positively correlated with silt and ρ and negatively with sand content. This digital map can be employed for a general overview investigation, for the whole studied area, for agricultural expansion and for environmental studies.
... A parametric logistic regression equation with four parameters (PL4) was used for fitting the laboratory measured values of SWCC. The general form of PL4 equation presented in equation (1 and 2) as describe by (Davis et al., 2002;Deming, 2015) wðhÞ ...
... Positive correlations with silt% and CaCO 3 % were due to increased pore ratio and, in contrast, negative correlation for q and sand%. The results parallel with other studies (Al-Qinna and Jaber, 2013; Chaudhari et al., 2013;Du, 2020;Mahdi, 2008;Mahdi and Naji, 2015;Saxton and Rawls, 2006;Sun et al., 2020). Equations (7) performed high identicality in estimating h s with a correlation (-0.929) and neglectable MSE (0.001)for training set. ...
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Al-Ahsa Oasis is one of the oldest and biggest agricultural regions in Saudi Arabia. Thirty-six soil samples representing most of the soil type in the region were collected and analysed in a laboratory for physical properties including particle size (sand%, silt%, clay%), saturation θs, and bulk density ρ. The soil-water characteristic curve (SWCC) was measured using the filter paper method. Intensive statistical analysis included correlation, stepwise multiple linear regression analysis (SWR), mean square error (MSE), and F-test were used to evaluate the potential PTFs. Silt (silt%) and bulk density (ρ) were achieved a high accuracy in prediction of (ρ) and saturation (θs) respectively. Both field capacity (FC) and wilting point (WP) were correlated significantly with θs with a very high prediction compatibility and MSE 0.004 and 0.001 respectively. Using tow levels of prediction demonstrated high correctness in predicting SWCC with correlation coefficient 0.986 and 0.952 with a low MSE equal to 0.0007 and 0.0028 respectively. The result of this study shown the high feasibility of developing a model for the prediction of SWCC using easily readable PTFs.
... For the determination of the WRC, different field and laboratory methods exist [9][10][11]. Analytical models [12] or regression equations-empirical formulas called pedo-transfer functions (PTF)-are used to predict the WRC values from easily measured or already available soil properties [13][14][15][16][17]. The majority of the PTFs for estimating the WRC use soil texture, bulk density and SOM content as predictors [1,13,17], although the necessity of the latter has been questioned [18] or shown to improve the estimations only for specific soil water potentials [19]. ...
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Soil water retention (SWR) is an important soil property related to soil structure, texture, and organic matter (SOM), among other properties. Agricultural management practices affect some of these properties in an interdependent way. In this study, the impact of management-induced changes of soil organic carbon (SOC) on SWR is evaluated in five long-term experiments in Europe (running from 8 up to 54 years when samples were taken). Topsoil samples (0–15 cm) were collected and analysed to evaluate the effects of three different management categories, i.e., soil tillage, the addition of exogenous organic materials, the incorporation of crop residues affecting SOC and water content under a range of matric potentials. Changes in the total SOC up to 10 g C kg−1 soil (1%) observed for the different management practices, do not cause statistically significant differences in the SWR characteristics as expected. The direct impact of the SOC on SWR is consistent but negligible, whereas the indirect impact of SOC in the higher matric potentials, which are mainly affected by soil structure and aggregate composition, prevails. The different water content responses under the various matric potentials to SOC changes for each management group implies that one conservation measure alone has a limited effect on SWR and only a combination of several practices that lead to better soil structure, such as reduced soil disturbances combined with increased SOM inputs can lead to better water holding capacity of the soil
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The determination of the soil pore size distribution, water retention curve, and derived parameters that control important processes in soils, such as water supply for plants; infiltration; water and solute movement in soils; erosion; plant nutrients and contaminants transport, etc, are challenging and the available methods are expensive , time-consuming and prone to bias and errors. The use of 1 H Nuclear Magnetic Resonance (NMR) relax-ometry to characterise the soil porosity and hydraulic properties through spin-lattice and spin-spin relaxometry results in an ill-posed problem with two correlated unknown quantities: the pore length scales, and surface relaxivity. To overcome this limitation of NMR relaxometry, we propose the use of a method that directly accesses the NMR diffusion modes governed only by the pore size, and therefore, independent of the unknown surface relaxivity. The manuscript describes an unprecedent application in Soil Science of the Decay due to Diffusion in Internal Field (DDIF) method to successfully determine the pore size distribution of undisturbed soil samples, as well as to estimate the water retention curves from the pore size distribution.
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Most soil hydraulic information used in Earth System Models (ESMs) is derived from pedo-transfer functions that use easy-to-measure soil attributes to estimate hydraulic parameters. This parameterization relies heavily on soil texture, but overlooks the critical role of soil structure originated by soil biophysical activity. Soil structure omission is pervasive also in sampling and measurement methods used to train pedotransfer functions. Here we show how systematic inclusion of salient soil structural features of biophysical origin affect local and global hydrologic and climatic responses. Locally, including soil structure in models significantly alters infiltration-runoff partitioning and recharge in wet and vegetated regions. Globally, the coarse spatial resolution of ESMs and their inability to simulate intense and short rainfall events mask effects of soil structure on surface fluxes and climate. Results suggest that although soil structure affects local hydrologic response, its implications on global-scale climate remains elusive in current ESMs. The effect of soil structure is not included in most Earth System Models. The authors here introduce and evaluate the consequences at local and global scale of modifying hydraulic properties of soils in response to biological activity—a process significantly changing soil structure.
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This paper presents the comparison of three different approaches to estimate soil water content at defined values of soil water potential based on selected parameters of soil solid phase. Forty different sampling locations in northeast of Iran were selected and undisturbed samples were taken to measure the water content at field capacity (FC), -33 kPa, and permanent wilting point (PWP), -1500 kPa. At each location solid particle of each sample including the percentage of sand, silt and clay were measured. Organic carbon percentage and soil texture were also determined for each soil sample at each location. Three different techniques including pattern recognition approach (k nearest neighbour, k-NN), Artificial Neural Network (ANN) and pedotransfer functions (PTF) were used to predict the soil water at each sampling location. Mean square deviation (MSD) and its components, index of agreement (d), root mean square difference (RMSD) and normalized RMSD (RMSDr) were used to evaluate the performance of all the three approaches. Our results showed that k-NN and PTF performed better than ANN in prediction of water content at both FC and PWP matric potential. Various statistics criteria for simulation performance also indicated that between kNN and PTF, the former, predicted water content at PWP more accurate than PTF, however both approach showed a similar accuracy to predict water content at FC.
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Agroecosystem models, regional and global climate models, and numerical weather prediction models require adequate parameterization of soil hydraulic properties. These properties are fundamental for describing and predicting water and energy exchange processes at the transition zone between solid earth and atmosphere, and regulate evapotranspiration, infiltration and runoff generation. Hydraulic parameters describing the soil water retention (WRC) and hydraulic conductivity (HCC) curves are typically derived from soil texture via pedotransfer functions (PTFs). Resampling of those parameters for specific model grids is typically performed by different aggregation approaches such a spatial averaging and the use of dominant textural properties or soil classes. These aggregation approaches introduce uncertainty, bias and parameter inconsistencies throughout spatial scales due to nonlinear relationships between hydraulic parameters and soil texture. Therefore, we present a method to scale hydraulic parameters to individual model grids and provide a global data set that overcomes the mentioned problems. The approach is based on Miller–Miller scaling in the relaxed form by Warrick, that fits the parameters of the WRC through all sub-grid WRCs to provide an effective parameterization for the grid cell at model resolution; at the same time it preserves the information of sub-grid variability of the water retention curve by deriving local scaling parameters. Based on the Mualem–van Genuchten approach we also derive the unsaturated hydraulic conductivity from the water retention functions, thereby assuming that the local parameters are also valid for this function. In addition, via the Warrick scaling parameter λ, information on global sub-grid scaling variance is given that enables modellers to improve dynamical downscaling of (regional) climate models or to perturb hydraulic parameters for model ensemble output generation. The present analysis is based on the ROSETTA PTF of Schaap et al. (2001) applied to the SoilGrids1km data set of Hengl et al. (2014). The example data set is provided at a global resolution of 0.25° at
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Studying and modeling the effects of soil properties and management on soil structure and near saturation water retention is vital for the development of effective soil and water conservation practices. The contribution of soil intrinsic properties and extrinsic conditions to structure stability was inferred, in quantitative terms, from changes in water retention curves near saturation (low matric potential, 0 - 50 cm, macropores > 60 μm) that were obtained by the high energy moisture characteristic (HEMC) method. The S - shaped water retention curves were characterized by the modified van Genuchten model that provided: (i) the model parameters α and n , which represent the location of the inflection point and the steepness of the water retention curve, respectively; and (ii) the soil structure index, SI=VDP/MS, where VDP is the volume of drainable pores, and MS is the modal suction. Model parameters, claculated by the soil - HEMC model, were related to soil properties and hence soil water retention properties were linked to measured characteristics in several field and laboratory experiments. Soil SI increased exponentially with the increase in α and the decrease in n , while the relationship between SI and α/n was linear. An improved description of the water retention and its link to pore and apparent aggregate size distribution, by using the model parameters α and n, could potentially assist in the selection of management practices for obtaining the most suitable type of soil structure depending on the desired soil function. Keywords: Structure stability, water retention, pore size, stability index, model.
The tangential model (TANMOD) is one of the few soil water retention curve (SWRC) models that can be applied in both unsaturated and saturated soils, from the positive suction range to the negative suction range, accounting for the effect of volume changes in the entrapped air in soil pores. The model has been successfully evaluated with relatively coarser soils. Its performance, however, has not been fully tested for various soil texture classes. In this study, we aim 1) to determine the TANMOD parameters for various soil texture classes, and 2) to assess the underlying relationship between the TANMOD parameters and the soil texture class. To address those objectives , the TANMOD was first fitted to 399 SWRC from 10 USDA soil texture classes in the UNSODA soil database. The model parameters consist of three coordinates (S re , s e), (S rm , s m), and (S rf , s f), three tangential slopes, c e , c m , and c f , along the curve. Multivariate analysis and several machine learning algorithms were respectively used to evaluate model parameters for each soil texture class and reveal the relation between the model parameters and the soil texture classes. The results demonstrated that the TANMOD fitted well from coarser soils to finer soils. Unique sets of the model parameters and their uncertainties are proposed for 10 USDA soil texture classes. Unsupervised learning algorithms, hierarchical cluster analysis and k-means clustering, failed to classify the TANMOD parameters while one of the supervised machine learning techniques, random forest, adequately classified the TANMOD parameters to the USDA soil texture classes. The accuracy of the classification based on the random forest model is 62.6%. The maximum tangential slope, c m , was the most important parameter in relation with the soil texture class. Consequently, the TANMOD parameters not only have their own physical meaning but also can be applied to various USDA soil texture classes.
Soil bulk density (ρb) variations influence soil hydraulic properties, such as the water retention curve (WRC), but they are usually ignored in soil water simulation models. We extend the van Genuchten WRC model parameters to account for ρb variations using a series of empirical expressions. WRC measurements made on eight soils with various ρb, and textures are used to calibrate these ρb-related empirical equations. Accordingly, two approaches are developed to estimate WRCs of soils at various ρb. Another eight soils with a wide range of ρb and textures are used to evaluate the accuracy of the new approaches. Approach 1 estimates WRCs for each soil at various ρb using a WRC measurement made at a reference ρb and the soil texture fractions. This approach gives reasonable WRC estimates for the eight validation soils, with an average root-mean-square error (RMSE) of 0.025 m³/m³ and an average determination coefficient (R²) of 0.94. For Approach 2, a WRC measurement made at a reference ρb and one additional water content-matric potential value measured at a different ρb value are used, which produces WRC estimates with an average RMSE of 0.017 m³/m³ and an average R² of 0.97. The methodology used in Approach 2 is also applied to the Brooks and Corey WRC model to obtain accurate and precise WRC estimates. The proposed approaches have the potential to be incorporated into simulation models for estimating soil hydraulic properties that are affected by transient and variable ρb.
Core Ideas We propose a model of mechanistic pore‐scale interactions of mucilage, water, and soil. Effect of mucilage on saturated hydraulic conductivity is stronger in coarse soils. Coarse soils require higher mucilage concentration to increase water content. Upscaling to macroscopic soil hydraulic properties remains challenging. The model was validated on measured water retention and saturated hydraulic conductivity Mucilage secreted by roots alters hydraulic properties of soil close to the roots. Although existing models are able to mimic the effect of mucilage on soil hydraulic properties for specific soils, it has not yet been explored how the effects of mucilage on macroscopic soil hydraulic properties depend on soil particle size. We propose a conceptual model of how mechanistic pore‐scale interactions of mucilage, water, and soil depend on pore size and mucilage concentration and how these pore‐scale characteristics result in changes of macroscopic soil hydraulic properties. Water retention and saturated hydraulic conductivity of soils with different ranges of particle sizes mixed with various mucilage concentrations were measured and used to validate the conceptual model. We found that (i) at low mucilage concentrations, the saturated conductivity of a coarse sand was a few orders of magnitude higher than that of a silt, (ii) at an intermediate concentration, the hydraulic conductivity of a fine sand was lower than of a coarse sand or a silt, and (iii) at a high concentration, all soils had a hydraulic conductivity of the same magnitude. At low matric potentials, mucilage increased the water content in all soilsin all soils. In coarser soils, higher mucilage concentrations were needed to induce an increase in water content of >0.05 g g –1 at low matric potentials. This study shows how pore‐scale interactions between mucilage, water, and soil particles affect bulk soil hydraulic properties in a way that depends on soil particle size. Including such effects in quantitative models of root water uptake remains challenging.
The determination of soil hydraulic properties is laborious and expensive, especially in large-scale applications. One often used substitute for measured hydraulic properties are pedotransfer function (PTFs) estimates. Most PTFs, however, are statistical models that tend to produce biased results for data outside their –often limited– calibration databases. In addition, most PTFs have been established on data derived from temperate regions causing the question whether such models are applicable to soils in tropical regions. This work aimed to evaluate the performance of the Splintex PTF to predict the hydraulic functions for sandy and clayey soils from several tropical and subtropical Brazilian datasets. Splintex is somewhat unique in that it is based on physical principles using a modification of the Arya-Paris method while allowing the estimation of van Genuchten parameters from limited data. In addition, Splintex has an option to include measured soil water retention points, in principle allowing it to produce accurate estimates for a variety of soils. Estimates by Splintex were compared with the empirical Rosetta PTF, which also has an option to use one (or two) retention points. Estimates by both PTFs were compared to observed retention data and field capacity, available water capacity, hydraulic conductivity, and diffusivity using metrics such as Pearson correlation (r), mean absolute error (MAE) and root mean square error (RMSE). Both Splintex and Rosetta yielded similar results and sometimes produced significant biases in estimated quantities. In the majority of cases it appears that Splintex produced somewhat better estimates than the 2001 version of Rosetta, indicating that Splintex is a viable, physically-based, alternative to estimating hydraulic properties.
Soil water‐holding capacity is an important component of the water and energy balances of the terrestrial biosphere. It controls the rate of evapotranspiration, and is a key to crop production. It is widely accepted that the available water capacity in soil can be improved by increasing organic matter content. However, the increase in amount of water that is available to plants with an increase in organic matter is still uncertain and may be overestimated. To clarify this issue, we carried out a meta‐analysis from 60 published studies and analysed large databases (more than 50 000 measurements globally) to seek relations between organic carbon (OC) and water content at saturation, field capacity, wilting point and available water capacity. We show that the increase in organic carbon in soil has a small effect on soil water content. A 1% mass increase in soil OC (or 10 g C kg ⁻¹ soil mineral), on average, increases water content at saturation, field capacity, wilting point and available water capacity by: 2.95, 1.61, 0.17 and 1.16 mm H 2 O 100 mm soil ⁻¹ , respectively. The increase is larger in sandy soils, followed by loams and is least in clays. Overall the increase in available water capacity is very small; 75% of the studies reported had values between 0.7 and 2 mm 100 mm ⁻¹ with an increase of 10 g C kg ⁻¹ soil. Compared with reported annual rates of carbon sequestration after the adoption of conservation agricultural systems, the effect on soil available water is negligible. Thus, arguments for sequestering carbon to increase water storage are questionable. Conversely, global warming may cause losses in soil carbon, but the effects on soil water storage and its consequent impact on hydrological cycling might be less than thought previously. Highlights We investigated how available water capacity can be increased with a 1% increase in soil organic carbon. We analysed data from 60 published studies and global databases with more than 50 000 measurements. The increase in organic carbon in soil has a small effect on soil water retention. A 1% mass increase in soil OC on average increased available water capacity by 1.16%, volumetrically.
Soil can be regarded as a source and a storage of water in soil-plant-atmosphere continum (SPAC). To make clear the characteristics of moisture retention of the soil in relation to other soil properties is part of the basic work of water dynamic analysis in SPAC. In this study, water retention characteristic curves and other soil properties were measured for 50 samples representing 5 different kinds of soil from the northeastern region of China.From the results, a new empirical equation for moisture characteristic curve was proposed. The parameters of the equation had physical meaning and could be estimated by the percentage of micro-aggregate and the organic matter content in the soil, and the geometric mean of soil particle size. Furthermore, an equation to describe the rate of soil water absorption was also proposed. Parameters in the equation depended upon the specific surface area of the soil, the organic matter content in the soil and the percentage of soil particles less than 0.01mm.