J. Jpn. Soc. Soil Phys.
土壌の物理性No. 157 p.19 ∼ 32 (2024)

Estimating soil hydraulic and thermal properties
using publicly available soil information
for future prediction of soil temperature and water content

Juri ASANO1 , Yuki KOJIMA2 , Chihiro KATO3 and Kohji KAMIYA2

Abstract: Determining the soil hydraulic and thermal
properties is necessary for predicting the future soil temperature and water content. However, it requires a longtime, enormous effort, and considerable expense, including soil sampling from the target site, laboratory testing,and optimization processing based on ﬁeld observations.
This study estimated soil hydraulic and thermal properties
using publicly available soil information and pedotransfer
functions, and examined their usefulness. Soil information was extracted from the Japanese Soil Inventory, andseveral pedotransfer functions were tested. The results ofsoil temperature and water content computations using theestimated soil hydraulic and thermal properties were compared with the computations using soil hydraulic and thermal properties obtained from previous studies. The estimation approach that minimized the error between the two
was selected as the optimal approach. Then, the future soil
temperature and water content were computed using thesoil hydraulic and thermal properties obtained by the optimal estimation approach and compared with the resultsof previous studies. The predictions of volumetric watercontent and soil temperature differed due to the estimatedsoil hydraulic and thermal properties, but there was no signiﬁcant difference in the magnitude of change between thepresent and future. This indicates that estimating soil hydraulic and thermal properties using publicly available soil
information and pedotransfer functions may be acceptable
if the estimation is limited to evaluating future changes.
Key Words : pedotransfer function, soil hydraulic properties, soil thermal properties, climate change, HYDRUS-1D

1. Introduction
In recent years, climate change due to global warm1 Graduate School of Agricultural and Life Sciences, The University ofTokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan.
2 Department of Civil Engineering, Gifu University, 1-1 Yanagido, Gifu
City, Gifu 501-1193, Japan. Corresponding author: Yuki KOJIMA.
3 Department of Agricultural and Environmental Engineering, Hirosaki
University, 3 Bunkyo-cho, Hirosaki City, Aomori 036-8561, Japan.
2024 年 4 月 1 日受稿 2024 年 6 月 22 日受理

ing has caused changes in temperature, precipitation, andprecipitation patterns. The Intergovernmental Panel on
Climate Change (IPCC) Sixth Assessment Report (IPCC,
2021) stated that the global average temperature for theten years from 2011 to 2020 is 1.09◦ C higher than thatbetween 1850 and 1900, primarily due to warming after
2012. Furthermore, they predicted that global warming
would increase rainfall intensity and that heavy rains andﬂoods would become more frequent and intense in mostparts of Asia. Climate change impacts the soil environment, i.e., soil temperature and water content (Amundson
et al., 2015). Changes in the soil temperature and water content can result in shifts in suitable areas for crop
cultivation (Sugiura and Yokozawa, 2004), decreased productivity in agriculture and ﬁsheries (Thiault et al., 2019),
impacts on soil ecosystems (e.g., Kaneda et al., 2016;
Fuentes and Porter, 2013; Onwuka, 2018), and an increasein greenhouse gas emissions due to soil microorganism activity (Schauﬂer et al., 2010). It is crucial to anticipate
future soil temperature and water content to modify crop
cultivation practices and improve crop varieties to continue
farming in response to climate change.
Some research is already underway to assess the effects of climate change on the soil temperature and water content with simulations of soil water and heat transfers using future climate data. For example, Kato andNishimura (2015) computed future soil water content ata soybean ﬁeld in Toyama Prefecture, Japan, using the
one-dimensional soil heat and mass transport analysis software HYDRUS-1D (PC-Progress, Prague, Czech Republic) (Šimůnek et al., 2016) in combination with a global
circulation model (GCM). The results highlighted a highsoil water content frequency, raising the risk of crop wet
damage. Using a similar approach, Tsuji et al. (2020) computed soil water content in ﬁelds in Yamaguchi Prefecture,
Japan. They found that soils are becoming drier in winter20

土壌の物理性and experiencing less drought in summer. Similarly, Asanoet al. (2023) assessed climate change’s impact on soil temperature and water content in both plains and mountainous
regions of Gifu Prefecture, Japan. They found that futurechange in soil water content is signiﬁcant in a mountainous
region, while future change in soil temperature is remarkable in a plain region. Furthermore, Yoshida et al. (2021)
and Slama et al. (2020) investigated the effects of climatechange on soil water content and soil salinity levels. Wanget al. (2021) evaluated future soil moisture changes affecting crop growth. Some research focused on forest soils,e.g., Horel et al. (2022) showed an increase in dry daysin forests, and Fernandez-Bou et al. (2020) found soil water content change will affect soil CO2 emissions in theforests.
While several studies have already predicted future soil
temperature and water content, there are few, and the targeted areas are limited. This is because information on soil
hydraulic properties, such as hydraulic conductivity andwater retention curve, and soil thermal properties, such as
thermal conductivity and volumetric heat capacity, are necessary when simulating soil temperature and water content.
The soil hydraulic and thermal properties are generally determined by sampling soil from the study area and performing a series of laboratory tests. For example, Asanoet al. (2023) determined them by the aforementioned processes and additional optimization based on on-site soil
monitoring data. However, this determination procedurerequires signiﬁcant time and effort. Predicting future soil
temperature and water content would be easier, and the targeted area could be wider if soil hydraulic and thermal
properties could be estimated without soil sampling andlaboratory tests.
We focused on a method to estimate soil hydraulic andthermal properties from readily available soil information.
This estimation method is known as the Pedotransfer function (PTF). The term “PTF” was coined by Bouma (1989)(McBratney et al., 2002). Bouma (1989) developed amethod to estimate time-consuming and costly soil hydraulic properties from soil texture and pH. Numerous
studies have been conducted on developing PTFs for soil
hydraulic properties (e.g., Schaap et al., 1998; Wösten etal., 1999) and thermal properties (e.g., de Vries, 1963;
Campbell, 1985). Although the PTFs are convenient, theirpracticality for evaluating climate change impacts on thesoil temperature and water content is unknown. Kato andNishimura (2017) tested the PTFs to estimate soil hydraulic parameters for soils in Toyama and Fukui cities,
Japan, and estimated future soil moisture in agriculturallands. They focused on the hydraulic properties and water content estimation; thus, thermal properties and tem-

第 157 号（2024）

perature dynamics in conjunction with the estimation of
hydraulic properties and water dynamics were not yet considered.
This study aimed to investigate the utility of estimating
soil hydraulic and thermal properties from publicly available soil information, speciﬁcally the Japanese Soil Inventory (JSI) (Takata, 2018; NARO, 2021), using multiple estimation approaches. The JSI is a dataset published by
the National Agriculture and Food Research Organization(NARO). This dataset contains information on the distribution of soil types and soil information for all of Japan.
We compared soil water content, matric potential, and temperature computed with the estimated soil hydraulic andthermal properties to those computed with the properties
determined with soil sampling, laboratory tests, and optimization.

2. Materials and Methods
2.1 Study sites
In this study, two experimental sites were selected: theGifu University experimental ﬁeld (35◦ 27’ 42.9” N, 136◦
44’ 21.5” E) (hereafter referred to as Gifu) and the Gifu
University Watershed Science Research Center Takayama
experimental site (36◦ 08’ 33.0” N, 137◦ 25’ 20.4” E)(hereafter referred to as Takayama). The locations of thesesites are shown in Fig. 1. Both ﬁeld sites are maintainedin bare ground conditions. A small amount of litter layer
was observed on the ground surface in Takayama. The
soil textures of Gifu and Takayama were sandy clay loam
and sandy loam. The soil hydraulic and thermal properties values at the study sites were measured by Asano et al.
(2023).
2.2 Overview of HYDRUS-1D
This study computed soil temperature and water content
using HYDRUS-1D. The HYDRUS-1D expresses onedimensional vertical liquid water movement in soil using

Fig. 1 The locations of the study sites (Gifu and Takayama).

論文：Estimating soil hydraulic and thermal properties using publicly available soil information

21

the Richards’ equation (Richards, 1931):
[ (
)]
∂θ
∂
∂h
=
K
+1
∂t
∂z
∂z

A = 0.65 − 0.78ρb + 0.60ρb 2(1)where θ is soil volumetric water content (m3 m−3 ), K is
hydraulic conductivity (m s−1 ), h is soil matric potential(m of water), t is time (s), and z is depth (m). While
HYDRUS-1D can consider water vapor movement in thesoil, vapor movement is negligible compared to liquid water movement in most cases except for dry conditions, andhence not considered in this study. The relationship between θ and h in unsaturated soils, i.e., water retention
curve, is necessary to solve Eq. (1). Although HYDRUS1D allows the selection of water retention curves from ﬁvemodels, we used the van Genuchten (1980) model (VG
model) as:Se =θ − θr= (1 + |α h|n )−mθs − θr(2)

where Se is effective saturation, θr and θs are residual andsaturated volumetric water content (m3 m−3 ), respectively,and α is an empirical parameter (m−1 ). The m and n are dimensionless empirical parameters, and m = 1 − n−1 . The
following equation expresses the K:[(

K = Ks Se 1 − 1 − Sel

1/m

HYDRUS-1D simulates soil temperature based on theheat conduction equation considering convective heattransfer due to liquid water movement:(4)

is volumetric heat capacity of soil (J m−3 ◦ C−1 ),where Cp
T is soil temperature (◦ C), λ is thermal conductivity (Wm−1 ◦ C −1 ), Cw is volumetric heat capacity of liquid water (J m−3 ◦ C−1 ), and ql is ﬂux density of liquid water (ms−1 ). We assumed that heat transfer related to water vapor movement was negligible. The thermal conductivity
λ can be selected from the Campbell (1985) model (hereafter, Campbell model) and the Chung and Horton (1987)
model (CH model) in HYDRUS-1D. Equation (5) presents
the Campbell model for unsaturated soil:[]
λ = A + Bθ − (A − F) exp − (Dθ )I

B = 1.06ρb(7)

The parameter D represents the water content at which
thermal conductivity increases sharply and is highly correlated with clay content. In this case, D is expressed by
Eq. (8), where mc is the volumetric fraction of clay (m3m−3 ).
D = 1 + 2.6mc −0.5

(8)When the soil particle density is 2.65 Mg m−3 for mineralsoil, F is given by:
F = 0.03 + 0.1ρb 2(9)The constant I can be set to 4:
I=4

(10)

Equation (11) represents the CH model:
(11)

(3)

where Ks is saturated hydraulic conductivity (m s−1 ), andl is a parameter representing pore connectivity (unitless).

()∂ Cp T∂T∂∂T
λ=−Cw ql∂t∂z∂z∂z

For mineral soils, B is

λ = b1 + b2 θ + b3 θ 0.5

)m ]2

(6)

(5)

where A, B, D, F, and I are coefﬁcients. In many mineralsoils, A is determined using the dry bulk density ρb :where b1 , b2 , and b3 are empirical parameters. The CHmodel is not a PTF; therefore, it is impossible to determine
b1 to b3 from soil information alone. The procedure to
determine them is described in 2.3.4.
The HYDRUS-1D determines surface boundary conditions by solving the surface energy balance based on the
input meteorological data (Saito et al., 2006). The surface
energy balance can be expressed as follows:
Rn − H − LE − G = 0(12)where Rn is net radiation (W m−2 ), H is sensible heat ﬂux(W m−2 ), L is latent heat of evaporation (J m−3 ), E is evaporation rate (m s−1 ), and G is soil heat ﬂux (W m−2 ). Precipitation and E from Eq. (12) are used as boundary conditions for water transfer computations, while G is used forheat transfer computations.
2.3 Estimation method of soil hydraulic andthermal properties
The soil hydraulic and thermal properties necessary for
HYDRUS-1D simulations were estimated from soil information obtained from JSI. JSI provides nationwide soil distribution and information for different soil classiﬁcations.
In this study, we acquired information on soil classiﬁcation, soil carbon content (OC), Ks , and θ values at pF= 0,22

土壌の物理性1.5, and 2.7. The information available in JSI is for different depths, but we assumed they represent the whole soil
proﬁle in this study.
Based on the information, we estimated sand-silt-clay
mass ratios (hereafter %SSC), ρb , Ks (the Ks value can beobtained from JSI, but we also examined a PTF for Ks ),parameters for water retention curve, and parameters for
λ models. Estimation was performed using multiple approaches for each parameter, outlined in Sections 2.3.1 to
2.3.4.
2.3.1 Estimation of %SSC
The JSI only provides soil classiﬁcation (Fig. 2), andexact values of %SSC are difﬁcult to determine. For example, Gifu and Takayama were identiﬁed as highly clayey
and clayey without speciﬁc silt-clay content values. Therefore, we extracted two types of %SSC: the %SSC of eachclassiﬁcation’s centroid (referred to as Method 1) and the%SSC at the center point of the line that minimizes clay
content considered to signiﬁcantly inﬂuence water retention (referred to as Method 2). Fig. 2 includes the %SSC of
Method 1 and Method 2 of highly clayey soil as an example. The %SSC determined by Method 1 are 25 % sand, 25% silt, and 50 % clay corresponding to the center of gravity of the upper triangle. In Method 2, % clay is ﬁxed at 25% which is the minimum value, and 37.5 % sand and 37.5
% silt correspond to the central of the base of the upper
triangle.
2.3.2 Estimation of ρ b
The ρb was estimated with the PTF proposed by Abdel-

第 157 号（2024）

baki (2018) from OC (%). Abdelbaki (2018) investigatedvarious ρb estimation approaches and proposed the mostsuitable one. The optimal estimation formula identiﬁed by
Abdelbaki (2018) is:ρb = (2.684 − 140.943 × 0.006) exp (−0.006OC) (13)The OC values obtained from JSI were used for Eq. (13).
2.3.3 Estimation of hydraulic property parameters
The three types of Ks values were prepared: i) the value
directly obtained from JSI, ii) the value obtained using
ROSETTA (Schaap et al., 2001) with the %SSC determined with Method 1 and ρb , and iii) the one obtainedusing ROSETTA with the %SSC determined with Method
2 and ρb . The JSI provides a median and variations of Ks .
We used the median value. The VG model parameters (θs ,θr , α , n) for the water retention curves were also determined with three approaches: i) by ﬁtting the VG model tothe θ values at multiple pF provided by JSI, ii) by estimating with ROSETTA (Schaap et al., 2001) from Method 1based %SSC and ρb , and iii) by estimating with ROSETTA
from Method 2-based %SSC and ρb . When estimating VG
model parameters by ﬁtting to pF-θ data provided by JSI,θr and α were ﬁxed at constant values due to the limited
number of θ data. The θr was ﬁxed at 0.1 m3 m−3 as soil
conditions are assumed never to reach a dry state of 0.1
m3 m−3 . The α values were ﬁxed to 0.01 m−1 for Gifu
and 0.1 m−1 for Takayama, which ensure convergence in
HYDRUS-1D computations. Combining the three Ks patterns with the three VG model patterns prepared in a totalof nine sets of soil hydraulic properties parameters for eachsampling point.
2.3.4 Estimation of thermal property parameters
The λ were estimated with either the Campbell model or
Lu et al. (2014) model (hereafter, Lu model) based on the%SSC values obtained from JSI and estimated ρb . Equation (14) represents the Lu model:()
λ = λdry + exp γ − θ −β(14)where λdry is the λ of air-dried samples, and β and γ areshape factors. The λdry can be estimated with the following
equation as revealed by Lu et al. (2007):Fig. 2 Triangular diagram of soil classiﬁcation in the Japanese
Soil Inventory. How the percentages of sand, silt, and clay of“highly clayey” soil are determined using Method 1 (center ofgravity of the soil texture diagram) and Method 2 (center of line
minimizing clay content) is illustrated as an example. Black circles represent Method 1, while white circles represent Method 2.

λdry = −0.56τ + 0.51

(15)

where τ represents soil porosity (m3 m−3 ), which can becomputed from ρb assuming that the soil particle density
is 2.65 Mg m−3 . The parameters β and γ are determinedwith the following equations:論文：Estimating soil hydraulic and thermal properties using publicly available soil information

β = 0.67 fcl + 0.24

(16)

γ = 1.97 fsa + 1.87ρb − 1.36 fsa ρb − 0.95

(17)where fcl (kg kg−1 ) and fsa (kg kg−1 ) are the mass fractions of clay and sand. Since the Lu model is not implemented in HYDRUS-1D, we approximated the Lu modelwith the CH model and determined the parameters b1 to b3
in Eq. (11).
The computation of Cp in Eq. (4) in HYDRUS-1D requires the volume fraction of soil solid. We obtained thisfraction by dividing ρb by the particle density (2.65 Mg
m−3 ).
2.4 Determining the optimal estimation approach for soil hydraulic and thermal properties
We computed the soil T , θ , and h from January 1, 2021,
to December 31, 2021, using HYDRUS-1D with the soil
hydraulic and thermal properties estimated with the approaches mentioned in 2.3. The computation was performed with a 200 cm long homogeneous soil column witha 2 cm node spacing. The initial time step for the simulation was 1 ×10−5 d. Daily meteorological data was used
to compute the surface boundary conditions. The bottom
boundary conditions were zero pressure head gradient (i.e.,free drainage) for water transfer and zero temperature gradient for heat transfer.
Meteorological data necessary for the boundary condition computations were obtained from the NARO Mesh
Agricultural Meteorological Data (Ohno et al., 2016;
NARO, 2024), created and made publicly available by the
NARO. This meteorological data set is a downscaled scenario dataset based on future predictions obtained by providing a warming scenario to GCM, downscaled to 1 km
mesh resolution. The NARO Mesh Agricultural Meteorological Data users must select the types of GCM and global
warming scenarios. We selected the MIROC5, the GCM
developed jointly by the University of Tokyo, the National
Institute for Environmental Studies, and the Japan Agency
for Marine-Earth Science and Technology for the GCM.
The RCP8.5 scenario, the most severe warming scenario
within the dataset, was selected. The Meteorological data
retrieved from the Mesh Agricultural Meteorological Datafor the simulations were daily maximum and minimum airtemperatures, daily cumulative precipitation, daily averagerelative humidity, daily average wind speed, and daily cumulative solar radiation.
Asano et al. (2023) conducted a series of laboratory tests
using soil samples collected at the Gifu and Takayama sites
and determined parameters for the soil hydraulic and thermal properties through an optimization process based on23

ﬁeld observations of soil water content and soil temperature. We also computed the T , θ , and h for the same
period using the parameters of Asano et al. (2023). We
considered these computations the reference values andcompared them with the computations with the parameters
determined with JSI and PTFs. The assessment was conducted by calculating the root mean squared error (RMSE)
for the soil T , θ , and h values computed with the parameters estimated in this study and with those retrieved from
Asano et al. (2023). The estimation approach that provided minimum RMSE was considered optimal for estimating soil hydraulic and thermal properties.
After determining the optimal approach for estimating
soil hydraulic and thermal properties estimation, we simulated the future soil T , θ , and h. The computation period
was set from 2011 to 2020 as the present and from 2091 to
2100 as the future. Again, we computed T , θ , and h using the parameters retrieved from Asano et al. (2023) andusing those estimated with the optimal approach for boththe present and future periods. In the case of the optimal
approaches for Gifu and Takayama differed, we also performed the same computations by swapping the estimation
approach for Gifu and Takayama. Namely, the hydraulicand thermal properties of Gifu were estimated using the
optimal approach for Takayama, and vice versa. This examination aimed to determine a more advantageous estimation approach among the two.
HYDRUS-1D can compute temporal changes of T , θ ,and h, and the computation results in this study are available every 0.01 d. However, in order to minimize the inﬂuence of unusual years such as El Niño events, studies
using GCM meteorological data generally average the results of computations over multiple years (e.g., Kato andNishimura, 2015; Yoshida et al., 2021). Thus, the averageof the output values for each month was obtained, and thenthe average of these values for ten years was computed.

3. Results and Discussion
3.1 Estimated soil hydraulic properties
Table 1 shows the %SSC values extracted from JSI, estimated ρb values using the Eq. (13), Ks , and the VG model
parameters. Additionally, values determined by Asano etal. (2023) are also included for comparison. Comparing the obtained parameters with those of Asano et al.
(2023), it was found that Ks was well estimated using
ROSETTA, while JSI tended to overestimate Ks . Regarding the VG model, θs was consistently smaller than Asanoet al. (2023) with any estimation approaches, while θr waslarger. The n values were comparable to those reported by
Asano et al. (2023). The PTF-based VG parameters repro-

土壌の物理性

24

第 157 号（2024）

Table 1 The soil texture extracted from the Japanese Soil Inventory (JSI), the estimated dry bulk density, saturated hydraulic conductivity, and van Genuchten (1980) (VG) model parameters for the Gifu and Takayama sites. Experimentally determined parameters
retrieved from Asano et al. (2023) were also included.
Dry bulk density (Mg m−3 )Soil texture (%)
Site

Gifu
Takayama

Asano et al.
(2023)

Method1 a)

Method 2 a)

Sand Silt Clay

Sand Silt Clay

Sand Silt Clay

60
80

25
40

37.5
42.5

16
9

24
11

25
40

50
20

37.5
42.5

Asano et al.
(2023)

Abdelbaki(2018) b)

1.36
0.78

1.39
1.29

25
15

Saturated hydraulic conductivity (m day−1 )
Site

Gifu
Takayama

VG model d)Asano et al.
(2023)JSIROSETTA(Method 1) c)

ROSETTA(Method 2) c)

0.34
6.99

1.64
15.55

0.12
0.27

0.14
0.34

Asano et al. (2023)

θs(m3 m−3 )θr (m3 m−3 ) α (m−1 ) n (-)

0.639
0.894

0.057
0.059

1.16
1.76

1.567
1.605

VG model d)
JSI data ﬁtting e)

Site

θs
θr
α
(m3 m−3 ) (m3 m−3 ) (m−1 )Gifu
Takayama

0.569
0.625

0.1
0.1

0.01
0.10

ROSETTA (Method 1) c)
n
(-)
1.21
1.24

θs
θr
α
(m3 m−3 ) (m3 m−3 ) (m−1 )
0.467
0.429

0.127
0.087

0.90
0.70

ROSETTA (Method 2) c)
n
(-)
1.28
1.43

θs
θr
α
(m3 m−3 ) (m3 m−3 ) (m−1 )
0.416
0.419

0.095
0.078

0.70
0.70

n
(-)
1.39
1.47

a) “Method 1” and “Method 2” indicate that texture is computed based on the center of gravity of the soil texture diagram and the center of line
minimizing clay content, respectively.
b) The bulk density is computed using the equation proposed by Abdelbaki (2018).
c) “ROSETTA” indicates parameters obtained with the software ROSETTA (Schaap et al., 2001) from soil sand, silt, and clay contents.
d) VG model parameters include residual water content (θr ), saturated water content (θs ), and empirical parameters (α and n).
e) “JSI data ﬁtting” indicates that the parameters are obtained by ﬁtting the VG model on the volumetric water content-pF values retrieved from JSI.

duced Asano et al. (2023) parameters better in Gifu thanin Takayama.
3.2 Optimal estimation approach for soil hydraulic properties
Table 2 shows the RMSE values between the θ and hfor 2021 computed using the estimated soil parameters andthose computed using Asano et al. (2023) parameters. The
RMSE values varied depending on the test site and computation item. In Gifu, Ks and VG model parameters estimated by ROSETTA with Method 2-based %SSC and ρb
resulted in the minimum RMSE values, which indicatesthis is the optimal estimation approach for the Gifu site.
The RMSE values were 0.0256 m3 m−3 for θ and 0.0018
m for h. While the optimal estimation approaches for bothθ and h were the same in Gifu, they varied in Takayama.
The combination of Ks published by JSI and VG model
parameters determined by ﬁtting the VG model with pF-θvalues published by JSI resulted in the minimum RMSE
value of 0.0277 m3 m−3 for θ . The combination of Ks

published by JSI and VG model parameters determined by
ROSETTA with Method 2-based %SSC and ρb yielded theminimum RMSE value of 0.2062 m for h. In this study,
we put more emphasis on θ and determined that the optimal estimation approach is the one showing the minimum
RMSE value of θ . Given that, the optimal estimation approach in Takayama was Ks values published by JSI and
VG model determined with the ﬁtting of VG model with
pF-θ values. Kato and Nishimura (2017) also showed thatthe optimal estimation approaches for soil hydraulic properties were not consistent in multiple locations with differing soil textural and cultivation conditions. The same trend
was conﬁrmed.
3.3 Estimated soil thermal properties
Table 3 shows the Campbell, Lu, and CH model parameters. The parameters of the CH model determined by
Asano et al. (2023) are also included in Table 3. Comparing the CH model parameters estimated with PTF andthose presented by Asano et al. (2023), it was observed

論文：Estimating soil hydraulic and thermal properties using publicly available soil information

25

Table 2 Root mean square error (RMSE) values of volumetric water contents and matric potentials computed using saturated hydraulic conductivity and van Genuchten (1980) (VG) model parameters extracted from Asano et al. (2023) and estimated. The “Rank”
indicates the ascending order of the RMSE.
Estimation Methods
Saturated hydraulicconductivity

Values published in
the JSI
Values published in
the JSI
Values published in
the JSI
Rosetta (Method 1) a)
Rosetta (Method 1) a)
Rosetta (Method 1) a)
Rosetta (Method 2) a)
Rosetta (Method 2) a)
Rosetta (Method 2) a)

Gifu

Takayama

Volumetric watercontent

Matricpotential

Volumetric watercontent

Matricpotential

RMSE
RANK
(m3 m−3 )

RMSE
RANK
(m)

RMSE
RANK
(m3 m−3 )

RMSE
RANK
(m)

JSI data ﬁtting b)

0.125570.241840.02771

0.8781

7

Rosetta (Method 1) a)

0.0404

3

0.236830.183980.21532

Rosetta (Method 2) a)

0.0491

40.334860.201190.2062

1

JSI data ﬁtting b)
Rosetta (Method 1) a)
Rosetta (Method 2) a)
JSI data ﬁtting b)
Rosetta (Method 1) a)
Rosetta (Method 2) a)

0.18310.08440.02730.17960.08180.0256

9
6
2
8
5
1

0.52000.37190.21100.4826
0.33420.1785

9
7
2
8
5
1

0.11270.08810.10410.10590.09470.1110

7
2
4
5
3
6

1.63140.4742
0.4612
1.57270.42140.4108

9
6
5
8
4
3

VG modela) “ROSETTA” indicates parameters obtained with the software ROSETTA (Schaap et al., 2001) from soil sand, silt, and clay contents. “Method 1”
and “Method 2” indicate that texture is computed based on the center of gravity of the soil texture diagram and the center of line minimizing clay
content, respectively.
b) “JSI data ﬁtting” indicates that the parameters are obtained by ﬁtting the VG model on the volumetric water content-pF values retrieved from JSI.

Table 3 Estimated thermal conductivity model parameters for the Campbell (1985) model, Lu et al. (2014) model, and Chung andHorton (1987) model. Experimentally determined parameters for Chung and Horton (1987) model retrieved from Asano et al. (2023)
were also included.
Campbell (1985) model
Method 1 a)
B
D

Site
A
Gifu
TakayamaE

1.566 1.470 0.397 6.814
1.587 1.366 0.371 6.814

A

Lu te al. (2014) model

Method 2 a)BDE

1.680 1.470 0.397 7.713
1.608 1.366 0.371 7.713

Method 1 a)
λdry
β
γ

Method 2 a)
λdry
β
γ

0.244 0.408 1.671
0.223 0.508 1.552

0.244 0.491 1.681
0.223 0.525 1.558

Chung and Horton (1987) model b)
Site
b1
Gifu
Takayama

0.000
0.000

Method 1 a)
b2
b3
0.128
1.020

2.222
1.165

Method 2 a)
b1
b2

b3

Asano et al. (2023)
b1
b2
b3

0.000
0.062

0.370
0.207

0.092
1.037

2.281
2.073

0.100
4.620

1.752
−4.372a) “Method 1” and “Method 2” indicate that texture is computed based on the center of gravity of the soil texture diagram and the center of line
minimizing clay content, respectively.
b) The parameters with Method 1 and Method 2 were obtained by the ﬁtting of Chung and Horton (1987) model on the thermal conductivity curves
estimated with the Lu et al. (2014) model.

that the estimated parameters of the Gifu site were close
to the parameters of Asano et al. (2023), while parameters of the Takayama site were not. This difference may
be attributed to the high organic content in the forest soilsof Takayama. There was no signiﬁcant difference between
Method 1 and Method 2.

3.4 Optimal estimation approach for soil thermal properties
Table 4 shows the RMSE values between T computedwith the thermal properties estimated with PTF and T computed with those presented by Asano et al. (2023), which
is treated as a reference. The results indicate that T simu-

土壌の物理性

26

第 157 号（2024）

Table 4 Root mean square error (RMSE) values of temperature computed using parameters retrieved from Asano et al.
(2023) and estimated in Gifu and Takayama. The “Rank” indicates the ascending order of the RMSE.
Estimation methods

Gifu

of thermal conductivity

RMSE (◦ C)

Takayama
RANK

RMSE (◦ C)

RANKCampbell (1985)model (Method 1) a)
Campbell (1985)model (Method 2) a)
Lu et al. (2014) (Method 1) a),b)
Lu et al. (2014) (Method 2) a),b)0.55550.6032
0.46130.47083
4
1
2

3.25983.53553.02012.90733
4
2
1a) “Method 1” and “Method 2” indicate that texture is computed based on the center of gravity of the soil texture diagram and thecenter of line minimizing clay content, respectively.
b) Because the Lu et al. (2014) model was not implemented in HYDRUS-1D, Chung and Horton (1987) model ﬁtted on the thermal
conductivity curves estimated with Lu et al. (2014) was used for the simulations.

Table 5 Optimal estimation approaches for soil hydraulic and thermal properties in Gifu and Takayama.
Site

Parameters

Optimal estimation method

Gifu

Saturated hydraulic conductivity
Water retention curve a)
Thermal conductivity
Saturated hydraulic conductivity
Water retention curve a)
Thermal conductivity

ROSEETA (Method 2) b)
ROSETTA (Method 2) b)
Lu et al. (2014) model (Method 1) b),c)
Values published in the Japanese Soil Inventory (JSI)JSI data ﬁtting d)
Lu et al. (2014) model (Method 2) b),c)

Takayamaa) van Genuchten (1980) model parameters.
b) “Method 1” and “Method 2” indicate that texture is computed based on the center of gravity of the soil texture diagram andthe center of line minimizing clay content, respectively.
c) Because the Lu et al. (2014) model was not implemented in HYDRUS-1D, Chung and Horton (1987) model ﬁtted on thethermal conductivity curves estimated with Lu et al. (2014) was used for the simulations.
d) “JSI data ﬁtting” indicates that the parameters are obtained by ﬁtting the VG model on the volumetric water content-pF
values retrieved from JSI.

lated with the Lu model (approximated by the CH model)
better ﬁts the reference values than the Campbell modelin both Gifu and Takayama. In Gifu, Lu model parameters estimated from Method 1-based %SSC were optimal,
with an RMSE value of 0.4613◦ C. The RMSE value wasslightly higher with Method 2-based %SSC (0.4708◦ C),
indicating a minor inﬂuence of the %SSC determinationfrom JSI information. There was also no signiﬁcant difference in RMSE values due to the difference in %SSC
determination for Takayama. The Method 2-based %SSC
resulted in a slightly lower RMSE of 2.9073◦ C compared
to 3.0201◦ C with Method 1-based %SSC. Simulations ofGifu showed better agreement with the reference values
than simulations of Takayama.
3.5 Estimation of future soil temperature, watercontent, and matric potential with the optimal parameters
Table 5 summarizes the optimal estimation approaches
for soil hydraulic and thermal properties parameters. They
include the Ks estimated with ROSETTA from Method
2-based %SSC and ρb , VG model parameters estimatedwith the same, and the λ estimated with the Lu modelfrom Method 1-based %SSC for Gifu. For Takayama, they
include the Ks retrieved from JSI, VG model parameters
determined by ﬁtting with pF-θ values published by JSI,and λ estimated with the Lu model from Method 2-based%SSC. Evaluations of the future soil T , θ , and h were performed with the optimally determined parameters.
Fig. 3 shows the present (average from 2011 to 2020)and future (average from 2091 to 2100) soil T , θ , and h at a
depth of 20 cm. The ﬁgure displays computations with the
optimal estimation approaches (Table 5) and those with parameters from Asano et al. (2023), which is considered asa reference. While the T computed with estimated parameters and the reference values agreed well both in the present
and future in Gifu (Fig. 3(a)), they differed in Takayama
approximately 3◦ C throughout the year both in the present
and future (Fig. 3(b)). The simulated θ and reference values of Gifu (Fig. 3(c)) showed minor differences from January to March and in December, both in the present and future. However, relatively large differences were observedin other months, with a maximum difference of about 0.02
m3 m−3 in August in the future. The simulated θ and reference values of Takayama (Fig. 3(d)) showed more sig-

論文：Estimating soil hydraulic and thermal properties using publicly available soil information

27

Fig. 3 The soil temperature (T ), volumetric water content (θ ), and matric potential (h) at 20 cm depth in Gifu and Takayama.
Each panel shows (a) T in Gifu, (b) T in Takayama, (c) θ in Gifu, (d) θ in Takayama, (e) h in Gifu, and (f) h in Takayama.
The “PTF” indicates the computations with soil hydraulic and thermal properties estimated with pedtransfer functions and theJapanese Soil Inventory, and “Reference” indicates the computations with soil hydraulic and thermal properties retrieved from
Asano et al. (2023). The “Present” and “Future” include the computations for 2011–2020 and for 2091–2100, respectively.

niﬁcant differences than those in Gifu, both in the present
and future. The difference in θ persisted at around 0.02
m3 m−3 from June to December. The simulated h of Gifu
(Fig. 3(e)) with estimated parameters and the reference
values were consistent. The differences in future change
due to the estimated parameter were at most 0.5 m. The
simulated h of Takayama (Fig. 3(f)) showed differences inboth present and future computations associated with theestimated parameters. Differences were particularly signiﬁcant from January to March in the present, showing an
average difference of 9 m. The difference decreases after
April but is still 6 m on average. The considerable difference in h can be attributed to the slight differences in water
retention curves, which results in large variation in h evenwhen θ value is similar.
The future prediction of soil T , θ , and h with the soil

28

土壌の物理性第 157 号（2024）

Fig. 4 The computed future change in soil temperature (∆T ), in volumetric water content (∆θ ), and in matric potential (∆h)
at 20 cm depth. Each panel shows (a) ∆T in Gifu, (b) ∆T in Takayama, (c) ∆θ in Gifu, (d) ∆θ in Takayama, (e)∆h in Gifu,and (f) ∆h in Takayama. The “PTF” indicates the computations with soil hydraulic and thermal properties estimated withpedtransfer functions and the Japanese Soil Inventory, and “Reference” indicates the computations with soil hydraulic andthermal properties retrieved from Asano et al. (2023).

hydraulic and thermal properties parameters from Asanoet al. (2023) was accurately reproduced with the estimatedsoil hydraulic and thermal properties only for T at the Gifu
site; however, T at the Takayama site as well as θ and hat both locations were not adequately reproduced. Thismay be attributed to the PTF reproducibility of the actual
soil hydraulic and thermal properties. Thus, we focusedon the future change of soil T , θ , and h, i.e., ∆T , ∆θ , and∆h, which represent the differences between present andfuture values. Fig. 4 illustrates ∆T , ∆θ , and ∆h in Gifu

and Takayama. The ∆T computed with parameters from
Asano et al. (2023) and those computed with the estimatedparameters were consistent in Gifu. The same trend wasfound in Takayama, while slight differences were observed
from March to May, with a maximum difference of 1.18◦ C(Fig. 4(a, b)). Similarly, ∆θ (Fig. 4(c, d)) showed minor
differences with a maximum difference of 0.0055 m3 m−3in Gifu and 0.0084 m3 m−3 in Takayama. It is an importantﬁnding that the ∆T and ∆θ can be accurately determinedwith the estimated soil hydraulic and thermal properties.

論文：Estimating soil hydraulic and thermal properties using publicly available soil information

29

Fig. 5 The computed soil temperature (T ) and volumetric water content (θ ) at 20 cm depth in Gifu and Takayama. Eachpanel shows (a) T in Gifu, (b) T in Takayama, (c) θ in Gifu, and (d) θ in Takayama. The “PTF” indicates the computations
with soil hydraulic and thermal properties estimated with the optimal estimation approaches for each site, and “Swapped”
indicates the computations with the soil hydraulic and thermal properties estimated with the swapped optimal estimation
approaches for the two sites.

The ∆h (Fig. 4 (e, f)) showed a relatively large differenceof up to 4.5 m (in Takayama in February). The possible
reasons might be the optimal estimation approaches for the
VG model were determined based on θ rather than h, anda small variation of the water retention curve can causea large difference in h. The ∆h was difﬁcult to simulate
with the estimated soil hydraulic and thermal properties
accurately.
It was found that using estimated soil hydraulic and thermal properties in the future prediction of soil T , θ does notpose any signiﬁcant issues if we focus on future changes
amount. However, the optimal estimation approaches forsoil hydraulic and thermal properties differ between Gifu
and Takayama. This fact may indicate that the optimal estimation approach varies depending on the location, which isundesirable. Thus, we performed the simulation by swapping the optimal estimation approach described in 2.4. Fig.
5 shows the computed T , θ at a depth of 20 cm by swapping the optimal estimation approach. Fig. 5 only shows
the results of 2021 and 2100 since other years showed similar trends. The T exhibited similar values regardless of theparameter estimation approach in both Gifu and Takayama(Fig. 5 (a, b)). Thus, the parameter estimation approach
may not be crucial for predicting future changes in T .
Swapping the estimation approach resulted in the overestimation of θ in Gifu (Fig. 5 (c)); conversely, θ was underestimated by the swapping in Takayama (Fig. 5 (d)). The
estimation method of λ was changed from Rank 1 to Rank
2 in both Gifu and Takayama (Table 4) due to parameter
swapping, so it was a relatively good-ﬁt estimation method
even after the swapping. The estimation method for θ was
replaced by the method from Rank 1 to Rank 7 in Gifu andfrom Rank 1 to Rank 6 in Takayama (Table 2), resulting in
low-ﬁtting methods. These factors may have affected theresults. Here, we also checked the future change quantity∆θ (Fig. 6). It was observed that the differences in ∆θ
due to the swapping of the parameter estimation approachwere minor in both Gifu and Takayama. The maximum
difference in Gifu was 0.025 m3 m−3 in February, while
the maximum difference in Takayama was 0.010 m3 m−3
in February. It was concluded that the estimation approachfor hydraulic property parameters is also not as crucial for土壌の物理性

30

第 157 号（2024）

Fig. 6 The future change in volumetric water content (∆θ ) at 20 cm depth in (a) Gifu and (b) Takayama. The “PTF” indicatesthe computations with soil hydraulic and thermal properties estimated with the optimal estimation approaches for each site,and “Swapped” indicates the computations with the soil hydraulic and thermal properties estimated with the swapped optimal
estimation approaches for the two sites.

predicting ∆θ , i.e., either of the easier approaches can beselected.

4. Conclusions
This study investigated the utility of estimating soil hydraulic and thermal properties from publicly available soil
information using multiple estimation approaches. We retrieved soil information from JSI and used PTFs to estimate the soil hydraulic and thermal properties. Soil T , θ ,and h were computed using the estimated parameters, andthe results were compared with the computations basedon the parameters provided by the literature. There were
errors in the T , θ and h due to the estimated parameters, while the results in Gifu were better than those in
Takayama. When the future change amounts from present
to future are focused, no considerable error was observedin both sites. This result suggests that focusing on future change amounts may eliminate the enormous effort ofcollecting soil samples from the study area and performing laboratory tests when determining soil hydraulic andthermal properties. Focusing on future change amounts
may also eliminate the need to use different estimation approaches of the soil hydraulic and thermal properties forvarious locations. Although validation at other locations
and under different surface conditions is needed, thesefacts are valuable for future studies on the effects of climate change on the soil T , θ at various sites.

reer Scientist 21K14940 from the Japan Society for the
Promotion of Science, Ogawa Science and Technology
Foundation research grants, and the Joint Usage/Research
Grant of the River Basin Research Center (2021-G-009),
Gifu University. The data used in this study were obtained
from the “Japanese Soil Inventory (NARO, National Agriculture and Food Research Organization)” and “The AgroMeteorological Grid Square Data (NARO, National Agriculture and Food Research Organization).”References
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第 157 号（2024）

This article is an open access article under theterms of the Creative Commons Attribution (CCBY) license (https://creativecommons.org/licenses/by/4.0/).

Hydrological Research Letters, 15(4): 92–97.

要旨

土壌水分量，地温の将来予測計算に必要な土壌水理特性，土壌熱特性の決定は，多くの時間と労力，費用を要する．本研究では，公開された土壌情報とペドトランスファー関数（PTF）を用いた土壌水理 ·熱特性推定法の有用性を検討した．土壌情報を日本土壌インベントリーから抽出し，複数の PTF で土壌
水理 · 熱特性を推定した．推定した土壌水理 · 熱特性と既往研究から得た土壌水理 · 熱特性を用いて土壌環境を計算し，両者の誤差が最小となる推定手法を最適とした．その後，最適推定手法で得た土壌水
理 · 熱特性を用いて将来土壌環境を計算し，既往研究の計算結果と比較した．その結果，地温や体積含水率の予測結果では両者の間で差が生じたが，現在から将来までの変化量では大差はなかった．将来変化量評価に限定すれば，公開された土壌情報と PTF によって土壌水理 · 熱特性を推定しても妥当な評価が可能であることがわかった．
キーワード：ペドトランスファー関数，土壌水理特性，土壌熱特性，気候変動，HYDRUS-1D