IJERD – International Journal of Environmental and Rural Development (2016) 7-1

Research article
erd

Predicting Soil Temperature Condition in Agricultural Land
under Climate Change in JapanCHIHIRO KATO*
Faculty of Agriculture and Life Science, Hirosaki University, Aomori, JAPAN
Email: chihirok@hirosaki-u.ac.jp

TAKU NISHIMURA
Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, JAPANReceived 11 October 2015

Accepted 11 April 2016(*Corresponding Author)

Abstract Decomposition of soil organic carbon as well as growth of crops are affected by soil
physical condition such as soil moisture and soil temperature. Thus predicting soil moisture andtemperature condition of arable lands under future climate change is important for both
mitigation and adaptation of climate change in agriculture. In this study, we attempted to
predict soil temperature condition in arbitrary arable lands in Japan. Soybean fields of Toyama
city, middle part of Japan facing Japan Sea were chosen as the experimental site. There,
monitoring of soil temperature and measurement of soil thermal properties which is a functionof soil moisture, was conducted. For the future application to arbitrary locations, the thermal
properties were also predicted with a mathematical model, by using soil physical properties
such as the ratio of sand: silt :clay, soil particle density and dry bulk density in soil physicaldatabase. Then, numerical simulation of soil moisture and temperature was conducted withpredicted soil hydraulic and thermal properties. The model with estimated thermal properties
described the measured soil temperature fairly well especially when the soil condition was wet.
Keywords climate change, thermal conductivity, soil temperature, soil database,numerical simulation

INTRODUCTION
Soil is a foundation of agriculture and ecosystem. Growth of crops and vegetables, and/or incidence ofinsect and pest as well as decomposition of soil organic matter are affected by soil physical condition
such as soil moisture and soil temperature (e.g. Allmaras et al., 1964; Singh and Dhaliwal, 1972;
Simunek and Suarez, 1993). Climate change including temperature rise and change in rainfall patterns
will alter soil physical conditions. For example, Bai et al. (2014) showed that soil temperature at depthof 50 cm has increased at an average rate of 0.79 ℃ per decade from 1982 to 2000 in Mojav Desert
region in USA and mentioned that the temporal changes of soil temperature was correlated with thoseof air temperature. The available observed data of soil temperature and soil moisture has been limited
spatially and temporarily compared to the meteorological data.
Numerical simulation of soil water and heat movement with GCM (General Circulation Model)
projections as boundary condition is one of the effective way to predict future soil physical condition.
Kato and Nishimura (2015) proposed the methods of temporal downscaling of GCM prediction,
especially the rainfall data, for producing boundary conditions for numerical simulations of soil
moisture, and applied the downscaled projections to prediction of future soil moisture of an agriculturalland in Japan. Kato and Nishimura (2016) attempted to estimate soil hydraulic parameters fornumerical simulation of soil moisture in arbitrary agricultural lands by using soil physical properties
database Solphy J (Eguchi et al., 2011). As for the soil temperature, Kato et al. (2011), for example,Ⓒ ISERD

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conducted the numerical simulation of soil temperature of agricultural field to reproduce the pasttemperature records with measured soil thermal conductivity. For the further application, it is beneficial
to predict soil temperature in arbitrary locations without measurement of soil thermal properties. Since
soil thermal properties are generally affected by soil physical properties such as soil texture, soil bulk
density, etc., soil physical properties databases are probably useful for estimation of soil thermal
properties and subsequent simulation of soil temperature.
Based on the above, in this study, we investigated the possibility of estimation of soil thermal
properties by using available soil physical properties databases and prediction of soil temperature
under climate change in arbitrary agricultural lands.
METHODOLOGY
Study Site
One of the plain agricultural field in Toyama city, the middle part of Japan facing Japan Sea, waschosen as the study site (Fig. 1). Hokuriku District, including Toyama city is the representative grain
growing area in Japan and has often been chosen as research sites for effects of climate change on
agriculture (MEXT, 2015). Recently, a multiple cropping system of paddy rice-barley-soybean in two
years has often been employing in the region around our study site. Barley and soy beans were grownfrom November 2010 to May 2011, and from June to October 2011, respectively, at Agricultural
Research Institute of Toyama Prefectural Agricultural, Forestry and Fisheries Research Center inToyama City. There, soil moisture and temperature at depths of 5, 10, 20, 30 and 40cm was monitoredcontinuously with ECH2O 5TE sensors (Decagon Devices, Inc., USA) from April to October, 2011.
Undisturbed 100 cm3 core samples and disturbed soil were sampled from a pit in the field and then soil
samples were brought back and their physical properties (soil texture, particle density, water retention,
saturated hydraulic conductivity) were measured in the laboratory (Kato and Nishimura, 2016). Soil
thermal conductivity λmeas was also measured with KD2 heat probe (Decagon Devices Inc. USA).
Japan

Hokuriku
District
ToyamaJapan Sea

Fig. 1 The location of Toyama City (Kato and Nishimura, 2016, modified)Fig.1 The location of Toyama City (Kato and Nishimura, 2016, modified)Numerical ModelHYDRUS-1D model has widely been used for calculation of soil moisture and temperature, and in thisstudy, the ver. 4.xx (Šimůnek et al., 2013) was used. Since heat is also transported with water, and soil
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IJERD – International Journal of Environmental and Rural Development (2016) 7-1

thermal properties often changes with soil moisture condition, both soil moisture and soil temperature
were simulated simultaneously. The governing equation of liquid and vapor water movement in soils
was an extended Richrads’ equation with root water uptake S(h) (Eq. (1)).
wT T h
wt

w ª
wh
wT º
K  K vh (  1)  K LT  K vT
S h
wx «¬
wx
wx »¼

(1)

where h is the pressure head [L], θT is the total volumetric water content, or the sum of the volumetricwater content of liquid (θ) and vapor (θv) water [L3L-3], respectively. T is temperature [K], K and KLT
are the isothermal and thermal hydraulic conductivity of the liquid phase [LT-1], Kvh and KvT are theisothermal and thermal vapor hydraulic conductivity, respectively[LT-1].
Heat transfer with vapor transport was described with heat flow equation (Eq. (2)).
wTwT
 L0 v
wt
wt

Cp T

wq T
wq
w ª
wT º
wT
OT
 Cw
 C v v  L0 v
«
»
wx ¬
wx ¼
wx
wx
wx

(2)

where C is the volumetric heat capacity and subscript p, w, and v mean porous medium, liquid waterand vapor, respectively, L0 is the volumetric latent heat of vaporization of liquid water [ML-1T-2] and qv
is the vapor flux density [LT-1]. λ is the apparent thermal conductivity of the soil [M LT-3K-1] which isthe sum of the thermal conductivity of the porous medium under no flow condition λ0 (θ) and themacrodispersivity. The details can be found in Šimůnek et al. (2013).
Soil Hydraulic Parameters
In this study, van Genuchten-Mualem (VG-M) model (van Genuchten, 1980) Eq. (3) and (4) was
employed for predicting water retention curves and unsaturated hydraulic conductivity.

Se

T Tr
Ts TrK (h)1  Dh

n m

(3)

>

K s S el 1  (1  S e1 m ) m

@

2

(4)

where θr and θs are the residual and saturated volumetric water content [L3L-3], Ks is the saturated
hydraulic conductivity [LT-1], Se is the effective saturation [dimensionless], α [L-1], l, m=1-1/n, and n[dimensionless] are empirical parameters.
Hydraulic parameters of VG-M model for soils of each layer were determined based on the digital
soil map (Takata et al., 2009) and the agricultural soil-profile physical properties database, Japan“SolphyJ” (Eguchi et al., 2011). Here, calculation area was divided into three layers according toSolphyJ database and dataset of water retention, i.e., volumetric water contents at suctions of 32, 500,
16000 and 320000 cmH2O of each soil layer were prepared. Then “RETC program” (Yates et al.,1992), which can predict parameters of soil hydraulic functions such as VG-M model by fitting
observed water retention by nonlinear least-squares method, was used for determining the VG-M
parameters. The saturated hydraulic conductivity “Ks” value was predicted with a neural network basedmodel “Rosetta” (Schaap et al., 1998). The recommended value 0.5 was employed as l. The details canbe found in Kato and Nishimura (2016).
Soil Thermal Parameters
Volumetric heat capacity C (θ) and thermal conductivity λ0 (θ) vary among soils. Considering theapplication to arbitrary locations, both C and λ 0 were determined with the soil properties database.
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IJERD – International Journal of Environmental and Rural Development (2016) 7-1

Volumetric heat capacity of soil can be described as the sum of heat capacities of the constituents
as follows (Šimůnek et al., 2013),

Csoil

CnT n  CoTo  CwT  Ca av | 1.92T n  2.51To  4.18T 106 (J m-3K-1)(5)where C means heat capacity and subscription of n, o, w and a are solid, organic matter, water and air,respectively and av is the volumetric fraction of air. θn can be found in SolphyJ database and θo can also
be obtained assuming that particle density of organic matter is 1.63 (g cm-3).
Models have been proposed to estimate soil thermal conductivity. In this study, Chung and Horton(1980) (C-H) model and Campbell (1985) model were employed.
Chung and Horton (1980) described soil thermal conductivity as follows (Eq.(6)).
OCH

b1  b2T  b3T 1 / 2(6)

where b1, b2 and b3 are empirical parameters. Chung and Horton (1980) proposed the sample set of
b1~b3 corresponding with “sand”, “loam”, and “clay” and hereafter we call those λCH as λsand, λloam andλclay, respectively.
According to Campbell (1985), soil thermal conductivity is describes as follows (Eq. (7)).
O campA^A  BT  A  D exp  CT

0.57  1.73T q  0.93T m
1  0.74T q  0.49T m

C 1  2.6T c

1 / 2, DE`

 2.8T n 1  T n ,20.03  0.7T n , E

B

2.8T n4(7)where subscription q, m and c represent quartz, minerals other than quartz, and clay, respectively, andρd and ρs are soil dry bulk density and soil particle density, respectively. θq, θm, and θc can be calculated
as equations (8) assuming that the entire sand fraction is originated from quartz, while silt and clay arefrom other minerals
θn =ρd/ρ , θq=θn×θSand (%) , θm＝θn – θq , θc=θn×θClay (%)(8)

where θsand and θclay are the volumetric fraction of sand and clay, respectively. Here, mass ratio of sandand clay in SolphyJ database were substitute for θsand and θclay, respectively. Though both ρd and ρs canbe found in Solphy J database, here, three different ρd (ρd1, ρd2, and ρd3) were employed for Eq. (8) as
follows since soil bulk density easily changes with cultivation.
ρd2 = ρd1 – 0.10, ρd3 = ρd2 - 0.10

(9)where dry bulk density value in Solphy J database were employed for ρd1. In this study, ρd1=1.02,
ρd2=0.92, and ρd3= 0.82 were used for the simulation. Hereafter we call λcamp which were predicted withρd1, ρd2, ρd3 as λ1.02, λ0.92, λ0.82, respectively.
Scenario Study
Using determined parameters, scenario study was conducted to predict soil moisture and temperatureof arable lands in Toyama under climate change. ELPIS-JP (Iizumi et al., 2012) is approximately 20
km scale daily GCM projection dataset of “50 time series” of 110 years. The “50 time series”
represents variability of meteorological phenomena incident to temporal downscaling of GCM
projections (Iizumi et al., 2012). Corresponding to Toyama weather station, MIROC 3.2 hires and A1Bof Toyama weather station data were employed for GCM and IPCC SRES Scenario, respectively.
Since temporal scale of soil water and heat movement in agricultural lands are usually shorter than aⒸ ISERD

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day, daily ELPIS-JP dataset was temporally downscaled into hourly or shorter scale by using weather
generator CLIGEN (Nick et al., 1995). Details of temporal downscaling methods can be found in Katoand Nishimura (2015). According to the climate scenario of the possible monthly rainfall depth of thesoybean growing period (June, July, August and September), expected maximum values in present(1981-2000) are 291, 405, 346 and 363 mm, respectively, and those values in future (2071 to 2090)
tend to increase to 1.3 ~ 1.6 times of those of present, except August. Average monthly air temperatureof four months from June in present are projected to be 24.4, 28.6, 30.4 and 25.9 ℃, respectively andare likely to rise 3~5 ℃ through a year in late 21st century. Here, we attempted to predict soil moistureand temperature with an assumption of maximum monthly rainfall in June both in “present” and“future” (Kato and Nishimura, 2015).
RESULTS AND DISCUSSION
Determined hydraulic parameters are shown in Table 1 and Kato and Nishimura (2016) reported thatthe model described soil moisture well with RMSE (root mean square error) of 0.01 cm3 cm-3.
Table 1 Determined soil hydraulic parameters of each layer
3

-3

3

-3

1

-1

LocationLevelθr (cm cm )θs(cm cm )αnKs (10 cm d )lToyama

1
2
3

0.027
0.029
0.009

0.470
0.361
0.468

0.043
0.032
0.046

1.11
1.11
1.12

18.9
11.6
72.0

0.5
0.5
0.5

Soil thermal diffusivity Ka, which is calculated by the ratio of thermal conductivity λa tovolumetric heat capacity Ca, is an indicator of the ability of soil to have temperature change with heat
inflow. Hereafter we call the thermal diffusivities calculated with λsand, λloam, λclay, λ1.02 , λ0.92, and λ0.82 as
Ksand, Kloam, Kclay, K1.02, K0.92, and K0.82, respectively. Figure 2 shows the comparison of soil thermal
diffusivities among those determined with (a) C-H model (Ksand, Kloam and Kclay) and (b) Campbell
model (K1.02, K0.92, and K0.82). Both figures also show Kmeas, which were calculated with measured soil
thermal conductivity λmeas fitted with C-H model. Compared with the measured values, C-H model
overestimated thermal conductivities and thus thermal diffusivities (Fig. 2 (a)). Especially, Ksand is fourtimes larger than Kmeas. This discrepancy is probably due to the difference in soil bulk density and typeof minerals which constitute the soil. Campbell model, which can partly consider some of physicalproperties, tended to describe the thermal diffusivities better than C-H model and the predicted values
were improved by assuming the lower dry bulk density, especially when soil moisture is high (Jury andHorton, 2004) (Fig. 2(b)).
1.6

Thermal diffusivity ( 10-6 m-2 s-1)

1.4

(a)

1.2

Ksand
Ksand

Kloam
Kloam

Kclay
Kclay

Kmeas
Kmeas

Thermal diffusivity ( 10-6 m-2 s-1)

1.6

1
0.8
0.6
0.4
0.2
0

1.4

(b)

K1.02
1st
layer (ρ d=1.02)
1stlayer(ρ d=0.9)
K0.92
1st
layer(ρ d=0.8)
K0.82
Measured
K

1.2

meas

1
0.8
0.6
0.4
0.2
0

0

0.2
0.4
Volumetric water content (cm3 cm-3)

0.6

0

0.2
0.4
Volumetric water content (cm3 cm-3)

0.6

Fig. 2 Estimated thermal diffusivities with (a) Chung and Horton and (b) Campbell modelsⒸ ISERD

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IJERD – International Journal of Environmental and Rural Development (2016) 7-1

Figure 3 shows the comparison of measured and simulated soil temperature at depth of 10cm inthe study field. Simulated values were calculated by using measured thermal conductivity λmeas. The
model described the measured soil temperature well with RMSE = 1.7 ℃.
Figure 4 shows the comparison of the simulated vertical distribution of soil temperature among
those calculated with five different thermal conductivity, or λsand, λ1.02, λ0.92, λ0.82 and λmeas on a (a) dryday (the sunny day after a few sunny days in succession) and (b) wet day (the day after a rainy day),and Fig.4(c) shows the simulated vertical distribution of volumetric water content of the dry day (a)and the wet day (b) of Fig. 4. Simulated soil temperature with λsand was larger than other simulated
results regardless of the soil moisture condition. On the other hand, simulated results with Campbell
model (λ1.02, λ0.92, and λ0.82) agreed well with that with λmeas under wet soil condition. It might bereflected that the thermal diffusivities K1.02, K0.92, and K0.82 at high water content are more similar toKmeas than those at low water content (Fig. 2). Campbell model gives smaller thermal diffusivity at low
water content due to the exponential function with water content as a variable (Eq.7). Probably thismade it quite difficult to evaluate thermal conductivity of soil under dry condition. Changes in dry bulk
density in the estimation of soil thermal conductivity did not affect predicted soil temperature so much(<1 ℃ ) compared to changes in soil moisture condition (Jury and Horton, 2004).Those results
indicated that in wetted seasons or humid regions such as Japan, soil temperature can be predicted withacceptable preciseness by using soil thermal conductivity which estimated with Campbell model byusing SolphyJ database.

Soil temperature [℃]

30

20

10

10cm_sim
Simulated

10cm measured
Observed

091
2011/4/1

111
4/21

131
5/11

151
5/31

171
6/20

Fig. 3 Comparison of the simulated and observed soil temperature at depth of 10 cm
Soil temperature （℃）
20
25

30

0

20

(a)5/21 12:00
(Dry)

10
λsand sand
C-H

30

1.02
λ1.02

40

0.9
λ0.92

50
60

Soil temperature (℃)
1520Volumetric water content（cm3 cm-3）

0.8
λ0.82
Measured
λmeas

0

25

0.2

0.4

0.6

0

0

Depth (cm)Depth (cm)1010(b) 5/10 12:00
(Wet)

10

20

30

λsandsand
C-H
λ1.02
1.02

40

0.9
λ0.92

50

λ0.82
0.8
λmeas
Measured60

Depth (cm)

15

(c)

20
30
40
50

(a) 5/21
5月21日(b) 5/10
5月10日

60

Fig. 4 Comparison of the simulated vertical distribution of the soil temperature predicted withestimated thermal conductivities on a dry day (a) and a wet day (b), and the simulated
vertical distribution of soil moisture of both days (c)Ⓒ ISERD

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IJERD – International Journal of Environmental and Rural Development (2016) 7-1

Soil temperature（℃）

353010cm depth

Future (2071-2090)

25
20
15

Present (1981-2000)

10
5
0

153
6/1

158
6/6

163
6/11

168
6/16

173
6/21

178
6/26

Fig. 5 Comparison of the simulated and observed soil temperature at depth of 10cm between“future” and “present”
Figure 5 shows the comparison of simulated soil temperature at depth of 10cm between “present”
and “future”. λ1.02 was employed in this scenario study. In the future, soil temperature may rise three tofive degrees accompanies with rising air temperature. In this way, the quantitative discussion about soil
temperature in the future in arbitrary agricultural lands maybe possible with estimated thermal
conductivity.
CONCLUSION
In this study, we investigated the proper method of estimating soil thermal properties by using digital
soil map and the soil physical database in order to predict soil temperature in arbitrary agriculturallands. Soil thermal conductivity could be estimated with mathematical model by using soil physicalproperties such as the ratio of sand: silt :clay, soil particle density and dry bulk density in the soil
physical database. Soil temperature was well described the past soil temperature records withnumerical simulation by using estimated parameters especially when the soil is wet. This result
indicated that it may be possible to predict soil moisture and soil temperature quantatively with the
combination of soil physical properties database and downscaled climate model projections especiallyfor the high humid regions such as Asian countries.
ACKNOWLEDGEMENTS
This study was supported by Research Program on Climate Change Adaptation (RECCA) of Ministryof Education, Culture, Sports, Science and Technology. We appreciate Toyama Prefectural
Agricultural Institute for their help in using the fields.
REFERENCES
Allmaras, R.R., Burrows, W.C. and Larson, W.E. 1964. Early growth of corn as affected by soil temperature. Soil
Sci. Soc. Am. Proc.,28 (2), 271-275.
Bai, Y., Scott, T.A. and Min, Q. 2014, Climate change implications of soil temperature in the Mojave Desert,
U.S.A. Front. Earth Sci., 8 (2), 302-308.
Campbell, G.S. 1985. Soil physics with basic; Transport models for soil-plant systems. Elsevier, New York, USA.
Chung, S.O. and Horton, R. 1987. Soil heat and water flow with a partial surface mulch. Water Resour. Res., 23,2175-2186.
Eguchi, S., Aoki, K. and Kohyama, K. 2011. Agricultural soil-profile physical properties database, Japan:
SolphyJ. Proceedings of Annual Meeting of JSDIRE. 302-303 (In Japanese).
Iizumi, T., Semenov, M.A., Nishimori, M., Ishigooka, Y. and Kuwagata, T. 2012. ELPIS-JP: A dataset of localscale daily climate scenarios for Japan. Phil. Trans. R. Soc. A, 370, 1121–1139.
Ⓒ ISERD

35

IJERD – International Journal of Environmental and Rural Development (2016) 7-1

Jury, W.A. and Horton, R. 2004. Soil physics 6th edition. John Wiley & Sons, Inc., Hoboken, New Jersey, USA.
Kato, C. and Nishimura, T. 2015. Temporal downscaling methods of GCM projections for predicting soil
moisture in agricultural land. Trans. JSIDRE, 295, (in Japanese with English abstract).
Kato, C., Nishimura, T., Imoto, H. and Miyazaki, T. 2011. Predicting soil moisture and temperature of Andisol
under monsoon climate in Japan. Vadose Zone J., 10, 541-551.
Kato, C. and Nishimura, T. 2016. Predicting soil moisture condition in arbitrary agricultural lands using thedigital soil map and soil physical properties database. Paddy and Water Environment (in press), doi:
10.1007/s10333-016-0537-z.
MEXT (Ministry of Education, Culture, Sports, Science and Technology). 2015. Research program of climatechange adaptation. https://www.restec.or.jp/recca/eng/staticpages/index/report_ninomiya_e.html, (Accessed
July 15, 2016).
Nicks. A.D., Lane, L.J. and Gander G.A. 1995. Weather generator. In: USDA-Water Erosion Prediction Project
Hillslope Profile and Watershed Model Documentation. NSERL Report, #10.
Schaap, M.G., Leij, F.J. and van Genuchten, M.Th. 1998. Neural network analysis for hierarchical prediction ofsoil water retention and saturated hydraulic conductivity. Soil Sci. Soc. Am. J, 62, 847-855.
Šimůnek, J. and Suarez, D.L. 1993. Modeling of carbon dioxide transport and production in soil: 1. Modeldevelopment. Water Resour. Res., 29, 487-497.
Šimůnek, J., Šejna, M., Saito, H., Sakai, M. and van Genuchten, M.Th. 2013. The HYDRUS-1D software
package for simulating the one-dimensional movement of water, heat, and multiple solutes in variablysaturated media.
Singh, N.T. and Dhaliwal, G.S. 1972. Effect of soil temperature on seedling emergence in different crops. Plant
Soil, 37, 441-444.
Takata, Y., Nakai, M. and Obara, H. 2009. Digital soil map of Japanese croplands in 1992. Jpn. Soc. Soil Sci.
Plant Nutr. 80, 502-505 (in Japanese with English abstract).
Van Genuchten, M.Th. 1980. A closed-form equation for predicting the hydraulic conductivity of unsaturated
soils. Soil Sci. Soc. Am. J. 44, 892.
Yates, S.R., van Genuchten, M.Th., Warrick, A.W. and Leij, F.J. 1992. Analysis of measured, predicted, andestimated hydraulic conductivity using the RETC program. Soil Sci. Soc. Am. J. 56, 347-354.

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