Validation of the process for generating soil moisture distribution
in the Tibetan Plateau*Nozomu Hirose1 , Toshio Koike1(1: Department of Civil Engineering, University of Tokyo)* Department of Civil Engineering, University of Tokyo, Japan, 113-8656,
7-3-1 Hongo, Bunkyo -ku, Tokyo, Japan. e -mail: hirose@hydra.t.u-tokyo.ac.jp
Abstract
Soil moisture plays two important roles in hydrology and atmospheric science.Many researchers pointed out theimportance of soil moisture in global and local scale based on the GCM simulation. Therefore, the temporal and spatial
variability of soil moisture has to be measured to estimate energy and water flux to the atmosphere. However, the land
surface shows a high spatial variability and heterogeneity compared to the atmo sphere. To estimate water and heat fluxes
from land surface to the atmosphere, the effect of soil moisture heterogeneity on water and heat fluxes should be considered.
To do so, we developed a land surface model which considers the soil moisture heterogeneity and showed its effect on theestimation of spatially averaged evaporation. To apply the developed model to the whole Tibetan Plateau, we have tovalidate it based on the field observation. Our objectives are two fold. One is to validate the generation process of the soil
moisture heterogeneity and the other is to apply the developed model to four observation sites.
At first, we confirm the process for generating the soil moisture distribution in the plain area, which is the basis of ourmodel. We identify two important relationships in this process based on the field observation. One is the effect of
micro-topography on soil moisture and the other is the interaction between soil moisture and thawing process in the frozen
soil. After and, to evaluate the model representation, the model is applied to four observation sites. The results are comparedwith the observed soil moisture distribution at each site. The model simulations agree with observed soil moisturedistribution.
Keyword: soil moisture distribution, permafrost, Tibetan Plateau.

1. Introduction
1.1. Importance of soil moisture on the global
climate system
Soil moisture plays an important role in both themeteorology and hydrology. It affects the evaporationfrom the land surface to the atmosphere and also affects theriver discharge through surface and subsurface runoff.
Therefore, soil moisture affects the global climate system.
An understanding of the effect of soil moisture helps in theprediction of global climate change, which leads toappropriate policies for preventing serious damage causedby floods and droughts. Many researchers have noted theimportance of soil moisture in the global climate system
according to simulation results of GCMs (GlobalCirculation Models).
The land surface is more heterogeneous than theatmosphere. To correctly estimate the water and heat flux
from the land surface to the atmosphere, it is necessary todevelop a model which considers the effect of the soil
moisture heterogeneity. Nakaegawa (2000) derived anequation of regionally-averaged surface heat fluxes andtheir aggregation criteria, and indicated the effects of theintrasubarea-scale distribution of the temperature and soil
moisture content using numerical experiments based on
their derived equations. The reader is referred to his paper
for the definition and explanation of the term
“intrasubarea”.
Chen(1991)
introducedaspatially-averaged equation for predicting the soil water
dynamics by comparing with the results from the spatiallyhorizontally-averaged Richards equation (SHARE).
However, previous studies have highlighted some of theproblems associated with this equation. The results arebased on model simulations, and have yet to be evaluatedusing field observations. The model developed in thesestudies does not include the physical processes involved in
the generation of the soil moisture heterogeneity.
Therefore, we have developed a model for considering thesoil moisture heterogeneity based on field observations in
the Tibetan Plateau, and evaluated its effect on thespatially -averaged evaporation (2002).

1.2. Estimation of the spatially-averaged
evaporation by considering the spatial variabilityof soil moisture
Recently, the advanced microwave radiometers AMSR
and AMSR-E have been launched. These satellites enable
us to observe the soil moisture at global coverage three orfour times per day. However, because of low spatial
resolution, they cannot observe the spatial variability ofsoil moisture in a footprint. Therefore, we need to predict
it using spatially-averaged high-temporal-resolution soil
moisture data retrieved from the microwave radiometers.
To reach our final target, first we need to understand thecharacteristics of the soil moisture variability using thedeveloped model. However, the model has yet to beevaluated.
In this paper, there are two objectives in the basic
research carried out to predict the soil moisture
heterogeneity based on microwave radiometer observations.
One is to evaluate the process for generating the soil
moisture heterogeneity based on field observations. The
model was developed considering this process. The otherTo evaluate the process for generating soil moisture
heterogeneity, we have implemented field observations
during the summer of 2002. The field observations
consist of horizontal observations for identifying therelationship between micro-topography and surface soilmoisture and vertical observations for understanding theland surface-to-atmosphere interaction.
To understand the horizontal variability of the
micro-topography and soil moisture, we observed theelevation and temporal variability of soil moisture before
and after a rainfall event at 10m intervals along a transect.
The elevation is measured by leveling. The temporalvariability in the soil moisture is measured using the Time
Domain Reflectometry (TDR) method.
Other field experiments have been carried out tounderstand the 1-D vertical land surface-atmosphereinteraction. It consists of meteorological measurements
using an Automatic Weather Station (AWS) and aboundary layer tower and vertical profiles of soil moistureand temperature under the surface using a Soil Moisture
and Temperature Measurement System (SMTMS). Thesesystems have been established at eight observation sites,and observations have been made since 1997.

100.15
0.10

50.05
0.00

0-0.05-0.10-5Topo: Obs-Run(30m)-0.15a) Amdo

Soil mois: Obs-Run(30m)

-0.20
3040

50

60

70

80

Difference of soil mois. [%]3. Evaluation of the process for generating the soil
moisture heterogeneity in plain areas
3.1. Field observations in the Tibetan Plateau

0.20

-1090

100

110

120

130

Distance[m]0.2010Topo: Obs-Run(30m)

0.15

Soil mois.: Obs-Run(30m)

0.10

50.05
0.00

0-0.05-0.10-5-0.15b) BJ-0.20

Difference of soil mois[%]The process for generating so il moisture heterogeneity ina plain area is different from that required for generating
the same information in a sloped area. Ishidaira etal(1998) on the one hand implemented field observations,and developed a model for sloped areas. They evaluatedthe processes in a sloped area based on field observations.
On the other hand, Hirose et al (2002) only speculated on
the generation process for plane areas. In this section, we
try to evaluate the processes using field observations.
The soil moisture heterogeneity in plane areas is assumed
to depend on the “micro-topography” during and after rain
events. In this paper, “micro-topography” refers to surface
undulations that do not appear in ordinary topographicalmaps. Concave regions tend to be wetter while convex
areas tend to be drier. Due to the larger thermal capacity ofsoil water and the larger amount of latent heat flux inwetter areas, the active layer remains shallow, even though
the conductivity of heat flow becomes large. In contrast,
a deeper active layer appears in the dryer area. The surface
soil of the shallow active layer remains wet for longer,
while one of the thick active layers dries up more rapidly
due to percolation to the thicker unsaturated zone.
To evaluate this process, we have identified two
important relationships using field observations. One is theeffect of micro-topography on soil moisture, and the otheris the interaction between soil moisture and the thawing
process in frozen soil.

To understand the hydrological processes in Tibet, weneed to separate the land area into sloped and plain areas.
In this paper, micro-topography refers to surface
undulations which consist of concavities and convexities in
the plain area. The difference between low and highelevations is less than 1 meter within 100-meter intervals.
On the other hand, it is more than 1 meter in sloped areas.
We focus on the plains at the Amdo and BJ sites. To
investigate the relationship between the micro -topography
and soil moisture, we apply a moving average to the soil
moisture change in order to reduce the short-term
fluctuations in the observed data. Fig. 1a) and b) show therelationship between the micro-topography and the soil
moisture at the Amdo and BJ sites. The thick and dottedlines indicate the difference between the observed andmoving average data of micro-topography and soil
moisture.
These figures show that the difference in the soil moistureis negative (positive) when the difference in the
micro -topography is positive (negative). The negative andpositive differences in the micro-topography represent theconcavity and convexity in the plains. These figuresindicate that the soil moisture in a concavity is larger thanin a convexity because the soil moisture in concavities
increases according to the surface water storage.
Therefore, they indicate the relationship between themicro -topography and the soil moisture in the plain.

Difference of Elev.[m]2. Soil moisture heterogeneity in the Tibetan
Plateau

3.2. Effect of micro-topography on soil moisture

Difference of Elev.[m]is to apply the model to the whole of the Tibetan region.

-100102030405060708090100

Distance[m]Fig.1: The relationship between topography and soil
moisture in a plane area. Fig.1a) and b) show the Amdo andBJ sites. The thick and dotted lines indicate the difference
between the observed and moving average data in themicro-topog raphy and the soil moisture.

Soil moisture [%]10200a)

8b)

50

50

100

100

Depth [cm]Depth [cm]Soil temperature [Degree]
02
46-2

30
00150
200
250

4/29
5/5

150
200
250

300

4/29
5/5300
Soil moisture [%]1020030

Soil temperature [Degree]
0246-2080c)d)

50

50

100

100

Depth [cm]Depth [cm]

150
200

150200

250

250

4/26
5/53004/26
5/5300

Fig.2: The vertical profiles of soil moisture (Fig.2a and c)and temperature (Fig.2b and d). The dotted and thick lines
indicate the initial and terminal conditions of the verticalprofiles
MJm-3K-1
1.5
21

2.5

00.20b)

50
0.15
100
Qs/Rn

In speculating the process involved in the generation of
the soil moisture heterogeneity, the following processenhances the difference in the soil moisture in concavities
and convexities. Higher (lower) soil moisture inconcavities (convexities) leads to smaller (larger) heatflows into the soil because of a large (small) heat capacityand evaporation. The thawing depth in the concavity isshallower than in the convexity. The higher soil moisture in
the concavity is kept because the melting water cannot
infiltrate the frozen table. Therefore, the difference in thesoil moisture between concavities and convexities
increases due to the interaction between the soil moistureand the thawing process in frozen soil.
To validate this interaction, we need to study to decrease
the heat flow into the soil by increasing evaporation andthe soil heat capacity.
At first, we investigated the effect of soil moisture on thesurface heat budget at the BJ site. We estimate the fluxes
based on the Bowen ratio method. Our results show thatthe ratio of the latent heat flux increases with soil moisture.
However, they indicate that the temporal variability of G
observed by the heat plate does not change with soilmoisture.
Secondly, we estimate the heat flow into the soil based on
the estimation method presented by Rouse (1982). Some
researchers have pointed out that the accura cy of the heat
plate observations is low. He mentions that there are two
problems associated with the heat plate observations.
One is the large difference between the heat capacity of theplate and that of soil. The other is that the heat platecannot measure the change in the heat flow caused by thewater flow within the soil. He presents a heat budget
equation which includes the thawing and freezing
processes in the soil. However, his equations are notapplied directly to the observed data because the thermal
conductivity and the soil ice content cannot be observed in
the field observations. Therefore, we address the small
temperature gradient in the observed data These observed
periods are seven days from Apr. 29 to May 5 in 1998 at
the Amdo site and ten days from Apr. 26 to May 5 in 2001
at the D66 site.
Fig.2 shows the vertical profiles of soil moisture (Fig.2aand c) and temperature (Fig.2b and d) which are addressed
to estimate the heat flow into the soil. Fig. 2a-b and c-dindicate the D66 and Amdo sites. In these figure, the doted
and thick lines indicate the initial and terminal conditionsof the vertical profiles. Fig.3a) shows the comparison
between the vertical profiles of heat capacity at the D66
and Amdo sites. The profiles of heat capacity are estimatedfrom the soil moisture profiles, as shown in Fig. 2a) and c).
This figure indicates the clear difference in the heat
capacity between the two sites. Fig.3b shows the ratio of
the estimated heat flow, Qs to Rn at the D66 and Amdosites. This figure shows that this ratio is smaller at theAmdo site compared with the D66 site. These figures
reveal that a high (low) soil moisture leads to a small(large) heat flow into the soil because of the high (low)heat capacity and the large (small) amount of evaporation.

Depth [cm]3.3. Interaction between the soil moisture and thethawing process in the frozen soil

150200250300a)

0.10
0.05

D66
Amdo

0.00
Amdo

D66

Fig.3: Comparison between the vertical profiles of heat
capacity at the D66 and Amdo sites

4. Validation of the model simulation
4.1. The model for considering the soil moisture
heterogeneity
Ishidaira and Toike (1998) developed a 1-D model toapply to the permafrost regions. It has a different numberof layers between the water and the heat flow in the model
calculations. For water flow, the calculation consists of fivelayers and uses the finite difference of the Richards
equation. The heat flow simulation implements a fine grid
(60 layers) based on the thermal diffusion equation. Oneof Its characteristics is the determination of the freezingboundary, independent of the five layers.
To consider the soil moisture heterogeneity, the modelneeds to include the surface water storage effect because of
the micro-topography. We add this to a tank model torepresent the water storage effect. The maximum height of
the water storage in this tank model depends on the10.8
0.6
d66

0.4
0.2

713
cal

001.2
Cumulative relative freq. [%]Cumulative relative freq. [%]a)102030
Soil moisture(%)

40c)

10.8
0.6

d66
0.4

728
cal

0.2
0102030
Soil moisture(%)

40500.4

Amdo630
cal0.2d)
1.00.80.6

BJ

0.4702cal

0.2
0.0001.2

102030
Soil moisture(%)

40

Cumulative relative freq. [%]

0.8
0.6
0.4

Amdo
725
cal

0.22030
Soil moisture(%)

4050

102030
Soil moisture(%)40e)
1.00.80.6050c)

0.8
0.6
Amdo
825
cal

0.2
0102030
Soil moisture(%)

40728
cal

0.2

1.2

0.4

BJ

0.4

0.001101.2b)

1005050

1.2102030
Soil moisture(%)

4050f)

10.80.6

BJ

0.4
826
cal0.200102030

4050

Soil moisture(%)

Fig.5: Comparison between the simulated and observed soilmoisture distribution at the Amdo (Fig.5a, b and c) and BJ(Fig.5d, e and f) sites

0.6
toutouhe
0.4
7/13
cal

0.2

Cumulative relative freq. [%]Cumulative relative freq. [%]0.6000102030
Soil moisture(%)

4050

Reference

1.2

10.8

0.850b)

01.2

1.2a)
1

Cumulative relative freq. [%]1.2

Cumulative relative freq. [%]Cumulative relative freq. [%]To estimate the model parameters, we make theassumption that a lot of the model parameters are the sameat the four observation sites, except for the distribution of
the maximum storage height. The model reproducibility
for changing the vertical profile of the soil moisture andtemperature is insufficient for comparing with the observed
data. The differences in the model parameters between
the observation sites need to be detected and taken into
consideration in future research plans.
The following procedures were carried out to determine
the model parameter and the distribution of the maximum
storage height. At first, we determined the model parameterto represent the vertical profile of the soil moisture andtemperature at each of the observation sites. Secondly,
we determined the maximum height distribution torepresent the observed soil moisture heterogeneity at eachof the observation sites. Thirdly, we compared the model
simulation with the observed data. Finally, we determined
the model parameters and the maximum height distribution
at each site by iteration.

1.2

Cumulative relative freq. [%]4.2. Estimation of the model parametersb), Toutouhe (Fig.4c and d), Amdo (Fig.5a, b and c) and BJ(Fig.5d, e and f) sites. In these figures, the thick and dottedlines indicate the observed and simulated soil moisture
distributions.
Its distribution is accumulated. Thesefigures show that the model simulation is in good
agreement with the observed data.

Cumulative relative freq. [%]micro-topography (surface undulations). Therefore, theboundary condition in the developed model is themaximum height distribution. This model neglects thelateral flow between grids. The overland flow out of thebasin is generated when the water storage is larger than themaximum height. In calculating the evaporation, thesurface mo isture availability, ß is unity when water storageoccurs.

d)

10.8
0.6

toutouhe

0.4

8/6
cal

0.2
00

10

2030
Soil moisture(%)

4050

Fig.4: Comparison between the simulated and observed soil
moisture distribution at D66 (Fig.4a and b), and Toutouhe(Fig.4c and d)

4.3. Validation of the model simulation at the fourobservation sites
We have evaluated the model predictability by comparing
the simulated soil moisture heterogeneity with the observed.
Fig. 4 and 5 show a comparison between simulated andobserved soil moisture distributions at the D66 (Fig.4a andNakaegawa, T., T.Oki and K.Musiake : The effects ofheterogeneity within an area on areally averaged evaporation.
Hydrol. Process. 14, 465-479, 2000.
Chen, Z, M.L. Kavvas and R.S.Govndaraju : Upscaling ofRichards equation for soil moisture dynamics to be utilized inmesoscale atmospheric models. Exchange Processes at theLand Surface for a Range of Space and Time Scales, IAHS,212, 125-132, 1991.
Nozomu Hirose, Koike Toshio and Ishidaira Hiroshi : Study on
Spatially Averaged Evaporation under Soil Moisture
Heterogeneity Affected by Permafrost Micro-topography, J.
Meteor. Soc. Japan, 80, 191-203, 2002.
Rouse. W.R : Microclimate of Arctic Tree Line 2. Soil
Microclimate of Tundra and Forest,
Water Resources
Research, 20, 67-73, 1984.
Ishidaira Hiroshi, Koike Toshio，Minjiao Lu，Nozomu Hirose:
Development of a 2-D soil model for heat and water transfer inpermafrost regions,
Annual J. Hydraulic Engineering,
JSCE，42, 133-138, 1998.