Validation study of MATSIRO land surface model using the observed data intropical monsoon climate of Thailand*Shin Miyazaki1 2, Wonsik Kim3, Yukiko Hirabayashi4, Shinjiro Kanae1, 5and Taikan Oki1
(1:IIS, University of Tokyo, Japan, 2: JST-CREST, Japan, 3: Seoul National University, Korea, 4: Yamanashi
University, Japan, 5: Research Institute of Humanity and Nature, Japan)* Institute of Industrial Science, University of Tokyo, Japan, 153-8505, Komaba,
Meguro Tokyo, Japan. e-mail: miyashin@iis.u-tokyo.ac.jp
Abstract
The objective of this study is to assess the performance of the land surface model, minimal advanced treatments ofsurface interaction and runoff (MATSIRO) in tropical monsoon climate of Thailand. The testing of MATSIRO was carriedout by offline mode (i.e., decoupled from complex interactions in the full 3D model). Driving and validation data sets wereprepared for a one year period in 2003 with 30-min time step, which we’ve obtained at Tak flux measurement (TFM) siteover mixed land use of tropical deciduous forest, grassland and paddy filed. The model was parameterized based on thestandard parameter values generally used within 3D version of MATSIRO namely GSWP2 data set. At TFM site there isclear seasonal variation of rainfall that is divided into dry season and rainy season. Sensitivity study of MATSIRO forvarious soil type showed that the simulated evapotranspiration (ET) for silt and sandy clay soil was well corresponded tothat of observed. As the soil consists of sandy clay soil according to our filed survey, we focused on the simulated result forsandy clay soil. The temporal change of simulated surface soil moisture was well corresponded to the observed surface soilmoisture when large amount of precipitation occurred. However, the reproducibility of surface soil moisture became bad in
prolonged dry period. The observed soil moisture in root zone was lower than that of simulated except the period of nearsaturation. During the period with successive rainfall, the simulated ET well reproduced the observation while the simulated
ET was quite lower than that of observed in dry season.
Keyword: Land Surface Model, Evapotranspiration, Soil moisture, Tropical monsoon climate, Hydraulic conductivity

1. Introduction
The land surface model (LSM) has great role on theland-atmosphere interaction in the general circulation
model (GCM). The intercomparison of LSMs has been
carried out in the international projects such as the projectfor intercomparison of land-surface parameterization
schemes (PILPS; Henderson-Sellers et al., 1995) and theglobal soil wetness project (GSWP; Dirmeyer et al., 1999).
The offline validation using observational data canidentify the serious problems and improve the realism ofthe LSM. So far, the offline validation of LSMs has been
implemented mainly in USA, Europe and Amazon.
However there were no such experiments in South-Eastern
Asia, where the contrast of dry and wet season is quiteremarkable. The objective of this study is to clarify thesensitivity of MATSIRO (Minimal Advanced Treatmentsof Surface Interaction and RunOff; Takata et al, 2003) for
various soil type at Tak flux measurement site (TFM; Kimet al, 2003, 16°56’N, 99°25’E; Fig.1 (a)). Moreover, weaimed to validate the MATSIRO on surface fluxes andconditions at TFM with 1D-offline simulation forimproving simulation in tropical monsoon climate.

2. Data, methods and model descriptions
The testing of MATSIRO was carried out offline mode
(i.e., decoupled from complex interactions in the full 3Dmodel). Driving and validation data sets were prepared fora one year period in 2003 with 30-min time step, which
we’ve obtained at 30m height of tower in TFM site overmixed land use of tropical deciduous forest, grassland andpaddy filed (Fig.1 (b)). The model was parameterizedbased on the standard parameter values generally usedwithin 3D version of MATSIRO namely data set ofGSWP-2 project (Dirmeyer et al., 2002). The spin-up
before simulation was carried out as recursive run of thedata in 2003 twice.

(a)(b)

TakTower

N10 kmFarm and grass land
Bare soil
Deciduous Forest
Early deciduous forest
Water
Paddy

Fig.1: (a) The location map of the TFM site and (b) theland cover around the site.
The data of observed surface fluxes was calculated byeddy-covariance method with coordinate rotation (Wilczaket al, 2001) and density correction (Webb et al., 1980).
The fluxes of MATSIRO are calculated from theenergy balance at the ground and canopy surfaces insnow-free and snow-covered portions considering thesubgrid snow distribution (Takata et al., 2003). InMATSIRO the stomatal resistance is evaluated on the basisof a photosynthesis-stomatal resistance model (e.g.,

0.45
0.4
0.35

120
P
Wz1_sim
Wz1_obs

100

0.3

80

0.25
60
0.2
0.15

40

Precipitation (mm)The soil moisture is the one of the most important factor
for controlling the soil evapotranspiration (ET). Table 1
shows the soil classification and hydraulic parameters in
the GSWP2 data set. In table 1, the W_sat is the saturationsoil moisture, Ks is the saturated hydraulic conductivity,
W_sat_matp is the saturated matric potential, B isempirical coefficient for slope of the moisturecharacteristic used for Clapp and Hornburger (1978). Theparameters shown here are based on the Cosby et al.
(1984).
Table1: Soil classification and hydraulic parameters in theGSWP2 data set
Soil
W_sa Ks
Phi_s B
classficationt(m/s)
at (m)
1
sand0.373 2.45E-5 -0.05
3.30
2loamy sand0.386 1.75E-5 -0.07
3.80
3sandy loam
0.419 8.35E-6 -0.16
4.34
4loam
0.476 2.36E-6 -0.65
5.25
5silt loam
0.471 1.10E-6 -0.84
3.63
6
silt
0.437 4.66E-6 -0.24
5.96
7
sandyclay 0.412 6.31E-6 -0.12
7.32
loam
8clay loam
0.478 1.44E-6 -0.63
8.41
9
silty clay loam 0.447 2.72E-6 -0.28
8.34
10 sandy clay
0.415 4.25E-6 -0.12
9.70
11 silty clay
0.478 1.02E-6 -0.58
10.78
12 caly0.450 1.33E-6 -0.27
12.93
Cosby et al. (1984) pointed out that the ratio of clay isthe discriminate factor for the B and Ks. The soil hydraulicparameters such as hydraulic conductivity and B parameterare closely related with the simulating soil moisture.
Therefore we’ve carried out the sensitivity test ofMATSIRO for ET using all soil type in GSWP2 data set.
Table 2 shows the results of sensitivity tests of MATSIRO
for cumulative ET during one year in 2003, dry season andrainy season with observed value. The annual precipitationin 2003, dry season and rainy season were 1154 mm, 134mm and 1020mm, respectively. Generally, the soil withhigh ratio of sand showed the larger ET than the soil withhigh ratio of clay. The results using soil type 6 (silt) and 10

Volumetric soil water content at 0-0.05m

3. Results and discussions(sandy clay) were well corresponded with observation.
According to our field survey around TFM site, the soil ofsurface several tens of centimeters consists of sandy clay
which is no. 10 soil in GSWP2 soil classification.
Therefore we will show the detailed comparison between
simulated using soil 10 and observed data in the following.
Table 2: Results of sensitivity tests of MATSIRO forevapotranspiration with observed value.
Soil No. E_year (mm) E_dry (mm) E_rain (mm)
1949
385
564
2
1005
427
578
3
1031
436
595
4
678
150
528
5496
73
423
6
870
277
593
7949
385
614
8
547
91
456
9716
158
558
10
842
232
610
11470
68
402
12
528
77
451
Obs
890
304
586
Figure 2 shows the time series of daily mean volumetricsoil water (W) content of simulated (soil-10) and observed
at 0.05m with observed precipitation. According to theseasonal variation of precipitation, we defined the first dry,rainy and second dry seasons in January to the end of May,
June to the end of October, and November and December
respectively. Temporal variation of simulated W was wellcorresponded with observed value. The increase of W after
the rainfall reproduced very well while the decrease ofsimulated W was slower than observed W. The discrepancy
between simulated W and observed value during dry
season and break of rainy season is likely related to thetreatment of soil water movement in MATSIRO when Wdecrease after rainfall and low.

0.1
200.050010111613121530
231740141650151
653
161563071573081
482
991
392
10 8
110 3
211 8
111 2
212 7
112 2
27

Farquhar et al., 1980; Ball, 1998; Collatz et al., 1990, 1991,1992) after the scheme in SiB2 (Sellers et al., 1996). A
simplified TOPMODEL (Stieglitz et al., 1997) is used tocalculate runoff. The governing equation of MATSIRO
uses Richards equation. The hydraulic conductivity andmatric potential are calculated based on Clapp andHornburger (1978) with the modification for frozen soil
(Takata et al., 2003). The depth of soil layer was set to 2m.
There are five soil layers (0-0.05m, 0.05-0.25m, 0.25-0.5m,0.5-0.75m, 0.75-2m) in MATSIRO. The root fraction using
in this study was arbitrarily defined as 0.35, 0.55 and 0.10
from surface to third layer. The land cover classification,soil type and leaf area index (LAI) were derived fromGSWP2 data set. The topographic parameters for
simplified TOPMODEL were estimated from GTOPO30
data.

DateFig. 2: Time series of daily mean volumetric water contentof simulated and observed at TFM in 2003.
To consider the transpiration, the total root zone soil
moisture is the most important factor. Figure 3 shows thetime series of daily mean simulated and observed W in rootzone layer. In MATSIRO the surface three layers (0-0.05m,0.05-0.25m and 0.25-0.5m) are considered as root zone forgrassland. The feature of temporal variation anddiscrepancy between simulated and observed in root zone

10Cumulative W in root zone (cm)

98
76543

Wz1-3_sum_sim
Wz1-3_sum_obs

2
1

613
121
530
231
740
141
650
151
653
1
61
563
0
71
573
0
81
482
991
392
8
1013
1028
1112
1127
12121227

1110

10DateFig. 3 Time series of daily mean volumetric soil watercontent of simulated and observed at TFM in root zone
layer in 2003
7

Evapotranspiration (mm/day)

6ET_sim
ET_obs

520140401206010080RoP80

10039410 8110 3211 8111 2212 7112 2
27929182

505161081737163615351

1650

414031302113117180
21600114020512040

660

Precipitation (mm/day)

0160

1Runoff (mm/day)180

10W was resemble to the surface W. In general, the observed
W in root zone was larger than the simulated W.
The time series of daily mean simulated and observed
evapotranspiration (ET) was shown in Fig. 3. In first dryseason, the simulated ET was about half of observed ET
except the period with increase of W after rainfall. The
simulated ET was corresponded to observed ET in rainyseason. In first half of second dry season, the simulated ET
was larger than the observed ET while the simulated ET
was corresponded to the observed ET in second half ofsecond dry season. These features were consistent with thevariation of W. It implies that the discrepancy of ET iscaused by the failure of simulation of W. The failure of W
in dry season likely related to the parameterization of soilhydraulic conductivity. As noted earlier, the hydraulicconductivity is estimated by Clapp and Hornburger (1978)
in MATSIRO. The dry end of moisture characteristic are
difficult to model precisely because the hydraulicconductivity is reduced by many orders of magnitude, evena rough approximation in this range may be sufficient(Clapp and Hornburger, 1978).

DateFig. 5: Time series of daily precipitation and simulated
runoff in 2003.

4. Concluding remarks
The 1D-offline test of MATSIRO was carried out atTFM site in tropical monsoon climate. The sensitivity testof MATSIRO for various soil types showed that thesimulated annual ET for silt and sandy clay was wellcorresponded to the observed ET. The soil at TFM site was
sandy clay defined by our filed survey. The detailed
analysis was made for simulated results for sandy soil. The
simulated surface W was well corresponded to theobserved surface W in rainy season or the period with
rainfall in dry season. The reproducibility of simulated
surface W was bad in dry season and the phase ofdecreasing W, which suggests the faire of simulation of
hydraulic conductivity in near dry end of soil. The
simulated totals root zone W also showed similar feature tothe surface soil moisture. The simulated ET in dry season
and the prolonged dry period had large discrepancy withobserved ET, while the simulated ET was wellcorresponded to the observed ET in rainy season and large
rain in dry season. Instead of Clapp and Hornburger (1978),the other theory for estimating hydraulic parameters suchas van Genuchten (1980) will be useful to clarify thereason for the problems in dry period.

References

4Ball, JT, PhD thesis, Stanford Univ., 89pp, 1998

3Clapp, R.B. and G. M. Hornburger, WRR, 14, 601-604, 1978.

Collatz et al., Plant Cell Environ., 13, 219-225, 1990.
Collatz et al., Agric, For. Meteorol., 54, 107-136, 1991.
Collatz et al., Aust. J. Plant Physiol. 19, 519-538, 1992.

2110
111613121530231740141650151653161
563
071
573081
482
991
392
10 8
110 3211 8
111 2212 7
112 2
270DateFig. 4: Time series of daily mean evapotranspiration ofsimulated and observed at TFM site in 2003.
The runoff is also important factor on the water budget.
Fig. 5 shows the time series of daily precipitation andsimulated total runoff. Almost all runoff consisted of theinfiltration excess runoff (Horton runoff). The runoff wasoccurred after the rainfall when it exceeded 20 mm per dayor successive rainfall.

Cosby BJ et al., WRR, 6, 682-690, 1984.
Dirmeyer, P.A., et al., BAMS, 80, 851-878, 1999.
Dirmeyer, P.A., et al., IGPO pub., 37, 65pp, 2002.

Farquhar GD, et al., Planta, 149, 78-90,1980
Hendrson-Sellers, A., et al., BAMS, 76, 489-503, 1995.
Kim, W et al, Korean Journal of Agricultural and Forest
Meteorology, 5, 116-127, 2003.
Sellers, P.J., et al., JC, 9, 676-705, 1996.
Takata et al, Global Planet. Change, 38, 209-222, 2003.
van Genuchten, M. Th., Soil Sci. Am. J., 44, 892-898, 1980.
Webb et al., Quat. J. Roy. Metorol. Soc., 106, 85-100, 1980.
Wilczak et al., BLM, 99, 127-150, 2001.