The Implementation Plan of GAME-Tropics
by
Japan sub-Committee for GAME-Tropics
Draft at July 1996

# 3.2.1 Scientific Background

## 3.2.1 (1) Scientific goals

Among GEWEX/GAME research components, GAME-T's role is to observe and investigate the energy and water cycle in the warm humid areas of the Asia Monsoon region, from tropics to sub-tropics. These areas are characterized by small seasonal change of temperature and a predominant diurnal cycle of temperature and precipitation. The annual variability of surface soil wetness is quite large where dry season is observed, and inter-annual variation of precipitation is as significant as diurnal cycle in the tropical area where the seasonal change is small. As a heat source for atmosphere, the release of latent heat in this region is largest on the earth, and it drives the circulation of the Asian Monsoon.

On the other hand, the density of population in this region is generally high, and the crop (rice) production supporting these large population is directly related to the variation of the precipitation during the Asian Monsoon. Therefore the seasonal prediction of precipitation by the construction of adequate combined land-atmosphere numerical model is not only challenging scientifically but also contributing to societal issues through improving the accuracy of water resources prediction. The high density of population is closely related to the relatively high density of the existing observational network of hydrological and meteorological stations.

The goal of GAME-T is to accomplish its role well considering these characteristics of the target area as one of key sub-programs of GEWEX/GAME.

## 3.2.1 (2) Scientific objectives

As a part of GEWEX/GAME, the objective of GAME-T is quantitative monitoring of vapor flux, precipitation, evapotranspiration, radiative flux and their seasonal, intra-seasonal and interannual variation at the target areas of the warm humid temperate region in the south-east Asia. Especially,

• the surface wetness which differs significantly in the dry and wet season, and
• the diurnal cycle of precipitation and other hydro-meteorological variables which is dominant in the tropical area
are focused on, and their effect on the energy and water flux from land surface including vegetation will be carefully observed and investigated. Of course,
• dynamical studies on the hierarchical structures of cloud-precipitation systems which consist of:
• individual clouds generated locally and micro-physically,
• cloud clusters or tropical cyclones moving from the South China Sea or the Bay of Bengal, and
• super clusters or convection centers organized by coupled effects of the Indian and Pacific Oceans,
• the better understanding of the effect of the interannual variation of precipitation on the yield of rice in this region, and
• improving the accuracy of the seasonal prediction of precipitation
are vital issues of the research in GAME-T.

In a global sense, Fig. 3.1 shows the correlation of rainfall in Thailand from May to July and the SOI of November at the same year. Their inter-annual variations are synchronizing and it is suggested that at least the energy and water cycle in Thailand can be a good measure of the variation of global climate system.

For the collection of existing hydro-meteorological observation, the target area of GAME-T covers as large as possible in the warm humid region of the South East Asia. Additional installation of observational instruments and enhanced observation will be put into practice in the ChaoPhraya river basin in Thailand, forest in Sarawak, Malaysia, and Sri Lanka (Fig. 3.2).

## 3.2.1 (3) Program Strategy

Corresponding to the strategy of GEWEX/GAME, water and energy cycles will be studied from several approaches.

1. Boundary layer observation will be carried out in order to measure the flux and the balance of energy and water at land surface. The study areas are selected as they will cover typical landscapes of the target region of GAME-T, such as forest, cultivated area (paddy field) and bare soil. Some new scientific information are expected to be obtained.
• The effect of soil wetness on the evaporation from land surface with vegetation, and the effect of rain water storage on runoff generation will be observed in the Chao Phraya river basin in Thailand where the difference between dry and wet season is large.
• The interaction between diurnal cycle of precipitation and evapotranspiration, especially from intercepted water, will be studied.
• A method to estimate evaporation from soil temperature data will be developed and validated.
• Large scale evapotranspiration will be estimated using the data of lower part of atmospheric boundary layer from frequent rawinsonde observation combined with satellite observation by NOAA/AVHRR/IR.
2. Ground truth data will be observed for the validation of earth observing satellites, and various hydro-meteorological information in large domain will be retrieved in order to estimate the flux and the balance of energy and water fluxes. In GAME-T,
• data from ground based radar, raingauges, and raindrop size distribution for TRMM,
• the area of water surface and the distribution of surface soil wetness for JERS-1/JSAR,
• surface soil wetness for RADARSAT,
• surface radiation and cloud for GMS/SVISSR, and
• landscape and the vegetation cover for NOAA/AVHRR
will be collected, observed and estimated. Algorithms should be developed and or implemented to retrieve precipitation using satellite remotesensing by GMS and/or TRMM.
3. Corresponding to the GAME strategy,
• flux observations by GAME/AAN/AWS at five points of monsoon forest, paddy field and shrubbery forest in Thailand, tropical forest in Sarawak, Malaysia, and vegetated area in Sri Lanka (Fig. 3.2).
will be done. Combining these observation with the hydro-meteorological data from the existing stations, longterm monitoring of the energy and water balance are planned. This observation aims the construction and the improvement of one dimensional model to estimate energy and water flux from land surface which may be covered with vegetation. This in GAME corresponds to that of PILPS in GEWEX.
4. In order to obtain better atmospheric data from four dimensional data assimilation, the rawinsonde observation should be enhanced in the GAME-T region where upper air sounding by rawinsonde is currently operated only once at 00 UTC (07 LST) in a day. Four times daily soundings by radiosonde are expected at least at three stations in Thailand, and two times daily or four times daily observations will be recommended at other stations in the target region of South-East Asia during the Intensive Observational Period of GEWEX/GAME. These special observations should be communicated through GTS line in real time, and 4DDA will be performed at Japan Meteorological Agency.
5. As a modelling study of the atmosphere, a meso-scale meteorological model, Code for Aggregating Nested Atmosphere and Environment (CANAE), is being developed and this will be nested in the atmospheric general circulation model developed by CCSR/NIES. Land-surface parameterization which calculates areal mean flux from each grid by weighted averaging of fluxes from a few typical landscapes will be employed in CCSR/NIES AGCM and CANAE. A distributed hydrological model which uses output from CANAE, such as precipitation and evapotranspiration, as its input will be independently developed and the model is expected to have an ability to consider the effect of artificial intake or storage.
6. Objective analysis will be carried out for obtained data sets in order to perform error checks and to make data handling easier for everybody including possible scientific users of the data outside of GAME project. Various data sets are objectively interpolated and extrapolated into 10km grids, at least in the Chao Phraya river basin, and gridded data of water and energy flux will be made. In a larger domain, energy and water balance estimated by 4DDA will be referred, and physical parameters such as precipitation, evapotranspiration and net radiation will be adjusted to satisfy the energy and water budget.
The year of 1998 is the target period of Intensive Observation, but preliminary observations will be carried out at 1996 and 1997, and additional observation is expected in 1999. Intensive observation, which cannot be done continuously for a long time, will focus on the comparison between the wet and dry seasons: two to three weeks each at dry season in March and at wet season in August. Intensive observation by rawinsonde are expected to grab the onset and the decaying of the South-West Asian Monsoon. These are planned from March to May and August to October. Ten year (1980-89) mean monthly rainfall in Thailand is shown in Fig. 3.3.
The rainy season starts late April to the beginning of May, and the interannual variation is large in May. During the dry season, from December to March, the rain is quite less and soil dries up very much. Intensive observations are planned considering these climatologies.

# 3.2.2 Strategy of the Experiments

GAME-T covers sub-tropical region of South-East Asia, including countries such as Philippine, Vietnam, Thailand, Malaysia, Singapore, Indonesia and Sri Lanka. Data analysis will cover the entire region but observations are planned mainly in Thailand, Malaysia and Sri Lanka. Because a lot of resources will be spent in Thailand, this implementation plan mainly introduce about what is planned for the GAME-T in Thailand and Malaysia (Fig. 3.4).

## 3.2.2 (1) 1-dimensional flux observation

One dimensional energy and water flux will be measured at these stations below in GAME-T. The distribution map of planned stations of flux observation are illustrated in Fig. 3.2 for whole GAME-T area. Net radiation and basic hydro-meteorological parameters will be continuously observed in stations described below, and time series of energy and water flux will be estimated and monitored.

Table 3.1: Land use in Thailand
RegionTotalForest Farm Holding LandUnclassified
North-Eastern105,534 13,624 57,71934,191
Northern 106,028 48,214 29,394 28,419
Central Plain64,938 15,192 28,629 21,117
Southern 44,197 8,406 17,334 18,457
Total320,697 85,436 133,076 102,184

Observing stations within the Chao Phraya river basin are a)-d) and h). From the statistics of land use classification in Thailand (1991, unit 1,000 rai = 160 ha), the Northern region and the Central Plain almost correspond to the upper and the lower part of ChaoPhraya basin, respectively. Further, the land use of the Farm Holding Land can be divided in detail.

Table 3.2: Land use classification of farm holding land in Thailand
North-Eastern 57,719 1,253 37,973 13,445 1,844 209 395 2,069 521
Northern 29,394 942 15,197 10,475 1,754 276 134 432 184
Central Plain 28,629 853 12,531 9,438 4,379 309 124 445 550
Southern 17,334 488 3,612 150 12,121 64 53 676 168
Total 133,076 3,536 69,313 33,519 20,098 858 707 3,621 1,423

From these tables, one can see that half of the upper Chao Phraya river basin is covered by forests, 30% is 'unclassified' and half of cultivated area, namely 15% of total, is paddy field. The landscapes of a), c) and b) are corresponding these major land uses. However, Kog-Ma experimental basin, a), is located in higher altitude compared to whole forest region in the ChaoPhraya river basin, and it may not represent the energy and water flux of forest region in the area. Shrubbery forest, c), is set up assuming that most 'unclassified' area corresponds to such landscape. In this region, shrubs in plain area is mixed with bare soil or small grasses. Further detailed study on the land use is required, especially investigating 'what is unclassified'.

Station h) is operated by the National Institute for Earth Science and Disaster Prevention (NIED, Japan), Research Development Corporation of Japan (JRDC, Japan) and Royal Irrigation Department (RID, Thailand). Basic time interval of operational hydro-meteorological data in Thailand is daily for TMD, RID, RFD and EGAT, but more than thirty agrometrological stations of TMD, three hourly data are manually recorded and soil temperature at several depths are regularly observed. The data from these agrometeorological stations may be used for flux estimation with additional measurement of net radiation. The experimental basin in Sri Lanka, g), will be serviced during the fiscal year of 1996, and the longterm monitoring will be started from FY 1997. Among these stations, a), b), c) and g) will be equipped with GAME/AAN-AWS and it is possible that f) will, too.

a)
Kog-Ma experimental basin, Thailand, the Monsoon Forest:
98.9 E 18.8 N, 1268 m (by RFD in Thailand, Prof. Suzuki)
b)
West Sukhothai along with Route 1113, Thailand, non-irrigated paddy field:
99.7 E 17.1 N, 50 m (by RID in Thailand, Prof. Aoki, Prof. Sunada and Mr. Hirota)
c)
EGAT Tower¡¢Thailand, shrubbery forest:
99.4 E, 16.9 N, 140 m (by EGAT in Thailand, Prof. Sunada, Dr. Sugita and Dr. Ohte)
d)
Moving observation in Y32/Y4 river basin, Thailand, various landscapes:
(by Prof. Aoki, Prof. Sunada and Mr. Hirota)
e)
Pasoh forest reserve, Malaysia, tropical rain forest:
102.3 E 3.0 N, 120 m (by FRIM in Malaysia¡¢FFPRI: Dr. Tani)
f)
Sarawak, Malaysia, tropical rain forest:
114.0 E, 4.2 N, 300m (by Prof. Suzuki)
g)
Experimental basin at University of Peradeniya, SriLanka, forest and grass land:
80.6 E, 7.3 N, 800 m (by University of Peradeniya in Sri Lanka¡¢Prof. Tanaka, Prof. Shimada and Prof. Tase)
h)
Khuwae-Noi meso-scale experimental basin, Thailand:
100.3-101.1E 16.8-17.7N, 2,100-45m (by NIED: Mr. Nakane, JRDC:Mr. Kurauchi and RID)

Table 3.3: Observed items at each observation station.
Item a) b) c) d) e) f) g) h)
Radiation ($R$) Budget@@@+@@@@
Downward Shortwave @@@+@@@.
Upward Shortwave @@@+@@@.
Downward Longwave @@@+@@@.
Upward Longwave @@@+@@@.
Vertical Profile of $R$+.+.++..
Wind Velocity ($v$) @@@+@@@@
Vertical Profile of $v$+++.+++.
Wind Direction @@@+@@@@
Temperature ($T$) @@@+@@@@
Vertical Profile of $T$ +++.+++.
Humidity ($q$) @@@+@@@@
Vertical Profile of $q$ +++.+++.
Surface $T$ by IR +++++++.
T in soil layers @@@+@@@@
Heat flow into ground @@@+@@@@
Pan evaporation .......@
Water temperature .@......
Water depth .@......
Sensible heat by SAT +++++++.
Latent heat by IR +++++++.
Soil moisture @@@.@@@@
Precipitation @@@+@@@@
Interception by crown +...+...
Stomata conductance +...++..
Leaf area index +...++..
Sap velocity +...++..
Rawinsonde obs. ++......
Data transmit by Sat. +++..++.
Hight (m) of obs. tower 5010100 10405030 6
Soil properties ++++++++
vegetation ++++++++
land use ++++++++
digital elevation map ++++++++
 @ : continuous observation + : only during the IOP . : not planned

Secondary products estimated from these observations are:

Output:
Longterm, hourly, at every station
• inter-calibrated radiation budget at land surface
• cross-calibrated heat transfer into soil layers
• sensible and latent heat flux by energy balance / Bowen method, at paddy fields
• sensible and latent heat flux by bulk method
• sensible and latent heat flux by bulk method / Penman-Monteith method, at forests
Output:
Longterm, hourly, at some station
• albedo
• sensible heat flux by GAME-AAN/AWS-SAT and latent heat flux by energy balance
Output:
IOP, hourly, at some station
• sensible heat flux by Sonic Anemometer-Thermometer(SAT) and latent heat flux by energy balance
• sensible heat flux by SAT and latent heat flux by covariance measurement by infrared
• sensible and latent heat flux by profile method
• roughness parameters in each direction (time independent)
• sensible and latent heat flux using lower boundary layer information obtained by rawinsonde

## 3.2.2 (2) Meso-scale process studies

The information described below will be available from GAME-T sub-program for the research in the field of meso-scale meteorology.

• upper air soundings by enhanced rawinsonde observation
• rain rates from ground based rain gauge
• radar images observed by conventional weather radar operated by TMD. They are expected to be updated in 1997 and plans are underway to make digital composite images. Currently operators are monitoring PPI images in most radar sites, but radars at Bangkok DonMuang International Airport and Phuket Island are replaced by a new radar system equivalent to WSR-88D of United States. Data from Phuket Island radar is determined by NASA to be one of the ground validation data for TRMM.
• Om Koi radar at 17 N 47'54", 98 E 25'57", 1,160m, operated by BRRAA (Bureau of the Royal Rainmaking and Agricultural Aviation). It is a S-band radar and range within 125 km are quantitatively used. Three dimensional scans are made each five minutes by changing its elevation angle up to 27 degree. Every radar data both reflectivity and doppler velocity are recorded in 8mm tape regardless of rain or no rain. This radar data is also determined as one of TRMM validation, and all the data will be sent and archived at NASA/GSFC.
• raindrop size distribution to be installed, traditional type disdrometer made in Switzerland.
• cloud parameters (temperature, humidity and cloud water) obtained by air craft observations by BRRAA. BRRAA has 32 airplanes called CASA, and there are approximately 100 pilots to operate them.
BRRAA's project itself is ``applied atmospheric resources research program'' (AARRP) Phase II from 1994 through 1998, and they are doing artificial rain enhancement by seeding clouds.
• From the middle of March to the middle of May, the bottom of the cloud is approximately 3,000 m and the cloud top is approximately 10,000 m. Ice crystal processes are dominating at the cloud top and iodo-silver (AgI) are used for seeding.
• After the onset of south-west Asian monsoon through October, the bottom of the cloud is approximately 1,333 m and the cloud top is approximately 5,000 m. No ice crystal processes are observed at the cloud top, and chlor-calcium (CaCl2) is used for seeding.
Therefore observed phenomena may be modified by artificial influences. The cooperative study will be appreciated that evaluate and validate the effect of seeding to rain enhancement using numerical model of the atmosphere which can resolve each cloud.

Using these information, these studies will be possible.

• Surface soil wetness on the development of atmospheric mixing layer,
• micro-physical and dynamical processes generating individual clouds,
• diurnal cycle of precipitation and local circulations,
• torrential rainfall associated with cloud clusters or tropical cyclones moving from the South China Sea or the Bay of Bengal,
• modulations of precipitating cloud clusters by super clusters or convection centers organized by coupled effects of the Indian and Pacific Oceans through equatorial planetary-scale waves,
• behaviors of the convection centers in the seasonal variation and the continental-scale circulation, and
• interannual and global changes of the seasonal and continental-scale rainfall variations.

However, promised output from GAME-T team is limited at present status. Specialists in meso-scale meteorology are all encouraged to join this subprogram for effective utilization of these valuable data.

Output:
Areal rainfall distribution obtained from weather radar with calibration using raingages will be prepared for hourly in approximately 1 to 5 km meshes.

## 3.2.2 (3) River-basin scale budget studies

Several river catchments are designated for water balance studies and hydrological modelling in the Chao Phraya Basin (Fig. 3.5). These catchments are selected to make hierarchical structure in terms of catchment size, as macro scale (-- 100,000 km$^2$), meso scale (10,000 km$^2$) and micro scale (1,000 km$^2$). Various hydro-meteorological parameters are objectively analyzed into 10km grids by interpolation and extrapolation using sensible and latent heat flux (3.2.2(1)), and precipitation distribution (3.2.2(2)), existing operational network (3.2.3). In addition to that, two kind of enhanced rawinsonde observations, installing in a hydrological experimental basin, and installing extra raingauges in the target area are planned.

Table 3.4: List of candidate basins for hydrological budget studies and modelling
ScaleGauging Station (Town)River Catchment area (km^2)ObservationRain gauges Land useRemarks
Micro Scale P.20 (Chiang Dao) Ping 1,355 Automatic Gauge and Stuff Gauge 5 Mountainous Forest main stream
P.4A (Mae Tang) Mae Tang (Ping) 1,902 Automatic Gauge and Stuff Gauge 15 Mountainous Forest PWRI
P.24A (Chom Thong)N.Mae Klang (Ping) 460 Stuff Gauge 8 Mountainous Forest, Paddy Doi.Inthanon irrigation, w-intake, much rainfall
P.14 (Oblung gorge)N.Mae Chaem 3,853 Automatic Gauge and Stuff Gauge ? Mountainous Forest uniform cross-section
Meso Scale EGAT Point (Hot)Ping 18,990 Automatic Gauge and Stuff Gaugemany Mountainous basinupstream of Bumibol reservoir
W.3A (Theon)Wang 8,985 Automatic Gauge and Stuff Gaugemany Mountainous basin major tributary
Macro Scale C.2 (Nakon Sawan)Chao Phraya110,569 Automatic Gauge and Stuff Gaugequite many various key point

(a)
Enhanced rawinsonde observation (I)
It is closely related to the flux estimation in a large domain. Evapotranspiration in large area will be estimated at least twice during daytime using rawinsonde observation, and the evapotranspiration at other time of the same day will be calculated using other continuous measurements at ground surface. From this method (Brutsaert and Sugita 1992, Sugita and Brutsaert 1991) daily (and diurnal cycle maybe with lesser accuracy) evapotranspiration will be obtained in large scale.
Place
flux observational station b) or c) in the section of 3.2.2(1), the middle of the ChaoPhraya river basin.
Frequency
4--6 times per day at 00, 03, 06, 09, 12, 18 UTC, it corresponds to 07, 10, 13, 16, 19, 01 LST.
Period
YEAR Period at
19962 weeks in August
19972 weeks in March and August, each
19984 weeks in March to May and August to September, each
19992 weeks at March and August, each

No data will be observed by current implementation plan at this station except for the above observational period. There is a TMD station at Phitsanulok, but pilot balloon observations by manual operation are performed up to 1,700 m for wind direction and speed.

Output:
Large scale evapotranspiration on a day of good weather condition during the scheduled period will be estimated.
(b)
Enhanced rawinsonde observation (II)
It aims to improve the accuracy of 4DDA data by increasing the number of observation from GAME-T region. It will also be possible to apply atmospheric water balance method to estimate large scale evapotranspiration and the change of total water storage within the region using rawinsonde data combined with precipitation and runoff data Oki et al. (1994).
Fig. 3.6 shows the good correspondence of seasonal cycle between atmospheric vapor flux convergence (adjustment was applied by a factor of 0.18) and monthly river discharge. Fig. 3.7 compares a few estimates of evapotranspiration, and such macro scale results will be validated by surface flux observations.
Place
 CHIANG MAI 98 59 E 18 47 N 312 m UBON RATCHATHANI 104 52 E 15 15 N 123 m BANGKOK METROPOLIS 100 34 E 13 44 N 2 m ( PHUKET 98 24 E 07 53 N 2 m ) ( SONGKHLA 100 36 E 07 12 N 4 m )
Frequency
: Four times daily at 00, 06, 12, 18 UTC
Period
YEAR Period at
19962 weeks in May and August
19974 weeks in March to May and and August to September, each
199812 weeks in March to May and August to October, each
19994 weeks in March to May and August to September, each

There is operational rawinsonde observation once daily at 00 UTC even out of enhanced observational period, and the vertical profiles of wind direction and velocity, temperature and humidity are observed up to approximately 25,000 m. During normal operation period, rawin observation are scheduled at 06 and 12 UTC and pilot balloon is used at 18 UTC. Both rawin and pilot balloon can observe only wind direction and wind velocity and the possible observational height by rawin observation is limited to approximately 15,000 m.

This enhanced rawinsonde observation will be carried out by National Space Development Agency of Japan as a part of ground validation experiment for ADEOS/TRMM in collaboration with TMD.

Output:
Fluxes of sensible heat and latent heat (water vapor) and their convergence to this region will be calculated from original rawinsonde data. Atmospheric water balance may be also applied.

(c)
Hydrological experimental basin
In correspondence to the flux station a), some existing weir will be improved at Kog-Ma hydrological experimental basin and discharge will be precisely observed. Katsesaert University will be mainly responsible in operation and management. Instrumentation of discharge-gauging stations may be installed in some meso-scale and micro-scale river basins (3.2.2(3)) if required.
Output:
Runoff data will be provided by 10 minutes to 1 hour intervals.

(d)
Installation of extra raingauges
The raingauges in the existing operational network tend to be located in residential areas, and are rare in mountaneous region. BRRAA is trying to distribute as evenly as possible, and approximately ten raingauges will be offered in order to support their activity. Another twenty rain gauges will be distributed in the Chao Phraya river basin in order to improve the mean areal rain rate.

For example in the mountaneous area, installing stations at 400 m, 700 m, 1260 m, and 1685 m are planned at Mt. Doi Pui where the Kog-Ma hydrological experimental basin is located. Temperature and humidity will be possibly measured simultaneously. In the Mae Chaem river basin where discharge is measured at P.14, there are many experimental basins of RFD and it will be possible to estimate areal mean rain rate accurately.

## 3.2.2 (4) Regional-nested modelling

Modelling studies can be divided in these sub-components. These are minimum requirement of modelling studies and further sophisticated approaches will be highly appreciated.

(a)
General circulation model : The latest version at that time of CCSR/NIES AGCM will be used.
(b)
Meso-scale meteorological model : CANAE model which uses the parameterization schemes in CCSR/NIES AGCM and converted into meso-scale dynamics is under development and will be used.
(c)
Land surface parameterization for atmospheric models : 'Mosaic' approach which classifies land surface of a grid into limited kinds and give a uniform forcing from atmosphere to each landscape and returns weighted mean flux to the atmosphere will be developed and used. Runoff routing model will be also coupled with these meteorological models.
(d)
Model to estimate the exchange of energy and water between land and atmosphere : In addition to the big leaf treatment of vegetation, which is sometimes used in the above c) type models, a model which explicitly describes the vertical profiles of flux and physical parameters within vegetation layer will be developed, validated and improved. Such kind of a model will be necessary for sparsely planted forest where energy and water are exchanged at both the crown of the trees and the bed of forest.
(e)
Runoff model for water resources prediction : A distributed rainfall runoff model which utilizes digital topographic map, land cover data, the distribution of soil property, etc., will be used. Areal hydro-meteorological information given by other sub-project of GAME-T will be used as input data. Artificial influences like intake or storage in reservoirs will be considered as possible.

Output:
These outputs are expected from this modelling studies in GAME-T.
1. Land surface parameterization schemes (c) will be calibrated or validated by estimated fluxes by detailed land surface model (d).
2. Run CANAE (b) using 4DDA data as initial and boundary condition and calibrate or validate against observation (3.2.2(2) and 3.2.2(3)).
3. Runoff model will be run by the output from above numerical experiment.
4. Do the similar numerical experiments of ii) and iii) but using output from AGCM or coupled general circulation model on global warming and disaggregate the prediction in regional scales.

# 3.2.3 Data collections

The hydro-meteorological data described below has been collected or will be collected from existing observational network. If not specified, data catalogue will be compiled and offered to scientific and engineering communities.

## 3.2.3 (1) Precipitation

Daily precipitation will be collected from TMD, RID and EGAT in Thailand totally at 300 points. Hourly data will be collected from BRRAA at 40 to 50 stations.

Hourly data is available in Malaysia from Malaysian Meteorological Service. Drainage and Irrigation Department of Malaysia is also observing precipitation. Daily precipitation are planned to be collected from Philippine, Vietnam, Singapore and Indonesia.

Monthly precipitation data at more than 50 stations in Sri Lanka has been collected from 1900 to 1994. This data set has been published as a data book.

Output:
Areal rainfall distribution will be obtained in combination with radar data and extra raingauge data.

## 3.2.3 (2) Discharge

Discharge data operationally measured are collected from RID and EGAT in Thailand.

Output:
Discharge or the water stage will be converted into runoff height by defining the watershed area.

## 3.2.3 (3) Temperature, humidity, wind direction and speed

In Thailand, TMD is observing and archiving above basic daily meteorological variables in digital form. RID and EGAT are also observing but there are no plan to collect data from them.

In Malaysia, MMS is archiving hourly data in digital form at major observational stations.

In Sri Lanka, a part of the basic meteorological data has already collected from 1900 to 1994.

There are no systematic network which observes radiation, however, sunshine duration is measured at major meteorological stations in Thailand.

In Sri Lanka, sunshine duration data from 1975 to 1994 has been collected at 24 stations, and another 10 stations are only from 1992 to 1994.

## 3.2.3 (5) Soil Moisture

There are no observational network of soil moisture in Thailand. LDD (Land Development Department) is joining to the project of AARRP Phase II by BRRAA (see 3.2.2(2)), and they started a study on drought prediction. Soil moisture will be observed at 50 stations by 5 day intervals in this study. They say reliable data will be obtained at least 20 of the 50 stations.

Daily tensio-meter data, which can be converted to soil moisture, at 8 depths in a soil layer have been collected at two sites in Sri Lanka from 1992 to 1994.

## 3.2.3 (6) Soil Temperature

Soil temperature is measured at 0, 5, 10, 20, 50, 100 cm depth by 3 hourly intervals at more than 30 agrometeorological stations of TMD in Thailand. These data will be collected.

In Sri Lanka, 30 minutes interval data at 6 depths from March to October 1993 at University of Peradeniya have been collected.

## 3.2.3 (7) Topographic Map

Topographic map of 1:250,000 is available in Thailand, and digital elevation data will be ready to be used in 1km grid unit.

In Sri Lanka, two scales of topographic map, 1:50,000 and 1 mile-1 inch, cover whole the island and they have been collected.

## 3.2.3 (8) Geological Map

Geological map of 1:250,000 is available in Thailand, and these will be digitized.

In Sri Lanka, a geological map of 1:1,000,000 has been collected.

## 3.2.3 (9) Land cover

Land use map of 1:250,000 is available in Thailand, and these will be digitized. Land cover will be classified by satellite remotesensing using NOAA/AVHRR or MOS1/MESSR, too.

In Sri Lanka, land use map and soil map of 1:1,000,000 have been collected.

# 3.2.4 Organization and coordination system of the experiment

## 3.2.4 (1) Research Organization in Japan

Disaster Prevention Research Institute (DPRI), Kyoto University
: Macro scale hydrological modelling in ChaoPhraya river basin and GIS management of hydrological and meteorological data
Faculty of Agriculture, Kyoto University
: Observation of hydrological processes in plain area
Faculty of Agriculture, Tokyo University of Agriculture and Technology
: Observation of hydrological processes in forests and measurement of energy and water flux at paddy fields
Faculty of Agriculture, University of Tokyo
: Land-atmosphere interaction at forest area in Thailand and Malaysia
Faculty of Education, Fukushima University
: Meso-scale phenomena in tropics associated with precipitation
Faculty of Engineering, Yamanashi University
: Variation of hydrological variables in plain area and estimation of hydrological variables by satellite remotesensing
Faculty of Integrated Arts and Sciences, Hiroshima University
: Measurements of soil moisture in a large domain
Faculty of Science, Kyoto University
: Orographical effect on meso-scale disturbances in tropical atmosphere
Faculty of Science, Tokyo Metropolitan University
: Large scale water balance
Forestry and Forest Products Research Institute (FFPRI)
: Observation in forestry experimental basin in Malaysia
Graduate School of Science, University of Tokyo
: Climatological study on the time-space variation of precipitation
Hokkaido National Agricultural Experiment Station
: Longterm monitoring of energy and water flux
Institute for Hydrospheric Atmospheric Sciences (IHAS), Nagoya University
: Data archive of atmospheric observation
Institute of Engineering Mechanics, University of Tsukuba
: Satellite remotesensing of hydrological variables
Institute of Geoscience, University of Tsukuba
: Estimation of large scale fluxes using satellite remotesensing and measurement of energy and water fluxes in Sri Lanka
Institute of Industrial Science (IIS), University of Tokyo
: Research management of GAME-T, GIS/RS data-base development and management, and meso-scale meteorological modelling.
National Institute for Environmental Studies
: Vegetation map estimated from satellite remotesensing
National Research Institute for Earth Science and Disaster Prevention
: Hydrological cycle in meso-scale experimental river basin
National Space Development Agency of Japan
: Three dimensional structure of energy and water cycle in tropics and TRMM ground validation experiments
Numerical Prediction Division, Japan Meteorological Agency
: Four dimensional data assimilation
Public Works Research Institute, Ministry of Construction
: Soil moisture ground validation experiment using RADARSAT and water balance study at Luang river basin
Radio Atmospheric Science Center (RASC), Kyoto University
: Study on the atmospheric disturbances in tropical atmosphere

## 3.2.4 (2) Research Organization outside Japan

Bureau of the Royal Rainmaking and Agricultural Aviation (BRRAA), Thailand
: Rain enhancement research and rainfall data collection.
Center for Water Research (CWR), The University of Western Australia
: Macroscale hydrological modelling
Department of Geography, University of Peradeniya, Sri Lanka
: Observation and study on energy and water cycle in Sri Lanka
Department of Irrigation and Drainage (DID), Malaysia
: Operational observation of hydro-meteorological data
Department of Meteorology and Hydrology, Myanmar
: Operational observation of hydro-meteorological data
Electric Generation Authority of Thailand (EGAT), Thailand
: Operational observation of hydro-meteorological data
Forestry Research Institute Malaysia (FRIM), Malaysia
: Study on energy and water cycle in forestry experimental basin
Katsesaert University, Thailand
: Observation at hydrological experimental basin and macro scale hydrological modelling
Malaysia Meteorological Service (MMS), Malaysia
: Operational observation of hydro-meteorological data
Meteorological Department, Singapore
: Operational observation of hydrological and meteorological data
Nangyang Technological University, Singapore
: Data analysis and modelling of hydrometeorological observations
National Research Council of Thailand (NRCT), Thailand
: Research coordination and satellite remotesensing
Royal Forestry Department (RFD), Thailand
: Enhanced observation in forestry experimental basin and rainfall observation in mountainous areas
Royal Irrigation Department (RID), Thailand
: Operational observation of hydrological and meteorological data and intensive observation at plain area of the ChaoPhraya river basin
Thai Meteorological Department (TMD), Thailand
: Operational observation of meteorological data, receiving NOAA satellite AVHRR data and enhanced rawinsonde observation

## 3.2.4 (3) Role Sharing

1-a)
Longterm monitoring at plain area: RID
1-b)
Intensive observation at plain area: FA U of Kyoto, FIAS Hiroshima U
1-c)
Longterm monitoring at forest: RFD, Katsesaert U
1-d)
Intensive observation at forest: FA U of Tokyo, FA Tokyo U of A and T
1-e)
Enhanced rawinsonde observation: TMD, NASDA
1-f)
Analysis of rawinsonde data: IHAS Nagoya U, NASDA
1-g)
Observation at Sri Lanka : DG U of Peradeniya
2-a)
Receiving NOAA/AVHRR data: TMD
2-b)
Land cover and vegetation map: NRCT
2-c)
Receiving GMS/SVISSR data: IIS U of Tokyo
2-d)
Estimation of rainfall distribution: FE Yamanashi U
3-a)
Artificial influence on water cycle: IIS U of Tokyo
3-b)
Effect of deforestation on water cycle in forest area: Katsesaert U
3-c)
Scaling in hydrological modelling: CWR U of Western Australia, Nangyang U
3-d)
Evapotranspiration processes : FA U of Tokyo
3-e)
Macroscale hydrological modelling : DPRI Kyoto U
3-f)
Distribution of soil moisture : FE Yamanashi U
3-g)
Meso scale meteorological modelling : IIS U of Tokyo
4-a)
Operational hydro-meteorological observation: RID, RFD, TMD, EGAT, MMS, DID
4-b)
Integrated database using GIS: IIS U of Tokyo, IHAS Nagoya U, DPRI Kyoto U
4-c)
Longterm variation of water balance in Sri Lanka: IG U of Tsukuba

## 3.2.5 References

Brutsaert and Sugita (1992)
Brutsaert, W. and M. Sugita (1992). Application of self-preservation in the diurnal evolution of the surface energy budget to determine daily evaporation. J. Geophys. Res. 97-D17, 18377-18382.

Oki et al. (1995)
Oki, T., K. Musiake, H. Matsuyama, and K. Masuda (1995). Global atmospheric water balance and runoff from large river basins. Hydrol. Proces. 9, 655-678.

Sugita and Brutsaert (1991)
Sugita, M. and W. Brutsaert (1991). Regional surface fluxes from remotely sensed skin temperature and lower boundary layer measurements. Water Resour. Res. 27, 747-752.

# 3.2.6 Acronyms and Abbreviations

 4DDA Four-Dimensional (4D) Data Assimilation AAN Asian AWS Network (GAME) AARRP applied atmospheric resources research program (by BRRAA) ADEOS Advanced Earth Observing Satellite (Japan) AGCM Atmospheric General Circulation Model AVHRR Advanced Very High Resolution Radiometer AWS Automated Weather Station BRRAA Bureau of the Royal Rainmaking and Agricultural Aviation CANAE Code for Aggregating Nested Atmosphere and Environment CCSR Center for Climate System Research, Univ. of Tokyo (Japan) CWR Center for Water Research, The University of Western Australia DID Department of Irrigation and Drainage (Malaysia) DPRI Disaster Prevention Research Institute, Kyoto University EGAT Electric Generation Authority of Thailand FFPRI Forestry and Forest Production Research Institute (Japan) FRIM Forestry Research Institute Malaysia FY Fiscal Year GAME GEWEX Asian Monsoon Experiment GAME-T GAME Tropics GEWEX Global Energy and Water Cycle Experiment GIS Geographic Information System GMS Geostationary Meteorological Satellite (Japan) GTS Global Telecommunication System IHAS Institute for Hydrospheric Atmospheric Sciences, Nagoya University IIS Institute of Industrial Science, Univ. of Tokyo IOP Intensive Observing Period IR Infrared ISP International Science Panel J-SAR JERS-1 Synthetic Aperture Radar JERS-1 Japanese Earth Remote-sensing Satellite-1 JRDC Research Development Corporation of Japan LDD Land Development Department (Thailand) LST Local Standard Time MESSR Multispectral Electronic Self-Scanning Radiometer MMS Malaysia Meteorological Service MOS-1 Marine Observation Satellite (Japan) NASA National Aeronautics and Space Administration (USA) NASDA National Space Development Agency of Japan NIED National Institute for Earth Science and Disaster Prevention (Japan) NIES National Institute for Environmental Studies (Japan) NOAA National Oceanic and Atmospheric Administration (USA) NRCT National Research Council of Thailand PILPS Project for Intercomparison of Land-Surface Parameterization Schemes PPI Plan Position Indicator RADARSAT Canadian Synthetic Aperture Radar Satellite RFD Royal Forestry Department (Thailand) RID Royal Irrigation Department (Thailand) RS remote sensing S-VISSR Stretched Visible/Infrared Spin-Scan Radiometer (GMS) SAT sonic anemometer thermometer SOI Southern Oscillation Index TMD Thai Meteorological Department, Thailand TRMM Tropical Rainfall Measuring Mission (U.S.-Japan) UTC Universal Time Coordinate WSR88D NEXRAD Next Generation Weather Radar (also known as WSR88D)

# Figures

Figure 3.1: Inter annual variation of rainfall in Thailand from May to July and the Southern Oscillation Index (SOI).
| Color PDF || | B/W PDF

Figure 3.2: Illustration of flux observation stations in whole GAME-T. | Color PDF || | B/W PDF

Figure 3.3: Mean monthly rainfall in Chao Phraya river basin. Mean from 1951 to 1988 is averaged from 7 obsering stations by Thai Meteorological Department. Error bars indicate the standard deviation.
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Figure 3.4: Comprehensive illustration of planned observation in Thailand and Malaysia.

Figure 3.5: Scales of Hydrologic Modelling for Chao Phraya River Basin.

Figure 3.6: Corrected Monthly vapor flux convergence and discharge (mm/month) in Chao Phraya River.
| Color PDF || | B/W PDF

Figure 3.7: Large scale monthly evapotranspiration (mm/month) in Chao Phraya river. Comparison among estimations by the atmospheric water balance method using corrected vapor flux convergence and precipitation, by ECMWF model forecast, and by Penman equation.
| Color PDF || | B/W PDF

Monday, 24-Nov-2003 09:46:22 JST( by Taikan OKI )