Long-term Water Balance Analysis Using Different Precipitation Products In Upper Chao
Phraya River, Thailand
1

2

2

SHUNJI KOTSUKI , KENJI TANAKA , and DAISUKE KOMORI
1
Graduate School of Engineering, Kyoto University
2
Disaster Prevention Research Institute, Kyoto University
3
Department of Civil Environmental Engineering, Tohoku University
Intermittent heavy rain from June 2011 led to massive flooding in the Chao Phraya River basin. An understanding ofwater-balance characteristics in the upper basin is required to revise the master plan for management of the entire basin.
In this study, the authors have tried to figure out the water balance characteristics in upper Chao Phraya River in Thailand.
The authors have conducted hydrological land surface analysis using SiBUC[1] with different precipitation products, which
are GPCC, APHRODITE, GPCP, GSMaP MWR, and collected observed data (K12). The main findings were as follows:(1) More than 80 % of precipitation was lost through evapotranspiration in upper basin. Simulated evapotranspiration were
almost the same, suggesting that evapotranspiration in this basin strongly depends not on precipitation but on othermetrological forcing such as temperature and radiations. (2) While the simulated runoff using K12 corresponded well witha runoff record at the Nakhon Sawan station, simulate runoff using GPCC and APHRODITE have overestimation andunderestimation to the runoff record. Because runoff ratio in the upper basin is low, small differences in precipitation data
resulted in large differences in runoff. These findings demonstrate the importance of the quality of precipitation data for
accurate simulations of the water balance in the upper basin.

Figure 1. Mean annual (a) precipitation, (b) simulated evapotranspiration, (c) simulated runoff (bars) and runoff ratio(triangles) in the five catchments averaged from 1998 to 2006. Red, blue, green purple, orange, and black bars
respectively represent collected precipitation (K12), GPCC, APHRODITE, GPCP, GSMaP_MWR, and observed discharge.
Keywords: Chao phraya river; Precipitation; Water resources management; Hydrological land surface model;
Evapotranspiration

Acknowledgments
The authors wish to thank the Royal Irrigation Department, Electricity Generating Authority and the Thai Meteorological
Department in Thailand for providing their valuable data sets. This work has been supported by a research project of the
JICA entitled “(IMPAC-T)” and the Japan Society for the Promotion of Science.

References[1] Tanaka, K (2004), Development of the New Land Surface Scheme SiBUC Commonly Applicable to Basin Water
Management and Numerical Weather Prediction Model. Doctoral Dissertation, Graduate School of Engineering,
Kyoto University (Kyoto), pp.289.

Long-term Water Balance Analysis Using Different Precipitation Products in Upper Chao
Phraya River, Thailand
SHUNJI KOTSUKI1 and KENJI TANAKA2
Graduate School of Engineering, Kyoto University
2
Disaster Prevention Research Institute, Kyoto University
1

Abstract
Intermittent heavy rain from June 2011 led to massive flooding in the Chao Phraya River basin. An understanding ofwater-balance characteristics in the upper basin is required to revise the master plan for management of the entire basin.
In this study, the authors have tried to figure out the water balance characteristics in upper Chao Phraya River in Thailand.
The authors have conducted hydrological land surface analysis using SiBUC with different precipitation products. The
main findings were as follows: (1) While precipitation has large difference between products, simulated evapotranspiration
using the products has small difference. Most of difference in precipitation translates to difference in runoff. Any error in
the precipitation translates to approximately the same absolute error in runoff over the Eastern Asia. (2) More than 80 % of
precipitation is lost through evapotranspiration in upper basin. Simulated evapotranspiration were almost the same,
suggesting that evapotranspiration in this basin strongly depends not on precipitation but on other metrological forcing
such as temperature and radiations. (3) While the simulated runoff using our collected precipitation corresponds well witha runoff record at the Nakhon Sawan station, simulate runoff using the APHRODITE product has underestimation to therunoff record. Because runoff ratio in the upper basin is low, small differences in precipitation data results in large
differences in runoff. These findings demonstrate uncertainties in precipitation have large impact on runoff simulation in
the upper Chao Phraya River basin.

Introduction
Intermittent heavy rain from June 2011 led to massive flooding in the Chao Phraya River basin, Thailand. The World Bank
estimates that the total economic damage and losses from the floods as of December 1, 2011, stands at 1.425 trillion Baht
(45.7 billion US$) [1]. These damages have affected not only Thailand but also industries in other countries such as Japan,
as has been widely reported in the press. Komori et al. reported using 15 weather stations that the total rainfall during the2011 rainy season was 1.439 mm, which was 143% of the average rainy season rainfall during the period from 1982 to
2002 [2]. After the massive flooding in 2011, The Thai government intends to revise the master plan (prepared in 1999) for
management of the entire river basin. Since the area of the upper basin is 68 % of the total basin, runoff from the upperbasin can cause flooding at the lower basin. An understanding of the water-balance characteristics in the upper basin isrequired if this revision is to be successful. The Japan International Cooperation Agency (JICA) launched a flood-control
project following the massive flooding. We joined the project through the Integrated Study on Hydro-Meteorological
Prediction and Adaptation to Climate Change in Thailand (IMPAC-T) project. The aim of the study was to determine thewater-balance characteristics in the upper Chao Phraya River basin using a hydrological model.
2

Figure 1(a) shows a diagram of the Chao Phraya River watershed, whose total area is about 160,000 km . The basin isdivided into an upper and a lower watershed by the narrow section at Nakhon Sawan. The area of the upper Nakhon
2
Sawan catchment is about 110,000 km , which is 68% of the total basin. The Ping, Wang, Yom, and Nan Rivers flow downfrom the northern mountainous area and merge at Nakhow Sawan. For the purposes of irrigation, power generation, and3flood control, the Bhumibol Dam (reservoir capacity 13.5 billion m ) was constructed on the Ping River, and the Sirikit Dam
3(reservoir capacity 9.5 billion m ) was constructed on the Nan River. Due to the Asian summer monsoon, rainfall
distribution over the basin differs distinctly between the rainy (May–October) and dry (November–April) seasons. Mean
rainfall during the rainy season accounts for about 90% of mean annual rainfall in the basin.

Figure 1. (a) Diagram of the Chao Phraya River watershed, and (b) mean annual precipitation of the six products from1998 to 2006. (b-1: APHRODITE, b-2: GPCC, b-3: H08, b-4: GPCP, b-5: GSMaP, and b-6: JRA25)
Many hydrological studies have been undertaken in the Chao Phraya River basin (e.g., Hanasaki et al.[3]). Additionally,
estimates of the impacts of climate change have been made using the outputs from global climate models (e.g. Kure et al.
[4]). However, few studies have reported the water-balance characteristics in the basin (e.g. Kotsuki and Tanaka [5,6]).
The main objective of this study was to determine the water-balance characteristics in the upper Chao Phraya River basinthrough runoff simulations. Precipitation is one of the most important climate variables for runoff simulation since it is the
predominant and ultimate source of water for the land surface water budget (Fekete and Vorosmarty [7]). Figure 1(b)shows the mean annual precipitation of the six precipitation products from 1998 to 2006 over Eastern Asia. Here, The
Global Precipitation Climatology Centre (GPCC; Rudolf et al. [8]), APHRODITE (Yatagai et al. [9]), and the H08 product(Hirabayashi et al. [10]) are produced by observed precipitation on the land surface. The Global Precipitation Climatology
Project (GPCP; Huffman et al. [11]) is the data derived from satellite and surface measurements. The Global Satellite
Mapping of Precipitation (GSMaP; Ushio et al. [12]) is produced by satellite observation data. JRA25 is the reanalyzed
precipitation data from a global climate model (Onogi et al. [13]). While the spatial distributions of precipitation coincidewith each other, there are differences in the amount of precipitation. It is important to understand the accuracy of a product,
the difference between precipitation products, and the impact that these differences in the amount of precipitation have on
simulated runoff.
We have conducted simulations using a hydrological model comprising a land-surface process and a river-routing process.
This paper describes the methodology used to conduct the simulations and the results of the model application to theupper Chao Phraya River basin. Through the simulations using different precipitation products, this paper reveals therunoff characteristics of the basin.

Methodology
Hydrological Model
A long-term water-balance analysis was conducted using the distributed hydrological model. The land-surface process
calculates the energy, radiation and water budgets on the land surface with seven meteorological forcings: temperature,
specific humidity, short-wave radiation, long-wave radiation, atmospheric pressure, wind speed, and precipitation. In thisstudy, the Simple Biosphere model including Urban Canopy (SiBUC; Tanaka, 2004 [14]) was used to calculate the landsurface process. Differences in land cover have a strong effect on energy, radiation, and water budgets. SiBUC uses themosaic approach to reflect land-cover mixtures. Because all model parameters can be determined with land-surface
products, the model does not require any parameter calibrations, normally. The river-routing process calculates the rivers
discharge in the channel using the kinematic-wave method, which uses the surface and sub-surface runoff data from theland-surface process.

- Data Sets
The hydrological simulations were conducted using different six precipitation data sets (APHRODITE, GPCC, H08, GPCP,
GPMaP, and JRA25). We also conducted the simulation using observed precipitation data. Precipitation data were
provided by the Royal Irrigation Department (RID) and the Thai Meteorological Department (TMD) through two projects:
IMPAC-T and GAME-T2 Data Center (GAME-T2). The number of precipitation stations per 1000 km2 of APHRODITE data
and the observed precipitation in each catchment are as shown in Table1c and 1d. Monthly-averaged diurnal variation of asatellite mapping of precipitation (GSMaP-TMI; Ushio et al. [12]) from 1998 to 2006 was used for each precipitation data.
Note that the intent of two simulations is not to discuss qualities of precipitation products, but to discuss the influence onrunoff of the small differences in precipitation data. Short-wave radiation, long-wave radiation, specific radiation, andtemperature data were recorded with the H08 product. Wind speed and atmospheric pressure were determined from
JRA25 reanalyzed data. Original meteorological data was used to change the resolution for the model mesh size using aninterpolating method.
Different land-cover fractions were determined from GLCC ver2.0 provided by USGS (US Geological Survey). Soilphysical parameters were identified using Ecoclimap provided by Meteo-France. A flow-direction map of the river was
produced using the steepest-slope method, which determines the flow direction of each mesh as the steepest slope
among four neighboring meshes. SRTM 90-m digital elevation data, which was provided by the Consultative Group on
International Agricultural Research (CGIAR), was used to determine the altitude used to determine flow directions.
Calculated flow-accumulation areas for the reported catchment area at five river stations are shown in Table 1a.
Catchments in the Chao Phraya River
Simulated results were produced for five river stations: the Bhumibol Dam for the Ping River, Ban Wang Man (W.4A) for
the Wang River, Ban Kaeng Luang (Y.6) for the Yom River, the Sirikit Dam for the Nan River, and Nakhon Sawan (C.2) forthe entire upper basin. River runoff records from 1981 to 2004 were collected at three stations: C.2, Y.6, and W.4A. These
runoff data were provided by RID and were obtained from the GAME-T2 Data Center. Inflow, release, pumped discharge,
and evapotranspiration data for the Bhumibol Dam and the Sirikit Dam were provided by the Electricity Generating
Authority of Thailand (EGAT).

Result and Discussion
Figure 2 shows difference between results using APHRODITE and other precipitation products in mean annual
precipitation, simulated evapotranspiration, and simulated runoff from 1998 to 2006 over Eastern Asia. Cold and warm
colors mean results using APHRODITE are lower and higher than results using other products. Large differences inprecipitation are shown in Figure 2a in Myanmar, Malay Peninsula, and Sumatera Islands. Although precipitation has large
difference as shown in Figure 2a, simulated evapotranspiration has small difference as shown in Figure 2b. Most ofdifference in precipitation translates to difference in runoff shown in Figure 2c. As Fekete et al. [7] pointed out, any error in
the precipitation translates to approximately the same absolute error in runoff in wet region, where the precipitation always
exceeds the potential evaporation.
Two hydrological simulations were conducted for a 24-year period using data from APHRODITE and the observed
precipitation in the Chao Phraya River basin. Figure 3 shows comparisons of monthly runoff from 1995 to 2004 at the C.2
station. Black, blue, and red lines represent the observed natural runoff, simulated runoff using APHRODITE data, andsimulated runoff using observed precipitation, respectively. Because our model did not include dam effects, the observed
runoff at the C.2 station were naturalized to remove the effects of the upper Bhumibol Dam and Sirikit Dam as follows:

N D C 2 = O D C 2 + [ I n f + P u m p − O u f ]B B , S K(1)

where ND is the naturalized runoff (m3s-1), OD is the observed runoff (m3s-1), Inf is the inflow runoff at dams (m3s-1), Pump
is the water pumped into dams (m3s-1), and Ouf is the discharge released from dams (m3s-1). The released discharge
includes water released from spillways and irrigation gates. Subscripted BB and SK mean the Bhumibol Dam and the
Sirikit Dam, respectively.

disc [m3/sec]

Figure 2. Difference in mean annual precipitation (a-1, a-2, a-3, a-4, a-5), simulated evapotranspiration (b-1, b-2, b-3, b4, b-5), and simulated runoff (c-1, c-2, c-3, c-4, c-5) from 1998 to 2006. Units of the figure are mm‧yr-1.The results
using GPCC minus that of APHRODITE are shown in a-1, b1, and c-1. The results using H08 minus that of
APHRODITE are shown in a-2, b-2, and c-2. The results using GPCP minus that of APHRODITE are shown in a-3, b-3,
and c-3. The results using GSMaP minus that of APHRODITE are shown in a-4, b-4, and c-4. The results using JRA25
minus that of APHRODITE are shown in a-5, b-5, and c-5. Cold color means precipitation, evapotranspiration, andrunoff using APHRODITE are lower than those of using other precipitation products.

6000 －obs －aphro －obsrain
4000
2000
0
95

96

97

98

99

C.2

00

01

02

03

04

3 -1

Figure 3. Comparison of monthly runoff from 1995 to 2004 at the Nakhon Sawan [m s ]. Black, blue, and red lines
represent observation, simulation using APHRODITE, and simulation using observed precipitation, respectively.
Figure 3 shows that the simulated runoff using the observed precipitation corresponded well to the naturalized runoff
compared with the simulated runoff using APHRODITE data. The Nash–Sutcliffe model efficiency coefficient, using 24years of monthly runoffs, was 0.70 for results using the observed precipitation and 0.47 for results using APHRODITE data.
These results were obtained without any parameter calibrations, as previously mentioned.
Table 1 presents the water-balance characteristics of the five upper catchments. Table 1b shows the observed averagerunoff during the 24-year study period. Table 1c and 1d shows simulated results over the same period using APHRODITE
data and the observed precipitation, respectively. Stat, Prec, Roff, and Rate refer to the number of precipitation stations2
per 1000 km , precipitation, runoff and runoff rate, respectively. Table 1c and 1d shows that more than 80% of the
precipitation was lost through evapotranspiration in the basin. Therefore the runoff ratios in both simulations were low.
Simulated runoff ratios using APHRODITE data were 0.10–0.11, whereas those calculated using the observed
precipitation were 0.15–0.18. In the five catchment areas, the average observed annual precipitation exceeded the values
from APHRODITE data by about 10%. It is considered that small difference in used precipitation stations of each productcauses the differences in annual precipitation. Because runoff ratios in the upper basins were low, a 10% difference in the
precipitation data resulted in 50–100% differences in runoff ratios. It is clear that the quality of the precipitation data is very
important for the simulation of water balance in the upper Chao Phraya River basin.

Table 1. Water-balance chracteristics of the upper catchments. Table (a) shows reported catchement areas and areas in2
the model [km ]. Table (b) shows the observed average runoff during the 24-year study period. Table (c) and (d) shows
simulated results using APHRODITE data and the observed precipitation. Stat, Prec, Roff and Rate refer to the number of
2-1-1
precipitation station per 1000 km , precipitation [mm･yr ], runoff [mm･yr ] and runoff rate[-].

Station(a) Catchment(b) Obs.

(c) Sim. (APHRODITE)(d) Sim. (observed prec.)Report

Model

Roff

Stat

Prec

Roff

Rate

Stat

Prec

Roff

Rate

C.2

110,569

110,070

195

1.18

1005

109

0.11

1.37

1144

192

0.17

Bhumibol Dam

26,40027,951

190

1.17

989

105

0.11

1.69

1101

169

0.15

Sirikit Dam

13,13014,439

415

0.69

1086

109

0.10

0.84

1246

212

0.17

Y.6

12,65813,786

200

1.17

992

106

0.11

1.07

1164

200

0.17

W.4A

10,507

10,677

89

1.58

935

97

0.10

1.69

1101

182

0.17

Figure 4. Comparison of annual runoff during the 24- years study period at (a) the C.2, (b) the Bhumobol Dam, (c) the
Sirikit Dam, (d) the Y.6, and (e) the W.4A [MCM･yr-1]. Black and red lines represent observed runoff and simulated runoff
using observed precipitation, respectively.
Figure 4 shows a comparison of annual runoff during the 24-year study period at (a) C.2, (b) the Bhumobol Dam, (c) the
Sirikit Dam, (d) Y.6, and (e) W.4A. Black and red lines represent observed runoff and simulated runoff using the observed
precipitation, respectively. It can be seen that simulated runoff using the observed precipitation corresponds well to theobserved runoff at C.2, the Bhumibol Dam, and Y.6. However, the simulated runoff was overestimated at W.4A and wasunderestimated at the Sirikit Dam. Because the simulated runoff rates in the five catchments were almost the same, thecause of the underestimation at the Sirikit Dam and the overestimation at W.4A was not due to the operation of thehydrological model. The overestimation at W.4A was considered to be caused by inundations in the lower Wang River.
Field interviews revealed that the W.4A station is located on a flood plain, which is inundated more than once a year. The
authors presume that the accuracy of precipitation data is responsible for the underestimation in the Nan River basin. As
mentioned previously, runoff in the upper Chao Phraya River basin is heavily dependent on the amount of precipitation.
Precipitation observation stations have now been installed through the IMPAC-T project in the Khuwaenoi basin, which islower part of Nan River basin. Although the Khuwaenoi basin is not included the Sirikit Dam catchment, the installations
are expected to lead to a better understanding of the Nan River basin.

Summary
In this study, the authors have conducted hydrological simulations using different six precipitation data. The long-termwater balance analysis has been performed using APHRODITE and the observed precipitation from 1981 to 2004 in theupper Chao Phraya River basin. The main findings are as follows:z Although precipitation has large difference between products, simulated evapotranspiration using products has
small difference. Most of difference in precipitation translates to difference in runoff. Any error in the precipitationtranslates to approximately the same absolute error in runoff over the Eastern Asia.\z Simulated runoff using the observed precipitation corresponded well with a naturalized runoff at the C.2 stationcompared with the runoff simulated using APHRODITE data. More than 80% of precipitation was lost throughevapotranspiration in the upper basin. Because runoff ratios in the upper basins were low, a 10% differences inprecipitation data resulted in a 50–100% difference in the runoff ratio.
z Although small differences were found between the APHRODITE data and the observed precipitation during therainy season, there were almost no differences between computed evapotranspiration using APHRODITE andobserved precipitation data. This suggests that the evapotranspiration in the rainy season depends not on
precipitation but on other meteorological parameters such as temperature and radiation.
These findings demonstrate uncertainties in precipitation have large impact on runoff simulation in the upper Chao Phraya
River basin.

Acknowledgments
The authors wish to thank the Royal Irrigation Department of Thailand, Electricity Generating Authority of Thailand and theThai Meteorological Department for providing their valuable data sets. This work has been supported by a research projectof the JICA entitled “Integrated study on hydroMeteorological Prediction and Adaptation to Climate change in Thailand(IMPAC-T)” and the Japan Society for the Promotion of Science.

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Hydrological Research Letters, 6, 41-46.
[3] Hanasaki N, Kanae S, Oki T, Musiake K. (2003), Simulating the discharge of the Chao Phraya River taking into
account reservoir operation. IAHS Publication, 281, 215-223.
[4] Kure S, and Tebakari T. (2012), Hydrological impact of regional climate change in the Chao Phraya River Basin,
Thailand. Hydrological Research Letters, 6, 53-58.
[5] Kotsuki S. and Tanaka K. (2013a), Impacts of Mid-Rainy Season Rainfall on Runoff into the Chao Phraya River,
Thailand. Journal of Disaster Research, 8-3, 397-405.
[6] Kotsuki S. and Tanaka K. (2013b), Uncertainties of precipitation products and their impacts on runoff estimates
through hydrological land surface simulation in Southeast Asian region. Hydrological Research Letters. (revision)[7] Fekete B. M. and Vorosmarty C. J. (2004), Uncertainties in precipitation and their impacts on runoff estimates.
Journal of Climate, 17, 294-304.
[8] Rudolf B, Andreas B, Udo S, Anja MC, Markus Z. (2010), GPCC status Report December 2010.
http://gpcc.dwd.de. Last access Mar 20, 2013.
[9] Yatagai A, Kamiguchi K, Arakawa O, Hamada A, Yasutomi N, Kitoh A. (2012), APHRODITE: Constructing a Longterm Daily Gridded Precipitation Dataset for Asia based on a Dense Network of Rain Gauges, Bulletin of American
Meteorological Society, 93, 1401-1415.
[10] Hirabayashi Y, Kanae S, Motoya K, Masuda K, Doll P. (2008), A 59-year (1948-2006) global near-surface
meteorological data set for land surface models. Hydrological Research Letters, 2, 36-40.
[11] Huffman G. J, Bolvin D T. (2013), Version 1.2 GPCP One-Degree Daily Precipitation Data Set Documentation.
http://precip.gsfc.nasa.gov. Last access Mar 20, 2013[12] Ushio T and Coauthors. (2009), A Kalman filter approach to the Global Satellite Mapping of Precipitation (GSMaP)
from combined passive microwave and infrared radiometric data. Journal of the Meteorological Society of Japan,
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[13] Onogi K. and Coauthors. (2007), The JRA-25 reanalysis. Journal of the Meteorological Society of Japan, 85, 369432.
[14] Tanaka K. (2004), Development of the New Land Surface Scheme SiBUC Commonly Applicable to Basin Water
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Kyoto University: Kyoto; 289