2012 AIT-NUS-ITB-KU JOINT SYMPOSIUM ON HUMAN SECURITY ENGINEERING
Bangkok, Thailand, November 19-20, 2012

Rainfall-Runoff Simulation for the 2011 Floodin Thailand and Water Resources Prediction
Research under a Changing Climate
Yasuto TACHIKAWA1, Supattana WICHAKUL2 and Kazuaki YOROZU3
1Associate Professor, Department of Civil and Earth Resources Engineering, Kyoto University(C1-116, Kyoto University Katsura Campus, Nishikyo-ku, Kyoto 615-8540, Japan)
E-mail: tachikawa@hywr.kuciv.kyoto-u.ac.jp
2Doctoral Student, Department of Civil and Earth Resources Engineering, Kyoto University(C1-116, Kyoto University Katsura Campus, Nishikyo-ku, Kyoto 615-8540, Japan)
E-mail: supattana@hywr.kuciv.kyoto-u.ac.jp
3Assistant Professor, Department of Civil and Earth Resources Engineering, Kyoto University(C1-116, Kyoto University Katsura Campus, Nishikyo-ku, Kyoto 615-8540, Japan)
E-mail: yorozu@hywr.kuciv.kyoto-u.ac.jp

The Thailand's Great Flood in 2011 resulted in the great calamity causing tremendous losses impacting
livelihood, social, and economics of the nation. A better understanding of the basin hydrological processes
is necessary for studying and predicting a future flood. Consequently, this study aims to develop a regional
distributed hydrological model for water resources situation prediction. The regional hydrologic model wascomposed of a runoff generation model with a concept of the variable infiltration capacity and a flowrouting model using the Kinematic Wave equation. The effects of dam control were also included in theflow routing model. By using the model, the effect of the existing dams operations is numerically evaluated.
The impact of climate change on river flow in Thailand was also analyzed by feeding future climate projection data into a distributed rainfall-runoff model. The projection data used consists of daily hydrologic
data downscaled by hourly precipitation for the present climate (1979-2003), the near future climate(2015-2039), and the future climate (2075-2099), which were simulated by a 20km spatial resolution
general circulation model (MRI-AGCM3.1S) developed by the Meteorological Research Institute, Japan
Meteorological Agency. It was found that a significant change of river flow appeared at the Pasak Riverbasin which is a tributariy of the Chao Phraya River. Then, a detail distributed hydrologic model was applied to the Pasak River basin to analyze the water storage of the Pasak Dam to access the reservoir could
satisfy the water demand under the future climate change scenario. It was suggested that water storage at
the Pasak Dam will not be able to meet the current water demand under the climate change scenario.
Key Words : Thailand, Chao Phraya River, Pasak River, water resources, climate change, river flow

average surface temperatures, and potential increaseof frequency of heavy rainfall, and so on based onlong term observations. The report also shows the
projections of climate change according to several
greenhouse gas emission scenarios and the impactsof climate change on water-related disasters andwater resources.
To cope with water-related disasters induced by

1. INTRODUCTION
Global warming will give us a serious impact on
our life. Frequencies and magnitudes of floods andsedimentation disasters are predicted to increase dueto the change of precipitation extremes. The IPCC,
the Intergovernmental Panel on Climate Change, 4thassessment report1),2) describes increase of global
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type relation of the discharge and the cross-sectionalarea:

climate change, both mitigation measures and adaptation measures are essential. For adaptation
measures, prediction of future water resources is a
key issue. The Chao Phraya River basin was found to
be one of the most affected flood prone basins inThailand in the near future and future climate3). To
project future river discharge using various GCM
outputs, we developed a distributed routing modelfor the entire Chao Phraya River basin, and foundthat a change of flow happened at the tributaries of
the Chao Phraya River and especially decrease on
October discharge at the Pasak River basin4). Thus,
we developed a water resources assessment model at
the Pasak River basin, which includes a detailed
distributed hydrologic model and a dam reservoir
water storage prediction model. An analysis showedthat water storage at the Pasak Dam will not be able
to meet the current water demand under a climatechange scenario. The distributed routing model isalso extended to a regional distributed hydrologic
model, which was confirmed to predict the Thailand’s Great Flood in 2011.

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5/3

(2)
is combined with the continuity equation (1) to routethe water, where i0 is slope; n is roughness coefficient; and B is width of the flow. The slope i0 is determined according to the watershed model. The
model parameters of the flow model are B and n. The
value of B is determined using the regression relationship B=aSc , where S is the catchment area, and a
and c are constant parameters. The value of n is determined to 0.03m-1/3s when the size of the catchment
is larger than 250km2 and 11.0m-1/3s when smaller
than 250km2. These values were tuned to reproduce
the observed data at two different catchments andapplied to all basins. Hereafter, we refer to the 1km
distributed flow routing model as 1K-FRM(http://hywr.kuciv.kyoto-u.ac.jp/products/1K-DHM/
1K-DHM.html).
/

2. DISTRIBUTED ROUTING MODEL(1) Watershed model
The topography of the catchment is modeled using
the eight direction method which assumes the flow
direction one-dimensionally to the steepest gradientdirection illustrated in Fig. 1. Each slope element
determined by the flow direction is represented by arectangle formed by the two adjacent nodes of gridcells. The watershed model is developed using the
1km resolution digital elevation model, DEM, included in HydroSHED5), which cover the globe with
about 100m spatial resolution. Figure 2 shows thedelineated catchments of the Chao Phraya Riverbasin (CPRB, 160,400km2) and the Pasak River basin (16,291km2) in Thailand using the channel network data in HydroSHED. The area, length, andgradient of each rectangular slope element used for
runoff and channel routing are calculated according
to the watershed model.

Fig. 1 Schematic drawing of a catchment modelling using
DEMs.

(2) Flow model
The kinematic wave model is applied to all rectangular slope and river elements to route the water todownstream according to the derived watershed
model. The continuity equation for each rectangularslope element is:(1)
where t is time; x is distance; A is cross-sectional
area; Q is discharge; and q(t) is the lateral inflow perunit length of slope or channel given as runoff generation provided by MRI-AGCM3.1S. The Manning

Fig. 2 Channel network data in HydroSHED for the Chao
Phraya River (red) and the Pasak River (blue).
2Based on the function of the infiltration capacity,
surface runoff, subsurface runoff and groundwaterrunoff are calculated for each grid cell and total
runoff for each grid cell is given to the flow routing
model 1K-FRM. The model was applied for the
CPRB at the 1/4 degree resolution and the model
represents about 560 (20 columns and 28 rows)
computational grid cells covering the basin, and 1-hrtime step of the calculation.

3. REGIONAL DISTRIBUTED HYDROLOGIC MODEL
The distributed routing model is extended to aregional distributed hydrologic model, which was
confirmed to predict the Thailand’s Great Flood in
2011. The regional distributed hydrological model
consists of a hydrologic model and a flow routing
model (1K-FRM). Both hydrologic and flow routing
models were founded as a grid-based model. In orderto reproduce the realistic runoff situation in the
CPRB, a dam operation model was combined in theflow routing model.

(2) Flow Routing Model
The distributed routing model 1K-FRM is used
for river routing. The quadratic function was applied
to flooded area where cross-section of the river was
accordingly changed with the over bank flow. The
criterion to distinguish the type of the cross section isset by the number of upstream grids. When thenumber is larger than 35,000 (about 35,000 km2) the
quadratic cross section is adopted for representing
the inundated areas. The 1K-FRM parameters are n,
B and a. In this study, we used the values of n and B
same as the original model, n = 0.03 m-1/3s and 11.0
m-1/3s for channel and slope flow, respectively. These
two values were determined and used in the Japanesecatchment4). To reproduce the inundation phenomenaof the flood 2011 in Thailand, we assumed thequadratic cross section shape to reproduce a flood
discharge properly.

(1) Hydrological Model
To develop a hydrologic model to generate surface and subsurface runoff6),7), we simplified the
Xinanjiang (XNJ) model8) by reducing the number ofparameters, and modifying sub-layers in the model
for surface and subsurface runoff generations. Additionally, the concept of the modified XNJ model, thetension water storage variation and aquifer condition
proposed by Nirupama et al.9) were adapted in thisstudy.
Based on assumption that infiltration capacities
over the study area vary due to variations in topography, soil, and land cover (refer to Fig. 3), the infiltration capacity i overs an area can be represented as
the following equation,(3) Dam Operation Model
As mentioned in the study area, flow in the Chao
Phraya River is significantly influenced by the dams
operation. Therefore, dam operation models for the
Bhumibol dam and Sirikit dam were embedded intosome particular grids of 1K-FRM where the dams
locate. An algorithm to develop a general reservoir
operating rules is a flexible function that can be adjusted for different dam features.
The kinds of information, which are required for
input to the dam operation model, are spillway capacity, downstream requirement, active storage,
min/max storage, and upper/lower rule curves. The
monthly operation basis of the dam model is to store
water in wet season (May-Dec.) and to release waterin dry season (Jan-Apr.). Finally, the overall
framework of the distributed hydrological model toachieve the simulated discharge in the river at eachfocused point can be schematized as in Fig.4.

where im represents the maximum infiltration capacity, A is the fraction of the cell area for the infiltration
capacity and takes values between 0 and 1, Ai is theimpervious portion of the cell, and b is an empiricalparameter showing a shape of the storage water capacity curve.

(4) Simulation Result
Figure 5 shows a spatially distributed river flow
discharge at CPRB on Oct. 14, 2011 using the calibrated regional distributed model. Figure 6 shows asimulated discharge at C.2 station. The model well
reproduces time and space distribution of river discharge.

Fig.3 The distribution of runoff and infiltration as a function ofgrid wetness and infiltration capacity.
3Input Data : Rainfall Data + Evapotranspiration

Hydrologic Model :Simplified XNJ Model with variableinfiltration capacity concept

Re se r vo ir Ope r at io n
M o de l :Bhumibol Dam and Sirikit Dam

Flow Routin g Model(1K-FRM)

Disch arge Prediction

Fig. 4 Framework of the distributed hydrological model.
Fig.6 Comparison of Simulated and Observed Daily Discharge at
C.2 Station.

Ping

Yom NanWang

The river discharge for the Chao Phraya Riverbasins is predicted by feeding the future climate
projection data into the 1K-FRM. The hydrologic
projection variables related to river discharge isshown in Figure 7. The inputted data to the distributed hydrologic model is daily surface runoff generation and daily sub-surface runoff generation data,
which are simulated by the land-surface processmodel embedded in the MRI-AGCM3.1S.
The time-scale of daily runoff generation data is
insufficient to reproduce the hourly flood peak discharge, thus it was downscaled using the time-seriesof hourly precipitation data of MRI-AGCM3.1S to
add the same hourly distribution pattern into the daily
surface runoff generation data. We confirmed thatriver discharge simulation with the timely
downscaled surface runoff generation and daily
subsurface runoff generation data successfully reproduced almost similar river discharge simulated byusing hourly precipitation, daily snowmelt, daily
evaporation and daily transpiraion12). Thus, thetimely downscaled surface runoff generation data
and daily subsurface runoff generation data wereused as inputted data to the distributed hydrologic
model.

C.2
Discharge in m3/s

Fig.5 A spatial distribution of estimated discharge on Oct 14, 2011.

4. IMPACT OF CLIMATE CHANGE ON
RIVER FLOW REGIME(1) GCM DATA USED FOR RIVER FLOWPROJECTION
The flow model was applied to all catchments and75 years runoff simulations were conducted. The
simulated river discharge data of daily maximum anddaily mean were stored for each day with about 1km
spatial resolution. The projection data used here issimulated by the general circulation model(MRI-AGCM3.1S) developed by the MeteorologicalResearch Institute in Japan. MRI-AGCM3.1S realizes 1920×960 of grid cells of about 20 km spatial
resolutions10),11). The products of MRI-AGCM3.1S
consists of various atmospheric and hydrologic variables of the present climate experiment(1979-2003), the near future climate experiment(2015-2039), and the future climate experiment(2075-2099), which were simulated under the SRESA1B scenario.

Fig. 7 Hydrologic projection data provided by MRI-AM20km
used for river discharge simulation.

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(2) IMPACT OF CLIMATE CHANGE ON
RIVER FLOW REGIME

5. WATER RESOURSES ASSESSMENT
MODEL AT THE PASAK RIVER BASIN

Runoff simulations of 75 years for the present
climate experiment, the near future climate experiment, and the future climate experimen were conducted for the area covering the entire Chao Phraya
River basin. Runoff simulation data of hourly
maximum and daily mean are stored for each daywith about 1km spatial resolution. The simulated
discharge data were analyzed to discuss the changeof the flood risk and water resources.
It was found that generally, the annual maximum
discharge of the main stream of the Chao Phraya
River does not change; however the one of the tributaries changes location to location4). Especially, we
found that the monthly discharge on October decreases at the Pasak River basin.
Figure 8 shows the last 10% daily flow duration
curve for each 25 years climate experiment at thelower reach of the Pasak River shown in Figure 1.
The figure shows the decrease tendency of the lowdischarge at the area. Figure 9 shows the projected
monthly discharge at the lower reach of the Pasak
River. The figure shows the clear pattern of the decrease of the October discharge.

Through the river discharge projection of the entire Chao Phraya River, it was revealed that the Pasak
River basin (Fig. 10) would be one of the hotspot
basins for the river discharge change. Thus, we focus
the catchment and developed a water resources assessment model to analyze the storage of the Pasak
Dam reservoir could satisfy the water demand under
the future climate change scenario13).
Figures 11 and 12 show the change of annual
precipitation and evapotranspiration simulated by
MRI-AGCM3.1S. Precipitation change is not significant but evapotranspiration clearly shows an increase trend because of temperature increase. Figures
13 to 15 show 25 years river flow simulation resultsof the inflow to the Pasak Dam reservoir for thecurrent, near future and future climate experiment byour detailed distributed hydrologic model.

Fig. 8 Change of the ratio of the mean October river discharge inthe future climate experiment with respect to the present climate
experiment.

Fig. 10 Detail topographic modeling using HydroSHED for a
distributed hydrologic model at the Pasak River basin.

Fig. 9 Change of the monthly discharge at the lower reach of the
Pasak River.
Fig. 11 Annual precipitation simulate by MRI-AGCM3.1S.

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Fig.12 Annual evapotranspiration simulate by MRI-AGCM3.1S.
Fig. 16 25 years river flow simulation results of the inflow to the
Pasak Dam reservoir for the current climate experiment.

Figure 16 shows the mean and standard deviationof simulated discharge for each climate experiment.
It is found that the inflow from June to mid-July of
the future experiment decreases about 20%, while theinflow from the mid-July to August increases and theone in October decreases much.
Using the simulated inflow, we investigated thechange of the Pasak dam reservoir storage. To consider the bias of the GCM data, two methods wereadopted. The first method is to obtain the ratio of themonthly inflow of the current climate experiment andthe future climate experiment for each month as:

Fig. 13 25 years river flow simulation of the inflow to the Pasak
Dam reservoir for the current climate experiment (1979-2003).

,,, / , ,

, / , ,

1, 12
1, 12

where , , , , and , are mean monthly inflow ofeach 25 years climate experiment. Then, the ratios
are multiplied to each month of the observed monthly
inflow as:
1, 12
,
,
, ,
,
1,
12
,
,
,

Fig. 14 25 years river flow simulation of the inflow to the Pasak
Dam reservoir for the near future experiment (2015-2039).

where , and , are modified monthly inflow data
for the near future and future climate experiment.
These inflow, mean monthly observed outflow andmean monthly evapotranspiration loss estimated by
the observed inflow and outflow were used to estimate the change of the dam storage. The simulation
was conducted for about 10 years data after the Pasak
dam completion of construction in 1999.
Figure 17 shows the change of the simulated
storage. The current climate experiment satisfies therule curve determined at the Pasak dam reservoir.
However, the simulated storage using the near andfuture climate experiments decreases year to year andafter 2 years and 7 months, the storage becomes zero
if the outflow pattern is the same.

Fig. 15 25 years river flow simulation of the inflow to the Pasak
Dam reservoir for the future climate experiment(2075-2099).

66. SUMARRYThe Thailand's Great Flood in 2011 resulted in
the great calamity causing tremendous losses impacting livelihood, social, and economics of the nation. A better understanding of the basin hydrologicalprocesses is necessary for studying and predicting afuture flood. Consequently, this study aims to develop a regional distributed hydrological model forwater resources situation prediction. The regionalhydrologic model was composed of a runoff generation model with a concept of the variable infiltration
capacity and a flow routing model using the Kinematic Wave equation. The effects of dam control
were also included in the flow routing model. By
using the model, the effect of the existing dams operations is numerically evaluated.
The impact of climate change on river flow in
Thailand was also analyzed by feeding future climate
projection data into a distributed rainfall-runoff
model. The projection data used consists of dailyhydrologic data downscaled by hourly precipitation
for the present climate (1979-2003), the near futureclimate (2015-2039), and the future climate(2075-2099), which were simulated by a 20km spatial resolution general circulation model (MRIAGCM3.1S) developed by the Meteorological Research Institute, Japan Meteorological Agency. It
was found that a significant change of river flow
appeared at the Pasak River basin which is atributariy of the Chao Phraya River. Then, a detail
distributed hydrologic model was applied to the
Pasak River basin to analyze the water storage of the
Pasak Dam to access the reservoir could satisfy thewater demand under the future climate change scenario. It was suggested that water storage at the Pasak
Dam will not be able to meet the current water demand under the climate change scenario.

Fig. 17 Change of the Pasak dan storage under climate change
scenario (case 1).

Fig. 18 Change of the Pasak dan storage under climate change
scenario (case 2).

To examine the simulation result, another simulation to use inflow projection time series was conducted. At first, the ratio of the observed inflow andthe current climate experiment were obtained as:, / , ,ACKNOWLEDGMENT: The GCM projection
data used for the river flow simulation was provided
by the Meteorological Research Institute, Japan. The
hydrologic observation data at the Pasak River basin
was provided by the Royal Irrigation Department,
Thailand.

1, 12Then, the ratio was multiplied to the 25 years inflow
projection for each climate experiment. Similar
simulation to the above were conducted for 25 years
data. Figure 18 shows the change of the simulated
storage. The current climate experiment satisfies therule curve, however, the simulated storage using thenear and future climate experiments decreases year toyear and after 6 years, the storage becomes zero if theoutflow pattern is the same.

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