THA 2019 International Conference on“Water Management and Climate Change towards Asia’s Water-Energy-Food Nexus and SDGs”
23– 25 January 2019, Bangkok, Thailand.

Assessment of runoff generation using the Simple Biosphere Model
including Urban Canopy for upper Chao Phraya River Basin,Thailand
Teerawat RAM-INDRA
Abstract Upper Chao Phraya river basin is consistingof four river basins: Ping, Wong, Yom, and Nan whichplay an essential role essential water to the central partof Thailand. Each sub-basin has a variety oftopography from mountainous area to flat plain. It is
usually difficult for field measurement with complex
terrain to directly measure the land and atmosphereinteraction. The land surface model (LSM) is analternative way to provide that information. In thisstudy, the runoff simulated by a land surface model
named Simple Biosphere Model including Urban
Canopy (SiBUC) for the regional scale of the upper
Chao Phraya River Basin. The forcing data aregathering from observation, reanalysis, and satellite
estimation. A kinematic wave routing model wasapplied for the runoff product and analyzed atrepresentative discharge observation stations for theupper Chao Phraya river basin. The validation isneeded to test the performance of the model in thestudy area. The result could provide more
understanding of the characteristic of runoff in theupper Chao Phraya river basin and should serve as thereference runoff generation data in the future research.

Introduction
A Global Circulation Model (GCM) is one of
the most reliable tools and widely used for research on
climate change and related fields including its impact
on the hydrologic cycle. Though GCM has a good
agreement with observation data in many necessary
parameters in large scale [1], there still have
uncertainties in climate predictions for three sources:internal variability, model uncertainty and scenario
uncertainty [2]. Bias correction techniques could be a
way to reduce uncertainty in GCMs output. Several
studies have developed bias correction methods toadjust GCM outputs. However, due to the north ofThailand sub-basin scale has a variety of topography
from the mountainous area in the northern part to flat
plain in the southern region. It is usually difficult forfield measurement with complex terrain to directly
measure the land and atmosphere interaction. To
provide those data, land surface model is another way.
Although, the land surface model need to evaluate
their performance before entrusting the result from thesimulation.
This paper presents the evaluation process ofland surface model name SiBUC over the upper part ofChao Phraya River Basin, Thailand by feeding therunoff generation to the flow routing model andcompare to the river discharge observation data at five
representative point for each sub-basin and the joint
position of Chao Phraya River.

Keywords Land surface model, Upper Chao Phraya
River basin, Runoff generation

Study Area

This study was performed on the fourtributaries
of Chao Phraya River in Thailand, Ping RiverBasin, Wang River Basin, Yom River Basin, andNan River Basin. The river flows through thenorthern part of Thailand to the southern part andmeets at the middle of the country. The size ofthe basin are around 34,500 km2, 10,800 km2,24,000 km2, and 34,700 km2 respectively. Figure
1 shows the river channel in upper Chao Phraya
and points of evaluation for each tributary, and all
the tributary joins together. The observationstations for each basin are select; Ping river:
inflow to Bhumibol Dam, Wang river: W.3A
stations, Yom river: Y.14, Nan river: inflow toSirikit Dam, and C.2.
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THA 2019 International Conference oon“Water Management and Climate Changge towards Asia’s Water-Energy-Food Nexus and SDGs”
23– 25 January 2019, Bangkok, Thailandd.

(1)(2)
The continuity eq
quation is defined in (1).
Where; t is time; x is spaace coordinate; A is flowcross-section area; Q is flow
w rate; qL is lateral flow perunit length from the side of the main flow or candetermine as rainfall inten
nsity or runoff generation
give vertically to the slopee. Equation (2) are derived
simplify momentum equatio
on with Manning equation
to rout the water. Where n isi manning coefficient; θ ischannel gradient.
C. Simulation setting
The cell size of SiB
BUC model are set into 10
km with aggregate all the in
nput surface data include ofland use, soil data, and veg
getation data to the modelresolution while the metho
odological data are utilized
as same as an original reesolution of JRA-55. The
simulation periods start froom 1 January 1991 to 31
December 2010 total of 20 years.
yFig. 1. upper Chao Phraya basiin and river discharge
observation stations (red dot) useed in the study
Hydrologic Model
A. Land surface model

As the output run
noff generate, they wereused as input to flow rou
uting model, 1K-FRM at
1 km spatial resolution. The simulation is set to
the natural condition of thet entire study area thatmeans no dam struccture includedand all
replication in one, withou
ut dividing the watershedarea.

This study is utilized land surface processmodel to produce runoff generaation. The land surface
process model proposed by Tanaaka [9] namely Simple
Biosphere including Urban Canopy (SiBUC)
developed from Simple Biospheere [10] with modify in[11] to more cooperate with cropping pattern insoutheast region were used in tthe study. The SiBUC
are using mosaic parameterizatioon approach to include
each land-use patch of the grid element to theatmosphere. The models are inncorporated with threesub-models (green area, urban aare and water body) todescribe each grid cell. The averrage surface fluxes foreach grid are from averaging thhe surface fluxes basedon each land-use weighted bby its sectional area.
SiBUC is using Richards’ equaation for soil moisture
store in three sub-soil layer and Darcy’s law toexpressed vertical exchanges bettween soil layers.

Data
A. Forcing data
SiBUC
needs
sevencomponents
meteorological data to drivve the model: precipitation,
air temperature, specific humidity,
hsurface pressure,wind speed, downward shortwave radiation, anddownward longwave raddiation. In this study,
precipitation data were obtainedofrom observation
stations of Royal Irrigatiion Department, Thailand(RID) and Thailand Meeteorological Department,
Thailand (TMD) across thet
study area and using
interpolation technique called inverse distanceweighting to make a spaatial precipitation dataset.
Other data are gathering from the product of theJapanese 55-year reanalysiss (JRA-55) [3] propose by
Japan Meteorological Ageency (JMA). The JRA-55
systems are configuration of data sources, quality
control, data selection for observations, data
assimilation system and fo
orecast model. The spatial
resolution of JRA-55 is TL3319L60 or corresponding toa grid interval of 60 km.

B. Flow routing model
The runoff generatioons are feed to a
distributed flow routing model; 1K- FRM to simulate
the river discharge. The modeels are based on onedimensional kinematic wave ddevelop in Hydrology
and Water Resources Research Laboratory, at Kyoto
University. The runoff generatiions are routed to thedownstream according to the ttopography data. Themodel and program for preparingg topography input canbeobtainedfrom1K-FRM
website(http://hywr.kuciv.kyoto-u.ac.jp/pproducts/1KDHM/1K-DHM.html).

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THA 2019 International Conference on“Water Management and Climate Change towards Asia’s Water-Energy-Food Nexus and SDGs”
23– 25 January 2019, Bangkok, Thailand.

B. Land use

moisture by the model. Still, the overall precipitation
excludes the urban area are only around 20% to 30%
are become runoff.

The land use data are based on MODIS landuse [4, 5]. These data sets are WGS 1984 coordinate
system reprojected product of Global Mosaics of thestandard MODIS land use data product (MCD12Q1) inthe IGBP Land Use Type Classification at a spatial
resolution of 10 km. Each cell class is modified tomatch on the land survey data from the Land
Development Department, Thailand (LDD) on thecategory of an urban area, cropland and irrigation area.
C. Elevation and slope
The elevation data used in the study are
obtained from Hydrological data and maps based onSHuttle Elevation Derivatives at multiple Scales(HydroSHEDS) with also used to create the slope data.
The HydroSHEDS is derived primarily from elevation
data of the Shuttle Radar Topography Mission(SRTM) at 90 km spatial resolution.

Fig. 2. runoff ratio

D. Soil data and vegetation dataB. Monthly discharge

The soil texture data are calculated from soil
physical properties from Digital Soil Map of theWorld (DSMW) [6] using criteria of twelve major soiltexture classifications defined by the USDA [7].
DSMW is a digitized version of the FAO-UNESCO
Soil Map of the World produced in the paper version.
For other soil data such as soil depth and root depth
are obtain from ECOCLIMAP [5]. ECOCLIMAP isincluded with an ecosystem classification and acoherent set of land surface parameters database at 1km resolution developed by Centre National deRecherchesMétéorologiques, France (CNRM-GAME).
The vegetation parameters used in this study are
extracting from MCD15A3H V006 [8] which arecombined fraction of Photosynthetically Active
Radiation (FPAR), and Leaf Area Index (LAI) datasets
at a 4-day time interval and 500 m spatial resolution.
The data were aggregated into 12-day interval to clear
out the missing data.

Monthly average discharge for 20 years isshown in Figure 4 for representative observation
discharge point of 4 tributary and C.2 station. The
simulation condition of the flow routing model for C.2station did not include the dam in the upstream. So, theriver discharge cannot directly compare and canassume that one of the reasons for causing the highly
overestimates from the simulation. Hence, theexplanations below will not be considerate with theC.2 station.
The result shows the hydrograph are followed
the pattern of the observation data with some missestimation on inflow to Bhumibol Dam, W.3A, andY.14. For inflow to Sirikit Dam, they see a substantial
underestimate between simulate result and observation
for the entire period. The average observation flow indry season for each river is 22%, 18%, 13% and 17%
for inflow to Bhumibol Dam, W.3A, Y.14, and inflow
to Sirikit Dam. When compared with the average flow
in wet season while the simulation result of average
flow in dry season to wet season is 21%, 13%, 17%,and 32% respectively. When comparing the average
flow of simulation with observation in wet season, thedifference is -12%, -24%, 33%, and 149%
respectively. The negative percentage represents
overestimate by simulate and positive are represent theunderestimation. From this result also show a large
underestimate value for inflow to Sirikit Dam thatrequires further analysis to understand the cause ofhuge amount of error.

ResultA. Runoff production
The SiBUC are given the result of runoff
generation. It can compare to precipitation in term of
runoff ratio to explain how much precipitation
becomes runoff by calculating annual runoff divide byannual precipitation. On the other hand, they can also
show how much the precipitation is lost through theevapotranspiration process. Figure 3 shows the annual
average runoff ratio of the study area from 1991 2010. The ratios vary from 0.1 to 0.9 based on the land
use tile. Most of the high value of the ratio is locatedin the urban area that considerate as impermeable
pavement which does not allow the evaporation of soil
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THA 2019 International Conference on“Water Management and Climate Change towards Asia’s Water-Energy-Food Nexus and SDGs”
23– 25 January 2019, Bangkok, Thailand.

(a) Inflow Bhumibol Dam(b) W.3A(d) Inflow Sirikit Dam(c) Y.14(e) C.2

Fig. 3. Monthly average discharge from 1991-2010.

(a) Inflow Bhumibol Dam(b) W.3A(d) Inflow Sirikit Dam(c) Y.14(e) C.2

Fig. 4. Cumulative distribution transformation for daily discharge from 1991-2010
With large difference in high and low flow
rates in the upper Chao Phraya River basin. We expect
a good estimation for extreme value to give theaccurate data for study relate to high and low value ofdischarge in the future. The daily result show most of
the stations have clear overestimate in the frequency of
low discharge. On the other hand, the frequency ofhigh discharge is somewhat overestimated whenconsidering inflow BB and W.3A, slightly
underestimate for inflow for SK and seem to good
match at Y.14.

C. Daily discharge
Figure 4 is present cumulative distributionfunction (CDF) of daily discharge for 20 years. The
CDF has illustratedthe probability that discharge
events are less or equal to a specified value based on
the normalized of daily discharge for all representation
observation stations. The value of normalizing daily
discharge can describe the high discharge are morethan 1 (Q/Qmean> 1) and for low discharge are less than1 (Q/Qmean< 1).

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THA 2019 International Conference on“Water Management and Climate Change towards Asia’s Water-Energy-Food Nexus and SDGs”
23– 25 January 2019, Bangkok, Thailand.

Concluding Summary

References

This paper present the performance of land
surface model: SiBUC on upper Chao Phraya River
Basin that includes 4 major tributaries: Ping, Wang,Yom, and Nan. Since the total runoff data are difficult
to obtain by measurement in the real field; the riverdischarge is used to evaluate instead of runoff
generated by the model. Discharge observation data ofeach river basin are used to representative comparison
point for each sub-basin.
The result seems to perform well for monthly
scale for inflow BB, W.3A, and Y.14 but still large
underestimate for inflow SK. In the daily scale, thesimulated performance for high and low discharge stillfound difficult to capture. From this result, the modelis needed to focus on particular sub-basins to adjust
the model parameter to improve the performance andcan generate runoff more accurate. The runoff
generation data from this study will provide as nearreference runoff data and use for the future study.
Since this study does not include large damein the simulation, we cannot directly compare thesimulation result to the observation data. Hence, thesignificant difference can be assumed that it affects thedam structure which comes to two solutions for thefuture work. First, neutralize the river by removing theeffect of Bhumibol dam and Sirikit dam as in [12]using the water balance equation and dam inflow data,
dam outflow data. Second, include the dam operation
function into the flow routing model.

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Acknowledgment
The authors are grateful to the Royal
Irrigation
Department,
Thailand,
theThai
Meteorological
Department,
The
Electricity
Generating Authority of Thailand, and the Land
Development Department, Thailand for provision ofobservation and survey data.

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