THA 2015 International Conference on“Climate Change and Water & Environment Management in Monsoon Asia”
28-30 January 2015, Bangkok, Thailand.

Application of a Land Surface Model for Bias Correction of Runoff
Generation Data from MRI-AGCM3.2S Dataset
DUONG Duc Toan 1, a *, TACHIKAWA Yasuto1,b and YOROZU Kazuaki1,cAbstract In climate change researches, generalcirculation models or global climate models (GCMs)
have been the most promising tools to project futurechanges and associated impacts in the hydrologicalcycle. However, there are biases in GCM outputs due tothe coarse spatial resolution, simplified physics
processes, numerical schemes, etc. Those biases should
be corrected before using GCM data in climate change
impact studies.
In this study, runoff generation data from the MRIAGCM3.2S dataset were fed into distributed flowrouting model 1K-FRM to project river discharge undera changing climate. Flow routing model 1K-FRM wasdeveloped in the Hydrology and Water Resources
Research Laboratory, Kyoto University. The MRIAGCM3.2S is the latest version of super-high-resolution
atmospheric general circulation model which was jointly
developed by Japan Meteorological Agency (JMA) andMeteorological Research Institute (MRI). Two river
basins located in Kyushu (Japan) were selected as study
areas, the Chikugo river basin and the Oyodo river
basin.
Since the observed runoff generation data is notavailable, the land surface model Simple Biosphereincluding Urban Canopy (SiBUC) was applied toreproduce runoff generation data to use in bias
correction of the MRI-AGCM3.2S’s output. SiBUC
model was developed in the Disaster Prevention
Research Insitute, Kyoto University. Corrected runoff
generation data were used to project river discharge andexamine the changes in river discharge in those twobasins under a changing climate.

Keywords
Land surface model, SiBUC, flow routing model, 1KFRM, bias correction, MRI-AGCM3.2S

1

Graduate School of Engineering, Kyoto University,
Kyoto, Japanaduong@hywr.kuciv.kyoto-u.ac.jpbtachikawa@hywr.kuciv.kyoto-u.ac.jpcyorozu@hywr.kuciv.kyoto-u.ac.jp

Introduction
Climate change is now widely accepted as a scientificfact. It is projected to have significant impacts onhydrology and water resources. The most common
approach to assess the hydrological impacts of global
climate change is to force hydrological models (HMs) orland surface models (LSMs) with output from generalcirculation models (GCMs). Therefore, the quality ofhydrological impact investigations largely depends on
the accuracy of GCMs in simulating climate data
(Hagemann et al., 2011).
Although there are considerable improvements in theperformance of GCMs in recent years, outputs of GCMs
still suffer from systematic errors, or biases, which canbe due to incomplete knowledge of climate systemprocesses, numerical schemes, parameterizations ofsmall scale (sub-grid scale) processes, and coarse spatial
resolution.
Removing these biases in GCM outputs is therefore
essential for improving the reliability of climate
projections and hydrological simulations forced byclimate model data. Several bias correction methods
have been developed and received much attention inclimate change impacts studies (e.g. Themeßl et al.,2011; Hagemann et al., 2011; Teutschbein and Seibert,2012).
In this work, river discharge in two river basins in
Kyushu area (Japan), Chikugo River basin and Oyodo
River basin, were projected by feeding the MRIAGCM3.2S runoff generation data into flow routing
model 1K-FRM. To improve the projection of riverdischarge using 1K-FRM, bias correction is considered
to apply to MRI-AGCM3.2S runoff generation data.
Due to the unavailability of observed runoff generation
data, an advanced land surface process model called
Simple Biosphere including Urban Canopy (SiBUC;
Tanaka, 2005) was applied to reproduce runoff
generation data based on meteorological andphenological records. Output of SiBUC model was used
as reference data for bias correction of MRI-AGCM3.2S
runoff generation data. Biases in GCM runoff
generation data were corrected using quantile-quantile
mapping bias correction method (Leimer et al., 2011).
Projected river discharge in Chikugo River basin andOyodo River basin from 1K-FRM using MRIAGCM3.2S runoff generation data, SiBUC runoff

THA 2015 International Conference on“Climate Change and Water & Environment Management in Monsoon Asia”
28-30 January 2015, Bangkok, Thailand.

generation data, and bias-corrected runoff generation
data were compared to examine the performance of land
surface process model and bias correction method inriver discharge projection.
Study area
The analysis in this study was performed for two river
basins in Kyushu area, Japan – Chikugo River basin andOyodo River basin. Fig. 1 shows the location of these
two river basins.

Land surface model SiBUC
The land surface process model Simple Biosphereincluding Urban Canopy (SiBUC) was presented by
Tanaka (2005) in Disaster Prevention Research Institute,
Kyoto University. SiBUC model uses mosaic approach,which couples independently each land-use patch of thegrid element to the atmosphere, to incorporate all kindof land-use to land surface scheme.
In SiBUC model, the surface of each grid cell is divided
into three land-use categories including green area(vegetation canopy and ground), urban area (urban
canopy and urban ground), and water body. Fig. 2
shows the schematic image of surface elements in
SiBUC model.

Fig. 1 Location of Chikugo River basin (blue) andOyodo River basin (red) in Kyushu area, Japan
Chikugo River flows through Oita, Fukuoka,
Kumamoto, and Saga prefectures in Kyushu area. Witha total length of about 143 km, it is the longest river on
Kyushu Island. The total basin area of Chikugo River isabout 2,800 km2.
Oyodo River runs through Kagoshima prefecture andMiyazaki prefectures with the basin area of about 2,230
km2. The length of Oyodo River basin is about 107 km.

The fractions of these three land-use categories are fixed
for each grid cell in SiBUC model. And surface fluxes
are obtained by averaging the surface fluxes over eachland-use weighted by its fractional area.

Flow routing model 1K-FRM

Data

1K-FRM is a distributed flow routing model based onkinematic wave theory (Hunukumbura et al. 2012). It
was developed by Hydrology and Water Resources
Research Laboratory of Kyoto University. The
kinematic wave model is applied to all slope units andrunoff is routed according to the flow direction
information. The basic form of the kinematic wave flow
equation is:∂A ∂Q+= q L ( x, t )
(1)
∂t ∂x

Topographic data
The topographic data used in flow routing model 1KFRM and land surface process model for two river
basins in Kyushu area were the 30 arc-second (1-km)
DEM and flow direction stored in HydroSHEDS(Lehner, 2006) for Asian regions. HydroSHEDS(Hydrological data and maps based on Shuttle Elevation
Derivatives at multiple scales) provides hydrographic
information for region and global-scale applications
based on high-resolution elevation data obtained by
NASA’s Shuttle Radar Topography Mission.

i0  1  m −1
5(2)
  , m=3n Bwhere, A(x, t) is the flow cross-sectional area, Q(x, t) is
the flow discharge, qL(x, t) is the lateral inflow per unit
length, i0 is the slope, n is the Manning roughness
coefficient, and B is the width of the flow.
Eq. (1) is the continuity equation. It is derived from theprinciple of mass conservation within a control volume.
Eq. (2) is derived from Manning's laws which are flow
resistance laws of open channel uniform flow.
Q = αA m , α =Fig. 2 Schematic image of SIBUC model

GCM runoff generation data
GCM data used for flow routing model 1K-FRM is 3hourly runoff generation data from super-highresolution atmospheric general circulation model MRIAGCM3.2S. It is the latest atmospheric GCMs based ona model jointly developed by the Japan Meteorological
Agency (JMA) and the Meteorological ResearchInstitute (MRI) (Mizuta et al., 2006). MRI-AGCM
provides data for three climate experiments: present
climate experiment (1979-2008), near future climate

THA 2015 International Conference on“Climate Change and Water & Environment Management in Monsoon Asia”
28-30 January 2015, Bangkok, Thailand.

experiment (2015-2044), and future climate experiment(2075-2104). The data used for future projection werebased on the Special Report on Emissions Scenarios(SRES) A1B scenario.
Meteorological data
Meteorological data to force land surface process model
SiBUC include seven components: precipitation, airtemperature, specific humidity, surface pressure, windspeed, long wave radiation, and short wave radiation. In
this study, the product of the Japanese 55-year
reanalysis (JRA-55) project was utilized to use as inputs
for SiBUC. However, JRA-55 precipitation and surface
radiation data are forecast data, not reanalysis data.
Therefore, other data sources were considered to use as
substitution. For precipitation data, the Asian
Precipitation - Highly-Resolved Observational Data
Integration Towards Evaluation of Water Resources(APHRODITE’s Water Resources) project was selected.
And the Surface Radiation Budget (SRB) dataset wasused to process long wave radiation and short wave
radiation data.
The Japanese 55-year Reanalysis (JRA-55) is the second
reanalysis project conducted by Japan Meteorological
Agency (JMA) (Ebita et al., 2011). In this project, asophisticated data assimilation system based on theoperational system as of December 2009 and newly
prepared dataset of past observations were used to
produce a high-quality homogeneous climate dataset.
The analysis period covers 50 years from 1958, whenregular radiosonde observation began on a global basis.
Table 1 and Table 2 summary information about JRA55 meteorological data used for land surface processmodel SiBUC.
Table 1 Parameters of surface analysis fields
Field parameter
Unit
Level
Ground or
PressurePa
surfaceTemperatureK
2m
Specific humidity
kg kg-1 2m
u-component of wind m s-1
10m
v-component of wind m s-1
10m

water

Table 6.2 Parameters of two-dimensional averagediagnostic fields
Field parameter
Unit
Level
mm
Ground or water
Total precipitation
day-1
surface
Downwardsolar
Ground or water
W m-2
radiation flux
surface
Downward long wave
Ground or water
W m-2
radiation flux
surface
The spatial resolution of JRA-55 data is 0.5625 degree.
Parameters of surface analysis fields have 6-hour
temporal resolution. And parameters of two-dimensional
average diagnostic fields have 3-hour temporal
resolution.

The
Asian
Precipitation
Highly-Resolved
Observational Data Integration Towards Evaluation of
Water Resources (APHRODITE's Water Resources)
project was conducted by the Research Institute for
Human and Nature (RIHN) and the MeteorologicalResearch Institute of Japan Meteorological Agency(MRI/JMA) from 2006 to develop state-of-the-art daily
precipitation datasets on high-resolution grids covering
the whole of Asia (Yatagai et al., 2012). The datasets are
created primarily with data obtained from a rain gauge
observation network. The APHRODITE’s Water
Resources dataset is the only long-term continentalscale daily product that contains a dense network ofdaily rain-gauge data for Asia, especially Japan.
Precipitation data to force land surface process modelfor Kuyshu area was extracted from the APHRO_JP
V1207 (Kamiguchi et al., 2010) dataset with spatial
resolution of 0.05 degree. Temporal resolution of
APHRO_JP precipitation data is daily.
Surface Radiation Budget (SRB) dataset is produced andarchived by the NASA Langley Research Center
Atmospheric Sciences Data Center (NASA/GEWEX). It
is produced on a 1 degree x 1 degree global grid using
satellite-derived cloud parameters and ozone fields,
reanalysis meteorology, and a few other ancillarydatasets. The SRB dataset contains 3-hourly long wave
and short wave radiative fluxes.
Soil, vegetation, and land use data
Phenological boundary conditions related to soil type,vegetation type and land use data for Kyushu area in
SiBUC model were collected from Ministry of Land,
Infrastructure, Transport and Tourism (MLIT, Japan)
field survey data and satellite databases such as
ECOCLIMAP product and GLASS Leaf Area Index
product.
Land use data for Kyushu area collected from MLIT
consist of 16 categories including paddy, farmland, fruit
farm, other farm, forest, waste land, building A, building
B, road, other land, lake, river A, river B, beach, andunknown. Surface parameters related to land use forgreen area, urban area, and water body in SiBUC model
was set based on this category. The spatial resolution of
MLIT land use data is 100 m.
Parameters for vegetation type were derived from the
GLASS Leaf Area Index (LAI) product, which
generated by the Center for Global Change Data
Processing and Analysis of Beijing Normal University(Xiao et al., 2013). The GLASS product is available
from 1982 to 2012 with temporal resolution of 8 days
and spatial resolution of 0.05 degree.
Soil parameters in SiBUC model such as root depth, soil
depth, soil texture class, etc. were set baed on
ECOCLIMAP product (Masson et al., 2003).
ECOCLIMAP is a database of surface parameters at 1km resolution which was implemented in the METEOFRANCE operation models.

THA 2015 International Conference on“Climate Change and Water & Environment Management in Monsoon Asia”
28-30 January 2015, Bangkok, Thailand.

Resolution and simulation period of SiBUC model
The output data grid of SiBUC was set in the same
coordinate, spatial resolution as MRI-AGCM3.2S runoff
generation data. Input data with finer spatial resolution
such as soil and vegetation were aggregated to create
20-km spatial resolution data. For coarser spatial
resolution data, value of the nearest grid was selected in
calculation.
SiBUC model was set to simulate runoff in Kyushu areafor 1982-2010 period based on the availability of input
data.
Bias correction of GCM runoff generation data
To correct biases in GCM runoff generation data,
quantile-quantile mapping (QQM) bias correction
method was selected. QQM was first introduced by
Brier and Panofsky (1968) as empirical transformation.
Methods based on quantile mapping are getting more
popular and widely used to correct climate model
outputs (e.g. Leimer et al., 2011; Vidal and Wade,2008). Themeßl et al. (2011) and Teutschbein and
Seibert (2012) compared various bias correction
methods and showed that QQM performs better thanothers.
In QQM bias correction method, GCM output andobservations are sorted for the same historical baseperiod to construct cumulative distribution functions(CDFs). These CDFs is used to define the quantiles ofsimulated values and observations. Then, GCM
simulated values is substituted with those of theidentical quantile from the observational dataset. Fig. 3
shows the schematic representation of quantile-quantile
mapping.

Fig. 3 Schematic representation of quantile-quantile
mapping
In this study, runoff data simulated by SiBUC model
were used as reference data to correct MRI-AGCM3.2S
runoff generation data. Bias correction was applied to
MRI-AGCM3.2S runoff generation data at each gridcell as follows:
Runoff generation data from MRI-AGCM3.2S dataset
and SiBUC model at the same grid and in the same
calendar month for the whole simulation period were
sorted from smallest to largest to construct cumulative
distribution functions.
Runoff generation data from MRI-AGCM3.2S dataset at
each quantile was corrected by SiBUC runoff data at theequivalent quantile.

MRI-AGCM3.2S corrected runoff generation data wererearranged following the original time order.
Results and discussions
Reproduction of runoff generation data using SiBUC
Two simulations for Kyushu area were carried out using
SiBUC model with different precipitation data sources,
JRA-55 and APHRO_JP. Fig. 4 shows the annual mean
runoff in Kyushu area simulated by SiBUC model.
Simulation using APHRO_JP precipitation data shows ahigher value of annual mean runoff compared to the one
using JRA-55 precipitation data.

Fig. 4 Annual mean runoff in Kyushu area simulatedusing JRA-55 (left) and APHRO_JP precipitation data(right) from 1982-2008 (unit: mm/year)
To examine the reproduction of runoff generation datausing land surface process model, river discharge inKyushu area were simulated using runoff generation
data given by SiBUC model. The runoff data simulated
by SiBUC model using JRA-55 and APHRO_JP
precipitation data hereinafter referred to as JRA-55
runoff data and APHRO_JP runoff data.
Flow duration curves for Oyodo River at Takaoka
station and for Chikugo River at Senoshita station wereconstructed using the total-period method and thecalendar-year method to compare simulated discharge
with observations. Observational data at two stations
mentioned above are available for 20 years period, from1982 to 2001.
Fig. 5 and Fig. 6 show the total period and calendar-year
flow duration curves for daily flow at Takaoka station,
Oyodo River. Flow duration curves for Chikugo River at
Senoshita are illustrated in Fig. 7 and Fig. 8
respectively.

Fig. 5 Total period flow duration curve of daily flow for
Oyodo Rirver at Takaoka

THA 2015 International Conference on“Climate Change and Water & Environment Management in Monsoon Asia”
28-30 January 2015, Bangkok, Thailand.

simulation using APHRO_JP runoff data still performs
better than others (see Fig. 7 and Fig. 8). Therefore, inbias correction part, APHRO_JP runoff data werechosen as reference data to correct biases in MRIAGCM3.2S runoff generation data.

Fig. 6 Calendar-year flow duration curve of daily flow
for Oyodo Rirver at Takaoka
As can be seen in Fig. 5, the flow duration curve fromsimulation using APHRO_JP runoff data was moreclose to observed data at Takaoka station, Oyodo Riverbasin. River discharges simulated using original MRI
runoff generation data and JRA-55 runoff data are lower
than the observations. The calendar-year flow duration
curves show the same pattern as the total-period flow
duration curves (Fig. 6).

Bias correction of runoff generation data
Biases in MRI-AGCM3.2S runoff generation data werecorrected with APHRO_JP runoff data using quantilequantile mapping method. Corrected MRI-AGCM3.2S
runoff generation data were fed into flow routing model
1K-FRM to examine the effect of bias correction of
runoff generation data on river discharge simulation.
Fig. 9 shows an example of the time series of MRIAGCM3.2S runoff generation data, APHRO_JP runoff
data, and corrected runoff generation data for 20 years
period (1982-2001) at one grid upstream of Takaoka
station, Oyodo River basin.

Fig. 9 An example of time series of runoff generation
data for 20 years period (1982-2001)

Fig 7 Total period flow duration curve of daily flow for
Chikugo Rirver at Senoshita

It can be seen that, after bias correction, the temporal
distribution pattern of corrected runoff generation data issimilar to that of original MRI-AGCM3.2S data.
However, comparing to reference data, the number of
events with high runoff depth in the corrected runoff
generation data is smaller but the density of high runoff
depth in each event is higher. It may result in less flood
events but higher peak discharge values.

Fig. 8 Calendar-year flow duration curve of daily flow
for Chikugo Rirver at Senoshita
At Senoshita station, Chikugo River basin, although all
the simulation showed an underestimation of thesimulated river discharges from the observation, theFig. 10 Total period flow duration curve of daily flow
for Oyodo Rirver at Takaoka

THA 2015 International Conference on“Climate Change and Water & Environment Management in Monsoon Asia”
28-30 January 2015, Bangkok, Thailand.

Fig. 11 Calendar-year flow duration curve of daily flow
for Oyodo Rirver at Takaoka
Flow duration curves for river discharge simulated using
corrected runoff generation data at Takaoka station,
Oyodo River basin, are illustrated in Fig. 10 and Fig. 11.
River discharge simulated using bias-corrected runoff
generation data show an improvement comparing tooriginal MRI-AGCM3.2S data. However, the peakdischarge values are overestimated in comparison withsimulation using reference runoff generation data. It
may arise from the differences in temporal distribution
pattern between corrected runoff generation data andreference data as mentioned above.
Conclusions
In this study, runoff generation data in the Kyushu area
were simulated using a land surface process model
SiBUC with reanalysis data. It was used as reference
data to correct biases in MRI-AGCM3.2S runoff
generation data.
SiBUC model showed a good performance inreproducing runoff generation data for Kyushu area. Ifhigh quality observed data are available, land surface
process model will be a useful tool to reproduce runoff
for a long-term period.
Bias correction of MRI-AGCM3.2S runoff generation
data were also performed and showed an improvement
in river discharge simulations. However, further works
need to be done in bias correction of runoff generation
data considering their temporal distribution pattern. The
spatial correlation between neigbour grid-cells are alsoneeded to be examined.
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