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 STUDY ON BIAS CORRECTION METHOD FOR RUNOFF
GENERATION DATA BASED ON REFERENCE DATACREATED BY LAND SURFACE MODELYUSUKE MIZUSHIMA1,a,
TACHIKAWA1,dKAZUAKI

YOROZU1,b,

Abstract Climate change has a great influence onhydrologiccycle and water resources. General
Circulation Model (GCM) is generally used forassessing the impact on natural disasters such as floods
and draughts due to climate change. To improve thereliability of future climate prediction by GCM, it iseffective to correct the bias of that output. From theviewpoint of water management, bias correction for
runoff generation data is required. In this study, bias
correction was performed for the MRI-AGCM3.2S 3hourly runoff generation data from 1982 to 2001, by
applying Quantile-Quantile Mapping (QQM) method.
The target area was Oyodo river basin in Kyushu
Island, Japan. Due to an unavailability of runoff
generation observation, land surface model SiBUC
was utilized for creating reference runoff generation
data by using meteorological data such as APHRO_JP
precipitation data and JRA-55 reanalysis data. The
effect of the bias correction was evaluated by
calculating the river discharge utilizing river routing
model, 1K-FRM. As a result, the simulated riverdischarge using bias-corrected runoff generation data
showed an improvement compared with that using theoriginal one. However, it was found thatthe simulated
river discharge using bias-corrected runoff wasoverestimated compared with that using reference data
especially on high flood events. Therefore, QQM
method was independently applied for high runoff
generation data and low data by setting thresholdvalues for reference data and original data,
respectively. The overestimation result was improvedby this advanced approach.

Department of Civil andEngineering, Kyoto University
Kyoto, JapanEarth

ICHIKAWA1,cand

YASUTO

Introduction
Climate change has a great influence onhydrologic cycle and water resources. General
Circulation Model (GCM) is generally used forassessing the impact on natural disasters such as floods
and draughts due to climate change.To improve thereliability of future climate prediction by GCM, it iseffective to correct the bias of that output.However,compared with bias correction for precipitation andtemperature, there are few studies on bias correction
related to river discharge.
Bias correction method for the MRIAGCM3.2S 3-hourly runoff generation data usingreference data created by land surface model SiBUC
was initially proposed by Duong[1]. Bias correction to
runoff generation data showed an improvement inriver discharge simulations. However,in bias
correction of runoff generation data, Duong pointed
out further works needed to be done, for example,
considering their temporal distribution pattern.
Based on these backgrounds, the purpose ofthis study is to revise bias correction method for runoff
generation data and simulate river discharge more
precisely.In this study, bias correction was performed
for the MRI-AGCM3.2S 3-hourly runoff generation byfollowingDuong’s study and the effect of bias
correction was evaluated by simulating river discharge
utilizing flow routing model 1K-FRM.
Methodology
Observational datasets are needed to apply bias
correction method to GCM runoff generation data.
However, runoff generation observation cannot be
available. Instead,in this study,reference data is createdby land surface modeland it is used as pseudoobservation. In addition, the effect of bias correction is
not evaluated with runoff itself but with simulated
river discharge by corrected runoff. Based on thesematters, the flow of this study is shown in Fig. 1.The
MRI-AGCM 3.2S 3-hourly runoff generation data isused as GCM runoff generation data. QuantileQuantile Mapping (QQM) method was selected as thebias correction method by following Duong’s
study.Then, the river discharges are simulated by using
original GCM runoff generation data, reference datafrom land surface model SiBUC and bias-correctedrunoff generation data. Duration curves are drawn by 3

Keywords Climate change, Bias correction, Runoff
generation data
1

YUTAKA

Resourcesamizushima.yusuke.88e@st.kyoto-u.ac.jp
yorozu@hywr.kuciv.kyoto-u.ac.jpcichikawa@hywr.kuciv.kyoto-u.ac.jpdtachikawa@hywr.kuciv.kyoto-u.ac.jpb40

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.

kinds of river discharge.The sim
mulated river dischargeusing bias-corrected runoff generration data is validated
referred to that using reference ddata.

Fig. 3 Schematic imaage of SiBUC model߲‫߲ܳ ܣ‬൅ൌ ‫ ୐ݍ‬ሺ‫ݔ‬ǡ ‫ݐ‬ሻሺͳሻ
߲‫ݔ߲ ݐ‬Fig. 1 The flow of tthis studyܳ ൌ ߙ‫ܣ‬௠ ǡ ߙ ൌ௠ͷඥ݅଴ ͳ ௠ିଵ൬ ൰ǡ ݉ ൌ ሺʹሻ݊ ‫ܤ‬͵where,‫ܣ‬ሺ‫ݔ‬ǡ ‫ݐ‬ሻ is the flow crross-sectional area,ܳሺ‫ݔ‬ǡ ‫ݐ‬ሻisthe flow discharge, ‫ ୐ݍ‬ሺ‫ݔ‬ǡ ‫ݐ‬ሻ‫ ݐ‬is the lateral inflow perunit length, ݅଴ is the slope, n is the Manning roughness
coefficient, and B is the wid
dth of the flow. Equation (1)is the continuity equationn. It is derived from theprinciple of mass conservatiion within a control volume.
Equation (2) is derived fromm Manning's laws which areflow resistance laws of open
n channel uniform flow.
C. Land Surface Model SiB
BUC
The land surface model Simple Biosphereincluding Urban Canopy waas developed by Tanaka [3]in Disaster Prevention Reesearch Institute in Kyoto
University. SiBUC modell uses mosaic approach,which couples independenttly each land-use patch of
the grid element to the atm
mosphere, to incorporate allkind of land-use to land surfface scheme.
In SiBUC model, each land surface grid isclassified into three land use
u categories, urban area,
water body, and green area.F
Fig. 3 shows the schematic
image of surface elements in
n SiBUC model.
The fractions of thhese land-use categories are
fixed for each grid cell in SiBUC
S
model. And surface
fluxes are obtained by aveeraging the surface fluxes
over each land-use weighteed by its fractional area.In
this study, the spatial resolu
ution is the same as that of
the MRI-AGCM 3.2S (20-kmm).
Fig. 4 shows the schematic image of water
budget in the green area moodel of SiBUC and Table. 1
shows the variables used
d in the model. Soil isexpressed by three layers annd the governing equations
for the three soil moisturees are based on Richards’
equation with forcing terrms of evapotranspiration(‫ܧ‬௦ ,‫ܧ‬ௗ௖ǡଵ ,‫ܧ‬ௗ௖ǡଶ ) and infiltraation (ܲଵ ). Equation (3) to(5) is the governing equatio
ons to soil moisture of threelayers.

Fig. 2 The study area- Oyoodo River basin
Simulation Design
A. Study Area
The target area in this study is Oyodo river
basin in Kyushu Island, Japanan. Fig. 2 shows thelocation of Oyodo river basin.O
Oyodo River is a firstclass river flowing from Kaggoshima prefecture to
Miyazaki prefecture. The total llength of Oyodo Riveris about 107 km and the catchm
ment area is about 2230
km2.
M
B. Flow Routing Model 1K-FRM
1K-FRM is a flow rouuting model based onkinematic wave theory. It was developed byHydrology and Water Resourcess Research Laboratoryof Kyoto University[2].1K-FR
RM calculates riverdischarges with each unit, sslope unit and riverunit.Different Manning roughneess coefficients are set
in each unit.The kinematic wavve model is applied to
all units and runoff is routed aaccording to the flow
direction information.The spatiial resolution of thismodel is 30 seconds (1-km).T he basic form of thekinematic wave flow equation is:

ͳ
ͳ
ܹଵ
ൌ
൤ܲ െ ܳଵǡଶ െ൫‫ ܧ‬൅ ‫ܧ‬ௗ௖ǡଵ ൯൨ሺ͵ሻߩ௪ ௦ߠ௦ ‫ܦ‬ଵ ଵ
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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.

2) Meteorological Data
The meteorological data to force land surface
model
SiBUC
includesseven components:
precipitation, air temperature, specific humidity,
surface pressure, wind speed, long wave radiation andshort wave radiation.
In this study, the product of the Japanese 55year reanalysis (JRA-55) project was utilized to use as
inputs to SiBUC. The Japanese 55-year Reanalysis(JRA-55) is the second reanalysisproject conducted by
Japan Meteorological Agency (JMA)
Table. 1 List of variables used in the green area modelof SiBUC
SymbolDefinitionUnitssoil moisture storesܹ௜root depthM‫ܦ‬௜soil water content at saturationߠ௦water density
kg m-3ߩ௪
direct evaporation of water from kg m-2‫ܧ‬௦
the surface soil layers-1‫ܧ‬ௗ௖ǡ௜

Fig. 4 Schematic image of water budget in the green
area model of SiBUC

ܲଵ

ܹଶ
ͳ
‫ܧ‬ௗ௖ǡଶൌ൤ܳ െ ܳଶǡଷ െ൨ሺͶሻߩ௪ߠ௦ ‫ܦ‬ଶ ଵǡଶܹଷͳൌൣܳ െ ܳଷ ൧ሺͷሻߠ௦ ‫ܦ‬ଷ ଶǡଷIn this model, after precipitation reaches theground, some of the water infiltrates through the soil
following Darcy’s law and the rest becomes surface
runoff. Sub-surface runoff is calculated from the thirdlayer of the soil. Runoff generation is the total sum of
surface runoff and sub-surface runoff. It is used as
input into flow routing model 1K-FRM.

abstraction of soil moisture bytranspiration
infiltration into the upper soillayerܳ௜ǡ௜ାଵ

flow between soil layerܳଷgravitational
recharge layer

drainagekg m-2
s-1
ms-1
ms-1
ms-1

from

using more sophisticated Data Assimilation system
based on the operational system as of December 2009,
and newly prepared past observations [5].
However, JRA-55 precipitation and surface
radiation data are forecast data, not reanalysis data.
Therefore, other data sources are considered to use as
substitution.
For precipitation data, the Asian Precipitation- Highly-Resolved Observational Data Integration
Towards.
Evaluationof
Water
Resources(APHRODITE’s Water Resources) project was
selected. APHRODITE’s Water Resources project was
conducted by the Research Institute for Human andNature (RIHN) and the Meteorological ResearchInstitute (MRI/JMA) from 2006 to develop state-ofthe-art daily precipitation datasets on high-resolution
grids covering the whole of Asia [6]. In this study,
precipitation data to force land surface model for
Kyushu area was extracted from the APHRO_JP
V1207 [7] dataset with spatial resolution of 0.05
degree. Temporal resolution of APHRO_JP
precipitation data is daily.
And the Surface Radiation Budget (SRB)
dataset was used as long wave radiation and shortwave radiation.Surface Radiation budget (SRB)
dataset is produced and achieved by the NASA
Langley Research Center Atmospheric Sciences Data
Center (NASA/GEWEX). It is produced on a 1

D. Data
1) GCM Runoff Generation Data
GCM data used for this study is 3-hourly
runoff generation data from the super-high resolution(20-km) atmospheric general circulation model MRIAGCM3.2S. It is one of the latest atmospheric GCMs
based on a model jointly developed by the Japan
Meteorological Agency (JMA) and the MeteorologicalResearch Institute (MRI) [4]. The MRI-AGCM
provides data for three climate experiments: present
climate experiment (1979-2008), near future climate
experiment (2015-2044), and future climate
experiment (2075-2104). The data used for future
projection were based on the Special Report on
Emissions Scenarios (SRES) A1B scenario. In thisstudy, only the output of present climate experiment is
used.

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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.

degreex1 degree global grid uusing satellite-derived
cloud parameters and ozonne fields, reanalysis
meteorology, and a few other aancillary datasets. The
SRB dataset contains 3-hourly long wave and shortwave radiative fluxes.

theriver discharge simulaated using bias-correctedrunoff generation data showeds
an improvementcompared with that simu
ulated using original one.
However, shown in Fig.6-bb whose horizontal axis islogarithm, there were also a problem that the riverdischarge simulated usinng bias-corrected runoff
generation was overestimatted in high peak discharge
as compared with that usingg reference data.

Fig. 5 Schematic representatioon of QQM method
noff Generation Data
Bias Correction For GCM Run
A. Application of Quantile-Quaantile Mapping(QQM)
Methodo 20 years at Kashiwada,
Fig. 6-a Duration curves of
Oyodo River basin(vertical axis islogarithm)

To correct biases in GC
CM runoff generation
data, quantile-quantile mapping ((QQM) bias correction
method was selected. The biass correction based on
QQM method has been widely uused to correct outputsof climate models.Teutschb
hbein and
Seibert[8]compared the various bias coorrection methods andshowed that the QQM method iis more effective thanthe other methods.
In QQM method, GCM outputs andobservations are sorted to construct cumulative
distribution functions (CDFs). Thhese CDFs are used todefine the quantiles of sim
mulated values andobservations. Then, GCM siimulated values aresubstituted with those of the idenntical quantile from theobservational dataset. Fig.5 sshows the schematic
representation of QQM method.
In this study, runoff datta simulated by SiBUC
model were used as reference daata to correct the MRIAGCM3.2S runoff generation ddata. The effect of bias
correction is evaluated by simuulating river dischargeusing flow routing model 1K-FR
RM.

o 20 years at Kashiwada,
Fig. 6-b Duration Curves of
Oyodo River basin (horizon
ntal axis is logarithm)
Fig. 7 shows a hhistogram of two kinds ofrunoff generation data, GC
CM outputs and referencedata, in July for 20 years. The red bar indicates theresult of GCM and the blueb
one indicates that ofreference data. Shown in Fiig. 7, the frequency of highrunoff amount is higher in reference data than that in
GCM outputs.Therefore, th
he runoff of reference data
which is assumed to be doominated by surface runoff
can be substituted with thee runoff of original GCM
outputs which is assumed to be dominated by subsurface runoff. Fig. 8 show
ws the time series of runoff
generation data in originall GCM outputs and biascorrected data. The red liine indicates the result oforiginal GCM outputs and th
he purple one indicates thatof bias-corrected data.Bothh lines show high runoff
value on July 8th, but relativ
vely large value can be seencontinuously in bias-corrected data after the flooding
event while relatively low value can be seen in original
GCM data after that. As showns
in Fig. 8, relatively

B. Results
Fig.6-a shows the durattion curves of 20 years
from 1982 to 2001 at Kashiw
wada station in Oyodo
River basin. The red line indicaatesthe simulated riverdischarge using original GCM rrunoff generation data,
the blue line indicates that using reference data and theblack line indicates the observaational river discharge.
It can be seen that the simulatedd river discharge usingreference data is closer to the oobservation in the highpeak discharge(larger than 100 m3s-1) compared withthat using original GCM outpputs. Therefore, land
surface model SiBUC is connsidered to reproduce
runoff better than the MRI-AG
GCM3.2S. In addition,
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THA 2019 International Conference on“Water Management and Climate Change towardds Asia’s Water-Energy-Food Nexus and SDGs”
23– 25 January 2019, Bangkok, Thailand.

large values that contribute to floodinng may appear
continuously. It is occurred due to thhe difference of
frequency distributions between originnal GCM data
and reference data.This is considered too be one of thecauses of overestimation in simulating rriver discharge.

contributing to flooding.Thresholdd values are set forreference data and original data, respectively.In thisstudy, the number of datawhich is larger than thethreshold value in original datasetss is set to be smaller
than the number which is larger than the thresholdvalue in reference datasets. As shownsin Table. 2,
twokinds of threshold patterns werew
applied in thisstudy.QQM method was applied
d for high runoff
generation data and low data independently.
iThis
method is called grouping QQM method.
m
Table. 2Pattern of threshold value
Threshold value ofT
Thresholdvalue ofreference data
original GCM
outputs
Pattern1 Applying 5% value ApplyingA
10% value
from largest on frrom largest on eachmof each celleach month of each monthcell
Pattern2 Applying
2.5% ApplyingA
10% value
value from largest frrom largest on eachmof each cellon each month of montheach cellFig. 7 Histogram of runoff generationn data in GCM
outputs and reference data in July for 200 years

B. Results
Fig.9 shows time series ofo 4 kinds of runoff
generation data. The red line ind
dicates the result oforiginal GCM outputs, the purple oneo indicates that ofsimple QQM method, light blue oneo indicates that ofpattern1, pink one indicates thaat of pattern2. By
applying grouping QQM methodd, itcan be possible
toreduce the amount of runoff gen
neration data at thetime of flooding, compared with sim
mple QQM method.
Fig. 8 Time series of runoff generation data in original
GCM outputs and bias-corrected data
Revision Of Bias Correction
A. Grouping QQM method
In order to improve the ovverestimation, arevision of bias correction was connsidered to beeffective. The purpose of this revision iis to reduce thefrequency of high runoff generatiion data that
contribute to flooding after bias corrrection and to
improve the overestimation of river disccharge.
The frequency of high runnoff amount ishigher in reference data than that in GC
CM outputs andafter bias correction, relatively largge values that
contribute to flooding may appear conttinuously and it
may cause overestimation in sim
mulating riverdischarge. Therefore, it is considered tto be needed toreduce the frequency of high runoff aamount in biascorrected runoff generation data for improving
overestimation results. In considerration of thedifference in frequency distribution in thhe two kinds ofdata, a threshold is set for each data annd divides eachdata into high runoff generation datta contributing
toflooding and low runoff generattion data not

Fig. 9 The effect of bias corrrection on runoff
generation data
Fig. 10 shows the duratio
on curvesof 20 years
from 1982 to 2001 at Kashiwadda station in Oyodo
River basin in 5 kinds of simulateed river discharge.In
addition to the colors of Fig. 9, thee blue line indicatesthe result of reference data. In the highhpeak discharge,
all the two patterns could redu
uce river discharge
compared with before revision. In PatternP
1, the resultsthat are closer to reference daata were obtained.
Therefore, they showed an improvvement in simulating
river discharge by applying groupin
ng QQM method.
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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.

References[1] D. D. Toan, “Assesssment of river dischargechanges in the Indochinna Peninsula region under achanging climate,” Dooctoral dissertation, Kyoto
University, 2014.
[2] Hydrology and water reesources laboratory, Kyoto
University,http://hywr.kkuciv.kyotou.ac.jp/products/1K-DH
HM/1K-DHM.html,access
2018/10/24.
[3] K. Tanaka, “Development of the new land surface
scheme SiBUC commmonly applicapble to basinwater management and numerical weather
prediction model,” Dooctoral dissertation, Kyoto
University, 2005.
[4] R. Mizuta, H. Yoshiimura, H. Murakami, M.
Matsueda, H. Endo,T.. Ose, K. Kamiguchi, M.
Hosaka, M. Sugi, S. Y
Yukimoto, S.Kusunoki, andA. Kitoh, “Climate Simulations Using MRIAGCM3.2 with 20-k
km Grid,”Journal of the
Meteorological Socieety of Japan, vol.90A,
pp.233-258, 2012.
[5] S. Kobayashi, Y. Ota, Y. Harada, A. Ebita, M.
Moriya, H. Onoda, K.. Onogi, H. Kamahori, C.
Kobayashi, H. Endo, K. Miyaoka, and K.
Takahashi, “The JRA
A-55 Reanalysis: General
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miguchi, O. Arakawa, A.
[6] A. Yatagai, K. Kam
Hamada,N.
YasutomiYandA.
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Fig. 10 The effect of bias correcttion on river discharge
Conclusions
Bias correction was peerformed for the MRIAGCM3.2S 3-hourly runoff ggeneration based onreference data created by land ssurface model SiBUC
and the effect of bias correctiion was evaluated bysimulating river discharge utilizinng flow routing model
1K-FRM. Bias correction applyying the QQM method
is effective by creating referrence data of runoff
generation data. However, it waas found that the riverdischarge simulated using bias--corrected runoff wasoverestimated compared with thaat using reference data
especially on high flood events.. Therefore, the QQM
method was revised by settingg threshold values forrunoff generation data. By this reevision, overestimationof river discharge isimproved aand river discharge issimulated more precisely.
As the improvement of tthe land surface model
progresses and the reproducibillity of reference data
improves, it can be expected thhat more reliable bias
correction is performed.

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