Proceedings of 2013 IAHR World Congress

Estimation of the Impact of Climate Change on Japanese Rice
Yield and Water Resources
Shunji Kotsuki
Ph. D. student, Kyoto University, Kyoto 611-0011, Japan. E-mail: kotsuki.shunji.42x@st.kyoto-u.ac.jp
Kenji Tanaka
Associate Professor, Disaster Prevention Research Institute, Kyoto Univ., Kyoto611-0011, Japan.
E-mail: tanaka.kenji.6u@kyoto-u.ac.jp

ABSTRACT: The aim of this study is to estimate the impact of climate change on Japanese rice yield andwater resources using a newly-developed Japanese Water Resources Model. The model is composed offive sub-modules: land surface hydrology, irrigation, rice growth, dam operation, and stream flow. Usingobserved meteorological data for the period of 1994 to 2003, simulated river discharge and rice yields
were verified. We estimate impact of climate change on river discharge, rice yields and water stress using
bias-corrected outputs from a super high-resolution global climate model under present, near-future, andlate 21st century climate conditions. The main findings were as follows: (1) Simulated rice yield and riverdischarge using observed meteorological forcings have shown good agreement with statistical data. The
snowfall correction and the dam operation module clearly improve the simulated river discharge. (2) The
seasonal discharge will change dramatically, especially at the end of the 21st century, in basins facing the
Sea of Japan that currently experience heavy snowfall. A depletion of annual snowfall would be result
from the warming temperature trend in many basins. (3) Climate change is predicted to strengthen thewater stress in many basins in the northeastern Japan, which uses large amounts of snowmelt water toprepare rice paddies for planting. Water stress would also increase in small basins. (4) Climate change
may mitigate low-temperature stress and expand the arable area, including into high-mountain areas andnorthern prefectures. Many prefectures facing the Sea of Japan are predicted to experience decrease inrice yield. The simulation clearly demonstrates the regions that should consider changing the period of
cultivation to mitigate the decrease in rice yields and increased water stress.
KEY WORDS: Climate change, Water resources model, Japanese river, Rice yield, human activity
1 INTRODUCTION
As the problems associated with climate change increase, evaluations of the likely impacts areurgently required, such as by climatological and hydrological modeling. The performance of generalcirculation models (GCMs) has improved, and GCM outputs have been used in hydrological models toassess the impacts of climate change. While many impact assessment studies for Japan using GCM
outputs have been reported, many have focused on a particular basin (e.g., Sato et al., 2011; Kim et al.,2011). Few impact assessment studies have covered the entire Japanese basin area to identify areas
vulnerable to climate change. The few previous studies that have examined all of Japan have assessedclimate change impacts on seasonal discharge, flood risk, and flood damage (Kazama et al., 2009;
Tachikawa et al., 2010), but none has investigated the water demand and supply balance. We developed
the novel Japanese Water Resources Model to assess the impact of climate change on seasonal discharge,
water demand-supply balances, and rice yield. This paper describes the validation of our model using
meteorological forcing with observed data and the impact of climate change on Japanese water resources,
estimated using bias-corrected GCM outputs.

2 METHODOLOGY
2.1 The Japanese Water Resources Model
The Japanese Water Resources Model proposed in this paper is composed of five sub-modules: land
surface hydrology, irrigation, rice growth, dam operation, and stream flow (Figure 1). The model has theadvantage of simulating not only the natural water cycle on land but also rice yields and the effects ofhuman activities such as dam operations, water withdrawal, and irrigation.
The rice growth module was developed from a field-scale model, the Simulation Model for
Rice-Weather Relationship (SIMRIW; Horie et al., 1995, Iizumi et al., 2009). Using temperature andshort-wave radiation as input data, the module simulates rice growth and rice yields by consideringhigh-temperature and low-temperature stresses on yield. The model parameters were originally at the field
scale for specific cultivars, but prefecture-specific parameters were optimized in the present study using
particle swarm optimization (PSO; Kennedy and Eberhart, 1995).
The land surface module calculates the energy, radiation, and water budgets on the land surface withseven meteorological forcings: temperature, specific humidity, short-wave radiation, long-wave radiation,
atmospheric pressure, wind speed, and precipitation. In this study, the Simple Biosphere Model including
Urban Canopy (SiBUC; Tanaka, 2004) was used to calculate the land surface process.
The basic function of the irrigation module is to maintain the water level in paddy fields and soil
moisture in croplands within an appropriate range that is defined for each growing stage. The water levelin paddy fields and soil moisture in croplands are calculated in the land surface module. The crop
calendar, which defines the planting date and harvesting date, is specified by the rice growth module.
The stream process calculates the river discharge using runoff calculated by the land surface process.
The river discharge is simulated with a kinematic wave equation. Intakes and drainages for agricultural,
domestic, and industrial water usage are considered in this process.
The dam operation module was developed to include the effect of 1,231 Japanese dams withvolumes over 100,000 m3. After classifying each dam’s objective into three types (flood protection, water
utilization, and multi-objective), the dam operation can be briefly described by the following equations:Qinf > Q flood Q 
Qbase =  flood  when  Qnorm Qinf ≤ Q flood =
Qouf max {Qbase , β ⋅ Qreq }

(1)

(2)

where Qinf is inflow [m3s-1], Qouf is terminal outflow [m3s-1], Qnorm is the maintenance outflow [m3s-1],
Qbase is the basic outflow [m3s-1], Qflood is the outflow during a flood [m3s-1], and Qreq is the outflow
requirement from downstream [m3s-1]. The outflow coefficient for requirement β was set as 2.5 in thisstudy. Equation (1) describes operations for flood protection and equation (2) describes operations forwater utilization, respectively. The maintenance outflow Qnorm is determined from the following equations
according to the objective of each dam:

Qnorm, f = min {Q flood , Qinf }(3)

Qlow for rainy season
Qnorm,u =  Qdry for dry season(4)

for St > Vu
for St ≤ Vu

Qnorm, f
Qnorm,m = Qnorm,u(5)

where Qlow is low-water outflow [m3s-1], Qdry is outflow during the dry season [m3s-1], Vu is the capacity ofthe dam [m3], and St is the storage of dam. The subscripts f, u and m indicate a flood protection dam, a2water utilization dam, and a multi-objective dam, respectively. The outflow during the dry season Qdry,
which is determined at the end of each rainy season, is calculated by the storage divided by the period
until the next rainy season. Following a river discharge analysis from 1994 to 2003 without dams, therainy season was determined for each dam as the period when the mean discharge of the month was
greater than the annual average discharge. Using the daily-discharge duration curve from the 10-year
simulation without the dam module, the 5% and 50% percentile values were set as the outflow during aflood Qflood and the low-water outflow Qlow, respectively.
2.2 Data sets
The meteorological forcing, statistical, and geophysical data used in this study are shown in Tables 1
and 2. Precipitation data were corrected using the following equation because precipitation gaugemeasurements underestimate snowfall (Yang et al., 1998):

CR =1 / (1 + 0.231 ⋅ Ws ) when {T < Tc },Tc =11.01 − 1.5e

(6)where CR is the gauge catch ratio [-], Ws is wind speed [ms-1], T is surface temperature [K], e is surface
vapor pressure, and Tc is critical temperature [K].
2.3 Validation using observed forcing
Using observed meteorological data for the period of 1994 to 2003, simulated river discharge andrice yields were validated. Model errors were calculated with the following equations:
Vs − VoBL =,Vo

Vs − Vo
1
MRE =
,
∑
N
Vo

2
1
RMSE = ∑ (Vs − Vo ) ,N

Nash =
1 − ∑ (Vs − Vo )2

∑ (V − V )
o

2

(7)

o

where Vs is the simulated value and Vo is the observed value.
2.3.1 Validation of the rice growth module
A comparison of the heading dates, harvesting dates, and yield simulated by our model and reported
by Iizumi et al. (2009) is shown in Table 3. “North pref.” signifies the averaged results for the northern
prefectures (Hokkaido and Tohoku District). “South pref.” signifies the averaged results for the southern
prefectures (Kyushu and Shikoku District). Although our parameter identification method was different
from that used by Iizumi et al., (2009), computed errors were almost the same, suggesting that theparameters of the rice growth module were identified correctly. Comparisons of statistical and simulated
rice yield from 1991 to 2004 in the north, east, middle, and west regions of Japan are shown in Figures(b-1), (b-2), (b-3), and (b-4). The simulated yields agreed well with the statistical data and reflected thedamage resulting from a cold summer in 1993.
Table 1 Meteorological forcing data sets
Observed precipitation
Kamiguchi et al., 2010
Observed other forcing
JMA, AMeDAS
MRI-AGCM20
Kusunoki et al., 2011
JMA: Japan Meteorological Agency
AMeDAS: Automated Meteorological Data Acquisition System

Table 2 Geophysical and statistical data sets
Flow direction
Global Drainage Basin Database(GDBD; Masutomi et al., 2009)
Channel slope
Soil Physical
Ecoclimap ver.1 (Meteo-France)
NDVI
Spot Vegetation 1km
Population
National Land Numerical Information
Dam
download service
Water usage

Figure 1 Schematic diagram of the Japanese Water
Resources Model used for this study. The modelcomprises five sub-modules: rice growth, hydrologicalland surface, irrigation, stream flow, and dam operation.

Land cover3

http://nlftp.mlit.go.jp/ksj-e/index.html

Table 3 Comparison of heading dates, harvesting dates, and yield simulated by our model and Iizumi et al., (2009)
Heading dates [day]Harvesting dates [day]Rice Yield [t/ha]
North pref. South pref.
North pref. South pref.
North pref. South pref.
R2
0.807
0.459
0.860
0.347
0.607
0.552
This
study
RMSE
1.3
1.5
1.7
1.9
0.45
0.36
R2
0.817
0.435
0.518
0.417
Iisumi(2009)
RMSE
2.3
3.2
0.49
0.36

Figure 2 Distribution of potential rice yield using observed forcing (a). Comparisons of statistical and simulated
rice yield from 1991 to 2004 in north, east, middle, and west regions of Japan are shown in Figure (b-1), (b-2), (b-3)
and (b-4). Black lines and red lines respectively show statistical and simulated rice yield. [unit: g/m2]

Figure 3 Seasonal validation of the monthly river discharge at major 19 first-grade rivers in Japan [unit: m3s-1].
Black lines with X-marks, blue lines with circles, and red lines with circles respectively represent observed
discharge, simulated discharge without and with correction of snowfall data.
42.3.2 Validation of river discharge
A seasonal validation of the monthly river discharge at 19 major first-grade rivers in Japan is shownin Figure 3. The Nash-Sutcliffe model efficiency coefficient (hereafter Nash) exceeds 0.8 in most rivers,
suggesting that our water resources model reproduced the seasonal discharge well. The effect of asnowfall correction is reflected in the difference between the blue (without correction) and red (withcorrection) lines in Figure 3. The snowfall corrections clearly improve the simulated river dischargeduring the snow melt season from March to June in northern Japanese rivers. The periods of snowmelt arealso reproduced in Figure 3, suggesting that accumulation and melting processes of snow on land surface
are calculated correctly in our Water Resources Model.
Water balance error and mean relative error were calculated for the 19 river stations to verify theeffects of snowfall correction and the dam operation module (Table 3-a). NS, SC, DA, and ND in thetable are the mean values with no snowfall correction, snowfall correction, no dam, and dam operation,
respectively. Blue and green labels in the table show the improvement of the simulation following thesnowfall correction and the dam operation module, respectively. Water balance error was improved formost stations following the snowfall correction. Seasonal river discharge was also improved by the dam
operation module. Clearly, the module describes the dam operation correctly. Actual dam operations haveto be used for simulations in specific river basins.
Table 3 Results at gauging stations of 19 major rivers of (a) validation using observed forcing, and (b) estimatedimpact of climate change on river discharge using GCM output. Water balance error (BL) and mean relative error(MRE) were calculated to verify effects of snowfall correction and the dam operation module in table 3-a. Water
balance error (BL) and Nash-Sutcliffe model efficiency coefficient (Nash) were calculated to estimate impact ofclimate change on river discharge. NS, SC, DA, ND respectively represent simulations without the snowfall
correction, with the snowfall correction, without the dam module, and with the dam module. Blue and green labelsin the table show the improvement of the simulation following the snowfall correction and the dam operation
module, respectively. Light and dark red colors show change and dramatic change in annual river discharge andseasonal discharge.
(a) Validation using observed forcing(b) Impact estimation
ClimatePresent (1994 ‒ 2003)
Future1
Future2Error function
BL
MRE
BL
Nash
BL
Nash
Model
NS,DA SC,DA SC,ND SC,DASC, DA1
Teshio
-39.6
-24.5
0.385
0.362
+ 7.8
0.944
+12.7
0.650
2
Tokachi
-19.3
- 8.1
0.235
0.198
+ 0.6
0.974
+11.1
0.871
3
Ishikari
-23.0
- 4.0
0.335
0.291
+ 9.2
0.950
+17.3
0.783
4
Yoneshiro
-31.6
-20.9
0.235
0.226
- 0.2
0.676
+ 9.2
-0.131
5
Omono
-30.1
-18.4
0.215
0.205
+ 5.6
0.477
+ 9.4
-0.455
6
Kitakami
-26.8
-18.8
0.199
0.195
+ 2.3
0.738
+ 5.3
0.433
7
Mogami
-34.4
-19.8
0.197
0.196
+ 6.5
0.554
+ 7.0
-0.961
8
Agano
-34.4
-19.8
0.238
0.231
+ 5.8
0.745
+ 3.1
-0.116
9
Shinano
-33.3
-22.0
0.214
0.203
+ 4.3
0.797
+ 5.9
-0.34810 Abukuma
-23.1
-16.0
0.282
0.261
+ 6.0
0.911
- 5.6
0.735
11
Naka
-14.5
-9.6
0.256
0.249
+ 7.5
0.890
- 0.3
0.804
12
Tone
+ 1.8
+ 9.7
0.269
0.282
+ 4.6
0.908
+ 6.1
0.803
13
Fuji
+49.1
+56.9
1.263
1.290
+ 2.8
0.934
+ 4.9
0.921
14
Tenryu
- 6.2
- 1.3
0.207
0.237
+ 3.5
0.955
+ 4.5
0.932
15
Kiso
- 3.6
+ 1.1
0.152
0.185
+ 1.9
0.975
- 1.1
0.852
16
Kizu
-23.8
-21.7
0.306
0.275
+ 4.3
0.781
+ 5.9
0.673
17
Gono
-31.5
-23.1
0.354
0.323
+ 2.5
0.722
+13.6
0.307
18
Yoshino
+ 6.2
+ 9.3
0.406
0.483
- 1.1
0.933
+ 1.2
0.957
19
Chikugo
-17.2
-15.2
0.282
0.267
-17.0
0.693
- 1.3
0.787
: improvement with snowfall correction: improvement with dam operation: change caused by climate change: dramatic change caused by climate change

53 ESTIMATION OF THE INPACT OF CLIMATE CHANGE
3.1 Bias correction of GCM data
The impacts of climate change on river discharge, rice yields, and water stress were estimated using
the forcing outputs from a super high-resolution global climate model, the Japan Meteorological ResearchInstitute atmospheric general circulation model with 20-km resolution (MRI-AGCM20; Kusunoki et al.,2011) under present (from 1979 to 2003), near future (from 2015 to 2039, hereafter future1), and late 21st
century (from 2075 to 2099, hereafter future2) conditions. Bias correction was undertaken for sevenmeteorological forcings using observed data with the following equation:
obs

GCM

Fy ,m ,d ,h ( x, y ) =FyGCM,m ,d ,h ( x , y ) + ( F present ,m ,riv ( x , y ) − F present ,m ,riv ( x , y ) )(8)－where F is the corrected forcing, Fobs is the observed forcing, FGCM is the forcing form the GCM, and F
present is the mean forcing from 1991 to 2004. The subscripts y, m, d, and h represent year, month, day andhour, and riv(x,y) is river basin which each mesh belongs to.
3.2 Effects on water resources
Water balance error and Nash were calculated to estimate the impact of climate change on riverdischarge (Table 3-b). The spatial distribution of Nash in Japanese basins is shown in Figure 4-a. The
table and figure suggest that the seasonal discharge will change dramatically, especially at the end of the
21st century, in basins facing the Sea of Japan that currently experience heavy snowfall. Changes inannual snowfall are shown in Figure 4-b, which indicates that a depletion of annual snowfall would result
from the warming temperature trend in many basins. A depletion of snowfall would increase the winterdischarge and decrease the spring discharge.
The cumulative withdrawal to demand ratio (CWD; Hanasaki et al., 2008) was calculated toevaluate the impact of changes in seasonal discharge on the water demand and supply balance. CWD
water stress in Japanese basins was calculated by the following equations:

CWD=i 100 ⋅ ∑ ωweek ,i / ∑ Dweek ,i(9)Figure 4 Changes in (a) seasonal discharge, (b) annual snowfall, and (c) the CWD water stress caused by climatechange. Figure (a-1), (b-1) and (c-1) show difference between near future climate (2015 ‒ 2039) and present
climate (1979 ‒ 2003). Figure (a-2), (b-2) and (c-2) show difference between end of 21th century climate (2075‒ 2099) and present climate. Changes in seasonal discharge were estimated by calculating Nash-Sutcliffe model
efficiency coefficient using averaged monthly discharge under the three climate conditions.

6 Dweek ,i 
Qweek ,i ≥ Dweek ,i 
(10)ωweek ,i =  when 

Q
else


 week ,i 
whereωweek is the weekly water intake [m3/week], Dweek is the weekly water demand [m3/week], and Qweek
is the weekly water supply [m3/week]. Agricultural, industrial, and domestic water demands were also
included.
Changes in the CDW water stress are shown in Figure 4-c. Cold and warm colors represent the mean
mitigated and strengthened water stresses caused by climate change, respectively. Climate change ispredicted to strengthen the water stress in many basins in the northeastern Japan (theTohoku District).
Because the Tohoku District uses large amounts of snowmelt water to prepare rice paddies for planting, adecrease in the spring discharge would increase water stress in this district. Water stress would also
increase in small basins, as shown in Figure 4-c. Rainfall intensity is predicted to increase due to thewarm temperature trends under future climate change. Thus, water stresses would also be strengthened inbasins that do not have large reservoirs. It is clear that many Japanese basins will face severe challenges
in water resource management under future climate scenarios.

3.3 Effects on rice yields
Changes in rice yield, absorbed short-wave radiation after the flowering stage, and low temperaturestresses on yields are shown in Figures 5-a, 5-b, and 5c. The hatches in Figure 5 indicate regions
incompatible with farming under present climate conditions because of cold temperatures. Regions withhatches are predicted to experience dramatic increases in rice yield, implying that climate change may
have the benefit of expanding the arable area, including into high-mountain areas and northern prefectures.
Figure 5-a indicates that rice yields will increase in many prefectures under future climate change
conditions. Regions where a decrease in rice yield is predicted correspond to those regions where
decreasing amounts of short-wave radiation are absorbed. A change in the timing of autumn rain may
decrease the amount of short-wave radiation absorbed. Many prefectures facing the Sea of Japan are
predicted to experience such a decrease in rice yield and absorbed short-wave radiation. Those prefectures
are also predicted to experience a dramatic change in seasonal river discharge. Prefectures facing the Sea
of Japan should consider changing the period of cultivation to mitigate the decrease in rice yields andincreased water stress. However, low-temperature stresses would be mitigated by the warming
temperature trend shown in Figure 5-c. Damage during cold summers in northern prefectures would be
mitigated under future climate change conditions.

Figure 5 Changes in (a) rice yield, (b) absorbed short-wave radiation (abSW) after flowering stage, and (c) low
temperature stresses (LTS) for yield. The hatches in the figures indicate aregions incompatible with farming underpresent climate condition because of cold temperature.
7

4 CONCLUSIONSIn this study, we newly developed the Japanese Water Resources Model to assess the impact ofclimate change on seasonal discharge, water balances, and rice yield. This paper has described thevalidation of our model using observed meteorological forcings and the impact of climate change on
Japanese water resources using GCM outputs. The main findings were as follows:
1) Simulated rice yield and river discharge using observed meteorological forcings have shown good
agreement with statistical data. The snowfall correction and the dam operation module clearly
improve the simulated river discharge.
2) The seasonal discharge will change dramatically, especially at the end of the 21st century, in basins
facing the Sea of Japan that currently experience heavy snowfall. A depletion of annual snowfall
would be result from the warming temperature trend in many basins.
3) Climate change is predicted to strengthen the water stress in many basins in the Tohoku District
located in the northeastern Japan. Because the Tohoku District uses large amounts of snowmelt waterto prepare rice paddies for planting, a decrease in the spring discharge would increase water stress inthis district. Water stress would also increase in small basins. It is clear that many Japanese basins
will face severe challenges in water resource management under future climate scenarios.
4) Climate change may mitigate low-temperature stress and expand the arable area, including into
high-mountain areas and northern prefectures. Many prefectures facing the Sea of Japan are predicted
to experience decrease in rice yield caused by depletion of the absorbed short-wave radiation.
Prefectures facing the Sea of Japan should consider changing the period of cultivation to mitigate thedecrease in rice yields and increased water stress.
ACKNOWLEDGEMENT
This work was conducted as a part of the SOUSEI program funded by the Ministry of Education, Culture,
Sports, Science and Technology (MEXT), Japan. This study was also supported by the Japan Society forthe promotion of Science (JSPS).
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