Simulating hydrological impact of environmental change in the Abukuma watershed,JapanTSUYOSHI KINOUCHI and KATUMI MUSIAKE
Department of Environment System Management, Fukushima University, 1 Kanayagawa,
Fukushima-Shi, Fukushima 960-1296, Japankinouchi@sss.fukushima-u.ac.jp
Abstract The WEP model was applied to the Abukuma watershed in Japan (5400km2). The
model is validated with observed discharges at several locations in 2002, and then applied tosimulations for 2003 and 2004 in a time step of 1 hour and a grid cell size of 500m. The
simulation results showed good agreements with observed in both low streamflow and floodhydrographs. The hydrological impacts of possible future environmental changes in thewatershed, i.e. increase in air temperature, rainfall intensity and patterns, land use alteration
and urbanization, are simulated using the WEP model. Evaporation, surface runoff andgroundwater recharge are found sensitive to the environmental changes, and the hydrographs
are also modified.
Key words WEP model; Abukuma watershed; environmental change; global warming; landuse; water budget
INTRODUCTION
Water resource management in watersheds is essential for both sustainable humansociety and nature conservation. However, growing economy, populations and urbanization
have been direct and indirect pressures on the basin-scale hydrological cycles in many
watersheds. Most significant indirect pressure would be the climate change caused by theglobal warming. Water-related hazards may increase in humid climate regions, while
droughts are more threats in dry zones. Land use is also the direct control factor ofhydrological cycle in the watershed and is dominated by human activities and climaticconditions. Under these changing conditions, physically distributed modeling is highly
required for hydrological prediction in watersheds with heterogeneous land surfaces, soils
and aquifers under uneven distribution conditions of rainfall to better estimate the impacts offuture environmental changes such as land use alteration, urbanization and climate change.
The WEP model (Water and Energy transfer Processes model), a grid-based distributedhydrological model developed by Jia et al. (2001), has a great potential for sustainable water
resource management on the watershed level. In this study, we applied the WEP model to theAbukuma Watershed, Japan, for assessing hydrological impacts of possible climate changeand land use alteration in the future.

DESCRIPTION OF THE ABUKUMA WATERSHED
Geographical, geological and meteorological conditions
The Abukuma River watershed is located in the northeast of Japan mainland, and has anarea of 5,400km2 of which forest and agricultural land use accounts for 79% and 18%,respectively. Most of the mainstream runs in the middle of the watershed from the south to
the north. Many tributaries run eastward or westward. The headwater starts from theAsahidake (elevation of 1,835m) and reaches down to the Pacific Ocean. The elevation of
the watershed ranges from the sea level to more than 2,000m on the west side, where a
volcanic mountain range is located. On the contrary, a peneplane is dominant on the east sideof the mainstream, forming mountains of low elevation and gentle river slope.
Geological conditions of thewatershed are characterized by thegranite and the granodiorite extendingon the east side and complex mixturesof the metamorphic rock, andesite andgranite on the west side. Diluvium andalluvial soils are developed on the westside due to the erosion of volcanic
products. Two ravines in themainstream
providemagnificent
landscapes, but limit the flood flow
capacity of the mainstream channel.
The watershed is located in thetemperatezone.
Mean
annualprecipitation is as much as 1,500mm
on the west side because of highelevation and snowfall, but less on theeast side (1,200mm) and the plain inthenortharea(1,100mm).
Precipitation in some mountainous
areas exceeds 2,000mm.
Fig. 1 Study area showing the Abukuma watershed andtwo lakes located outside the watershed.

Socio-economic condition
The total population in the watershed is 1.38 million. Large urban cities were developedon the fluvial basins nearby the mainstream, probably due to the access to the main water. At
present, these cities utilize the mainstream for wastewater discharges, rather than domestic
and industrial water uptake. Certain amount of drinking water depends on tributary rivers.

Problems in the Abukuma River watershed
The Abukuma River watershed experienced several devastating floods in the past, suchones happened in August 1986, August 1998 and July 2002. Following the flood in August
1998, which resulted in 20 deaths and injuries, 3,590 inundated households, and 69 collapsed
houses, an intensive river improvement project was conducted by the embankment, dredging,installation of drainage pumps, etc. to raise the safety level against flooding. However, theriver is still vulnerable to floods due to low flood flow capacity in middle to upper reaches
and some bottleneck reaches. Furthermore, the recent trend of intensified rainfall is likely tocause flood damages along tributaries. In addition, the Abukuma River is recognized as oneof the worst quality stream among major watersheds located in the Tohoku region (Kinouchi& Musiake, 2006). Water resources are not abundant, as the precipitation is less than theJapan’s average and water use in some areas are dependent on the diversion from outside thewatershed.
MODEL APPLICATION
Input data and parameters
WEP model requires a variety of input data as listed in Table 1. For each computationalmesh (500m by 500m), thirteen land use types are reclassified into five categories; tallvegetation (forest and urban trees), short vegetation (grassland, paddy and other agriculture),bare soil land, water body, and impervious area (structures and ground), referring to previousstudies (Jia et al., 2002, Jia et al., 2005).
Table 1 List of main input data used for WEP model simulation
Category
Item
SourceLand surface conditions
Watershed boundary
Digital map* (250m by 250m DEM)
Topography
National digital information* (100m byLand use
100m raster data of 13 land use types,
Soil type1km resolution for soil types)
Soil depth
Geologic map (borehole data)
Meteorological andPrecipitation (31 stations)
Hourly data from MLIT** andhydrological conditions
Relative humidity (3 station)
AMeDAS***
Air temperature, Wind velocity,
Sunshine duration (12 stations)River network andStream channel
Digital Map, National digital information,
hydraulic dimensions
River profiles
Survey by the MLIT
Water use
Diverted water volume
Report of daily water diversion from theInawashiro Lake and the Hatori LakeArea for diverted irrigation
Irrigation districts map
Anthropogenic conditions
Population
National census data by the Ministry ofInternal Affairs and Communications*Products of the Geographic Survey Institute, **Ministry of Land, Infrastructure, Transport and Tourism***Automated meteorological data acquisition system by the National Meteorological Agency

The Thiessen polygon method was applied to determine precipitation and othermeteorological variables of each grid cell from gauged values. Air temperature at each grid
was corrected from measured one using the lapse rate (0.65°C/100m) and the elevation of
the grid. No correction was made for the precipitation. The degree-day snowmelt method
was included in the WEP model with the melt factor and the base temperature being
1.0mm/°C/day and 2°C, respectively.
The topsoil in the watershed is classified into ten types based on the national digitalinformation. The hydraulic properties of each type are given from the soil sample test andsensitivity check (Fig. 2, Table 2). Exceptions are set for the topsoil overlaid on somegeological types such as granite, loam and gravel (Fig. 2). Spatial distribution of thethickness and stratigraphy of the ground was given from the hydrogeological maps andborehole data at 986 locations within the watershed (Fig. 3). The extent of the ground wasconsidered as unconfined aquifer because no distinct aquitard was found. Permeability andstorage coefficient are aggregated, giving those values for each ground soil layer.

Saturated hydraulic conductivity (cm/s)

1.0E-20～20cm
20～60cm
60～200cm
1.0E-3

1.0E-4

1.0E-5

1.0E-6
Forest soils Podozol Lowland
PeatGranite
GravelRegosol Kuroboku
soils
Paddy
Sand Pyroclastic
Gley soils soils
Loammaterial
Red and Yellow
soils
Classified by subsurface geology

Fig. 2 Soil physical parameters (1).

Fig. 3 Distribution of soil and aquifer depths.

Table 2 Soil physical parameters (2).
Soil or land categoryForest soils, Gley soils, Red and yellow soils,Regosol, Podozol, Lowland soils
Kuroboku soils
Peat, Paddy
Urban

Saturated and residual moisture contents
0–20cm20–60cm
60–140cm
0.70 / 0.40

0.55 / 0.20

0.55 / 0.20

0.70 / 0.30
0.80 / 0.44
0.80 / 0.44

0.70 / 0.30
0.80 / 0.44
0.80 / 0.44

0.70 / 0.30
0.39 / 0.12
0.80 / 0.44

The Manning’s roughness of overland flow is given as a harmonic average of theroughness coefficient for each land use type (Table 3). The overland flow is routed along thesteepest downward one among eight directions to its adjacent cells using the kinematic wave
method. The Manning roughness values of the mainstream are the estimates based on theinverse simulation of observed floods (Table 3), and the value for the remaining streams isset to 0.033. Dam operation rules are incorporated into the model. Unit water volume for
irrigation and household is set to 200m3/ha/day and 300litter/person/day, respectively.
Irrigation water is provided from local streamflow, except for two districts where irrigationwater is diverted from two lakes located outside of the Abukuma watershed (see Fig. 1).
Table 3 Equivalent roughness of each land use type.
Land use

ForestGrassland
Cropland

Paddy

Bare lands

UrbanInland
waterStreamchannel

Manning’s n

0.13

0.08

0.05

0.10

0.02

0.01

0.022–0.037

Validation and application
The model was run for a simulation from 2002 to 2004 in a time step of 1 hour (exceptfor overland flow and river flow routing with a time step of 10 minutes) and a grid cell sizeof 500m×500m. River channels are divided into computational elements with thelongitudinal length of 395 - 770m. Hourly flow discharge at representative water levelmeasurement sites is compared with simulated results. Principle parameters are manuallyvalidated for the simulation results of 2002.
Annual and flood hydrographs Variations in simulated hourly streamflow agreed wellwith simulated at most measurement stations throughout years (Fig. 4). Due to snowmelt, thespring streamflow At Yagita station of the Ara river increases in both simulation andmeasurement (Fig. 6). Biggest floods during the simulation periods are compared between
simulation and measurement (Fig. 5 and 7). The peak discharge at each station is also well
reproduced. The difference is attributable to the flooding along the mainstream, as the model
does not represent the flooding phenomenon. The flood in July 2002 caused inundation inlarge areas, which resulted in detaining and reducing flood discharge.
Hourly discharge (m3/s)

1600
Motomiya

Simulated

Observed

1200
800
400
0
1/1

1/31

3/2

4/1

5/1

5/31

6/30

7/30

8/29

9/28

10/28

11/27

12/27

6/30

7/30

8/29

9/28

10/28

11/27

12/27

Hourly discharge (m3/s)

2000
YahataSimulatedObserved

1500
1000
500
0
1/1

1/31

3/2

4/1

5/1

5/31

Fig. 4 Annual hydrographs at mainstream stations (Motomiya and Yahata, 2002).

3000

Simulated
Observed

Hourly discharge (m3/s)

3000
2000
1000
07000

Hourly discharge (m3/s)

Motomiya

7/9

7/11

7/13

7/15

7/19

Simulated
Observed

Yahata6000

7/17

Hourly discharge (m3/s)

Hourly discharge (m3/s)

4000

5000
4000
3000
2000
1000
0
7/9

7/11

7/13

7/15

7/17

Simulated
Observed

Motomiya

2000

1000

0
9/28
4000

9/30

10/2

10/4

10/6

Simulated
Observed

Yahata

3000
2000
1000
0
9/287/19

9/30

10/2

10/4

10/6Fig. 5 Flood hydrographs at mainstream stations in July and October 2002.
Hourly discharge (m3/s)

120
Yagita100

Simulated

Observed

80
60
40
200
1/1

1/31

3/2

4/1

5/1

5/31

6/30

7/30

8/29

9/28

10/28

11/27

12/27

Fig. 6 Annual hydrograph at Yagita, Ara river in 2003.

1000

010/3

10/5

10/7

10/9

10/11

10/13

Simulated
Observed

Yahata

3000
2000
1000
010/3

10/5

10/7

10/9

10/11

3000
Hourly discharge (m3/s)

2000

4000
Hourly discharge (m3/s)

Simulated
Observed

Motomiya

10/13SimulatedObserved

Motomiya

2000

1000

010/194000

Hourly discharge (m3/s)

Hourly discharge (m3/s)

3000

10/20

10/21

10/22

10/23

10/24

Simulated
Observed

Yahata

3000
2000
1000
010/19

10/20

10/21

10/22

10/23

10/24

Fig. 7 Flood hydrographs at mainstream stations in October 2004.

Low flow, accumulated runoff and annual water budget Low flow is well simulated
by considering the snowmelt, irrigation water diversion, and spatial distribution of groundsoil characteristics. Accumulated runoff is a little overestimated due partly to the initialvariable settings. Annual water budgets in 2003 and 2004 are shown in Fig. 8. Subsurface
flow is larger than other components, which implies the contribution of runoff from forestedlands. Surface runoff and groundwater outflow are almost comparable. Diverted water forirrigation is small in the annual budget, but it should be noted that the irrigation period islimited to about 4 months and the impact of diversion should be large in the irrigation
periods.
ET450

ET555

PR
1261

DW 37

PR1438DW 26

194TW & IW328

SR428

28

WD 28
SF & LF 449

223

TW & IWSRWD
SF & LF 571
348.5
GW(2003)383.2
GW(2004)

Fig. 8 Schematic of annual water budgets in the Abukuma watershed (left: 2003, right: 2004, unit: mm).
ET: evapotranspiration, PR: precipitation, DW: diverted water, TW&IW: tap water and irrigation water,
SR: surface runoff, WD: wastewater discharge, SF&LF: subsurface and lateral flows, GW: groundwater
outflow.

15

Change in water budget component (%)

Change in water budget component (%)

HYDROLOGICAL IMPACTS OF ENVIRONMENTAL CHANGE
Impacts of future climate change were estimated giving possible scenarios of air
temperature increase and precipitation variations in the future, which are based on GCM
simulation results. 12% increase in evapotranspiration is predicted due to the 4°C air
temperature increase alone (Fig. 9). By contrast, the groundwater recharge is reduced byabout 10%, leading the reduction of runoff components (Fig. 9). More than 10% increases ingroundwater recharge and surface runoff are simulated due to the 5.1% increase in annual
precipitation, which was given based on the GCM derived ratios of monthly precipitation for
2091-2100 and 1991-2000. In case of the air temperature and precipitation increases, someoffsetting relations are found for runoff components and evapotranspiration.

ETDRSR
SF&LF
GW
GWR

105(+2°C)

0(+4°C)(Present)-5-10-15
20212223

Annual mean temperature (°C)

24251412108ET
DRSR
SF&LF
GW
GWR

6420-2(1261mm)(1325mm, +5.1%)(1325mm, +5.1%)

present

Rainfall variation

Rainfall variation& 4°C up-4Fig. 9 Percentage of change in water budget components from those for the present case simulation result;
Impacts of temperature increase (left), rainfall variations and their composite (right). DR: direct runoff(=SR+SF&LF), GWR: groundwater recharge. See Fig. 8 for other abbreviations.

Change in water budget component (%)

20
Impacts of deforestation, river
15
10
improvement and urbanization
5were also simulated. As major
0-5
cities in this watershed were-10ETDR-15
developed
over
relatively
SR-20
SF&LF
GW
-25
permeable
geologicalareas,
GWR
-30
-35
significant hydrological impacts
presentForest
Reducedto grassland
permeabilityare found for surface runoff (notshown here). Channelization of Fig. 10 Percentage of change in water budget components
tributaries is found to be one of from those for the present case simulation result; impact ofland use alteration. DR: direct runoff (=SR+SF&LF), GWR:
responsible factors for quicker groundwater recharge. See Fig. 8 for other abbreviations.
peak flood discharge, in addition
to natural hydrological response due to geological and soil characteristics. Deforestation
impact was simulated by assuming that all forested lands are turned into grasslands. Becauseof the reduced water consumption by transpiration of forets, groundwater recharge isincreased (Fig. 10). Streamflow during floods tends to increase, but very little impact isfound for the low flow. Assuming the permeability of the topsoil is reduced to one fifth, asignificant increase in surface runoff was found to occur as a result of devastated forests.

CONCLUSION
This study investigated hydrological impacts of possible environmental changes in theAbukuma watershed in Japan. The WEP model was successfully applied to the watershed,and predicted the impacts of climate change and land use alteration on the water budget.
Acknowledgement Authors wish to acknowledge the suggestions and supports given byProf. Yangwen Jia from IWHR, China.
REFERENCES
Jia, Y., Ni, G., Kawahara, Y. and Suetsugi, T. (2001) Development of WEP model and its
application to an urban watershed, Hydrological Processes, Vol.15, No.11, 2175-2194.
Jia, Y., Ni, G., Yoshitani, J., Kawahara, Y. and Kinouchi, T. (2002) Coupling simulation ofwater and energy budgets and analysis of urban development impact, Jour. ofHydrologic Eng., ASCE, Vol.7, No.4, 302-311.
Jia, Y., Kinouchi, T. and Yoshitani, J. (2005) Distributed hydrologic modeling in a partiallyurbanized agricultural watershed using water and energy transfer process model. Jour. ofHydrologic Eng., ASCE, Vol. 10. No. 4, 253-263.
Kinouchi, T. and Musiake, K. (2006) Long-term change of stream water quality as aconsequence of watershed development and management, Proceedings of 3rd APHW
Conference.