Journal of Natural Disaster Science, Volume 27, Number 2, 2005, pp85-94

85

Distributed hydrologic simulations to analyze the impacts of land use changes
on flood characteristics in the Yasu River basin in Japan

1

T. A. KIMARO1, Y. TACHIKAWA2 and K. TAKARA2
Water Resources Engineering Department, Faculty of Civil Engineering and the Built
Environment, College of Engineering and Technology, University of Dar es Salaam
2
Disaster Prevention Research Institute, Kyoto University(Received for 10 Jun., 2005 and in revised from 10 Jan., 2006)

ABSTRACTIn most parts of the world, land use data from remote sensing have been accumulated for more than 30 years.
Historical images of land use can be used to know the location and types of land use change as a step towards
investigating the impact of such change on floods. In this paper, we present a distributed watershed model developed for using land use data for investigating the impacts of land use changes on floods. The output of the model
includes hydrographs and runoff maps simulated with different land use data to show the impact of land usechange. The model has been applied in the Yasu River basin in Japan to evaluate the impact of land use changes
between 1976 and 1997. The results show that land use changes during this period can be associated with anincrease of up to 18% in peak flow and decrease in travel time. The changes in flood peaks appear to be largely
influenced by the spatial distribution of land use change, and the changes are explained by a physically based distributed hydrological model considering the spatiotemporal distributions of surface water storage and surface
roughness derived from land use data.

1. INTRODUCTION
Although there are many studies on a hydrologic model utilizing land use data, only a few of these have focused on explaining
the effects of land use change on river flow with hydrologic modeling. This fact is partly represented in the remarks of Beven (2000)
that techniques for analysis of the effects of land use change on amodeled hydrological response are still very much at an earlystage. A review of the few studies available shows that general
conclusions and guidelines are difficult to establish because thecase studies presented differ in key issues such as temporal andspatial scale of investigation, type of hydrological model andmodel sensitivity to land use change. The results of investigations
are influenced by these factors, and their applications may be limited to the specific conditions under which they were obtained. One
important limitation of studies on hydrological impacts of land usechange is the formulation of a realistic land use scenario for analysis. To answer the question of how much river flow will be affected by a 50% change of forest, a researcher may decide to convert
50% of forested area into another land use type. However, thebasin response will depend on the land use to be replaced, and how
the land use will change is very uncertain.
Generally speaking, the hydrological impacts of land usechange depend not only on the overall changes in land use types
but also in their spatial distribution (Schumann and Schultz, 2000).
To predict these effects meaningfully, one needs to estimate land
use changes properly by using archived remotely sensed land use
data. Another important problem in investigating the hydrological
KEY WORDS: Land use change, Distributed hydrological model, Floods

impacts of land use changes is the modeling of hydrologicalprocesses that are affected by land use change. This problem needs
to be addressed by appropriate physically based modeling ofhydrologic components to capture the effects of land use changes
on a simulated hydrograph. It is desirable that a model used ininvestigation is able to reasonably simulate a river hydrograph during a flood event using land use-dependent parameters derived
from concurrent land use data, before it is used to predict changes
in runoff induced by land use change. The scale of investigation
also needs to be considered because effects of land use changes are
not expected to be the same at different spatial and temporal scalesof analysis.
The availability of fine-resolution land use data from satellite
remote sensing in recent years has prompted studies on rainfallrunoff simulations aimed at investigating the impacts of land usechange and change on river flow. A few examples of studies inthis area include Dagnachew et al. (2003), Niehoff et al. (2002),
Kim et al. (2002) and Schumann and Schultz (2000). An importantdifference between this study and other similar works is that landuse data are rigorously used in runoff simulations with moreemphasis on evaluating the impacts of observed land use changes
on flood runoff. Somewhat similar works by Schumann and
Schultz (2000), Dagnachew et al. (2003) and Niehoff et al. (2002)
either did not emphasize specifically the evaluation of the hydrological impacts of land use change, or differed from this study inthe approach and scale of investigation. A more specific differencein the approach is that these studies and many other previous works
relied on scenario analysis to describe the impacts of land use

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changes rather than using observed land use changes as emphasized in the current work. Schumann and Schultz (2000) comparedriver flow simulated with different land use data in the Sauer basin.
Their study did not directly compare simulated runoff and observed
runoff under the influence of concurrent land use data. Althoughchanges in runoff could be detected between periods where land
use changes are expected to have occurred, changes in runoff during the same period could not be established. The difference
between the work and this research also concerns the scale ofinvestigation and modeling approach. The Hydrologically Similar
Units (HSU) concept was adopted in their work to characterizespatial variability, and runoff simulations were performed on adaily time step. A similar scale of investigation was used by
Dagnachew et al. (2003), while this study uses a grid-based modelwith 500-m spatial resolution and 1-hour time resolution to represent flood runoff processes in detail. The models used by
Schumann and Schultz (2000) as well as that used by Dagnachew
et al. (2003) required model parameter calibration that may introduce land use-based parameters and affect the contrast between thesimulation results of different land use data. In this study, previously determined model parameters are used to avoid this problem.
Although the main objective of investigating the hydrologicalimpacts of land use changes is to quantify their effects, most previous works in this area have rather presented or analyzed the sensitivity of model output to land use changes. These researches aredifferent from evaluating the real impact of changes, requiring
more rigorous use of concurrent land use data in simulation. The
use of the assumed land use scenario can be suitable for the formercase but not for the latter. The analysis also needs to be carefully
designed to show that predicted changes in runoff are consistent
with observed changes in land use and runoff.
The emphasis of this paper is to quantify the effects of land
use changes observed in the study basin over a period of 20 years.
Concurrent land use and runoff data are used in runoff simulations
to study changes to flood hydrographs during this period using a
distributed rainfall runoff model. This paper presents the details of
the model used and results of evaluation of the impacts of land usechanges on flood hydrographs in the Yasu River basin, Japan,
between 1976 and 1997. Land use changes are determined withland cover data generated based on a topography map in 1976 andremote sensing data in 1997 with 100-m resolution.
2.. MODELING METHODOLOGY
The effects of land use changes on river flow are well appreciated but less understood. The main problem is that runoff generation is a spatially distributed process that depends on several
influencing factors including slope, soil type, surface roughness,
imperviousness, surface storage as well as distribution of rainfall
and initial conditions. Some of these factors such as roughness,
imperviousness and surface storage depend on land use. Their
effects on simulated runoff are accounted for by using physicalprocess models parameterized with land use data.
The main focus of the modeling work in this study is to develop a watershed model that can be used for detecting the impacts ofland use change on floods. A distributed watershed model taking
into account the effects of surface factors on flood simulation isdeveloped and applied in the study basin. The model consists of

two main components for surface water balance and routing as
described below.
2.1 Surface Water Balance Model
A grid-based surface water balance model is used to estimate
the amount and onset of runoff from each grid under the influenceof land cover. This component simulates the effect of surface
detention storage and the degree of surface perviousness on runoff
generation. Maximum surface storage Dl is defined as the limiting
holding capacity of the ground that is filled before the occurrenceof surface runoff. A large value of surface storage is assigned forland cover such as forest or paddy fields to delay runoff occurrenceat the beginning of a rainstorm. On the other hand, a small value isassigned to quickly draining surfaces such as urbanized areas. The
value of Dl depends on land use, and the following simple relationship is assumed to generate spatially distributed Dl values for different land use classes.
Dl =

nl
D
np p

(1)

where Dl and Dp are the maximum surface storage for land use
class l and for paddy, respectively. Parameters nl and np are theManning s roughness values of land use class l and that of paddy.
The infiltration intensity to the ground is calculated using theGreen and Ampt formula for unsteady rainfall events (Mein andLarson, 1973; Chu, 1978). The governing equations for infiltration
rate fp and cumulative infiltrated depth F is:MSav ˆ
Ê
f p = K s Á1.0 +
˜
Ë
F ¯

(2)

Ê
F ˆ
F - MSav lnÁ1.0 +˜ = Ks t - t p + t p 'MSav ¯
Ë

(

)

(3)

where M is the initial soil moisture deficit equal to difference
between the saturated soil moisture content or porosity θs and initial soil moisture content θ. Sav is the average suction head acrossthe wetting front; Ks is the saturated hydraulic conductivity; t is theactual time; and tp is the time to ponding. The shift in time scale tp'
is defined to consider unponded conditions at the beginning of theinfiltration process. Equations 2 and 3 are derived by applying
Darcy s law to the situation of infiltration under ponded conditions
from the start of the infiltration process (Mein and Larson, 1973).
For unsteady rainfall events whereby the surface is not ponded
at the beginning of the event, the infiltration rate is assumed to beequal to rainfall intensity whenever such unponded conditions areencountered. This means infiltration occurs at the same rate as
rainfall intensity. All parameters of the infiltration model dependon the soil type (Rawls et al., 1993). Equation 3 is solved iteratively using the Newton-Rapson method to determine F at the end of
the current simulation step. The infiltration rate calculated by
Equation 2 is used to determine net rainfall intensity rnet, which issupplied to the routing model (see Fig. 1 and the following section)
as described by Equation 4.
Ï0
rnet = ÌÓr - f p

if

F < Dlif

F = Dl(4)

DISTRIBUTED HYDROLOGIC SIMULATIONS TO ANALYZE THE IMPACTS OF LAND USE CHANGESON FLOOD CHARACTERISTICS IN THE YASU RIVER BASIN IN JAPAN

2.2 Distributed routing model
Distributed runoff routing aims to capture the impact of surface conditions such as roughness and slope as well as artificial
effects such as diversions and reservoirs on simulated river flow.
This enables the effects of changes in surface conditions on stream
flow to be investigated and is also necessary for investigating theeffects of land use changes on floods.
A distributed routing model in this study is constructed bylinking grid-based models routing net rainfall rnet calculated using
the surface water balance model described in the previous section.
A schematic layout of the distributed routing model is given in Fig.
1.
The Digital Elevation Model (DEM) is used to construct thewhole watershed model in the same outlay as shown in Fig. 1. A
kinematic wave model is used to route flow over each grid with
upstream boundary conditions defined by inflow from upstream
grids that drain into the particular grid. This creates a watershed
model accumulating flow along the DEM-defined flow paths thatcan simulate flow routing under the real morphological conditionsof the watershed. DEM data are also used to define the slope, areaand length of each grid cell.
Although any other model can be used to define the routing
process, the kinematic wave model seems to be preferred by many
researchers in constructing distributed routing models. Examples
can be seen in a number of models including the KINEROS model(Smith et al., 1995), LISFLOOD model (De Roo et al., 2000,
2001) and Cell Distributed Model (Takara et al., 2002; Kojima etal., 2003). The use of the kinematic wave model is also advantageous in the study since its main parameter, the Manning s roughness coefficient, depends on land use (Engman, 1986). This helps
to include the effect of land use on the routing process.
The kinematic wave model is implemented at each gridassuming the grid as a flow plane sloping in the direction defined87

by the digital elevation flow map (Kimaro et al., 2002, Kimaro,2003). The upstream boundary condition qin (see Fig. 1) is definedby the sum of flows qo from all upstream grids. The grid model
also receives net runoff depth rnet calculated by the surface waterbalance model as lateral inflow. In the present formulation, rnet is
calculated prior to running the routing model. The governing equation of the routing component considering unit width of grid is∂h ∂q+= rnet∂t ∂x(5)where h(x,t) is the flow depth, q(x, t) is the flow discharge per unit
width, and rnet(t) is the output of the surface water balance model.
The meaning of rnet in this equation is the lateral inflow per unit
length along flow direction x per unit width perpendicular to x.
Equation 5 is a continuity equation obtained by applying the massbalance principle to infinitesimal control volume along a flow
plane. This equation is combined with momentum equation adopting the kinematic wave assumptions:
q=

So 53
h
n

(6)

where So is the slope of the grid and n is the Manning s roughness
coefficient. Equation 6 assumes that uniform flow over a flow
plane governed by Manning s equation and that the width of flow
is much larger compared to the flow depth. Equations 5 and 6 are
combined and solved to obtain flow discharge q and flow depth hby various finite difference schemes (see, for example, Beven,
1979; Chow et al., 1988 for details). The solution scheme requires
upstream boundary conditions that are defined to maintain the continuity of flow from upstream grids to the outlet along the DEM
flow paths as follows:qin (t ) = Â qo,up (t )(7)where qin(t) is the upstream boundary condition for any grid andΣqo,up(t) is the sum of outflow from upstream grids draining intothat grid as defined by the flow direction map. qin(t) for all cells at
the upstream watershed boundary is assumed to be zero. The
watershed model is constructed using object-oriented programming
features using C++ with the advantage of flexibility to change,
increase or decrease the process models within the watershed
model as desired for different modeling objectives (Kimaro, 2003).
3. STUDY AREA AND DATA SETS

Fig. 1 Schematic representation of distributed routing model.

The study area in this research is the Yasu River basin locatedon the main island (Honshu) in Japan. The watershed drains into
Japan s largest freshwater lake, Lake Biwa. The altitude in thebasin ranges from 97 to 1300 amsl. The catchment area upstreamof the Yasu gauging point is 377.1 km2. Annual rainfall varies
from 1300 mm in the lower part of the catchment to 1900 mm in
the upper part due to influence of the attitude. The basin receives
much of the rainfall in June and July due to the seasonal rain front
and in September and October due to typhoons. A large part of thebasin (60%) consists of mountainous terrain covered by evergreen
forest. The main land cover in the lower part of the basin is paddy
fields. This area also has a slowly growing urban center that tends
to concentrate along the main tributaries of the Yasu River as discussed later in Section 3.1. The map of the Yasu River basin

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T. A. KIMARO, Y. TACHIKAWA AND K. TAKARA

showing the locations of gauging stations and the river network is
given in Fig. 2.
3.1 Land use data and land use changes
Land use data in the study basin are available from the Japan
Geographical Survey Institute (GSI). These data are available at
100-m spatial resolution and are classified into 15 land use classes
developed for 1976 and 11 land use classes for 1997, which cover
all of Japan. The data (see Fig. 3) clearly show forested areas,
paddy fields, river channels, built-up areas, golf courses, roads andagricultural land.
Land use data were processed to quantify the coverage of different land use classes as well as changes that occurred between
1976 and 1997. The results of this analysis are shown in Figs. 3-6.
Forest and paddy fields cover the largest part of the basin. The
upper part, the Kashiki subcatchment, is predominantly covered byforest and the main land use in the lower part is paddy fields.
These two land use classes experienced only small changes
between 1976 and 1997 as shown in Fig. 6. The coverage of forest
decreased from 63.4% of the whole basin in 1976 to 60.9% in
1997. The area occupied by paddy fields also decreased slightly
from 18.7% in 1976 to 17.3% in 1997.
It may be noted from the map of land use change in Fig. 5 that
the Ukawa tributary experienced more changes in land use compared to the Kashiki tributary. The pattern of urbanization is also
clearly mapped in this figure, and it shows that urbanization tends
to grow into the Ukawa subbasin from the lower part of the basin
and is concentrated along the river. There are also indications that
extension of a major communication route towards the southeastern part of the basin (see Fig. 4) has stimulated the urbanization ofthis area. The notable change in land use within the forest area isconversion to golf courses, which can be distinguished clearly
between the two land use maps for 1976 and 1997 in Figs. 3 and 4.
Land use data also show that the percentage coverage of the urban
area increased from 4.6% in 1976 to 6.9% in 1997, which is equivalent to 50% growth. The distribution of land use changes in thebasin between 1976 and 1997 is shown in Fig. 5.
3.2 Rainfall data
The Yasu River basin is covered by an extensive network for
rainfall gauges. Hourly rainfall data are available from gauges
operated by the Ministry of Land Infrastructure and Transport(MLIT), and the Japan Meteorological Agency collected throughthe so-called AMEDAS (Automatic Data Acquisition System) stations. Fig. 2 includes the stations used in this study. There are two
rainfall seasons in the basin. The first extends from June to July
during the rainy season and the second is from the end of August to
September during the typhoon season. Floods are usually experienced during the latter season, which has more intense rainfall
caused by typhoons.
Distributed rainfall data at spatial resolution for the distributedhydrologic model (500 m) were generated by the ordinary Kriging
method for each event using these stations. The spatial distributions of rainfall during major flood events show considerable variability influenced by topography. Most of the precipitation isobserved on the highlands towards the northeastern part of thewatershed (the Ogawara station) in the Kashiki subbasin. This tributary contributes more to flood runoff at the lower part because ofhigh precipitation and steep river gradient that cause fasterdrainage. Rainfall intensity during typical flood events ranges
between 15 mm/h in the lower part to 70 mm/h in the upper mountainous area.
3.3 Stream flow and flood records
The stream flow data used in this study were obtained from
the Ministry of Land Infrastructure and Transportation.
Continuous water level records and regularly updated rating curves
for the Yasu gauging site from 1975 to 2000 were collected fromthe Lake Biwa River office of the MLIT. MLIT also keeps arecord of high flows during typhoon events at the Yasu gauging
site, which can be used as independent source of data to verify theaccuracy of stream gauge data for high flows. The two sources ofFig. 2 Hydrometeorological gauges and river network in the Yasu River basin.

DISTRIBUTED HYDROLOGIC SIMULATIONS TO ANALYZE THE IMPACTS OF LAND USE CHANGESON FLOOD CHARACTERISTICS IN THE YASU RIVER BASIN IN JAPANFig. 3 Land use classification in the Yasu River basin in 1976.

Fig. 4 Land use classification in the Yasu River basin in 1997.

Fig. 5 Pattern of land use change from 1976 to 1997 in the Yasu River basin.

89

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T. A. KIMARO, Y. TACHIKAWA AND K. TAKARA

data compare well for the flood events used in this study. A continuous stream flow database was compiled that can be accessed to
extract stream flow during any flood event for comparison withsimulated river flow. Table 1 lists the major floods that haveoccurred in the Yasu River basin.
According to the observed data, the Yasu watershed responds
very quickly to intense rainfall such as during typhoons events.
Peak river flow is observed within 3 to 4 hours after the peak ofrainfall, and the flow recedes to almost zero soon after the flood
peak. This behavior indicates a relative dominance of surface
runoff or sub-surface water transfer over a very small distance,with a short residence time in the soil layer as suggested by(Gaume et al., 2004). There is also an indication that floods inmore recent years at the Yasu site have sharp rises and quick recessions compared to the past (Figs. 7 and 8). This situation may becaused by changes in the storage characteristics of the watershed,including possible effects of land use changes.
4. MODEL APPLICATION AND RESULTS
The hydrologic model described in Section 2 is applied to the
Yasu River basin (377.1 km2) to investigate the effects of land usechanges using selected flood events between 1976 and 1997. The
target of the simulation experiment was to study the qualitative andquantitative changes on flood hydrographs due to land use change
during this period.

The effects of land use changes are studied from hydrographs
simulated at the Yasu gauging site where observed flow is available for comparison with simulation. To trace locations withremarkable changes in runoff generation, differential runoff maps
produced with different land use are used. The model is applied
without the calibration of parameters except for tuning the initialsoil moisture deficit M as an initial condition and the saturated
hydraulic conductivity Ks to obtain the best fit of simulated hydrographs. The initial soil moisture deficit M is assumed dependingon the events with spatially uniform distribution. For the 1975 and1994 flood events, the value of M was set to 0.187 and 0.242,
respectively. Table 2 summarizes the model parameter values
used in the flood simulations.
The procedure adopted to study changes to flood generationand propagation as a result of land use changes is as follows:(1) Simulation is performed using the land use map A for flood
events for which land use map A (land use in 1976) isbelieved to be representative of the prevailing land use condition. Infiltration parameter Ks and initial soil moisture deficit
M are tuned to produce the best representation of the floodhydrograph by visual inspection of the observed and estimatedhydrographs.

Fig. 7

Typical flood hydrographs in the Yasu River basin
between 1976 and 1985.

Fig. 8

Typical flood hydrographs in Yasu River basin between
1993 and 1995.

Fig. 6 Percentage coverage of different land use classes in1976 and 1997 in the Yasu River basin.

Table 1 Major floods at Yasu gauging site.

DISTRIBUTED HYDROLOGIC SIMULATIONS TO ANALYZE THE IMPACTS OF LAND USE CHANGESON FLOOD CHARACTERISTICS IN THE YASU RIVER BASIN IN JAPAN(2) After obtaining satisfactory results in the step above, similar
initial conditions and rainfall input are used with the land use
map B (land use in 1997) in runoff simulations. Changes inflood peaks and timing are determined from the simulation
results using the two different land use maps.
(3) The differential runoff map generated using runoff maps produced with land use A and B is used to locate areas with91

remarkable changes in runoff generation to reveal the effectsof spatially distributed land use changes. The results are discussed with a map of land use change between A and B
to understand the type of land use changes and their impact onrunoff generation processes.

Table 2 Parameter values used for flood simulations.

Fig. 9 Simulation results for flood event in July 1975 with 1976 and 1997 land use data.

Fig. 10 Simulation results for flood event in September 1994 with 1976 and 1997 land use data.

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T. A. KIMARO, Y. TACHIKAWA AND K. TAKARA

4.1 Changes of flood peaks in the basin
A total of five flood events between 1975 and 1994 were simulated with land use data for 1976 and 1997 to investigate changes
in flood peaks. Applicable land use data at the time of occurrenceof the flood are those that are captured within the shortest timefrom the date of occurrence of the flood. For example, for a flood
event in 1980, land use data of 1976 are assumed, while for a flood
event in 1994, land use data of 1997 are assumed.
The results of runoff simulations with 1976 and 1997 land use
data for different flood events are shown in Figs. 9 and 10 and arealso summarized in Table 3. The distributed hydrological model
has reasonable ability to predict flood peaks and timing as can beseen in observed and estimated flow with the land use condition in1976 in Fig. 9 and observed and estimated flow with the land use
condition in 1997 in Fig. 10. In these figures, both the peak andtiming of floods are well reproduced when the land use data mostrepresentative of the land use situation at the time of flood occurrence are used. The model matches well the rising limb of thehydrograph, which represents a quick flow, while the falling limb
is not well reproduced. This is due to subsurface processes that are
not considered in the current hydrologic model. However, theresults suggest that quick overland flow as modeled in the current
study may be used to estimate flood peak and timing.
It is found in Figs. 9 and 10 and Table 3 that the runoff simulation results using 1976 land use data give consistently lower
flood peaks and delayed peak timing compared to those using 1997
land use data. The land use changes over a period of 20 years from1976 to 1997 results in the highest increase in flood peak simulated
being 18.2% and the lowest being 7.41%. Although the changes
are small in this case, this only depends on the magnitude of land
use change. Definitely more changes in land use leads to morechanges in simulated runoff, which could be captured using the
proposed hydrologic model structure.
4.2 Spatial changes in runoff generation
The effects of localized land use changes on floods need to be
examined in order to control the spatial distribution of land use or
evaluate the impacts of different spatial patterns of activity within
the basin. As the proposed hydrologic model simulates the spatially distributed process of runoff generation, the difference in spatially distributed runoff production maps produced with different
land use patterns delineates the areas where runoff changes haveoccurred. The analysis reveals the effects of different types of land

use change at different locations on flood magnitude and timing offlood routing. Runoff production maps using the land use patternsof 1976 and 1997 were generated for the 1994 flood simulation bysumming the total flow volume from the beginning of the flood
event to the end for all grid cells. A difference in the runoff production maps by subtracting the cumulative runoff volume for eachcorresponding cell is shown in Fig. 11(a).
To understand how the land use change affects runoff generation and propagation, the difference in land use map is also madeas shown in Fig. 11(b). It is found that more runoff tends to begenerated from the southern part of the catchment, which also
experienced a higher concentration of land use changes. A large
area of the basin on the northeastern part does not show increase in
runoff. Apparently, the Ukawa subcatchment has experienced
more urban activities as shown by the increase in concentrated
built-up areas and golf courses. This is causing a gradual change inthe rainfall runoff relationship as revealed by both flood runoff
simulations and land use data analysis. The use of differential
maps for runoff productions and land use patterns seem to be suitable for evaluating the impacts of spatially distributed land usechanges on floods.
5. CONCLUSIONSThis study investigated the effects of land use changes onflood characteristics using a distributed hydrological model andland use data. The distributed hydrological model consists of infiltration excess overland flow process defined by the Green and
Ampt infiltration model and routing process with a DEM-based
kinematic wave model, which well represents the rising limb offlood hydrographs, peak discharge and timing of floods. The
results encourage the utilization of land use data in hydrologic
modeling to describe the impact of land use changes on floods.
The results of the study also indicate that a DEM-based kinematicwave routing method provides an effective way of understanding
the effects of the spatially distributed runoff-generating process on
stream flow. The model is especially suitable for investigating theimpacts of land use changes because it takes into account the effectof land use change on the flow generation mechanism.
Despite the complexity of analyzing the effects of land usechange on catchment response, the simulation study shows theimpact of land use change on the distributed overland flow process.
In this case study, the impact of land use change on flood forma-

Table 3 Summary of simulation for different events with 1976 and 1997 land use data.

DISTRIBUTED HYDROLOGIC SIMULATIONS TO ANALYZE THE IMPACTS OF LAND USE CHANGESON FLOOD CHARACTERISTICS IN THE YASU RIVER BASIN IN JAPAN93

Fig. 11 (a) Differential runoff production map induced by land use change from1976 to 1997 for the 1994 flood event. (b) Differential land use mapthat shows the cells converted to different land use cells between 1997
and 1976. Note that the central southern part of the basin (Ukawa subcatchment) has a high concentration of changed pixels.

tion in the Yasu River basin does not appear to be alarming due toa low degree of land use change. However, the proposed distributed model well captures flood change and, if it is applied in a
drastic land use change environment, a greater understanding of theeffects of land use changes on floods can be gained.
ACKNOWLEDGEMENTS
This research was supported by the Grant-in-Aid for Scientific
Research C 18560497 (PI: Y. Tachikawa, DPRI, Kyoto Univ.) andthe CREST program of the Japan Science and Technology Agency(PI: K. Takara). The authors wish to express their thanks to MLIT
for providing hydrological data.

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