Annual Journal of Hydraulic Engineering, JSCE, Vol.52, 2008, February
Annual Journal of Hydraulic Engineering, JSCE, Vol.52, 2008, February

A SIMPLIFIED FLOOD INUNDATION MODELINTEGRATING WITH RAINFALL-RUNOFFPROCESSES USING GLOBALLY AVAILABLETOPOGRAPHIC DATA
Nanshan ZHENG1, Yasuto TACHIKAWA2 and Kaoru TAKARA3
1Student Member of JSCE, Graduate student, Dept. of Urban and Environmental Eng., Kyoto University(Kyoto 615-8540, Japan) E-mail: zhengnanshan@z05rcl.mbox.media.kyoto-u.ac.jp
2Member of JSCE, Dr. Eng., Associate Professor, Dept. of Urban and Environmental Eng., Kyoto University(Kyoto 615-8540, Japan)
3Fellow of JSCE, Dr. Eng., Professor, DPRI, Kyoto University (Uji 611-0011, Japan)

Flood hazard is one of the most harmful disasters in the world, and it is significant to obtain
information on flood characteristics for hazard mitigation as well as vulnerability assessment. This paper
develops a model for simulating flood inundation integrating with rainfall-runoff processes. The model isdesigned to operate with the Shuttle Radar Topography Mission (SRTM) DEM data and other globally
open data sets, based on the simplified process representation capable of simulating flood inundation. It
consists of a one-dimensional diffusion wave representation for channel flow and a two-dimensional
diffusion wave approximation of overland flow solved using an explicit finite difference scheme. In themodel, exchange of flows between river channel and overland surface is calculated through Manning’s
formula in response to the relative water surface elevation. Simulated results in the Maruyama Riverbasin show an acceptable agreement with the observed flooded area. Hence the flood vulnerable area isable to be identified and delineated. This research is a fundamental step to assess flood hazard
vulnerability at any global catchments.
Key Words: Flood inundation, flood modeling, geographical information system, SRTM

1. INTRODUCTION
As the occurrence of flood event has becomecommon in many parts of the world, flood events
during previous years have raised public, politicaland scientific awareness of flood risk and flood
prevention1). The frequency, distribution and causesof floods over the last thirty years has been analyzed
and reported by the Dartmouth Flood Observatory,
and a five-fold increase in the number of floods per
year has been observed since the 1980s2). Flooding
threats are likely to increase given current climatechange predictions. Whilst much more people livein floodplain or low-lying areas, society becomes
more exposed to flood events. Consequently
strategies to cope with flood disasters, for example
structural measures such as reservoirs and levee
projects, and non-structural measures such as
floodplain regulations, insurance, emergency
preparedness etc. are taken. These countermeasures
rely on flood prediction capabilities, and especially,

the ability to delineate potential flood inundation
areas is one of the most important requirements.
Recently a shift in paradigms is observed from atechnical oriented flood protection towards floodrisk management, and the change in focusing from
‘impacts-led’ approach to ‘vulnerability-led’approach and living with the risk are recognized3). It
is necessary and significant to study on regional
vulnerability for flood hazard.
During the past two centuries, scientists andengineers have sought to develop models torepresent and predict the behavior of water flow
within canals, rivers and watershed4). To date withthe application of geographical information system(GIS) and remote sensing techniques to water
resources management, more and more researchers
are involved in developing a distributedhydrological model to simulate runoff and stream
flow in watershed. On the other hand, numerical
hydraulic models of overland flow provide thecapability to simulate flooding based on a scenario- 61 -

discharge, the estimation of reach scale floodinundation has been studied5), 6), especiallynumerical inundation hydrodynamic model in urban
area due to heavy rainfall has been developed7), 8).
However, as Van Roon and Knight have pointed
out that floodplain management is being seen
increasingly in a catchment wide context9), it issignificant to develop an integrated model
applicable to any catchments for flood vulnerability
assessment. Thus, this study proposes a grid cellbased simplified flood inundation simulation model
integrating with rainfall runoff processes to obtain aspatial distribution of flood hazard vulnerability.
Topographic data are crucial for flood inundation
modeling, thus much interest has focused on the useof highly accurate topographic data such as airborne
laser altimetry data or LiDAR to parameterize floodinundation models5). However, the data availability
is not always feasible because of given time andbudgetconstraints.
The
first
near-global
high-resolution digital elevation model of the Earthhas been released following the successful Shuttle
Radar Topography Mission (SRTM) in the year of
2000, which is especially valuable in the Earthsciences and has a wide range of application fields.
In this study the availability of SRTM DEM as asource of terrain data for flood modeling is focused.
The objectives of the present study are: (a) todevelop flood inundation model integrating with
rainfall-runoff processes for large areas; (b) to takeadvantage of globally available data to simulate
flood inundation for further study of flood hazard
vulnerability assessment at any catchments; and (c)to emphasize on the development of a distributedmodel in catchment context, which has theadvantages of being spatially explicit, physically
based, and adapting to data poor regions for floodinundation simulation.
According to the simulation result, flood
characteristics were well captured and the flood
vulnerable area was identified and delineatedefficiently. In the coming sections, model
development methodology, characteristics of study
site, and model application are described in detail.

flows into the river channel. When the bankful flow
depth is reached in the river channel, water flows
out of the river channel into adjacent surface areas(Fig. 1). Therefore these surface areas act either as
temporary tanks for flood water or additional routes
for flow conveyance. In this process, the water
surface elevation for river channel is calculatedusing a one dimensional channel flow model.
Overland surface flow routing over the wholewatershed is simulated by a 2-dimensional model,
which is to treat each grid as a storage cell, and thechange in cell volume over time is equal to therainfall excess and fluxes into or out of it. Whilst at
the interface between river channel and overland
surface area, the exchange of flow is simply
simulated in response to the relative water surface
elevation, i.e., surface flow can be either away fromriver or into river7), 10) (Fig. 2).

Qup
Qleft

j
Qright
Qdown

∆y

y

x

River

∆x

Fig. 1 Schematic drawing of 2-D overland flood routing andchannel routing.
Grid cell 1
Grid cell 2d1

2. METHODOLOGY(1) Model descriptions
Spatiotemporal information of flood events is of
the utmost importance for flood disaster mitigation
as well as for flood vulnerability assessment. A
flood inundation simulation model integrating with
rainfall-runoff processes is developed in this study,
which is based on simplified process representation
to obtain simulation of flood water depth and hence
inundation extent. The model formulation is
described below.
Overland flow which originates fromprecipitation or saturation excess on the soil, surface

i

d2

h1
h2

g1

g2

Fig. 2 Flow between grid cell 1 and 2 (d, depth of water in thecell; g, ground elevation; and h, water surface elevation in thecell).

- 62 -

The processes of overland flow and channel flow
can be modeled using approximations of the Saint
Venant equations11). Gonwa and Kavvas12) andothers have demonstrated that the diffusion wave
approximation to the full Saint Venant equations isappropriate for many cases of practical interest. In
some simulation models based on kinematic wave
approximation, flow directions for the stream
network are generated from DEM data. However, it
can not deal with the backwater situations.
Therefore diffusive wave approximations are
employed as the governing equations for both
overland and river channel flows.
(2) Governing equations
Hydrologic and hydraulic modeling requires aclear understanding the hydrologic cycle atcatchment scale. The model proposed here is basedon raster data structures, and grid cells are adopted
to describe distributed watershed characteristics
using such as elevation, land use, soil type, etc.,
where
flood
inundation
integrating
withrainfall-runoff processes are simulated for every
grid cell.
The rainfall excess ( Re ) over the cells is
assumed to equal to the sum of the amounts ofprecipitation to the ground ( R g ), interception ( Vi ),and infiltration ( V f ). For the sake of simplicity,
being an event based flood runoff and inundation
model, evapotranspiration is not taken into
consideration. Therefore the rainfall excess iscalculated by Eq. (1).
Re = R g − Vi − V f(1)
In this research, during the simulation, if therainfall intensity is less than the intensity ofinterception, the rainfall rate is set to zero and theremainder of the interception is removed from therainfall in the following time increments until theinterception depth has been satisfied. The
infiltration is obtained from the one-layer
Green-Ampt infiltration model, assuming the soils
are homogeneous, deep, and well-drained withineach grid cell13), 14).
Overland flow is governed by the continuity andmomentum equations: Eq. (2) and Eq. (3) 5), 7). To
solve the overland flow equations, explicit finite
difference method is selected. In order to solve thecontinuity equation relating flow into or out of agrid cell and its change in volume, momentum
equation for each direction is calculated according
to Manning’s formula:
i, j

dh
dt

=

Qup + Q down + Qleft + Q right + R e
∆x∆y

53
12
h
i − 1, ji, j−hflow h
i, j
Q =±
∆y
x
n
∆x

(2)
(3)

where h i , j is the water free surface height at the gridcell ( i, j ); t is time; ∆x and ∆y are grid cell sizes;

n is the Manning’s friction coefficient; Qx andQ y are the volumetric flow rates between grid cells

in x and y directions, whose sign depends on theflow direction. Qy is defined analogously to Eq.
(3); Qup , Qdown , Qleft and Qright are the flow rates(either positive or negative) from the up, down, left
and right adjacent cells, respectively, which can be
represented by Qx or Qy accordingly (Fig. 1).
The flow depth, h flow , represents the depth throughwhich water can flow between two cells.
To obtain water which flows into or out of river
channel at the interface between river channel andfloodplain area or low-lying area, the exchange offlow is calculated using Manning’s formula (Eq.
(3)), as overland flow calculation.
In this study, the location of channel network ispredetermined by using ArcGIS software, and ariver channel may run not only in the x or y
direction but also along the diagonal directions.
Under this setting, channel flow is also represented
by the continuity and momentum equations (Eq. (4)and Eq. (5)).
∂Q ∂A
+
=q
∂x ∂t
S0 −

n 2 P 4 3Q 2
10 3

A⎡ ∂h ⎤−⎢ ⎥ =0⎣ ∂x ⎦(4)(5)

where x is distance along the longitudinal axis; A
is cross-sectional area; Q is discharge; q is lateralflow into or out of river channel; P is wetted
perimeter of channel flow; S 0 is bed slope.
(3) Model development approach
The primary objective of this research is todevelop an integrated flood inundation simulation
model to predict and assess flood hazard. Here aloose coupling method of program development isselected. In such case, GIS and flood inundation
simulation model remain separate, but loosely
linked through data input and output operation.
The model program is developed according to
the methodology mentioned in the above under theenvironment of Microsoft Visual C++ and Windows
operating system, in which the hydrologic processes
including precipitation, interception and infiltration
are considered.

3. MODEL APPLICATION TO THE
MARUYAMA RIVER BASIN(1) Study site
The integrated model described in the above
section is applied to the Maruyama River basin,
which is located in the Hyogo Prefecture, Japan (Fig.
3). Within the mountain boundary there are hills andlower lands. The Maruyama River (Class A river
system in Japan) flows from its source of Maruyama
mountain, to Tajima district and then runs
northward through the Toyooka basin to the Sea of- 63 -

Japan, whose mainstream length is 68 km, and thetotal length of its tributary is 638 km. The area ofthe Maruyama River catchment is 1,330 km2 withthe population of 152,000, where the mountain area
accounts for 86%, and about 30% residents inhabit
in the downstream floodplain area. From the river
outlet to the upstream 16 km, the river bed slope isvery flat (1/10,000). Upstream of this part, the bed
slope is 1/500. The elevation ranges from 7 m to1488 m. The mean annual precipitation is about
2000 mm. There are some tributary channels such as
the Izushi river, and the Inaba river. The special
topographic conditions result in this area being
vulnerable to flooding, even though levee breachingdoes not occur15).
From 19th to 21st October 2004, Typhoon No.
23 hit the Japanese islands, which brought
precipitation as much as 278 mm in the Toyooka
area over a two-day period, the heavy amount ofwater flowed into the mainstream of Maruyama
River, which caused severe flooding and levee
breaching15), 16).

Fig. 3 The DEM of the study area with the river channel
generated.

(2) Data sets and pre-processing
Generally distributed flood and inundation
simulation models require four key data: (a)topographic data or DEMs to construct the model
grid; (b) bulk flow data to provide model inflow andoutflow boundary conditions; (c) an estimate ofeffective friction parameter for each model cell; and(d) validation data. Here, the required datapreparations are introduced as follow:a) SRTM DEMs
DEM data is downloaded from USGS website,
its original resolution is around 90 meters, and in
this study it has been extracted, projected andre-sampled with the resolution of 200 meters, inwhich the numbers of row and column are 259 and
250 respectively (Fig. 3). SRTM DEMs are basedon interferometry, which involves the reception of abackscattered radar signal by two antennae. The
signals include radar speckle, or random noise,
which degrades relative vertical accuracy
particularly on floodplains17). Hence, here filter toremove radar speckle is adopted to enhance theutility of SRTM for flood modeling.
b) River channel delineation and channel
parameterization
River channel (Fig. 3) is derived from DEM data
after pit removal in ArcGIS software environment.
The river channel is discretized to route on grid cells,
and utilized combining with the catchment DEM.
For model parameterization, channel cross-sectional
geometry is specified as trapezoidal. The required
parameter values at each grid cell along the river
channel are bank height, channel width, and sideslope, which can be specified at each grid. Herespatially variable channel parameters are supplied
according to documents for the Maruyama Riverbasin. The channel width ranges from 20 m to 150
m, which is extracted under Google Earthenvironment (Table 1).
c) Some other additional data
In order to apply the proposed model to any
catchments on the globe, land cover and land use
classification data is downloaded from GLCF
(Global Land Cover Facility) website, whose
resolution is 1 km. After re-projection, re-sampling
and extraction, these data are overlaid to thewatershed grid system. In the future, in order to gethigher resolution data, MODIS satellite data can be
employed. Precipitation (Fig. 4) is collected fromfield measurements. There are eight rainfall stations
distributed in the study area. The inverse distanceweighted method is used to interpolate precipitationfor each grid cell. Other input parameters such as
Manning’s roughness coefficient are provided fromthe published literature13), 18). The roughness values
for the overland surface grid cells and interception
storage capacity are specified based on land use
classification (Table 2). Soil type is extracted from
Soil map of Japan (1:1,000,000).

- 64 -

Rainfall (mm/h)
0
5
10
15
20
25
30
35
40

10/19 12:00

10/20 12:00

Time

Fig. 4 Rainfall in the Maruyama River Basin.

Table 1 Example of river channel properties (Note: Channel number is the number of generated river channel segment).
Initial water depth
Manning’s
Side slope
Width (m)Channel number
Bank height (m)(m)
coefficient
(H:V)
1
2
3
4

4
5
8
3

0.10 (Trapzoid)
0.00 (Rectangle)
0.00 (Rectangle)
0.25 (Trapzoid)

30
48
150
25

0.5
0.6
1.0
0.5

0.03
0.03
0.03
0.03

Table 2 Manning’s roughness coefficients of different land use classification.
Deciduous
Deciduous
Evergreen
Evergreenbroadleaf
needleleaf
broadleaf
needleleaf
forestforestforestforestLand use
Manning’s
coefficientInterception (mm)Land use
Manning’s
coefficientInterception (mm)Mixedforests

0.040

0.040

0.040

0.040

0.040

3
Closed shrublands

3Open shrublands

3
Woody savannas

3
Savannas3
Croplands

0.035

0.035

0.030

0.030

0.030

3

1

1.5

1.5

1.5

LegendObserved inundation

iv
er

th
eN
as

ar
iv
er

Simulated inundation

ya
m
ar

0.0-0.6meMaru

0.6-2.3merthe Izushi rivth

2.3-4.7m>4.7m
River channel
generated

b)

a)

c)

Fig. 5 The comparison of observed flood inundation and simulated results. a) Simulated inundation in study catchment; b) The
regional flood inundation extent magnified; and c) The comparison of observed inundation extent digitized from document16) andsimulated inundation extent shown in b).

(3) Result analysis
Typhoon 23 in the year of 2004 brought a heavy
rainfall in the Maruyama River basin, which
resulted in large flood inundation at Toyooka cityarea. In this research, the integrated model is applied
to study this flooding case. Although the model
simulation results are composed of flood discharge
and distributed flood inundation depth, here thecomparison of the flood inundation extent isemphasized, because flood extent data has proved to
be a valuable tool for calibrating and validating
numerical hydraulic models10). In Fig.5, the final
spatial distribution of flood inundation is shown,which indicates the model can simulate the flooding
process and identify the flood vulnerable area.
The simulated flood extent inside of blue broken
line at Fig. 5a, magnified as Fig. 5b, is similar to
the inundation extent observed (Fig. 5c), in which

the flood inundation area observed is about 41 km2,
and the simulated inundation area is 42.49 km2.
According to the overlay analysis of simulated
extent and observed extent, around 30.22 km2 insideof the observed inundation area is located in thesimulated inundation extent, which shows theconsistency ratio is about 75%. In the simulation theriver channel is generated from DEM, which is alittle different from the actual river, therefore it has
an effect on the spatial distribution of floodinundation, and it is not easy to compare the spatial
distribution of flood inundation directly. However,based on the analysis of spatial distribution of floodinundation at Toyooka city area, it shows anacceptable agreement. Further analyses about theaccuracy and uncertainty of flood inundation
simulation can be studied when more detail datasuch as river channel and flood extent observation- 65 -

are gathered.
The comparison of river water depth at theTachino station after 7:00am on October 20th, 2004
is shown in Fig. 6. The peak of water depth and itschange are well reproduced, which reflect thismodel is effective. The descent of simulated water
depth is not very rapid, which is because the model
does not include levee breaching at present, but thisis able to be incorporated into model for scenario
analysis.
Water depth-Rainfall
Simulated

Observed

12

0

10

20

8

40

6

60

4

80

2

100

0

120
1

9

17

25

33

41

Rainfall (mm)Water depth (m)Rainfall

49

Time (hour)Fig. 6 Water depth comparison (Site: Tachino station)

4. CONCLUSIONSIn this study, the physically based simplified
flood inundation simulation model coupled withhydrologic processes for catchment scale context isdeveloped with the application of globally available
data. The model calibration results demonstrate thatthe overall model performance is acceptable, which
provides a feasible modeling tool to simulate theflood inundation and is of significance to flood
vulnerability assessment, especially focusing on
overflow flood. All the parameters employed in themodel are physically based, and almost model input
data can be collected from open source data,
therefore it is possible to apply the model for flood
hazard management in data poor environment.
However, the precision of model simulation not
only depends on DEM accuracy, but also relies onhydrologic and hydraulic model structure. At
present, the hydrologic model only takes surface
water flow into consideration. In order to improvethe model applicability for various hydrologic
environments, the subsurface flow and other detail
rainfall-runoff process models need to be coupled in
the further study.

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(Received September 30, 2007)- 66 -