Journal of Water Resource and Protection, 2013, 5, 469-473 Published Online April 2013 (
Identification of Inundation Hazard Zones in Manas
Basin, China, Using Hydrodynamic Modeling and
Remote Sensing
Like Ning1,2, Hailong Liu1*, Anming Bao2
1Water Resources and Architectural Engineering College of Shihezi University, Shihezi, China
2Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, China
Email: *
Received January 9, 2013; revised February 17, 2013; accepted February 27, 2013
Copyright © 2013 Like Ning et al. This is an open access article distributed under the Creative Commons Attribution License, which
permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
A two-dimensional hydrodynamic model, Floodarea was applied to simulate the flood inundation area and flood depth
in Manas basin, China. Two scenes of Land sat TM images were also used in this research. One image was used to pro-
duce the spatial distributed manning roughness to feed the model, the other one was used to delineate the actual inun-
dated area by a modified NDWI method. The model and the manning roughness were validated by the comparison of
simulated flood inundation extent and the corresponding actual inundated area obtained from Landsat image. The re-
sults show that the actual inundation extent obtained from Landsat image was 240.45 km2, and the modeled inundation
area was 276.15 km2. It indicates that manning roughness ranging from 0.025 to 0.833 is appropriate in the basin. In
addition, the mo deled flood depth v aried from 0 to 7.77 m. Taking land use into account, five hazard zones were identi-
fied in the study area. This study would be beneficial to flood control and disaster reduction.
Keywords: Flood Inundation; Floodarea; Remote Sensing; Modified NDWI
1. Introduction
Flood is one of the most recurring and devastating natu-
ral hazards [1]. Human interventions and climate change
have significantly effect on it [2]. Many researches show
that flood intensity and frequency would increase in the
future, and it must be threaten many regions of the world
[3]. Accurate information on the floods is very necessary
for controlling potential hazards and risks.
Hydrodynami c m odel l i ng and remote sensing dat a have
been widely applied to predict and evaluate the flood risk
[4,5]. An integrated hydraulic modelling based on TELE-
MAC-2D and remote sensing data has been used to in-
vestigate the floodplain flow process by Bates et al. [6].
Patro simulated the flood inundation extent and flooding
depth using MIKE FLOOD and an IRS-1D WiFS image
[7]. The remote sensing can not only provide actual flood
extent, but also spatial distributed parameters. They are
indispensable to flood inundation models, such as the
manning roughness [7].
Manas basin, a typical flood-prone inland river basin,
locates in the northwest of China. It is the core of the re-
gional economic zone in Xinjiang, China. However, flood
events occurred frequently and caused great losses [8]. A
catastrophic flood on record happened in Manas basin on
the mid-July and early August in 1999 [9,10]. The flood
lasted for 23 days, from July 14 to August 5. This catas-
trophic flood made 17 thousand people stricken, caused
the direct economic loss about 21 million (USD), and
flooded tens of thousands of field. Some water conser-
vancy facilities were damaged during the flood.
The aim in this paper is to reproduce the devastating
flood occurred 1999 and identity the hazard zones in
Manas basin. The modified NDWI was introduced to de-
lineate the actual flood inundation area from the Landsat
TM image. The Floodarea model has been widely used to
simulate the flood inundation. The main objects of this
study are as follows: 1) to construct a GIS-based model
Floodarea for study area and 2) to identity the hazard
zones according to the simulated flood depth and in the
Manas rive r basin.
2. Methodology and Study Area
2.1. Hydrodynamic Model
The Floodarea, a two-dimensional hydrodynamic model,
developed by Geomer Company, has been applied for
*Corresponding a uthor.
opyright © 2013 SciRes. JWARP
flood inundation widely [11-15]. It is completely embed-
ded into the graphical user interface of ArcGIS desktop
[16]. Floodarea model takes full advantage of ESRI
Grids on hydrodynamic modeling, accomplishes the data
fusion of hydrologic and hydrodynamic. Drainage net-
work grids with water levels, hydrographs and rainstorm
feed the model with floods. The gradient is defined by
the difference between the lowest water level and the
highest terrain elevation found in the cell and the neigh-
boring cells. The smallest iteration time step can be ad-
justed dynamically. An important control criterion for
this adjustment is the amount of water available. If the
discharge rates become too larg e co mpared with the avai-
lable volume, the iteration time step would be reduced.
Only water level changes exceeding 1 mm are considered
by that control mechanism. If the volumes exchanged be-
tween cells are very small, the iteration time step would
increase automatically. This permanent optimization kee ps
processing time at a minimum [16,17].
Roughness value is very important for the accurate
simulation. The hydrodynamic approach was used to cal-
culate the inundation area in this study. The discharge
volume to the neighboring cells was calculated using the
Manning-St ri ckler formul a (Equat i on (1 )) .
23 12
QARI n (1)
where Q is flood discharge (m3/s), A is the flood section
area of the watercourse (m2), n is the roughness, R is the
hydraulic radius, RAX
, X is the wetted perimeter of
flood section, and I is the hydrau lic gradient.
The flow depth during an iteration interval is taken
from the difference between water level and maximum
terrain elevation along th e flow path (Equ a tion (2 )).
flow depthwater
max elevation
2.2. Flood Extent
Water can be distinguished from other feature types
based on its strong spectral absorption characteristics.
McFeeters [18] proposed a Normalized Difference Water
Index method for Landsat TM to make a dis-
tinction between water features and other feature types.
However, the extracted water information in water re-
gions with built-up land background was often mixed.
Some built-up land features and self-shadowed areas are
misclassified as water bodies.
Here we choose the modified to
identify water bodies. The MND WI was a further deve-
lopment of NDWI by Xu [19]. It can be defined as fol-
where Green is a green band such as TM2, MIR is a
middle infrared ban d suc h as TM 5.
The MNDWI can not only reveal subtle features of
water more efficiently than NDWI, but also remove sha-
dow effects on water [19].
2.3. Generation of Manning Roughness
In order to get a spatially-distributed manning roughness
image (friction coefficients), a friction coefficients me-
thod was introduction [7]. In this method, a representa-
tive manning image (friction coefficients) can be derived
from land cover. Firstly, the land cover was classified us-
ing maximum likelihood (ML) and fuzzy c-means meth-
ods. Two images of land cover proportions were obtained
for each land cover class. Then friction coefficients were
generated for the two classifications. In the ML classifi-
cation, friction coefficients were estimated from land
cover using a look-up table. In the fuzzy c-means image,
pixels represent the proportion of each class. The images
of land cover pr oportions g enerated for a study ar ea were
combined into a single image of friction using:
2.4. Study Area
Manas Basin lies to the north of the Tianshan Mountains
and covers a surface area about 2 be-
tween latitudes 43˚20'N - 45 ˚55'N and longitudes 85˚00'E
- 87˚00'E (Figure 1). Manas River, the largest inland
river at the north slope of Tianshan Mountain, has 10 tri-
butaries. Its geomorphological types include alpine, low
mountains, alluvial-fan plain and desert. The average an-
nual temperature is 6.5˚C. The average annual precipi-
tation ranges from 100 to 200 mm. The average annual
evaporation ranges from 1500 to 2000 mm. Kensiwate
station is the con trol station at the confluence of tributar-
ies. The average annual runoff is about 12.8 × 108 m3,
and the annual runoff is 39.7 m3/s. The main soil is grey
cinnamon soil.
2.5. Basis Data
The typical catastrophic flood hydrograph, the Digital
Elevation Model data (DEM) and the Manning grid are
essential input for the model. More details as follows:
1) A high resolution digital elevation model (DEM),
with a horizontal grid resolution of 5 × 5 m, was adopted
in this study. It was produced by d igitizing a 1:10,0 00 to-
pographic map, which is provided by the Surveying and
Mapping Bureau of Xinjiang Uygur Autonomous Region.
The production process was carried out in accordance
with the standards of the Nation Bureau of Surveying and
Copyright © 2013 SciRes. JWARP
Figure 1. Relief map of the study area.
Mapping on the establishment of a digital elevation mo-
del data.
2) The hydrograph. The flood hydrograph during the
flood in 1999 fed the model, which is greater than the
100-year return period. It lasted for 23 days from July 14
to August 5, with a peak discharge about 1041 m3/s.
3) Two sets of Landsat 5 TM, with spatial resolution
of 30 m, were selected. The image on July 4, 1999 was
used to classify the types of land use. It is the basis of
manning roughness. And the image on August 5, 1999
was applied to delineate the actual inundation after the
flood. The images were registered according to the DEM
before flood inundation simulation.
3. Results Figure 2. The distribution of land cover and manning r ough-
ness in the study area.
3.1. Land Use Interpretation and Manning
Roughness Calculation Table 1. Land cover and manning values for each class.
We obtained seven types of land use accordin g to the TM
image before the flood, which include forest, grass, rural
area, unused land, urban area, water and farming land.
The classified result is shown in Figure 2 (left). Table 1
summaries the area proportions of different land cover
classes. It is seen that the most of upper reaches of the
study area is grass. In the low reaches, the land cover is
dominated by farming and unused land. Also, the man-
ning values for each class are listed in Table 1. The man-
ning values of farming land, forest, grass, water, rural
area, urban area and unused land were 0.035, 0.083,
number Class
description Proportion Manning
1 Forest 1.73% 0.083
2 Grass 41.60% 0.033
3 Rural 0.71% 0.080
4 Unused 26.99% 0.050
5 Urban 0.28% 0.025
6 Water 0.82% 0.030
7 Farming 27.87% 0.035
Copyright © 2013 SciRes.
0.033, 0.030, 0.080, 0.050, and 0.025, respectively.
Based on the classification, the manning roughness was
calculated by Equation (4). The spatial distribution of
manning roughness was illustrated in Figure 2 (right).
3.2. Flood Simulation
The flood was simulated using Floodarea with the man-
ning roughness image derived from Landsat TM and the
observed flood hydrograph. The modeled flood inunda-
tion area is shown in Figure 3 (right). It is observed that
the initiation of flooding in the study area was caused at
the junction of Beijiang railway and the Manas river,
where there is a major bridge across the river. In addition,
the actual flood inundation area was delineated using
MNDWI, as shown in Figure 3 (left).
The actual flood inundation was compared with the
simulated flood inundated area in the same period. From
the interpretation result of TM using MNDWI, the flood
inundation extent is 240.45 km2. The simulated flooding
extent is 276.15 km2. The simulated area is more than
14.8% of the actual flood inundation. if the percentage is
less than 15% according to the criteria, the model perfor-
mance is good [20]. So this indicates that there is a close
relationship between them. And the model performance
and manning roughness are acceptable.
The spatial distribution of flood depth was showed in
Figure 3 (right). The flood depth varied from 0.05 m to
7.77 m, which were less than 2 m in the most of inun-
dation area. The area of more than 4 m mainly existed in
the main channel. The depth in the lower area from the
Wuyi road was mainly about 0.5 m.
3.3. Identification of Inundation Hazard Zones
Inundation hazard zones were identified based on the
Figure 3. Comparison of flood inundated areas obtained
from landsat (left) and floodarea (right).
simulated flood inundation and the land use of the study
area. There were five hazard zones in the study area (Fig-
ure 4). Zone A was the junction of major channel and
floodplain. During the flood, the bridge across the river
in zone A was destroyed, and water spread. The other
four hazard zones have some characteristics in common.
Firstly, the water in these zones was deep. The flood
depth was 1.04 m, 0.77 m, 0.65 m and 0.32 m for zone B
to Zone E. Secondly, four zones were located in the
junction of different land use types. Zone B was the junc-
tion of farming and unused land. Zone C was the junction
of farming and forest. Zone D mixed of farming and
grass, and Zone E was the transitional zone of farming
and forest, grass. Thirdly, four hazard zones were all lo-
cated where the channel is winding.
4. Conclusions
The present study aimed at reproducing a devastating
flood occurred in 1999 and identifying the hazard zones
in Manas basin, China. A two-dimensional hydrody-
namic model, Floodarea was used to simulate the flood
inundation area and flood depth. The validation was con-
ducted by the comparison of actual flood extent and mo-
deled inundation. The simulated inundation area is
276.50 km2, and the actual inundation extent delineated
by MNDWI is 240.45 km2. The simulated inundation
area is more than 14.8% of the actual one. The validation
results show that the model performs satisfactory in re-
Figure 4. Identification of hazard zones in the Manas Basin.
Copyright © 2013 SciRes. JWARP
Copyright © 2013 SciRes. JWARP
producing the serious flood, and the spatial distributed
manning roughness is appropriate. The manning rough-
ness varied from 0.25 to 0.833 in the study area. And the
flood depth varied 0 to 7.77 m in the study area. Further-
more, five hazard zones were identified according to the
simulated flood depth, and the land use classified from
the Landsat image.
The spatial distributed manning roughness image may
be used to simulate the flood inundation extent and flood
depth in the study area caused by floods. And the iden-
tification of hazard zones has been adopted for Manas
basin water resources management and flood preventing
5. Acknowledgements
This work was supported by the National 973 Key Pro-
ject of China (2010 CB951004), the National Natur al Sci-
ence Foundation of China (41161008), National support
project (2012BAH27B03) and Team innovation project
of Shihezi University (2011ZRKXTD-0304).
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