Identification of Inundation Hazard Zones in Manas Basin, China, Using Hydrodynamic Modeling and Remote Sensing

doi:10.4236/jwarp.2013.54046

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Journal of Water Resource and Protection, 2013, 5, 469-473

http://dx.doi.org/10.4236/jwarp.2013.54046 Published Online April 2013 (http://www.scirp.org/journal/jwarp)

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: *[email protected]

Received January 9, 2013; revised February 17, 2013; accepted February 27, 2013

permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

ABSTRACT

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.

L. K. NING ET AL.

470

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



level

elevation





NDWI



WI MNDWI



, 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)

2.2. 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-

lows:



Green

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:

Green

NDWI MIR MIR (3)















.6510km

(4)

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

L. K. NING ET AL.

JWARP

471

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,

Class

number Class

description Proportion Manning

value

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

L. K. NING ET AL.

472

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.

L. K. NING ET AL.

473

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

strategies.

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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