Hydrologîcal Sciences-Jonrnal-des Sciences Hydrologiques, 45(3) Jane 2000
337
Development of flood hazard maps of Bangladesh
using NOAA-AVHRR images with GIS
MD MONIRUL ISLAM & KIMITERU SADO
Department of Civil Engineering, Kitami Institute of Technology, 165 Koen-cho,
Kitami 090-8507, Japan
e-mail: [email protected]
Abstract Flood hazard maps were developed using remote sensing (RS) data for the
historical event of the 1988 flood with data of elevation height, and geological and
physiographic divisions. Flood damage depends on the hydraulic factors which
include characteristics of the flood such as the depth of flooding, rate of the rise in
water level, propagation of a flood wave, duration and frequency of flooding,
sediment load, and timing. In this study flood depth and "flood-affected frequency"
within one flood event were considered for the evaluation of flood hazard assessment,
where the depth and frequency of the flooding were assumed to be the major
determinant in estimating the total damage function. Different combinations of
thematic maps among physiography, geology, land cover and elevation were
evaluated for flood hazard maps and a best combination for the event of the 1988
flood was proposed. Finally, the flood hazard map for Bangladesh and a flood risk
map for the administrative districts of Bangladesh were proposed.
Mise au point des cartes d'exposition au risque de crue du Bangla
Desh à l'aide d'images NOAA AVHRR et d'un SIG
Résumé Des cartes d'exposition au risque de crue ont été mises au point en utilisant
des données altimétriques, géologiques et physiographiques obtenues par télédétection
lors de la crue historique de 1988. Les dégâts provoqués par les crues dépendent de
différents facteurs hydrauliques tels que le niveau de l'eau, la vitesse de montée du
niveau, la propagation de l'onde de crue, la durée et la fréquence des crues, la charge
en sédiments et le moment de la crue. Dans la présente étude, nous nous basons sur le
niveau et la fréquence de crue pour évaluer le risque, car ces facteurs ont été
considérés comme principaux dans l'estimation totale des dommages. Différentes
combinaisons de cartes thématiques dont la physiographie, la géologie, l'occupation
des sols et l'altitude ont été évaluées pour établir des cartes d'exposition au risque de
crue et la meilleure combinaison relative à la crue de 1988 a été retenue. Une carte des
risques de crues pour le Bangla Desh et une carte d'exposition au risque de ses
différents districts administratifs sont enfin proposées.
INTRODUCTION
Bangladesh is a flood-prone country. Floods have had an impact on society from time
immemorial (Kundzewicz & Takeuchi, 1999), and they represent neither a new nor a
recent phenomenon in Bangladesh. Heavy monsoon rainfall occurs in summer from
June to October and average rainfall varies from 1200 mm in the west to 5800 mm in
the northeast (Rahman, 1996). The three mighty rivers, Brahmaputra, Ganges and
Meghna, enter Bangladesh from India through the north, northwest and northeast of the
country, respectively. High magnitude floods strike on a regular basis in the drainage
basins of these three rivers in Bangladesh, India and its peninsula (Bhattacharyya,
1997; Kale & Pramod, 1997; Muramoto, 1988; Rahman, 1996) because of the passages
Open for discussion until 1 December
2000
338
Md Monirul Islam & Kimiteru Sado
of depression and cyclone storm during the monsoon season. Floods are the most
significant natural hazard causing suffering to a large number of people and damage to
property in Bangladesh. Flood forecasting and warning systems were adopted in 1972
(Ministry of Foreign Affairs, Japan, 1989). The warning systems have subsequently
been expanded and enhanced, but are still insufficient. In recent years, various flood
control and management measures have been adopted, but flooding in such large rivers
profoundly challenges flood hazard management, because of the inadequacy of
conventional data and the spatio-temporal variability of floods. Flood studies in this
tropical area are greatly impaired by the inadequate long-term flood records and the
lack of sophistication of measurement procedures. As well as the three major
international rivers, there are more than 230 smaller rivers, including tributaries and
distributaries, in Bangladesh. Bangladesh lies in the farthest downstream area of the
three river basins and thus has limited control over the Ganges, Brahmaputra and
Meghna rivers. Moreover, for adequate and timely flood forecasting, Bangladesh
sometimes depends on information from the surrounding countries. The frequently
occurring floods are very costly in terms of human hardship and economic loss.
Therefore, the ability to estimate damages associated with a flood event is very
important and necessary to evaluate future alternative flood control policies. The
severity of the historical flood event of 1988 prompted the Bangladesh Government to
undertake a review of flood policy and flood protection measures with different foreign
donor agencies (Oya, 1993; World Bank, 1989). The historic floods were monitored by
different governmental agencies of Bangladesh (Bangladesh Government & UNDP,
1989) and some results were published using National Océanographie and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer
(AVFIRR) imagery (Ali et al, 1989; Choudhury, 1989; Islam & Sado, 1998a,b; Islam
& Sado, 2000; Ochi et al, 1991; Rahman et al, 1991; Rasid & Pramanik, 1990).
Flood hazard assessment using geographic information system (GIS) approaches is
still very crucial for the study of historical events. Furthermore, digital flood hazard
maps for whole area of the country and flood risk maps for the administrative districts
of Bangladesh are not available for any historical events. Therefore, in a round-table
discussion following the 1998 flood event, experts from different fields recommended
the need for flood hazard maps for proper planning and management against future
flood disaster (Center for Alternatives, 1998; Nishat, 1998).
Because flood is a wave phenomenon, different inundated areas are presented on
the satellite images which are taken at different times during the flood. The time of
acquisition of satellite data does not generally coincide with the time of flood peak or
maximum inundated area. Therefore the date and time of data collection and recurrent
periods of satellite imaging are important for investigation of satellite data (Oberstadler
et al, 1997). Furthermore, the routine measurement and the estimation of hydrological
parameters including flood-related parameters, could be useful in the areas ranging
from global scale to local or regional scales, depending on the spatial resolution and
recurrent period (Schultz, 1994; Tholey et al, 1997). NOAA data have been used to
observe floods which inundated large areas (Islam & Sado, 1998a,b, 2000; Wiesnet et
ah, 1974), while European Remote Sensing Satellite (ERS), Landsat, Marine
Observation Satellite (MOS) and Système Probatoire d'Observation de la Terre
(SPOT) have been used to observe regional or local floods (Oberstadler et al, 1997;
Development of flood hazard maps of Bangladesh
339
Profeti & Macintosh, 1997; Sado & Islam, 1997; Tholey et al, 1997). NOAAAVHRR, Landsat Thematic Mapper (TM), MOS Multispectral Electronic SelfScanning Radiometer (MESSR) and SPOT-High Resolution Visible (HRV) are passive
systems and can only operate effectively during daytime in cloudless conditions, while
a synthetic aperture radar (SAR) system like ERS is active and can deliver day and
night coverage in all weather. Precise mapping of the flood extent is expected to be
made possible by improved spatial resolution, more spectral bands, but, most
importantly, by a synergistic RS approach utilizing either visible or infrared data and
SAR data (Engman, 1996; Smith, 1997). GIS technology is essential in the
development of automated methods for quantifying the spatial variability of flood
hazard and flood related problems, and it has been widely used in supporting surface
water modelling and flood hazard exposure (Boyle et al, 1998; Greene & Cruise,
1995; Maidment, 1993; Paudyal, 1996; Ross & Tara, 1993; Schultz, 1994).
This study focuses on the historical event of the 1988 flood in the Ganges,
Brahmaputra and Meghna river basins in Bangladesh, because this was the most
devastating environmental disaster in the memorable history of the country. The
purpose of this paper is to illustrate the development of a flood hazard map which is
enhanced by GIS technology using NOAA-AVHRR data. Hazard assessment focuses
on the hazard ranks, from normal to severe, posed by the 1988 flood event. After the
identification of flood hazard ranks, formulation of the risk areas for each
administrative district is performed.
PREPARATION OF THE DATA FOR THE STUDY
Four NOAA-AVHRR data sets were used for this study: one (20 January 1988) for the
dry season and three (18 September, 24 September and 8 October 1988) for the flood
season. NOAA-AVHRR cannot receive the radiance from a cloud covered ground
surface, as this interrupts the actual estimation of flooded area. Therefore the low cloud
covered data were employed to estimate flooded area, "flood-affected frequency"
within one flood event and classes of flood depth. The notion of flood-affected
frequency is introduced here. It is determined for each pixel as the ratio of the number
of NOAA images within the same flood event of 1988 showing inundation to the total
number of NOAA images available for this flood. It is threfore, linked to flood
duration. The cloud covered pixels were interpreted by using the algorithm that was
recently developed for recovery of cloud covered pixels as water or non-water (Islam &
Sado, 2000). Digital elevation data, physiographic division, geological division,
administrative division and drainage network data were prepared. Finally, these digital
data were combined with the NOAA-AVHRR data within a GIS approach.
FLOOD-AFFECTED FREQUENCY AND FLOOD DEPTH SEEN THROUGH
NOAA-AHVRR DATA
The 1988 flood in Bangladesh lasted for long period. It commenced in early July
(Rasid & Pramanik, 1990) and peak flood levels were reached at Bahadurabad on
340
Md Monirul Islam & Kimiteru Sado
30 August, Hardinge Bridge on 2 September, Dhaka on 4 September, and Bhairab
Bazar on 10 October, and remained above danger level for 15, 22, 23 and 76 days,
respectively. The most damaging aspect of the flood was the destruction of people's
means of livelihood because of interruption of transportation and communication, and
submergence of houses due to the long duration of the flood. Human suffering was
intensified as the slowly receding water prevented people returning from flood shelters
to their homes. The rice grown in the flood season is mainly cultivated during JulyNovember (54%) and April-August (20%) (Ministry of Foreign Affairs, Japan, 1989).
However, paddy cannot tolerate submergence for a long time. Consequently, the floodaffected frequency and the flood depth basically determine the extent of flood damage
in Bangladesh. Flood-affected frequency and flood depth were estimated by using the
images of 18 September, 24 September and 8 October 1988 within one event of 1988.
Water and non-water areas were estimated using the images of 18 September,
24 September and 8 October 1988 for the flood season and 20 January 1988 for the dry
season by means of ISODATA clustering of unsupervised classification and various
supervised classifications. ISODATA clustering is an iterative non-hierarchical clustering which uses minimum spectral distance to assign a cluster for each candidate
pixel. Initially all images were categorized into various classes by both supervised and
unsupervised classification; then the classes were divided into three categories: nonwater, water and cloud, and finally into two categories: water and non-water, after the
interpretation of cloud covered pixels.
An inundated area that did not appear in any of the above mentioned three images
(i.e. 8 in Fig. 1) was considered to be a non-hazard area and that which appeared in a
single image (5, 6 or 7 in Fig. 1) was considered to be a low hazard area. The common
inundated area that appeared in two images (2, 3 or 4 in Fig. 1) and that which
appeared in all three images (1 in Fig. 1) were considered to be medium and high
hazard areas, respectively. Flood-affected frequency within one event corresponding to
damage ranking were denoted as class 1, class 2, class 3 and class 4 for non-hazard,
low, medium and high hazard areas, respectively.
Flood water depths were classified as shallow, medium and deep by using the
maximum likelihood method of supervised classification for the same images. Training
18 September 1988 24 September 1988
8 October 1988
Fig. 1 Schematic concept of flood-affected frequency analysis by three images within
one event of 1988.
Development of flood hazard maps of Bangladesh
341
Table 1 Slope, S, and intercept, /, for NOAA-10 satellite, and calculated mean albedo for different
categories of water depth.
NOAA-10:
S (%/count) /(%)
Bandai
Band_2
0.10588
0.106073
-3.52794
-3.47665
Albedo (%):
No water
Shallow
6.80
10.65
5.13
9.44
Medium
4.52
4.80
Deep
4.59
3.43
areas for shallow, medium and deep flood were selected on these images according to
the differences in colours and grey scales for different categories of depth, and these
differences were interpreted after superimposing the NOAA images onto a digital
elevation image of Bangladesh. If deep flood appeared for a pixel in a single image
then it was considered as "deep"; and if medium flood appeared in a single image for
the pixel that was represented as shallow flood by two other images, then it was
considered as "medium flood". The priority was given for the highest of three
categories of flood depth corresponding to three images. Furthermore, the rankings for
flood water depth were denoted as class 1, class 2, class 3 and class 4 for no flooding,
shallow, medium and deep flooding, respectively.
In order to examine the classified results of water depth, the average albedo was
evaluated. Table 1 shows the average albedo for the different categories of flood depth
which were evaluated using the image of 18 September 1988. Albedo can be estimated by:
where, A, C, S, and / are percentage albedo, digital number, slope and intercept,
respectively, for i band (Lauritson et a/., 1979). The values of slope and intercept
(SeaSpace Corporation, 1995) are also shown in Table 1. The albedo for shallow water is
higher than that for medium and deep flood and the albedo for medium flood is higher
than that for deep flood, except for Band_l (where they are nearly equal), which can be
considered as showing high turbidity of deep water with large amounts of discharge per
unit width. Albedo is generally related to turbidity and water depth, but here albedo
results were considered for water depth only, because deeper flood waters generally have
more turbidity than shallower waters due to the high water velocity during a flood. The
results of albedo estimation show good agreement with the effective absorption of
submerged sunlight by suspended solids for different depths of water.
FLOOD DAMAGE SEEN THROUGH PHYSIOGRAPHIC AND
GEOLOGICAL DIVISIONS AND ADMINISTRATIVE DISTRICTS
To estimate the flooded area, two categories of the three images, water and non-water,
were combined to obtain the maximum water area. Then this combined image was
superimposed onto the two categories of dry season image. The water areas which
appeared in both images are considered to be normal water (river, lake, pond, etc.) and
the non-water areas are considered to be non-flooded areas. Thus, the areas which
represent water areas in the combined image but non-water areas in the dry season
image are considered to be flooded areas. Earlier studies have shown that the estimated
flooded areas of these combined images, using various supervised and unsupervised
342
Md Monirul Islam & Kimiteru Sado
classifications, range from 47% to 50% (Islam & Sado, 1998a,b). In this study only
ISODATA clustering results were used.
Physiographic divisions of Bangladesh
Bangladesh may be divided into five main physiographic regions, with various subdivisions. Table 2 shows the occupied area for each division as a percentage of the
Table 2 Portion of total area and flooded area (%) for each physiographic division.
ID Physiographic division
North Bengal region:
1 Tetulia Dinajpur alluvial fan
2 New Tista flood plain
3 Old Tista flood plain
4 Old Lower Tista flood plain
5 Northern Barind outliner
6 Eastern Barind Tract
7 Inundated Eastern Barind Tract (Chalan Beel)
8 Central Barind Tract
9 Western Jamuna flood plain
10 Northern Ganges flood plain
Northeastern region:
11 Old Brahmaputra flood plain
12 Madhupur Tract
13 Eastern Jamuna flood plain
14 Sylhet Mymensingh Haor area
15 Surma-Kusiyara flood plain
16 Northern Mymenshing hilly area
17 Sylhet hilly area
18 Very old Brahmaputra flood plain
19 Titas River valley
Tippera Comilla region:
20 Tippera Comilla flood plain
Southwestern region:
21 Southwest Ganges flood plain (inactive)
22 Southeast Ganges flood plain (active)
23 Old coastal marshes
24 Coastal sediment and island (non-salinized)
25 Salinized coastal sediment
26 Coastal sediment with mangrove
Chittagong region:
27 Coastal plain
28 Marine coastal plain
29 Chittagong island plain
30 Chittagong ridge valley
31 Water course/river
Total
ID: identification number for each division.
Portion of total area (%) Flooded area (%)
2.92
4.48
2.26
1.15
0.77
6.05
0.61
2.52
1.58
1.04
14.24
43.64
23.36
76.50
12.85
49.77
99.04
22.42
97.17
80.50
9.01
3.27
1.91
4.25
4.02
2.57
1.67
0.40
0.52
74.35
48.98
99.34
95.26
72.99
48.98
51.26
79.41
78.68
2.73
42.73
7.23
4.86
2.78
7.10
1.89
4.07
24.11
77.93
69.57
26.60
12.57
14.54
0.92
1.29
1.24
9.96
4.92
100.00
18.67
24.00
16.30
6.93
50.65
Development of flood hazard maps of Bangladesh
343
total land of Bangladesh, and the flooded area as a percentage of each physiographic
division, which was obtained by superimposing the combined flooded area image onto
the physiographic image. The areas highly affected by the 1988 flood were the inundated eastern Barind Tract, the western Jamuna flood plain and the northern Ganges
flood plain of the North Bengal region, the eastern Jamuna flood plain, the Sylhet
Mymensingh haor area, the very old Brahmaputra flood plain and the Titas River
valley of the North Eastern region, and the southeast Ganges flood plain (active) of the
South Western region. Comparatively, the Chittagong and Tippera Comilla regions
were less affected.
Geological divisions of Bangladesh
Table 3 shows the nine geological groups: coastal deposits, deltaic deposits, alluvial
deposits, alluvial fan deposits, residual deposits, bed rock, Tipam group, Surma group
and Jainta formation, which occur in Bangladesh. Each geological group is sub-divided
into several categories. The occupied area as a percentage of the total land area of the
country and the percentage flood-affected area for each geological category are also
shown in Table 3. The deltaic sand sub-division of the deltaic deposits, and the marsh
clay and peat, alluvial silt, and alluvial silt and clay sub-divisions of the alluvial
deposits were highly affected by the flood of 1988. Each geological category has its
own characteristics for crop cultivation, irrigation, and habitat. Seventy percent of
people in Bangladesh depend on agriculture, which plays vital role in the economic
development of the country. The damage assessment for the physiographic and geological divisions should provide helpful information about the losses of seasonal crops.
Administrative districts of Bangladesh
There are 64 administrative districts in Bangladesh (shown in Table 4). Every district
has its own characteristics. Many people are forced to live on and cultivate flood-prone
land. There is intermittent water shortage in some districts of northern and central
Bangladesh because of falling water tables, land degradation, deforestation and overpopulation (Mirza, 1997). Natural hazards such as cyclones with strong surges strike
the southern parts of the country, and many districts flood routinely during the summer
monsoon season. Table 4 shows the administrative districts with their occupied area as
a percentage of the total land area, and the 1988 flood-affected area as a percentage for
each district. District damage ranks (DDR) from 1 to 10 were allocated by using the
flood-affected area percentage: 0-10% was denoted as district damage rank 1, 10-20%
as damage rank 2, and so on. Highly flood-affected districts are Serajganj, Gopalganj,
Kishoreganj and Bramanbaria having damage rank 10, and Rajbari, Faridpur,
Madaripur, Jamalpur, Netrokona, Sunamganj, Manikganj and Dhaka having damage
rank 9. Khagrachari and Bandarban districts are almost flood free areas because of the
upland hilly areas in the southeastern parts of the country. Some of the northwestern,
northeastern and southeastern parts of Bangladesh, near the border areas, have high
elevation; all others districts—the cultivated areas—have low elevation.
344
Md Monirul Islam & Kimiteru Sado
Table 3 Portion of total area and flooded area (%) for each geological division.
ID Geological division
Coastal deposits:
Beach and dune sand
Deltaic deposits:
Mangrove swamp deposits
Tidal mud
Tidal deltaic deposits
Estuarine deposits
Deltaic silt
Deltaic sand
Alluvial deposits:
Marsh clay and peat
9 Alluvial sand
10 Alluvial silt
11 Alluvial silt and clay
12 Chandina alluvium
13 Valley alluvium and colluvium
Alluvial fan deposits:
14 Young gravelly sand
15 Old gravelly sand
Residual deposits:
16 Barial clay residuum
17 Madhupur clay residuum
Bed rock:
18 St. Martin's lime stone (Pleistocene)
19 Dihing and Dupi Tila formation undivided
20 Dihing formation (Pleistocene and Pliocene)
21 Dupi Tila formation (Pleistocene and Pliocene)
Tipam group:
22 Girujan clay (Pleistocene and Neogene)
23 Tipam sand and stone
Surma group:
24 Boka Bill formation (Neogene)
25 Bhuban formation (Miocene)
26 Barail formation (Oligocène)
Jainta formation:
27 Kopili formation (late Eocene)
28 Lake, water course/river
Total
Portion of total area (%) Flooded area (%)
1.50
42.35
4.00
0.67
8.13
3.01
8.68
1.25
10.83
32.66
22.38
39.20
45.58
74.26
11.69
0.00
10.39
10.57
4.65
3.56
77.42
0.00
76.76
80.08
44.45
10.53
4.89
3.03
27.71
13.33
5.12
2.37
17.64
35.20
0.00
0.96
0.46
0.39
0.00
46.15
0.55
29.06
0.89
0.06
15.88
0.00
4.83
2.39
0.45
9.23
9.22
4.47
0.00
6.06
100.00
0.00
60.61
ID: identification number for each division.
FLOOD HAZARD MAP
Flood hazard maps were developed using land cover, elevation, physiographic and
geological features and drainage network data. Flood-affected frequency and flood
Development of flood hazard maps of Bangladesh
345
Table 4 Portion of total area and flooded area (%) and districts damage ranks for the administrative
districts.
ID District name Portion of
total area
(%)
0.93
1 Panchagar
1.28
2 Takurgaon
1.20
3 Nilpharaari
4 Lalmonirhat
0.87
1.44
5 Kurigram
1.57
6 Rangpur
2.55
7 Dinajpur
1.51
8 Gaibandha
0.68
9 Joypurhat
2.31
10 Naogaon
2.12
11 Bogra
1.16
12 Nawabganj
1.74
13 Rajshahi
1.33
14 Natore
1.73
15 Serajganj
1.65
16 Pabna
1.32
17 Kushtia
0.53
18 Meherpur
0.82
19 Chuadanga
1.35
20 Jhenaido
0.71
21 Magura
1.82
22 Jessore
0.70
23 Narail
24 Satkhira
2.79
2.98
25 Khulna
2.71
26 Bagerhat
0.84
27 Rajbari
1.39
28 Faridpur
0.80
29 Shariatpur
0.81
30 Madaripur
1.09
31 Gopalganj
1.67
32 Barisal
Total
Flooded
area
(%)
8.72
13.21
16.96
16.81
64.20
17.60
15.08
68.62
13.95
50.29
53.55
74.80
50.80
59.53
91.66
73.57
35.35
22.75
32.21
12.87
37.80
18.37
66.67
12.13
19.84
25.61
89.97
88.88
78.93
89.13
92.88
50.63
DDR
ID
1
2
2
2
7
2
2
7
2
6
6
8
6
6
10
8
4
3
4
2
4
2
7
2
2
3
9
9
8
9
10
6
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
District name Portion of
total area
(%)
Perojpur
1.00
Jhalakhati
0.54
Patuakhali
2.03
1.18
Bagura
Bhola
1.60
Sherpur
0.90
Jamalpur
1.40
Mymensingh
2.85
Netrokona
1.98
Kishoreganj
1.81
Sunamganj
2.47
2.13
Sylhet
Moulvibazar
2.09
Habiganj
1.97
2.30
Tangail
Manikganj
0.98
Gazipur
1.20
Norshingdi
0.73
Dhaka
1.13
Narayanganj
0.51
Munshiganj
0.71
Bramanbaria
1.30
Comilla
2.05
Chandpur
1.17
Lakshmipur
1.05
2.10
Noakhali
Feni
0.60
Khagrachari
1.99
Rangamati
3.13
Chittagong
3.59
Bandarban
3.18
Cox's bazar
1.90
100.00
Flooded
area
(%)
35.01
19.03
22.99
25.36
16.80
54.90
87.52
49.41
81.20
92.48
86.29
75.04
65.64
78.06
72.56
89.57
50.77
78.25
87.23
78.88
71.06
92.72
57.40
50.86
17.83
32.53
15.50
1.22
12.08
19.50
4.95
22.18
DDR
4
2
3
3
2
6
9
5
9
10
9
8
7
8
8
9
6
8
9
8
8
10
6
6
2
4
2
1
2
2
1
3
ID: identification number for each district; DDR: district damage rank, based on flood-affected area
percentage for each district.
depth were used as hydraulic components. To assess the flood hazard, a model was
considered, the schematic concept of which is shown in Fig. 2. Flood-affected
frequency, flood depth and land cover categories were estimated from NOAA-AVHRR
data. Digital physiographic, geological, elevation, land cover and drainage network
data were considered within a GIS approach.
Flood hazard rank assessment
Hazard ranks were decided based on a weighted score for the physiographic,
geological, land cover and elevation data for each pixel of the land area of Bangladesh.
Md Monirul Islam & Kimlteru Sado
346
Digital elevation data
Geologic map
Physiographic map
Drainage map
Satellite imagery
(NOAA AVHRR )
Programs for data processing
Flood affected
frequency
Flood
depth
Land cover
Physiography
Geology
Elevation Drainage
network
Flood hazard
map by flood
affected frequency
Flood hazard
map by flood
water depth
Fig. 2 Schematic concept of a model for flood hazard assessment.
A weighted score was estimated by:
Weightedscore = 0.0xclassl + 1.0xclass2 + 3.0xclass3 + 5.0xclass4
(2)
where, class 1 to class 4 represent the area percentage occupied by the categories of
each hydraulic component for each ID category of the respective GIS component. The
coefficients of 0.0, 1.0, 3.0 and 5.0 for classes 1-4 in equation (2) were used to
describe the weight for flood damage. The acquired area percentages for each class for
the physiographic divisions, based on flood water depth, are shown in Table 5 with the
calculated weighted score. Points for each ID category were estimated on the basis of
linear interpolation between 0 and 100, where 0 corresponds to the lowest (0) and 100
to the highest (483.27) score.
In order to quantify the flood hazard, the four rankings for flood damage
(HR 1-4) were obtained from the allocated points by considering the interactive effect
between two of the thematic data sets of physiography, geology, land cover and
elevation. The three rankings (HR 1-3) were obtained from the points to consider the
interactive effect of three among the four thematic data sets. Hazard ranks were fixed
according to the corresponding value of the point: when categorized from 1 to 4, points
0-25 were given hazard rank 1, 25-50 rank 2, 50-75 rank 3 and 75-100 rank 4, but
when categorized from 1 to 3, points 0-33 corresponded to hazard rank 1, 33-66 rank
2 and 66-100 rank 3. Similarly hazard ranks were determined using the same algorithm
for physiographic divisions (31 divisions), using flood-affected frequency, and for the
geological divisions (28 divisions), land cover classification (9 categories) and
elevation height intervals (9 intervals), using flood-affected frequency and flood water
depth.
Ten combinations of thematic maps were prepared for both flood-affected
frequency and flood depth. Figure 3 shows the combinations of thematic data
Development of flood hazard maps of Bangladesh
347
Table 5 Flood hazard ranks for physiographic divisions calculated using flood water depth.
ID
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
Class 1
72.48
39.88
64.40
20.86
75.60
46.71
0.27
71.57
2.30
12.19
22.10
47.15
0.00
1.15
27.68
50.66
43.73
2.10
5.17
52.18
68.88
16.04
26.67
50.91
62.34
64.60
63.11
89.34
97.62
89.20
8.06
Class 2
12.54
7.62
8.54
0.95
5.77
0.40
0.00
4.24
0.21
0.16
0.05
0.62
0.00
0.00
0.90
3.51
1.67
0.21
0.00
14.50
4.61
0.45
1.12
18.50
9.45
3.37
29.96
1.64
1.09
4.24
4.46
Class 3
12.36
43.62
25.86
54.10
13.94
35.79
26.65
19.09
62.39
50.93
54.86
32.42
65.08
5.49
30.22
15.33
25.28
55.88
26.98
25.79
22.24
51.41
38.13
16.28
12.34
13.53
5.83
2.42
0.61
0.22
3.71
Class 4
2.62
8.88
1.19
24.08
4.68
17.10
73.08
5.11
35.10
36.72
23.00
19.81
34.92
93.36
41.20
30.50
29.32
41.81
67.85
7.53
4.27
32.10
34.07
14.31
15.86
18.49
1.09
6.61
0.68
6.34
71.29
Score
62.74
182.88
92.09
283.67
71.02
193.25
445.33
87.02
362.87
336.56
279.62
196.93
369.85
483.27
297.55
202.03
224.12
376.89
420.19
129.53
92.68
315.17
285.87
138.87
125.80
136.43
52.91
41.92
6.32
36.61
416.82
Point
12.98
37.84
19.06
58.70
14.70
39.99
92.15
18.01
75.09
69.64
57.86
40.75
76.53
100.00
61.57
41.81
46.37
77.99
86.95
26.80
19.18
65.22
59.15
28.74
26.03
28.23
10.95
8.67
1.31
7.57
86.25
HR1-4
1
2
1
3
1
2
4
1
4
3
3
2
4
4
3
2
2
4
4
2
1
3
3
2
2
2
1
1
1
1
4
H
1
2
1
2
1
2
3
1
3
3
2
2
3
3
2
2
2
3
3
1
1
2
2
1
1
1
1
1
1
1
3
Note: Classes 1-4 represent the area percentage of no flooding, shallow, medium and deep floods,
respectively.
considered and the concepts of ranking matrix in two-dimensional and threedimensional multiplication modes for the development of flood hazard maps.
Best combination of thematic data for a flood hazard map
The best combination of thematic data for a flood hazard map was examined among
the ten combinations of thematic data shown in Fig. 3 for flood-affected frequency and
flood depth using a square matrix examination. Table 6 shows the square matrix for the
combination of land cover classification, and physiographic and geological divisions
for flood-affected frequency and flood depth. The columns and rows of the matrix
represent the number of pixels for each flood hazard rank of the flood hazard maps
derived using flood depth (D) and flood-affected frequency (F), respectively. The
348
Md Monirul Islam & Kimiteru Sado
Z 1. Land cover
2. Elevation
HR
1
2
3
4
Physiography
1
1
2
3
4
2
2
4
6
8
3
3
6
9
12
4
4
8
12
16
(b) Ranking matrix for two
dimensional multiplication mode
X
Geology
Combination by 2 among 4
4
C
2
4!
= — = 6
Combination by 3 among 4
4!
4 C s
3!1!
•
(a) Thematic map combinations
HR 1
1 1
2 2
3 3
2
2
4
6
"
HR 1
1 1
2 2
3 3
2
2
4
6
3
3
6
9
XY
3 4
3 4
6 8
9 12
6
6
12
18
9
9
18
27
(c) Ranking matrix for three
dimensional multiplication mode
Fig. 3 Concept of two- and three-dimensional ranking matrix in multiplication mode
for different combination of thematic maps.
Table 6 Square matrix examination for the combination of thematic data: land cover, physiographic and
geological data.
1
2
3
6
4
8
12
9
18
27
Total
F ^ \
1
2
3
4
6
8
9
12
18
27
5119
858
0
51
0
0
0
0
0
0
0
0
9558
4749
11396
Total
5170
10545
0
129
0
0
0
0
0
0
2967
1749
0
26
7112
1455
0
0
0
0
0
0
0
0
0
0
0
17626
11828
0
4
330
0
181
0
1444
36
0
0
0
0
1374
14476
2489
1348
1953
8650
0
0
0
6
0
1297
1224
745
0
0
0
0
1460
0
1607
2324
0
0
0
0
3079
0
0
74
0
8657
1333
7366
13626
1889
4603
0
0
0
0
15854 23785
0
0
0
0
0
0
0
554
2637
9769
12960
8948
20223
13968
10152
14616
3734
4412
16577
16337
9769
118736
D: flood water depth, F: flood-affected frequency; summation of diagonal elements is 65.98% of total
pixels.
diagonal pixels denote that the pixels occupy the same hazard ranks whether developed
using flood-affected frequency or flood depth.
Summations of diagonal elements for the possible combinations, 4 C 2 and 4C3, are
shown in Table 7. Possible combinations show that the summations of diagonal
elements range from 37% to 66%. The combination which shows the maximum total
Development of flood hazard maps of Bangladesh
349
Table 7 Summations of diagonal elements of square matrixes for determining the best combination of
thematic data for flood hazard map.
ZDE (%)
X
Y
Zl
Z2
65.98
A
A
A
64.74
A
A
52.83
A
A
A
48.25
A
A
47.61
A
A
47.11
A
A
A
44.49
A
A
44.43
A
A
44.23
A
A
A
36.51
A
A
DE: diagonal element, X: physiography, Y: geology, Zl: land cover, Z2: elevation and A: thematic map
considered.
pixels for diagonal elements is considered as the best—in this case, the combination of
physiography, geology and land cover. The combination of physiography and geology
is the second best, whilst that of land cover and elevation is the worst.
Therefore a flood hazard map may be derived from land cover classification,
physiographic and geological divisions using either flood-affected frequency or flood
depth. Table 6 also shows the comparison among the number of pixels which were
occupied by each flood hazard rank for the two different components: flood-affected
frequency and flood depth. If there is perfect congruence between these two flood
hazard maps then the non-diagonal elements of the matrix in Table 6 would be zero.
However, there are a lot of non-zero elements above diagonal elements for the hazard
rank of flood depth. Hazard ranks evaluated by depth show a deviation in the marginal
distribution toward higher ranks compared with hazard ranks evaluated by frequency.
The flood hazard map for depth occupies larger areas in higher ranks compared with
the flood hazard map for frequency.
Developed flood hazard maps
Flood hazard maps which were developed using ranking matrixes do not show water
courses. Therefore, the drainage map was overlaid onto the developed hazard maps,
and the final hazard maps with water courses are shown in Fig. 4(a) and (b). Hazard
ranks were considered from 1 to 27 which were estimated by the ranking matrixes of
three-dimensional multiplication mode. Comparing Fig. 4(a) and (b), 7 1 % of pixels
show the same hazard ranks and 29% are different. Some differences may be seen:
(a) in the northwestern upstream part of Brahmaputra River basin (Fig. 4(a) shows the
areas ranked as 2 but Fig. 4(b) shows them ranked as 2, 3, 4 and 6); (b) the Madhupur
Jungle areas in the central part of the country (rank 2 and 3 in Fig. 4(a), rank 4 in Fig.
4(b)); (c) the northeastern areas of the Ganges River basin near the Barrind tract (rank
12 in Fig. 4(a), rank 12, 18 and 27 in Fig. 4(b)); (d) the mangrove areas in the
southwest (rank 1 in Fig. 4(a), rank 4 in Fig. 4(b)); and (e) the northeastern upstream
Md Monirul Islam & Kimiteru Sado
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Development of flood hazard maps of Bangladesh
351
part of the Meghna River (rank 4, 6 and-18 in Fig. 4(a), rank 18 and 27 in
Fig. 4(b)). These differences are mainly due to the consideration of the two different
components, flood-affected frequency and flood depth, for the same flood event. The
new flood hazard maps developed herein show the different results for some areas
compared with a previously evaluated hazard map (Islam & Sado, 2000), which was
developed considering a ranking matrix of only two-dimensional multiplication mode
using flood-affected frequency with land cover and elevation height data (combination
no. 10 in Table 7). Therefore, it is concluded that these new flood hazard maps provide
more accurate information compared with the earlier one due to the consideration of
flood-affected frequency and flood water depth with land cover, physiographic and
geological data. In addition, the new flood hazard maps contain more hazard ranks
because of the three-dimensional multiplication mode of the ranking matrix evaluated
by land cover, physiographic and geological data.
DEVELOPMENT OF A FLOOD RISK MAP FOR ADMINISTRATIVE
DISTRICTS
A flood risk map was derived for the administrative districts of Bangladesh using the
developed flood hazard maps (Fig. 4). The mean hazard rank, HR, for each
administrative district was estimated by:
• Xn^
HR =^=—
(3)
i
where, «, is the number of pixels occupied by the ith hazard rank for each
administrative district, and r,- is the z'th hazard rank value. The risk ranks from 1 to 5
were fixed by the corresponding value of the mean hazard rank (risk rank 1: mean
hazard ranks 1-5, risk rank 2 for 5-10, risk rank 3 for 10-15, risk rank 4 for 15-20 and
risk rank 5 for 20-25). Higher rank means higher risk of a flood occurring in an
administrative district. Table 8 shows the occupied districts against risk ranks for
flood-affected frequency and flood water depth. Seventy-five percent of districts were
Table 8 Square matrix examination for risk ranks of flood-affected frequency and flood water depth on
administrative districts.
D
1
2
3
4
16
0
0
0
0
16
3
13
0
0
0
16
1
4
11
0
0
16
0
0
7
4
0
11
5
Total
F
1
2
3
4
5
Total
0
0
0
1
4
5
20
17
18
5
4
64
D: flood water depth, F: flood-affected frequency; summation of diagonal elements is 75.00% of total
districts.
Md Monirul Islam & Kimiteru Sado
352
occupied by the same ranks and 25% of districts differed from each other for floodaffected frequency and flood depth, which were considered independently. For the
development of a flood risk map for the administrative districts, the interactive effect
of flood-affected frequency and flood depth were considered. The flood risk map
shown in Fig. 5 was developed by the ranking matrix of two-dimensional
multiplication mode, where risk ranks are 1, 2, 3, 4, 5, 6, 8, 9, 10, 12, 15, 16, 20 and
25. In this flood risk map, administrative districts were grouped as lowest risk (risk
rank 1, 2, 3, 4, 5), low risk (risk rank 6, 8, 9, 10), medium risk (risk rank 12, 15), high
risk (risk rank 16, 20) and very high risk (risk rank 25) zones, considering their
similarity of flood risk rather than their geographical position location.
The flood hazard map represents the magnitude of flood damage for each pixel
which has been classified by flood water depth or flood-affected frequency independently, while the flood risk map represents the magnitude of more severe flood damage
89°
90°
92°
91°
93° E
—K
F l o o d risk z o n e
_ l _
WÊÊÊÊ Lowest risk
Low risk
M e d i u m risk
H i g h risk
*!*$•. Very h i g h risk
26° N
26° N
25°
25°
24°
24" •
23°
23°
l
1
1
.i
22° -
-J
).
-i
h
s l
S 1
» It 1 0
'! 1 '.' I ~>
22°
4 8 12 J6 20
51015 20 25
Ranking matrix
21°
®
21 =
— I —
88°
89°
T
90°
Fig. 5 Flood risk map for administrative districts.
91°
92°
Development of flood hazard maps of Bangladesh
353
due to the interactive effect of depth and frequency. Thus the flood risk map represents
the priority to be given to flood countermeasures in each administrative district. The
districts of Sunamganj, Kishoreganj, Serajganj, Manikganj and Narayanganj lie in very
high risk areas which cover 7.50% of the total area of the country; Sylhet, Hobiganj,
Bramanbaria, Netrokhona, Norshingdi, Tangail, Natore, Pabna, Dhaka, Monshiganj
and Gopalganj lie in high risk areas which cover 16.34% of the total area. Medium and
low risk areas cover 23.02 and 24.54%, respectively, and 28.60% areas are at the
lowest risk.
The authors have also reported that Dhaka City, the capital of Bangladesh, was
highly affected by the flood of 1988 (Sado & Islam, 1997). For the effective planning
of flood defences and the safety of the people living in high risk areas, useful
information can be provided using this risk map. Priority can be given to the
developments necessary in the administrative districts which are in high risk areas. Aid
can be provided and necessary advance action taken for a future flood event by
understanding this flood risk map. This flood risk map is illustrated using a new
generalized technique prepared in digital form. Therefore, information can be shared
among the various agencies at various organizational levels for their further analyses
and uses.
CONCLUSIONS
Flood hazard assessment can be performed using NOAA-AVHRR data with
administrative districts, and physiographic, geological, elevation and drainage network
data. Flood-affected frequency and flood water depth are essential components for the
evaluation of flood hazard. In this study, the categories of flood-affected frequency and
flood water depth were estimated using NOAA satellite data. Flood hazard rank
assessment was undertaken on the basis of land cover classification, physiographic
divisions, geological divisions, elevation intervals and administrative districts. All
these data and maps were developed in digital form and can be used as a GIS database
in other fields.
The study shows that 71% of hazard ranks in the area are the same for the best
combination of thematic data whether they have been estimated with regard to floodaffected frequency or flood water depth, and 75% of the administrative districts fall
within the same risk zones when estimated using either flood-affected frequency or
flood water depth. However, planning flood defences after the flood hazard map
developed using flood depth could result in higher safety than if the other map is used.
Finally, flood risk assessment was performed using both flood hazard maps for the
administrative districts of Bangladesh considering the synergistic effect of floodaffected frequency and flood water depth. The study shows that 7.50% of areas are at
very high risk and 16.34% are at high risk. The capital city also lies in a high risk area.
The results described in this study should provide helpful information about flood
risk management and should be useful in assigning priority for the development of very
high risk and high risk areas. In addition, the study may have considerable management
implications for emergency preparedness, including aid and relief operations in high
risk areas in the future. Flood hazard and flood risk maps may also help the responsible
354
Md Monirul
Islam & Kimiteru
Sado
authorities to better comprehend the inundation characteristics of the flood plains, the
protection of which is their responsibility. In addition, the general public will be made
aware of the imagery of flooding which helps in understanding the risk of flood.
Finally, these types of flood hazard and risk map in digital form can be used as a
database to be shared among the various government and non-government agencies
responsible for the construction and development of flood defence.
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Received 3 August 1999; accepted 21 January 2000