Date: Fri, 12 Sep 2008 03:40:19 +0530
From: "JaiBihar .com" <[EMAIL PROTECTED]>
Subject: GIS in Flood Hazard Mapping: a case study of Kosi River Basin
GIS in Flood Hazard Mapping: a case study of Kosi River Basin G Venkata
Bapalu, Rajiv Sinha - September 12, 2008
Flood Hazard Mapping is a vital component for appropriate land use planning
in flood-prone areas. It creates easily-read, rapidly-accessible charts and
maps which facilitates the administrators and planners to identify areas of
risk and prioritize their mitigation/ response efforts. This article presents
an efficient methodology to accurately delineate the flood-hazard areas in the
Kosi River Basin, North Bihar, India in a GIS environment. We have used one of
the multi-criteria decision-making techniques, Analytical Hierarchical Process
(AHP) which provides a systematic approach for assessing and integrating the
impact of various factors, involving several levels of dependent and
independent, qualitative and quantitative information. We present a novel
methodology for computing a composite index of flood hazard derived from
topographical, land cover, geomorphic and population related data. All data are
finally integrated in a GIS environment to prepare a final Flood
Hazard map. This flood hazard index computed from AHP method not only
considers susceptibility of each area to be inundated but also takes into
account the factors that are inherently related to flood emergency management.
Floods are probably the most recurring, widespread, disastrous and frequent
natural hazards of the world. India is one of the worst flood-affected
countries, being second in the world after Bangladesh and accounts for one
fifth of global death count due to floods. About 40 million hectares or nearly
1/8th of India's geographical area is flood-prone. The plains of north Bihar
are some of the most susceptible areas in India, prone to flooding. A review by
Kale (1997) indicated that the plains of north Bihar have recorded the highest
number of floods during the last 30 years. The total area affected by floods
has also increased during these years. Drained by two major rivers, the Kosi
and Gandak, and several smaller systems such as Burhi Gandak, Baghmati and
Kamla-Balan, the plains of north Bihar have experienced extensive and frequent
loss of life and property over the last several decades (Sinha and Jain, 1998).
The Kosi River (The Sorrow of Bihar) is well-known in India
for rapid and frequent avulsions of its course and the extensive flood damages
it causes almost every year. The Kosi is one of the major tributaries of the
Ganga River, and rises in the Nepal Himalayas. After traversing through the
Nepal Himalayas, it enters India near Bhimnagar. Thereafter, it flows through
the plains of north Bihar and joins the Ganga River near Kursela, after
traversing for 320 km from Chatra. The river has been causing a lot of
destruction by lateral movement and extensive flooding. As its waters carry
heavy silt load and the river has a steep gradient, the river has a tendency to
move sideways. Thus, in about 200 years the river has moved laterally by about
150 km (Gole and Chitale,1966; Wells and Dorr, 1987). To check the lateral
movement as well as for flood control, embankments on both sides of the river
were constructed, five to sixteen km apart. Although this has confined the
lateral shift of the river within the embankments, but the problem of
flooding is still a challenge in this area. The problem of river flooding is
getting more and more acute due to human intervention in the flood plain at an
ever increasing scale. There must be a realization that minimizing the risk and
damage from floods may be more rational way of flood management rather than
formulating structural measures along the dynamic rivers such as the Kosi.
In this scenario, the regulation of flood hazard areas coupled with enactment
and enforcement of flood hazard zoning could prevent damage of life and
property from flooding in short term as well as in long term. Flood management
and control are necessary not only because floods impose a curse on the
society, but the optimal exploitation of the land and proper management and
control of water resources are of vital importance for bringing prosperity in
the predominantly agricultural based economy of this diversely populated
country. This cannot become technically feasible without effective flood hazard
maps. Flood hazard mapping and flood inundation modeling are the vital
components in flood mitigation measures and land use planning, and are
prerequisites for the flood insurance schemes. This article presents a
multi-parametric analysis to compute a composite index of flood hazard and to
produce a Flood Hazard map. The primary data used for this study were obtained
from
three sources. The first set of data includes topographic maps, district level
maps, and census data of 1991 for the regional divisions of Bihar are obtained
from the Survey of India, National Atlas & Thematic Mapping Organization
(NATMO), and District Statistical Office, Saharsa respectively. The second set
of data includes the digital elevation data (GTOPO30), a global digital
elevation model (DEM) from U.S. Geological Survey's EROS Data Center in Sioux
Falls, South Dakota and the DEM derived from the toposheets of the study area.
The third set of data is the digital remote sensing images for the study area
(IRS-1D, LISS III) obtained from the National Remote Sensing Agency, Hyderabad.
Integrated analysis of GIS & Analytical Hierarchical Process (AHP) In this
study, ArcView GIS 3.2a was used for working with grids and shape files. The
sequence of operations followed is schematically shown in Figure 1. Firstly,
the DEM of the study area was generated using the spot height data collected
from the topomaps in ILWIS 3.2 Image Processing software. Then, it was
converted into a point map and exported into ArcView GIS; where this vector
elevation map is converted into raster grid format for overlay analysis.
Secondly, the satellite imagery was georeferenced and registered to the
geographic space. On this georeferenced image, image classification algorithms
like GMLC, NDVI were applied to extract the land cover and vegetation
information. Also, onscreen digitization process was carried out to delineate
the geomorphic features from the image. All these classified and processed
images were then exported into ArcView GIS for conducting overlay analysis.
Thirdly,
demographic data like population density (delineated from Census data in
ArcView GIS) was also included in the overlay analysis. Some extra GIS
operations like buffering were applied on some of the above obtained data to
derive some new important data which in turn used in overlay analysis. Finally,
all data was integrated in a GIS environment using AHP method discussed next.
The study is carried out using ENVI 4.0, ERDAS IMAGINE 8.5, ILWIS 3.2, Arcview
GIS 3.2a, Arcview GIS Spatial Analyst Extension with Model Builder, Arcview
GIS 3D Analyst Extension
Analytical Hierarchical Process (AHP) is a multi-criteria decision making
technique, which provides a systematic approach for assessing and integrating
the impacts of various factors, involving several levels of dependent or
independent, qualitative as well as quantitative information. It is a
methodology to systematically evaluate, often conflicting, qualitative criteria
(Saaty, 1980). Like other multi-attribute decision models, AHP also attempts to
resolve conflicts and analyze judgments through a process of determining the
relative importance of a set of activities or criteria by pairwise comparison
of these criteria on a 9-point scale. In order to do this, a complex problem is
first divided into a number of simpler problems in the form of a decision
hierarchy (Erkut and Moran, 1991). AHP is often used to compare the relative
preferences of a small number of alternatives concerning an overall goal. AHP
is becoming popular in decision-making studies where conflicting
objectives are involved. Recently, Siddiqui et al., (1996) introduced a new
method known as Spatial AHP to identify and rank areas that are suitable for
a landfill, using knowledge based user preferences and data contained in GIS
maps.
The goal or the objective of this research is the mapping of flood hazard
zones in the Lower Kosi river basin. The decision factors to relate attribute
to suitability concerning a particular goal are the factors controlling flood
hazard in the study area. The primary decision factors considered in this study
are geomorphic features, elevation, vegetation, land cover, distance to active
channels, and population density (Fig. 2). Once the decision factors are
identified and selected, sub-factors and even sub-sub-factors are identified to
describe these criteria better. For example, the geomorphologic factor was
sub-divided into nine sub-factors as shown in Figure 2. Similarly, the other
decision factors like vegetation, land cover, elevation, distance, and
population density are sub-divided into sub-factors.
The RIWs are the normalized eigen vectors corresponding to the maximum eigen
values of the pair-wise comparison matrices constructed at each level of the
decision hierarchy. The RIW assigned to each hierarchy element was determined
by normalizing the eigen vector of the decision matrix. Eigen vectors were then
estimated by multiplying all the elements in a row and taking the nth root of
the product, where n is the number of row elements (Saaty, 1980). Normalization
of the eigen vectors was accomplished by dividing each eigen vector elements to
the decision factor.
For the hierarchy represented in the Figure 2, the relative importance
weightage of level 2 decision factors like population density, distance,
elevation, vegetation, land cover, geomorphic features was determined by
comparing the decision factors pairwise. This was followed by pairwise
comparison within each level 3-decision factor. An attempt has been made to
resolve conflicts and analyze judgement by a process of determining the
relative importance of decision factors related to this study by pairwise
comparison of these factors on a nine point scale.
Flood Hazard Index (FHI) The FHI for each pixel was determined by
aggregating RIWs at each level of the hierarchy. FHI for all raster cells in
all the thematic layers were determined simultaneously using overlay analysis
conducted in Model Builder of ArcView GIS3.2a. Higher the FHI value, higher is
the flood hazard for that pixel.
FHI was calculated by multiplying the RIWs of level 3-decision factor by the
associated RIWs of the level 2 factors at each level and summing the values of
all grouped elements. Since our problem is defined in three level hierarchies,
the simplified equation for 3 level hierarchies is:
Where,
FHI = Flood Hazard Index,
N2 = the number of level 2 decision factor,
RIWi2 = relative importance weight of level 2 decision factor i.
RIW ij 3 = relative importance weight of level 3 sub-factor j of level 2
decision factor i.
If the decision hierarchy has more or fewer levels, the formula must be
modified appropriately.
The FHI values as obtained from the above equation for the study area in the
lower Kosi
River basin were classified into low, medium, high and very high hazard based
on histogram distribution and the final Flood Hazard Map is shown in figure 3.
Higher values of FHI signify more susceptibility to floods and are the places
of potential flood threat. The flood hazard map obtained by overlaying various
thematic layers in a GIS environment is showing very satisfactory results when
compared to the inundation map derived from the MODIS flood inundation map of
July 25, 2004 for the Kosi basin. The source of this MODIS flood inundation map
is the Dartmouth Flood Observatory, Dartmouth College, Hanover NH 03755, USA.
This inundation map is showing the flooded areas form the period 1988 to 2004
which served as the latest information source for the validation of the current
research work. Not only that the inundation areas coincide in the final flood
hazard map but the severity of the hazard areas is also reflected. A comparison
of the flood hazard map with July 25th,
MODIS flood inundation image reflects the following:
The western part of the study area is under high and very high flood hazard
zone. The population density is also high (701-1000, > 1001 persons per sq.km)
in this region. This area is frequently inundated as is evident from the MODIS
inundation map.
Distance to active channels (which are the main sources of flood discharge)
is playing an important role in the control of flood hazard in the study area.
Geomorphic features like active channels, inactive channels, channel bars,
water-logged areas, oxbow-lakes, moist sand are undergoing rapid modifications
due to channel avulsion, meandering cut-offs in the study area. Such kind of
dynamic behaviour also contributes to frequent and extensive flooding in the
area.
The entire region is a plain having a gentle slope from north-west to
south-east. The elevation in the study window gradually reduces from 49m in the
north to 32m in the south and as a result a number of marshes and swamps have
developed in this region. Moreover, a number of tributaries join the Kosi in
this region. As a result, this area has very high flood hazard index.
The inundation limit or extent of MODIS inundation map of 2004 and previous
years closely match with the hazard areas in the map validating (or confirming)
the logic followed in the analysis and the model developed; reflecting flood
effects for operational years.
Hazard areas mapped are as per the integrated effect of different parameters
and not just on the basis of a few years of inundation data. Thus, the
potential of flood hazard of the areas is based on an integrated analysis and
not merely on a hydrological phenomenon.
There are some areas which have not been inundated during the last 1-2 years
but they still fall under high and very hazard areas e.g. parts of Kiratpur,
Gouraboram, Biraul, Mahisi, Kusheswarasthan west, Alauli development blocks.
This suggests that the future potential of flooding is high in these regions
and adequate measures should be taken to protect these areas.
The development blocks viz. Singheshwar, Madhepura, Sour-Bazar,
Eastern-parts of Kahara, Simri Bakthiarpur, Supaul which fall under low hazard
areas and have also been inundated for the last 10 years indicate much lower
probability of flooding in future. This is perhaps a manifestation of gradual
migration of the Kosi river towards west during the last 200 years.
Final remarks The research presented in this article formulates an efficient
methodology to accurately delineate the flood hazard areas in the lower Kosi
River basin, North Bihar, India. This study represents some exploratory steps
towards developing a new methodology for inexpensive, easily-read,
rapidly-accessible charts and maps of flood hazard based on morphological,
topographical, demographical related data. The study has also focused on the
identification of factors controlling flood hazard in the study area. It
accomplishes this goal by combining Spatial AHP technique with GIS-based
overlay analysis.
Such efforts should be a part of non-structural measures of flood management
to reduce short term and long-term damages and to bring awareness among the
scientific community on the potential need of this research. The basic merit of
this methodology lies in its simplicity and low cost. This is one of the
initial projects attempted; the lessons learned from this pilot effort can be
applied to a larger area encompassing the entire Kosi River basin and other
river basins of the country.
References cited
Erkut and Moran (1991). "Locating Obnoxious Facilities in the Public
Sector: An Application of the Analytic Hierarchy Process to Municipal Landfill
Siting Decisions", Socio-Economic Planning Sciences 25/2, 89-102.
Gole, C.V. and Chitale, S.V. (1996), Inland delta building activity of Kosi
river, Journal of the Hydraulic Division, Proceedings of the American Society
of Civil Engineers, vol. 2, pp. 111-126.
Kale, V.S. (1997) Flood studies in India: A brief review. Journal of the
Geological Society of India, 49, 359-370.
Wells, Neil A., and Dorr, John A. (1987). Shifting of the Kosi River,
northern India. Geology, 15(3), 204-207.
Saaty, T.L., (1980). The Analytic Hierarchy Process. McGraw-Hill, New York,
20-25.
Siddique, M. Z., Jess, W. Everett and Baxter, E. Vieux., (1996). Landfill
siting using Geographic Information System: A Demonstration. Journal of
Environmental Engineering. ASCE, 122(6), 515-523.
Sinha, R and Jain, V. (1998). Flood hazards of north Bihar rivers,
Indo-Gangetic plains. In: Kale, V. S. (Ed) Flood Studies in India, Memoir
Geological Society of India, 41, 27-52.
Bihar Group
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