Spatial analysis of flood disaster in Delta state, Nigeria

Spatial Analysis of Flood Disaster in Delta State, Nigeria
G.O. Enaruvbe
Regional Centre for Training in Aerospace Surveys (RECTAS),
Obafemi Awolowo University, Ile-Ife, Nigeria
email: enaruvbe@gmail.com
&
G.U. Yesuf
Regional Centre for Training in Aerospace Surveys (RECTAS),
Obafemi Awolowo University, Ile-Ife, Nigeria
Abstract
Flooding has become more prevalent over the past decades in Nigeria. The recent incidence of flood
disaster in the country has been attributed to increased rainfall and infrastructure failure. Coastal farming
communities are the most adversely affected as their farms and fishing implements were inundated or
washed away by the floodwater. An important aspect of flood disaster management is the provision of
timely information, necessary for mitigation of the impact of the disaster. Using ASTER data, the digital
elevation model of the State was generated in ArcGIS. Land use classes were derived from visual image
interpretation of google earth images using the Multi-Resolution Land Cover classification(MRLC)
system. As a result of field survey, a buffer of 2km was created around River Niger and other inland rivers
in the State. This was used in ascertaining the extent of the flooded area in the state. The result reveals that
an area of over 7,950km2
or over 49% of the riverine and coastal area of the State was flooded, affecting
more than 50 coastal and riverine communities and about 137.1 hectares of farmland across the State. The
research concludes that the use of ASTER data and GIS seems effective in identifying and mapping areas
prone to flooding. It is recommended that flood mapping should be integrated into land use planning as a
strategy for mitigating damage caused by flood disaster in developing countries.
Keyword: Flood, Disaster, ASTER, Climate change, Farming
Introduction
Flood disasters are among the most destructive natural disaster in history. Damage caused by flood to
agriculture, homes and public facilities around the world runs into several millions of dollars annually. In
most cases, flooding occurs when rivers overflow their banks as a result of excessive rainfall, dam failure,
or obstruction of river channel resulting from encroachment.
IRPG Vol. 11, No. 1, pp. 52 - 58, 2012. Copyright (c) Department of Geography, O.A.U.Ile-Ife, Nigeria. All rights reserved

Enaruvbe & Yesuf / Ife Research Publications in Geography Vol. 11, No. 1, 2012 53
The devastating effect of flood on the means of livelihood, particularly in the developing countries is
evident in the literature (Karki, Shrestha, Bhattarai, and Thapa, 2011). The incidence of flooding has
become more frequent and severe around the world, a situation that has been attributed to climate change
and sea-level rise (Clark et al., 1998). Several flood disasters have occurred in Nigeria in the recent past.
These include the Sokoto flood disaster of September, 2010; Ibadan flood of August 2011 and the
September 2012 flood disaster which can be described as one of the most devastating in the last half
century. The September, 2012 flood affected several states in Nigeria including Adamawa, Kogi, Delta,
Bayelsa and Rivers States, displacing millions of people in the process. This flood has rendered millions of
people homeless and their means of livelihood destroyed. The social and economic impact of the recent
flood incident, particularly on agricultural production and social infrastructure, cannot be overemphasized,
yet the long term impacts of the recent flood in Nigeria could be more severe.
As a result of the increasing intensity and frequency of flooding in the recent past, attempts have been made
to investigate the natural and human causes of flood and flood related disasters. Karki et al.(2011) mapped
flood risk vulnerability pattern in flood plain of the Kankai River, Nepal using a combination of flood
simulation model, remote sensing data and socio-economic data. Eni, Atu, Oko, and Ekwok, (2011) also
used interview method in analysing the impact of flood on farmlands in Cross River state, Nigeria. Sanyal
and Lu, (2004) however, derived flood hazard index map of vulnerability levels by incorporating
population density, flood frequency, transportation network, access to drinking water and the availability
of high ground in mapping maximum risk zones.
Though several scholars have analysed the problem of flooding in Nigeria (Atedhor, Odjugo, and Uriri,
2011; Eni et al. 2011; Etuonovbe, 2011; Dabara, 2012), most of the research, however, focused on the
health and social impacts of flooding. Apart from Eni et al. (2011) who investigated the impact of flooding
on farmlands in Cross River State, Nigeria using a combination of interview and laboratory methods, the
implications of flood disaster on food production in the country seems not to have been investigated.
The increasing intensity and frequency of flood disaster in the recent past calls for an approach that is
timely and cost effective. It has been shown that the integration of Remote Sensing and Geographic
Information Systems (GIS) provide valuable and timely spatial information in the event of a natural
disaster. This approach has proved to be a very important tool in the evaluation and management of natural
disaster. Pradhan, (2009) analysed flood risk areas in the east coast of Malaysia using GIS and statistical
models. Though effective, this method may be time consuming as every part of the affected area need to be
visited for the purpose of collecting GPS data for elevation mapping and modeling. Advanced Space-borne
Thermal Emission and Reflection Radiometer (ASTER) image and Shuttle Radar Topography Mission
(SRTM) images provide a means of generating the digital elevation model (DEM) of the landscape and
therefore, it can give an estimate of flood depth in areas inundation by flood water.
Land use maps generated from image processing in a GIS environment provides useful estimates of the
damage caused by flood disaster (Ostir, Veljanovski, Podobnikar, and Stancic, 2003). These maps give a
strong direct impression of the spatial pattern of flood disaster (Merx, Thieken, and Gocht, 2007). Similarly,
remote sensing is important in every phase of disaster management (Joyce, Belliss, Samsonov, McNeill,
and Glassey, 2009) providing a rapid-response data source for mapping natural hazards and disaster.
Though there may be difficulties associated with data acquisition, Joyce et al. (2009) note that remote
sensing provide a useful means of monitoring natural disaster and emergency services. National and local
flood risk mapping initiative have been adopted in some countries to improve and increase information
available on flood risk (Merx et al. 2007). Information derived from disaster mapping efforts are important
in providing an alert system and for the design of evacuation strategies in the event of a flood disaster. This
research, therefore, seeks to analyse the impact of flood on farming communities in Delta State using
remote sensing data and Geographic Information Systems techniques.

Study Area
Delta State lies approximately between longitudes 5°00 and 6°45'E and latitudes 5°00 and 6°30'N. It is one
of the oil-producing States of Nigeria and is divided into twenty-five local government councils with a total
land area of 16,842 km2
. It has a population of 4,112,445 according to the 2006 National Population
Commission (NPC, 2006) with a large proportion of the population engaged in farming and fishing
activities. The State shares boundaries with Edo to the north, Ondo to the northwest, Anambra to the east
and Bayelsa and Rivers State to the southeast. On its southern flank is 160 km of the coastline of the Bight
of Benin. The State is criss-crossed by many rivers and streams including the River Niger which separates
it from Anambra, Bayelsa and Rivers States. Many communities, especially in the southern part of the State
are lowland, coastal areas. The rainfall pattern of the area is seasonal, characterised by wet season, which
lasts between March and October while the dry season begins in November and ends in February in the
northern delta but all year round in the coastal region. A short-dry-season usually interrupts the wet season
in late July or August. Mean annual rainfall ranges from 4000mm in the coastal areas to 1500mm in the
northern part of the region. Temperatures are generally high and relatively constant all year round. Average
monthly maximum and minimum temperatures vary from 28°C to 38 °C and 21°C to 23°C respectively
(NDDC, 2005).
Materials and Methods
Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) global digital elevation
model (30m resolution) was downloaded from the Global Land Cover Facility site (GLCF) and contours
were generated to determine the pattern of vulnerability of the State to flooding using elevation as criteria.
Elevation has been shown to be an important consideration in flood risk mapping in coastal areas (Dewan,
Islam, Kumamoto, and Nishigaki, 2004; Sanyal and Lu, 2004; Sanyal and Lu, 2005). ASTER scenes
covering Delta State were mosaicked in ArcGIS, and transformed to the projected coordinate system
-WGS 1984 UTM Zone 32N. Settlement and farmland in the study area were extracted using the visual
image interpretation of google earth image and on-screen digitizing. The multi-resolution land
characterization regional and land cover classification (MRLC) system (Vogelmann, Sohl, Campbell, and
Shaw, 1998) was adopted in categorizing the land use types in the State. On the basis of the MRLC
classification systems, the State was classified into four major land use classes (Water body, Settlement,
Bare surface and Vegetated surface).
Some of the affected communities in the riverine areas of the State were visited about two weeks after the
flood disaster to validate the classified image and determine the level and extent of the flood in the area.
The communities visited include Uzere, Asaba, Ossissa, Aboh, Uvwie, Gbekebo, Warri, Evwreni and
Burutu. The height of the flood mark on buildings were measured and their geographic location was
captured using a hand-held GPS receiver. Oral interview was also conducted to ascertain the impact of the
flood on the means of livelihood of the people, particularly on their farm land and fish ponds. Information
on the impact of the flood on social amenities such as schools in the communities were also solicited.
The risk map of the study area was generated using a combination of elevation derived from the ASTER
image as well as measurements and information obtained from the field survey. The elevation classes
derived was based on information extracted from the ASTER images and field survey. It was observed that
elevation in the State ranges between less than 2m above mean sea-level in the southern part of the State
and along the River valleys, to above 100m in the northern part. Field observations indicate that apart from
the communities around the River Niger valley which has a broad floodplain and dense distributaries, most
communities affected by the flood were within 2km of River Niger and its tributaries. A buffer of 2km was
therefore generated to determine flooded settlements and farmlands in the State. On the basis of the buffer,
the State was therefore demarcated (Figure 2) into flooded and non-flooded areas (Sanyal and Lu, 2005;
Dewan et al., 2007).

Results and Discussion
The digital elevation model (DEM) generated from the ASTER image is shown in Figure 1. The Figure
shows that elevation in most parts of the State, particularly the southern part, is generally less than 30m
above sea level. The low elevation of the State, coupled with its proximity to River Niger may have further
exposed the communities in the State to flooding and tidal incursion. Many of the communities in the
coastal area of the State are either farming or fishing communities. For instance, communities such as
Burutu, Aboh, Onya and Patani are mainly fishing communities while others such as Obikwele, Ossissa
and Umudike are farming communities along the River Niger floodplain.
Figure 1: Digital Elevation Model
Analysis (Figure 2) shows that about 7,950km2
, representing more than 49% of the riverine and coastal
communities of Delta State were affected by the 2012 flood disaster. More than 50 communities and about
137.1hectares of farmland were affected across the State. Riverine communities such as Burutu, Gbekebo,
Uzere, Patani, Asaba-Assay and Akri-Ogidi were severely affected. At Gbekebo for instance, it was
observed that flood water got to a height of about 1meter in some areas. This resulted in many fish ponds
being washed away as the entire community was inundated. Similarly, farms and fish ponds were also
washed away at Uzere, Patani, Evwreni and Aboh on the River Niger floodplain. Farmlands, schools, roads
and other social facilities were also flooded in Asaba, Okpanam, Ebuh and Ilah in the northern part of the
State. Most parts of Warri, Sapele, Ughelli and Obiaruku were also inundated. However, communities with
high elevation such as Agbor, Ubulu-uku, Obior and Mbiri and environs at the north and northwestern part
of the State were not affected by the flood.
The volume of flood water that reached the State from the combined effect of excessive rainfall and the
released of water from Kainji dam in Nigeria and Lagdo dam in Cameroun resulted in the rapid rise of
water levels in both River Niger and Benue respectively, consequently resulting in flooding of settlements
along their floodplain. Islam and Sabo (2000) noted that the quantity of water and rate of rise in water level
influences the damage cause by flooding. Wang, Colby, and Mulcahy, (2002) mapped flood in the United
States using Landsat TM and noted that DEM assists in identifying flooded areas in coastal areas and in
areas of large spatial extents with relatively flat topography such as exists in Delta state. Similarly,
Pradhan, (2009) has noted that the use of DEM was effective in delineating areas vulnerable to flooding
and Meyer et al. (2009) has argued that maps provide a tool for assessment of the impact of flooding in
local communities.

The findings demonstrate the level of exposure of the state to flood disaster and also underscore the need
for measures to be taken that will minimise the impact of future flood events. Similarly, the findings
support Sanyal and Lu, (2005 and 2004) who demonstrated the use of ASTER images for generating DEM
for flood risk assessment. Meyer et al., (2009) also demonstrated the use of remote sensing and GIS in the
integration of data from various sources in a multi-criteria flood risk mapping.
Flooding and food production
The flooding situation as observed in Figure 2 indicates that farmland and fish ponds and other social
facilities in the affected local communities were severely damaged. It has also been noted that land
degradation and nutrient dynamics are other effects of flooding (Rosenzweig, Iglesius, Yang, Epstein, and
Chivian, 2001). This situation may lead to scarcity and high cost of food in the near future. The impact of
extreme climate variation on food production has been highlighted by Rosenzweig and Parry, (1994) and
Rosenzweig et al.,(2001). They note that most crops are sensitive to such extreme conditions as a decrease
in rainfall, high temperature and flooding. Other impacts are termed indirect and these include influence on
soil processes, nutrient dynamics and pest organisms. These conditions have been observed to significantly
affect soil productivity and crop yield.
Rosenzweig et al. (2001) further states that these impacts of climate change and the accompanying extreme
conditions increases the vulnerability of the population to hunger and malnutrition. Rosenzweig and Parry,
(1994) and Parry, Rosenzweig, Iglesias, Livermore, and Fischer, (2004) noted the impact of global climate
change on food production. They observed that though global food production seems stable, there is
variation in regional production and between the developed and developing countries as the developed
countries produce more food. This disparity is likely to continue to increase and may be worsened by
extreme climatic events.
Figure 2: Flood inundated area

Conclusion and Recommendation
Remote sensing and Geographic Information Systems techniques were adopted in examining the impact
and extent of the recent flood disaster on agriculture and human settlement in the study area. The use of
ASTER images for the generation of DEM seems effective in mapping flood prone areas in the study area.
The study demarcated the State into flooded and non-flooded area on the basis of elevation generated from
the ASTER image and 2 kilometer buffer created around rivers in the State. The results of the study
indicate that a large part of coastal and Riverine communities in the state were affected by the flood,
inundating many farmlands and washing away fish ponds and fishing equipment.
The study demonstrates the need for mapping flood prone areas as an important aspect of land use planning
and evaluation. This is more so in developing countries such as Nigeria where emergency and disaster
response may not be very swift. Flood mapping may go a long way in developing flood disaster response
strategies and reduce the damage resulting from flooding and other extreme natural events.
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