An Assessment and Mapping of Coastal Flooding in Niger-Delta; a case study of Bonny, Okrika and Ogu/Bolo of Rivers State

Background: This research looks at the assessment and mapping of coastal ooding in Niger-Delta region of Nigeria, staring at the three local governments (Bonny, Okrika, and Ogu/Bolo) in Rivers State that lies at intervals the shores of Atlantic Ocean. Result: At the analysis it was found that most of the study areas lies at the creek and with the steady rise of the sea level, as a result of increase in temperature, most of the study area will have steady occurrence of ooding.


Background
Since the start of the 20th century, the mean global sea level has been rising. Between 1900 and 2016, the sea level rose by 16-21 cm (6.3-8.3 in). More precise data gathered from satellite radar measurements reveal an accelerating rise of 7.5 cm (3.0 in) from 1993 to 2017, which is a trend of roughly 30 cm (12 in) per century. This acceleration is due mostly to human-caused global warming, which is driving thermal expansion of seawater and the melting of land-based ice sheets and glaciers. Between 1993 and 2018, thermal expansion of the oceans contributed 42% to sea level rise; the melting of temperate glaciers, 21%; Greenland, 15%; and Antarctica 8%. Climate scientists expect the rate to further accelerate during the 21st century. Human activities have modi ed the environment. Substantial population growth, accelerated socio-economic activities and migration have exaggerated these environmental changes over the last decade. The impacts of these changes on urban climate have become evident in global, regional, and local trends in contemporary atmospheric temperature, humidity, rainfall records and other relevant climatic indicators. For many cities in both the developed and developing nations, urban ooding has been, and continues to be, a major environmental problem. Between 1950 and 2017, the record of ood events registered showed that about 2% occurred in 1950s and increased rapidly to 52.2% by the end of 2017, endangering lives and causing property damage in the process. In the last decade, ood has affected more than 1.4 billion people and accounted for the death of about 100,000 people. Also, between 1998 and 2018, more than 3,136 ood disasters have occurred around the world, with an estimated total damage of US$556 billion Sea level rises can in uence human populations considerably in coastal and island regions. Widespread coastal ooding is expected with several degrees of warming sustained for millennia. Further effects are higher storm-surges and more dangerous tsunamis, displacement of populations, loss and degradation of agricultural land and damage in cities. Natural environments like marine ecosystems are also affected, with sh, birds and plants losing parts of their habitat. Flood is one of the most devastating natural hazards, with a high rate of occurrence affecting many countries worldwide and causing huge economic and great human loss annually (Thilagavathi, et al., 2011;Willby and Keenan, 2012 Komolafe et al., 2019) and it is estimated they have generated more than 30% of all disasters globally between 1945 and 1986 (Glickman et al., 1992). A variety of factors in uence ood occurrence in cities such as urbanization without corresponding upgrade of drainage infrastructure, limited capacity of river channels, settlements in low areas, and ood plain encroachment are common phenomena attributed to the increasing ood impacts. The manifestation of climate change which is evident in extreme rainfalls has equally contributed to the frequency, severity and intensity of the problem. The effects of sea-level rise such as the coastal processes of inundation and erosion are of great economic and ecological signi cance considering the intensive and irreversible changes most likely to occur in the coastal ecosystems.
The study areas are Bonny, Okrika and Ogu/Bolo local government of Rivers State. These areas are situated on an island south of Port Harcourt, making them a suburbs of the much larger city and are lay on the coastal area of the Atlantic Ocean, and many times they experiences oods around them. The identi cation of areas prone to ood can contribute signi cantly to ood risk management and land use planning such that, potent ood management policies could be put in place, ood defense infrastructure could be constructed and urban development in ood prone areas could be duly monitored.
This study aims to address the impact of ood on the coastal Environment, and is achieved with the following objectives; Identify the Land use changes of the study area.
Investigate and analyze the soil type and vegetation of the area.
Identify and mapping of potential coastal ood risk areas. The dataset used for this study was secondary data which contains spatial and non-spatial attributes.
The dataset includes Landsat 8 Operational Land Imager (OLI) images, topographic maps, temperature and SRTM DEM data. Software packages include; ArcGIS version 10.5, Idrisi. intersected with a template containing water related features (coastlines, lakes, and double-lined rivers). It was acquired from Food and Agricultural Organization.

Software Used
The software used is as listed; ArcGIS 10.5: The spatial plus statistical analyst extensions of the ArcGIS 10.5 version will be applied to take out both the spatial analyses and the spatial statistical analyses, and also supervised classi cation was being carried out in Arcgis which was applied to examine the land-use/land-cover between the years of observation.
Microsoft Excel: is a spreadsheet program use to estimate and analyze numerical data. The software was being applied to transmit out the statistical investigation.
To extract the study area, the topographic map covering the Enugu was georeferenced in the ArcGIS environment. The methodology adopted for this work was categorized into four stages (a) Digitized soil mapping (b) DEM classi cation (c) Land use/ Land cover (LULC). (d) Flood risk mapping.
A. Digitized soil mapping The creation and the population of a geographically referenced soil database generated at a given resolution by using eld and laboratory observation methods coupled with environmental data through quantitative relationship. Digital soil mapping is the prediction of soil classes or properties from point data using a statistical algorithm. Data was collected at fao website and was downloaded in shape le format, which was analyzed in Arcgis software.

B. DEM classi cation.
DEM is a digital model that provides topographic information. The classi cation will be based on Terrestrial and Non Terrestrial data.
C. Land use/land cover: IDRISI was used for Image Analysis this include: Pre-processing -operations before the key statistics study and removal of evidence, are referred to as radiometric or geometric corrections.
Image Enhancement -increase the form of imagery to promote graphic understanding and examination.
Spatial ltering enhances precise longitudinal con gurations in an image.
Image Transformation -mutual processing of statistics from numerous spectral bands. Arithmetic procedures (i.e. deduction, tallying, duplication, partition) are achieved to syndicate and convert the unique bands into "different" images that enhanced presentation or focus assured topographies in the sight. Techniques: Spectral or band rationing and principal components analysis.
Image Sorting and Study -regularly executed on multi-channel statistics groups (A) this procedure consign small pixels in an image to a speci c class or subject.

Data Analysis, Results And Discussion
All the factors contributing the causes of ooding were delineated & examined, drainage, soil types, slope, landuser/land cover, and geomorphology, their results are discussed intensively as follows: Soil mapping: Soil texture is the "feel" of the soil when a moist quantity is manipulated between thumb and fore nger. It is one of the more useful tests in evaluating soil. Sands because of their large grain size allow faster permeability of water than clays. Sands hold very little water. Loam soils contain sand, silt and clay in such proportions that stickiness and non-adhesiveness is in balance -so the soils are mouldable but not sticky. The in ltration rate is the velocity or speed at which water enters into the soil. It is usually measured by the depth (in mm) of the water layer that can enter the soil in one hour. Loam soil has an in ltration rate of 10-20 mm/hr, while Sandy-loam soil has in ltration rate of 20-30 mm/hr. these are the two soil texture in the study area while the third is creeks alone the river banks. Slope and Elevation: Slope discovers the steepest downhill slope for a position on a surface. The Slope command captures an input surface raster as well as calculates an output raster containing the slope at every cell. The decrease in the slope value, the atter the terrain, and the increase in the slope value, the steeper the terrain the output slope raster may be calculated as percentage slope or degree of slope.
Flooding occurs due to slope failure, a stable slope is one whose resisting forces are more than the downslope driving forces, Flood occurs when gravity and other driving forces exceed the strength of slope

Flow Direction
The factor in deriving hydrologic characteristics of a surface is the ability to establish the trend of ow from each cell in the raster. This is done with the Flow Direction tool. This tool captures a surface in the form of input and outputs raster showing the trend of ow in every cell. If the Output drop raster option is selected, an output raster is formed showing a ratio of the highest alteration in elevation from every cell along with the trend of ow to the path length amid centers of cells which is shown in percentages. If the Force the entire edge cells to ow outward option is selected, the entire cells along the edge of the surface raster will ow outward from the surface raster. Land use/land cover: Each composed image was ordered into 4 classes: built-up areas, vegetation, Roads, and water body. The outcome of the classi cations of land cover is found in Fig. 4 ft) this century is "physically plausible". A conservative estimate of the long-term projections is that each Celsius degree of temperature rise triggers a sea level rise of approximately 2.3 meters. Elevation of raster is essentially the grid of cells, each cell containing an elevation above mean sea level, so we want to know what will be ooded, so if we assume that the sea level will rise by 15ft in future for a variety of reason like increase in temperature, more than 50% of some of the parts of Okrika will experience frequent ooding in years to come. While less than 50% of parts of Bonny and Ogu/Bolo will experience ooding in years to come.

Adaptation And Conclusion
Coastal ooding is a sudden and abrupt inundation of a coastal environment caused by a short-term increase in water level due to a storm surge and extreme tides. The magnitude and extension depend on the coastal topography, storm surge conditions and broader bathymetry of the coastal area. Flooding constitutes a particular challenge in low-lying areas as deltas and coastal plains and land subsidence caused by sediment de cits or ground water extraction can further exacerbate the problem. Technical measures for ood control include dike construction, maintenance of natural dune systems, protection of coastal ecosystems, planting of vegetation around the coastal areas and different ood proo ng and accommodation activities. Generally, even moderate ooding hazards should be taken very seriously due to the potential disastrous consequences, and ood protection is a key aspect in coastal Disaster Risk Reduction. This will help to improve in the management of these challenges in a holistic fashion.
The study presented the ood estimate mapping of Bonny, Okrika, and Ogu/Bolo of Rivers State whereby several villages are severely ooded leaving people homeless, destroyed properties, agricultural lands, posing a health risk to the inhabitants. The combination of spatial data sets and multi-criteria analysis with geospatial application software to arrive at results such as land-use landcover map, soil type map, and ood mapping. This makes the methodology used in this study very exible to be utilized in any region provided that speci ed local factors are taken into account. From the geospatial analysis, the result revealed that Okrika has the higher tendencies of ooding impacts, while Bonny and Ogu/Bolo also get ooded but not as Okrika. This spatial analytical capacity utilized in this study will not only mitigate the consequences of ooding, rather it can be utilized as a decision support system for planning, preparedness, prevention, mitigation, response, recovery for the possible occasion of ood activities in the study area. Authors' contributions: Various works involving the analysis and eldwork were done by the main and co authors in this research.