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The hydrological modeling of Ogbunabali floodplain using remote sensing and geographic information techniques
The study aims to determine the floodplain hydraulic and hydrological spatial pattern using digital elevation models (DEMs) from topographic survey data. |
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Abstract
Floodplain is a natural ecosystem that has both negative and positive effects on man. It provides land for development, transportation, recreation, agriculture, and hydro power generation. The negative effects are those associated with urban flooding when the land is inundated by water from the rivers, streams, creeks, runoff etc. These effects enable floodplains management programmes to be put in place by the Federal, State, and Local authorities in many countries of the world, mostly, the developed nations. This study was focused on the hydrology and hydraulic modeling and the development of digital database of floodplain of Ogbunabali Port Harcourt, Rivers Sate, Nigeria. The software used was ESRI’s ArcGIS 10.1 and SURFER 10 and the dataset was the topographic survey data obtained at 30m interval and SPOT image with 2.5m x 2.5m spatial resolution. The data was downloaded from total station and saved in excel spreadsheet in xyz coordinates. The elevation data was used to model contour, slope, Triangular Irregular Network, flow direction and flow accumulation which depicted the hydraulic pattern of the floodplain. The hydraulic characteristics of the models were the same and showed that flows came from high gradient (steep slope) to the lower gradient (gentle slope). Similarly, the floodplain digital database consists of four land use/ land cover which were water body, dumpsite, nypa palm, and built-up area. The total built-up area was 4.413ha, dumpsite 0.626ha, nypa palm 16.583ha, and water body 21.324ha. The database is necessary for the estimation of flood rate damage. High resolution satellite images and 2D floodplain software was used to map all floodplains in Port Harcourt for flood disaster management. In conclusion, the digital database of all floodplains in Port Harcourt City should be developed for the effective management of flood disaster in the city.
I. Introduction
Floodplain according to (FEMA 480, 2005) is defined as any land area susceptible to being inundated by flood waters from any source. The source of floodplain water may be rivers, streams, creeks, lagoons, drainage systems, and runoff water from urban areas. Floodplain has been a major characteristic of most coastal communities with tributaries from the main Atlantic Ocean. Most settlements (city, town, village and hamlet) situated along water channels, creeks, lagoons, and river estuaries in the Niger Delta region. Floodplain may vary in areal extent from one location to another and may vary in term of biodiversities abundance. Generally, identify as a dry area adjacent to wetlands, low lying areas with poor drainage capability, and small water pond (Department of Natural Resources, 2006). It is formed by deposit of lateral and vertical accretion (Wolman and Leopold, 1957). In addition, materials deposited in floodplain are eroded from upland areas of the drainage basin and from overbank flow.
Floodplain is one of the fertile ecosystems and contain cultural and natural resources that have values to the society. Its functions are enormous and include agricultural activities, water supply, hydropower development, aesthetic beauty, and site for transportation routes (Task Force on the Natural and Beneficial Function of the Floodplain, 2002). It also serves as route for discharge of excess water, and a suitable site for human infrastructural development (Association of Floodplain Managers, 2008). Floodplains also provide groundwater recharge, filter sediment and contaminants, recreational site, and habitat for flora and fauna (West Virginia Quick Guide, 2009). However, most of these functions are gradually degraded due to anthropogenic activities such as mining, intensive agricultural activities, and infrastructural development.
Floodplains a hydrologically important, environmentally sensitive and ecologically productive area is supported with articulated management plan to ensure full utilization of its potential. In Unites States, floodplain management was promulgated by the passage of the National Flood Insurance Act of 1968 (Lynn, 2009). The Act established National Flood Insurance Program (NFIP) administered by the Federal Emergency Management Agency (FEMA). Floodplains management outline regulatory framework for the use, mapping, mitigation, and administration of floodplain areas. Floodplains management is an intergovernmental approach involving the federal, state, and local authority to achieve the stated goals.
Floodplains management is best achieved from detail base map depicting all features in the area and a topographic data to delineate hydrology and hydraulic characteristics of the area. The base map is produced from the horizontal coordinates obtained from field survey and are used to depict streets, railway lines, stream networks, settlements, and agricultural lands and others features located in the floodplain. Mapping of floodplains may be carried out using conventional survey methods or through aerial photography. The topographic data models the hydrology and hydraulic (H & H) pattern of floodplains which may be used in the determination of flood discharge and frequency, and flood elevation and floodway. Floodplains modeling can be carried out using 1D (HEC-RAS, 2016) and 2D (DHI, 2007) software packages.
This study used DEM from topographic survey to model the hydrology of Ogbunabali floodplain and geographic information systems defined by (Charles and Paul, 2008) as a system of hardware, software, data and organizational structure for collecting, storing, manipulating, and spatially analyzing georeferenced data, and displaying the information resulting from these processes using map.
Floodplains encroachments by human activities are responsible for flash flood in urban areas (FEMA 480, 2005). Floodplains channels may be block by solid waste generated from residential, industrial, and commercial areas. The case is not different from Port Harcourt City where floodplains channels has been completely covered by solid waste like bottles, plastics, metal objects, papers from schools and factories etc. These humans induce activities on floodplain has been responsible for flood cases in the city. For example, the 2017 flood in some parts of Port Harcourt City causing lost of valuable properties in millions of naira is a good example. The flood affected the office of the Federal Road Safety Commission (FRSC) along Port Harcourt / Aba expressway and other public and private residence in D/Line, Nkpogu, Diobu and Borokiri (Leadership News Paper, July 29 2017). The FRSC office is situated within buffer radius of 30m from the Ntawogba creek. The flood was caused by the blockage of Nwaja and Ntawogba (two major creeks that traverse Port Harcourt City) creeks (Vanguard News Paper, July 24 2017) which resulted to the dumping of solid waste in the creeks according to press release by the Rivers State Waste Management Agency (RIWAMA) boss Brother Felix Obuah. In providing solutions to the problems, this study was carried out using topographic data to model the hydraulic and hydrology of Ogbunabali floodplain and to create an inventory of all features in the area that will guarantee effective floodplain mapping and management.
The study is structured to achieve the following objectives: (i) to determine the floodplain hydraulic and hydrological spatial pattern using digital elevation models (DEMs) from topographic survey data. (ii) to develop digital floodplain base map (DFBM) of the study area.
1.2 Justification of the Study
The creation of digital database of Ogbunabali floodplain will further advance the awareness of flood impact on properties in the area. Also, the floodplain base map will assist policy makers to provide up to date reports on the extent and damage cause due to flooding. The base map will assist government agencies in identifying flood risk and vulnerable areas based on the floodplain hydrological and hydraulic characteristics.
Several researchers have used different approach in floodplain study with emphases on flood risk zone and vulnerability mapping. (Samarasinghe et al, 2010) used HEC-HMS and HEC-RAS GIS software and remote sensing data to validate flood information forecast, planning and management in Kalu-Ganga River basin, Sri Lanka. The dataset used for the study include; satellite image data, topographic data, hydro-metrological data, and census data. Also, (Lawal et al, 2014) used Minimum Distance Algorithm to develop the extent of flood and compared their effect in flood generation in the state of Perlis, Malaysia. Datasets applied are geological map, topographic map, and SPOT image and these data were processed using GIS software. The study concluded that correlation exist between extracted model and the flood factors. (Bera et al, 2012) used Landsat ETM+, TM, LISS-111, STRM, geological map, climatic data, soil map, groundwater data, rainfall data, and population datasets and ERDAS IMAGINE 9.2, ArcGIS 9.2, and PCI Geomatica-9.1 software to generate flood risk and vulnerability map of Mongalkote block in Eastern India. The study identified five vulnerable flood areas in the study area. Similarly, (Muhammad and Iyortim, 2013) study the middle course of River Kaduna, Nigeria flood that have claimed several lives and properties using high resolution image, field interview observation, and DEM data. The flood vulnerable areas were model using ArcGIS software and the results overlay on image to show affected properties.
The reviews above from various researchers focused on mapping vulnerable areas and much has not been done in knowing floodplains hydraulic pattern which is main input in the management process of floodplains. In this study the emphasis is on modeling hydrology and hydraulic nature of Ogbunabali, Port Harcourt floodplain using elevation data obtained from field survey, and ArcGIS 10.1 was utilized to perform the modeling. The study also, develop digital floodplain database from satellite image to aid its management.
II. Literature review
The section discussed the general methods of delineating floodplains and the available methods of acquiring digital terrain models (DEMs) for studying floodplains hydrology and hydraulic patterns.
2.1 Methods of Generating Floodplain Map
2.1.1 Conventional Survey Method
Floodplain may be map using traditional survey methods to obtained planimetric points defining the area. The traditional survey method involves the use of Theodolite or Total station to carry out measurements of details within floodplains. The Theodolite or Total station may be 1” or 2” instrument to approve the measurement accuracy. The observed data may be recorded directly in the field sheets or downloaded from the memory in the case of Total station. Conventional floodplain mapping methods may be suitable for small area and less difficult terrain. According to (FEMA 480, 2005) floodplain maps are used for the regulating of new flood prone areas, insurance policies, and granting of loan by the lenders and federal agencies.
2.1.2 Remote Sensing Method
With the advancement in technology, remote sensing is now used in the mapping of floodplains because of large area coverage capability and in accessing inaccessible parts of the globe. Remote sensing data depending on altitude may be high, medium, and low spatial resolution. According to (Dano et al, 2011) satellite imageries for floodplain delineation are categories as optical sensor example Landsat image, and microwave sensor example Radar satellite image. Landsat image is mostly used in floodplain mapping because they can be downloaded free-of-charge from its website in any part of the globe. Landsat satellite was launched into orbit on July 23, 1972 (Anji 2008) for environmental studies.
Floodplain can also be mapped using SPOT image (Lawal et al, 2014). SPOT image is a 2.5m x 2.5m spatial resolution image launched by France in 22 February, 1986 (Richards and Xiuping, 2006). Others high spatial resolution image for floodplain mapping includes Quick Bird, and IKONOS with 0.61m and 1m spatial resolution respectively. High cost of these images has prevented its use for floodplains mapping.
Remote sensing data can be used to generate different types of floodplain maps such as Flood Hazard Boundary Map (FHBM), and Flood Insurance Rate Map (FIRM) (FEMA 480, 2005). The various maps are useful in floodplain management and planning.
2.2 Sources of data for floodplain Hydrological modeling
2.2.1 DEM from traditional Survey technique
Traditional surveying technique is the oldest method of acquiring digital elevation model (DEM) of the earth’s surface. The traditional survey techniques of observing DEM includes the used of differential global positioning system (DGPS), leveling instrument, total station and theodolite (Zhilin et al, 2005). The used of theodolite instrument in acquiring DEM is now in extinction and obsolete in the field of spatial information science and surveying. Total station instrument is capable of measuring x, y, z of points on the earth’s surface using observed distance, bearing, and the control coordinates used of theodolite instrument in acquiring DEM is now in extinction and obsolete in the field of spatial information science and surveying. Total station instrument is capable of measuring x, y, z of points on the earth’s surface using observed distance, bearing, and the control coordinates.
Today, DGPS is being used in acquiring DEM data accurately over an area and gradually replacing total station and theodolite. DGPS works using the principle of trilateration to fix its position by making measurement to minimum of four satellites orbiting the earth.
Traditional DEM is created by observing regularly or irregularly spaced horizontal coordinates (x, y) and vertical coordinate (z) points on the earth’s surface. The grid interval of the observation represents spatial resolution of the DEM data. (Heywood et al, 2006) stated that traditional survey method of obtaining elevation data is more accuracy since the observations are connected to a known control and covered relatively small area.
2.2.2 DEM from photogrammetric data
Photogrammetry is another method of acquiring digital elevation model (DEM) of surface locations. Punmia et al., (2005) defined photogrammetry as the science and art of obtaining accurate measurements by use of photographs taken from specific altitude for the purpose of constructing topographic maps, classification of soil, geological mapping, and for military operations. This field of study started in the 19th century by Aime Laussedat, an officer in the Engineering Corps of the French Army and is being regarded as the father of photogrammetry (Zhilin et al, 2005). In 1849 Laussedat justified the use of photograph to prepare topographic map and this might mark the beginning of application of photogrammetry in topographic mapping.
2.2.3 DEM from LiDAR data
LiDAR (also called LADAR or laser altimetry) is an acronym for light detection and ranging (NOAA, 2012). LiDAR is an airborne system for large area coverage and some are used as ground-based stationary and mobile platforms for data collections. It is an elevation data source fitted with an active laser pulse which enhanced its efficiency in working day and night (Hiremath and Kodge 2010, www. aerometric.com/ LiDAR). LiDAR data are mostly collected at night and under clear weather conditions. It operated in the nearinfrared region of the electromagnetic spectrum, while the bathymetric LiDAR operated in the green laser wavelength with greater penetration of water and the ability to detect bottom features. Unlike other remote sensing systems, LiDAR records ground elevation in thick vegetation areas through the canopy holes. The absolute accuracy of LiDAR system ranges from 10cm to 20cm for most recent data and 15cm to 30cm for older data LiDAR (NOAA, 2012).
LiDAR computes x, y, z (eastings, northings and height) of target features using time difference between transmitted and return laser pulse, the transmitted angle of the pulse, and location of the sensor above the earth surface. The horizontal resolution of points spacing is between 1m to 2m but higher LiDAR may have eight points per 1m coverage.
LiDAR system has been used in different application areas. It is used in the mapping of North Carolina floodplain (North Carolina Cooperating Technical State, 2003), delineation of vulnerable areas to sea level fluctuation (Dean, 2009), and in shoreline extraction (Lee et al, 2010). Others applications areas includes forestry and infrastructure inventories (Jay, 2010). LiDAR data can be used in the following surface modeling such as; contour map, slope model, triangular irregular network, cross section determination, flow direction and accumulation models, watershed delineation to mention a few.
III. Methodology
The chapter focused on two main subheadings which is (a) identification of dataset and software used for the study and (b) the data processing techniques adopted. The dataset and software were set of data (primary or secondary) and software (vector or raster) required for the study. While data processing involves all the processing methods such as digitizing, surface modeling and plotting of maps carried out in the study. Data processing was an office-based operation. The methodology was designed to achieve all the stated objectives of the study.
3.1 Dataset and Software
The study was carried out using elevation data at 25m grid interval obtained from Total station traverse. Secondly, high resolution SPOT satellite image clipped from Google Earth with a spatial resolution of 2.5m x 2.5m was used to digitize features within the study area floodplain.
Similarly, the following software were used to facilitate this study and they are;
a. ESRI’s ArcGIS 10.1 vector base GIS software was used to digitized the features in the study area and creation of geodatabase, hydraulic modeling of the floodplain and compilation of maps.
b. SURFER 10 was used to generate additional digital terrain model (DTM) such as wireframe and flow direction models which are significance in the interpretation of floodplain hydrology.
The data processing ability of software was supported by computer application programs for specific task.
3.1.1 Control Establishment
Three control points were established on stable locations at close proximity to the project site. The control points were established such that they are intervisible during observations. The control points were established and observed using Promark 3 DGPS in static mode. The observations were taken for 30mins per station for accurate position fixing.
3.1.2 Total Station Traversing
Traversing from the established control points was carried out in other to determine eastings, northings and elevation (X, Y, Z) of the ground points (Kavanagh, 2010) of the study area. The observations were taken at grid interval of 25m throughout the study location. The observations were taken using Leica 805 Total station instrument and its accessories. Prior to this observation, the Total station was calibrated at SHELL calibration base and was found to satisfy the requirement for this project. The observations were recorded directly in the instrument and downloaded using Leica Survey Office (Leica instrument downloading software). The observations were exported to excel spread sheet for further analysis of floodplain hydraulic modeling.
3.2 Data Processing
3.2.1 Generation of Triangular Irregular Network (TIN)
Triangular irregular network (TIN) is a digital elevation model (Sulebak, 2000) and is one of the methods of hydraulic modeling of floodplain. TIN model produced a network of triangular surfaces based on interpolated points with the vertices representing peaks, depressions and passes, and the triangular edges represents ridges and valleys (Heywood et al, 2006). TIN model can be generated using different software, for example, (Muhammad, 2006) used Global Mapper software but in this study ArcGIS 10.1 was used to generate TIN.
TIN model was produced from the points data download from Total station and saved in MS excel in eastings, northings and elevation column. The data was added to ArcGIS window using add data button and was later converted to shape file format. By enabling the 3D Analyst tools from the Arc Toolbox, create TIN module was double click and the shape file was selected as input feature class. The mass points were selected as the surface feature types to represents the geometry of the imported points. The output TIN was generated using default nine (9) classes of equal interval to represents the floodplain terrain. The TIN operation screen print from ArcGIS window is shown in figure 2.
3.2.2 Slope Model
Slope model is very useful in hydraulic modeling of floodplain. It describes the topography of the study area from the interpolated points. The TIN created above was converted to raster by double clicking TIN to Raster from the 3D Analyst Tools. The slope model was generated by double clicking slope from the raster surface module and the converted TIN to raster selected as input file. The output file was generated using degree of slope as output measurement with nine (9) classes of equal interval. The screen print of slope generation dialogue box from ArcGIS was shown in figure 2.
3.2.3 Contour Model
Contour line joins all points of equal elevation and perhaps one of the traditional applications of digital terrain models (DTMs). It is based on interpolation principle (Zhilin et al, 2005) where values are generated at unknown locations within the study area. Contour models are used to delineate linear features such as banks and channel thalweg and point feature such as hills and sinks in the floodplain.
It is generated by double clicking contour from the raster surface module in the 3D Analyst Tools. In the dialogue box the raster model created earlier represents input raster and a contour interval of 0.10m was specified to produce contour model. Contour generation dialogue box is shown in figure 3.
3.2.4 Flow Direction Model
The flow direction is a digital terrain model used in floodplain hydraulic modeling. It shows the direction of surface and groundwater flow within floodplain. Flow direction was generated using blank grid file in SURFER 10 software.
3.2.5 Creation of Floodplain Geo-database
Floodplain mapping and management is facilitated through creation of geo-database for the feature class. It is a relational database storing floodplain spatial and attributes data (Francisco et al, 2011). Geo-database is managed by the database management system (DBMS) (Otto and Rolf 2009) and of different types. The floodplain geo-database according to (Lynn, 2009) can provide the following advantages:
a. Removal of redundancy
b. Concurrency control
c. Transferability
d. And data standardization.
The geo-database was created in ArcGIS 10.1 by digitizing features in the floodplain study area. In this floodplain, four features’ types such as water body, built-up, nypa palm, and dumpsite were digitized from the SPOT satellite image. However, the digitization from image was validated by the traverse observation that was used to produce the final output map.
3.3 Study Area
Ogunabali floodplain in Port Harcourt local government Area, Rivers State, Nigeria is located on longitude 279745mE – 280246mE and latitude 530161mN – 531922mN in the WGS-1984, UTM Zone 32N coordinates system. The floodplain has a total area of 42.943ha and perimeter of 4657.99m and it is narrow in the north and wider in the southern part. It is bounded by Elekahia in the north, Nkpogu in the east, Amadi-ama in the south, and Ogunabali in the west. The floodplain is tributary of Amadi-ama River (salt and tidal river) that flows to and from Nwaja creek. The creek (Nwaja) has been a source of flash flooding in the area over the years as result of channel blockage. The floodplain is currently used in some part as dumpsites, and building of slum settlements along water course. The remaining parts are covered by water body and Nypa palm vegetation.
The elevation of the floodplain ranges from -0.32m to 8.1m as obtained from filed survey data. The geology of the floodplain is characterized by sedimentary rocks of the Niger Delta (Youdeowei and Nwankwoala, 2016). The mean temperature in Port Harcourt city ranges from 30.0 – 33.0°C and annual rainfall ranges between 2100 – 4600mm (NIMET, 2011). The prevailing rainfall has been responsible for flood cases in the city. This study will proffer solutions on flood management using GIS software and remote sensing approach.
IV. Results and Discussion
This section presented the results of the analysis of floodplain from the observed topographic survey data. The section was sub-divided to address outlined study objectives.
4.1 Modeling Floodplain Elevation
Figure 5 is the spot heights from topographic survey of the floodplain. The data was acquired using total station instrument in coordinate mode. The minimum, maximum and mean elevation were -0.32m, 8.10m, and 1.21m respectively.
Similarly, figure 6 is the contour model of Ogbunabali floodplain. The contour model was produced at contour interval (CI) of 0.10m and the contour line and values represented in brown colour. Contour model is a 2.5D representation of the topography utilized in floodplain modeling.
Triangular Irregular Network (TIN) model is another digital elevation model technique used in floodplain hydrology as shown in figure 7 below. TIN model was produced from the topographic data using the default nine (9) class intervals. The classes are represented using different colours, for example, the maximum elevation with values ranges from 6.831m – 7.724m is shown in Arctic white colour, it is followed by 5.938m – 6.831m as shown in gray colour. The least TIN elevation values range from -0.313m – 0.580m represented by Beryl green colour.
Figure 8 is the slope model of the floodplain produced from the converted TIN model to raster surface. The slope model was produce using the degree of slope and classified into nine (9) default classes. The slope values were presented from the smallest range to the highest range with different colours. The first and the least degree of slope range from 0.00 – 0.47 degrees, followed by 0.47 – 1.10 degrees. The highest slope ranges from 10.33 – 13.31 degrees as shown in red colour.
4.2 Modeling Floodplain Hydrology and Hydraulic Patterns
Figure 9 is the flow accumulation model of the floodplain. Flow accumulation model creates a raster of accumulated flow into each cell. It was produced from flow direction raster model obtained from digital elevation model (DTM) using hydrology tool in the ArcGIS 10.1 spatial analyst tools. The model was reclassified into five classes from minimum (0.00) to the maximum (745) accumulated flow using natural breaks interval. The minimum accumulated flow cells range from 0.00 – 23.37 represented in white colour followed by 23.37 – 90.57 accumulated flow. The third-class ranges from 90.57 – 189.90 which was followed in the order by 189.90 – 341.82 represented with medium apple green colour. The final class ranges from 341.82 – 745.00 as shown in Mars red colour.
Figure 10 is the flow direction model overlay on contour model of the floodplain. Flow direction model was produced from SURFER 10 using 1-Grid Vector Map command. The model arrows show the direction of water flow in the floodplain. Also, the length of the arrow depends on the magnitude, or steepness of the slope. From the model high flow is represented with longer arrows with magnitude 0.102 while low flow is shown by shorter arrows with magnitude 0.00008 as shown in the model legend.
4.3 Floodplain Digital Database
Floodplain geodatabase was created in ArcGIS 10.1 using SPOT image and the data from field survey and the map is shown in figure 11 below. The floodplain is made up of built-up areas, nypa palm vegetation, water body, and dumpsite in vector polygons. The area in hectares of each feature is shown in table 2 below.
4.4. Discussion of Results
4.4.1 Hydrology and Hydraulic patterns of the Floodplain
The hydraulic modeling of the floodplain shows that the flow patterns are in different direction of the upland area. The contour lines were crowded at three locations namely: extreme north, east and southwest positions, indicating steep slope. However, at the central position of the floodplain, the contour lines are evenly spaced, indicating gentle slope topography. It then implies that water and materials will flow from steep slope to gentle slope. Similarly, TIN model also shows steep slope at the edges with values 3.259m to 8.831m and gentle slope with values -0.313 to 2.366m in the floodplain centre. The maximum slope angle occurs at the edges with value 10.33 – 13.31 degrees as shown in red colour on the slope model. The slope angle at the centre varies from 0.00 – 0.47 and from 0.47 – 1.10 degrees respectively. The hydraulic pattern of the floodplain is further explained using flow direction and accumulation models to represents the actual flow in the area.
The flow direction model shows high flow magnitude on the steep slope represented by long arrows with value 0.102 magnitude. The arrows are pointing towards the floodplain centre with gentle slope (small slope angle). Also, at the floodplain centre the arrows are smaller which indicates that flow magnitude is smaller compared to the edges. The directions of the longer arrows reflect the topography and proved that flood water will flow from steep slope (higher gradient) to gentle slope (lower gradient) area.
The flow accumulation model shows the maximum flow accumulated cell with value ranges from 341.82 – 745.00 in red colour at the southern map area. This high flow accumulation raster cells can be used to channel water from the floodplain. In most cases they defined stream flow in the floodplain and are the resultant of flows from other directions.
4.4.2 Digital Database of the Floodplain
The digital database of the floodplain shows total built-up area as 4.413ha, located at the fringes of the floodplain. The builtup are located along the water course and from the activities of gradual land reclamation. Dumpsite with total area 0.626ha was found within the floodplain while some are being dumped indiscriminately into the water body. The dumpsite is gradually covering the available flow channel as observed at 662m southward. Similarly, the nypa palm predominantly located in the south with a total area of 16.583ha and is the second largest feature in the floodplain. Such a database will be useful in the management and planning of the floodplain resources and features. It would provide inventories of all features for effective floodplain development. In addition, floodplain database is used to implement the National Floodplain Insurance Program (NFIP) which provides protection of lives and properties for those living in floodplains (West Virginia Quick Guide, 2009). Also, the database will depict flood vulnerable areas, risk zones on Special Flood Hazard Area (SFHA) map. Above all, it will provide relevant information on flood forecasting and early warning to those living in floodplains.
V. Conclusion
The knowledge of floodplain hydrology and hydraulic (H&H) characteristics is essential for the effective management of its resources. The H&H data are incorporated into the floodplain digital database which guaranteed automatic data storage, editing, and retrieval. The study utilized contour model, Triangular Irregular Network (TIN) model, slope model, flow direction and accumulation models derive from topographic survey data of the Ogbunabali floodplain. The hydraulic patterns derive from the models are the same and shows that the flows are from the higher slope along the edges to the lower slope of the floodplain centre.
VI. Recommendations
For further study, the following recommendations were made: 1. That the digital database of all floodplains in Port Harcourt city should be developed for the effective management of flood disaster in the city.
2. That high resolution satellite image should be used to create floodplain database that will aid estimation of flood damage.
3. Establishment of insurance policy for individual living within floodplains areas in the city based on the floodplain base map.
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The paper is originally published in Journal of Research in Environmental and Earth Sciences Volume 9 ~ Issue 12 (2023) pp: 19-35 with open access at www. questjournals.org. © The author(s) 2023.
The paper is republished with authors’ permission.
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