Erosion Data

The data needed to estimate erosion includes depth of closure, wave data (used to calculate significant wave heights), sea level rise estimates for Nigeria, beach width and berm data. Uncertainties are incorporated into the calibration of these data which forms the parameters of the Bruun model. These data form the parameters of the Bruun model.

Depth of closure

Nicholls et al. (1998) define the depth of closure as the boundary between the upper and lower shoreface which can be used to deduce a seaward limit to significant cross-shore sediment transport. Depth of closure is widely used within coastal engineering as an

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empirical measure of the seaward limit of significant cross-shore sediment transport on sandy beaches (Nicholls et al., 1998). The depth of closure is applied in the estimation of coastal budgets, numerical models of coastal change, beach nourishment design and the disposal of dredged material (Masselink and Hughes, 2003). The prediction of the depth of closure remains a difficult task as there are limited models to predict it (Nicholls et al., 1998).The depth of closure can be determined if high-quality, repetitive morphological surveys of the shoreface are available (Masselink and Hughes, 2003).

Uncertainty revolves with the major parameter of the depth of closure, which is the significant wave height. This uncertainty was accounted for by conducting a basic sensitivity analysis in section 4.4. For this study, these data are not available; hence the equation proposed by Hallermeier (1981) was employed (see section 3.4.3.1).

Wave data

In estimating the depth of closure using the Hallermeier (1981) equation, wave data is crucial. The Global Wave Statistics Online (BMT Fluid Mechanics, 2010) database provided the wave data for the study established on long-term (more than 130 years) wind and wave statistics for all the world‘s ocean. The wave data is based on visual observations of wind speed and wave height obtained from the UK Meteorological Office. Other sources of data could be considered and the sources of data currently available are usually classified into instrumental (including remote sensing from satellites); hindcast (estimated from wind field analysis); and visual (BMT Fluid Mechanics, 2010). The database covers over a hundred worldwide and 31 European sea areas; and is populated with results such as wind speed probabilities, extreme wave heights, wave height and period joint probabilities, and storm and calm persistence statistics (BMT Fluid Mechanics, 2010). However, with 104 sea areas for the whole world, that for Nigeria extends from Sierra Leone to Cameroon (Abbott et al., 2011).

This is a major uncertainty with the wave data. A basic sensitivity analysis conducted in

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section 4.4 of this study uses other wave heights obtained from different sources (e.g.

Surfline/Wavetrak, Inc, 2011; and Surf- forecast, 2011) to determine the extent of uncertainty of the sensitivity analysis. The data was used to estimate the mean annual significant wave height and wave period for the Nigerian coast.

Sea level rise estimates

This data was obtained from the Hydrographic Office of the Nigerian Navy. The details of this is mentioned in section 2.2.1

Width of Shoreface

The Google Earth satellite image was used to determine the width of the shoreface. The coastline width is a consequence of the tidal range and the slope of the beach. Without knowing the time (tidal state) of the Google Earth image, an estimate of width of shoreface was measured from the water line – relating to the depth of closure to stable features. In situ measurements were carried out and these measurements were verified from the Satellite image. Uncertainties exist in the measurement and with the satellite image with resolution of about 15 metres. For example, a location along the coast suggests that the width of shoreface is about 43 metres but the measurement on the satellite image records it as 41 metres. Comparing the results of the measurement in the in situ data to the satellite image for the locations shows approximately +/- 5 metres difference. This forms the basis of the sensitivity analysis conducted in section 4.4.

Measurement on the satellite image involves dividing the coastline into a segment of 5km, 10km and other lengths depending on the attribute of a specific segment of the coastline. Within each segment, three measurements were taken at three sites, which were then averaged to give the width of the shoreface for each coastal segment. Figure 3.1 shows the width of shoreface in the illustration of the Bruun rule.

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Figure 3.1: (Source: Gutierrez et. al., 2009): The basic dimensions of the shoreface illustrating the Bruun model. L* is the width of shoreface also represented as w.

Berm height data

This is another parameter useful in computing shoreline changes. Berms are the first line of defence of the beach which protects the backshore and coastal dunes from erosion under mild wave conditions and during the early phase of a storm (Masselink and Hughes, 2003). Berms are dynamic and respond rapidly to change in wave conditions; indeed large wave height or period results in higher berms (Masselink and Hughes, 2003). Estimating the berm height for this study involves the use of validated equations since there was no data available. It also involves estimating the wave breaker height as it is embedded in the equation proposed by Takeda and Sunamura (1982). The uncertainties revolving the estimation of the berm height include the calibration of the significant wave height and the wave period. The sensitivity analysis conducted in section 4.4 accounted for these uncertainties.

80 3.4.2.2 Inundation Data

Table 3.1 is the summary of the data that was used to estimate the extent of inundation on the four coasts.

Elevation

The elevation data is from the Shuttle Radar Topographic Mission (SRTM) digital elevation model (DEM) with a global coverage. The horizontal grid spacing is 3 arc-seconds (approximately 90 metres at the equator). The horizontal coordinate system is referenced to the World Geodetic System 84 (WGS84) and has a vertical spacing of 1 m.

The absolute horizontal accuracy is +/-20m at 90% confidence level, the vertical accuracy is +/-6.13 m at 95% confidence level (CIAT, 2005). The vertical accuracy represents the uncertainty in the SRTM elevation data. The uncertainty in the elevation dataset is accounted for in sections 5.5 and 7.2.2.1 the uncertainty in the elevation dataset.

Table 3.1: Summary of Data Sources Dimension Dataset

The GPWv3 is the most detailed version of the GPW and provides globally consistent

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and spatially explicit human population information and data for use in research, policy making, and communications (The Center for International Earth Science Information Network and International Centre for Tropical Agriculture, 2005). The GPW adopts a simple population algorithm gridded at 30 arc-seconds (approximately 1 km at the equator). The spatial reference is WGS84.

Economic Activity (Gross Domestic Product)

In estimating economic activity that will be at risk in the event of rising sea levels, the spatially explicit socio-economic data of the Greenhouse Gas Initiative Program was employed. The data is a demographic-economic development scenario for the period 1990-2100 with a ten-year interval and based on three scenarios. The resolution level for this spatial dataset is 30 arc-seconds and the grid coordinate system is un-projected latitude/longitude. The data is given per grid cell and each is quantified in monetary terms in US$1990. From the estimations made, period 2010 was selected.

Urban Extent

This research uses the Global Rural-Urban Mapping Project (GRUMP). The alpha edition used for this study is a development on GPWv3 with the incorporation of urban and rural information, providing new insights into urban population distribution and the global extents of human settlements (CIESIN & CIAT, 2005). Just like the GPWv3 it provides globally consistent and spatially explicit human population information and data for use in research, policy making, and communications (CIESIN & CIAT, 2005).

The resolution of the GRUMP is 30 arc-seconds (1 km) and its horizontal datum is the WGS84. The cell value is integer, where 1 = rural and 2 = urban

Agricultural Extent

The dataset for agricultural extent is the PAGE Global Agricultural Extent version 2 with a 1 km resolution (World Resources Institute and The International Food Policy

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Research Institute, 2005). This dataset identifies approximately 200 seasonal land cover regions (SLCRs) per continent based on the interpretation of a series of satellite images captured every 10 days over the period April 1992 to March 1993. The horizontal coordinate system is in decimal degrees with abscissa and ordinate resolution of 1 km at the equator, and the cell size is 1 km. For the geodetic model, the horizontal datum is Clarke1866. The dataset contains 18 classes: Table 3.2 shows codes and the classes of the agricultural element.

Wetland

The wetland data Global Lakes and Wetlands Database version 3 (GLWD-3) used for this study was developed by Lehner and Döll (2004). The GLWD-3 dataset is a global raster map that comprises lakes, reservoirs, rivers, and different wetland types (Table 3.3) at 1 km resolution. The dataset could be used as an estimate of wetland extents, and to identify large-scale wetland distributions and wetland complexes (Lehner and Döll, 2004).

Table 3.2: Label codes for Agricultural elements

Cell Codes Label

10 Cropland

11 Plantations

13 Cropland / Pasture

14 Agriculture with forest

41 Primarily Forest (>60%)

42 Primarily Grassland (>60%)

60 Non-vegetated / Sparsely vegetated

83 Table 3.3: Label codes for Wetland elements Cell Value Lake or Wetland Type

1 Lake

3 River

4 Freshwater Marsh, Floodplain

5 Swamp Forest, Flooded Forest

6 Coastal Wetland (incl. Mangrove, Estuary, Delta, Lagoon)