The major use of sea level rise assessments has been in identifying coastal population (Kettle, 2010) and land at risk (Gesch et al., 2009). However, numerous sources and types of uncertainty, which limit confidence in the accuracy of modelled results are embedded in the data and assumptions used to develop these assessments (Kettle, 2010).
The sources and types of uncertainties that compromise the accuracy of sea level rise assessments usually arise from the following: measuring and monitoring sea level errors (Woodworth, 2006). Others include: determining trends (Jevrejeva et al., 2006);
estimating trajectories of change (Bindoff et al., 2007, IPCC.,2007); predicting social change (Moser, 2005); predicting shoreline change (Slott et al., 2006); and using inadequate data and methods to quantify the impacts (Kettle, 2010). Many sea level rise assessments have been unable to provide detailed information concerning the impacts in coastal environments. This is because most of these assessments have not presented the impacts with the degree of confidence and uncertainty in the elevation data employed that is optimal for decision-making (Gesch et al., 2009). Many elevation dataset used
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for sea level rise assessments are poorly suited for detailed inundation mapping especially regions with gently sloping landscape (Ericson et al., 2006; Rowley et al., 2007; McGranahan et al., 2007). Gesch et al., (2009) also noted that the elevation datasets have elevations rounded up only to whole metre intervals, which renders the overall vertical accuracy to be poor when, compared to the intervals of predicted sea level rise over the next century. Vertical accuracy is an expression of the overall quality of the elevation dataset in comparison to the true ground elevations at corresponding locations and for proper quantitative use of elevation data, its vertical accuracy must be known and understood (Gesch et al., 2009). Digital Elevation Models (DEMs) are often used in sea level rise assessments because they are computationally efficient and inexpensive to obtain (Kettle, 2010). Typically, they use low-resolution data due to the unavailability of higher resolution data (Kettle, 2010). Most DEMs are rounded up to a whole feet or metres, referenced to mean sea level with horizontal resolution of about 30 and 90 metres, while some to 1 kilometre. Errors in DEMs are a function of the collection process, processing, quality control of data and geographic characteristics of the land (Hodgson et al., 2003). Many DEMs have global or near-global extent and many studies (Small and Nicholls, 2003; Ericson et al., 2006; Gesch et al., 1999;
Rowley et al. 2007; and Hastings and Dunbar, 1998) have used them (GTOPO30) in sea level rise assessments.
The Shuttle Radar Topographic Mission (SRTM) elevation dataset has broader coverage and improved resolution over the GTOPO30 and is available at about 90-metre resolution with near global coverage (Gesch et al., 2009). Various studies, for example McGranahan et al., (2007); Demirkesen et al., (2007, 2008) have employed the SRTM elevation dataset for their sea level rise assessments; the former for estimates of population at risk and the latter land use/land cover classes in the delineated vulnerable
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areas. Many of these studies acknowledged the limitations of their results because of the source data they used, and clearly list the caveats for proper use of the maps, which indicates that the maps are useful in depicting broad implications of sea level rise, but not appropriate for site-specific decision-making (Gesch et al., 2009). With the numerous researches in sea level rise assessments, significant progress still needs to be made to improve the science-based information needed for decision-making. This is because in most sea level rise assessments, the quality of the available input data and the common tendency to overlook the consequences of coarse data resolution and large uncertainties ranges has hindered the usefulness and applicability of many results (Gesch et al., 2009).
Among the limitations of sea level rise studies, include the use of lower resolution DEMs with poor vertical accuracies. There is need for better elevation information to give credence to SLR assessments. Another major limiting factor in sea level rise assessments is the lack of consideration of uncertainty of input elevation data. There is a need for rigorous accuracy testing for vertical errors and its measurement in elevation datasets (Gesch et al., 2009). The overall vertical error is a measure of the uncertainty of the elevation information. In a sea level rise analysis carried out by Kettle (2010), to investigate how uncertainty in DEMs and future SLR lead to different estimates of the population at risk throughout Charleston County, South Carolina, three scenarios were illustrated to represent the projected range of SLR by 2100. The scenarios are 37 cm, 80 cm and 2m for low, medium and high scenarios respectively. Results indicate that uncertainty within DEMs and SLR contributes to substantially different estimates of population at risk (Table 2.1). The uncertainty in DEMs alone contributes to estimates of population at risk that range from 2 to 104,000 people for the 37 cm SLR scenario.
Results also illustrate the sensitivity of DEMs to different SLR scenarios. Specifically,
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these DEMs did not reveal a difference between the 37 and 80 cm SLR scenarios. This is because elevation units are reported in whole metres and thus lack the sensitivity to detect changes in sea level that occur between integers.
Table 2.1 Population at risk for different SLR scenarios and DEMs Modelled population at risk to SLR
SLR Scenario Over-predicted Elevation
Reported Elevation
Under-predicted Elevation
37 cm 104,200 50,351 2
80 cm 104,200 50,351 2
2 m 166,621 98,365 32,646
Source: (Kettle, 2010)
Another example to illustrate the importance of the accounting for vertical uncertainty in sea level rise vulnerability assessment is the study carried out by Gesch (2009). In his assessment, four elevation datasets were used to compare delineated areas in a 1-metre sea level rise scenario. The details are in Table 2. Even in the NED dataset that has an approximate horizontal resolution of 30 metres, the delineation of the 1-metre (m) zone is more than double when the elevation uncertainty is considered. This therefore calls into question the reliability of any conclusions drawn from the delineations. From Table 2.2, the DEMs do not have the capability to accurately delineate a 1-metre sea level rise inundation zone. Lidar is more appropriate because it has less uncertainty. This has necessitated SLR assessment to incorporate a range of values in reporting the size of the inundation area for a given sea level rise scenario, especially for sites where high accuracy lidar data are not available (Gesch 2009).
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One other major sea level rise assessment study is in estimating the extent of shoreline retreat. Many models exist currently but all their parameters are subject to uncertainties.
These parameters are discussed in section 3.4.1.1 in relation to the model used to estimate shoreline retreat along the Nigerian coast.
Table 2.2 The area of potential inundation from a 1-metre sea-level rise as calculated from four elevation datasets, as well as the area of inundation when the uncertainty of the elevation data is considered.
Elevation Data Area ≤=1 dataset. Fonteh et al.(2009) obtained tide data from TOPEX/Poseidon satellite which indicates a sea level rise of between 1.8 to 2.2 mm/yr. in Calabar, Nigeria for the period 1948-2003. The Revised Local Reference Level (RLRL) data and satellite altimetry data between 1993-2003 indicates a relative SLR of 3.1 mm/yr. with a range of lower 95%
confidence levels and upper 95% confidence levels of 2.3 and 3.8 mm respectively (IPCC, 2007c). With the tidal predictions obtained from the Nigerian Navy (2008), the upper level is approximately 4.5 mm/yr. If this rate of change continues until 2100, then sea level would have risen to 40.5 cm (0.4 m). This is lower than the IPCC estimates of 59 cm by year 2100. However, with the discussion forgoing, increase in GHG emissions and contributions from Greenland and Antarctica will ensure sea level rise estimates to exceed the IPCC high estimates of 86 cm by 2100.
20 2.2.3 Need for Coastal Management
Coastal communities and habitats will be increasingly affected by climate change impacts due to sea level rise (Field et al., 2007). Coastal systems will be affected. There could be land loss through inundation; erosion of coastal lands; migration of coastal landforms and habitats; increased frequency and extent of storm-related flooding;
wetland losses; and increased salinity in estuaries and coastal freshwater aquifers (Williams et al., 2009). Other impacts that could exacerbate the impacts of sea level rise include severe droughts and storm intensity, and continued rapid coastal development (Nicholls et al., 2007). Human induced impacts also are detrimental to the success of the coast (Sutherland, 2004). With increasing SLR, the effects, which are cumulative, will be felt on both the natural ecosystems and human developments; hence, the need for new and innovative coastal zone management and planning approaches to be employed on the coast, otherwise there will be increasing vulnerability of coastal development and coastal population (Williams et al., 2009).
The need for coastal zone management stems from clear evidence that coastal resources are being compromised; coastal uses are in conflict; or the coastal environment is facing destruction from natural hazards and man-made activities (Sutherland, 2004). Coastal zone management is vital in preventing the weakening and devaluing of coastal resources and making coastal regions susceptible to sea-level rise (Sutherland, 2004).
This is supported by (Watson et al., 2001). He stated
―Integrated coastal zone management (ICZM) is an iterative and evolutionary process for achieving sustainable development by developing and implementing a continuous management capability that can respond to changing conditions, including the effects of climate change‖.
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ICZM is therefore an effective tool for managing the coast as well as an adaptation strategy for sea level rise. The main goals in managing coastal zones are to enable sustainability of the various coastal resources, the livelihood of the community that depends on these resources now and in the future, and mitigating the adverse effects of climate change and its effects such as sea level rise. ICZM is valuable because it has been regarded as the means of by which sustainable development can be achieved on the coast. Sutherland (2004) stressed that ICZM is a tool for good governance of coastal spaces and an adaptation strategy for sea level rise.