Spatial vulnerability analysis

Analysis of current scenarios used

This study concentrated on the two extreme RCP scenarios (RCP2.6 and RCP8.5), as appraised in Table 2.1. The prioritised scenarios were then analysed under the different periods to 2030, 2060 and 2090. The process was aimed at establishing the various SLR effects on the high tide on the coastline or especially during a storm surge. The analysis gives an indication of the extent of flooding due to escalating SLR under the different scenarios and years. This enabled an assessment of spatial extent of land and settlement areas threatened under the different global and local climate forcing, thus facilitating an understanding of how to prioritise mitigation and adaptation measures for both islands.

The projected seasonal temperature and precipitation changes for the SRES scenarios were customised/adapted from global averages to local level estimates. However, given that the RCPs are yet to be downscaled to the local level regional-scale climate information from IPCC (?) was used as the secondary data for this purpose. Projected changes in average monthly temperature, precipitation and sea surface temperatures up to 2100 were derived as secondary data from IPCC.

Analysis of inundation/flood risk areas

The maps used in the analysis were secured and collected from the USGS, NASA LD, and AAP depository, with the landsart images as well as the digital elevation models (DEM) for both the Mombasa and Lamu islands being prioritised for initial analysis. Secondary data on land-use were collected from the Regional Center for Mapping of Resources for Development (RCMRD) in Nairobi Kenya. The analysis of the areas under threat of inundation due to escalation in SLR based on the 1 in 100 years repeat cycle scenarios were done using the Global Mapper software. The GIS software had the capacity to handle large rasta files as compared to ArcGis and was best suited for the initial flood risk analysis. Image analysis for different flood zone areas were projected and the results identified and discussed towards the key findings.

Delineated inundation zones were subsequently overlaid with indicators for land area, population, as well as urban infrastructure/assets in order to facilitate the modelling of the effects of SLR under different scenarios and the available topographic maps. The derived scenarios are based on the prioritised IPCC SLR scenarios. Finally the surface indicator maps (LECZ and population density maps) were overlaid with the inundation zone layer in order to determine the spatial exposure of each of the two indicators.

Calculating population at risk

Two geographic data sets were required for this assessment. The first set delineates the extent of the LECZ itself. The study adopted the definition of LECZ as land area contiguous with the coastline up to a 10-metres elevation, based on the measure from the Shuttle Radar Topography Mission (SRTM) elevation data set that were localized for Mombasa and Lamu island. The second data set required for this analysis is a population grid for each of the islands and associated with each grid cell and land area. Estimates for the population was collected and analysed from the 2009 census data sets, which were allocated to urban extents or surrounding rural areas.

For the assessment of exposed assets, the exposed population was translated to the amount of capital per inhabitant. This capital per inhabitant is computed from the GDP per capita in each county and an estimate of the ratio of “produced capital” to GDP. The ratio of produced capital to GDP is calculated using the World Bank dataset published with the “Changing Wealth of the Nations” report. The resulting ratio was an average of 2.8 and was applied to both counties. The analysis was based on the 30-year periods (2030,2060, and 2090) based on and described under the pessimistic scenario (RCP 8.5) and the optimistic scenario (RCP 2.6). Being at the equator, Mombasa and Lamu Islands are not expected to experience critical escalation in tropical storms. Scenario 1 (RCP 8,5) is based on current linear SLR. Scenario 2 (RCP 2.6) is based on a global reduction in population as well as an adaptive society. The analysis helped in determining the spatial exposure of each city-Island under each of the two scenarios.

Vulnerability to future SLR analysis

For the co-production of the map using the STRM database, the LECZ maps for the island were produced (see Figure C.4 for Mombasa island and Figure C.6 for Lamu Island). These maps were then subjected to inundation and alternative storm-surge (wave height) scenarios analysis based on application of the equation by Nicholls under the RCP2.6 and RCP8.5 for the periods to 2030, 2060 and 2090 as recorded by IPCC. Secondly, the county surface maps for each exposure indicator were constructed to determine exposed population and urban extent.

These surface indicator maps were then overlaid with the inundation zone layer. The overlaying of the maps helped in deriving the spatial exposure of each of the two indicators (population and urban extent) under inundation threat for the two counties. More detailed descriptions of these steps are as follows.

The calculation of storm surges (extreme sea levels) followed the method outlined by ? and also applied in several global studies such as ?? where storm surges are

calculated as follows: FSS = S100 + SLR + ( UPLIFT∗ 100yrs 1000 ) + SUB + (S100∗ x), (3.3.1) Where

• FSS= Future sea storm

• S100=1-in-100 years surge height(m),

• SLR=sea-level rise (based on the IPCC prediction), • UPLIFT=continental uplift/subsidence in mm/year, • SUB=0.5mm (applies to deltas only),

• x=0.1, or increase of 10% applied only in coastal areas currently prone to

tsunamis and tropical cyclones.

Calculating assets exposed to SLR effects

For the analysis of exposed assets, values for 2015 National per capita GDP and the Purchasing Power Parity (PPP) were obtained from the International Monetary Fund database. PPP values are recognized and used as a reliable standardised value indicator for economic comparison across economies/countries. Normally, GDP is assumed to be equal throughout the country thus urban GDP per capita equals rural GDP per capita in a given year. For analysis of future cities, the relationship is also assumed to hold. The assets exposed and at risk are calculated as shown in Equation (3.3.2 below. The national per capita GDP is preferred to the local per capita which would be translated regarding the coastal contribution to the national GDP (see for example ??). In estimating the losses, a method suggested by Nicholls and adopted by ? and ? was used and is described as follows

Ea= Ep∗ GDPpercapitaP P P ∗ 2.8. (3.3.2)

Where, Ea= Exposed assets, Ep= Exposed populations, and

GDPpercapitappp= the country’s per capita Gross Domestic Product(GDP)

purchasing power parity (ppp)

According to ? the factor of 2.8 translates to per capita GDP, i.e. the annual production of the economy divided by population, to the per capita value of assets. In earlier studies this factor was set at 5 but was later revised downwards to the factor of 2.8 based on current GDP values. In their argument, annual investments usually represent, on average, about 25 percent of GDP. Since economic resources in cities include buildings, transport infrastructures, utility

infrastructures, and other long-lived assets, assuming a lifetime of 40 years for these investments was deemed to be acceptable. Assuming also that per capita asset value in the city is growing by 3 percent a year, a rapid calculation suggests that the value of these assets is between 4 and five times per capita GDP. Consistent with this calculation, previous experience from studies of historical losses from flooding events have been undertaken, shows that losses accrued from flood events are within five times greater than the GDP of the affected areas.

This factor of 2.8, however, is likely to underestimate the property at risk particularly for developing regions, where there is a greater inequality between cities/towns and rural areas. That being the case, the calculation of the exposed assets in this study gives an initial estimate of the value of the infrastructure and losses that may result due to climate-related SLR impacts. The urban GDP per capita is assumed to grow at the same rate as national (or regional) GDP throughout the period to 2090. Equally, urban GDP is assumed to be equivalent to rural GDP per capita throughout the study periods.

Total GDP for each city is therefore, the product of projected urban population and per capita GDP in 2090 for the two counties. The analysis for the future projections is based on the population projections until 2100 as well as the poverty index derived from the GDP of Mombasa County as well as Lamu County. These projections are also in tandem with the individual county population projections. The population of the two islands and the countries Gross Domestic Product (GDP) is used in the analysis of the number and extent of exposed infrastructure, people and associated economic assets. The population density maps were overlayed on the digital elevation model after the development of the analyzed elevation contours to ascertain population living in the LECZ and within the susceptible areas.

The population of each sub-county within the island county was then estimated. The analysis of the exposure is based on the 1 to 100-year return episode of extreme water events and in the absence of sea defense mechanisms along the coastline. The scenario modeling process developed by Nicholls is summarized in Figure 3.8.

Figure 3.8: Conceptual framework for spatial inundation scenario modelling.