WPS4136 The Impact of Sea Level Rise on Developing Countries: A Comparative Analysis By Susmita Dasgupta* Benoit Laplante** Craig Meisner* David Wheeler*** and Jianping Yan** World Bank Policy Research Working Paper 4136, February 2007 The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org. *Development Research Group, World Bank. **Independent consultants, Canada. *** Senior Fellow, Center for Global Development Correspondence should be addressed to: Dr. Susmita Dasgupta, World Bank, 1818 H Street, NW, Washington, DC 20433, sdasgupta@worldbank.org. Acknowledgements: Funding for this project was provided by the Canadian Trust Fund (TF030569) sponsored by the Canadian International Development Agency (CIDA). We would also like to extend our special thanks to Piet Buys, Uwe Deichmann and Jillian Kingston for their guidance and valuable help. Abstract Sea level rise (SLR) due to climate change is a serious global threat: The scientific evidence is now overwhelming. Continued growth of greenhouse gas emissions and associated global warming could well promote SLR of 1m-3m in this century, and unexpectedly rapid breakup of the Greenland and West Antarctic ice sheets might produce a 5m SLR. In this paper, we have assessed the consequences of continued SLR for 84 developing countries. Geographic Information System (GIS) software has been used to overlay the best available, spatially-disaggregated global data on critical impact elements (land, population, agriculture, urban extent, wetlands, and GDP) with the inundation zones projected for 1-5m SLR. Our results reveal that hundreds of millions of people in the developing world are likely to be displaced by SLR within this century; and accompanying economic and ecological damage will be severe for many. At the country level, results are extremely skewed, with severe impacts limited to a relatively small number of countries. For these countries (e.g., Vietnam, A.R. of Egypt, and The Bahamas), however, the consequences of SLR are potentially catastrophic. For many others, including some of the largest (e.g., China), the absolute magnitudes of potential impacts are very large. At the other extreme, many developing countries experience limited impacts. Among regions, East Asia and Middle East/North Africa exhibit the greatest relative impacts. To date, there is little evidence that the international community has seriously considered the implications of SLR for population location and infrastructure planning in developing countries. We hope that the information provided in this paper will encourage immediate planning for adaptation. 2 I. Introduction As noted by the International Panel on Climate Change (IPCC, 2001b), climate change will have many negative effects, including greater frequency of heat waves; increased intensity of storms, floods and droughts; rising sea levels; a more rapid spread of disease; and loss of biodiversity. Sea level rise (SLR) poses a particular threat to countries with heavy concentrations of population and economic activity in coastal regions. Until recently, studies of SLR typically predicted a 0-1 meter rise during the 21st century (Church et al. 2001, IPCC Third Assessment, 2001). The three primary contributing factors have been cited as: (i) ocean thermal expansion; (ii) glacial melt from Greenland and Antarctica (plus a smaller contribution from other ice sheets); and (iii) change in terrestrial storage. Among these, ocean thermal expansion was expected to be the dominating factor behind the rise in sea level. However, new data on rates of deglaciation in Greenland and Antarctica suggest greater significance for glacial melt, and a possible revision of the upper-bound estimate for SLR in this century. Since the Greenland and Antarctic ice sheets contain enough water to raise the sea level by almost 70 m (Table 1), small changes in their volume would have a significant effect.1 Table 1: Physical characteristics of ice on Earth. Glaciers Ice caps Glaciers and Greenland Antarctic ice ice caps * ice sheet sheet Number > 160,000 70 Area (10 km2) 6 0.43 0.24 0.68 1.71 12.37 Volume (10 km3) 6 0.08 0.10 0.18 ± 0.04 2.85 25.71 Sea-level rise equivalent (m) 0.24 0.27 0.50 ± 0.10 7.2 61.1 Accumulation (sea-level equivalent, mm/yr) 1.9 ± 0.3 1.4 ± 0.1 5.1 ± 0.2 Source: Church et al. (2001), Table 11.3 Data sources: Meier and Bahr (1996), Warrick et al. (1996), Reeh et al. (1999), Huybrechts et al. (2000). * - does not include Greenland and Antarctic ice sheets (represented in the next columns) Since the IPCC Third Assessment Report in 2001, there has been an increased effort to improve measures of mass loss for the Greenland ice sheet and its contribution to SLR. Using satellite interferometry observations, Ringot and Kanagaratnam (2006) detected widespread glacier flow acceleration in the lower latitudes between 1996 and 2000, and rapid extension to higher latitudes by 2005. When combined with surface loss estimates 1 If the Greenland ice sheet were to melt completely, it would raise average sea level by approximately 7 meters (Church et al. 2001). 3 by Hanna et al. (2005), they calculated a total loss double that in the previous decade. Comparing this rate of contribution of Greenland's ice sheet to SLR with the IPCC estimate for the 20th century, the new measures are roughly two to five times greater. In another study of mass loss for Greenland using repeat altimetry, Krabill et al. (2004) found that between 1993-1994 and 1998-1999, the ice sheet was losing 54 ± 14 gigatons of ice per year (Gt/yr). In contrast, net mass loss over the 1997-2003 interval averaged 74 ± 11 Gt/yr. At these rates of net mass loss, the contribution of the Greenland ice sheet to SLR is roughly double the rate assumed in the IPCC Third Assessment (2001) report2. In Antarctica, using the Gravity Recovery and Climate Experiment (GRACE) satellites, Velicogna and Wahr (2006) have determined mass variations of the entire Antarctic ice sheet during 2002-2005.3 Their results indicate that the mass of the ice sheet decreased significantly, at a rate of 152 ± 80 cubic kilometers of ice per year; most of this loss came from the West Antarctic ice sheet (WAIS). This rate is several times greater than that assumed in the IPCC Third Assessment, and the IPCC admitted that its final estimate did not take into account the dynamic changes in the WAIS. Increasing concern also attaches to the stability of the WAIS, which currently rests on bedrock below sea level. Mercer (1978) speculated that human-induced global warming could cause the WAIS to be released into the ocean by a sliding mechanism (also referred to as WAIS collapse). This would cause a rapid rise in sea level, since it would be triggered solely through a displacement of the WAIS without its having to melt. Were the WAIS to collapse, it would raise average sea level by approximately 5 to 6 meters (Tol et al., 2006). While there remains considerable uncertainty about the above scenarios, and the time horizon over which they may unfold, recent research and expert opinion indicate that significant SLR may occur earlier than previously thought.4 This has prompted a number of researchers to model the estimated impact of significant increases in SLR (these are sometimes termed `extreme climate scenarios'). A number of studies have provided estimates of the potential impacts for specific developed countries (e.g. France, the 2360 gigatons of ice correspond approximately to 1 mm of sea level. 3The GRACE result for total Antarctic ice mass change includes complete contributions from such regions as the EAIS coastline and the circular cap south of 82°S, which has not been completely surveyed with other techniques. 4See Vaughan and Spouge, 2002. 4 Netherlands, Poland, Singapore and the United States)5; developing countries (e.g. Bangladesh, Benin, China, Nigeria, and Senegal)6; or specific areas of individual countries (e.g. deltas of the Nile and Bengal; Rhine Delta, Thames Estuary and Rhone Delta)7. Only a limited number of studies have assessed the impacts of SLR on a broader regional or world scale. Such studies include: Darwin and Tol (1999), Hoozemans et al. (1993), Nicholls and Mimura (1998), Nicholls et al. (2004), Nicholls and Lowe (2006), and Nicholls and Tol (2006). Some of these studies examine the impact of `extreme climate scenarios' such as a 5 meter SLR (e.g. Nicholls et al., 2004). However, while indicators of impacts generally include land loss, population affected, capital loss value and wetlands loss, different studies have used different subsets of indicators or regions, making it difficult to compare the relative magnitude of impacts across countries or regions.8 This paper provides a broader comparison, by assessing the impacts of SLR for all developing countries using a homogeneous set of indicators, and for multiple SLR scenarios. To our knowledge, this is the first such exercise. Mendelsohn et al. (2006) provide complementary evidence, by examining the market impacts of climate change on rich and poor countries for a number of different climate scenarios. However, their work does not assess the impact of SLR on multiple physical and economic indicators. For this study, we group 84 coastal developing countries into 5 regions (corresponding to the 5 regional departments of the World Bank):9 Latin America and the Caribbean (25 countries); Middle East and North Africa (13); Sub-Saharan Africa (29); East Asia (13); and South Asia (4). For each country and region, we assess the impact of SLR using the following 6 indicators: land, population, gross domestic product (GDP), urban extent, agricultural extent, and wetlands. Finally, these impacts are calculated for SLR scenarios ranging from 1 to 5 meters. 5See Baarse et al. (1994), Bijlsma et al. (1996), Mendelsohn and Neumann (1999), Ng and Mendelsohn (2005), Olsthoorn et al. (2002), and Zeidler (1997). 6Adam (1995), Dennis et al. (1995), French et al. (1995), Han et al. (1995), and Warrick et al. (1996). 7Tol et al. (2005), Yim (1995). 8For example, the regional assessments presented in Nicholls and Mimura (1998) cover four regions: Europe; West Africa; South, South-East, and East Asia; and the Pacific Small Islands. It does not include Latin America and the Caribbean or other regions of Africa. 9Hoozemans et al. (1993) divided the globe (including developed countries) into 20 regions. 5 At the outset, we acknowledge that this analysis has limitations. First, we do not assess the likelihood of alternative SLR scenarios. We take each scenario as given, and assess the impacts using our 6 indicators for each of the 84 developing countries and 5 regions. Second, we assess the impacts of SLR using existing populations, socio-economic conditions and patterns of land use, rather than attempting to predict their future states. Since human activity is generally increasing more rapidly in coastal areas, our approach undoubtedly underestimates the future impacts of SLR in most cases. This underestimation will be greatest for SLR impacts on population and GDP in absolute terms (number of people impacted or $ of GDP impacted), Third, our study is conservative because we do not consider storm surge augmentation. Even a small increase in sea level can significantly magnify the impact of storm surges, which occur regularly and with devastating consequences in some coastal areas. Despite these limitations, we believe that our comprehensive baseline estimates of SLR impacts can assist policymakers and international development institutions in allocating resources for adaptation to climate change. In particular, we believe that our specific estimates, based on existing coastal conditions, are more likely to interest decision- makers than estimates based on projections of future coastal population, economic activity, etc. In the next section, we describe the methodology and data sources used to estimate the impact of SLR in developing countries. We present our results in Section III, at the global, regional and country levels. Section IV provides a summary and conclusions. II. Methodology and data sources II.1 Data Sources We employed geographic information system (GIS) software to overlay the critical impact elements (land, population, agriculture, urban extent, wetlands, and GDP) with the inundation zones projected for 1-5 m. SLR. We used the best available, spatially- disaggregated data sets from various public sources, including the Center for Environmental Systems Research (CESR), the Center for International Earth Science Information Network (CIESIN), the International Centre for Tropical Agriculture (CIAT), 6 the International Food Policy Research Institute (IFPRI), the National Aeronautics and Space Administration (NASA), the National Oceanographic and Atmospheric Administration (NOAA), and the World Bank. Table 2 summarizes the data sources for assessments of inundation zones and impacts. Table 2 Summary of Data Sources Dimension Dataset Unit Resolution Source(s) Name Coastline and WVS 1:250,000 NOAA/NASA country boundary Elevation SRTM 90m km2 90m CIAT DEM V2 Population GPW-3 Population 1km CIESIN counts Economic GDP2000 million US 5km World Bank, based on activity dollars Sachs et al. (2001) Urban extent GRUMP V1 km2 1km CIESIN Agricultural GAE-2 km2 1km IFPRI extent Wetlands GLWD-3 km2 1km CESR, Lehner, B. and Döll, P. (2004) II.2 Methodology The country indicator database was developed by following the six-step procedure described below. II.2.1 Preparing country boundaries and coastlines Country coastlines were built by sub-setting polygons from the World Vector Shoreline (polygon), a standard National Geospatial Intelligence Agency (formerly Defense Mapping Agency) product at a nominal scale of 1:250,000. It contains worldwide coverage of shorelines and international boundaries. The subset country coastlines were also used as a mask for calculating country totals for the selected exposure indicators. 7 II.2.2 Building coastal terrain models (DTM) Coastal terrain models were derived from the CIAT SRTM 90 meters digital elevation model (DEM) data (Version 2), released in 2005.10 Zipped data files were downloaded from the CIAT website, and then converted into raster format, and mosaiced in terms of country boundaries in the ArcGIS environment. II.2.3 Identifying inundation zones Inundation zones were derived from the coastal terrain model (DTM) by setting the value of pixels in the DTM to 1 for the different SLR scenarios examined in this study. Pixels that are apparently not connected to coastlines, such as inland wetlands and lakes, were masked out manually. II.2.4 Calculating exposure indicators Estimates for each indicator were calculated by overlaying the inundation zone with the appropriate exposure surface dataset (land area, GDP, population, urban extent, agriculture extent, and wetland). Exposure surface data were collected from various public sources. Unless otherwise indicated, latitude and longitude are specified in decimal degrees. The horizontal datum used is the World Geodetic System 1984 (WGS 1984). For area calculation, all units are projected to World Equal Area. For the exposure grid surfaces, two GIS models were built for calculating the exposed value. Because the values of the pixels in GDP and population surfaces are respectively in millions of US dollars and number of people, the exposure is calculated by multiplying the exposure surface with the inundation zone and then summing up by multiplying grid count and value. Exposure indicators, such as land surface, urban extent, agriculture extent and wetland are measured in square kilometers. II.2.5 Adjusting absolute exposure indicators For exposure indicators such as land area, population and GDP, which have measured country totals available, the exposed value is adjusted to reflect its real value by using the following formula: 10 Shuttle Radar Topographic Mission. 8 Vadj = CTmea Vcal CTcal where Vadj ­ Exposed value adjusted; Vcal ­ Exposed value calculated from exposure grid surfaces; CTmea ­ Country total obtained based on statistics; CTcal ­ Country total calculated from exposure grid surface. II.2.6 Conducting data quality assurance and control Quality control was conducted to adjust for errors caused by overlaying grid surfaces of different resolutions, such as the 90-meter resolution inundation zone with 1-kilometer or 5-kilometer exposure grid surfaces. The following procedure was employed: 1) Calculate the country total from the grid surface using the country boundary; 2) Calculate the aspect exposure that is under 5-meter SLR; 3) Calculate the aspect exposure that is over 5-meter SLR; 4) Compare the country total with the sum of both aspect exposures. If the difference is less that 5%, the calculated aspect exposure was considered within the error tolerance. If not, the exposure calculation was reviewed and estimates revised until the 5% difference threshold was reached. A more detailed description of each dataset is provided in Appendix 1. III. Results In the first sub-section below, we present results at the global level for the 84 developing countries included in this analysis. In sub-section III.2, we present the results for each of the 5 regions and, individually, for each of the 84 countries. Our results indicate that for a number of countries, even a 1-meter SLR would have a very significant impact. III.1 Global results Table 3 indicates that approximately 0.3% (194,000 km2) of the territory of the 84 developing countries would be impacted by a 1-meter SLR. This would increase to 1.2% 9 in a 5m SLR scenario. Though this remains relatively small in percentage terms, approximately 56 million people (or 1.28% of the population) of these countries would be impacted under a 1m SLR scenario. This would increase to 89 million people for 2m SLR (2.03%), and 245 million people (5.57%) for 5m SLR. The impact of SLR on GDP is slightly larger than the impact on population, because GDP per capita is generally above average for coastal populations and cities. Wetlands would experience significant impact even with a 1m SLR. Up to 7.3% of wetlands in the 84 countries would be impacted by a 5m SLR. As shown in the next section, these impacts are not uniformly distributed across the regions and countries of the developing world. The impacts are particularly severe in a limited number of countries. Table 3 Impacts of sea level rise: Global level 1m 2m 3m 4m 5m Area (Total = 63,332,530 sq. km.) Impacted area 194,309 305,036 449,428 608,239 768,804 % of total area 0.31 0.48 0.71 0.96 1.21 Population (Total = 4,414,030,000) Impacted population 56,344,110 89,640,441 133,049,836 183,467,312 245,904,401 % of total population 1.28 2.03 3.01 4.16 5.57 GDP (Total = 16,890,948 million USD) Impacted GDP (USD) 219,181 357,401 541,744 789,569 1,022,349 % of total GDP 1.30 2.12 3.21 4.67 6.05 Urban extent (Total = 1,434,712 sq. km.) Impacted area 14,646 23,497 35,794 50,742 67,140 % of total area 1.02 1.64 2.49 3.54 4.68 Agricultural extent (Total = 17,975,807 sq. km.) Impacted area 70,671 124,247 196,834 285,172 377,930 % of total area 0.39 0.69 1.09 1.59 2.10 Wetlands area (Total = 4,744,149 sq. km.) Impacted area 88,224 140,355 205,697 283,009 347,400 % of total area 1.86 2.96 4.34 5.97 7.32 10 III.2 Regional results In this sub-section, we examine results for Latin America and the Caribbean, Middle East and North Africa, Sub-Saharan Africa, East Asia, and South Asia.11 To facilitate the reading of these results, we follow a similar structure of presentation for all regions. (i) Latin America and the Caribbean region As shown in Table 4, the impact of SLR in Latin America and the Caribbean is relatively similar to the impact noted earlier for all developing countries insofar as land area, agriculture and wetlands are concerned. However, a much smaller percentage of the region's population and GDP would be impacted (0.57% and 0.54% respectively for 1m SLR, vs. 1.28% and 1.30% respectively worldwide). The same holds for the impact on urban infrastructure. Table 4 Impacts of sea level rise: Latin America & Caribbean region 1m 2m 3m 4m 5m Area (Total = 18,806,598 sq. km.) Impacted area 64,632 101,736 149,183 193,786 234,117 % of total area 0.34 0.54 0.79 1.03 1.24 Population (Total = 501,550,000) Impacted population 2,873,505 4,732,734 7,247,905 10,268,489 13,472,827 % of total population 0.57 0.94 1.45 2.05 2.69 GDP (Total = 3,649,731 million USD) Impacted GDP (USD) 19,663 36,201 49,447 67,403 86,850 % of total GDP 0.54 0.99 1.35 1.85 2.38 Urban extent (Total = 505,477 sq. km.) Impacted area 3,080 5,212 8,090 11,614 15,294 % of total area 0.61 1.03 1.60 2.30 3.03 Agricultural extent (Total = 4,889,156 sq. km.) Impacted area 16,104 29,514 47,003 66,330 85,959 % of total area 0.33 0.60 0.96 1.36 1.76 Wetlands area (Total = 1,651,735 sq. km.) Impacted area 22,314 38,782 60,876 85,734 108,476 % of total area 1.35 2.35 3.69 5.19 6.57 11 These differ from the 20 coastal regions used in Nicholls and Nomura (1998). Our selection of these 5 regions is simply explained by the fact that those regions correspond to the 5 regional departments of the World Bank. 11 When the results are examined at the country level, one notes very significant differences within the region. As is starkly revealed in Figure 1a, The Bahamas would experience the largest percentage of impacted land: Even with a 1m SLR, approximately 11% of the land area of The Bahamas would be impacted. This percentage reaches in excess of 60% under a 5m SLR scenario. Cuba and Belize would also experience significant impacts, albeit at a much reduced scale when compared with The Bahamas. Figure 1a Latin America & Caribbean: Country area impacted12 1 meter 2 meter 3 meter 4 meter 5 meter 70 60 ea)rA( 50 40 pact 30 mI %20 10 0 sa ab ) a ur mahaB Cu lizeeB coiR e co sa y iti ci ur aib Ha dor Fr( nai liza al Pe to m xie maan eli Ch roda aciR nt Br Ja M lv mol e eruP guaaraciN acia manir anay Su ondH Gu uguarU publeR Pa Sa uacE naa Th nac aleuzeneV El staoC gerA Co atemuG nii ed uiG h B. nce omD R. Fr Figures 1b and 1c show the impact of SLR on population. With a 1m SLR, the populations of Suriname, Guyana, French Guiana, and The Bahamas would be most severely impacted (as a percentage of national population): 7.0%, 6.3%, 5.4% and 4.5% respectively. These percentages increase rapidly, reaching 30% in Suriname and 25% in Guyana for a 3m SLR. Approximately half of the population of these countries would be impacted with a 5m SLR. In terms of economic activity (Figure 1d), the impact of a 1m SLR on Suriname, Guyana, and The Bahamas' GDP is expected to reach approximately 5%. With a 3m SLR, impacted GDP reaches 20% in Suriname, and approximately 15% in both Guyana and 12Note that Puerto Rico is officially a Territory, and not a country. 12 The Bahamas. Guyana would exhibit the largest percentage of urban extent impacted (Figure 1e). It reaches 10% with a 1m SLR, and increases to 22% and 38% with a 2m and 3m SLR. Figure 1b Latin America & Caribbean region: Exposed population (5m SLR) 13 Figure 1c Latin America & Caribbean: Population impacted 1 meter 2 meter 3 meter 4 meter 5 meter 60 n)oitla 50 puoPlatoT(t 40 30 20 Impac 10 % 0 e manir anay sa e coiR ya r ab ani a iti sa ru aal Gu Su mahaBe lizeB )rF( caai liza Cu nt licb ocix Br mbia Ha auga Pe ot Pa uianaG eruP ugurU uadocE man ilehC em Jam gerA Th aleuzeneVed pueR Me caiRa rodavl Sa nac looC stoC nduroH cariN El atuG ench B. Fr R. minioD Figure 1d Latin America & Caribbean: GDP impacted 1 meter 2 meter 3 meter 4 meter 5 meter 40 35 )PDG(tca 30 25 20 mpI 15 % 10 5 0 e manir sa r coiR do ab y a ua liz ac ina ur sa ia iti al Su mahaBe anay )rF( ezil Cu man Bra Pe ur mb Ha aug ilehC Gu naiauG Be ot caiRa ema Pa eruP uacE ugrU mai xicoe Ja gentrA M raac na Ni El stoC atuG Th hcne aleuzeneVed iclbupeR ondH looC rodavlaS B. nici Fr R. moD 14 Figure 1e Latin America & Caribbean: Urban extent impacted 1 meter 2 meter 3 meter 4 meter 5 meter 70 )t 60 en xtE 50 anbrU( 40 30 actp 20 mI %10 0 a e r y a iti a yanuG manir sa do ac ab aib cil ur sa Su mahaB )rF( ezil anai coiR ua ocix liza Pe caiR al Be ot uacE Ha ugrU man ai Cu Br ilehC Pa Jam Me mol rodavl e Gu eruP aleuzeneV anitneg Ar Co pubeR staoC nduroH guaaraciN Sa emtauG Th ch ed nac El renF B. nii R. omD The Bahamas' agricultural extent exhibits the highest impact (Figure 1f). It is of interest to note that while Argentina's area, population and GDP would not be significantly impacted by SLR, its agricultural extent would be significantly impacted. Finally, this analysis reveals that wetlands of the region would be severely impacted by SLR (Figure 1g). With a 1m SLR, approximately 30% of Jamaica's and Belize's wetlands would be impacted. With a 5m SLR, most of The Bahamas' and Belize's wetlands would be impacted, as well as more than half of Cuba's wetlands. 15 Figure 1f Latin America & Caribbean: Agricultural extent impacted 1 meter 2 meter 3 meter 4 meter 5 meter 45 40 )erut 35 ulci 30 grA(tc 25 20 pa 15 mI 10 % 5 0 sa e o co a y ) ci tii mahaB manir nai nt caia ezil anay sa ur m Be ubaC dor iab ala liza eli exi icR ur m man aciR roda Fr( Ha Pe M a lv Br Ch Su gerA Ja Gu augarac uacE otre Pa uguarU naa e Ni Pu aleuzeneV ondH looC stoC ublpeR atemuG Th ed SalE uiG h nac B. nce nii R. Fr omD Figure 1g Latin America & Caribbean: Wetlands impacted 1 meter 2 meter 3 meter 4 meter 5 meter 90 80 nd)alte 70 60 W(tc 50 40 pa 30 mI 20 % 10 0 sa mahaB ezil ab ca ya sar iti ci e a r a ur ai Ha gua )rF( ani ac iab liza do al Be Cu ocix Ri Pe coiR Me Jam ugurU anay nt m Br ilehC nduoH ublpeR manir man Gu Pa ats uacE rodavl em ot Su Sa at The nac raaciN anaiuG gerA Co looC aleuzeneV Gu nii ch ed El eruP omD enrF B. R. 16 (ii) Middle East and North Africa Table 5 reveals that, while the land area of the Middle East and North Africa region would be less impacted by SLR than the developing world generally (0.25% vs. 0.31% with a 1m SLR), all other indicators suggest more severe impacts of SLR in this region. In particular, with a 1m SLR, 3.2% of its population would be impacted (vs. 1.28% worldwide), 1.49% of its GDP (vs. 1.30% worldwide), 1.94% of its urban population (vs. 1.02% worldwide), and 3.32% of its wetlands (vs. 1.86% worldwide). Except for land area, the impacts of SLR are much more severe in this region than in Latin America and the Caribbean. Table 5 Impacts of sea level rise: Middle East and North Africa region 1m 2m 3m 4m 5m Area (Total = 10,050,556 sq. km.) Impacted area 24,654 33,864 43,727 53,615 63,120 % of total area 0.25 0.34 0.44 0.53 0.63 Population (Total = 259,396,000) Impacted population 8,307,472 10,912,744 13,684,993 16,454,655 19,439,678 % of total population 3.20 4.21 5.28 6.34 7.49 GDP (Total = 1,404,470 million USD) Impacted GDP (USD) 20,870 30,365 39,037 46,209 54,853 % of total GDP 1.49 2.16 2.78 3.29 3.91 Urban extent (Total = 190,030 sq. km.) Impacted area 3,679 5,037 6,529 7,951 9,384 % of total area 1.94 2.65 3.44 4.18 4.94 Agricultural extent (Total = 354,294 sq. km.) Impacted area 4,086 6,031 8,007 9,819 11,451 % of total area 1.15 1.70 2.26 2.77 3.23 Wetlands area (Total = 342,185 sq. km.) Impacted area 11,361 14,758 18,224 21,417 24,277 % of total area 3.32 4.31 5.33 6.26 7.09 When examined at the country level, only Qatar's land area would experience a significant impact (Figure 2a). 17 Figure 2a Middle East and North Africa region: Country area impacted 1 meter 2 meter 3 meter 4 meter 5 meter 14 12 ea)rA( 10 8 actp 6 mI 4 % 2 0 rat se ai na ar a Qa atir tiaw tpygE Liby aireg mE Ku Tunis R. narI.RI. ne Om haaS meY iabarA occor Al A. abrA edtinU hsinapSr of p.eR udiaS Mo me For The A.R. of Egypt's population would be most severely impacted by SLR (Figures 2b and 2c). With a 1m SLR, approximately 10% of the A.R. of Egypt's population would be impacted. Most of this impact takes place in the Nile Delta; it reaches 20% with a 5m SLR. Approximately 5% of the population of United Arab Emirates and Tunisia would be impacted by a 1m SLR. The A.R. of Egypt's GDP would also be significantly impacted by SLR (Figure 2d). This is partly explained by the impact of SLR on the A.R. of Egypt's agricultural extent. Indeed, most of the impact of SLR on the agricultural sector of the region would take place in the A.R. of Egypt which would experience a severe impact (Figure 2f). Even with a 1m SLR, approximately 12.5% of the A.R. of Egypt's agricultural extent would be impacted; this percentage reaches 35% with a 5m SLR. The A.R. of Egypt's agricultural sector may thus experience severe disruption as a result of SLR. 18 Figure 2b Middle East and North Africa region: Exposed population (5m SLR) 19 Figure 2c Middle East and North Africa region: Population impacted 1 meter 2 meter 3 meter 4 meter 5 meter on)ita 25 20 Popul 15 al Tot( 10 ct 5 pa mI 0 % t ypgE es rat ia atri ayb na Qa Li Om occor tiaw ne naIr. aireg R. A. mE isnuT iabarAidu Ku Mo abrA Sa meYfo.p I.R Al raahaS hsi Re edtinU anpS er mroF Figure 2d Middle East and North Africa: GDP impacted 1 meter 2 meter 3 meter 4 meter 5 meter 18 16 )PDG( 14 12 10 actp 8 mI 6 % 4 2 0 t ypgE rat ais s tiaw a by na ne ar Qa R. uniT ateri Li Ku Om occor naIr. A. mE Mo .RI ba meYfo.p aireg Al iabarAidu haaS Ar Sa Re edti hsinapSre Un Form The urban extent of the region would also be significantly impacted (Figure 2e). In the A.R. of Egypt, Libya, United Arab Emirates, and Tunisia, the impact reaches approximately 5% with a 1m SLR, 6 to 7% with a 2m SLR, and approximately 10% with a 5m SLR. The wetlands of Qatar, and to a lesser extent Kuwait, Libya, and United Arab Emirates would be significantly impacted by SLR (Figure 2g). 20 Figure 2e Middle East and North Africa: Urban extent impacted 1 meter 2 meter 3 meter 4 meter 5 meter 14 )tnetxE 12 10 nabrU(tcap 8 6 4 Im % 2 0 es atri tpygE a a by ais rat na Li Qa tiaw ne Om occor ar aireg mE R. Tuni biarA Ku meY Mo haaS narI.RI. Al barA A. udiaS of hs p.eR ni edtinU paSre m For Figure 2f Middle East and North Africa: Agricultural extent impacted 1 meter 2 meter 3 meter 4 meter 5 meter e)rutl 40 35 cuirgA( 30 25 20 15 actp 105 mI % 0 tpygE.R ais uniT aireg ayb na ne a ra s ara Li tiaw Al Om occor Mo naIr.RI. meY biarA Qat teari Ku ahS A. of mE p.eR udiaS ba shi Ar deti anpS er Un mroF 21 Figure 2g Middle East and North Africa: Wetlands impacted 1 meter 2 meter 3 meter 4 meter 5 meter 80 70 )d 60 anl et 50 W( 40 actp 30 mI 20 % 10 0 ra tiaw Qat setari a by aisi Li tpygE a Ku mE Tun biarA R. narI.RI. ar ne an haaS occo meY Om aireg Mor Al ba A. Ar udiaS hsin of deti paSre p.eR Un mr Fo (iii) Sub-Saharan Africa Of all regions, Sub-Saharan Africa has the least impact. As indicated in Table 6, less than Ľ of 1% of the region's GDP would be impacted by a 1m SLR, while its agricultural extent would generally remain free of any impact. Only a very small percentage of the region's area and agricultural extent would be impacted, even with a 5m SLR, and less than 1% of the population would be impacted with a 3m SLR. 22 Table 6 Impacts of sea level rise: Sub-Saharan Africa 1m 2m 3m 4m 5m Area (Total = 16,137,438 sq. km.) Impacted area 18,641 28,083 42,645 59,661 77,253 % of total area 0.12 0.17 0.26 0.37 0.48 Population (Total = 463,121,000) Impacted population 2,098,795 3,651,629 4,303,289 8,471,790 11,040,978 % of total population 0.45 0.79 0.93 1.83 2.38 GDP (Total = 963,974 million USD) Impacted GDP (USD) 2,228 4,368 7,143 10,897 13,722 % of total GDP 0.23 0.45 0.74 1.13 1.42 Urban extent (Total = 109,372 sq. km.) Impacted area 430 742 1,268 1,853 2,449 % of total area 0.39 0.68 1.16 1.69 2.24 Agricultural extent (Total = 4,236,159 sq. km.) Impacted area 1,646 3,404 6,595 11,231 16,145 % of total area 0.04 0.08 0.16 0.27 0.38 Wetlands area (Total = 805,030 sq. km.) Impacted area 8,902 13,551 20,625 29,078 37,864 % of total area 1.11 1.68 2.56 3.61 4.70 Within the region, the countries with greatest land area impact would be The Gambia and, to a lesser extent, Guinea-Bissau (reference Figure 3a). However, even in these two countries, the impact remains well below 4% with a 2m SLR, and it never raises above 10% except for The Gambia with a SLR of 4m or greater. Figure 3a Sub-Saharan Africa: Country area impacted 1 meter 2 meter 3 meter 4 meter 5 meter 16 14 12 ea)rA( 10 actp 8 mI 6 % 4 2 0 aib maG e Th uassiB-aeni l e ga go lia fo on que pei nin aeni ana aerit ituo ayn aibi alog nad ogn ogn neeS Le bi Be racs To Gu ga Gh Er bonaG aireg aireb tsaoC ma Ni Li Ke m An acirfA noore Su Co Co Gu arreiS ma ncirP ainatiru jibD eaniuG m da So Na R. oz M dna Ma Ma e omT alirotauqE yro htu Iv So licbupeR Ca D. oaS deitnU,ainaznaT 23 Both The Gambia and Mauritania would experience a significant population impact (Figures 3b and 3c), reaching approximately 8% in Mauritania with a 1m SLR. For most countries in the region, population impacted remains below 5% even with a 5m SLR. Mauritania's GDP would also experience the largest impact, reaching slightly below 10% with a 1m SLR (Figure 3d). Note that approximately 5% of Benin's GDP would also be impacted by a 1m SLR. Urban extent is most impacted in Mauritania (Figure 3e), while agricultural extent is most impacted in The Gambia, Guinea-Bissau, and Mauritania (Figure 3f). Approximately 15% of Benin's wetlands would be impacted by a 1m SLR (Figure 3g). When SLR reaches 5m, The Gambia's and Senegal's wetlands are those most affected. Figure 3b Sub-Saharan Africa: Exposed population (5m SLR) 24 Figure 3c Sub-Saharan Africa: Population impacted 1 meter 2 meter 3 meter 4 meter 5 meter 25 ) onital 20 opuPla 15 Tot(tcap 10 mI 5 % 0 aib maG e ianaitrua au n go ar fo ssiB arieb nin que aen bo aila pei ogn nad ayn To one ana aibi m alog aerti Li Be lagen tiuobi bi aireg tsaoC sca An Co oonre Su Er acirfA Ke ognoC Se Dj Gui Ga Ni Le Gh Nam ag So eaniuG M Th ea-niuG ma Moz arreiS yro ncirP R. Iv ad M nda e m To alriotauqE maC licbupeR htu So D. oaS deitnU, ianaznaT Figure 3d Sub-Saharan Africa: GDP impacted 1 meter 2 meter 3 meter 4 meter 5 meter 30 )P 25 20 (GDtcap 15 Im 10 % 5 0 ianaitrua aib nin maG ana no go ayn liaa a aibi Be ssaui lagen ueq eani bi astoC aireg aireb alog ituo nad ret ogn Gu Ni Gh ascar m Li An jibD noore To ogn m Gab Co Ke ainazn acifrA Su Eri Co e Se yr enoeL ag moS eaniuG Na peicnirP M Th ea-Bni ma R. voI arre Ta ad Ca Moz Si M aliro uthoS D. dna Gu atuqE e omT oaS 25 Figure 3e Sub-Saharan Africa: Urban extent impacted 1 meter 2 meter 3 meter 4 meter 5 meter 40 35 30 )t en xtE 25 anbrU( 20 actp 15 mI % 10 5 0 ianaitrua aib air maG be lagen i eani e n an fo ssau Be ueqib ogoT iala m astoC scara bo alog pei aireg aerit aibi ogn m acirfA nad ayn Li Se Gu Bi outbijD nin oneL So yr Ga ag An Ni Er Gha eaniuG ngooC Co noore Su Ke Na m h M The ea-niuG ma Moz rraeiS ncirP voI ad Ca M dna alir iclbupeR R..D utoS e m To atouqE oaS deitnU,ainaznaT Figure 3f Sub-Saharan Africa: Agricultural extent impacted 1 meter 2 meter 3 meter 4 meter 5 meter 14 )erut 12 ulci 10 grA( 8 ct 6 pa 4 mI % 2 0 u l e lia ra go ai n mbiaaGe saisB- of itu aniait nin sc aeni nob ana ayn alog ogn ogn da aibi ura negaeS Ca Th ineauG M oneLarre euqib Be To arieg noore acifrA m aerti maz maoS ga Gu Ga Gh eaniuG Ke Ni An Liber m Co Co Su Er daa Na jiboD uth R. inciperP Si Mo M aliro astoCyrovI publiceR So D. atuqE detinU,aina dna Tome oaS Tanz 26 Figure 3g Sub-Saharan Africa: Wetlands impacted 1 meter 2 meter 3 meter 4 meter 5 meter 50 45 )d 40 anl 35 et 30 W( 25 actp 20 mI 15 %10 5 0 aib l ga nin m aeni e t air no i a n fo on ana aerti ayn liaa aibi Ga neeS Be Le sausi asoC be racs Togo aireg m Gu Li ga Gh euqib Gab Er acirfA Ni Ke ngolA oonre ongoC udaS e Th arreiS eaniuG So boutijD ea-BniuG ianaitrua M yrovI daa maz Nam M Mo alriotauqE htu maC epicnirP So licbupeR ongoC.R.D deitnU, dna e m To ianaznaT oaS (iv) East Asia East Asia would be significantly impacted by SLR. At 5m SLR, East Asia is the most severely impacted region in the developing world. For 1m to 5m SLR, the impacted population is 2% to 8.6%, while impacted GDP is 2.09% to 10.2%. Urban extent and wetlands are also significantly impacted by SLR (Table 7). 27 Table 7 Impacts of sea level rise: East Asia 1m 2m 3m 4m 5m Area (Total = 14,140,767 sq. km.) Impacted area 74,020 119,370 178,177 248,970 325,089 % of total area 0.52 0.84 1.26 1.76 2.30 Population (Total = 1,883,407,000) Impacted population 37,193,866 60,155,640 90,003,580 126,207,275 162,445,397 % of total population 1.97 3.19 4.78 6.70 8.63 GDP (Total = 7,577,206 million USD) Impacted GDP (USD) 158,399 255,510 394,081 592,598 772,904 % of total GDP 2.09 3.37 5.20 7.82 10.20 Urban extent (Total = 388,054 sq. km.) Impacted area 6,648 11,127 17,596 25,725 34,896 % of total area 1.71 2.87 4.53 6.63 8.99 Agricultural extent (Total = 5,472,581 sq. km.) Impacted area 45,393 78,347 121,728 174,076 229,185 % of total area 0.83 1.43 2.22 3.18 4.19 Wetlands area (Total = 1,366,069 sq. km.) Impacted area 36,463 56,579 79,984 110,671 130,780 % of total area 2.67 4.14 5.86 8.10 9.57 As shown in Figure 4a, Vietnam is the most seriously impacted by SLR: Up to 16% of its area would be impacted by a 5m SLR, making it second only to The Bahamas among countries analyzed for this paper. Most of this impact is in the Mekong and Red River Deltas. Note in Figure 4b that most of Vietnam's land area southwest of Ho Chi Minh City would be severely impacted by SLR. Large percentages of Vietnam's population and economic activity are located in these two river deltas. As shown in Figures 4c and 4d, 10.8% of Vietnam's population would be impacted by a 1m SLR. This is the largest percentage of impacted population among all 84 countries (A.R. of Egypt follows with 10.56%). Vietnam's impacted population would reach 35% with a 5m SLR. The impacts of SLR on Vietnam's GDP (Figure 4e) and urban extent (Figure 4f) closely follow the impact on its population. 28 Figure 4a East Asia: Country area impacted 1 meter 2 meter 3 meter 4 meter 5 meter 18 16 14 )aer 12 (Atcap 10 8 Im % 6 4 2 0 mante a inhC ra ais m ndali one aidob s ai ne aer aer ays ienu ani Ko weN al Br Ch aeni Vi na yan M ndI Tha m ppiil Ca iwaT hiP Kofo.p Re R..P.D M aup Gu Pa 29 Figure 4b Inundation zone: Vietnam 30 Figure 4c East Asia: Exposed population (5m SLR) Figure 4d East Asia: Population impacted ) 1 meter 2 meter 3 meter 4 meter 5 meter onit 45 la 40 puoPla 35 30 25 otT(tc 20 15 10 pa 5 Im 0 % mante dnali ra m nawi ani a se ani a htr h i Ch odib inp aise Ch ysia aer aer weN ae Vi Ta No Ko outS Ko Tha Myan maC unerB lipihP donnI Mal aup in Gu Pa 31 Thailand's GDP would also experience a significant impact. However, as shown in Figure 4e, this impact would be significant only with a 4m or 5m SLR. Among the other indicators, Vietnam's agricultural extent would be the most severely impacted in East Asia (Figure 4g). Note that Myanmar's agricultural extent, as well as wetlands (Figure 4h) would also be significantly impacted. Most of Vietnam's wetlands would also be impacted by SLR. Figure 4e East Asia: GDP impacted 1 meter 2 meter 3 meter 4 meter 5 meter 40 35 )PD 30 25 (Gtcap 20 15 Im % 10 5 0 mante dnalia ar ani m Ch aidob aise nawi . ani a se i ysia inp Ch P.R.D aer fo. aer Ko Ko unerB weN aeni Vi Th yan M Cam donnI Ta al Rep M lipi aup Gu Ph Pa Figure 4f East Asia: Urban extent impacted 1 meter 2 meter 3 meter 4 meter 5 meter 45 40 )t en 35 xtE 30 anbrU( 25 20 actp 15 mI 10 % 5 0 mant ndali ais a s ai inhC ne ra aer ani a ays ienu ae Ko Ch bodi Br orK weN aeni Vie Tha ndoneI na ppiil al mna iwaT hiP M My R..P maC of D. p.eR puaaP Gu 32 Figure 4g East Asia: Agricultural extent impacted 1 meter 2 meter 3 meter 4 meter 5 meter 25 )erutluicrg 20 15 (Atcap 10 Im % 5 0 mant ra ais ani aer of Vie mna ne aidob nawi ani Ch ndalia Ch aisyal R..P D. Ko senip aer ienu weN Ko Br aen My donI m Ta p.eR Th Ma ilip Ca aup Gui Ph Pa Figure 4h East Asia: Wetlands impacted 1 meter 2 meter 3 meter 4 meter 5 meter 100 90 )dnlate 80 70 60 W(tcap 50 40 Im 30 % 20 10 0 mante ra s ndali of ne aer a ais ai ani i Vi mna nawi ani Ch bodi ays Ch weN Ta ppiil p.eR aer Ko al unerB aeni My Tha hiP Ko.R.P maC ndoneI M D. puaaP Gu 33 (v) South Asia For a 1m SLR, the impacts of SLR in South Asia resemble those noted in Sub-Saharan Africa, albeit slightly higher. Except for land area, the impacts of SLR in South Asia are smaller than worldwide impacts (Table 8). Table 8 Impacts of sea level rise: South Asia 1m 2m 3m 4m 5m Area (Total = 4,197,171 sq. km.) Impacted area 12,362 21,983 35,696 52,207 69,225 % of total area 0.29 0.52 0.85 1.24 1.65 Population (Total = 1,306,556,000) Impacted population 5,870,472 10,187,694 17,810,069 22,065,103 39,505,521 % of total population 0.45 0.78 1.36 1.69 3.02 GDP (Total = 3,295,567 million USD) Impacted GDP (USD) 18,021 30,957 52,036 72,462 94,020 % of total GDP 0.55 0.94 1.58 2.20 2.85 Urban extent (Total = 241,779 sq. km.) Impacted area 809 1,379 2,311 3,599 5,117 % of total area 0.33 0.57 0.96 1.49 2.12 Agricultural extent (Total = 3,023,617 sq. km.) Impacted area 3,442 6,951 13,501 23,716 35,190 % of total area 0.11 0.23 0.45 0.78 1.16 Wetlands area (Total = 579,130 sq. km.) Impacted area 9,184 16,685 25,988 36,109 46,003 % of total area 1.59 2.88 4.49 6.24 7.94 Within South Asia, Bangladesh would experience the largest percentage share of land area impacted (Figure 5a). However, this impact exceeds 5% only when SLR is in excess of 3m. With a 1m SLR, the populations of Bangladesh and Sri Lanka experience similar percentage impacts (Figures 5b and 5c). However, as the sea level rises, the impact on Bangladesh's population becomes more important. A similar situation prevails for GDP (Figure 5d). 34 Figure 5a South Asia: Country area impacted 1 meter 2 meter 3 meter 4 meter 5 meter 12 a)erA( 10 8 ctap 6 mI 4 % 2 0 Bangladesh Sri Lanka Pakistan India Sri Lanka's urban extent would experience a slightly larger impact than Bangladesh's (Figure 5e), while Bangladesh's agricultural extent would experience a much larger impact than any other South Asian country's (Figure 5f). Insofar as wetlands are concerned, Pakistan would experience the largest impact in South Asia (Figure 5g). 35 Figure 5b South Asia: Exposed population (5m SLR) 36 Figure 5c South Asia: Population impacted 1 meter 2 meter 3 meter 4 meter 5 meter 10 9 n)oita 8 7 pul Pola 6 5 Tot(tc 4 pa 3 mI 2 % 1 0 Bangladesh Sri Lanka India Pakistan Figure 5d South Asia: GDP impacted 1 meter 2 meter 3 meter 4 meter 5 meter 10 9 8 )PDG(tcap 7 6 5 4 Im % 3 2 1 0 Bangladesh Sri Lanka India Pakistan 37 Figure 5e South Asia: Urban extent impacted 1 meter 2 meter 3 meter 4 meter 5 meter 12 10 )tnetxE 8 nabru(tcap 6 4 Im % 2 0 Bangladesh Sri Lanka India Pakistan Figure 5f South Asia: Agricultural extent impacted 1 meter 2 meter 3 meter 4 meter 5 meter 12 )e 10 urt ulci 8 grA(tc 6 pa 4 mI % 2 0 Bangladesh Sri Lanka Pakistan India 38 Figure 5g South Asia: Wetlands impacted 1 meter 2 meter 3 meter 4 meter 5 meter 30 25 )dnlatew(tcap 20 15 Im 10 % 5 0 Pakistan Sri Lanka Bangladesh India III.3 Summary of results (i) World and regional results As shown in Table 9, East Asia and the Middle East and North Africa would experience the largest percentage impacts from SLR. Population impact is larger in the latter for a 1m SLR, but larger in East Asia for a 5m SLR. Similar results hold for the impacts on urban extent, agricultural extent, and wetlands. Impact on GDP is much larger in East Asia than in any other region of the world, reaching 10.2% with a 5m SLR. 39 Table 9 Summary of world and regional impacts World LA MENA SSA EA SA Indicators 1m SLR Area 0.31 0.34 0.25 0.12 0.52 0.29 Population 1.28 0.57 3.20 0.45 1.97 0.45 GDP 1.30 0.54 1.49 0.23 2.09 0.55 Urban extent 1.02 0.61 1.94 0.39 1.71 0.33 Ag. extent 0.39 0.33 1.15 0.04 0.83 0.11 Wetlands 1.86 1.35 3.32 1.11 2.67 1.59 5m SLR Area 1.21 1.24 0.63 0.48 2.30 1.65 Population 5.57 2.69 7.49 2.38 8.63 3.02 GDP 6.05 2.38 3.91 1.42 10.2 2.85 Urban extent 4.68 3.03 4.94 2.24 8.99 2.72 Ag. extent 2.10 1.76 3.23 0.38 4.19 1.16 Wetlands 7.30 6.57 7.09 4.70 9.57 7.94 LA: Latin America and Caribbean; MENA: Middle East and North Africa; SSA: Sub-Saharan Africa; EA: East Asia; SA: South Asia. (ii) Most impacted countries Figures 6a to 6f summarize our results for each indicator by presenting the top 10 impacted countries (as a percentage of their national values). For this purpose, we use the 1m SLR scenario. For land area (Figure 6a), The Bahamas is by far the most impacted country, with close to 12% of its area affected. Around 10% of Vietnam's and the A.R. of Egypt's populations would be impacted with a 1m SLR (Figure 6b). Vietnam would also see 10% of its GDP (Figure 6c) and urban extent (Figure 6d) impacted. Mauritania's GDP would be significantly impacted. The A.R. of Egypt's agricultural extent would experience the largest percentage impact, reaching approximately 13%. Finally, nearly 28% of Vietnam, Jamaica and Belize's wetlands would be impacted by a 1m SLR. Among all of the indicators used in this paper, Vietnam ranks among the top 5 most impacted countries, with the A.R. of Egypt, Suriname and The Bahamas consistently ranking among the highest. 40 Figure 6a Most impacted countries: Land area 14 12 10 ed actp mi 8 ea ar 6 d anl % 4 2 0 mas tar lize Rico ba bia ica Baha Vietnam Qa Be ngladesh The Puerto Cu TaiwanChina Gam Jama The Ba Figure 6b Most impacted countries: Population 12 10 detcap 8 mi onital 6 opup 4 % 2 0 Vietnam ypt (Fr) n Eg yana mas Beni A.R. Mauritania Suriname Gu Tunisia Baha French Guiana UnitedArabEmiratesThe 41 Figure 6c Most impacted countries: GDP 12 10 ed 8 actp mi 6 PDG % 4 2 0 VietnamMauritaniaA.R. Egypt e Surinam Benin yana (Fr) Gu Tunisia Ecuador TheBahamas FrenchGuiana Figure 6d Most impacted countries: Urban extent 12 10 detcap 8 mi aera 6 n 4 urba % 2 0 Vietnam Guyana (Fr)Mauritania ypt as A.R. Eg Libya Emirates Tunisia Suriname Baham French Guiana Arab e Th ited Un 42 Figure 6e Most impacted countries: Agriculture extent 14 12 10 ed actp 8 mi ea arr 6 ag % 4 2 0 A.R. Egypt Vietnam riname as r ham xico China Me anma Su Jamaica My Guyana TheBa Argentina Taiwan Figure 6f Most impacted countries: Wetlands 35 30 detc 25 pa mi 20 ndsaltew 15 % 10 5 0 Vietnam Jamaica lize ar as nin ina Be Qat Libya Uruguay xico Ch Me Be TheBaham Taiwan 43 IV. Conclusions Even if greenhouse gas (GHG) emissions were stabilized in the near future, thermal expansion and deglaciation would continue to raise the sea level for many decades. Continued growth of GHG emissions and associated global warming could well promote SLR of 1m-3m in this century, and unexpectedly rapid breakup of the Greenland and West Antarctic ice sheets might produce a 5m SLR. In this paper, we have assessed the consequences of continued SLR for 84 developing countries. Our results are extremely skewed, with severe impacts limited to a relatively small number of countries. For these countries (e.g., Vietnam, A.R. of Egypt, The Bahamas), however, the consequences of SLR are potentially catastrophic. For many others, including some of the largest (e.g., China), the absolute magnitudes of potential impacts are very large. At the other extreme, many developing countries experience limited impacts. Among regions, East Asia and Middle East/North Africa exhibit the greatest relative impacts. In this conclusion, we would like to highlight two important implications of our findings. First, the overall magnitudes for the developing world are sobering: Within this century, hundreds of millions of people are likely to be displaced by SLR; accompanying economic and ecological damage will be severe for many. The world has not previously faced a crisis on this scale, and planning for adaptation should begin immediately. Second, international resource allocation strategies should recognize the skewed impact distribution that we have documented in this paper. Some countries will be little-affected by SLR, while others will be so heavily impacted that their national integrity may be threatened. Given the scarcity of available resources, it would seem sensible to allocate aid according to degree of threat. Under the provisions of the United Nations Framework Convention on Climate Change (UNFCCC), some work has begun on National Adaptation Programmes of Action (NAPAs). These are intended to facilitate the identification of priority activities, including adaptation to SLR, for the least-developed countries. To date however, only 8 countries have developed comprehensive NAPAs: Bangladesh, Bhutan, Comoros, Djibouti, Malawi, Mauritania, Niger and Samoa. Of these, five are coastal states with potential SLR problems. For comparison, our summary above reveals 10 poor countries that will be very severely impacted by SLR. With the exceptions of Bangladesh and Mauritania, 44 none are included in the NAPA list above: Benin, Guyana, Suriname, A.R. of Egypt, The Gambia, Guinea-Bissau, Mauritania, Vietnam, Bangladesh and Sri Lanka. Many other developing countries will also experience significant impacts. We should reiterate that these results are not speculative: The current atmospheric concentration of GHG's is sufficient to drive global warming well into the next century, and much higher concentrations will undoubtedly be reached before any global agreement can be implemented. For precautionary planning purposes, SLR in the range of 1m ­ 3m should therefore be regarded as realistic. To date, however, there is little evidence that the international community has seriously considered the implications for population location and infrastructure planning in many developing countries. 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Yim, W., 1995: Implications of sea-level rise for Victoria Harbour, Hong Kong, Journal of Coastal Research, 14, 167-189. Zeidler, R.B., 1997: Climate change variability and response strategies for the coastal zones of Poland, Climate Change, 36, 151-173. Zuo, Z., and J. Oerlemans, 1997: Contribution of glacier melt to sea level rise since AD 1865: a regionally differentiated calculation. Climate Dynamics, 13: 835-845. 48 Appendix 1: Data description Dataset: Coastline and Country Boundary Source: The National Geospatial-Intelligence Agency (formerly Defense Mapping Agency) URL: http://rimmer.ngdc.noaa.gov/mgg/coast/getcoast.html Description: The World Vector Shoreline dataset is a digital data file at a nominal scale of 1:250,000, containing worldwide coverage of shorelines and international boundaries. The World Vector Shoreline is a standard National Geospatial-Intelligence Agency (formerly Defense Mapping Agency) product. The Pacific Disaster Center performed significant editing on the original dataset to correct numerous topology errors. The international boundaries were also removed, resulting in a dataset of world shorelines and country boundaries. The main source material for the WVS was the DMA's Digital Landmass Blanking (DLMB) data which was derived primarily from the Joint Operations Graphics and coastal nautical charts produced by DMA. The DLMB data consists of a land/water flag file on a 3 by 3 arc-second interval grid. This raster data set was converted into vector form to create the WVS. For areas of the world not covered by the DLMB data (e.g. the Arctic and Antarctic), the shoreline was taken from the best available hard copy sources at a preferred scale of 1:250000. The WVS data are stored in chain-node format. Pacific Disaster Center processing: Data were downloaded in an ArcINFO Generate format from the NOAA/NGDC Coastline Extractor in 37 sections. The files were converted to ArcINFO line coverages using the GENERATE command and were appended to make a world-wide coverage. The data were re-projected from Geographic to Mercator with a central meridian of 150 degrees and the WGS 1984 Datum. Significant editing was performed on the original dataset to correct numerous topology errors. The line coverage was cleaned with a fuzzy tolerance of 30 meters and a dangle tolerance of 100 meters. Where dangling nodes occurred, line segments were connected in order to build polygons. The lines were connected only to fix topology errors and may not be geographically correct. The international boundaries were also removed, resulting in a dataset of world shorelines and country boundaries Dataset: SRTM 90m DEM data Version 2 Source: International Centre for Tropical Agriculture (CIAT) URL: http://gisweb.ciat.cgiar.org/sig/90m_data_tropics.htm Description: The data are in ARC GRID format, in decimal degrees and datum WGS84. They are derived from the USGS/NASA SRTM data. CIAT have processed this data to provide seamless continuous topography surfaces. Areas with regions of no data in the original SRTM data have been filled in using interpolation methods. Dataset: GDP Source: DECRG of the World Bank based on Sachs et al. (2001) Description: GDP grid dataset was initially produced by DECRG for the World Bank/Columbia University Global Natural Disaster Hotspots Project. The total level of economic activity at the national level is measured by the Gross Domestic Product (GDP), the annual market value of final goods and services produced by a country. For about 50 countries, more than half developing or transitional economies (including Bangladesh, Brazil, China, India, Indonesia, and Mexico), GDP data are available for sub-national units. Following Sachs et al. (2001), DECRG applied these sub-national 49 estimates to population density, using the World Bank estimates of GDP based on Purchasing Power Parity (PPP) for 2000. Dataset: Population Source: Center for International Earth Science Information Network (CIESIN) URL: http://sedac.ciesin.columbia.edu/gpw Description: An estimate of population for the year 2000, developed as part of GPW version 3, is used to characterize the "current" distribution of population. Although population distribution is likely to change in the future due to differential rates of population change, including urban and coastal migration and different fertility and mortality rates, at present we have little basis for projecting these changes into the future (Gaffin et al., 2004). Dataset: Urban extent (GRUMP Version 1) Source: Center for International Earth Science Information Network (CIESIN), Columbia University; International Food Policy Research Institute (IPFRI); the World Bank; and Centro Internacional de Agricultura Tropical (CIAT). URL: http://sedac.ciesin.columbia.edu/gpw/ Description: The data is the alpha release of version 1 of the GRUMP product. The data are stored in geographic coordinates of decimal degrees based on the World Geodetic System spheroid of 1984 (WGS84), 30 arc-second (1-km) resolution. The cell value is integer, where 1 = rural and 2 = urban. Large water bodies have been masked and thus appear as "no data". Dataset: Agriculture extent (PAGE Global Agricultural Extent version 2) Source: the International Food Policy Research Institute URL: www.ifpri.org Description: PAGE Global Agricultural Extent version 2 (1km resolution) was created based on version 2 of the Global Land Cover Characteristics Dataset (GLCCD v2.0, USGS/EDC 2000). The methods used to create this dataset were the same as those employed to create the original PAGE Global Agricultural Extent. This dataset identifies approximately 200 seasonal land cover regions (SLCRs) per continent (e.g., 167 for South America and 205 for North America) based on the interpretation of a series of satellite images captured every 10 days over the period April 1992 to March 1993. Table 3 Cell codes for Cell Codes Label 10 Cropland 11 Plantations 12 Pasture 13 Cropland / Pasture 14 Agriculture with forest 15 Agriculture with other vegetation 20 Agriculture / Forest mosaic 21 Agriculture / Other mosaic 30 Forest with agriculture 31 Other vegetation with agriculture 40 Agriculture / 2 other land cover types 41 Primarily Forest (>60%) 41 Primarily Grassland (>60%) 50 50 Other vegetation: wetlands, mangroves 60 Non-vegetated / Sparsely vegetated 70 In-land water 80 Ocean 100 Islands Dataset: Wetlands (GLWD-3) Source: Lehner, B. and Döll, P. (2004): Development and validation of a global database of lakes, reservoirs and wetlands. Journal of Hydrology, 296/1-4, p.1- 22. URL: http://www.wwfus.org/science/data.cfm Description: The GLWD-3 dataset comprises lakes, reservoirs, rivers, and different wetland types (Table 4) in the form of a global raster map at 30-sec resolution. It was produced by the Center for Environmental Systems Research (CESR), the University of Kassel, Germany, and the World Wildlife Fund, Washington DC, USA (Lehner and Döll 2004), based on various data sources, mostly 1970s to 1990s. The GLWD-3 dataset may serve as an estimate of wetland extents for global hydrology and climatology models, or to identify large-scale wetland distributions and important wetland complexes. It is supplemented by GLWD-1 and GLWD-2. Table 4 Cell codes for GLWD-3 dataset. Cell Value Lake or Wetland Type 1 Lake 2 Reservoir 3 River 4 Freshwater march, floodplain 5 Swamp forest, flooded forest 6 Coastal wetland (incl. mangrove, estuary, delta, lagoon) 7 Pan, brackish/saline wetland 8 Bog, fen, mire (peatland) 9 Intermittent wetland/lake 10 50-100% wetland 11 25-50% wetland 12 Wetland complex (0-25% wetland) 51