d i s c u s s i o n pa p e r n u m B e r 6 august 2010 d e v e l o p m e n t a n d c l i m at e c h a n g e d i s c u s s i o n pa p e r s 1 56664 d e v e l o p m e n t a n d c l i m a t e c h a n g e Costs of Adaptation Related to Industrial and Municipal Water Supply and Riverine Flood Protection D I S C u S S I O N PA P E r N u M B E r 6 AuGuST 2010 D E V E L O P M E N T A N D C L I M A T E C H A N G E Costs of Adaptation Related to Industrial and Municipal Water Supply and Riverine Flood Protection Philip J. Ward, Pieter Pauw, Luke M. Brander, Jeroen, C.J.H. Aerts Institute for Environmental Studies (IVM), VU University Amsterdam The Netherlands, philip.ward@ivm.vu.nl and Kenneth M. Strzepek Civil, Environmental & Architectural Engineering Faculty, University of Colorado at Boulder, USA, strzepek@spot.colorado.edu Papers in this series are not formal publications of the World Bank. They are circulated to encourage thought and discussion. 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All dollars are U.S. dollars unless otherwise indicated. III Table of ConTenTs 1. Introduction 1 1.1 Main Aims of the Consultancy 1 1.2 Structure of the Paper 1 2. Context 3 2.1 What are the Potential Impacts of Climate Change on the Sector? 3 2.1.1 Physical effects of climate change on the water cycle 3 2.1.2 Impacts of climate change and non-climate drivers on water supply and flooding 4 2.2 Who (across and within Countries) is likely to be Most Affected? 7 2.3 Uncertainties in Climate Change Impacts 9 2.4 What Experience is there with Adaptation in the Sector? 10 2.4.1 Autonomous adaptation 11 2.4.2 Public Sector investment 15 2.4.3 "Soft" adaptation ­ policies and regulations 17 2.4.4 Reactive adaptation 19 2.5 What is the Nature and Extent of Adaptation/Development Deficit in this Sector? 19 2.6 Adaptive Capacity 20 2.7 Summary 21 3. Literature review 22 3.1 Previous Studies on the Costs of Climate Change 22 3.2 Previous Studies on the Costs of Adaptation to Climate Change 23 3.3 How our Study Complements Existing Work 24 4. Methodology 26 4.1 Geographical and Temporal Scale 26 4.2 Scenarios 27 4.3 Rainfall­Runoff Simulations 27 4.4 Industrial and Municipal Water Supply Methodology 28 4.5 Riverine Flood Protection 32 5. results and discussion 34 5.1 Costs of Climate-Change-Related Adaptation 34 5.1.1 Water supply 34 IV A D A PTATION rELATED TO INDu ST rIAL AND MuNICIPAL WATE r Su PPLy AND rIVE r INE FLOOD PrOTE C T I O N 5.1.2 Riverine flood protection 39 5.1.3 Total costs of water resources adaptation 40 6. Conclusions, Limitations, and recommendations 43 6.1 Main Conclusions 43 6.2 Limitations and Recommendations 44 7. references 47 8. Appendixes 59 Appendix 1. Water Supply Results. GCM: CSIRO. Discount Rate: 0% 60 Appendix 2. Water Supply Results. GCM: CSIRO. Discount Rate: 3% 62 Appendix 3. Water Supply Results. GCM: CSIRO. Discount Rate: 5% 64 Appendix 4. Water Supply Results. GCM: CSIRO. Discount Rate: 7% 66 Appendix 5. Water Supply Results. GCM: NCAR. Discount Rate: 0% 68 Appendix 6. Water Supply Results. GCM: NCAR. Discount Rate: 3% 70 Appendix 7. Water Supply Results. GCM: NCAR. Discount Rate: 5% 72 Appendix 8. Water Supply Results. GCM: NCAR. Discount Rate: 7% 74 Appendix 9. Riverine Flood Protection Results. GCM: CSIRO. Discount Rate: 0% 76 Appendix 10. Riverine Flood Protection Results. GCM: CSIRO. Discount Rate: 3% 78 Appendix 11. Riverine Flood Protection Results. GCM: CSIRO. Discount Rate: 5% 80 Appendix 12. Riverine Flood Protection Results. GCM: CSIRO. Discount Rate: 7% 82 Appendix 13. Riverine Flood Protection Results. GCM: NCAR. Discount Rate: 0% 84 Appendix 14. Riverine Flood Protection Results. GCM: NCAR. Discount Rate: 3% 86 Appendix 15. Riverine Flood Protection Results. GCM: NCAR. Discount Rate: 5% 88 Appendix 16. Riverine Flood Protection Results. GCM: NCAR. Discount Rate: 7% 90 TABLes 4.1 Construction costs of reservoir storage capacity per cubic meter in the united States (In 2005 u.S. Dollars) 30 4.2 Storage-cost relations between mean FPu slope, and average reservoir storage costs per cubic meter. The cost in 2005 u.S. Dollars (y) is a function of the mean FPu slope in degrees (X). 31 5.1 Average annual costs over the period 2010­50 of adaptation in the industrial and municipal water supply sector, based on the best estimate (above), small dams (middle), and large dams (below) scenarios for reservoir construction 35 5.2 Total increase in reservoir storage capacity (cubic kilometers) between present and 2050 under the baseline & CC scenario for CSIrO and NCAr GCMS (best estimate) 38 5.3 Average annual costs of adaptation in terms of riverine flood protection over the period 2010­50 39 5.4 Average annual water resources adaptation costs over the period 2010­50 41 5.5 Average annual adaptation costs in the water resources sector as a percentage of world GDP in 2007 42 D E V E L O P M E N T A N D C L I M AT E C H A N G E D I S C u S S I O N PA P E r S V Figures 2.1 Projected Changes (percent) in municipal and industrial water demand per Food Producing unit (FPu), 2005 and 2030 (above), and 2005 and 2050 (below), due to non-climatic drivers according to the socioeconomic scenario used throughout the EACC study. 6 4.1 Map showing the FPus (Food Producing units) used as the basic geographical unit of study in this project (as developed by IFPrI and IWMI) 27 4.2 Example of a typical storage-yield curve for a hypothetical basin 29 4.3 Location of the reservoirs in our data base of reservoir construction costs and storage capacity 32 4.4 reservoir storage of industrial and municipal water supply reservoirs in each of the reservoir size classes shown in Table 4.1, Expressed as a percentage of the total reservoir storage of industrial and municipal water supply reservoirs 33 5.1 Cumulative costs (2005 u.S. Dollars) of climate-change-related adaptation in the water supply sector in developing countries (DCS) and non-developing countries (non-DCS) for (A) CSIrO, and (B) NCAr 36 5.2 Cumulative costs (2005 u.S. Dollars) of climate-change-related adaptation in the water supply sector in the world bank regions for (A) CSIrO (net), (B) CSIrO (gross), (C) NCAr (net), and (D) NCAr (gross) 37 5.3 Annual costs (2005 u.S. Dollars) of adaptation in the water supply sector per 5-year period for the baseline (solid lines) and baseline & CC (dotted lines) scenarios for (A) CSIrO, and (B) NCAr 37 5.4 Cumulative costs (2005 u.S. Dollars) of climate-change-related adaptation for riverine flood protection for (A) CSIrO and (B) NCAr 40 5.5 Cumulative costs (2005 u.S. Dollars) of climate-change-related adaptation for riverine flood protection in the world bank regions for (A) CSIrO (net), (B) CSIrO (gross), (C) NCAr (net), and (D) NCAr (gross) 41 BOXes 2.1 Impacts of climate change on water quality 4 2.2 Climate vulnerability index 9 2.3 uncertainty in flood return period estimates 10 2.4 Costs of several adaptation measures in water supply and demand 12 2.5 rainwater harvesting and local watershed management ­ the case of Laporiya, India 14 2.6 Local knowledge and ownership in water storage: The Kitui Sand Dams in Kenya 14 2.7 Integrated water resources management 18 5.1 Pros and cons of water supply through reservoir storage 38 1 1. InTroduCTIon iii. Estimate climate change adaptation costs in the industrial and municipal water supply sector; iv. Estimate climate change adaptation costs for river- This background paper describes the work carried out ine flood protection. on one component of a larger World Bank study enti- tled The Economics of Adaptation to Climate Change The potential effects of climate change on the hydro- (EACC), whose aim is to estimate the costs of adapt- logical cycle are also expected to lead to changes in ing to climate change in developing countries over the other water-related sectors, such as health (Confalonieri period 2010­50. The overall objective of the EACC et al. 2007; Kabat et al. 2003); agriculture (Bates et al. study is to help decision makers in developing coun- 2008; Easterling et al. 2007; Kabat et al. 2003); industry, tries to better understand and assess the risks posed by transport, and energy supply (Wilbanks et al. 2007); climate change and to better design strategies to adapt ecosystem services (Fischlin et al. 2007); fisheries to climate change. The study is further intended to (Easterling et al. 2007; FAO 2009); and forestry inform the international community's efforts, including (Easterling et al. 2007). The impacts of climate change UNFCCC and the Bali Action Plan, to provide access on these sectors are investigated separately in other to adequate, predictable, and sustainable support, and contributions to the EACC project, and are not to provide new and additional resources to help the discussed further here. Importantly, while agricultural most vulnerable developing countries meet adaptation irrigation withdrawals account for almost 70 percent of costs. global water withdrawals, and 90 percent of global consumptive water use (Shiklomanov and Rodda 2003), 1.1 MaI n aIMs of T he ConsulTanC y irrigation is considered in the EACC study as part of agricultural sector work. Hence, in this paper we assume Within the framework of the EACC study, the World no changes in agricultural water demand in order to Bank has commissioned this research to examine the avoid double-counting. It should also be noted that costs of adaptation to climate change in developing coastal and estuarine water resource issues are not countries for (a) industrial and municipal raw water considered here, since they too are addressed elsewhere supply, and (b) riverine flood protection. in the EACC project. This paper provides the main findings of this work, and All costs in this report are given in 2005 U.S. dollars, addresses the following research aims of the unless otherwise stated. consultancy: 1 . 2 s T r uC Tu r e o f Th e pa p e r i. Develop a data base of adaptation policies, pro- grams, and projects that can be used in the water In Section 2, the background context of the study is supply sector; developed, outlining the potential impacts of climate ii. Develop a data base of adaptation policies, pro- change on water supply and riverine flooding, which grams, and projects that can be used for riverine regions and population groups are likely to be most flood protection; severely affected by climate change, and what 2 A D A PTATION rELATED TO INDu ST rIAL AND MuNICIPAL WATE r Su PPLy AND rIVE r INE FLOOD PrOTE C T I O N experience exists in the sectors in terms of adaptation. provided in section 3. In section 4, we present the We also provide information on, and examples of, methods used to address aims iii and iv of the consul- adaptation policies, programs, and projects that can be tancy; that is, estimating the costs of adaptation to used to adapt to climate change in terms of water climate change in terms of industrial and municipal supply and flood protection, thereby addressing aims water supply and riverine flood protection. The results i and ii. of the quantitative assessment are presented and discussed in section 5. Finally, conclusions, limitations, A summary of previous research on economic aspects of and recommendations of the study are presented in climate-change-related adaptation in the water sector is section 6. 3 2. ConTexT negative trends are observed over western Africa and the Sahel. Over the extra-tropical land masses of the Southern Hemisphere, there are no strong overriding trends. However, increasingly wet condi- 2.1 WhaT are The poT en TIal IMpa C Ts tions have been observed over southeastern areas of of Cl IMaTe Change on T he South America and the Amazon, while negative se CTor? trends have been observed over parts of the conti- nent's western coast. In the 21st Century, mean There is a large body of research available on the annual precipitation is projected to increase at high impacts of climate change on the water sector. Here, we latitudes and in some tropical monsoon regions, and provide an overview of some key findings, based on the decrease in some subtropical and lower mid-latitude Fourth Assessment Report (AR4) of the regions, except for increases in eastern Asia. Intergovernmental Panel on Climate Change · Heavy precipitation events have shown a wide- (Kundzewicz et al. 2007) and a number of other sources. spread increase in occurrence during the second half First, we briefly outline the main physical effects of of the 20th century, even in regions where mean observed and projected climate change on the water annual precipitation has decreased. In the 21st cycle, and then we provide an overview of the impacts Century, it is very likely that heavy precipitation of those changes on water supply and flooding. The events will become more frequent, especially in impact of climate change on these two aspects of water tropical and high-latitude regions that experience is also strongly dependent on socioeconomic develop- increases in mean precipitation. ment, as discussed in section 2.1.2. · The water vapor content of the troposphere has increased in recent decades, which is consistent with 2.1.1 physical effects of climate change on the observed global warming and near-constant rel- the water cycle ative humidity. Potential evapotranspiration is projected to increase almost everywhere due to There is now strong evidence that climate change is increasing temperatures, and an increase in the affecting the hydrological cycle. The IPCC AR4 water-holding capacity of the atmosphere (due to (Kundzewicz et al. 2007) and the IPCC technical paper projected higher temperatures combined with little on climate change and water (Bates et al. 2008), provide change in relative humidity). comprehensive reviews of recent research on observed · Globally, soil moisture has decreased, especially in and projected hydrological changes over the last several the tropics and subtropics. Annual mean soil mois- decades. Here we present a summary of these findings: ture is projected to decrease in the subtropics and the Mediterranean regions, and at high latitudes · Mean annual precipitation over land has generally where snow cover diminishes. On the other hand, increased over the 20th century between 30°N and soil moisture is projected to increase in East Africa, 85°N, but notable decreases have occurred in the central Asia, and some regions with increased past 40 years from 10°S to 30°N. The strongest precipitation. 4 A D A PTATION rELATED TO INDu ST rIAL AND MuNICIPAL WATE r Su PPLy AND rIVE r INE FLOOD PrOTE C T I O N · Snow cover, permafrost, seasonally frozen areas, and decrease over some dry regions at mid- ground, and glaciers have all shown significant latitudes and in the dry tropics. decreasing trends globally; this is of concern since · Changes in the seasonal discharge pattern are the cryosphere stores about 75 percent of the world's expected in many regions. A very robust finding is freshwater, and more than one-sixth of the world's that warming will lead to changes in the seasonality population lives in glacier- or snowmelt-fed river of river flows in areas where much winter season basins (Stern 2006). In the 21st century, increases in precipitation currently falls as snow, with spring global temperatures are projected to lead to further flows decreasing because of reduced or earlier snow- decreases in the various components of the melt, and winter flows increasing. The discharge of cryosphere. rivers draining areas covered by glaciers or snow · The effects of climate change on groundwater are may increase and experience changes in seasonality less well studied. Groundwater levels in shallow in the short term as the cryosphere responds to aquifers are affected by changes in climate via the warming, and decrease in the longer term as the recharge process (Chen et al. 2002). Although there area and volume covered by glaciers or snow is is an observed decreasing trend in groundwater lev- reduced. els during the last few decades, this has been mainly · Although the costs of adaptation to changes in attributed to overextraction; little is known of the water quality are not assessed in this study, this impacts of future climate change. will also be affected by climate change. Climate · Observed annual runoff and river discharge show change is expected to worsen many forms of water a broadly coherent pattern of change at the global pollution as a result of higher water temperatures, scale, with increases in some regions (for example, increased precipitation intensity, and low flow peri- high latitudes) and decreases in others (for example, ods. In Box 2.1, the main findings of the IPCC on parts of west Africa, southern Europe, and south- water quality are summarized. ernmost South America). Runoff trends are, how- ever, not always consistent with precipitation 2.1.2 Impacts of climate change and non-climate changes; they are also influenced by land use and drivers on water supply and flooding temperature change, and human interventions such as reservoir impoundment. Annual average river Water supply. Section 2.1.1 gives an overview of how runoff is projected to increase as a result of climate the availability of water is expected to change as a change at high latitudes and in some wet tropical result of climate change. However, many other box 2.1. IMpaCTs of ClIMaTe Change on WaTer qualITy Climate change is expected to worsen many forms of water pollution, including the load of sediments, nutrients, dissolved organic carbon, pathogens, pesticides, and salt, as well as thermal pollution, as a result of higher water temperatures, increased precipitation intensity, and low flow periods (Kundzewicz et al. 2007). This may promote algal blooms (Hall et al. 2002; Kumagai et al. 2003), and increase the bacteri- al and fungal content of water (Environment Canada 2001). In turn, these processes will impact on ecosystems, human health, and the reli- ability and operating costs of water systems. rising temperatures are likely to lower the water quality of lakes (Kundzewicz et al. 2007), as a result of increased thermal stability, resulting in reduced oxygen concentrations and an increased release of phosphorus from sediments (Nicholls 1999). However, rising temperatures can also improve water quality during the winter and spring due to the earlier break-up of ice; this can lead to higher oxygen levels, and as a result a reduction in winter fish-kill (Kundzewicz et al. 2007). More intense rainfall may lead to an increase in suspended solids (increasing turbidity) in lakes and reservoirs due to increased soil fluvial erosion (Leemans and Kleidon 2002), thereby increasing the delivery of adsorbed pollutants such as phosphorous and heavy metals (Boers 1996; Bouraoui et al.,2004; De Wit and Behrendt 1999; Mimikou et al. 2000; Neff et al. 2000; Verstraeten et al. 2002). However, soil erosion and the delivery of sediments to rivers is also highly dependent on land use (Houben et al. 2006; Toy et al. 2002; Van rompaey et al. 2002; Ward et al. 2009). In addition, more frequent heavy rainfall events may overload the capacity of sewer systems and water and wastewater treatment plants more often. Increased occurrences of low flows will lead to decreased contaminant dilution capacities, and therefore higher pollutant concentrations. Water quality deterioration is expected to be a particular problem in many arid and semi-arid areas, where climate change is likely to increase salinization of shallow groundwater due to increased evapotranspiration (Kundzewicz et al. 2007); as stream flow is projected to decrease in many semi-arid areas, the salinity of rivers and estuaries will increase. D E V E L O P M E N T A N D C L I M AT E C H A N G E D I S C u S S I O N PA P E r S 5 non-climatic drivers also affect the availability of Agriculture is currently the largest sector in terms of freshwater resources at the global scale (UN 2003). For water withdrawal and use: irrigation water withdrawals example, the availability of water is strongly influenced account for almost 70 percent of global withdrawals, and by land-use change (Andréassian 2004; Calder 1993; 90 percent of global consumptive water use Iroumé et al. 2005; Mahe et al. 2005; Scott et al. 2005; (Shiklomanov and Rodda 2003). Research into the non- Ward et al. 2008), but also by the construction of climate-change-related changes in irrigation water reservoirs and other retaining features (Ward and demand in the future show differing results. An FAO Robinson 1999; WCD 2000). Moreover, water study shows an increase in irrigation water withdrawals supply--that is, the water that is supplied to sectoral of 14 percent by 2030 for developing countries users to meet their water demands--is not only (Bruinsma 2003), while the four Millennium Ecosystem affected by changes in water availability, but also by Assessment scenarios show much smaller increases at changes in water demand. the global scale, ranging between 0­6 percent by 2030, and 0­10 percent by 2050 (Millennium Ecosystem The IPCC (Bates et al. 2008) states that the increase in Assessment 2005a,b). These increases are smaller in rela- household and industrial water demand due to climate tive terms than those projected for domestic and indus- change is likely to be rather small, for example less than trial water use; this is, based on the idea that the value of 5 percent by 2050 at selected locations (Downing et al. water will be much higher for the latter uses 2003; Mote et al. 1999). Nevertheless, large increases in (Kundzewicz et al. 2007). According to the IPCC global municipal and industrial water demand are (Kundzewicz et al. 2007), there were no global estimates expected as a result of non-climatic drivers, mainly of the impacts of climate change on irrigated water population growth and economic development, and also demand at the time of publication. However, Bates et al. changing societal views on the value of water (2008) state that higher temperatures and increased (Kundzewicz et al. 2007). There are many plausible precipitation variability would, in general, lead to scenarios of future domestic and industrial water increased irrigation water demand, even if the total demand available, but their results can vary strongly precipitation in the growing season does not change. (Alcamo et al. 2000; Gleick 2003; Millennium Nevertheless, the water demand of the agricultural sector Ecosystem Assessment 2005a,b; Seckler et al. 1998; will clearly be affected by changes in irrigation methods Vörösmarty et al. 2000). In Figure 2.1, the changes in and their effectiveness, as well as the price of water. water demand due to non-climatic drivers between 2005 and 2030, and between 2005 and 2050, are shown The differences between water availability and water for 281 FPUs (Food Producing Units) based on the demand can lead to situations of water stres, which is water-demand scenario used throughout the EACC defined by the IPCC as per capita water availability study. The method used to produce these scenarios is below 1,000 m3 per year (Bates et al. 2008). Global described in the main EACC report. The FPUs of assessments of water stress suggest that the current IFPRI (The International Food Policy Research population living in water-stressed basins is between Institute) and IWMI (International Water Management 1.4 to 2.1 billion (Alcamo et al. 2003a,b; Arnell 2004; Institute) divide the world into 281 sub-basins, where Oki et al. 2003; Vörösmarty et al. 2000). Most research each sub-basin represents a hybrid between river basins has found that levels of water stress will increase in the and economic regions, and are discussed in Section 4.1. future as a result of both climatic and non-climatic This scenario shows an increase in water demand in drivers, although there are large differences in estimates most parts of the world, including OECD countries; across studies. Arnell (2004), who accounted for popu- this latter finding is contrary to the findings of some lation growth and the impact of climate change, found projections, for example Gleick (2003), which show a that the number of people projected to experience an decrease in this region. The maps in Figure 2.1 clearly increase in water stress is between 0.4­1.7 billion in the show that the increase in water demand is expected to 2020s, and between 1.0­2.7 billion in the 2050s; this is be greatest in the developing world. The results of the based on the Special Report on Emission Scenarios Millennium Ecosystem Assessment (2005a,b) show a (SRES) A2 population scenario for the 2050s (IPCC global increase in domestic and industrial water demand 2000). When environmental flows--that is, the amount of between 14 and 83 percent by 2050. of water required to sustain functioning 6 A D A PTATION rELATED TO INDu ST rIAL AND MuNICIPAL WATE r Su PPLy AND rIVE r INE FLOOD PrOTE C T I O N fIgure 2.1. projeCTed Changes (perCenT) In MunICIpal and IndusTrIal WaTer deMand per food produCIng unIT (fpu), 2005 and 2030 (above), and 2005 and 2050 (beloW), due To non-ClIMaTIC drIvers aCCordIng To The soCIoeConoMIC sCenarIo used ThroughouT The eaCC sTudy. Note: The fpus divide the world into 281 sub-basins, where each sub-basin represents a hybrid between river basins and economic regions (see section 4.1). ecosystems--are incorporated, the degree of water Changes in water availability and demand can have stress may increase further (Smakhtin et al. 2003). serious impacts on the frequency and intensity of Based on these and other studies, the IPCC concluded droughts. The term drought can refer to: meteorologi- with high confidence that globally the negative impacts cal drought (precipitation well below annual/seasonal of future climate change on freshwater systems and average); hydrological drought (low river flows and ecosystems are expected to outweigh the benefits water levels in rivers, lakes, and groundwater); agricul- (Kundzewicz et al. 2007). However, it should be noted tural drought (low soil moisture with adverse effects on that these studies do not consider the role of adaptation agricultural output); and environmental drought (a (planned and autonomous) in reducing water stress. combination of the above) (Kundzewicz et al. 2007). Regional patterns of water stress are discussed further Since the 1970s, droughts have become more common, in section 2.2. especially in the tropics and subtropics. According to D E V E L O P M E N T A N D C L I M AT E C H A N G E D I S C u S S I O N PA P E r S 7 the IPCC, it is more likely than not that there is a Traditionally, flood assessments have examined how human contribution to this trend (IPCC 2007). climatic, hydrological, and socioeconomic changes may Furthermore, it is likely that the area affected by affect the probability of flooding, as discussed above. drought will increase in the future, with a tendency for However, there is currently an international shift toward drying of mid-continental areas during summer, lead- a more integrated system of flood risk assessment ing to an increased risk of droughts in those regions (Büchele et al. 2006; Merz et al. 2004), whereby flood (Bates et al. 2007). A single-model study of global risk can be defined as the probability of flooding multi- drought frequency suggests that the percentage of land plied by the potential flood damage (Smith 1994). experiencing extreme drought at any one time will These potential damages can be measured in terms of increase 10- to 30-fold by the 2090s, under SRES economic damage and the loss of human lives. Flood scenario A2 (Burke et al. 2006). risk is therefore not only determined by the factors listed above that control flood probability, but also by Riverine flooding. The changes in climate and hydro- the scale and type of developments in flood-prone areas logical parameters discussed in section 2.1.1 are (Kundzewicz and Schellnhuber 2004). The economic expected to lead to changes in the frequency and value of developments in flood-prone areas, as well as intensity of riverine floods (Vörösmarty et al. 2000; population density, is expected to increase in the future Wetherald and Manabe 2002). The observed changes (Bouwer et al. 2007), despite new spatial planning poli- in precipitation intensity suggest that climate change cies and legislation adopted by some countries to mini- may already have had an impact on floods. Globally, mize developments in flood-prone areas (Pottier et al. the number of great inland flood catastrophes during 2005). However, while modeling studies of flood the period 1996­2005 was twice as many as the damage and risk have been carried out at local to basin decadal average in the period 1950 to 1980 scales (Dutta et al. 2003; Hall et al. 2005; IKSR 2001; (Kundzewicz et al. 2007). However, there is no clear- Lekuthai and Vongvisessomjai 2001; Nascimento et al. cut evidence of a climate-related increasing trend in 2006), data limitations, scale issues, and current meth- flood frequency during the last decades of the 20th odologies do not (yet) allow flood damage and risk Century (Kundzewicz et al. 2005; Schiermeier 2006), modeling at the global scale. as the occurrence of flooding is also dependent on many other factors such as land use change 2 . 2 W h o ( aCr o s s a n d W I Th I n (Andréassian 2004; Brown et al. 2005; Calder 1993; C o u nTr I e s ) I s lI k e ly To b e M o sT EEA 2001; Fahey 1994; Gentry and Parody 1980; a f f eC Te d ? Jones 2000; Mahe et al. 2005; Robinson et al. 1991; Ward et al. 2008), river confinement (Kundzewicz and The impacts of climate change on the water sector will Schellnhuber 2004; Milly et al. 2002), and the pres- be felt in both developed and developing countries. ence/absence of other adaptation measures (Bates et al. However, as stated by IPCC (2007), many regions of 2008; Deltacommissie 2008). Nevertheless, many stud- the developing world are particularly vulnerable. ies show that the projected increases in precipitation Vulnerability is defined by IPCC as the degree to which totals and intensity in many river basins in the 21st geophysical, biological, and socioeconomic systems are century are expected to lead to an increase in the susceptible to, or unable to cope with, adverse effects of frequency of flood events (Huntington 2006; Kleinen climate change, including climate variability and and Petschel-Held 2007; Milly et al. 2002, 2005; extremes. It is a function of, among other things, the Palmer and Räisänen 2002; Voss et al. 2002). For character, magnitude, and rate of climate variation and example, Milly et al. (2002) found that for fifteen out climate change to which a system is exposed, its sensi- of sixteen large basins that they modeled worldwide, a tivity, and its adaptive capacity. quadrupling of CO2 would lead to the much more frequent recurrence of the current 1/100 year peak Arnell et al. (2001) identified several factors, both phys- flow. For some areas, they found that the current 1/100 ical and societal, which have been associated with high year peak flow may occur once in every 2 to 5 years by levels of vulnerability in the water sector. A number of 2100 as a result of climate change. these are summarized below: 8 A D A PTATION rELATED TO INDu ST rIAL AND MuNICIPAL WATE r Su PPLy AND rIVE r INE FLOOD PrOTE C T I O N Physical Features Africa is one of the regions of the world that is poten- · A current hydrological and climatic regime that is tially most vulnerable to climate change in terms of marginal for agriculture and livestock water supply, as a large share of the economy of African · A highly seasonal hydrological setting countries tends to be in climate-sensitive sectors (Smith · High rates of sedimentation in reservoirs and Lenhart 1996). Climate change research in Africa · Topography and land-use patterns that promote soil suggests that the population at risk of increased water erosion and flash flooding stress will be between 75­250 million by the 2020s, and · 350­600 million by the 2050s (for the full range of · Lack of variety in climatic conditions across a SRES scenarios). However, the impact of climate region, leading to an inability to relocate activities change on water supply will not be uniform across the in response to climate change continent; the results of six climate models show that the climatic impact on water stress is a likely increase in Societal Characteristics the number of people living under water-stressed condi- · Poverty and low incomes, which prevent long-term tions in northern and southern Africa, with a decrease planning at the household level in eastern and western Africa (Arnell 2004). · Lack of infrastructure, or poor maintenance of existing infrastructure Between the 1950s and the 1990s, annual economic losses · Lack of human capital skills for system planning from large extreme events have increased tenfold. In and management terms of flood losses, the developing world, and particu- · Lack of appropriate, empowering institutions larly South and East Asia, have been the hardest hit · Absence of appropriate land-use planning (Kabat et al. 2003). Although these increases in losses are · High population densities and other factors that also attributable to a myriad of non-climatic drivers,-- inhibit population mobility including population growth, expansions into flood-prone · Increasing demand for water because of rapid popu- areas, land use changes, and manipulation of water within lation growth channels--climatic factors are partly responsible. · Conservative attitudes toward risk According to Kleinen and Petschel-Held (2007), up to 20 · Lack of formal links among various parties involved percent of the world's population will live in river basins in water management that are likely to be affected by climate-change-related increases in flood hazards by the 2080s. Many of these factors are prevalent in large parts of the developing world, and so the negative impacts of Floodplain areas downstream from glaciers are likely to climate change are likely to be greatest there. This is be particularly vulnerable to increasing flood hazards supported by the results of several global estimates of and flood risks in the coming decades (Bates et al. vulnerability, which show medium to high vulnerability 2008). The rapid melting of glaciers, due to increased in large parts of the developing world (Alcamo and temperatures, can lead to river flooding and to the Heinrichs 2002; Raskin et al. 1997; Sullivan 2006); see, formation of glacial melt-water lakes, which can lead to for example, Box 2.2. a serious threat of outburst floods (Coudrain et al. 2005). Moreover, these areas may face increased water Water-stressed river basins are mainly located in north- stress in the longer term, since the reduction or disap- ern Africa, the Mediterranean, the Middle East, the pearance of glacier mass will lead to reductions in Near East, southern Asia, the United States, Mexico, glacial melt-water supply. north-eastern Brazil, and the west coast of South America (Bates et al. 2008). A number of global-scale The impacts of climate change, both in terms of water (Alcamo and Heinrichs 2002; Arnell 2004), national- supply and flooding, may be particularly strongly felt in scale (Thomson et al. 2005), and basin-scale assess- transboundary river basins. In many developing coun- ments (Barnett et al. 2004) show that semi-arid and tries, sharing the water resources of river basins arid basins are the most vulnerable with respect to water between riparian states, and coordinating flood stress as a result of climate change. management, remains challenged by weak institutional D E V E L O P M E N T A N D C L I M AT E C H A N G E D I S C u S S I O N PA P E r S 9 box 2.2. ClIMaTe vulnerabIlITy Index Vulnerability to global changes depends on a combination of factors. For the water resources sector, vulnerability is not only influenced by the quantity of water available, but also by a range of social, economic, and environmental factors that affect the ability to cope with chang- ing conditions. In order to identify the most vulnerable regions, a new approach to this problem has been developed through the application of a Climate Vulnerability Index (CVI). The CVI is a holistic methodology for water resources evaluation in keeping with the sustainable live- lihoods approach used by many donor organizations to evaluate development progress. The scores of the index range are on a scale of 0 to 100, with the total score being a weighted average of six major components, namely: resources, access, capacity, use, environment, and geospatial. The map of CVI scores below is an illustrative result only, but it does demonstrate the technique's power. Estimated CVI scores for 148 countries high (52.0­60.0) Medium high (44.0­51.9) Medium (36.0­443.9) Medium low (28.0­35.9) low (20.0­27.9) no data Source: http://ocwr.ouce.ox.ac.uk/research/wmpg/cvi/cvi_leaflet.pdf arrangements and inadequate infrastructure. Regardless 2 . 3 u nC e rTaI nT Ie s I n Cl I M aTe Ch a n g e of the level of economic development, climate change I M pa C Ts poses a threat to these basins. Projected changes in water resources and flood frequency due to climate There are significant uncertainties in projections of the change can impact the water balance and consequently impacts of climate change on water resources. the hydro-political balance (World Bank 2009). Wolf Uncertainties in the impacts of climate change on the et al. (2003) indicate that historically extreme conflicts hydrological cycle can arise from a myriad of sources, over water have been more common in water-scarce including uncertainties in: the internal variability of the regions where extreme conditions characterized by high climate system; in the future greenhouse gas and aero- interannual variability occur. sol emission scenarios (and in the scenarios of popula- tion, economic development, and technological change The extent to which people will be affected by climate that generate them); in the translation of these emis- change is also strongly related to their adaptive capacity, sions scenarios into climate change by climate models; which is influenced by economic and natural resources, in the methods used to downscale climate model data social networks, entitlements, institutions and gover- to the lower resolutions required in hydrological impact nance, human resources, and technology (Adger et al. assessments; and in the hydrological models used to 2007); this is discussed further in section 2.6. simulate the impacts of climate change on the 10 A D A PTATION rELATED TO INDu ST rIAL AND MuNICIPAL WATE r Su PPLy AND rIVE r INE FLOOD PrOTE C T I O N hydrological cycle. The largest of these uncertainties in Adaptation to changing conditions in water availability climate change impact assessments on water resources and demand has always been at the core of water are due to the uncertainty in precipitation inputs (Bates management (Adger et al. 2007). Traditionally, water et al. 2008). managers and users have relied on historical experience when planning water supplies and distribution When assessing the impacts of climate change on (UNFCCC 2007). Water supply management has water supply, further uncertainties are added with mainly concentrated on meeting increasing water regard to the socioeconomic scenarios used to project demand, and flood defense measures have assumed future water demand (Bates et al. 2008). When assess- stationary flood recurrence periods. However, under a ing flood hazards, with or without climate change, changing climate, these assumptions are no longer valid uncertainties are added through the extrapolation of (Kundzewicz et al. 2007). Therefore, current water relatively short observed or simulated time-series of management practices need to be redesigned, and the discharge to estimate the return period of rare flood procedures for designing water-related infrastructure events. Even in basins for which relatively long and need to be revised. Otherwise, systems may be wrongly reliable observed records exist (e.g., 100 years), the conceived, and under- or overdesigned, with either extrapolation of these data to predict events with longer inadequate performance or excessive costs as a result. return periods remains problematic for the current However, necessary adaptation to climate change in the climate, let alone under scenarios of future climate water sector goes beyond structural measures, but also change (Box 2.3). includes forecasting/warning systems, insurance instru- ments, and a large variety of means to improve water 2.4 WhaT experIenC e Is There W I Th use efficiency and related behavioral change, economic adapTaTI on In T he seCTor? and fiscal instruments, legislation, and institutional change (Kundzewicz et al. 2008). Even if emissions of anthropogenic greenhouse gases were stabilized today, human-induced changes in Although climate change is not directly addressed in climate will continue for many centuries (IPCC 2007). the eight Millennium Development Goals (MDGs), Therefore, in addition to mitigation, it is essential to most of them are directly or indirectly related to water develop adequate adaptation measures to moderate the (Kundzewicz et al. 2007). Hence, adaptation to climate impacts and realize the opportunities associated with change in the water sector (as in all sectors) should be climate change. There are many definitions of what carried out in a manner that is synergistic with develop- constitutes adaptation to climate change; these are ment priorities in general (Adger et al. 2007; discussed in the main EACC report. Kundzewicz et al. 2007; Ribot et al. 1996), and climate box 2.3. unCerTaInTy In flood reTurn perIod esTIMaTes Traditional flood management practices rely strongly on technical engineering capacity for reducing the probability of a flood, whereby flood defenses are designed to withstand a so-called design discharge; that is, the discharge that occurs, on average, once in a given number of years, or the so-called return period (TAW 2000). In the Netherlands, which has one of the world's most advanced flood management sys- tems, the design discharge for embanked river sections is based on a return period of 1,250 years. For the Meuse river, which enters the Netherlands at Borgharen (flowing from Belgium), the method used to determine the design discharge involves obtaining and analyzing observed annual maximum discharges at Borgharen since 1911. Several theoretical distribution functions are fitted to these observed maxi- ma, and are then used to make an extrapolation to the required exceedance probability. The design discharge is then calculated based on a combination of different extreme-value statistics distribution functions, where the weights are determined by Bayesian analysis. However, the estimation of discharges with return periods of 100­10,000 years via statistical extrapolation based on about 100 years of discharge observations introduces large uncertainties (De Wit and Buishand 2007). Firstly, it is not known how representative the ca. 100-year observed discharge record is. For example, the discharge with a return period of 1,250 years, as estimated in 1996 and 2001, was 3650 m3s-1 and 3800 m3s-1 respectively. This increase is mainly due to the extension of the observed record with a relatively wet period from 1996­2001. This problem is even greater in most basins, since the length of the observed discharge record of the Meuse is relatively long compared to average. Furthermore, the choice of frequency distributions used introduces even more uncertainty. D E V E L O P M E N T A N D C L I M AT E C H A N G E D I S C u S S I O N PA P E r S 11 policy should be embedded in general development measures in this section. An overview of the costs policy. associated with several adaptation measures, both on the supply and demand side, can be found in Box 2.4. In this section, we provide an overview of a number of Options to counteract increasing flood risk can be available adaptation measures in terms of water supply divided into two categories: either to modify the prob- and riverine flood protection. A few points should be ability of flooding, or to modify the flood damage in noted. First, in the EACC study, the World Bank differ- the event of flooding. Each adaptation measure has entiates between four categories of adaptation measures: advantages and disadvantages, and the choice for one autonomous adaptation, public sector investment, "soft" measure or another is site-specific and should be adaptation, and reactive adaptation. Hence, we have used tailor-made: there is no single one-size-fits-all these categories in this assessment. However, these cate- measure (WCD 2000; Bates et al. 2008). gories are clearly not mutually exclusive; most forms of adaptation fall into several of these categories. Second, 2.4.1 autonomous adaptation the list of adaptation options is not exhaustive, because the measures available are so diverse, and novel adapta- A distinction is frequently made between autonomous tion measures are constantly being developed. Third, as and planned adaptation. Autonomous adaptation already stated, adaptation to climate change should be measures are those that do not necessarily constitute carried out in the framework of sustainable development conscious responses to climate change, but result from in general. Undertaking interventions that create more changes to meet altered demands, objectives, and expec- stress in the long term does not help to reduce vulnera- tations that, while not deliberately designed to cope with bility to climate change. Fourth, some measures that can climate change, may lessen the (likely) consequences of be taken to improve the situation in the water sector that change (Feenstra et al. 1998). Farm-level adaptation should be considered regardless of climate change. For is often referred to as "autonomous," as farmers under- example, measures to reduce water transmission losses-- take action without government intervention, such as a for example, reducing pipe leakage--have huge impacts decision to change the time of planting or change crop on water supply, but we do not list this as an adaptation type. However, from a farmer's perspective this is not a to climate change per se. spontaneous action, but is likely to have involved some serious consideration and advance planning (Feenstra et Adaptation options designed to ensure water supply al. 1998). Autonomous adaptation is widely interpreted during average and drought conditions require inte- as incorporating actions taken by private actors rather grated demand-side and supply-side strategies (Bates than governments; in fact, autonomous and private et al. 2008). Supply-side options generally involve adaptation measures are generally one and the same increases in storage capacities or abstraction of (partly) (Smit and Pilifosova 2003). Such adaptations are wide- untapped sources, and can therefore have adverse envi- spread in the water sector, although with varying degrees ronmental consequences (Bates et al. 2008; of effectiveness in coping with climate change. Kundzewicz et al. 2007). Some supply-side options may also be inconsistent with climate change mitiga- In the conventional way of identifying planned adapta- tion measures because they involve high energy tion needs, the occurrence of autonomous adaptation is consumption, such as desalination, and to a lesser assumed up to a certain level. Policy response and extent, pumping. Conversely, demand-side options planned adaptations are then only needed for the resid- may lack practical effectiveness because they rely on ual impact of climate change; that is, the impact that the cumulative actions of individuals (Bates et al. appears to remain after autonomous adaptation has 2008; Kundzewicz et al. 2007). As agriculture is the taken place (Smit and Pilifosova 2003). The extent to largest sector in terms of land occupation, a large which autonomous, private, or market adaptation can sector in terms of GDP in developing countries reduce the societal impacts of climate change to an (Kandlikar and Risbey 2000; Nhemachena and acceptable or non-dangerous level is an issue of great Hassan 2007), and irrigation is responsible for 70 interest (Smit and Pilifosova 2003). Researching this percent of global water withdrawals, we include a issue automatically leads to the question of adaptive number of water-related farm-level adaptation capacities (Adger 2003) (see section 2.6). 12 A D A PTATION rELATED TO INDu ST rIAL AND MuNICIPAL WATE r Su PPLy AND rIVE r INE FLOOD PrOTE C T I O N box 2.4. CosTs of several adapTaTIon Measures In WaTer supply and deMand Most of the estimates of the costs of both supply and demand adaptation options in the table below are taken from the final report of the World Commission on Dams (WCD) (Sutherland and Fenn 2000), with additional information from Zhou and Tol (2005). The costs in the WCD were originally reported in uSD2000, and have been adjusted to uSD2005 using the WDI Deflator Index of the World Bank. Of these options, it can be seen that small-scale rainwater harvesting measures tend to provide a cost-effective form of water supply adaptation. Moreover, many of these schemes are easy to implement. The recycling of water is also an economically viable and sustainable option in many municipal and industrial situations. Zhou and Tol (2005) state that while there are large differences depending on the method used, desalination is feasible today at a cost of $1.00/m3 for seawater, and $0.60/m3 for brackish water; this cost is expected to continue to decline in the future as technology progresses. The latter study also estimates the cost of transporting water at ca. 0.05­0.06m3 per 100 km in terms of horizontal transport, and ca. 0.05­0.06m3 per 100 m of vertical lift. Transport makes desalinated water prohibitively expen- sive in highlands and continental interiors, but not elsewhere. Option Location Cost (USD 2005) Cost basis Source Rainwater harvesting 1-2-1 rainwater project China 0.10/m3 30 yrs life; replacement after 15 Sutherland and Fenn, 2000 yr. rain 4 months/yr rainwater jars Thailand 0.03/m3 30 yrs life; replacement after 10 Sutherland and Fenn, 2000 & 20 yrs. rain 4 months/yr rooftop to tanks Sri Lanka 0.25/m3 30 yrs life; replacement after 10 Sutherland and Fenn, 2000 & 20 yrs. rain 4 months/yr Recycling recycling California uSA 0.39/m3 Sutherland and Fenn, 2000 recycling Durban South Africa 0.03/m3 Sutherland and Fenn, 2000 Discrete private sector Haiti 0.94/m3 Based on breakdown of water Sutherland and Fenn, 2000 distribution; purchase of charge to cover costs water from mains at one point ultra-low toilet retrofitting uSA 0.18/m3 uSD2000 200/acre foot Sutherland and Fenn, 2000 Metering uK 1.13/m3 Sutherland and Fenn, 2000 Desalination Desalination seawater Global 1.00/m3 Zhou & Tol, 2005 Desalination brackish Global 0.60/m3 Zhou & Tol, 2005 water Water transport Global 0.05-0.06/100km Cost per 100 km horizontal Zhou & Tol, 2005 transport or 100 m vertical lift Some of the main constraints of autonomous adaptation fully priced (see section 2.4.3). Examples of water effi- are a private deficiency of information and access to cient technologies include ultra-low-flow toilets, low- resources, relatively high adaptation costs, and the flow showers, hand basin spray taps, and waterless incurrence of residual damages. Furthermore, autono- urinals (Sutherland and Fenn 2000). The market uptake mous adaptation can lead to a perception of reduced for such technologies can be influenced by policy risk to climate change, which is not always correct (section 2.4.3). (Smit and Pilifosova, 2003). Diversifying crop types and varieties, including 2.4.1.1 Demand-sideautonomousadaptation substitution. This measure has the potential to reduce Improving water efficient technologies. This adapta- a farmers' exposure to climate change and to increase tion measure can be considered as autonomous, since the flexibility of farm production. Substitution to water efficient technologies are developed by private more drought-tolerant crops can greatly increase the parties, and purchased by private individuals in order to coping ability during droughts (Gbetibouo 2009; reduce consumption and thus cost. Hence, these Nhemachena and Hassan 2007; Smit and Skinner measures may perform better where water services are 2002). D E V E L O P M E N T A N D C L I M AT E C H A N G E D I S C u S S I O N PA P E r S 13 Diversifying livestock types and varieties, including Changing the timing of production. By changing the substitution. This measure is essentially the same as timing of seeding, farmers can maximize farm produc- the previous one, but for livestock farmers. It has the tivity during the growing season and avoid heat stress potential to reduce a farmers' exposure to climate and moisture deficiencies. However, for this to work, change and to increase the flexibility of farm production there needs to be some stability in the seasons, or long- (Smit and Skinner 2002). term weather forecasts; otherwise, farmers do not know when they should plant (Smit and Skinner 2002). Planting trees. Reduces exposure to climate change and Farmers can also adapt to changing seasons by spread- increases the flexibility of farm production (Deressa et ing their seeding activities over a longer period. al. 2009). However, to plant trees and to harvest fruit is a multiple-year investment; if the tree dies, it takes years 2.4.1.2 Supply-sideautonomousadaptation before a new tree produces fruits or other tangible prod- Rainwater harvesting. Surface runoff can cause ucts. This makes the planting of trees as an adaptive erosion, a loss of soil nutrients, and a loss of soil mois- option vulnerable to climatic extremes. ture holding capacity. Furthermore, the rapid runoff of water and overextraction of groundwater can lead to Changing the intensity of production. If precipitation decreased groundwater tables. Micro-catchment water is a limiting factor in crop growth, a change in the harvesting systems help to increase soil moisture reten- amount of precipitation can create chances to plant tion and groundwater levels by increasing groundwater more or force a farmer to plant less. A decreasing trend recharge, and reducing the loss of runoff (Vohland and in precipitation could be coped with by planting less, so Barry 2009). However, in some cases the space put aside as to ensure that the planted crops at least reach a for these measures cannot be used for growing crops, mature state. This adaptive measure reduces exposure to and can lead to farmer opposition (Smit and Skinner climate change, and increases flexibility of farm produc- 2002). When implemented as part of a sustainable tion (Smit and Skinner 2002). water management plan at the local level, rainwater harvesting has great potential (Box 2.5). Rainwater Changing the location of production. A more drastic harvesting can also be carried out at the household level, measure to cope with climate change is to alter land use for example by capturing rainwater on roofs and collect- by changing the location of production to more suitable ing this in tanks. This technique is gaining popularity in lands. Moving away from marginal areas has the poten- all parts of the world, developed and developing coun- tial to reduce soil erosion and improve moisture and tries alike (Bates et al. 2008); costs vary geographically, nutrient retention. However, it may reduce the acreage but are generally low (Box 2.4). of land that is available for agricultural production (Smit and Skinner 2002), and there may not be land Small-scale catchments and storage of precipitation. available to move to, leading to conflicts. Small-scale storage of precipitation--for example, in sand dams, tanks, or small storage dams--can increase mois- Alternative fallow and tillage practices. This ture retention despite decreasing precipitation and measure has the potential to reduce soil erosion and increasing evaporation, and has the benefit that the stor- improve moisture and nutrient retention (Smit and age constructions usually have a long lifetime (Bates et al. Skinner 2002). 2008). Sand dams, as one example of small-scale storage, also provide a means to bridge (prolonged) dry periods, as Increasing the efficiency of irrigation. Measures that they provide a means to keep crops, livestock, and trees improve the efficiency of irrigation systems allow for alive (Lasage et al. 2008; Pauw et al. 2008) (Box 2.6). greater flexibility as water consumption reduces while crop yields are maintained (Smith and Lenhart 1996). Prospecting and extraction of groundwater. This These measures could be technical--for example, measure can be applied both autonomously and as a changing sprinkler irrigation to drip-irrigation-- but public sector adaptive measure. However, abstraction of changing the timing of irrigation toward less sunny (partly) untapped sources can have adverse environ- hours of the day is also beneficial. mental consequences (Bates et al. 2008; Kundzewicz et 14 A D A PTATION rELATED TO INDu ST rIAL AND MuNICIPAL WATE r Su PPLy AND rIVE r INE FLOOD PrOTE C T I O N box 2.5. raInWaTer harvesTIng and loCal WaTershed ManageMenT ­ The Case of laporIya, IndIa rainwater harvesting, in combination with good watershed management practices, can be used to address water supply problems. For example, the village of Laporiya in India has adopted such practices, and learned not only how to survive despite low and variable rainfall, but to thrive despite these climatic conditions. The village lies in the state of rajasthan, about 100 kilometers west of Jaipur. Its climate is semi-arid; it receives an average of 500 mm of rainfall per year, and rainfall is highly variable. The 2001 Census of India states that 93 per- cent of households own some agricultural land. With no perennial rivers in its vicinity, agriculture in Laporiya is rainfed. However, given the variability in annual precipitation, agriculture is a risky activity. Therefore, in order to achieve the sustainability of its water resources, Laporiya has developed an innovative rainwater harvesting technique, known to many as "Laporiya's rectangles," that has allowed it to harvest rainfall to provide surface water for irrigation, water to recharge groundwater reservoirs, and to enable grass production on common grazing lands. One of the main elements of the system is a series of shallow, slightly sloped, three-sided embankments (on average 66 meters wide, 132 meters long, and 1.5 meters high), which collect water with a depth of between 2.5­23.0 cm (Laporiya's rectangles, or "chaukas"). This water slowly seeps into the ground to increase the recharge of groundwater. The design also encourages grass production and soil moisture reten- tion. These chaukas are generally built in a series, following the natural slope of the land to eventually reach village ponds. Miniature dams are also built in the system, which help to collect water for irrigation and animal drinking water. Additionally, separate tanks are established to provide irrigation and drinking water, and to serve as a percolation tank. Though no precise figures are available, Laporiya's water harvesting structures are generally low-cost, use mainly local materials, and have been built in part through household labor donations. The village has become self-sufficient in its drinking water needs, despite nine consecutive years of drought, and has experienced increases in agricultural and livestock productivity and per capita incomes. The project was initiated by a local NGO, and is managed at the village level; the impor- tance of stakeholder participation and local knowledge and ownership is a key to the project's success (Narain 2008). box 2.6. loCal knoWledge and oWnershIp In WaTer sTorage: The kITuI sand daMs In kenya The Kitui sand dams in Kenya are an example of how communities can use their knowledge on water to cope with droughts. It also provides an example of a novel approach to dam building which avoids many of the disadvantages of large dams. Kitui district is a semi-arid region, 135 km east of Nairobi. The area is characterized by highly erratic and unreliable rainfall, with two rainy seasons providing 90 percent of the annual rainfall. Historical analysis of metrological data shows that climate (change) is already an issue in the Kitui district. Since 1990, a local NGO (Sahelian Solution Foundation, SASOL) has been assisting local communities in building over 500 small-scale (3­50 m wide) sand dams to store water in artificially enlarged sandy aquifers. Sand dams are small concrete structures built in ephemeral rivers to store excess rainfall to overcome periods of drought. This old technique differs from traditional dams by storing water within the sand and gravel particles, which are accumulated against the dam wall. Hence, the term "sand" refers to the sand behind the dam that holds the water. The sand pre- vents high evaporation losses and contamination. Since the start of the project more than 67,500 people in Kitui have better access to safe drinking water, at an average investment of less than $35 per person. Physical restrictions prevent dams from being built everywhere, and this causes disparities within the community. Households with dams now live on average 1700 m closer to their primary water sources and save 100 minutes per day on fetching water. In turn, the increased water availability and the saved time have brought tremendous positive social and economic changes, most of which are agricultural. However, the situation of households without dams has deteriorated due to poor rains in recent years (Pauw et al. 2008). The dams are built by the communities; SASOL only facilitates fundraising for the dam materi- als and engineering. Hence, communities have ownership and are committed to maintenance and efficient use of the dams. The good prac- tices and experiences in Kitui should be matched with the needs and circumstances in other areas in Kenya and other developing (semi-arid) countries. International organizations could use their networks and resources to scale up this project, which shows that local action can be a cost-effective way of addressing water supply and drought issues on a larger scale (Lasage et al. 2008). al. 2007). Costs can vary greatly depending on many such as drip irrigation, can be used. However, a clear factors, including: depth of the borehole; bedrock constraint of these measures is that water needs to be material; and the chance of drilling a successful available to implement irrigation practices (Nhemachena borehole. and Hassan 2007; Smit and Skinner 2002). Implementing irrigation practices. Irrigation can lead A 2.4.1.3. utonomousadaptationforriverineflood to increased soil moisture retention in the case of protection decreasing precipitation and increasing evaporation. Miniature protection barriers. Dikes and barriers are Alternatively, more efficient forms of irrigation practice, usually built on a large scale through public expenditure. D E V E L O P M E N T A N D C L I M AT E C H A N G E D I S C u S S I O N PA P E r S 15 However, small barriers--for example, earthen barriers, insurance system, especially in rural and developing wood walls--can be placed around dwellings at low cost areas (Bergkamp et al. 2003). Constraints for the insur- in flood-prone areas to offer some protection in the ance industry include a lack of data availability on event of flooding (UNFCCC 2006). A drawback is that hazards and exposures in developing countries. such adaptation measures may provide little/no protec- Nevertheless, successful schemes against disaster losses tion in the event of major flood events with large flood in developing countries do exist, such as in Columbia, depths (Marfai and King 2008). where microentrepreneurs offer affordable and easy-to- understand life and property microinsurance to the Move valuable things upwards. One way to greatly most vulnerable (Bouwer et al. 2007). As a result of reduce the damages caused by floods is to move valuable climate change, demand for insurance products is items upwards. For example, granaries can be estab- expected to increase (ABI 2004; Dlugolecki and Lafeld lished in treetops (Patt and Schröter 2008). A simple 2005; Mills et al. 2005; Valverde and Andrews 2006). yet effective way in which people adapt to flooding at Recent research shows that homeowners are willing to the household level is to move valuable items from the take measures to reduce their own vulnerability in ground floor up to higher floors during floods. This exchange for lower insurance premiums (Botzen et al. method is adopted by many households in coastal cities 2009), showing that insurance schemes can help to of Indonesia, although clearly a requirement is that promote proactive adaptation. A possible constraint is there is a dry level to which valuables can be moved that consumers have low risk awareness, which makes (Marfai and King 2008). premiums seem expensive (UNFCCC 2007). Also, in general the government will always be the reinsurer of Flood-proofing of buildings. Buildings in flood-prone last resort, and it is therefore important to formalize the areas can be built in such a way as to make them less nature of this public-private partnership. vulnerable to the effects of flooding. Buildings can be modified in several ways to reduce the risk of floodwa- 2.4.2 public sector investment ter penetration, including: waterproofing walls; fitting temporary closure devices; and building boundary walls; Implementing adaptation measures involves many orga- or to reduce the effects should water enter the building, nizations, institutions, and individuals, but in practice such as by routing and locating electrical sockets at the responsibility often falls on the public sector higher levels. Indigenous adaptations include building (UNFCCC 2006). Public sector climate change invest- houses on stilts, and sometimes on raised mounds ments are the result of deliberate policy decisions that (Green et al. 2000). specifically take climate change into account. In identi- fying and evaluating which adaptations are most attrac- Resettlement. An autonomous reaction to flooding can tive, consideration must be given to how they relate to be to move to higher ground or less flood-prone areas. ongoing decision-making processes, constraints, stimuli However, floodplains are generally very fertile, and in and decision criteria (Smit and Skinner 2002). Most of some cases a scarcity of land may prevent people from the public sector adaptation options given below are moving to less-flood-prone areas. Moreover, people may implemented on a larger scale than autonomous have cultural or economic bonds with the floodplain adaptation. area in which they live, and a perception of low risk (Marfai and King 2008). Examples exist in developing 2.4.2.1 Demand-sidepublicsectoradaptation countries of communities who temporarily migrate Crop development. The agronomical development and during high-flow events, such as in Bangladesh, where provision of new crop varieties--including types, culti- some rural households dismantle their homes and move vars, and hybrids--has the potential to provide crop them by boat to higher ground as river levels rise choices better suited to different temperature, moisture, (Green et al. 2000). and other conditions associated with climate change. Development of new varieties could also have a mitigat- Flood insurance. This measure can help to cover the ing function in terms of CO2 sequestration in the soil economic costs of the impacts of climate-related (Downing et al. 1997; Patt and Schröter 2008; Smit and hazards. However, it is hard to organize a good Skinner 2002). 16 A D A PTATION rELATED TO INDu ST rIAL AND MuNICIPAL WATE r Su PPLy AND rIVE r INE FLOOD PrOTE C T I O N Increase the efficiency of irrigation. Measures that droughts or other problems of water supply, especially improve the efficiency of irrigation systems allow for for highly valued water uses such as urban supply. greater flexibility as water consumption declines while However, transferring water from one basin to another crop yields are maintained (Smith and Lenhart 1996). can cause large environmental damage (Smith and Public sector investment in the large-scale subsidized Lenhart 1996; Kundzewicz et al. 2007). Zhou and Tol supply of efficient irrigation equipment could help (2005) estimate that a 100 m vertical lift in transporta- private farmers to alter their irrigation systems. tion is about as costly as a 100 km horizontal transport (about $0.0.5­$0.06/m3). Weather and climate information systems. Seasonal estimates have the potential to aid risk assessment and Small-scale catchment and storage of precipitation; production decisions over several months, especially implementation of irrigation practices; prospecting during climatological deviations like El Niño and La and extraction of groundwater. These can be carried Niña (Smit and Skinner 2002). Seasonal and longer- out as smaller-scale autonomous adaption measures (see term climate projections can inform farmers about the section 2.4.1.2), but also on a larger scale via public- probability of extreme events and likely rainfall patterns sector-driven investments. and temperatures. However, developing seasonal predic- tions is difficult as they come with many uncertainties, P 2.4.2.3 ublicsectoradaptationforriverineflood and their applicability is dependent on a difficult itera- protection tive process of gaining credibility, legitimacy, cognition, Dams. In the appropriate circumstances, dams can be and timing. Sometimes the projections do not contain a highly effective way of reducing downstream flood enough new information, or the scale of forecasting is losses, by providing reservoir storage to retain water not useful (Molua 2002; Patt and Gwata 2002; during peak flow events (Green et al. 2000). UNFCCC 2006). However, there are also numerous disadvantages, and dams are not an appropriate form of adaptation in all 2.4.2.2 Supply-sidepublicsectoradaptation cases. These issues are discussed further in section Increasing reservoir storage capacity. Reservoirs are used 5.1.1. extensively throughout the world to store surface water, thus providing a buffer during the dry season. For a Dikes. Large-scale dikes, as opposed to household or discussion of the main advantages and disadvantages of small-scale barriers, are usually constructed and main- this measure, see Box 5.1. tained by the public sector. They are most likely to be used for floodplains that are already intensively used, Desalination. This technological measure can be used such as urban areas and rural areas in non-urbanized at all spatial scales, and can potentially lead to an regions with a history of flood alleviation. A problem "unlimited" availability of freshwater. However, at pres- with dikes is that they may be destroyed by erosion as ent the production of desalinized water is, in general, the river dynamically adapts to changing flows, and energy intensive and relatively expensive, about $1.00/ therefore bank protection and maintenance has to be m3 for sea water and $0.60 for brackish water (Bates et undertaken regularly (Green et al. 2000). al. 2008), although the costs are expected to decrease in the future (Zhou and Tol 2005). Polders. Polders are areas of land surrounded by dikes, known as a ring dike. The ring dike protects the area of Recycling and reuse of (municipal) wastewater. This the floodplain within the dike from riverine flooding, measure can be cost-effective in the long term as it also up to a given design discharge. A problem is that they reduces demand. However, the installation of distribu- interfere with the natural drainage patterns within the tion systems can initially be expensive compared to protected area, and therefore while providing protection other water supply alternatives (Bates et al. 2008). up to a nominal level from flooding from the main river, they may still be flooded by extreme precipitation events Inter-basin transfer of water. Where feasible, this may or local tributaries flowing through the area (Green et be an effective and flexible response to regional al. 2000). D E V E L O P M E N T A N D C L I M AT E C H A N G E D I S C u S S I O N PA P E r S 17 Altering river channel morphology. In the past it was of buildings, thus reducing the pressure on the drain- common to shorten the length of rivers by cutting off age system and the risk of flooding (Bates et al. 2008). meanders and carrying out channel straightening. An extension to this is the concept of green roofs, However, the trend at present is in the opposite direc- which are roofs made of a system of manufactured tion, restoring meanders and avoiding straightening. layers to support the growth of vegetation. Green roofs Decreasing river length increases the gradient, thus have been proven to slow the rate of runoff from the increasing flow velocities and reducing the storage roof (Aerts et al. 2009). capacity of the channel, thereby increasing peak flows. Another option is to increase the capacity of the river Flood forecasting and warning. A flood forecast is a channel by widening, deepening, or reducing resistance; prediction of future flooding; it becomes a flood warning increasing the discharge capacity of rivers can also be when it is received by those who need it in a usable form. carried out by constructing bypass channels to be used Flood warnings must be disseminated within and during peak events. The latter approaches may require between agencies, and need to be delivered to the flood- large investments in dredging to maintain the required plain occupants, along with advice on what measures are depths, and bypass channels can negatively impact on to be taken (Green et al. 2000; UNFCCC 2007). Clearly, the land they cut through (Green et al. 2000). the occupants of floodplains must be informed of the actions to be taken prior to the issuing of warnings, since Detention basins. Like dams, detention basins help to flood warnings are often only available with a limited alleviate flood losses by providing additional water stor- time span before the flood event (Green et al. 2000). age capacity during times of high flow (Green et al. 2000). The concept of giving more room to rivers is Emergency planning, evacuation, and disaster relief. rapidly assuming a key role in flood hazard manage- In developing a flood hazard management policy, it is ment (Aerts and Droogers 2009). Natural wetlands can essential to consider how all floods will be managed be excellent forms of detention basins, and artificial and not just some; that is, not just those up to a nomi- wetlands are also increasingly being constructed to store nal design standard. This means that it is necessary to flood waters (Green et al. 2000). In river basins where design for failure, and how to respond in the event of land is scarce, multi-functional land use planning disastrous flooding. Emergency planning, evacuation, options can be implemented whereby the land use in and flood relief require preparations, including draw- risk-prone areas is harmonious with intermittent inun- ing up contingency plans; training emergency planners dation. For such an approach to be successful, intensive and managers; rehearsing the emergency response; multi-stakeholder participatory approaches must be raising public awareness; educating people on the undertaken in all stages of planning, development, and measures they can take to reduce their vulnerability; implementation (Aerts and Droogers 2009). However, and informing people of the location of shelters and this can be problematic when flood waters are polluted, evacuation centers, and means of evacuation (Green et since the pollutants will be deposited in flood retention al. 2000). basins. Hence, this should be considered in combination with water quality alleviation measures. 2.4.3 "soft" adaptation ­ policies and regulations Increase detention storage in urban areas. In urban To address the broad range of uncertainties involved in areas, buildings and paved impermeable surfaces climate change projections, anticipatory adaptation poli- greatly reduce the rate of infiltration to the soil and cies should be flexible. As such, a policy may be either groundwater, and increase the rate at which water robust, meaning that it allows the system to continue reaches watercourses (Green et al. 2000). Several functioning under a wide range of conditions, or resil- options are available to increase the storage area for ient, meaning that it allows the system to quickly adapt water in cities, including: infiltration trenches and to changed conditions (Smith and Lenhart 1996). The soakaways; permeable or porous pavements for roads adaptation measures described below have not been and car parks; and small retention ponds (Green et al. strictly divided into water supply and flood protection 2000). Excess precipitation can also be stored on roofs options, since some are relevant in both cases. 18 A D A PTATION rELATED TO INDu ST rIAL AND MuNICIPAL WATE r Su PPLy AND rIVE r INE FLOOD PrOTE C T I O N Develop integrated water management options. Support water-efficient technologies. Many water- Basinwide management plans integrate water as a efficient technologies exist that can help to reduce resource and ensure the representation of all users. municipal water demand (see section 2.4.1.1). Policy River basin management plans are already developed measures that can be employed to assist the market all over the world, but are not always effectively penetration of these measures include: appliance implemented, especially in large areas where commu- exchange programs; subsidies to manufacturers; subsi- nication is difficult. Another drawback is that the dies to consumers; and encouraging or requiring the use design and investment cycle of an integrated water of water-saving technologies in new and/or existing management plan is framed so that major projects are buildings (Sutherland and Fenn 2000). expected to be operational for several decades (Downing et al. 1997). With the uncertainties that Establish set-back zones. Relocation of housing and still surround the impacts of climate change, a lack of other susceptible types of land use will reduce the flexibility could potentially lead to shortcomings in losses of climate-related hazards (Bates et al. 2008), management plans. A key aspect in this regard is that in particular flood events. However, relocating people of integrated water resources management, which is is a difficult process. Patt and Schröter (2008) show discussed in Box 2.7. that a relocation program in Mozambique failed because the people perceived the risks of flooding Water pricing. The economic incentive of water pricing differently from policy makers, and they moved back can reduce the demand for water and promote water to the areas they were relocated from. Also, in many reuse (Bates et al. 2008). It could also facilitate a more developing countries, the most vulnerable locations rapid and efficient response to climate change than is are inhabited by poor communities; in order to the case under more rigid schemes for water allocation promote resettlement to less vulnerable areas, these (Smith and Lenhart 1997). However, countries and people must be provided with new houses and subnational jurisdictions differ considerably in the economic opportunities in less vulnerable areas. extent to which their laws, administrative procedures, Forced removal is politically unacceptable, and and documentation of water rights facilitate market- vulnerable people living on floodplains may prefer to based water transfers, while protecting other water uses stay where they are, since their livelihoods may be and environmental values (Downing et al. 1997; based on access to the river and floodplains, and their Kundzewicz et al. 2007). Governments need to facilitate resources. It must be appreciated that flood risks are the cost pricing for water services, and appropriate not the only risk to life and property for these mechanisms must be implemented to protect the poor communities (Green et al. 2000). (Hooper 2006). box 2.7. InTegraTed WaTer resourCes ManageMenT A much-heralded approach to water resources planning is the concept of integrated water resources management (IWrM). In 2002, at the Johannesburg World Summit on Sustainable Development, the Technical Advisory Committee of the Global Water Partnership defined IWrM as "... a process, which promotes the coordinated development and management of water, land and related resources in order to maximize the resultant economic and social welfare in an equitable manner without compromising the sustainability of vital ecosystems ...", and emphasized that water should be managed in a basin-wide context, under the principles of good governance and public participation (rahaman and Varis 2005). IWrM strives for the sustainability of all aspects of water resource management, including policy, management, and implementation. It involves applying knowledge from various disciplines as well as the insights from diverse stakeholders to devise and implement efficient, equitable, and sustainable solutions to water and development problems. It is a comprehensive participatory planning and implementation tool for managing and developing water resources in a way that balances social and economic needs, and that ensures the protection of ecosystems for future generations. Successful integrated water management strategies include capturing society's views; reshaping planning processes; coordinating land and water resources management; recognizing water quantity and quality linkages; con- junctive use of surface water and groundwater; protecting and restoring natural systems; and including consideration of climate change. In addition, integrated strategies explicitly address impediments to the flow of information (Moench et al. 2003). IWrM should be an instrument to explore adaptation measures to climate change, but in this respect it is still in its infancy (Bates et al. 2008). D E V E L O P M E N T A N D C L I M AT E C H A N G E D I S C u S S I O N PA P E r S 19 Phase out development in exposed areas. A legal take action in the emergency response and recovery agreement that restricts development is less radical than phase of a flood, as demonstrated by projects in the establishment of a set-back zone (UNFCCC 2006). Nicaragua and Mozambique (Maskrey 1989). However, it is only applicable in areas where there is a longer period of time to react and where there is abso- 2.4.4 reactive adaptation lute certainty that the area will be exposed in the future. Adaptation that is triggered by past events is often Source control / land use planning. Land use change called reactive. In a sense, reactive adaptation has by upstream can cause changes in discharge and flood definition an anticipatory character, as the adaptive peaks downstream. Hence, policies regarding land use action is taken based on some assessment of likely change, particularly those that aim to reduce or elimi- conditions in the future (Adger et al. 2005). Smit and nate deforestation, can be used to ameliorate flood Pilifosova (2003) argue that autonomous adaptation is control at the source (Andréassian 2004; Calder 1993). mostly reactive. The division between proactive and However, although afforestation normally results in reactive adaptation is therefore extremely fuzzy. Many increased evapotranspiration, it is not clear whether it of the adaptation measures listed in sections 2.4.1 to reduces the risk of the most extreme events (Cosandey 2.4.3 can indeed be taken either reactively or et al. 2005; Green et al. 2000). proactively. Community involvement and participation. As stated 2 . 5 W h aT I s Th e n aTu r e a n d e xTe nT in several of the above examples, community involvement o f a d a pTaT Io n / d e v e l o pM e nT and the participation of all relevant stakeholders greatly d e fI C I T In Th Is s eC To r ? improves the chance of developing and implementing sustainable plans in terms of both water supply and Although climate change is not directly addressed in flooding (Bates et al. 2008). In this regard, NGOs can the eight Millennium Development Goals (MDGs), play a key role. In addition to raising public awareness, most of them are directly or indirectly related to water they can act as intermediaries, identifying technologies, (Kundzewicz et al. 2007). Hence, adaptation to climate facilitating investments, and providing management, change in the water sector (as in all sectors) should be technical, and other assistance (UNFCCC 2006). An carried out in a manner that is synergistic with develop- important part of this process is the incorporation of ment priorities in general (Adger et al. 2007; indigenous knowledge and practices for sustainable water Kundzewicz et al. 2007; Ribot et al. 1996), and climate (Bates et al. 2008) (see Boxes 2.5 and 2.6). policy should be embedded in general development policy. Indeed, many believe that the best hope for Education. Education and information are important adaptation to climate change is through development. elements in a long-term commitment to sustainable Development enables an economy to diversify and development and adaptation in water resources (Green become less dependent on sectors such as (rainfed) agri- et al. 2000; Sutherland and Fenn 2000). culture that are more likely to be affected by climate change. At the same time, adaptation to climate change Information exchange system. The exchange of infor- is seen as essential for development; unless developing mation, knowledge, and experience on adaptation countries can adapt to changes in climate, they are should be shared at all levels, from local to international. unlikely to develop (Narain 2008). Policies and programs that encourage such exchange can play a key role in adaptation and development in The high vulnerability of many developing countries to general (Bates et al. 2008). water stress, drought, and flooding (see section 2.2) demonstrates that adaptation is needed already to Help and counseling. Flood hazard plans should reduce vulnerabilities under current climate and climate account for post-flooding assistance and counseling variability. Adaptation to projected changes in future (Green et al. 2000). There are strong benefits in mobi- climate often has the ancillary benefit of reducing lizing community-based organizations and NGOs to vulnerability with respect to current climate variability 20 A D A PTATION rELATED TO INDu ST rIAL AND MuNICIPAL WATE r Su PPLy AND rIVE r INE FLOOD PrOTE C T I O N (Fankhauser 2006), and therefore contributes to the Like exposure and vulnerability, adaptive capacity is goals of sustainable development. Indeed, adaptation unevenly distributed between countries, and is highly policies in water resource management can be designed differentiated within countries. There are individuals to explicitly provide ancillary benefits in other sectors, and groups within all societies that have insufficient including agriculture, forestry, recreation, and ecosystem capacity to adapt to climate change (Adger et al. services (EEA 2007). 2007). One early finding in hazard research is that within countries, people's ability to adapt, and their From a temporal perspective, adaptation to climate risks access to adjustments, reflect existing divisions can be viewed at three levels, including responses to between rich and poor, powerful and powerless, ethnic (a) current variability (which also reflects learning from or gender-favored, and ethnic or gender-denied (Kates past adaptations to historical climates); (b) observed 2000). Recent analyses in Africa, Asia, and Latin medium- and long-term trends in climate; and America show that marginalized, primary resource- (c) anticipatory planning in response to model-based dependent livelihood groups are particularly vulnera- scenarios of long-term climate change. The responses ble to climate change impacts if their natural resource across the three levels are often intertwined (Adger et base is severely stressed and degraded, or if their al. 2007). The level of adaptation that will be carried out governance systems are in, or near, a state of failure. in a country essentially depends on an evaluation of the For example, women in subsistence farming commu- (expected) costs and benefits of adaptation by the rele- nities in southern Africa are disproportionately vant stakeholders. Such an evaluation does not have to burdened with the costs of recovery and coping with be in the form of a formal cost-benefit analysis, but drought (Adger et al. 2007). some evaluation of gains and losses must be assumed (EEA 2007). Adger et al. (2007) and Bates et al. (2008) describe five main limits on adaptive capacity, many of which are It should also be noted that, if done badly, adaptation specifically relevant to developing countries, namely: can increase the adverse effects of climate change, or have negative effects on sustainable development in · Physical or ecological. It may not be possible to pre- general, thus increasing the so-called adaptation deficit. vent adverse effects of climate change through This is called maladaptation, and is defined by the either technical means or institutional changes; for IPCC (2001) as "... any changes in natural or human example, it may be impossible to adapt where rivers systems that inadvertently increase vulnerability to dry up completely. climatic stimuli; an adaptation that does not succeed in · Technical, political, or social. For example, it may reducing vulnerability, but increases it instead... ." be difficult to find acceptable sites for new reser- voirs, or for water users to consume less. 2.6 a dap TIve C apa CITy · Economic. An adaptation strategy may simply be too costly in relation to its benefits. The level of adaptation that will be carried out in a · Cultural and institutional. Cultural and institu- particular country, region, or locality is not only depen- tional factors include the institutional context dent on the kind and magnitude of the change in within which water management operates, the low climatic conditions, but is dependent on adaptive capac- priority given to water management, lack of coordi- ity. Adaptive capacity is the ability or potential of a nation between agencies, tensions between different system to respond successfully to climate variability and scales, ineffective governance, and uncertainty over change, and includes adjustments in both behavior and future climate change, but also the existence of vio- in resources and technologies (Adger et al. 2007). All lent conflicts and the spread of infectious disease. societies are fundamentally adaptive (Adger 2003), but · Cognitive and informational. For example, water the presence of a sufficient level of adaptive capacity is a managers may not recognize the challenge of cli- necessary condition for the design and implementation mate change, or may give it low priority compared of effective adaptation strategies to reduce the likeli- to other challenges. A key informational barrier is hood and magnitude of the negative impacts of climate the lack of access to methodologies to cope with cli- change (Brooks and Adger 2005). mate change. D E V E L O P M E N T A N D C L I M AT E C H A N G E D I S C u S S I O N PA P E r S 21 2.7 s uMM ary There are, however, considerable uncertainties in assess- ing the impacts of climate change on water resources. There is already much evidence that climate change in These uncertainties come from a whole range of recent decades has had many physical impacts on the sources. The largest uncertainty in terms of climate hydrological cycle. These impacts are expected to change impact assessments on water resources remains continue and intensify over the course of the 21st the uncertainty in precipitation inputs. century. This will affect all water-related sectors, includ- ing water supply, flooding, agriculture, health, industry, Despite these uncertainties, the observed trends in transport, energy supply, ecosystem services, fisheries, water resource change are compelling. Impacts will and forestry. There will be positive as well as negative continue to increase in the 21st century regardless of impacts, but overall the negative effects are expected to whether greenhouse gas emission mitigation takes place. outweigh the benefits. Hence, in addition to mitigation, it is essential to develop adequate adaptation measures to moderate the The availability of water will increase in some parts of impacts and realize the opportunities associated with the world, and decrease in others. However, water climate change. supply is dependent on both demand and availability. Water demand is expected to increase greatly in most All adaptation options have their own pros and cons in parts of the world, with especially large proportional several aspects, including economic, ethical, technological, increases in municipal and industrial demand. Water- social, political, and environmental. The correct measure stress is expected to increase as a result of both climatic or combination of measures is always site- and context- and non-climatic drivers; furthermore, the frequency, specific. Although climate change is not directly magnitude, and geographical extent of droughts are addressed in the eight Millennium Development Goals, projected to increase. most of them are directly or indirectly related to water. Hence, adaptation to climate change should be carried Due to projected increases in precipitation intensity, out in a manner that is synergistic with development increases in the frequency and magnitude of riverine priorities in general, and climate policy should be embed- flood events are expected in many parts of the world. ded in general development policy. Adaptation measures Non-climatic drivers such as land-use change, river should address economic, environmental, and social confinement, and other measures also play a crucial welfare in an equitable manner, and should address issues role in flood probability. Flood risk is the product of in a basinwide context, following the principles of good flood probability and the damages caused by flooding. governance. Furthermore, key facets are public participa- The damages associated with floods (both economic tion and the use of local knowledge in planning, develop- and loss of life) are expected to increase due to ment, and maintenance of adaptation strategies, whether augmented economic values and population density in they be structural or policy measures. These issues are flood-prone areas. addressed in the concept of integrated water resource management, which should be used as an instrument to The impacts of climate change on the water sector will explore adaptation measures to climate change. be felt in both developed and developing countries, but the worst impacts are expected in the latter due to a The level of adaptation that will be carried out in a combination of physical and social characteristics. As a particular country, region, or locality is not only depen- result of climate change, water stress is expected to be dent on the kind and magnitude of the change in worst in semi-arid and arid river basins. Africa is espe- climatic conditions, but also on the way people are able cially vulnerable to climate change in terms of water to prepare or react: their adaptive capacity. supply. Although many areas of the world are expected Marginalized, natural-resource-dependent livelihood to be hit by increased flood risk, the highly populated groups are particularly vulnerable to climate-change mega-basins of Asia may be affected worst. impacts. 22 3. lITeraTure revIeW increases in loss are also attributable to several non- climatic drivers, climatic factors are also partly responsi- ble (Kundzewicz et al. 2007). In this section, we provide a summary of some of the key works that have been carried out with regard to the Efforts to quantify the economic impacts of future economic aspects of climate change and adaptation in climate-related changes in water resources are hampered the fields of water supply and flood protection. At a by a lack of data, the uncertainties described in section regional and global scale, such analyses are currently 2.3, and by the fact that the estimates are highly sensi- limited (Adger et al. 2007; EEA 2007; Kuik et al. 2008). tive to both the cost estimation methods and the differ- One of the reasons for this is that it is first necessary to ent assumptions used with regard to the allocation of have a thorough understanding of (a) the expected changes in water availability across various types of physical impacts of climate change on the hydrological water use (Changnon 2005; Schlenker et al. 2005; cycle, and (b) the impacts of those changes on water- Young 2005). In some regions, hydrological changes related sectors, before it is possible to assess the costs of may have impacts that are positive in some aspects and climate change and adaptation. We first summarize a negative in others; for example, increased annual runoff number of studies that have estimated the possible may produce benefits for a variety of both in-stream impacts of climate change on the water sector in and out-of-stream water users by increasing renewable economic terms, and then review studies that have water resources, but may simultaneously increase flood specifically examined the costs of adaptation. risk. Overall, the IPCC states that it is very likely that the costs of climate change to the water sector will outweigh the benefits globally (Bates et al. 2008). 3.1 p rev I ous s Tud Ies on T he Co s Ts of ClIM aTe Change Most of the studies examining the economic impacts of climate change on the water sector have so far been Between the 1950s and the 1990s, the annual economic carried out at the local, national, or river-basin scale, losses from large extreme events, including floods and and the global distribution of such studies is skewed droughts, increased tenfold, with the developing world toward developed countries (Chen et al. 2001; Choi and being hardest hit (Kabat et al. 2003). Currently, flood Fisher 2003; Dore and Burton 2001; Evans et al. 2004; damage constitutes about a third of the economic losses Hall et al. 2005; Kirshen et al. 2005, 2006; Middelkoop inflicted by natural hazards worldwide (Munich Re et al. 2001; Schreider et al. 2000). Nevertheless, studies 2005), and the economic losses associated with floods that have assessed the economic impacts of climate vari- worldwide have increased by a factor of five between the ability on floods and droughts in developing countries periods 1950­80 and 1996­2005 (Kron and Berz 2007). have found these to be substantial. For example, the cost From 1990 to 1996 alone, there were six major floods to Kenya of two extreme events--the floods associated throughout the world in which the number of fatalities with the 1997/8 El Niño event and the drought associ- exceeded 1,000, and 22 floods with losses exceeding ated with the 1998­2000 La Niña event--show a cost $1 billion each (Kabat et al. 2003). Although these to the country of 11 percent of its GDP for the former, D E V E L O P M E N T A N D C L I M AT E C H A N G E D I S C u S S I O N PA P E r S 23 and 16 percent of GDP for the latter (World Bank developed a model of climate change impacts that 2006a). According to the same study, floods and accounts for the dynamics of climate change and the droughts are estimated to cost Kenya about 2.4 percent systems affected by it. For the water resources sector, of its GDP annually, and water resources degradation a many simplifying assumptions were made to develop a further 0.5 percent. As these are likely to become more simple ad hoc model of the impacts of climate change on pronounced with climate change, economic costs can be water resources. This model shows a loss to world GDP expected to be more substantial in the future, holding ranging from 0.5­1.5 percent by 2200 (Tol et al. all other factors constant. For Ethiopia, economywide 2002b). The authors clearly acknowledge and highlight models incorporating hydrological variability show a the caveats of these studies, stating that the results are drop in projected GDP growth by up to 38 percent indications of potential pressure points and relative compared to when hydrological variability is not vulnerabilities, and should not be used as predictors or included (Mogaka et al. 2006). However, it is not as input to decision analyses. hydrological variability per se that causes the problem, but rather an extreme vulnerability to it due to a lack of the necessary capacity, infrastructure, and institutions to 3 . 2 p r e vI o u s sTu d Ie s o n T h e C o sT s mitigate the impacts (Grey and Sadoff 2007). Similarly, o f a d a pTaT Io n To ClI M aT e Ch a n g e future flood damages will depend not only on changes in the climate regime, but also on settlement patterns, Considering the importance of adapting to climate land-use decisions, flood forecasting quality, warning change in the water sector, the literature on this topic is and response systems, and other adaptive measures limited (EEA 2007; Kuik et al. 2008). Estimates of the (Andréassian 2004; Calder 1993; Changnon 2005; costs of adaptation to climate change across sectors at Mileti 1999; Pielke and Downton 2000; Ward and the global scale were not available until 2006. Since Robinson 1999; Ward et al. 2008; WCD 2000). then, several multisectoral estimates of these costs have become available (Oxfam 2007; Stern 2006; UNDP At the regional scale, the Association of British Insurers 2007; UNFCCC 2007; World Bank 2006b). These (ABI) estimated the financial costs of climate change studies are discussed in the main EACC report, and are through its effects on extreme storms (hurricanes, therefore not discussed further here. typhoons, and windstorms) by using insurance catastro- phe models. They found that climate change could At the local, national, and river basin level, the increase the annual cost of flooding in the U.K. almost geographical distribution of research is skewed toward 15-fold by the 2080s under high-emission scenarios. If developed countries, although examples do exist in climate change increased European flood losses by a developing countries. Examples include the costs of similar magnitude, they estimate that costs could adaptation measures to maintain water quality in the increase by up to $120­$150 billion, for the same high- Assabet River near Boston, Massachusetts in the U.S. emission scenarios (ABI 2005). (Kirshen et al. 2006); the costs of adaptation to main- tain the availability of drinking water supply and the An early global study by Fankhauser (1995) estimated capacity of treating wastewater in Toronto, Canada the regional impacts of a temperature increase of 2.5°C (Dore and Burton 2001); water management adaptation in various sectors, converted these to dollars, and then costs and benefits for the Berg River in South Africa summed them to the global level. For the global water through the establishment of an efficient water market sector, this yielded an estimated loss of about $47 and an increase in water storage by constructing a new billion (in 1995 U.S. dollars). Tol (2002a) derived dam (Callaway et al. 2007); the costs of defending the benchmark estimates of the costs of climate change in Netherlands against increased river and coastal flooding several sectors based on a review of climate change as a result of climate change (Aerts et al. 2008); the literature. For the water resources sector, this led to a costs of adaptation to reduce flood damage in the Rhine loss of about $84 billion (in 2002 U.S. dollars) for the basin in Europe (EEA 2007); and the costs of diverting world as a whole for a global temperature increase of water and building new water infrastructure at an accel- 1.0°C. In an accompanying study, Tol (2002b) erated pace in order to cope with a reduction in water 24 A D A PTATION rELATED TO INDu ST rIAL AND MuNICIPAL WATE r Su PPLy AND rIVE r INE FLOOD PrOTE C T I O N yields and supply in Quito, Ecuador, as a result of water. The results suggest that the adaptation costs will glacier retreat (Vergara et al. 2007). amount to about $531 billion (in 2000 U.S. dollars) in total for the period up to 2030. Of this, 85 percent is A regional study of the effects of climate change on estimated to be required in developing countries, water supply is available for Sub-Saharan Africa mainly Asia and Africa. These costs refer to the adap- (Muller 2007). This research estimated the costs of tation costs associated with both socioeconomic and adapting urban water infrastructure in the region to climatic changes. The assessment of Kirshen (2007) climate change to be $2­$5 billion per year. This study was subsequently modified in UNFCCC (2007). In assumes that: (a) reliable yields from dams will reduce at this study, two further costs were included, namely the the same rate as stream flow (for example, a 30 percent increased cost of reservoir construction since the best reduction in stream flow will mean a 30 percent reduc- locations have already been taken, and unmet irrigation tion in reliable yield); (b) where waste is disposed into demands. This report suggests that the total costs of streams, a reduction in stream flow by x percent will adaptation worldwide for the period up to 2030 will be mean that the pollutant load must be reduced by x $639 billion under SRES B1 and $797 billion under percent; and (c) power generation reduces linearly with SRES A1b. It is assumed that 25 percent of these costs stream flow. The costs of adapting existing urban water are specifically related to climate change, and hence the storage facilities are estimated at $0.05­$0.15 billion/ cost of adaptation to climate change worldwide in the year, and the costs of additional new developments are water supply sector up to 2030 is estimated at $9­$11 estimated at $0.015­$0.05 billion/year. For wastewater billion per annum. Of these costs, 85 percent are esti- treatment, the adaptation costs of existing facilities are mated to be required by developing countries estimated at $0.1­$0.2 billion/year, and the costs of (UNFCCC 2007). additional new facilities are estimated at $0.075­$0.2 billion/year. 3 . 3 h o W o u r sTu d y C o M p l eM e nTs e xI s T In g Wo r k To date, there is only one assessment of the costs of adaptation in water resources at the global level In terms of estimating the costs of adaptation to (Kirshen 2007). This study estimates the global costs of climate change for industrial and municipal water adaptation associated with additional water infrastruc- supply, our study builds on the work of Kirshen (2007) ture needed by 2030 to provide a sufficient water in several ways: (a) the method used to estimate the supply, given present and future projected water costs of additional reservoir storage requirements is demands and supplies in more than 200 countries. more detailed; (b) the study of Kirshen examined the Costs are estimated for four main water supply utilities: combined costs of adaptation to socioeconomic devel- additional surface storage reservoirs; additional ground- opment and climate change. This was subsequently water wells; desalination plants; and water reclamation updated by UNFCCC (2007), who attempted to delin- technologies. The study first compares future projected eate the climate-change-related costs, by assuming water demands from different sectors to water supplies. them to be 25 percent of the total costs. In the present Next, the need for additional supply infrastructure is study, we carry out socioeconomic baseline analyses determined, based on an assumed international legisla- without climate change, and analyses with socioeco- tion that would limit water withdrawals in 2050 to 40 nomic baseline changes and climate change, and percent of total available national water resources. If a climate change only, in order to derive an improved country's water withdrawal requirements can be delineation; and (c) the time-horizon of our study (to covered by its internal water availability, the additional 2050) is longer than in the Kirshen study (to 2030). reservoir storage and wells required (and their costs) are The work of Kirshen does, however, provide more determined. If a country cannot meet the international detailed information on the costs of water supply legislation (that is, withdrawal requirements exceed 40 through additional groundwater wells, desalination percent of mean annual flows), it resorts to (in order of plants, and water reclamation technologies, as these are priority) desalination for domestic/commercial demand, lumped into one single category--alternative supply reclaimed water for irrigation demand, and virtual measures--in the present study. D E V E L O P M E N T A N D C L I M AT E C H A N G E D I S C u S S I O N PA P E r S 25 We are not aware of any previous studies on estimating methodologies and data, and the time available for this the global costs of adaptation related to riverine flood assessment, render a flood-risk-based analysis impossi- protection. The results presented in this study should ble in this study (see section 6.2). The results do, therefore be treated as preliminary indicative costs. however, provide a benchmark against which future Many limitations remain, not least the fact that flood studies can be compared. risk (probability x damage) is not assessed. Current 26 4. MeThodology The cost estimates were then aggregated from the FPU level to the country level, and then to the level of the six World Bank regions, namely: East Asia & Pacific In this section, we describe the methods used to address (EAP); Europe & Central Asia (ECA); Latin America the third and fourth aims of this consultancy, namely: & Caribbean (LAC); Middle East & North Africa (MENA); South Asia (SA); and Sub-Saharan Africa · Estimate the climate change adaptation costs in the (SSA). Additionally, we report the results for all coun- industrial and municipal water supply sector; tries in these regions together (DC; Developing · Estimate the climate change adaptation costs for Countries), and for countries that do not belong to one riverine flood protection. of the World Bank regions (Non DC; non-developing countries). The results are not presented for individual In section 4.1, the geographical and temporal scale of FPUs or countries, since the aim of this work is to the study is described; in section 4.2, we describe the provide an estimate of the costs of adaptation to climate scenarios used. The model and method used to simulate change at the global scale. Moreover, the costs of indi- changes in the hydrological cycle are described in vidual FPUs or countries may be greatly under- or over- section 4.3. The methods used to estimate the costs of estimated, as is the case with any study with a focus on adaptation in terms of industrial and municipal water the global level. Instead, more detailed and complemen- supply and riverine flood protection are described in tary country case studies are being carried out as part of sections 4.4 and 4.5 respectively. the EACC project. 4.1 geograph ICal and Te Mpora l Throughout the EACC study, the year 2050 is used as s Cale the primary time horizon for analysis. This time hori- zon was chosen for a number of reasons, including: All analyses were initially carried out at the geographi- (a) this is a relevant timeframe for current infrastructure cal scale of the food producing units (FPUs) of IFPRI planning; and (b) beyond 2050, uncertainties in projec- (The International Food Policy Research Institute) and tions increase dramatically. For some of the EACC IWMI (International Water Management Institute). analyses, the year 2030 is also used, which allows for These FPUs divide the world into 281 sub-basins some assessment of the temporal evolution of costs (Figure 4.1), where each sub-basin represents a hybrid between now and 2050. The changes in climate at 2030 between river basins and economic regions. The world and 2050 are relative to the climate in the period 1961­ is divided into (a) 125 major river basins of various 90, as derived from data sets of the Climate Research sizes; and (b) 115 economic regions made up mostly of Unit (CRU), and described in the main EACC report. single nations with a few regional groupings of nations; The years 2030 and 2050 represent decadal averages of the intersection of these two maps is used to create the monthly climate model output. In other words, when FPUs (Cai and Rosegrant 2002; De Fraiture 2007; reporting climate in 2030 or 2050, this actually refers to Rosegrant et al. 2002). the average climate in the period 2025­35 or 2045­55, respectively. D E V E L O P M E N T A N D C L I M AT E C H A N G E D I S C u S S I O N PA P E r S 27 fIgure 4.1. Map shoWIng The fpus (food produCIng unITs) used as The basIC geographICal unIT of sTudy In ThIs projeCT (as developed by IfprI and IWMI) 115 Regions X 126 H2O Basins 281 Food Producing Units Source: IfrpI 2009. 4.2 sC enarIos across the sectoral studies; these scenarios are described in the main EACC report. The climate scenario data As stated in section 2, climate change adaptation should used in this study were from two GCMs carried out for be carried out in the context of sustainable development the Fourth Assessment Report (AR4) of IPCC (IPCC, in general. Hence, the impacts of climate change, and 2007) using the SRES emissions scenario A2. The the costs of adapting to those impacts, should be GCMs used are NCAR_CCSM3 and CSIRO_MK3, assessed relative to a baseline socioeconomic situation in hereinafter referred to as NCAR and CSIRO the future, rather than relative to current conditions. respectively. Using this approach, one should firstly estimate the costs of adapting to some notional level under the 4 . 3 r aI n fa l l - r u n o f f sI M u l aT Io n s future socioeconomic baseline, without climate change. Then, the costs of adapting to the same notional level The effects of climate change on the water cycle were can be assessed under the future socioeconomic baseline, assessed using the rainfall-runoff model CLIRUN-II, but including the impacts of climate change. The costs run on a monthly time-step at a spatial resolution of of adapting to climate change are then the residual of 0.5° x 0.5°. The key parameters simulated by the baseline costs with climate change, and the baseline CLIRUN-II and used in the present study were costs without climate change. monthly runoff and the magnitude of the 10-year and 50-year maximum monthly runoff (10-year and 50-year In the EACC study, standard scenarios of socioeco- monthly floods). A description of the rainfall-runoff nomic development and climate change have been used model, and the methods used to simulate these runoff 28 A D A PTATION rELATED TO INDu ST rIAL AND MuNICIPAL WATE r Su PPLy AND rIVE r INE FLOOD PrOTE C T I O N parameters, is provided in an accompanying EACC withdrawals is not allowed to increase above 80 background paper. percent of total runoff. The figure of 80 percent was reached through expert judgment during a 4.4 IndusTrIal and MunICIpal WaTe r World Bank consultation. However, this standard is supply Me T hodology arbitrary, and underscores the need for basin-scale planning when actual water management plans are In this study, the cost of adaptation is defined as the being developed and implemented, and/or; cost of providing enough raw water to meet future ii. The cost of supplying water from reservoir yield is industrial and municipal water demand, based on coun- in excess of $0.30/m3. In these cases, supply was try-level demand projections until 2050. The costs are assumed to be met through a combination of alter- estimated for the following scenarios: native backstop measures--such as recycling, rain- water harvesting, or desalination--at a cost of a. Socioeconomic baseline (Baseline): accounts for $0.30/ m3. This cost represents an estimate of the changes in industrial and municipal water demand average cost of these alternative measures (see between the present and 2030, and between the Box 2.4). present and 2050. The demand projection was derived from the socioeconomic scenario described The required adaptation measures were determined first in the main EACC report. The percentage change for the year 2030, and then for the year 2050. It was in industrial and municipal water demand per FPU then assumed that the required measures are imple- is shown in Figure 2.1. mented linearly between 2010 and 2030, and again b. Baseline and climate change (Baseline & CC): between 2030 and 2050. We assume operations and assumes the changes in water demand described maintenance (O&M) costs of reservoirs of 2 percent of above, and also accounts for changes in water avail- construction costs per annum, as stated in Palmieri et al. ability due to climate change, as simulated using the (2001), and as the mean of the estimate of 1­3 percent rainfall-runoff model CLIRUN-II. stated in WCD (2000). c. Climate change only (CC): difference between baseline and baseline & CC scenario. Estimating the additional reservoir storage capacity requirement. The additional reservoir storage capacity Increased water demand between present and the future required (compared to the present) was calculated using scenarios was assumed to be met primarily through storage-yield curves. Storage-yield curves show the stor- reservoir yield by increasing the capacity of surface age capacity that is needed to provide a given firm yield reservoir storage, except for when: and reliability of water supply over the course of a year; that is, basin yield. Basin yield is a better indicator of i. Increasing supply from reservoir yield would the reliability of water supply than annual runoff, as increase withdrawals to more than 80 percent of annual runoff only provides information on changes in river runoff. If current water withdrawals are annual water availability, and not on its variability or already in excess of 80 percent of total runoff, we accessibility for water supply needs (World Bank 2009). did not allow the percentage of withdrawals to In Figure 4.2, typical storage-yield curves are shown for increase further. There is an extensive literature a hypothetical basin. The blue line shows the reservoir devoted to determining how much water a river storage required to provide different yields, given the needs to sustain a healthy aquatic ecosystem and mean annual runoff (inflow) indicated by the dotted ecosystem function (for a review, see Tharme 2003). blue line. Climate change can cause changes in mean In reality, assessments must be carried out at the basin runoff and in runoff variability, and hence can local level, based on local physical, socioeconomic, change the shape of the storage-yield curve. For exam- and political considerations. In a global study of this ple, the red line shows the potential effects of reduced nature, it is not possible to address such consider- annual runoff and/or variability on the storage-yield ations as much more rigorous analyses are required curve. Given a lower mean annual runoff, more reservoir (Vogel et al. 2007), and hence a generalized rule of storage capacity is required to achieve the same yield. thumb was applied whereby the percentage of water The difference between K and K' is the additional D E V E L O P M E N T A N D C L I M AT E C H A N G E D I S C u S S I O N PA P E r S 29 storage-yield curve, and the maximum value of St from fIgure 4.2. exaMple of a TypICal these values is the storage required for the time-series of sTorage-yIeld Curve for a inflows used. In the present study, monthly values were hypoTheTICal basIn used to calculate the storage requirement, as using only annual values can lead to an overestimation of each Yield (MCM/Yr) 2005 Average Annual Flow storage level since the storage may not be able to handle the within-year monthly variability of inflow. The monthly time-series of inflow and net evaporation were derived from the CLIRUN-II simulations. Y2005 2050 Average Annual Flow Y2050 Estimates of current reservoir storage per FPU were previously calculated for IWMI's WATERSIM model, based on data from the World Register of Dams (ICOLD 1998). This version of the ICOLD data base provides the storage capacity of more than 25,000 dams around the world. However, the data are not georefer- K K' Storage (MCM) enced, and are not available as a GIS data base. Source: World bank 2009. Therefore, Strzepek (in prep.) carried out an exhaustive Notes: due to climate change, the discharge in the year 2050 (dotted red analysis to identify and plot the geographical coordi- line) is lower than in 2005 (dotted blue line), and therefore the storage-yield nates of the reservoirs accounting for 90 percent of total curve is lower (diminishing returns of yield for the same level of storage). The distance between k and k' shows the additional storage requirement that is reservoir storage per country, and then used a GIS to needed to compensate for the loss in basin yield between 2005 and 2050 reassign and map these to the FPUs used in this study. due to climate change. ICOLD has a more recent data set (ICOLD 2003), but in terms of the current analysis there is little value- added in repeating this exhaustive and time-intensive storage needed to compensate for the loss in basin yield exercise, given that the amount of storage added between 2005 and 2050 due to climate change. An between 1998 and 2003 was small at the global scale, increase in demand for water due to changes in the relative to the total stock that already existed in 1998 socioeconomic situation of a basin can also affect the (ICOLD 2003), and given the time and resources avail- yield and storage requirement irrespective of changes in able for this study. climate. If the demand for water increases in a basin, one moves upward along the storage-yield curve, since Estimating the cost of reservoir storage construction. an increase in demand requires a higher yield, and Ideally, a global data base showing the construction costs hence a higher storage capacity. of existing reservoirs would allow us to calculate average construction costs per country. However, such a data For this study, storage-yield curves were developed for base does not exist. Hence, in this study we used and each FPU, and for each climate scenario. The curves modified a methodology developed jointly by the U.S. were established using a modified version (Wiberg and Army Corps of Engineers (USACOE) and the U.S. Strzepek 2005) of the sequent peak algorithm approach Bureau of Reclamation (USBR) to estimate the (Thomas and Fiering 1963), whereby: construction costs of reservoir storage per FPU. The method was modified by Löf and Hardison (1966), who { Rt + Et ­ 1 ­ Pt ­1 ­ Qt + St­1 if positive developed a table of storage-cost relationships for 11 St = O otherwise (Eq. 1) size classes of reservoirs and 10 physiographic zones in the United States. These tables were subsequently where S is the reservoir storage capacity, R is the release, updated by Wollman and Bonem (1971) based on 1960s E is the evaporation above the reservoir, P is the precip- U.S. construction technology. The data used were both itation above the reservoir, Q is the inflow, and t is the current time period. Equation 1 is applied for each 1 http://www.iwmi.cgiar.org/Tools_And_Resources/Models_and_ period in the time-series used to construct the Software/WATSIM/index.aspx 30 A D A PTATION rELATED TO INDu ST rIAL AND MuNICIPAL WATE r Su PPLy AND rIVE r INE FLOOD PrOTE C T I O N geographically comprehensive, since they are based on vegetation, water, and climate. For many parts of the the physiographic zones of the entire United States, and world, detailed information on all of these parameters are very complete. During the period 1940 to 1970, over are not readily available, which makes it difficult to 60 percent of all U.S. reservoir storage capacity was built, assign FPUs to a physiographic zone. However, and most of it under the direction of the USACOE and Strzepek (in prep.) found that the most important the USBR. Recent work shows that in the period 1970 single factor explaining the differences in reservoir stor- to the present neither these relative cost relations nor the age construction costs is the slope of the basin in which costs in real terms have changed substantially in the U.S. the reservoir is situated, and derived relationships (Wiberg and Strzepek 2005). Hence, we updated the between mean FPU slope and construction costs per latter tables to obtain construction costs per cubic meter cubic meter for each of the 11 size classes used in the in 2005 U.S. dollars (Table 4.1). original work. We used these relationships (updated to 2005 U.S. dollars; Table 4.2) to estimate the costs of Given that the spectrum of physiographic zones within construction per FPU. The mean slope per FPU was the U.S. encompasses many of the physiographic zones derived from high-resolution digital elevation models: that are found elsewhere in the world, it is assumed that HYDRO1k2 for most regions; and GTOPO303 for these relative cost structures between physiographic Greenland and Australia (since these are not available zone and reservoir size will be similar around the globe in the former data set). (Wiberg and Strzepek 2005), even though the absolute costs will vary due to differences between countries in Construction index multipliers derived from the work heavy civil engineering construction costs. Therefore, of Compass International Consultants Inc. (2009) were these relative cost structures can be transferred to FPUs then applied to each FPU to account for cost differ- outside the U.S. by using indices to adjust for the rela- ences in heavy civil engineering construction. These tive difference in heavy civil engineering construction multipliers are available for 88 countries, and provide a between the U.S. and the FPU in question. factor by which U.S. construction costs can be multi- plied to estimate mean construction costs in those The physiographic zone classifications used by Löf and Hardison (1966) are based on a complex combination of factors that characterize the region in which they are 2 http://edc.usgs.gov/products/elevation/gtopo30/hydro/index.html located, namely: slope, geology, landform, soil, 3 http://edc.usgs.gov/products/elevation/gtopo30/gtopo30.html Table 4.1. ConsTruCTIon CosTs of reservoIr sTorage CapaCITy per CubIC MeTer In The unITed sTaTes (In 2005 u.s. dollars) Size class (million m3) Class Class Class Class Class Class Class Class Class Class Class I II III IV V VI VII VIII IX X XI Physiographic zone 0­25 25­49 49­74 74­123 123­247 247­493 493­1233 1233­2467 2467­4934 4934­12,335 > 12,335 A 1.21 1.00 0.92 0.85 0.76 0.69 0.59 0.53 0.46 0.40 0.32 B 1.05 0.84 0.76 0.69 0.61 0.53 0.45 0.40 0.34 0.29 0.23 C 0.93 0.71 0.63 0.58 0.50 0.42 0.35 0.29 0.26 0.21 0.16 D 0.84 0.63 0.55 0.49 0.41 0.34 0.27 0.22 0.19 0.15 0.09 E 0.82 0.61 0.52 0.45 0.38 0.32 0.25 0.20 0.17 0.13 0.08 F 0.76 0.56 0.48 0.42 0.34 0.29 0.23 0.18 0.15 0.11 0.06 G 0.75 0.54 0.46 0.40 0.32 0.27 0.20 0.16 0.13 0.10 0.05 H 0.65 0.45 0.38 0.33 0.27 0.21 0.16 0.13 0.10 0.08 0.04 I 0.50 0.34 0.29 0.24 0.20 0.16 0.12 0.10 0.08 0.05 0.03 J 0.32 0.23 0.19 0.17 0.13 0.11 0.08 0.06 0.05 0.04 0.02 D E V E L O P M E N T A N D C L I M AT E C H A N G E D I S C u S S I O N PA P E r S 31 Table 4.2. sTorage-CosT relaTIons beTWeen Mean fpu slope, and average reservoIr sTorage CosTs per CubIC MeTer. The CosT In 2005 u.s. dollars (y) Is a funCTIon of The Mean fpu slope In degrees (x). Reservoir size class Storage capacity (million m3) Storage-cost relation R2 I 0­25 y = 0.0192x2 + 0.0525x + 0.5677 0.566 II 25­49 y = 0.0288x2 ­ 0.0043x + 0.4348 0.607 III 49­74 y = 0.0332x2 ­ 0.0303x + 0.3886 0.623 IV 74­123 y = 0.0361x2 ­ 0.0508x + 0.3567 0.614 V 123­247 y = 0.0363x2 ­ 0.0592x + 0.3019 0.638 VI 247­493 y = 0.0359x2 ­ 0.0655x + 0.2569 0.653 VII 493­1233 y = 0.0357x2 ­ 0.0750x + 0.2150 0.663 VIII 1233­2467 y = 0.0353x2 ­ 0.0804x + 0.1849 0.685 IX 2467­4934 y = 0.0333x2 ­ 0.0807x + 0.1625 0.684 X 4934­12,335 y = 0.0326x2 ­ 0.0847x + 0.1392 0.699 XI > 12,335 y = 0.0306x2 ­ 0.0874x + 0.1084 0.697 88 countries. For countries for which no construction reservoirs were Aylward et al. (2001) and references cost multiplier is available, we used the mean multiplier therein; World Bank (1996); Merrow and Shangraw Jr. of all listed countries in the same World Bank region. (1990); and World Bank project performance assess- As the construction cost index is available at the coun- ment reports, implementation completion and results try level, while the spatial unit of analysis in this study reports, performance audit reports, and project comple- is the FPU, we calculated a construction cost multiplier tion reports. From these sources we established, where for each FPU as follows. Most FPUs only contain land possible, the total project costs, reservoir construction area of one country; for these FPUs the construction costs, valuation currency, and year of valuation. In some index of the relevant country was used. For those FPUs cases, the year of valuation was unclear, or significantly covered by several countries, the construction index of different cost estimates were given in different sources; the FPU was calculated based on the proportion of the these reservoirs were excluded from further analysis. We FPU covered by each country, such that: also excluded reservoir projects that were constructed over a number of phases, and for which it was not CIFPU = n (CICTRYi x ACTRYi) + (CICTRYi+1 x ACTRYi+1)... i possible to separate the costs associated with each proj- (CICTRYn x ACTRYn) (Eq. 2) ect phase. We then established the storage capacity of each of the reservoirs. The primary source for this where CI is the value of the construction index multi- purpose was the World Register of Dams (ICOLD plier for a given FPU, and A is the proportion of the 2003). However, we also referred to the data base of surface area of that FPU covered by a given country Chao et al. (2008), which provides corrections to the (CTRY). ICOLD data base for a number of dams, using the latter source where discrepancies existed. In all cases, we Validation of the reservoir storage cost estimation checked the value of storage capacity in ICOLD with method. In order to validate this method we used it to the capacity stated in the original source; where a large estimate the construction costs of 85 existing reservoirs discrepancy existed, we removed these reservoirs from for which we identified the costs and storage capacities further analyses. For 40 percent of the reservoirs, from published literature, and compared the actual specific estimates were given of both total costs and recorded costs to our cost estimates. The main sources construction costs, while for 60 percent of the reservoirs that we used to construct the data base of 85 costed only total costs could be derived. For the reservoirs for 32 A D A PTATION rELATED TO INDu ST rIAL AND MuNICIPAL WATE r Su PPLy AND rIVE r INE FLOOD PrOTE C T I O N which both total costs and construction costs were (or even per region or globally). Therefore, for this known, we calculated the ratio of construction costs to study the cost estimates were carried out using three total costs, and used this to estimate the construction scenarios of future reservoir size distribution: (1) small costs of the remaining reservoirs. dams (all future reservoirs have a storage capacity under 25 million m3); (2) large dams (all future reservoirs This filtering process resulted in a data base of 85 have a storage capacity greater than 12,335 million costed reservoirs (Figure 4.3), with a good geographical m3); and (3) best estimate. In the latter scenario, we coverage over most of the World Bank regions assume that future reservoir construction will follow (although relatively few reservoirs in MENA (3) and the same size distribution as in the 21st Century in the SA (5)). Summing the total observed and estimated United States, based on the size distribution of indus- construction costs for all of these reservoirs (about trial and municipal water supply storage reservoirs in $58.0 billion and $65.3 billion, respectively), and divid- the Major Dams of the United States data set ing these by the total storage capacity (about 498.0 (National Atlas of the United States 2006)) km3), led to an estimated cost of reservoir storage of (Figure 4.4). The small dams and large dams scenarios $0.116/m3 based on the observed costs, compared to do not represent realistic future scenarios; instead they $0.130 based on our estimation method. Hence, the give the maximum and minimum values associated method used provides a good estimate of the costs of with the method. It would be prudent to use distribu- reservoir construction globally. tions of storage capacity based on analyses of each country separately, but comprehensive georeferenced Estimation of future reservoir storage capacity distri- reservoir data bases for this purpose are not currently butions. The cost of reservoir construction depends available (see section 6.2). strongly on the capacity of the reservoir, as shown in Table 4.1. Therefore, in order to estimate the costs of 4 . 5 r I v e rI n e f l o o d p r oTe C T Io n adding reservoirs in the future, information is required on how large those reservoirs will be, in terms of capac- In this study, the cost of adaptation in terms of riverine ity. However, there is no global data base of planned flood protection is defined as the cost of providing flood dam or reservoir projects that can be used to assess the protection against the 50-year monthly flood in urban future size class distribution of reservoirs per country areas, and the 10-year monthly flood in agricultural fIgure 4.3. loCaTIon of The reservoIrs In our daTa base of reservoIr ConsTruCTIon CosTs and sTorage CapaCITy D E V E L O P M E N T A N D C L I M AT E C H A N G E D I S C u S S I O N PA P E r S 33 areas. The costs were estimated under the following scenarios: fIgure 4.4. reservoIr sTorage of IndusTrIal and MunICIpal WaTer supply Socioeconomic baseline (Baseline): costs of providing reservoIrs In eaCh of The reservoIr riverine flood protection based in 2050, without climate sIze Classes shoWn In Table 4.1, change. Assumes no flood protection is in place at expressed as a perCenTage of The present. ToTal reservoIr sTorage of IndusTrIal and MunICIpal WaTer supply reservoIrs Baseline and climate change (Baseline & CC): assumes that the costs of adaptation in the baseline will increase 35 or decrease by the same percentage as the percentage 30 change in magnitude of the 50-year monthly flood (for 25 urban areas) or the 10-year monthly flood (for agricul- 20 % tural areas). 15 10 Climate change only (CC): difference between baseline 5 0 and baseline & CC scenario. I II III IV V VI VII VIII IX X XI Reservoir size class The magnitude of the 10-year and 50-year monthly Source: national atlas of the united states, 2006. flood was estimated using the rainfall-runoff model Note: data are taken for all reservoirs listed in the Major dams of the united CLIRUN-II, using the methodology described in the states data set. accompanying EACC background paper. The costs of adaptation were based on providing riverine flood protection to the given nominal standards, via a system of dikes and polders. The costs of providing these stan- The required adaptation measures were calculated for dards of flood protection were assumed to be $50,000 the year 2050, and were then assumed to be imple- per km2 for urban areas, and $8,000 per km2 for agri- mented linearly in the intervening 40 years. We assume cultural areas. These cost estimates were derived O&M costs of 0.5 percent of construction costs per through a review of World Bank project performance annum, which is consistent with the maintenance costs assessment reports, implementation completion and of river dikes in coastal areas in the coastal sector work results reports, performance audit reports, and project of the EACC study. completion reports. Land use data were taken from the data base of the Center for Sustainability and the Global Environment (SAGE)4 (Foley et al. 2003; Leff 2003; Ramankutty and Foley 1998). 4 www.sage.wisc.edu 34 5. resulTs and dIsCussIon 5.1.1 Water supply The estimated costs of adaptation in the industrial and In this section we present the main results of the municipal water supply sector per 5- year period in the analytical work. The limitations of the study, and baseline, baseline & CC, and CC scenarios, for discount accompanying recommendations are given in section rates of 0 percent, 3 percent, 5 percent, and 7 percent, can 6.2. Maps showing the change in industrial and be found in Appendixes 1 to 8. For the climate change municipal water demand per FPU in the EACC scenarios, both net and gross costs are shown. The overall socioeconomic scenario between 2005 and 2030, and costs are highly sensitive to the choice of discount rate. between 2030 and 2050, can be found in Figure 2.1. In this section, the results are given for a discount rate of The changes in the key climatic and hydrological vari- 0 percent (consistent with the sectoral results in the main ables that drive our adaptation cost analyses are EACC report), and the reader is referred to the appen- discussed in an accompanying EACC background dixes for results relating to other discount rates. paper. The average annual costs of adaptation in the industrial 5.1 Cos Ts of Cl IMaTe-Change- and municipal water supply sector over the period relaT ed adapTaTIon 2010­50 are shown in Table 5.1. The net and gross adaptation costs differ greatly. The total gross costs for Using the methods discussed in section 4, the annual all developing countries are almost twice as large as the costs of adaptation in relation to water supply and total net costs. This is because many countries will riverine flood protection were estimated. In this report, benefit from climate change (in relation to the baseline) the climate-change-related adaptation results are in terms of water supply. However, as shown by the net calculated using two approaches. In the first approach, cost estimates, globally the total costs outweigh the the costs (positive and negative) are calculated for each avoided costs (benefits). country, and then summed for each 5-year period, before being aggregated to World Bank regions. Thus, The cost estimates are affected by the choice of scenario the results per region refer to the net total of positive of future reservoir storage size. Using the CSIRO and negative costs, and can therefore be negative (i.e. model, the total climate-change-related adaptation cost avoided costs, or benefits) for certain regions. In the in all developing countries varies by a factor of 2.7 (net), second approach, the annual negative costs at the or 2.0 (gross), between the large dams and small dams country level are first set to zero, so as only to show scenarios. For the NCAR model, these costs only vary the strict costs of adaptation in that year. These results by a factor of 1.3. The best estimate scenario suggests are then summed to 5-year periods, and then aggre- costs slightly higher than the large dams scenario (by a gated to World Bank regions. In the following discus- factor of 1.3 to 1.5). sion we refer to the results of the former approach as net costs, and to the results of the latter approach as The results in Table 5.1 also show large differences gross costs. between regions and between GCMs; these can be seen D E V E L O P M E N T A N D C L I M AT E C H A N G E D I S C u S S I O N PA P E r S 35 Table 5.1. average annual CosTs over The perIod 2010­50 of adapTaTIon In The IndusTrIal and MunICIpal WaTer supply seCTor, based on The besT esTIMaTe (above), sMall daMs (MIddle), and large daMs (beloW) sCenarIos for reservoIr ConsTruCTIon Average annual adaptation costs in the water supply sector (2010­2050) - USD billions Baseline CC Net costs CC Gross costs CSIRO NCAR CSIRO NCAR Best estimate EAP 20.79 0.55 0.26 2.14 3.13 ECA 2.15 ­0.37 0.86 0.49 1.62 LAC 3.04 1.46 5.20 2.88 5.26 MENA 6.74 ­0.39 0.00 0.22 0.55 SA 28.73 2.38 ­2.32 5.94 1.82 SSA 5.08 7.31 5.89 7.58 6.16 Total DC 66.53 10.94 9.89 19.26 18.54 Total Non DC 6.44 1.24 2.06 3.48 3.24 Small dams EAP 22.10 0.40 ­1.61 2.17 2.34 ECA 3.29 ­0.96 0.92 0.45 2.66 LAC 4.57 7.76 5.03 9.96 5.37 MENA 6.89 0.07 0.62 0.69 1.17 SA 28.73 2.28 ­2.43 5.90 1.67 SSA 5.32 13.62 8.75 13.90 9.12 Total DC 70.89 23.17 11.29 33.08 22.35 Total Non DC 7.20 2.12 4.75 5.02 5.95 Large Dams EAP 20.79 0.55 0.26 2.14 3.13 ECA 2.15 ­0.37 0.86 0.49 1.62 LAC 3.04 1.46 5.20 2.88 5.26 MENA 6.74 ­0.39 0.00 0.22 0.55 SA 28.60 1.30 ­2.46 4.86 1.67 SSA 5.09 5.95 4.64 6.22 4.92 Total DC 66.40 8.50 8.50 16.82 17.14 Total Non DC 6.72 0.95 1.78 3.48 3.24 Note: results are shown for the baseline, and for the CC scenarios. discount rate = 0%. for the full results per 5-year periods, and for other discount rates (3%, 5%, 7%), please refer to appendix 1 to appendix 8. more clearly in Figure 5.1 and Figure 5.2. For both 2010­50 (Table 5.1) to be highest for the SSA region. GCMs, the costs of adaptation are much higher for As shown in Figure 5.2 and Table 5.1, there are net developing countries than for non-developing countries. avoided costs in some regions. For the best estimate Although the total costs across all developing countries scenario, MENA and ECA show net avoided costs are similar for CSIRO and NCAR, they vary greatly at using CSIRO, and SA shows net avoided costs using the scale of World Bank regions (Figure 5.2). However, NCAR. The regional differences between the GCMs the results forced by both GCMs show the climate- clearly highlights the large uncertainties involved in change-related adaptation cost over the entire period GCM projections of the future. 36 A D A PTATION rELATED TO INDu ST rIAL AND MuNICIPAL WATE r Su PPLy AND rIVE r INE FLOOD PrOTE C T I O N fIgure 5.1. CuMulaTIve CosTs (2005 u.s. dollars) of ClIMaTe-Change-relaTed adapTaTIon In The WaTer supply seCTor In developIng CounTrIes (dCs) and non- developIng CounTrIes (non-dCs) for (a) CsIro, and (b) nCar (a) CSIRO (b) NCAR 800 800 Cumulative costs (USD billions) Cumulative costs (USD billions) 600 600 400 400 200 200 0 0 2015 2020 2025 2030 2035 2040 2045 2050 2015 2020 2025 2030 2035 2040 2045 2050 DC Net costs DC Gross costs Non DC Net costs Non DC Gross costs Note: discount rate = 0%. Besides geographical differences, Figures 5.1 and 5.2 scenario by 2050 under both GCMs. These changes are show considerable temporal differences in cost patterns, due to the relatively large increase in water availability with clear differences between the first half of the (runoff ) in some basins between 2030 and 2050, which period (2010­30) and the second half of the period means that a greater yield can be supplied by existing (2030­50). The abruptness of these changes derive for a (and new) reservoir storage. large part from the fact that climate change data in this study were available for 2030 and 2050, with the While Figure 5.3 shows that the adaptation costs asso- required adaptation measures assumed to be imple- ciated with additional reservoir construction in the mented linearly first between 2010 and 2030 (based on future in the baseline and baseline & CC scenarios are the change in demand and water availability between much lower than those associated with alternative those periods), and then between 2030 and 2050 (based measures, the projected increase in reservoir capacity is on the change in demand and water availability between still substantial (Table 5.2). The total differences those periods). Nevertheless, both models suggest that between the two GCMs are small, although there is (net) costs will be higher in the period 2010­30 than in more variation between the GCMs when comparing the period 2030­50. These changes can be explained individual World Bank regions. For the sake of compar- with reference to Figure 5.3, which shows the individual ison, a recent study of current reservoir storage world- adaptation costs in all developing countries associated wide, based on 29,484 named reservoirs, estimates the with adding reservoir storage capacity to increase yield, current surface storage capacity of the world's reservoirs and providing water from alternative supply measures, to be about 8,300 km3 (Chao et al. 2008). for both the baseline and baseline & CC scenarios. Most of the baseline water supply adaptation costs While the IPPC states that new reservoirs are expected pertain to supplying water from alternative supply to be built in developing countries in the coming measures. In both cases, the total baseline costs increase century (Bates et al. 2008), the question of adding new strongly over the entire period 2010­50, due to increas- surface water storage, by building new dams, is politi- ing populations and economic development. However, cally sensitive; many groups and stakeholders have the net difference between baseline costs and baseline & strong feelings either for or against this strategy (WCD CC costs becomes smaller after 2030. The costs of 2000). Nevertheless, a global-scale estimate of the direct water supply from alternative water measures in the costs associated with pursuing such a strategy has not baseline & CC scenario fall below those of the baseline previously been carried out, and hence this research D E V E L O P M E N T A N D C L I M AT E C H A N G E D I S C u S S I O N PA P E r S 37 fIgure 5.2. CuMulaTIve CosTs (2005 u.s. dollars) of ClIMaTe-Change-relaTed adapTaTIon In The WaTer supply seCTor In The World bank regIons for (a) CsIro (neT), (b) CsIro (gross), (C) nCar (neT), and (d) nCar (gross) (a) CSIRO (Net costs) (c) NCAR (Net costs) 350 350 Cumulative costs (USD billions) Cumulative costs (USD billions) 300 300 250 250 200 200 150 150 100 100 50 50 0 0 ­50 ­50 ­100 ­100 2015 2020 2025 2030 2035 2040 2045 2050 2015 2020 2025 2030 2035 2040 2045 2050 (b) CSIRO (Gross costs) (d) NCAR (Gross costs) 350 350 Cumulative costs (USD billions) Cumulative costs (USD billions) 300 300 250 250 200 200 150 150 100 100 50 50 0 0 ­50 ­50 ­100 ­100 2015 2020 2025 2030 2035 2040 2045 2050 2015 2020 2025 2030 2035 2040 2045 2050 EAP ECA LAC MENA SA SSA Note: discount rate = 0%. a downward sloping cost curve signifies net avoided costs. fIgure 5.3. annual CosTs (2005 u.s. dollars) of adapTaTIon In The WaTer supply seCTor per 5-year perIod for The baselIne (solId lInes) and baselIne & CC (doTTed lInes) sCenarIos for (a) CsIro, and (b) nCar (a) CSIRO (b) NCAR 100 100 USD per annum (billions) USD per annum (billions) 80 80 60 60 40 40 20 20 0 0 2010­2015 2015­2020 2020­2025 2025­2030 2030­2035 2035­2040 2040­2045 2045­2050 2010­2015 2015­2020 2020­2025 2025­2030 2030­2035 2035­2040 2040­2045 2045­2050 Baseline ­ Storage Baseline & CC ­ Storage Baseline ­ Alternative Baseline & CC ­ Alternative Note: The red lines represent the costs of additional reservoir storage requirements, and the blue lines represent the additional costs of water supply using alternative measures. discount rate = 0%. 38 A D A PTATION rELATED TO INDu ST rIAL AND MuNICIPAL WATE r Su PPLy AND rIVE r INE FLOOD PrOTE C T I O N discussion of some of the pros and cons of reservoirs Table 5.2. ToTal InCrease In reservoIr and dams is presented in Box 5.1. Such a discussion sTorage CapaCITy (CubIC kIloMeTers) could fill an entire volume, which is beyond the scope of beTWeen presenT and 2050 under The this paper; for a comprehensive overview the reader is baselIne & CC sCenarIo for CsIro and referred to specific studies on this issue, such as the nCar gCMs (besT esTIMaTe) report of World Commission on Dams (WCD 2000). There is a clear need to assess the suitability of dams Increase in reservoir storage capacity between and reservoirs at the project level. The WCD set out 26 present and 2050 under baseline & CC sce- guidelines for decision making on the provision of water nario (cubic kilometers) and energy resources (WCD 2000), which highlights CSIRO NCAR the need to consider a full range of economic, social, Best estimate Best estimate and environmental costs and benefits associated with a EAP 469 647 whole range of measures when assessing water resource ECA 77 95 development (and adaptation) options. A key element is LAC 701 789 MENA 26 40 the need to involve all key stakeholders and policies, SA 298 220 including government, civil society, private sector, SSA 983 420 professional organizations, multilateral and bilateral Total DC 2555 2212 organizations, international standards, and international Total Non DC 426 591 agreements. Examples of community-based water proj- Total 2981 2803 ects in developing countries that have included the use of small dam structures to avoid some of the negative impacts of dams are discussed in Boxes 2.5 and 2.6. adds to the discourse on this topic. Given this signifi- Our estimates of climate-change-related adaptation cant increase in reservoir storage capacity, a short costs in the water supply sector are generally higher box 5.1. pros and Cons of WaTer supply Through reservoIr sTorage Direct and planned benefits of reservoirs include: the provision of water supply for municipal and industrial water use and irrigation (some 30­40 percent of irrigated land worldwide relies on dams); flood control--globally, 8 percent of dams are reported as having flood alleviation as one of their purposes (Green et al. 2000); hydroelectric power generation--dams generate about 19 percent of the world's electricity (WCD 2000); and navigation (Brown et al. 2009). Indirectly, dams can have the following impacts: increased agricultural yields; local and regional economic diversification; increased fish productivity in reservoirs; local employment and skills development; rural electrification; and the expansion of physical and social infrastructure such as roads and schools. In some cases, reservoir construction has led to the creation of productive fringing wetland ecosystems with fish and waterfowl habitat opportunities, although the ecological impacts of dams have gen- erally been more negative than positive (WCD 2000). recent research also shows that the impoundment of water on land in artificial reser- voirs worldwide may have reduced the magnitude of global sea level rise by about 30 millimeters (Chao et al. 2008). on the other hand, dams have had many negative impacts, particularly in environmental and social terms (petts 1984; poff and hart 2002; poff et al. 1987; Ward and stanford 1979; WCd 2000). large dams have mainly had negative impacts on the environment, including: loss of forest and wildlife habitat; loss of species populations; environmental degradation of upstream areas due to the inundation of reservoir areas; emissions of greenhouse gases from reservoirs due to decaying vegetation and carbon inflow; loss of aquatic biodiversity and productivity in upstream and downstream fisheries; and loss of ecosystem services in downstream floodplains, wetlands, riverine, and estuarine ecosystems. In addition, it is estimated that 0.5­1.0 percent of the total freshwater storage capacity of existing dams is lost each year to sedimentation (Clarke 2000). Moreover, waterlogging and soil salinity in irrigated systems are becoming serious problems globally; 20 percent of the case-study dams assessed by WCd with an irrigation component reported impacts from waterlogging. Many negative social impacts have been associated with the large-scale use of dams in the 20th century. for example, over 40­80 million people have been displaced worldwide. Many of the displaced were not officially recognized as such, and therefore not resettled or compensated; where compensation was provided, it often proved inade- quate. Those who were resettled have rarely had their livelihoods restored, as resettlement programs have focused on physical relocation rather than on the economic or social development of the displaced. In addition, millions of people living downstream from dams have suffered serious harm to their livelihoods and had the future productivity of their resources put at risk. groups that have suffered disproportionate negative impacts include indigenous and tribal people, vulnerable ethnic minorities, and women (WCd 2000). dams and reservoirs have also frequently been associated with the loss of cultural resources and heri- tage (Cernea 1999; goldsmith and hildyard 1986). D E V E L O P M E N T A N D C L I M AT E C H A N G E D I S C u S S I O N PA P E r S 39 than those of Kirshen (2007) and UNFCCC (2007). separately in the appendixes. The total costs of urban Those studies state that 85 percent of total climate- flood protection are much higher than those of agri- change-adaptation costs will be required by developing cultural flood protection. For example, for all develop- countries, similar to the 80­90 percent estimated in our ing countries, over the period 2010­50, urban flood study. UNFCCC estimate the annual average climate- protection accounts for 91 percent of the total costs change-related adaptation costs up to 2030 for develop- for CSIRO and 94 percent for NCAR. As with water ing countries to be about $7.7 billion for the SRES B1 supply, the costs are sensitive to the discount rate scenario, and $9.6 billion for the A1b scenario. It is used. In this section, the results are given for a difficult to compare these costs directly with those in discount rate of 0 percent (consistent with the sectoral the present study, since the UNFCCC study projected results in the main EACC report), and the reader is the combined costs of adaptation due to both climate referred to the appendixes for results relating to other and socioeconomic change, and then assumed that discount rates. climate change costs account for 25 percent of this total, whereas we have estimated the climate-change-related The average annual costs of adaptation for riverine costs in relation to a socioeconomic baseline. For the flood protection over the period 2010­50 are shown in period up to 2030 only, we estimate annual climate- Table 5.3. These are simply the additional costs of change-related net costs at $9 billion (CSIRO) and $17 providing flood protection measures against monthly billion (NCAR), and annual gross costs at $15 billion floods with a nominal return period (that is, 50 years (CSIRO) and $21 billion (NCAR). and 10 years for urban and agricultural areas, respec- tively), but do not consider the damages that would be 5.1.2 riverine flood protection caused by flood events with longer return periods. The differences between the net and gross cost estimates are The estimated costs of adaptation for riverine flood smaller than in the case of water supply adaptation protection per 5- year period in the baseline, baseline costs. This is because even in many basins where mean & CC, and CC scenarios, for discount rates of 0 annual runoff is expected to decrease in the future, the percent, 3 percent, 5 percent, and 7 percent, can be occurrence of extreme events (both high and low flows) found in Appendixes 9 to 16. All of the flood protec- is expected to increase (Kundzewicz et al. 2007). Hence, tion cost estimates are made up of an urban and an fewer basins are projected to encounter "benefits" in agricultural component, the costs of which are shown terms of flood protection. Table 5.3. average annual CosTs of adapTaTIon In TerMs of rIverIne flood proTeCTIon over The perIod 2010­50 Average annual adaptation costs of flood protection (2010­2050) - USD billions Baseline CC Net costs CC Gross costs CSIRO NCAR CSIRO NCAR EAP 8.59 1.57 0.78 1.57 0.94 ECA 13.60 0.67 1.39 0.93 1.66 LAC 10.36 1.74 0.29 2.05 0.96 MENA 5.12 0.46 ­0.29 0.55 0.11 SA 6.17 1.64 0.96 1.67 1.09 SSA 4.72 ­0.16 0.33 0.25 0.41 Total DC 48.55 5.92 3.45 7.00 5.16 Total Non DC 49.77 6.18 1.40 7.32 1.99 Note: results are shown for the baseline, and for the CC scenarios. discount rate = 0%. for the full results per 5-year period, and for other discount rates (3%, 5%, 7%) please refer to appendixes 9 to 16. 40 A D A PTATION rELATED TO INDu ST rIAL AND MuNICIPAL WATE r Su PPLy AND rIVE r INE FLOOD PrOTE C T I O N For the CSIRO model, the climate-change-related costs for water supply and riverine flood protection were adaptation costs are of a similar magnitude for develop- first summed for each country, before (b) setting all net ing and non-developing countries (Figure 5.4). For negative costs to zero, and (c) aggregating to the World NCAR, the total costs for developing countries are Bank regions. The average annual net costs for all greater than those for non-developing countries by a developing countries are $13.3­$16.9 billion, and aver- factor of about 2.5. There are also large differences age annual gross costs are $20.2­$22.8 billion. The between the results of the two GCMs at the scale of the adaptation costs are substantially greater for developing World Bank regions (Figure 5.5). In some cases, even countries than for non-developing countries under both the sign of the change is different between the models. CSIRO and NCAR. There are large geographical This is the case for both MENA (net costs for CSIRO differences between the cost estimates derived using the and net avoided costs for NCAR), and SSA (net two GCMs, but both suggest that the climate-change- avoided costs for CSIRO and net costs for NCAR). related adaptation costs will be greatest in the SSA These differences are associated with the regional region. differences in the magnitude (and sign) of change of the 50- year and 10- year maximum monthly runoff The cost estimates in this study are subject to several between the two models, again highlighting the uncer- limitations (see section 6.2). Nevertheless, they are rela- tainty involved in the use of GCM results. tively small in relation to world GDP. In Table 5.5, the baseline and climate-change-related water resources 5.1.3 Total costs of water resources adaptation adaptation costs are shown (the water supply costs are based on the best estimate scenario) in relation to world So far, the costs of adaptation have been discussed indi- GDP (based on world GDP in 2007 from the World vidually for water supply and riverine flood protection. Bank WDI index, in 2005 U.S. dollars; that is, $51,528 In Table 5.4, the adaptation costs are shown for water billion). The global cost estimates (developing and non- supply and riverine flood protection together, here developing countries combined) of climate-change- termed water resources adaptation. In this case, the net related adaptation in the water resources sector amount costs are obtained by summing the total net costs for to 0.04­0.06 percent of world GDP. The baseline adap- water supply and riverine flood protection. However, for tation costs are significantly higher, but still low (0.33 gross costs the following method was adopted: (a) net percent). fIgure 5.4. CuMulaTIve CosTs (2005 u.s. dollars) of ClIMaTe-Change-relaTed adapTaTIon for rIverIne flood proTeCTIon for (a) CsIro and (b) nCar (a) CSIRO (b) NCAR 300 300 Cumulative costs (USD billions) Cumulative costs (USD billions) 250 250 200 200 150 150 100 100 0 0 2015 2020 2025 2030 2035 2040 2045 2050 2015 2020 2025 2030 2035 2040 2045 2050 DC Net costs DC Gross costs Non DC Net costs Non DC Gross costs Note:dC = developing countries; non dC = non-developing countries. D E V E L O P M E N T A N D C L I M AT E C H A N G E D I S C u S S I O N PA P E r S 41 fIgure 5.5. CuMulaTIve CosTs (2005 u.s. dollars) of ClIMaTe-Change-relaTed adapTaTIon for rIverIne flood proTeCTIon In The World bank regIons for (a) CsIro (neT), (b) CsIro (gross), (C) nCar (neT), and (d) nCar (gross) (a) CSIRO (Net costs) (c) NCAR (Net costs) 80 80 Cumulative costs (USD billions) Cumulative costs (USD billions) 60 60 40 40 20 20 0 0 ­20 ­20 2015 2020 2025 2030 2035 2040 2045 2050 2015 2020 2025 2030 2035 2040 2045 2050 (b) CSIRO (Gross costs) (d) NCAR (Gross costs) 80 80 Cumulative costs (USD billions) Cumulative costs (USD billions) 60 60 40 40 20 20 0 0 ­20 ­20 2015 2020 2025 2030 2035 2040 2045 2050 2015 2020 2025 2030 2035 2040 2045 2050 EAP ECA LAC MENA SA SSA Note: discount rate = 0%. Table 5.4. average annual WaTer resourCes adapTaTIon CosTs over The perIod 2010­50 Average annual water resources adaptation costs (2010­2050) - USD billions Baseline CC Net costs CC Gross costs CSIRO NCAR CSIRO NCAR EAP 29.4 2.1 1.0 3.3 3.4 ECA 15.8 0.3 2.3 1.2 2.5 LAC 13.4 3.2 5.5 3.9 5.6 MENA 11.9 0.1 -0.3 0.6 0.4 SA 34.9 4.0 -1.4 6.3 2.0 SSA 9.8 7.2 6.2 7.4 6.4 Total DC 115.1 16.9 13.3 22.8 20.2 Total Non DC 56.2 7.4 3.5 8.5 4.3 Note:results are shown for the baseline, and for the CC scenarios. discount rate = 0%. 42 A D A PTATION rELATED TO INDu ST rIAL AND MuNICIPAL WATE r Su PPLy AND rIVE r INE FLOOD PrOTE C T I O N Table 5.5. average annual adapTaTIon CosTs In The WaTer resourCes seCTor as a perCenTage of World gdp In 2007 Annual water resources adaptation costs as a % of world GDP in 2007 Baseline CC Net costs CC Gross costs CSIRO NCAR CSIRO NCAR EAP 0.06 0.00 0.00 0.01 0.01 ECA 0.03 0.00 0.00 0.00 0.00 LAC 0.03 0.01 0.01 0.01 0.01 MENA 0.02 0.00 0.00 0.00 0.00 SA 0.07 0.01 0.00 0.01 0.00 SSA 0.02 0.01 0.01 0.01 0.01 Total DC 0.22 0.03 0.03 0.04 0.04 Total Non DC 0.11 0.01 0.01 0.02 0.01 43 6. ConClusIons, lIMITaTIons, and 6 . 1 M a I n C o n Cl u sI o n s reCoMMendaTIons A myriad of adaptation measures are available in terms of both water supply and riverine flood protection, with Even if emissions of anthropogenic greenhouse gases new innovative adaptation options continually being were stabilized today, human-induced changes in developed. A (nonexhaustive) data base of adaptation climate will continue for many centuries (IPCC 2007). options is provided in section 2.4. Therefore, in addition to mitigation, it is essential to develop adequate adaptation measures to moderate the In the water supply sector, adaptation measures can be impacts and realize the opportunities associated with divided into supply-side and demand-side measures. climate change. However, on a global scale, sectoral and Ideally, adaptation options designed to ensure water cross-sectoral studies of the economic aspects of climate supply during average and drought conditions should change and adaptation are very limited (Adger et al. integrate strategies on both sides of this spectrum 2007; EEA 2007; Kuik et al. 2008). Hence, the EACC (Bates et al. 2008). In terms of flood protection, adapta- study has been carried out to estimate the costs of tion options can either reduce the probability or magni- adapting to climate change in developing countries over tude of flood events (i.e., reduce the flood hazard) or the period 2010­50. can reduce the impacts of floods. Traditionally, flood protection measures in most parts of the world have This background paper examines adaptation in terms of concentrated on reducing the probability of flooding, the water supply sector and riverine flood protection, often by providing structural measures designed to and addresses the following research aims: protect against a flood with a given return period. However, there is currently an international shift toward · Develop a data base of adaptation policies, pro- a more integrated system of flood risk assessment, grams, and projects that can be used in the water whereby flood risk can be defined as the probability of supply sector; flooding multiplied by the potential flood damage. · Develop a data base of adaptation policies, pro- Under this approach, adaptation should consider struc- grams, and projects that can be used for riverine tural and non-structural measures that address both flood protection; flood probability and impact. · Estimate the climate change adaptation costs in the industrial and municipal water supply sector; The costs of adaptation measures vary greatly according · Estimate the climate change adaptation costs for to location and type of measure. This is not only true riverine flood protection. for the direct construction, implementation, and/or O&M costs, but also for the indirect costs, such as envi- The main conclusions related to these aims are provided ronmental and socioeconomic costs. In addition, adap- in section 6.1. Key limitations of the study, with accom- tion measures may provide secondary benefits at all panying recommendations for future research, are levels from household to international. Again, these summarized in section 6.2. benefits vary according to location and type of measure. 44 A D A PTATION rELATED TO INDu ST rIAL AND MuNICIPAL WATE r Su PPLy AND rIVE r INE FLOOD PrOTE C T I O N The correct measure or combination of measures is The adaptation costs are substantially greater for devel- always site- and context-specific. Nevertheless, a key oping countries than for non-developing countries. factor in adaptation planning is the need to adapt in While there are large geographical differences between ways that are synergistic with development, and that do the cost estimates derived using the two GCMs, both not lead to increases in the adaptation deficit (that is, suggest that the overall costs will be greatest in the maladaptation). Although climate change is not directly Sub-Saharan Africa region. addressed in the eight Millennium Development Goals (MDGs), most of them are directly or indirectly related The results support the notion that the negative impacts to water, and therefore linked to the issue of climate of climate change in the water resources sector will change. Adaptation measures should address economic, generally be greater in developing countries than in environmental, and social welfare in an equitable non-developing countries, and that the costs of adapta- manner, and should address issues in a basin-wide tion will be greatest in Sub-Saharan Africa. They also context, following the principles of good governance. underline the importance of mainstreaming climate Key facets are public participation and the use of local change adaptation into general development practices, knowledge in the planning, development, and mainte- since the adaptation costs in the climate change scenar- nance of adaptation strategies, whether they be struc- ios are small compared to the baseline adaptation costs. tural or policy measures. These issues are addressed in Moreover, the costs are relatively small in relation to the concept of integrated water resource management, world GDP, even those associated with adapting to the which should be used as an instrument to explore adap- baseline level. tation measures to climate change. 6 . 2 l I M I TaT Io n s a n d r eC o M M e n d aT I o n s Our best estimate of the annual costs of climate change adaptation in developing countries in the industrial and Due to the global nature of this study, the results municipal raw water supply sector is between $9.9­ provide a broad estimate of the possible costs of adapta- $10.9 billion (net), and $18.5­$19.3 billion (gross). tion to climate change The absolute numbers should be These costs are much higher than those estimated for treated with caution, but they give an indication of the non-developing countries. In terms of climate-change- magnitude of the problem globally. The relative propor- related adaptation costs for riverine flood protection, tions of the total costs per World Bank region give an our annual estimates for developing countries are indication of the relative scale of the problem among between $3.5­$5.9 billion (net), and $5.2­$7.0 billion regions. As more information becomes available on the (gross). Most of these costs (>90 percent) are associated costs of adaptation measures at the global scale, more with the provision of flood protection in urban areas. comprehensive assessments of costs (and benefits) of There are large differences between the two GCMs different approaches can be carried out and compared used in terms of the regional and temporal distribution to the outcomes of this study. In the case of riverine of costs, both for water supply and riverine flood flood protection, the estimates provide, to the best of protection. This highlights the large uncertainties our knowledge, the first assessment of the costs of involved in GCM projections of the future. climate-change-related adaptation at the global scale. In this section, a number of the main limitations of this The combined annual costs of adaptation in developing study are summarized, as well as recommendations on countries for water supply and riverine flood protection, how future research could address these issues. here defined as water resources adaptation costs, are between $13.3­$16.9 billion (net), and $20.2­$22.8 · There are significant uncertainties in projections of billion (gross). These estimates are small in relation to the impacts of climate change on water resources. total world GDP, at about 0.03­0.04 percent. The esti- Uncertainties about the impacts of climate change mated annual cost of adaptation in the baseline scenario on the hydrological cycle include uncertainties in (that is, without climate change) in developing countries the internal variability of the climate system; in the is significantly higher ($115.1 billion). Nevertheless, this future greenhouse gas and aerosol emission scenar- is still small in relation to world GDP (0.22 percent). ios (and in the scenarios of population, economic D E V E L O P M E N T A N D C L I M AT E C H A N G E D I S C u S S I O N PA P E r S 45 development, and technological change that gener- bases of planned future reservoirs exist, and it is ate them); in the translation of these emissions sce- therefore not possible to accurately predict the size narios into climate change by climate models; and of reservoirs that will be built in the future. Our in the hydrological models used to simulate the best estimate is based on the size distribution of impacts of climate change on the hydrological cycle. existing water supply reservoirs in the United As with all modeling studies, these uncertainties States. It would be prudent to use distributions of and model errors permeate through the entire mod- storage capacity based on analyses of each country eling chain. The largest of these uncertainties in separately, but comprehensive georeferenced reser- climate change impact assessments on water voir data bases are currently not available. At pres- resources are due to the uncertainty in precipitation ent, the most comprehensive is the Global Lakes inputs (Bates et al. 2008). In order to gain a better and Wetlands Database (Lehner and Döll 2004), insight into the size of the uncertainty associated but this only contains 740 reservoirs. Currently, the with these factors, future research should assess the Global Reservoir and Dam (GRanD) data base is impacts under a greater range of GCMs and emis- being developed as part of the Global Water System sions scenarios. As research continues into improv- Project (GWSP) (http://www.gwsp.org/current_ ing climate models and model parameterization, activities.html). Once available, this will initially and computational power continues to increase, contain about 7,000 reservoirs, and could be used to these uncertainties should be reduced. improve assumptions pertaining to the future distri- · As well as the uncertainties in future climate bution of reservoir size classes. change impact projections described above, uncer- · The relative marginal costs of dam construction tainties exist in the projections of baseline changes may increase in the future because existing dams (that is, without climate change). For this study, we and reservoirs are likely to have used many of the used only one scenario of future socioeconomic most cost-effective locations. change in order to estimate future water demand. · There is an extensive literature devoted to deter- In terms of riverine flood protection, the baseline mining how much water a river needs to sustain a costs are dependent on the protection level to be healthy aquatic ecosystem and ecosystem function afforded by flood defense measures. In this study, (for a review, see Tharme 2003). When planning we estimate the baseline costs as the costs of pro- adaptation measures in the water sector, assessments viding flood protection against a 50-year monthly must consider how to ensure that a minimum eco- flood in urban areas, and against a 10- year logical function is maintained. In reality, this must monthly flood in agricultural areas. In many basins, be carried out at the local level, based on local phys- this protection level is already provided, especially ical, socioeconomic, and political considerations. In in many non-developing countries, and hence the a global study of this nature, it is not possible to baseline costs will be overestimated. Nevertheless, address such considerations, and hence a general- the aim of this study is primarily to assess the costs ized rule of thumb was applied whereby the per- of climate-change-related adaptation. centage of water withdrawals is not allowed to · We have only examined the direct construction, increase above 80 percent of total runoff. However, implementation, and O&M costs associated with this standard is arbitrary, and underscores the need the adaptation measures considered. All adaptation for basin scale planning when actual water manage- measures entail other costs, both direct and indi- ment plans are being developed and implemented. rect. Similarly, we do not consider the possible · We did not account for the effects of sedimentation direct and indirect secondary economic benefits of on reservoir storage capacity, since there are no data adaptation measures. As such, the study does not bases describing regional rates of this phenomenon. provide an economic cost-benefit analysis, but an This will lead to an underestimation of costs, since assessment of the construction, implementation, either (a) more capacity will need to be added to and O&M costs of these adaptation strategies. replace lost capacity; (b) more supply will have to be · The cost of adding reservoir storage capacity varies met through alternative measures; and/or (c) expen- greatly with reservoir size. No comprehensive data sive dredging activities will have to be carried out. 46 A D A PTATION rELATED TO INDu ST rIAL AND MuNICIPAL WATE r Su PPLy AND rIVE r INE FLOOD PrOTE C T I O N · Demand-side adaptations are not explicitly costed of adaptation related to climate change in terms of in this study, since the demand projections already riverine flooding are estimated in terms of the costs account for some increase in efficiencies over time, of retaining a nominal flood protection standard. and this could lead to double counting. However, it The time and resources available for the current should be noted that there is substantial scope for study, and existing data sets and methodologies, did economizing on the consumption of water (Zhou not permit us to carry out a flood-risk-based assess- and Tol 2005). ment of the costs (and benefits) of adaptation in · We do not account for water trading between coun- terms of flood management. Such an assessment tries; in some cases, this could lead to a more effi- would require the combination of a hydrological and cient use of water resources. Such arrangements hydrodynamic model at the global scale to produce need to be negotiated and formalized in agreements global flood hazard maps, and stage damage rela- and treaties between riparian states, and cannot be tions to relate flood depth to (economic) damage. implemented in such a global modeling exercise. · In some areas (especially non-developing countries), · We do not account for the adaptation costs associ- the climate-change-related adaptation costs for riv- ated with climate-change-related alterations in erine flood protection will be overestimated since water quality. Climate change is expected to worsen the existing flood management measures already many forms of water pollution. 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Water and climate change: understand- ing the risks and making climate-smart investment deci- sions. Washington, DC; World Bank. 59 8. appendIxes 60 A D A PTATION rELATED TO INDu ST rIAL AND MuNICIPAL WATE r Su PPLy AND rIVE r INE FLOOD PrOTE C T I O N appendIx 1. WaTer supply resulTs. gCM: CsIro. dIsCounT raTe: 0% Annual costs (averaged over 5-year periods) in USD billion Baseline Baseline & CC CC (net) CC (gross) Small Best Large Small Best Large Small Best Large Small Best Large EAP 8.44 8.67 8.67 9.34 9.94 9.94 0.90 1.27 1.27 1.47 2.06 2.06 ECA 1.06 1.37 1.37 0.61 1.03 1.03 ­0.44 ­0.34 ­0.34 0.06 0.19 0.19 LAC 1.40 2.00 2.00 0.41 0.41 0.41 ­0.99 ­1.59 ­1.59 0.12 0.12 0.12 2010­2015 MENA 1.14 1.22 1.22 1.16 1.15 1.15 0.01 ­0.07 ­0.07 0.17 0.09 0.09 SA 5.24 5.24 5.53 8.18 8.18 9.75 2.94 2.94 4.22 3.51 3.51 4.79 SSA 0.91 1.09 1.10 4.45 3.71 3.66 3.55 2.63 2.55 3.70 2.89 2.81 Non DC 2.56 2.94 3.63 4.30 4.88 4.88 1.74 1.95 1.25 2.18 2.41 2.41 All DCs 18.18 19.59 19.90 24.16 24.43 25.94 5.97 4.84 6.04 9.03 8.85 10.05 EAP 13.65 13.23 13.23 15.52 15.22 15.22 1.87 1.99 1.99 2.30 2.45 2.45 ECA 1.93 1.77 1.77 1.22 1.22 1.22 ­0.71 ­0.56 ­0.56 0.09 0.21 0.21 LAC 2.21 2.26 2.26 0.51 0.51 0.51 ­1.70 ­1.75 ­1.75 0.13 0.13 0.13 2015­2020 MENA 2.93 2.94 2.94 2.96 2.76 2.76 0.03 ­0.18 ­0.18 0.44 0.22 0.22 SA 12.11 12.11 12.34 16.77 16.77 17.74 4.66 4.66 5.40 5.94 5.94 6.68 SSA 1.96 2.05 2.06 8.41 5.51 5.24 6.45 3.47 3.18 6.69 3.78 3.49 Non DC 4.03 4.10 4.77 6.24 5.85 5.85 2.21 1.75 1.08 3.17 2.64 2.64 All DCs 34.79 34.36 34.60 45.40 41.99 42.68 10.61 7.64 8.08 15.59 12.73 13.17 EAP 18.86 17.79 17.79 21.70 20.51 20.51 2.84 2.72 2.72 3.41 3.31 3.31 ECA 2.81 2.18 2.18 1.83 1.40 1.40 ­0.98 ­0.78 ­0.78 0.16 0.23 0.23 LAC 3.03 2.52 2.52 0.61 0.61 0.61 ­2.41 ­1.90 ­1.90 0.14 0.14 0.14 2020­2025 MENA 4.72 4.66 4.66 4.77 4.36 4.36 0.05 ­0.30 ­0.30 0.71 0.36 0.36 SA 18.97 18.97 19.14 25.36 25.36 25.72 6.38 6.38 6.58 8.36 8.36 8.56 SSA 3.02 3.01 3.02 12.37 7.32 6.82 9.36 4.31 3.80 9.69 4.67 4.16 Non DC 5.50 5.26 5.90 8.17 6.82 6.82 2.67 1.56 0.92 4.25 2.88 2.88 All DCs 51.40 49.12 49.30 66.65 59.55 59.42 15.25 10.43 10.12 22.47 17.07 16.76 EAP 24.07 22.35 22.35 27.88 25.79 25.79 3.81 3.45 3.45 4.52 4.16 4.16 ECA 3.68 2.58 2.58 2.44 1.58 1.58 ­1.24 ­1.00 ­1.00 0.23 0.24 0.24 LAC 3.84 2.77 2.77 0.72 0.72 0.72 ­3.12 ­2.06 ­2.06 0.16 0.16 0.16 2025­2030 MENA 6.50 6.38 6.38 6.58 5.97 5.97 0.07 ­0.41 ­0.41 0.98 0.49 0.49 SA 25.84 25.84 25.94 33.94 33.94 33.71 8.10 8.10 7.77 10.79 10.79 10.45 SSA 4.07 3.96 3.97 16.34 9.12 8.40 12.26 5.15 4.42 12.68 5.57 4.84 Non DC 6.98 6.41 7.03 10.11 7.78 7.78 3.13 1.37 0.75 5.41 3.11 3.11 All DCs 68.01 63.89 64.00 87.89 77.12 76.16 19.89 13.23 12.16 29.36 21.41 20.34 EAP 25.70 22.89 22.89 26.98 23.92 23.92 1.28 1.02 1.02 3.29 2.85 2.85 ECA 3.77 2.04 2.04 3.29 1.86 1.86 ­0.48 ­0.18 ­0.18 0.66 0.68 0.68 LAC 5.21 3.29 3.29 10.32 7.37 7.37 5.11 4.09 4.09 6.86 4.88 4.88 2030­2035 MENA 7.98 7.80 7.80 8.05 7.29 7.29 0.07 ­0.51 ­0.51 0.97 0.39 0.39 SA 32.91 32.91 32.38 38.21 38.37 34.41 5.29 5.46 2.02 7.89 7.98 4.55 SSA 5.48 5.15 5.14 22.83 14.49 13.30 17.35 9.34 8.16 17.60 9.56 8.38 Non DC 7.95 6.83 6.81 10.02 7.44 7.44 2.08 0.60 0.63 5.76 3.38 3.38 All DCs 81.07 74.08 73.54 109.68 93.31 88.15 28.61 19.23 14.61 37.27 26.35 21.73 (Continued on next page) D E V E L O P M E N T A N D C L I M AT E C H A N G E D I S C u S S I O N PA P E r S 61 appendIx 1. (continued) EAP 27.20 25.01 25.01 26.58 24.52 24.52 ­0.61 ­0.49 ­0.49 1.60 1.53 1.53 ECA 4.06 2.23 2.23 3.19 2.13 2.13 ­0.87 ­0.10 ­0.10 0.73 0.74 0.74 LAC 6.08 3.55 3.55 19.49 8.08 8.08 13.41 4.53 4.53 15.47 5.38 5.38 2035­2040 MENA 9.30 9.05 9.05 9.39 8.52 8.52 0.09 ­0.53 ­0.53 0.82 0.19 0.19 SA 38.91 38.91 38.44 40.03 40.22 36.85 1.12 1.30 ­1.59 4.53 4.61 1.72 SSA 7.26 6.81 6.80 25.93 17.08 15.12 18.67 10.27 8.32 18.94 10.47 8.53 Non DC 9.07 7.74 7.68 10.96 8.49 8.49 1.89 0.74 0.81 6.12 3.93 3.93 All DCs 92.81 85.57 85.09 124.62 100.54 95.22 31.81 14.97 10.13 42.10 22.93 18.09 EAP 28.69 27.13 27.13 26.18 25.12 25.12 ­2.51 ­2.00 ­2.00 0.39 0.39 0.39 ECA 4.36 2.42 2.42 3.09 2.39 2.39 ­1.26 ­0.03 ­0.03 0.79 0.80 0.80 LAC 6.94 3.82 3.82 28.66 8.79 8.79 21.72 4.97 4.97 24.09 5.88 5.88 2040­2045 MENA 10.62 10.30 10.30 10.73 9.74 9.74 0.11 ­0.56 ­0.56 0.71 0.02 0.02 SA 44.91 44.91 44.50 41.86 42.06 39.29 ­3.05 ­2.85 ­5.21 3.58 3.64 1.29 SSA 9.04 8.48 8.46 29.03 19.66 16.95 20.00 11.19 8.48 20.28 11.39 8.68 Non DC 10.20 8.65 8.54 11.90 9.53 9.53 1.70 0.88 0.99 6.47 4.47 4.47 All DCs 104.55 97.06 96.63 139.56 107.77 102.28 35.01 10.71 5.65 49.84 22.12 17.06 EAP 30.19 29.24 29.24 25.78 25.72 25.72 ­4.40 ­3.52 ­3.52 0.38 0.38 0.38 ECA 4.65 2.61 2.61 2.99 2.65 2.65 ­1.65 0.04 0.04 0.86 0.86 0.86 LAC 7.81 4.09 4.09 37.84 9.49 9.49 30.03 5.40 5.40 32.70 6.37 6.37 2045­2050 MENA 11.93 11.55 11.55 12.07 10.97 10.97 0.14 ­0.58 ­0.58 0.77 0.02 0.02 SA 50.91 50.91 50.56 43.69 43.90 41.73 ­7.22 ­7.01 ­8.83 2.62 2.67 0.86 SSA 10.81 10.14 10.13 32.13 22.25 18.77 21.32 12.11 8.64 21.64 12.30 8.83 Non DC 11.33 9.56 9.41 12.85 10.58 10.58 1.52 1.02 1.17 6.82 5.01 5.01 All DCs 116.30 108.54 108.18 154.50 114.99 109.35 38.20 6.45 1.17 58.96 22.60 17.32 62 A D A PTATION rELATED TO INDu ST rIAL AND MuNICIPAL WATE r Su PPLy AND rIVE r INE FLOOD PrOTE C T I O N appendIx 2. WaTer supply resulTs. gCM: CsIro. dIsCounT raTe: 3% Annual costs (averaged over 5-year periods) in USD billion Baseline Baseline & CC CC (net) CC (gross) Small Best Large Small Best Large Small Best Large Small Best Large EAP 7.67 7.89 7.89 8.49 9.05 9.05 0.82 1.15 1.15 1.35 1.88 1.88 ECA 0.96 1.25 1.25 0.56 0.95 0.95 ­0.40 ­0.31 ­0.31 0.05 0.17 0.17 LAC 1.27 1.83 1.83 0.37 0.37 0.37 ­0.90 ­1.46 ­1.46 0.11 0.11 0.11 2010­2015 MENA 1.03 1.10 1.10 1.04 1.03 1.03 0.01 ­0.06 ­0.06 0.15 0.08 0.08 SA 4.73 4.73 4.99 7.40 7.40 8.85 2.68 2.68 3.85 3.19 3.19 4.36 SSA 0.82 0.98 1.00 4.04 3.38 3.33 3.22 2.40 2.33 3.36 2.63 2.57 Non DC 2.32 2.68 3.31 3.92 4.46 4.46 1.59 1.78 1.15 1.98 2.21 2.21 All DCs 16.48 17.78 18.06 21.90 22.19 23.58 5.42 4.40 5.51 8.20 8.07 9.18 EAP 10.73 10.41 10.41 12.21 11.98 11.98 1.47 1.57 1.57 1.81 1.93 1.93 ECA 1.52 1.40 1.40 0.96 0.96 0.96 ­0.56 ­0.44 ­0.44 0.07 0.16 0.16 LAC 1.74 1.78 1.78 0.40 0.40 0.40 ­1.34 ­1.38 ­1.38 0.10 0.10 0.10 2015­2020 MENA 2.30 2.31 2.31 2.32 2.16 2.16 0.03 ­0.14 ­0.14 0.34 0.17 0.17 SA 9.50 9.50 9.68 13.17 13.17 13.94 3.67 3.67 4.26 4.67 4.67 5.26 SSA 1.54 1.61 1.62 6.61 4.34 4.12 5.07 2.73 2.50 5.26 2.98 2.75 Non DC 3.17 3.23 3.75 4.91 4.61 4.61 1.74 1.39 0.86 2.50 2.09 2.09 All DCs 27.33 27.01 27.20 35.67 33.01 33.57 8.34 6.01 6.37 12.25 10.02 10.38 EAP 12.81 12.09 12.09 14.74 13.93 13.93 1.93 1.85 1.85 2.32 2.25 2.25 ECA 1.91 1.48 1.48 1.24 0.95 0.95 ­0.66 ­0.53 ­0.53 0.11 0.15 0.15 LAC 2.06 1.71 1.71 0.42 0.42 0.42 ­1.64 ­1.29 ­1.29 0.10 0.10 0.10 2020­2025 MENA 3.20 3.16 3.16 3.24 2.96 2.96 0.04 ­0.20 ­0.20 0.48 0.24 0.24 SA 12.88 12.88 12.99 17.21 17.21 17.47 4.34 4.34 4.48 5.68 5.68 5.82 SSA 2.05 2.04 2.05 8.40 4.97 4.63 6.35 2.93 2.58 6.58 3.18 2.83 Non DC 3.74 3.57 4.01 5.55 4.64 4.64 1.81 1.07 0.63 2.89 1.96 1.96 All DCs 34.90 33.36 33.48 45.25 40.45 40.36 10.35 7.09 6.88 15.26 11.60 11.39 EAP 14.11 13.11 13.11 16.35 15.13 15.13 2.24 2.02 2.02 2.65 2.44 2.44 ECA 2.16 1.52 1.52 1.43 0.93 0.93 ­0.73 ­0.59 ­0.59 0.14 0.14 0.14 LAC 2.25 1.63 1.63 0.42 0.42 0.42 ­1.83 ­1.21 ­1.21 0.09 0.09 0.09 2025­2030 MENA 3.81 3.74 3.74 3.85 3.50 3.50 0.04 ­0.24 ­0.24 0.57 0.29 0.29 SA 15.14 15.14 15.21 19.90 19.90 19.76 4.75 4.75 4.56 6.33 6.33 6.13 SSA 2.39 2.32 2.33 9.58 5.35 4.92 7.19 3.02 2.60 7.43 3.27 2.84 Non DC 4.09 3.76 4.13 5.93 4.57 4.57 1.84 0.81 0.44 3.17 1.83 1.83 All DCs 39.87 37.46 37.52 51.53 45.22 44.66 11.66 7.76 7.14 17.21 12.56 11.94 EAP 5.61 5.21 5.21 5.04 4.92 4.92 ­0.57 ­0.28 ­0.28 0.26 0.47 0.47 ECA 0.60 0.56 0.56 0.79 0.79 0.79 0.19 0.23 0.23 0.37 0.36 0.36 LAC 1.42 1.55 1.55 3.91 3.62 3.62 2.49 2.07 2.07 2.86 2.48 2.48 2030­2035 MENA 1.10 1.13 1.13 1.09 1.05 1.05 0.00 ­0.08 ­0.08 0.10 0.03 0.03 SA 5.37 5.37 5.23 6.27 6.35 5.47 0.90 0.99 0.24 1.26 1.35 0.60 SSA 0.94 0.97 0.97 5.38 4.68 4.54 4.44 3.71 3.57 4.50 3.78 3.64 Non DC 1.86 1.91 2.06 2.46 2.84 2.84 0.60 0.93 0.78 1.51 1.73 1.73 All DCs 15.02 14.79 14.65 22.48 21.42 20.40 7.46 6.63 5.75 9.35 8.46 7.58 (Continued on next page) D E V E L O P M E N T A N D C L I M AT E C H A N G E D I S C u S S I O N PA P E r S 63 appendIx 2. (continued) EAP 3.20 3.12 3.12 2.24 2.55 2.55 ­0.96 ­0.57 ­0.57 0.17 0.17 0.17 ECA 0.20 0.39 0.39 0.48 0.69 0.69 0.28 0.30 0.30 0.40 0.35 0.35 LAC 1.02 1.40 1.40 3.72 3.39 3.39 2.70 1.99 1.99 2.91 2.36 2.36 2035­2040 MENA 0.27 0.34 0.34 0.26 0.31 0.31 ­0.01 ­0.02 ­0.02 0.04 0.03 0.03 SA 2.17 2.17 2.07 2.30 2.38 1.86 0.13 0.21 ­0.21 0.80 0.88 0.46 SSA 0.45 0.53 0.53 3.44 3.73 3.69 2.99 3.20 3.16 3.04 3.26 3.21 Non DC 1.17 1.37 1.54 1.57 2.39 2.39 0.40 1.02 0.85 1.03 1.64 1.64 All DCs 7.30 7.94 7.85 12.44 13.05 12.50 5.14 5.11 4.65 7.37 7.05 6.59 EAP 2.85 2.76 2.76 1.98 2.24 2.24 ­0.87 ­0.51 ­0.51 0.15 0.14 0.14 ECA 0.16 0.34 0.34 0.45 0.63 0.63 0.29 0.29 0.29 0.38 0.33 0.33 LAC 0.91 1.29 1.29 3.25 3.18 3.18 2.33 1.89 1.89 2.52 2.23 2.23 2040­2045 MENA 0.20 0.26 0.26 0.19 0.24 0.24 ­0.01 ­0.02 ­0.02 0.03 0.03 0.03 SA 1.79 1.79 1.69 1.98 2.06 1.53 0.20 0.27 ­0.16 0.76 0.83 0.40 SSA 0.36 0.43 0.44 3.09 3.39 3.38 2.73 2.96 2.94 2.78 3.01 2.99 Non DC 1.04 1.23 1.38 1.40 2.16 2.16 0.36 0.93 0.78 0.94 1.51 1.51 All DCs 6.27 6.87 6.78 10.94 11.74 11.20 4.67 4.87 4.43 6.61 6.57 6.12 EAP 2.58 2.47 2.47 1.80 2.01 2.01 ­0.78 ­0.46 ­0.46 0.12 0.14 0.14 ECA 0.14 0.30 0.30 0.42 0.58 0.58 0.28 0.27 0.27 0.35 0.31 0.31 LAC 0.83 1.18 1.18 2.87 2.96 2.96 2.04 1.77 1.77 2.20 2.09 2.09 2045­2050 MENA 0.16 0.22 0.22 0.15 0.20 0.20 ­0.01 ­0.02 ­0.02 0.03 0.02 0.02 SA 1.55 1.55 1.46 1.79 1.86 1.33 0.24 0.31 ­0.12 0.71 0.78 0.35 SSA 0.30 0.37 0.37 2.81 3.10 3.10 2.51 2.73 2.73 2.55 2.77 2.77 Non DC 0.93 1.11 1.24 1.25 1.95 1.95 0.32 0.85 0.71 0.86 1.39 1.39 All DCs 5.55 6.09 6.00 9.83 10.70 10.18 4.28 4.60 4.17 5.97 6.11 5.68 64 A D A PTATION rELATED TO INDu ST rIAL AND MuNICIPAL WATE r Su PPLy AND rIVE r INE FLOOD PrOTE C T I O N appendIx 3. WaTer supply resulTs. gCM: CsIro. dIsCounT raTe: 5% Annual costs (averaged over 5-year periods) in USD billion Baseline Baseline & CC CC (net) CC (gross) Small Best Large Small Best Large Small Best Large Small Best Large EAP 7.22 7.43 7.43 7.98 8.52 8.52 0.77 1.08 1.08 1.27 1.78 1.78 ECA 0.90 1.18 1.18 0.52 0.89 0.89 ­0.38 ­0.29 ­0.29 0.05 0.16 0.16 LAC 1.20 1.73 1.73 0.35 0.35 0.35 ­0.84 ­1.38 ­1.38 0.10 0.10 0.10 2010­2015 MENA 0.96 1.02 1.02 0.97 0.97 0.97 0.01 ­0.06 ­0.06 0.14 0.07 0.07 SA 4.42 4.42 4.67 6.94 6.94 8.31 2.52 2.52 3.64 3.00 3.00 4.11 SSA 0.77 0.92 0.94 3.79 3.19 3.14 3.02 2.26 2.20 3.15 2.48 2.42 Non DC 2.19 2.52 3.13 3.69 4.21 4.21 1.50 1.69 1.09 1.87 2.08 2.08 All DCs 15.47 16.71 16.98 20.56 20.86 22.18 5.09 4.14 5.20 7.72 7.60 8.66 EAP 9.19 8.92 8.92 10.45 10.26 10.26 1.26 1.34 1.34 1.55 1.65 1.65 ECA 1.30 1.20 1.20 0.82 0.82 0.82 ­0.48 ­0.38 ­0.38 0.06 0.14 0.14 LAC 1.49 1.53 1.53 0.35 0.35 0.35 ­1.14 ­1.18 ­1.18 0.09 0.09 0.09 2015­2020 MENA 1.96 1.97 1.97 1.99 1.85 1.85 0.02 ­0.12 ­0.12 0.29 0.15 0.15 SA 8.12 8.12 8.28 11.26 11.26 11.93 3.14 3.14 3.65 4.00 4.00 4.50 SSA 1.32 1.38 1.39 5.66 3.72 3.53 4.34 2.34 2.15 4.50 2.55 2.36 Non DC 2.71 2.76 3.22 4.20 3.96 3.96 1.49 1.19 0.74 2.14 1.79 1.79 All DCs 23.39 23.11 23.28 30.52 28.26 28.74 7.14 5.14 5.46 10.49 8.58 8.89 EAP 9.97 9.41 9.41 11.47 10.85 10.85 1.50 1.44 1.44 1.80 1.75 1.75 ECA 1.48 1.15 1.15 0.97 0.74 0.74 ­0.52 ­0.41 ­0.41 0.08 0.12 0.12 LAC 1.60 1.33 1.33 0.33 0.33 0.33 ­1.28 ­1.01 ­1.01 0.08 0.08 0.08 2020­2025 MENA 2.49 2.46 2.46 2.52 2.30 2.30 0.03 ­0.16 ­0.16 0.37 0.19 0.19 SA 10.01 10.01 10.10 13.39 13.39 13.59 3.38 3.38 3.49 4.42 4.42 4.53 SSA 1.59 1.59 1.59 6.54 3.87 3.61 4.94 2.28 2.01 5.12 2.48 2.21 Non DC 2.91 2.78 3.12 4.32 3.61 3.61 1.41 0.83 0.49 2.25 1.53 1.53 All DCs 27.15 25.96 26.05 35.21 31.48 31.41 8.06 5.52 5.36 11.87 9.03 8.87 EAP 9.98 9.27 9.27 11.56 10.70 10.70 1.58 1.43 1.43 1.87 1.73 1.73 ECA 1.53 1.07 1.07 1.01 0.66 0.66 ­0.52 ­0.42 ­0.42 0.10 0.10 0.10 LAC 1.59 1.15 1.15 0.30 0.30 0.30 ­1.30 ­0.86 ­0.86 0.06 0.06 0.06 2025­2030 MENA 2.69 2.64 2.64 2.72 2.47 2.47 0.03 ­0.17 ­0.17 0.40 0.20 0.20 SA 10.71 10.71 10.75 14.07 14.07 13.98 3.36 3.36 3.22 4.47 4.47 4.34 SSA 1.69 1.64 1.65 6.77 3.78 3.48 5.08 2.14 1.84 5.26 2.31 2.01 Non DC 2.89 2.66 2.92 4.19 3.23 3.23 1.30 0.57 0.31 2.25 1.29 1.29 All DCs 28.19 26.49 26.54 36.44 31.98 31.59 8.25 5.49 5.05 12.17 8.88 8.44 EAP 2.93 2.76 2.76 2.47 2.51 2.51 ­0.46 ­0.25 ­0.25 0.13 0.22 0.22 ECA 0.26 0.32 0.32 0.43 0.49 0.49 0.16 0.17 0.17 0.25 0.24 0.24 LAC 0.80 0.99 0.99 2.34 2.32 2.32 1.55 1.33 1.33 1.73 1.59 1.59 2030­2035 MENA 0.43 0.47 0.47 0.43 0.43 0.43 0.00 ­0.03 ­0.03 0.03 0.00 0.00 SA 2.39 2.39 2.32 2.84 2.89 2.42 0.44 0.50 0.11 0.69 0.74 0.35 SSA 0.42 0.47 0.47 2.88 2.76 2.71 2.46 2.29 2.25 2.49 2.33 2.29 Non DC 1.00 1.09 1.20 1.34 1.74 1.74 0.34 0.66 0.55 0.84 1.11 1.11 All DCs 7.24 7.39 7.31 11.38 11.40 10.89 4.14 4.02 3.58 5.33 5.13 4.69 (Continued on next page) D E V E L O P M E N T A N D C L I M AT E C H A N G E D I S C u S S I O N PA P E r S 65 appendIx 3. (continued) EAP 1.81 1.75 1.75 1.29 1.45 1.45 ­0.52 ­0.30 ­0.30 0.10 0.10 0.10 ECA 0.10 0.22 0.22 0.28 0.40 0.40 0.18 0.18 0.18 0.23 0.21 0.21 LAC 0.57 0.82 0.82 1.96 1.98 1.98 1.39 1.16 1.16 1.50 1.38 1.38 2035­2040 MENA 0.12 0.16 0.16 0.11 0.14 0.14 ­0.01 ­0.01 ­0.01 0.02 0.01 0.01 SA 1.08 1.08 1.02 1.25 1.30 0.98 0.17 0.22 ­0.04 0.48 0.53 0.27 SSA 0.21 0.26 0.26 1.92 2.12 2.12 1.71 1.86 1.85 1.74 1.89 1.88 Non DC 0.65 0.78 0.88 0.89 1.38 1.38 0.24 0.60 0.50 0.59 0.95 0.95 All DCs 3.89 4.29 4.24 6.81 7.39 7.07 2.92 3.10 2.84 4.07 4.12 3.85 EAP 1.49 1.43 1.43 1.06 1.18 1.18 ­0.43 ­0.25 ­0.25 0.08 0.09 0.09 ECA 0.08 0.18 0.18 0.24 0.33 0.33 0.16 0.15 0.15 0.20 0.18 0.18 LAC 0.47 0.68 0.68 1.59 1.68 1.68 1.11 1.00 1.00 1.21 1.18 1.18 2040­2045 MENA 0.09 0.12 0.12 0.08 0.11 0.11 ­0.01 ­0.01 ­0.01 0.01 0.01 0.01 SA 0.86 0.86 0.81 1.03 1.07 0.78 0.17 0.21 ­0.03 0.41 0.45 0.21 SSA 0.16 0.20 0.21 1.60 1.77 1.77 1.44 1.56 1.57 1.46 1.59 1.59 Non DC 0.53 0.64 0.72 0.73 1.13 1.13 0.19 0.50 0.41 0.49 0.80 0.80 All DCs 3.15 3.48 3.43 5.60 6.15 5.86 2.44 2.67 2.43 3.37 3.50 3.26 EAP 1.23 1.17 1.17 0.87 0.97 0.97 ­0.36 ­0.21 ­0.21 0.06 0.08 0.08 ECA 0.07 0.14 0.14 0.20 0.28 0.28 0.14 0.13 0.13 0.17 0.15 0.15 LAC 0.39 0.57 0.57 1.30 1.42 1.42 0.90 0.86 0.86 0.98 1.01 1.01 2045­2050 MENA 0.06 0.09 0.09 0.06 0.08 0.08 ­0.01 ­0.01 ­0.01 0.01 0.01 0.01 SA 0.69 0.69 0.64 0.85 0.88 0.62 0.16 0.19 ­0.02 0.35 0.38 0.17 SSA 0.13 0.16 0.16 1.33 1.47 1.48 1.20 1.31 1.32 1.22 1.33 1.34 Non DC 0.44 0.53 0.59 0.60 0.93 0.93 0.16 0.41 0.34 0.41 0.67 0.67 All DCs 2.57 2.83 2.79 4.61 5.10 4.85 2.04 2.27 2.06 2.79 2.96 2.75 66 A D A PTATION rELATED TO INDu ST rIAL AND MuNICIPAL WATE r Su PPLy AND rIVE r INE FLOOD PrOTE C T I O N appendIx 4. WaTer supply resulTs. gCM: CsIro. dIsCounT raTe: 7% Annual costs (averaged over 5-year periods) in USD billion Baseline Baseline & CC CC (net) CC (gross) Small Best Large Small Best Large Small Best Large Small Best Large EAP 6.80 7.01 7.01 7.52 8.03 8.03 0.72 1.02 1.02 1.20 1.68 1.68 ECA 0.85 1.12 1.12 0.49 0.84 0.84 ­0.36 ­0.27 ­0.27 0.05 0.16 0.16 LAC 1.13 1.63 1.63 0.33 0.33 0.33 ­0.80 ­1.30 ­1.30 0.10 0.10 0.10 2010­2015 MENA 0.90 0.96 0.96 0.91 0.91 0.91 0.01 ­0.05 ­0.05 0.13 0.07 0.07 SA 4.15 4.15 4.38 6.52 6.52 7.82 2.37 2.37 3.44 2.82 2.82 3.88 SSA 0.72 0.87 0.88 3.56 3.01 2.96 2.84 2.14 2.08 2.97 2.35 2.29 Non DC 2.06 2.38 2.95 3.48 3.98 3.98 1.42 1.60 1.03 1.76 1.97 1.97 All DCs 14.54 15.74 15.99 19.34 19.64 20.90 4.79 3.91 4.91 7.27 7.18 8.18 EAP 7.90 7.66 7.66 8.98 8.82 8.82 1.08 1.15 1.15 1.33 1.42 1.42 ECA 1.12 1.03 1.03 0.71 0.71 0.71 ­0.41 ­0.32 ­0.32 0.05 0.12 0.12 LAC 1.28 1.32 1.32 0.30 0.30 0.30 ­0.98 ­1.02 ­1.02 0.08 0.08 0.08 2015­2020 MENA 1.68 1.69 1.69 1.70 1.59 1.59 0.02 ­0.11 ­0.11 0.25 0.13 0.13 SA 6.97 6.97 7.10 9.67 9.67 10.24 2.70 2.70 3.14 3.43 3.43 3.87 SSA 1.13 1.18 1.19 4.86 3.20 3.04 3.73 2.01 1.85 3.87 2.20 2.03 Non DC 2.33 2.38 2.77 3.61 3.41 3.41 1.28 1.03 0.64 1.84 1.54 1.54 All DCs 20.08 19.85 20.00 26.21 24.27 24.69 6.13 4.42 4.69 9.01 7.37 7.65 EAP 7.80 7.36 7.36 8.98 8.49 8.49 1.17 1.13 1.13 1.41 1.37 1.37 ECA 1.16 0.90 0.90 0.76 0.58 0.58 ­0.40 ­0.32 ­0.32 0.07 0.09 0.09 LAC 1.25 1.05 1.05 0.25 0.25 0.25 ­1.00 ­0.79 ­0.79 0.06 0.06 0.06 2020­2025 MENA 1.95 1.92 1.92 1.97 1.80 1.80 0.02 ­0.12 ­0.12 0.29 0.15 0.15 SA 7.83 7.83 7.90 10.47 10.47 10.63 2.64 2.64 2.73 3.46 3.46 3.55 SSA 1.25 1.24 1.25 5.11 3.03 2.82 3.87 1.79 1.58 4.00 1.94 1.73 Non DC 2.28 2.18 2.45 3.38 2.83 2.83 1.11 0.65 0.38 1.76 1.20 1.20 All DCs 21.24 20.31 20.39 27.54 24.63 24.58 6.30 4.32 4.20 9.29 7.07 6.95 EAP 7.11 6.60 6.60 8.24 7.62 7.62 1.13 1.02 1.02 1.34 1.23 1.23 ECA 1.09 0.76 0.76 0.72 0.47 0.47 ­0.37 ­0.30 ­0.30 0.07 0.07 0.07 LAC 1.14 0.82 0.82 0.21 0.21 0.21 ­0.92 ­0.61 ­0.61 0.05 0.05 0.05 2025­2030 MENA 1.92 1.88 1.88 1.94 1.76 1.76 0.02 ­0.12 ­0.12 0.29 0.14 0.14 SA 7.62 7.62 7.66 10.02 10.02 9.96 2.40 2.40 2.30 3.19 3.19 3.09 SSA 1.20 1.17 1.17 4.82 2.70 2.48 3.62 1.52 1.31 3.75 1.65 1.43 Non DC 2.06 1.90 2.08 2.99 2.31 2.31 0.93 0.41 0.22 1.60 0.92 0.92 All DCs 20.08 18.87 18.90 25.95 22.78 22.50 5.87 3.91 3.60 8.67 6.33 6.02 EAP 1.70 1.62 1.62 1.37 1.44 1.44 ­0.33 ­0.18 ­0.18 0.09 0.12 0.12 ECA 0.13 0.19 0.19 0.25 0.31 0.31 0.12 0.12 0.12 0.17 0.16 0.16 LAC 0.48 0.64 0.64 1.47 1.50 1.50 0.98 0.87 0.87 1.09 1.03 1.03 2030­2035 MENA 0.20 0.22 0.22 0.19 0.21 0.21 0.00 ­0.02 ­0.02 0.01 0.00 0.00 SA 1.24 1.24 1.20 1.49 1.53 1.25 0.25 0.29 0.06 0.41 0.44 0.21 SSA 0.22 0.26 0.26 1.70 1.71 1.70 1.48 1.46 1.44 1.50 1.48 1.47 Non DC 0.59 0.66 0.74 0.80 1.10 1.10 0.21 0.44 0.37 0.51 0.72 0.72 All DCs 3.98 4.17 4.13 6.48 6.71 6.42 2.50 2.54 2.29 3.26 3.24 2.99 (Continued on next page) D E V E L O P M E N T A N D C L I M AT E C H A N G E D I S C u S S I O N PA P E r S 67 appendIx 4. (continued) EAP 1.06 1.03 1.03 0.76 0.85 0.85 ­0.30 ­0.17 ­0.17 0.06 0.06 0.06 ECA 0.06 0.13 0.13 0.17 0.24 0.24 0.11 0.11 0.11 0.14 0.12 0.12 LAC 0.33 0.48 0.48 1.11 1.17 1.17 0.78 0.69 0.69 0.84 0.81 0.81 2035­2040 MENA 0.06 0.08 0.08 0.06 0.08 0.08 0.00 ­0.01 ­0.01 0.01 0.01 0.01 SA 0.60 0.60 0.57 0.73 0.76 0.57 0.12 0.15 ­0.01 0.29 0.31 0.16 SSA 0.11 0.15 0.15 1.12 1.24 1.24 1.01 1.10 1.10 1.02 1.11 1.11 Non DC 0.38 0.45 0.51 0.52 0.81 0.81 0.14 0.35 0.29 0.35 0.56 0.56 All DCs 2.24 2.47 2.44 3.95 4.33 4.15 1.71 1.86 1.70 2.36 2.43 2.28 EAP 0.80 0.77 0.77 0.57 0.63 0.63 ­0.23 ­0.13 ­0.13 0.04 0.05 0.05 ECA 0.04 0.10 0.10 0.13 0.18 0.18 0.09 0.08 0.08 0.11 0.10 0.10 LAC 0.25 0.37 0.37 0.83 0.91 0.91 0.57 0.54 0.54 0.62 0.64 0.64 2040­2045 MENA 0.04 0.06 0.06 0.04 0.05 0.05 0.00 ­0.01 ­0.01 0.01 0.01 0.01 SA 0.44 0.44 0.42 0.55 0.57 0.41 0.10 0.13 0.00 0.22 0.24 0.11 SSA 0.08 0.10 0.11 0.85 0.94 0.95 0.77 0.84 0.84 0.78 0.85 0.86 Non DC 0.28 0.34 0.39 0.39 0.61 0.61 0.11 0.27 0.22 0.26 0.43 0.43 All DCs 1.66 1.84 1.81 2.97 3.29 3.13 1.30 1.45 1.32 1.78 1.88 1.76 EAP 0.60 0.57 0.57 0.43 0.47 0.47 ­0.17 ­0.10 ­0.10 0.03 0.04 0.04 ECA 0.03 0.07 0.07 0.10 0.13 0.13 0.07 0.06 0.06 0.08 0.07 0.07 LAC 0.19 0.28 0.28 0.62 0.70 0.70 0.43 0.42 0.42 0.46 0.49 0.49 2045­2050 MENA 0.03 0.04 0.04 0.03 0.04 0.04 0.00 0.00 0.00 0.00 0.00 0.00 SA 0.33 0.33 0.30 0.41 0.43 0.30 0.08 0.10 ­0.01 0.17 0.19 0.08 SSA 0.06 0.08 0.08 0.65 0.72 0.72 0.59 0.64 0.65 0.60 0.65 0.66 Non DC 0.21 0.26 0.29 0.29 0.46 0.46 0.08 0.20 0.17 0.20 0.32 0.32 All DCs 1.24 1.37 1.35 2.23 2.49 2.36 0.99 1.12 1.02 1.35 1.45 1.35 68 A D A PTATION rELATED TO INDu ST rIAL AND MuNICIPAL WATE r Su PPLy AND rIVE r INE FLOOD PrOTE C T I O N appendIx 5. WaTer supply resulTs. gCM: nCar. dIsCounT raTe: 0% Annual costs (averaged over 5-year periods) in USD billion Baseline Baseline & CC CC (net) CC (gross) Small Best Large Small Best Large Small Best Large Small Best Large EAP 8.44 8.67 8.67 12.54 13.30 13.30 4.11 4.62 4.62 4.20 4.75 4.75 ECA 1.06 1.37 1.37 1.62 2.01 2.01 0.57 0.64 0.64 1.17 1.48 1.48 LAC 1.40 2.00 2.00 6.64 8.13 8.13 5.25 6.13 6.13 5.28 6.17 6.17 2010­2015 MENA 1.14 1.22 1.22 1.13 1.10 1.10 ­0.01 ­0.12 ­0.12 0.17 0.06 0.06 SA 5.24 5.24 5.53 5.73 6.11 7.07 0.48 0.87 1.54 0.65 1.01 1.68 SSA 0.91 1.09 1.10 4.96 4.64 4.62 4.05 3.56 3.52 4.15 3.69 3.65 Non DC 2.56 2.94 3.63 5.70 6.19 6.19 3.15 3.26 2.56 3.53 3.65 3.65 All DCs 18.18 19.59 19.90 32.62 35.29 36.23 14.44 15.70 16.34 15.61 17.17 17.80 EAP 13.65 13.23 13.23 16.90 17.73 17.73 3.25 4.49 4.49 3.46 4.73 4.73 ECA 1.93 1.77 1.77 2.40 2.46 2.46 0.47 0.69 0.69 1.61 1.67 1.67 LAC 2.21 2.26 2.26 8.91 9.02 9.02 6.70 6.76 6.76 6.74 6.80 6.80 2015­2020 MENA 2.93 2.94 2.94 2.88 2.60 2.60 ­0.05 ­0.34 ­0.34 0.43 0.14 0.14 SA 12.11 12.11 12.34 12.11 12.53 13.35 0.00 0.42 1.02 0.56 0.93 1.52 SSA 1.96 2.05 2.06 8.47 6.88 6.80 6.51 4.83 4.74 6.68 5.00 4.91 Non DC 4.03 4.10 4.77 8.55 7.28 7.28 4.52 3.19 2.52 5.24 3.91 3.91 All DCs 34.79 34.36 34.60 51.67 51.22 51.96 16.87 16.86 17.36 19.47 19.28 19.77 EAP 18.86 17.79 17.79 21.26 22.15 22.15 2.40 4.37 4.37 2.73 4.72 4.72 ECA 2.81 2.18 2.18 3.19 2.92 2.92 0.38 0.74 0.74 2.05 1.87 1.87 LAC 3.03 2.52 2.52 11.18 9.90 9.90 8.15 7.38 7.38 8.19 7.43 7.43 2020­2025 MENA 4.72 4.66 4.66 4.63 4.11 4.11 ­0.09 ­0.55 ­0.55 0.68 0.22 0.22 SA 18.97 18.97 19.14 18.49 18.95 19.64 ­0.49 ­0.02 0.50 0.79 1.16 1.68 SSA 3.02 3.01 3.02 11.98 9.12 8.97 8.96 6.11 5.95 9.20 6.32 6.16 Non DC 5.50 5.26 5.90 11.40 8.37 8.37 5.89 3.12 2.48 6.95 4.17 4.17 All DCs 51.40 49.12 49.30 70.71 67.15 67.69 19.31 18.03 18.39 23.64 21.71 22.07 EAP 24.07 22.35 22.35 25.61 26.58 26.58 1.54 4.24 4.24 2.03 4.70 4.70 ECA 3.68 2.58 2.58 3.97 3.37 3.37 0.28 0.79 0.79 2.49 2.06 2.06 LAC 3.84 2.77 2.77 13.44 10.78 10.78 9.60 8.01 8.01 9.75 8.06 8.06 2025­2030 MENA 6.50 6.38 6.38 6.37 5.61 5.61 ­0.13 ­0.77 ­0.77 0.94 0.30 0.30 SA 25.84 25.84 25.94 24.87 25.37 25.92 ­0.97 ­0.47 ­0.02 1.02 1.40 1.84 SSA 4.07 3.96 3.97 15.49 11.36 11.15 11.42 7.39 7.17 11.72 7.63 7.41 Non DC 6.98 6.41 7.03 14.24 9.46 9.46 7.27 3.05 2.43 8.66 4.44 4.44 All DCs 68.01 63.89 64.00 89.75 83.07 83.42 21.74 19.19 19.41 27.95 24.15 24.38 EAP 25.70 22.89 22.89 19.87 20.19 20.19 ­5.84 ­2.70 ­2.70 1.43 1.46 1.46 ECA 3.77 2.04 2.04 3.99 2.77 2.77 0.21 0.73 0.73 2.34 1.30 1.30 LAC 5.21 3.29 3.29 11.15 6.59 6.59 5.94 3.31 3.31 5.99 3.35 3.35 2030­2035 MENA 7.98 7.80 7.80 8.27 7.33 7.33 0.29 ­0.47 ­0.47 1.22 0.46 0.46 SA 32.91 32.91 32.38 31.04 30.86 29.36 ­1.88 ­2.06 ­3.02 2.59 2.59 1.62 SSA 5.48 5.15 5.14 15.84 11.12 10.44 10.36 5.98 5.30 10.73 6.24 5.57 Non DC 7.95 6.83 6.81 13.42 7.25 7.25 5.48 0.42 0.45 6.76 1.69 1.69 All DCs 81.07 74.08 73.54 90.15 78.86 76.68 9.08 4.78 3.14 24.30 15.40 13.76 (Continued on next page) D E V E L O P M E N T A N D C L I M AT E C H A N G E D I S C u S S I O N PA P E r S 69 appendIx 5. (continued) EAP 27.20 25.01 25.01 21.22 21.50 21.50 ­5.98 ­3.51 ­3.51 1.53 1.51 1.51 ECA 4.06 2.23 2.23 5.08 3.14 3.14 1.02 0.91 0.91 3.11 1.41 1.41 LAC 6.08 3.55 3.55 9.82 6.88 6.88 3.74 3.33 3.33 3.80 3.38 3.38 2035­2040 MENA 9.30 9.05 9.05 10.27 9.19 9.19 0.97 0.14 0.14 1.50 0.67 0.67 SA 38.91 38.91 38.44 35.21 35.01 33.64 ­3.70 ­3.91 ­4.80 2.59 2.54 1.64 SSA 7.26 6.81 6.80 17.23 13.00 11.19 9.97 6.19 4.39 10.45 6.52 4.72 Non DC 9.07 7.74 7.68 13.76 8.53 8.53 4.69 0.79 0.86 6.09 2.18 2.18 All DCs 92.81 85.57 85.09 98.84 88.72 85.54 6.02 3.15 0.46 22.98 16.03 13.34 EAP 28.69 27.13 27.13 22.57 22.80 22.80 ­6.12 ­4.32 ­4.32 1.63 1.57 1.57 ECA 4.36 2.42 2.42 6.18 3.51 3.51 1.82 1.09 1.09 3.88 1.52 1.52 LAC 6.94 3.82 3.82 8.49 7.17 7.17 1.55 3.35 3.35 1.70 3.41 3.41 2040­2045 MENA 10.62 10.30 10.30 12.27 11.05 11.05 1.66 0.76 0.76 1.86 0.95 0.95 SA 44.91 44.91 44.50 39.39 39.15 37.93 ­5.52 ­5.76 ­6.58 2.60 2.48 1.67 SSA 9.04 8.48 8.46 18.62 14.88 11.94 9.58 6.41 3.48 10.16 6.80 3.88 Non DC 10.20 8.65 8.54 14.10 9.81 9.81 3.90 1.16 1.27 5.44 2.69 2.69 All DCs 104.55 97.06 96.63 107.52 98.58 94.41 2.97 1.52 ­2.22 21.82 16.73 12.99 EAP 30.19 29.24 29.24 23.92 24.11 24.11 ­6.27 ­5.13 ­5.13 1.72 1.63 1.63 ECA 4.65 2.61 2.61 7.27 3.89 3.89 2.63 1.27 1.27 4.65 1.63 1.63 LAC 7.81 4.09 4.09 7.16 7.46 7.46 ­0.65 3.37 3.37 1.54 3.47 3.47 2045­2050 MENA 11.93 11.55 11.55 14.28 12.92 12.92 2.34 1.37 1.37 2.59 1.60 1.60 SA 50.91 50.91 50.56 43.57 43.30 42.21 ­7.34 ­7.61 ­8.35 2.61 2.43 1.69 SSA 10.81 10.14 10.13 20.01 16.76 12.70 9.20 6.62 2.57 9.87 7.08 3.03 Non DC 11.33 9.56 9.41 14.44 11.09 11.09 3.11 1.53 1.68 4.93 3.20 3.20 All DCs 116.30 108.54 108.18 116.21 108.44 103.28 ­0.08 ­0.11 ­4.90 22.99 17.85 13.05 70 A D A PTATION rELATED TO INDu ST rIAL AND MuNICIPAL WATE r Su PPLy AND rIVE r INE FLOOD PrOTE C T I O N appendIx 6. WaTer supply resulTs. gCM: nCar. dIsCounT raTe: 3% Annual costs (averaged over 5-year periods) in USD billion Baseline Baseline & CC CC (net) CC (gross) Small Best Large Small Best Large Small Best Large Small Best Large EAP 7.67 7.89 7.89 11.44 12.13 12.13 3.77 4.24 4.24 3.85 4.35 4.35 ECA 0.96 1.25 1.25 1.48 1.84 1.84 0.52 0.59 0.59 1.06 1.35 1.35 LAC 1.27 1.83 1.83 6.06 7.44 7.44 4.79 5.61 5.61 4.82 5.64 5.64 2010­2015 MENA 1.03 1.10 1.10 1.02 0.99 0.99 ­0.01 ­0.11 ­0.11 0.15 0.06 0.06 SA 4.73 4.73 4.99 5.17 5.53 6.41 0.45 0.80 1.42 0.59 0.93 1.55 SSA 0.82 0.98 1.00 4.50 4.23 4.21 3.69 3.24 3.21 3.78 3.36 3.33 Non DC 2.32 2.68 3.31 5.19 5.66 5.66 2.87 2.98 2.35 3.22 3.34 3.34 All DCs 16.48 17.78 18.06 29.68 32.15 33.02 13.20 14.37 14.95 14.26 15.70 16.28 EAP 10.73 10.41 10.41 13.31 13.96 13.96 2.58 3.55 3.55 2.74 3.74 3.74 ECA 1.52 1.40 1.40 1.89 1.94 1.94 0.37 0.54 0.54 1.27 1.32 1.32 LAC 1.74 1.78 1.78 7.02 7.12 7.12 5.28 5.33 5.33 5.31 5.36 5.36 2015­2020 MENA 2.30 2.31 2.31 2.26 2.04 2.04 ­0.04 ­0.26 ­0.26 0.33 0.11 0.11 SA 9.50 9.50 9.68 9.51 9.84 10.49 0.00 0.34 0.81 0.44 0.73 1.20 SSA 1.54 1.61 1.62 6.66 5.42 5.35 5.12 3.81 3.73 5.25 3.94 3.86 Non DC 3.17 3.23 3.75 6.73 5.74 5.74 3.56 2.52 1.99 4.13 3.09 3.09 All DCs 27.33 27.01 27.20 40.64 40.32 40.91 13.31 13.31 13.71 15.34 15.21 15.60 EAP 12.81 12.09 12.09 14.45 15.06 15.06 1.64 2.98 2.98 1.87 3.22 3.22 ECA 1.91 1.48 1.48 2.16 1.98 1.98 0.26 0.50 0.50 1.39 1.27 1.27 LAC 2.06 1.71 1.71 7.60 6.74 6.74 5.54 5.03 5.03 5.57 5.06 5.06 2020­2025 MENA 3.20 3.16 3.16 3.14 2.79 2.79 ­0.06 ­0.38 ­0.38 0.46 0.15 0.15 SA 12.88 12.88 12.99 12.55 12.86 13.33 ­0.33 ­0.01 0.34 0.54 0.79 1.14 SSA 2.05 2.04 2.05 8.14 6.20 6.10 6.09 4.16 4.05 6.25 4.30 4.19 Non DC 3.74 3.57 4.01 7.74 5.70 5.70 4.01 2.13 1.69 4.72 2.84 2.84 All DCs 34.90 33.36 33.48 48.04 45.64 46.01 13.14 12.28 12.52 16.08 14.78 15.03 EAP 14.11 13.11 13.11 15.03 15.60 15.60 0.91 2.49 2.49 1.19 2.76 2.76 ECA 2.16 1.52 1.52 2.33 1.98 1.98 0.17 0.46 0.46 1.46 1.21 1.21 LAC 2.25 1.63 1.63 7.89 6.33 6.33 5.63 4.70 4.70 5.72 4.73 4.73 2025­2030 MENA 3.81 3.74 3.74 3.74 3.29 3.29 ­0.08 ­0.45 ­0.45 0.55 0.17 0.17 SA 15.14 15.14 15.21 14.58 14.87 15.20 ­0.57 ­0.27 ­0.01 0.60 0.82 1.08 SSA 2.39 2.32 2.33 9.08 6.66 6.54 6.69 4.34 4.21 6.87 4.48 4.35 Non DC 4.09 3.76 4.13 8.35 5.56 5.56 4.26 1.79 1.43 5.08 2.61 2.61 All DCs 39.87 37.46 37.52 52.63 48.73 48.93 12.77 11.27 11.41 16.40 14.18 14.31 EAP 5.61 5.21 5.21 4.70 4.87 4.87 ­0.91 ­0.33 ­0.33 0.69 0.71 0.71 ECA 0.60 0.56 0.56 0.84 0.93 0.93 0.24 0.37 0.37 0.54 0.56 0.56 LAC 1.42 1.55 1.55 3.19 3.17 3.17 1.77 1.62 1.62 1.80 1.65 1.65 2030­2035 MENA 1.10 1.13 1.13 1.21 1.15 1.15 0.11 0.01 0.01 0.18 0.09 0.09 SA 5.37 5.37 5.23 5.56 5.47 5.05 0.19 0.10 ­0.18 1.05 1.01 0.73 SSA 0.94 0.97 0.97 2.84 2.43 2.35 1.90 1.46 1.38 1.99 1.55 1.47 Non DC 1.86 1.91 2.06 2.98 2.71 2.71 1.12 0.80 0.65 1.29 0.96 0.96 All DCs 15.02 14.79 14.65 18.34 18.03 17.53 3.32 3.24 2.87 6.25 5.56 5.19 (Continued on next page) D E V E L O P M E N T A N D C L I M AT E C H A N G E D I S C u S S I O N PA P E r S 71 appendIx 6. (continued) EAP 3.20 3.12 3.12 2.83 2.98 2.98 ­0.36 ­0.14 ­0.14 0.62 0.64 0.64 ECA 0.20 0.39 0.39 0.49 0.74 0.74 0.29 0.35 0.35 0.36 0.50 0.50 LAC 1.02 1.40 1.40 2.17 2.82 2.82 1.15 1.42 1.42 1.48 1.44 1.44 2035­2040 MENA 0.27 0.34 0.34 0.40 0.45 0.45 0.13 0.11 0.11 0.14 0.12 0.12 SA 2.17 2.17 2.07 2.57 2.48 2.17 0.40 0.31 0.10 0.96 0.90 0.69 SSA 0.45 0.53 0.53 1.25 1.43 1.37 0.80 0.91 0.84 1.00 0.99 0.94 Non DC 1.17 1.37 1.54 1.70 2.25 2.25 0.52 0.88 0.71 0.69 0.91 0.91 All DCs 7.30 7.94 7.85 9.71 10.90 10.53 2.41 2.96 2.68 4.55 4.59 4.32 EAP 2.85 2.76 2.76 2.46 2.59 2.59 ­0.39 ­0.17 ­0.17 0.56 0.58 0.58 ECA 0.16 0.34 0.34 0.41 0.66 0.66 0.25 0.31 0.31 0.30 0.45 0.45 LAC 0.91 1.29 1.29 1.99 2.55 2.55 1.07 1.25 1.25 1.34 1.28 1.28 2040­2045 MENA 0.20 0.26 0.26 0.30 0.35 0.35 0.10 0.09 0.09 0.11 0.10 0.10 SA 1.79 1.79 1.69 2.21 2.12 1.82 0.43 0.33 0.12 0.89 0.84 0.63 SSA 0.36 0.43 0.44 1.06 1.23 1.20 0.71 0.80 0.77 0.87 0.87 0.84 Non DC 1.04 1.23 1.38 1.51 2.00 2.00 0.47 0.77 0.62 0.62 0.80 0.80 All DCs 6.27 6.87 6.78 8.43 9.49 9.16 2.16 2.62 2.38 4.07 4.11 3.87 EAP 2.58 2.47 2.47 2.16 2.28 2.28 ­0.41 ­0.20 ­0.20 0.51 0.52 0.52 ECA 0.14 0.30 0.30 0.35 0.59 0.59 0.21 0.28 0.28 0.25 0.40 0.40 LAC 0.83 1.18 1.18 1.82 2.29 2.29 1.00 1.11 1.11 1.21 1.13 1.13 2045­2050 MENA 0.16 0.22 0.22 0.24 0.29 0.29 0.08 0.07 0.07 0.09 0.08 0.08 SA 1.55 1.55 1.46 1.97 1.88 1.58 0.42 0.33 0.13 0.83 0.78 0.57 SSA 0.30 0.37 0.37 0.94 1.08 1.08 0.64 0.71 0.71 0.77 0.78 0.77 Non DC 0.93 1.11 1.24 1.36 1.79 1.79 0.43 0.68 0.55 0.55 0.70 0.70 All DCs 5.55 6.09 6.00 7.49 8.41 8.11 1.93 2.32 2.10 3.66 3.69 3.47 72 A D A PTATION rELATED TO INDu ST rIAL AND MuNICIPAL WATE r Su PPLy AND rIVE r INE FLOOD PrOTE C T I O N appendIx 7. WaTer supply resulTs. gCM: nCar. dIsCounT raTe: 5% Annual costs (averaged over 5-year periods) in USD billion Baseline Baseline & CC CC (net) CC (gross) Small Best Large Small Best Large Small Best Large Small Best Large EAP 7.22 7.43 7.43 10.79 11.44 11.44 3.57 4.01 4.01 3.65 4.11 4.11 ECA 0.90 1.18 1.18 1.39 1.73 1.73 0.49 0.55 0.55 1.00 1.28 1.28 LAC 1.20 1.73 1.73 5.71 7.03 7.03 4.52 5.30 5.30 4.55 5.33 5.33 2010­2015 MENA 0.96 1.02 1.02 0.95 0.92 0.92 ­0.01 ­0.10 ­0.10 0.14 0.05 0.05 SA 4.42 4.42 4.67 4.85 5.19 6.02 0.43 0.76 1.34 0.56 0.88 1.46 SSA 0.77 0.92 0.94 4.24 3.98 3.97 3.47 3.06 3.03 3.55 3.17 3.14 Non DC 2.19 2.52 3.13 4.89 5.34 5.34 2.70 2.82 2.22 3.03 3.15 3.15 All DCs 15.47 16.71 16.98 27.93 30.29 31.11 12.46 13.58 14.13 13.46 14.83 15.38 EAP 9.19 8.92 8.92 11.41 11.97 11.97 2.22 3.05 3.05 2.36 3.21 3.21 ECA 1.30 1.20 1.20 1.62 1.67 1.67 0.32 0.47 0.47 1.08 1.13 1.13 LAC 1.49 1.53 1.53 6.01 6.11 6.11 4.52 4.58 4.58 4.55 4.60 4.60 2015­2020 MENA 1.96 1.97 1.97 1.93 1.75 1.75 ­0.03 ­0.23 ­0.23 0.29 0.10 0.10 SA 8.12 8.12 8.28 8.13 8.42 8.98 0.01 0.29 0.70 0.37 0.62 1.03 SSA 1.32 1.38 1.39 5.70 4.64 4.58 4.38 3.26 3.20 4.50 3.38 3.31 Non DC 2.71 2.76 3.22 5.76 4.93 4.93 3.05 2.16 1.71 3.53 2.65 2.65 All DCs 23.39 23.11 23.28 34.80 34.54 35.04 11.42 11.43 11.77 13.15 13.05 13.39 EAP 9.97 9.41 9.41 11.25 11.73 11.73 1.28 2.32 2.32 1.46 2.51 2.51 ECA 1.48 1.15 1.15 1.69 1.55 1.55 0.20 0.39 0.39 1.09 0.99 0.99 LAC 1.60 1.33 1.33 5.92 5.25 5.25 4.32 3.92 3.92 4.34 3.94 3.94 2020­2025 MENA 2.49 2.46 2.46 2.44 2.17 2.17 ­0.05 ­0.29 ­0.29 0.36 0.12 0.12 SA 10.01 10.01 10.10 9.76 10.01 10.37 ­0.25 ­0.01 0.27 0.42 0.62 0.89 SSA 1.59 1.59 1.59 6.33 4.82 4.75 4.74 3.24 3.15 4.86 3.34 3.26 Non DC 2.91 2.78 3.12 6.03 4.44 4.44 3.12 1.66 1.32 3.68 2.22 2.22 All DCs 27.15 25.96 26.05 37.39 35.53 35.82 10.24 9.57 9.76 12.53 11.52 11.71 EAP 9.98 9.27 9.27 10.63 11.04 11.04 0.65 1.77 1.77 0.85 1.96 1.96 ECA 1.53 1.07 1.07 1.65 1.40 1.40 0.12 0.33 0.33 1.03 0.86 0.86 LAC 1.59 1.15 1.15 5.58 4.48 4.48 3.99 3.33 3.33 4.05 3.35 3.35 2025­2030 MENA 2.69 2.64 2.64 2.64 2.33 2.33 ­0.05 ­0.32 ­0.32 0.39 0.12 0.12 SA 10.71 10.71 10.75 10.31 10.51 10.75 ­0.40 ­0.19 ­0.01 0.42 0.58 0.77 SSA 1.69 1.64 1.65 6.42 4.71 4.62 4.73 3.07 2.98 4.86 3.17 3.08 Non DC 2.89 2.66 2.92 5.91 3.93 3.93 3.02 1.27 1.01 3.59 1.85 1.85 All DCs 28.19 26.49 26.54 37.23 34.47 34.62 9.04 7.98 8.08 11.60 10.04 10.13 EAP 2.93 2.76 2.76 2.53 2.64 2.64 ­0.40 ­0.12 ­0.12 0.44 0.45 0.45 ECA 0.26 0.32 0.32 0.43 0.56 0.56 0.16 0.24 0.24 0.28 0.35 0.35 LAC 0.80 0.99 0.99 1.84 2.03 2.03 1.04 1.04 1.04 1.06 1.06 1.06 2030­2035 MENA 0.43 0.47 0.47 0.49 0.49 0.49 0.06 0.03 0.03 0.08 0.04 0.04 SA 2.39 2.39 2.32 2.63 2.58 2.34 0.24 0.18 0.02 0.65 0.62 0.46 SSA 0.42 0.47 0.47 1.35 1.26 1.24 0.92 0.80 0.77 0.97 0.85 0.83 Non DC 1.00 1.09 1.20 1.57 1.65 1.65 0.58 0.57 0.46 0.64 0.63 0.63 All DCs 7.24 7.39 7.31 9.27 9.56 9.30 2.04 2.17 1.98 3.48 3.38 3.19 (Continued on next page) D E V E L O P M E N T A N D C L I M AT E C H A N G E D I S C u S S I O N PA P E r S 73 appendIx 7. (continued) EAP 1.81 1.75 1.75 1.61 1.69 1.69 ­0.20 ­0.06 ­0.06 0.36 0.37 0.37 ECA 0.10 0.22 0.22 0.26 0.42 0.42 0.15 0.20 0.20 0.19 0.29 0.29 LAC 0.57 0.82 0.82 1.29 1.65 1.65 0.72 0.83 0.83 0.87 0.84 0.84 2035­2040 MENA 0.12 0.16 0.16 0.17 0.21 0.21 0.06 0.05 0.05 0.06 0.06 0.06 SA 1.08 1.08 1.02 1.37 1.31 1.13 0.29 0.23 0.11 0.56 0.52 0.40 SSA 0.21 0.26 0.26 0.68 0.78 0.77 0.47 0.52 0.51 0.56 0.57 0.55 Non DC 0.65 0.78 0.88 0.97 1.28 1.28 0.32 0.51 0.41 0.39 0.52 0.52 All DCs 3.89 4.29 4.24 5.37 6.06 5.87 1.48 1.77 1.64 2.60 2.65 2.51 EAP 1.49 1.43 1.43 1.29 1.35 1.35 ­0.21 ­0.08 ­0.08 0.30 0.31 0.31 ECA 0.08 0.18 0.18 0.20 0.34 0.34 0.12 0.16 0.16 0.15 0.23 0.23 LAC 0.47 0.68 0.68 1.08 1.35 1.35 0.61 0.67 0.67 0.72 0.68 0.68 2040­2045 MENA 0.09 0.12 0.12 0.13 0.16 0.16 0.04 0.04 0.04 0.05 0.04 0.04 SA 0.86 0.86 0.81 1.11 1.06 0.90 0.26 0.21 0.09 0.47 0.44 0.33 SSA 0.16 0.20 0.21 0.54 0.63 0.63 0.38 0.42 0.42 0.45 0.46 0.46 Non DC 0.53 0.64 0.72 0.80 1.05 1.05 0.26 0.41 0.32 0.32 0.41 0.41 All DCs 3.15 3.48 3.43 4.35 4.90 4.74 1.20 1.42 1.31 2.13 2.17 2.05 EAP 1.23 1.17 1.17 1.03 1.09 1.09 ­0.20 ­0.09 ­0.09 0.24 0.25 0.25 ECA 0.07 0.14 0.14 0.16 0.28 0.28 0.09 0.14 0.14 0.11 0.19 0.19 LAC 0.39 0.57 0.57 0.90 1.11 1.11 0.50 0.54 0.54 0.59 0.55 0.55 2045­2050 MENA 0.06 0.09 0.09 0.10 0.12 0.12 0.03 0.03 0.03 0.04 0.03 0.03 SA 0.69 0.69 0.64 0.91 0.87 0.72 0.22 0.18 0.08 0.40 0.38 0.27 SSA 0.13 0.16 0.16 0.44 0.50 0.51 0.31 0.34 0.35 0.36 0.37 0.38 Non DC 0.44 0.53 0.59 0.65 0.85 0.85 0.21 0.33 0.26 0.26 0.33 0.33 All DCs 2.57 2.83 2.79 3.53 3.97 3.83 0.96 1.13 1.04 1.74 1.77 1.67 74 A D A PTATION rELATED TO INDu ST rIAL AND MuNICIPAL WATE r Su PPLy AND rIVE r INE FLOOD PrOTE C T I O N appendIx 8. WaTer supply resulTs. gCM: nCar. dIsCounT raTe: 7% Annual costs (averaged over 5-year periods) in USD billion Baseline Baseline & CC CC (net) CC (gross) Small Best Large Small Best Large Small Best Large Small Best Large EAP 6.80 7.01 7.01 10.19 10.81 10.81 3.39 3.79 3.79 3.46 3.90 3.90 ECA 0.85 1.12 1.12 1.31 1.64 1.64 0.47 0.52 0.52 0.95 1.21 1.21 LAC 1.13 1.63 1.63 5.40 6.65 6.65 4.27 5.01 5.01 4.30 5.04 5.04 2010­2015 MENA 0.90 0.96 0.96 0.89 0.87 0.87 ­0.01 ­0.09 ­0.09 0.13 0.05 0.05 SA 4.15 4.15 4.38 4.55 4.87 5.66 0.41 0.72 1.27 0.54 0.84 1.39 SSA 0.72 0.87 0.88 3.99 3.76 3.74 3.27 2.89 2.86 3.35 3.00 2.97 Non DC 2.06 2.38 2.95 4.61 5.06 5.06 2.55 2.67 2.10 2.86 2.98 2.98 All DCs 14.54 15.74 15.99 26.33 28.59 29.36 11.79 12.85 13.37 12.72 14.03 14.55 EAP 7.90 7.66 7.66 9.81 10.29 10.29 1.92 2.63 2.63 2.04 2.77 2.77 ECA 1.12 1.03 1.03 1.39 1.43 1.43 0.28 0.40 0.40 0.93 0.98 0.98 LAC 1.28 1.32 1.32 5.17 5.26 5.26 3.89 3.94 3.94 3.92 3.96 3.96 2015­2020 MENA 1.68 1.69 1.69 1.66 1.50 1.50 ­0.03 ­0.19 ­0.19 0.25 0.08 0.08 SA 6.97 6.97 7.10 6.98 7.23 7.71 0.01 0.26 0.60 0.32 0.54 0.89 SSA 1.13 1.18 1.19 4.90 3.99 3.94 3.77 2.81 2.75 3.86 2.90 2.85 Non DC 2.33 2.38 2.77 4.95 4.24 4.24 2.62 1.86 1.47 3.04 2.28 2.28 All DCs 20.08 19.85 20.00 29.91 29.70 30.13 9.83 9.84 10.13 11.32 11.23 11.52 EAP 7.80 7.36 7.36 8.81 9.19 9.19 1.01 1.82 1.82 1.15 1.97 1.97 ECA 1.16 0.90 0.90 1.32 1.21 1.21 0.16 0.31 0.31 0.85 0.78 0.78 LAC 1.25 1.05 1.05 4.63 4.12 4.12 3.38 3.07 3.07 3.40 3.09 3.09 2020­2025 MENA 1.95 1.92 1.92 1.91 1.70 1.70 ­0.04 ­0.23 ­0.23 0.28 0.09 0.09 SA 7.83 7.83 7.90 7.63 7.83 8.12 ­0.20 0.00 0.21 0.33 0.48 0.70 SSA 1.25 1.24 1.25 4.95 3.78 3.72 3.71 2.53 2.47 3.81 2.62 2.55 Non DC 2.28 2.18 2.45 4.72 3.48 3.48 2.44 1.30 1.03 2.88 1.74 1.74 All DCs 21.24 20.31 20.39 29.26 27.81 28.04 8.02 7.50 7.65 9.81 9.02 9.18 EAP 7.11 6.60 6.60 7.58 7.87 7.87 0.47 1.26 1.26 0.60 1.40 1.40 ECA 1.09 0.76 0.76 1.17 1.00 1.00 0.08 0.23 0.23 0.74 0.61 0.61 LAC 1.14 0.82 0.82 3.98 3.20 3.20 2.84 2.38 2.38 2.88 2.39 2.39 2025­2030 MENA 1.92 1.88 1.88 1.88 1.66 1.66 ­0.04 ­0.23 ­0.23 0.28 0.09 0.09 SA 7.62 7.62 7.66 7.34 7.49 7.65 ­0.28 ­0.14 0.00 0.30 0.41 0.55 SSA 1.20 1.17 1.17 4.58 3.36 3.30 3.37 2.19 2.12 3.46 2.26 2.19 Non DC 2.06 1.90 2.08 4.21 2.80 2.80 2.15 0.91 0.72 2.56 1.32 1.32 All DCs 20.08 18.87 18.90 26.52 24.56 24.67 6.44 5.69 5.76 8.27 7.16 7.23 EAP 1.70 1.62 1.62 1.50 1.57 1.57 ­0.20 ­0.05 ­0.05 0.28 0.29 0.29 ECA 0.13 0.19 0.19 0.24 0.35 0.35 0.11 0.15 0.15 0.16 0.23 0.23 LAC 0.48 0.64 0.64 1.13 1.31 1.31 0.65 0.67 0.67 0.68 0.68 0.68 2030­2035 MENA 0.20 0.22 0.22 0.24 0.25 0.25 0.04 0.02 0.02 0.04 0.03 0.03 SA 1.24 1.24 1.20 1.43 1.39 1.25 0.19 0.15 0.05 0.42 0.40 0.30 SSA 0.22 0.26 0.26 0.74 0.73 0.73 0.51 0.48 0.47 0.54 0.51 0.50 Non DC 0.59 0.66 0.74 0.92 1.04 1.04 0.33 0.38 0.31 0.36 0.41 0.41 All DCs 3.98 4.17 4.13 5.28 5.61 5.45 1.30 1.44 1.33 2.13 2.14 2.03 (Continued on next page) D E V E L O P M E N T A N D C L I M AT E C H A N G E D I S C u S S I O N PA P E r S 75 appendIx 8. (continued) EAP 1.06 1.03 1.03 0.94 0.99 0.99 ­0.12 ­0.03 ­0.03 0.21 0.22 0.22 ECA 0.06 0.13 0.13 0.15 0.25 0.25 0.09 0.12 0.12 0.11 0.17 0.17 LAC 0.33 0.48 0.48 0.77 0.97 0.97 0.44 0.49 0.49 0.52 0.50 0.50 2035­2040 MENA 0.06 0.08 0.08 0.09 0.11 0.11 0.03 0.03 0.03 0.03 0.03 0.03 SA 0.60 0.60 0.57 0.78 0.75 0.64 0.18 0.15 0.07 0.33 0.31 0.23 SSA 0.11 0.15 0.15 0.39 0.45 0.45 0.28 0.31 0.31 0.33 0.33 0.33 Non DC 0.38 0.45 0.51 0.57 0.75 0.75 0.19 0.30 0.24 0.23 0.30 0.30 All DCs 2.24 2.47 2.44 3.13 3.53 3.42 0.89 1.06 0.98 1.52 1.56 1.48 EAP 0.80 0.77 0.77 0.69 0.72 0.72 ­0.11 ­0.04 ­0.04 0.16 0.17 0.17 ECA 0.04 0.10 0.10 0.11 0.18 0.18 0.06 0.09 0.09 0.08 0.13 0.13 LAC 0.25 0.37 0.37 0.58 0.73 0.73 0.33 0.36 0.36 0.39 0.36 0.36 2040­2045 MENA 0.04 0.06 0.06 0.06 0.08 0.08 0.02 0.02 0.02 0.02 0.02 0.02 SA 0.44 0.44 0.42 0.59 0.56 0.47 0.14 0.12 0.06 0.25 0.24 0.18 SSA 0.08 0.10 0.11 0.29 0.33 0.34 0.21 0.23 0.23 0.24 0.25 0.25 Non DC 0.28 0.34 0.39 0.43 0.56 0.56 0.14 0.22 0.17 0.17 0.22 0.22 All DCs 1.66 1.84 1.81 2.31 2.61 2.52 0.65 0.77 0.71 1.14 1.16 1.10 EAP 0.60 0.57 0.57 0.50 0.53 0.53 ­0.10 ­0.04 ­0.04 0.12 0.12 0.12 ECA 0.03 0.07 0.07 0.08 0.14 0.14 0.04 0.07 0.07 0.05 0.09 0.09 LAC 0.19 0.28 0.28 0.44 0.54 0.54 0.25 0.26 0.26 0.29 0.27 0.27 2045­2050 MENA 0.03 0.04 0.04 0.04 0.06 0.06 0.01 0.01 0.01 0.02 0.01 0.01 SA 0.33 0.33 0.30 0.44 0.42 0.35 0.11 0.09 0.04 0.19 0.18 0.13 SSA 0.06 0.08 0.08 0.21 0.24 0.25 0.15 0.17 0.17 0.18 0.18 0.19 Non DC 0.21 0.26 0.29 0.32 0.41 0.41 0.11 0.16 0.13 0.13 0.16 0.16 All DCs 1.24 1.37 1.35 1.71 1.92 1.86 0.48 0.56 0.51 0.85 0.86 0.82 76 A D A PTATION rELATED TO INDu ST rIAL AND MuNICIPAL WATE r Su PPLy AND rIVE r INE FLOOD PrOTE C T I O N appendIx 9. rIverIne flood proTeCTIon resulTs. gCM: CsIro. dIsCounT raTe: 0% Annual costs (averaged over 5-year periods) in USD billion CC Baseline Baseline & CC CC (net) (gross) Urban Agric. Total Urban Agric Total Urban Agric Total Total EAP 7.4 0.5 7.9 8.7 0.6 9.3 1.3 0.1 1.4 1.4 ECA 11.7 0.8 12.5 12.3 0.9 13.1 0.6 0.1 0.6 0.9 LAC 9.2 0.3 9.5 10.7 0.4 11.1 1.5 0.1 1.6 1.9 2010­2015 MENA 4.6 0.1 4.7 5.1 0.1 5.1 0.4 0.0 0.4 0.5 SA 5.3 0.4 5.7 6.7 0.5 7.2 1.4 0.1 1.5 1.5 SSA 4.0 0.3 4.3 3.9 0.3 4.2 ­0.2 0.0 ­0.1 0.2 Non DC 44.9 0.9 45.8 50.5 1.0 51.5 5.6 0.1 5.7 6.7 All DCs 42.3 2.4 44.7 47.4 2.8 50.1 5.1 0.3 5.5 6.4 EAP 7.5 0.6 8.1 8.9 0.7 9.6 1.4 0.1 1.5 1.5 ECA 12.0 0.8 12.8 12.6 0.9 13.5 0.6 0.1 0.6 0.9 LAC 9.4 0.3 9.8 11.0 0.4 11.4 1.6 0.1 1.6 1.9 2015­2020 MENA 4.8 0.1 4.8 5.2 0.1 5.3 0.4 0.0 0.4 0.5 SA 5.4 0.4 5.8 6.9 0.5 7.4 1.4 0.1 1.6 1.6 SSA 4.1 0.3 4.5 4.0 0.3 4.3 ­0.2 0.0 ­0.2 0.2 Non DC 46.1 0.9 46.9 51.8 1.0 52.8 5.7 0.1 5.8 6.9 All DCs 43.3 2.5 45.8 48.5 2.8 51.4 5.2 0.4 5.6 6.6 EAP 7.7 0.6 8.3 9.1 0.7 9.8 1.4 0.1 1.5 1.5 ECA 12.3 0.8 13.1 12.9 0.9 13.8 0.6 0.1 0.6 0.9 LAC 9.7 0.3 10.0 11.3 0.4 11.7 1.6 0.1 1.7 2.0 2020­2025 MENA 4.9 0.1 4.9 5.3 0.1 5.4 0.4 0.0 0.4 0.5 SA 5.5 0.4 6.0 7.0 0.5 7.5 1.5 0.1 1.6 1.6 SSA 4.2 0.3 4.6 4.1 0.3 4.4 ­0.2 0.0 ­0.2 0.2 Non DC 47.2 0.9 48.1 53.0 1.0 54.0 5.9 0.1 6.0 7.1 All DCs 44.3 2.6 46.9 49.7 2.9 52.6 5.4 0.4 5.7 6.8 EAP 7.9 0.6 8.5 9.3 0.7 10.0 1.4 0.1 1.5 1.6 ECA 12.6 0.8 13.4 13.2 0.9 14.1 0.6 0.1 0.7 0.9 LAC 9.9 0.4 10.2 11.5 0.4 12.0 1.7 0.1 1.7 2.0 2025­2030 MENA 5.0 0.1 5.1 5.4 0.1 5.5 0.5 0.0 0.5 0.5 SA 5.7 0.4 6.1 7.2 0.5 7.7 1.5 0.1 1.6 1.6 SSA 4.3 0.3 4.7 4.2 0.3 4.5 ­0.2 0.0 ­0.2 0.2 Non DC 48.3 0.9 49.2 54.3 1.0 55.3 6.0 0.1 6.1 7.2 All DCs 45.4 2.6 48.0 50.9 3.0 53.9 5.5 0.4 5.9 6.9 EAP 8.1 0.6 8.7 9.6 0.7 10.3 1.5 0.1 1.6 1.6 ECA 12.9 0.9 13.8 13.5 0.9 14.4 0.6 0.1 0.7 0.9 LAC 10.1 0.4 10.5 11.8 0.4 12.2 1.7 0.1 1.8 2.1 2030­2035 MENA 5.1 0.1 5.2 5.6 0.1 5.7 0.5 0.0 0.5 0.6 SA 5.8 0.4 6.2 7.4 0.6 7.9 1.5 0.1 1.7 1.7 SSA 4.4 0.3 4.8 4.3 0.3 4.6 ­0.2 0.0 ­0.2 0.3 Non DC 49.4 0.9 50.3 55.5 1.1 56.6 6.1 0.1 6.2 7.4 All DCs 46.4 2.7 49.1 52.0 3.1 55.1 5.6 0.4 6.0 7.1 (Continued on next page) D E V E L O P M E N T A N D C L I M AT E C H A N G E D I S C u S S I O N PA P E r S 77 appendIx 9. (continued) EAP 8.3 0.6 8.9 9.8 0.7 10.5 1.5 0.1 1.6 1.6 ECA 13.2 0.9 14.1 13.8 1.0 14.8 0.6 0.1 0.7 1.0 LAC 10.3 0.4 10.7 12.1 0.4 12.5 1.7 0.1 1.8 2.1 2035­2040 MENA 5.2 0.1 5.3 5.7 0.1 5.8 0.5 0.0 0.5 0.6 SA 5.9 0.4 6.4 7.5 0.6 8.1 1.6 0.1 1.7 1.7 SSA 4.5 0.3 4.9 4.4 0.4 4.7 ­0.2 0.0 ­0.2 0.3 Non DC 50.5 1.0 51.5 56.8 1.1 57.9 6.3 0.1 6.4 7.6 All DCs 47.5 2.7 50.2 53.2 3.1 56.3 5.7 0.4 6.1 7.2 EAP 8.4 0.6 9.1 10.0 0.7 10.7 1.5 0.1 1.7 1.7 ECA 13.5 0.9 14.4 14.1 1.0 15.1 0.6 0.1 0.7 1.0 LAC 10.6 0.4 10.9 12.3 0.5 12.8 1.8 0.1 1.8 2.2 2040­2045 MENA 5.3 0.1 5.4 5.8 0.1 5.9 0.5 0.0 0.5 0.6 SA 6.1 0.5 6.5 7.7 0.6 8.3 1.6 0.1 1.7 1.8 SSA 4.6 0.3 5.0 4.5 0.4 4.8 ­0.2 0.0 ­0.2 0.3 Non DC 51.6 1.0 52.6 58.0 1.1 59.1 6.4 0.1 6.5 7.7 All DCs 48.5 2.8 51.3 54.4 3.2 57.6 5.9 0.4 6.3 7.4 EAP 8.6 0.6 9.3 10.2 0.8 11.0 1.6 0.1 1.7 1.7 ECA 13.8 0.9 14.7 14.4 1.0 15.4 0.6 0.1 0.7 1.0 LAC 10.8 0.4 11.2 12.6 0.5 13.1 1.8 0.1 1.9 2.2 2045­2050 MENA 5.5 0.1 5.5 5.9 0.1 6.0 0.5 0.0 0.5 0.6 SA 6.2 0.5 6.7 7.8 0.6 8.4 1.7 0.1 1.8 1.8 SSA 4.7 0.4 5.1 4.6 0.4 4.9 ­0.2 0.0 ­0.2 0.3 Non DC 52.7 1.0 53.7 59.3 1.1 60.4 6.6 0.1 6.7 7.9 All DCs 49.6 2.9 52.4 55.6 3.3 58.8 6.0 0.4 6.4 7.6 78 A D A PTATION rELATED TO INDu ST rIAL AND MuNICIPAL WATE r Su PPLy AND rIVE r INE FLOOD PrOTE C T I O N appendIx 10. rIverIne flood proTeCTIon resulTs. gCM: CsIro. dIsCounT raTe: 3% Annual costs (averaged over 5-year periods) in USD billion CC Baseline Baseline & CC CC (net) (gross) Urban Agric. Total Urban Agric Total Urban Agric Total Total EAP 6.7 0.5 7.2 8.0 0.6 8.6 1.2 0.1 1.3 1.3 ECA 10.7 0.7 11.5 11.2 0.8 12.0 0.5 0.1 0.6 0.8 LAC 8.4 0.3 8.7 9.8 0.4 10.2 1.4 0.1 1.5 1.7 2010­2015 MENA 4.3 0.1 4.3 4.6 0.1 4.7 0.4 0.0 0.4 0.5 SA 4.8 0.4 5.2 6.1 0.5 6.6 1.3 0.1 1.4 1.4 SSA 3.7 0.3 4.0 3.6 0.3 3.8 ­0.2 0.0 ­0.1 0.2 Non DC 41.2 0.8 41.9 46.3 0.9 47.1 5.1 0.1 5.2 6.2 All DCs 38.7 2.2 40.9 43.4 2.5 45.9 4.7 0.3 5.0 5.9 EAP 6.0 0.4 6.4 7.0 0.5 7.6 1.1 0.1 1.2 1.2 ECA 9.5 0.6 10.1 9.9 0.7 10.6 0.4 0.0 0.5 0.7 LAC 7.4 0.3 7.7 8.7 0.3 9.0 1.2 0.1 1.3 1.5 2015­2020 MENA 3.8 0.1 3.8 4.1 0.1 4.2 0.3 0.0 0.3 0.4 SA 4.3 0.3 4.6 5.4 0.4 5.8 1.1 0.1 1.2 1.2 SSA 3.3 0.2 3.5 3.1 0.3 3.4 ­0.1 0.0 ­0.1 0.2 Non DC 36.4 0.7 37.1 40.9 0.8 41.7 4.5 0.1 4.6 5.5 All DCs 34.2 2.0 36.2 38.3 2.2 40.6 4.1 0.3 4.4 5.2 EAP 5.3 0.4 5.7 6.2 0.5 6.7 1.0 0.1 1.0 1.0 ECA 8.4 0.6 8.9 8.8 0.6 9.4 0.4 0.0 0.4 0.6 LAC 6.6 0.2 6.8 7.7 0.3 8.0 1.1 0.0 1.1 1.3 2020­2025 MENA 3.3 0.0 3.4 3.6 0.1 3.7 0.3 0.0 0.3 0.4 SA 3.8 0.3 4.1 4.8 0.4 5.1 1.0 0.1 1.1 1.1 SSA 2.9 0.2 3.1 2.8 0.2 3.0 ­0.1 0.0 ­0.1 0.2 Non DC 32.1 0.6 32.8 36.1 0.7 36.8 4.0 0.1 4.1 4.8 All DCs 30.2 1.7 32.0 33.9 2.0 35.9 3.7 0.2 3.9 4.6 EAP 4.6 0.3 5.0 5.5 0.4 5.9 0.9 0.1 0.9 0.9 ECA 7.4 0.5 7.9 7.8 0.5 8.3 0.3 0.0 0.4 0.5 LAC 5.8 0.2 6.0 6.8 0.2 7.0 1.0 0.0 1.0 1.2 2025­2030 MENA 2.9 0.0 3.0 3.2 0.0 3.2 0.3 0.0 0.3 0.3 SA 3.3 0.3 3.6 4.2 0.3 4.5 0.9 0.1 1.0 1.0 SSA 2.6 0.2 2.7 2.5 0.2 2.7 ­0.1 0.0 ­0.1 0.1 Non DC 28.4 0.5 28.9 31.9 0.6 32.5 3.5 0.1 3.6 4.3 All DCs 26.7 1.5 28.2 29.9 1.8 31.7 3.2 0.2 3.4 4.1 EAP 4.1 0.3 4.4 4.8 0.4 5.2 0.8 0.1 0.8 0.8 ECA 6.5 0.4 7.0 6.8 0.5 7.3 0.3 0.0 0.3 0.5 LAC 5.1 0.2 5.3 6.0 0.2 6.2 0.9 0.0 0.9 1.0 2030­2035 MENA 2.6 0.0 2.6 2.8 0.0 2.9 0.2 0.0 0.2 0.3 SA 2.9 0.2 3.2 3.7 0.3 4.0 0.8 0.1 0.8 0.9 SSA 2.3 0.2 2.4 2.2 0.2 2.3 ­0.1 0.0 ­0.1 0.1 Non DC 25.0 0.5 25.5 28.2 0.5 28.7 3.1 0.1 3.2 3.8 All DCs 23.5 1.4 24.9 26.4 1.5 27.9 2.8 0.2 3.0 3.6 (Continued on next page) D E V E L O P M E N T A N D C L I M AT E C H A N G E D I S C u S S I O N PA P E r S 79 appendIx 10. (continued) EAP 3.6 0.3 3.9 4.3 0.3 4.6 0.7 0.0 0.7 0.7 ECA 5.8 0.4 6.1 6.0 0.4 6.5 0.3 0.0 0.3 0.4 LAC 4.5 0.2 4.7 5.3 0.2 5.5 0.8 0.0 0.8 0.9 2035­2040 MENA 2.3 0.0 2.3 2.5 0.0 2.5 0.2 0.0 0.2 0.2 SA 2.6 0.2 2.8 3.3 0.2 3.5 0.7 0.1 0.7 0.8 SSA 2.0 0.1 2.1 1.9 0.2 2.1 ­0.1 0.0 ­0.1 0.1 Non DC 22.1 0.4 22.5 24.8 0.5 25.3 2.7 0.0 2.8 3.3 All DCs 20.8 1.2 22.0 23.3 1.4 24.6 2.5 0.2 2.7 3.2 EAP 3.2 0.2 3.4 3.8 0.3 4.0 0.6 0.0 0.6 0.6 ECA 5.1 0.3 5.4 5.3 0.4 5.7 0.2 0.0 0.3 0.4 LAC 4.0 0.1 4.1 4.7 0.2 4.8 0.7 0.0 0.7 0.8 2040­2045 MENA 2.0 0.0 2.0 2.2 0.0 2.2 0.2 0.0 0.2 0.2 SA 2.3 0.2 2.5 2.9 0.2 3.1 0.6 0.0 0.7 0.7 SSA 1.8 0.1 1.9 1.7 0.1 1.8 ­0.1 0.0 ­0.1 0.1 Non DC 19.5 0.4 19.8 21.9 0.4 22.3 2.4 0.0 2.5 2.9 All DCs 18.3 1.1 19.4 20.5 1.2 21.7 2.2 0.2 2.4 2.8 EAP 2.8 0.2 3.0 3.3 0.2 3.6 0.5 0.0 0.5 0.6 ECA 4.5 0.3 4.8 4.7 0.3 5.0 0.2 0.0 0.2 0.3 LAC 3.5 0.1 3.6 4.1 0.2 4.3 0.6 0.0 0.6 0.7 2045­2050 MENA 1.8 0.0 1.8 1.9 0.0 2.0 0.2 0.0 0.2 0.2 SA 2.0 0.2 2.2 2.6 0.2 2.7 0.5 0.0 0.6 0.6 SSA 1.5 0.1 1.7 1.5 0.1 1.6 ­0.1 0.0 ­0.1 0.1 Non DC 17.2 0.3 17.5 19.3 0.4 19.7 2.1 0.0 2.2 2.6 All DCs 16.1 0.9 17.1 18.1 1.1 19.1 1.9 0.1 2.1 2.5 80 A D A PTATION rELATED TO INDu ST rIAL AND MuNICIPAL WATE r Su PPLy AND rIVE r INE FLOOD PrOTE C T I O N appendIx 11. rIverIne flood proTeCTIon resulTs. gCM: CsIro. dIsCounT raTe: 5% Annual costs (averaged over 5-year periods) in USD billion CC Baseline Baseline & CC CC (net) (gross) Urban Agric. Total Urban Agric Total Urban Agric Total Total EAP 6.4 0.5 6.8 7.5 0.6 8.1 1.2 0.1 1.2 1.3 ECA 10.1 0.7 10.8 10.6 0.7 11.4 0.5 0.1 0.5 0.7 LAC 8.0 0.3 8.2 9.3 0.3 9.6 1.3 0.1 1.4 1.6 2010­2015 MENA 4.0 0.1 4.1 4.4 0.1 4.5 0.4 0.0 0.4 0.4 SA 4.6 0.3 4.9 5.8 0.4 6.2 1.2 0.1 1.3 1.3 SSA 3.5 0.3 3.8 3.4 0.3 3.6 ­0.1 0.0 ­0.1 0.2 Non DC 38.9 0.7 39.6 43.7 0.8 44.6 4.8 0.1 4.9 5.8 All DCs 36.6 2.1 38.7 41.0 2.4 43.4 4.4 0.3 4.7 5.6 EAP 5.1 0.4 5.5 6.0 0.4 6.5 0.9 0.1 1.0 1.0 ECA 8.1 0.5 8.7 8.5 0.6 9.1 0.4 0.0 0.4 0.6 LAC 6.4 0.2 6.6 7.5 0.3 7.7 1.1 0.0 1.1 1.3 2015­2020 MENA 3.2 0.0 3.3 3.5 0.1 3.6 0.3 0.0 0.3 0.3 SA 3.7 0.3 3.9 4.6 0.3 5.0 1.0 0.1 1.1 1.1 SSA 2.8 0.2 3.0 2.7 0.2 2.9 ­0.1 0.0 ­0.1 0.2 Non DC 31.2 0.6 31.8 35.1 0.7 35.8 3.9 0.1 4.0 4.7 All DCs 29.4 1.7 31.1 32.9 1.9 34.8 3.5 0.2 3.8 4.5 EAP 4.1 0.3 4.4 4.9 0.4 5.2 0.8 0.1 0.8 0.8 ECA 6.5 0.4 7.0 6.8 0.5 7.3 0.3 0.0 0.3 0.5 LAC 5.1 0.2 5.3 6.0 0.2 6.2 0.9 0.0 0.9 1.0 2020­2025 MENA 2.6 0.0 2.6 2.8 0.0 2.9 0.2 0.0 0.2 0.3 SA 2.9 0.2 3.2 3.7 0.3 4.0 0.8 0.1 0.8 0.9 SSA 2.3 0.2 2.4 2.2 0.2 2.3 ­0.1 0.0 ­0.1 0.1 Non DC 25.1 0.5 25.5 28.2 0.5 28.7 3.1 0.1 3.2 3.8 All DCs 23.6 1.4 24.9 26.4 1.5 28.0 2.8 0.2 3.0 3.6 EAP 3.3 0.2 3.5 3.9 0.3 4.2 0.6 0.0 0.6 0.6 ECA 5.2 0.4 5.6 5.5 0.4 5.9 0.2 0.0 0.3 0.4 LAC 4.1 0.1 4.3 4.8 0.2 5.0 0.7 0.0 0.7 0.8 2025­2030 MENA 2.1 0.0 2.1 2.3 0.0 2.3 0.2 0.0 0.2 0.2 SA 2.4 0.2 2.5 3.0 0.2 3.2 0.6 0.0 0.7 0.7 SSA 1.8 0.1 1.9 1.7 0.1 1.9 ­0.1 0.0 ­0.1 0.1 Non DC 20.1 0.4 20.5 22.6 0.4 23.0 2.5 0.0 2.5 3.0 All DCs 18.9 1.1 20.0 21.2 1.2 22.4 2.3 0.2 2.4 2.9 EAP 2.6 0.2 2.8 3.1 0.2 3.3 0.5 0.0 0.5 0.5 ECA 4.2 0.3 4.5 4.4 0.3 4.7 0.2 0.0 0.2 0.3 LAC 3.3 0.1 3.4 3.9 0.1 4.0 0.6 0.0 0.6 0.7 2030­2035 MENA 1.7 0.0 1.7 1.8 0.0 1.8 0.2 0.0 0.2 0.2 SA 1.9 0.1 2.0 2.4 0.2 2.6 0.5 0.0 0.5 0.5 SSA 1.5 0.1 1.6 1.4 0.1 1.5 ­0.1 0.0 ­0.1 0.1 Non DC 16.1 0.3 16.4 18.1 0.3 18.5 2.0 0.0 2.0 2.4 All DCs 15.1 0.9 16.0 17.0 1.0 18.0 1.8 0.1 2.0 2.3 (Continued on next page) D E V E L O P M E N T A N D C L I M AT E C H A N G E D I S C u S S I O N PA P E r S 81 appendIx 11. (continued) EAP 2.1 0.2 2.3 2.5 0.2 2.7 0.4 0.0 0.4 0.4 ECA 3.4 0.2 3.6 3.5 0.2 3.8 0.2 0.0 0.2 0.2 LAC 2.6 0.1 2.7 3.1 0.1 3.2 0.4 0.0 0.5 0.5 2035­2040 MENA 1.3 0.0 1.4 1.5 0.0 1.5 0.1 0.0 0.1 0.1 SA 1.5 0.1 1.6 1.9 0.1 2.1 0.4 0.0 0.4 0.4 SSA 1.2 0.1 1.2 1.1 0.1 1.2 0.0 0.0 0.0 0.1 Non DC 12.9 0.2 13.2 14.5 0.3 14.8 1.6 0.0 1.6 1.9 All DCs 12.1 0.7 12.8 13.6 0.8 14.4 1.5 0.1 1.6 1.9 EAP 1.7 0.1 1.8 2.0 0.1 2.1 0.3 0.0 0.3 0.3 ECA 2.7 0.2 2.9 2.8 0.2 3.0 0.1 0.0 0.1 0.2 LAC 2.1 0.1 2.2 2.5 0.1 2.6 0.4 0.0 0.4 0.4 2040­2045 MENA 1.1 0.0 1.1 1.2 0.0 1.2 0.1 0.0 0.1 0.1 SA 1.2 0.1 1.3 1.5 0.1 1.7 0.3 0.0 0.3 0.4 SSA 0.9 0.1 1.0 0.9 0.1 1.0 0.0 0.0 0.0 0.1 Non DC 10.3 0.2 10.5 11.6 0.2 11.8 1.3 0.0 1.3 1.5 All DCs 9.7 0.6 10.3 10.9 0.6 11.5 1.2 0.1 1.3 1.5 EAP 1.4 0.1 1.5 1.6 0.1 1.7 0.2 0.0 0.3 0.3 ECA 2.2 0.1 2.3 2.3 0.2 2.4 0.1 0.0 0.1 0.2 LAC 1.7 0.1 1.8 2.0 0.1 2.0 0.3 0.0 0.3 0.3 2045­2050 MENA 0.9 0.0 0.9 0.9 0.0 0.9 0.1 0.0 0.1 0.1 SA 1.0 0.1 1.0 1.2 0.1 1.3 0.3 0.0 0.3 0.3 SSA 0.7 0.1 0.8 0.7 0.1 0.8 0.0 0.0 0.0 0.0 Non DC 8.3 0.2 8.4 9.3 0.2 9.5 1.0 0.0 1.0 1.2 All DCs 7.8 0.4 8.2 8.7 0.5 9.2 0.9 0.1 1.0 1.2 82 A D A PTATION rELATED TO INDu ST rIAL AND MuNICIPAL WATE r Su PPLy AND rIVE r INE FLOOD PrOTE C T I O N appendIx 12. rIverIne flood proTeCTIon resulTs. gCM: CsIro. dIsCounT raTe: 7% Annual costs (averaged over 5-year periods) in USD billion CC Baseline Baseline & CC CC (net) (gross) Urban Agric. Total Urban Agric Total Urban Agric Total Total EAP 6.0 0.4 6.5 7.1 0.5 7.7 1.1 0.1 1.2 1.2 ECA 9.6 0.6 10.3 10.1 0.7 10.8 0.5 0.1 0.5 0.7 LAC 7.5 0.3 7.8 8.8 0.3 9.1 1.3 0.1 1.3 1.5 2010­2015 MENA 3.8 0.1 3.9 4.2 0.1 4.2 0.3 0.0 0.4 0.4 SA 4.3 0.3 4.7 5.5 0.4 5.9 1.2 0.1 1.2 1.3 SSA 3.3 0.2 3.6 3.2 0.3 3.4 ­0.1 0.0 ­0.1 0.2 Non DC 36.8 0.7 37.5 41.4 0.8 42.2 4.6 0.1 4.7 5.5 All DCs 34.6 2.0 36.6 38.8 2.3 41.1 4.2 0.3 4.5 5.3 EAP 4.4 0.3 4.7 5.2 0.4 5.6 0.8 0.1 0.9 0.9 ECA 7.0 0.5 7.5 7.4 0.5 7.9 0.3 0.0 0.4 0.5 LAC 5.5 0.2 5.7 6.4 0.2 6.7 0.9 0.0 1.0 1.1 2015­2020 MENA 2.8 0.0 2.8 3.0 0.0 3.1 0.3 0.0 0.3 0.3 SA 3.2 0.2 3.4 4.0 0.3 4.3 0.8 0.1 0.9 0.9 SSA 2.4 0.2 2.6 2.3 0.2 2.5 ­0.1 0.0 ­0.1 0.1 Non DC 26.9 0.5 27.4 30.3 0.6 30.8 3.3 0.1 3.4 4.0 All DCs 25.3 1.5 26.8 28.4 1.7 30.0 3.1 0.2 3.3 3.9 EAP 3.2 0.2 3.5 3.8 0.3 4.1 0.6 0.0 0.6 0.6 ECA 5.1 0.3 5.5 5.4 0.4 5.7 0.2 0.0 0.3 0.4 LAC 4.0 0.1 4.2 4.7 0.2 4.9 0.7 0.0 0.7 0.8 2020­2025 MENA 2.0 0.0 2.1 2.2 0.0 2.2 0.2 0.0 0.2 0.2 SA 2.3 0.2 2.5 2.9 0.2 3.1 0.6 0.0 0.7 0.7 SSA 1.8 0.1 1.9 1.7 0.1 1.8 ­0.1 0.0 ­0.1 0.1 Non DC 19.7 0.4 20.0 22.1 0.4 22.5 2.4 0.0 2.5 2.9 All DCs 18.5 1.1 19.5 20.7 1.2 21.9 2.2 0.2 2.4 2.8 EAP 2.3 0.2 2.5 2.8 0.2 3.0 0.4 0.0 0.5 0.5 ECA 3.7 0.3 4.0 3.9 0.3 4.2 0.2 0.0 0.2 0.3 LAC 2.9 0.1 3.0 3.4 0.1 3.6 0.5 0.0 0.5 0.6 2025­2030 MENA 1.5 0.0 1.5 1.6 0.0 1.6 0.1 0.0 0.1 0.2 SA 1.7 0.1 1.8 2.1 0.2 2.3 0.4 0.0 0.5 0.5 SSA 1.3 0.1 1.4 1.2 0.1 1.3 ­0.1 0.0 0.0 0.1 Non DC 14.3 0.3 14.6 16.1 0.3 16.4 1.8 0.0 1.8 2.1 All DCs 13.5 0.8 14.3 15.1 0.9 16.0 1.6 0.1 1.7 2.1 EAP 1.7 0.1 1.8 2.0 0.1 2.2 0.3 0.0 0.3 0.3 ECA 2.7 0.2 2.9 2.9 0.2 3.1 0.1 0.0 0.1 0.2 LAC 2.1 0.1 2.2 2.5 0.1 2.6 0.4 0.0 0.4 0.4 2030­2035 MENA 1.1 0.0 1.1 1.2 0.0 1.2 0.1 0.0 0.1 0.1 SA 1.2 0.1 1.3 1.6 0.1 1.7 0.3 0.0 0.4 0.4 SSA 0.9 0.1 1.0 0.9 0.1 1.0 0.0 0.0 0.0 0.1 Non DC 10.5 0.2 10.7 11.8 0.2 12.0 1.3 0.0 1.3 1.6 All DCs 9.8 0.6 10.4 11.0 0.6 11.7 1.2 0.1 1.3 1.5 (Continued on next page) D E V E L O P M E N T A N D C L I M AT E C H A N G E D I S C u S S I O N PA P E r S 83 appendIx 12. (continued) EAP 1.2 0.1 1.3 1.5 0.1 1.6 0.2 0.0 0.2 0.2 ECA 2.0 0.1 2.1 2.1 0.1 2.2 0.1 0.0 0.1 0.1 LAC 1.6 0.1 1.6 1.8 0.1 1.9 0.3 0.0 0.3 0.3 2035­2040 MENA 0.8 0.0 0.8 0.9 0.0 0.9 0.1 0.0 0.1 0.1 SA 0.9 0.1 1.0 1.1 0.1 1.2 0.2 0.0 0.3 0.3 SSA 0.7 0.1 0.7 0.7 0.1 0.7 0.0 0.0 0.0 0.0 Non DC 7.6 0.1 7.8 8.6 0.2 8.7 0.9 0.0 1.0 1.1 All DCs 7.2 0.4 7.6 8.0 0.5 8.5 0.9 0.1 0.9 1.1 EAP 0.9 0.1 1.0 1.1 0.1 1.2 0.2 0.0 0.2 0.2 ECA 1.4 0.1 1.5 1.5 0.1 1.6 0.1 0.0 0.1 0.1 LAC 1.1 0.0 1.2 1.3 0.0 1.4 0.2 0.0 0.2 0.2 2040­2045 MENA 0.6 0.0 0.6 0.6 0.0 0.6 0.1 0.0 0.1 0.1 SA 0.7 0.0 0.7 0.8 0.1 0.9 0.2 0.0 0.2 0.2 SSA 0.5 0.0 0.5 0.5 0.0 0.5 0.0 0.0 0.0 0.0 Non DC 5.6 0.1 5.7 6.2 0.1 6.4 0.7 0.0 0.7 0.8 All DCs 5.2 0.3 5.5 5.9 0.3 6.2 0.6 0.0 0.7 0.8 EAP 0.7 0.0 0.7 0.8 0.1 0.8 0.1 0.0 0.1 0.1 ECA 1.1 0.1 1.1 1.1 0.1 1.2 0.0 0.0 0.1 0.1 LAC 0.8 0.0 0.9 1.0 0.0 1.0 0.1 0.0 0.1 0.2 2045­2050 MENA 0.4 0.0 0.4 0.5 0.0 0.5 0.0 0.0 0.0 0.0 SA 0.5 0.0 0.5 0.6 0.0 0.6 0.1 0.0 0.1 0.1 SSA 0.4 0.0 0.4 0.3 0.0 0.4 0.0 0.0 0.0 0.0 Non DC 4.0 0.1 4.1 4.6 0.1 4.6 0.5 0.0 0.5 0.6 All DCs 3.8 0.2 4.0 4.3 0.3 4.5 0.5 0.0 0.5 0.6 84 A D A PTATION rELATED TO INDu ST rIAL AND MuNICIPAL WATE r Su PPLy AND rIVE r INE FLOOD PrOTE C T I O N appendIx 13. rIverIne flood proTeCTIon resulTs. gCM: nCar. dIsCounT raTe: 0% Annual costs (averaged over 5-year periods) in USD billion CC Baseline Baseline & CC CC (net) (gross) Urban Agric. Total Urban Agric Total Urban Agric Total Total EAP 7.4 0.5 7.9 8.0 0.6 8.6 0.7 0.1 0.7 0.9 ECA 11.7 0.8 12.5 12.9 0.9 13.8 1.2 0.1 1.3 1.5 LAC 9.2 0.3 9.5 9.4 0.4 9.8 0.2 0.0 0.3 0.9 2010­2015 MENA 4.6 0.1 4.7 4.4 0.1 4.4 ­0.3 0.0 ­0.3 0.1 SA 5.3 0.4 5.7 6.1 0.5 6.6 0.8 0.1 0.9 1.0 SSA 4.0 0.3 4.3 4.3 0.3 4.6 0.3 0.0 0.3 0.4 Non DC 44.9 0.9 45.8 46.2 0.9 47.1 1.3 0.0 1.3 1.8 All DCs 42.3 2.4 44.7 45.2 2.7 47.9 2.9 0.3 3.2 4.7 EAP 7.5 0.6 8.1 8.2 0.6 8.8 0.7 0.1 0.7 0.9 ECA 12.0 0.8 12.8 13.2 0.9 14.1 1.2 0.1 1.3 1.6 LAC 9.4 0.3 9.8 9.7 0.4 10.0 0.2 0.0 0.3 0.9 2015­2020 MENA 4.8 0.1 4.8 4.5 0.1 4.6 ­0.3 0.0 ­0.3 0.1 SA 5.4 0.4 5.8 6.2 0.5 6.7 0.8 0.1 0.9 1.0 SSA 4.1 0.3 4.5 4.4 0.3 4.8 0.3 0.0 0.3 0.4 Non DC 46.1 0.9 46.9 47.4 0.9 48.3 1.3 0.0 1.3 1.9 All DCs 43.3 2.5 45.8 46.3 2.8 49.0 3.0 0.3 3.3 4.9 EAP 7.7 0.6 8.3 8.4 0.6 9.1 0.7 0.1 0.8 0.9 ECA 12.3 0.8 13.1 13.6 0.9 14.5 1.3 0.1 1.3 1.6 LAC 9.7 0.3 10.0 9.9 0.4 10.3 0.3 0.0 0.3 0.9 2020­2025 MENA 4.9 0.1 4.9 4.6 0.1 4.7 ­0.3 0.0 ­0.3 0.1 SA 5.5 0.4 6.0 6.4 0.5 6.9 0.9 0.1 0.9 1.0 SSA 4.2 0.3 4.6 4.5 0.4 4.9 0.3 0.0 0.3 0.4 Non DC 47.2 0.9 48.1 48.5 0.9 49.4 1.4 0.0 1.4 1.9 All DCs 44.3 2.6 46.9 47.4 2.8 50.2 3.0 0.3 3.3 5.0 EAP 7.9 0.6 8.5 8.6 0.7 9.3 0.7 0.1 0.8 0.9 ECA 12.6 0.8 13.4 13.9 0.9 14.8 1.3 0.1 1.4 1.6 LAC 9.9 0.4 10.2 10.1 0.4 10.5 0.3 0.0 0.3 0.9 2025­2030 MENA 5.0 0.1 5.1 4.7 0.1 4.8 ­0.3 0.0 ­0.3 0.1 SA 5.7 0.4 6.1 6.5 0.5 7.0 0.9 0.1 0.9 1.1 SSA 4.3 0.3 4.7 4.6 0.4 5.0 0.3 0.0 0.3 0.4 Non DC 48.3 0.9 49.2 49.7 0.9 50.6 1.4 0.0 1.4 2.0 All DCs 45.4 2.6 48.0 48.5 2.9 51.4 3.1 0.3 3.4 5.1 EAP 8.1 0.6 8.7 8.8 0.7 9.5 0.7 0.1 0.8 0.9 ECA 12.9 0.9 13.8 14.2 1.0 15.2 1.3 0.1 1.4 1.7 LAC 10.1 0.4 10.5 10.4 0.4 10.8 0.3 0.0 0.3 1.0 2030­2035 MENA 5.1 0.1 5.2 4.8 0.1 4.9 ­0.3 0.0 ­0.3 0.1 SA 5.8 0.4 6.2 6.7 0.5 7.2 0.9 0.1 1.0 1.1 SSA 4.4 0.3 4.8 4.7 0.4 5.1 0.3 0.0 0.3 0.4 Non DC 49.4 0.9 50.3 50.8 0.9 51.8 1.4 0.0 1.4 2.0 All DCs 46.4 2.7 49.1 49.6 3.0 52.6 3.2 0.3 3.5 5.2 (Continued on next page) D E V E L O P M E N T A N D C L I M AT E C H A N G E D I S C u S S I O N PA P E r S 85 appendIx 13. (continued) EAP 8.3 0.6 8.9 9.0 0.7 9.7 0.7 0.1 0.8 1.0 ECA 13.2 0.9 14.1 14.5 1.0 15.5 1.3 0.1 1.4 1.7 LAC 10.3 0.4 10.7 10.6 0.4 11.0 0.3 0.0 0.3 1.0 2035­2040 MENA 5.2 0.1 5.3 4.9 0.1 5.0 ­0.3 0.0 ­0.3 0.1 SA 5.9 0.4 6.4 6.8 0.5 7.4 0.9 0.1 1.0 1.1 SSA 4.5 0.3 4.9 4.8 0.4 5.2 0.3 0.0 0.3 0.4 Non DC 50.5 1.0 51.5 52.0 1.0 52.9 1.5 0.0 1.4 2.1 All DCs 47.5 2.7 50.2 50.7 3.0 53.8 3.3 0.3 3.6 5.3 EAP 8.4 0.6 9.1 9.2 0.7 9.9 0.8 0.1 0.8 1.0 ECA 13.5 0.9 14.4 14.8 1.0 15.8 1.4 0.1 1.5 1.7 LAC 10.6 0.4 10.9 10.8 0.4 11.3 0.3 0.0 0.3 1.0 2040­2045 MENA 5.3 0.1 5.4 5.0 0.1 5.1 ­0.3 0.0 ­0.3 0.1 SA 6.1 0.5 6.5 7.0 0.5 7.5 0.9 0.1 1.0 1.1 SSA 4.6 0.3 5.0 5.0 0.4 5.3 0.3 0.0 0.3 0.4 Non DC 51.6 1.0 52.6 53.1 1.0 54.1 1.5 0.0 1.5 2.1 All DCs 48.5 2.8 51.3 51.9 3.1 55.0 3.3 0.3 3.7 5.5 EAP 8.6 0.6 9.3 9.4 0.7 10.1 0.8 0.1 0.8 1.0 ECA 13.8 0.9 14.7 15.2 1.0 16.2 1.4 0.1 1.5 1.8 LAC 10.8 0.4 11.2 11.1 0.4 11.5 0.3 0.0 0.3 1.0 2045­2050 MENA 5.5 0.1 5.5 5.1 0.1 5.2 ­0.3 0.0 ­0.3 0.1 SA 6.2 0.5 6.7 7.2 0.5 7.7 1.0 0.1 1.0 1.2 SSA 4.7 0.4 5.1 5.1 0.4 5.5 0.3 0.0 0.4 0.4 Non DC 52.7 1.0 53.7 54.3 1.0 55.3 1.5 0.0 1.5 2.1 All DCs 49.6 2.9 52.4 53.0 3.2 56.2 3.4 0.3 3.7 5.6 86 A D A PTATION rELATED TO INDu ST rIAL AND MuNICIPAL WATE r Su PPLy AND rIVE r INE FLOOD PrOTE C T I O N appendIx 14. rIverIne flood proTeCTIon resulTs. gCM: nCar. dIsCounT raTe: 3% Annual costs (averaged over 5-year periods) in USD billion CC Baseline Baseline & CC CC (net) (gross) Urban Agric. Total Urban Agric Total Urban Agric Total Total EAP 6.7 0.5 7.2 7.3 0.6 7.9 0.6 0.1 0.7 0.8 ECA 10.7 0.7 11.5 11.8 0.8 12.6 1.1 0.1 1.2 1.4 LAC 8.4 0.3 8.7 8.6 0.3 9.0 0.2 0.0 0.2 0.8 2010­2015 MENA 4.3 0.1 4.3 4.0 0.1 4.1 ­0.2 0.0 ­0.2 0.1 SA 4.8 0.4 5.2 5.6 0.4 6.0 0.7 0.1 0.8 0.9 SSA 3.7 0.3 4.0 3.9 0.3 4.3 0.2 0.0 0.3 0.3 Non DC 41.2 0.8 41.9 42.3 0.8 43.1 1.2 0.0 1.2 1.7 All DCs 38.7 2.2 40.9 41.4 2.5 43.8 2.7 0.3 2.9 4.3 EAP 6.0 0.4 6.4 6.5 0.5 7.0 0.5 0.1 0.6 0.7 ECA 9.5 0.6 10.1 10.5 0.7 11.2 1.0 0.1 1.0 1.2 LAC 7.4 0.3 7.7 7.6 0.3 7.9 0.2 0.0 0.2 0.7 2015­2020 MENA 3.8 0.1 3.8 3.5 0.1 3.6 ­0.2 0.0 ­0.2 0.1 SA 4.3 0.3 4.6 4.9 0.4 5.3 0.7 0.1 0.7 0.8 SSA 3.3 0.2 3.5 3.5 0.3 3.8 0.2 0.0 0.2 0.3 Non DC 36.4 0.7 37.1 37.4 0.7 38.1 1.1 0.0 1.0 1.5 All DCs 34.2 2.0 36.2 36.6 2.2 38.7 2.4 0.2 2.6 3.8 EAP 5.3 0.4 5.7 5.7 0.4 6.2 0.5 0.0 0.5 0.6 ECA 8.4 0.6 8.9 9.2 0.6 9.9 0.9 0.1 0.9 1.1 LAC 6.6 0.2 6.8 6.8 0.3 7.0 0.2 0.0 0.2 0.6 2020­2025 MENA 3.3 0.0 3.4 3.1 0.0 3.2 ­0.2 0.0 ­0.2 0.1 SA 3.8 0.3 4.1 4.4 0.3 4.7 0.6 0.0 0.6 0.7 SSA 2.9 0.2 3.1 3.1 0.2 3.3 0.2 0.0 0.2 0.3 Non DC 32.1 0.6 32.8 33.1 0.6 33.7 0.9 0.0 0.9 1.3 All DCs 30.2 1.7 32.0 32.3 1.9 34.2 2.1 0.2 2.3 3.4 EAP 4.6 0.3 5.0 5.1 0.4 5.4 0.4 0.0 0.5 0.5 ECA 7.4 0.5 7.9 8.2 0.6 8.7 0.8 0.1 0.8 1.0 LAC 5.8 0.2 6.0 6.0 0.2 6.2 0.2 0.0 0.2 0.6 2025­2030 MENA 2.9 0.0 3.0 2.8 0.0 2.8 ­0.2 0.0 ­0.2 0.1 SA 3.3 0.3 3.6 3.8 0.3 4.1 0.5 0.0 0.6 0.6 SSA 2.6 0.2 2.7 2.7 0.2 2.9 0.2 0.0 0.2 0.2 Non DC 28.4 0.5 28.9 29.2 0.5 29.7 0.8 0.0 0.8 1.2 All DCs 26.7 1.5 28.2 28.5 1.7 30.2 1.8 0.2 2.0 3.0 EAP 4.1 0.3 4.4 4.5 0.3 4.8 0.4 0.0 0.4 0.5 ECA 6.5 0.4 7.0 7.2 0.5 7.7 0.7 0.0 0.7 0.8 LAC 5.1 0.2 5.3 5.3 0.2 5.5 0.1 0.0 0.1 0.5 2030­2035 MENA 2.6 0.0 2.6 2.4 0.0 2.5 ­0.1 0.0 ­0.1 0.1 SA 2.9 0.2 3.2 3.4 0.3 3.7 0.5 0.0 0.5 0.6 SSA 2.3 0.2 2.4 2.4 0.2 2.6 0.1 0.0 0.2 0.2 Non DC 25.0 0.5 25.5 25.8 0.5 26.2 0.7 0.0 0.7 1.0 All DCs 23.5 1.4 24.9 25.2 1.5 26.7 1.6 0.2 1.8 2.6 (Continued on next page) D E V E L O P M E N T A N D C L I M AT E C H A N G E D I S C u S S I O N PA P E r S 87 appendIx 14. (continued) EAP 3.6 0.3 3.9 3.9 0.3 4.2 0.3 0.0 0.4 0.4 ECA 5.8 0.4 6.1 6.3 0.4 6.8 0.6 0.0 0.6 0.7 LAC 4.5 0.2 4.7 4.6 0.2 4.8 0.1 0.0 0.1 0.4 2035­2040 MENA 2.3 0.0 2.3 2.2 0.0 2.2 ­0.1 0.0 ­0.1 0.0 SA 2.6 0.2 2.8 3.0 0.2 3.2 0.4 0.0 0.4 0.5 SSA 2.0 0.1 2.1 2.1 0.2 2.3 0.1 0.0 0.1 0.2 Non DC 22.1 0.4 22.5 22.7 0.4 23.1 0.6 0.0 0.6 0.9 All DCs 20.8 1.2 22.0 22.2 1.3 23.5 1.4 0.1 1.6 2.3 EAP 3.2 0.2 3.4 3.5 0.3 3.7 0.3 0.0 0.3 0.4 ECA 5.1 0.3 5.4 5.6 0.4 6.0 0.5 0.0 0.6 0.7 LAC 4.0 0.1 4.1 4.1 0.2 4.2 0.1 0.0 0.1 0.4 2040­2045 MENA 2.0 0.0 2.0 1.9 0.0 1.9 ­0.1 0.0 ­0.1 0.0 SA 2.3 0.2 2.5 2.6 0.2 2.8 0.4 0.0 0.4 0.4 SSA 1.8 0.1 1.9 1.9 0.1 2.0 0.1 0.0 0.1 0.2 Non DC 19.5 0.4 19.8 20.0 0.4 20.4 0.6 0.0 0.6 0.8 All DCs 18.3 1.1 19.4 19.6 1.2 20.7 1.3 0.1 1.4 2.1 EAP 2.8 0.2 3.0 3.1 0.2 3.3 0.3 0.0 0.3 0.3 ECA 4.5 0.3 4.8 4.9 0.3 5.3 0.5 0.0 0.5 0.6 LAC 3.5 0.1 3.6 3.6 0.1 3.7 0.1 0.0 0.1 0.3 2045­2050 MENA 1.8 0.0 1.8 1.7 0.0 1.7 ­0.1 0.0 ­0.1 0.0 SA 2.0 0.2 2.2 2.3 0.2 2.5 0.3 0.0 0.3 0.4 SSA 1.5 0.1 1.7 1.6 0.1 1.8 0.1 0.0 0.1 0.1 Non DC 17.2 0.3 17.5 17.7 0.3 18.0 0.5 0.0 0.5 0.7 All DCs 16.1 0.9 17.1 17.2 1.0 18.3 1.1 0.1 1.2 1.8 88 A D A PTATION rELATED TO INDu ST rIAL AND MuNICIPAL WATE r Su PPLy AND rIVE r INE FLOOD PrOTE C T I O N appendIx 15. rIverIne flood proTeCTIon resulTs. gCM: nCar. dIsCounT raTe: 5% Annual costs (averaged over 5-year periods) in USD billion CC Baseline Baseline & CC CC (net) (gross) Urban Agric. Total Urban Agric Total Urban Agric Total Total EAP 6.4 0.5 6.8 6.9 0.5 7.5 0.6 0.1 0.6 0.7 ECA 10.1 0.7 10.8 11.2 0.8 11.9 1.0 0.1 1.1 1.3 LAC 8.0 0.3 8.2 8.2 0.3 8.5 0.2 0.0 0.2 0.8 2010­2015 MENA 4.0 0.1 4.1 3.8 0.1 3.8 ­0.2 0.0 ­0.2 0.1 SA 4.6 0.3 4.9 5.3 0.4 5.7 0.7 0.1 0.8 0.9 SSA 3.5 0.3 3.8 3.7 0.3 4.0 0.2 0.0 0.3 0.3 Non DC 38.9 0.7 39.6 40.0 0.7 40.8 1.1 0.0 1.1 1.6 All DCs 36.6 2.1 38.7 39.1 2.3 41.4 2.5 0.2 2.8 4.1 EAP 5.1 0.4 5.5 5.6 0.4 6.0 0.5 0.0 0.5 0.6 ECA 8.1 0.5 8.7 9.0 0.6 9.6 0.8 0.1 0.9 1.1 LAC 6.4 0.2 6.6 6.6 0.2 6.8 0.2 0.0 0.2 0.6 2015­2020 MENA 3.2 0.0 3.3 3.0 0.0 3.1 ­0.2 0.0 ­0.2 0.1 SA 3.7 0.3 3.9 4.2 0.3 4.6 0.6 0.0 0.6 0.7 SSA 2.8 0.2 3.0 3.0 0.2 3.2 0.2 0.0 0.2 0.3 Non DC 31.2 0.6 31.8 32.1 0.6 32.7 0.9 0.0 0.9 1.3 All DCs 29.4 1.7 31.1 31.4 1.9 33.3 2.0 0.2 2.2 3.3 EAP 4.1 0.3 4.4 4.5 0.3 4.8 0.4 0.0 0.4 0.5 ECA 6.5 0.4 7.0 7.2 0.5 7.7 0.7 0.0 0.7 0.8 LAC 5.1 0.2 5.3 5.3 0.2 5.5 0.1 0.0 0.1 0.5 2020­2025 MENA 2.6 0.0 2.6 2.4 0.0 2.5 ­0.1 0.0 ­0.1 0.1 SA 2.9 0.2 3.2 3.4 0.3 3.7 0.5 0.0 0.5 0.6 SSA 2.3 0.2 2.4 2.4 0.2 2.6 0.1 0.0 0.2 0.2 Non DC 25.1 0.5 25.5 25.8 0.5 26.3 0.7 0.0 0.7 1.0 All DCs 23.6 1.4 24.9 25.2 1.5 26.7 1.6 0.2 1.8 2.6 EAP 3.3 0.2 3.5 3.6 0.3 3.9 0.3 0.0 0.3 0.4 ECA 5.2 0.4 5.6 5.8 0.4 6.2 0.5 0.0 0.6 0.7 LAC 4.1 0.1 4.3 4.2 0.2 4.4 0.1 0.0 0.1 0.4 2025­2030 MENA 2.1 0.0 2.1 2.0 0.0 2.0 ­0.1 0.0 ­0.1 0.0 SA 2.4 0.2 2.5 2.7 0.2 2.9 0.4 0.0 0.4 0.4 SSA 1.8 0.1 1.9 1.9 0.1 2.1 0.1 0.0 0.1 0.2 Non DC 20.1 0.4 20.5 20.7 0.4 21.1 0.6 0.0 0.6 0.8 All DCs 18.9 1.1 20.0 20.2 1.2 21.4 1.3 0.1 1.4 2.1 EAP 2.6 0.2 2.8 2.9 0.2 3.1 0.2 0.0 0.3 0.3 ECA 4.2 0.3 4.5 4.6 0.3 4.9 0.4 0.0 0.5 0.5 LAC 3.3 0.1 3.4 3.4 0.1 3.5 0.1 0.0 0.1 0.3 2030­2035 MENA 1.7 0.0 1.7 1.6 0.0 1.6 ­0.1 0.0 ­0.1 0.0 SA 1.9 0.1 2.0 2.2 0.2 2.4 0.3 0.0 0.3 0.4 SSA 1.5 0.1 1.6 1.5 0.1 1.7 0.1 0.0 0.1 0.1 Non DC 16.1 0.3 16.4 16.6 0.3 16.9 0.5 0.0 0.5 0.7 All DCs 15.1 0.9 16.0 16.2 1.0 17.2 1.0 0.1 1.1 1.7 (Continued on next page) D E V E L O P M E N T A N D C L I M AT E C H A N G E D I S C u S S I O N PA P E r S 89 appendIx 15. (continued) EAP 2.1 0.2 2.3 2.3 0.2 2.5 0.2 0.0 0.2 0.2 ECA 3.4 0.2 3.6 3.7 0.3 4.0 0.3 0.0 0.4 0.4 LAC 2.6 0.1 2.7 2.7 0.1 2.8 0.1 0.0 0.1 0.3 2035­2040 MENA 1.3 0.0 1.4 1.3 0.0 1.3 ­0.1 0.0 ­0.1 0.0 SA 1.5 0.1 1.6 1.8 0.1 1.9 0.2 0.0 0.3 0.3 SSA 1.2 0.1 1.2 1.2 0.1 1.3 0.1 0.0 0.1 0.1 Non DC 12.9 0.2 13.2 13.3 0.2 13.5 0.4 0.0 0.4 0.5 All DCs 12.1 0.7 12.8 13.0 0.8 13.7 0.8 0.1 0.9 1.4 EAP 1.7 0.1 1.8 1.8 0.1 2.0 0.2 0.0 0.2 0.2 ECA 2.7 0.2 2.9 3.0 0.2 3.2 0.3 0.0 0.3 0.4 LAC 2.1 0.1 2.2 2.2 0.1 2.3 0.1 0.0 0.1 0.2 2040­2045 MENA 1.1 0.0 1.1 1.0 0.0 1.0 ­0.1 0.0 ­0.1 0.0 SA 1.2 0.1 1.3 1.4 0.1 1.5 0.2 0.0 0.2 0.2 SSA 0.9 0.1 1.0 1.0 0.1 1.1 0.1 0.0 0.1 0.1 Non DC 10.3 0.2 10.5 10.6 0.2 10.8 0.3 0.0 0.3 0.4 All DCs 9.7 0.6 10.3 10.4 0.6 11.0 0.7 0.1 0.7 1.1 EAP 1.4 0.1 1.5 1.5 0.1 1.6 0.1 0.0 0.1 0.2 ECA 2.2 0.1 2.3 2.4 0.2 2.5 0.2 0.0 0.2 0.3 LAC 1.7 0.1 1.8 1.7 0.1 1.8 0.0 0.0 0.0 0.2 2045­2050 MENA 0.9 0.0 0.9 0.8 0.0 0.8 0.0 0.0 0.0 0.0 SA 1.0 0.1 1.0 1.1 0.1 1.2 0.1 0.0 0.2 0.2 SSA 0.7 0.1 0.8 0.8 0.1 0.9 0.0 0.0 0.1 0.1 Non DC 8.3 0.2 8.4 8.5 0.2 8.7 0.2 0.0 0.2 0.3 All DCs 7.8 0.4 8.2 8.3 0.5 8.8 0.5 0.1 0.6 0.9 90 A D A PTATION rELATED TO INDu ST rIAL AND MuNICIPAL WATE r Su PPLy AND rIVE r INE FLOOD PrOTE C T I O N appendIx 16. rIverIne flood proTeCTIon resulTs. gCM: nCar. dIsCounT raTe: 7% Annual costs (averaged over 5-year periods) in USD billion CC Baseline Baseline & CC CC (net) (gross) Urban Agric. Total Urban Agric Total Urban Agric Total Total EAP 6.0 0.4 6.5 6.6 0.5 7.1 0.5 0.1 0.6 0.7 ECA 9.6 0.6 10.3 10.6 0.7 11.3 1.0 0.1 1.0 1.2 LAC 7.5 0.3 7.8 7.7 0.3 8.0 0.2 0.0 0.2 0.7 2010­2015 MENA 3.8 0.1 3.9 3.6 0.1 3.6 ­0.2 0.0 ­0.2 0.1 SA 4.3 0.3 4.7 5.0 0.4 5.4 0.7 0.1 0.7 0.8 SSA 3.3 0.2 3.6 3.5 0.3 3.8 0.2 0.0 0.2 0.3 Non DC 36.8 0.7 37.5 37.9 0.7 38.6 1.1 0.0 1.1 1.5 All DCs 34.6 2.0 36.6 37.0 2.2 39.2 2.4 0.2 2.6 3.9 EAP 4.4 0.3 4.7 4.8 0.4 5.2 0.4 0.0 0.4 0.5 ECA 7.0 0.5 7.5 7.7 0.5 8.3 0.7 0.0 0.8 0.9 LAC 5.5 0.2 5.7 5.7 0.2 5.9 0.1 0.0 0.2 0.5 2015­2020 MENA 2.8 0.0 2.8 2.6 0.0 2.7 ­0.2 0.0 ­0.2 0.1 SA 3.2 0.2 3.4 3.6 0.3 3.9 0.5 0.0 0.5 0.6 SSA 2.4 0.2 2.6 2.6 0.2 2.8 0.2 0.0 0.2 0.2 Non DC 26.9 0.5 27.4 27.7 0.5 28.2 0.8 0.0 0.8 1.1 All DCs 25.3 1.5 26.8 27.0 1.6 28.7 1.7 0.2 1.9 2.8 EAP 3.2 0.2 3.5 3.5 0.3 3.8 0.3 0.0 0.3 0.4 ECA 5.1 0.3 5.5 5.6 0.4 6.0 0.5 0.0 0.6 0.7 LAC 4.0 0.1 4.2 4.1 0.2 4.3 0.1 0.0 0.1 0.4 2020­2025 MENA 2.0 0.0 2.1 1.9 0.0 1.9 ­0.1 0.0 ­0.1 0.0 SA 2.3 0.2 2.5 2.7 0.2 2.9 0.4 0.0 0.4 0.4 SSA 1.8 0.1 1.9 1.9 0.1 2.0 0.1 0.0 0.1 0.2 Non DC 19.7 0.4 20.0 20.2 0.4 20.6 0.6 0.0 0.6 0.8 All DCs 18.5 1.1 19.5 19.7 1.2 20.9 1.3 0.1 1.4 2.1 EAP 2.3 0.2 2.5 2.6 0.2 2.8 0.2 0.0 0.2 0.3 ECA 3.7 0.3 4.0 4.1 0.3 4.4 0.4 0.0 0.4 0.5 LAC 2.9 0.1 3.0 3.0 0.1 3.1 0.1 0.0 0.1 0.3 2025­2030 MENA 1.5 0.0 1.5 1.4 0.0 1.4 ­0.1 0.0 ­0.1 0.0 SA 1.7 0.1 1.8 1.9 0.1 2.1 0.3 0.0 0.3 0.3 SSA 1.3 0.1 1.4 1.4 0.1 1.5 0.1 0.0 0.1 0.1 Non DC 14.3 0.3 14.6 14.8 0.3 15.0 0.4 0.0 0.4 0.6 All DCs 13.5 0.8 14.3 14.4 0.9 15.3 0.9 0.1 1.0 1.5 EAP 1.7 0.1 1.8 1.9 0.1 2.0 0.2 0.0 0.2 0.2 ECA 2.7 0.2 2.9 3.0 0.2 3.2 0.3 0.0 0.3 0.4 LAC 2.1 0.1 2.2 2.2 0.1 2.3 0.1 0.0 0.1 0.2 2030­2035 MENA 1.1 0.0 1.1 1.0 0.0 1.0 ­0.1 0.0 ­0.1 0.0 SA 1.2 0.1 1.3 1.4 0.1 1.5 0.2 0.0 0.2 0.2 SSA 0.9 0.1 1.0 1.0 0.1 1.1 0.1 0.0 0.1 0.1 Non DC 10.5 0.2 10.7 10.8 0.2 11.0 0.3 0.0 0.3 0.4 All DCs 9.8 0.6 10.4 10.5 0.6 11.1 0.7 0.1 0.7 1.1 (Continued on next page) D E V E L O P M E N T A N D C L I M AT E C H A N G E D I S C u S S I O N PA P E r S 91 appendIx 16. (continued) EAP 1.2 0.1 1.3 1.4 0.1 1.5 0.1 0.0 0.1 0.1 ECA 2.0 0.1 2.1 2.2 0.1 2.3 0.2 0.0 0.2 0.3 LAC 1.6 0.1 1.6 1.6 0.1 1.7 0.0 0.0 0.0 0.1 2035­2040 MENA 0.8 0.0 0.8 0.7 0.0 0.8 0.0 0.0 0.0 0.0 SA 0.9 0.1 1.0 1.0 0.1 1.1 0.1 0.0 0.1 0.2 SSA 0.7 0.1 0.7 0.7 0.1 0.8 0.0 0.0 0.1 0.1 Non DC 7.6 0.1 7.8 7.8 0.1 8.0 0.2 0.0 0.2 0.3 All DCs 7.2 0.4 7.6 7.7 0.5 8.1 0.5 0.0 0.5 0.8 EAP 0.9 0.1 1.0 1.0 0.1 1.1 0.1 0.0 0.1 0.1 ECA 1.4 0.1 1.5 1.6 0.1 1.7 0.1 0.0 0.2 0.2 LAC 1.1 0.0 1.2 1.2 0.0 1.2 0.0 0.0 0.0 0.1 2040­2045 MENA 0.6 0.0 0.6 0.5 0.0 0.5 0.0 0.0 0.0 0.0 SA 0.7 0.0 0.7 0.8 0.1 0.8 0.1 0.0 0.1 0.1 SSA 0.5 0.0 0.5 0.5 0.0 0.6 0.0 0.0 0.0 0.0 Non DC 5.6 0.1 5.7 5.7 0.1 5.8 0.2 0.0 0.2 0.2 All DCs 5.2 0.3 5.5 5.6 0.3 5.9 0.4 0.0 0.4 0.6 EAP 0.7 0.0 0.7 0.7 0.1 0.8 0.1 0.0 0.1 0.1 ECA 1.1 0.1 1.1 1.2 0.1 1.2 0.1 0.0 0.1 0.1 LAC 0.8 0.0 0.9 0.9 0.0 0.9 0.0 0.0 0.0 0.1 2045­2050 MENA 0.4 0.0 0.4 0.4 0.0 0.4 0.0 0.0 0.0 0.0 SA 0.5 0.0 0.5 0.5 0.0 0.6 0.1 0.0 0.1 0.1 SSA 0.4 0.0 0.4 0.4 0.0 0.4 0.0 0.0 0.0 0.0 Non DC 4.0 0.1 4.1 4.2 0.1 4.2 0.1 0.0 0.1 0.2 All DCs 3.8 0.2 4.0 4.1 0.2 4.3 0.3 0.0 0.3 0.4 The World Bank Group 1818 H Street, NW Washington, D.C. 20433 USA Tel: 202-473-1000 Fax: 202-477-6391 Internet: www.worldbank.org/climatechange