The CHANGING 2024 WEALTH of NATIONS TECHNICAL REPORT Building Coastal Resilience with Mangroves: The Contribution of Natural Flood Defenses to the Changing Wealth of Nations 20 30 40 60 80 100 120 140 160 © 2024 International Bank for Reconstruction and Development / The World Bank 1818 H Street NW Washington DC 20433 Telephone: 202-473-1000 Internet: www.worldbank.org This work is a product of the staff of The World Bank with external contributions. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of The World Bank, its Board of Executive Directors, or the governments they represent. The World Bank does not guarantee the accuracy, completeness, or currency of the data included in this work and does not assume responsibility for any errors, omissions, or discrepancies in the information, or liability with respect to the use of or failure to use the information, methods, processes, or conclusions set forth. 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Design and layout: Clarity Global Strategic Communications www.clarityglobal.net Acknowledgements Authors: Pelayo Menéndez, UCSC; Michael W. Beck, UCSC; Sheila Abad, Instituto de Hidráulica Ambiental-IH Cantabria; Iñigo J. Losada, Instituto de Hidráulica Ambiental-IH Cantabria This report has drawn extensively on previous World Bank valuation work, including that the development of valuation guidelines for a comprehensive assessment of the coastal protection benefits derived from these natural assets led by Michael W. Beck and Glenn-Marie Lange and the practical implementation at the national level for mangroves in the Philippines and Jamaica (Menendez et al. 2018; Ortega et al. 2019). The authors also acknowledge past support in the development of this work from The Nature Conservancy, AXA Research Fund, and the Center for Coastal Climate Resilience. The report benefited from the thoughtful guidance and input of Stefanie Onder, Borja Gonzalez Reguero, and Alexis Rivera Ballesteros of the World Bank. This technical report was produced as input to the upcoming Changing Wealth of Nations 2024 report. The Changing Wealth of Nations flagship series is produced by the World Bank and provides the most comprehensive accounting of the wealth of nations, an in-depth analysis of the evolution of wealth, and pathways to build wealth for the future. The flagship series—and the accompanying global database— firmly establishes comprehensive wealth as a measure of sustainability and a key component of country analytics. Each iteration expands the coverage of wealth accounts and improves our understanding of the quality of all assets, notably, natural capital. In addition, each report provides a new set of tools and analysis to help policy makers mainstream wealth and its components into economic analysis and guide decision-making at the country and global scale. This report received financial support from the Global Program on Sustainability (GPS) trust fund and the PROBLUE trust fund. BUILDING COASTAL RESILIENCE WITH MANGROVES I l EXECUTIVE SUMMARY Executive summary With the escalation of coastal risks caused by storms distribution of flood damage (risk) and the benefits and climate change, the demand for coastal defenses of avoided damages (habitat benefits). is on the rise. Global studies using risk models We assessed flood risk and mangrove benefits in (Beck et al. 2018; Menendez et al. 2020) have shown 1996, 2010, 2015, and 2020 and evaluated changes that mangroves and coral reefs can provide highly in flood risks and mangrove benefits between these valuable coastal protection services by reducing years. We assessed risks and mangrove benefits waves and storm surges, and acting as a first line for 121 nations with mangroves covering about of defense against flooding and erosion. These natural coastal protection services were included 700,000 kilometers (km) of (sub)tropical coastlines, for the first time in the 2021 edition of the World 97 of which showed either economic or social Bank’s The Changing Wealth of Nations (CWON), benefits from mangroves. We summarize results by which covered the changing value of mangroves as country, but these models and values can be used coastal protection assets from 1995–2015. Here, we to understand risks and benefits within countries have included new data for 2020 on global mangrove at the provincial and even municipal levels. These distribution, and assessed current flood risks and the are fully quantitative risk models compared to benefits of mangroves in reducing floods. We also other index-based approaches for assessing coastal have re-analyzed historic flood risk and mangrove vulnerability and ecosystem services. benefits (1995–2020) as there were updates to past The present value of the flood reduction benefits from ecological (mangrove distributions) and economic mangroves (100-year assets at a 4 percent discount rate) (Penn World Table (PWT)) data. in 2020 is $855 billion. The countries with the greatest To evaluate the contribution of natural capital assets present value of mangroves for flood reduction are to the wealth of (sub)tropical nations, we use peer- China, Vietnam, Australia, the United States (US), and reviewed models of flood risks and habitat benefits. India (all countries here and after are listed in rank Our approach uses a combination of process- order). Annual flood risk on mangrove coastlines in based storm and hydrodynamic models, which are 2020 is most significant in China, the US, Australia, described in Menendez et al. 2020. Specifically, we Taiwan, and Vietnam. Each of these countries has use these models to determine the area and depth more than 98,000 people exposed to coastal flooding of flooding with and without mangroves for five yearly and more than $4.7 billion in assets at risk storm frequency events (one storm in 5, 10, 25, 50, from flooding annually on mangrove coastlines. The and 100 years), which are driven by local storm data. increase in the wealth of mangroves for flood risk To assess the value of mangroves as natural capital reduction from 1996–2010 was $130 billion, while assets, we overlay flood extent and depth data onto from 2010–2020 the increase was $502 billion. The historical data on populations and asset values (PWT countries receiving the most significant increases in 10.0, (Feenstra et al. 2015)), adjusted to constant 2020 wealth in absolute values in 2020 (US dollars) were US dollars. This enables us to identify a probabilistic China, Vietnam, Australia, the US, and India, and in II BUILDING COASTAL RESILIENCE WITH MANGROVES EXECUTIVE SUMMARY l relative values (percent) were South Africa, Guyana, and property increased by 33 percent and 104 percent Vanuatu, Grenada, and St Lucie. globally. These national increases in risk are created by the mangrove loss (0.66 percent) and increases in The period from 1996–2010 covers some of the most population (12 percent) and capital stock (40 percent). significant losses of mangroves in recorded history The countries with the most significant increases from the rapid expansion of shrimp aquaculture in in people at flood risk were China, Vietnam, India, the 2000s and the more consistent losses from coastal Bangladesh, and Indonesia (more than 347,000 people development. The global loss of mangroves was 4 flooded/year/country). The countries with the most percent from 1996–2010, but was more than 30 percent significant increases in economic risk were China, the in some countries, such as Sudan, Turks and Caicos, US, Australia, Taiwan, and Vietnam (more than $2,700 Oman, Djibouti, and Sri Lanka. Over this period, the million/year/country). total mangrove loss in hectares (absolute value) was most significant in Indonesia, Mexico, Australia, However, in the period 2010–2020, for the first time, Myanmar, and Cuba. From 1996–2010 annual coastal mangrove benefits increased more than flood risk. flood risk increased dramatically to people (32 Within this decade, more than 61 percent of people percent) and property (122 percent). Multiple factors received direct flood benefits, and mangroves protect contributed to this increase, including mangrove loss more than 109 percent of capital stock from coastal (4 percent) and population increases (21 percent), flooding. Vietnam, Bangladesh, India, China, and and capital stock increases (72 percent) in these Cameroon experienced the most significant increase countries. Despite the loss of mangrove habitats, in people protected by mangroves (more than 147,000 their flood reduction benefits (i.e., their value to people/year/country). Some countries experienced a national wealth) increased significantly. Mangroves decline in people protected by mangroves, including protected 22 percent more people and 59 percent Malaysia, Myanmar, Taiwan, Pakistan, and Colombia. more capital stock value in 2010 than in 1996 because In economic terms, China, Vietnam, Australia, the US, of increased populations and asset values in areas and Bangladesh saw the most significant increases protected by mangroves. The countries receiving the in mangrove flood protection benefits of more than most significant increase in mangrove flood reduction $1,170 million/year. Jamaica, Timor Leste, Belize, and benefits to national wealth from 1996–2015 were Pakistan experienced decreased mangrove benefits Vietnam, China, Puerto Rico, India, and Indonesia. (a decrease of $0.8 million/year/country) in annual expected flood protection benefits from mangroves. Mangrove cover declined globally from 2010–2020, but the decrease was small overall (0.66 percent). The most These results indicate that mangroves can have significant gains in mangrove habitat were observed significant and highly valuable benefits for national in Oman, Turks and Caicos Islands, Mauritania, Togo, flood risk reduction and climate adaptation. These and Djibouti, with an increase in cover of more than values can be used to inform investments in the 10 percent. However, some countries still suffered conservation, restoration, and management of these loss rates higher than 10 percent, including Saudi habitats. Arabia, Taiwan, Sudan, Pakistan, and Jamaica. From 2010–2020, the annual coastal flood risk to people BUILDING COASTAL RESILIENCE WITH MANGROVES III TABLE OF CONTENTS EXECUTIVE SUMMARY II TABLES V FIGURES V ACRONYMS AND ABBREVIATIONS VI 1. INTRODUCTION 1 2. WATER VALUE IN THE CONTEXT OF CWON 4 2.1 Methods in Brief 4 2.2 Study Domain Description 6 2.3 The Philippines: Baseline Case to Build Global Models 7 2.4 Methods in Detail 9 2.5 Model Limitations and Assumptions 14 3. DATASETS 16 3.1 Overview 16 3.2 Mangrove Data 18 3.3 Population Data 19 3.4 Capital Stock Data 19 3.5 Consumer Price Index 20 3.6 Damage Functions 20 4. RESULTS 21 4.1 Mangrove Cover 21 4.2 Overall Results: Flood Risk and Benefits 22 4.3 Mangrove Asset Value and Changing Wealth 29 5. DISCUSSION 32 6. RECOMMENDATIONS 35 REFERENCES 36 APPENDIX 1: Data Review Process 40 APPENDIX 2: Data Tables 48 IV BUILDING COASTAL RESILIENCE WITH MANGROVES TABLES TABLE 2.1: Risk maps summary table 14 TABLE 3.1: List of datasets used in the analysis 16 TABLE 4.1: Top 20 countries in mangrove asset value (100yrs at 4% discount) and annual expected benefit for flood protection 31 TABLE A1: Flood risk and flood reduction benefits of mangroves across 97 countries in 1996 48 TABLE A2: Flood risk and flood reduction benefits of mangroves across 97 countries in 2010 51 TABLE A3: Flood risk and flood reduction benefits of mangroves across 97 countries in 2020 54 TABLE A4: Absolute changes in flood risk and flood reduction benefits of mangroves across 97 countries between 1996–2010 57 TABLE A5: Absolute changes in flood risk and flood reduction benefits of mangroves across 97 countries between 2010–2020 60 TABLE A6: Percentage changes in flood risk and flood reduction benefits of mangroves across 97 countries between 1996–2010 63 TABLE A7: Percentage changes in flood risk and flood reduction benefits of mangroves across 97 countries between 2010–2020 66 FIGURES FIGURE 2.1: Key steps and data for estimating the flood protection benefits provided by mangroves 6 FIGURE 2.2: The geographic subdivisions for hydrodynamic models 7 FIGURE 2.3: CWON risk assessment methodology 11 FIGURE 3.1: Differences in global mangrove cover between GMW 2.0 and GMW 3.0 18 FIGURE 3.2: Flood depth damage curves for people and stock 20 FIGURE 4.1: Mangrove area in the top 20 countries with more mangroves in 1996 21 FIGURE 4.2: Global flood risk and mangrove flood protection benefits to people and capital stock (1996– 2020) 22 FIGURE 4.3: Changes in total population, total capital stock, and total area of mangroves across 97 countries (1996–2020) 24 FIGURE 4.4: Flood risk and mangrove benefits to people and capital stock by income level 24 FIGURE 4.5: Flood risk and mangrove benefits to people and capital stock by World Bank region 26 BUILDING COASTAL RESILIENCE WITH MANGROVES V ACRONYMS AND ABBREVIATIONS CWON Changing Wealth of Nations EU European Union GDP Gross domestic product GHS-POP Global Human Settlement population grid GMW Global Mangrove Watch IDB Inter-American Development Bank JRC Joint Research Centre KM Kilometer M Meter PV Present value PWT Penn World Table SEEA System of Environmental-Economic Accounting SNA System of National Accounts SRTM-DTM Shuttle Radar Topography Mission UNDRR United Nations Office for Disaster Risk Reduction UNFCCC United Nations Framework Convention on Climate Change US United States VI BUILDING COASTAL RESILIENCE WITH MANGROVES INTRODUCTION l 1 Introduction Rigorously evaluating flood reduction benefits risks and fails to adapt to changing environments. and identifying where natural coastal defenses There is growing interest in nature-based defenses, provide the most significant benefits can play an as seen in significant attention from international important role in informing policies for adaptation, bodies such as the United Nations Framework sustainable development, and environmental Convention on Climate Change (UNFCCC), the restoration. Governments worldwide are investing United Nations Office for Disaster Risk Reduction billions of dollars in reducing the risks of flooding, (UNDRR), the European Union (EU), and national erosion, and extreme weather events related to and multinational agencies and organizations such coastal development and climate change. However, as the Army Corps of Engineers, the World Bank, most investments in coastal protection support “gray and the Inter-American Development Bank (IDB) infrastructure” that remains vulnerable to coastal 1 (see Box 1.1). Box 1.1: Growing interest in nature-based defenses across international bodies here is a growing interest in nature-based defenses from multiple international bodies. The T Paris Agreement, for example, adopted under the UNFCCC, emphasizes the role of ecosystems and nature-based solutions in building resilience to climate change. The UNDRR, as part of the United Nations, focuses on reducing disaster risk and promoting resilience worldwide. The Sendai Framework for Disaster Risk Reduction, adopted by UN member states in 2015, highlights the significance of nature-based solutions in disaster risk reduction efforts. It encourages the integration of ecosystem-based approaches into disaster risk management strategies. The EU has various policies and initiatives promoting nature-based solutions. Its Biodiversity Strategy for 2030 aims to protect and restore ecosystems, including using nature-based solutions. The EU’s Green Deal and the European Green Infrastructure Strategy also emphasize the role of nature- based approaches in achieving environmental sustainability and resilience. The World Bank has been actively involved in promoting nature-based solutions in its projects and initiatives. It supports initiatives such as the Global Partnership for Oceans and the Forest Carbon Partnership Facility, which incorporate nature-based approaches in their strategies for sustainable development and climate action. The IDB focuses on promoting economic and social development in Latin America and the Caribbean. While it may not have specific agreements dedicated solely to nature-based defenses, the IDB has supported numerous projects and initiatives that incorporate nature-based solutions to address environmental challenges and enhance resilience in the region. 1 Gray infrastructure refers to structures such as dams, seawalls, roads, pipes, or water treatment plants. BUILDING COASTAL RESILIENCE WITH MANGROVES 1 l INTRODUCTION The wealth accounts produced by the World Bank’s by quantifying the value of mangrove assets over CWON work program can be used to inform many a period of two decades (1995 to 2015; Beck et al. policy decisions, including those on risk reduction, 2022). adaptation, development and natural resource Mangroves play a crucial role in safeguarding management. The measurement of wealth is coastlines by mitigating the risk of flooding and enhanced with each update of the CWON database erosion. The aerial roots of mangroves act as a by broadening the scope and improving the quality barrier to retain sediments, thereby preventing of all assets, with particular emphasis on natural erosion. Moreover, the roots, trunks, and canopy capital and human capital. The five asset categories included in the comprehensive wealth accounts are of mangroves reduce the force of oncoming wind produced capital, non-renewable natural capital, and waves, and this, in turn, mitigates the risk of renewable natural capital, human capital, and net flooding. According to the World Bank (2016), a foreign assets. 500-meter-wide mangrove forest can reduce wave heights by 50–100 percent in general, while during Previous wealth estimates were limited in their cyclones the reduction can go up to 60–90 percent assessment of renewable natural capital due to data (Narayan et al. 2010). This reduction in wave limitations, with the focus being on agricultural heights can result in relatively small reductions land, forests, and protected areas. However, CWON in water levels that can prevent property damage 2021 expanded the coverage of renewable natural and flooding, particularly in low-lying areas. capital to include marine fisheries, a pilot account for Mangroves contribute to long-term coastal stability renewable energy, and additional ecosystem services by promoting sedimentation, decreasing erosion, from forests, especially mangroves. The upcoming and maintaining tidal channels. They also provide CWON 2024 report will build on the findings of resources to support fisheries, building materials, CWON 2021 and extend the analysis further. ecotourism, and trade, thus promoting sustainable livelihoods and reducing social vulnerability. Recent global studies employing integrated ecological, engineering, and economic models have Mangroves are natural barriers that provide crucial highlighted the crucial coastal protection services protection to people and property from storms. provided by mangroves and coral reefs (Beck et However, policy makers often fail to account for al. 2018; Menendez et al. 2020). In 2016, the World these benefits, leading to the continued loss of these Bank formulated guidelines for a comprehensive habitats. At present, , losses from mangroves are assessment of the coastal protection benefits not factored into decision-making. In addition, the derived from these natural assets (Beck et al. 2016). benefits of managing or restoring coastal natural These guidelines were successfully implemented capital remain unrecognized. and refined at the national level for mangroves in the Philippines and Jamaica (Menendez et al. 2018; We use new data on historical mangrove distribu- Ortega et al. 2019) and have since been extended to tions from 1996 to 2020 to assess changes in the value assess the value of reefs and mangroves worldwide of coastal protection benefits over time. Overall, 19 (Beck et al. 2018; Losada et al. 2018; Menendez et al. percent of the world’s mangroves was lost between 2020). In 2021, the CWON report broke new ground 1980 and 2005 (Spalding et al. 2010). Though the rate 2 BUILDING COASTAL RESILIENCE WITH MANGROVES INTRODUCTION l of loss has slowed over the past decade (according robust evaluation of shoreline protection services. to Global Mangrove Watch data, Bunting et al. 2022), The next section is dedicated to the datasets used mangroves still face significant threats. When man- in this assessment. It provides detailed descriptions groves are degraded or destroyed, coastlines become of each dataset employed, including information on more vulnerable to the destructive effects of waves their sources, resolution, and temporal coverage. and storm surges, putting more people and property This section serves as a valuable reference at risk from storms, floods, and rising sea levels. for understanding the data underpinning the In this study, we applied a methodological approach subsequent analyses, which is presented in the to assess the value of shoreline protection services results section. The latter showcases the findings provided by mangroves. Our approach involved related to shoreline protection services provided by using global flood risk models to simulate the impact mangroves, highlighting key trends, spatial patterns, of waves and surge generated by tropical storms as and temporal changes in asset value. The results they interacted with mangrove shorelines. Through shed light on the significance and effectiveness of this modeling, we evaluated the socioeconomic risk mangroves as a natural defense against flooding. associated with flooding and quantified the flood Lastly, the discussion section engages in a protection service offered by mangroves. To capture comprehensive discussion of the results and temporal dynamics, we used mangrove extent layers methods employed in this assessment. It examines from multiple time points, spanning the period the implications of the findings, identifies from 1996–2020. By incorporating these temporal potential limitations, and explores the strengths dimensions, we were able to measure how the and weaknesses of the applied methodologies. asset value of mangroves changed over time, thus This section facilitates a deeper understanding providing insights into the evolving importance of of the analysis and its implications. The final mangroves for shoreline protection. section concludes the report by providing key The rest of this report is structured as follows: recommendations based on the findings and discussions. It offers actionable insights for policy The first section focuses on the methods employed makers, stakeholders, and practitioners involved in this study and provides a comprehensive in coastal management, disaster risk reduction, description of the study domain. It outlines the and climate adaptation. The recommendations aim specific approaches used to model the effect of to enhance the integration of shoreline protection waves and surge on mangrove shorelines, as well as services provided by mangroves into decision- the evaluation of the socioeconomic risk associated making processes and promote sustainable practices with flooding and the quantification of mangroves’ for coastal resilience. flood protection service. It provides an overview of the main updates in data and models used in this new assessment. It highlights the advancements made in terms of data sources, methodologies, and modeling techniques, which contribute to a more BUILDING COASTAL RESILIENCE WITH MANGROVES 3 l METHODS 2 Methods 2.1 METHODS IN BRIEF recommended for the assessment of coastal protection services from habitats (Figure 2.1). This In this section we describe the methods and data figure illustrates the process of estimating the flood sources for estimating flood risk, flood protection protection benefits provided by mangroves. It starts benefits, and the asset value of mangroves locally, with assessing offshore dynamics and sea states, nationally, and globally. The flood protection then considers nearshore hydrodynamics and their benefits provided by mangroves are assessed as the impact on waves. The figure highlights the role of flood damages avoided to people and capital stock mangroves as habitat and their effect on reducing by keeping mangroves in place (Beck et al. 2018; wave run-up. It further extends to estimating Losada et al. 2017; Menendez et al. 2020; Beck et al. flood heights inland for different return periods, 2022). This section extensively relies on the findings comparing scenarios with and without mangroves. and methodologies presented in the technical Finally, the consequences of flooding, including report for CWON 2021, published in 2022 by Beck land damage, affected population, and built capital, et al. The detailed explanations and methodologies are evaluated. Many aspects of these models such employed are extensively covered in Beck et al. 2022 as connections between wind, waves, run-up, and and are reproduced here for completeness and to flooding have been extensively validated (Menéndez ensure a thorough understanding of the approach et al. 2018, 2019; Menéndez et al. 2020). used in this study. We first developed key mangrove-specific aspects We couple offshore storm models with coastal of the flood models in the Philippines, a country process and flood models to measure the flooding with over 36,000km of heavily populated coastlines, that occurs: (i) with and without mangroves, (ii) high risks from cyclones, and more than 200,000 under cyclonic and non-cyclonic storm conditions, hectares of coastal mangroves. We use these (iii) by storm frequency (return period) across the models to generate a dataset of several thousand globe. These flood extents and depths are used simulations to describe the physical relationships to estimate the annual expected flood damage to between tropical cyclones, offshore wave climate, people and capital stock, and hence the expected mangrove extent and geometry, and extreme water benefits of mangroves in social (people protected) levels (i.e., flood height) along the shoreline for five and economic terms (capital stock protected). storm frequency events (one storm in 5, 10, 25, 50, and 100 years) driven by local storm data. Our estimates are based on a set of global statistical models, hydrodynamic process-based models, and This dataset is then used to estimate how mangroves socioeconomic data. All these processes are grouped modify extreme water levels for every kilometer of into five steps following the Averted Damages mangrove shoreline globally. Global flood depths (Expected Damage Function) approach, commonly and extents are then estimated by intersecting the used in engineering and insurance sectors and global extreme water levels with a global topography 4 BUILDING COASTAL RESILIENCE WITH MANGROVES METHODS l dataset, at 90-meter (m) resolution, from the Shuttle these ecosystem services is properly accounted for. Radar Topography Mission. Finally, we overlay Second, the Averted Damages Approach employs the resulting maps of flood depths and extents on quantitative methods and tools that are consistent socioeconomic asset information downscaled to 90 with the rigorous assessment and accounting x 90 meters. Flooded socioeconomic assets are then practices recommended by the SNA and SEEA assessed by flood depth to identify flood damages frameworks. By using process-based models (risk) and avoided damages (mangrove benefits). and statistical tools, the approach adheres to the The Averted Damages Approach provides a rigorous principles of accuracy, reliability, and transparency foundation for estimates of flood risk and habitat that are essential for economic and environmental benefits (Barbier 2015; Beck & Lange 2016; Pascal accounting. et al. 2016; van Zanten et al. 2014). We have Furthermore, the Averted Damages Approach chosen this approach over others because it is (i) considers the impacts of extreme events, which quantitative in contrast to other approaches, which aligns with the guidelines of the SEEA in assessing use indicator (expert) scores to assess shoreline the economic consequences of natural disasters. vulnerability (e.g., (Silver et al. 2019)), (ii) it uses By capturing the potential damages and costs that process-based models and statistical tools to assess would occur in the absence of natural coastal hydrodynamics, (iii) it uses the methods and tools of habitats, the approach provides a comprehensive risk agencies, insurers and engineers (Narayan et al. evaluation of the risk reduction and resilience- 2016, 2017; Reguero et al. 2018), (iv) it is consistent building benefits associated with these habitats. with approaches for national accounting (Beck et al. 2016), and (v) it accurately captures impacts of By ensuring consistency with SNA and SEEA extreme events. standards and guidelines, the Averted Damages Approach facilitates the integration of its findings The Averted Damages Approach aligns with the into broader economic accounts and decision- System of National Accounts (SNA) and the System making processes. The approach provides a robust of Environmental-Economic Accounting (SEEA) and credible framework for valuing the ecosystem standards and guidelines in several ways. First, it services and benefits provided by natural recognizes the importance of valuing ecosystem coastal habitats, thereby contributing to a more services and natural assets within economic comprehensive understanding of their economic assessments, which is a fundamental principle of significance, and supporting informed policy both the SNA and SEEA frameworks. By explicitly and management decisions at the intersection of incorporating the estimation of averted damages economics and the environment. and the value of flood protection provided by natural coastal habitats like mangroves, the approach ensures that the economic significance of BUILDING COASTAL RESILIENCE WITH MANGROVES 5 l METHODS FIGURE 2.1: KEY STEPS AND DATA FOR ESTIMATING THE FLOOD PROTECTION BENEFITS PROVIDED BY MANGROVES OFFSHORE NEARSHORE HABITAT IMPACTS CONSEQUENCES DYNAMICS DYNAMICS Impact with mangroves Impact without mangroves Offshore Nearshore Onshore Note: Step 1, Offshore Dynamics: Oceanographic data are combined to assess offshore sea states. Step 2, Nearshore Dynamics: Waves are modified by nearshore hydrodynamics. Step 3, Habitat: Effects of mangroves on wave run-up are estimated. Step 4, Impacts: Flood heights are extended inland along profiles (every 1km) for 1 in 5, 10, 25, 50, and 100-year events with and without mangroves. Step 5, Consequences: The land, people, and built capital damaged under the flooded areas are estimated. Source: Beck et al. 2019. 2.2 STUDY DOMAIN DESCRIPTION level divides the 700,000 km of coastline into 68 sub- regions considering coastline transects of similar This global study covers 700,000km of mangrove coastal typology (e.g., islands and continental coastlines. For computational purposes, we divided coasts) and similar ecosystem characteristics the global domain into three levels (Figure 2.2). The (Figure 2.2b). The third level is at the national scale, first level is the division into six macro-regions, defining country-side units (Figure 2.2c). Within corresponding to the five ocean basins of tropical these nationwide units, we create cross-shore cyclone generation (Knapp et al., 2010): East Pacific, profiles perpendicular to the mangrove habitats for North Atlantic, North Indian, South Indian, West each kilometer of coastline, totaling 700,000 profiles Pacific and South Pacific (Figure 2.2a). The second (Figure 2.2d). 6 BUILDING COASTAL RESILIENCE WITH MANGROVES METHODS l FIGURE 2.2: THE GEOGRAPHIC SUBDIVISIONS FOR HYDRODYNAMIC MODELS Note: (a) Macro-regions with the global mangrove cover in red, (b) Sub-regions in the Atlantic Ocean basin, (c) Local study units every 20km of coastline in the Northern Caribbean sub-region, (d) Profiles every 1km of coastline in the North of Cuba. Source: Beck et al. 2022. 2.3 THE PHILIPPINES: • Almost 10 percent (548 events) of the global BASELINE CASE TO BUILD tropical cyclone records from the International GLOBAL MODELS Best Track Archive for Climate Stewardship (IBTrACS) database affected the Philippines The Philippines is the baseline case used to develop (Knapp et al. 2010 and Knapp et al. 2018). the global statistical models of the relationships • The islands of the Philippines present high between tropical cyclone parameters, oceanographic climatic variability and are at particularly high variables, and onshore flood extents and depths on risk from natural hazards like typhoons and mangrove coastlines. The Philippines is an excellent regular storms, which are the cause of 80 percent pilot case for valuation of the coastal protection of the total losses from disasters (National ecosystem service provided by mangroves because: Economic and Development Authority 2017). BUILDING COASTAL RESILIENCE WITH MANGROVES 7 l METHODS • The Philippines ranks in the top 15 most Cyclone Model: Offshore and nearshore mangrove habitat-rich countries, with 2,630km2 of dynamics generated by tropical cyclones mangroves (in 2010), representing 2 percent of the world total (Giri et al. 2011). Offshore waves and storm surge generated by tropical cyclones were numerically simulated in • These mangrove habitats show extensive variation the Philippines by using Delft3D modules “Flow” in both cross-shore width (0.1km and 8km wide) and average depth (Menéndez et al. 2018). (“Delft3D-FLOW User Manual” 2006) and “Wave” (“Delft3D-WAVE User Manual” 2000). Both modules By encompassing almost all possible geometries, were run simultaneously in a two-dimensional grid the Philippines dataset provides a comprehensive of 5km cell-size with a time step of 30s, forced with basis for extrapolation. To account for variations in hourly wind data and sea level pressure fields in a climate and coastal conditions, synthetic tropical model that considers the non-linear interaction cyclones were modeled beyond the specific climate processes of tide, wind setup, inverse barometers, patterns found in the Philippines. These synthetic and wave setup. The model was validated by cyclones cover a broad range of magnitudes, comparing the storm surge generated by typhoon ensuring that the modeling approach captures Rammasun in the bays of Legaspi and Subic. We the variability and potential scenarios that can be used tidal gauge data from the Global Sea Level encountered in mangrove areas worldwide. Observing System (GLOSS, http://www.gloss- sealevel.org) for validation. We valued the flood protection service of mangroves in the Philippines by using the numerical model Using the results of the numerical simulations Delft3D, considering both cyclonic and non- carried out with the Delft3D model in the cyclonic storm conditions, for scenarios with and Philippines we looked for statistical relationships without mangroves. We used these results to build between cyclone parameters and oceanographic two global statistical models. The first, a “Cyclone variables to create a new predictive model, where Model”, was developed to describe specific offshore the key oceanographic variables that affect on-shore and nearshore ocean dynamics produced by tropical flooding (wave height, wave period, storm tide and cyclones (wave height, peak period, storm surge, duration of the storm peak) were predicted based and storm duration), and the second, a “Mangroves on cyclone parameters (distance, wind speed, track & Flood Height Model”, estimated how the presence velocity, wind angle of incidence). In the Philippines, and profile of mangrove habitats influences the we simulated 548 storm events creating a database of total water level during storm conditions on the 58 million results. We randomly selected 90 percent shoreline. Further details about the Philippines- of the generated results to build our predictive based models can be found in Menéndez et al. (2018, model and used the other 10 percent for validating 2020) and the two models are summarized below. the predictive models. We examined the correlation between the physical tropical cyclone parameters and the oceanographic variables for two coastal area typologies: areas directly exposed to tropical 8 BUILDING COASTAL RESILIENCE WITH MANGROVES METHODS l cyclones and areas protected from the direct impact dynamic conditions (wave height, wave period, of tropical cyclones. Based on these variables, we storm surge, and astronomical tide) were subdivided then developed a best fit regression model to predict into two groups: (1) those produced by less intense the oceanographic variables. local climate or extreme climate generated far away from the study area (regular climate or non-cyclonic Mangroves & Flood Height Model: conditions) and (2) those produced by local extreme The role of coastal habitats in events (tropical cyclones or cyclonic conditions). nearshore dynamics 1A. Non-cyclonic climate: Deep water ocean The Mangroves & Flood Height Model predicts the dynamics produced by any climate condition other effects of mangrove forest characteristics on flood than tropical cyclones was analyzed as non-cyclonic heights at the shoreline. Coastal vegetation provides climate. Non-cyclonic climate was defined by resistance to the energy and flow of waves and water different datasets within the period 1979 to 2010: a as they come onshore, which is modeled by using a global wave reanalysis (Reguero et al. 2012; Perez friction factor based on the Manning coefficient (n). et al. 2017), a global storm surge reanalysis (Cid We assigned different friction factors to sandy soil et al. 2017), astronomical tide (Pawlowicz et al. (n=0.02), mangroves (n=0.14), and coral reefs (n=0.05) 2002; Egbert & Erofeeva 2002), and mean sea level (Prager 1991; Zhang et al. 2012).2 One-dimensional compiled from historical numerical reconstruction numerical propagations, on cross-shore profiles and satellite altimetry (Church et al. 2004). Waves perpendicular to the mangrove shoreline, were and sea level conditions due to tropical cyclones carried out using the Delft3D model to obtain flood were excluded and studied separately to avoid heights along the coast. We used these numerical double counting, resulting, finally, in 32 years’ time results to create two interpolation tables for cyclonic series of only non-cyclonic climate. The 32-year and non-cyclonic storm conditions that correlate the long time series of wave data (1979 to 2010) included oceanographic information at the seaward side of 280,000 sea states (one sea state represents one hour the profile (wave height, wave period, weather storm of wave height, peak period, and total water level). tide, and storm peak duration) and the characteristics We reduced the number of sea-state propagations of the mangrove profiles (width and slope) with the by considering only the 3,787 non-repeated flood height. These tables contain 37,500 tropical combinations of wave height, peak period, and cyclone simulations (50 cyclones x 750 profiles) and total water level and, then, applying the Maximum 90,000 non-cyclonic climate simulations (120 sea Dissimilarity Algorithm to identify 120 sea states states x 750 profiles). to be propagated with the Snell law and shoaling equation across coastal profiles. All of the models and data in the non-cyclonic climate analysis, 2.4 METHODS IN DETAIL including waves and storm surge, have been globally Stage 1: Offshore tropical cyclone and non- validated (e.g., Reguero et al. 2012; Perez et al. 2017; cyclonic climate sea states. The offshore hydro- Cid et al. 2017). The non-cyclonic climate analysis 2 Data for the three bottom types was used, but the only with/without scenario comparison was done with the mangroves. BUILDING COASTAL RESILIENCE WITH MANGROVES 9 l METHODS covers most of the storm and flood risk conditions 2B. Tropical cyclones: We used the Cyclone globally (including tropical depressions and storms) Model described above to estimate key nearshore except for cyclone conditions, which represent parameters from cyclones including wave height comparatively small (spatio-temporally) but locally (Hs), peak period (Tp), storm surge (SS) and the time intense conditions. duration of the meteorological tide (Tss). 1B. Tropical cyclones: Tropical cyclones were Stage 3: Modeling the role of coastal habitats in considered separately from non-cyclonic climate if nearshore dynamics, flood height. We used the the 10-minute sustained wind speeds (W10m) exceed Mangroves & Flood Height Model (described above) 118km/h. Tropical depressions (W10m ≤ 62km/h) to estimate flood height given mangrove length and and tropical storms (63km/h ≤ W10m ≤ 118km/h) depth, significant wave height, peak period, and total are included in the non-cyclonic climate models. water level at the head of each cross-shore profile. For historical tropical cyclones, we use the IBTrACS After calculating the historical time series of flood database (Knapp et al. 2018), which provides six- height (1979 to 2010), we applied an extreme value hourly data for wind speed, atmospheric pressure, analysis to obtain 1 in 5-, 10-, 25-, 50-, and 100-year and track position, and contains regularly updated extreme sea levels at the coast. To do that, we selected storm data (https://www.ncdc.noaa.gov/ibtracs/). maximum values over a threshold (minimum 1 event every 5 years) and then adjusted these values Stage 2: From offshore dynamics to shallow to a Generalized Pareto-Poisson distribution. One- water. We obtained ocean hydrodynamics on the dimensional wave and surge propagation model seaward side of each cross-shore profile cyclonic through the cross-shore profiles is commonly used and non-cyclonic conditions. Waves interact with even in site-based models (Beck et al. 2018). The the bottom and other obstacles (e.g., islands) as consideration of non-linear effects is only possible they approach the coast and modify height and using computationally expensive phase resolving direction through shoaling, refraction, diffraction, models (e.g., XBeach) at local scales (e.g., bays). This and breaking processes. modeling approach is not feasible at the global scale because of computational capacity and the lack of 2A. Non-cyclonic climate: For non-cyclonic climate, high-resolution bathymetric data, and especially several thousand offshore ocean parameters if risk is to be evaluated probabilistically across representing non-extreme sea states are clustered multiple events and scenarios (Beck et al. 2018). into 120 representative sea states using a Maximum Dissimilarity Algorithm (Camus et al. 2011a; Camus Stage 4: Calculating impacts: Flooding maps. et al. 2011b). Wave and water level conditions at the To estimate flooding, we used a modified bathtub seaward point of each profile are then associated flooding model, which includes a hydraulic with the nearest non-cyclonic climate sea state. connectivity requirement for flooding connected From this, a regression model was created that points across the coastal topography. We used 1 equates offshore non-cyclonic climate sea states in- 5-, 10-, 25-, 50- and 100-year flood heights every with wave and water level conditions at the seaward 1km of coastline, a global topography dataset, end of a profile. 20 Desalination is recorded as the abstraction of saltwater. 10 BUILDING COASTAL RESILIENCE WITH MANGROVES METHODS l at 90m horizontal resolution, from the Shuttle of (1) people exposed, (2) capital stock exposed, Radar Topography Mission (Farr et al. 2007) and (3) people at evacuation risk, and (4) losses of a bathtub method for flooding based on hydraulic capital stock. We intersected the flood maps with connectivity between pixels. The final product of population data from the new Global Human this stage were 50 global flood maps representing Settlement population grid (GHS-POP) released in 5 return periods (1 in 5-, 10-, 25-, 50- and 100-year 2022. The dataset is a spatial population raster that events), 2 storm conditions (cyclonic and non- depicts the distribution of residential population, cyclonic), and 5 mangrove extents (1996, 2010, expressed as the number of people per cell (https:// 2015, 2020, and no mangroves). ghsl.jrc.ec.europa.eu/ghs_pop2022.php) and capital stock data from the PWT version 10.0 (https://www. Stage 5: Assessing global flood consequences rug.nl/ggdc/productivity/pwt/). The workflow of in areas protected by mangroves. The expected the exposure and risk assessment methodology is flood risk and benefits provided by mangroves were summarized in Figure 2.3. presented in social and economic terms. In this step, we calculated the effects of flooding in terms FIGURE 2.3: CWON RISK ASSESSMENT METHODOLOGY BUILDING COASTAL RESILIENCE WITH MANGROVES 11 l METHODS We followed a multi-step approach to obtain people determine the amount of capital assets available and capital stock exposure and risk: on a per-person basis in each nation. Subsequently, we integrated this country-level information with Step 1. Global population and capital stock our previously calculated data, which specified the distribution: Population distribution and density is population residing within each 250x250m grid cell obtained from the spatial raster GHS-POP R2022A. across the globe. By doing so, we could gauge the This dataset contains global residential population total capital stock associated with each grid cell, estimates at 1km resolution for 1975, 1990, 2000, considering the population it encompasses. To create 2015, and 2020. We downscaled the population a comprehensive and highly detailed global capital rasters from 1km to 250m resolution in order to stock distribution map, we applied these calculations match the past GHS-POP R2019, which are available at a fine-grained resolution of 250m. The result is a for the years 1975, 1990, 2000, and 2015 at 250m. raster representation that provides an in-depth view To obtain population estimates for the years 1996 and of how capital assets are distributed across the world 2010 in our analyses, we interpolated the global data at this level of granularity. between 1990 to 2000 and 2000 to 2015, respectively. Step 2. Resampling coastal flooding grids (at 90m) Then we calibrate both, 1996 and 2010 interpolated to population and capital stock resolution (250m): grids, with nationwide population statistics from To overlay flood and assets maps, both must be at the the World Bank (World Bank Data). The calibration same horizontal resolution. We upscaled the flood consists of adjusting the total people per country rasters from 90m to 250m to be consistent with the from the interpolated grids to the World Bank data. resolution of the global population and asset data. In the case of 2020, we had to reduce the population We use the ArcGIS toolbox function, “resample”, raster from 1km to 250m by equally distributing for the spatial redistribution of the flooding grids. people across each 250x250m pixel, but no further Three main reasons support this approach: (1) By adjustments were needed. upscaling flood risk to 250m we are still working with Global capital stock was calculated using PWT 10.0. sufficiently high-resolution spatial risk to aggregate This table provides information on relative levels of results at both national level and at any local level income, output, input, and productivity. It includes higher resolution; (2) working with 250m global 182 countries and covers 70 years (1950 to 2019). rasters rather than 90m reduces the size of the intermediate files and therefore the computational For our economic analyses we used the national data demand of the overlying process; and (3) we do of capital stock (“rnna” variable) at constant 2017 not need to re-calibrate the new rescaled rasters by national prices and transformed these into constant adjusting the total population and total capital stock 2020 national prices by using country-based consumer per country at 90m resolution to those at 250m. price index data from the World Bank (https://data. worldbank.org/indicator/FP.CPI.TOTL). To derive a Step 3. Exposure. People and stock in flooded areas: comprehensive understanding of the global capital Here we calculate the number of people and then stock distribution, our methodology involved several the capital stock exposed to coastal flooding in 1996, steps. Initially, we computed the capital stock per 2010, 2015, and 2020 with and without mangroves. We capita for each country. This step allowed us to first reclassify the flooding raster into 1 and 0 values. 12 BUILDING COASTAL RESILIENCE WITH MANGROVES METHODS l We assign 1 to flooded pixels with water, and 0 to Step 5. Risk. People evacuated and capital stock dry pixels. Then we multiply population rasters by loss due to coastal flooding: To calculate risk, we the reclassified flood raster and obtain the global multiply damage coefficient rasters by people and distribution of people exposed to coastal flooding. The capital stock exposure rasters. A total of 160 risk capital stock exposed to flooding is then calculated maps for the different conditions and scenarios are by multiplying people exposed by capital stock per generated as a combination of asset type (x2), year capita at national level (PWT 10.0). The exposure (x4), storm condition (x2), ecosystem presence (x2), layers will inform how many people and assets are and return period (x5) (see Table 2.1). in flooding areas, but not the real damage to people Step 6. Nationwide aggregation results: Risk to and the real economic loss (risk). Calculating flood people and capital stock is aggregated at national risk requires that we estimate flood damages using scale. We first create a 10km external buffer for each damage functions, which relate flood damages at a country and find the pixels that lay into each country location to the flood depth at that location. buffer boundary. We calculate the total number Step 4. Damage coefficients: Flood damage depends of people and the total capital stock value of each on the water depth and the type of asset. We use country under each scenario. different damage functions for population and capital Step 7. Annual expected risk and benefits: In stock. For people, the damage function assumes that, addition to assessing risk for specific events (such as in a grid cell, people are not affected by water below a 100-year storm event), we also examined average 30cm in depth and all people are affected by flood annual expected damages and benefits provided by water depths greater than 30cm. This a commonly mangroves. To estimate annual risk, we integrated used threshold in civil protection services to decide the values under the extreme value distribution when people must be evacuated (Shao et al. 2015). curves that compare capital stock damaged, or For capital stock, we combined data from JRC people affected, by storm return period—in other (Huizinga et al. 2017) and Hazus (Scawthorn et al. 2006) words, the integration of the expected damage with flood depth damage curves. The best combination of the probability of the storm events. these curves globally results in a damage function that Step 8. 100-year asset value calculation: We ramps up linearly from 0 to 50 percent of damage when calculated the present value of mangrove benefits water depth is below 1m. Then damage increases at over a period of 100 years. We assumed a constant a slower rate from 50 percent at 1m water depth to benefit flow and 4 percent discount rate to obtain the 100 percent at 5m. We use these curves to calculate 100-year asset value (Eq. 2). a global raster of damage coefficients to people and capital stock. In prior work, we tested the use of i=100 various damage curves (including complex damage AEB functions) for population, residential, and industrial PV= ∑ (1+r) i (2) i=1 stock from Hazus in the Philippines (Menendez et al. 2018), and we found that the results were not Where PV is the present value, AEB are the annual significantly different from approaches using simpler expected benefits, r is the discount rate (4%) and “i” is curves such as those in Figure 3.2. each year within the life cycle period (i=1-100 years). BUILDING COASTAL RESILIENCE WITH MANGROVES 13 l METHODS TABLE 2.1: RISK MAPS SUMMARY TABLE MANGROVE RETURN YEARS ASSETS STORM CONDITION SCENARIO PERIOD 1996 People With mangroves Cyclonic (tropical cyclones) 1 in 5 years 2010 Capital stock Without mangroves Non-cyclonic (regular climate) 1 in 10 years 2015 1 in 25 years 2020 1 in 50 years 1 in 100 years 2.5 MODEL LIMITATIONS The greatest sources of uncertainty in coastal AND ASSUMPTIONS flood risk modeling are estimates of topography and bathymetry. Given that flooding and damage Our efforts represent state of the art process-based from tropical storms are among the greatest risks assessments of flood risk and mangrove benefits to people and property, better elevation and depth globally. For most countries with mangroves, these data is urgently needed. Fortunately, in the past represent the best data and models for mangrove decade there has been a substantial increase in benefits, and for many of these countries the best the availability of high-resolution coastal elevation national-level estimate of flood risk. For this global data through the widespread use of LIDAR (light study, we have developed a dataset of several detection and ranging). Nearshore bathymetry, thousand simulations to describe how mangroves however, remains a major gap, though there are modify extreme water levels at the shoreline, advances in remote sensing that could help. for every kilometer of mangrove coastline in the world. This approach is highly efficient and allows Other sources of uncertainty rely on socioeconomic us to estimate coastal flood risk globally for new estimates. The changes in flood risk due to changes scenarios of mangrove presence and extent. in the socioeconomics are more than twofold greater than the sensitivity to changes in the hydrodynamic As with all global models, there is the potential conditions. that individual countries may have better data, for example, on mangrove habitats or bathymetry Our coastal flooding analyses have several significant, (measuring the depth of water), which could be combined improvements over other recent global used for higher resolution analyses of risks and flooding analyses including: benefits. For mangrove coverage, we have only the extent of mangroves, for example, not the age, • Generation of global flood maps at 90m resolution. structure, density, species, degree of degradation, • Consideration of hydraulic connectivity in the and other factors that can affect the capacity of flooding of land. mangroves to reduce flooding. We use mangrove length and water depth (or elevation) across each • The use of 30 years of wave, surge, tide, and sea 1km cross-shore profile as input to the model. level data. 14 BUILDING COASTAL RESILIENCE WITH MANGROVES METHODS l • Reconstruction of the flooding height time series and 2020. Adjustments within the time period would and associated flood return periods. drastically shorten the data record, which is not a standard approach for risk and flood modeling. Our global flood risk models also include the latest datasets released of mangroves, population Caveats in the interpretation if the data is used at distribution, and economics. Remaining constraints a subnational scale: for this global coastal flooding model include the consideration of flooding as a one-dimensional • Data availability and accessibility: Working process and the difficulty in representing flooding with high-resolution data at a global scale can well in smaller islands.3 be challenging because it is based on other spatial explicit data that is at a coarse scale (e.g., In the previous analysis (CWON 2021, Beck et al. population data at 1km resolution or economic 2022b), we modeled 111 countries with mangroves data at national scale resolution), which can and only 92 benefited from mangroves. In this introduce uncertainties and gaps in the analysis. new updated analysis, we modeled 121 countries • Data accuracy and precision: While high- with mangroves and 97 countries benefited from resolution data provides more detailed spatial mangroves. More details about the selection information, it is essential to assess the accuracy of countries providing mangrove benefits are and precision of the data. The quality and presented in Appendix 1. reliability of the input data, including elevation We do not account for changes in sea level from models and socioeconomic data, can influence the 1996 to 2020; these effects would be small over accuracy of the flood risk assessment. this time period and would have minor impacts on • Validation challenges: Validating high-resolution flooding. We also calculate the (local) distributions global data presents significant challenges due of extreme sea levels across this entire time period. to the lack of comprehensive ground truth data The statistical distributions representing extreme at such a fine-grained resolution for the entire sea levels were kept unchanged throughout the globe. The availability of local validation data, entire time period under consideration. Keeping the such as historical flood records or detailed extreme sea levels distribution constant simplifies flood damage assessments, is often limited the analysis by assuming homoscedasticity or inconsistent across different regions. This (variability remains constant over time). This makes it challenging to validate and verify the simplification can be helpful for certain analytical accuracy of the high-resolution global flood risk purposes, especially when you want to focus on other model. As a result, there may be uncertainties variables or factors without the added complexity in the model’s performance and limitations in of modeling changing variances. This is the case of assessing its reliability for specific locations or this analysis, where we focus on modeling the effect subnational areas. Rigorous validation efforts, of changes in mangrove distribution. where possible, should be conducted to assess the We are assuming that the 30-year historical data of model’s performance and ensure its suitability for waves and cyclones is applicable to 1996, 2010, 2015, subnational applications. 3 The challenges encountered in modeling are primarily attributed to the intricate nature of building a one-dimensional model for regions characterized by very small islands and highly fragmented coastlines. The resolution of the model is not the limiting factor; rather, it is the complex and geographically dispersed nature of the coastal features that pose significant difficulties in the modeling process. BUILDING COASTAL RESILIENCE WITH MANGROVES 15 l DATA S E T S 3 Datasets 3.1 OVERVIEW significance, we aim to provide a transparent and comprehensive account of the data sources used in The dataset overview section provides a this assessment. comprehensive view of the various datasets used in this study to assess coastal flood risk and the Several databases used in the assessment of associated impacts. This section begins with a mangrove coastal protection benefits have been detailed table listing all the datasets employed updated and improved since the CWON 2021 report (Table 3.1), followed by a thorough explanation of (World Bank 2021), in particular the mangrove key datasets that play a crucial role in the analysis. cover maps and socioeconomic data. Due to The datasets discussed include mangrove data, these changes, the results corresponding to the population data, capital stock, consumer price index, previous analysis (1996 to 2015) were updated, and damage functions. Each dataset contributes and they changed quantitatively (slightly) but not essential information to our understanding of qualitatively. The main changes in the underlying coastal vulnerability and the estimation of potential datasets are explained. damages. By presenting these datasets and their TABLE 3.1: LIST OF DATASETS USED IN THE ANALYSIS SPATIAL TEMPORAL DATABASE DOMAIN DOMAIN DATABASE INSTITUTION VARIABLE WEB LINK NAME AND AND RESOLUTION RESOLUTION Tropical Wind speed 1841-2023 IBTRrACS NOAA Global (10 km) IBTrACS cyclones Long, lat, time (3 hours) 1979-2010 Waves GOW 2.0 IH Cantabria Hs, Tp, Dm Global (250km) GOW 2.0 (1 hour) Global (4km Astronomical Astronomical GOT IH Cantabria coast, 18km Any (1 hour) GOT tide tide elevation open ocean) Extension of Storm surge 1871-2010 Storm surge IH Cantabria Global (200km) Storm Surge DAC level (6 hours) 16 BUILDING COASTAL RESILIENCE WITH MANGROVES DATA S E T S l SPATIAL TEMPORAL DATABASE DOMAIN DOMAIN DATABASE INSTITUTION VARIABLE WEB LINK NAME AND AND RESOLUTION RESOLUTION Mean sea Sea Level CSIRO Marine Mean sea Global 1950-2000 MSL level Rise Research level (100km) (1 month) GEBCO GEBCO Global (450m) Bathymetry Allen Coral Allen Coral Water depth No GEBCO Global (10m Atlas Atlas Topography SRTM PLUS NASA Land elevation Global (90m) No SRTM Global Mangrove 1996-2020 Mangroves Mangrove UN-WCMC Global (1km) GMW 3.0 distribution (annual) Watch 3.0 GHS-POP – European Global (250m 1975-2020 Population # of people GHS-POP R2022A Commission and 1km) (annual) Capital stock Penn World The World at constant Global 1950-2019 Capital stock PWT 10.0 Table 10.0 Bank 2017 national (country) (annual) prices Consumer The World Consumer Global 1960-2021 CPI CPI price index Bank price index (country) (annual) Damage People People % damage to function Self-produced Global No evacuation damage curve people people standard Self-produced from: Damage FEMA-Hazus Capital stock % damage to function Fema-Hazus Global No damage curve capital stock stock JRC JRC-European Commission BUILDING COASTAL RESILIENCE WITH MANGROVES 17 l DATA S E T S 3.2 MANGROVE DATA this new report we used GMW 3.0 to update 1996 to 2015 mangrove coverage and added a new time Global Mangrove Watch (GMW) released an updated point corresponding to 2020. The most significant version (GMW 3.0) of global mangrove coverage in difference in these datasets is that the improved August 2022 (Bunting et al. 2022). This release includes GMW 3.0 shows a consistently greater global new mangrove distribution maps corresponding to coverage than GMW 2.0 (about 7 percent greater, see the 2016 to 2020 period and updates/improvements Figure 3.1). To consistently assess mangrove benefits of the historical mangrove distribution data for the over time, all data (not just 2020) were updated, the period 1996 to 2015. In CWON 2021, we used GMW flood models were re-run, and new assessments of 2.0 for the time periods 1996, 2010, and 2015. In risk and benefits were developed. FIGURE 3.1: DIFFERENCES IN GLOBAL MANGROVE COVER BETWEEN GMW 2.0 AND GMW 3.0 Global Mangrove Area [Ha} 16 500 000 16 000 000 15 500 000 15 000 000 14 500 000 14 000 000 1990 1995 2000 2005 2010 2015 2020 GMW (v 2.0) GMW (v 3.0) Despite observing changes in the total area of The GMW has generated a global baseline map of mangroves, we discovered that these alterations mangroves for 2010 using ALOS PALSAR and Landsat had minimal impact on the results. This is due to (optical) data, and changes from this baseline for the fact that the crucial factor driving variations seven epochs between 1996 and 2020 derived from in coastal flooding, which is the cross-shore width JERS-1 SAR, ALOS PALSAR, and ALOS-2 PALSAR-2. of the mangrove forest, remained relatively stable Annual maps are planned from 2018 and onwards. or exhibited minimal change in the new data. It The primary objective of the GMW has been to is important to emphasize that modifications in provide countries with mangrove extent and change the overall area of mangroves do not necessarily maps to help safeguard against further mangrove correspond to changes in flood risk, as the forest loss and degradation. The GMW aims to effectiveness of mangroves’ protection services is provide geospatial information about mangrove primarily influenced by the cross-shore width, which extent and changes to the Ramsar Convention, exhibited limited variation. national wetland practitioners, decision-makers, and nongovernmental organizations. 18 BUILDING COASTAL RESILIENCE WITH MANGROVES DATA S E T S l 3.3 POPULATION DATA 250m resolution. There were 22 tropical nations that had mangroves but were not included in the PWT; we Global exposure data for people was obtained from filled most of these gaps with national data from the GHS-POP grid dataset from the European Commission World Bank. There were a few remaining countries (https://ghsl.jrc.ec.europa.eu/ghs_pop2022.php). 4 and territories that we were not able to include in the This new package, released in 2022 (GHS R2022A, analyses due to the lack of economic data, including (Schiavina et al. 2022)) substitutes the previous Eritrea, French Guiana, New Caledonia, Micronesia, version (GHS R2019) and provides estimates of global Palau, Somalia, Guadeloupe, Martinique, Timor populations and their distribution for 1975, 1990, Lester, Mayotte, Samoa, US Virgin Islands, Saint 2000, 2015, and 2020, as well as future projections to Martin, and American Samoa. 2025 and 2030. GHS R2022A matches or outperforms other data sources for accuracy in epochs 2018 and The new updated version of the PWT (10.0, Feenstra 2020 and matches or outperforms also all the other et al. 2015) was released in June 2021. We used the single epochs (1975, 1990, 2000, and 2015) included capital stock at constant national prices (“rnna”) in the previous release GHS R2019. The global and total population (“pop”) to calculate the gross distribution of population used for 1996, 2010, and domestic product (GDP) per capita ratio at national 2015 is 250m resolution (GHS R2019), while 2020 level. Several changes have been introduced in population distribution released in 2022 (GHS the most recent version of PWT 10.0 relative to the R2022A) with the new version is at 1km resolution. version used in the previous CWON report (PWT 9.1). These differences result in changes in the economic valuation of mangroves worldwide. The main updates 3.4 CAPITAL STOCK DATA are described below: This study uses data from PWT 10.0 from the • Capital stock in PWT 9.1 was in constant 2011 Groningen Growth and Development Center national prices, while capital stock in PWT 10.0 (https://www.rug.nl/ggdc/productivity/pwt/). This version is in constant 2017 national prices. is a database with information on relative levels of income, output, input, and productivity. The table • In PWT 9.1, the time series was 1950 to 2017. covers 182 countries and 70 years (1950–2019). We PWT 10.0 covers the period 1950 to 2019. used the nationwide data of capital stock at constant 2017 national prices and transformed these into • Capital stock was recalculated in some countries constant 2020 national prices by using country-based (e.g. China, Sudan) using an outdated nominal consumer price index. Then, we calculated the stock value for GDP. Capital stock values are per capita at each country and multiplied these remarkably higher than previously estimated national values by the population located at each grid in some countries. cell. We then obtained the global stock distribution at 4 The Global Urban Footprint primarily focuses on urban areas and their extent, which may not accurately capture the distribution and density of human settlements in coastal regions. The Global Human Settlement dataset, on the other hand, provides more comprehensive coverage of human settlements, including both urban and rural areas. When assessing the impact of coastal storms, it is essential to consider not only the built environment but also the vulnerability and exposure of the population. The Global Human Settlement dataset incorporates population distribution and density, allowing for a more accurate estimation of the number of people at risk from coastal flooding. BUILDING COASTAL RESILIENCE WITH MANGROVES 19 l DATA S E T S 3.5 CONSUMER PRICE INDEX For this new analysis, we first transformed capital The adjustment applied in this analysis is stock in constant 2017 national prices to capital stock described by the equation below. in constant 2020 national prices using the 2021 updated country-based consumer price index from the World Where V is the capital stock value in any given Bank. The index is the most widely used measure of year’s constant national price and CPI is the inflation and is sometimes viewed as an indicator of consumer price index. the effectiveness of government economic policy. V 2017 CPI 2017 CPI 2020 = ➞ V 2020 = V 2017 . V 2020 CPI 2020 CPI 2017 3.6 DAMAGE FUNCTIONS aim to address flooding effects on property globally, developing a consistent database of depth-damage Global flood depth damage functions are needed curves. We used both, JRC and Hazus to calculate to evaluate the sensitivity of people and property capital stock damages (Figure 3.2). Capital stock damaged at different flood levels. The report from damage results from the integration of global JRC the EU Joint Research Centre (JRC) collected data damage functions, calibrated with Hazus damage from Africa, Asia, Oceania, North America, South curves for different building types. America, and Central America and proposed damage functions for residential and industrial stock in each The damage curve for people was built based on a region (Huizinga et al. 2017). We refer to these as commonly used threshold in civil protection services JRC damage functions. These damage functions are to decide when people must be evacuated. It indicates a better alternative to damage curves from FEMA that no people are affected in areas with less than Hazus (Scawthorn et al. 2006), which were based only 30cm water depth, and 100 percent of people affected on US collected data but were frequently extrapolated in areas with water depth above 30cm (Figure 3.2). for use in other geographies. JRC damage functions FIGURE 3.2: FLOOD DEPTH DAMAGE CURVES FOR PEOPLE AND STOCK 20 BUILDING COASTAL RESILIENCE WITH MANGROVES R E S U LT S l 4 Results 4.1 MANGROVE COVER had previously experienced significant mangrove loss (Appendix 2, tables A5 and A7), now saw gains Mangrove extent declined significantly at the end between 2 percent and 25 percent in mangrove area of last century and the first decade of the current (e.g., Turks and Caicos, Oman, Djibouti, Peru, China, century. According to the GMW dataset, almost 4 and Thailand). In contrast, there are still nations percent of global mangrove cover was lost between where mangroves experienced significantly higher 1996–2010, from 158,000km2 to 151,000km2. Countries losses in the last decade than within the period 1996 such as Indonesia, Mexico, Australia, Myanmar, Cuba, to 2010 (Appendix 2, tables A5 and A7): Saudi Arabia, Nigeria, Bahamas, and Brazil lost the most mangrove Taiwan, Jamaica, Honduras, Nicaragua, Puerto Rico, forest in the late 1990s and early 2000s (Figure 4.1b). and Costa Rica experienced an increasing mangrove In this period, a few countries saw gains in mangroves loss rate between 5 percent and 25 percent. (Appendix 2, tables A4 and A6), which included countries in in Africa (Suriname, the Gambia, Guyana, The change in the coverage of mangroves is spatially Senegal, Gabon, Liberia, and Cameroon) and Central variable, which affects the protection provided by America (Nicaragua, Honduras, El Salvador, Panama, mangroves. The effect on flood height at the shore is and Costa Rica). the best way to consistently compare how mangrove loss (and gain) affects flooding. However, from 2010 to 2020, only 0.66 percent of global mangrove cover was lost. Many countries that FIGURE 4.1: MANGROVE AREA IN THE TOP 20 COUNTRIES WITH MORE MANGROVES IN 1996 a) Mangrove area [ha] BUILDING COASTAL RESILIENCE WITH MANGROVES 21 l R E S U LT S b) Changes in mangrove area [ha] Note: Panel (a) shows the total hectares of mangrove in 1996, 2010, and 2020. Panel b) shows the percentage change in mangrove area from two-time frames: 1996 to 2010 and 2010 to 2020 (top 20 countries with the largest mangrove cover shown). 4.2 OVERALL RESULTS: FLOOD RISK AND BENEFITS 4.2.1 Flood Risk and Mangrove Benefits Globally Aggregated FIGURE 4.2: GLOBAL FLOOD RISK AND MANGROVE FLOOD PROTECTION BENEFITS TO PEOPLE AND CAPITAL STOCK (1996– 2020) Note: The results are divided into two periods: 1996–2010, 2010–2020 22 BUILDING COASTAL RESILIENCE WITH MANGROVES R E S U LT S l We observed different trends in flood risk and flood Relative contribution of the socioeconomic benefits across two different periods (1996 to 2010 development and mangrove decline in flood risk: and 2010 to 2020). From 1996 to 2010, when mangrove The impact of flood risk caused by a combination of loss was more severe globally (about 4 percent), flood economic growth and changes in mangrove forests risk increased more than mangrove benefits (32– is not straightforward. This means that we cannot 122 percent increase in risk versus a 22–59 percent simply measure how much each factor contributes increase in benefits, see Figure 4.2). In contrast, to the overall increase in flood risk. For example, from 2010 to 2020, when mangrove loss slowed between 1996 and 2010, there was a 3.8 percent down (about 0.7 percent), flood risk increased less reduction in mangrove cover, a 21 percent increase than mangrove benefits (33–103 percent increase in population, and a 72 percent rise in capital stock in risk versus a 61–142 percent increase in benefits, (Figure 4.3). These changes led to a 32 percent see Figure 4.2). As coastal flooding increases slowed increase in risk to people and a 122 percent increase down, more people started living in mangrove- in losses of capital stock due to flooding. Then, from protected areas, resulting in high benefits and higher 2010 to 2020, despite a smaller mangrove loss (0.66 potential risks in the future if we experience similar percent), a 12 percent growth in population, and a losses to those experienced from 1996 to 2010. 40 percent increase in capital stock, there was still There are two reasons that explain this global a 33 percent increase in the risk to people and a 103 pattern change: percent rise in capital stock losses (Figure 4.3). This complex impact happens because more people and • Flood extent in 2010 increased significantly due to valuable assets are moving into areas that used to the 4 percent mangrove loss, rapidly reducing the be protected by mangroves but are now at risk of land area protected by mangroves and preventing flooding due to a larger flooded area. people from settling in mangrove-protected zones. • Flood extent did not increase significantly from 2010 to 2020, resulting in more people and economic opportunities in the mangrove- protected zones than in previous decades. BUILDING COASTAL RESILIENCE WITH MANGROVES 23 l R E S U LT S FIGURE 4.3: CHANGES IN TOTAL POPULATION, TOTAL CAPITAL STOCK, AND TOTAL AREA OF MANGROVES ACROSS 97 COUNTRIES (1996–2020) 7, 000 100,000 90,000 6, 000 Millions Billions +12% 80,000 5, 000 +21% +40% 70,000 60,000 4, 000 +72% 50,000 # of people km2 3, 000 USD 40,000 157,000 300,000 2, 000 -3.8% 151,000 20,000 -0.7% 1, 000 150,000 10,000 0 0 1990 1995 2000 2005 2010 2015 2025 Year Total pop Total stock Mangrove area 4.2.2 Flood Risk and Mangrove Benefits by Income Level FIGURE 4.4: FLOOD RISK AND MANGROVE BENEFITS TO PEOPLE AND CAPITAL STOCK BY INCOME LEVEL 24 BUILDING COASTAL RESILIENCE WITH MANGROVES R E S U LT S l If we analyze the effect of mangrove loss and If we focus on the two different periods (1996 socioeconomic growth by countries split by income to 2010 and 2010 to 2020) in Figure 4.4, we find level, we observe the following: differences in patterns than those observed at global scale (Figure 4.2): • While upper-middle income countries present the highest risk values (panels (a) and (d) in Figure • Upper-middle and low-income countries present 4.4), lower-middle income countries show the the same pattern observed at global scale, where highest values in terms of benefits provided by flood risk increases more than mangrove benefits mangroves (panels (b) and (e)). This means that in 1996 to 2010, and where flood risk increases less mangroves provide more benefits to lower-income than benefits in 2010 to 2020 (panel (a) versus (b) communities because in those countries more and panel (d) versus (e) in Figure 4.4). people live in the mangrove-protected belt. • High-income countries present a reverse pattern • In low-income countries, each hectare of in terms of people, where the number of people mangrove protects more people from flooding at flood risk increased less than the number of (panel (c)) than capital stock (panel (f)). people receiving benefits from mangroves in Accounting for the social impact of flooding 1996 to 2010 and flood risk increased more than allows us to highlight the importance of mangrove benefits in 2010 to 2020 (panel (a) versus (b) and habitat defending low-income communities. panel (d) versus (e) in Figure 4.4). This is because coastal population growth in the last decade in • The opposite happens in high-income countries, high-income countries tends to settle in areas that where the economic protection of mangroves are potentially flooding under extreme events. (panel (f)) is higher than the social protection (panel (c)). BUILDING COASTAL RESILIENCE WITH MANGROVES 25 l R E S U LT S 4.2.3 Flood Risk and Mangrove Benefits by World Bank Region FIGURE 4.5: FLOOD RISK AND MANGROVE BENEFITS TO PEOPLE AND CAPITAL STOCK BY WORLD BANK REGION By analyzing the time evolution of flood risk and value of this region exceeds $30,000/hectare in 2020, mangrove benefits by World Bank region, we found while the North America mangroves provide the that the East Asia and Pacific region concentrates second highest value of flood protection of $10.500/ 90 percent of the global flood risk and benefits to hectare (panel (f) in Figure 4.5). On the opposite side, people and capital stock (Figure 4.5). However, when the lowest unitary value of flood protection benefits these risks and benefits are weighed by the area of from mangroves occurs in Sub-Saharan Africa, where mangrove, the Middle East and North Africa region each hectare of mangrove protected $195 in capital stands out due to the lower number of mangrove stock in 2020; and Latin America and the Caribbean, forests providing valuable protection. The per hectare with a value of $880/hectare (panel (f) in Figure 4.5). 26 BUILDING COASTAL RESILIENCE WITH MANGROVES R E S U LT S l The Middle East and North Africa is the region with From 1996 to 2010, 0.98 million more people (22 the most people protected by hectare of mangrove percent) received flood reduction benefits from (panel (c) in Figure 4.5). In 2020, each hectare of mangroves annually (Figure 4.2). The countries mangrove protected an average of 10 people in the receiving the most flood risk reduction benefits Middle East and North Africa. In contrast, in Sub- to their coastal communities from mangroves in Saharan Africa and Latin America and the Caribbean, 2010 were Vietnam, India, China, Indonesia, and only 0.17 people received direct protection from the Philippines (Appendix 2, Table A2). In each of mangroves in 2020. these countries, mangroves are predicted to reduce flood risk to more than 250,000 people annually. The 4.2.4 Flood Risk and Mangrove countries where mangrove benefits increased the Benefits by Country most from 1996–2010 were Vietnam, India, Brazil, China, and Indonesia. In each of these countries, the 1996–2010 increase in people protected by mangroves exceeded Flood risk increased substantially from 1996 to 2010. 57,000 annually (Appendix 2, Table A4). However, 20 The increase in risk was driven by population and countries experienced a reduction in flood benefits, economic growth in coastal areas and countries, as such as Thailand, Guyana, Puerto Rico, Haiti, and well as from an increase in flooding due to mangrove Jamaica, with more than 1,300 people at greater risk decline on many coastlines. The countries with the in 2010 than in 1996 (Appendix 2, Table A4). most people annually affected by coastal flooding in From 1996 to 2010, we estimated that annual flood 2010 were China, Vietnam, India, the Philippines, reduction benefits from mangroves to capital and Indonesia. Each of these five countries had stock increased by $5.3 billion (59 percent) (Figure more than 400,000 people at risk of flooding annually 4.2). The highest mangrove benefits in 2010 were (Appendix 2, Table A2). The countries with the measured in Vietnam, China, Australia, the US, and greatest percentage increase in annual flood risk Indonesia, with all five countries receiving more than to people from 1996 to 2010 were Eritrea, Angola, $80 million in annual capital stock protection from Tonga, Qatar, and Thailand (Appendix 2, Table A6). mangroves (Appendix 2, Table A2). The increase All these countries had a more than two-fold risk to from 1996 to 2010 was particularly high in Vietnam, people in a 15-year period (1996–2010). China, Puerto Rico, India, and Indonesia, with The countries leading the economic risk ranking annual flood reduction benefits from mangroves to in 2010 were China, the US, Taiwan, Vietnam, and capital stock increasing by at least $246 per country Australia. Each of these five countries was predicted (Appendix 2, Table A4). Twenty-two countries, such to have more than $1.6 billion in flood losses annually as Thailand, the US, the United Arab Emirates, in 2010 (Appendix 2, Table A2). In relative terms, the Philippines, and Jamaica present the reverse economic flood risk increased the greatest in African pattern, where mangrove benefits declined between countries (Sudan, Angola, Tanzania, Mozambique), $5.5 million and $100 million (Appendix 2, Table A4). where they had at least 1.5 times more economic losses in the 15-year period 1996 to 2010 (Appendix 2, Table A6). BUILDING COASTAL RESILIENCE WITH MANGROVES 27 l R E S U LT S 2010–2020 Vietnam, where annual flood risk was predicted to exceed $4.5 billion per country in all these countries Between 2010 and 2020, the changes in flood risk in 2020 (Appendix 2, Table A3). The countries with were driven almost exclusively by socioeconomic the highest relative increase in the economic risk of growth rather than mangrove degradation. The total flooding between 2010 and 2020 were Grenada, the population in countries with mangroves increased by Gambia, Guyana, and Equatorial Guinea (Appendix 2, 12 percent while total capital stock grew by 40 percent Table A7); in each of these countries annual economic in this period. In contrast, mangrove degradation flood risk multiplied by more than 15 from 2010 to slowed down to 0.66 percent globally (Figure 4.3). 2020. In the same period, there are seven countries This overall pattern does not apply everywhere, as where flood risk to capital stock decreased. The top we will describe below. five countries are Sudan, Somalia, Kiribati, Seychelles, From 2010 to 2020, the countries with the most people and Timor Leste, all of them with more than annually affected by coastal flooding in 2020 are the 47 percent in flood risk reduction (Appendix 2, Table same as those in 2010. China, Vietnam, India, the A7). Three of these five countries did not experience Philippines, and Indonesia are the top five; all of them significant mangrove loss greater than 0.22 percent exceeding 700,000 people at risk of flooding annually from 2010 to 2020. (Appendix 2, Table A3). However, the highest percent From 2010 to 2020, 3.4 million (61 percent) more increases in flood risk to people were observed in people received annual flood reduction benefits from Grenada, the Gambia, Mayotte, and the US Virgin mangroves (Figure 4.2). The countries receiving the Islands (Appendix 2, Table A7). All these countries most benefits to coastal populations in 2020 were multiplied by 15 the risk in the 10-year period (2010– Vietnam, India, China, Bangladesh, and Indonesia;, 2020). In two nations, Eritrea and Kiribati, flood risk with all five countries exceeding 450,000 people to people decreased between 2010 and 2020, where we observed a 50 percent and 39 percent risk reduction protected annually by mangroves (Appendix 2, respectively (Appendix 2, Table A7). This decline Table A3). The countries where mangrove benefits is mostly explained by a reduction in the coastal increased the most from 2010 to 2020 were Vietnam, population, but it can also be partially explained Bangladesh, India, China, and Cameroon. In each of by the improvement in mangrove conservation and these countries, the increase in people protected by restoration. For example, Kiribati experienced no mangroves exceeded 145,000 more people protected mangrove loss between 2010 and 2020, according to in 2020 than in 2010 (Appendix 2, Table A5). However, the GMW dataset. there were seven countries where mangrove benefits declined in this five-year period, such as in Malaysia, From 2010 to 2020, the economic risk in mangrove Myanmar, Taiwan, Pakistan, Colombia, Jamaica, and areas increased more significantly than the social French Polynesia (Appendix 2, Table A5). risk. We predicted that annual economic flood risk increased by 103 percent ($91 billion) from 2010 to From 2010 to 2020, we estimated that annual flood 2020 across all mangrove coastlines (Figure 4.2), reduction benefits from mangroves to capital stock particularly in China, the US, Australia, Taiwan, and increased by $20.5 billion (142 percent) globally 28 BUILDING COASTAL RESILIENCE WITH MANGROVES R E S U LT S l (Figure 4.2). The countries receiving the highest The previous sections described how coastal economic benefits from mangroves in 2020 were protection services are measured and valued over China, Vietnam, Australia, the US, and India, with past periods. We now turn to the second component the five countries exceeding $1,620 million in flood of asset valuation, projections about the generation protection benefits every year (Appendix 2, Table of protection services by mangroves in the future. A3). The highest increase in benefits between 2010 This requires the following information: and 2020 was observed in China, Vietnam, Australia, • A discount rate: 4 percent is used to discount all the US, and Bangladesh, each with more than $1,170 other assets in CWON so we will apply the same million more benefits in 2020 than in 2010 (Appendix discount to mangrove assets. 2, Table A5). On the other hand, the four countries with the highest reduction in economic benefits of • Lifetime over which coastal protection services mangroves were Jamaica, Timor Leste, Belize, and will be valued: Following international accepted Pakistan, all of them with a benefit reduction up to approaches and best practice for valuing natural $7.5 million (Appendix 2, Table A5). capital and ecosystem assets (United Nations et al. 2014 and 2021; United Nations Department of Economics, 2021; Ramachandra, 2021), we 4.3 MANGROVE ASSET VALUE have assumed a 100-year project lifespan for AND CHANGING WEALTH these coastal habitat assets, which is consistent Following the same approach as the previous CWON with other infrastructure assets. Given that report, we are interested in the annual benefits these habitats have survived for millennia in from mangroves and the asset value of mangroves. these environments (and past sea levels), this is Some assets, like manufactured capital, are traded a reasonable assumption. Of course, it will be a in markets with market prices established that management choice to help keep these natural defenses in place over this time period. represent the value of the asset. But mangroves and the services they provide, like much of natural The present value (2020) of the global flood reduction capital, are not (yet) traded in markets. benefits from mangroves (100-year asset at 4 percent discount rate) is $855 billion. The countries with For assets like mangroves that do not have market the greatest present value of mangroves for flood prices, we rely on the economic definition of asset reduction are China, Vietnam, Australia, the US, and value: the discounted sum of the services mangroves India (Table 4.1). can be expected to generate over their lifetime. This approach has two major components, i) estimation The increase in the wealth of mangroves for flood of the value of services using non-market valuation risk reduction from 1996 to 2010 was estimated to be techniques like the expected damage function that over $130 billion (Table 4.1). The countries receiving was applied to estimate annual flood protection the greatest increases in wealth in absolute values benefits, and ii) projections of the generation of (US dollars) are Vietnam, China, Puerto Rico, India, these services in the future. and Indonesia (Appendix 2, Table A4), and in relative BUILDING COASTAL RESILIENCE WITH MANGROVES 29 l R E S U LT S values (percent) were Somalia, Djibouti, the Solomon The increase in the wealth of mangroves for flood Islands, Vietnam, and Equatorial Guinea (Appendix 2, risk reduction from 2010 to 2020 was estimated to be Table A6). Despite mangrove losses, most countries over $502 billion (Table 4.1). The countries receiving still saw increases in the asset value or wealth of the greatest increases in wealth in absolute values mangroves for flood risk reduction, largely because (US dollars) are China, Vietnam, Australia, the US, of overall increases in flood risk (growth of people and Bangladesh (Appendix 2, Table A5), and in and assets on coastlines). However, some nations (22) relative values (percent) were South Africa, Guyana, saw overall losses in mangrove wealth for flood risk Vanuatu, Grenada, and St Lucie (Appendix 2, Table reduction, including, most notably, Thailand, the US, A7). The countries that presented overall losses Timor Leste, the United Arab Emirates, and Ecuador (four) in mangrove wealth for flood risk reduction (Appendix 2, Table A4). were Jamaica, Timor Leste, Belize, and Pakistan (Appendix 2, Table A5). 30 BUILDING COASTAL RESILIENCE WITH MANGROVES R E S U LT S l TABLE 4.1: TOP 20 COUNTRIES IN MANGROVE ASSET VALUE (100YRS AT 4% DISCOUNT) AND ANNUAL EXPECTED BENEFIT FOR FLOOD PROTECTION ANNUAL ANNUAL EXPECTED CHANGE IN CHANGE IN EXPECTED BENEFIT PRESENT PRESENT PRESENT PRESENT PRESENT COUNTRY BENEFIT 2020 VALUE 1996 VALUE 2010 VALUE 2020 VALUE VALUE 2020 (CAPITAL 1996–2010 2010–2020 (# PEOPLE) STOCK) China 764,842 7,463,666,893 22,325,626,254 57,590,390,949 182,897,149,728 157.96% 217.58% Vietnam 3,673,309 7,418,425,588 21,322,836,434 80,523,754,704 181,788,511,594 277.64% 125.76% Australia 111,606 5,072,180,073 39,671,971,954 41,626,768,977 124,293,767,602 4.93% 198.59% The US 64,247 2,436,538,383 31,208,751,490 29,457,160,897 59,707,370,632 -5.61% 102.69% India 1,144,050 1,620,261,180 10,407,317,477 18,292,841,975 39,704,498,591 75.77% 117.05% Indonesia 489,596 1,532,916,500 13,879,234,598 19,908,597,438 37,564,117,295 43.44% 88.68% Bangladesh 621,358 1,354,057,650 2,260,971,180 4,473,494,384 33,181,181,355 97.86% 641.73% Mexico 161,141 1,311,000,444 11,581,011,139 13,741,068,381 32,126,064,565 18.65% 133.80% Puerto Rico 17,258 996,394,598 9,324,186,523 19,060,043,528 24,416,648,625 104.42% 28.10% Brazil 265,511 773,487,112 7,430,052,752 9,297,772,021 18,954,300,904 25.14% 103.86% Philippines 327,137 591,802,185 12,182,335,903 12,045,729,307 14,502,111,950 -1.12% 20.39% Suriname 69,710 505,220,635 4,950,349,657 7,299,683,830 12,380,431,154 47.46% 69.60% Japan 2,485 411,913,542 1,202,150,313 913,169,304 10,093,940,934 -24.04% 1005.37% U. A. Emirates 111,891 308,394,623 7,988,337,549 7,316,262,099 7,557,209,927 -8.41% 3.29% Taiwan 70,762 271,882,632 3,288,095,836 6,250,250,190 6,662,483,625 90.09% 6.60% New 1,384 251,396,603 865,343,094 2,833,844,905 6,160,473,504 227.48% 117.39% Caledonia New Zealand 1,186 223,056,032 1,023,807,610 1,104,894,922 5,465,987,840 7.92% 394.71% Thailand 23,646 211,367,279 4,626,181,186 2,160,197,680 5,179,554,960 -53.30% 139.77% Cuba 9,227 157,240,605 1,175,008,404 1,114,094,784 3,853,180,868 -5.18% 245.86% Ecuador 16,398 144,993,398 1,417,540,996 1,006,511,124 3,553,063,073 -29.00% 253.01% World total 8,884,573 34.88 222,281 352,638 854,824 58.64% 142.41% BUILDING COASTAL RESILIENCE WITH MANGROVES 31 l DISCUSSION 5 Discussion Traditional methods for measuring economic protected areas. This coastal population migration progress, such as GDP, mainly account for market puts substantially more people at risk of flooding if goods and services and overlook the non-market mangrove habitat is not maintained by nations. As contributions of nature. As a consequence, the more people settle in mangrove-protected areas, benefits provided by habitats are undervalued, as the contrast between the escalating flood risk in the only those services that can be directly taken from the absence of mangroves and the moderated flood risk ecosystem are assessed, such as the fish and timber with mangroves becomes more evident, resulting harvests. Essential non-market services such as flood in the observed trend of flood protection benefits protection and blue carbon sequestration, which outpacing flood risk for the first time. depend on habitat preservation, are seldom evaluated. Interestingly, the highest per hectare economic value To prevent the depletion of natural capital and ensure of mangroves occurs in high-income countries, the provision of crucial ecosystem services, policy while the highest per hectare number of people makers and land managers should incorporate protected takes place in lower-middle income these values into their decision-making processes. countries. This trend can be attributed to variations By conducting better valuations, decision-makers in both mangrove ecosystems and socioeconomic can better meet their management objectives for conditions. High-income countries often have more environmental conservation and hazard mitigation. developed infrastructure and industries that benefit In recent years, there have been updates to the data on from mangroves’ ecological services, whereas lower- mangroves, populations, and the PWT. These updates middle income countries may rely heavily on these have changed specific quantitative estimates of risk ecosystems for direct livelihoods and protection, even and mangrove benefits, but the overall patterns and though the economic value per unit area may be lower. country rankings have remained unchanged (i.e., no Currently, the global flood reduction benefits from qualitative changes in results). Unfortunately, flood mangroves have a present value of $855 billion. risks continue to rise in most countries owing to The countries with the greatest present value of continued growth of coastal populations and built- mangroves for flood reduction are China, Vietnam, up infrastructure. Australia, the US, and India. The overall change in However, mangrove losses did slow after 2010 and, the wealth of mangroves for flood risk reduction in some countries, mangroves are increasing. from 1996 to 2010 was estimated at over $130 billion, As a result, the value of mangroves for flood risk and from 2010 to 2020 over $502 billion. The rapid reduction continues to increase. For the first time, increase in asset value observed globally is attributed in the period 2010 to 2020, flood protection benefits to the dual impact of the protective function of from mangroves increased faster than flood risk. The mangroves and the high population density in phenomenon of flood protection benefits increasing mangrove-protected areas. Even narrow stretches of at a higher rate than flood risk can be attributed to the mangroves (some as small as 500m) play a significant significant population migration towards mangrove- role in diffusing a considerable amount of wave 32 BUILDING COASTAL RESILIENCE WITH MANGROVES DISCUSSION l and storm energy—up to 99 percent. Even a minor flooded area. These findings underscore the complex reduction in the extent of mangrove coverage can dynamics at play, highlighting the close interaction have dire consequences, rendering coastal regions between economic growth, changes in mangrove susceptible to substantial destruction during storm forests, and the risk of flooding. It is evident that a occurrences. Notably, flood protection does not deeper understanding of this non-linear interaction follow a straightforward linear pattern. is paramount for informed decision-making and effective risk management strategies. Countries that have seen a swift escalation in both risks and benefits since 2010 or 2015 also A limiting aspect of these global economic analyses house a substantial number of people within of changing wealth is the use of the PWT for the regions protected by mangroves. These valuable economic risk calculation, instead of other spatially ecosystems tend to be situated in environmentally explicit asset databases (such as GAR 15 used in delicate zones, which, coincidentally, attract Menendez et al. 2020). The PWT dataset provides human settlements due to their proximity to water aggregated capital stock data at the country level, resources and economic opportunities. which may not capture the spatial heterogeneity of capital distribution within specific coastal areas. The interplay between socioeconomic growth This lack of spatial resolution can limit the accuracy and changes in mangrove cover yields a non- of flood risk assessments that require fine-grained linear effect on flood risk. This complexity hinders information on asset distribution and can restrict a straightforward estimation of the individual the precision of flood risk estimates and hinder contributions of each factor to the overall change the identification of vulnerable areas. The dataset in flood risk. For instance, from 1996 to 2010 there assumes homogeneity in capital stock across sectors is a 4 percent decline in mangrove cover, a 21 within a country. However, coastal areas often percent surge in population, and a substantial 72 exhibit variations in infrastructure and capital percent increase in capital stock. These alterations assets based on their specific characteristics, such as resulted in a notable 32 percent escalation in risk population density, urbanization, and vulnerability. to the population and an even more pronounced Failure to account for such variations may lead to 122 percent amplification in capital stock losses inaccurate flood risk estimates. However, the PWT attributed to flooding. In the period from 2010 to 2020, dataset provides capital stock data over a longer time there is a relatively modest 0.66 percent reduction in horizon, allowing for analyses and comparisons mangrove cover, a 12 percent growth in population, across different decades or even centuries. This and a 40 percent expansion in capital stock. Despite extended temporal coverage is valuable when these seemingly lesser changes, the impact on flood examining long-term trends, assessing the evolution risk remains significant. During this period, there of capital accumulation, and understanding the was an additional 33 percent rise in the risk posed dynamics of economic development over time. to the population, accompanied by a substantial 103 percent increase in capital stock losses. The observed The values that we provide are based on process-based non-linear effect is intricately linked to the influx of evaluations of flood risk and mangrove benefits, and population and capital stock into areas previously the quality of the final estimates depends on the safeguarded by mangroves, rendering these regions available input data. Through our prior research more susceptible to flooding due to an expanded and sensitivity analyses, we have determined that BUILDING COASTAL RESILIENCE WITH MANGROVES 33 l DISCUSSION topography estimates (Menéndez et al. 2019) present • Resolution effects: The downscaling process can the largest source of uncertainty in coastal flood risk lead to variations in data quality and detail across assessments. In addition, nearshore bathymetry regions. Some areas may benefit from higher remains a significant gap, despite advances in resolution data, while others may experience a remote sensing that could enhance flood risk loss of information. assessments. While our results are based on global • Spatial bias: The global models used for mangrove distribution data that are being improved, downscaling may have inherent spatial biases or we still lack information on other critical factors, limitations that persist even at finer resolutions. such as mangrove density, species, and degree of Interpreters should be cautious of these biases degradation, which can affect mangroves’ ability to and their potential impact on the accuracy of reduce flooding. In addition, limitations for global subnational data. coastal flooding models still exist, including the inability to consider flooding as a multidimensional • Comparative analysis: When interpreting process and the difficulty in accurately representing subnational data aggregated from global sources, it flooding in small islands. is essential to maintain a comparative perspective. This allows for the identification of regional Mangroves provide a wide range of additional benefits, outliers, trends, and anomalies that may not be from fisheries to carbon mitigation, which can be apparent when solely focusing on national or valued and combined with flood reduction values global levels. to bolster the case for cost-effective investments in mangrove conservation and restoration. In Appendix 1, we have provided a comprehensive document detailing the intricacies of the data, along Considering these limitations, a pragmatic approach with a thorough explanation of the meticulous cleaning to using the data is advisable. Since economic and processing procedures that were undertaken. data is available at the national scale—the highest This supplementary resource aims to transparently resolution available—the focus should be on present the caveats associated with the data, shedding assessing relative changes between different years light on the measures employed to ensure its accuracy and scenarios, rather than focusing only on absolute and reliability. values. This approach helps mitigate the influence of underlying socioeconomic data variations, While this report primarily focuses on mangroves, which can substantially impact absolute figures. it is important to acknowledge that the assessment Interpreting subnational spatial data requires of coastal protection provided by natural assets is careful consideration of both its advantages and incomplete without considering the role of coral reefs limitations. Subnational data provides a finer level in (sub)tropical nations and marshes and oyster reefs in of detail and granularity compared to national-level other nations. Coral reefs serve as natural submerged data, which can offer valuable insights into regional breakwaters, effectively breaking waves and variations and disparities. when working with attenuating wave energy (Lowe et al. 2005; Monismith subnational spatial data derived from global models 2007). Research by Ferrario et al. (2014) highlights that downscaled to finer resolutions and then aggregated coral reefs can reduce incoming wave energy by up at the national scale, it introduces several nuanced to 97 percent. Coral reefs and other coastal habitats considerations for interpretation: should be included in future assessments. 34 BUILDING COASTAL RESILIENCE WITH MANGROVES R E C O M M E N DAT I O N S l 6 Recommendations These findings demonstrate the important role that many countries, and while best practices are still mangroves play in protecting against flood risks evolving, current approaches are well developed. and the significant economic value they hold. The • Efforts to restore mangroves should be expanded, results can inform decision-making on adaptation, and the results of this study can be used alongside development, and environmental conservation. The project costs to identify areas where mangrove effectiveness of mangroves in reducing flood risks restoration could yield significant returns on presents opportunities to support their conservation investment (e.g., Beck et al. 2022). and restoration. Using these values, mangrove management can be supported by public funding • Accounting for the flood reduction benefits of for hazard mitigation, disaster recovery, and climate mangroves as natural asset values can inform adaptation. Public and private funding opportunities hazard mitigation and disaster recovery spending. for mangrove management include blue bonds, infrastructure bonds, and insurance, among other • These valuations enable funding to be prioritized options (e.g., Airoldi et al. 2021). By assessing spatial for adaptation and help identify priority sites for variation in mangrove flood protection benefits, we mangrove coastal protection, either as standalone can identify areas where managing mangroves could solutions or as part of hybrid approaches that yield the greatest returns. combine mangrove natural defenses with built infrastructure. Overall, we have identified key considerations and recommendations for mangrove management for risk • Rigorous valuations support innovative finance reduction based in part on our results and our work opportunities for mangrove conservation and with field practitioners and decision-makers. restoration. For example, these results can inform risk models used by the insurance industry, which • Conserving existing mangroves is crucial as they underpin the development of habitat insurance, make a significant contribution to national wealth blue bonds, and insurance incentives such as and their value is expected to grow over time. community and household premium reductions. • Mangrove restoration is a well-established practice • These values can also support the development that can be effectively implemented on a large of catastrophic hazard bonds, resilience bonds, scale, with public and private funding. 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Estuarine, Coastal and https://seea.un.org/ecosystem-accounting. Shelf Science, 102, 11–23. 2020 BUILDING COASTAL RESILIENCE WITH MANGROVES 39 l APPENDIX 1 Appendix 1: Data Review Process OVERALL REMARKS • IMPORTANT: Any difference between CWON 2021 and CWON 2023 observed after 2015 must be ignored. The new CWON 2023 data between 2015-2020 is based on the 2020 data point, while the old CWON 2021 data between 2015-2020 is an extrapolation based on the period 2010-2015. • Total # of countries in CWON 2023 before cleaning: 121 (111 in CWON 2021) • Total # of countries in CWON 2023 AFTER cleaning: 97 (92 in CWON 2021) • Total # of data points that need to be revised (see analysis of outliers below): 6 ✓ YEM (1996), PHL (2015), DOM (2015), PAN (2010), SLV (2015), LKA (2015) • The missing countries in 2021 (10): ✓ BMU, COG, COK, GUM, KIR, MDV, MHL, PYF, TUV, WLF blue=to remove because less 100 ha mangroves • Countries removed from 2023 analysis (24): ✓ ABW, AIA, ASM, BHR, BMU, BRB, COD, COK, COM, DMA, EGY, GNB GUM, HKG, IRN, KNA, MAF, MDV, MHL, STP, TUV, VCT, VGB, WLF • Countries that will have different results in 2023 vs 2021 because there was no mangrove data in CWON 2021 (3): ✓ BEN, MAC, TGO (updated mangrove data in CWON 2023) • Countries that will have different results (>10% difference) in 2023 vs 2021 because of significant changes in mangroves or population data in 2023 vs CWON 2021 (6): ✓ MOZ, THA, SYC, VEN, SLB, JAM • Countries that will have different results (>10% difference) in 2023 vs 2021 because of significant changes in mangroves or stock data in 2023 vs CWON 2021 (8): ✓ SYC, SGP, ATG, BRN, SYC, HND, JAM, SDN • Countries with no population or economic data in PWT 10.0 (21): ✓ ANT, CUB, ERI, FSM, GLP, GUF, KIR, MTQ, MYT, NCL, PLW, PNG PRI, PYF, SLB, SOM, TLS, TON, VIR, VUT, WSM 40 BUILDING COASTAL RESILIENCE WITH MANGROVES APPENDIX 1 l DATA CLEANING 1. REMOVE: Countries with Ha_mang<100 • The global model isn’t sensitive to countries with few mangroves. • Countries (18): ABW, AIA, ASM, BHR, BMU, BRB, COK, COM, DMA, GUM KNA, MAF, MDV, MHL, STP, TUV, VGB, WLF • WHAT WE DO: We will check if these countries have risk values and neglect those. • Why? We don’t have accurate precision, and our global model is not sensitive to areas with very few mangroves. 2. REMOVE: Countries with Ha_mang<100 and no risk value • All the countries with less than 100 ha mangroves don’t have risk value • Countries (18): ABW, AIA, ASM, BHR, BMU, BRB, COK, COM, DMA, GUM KNA, MAF, MDV, MHL, STP, TUV, VGB, WLF • WHAT WE DO: Neglect these countries and remove them from the analysis. • Why? We don’t have accurate precision, and our global model is not sensitive to areas with very few mangroves. 3. REMOVE: Countries with risk to stock but no risk to people • This is a bug in the geospatial analysis. • Countries: GNB • WHAT WE DO: Remove this country from the analysis. • Why? Wrongly taking stock risk values from the contiguous country (Guinea). 4. REMOVE: Countries with benefits to stock but no benefits to people • This is a bug in the geospatial analysis. • Countries: COD, EGY, HKG, IRN, VCT • WHAT WE DO: Remove these countries from the analysis. • Why? Same as the previous point, wrongly taking stock risk values from the contiguous countries. 2020 BUILDING COASTAL RESILIENCE WITH MANGROVES 41 l APPENDIX 1 DIFFERENCE 2023 VS 2021 AFTER CLEANING Only differences > 10% 1. Risk to people These countries were not captured in the 2021 report. Country ‘BEN’ ‘BEN’ ‘BEN’ ‘TGO’ ‘TGO’ ‘TGO’ Year 1996 2010 2015 1996 2010 2015 % diff Inf Inf Inf Inf Inf Inf Abs diff 23699 33041 38421 1425 1452 1132 Differences are explained because: • Countries with no accurate mangrove data in the CWON 2021 but accurate now (BEN, TGO) 2. Benefits to people Country ‘BEN’ ‘BEN’ ‘BEN’ ‘TGO’ ‘LKA’ ‘SLB’ ‘PHL’ ‘JAM’ ‘MYT’ ‘FSM’ ‘JPN’ ‘MOZ’ ‘NZL’ ‘DOM’ ‘THA’ ‘SYC’ ‘VEN’ ‘FJI’ Year 1996 2010 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 % diff Inf Inf Inf Inf 362 72 71 37 33 29 28 15 15 14 13 12 11 10 Abs diff 164 166 3350 422 1686 1583 132912 970 59 59 213 3545 46 385 1472 72 1793 191 % diff 2445 3328 1631 5608 6 1 4 4 16 9 4 3 3 0 15 112 1 4 Mang % diff 0 0 0 1 1 100 0 1 NaN NaN 0 3 0 2 0 1 3 3 Pop Differences are explained because: • Countries with no accurate mangrove data in the CWON 2021 but accurate now (BEN, TGO) • Update in mangrove data and pop data since CWON 2021 release, most likely impacting coastal communities (MOZ, THA, SYC, VEN, SLB, JAM) • Very small absolute value difference (MYT, FSM, JPN, NZL, DOM, FIJ) What needs to be revisited: • Outliers: LKA (2015) and PHL (2015) 42 BUILDING COASTAL RESILIENCE WITH MANGROVES APPENDIX 1 l 3. Risk to stock Country ‘BEN’ ‘BEN’ ‘BEN’ ‘TGO’ ‘TGO’ ‘TGO’ ‘SGP’ ‘SGP’ Year 1996 2010 2015 1996 2010 2015 2010 1996 % diff Inf Inf Inf Inf Inf Inf 35 10 Abs diff 34374174 43522276 55097573 1990829 1694357 1374299 187249 368951 % diff Mang 2445 3328 1631 6416 6605 5608 14 18 % diff Stock 33 14 22 30 27 0 44 50 Differences are explained because: • Countries with no accurate mangrove data in the CWON 2021 but accurate now (BEN, TGO) • Update in mangrove data and pop data in this report vs CWON 2021, most likely impacting coastal communities (SGP) 4. Benefits to stock Country ‘BEN’ ‘BEN’ ‘BEN’ ‘TGO’ ‘YEM’ ‘SYC’ ‘PHL’ ‘DOM’ ‘SGP’ ‘ATG’ ‘BRN’ Year 1996 2010 2015 2015 1996 2010 2015 2015 1996 2010 1996 % diff Inf Inf Inf Inf 245 181 133 114 84 46 45 Abs diff 518272 426760 3052774 639490 487666 1471938 241443518 3125583 737901 1360157 683702 % diff Mang 2445 3328 1631 5608 17 112 4 0 18 5 1 % diff Stock 33 14 22 0 64 63 34 6 50 80 29 Country ‘PAN’ ‘ATG’ ‘SLV’ ‘SGP’ ‘SYC’ ‘BRN’ ‘LKA’ ‘HND’ ‘JAM’ ‘SDN’ Year 2010 1996 2015 2010 1996 2010 2015 2015 2015 1996 % diff 43 36 36 22 14 13 13 12 12 10 Abs diff 1979824 1146874 483601 432780 635805 176484 940306 640763 5575617 3705 % diff Mang 2 5 1 14 110 0 6 3 4 250 % diff Stock 4 71 11 44 63 34 37 14 66 637 Differences are explained because: • Countries with no accurate mangrove data in the CWON 2021 but accurate now (BEN, TGO) • Update in mangrove data and pop data since CWON 2021 release, most likely impacting coastal communities (SYC, SGP, ATG, BRN, SYC, HND, JAM, SDN) What needs to be revisited: 2020 • Outliers: YEM (1996), PHL (2015), DOM (2015), PAN (2010), SLV (2015), LKA (2015) BUILDING COASTAL RESILIENCE WITH MANGROVES 43 l APPENDIX 1 OUTLIERS YEM (1996): The most % difference observed in benefits to stock in 1996 is not significant in magnitude compared to the benefit to stock value observed in the following years. PHL (2015): The 2015 time point calculated in CWON 2021 is incorrect. The “without mangrove” scenario has been revisited, and the new 2015 value is correct. 44 BUILDING COASTAL RESILIENCE WITH MANGROVES APPENDIX 1 l DOM (2015): Same issue as in the Philippines. The 2015 value of the “without mangrove” scenario in CWON 2021 is incorrect and the new value obtained in CWON 2023 must be used. PAN (2010): Same issue as in the Philippines. The 2010 value of the “without mangrove” scenario in CWON 2021 is incorrect and the new value obtained in CWON 2023 must be used. BUILDING COASTAL RESILIENCE WITH MANGROVES 45 l APPENDIX 1 LKA (2015): The 2015 value of people benefits “without the mangrove” scenario in CWON 2021 is underestimated, resulting in an incorrect low benefit value. The new value obtained in CWON 2023 must be used. OTHER CAVEATS EXPLAINED 1. Countries with risk value>0 but benefits=0 • This is because mangroves are not located in populated areas. • Countries: COG, TGO • WHAT WE DO: Nothing; this is something that can happen. After reviewing these countries, we observed that no mangrove-protected areas are populated. 2. Countries with no pop and economic data but risk value • This is because we still don’t have socioeconomic data from the PWT, but we have risk to people because the GHS pop dataset used in the analysis has people distributed in these countries: • Countries (21): ANT, CUB, ERI, FSM, GLP, GUF, KIR, MTQ, MYT, NCL, PLW, PNG. PRI, PYF, SLB, SOM, TLS, TON, VIR, VUT, WSM • WHAT WE DO: We filled the gaps with updated PWT in the countries in green. The countries in black don’t have population or capital stock data yet. 46 BUILDING COASTAL RESILIENCE WITH MANGROVES APPENDIX 1 l 3. Countries that increase rapidly after 2010 • ANT, ATG, BEN, BRN, CIV, COG, DJI, ERI, GAB, GRD, JPN, KIR, LBR, NZL, PLW, PYF 4. Countries that increase rapidly after 2015 • ANT, AUS, BGD, CIV, CMR, COG, DOM, ECU, GMB, GNQ, GRD, LCA, NIC, PER, QAT The rapid increase in stock risk observed in these countries is attributed to the dual impact of the protective function of mangroves and the high population density in mangrove-protected areas: • Protective Role of Mangroves: Like a seawall that is overtopped when a flood height level is exceeded, even narrow strips of mangroves (as small as 500m) can dissipate a significant portion of wave and storm energy, up to 99 percent. Even a small reduction in mangrove coverage can lead to catastrophic consequences, leaving the coastal areas vulnerable to severe damage during storm events. In the countries listed that experience a rapid increase in risk and benefits, we identified key coastal areas with narrow strips of mangroves where flooding was triggered after 2010 and 2015. Flood protection is not linear. • High Population Density in Mangrove Protected Areas: The countries experiencing a rapid increase in stock risk after 2010 or 2015 also exhibit a high number of people living in mangrove- protected areas. Mangroves are often located in ecologically sensitive regions, which also happen to be attractive areas for human settlements due to their proximity to water resources and livelihood opportunities. BUILDING COASTAL RESILIENCE WITH MANGROVES 47 APPENDIX 2 l Appendix 2: Data Tables TABLE A1: FLOOD RISK AND FLOOD REDUCTION BENEFITS OF MANGROVES ACROSS 97 COUNTRIES IN 1996 1996 POP STOCK ID COUNTRY ISO3 YEAR MANG HA POP POP EXP POP RISK POP BEN STOCK STOCK EXP STOCK RISK STOCK BEN PV 100yr 4% BEN HA BEN HA 34 China CHN 1996 29066 1251636186 10952010 10604835 428470 14,74 5169485084548 39516162493 23042962985 911064157 22 325 626 254 31344,67 179 Vietnam VNM 1996 198962 76068743 1530797 1359172 2000395 10,05 214129906818 1235672038 571426313 870142310 21 322 836 434 4373,41 79 India IND 1996 510157 982365243 585153 463970 625398 1,23 2409260145291 941166913 341884558 424701812 10 407 317 477 832,49 135 Philippines PHL 1996 285316 71401749 481894 411847 214885 0,75 356043205229 2315826893 792246178 497136764 12 182 335 903 1742,41 78 Indonesia IDN 1996 3138690 200756590 520634 344350 306753 0,1 1326463183831 3052363830 635608884 566383806 13 879 234 598 180,45 169 Taiwan TWN 1996 197 21441432 223872 201491 51363 260,73 478732074637 7524647650 1869931506 134180615 3 288 095 836 681119,87 114 Myanmar MMR 1996 596955 44452206 250183 198328 76106 0,13 49952821264 33433992 12591490 4363933 106 938 174 7,31 25 Brazil BRA 1996 1148395 164614688 181016 167462 71223 0,06 1512112407908 2620583832 730392604 303205593 7 430 052 752 264,03 16 Bangladesh BGD 1996 527667 117649932 162224 136294 161867 0,31 223833397553 183252954 87590645 92265712 2 260 971 180 174,86 174 United States USA 1996 241100 268335003 132078 120101 32580 0,14 12778466659459 22857664786 7277327214 1273566732 31 208 751 490 5282,32 United Arab 5 ARE 1996 7591 2539126 85847 78235 38101 5,02 241257321709 12767127465 510208951 325988079 7 988 337 549 42944,02 Emirates 177 Venezuela VEN 1996 302537 22385650 80524 74482 11953 0,04 - 92 Cambodia KHM 1996 73406 10982917 70244 64045 35576 0,48 16862151815 54226683 22499299 20602220 504 857 380 280,66 122 Malaysia MYS 1996 590057 21017613 62369 48310 27050 0,05 292432185232 883764750 136971054 88054789 2 157 782 516 149,23 72 Guyana GUY 1996 39190 760795 43361 36794 36035 0,92 2751373772 302688563 90807 94813 2 323 392 2,42 125 Nigeria NGA 1996 920270 110668794 55582 35130 31528 0,03 384814447644 272351479 71737782 46054017 1 128 553 640 50,04 108 Madagascar MDG 1996 282644 13902688 37422 34522 9718 0,03 23797772325 40952686 17130979 6745564 165 300 039 23,87 110 Mexico MEX 1996 1063983 93147044 41261 29193 64623 0,06 1402920029426 976238225 329784139 472597903 11 581 011 139 444,18 64 Ghana GHA 1996 18645 17462496 33950 26640 12224 0,66 50578879417 201858068 90582434 46736826 1 145 285 874 2506,67 9 Australia AUS 1996 1051177 18189277 28685 23843 32329 0,03 643221785094 4227517611 1338157923 1618933833 39 671 971 954 1540,12 14 Benin BEN 1996 3054 6094259 30496 23699 164 0,05 10641466497 79518215 33990452 512486 12 558 469 167,81 144 Saudi Arabia SAU 1996 9027 19033845 27348 23306 3971 0,44 852733583618 811353376 131214725 18957358 464 550 039 2100,07 131 Oman OMN 1996 481 2236654 22888 18853 3450 7,17 65669326462 494625624 78944233 5247658 128 593 854 10909,89 117 Mozambique MOZ 1996 325865 15960442 19821 18830 24389 0,07 8529292846 3593197 1606752 2866283 70 238 262 8,8 184 Somalia SOM 1996 6083 7472450 20634 18683 149 0,02 3487368934 9629823 8719297 69538 1 704 029 11,43 163 Thailand THA 1996 274943 60130186 54408 18306 30207 0,11 656305777631 811189417 131884749 188785202 4 626 181 186 686,63 39 Colombia COL 1996 299604 37076387 21151 18073 14378 0,05 298293921856 227682040 61802282 35358071 866 449 494 118,02 35 Côte d’Ivoire CIV 1996 6901 14665127 19241 14890 2532 0,37 42943611117 53036015 13902328 3671123 89 960 865 531,97 132 Pakistan PAK 1996 107096 127349290 18381 13954 26719 0,25 431043097352 32898784 13185260 14919116 365 592 923 139,31 180 Yemen YEM 1996 2007 15469274 15034 13551 22 0,01 47190213839 122660860 42807284 571877 14 013 845 284,94 BUILDING COASTAL RESILIENCE WITH MANGROVES 48 APPENDIX 2 l 88 Japan JPN 1996 1025 126644094 15436 13447 1491 1,45 4561300434281 2419961123 542998754 49057350 1 202 150 313 47860,83 146 Senegal SEN 1996 233472 8912861 17369 11037 7127 0,03 20977338722 42343466 14571306 9983782 244 652 568 42,76 51 Dominican DOM 1996 19881 7952763 10030 8778 1123 0,06 60753381133 71422063 30261167 4010098 98 267 447 201,71 Republic 187 Kiribati KIR 1996 146 82832 7917 7402 346 2,37 283413912 27088419 25326320 1183857 29 010 415 8108,61 185 PuertoRico PRI 1996 8550 3724655 9149 7011 18565 2,17 76339157370 187514535 143694874 380501404 9 324 186 523 44503,09 86 Jamaica JAM 1996 10551 2558637 8733 6890 4626 0,44 25502273532 221413680 84185356 53758474 1 317 351 351 5095,11 186 Cuba CUB 1996 378885 10963031 8097 6632 5114 0,01 102791263482 75918864 62182772 47949743 1 175 008 404 126,55 118 Mauritania MRT 1996 1461 2372901 8915 6157 4801 3,29 12618968152 18244867 6207254 5193456 127 265 634 3554,73 76 Haiti HTI 1996 20990 7887304 6538 5430 11886 0,57 14635512145 15287876 5744449 13244329 324 552 269 630,98 188 SolomonIslands SLB 1996 52098 386069 4790 4159 800 0,02 319912860 3969194 3446321 662913 16 244 682 12,72 53 Ecuador ECU 1996 170891 11703174 6718 3973 7881 0,05 105853434221 130946024 28158393 57847013 1 417 540 996 338,5 158 Seychelles SYC 1996 227 77594 3883 3702 140 0,62 1051999110 140270506 33943560 3941509 96 586 674 17363,48 170 U.R. of Tanzania: TZA 1996 124413 29630526 5381 3680 7128 0,06 39216143965 5045154 1813584 3902200 95 623 407 31,36 Mainland 90 Kenya KEN 1996 57085 28589451 4480 3612 11736 0,21 113272373079 9925279 3953566 13044870 319 664 526 228,52 100 Sri Lanka LKA 1996 25861 18367288 5683 3477 2867 0,11 102812165880 24633586 7837000 6014139 147 376 470 232,56 19 Bahamas BHS 1996 164813 283978 3102 2894 693 0 8046482546 216342054 83454246 24120873 591 081 969 146,35 189 PapuaNew- PNG 1996 467722 4785895 4330 2612 3490 0,01 11164843503 10101302 6093442 8141696 199 512 252 17,41 Guinea 42 Costa Rica CRI 1996 38153 3632362 2640 2548 1090 0,03 42633054668 33143048 14991188 6259859 153 397 839 164,07 65 Guinea GIN 1996 262094 7463782 3551 2193 7351 0,03 17397195316 2575623 1003020 2922949 71 626 862 11,15 134 Peru PER 1996 13386 24753824 2403 2112 71 0,01 154839364798 20286338 9995109 227682 5 579 347 17,01 145 Sudan SDN 1996 1488 24782383 6704 2111 10 0,01 46872516272 1721176 342386 29458 721 868 19,8 130 New Zealand NZL 1996 29027 3717349 2360 1975 779 0,03 101942638469 219787874 103597061 41779541 1 023 807 610 1439,33 133 Panama PAN 1996 155860 2796291 1957 1743 1886 0,01 37312068291 28325504 10704552 8485873 207 946 309 54,45 27 Brunei Darus- BRN 1996 18434 304622 2380 1658 780 0,04 20100671610 220416669 20805850 1860202 45 584 248 100,91 salam 59 Fiji FJI 1996 48541 784386 1842 1615 1428 0,03 7594613415 10168468 4032108 4691945 114 976 108 96,66 48 Djibouti DJI 1996 1000 643654 2637 1586 85 0,09 1253541723 2441388 181171 27509 674 108 27,51 162 Togo TGO 1996 1238 4348805 2386 1425 0 0 6646678002 5133260 1975868 0 - 0 190 NewCaledonia NCL 1996 33008 197564 1583 1367 966 0,03 7222113698 57867860 49971804 35312921 865 343 094 1069,83 74 Honduras HND 1996 90153 5874809 1355 1087 1788 0,02 27342997566 7280634 2801232 5156541 126 361 032 57,2 191 Guadeloupe GLP 1996 3413 0 1367 1082 789 0,23 0 0 0 0 - 0 181 South Africa ZAF 1996 2566 42241011 1140 1014 33 0,01 401155337743 13133499 7488676 75713 1 855 347 29,51 192 FrenchGuiana GUF 1996 76023 0 1000 975 658 0,01 0 0 0 0 - 0 148 Sierra Leone SLE 1996 182270 4312666 3431 950 3301 0,02 5455463412 3091113 543196 1669979 40 922 834 9,16 152 Suriname SUR 1996 81563 448213 1803 940 26334 0,32 4182226847 30826452 7345366 202013869 4 950 349 657 2476,78 195 Vanuatu VUT 1996 1585 174714 946 930 21 0,01 596502907 3229803 3175176 71698 1 756 959 45,24 BUILDING COASTAL RESILIENCE WITH MANGROVES 49 APPENDIX 2 l MicronesiaFed- 194 FSM 1996 9084 110785 987 878 212 0,02 0 0 0 0 - 0 eratedStatesof 193 TimorLeste TLS 1996 1373 831269 999 870 1524 1,11 27214769735 32706085 28482777 49893968 1 222 651 636 36339,38 Antigua and 8 ATG 1996 860 70173 883 827 367 0,43 887477907 36315031 8301412 4405696 107 961 576 5122,9 Barbuda 149 El Salvador SLV 1996 52696 5689938 884 780 381 0,01 34428063067 4820840 2417587 1411454 34 587 679 26,78 22 Belize BLZ 1996 71678 213664 804 738 3725 0,05 1039463186 6374885 3147061 18016148 441 485 689 251,35 197 Eritrea ERI 1996 9303 2264073 681 681 1141 0,12 0 0 0 0 - 0 98 Liberia LBR 1996 20647 2160478 2437 648 1560 0,08 769809086 4059217 558342 1378195 33 772 667 66,75 36 Cameroon CMR 1996 210832 13970813 990 637 6034 0,03 36806291639 2789470 651513 9714234 238 047 294 46,08 126 Nicaragua NIC 1996 89009 4741578 669 538 771 0,01 19394689687 4074568 1508706 2221245 54 431 606 24,96 38 Congo COG 1996 2481 2785810 458 458 0 0 10156227143 3505331 2213260 0 - 0 200 Martinique MTQ 1996 1921 0 459 446 42 0,02 0 0 0 0 - 0 Equatorial 68 GNQ 1996 33836 515853 484 397 35 0 2583910335 1358673 513859 217805 5 337 311 6,44 Guinea Trinidad and 166 TTO 1996 11867 1257549 414 386 792 0,07 17749195244 2451489 1677183 2525316 61 882 866 212,8 Tobago 2 Angola AGO 1996 53180 14400719 470 366 411 0,01 47896152703 4471571 2371291 1700985 41 682 636 31,99 147 Singapore SGP 1996 16954 3638187 512 360 183 0,01 161990287534 46725153 3282808 1521302 37 279 504 89,73 199 Samoa WSM 1996 234 176713 464 297 87 0,37 0 0 0 0 - 0 99 Saint Lucia LCA 1996 163 148834 289 283 15 0,09 1523584982 5393593 3290866 181294 4 442 609 1112,23 61 Gabon GAB 1996 195378 1112955 360 237 347 0 20138303520 5477582 1047139 1303022 31 930 553 6,67 201 FrenchPolynesia PYF 1996 122 235189 321 209 144 1,18 - 71 Guatemala GTM 1996 29678 10646674 214 207 331 0,01 69970120891 1420540 1042316 1188422 29 122 280 40,04 Turks and Caicos 160 TCA 1996 20631 16926 201 176 142 0,01 204648198 5763406 2908073 2455669 60 176 166 119,03 Islands 140 Qatar QAT 1996 433 522531 109 101 144 0,33 36783433466 7741979 2411838 3054404 74 848 167 7054,05 UnitedStatesVir- 203 VIR 1996 281 108095 135 82 77 0,27 0 0 0 0 - 0 ginIslands 206 Palau PLW 1996 5656 17732 87 78 12 0 0 0 0 0 - 0 202 Tonga TON 1996 1055 100195 142 53 456 0,43 504796739 715416 267022 2297393 56 297 613 2177,62 207 Mayotte MYT 1996 677 0 54 43 117 0,17 0 0 0 0 - 0 NetherlandsAn- 209 ANT 1996 292 0 16 16 98 0,34 0 0 0 0 - 0 tilles 66 Gambia GMB 1996 74099 1164091 555 14 638 0,01 2915091706 551269 9018 338159 8 286 586 4,56 70 Grenada GRD 1996 210 101001 4 4 9 0,04 880388905 90171 37565 125007 3 063 296 595,27 44 Cayman Islands CYM 1996 4684 34065 0 0 0 0 2453120847 0 0 0 - 0 China, Macao 105 MAC 1996 134 393373 0 0 0 0 18337045569 0 0 0 - 0 SAR 120 Mauritius MUS 1996 553 1141948 0 0 0 0 11349000312 0 0 0 - 0 BUILDING COASTAL RESILIENCE WITH MANGROVES 50 APPENDIX2 l TABLE A2: FLOOD RISK AND FLOOD REDUCTION BENEFITS OF MANGROVES ACROSS 97 COUNTRIES IN 2010 2010 POP POP STOCK ID COUNTRY ISO3 YEAR MANG HA POP POP EXP POP RISK STOCK STOCK EXP STOCK RISK STOCK BEN PV 100yr 4% BEN BEN HA BEN HA 34 China CHN 2010 24383 1368810615 14584698 14171614 486224 19,94 14446856973732 202060401635 64918046096 2350148676 57 590 390 949 96384,72 79 India IND 2010 495010 1234281170 806733 644363 774727 1,57 5772294531564 2600992178 733721444 746494296 18 292 841 975 1508,04 174 United States USA 2010 233110 309011475 154658 141312 36899 0,16 18069244155380 32524514401 8965434799 1202087823 29 457 160 897 5156,74 78 Indonesia IDN 2010 2941621 241834215 626755 435851 364345 0,12 2124785018064 5836084091 1106307898 812430045 19 908 597 438 276,18 25 Brazil BRA 2010 1127630 195713635 181407 167734 136150 0,12 2333846837042 3101619339 823564625 379423481 9 297 772 021 336,48 132 Pakistan PAK 2010 92278 179424641 30753 22992 30716 0,33 728280171563 60689017 13479473 16161086 396 027 396 175,13 125 Nigeria NGA 2010 895792 158503197 87349 56413 39402 0,04 921748695649 367104777 90202309 34497476 845 360 615 38,51 16 Bangladesh BGD 2010 530019 147575430 257493 205842 160900 0,3 482195626425 639539152 277450025 182554359 4 473 494 384 344,43 88 Japan JPN 2010 1002 128542353 16489 14455 1003 1 4980063550992 3046502318 668524486 37264613 913 169 304 37190,23 110 Mexico MEX 2010 1002966 114092963 54790 41671 75238 0,08 1964671677829 1607402218 497507398 560745519 13 741 068 381 559,09 135 Philippines PHL 2010 270166 93966780 567216 500565 249445 0,92 638113930890 3117779988 877535502 491562122 12 045 729 307 1819,48 179 Vietnam VNM 2010 187530 87967651 1766447 1553534 2459564 13,12 528931202305 4966744132 2064699763 3286013385 80 523 754 704 17522,6 163 Thailand THA 2010 258898 67195028 74533 55307 8521 0,03 1008888839845 1224148952 296940857 88153347 2 160 197 680 340,49 181 South Africa ZAF 2010 2616 51216964 1306 1158 39 0,01 620348171335 17102393 9675366 100823 2 470 668 38,54 114 Myanmar MMR 2010 552084 50600818 343004 276050 86041 0,16 217607973209 254676149 47728715 14605052 357 896 785 26,45 39 Colombia COL 2010 289011 45222700 32704 28750 18489 0,06 454385085611 390738781 90454480 44572515 1 092 249 435 154,22 U.R. of Tanzania: 170 TZA 2010 121556 43094401 8677 6338 7939 0,07 87287318838 14150753 5399291 6912988 169 402 764 56,87 Mainland 90 Kenya KEN 2010 56274 42030676 7681 6727 11930 0,21 184872696083 18171321 7978539 11298533 276 870 540 200,78 145 Sudan SDN 2010 832 34545013 9454 4263 21 0,03 108619347133 15808043 2375067 42952 1 052 539 51,63 134 Peru PER 2010 11168 29027674 3191 2765 91 0,01 294321054297 39880186 17093842 434410 10 645 217 38,9 Venezuela (Boli- 177 varian Republic VEN 2010 299267 28439940 110079 100512 16086 0,05 - of) 122 Malaysia MYS 2010 584776 28208035 70672 48361 52769 0,09 527114109460 1261493186 158256759 100018357 2 450 949 738 171,04 144 Saudi Arabia SAU 2010 8112 27421461 41229 34961 5625 0,69 1255991382365 1616170391 196394747 21347293 523 115 394 2631,57 64 Ghana GHA 2010 17961 24779619 41756 32905 14766 0,82 104314081136 278614909 125717540 63635518 1 559 388 305 3542,98 117 Mozambique MOZ 2010 314551 23531574 25416 23238 27501 0,09 25520952048 9131978 3915405 6102208 149 534 601 19,4 2 Angola AGO 2010 52341 23356246 2459 2041 1476 0,03 131616570361 21449056 7290192 5621128 137 745 736 107,39 180 Yemen YEM 2010 1675 23154855 23313 21104 33 0,02 91774642577 326965294 94138600 620486 15 205 009 370,44 169 Taiwan TWN 2010 212 23140948 210227 186109 83358 393,2 906267082174 12125102815 2341735210 255060210 6 250 250 190 1203114,2 9 Australia AUS 2010 1000126 22154679 34063 28652 39175 0,04 1015941161100 6807717905 1605608107 1698705190 41 626 768 977 1698,49 108 Madagascar MDG 2010 275202 21151640 61812 56818 17118 0,06 36200947474 74240070 27919724 9856259 241 527 617 35,81 35 Côte d’Ivoire CIV 2010 6873 20532950 25464 20592 2663 0,39 56103173740 60885325 16670224 3283295 80 457 141 477,71 BUILDING COASTAL RESILIENCE WITH MANGROVES 51 APPENDIX 2 l 36 Cameroon CMR 2010 211898 20341241 1502 898 11990 0,06 64309759552 4544770 968674 10168552 249 180 357 47,99 100 Sri Lanka LKA 2010 17938 20261737 6404 3860 3102 0,17 210171711947 47200552 10479399 10579614 259 253 430 589,79 53 Ecuador ECU 2010 159993 15011117 8692 7683 7680 0,05 164418793296 168935748 36653011 41073706 1 006 511 124 256,72 71 Guatemala GTM 2010 29304 14630417 248 225 380 0,01 114578203710 1954303 1431278 1585539 38 853 632 54,11 92 Cambodia KHM 2010 68007 14312212 77276 73030 39337 0,58 49146059442 126045593 56069026 45355713 1 111 441 702 666,93 146 Senegal SEN 2010 234932 12678148 27342 17289 11430 0,05 37557726405 79478324 15997476 19351007 474 196 407 82,37 184 Somalia SOM 2010 5965 12026649 23743 21408 904 0,15 4160448440 8213554 7405794 312726 7 663 350 52,43 186 Cuba CUB 2010 350286 11290417 9018 7076 5431 0,02 94514323693 75491470 59234602 45463980 1 114 094 784 129,79 65 Guinea GIN 2010 260150 10192176 3784 2160 9089 0,03 27507941015 4727164 1707976 6249574 153 145 805 24,02 76 Haiti HTI 2010 19114 9949322 13597 12302 9722 0,51 16072658887 48966039 20698229 19385464 475 040 776 1014,2 Dominican 51 DOM 2010 19390 9695121 14292 12631 1294 0,07 127578324321 197898473 55712393 6360643 155 867 550 328,04 Republic 14 Benin BEN 2010 2742 9199259 41889 33041 166 0,06 19074085139 100595504 43036432 421996 10 341 012 153,9 United Arab 5 ARE 2010 7717 8549988 232333 213573 72943 9,45 456217128221 17951901093 450554648 298562024 7 316 262 099 38688,87 Emirates 74 Honduras HND 2010 92650 8317470 2328 2051 2025 0,02 45929748146 15462756 6534975 7237867 177 363 924 78,12 PapuaNew- 189 PNG 2010 467085 7583269 4771 2957 3908 0,01 28486216511 17922052 11107840 14680230 359 739 021 31,43 Guinea 162 Togo TGO 2010 1274 6421679 2365 1452 0 0 8541388683 4237072 1681624 0 - 0 148 Sierra Leone SLE 2010 180334 6415634 4003 1352 3343 0,02 9980542633 2680981 549464 1298506 31 819 888 7,2 149 El Salvador SLV 2010 53459 6183875 788 659 420 0,01 47421516609 6644294 3262820 2119818 51 946 138 39,65 126 Nicaragua NIC 2010 91642 5824065 873 717 949 0,01 31108024102 5884245 2139419 2821134 69 131 886 30,78 147 Singapore SGP 2010 16363 5131172 1209 1006 317 0,02 347963239672 149117480 327786 2243956 54 988 140 137,14 42 Costa Rica CRI 2010 38496 4577378 2792 2695 1496 0,04 79169997461 54694026 17673123 5731427 140 448 613 148,88 130 New Zealand NZL 2010 30193 4370062 2749 2324 809 0,03 149768077644 336043977 144691584 45088552 1 104 894 922 1493,34 38 Congo COG 2010 2449 4273731 744 744 0 0 18075276601 6161965 2192821 0 - 0 98 Liberia LBR 2010 20701 3891356 3972 1087 2652 0,13 5091835958 4477524 636782 1575033 38 596 182 76,08 185 PuertoRico PRI 2010 8625 3721525 11230 9566 14972 1,74 193334928149 583403643 496958081 777802257 19 060 043 528 90179,97 133 Panama PAN 2010 156292 3642687 2591 2193 3010 0,02 80489135288 59395261 9508379 6462343 158 359 709 41,35 118 Mauritania MRT 2010 1359 3494195 10399 7180 5855 4,31 18002350551 30747988 10718815 9012928 220 861 792 6632,03 197 Eritrea ERI 2010 7243 3147727 9180 9180 0 0 0 0 0 0 - 0 131 Oman OMN 2010 311 3041434 40607 33764 5185 16,67 100989599199 1284848368 107246179 3344165 81 948 760 10752,94 86 Jamaica JAM 2010 10621 2810460 8767 7605 3282 0,31 26747398296 244536034 108789282 46400510 1 137 044 451 4368,75 140 Qatar QAT 2010 434 1856327 443 397 580 1,34 203025574388 53303315 4903216 4547543 111 437 537 10478,21 66 Gambia GMB 2010 75168 1793196 405 7 560 0,01 4906805141 585784 6724 414732 10 163 007 5,52 61 Gabon GAB 2010 195438 1624140 384 274 422 0 21594976839 4982945 972298 1455760 35 673 397 7,45 Trinidad and 166 TTO 2010 11395 1328147 712 686 415 0,04 42641951800 5277469 1955300 632082 15 489 169 55,47 Tobago 120 Mauritius MUS 2010 391 1247955 0 0 0 0 21047836494 0 0 0 - 0 BUILDING COASTAL RESILIENCE WITH MANGROVES 52 APPENDIX 2 l 193 TimorLeste TLS 2010 1347 1088486 1385 1174 1522 1,13 14129126078 17978035 15239143 19756368 484 129 778 14666,94 Equatorial 68 GNQ 2010 34004 943639 649 527 59 0 33789655963 20353066 442089 755486 18 513 184 22,22 Guinea 59 Fiji FJI 2010 48767 859818 1356 1128 1813 0,04 9392392987 9415484 3950336 6732854 164 988 581 138,06 48 Djibouti DJI 2010 693 840198 4293 2554 110 0,16 2533400848 10732907 769688 115938 2 841 061 167,3 72 Guyana GUY 2010 40390 749436 53542 43699 29326 0,73 6101135187 421116891 126335 82746 2 027 691 2,05 188 SolomonIslands SLB 2010 52275 540394 6963 5658 1444 0,03 951595572 12261350 9963337 2542782 62 310 870 48,64 China, Macao 105 MAC 2010 124 538219 0 0 0 0 52564328374 0 0 0 - 0 SAR 152 Suriname SUR 2010 84943 529131 3888 2513 32112 0,38 7295609793 94614808 28486494 297885498 7 299 683 830 3506,89 Brunei Darus- 27 BRN 2010 18582 388646 2938 2138 986 0,05 23901175438 274706284 23770897 1305590 31 993 482 70,26 salam 19 Bahamas BHS 2010 142069 354942 4342 4106 821 0,01 10700438474 471402538 168473459 39444371 966 584 272 277,64 22 Belize BLZ 2010 67513 322464 1438 1299 5490 0,08 1978480722 12936012 5276016 21180817 519 035 899 313,73 201 FrenchPolynesia PYF 2010 119 283788 383 256 162 1,36 - 190 NewCaledonia NCL 2010 32148 249750 1759 1503 1061 0,03 27221465561 191721954 163819270 115643543 2 833 844 905 3597,22 195 Vanuatu VUT 2010 1601 245453 857 838 19 0,01 1849825928 6458674 6315483 143191 3 508 895 89,44 199 Samoa WSM 2010 240 194672 380 270 61 0,25 0 0 0 0 - 0 99 Saint Lucia LCA 2010 162 174085 284 278 16 0,1 2022826606 8961406 3342898 405282 9 931 435 2501,74 UnitedStates- 203 VIR 2010 273 108357 132 76 78 0,29 0 0 0 0 - 0 VirginIslands 187 Kiribati KIR 2010 146 107995 7067 6418 424 2,9 431646817 28246197 25652199 1694692 41 528 426 MicronesiaFed- 194 FSM 2010 8997 107588 752 666 137 0,02 0 0 0 0 - 0 eratedStatesof 202 Tonga TON 2010 1071 107383 261 221 341 0,32 964490813 2344245 1984974 3062788 75 053 617 2859,75 70 Grenada GRD 2010 209 106233 3 3 10 0,05 1382088016 127813 54350 208927 5 119 756 999,65 158 Seychelles SYC 2010 227 91264 4182 4017 149 0,66 1681786978 209231209 36193510 1734235 42 497 427 7639,8 Antigua and 8 ATG 2010 841 88028 765 713 340 0,4 1243184861 50485210 22896669 4396622 107 739 218 5227,85 Barbuda 44 Cayman Islands CYM 2010 4459 56672 0 0 0 0 3830571472 0 0 0 - 0 Turks and Caicos 160 TCA 2010 12429 32660 433 408 89 0,01 459623272 38132727 19684138 5887262 144 267 349 473,67 Islands 206 Palau PLW 2010 5702 18540 104 104 2 0 0 0 0 0 - 0 192 FrenchGuiana GUF 2010 77846 0 1377 1327 1216 0,02 0 0 0 0 - 0 207 Mayotte MYT 2010 692 0 93 73 164 0,24 0 0 0 0 - 0 NetherlandsAn- 209 ANT 2010 237 0 27 27 144 0,61 0 0 0 0 - 0 tilles 200 Martinique MTQ 2010 1927 0 456 445 53 0,03 0 0 0 0 - 0 191 Guadeloupe GLP 2010 3431 0 1472 1334 484 0,14 0 0 0 0 - 0 352 638 497 469 BUILDING COASTAL RESILIENCE WITH MANGROVES 53 APPENDIX 2 l TABLE A3: FLOOD RISK AND FLOOD REDUCTION BENEFITS OF MANGROVES ACROSS 97 COUNTRIES IN 2020 2020 POP STOCK ID COUNTRY ISO3 YEAR MANG HA POP POP EXP POP RISK POP BEN BEN STOCK STOCK EXP STOCK RISK STOCK BEN PV 100yr 4% BEN HA HA 34 China CHN 2020 24926 1439919586 17237935 16077932 764 842 30,68 22066954093611 503328178904 121390511628 7 463 666 893 182 897 149 728 299433 79 India IND 2020 492571 1380193228 1559486 1210902 1 144 050 2,32 10544231204789 7721477376 1733514554 1 620 261 180 39 704 498 591 3289,4 174 United States USA 2020 231525 331033569 378332 329242 64 247 0,28 22583187924602 82306728605 21906039066 2 436 538 383 59 707 370 632 10523,87 78 Indonesia IDN 2020 2913232 273580593 1145396 783119 489 596 0,17 3551482344809 15221306577 2578974892 1 532 916 500 37 564 117 295 526,19 132 Pakistan PAK 2020 82083 220902350 54119 42299 21 578 0,26 1114688608941 103165962 19940747 15 305 644 375 064 791 186,47 25 Brazil BRA 2020 1138408 212629731 387813 348868 265 511 0,23 2507912321150 7441494892 1789208830 773 487 112 18 954 300 904 679,45 125 Nigeria NGA 2020 887472 206052515 201334 133742 94 983 0,11 1242930041098 964197244 208762747 93 448 379 2 289 952 434 105,3 16 Bangladesh BGD 2020 531711 164715614 785889 657059 621 358 1,17 944499321470 3493814052 1423614981 1 354 057 650 33 181 181 355 2546,6 110 Mexico MEX 2020 1004711 128960270 139412 104691 161 141 0,16 2423514373478 4625531937 1191043651 1 311 000 444 32 126 064 565 1304,85 88 Japan JPN 2020 1016 126518410 37717 33178 2 485 2,45 5463369531448 7340739543 1337335759 411 913 542 10 093 940 934 405426,71 135 Philippines PHL 2020 274939 109581836 921223 817650 327 137 1,19 1152134708370 6636454709 1587984994 591 802 185 14 502 111 950 2152,49 179 Vietnam VNM 2020 186363 97378250 3054035 2467538 3 673 309 19,71 976490541756 13368713959 4763444998 7 418 425 588 181 788 511 594 39806,32 163 Thailand THA 2020 264930 69822711 196404 124711 23 646 0,09 1368329275145 3799821323 840303431 211 367 279 5 179 554 960 797,82 181 South Africa ZAF 2020 2654 59324022 10060 8866 1 603 0,6 712029114557 140434889 76226762 19 888 539 487 368 628 7493,8 U.R. of Tanza- 170 TZA 2020 120155 58026159 24907 19894 10 681 0,09 166628255923 65544673 27787778 15 381 416 376 921 584 128,01 nia: Mainland 114 Myanmar MMR 2020 546707 54382520 557333 469825 63 024 0,12 393081087972 1323031211 186443130 71 031 161 1 740 618 529 129,93 90 Kenya KEN 2020 56260 53755381 19316 15358 34 183 0,61 316018342362 55593858 16973713 30 239 949 741 029 920 537,5 39 Colombia COL 2020 289936 51017838 85513 71822 15 055 0,05 646151159225 1305068446 203611042 71 735 854 1 757 887 030 247,42 145 Sudan SDN 2020 684 43824943 8327 6649 3 564 5,21 154300616960 17291685 2239910 999 635 24 496 055 1461,45 144 Saudi Arabia SAU 2020 6127 34834300 105468 82993 17 490 2,85 1679407656786 5240755693 494669022 103 970 766 2 547 803 517 16969,28 134 Peru PER 2020 11845 33031646 8609 8188 659 0,06 429569152836 153187717 66167515 5 575 666 136 631 690 470,72 2 Angola AGO 2020 51756 32840803 12342 11775 6 739 0,13 151786681522 107089034 25544965 25 724 543 630 379 900 497,03 122 Malaysia MYS 2020 579537 32371521 134338 106284 22 448 0,04 858632009547 3247177863 326202137 101 506 964 2 487 428 051 175,15 117 Mozambique MOZ 2020 304897 31236068 50833 41997 28 845 0,09 41999747798 48988659 21831867 20 673 476 506 603 509 67,8 64 Ghana GHA 2020 18391 31068610 92572 59157 18 397 1 196992841710 598480629 238007943 104 120 600 2 551 475 199 5661,5 180 Yemen YEM 2020 1566 29825161 59110 50926 6 239 3,98 40531798299 764720226 307617994 29 685 563 727 444 692 18956,3 Venezuela 177 (Bolivarian VEN 2020 300542 28144540 144021 128871 19 847 0,07 - Republic of) 108 Madagascar MDG 2020 275833 27676301 113228 103671 20 020 0,07 50127560367 123876405 31042035 13 542 987 331 870 883 49,1 36 Cameroon CMR 2020 211303 26536493 11569 9922 159 861 0,76 100262884445 38220698 13402059 86 809 675 2 127 270 999 410,83 35 Côte d’Ivoire CIV 2020 6453 26363858 87478 82261 32 548 5,04 138705040325 266796665 96549850 12 464 196 305 435 110 1931,54 9 Australia AUS 2020 988392 25508244 119134 98148 111 606 0,11 1247675517506 26021029061 5373035758 5 072 180 073 124 293 767 602 5131,75 BUILDING COASTAL RESILIENCE WITH MANGROVES 54 APPENDIX 2 l 169 Taiwan TWN 2020 167 23611974 469337 389704 70 762 423,72 1195742591991 31096278116 5051365408 271 882 632 6 662 483 625 100 Sri Lanka LKA 2020 18119 21418703 51794 45376 3 985 0,22 331303280206 604177527 153022483 17 583 436 430 882 082 970,44 71 Guatemala GTM 2020 29554 17915095 1757 1684 996 0,03 162903260127 13715393 8035086 4 633 208 113 536 757 156,77 53 Ecuador ECU 2020 162785 17662966 35865 30509 16 398 0,1 209442935936 798905577 142529876 144 993 398 3 553 063 073 890,7 146 Senegal SEN 2020 232995 16738405 50280 30551 32 980 0,14 61323779034 175016917 35195408 43 351 208 1 062 321 309 186,06 92 Cambodia KHM 2020 69351 16723292 101530 92456 71 430 1,03 97311570007 269136301 116612032 110 580 366 2 709 771 758 1594,5 184 Somalia SOM 2020 5886 16537016 26538 21804 2 039 0,35 4641219516 7448060 6119432 572 258 14 023 182 97,22 65 Guinea GIN 2020 253417 13128199 14768 7795 21 210 0,08 49747828142 33535451 10941961 35 497 912 869 876 298 140,08 14 Benin BEN 2020 2970 12117258 91341 70975 7 469 2,51 44453903282 244459495 101259778 6 809 478 166 866 252 2292,75 76 Haiti HTI 2020 18837 11402976 32329 29641 32 354 1,72 19346133743 140521875 70343431 82 257 844 2 015 728 385 4366,82 186 Cuba CUB 2020 342417 11300698 33617 29447 9 227 0,03 192579234168 572879314 501816853 157 240 605 3 853 180 868 459,21 Dominican 51 DOM 2020 19270 10850775 45178 42807 6 554 0,34 213350265311 861913506 198454163 14 110 676 345 782 101 732,26 Republic United Arab 5 ARE 2020 7393 9910099 323118 249755 111 891 15,13 647638636076 27732260735 499717857 308 394 623 7 557 209 927 41714,41 Emirates 74 Honduras HND 2020 85226 9904712 7825 6683 2 721 0,03 64976689587 55174221 23723407 8 703 728 213 284 846 102,13 PapuaNew- 189 PNG 2020 461059 9749640 26358 22150 8 133 0,02 46690143258 126226076 106074345 38 948 201 954 425 626 84,48 Guinea 162 Togo TGO 2020 1455 8275639 8215 4973 4 786 3,29 19770572881 17086086 5917452 2 349 117 57 565 110 1614,51 148 Sierra Leone SLE 2020 172741 7976280 16630 10778 6 660 0,04 15545890652 13878968 6386342 3 624 628 88 821 506 20,98 126 Nicaragua NIC 2020 86057 6625503 6220 5268 8 175 0,09 39336029220 47887833 19871485 16 609 848 407 024 309 193,01 149 El Salvador SLV 2020 51920 6486360 2655 2324 1 222 0,02 58771070279 26116400 15432669 2 211 573 54 194 594 42,6 147 Singapore SGP 2020 15222 5851175 6372 5046 1 192 0,08 490410857871 960621673 8122326 4 813 679 117 959 199 316,23 38 Congo COG 2020 2500 5516657 1001 1001 1 510 0,6 16310826822 11226038 7336084 1 686 750 41 333 807 674,7 131 Oman OMN 2020 387 5120499 51012 38018 14 581 37,68 132034377473 1579581787 129390724 30 014 604 735 507 841 77557,12 42 Costa Rica CRI 2020 37034 5095681 4923 4394 2 729 0,07 109928199585 119030888 36367818 16 181 122 396 518 378 436,93 98 Liberia LBR 2020 19726 5055775 18142 11220 5 823 0,3 7266664186 25975300 7704047 5 179 471 126 922 932 262,57 130 New Zealand NZL 2020 29676 4822995 24559 23126 1 186 0,04 201472933694 3402247113 1315012005 223 056 032 5 465 987 840 7516,38 118 Mauritania MRT 2020 1563 4648079 21934 16060 6 652 4,26 26841093079 105178233 31499215 22 558 559 552 797 466 14432,86 133 Panama PAN 2020 152860 4316009 9396 8837 4 742 0,03 142258286551 432989773 29488915 14 716 690 360 632 474 96,28 197 Eritrea ERI 2020 7175 3555868 4628 4570 6 838 0,95 0 0 0 - - 0 185 PuertoRico PRI 2020 8200 3281538 17793 14668 17 258 2,1 189460350901 1027282946 846860352 996 394 598 24 416 648 625 121511,54 86 Jamaica JAM 2020 9767 2961711 12126 9981 2 804 0,29 29330849256 342650890 157691761 38 824 287 951 389 114 3975,05 140 Qatar QAT 2020 460 2882452 3810 3051 6 533 14,2 283597278193 655662501 43318936 57 131 953 1 400 018 451 124199,9 66 Gambia GMB 2020 74425 2415318 3197 2784 1 687 0,02 6459215166 4096410 1778693 1 397 478 34 245 197 18,78 61 Gabon GAB 2020 194197 2225883 2293 2162 1 557 0,01 31052975545 35277709 4512528 2 956 009 72 436 998 15,22 Equatorial 68 GNQ 2020 33795 1402997 4245 3803 1 290 0,04 24417268575 113355638 7067366 11 432 764 280 159 870 338,3 Guinea BUILDING COASTAL RESILIENCE WITH MANGROVES 55 APPENDIX 2 l Trinidad and 166 TTO 2020 11169 1400103 2831 3000 567 0,05 39559069658 51048847 16350499 6 937 261 169 997 574 621,12 Tobago 193 TimorLeste TLS 2020 1344 1299995 2700 2188 3 698 2,75 4782237753 9932378 8048905 13 603 679 333 358 140 10121,78 120 Mauritius MUS 2020 396 1272151 0 0 - 0 30158458634 0 0 - - 0 48 Djibouti DJI 2020 779 988197 5709 4169 6 547 8,4 5945590252 27144682 944435 1 075 824 26 363 066 1381,03 59 Fiji FJI 2020 48811 896423 6302 5564 3 911 0,08 12606096425 55773758 26223658 18 000 774 441 108 949 368,79 72 Guyana GUY 2020 38703 786526 73204 56761 33 613 0,87 8928041978 596287984 26437556 10 643 910 260 829 004 275,02 188 SolomonIslands SLB 2020 51928 691191 14244 12568 4 837 0,09 2268082456 46740433 41240786 15 872 190 388 948 000 305,66 105 China, Macao SAR MAC 2020 122 649254 0 0 - 0 62706830508 0 0 - - 0 152 Suriname SUR 2020 85438 586754 6293 4230 69 710 0,82 7834100992 169048742 42509610 505 220 635 12 380 431 154 5913,3 Brunei Darus- 27 BRN 2020 18707 437607 5115 4636 1 602 0,09 25333602841 608827310 47223278 7 818 968 191 603 803 417,97 salam 22 Belize BLZ 2020 68012 397635 3628 3252 5 925 0,09 2333555212 35366730 13669276 19 687 220 482 435 306 289,47 19 Bahamas BHS 2020 145902 393327 7569 6953 2 378 0,02 12077227252 901310859 321316069 133 727 487 3 276 991 935 916,56 195 Vanuatu VUT 2020 1532 311685 6177 5656 1 607 1,05 2899356943 57459704 52613257 14 948 639 366 316 384 9757,6 French 201 PYF 2020 126 301920 2115 1753 122 0,97 - Polynesia 190 NewCaledonia NCL 2020 33481 271130 6612 5427 1 384 0,04 49249393849 1201036374 985787115 251 396 603 6 160 473 504 7508,63 199 Samoa WSM 2020 231 214929 579 543 2 568 11,12 0 0 0 - - 0 99 Saint Lucia LCA 2020 162 183691 1178 1108 1 933 11,93 2381591197 40887955 24267845 30 791 157 754 537 271 190068,87 187 Kiribati KIR 2020 146 126463 4934 3919 890 6,1 581128449 22672938 18008765 4 089 768 100 219 761 28012,11 70 Grenada GRD 2020 209 112552 1616 1594 623 2,98 1889054893 60951484 37890019 18 732 052 459 028 915 89627,04 MicronesiaFeder- 194 FSM 2020 8711 112106 3307 2962 420 0,05 0 0 0 - - 0 atedStatesof UnitedStatesVir- 203 VIR 2020 274 106290 1552 1395 257 0,94 0 0 0 - - 0 ginIslands 202 Tonga TON 2020 1039 105254 1245 896 1 404 1,35 1380935130 16334431 11755543 18 420 515 451 394 702 17729,08 158 Seychelles SYC 2020 227 98382 5641 5287 652 2,87 2646840504 326410103 25239839 10 276 077 251 815 257 45269,06 Antigua and 8 ATG 2020 869 97950 1231 1050 452 0,52 1675958120 93134444 38271361 14 072 879 344 855 886 16194,34 Barbuda 44 Cayman Islands CYM 2020 4432 65722 0 0 - 0 4949331518 0 0 - - 0 Turks and 160 TCA 2020 15037 38717 899 827 1 641 0,11 699238769 74687008 47566667 71 921 778 1 762 443 098 4782,99 Caicos Islands 206 Palau PLW 2020 5687 17972 561 584 253 0,04 0 0 0 - - 0 192 FrenchGuiana GUF 2020 83100 0 3646 3484 6 074 0,07 0 0 0 - - 0 207 Mayotte MYT 2020 675 0 2270 2167 1 335 1,98 0 0 0 - - 0 NetherlandsAn- 209 ANT 2020 228 0 526 458 6 468 28,37 0 0 0 - - 0 tilles 200 Martinique MTQ 2020 1944 0 2419 2384 620 0,32 0 0 0 - - 0 191 Guadeloupe GLP 2020 3415 0 2766 2631 1 016 0,3 0 0 0 - - 0 BUILDING COASTAL RESILIENCE WITH MANGROVES 56 APPENDIX 2 l TABLE A4: ABSOLUTE CHANGES IN FLOOD RISK AND FLOOD REDUCTION BENEFITS OF MANGROVES ACROSS 97 COUNTRIES BETWEEN 1996-2010 1996 vs 2010 (ABSOLUTE VALUE) POP MANG POP POP POP STOCK ID COUNTRY ISO3 YEAR POP BEN STOCK STOCK EXP STOCK RISK STOCK BEN PV 100yr 4% HA EXP RISK BEN BEN HA HA 79 India IND 1996 -15147 251915927 221580 180393 149329 0,34 3363034386273 1659825265 391836886 321792484 7885524497,711 675,55 34 China CHN 1996 -4683 117174429 3632688 3566779 57754 5,2 9277371889184 162544239142 41875083111 1439084519 35264764694,912 65040,05 132 Pakistan PAK 1996 -14818 52075351 12372 9038 3997 0,08 297237074211 27790233 294213 1241970 30434473,604 35,82 125 Nigeria NGA 1996 -24478 47834403 31767 21283 7874 0,01 536934248005 94753298 18464527 -11556541 -283193025,616 -11,53 78 Indonesia IDN 1996 -197069 41077625 106121 91501 57592 0,02 798321834233 2783720261 470699014 246046239 6029362839,948 95,73 174 United States USA 1996 -7990 40676472 22580 21211 4319 0,02 5290777495921 9666849615 1688107585 -71478909 -1751590593,363 -125,58 25 Brazil BRA 1996 -20765 31098947 391 272 64927 0,06 821734429134 481035507 93172021 76217888 1867719269,005 72,45 16 Bangladesh BGD 1996 2352 29925498 95269 69548 -967 -0,01 258362228872 456286198 189859380 90288647 2212523204,189 169,57 135 Philippines PHL 1996 -15150 22565031 85322 88718 34560 0,17 282070725661 801953095 85289324 -5574642 -136606596,619 77,07 110 Mexico MEX 1996 -61017 20945919 13529 12478 10615 0,02 561751648403 631163993 167723259 88147616 2160057241,681 114,91 U.R. of Tanza- 170 TZA 1996 -2857 13463875 3296 2658 811 0,01 48071174873 9105599 3585707 3010788 73779356,921 25,51 nia: Mainland 90 Kenya KEN 1996 -811 13441225 3201 3115 194 0 71600323004 8246042 4024973 -1746337 -42793986,434 -27,74 179 Vietnam VNM 1996 -11432 11898908 235650 194362 459169 3,07 314801295487 3731072094 1493273450 2415871075 59200918270,124 13149,19 145 Sudan SDN 1996 -656 9762630 2750 2152 11 0,02 61746830861 14086867 2032681 13494 330670,456 31,83 181 South Africa ZAF 1996 50 8975953 166 144 6 0 219192833592 3968894 2186690 25110 615320,525 9,03 2 Angola AGO 1996 -839 8955527 1989 1675 1065 0,02 83720417658 16977485 4918901 3920143 96063100,284 75,4 144 Saudi Arabia SAU 1996 -915 8387616 13881 11655 1654 0,25 403257798747 804817015 65180022 2389935 58565354,778 531,5 39 Colombia COL 1996 -10593 8146313 11553 10677 4111 0,01 156091163755 163056741 28652198 9214444 225799940,979 36,2 180 Yemen YEM 1996 -332 7685581 8279 7553 11 0,01 44584428738 204304434 51331316 48609 1191163,496 85,5 117 Mozambique MOZ 1996 -11314 7571132 5595 4408 3112 0,02 16991659202 5538781 2308653 3235925 79296338,880 10,6 64 Ghana GHA 1996 -684 7317123 7806 6265 2542 0,16 53735201719 76756841 35135106 16898692 414102430,513 1036,31 108 Madagascar MDG 1996 -7442 7248952 24390 22296 7400 0,03 12403175149 33287384 10788745 3110695 76227577,855 11,94 122 Malaysia MYS 1996 -5281 7190422 8303 51 25719 0,04 234681924228 377728436 21285705 11963568 293167221,842 21,81 163 Thailand THA 1996 -16045 7064842 20125 37001 -21686 -0,08 352583062214 412959535 165056108 -100631855 -2465983505,857 -346,14 36 Cameroon CMR 1996 1066 6370428 512 261 5956 0,03 27503467913 1755300 317161 454318 11133062,134 1,91 114 Myanmar MMR 1996 -44871 6148612 92821 77722 9935 0,03 167655151945 221242157 35137225 10241119 250958610,825 19,14 Venezuela 177 (Bolivarian VEN 1996 -3270 6054290 29555 26030 4133 0,01 0 0 0 0 0 0 Republic of) United Arab 5 ARE 1996 126 6010862 146486 135338 34842 4,43 214959806512 5184773628 -59654303 -27426055 -672075450,271 -4255,15 Emirates 35 Côte d’Ivoire CIV 1996 -28 5867823 6223 5702 131 0,02 13159562623 7849310 2767896 -387828 -9503724,751 -54,26 184 Somalia SOM 1996 -118 4554199 3109 2725 755 0,13 673079506 -1416269 -1313503 243188 5959321,696 41 BUILDING COASTAL RESILIENCE WITH MANGROVES 57 APPENDIX 2 l 134 Peru PER 1996 -2218 4273850 788 653 20 0 139481689499 19593848 7098733 206728 5065869,433 21,89 71 Guatemala GTM 1996 -374 3983743 34 18 49 0 44608082819 533763 388962 397117 9731351,687 14,07 9 Australia AUS 1996 -51051 3965402 5378 4809 6846 0,01 372719376006 2580200294 267450184 79771357 1954797023,286 158,37 146 Senegal SEN 1996 1460 3765287 9973 6252 4303 0,02 16580387683 37134858 1426170 9367225 229543839,231 39,61 92 Cambodia KHM 1996 -5399 3329295 7032 8985 3761 0,1 32283907627 71818910 33569727 24753493 606584321,141 386,27 53 Ecuador ECU 1996 -10898 3307943 1974 3710 -201 0 58565359075 37989724 8494618 -16773307 -411029871,214 -81,78 14 Benin BEN 1996 -312 3105000 11393 9342 2 0,01 8432618642 21077289 9045980 -90490 -2217457,359 -13,91 PapuaNew- 189 PNG 1996 -637 2797374 441 345 418 0 17321373008 7820750 5014398 6538534 160226769,113 14,02 Guinea 65 Guinea GIN 1996 -1944 2728394 233 -33 1738 0 10110745699 2151541 704956 3326625 81518942,289 12,87 74 Honduras HND 1996 2497 2442661 973 964 237 0 18586750580 8182122 3733743 2081326 51002891,543 20,92 148 Sierra Leone SLE 1996 -1936 2102968 572 402 42 0 4525079221 -410132 6268 -371473 -9102945,492 -1,96 162 Togo TGO 1996 36 2072874 -21 27 0 0 1894710681 -896188 -294244 0 0 0 76 Haiti HTI 1996 -1876 2062018 7059 6872 -2164 -0,06 1437146742 33678163 14953780 6141135 150488507,016 383,22 88 Japan JPN 1996 -23 1898259 1053 1008 -488 -0,45 418763116711 626541195 125525732 -11792737 -288981008,359 -10670,6 100 Sri Lanka LKA 1996 -7923 1894449 721 383 235 0,06 107359546067 22566966 2642399 4565475 111876960,297 357,23 Dominican 51 DOM 1996 -491 1742358 4262 3853 171 0,01 66824943188 126476410 25451226 2350545 57600102,868 126,33 Republic 98 Liberia LBR 1996 54 1730878 1535 439 1092 0,05 4322026872 418307 78440 196838 4823514,993 9,33 169 Taiwan TWN 1996 15 1699516 -13645 -15382 31995 132,47 427535007537 4600455165 471803704 120879595 2962154354,251 521994,33 147 Singapore SGP 1996 -591 1492985 697 646 134 0,01 185972952138 102392327 -2955022 722654 17708635,545 47,41 38 Congo COG 1996 -32 1487921 286 286 0 0 7919049458 2656634 -20439 0 0 0 140 Qatar QAT 1996 1 1333796 334 296 436 1,01 166242140922 45561336 2491378 1493139 36589369,698 3424,16 118 Mauritania MRT 1996 -102 1121294 1484 1023 1054 1,02 5383382399 12503121 4511561 3819472 93596157,530 3077,3 126 Nicaragua NIC 1996 2633 1082487 204 179 178 0 11713334415 1809677 630713 599889 14700279,343 5,82 42 Costa Rica CRI 1996 343 945016 152 147 406 0,01 36536942793 21550978 2681935 -528432 -12949225,630 -15,19 197 Eritrea ERI 1996 -2060 883654 8499 8499 -1141 -0,12 0 0 0 0 0 0 133 Panama PAN 1996 432 846396 634 450 1124 0,01 43177066997 31069757 -1196173 -2023530 -49586600,621 -13,1 131 Oman OMN 1996 -170 804780 17719 14911 1735 9,5 35320272737 790222744 28301946 -1903493 -46645094,056 -156,95 130 New Zealand NZL 1996 1166 652713 389 349 30 0 47825439175 116256103 41094523 3309011 81087311,237 54,01 66 Gambia GMB 1996 1069 629105 -150 -7 -78 0 1991713435 34515 -2294 76573 1876421,288 0,96 61 Gabon GAB 1996 60 511185 24 37 75 0 1456673319 -494637 -74841 152738 3742844,537 0,78 149 El Salvador SLV 1996 763 493937 -96 -121 39 0 12993453542 1823454 845233 708364 17358459,110 12,87 Equatorial 68 GNQ 1996 168 427786 165 130 24 0 31205745628 18994393 -71770 537681 13175872,366 15,78 Guinea 186 Cuba CUB 1996 -28599 327386 921 444 317 0,01 -8276939789 -427394 -2948170 -2485763 -60913619,822 3,24 193 TimorLeste TLS 1996 -26 257217 386 304 -2 0,02 -13085643657 -14728050 -13243634 -30137600 -738521857,777 -21672,44 86 Jamaica JAM 1996 70 251823 34 715 -1344 -0,13 1245124764 23122354 24603926 -7357964 -180306900,441 -726,36 48 Djibouti DJI 1996 -307 196544 1656 968 25 0,07 1279859125 8291519 588517 88429 2166952,556 139,79 SolomonIs- 188 SLB 1996 177 154325 2173 1499 644 0,01 631682712 8292156 6517016 1879869 46066187,960 35,92 lands BUILDING COASTAL RESILIENCE WITH MANGROVES 58 APPENDIX 2 l China, Macao 105 MAC 1996 -10 144846 0 0 0 0 34227282805 0 0 0 0 0 SAR 22 Belize BLZ 1996 -4165 108800 634 561 1765 0,03 939017536 6561127 2128955 3164669 77550210,671 62,38 120 Mauritius MUS 1996 -162 106007 0 0 0 0 9698836182 0 0 0 0 0 Brunei Darus- 27 BRN 1996 148 84024 558 480 206 0,01 3800503828 54289615 2965047 -554612 -13590766,504 -30,65 salam 152 Suriname SUR 1996 3380 80918 2085 1573 5778 0,06 3113382946 63788356 21141128 95871629 2349334172,500 1030,11 59 Fiji FJI 1996 226 75432 -486 -487 385 0,01 1797779572 -752984 -81772 2040909 50012472,998 41,4 19 Bahamas BHS 1996 -22744 70964 1240 1212 128 0,01 2653955928 255060484 85019213 15323498 375502303,123 131,29 195 Vanuatu VUT 1996 16 70739 -89 -92 -2 0 1253323021 3228871 3140307 71493 1751935,893 44,2 Trinidad and 166 TTO 1996 -472 70598 298 300 -377 -0,03 24892756556 2825980 278117 -1893234 -46393697,271 -157,33 Tobago 190 NewCaledonia NCL 1996 -860 52186 176 136 95 0 19999351863 133854094 113847466 80330622 1968501811,551 2527,39 FrenchPoly- 201 PYF 1996 -3 48599 62 47 18 0,18 0 0 0 0 0 0 nesia 99 Saint Lucia LCA 1996 -1 25251 -5 -5 1 0,01 499241624 3567813 52032 223988 5488825,715 1389,51 187 Kiribati KIR 1996 0 25163 -850 -984 78 0,53 148232905 1157778 325879 510835 12518011,163 3498,87 Cayman 44 CYM 1996 -225 22607 0 0 0 0 1377450625 0 0 0 0 0 Islands 199 Samoa WSM 1996 6 17959 -84 -27 -26 -0,12 0 0 0 0 0 0 Antigua and 8 ATG 1996 -19 17855 -118 -114 -27 -0,03 355706954 14170179 14595257 -9074 -222358,361 104,95 Barbuda Turks and Cai- 160 TCA 1996 -8202 15734 232 232 -53 0 254975074 32369321 16776065 3431593 84091183,024 354,64 cos Islands 158 Seychelles SYC 1996 0 13670 299 315 9 0,04 629787868 68960703 2249950 -2207274 -54089247,156 -9723,68 202 Tonga TON 1996 16 7188 119 168 -115 -0,11 459694074 1628829 1717952 765395 18756003,707 682,13 70 Grenada GRD 1996 -1 5232 -1 -1 1 0,01 501699111 37642 16785 83920 2056459,516 404,38 206 Palau PLW 1996 46 808 17 26 -10 0 0 0 0 0 0 0 UnitedStates- 203 VIR 1996 -8 262 -3 -6 1 0,02 0 0 0 0 0 0 VirginIslands 192 FrenchGuiana GUF 1996 1823 0 377 352 558 0,01 0 0 0 0 0 0 207 Mayotte MYT 1996 15 0 39 30 47 0,07 0 0 0 0 0 0 Netherland- 209 ANT 1996 -55 0 11 11 46 0,27 0 0 0 0 0 0 sAntilles 200 Martinique MTQ 1996 6 0 -3 -1 11 0,01 0 0 0 0 0 0 191 Guadeloupe GLP 1996 18 0 105 252 -305 -0,09 0 0 0 0 0 0 185 PuertoRico PRI 1996 75 -3130 2081 2555 -3593 -0,43 116995770779 395889108 353263207 397300853 9735857004,333 45676,88 Micronesi- 194 aFederated- FSM 1996 -87 -3197 -235 -212 -75 0 0 0 0 0 0 0 Statesof 72 Guyana GUY 1996 1200 -11359 10181 6905 -6709 -0,19 3349761415 118428328 35528 -12067 -295701,823 -0,37 BUILDING COASTAL RESILIENCE WITH MANGROVES 59 APPENDIX 2 l TABLE A5: ABSOLUTE CHANGES IN FLOOD RISK AND FLOOD REDUCTION BENEFITS OF MANGROVES ACROSS 97 COUNTRIES BETWEEN 2010-2020 2010 VS 2020 (ABSOLUTE VALUE) POP MANG STOCK BEN ID COUNTRY ISO3 YEAR POP POP EXP POP RISK POP BEN BEN STOCK STOCK EXP STOCK RISK STOCK BEN PV 100yr 4% HA HA HA 179 Vietnam VNM 1996 -1167 9410599 1287588 914004 1213745 6,59 447559339451 8401969827 2698745235 4132412203 101264756890,334 22283,72 34 China CHN 1996 543 71108971 2653237 1906318 278618 10,74 7620097119879 301267777269 56472465532 5113518217 125306758779,503 203048,28 185 PuertoRico PRI 1996 -425 -439987 6563 5102 2286 0,36 -3874577248 443879303 349902271 218592341 5356605096,990 31331,57 79 India IND 1996 -2439 145912058 752753 566539 369323 0,75 4771936673225 5120485198 999793110 873766884 21411656616,165 1781,36 78 Indonesia IDN 1996 -28389 31746378 518641 347268 125251 0,05 1426697326745 9385222486 1472666994 720486455 17655519857,237 250,01 169 Taiwan TWN 1996 -45 471026 259110 203595 -12596 30,52 289475509817 18971175301 2709630198 16822422 412233434,240 424925,51 152 Suriname SUR 1996 495 57623 2405 1717 37598 0,44 538491199 74433934 14023116 207335137 5080747324,259 2406,41 16 Bangladesh BGD 1996 1692 17140184 528396 451217 460458 0,87 462303695045 2854274900 1146164956 1171503291 28707686971,115 2202,17 110 Mexico MEX 1996 1745 14867307 84622 63020 85903 0,08 458842695649 3018129719 693536253 750254925 18384996184,733 745,76 190 NewCaledonia NCL 1996 1333 21380 4853 3924 323 0,01 22027928288 1009314420 821967845 135753060 3326628599,160 3911,41 9 Australia AUS 1996 -11734 3353565 85071 69496 72431 0,07 231734356406 19213311156 3767427651 3373474883 82666998624,832 3433,26 25 Brazil BRA 1996 10778 16916096 206406 181134 129361 0,11 174065484108 4339875553 965644205 394063631 9656528882,469 342,97 92 Cambodia KHM 1996 1344 2411080 24254 19426 32093 0,45 48165510565 143090708 60543006 65224653 1598330056,355 927,57 64 Ghana GHA 1996 430 6288991 50816 26252 3631 0,18 92678760574 319865720 112290403 40485082 992086893,810 2118,52 19 Bahamas BHS 1996 3833 38385 3227 2847 1557 0,01 1376788778 429908321 152842610 94283116 2310407663,028 638,92 122 Malaysia MYS 1996 -5239 4163486 63666 57923 -30321 -0,05 331517900087 1985684677 167945378 1488607 36478313,042 4,11 114 Myanmar MMR 1996 -5377 3781702 214329 193775 -23017 -0,04 175473114763 1068355062 138714415 56426109 1382721744,458 103,48 146 Senegal SEN 1996 -1937 4060257 22938 13262 21550 0,09 23766052629 95538593 19197932 24000201 588124901,436 103,69 39 Colombia COL 1996 925 5795138 52809 43072 -3434 -0,01 191766073614 914329665 113156562 27163339 665637594,954 93,2 PapuaNew- 189 PNG 1996 -6026 2166371 21587 19193 4225 0,01 18203926747 108304024 94966505 24267971 594686605,018 53,05 Guinea 76 Haiti HTI 1996 -277 1453654 18732 17339 22632 1,21 3273474856 91555836 49645202 62872380 1540687608,849 3352,62 100 Sri Lanka LKA 1996 181 1156966 45390 41516 883 0,05 121131568259 556976975 142543084 7003822 171628651,086 380,65 2 Angola AGO 1996 -585 9484557 9883 9734 5263 0,1 20170111161 85639978 18254773 20103415 492634164,414 389,64 118 Mauritania MRT 1996 204 1153884 11535 8880 797 -0,05 8838742528 74430245 20780400 13545631 331935674,071 7800,83 Turks and 160 TCA 1996 2608 6057 466 419 1552 0,1 239615497 36554281 27882529 66034516 1618175748,357 4309,32 Caicos Islands 65 Guinea GIN 1996 -6733 2936023 10984 5635 12121 0,05 22239887127 28808287 9233985 29248338 716730493,358 116,06 130 New Zealand NZL 1996 -517 452933 21810 20802 377 0,01 51704856050 3066203136 1170320421 177967480 4361092918,926 6023,04 117 Mozambique MOZ 1996 -9654 7704494 25417 18759 1344 0 16478795750 39856681 17916462 14571268 357068907,727 48,4 22 Belize BLZ 1996 499 75171 2190 1953 435 0,01 355074490 22430718 8393260 -1493597 -36600592,987 -24,26 108 Madagascar MDG 1996 631 6524661 51416 46853 2902 0,01 13926612893 49636335 3122311 3686728 90343265,943 13,29 U.R. of Tanzania: 170 TZA 1996 -1401 14931758 16230 13556 2742 0,02 79340937085 51393920 22388487 8468428 207518819,647 71,14 Mainland BUILDING COASTAL RESILIENCE WITH MANGROVES 60 APPENDIX 2 l 144 Saudi Arabia SAU 1996 -1985 7412839 64239 48032 11865 2,16 423416274421 3624585302 298274275 82623473 2024688123,006 14337,71 Dominican 51 DOM 1996 -120 1155654 30886 30176 5260 0,27 85771940990 664015033 142741770 7750033 189914550,893 404,22 Republic 74 Honduras HND 1996 -7424 1587242 5497 4632 696 0,01 19046941441 39711465 17188432 1465861 35920922,335 24,01 59 Fiji FJI 1996 44 36605 4946 4436 2098 0,04 3213703438 46358274 22273322 11267920 276120368,300 230,73 188 SolomonIslands SLB 1996 -347 150797 7281 6910 3393 0,06 1316486884 34479083 31277449 13329408 326637129,673 257,02 140 Qatar QAT 1996 26 1026125 3367 2654 5953 12,86 80571703805 602359186 38415720 52584410 1288580914,316 113721,69 132 Pakistan PAK 1996 -10195 41477709 23366 19307 -9138 -0,07 386408437378 42476945 6461274 -855442 -20962605,352 11,34 202 Tonga TON 1996 -32 -2129 984 675 1063 1,03 416444317 13990186 9770569 15357727 376341084,734 14869,33 147 Singapore SGP 1996 -1141 720003 5163 4040 875 0,06 142447618199 811504193 7794540 2569723 62971059,538 179,09 149 El Salvador SLV 1996 -1539 302485 1867 1665 802 0,01 11349553670 19472106 12169849 91755 2248456,183 2,95 126 Nicaragua NIC 1996 -5585 801438 5347 4551 7226 0,08 8228005118 42003588 17732066 13788714 337892422,742 162,23 Equatorial 68 GNQ 1996 -209 459358 3596 3276 1231 0,04 -9372387388 93002572 6625277 10677278 261646686,682 316,08 Guinea 187 Kiribati KIR 1996 0 18468 -2133 -2499 466 3,2 149481632 -5573259 -7643434 2395076 58691334,978 16404,63 36 Cameroon CMR 1996 -595 6195252 10067 9024 147871 0,7 35953124893 33675928 12433385 76641123 1878090642,256 362,84 71 Guatemala GTM 1996 250 3284678 1509 1459 616 0,02 48325056417 11761090 6603808 3047669 74683125,789 102,66 184 Somalia SOM 1996 -79 4510367 2795 396 1135 0,2 480771076 -765494 -1286362 259532 6359831,400 44,79 99 Saint Lucia LCA 1996 0 9606 894 830 1917 11,83 358764591 31926549 20924947 30385875 744605836,403 187567,13 134 Peru PER 1996 677 4003972 5418 5423 568 0,05 135248098539 113307531 49073673 5141256 125986473,124 431,82 98 Liberia LBR 1996 -975 1164419 14170 10133 3171 0,17 2174828228 21497776 7067265 3604438 88326749,575 186,49 61 Gabon GAB 1996 -1241 601743 1909 1888 1135 0,01 9457998706 30294764 3540230 1500249 36763600,240 7,77 48 Djibouti DJI 1996 86 147999 1416 1615 6437 8,24 3412189404 16411775 174747 959886 23522005,467 1213,73 70 Grenada GRD 1996 0 6319 1613 1591 613 2,93 506966877 60823671 37835669 18523125 453909159,549 88627,39 66 Gambia GMB 1996 -743 622122 2792 2777 1127 0,01 1552410025 3510626 1771969 982746 24082189,744 13,26 195 Vanuatu VUT 1996 -69 66232 5320 4818 1588 1,04 1049531015 51001030 46297774 14805448 362807488,392 9668,16 180 Yemen YEM 1996 -109 6670306 35797 29822 6206 3,96 -51242844278 437754932 213479394 29065077 712239682,737 18585,86 181 South Africa ZAF 1996 38 8107058 8754 7708 1564 0,59 91680943222 123332496 66551396 19787716 484897960,736 7455,26 145 Sudan SDN 1996 -148 9279930 -1127 2386 3543 5,18 45681269827 1483642 -135157 956683 23443515,956 1409,82 Venezuela 177 (Bolivarian VEN 1996 1275 -295400 33942 28359 3761 0,02 0 0 0 0 0 0 Republic of) 192 FrenchGuiana GUF 1996 5254 0 2269 2157 4858 0,05 0 0 0 0 0 0 207 Mayotte MYT 1996 -17 0 2177 2094 1171 1,74 0 0 0 0 0 0 NetherlandsAn- 209 ANT 1996 -9 0 499 431 6324 27,76 0 0 0 0 0 0 tilles FrenchPoly- 201 PYF 1996 7 18132 1732 1497 -40 -0,39 0 0 0 0 0 0 nesia 200 Martinique MTQ 1996 17 0 1963 1939 567 0,29 0 0 0 0 0 0 UnitedStates- 203 VIR 1996 1 -2067 1420 1319 179 0,65 0 0 0 0 0 0 VirginIslands BUILDING COASTAL RESILIENCE WITH MANGROVES 61 APPENDIX 2 l 162 Togo TGO 1996 181 1853960 5850 3521 4786 3,29 11229184198 12849014 4235828 2349117 57565109,729 1614,51 38 Congo COG 1996 51 1242926 257 257 1510 0,6 -1764449779 5064073 5143263 1686750 41333807,058 674,7 44 Cayman Islands CYM 1996 -27 9050 0 0 0 0 1118760046 0 0 0 0 0 China, Macao 105 MAC 1996 -2 111035 0 0 0 0 10142502134 0 0 0 0 0 SAR 120 Mauritius MUS 1996 5 24196 0 0 0 0 9110622140 0 0 0 0 0 206 Palau PLW 1996 -15 -568 457 480 251 0,04 0 0 0 0 0 0 199 Samoa WSM 1996 -9 20257 199 273 2507 10,87 0 0 0 0 0 0 MicronesiaFed- 194 FSM 1996 -286 4518 2555 2296 283 0,03 0 0 0 0 0 0 eratedStatesof 191 Guadeloupe GLP 1996 -16 0 1294 1297 532 0,16 0 0 0 0 0 0 197 Eritrea ERI 1996 -68 408141 -4552 -4610 6838 0,95 0 0 0 0 0 0 Antigua and 8 ATG 1996 28 9922 466 337 112 0,12 432773259 42649234 15374692 9676257 237116668,081 10966,49 Barbuda 72 Guyana GUY 1996 -1687 37090 19662 13062 4287 0,14 2826906791 175171093 26311221 10561164 258801313,229 272,97 14 Benin BEN 1996 228 2917999 49452 37934 7303 2,45 25379818143 143863991 58223346 6387482 156525240,004 2138,85 148 Sierra Leone SLE 1996 -7593 1560646 12627 9426 3317 0,02 5565348019 11197987 5836878 2326122 57001617,277 13,78 35 Côte d’Ivoire CIV 1996 -420 5830908 62014 61669 29885 4,65 82601866585 205911340 79879626 9180901 224977969,798 1453,83 42 Costa Rica CRI 1996 -1462 518303 2131 1699 1233 0,03 30758202124 64336862 18694695 10449695 256069765,496 288,05 Brunei Darus- 27 BRN 1996 125 48961 2177 2498 616 0,04 1432427403 334121026 23452381 6513378 159610321,358 347,71 salam 90 Kenya KEN 1996 -14 11724705 11635 8631 22253 0,4 131145646279 37422537 8995174 18941416 464159380,085 336,72 Trinidad and 166 TTO 1996 -226 71956 2119 2314 152 0,01 -3082882142 45771378 14395199 6305179 154508405,072 565,65 Tobago 131 Oman OMN 1996 76 2079065 10405 4254 9396 21,01 31044778274 294733419 22144545 26670439 653559080,949 66804,18 133 Panama PAN 1996 -3432 673322 6805 6644 1732 0,01 61769151263 373594512 19980536 8254347 202272764,957 54,93 158 Seychelles SYC 1996 0 7118 1459 1270 503 2,21 965053526 117178894 -10953671 8541842 209317829,644 37629,26 186 Cuba CUB 1996 -7869 10281 24599 22371 3796 0,01 98064910475 497387844 442582251 111776625 2739086083,530 329,42 135 Philippines PHL 1996 4773 15615056 354007 317085 77692 0,27 514020777480 3518674721 710449492 100240063 2456382643,289 333,01 86 Jamaica JAM 1996 -854 151251 3359 2376 -478 -0,02 2583450960 98114856 48902479 -7576223 -185655337,017 -393,7 125 Nigeria NGA 1996 -8320 47549318 113985 77329 55581 0,07 321181345449 597092467 118560438 58950903 1444591818,896 66,79 88 Japan JPN 1996 14 -2023943 21228 18723 1482 1,45 483305980456 4294237225 668811273 374648929 9180771629,429 368236,48 53 Ecuador ECU 1996 2792 2651849 27173 22826 8718 0,05 45024142640 629969829 105876865 103919692 2546551948,244 633,98 United Arab 5 ARE 1996 -324 1360111 90785 36182 38948 5,68 191421507855 9780359642 49163209 9832599 240947828,634 3025,54 Emirates 193 TimorLeste TLS 1996 -3 211509 1315 1014 2176 1,62 -9346888325 -8045657 -7190238 -6152689 -150771637,775 -4545,16 174 United States USA 1996 -1585 22022094 223674 187930 27348 0,12 4513943769222 49782214204 12940604267 1234450560 30250209734,834 5367,13 163 Thailand THA 1996 6032 2627683 121871 69404 15125 0,06 359440435300 2575672371 543362574 123213932 3019357280,095 457,33 BUILDING COASTAL RESILIENCE WITH MANGROVES 62 APPENDIX 2 l TABLE A6: PERCENTAGE CHANGES IN FLOOD RISK AND FLOOD REDUCTION BENEFITS OF MANGROVES ACROSS 97 COUNTRIES BETWEEN 1996-2010 1996 vs 2010 (PERCENTAGE) MANG POP POP BEN STOCK BEN ID COUNTRY ISO3 YEAR POP POP RISK POP BEN STOCK STOCK EXP STOCK RISK STOCK BEN PV 100yr 4% HA EXP HA HA 140 Qatar QAT 1996 0,002 2,553 3,064 2,931 3,028 3,061 4,519 5,885 1,033 0,489 0,489 0,485 United Arab 5 ARE 1996 0,017 2,367 1,706 1,730 0,914 0,882 0,891 0,406 -0,117 -0,084 -0,084 -0,099 Emirates Turks and Caicos 160 TCA 1996 -0,398 0,930 1,154 1,318 -0,373 0 1,246 5,616 5,769 1,397 1,397 2,979 Islands 68 Equatorial Guinea GNQ 1996 0,005 0,829 0,341 0,327 0,686 0 12,077 13,980 -0,140 2,469 2,469 2,450 98 Liberia LBR 1996 0,003 0,801 0,630 0,677 0,7 0,625 5,614 0,103 0,140 0,143 0,143 0,140 44 Cayman Islands CYM 1996 -0,048 0,664 0 0 0 0 0,562 0 0 0 0 0 2 Angola AGO 1996 -0,016 0,622 4,232 4,577 2,591 2 1,748 3,797 2,074 2,305 2,305 2,357 184 Somalia SOM 1996 -0,019 0,609 0,151 0,146 5,067 6,5 0,193 -0,147 -0,151 3,497 3,497 3,587 189 PapuaNewGuinea PNG 1996 -0,001 0,585 0,102 0,132 0,120 0 1,551 0,774 0,823 0,803 0,803 0,805 66 Gambia GMB 1996 0,014 0,540 -0,270 -0,5 -0,122 0 0,683 0,063 -0,254 0,226 0,226 0,211 38 Congo COG 1996 -0,013 0,534 0,624 0,624 0 0 0,780 0,758 -0,009 0 0 0 108 Madagascar MDG 1996 -0,026 0,521 0,652 0,646 0,761 1 0,521 0,813 0,630 0,461 0,461 0,500 14 Benin BEN 1996 -0,102 0,509 0,374 0,394 0,012 0,2 0,792 0,265 0,266 -0,177 -0,177 -0,083 22 Belize BLZ 1996 -0,058 0,509 0,789 0,760 0,474 0,6 0,903 1,029 0,676 0,176 0,176 0,248 180 Yemen YEM 1996 -0,165 0,497 0,551 0,557 0,5 1 0,945 1,666 1,199 0,085 0,085 0,300 148 Sierra Leone SLE 1996 -0,011 0,488 0,167 0,423 0,013 0 0,829 -0,133 0,012 -0,222 -0,222 -0,214 162 Togo TGO 1996 0,029 0,477 -0,009 0,019 0 0 0,285 -0,175 -0,149 0 0 0 117 Mozambique MOZ 1996 -0,035 0,474 0,282 0,234 0,128 0,286 1,992 1,541 1,437 1,129 1,129 1,205 118 Mauritania MRT 1996 -0,070 0,473 0,166 0,166 0,220 0,310 0,427 0,685 0,727 0,735 0,735 0,866 90 Kenya KEN 1996 -0,014 0,470 0,715 0,862 0,017 0 0,632 0,831 1,018 -0,134 -0,134 -0,121 61 Gabon GAB 1996 0,000 0,459 0,067 0,156 0,216 0 0,072 -0,090 -0,071 0,117 0,117 0,117 36 Cameroon CMR 1996 0,005 0,456 0,517 0,410 0,987 1 0,747 0,629 0,487 0,047 0,047 0,041 U.R. of Tanzania: 170 TZA 1996 -0,023 0,454 0,613 0,722 0,114 0,167 1,226 1,805 1,977 0,772 0,772 0,813 Mainland 144 Saudi Arabia SAU 1996 -0,101 0,441 0,508 0,500 0,417 0,568 0,473 0,992 0,497 0,126 0,126 0,253 125 Nigeria NGA 1996 -0,027 0,432 0,572 0,606 0,250 0,333 1,395 0,348 0,257 -0,251 -0,251 -0,230 146 Senegal SEN 1996 0,006 0,422 0,574 0,566 0,604 0,667 0,790 0,877 0,098 0,938 0,938 0,926 64 Ghana GHA 1996 -0,037 0,419 0,230 0,235 0,208 0,242 1,062 0,380 0,388 0,362 0,362 0,413 74 Honduras HND 1996 0,028 0,416 0,718 0,887 0,133 0 0,680 1,124 1,333 0,404 0,404 0,366 147 Singapore SGP 1996 -0,035 0,410 1,361 1,794 0,732 1 1,148 2,191 -0,900 0,475 0,475 0,528 132 Pakistan PAK 1996 -0,138 0,409 0,673 0,648 0,150 0,32 0,690 0,845 0,022 0,083 0,083 0,257 195 Vanuatu VUT 1996 0,010 0,405 -0,094 -0,099 -0,095 0 2,101 0,100 0,989 0,997 0,997 0,977 BUILDING COASTAL RESILIENCE WITH MANGROVES 63 APPENDIX 2 l 35 Côte d’Ivoire CIV 1996 -0,004 0,400 0,323 0,383 0,052 0,054 0,306 0,148 0,199 -0,106 -0,106 -0,102 188 SolomonIslands SLB 1996 0,003 0,400 0,454 0,360 0,805 0,5 1,975 2,089 1,891 2,836 2,836 2,824 145 Sudan SDN 1996 -0,441 0,394 0,410 1,019 1,1 2 1,317 8,184 5,937 0,458 0,458 1,608 197 Eritrea ERI 1996 -0,221 0,390 12,480 12,480 -1 -1 0 0 0 0 0 0 71 Guatemala GTM 1996 -0,013 0,374 0,159 0,087 0,148 0 0,638 0,376 0,373 0,334 0,334 0,351 105 China, Macao SAR MAC 1996 -0,075 0,368 0 0 0 0 1,867 0 0 0 0 0 65 Guinea GIN 1996 -0,007 0,366 0,066 -0,015 0,236 0 0,581 0,835 0,703 1,138 1,138 1,154 131 Oman OMN 1996 -0,353 0,360 0,774 0,791 0,503 1,325 0,538 1,598 0,359 -0,363 -0,363 -0,014 122 Malaysia MYS 1996 -0,009 0,342 0,133 0,001 0,951 0,8 0,803 0,427 0,155 0,136 0,136 0,146 135 Philippines PHL 1996 -0,053 0,316 0,177 0,215 0,161 0,227 0,792 0,346 0,108 -0,011 -0,011 0,044 193 TimorLeste TLS 1996 -0,019 0,309 0,386 0,349 -0,001 0,018 -0,481 -0,450 -0,465 -0,604 -0,604 -0,596 48 Djibouti DJI 1996 -0,307 0,305 0,628 0,610 0,294 0,778 1,021 3,396 3,248 3,215 3,215 5,081 187 Kiribati KIR 1996 0 0,304 -0,107 -0,133 0,225 0,224 0,523 0,043 0,013 0,432 0,432 0,432 92 Cambodia KHM 1996 -0,074 0,303 0,100 0,140 0,106 0,208 1,915 1,324 1,492 1,201 1,201 1,376 133 Panama PAN 1996 0,003 0,303 0,324 0,258 0,596 1 1,157 1,097 -0,112 -0,238 -0,238 -0,241 53 Ecuador ECU 1996 -0,064 0,283 0,294 0,934 -0,026 0 0,553 0,290 0,302 -0,290 -0,290 -0,242 27 Brunei Darussalam BRN 1996 0,008 0,276 0,234 0,290 0,264 0,25 0,189 0,246 0,143 -0,298 -0,298 -0,304 Venezuela (Bolivari- 177 VEN 1996 -0,011 0,270 0,367 0,349 0,346 0,25 0 0 0 0 0 0 an Republic of) 190 NewCaledonia NCL 1996 -0,026 0,264 0,111 0,099 0,098 0 2,769 2,313 2,278 2,275 2,275 2,362 76 Haiti HTI 1996 -0,089 0,261 1,080 1,266 -0,182 -0,105 0,098 2,203 2,603 0,464 0,464 0,607 42 Costa Rica CRI 1996 0,009 0,260 0,058 0,058 0,372 0,333 0,857 0,650 0,179 -0,084 -0,084 -0,093 79 India IND 1996 -0,030 0,256 0,379 0,389 0,239 0,276 1,396 1,764 1,146 0,758 0,758 0,811 Antigua and 8 ATG 1996 -0,022 0,254 -0,134 -0,138 -0,074 -0,070 0,401 0,390 1,758 -0,002 -0,002 0,020 Barbuda 16 Bangladesh BGD 1996 0,004 0,254 0,587 0,510 -0,006 -0,032 1,154 2,490 2,168 0,979 0,979 0,970 19 Bahamas BHS 1996 -0,138 0,250 0,400 0,419 0,185 0 0,330 1,179 1,019 0,635 0,635 0,897 126 Nicaragua NIC 1996 0,030 0,228 0,305 0,333 0,231 0 0,604 0,444 0,418 0,270 0,270 0,233 110 Mexico MEX 1996 -0,057 0,225 0,328 0,427 0,164 0,333 0,400 0,647 0,509 0,187 0,187 0,259 39 Colombia COL 1996 -0,035 0,220 0,546 0,591 0,286 0,2 0,523 0,716 0,464 0,261 0,261 0,307 51 Dominican Republic DOM 1996 -0,025 0,219 0,425 0,439 0,152 0,167 1,100 1,771 0,841 0,586 0,586 0,626 9 Australia AUS 1996 -0,049 0,218 0,187 0,202 0,212 0,333 0,579 0,610 0,200 0,049 0,049 0,103 181 South Africa ZAF 1996 0,019 0,212 0,146 0,142 0,182 0 0,546 0,302 0,292 0,332 0,332 0,306 201 FrenchPolynesia PYF 1996 -0,025 0,207 0,193 0,225 0,125 0,153 0 0 0 0 0 0 78 Indonesia IDN 1996 -0,063 0,205 0,204 0,266 0,188 0,2 0,602 0,912 0,741 0,434 0,434 0,531 BUILDING COASTAL RESILIENCE WITH MANGROVES 64 APPENDIX 2 l 25 Brazil BRA 1996 -0,018 0,189 0,002 0,002 0,912 1 0,543 0,184 0,128 0,251 0,251 0,274 152 Suriname SUR 1996 0,041 0,181 1,156 1,673 0,219 0,188 0,744 2,069 2,878 0,475 0,475 0,416 158 Seychelles SYC 1996 0 0,176 0,077 0,085 0,064 0,065 0,599 0,492 0,066 -0,560 -0,560 -0,560 130 New Zealand NZL 1996 0,040 0,176 0,165 0,177 0,039 0 0,469 0,529 0,397 0,079 0,079 0,038 134 Peru PER 1996 -0,166 0,173 0,328 0,309 0,282 0 0,901 0,966 0,710 0,908 0,908 1,287 99 Saint Lucia LCA 1996 -0,006 0,170 -0,017 -0,018 0,067 0,111 0,328 0,661 0,016 1,235 1,235 1,249 179 Vietnam VNM 1996 -0,057 0,156 0,154 0,143 0,230 0,305 1,470 3,019 2,613 2,776 2,776 3,007 174 United States USA 1996 -0,033 0,152 0,171 0,177 0,133 0,143 0,414 0,423 0,232 -0,056 -0,056 -0,024 114 Myanmar MMR 1996 -0,075 0,138 0,371 0,392 0,131 0,231 3,356 6,617 2,791 2,347 2,347 2,618 163 Thailand THA 1996 -0,058 0,117 0,370 2,021 -0,718 -0,727 0,537 0,509 1,252 -0,533 -0,533 -0,504 100 Sri Lanka LKA 1996 -0,306 0,103 0,127 0,110 0,082 0,545 1,044 0,916 0,337 0,759 0,759 1,536 199 Samoa WSM 1996 0,026 0,102 -0,181 -0,091 -0,299 -0,324 0 0 0 0 0 0 86 Jamaica JAM 1996 0,007 0,098 0,004 0,104 -0,291 -0,295 0,049 0,104 0,292 -0,137 -0,137 -0,143 59 Fiji FJI 1996 0,005 0,096 -0,264 -0,302 0,270 0,333 0,237 -0,074 -0,020 0,435 0,435 0,428 34 China CHN 1996 -0,161 0,094 0,332 0,336 0,135 0,353 1,795 4,113 1,817 1,580 1,580 2,075 120 Mauritius MUS 1996 -0,293 0,093 0 0 0 0 0,855 0 0 0 0 0 149 El Salvador SLV 1996 0,014 0,087 -0,109 -0,155 0,102 0 0,377 0,378 0,350 0,502 0,502 0,481 169 Taiwan TWN 1996 0,076 0,079 -0,061 -0,076 0,623 0,508 0,893 0,611 0,252 0,901 0,901 0,766 202 Tonga TON 1996 0,015 0,072 0,838 3,170 -0,252 -0,256 0,911 2,277 6,434 0,333 0,333 0,313 166 Trinidad and Tobago TTO 1996 -0,040 0,056 0,720 0,777 -0,476 -0,429 1,402 1,153 0,166 -0,750 -0,750 -0,739 70 Grenada GRD 1996 -0,005 0,052 -0,25 -0,25 0,111 0,25 0,570 0,417 0,447 0,671 0,671 0,679 206 Palau PLW 1996 0,008 0,046 0,195 0,333 -0,833 0 0 0 0 0 0 0 186 Cuba CUB 1996 -0,075 0,030 0,114 0,067 0,062 1 -0,081 -0,006 -0,047 -0,052 -0,052 0,026 88 Japan JPN 1996 -0,022 0,015 0,068 0,075 -0,327 -0,310 0,092 0,259 0,231 -0,240 -0,240 -0,223 UnitedStatesVirgin- 203 VIR 1996 -0,028 0,002 -0,022 -0,073 0,013 0,074 0 0 0 0 0 0 Islands 185 PuertoRico PRI 1996 0,009 -0,001 0,227 0,364 -0,194 -0,198 1,533 2,111 2,458 1,044 1,044 1,026 72 Guyana GUY 1996 0,031 -0,015 0,235 0,188 -0,186 -0,207 1,217 0,391 0,391 -0,127 -0,127 -0,153 MicronesiaFeder- 194 FSM 1996 -0,010 -0,029 -0,238 -0,241 -0,354 0 0 0 0 0 0 0 atedStatesof 192 FrenchGuiana GUF 1996 0,024 0 0,377 0,361 0,848 1 0 0 0 0 0 0 207 Mayotte MYT 1996 0,022 0 0,722 0,698 0,402 0,412 0 0 0 0 0 0 209 NetherlandsAntilles ANT 1996 -0,188 0 0,688 0,688 0,469 0,794 0 0 0 0 0 0 200 Martinique MTQ 1996 0,003 0 -0,007 -0,002 0,262 0,5 0 0 0 0 0 0 191 Guadeloupe GLP 1996 0,005 0 0,077 0,233 -0,387 -0,391 0 0 0 0 0 0 BUILDING COASTAL RESILIENCE WITH MANGROVES 65 APPENDIX 2 l TABLE A7: PERCENTAGE CHANGES IN FLOOD RISK AND FLOOD REDUCTION BENEFITS OF MANGROVES ACROSS 97 COUNTRIES BETWEEN 2010-2020 2010 vs 2020 (PERCENTAGE) POP PV 100yr STOCK ID COUNTRY ISO3 YEAR MANG HA POP POP EXP POP RISK POP BEN STOCK STOCK EXP STOCK RISK STOCK BEN BEN HA 4% BEN HA 179 Vietnam VNM 1996 -0,006 0,107 0,729 0,588 0,493 0,502 0,846 1,692 1,307 1,258 1,258 1,272 34 China CHN 1996 0,022 0,052 0,182 0,135 0,573 0,539 0,527 1,491 0,870 2,176 2,176 2,107 185 PuertoRico PRI 1996 -0,049 -0,118 0,584 0,533 0,153 0,207 -0,020 0,761 0,704 0,281 0,281 0,347 79 India IND 1996 -0,005 0,118 0,933 0,879 0,477 0,478 0,827 1,969 1,363 1,170 1,170 1,181 78 Indonesia IDN 1996 -0,010 0,131 0,828 0,797 0,344 0,417 0,671 1,608 1,331 0,887 0,887 0,905 169 Taiwan TWN 1996 -0,212 0,020 1,233 1,094 -0,151 0,078 0,319 1,565 1,157 0,066 0,066 0,353 152 Suriname SUR 1996 0,006 0,109 0,619 0,683 1,171 1,158 0,074 0,787 0,492 0,696 0,696 0,686 16 Bangladesh BGD 1996 0,003 0,116 2,052 2,192 2,862 2,9 0,959 4,463 4,131 6,417 6,417 6,394 110 Mexico MEX 1996 0,002 0,130 1,544 1,512 1,142 1 0,234 1,878 1,394 1,338 1,338 1,334 190 NewCaledonia NCL 1996 0,041 0,086 2,759 2,611 0,304 0,333 0,809 5,264 5,018 1,174 1,174 1,087 9 Australia AUS 1996 -0,012 0,151 2,497 2,426 1,849 1,75 0,228 2,822 2,346 1,986 1,986 2,021 25 Brazil BRA 1996 0,010 0,086 1,138 1,080 0,950 0,917 0,075 1,399 1,173 1,039 1,039 1,019 92 Cambodia KHM 1996 0,020 0,168 0,314 0,266 0,816 0,776 0,980 1,135 1,080 1,438 1,438 1,391 64 Ghana GHA 1996 0,024 0,254 1,217 0,798 0,246 0,220 0,888 1,148 0,893 0,636 0,636 0,598 19 Bahamas BHS 1996 0,027 0,108 0,743 0,693 1,896 1 0,129 0,912 0,907 2,390 2,390 2,301 122 Malaysia MYS 1996 -0,009 0,148 0,901 1,198 -0,575 -0,556 0,629 1,574 1,061 0,015 0,015 0,024 114 Myanmar MMR 1996 -0,010 0,075 0,625 0,702 -0,268 -0,25 0,806 4,195 2,906 3,863 3,863 3,912 146 Senegal SEN 1996 -0,008 0,320 0,839 0,767 1,885 1,8 0,633 1,202 1,200 1,240 1,240 1,259 39 Colombia COL 1996 0,003 0,128 1,615 1,498 -0,186 -0,167 0,422 2,340 1,251 0,609 0,609 0,604 189 PapuaNewGuinea PNG 1996 -0,013 0,286 4,525 6,491 1,081 1 0,639 6,043 8,550 1,653 1,653 1,688 76 Haiti HTI 1996 -0,014 0,146 1,378 1,409 2,328 2,373 0,204 1,870 2,399 3,243 3,243 3,306 100 Sri Lanka LKA 1996 0,010 0,057 7,088 10,755 0,285 0,294 0,576 11,800 13,602 0,662 0,662 0,645 2 Angola AGO 1996 -0,011 0,406 4,019 4,769 3,566 3,333 0,153 3,993 2,504 3,576 3,576 3,628 118 Mauritania MRT 1996 0,150 0,330 1,109 1,237 0,136 -0,012 0,491 2,421 1,939 1,503 1,503 1,176 Turks and Caicos 160 TCA 1996 0,210 0,185 1,076 1,027 17,438 10 0,521 0,959 1,416 11,217 11,217 9,098 Islands 65 Guinea GIN 1996 -0,026 0,288 2,903 2,609 1,334 1,667 0,808 6,094 5,406 4,680 4,680 4,832 130 New Zealand NZL 1996 -0,017 0,104 7,934 8,951 0,466 0,333 0,345 9,124 8,088 3,947 3,947 4,033 117 Mozambique MOZ 1996 -0,031 0,327 1,000 0,807 0,049 0 0,646 4,365 4,576 2,388 2,388 2,495 22 Belize BLZ 1996 0,007 0,233 1,523 1,503 0,079 0,125 0,179 1,734 1,591 -0,071 -0,071 -0,077 108 Madagascar MDG 1996 0,002 0,308 0,832 0,825 0,170 0,167 0,385 0,669 0,112 0,374 0,374 0,371 U.R. of Tanzania: 170 TZA 1996 -0,012 0,346 1,870 2,139 0,345 0,286 0,909 3,632 4,147 1,225 1,225 1,251 Mainland 144 Saudi Arabia SAU 1996 -0,245 0,270 1,558 1,374 2,109 3,130 0,337 2,243 1,519 3,870 3,870 5,448 BUILDING COASTAL RESILIENCE WITH MANGROVES 66 APPENDIX 2 l 51 Dominican Republic DOM 1996 -0,006 0,119 2,161 2,389 4,065 3,857 0,672 3,355 2,562 1,218 1,218 1,232 74 Honduras HND 1996 -0,080 0,191 2,361 2,258 0,344 0,5 0,415 2,568 2,630 0,203 0,203 0,307 59 Fiji FJI 1996 0,001 0,043 3,647 3,933 1,157 1 0,342 4,924 5,638 1,674 1,674 1,671 188 SolomonIslands SLB 1996 -0,007 0,279 1,046 1,221 2,350 2 1,383 2,812 3,139 5,242 5,242 5,284 140 Qatar QAT 1996 0,060 0,553 7,600 6,685 10,264 9,597 0,397 11,301 7,835 11,563 11,563 10,853 132 Pakistan PAK 1996 -0,110 0,231 0,760 0,840 -0,297 -0,212 0,531 0,700 0,479 -0,053 -0,053 0,065 202 Tonga TON 1996 -0,030 -0,020 3,770 3,054 3,117 3,219 0,432 5,968 4,922 5,014 5,014 5,200 147 Singapore SGP 1996 -0,070 0,140 4,270 4,016 2,760 3 0,409 5,442 23,779 1,145 1,145 1,306 149 El Salvador SLV 1996 -0,029 0,049 2,369 2,527 1,910 1 0,239 2,931 3,730 0,043 0,043 0,074 126 Nicaragua NIC 1996 -0,061 0,138 6,125 6,347 7,614 8 0,264 7,138 8,288 4,888 4,888 5,271 68 Equatorial Guinea GNQ 1996 -0,006 0,487 5,541 6,216 20,864 0 -0,277 4,569 14,986 14,133 14,133 14,225 187 Kiribati KIR 1996 0 0,171 -0,302 -0,389 1,099 1,103 0,346 -0,197 -0,298 1,413 1,413 1,413 36 Cameroon CMR 1996 -0,003 0,305 6,702 10,049 12,333 11,667 0,559 7,410 12,835 7,537 7,537 7,561 71 Guatemala GTM 1996 0,009 0,225 6,085 6,484 1,621 2 0,422 6,018 4,614 1,922 1,922 1,897 184 Somalia SOM 1996 -0,013 0,375 0,118 0,018 1,256 1,333 0,116 -0,093 -0,174 0,830 0,830 0,854 99 Saint Lucia LCA 1996 0 0,055 3,148 2,986 119,813 118,3 0,177 3,563 6,260 74,975 74,975 74,975 134 Peru PER 1996 0,061 0,138 1,698 1,961 6,242 5 0,460 2,841 2,871 11,835 11,835 11,101 98 Liberia LBR 1996 -0,047 0,299 3,567 9,322 1,196 1,308 0,427 4,801 11,098 2,288 2,288 2,451 61 Gabon GAB 1996 -0,006 0,370 4,971 6,891 2,690 0 0,438 6,080 3,641 1,031 1,031 1,043 48 Djibouti DJI 1996 0,124 0,176 0,330 0,632 58,518 51,5 1,347 1,529 0,227 8,279 8,279 7,255 70 Grenada GRD 1996 0 0,059 537,667 530,333 61,3 58,6 0,367 475,880 696,148 88,658 88,658 88,658 66 Gambia GMB 1996 -0,010 0,347 6,894 396,714 2,013 1 0,316 5,993 263,529 2,370 2,370 2,402 195 Vanuatu VUT 1996 -0,043 0,270 6,208 5,749 83,579 104 0,567 7,897 7,331 103,396 103,396 108,097 180 Yemen YEM 1996 -0,065 0,288 1,535 1,413 188,061 198 -0,558 1,339 2,268 46,842 46,842 50,172 181 South Africa ZAF 1996 0,015 0,158 6,703 6,656 40,103 59 0,148 7,211 6,878 196,262 196,262 193,442 145 Sudan SDN 1996 -0,178 0,269 -0,119 0,560 168,714 172,667 0,421 0,094 -0,057 22,273 22,273 27,306 Venezuela (Bolivari- 177 VEN 1996 0,004 -0,010 0,308 0,282 0,234 0,4 0 0 0 0 0 0 an Republic of) 192 FrenchGuiana GUF 1996 0,067 0 1,648 1,625 3,995 2,5 0 0 0 0 0 0 207 Mayotte MYT 1996 -0,025 0 23,409 28,685 7,140 7,25 0 0 0 0 0 0 209 NetherlandsAntilles ANT 1996 -0,038 0 18,481 15,963 43,917 45,508 0 0 0 0 0 0 201 FrenchPolynesia PYF 1996 0,059 0,064 4,522 5,848 -0,247 -0,287 0 0 0 0 0 0 200 Martinique MTQ 1996 0,009 0 4,305 4,357 10,698 9,667 0 0 0 0 0 0 UnitedStatesVirgin- 203 VIR 1996 0,004 -0,019 10,758 17,355 2,295 2,241 0 0 0 0 0 0 Islands 162 Togo TGO 1996 0,142 0,289 2,474 2,425 0 0 1,315 3,033 2,519 0 0 0 38 Congo COG 1996 0,021 0,291 0,345 0,345 0 0 -0,098 0,822 2,346 0 0 0 BUILDING COASTAL RESILIENCE WITH MANGROVES 67 APPENDIX 2 l 44 Cayman Islands CYM 1996 -0,006 0,160 0 0 0 0 0,292 0 0 0 0 0 105 China, Macao SAR MAC 1996 -0,016 0,206 0 0 0 0 0,193 0 0 0 0 0 120 Mauritius MUS 1996 0,013 0,019 0 0 0 0 0,433 0 0 0 0 0 206 Palau PLW 1996 -0,003 -0,031 4,394 4,615 125,5 0 0 0 0 0 0 0 199 Samoa WSM 1996 -0,038 0,104 0,524 1,011 41,098 43,48 0 0 0 0 0 0 MicronesiaFeder- 194 FSM 1996 -0,032 0,042 3,398 3,447 2,066 1,5 0 0 0 0 0 0 atedStatesof 191 Guadeloupe GLP 1996 -0,005 0 0,879 0,972 1,099 1,143 0 0 0 0 0 0 197 Eritrea ERI 1996 -0,009 0,130 -0,496 -0,502 0 0 0 0 0 0 0 0 Antigua and Bar- 8 ATG 1996 0,033 0,113 0,609 0,473 0,329 0,3 0,348 0,845 0,671 2,201 2,201 2,098 buda 72 Guyana GUY 1996 -0,042 0,049 0,367 0,299 0,146 0,192 0,463 0,416 208,265 127,634 127,634 133,156 14 Benin BEN 1996 0,083 0,317 1,181 1,148 43,994 40,833 1,331 1,430 1,353 15,136 15,136 13,898 148 Sierra Leone SLE 1996 -0,042 0,243 3,154 6,972 0,992 1 0,558 4,177 10,623 1,791 1,791 1,914 35 Côte d’Ivoire CIV 1996 -0,061 0,284 2,435 2,995 11,222 11,923 1,472 3,382 4,792 2,796 2,796 3,043 42 Costa Rica CRI 1996 -0,038 0,113 0,763 0,630 0,824 0,75 0,389 1,176 1,058 1,823 1,823 1,935 27 Brunei Darussalam BRN 1996 0,007 0,126 0,741 1,168 0,625 0,8 0,060 1,216 0,987 4,989 4,989 4,949 90 Kenya KEN 1996 -0,000 0,279 1,515 1,283 1,865 1,905 0,709 2,059 1,127 1,676 1,676 1,677 166 Trinidad and Tobago TTO 1996 -0,020 0,054 2,976 3,373 0,366 0,25 -0,072 8,673 7,362 9,975 9,975 10,197 131 Oman OMN 1996 0,244 0,684 0,256 0,126 1,812 1,260 0,307 0,229 0,206 7,975 7,975 6,213 133 Panama PAN 1996 -0,022 0,185 2,626 3,030 0,575 0,5 0,767 6,290 2,101 1,277 1,277 1,328 158 Seychelles SYC 1996 0 0,078 0,349 0,316 3,376 3,348 0,574 0,560 -0,303 4,925 4,925 4,925 186 Cuba CUB 1996 -0,022 0,001 2,728 3,162 0,699 0,5 1,038 6,589 7,472 2,459 2,459 2,538 135 Philippines PHL 1996 0,018 0,166 0,624 0,633 0,311 0,293 0,806 1,129 0,810 0,204 0,204 0,183 86 Jamaica JAM 1996 -0,080 0,054 0,383 0,312 -0,146 -0,065 0,097 0,401 0,450 -0,163 -0,163 -0,090 125 Nigeria NGA 1996 -0,009 0,300 1,305 1,371 1,411 1,75 0,348 1,626 1,314 1,709 1,709 1,734 88 Japan JPN 1996 0,014 -0,016 1,287 1,295 1,478 1,45 0,097 1,410 1,000 10,054 10,054 9,901 53 Ecuador ECU 1996 0,017 0,177 3,126 2,971 1,135 1 0,274 3,729 2,889 2,530 2,530 2,470 United Arab 5 ARE 1996 -0,042 0,159 0,391 0,169 0,534 0,601 0,420 0,545 0,109 0,033 0,033 0,078 Emirates 193 TimorLeste TLS 1996 -0,002 0,194 0,949 0,864 1,430 1,434 -0,662 -0,448 -0,472 -0,311 -0,311 -0,310 174 United States USA 1996 -0,007 0,071 1,446 1,330 0,741 0,75 0,250 1,531 1,443 1,027 1,027 1,041 163 Thailand THA 1996 0,023 0,039 1,635 1,255 1,775 2 0,356 2,104 1,830 1,398 1,398 1,343 BUILDING COASTAL RESILIENCE WITH MANGROVES 68