metadata: authors: 0: author: Bangalore,Mook
1: author: Smith,Andrew I
2: author: Veldkamp,Ted
count: Vietnam
docna: 0: docna: Exposure to floods, climate change, and
            poverty in Vietnam
docty: Policy Research Working Paper
subtopic: Climate Change Impacts,Science of Climate Change,Climate Change and Health,Social Aspects of Climate Change,Climate Change and Environment,Inequality,Global Environment,Natural Disasters
teratopic: Public Sector Development,Water Resources,Environment,Science and Technology Development,Health, Nutrition and Population,Poverty Reduction,Governance,Urban Development,Communities and Human Settlements,Macroeconomics and Economic Growth
abstracts: cdata!: With 70 percent of its population living
            in coastal areas and low-lying deltas, Vietnam is exposed to
            many natural hazards, including river and coastal flooding.
            These hazards are expected to worsen due to climate change,
            and the impacts of any change in hazard magnitude may be
            particularly acute in this region. This paper examines the
            exposure of the population and poor people in particular to
            current and future flooding at the country level, using new
            high-resolution flood hazard maps and spatial socioeconomic
            data. The paper also examines flood exposure and poverty at
            the local level within Ho Chi Minh City. The national-level
            analysis finds that a third (33 percent) of today’s
            population is already exposed to a flood, which occurs once
            every 25 years, assuming no protection. For the same return
            period flood under current socioeconomic conditions, climate
            change may increase the number exposed to 38 to 46 percent
            of the population. Climate change impacts can make frequent
            events as important as rare ones in terms of exposure: for
            instance, the estimates suggest a 25-year flood under future
            conditions can expose more people than a 200-year flood
            under current conditions. Although poor districts are not
            found to be more exposed to floods at the national level,
            the city-level analysis of Ho Chi Minh City provides
            evidence that slum areas are more exposed than other parts
            of the city. The results of this paper show the benefits of
            investing today in flood risk management, and can provide
            guidance as to where future investments may be targeted.
            Furthermore, while the main strategy in Vietnam today to
            manage flood risk is to reduce exposure, the increase in
            exposure estimated in this paper provides support that
            alternative strategies to reduce vulnerability (such as
            financing for floor-raising) or improve the ability-to-adapt
            of households (such as social safety nets) may warrant
            increased attention.
geo_reg_key: 517198,517191
repnme: repnme: Exposure to floods, climate change, and
            poverty in Vietnam
display_title: Exposure to floods, climate change,
            and poverty in Vietnam
listing_relative_url: /research/2016/07/26597551/exposure-floods-climate-change-poverty-vietnam
topicv3: Climate Change Impacts m1326159 1773,Poverty m1326687 695,Climate Change m1326829 567,Environment, Natural Resources and Blue
            Economy m1327391 566,Flood m1503315 3216
publishtoextweb_dt: 2016-07-25T00:00:00Z
historic_topic: Public Sector Development,Water Resources,Environment,Science and Technology Development,Health, Nutrition and Population,Poverty Reduction,Governance,Urban Development,Communities and Human Settlements,Macroeconomics and Economic Growth
no_of_pages: 29
author_keys: 0: author_key: 1719153
model_extractions: WPS7765 Policy Research Working Paper 7765 Climate Change and Poverty in Vietnam _Background Paper_ Exposure to Floods, Climate Change, and Poverty in Vietnam _Mook Bangalore Andrew Smith Ted Veldkamp_ Development Economics Environment and Natural Resources Global Practice Group & Climate Change Cross-Cutting Solutions Area July 2016
Policy Research Working Paper 7765

# **Abstract**

With 70 percent of its population living in coastal areas and low-lying deltas, Vietnam is exposed to many natural hazards, including river and coastal flooding. These hazards are expected to worsen due to climate change, and the impacts of any change in hazard magnitude may be particularly acute in this region. This paper examines the exposure of the population and poor people in particular to current and future flooding at the country level, using new high-resolution flood hazard maps and spatial socioeconomic data. The paper also examines flood exposure and poverty at the local level within Ho Chi Minh City. The national-level analysis finds that a third (33 percent) of today’s population is already exposed to a flood, which occurs once every 25 years, assuming no protection. For the same return period flood under current socioeconomic conditions, climate change may increase the number exposed to 38 to 46 percent of the population. Climate change impacts can make frequent events as important as rare ones in terms of exposure: for instance, the estimates suggest a 25-year flood under future conditions can expose more people than a 200-year flood under current conditions. Although poor districts are not found to be more exposed to floods at the national level, the city-level analysis of Ho Chi Minh City provides evidence that slum areas are more exposed than other parts of the city. The results of this paper show the benefits of investing today in flood risk management, and can provide guidance as to where future investments may be targeted. Furthermore, while the main strategy in Vietnam today to manage flood risk is to reduce exposure, the increase in exposure estimated in this paper provides support that alternative strategies to reduce vulnerability (such as financing for floor-raising) or improve the ability-to-adapt of households (such as social safety nets) may warrant increased attention.

This paper is a product of the World Bank Environment and Natural Resources Global Practice Group and the Climate Change Cross-Cutting Solutions Area and is a background paper for the World Bank work on “Climate Change and Poverty in Vietnam.” It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http:// econ.worldbank.org. The authors may be contacted at mbangalore@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Exposure to Floods, Climate Change, and Poverty in Vietnam

Mook Bangalore<sup>1</sup> , Andrew Smith<sup>2</sup> , Ted Veldkamp<sup>3</sup> 1 Climate Policy Team, World Bank, Washington, DC, USA 2 SSBN Ltd, Bristol, UK 3 Institute for Environmental Studies, Vrije Universiteit, Amsterdam

## **Acknowledgements**

This work is part of the programmatic AAA on Vietnam Climate Resilience and Green Growth (P148188) and was developed under the oversight of Christophe Crepin. It contributed to the global program on Climate Change and Poverty (P149919) under the oversight of Stephane Hallegatte. The authors thank Abigail Baca, Christophe Crepin, Chandan Deuskar, Stephane Hallegatte, Stuart Hamilton, Pam McElwee, Madhu Raghunath, Maurice Rawlins, Ulf Narloch, Dzung Huy Nguyen, and Vo Quc Tuan for valuable comments and feedback. The authors may be contacted at <u>mbangalore@worldbank.org</u>

**JEL codes:** Q54, I30, Q50

**Keywords:** Floods, Poverty, Vietnam, Exposure, Urban Development
# **1. Introduction**

Vietnam is a rapidly developing country highly exposed to natural hazards. One of the major natural risks the country faces is riverine and coastal flooding, due to its topography and socioeconomic concentration: Vietnam’s coastline is 3,200 kilometers long and 70 percent of its population lives in coastal areas and low‐lying deltas (GFDRR 2015). Furthermore, climate change is expected to increase sea levels and the frequency and intensity of floods, globally and in Southeast Asia (IPCC 2014; World Bank 2014). Given the country’s concentration of population and economic assets in exposed areas, Vietnam has been ranked among the five countries most affected by climate change: a 1 meter rise in sea level would partially inundate 11 percent of the population and 7 percent of agricultural land (World Bank and GFDRR 2011; GFDRR 2015).

Even though climate change impacts are expected to primarily occur in the future, flooding already causes major problems in Vietnam, with some segments of the population more vulnerable than others (Adger 1999; World Bank 2010; World Bank and Australian AID 2014). In particular, evidence suggests poor people are more vulnerable than the rest of the population to natural disasters such as floods, as their incomes are more dependent on weather, their housing and assets are less protected, and they are more prone to health impacts (Hallegatte et al. 2016, Chapter 3). Poor people also have a lower capacity to cope with and adapt to shocks due to lower access to savings, borrowing, or social protection; and climate change is likely to worsen these trends (Hallegatte et al. 2016, Chapter 5).

Therefore, it is important to quantify how many people are exposed to floods, how this distribution of exposure falls upon regions and socioeconomic groups, and how climate change may influence these trends. Employing flood hazard maps and spatial socioeconomic data, this paper examines these questions in the context of Vietnam:

1. How many people are exposed currently? How might this change under climate change?

2. Where is exposure highest currently? How might this change under climate change?

3. How many poor people are exposed currently? How might this change under climate change?

Furthermore, given that the dynamics of poverty and natural disasters (and particularly, floods) occur at the local level, analyses at the national scale (or even at the province or district level) may miss important mechanisms and small‐scale differences, from one city block to the next. To complement the country‐ level analysis, we also focus at the local level within Ho Chi Minh City (HCMC), a city with high flood exposure. Here, we combine high‐resolution flood hazard data with spatial data on slum location, urban expansion, and migration, to examine the distribution of exposure across poor and non‐poor locations. While many studies have examined flood risk in Vietnam, many have only focused on hazard mapping. The contribution of this paper is to include the socioeconomic dimensions and examine how flood exposure is distributed across poor and non‐poor locations, at the country and city levels.

The national‐level analysis finds that a third (33%) of today’s population is already exposed to a 25 year event (an event with a probability of occurrence of 0.04), assuming no protection. For the same return 2
period flood under current socioeconomic conditions, climate change may expose 38‐46% of the population, depending on the severity of sea level rise. Climate change impacts may make frequent events as important as rare ones in terms of exposure: for instance, a 25‐year flood under future climate conditions exposes more people than a 200‐year flood under current conditions. While poor districts are not found to be more exposed to floods at the national level, the city‐level analysis of HCMC provides evidence that 68‐85 percent of slum areas are exposed to floods, a higher percentage than the rest of the city. In addition to showing the benefits of investing today in flood protection, this paper provides policy implications for the design of flood risk management strategies in Vietnam.

# **2. Literature review**

In the last 30 years, floods worldwide have killed more than 500,000 people and resulted in economic losses of more than US$500 billion (Kocornik‐Mina et al. 2015). It is therefore no surprise that a number of studies have examined the population and economic assets exposed to flood risk. At the global level, it is well documented that an increasing share of the population and economic assets lie in areas exposed to riverine and coastal flood risk today, and these trends show no sign of slowing down (UN‐ISDR 2015; Ceola, Laio, and Montanari 2014; Jongman et al. 2014). To compound these socioeconomic changes, climate change is expected to intensify many hazards and further increase exposure: the number of people exposed to river floods could increase by 4‐15% in 2030 and 12‐29% in 2080 (Winsemius et al. 2015).

But only a handful of global studies have examined how this distribution of flood exposure differs between rich and poor. Kim (2012) assesses these dynamics at the country‐level, and finds that poor countries tend to be more exposed to natural disasters, including floods, compared to rich countries. More recently, (Winsemius et al. 2015) examined whether poor people _within countries_ are more exposed to flood risk, and found that this was the case for 60% of the 52 countries sampled.

Within Vietnam, studies suggest that floods significantly impact poverty, both quantitatively at the national level using household survey data (Bui et al. 2014) and qualitatively through focus group interviews at the local level in Ho Chi Minh City (World Bank and Australian AID 2014). One study within Vietnam examines the exposure of poor and non‐poor people to floods and found that a disproportionate number of poor people live in highly‐flooded areas of the Mekong Delta (Nguyen 2011).

At a more local scale and especially within cities, land and housing markets often push poorer people to settle in riskier areas. Where markets factor in hazard risks, housing is cheaper where risk is higher (Husby and Hofkes 2015). And, because poorer people have fewer financial resources to spend on housing and a generally lower willingness and ability to pay for safety, they are more likely to live in at‐risk areas (Lall and Deichmann 2012; Fay 2005; Hallegatte et al. 2016).

Empirically, this higher exposure to flood risk for poor urban dwellers is found in about 75% of the countries examined by (Winsemius et al. 2015), and also when using high‐resolution data on household location and flood hazards in Mumbai, India (Patankar 2015). This high exposure of the urban poor to floods has severe implications on the health of children and economic outcomes of adults, as evidenced in HCMC (World Bank and Australian AID 2014).

3
This paper provides an in‐depth case study of floods, poverty, and climate change in Vietnam and Ho Chi Minh City, examining the exposure of the total population, and poor people in particular to current and future flood risk. It makes two contributions; the first is that it combines state‐of‐the‐art hazard maps with socioeconomic data to examine distributional impacts of floods at the national‐level in Vietnam. Most previous analyses of floods and climate change in Vietnam at the national‐level have focused on hazard mapping and not its distributional impacts (Institute of Strategy and Policy on Natural Resources and Environment 2009; Ministry of Natural Resources and Environment 2009). The second contribution is the paper’s analysis of flood exposure and poverty at national and local levels: most previous analyses have focused on one or the other (Winsemius et al. 2015; World Bank and Australian AID 2014).

# **3. Data**

To examine population and poverty‐specific exposure to floods, we employ spatial data defining flood hazard and a number of socioeconomic characteristics representing poverty and population density.

# **3.1. Flood hazard data**

# **3.1.1. Flood hazard maps for Vietnam developed for this study**

For this study, we developed flood hazard maps representing riverine, flash‐flood and coastal flood risk for Vietnam. These flood hazard maps estimate the inundation depth at a grid cell level of 3 arc‐seconds, (~ 90m) and provide coastal surge hazard layers, along with pluvial and fluvial layers. The maps provide information on the extent and depth of flood hazard for a specific location. For the coastal component, we explicitly model four return periods – 25, 50, 100, and 200 year events, under current and future climate conditions.

There is a significant amount of uncertainty with regards to how much sea level will rise. For that reason we model three future climate scenarios per return period: a low, medium, and high scenario (Table 1), using estimates from the IPCC (IPCC 2014; IPCC 2007). For the fluvial and pluvial hazards, future climate scenarios were not explicitly simulated owing to the complexity and considerable uncertainties that arise (Smith et al. 2014).<sup>1</sup> Although robust modeling of the magnitude of future extreme rainfall is not yet possible, heavy rainfall is expected to increase in a warmer climate, owing to the increased water holding capacity of the atmosphere. Therefore instead of a direct modeling approach, future climate scenarios were inferred by taking flood hazard maps derived under current climate conditions for different return periods, and using

> 1 These uncertainties largely arise from climate models; global climate models (GCMs) struggle to represent the physical processes that produce extreme rainfall. Indeed even in higher resolution regional climate models (RCMs), heavy rainfall events are poorly represented. As a result the modelled rainfall data must be ‘corrected’, in order to render it realistic. The fact that the underlying models themselves cannot represent flood driving rainfall means that there is little confidence in the projections that they produce. Moreover, at the national scale there is very little river gauge data available in Vietnam. Therefore rainfall‐runoff models, required to transform rainfall projections into river discharge values, would be largely un‐calibrated. This adds an additional source of significant modeling uncertainty to the model cascade. The combination of poorly represented extreme rainfall in climate models, coupled with uncalibrated rainfall‐runoff models, would largely render any projections of future flood risk impractical, owing to the significant uncertainties that arise.

4
them as a proxy for future climate scenarios. The return period hazard maps used for each of the future scenarios are outlined in Table 2. Although simplistic, this method allows areas that may be impacted by increasing riverine and extreme rainfall driven flooding to be identified. Clearly there are some significant assumptions and uncertainties arising from this method. However, given the impracticalities of modeling future flood risk in Vietnam, this approach provides a plausible and practical attempt to estimate changing flood risk at the national scale.

For each of the four return periods, four scenarios are modeled (historical, future with low sea level rise, future with medium sea level rise, and future with high sea level rise), combining the coastal and fluvial/pluvial hazard layers (Table 2). Importantly, the flood hazard models do not include flood protection (such as dikes and drainage systems), which can make a large difference in the flood hazard particularly in well‐protected areas. In these well‐protected areas, our flood maps may over‐estimate the flood hazard. For full details on the methodology used to produce these hazard maps, see Appendix 1.

_Table 1. Future scenarios used for Vietnam coastal modeling. RCP stands for Representative Concentration Pathway. We use two RCPs from the recent Intergovernmental Panel on Climate Change (IPCC) report (IPCC 2014) to represent a low climate change and a high climate change scenario. RCP2.6 is a low scenario consistent with temperature increases of 2°C, while RCP8.5 is a high scenario consistent with temperature increases of 4°C. The A1B scenario was taken from a previous IPCC report (IPCC 2007) and represents a medium climate change scenario, in between RCP2.6 and RCP8.5._

|**Simulations**|**Scenario**|**Percentile**|**SLR ‐2100(m)**|
|---|---|---|---|
|Low|RCP 2.6|0.5|0.28|
|Medium|A1B|0.05|0.6|
|High|RCP 8.5|0.95|0.98|

_Table 2. Hazard map scenarios for which the modeling was conducted for Vietnam_

|**Scenario**|**Coastal**|**Fluvial/Pluvial**|
|---|---|---|
|1 in 25|1 in 25|1 in 25|
|1 in 25 Future – Low|1 in 25 + 28cm|1 in 50|
|1 in 25 Future – Medium|1 in 25 + 6cm|1 in 75|
|1 in 25 Future – High|1 in 25 + 98cm|1 in 100|
|1 in 50|1 in 50|1 in 50|
|1 in 50 Future – Low|1 in 50 + 28cm|1 in 75|
|1 in 50 Future – Medium|1 in 50 + 6cm|1 in 100|
|1 in 50 Future – High|1 in 50 + 98cm|1 in 200|
|1 in 100|1 in 100|1 in 100|
|1 in 100 Future – Low|1 in 100 + 28cm|1 in 200|
|1 in 100 Future – Medium|1 in 100 + 6cm|1 in 250|
|1 in 100 Future – High|1 in 100 + 98cm|1 in 500|
|1 in 200|1 in 200|1 in 200|
|1 in 200 Future – Low|1 in 200 + 28cm|1 in 250|
|1 in 200 Future – Medium|1 in 200 + 6cm|1 in 500|
|1 in 200 Future – High|1 in 200 + 98cm|1 in 1000|

5
For most of the analyses, the “combined” maps are used, which include both coastal and the fluvial/pluvial floods. For instance, the combined maps for the 25‐year return period flood (under current conditions, and low, medium, and high future conditions) are presented in Map 1. A Google Earth image of Ho Chi Minh City with the flood map for a 25‐year return period with high climate change is presented in Map 2.

_Map 1. A visual of what the combined hazard maps (which include coastal and fluvial/pluvial) look like. The map presented here is the worse‐case scenario we simulate, a 200‐year return period flood with high sea level rise._ _Map 2. Google Earth image of the flood maps overlaid with the built environment in Ho Chi Minh City, for the 25‐year return period under high climate change._ 6
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data), we decided to use extent since our flood data assumes no protection. Protection is more likely to impact the depth, rather than the extent, of the flood results.<sup>2</sup> We then overlay this flood layer with the population density data set, to estimate the number of people per population grid cell that are exposed to floods. As the population density data set is at a lower resolution (1km) than the flood data (90m), we estimate the percentage of the population grid cell which is flooded, and multiply this percentage by the population in that grid cell. For instance, if a population grid cell has 500 people, and 10% of that cell is flooded (based on the flood data), then we estimate 50 people to be exposed to floods in that cell. In doing so, we assume that the population is evenly distributed within a grid cell.

We run this analysis for all the scenarios presented in Table 2, and aggregate our results at the district level to estimate the number of people affected. To include the poverty dimension, we use the poverty headcount rate in each district to estimate the percentage of poor people exposed. For instance, if 20,000 people are exposed to floods in District X, and District X has a poverty headcount rate of 20%, 1,000 poor people are exposed to floods in that district. In this analysis, we assume that poverty is evenly distributed within a district.

# **4.2. Slum and urban expansion exposure in Ho Chi Minh City**

For the HCMC analysis, we estimate the general exposure to flooding, for the whole province of HCMC and in each of its 24 districts. The flood maps used here are based on a model of HCMC, and are not the same map as used in the figurative example in Section 4.1.

Exposure to flooding was again evaluated using flood extent (we also evaluate flood depth, for full results, see Appendix 2). We examine the flood extent in three areas: for all urban areas (the whole HCMC province), for those areas defined as potential slums (from the PUMA data set), to examine how exposure to floods is different in slum areas. We do the same for areas defined as urban expansion locations (also from the PUMA data set) to evaluate whether new urban developments within the province of HCMC take place in flood prone areas.

Again we use a number of events, from the case of regular flooding (10‐year event) to more extreme flooding events (1000‐year event). Moreover, we examine how this exposure changes due to climate change (proxied by sea level rise changes), by running the analysis with flood hazard maps taking into account a 30 cm sea level rise. In each district and across the whole city, we examine the percentage of area within each of the three categories (all urban areas, slums, and urban expansion areas) that is exposed to floods (that is, where flood depth > 0cm) and the percentage which is not exposed to floods (that is, where flood depth = 0cm). We then compare these values across the three categories.

# **5. Results**

> 2 There is also  a good reason for examining extent over depth, in terms of the hazard modeling; flood depths within a large scale flood model are very uncertain, and there is much more certainty about extents.

10
# **5.1. National‐level analysis for poverty and exposure to floods**

# **5.1.1. Flood risks (with and without climate change)**

For the entire country of Vietnam, at the district level, we estimate the total number of people and the share of the population who are exposed to floods. In the results presented, we examine the four scenarios for the 25‐year, 50‐year, 100‐year and 200‐year return period flood – a historical scenario, and three scenarios representing future climate: a low, medium, and high scenarios.

We aggregate the results at the country level.<sup>3</sup> A third (33%) of today’s population is already exposed to a 25‐year flood in Vietnam, assuming no protection (such as dikes and drainage systems), which can make a large difference in the flood hazard particularly in well‐protected areas. In these well‐protected areas, our flood maps may over‐estimate the flood hazard.

When including climate change, this percentage increases by 13‐27%, depending on the severity of sea level rise. This increase in exposure is due to the concentration of the population in coastal areas. For the 50‐year flood, more than a third (38%) of today’s population is already exposed. Given climate change, this number is expected to increase by 7‐21% (resulting in overall exposure of between 40 and 48%) for the same return period (50‐year). For a 100‐ and 200‐year flood under a high climate scenario, more than half of the population is exposed.

Climate change impacts can be seen in these exposure numbers – for instance, a 50‐year flood with medium climate change impacts has the same exposure of a 200‐year historical flood (at 44%), while almost half the country’s population (48%) is exposed to a 50‐year flood with high climate impacts. Full results are presented in Table 3.

_Table 3. Population exposed to flood risk in Vietnam, across the 16 flood hazard scenarios examined._

|**Scenario**|**Exposure**||**Return**|**period**||
|---|---|---|---|---|---|
|||25|50|100|200|
|Hitil|Estimated population exposed (m)|30.17|34.30|38.35|40.43|
|sorca|Percentage of today’spopulation|33%|38%|42%|44%|
|li|Estimated population exposed (m)|34.78|36.87|40.91|42.32|
|Low cmate<br>h|Percentage of today’s population|38%|40%|45%|46%|
|cange|Increase due to climate change|13%|7%|6%|4%|
|Medium|Estimated population exposed (m)|38.03|40.22|43.34|45.16|
|climate|Percentage of today’s population|42%|44%|48%|50%|
|change|Increase due to climate change|21%|15%|11%|10%|
||Estimated population exposed (m)|41.46|43.36|46.13|48.72|
|High climate<br>h|Percentage of today’s population|46%|48%|51%|53%|
|cange|Increase due to climate change|27%|21%|17%|17%|

3 Results presented are similar to a previous study analyzing the exposure to a 100‐year return period flood without climate change impacts, which finds 40 million people to be exposed to that event (Jongman et al. 2014).

11
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[) 20 - 40% (5 40 - 60% HI 60 - 80% HH 80 - 99%
impact of climate change on the number of poor people exposed is evident. For instance, a 25‐year event with high climate change impacts has the same exposure as a 200‐year historical event (at around 41% of poor people being exposed).

_Table 4. Number and percentage of poor exposed to flood risk in Vietnam, across the 16 flood hazard scenarios examined._

|**Scenario**|**Exposure**||**Return**<br>|**period**<br>||
|---|---|---|---|---|---|
|||25|50|100|200|
|Historical|Estimated poor exposed (million)|5.28|6.19|6.88|7.24|
||Percentage of today’spoor|30%|35%|39%|41%|
|Low climate|Estimated poor exposed (million)|6.27|6.64|7.32|7.54|
|<br>chane|Percentage of today’s poor|35%|37%|41%|42%|
|g|Increase due to climate change|16%|7%|6%|4%|
|Medium|Estimated poor exposed (million)|6.80|7.16|7.69|8.00|
|climate|Percentage of today’s poor|38%|40%|43%|45%|
|change|Increase due to climate change|22%|14%|11%|10%|
|Hih lit|Estimated poor exposed (million)|7.33|7.66|8.14|8.56|
|g cmae<br>chane|Percentage of today’s poor|41%|43%|46%|48%|
|g|Increase due to climate change|28%|19%|16%|15%|

Based on the statistics provided in Table 4, there is no strong signal that poor people are more exposed than non‐poor people, at the national level. However, this may not be the case in specific regions or within specific districts.

To examine which districts have a confluence of poverty and flood risk, we classify both each district’s poverty headcount rate and flood exposure into three categories: low, medium, and high. We create 3 quantiles for each. We examine both absolute and relative numbers, overlaying the number of poor and number of flood exposed, and the percentage of poor and percentage of flood exposed.

The results suggest that areas of the Northern Mountains and the Mekong Delta exhibit districts with high flood and high poverty (darkest shade of brown in Map 12). The results are slightly different when comparing relative and absolute numbers. When using absolute (the number of poor and number of flood exposed) more areas of high flood and poverty are visible in the Mekong and Red River Delta, as well as along the eastern coasts.

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We examine these dynamics in Ho Chi Minh City, using high‐resolution local‐scale flood maps designed specifically for HCMC (Lasage et al. 2014) and proxy for poverty using the spatial location of potential slums from the Platform for Urban Management and Analysis (PUMA) data set (World Bank 2015). The PUMA data set also has information on locations of urban expansion from 2000 to 2012. We therefore examine exposure to flooding in all three locations – urban areas as a whole, potential slum locations, and areas of urban expansion. The results we present below are for all districts in HCMC; results for only districts with slum areas are similar and thus not reported.

We find that a relatively high percentage of the potential slum areas are exposed to floods, ranging from 68.9% (for a 10‐year return period) up to 83.3% (for a 1000‐year return period). When considering all urban areas of HCMC, exposure to flooding is lower: 63% (for a 10‐year return period) up to 68.3% (for a 1000‐year return period). A sea level rise of 30 cm increases the extent of flooded areas the most in slum areas and for a low‐probability but recurrent flood (10‐year flood). For a 10‐year flood and looking only within slum areas, we find an increase in exposure of 15 percentage‐points due to sea level rise, compared to a difference of 5.7 percentage‐points when looking at the entire urban area of HCMC. These results, as presented in Figure 1, suggest slum areas to be more exposed to floods than non‐slum areas.<sup>4</sup> Due to cognitive biases, it can be hypothesized that flood risk from frequent events (like the 10‐year return period event) are more likely to be remembered than a rare event (like the 1000‐year return period event) and thus more likely to be included in land values. If this logic holds, it is likely that potential slum areas should exhibit a higher exposure than other areas for frequent events. However, in our analysis we find the opposite: that the difference between slum and non‐slum increases as the return period event gets rarer.

When looking at the areas of urban expansion we find that a large share, 72.2% under a flooding event with a 10‐year year return period up to 74.4% in case of a 1000‐year return period, of these areas is located in areas prone to flooding (Figure 1).

> 4 Disaggregated results per district can be found in Appendix 2. Results using depth as an indicator for flooding is also presented in Appendix 2.

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<!-- Start of picture text --> 90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>All urban areas Potential slum areas Urban expansion areas<br>RP10 RP25 RP50 RP100 RP1000<br>RP10_SLR RP25_SLR RP50_SLR RP100_SLR RP1000_SLR<br>% of area exposed<br><!-- End of picture text --> _Figure 1. Slum areas tend to be more exposed than the average, across all flood scenarios. SLR means the scenario includes a 30cm sea level rise due to climate change. RP denotes the turn period event._

# **6. Limitations**

The findings presented should be interpreted considering a number of caveats.

While we use current and future flood data, we only use current population and poverty data, as reliably projecting these socioeconomic trends spatially into the future is almost impossible. Changes in these trends – among many other factors – can lower socioeconomic vulnerability even as the climate change hazard increases (Hallegatte et al. 2016). Along these lines, while we examine which regions within Vietnam have the highest flood exposure, we do not examine the determinants of vulnerability (other than poverty). Recent analyses suggest that the Northwest, Central Highlands, and Mekong River Delta have the greatest socioeconomic vulnerability (World Bank 2010).

In the flood hazard maps developed for this paper, we assume no protection due to a lack of data and as a result the hazard maps present an upper bound of flood risk. Work is currently ongoing to develop a global database of flood protection, and this information can be mobilized for future work (Scussolini et al. 2015). For the national‐level analysis, flooded areas are defined as any area with inundation higher than 0. We have not yet explored the depth dimension, although the flood hazard maps developed for this study allow for this potential in future work.

For the HCMC analysis, the location of the slum areas in the PUMA data set are mainly restricted to the old town. Furthermore, slum areas are often difficult to define and the data we have likely does not capture all slum areas within HCMC. Finally, the urban expansion data set does not make a distinction between the urban expansion of residential areas or infrastructure (roads, etc.).

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In terms of the hazard, the flood maps for HCMC show flood depth and extent from the river and from sea (when looking at the sea level rise scenario). Pluvial flooding and possible ‘sink’‐areas in the city are not taken into account. Moreover, the lowest return period we have flood maps for is not low, compared to what is experienced in the city. Some areas of HCMC are flooded every year. Since this analysis used a flood with a 10‐year return period as the flooding scenario with the highest recurrence interval we were not able to capture the relative differences in exposure to these yearly/bi‐annual flooding events (and we hypothesize that poor people are relatively more exposed to these types of flooding than the general population).

# **7. Policy Recommendations**

Despite the limitations, this analysis presents some initial findings on what exposure to floods looks like in Vietnam, how it may change under a changing climate, and the exposure of poor people. The results from this paper have implications regarding infrastructure development, land use planning, and strategies to manage flood risk.

First, the results of this paper suggest that climate change is likely to increase the number of people exposed to floods, especially in the Mekong and Red River Deltas. Climate change impacts can make frequent events as important as rare ones in terms of exposure: for instance, a 25‐year flood under future conditions of high sea level rise exposes more people than a 200‐year flood under current conditions. In addition to showing the benefits of investing in flood risk management today, these results also suggest that new investments in flood protection (whether natural protection through mangroves or physical protection through dikes and drainage systems) should be planned with climate change considerations (e.g. where will flood be the worst in the future and how can new infrastructure withstand future levels of flood).

However, current planning approaches in Vietnam have not yet adequately taken these existing and future floods into account (IMHEN and UNDP 2015). For example, the city of Long Xuyen in the Mekong Delta has based its dike infrastructure around the city on historical floods levels only, with no inclusion of future climate change‐induced water levels (This Report, 2016). Qualitative surveys in Long Xuyen suggest existing defenses have already proved inadequate in recent flooding (This Report, 2016). Investments in climate‐informed flood protection taken now reduce flood exposure, but can also save money in the long‐ run by reducing the amount spent on recovery and reconstruction for future floods. And while it is challenging to integrate into project planning, innovative approaches such as decision‐making under uncertainty can provide support to decision‐makers on how to design flood projection with climate change in mind (Hallegatte et al. 2012).

Furthermore, while infrastructure investment can protect certain areas, it may increase exposure in other areas. for example, upstream dams in the Mekong Delta can increase the strength and velocity of downstream floods (This Report, 2016). Thus, flood risk assessment needs to be integrated across policy sectors (agriculture, industry, infrastructure, defense, urban planning) before development decisions are made, and more comprehensive approaches, like integrated coastal zone management and planning, would help to reduce exposure of people and assets in Vietnam.

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Second, this paper found that that while poor people do not appear to be more exposed to floods than non‐poor people at the national level, this is not the case at the local level within a city. The results presented suggest potential slum areas are more exposed to floods than non‐slum areas, and the exposure differential increases with sea level rise. As a result, risk‐sensitive land‐use planning may be a priority to ensure development takes place in safer areas.

In many cases, risk‐sensitive land‐use planning involves resettlement, which is the major ex‐ante hazard adaptation mechanism employed in Vietnam currently, especially in the Mekong Delta. Furthermore, recent research in Tan Chau district suggests the resettlement policy enacted in 2002 may have made households worse‐off. Inadequate financing resulted in households paying for their new settlements out‐ of‐pocket; many households who were farmers and fishers did not have adequate land, transportation and market access, and inadequate livelihood support was provided to them (This Report, 2016). Where resettlement policies are enacted, it is imperative that such policies are paired with livelihood and financing support.

The estimates of increasing exposure provided in this paper also provide support for increased attention towards strategies which reduce vulnerability or increase the ability of households to adapt to floods. Strategies such as government subsidies for household‐level flood protection (like raising of floors), improved financial inclusion, and better observation systems and early warning, and resilient agricultural practices can reduce the asset and income losses associated with floods (Hallegatte et al. 2016). And when hit, targeted social protection (which can support the affected population quickly after a large flood) can hasten recovery (Hallegatte et al. 2016, Chapter 5). Such policy measures may be targeted in areas with higher future exposure (geographical targeting) as well as to individuals and households classified as poor and near poor who experience flooding (individual targeting). Areas such as the Northern Mountains have high poverty and are expected to experience an increase in flood exposure. While infrastructure protection can be costly in these remote and sparsely‐populated areas, strategies to reduce vulnerability or improve the ability‐to‐adapt of households can reduce flood impacts.

Risk management policies are best designed as holistic strategies that combine many of these levers – from risk‐sensitive land‐use planning to flood protection to investments in social protection (World Bank 2013). But each strategy undertaken will be context‐specific and based on local conditions. The results of this paper provide some insights to inform the implementation of risk management policies in Vietnam and suggest that such investments can better manage current and future flood risks if action is taken today.

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# **9. Appendix**

# **9.1. Appendix 1. National‐level: Flood Hazard Modeling Details**

All the data were produced using the SSBN global flood model, producing flood hazard data at 90m resolution. The SSBN global model couples a flood frequency analysis conducted at the global scale, with a fully 2‐D hydraulic modeling framework. Extreme river discharges are derived from a Flood Frequency Analysis (FFA), applied at the global scale (Smith, Sampson, and Bates 2015). The model also explicitly simulates in‐channel flow, with the FFA also used to estimate bankfull discharge across the channel network (defined as the 1.1 – 2 year event depending on climate zone); these values are used to calibrate the channel conveyance capacity within the hydraulic modeling framework. A number of global data sets are used to derive the inputs to the hydraulic model. Firstly, the Hydrosheds variant of SRTM is used, both at 3 and 30 arc second resolutions. A number of additional corrections are applied to the terrain data including a systematic vegetation correction procedure in vegetated areas and an urban correction procedure in urbanized areas. A detailed description of the modeling framework is provided by (Sampson et al. 2015).

For the coastal simulations, input boundary conditions were derived using estimates of return period surge heights, taken from (Chinh, JianCheng, and Bui 2014). Storm surge hydrographs for each recurrence interval were taken for four tidal gauges located along the Vietnamese coastline. Coastal boundary conditions for the hydraulic model were derived by linearly interpolating between the gauge locations. The hydraulic model was set up so that a coastal boundary condition was implemented for each ‘land’ cell located next to the coast. In addition to the coastal boundary conditions, large river channels were also 24
included in the simulations, using a sub‐grid channel network set‐up (Neal, Schumann, and Bates 2012). In channel flow was estimated to be 0.5*bankfull Q; rivers were estimated to be at 50% channel capacity.

Coastal simulations under future climate conditions were undertaken using the latest projections of global mean sea‐level rise, outlined in the Fourth Assessment Report (AR5) and Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC) (IPCC 2014; IPCC 2007). Estimates of sea‐level changes were taken and used to directly perturb the boundary conditions used in the simulations under current conditions. In order to incorporate uncertainty, simulations were undertaken for a range of projected changes, represented here as Low, Medium and High sea‐level rise (SLR) projections ( Table 1). The simulations are all conducted assuming that no flood defenses are in place; clearly flood defenses are not represented in the available terrain data. Therefore, these simulations should be considered as an upper bound of flood risk in the country.

The possibility of including a storm surge intensity component to the future projections was also discussed, but there are significant uncertainties around quantifying storm intensity changes that would preclude any reasonable modeling being undertaken (Seneviratne et al. 2012). It also seems that changes in surge extremes are going to be largely driven by sea‐level rise (Lowe et al. 2010). That being said, there are studies attempting to quantify changes in storm surge intensity; Lin et al. (2012) reported in a study focused on the North Atlantic, that in some cases changing storm surge intensity was comparable to changing sea‐level rise. Such changes would effectively double change in hazard intensities presented here. As of yet, we have not included this in the simulations due to the uncertainty.

# **9.2. Appendix 2. City‐case: HCMC**

# **9.2.1. Results for flood extent across districts**

Figure 2 presents the differences in relative flooding extent (%) for a flooding event with a 10 year or 50 year return period representing the current and future conditions. Overall, we find that both under current conditions and given a 30 cm sea level rise, relative flooding extent is higher in slum and urban expansion areas than in the non‐slum areas, both when looking at the HCMC‐city level and at the level of districts. This can serve as a first‐order indicator that these slum and urban expansion areas are relatively often located in flood prone areas.

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- @ Sprawl areas - Future conditions

— Non-slum areas - Future conditions
<!-- Start of picture text --> 180<br>160 s<br>as . . si<br>140 = 2 aT ii<br>. x<br>== re. =s - 3 . ps .<br>= 100 * 2 : e $<br>3<br><!-- End of picture text --> <!-- Start of picture text --> 3=5 80° . .- . i ;.1 aT bd7. fe. . . =. »° >re ’<br>z 40 i + °<br>i é pa e 1: ,<br>20 = ° . id - °<br>Fi ° ° r] CI] ba °<br>of =. . . LEr he. . °a . . ° ° * ° °<br>District 1 District Thu Duc District$ GoVap Binh Tan Binh TanPhu Phu District2 District 3 District District District 4 District5 District 6 District& Binh Tan District7 CuChi Hoc Binh =NhaBe CanGio HCMC<br>12 Thanh Nhuan 10 11 Mon = Chanh<br><!-- End of picture text --> <!-- Start of picture text --> 180<br>160 « .+ * a<br>8<br>140 5%: .<br>?<br>- s 1 id a<br>=§ 10 s . ° 4<br>b i<br>= 100 . .<br>7<br>$<br><!-- End of picture text --> <!-- Start of picture text --> 580 a o s 77 . = 4<br>5 60 7 5 2 . + ° £ ij<br>£ a3 = - o ih at<br>z 4020 °a ° : . id e , aase . .<br>é - . 5 =<br>of Ae. . . rf. if. rf. =° . e ° ° . .<br>District 1 District Thu Duc District$ GoVap Binh TanBinh TanPhu Phu District2 District 3 District District Distr4 D i strictct 5 District6 District 8 Binh Tan District7 Cuchi Hoc Binh =NhaBe CanGio HCMC<br>12 Thanh ANhuan 10 1 Mon = Chanh<br><!-- End of picture text --> @ Sprawl areas - Current conditions — Non-slum areas - Current conditions @ Slum areas - Future conditions

- @ Sprawl areas - Future conditions

— Non-slum areas - Future conditions