An Evaluation of Coastal Flooding Risk Due to Storm Surge Under Future Sea Level Rise Scenarios in Thua Thien Hue Province, Vietnam

An Evaluation of Coastal Flooding Risk due to Storm Surge under Future Sea Level Rise Scenarios in Thua Thien Hue Province, Vietnam

Thu Nguyen, PhD

A Thesis

Geoscience

Submitted to the Graduate Faculty of Texas Tech University in Partial Fulfillment of the Requirements for the Degree of

DOCTOR OF PHILOSOPHY

Approved

Dr. Jeffrey Lee Chair of Committee

Dr. Katharine Hayhoe Dr. Cao Guofeng Dr. Kevin Mulligan

Mark Sheridan Dean of the Graduate School

December 2017

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ACKNOWLEDGEMENTS

The most important acknowledgement of gratitude I wish to express is to my committee chair, Dr. Jeff Lee at Department of Geosciences, who has the attitude and the substance of a genius. Dr. Lee has motivated me for this research and their constant guidance and supports. He granted me various conveniences without which it would not have been possible to do the work.

I would like to express my profound appreciation to my adviser, Dr. Katharine Hayhoe at Department of Political Science, who inspired me this research. Dr. Hayhoe has put her immense knowledge, valuable experiences and wisdom at my disposal.

I would like to thank my committee members, Dr. Cao Guofeng and Dr. Kevin Mulligan, for their practical advice in my research work and suggested many important addition and improvement.

Special thanks to my dear friends, especially to Mr. Dien Xuan Nguyen in Vietnam, who provided me with the most important inputs including data, software and even training. Without his help and inputs this work would not have been done.

And finally, I would like to say this thesis is dedicated to my husband and children who have always stood by me and support me unconditionally. Without their love, support and hardship over years none of this work would have been possible. They have always been there for me and I am thankful for everything they have helped me achieve.

Thanks for all your encouragement!

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TABLE OF CONTENTS ACKNOWLEDGEMENTS ...... ii ABSTRACT ...... v LIST OF TABLES ...... vi LIST OF FIGURES ...... vii MOTIVATION FOR THE WORK ...... viii CHAPTER ONE ...... 1 INTRODUCTION...... 1 1. Background ...... 1 2. Climate Change in Vietnam ...... 3 2.1 Climate change scenarios ...... 4 2.2 Sea level rise scenarios ...... 7 2.3 Impacts of Climate Change ...... 10 3. Dissertation Objectives ...... 12 CHAPTER TWO ...... 13 LITERATURE REVIEW ...... 13 1. Sea Level Rise ...... 13 2. Tropical Storm/Storm Surge ...... 15 3. Land subsidence ...... 16 4. Coastal Flooding ...... 17 5. Vertical uncertainty in elevation-based SLR assessment ...... 18 6. Reliability of MIKE 21 FM model in inundation modeling ...... 20 7. Justification ...... 21 CHAPTER THREE ...... 23 STUDY AREA ...... 23 1. Geographical Characteristics...... 23 2. Disaster context and their impacts in Thua Thien Hue ...... 25 CHAPTER FOUR ...... 29 METHODOLOGY ...... 29 1. Data Sets and Pre-Processing ...... 30

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1.1 Data sets for MIKE 21 FM modeling ...... 31 1.2 Data source for impact analysis using ArcGIS tools ...... 33 2. Approach ...... 34 CHAPTER FIVE ...... 52 RESULTS ...... 52 1. Simulation of coastal flooding ...... 52 2. Analysis of Impacts ...... 52 CHAPTER SIX ...... 64 CONCLUSIONS ...... 64

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ABSTRACT

This study attempts to quantify the impacts of coastal flooding on land area, population and urban infrastructure that would result from an extreme tropical storm occurring over a range of for future sea level rise scenarios. This case study follows a storm named Xangsane, that occurred in Thua Thien Hue, a central province in Vietnam, in 2006. It compares the impacts caused by different scenarios of storm surges and sea levels simulated on two different datasets, a national one and a global one. A 2-dimentional hydrodynamic model is used to simulate the storm surge and coastal flooding. The numerical simulation is coupled with ArcGIS tools to analyze the level and extent of inundation areas as well as their influences on population and urban infrastructure (transportation system, schools, hospitals, and historical heritage sites).

Projection indicates that the sea level rise (SLR) of 1 m and 2 m can cause huge impacts for transportation infrastructure and buildings. The impact increase on transportation infrastructure is considerably significant, from 200% to 400% increase on all types of road across different storm surge scenarios. The impact on railroads is the least, about 32 km to 50 km in SLR of 1 m and 2 m, but its impact percentage is the highest among all other categories. The maximum railroad impact increases nearly 2000% for SLR of 1 m and 3500% for SLR of 2 m with or without storm surge. For land area, building (school and hospital), and special infrastructure, the impacts increase up to 450% in SLR of 1 m and 2 m simulated on both datasets (the national and the global). The risk of flooding is closely related to the SLR more than storm surges. The impact of projected sea levels increases the need for flood control measures in Thua Thien Hue province.

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LIST OF TABLES Table 1: Relative frequency of natural disasters in Vietnam (CCFSC, 2005) ...... 3 Table 2: Summary of original data and their sources ...... 31 Table 3: Study Scenarios ...... 36 Table 4: Mesh Generation Parameter ...... 38 Table 5: Parameters of the MIKE 21 flow model ...... 40 Table 6: Total inundated area (km2) in different scenarios ...... 53 Table 7: Storm Making Land-falling_National Data 10K ...... 54 Table 8: Storm Moving Along The Coast_National Data 10K...... 55 Table 9: Storm Making Land-falling_Global Data SRTM ...... 57 Table 10: Storm Moving Along the Coast_Global Data SRTM ...... 58 Table 11: No Storm Surge_SRTM ...... 60 Table 12: No Storm Surge_10K ...... 61

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LIST OF FIGURES Figure 1: Change in Average Temperature (0C) (IMHEN, 2016) ...... 5 Figure 2: Changes in Annual Average Rainfall (%) (IMHEN, 2016) ...... 7 Figure 3: Sea Level Rise under the Lower Emission Scenario (cm) ...... 8 Figure 4: Sea Level Rise in Medium Emission Scenario (cm) ...... 8 Figure 5: Sea Level Rise in High Emission Scenario (cm) ...... 8 Figure 6: The Risk of Flooding with Rising Sea Level of 100cm (IMHEN, 2016) ...... 9 Figure 7: The Trend of Flood and Storm Occurrence in Vietnam during 1975 to 2013...... 10 Figure 8: Mapping and Ranking the Disaster Frequency, Disaster Damages and Killed People in Vietnam during 1990-2016 ...... 11 Figure 9: Administrative Map of Thua Thien Hue Province……………………………………..23 Figure 10: Statistics of Population and Land Area in Thua Thien Hue Province in 2013 ...... 24 Figure 11: The Historic Flood in Hue in 1999 ...... 25 Figure 12: Flood-related losses in Thua Thien Hue Province, Vietnam during 1993 – 2012 ...... 26 Figure 13: Flood-related losses in Thua Thien Hue Province, Vietnam during 1993 – 2012 ...... 26 Figure 14: Inundated Heritage Sites in a flood 2011 (Photo credit: laodong.com.vn) ...... 28 Figure 15: Demonstration of different data layer used in coastal flooding model ...... 33 Figure 16: Different Storm Track Assumptions ...... 35 Figure 17: Demonstration of Mesh Generation in MIKE 21 FM Window ...... 39 Figure 18: Observed Tidal Water Level Measured at Son Tra Station in 2006 ...... 41 Figure 19: Comparison of Observed Water Level and Simulated Water Level ...... 41 Figure 20: Land-Falling Storm with SLR 1 m Simulation on 10K Dataset ...... 42 Figure 21: Moving Along Storm with SLR 1 m Simulation on 10K Dataset ...... 43 Figure 22: Land-Falling Storm with SLR 1 m Simulation on SRTM Dataset ...... 43 Figure 23: Moving Along Storm with SLR 1 m Simulation on SRTM Dataset ...... 44 Figure 24: Comparison of No SLR Inundation Scenarios on SRTM and 10K Datasets ...... 45 Figure 25: Comparison of No Storm Surge Inundation Scenarios on SRTM and 10K Datasets .. 45 Figure 26: Comparison of 1 m-SLR Inundation Scenarios on SRTM and 10K Datasets ...... 46 Figure 27: Comparison of No SLR Inundation Scenarios on 10K Dataset ...... 46 Figure 28: Comparison of Inundation Scenarios with the different SLR scenarios and the same simulated storm track on 10K Dataset ...... 47 Figure 29: Comparison of Inundation Scenarios with the same SLR scenarios and the different simulated storm track ...... 48 Figure 30: Population impacted by different Storm Scenarios with 1-m SLR ...... 49 Figure 31: Impacts caused by different SLR Scenarios 10K Dataset ...... 50 Figure 32: Impacts illustrated on different datasets (SRTM and 10K) ...... 50 Figure 33: Impacts by land-falling storm _10K dataset ...... 54 Figure 34: Impacts by moving along storm _10K dataset ...... 56 Figure 35: Impacts by land-falling storm _SRTM ...... 57 Figure 36: Impacts by moving along storm _SRTM ...... 58 Figure 37: Impact comparison between two datasets 10K and SRTM ...... 59 Figure 38: No Storm Surge with Different SLR Scenarios _10K dataset ...... 61 Figure 39: No Storm Surge with Different SLR Scenarios _SRTM ...... 62 Figure 40: Impact comparison among different storm surge scenarios ...... 62 Figure 41: Impact comparison between No Storm Surge and No SLR _10K dataset ...... 63

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MOTIVATION FOR THE WORK

I was born in Quang Ngai, a small coastal province in the Central region of Vietnam. Every year, I witnessed and suffered several storms that came inland and caused flooding. My small, old house was always inundated. Though I had no idea when I was young how much damage and how many deaths a storm or a flood can cause, my brothers and sister and I trembled and were scared whenever my mother told us to hide under the bed to avoid the sweep of the storm or to move up to the roof to escape the flood. These memories are unforgettable and help motivate my research into the risks posed by storm- caused coastal flooding in the region, especially given the high vulnerability of its people and infrastructure. The overall goal of my research is to help mitigate the impacts of climate change, especially by reducing the risks of storm-related disaster and poverty in Vietnam. This study provides a model for flooding caused by storms and sea-level rise, which I hope will aid in the planning and development of the coastal region.

I chose Thua Thien Hue province for this study for several reasons. First, Thua Thien Hue province has a relative abundance of available data for analysis. Second, as one of the central provinces in the coastal region, Thua Thien Hue has experienced the most water-related deaths and damage in the country and has the second-highest frequency of water-related disasters in the country. Third, with typical geographical characteristics, Thua Thien Hue can serve as an example of the whole region.

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CHAPTER ONE

INTRODUCTION

1. Background Vietnam is a coastal country located in the tropical region of Southeast Asia with a total population of over 90 million people, of which about 10 million live in the coastal zone and are potentially subjected to the impacts of a rising sea level. Since the political and economic reforms launched in 1986, this developing country has made many remarkable advances: from being one of the poorest countries in the world, it became a lower middle-income country with per capita income of US$2,100 by the end of 2015 (World Bank, 2016); the poverty rate has decreased from 58 percent in 1993 to 14.5 percent in 2008; and population growth rate has slowed from 1.16 in 2002 to 1.04 in 2011 (World Bank, 2010). In addition to improving most welfare indicators, Vietnam has also made good progress towards achieving the ten original Millennium Development Goal targets (UN, 2014).

Geographically, Vietnam is divided into three parts: North, Central and South. Central Vietnam, bridging the North and the South, is a long land strip located by the coast. The central part is one of three metro regions of Vietnam, where a large proportion of the population, infrastructure, and economic production including tourism, industry, and irrigated agriculture are tightly packed along the coast. It holds the long-term economic future of Vietnam because of many factors. Firstly, it contains many clusters of historical sightseeing and ancient towns recognized as UNESCO World Heritage Sites in Thua Thien Hue and Hoi An. Furthermore, Central Vietnam has a coastal range and numerous lovely beaches allowing cities like Danang, Quy Nhon and Nha Trang to develop a tourist economy. Secondly, Central Vietnam is well identified by the development of agriculture and both light and major heavy industrial clusters. While Danang and Nha Trang are very famous cities for their sandy beaches, Quang Ngai is known for the first major oil refinery in the country, Dak Lak is a place for major coffee planting and processing and bauxite mining, and Binh Dinh known for its major wood furniture industrial area. Moreover, auto parts and IT industries are also discovering Central Vietnam for its potential. Thirdly, it

1 Texas Tech University, Thu Nguyen, December 2017 has the most convenient transportation system in the country. It has airports, railroads, and ports in the cities where it takes just few minutes to get there. Indeed, Central Vietnam holds the most promise for new economic growth in Vietnam. Despite its advances, Vietnam is still highly vulnerable to the adverse effects of global warming and climate change. Some studies have reported sea level rise in Vietnam. According to the United Nations Environment Programme (UNEP 1993), sea level in Vietnam increased 5 cm from mid-1960 to 1990. The General Department of Meteorology and Hydrology estimated that sea levels were rising with an average speed of 2 mm per year. The forecasts of the extent of sea level rise in the future are very different, including the publication of national reports determining sea level rise increase by 1 meter by 2100 (MONRE, 2003; Hoang, 2005). Weather patterns are changing, with increasingly powerful and more frequent storms along the coast, increased frequency of extreme weather events, and onset of desertification further inland, while sea level rise is driving the intrusion of saline water into the Mekong Delta (Hoang, 2005). Both observed and projected future changes are putting Vietnam’s ability to achieve the rest of the MDG targets under enormous constraints.

Vietnam has an extensive coastline, two major river deltas, and mountainous areas on its eastern and northeastern borders. This long narrow country covers 329,241 square kilometers of mainland and shares its inland border of approximately 3,730 km with China, Laos, and Cambodia. It also meets the ocean in the east, south and southeast with about 1 million square kilometers of territorial sea and about 3,260 kilometers of coastline. Because of its geographical position, Vietnam is heavily exposed to the risks of weather variability and climate change. Vietnam suffers from many kinds of disasters, such as: floods, tropical depressions and other storms, storm surge, flash floods, hail, drought, landslides, and forest fires. Among these, storms and floods are the most frequent disasters. The table below shows the relative frequency of different kinds of natural disasters occurring in Vietnam.

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Table 1: Relative frequency of natural disasters in Vietnam (CCFSC, 2005)

High Medium Low

Flood, Inundation Hail rain Earthquake

Typhoon, Tropical Forest fire Accident (technology) depression

Landslide Frost Flash flood

Tornado Salt water intrusion

Drought

Table 1 shows that floods and flash floods, typhoons, and tropical depression are the most frequent extreme climate events occurring in Vietnam.

Climate change effects, such as mean SLR and weather extremes (storms, floods, storm surges, droughts, heat waves) pose risks for development in Vietnam. With its huge coastal and lowland region, and most of its major tourist destinations and economic development zones concentrated on the coast, Vietnam’s development potential in many sectors is threatened by climate change effects. Climate change impacts have the potential to increase poverty and even cause social and political turmoil.

2. Climate Change in Vietnam Scenarios of climate change and sea level rise for Vietnam were first published in 2009 by Ministry of Natural Resources and Environment based on results of national and international research. Then they were issued again in 2011 associated with National Strategy on Climate Change, which identified timeline targets and priority. The Ministry of Natural Resources and Environment has updated scenarios of climate change and sea level rise associated with the reliable data sources, the specific climatic conditions of Vietnam and the products of climate models (IMHEN, 2016).

Climate change and sea level rise scenarios for Vietnam were updated in 2016. These scenarios were developed based on many key outcomes. Those are the Fifth Report

Texas Tech University, Thu Nguyen, December 2017 assessment (AR5) of the Intergovernmental Panel on Climate Change (IPCC), the meteorological data monitored and sea water levels, the topographic mapping data, high resolution global and regional models and the atmospheric models. Besides, they also include the recent trends of change in climate and sea level rise in Vietnam, contributions from the Scientific Research Institute of Hydrometeorology and Climate Change (SRIHCC) and from the Advisory Council of the National Committee on Climate Change, findings from research institutes and universities in Vietnam. SRIHCC Institute has made significant contributions in the development of scenarios in 2009 and 2011 and responsible in leading the study and development of climate change scenarios in 2016 (IMHEN, 2016).

In the fifth report, IPCC has developed scenarios based on a new approach of standard emission scenarios called benchmark emissions scenarios or representative concentration pathways (RCP). RCP scenarios focus on the concentration of greenhouse gases, but not the emissions process based on assumptions on social, economic, technological, demographic development as it did in SRES. In other words, RCP makes assumptions on the destination, creating more conditions and choices in the process of development of economy, technology, population (IPCC, 2014). There are four scenarios RCP, including RCP2.6, RCP4.5, RCP6.0, and RCP8.5.

2.1 Climate change scenarios Climate change scenarios in Vietnam have been developed for seven specific climate regions based on their different geographic conditions: North West, North East, Red River Delta, North Central Regions, South Central Coastal, Central Highlands, and the South Regions (MONRE, 2012). Climate change scenarios for Vietnam can be summarized as follows: a. Temperature: The average air surface temperature (temperature) yearly and seasonal (winter, spring, summer, autumn) in all regions of Vietnam are on the uptrend compared to the base period (1986-2005). The increase depends on the RCP scenarios and climate zones. More particularly, the standard increase in average temperature is from 1.3 to 1.7oC in the mid-21st century and from 1.7 to 2.4oC at the end of the century with RCP4.5 scenarios. With RCP8.5 scenarios, the annual average temperature in the middle of the 21st

Texas Tech University, Thu Nguyen, December 2017 century increases from 2.0 to 2.3oC in the north and from 1.8 to 1.9oC in the south. By the end of the century, it increases from 3.3 to 4.0oC in the north and from 3.0 to 3.5oC in the south. In general, temperature is higher in the north than in the south (Figure 1).

Figure 1: Change in Average Temperature (0C) (IMHEN, 2016) b. Rainfall: The average annual rainfall tends to increase over the base period in all regions and all scenarios. Dry season rainfall in some areas tends to decline by the end of the 21st century. With the scenario RCP4.5, average annual rainfall tends to increase in

Texas Tech University, Thu Nguyen, December 2017 most regions, commonly from 5 to 15%. Some coastal provinces in North Delta, North Central, Central region may rise above 20%. c. Extremes: In the 21st century, extreme temperatures tend to increase over the period 1986-2005 average in all regions of Vietnam, all the scenarios. With RCP4.5 scenarios, by the end of the 21st century, the average temperature year uptrend from 1.7 to 2.7oC, the highest increases are in Northeast, northern Plains; lowest in the South-Central region and the South. Meanwhile, the average low temperature in the end of the century it has increased from 1.8 to 2.2oC Extreme rainfall tends to increase. The largest one-day rainfall has increased and the whole territory of Vietnam with an increase of popularity from 10 to 70%. The increase mostly is in the Northeast, Central (from Thua Thien Hue to Quang Nam) and the Southeast (Figure 2). Relating to climate extremes in Vietnam, it is projected that by the end of the 21st century, the number of days with maximum temperature of over 35 °C would increase from 15 to 30 days in almost all regions in the country based on a mid-range emission scenario (MONRE, 2012). The future general trend for maximum daily precipitation increases in the North regions but decrease in the South and South Central (MONRE, 2012).

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Figure 2: Changes in Annual Average Rainfall (%) (IMHEN, 2016)

2.2 Sea level rise scenarios Sea level rise scenarios for Vietnam are developed based on the same three scenarios, for seven climate regions along the coast of Vietnam, namely: (1) from Mong Cai to Hon Dau; (2) from Hon Dau to Ngang Pass; (3) from Ngang Pass to Hai Van Pass; (4) from Hai Van Pass to Dai Lanh Cape compression; (5) from Dai Lanh Cape to Ke Ga Cape; (6) from Ke Ga Cape to Ca Mau Cape; and (7) from Ca Mau Cape to Kien Giang (MONRE, 2012).

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Sea Level Rise in Low Emission Scenario (cm)

60.00

40.00

20.00

0.00 2030 2040 2050 2060 2070 2080 2090 2100

Mong Cai-Hon Dau Hon Dau-Deo Ngang Deo Ngang-Deo Hai Van Deo Hai Van-Mui Dai Lanh Mui Dai Lanh-Mui Ke Ga Mui Ke Ga-Mui Ca Mau Mui Ca Mau-Kien Giang

Figure 3: Sea Level Rise under the Lower Emission Scenario (cm)

Sea Level Rise in Medium Emission Scenario (cm)

80.0 60.0 40.0 20.0 0.0 2020 2030 2040 2050 2060 2070 2080 2090 2100 Mong Cai-Hon Dau Hon Dau-Deo Ngang Deo Ngang-Deo Hai Van Deo Hai Van-Mui Dai Lanh Mui Dai Lanh-Mui Ke Ga Mui Ke Ga-Mui Ca Mau Mui Ca Mau-Kien Giang

Figure 4: Sea Level Rise in Medium Emission Scenario (cm)

Sea Level Rise in High Emission Scenario (cm)

100.0 80.0 60.0 40.0 20.0 0.0 2020 2030 2040 2050 2060 2070 2080 2090 2100

Mong Cai-Hon Dau Hon Dau-Deo Ngang Deo Ngang-Deo Hai Van Deo Hai Van-Mui Dai Lanh Mui Dai Lanh-Mui Ke Ga Mui Ke Ga-Mui Ca Mau Mui Ca Mau-Kien Giang

Figure 5: Sea Level Rise in High Emission Scenario (cm)

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If sea level rises 1 meter, approximately 17.57% area of the Red River Delta, 1.47% of the central coastal provinces from Thanh Hoa to Binh Thuan, 17.84% of Ho Chi Minh City and 4.79% of Ba Ria - Vung Tau are at risk of flooding. The Mekong Delta is an area at high flood risk (39.40% of area), in which Kien Giang province has the highest flood risk (75% of the area). The islands with the highest risk of flooding include Van Don island cluster, Con Dao cluster and Phu Quoc Island. The flooding risk at the natural islands of the Spratly islands is not very high; whereas, the flooding risk is greater in Paracel Islands, especially at island groups of Triton and Crescent (Figure 6). a) b)

Figure 6: The Risk of Flooding with Rising Sea Level of 100cm (IMHEN, 2016) a) Vietnam Coastal Areas; b) Red River Delta; c) Mekong River Delta

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2.3 Impacts of Climate Change

The combination of natural variability and ongoing climatic change has increased the number of disaster events and caused tremendous losses in human lives and properties because of devastating natural disasters in Vietnam. The trend was increasing with major disaster events continuously occurred and badly affecting all regions in the country, especially the coastal provinces in the central region (Figure 7).

Occurrence of flood and storm in Vietnam 12

0 1975 1980 1985 1990 1995 2000 2005 2010 2015

Figure 7: The Trend of Flood and Storm Occurrence in Vietnam during 1975 to 2013. Source: EMDAT (http://www.emdat.be/)

The database extracted from EM-DAT shows that between 1990 and 2016, there were about 161 weather-related extreme events (flood, storms, landslide and drought) in Vietnam. They killed 12,091 persons, cost US$11,741,639 thousand in damages, and affected 49,160,571 people. The most affected provinces were those along the central coast, including Thanh Hoa, Quang Ngai, Binh Dinh because the weather-related extreme events appeared to take place here more frequently than any other areas. As a result, these central and coastal provinces were more vulnerable to the disasters in terms of people killed and damage cost. See maps below.

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Figure 8: Mapping and Ranking the Disaster Frequency, Disaster Damages and Killed People in Vietnam during 1990-2016. (Source: EMDAT)

A social analysis conducted by the World Bank to examine the vulnerability to climate change at the regional level in Vietnam indicated that “exposure to the effects of climate change is highest in the central coastal regions and in the Mekong River Delta” (World Bank, 2010). The Central Coast region of Vietnam has the most exposure to climate change impacts, especially vulnerable to storms and flooding. Vietnam directly suffers from the Asian monsoon regime that causes about 6 to 10 storms and tropical depressions annually. Specifically, over the past 50 years, Vietnam has suffered more than 400 storms and tropical depressions. Estimated material loss accounts for 1.5% GDP per year (3rd AIPA). About 70% of population is presently at risk of typhoons and tropical storms, flooding, landslides and droughts (Hoang, 2012). In 2010, Vietnam was affected by 6 storms (4 at sea and 2 on-land) and 4 historical floods, resulting in the death of 366 persons, 96 persons missing, 493 persons wounded, and over 300 thousand hectares of farming production damaged (3rd AIPA). Besides, most of the development of industrial, construction and service sectors and urbanization are concentrated in coastal provinces. This could lead to the increased exposure to weather-

Texas Tech University, Thu Nguyen, December 2017 related risks. Therefore, addressing this increasing complex situation, reduction of disaster risks and climate change impacts has become one of the most important tasks of Vietnam.

3. Dissertation Objectives The overall aim of this study is to assess the vulnerability of Thua Thien Hue province due to coastal flooding from storm surges and projected sea level rise. A series of scenarios under present and possible future sea level conditions will be considered in this study to determine the range of potential climate change impacts. To achieve this, the specific objectives of this study are:

• To develop a clear-cut, applicable and accurate two-dimensional flooding model of the central coast area in Vietnam. This will be based on the high-resolution process- based numerical model MIKE 21 FM offered by the Danish Hydraulic Institute (DHI), including the simulation of tide, wind and spectral wave, and building parameters for the model.

• To develop a series of inundation scenarios relative to storm surges and sea levels in the area using numerical model for storm surge simulations and producing inundation maps through simulating an extreme storm event plus sea level rise.

• To estimate the flooding risks caused by storm surges and sea level rises in the area using ArcGIS tools through overlaying inundation maps with related inventory layers.

• To evaluate possible climate change implications in the area by identifying and analyzing the coastal flooding impacts and possible flood defenses using ArcGIS tools. To determine the net impact of SLR.

• To contribute to the task of protection and prevention of storm induced coastal flooding in the SLR condition in the region.

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CHAPTER TWO

LITERATURE REVIEW

1. Sea Level Rise Sea level rise (SLR) has become a serious concern to coastal communities for several reasons. Higher sea level can bring coastal flooding deeper in-land. The damages and impacts are much more when the powerful storms and flooding inundate the land. It can cause erosion, saltwater intrusion, and many other water–related hazards. Moreover, the coastal lowlands are usually developed areas with dense populations and industrial sites. Therefore, the potential impacts of SLR are very high.

In the early 21st century, the rate of global sea-level rise was found to be increasing more sharply than the relatively stable rates of previous years (Church and White, 2011; Gehrels and Woodworth, 2012; IPCC, 2013). This onset of increased sea-level rise coincided with increasing global temperature (Kemp et al., 2011). As projected, global sea level will continue rising in the upcoming centuries, with a mean global increase that could approach or exceed 1 m by 2100 (IPCC, 2013; Parris et al., 2012). SLR is also expected to speed up through the 21st century. There are varied projections of global sea level rise. The Intergovernmental Panel on Climate Change’s (IPCC) Fifth Assessment Report (AR5) projected a global-average sea level rise of 28 to 61 cm for a scenario of extreme emissions reductions (RCP 2.6) and 52 to 98 cm for an unmitigated growth of emissions (RCP 8.5) by 2100. Most experts estimate a higher sea-level rise by 2100 than the IPCC AR5 projects. For example, most recently, the National Oceanic and Atmospheric Administration (NOAA) for the U.S. National Climate Assessment projected a mid-range of 0.5 to 1.2 m, with credible lower and upper limits of 0.2 m and 2.0 m (Parris et al., 2012). Schaeffer et al. (2012) estimated 2.0 m of global rise from 2000 to 2300 for the low temperature scenario. The wide range in projected changes in global SLR result from a large uncertainty that influences projections. The significant uncertainties are identified as including poor glacier inventory and lack of hypsometric data (Radic et al., 2014), unknown future greenhouse gas emission rates/uncertain future warming scenarios, incomplete understanding of ice melt dynamics and possible future changes in ocean circulation patterns, or the complex processes and feedback mechanisms that cause sea level to rise

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(Horton et al., 2014). To deal with this uncertainty most predictions of future sea level rise, therefore, are given as ranges.

On the other hand, there is a shared agreement that the global sea-level will continue to rise during the 21st century. Regionally, relative sea level rise at individual areas will vary from the global average. More specifically, the rise is above the average at low latitudes (in the Western Pacific and Indian Ocean) and below the average at high latitudes; For instance, the regional variations are up to 20% higher than the mean along the East Asian coast and in the Indian Ocean, and up to 30% lower than the mean in mid-latitude Northern America and Europe (30–500 N) (Perrette et al., 2013). Generally, the densely populated regions affected by coastal flooding from tropical storms have experienced a rate of SLR close or greater than the global average (Jonathan et al., 2013).

The different regions may experience varied rise rates and the effects of sea level rise also vary greatly among countries, even for countries in a same region. The countries in the region of Southeast Asia would experience the higher end of the projection (Liem, 2008). For Vietnam, the data measured along Vietnam coast shows that sea level rate was 3 mm/year during the period of 1993 and 2008, which is comparable with the global tendency (Le et al., 2011). Sea level at Hon Dau station increased about 20cm over the past 50 years. There have been some other studies reporting sea level rise in Vietnam. Historically, UNEP (1993) found the sea levels around Viet Nam rose by 5cm between the 1960s and 1990s and the Hydro-meteorological General Department estimated the seawater level at an average increased rate of 2 mm per year.

Vietnam is ranked as one of the top five countries the most affected under a 1-meter SLR. Vietnam's physical geography makes it more vulnerable to sea-level rise than most countries because of its long coast line (3260 km), threats of coastal hazards such as typhoons and erosion, and its major population located very close to the two main deltas of Vietnam, the Red and Mekong Rivers (Narins et al., 2010). Because of geographical features of deltas in Vietnam and their proximity to high population areas, a 1-meter rise in the sea level along the coast of Southeast Asia would potentially result in a 5% land loss,

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about 11% population displacement, over 10 % of GDP reduced, about 11% of urban areas and 29% of wetlands affected in Vietnam (Dasgupta, 2007). Also, many other sectors can be impacted or permanently inundated, including infrastructure (4.3% of national and local roads permanently inundated), industry (manufacturing in 20 provinces affected), settlements (2% permanently inundated), forests (8% permanently inundated), water bodies, and protected areas (36 out of 190 terrestrial protected areas and important national wetlands affected). The findings show that areas in the Mekong Delta and South-East Region, where most of the poor concentrated, are the highest vulnerable and affected ones (Carew-Reid, 2008).

2. Tropical Storm/Storm Surge At the end of the 21st century, global warming is projected not to lead to an increase in frequency of tropical cyclones but an increase in stronger storms (Knutson, 2010; Murakami et al., 2011). The projections for changes in the number of tropical cyclones range from −6 to −34% globally, with increases in mean tropical cyclone global wind speed ranging between 2 to 11% (Knutson, 2010). Worldwide, there are annually about 90 tropical cyclones and they are not distributed equally among the various basins (Frank and Young, 2007). There is a decrease trend for Atlantic hurricanes but an increase trend for tropical cyclone activity over the Western North Pacific (Landsea et al., 1996). According to “Unisys Weather”, nearly half of worldwide events are occurring in Western Pacific Ocean. While about one-fifth of these tropical cyclones make landfall yearly with the intensity of a hurricane (defined by wind speeds ≥ 33 ms−1), Vietnam gets from two to six land-falling ones with different categories but huge coastal impacts are caused largely by this significant subcategory of storms (Weinkle et al., 2012). There is a significant increase in the frequency for the strongest tropical cyclones (categories 4 and 5) in the western Pacific over the last three decades noticed (Webster et al., 2005). However, there is significantly greater uncertainty with respect to how tropical cyclone activity will differ among regions. Still, not all ocean basins may experience an increase in tropical cyclone intensity. Uncertainties in model projections of future tropical cyclone activity are inevitable (Brown et al. 2007; Butler et al. 2012). These uncertainties arise due to uncertainties in “how the large-scale tropical climate will change and uncertainties in the implications of these changes for tropical cyclone activity” (Knutson,

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2010). Besides, the poor knowledge of the hurricane characteristics (track, wind speed, forward speed, etc.) and physical parameters and inputs such as bottom friction and bathymetry are important factors that contribute to the difficulties of an accurate projection (Thomas, 2015). These uncertainties need to be addressed to increase confidence in regional and global tropical cyclone projections

The more intense tropical storms are, the more extreme the storm surges they produce. Severe storm surges are becoming a global hazard, affecting low-lying coastlines and their communities all over the world. As huge risks, the tropical storm surges cause coastal inundation which in turns result in a large number of deaths and economic loss for coastal countries. For instance, the Bhola cyclone surge, which made landfall in Bangladesh in November of 1970, caused between 300,000 and 500,000 deaths (Murty et al., 1986). Hurricane Katrina killed at least 1,200 people in Louisiana and Mississippi in 2005 (Blake et al., 2011). Ketsana typhoon resulted in over 400 people in the Philippines, Vietnam and Cambodia in September 2009 and left about 118.6 million US dollars’ worth of damages for Philippines and 120 million dollars’ worth for Vietnam (Clark, 2009). Again, in 2013, the Haiyan Typhoon caused over 6,000 deaths in Philippines (Höllt et al., 2015).

3. Land subsidence Enhanced land subsidence together with increased rates of SLR will further strengthen tropical cyclone flooding. Rapid subsidence is common along populated deltaic and coastal plain systems, owing to exploitation of groundwater, oil and gas, and reductions in fluvial sediment supply (Woodruff et al., 2013). Human-induced land subsidence rates were recorded exceeding an average of 1 cm yr−1 in such megacities as Tokyo, Japan (5 m of subsidence from 1930 to 1995); Osaka, Japan (2.8 m from 1935 to 1995); Tianjin, China (3.1 m from 1959 to 2003); Shanghai, China (2.8 m of subsidence between 1921 and 1995); and Manila, Philippines (>1 m of subsidence among 1991 and 2003) (Nicholls, 1995 & Rodolfo, 2006). It is estimated that by 2100 half of Shanghai being flooded by extreme storm-water levels due to additional land subsidence along the Yangtze River delta plus a 4.3 m projected relative rise in sea level (Rodolfo, 2006). These assumptions do not take any countermeasures to alleviate artificial causes of land subsidence.

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In Vietnam, land subsidence is also a significant factor compounded by the threat of SLR contributing to enhancing tropical storm flooding impacts. Increases in tropical cyclone flooding impacts are projected at the Red River Delta where SLR rates are expected to significantly exceed the global average and speedy rates of land subsidence is occurring (Neumann, 2012). Major land subsidence in two main deltas of Vietnam, the Red River and Mekong River ones, is contributing to posing a flood inundation hazard to its coastal communities and infrastructure. While groundwater exploitation is identified as a major cause of land subsidence in the Mekong River Delta (Erban et al., 2014); rapid urbanization, including population growth, the increase of built-up surfaces and the type of building foundation, increased natural resource demand, and even limited underground pumping withdrawal capacity, is considered as a severely destructive root to land subsidence in Red River (Dang et al., 2014).

So far, there have not been many studies of land subsidence in Vietnam, especially no research of land subsidence rates in the central coastline can be found. It seems hardly to find any database of land subsidence rates in Vietnam; therefore, this paper will exclude land subsidence factor in determining coastal flooding impacts in the region. Future papers are expected to give a light to the issues.

4. Coastal Flooding Today, coastal flooding due to sea-level rise combined with storm surge and shoreline subsidence is severely influencing coastal communities. In all the regions they affect, tropical storms are usually the primary factor that can cause devastating coastal floods. Once big tropical storms come further in-land, they bring along strong wind, heavy rainfall, and extreme storm surges, which cause a large-scale coastal inundation. Coastal population are becoming more prone to extreme flooding from tropical cyclones (Peduzzi et al., 2012).

Coastal flooding related to land-falling tropical cyclones depends mainly on two factors: the likelihood of tropical cyclone occurrence and relative sea level (Woodruff et

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al., 2013). Therefore, accurate predictions of future flood risk must take two factors together into consideration. Many studies have suggested that coastal water levels are comparable to storms with SLRs. Instead of different SLR rates, storm intensity, and time among studies, the overall agreement is an increase in future flood levels. Besides, regardless of uncertainty in projections of tropical cyclones, future coastal flooding from tropical cyclones is expected to increase because of accelerated sea level rise (Woodruff et al., 2013). Vietnam is becoming more vulnerable to the impact of changing climate and frequent disasters, namely flooding, owing to its geographical location and long densely populated coastline. Every year, Vietnam suffers directly from 6 to 10 storms and tropical depressions often occurring from June to November (Razafindrabe et al., 2014). Those frequently strike the central and northern parts of Vietnam with strong wind, heavy rain and extreme floods during September and October (SRV 2004). Flooding is a normal occurrence in many regions of Vietnam, and its severity has recently escalated because the conditions inducing flooding are intensifying, at both the local and global levels. During 1989 to 2010, flood is the most reported event in the historical disaster database with 48% of the total record of all types of disasters, and accounts for 67% of deaths, the most deaths of all (Nhu et al., 2011). There is significant evidence showing the importance of damages and loss in various parts of Vietnam due to flooding. The heaviest casualties recorded in the country were caused by the 1999 floods, which killed some 750 people (Clark, 2009) and resulted in a damage worth of more than 3,773 billion VND (approx. US $194 million);

whereas, typhoons Xangsane and Durian both left at least 70 dead in 2006 (Clark, 2009).

Instead, Ketsana 2009 was considered as the worst disaster to hit the central region of Vietnam. It left 163 people dead and 11 people missing and led to an economic loss evaluated at US $785 million. More than 17,000 houses were devastated and about 4,000 classrooms scratched. There were 10 provinces affected and some 200,000 hectares of

cropland destroyed (Clark, 2009). As a result, flooding remains one of the major water- related disaster risks in Vietnam.

5. Vertical uncertainty in elevation-based SLR assessment Over the last three decades, there have been many studies conducted on the effects of sea level rise using the elevation data, most often in the form of digital elevation models 18

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(DEMs) (Dasgupta et al., 2009, 2011; Hereher, 2010; Neumann et al., 2010; Lichter et al., 2011; Nicholls et al., 2011; Zhang et al., 2011; Curtis and Schneider, 2011). However, elevation data are identified as the main source of uncertainty due to varied measuring techniques, measuring methods and the different technologies. It is much more uncertain for elevation-based sea level rise assessment when it is combined with other variable sources including “water-level data (from long-term tide gages), the mathematically modeled tidal datum to which water levels are referenced vertically, and sea level trends or projections” (Gesch, 2013). Despite the fact, insufficient studies have scrutinized the effects of elevation uncertainty on the sea level rise assessment. Usually, it is easy and simple to map the water level on a coastal DEM to identify the vulnerability of land and its consistent resources; however, the reliable findings and the usefulness of the elevation- based assessment will be suspicious due to the uncertainty of the coastal topography mapped (Gesch, 2013). Therefore, some recent studies have emphasized the importance in the qualities of underlying DEMs as well as the understanding and properly applying of those DEMs in impact assessment of climate change (NOAA, 2010; Gesch, 2009; Gesch et al., 2009).

There are few ways to deal with the vertical uncertainty on elevation-based sea level rise vulnerability analysis. For different errors, there are different ways. Particularly, “the uncertainty in sea level trend or projections can be handled by mapping and analyzing a range of increased water levels that result from climate model simulations” as was done in IPCC assessment report (Gesch, 2013). The error associated with water-level data could be lessened if “the local water level information can be included” (Marbaix and Nicholls, 2007; Poulter and Halpin, 2007 as cited in Gesch, 2013). For detailed sea-level rise assessment, Leatherman (2001) suggested, “DEMs should be referenced to the mean high water (MHW) datum to distinguish the areas occasionally inundated by tides (as cited in Gesch, 2013). Some other studies employ varied high-resolution datasets to compare or calculate the standard errors for accuracy assessment (Gesch, 2014). Besides, it is well known that elevation errors are best controlled using a geospatial model named regression- kriging and sequential Gaussian simulation in ArcGIS. These tools allow the consideration of spatial correlation in elevation errors, “which has a significant impact on spatial- interaction analyses, such as inundation modelling” (Leon et al., 2014).

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6. Reliability of MIKE 21 FM model in inundation modeling MIKE 21 FLOW MODEL or Flexible Mesh (FM) by Danish Hydraulic Institute (DHI) is a 2D hydrodynamic modeling using flexible mesh bathymetry. The application of MIKE 21 includes such modules as MIKE 21FM HD (Hydrodynamic), MIKE 21 SW (Spectral Wave), MIKE 21 NSW (Near shore Spectral Wave), MIKE 21 FM Tidal, MIKE 21 Toolbox. The typical application fields of MIKE 21 FM includes assessment of hydrographical conditions in non-stratified waters, coastal flooding and storm surge, inland flooding and overflow, forecast and warning systems. The hydrodynamic module of MIKE 21 FM simulates unsteady flow considering bathymetry, sources and external forcing. This model permits spatial varying resolution, so that the complex tidal channels and local topographic features that may influence the tidal and wave dynamics can be sufficiently resolved (DHI, 2016).

The application of MIKE 21 in flooding inundation modeling is popular worldwide. Specifically, Madsen and Jakobsen (2004) took advantages of a two-dimensional hydrodynamic model MIKE 21 in replicating the sea-level rise combined with the 1991 Bangladesh storm surge. Kumar et al. (2008) employed the MIKE 21 model to run simulations of storm surge in the case of the severe cyclone of November 1989 for the Andaman Islands in the Indian Ocean. Similarly, Kuang et al., (2014) applied MIKE 21 model in mapping impacts of potential future sea-level rise at the Yangtze River Estuary. Moreover, Wang et al., (2012) suggested the use of MIKE 21 in evaluating the combined effects of sea level rise, land subsidence, and storm surges on the coastal areas of Shanghai, China. Additionally, MIKE 21 is also combined with ArcGIS to develop an integrated methodology for flood prediction in Eskilstuna community, Sweden (Yang &Rystedt, 2002).

In addition to the reliability of the MIKE 21 model, it is considered as the most accessible and applicable because it is spreading and training around the world. Presently, DHI has offices in more than 30 countries, where they are providing training at-site and online locally in the various native languages (DHI, 2016). In addition, they provide updated software and technical support. In Vietnam, MIKE 21 FM is widely used in modeling flooding inundation in the Central Institutions and even by JICA projects.

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Because of its popularity and reliability, I use MIKE 21 models to simulate the combined effect of a disaster chain of sea level rise and storm surge on a coastal area of Thua Thien Hue province in Central region of Vietnam for this paper.

7. Justification The study aims to assess the vulnerability of population and infrastructure along Vietnam’s central coast due to tropical storm flooding in sea-level rise conditions. The importance of the study is justified by following main reasons: Vietnam is ranked as one of the top five countries most affected by a 1-meter SLR and its long coast line makes it more vulnerable to sea-level rise than most countries (Narins et al., 2010). The relative rates of SLR in Vietnam are expected to significantly exceed the global average and the enhanced rates of SLR will further intensify tropical cyclones and coastal flooding and can cause substantial increases in their impacts in the regions they affect (Woodruff et al., 2013). Central Vietnam with its topography (skinny strip of land exposed to open ocean) is considered as the most susceptible part to the impacts of sea level rise, tropical storms, and coastal flooding.

Tropical storm-induced flooding occurrence is normal in many regions of Vietnam, but those which are extreme, with water levels equivalent to or exceeding historic flood levels have recently been striking more frequently and are particularly destructive in the central provinces in Vietnam (Oanh et al., 2011 & SRV 2004). The illustrations are

Xangsane occurring in Ha Tinh and Thua Thien Hue provinces in 2006; and, Ketsana affecting Da Nang, Quang Ngai, and Binh Dinh provinces in 2009 (EM-DAT database). Those storms were considered as the worst ones to hit Vietnam and left overwhelming devastating damage.

There is significant evidence showing the importance of loss and damages in central Vietnam due to coastal flooding. Among all types of disasters during 1989 to 2010, according to a preliminary analysis of flood and storm disaster data in Viet Nam, increased flood severity accounts for the largest share of the total record, followed by hailstorm, storm, flash flood, and tropical cyclone. These four disasters, together with flood are

Texas Tech University, Thu Nguyen, December 2017 responsible for nearly 90% of loss of life in the country (Oanh et al., 2011). However, most of the loss and damages are concentrated in the central provinces. Again, according the above analysis and over the same period of time, Quang Ngai province has the highest number of deaths (924) caused by those disasters and Thua Thien Hue province has the second highest number of fatalities (572). Whereas, the provinces with the most houses destroyed and damaged are Quang Binh (331,055 houses), Thanh Hoa (103,646 houses), and Ha Tinh (96,172 houses) (Oanh et al., 2011). Central Vietnam is considered the most exposed to the effects of climate change (World Bank, 2010). Its coastal provinces are becoming more prone to the impact of changing climate and frequent disasters, mostly storms and floods, due to not only its physical geography but also its major densely populated and developing location. Central Vietnam with its one third of the 90 million Vietnamese people serves as “a real strategic backbone” in land and culture. Central Vietnam is not only famous for beautiful beaches and ancient towns like Hoi An and Hue, which is recognized by as World Heritage Site by UNESCO but also well known by linked industrial clusters and very convenient transportation including airports, ports and railways. These all are attracting investment and development in the region. Central Vietnam also has heard by its first major oil refinery in Vietnam, Dung Quat and IT industries. The region is developing much faster than the other two metro regions of Vietnam and most of the development is happening along its coastal lowland range. This makes Central Vietnam more vulnerable to enhanced tropical storm flooding due to the sea level rise because the region might suffer more in term of lost human lives, property, and economy.

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CHAPTER THREE

STUDY AREA

1. Geographical Characteristics Thien Thien Hue (TTH) is one of coastal metropolitan provinces in the central region of Vietnam. Geographically, the province is bordering Quang Tri province in the north and Dang Nang City in the south, exposing to the East Sea of Vietnam on the east, and facing Laos on the west. Its total area is 5,033.2 km2 and total population of about 1 million people (Statistical Year Book, 2013).

Figure 9: Administrative Map of Thua Thien Hue Province. (Data source: Key Laboratory of River and Coastal Engineering, Vietnam Academy for Water Resources)

In term of administrative organization, TTH province comprises six districts (Phong Dien, Quang Dien, Phu Vang, Phu Loc, A Luoi, and Nam Dong), two towns (Huong Thuy and Huong Tra), and a metropolitan of Hue city. It is located on the main traffic axis North – South and on the corridor East – West connecting Myanmar, Thailand, Laos, and Vietnam along the Route Number 9. It has a coastline of 127 km long, Thuan An Port and Chan May Deep Water Port, Phu Bai Airport, and an inter-state railway station,

Texas Tech University, Thu Nguyen, December 2017 all of which are very supportive for the development of industry, commerce, and services in the central as well as in highland region. Because of its opportune position, TTH is determined as one of the central key economic and tourist zones in the region (TTHPPC, 2005).

Figure 10: Statistics of Population and Land Area in Thua Thien Hue Province in 2013 (Statistical Year Book, 2013)

Located in the central part of the country on the north side of the Hai Van Mountain, Thua Thien Hue has a variety of resources including forested mountains and hills, rivers, streams, paddy rice fields, marine areas and coastal lagoons, and mineral resources. The province is not only famous for its fascinating natural landscapes but also for its cultural heritage of ancient Capital Hue. Hue is maintaining “a vast physical cultural treasure with an ancient historical place complex and thousands of old pagodas”. The capital holds “a unique national architecture and non-material cultural treasure with all kinds of festivals, religions, traditional and popular festivals” (Web Portal of TTH, 2016). Moreover, the ancient Hue City and Huong River are both World Heritage Sites, Hue Court Music has just been announced to be an Intangible Cultural Heritage Work (Web Portal of TTH, 2016). Hue is a cultural phenomenon of Vietnam and the World because of tangible and intangible cultural vestige values. The essential geography, inherited natural and cultural advantages are the important properties for Thua Thien Hue to enhance a sustainable tourism development.

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Thua Thien Hue position is in the monsoon tropical climate and it has distinct dry and wet seasons, with the annual average temperature of about 25 – 260C. During the dry season, the lengthy low rainfall causes salinity that badly affects agriculture, lagoon ecology and aquatic resources and interrupts ecosystems upstream. The wet season follows the dry one with very intense rainfall, which usually comes with typhoon, and storms and causes devastating floods. The highest rainfall on record measured in the Huong River Basin is 731 mm daily, 2.4 mm monthly and 5.9 mm annually (Ngu et al., 2004).

2. Disaster context and their impacts in Thua Thien Hue Thua Thien Hue Province has been repeatedly impacted by many types of water related disasters such as typhoons, storms, and floods. Recently, such disasters have been increasing in both occurrence and influence, resulting in substantial socio-economic disorder and loss of life, seriously affecting upstream and downstream infrastructure and ecology, damaging World Heritage Sites and interrupting people's livelihoods and properties.

Figure 11: The Historic Flood in Hue in 1999 (Photo credit: Sai Don Giai Phong Newspaper)

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According to an Annual Report on flood prevention in the region (2013), floods in Thua Thien Hue from 1993 to 2012 resulted in 554 deaths and property damage of 8,872 billion VND (about 398 million USD). Among those, the floods in 1999 killed the most (352 people) and cost nearly two thousand billion VND (about 80 million USD) of damages, while the flood in 2006 is considered the costliest, nearly three thousand billion VND (about 131.5 million USD).

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Figure 12: Flood-related losses in Thua Thien Hue Province, Vietnam during 1993 – 2012

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Figure 13: Flood-related losses in Thua Thien Hue Province, Vietnam during 1993 – 2012 (Annual Report on Flooding Prevention in TTH, 2013)

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There are other examples of damages caused by major floods and storms in Thua Thien Hue province. For instance, according to the Office of the Committee for Flood and Storm Control of Thua Thien Hue Province, the floods in 1983 killed 252 people, injured 115 people, destroyed 2,100 houses, washed 1,511 houses away, and washed away 2,566 buffalo and 20,000 pigs. The historic flood in early November 1999 with flood level ranging from 3.2 m to 4.9 m cost total loss of VND 1,700 billion. It killed 352 people, injured 305 people, 25,015 houses washed away, 1,027 schools collapsed, and killed 160,537 cattle and 879,776 poultry heads. And especially, the flood of November 25–27, 2004 left 10 people dead and caused loss of over VND 208 billion. Apart from flood damages, typhoons or tropical storms also contributed to degrade the province. Particularly, Typhoon Cecil made land falling in Quang Tri and Thua Thien Hue on 16 October 1985 and collapsed 214,000 houses, 2,000 classrooms and 200 health facilities; broke 600 high-voltage electricity pylons; sunk thousands of fishing boats; killed 840 people; made 100 people missing and 200 people injured. Typhoon Ed on 18 October 1990: 18 deaths and property loss of VND 56,540 billion. More seriously, Typhoon Xangsane in early October 2006 was the most recent storm to hit and devastate the Thua Thien-Hue Province causing flooding across the province with ten deaths and the most damage cost of over VND 2,900 billion (Nguyen & Phan, 2000).

Currently, Thua Thien Hue province is one of the regions most exposed to floods, storms and tropical depressions, of which floods cause the greatest damage. An assessment of the vulnerability of each sector indicated that climate-related disaster risks often cause the most disturbing damage to urban infrastructure system including roads, railroads, public facilities, and major historical heritages in the city (Climate Action Plan, 2014).

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Figure 14: Inundated Heritage Sites in a flood 2011 (Photo credit: laodong.com.vn)

Thua Thien Hue is at high risk from natural disasters and other climate change related impacts, as much of the province's infrastructure and industry are concentrated in the coastal plains. Human settlements are very dense within 25 km of the coast and along the Perfume River Delta (TTHPPC, 2005). Moreover, most of the population is living near the poverty line and their livelihoods depend heavily on the water and natural resources of the river basin. Besides, the low level of response of local people and their incomplete sources of income, along with their reluctance or non-resettlement, all contribute to great harm to human life and properties and inability to recover in the event of a major storm or flood (TTHPPC, 2005).

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CHAPTER FOUR

METHODOLOGY

There are a several studies which investigate the combined impacts of storm surge induced coastal flooding, sea level rise, and land subsidence in Southeast Asia. They have been exploring different aspects of the problem. For example, recently Chen & Liu (2016) have used a semi-implicit Eulerian–Lagrangian Finite-Element (SELFE) model, which is a three-dimensional hydrodynamic model developed at the Center for Coastal Margin Observation and Prediction as an open source community model. They revised and employed SELFE to calculate the storm surge and the potential inundation areas in the southern coast of Taiwan. Another related study on the combined effects of three phenomena of sea level rise, land subsidence, and storm tides employed “an established diffusion-based flood inundation model” (FloodMap), which combined a 1D river flow model and a 2D floodplain flow model to simulate the flood process and produce flood potential maps for the Huangpu River in Shanghai, China (Yin et al., 2013). Others used different types of software for inundation simulation, including GeoCA-Urban software package applied for China (Yin et al., 2011), MWH Soft model (formerly Wallingford software) combined with InforWorks River Simulation, which involves ArcGIS software, implemented for Malaysia (Mah et al., 2011), or ArcGIS software only employed for coastal inundation due to sea level rise in Indonesia (Marfai & King, 2008).

There are not many studies related to this topic for Vietnam. Searching for existing literature, I could find very few related studies done recently and most of them took advantage of ArcGIS software for their study methods. For instance, Isaac Boateng (2012) applied ArcGIS to assess the coastal vulnerability to climate change in Vietnam. Similarly, Nguyen & Woodroffe (2015) also integrated ArcGIS to determine the vulnerability to sea level rise in the Mekong Delta in Vietnam. Other studies also used ArcGIS to “map impacts upon agriculture from extreme floods” in Central Vietnam (Chau et al., 2013), or even ArcGIS raster model to simulate the flood in Perfume River Basin, Thua Thien Hue province, Vietnam (Villegas, 2004). One study of storm surge impacts on population for Red River Delta in the North applied the Sea, Lake, and Overland Surges from Hurricanes

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(SLOSH) model used by the National Weather Service (NWS) to define the maximum surges (Neumann et al., 2012). Another study on climate change impact on flood hazard in the Mekong Delta used a 1D hydrodynamic model named MIKE 11 to simulate a flood event (Dinh et al., 2012). For some reason, it is hard to find any related studies in which they could use some applicable numerical models in their research methods. It seems that ArcGIS is more popular for the researchers to conduct their studies in Vietnam.

In this research, a new research method is developed, a combination of a hydrodynamic model and ArcGIS software. The model named MIKE 21 FM is coupled with ArcGIS tools to simulate storm surge corresponding to the historical Xangsane storm that occurred in 2006 with different storm tracks to demonstrate the inundation areas. More importantly, the availability of data also contributes to my making decision of research area of Thua Thien Hue province. I hope that my study will make significant contributions to providing the awareness of the importance of sea level rise, tropical storms, storm surges and coastal flooding impacts in the region as well as help reduce the damages, limit the loss of lives and property caused by them, and facilitate social economic development in the region.

1. Data Sets and Pre-Processing There are generally two main types of data sets used in this study: spatial and non- spatial ones. Spatial data are digital elevation model (DEM), XYZ elevation text files, bathymetry shape files, coastline digitalized shape file, and property shape files with value attributes (transportation infrastructure layers). Non-spatial data include population figures created from Statistical Year Book issued in 2014 and historical weather data. Most of the data sets are available in many different sources and they must be edited and preprocessed before they can be used as inputs for the hydrological modeling and for analysis using ArcGIS software.

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Table 2: Summary of original data and their sources

Coverage Format Source Name NGA SRTM 1 Raster Global Data Explorer at https://gdex.cr.usgs.gov/gdex/ arcsec Data available from the U.S. Geological Survey. See USGS Visual Identity System Guidance (link is external) for further details. SRTM15_PLUS Raster National Oceanic and Atmospheric Administration at https://www.ngdc.noaa.gov/mgg/announcements/annou nce_predict.html In-land XYZ files Key Laboratory of River and Coastal Engineering, topography Vietnam Academy for Water Resources (10K) Nearshore Shape Vietnam Ocean Data and Information Center at topography files http://vodic.vn/trang-chu-vi.aspx Provincial KML file Google Earth Pro coastline Population Figure Created from Statistic Book 2014 Transportation Shape file Downloaded from https://gis.thuathienhue.gov.vn/ Infrastructure Historical tidal Text Key Laboratory of River and Coastal Engineering, data Vietnam Academy for Water Resources.

Historical Text http://www.solar.ifa.hawaii.edu/Tropical/tropical.html Xangsane data http://www.jma.go.jp/jma/jma-eng/jma-center/rsmc- hp-pub-eg/trackarchives.html http://weather.unisys.com/hurricane/w_pacific/2006H/i ndex.php

1.1 Data sets for MIKE 21 FM modeling The Shuttle Radar Topography Mission (SRTM) 1 arc second is global elevation bathymetric data that provide worldwide coverage of void filled data at a resolution of 1 31

Texas Tech University, Thu Nguyen, December 2017 arc-second (30 meters) and offer open distribution of this high resolution global data set. The National Geospatial Intelligence Agency (NGA) worked with the National Aeronautics and Space Administration (NASA) to secure the radar data to generate the first near-global set of land elevations. The SRTM 1 Arc-Second Global (30 meters) data set released 2014 (USGS, 2015). The data for the study area is downloaded for vertical accuracy assessment. For use in the hydrodynamic model, the data must be re-processed using ArcGIS tools. After downloaded, the SRTM raster data firstly is re-projected to have the same projection with the original topography data. The SRTM raster layer is clipped to match with the original studied layer. It is then processed and transformed to be fit in the hydrodynamic modelling. The SRTM dataset is used for vertical accuracy assessment beside to another national dataset collected inside the country of Vietnam.

SRTM15_PLUS is further ocean data obtained from National Oceanic and Atmospheric Administration (NOAA), National Centers for Environmental Information, and from the website of Satellite Geodesy, Scripps Institution of Oceanography, University of California San Diego. This is a new global bathymetry data at 15 arc second resolution for resolving seafloor fabric named SRTM15_PLUS. This SRTM15_PLUS provides the foundational bathymetry layer for Google Earth. This data is used in the hydrodynamic model for creating waves and surges when the simulated storm is forming.

The national in-land topography elevation dataset is xyz data type. Under the fund of the National Target Program to Respond to Climate Change, the elevation points were extracted and digitized from the topographic map contours with the scale of 1/10,000 (10K). The data later was calibrated with some observed elevation points by Japan International Cooperation Agency (JICA) to serve for a project named “Building Disaster Resilient Societies in Central Region in Vietnam”. This data is also used to simulate and built flood hazard maps in other JICA projects (Figure 15)

The nearshore topography data (Figure 15) is provided by Vietnam Ocean Data and Information Center after face to face meetings and ID verification. The bathymetry data is collected with the scale 1/50,000 and sea level of about 30km deep. The methods and the technology used in achieving data is not revealed by provider.

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The coastline along the province is digitalized from Google Earth Pro into KML file and exported into a shape file before imported into MIKE 21 FM to verify the data. It shows the data is correctly overlaid. The model simulation acquires the historical tidal data in Son Tra station from Key Laboratory of River and Coastal Engineering, Vietnam Academy for Water Resources

Figure 15: Demonstration of different data layer used in coastal flooding model

1.2 Data source for impact analysis using ArcGIS tools Data used for impact analysis include transportation infrastructure (road and railroad), public infrastructure (hospital, school, and other special infrastructures), land area and population. In addition to the population data by district created from Statistic

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Book 2014, the transportation infrastructure is available at the provincial electronic website. Other data such as land area, land use, school, special infrastructure is provided by Key Laboratory of River and Coastal Engineering, Vietnam Academy for Water Resources.

2. Approach This study aims to develop a comprehensive understanding of the possible effects of SLR on storm surge flooding in the coastal zone of Thua Thien Hue province. In this study, the storm surge during the event of Typhoon Xangsane in 2006 is numerically modeled and simulated to map the inundated areas in the province. The historical Xangsane made land on the south side of Thua Thien Hue on 1 October 2006 and caused distressing flooding across the province. Xangsane was evaluated as the worst disaster to hit Thua Thien Hue from 1993 to 2012, and left the massive impacts on people and properties.

In the study, a hydrodynamic model named MIKE 21 Flexible Mesh (FM) will be utilized to simulate storm surge-caused coastal flooding. MIKE 21 FM is a numerical and two-dimensional hydrodynamic model with its tools used to assess the hydrodynamic condition incorporated with projected sea levels. MIKE 21 FM is one of the software products in the package of MIKE ZERO Release 2016 created by the Danish Hydraulic Institute (DHI), which is an international engineering firm specializing in the development of environmental modeling software. The tools are applied under a range of future SLR scenarios to indicate both spatial variability of risk and changes in flood characteristics between present time and projected sea levels. I use MIKE 21 flow model to simulate flooding caused by SLR and storm surges following the Xangsane storm event in 2006. I examine the five scenarios of sea level rise, including No SLR, 0.25 m, 0.50 m, 1 m, and 2 m simulated on two different datasets, National data (10K) collected from Vietnam and SRTM 1 arc second downloaded from U.S Geological Survey website. These scenarios are simulated twice on two different assumed storm tracks, containing storm land falling directly into the province and storm moving along the provincial coastline. There are also three scenarios of SLR, 0.50 m, 1 m, and 2 m simulated on the two datasets in the condition of no storm surge. To compare the level of inundation as well as the impact caused by different factors, one scenario is simulated based on actual tide measured in Son Tra

Texas Tech University, Thu Nguyen, December 2017 station, and based on the wind direction and wave of Xangsane storm occurred in 2006, without sea level rise added. So, there are 27 simulations total on five SLR scenarios and four storm track scenarios (historical track, no storm surge, land-falling, and moving along tracks) and on two datasets.

Figure 16: Different Storm Track Assumptions

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Table 3: Study Scenarios

Scenario/Dataset No SLR 0.25 m 0.50 m 1 m 2 m

Historical Xangsane 10K storm and tide Storm Land-Falling 10K 10K 10K 10K 10K

Condition SRTM SRTM SRTM SRTM SRTM

Storm Along the Coast 10K 10K 10K 10K 10K

Condition SRTM SRTM SRTM SRTM SRTM

No Storm Surge 10K 10K 10K

SRTM SRTM SRTM

The study evaluates the impacts of five SLR scenarios combined with or without four storm tracks for the central part of Vietnam. These SLR scenarios are applied in the study based on the range of SLR projected for the country (See Figure 3, 4, and 5 and because they fit in the RCP projections (IPCC, 2014). The fifth scenario is out of the range but still added into the analysis to determine the significant impacts. The net impacts of SLR is compared on the top of the storm surge and the local tidal water surface to estimate significant impacts with and without SLR scenarios.

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The first step of data collection involves the complex tasks of data researching, professional networking and traveling to Vietnam to obtain data. The data acquired includes different topographical (terrain) data, historical data and resource data. The step also takes in the creation, correction, standardization and formation of data to fit in the hydrodynamic model and study scope. The step employs ArcGIS tools to do the creation, transformation and projection of topographical data and resource data for the study.

The second step includes the steps of model setup and trial model running. The model set-up involves creating inputs for the model, as generating mesh area and simulating tide, wind, and wave. Besides, setting parameters and boundary conditions for the model also plays an important role in running the model successfully:

Computational Mesh Area

The mesh area is very large because it spreads from in-land area to the open sea. The computational domain is about 400,000 km in the east-west direction from Thua Thien Hue to the open sea and about 340,000 km in the north-south direction from South China Sea down. However, the mesh domain for in-land area is much smaller, about 80 km from inland to the shore and about 80 km length of central coastline. After specifying the computational area, varied priorities for mesh/grid area and mesh dimension must be determined. It is divided by an unstructured triangular mesh with 60,695 nodes and 119,834 elements. The mesh size generally decreases from the open sea to the near shore (Figure 18). The largest mesh is located at the east ocean boundary with a length of 3,427 m, the second larger mesh is the west near-shore area with the length of 960 m, the smaller is for the lagoon at the length of 820 m, and the smallest mesh is for in-land area with a length of 142 m.

Table 4: Mesh Generation Parameter

Input Value Open-sea Domain 400,000 km x 340,000 km Inland Domain 80 km x 80 km Node 60,695 Element 119,834 Largest mesh length 3,427 m

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Second larger mesh length 960 m Smaller mesh length 820 m Smallest mesh length 142 m

Figure 17: Demonstration of Mesh Generation in MIKE 21 FM Window

Simulation of Tides, Wind, and Wave Tide, Wave and Wind field are generated using Tidal, Nearshore Spectral Wave (NSW) and Wind Module in MIKE 21 Toolbox. The inputs for the tidal simulation are the actual tide levels recorded at Son Tra station during the Xangsane event at the end of September and beginning of October 2006. The wave and wind simulations are based on the historical data of Xangsane storm. Boundary Conditions and Parameter Settings: Open boundary conditions are specified as time-varying tidal levels at three open sea boundaries and a constant runoff at river mouth. The parameters for model calibration comprise the value of wind friction and those of the waves and tides. While testing the model trials, the wind friction value has been adjusted to reach desired results.

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Table 5: Parameters of the MIKE 21 flow model

Parameter Value

Module Cyclone Wind Generation, MIKE 21 NSW, MIKE 21 Flow Model FM (Hydrodynamic Module) Bathymetry Unstructured triangular mesh Simulation period From: 30/09/2006 7:00 AM to: 01/10/2006 6:00 AM Time step 600 secs Number of time steps 210 Enable Flood and Dry Yes Initial surface level 0.00 m Wind friction 7 m/s = 0.001255; 25 m/s = 0.002425 Eddy viscosity 0.28 Bed resistance 32 m1/3/s Result file dfsu format

Model Verification There is no data found of the extent and level of inundation caused by the Xangsane storm in 2006 in Thua Thien Hue Province to verify the model. Therefore, the model is calibrated using the observed water level at Son Tra station during the time when the Xangsane storm occurred.

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Figure 18: Observed Tidal Water Level Measured at Son Tra Station in 2006

SON TRA WATER LEVEL COMPARISION 2 Observed Data Simulation 1.5

0.5 WATER LEVEL (M)LEVELWATER

-0.5

TIME Figure 19: Comparison of Observed Water Level and Simulated Water Level

The study uses the tide data recorded at Son Tra station from 6:00 AM September 29th, 2006 to 12:00 PM October 2nd, 2006 to verify the model (Figure 8). The results show that in both cases the high tide simulation is close, except the peak. The differences between observed and simulated water level at the highest point is about 3 cm higher in the observed and the time when the peak in simulated model appears about three hours before the

Texas Tech University, Thu Nguyen, December 2017 observed. The time when the maximum water levels appears is three hours separate while the storm’s path is valid in every six hours, so there is not enough detail to review it. However, the general result can be acceptable.

In the third step, the study uses the hydrodynamic MIKE 21 FM to simulate the flooding caused by storm surges combined with the historical Xangsane storm surges in 2006 in the condition of different projected sea levels. After the smooth trials of lots of simulation running, to achieve the required results it is necessary to prepare and run 27 simulations of flooding scenarios using different assumed storm paths on two individual topographical terrain data sets. The first dataset is the national one collected from some local departments inside Vietnam and the other is Shuttle Radar Topography Mission (SRTM) 1 arcsecond downloaded from the U.S. Geological Survey. The output of this step is the modeling results of different inundation scenarios linked to the varied combined effects of SLR and storm surge (Figure 22, 23, 24, and 25).

Figure 20: Land-Falling Storm with SLR 1 m Simulation on 10K Dataset

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Figure 21: Moving Along Storm with SLR 1 m Simulation on 10K Dataset

Figure 22: Land-Falling Storm with SLR 1 m Simulation on SRTM Dataset

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Figure 23: Moving Along Storm with SLR 1 m Simulation on SRTM Dataset

The fourth step consists of transforming the simulated results into mapping varied flooding scenarios using ArcGIS tools. At this stage, a series ArcGIS tools, which include data extraction, raster clipping, conversion, reclassification, and reprojection, are employed to manipulate the simulated output in different approaches. The final products of this step are a sequence of 27 inundated maps produced associated with different scenarios of sea level rise, varied scenarios of storm tracks, and with two different datasets (Illustrations in Figure 26, 27, 28, and 29).

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Figure 24: Comparison of No SLR Inundation Scenarios on SRTM and 10K Datasets

Figure 25: Comparison of No Storm Surge Inundation Scenarios on SRTM and 10K Datasets

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Figure 26: Comparison of 1 m-SLR Inundation Scenarios on SRTM and 10K Datasets

Figure 27: Comparison of No SLR Inundation Scenarios on 10K Dataset

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Figure 28: Comparison of Inundation Scenarios with the different SLR scenarios and the same simulated storm track on 10K Dataset

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Figure 29: Comparison of Inundation Scenarios with the same SLR scenarios and the different simulated storm track

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This fifth step involves the analyzing and estimating the impacts caused by simulated flooding scenarios on human and resources. The step also employs ArcGIS tools, which can help overlay maps of inundation area with variables of land use, transportation infrastructure, and population to assess the effects caused by climate disturbances.

Figure 30: Population impacted by different Storm Scenarios with 1-m SLR

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Figure 31: Impacts caused by different SLR Scenarios 10K Dataset

Figure 32: Impacts illustrated on different datasets (SRTM and 10K)

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Based on the simulated inundation scenarios and analysis of the impacts, the last step provides the recommendations and suggestions for local authorities, development planners, policy makers, and the government on the future risks of coastal flooding, storm surge, and SLR potentially exposed to the region. All stakeholders should be aware of the risks so that they could make strategies for area development and planning as well as for sound policymaking.

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CHAPTER FIVE

RESULTS

1. Simulation of coastal flooding We use an unsteady, two-dimensional MIKE 21 flow model to simulate flooding because of sea level rise and storm surges. We examined flooding following the Xangsane storm surge event in 2006. The results of 27 numerical simulation of flooding scenarios are processed. The outputs for each of the simulated scenarios in selected time steps include: • Video animations of flooding evolution in time and space. • Extent of flooding in form of ESRI polygon SHP file. • Maps of water depth in form of ESRI grids with 2x2 m resolution. The above-mentioned model results are manipulated and then overlaid with inventory ArcGIS layers including land area, hospital, school, and special infrastructure, road, rail road, and population. The simulation result shows that some inland areas are inundated because sea water level rises and spills into lagoon, and overflows onto the low-lying areas, causing flooding. Indeed, theses low-lying areas have relatively low ground level, ranging from 0.5 m to 2 m. Hence, when storm surges appear to be about 1.5 m, that the areas are flooded is reasonable. However, flooded duration is not very long, just a few hours, so the damages it causes are not very significant.

2. Analysis of Impacts The impacts caused by different SLR scenarios and storm surge scenarios are mapped and analyzed using ArcGIS tools. The result show that the simulated storm flood level in the study area increases significantly due to the rise in sea level that results in the dramatically increasing impact in higher sea level rise scenarios (Figure 34 and 35; Table 6, 7 and 8).

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Table 6: Total inundated area (km2) in different scenarios

Scenarios of /Storm No SLR 0.25 m 0.5 m 1 m 2 m

Track/SLR

10K_Landfalling 187 183 290 478 640 Percentage Increase 55% 155% 242% 10K_Alongmoving 234 307 397 501 672 Percentage Increase 30% 69% 113% 186% SRTM_Landfalling 88 100 111 143 320 Percentage Increase 13% 25% 62% 260% SRTM_AlongMoving 95 103 114 157 339 Percentage Increase 8% 20% 65% 254%

There are great differences in total inundated area when the sea levels rise from 0 to 2 m. Although those jumps in the first scenarios of 0.25 m and 0.50 m across the different storm tracks and different datasets are not noticeably, the total inundated area incredibly increases in the 1 m and 2 m SLR scenarios, about 150% and 250% respectively. It seems that when the storm moving along the near-shore coast, it causes more impacts and more land inundated than the storm making land-falling does, but the difference is not very much significant. Comparing between two datasets, national data (10K) and global data (SRTM), the total inundated land area analyzed in 10K data increases from 100% to 150% more.

Figure 34 and Table 7 show result of impacts analyzed in different simulated scenarios of coastal storm flooding, including the original Xangsane storm and the assumed land-falling storm in the study area with and without sea levels on the 10K dataset. If we compare the impacts caused by the two scenarios, original Xangsane storm and the one without SLR, we do not see the substantial difference except the railroad and the road inundated. While road effected by the Xangsane storm is about 69% more than the one by No SLR scenario, the railroad inundated is 855% more compared to the one caused No SLR scenario. It is accurate result since the Xangsane made landfalling into the south side of the province where the interstate highway, interstate railroad and train station located.

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The No SLR assumes the storm making landfalling into the city where there is no railroad at all.

With SLR, the impacts on transportation infrastructure are increasing dramatically high. It is the highest increase for the railroad when 1,827% and 3,504% estimated increase in SLR of 1 m and 2 m comparing with the No SLR scenario. Almost 250% of impacted roads increased in SLR of 1 m and 390% increase in SLR of 2 m. Following are the hospital and school categories which also make the high rise reaching close to 300% in SLR of 1 m and 450% in SLR of 2 m. Land inundated and population impacted increase more than twice in SLR of 1 m and more than 3 times in SLR of 2 m.

LAND-FALLING STORM % 45 40 35 30 25 20 15 10 5 0

Original Xangsane No SLR 0.25 m 0.5 m 1 m 2 m

Figure 33: Impacts by land-falling storm _10K dataset

Table 7: Storm Making Land-falling_National Data 10K

Impact/ Historical No SLR 0.25 m 0.50 m 1 m 2 m Scenario of SLR Xangsane data Hospital (unit) 0 8 15 22 31 42 School (unit) 59 69 111 162 259 385

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Special 48 36 49 64 92 174 Infrastructure (unit) Railroad (km) 21 1 4 13 32 52 Road (km) 1,460 863 1,003 1,683 2,998 4,221 Land Area (sq.km) 162 187 183 290 478 640 Population (person) 148,633 162,344 165,546 235,034 362,339 582,737

Table 8 and Figure 35 below illustrate the impacts caused by the storm moving along the provincial near shore coast. The finding also displays that the transportation infrastructure is effected the most. Although the total road inundated is a weighty number (4,391.62 km) comparing to the total railroad impacted (52.12 km), the increase assessed for railroad is 400% in the SLR of 1 m and 700% for the 2 m, comparing to 180% and 270% for the impacted road in the SLR of 1 m and 2 m. In this scenario, school is impacted more in SLR 1 m and 2 m with 300% and 400% increase. The remaining categories have the same increase comparing with the above scenario, almost twice and 3 times in SLR of 1 m and 2 m for hospital, special infrastructure, land area, and population. The result is noticed when the storm’s pathway is altered such that instead of making land, it moves along the provincial coast, it seems to affect more people and property even though it is not significant.

Table 8: Storm Moving Along The Coast_National Data 10K

Impact/ Historical No SLR 0.25 m 0.50 m 1 m 2 m Scenario of SLR Xangsane data Hospital (unit) 0 13 20 24 33 41 School (unit) 59 100 137 196 299 394 Special Infrastructure 48 52 68 77 113 177 (unit) Railroad (km) 21 6 10 18 32 52 Road (km) 1,460 1,178 1,751 2,375 3,297 4,392

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Land Area (sq.km) 162 235 307 398 501 672 Population (person) 148,633 215,598 252,164 323,352 474,294 598,354

NEAR SHORE ALONG MOVING STORM % 40 35 30 25 20 15 10 5 0 Hospital School Special Railroad Road (km) Land Area Population (unit) (unit) Infras. (km) (sq.km) (person) (unit)

Original Xangsane No SLR 0.25 m 0.5 m 1 m 2 m

Figure 34: Impacts by moving along storm _10K dataset

Tables 9 and 10 together with Figures 36 and 37 are those which demonstrate the impacts caused by two scenarios of storm paths like above but simulated on a different dataset, global dataset called SRTM. The result also shows the big difference of impacts among varied SLR scenarios. Similarly, the biggest jump is for railroad impact when about 250% and more than 1,500% increase projected for SLR of 1 m and 2 m for both two storm surge scenarios. The second peak is for the hospital category. Compared with No SLR scenario, the hospital impact rises 150% and 700% in SLR of 1 m and 2 m in the case of storm making landfall and100% and 460% when storm moving along the near shore coast. The impact growth of remaining categories ranges from around 50% to 250% in SLR of 1 m and 2 m for both storm surge scenarios. Unlike the findings shown on national dataset (10K), the impact increase is just one third in SLR of 1 m but almost 5 or 6 times more than the one in No SLR scenario.

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Table 9: Storm Making Land-Falling_Global Data SRTM

Impact/Scenario of No SLR 0.25 m 0.50 m 1 m 2 m

SLR

Hospital (unit) 2 3 4 5 16 School (unit) 44 48 55 66 154 Special 22 33 34 42 55 Infrastructure (unit) Railroad (km) .4 .5 .5 1.5 6.5 Road (km) 420 470 524 676 1,639 Land Area (sq.km) 89 101 112 144 320 Population (person) 89,074 99,954 110,089 133,199 249,701

LAND-FALLING STORM_SRTM % 70 60 50 40 30 20 10 0

No SLR 0.25 m 0.5 m 1 m 2 m

Figure 35: Impacts by land-falling storm _SRTM

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NEAR SHORE ALONG MOVING STORM_SRTM % 70 60 50 40 30 20 10 0

No SLR 0.25 m 0.5 m 1 m 2 m

Figure 36: Impacts by moving along storm _SRTM

Table 10: Storm Moving Along the Coast_Global Data SRTM

Impact/Scenario No SLR 0.25 m 0.50 m 1 m 2 m of SLR

Hospital (unit) 3 3 4 6 17 School (unit) 47 47 52 68 162 Special 21 33 34 41 54 Infrastructure (unit) Railroad (km) .6 .6 1.4 2 10 Road (km) 453 482 538 715 1,744

Land Area 95 104 115 158 339 (sq.km) Population 96,549 103,112 111,386 143,786 271,076 (person)

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Comparing the inundation and the impacts between the two datasets, there are far more impacts and inundation shown on 10K dataset. The level of impact on 10K dataset is at least three times more than the one on SRTM (for population and special infrastructure) and at most 10 times more for railroad category. For other categories, they are almost four times influenced more in 10K dataset than in SRTM dataset (Figure 38). This reveals that there is a big difference of elevation between two datasets. In order to examine which dataset can give a more accurate result, I have to review the literature to see what other researches or studies have found about the global SRTM data. In several publications from United States Geological Survey (USGS) about the vertical accuracy assessment among different datasets, the outcomes show that SRTM dataset is less accurate than some others including the NED and ASTER GDEM (Gesch at all, 2012 and 2014). Since it is extracted from schemes which collect the surface data “without measuring ground elevations in the presence of buildings and vegetation canopies”, it has “a positive elevation bias in built-up and forested areas” (Gesch and others, 2014). Whereas, the national dataset 10K is calibrated with observed elevation points by JICA and is also used popularly to simulate and built flood hazard maps in many different national and international projects. Therefore, we better give more trust to the results analyzed on 10K dataset.

DIFFERENCE BETWEEN TWO DATASET % Land-Falling Storm with SLR 1 m

100 80 60 40 20 0

National Data-10K Global Data-STRM

Figure 37: Impact comparison between two datasets 10K and SRTM

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The simulations are done again on both datasets without the storm surge to see how the impact increase without the storm surge but with the SLR. The SLR scenarios in this case are just 0.5 m, 1 m, and 2 m (Table 11, 12, and Figure 39, 40). For both datasets, the percentage increase in railroad impact is still the highest. Comparing to the SLR of 0.5 m, the railroad impact rise almost 450% and 800% in SLR of 1 m and 2 m on 10K dataset. More significantly, it reaches the peak at 3,500% of impact increase in railroad for SLR of 2 m on 10K dataset. For 10K dataset, the impact increase on other categories is very stable for both SLR of 1 m and 2 m. Its increase ranges around 50% to 100% for SLR of 1 m and around 120% to 200% for SLR of 2 m. Differently, there is a large distance between SLR of 1 m and 2 m on SRTM dataset. Apart from the railroad impact, the other varies from 60% to 400% in SLR of 1 m and from 450% to 1,330% in SLR of 2 m. The impact increase percentage is four to ten times more in SLR of 2 m than 1 m.

Table 11: No Storm Surge_SRTM

Impact/Scenario 0.50 m 1 m 2 m

of SLR

Hospital (unit) 3 15 43 School (unit) 50 141 409 Special 33 55 182 Infrastructure (unit) Railroad (km) 1.6 6.5 71 Road (km) 501 1,416 4,946 Land Area 107 277 919 (sq.km) Population 103,545 232,228 656,559 (person)

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Table 12: No Storm Surge_10K

Impact/Scenario 0.50 m 1 m 2 m

of SLR

Hospital (unit) 14 31 42 School (unit) 128 251 380 Special 55 96 170 Infrastructure (unit) Railroad (km) 5.6 30 51 Road (km) 1,482 2,860.6 4,229.1 Land Area 286 469 652 (sq.km) Population 244,414 379,056 602,419 (person)

NO STORM SURGE_10K % 60 50 40 30 20 10 0

0.5 m 1 m 2 m

Figure 38: No Storm Surge with Different SLR Scenarios _10K dataset

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NO STORM SURGE_SRTM % 90 80 70 60 50 40 30 20 10 0

0.5 m 1 m 2 m

Figure 39: No Storm Surge with Different SLR Scenarios _SRTM

In Figure 41 below, the analysis shows that with or without the storm surge the impact increase insignificantly. The impact can only rise dramatically with a certain sea level (Figure 42).

DIFFERENCE IN STORM SURGE SCENARIOS W/SLR 1 M_10K DATASET

No Surge Land-Falling Storm Near Shore Along Moving Storm

LAND AREA (SQ.KM) 469.6 478.24 501.45

R O A D ( K M ) 2,860.60 2,998.04 3,296.60

RAILROAD (KM) 30.4 32.52 32.59

SPECIAL INFRAS. (UNIT) 96 92 113

SCHOOL (UNIT) 251 259 299

HOSPITAL (UNIT) 31 31 33

Figure 40: Impact comparison among different storm surge scenarios

Comparing the scenarios of No SLR with No Storm Surge with SLR of 1 m, the result shows that the sea levels really cause much more impact on land, population, and

Texas Tech University, Thu Nguyen, December 2017 transportation infrastructure. The analysis displays that the scenario of No Storm Surge with SLR of 1 m can result in double or three times more impacts than the scenarios of No SLR with the two different storm path surges. For railroad impact, it can even cause 20 times more than the one with land-falling storm and five times more than the one with storm moving along the coast (Figure 42). In conclusion, the sea levels are the key factor that leads to flooding devastating impacts.

NO SLR VS. NO STORM SURGE % 80 70 60 50 40 30 20 10 0

NoSLR/Land-Falling Storm NoSLR/Moving-Along Storm No Storm Surge/SLR 1m

Figure 41: Impact comparison between No Storm Surge and No SLR _10K dataset

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CHAPTER SIX

CONCLUSIONS

The coastal area in the central part of Vietnam seems to be facing a variety of weather risks related to climate change, including higher frequency and intensification of tropical cyclones, higher storm surges, and even an accelerated rise in sea level. This research investigates the vulnerability of the coastal zone in Vietnam against coastal flooding related to intensification of storm surges under different scenarios of sea level rise. A 2D hydrodynamic model named MIKE 21 FM is employed to simulate the storm and storm surges associated with the historical storm Xangsane in 2006. Some other tools in the package of MIKE ZERO product are used to map the inundated area when the storm passes over. A detailed GIS analysis is applied to determine the impacts caused by storm surge associated with and without a rise in sea-level in the region. There are 27 scenarios taken into the study for analysis: 20 scenarios of SLR of No SLR, 0.25 m, 0.5 m, 1 m, and 2 m simulated on two different storm paths with two different datasets, 6 scenarios of No Storm Surge with SLR of 0.5 m, 1 m, and 2 m simulated on two datasets, and one simulation on the original Xangsane storm.

The results comparing impacts caused by No SLR with a SLR of 1 m indicates that there is much greater loss in people and properties in the scenarios with sea level rise. With sea level rise of 1 m, the impact on transportation infrastructure is very significant, from 200% to 400% increase, especially the effect on all types of road, railroad and inundated land area. Roads always are heavily damaged across all scenarios. Since there is only one interstate railroad along the whole country and it is lowland elevation in the study area, the damage is not much for scenarios of low SLR. However, when sea level rises to a certain one, like 1 m or 2 m, the impact percentage increases considerably, even almost 2000% and 3500% for SLR of 1 m and 2 m with or without storm surge. For land area, building (school and hospital), and special infrastructure, the impact increase around 250% to 450% in SLR of 1 m and 2 m for national dataset and about 50% to 400% for SRTM dataset.

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There are some conclusions drawn from the analysis. While the road impact is highest the railroad impact percentage increases the most across all scenarios. Comparing between the scenarios of No SLR and No Storm Surge with SLR, the latter one really makes the greater impact. The scenarios of different storm surge paths without SLR do not make any significant increase in impact. That means the sea levels really enhance and accelerate the coastal flooding and cause much more impacts. Compared to the No SLR scenario, the impact increase trend is double and three times in SLR of 1 m and 2 m for national dataset but it is just half and even six times for SRTM dataset. So, the increase jump is not very high in SLR of 1 m but then it goes much higher in SLR of 2 m for SRTM dataset.

The combined effect of storm surges and SLR certainly gives rise to the high risk of flooding in the research area (particularly in SLR of 1 m and 2 m). Based on the results of this study, we recommend several strategies for Thua Thien Hue area:

1. Urban planning and urban infrastructure planning is of great importance to building climate resilience. Therefore, the city planning authorities should consider the impacts of climate-related SLR in the region for future city planning and development. The integration of climate change into land-use plans, urban construction and infrastructure development plans (especially transportation, public facilities such as schools, hospitals, health clinics, historical and cultural heritage sites), tourism sector plans and socio- economic development plans is essential. Besides, the integration of climate uncertainties and climate resilience into development plans and urban plans is also needed.

2. The drainage system plays an important role in responding to floods in the area. The city should take innovative solutions of preventive measures and intervention plans into account to effectively manage the operation of the system, especially in major flood events or reservoir failure events.

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