数値シミュレーションモデルを用いたサイゴン川下 流域カンジオ湾の気候変動に伴う環境評価と洪水制 御
ブゥ, ティ, ホワイ, トゥ
https://doi.org/10.15017/2534498
出版情報：Kyushu University, 2019, 博士（農学）, 課程博士 バージョン：
権利関係：
Environmental Assessment and Flood Control in the Can Gio Bay Area located in the Lower Saigon
River using Numerical Simulation Models in Context of Climate Change
VU THI HOAI THU
2019
CONTENTS
Chapter 1 Introduction 1
1. 1 Background 1
1. 1. 1 Climate Change and Global Warming 1
1. 1. 2 Statements of the Problems 3
1. 1. 3 Necessity of Research 6
1. 2 Objective of the Study 7
1. 3 Literature Review 8
1. 3. 1 Probable Approach 8
1. 3. 2 Reviewed Papers 8
Chapter 2 Targeted Area - Can Gio Bay 13
2. 1 Physical Characteristic 13
2. 1. 1 Geography Location 13
2. 1. 2 Terrain 15
2. 1. 3 Geology 15
2. 1. 4 Climatology 15
2. 1. 5 Hydrology 16
2. 1. 6 Tidal Regime 17
2. 1. 7 Salinity Variation 18
2. 2 Can Gio Mangrove Forest 19
2. 3 Social-Economic Aspect 21
Chapter 3 Impact of Sea Level Rise and Sea Dike Construction on
Hydrodynamic Regime and Inundated Area in Can Gio Bay 23
3. 1 Introduction 23
3. 2 Methodology 26
3. 2. 1 Two-Dimensional Depth-Averaged Model 26
3. 2. 2 Wetting-and-Drying Scheme 28
3. 3 Boundary Conditions and Data Used 29
3. 4 Validation of the Model 33
3. 4. 1 Model Validation for Water Level 33
3. 4. 2 Model Validation for Inundated Area 35
3. 5 Scenario Analysis 36
3. 5. 1 Baseline Scenario 36
3. 5. 2 Sea Level Rise Scenarios 37
3. 5. 3 Sea Dike Construction Scenarios 38
3. 5. 4 Proposed Modeling for Scenarios 40
3. 6 Results and Discussions 44
3. 6. 1 Impacts on Hydrodynamic Regime 44
3. 6. 2 Impact on the Inundated Area 51
3.7 Conclusion 54
Chapter 4 Assessment of Spatial-Temporal Distribution of Observed
Salinity in Can Gio Bay 57
4.1 Introduction 57
4. 2 Observation Method of Salinity 58
4. 2. 1 Temporal Salinity Variation 59
4. 2. 2 Horizontal Spatial Salinity Distribution 61
4. 2. 3 Salinity Vertical Profiles 62
4. 3 Salinity Field Observation in 2017 63
4. 3. 1 Temporal Salinity Variation 63
4. 3. 2 Horizontal Spatial Salinity Distribution 64
4. 3. 3 Salinity Vertical Profiles 66
4. 4 Salinity Field Observation in 2018 70
4. 4. 1 Temporal Salinity Variation 70
4. 4. 2 Horizontal Spatial Salinity Distribution 71
4. 4. 3 Salinity Vertical Profiles 75
4. 5 Comparing Salinity of the Years 2017 and 2018 84
4. 6 Conclusion 86
Chapter 5 Impact of Sea Level Rise and Sea Dike Construction on
Salinity Regime in Can Gio Bay 88
5. 1 Introduction 88
5. 2 Methodology 91
5. 3 Boundary Conditions and Data Used 92
5. 4 Validation of the Model 96
5. 4. 1 Validation of the Hydrodynamic Model 96
5. 4. 2 Validation of the Convective-Dispersive Model 97
5. 5 Scenarios Analysis 99
5. 6 Results and Discussions 102
5. 6. 1 Spatial-Temporal Salinity Distribution in Can Gio Mangrove Forest 102
5. 6. 2 Effects of Sea Level Rise on Salinity Distribution 105
5. 6. 3 Effects of Sea Level Rise and Sea Dike Construction on Salinity
Distribution 110
5. 6. 4 Effects on Mangrove Habitats 116
5. 7 Conclusion 117
Chapter 6 General Conclusion 119
Acknowledgments 125
References 127
Chapter 1 Introduction 1. 1 Background
1. 1. 1 Climate Change and Global Warming
Nowadays, climate change is trusted as extraordinary challenges to coastal cities located in river basins with the influence of sea tide regime, especially in low- lying urban deltas (Arjan et al., 2010). The predicted changes in sea level, river discharge, and weather extremes, combined with increasing potential impacts due to population growth and rapid urbanization, enlarge concerns to make cities
“climate-proof”. Adverse effects of climate change are inevitable, but the strategic orientation for adaptation to mitigation of fatality, economic and social damages will be much-needed.
Resilience is frequently used in various fields, including ecology, economic, and engineering (Gersonius, 2008). Obviously, there were a number of different definitions be offered, of which a synthesis report of climate risks and adaptation in Asia coastal megacities of World Bank (2010) defined that
“Resilience is a system’s ability, community or society exposed to hazards to resist, accommodate, absorb and recover from the hazard' effects in a timely and efficient manner”. However, in the context of flood management, resilience is defined as a system's capacity, society potentially exposed to hazards, to adapt by resisting or changing, to catch and maintain an acceptable level of function and structure, and resilience of cities translates into a new paradigm for urbanization and influences the way to understand, manage urban hazards, urban planning (e.g., Bonanno, 2004; Butler et al., 2007; Werner and Smith, 1982). Building urban resilience is a part of a broader effort of national key project entitled
“Study on integrated measures for flood control in the downstream area of the Saigon-Dongnai River basin and vicinities” by Nguyen (2014), which particularly aimed to increase the resilience of cities located at the downstream to disasters and anticipated climate change impacts by utilize a risk-based approach in public investment decision-making process. The subject of the initiative is to determine practical tools and a scalable methodology for risk assessment, which can be utilized for city-level investment decisions (Klinke and Renn, 2006).
On the other hand, the Government of Ho Chi Minh City and the Ministry of Agriculture and Rural Development (MARD) of Vietnam require the relevant agencies to evaluate if watercourses, river basins, and coastlines are at hazard risk from flooding, to map the flood extents for separated areas, the assets and human at risk in these areas, and to take adequate and coordinated measures to reduce flood risk (Nguyen, 2014). This is an understanding of a much difficult task, and essential investments and other efforts have been made by various stakeholders, experts, and governments to construct flood risk management plans focused on prevention, protection, and preparedness.
Climate change classifies mainly by global warming and sea level rise (SLR). According to the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report in 2007 (IPCC, 2007), the coastal countries in Southeast Asia are highly vulnerable to climate change. Notably, Vietnam is one of ten states where are strongly affected by climate change, especially, the SLR.
According to the report “Climate change, SLR scenarios for Vietnam” of the Ministry of Natural Resources and Environment of Vietnam (MONRE, 2009), the average temperature of Vietnam increased about 0.5 oC to 0.7 oC during the last 50 years (1958–2007) and it will be expected to rise between 2.5
oC to 3.7 oC in the 2100s. Comparing the period 1931–1940, the annual temperature in the Ho Chi Minh City in the period 1991–2000 was higher 0.6 oC.
The change in heat environment leads to the changing of precipitation. In the period from 1958 to 2007, although the average rainfall decreased by 2 % in the whole country, the annual rainfall increased in the South of Vietnam. In the summer, heavy rainfall will become more frequent; storms will become more intense and frequent. Moreover, in the past 50 years, sea level rose approximately 0.20 m at the Hon Dau, which is located in theHai Phong Province. The data from 1979 to 2006 at the Vung Tau Station, located in the Ba Ria Vung Tau Province shows that the average sea level rose by 0.13 m.
1. 1. 2 Statements of the Problems
Ho Chi Minh City (HCMC) is considered as the biggest city in the South of Vietnam. It is located in the delta area formed by the Saigon River and Dongnai River as shown in Fig. 1.1, which have the characteristics of the tidal river. The HCMC plays a vital role in the economy of Vietnam. The area of the city is only 0.6 % of Vietnam; however, the city population is 8.3 % compared with the whole country. It takes 20.2 % of GDP. The HCMC is the attracted place for migrants from rural areas. From 1975 to 2000, its population has doubled from 2.5 million to 5.17 million people. Up to now, the population of the HCMC is eight million people with a rate of 2 % urban growth. Urbanization has been rapid because of the economic growth and the population increase.
Fig. 1.1 Location of the Ho Chi Minh City.
Fig. 1.2 Causes of increasing inundation to the HCMC.
By unique topography, the HCMC is located in a flat, low-lying area.
Thus the HCMC is frequently inundated under influences of the tidal regime from the East Sea and flood discharges from upstream. In the recent decade, the HCMC has been increasingly facing with serious inundation problem. Figure 1.2 shows the key causes of increasing inundation, consisting of urbanization, land subsidence, heavy rainfall, flooding from the upstream inflow discharge and the SLR (Nguyen et al., 2015). To be specific in 2000, this historical flood event caused severe flooding in the HCMC with approximately 42.3 % of the area inundated, of which, about 90 % of the Can Gio Bay area submerged. Also, the climate change is occurring and affecting the region, as evidenced by ultra- abnormal typhoon strength and rising sea level. Of particular significance, the highest water level recorded in 61 years was 1.68 m at the Phu An Station, which is located the downstream of the HCMC, occurred on October 20, 2013, and this natural disaster resulted in serious inundation in the HCMC and caused fatalities and severe economic and social damage.
To cope with the significant challenges from rapidly increasing inundation in the HCMC, the Vietnam Government had done some projects to protect the HCMC. Notably, Japan International Cooperation Agency did the upgrade project of Nhieu Loc – Thi Nghe drainage system in 2001 to protect the center of
Ho Chi Minh city Urbaniza-
tion
Sea Level Rise
Flooding from upstream
Heavy rainfall
Land subsidence
the HCMC from heavy rainfall (JICA, 2001). However, inundated areas have not only been none-declining but also been increasing number of inundated sites.
Next, the Project 1547/QĐ-TTg, proposed by the HCMC Government in 2008 (Vietnamese Government, 2008) aiming to protect center urban areas of the HCMC from tide and flood impact. In this project, the targeted area was divided into three part. Nevertheless, there was much attention to this project due to discredit about real protection capability of this project.
Then, the project of super sea dike proposed in 2010 with an idea to construct sea dike as shown in Fig. 1.3 connecting the Go Cong to the Vung Tau in the Can Gio Bay area with 33 km length. Besides that, the sea dike connecting the Go Cong to the Can Gio was also suggested. This sea dike is 5 km shorter than the Go Cong - Vung Tau sea dike. This project aims to control flood, mitigate inundation and anti-salinity for whole the HCMC area in short-term and long-term under impacts of climate change, especially SLR. However, until now there are many arguments to debate negative and positive effects on
Fig. 1.3 Projects of sea dike construction. (a) a sea dike from the Go Cong to the Vung Tau (GCVT) protected all parts of the Can Gio mangrove forest
area; (b) a sea dike from the Go Cong to the Can Gio (GCCG) protected some parts of the Can Gio Mangrove forest area.
environmental water in the rivers as well as in the Can Gio mangrove forest. The Can Gio Bay area is situated at the end of the Saigon River where connecting coastal zone into the East Sea. It is a famous district for the mangrove swamp as mangrove biosphere reserve for the HCMC. It plays an essential role in the ecological system; it has been recognized as an international biosphere reserve zone by UNESCO (2000). Somehow, this project also received much attention from researchers. To construct this sea dike, it is a necessity to extend the time to pay more attention to later deeply researches.
Many researchers have attempted to analyze the positive and negative impacts of the sea dike construction. Most of these attempts have focused on studying the capability for controlling the water level in the urban area, and flood discharge from the upstream region (Nguyen et al., 2015), but changes in water level, inundation, salinity and its impacts to the Can Gio Bay area have received less attention. During the past decades, several critical studies have been conducted in this area, which carried out new valuable results. These noteworthy outcomes include Nguyen (2014) and Ngoc et al. (2013), but these studies only pointed out the changes of water level under the impacts of sea dike construction and did not consider inundation in the wetland area and salinity change in the main rivers of the Can Gio Bay area.
1. 1. 3 Necessity of Research
Hydrodynamics study is critical in managing and designing hydraulic construction systems in the Can Gio Bay area. Inundation causes damages to local houses, shrimp fields, mangrove forests. The main reasons causing inundation are the tidal regime from the East Sea, and the upstream inflow from the rivers combined with the heavy rainfall, especially 80 % of total precipitation occurred in the rainy season.
If the sea dike would be constructed, the characteristics of the Saigon River mouth would be influenced by changing the structure, and the Can Gio mangroves forest would have both negative and positive impacts. The flow in the Can Gio Bay would be influenced under high pressure due to the variation of
river inflow and tidal current. Besides, the SLR would also be essential factors which impact the flow and the inundation area of the Can Gio Bay.
Furthermore, the Can Gio mangrove forest is an essential part of the HCMC and other surrounding areas. Salinity is the main factor which gives an impact on the development of mangrove trees. Understanding temporal salinity variation and spatial salinity distribution in the Can Gio Bay area is an essential condition for sustainable development of the Can Gio mangrove forest. In addition, the sea dike construction and the SLR also make a change of salinity distribution in the Can Gio mangrove forest.
1. 2 Objective of the Study
This study gives an understanding of hydrodynamics in the Can Gio Bay such as the change of water flow and water velocity under the significant difference of tidal level and upstream inflow of the rivers. Moreover, this study gives an understanding clearly about salinity regime in the Can Gio Bay area.
The objectives of this research are:
Assessing the impact of climate change - sea level rise, and strategic development- sea dike on water flow and flooding inundation.
Assessing the impact of SLR and sea dike construction on salinity distribution on the mangrove forest in the Can Gio Bay area.
First of all, the study area – the Can Gio Bay is briefly explained in the Chapter 2. The Chapter 3 describes the impact of SLR and sea dike construction on the hydrodynamic regime and inundation in the Can Gio Bay. The study sites, methodology, all data obtained and used in simulations, the two-dimensional depth-averaged hydrodynamic model, a wetting-and-drying scheme for discerning tidal flats and achieved results are presented in this chapter. The Chapter 4 describes the salinity field observations in two periods between 2017 and 2018. Data loggers’ surveying achieved the salinity field data including temporal salinity time series, spatial salinity distribution and salinity vertical profiles, which are discussed in detail in this chapter. The Chapter 5 deals with the impact of SLR and sea dike construction on the salinity regime in the Can
Gio Bay. The convective-dispersive model is combined in the hydrodynamic model to simulate temporal salinity variation and spatial salinity distribution in the Can Gio Bay. The achieved field observation data in the Chapter 4 are used for validating salinity model in the Chapter 5. Finally, general conclusions and recommendations for widespread implementation of the above-described studies are pointed out in the Chapter 6.
1. 3 Literature Review 1. 3. 1 Probable Approach
To develop a model which can simulate the water flow and access the inundated area of the Can Gio Bay, the approach should be considered, and values of the process should be determined as bellows:
Select suitable models and then apply and develop this model to achieve good performance by evaluating the parameter values of the model.
The obtained results of this model are calculated from observed data and available historical records and estimated by the error indicators and effective performance.
Based on the achieved results the impact of tide and upstream inflow to the inundated area and salinity distribution on the Can Gio Bay area is assessed.
The scenarios of SLR and sea dike construction are set up in this study based on the actual conditions and data of the HCMC Government and the Vietnamese Government.
1. 3. 2 Reviewed Papers
In estuaries, combining of river mouth and sea, mixing between fresh and saline water, effecting of river discharge, tides, winds, waves, and offshore currents make an incredibly complex hydrodynamic process. Mathematical modeling has been emerged as a powerful tool in water resources management; it allows simulation of environmental water and prediction of the possible impacts due to the anticipated changes. To understand the hydrodynamic processes, several numerical models have been published. These models include HAMSOM
(Backhaus, 1983), POM (Blumberg and Mellor, 1987), FVCOM (Chen et al., 2003) and SWAN (Booij et al., 1999).
Walters and Cheng (1979) used a finite element model to compute the tidal currents in an estuary. This model used triangular, isoparametric elements with basic quadratic functions for two horizontal velocity components and a primary linear function for water surface elevation. The model tests had been made in the San Francisco Bay, California (South Bay) and the Carquinez Strait (West Bay) which owned the complex bathymetry, and showed that the generally shallow basins dictated the hydrodynamic characteristics of the bays.
Chau et al. (1996) simulated the flow exchange or tidal flushing of the natural water bodies by a grid "block" technique successfully to overcome the difficulty of complex topography with a depth-averaged two-dimensional flow.
A new finite difference mathematical model of depth-averaged flow was developed. A numerically generated boundary-fitted orthogonal curvilinear grid system developed for water bodies.
Hu and Kot (1997) introduced a numerical model of the tide in the Pearl River Estuary with a moving boundary. The horizontal two-dimensional simulation of tide was presented. A drying and flooding procedure was applied to simulate the drying when the water level was low, and the flooding when the water level was high. The influence of numerical solution in space-discretization and time-discretization of the convection terms was investigated.
Uchiyama (2004) developed a numerical model based on the Princeton Ocean Model with the wetting-and-drying scheme. This study incorporated a wetting-and-drying scheme into three-dimensional Princeton Ocean Model to simulate hydrodynamics in estuaries including intertidal areas such as mudflats and salt marshes, which has shown that the dominant factors of hydrodynamics were the wetting-and-drying processes.
Hiramatsu et al. (2005) developed a two-dimensional depth-averaged finite difference numerical models to predict the tidal flow velocity and suspended sediment concentration in the Ariake Sea, located in the Kyushu Island, Japan. An operator-splitting technique was utilized for time integration of
the convective-dispersive equation. The results of this study showed that the models could be used to predict the tidal flow velocity and the spatial distribution of sediment concentration. Tabata et al. (2013) also applied a two-dimensional depth-averaged model for simulating the convective-dispersion of the larvae of the pen shell Atrina pectinata in the Ariake Sea.
Water quality models are essential tools to support optimizing aquatic ecosystem rehabilitation programs and assess their efficiency. Chen et al. (2008) developed a two-dimensional water quality model in the Daqinghe River Mouth of Dianchi Lake, China based on the flow conditions. The numerical solutions used the Alternating Direction Implicit (ADI) method to solve for the hydrodynamics module. Huiting et al. (2018) studied about hydrodynamic and salinity transport processes in the Pink Beach wetlands of the Liao River estuary, China by using MIKE 21 hydrodynamic and salinity model. The MIKE 21 model is one of the most widely used hydrodynamic models. The hydrodynamic module of the MIKE 21 model is based on the numerical solution of the depth-integrated incompressible flow with Reynolds-averaged mass conservation and momentum equations.
Mangrove is an ecological system referring to a taxonomically diverse association of trees and shrubs which form the dominant vegetation in tidal, wetlands within tropical and subtropical coasts. It is an unique ecosystem that flourish in the intertidal zone from the mean sea level to the highest spring tides in the tropics and subtropics. In many mangrove forests, variation in tree height and productivity along environmental gradients is a common phenomenon. The ecophysiology of mangroves is a significant concern of a number of researches such as Marilyn (1988) and Gonasageran et al. (2011). Globally, mangroves are generally undervalued, poorly managed, and overexploited (Ewel et al., 1998).
The raton 35 % of the world’s mangrove forests have been destroyed by human activities over the last two decades. Mangrove conservation and sustainable utilize as a zone of critical transition between land area, and sea area needs to be better evaluated (Ewel et al., 2001; Saenger, 2002). The human impacts and global change have prompted worldwide scientific interest to understand the
ecology and eco-physiological requirements of mangrove establishment, persistence, and development (Robertson and Alongi, 1992; Saenger, 2002). The ground surface area of mangrove forest is often covered with saplings and seedlings of overstory species; however, there is a conspicuous lack of some species such as herbaceous, shrub, and vine in many forests.
Mangrove ecosystem can be considered that its area can extend rapidly in response to regional topographical and climate changes. Mangroves naturally complete their life cycles under salinity conditions (Flowers and Yeo, 1986). The optimal salinity for mangrove growth depends on the kind of plant. For example, the optimal salinity for the growth of Acaryochloris marina was substantially lower, ranging from 10 psu to 25 psu seawater (Downton, 1982; Clough, 1984;
Ball, 1988). A vital factor to mangrove ecosystems is the extent of the SLR. It is complicated to generalize about the effect of climate change on the mangrove ecosystem. However, all mangrove systems occur somewhere with the different low and high salinity, which makes it clear that they are likely to be significantly influenced by any changes of salinity as well as the change of sea level. Different mangrove species appear in a separate area, which points out the preference for the degree of salinity of the surrounding environment. Mangrove ecosystems accumulate peat or mud, which gives them the capacity to adjust to the SLR. If the sediment accretion rate equals the rate of SLR, then inundation preferences of the different mangroves species can be maintained. Besides, the growth rate of mangroves is critically related to the availability of water to the trees, and this is reflected in the soil water content and soil salinity. As most mangroves are tidally inundated, soil water content only becomes a problem when the inundation is occasional and the rainfall very limited. Soil salinity, however, characterizes the mangrove habitat and growth of some mangroves have been shown to be maximal under relatively low salinities (Clough, 1984). As the salinity of the soil increases, the mangroves face the problems of rising salt levels in the tissues and decreasing availability of water. The increasing salt levels in the issue may bring about a lessening in the net assimilation rate per unit leaf area (Ball, 1988) and therefore reduce growth.
Salinity is one of the essential drivers in mangrove establishment and development (e.g., Ball, 2002). Mangroves can grow in a range of various salinities, extending from primarily freshwater environments into hypersaline areas (Chapman, 1944). Ball (1988) described this overlap by first making observe that mangrove species vary widely in their abilities to deal with salinity and rarely partition within a narrow functional niche.
In the Can Gio Bay, there are a lot of researches, however, most of the studies are related to the ecosystem of the Can Gio mangrove forest such as Claudia and Vo (2013) that assessed the ecosystem of the Can Gio mangrove biosphere reserve by combining earth-observation- and household-survey-based analyses. Luong (2011) conducted the study on the numerical investigation on the sediment transport trend and they gave a mathematical model to simulate the sediment transport under current effects by tides and winds.
There are some studies using the satellite data to apply in the Can Gio mangrove forest. Binh et al. (2008) used multi-temporal remote sensing data to detect mangrove change and manage them. Luong (2011) used the satellite data by remote sensing and geographical information system technology to assess the change of mangrove forest in the Can Gio Bay for monitoring the mangrove forest. Vien et al. (2014) reported details about the flora and fauna in the Can Gio mangrove forest. Luong (2011) monitored spatial-temporal changes of mangrove forests using the Landsat data during four periods: 1989-1996, 1996-2003, 2003- 2009, and 2009-2014, and processed by three main steps: data pre-processing, mangrove extraction and accuracy assessment. Furthermore, the erosion and deposition in the Can Gio mangrove forest were also considered. In detail, Vien and Le (2014) assessed the erosion from 1953 to 2010 in the Can Gio mangrove.
Huynh et al. (2014) studied about monitoring the riverbank erosion.
The environmental value of mangrove tree in the Can Gio Bay area was studied by some researches. Pham and Vien (2014) studied the effects of thinning on CO2 absorption capacity of the Rhizophora apiculata plantations in the forest.
Carbon sequestration of the Ceriops zippeliana was considered by Cao and Vien (2014).
Chapter 2 Targeted Area - Can Gio Bay 2. 1 Physical Characteristic
2. 1. 1 Geography Location
The Can Gio Bay area shown in Fig. 2.1 lies between 10° 22′ 14″ – 10° 40′ 09″
N latitude and 106° 46′ 12″ – 107° 00′ 59″ E longitude and approximately 65 km south of the HCMC in Vietnam (UNESCO/ MAB, 2000). The region is bounded by the Nha Be District to the north, the East Sea to the south, the Dong Nai and the Ba Ria-Vung Tau Provinces to the east, and the Long An and the Tien Giang Provinces to the west.
Fig. 2.1 Location of the Can Gio Bay and main rivers in Can Gio Bay area.
2. 1. 2 Terrain
The topography of the Can Gio Bay area is generally flat and low-lying. Its terrain is formed by alluvial deposits from the Soai Rap, the Long Tau, the Dong Tranh, the Nga Bay, the Thi Vai, and the Dong Tranh and the Ganh Rai estuaries (UNESCO/ MAB, 2000; Tuan et al., 2002). The topography in this area can be classified into five types based on the tidal level (above sea level): (1) unflooded upland (2 m – 10 m), (2) multi-year cycle flooded lowland (1.5 m – 2.0 m), (3) monthly cycle flooded lowland (0.5 m – 1.5 m), (4) daily cycle flooded lowland (0.3 m – 0.5 m), and (5) lower tide accretion area (< 0.3 m) (Miyagi et al., 2014).
2. 1. 3 Geology
The soil in the Can Gio Bay area has been created by combining alluvial clay deposition, vitriolic processes, and brackish water. Four main soil types can be found including saline soil, saline soil with low alum content, saline soil with high alum content and soft sandy soil with mud deposits at the seashore (Tuan et al., 2002). The salinity intrusion in soil is dramatically and directly effected from the sea and depended on the influences of the tides.
The soil types in the Can Gio Bay area are somewhat limiting for human use. The deeper soil layers are as yet highly un-compacted thus unable to provide a solid foundation, and they have a high content of various sulfur oxides, which are detrimental to agriculture, and a high NaCl salt content.
2. 1. 4 Climatology
The Can Gio Bay area has a tropical climate with typically monsoonal of two distinctive seasons (Tuan et al., 2002), a dry season from November to May and a rainy season from June to October. Rainfall, humidity, temperature, and hours of sunshine are variable between the dry and the wet seasons.
There are two main wind directions. Wind directions of the Can Gio Bay area during the rainy season are southwest with the most robust velocity in July and August, and during the dry season are northeast with the strong wind in February and March.
The Can Gio Bay area has an annual average rainfall from 1300 mm – 1400 mm with the highest monthly average from 300 mm – 400 mm in September. The rainfall in the Can Gio Bay area is lowest in the HCMC area and decreases gradually southwards.
The humidity is around 4 % – 8 % and higher than in other areas of HCM.
During the rainy season, humidity is from 79 % – 83 %, getting a maximum in September with 83 %. In the dry season, humidity varies from 74 % – 77 %, with a minimum in April around 74 %. The average evaporation is highest in June with 173.2 mm/month and lowest in September with 83.4 mm/month.
The daily average temperature amplitude varies from 5 oC – 7 oC, however, less than 4 oC over a month. The monthly average temperatures are at their highest around 37 oC at the end of the dry season from March to May, and at their lowest form around 18 oC at the end of the rainy season from December to January. The monthly averages are ranging from 25.5 oC – 29.0 oC. The yearly average temperature is 25.8 oC, measured at the Do Hoa Gauging Station. There is a very slight temperature decrease from north to south, which is barely perceptible.
The sunshine hours are from 5 hr – 9 hr per day. The average daily radiation is always above 300 cal/cm2/d. The maximum monthly average appears in March at 14.2 kcal/cm2/month. There is a noticeable reduction, particularly between the periods from September to December in the monthly amounts of radiation from 14 kcal/cm2/month to 10 kcal/cm2 /month.
2. 1. 5 Hydrology
The Can Gio Bay area has a very complex network of rivers, referenced in Fig.
2.1, ranging in length from 10 km – 70 km with depth up to 30 m as shown in Table 2.1. The rivers account for 31.8 % of the total area (Luong, 2011).
Freshwater from the Saigon-Dongnai River is carried toward the sea via its two main branches, the Long Tau River and the Soai Rap River which have a lot of small branches and creeks such as the Dua, the Dong Tranh Rivers and subordinate branches as the Thi Vai River. Most of the water in the Can Gio Bay
area is generally brackish due to the mixing of freshwater and seawater, which is of great importance to the area’s two main estuaries, the Dong Tranh and the Ganh Rai.
The majority of those rivers flow in a generally southeasterly direction and their courses affect local topography and change in vegetation. The Long Tau and the Soai Rap Rivers, the two main terminal branches, effect to the hydrographic regime of other subsidiary branches.
In the Can Gio Bay area, there were some observed water level stations recorded by the Can Gio mangrove forest projects. Especially, the Soai Rap Station was located in the Soai Rap River. The Dong Tranh Station was situated in the Dong Tranh River. Furthermore, the Nga Bay Station was established in the Long Tau River and in the core of the mangrove forest. In addition, the Thi Vai Station was located in the Thi Vai River. Each station was situated in particular area to study about hydrodynamic of the Can Gio Bay area.
2. 1. 6 Tidal Regime
The Can Gio mangrove forest is located on a zone with a bi-diurnal tidal regime, i.e., two ebb and flow tides per day (Luong, 2011). Tidal amplitudes are from about 2 m at the mean tide to 4 m during the spring tide. It has been evidently observed that the two daily high and low tides different in height. Maximum tidal amplitudes in the range of about 4.0 m – 4.2 m are the highest observed in Vietnam. Tidal amplitude reduces with distance north, i.e., inland, relating to the proximity to the East Sea. The tidal regime of the Can Gio Bay area is based on the tidal regime at the Vung Tau tidal observation Station as shown in Fig. 2.1.
Table 2.1 The main rivers of the Can Gio Bay.
Name of River Length (km) Width (km) Depth (m)
Soai Rap 44.00 3.10 <10–20
Dong Tranh 67.50 1.80 01–25
Long Tau 32.00 0.55 10–25
Dua 10.00 0.90 10–30
Thi Vai 12.00 0.60 10–20
This is only one tidal observation station set up from the Binh Thuan Province to the Ca Mau Province. The location of the Vung Tau Station is at longitude 107°
04' E and latitude 10° 20' N.
In the Can Gio Bay area, the maximum tide occurs in October or November, and the minimum in April or May. During the 29th and 3rd day of the month on lunar calendar and during the 14th and 18th day of the month, the total area of the Can Gio mangrove forest is flooded twice a day at high tide. On the 8th and 25th day of the month on lunar calendar, the low tide is at its minimum (Nguyen et al., 2011). Inundation frequencies at different ground levels were determined based on a tidal chart of the Vung Tau Station. The semidiurnal tides are 2 m – 4 m in amplitude, with maximum and minimum tidal amplitudes occurring from October to November and from April to May (Binh et al., 2008).
Affecting the Can Gio Bay area, the East Sea tide has a semidiurnal regime with two high-water levels (+0.7 m – +2.4 m) and two low-water levels (−0.5 m – −1.5m). This tidal regime, combined with the shifting topography, produces varying flood depths and times depending on location. Lugo (1986) found that the frequency of tidal flooding and salinity levels determined mangrove density and type in the forest. The hydrodynamic regime is a crucial factor in forming species distribution, productivity, and nutrient cycles in mangrove ecosystems. A combination of tidal currents, tidal circulation, and water exchange affect the geomorphology in which mangrove species can be established, including the erosion and the deposition of soil or the formation of mudflats.
2. 1. 7 Salinity Variation
The Can Gio Bay area faces several environmental issues, particularly changes in water quality that relate to salinity change and can lead to detrimental effects on the mangrove forest as salinity is a critical ecological factor for plant growth and productivity (Allakhverdiev et al., 2000). Salinity fluctuations in this area are directly correlated with the combined effects of the tidal regime and the Saigon and the Dongnai Rivers' flow; the salinity is highest during high tide and lowest
during low tide, while higher river flow reduces salinity by pushing seawater further seaward. Other factors influencing salinity levels include regulated flows from the upstream Tri An and Dau Tieng Reservoirs and the depth of rivers. Due to morphological differences between the Soai Rap and the Long Tau Rivers, these channels are affected differently by the tidal regime of the East Sea: the former has a lower salinity than the latter because of its shallow cross-section. In other words, the sea’s tidal impact on the shallower Soai Rap River is less than on the deeper Long Tau River. The salinity in the Can Gio Bay area also differs depending on the seasons. Salinity increases in the dry season as seawater penetrates further inland, peaking in March and April at 19 psu – 20 psu in the north, and 26 psu – 30 psu near the sea. Salinity declines in the rainy season, bottoming out from August to November at only 4 psu – 8 psu in the mangrove forest (Hong, 2004). Salinity is an essential key to environmental research and management in fields such as climate science and oceanography, water quality monitoring, and coastal management.
2. 2 Can Gio Mangrove Forest
The Can Gio Bay includes the Can Gio mangrove forest considered as "Green- lung" of the HCMC and the surrounding area. The Can Gio mangrove forest was located on the alluvium of the Saigon and the Dongnai Rivers. The development of mangrove forest is dependent on high precipitation and a high density of rivers interweaving the area and providing an abundant and plentiful supply of alluvium in the estuarine region.
Nearly all of the mangroves were destroyed because of chemical warfare in 1965–1970. Since that time, the local people have successfully rehabilitated almost a thousand hectares of mangroves. In 2000, the Can Gio mangrove forest was designated as an International Mangrove Biosphere Reserve by the United Nations Educational, Scientific and Cultural Organization / Man and the Biosphere Programme (UNESCO/ MAB). With 77 different mangrove species (35 true mangroves and 42 associates), the Can Gio mangrove forest has contributed environmental importance and economic benefit in this coastal
region but remains very vulnerable to impacts from natural and human driving forces (UNESCO/ MAB, 2000). The mangrove forest located within this district is respected as one of the most beautiful mangrove forests in the Southeast Asia.
In global studies, it is usually classified as a ‘Mangrove afforestation and re- forestation area’ (Blasco et al., 2001). The mangrove forest area of the Can Gio Biosphere Reserve has a very high biodiversity with various kind of fauna and flora: approximately 200 species of fauna and 52 species of flora. In the southern part of the Can Gio mangrove forest, three dominant species are namely Avicennia alba, Rhizophora apiculata, and Phoenix paludosa. The most common mangrove species is the Rhizophora apiculata. The Avicennia alba species is often found in areas of high salinity. The Phoenix paludosa are usually found on the elevated ground, forming mixed communities with other mangrove species such as the Acrostitum aureum and the Nypa fruticans. Further plant species found in the Can Gio region are the Bruguiera gymnorrhiza, the Bruguiera parviflora, the Ceriopssp, the Kandelia candel, the Rhizophora mucronata, the Sonneratia alba, the Sonneratia ovata, the Sonneratia casedar, the Avicennia alba, the A. officinalis, the A. lanata, the Aegiceras majus, the Thespesia populnea, the Hibiscus tiliaceus, the Lumnitzerara cemose, the Xylocarpus granatum, and the Excoecaria agallocha (Binh et al., 2008). Mangroves and seagrass beds act as a breeding ground for many other species of mollusks, crustaceans, fish, amphibians, and birds as well as terrestrial animals.
Mangrove habitats are often varied depending on various environmental conditions including climate, hydrology, geophysiology, geomorphology, and petrology (Chapman, 1944). Mangroves grow well in fine-grained sediment and active deltaic plains that receive abundant fresh water. However, frequent flooding and an adverse tidal regime can negatively affect their growth. The coastline of southern Vietnam offers suitable hydrological, hydrodynamic regimes, and soil characteristics to support one of the most extensive and diverse mangrove forests in the world.
2. 3 Social-Economic Aspect
The Can Gio mangrove forest located 50 km southeast of the HCMC is considered an international biosphere reserved zone, the Can Gio Mangrove Biosphere Reserve (UNESCO/ MAB, 2000). Situated in the deltaic confluence of the Saigon, the Dongnai and the Vam Co Rivers, the mangrove forest plays an important key in protecting the environment of the HCMC and the surrounding area, providing useful products such as fuelwood and construction poles, and ecosystem services such as coastal protection and carbon sequestration. The mangrove forest reduces the effects of coastal storms and erosion, and provides a habitat for many species, including marine life of economic value. It is also potentially valuable for developing ecotourism. The Can Gio Bay area is an essential environmental resource for the surrounding area, hosting about 105 plant species and 150 species of aquatic fauna. The area also supports various wildlife such as wild pigs, monkeys, otters, saltwater crocodiles, many species of snakes, and abundant birds including rare species. Moreover, the Can Gio Bay area plays a crucial role in the developing economy around the HCMC, supporting jobs in aqua agriculture, fisheries, shrimping, and ecotourism.
Approximately 58000 people are living within the boundaries of the biosphere reserve, 54000 people of which live in the transition area. The local people are from different origins, and have a mixture of cultures and ways of life. The main economic activities are fisheries, agriculture, aquaculture, and salt production.
Around 20 % of the Can Gio Bay area inhabitants are aquaculture shrimp farmers, who mainly live in the transition zone. Some residents have been allocated with forests for protection for around 30 years, and they can utilize a small portion of the land for aquaculture and salt production. Other residents, engaged in miscellaneous occupations, have no property and must earn their living by catching crabs and mollusks, and collecting firewood. The area also hosts the main shipping channel via the Long Tau and the Soai Rap Rivers, allowing ships of less than 20000 tons carrying the capacity to enter the HCMC at the ports of the Cat Lai and the Phu Huu.
The Can Gio Bay area is the poorest district of the HCMC. The Can Gio mangrove forest is expected to be a site where sustainable development, conservation, and cultural, socio-economic activities in forestry and fishery management systems can be tested, refined, demonstrated and implemented.
Although reforestation in the Can Gio Bay area was quite successful, the problematic issue remains that poor local people tend to generate their income by utilization of the direct values of mangrove forest by cutting wood, destroying mangrove forest land to serve for shrimp farming. Further threats to the Can Gio mangrove forest is shoreline erosion induced by large freight ships steering directly through the reserve to the HCMC harbor. Tourism also brings the accumulation of solid and liquid wastes, noise and light pollution, as well as increases danger of land speculation.
Chapter 3 Impact of Sea Level Rise and Sea Dike Construction on Hydrodynamic Regime and Inundated Area in Can Gio Bay 3. 1 Introduction
Inundation is a severe issue in many locations worldwide, including the HCMC, which is located in an area of flat, low-lying terrain in the Saigon–Dongnai River basin. The HCMC is the most important city in southern Vietnam, rapidly industrializing and with a population of 7.5 million people (World Bank, 2010).
Because of the frequency of heavy rainfall, river flooding, and sea level rise (SLR), combined with land subsidence, the HCMC ranks fourth globally among the coastal cities most threatened by climate change (Nicholls et al., 2008).
Severe flooding becomes more dangerous and frequent when upstream flooding and heavy rainfall combined with the spring tide. The HCMC has attempted several projects to address this problem, but none have been successful. The HCMC Environmental Sanitation Project improved the drainage and sewer system of the city to handle extreme rainfall, and a network of levees and barriers called "Project 1547/QD-TTg" (Vietnamese Government, 2008) was designed and is now under construction. However, these projects may only resolve flooding problems in the short term, and drainage remains difficult because there was less attention, paid to water level increases in rivers around the city caused by the upstream flood discharge and the SLR. Thus, no previous countermeasures have fully solved the flooding problems in the overall HCMC area.
To solve flooding problems in all areas of the HCMC, a sea dike project with two types of sea dike, one connecting the Go Cong to the Vung Tau and one connecting the Go Cong to the Can Gio, as shown in Fig. 3.1, was proposed in 2010 and the feasibility of this proposal has been debated. Nevertheless, no alternative solution has been proposed, and this sea dike project is being subjected to a feasibility study by the Vietnamese Government as a long-term solution to lower water levels around the city to facilitate more efficient drainage.
In recent years, especially in 2016, the HCMC has experienced severe flooding, and many streets were inundated at depths of 0.5 m – 1.5 m. Consequently, many
scientists and the Vietnamese Government have agreed to speed up the implementation of the sea dike feasibility study. Hoang and Khang (2015) considered this sea dike construction could fix problems through limited spatial planning of levees and barriers in the inner city and could regulate water storage by reducing flood flows from upstream and controlling rising sea water levels resulting from climate change.
As shown in Figs. 2.1 and 3.1, the Can Gio Bay is located approximately 50 km southeast of the HCMC, and is situated in a deltaic confluence of the Saigon, the Dongnai, and the Vam Co Rivers; this confluence also includes smaller rivers such as the Soai Rap, the Dong Tranh and the Nga Bay Rivers, forming a network of waterways (HCMC People's Committee, 2012). A large mangrove forest in the Can Gio Bay was added to the World Network of Biosphere Reserves (UNESCO/ MAB, 2000). The mangrove forest plays a crucial role in protecting the environment of the HCMC and the surrounding area,
Fig. 3.1 Site map of the Can Gio Bay study area, showing the topography and bathymetry of the Can Gio Bay observed in 2006, the location of the Can Gio mangrove forest, shoreline, the Go Cong –Vung Tau sea dike, the Go Cong – Can Gio sea dike, and measurement points of river discharge and water levels.
providing useful products such as fuelwood and construction poles, and ecosystem services such as coastal protection and carbon sequestration. The mangrove forest reduces the effects of coastal storms and erosion and provides a habitat for many species, including marine life of economic value; it is also potentially valuable for developing ecotourism. The proposed sea dike would affect the hydrodynamic regime of the estuaries, and the change in the tidal regimes would adversely affect coastal ecology, especially in the Can Gio mangrove forest. Assessing the impact of sea dike construction on the hydrodynamic regime and inundation of the Can Gio Bay area would be therefore critical to the sea dike project.
Several studies that analyzed the inundation in the HCMC center have been conducted (Storch and Downes, 2011; Lasage et al., 2014; World Bank, 2010). However, the inundation in the Can Gio Bay has received less attention.
For example, the current and future risks in the HCMC were quantified by Storch and Downes (2011) by combining urban development and SLR scenarios. Future SLRs and urban growth scenarios were studied by Lasage et al. (2014), and various adaptive measures were suggested based on cost-benefit analyses for the HCMC. Further, many researchers have attempted to analyze the positive and negative impacts of sea dike construction. However, most of them focused on investigating the capability to control the water level in urban areas and flood discharge from upstream areas (Nguyen et al., 2015). In the past decades, several important studies have been conducted in this area and new and valuable results obtained. These noteworthy outcomes include the findings of Nguyen (2014) and Ngoc et al. (2013). However, these studies only identified the water level change under the impact of sea dike construction; inundation in the wetlands of the Can Gio coastal area was not considered.
In this chapter, how sea dike construction impacts the hydrodynamic regime and the fluctuation in inundated areas of the Can Gio Bay is examined by using a two-dimensional hydrodynamic model combined with a wetting-and- drying scheme to assess the feasibility of two sea dike types, the Go Cong - Vung Tau sea dike and the Go Cong - Can Gio sea dike. Specifically, the study
examines the effects of those sea dike types under operating the sea dike gate by controlling the maximum water level in the interior of the Can Gio Bay, based on the relationship between the water levels inside and outside the sea dike gate.
3. 2 Methodology
3. 2. 1 Two-Dimensional Depth-Averaged Model
Recently, three-dimensional models are widely used for simulating coastal area (e.g., Chen et al., 2003; Jiang et al., 2007; Reza et al., 2016). However, this study only focused on assessing the water level and inundation area of the Can Gio Bay. Thus the two-dimensional model was the most effective. Besides that, because the water depth in the Can Gio Bay is extensively shallow and the flow of the Can Gio Bay is driven mostly by tidal flow and flood water flow, horizontal velocity components are much greater than vertical ones. In addition to that, the impact of the SLR and the sea dike construction on the water level and inundation area in the Can Gio mangrove forest was evaluated using numerical simulations, in which the evaluation absolutely needed the change of horizontal water level and inundation area, but the change of vertical profile was not necessarily required. Therefore, to identify the change of water level and inundation area, the horizontally two-dimensional depth-averaged model for shallow water was effectively used (e.g., Hu and Kot, 1997). This model was developed by integrating a three-dimensional continuity equation and using Reynolds-averaged Navier-Stokes equations assuming the Boussinesq approximation and hydrostatic pressure.
To simulate the process of hydrodynamics for the shallow water areas, a horizontal two-dimensional mathematical model (e.g., Hiramatsu et al., 2005 ; Tabata et al., 2013) was developed by the finite difference method. This model is based on the two-dimensional continuity equation (3.1) and momentum equation (3.2, 3.3) for simulating the flow of the Can Gio Bay. The Governing equations of the model were as follows.
The continuity equation was used to calculate the water level:
U h
V h
0t x y
(3.1)
The momentum equations were used to calculate the velocity in the x- and y- directions:
2 2
2 2
1 sx bx
h
U U U U U
U V fV g A
t x y x x y h
(3.2)
2 2
2 2
1 sy by
h
V V V V V
U V fU g A
t x y x x y h
(3.3) As shown in Fig. 3.2, is the water level;
h is the bottom elevation; t is the time; f is the Coriolis parameter,indicating the rotation effect of earth; g is the acceleration due to gravity;
2 2
s sx sy
is the surface shear stress;b
bx2by2
is the bottom shearstress; is the density of water; U and V are the depth-averaged horizontal velocity components in the x- and y-directions in the Cartesian coordinate system, respectively, and Ah is the coefficient of eddy viscosity determine by the Smagorinsky model (Smagorinsky, 1963):
2 / 2 1 2
2 1 2
1
y V y
U x V x
A U S
Ah m G (3.4)
Where Sm is the Smagorinsky coefficient and AG is the mesh area.
In the equations 3.2 and 3.3, the surface shear stress due to wind could be ignored. The bottom shear stress was calculated using the Manning roughness law by the following equation:
2 2
1/3
b gn W
h
(3.5) Where n is the Manning’s coefficient of roughness which depends on topography of the study area and W is the horizontal velocity.
Fig. 3.2 Coordinate system.
By using the equation 3.5, the equationss 3.2 and 3.3 could be rewritten as the following:
2 2 2 2 2
4/3
2 2
h
U U U U U gn U U V
U V fV g A
t x y x x y h
(3.6)
2 2 2 2 2
2 2 4/3 h
V V V V V gn V U V
U V fU g A
t x y x x y h
(3.7) The most common approach of the spatial discretization of the shallow- water equations is finite differences with a rectangular grid with grid sizes of x and y and the spatial derivatives are approximated by finite differences as shown in Vreugdenhil (1994). In this case, the staggered grid system has been effectively applied in the past researches (e.g., Hiramatsu et al., 2005 ; Tabata et al., 2013) using the leap-frog scheme for time discretization and a central differential scheme for spatial discretization.
3. 2. 2 Wetting-and-Drying Scheme
In the hydrodynamic model, the wetting and drying process is essential because it shows the movement of the flow from the wet to dry in the area and contrast.
This process is very common in the coastal area. It affects directly the accuracy of the simulation results and the treatment for the wet-and-dry process in a complex topography could cause numerical errors. The Can Gio mangrove forest is a flat tidal wetland area, subject to alternate wetting and drying. It is therefore necessary to incorporate a wetting-and-drying scheme into the model.
Furthermore, two-dimensional numerical models should include a wetting-and- drying process with moving-boundary techniques (Sabbagh-Yazdi et al., 2008).
The accuracy and stability of the hydrodynamic model are affected by the numerical solution for the wetting-and-drying point in the modeled area. Many researchers have provided solutions for the wetting-and-drying area, including Leendertse (1970, 1987), Stelling (1984), Falconer and Owens (1987), and Uchiyama (2004). This study used the method of Uchiyama (2004) to solve the wetting-and-drying grid in the model. In this model, the land mask function
(LMF) to determine and mask all land meshes during model simulations was defined by two values: one for the wet area and zero for the dry area.
In the wetting-and-drying scheme, the water depth Di,j (=hi,j + i,j) at a grid cell (i, j) was compared with a threshold depth dth and a minimum depth dmin
which were determined depending on the topography of the study area. If the water depth was greater than the threshold depth dth, this mesh became to be wet and was remained in the computational domain. Otherwise, the grid cell was regarded as potentially dry. Then, the four cells located next to the potentially dry cell would be tested by the following three conditions:
1) min (i-1,j, i+1,j, i,j-1, i,j+1) i,j , (3.8) 2) min (Di-1,j, Di+1,j, Di,j-1, Di,j+1 ) dth, (3.9) 3) max( LMFi-1,j, LMFi+1,j, LMFi,j-1, LMFi,j+1 ) = 0. (3.10) If satisfying at least one condition, the potential dry grid was considered as land area and would be removed from the computational domain; the LMF was set to be zero. If not satisfy any condition, the water depth Di,j at each cell was compared with dmin. If Di,j was smaller than or equal to dmin, the cell was considered as wet; the LMF was set to be zero. In contrast, the LMF was set to be one.
3. 3 Boundary Conditions and Data Used
According to recorded data, the largest recorded flood hit the HCMC in 2000, during which an unusual combination of heavy rainfall, maximal upstream flood discharge, and high tides occurred simultaneously, causing an extreme historical flooding disaster for the HCMC. The heavy rainfall occurred during five days at the end of October 2000. Maximum daily precipitation exceeding 150 mm, combined with high spring tides that resulted in massive flood discharge from upstream dumping, caused flooding in approximately 42 % of the HCMC area;
notably, 90 % of the Can Gio Bay area was inundated as a result of historical flooding over a period of 80 years. The simulated period of evaluation for the model comprised ten days from 00:00 on October 20, 2000 to 24:00 on October 30, 2000. This period was selected because it contained the largest inundation
occurring in 2000. In addition, the simulation period from 00:00 on August 7 to 00:00 on August 14, 2009 was also utilized for the validation of the hydrodynamic model.
The data used in the model are shown in Table 3.1.
First, the latest digital elevation model (DEM) of the Can Gio Bay with 50 m resolution shown in Fig. 3.1, which was collected in 2006 by the Division of Science, Technology and International Affairs, South Campus of Thuyloi University through the key national project of DTDL.2011-G/38 (Nguyen, 2014), was used to provide the seabed and ground elevations for simulations of validation and scenario analyses.
Next, the hourly observed sea water level for the entire year of 2000 and 2009 at the Vung Tau Station was utilized to calculate the harmonic constants for sea boundaries. Figure 3.8 shows the observed sea water level at the Vung Tau Station from October 20 to October 30, 2000 as a typical period with the
Table 3.1 Data used in the model.
Items/Data Location Period Purpose
DEM Can Gio Bay 2006
Seabed and ground
elevations for validation and scenario analyses Observed water
level Vung Tau
2000/10/20 -
2000/10/30 Sea boundary for scenario analyses 2009/08/07 -
2009/08/14 Sea boundary for validation
River discharge Phu Xuan, Vam Co, Cua Tieu, Cua Dai, and Thi Vai River
2000/10/20 - 2000/10/30
Inflow boundary for scenario analyses 2009/08/07 -
2009/08/14
Inflow boundary for validation Observed water
level
Soai Rap, Dong Tranh, Nga Bay, and Thi Vai River
2009/08/07 -
2009/08/14 Validation Observed
maximum inundated area
Can Gio Bay area 2000/10/20 -
2000/10/30 Validation
