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Quantitative risk measures of flood damage under the impacts of climate change

Quan Van Dau, Kittiwet Kuntiyawichai *

Water Resources Engineering, Department of Civil Engineering, Faculty of Engineering,

Khon Kaen University, Khon Kaen, 4002, Thailand

Abstract:

Flood is one of the most frequent hazardous natural disasters, which causes economic

depression, damages to livelihood and properties. Quantitative of the flood damage is

considered to be significant in minimizing potential losses from the natural hazard. In this

attempt, the paper aims to investigate the feasibility of assessing flood simulations and flood

damage that corresponds to the impacts of climate change in the central region of Vietnam.

The system was developed using the outputs of the Hadley Centre Coupled Model, version 3

(HadCM3) for A2 and B2 scenarios, a coupling of hydrological model (HEC – HMS) and

hydrodynamic model (HEC – RAS). In addition, the flood damage reduction analysis model

(HEC – FDA) was also considered for estimating flood damage. The findings indicated that

the weather will become hotter in the future with the temperature increasing from 0.4°C to

2.2°C and 0.19°C to 0.6°C correspond to A2 and B2 scenarios, respectively. Consequently,

the annual rainfall will also increase from 3.3% to 14.5% and 3.6% to 6.8% correspond to A2

and B2 scenarios, respectively. The results also demonstrated that runoff and water level can

potentially increase in the future and it might play a large role in destruction of crops,

transportation, water supply, and communities in the basin. The uncertainty of flood damage

costs was also estimated approximately $18,512.91 million to $19,148.59 million from the

decades of 2020s to 2080s. Additionally, the outcomes of this study can be applied for the

entire Huong River Basin in order to mitigate the flood problems in the future.

Keywords: Food damage, Climate change, Downscaling, HEC – HMS, HEC – RAS. * Corresponding author. Tel: +66 817 184 330; Fax: +66 4336 2142

Email address: Kittiwet_k@hotmail.com

1. Introduction

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Flood damage is an essential issue in water engineering science and economic

development. It can be considered in term of directly flood damage such as resulting from the

physical contact with the flood water, and indirectly flood damage occurred on time and after

the event which is related to cost of alternative accommodation and loss of income .

According to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change

(IPCC), the global temperatures are likely to increase by 0.3°C to 4.8°C under the

Representative Concentration Pathway (RCP) 2.6 to 8.5 scenarios by the end of the century .

Flood damage might be occurred seriously when associated with high amount of carbon-

dioxide (CO2) concentration. In this sense, determining the flood damage would be very

significant in order to quantify the influences of flood that may threat in the future. By taking

the impacts of climate change in this paper, the General Circulation Model (GCM) is

considered as the most common source to evaluate the hydrological impact studies. Since,

GCMs remain limited as these coarse resolution and uncertainty, to capture many important

regional parameters. Thus, the statistical downscaling model is subsequently applied in this

study to transfer the coarse spatial outputs to the finer resolution of regional level.

Recently, there are several studies carried out regarding flood damage such as: Chen

(2007) used the hydrodynamic model HEC - RAS to develop an inundation mapping, while

integrated the depth-damage function to estimate the flood damage, where

is damage factor, is maximum damage in USD/m2; Mohamma di et a l. (2014)

estimated flood damage using flood damage reduction analysis HEC – FDA model in the

Neka River; Van Ooteg em et a l. (2015) constructed four multivariate damage models for

pluvial flood, as well as the Tobit-estimation technique on identifying victims; McGra th et a l.

( 2015) studied the sensitivity analysis of flood damage in Fredericton based on the U.S.

FEMA’s standardized methodology, etc.

In this paper, the potential impacts of climate changes in the central region of

Vietnam are investigated using the Statistical Downscaling Model (SDSM). In addition, to

evaluate the hydrological processes, a coupling of the hydrological (HEC – HMS) and

hydrodynamic models (HEC – RAS) are implemented. As the expected outcomes, flood

damage is also analyzed using the HEC - FDA model to quantify flood-risks in the future.

2. Study area

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The Huong River Basin located in Thua Thien Hue Province, Vietnam was selected as

the case of study area. Geographically, it contains a total area of 2,830 km2 with a length of

104 km from the upstream of Thuong Nhat station to Thuan An outlet. There are three major

reservoirs: Huong Dien, Binh Dien and Ta Trach, which are situated along the tributaries of

the Bo, Huu Trach, and Ta Trach rivers, respectively. The river basin accounts almost 80% of

the entire area, it is mainly mountainous areas with its height varies from 200 m to 1,850 m in

comparison to the mean sea level (MSL). The river basin also accommodated the Tam Giang

– Cau Hai lagoon with 68 km of length, and 22,000 hectares of water surface areas.

The Huong River Basin is considered to have an abundant climate characteristic, with

the mean annual rainfall approximately from 2,500 mm to 3,500 mm vary from the coastal

areas to the hilly areas. The annual average temperature is measured approximately from

21°C to 26°C with the highest recorded temperature of 41.3°C at Hue City. The Huong River

Basin is known to be influenced by major of extreme natural disasters include of tropical

cyclones and flood as well as inundation at the downstream of the basin.

Fig. 1 The Huong River Basin, Vietnam (Thua Thien Hue Hydro-Meteorology and

Forecasting Center, Vietnam, 2014)

3. Data and Methods

3.1 Data collection

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The meteorological data consist of daily rainfall, maximum and minimum

temperatures during 1976 to 2012 obtained from Thua Thien Hue Hydro-Meteorology and

Forecasting Center (TTH – HMFC). In additional, the observed discharge and water level

from 2009 to 2014 were also collected from the TTH – HMFC for hydrology - hydraulic

modeling simulations. As representative the topography for environmental modeling, the

spatial geo-referenced data set of the Digital Elevation Model (DEM) with 10 m x 10 m

resolution generated from 1:25,000 topographic contour was implemented. The Curve

Number (CN) values were also determined based on land use and soil maps generated in

2014 from the Department of Resources and Environment of Thua Thien Hue Province.

For global climate data, the daily predictors were derived from the National Centers

for Environmental Prediction (NCEP) re-analysis during the period of 1961 to 2001.

Moreover, the Hadley Centre Coupled Model, version3 (HadCM3) daily outputs for the

period 1961 to 2099 were obtained from the Met Office Hadley Centre, England. HadCM3

was chosen since there is unavailable any specific GCM models at present in Vietnam, the

selection based on the previous studies including the central region of Vietnam and the

Southeast Asia . Moreover, according to the World Bank, Vietnam population will increase

over 100 million people in the middle of the century and it tends to be higher in the end of the

21st century. Therefore, the high scenario (A2) and medium scenario (B2) were selected for

this study area. The A2 scenario is described a very heterogeneous world, the increasing of

the population and slow to economic; whereas, the B2 scenario emphasized on local solutions

to economic and increasing of the population .

3.2 Statistical Downscaling Model

The SDSM model is defined as multiple regression-based method, which was

introduced by Wil by et a l. (2002) . The model was developed to convert the resolution from

large scale predictor variables into local climate variables. Initially, the NCEP reanalysis

predictors were selected to identify sensible relationships between the predictor variables and

observed data. A correlation and partial correlation analyses were also applied to investigate

inter-variable correlations of the potential utilization of predictor and predictand (rainfall,

temperature) relationships. Regarding calibration, rainfall was modeled as a conditional

process because the amounts of rainfall depend on wet-day occurrence while the temperature

was modeled as an unconditional process. Alternatively, the transformation of the “fourth

roots” was used to account for the skewed nature of the rainfall distribution. It suggested that

the climatological baseline period for 30-year such as the period of 1961 to 1990. However,

because of the lack of observation data, the period of 1976 to 1990 was used as a baseline

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period in this study. The model was simulated for daily rainfall, maximum and minimum

temperatures, i.e. the period of 1976 to 1990 for calibration and 1991 to 2001 period for

validation.

3.3 Hydrology model HEC – HMS

The Hydrologic Engineering Center – Hydrologic Modeling System (HEC – HMS)

model was developed by the U.S. Army Corps of Engineer. It was used to utilize the rainfall

– runoff processes of river basins and it was widely applied in many hydrological studies. To

represent the behavior of the river basin, HEC – HMS contains runoff volume, direct runoff,

baseflow and channel flow .

There are several loss methods that are designed in the HEC – HMS model to

calculate actual infiltration from sub-basins. Among the remaining loss methods, the Soil

Conservation Service - Curve Number (SCS – CN) method was used for the event-based

simulation due to its simplicity manner. In addition, the SCS – CN method provides better

results in comparison to the other methods in this study area. The Snyder Unit Hydrograph

(SUH) is well known as a common method used to define how excess rainfall is transformed

into runoff in sub-basins. SUH calculated runoff by utilizing a standard unit hydrograph,

which comprises of five characteristics, i.e. peak discharge, basin lag, basin time, and the

widths of the unit hydrograph at 50% and 70% of peak discharge . This method has recently

been proposed by in the same river basin, and it was selected in this study area. For

calculating the actual subsurface flow from sub-basins, the recession baseflow method was

considered due to its capability to automatically reset after each flood event; and this method

does not conserve mass for each sub-basin. Recession baseflow is broadly practiced in many

hydrological studies and it can be applied for both event and continuous simulations.

Alternatively, the Muskingum routing method was also applied to represent a segment of a

river. This method is examined as one of the most popular methods at present, since it used a

simple conservation mass method to route flow along the channel.

Moreover, there were three stations selected to represent the hydrological processes

applied to model evaluation, i.e. Phu Oc station, Binh Dien and Ta Trach reservoirs. At Phu

Oc station, due to lacking of observed flows in the Bo River, the observed daily water level

data measured at Phu Oc station for the period of 2009 to 2010 was used for calibration, and

2011 to 2012 period was used for validation. The observed daily inflow to Binh Dien

reservoir during the period of 2010 to 2011 was used for calibration and the year 2012 was

used for validation. For Ta Trach reservoir which was just operated in May 2014, the

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available observed inflow data for the period of September to October 2014 was used for

calibration, while the period of November to December 2014 was used for validation.

3.4 Hydrodynamic model HEC – RAS

The Hydrologic Engineering Center – River Analysis System (HEC – RAS) is a

hydrodynamic model developed by the U.S. Army Corps of Engineers. It was designed for

flood prone determination and hydraulic analysis of river channel. The model includes four

river analysis components such as the sediment transport, water quality, steady flow and

unsteady flow .

To carry out a model performance, the unsteady flow analysis was implemented in

HEC – RAS model. Simulation of unsteady flow uses dynamic Saint-Venant equation to

simulated one dimensional flow through the channel. The Manning’s n values were adjusted

vary from 0.12 to 0.15 for the left and right of the river banks, and from 0.032 to 0.04 for the

main of channel. In addition, the discharges at the outlets of Huong Dien, Binh Dien and Ta

Trach reservoirs were utilized as the upstream boundary conditions, and tidal water level at

Thuan An outlet was employed as the downstream boundary condition. Two streamflow

stations located at the downstream of the river basin include of Kim Long and Phu Oc

stations, were chosen to evaluate the model performances. The period of 2009 to 2010 was

used for calibration and 2011 to 2012 period was used for validation.

3.5 Risk-based analysis of flooding

To carry out the preliminary risk-based analysis of flooding in the Huong River Basin,

the Hydrologic Engineering Center - Flood Damage Reduction Analysis (HEC - FDA) model

developed by the U.S. Army Corps of Engineering was considered. HEC - FDA provided

state-of-the-art analysis to formulate and evaluate flood damage reduction, it used the risk-

based analysis to compute expected annual damage (EAD) . EAD was defined as the mean

damage derived from the damage-exceedance probability function as shown in Fig.2. To

compute EAD, the uncertainty of discharge-exceedance probability, state-discharge and

damage-stage functions were quantified and associated with economic performance analysis .

Mathematically, EAD can be calculated using Equation 1.

Discharge (m3/s)

Stage (m) Probability

Damage ($)

III

III IV

EAD

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Fig 2 Expected annual damage computation sensitivity analysis

(1)

Where: D is the damage in year; f(D) is the probability density function

Furthermore, to perform the interaction between the hydrology - hydraulic and

economic relationships, the Monte Carlo simulation was applied. The Monte Carlo

simulation method was defined as the numerical integration approach, it used the input

probability and function uncertainties to determine a value of flood damage for many

possible scenarios. Based on the hydrodynamic model and flood inundation mapping, water

surface profiles (WSP) for the river basin was developed, which consists of eight profiles (i.e.

0.50-, 0.20-, 0.10-, 0.04-, 0.02-, 0.01-, 0.004-, and 0.002-exceedance probability flood

events). In this study, WSP was derived from the hydrodynamic model HEC - RAS include

of discharges and stages-based simulated with the unsteady flow analysis.

A discharge - probability function was developed using a graphical exceedance

probability method from WSP. The graphical method was selected since it is widely used for

managed flow conditions, particularly well-suited for the unsteady flow analysis. This

method was relied on the “equivalent record length and error limit curves” using the “less

simple” approximation to the Order Statistics method, random sampling of the graphical

relationship can be determined. A stage-discharge function was performed to transform the

flow into water level for each exceedance probability. A normal-distribution type of the

probability density function (PDF) and the standard deviation (SD) were applied. A damage-

stage function represents the relationship between depths of water to the cost of damages

were developed by classifying into five categories (e.g. agriculture, industry, infrastructure,

irrigation and resident). The classification and structure inventory development based on the

observed data taken from Hinh (2009) . In addition, the flood damage in 2009 was assigned

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as the base structure module for model simulation. The model was operated separately for the

difference periods during the decade of 2020s, 2050s, and 2080s.

3.6 Model evaluation criteria

For the assessment of model performances, there are four statistical measures for

judging the goodness of fit of model simulations were used in this study include

determination coefficient (R2), Nash – Sutcliffe coefficient (ENS), Root Mean Square Error

(RMSE), and Percent Error in Peak (PEP).

(2)

(3)

(4)

(5)

Where: is observed data; is mean of observed data; is simulated data; is mean of

simulated data; n is total number of variable; is simulated peak; is observed peak

4. Results and discussions

4.1 Evaluation of the statistical downscaling model

In statistical downscaling, the finding of sensible predictors is an essential part and is

the most time consuming step. According to Wil by et a l. (2007) , the predictors contain higher

correlation value implies a higher degree of association. In fact, rainfall downscaling is

necessarily more problematic compared with the temperature due to its coarse spatial

resolutions, poor resolved by regional-scale predictors, and containing many regional driving

factors. For instance, the results expressed that relative correlation for the mean temperature

at 2 m was given of 0.7, whereas, it indicated an underestimated for daily rainfall with

correlation values donated of 0.1. The obtained findings can also be collated to Shrest ha et a l.

(2014) , and Hu et a l. (2013) , where downscaling of rainfall is an argumentative but still fairly

representative. Nevertheless, the results demonstrated a good outcome for mean monthly

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rainfall and temperatures as exhibited in Fig. 3. The R2 and RMSE values for rainfall during

the calibration and validation also ranged between 0.88 to 0.98, and 3.72 to 1.92,

respectively. In view of maximum and minimum temperature, the R2 and RMSE values

ranged between 0.88 to 0.99, and 1.06 to 0.05, respectively. Based on the statistical indices, it

can be reasoned that SDSM was performed well in this study.

Fig 3 The calibration results for (a) mean monthly rainfall and (b) mean monthly

temperature during 1976 – 1990 at Hue station.

4.2 Projection of future climate for rainfall and temperature

The projection for future mean annual rainfall indicated increase trend under

consideration with the A2 and B2 scenarios in the Huong River Basin (Fig 4). The results

implied that rainfall tends to increase during the summer season from January to August,

particularly, it showed higher in February with 60% increasing for A2 scenario and 37%

increasing for B2 scenario; meanwhile, mean monthly rainfall seem to be decrease about 7%

– 12% in April for the both scenarios. During the monsoon season, rainfall is expected to

decrease by an approximately 2.4% to 8.5% in October, and possibly increase by 3% to

16.8% in November to December. As the investigation indicates, the overall findings

suggested that the mean annual rainfall will increase between 3.3% to 14.5% for A2 scenario

and 3.6% to 6.8% for B2 scenario from the decades of 2020s, 2050s and 2080s in comparison

to the baseline period.

a) b)

a) b)

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Fig. 4 Mean monthly rainfall changes for the decades of 2020s, 2050s and 2080s in

comparison to the baseline period under (a) A2 scenario and (b) B2 scenario

The results also interpreted that temperatures will increase and the weather will be

hotter in the future. As illustrated in Fig. 4, it can be observed that mean monthly temperature

changes for the A2 scenario is higher compared with the B2 scenario. Particularly,

temperature suggested to increase in January and May between 0.5°C to 2.2°C, and slightly

decrease in March and July between 0.2°C to 0.4°C. The study also exposed an increment of

temperature during the monsoon season by 5.3°C and 1.68°C for A2 and B2 scenarios,

respectively. It can be concluded that the mean annual temperature will increase in the future

with a range of 0.4°C to 2.2°C and 0.19°C to 0.6°C under the emissions of A2 and B2

scenarios, respectively.

Fig. 4 Mean monthly temperature changes for the decade of 2020s, 2050s and 2080s in

relation to the baseline period under (a) A2 scenario and (b) B2 scenario

4.3 Hydrologic HEC –HMS model evaluation

The calibration and validation processes were applied for the three stream stations

(i.e. Ta Trach, Binh Dien and Phu Oc stations) to prove the reliability and stability of the

model. As showed in Fig.5, the results performed a good achievement between observed and

simulated in terms of trends and magnitudes, except a certain period of November 18-19,

2011 at Ta Trach station, which illustrate a clear difference due to the effect of tropical

northeast monsoon as reported by . The outcomes also gave the PEP value ranges from 1% to

8.5%, 1% to 10% and 14% to 20% during the calibration and validation periods for Ta Trach,

Binh Dien and Phu Oc stations, respectively. Based on the ENS and R2 statistical values as

exhibited in Table 1, it can be evinced that HEC – HMS performed well during the

simulation.

a) b)

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c) d)

a) b)

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Fig. 5 The HEC – HMS model calibration results (a, c, e) and validation results (b, d, f) at Ta

Trach, Binh Dien, and Phu Oc stations, respectively

Table 1 Statistical indices of HEC – HMS model performance

StationCalibration Validation

R2 ENS R2 ENS

Phu Oc 0.85 0.84 0.85 0.78Binh Dien 0.85 0.83 0.84 0.77Ta Trach 0.94 0.91 0.91 0.89

4.5 HEC – RAS model for flood mapping

A total 79 cross sections were imported into HEC – RAS model include observed data

and DEM with 10m x 10m resolutions. The results clear exposed that HEC – RAS model

performed well in simulating water levels at both Phu Oc and Kim Long stations during the

calibration and validation processes (Fig.6). Despite the correlation values for validation

process at Phu Oc station was rather low due to the effect of unprecedented levels of

overflow from the Bo River which influenced its flow pattern. However, based on the

statistical indices summarized in Table 2, it was considered that HEC – RAS model is

suitable to simulate flood regimes in the Huong River Basin.

e) f)

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Fig. 6 The HEC – RAS model calibration results (a, c) and validation results (b, d) at Kim

Long and Phu Oc stations, respectively

Table 2 Statistical indices of HEC – RAS model performance

StationCalibration Validation

R2 ENS R2 ENS

Phu Oc 0.74 0.73 0.67 0.64Kim Long 0.78 0.77 0.79 0.74

4.6 Flood simulation for future emission scenarios

As representative worst case projected high of CO2 concentration, the A2 scenario

was selected for flood simulations. The results manifested trends in increasing water level

and flow magnitude at Phu Oc and Kim Long stations under the impacts of climate change

(Table 4). Increasing water level and discharge can logically lead to increase flood

occurrences and risk damages in the Huong River Basin in the future. Furthermore, in order

to identify the flood prone areas for the basin, the flood inundation mapping in the decades of

2020s, 2050s, and 2080s were delineated as presented in Fig 7.

Table 4 Maximum water levels and discharges under A2 scenario

Period Phu Oc Kim Long

a) b)

c) d)

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(m+MSL) (m3/s) (m+MSL) (m3/s)

2020s 1.39 224.53 0.83 434.522050s 1.58 267.67 0.87 462.992080s 2.01 380.88 1.15 604.03

Where: is the maximum flood level (m+MSL); is the maximum discharge at

selected cross section (m3/s)

Fig. 7 Flood inundation extent of the Huong River Basin based on (a) satellite imagery

taken on 11th June 1999 (b) HEC – RAS delineated flood inundation extent for the

decade of 2020s (c) HEC – RAS delineated flood inundation extent for the decade of

2050s (d) HEC – RAS delineated flood inundation extent for the decade of 2080s

under A2 emission scenario

4.7 Flood damages estimation

From the flood inundation mapping, the flooded areas were identified and taken into

account to quantify the flood damage costs. Consequently, the sensitivity analysis of the

relationship between discharge-exceedance probability, stage-discharge, damage-stage, and

mean damage-exceedance probability were generated using the Monte Carlo simulation as

presented in Fig. 8 below.

a) b)

c) d)

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Fig. 8 EAD computation sensitivity analysis for the relationships of (a) discharge-

exceedance probability function (b) stage-discharge function (c) damage-stage

function (d) mean damage-exceedance probability at Tuan-Kim Long reach

Based on the outcome of study, the results demonstrated an agreement of the flood

damage costs between observed and modeled in 2009 (Table 5). It also can be realized that

the flood damages for the infrastructure type was ranked as the highest damages compared

with the other types of damages. Furthermore, the findings expressed an increment trend of

the flood damage costs approximately from $18,512.91 million to $19,148.59 million

corresponds to the decades of 2020s to 2080s, respectively.

Table 5 Flood damage estimation for the Huong River Basin under future emission

scenarios

Damage categories

Observed($1,000)

Modeled($1,000)

2020s($1,000)

2050s($1,000)

2080s($1,000)

Resident 3,252.53 3,255.38 3,127.33 3,211.12 3,311.88

Agriculture 4,149.77 4,146.78 3,977.73 4,003.60 4,093.45Irrigation 4,261.93 4,252.97 4,100.86 4,136.86 4,243.04Infrastructure 6,168.58 6,155.14 5,960.83 5,990.97 6,091.23

Industry 1,401.95 1,406.78 1,346.16 1,355.20 1,408.99

a) b)

c) d)

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Total 19,234.76 19,217.05 18,512.91 18,697.75 19,148.59

Note: 1 USD = 17,800 VND in 2009

5. Conclusions

In this paper, an assessment of flood simulation as well as flood damage was carried

out in the Huong River Basin. The results explored that HadCM3 model provided a realistic

finding in estimating the potential impacts of climate change in this study area. From the

performed research, it is possible to conclude that the application of hydrological model HEC

– HMS and hydraulic model HEC – RAS are suitable for the Huong River Basin.

Alternatively, the flood damage reduction analysis HEC – FDA was also demonstrated good

performances in quantitative flood damage costs. From the main finding of this study, the

results suggested that the weather will be hotter in the future, which ranges between 0.4°C to

2.2°C and 0.19°C to 0.6°C for A2 and B2 scenarios, respectively. Moreover, the annual

rainfall will also increase from 3.3% to 14.5% and 3.6% to 6.8% correspond to A2 and B2

scenarios, respectively. Increasing of temperature and rainfall will significantly influence

discharges and water levels in the basin, which might cause negative impacts, including loss

of human life, damage to property and destruction of crops, etc. As the results suggest, the

quantitative of flood damage costs were estimated approximately from $18,512.91 million to

$19,148.59 million in the future. Finally, the outcomes of this research can be used as a

guideline for adaptation measures corresponding to climate changes in the other parts of

Vietnam.

Acknowledgements

The author would like to acknowledge the generous financial support of the Khon

Kaen University Scholarship for ASEAN and GMS Countries’ Personnel program. The

author’s thanks are also extended to the Thua Thien Hue Hydro-meteorology Forecasting

Center for its support in providing the meteorological data.

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