Climate Change Vulnerability of Agriculture in Coastal Communes of Quang Tri Province, Vietnam45233

Climate Change Vulnerability of Agriculture in Coastal Communes of Quang Tri Province, Vietnam Son Hoang Nguyen 1 , Cham Dinh Dao 23 , Hang Anh Phan 4 , Quan Trong Nguyen 1 , Toai Anh

Climate Change Vulnerability of Agriculture in

Coastal Communes of Quang Tri Province, Vietnam Son Hoang Nguyen (1) , Cham Dinh Dao (2)(3) , Hang Anh Phan (4) , Quan Trong Nguyen (1) , Toai Anh

Le (1)

(1) University of Education, Hue University, Thua Thien Hue, Vietnam

(2) VAST Institute of Geography, Hanoi, Vietnam

(3) VAST Graduate University of Science and Technology, Hanoi, Vietnam

(4) University of Science, Hue University, Thua Thien Hue, Vietnam

* Correspondence: nguyenhoangson@dhsphue.edu.vn

Abstract: Due to its long coastline, Vietnam is regarded as one of the countries seriously affected by

climate change If the sea level rises by 1 meter, 40% of the Mekong Delta area, 10% of the Red River Delta area will be flooded, directly affecting 20-30 million people Particularly, in Quang Tri province, the coastal area is the place where people mainly live on agriculture and they are at risk of being severely affected by climate change With the application of GIS and remote sensing, evaluating the vulnerability of agricultural production activities in coastal communes in Quang Tri province becomes more effective The research process has identified the Sensitivity Index (S - Sensitivity) (including traffic access index; the impacts of residential areas; the impacts of industrial zones; the level of community dependence) , Exposure index (E - Exposure) (including sea level rise until 2100; temperature change until 2100), Adaptive Capacity index (AC - Adaptive capacity) (including slope index ; morphology), thereby synthesizing the vulnerability index due to the impacts of climate change on agricultural production (V - Vulnerability to climate change)

Keywords: Climate change; vulnerability level; agricultural production; coastal; Quang Tri

1 Introduction

Currently, climate change and its impacts are becoming a significant research area Without proper adaptation strategies, climate change will lead to considerable environmental changes and have serious impacts on various countries all over the world Besides, climate change also makes multidimensional impacts on humanity in several socio-economic aspects such as agriculture, human health, tourism activities, labor shortage and widespread epidemics In particular, agricultural production is the most seriously affected area including changing the structure of crops; cultivation and husbandry; catching and aquaculture and the risk of new epidemics affecting plants and animals Thus, scientific researches on climate change and its impacts on agricultural production should take into consideration the vulnerability in many areas, especially in coastal ones

There have been several approaches to assess the vulnerability and there are many evaluative reports and compare these methods over the past few decades, such as summary reports on vulnerable situation caused by the climate change and impact assessment tools (Balangue 2013); Handbook about vulnerable situation of current and next generation and vulnerability assessment tools (Carg et al 2007); Assessment of the vulnerable situation: An overview of approaches (Morgan 2011); Guidelines for assessing the vulnerable situation, impact and have ability to adapt to climate change (Provia 2013); Summary report on tools and methodologies for assessing vulnerability and ability to adapt to climate change (Rizvi

et al 2014); Overview of methods and tools for adapting to climate change (Schipper et al

2010); A brief report on methods and tools for assessing impacts, vulnerable situation and capacity to adapt to climate change (UNFCCC 2008) Recent vulnerability assessment reports conducted in Southeast Asia are a typical example These reports focus mainly on flooded areas in the lower Mekong region (ICEM 2011); The report assesses the ranking of vulnerabilities of some provinces to identify the most vulnerable provinces in the lower Mekong Delta (USAID Mekong ARCC 2013); a Mekong tributary (WWF 2014); a simple ecosystem like Ramsar wetland (Meynell et al 2014), or urban center (ICEM 2015) These studies are almost complex, involving detailed assessment of vulnerability and implementation of large-scale interventions such as the whole region, the nation and the area based on a combination of diversity of ecosystems, livelihoods, infrastructure and economic assets Meanwhile, many studies have shown the necessity to conduct vulnerability assessment and climate change adaptation based on social factors

In Vietnam, from 2001 to 2005, the study and assessment of vulnerability of coastal areas in South Central Vietnam is considered as a scientific basis for mitigating disasters, sustainable land use planning carried out in the period 2001-2002 (Nhuan 2002) or a project

to investigate and evaluate the vulnerability of Vietnam's resources - environment and marine meteorological conditions Since Vietnam joined the signing of the United Nations Framework Convention on Climate Change (UNFCCC) in 1992 and the Kyoto Protocol in

1998, climate change has been frequently studied Ministry of Natural resources and environment are Vietnamese government units that preside over activities related to climate change Up to now, the Ministry of Natural resources and Environment has developed three scenarios of the climate change and sea level rise in Vietnam, respectively published in 2009,

2012 and 2016 However, the research of assessing the vulnerability in Vietnam, began in the end of the 20th century Studies approach different fields of natural systems, society, communities and coastal resources on the scale of research from region / region to the coastal zone of Vietnam

Natural disasters and the environmental pollution in coastal areas are forecasted and

a map of vulnerability for the entire coastal area of Vietnam has been developed, including regions: North, Central, Southwest, South, Truong Sa Islands until 2015, 2020 and the scenario of sea level rise of 0.5 metre and 1metre (Nhuan 2011)

World Wide Fund For Nature (WWF) has conducted a research project named

"Synthetic rapid assessment of vulnerability and adaptability to climate change in three coastal districts, Ben Tre province" year 2012 and the project "Assessing the vulnerability to climate change of ecosystems in Vietnam" in 2013 WWF has the same approach view with author Nhuan, vulnerability assessment method in this project is determined based on the determination of exposure, sensitivity and adaptive capacity index

In recent years, some coastal communes in Quang Tri, Vietnam are often directly affected by several strong types of natural disasters, including storms, floods, droughts, saline intrusion, which lead to severe damage, especially on productivity and quality in agricultural production It has direct impact on people's livelihoods: agricultural land area

is abandoned or changing the purpose of use is increasing This causes instability of local food security, poverty increase and social evils in the area as well

In order to not only improve the lives of residents but also find a new direction for the agricultural sector in coastal communes against the impacts of climate change, it is necessary to have territorial organizations of agricultural production and suitable livelihoods models to climate change to promote socio-economic development, improve the life quality for local people Therefore, the evaluation of the vulnerability level of agricultural production due to climate change in coastal communes in Quang Tri plays a significant role in scientific and practical aspects

2 Methodology

2.1 Data collection and analysis

Data and maps of natural conditions, climate change and its impacts on agricultural production; livelihood, socio - economic information in the coastal communes in Quang Tri province related to climate change; documents of project or programs which are about socio

- economic development, agricultural development adapting to climate change in coastal area are collected All of information related to research subjects and areas is approached and suitably applied in research process

2.2 Mapping, remote sensing and geographic information system (GIS)

Applying cartography, remote sensing techniques on the basis of aerial photographic images and satellite images in different periods can assess the degree of changes in natural characteristics and agricultural production activities due to the impacts

of climate change Using geographic information system (GIS) to update meteorological and hydrological documents, information on natural environment changes on the surface, storage of database systems, parts of maps helping for research, proposing solutions to solve and adapt to climate change and update documents conveniently and quickly

2.3 Determining vulnerability due to climate change impacts

a The components of vulnerability

- Sentivity: According Kleynhans (1999), ecological sentivity is the ability of suffering

a specific impact (such as environmental change) and recovering after suffering the impact The smaller the resistance and the ability can keep the system balanced, the more sensitive

it is, and vice versa

- Exposure: According to Cutter (2000) and Nhuan Trong Mai (2008), the density of vulnerability is the density of vulnerable objects determined by the distribution and the role

of the vulnerable objects Another definition of exposure is the level of exposure of the study subject with the factors affecting it in different directions depending on the element

- Adaptive capacity: According to IPCC, Adaptive capacity is the capacity of a system in order to adapt to climate change (including negative climate changes), to minimize damages, exploit beneficial elements or to adapt to the impacts of climate change (IPCC 2007) (table 1)

Table 1 Weight of indexes

TT Index Weight Result

b Standardized method of variables

Standardize statistics: Use inherited statistics from relevant branches’ data, then quantify and use a calculation formula to standardize and bring the index from 0 to 100

Spatial analysis: Using spatial analysis tools in GIS to build variables for analysis and evaluation process

Integrating information and multiplying weighted information layers: Using variables which indexes have been standardized to 0-100 for integration through algorithms

to synthesize and calculate key indexes and sub-indexes

Value variables can be understood as a quantity that is included in the formula to calculate a value to be searched The selection of variables to assess vulnerability depends

on the theory and approach method, along with expert opinions Choosing different variables will give different results

For each variable, because it is measured by different quantities (for example, the temperature variable is measured by degrees Celsius, the impact degrees; or the AC index

is measured by socio - economic factors), we have to put quantities into one axis (same unit)

in order to be able to evaluate The unit here is the evaluation index Thus, we apply the following formula (1) (2) (WWF 2013)

(1)

(2)

For the variables shown as low as possible, the formula (1) should be applied to standardize, whereas, with the higher variables as possible, the formula (2) should be applied

to standardize

c Building and standardizing variables:

Variables in evaluation of vulnerability are determined based on natural and socio-economic impacts The key indexes are identified based on the theoretical views of the IPCC and applied by many scientists The variables used in the evaluation include:

- Sensitive index S: Traffic access index; Impact of residential areas; Impact of industrial parks; Dependence level of the community

- Exposure index E: Sea level rise to 2100; Temperature change to 2100

- Adaptive Capacity index AC: Slope; Shape

d Determine the weight and calculate the vulnerability index

* Determine the weight

Evaluating the weight based on experts’ opinions The result is calculated by the formula (3) Example: Weight of traffic access: 3/(3+6+5+6) = 0.15

In which: Xi: weight of index (i=1; 2…n)

(3)

*Method of index in evaluating vulnerability

V (Vulnerability) can be seen as expressed as a function of the exposure level (Exposure) - the degree to which climate change affects the system; S (sensitivity level) - the degree to which the system is affected and the adaptive capacity AC - is the ability of the system to be adjustable (4)

V=

In which:

V: vulnerability index

E: exposure index

S: sensitivity index

AC: adaptive capacity index

(5)

Based on the analysis of vulnerable indexes, the project conducts vulnerability evaluation through the synthetic formula of calculating vulnerability index proposed by IPCC (5) (IPCC 2007)

3 Study area

The coastal communes in Quang Tri has an area about 14.193,93 ha, which accounts for 3.01% natural area of the province, including 12 communes of Hai Lang district (Hai An, Hai Khe communes), Trieu Phong district (Trieu Van, Trieu An, Trieu Lang, Trieu Do communes), Gio Linh district (Trung Giang, Gio Hai, Gio Viet, Gio Mai communes), Vinh Linh district (Vinh Thai, Vinh Giang communes) (figures 1)

In Quang Tri province, the coastal area is popular for plain, abrasive, accumulation, and sand dunes The plain does not form a continuous band but sometimes breaks due to the protruding branches or hills Absolute height is about 20m or less, including 3 types of terrain: accumulation plains and coastal sand dunes

In 2018, the population of coastal communes of Quang Tri province is 54,003 people, accounting for 8.3% of the population of Quang Tri province; population density here is 236.3 people/km2, 1.86 times higher than the province's population density (126.7 people/km2) (Quang Tri Statistical Office 2019) Population structure by age: the study area has a young population structure, but for the agriculture-forestry-fishery production sector, according to the working age, the structure is aging

Figures 1 Location of coastal communes in Quang Tri province, Vietnam

3.1 Temperature

In the coastal communes in Quang Tri province, the average temperature in the period of 1993 - 2018 is around 24.5oC (table 2)

Table 2 Average temperature in meteorological stations during 1993-2018 (0 C)

T

Statio

n

temperatu

re

1 Con

Co

20

6

21

1

22

1

24

8

27

7

29

7

29

5

29

4

28

1

26

5

24

8

22

1

25.5

2 Dong

Ha

19

6

20

8

22

7

26

0

28

3

29

9

29

5

28

8

27

0

25

3

23

1

20

4

25.1

3 Khe

Sanh

18

1

19

5

21

6

24

5

25

7

26

0

25

4

25

0

24

3

23

1

21

2

18

6

22.8

Compared to the standard average temperature of 1975-2018, the average annual temperature in the period of 1993-2018 is mostly higher, from 0.10C in 1994 and 2013; 0.30C

in 1997 and 2009; 0.20C in 2001 and 2007; 0.40C in 2003, 2006, and 2012; 0.70C in 2010; the highest temperature was in 1998, higher than average temperature 0.90C (table 3)

Table 3 Characteristics of monthly and annually average temperature in the province in the period

1975-2018 (Unit: 0C, Average temperature: AT)

3.2 Rainfall

Due to differentiation depending on geographical location and local climate characteristics, rainfall at different stations in the coastal communes of Quang Tri province

is different The data of rainfall collected from Vinh Linh, Gia Vong, Dong Ha, Thach Han, Cua Viet, Huong Hoa and Ba Long stations in Quang Tri province shows that the annual rainfall is in the range of 2,000 - 2,800 mm Rainfall in 3 months of rainy season accounts for 68-70% of annual rainfall (table 4)

Table 4 Average rainfall in some years (Unit: mm)

Vinh

Linh

129.9 83.3 48.6 51.9 100.5 97.8 94.3 125.3 420.2 766.0 462.3 227.0 2.614,1

Gia

Vong

60.1 47.9 35.4 64.1 143.6 101.4 78.7 155.0 509.7 695.9 456.4 188.0 2.536,3

Dong

Ha

48.2 34.1 30.8 60.7 119.3 83.0 65.7 163.2 388.9 683.9 429.0 175.2 2.291,8

Thach

Han

84.3 60.7 48.9 63.0 135.0 105.7 82.9 135.3 476.4 710.6 438.6 240.7 2.627,3

Cua

Viet

57.6 48.6 33.1 50.8 102.6 63.4 68.1 150.3 398.6 574.3 415.7 219.6 2.187,8

Huong

Hoa

83.6 61.7 47.8 97.8 191.5 171.7 148.9 219.1 585.8 778.0 227.7 95.7 2.779,9

Khe

Sanh

16.7 19.2 29.7 89.8 158.9 210.8 187.8 295.9 376.7 455.0 175.8 64.7 2.118,6

Ba Long 99.8 90.1 51.0 71.7 156.6 156.8 74.2 173.1 473.4 762.0 411.8 227.8 2.794,3 During the dry season from December to April, there are usually light rains from 7

to 8 days with rainfall from 20-30 mm The rainy season starts from September to November; sometimes the rainy season lasts until December Due to the terrain characteristics, the rain

in the rainy season is rarely equally spread all over the province

The average annual rainfall during 1993 - 2018 in Quang Tri province (the average rainfall across the province by weighting method) does not clearly show the increase or decrease trend Compared to the standard period of 1975-2018 (2,325 mm), the number of years with higher rainfall is 12 years on average, including 1995, 1996, 1998, 1999, 2001, 2002,

2005, 2007, 2008, 2009, 2011, 2013, and 2015 The year with the highest rainfall exceeded 2011 were 653 mm, followed by 1999, 594 mm in excess and 469 mm in 2013 The year with the highest standard rainfall loss was 2004 with a decrease of 555 mm, followed by 1993 with a decrease of 513 mm and in 1998, it decreased 498 mm

3.3 Sea level rise

As Quang Tri is a coastal province in the central region of Vietnam, it is generally affected by sea level rise on a global scale in general and Vietnam in particular By analyzing sea level data at Hon Dau and Vung Tau from 1957 to the present, it is clear that in about 40 years, the increase trend of sea level is real with a rising water level of 2.3 mm/year on the big plains in Vietnam

According to the scenario of Quang Tri climate change and sea level rise by 2020 when the sea level rises from 8 to 9 cm, the national highways and provincial roads are not seriously influenced by sea level rise However, by 2100 when the sea level rise is 51 - 63 cm, there will be about 2.67% of the national highway length and 8.23% of the provincial road length will be affected by frequent flood; the worst case is that railway will be affected 0.21% Besides, it also affects the ability of flood in coastal roads

3.4 Extreme weather phenomena

Climate change has some impacts on extreme weather events and they can be divided into the following groups:

* Extremes of weather and climate variables (temperature, rain, wind );

* Extreme weather and climate events (monsoon, El Nino, storm );

* The phenomena affects natural physical environment (drought, flood, extreme sea level )

In general, the identification and definition of weather and climate phenomena in terms of risk management is very complicated and depends on the specific purpose This aspect focuses on the collection and synthesis of extreme weather data and is defined as the occurrence of values higher or lower than the threshold value of a weather or climate element, near the upper limits, or below the range of observed values of that element The data set for the study which is used based on actual monitoring data at meteorological and hydrological stations updated to 2018 The mentioned phenomenal include:

- Absolute maximum temperature (Tx), absolute minimum (Tm):

According to statistics in the period of 1993-2015, the annual average maximum temperature in Dong Ha station is about 29.50C, higher than the average period (1973-2015)

of 0.20C, and higher than the average time in the period 1973-1992 was 0.40C (MONRE 2016)

In particular, the average maximum temperature data in the period of 2003-2015 tends to increase slightly comparing with the average period of 1993-2002 For annual average minimum temperature, at Dong Ha station, the value is about 22.60C, higher than the standard period 0.20C, and higher than in 1973- 1992 was 0.50C Similar to the peak temperature changes, the average minimum temperature in the period of 2003-2015 showed

a slight increase comparing with the average of 1993-2002 periods Thus, both the annual average maximum and minimum temperatures in the period of 2003-2015 are higher than the average maximum and minimum temperatures in the rest of periods

- Storms, tropical depressions:

The characteristic of storms and tropical depressions in Quang Tri varies greatly depending on the storm and the period when it lands There are years without storms but there are years with 2 to 3 storms (1964; 1996) On average, there is about 1.2 to 1.3 storms Quang Tri coastal area has up to 78% of storms and tropical depressions in the East Sea, causing heavy rains and flood in rivers and flooding coastal plains of Quang Tri or valley areas or on some parts of the Thach Han River Storm landing usually lasts from 8 to 10 hours but the accompanying rain usually lasts up to 3 days, causing floods and flash floods, which leads to serious damage to people and property

According to the statistical results, generally, the number of storms and tropical depression directly affecting Quang Tri province tended to decrease slightly but the level of decrease was not considerable In some years Quang Tri province was not affected by any storms Other years this province was affected by 1 to 3 storms and tropical depressions In the period of 1962-2009, in 1964 and 1984, there were 2 years that the province was directly affected by 4 storms and tropical depressions

- Floods and flash floods:

Due to the high slope and short river system, floods occur quickly and fiercely, combining with heavy rains, vegetation cover and weak soil structure places can cause flash floods Floods and flash floods greatly affected the province's economic development For

example, floods from September 29 to October 5, 2010 caused floods and flash floods for Ha Tinh, Nghe An, Quang Binh, Quang Tri and Thua Thien Hue provinces The heavy rain starting on September 29, 2010 caused floods and flash floods across the Ngan Sau - Ngan Pho river basin Due to heavy rains, the upstream water level concentrates quickly In Quang Tri, more than 2,000 houses were flooded, many rice field areas in the Dong Ha city and Gio Linh district were flooded, one person died and many infrastructure works were damaged

- Thunderstorms, whirlwinds, rain and hail, fog:

Due to the climate characteristics, the coastal communes in Quang Tri province have relatively large number of thunderstorms According to Dong Ha meteorological observation station from 1975-2013, on average, there are about 67.3 thunderstorms a year Especially, there are years when the number of thunderstorms is more than 100 days (in

1980, 1981 there were 104 thunderstorms)

Whirlwind is a phenomenon where the wind accelerates suddenly, the direction changes suddenly, the air temperature drops sharply, and the humidity increases rapidly with thunderstorms, showers or hail Tornado is vortices in which the wind in the circulation is small in the tens or hundreds of meters Tornado is small swirling swirls, which often occur when the atmosphere is turbulent and basically unpredictable

According to statistics of the number of foggy days at Dong Ha station from 1975 to

2018, on average, there are about 17 days of fog a year The foggiest time is usually in January, February and March, while June, July and August there is not usually foggy Incomplete statistics at Khe Sanh station, average from 2007 to 2013 up to 126.6 days / year This is also reasonable because Khe Sanh station represents for the mountainous region with

a mild climate

4 Results

4.1 Sensitivity

- Traffic access index: Sensitive index of agricultural production activities and roads is

built from the separation of traffic road information of topographic maps Calculating the distance with the maximum value of 10km, the meaning of this index indicates that the closer to the road that the agricultural production activities are, the more sensitive and vulnerable they will be

- Impacts of residential areas: The sensitivity index of agricultural production activities

and residential areas is built from the separation of population and urban information layers

of the current state of land cover map, and then calculate the distance with the maximum value of 15km, the meaning of this index indicates that the closer to residential areas that agricultural activities are, the more sensitive and vulnerable they will be

- Impacts of industrial zones: The sensitivity index of agricultural production activities

and industrial zones is built from the separation of information on the current status of industrial zones of the current land use map Calculating the distance with the maximum value of 25km, the meaning of this index indicates that the closer to industrial zone that agricultural production activity are, the more sensitive and vulnerable they will be