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 (2)(3), Hang Anh Phan (4), Quan Trong Nguyen (1), Toai Anh Le (1) (1) University of Education, Hue University, Thua Thien Hue, Vietnam 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 (2) 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 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 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 socioeconomic 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 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 Weight of indexes TT Index Weight Result I Sensitivity Index 0.3 Traffic access index 0.15 Impact of residential areas 0.3 Impact of industrial parks 0.25 Dependence level of the community 0.3 II Exposure index 0.4 Sea level rise to 2050 0.5 Temperature change to 2050 0.5 III Adaptive capacity 0.3 Slope 0.45 Shape 0.55 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 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 socioeconomic 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 (3) In which: Xi: weight of index (i=1; 2…n) *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= (4) V= In which: V: vulnerability index (5) E: exposure index S: sensitivity index AC: adaptive capacity index 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) 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 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 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 Average temperature in meteorological stations during 1993-2018 (0C) T Statio T n 10 11 12 Average temperatu re Con Co 1 7 5 Dong 19 20 22 26 28 29 29 28 27 25 23 20 Ha Khe 18 19 21 24 25 26 25 25 24 23 21 18 Sanh 20 21 22 24 27 29 29 29 28 26 24 22 25.5 25.1 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 Characteristics of monthly and annually average temperature in the province in the period 1975-2018 (Unit: 0C, Average temperature: AT) Year AT Year AT Year AT Year AT 1975 24.3 1986 24.3 1997 24.7 2008 23.8 1976 24 1987 25.1 1998 25.4 2009 24.4 1977 24.1 1988 24.4 1999 24.4 2010 24 1978 24.1 1989 24 2000 24.2 2011 24.2 1979 24.5 1990 24.5 2001 24.6 2012 24.4 1980 24.7 1991 24.9 2002 24.3 2013 24.5 1981 24.5 1992 24.2 2003 24.8 2014 24.0 1982 24.4 1993 24.4 2004 24.1 2015 25.4 1983 24.3 1994 24.5 2005 24 2016 24.4 1984 23.7 1995 24.2 2006 24.5 2017 24.6 1985 23.8 1996 24 2007 24.4 2018 24.3 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 months of rainy season accounts for 68-70% of annual rainfall (table 4) Table Average rainfall in some years (Unit: mm) Station 10 11 12 Year Vinh 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 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 Linh Gia Vong Dong 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 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 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 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 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 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 Ha Thach Han Cua Viet Huong Hoa Khe Sanh Ba Long During the dry season from December to April, there are usually light rains from to 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 to 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 to 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 to 10 hours but the accompanying rain usually lasts up to 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 to storms and tropical depressions In the period of 1962-2009, in 1964 and 1984, there were years that the province was directly affected by 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 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 - Dependence level of the community: The criteria of agriculture, forestry and fishery labor show the dependence on the affected field, the number of laborers/ total population of localities reflects the economic dependence that locality to the agricultural sector The ratio of agriculture, forestry and fishery/total population is determined based on survey data of the General Statistics Office in each commune The lower the rate is, the greater the level of community dependence on agricultural production, the more sensitive and vulnerable it will be (table 5) (figure 2) Table Sensitive index variables used in vulnerability evaluation Main index Sub index Meaning Source Formula for standardization This index indicates that the closer to the road that the Traffic access agricultural production Space index activities are, the more analysis (2) sensitive and vulnerable they will be This index indicates that the Impacts of residential ares agricultural activity is, the more sensitive and vulnerable Space analysis (2) they will be Sensitivity index closer to residential areas that This index indicates that the Impacts of closer to industrial zone that industrial agricultural production zones activity are, the more sensitive Space analysis (2) and vulnerable they will be This indicates the level of livelihood dependence on Dependence agricultural production (Index level of the is determined from the community number of agricultural, forestry and fishery / total population) Statistics General Statistics Office (1) Figure Map of Sensitivity (S) of agricultural production activities 4.2 Exposure Sea level rise and temperature change are two main indexes of climate change The IPCC organization has demonstrated that temperature increase lead to sea level rise Therefore, two main indexes are applied to evaluate vulnerability - Sea level rise to 2100: Sea level rise index is based on the climate change scenario developed and published by the Ministry of Natural Resources and Environment in 2016 The data of average sea level rise according to the scenario RCP 4.5 calculated by 2100 is 53 cm (32 - 75) for the Quang Tri area Combined with the DEM elevation numerical model, it is possible to determine which areas will flood till 2100 (MONRE 2016) Temperature changes to 2100: In 2100, in Quang Tri province according to RCP scenario 4.5, the temperature increase at different places in the region can range from 1.3 to 2.7oC (period 2080 - 2099); the most common increase will be 1.9oC (table 6) (figure 3) (MONRE 2016) Table Variables of the exposure index used in vulnerability assessment Main index Sub index Meaning Determine the level of impacts of sea Sea level rise level rise on to 2100 agricultural production activities Exposure Determine the level of impact of Temperature temperature changes to changes on 2100 agricultural production activities Source Formula for standardization - Sea level rise scenario to 2050 (based on climate change scenario of Ministry of Natural (1) Resources and Environment) - DEM elevation model Scenario of temperature change to 2050 (based on climate change scenarios of the Ministry of Natural Resources and Environment) Figure Map of Expose (E) of agricultural production activities (1) 4.3 Adaptive capacity - Slope: The slope index is built from a DEM height model, the meaning of this index is that the lower slope areas that agricultural production activities are, the higher the adaptive capacity will be - Shape: The shape index is determined by the steps: calculating the area and circumference of agricultural production activities, dividing the area by pi (3.14) and taking the square root to determine the radius of the circle The standard corresponds to the area of the agricultural production activity, and then calculates the circumference of the circle corresponding to the standard radius; divide the circumference of agricultural activities by the circumference of the circle The larger this index shows, the longer the shape of agricultural production, the more vulnerable it will be (table 7) (figure 4) Table Adaptive Capacity index variables used in vulnerability evaluation Main index Sub index Meaning Source Formula for standardization The higher slope in Slope Adaptive agricultural production of areas is, the more Analysis from DEM (2) adaptable will be Capacity (AC) Shape Agricultural production Analysis from the has a long and fragmental distribution map of structure, which is less ecosystems in the able to adapt to the core coastal area of Quang areas Tri province (1) Figures Map of Adaptive Capacity (AC) of agricultural production activities 4.4 Vulnerability Formula for calculating vulnerability index (4) (5) (WWF 2013) In the tables and 5, it is shown that there are 2.498,66 ha, accounting for 17.60% of the area of agricultural production activities in coastal communes in Quang Tri province, which is highly vulnerable, concentrated in Trieu An, Gio Viet, Gio Mai, Trieu Do, Trung Giang communes The high level of vulnerability has an area of 6.099,44ha, equivalent to 42.97% of Gio Mai, Gio Viet, Gio Hai, Vinh Giang and Trieu Van communes The average level of vulnerability accounts for 3.943,86 ha, equivalent to 27.79% The very low level of damage, accounting for 189.83 ha, equivalent to 1.34% of the communes of Hai An and Hai Khe Thus, the area with high or higher vulnerability level accounts for 60.57% of the total area of the research area (table 8) (figure 5) Table Areas and vulnerability level rate of agricultural production activities in coastal communes in Quang Tri province Vulnerability S E AC (Sensitivity) (Exposure) (Adaptive capacity) V (Vulnerability to climate change) (ha) (%) (ha) (%) (ha) (%) (ha) (%) Very low 141.01 0.99 6,123.76 43.14 625.73 4.41 189.83 1.34 Low 165.63 1.17 1,442.27 10.16 549.91 3.87 1,462.14 10.30 Average 6,925.2 48.79 507.45 3.58 1,884.2 13.27 3,943.86 27.79 High 3,008.16 21.19 5,971.17 42.07 6,638.47 46.77 6,099.44 42.97 Very high 3,953.93 27.86 149.28 1.05 4,495.62 31.67 2,498.66 17.60 Total 14,193.93 100 14,193.93 100 14,193.93 100 14,193.93 100 Figures Map of Vulnerability (V) of agricultural production activities Conclusions and recommendation 5.1 Conclusions Up to now, evaluating vulnerability still remains a difficult problem Applying GIS tools allows the collection of information from multi-field and standardized expression on the characteristics of space, meets the current and synchronous information requirements of vulnerability assessment to serve scientific research study and makes a reasonable management and use policy Through the evaluation process, it was shown that due to the impacts of climate change, the majority of agricultural production in coastal communes in Quang Tri province has a high level of vulnerability, indicating that 42.97% of the study area rescue has a high degree of injury and 17.60% has a very high degree of injury This raises the problem that requires suitable solutions to adapt to the impact of climate change on agriculture in particular and the economy in general 5.2 Recommendations On the basis of analyzing the impacts of climate change, the adaptability and vulnerability of agriculture has many structural and non-structural measures to adapt to climate change; however, it is necessary to focus on the following primary solutions: - Solutions to save surface water sources along with water source reserves: + Protecting and developing vegetation cover: Strict protection and development of coastal protection forests to prevent natural disasters, coastal erosion, river bank erosion, anti-sand and sand phenomenon, big flow restrictions in the rainy season + Exploiting water sources from natural rivers and lakes in combination with the construction of suitable artificial lakes and dams to ensure enough supply for production activities + Strictly and economically managing water sources used for agricultural production, completing irrigation water systems to limit water loss and leakage - Solutions to modernize agricultural production along the direction of adaptation: + Developing seed sources: Researching and developing a new seed group capable of adapting to the impacts of climate change + Flexibility for the seasons and production objects: Building a reasonable seasonal calendar based on the changing nature of the weather and climate Replacing objects of low productivity and poor adaptability to those of higher economic efficiency + Mechanizing production: Investment in mechanization in stages of the production process, especially for the cultivation industry in order to decrease dependence on natural conditions, take initiative in seasonal activities and mitigate losses + Modernizing agricultural infrastructure and materials + Improving production techniques: Promoting research, piloting and spreading mass deployment of techniques, forms, models of production adapting to climate change in accordance with the targets of sustainable development + Reasonable production plans: Due to the trend of erratic movements of extreme weather events and climate change, it is important to have reasonable plans in proposing future agricultural production plans In order to bring the long-term, sustainable and reasonable between environment and agricultural production + Weather forecast and climate changes in a timely and accurate manner along with raising people’s awareness and capacity to adapt to and change climate References Allison, E.H., A.L.Perry,M.C Badjeck, W.N Adger, K Brown, D Conway, A.S Halls, G.M.Pilling, J.D Reynolds, N L Andrew, and N K Dulvy (2009) Climate change and fisheries: a comparative analysis of the relative vulnerability of 132 countries, Fisheries 10, pp 173-196 Balangue, Tonie O (2013) Summary report on vulnerability caused by climate change and impact assessment tools, Climate Change Committee Phillipines,168 pages Amit Garg, Ashish Rana and P.R Shukla, Manmohan Kapshe, K Narayanan, D Parthasarathy, and Unmesh Patnaik (2007) Handbook of Current and Next Generation Vulnerabilities and Vulnerability Assessment Tool, New York: Routledge, 98 pages ICEM (2015) Assessment of vulnerability and adaptation planning of the construction industry in urban areas in the Mekong Basin ADB and Nordic Development Fund Hanoi, ISBN 978-0-9924435-5-9, 75 pages IPCC (Intergovernmental Panel on Climate Change) (2007) Fourth Assessment Report, Working Group II report Impacts: Adaptation and Vulnerability, 987 pages Meynell, P.J., Thongsavath, O., Xeuasing, K., Vannalath, V., and Glémet, R (2014) Assessment of the vulnerability of Beung Kiat Ngong Ramsar site, Lao PDR Vientiane, Lao PDR: IUCN, 127 pages MONRE (Vietnam Ministry of Natural Resources and Environment) (2012) Scenario of climate change and sea level rise for Vietnam Ha Noi: Environment - 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