INTRODUCTION
Drought and saline intrusion under Climate Change context in Viet Nam
Climate change (CC) represents one of the most significant challenges facing humanity today, as it leads to rising temperatures, sea level rise (SLR), droughts, flooding, and saltwater intrusion These effects pose a substantial threat to agriculture, key industries, and the socio-economic systems of numerous countries worldwide.
The Intergovernmental Panel on Climate Change (IPCC) projects that by the end of the 21st century, global average temperatures will rise between 2.1℃ and 3.5℃ under the SSP2-4.5 scenario Additionally, sea levels are expected to increase by 0.44m to 0.76m, and potentially reach 0.63m to 1.01m in scenarios with very high greenhouse gas emissions (SSP5-8.5) (IPCC, 2021).
By the end of the 21st century, Vietnam is projected to experience a temperature rise of 1.9℃ to 2.4℃ in the North and 1.7℃ to 1.9℃ in the South, with sea levels expected to rise between 57cm and 73cm, according to the RCP 4.5 scenario by the IPCC (2021) This global warming is contributing to the increased intensity of natural disasters, including storms, floods, and droughts Research by Nguyen et al (2021) indicates that droughts are becoming more prolonged, severe, and intense during the dry season and transitional months in regions such as the Red River Delta, North Central, and South Central Vietnam Notably, the risk of drought is anticipated to escalate in the Red River Delta and South Central sub-regions.
Drought is increasingly prevalent and exhibits significant spatial variability across different climatic regions (Phan et al., 2013) A study by Phan (2010) employed statistical methods to evaluate drought variation across seven climatic sub-regions in Vietnam, revealing a general increase in drought occurrences with uneven severity The North Central and South Central sub-regions experience the highest frequency, severity, and intensity of drought, while the Northwest sub-region shows the lowest frequency of drought events (Phan, 2010).
Temperature increase, shortage water and sea level rise are cause increasing area of saline intrusion in the coastal area of Red River Delta (RRD), Mekong River Delta
(MRD) (Pham , et al., 2012; Tran , et al., 2019) The saltwater intrusion is spreading and it’s a major threat to agriculture sector and other sectors (Pham , et al., 2012; Hagenvoort, et al., 2013).
Necessity of research
Climate change (CC) is currently one of the most pressing challenges facing humanity, characterized by rising temperatures, sea level rise (SLR), droughts, flooding, and increased saltwater intrusion The ongoing SLR is significantly contributing to year-round saline intrusion, impacting ecosystems and communities alike (Hagenvoort et al., 2013; Bricheno et al.).
In recent years, the combination of sea level rise (SLR) and drought has intensified saline intrusion, significantly impacting various sectors, particularly agriculture (Williams et al., 2003; Rice et al., 2012; Bhattachan et al., 2019) This phenomenon has notably affected low-relief landforms such as the Red River Delta (RRD) and the Mekong River Delta (VMRD), with the latter being the most extensively studied region regarding saline intrusion The VMRD, being the lowest area among the three deltas globally, is at a heightened risk of severe consequences from climate change over the next 30 to 50 years.
According to MONRE (2021), a 100 cm rise in sea level by the end of the century could result in 47.29% of the Mekong River Delta being permanently flooded Additionally, drought is expected to become more frequent and severe, with significant spatial variability across different climate regions (Phan et al., 2013; Nguyen et al., 2021; Shahid, 2011) This phenomenon, characterized by a prolonged shortage of rainfall compared to long-term averages, accumulates over time and manifests in serious impacts once it is observed While climate change influences drought on a global scale, its effects vary significantly by region (Phan, 2010) Changes in temperature, precipitation, humidity, and evaporation patterns have further altered the duration, frequency, intensity, and spatial extent of drought events.
Drought-induced saline intrusion, exacerbated by climate change, leads to significant economic losses, ranking among the most damaging natural disasters (Sam et al., 2008; Leng et al., 2015) Prolonged droughts can result in severe crop failures, alter agricultural ecosystems, and threaten the viability of high-value crops In Vietnam, the impact of drought is particularly pronounced.
Natural disasters, particularly droughts, rank as the third most damaging events after storms and floods (DMC, 2011a) The Vulnerable Mountain Regions and Districts (VMRD) have been significantly impacted by climate change, leading to increased average temperatures, a rise in extreme weather events, sea level rise (SLR), and saline intrusion, all of which threaten agricultural activities (IPCC, 2007) This region faces severe risks from drought and saline intrusion, which jeopardize livelihoods, food security, and water supply (UNCT, 2020) For example, in 1982, drought devastated 180,000 hectares of crops, and in Soc Trang province, 95% of farmers have reported damage due to these challenges (Truong et al.).
According to a 2011 report by MONRE, over 650,000 hectares of rice were cultivated in the coastal region of the VMRD, yet approximately 100,000 hectares, or 15.38%, face a significant risk of saline intrusion during the dry season each year.
The VMRD has been significantly affected by drought and saline intrusion, impacting 40% of its arable land (IMHEN & UNDP, 2015) The period from 2015 to 2016 marked the most severe saline intrusion in 90 years, with saline water penetrating up to 90 kilometers inland (FAO, 2021) This led to a reduction in paddy production by 1.1 million tons and damaged 477,113 hectares of agricultural land In Soc Trang province alone, over 81,000 hectares of shrimp farming were lost, contributing to a total of 247,711 hectares of agricultural land affected, which represents 51.79% of the total agricultural damage in the VMRD (FAO).
2016, Nguyen, et al., 2017) In 2019-2021, 35,602.5 hectares of rice in VMRD was suffered damage by drought and saline intrusion (VDMA, 2020)
Over 100 drought indices have been developed globally, with researchers selecting different indicators based on climate regions (Zargar et al., 2011) In Vietnam, studies indicate that the Standardized Precipitation Index (SPI) effectively captures drought variability in the Vietnam Mekong River Delta (VMRD) and the Southern region overall (Nguyen).
Research on drought and salinity intrusion in the Mekong Delta has been conducted by Nguyen (2016) and the UNCT (2020), highlighting the region's environmental challenges Additionally, Tue et al (2015) explored drought conditions in the Central Highlands from 1990 to 2005 using the Standardized Precipitation Index (SPI), revealing significant findings regarding the area's climate patterns.
4 regional climate models data successfully used spatial and temporal distributions to recorded the most severe drought events from 1998 and 2005 (Vu, et al., 2015)
Lee et al (2018) examined drought trends and characterization in VMRD from 1984 to 2015 using SPI and SPEI with various precipitation datasets, finding a decrease in drought frequency but an increase in the spatial distribution of moderate to severe droughts Expanding the study, Stojanovic et al (2020) analyzed dry conditions in Vietnam from 1980 to 2017, also utilizing SPI and SPEI, and discovered significant regional variations in drought duration and severity.
Drought has significant impacts on various sectors, as evidenced by historical events such as the 1982-1983 drought, which affected the environment and socio-economic conditions (Nguyen, 2007) A study by Tran et al (2018) utilized the Standardized Precipitation Index (SPI) to assess the effects of drought on rice land use Additionally, the droughts in Vietnam from 2008 to 2017 notably impacted the coffee sector, reducing output and farm profits, highlighting the importance of drought detection as a critical issue (Byrareddy et al., 2021).
Previous research on drought has primarily focused on two areas: the mechanisms, causes, and trends of drought, and its impacts on various sectors However, there is a notable lack of publications addressing the characteristics of drought and its specific effects on agriculture (Vu et al., 2014) Notably, there have been no studies that simultaneously analyze historical drought patterns while examining the relationship between drought changes and saline intrusion, nor have there been assessments of the combined impacts of drought and saline intrusion on agricultural activities in the VMRD Consequently, this thesis aims to fill that gap.
" Assessment of drought change and its impact on salinity intrusion and agricultural activity over Mekong River Delta of Vietnam".
Research question and hypothesis
What are changes in drought characteristics in VMRD?
The drought’s characteristics experienced an increase in frequency, duration, severity and intensity
What are the effects of drought change on saline intrusion and agricultural activities?
Drought appeals to have a significant effect on saline intrusion and agriculture activity Drought and saline intrusion have positive correlation In contrast, drought and agriculture activity have negative correlation
What is the level of drought risk in Soc Trang province?
Soc Trang have high level of drought risk
Objectives of the research
This study focuses on three key objectives: first, to analyze the changes in drought patterns within the Vietnam Mekong River Delta (VMRD); second, to evaluate the connection between drought occurrences and factors such as saline intrusion and agricultural activities; and third, to assess the drought risk specifically for Soc Trang province, which serves as a representative area of the VMRD.
Matrix of learning outcomes for the master's thesis
This study could lead to several results and outcome listed below:
- Result 1 (R1): Deeply understand about spatio-temporal variability of drought over VMRD
- Result 2 (R2): Analysis of drought impact for saline intrusion and agriculture activity in the VMRD
- Result 3 (R3): Solutions, recommendations for the adaptation in the VMRD
- Outcome 1 (O1): Orientation for further researches
Table 1.2 Matrix of learning outcomes for the master’s thesis
PLOs of MCCD (Details in
Result of the master’s thesis
Other outcomes of the Mater’s thesis
PLOs of MCCD (Details in
Result of the master’s thesis
Other outcomes of the Mater’s thesis
Scope of research
The VMRD of Vietnam, encompassing 13 provinces, serves as the study area for evaluating drought changes, with Soc Trang province specifically selected for assessing drought risk and its effects on agriculture and saline intrusion Soc Trang has faced significant drought challenges, marked by increasing frequency, severity, and duration This study will analyze drought changes from 1980 to 2020, utilizing data from the last 21 years (2000-2020) to evaluate the impact on agricultural activities and saline intrusion Additionally, four critical dry periods—2004-2005, 2008-2009, 2015-2016, and 2019-2020—will be examined to assess drought risk in Soc Trang province.
Study area
The Mekong Delta Region (VMRD) is the largest delta in southern Vietnam, featuring a coastline that exceeds 732 kilometers It is bordered by the Gulf of Thailand to the west, the East Sea to the east, and shares its northern boundary with Cambodia and Ho Chi Minh City to the northwest The region includes Can Tho city and encompasses 12 provinces: Long An, Tien Giang, Ben Tre, Vinh Long, Tra Vinh, Hau Giang, Soc Trang, Dong Thap, An Giang, Kien Giang, Bac Lieu, and Ca Mau.
Figure 1.1.Vietnamese Mekong River Delta base map
As of 2020, the General Statistics Office reported that the region has a population of 17,318,600 and covers an area of 40,816 km², representing 12.32% of Vietnam's total land area The agricultural land in VMRD spans 2.615 million hectares, making up 64% of the region's natural area, with key activities including rice cultivation and aquaculture Additionally, the agricultural sector in VMRD employs 43.3% of the country's total population.
The terrain features a flat landscape that slopes gently from north to south, characterized by two significant low-lying regions: Dong Thap Muoi and the northeastern area of the Ca Mau peninsula This geographical setup creates fertile plains with an extensive canal system, bordered by the sea on three sides and boasting a long coastline, making it an excellent location for agricultural development.
Vietnam's Rice Bowl plays a crucial role in the country's economy, contributing over 40% of agricultural productivity and 54% of rice production It is responsible for 90% of the nation's rice exports and a significant portion of aquatic and fruit goods In 2019, the Vietnam Mekong River Delta (VMRD) accounted for 17.7% of the national GDP (Vu et al., 2020).
Soc Trang is geographically located between 9°14'N and 9°56'N latitude and between 105°34'E and 106°18'E longitude This coastal province located in the southeastern
The MRD covers a total area of 331.2 thousand hectares, with 212.6 thousand hectares designated for agriculture and 10.2 thousand hectares for forestry as of 2020 (GSO, 2020) The province has a population of 1,195,741, with 68% residing in rural areas Agriculture plays a crucial role in the region, with approximately 72% of the population and 63% of the workforce engaged in agricultural activities, making it the primary source of income for over 70% of the residents.
As of 2009, Soc Trang province comprises 9 districts, later expanding to 11 with the addition of Tran De and Chau Thanh The province contributes 40.5% to the regional Gross Regional Domestic Product (DRGP), with agriculture, forestry, and fisheries accounting for 45.10% of this figure Agricultural land spans 279,398 hectares, representing 84.71% of the total land area, of which 182,984 hectares (65.5%) are dedicated to rice cultivation, while the remainder is allocated for vegetables, short-term industrial crops, perennial crops, and fruit trees (GSO, 2020).
The VMRD region experiences a tropical monsoon climate characterized by two distinct seasons: a rainy season from May to October and a dry season from November to April Average yearly temperatures range from 24 to 29℃, with peak temperatures of 29℃ recorded in April Precipitation levels drop below 100mm in April and May, while rainfall increases from May, reaching its highest in August and September before tapering off During the dry season, rainfall decreases significantly, often falling below 100mm, and temperatures remain between 25-29℃, with some areas experiencing little to no rain from January to March This pattern of rising temperatures and diminishing rainfall has led to severe water shortages in VMRD, raising concerns about potential droughts and their socio-economic impacts on the region.
Figure 1.2 Average temperature in MRD Figure 1.3 Average precipitation in MRD
Structure of the thesis
The thesis contains five main parts involve: (1) Introduction; (2) Literature review; (3) Data and Methodology; (4) Results and discussions; and (5) Conclusion
Chapter 1: Introduction - presents the overview of the research containing basic and key ideas of the thesis such as the necessity of research, research question, research hypothesis, research objective, the scope of research
Chapter 2: Literature review - concentrating on briefly displaying previous study- related academic works
Chapter 3: Data and Methodology - concentrating on the data and methodology, as well as an explanation on how each characteristic, indicator or component was determined
Chapter 4: Results and discussion - analysis of drought characteristics, the relationship between these characteristics, drought risk and discussion about them under CC context in VMRD
Chapter 5: Conclusion – is the final chapter of the thesis, which includes highlights and new findings.
Logical framework
The study's logical framework, illustrated in Figure 1.4, focuses on four key characteristics of drought: frequency, duration, severity, and intensity, all assessed using the Standard Precipitation Index (SPI) Additionally, Pearson’s Correlation Coefficient will be employed to analyze the relationship between drought conditions and salinity levels.
Moc Hoa Chau Doc Cao Lanh My Tho
Ba Tri Can Tho Rach Gia Soc Trang
Moc Hoa Chau Doc Cao Lanh My Tho Ba TriCan Tho Rach Gia Soc Trang Bac Lieu Ca Mau
The risk analysis process evaluates the likelihood and potential impact of drought on agricultural activities The drought risk index is determined by assessing hazard, exposure, and vulnerability Based on the findings from the drought risk assessment, both short-term and long-term adaptation strategies are recommended to mitigate the effects of drought.
Percentage planted area of paddy
Solution: Short-term, long-term
Percentage of high school graduates (%)
Figure 1.4 Logical framework of the research
LITERATURE REVIEW
Definition of drought
Drought is a gradual and complex natural hazard that develops over an extended period, leading to various interpretations of its definition Research by Wilhite and Glantz (1985) identified over 150 definitions of drought, highlighting the significant diversity in understanding this phenomenon Yevjevich (1969) emphasized that this lack of consensus on what constitutes drought poses a significant challenge to drought research, underscoring the importance of establishing a common definition for effective study and management.
Some definitions of drought are often described below:
U.S Weather Bureau (1953) A drought is usually defined as a “period of dry weather sufficient in length and severity to cause at least partial crop failure"
Drought occurs when there is insufficient rainfall, as noted by Thomas (1962) The likelihood of experiencing drought is significantly influenced by the balance between local water reserves and the annual water needs of the region (Thomas, 1962).
In 1965, Palmer characterized drought as a prolonged period, typically lasting months or years, during which the actual moisture supply consistently falls below the expected climatic levels for a specific location.
According to the World Meteorological Organization (WMO), drought is defined as a prolonged absence or significant deficiency of precipitation, leading to a period of unusually dry weather that creates a serious hydrological imbalance.
Drought is characterized by a prolonged absence of precipitation, often lasting a season or longer, leading to significant water shortages, as defined by the National Drought Mitigation Center (NDMC) in New Zealand.
| National Drought Mitigation Center, et al.)
According to Wilhite (2000), although drought is related to climatic factors like high temperatures, high winds, low relative humidity, and precipitation
Drought is fundamentally defined as a significant decrease in precipitation over an extended duration, typically lasting a season or longer (Wilhite et al., 2007).
IPCC (2012) generally defines drought as “a period of abnormally dry weather long enough to cause a serious hydrological imbalance.” (IPCC, 2012)
According to the Merriam-Webster Dictionary drought is "a period of dryness especially when prolonged."
In Vietnam, drought is defined as a significant and prolonged lack of rainfall, leading to decreased moisture in the air and soil This phenomenon results in diminished river and stream flows, reduced water levels in ponds, lakes, and underground aquifers Consequently, drought adversely impacts plant growth and development, leading to crop failures, environmental degradation, increased poverty, and the spread of diseases.
In essence, drought originates from insufficient rainfall over a prolonged period of time, leading to insufficient water for daily life and ecological environment
Drought is primarily caused by a prolonged or temporary lack of rainfall in specific regions, particularly in arid and semi-arid areas where low rainfall persists over time (Nguyễn et al., 2002) A significant decrease in rainfall compared to long-term averages can lead to drought conditions Additionally, the El Niño phenomenon, characterized by abnormal warming of sea surface temperatures in the central and eastern equatorial Pacific Ocean, contributes to reduced rainfall, higher temperatures, and increased evaporation, making many areas susceptible to drought, especially near the equatorial Pacific (Wilhite et al., 2007; Nguyễn et al., 2017) Human activities, such as deforestation and poor land and water management, further exacerbate drought conditions, while large-scale atmospheric circulation changes also play a role in regional drought occurrences.
13 changing the vegetation cover, vegetation, excessive use of water resources, etc.) can aggravate drought (Xue, et al., 1993; Bruce, 1994)
Drought can be classified in various ways, but according to the World Meteorological Organization (WMO) and the American Meteorological Society (AMS), as well as Wilhite, there are four main types based on their characteristics and impacts: meteorological drought, hydrological drought, agricultural drought, and socioeconomic drought.
Meteorological drought occurs when there is a significant lack of precipitation, disrupting the balance between precipitation and evaporation over a specific period Factors such as radiation intensity, temperature, wind speed, and air humidity influence evaporation rates Consequently, conditions characterized by high temperatures, strong winds, and dry weather exacerbate drought conditions Precipitation is the primary variable utilized in the analysis of meteorological drought.
Hydrological drought occurs when there is an imbalance between surface water and groundwater, often assessed through streamflow data (Dracup et al., 1980; Panu et al., 2009) Key geographical factors significantly influence the occurrence of hydrological droughts, which typically manifest after meteorological and agricultural droughts due to prolonged water scarcity.
Agricultural drought refers to a phase characterized by diminished soil moisture and decreased crop yields, independent of surface water availability Typically, it follows meteorological drought and precedes hydrological drought Plant growth is influenced by weather conditions, the specific biological traits of each plant, its growth stage, and soil characteristics Consequently, agricultural drought indices are formulated using a blend of rainfall, temperature, and soil moisture data.
(iv) Socio-economic drought is the shortage of water sources for socio-economic activities It occurs when water demand exceeds water supply Therefore, studying the
14 socio-economic drought does not only require studying the availability of water data from hydro meteorological variables, but also needs data on water demand (AMS,
Figure 2.1 The general sequence for the occurrence of different drought types Source: National Drought Mitigation Center, University of Nebraska-Lincoln, USA
Drought Characteristics
The characteristics of drought are difficult to identify and quantify (Wilhite, Hayes, & Svoboda, 2000) Drought is different from other natural disasters (e.g flood, storm, earthquake, etc.) in the following aspects:
Invisible impact over a wide range
The beginning and end of drought is difficult to determine
There is no single definition of what counts as drought
Drought is usually investigated using four characteristics: frequency, duration, intensity and severity (Wilhite, et al., 2000)
The average time between drought episodes with a severity equal to or greater than a threshold is characterized as drought frequency
Drought duration is the time from the beginning to the end of a drought Depending on the region, drought duration can vary between a week up to a few years
Drought intensity defines as the ratio of the severity of the drought to the duration
Drought severity can be defined in two ways: Either as the extent of the precipitation deficit (i.e., magnitude) or as the severity of the damages caused by the deficit.
Association among drought, and saline intrusion and agricultural activity
2.3.1 Association among drought, and saline intrusion and agricultural activity on a global scale
Recent studies indicate a significant rise in the intensity, frequency, severity, and duration of droughts globally, particularly in tropical and subtropical regions since 1970 This trend is attributed to higher temperatures and decreased precipitation in continental areas, leading to more severe and prolonged drought conditions.
In Europe, drought assessments are based on three key characteristics: frequency, duration, and severity Research indicates that these droughts have become longer and more intense over the past 30 years (Jonathan Spinoni, 2016) Notable severe droughts occurred in 1976 and 1991, highlighting the increasing challenges posed by climate variability in the region.
From 2003 to 2005, severe droughts significantly impacted the agricultural sector, leading to a reduction in the yields of paddy, vegetables, and fruit trees, with estimated losses reaching €100 billion over the past 30 years The 2005 drought on the Iberian Peninsula alone caused a 10% decrease in Europe's total grain production.
Experts have identified that droughts and floods can arise from different climate scenarios In Southern and Southeastern Europe, there has been a notable decline in average precipitation coupled with heightened evapotranspiration, leading to extended periods of drought.
A study by Zubietia and co-author (2021) in Peru examined the frequency and intensity of droughts and their effects on the agricultural sector The findings revealed that between 2000 and 2010, droughts became increasingly intense, frequent, and prolonged, leading to detrimental consequences for agriculture, including crop losses and a rise in pests and diseases.
Research in China highlights a significant increase in drought occurrences, with Bordi et al (2004) analyzing dry and wet spells in Eastern China from 1951 to 2000 Dai et al (2004) noted a rise in temperature and drought frequency since 1970, predicting longer and more severe droughts in the future Leng et al (2015) estimated that 51 droughts, predominantly long-term, are expected to occur Notably, severe droughts in southern China in 1997, 1999, and 2002 impacted 40 million hectares of agricultural land (Zhang, 2003) The IPCC (2008) attributes water scarcity in Asia to rising temperatures, reduced rainy days, and increased El Niño frequency, leading to decreased agricultural yields over recent decades (Bates et al., 2008).
Research indicates that drought significantly impacts food security in India, as evidenced by the 2002 drought, which caused a staggering drop of 38.7 million tons in food grain production, affecting 300 million people and 150 million animals due to shortages in food, fodder, and water Furthermore, a report by Carolinas Integrated Sciences and Assessments (CISA) highlights that the increasing frequency and duration of droughts contribute to the inland spread of saline intrusion, worsening the situation by decreasing the availability of fresh water needed to mitigate salt accumulation in soil and groundwater.
2.3.2 Association among drought, and saline intrusion and agricultural activity in Viet Nam
Drought ranks as the third most destructive natural disaster in Vietnam, following storms and floods (DMC, 2011a) This critical issue has garnered significant attention from researchers, including a study by Nguyen Duc Ngu et al (1995), which assessed the severity of drought across various regions in the country The findings revealed that the Northwest, Northeast, and Northern Delta regions experience severe drought conditions during winter, while the North Central region also faces significant drought challenges.
Drought primarily affects Vietnam during the summer months, but regions such as the South, South Central, and Central Highlands experience prolonged droughts in both winter and spring Additionally, the South Central Coast faces drought conditions in both spring and summer Overall, drought is a widespread issue impacting various areas across the country (Nguyễn et al., 2013).
A drought study conducted by Nguyen Van Thang, utilizing the Palmer Drought Severity Index (PDSI) from 1961 to 2010, revealed that 73.5% of the years within this 36-year period experienced drought conditions, with the Northern Delta provinces facing 29 instances of drought (Nguyễn, 2015) Additionally, Tran Thuc (2012) noted that the Southern region of the South Central area encountered more severe droughts between 1981 and 2005 compared to other regions (Trần, 2012).
In their 2015 study, Nguyen Duc Thang, Truong Duc Tri, and colleagues employed linear regression to analyze climate change trends and extreme weather events across Vietnam Their research indicates that by the end of the 21st century, the frequency of hot days is expected to rise nationally, particularly in the Northern Delta, South Central, and Southern regions Additionally, while the overall number of droughts may not increase, their duration is projected to extend significantly, especially in the Central Highlands and Southern areas of Vietnam.
The VMRD area is significantly impacted by climate change, with increasing frequency and severity of drought posing serious risks to the local population This has led to extensive research focused on drought-related issues in the region, highlighting the importance of addressing this pressing concern.
Mai Kim Lien, Tran Hong Thai et al, (2016) used the SPI to compare drought characteristics of provinces in the VMRD The results show that the areas of Ca Mau,
My Tho and Chau Doc province have a higher frequency and intensity of drought than other provinces
A study on the development of drought maps in the VMRD region, considering climate change, utilizes rainfall and temperature data from A2 and B2 scenarios This research aims to evaluate the current status of meteorological drought and the impact of climate change on drought conditions in VMRD.
An analysis of a 33-year data series reveals that the Standardized Precipitation Index (SPI) exhibits both spatial and temporal variations While the frequency of drought occurrences has remained stable, the intensity of droughts varies significantly across different regions of the VMRD (Trần et al., 2015; Nguyễn et al., 2017).
Drought is unpredictable, making the development of effective forecasting models essential for efficient water resource management and damage mitigation Nguyen et al (2012) introduced advanced meteorological drought forecasting technology at the VMRD, resulting in a high-quality model that adheres to WMO and Obukov standards Combining historical drought data with robust forecasting models is vital for effectively addressing drought challenges and minimizing its impacts.
Assessment of drought risk
Drought is the most common natural disaster globally, and its duration, severity, and intensity are projected to worsen due to climate change (MONRE, 2020; Dabanli, 2018) The socio-economic impacts of drought are particularly severe in the agricultural sector, prompting scientists to focus on assessing drought risk and developing effective solutions to mitigate damage While various methods exist for quantifying drought risk, there is no consensus on the best approach, as each method has its own advantages and limitations that vary by region Currently, two primary methods for drought risk assessment are widely used: (i) assessing risk based on Hazard and Vulnerability and (ii) [insert second method].
Drought risk assessment combines 3 components (i.e hazard, exposure, and vulnerability)
Assessing drought risk in Semnan province, Iran, highlights the dependence of risk areas on drought hazards and socioeconomic conditions, emphasizing the importance of adaptation strategies (Nasrollahi et al., 2018) Similarly, Dabanli (2018) evaluated drought risk in Turkey, identifying six cities with moderate risk and one city, Konya, facing high risk These findings provide a crucial foundation for developing adaptive measures and planning techniques for effective drought risk management.
Blauhut et al (2016) utilized the enhanced EDII database to create a drought risk map for Europe, employing drought indices like the Standardized Precipitation Index (SPI) and Soil Moisture Anomaly (ΔpF) as quantitative measures of drought hazard Their research assessed drought risk by considering both hazard and vulnerability, revealing notable regional and sector-specific differences in drought risk across the continent.
In China, drought risk assessment utilizes three key components: hazard, exposure, and vulnerability Hazards are identified through climate anomalies, while exposure encompasses socioeconomic and agricultural conditions, and vulnerability is linked to land use Research indicates that vulnerable ecoregions are at a high risk of drought in both historical and future contexts (Chou et al., 2019) However, this research primarily focuses on the disaster itself, neglecting the interrelationship between the disaster-tolerant environment and resilience, leading to an emphasis on hazards over other critical components.
Vietnam is highly vulnerable to drought, prompting extensive research by scientists on this critical issue Recent studies have concentrated on two main areas: the impact of drought on socioeconomic conditions and the associated risks, as well as strategies for adaptation and mitigation.
Tuan et al (2019) highlighted that traditional indicators for quantifying drought risk fail to fully capture the socioeconomic, infrastructure, and population impacts of drought To address this gap, the researchers incorporated infrastructure and population variables to evaluate vulnerability, alongside hazard and exposure metrics for a comprehensive assessment of drought risk Utilizing data from 14 stations over a 39-year period, their findings revealed that the highest risk levels were concentrated in five provinces in the northeastern Southwest region, with 73.68% of the VMRD categorized as being at moderate risk.
Huong et al (2020) conducted an assessment of drought risk in Mid-Central Vietnam, quantifying it through the factors of hazard, exposure, and susceptibility Their findings indicated that the region faces a high level of risk, significantly impacting socioeconomic conditions, the environment, and local ecosystems.
Le et al (2021) presented a comprehensive analysis of drought risk in the Highlands and South of Vietnam, incorporating three key components: hazard, exposure, and vulnerability They assessed drought hazards through indices like the Standardized Precipitation Index (SPI) and surface soil moisture (SSM) Additionally, drought exposure and vulnerability were evaluated based on land use and socio-economic factors The study identified that the provinces most at risk are located in the Vietnam Mekong River Delta (VMRD) region (Le et al., 2021).
DATA AND METHOD
Data used
This study analyzed secondary data from 10 rainfall monitoring stations in the VMRD region, covering the period from 1980 to 2020 The data comprises monthly precipitation records from stations located in the provinces of Long An, An Giang, Kien Giang, Dong Thap, My Tho, Can Tho, Ben Tre, Soc Trang, Bac Lieu, and Ca Mau, as detailed in Table 3.1.
Table 3.1 List of rainfall monitoring stations in VMRD, Viet Nam
This study investigates the impact of drought on agricultural activities by analyzing key indicators such as paddy yield and the extent of land affected by saline intrusion across four distinct periods: 2004-2005 (baseline), 2008-2009 (mild drought), and 2015-2016 and 2019-2020 (severe droughts) Salinity data was collected from eight monitoring stations: An Lac Tay, Dai Ngai, Soc Trang, Long Phu, Tran De, Thanh Thoi Thuan, Thanh Phu, and Nga Nam Additionally, the research utilized data from various reputable sources, including the General Statistics Office, the National Center for Hydro-Meteorological Forecasting, and reports from organizations such as IPCC, FAO, GIZ, and the World Bank.
Drought Index
The Standardized Precipitation Index (SPI), developed by McKee et al in 1993, serves as a crucial tool for assessing and monitoring varying dry or wet conditions based on precipitation levels across different time scales This index quantifies precipitation data as standard deviations from long-term averages, which are calculated from a minimum of 30 years of monthly precipitation records The SPI is instrumental in providing insights into climatic trends and water resource management.
𝜎 (1) where: R: Precipitation at a particular time scale, 𝑅̅ long term average precipitation; 𝜎: standard deviation
The Standardized Precipitation Index (SPI) can be calculated over various time scales, including 1, 3, 6, 9, 12, 24, and 48 months, as noted by McKee (1993) and the WMO (2012) The SPI-1 month is significant for assessing agricultural drought, reflecting short-term soil moisture conditions and drought stress In contrast, SPI-3 month provides a seasonal estimate of precipitation and its impact on overall crop yield, while SPI-6 month is utilized to evaluate groundwater effects, effectively illustrating precipitation patterns over time.
1992) Due to the regional characteristics of the VMRD and the assessment of its suitability to study meteorological drought, SPI-6 month is the optimal choice (Mai , et al., 2016)
The Gamma function utilizes historical precipitation data to analyze the correlation between probability and precipitation levels It calculates the normally distributed probability density using an inverse normal distribution with a mean of 0 and a standard deviation of 1, represented mathematically as g(x) = 1.
The Gamma function incorporates parameters α and β, which are estimated for each station across various time scales, including 1, 3, 6, 9, and 12 months, as well as weekly intervals throughout the year These parameters, α and β, are optimally estimated, as demonstrated in Equation 3.
With A= ln(χ) - ∑ ln (χ) ̅̅̅̅̅̅̅̅̅̅ n , Where, χ: precipitation, n: station number
The cumulative probability with χ = 0 become: H(χ) = q + (1 − q)G(χ)
Where: q: the probability; H(x): Cumulative probability
The Standardized Precipitation Index (SPI) is derived from a standard normal distribution function with a mean of zero and a standard deviation of one, utilizing precipitation probability records across various time series This index effectively estimates drought conditions at different scales, enabling early warnings and assessments of drought characteristics while suggesting mitigation strategies to minimize damage An SPI value greater than zero indicates wet conditions, whereas a value less than zero signifies drought The World Meteorological Organization (WMO) and McKee et al (1993) have employed the SPI to categorize moisture levels and dryness, as outlined in Table 3.2.
Table 3.2 Drought classification based on SPI values
SPI Degrees of drought Cumulative Probability
A drought index is essential for assessing changes in drought conditions, as identified by Wilhite et al (2000) This index is determined by four key characteristics: frequency, duration, severity, and intensity.
Drought frequency refers to the total number of droughts occurring within a specified study period, which can be measured annually or over a decade In this research, the frequency is quantified using the formula: f 𝑛.
𝑁 , where n is the number of months due and N is the total number of months in the study period (41 year) (Hamal, et al., 2020; Ghosh, 2018)
(ii) The duration of each drought event is determined by the number of months between its onset and end (Ghosh, 2018)
Drought severity is determined by summing the SPI drought index values for each drought occurring during the dry season, which spans from November of the previous year to April of the following year.
(iv) Drought intensity is determined by how severe the drought is and how long it lasts
It is determined as the ratio of drought severity to drought duration
Pearson’s Correlation Coefficient and linear regression
In 1888, Galton introduced the concept of correlation, which Pearson later expanded upon in 1896 with the development of the Pearson Product-Moment correlation coefficient (r) This coefficient is essential for assessing the statistical relationship between two continuous variables and is considered the most reliable method for analyzing variable associations Its significance lies in providing a robust framework for gathering evidence and evaluating the impact of drought.
In this research, Pearson’s Correlation Coefficient Tests was conducted based on correlation charts and Correlation Coefficient (r), which can be calculated by: r = ∑ (𝑥 𝑖 −𝑥̅)(𝑦 𝑖 −𝑦̅)
𝑥̅ and 𝑦̅ are the mean values of the variables x (Severe drought) and y (Area land affected by Salinity by 8g/l, 12g/l and production of paddy, agriculture land area)
r > 0: It indicates that two variables have a positive correlation
r < 0: It indicates that two variables have a negative correlation
When the correlation coefficient (r) is equal to 1 or -1, it indicates a perfect linear relationship between the variables x and y, allowing for the calculation of y for any given x Conversely, an r value of 0 signifies that there is no correlation between the two variables According to Cohen (1988), r values serve as a measure of correlation and are categorized accordingly.
This study examines the relationship between two variables, proposing a null hypothesis (H0) that suggests they are unrelated In contrast, the alternative hypothesis (H1) posits a relationship exists A statistically significant p-value of less than 0.05 supports the alternative hypothesis, while a p-value greater than 0.05 indicates that the null hypothesis should be retained.
This study employed linear regression to analyze the relationship between drought, saline intrusion, and agricultural activities The dependent variables included the percentage of area affected by salinity levels of 8g/l and 12g/l, as well as paddy production and agricultural land area Drought severity served as the independent variable For paddy crops, a critical salinity level exceeding 4g/l is detrimental to plant survival (Vu et al., 2019; Apel et al., 2020) Salinity levels of 8g/l and 12g/l represent severe conditions that adversely impact agriculture The linear relationship is represented by the equation: y = 𝛽 0 + 𝛽 1 × x.
In a regression analysis, the dependent variable (y) is influenced by the independent variable (x), with 𝛽 0 representing the slope and 𝛽 1 the intercept This relationship indicates that for every one-unit increase in x, y increases by 𝛽 0 + 𝛽 1 units Specifically, severe droughts can contribute to a significant increase in saline intrusion and agricultural activities, quantified as an increase of at least 1 unit The R-squared (R²) value, which ranges from 0 to 1, is a critical metric in this context; values above 0.5 suggest that over 50% of the variability in y can be explained by changes in x, highlighting the independence and explanatory power of the model.
This research focuses on climate statistical analysis using time series, which often leads to the presence of autocorrelation in the model To ensure the validity of the findings, a correlation test, specifically the Durbin-Watson Test, will be conducted to confirm the absence of autocorrelation within the model.
This research utilized Pearson’s Correlation Coefficient Tests and linear regression analysis, conducted using R-studio Detailed commands and methodologies can be found in the appendix.
Drought risk assessment
Pearson’s Correlation Coefficient test plays a crucial role in evaluating the effects of drought on saline intrusion and agricultural activities This research focuses on assessing drought risk, which involves calculating the potential consequences of drought on these factors The risk assessment process entails a comprehensive estimation of both the likelihood of drought occurrence and the severity of its potential impacts (Bizottság, 2010).
According to the IPCC, 2014 hazard defined:
Natural or human-induced events can lead to significant risks, including loss of life, injuries, and health issues, as well as damage to property, infrastructure, and ecosystems These events can disrupt livelihoods and essential services, highlighting the importance of understanding and mitigating such risks for better community resilience and environmental protection.
Indicators for hazard assessment included drought severity and salinity levels of 8g/l and 12g/l Drought severity, indicative of water stress, is measured using total SPI values below -0.5 during the dry season (November to April) Additionally, the severity of saline intrusion is assessed by evaluating the proportion of land affected by salinity levels of 8g/l and 12g/l, in comparison to the base rate of 4g/l.
According to IPCC, 2014 exposure is defined as:
The presence of individuals, livelihoods, species, ecosystems, and essential resources in areas that may be negatively impacted is critical (IPCC, 2014) This includes environmental functions, services, infrastructure, and various economic, social, or cultural assets that could face adverse effects.
The Drought Exposure Index (DEI), as defined by the IPCC, is calculated using three key factors: the percentage of agricultural land relative to the total land in a district, the percentage of paddy planted area within the district, and the percentage of the rural population These elements are essential for analyzing drought exposure, as they reflect the degree of human dependence on natural resources (Lê et al., 2019) The risk of drought is directly proportional to these indicators, with three key signs indicating an increased level of drought danger.
Vulnerability refers to the inclination to be negatively impacted, incorporating aspects such as sensitivity to harm and an inadequate ability to adapt or cope with adverse conditions (IPCC, 2014).
The assessment of the Drought Vulnerability Index (DVI) in relation to socioeconomic conditions involves three critical indicators: (i) the multi-dimensional poverty rate, which reflects individuals' ability to manage drought impacts; (ii) the contribution of agriculture to GDP, indicating local reliance on the agricultural sector; and (iii) the percentage of high school graduates, which signifies the community's adaptive capacity to utilize technology, such as smartphones, for drought warnings.
Summary of indicators of all components used for estimating DHI, DEI and DVI is presented in Table 3.3
Table 3.3 Indicators and data sources of components
Severity of drought Total SPI values under threshold
The weight of rate area effect by salinity 8g/l and base rate (area effect by salinity 4g/l)
The weight of rate area effect by salinity 12g/l and base rate (area effect by salinity 4g/l)
The percentage of agricultural land to the total land of district, %
Positive Soc Trang statistical yearbook
The percentage of planted area of paddy to the total land of district, %
Positive Soc Trang statistical yearbook
The percentage of Population rural to total population of province, %
Positive Soc Trang statistical yearbook
The multi-dimensional poverty line based on poverty threshold, %
Positive Soc Trang statistical yearbook Agriculture contribution to GDP
Percentage of GDP, % Positive Soc Trang statistical yearbook
Percentage of high school graduates
Percentage of high school graduates
Negative Soc Trang statistical yearbook
Step 2: Data preparation and indicator estimation
To ensure consistency in data analysis, it is essential to normalize the selected indicators that vary in measurement units and scales This research employs the min-max normalization method, which converts the measured values into a standardized range of 0 to 1 Positive correlation indicators are calculated using equation (6), while negative correlation indicators are computed with equation (7).
Which 𝑠 𝑖 – normalized indicator value, xi – indicator value for district i, 𝑥 𝑚𝑖𝑛 and
𝑥 𝑚𝑎𝑥 – minimum and maximums indicator before normalization ( Le, et al., 2021)
After normalizing the data, Drought Hazard Index (DHI), Drought Exposure Index (DEI) and Drought Vulnerability Index (DVI) are calculated by following formula (8),
(9) and (10) Each indication is standardized to a common distribution interval of 0 to
Where n: number of Hazard indicators; 𝑠 𝑖 – normalized hazard indicator value The DHI is re-scaled into four classes: Low (DHI ≤ 0.250); Moderate (0.251< DHI ≤ 0.500); High (0.501< DHI ≤ 0.750); Very high (0.751< DHI ≤ 1.000)
Where n: number of Exposure indicators, 𝑠 𝑖 – normalized exposure indicator value
The DEI is re-scaled into four classes: Low (DEI ≤ 0.250); Moderate (0.251< DEI ≤ 0.500) ; High (0.501< DEI ≤ 0.750); Very high ( 0.751< DEI ≤ 1.000)
Where n: number Vulnerability indicators; 𝑠 𝑖 – normalized vulnerability indicator value The DVI is re-scaled into four classes: Low (DVI ≤ 0.250); Moderate (0.251< DVI ≤ 0.500); High (0.501< DVI ≤ 0.750); Very high ( 0.751< DVI ≤ 1.000)
Drought risk assessment can be conducted through various methods, including expert consultation and the weighted average method; however, the average method is considered the most effective Consequently, the Drought Risk Index (DRI) was calculated using the average method, which incorporates three key components: Drought Hazard Index (DHI), Drought Exposure Index (DEI), and Drought Vulnerability Index (DVI), as outlined in equation (11) (Le et al., 2021).
To ensure consistency across varying unit effects, all indicators must be standardized to a common scale of 0 to 1 Subsequently, the DRI is re-scaled into four distinct levels (Dabanli, 2018).
This study utilized ArcMap 10.7.1 to visualize the drought risk in the research area, employing secondary data processed within the WGS84/UTM zone 48N coordinate system A base map was developed using district and province boundary information, which was transformed into a shapefile with added georeferencing Subsequently, DHI and DRI data were input and visualized, followed by editing the geometry layout prior to exporting the final map.
RESULTS AND DISCUSSION
Spatio-temporal variability of drought over MRD
Figure 4.1 illustrates the fluctuations of drought events in VMRD as identified by the Standardized Precipitation Index (SPI) across various stations The findings reveal that the majority of drought and wetness events at all stations align with periods of negative and positive SPI values, respectively.
Figure 4.1 Standardized Precipitation index (SPI-6 months) (1980-2020)
Recent data indicates that droughts have increased in frequency across nearly all monitoring stations, with occurrences ranging from 19 to 31 times over the past 41 years, averaging about 5 to 8 events per decade, particularly affecting coastal regions like Bac Lieu.
In the VMRD region, drought events occur annually at a rate of 0.46 to 0.76 times, with Ca Mau, Can Tho, Moc Hoa, and Soc Trang being significantly affected Notably, Bac Lieu and Soc Trang experience a higher frequency of drought, recording 31 and 22 events over a span of 41 years, translating to approximately 0.76 and 0.53 drought events per year, respectively.
Between 1980 and 1999, and 2000 to 2020, total drought episodes varied from 8 to 28 over two decades A comparison of these periods indicates a slight rise in drought events, increasing from 1 to 4 occurrences in the provinces of An Giang, Can Tho, Soc Trang, and Ca Mau.
1980 and 1999 In contrast, droughts decreased by 1-5 events per 20 years in Long An, Dong Thap, My Tho, Bac Lieu, Ba Tre, and Kien Giang provinces
Table 4 1 Number of drought events for decade (1980-2020)
Table 4.1 represents the number of drought events occurred during period of 1980–
The analysis of drought stages from 1980 to 2020 reveals significant fluctuations in drought trends across four distinct periods: 1980–1989, 1990–1999, 2000–2009, and 2010–2020 Notably, the period from 2010 to 2020 has experienced a marked increase in drought events, particularly in regions such as Moc Hoa, Can Tho, Bac Lieu, and Ca.
Mau station, which go up to 8, 10 and 11 events/decade In general, the drought trend has been increased during the period 1980-2020
Drought frequency was assessed by analyzing the number of drought months relative to the total months considered, revealing significant occurrences in Soc Trang, An Giang, Dong Thap, and My Tho provinces The analysis showed an average drought frequency of 26.17% during the dry season, with November in Soc Trang and April in My Tho experiencing the highest rates Interestingly, Ba Tri and Rach Gia stations reported more frequent droughts during the rainy season, averaging 30% While the impact of droughts is more pronounced in the dry season due to their severity, they also manifest during the wet season, as indicated by the SPI index reflecting below-average precipitation This trend suggests that water shortages and severe drought conditions are likely to escalate in the subsequent dry seasons.
Table 4.2.Drought frequency classify by months in VMRD (1980-2020)
Figure 4.2 illustrates the regional distribution of drought occurrences, highlighting that moderate drought events account for approximately 48% of occurrences in the VMRD, with the highest frequencies noted for this category.
Month Moc Hoa Chau Doc Cao Lanh My Tho Can Tho Soc Trang Bac Lieu Ca Mau Ba Tri Rach Gia
36 found (51-60%) in Rach Gia, Soc Trang and Kien Giang province (Fig 4.2) Besides,
My Tho and Chau Doc are two provinces significantly affected by severe drought events, with a frequency ranging from 30% to 35% Additionally, the highest occurrences of extreme drought, indicated by SPI values below -2, have been recorded in My Tho and An Giang provinces.
Between 1980 and 2020, the average occurrence of droughts was 48.11% for moderate droughts, 25.11% for severe droughts, and 28.33% for extreme droughts, indicating that moderate droughts were more prevalent than their severe and extreme counterparts Notably, the Soc Trang and Rach Gia stations reported the highest frequencies of moderate droughts at 51% and 60%, respectively.
Figure 4.2 Drought frequency in VMRD (1980-2020)
Research by Lee et al (2018) indicated that moderate drought was more prevalent than extreme drought in the VMRD from 1984 to 2015 However, recent findings show that extreme drought has surpassed severe drought levels, with a longer data series than previously studied This suggests that drought conditions are likely to intensify compared to earlier periods.
The analysis of drought events over a 41-year period reveals an increasing trend in drought duration across all provinces in the VMRD region Notably, Kien Giang, Soc Trang, Rach Gia, and Bac Lieu provinces experience longer drought durations, attributed to their less dense river systems compared to other provinces.
Moderate Drought Severe Drought Extreme drought
Evaporation from oceans and rivers is a crucial component of the water cycle, significantly impacting rainfall levels in densely riverine areas (USGS, 2019) Consequently, provinces like Kien Giang, Soc Trang, Rach Gia, and Bac Lieu experience longer drought durations compared to regions such as Vinh Long, Tien Giang, and Ben Tre.
Figure 4.3 Drought Severity, Duration and Intensity in VMRD (1980-2020)
The average drought duration in the VMRD is 9 months, with the longest recorded drought lasting 20 months Between 1980 and 2004, most drought events lasted less than 6 months, except for the notable drought in 1990, which persisted for 12 months.
From 2004 onwards, typical drought events lasted longer, with a duration of over than
Droughts lasting between 6 to 12 months have been documented in various years, notably from 2009 to 2010, 2015 to 2016, and 2019 to 2020 The significant drought of 2019-2020 extended for over a year, suggesting its onset in the dry season of 2019 and concluding in 2020 Research by Duc et al (2022) highlights that even mild or moderate droughts can have prolonged and severe impacts on both the environment and human populations (Tran et al., 2022).
Drought severity refers to the extent of reduced precipitation compared to average levels, leading to significant water shortages Analysis of drought events in the VMRD, illustrated in Figure 4.3, reveals that the cumulative negative Standardized Precipitation Index (SPI) over a span of 41 years indicates highly severe drought conditions, particularly noted in the years 2009-2010 and 2015 across all VMRD provinces.
Relationship between drought and saline intrusion and agricultural activity
Table 4.5 presents the descriptive statistics for five key variables in this study, which explores the relationship between drought and saline intrusion in agricultural activities The variables include the severity of drought (X), the area of land impacted by salinity at 4g/l (Y1), 8g/l (Y2), and 12g/l (Y3), along with rice production (Y4) and the total area of agricultural land (Y5).
Table 4.5 Descriptive statistics of variables
Variable Median Mean Standard deviation Min Max
4.2.1 Relationship between drought and saline intrusion
The relationship between saline intrusion and drought changes in Soc Trang province is explored by analyzing the correlation between drought severity, indicated by a Standardized Precipitation Index (SPI) of less than -0.5, and the extent of land impacted by varying salinity levels (4g/l, 8g/l, and 12g/l) The correlation coefficients (r) for these factors are illustrated in Figure 4.4 and detailed in Table 4.6.
Figure 4.4 Correlation coefficients between drought severity and area of land affected by salinity a 4g/l; b.8g/l; c
The findings indicate a positive correlation between drought severity and saline intrusion, with high correlation coefficient values presented in Table 4.6 Specifically, at salinity levels of 4g/l, 8g/l, and 12g/l, the correlation remains notably strong.
A strong positive correlation of 0.56, with a confidence level exceeding 95%, indicates that as drought severity increases, the area of land affected by salinity rises significantly, particularly at 4g/l Further correlation analysis at higher salinity levels (8g/l and 12g/l) reveals even stronger positive correlations of 0.567 and 0.617, respectively Consequently, we can conclude that at least 50% of the increase in saline intrusion can be attributed to the rising probability of drought in the region.
The equation (1) in Table 4.6 indicated that when drought severity increases 1 unit, area of land affect by salinity 4g/l increases 2459.33 unit (2423.37+35.96*1)
Higher salinities of 8g/l and 12g/l exhibit strong correlation coefficients, indicating that changes in drought conditions will also affect saline intrusion Specifically, equations (2) and (3) demonstrate that a 1-unit increase in drought leads to an increase of 1986.86 hectares of land affected by salinity at 8g/l and 1569.45 hectares at 12g/l.
Table 4.6 Result of Pearson's Correlation Coefficients and Linear regression between drought severity and area of land affected by saline intrusion
Strength of correlation p-value Equation R-
4.2.2 Relationship between drought and agricultural activity
Table 4.7 and Figure 4.5 indicate a significant negative correlation between drought severity and rice production, with a p-value of less than 0.05 This suggests that as drought severity increases, both rice production and the area of agricultural land decline.
Figure 4.5 Correlation coefficients a Drought severity and rice production; b
Drought severity and agriculture land area
Equation (4) and (5) in Table 4.7 quantified the relationship between drought and agricultural activity with negative correlation If drought increases 1 unit, rice x
Rice production has decreased by 804.02 units, while the agricultural land area has contracted by 276,650.8 units However, the R-square values of 0.25 and 0.28 indicate that variations in rice production and agricultural land area are influenced not only by drought conditions but also by various human-related factors.
Table 4.7 Result of Pearson's Correlation Coefficients and Linear regression between drought severity and rice production, agriculture land area
Strength of correlation p-value Equation R-
Statistical research in climate often involves time series analysis, which can lead to autocorrelation in models However, the Durbin-Watson Test results indicate that autocorrelation is not present in this model Detailed results and the command for the Durbin-Watson Test can be found in Appendix C.
Drought Risk assessment
DHI is a crucial indicator for assessing the potential occurrence of drought and saline intrusion, which significantly impact both nature and human activities It is calculated based on drought severity and saline intrusion levels The observed drought severity during four key events—2004-2005, 2008-2009, and 2015—was recorded at 16.4, 2.97, 18.42, and 59.93 units, respectively.
Between 2016 and 2020, Soc Trang province experienced varying lengths of drought, notably extending over six months in 2015-2016 and 2019-2020, while being shorter in 2004-2005 and 2008-2009 This analysis focuses on four significant drought events (2004-2005, 2008-2009, 2015-2016, and 2019-2020) to evaluate changes in drought hazard levels across the region over time The aim is to identify sensitive factors contributing to drought risk in Soc Trang province.
Figure 4.6 illustrates the varying levels of drought hazard across Soc Trang province, highlighting significant changes over the years Between 2004 and 2005, most districts faced high drought hazard levels, with only Soc Trang and Ke Sach towns reporting minimal hazards of 0.051 and 0.101 units, respectively However, during the 2008-2009 period, which experienced milder drought conditions, districts like Thanh Tri, My Xuyen, Cu Lao Dung, and Vinh Chau recorded high hazard levels of 0.541, 0.493, and 0.654, while Ke Sach, My Tu, and Soc Trang had notably low scores of 0.015, 0.021, and 0.000 This discrepancy is attributed to the geographic location of the districts, with those farther from the sea less impacted by drought and saline intrusion The drought hazard in Soc Trang province was deemed extremely high during the 2019-2020 season, marking the most severe drought in the 41-year study period.
Figure 4 6 Drought Hazard Index map over Soc Trang province
Coastal districts are at a significant risk of hazards, while inland areas such as Soc Trang, Chau Thanh, Ke Sach, and My Tu experience only mild impacts from drought and saltwater intrusion, resulting in low to moderate hazard levels.
4.3.2 Drought Exposure Index (DEI) and Drought Vulnerability Index (DVI)
Drought exposure was evaluated using three key indicators: the percentage of agricultural land, the percentage of paddy planted area, and the percentage of the rural population The findings indicated that all districts experienced very high drought exposure, with My Tu, Long Phu, and Thanh Tri districts scoring over 0.75 units across four stages This significant exposure level can be attributed to their heavy reliance on agriculture and a high rural population.
Table 4.8 Drought Exposure Index (DEI) over Soc Trang Province
Soc Trang Moderate Low Low Moderate
Chau Thanh - - Very high Very high
Ke Sach High Very high High Very high
My Tu Very high Very high Very high Very high
Cu Lao Dung Moderate Moderate Moderate Moderate
Long Phu Very high Very high Very high Very high
My Xuyen Very high Very high Very high High
Nga Nam Very high Very high High High
Thanh Tri Very high Very high Very high Very high
Vinh Chau High High High Low
Tran De - - Very high Very high
Table 4.9 indicates that over four periods, the DVI in nearly all districts falls within a moderate to high range, primarily due to elevated multi-dimensional poverty rates and agriculture's significant contribution to the GDP, highlighting the reliance of local livelihoods on agricultural activities The value "-" signifies non-availability as Chau Thanh and Tran De were not recognized as districts in Soc Trang province prior to 2009.
In Soc Trang Province, agriculture plays a crucial role in the economy, particularly in Thanh Tri district, where it accounted for 70% of GDP during 2019-2020 and boasts impressive rice yields of 5.2 to 6.4 tons per hectare Despite these agricultural successes, the district faces significant poverty rates, highlighting the community's heavy reliance on agriculture for livelihood Additionally, three districts in the province exhibit very high Development Vulnerability Index (DVI) scores, while Soc Trang and Cu Lao Dung districts show intermediate scores, attributed to the province's relatively better socio-economic status and a lower agricultural contribution to GDP compared to other regions.
Table 4 9 Drought Vulnerability Index (DVI) over Soc Trang Province
Soc Trang Moderate Low Moderate Moderate
Ke Sach High High High High
My Tu High High Very high High
Cu Lao Dung Moderate Moderate Moderate Low
Long Phu High High High High
My Xuyen High High High Moderate
Nga Nam Moderate High High High
Thanh Tri Moderate High Very high Very high
Vinh Chau Moderate High High High
The analysis indicates that the drought hazard during 2015-2016 was more severe than that of 2019-2020, although vulnerability to drought in 2019-2020 was lower This disparity is attributed to the intense drought in 2015-2016, which significantly impacted agriculture, the economy, and the environment As a result, valuable lessons were learned, leading to improved adaptation and mitigation strategies These strategies include enhanced forecasting and early warning systems, adjustments to cultivation calendars, and proactive measures by local communities Additionally, it is essential for local authorities and residents to focus on repairing, completing, and expanding irrigation systems, dams, and dikes to better manage future droughts.
47 water preventive embankments, proactive water storage soon when drought and salinity intrusion occur, etc (Nguyen, 2017)
Drought risk assessment involves evaluating the likelihood and severity of drought risks by assigning scores to each risk This method effectively quantifies the probability of drought occurrences and the potential impacts associated with them (Bizottság, 2010).
In this paper, DRI is generated based on 3 components DHI, DEI and DVI The drought risk map is generated based on DRI and presented in Figure 4.7 a.2004-2005 b.2008-2009 c.2015-2016 d.2019-2020
Figure 4.7 Drought Risk Index map over Soc Trang province a 2004-2005 period; b: 2008-2009 period; c 2015-2016 period; d 2019-2020 period
In the 2004-2005 period, 7/9 districts were exposed to the high drought risk, account for 77.77% and 2 districts (Soc Trang, Cu Lao Dung district) were exposed to the
In 2008-2009, the drought risk was moderate and comparable to previous years, but the drought severity in Soc Trang and Ke Sach diminished, leading to a decrease in their drought risk scores.
Between 2015 and 2016, Soc Trang province experienced drought in 9 out of 11 districts, representing 81.8% of the area, which led to a notable rise in the risk score by 15.14% compared to the prior period.
During the 2019-2020 period, Thanh Tri and Long Thanh districts were identified as high drought risk areas, while Soc Trang town exhibited the lowest risk score in the province due to its stronger economic stability and reduced reliance on agriculture Despite this, Soc Trang still faced a significant risk score of over 81.8 percent, consistent with previous assessments.
In the 2019-2020 stage, research identified significant hazards in six coastal districts: Cu Lao Dung, Tran De, Vinh Chau, My Xuyen, Thanh Tri, and Long Phu While drought risk scores were generally low across most districts, Thanh Tri and Long Phu exhibited very high drought risks due to elevated Drought Hazard Index (DHI), multi-dimensional poverty rates, and a greater reliance on agriculture for GDP Conversely, Soc Trang, one of the province's wealthiest areas with a lesser dependence on agriculture, was the only district experiencing a moderate drought risk.
Drought risk is influenced more by vulnerability than by hazard, indicating that coastal districts may experience greater impacts from drought and saline intrusion compared to other areas, although their risk levels are not necessarily higher The Drought Vulnerability Index (DVI) reflects socio-economic conditions and the capacity for resilience and adaptation to drought and saline intrusion, suggesting that a lower DVI correlates with a higher risk.
Recommendation
Climate change significantly threatens the VMRD through rising sea levels, flooding, and severe droughts Recent data indicates an increasing trend in drought duration and intensity, with the most devastating droughts recorded in 2015-2016 and 2019-2020, resulting in substantial damage to the region.
49 occurred, the government and local government suggested solution, which offered effective responses and stable development to VMRD and they learnt a lot of lessons and reaction solutions
- The foundation for timely adaptation and mitigation programs is improved forecasting and early warning capacity
- Promoting and instilling a sense of responsibility individuals, organize for properly managing, exploiting, and saving water
- Optimizing the farming system: applying water-saving irrigation technology, changing crop structure (Nguyen, 2017)
To effectively address drought and saline intrusion, ministries and local authorities should develop systematic seasonal calendars and cultivated area maps, along with comprehensive prevention and response plans that are informed by forecasts, early warnings, and climate scenarios.
The government must prioritize support for drought-prone areas like Thanh Tri and Long Phu districts by enhancing economic development initiatives to decrease poverty rates Additionally, restructuring agricultural practices and transitioning to drought-resistant and salt-tolerant crops will boost economic value in these vulnerable regions.
The VMRD region faced its most severe droughts on record during the 2015-2016 and 2019-2020 periods, prompting both government and NGOs to invest in the development and enhancement of irrigation infrastructure Despite these efforts, challenges such as construction delays and potential issues persist (Nguyen et al., 2018) To expedite progress, it is crucial for contractors to address these problems effectively, particularly in key projects like the Cai Lon-Cai Be Irrigation Project and the Ben Tre Water Management Project.
- Reduce GHGs emissions by encourage, priority the use of biogas in rural areas (Nguyen, 2017), encourage investors to develop renewable energy as well, decrease number of coal-fired power plant
- Forests are crucial for reducing and adapting to climate change in general, as well as drought and saltwater intrusion in particular As a result, forest development and protection are critical
- Besides, drought and salinity intrusion damage insurance will be created by the government and NGOs, which will enable carriers share losses and improve community education