INTRODUCTION
Drought and saline intrusion under Climate Change context in Viet Nam
Climate change (CC) is currently one of the most significant challenges facing humanity, leading to rising temperatures, sea level rise (SLR), droughts, and flooding These impacts threaten agriculture, key industries, and the socio-economic systems of numerous countries globally.
According to the Intergovernmental Panel on Climate Change (IPCC), by the end of the
By the end of the 21st century, global average temperatures are projected to rise between 2.1℃ and 3.5℃ under the SSP2-4.5 scenario Concurrently, sea levels are expected to increase by 0.44m to 0.76m, with potential rises of 0.63m to 1.01m in high greenhouse gas emissions scenarios (SSP5-8.5) (IPCC, 2021).
According to the RCP 4.5 scenario by the IPCC (2021), Vietnam is projected to experience an average temperature increase of 1.9℃ to 2.4℃ in the North and 1.7℃ to 1.9℃ in the South by the end of the 21st century, along with a sea level rise of 57cm to 73cm This global warming trend is contributing to the heightened intensity of natural disasters, including storms, floods, and droughts Research by Nguyen et al (2021) indicates that drought conditions are becoming more prolonged, severe, and intense, particularly during the dry season and transitional months between dry and wet seasons in regions such as the Red River Delta and South Central Vietnam, where the risk of drought is expected to increase significantly.
Drought is increasingly prevalent and varies significantly across different climatic regions, as highlighted by Phan et al (2013) A study by Phan (2010) utilized statistical methods to analyze drought variations across seven climatic sub-regions in Vietnam, revealing an overall increase in drought occurrences, albeit with varying severity The North Central and South Central sub-regions experience the highest frequency and intensity of drought, while the Northwest sub-region shows the least impact Notably, the North and South Central regions face the most severe drought conditions, according to Phan et al (2019).
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 saltwater intrusion The continuous increase in sea levels is contributing to year-round saline intrusion, posing significant risks to ecosystems and communities (Hagenvoort et al., 2013; Bricheno et al.).
Sea level rise (SLR) combined with drought is exacerbating saline intrusion, significantly impacting various sectors, particularly agriculture The low-relief landforms of the Red River Delta (RRD) and the Mekong River Delta (VMRD) are particularly vulnerable, with the VMRD being the most studied region regarding saline intrusion This heightened focus is due to the VMRD's status as the lowest area among the world's three deltas at high risk of severe effects from climate change over the next 30 to 50 years.
According to the MONRE (2021), a projected sea level rise (SLR) of 100 cm by the end of the century under the extreme RCP8.5 scenario could result in 47.29% of the Mekong River Delta (MRD) being permanently flooded Concurrently, drought conditions are expected to intensify, exhibiting significant spatial fluctuations and regional differences (Phan et al., 2013; Nguyen et al., 2021; Shahid, 2011) As a severe extreme weather event, drought is anticipated to become more frequent and intense (IMHEN & UNDP, 2015), primarily due to insufficient rainfall compared to long-term averages This complex phenomenon develops gradually, with its impacts becoming evident only after prolonged periods, leading to severe consequences While climate change affects drought globally, its effects vary across different climate regions (Phan, 2010) Notable changes in temperature, precipitation, humidity, and evaporation have altered the duration, spatial extent, frequency, and intensity of drought events.
Drought-induced saline intrusion, exacerbated by climate change, is leading to significant economic losses, making it one of the most damaging natural disasters (Sam et al., 2008; Leng et al., 2015) Prolonged drought conditions can result in crop failures and alter agricultural ecosystems, threatening the viability of high-value crops In Vietnam, these challenges are becoming increasingly pronounced due to persistent drought.
Natural disasters, particularly drought and saline intrusion, rank as the third most damaging events after storms and floods (DMC, 2011a) The Vulnerable Marine and River Delta (VMRD) regions are significantly impacted by climate change, facing increased average temperatures, more frequent extreme weather events, sea level rise, and saline intrusion (IPCC, 2007) These changes pose severe risks to agricultural activities, threatening livelihoods, food security, and water supply (UNCT, 2020) For example, in 1982, a drought devastated 180,000 hectares of crops, with Soc Trang province experiencing particularly severe effects, where 95% of farmers reported damage due to drought and saline intrusion (Truong et al., 2015).
According to the 2011 MONRE report, over 650,000 hectares of rice were cultivated in the coastal region of the VMRD, with approximately 100,000 hectares, or 15.38%, facing a significant risk of saline intrusion during the dry season each year.
The VMRD has been significantly impacted by drought and saline intrusion, affecting 40% of its arable land (IMHEN & UNDP, 2015) Notably, the region experienced its most severe saline intrusion in 90 years during the 2015-2016 period, 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 caused damage to 477,113 hectares of agricultural land In Soc Trang province alone, over 81,000 hectares of shrimp farms were lost, contributing to 51.79% of the total agricultural land damage in the VMRD, which amounted to 247,711 hectares (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, but researchers select different indicators based on climate regions (Zargar et al., 2011) In Vietnam, studies indicate that the Standardized Precipitation Index (SPI) effectively captures drought variability, particularly in the VMRD and the Southern region overall (Nguyen).
Research on environmental challenges in Vietnam highlights significant studies on drought and salinity intrusion in the Mekong Delta (Nguyen, 2016; UNCT, 2020) and an investigation into drought in the Central Highlands from 1990 to 2005 using the Standardized Precipitation Index (SPI) by Tue et al (2015), which revealed critical findings on these issues.
3 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 the VMRD from 1984 to 2015, utilizing SPI and SPEI with various precipitation datasets Their research indicated a decrease in drought frequency but an increase in the spatial distribution of moderate to severe droughts Similarly, Stojanovic et al (2020) analyzed dry conditions in Vietnam from 1980 to 2017, also employing SPI and SPEI, and found significant regional variations in drought duration and severity.
Drought impacts have been extensively studied, particularly during significant dry periods such as the 1982-1983 event, which affected both the environment and socio-economic sectors (Nguyen, 2007) A notable study by Tran et al (2018) utilized the Standardized Precipitation Index (SPI) to assess drought effects on rice land use Furthermore, the drought episodes in Vietnam from 2008 to 2017 had profound consequences on the coffee sector, influencing both output and farm profits, highlighting the critical importance of drought detection (Byrareddy et al., 2021).
Previous research on drought can be categorized into two main areas: the mechanisms, causes, and trends of drought, and its impacts on various sectors Notably, there is a lack of studies focusing on the characteristics of drought and its effects on agriculture Additionally, there have been no comprehensive analyses that explore the relationship between historical drought patterns and saline intrusion, nor have there been assessments of the combined effects of both factors on agricultural activities in the VMRD Hence, this thesis aims to address these gaps in the existing literature.
" 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 The drought’s characteristics experienced an characteristics in VMRD? increase in frequency, duration, severity and intensity.
Drought has a significant impact on saline intrusion and agricultural activities, demonstrating a positive correlation between the two However, there is a negative correlation between drought conditions and agricultural productivity Understanding these relationships is crucial for developing effective strategies to mitigate the effects of drought on agriculture and manage saline intrusion.
What is the level of drought Soc Trang have high level of drought risk risk in Soc Trang province?
Objectives of the research
This study focuses on three key objectives: first, to analyze the changes in drought conditions in the Vietnam Mekong River Delta (VMRD); second, to evaluate the correlation between drought occurrences, saline intrusion, and agricultural activities; and third, to assess the drought risk specifically for Soc Trang province, which serves as a representative area within 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 Other outcomes of the thesis Mater’s thesis
PLOs of MCCD (Details in Result of the master’s Other outcomes of the thesis Mater’s thesis
Scope of research
The study area for assessing drought changes encompasses the entire VMRD of Vietnam, comprising 13 provinces, with Soc Trang province specifically selected for evaluating drought risk and its effects on agriculture and saline intrusion Soc Trang has faced significant drought challenges, characterized by increasing frequency, severity, and duration The analysis will cover drought changes from 1980 to 2020, while the impact on agricultural activities and saline intrusion will be assessed using data from the years 2000 to 2020 Additionally, four critical dry periods—2004-2005, 2008-2009, 2015-2016, and 2019-2020—will be examined to evaluate the drought risk in Soc Trang province.
Study area
The Mekong River Delta (VMRD) is the largest delta in southern Vietnam, featuring a coastline that spans over 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 comprises one city, Can Tho, and twelve provinces, including 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.
The Vietnamese Mekong River Delta, covering an area of 40,816 km², is home to approximately 17.3 million people, representing 12.32% of Vietnam's total land area, according to the General Statistics Office (GSO, 2020) This region boasts 2.615 million hectares of agricultural land, which constitutes 64% of its natural area, primarily dedicated to rice cultivation and aquaculture Notably, in 2020, the agricultural sector employed 43.3% of the total workforce in Vietnam.
The terrain of the region is predominantly flat, descending from north to south, featuring two significant low-lying areas: Dong Thap Muoi and the northeastern part of the Ca Mau peninsula This geography creates fertile plains and a dense network of canals, bordered by the sea on three sides and boasting an extensive coastline, which collectively provide an excellent foundation for agricultural development VMRD is recognized for its agricultural potential in this area.
Vietnam's "Rice Bowl" is a vital region, contributing over 40% of the nation's agricultural productivity and 54% of its rice output It plays a crucial role in the economy, accounting for 90% of rice exports and a significant portion of aquatic and fruit products In 2019, the Vietnam Mekong River Delta (VMRD) contributed 17.7% to the country's 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, including 212.6 thousand hectares designated for agriculture and 10.2 thousand hectares for forestry as of 2020 With a population of 1,195,741, approximately 68% reside in rural areas, highlighting the province's reliance on agriculture The agricultural sector employs around 72% of the population and constitutes 63% of the workforce, serving as the primary source of income for over 70% of the residents.
In 2009, Soc Trang province comprised nine districts, which later expanded to eleven with the establishment of Tran De and Chau Thanh Agriculture, forestry, and fisheries play a significant role in the region's economy, contributing to 40.5% of the province's Gross Regional Domestic Product (GRDP) in relation to the Vietnam Maritime Region Development (VMRD).
Agricultural land comprises 279,398 hectares, accounting for 84.71% of the total area, with 182,984 hectares (65.5%) dedicated to rice cultivation The remaining land is allocated for vegetables, short-term industrial crops, perennial crops, and fruit trees (GSO, 2020).
The VMRD experiences a tropical monsoon climate characterized by a rainy season from May to October and a dry season from November to April Average yearly temperatures range from 24 to 29℃, with April seeing maximum temperatures of 29℃ Precipitation is typically below 100mm in April and May, while it peaks in August and September During the dry season, rainfall decreases significantly, often falling below 100mm, and temperatures remain steady between 25-29℃ From January to March, many areas face almost no rainfall, leading to rising temperatures and severe water shortages If these conditions continue, the region risks drought, which could have significant socio-economic impacts.
Moc Hoa Chau Doc Cao Lanh My Tho
Moc Hoa Chau Doc Cao Lanh My Tho Ba Tri
Ba Tri Can Tho Rach Gia Soc Trang
Can Tho Rach Gia Soc Trang Bac Lieu Ca Mau
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 evaluating drought changes through four key characteristics: frequency, duration, severity, and intensity, utilizing the Standard Precipitation Index (SPI) Additionally, Pearson’s Correlation Coefficient will be applied to assess the relationship between drought conditions and salinity levels.
The risk analysis process involves a thorough evaluation of the likelihood of drought occurrences and their potential impacts on agricultural activities The drought risk index is determined by assessing hazard, exposure, and vulnerability Based on the findings of the drought risk assessment, both short-term and long-term adaptation strategies are recommended to mitigate the effects of drought.
Frequency Duration Severity Intensity Intrusion activities
Drought Severity Percentage Agricultural Multi-dimensional
Severity of salinity Land poverty rate (%)
Percentage planted area Agriculture 8g/l of paddy contribution to GDP
Percentage people of Percentage of high 12g/l rural school graduates (%)
Solution: Short-term, long-term
Figure 1.4 Logical framework of the research
LITERATURE REVIEW
Definition of drought
Drought is a gradual and insidious natural hazard that develops over time, leading to various interpretations and definitions Research by Wilhite and Glantz (1985) identified over 150 definitions of drought, highlighting the complexity of the term Yevjevich (1969) emphasized that this lack of consensus on what constitutes drought poses significant challenges in drought research Therefore, establishing a unified definition is crucial for effective study and management of drought conditions.
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" (USWB, 1953)
Drought occurs when there is insufficient rainfall, as noted by Thomas (1962) The likelihood of experiencing drought is significantly influenced by the balance between water reserves and the annual water needs of a region (Thomas, 1962).
In 1965, Palmer described drought as a prolonged period, typically lasting months or years, during which the actual moisture supply in a specific location significantly falls below the expected climatic levels.
According to the World Meteorological Organization (WMO), drought is defined as a prolonged period characterized by a significant lack of precipitation, leading to an abnormal dry spell that can result in serious hydrological imbalances.
Drought is characterized by an extended period of insufficient rainfall, often lasting a season or longer, leading to significant water shortages, as defined by the National Drought Mitigation Center (NDMC) in New Zealand.
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 natural 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 extended lack of rainfall, leading to decreased moisture in the air and soil, diminished river and stream flows, and reduced water levels in ponds, lakes, and underground aquifers This phenomenon adversely impacts plant growth and development, resulting in crop failures, environmental degradation, and increased poverty and disease (Nguyen Duc Ngu, 2002).
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 significant and persistent shortage of rainfall in specific areas, particularly in arid and semi-arid regions where low rainfall over extended periods can lead to severe dryness (Nguyễn et al., 2002) Factors contributing to drought include substantial drops in rainfall compared to long-term averages and natural phenomena like El Niño, which raises sea surface temperatures in the equatorial Pacific, resulting in decreased rainfall and increased evaporation (Wilhite et al., 2007; Nguyễn et al., 2017) Additionally, human activities such as deforestation and poor land and water resource management exacerbate drought conditions, alongside large-scale changes in atmospheric circulation that vary by region.
12 changing the vegetation cover, vegetation, excessive use of water resources, etc.) can aggravate drought (Xue, et al., 1993; Bruce, 1994).
Drought can be classified into four distinct types based on its characteristics and impacts: Meteorological drought, which refers to a prolonged period of below-average precipitation; Hydrological drought, which affects water supply and availability; Agricultural drought, impacting crop production and soil moisture; and Socioeconomic drought, which considers the effects of water scarcity on the economy and society This classification is supported by the World Meteorological Organization and the American Meteorological Society (2004) as well as Wilhite (1985).
Meteorological drought occurs when there is insufficient precipitation to maintain a balance between precipitation and evaporation in a specific area over a certain timeframe Factors such as radiation intensity, temperature, wind speed, and humidity significantly influence evaporation rates Consequently, conditions characterized by high sunshine, elevated temperatures, strong winds, and low humidity contribute to increased drought severity Precipitation is the primary variable used for analyzing meteorological drought (Santos, 1983; Eltahir, 1992; WMO, 1992).
Hydrological drought occurs when there is an imbalance between surface water and groundwater, often identified through streamflow data (Dracup et al., 1980; Panu et al., 2009) Geographical factors play a significant role in influencing hydrological droughts, which typically manifest after meteorological and agricultural droughts due to prolonged water shortages.
Agricultural drought refers to a phase characterized by decreased soil moisture and diminished crop yields, independent of surface water availability Typically, it follows meteorological drought and precedes hydrological drought Plant growth is influenced by various factors, including weather conditions, the unique biological traits of each plant, their growth stages, and soil characteristics Consequently, agricultural drought indices are formulated by integrating data on rainfall, temperature, and soil moisture (IPCC, 2012).
(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
13 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, 2004).
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 across continental areas, contributing 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 drought events occurred in 1976 and 1991, highlighting the increasing impact of these climatic challenges across the region.
Between 2003 and 2005, severe droughts had long-lasting effects on the agricultural sector, significantly reducing the yields of paddy, vegetables, and fruit trees, with an estimated economic impact of €100 billion over the past 30 years Notably, the 2005 drought on the Iberian Peninsula led to a 10% decrease in total grain production across Europe (Mishra).
Experts indicate that droughts and floods may arise under different climate scenarios, with regions in Southern and Southeastern Europe facing decreased average precipitation and heightened evapotranspiration, leading to extended periods of drought.
A study by Zubietia and co-author (2021) in Peru analyzed the changes in drought frequency and intensity and their effects on the agriculture sector The findings revealed that between 2000 and 2010, droughts became increasingly intense, frequent, and prolonged, leading to detrimental consequences for agricultural activities, including crop loss and the rise of pests and diseases (Zubieta et al., 2021).
Research in China highlights the increasing severity and frequency of droughts, particularly since 1970, with studies indicating a rise in temperature and prolonged dry spells (Bordi et al., 2004; Dai et al., 2004) Projections suggest that China will face more droughts, with an estimated 51 occurrences, predominantly long-term (Leng et al., 2015) The southern regions have experienced significant drought impacts, notably in 1997, 1999, and 2002, affecting 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 events, which have contributed to declining 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 resulted in a staggering loss of 38.7 million tons of food grain, 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, further complicating the issue by diminishing the availability of fresh water needed to leach salts from soil and groundwater.
2.3.2 Association among drought, and saline intrusion and agricultural activity in Viet Nam
In Vietnam, drought ranks as the third most destructive natural disaster, 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 drought severity across various regions The findings revealed that the Northwest, Northeast, and Northern Delta 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 prolonged droughts are also observed in the South, South Central, and Central Highlands during winter and spring Additionally, the South Central Coast experiences drought conditions in both spring and summer Overall, drought occurrences are widespread across various regions of Vietnam (Nguyễn et al., 2013).
A drought study by Nguyen Van Thang, utilizing the Palmer Drought Severity Index (PDSI) from 1961 to 2010, revealed that drought conditions occurred in 36 years, representing 73.5% of the analyzed period, with the Northern Delta provinces experiencing 29 instances of drought (Nguyễn, 2015) Additionally, research by Tran Thuc (2012) indicated that the Southern region of the South Central area faced 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 extremes in Vietnam The research indicates that by the end of the 21st century, there will be a significant increase in the frequency of hot days nationwide, particularly in the Northern Delta, South Central, and Southern regions Additionally, droughts are expected to occur more frequently and last longer across most climate zones in Vietnam, especially in the Central Highlands and Southern areas, although the overall number of droughts may not increase.
The VMRD region is significantly impacted by climate change, with increasing and severe droughts posing a major threat to communities Consequently, extensive research is being conducted on drought-related issues in this area, highlighting various notable projects aimed at addressing these challenges.
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 creation of drought maps in the VMRD region examines the impact of climate change by utilizing rainfall and temperature data from A2 and B2 scenarios This research aims to evaluate the current status of meteorological drought and how climate change influences drought conditions in VMRD.
An analysis of a 33-year data series reveals that the Standardized Precipitation Index (SPI) exhibits variations in both spatial and temporal dimensions While the frequency of drought occurrences remains stable, the intensity of droughts fluctuates across different regions of the VMRD (Trần et al., 2015; Nguyễn et al., 2017).
Drought is inherently unpredictable, making the development of effective forecasting models essential for the sustainable management of water resources and minimizing potential damages Nguyen et al (2012) introduced a meteorological drought forecasting technology at the VMRD, resulting in a high-quality model that aligns with WMO and Obukov standards The integration of historical drought studies with robust forecasting models is vital for enhancing responses to drought and mitigating its impacts.
Assessment of drought risk
Drought is the most common natural disaster globally, with its duration and intensity expected to worsen due to climate change (MONRE, 2020; Dabanli, 2018) The socio-economic impacts of drought are particularly severe in agriculture, prompting scientists to focus on assessing drought risk and developing solutions to mitigate its effects While various approaches exist for quantifying drought risk, there is no universal consensus on the methods, as each has unique advantages and limitations suited to specific regions Currently, two primary methods for drought risk assessment are widely utilized: (i) assessing drought risk based on Hazard and Vulnerability and (ii) [missing second method].
Drought risk assessment combines 3 components (i.e hazard, exposure, and vulnerability).
A study on drought risk assessment in Semnan province, Iran, highlights that risk areas are influenced by both drought hazards and socioeconomic conditions, emphasizing the importance of adaptation and mitigation strategies (Nasrollahi et al., 2018) Similarly, Dabanli (2018) evaluated drought risk in Turkey, identifying six cities at moderate risk and one city, Konya, at high risk These findings provide a crucial basis for developing effective adaptive measures and planning techniques for managing drought risks.
Blauhut et al (2016) utilized the enhanced EDII database to develop 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 throughout Europe.
In China, drought risk assessment utilizes three key components: hazard, exposure, and vulnerability Hazards are identified through climate anomalies, while exposures encompass socioeconomic and agricultural conditions, and vulnerability is linked to land use Recent findings indicate that vulnerable ecoregions are at a high risk of drought in both historical and future contexts (Chou et al., 2019) However, this research primarily emphasizes the disaster itself, neglecting the crucial interrelationship between disaster-tolerant environments and resilience, leading to a focus on hazards over other significant components.
Vietnam is highly vulnerable to drought, making it a focal point for scientific research Recent studies have concentrated on two key areas: the impacts of drought on socioeconomic conditions and drought risk assessment, as well as exploring effective adaptation and mitigation strategies.
Tuan et al (2019) highlighted that existing indicators for measuring drought risk fail to fully account for socioeconomic factors, infrastructure, and population impacts To address this, the researchers incorporated infrastructure and population variables to better represent vulnerability, alongside hazard and exposure metrics for a comprehensive drought risk assessment Utilizing data from 14 stations over a 39-year period, the study revealed that the highest drought risk was concentrated in five provinces in the northeast of the Southwest region, with 73.68% of the Vulnerability to Multi-Risk Drought (VMRD) categorized as being at moderate risk.
Huong et al (2020) evaluated drought risk in Mid-Central Vietnam, quantifying it through hazard, exposure, and susceptibility metrics Their findings indicated that the region faces a high risk of drought, significantly impacting socioeconomic conditions, the environment, and local ecosystems.
Le et al (2021) identified drought risk in the Highlands and South of Vietnam by integrating three key components: hazard, exposure, and vulnerability They assessed drought hazard 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 revealed that the provinces most at risk were located in the Vietnam Mekong River Delta (VMRD).
DATA AND METHOD
Data used
This study analyzed secondary data obtained from 10 rainfall monitoring stations across the VMRD region, covering the period from 1980 to 2020 The monthly precipitation data includes measurements 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 examines 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 drought) 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 incorporates data from reputable sources, including statistical yearbooks, the General Statistics Office, the National Center for Hydro-Meteorological Forecasting, and various publications 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 vital tool for assessing and monitoring dry or wet conditions through precipitation data across various time scales This index is expressed in standard deviations, comparing observed precipitation to long-term averages spanning over 30 years To calculate the SPI, monthly precipitation data is collected over a minimum period of 30 years, utilizing a specific equation for accurate results.
SPI = − where: R: Precipitation at a particular time scale, ̅ long term average precipitation; : standard deviation.
The Standardized Precipitation Index (SPI) can be calculated using various time scales, including 1, 3, 6, 9, 12, 24, or 48 months (McKee et al., 1993; WMO, 2012) The SPI-1 month is particularly relevant for assessing agricultural drought, as it reflects short-term soil moisture conditions and drought stress In contrast, the SPI-3 month provides a seasonal estimate of precipitation and its impact on overall crop yield For evaluating groundwater effects, the SPI-6 month is utilized, effectively demonstrating precipitation trends over specific time frames.
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 illustrate the correlation between probability and precipitation levels By employing an inverse normal distribution with a mean of 0 and a standard deviation of 1, the probability density is calculated, represented as g(x) = 1 χ −1 −χ/β τ( ).
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 Additionally, the optimal estimation of the α and β parameters is demonstrated in Equation 3.
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, allowing for the calculation of precipitation probabilities across various time scales This index is instrumental in estimating drought conditions, offering early warnings and assessing drought characteristics to mitigate potential damage An SPI value greater than zero indicates wet conditions, while a value less than zero signifies drought The World Meteorological Organization (WMO) and McKee et al (1993) utilized the SPI to categorize moisture levels, facilitating a better understanding of dryness and wetness (refer to Table 3.2).
Table 3.2 Drought classification based on SPI values
SPI Degrees of drought Cumulative Probability
A drought index is a crucial variable for assessing changes in drought conditions, as identified by Wilhite et al (2000) This index is determined by evaluating four key characteristics: frequency, duration, severity, and intensity.
Drought frequency refers to the total number of droughts observed during a specific study period, which can also be expressed as the annual or decadal count of droughts In this research, drought frequency is calculated using the formula f = n/N, where 'n' represents the number of months with drought conditions, and 'N' signifies the total number of months over the 41-year study period (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 aggregating the SPI drought index values for each drought event occurring during the dry season, which spans from November of the previous year to April of the current 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, Francis Galton introduced the concept of correlation, which was later refined by Karl Pearson in 1896 through the development of the Pearson Product-Moment Correlation Coefficient (r) This coefficient is essential for evaluating the statistical relationship between two continuous variables, making it one of the most reliable methods for analyzing variable associations Its application is crucial for gathering evidence and forming a basis for assessing the impacts 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 ∑ = ( − ̅)( − ̅)
√∑ = ( − ̅) 2 ∑ = ( − ̅) 2 ̅ and ̅ 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 r equals 1 or -1, it indicates a perfect linear correlation between the two variables (x, y), allowing for the calculation of y for any given x Conversely, an r value of 0 signifies no correlation between x and y According to Cohen (1988), these r values are essential for understanding the strength and nature of correlation.
This study examines the relationship between two variables, positing a null hypothesis (H0) that suggests they are unrelated Conversely, the alternative hypothesis (H1) proposes a significant relationship A p-value of less than 0.05 indicates statistical significance, leading to the acceptance of the alternative hypothesis, while a p-value greater than 0.05 supports the retention of the null hypothesis.
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, while drought severity served as the independent variable Critical salinity levels for paddy survival exceed 4g/l, as noted by Vu et al (2019) and Apel et al (2020) Salinity levels of 8g/l and 12g/l represent severe conditions that significantly impact agriculture The relationship can be mathematically represented by the linear equation: y = 0 + 1 × x.
In a statistical model, the dependent variable (y) is influenced by the independent variable (x), where the slope (0) and intercept (1) indicate that a one-unit increase in x results in a corresponding increase in y by 0 + 1 units This suggests that severe drought conditions, exceeding at least 1 unit, contribute to an increase of 0 + 1 units in saline intrusion or agricultural activities The R-squared (R²) value, which ranges from 0 to 1, is expected to be greater than 0.5, indicating that more than 50% of the variability in y can be explained by changes in x This percentage reflects the degree of independence in the variability or change of the dependent variable as explained by R².
This statistical research on climate utilizes time series analysis, which often leads to the occurrence of autocorrelation in the model To ensure the validity of the findings, a correlation test, specifically the Durbin-Watson Test, is necessary to confirm the absence of autocorrelation in the model.
This research utilized Pearson’s Correlation Coefficient Tests and linear regression analysis, conducted through R-studio, with detailed commands provided in the appendix.
Drought risk assessment
Pearson’s Correlation Coefficient test plays a crucial role in evaluating the impact of drought on saline intrusion and agricultural activities This research focuses on assessing drought risk, which involves calculating the potential consequences and impact levels of drought on these factors The risk assessment process includes a thorough 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 or trends can lead to significant risks, including loss of life, injuries, and health impacts, while also causing damage to property, infrastructure, and livelihoods, as well as affecting service provision, ecosystems, and environmental resources (IPCC, 2014).
Hazard assessment utilized key indicators, including drought severity and salinity levels of 8g/l and 12g/l Drought severity, indicative of water stress, is assessed using total Standardized Precipitation Index (SPI) values below -0.5 during the dry season from November to April Meanwhile, the impact of saline intrusion is evaluated by measuring the area affected by salinity levels of 8g/l and 12g/l, alongside the baseline area impacted by salinity of 4g/l.
According to IPCC, 2014 exposure is defined as:
The presence of individuals, communities, diverse species, and ecosystems, along with vital environmental functions, services, and resources, as well as infrastructure and socio-cultural assets, is crucial in areas that may face negative impacts.
The Drought Exposure Index (DEI), as defined by the IPCC, is calculated using three key factors: the percentage of agricultural land in relation to the total land of a district, the percentage of paddy planted area compared to the district's total land, and the percentage of the rural population These metrics are vital for analyzing drought exposure, as they reflect the extent of human dependence on natural resources A higher DEI indicates an increased risk of drought, highlighting the critical need for effective drought management strategies.
Vulnerability refers to the likelihood of being negatively impacted, encompassing various factors such as sensitivity to harm and the inability to cope and adapt effectively.
The Drought Vulnerability Index (DVI) assessment incorporates three critical socioeconomic indicators: the multi-dimensional poverty rate, which highlights the community's ability to withstand drought impacts (Le et al., 2021); the agricultural contribution to GDP, reflecting local dependence on agriculture (Le et al., 2021); and the percentage of high school graduates, which signifies the population's adaptive capacity through technology, such as smartphones, for drought warning (Lê et al., 2019).
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
Component Indicator Description to overall source drought
Severity of drought Total SPI values under Positive Calculate threshold by author
The weight of rate area Positive Soc Hazard Severity of salinity effect by salinity 8g/l and Trang’s
8g/l base rate (area effect by stations salinity 4g/l)
The weight of rate area Positive Soc Severity of salinity effect by salinity 12g/l Trang’s
12g/l and base rate (area effect stations by salinity 4g/l)
% Agricultural The percentage of Positive Soc Trang agricultural land to the statistical land total land of district, % yearbook
% Planted area of The percentage of planted Positive Soc Trang
Exposure area of paddy to the total statistical paddy land of district, % yearbook
The percentage of Positive Soc Trang
% Population rural Population rural to total statistical population of province, % yearbook
Multi-dimensional The multi-dimensional Positive Soc Trang poverty line based on statistical poverty rate poverty threshold, % yearbook
Agriculture Percentage of GDP, % Positive Soc Trang
Percentage of high Percentage of high school Negative Soc Trang graduates statistical school graduates yearbook
Step 2: Data preparation and indicator estimation
To ensure consistency across various indicators with differing measurement units and scales, normalization of the data is essential This research employs the min-max method to convert 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– normalized indicator value, x i – indicator value for district i, and – 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 1.
Where n: number of Hazard indicators; – 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, – 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; – 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 can be evaluated through various methods, such as expert consultation and the weighted average method However, the average method is considered the most effective approach Consequently, the Drought Risk Index (DRI) was calculated using the average method, incorporating 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 comparability, all indicators must be adjusted to a uniform unit Each indicator is standardized to a common distribution range of 0 to 1 Subsequently, the DRI is categorized into four distinct levels (Dabanli, 2018).
This study utilized ArcMap 10.7.1 to visualize drought risk in the research area, employing secondary data processed within the WGS84/UTM zone 48N geographic coordinate system A base map was developed, incorporating district and province boundaries, which were transformed into shapefiles with added georeferencing Subsequently, DHI and DRI data were inputted 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 variation of drought events across VMRD as determined by the Standardized Precipitation Index (SPI) for all stations The findings reveal that the majority of drought and wetness events at these stations correspond 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 events occurring 19-31 times over a 41-year period, averaging approximately 5-8 droughts per decade, particularly affecting coastal regions like Bac Lieu.
In the VMRD region, including Ca Mau, Can Tho, Moc Hoa, and Soc Trang, drought events occur annually at a rate of 0.46 to 0.76 Notably, Bac Lieu and Soc Trang experience a higher frequency of drought, with 31 and 22 occurrences over 41 years, translating to approximately 0.76 and 0.53 drought events per year, respectively.
Between 1980 and 1999, and from 2000 to 2020, the total number of drought episodes varied from 8 to 28 over two decades A comparison of these periods indicates a slight increase in drought events, with An Giang, Can Tho, Soc Trang, and Ca Mau provinces experiencing an uptick of 1 to 4 additional occurrences.
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–
Between 1980 and 2020, four distinct drought stages were observed, revealing significant fluctuations in drought trends across the study area Notably, the period from 2010 to 2020 experienced a marked increase in drought events, particularly affecting 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.
The analysis of drought frequency revealed a significant occurrence in Soc Trang, An Giang, Dong Thap, and My Tho provinces, with an average drought frequency of 26.17% during the dry season Notably, Soc Trang in November and My Tho in April experienced the highest drought frequencies In contrast, Ba Tri and Rach Gia stations reported more frequent drought events during the rainy season, averaging 30% The impact of droughts is more pronounced in the dry season due to their severity, although they can also occur in wet seasons, as indicated by the SPI index which highlights a lack of precipitation relative to averages This pattern suggests that water shortages may worsen in future dry seasons, leading to increasingly severe drought conditions.
Table 4.2.Drought frequency classify by months in VMRD (1980-2020)
Drought frequency (%) Month Moc Hoa Chau Doc Cao Lanh My Tho Can Tho Soc Trang Bac Lieu Ca Mau Ba Tri Rach Gia
Figure 4.2 illustrates the regional distribution of drought occurrences, highlighting moderate, severe, and extreme drought frequencies across the VMRD Moderate drought events account for approximately 48% of occurrences in this region, indicating the highest frequency of drought events.
35 found (51-60%) in Rach Gia, Soc Trang and Kien Giang province (Fig 4.2) Besides,
My Tho and Chau Doc are two provinces that experienced severe drought events with frequency of 30-35% Moreover, Frequency of extreme drought events with SPI values
< -2 were also observed the highest in My Tho and An Giang provinces.
Between 1980 and 2020, moderate droughts occurred with an average frequency of 48.11%, surpassing severe and extreme droughts, which had frequencies of 25.11% and 28.33%, respectively Notably, the highest incidence of moderate drought was documented in Soc.
Trang and Rach Gia station (51 and 60%).
0 Moc Chau Cao My Can Soc Bac Ca Ba Tri Rach Hoa Doc Lanh Tho Tho Trang Lieu Mau Gia
Moderate Drought Severe Drought Extreme drought
Figure 4.2 Drought frequency in VMRD (1980-2020)
According to Lee et al (2018), moderate drought occurred the highest and extreme drought occurred less than moderate and extreme drought in VMRD for a period of
Between 1984 and 2015, research indicated that extreme drought events surpassed severe drought occurrences, with a longer data series than previously analyzed This suggests that drought conditions are likely to intensify in the future compared to earlier periods.
The analysis of drought events over a 41-year period in the VMRD region reveals an increasing trend in drought duration, particularly in the provinces of Kien Giang, Soc Trang, Rach Gia, and Bac Lieu These provinces, characterized by a less dense river system compared to others, experience longer drought periods, highlighting the impact of geographical factors on drought severity.
Evaporation from oceans and rivers is a key component of the water cycle, significantly influencing rainfall patterns Regions with dense river systems experience higher evaporation rates, leading to increased precipitation Consequently, provinces like Kien Giang, Soc Trang, Rach Gia, and Bac Lieu endure longer drought periods compared to others such as Vinh Long, Tien Giang, and Ben Tre.
Figure 4.3 Drought Severity, Duration and Intensity in VMRD (1980-2020)
The VMRD experiences an average drought duration of 9 months, with the longest recorded drought lasting 20 months Between 1980 and 2004, drought events typically lasted less than 6 months, except for the notable 1990 drought, which extended 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 observed in various years, notably in 2009-2010, 2015-2016, and 2019-2020 The significant drought of 2019-2020 persisted for over a year, suggesting it began in the dry season of 2019 and continued into 2020 Research by Duc et al (2022) highlights that even mild or moderate droughts can endure long enough to cause 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, resulting in a significant water shortage According to Figure 4.3, the drought events in the VMRD region, assessed through the cumulative negative SPI values over 41 years, reveal that the provinces experienced particularly severe droughts during the years 2009-2010 and 2015.
Between 2016 and 2020, drought severity in the VMRD reached alarming levels, peaking in the 2019-2020 period The average drought severity value is typically around 6.48, but Soc Trang province recorded an unprecedented high of 59.93, nearly nine times the average This extreme drought in Soc Trang has significant implications, severely impacting various sectors including agriculture, socio-economics, and the environment.
Relationship between drought and saline intrusion and agricultural activity
Table 4.5 presents the descriptive statistics for the five key variables examined in this research, which explores the relationship between drought and saline intrusion in agricultural activity 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 agricultural land area (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 investigated by analyzing the correlation between drought severity, indicated by a Standardized Precipitation Index (SPI) of less than -0.5, and the extent of land affected by varying salinity levels (4g/l, 8g/l, and 12g/l) The correlation coefficients (r) illustrating this relationship are presented in Figure 4.4 and 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 12g/l c
The findings indicate a positive correlation between drought severity and saline intrusion, as evidenced by high correlation coefficient values in Table 4.6 Specifically, at salinity levels of 4g/l, 8g/l, and 12g/l, the correlation is notably strong.
A strong positive correlation of 0.56, exceeding the 95% confidence level, indicates that as drought severity increases, the area of land affected by salinity (4g/l) significantly rises in the region To further investigate the relationship between drought and saline intrusion on salinity levels, we conducted additional correlation analyses for higher salinity levels of 8g/l and 12g/l The results revealed even stronger positive correlations of 0.567 and 0.617, respectively.
Therefore, we can conclude that at least 50% (square of correlation coefficient) of saline intrusion increase is due to the increasing 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 demonstrated significant correlation coefficients, indicating that changes in drought conditions directly influence saline intrusion Specifically, equations (2) and (3) reveal that a 1-unit increase in drought leads to an increase of 1986.86 hectares affected by 8g/l salinity and 1569.45 hectares affected by 12g/l salinity.
Table 4.6 Result of Pearson's Correlation Coefficients and Linear regression between drought severity and area of land affected by saline intrusion
Variable of p-value Equation coefficient Square correlation (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 correlation coefficient showing p