Introduction
Research framework
Recent years have seen a rise in extreme weather and climate events, notably increasing the occurrence of natural disasters such as floods and severe droughts (Cunha et al., 2019) According to the Centre for Research on the Epidemiology of Disasters (CRED) (Wallemacq), these escalating climate-related events pose significant risks to communities worldwide, emphasizing the urgent need for effective disaster preparedness and climate resilience strategies.
Between 1998 and 2017, extreme weather events and geophysical disasters resulted in the deaths of 1.3 million people and affected approximately 4.4 billion individuals worldwide The majority of these disasters—ranking at 91%—were caused by floods, storms, droughts, and other extreme weather phenomena, highlighting the significant impact of climate-related events on global communities.
Drought is a silent but pervasive hazard resulting from water scarcity, with severe impacts on agriculture, water supply, and the environment, leading to significant economic losses and ecosystem damage It occurs across all climatic zones, affecting natural habitats, ecosystems, society, and the economy worldwide Drought conditions negatively impact vegetation, soil, air quality, and wildlife, increasing the risk of forest fires, land degradation, and forest mortality, while reducing primary productivity and altering biodiversity.
Recent years have seen an increase in the frequency and severity of droughts, driven by changes in rainfall patterns that include more intense rainfall events but fewer wet days These prolonged droughts are becoming more common, especially over longer time scales, significantly impacting agriculture worldwide Studies have documented a notable decline in agricultural productivity across regions such as Asia, primarily due to these increasingly severe drought conditions (Bakker and Downing, 2000; Dahal et al., 2016) For instance, the 2006 Australian drought led to a 36% reduction in the national winter cereal crop, plunging many farmers into financial crises (Wong et al., 2010).
The 2015-2016 drought in Eswatini had severe adverse effects, resulting in a 30% decline in incomes, particularly impacting the agricultural sector (Tfwala et al., 2020) Similarly, many regions in Brazil have recently experienced significant drought conditions, highlighting the increasing frequency and impact of climate-related water shortages worldwide.
Over the past 60 years, some of the most extreme droughts have severely impacted water resources, with notable events in 2014 when a severe drought affected the water supply of 28 million people in southeastern Brazil (Bevacqua et al., 2021; Cunha et al., 2019; Melo et al., 2016).
From 1980 to 2019, the United States experienced 26 drought events, resulting in economic losses of at least $249 billion, with each event averaging over $9.6 billion in damages (NOAA, 2020) A recent study led by UC Merced researchers estimates that the 2021 drought directly impacted California's agriculture sector, causing approximately $1.1 billion in losses and the displacement of around 8,750 full- and part-time jobs (Lorena Anderson, 2022; Medellín-Azuara et al., 2022).
Drought events in Thailand, Cambodia, Laos, and Vietnam severely impact the socio-economic stability of these countries, affecting 85% to 90% of livelihoods in rural communities of the Lower Mekong Basin (MRC, 2019) The region has experienced extreme droughts in 1992, 1999, 2003, and 2015-2016, leading to significant economic losses from crop damage and environmental degradation (MRC, 2019) The 2016 drought alone caused approximately $1.7 billion USD in damages in Thailand, water shortages affecting 18 of 25 provinces, and impacted 2.5 million people in Cambodia In Vietnam, the drought costs were around $669 million USD, with total recovery expenses estimated at approximately $1.5 billion USD, highlighting the severe socio-economic consequences of recurring droughts in the Lower Mekong Basin (MRC, 2019).
El Niño weather events have increased in frequency over the past 50 years, leading to a rise in typhoons, floods, and droughts (Oxfam, 2008) In recent years, southern Vietnam has experienced more prolonged droughts, impacting local agriculture and water resources Specifically, Hoc (2002) reports a series of droughts in the Central Highlands from 1994 to 1998 that severely affected winter-spring crops, highlighting the vulnerability of this region to climate variability.
In 2016, El Niño was identified as the cause of Vietnam’s most severe drought in 90 years, impacting 52 out of 63 provinces and leading to 18 provinces declaring states of emergency (FAO, 2016a) Rising temperatures and reduced rainfall during dry seasons, such as spring and summer in the Southern Central region, spring in the South, and winter in the North, are expected to increase drought frequency and intensity in Vietnam (Tran et al., 2016).
Drought is a complex phenomenon whose precise definition depends on various factors, including hydrological, economic, environmental, and social influences, as well as related processes and impacts (Barbosa et al., 2020) It is primarily caused by lack of precipitation, high temperatures, overuse of water resources, and overpopulation Importantly, drought differs from aridity, a stable climate characteristic, and from water shortage, which occurs when available water resources cannot meet long-term demand (Barbosa et al., 2020) Drought can be classified into four main types: meteorological, hydrological, agricultural, and socioeconomic (Thao et al., 2019; Wilhite, 2000; Wilhite and Glantz, 1985), each with distinct impacts and implications for water management and policy.
Meteorological drought refers to a period marked by significantly below-average precipitation levels for a specific region, disrupting long-term climate patterns (Barbosa et al., 2020) This type of drought is a critical indicator of climate variability and initial stages of drought development Prolonged meteorological drought can progress into more severe drought categories, including agricultural, hydrological, and socioeconomic droughts, impacting water resources, crop production, and community livelihoods Understanding meteorological drought is essential for early warning systems and effective drought management strategies.
Agricultural drought occurs due to meteorological drought characteristics that lead to reduced precipitation and soil moisture shortages These conditions result in limited water availability for irrigation, adversely impacting both natural vegetation and crop production Factors such as discrepancies between actual and potential evapotranspiration further exacerbate water deficits, making agricultural drought a significant threat to food security and crop yields (Aksoy et al., 2018).
Hydrological drought occurs when periods of precipitation deficit lead to a decline in surface and subsurface water supplies, affecting streamflow, reservoir and lake levels, and groundwater tables (Barbosa et al., 2020).
A socioeconomic drought occurs when the demand for essential economic goods and services, such as fruits, vegetables, grains, meat, and hydroelectric power, cannot be met due to a weather-related shortage in water availability This type of drought links the supply and demand of these goods directly to meteorological, hydrological, and agricultural drought conditions (Wilhite, 2000) It highlights how climate variability and water scarcity impact economic sectors, leading to shortages that affect both producers and consumers.
Besides four classical definitions of drought, Crausbay et al (2017) proposed a new type of drought – ecological drought – combining the ecological, climatic, hydrological, socioeconomic and cultural dimensions of drought They
Aims and objectives
Drought is a natural disaster that cannot be prevented, but its damages can be mitigated through effective adaptation and mitigation strategies The extent of drought damage depends on the severity of the drought itself and the interaction between socio-economic and ecological vulnerabilities within vulnerable systems (UNDP, 2021) Understanding livelihood vulnerability to drought is crucial for building resilience and developing appropriate management and policy strategies Additionally, considering local perceptions and coping capacities regarding droughts and climate change is essential to assess their ability to reduce vulnerability When communities accurately perceive risk and possess strong adaptive and coping capacities, drought damages can be minimized; conversely, a lack of risk awareness and limited adaptation capacity can lead to increased vulnerability and greater drought impacts.
In the Central Highlands region, agriculture is the primary income source, playing a vital role in reducing population vulnerability While droughts currently lead to inevitable declines in farmers' incomes, implementing a crop yield forecasting tool represents a crucial step toward anticipating low-production years Such an effective tool can enable Vietnamese authorities to better prepare and support vulnerable populations before humanitarian crises escalate, ultimately helping to mitigate the impacts of agricultural droughts and strengthen community resilience.
This dissertation aims to deepen the understanding of population vulnerability to drought and other extreme weather events in the Central Highlands of Vietnam, with a focus on the impacts of climate change By analyzing the factors contributing to community resilience and risk, the study seeks to inform effective adaptation strategies Addressing climate change's influence on regional weather patterns is crucial for developing sustainable solutions to mitigate vulnerabilities Ultimately, the research emphasizes the importance of targeted policies to enhance the resilience of local populations facing increasing climate-related threats.
The purpose of the study was reached by undertaking the following tasks:
Assessing livelihood vulnerability to drought for selected areas using standard international vulnerability indices
Understanding local people's perceptions of drought and investigating the differences between their perceptions and meteorological recorded data in the selected areas
Performing regional coffee yield forecasting using remote sensing data and statistical yield forecast models.
The dissertation outline
This thesis comprises a set of scientific papers, including one accepted and two submitted for publication Chapter 1 provides the study’s background, objectives, and an overview of the research conducted In Chapter 2, the focus is on assessing livelihood vulnerability to drought in the study area, utilizing the Livelihood Vulnerability Index (LVI) and the Vulnerability Index-Intergovernmental Panel on Climate Change (VI-IPCC) methods This chapter, published in the International Journal of Disaster Risk Science, offers an in-depth analysis of household vulnerability to drought and is based on the structure of the first paper.
Paper I: Nguyen Thi Thanh Thao, Dao Nguyen Khoi, Tran Thanh Xuan,
This study investigates local residents' awareness and perceptions of drought in the Dak Nong province of the Lower Mekong River Basin, emphasizing that community perceptions significantly influence the effectiveness of drought mitigation strategies It compares local perceptions with meteorological data to identify discrepancies and better understand drought dynamics The research includes an analysis of meteorological records, focusing on monthly precipitation and temperature patterns, extreme weather events, and long-term trends Using the Mann-Kendall trend test and Standardized Precipitation Indices, the study assesses drought severity and occurrence, providing a comprehensive overview of climate variability and its impact on local livelihoods.
Paper II: Nguyen Thi Thanh Thao, Dao Nguyen Khoi, Luong Van Viet,
Joost Wellens, Marie Lang, Bernard Tychon: Comparing local people’s
This study explores nine key perceptions of climate change and drought in Dak Lak province, Vietnam, based on scientific observations within the Lower Mekong Basin It highlights how local communities perceive environmental changes and the impacts of climate variability on their livelihoods The research emphasizes the importance of integrating scientific data with local knowledge to enhance climate resilience strategies Findings from this case study contribute valuable insights to the fields of environment, development, and sustainability, underscoring the need for targeted adaptation policies in the Mekong region The paper is intended for publication in the Environment, Development and Sustainability (ENVI) journal, aiming to inform policymakers and stakeholders about the perceptual dimensions of climate change in Vietnam.
To mitigate the impacts of drought on agricultural communities, providing decision-makers with reliable tools such as predictive models is essential for forecasting crop yields and production This enables proactive measures to safeguard livelihoods and implement targeted support strategies In this study, coffee—a primary crop in the region—is the focus, with Chapter 4 presenting an early regional prediction of coffee yield using a combination of statistical methods and satellite remote sensing data, including vegetation biophysical variables like NDVI, LAI, and FAPAR The methodology in Chapter 4 follows the framework established in Paper III to enhance accuracy and applicability of yield predictions under drought conditions.
Paper III: Nguyen Thi Thanh Thao, Dao Nguyen Khoi, Antoine Dennis,
Luong Van Viet, Joost Wellens, Bernard Tychon: Early Prediction of Coffee Yield in the Central Highlands of Vietnam Using a Statistical Approach and Satellite Remote Sensing Vegetation Biophysical
2975; https://doi.org/10.3390/rs14132975 Published: 22 June 2022 Finally, Chapter 5 presents a summary and the conclusions of the study Recommendations for future work are also presented
Figure 1.1 Outline of the thesis
Background of the study and objectives
Chapter 2 Assessment of livelihood vulnerability to drought in the studies area
Calculating the livelihood vulnerability index (LVI)
Calcuating the livelihood vulnerability index – IPCC (LVI-IPCC)
Assessment the vulnerbility of study areas
Chapter 3 Comparing local people’s perception of climate change and drought with scientific observations in the study area
Analyze local people’s perceptions on climate change and related drought Meteorological data analysis (eight precipitation extremes: RX1day, RX5day, R95p, SDII, R20mm, R25mm, CDD, CWD)
Trend analysis of annual, monthly and seasonal rainfall, temprature,
Identify the onset and cessation of the rainy season
Trend of the onset and cessation of the rainy season
Investigate the differences between the perceptions of local people and meteorological recorded data in this area
Chapter 4 Early prediction of coffee yield in the Central Highlands of Vietnam using statistical approach and satellite remote sensing vegetation biophysical variables
Chapter 5 General conlusion and outlook
Assessment of livelihood vulnerability to drought in the
Introduction
Drought is a recurring natural disaster that significantly impacts water resources and socio-economic stability It arises from a substantial hydrological deficit caused by climatic factors like decreased rainfall or human activities such as land-use changes Drought is primarily categorized into four major types: meteorological, hydrological, agricultural, and socio-economic, each affecting ecosystems and communities differently Understanding these types is essential for implementing effective drought management and mitigation strategies.
Droughts, classified into agricultural and socio-economic types based on their impacts, have become more frequent and severe due to climate change, posing significant challenges to socio-economic development, particularly in agriculture-dependent developing countries (Thilakarathne & Sridhar, 2017; IPCC, 2013a) Vulnerable populations, especially those relying heavily on agricultural activities, are at increased risk, highlighting the need for comprehensive vulnerability assessments to inform effective climate change adaptation and disaster risk reduction strategies (Panthi et al., 2016) According to the IPCC (2001), vulnerability refers to the degree to which physical, biological, and social systems are exposed to and unable to cope with the adverse effects of climate variability Assessing vulnerability involves systematic approaches that consider the complex interactions between humans and their environment, encompassing both physical and social dimensions (Hahn et al., 2009).
In recent years, studies on vulnerability assessment in the context of climate change natural disasters have been gained more attentions from scientists
Various approaches to vulnerability assessment include historical narrative, comparative analysis, statistical analysis, indicator-based methods, and agent-based modeling, with the indicator-based approach being the most widely used for evaluating vulnerability to climate change and natural disasters (Mohmmed et al., 2018; Pandey and Jha, 2012; Salik et al., 2015) Over the past decade, the Livelihood Vulnerability Index (LVI) has become a popular and effective tool for assessing farmers' vulnerability to climate change and natural disasters worldwide (Addisu Legese et al., 2016; Panthi et al., 2016; Adu et al., 2018; Oo et al., 2018; Williams et al., 2018) Developed by Hahn et al (2009) based on the IPCC’s definition of vulnerability, the LVI incorporates variables that measure smallholder farmers' exposure, sensitivity, and adaptive capacity to hazards such as droughts and floods The LVI enables researchers to monitor vulnerability over time and space, identify underlying contributing processes, prioritize strategies for vulnerability reduction, and evaluate the effectiveness of these strategies across different social and ecological contexts.
Climate change and natural disasters impact different environments uniquely, as highlighted by Shah et al (2013) According to Panthi et al (2016), the effects of these phenomena vary across regions, making localized vulnerability assessments essential for effective disaster management and climate adaptation strategies.
Vietnam, a tropical and developing country in Southeast Asia, is one of the most vulnerable hotspots affected by climate change and natural disasters such as droughts and floods (IMHEN and UNDP, 2015; IPCC, 2013b) Over the past two decades, Vietnam has experienced approximately 216 natural disasters, causing an annual loss of about 0.55% of its Gross Domestic Product (Eckstein et al., 2017) The years 2015-2016 saw the most severe prolonged drought in the past 90 years, particularly impacting the Central Highlands and severely damaging agricultural production and farmers’ incomes (UNDP, 2016a) This region is vital to Vietnam’s economy as the largest coffee producer, with the country being the world's second-largest coffee exporter However, recent studies indicate that droughts in the Central Highlands are intensifying and prolonging, threatening agriculture and livelihoods in the area (Sam et al., 2018) Poor and farming communities are especially vulnerable to climate change and natural disasters due to their limited adaptive capacity, yet the livelihood impacts of drought on farmers in Vietnam, particularly in the Central Highlands, remain underreported This knowledge gap hampers the development of effective strategies to enhance farmers’ resilience and welfare amid increasing drought risks.
This study aims to assess the livelihood vulnerability of farmers to drought in Krong No District, Dak Nong Province, Vietnam's Central Highlands Five communities—Quang Phu, Nam N’dir, Dak Nang, Duc Xuyen, and Dak D’ro—were selected for investigation due to their high vulnerability during the severe droughts of 2015-2016 The findings will support local governments in developing effective drought adaptation strategies to improve farmers’ resilience and adaptive capacity in the region.
Study area
Krong No District is situated in the Central Highlands of Vietnam, covering an area of 813 km² and characterized by an average altitude exceeding 2,000 meters Located between latitudes 12°15′–12°30′N and longitudes 107°45′–108°05′E, Krong No is a high-altitude district known for its unique geographical features As of recent records, the district has a population of approximately 70,604 residents, contributing to its vibrant local community.
Krong No District in Dak Nong experiences a tropical monsoon climate with distinct dry and wet seasons Rainfall is highly seasonal, concentrating during the monsoon months from April or May to November, with July, August, and September receiving the highest precipitation of up to 320 mm The average annual temperature is approximately 25°C, while during the dry season months of January and February, temperatures drop to around 20°C with minimal precipitation of about 4–5 mm Humidity remains relatively high year-round, averaging around 76%, reaching its peak at 89% in August.
Figure 2.1 Location of the study area
Methodology
This study utilized the Livelihood Vulnerability Index (LVI) and VI-IPCC to assess household vulnerability to drought in Krong No District, Dak Nong Province These indices were selected due to their extensive application in evaluating climate change and disaster vulnerability in various studies (e.g., Addisu Legese et al., 2016; Panthi et al., 2016; Adu et al., 2018; Oo et al., 2018) The analysis highlights the importance of these tools in understanding regional climate resilience, with the study area including the grey-shaded Dak Nong Province and the specific focus on Krong No District.
Williams et al 2018) In the following subsections, we provide the detailed methods of LVI and VI-IPCC used in this study
According to Hahn et al (2009), the Livelihood Vulnerability Index (LVI) encompasses seven key components: sociodemographic profile, livelihood strategies, social networks, health, food, water, and natural hazard-induced disasters and climate variability In this study, the LVI was calculated by assessing these components, each comprising various subcomponents derived from survey data collected from households affected by the 2015–2016 droughts To ensure comparability, subcomponents measured on different scales were first standardized using the Human Development Index (HDI) methodology This comprehensive approach helps identify the most vulnerable households and informs targeted resilience strategies in drought-prone regions.
S max −S min (1) where Sc is the original value of the subcomponent for community c, Smin and Smax are minimum and maximum values reflecting low and high vulnerability of this subcomponent
Table 2.1 Major components and sub-components comprising the Livelihood Vulnerability Index (LVI) developed for 5 communities in Krong No district, Dak Nong province
Subcomponents Unit Explanation of subcomponents relative to LVI
SDP1 - Ratio of dependent people
- Higher value reflects less capacity to adapt
SDP2 - Percentage of female- headed households
% Higher value reflects less capacity to adapt Women typically have less adaptive capacity
SDP3 - Percentage of household heads who have not attended school
% Higher value reflects less capacity to adapt Education makes people more aware and able to adjust to change in environmental conditions
Index which was constructed as the inverse of the number of livelihood activities of
- Higher value reflects more capacity to adapt Income diversification increases adaptive capacity
LS2 - Percentage of households depending only on agriculture as a source of income
% Higher value reflects less capacity to adapt Households depending only on agriculture are more vulnerable
LS3 - Agricultural livelihood diversification index, which was constructed as the inverse of the number of crops cultivated by a household + 1
- Higher value reflects more capacity to adapt
Diverse crops reduce the risk of major losses
Food (F) F1 - Percentage of households depending only on their farming products as a source for food
% Higher value indicates vulnerable Limited source for food
Higher value indicates less vulnerable
F3 - Percentage of households struggling for food Proportion of households reported that they had at least one month struggling for food
% Higher value indicates more vulnerable
SN1 - Percentage of households not having access to communication media (TV/radio, telephone)
% Higher value indicates more vulnerable
Communication media makes people aware of hazard occurrence and having better preparation
SN2 - Percentage of households not having access to local government service
% Higher value indicates more vulnerable
These services strengthen adaptive capacity
SN3 - Percentage of households not having access to funds from government or other organizations
% Higher value indicates more vulnerable
Funds sources strengthen adaptive capacity
Health (H) H1 - Average distance to health facility km Higher value indicates more vulnerable
H2 - Percentage of households with family member with chronic illness
% Higher value indicates more vulnerable
People with chronic illness are more sensitive
H3 - Percentage of households not participating in health insurance
% Higher value indicates more vulnerable
Water (W) W1 - Percentage of households using natural water sources from well or stream
% Higher value indicates more vulnerable
W2 - Percentage of households not having stable water from a water treatment plant
% Higher value indicates more vulnerable
Family with unstable water supply is more sensitive
W3 - Storage water volume of households m 3 Higher value indicates more vulnerable
Drought (D) D1 - Frequency of drought (6- month Standardized Precipitation
% Higher value reflects more exposure
D2 - Mean standard deviation of monthly precipitation
- Higher variability implies higher exposure
D3 –Mean standard deviation of monthly maximum temperature
- Higher variability implies higher exposure a 1 USD = 23.25 VND (exchange rate on 2 September 2019); SPI6 = 6-month standardized precipitation index
After standardizing subcomponents, major component index is calculated by the following equation
𝑀𝑗𝑐= ∑ 𝑛 𝑖=1 𝑖𝑛𝑑𝑒𝑥𝑆 𝑛 𝑐 (2) where n is the number of sub-components in each major components and
Mjc is value of major component j for community c The LVI for community was calculated using the following equation:
∑ 𝑛 𝑖=1 𝑤 𝑀𝑖 (3) where wMi is the weight of each major component, which was estimated by the number of subcomponents that make up each major component
Using a radar chart, we compared the vulnerability levels of each major component across different communities after calculating the major components and the Livelihood Vulnerability Index (LVI) The LVI was scaled from 0 to 1, where 0 indicates the least vulnerability and 1 the most vulnerable, providing a clear visualization of community resilience.
2.3.2 IPCC – Vulnerability index (LVI-PCC)
This study employs the VI-IPCC framework to evaluate livelihood vulnerability, emphasizing three key components: exposure, adaptive capacity, and sensitivity Drought is categorized under exposure, while water, food, and health sectors fall under sensitivity Additionally, the socio-demographic profile, livelihood strategies, and social networks are analyzed as determinants of adaptive capacity, providing a comprehensive assessment of vulnerability (Table 2.2).
Table 2.2 Major components are framed under Exposure, Sensitivity and Adaptive capacity contributing factors to vulnerability
IPCC contributing factors to vulnerability Major components
Exposure Natural disaster and climate variability
Adaptive capacity Socio-demographic profile
This study assesses climate vulnerability by utilizing rainfall data from three rain gauges within the study area to measure exposure Sensitivity is evaluated based on the current conditions of food, water security, and health status in Dak Nong province Adaptive capacity is quantified through socio-demographic profiles, livelihood strategies, and existing social networks The VI-IPCC index is calculated using the same sub-components as the LVI index, employing specific equations to provide a comprehensive measure of climate vulnerability.
The VI-IPCC index is calculated as follows
VI – IPCC = (exposure – adaptive capacity) x sensitivity (4)
The VI-IPCC index ranges from -1 (least vulnerable) to 1 (most vulnerable) IPCC-defined contributing factor (exposure, adaptive capacity and sensitivity) is calculated as below:
CFc represents the contributing factors for community c, as defined by the IPCC, encompassing exposure, adaptive capacity, and sensitivity Each factor is weighted by wMi, which signifies the importance of each major component Mci within the community The variable n indicates the total number of major components considered in each contributing factor, ensuring a comprehensive assessment of community vulnerability.
The study evaluates vulnerability by calculating contributing factors—exposure, adaptive capacity, and sensitivity—alongside the VI-IPCC index These results are visually represented using a vulnerability triangle diagram, enabling clear comparison across two or more study areas Each vertex of the triangle illustrates one of the contributing factors, providing an intuitive understanding of their relative impacts on overall vulnerability This method facilitates effective analysis of climate risk, supporting targeted adaptation strategies.
This study analyzed data from both primary and secondary sources, including monthly precipitation records from three rain gauges at Lak, Duc Xuyen, and Dak Nong stations, collected from 1981 to 2016 by the Vietnam Hydro-Meteorological Data Center (HMDC) To assess drought frequency, the Standardized Precipitation Index (SPI) was employed, with a 6-month timescale chosen for its effectiveness in capturing seasonal meteorological droughts The SPI calculation methodology follows the procedures outlined by McKee et al., providing a reliable measure for drought assessment.
Primary data was collected through a structured household questionnaire survey focused on LVI components and sub-components The questionnaire gathered comprehensive socio-economic, demographic, and livelihood information at both community and district levels in Krong No district, ensuring a thorough understanding of local conditions and factors influencing livelihoods.
A household survey was conducted in April 2016 across five communities in Krong No district, involving 250 randomly selected households, with approximately 50 households from each community—Quang Phu, Nam N’dir, Dak Nang, Duc Xuyen, and Dak D’ro The survey aimed to assess perceptions of drought and climate change, as well as explore adaptive solutions and interventions that stakeholders can implement to mitigate drought impacts The sample size was determined with a 95% confidence level, ±10% precision, and an assumed 50% coverage, using the probability proportional to size sampling method.
The survey involved 20 trained interviewers who conducted interviews with household heads or other experienced members of selected households, each lasting approximately 30 minutes in Vietnamese Data collection focused on gathering information on specific indicators outlined in Table 2.1, with data subsequently inputted, verified, and analyzed using MS Excel 16.0 The primary objective of the survey was to collect comprehensive data on these key indicators to inform research and decision-making.
Results and discussion
The survey data is summarized in Table 2.3, detailing indices across five communes, including maximum and minimum values Table 2.4 presents the Livelihood Vulnerability Index (LVI) for these communities and Krong No district, after standardization and aggregation into seven key components: Socio-demographic profile (SDP), Livelihood strategies (LS), Food (F), Water (W), Health (H), Social networks (SN), and Drought (D) Additionally, Table 3 displays the VI–IPCC results for Krong No district, where the seven components are consolidated into three main factors: Adaptive capacity (SDP, LS, SN), Sensitivity (H, F, W), and Exposure (Drought).
Table 2.3 The result of values of LVI subcomponents for the five communities in the
Krong No District in Dak Nong Province, Vietnam
SDP = Sociodemographic profile; LS = Livelihood strategy; F = Food; W = Water;
H = Health; SN = Social networks; D = Drought
Table 2.4 LVI components calculation for five communes in Krong No district
SDP = Sociodemographic profile; LS = Livelihood strategy; F = Food; W = Water;
H = Health; SN = Social networks; D = Drought; LVI = Livelihood vulnerability index
Table 2.5 VI-IPCC contributing factors calculation for five communities and Krong No district in Dak Nong Province, Vietnam
Adaptive capacity (AC) 0.496 0.631 0.607 0.505 0.586 0.565 Sensitivity (S) 0.586 0.457 0.522 0.564 0.449 0.515 Exposure (E) 0.399 0.387 0.370 0.369 0.367 0.378
The VI-IPCC (Vulnerability Index—Intergovernmental Panel on Climate Change) is scaled and ranges from − 1 (least vulnerable) to + 1 (most vulnerable)
The results show that the vulnerability of Krong No District's five communities is moderate, with LVI and VI-IPCC indices valued at 0.444 and −0.096, respectively, within their respective vulnerability scales of 0 to 1 and -1 to 1.
22 that households of the Quang Phu community are the most vulnerable, followed by Nam N’dir, Dak Nang, Duc Xuyen, and Dak D’ro communities (Tables 2.3, 2.4)
2.4.1 Drought in the Krong No district
Using the Thiessen polygon method to analyze the spatial correlation between station rainfall and drought, the Duc Xuyen and Lak stations are identified as having rainfall closely linked to drought occurrences in the study region The Duc Xuyen station’s rainfall is strongly correlated with droughts across the Dak D’ro community, most of Nam N’dir and Duc Xuyen communities, and parts of Dak Nang community, indicating its significant influence over these areas Conversely, the Lak station’s rainfall shows a close relationship with droughts in the Quang Phu community and the remaining parts of Dak Nang and Duc Xuyen communities Drought severity assessments reveal that areas associated with Duc Xuyen are more heavily impacted by droughts, while Quang Phu, linked to the Lak station, experiences the least drought influence Overall, Krong No District faces a drought frequency of approximately 16%, with over 66% of the area experiencing moderate drought, about 20% suffering severe drought, and nearly 14% subjected to extreme drought, highlighting the urgent need for targeted drought mitigation strategies, especially in regions influenced by the Duc Xuyen station.
Figure 2.2 Drought frequency in three rain gauges in Krong No district
Table 2.6 The result of SPI6 in Krong No district
* SPI drought class classification (T.B McKee et al., 1993)
Household food security in the study area is fragile due to heavy dependence on farming income, with nearly 30% of Krong No District households facing food shortages Our survey indicates that food scarcity peaks between January and May, especially in March, coinciding with the period before harvest (Fig 2.3) During these months, households also experience water shortages for domestic use and irrigation, with the same seasonal peak in March Drought conditions during this critical period severely impact crop yields and water availability, exacerbating difficulties for local residents and jeopardizing their livelihoods.
Figure 2.3 Percentage of households struggling for food and lacking water in Krong No district
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Struggling for food Lacking water
2.4.2 Livelihood’s vulnerability in the Krong No district
Figure 4a highlights the seven major components influencing LVI in Krong No District, with water (0.774) and livelihood strategies (0.661) identified as the primary factors increasing vulnerability The sociodemographic profile of households in Krong No is relatively strong, showing a low vulnerability score (0.168) Improvements over the past 25 years, driven by successful family planning programs, have reduced burdens associated with large families and female-headed households Additionally, the increase in small-sized families and school attendance over the last decade has contributed significantly to mitigating the district's overall vulnerability.
The vulnerability assessment of Krong No District in Dak Nong Province, Vietnam, is visually represented through two key diagrams Figure 2.4a displays the Vulnerability Spider Diagram of the Livelihood Vulnerability Index (LVI), highlighting major components such as sociodemographic profile (SDP), livelihood strategies (LS), food security (F), water access (W), health (H), and social networks (SN) Meanwhile, Figure 2.4b presents the Vulnerability Index-Intergovernmental Panel on Climate Change (VI-IPCC) pyramid diagram, illustrating the primary factors contributing to climate-related vulnerability, including drought (D) These diagrams collectively facilitate a comprehensive understanding of the district’s multifaceted vulnerabilities affecting local livelihoods.
AC = Adaptive capacity; E = Exposure; S = Sensitivity
Water scarcity remains a critical issue in the district, with approximately 70% of households lacking adequate water for domestic use and irrigation during the dry season Our survey highlights that three out of five communities experience complete water deficiency, underscoring the urgent need for sustainable water management solutions Addressing this challenge is vital for improving living conditions and supporting agricultural productivity in the region.
Approximately 25% of households rely on natural water sources without access to a centralized water supply system, making the district highly vulnerable during prolonged dry seasons This vulnerability is compounded by the community’s heavy dependence on farming and persistent rural poverty Households in Krong No predominantly rely on undiversified livelihood strategies, primarily farming and small-scale livestock, leaving them vulnerable even in normal conditions With over 80% of families depending solely on agriculture, droughts cause severe crop failures and income loss, leading to food and water shortages, increased disease, and a cycle of poverty The lack of stable employment further exacerbates their vulnerability, highlighting the urgent need for diversified income sources and improved water infrastructure.
The VI-IPCC index also indicates that the vulnerability of the district is at a medium level (− 0.096) based on the vulnerability scale of − 1 to + 1 The VI- IPCC result is presented in a spider chart (Fig 2.4a) and in different format using three contributing factors (calculated in Eq 4), aggregated from the seven major components in Fig 2.4a, and displayed in a pyramid chart (Fig 2.4b) In general, the adaptive capacity of households just surpasses the average (approximately 0.6), but is not strong enough to respond to the impacts of drought Sensitivity shows that the living standard of the community is still low and needs more support from the government The VI-IPCC indicates that both adaptive capacity (AC) and sensitivity (S) should be taken into consideration during drought mitigation efforts in which sensitivity (water, food, and health) should be prioritized The result suggested that household adaptive capacity also needs to be addressed directly, because community capacity is the key to solving economic, social, and environmental problems Oo et al (2018), in addressing similar issues in Myanmar, stated that lack of households adaptive capacity is a main cause of high vulnerability to the impacts of climate change and disasters Studies in West Africa and in the Himalayas indicated that knowledge (Obayelu et al 2014) and income (Aryal et al 2014) are key factors in determining household adaptive capacity and reducing household vulnerability When
A strong community relies on the integration of knowledge and the economy, empowering society to improve its quality of life through proactive efforts Robust social institutions play a vital role in resilience, enabling communities to withstand and recover from prolonged droughts By combining these elements, societies can enhance their capacity to mitigate drought damages and promote sustainable development.
2.4.3 Livelihood’s vulnerability of five communes
The vulnerability assessment of the five communities in Krong No District highlights Quang Phu (LVI = 0.510) and Nam N’dir (LVI = 0.486) as the most vulnerable, with specific issues identified through detailed analysis Water availability and diversified livelihood strategies are critical concerns across all communities, with Dak Nang, Quang Phu, and Nam N’dir showing the highest water vulnerability, indicated by very high LVI values of 0.884, 0.855, and 0.839, respectively, exacerbated by prolonged dry seasons and droughts Additionally, the communities face high vulnerability in livelihood strategies, especially Quang Phu and Dak Nang, necessitating targeted interventions The social network component reveals that Quang Phu and Nam N’dir lack adequate communication facilities and support policies, making them priority areas for social support, food security, and healthcare enhancements Effective policies tailored for Quang Phu should prioritize water management, livelihoods, and drought mitigation, while for Nam N’dir, the focus should also include strengthening social networks and food security Implementing these strategic policies is essential to mitigate vulnerabilities and improve resilience in these communities.
Figure 2.5 highlights that livelihood vulnerability in the five communities primarily stems from two key factors: adaptive capacity (AC) and sensitivity (S) Adaptive capacity considers household sociodemographic profiles, livelihood strategies, and social networks, while sensitivity addresses water, food, and health components The figure also reveals notable differences between communities, particularly between Quang Phu and Nam N’dir compared to others Rankings from low to high capacity are Quang Phu > Nam N’dir > Duc Xuyen > Dak Nang > Dak D’ro, with Dak D’ro demonstrating the strongest adaptive capacity due to its proximity to the district center, unlike Quang Phu, which is farther from local administrative support The impacts of drought are indirectly reflected through the sensitivity components, with the order from high to low being Quang Phu > Nam N’dir > Dak Nang > Dak D’ro > Duc Xuyen Overall, Quang Phu and Nam N’dir emerge as the primary vulnerability hotspots within Krong No District.
Duc Xuyen Nam N’dir Dak Nang
The analysis of Figure 2.5 highlights the major contributing factors to livelihood vulnerability in Krong No District, Dak Nong Province, Vietnam Key components of the Livelihood Vulnerability Index (LVI) include sociodemographic profile, livelihood strategies, food security, water resources, health conditions, and social networks Similarly, the Vulnerability Index based on the Intergovernmental Panel on Climate Change (VI-IPCC) emphasizes critical factors such as drought impacts and socio-economic resilience These insights identify the primary elements affecting community vulnerability, providing a comprehensive understanding essential for targeted intervention strategies.
AC = Adaptive capacity; E = Exposure; S = Sensitivity
Discussion
Water availability and effective livelihood strategies are the key determinants of vulnerability among the five communities surveyed in Krong No District Most households depend on natural water sources due to the absence of a centralized water supply system, and their livelihoods are predominantly reliant on agriculture, limiting diversification and increasing susceptibility to climate change impacts, particularly water shortages during the dry season High agricultural water reliance exacerbates existing water problems, which are projected to worsen with future decreases in streamflow, especially in dry periods To address drought-related challenges, communities need to explore alternative water sources such as wells, ponds, and rainwater harvesting systems Implementing advanced water management practices—including drip irrigation, irrigation supplements, and stress-tolerant crop varieties—is essential to mitigate current and future water scarcity issues and build resilient livelihoods.
Duc Xuyen Nam N’dir Dak Nang Dak D’ro Quang Phu
The five surveyed communities primarily rely on farming as their main livelihood, making their income highly susceptible to adverse effects from droughts Low livelihood diversification indices (LS1 and LS3) indicate a high vulnerability of existing livelihood strategies, aligning with the findings of Aryal et al (2014) and Oo et al (2018) According to Antwi-Agyei et al (2013), households with more than two income sources are better equipped to withstand vulnerabilities, highlighting the importance of diversified livelihoods To enhance household resilience against drought impacts, it is recommended to adopt livelihood diversification strategies that combine both farming and nonfarming activities.
The LVI and VI-IPCC indices effectively assess household vulnerability across the five study areas, enabling comparison of vulnerability levels between different sites within these regions However, these indices may not be directly comparable to other studies in distant regions due to differing subcomponents and contextual factors Hahn et al (2009) emphasized that the choice of subcomponents significantly influences the outcomes of household livelihood vulnerability assessments to climate change and natural hazards.
The local environment significantly influences the framing and design of subcomponents in vulnerability indices Selecting appropriate subcomponents presents a key challenge in developing effective vulnerability assessments To ensure accuracy and relevance, this study highlights the importance of comprehensive literature reviews, expert consultations, and stakeholder engagement when designing subcomponents for vulnerability indices such as the LVI and VI-IPCC Incorporating these methods enhances the reliability and contextual appropriateness of vulnerability assessments.
Conclusion
This study assesses the livelihood vulnerability of farmers in Krong No District, Dak Nong Province, in Vietnam's Central Highlands, using two key indices: the Livelihood Vulnerability Index (LVI) and the Vulnerability Index based on the IPCC framework (VI-IPCC) The findings reveal that both LVI and VI-IPCC scores indicate a high level of vulnerability among local farmers, highlighting the need for targeted resilience-building strategies in this region.
No district experienced a medium level of livelihood vulnerability due to drought, with vulnerability indices of 0.444 and -0.096 Among the five surveyed communities, the Quang Phu community exhibited the highest overall vulnerability, as indicated by the LVI and VI-IPCC values derived from major components.
This study highlights that drought, with indices of 0.510 and -0.057, significantly impacts the district and its communities, particularly Nam N’dir, Dak Nang, Duc Xuyen, and Dak D’ro Water sensitivity and livelihood strategies are identified as the primary factors contributing to high vulnerability to drought effects To mitigate these risks, the study recommends increasing investment in water management practices and promoting livelihood diversification Future research will evaluate the effectiveness of policy interventions aimed at reducing community vulnerability and enhancing resilience to drought conditions.
This research is funded by the Vietnam National Foundation for Science and Technology Development (NAFOSTED) under Grant Number 105.06- 2013.09
Table S 1 The influence of rainfall station per commune according to the Thiessen Polygon Method
Phu Dak D’ro Dak Nang Nam N’dir Duc Xuyen
Comparing local people’s perceptions of climate change and
Introduction
Climate variability is a global issue, leading to increased unpredictability of extreme weather events worldwide (Eckstein et al., 2019) This growing unpredictability underscores the urgent need for effective climate adaptation and mitigation strategies to protect communities and ecosystems from the adverse impacts of climate change.
According to the IPCC (2018), regional climate change impacts include extreme temperature increases, more frequent and intense precipitation events, and heightened drought occurrences Between 1999 and 2018, over 495,000 deaths and USD 3.54 trillion in economic losses were caused by more than 12,000 extreme weather events worldwide (Eckstein et al., 2019) Drought, as a severe weather event, occurs when prolonged periods of deficient precipitation lead to declining surface and subsurface water levels, disrupting hydrological balances and negatively affecting land resources, ecosystems, and societies (Rezaei et al., 2016).
In recent years, the Lower Mekong Basin, including Thailand, Cambodia, Laos and Vietnam, experienced extreme drought in 1992, 1999, 2003 and 2015–
The 2016 extreme drought resulted in significant economic losses and environmental impacts across Southeast Asia In Thailand, the drought caused USD 1.7 billion in damages, while in Cambodia, 18 out of 25 provinces experienced water shortages affecting 2.5 million people Vietnam also faced substantial costs, with damages estimated at USD 669 million and recovery expenses reaching approximately USD 1.5 billion These events highlight the severe consequences of drought on agriculture, livelihoods, and the environment in the region.
Vietnam is highly vulnerable to climate change and natural disasters such as droughts and floods, causing significant economic losses—approximately 226 disasters between 1999 and 2018 resulted in a 0.47% reduction in annual GDP The 2015–2016 dry season, intensified by El Niño, brought the most severe droughts in 90 years, severely impacting agriculture and farmers’ incomes, especially in the Central Highlands The Central Highlands, a vital part of the Lower Mekong Basin, is Vietnam’s leading coffee producer and second-largest export region, making its climate resilience critical to the national economy Recent studies warn that droughts in the region are expected to become increasingly extreme and prolonged in the near future, posing significant challenges for sustainable development.
Drought continues to threaten agriculture and livelihoods in this region, but its impacts can be mitigated through technological innovations and appropriate adaptation strategies Effective adaptation requires understanding local perceptions of climate change and drought, as farmers' views and responses are central to successful resilience efforts Developing suitable strategies involves recognizing how communities perceive and experience climate hazards, emphasizing the importance of comparing local knowledge with scientific observations Incorporating traditional knowledge and local perceptions can enhance drought risk management, improve preparedness, and reduce vulnerability by aligning adaptation measures with community insights and responses.
The integration of local and scientific knowledge remains limited due to an incomplete understanding of local knowledge systems and the absence of effective approaches and tools to combine both sources This gap hampers comprehensive environmental management and decision-making processes, emphasizing the need for innovative methods to bridge these knowledge systems (Adger and Pulhin, 2014; Kahsay et al., 2019; Kettle et al., 2014).
This study aimed to analyze local residents’ perceptions of climate change and droughts in Dak Lak province, Central Highlands of Vietnam The research focused on three districts—Buon Don (Eanuol, Cuor Knia, and Tan Hoa communes), Cu M’gar (Ea Kiet, Ea Tar, and Ea M’droh communes), and Easup (Eale and Cu Kbang communes)—chosen for their vulnerability to climate impacts Understanding community perspectives in these specific areas provides valuable insights into the impacts of climate change on local livelihoods This research contributes to developing targeted strategies for climate adaptation and resilience in the Central Highlands region.
The 2015–2016 drought notably impacted 34 areas, highlighting the severity of the climate crisis in that period (FAO, 2016b) A study comparing local residents’ perceptions of climate change and droughts with meteorological data aims to provide valuable insights for developing effective drought-adaptation strategies Such research supports local governments in enhancing the adaptive capacities of communities most vulnerable to climate variability, ultimately strengthening resilience against future drought events.
Study area
Dak Lak province, located in the Central Highlands of Vietnam within the Lower Mekong River Basin, spans 13,125 km² and has a diverse population of 2.127 million people (2019), comprising 44 ethnic groups, with the Kinh ethno group making up 70% The remaining 30% includes ethnic minorities such as Ede, M’nong, Thai, Tay, and Nung Agriculture is the primary livelihood in Dak Lak, characterized by a climate that varies with altitude—from hot and humid at 400–800 meters to cooler heights above 800 meters—and seasonal rainfall impacts its agricultural productivity The region experiences two distinct seasons: a rainy season from May to October with 80–85% of annual rainfall, and a dry, sunny season from November to April accounting for 15–20% of annual precipitation Geographically situated between 107°28'57"–108°59'37" E longitude and 12°9'45"–13°25'06" N latitude, Dak Lak's elevation ranges from 400 to 800 meters, supporting perennial crops such as coffee, pepper, cashew, and fruits, as well as seasonal crops like rice, maize, sweet potato, vegetables, sugarcane, groundnut, and soybean.
Figure 3.1 Location of Dak Lak province and the three surveyed districts
Methodology
During March and April 2019, field surveys and data collection were conducted at the commune level in Dak Lak province to assess climatic phenomena and their effects on local farming practices The structured questionnaire gathered socio-economic, demographic, and agricultural information across eight communes in three districts—Buon Don, Cu M’gar, and Ea Sup—particularly focusing on climate change, drought events, and adaptation strategies implemented to mitigate drought impacts in vulnerable areas affected by the 2015–2016 drought Prior to data collection, the questionnaire was tested with ten respondents and reviewed by experts to ensure clarity and accuracy A total of 354 households were surveyed, selected based on the population size of each commune, using a probability proportional to size sampling method at a 95% confidence level with a 5.21% margin of error, to provide reliable insights into drought resilience and farming adaptations.
A total of 36 households were selected through simple random sampling, with each respondent interviewed in Vietnamese for approximately 30 minutes The number of households targeted in each commune, as shown in Table A.1, was based on the population size of each area to ensure representative sampling In cases of non-response, interviewerscontinued with the next household to meet the target number in each commune The survey data analyzed in this research is presented in Table A.2, providing comprehensive insights into the study population.
The survey data were analyzed using Microsoft Excel 2010 for descriptive statistics, with missing data addressed through simple deletion (Acock, 2005) To better understand household profiles and local perceptions of drought-related climate change, Barnard’s exact test (Erguler, 2016) was conducted using RStudio (version 3.6.2) The analysis suggests that higher education and literacy levels enable individuals to access diverse information sources, leading to a better understanding of climate change and drought issues (Habiba et al., 2012; Manandhar et al., 2015) A statistically significant difference at the 95% confidence level (p-value < 0.05) indicates notable disparities in perceptions based on education and literacy.
The null hypothesis was rejected at a significance level of ≤ 0.05, indicating significant differences The study compared local people's perceptions with meteorological data analysis to evaluate discrepancies between community experiences and measured climate change data related to drought and extreme events.
This study analyzed meteorological data from 1981 to 2018, including daily precipitation records from three rain gauges (Buon Me Thuot, Ban Don, and Buon Ho) and temperature data from the Buon Ma Thuot weather station, sourced from the Vietnam Hydro-Meteorological Data Centre The data were used to evaluate monthly distribution patterns of precipitation and temperature, identify extreme precipitation events, and analyze long-term trends in both variables The Mann-Kendall trend test, conducted with RStudio version 3.6.2, was employed to detect significant trends in the meteorological data over the study period.
‘Kendall’ package (McLeod, 2011) In addition, descriptive statistics such as minimum, maximum, median and mean values, standard deviation (STD),
37 coefficient of variation (CV), skewness and kurtosis were analysed to better understand the data characteristics Furthermore, precipitation extremes (Table 1) were estimated using Rclimdex 1.1 (version 1.0) (Wang and Feng, 2004)
Table 3.1 List of eight precipitation extremes used in this study
Types Indices Name Definitions Unit
RX1day Max 1-day precipitation amount
Monthly maximum 1-day precipitation mm
RX5day Max 5-day precipitation amount
Monthly maximum 5-day precipitation mm
R95p Very wet days Annual total PRCP when precipitation >95 th percentile mm
SDII Simple daily intensity index
Annual total precipitation divided by the number of wet days mm/day
R20mm Number of heavy precipitation days
Annual count of days with daily precipitation ≥20mm days
R25mm Number of very heavy precipitation days
Annual count of days when precipitation ≥25mm days
CDD Consecutive dry days Maximum number of consecutive days with precipitation