From National to Local: A Comprehensive Vulnerability Assessment Framework for Climate Change Adaptation in Bhutan

doi:10.21203/rs.3.rs-5037109/v1

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From National to Local: A Comprehensive Vulnerability Assessment Framework for Climate Change Adaptation in Bhutan

https://doi.org/10.21203/rs.3.rs-5037109/v1

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Climate change has evolved into a dire global crisis, affecting communities and nations worldwide. Bhutan, a small landlocked country in the fragile eastern Himalayas, faces significant challenges due to climate change. To effectively plan for climate change adaptation, it is crucial to assess local vulnerabilities and understand community-specific needs at a finer spatial level. National-level studies often fail to capture the location specificity of smaller areas, highlighting the need for localized assessments. Therefore, this study focuses on the climate change vulnerability assessment of nine Southwestern districts in Bhutan. It integrates quantitative analysis with qualitative information obtained from primary field surveys. The primary objective is to mainstream climate change adaptation and mitigation measures into the developmental plans of these districts based on this detailed vulnerability assessment. Another key objective is to prioritize adaptation strategies for enhancing community resilience in areas highly vulnerable to climate change. The study assesses communities' exposure, sensitivity, and adaptive capacity using 40 indicators (six for exposure, 14 for sensitivity, and 20 for adaptive capacity) to compute vulnerability indexes. The results indicate that Dagana (-0.72) and Sarpang (-0.71) districts are the most vulnerable, due to their high exposure and sensitivity. Conversely, Tsirang (-0.35), Thimphu (-0.36), and Haa (-0.38) districts are less vulnerable, attributed to higher adaptive capacity. The study recommends incorporating climate change adaptation and mitigation measures into district development plans. Prioritizing adaptation strategies based on district-specific vulnerabilities is essential for enhancing community resilience. This research contributes to informed decision-making and effective climate change adaptation in Bhutan.

Climate Analysis and Modeling

Climate Change

Vulnerability assessment

Eastern Himalayas

Adaptation strategies Exposure

Sensitivity

Resilience

Green House Gas emissions from industrial developments coupled with the loss of forests have led to global warming (FAO, 2010). The global average temperature has risen by almost 0.9°C in the past century and is projected to rise by 2 to 3°C in the next hundred years (Stocker et al., 2013). In 2020, the global mean temperature was 1.2 ± 0.1°C above the 1850–1900 baseline (WMO, 2020). The earth is predicted to be 1.4–4.4°C hotter than pre-industrial levels by the end of this century, as per the IPCC Sixth Assessment Report (AR6) summary for policy makers (IPCC, 2021). Asia-Pacific is the most disaster-prone region in the world as it is more vulnerable to climate change risks than other regions, given its dependence on the natural resources and agriculture sectors, densely populated coastal areas, weak institutions, and a considerable proportion of its population under poverty (Anbumozhi et al., 2012). The Himalayan ecosystem is also changing rapidly and is expected to exacerbate with the predicted increase in mean temperature by 0.3–4.8°C by 2100 (IPCC, 2013). Over-exploitation of natural resources coupled with unsustainable development and remoteness of the communities lowers the resilience of the communities (Gerlitz et al., 2017). Bhutan is a small, landlocked, developing country with a total area of 38,394 km² and a population of 745000 (PHCB, 2017). Characterized by rugged mountainous terrain, the landscape elevation ranges from 160 meters at the foothills to more than 7000 meters in the mountains. Due to limited economic development, low population pressure and strong commitment to sustainable development, the country still has extensive forest cover, approximately 69% of the land area (DoFPS, 2023). Despite a large forest reserve, Bhutan continues to be impacted by climate change and its impacts. Bhutan is located in the Himalayan ranges, and the warming trend is expected to be more than 0.3°C (Wester et al., 2018). Climate models under RCP4.5 show a likely increase in Bhutan’s annual average temperature by 0.8°C-1.6°C from the year 2021 to 2050 and by 1.6°C-2.8°c during 2070–2099. Seasonal changes during the year 2021–2050 show an increase in the range of 0.9°C – 1.8°C all over Bhutan. More significant warming is indicated during the spring (MAM) and winter (DJF) seasons. The country is expected to experience an increase in temperature, with a more considerable increase projected in the highlands. Under the RCP 8.5 scenario, an increase in annual mean temperature of 0.8°C – 2.0°C is expected all over Bhutan between the year 2021–2050, with increases of more than 2.8°C towards the end of the century (2070–2099). More pronounced changes can be noted towards the end of the century in the range of 3.2°C – 5.6°C (NCHM, 2019).

1.1 Climate and Agriculture on Bhutan

The southwestern monsoon dominates the landscape’s climate, originating from the Bay of Bengal, accounting for 60–90% of the annual precipitation (Sharma et al., (2021). Generally, the monsoon starts in June and lasts until early September. The landscape has a total population of 441371, which is about 60 % of the country's ttal population (NSB, 2023). About 62 % of the population ives in rural areas practicing subsistence farming with minimal landholdings (PHCB, 2017). The warm and favorable climate along the middle valleys also harbors some of the most fertile and productive agricultural lands in the country. Agriculture and livestock farming are the main livelihood activities of people living within the landscape. Over the years, it has evolved, characterized by the diversity of ecological conditions and a high degree of self- reliance. Agriculture is labour intensive with a relatively low intensity of use of external farm inputs. Among the holdings using agricultural inputs, most of the households use farmyard manure or compost. Farm mechanization is still low; about 50 % of the holdings use at least oe or more types of machines or equipment (Wangdi, 2022).

1.2 Climate change impacts in Bhutan

In recent years, the landscape has also been impacted by varying degrees of natural disasters, notably floods, soil erosion, drying drinking and irrigation water sources, and bird flu. Human-wildlife conflicts within the landscape are a prominent and ensuing threat to the livelihoods of communities. Elephants in the south, Tiger in the central, and Bears in the west are the main conflicting wildlife species causing large-scale property, livestock, and crop depredation in the landscape (Wangdi, 2022). Human casualties and deaths due to HWC (human-wildlife conflicts) are reported annually from the region. Although HWC is prevalent throughout the country, the southern region suffers heavily from crop and property damage by elephants. The damages caused by elephants have contributed to economic loss for farmers in Southern Bhutan. Among the Southern Districts affected by the Human Elephant Conflict (HEC), Sarpang is the most affected district in the region, with about 263 conflict cases reported from twelve of the fourteen gewogs from the period 2016–2017 (Wangdi, 2022). There are also multiple reports on decreasing cash crop yield, especially orange and cardamom within the landscape, which has significantly impacted the communities' income. Damages to infrastructures like roads, irrigation channels, and living houses due to heavy rainfall in the monsoon within the landscape are one of the highest in the country annually.

1.3 Rationale and objectives

Climate Change has already led to significant changes in the country’s natural environment through increasingly variable and intense precipitation patterns and higher temperatures contributing to more frequent and intense extreme weather events. This, in turn, has led to increased frequency and intensity of hazards like droughts, floods, and landslides, alongside slower onset changes like retreating glaciers, changes in plant and animal phenology, vegetation composition, and species migration patterns. The most vulnerable sectors and resources to these effects are water resources, agriculture, forests and biodiversity. Although water resources are abundant, a recent report on drying up springs and water sources indicates that about 0.9% have already dried up, while 25.1% are drying in Bhutan. Impacts on natural resources and on dependent livelihoods are particularly significant and acute for local rural communities. Bhutan is predominantly an agrarian economy, with more than 62.2% of the population living in rural areas (PHCB, 2017). Nearly 67% of the total households in the country are in rural areas, and female heads make up 32.9% of households in the country. Approximately 39% of the rural population are illiterate without any formal education. The average landholding is 2.7 acres per household, and 34% of the households do not own any land (NSB, 2022). Most farms are small to medium-sized and family-owned, with a large portion of the agricultural production covering subsistence needs. Poverty is largely a rural phenomenon, with 87% of all poor individuals living in rural Bhutan (NSB, 2023). Therefore, rural communities in Bhutan's rural areas are particularly highly vulnerable to climate change impacts.

Though climate change is a global phenomenon, its manifestation and impacts vary locally, and so do the adaptation capacities of the communities. For effective planning for climate change adaptation, an assessment of local vulnerabilities is required to know the community's needs and priorities at the local level. Studies done at the national level do not adequately capture the location specificity of smaller areas. Therefore, there is a need for detailed investigations at a finer spatial level. There are very few studies into vulnerability analysis in Bhutan at the national/regional level or at the household level. In this direction, this study focuses on the climate change vulnerability assessment of nine southwestern districts of Bhutan by integrating quantitative analysis with qualitative information obtained from primary field surveys.

Given the increasing impacts of Climate Change globally and regionally, the primary objective of this study is to assess the Climate Change Vulnerability of rural communities residing in nine Southwestern districts of the country. The objective of this study is also to mainstream climate change adaptation and mitigation measures into climate-smart sustainable management plans based on the findings and observations of the vulnerability assessment. The following are the main objectives of the Climate Change Vulnerability Assessment.

Assess the vulnerability of nine Southwestern districts of Bhutan based on social vulnerability.

Assess community-based capacities for coping and adaptation measures and identify key entry points for intervention.

Prioritize and recommend adaptation strategies for the enhancement of community resilience to climate change.

2.1 Study Area: Location

The Climate Change vulnerability assessment was conducted in rural areas of nine Southwestern districts of Bhutan falling outside the country's Protected Area system (Fig. 1). The nine districts further comprise 85 administrative blocks known as Gewogs, which are the smallest unit of administration in the Bhutanese governmental structure. The nine districts are Samtse, Chhukha, Sarpang, Tsirang, Dagana and Zhemgang, Thimphu, Paro and Haa, covering a total area of 9835 km^2, which is approximately 27% of the total geographical area of the country. Thimphu, Paro and Haa districts are in the extreme Western part of the country with a predominantly temperate climate, while the remaining districts are located along the lower foothills of the southern belt with a predominantly subtropical to tropical climate. With average elevation ranging from 97 to 5720 masl, the landscape cuts across all the six major agro-ecological zones of Bhutan, from wet subtropical in the south with relatively high rainfall to dry alpine meadows which remains mostly under snow cover around the year. The variation in climate, weather and forest ecosystems along the landscape is significant due to changes in microclimates along the landscape.

2.2 Land Use Types

With about 83% of the total landscape area under forest cover, forest is the major land use type in the study area. Only about 5.6% of the total landscape area is under agriculture, comprising both wetland and dry lands. About 1% of the total landscape area is under water bodies, including rivers, streams, lakes, water catchment areas and wetlands (NFI, 2023). With forest as the major land use type in the landscape, it comprises of eight different types of forest, viz.: Warm Broadleaved Forest, Cool Broadleaved Forest, Subtropical Forest, Fir Forest, Evergreen Oak Forest, Hemlock Forest, Blue pine Forest and Chirpine Forest. Warm Broadleaved Forest is the predominant forest type in the landscape, followed by Cool Broadleaved Forest and Subtropical Forest (Table 1).

Table 1

Land use types and forest types of the study areas
Land use Types	Area (Ha)	%	Forest Types	Area (Ha)	%
Forests	815886.23	83.1	Warm Broadleaved Forest	265391.03	33.67
Shrubs	62223.24	6.3	Cool Broadleaved Forest	136549.3	17.32
Cultivated Agriculture	55423.52	5.6	Subtropical Forest	127064.75	16.12
Meadows	16862.64	1.7	Fir Forest	89029.4	11.29
Alpine Scrubs	9895.93	1	Evergreen Oak Forest	79788.41	10.12
Water Bodies	9615.62	1	Hemlock Forest	45128.1	5.73
Rocky Outcrops	4699.44	0.5	Blue Pine Forest	42367.95	5.37
Built up	4110.53	0.4	Chirpine Forest	2927.49	0.37
Landslides	1583.1	0.2
Snow and Glacier	572.84	0.1
Non-Built up	354.5	0

Forests play a vital role in the livelihood of the landscape population and are intrinsically intertwined with the social, cultural and traditions of the communities. The landscape has a total of 357 Community forests, which are solely managed by the communities to meet their tangible and intangible livelihood needs (DoFPS, 2022).

2.3 Climate data of the study area

The study of climate science and research is still a challenge for Bhutan despite the efforts to generate observation data from the 1990s. It is mainly attributed to the paucity of historical climate data coupled by a lack of robust observational network, resources, technology, and capacity to undertake climate research (NCHM, 2019). For this study, the freely available climate datasets from the Regional Database System (RDS) of the International Centre for Integrated Mountain Development (ICIMOD) were used to understand the historical temperature and future climate projections of the study area. The RDS of ICIMOD is a one-stop data portal for the Hindu Kush Himalaya, which ensures the integrated management of centre-wide data and information incorporating geospatial, socioeconomic, and multi-thematic data at different levels. Additionally, global datasets of WorldClim (https://www.worldclim.org/) have been used to understand the historical precipitation of the study area.

2.4 Historical Temperature and Future Climate Projection

CMIP6 climate datasets at a spatial resolution of 100 km are widely used for future climate projections. However, for the purpose of this study, the HI-AWARE high-resolution Reference Climate Dataset for the Indus, Ganges and Brahmaputra River Basins under RCP 4.5 (Representative Concentration Pathways 4.5) was used for future climate projection of the study area.

Table 2

Projected mean temperature increases from historical mean temperature
Historical Temperature (1981–2010) in °C				Projected Future Temperature (2011–2050) in °C
Districts	Mean Temp	Maximum Temp	Minimum Temp	Mean Temp	Max Temp	Min Temp	Mean temp. increase from historical mean temp.
Dagana	7.52	14.54	-3.60	8.26	14.38	0.34	0.73
Gedu	7.43	14.71	-3.70	8.21	14.39	0.15	0.79
Paro	2.63	10.97	-10.01	3.50	10.29	-5.15	0.88
Samtse	10.22	17.53	-0.73	11.36	17.10	3.44	1.14
Sarpang	8.56	15.48	-2.67	9.23	15.47	1.30	0.67
Thimphu	1.30	9.44	-11.46	2.49	9.26	-6.15	1.19
Tsirang	9.38	16.05	-1.66	10.65	16.52	3.02	1.27
Zhemgang	12.20	19.01	1.15	12.84	19.02	5.17	0.64
Haa	8.82	16.25	-2.41	9.31	15.23	1.23	0.49

The data is a bias-corrected future climate dataset for daily mean air temperature (Tavg), daily maximum air temperature (Tmax), daily minimum air temperature (Tmin) and daily precipitation (P). This dataset has a daily temporal resolution spanning 90 years from 1st January 2011 to 31st December 2100 and a spatial resolution of 5 x 5 km. The projected mean temperature increase ranges from 0.49°C (lowest) to 1.27°C (highest) as compared with the historical mean temperature (Table 2). This indicates continued warming in the immediate future.

2.5 Data collection

For this assessment, a comprehensive socio-economic survey based on a pre-designed survey questionnaire was conducted across 7173 households, which represents approximately 25% of households from each district of the study area. This robust sampling approach ensures that the study’s findings reflect a diverse cross-section of the population. The survey respondents comprised 60% (4295) male and 40% (2878) female. Approximately 72% of the respondents are above 60 years of age, while around 22% are between 30 and 59 years. Only 6% of the respondents were below 29 years old. Nearly 33% of the households are headed by women while about 70% of the households are headed by the male. Women head more than 60% of the households in Paro and Thimphu District, unlike in other districts where most of the households are headed by males (Table 3).

Table 3

Number of households surveyed with male and female respondents
Districts	Female	Male	Total Survey respondent (n)	% of Female-headed household
Dagana	332	633	965	34
Chhukha	325	701	1026	32
Paro	613	346	959	64
Samtse	305	1484	1789	17
Sarpang	172	459	631	27
Thimphu	235	159	394	60
Tsirang	177	713	890	20
Zhemgang	195	217	412	47
Haa	24	83	107	22
Total	2378	4795	7173	33

2.6 Methodology

2.6.1 Climate Vulnerability Assessment and Conceptual Framework

The concept of climate change vulnerability is complex and multifaceted, encompassing exposure, sensitivity, and adaptive capacity (Laitonjam, 2018), leading to a multitude of definitions and interpretations of the term vulnerability with no one universally accepted definition of vulnerability (MoEFC, 2014). Vulnerability is influenced by a range of factors, including environmental, socioeconomic, institutional, and political structures (Pricope et al., 2013). The one-size-fits-all vulnerability label is not sufficient. Measuring vulnerability is particularly misleading, as this is impossible and raises false expectations. It is important to note that vulnerability is a theoretical concept and cannot be directly measured or observed (Hinkel, 2011). The only consensus that seems to exist is that vulnerability is bound to a specific location and context. Further, all vulnerability assessments are relative and not absolute (Adger et al., 2003). The IPCC Third Assessment Report (TAR) describes vulnerability as “The degree to which a system is susceptible to, or unable to cope with, adverse effects of climate change, including climate variability and extremes” (IPCC, 2003). This concept of IPCC is adopted for the purpose of this assessment, which was used for such similar studies (Hahn et al., 2009; Kumar et al., 2016; Lung et al., 2013; Metzger et al., 2006).

CV = f (E, S, AC) ………………………………………………………………………... Eq. (1)

Where CV (Vulnerability) is expressed as a function of exposure (E), sensitivity (S) and adaptive capacity (AC) (Brooks et al., 2005; IPCC, 2003; KC et al., 2015). It is used primarily to refer to the vulnerability of climate change impacts.

Exposure is defined in these reports as the nature and degree to which a system is exposed to significant climatic variations. Sensitivity is the degree to which a system is affected, either adversely or beneficially, by climate-related stimuli. The effect may be direct, such as a change in crop yield in response to a change in mean, range, or variability of temperature, or indirect, such as damages caused by an increase in the frequency of coastal flooding due to sea level rise (Brooks, 2003). Adaptive capacity is the system's flexibility to adjust to climate change and cope with the consequences. The system's capacity to adapt to the system depends on the ownership and access to assets (Piya et al., 2016). Vulnerability is influenced by adaptive capacity, which has an inverse relation with vulnerability (Giupponi et al., 2019). The conceptual framework for assessing vulnerability and developing adaptation measures is represented in Fig. 2.

Each district was considered as the system of interest for which climate change vulnerability was assessed. The individual blocks (Gewogs) of the districts were selected as the assessment’s unit of measurement. Three conceptual approaches of vulnerability are typically used: socio-economic (Neil Adger, 1999), biophysical (Füssel & Klein, 2006) and integrated approaches (Nelson et al., 2010; O’Brien et al., 2004; Piya et al., 2016). The socio-economic approach involves analysis of society's social, political and economic aspects (Choden et al., 2020). It is associated with the well-being of individuals, communities and society (UNISDR, 2004). Thus, this method was employed in this study

2.6.2 Selection and description of indicators

Vulnerability assessments can be qualitative or quantitative, which include indicator-based and econometric-based methods (Choden et al., 2020; Gerlitz et al., 2017; Maiti et al., 2015). The ‘econometric method’ analyzes the level of vulnerability of different social groups using household-level socio-economic survey data, while the ‘indicator method’ selects the indicators from potential indicators and systematically combines them to indicate the level of vulnerability (Deressa et al., 2008). Numerous studies used ‘indicator method’ to assess social vulnerability to climate change (Brooks, 2003; Choden et al., 2020; Hahn et al., 2009; Maiti et al., 2015; Nelson et al., 2010; Piya et al., 2016). Similarly, the indicator-based method was applied for the present assessment. The indicators for exposure, sensitivity, and adaptive capacity were identified and listed in the literature on similar studies (cf. Maiti et al., 2015; Choden et al., 2020). Consultations with experts from Ugyen Wangchuck Institute for Forestry Research and Training and field officials of the study area were also held in a series of meetings and workshops to finalize the indicators. The indicators were thoroughly discussed based on their potential impacts and contribution to exposure, sensitivity and adaptive capacity.

Following O’Brien et al. (2004), exposure is represented as “either long-term changes in climate conditions or by changes in climate variability, including the magnitude and impacts of extreme events.” This definition includes climate change and extreme events, which form the central focus of this study. Perception of historical changes in climate variables, occurrence of extreme events and its impacts were taken as the indicator for the exposure (Piya et al., 2016). The climatic variables include temperature extremes and their impacts, shifts in rainfall seasonality, and occurrence of extreme events. Climate-related extreme events are landslides, flashfloods, Glacial Lake Out Burst Floods (GLOF), windstorms, occurrence of seasonal droughts and heat waves. Large scale destruction of agricultural crops due to windstorms is also reported from most parts of the study area annually. Since most of the households in the study area practice rainfed agriculture, seasonal droughts caused by delay or absence of rainfall in monsoon seasons significantly impact agriculture food production, particularly paddy cultivation, one of the main staple foods in the study area. These indicators indicate the extent of exposure of the communities as a whole and are applicable across multiple sectors.

Sensitivity is measured by the “degree to which a system is modified or affected by an internal or external disturbance or set of disturbances” (Gallopin, 2003). This approach to sensitivity considers the cumulative impacts of past climate hazards on livelihoods as a proxy for future sensitivity, as the households facing higher impacts are the ones that will be more sensitive in the future. For this study, climate-sensitive sectors like agriculture, water, livestock, forest, health, and infrastructures were used to determine their sensitivity. Decrease in crop yield, increase in incidences of pests and diseases, increased infestations by invasive weeds both in agricultural and forest lands, change in forest cover and compositions over the last ten years, changes in the availability (volume) of irrigation and drinking water, change in the wildlife population, death of family members due to vector or water-borne disease represent sensitivity for the purpose of this study. It is hypothesized that higher impacts of past climatic disasters increase the sensitivity of households and, more so, to poorer sections of the community. Changes in wildlife populations were considered sensitivity indicators, specifically from the point of view of human wildlife conflict (HWC). Several studies have shown that climate change is one of the drivers of HWC, causing changes in plant phenology and shifts in habitat. A study by Abrahms (2021) highlighted that climate change impacts the availability of resources, thereby causing the congregation of wildlife and people to crowded spaces, causing conflicts.

The adaptive capacity of the households was considered as the summation of five types of livelihood assets physical, human, social, natural and financial (Gerlitz et al., 2017; Maiti et al., 2015; Piya et al., 2016). A diversified asset would allow for substitution among the assets to switch from one livelihood activity to another with the changing impacts of climate change (Piya et al., 2019a). Physical assets are represented by four indicators viz, access to socio-economic facilities, access to basic household facilities, access to road, and communication media. Access to these facilities enhances the adaptive capacity of households to cope with the impacts of climate change. The ownership of a mobile phone/radio/television increases adaptive capacity through access to weather-related information. Access to roads is assumed to be proportionately related to adaptive capacity as households located far from roads will be at a disadvantage for reasons such as lack of opportunity for income generation due to lack of markets or inability to access service centers such as hospitals at a time of emergency (Piya et al., 2019). There are four indicators under human assets viz, literacy of the head of the household, vocational skills of the household members, gender of the household head and awareness of climate change adaptation. Labor exchanges, which is an age-old practice followed in rural villages, increase the adaptive capacity as it not only enhances the social bond in the community but also meets the labour requirements at minimal costs as opposed to hiring and mechanization. There are seven indicators under natural assets: household landholding, the status of forest and wetland conditions surrounding the households, availability of different types of forest resources, availability of alternative sources of drinking and irrigation water and food self-sufficiency of the households, all of which increases the adaptive capacity of households. Financial assets include access to credit facilities and availability of savings. A total of 6 indicators for exposure, 14 indicators for sensitivity, and 21 indicators for adaptive capacity were used to assess the vulnerability (Table 4).

Each finalized indicator was then quantified by providing and deciding on a measurable parameter. Most indicators were given a score of 0 to 2 based on the impacts caused to the communities. The scoring was decided jointly through consultation with field officials representing respective districts. A detailed description of the indicator is given in Table 4.

2.6.3 Vulnerability Analysis

The latent variables (Exposure, Sensitivity, and Adaptive capacity) were captured based on indicators by constructing a confirmatory factor analysis (CFA) model using the lavaan package in R. CFA was used for this study as all the variables are categorical. One factor CFA was employed with the assumption that each latent variable associated with indicator variables is a reliable estimate measure of respective latent variables. All the indicators used to estimate the latent variables are fundamental elements in a CFA, and the covariance between the observed variables forms the fundamental components of the CFA. The observed population covariance matrix 𝛴 is a matrix of bivariate covariance that determines how many total parameters can be estimated in the model. The model implied matrix 𝛴(𝜃) has the same dimensions as 𝛴. The model-implied covariance is defined as:

𝛴(𝜃) = 𝐶𝑜𝑣(𝑦) = 𝛬𝛹𝛬^′ + 𝛩 𝜖 ……………………………………………………………. Eq. (2)

This means that theta 𝛩 is composed of parameters 𝛬, 𝛹, 𝛩, which corresponds to the loadings, the covariance of latent variables and the covariance of residual errors. For estimation of this model, the marker method was used, whereby it fixes variance of each factor to one but freely estimates all loadings. The constructed model was diagnosed for robustness using RMSEA and observed the value of 0.061 and a P-value less than 0.001, indicating that the model is robust. The robust model is used to predict latent variable values for each observation. The predicted values are then normalized to bring the values within the comparable range. The min-max method was used for data normalization, where the formula (Value-Min)/(Max-Min) was used. The following formula was used to determine the vulnerability index:

𝑉𝑢𝑙𝑛𝑒𝑟𝑎𝑏𝑖𝑙𝑖𝑡𝑦 (𝑉) = 𝐴𝑑𝑎𝑝𝑡𝑖𝑣𝑒 𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦 (𝐴𝐶) − (𝐸𝑥𝑝𝑜𝑠𝑢𝑟𝑒 (𝐸) + 𝑆𝑒𝑛𝑠𝑖𝑡𝑖𝑣𝑖𝑡𝑦(𝑆))

The overall vulnerability index facilitates inter areas comparison. A higher value of the vulnerability index indicates lower vulnerability. A negative index value indicates the net effect of adaptive capacity, exposure and sensitivity (Maiti et al., 2015).

Table 4: Description of indicators used in the assessment

Component	Indicator	Description and Scoring of the Indicator	Unit	Relation
Exposure	Extreme Temperature	Occurrence of temperature extremes (1 = Experienced either heat or cold; 2 = Experienced both head and cold; 0 = Have not experienced it/have no idea)	Ordinal Value	+
	Extreme Temperature Impacts	Impacts of extreme temperature experienced (0 = No impacts are experienced; 1 = 1–3 impacts are experienced; and 2 = if more than 3 impacts are experienced)	Ordinal Value	+
	Rainfall Seasonality	Shift in rainfall seasonality (0 = No occurrence or no information provided; 1 = Occurred)	Ordinal Value	+
	Changing rainfall seasonality impacts	The number of impacts experienced due to changes in rainfall seasonality (0 = No impacts are experienced; 1 = 1–3 impacts are experienced; and 2 = 3 or more impacts are experienced)	Ordinal Value	+
	Occurrence of extreme events	The number of extreme events occurred (0 = No occurrence; 1 = 1–3 extreme events occurred; and 2 = if more than 3 extreme events occurred).	Ordinal Value	+
	Impacts of extreme events	The number of impacts experienced due to extreme events (0 = no impacts are experienced; 1 = 1–3 impacts are experienced; and 2 = more than 3 impacts are experienced)	Ordinal Value	+
Component	Indicator	Description and Scoring of the Indicator
Sensitivity	Crop Yield	Decrease in crop productivity (0 = increase in crop yield; 1 = No change; 2 = decrease in crop yield)	Ordinal Value	+
	Pest Diseases	Occurrence of pests and diseases (0 = no information on occurrence; 1 = 1–2 types of pests and disease experienced; 2 = 3 or more types of pests and disease experienced)	Ordinal Value	+
	Invasive Weeds	Types of invasive weeds observed (0 = no information or its types; 1 = 1–2 types observed; 2 = 3 or more types observed)	Ordinal Value	+
	Invasive Weeds Occurrence	Type of land affected due to occurrence of invasive weeds: (1 = either agriculture land or forest land; 2 = both types of land affected; 0 = No occurrence observed either in agricultural land or forest land)	Ordinal Value	+
	Drinking Water	Change in drinking water volume (2 = dried up; 1 = decreased; 0 = no change or uncertain)	Ordinal Value	+
	Irrigation Water	Change in irrigation water volume (2 = dried up;1 = decreased; and 0 = no change or uncertain)	Ordinal Value	+
	Forest Cover	Change in forest cover (2 = decreased; 1 = no change; 0 = increased)	Ordinal Value	+
	Forest Composition	Change in forest composition (2 = change observed; 1 = uncertain; 0 = no change)	Ordinal Value	+
	Wildlife Population	Change in wildlife population (2 = increased; 1 = no change; 0 = decreasing)	Ordinal Value	+
	Vector Diseases	Occurrence of vector borne disease (2 = increased, 1 = no change; and 0 = decreased)	Ordinal Value	+
	Water Diseases	Occurrence of water borne disease (2 = increased; 1 = no change; and 0 = decreased)	Ordinal Value	+
	Family Fatality from Water and Vector Diseases	Family fatality due to water and vector diseases (1 = yes; and 0 = no)	Ordinal Value	+
	Pastureland	Pastureland conditions (2 = degraded; 0 = improved (0); 1 = no change or uncertain)	Ordinal Value	+
	Impact on Infrastructure	Number of infrastructures impacted due to climate hazard (1 = one; 2 = two or more are impacted; and 0 = if none are impacted)	Ordinal Value	+
Component	Indicator	Description and Scoring of the Indicator
Human Asset	Household head literacy	Literacy of household head (0 = illiterate; 1 = secondary education, including monastic education; and 2 = bachelor's degree and above)	Ordinal Value	+
	Vocational Skill	Vocational skills of household members (0 = none or zero; 1 = 1 to 3 skills; and 2 = 3 or more skills)	Ordinal Value	+
	Gender of Head of the Household	Gender of the Head of the Household (1 = male and 0 = female)	Ordinal Value	+
	Climate Awareness	Awareness on climate change and adaptation (0 = not aware; 1 = aware).	Ordinal Value	+
	Waste Awareness	Awareness on waste management (2 = aware or moderately aware; 1 = limited awareness; and 0 = not aware)	Ordinal Value	+
Social Asset	Group membership	Membership in social group (1 = yes; and 0 = No)	Ordinal Value	+
	Farm Labor	Availability of farm labour types (0 = when no or atleast 1 type of labor is available; 1 = 2–3 types of farm labours available; and 2 = more than 3 types of farm labours available)	Ordinal Value	+
	Group Membership Types	Membership to community group (0 = no membership; 1 = membership in 1–2 community groups; and 2 = membership in more than 2 community groups	Ordinal Value	+
Natural asset	Forest Status	Condition of forest in the locality (2 = good condition; 1 = uncertain; and 0 = degraded condition)	Ordinal Value	+
	Wetland Status	Condition of wetland in the locality (2 = good condition;1 = uncertain; and 0 = degraded condition)	Ordinal Value	+
	Forest Produce Type	Types of forest produce available (1 = 1–2 available; 3 = more than 3 types available; and 0 = if no produce is utilized or available)	Ordinal Value	+
	Land Holding	Extend of landholding (0 = no land holding, 1 = landing holding from 1 acre to 3.45 acres; and 2 = > 3.45 acres land holding	Ordinal Value	+
	Water Availability	Availability of drinking water (0 = acute water shortage; 1 = when it is available for 3 to 6 months; and 2 = availability all year round)	Ordinal Value	+
	Alternative Water Source	Availability of alternative water source (1 = yes; and 0 = no)	Ordinal Value	+
	Food Self- sufficiency	Food self-sufficiency (2 = complete self-sufficiency ;1 = partial or mostly self-sufficiency; and 0 = no self-sufficiency)	Ordinal Value	+
Financial Asset	Credit facility	Access to credit facility (1 = available; 0 = not available)	Ordinal Value	+
Financial Asset	Savings	Availability of savings (2 = savings > Nu. 120,000; 1 = savings < Nu. 120,000; and 0 = having no savings)	Ordinal Value	+
Physical asset	Facility Access	Access to socio-economic facilities (0 = access to1-3 facilities; 1 = access to 3–4 facilities; and 2 = access to more than 4 facilities)	Ordinal Value	+
	Household facility Access	Access to household facilities (0 = access to < 2 facilities; 1 = access to 3–4 facilities; and 2 = access to more than 4 facilities)	Ordinal Value	+
	Road access	Access to types of roads (2 = paved road; 1 = unpaved road; and 0 = no access to road)	Ordinal Value	+
	Communication Medium	Access to communication medium (1 = access to 1 medium, 2 = access to 2 or more medium; and 0 = no access to communication medium)	Ordinal Value	+

3.1 Exposure

The exposure is estimated by summation of 6 indicators. All six indicators showed positive contribution to exposure as hypothesized and were found to be significant (p < 0.001). Thus, all indicators were used to determine the level of exposure. The impacts of extreme temperatures, rainfall seasonality, and extreme events had a greater influence on the overall exposure index compared to the contribution from temperature extremes alone. The absolute values of model weights revealed that the rainfall seasonality and its impacts contributed the most to the overall exposure index, followed by extreme events (Table 5).

Table 5

Weights and significance of indicators on exposure
Exposure Indicator	Weight	Std. Err	Z- Value	Sig. (P - Value)
Extreme Temperature	1.000
Extreme Temperature Impact Types	3.050	0.199	15.324	0.000
Rainfall Seasonality	4.057	0.246	16.483	0.000
Rainfall Seasonality Impact Types	6.845	0.416	16.439	0.000
Extreme Events	2.377	0.161	14.798	0.000
Extreme Event Types	3.111	0.208	4.966	0.000

The third national communication of Bhutan, 2020 reported a decreasing trend in rainfall with high variability. Most studies in Bhutan reported fluctuations and erratic rainfall (NBC, 2011; NEC, 2016; Phurba Lhendup, 2011; Tshering et al., 2011; Wangdi & Kusters, 2012). According to past records, there are frequent and widespread windstorm occurrences in Bhutan. The windstorms had mostly affected rural households and their livelihood. Unseasonal and intense rainfall and hailstorms can not only reduce agricultural productivity but also cause large-scale damage to the crops, thereby affecting farmers who are caught unprepared. Erratic rainfall patterns are already affecting agricultural productivity, as most farmers rely entirely on monsoons for irrigation. As a result, farmers are increasingly reporting crop yield instability, production loss, crop quality degradation, and decreased water availability for farming and irrigation. In addition, changes in precipitation patterns have a short-, medium-, and long-term impact on water availability for drinking, with cycles of flooding during monsoons and shallow flows and drying streams during other seasons. Paddy is the leading staple food of the study area and plays a major role in food security of the communities. Its cultivation is mainly dependent on monsoon rainfall. Paddy is grown in irrigated, rain-fed, and upland ecosystems. Due to its water-intensive nature, Paddy cultivation is particularly vulnerable to climate change despite it playing a pivotal role in the food security of the communities in the study area. Dagana and Tsirang are highly exposed districts compared to other districts in the landscape. The majority (< 90%) of the households in these districts have observed extreme temperatures, changes in rainfall seasonality, and extreme events like flash floods, seasonal droughts, and windstorms. Samtse, Haa, Paro and Zhemgang have relatively lower exposure as compared to other districts, with Haa as the least exposed district in the landscape.

3.2 Sensitivity

Sensitivity components were considered as a summation of fourteen indicators. All indicators that contributed to sensitivity positively were found to be significant (p > 0.05). The weightage contribution of the invasive weeds and their occurrence to the sensitivity index was the highest, followed by pest and disease occurrence and changes in crop yield (Table 6).

Table 6

Weights and significance of indicators on sensitivity
Sensitivity Indicator	Weight	Std. Err	Z - Value	Sig. (P - Value)
Crop Yield	1.000
Pest and Diseases	2.724	0.322	8.459	0.000
Invasive Weeds	6.259	0.732	8.548	0.000
Invasive weeds Occurrence	6.081	0.706	8.612	0.000
Drinking Water	0.599	0.101	5.929	0.000
Irrigation Water	0.382	0.081	4.713	0.000
Forest Cover	0.945	0.164	5.770	0.000
Forest Composition	0.943	0.169	5.593	0.000
Wildlife Population	0.023	0.098	0.237	0.000
Vector Borne Diseases	-0.475	0.105	-4.521	0.000
Water Borne Diseases	-0.634	0.111	-5.721	0.000
Family Fatality	0.120	0.026	4.671	0.000
Pastureland	0.348	0.082	4.273	0.000
Infrastructure Impacts	0.930	0.129	7.196	0.000

Vector and waterborne diseases contributed the least to the overall sensitivity index. The occurrence of invasive weeds impacts on infrastructures, and changes in wildlife populations were listed as the main contributors to sensitivity for seven of the nine districts. These indicators were featured as the top three indicators, with a score of 2 for each district. Invasive alien plants are harmful nonnative plant species that humans have introduced outside of their “natural” geographical range. Climate change can facilitate alien plant invasions by altering background environmental conditions, by increasing disturbance through extreme climatic events, and through human responses to climate change. While all plants, regardless of whether they are native or alien, will likely be affected by environmental change, it is widely expected that climate change will favor invasive alien plants at the expense of native plants (Turbelin & Catford, 2021). Though limited studies on invasive and alien species have been conducted in Bhutan, a study conducted by the National Biodiversity of Bhutan concludes that Invasive species pose the greatest threat to agricultural land and reduced crop production in Bhutan (NBC, 2021). The impact of climate change on invasive alien species is a major driver of pastoral vulnerability and agricultural land degradation (Bogale & Tolossa, 2021). It results in loss of livestock forage and reduced crop production. Vicente et al. (2019) emphasize the need for early detection and management of invasive alien plant species in agricultural and agro-forest landscapes, particularly in the context of climate change. Ageratina Adenophora, Alternanthera sissilis and Blyxa auberthii are the top three invasive plants reported from the study area, occurring mostly in agricultural fields (Fig. 3).

Though there are not many studies on the relationship between climate change and HWC, there are reports that highlight their interaction and complexity. A systematic literature review study on HWC in the Hindu Kush Himalayas (HKH) bySharma et al. (2021) revealed that climate change is one of the drivers of HWC, causing changes in plant phenology and shift in habitat. Abrahms (2021) affirms that climate change exacerbates human-wildlife conflicts by intensifying resource scarcity and forcing people and wildlife to share increasingly crowded spaces. These clashes largely stem from the co-occurrence of humans and wildlife seeking limited resources in shared landscapes and often have unforeseen consequences.G. Otiang’a-Owiti, (2011) says that climate change will alter the location and nature of the geographical environment, and wildlife will be forced to migrate to new areas as a way of adapting or face extinction. As there are limited natural places left for wildlife to move to, this will likely bring wildlife into more densely populated human areas and create situations of human-wildlife conflict. The recovery and expansion of wildlife populations, combined with environmental changes such as climate change, are also contributing to the rising frequency and severity of these conflicts (Treves, 2008). These conflicts are further compounded by the impacts of wildlife and changing climate on food security, particularly in regions where human and wildlife populations compete for limited resources (Salerno et al., 2021). Climate change causes socio-ecological impacts and conflicts, such as HWC, which reduces adaptation capacity and increases vulnerability (Gupta et al., 2017). Human-wildlife conflict is a persistent and major issue in Bhutan, whereby substantial damages to agricultural crops, livestock and human properties are caused by conflicting wildlife species annually. Tiger, Leopard, Wild pig, Dhole, Asian elephants and Himalayan Black bears are the most conflicting wildlife species in the study area, causing livestock depredation, crop depredation, and human property damage (Ref. Tables 7, 8 & 9). Asian elephants are one of the main conflicting species causing large-scale damage to human property. They are mostly confined to the Sarpang and Samtse Districts in the southern parts of the country (Table 9). Similarly, Tiger, Dhole, Leopard and Himalayan Black bear are the main conflicting wildlife species causing livestock depredations, especially in central and western districts like Zhemgang, Haa, Paro, Chhukha and Thimphu (Table 7).

Table 7

Incidences of livestock depredation by conflicting wildlife species
Districts	Asiatic black bear	Barking deer	Birds	Elephant	Gaur	Langurs	Macaque	Others	Porcupine	Rabbit	Rodents	Sambar	Wild Pig
Dagana	1	166	108	60	3	21	315	73	72	4	59	6	230
Gedu	144	113	53	31	1	26	280	105	160	16	15	32	485
Samtse	48	68	53	84		27	458	194	216	28	45	6	347
Sarpang	1	15	16	137		9	18	37	3	77	2	3	265
Thimphu	27	7	12			6		9				9	169
Tsirang	11	139	144	2		27	106	100	63	3	57	9	492
Zhemgang	59	65	53			29	83	33	90	1	37	71	374
Haa	44	29	3			1	2	6	16		4	10	14
Total	335	602	442	314	4	146	1262	557	620	129	219	146	2376

3.3 Adaptive Capacity

Adaptive components were considered as a summation of twenty indicators. Table 10 represents the weights of each indicator to adaptive capacity. As compared with household-head literacy, indicators such as vocational skills, household group membership and its type, availability of different forest product types, availability of water and alternative water sources, food self-sufficiency, access to credit facilities, availability of household savings, access to socio-economic facilities and basic household facilities contributed more to the adaptive capacity index. Socio-economic facilities include access to roads, community centers, departmental stores, markets, shops, hospitals, and schools, while access to basic household facilities includes access to electricity, toilets, cooking gas, tapped water etc. Indicators such as forest and wetland conditions around the households had no significant contribution to the overall adaptive capacity index; however, the insignificant indicators were not dropped since the model performance was robust (p < .001).

Table 8

Incidences of crop depredation by conflicting wildlife species
Districts	Civet	Common leopard	Dhole	Eagle	Himalayan Black Bear	Leopard cat	Monitor lizard	Snake	Snow leopard	Tibetan wolf	Tiger	Yellow-throated marten
Dagana	10	17	50	15	12	11		9		5	21	8
Chhukha	1	12	77	22	46	14	3	4		9	13	3
Samtse	6	37	28	28	15	60	4	8	4	4	3	1
Sarpang	5	3	5	3	1	1		2	1
Thimphu		1	9	1	10					5	20
Tsirang	5	8	13	15	13	9		4			2	5
Zhemgang		27	56	9	4	3				2	45	6
Haa			13		8	1				4	4
Total	27	105	251	93	109	99	7	27	5	29	108	23

Table 9

Incidences of property damages by conflicting wildlife species
Office	Asian Elephant	Asiatic Black Bear	Langurs	Leopard cat	Macaque	Wild pig
Dagana	18		2	2	33	13
Gedu	15	11	1		15	41
Samtse	22	4	2		27	14
Sarpang	78					11
Thimphu	1	11	1			9
Tsirang		3	1	1	2	27
Zhemgang		3	11		6	5
Haa		3
Total	134	35	18	3	83	120

Table 10

Weights and significance of indicators on adaptive capacity
Adaptive Capacity Indicator	Weight	Std. Err	Z - Value	Sig. (P - Value)
Human Asset
Literacy of Household Head	1.000
Vocational Skills	2.776	0.679	4.087	0.000
Gender of Household Head	0.503	0.239	2.106	0.035
Climate Awareness	0.432	0.140	3.088	0.002
Waste Awareness	1.788	0.572	3.125	0.002
Social Asset
Community Group Membership	15.974	3.645	4.382	0.000
Community Group Membership Types	20.746	4.745	4.372	0.000
Natural Asset
Forest Status	0.036	0.366	0.098	0.922
Wetland Status	-0.383	0.313	-1.224	0.221
Forest Produce Type	2.413	0.631	3.826	0.000
Landholding	0.952	0.254	3.749	0.000
Water Availability	1.756	0.522	3.365	0.001
Alternative Water Sources	1.693	0.442	3.833	0.000
Financial Asset
Food Self-Sufficiency	1.919	0.516	3.717	0.000
Credit Access	2.127	0.532	3.999	0.000
Savings	1.218	0.331	3.675	0.000
Physical Asset
Access to Facilities	1.704	0.509	3.349	0.001
Access to Household Facilities	2.185	0.522	4.185	0.000
Road Access and Conditions	0.710	0.336	2.115	0.034
Communication Medium Types	0.917	0.311	2.953	0.003

3.4 Vulnerability Index

The vulnerability index scores of individual districts are presented in Table 11 and Fig. 4. Districts with higher negative scores on the vulnerability index were more vulnerable. However, a lower negative score on the vulnerability index do not mean that those districts are not vulnerable, but they are comparatively less vulnerable.

Table 11

Vulnerability Index of districts
	Districts	Adaptive capacity	Exposure	Sensitivity	Vulnerability
1	Dagana	0.32	0.56	0.48	-0.72
2	Sarpang	0.16	0.39	0.48	-0.71
3	Gedu	0.30	0.55	0.44	-0.69
4	Paro	0.28	0.50	0.44	-0.66
5	Zhemgang	0.32	0.37	0.52	-0.57
6	Samtse	0.23	0.41	0.26	-0.44
7	Thimphu	0.37	0.48	0.26	-0.38
8	Haa	0.33	0.42	0.27	-0.36
9	Tsirang	0.52	0.56	0.31	-0.35

Based on the vulnerability index of respective districts, it is found that Dagana and Sarpang districts are the most vulnerable districts, with vulnerability index of -0.72 and − 0.71, respectively. The vulnerability of these districts is mainly attributed to their high exposure and sensitivity. On the other hand, Tsirang, Thimphu and Haa districts with vulnerability index of -0.35, -0.36, and − 0.38 are found to be relatively less vulnerable districts with relatively higher adaptive capacity. Similar findings were reported in the nationwide climate vulnerability and capacity assessment conducted as a part of the National Adaptation Plan (NAP) formulation process (NECS, 2021). The study found Sarpang and Dagana District to be the districts with high risk from climate impacts in the country.

3.5 Impacts and coping mechanism

The common impacts of extreme temperatures reported by the households in the communities are; a decrease or loss of crop yield, an increasing number of pests and diseases, death of livestock, decline in milk production, drying up of water resources, and heat stress. To cope with these impacts, commonly practiced coping mechanisms by the communities are crop rotation, switching to heat/cold resistant crops, rainwater harvesting, irrigation, change of crop types, cover cropping, change in grazing areas and engaging in other business activities for alternative revenue generation. Similarly, the common impacts of change in rainfall seasonality are a decrease in crop yield, a decrease in soil quality, a decrease in water availability for irrigation and drinking, range shifts of wild animals and plants, and an increase in the number of pests and diseases. Besides change in crop types and cropping seasons, the communities also resort to use of chemical fertilizers with irrigation to boost the agricultural production to cope with the impacts of changing rainfall seasonality. Local government offices are the central institutions that assist people in coping with climate change impacts and disasters. Communities reported that the District disaster office, District kidu office, Gewog RNR extension offices (agriculture, livestock, and forestry), community center, and health offices assisted them in dealing with climate change impacts. Other agencies such as financial and insurance offices, non-government organizations (Tarayana Foundation and United Nations Development Program), Department of Disaster Management (DDM), National Center for Hydrology and Meteorology (NCHM), Department of Forest and Park Services (DoFPS), Department of Agriculture (DoA), and Department of Livestock (DoL) are also mentioned to have assisted communities in adapting to climate change. These commonly practiced coping mechanisms could be considered while developing climate change adaptation plans. The institutional capacities of local government and other relevant agencies should be built to assist communities in undertaking climate change mitigation and adaptation measures.

Bhutan is ranked the 38th most vulnerable country and 62nd most ready country to respond effectively to climate change by the Global Climate Change Adaptation Index (ND-GAIN) in 2021. The Climate Change Policy of the Kingdom of Bhutan (2020) underpins the increasing susceptibility of the country to climate-related risks and extreme events such as glacial lake outburst floods, windstorms and erratic rainfall patterns, causing landslides, flash floods, outbreaks of pest and diseases and changing water availability. Taking account of the risks, one of the policy objectives is to take all measures to protect the health, lives, livelihoods and happiness of the people from the adverse impacts of climate change by building adaptive capacity and resilience to reduce vulnerability by integrating adaptation actions into the developmental planning process at all levels (NEC, 2020). The third national communication of Bhutan to the United Nations Framework Convention on Climate Change (UNFCCC) underscores water, agriculture, forest and biodiversity, energy and human health as the key risk areas cross-cutting across various sectors in the country. The National Adaptation Plan of the Kingdom of Bhutan (2023) highlights drying water sources, floods, and landslides as the current risks and hazards which need to be addressed with urgency. It also highlights that the current significant trends in climate change and its possible negative impacts makes it imperative to comprehensively address agriculture adaptation (NEC, 2023)

This study specifically assessed the vulnerability of communities residing in the nine South-western districts of Bhutan with the aim of developing and integrating climate change adaptation strategies into the developmental plans of these districts. It provides a diagnostic tool to understand the resilience of communities based on the critical socio-economic parameters and guide adaptation planning, implementation, and investment. The index can be used as a baseline to monitor and track vulnerability changes and evaluation of future adaptation. Vulnerability was calculated as the net effect of exposure and sensitivity on the adaptive capacity. Based on the findings and observation of this assessment, the relevant authorities can develop climate change adaptation and mitigation strategies and action plans, which could be included within the broader developmental plans of the districts for effective implementation.

The adaptation strategies must focus on lowering the exposure and sensitivity index and increasing adaptive capacities. Climate change adaptation and mitigation measures must be taken to reduce the impacts of climate-induced events like landslides, flash floods, windstorms, changes in rainfall seasonality and temperature extremes. Adaptation actions must also focus on tackling Human Wildlife Conflicts, securing drinking and irrigation water, and managing forest resources, including pastureland, especially in the context of increasing invasive species, as these were found to be commonly reported indicators that raised the climate sensitivity of most districts. Similarly, the adaptive capacity of the communities must be strengthened to build climate resilience.

Common coping mechanisms and local knowledge already being practiced by communities to deal with climate change impacts could be incorporated into the adaptation plans. The adaptation priorities identified in the National Adaptation Plan of Bhutan (2023), the Third National Communication (TNC), sector-specific climate risk assessments carried out as part of the National Adaptation Plan (NAP), and the Bhutan REDD + strategy could be used as a guiding document to develop adaptation plan for respective districts. The climate change adaptation priorities identified in these national documents, which are synthesized based on more comprehensive consultations and studies, are very relevant for the communities and have a greater chance of securing financial and technical support. So, here, based on this study’s findings, following general recommendations are proposed.

Firstly, adaptation measures should be implemented to reduce exposure to climate-induced events like landslides, shifts in rainfall, and extreme temperatures, and simultaneously, these measures should be prioritized according to the specific impacts and severity experienced in each district. Secondly, reducing human-wildlife conflict (HWC) is crucial to protect crop and livestock livelihoods in rural communities. At the same time, enhancing climate-smart agriculture and livestock farming through diversification, climate-resistant breeds, and sustainable land management will help communities adapt to climate uncertainties. Thirdly, securing water resources and promoting efficient use through technologies like rainwater harvesting and integrated watershed management is essential. In fourth, support effective coping mechanisms that are already being practiced by communities in dealing with climate-related issues and impacts. Also, they should be prepared in undertaking measures to reduce forest fires, the spread of invasive plants, and outbreaks of forest pests and diseases. These efforts will help promote a healthy forest ecosystem and sustain natural resources. Further, building capacity and awareness among communities, local governments, and stakeholders is vital. Moreover, enhancing adaptive capacity by developing human, social, natural, financial, and physical assets will foster resilience. Finally, develop climate change adaptation plans for respective districts, taking into consideration the findings of this assessment report and other relevant documents. This will ensure that adaptation strategies are tailored to the specific needs and vulnerabilities of each district.

Ethics Approval The study has been approved by Ugyen Wangchuk Institute for Forestry Research and Training (UWIFoRT) under the Department of Forest and Park Services, Ministry of Energy and Natural Resources, Bhutan Institutional Review Board (IRB) / ethics committee, which has thoroughly reviewed the project to ensure compliance with ethical standards. This approval confirms that there are no violations of ethical principles in the conduct of this research. Participant Consent All participants involved in the study have provided informed consent to participate. This includes permission to publish findings ensuring their rights and privacy are respected. In cases where the ethics committee waived the need for individual consent, this has been duly noted and approved.

Conflict of Interest:

The authors declare no conflict of interest.

Author contribution

statement: Study conception and design by TNW; Methodology Implementation by TNW; Experiment execution by TNW and YD; Data collection by TNW and YD; Data analysis/Interpretation by TNW and YD; Manuscript writing/ revision by TNW and YD. All authors gave final approval.

Acknowledgement:

We acknowledge the IKI, Germany for providing Funding and necessary logistic support during the research time.

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The authors declare potential competing interests as follows: Conflict of Interest: The Authors Declare No Conflict of Interest.

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From National to Local: A Comprehensive Vulnerability Assessment Framework for Climate Change Adaptation in Bhutan

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Abstract

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1. Introduction

1.1 Climate and Agriculture on Bhutan

1.2 Climate change impacts in Bhutan

1.3 Rationale and objectives

2. Methods and Materials

2.1 Study Area: Location

2.2 Land Use Types

2.3 Climate data of the study area

2.4 Historical Temperature and Future Climate Projection

2.5 Data collection

2.6 Methodology

2.6.1 Climate Vulnerability Assessment and Conceptual Framework

2.6.2 Selection and description of indicators

2.6.3 Vulnerability Analysis

3. Results and discussion

3.1 Exposure

3.2 Sensitivity

3.3 Adaptive Capacity

3.4 Vulnerability Index

3.5 Impacts and coping mechanism

4. Conclusion and recommendation

Declarations

Conflict of Interest:

Author contribution

Acknowledgement:

References

Additional Declarations

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