Climate and Development

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Mapping the need for adaptation: assessing drought vulnerability using the livelihood vulnerability index approach in a mid-hill region of Nepal

Janardan Mainali & Narcisa G. Pricope

To cite this article: Janardan Mainali & Narcisa G. Pricope (2018): Mapping the need for adaptation: assessing drought vulnerability using the livelihood vulnerability index approach in a mid-hill region of Nepal, Climate and Development, DOI: 10.1080/17565529.2018.1521329 To link to this article: https://doi.org/10.1080/17565529.2018.1521329

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RESEARCH ARTICLE Mapping the need for adaptation: assessing drought vulnerability using the livelihood vulnerability index approach in a mid-hill region of Nepal Janardan Mainali a,b and Narcisa G. Pricope a aDepartment of Ocean and Earth Sciences, University of North Carolina Wilmington, Wilmington, NC, USA; bResearch and Development Society, Kirtipur, Nepal

ABSTRACT ARTICLE HISTORY For effective development and adaptation interventions in resource-poor regions to take place, it is critical Received 18 October 2016 to identify, at the highest spatial scale possible, regions of higher priority based on current needs and Accepted 28 August 2018 vulnerabilities. The index-based assessment of vulnerability to climate change and variability is typically KEYWORDS used to identify administrative-level regions of high vulnerability using various socioeconomic and Biophysical; socioeconomic; biophysical datasets. One method that combines both approaches at the community level consists of resilience; development collecting highly resolved socio-economic data and using the livelihood vulnerability index (LVI) to needs; climate change assess population vulnerability to increased climate variability and shocks. We use this mixed-methods approach in mapping climate vulnerability of ten drought-prone villages in the central-east mid-hill region of Nepal. We integrate data from over 900 household surveys and national-level databases and identify spatial patterns in the different components of climate vulnerability. We assess to what extent climatic extremes or people’s socioeconomic capacity contribute to vulnerability and may shape development needs at the sub-district scale. We find that the majority of our study area falls in the high vulnerability category with significant spatial variation. In some villages, there are different vulnerability classes in different wards, indicating that even within the lowest administrative units, there is a significant spatial variation in the level of vulnerability. Livelihood strategies, water availability, and topographic components played the most important role in determining overall vulnerability and we measure strong interconnections among different components. The interconnectedness nature of different vulnerability components is creating a self-reinforcing downward spiral of vulnerability that traps local communities in a state of heightened vulnerability. We conclude that adaptation strategies in highly vulnerable regions should include careful consideration of different livelihoods and environmental components, their fine-scale spatial variations, and interconnections.

1. Introduction Development Index (HDI) to classify countries based on Climate vulnerability is the characteristic of a system deter- their gross national product, their education index, and life mined by its exposure-character, magnitude, and rate of cli- expectancy at birth (Anand & Sen, 1994; HDI, 2016). Environ- mate variation, sensitivity- system’s susceptibility or harm mental aspects are also now being increasingly considered in associated with environmental and social changes and adaptive order to understand how they alter and control the quality of capacity- the system’s capacity to withstand variability and life as human life is inherently linked to the surrounding changes (Adger, 2006; Field et al., 2014). The climate vulner- environment. Early on, Cutter, Boruff, and Shirley (2003) pro- ability concept comprises information related to the physical posed a social vulnerability index to assess human vulnerability environment such as the rate of temperature change, precipi- to environmental hazards. This approach was then used in the tation change, hazard frequency and concurrently uses other assessment of vulnerability to various hazards such as hurri- socioeconomic information such as income level, occupation, canes, floods, and droughts (Bjarnadottir, Li, & Stewart, 2011; resource access, and others to determine the vulnerability status Flanagan, Gregory, Hallisey, Heitgerd, & Lewis, 2011; Rygel, of people living in a particular location. Climate vulnerability O’Sullivan, & Yarnal, 2006; Tucker et al., 2015). Typically, science is now receiving increased attention as a result of its social vulnerability assessments include either socioeconomic ability to contribute to identifying, monitoring, and estimating or biophysical or both type of indicators, based on the type the extent of harm to social and economic systems (Stern, Ebi, of assessments. A more inclusive concept that encapsulates Olson, Steinbruner, & Lempert, 2013). both such components, usually as a single numeric value, is Researchers and development practitioners are using var- the climate vulnerability index (Sullivan & Meigh, 2005). This ious approaches to quantifying the quality of life of human approach also uses more or less similar indicators and aggrega- populations. The United Nations has been using the Human tion methods as that of the social vulnerability index but uses

CONTACT Janardan Mainali [email protected] Department of Ocean and Earth Sciences, University of North Carolina Wilmington, Wilmington, NC, USA Research and Development Society, Kirtipur, Nepal Supplemental data for this article can be accessed here https://doi.org/10.1080/17565529.2018.1521329. © 2018 Informa UK Limited, trading as Taylor & Francis Group 2 J. MAINALI AND N. G. PRICOPE data collected from primary sources belonging to a variety of LVI with the Livelihood Effect Index and reported a valid socioeconomic and biophysical indicators. The vulnerability reflection of vulnerability in the villages compared in Mustang status of a region is assessed by interpreting the vulnerability district, Nepal. Gentle, Thwaites, Race, and Alexander (2014) index and recommendations for adaptation activities to dimin- also compared communities in Lamjung district Nepal using ish vulnerability and enhance resilience are then made based on the LVI approach. A modification of this approach as this assessment. Multidimensional Livelihood Vulnerability Index has The spatial variation of this vulnerability index and com- recently been used to analyze household level vulnerability ponents can be assessed using geographic information system in different parts of Hindu Kush Himalaya region (Gerlitz (GIS) in order to spatially and explicitly prioritize the adap- et al., 2017). tation need of a community (Preston, Yuen, & Westaway, The LVI approach was proposed by Hahn et al. (2009)asa 2011). Different indicators of climate vulnerability like climatic modified form of the Sustainable Livelihood Approach pre- variables, hazards, the condition of land, socioeconomic con- viously employed by the United Nations. The Sustainable Live- ditions and access to resources are broadly classified as bio- lihood approach uses the natural, social, financial, physical, and physical and socioeconomic factors (Füssel, 2007). These human capital to design development programing at the com- factors act in conjunction to determine the characteristic of munity level (Chambers & Conway, 1991; Hahn et al., 2009). any socio-ecological system towards the susceptibility or The livelihood vulnerability index approach integrates climate harm due to the climatic variability and hazard. These factors exposure and accounts for household adaptation practices on are location specific and vary from household to household, top of the sustainable livelihood approach (Chambers & Con- community to community, and country to country. Therefore way, 1991; Hahn et al., 2009.). It uses multiple indicators to regions that appear socio-economically similar may be experi- assess exposure to natural disasters and climate variability, encing different levels of vulnerability if they are located in social and economic characteristics of households that affect different places (Turner et al., 2003). The location-specific adaptive capacity, and current health, food, and water resource information regarding the factors contributing it is of foremost characteristics that determine sensitivity to climate change importance to understanding climate vulnerability. Recent impacts. The LVI approach relies heavily on data collected advances in GIS technology and availability of digital data in from primary sources with indicators divided into the socio- the public domain have made collection and analysis of demographic profile, livelihood, health, social networks, food, spatially explicit data for climate vulnerability assessments water, and natural disasters and climate variability. more feasible (Preston et al., 2011). Nonetheless, performing In this study, we use the Livelihood Vulnerability Index spatial assessments of climate vulnerability in developing approach to mapping the climate vulnerability condition of countries beyond the district level remains challenging due to drought-prone villages lying along the Sunkoshi River in the unavailability of appropriate data, methodologies to link central-east mid-hill region of Nepal. We add two novel com- socioeconomic data to varying resolution biophysical datasets, ponents, spatially-explicit climate, and topographic variables, and poor technological infrastructure. There are secondary to the livelihood vulnerability index approach and use geo- data-based assessments which can analyze regions beyond the graphic information system (GIS) modeling to analyze and district level but most of them only use interpolated socioeco- map climate vulnerability of local communities to obtain highly nomic data making results less accurate, especially on a local resolved outputs that account for both exposure and sensitivity scale (de Sherbinin et al., 2014). Availability of local socioeco- variables. We have three main objectives: 1) identify the spatial nomic data, therefore, is a major constraint. The increased patterns of different components of climate vulnerability and availability of the latter would make it easier for assessments test whether the pattern is similar for different components, of climate vulnerability in the context of a plethora of global 2) determine whether it is climatic extremes or people’s socio- high-resolution data related to remote sensing-based climate, economic capacity that determines the adaptation and develop- topography, and other biophysical variables available in the ment needs in our study area and, 3) assess how different public domain (de Sherbinin et al., 2015). components are interconnected to control the vulnerability of One way of overcoming the issue of poor socioeconomic the region in a climatically sensitive mid-hill region that is data availability is collecting socio-economic data from region- characteristic for approximately 30% of the land area of Nepal. ally representative areas and the use of composite approaches to assess human vulnerability to climate change (Hahn, Riederer, 2. Methods & Foster, 2009). This method that relies heavily on the liveli- 2.1. Study area hood vulnerability index (LVI), however, has so far only been used to analyze climate vulnerability of discrete locations, This study performs an assessment of drought vulnerability of such as two districts in Mozambique (Hahn et al., 2009). A 10 different villages in Ramechhap district in Central-east similar approach was used to assess two different communities Nepal (Figure 1). Nepal is a mountainous country lying along in Trinidad and Tobago by Shah, Dulal, Johnson, and Baptiste the Himalaya, between India and China. It is one of the most (2013), three different villages in different physiographic region vulnerable countries to climatic impacts due to the ubiquitous of Kaligandaki Basin of Nepal by Panthi et al. (2015), vulner- presence of climatic hazards such as landslides, floods, ability of different transhumance community in different droughts and glacial lake outburst floods, as well as poor parts of Nepal by Aryal, Cockfield, and Maraseni (2014), and social, economic and institutional capacity (Aryal, 2012; different district blocks of Bihar, India by Madhuri, Tewari, Krishnamurthy, Lewis, & Choularton, 2014; Pathak, Gajurel, and Bhowmick (2014). Urothody and Larsen (2010) compared & Mool, 2010). CLIMATE AND DEVELOPMENT 3

Figure 1. Map of study area.

Recent sub-national assessments of spatial variation of cli- across the region has warranted a scientific assessment mate vulnerability of Nepal have revealed Ramechhap dis- using empirical data (Bhuju et al., 2013). trict as one of the most vulnerable districts in terms of Although two snow-fed, perennial rivers, the Sunkoshi and climate change effects (Mainali & Pricope, 2017a; Ministry Tamakoshi, flow through the region, people living at higher of Environment, 2010). This district’s vulnerability is due elevations are suffering from drought as this region receives to the presence of recurring droughts and people’s low less than 1000 mm of rainfall per year (Figures 1 and 2). socioeconomic adaptation capacity. This district’s local According to local people, this rainfall amount has been reced- people, governmental and nongovernmental organizations ing in recent years. People are experiencing an acute shortage of are not familiar with climate change and its impacts on water, be it in the form of drinking water or water typically used this region and possible efforts that can be made to reduce for irrigation purposes. These shortages have serious impli- their vulnerability (Baral, Bhuju, Shrestha, & Yonjan- cations for agriculture and food security, forest resources, and Shrestha, 2012). Moreover, a local and regional lack of human and livestock health (Bhuju et al., 2013). This research, understanding of the effects of climate change, variability therefore, aims to assess drought vulnerability at 10 village and climatic extremes and insufficient spatial information development committees (total of 272 square kilometers and about potential variations in climate vulnerability levels more than 40,000 people) lying along the Sunkoshi river. 4 J. MAINALI AND N. G. PRICOPE

Figure 2. Average monthly precipitation pattern of Manthali weather station near our study area (2004–2014) (DHM Nepal, 2016).

This region is characterized by a subtropical climate with topography and climate were acquired from the global and 30-degree centigrade maximum temperature during summer national database (Table 2). Our questionnaire was based on and around 10-degree centigrade minimum temperatures questions designed to derive the livelihood vulnerability index during winter. This region rarely receives any snowfall and (Hahn et al., 2009) tailored to be relevant to the drought- average annual precipitation is less than 1000 mm per year. prone middle hill of Nepal. The questionnaire is divided into The majority (80%) of rainfall occurs during monsoon season the seven major components: socio-demographic profile, liveli- (June, July, and August) (Figure 2). hood strategies, social networks, health, food, water, and natu- The total population of all these VDCs is 40,428 (Table 1). ral disasters. A novel contribution of our spatially explicit These ten villages are among the eighteen most drought vulner- approach is the addition of climate variability and topography able VDCs in the Ramechhap district (Shrestha et al., 2010) components from secondary sources. along the Sunkoshi river (Figure 1). The major occupation of the people living in this region is agriculture and animal hus- bandry. As income from agriculture and animal husbandry is 2.3. Field data collection not sufficient for most families, a large proportion of the predo- In order to obtain as representative sample as possible, we minantly male population works in different cities in Nepal and sampled about 10% of all households in our study area. To abroad, especially in the Middle East countries (Pers. Comm.). do so, we acquired household data for each ward of our study villages from the Department of Survey, Government 2.2. The livelihood vulnerability index approach of Nepal (CBS, 2012). We employed a random systematic sampling approach (Madow, 1946) to collect the data from We use the livelihood vulnerability index approach to assess the samples beginning with a random house near the edge vulnerability and map spatially explicit outcomes at high resol- of the ward, and subsequently, the second household was ff ution for di erent wards of village development committees. sampled from the 8th house if there were 80 households, ff We use nine di erent components of livelihood vulnerability 7th house if there were 70 households, and so on. From index and perform a composite aggregation to derive the cli- each ward, a few extra household samples were also collected ff mate vulnerability index. Among them, the seven di erent whenever possible. The total numbers of samples collected in groups of data pertaining to household vulnerability were col- each village development committee (VDC) are listed in fi lected in the eld using a standard questionnaire, while Table 3.

Table 1. Area and demographic characteristics of the study villages (CBS, 2012). 2.4. Collection of biophysical data VDC Area (Square Kilometer) Number of Households Population Bethan 18.13 1090 4634 We used biophysical data in climate and topography com- Majuwa 17.25 453 2293 ponents. The climate data include precipitation and tempera- Rakathum 20.66 716 3434 Bhirpani 26.95 764 3253 ture. The topographic data include slope derived from digital Pakarbas 31.29 1177 6392 elevation model, land cover data derived from Landsat pro- Bhaluwajor 31.74 715 3496 ducts, and Normalized Difference Vegetation Index (NDVI) Ramechhap 38.68 1153 5222 Rampur 38.04 858 4101 derived from MODIS data. We used one-kilometer resolution Sukajor 28.98 756 3538 data created for the national vulnerability assessment of Khaniyapani 21.07 794 4065 Nepal for this purpose (Mainali & Pricope, 2017a). Details on Total 272.79 8476 40,428 the data sources and data processing of the biophysical data CLIMATE AND DEVELOPMENT 5

Table 2. Livelihood vulnerability index sub components. Major Component Sub-component Source Socio-Demographic Profile Percentage of household with female head Survey Age of household head Survey Education level of household head Survey Percentage of dependent population (less than 15 and more than 65 years of Survey age) Livelihood Strategies Percentage of household with family member working outside of the Survey community Percentage of households dependent solely on agriculture Survey Livelihood diversification Index (1/(number of occupation)) Survey Social Network Proportion of household who didn’t get help from their neighbor in past 12 Survey months Proportion of people who have not borrowed or lent money in past month Survey Percentage of households that have not asked government assistance Survey Food Percent of households solely dependent on family farm for food Survey Average number of months households struggle to find food (12- Survey own_production) Percent of households that do not save crops Survey Percent of households that do not save seeds Survey Average crop diversification index (1/ (Number of crops grown +1) Survey Water Percent of households reporting water conflicts Survey Percent of households that utilize a natural water source Survey Average time to water source in minute Survey Percent of households that do not have a consistent water supply Survey The inverse of the average number of liters of water stored per household. Survey Disaster Average number of floods, landslide, and drought events in past 6 years Survey Percent of households that did not receive a warning about the pending Survey natural disasters Percent of households with an injury or death as a result of the most severe Survey natural disaster in the past 6 years. Health Travel time in minutes to reach the nearest health post. Survey Percentage of household with a member with chronic diseases Survey Percentage of household with members who left work or school due to illness Survey in past week Climate Inverse of Average annual precipitation from 1981 January to 2014 (Funk et al., 2014; Mainali & Pricope, 2017b) Coefficient of variation of monsoon precipitation from 1981 to 2014 (Funk et al., 2014; Mainali & Pricope, 2017b) Rate of change of maximum temperature from 1977 to 2012 (DHM Nepal, 2016; Mainali & Pricope, 2017b) Topography The standard deviation of Normalized Difference Vegetation Index of the (Mainali & Pricope, 2017b; MODIS13Q1, 2016) average of July, August and September from 2000 to 2014. Land cover rank (Mainali & Pricope, 2017b; Uddin et al., 2015) Slope (Mainali & Pricope, 2017b; SRTM, 2016)

are available from the (Mainali & Pricope, 2017a) and can be In Equation 1 above, Sw is the average data of a particular downloaded from the data publication (Mainali & Pricope, variable for a ward, Smin is minimum value in all wards and 2017b). Smax is the maximum value in all wards. This standardization will result in the data with value zero to one: zero being the least vulnerable and one being the most vulnerable for each 2.5. Spatial and statistical analysis variable. We use 0 and 100 as the minimum and maximum value with the data in percentage while minimum and the We used a ward boundary shapefile of our study area acquired maximum value recorded in the household level data for from Department of Survey, Government of Nepal to subset all rest of the socioeconomic variables. For the biophysical socioeconomic and biophysical data to the extent of our study data, we use country-wide national range as maximum and area. The socioeconomic data were aggregated to the level of minimum value. village development committee while biophysical data were aggregated to the level of the ward and were stored in the attri- fi bute table of the polygon shape le. The calculation of vulner- Table 3. Number of sampled households in each village development committee ability indices was performed in a GIS environment using the (CBS, 2012). Field Calculator tool of ArcGIS. Number of Sampled Percentage This research uses a widely utilized approach to standardiz- VDC Households households Household (%) ing data because we worked with multiple datasets of different Bethan 1090 114 10.45 ff Majuwa 453 69 15.23 numeric scales and units, and belonging to di erent disciplines. Rakathum 716 81 11.31 The data standardization approach is similar to the one used in Bhirpani 764 85 11.1 the calculation of human development index (Anand & Sen, Pakarbas 1177 128 10.87 Bhaluwajor 715 81 11.32 1994), and is also employed widely to calculate the climate vul- Ramechhap 1153 113 9.8 nerability index (Hahn et al., 2009; Sullivan & Meigh, 2005). Rampur 858 85 9.9 − Sukajor 756 80 10.5 = Sw Smin Khaniyapani 794 84 10.57 Indexsw (1) Total 8476 920 10.85 Smax − Smin 6 J. MAINALI AND N. G. PRICOPE

2.6. The LVI calculation – the composite index approach In equation 5, Sensitivity is sensitivity index for the ward w, After each variable was standardized, the sub-components were w H is the average of health subcomponent, W is the average of averaged using the following equation (Hahn et al., 2009). w w
water subcomponent and, TOPOw is the average of the topo- n index graphic subcomponent. The resultant sum is divided by 16, = i=1 sdi Mw (2) ff n sum of the weights of the di erent sub components.

Where M is one of the seven major components for 4∗SDPw + 3∗LSw + 3∗SNw w Adaptive = (7) ward w (Socio-Demographic Profile (SDP), Livelihood Strat- w 10 egies (LS), Social Networks (SN), Health (H), Food (F), Here Adaptivew is the adaptive capacity index of the ward w, Water (W), Natural Disasters (D), Climate Variability SDPw is the average of Socio demographic Profile, LSw is the (CV), or Topography (TOPO)), indexs i represents the sub- d average of Livelihood Strategies, and SNw is the average of component indexed by i, that make up each major com- Social Network subcomponent for the ward w. The resultant ponent and n is the number of sub component in each sum is divided by 10, sum of the weights of the different sub major components. components. After each sub-component was derived, the final livelihood vulnerability index was calculated using the follow- ing equation. 3. Results and discussion = LVIw 3.1. Components of the composite livelihood

WSDPSDPw + WLSLSw + WSW SNw + WHHHw vulnerability index

+WFFw + WwWw + WDDw + WCV CVw + WTOPOTOPOw We aggregated survey results at the village development com- WSDP + WLS + WSW + WH + WF + Ww + WD + WCV + WTOPO mittee level to make it a statistically robust assessment. We (3) used twenty-six different variables divided into seven different components collected using the survey questionnaire: socio- where LVIw is the Composite Livelihood Vulnerability Index demographic profile, livelihood strategies, social networks, for a ward w, equals the weighted average of the nine major health, food, water, and natural disasters. The topography components. The weights of each major component, WMi, and climate components were acquired from national and glo- are determined by the number of sub-components that make bal databases. The average values of each component and sub- up each major component. components are presented in Table 4. Among the different variables included into the socio-demo- fi 2.7. The LVI calculation – the IPCC framework approach graphic pro le component, we found that the percentage of households with a female head of household ranged from fi ff We classi ed di erent components of vulnerability into about 5 in Bethan village to 30 in Rampur, while this percentage exposure, sensitivity, and adaptive capacity and used equation for the country of Nepal is about 25 (Statistical Pocket Book, 4 to derive a livelihood vulnerability index according to the 2014). The average age of household heads is consistent across fi IPCC de nition (Hahn et al., 2009; IPCC, 2001). the different villages. The lowest is about 49 in Bhaluwajor with IPCC - Livelihood Vulnerability = ([Exposure] the highest being the 58 in Sukhajor village. This range is larger than the national average of 45 years (Statistical Pocket Book, − [Adaptive Capacity])∗[Sensitivity] (4) 2014). The percentage of the household heads with secondary education is also highly variable with only 30 in Sukhajor to Disaster (D) and Climate Variability (CV) components were 62 in Rampur VDC. The percentage of the dependent popu- used to calculate the exposure component (Equation 5). Health lation is also fairly homogenous (30 to 39) among different vil- (H), Food (F), Water (W) and Topography (TOPO) com- lages in this area. This range is significantly higher than the ponents were used to derive the Sensitivity (Equation 6) national average of 17.5 (Statistical Pocket Book, 2014). The while Socio-demography Profile (SDP), Livelihood Strategies standardized value of socio-demographic profile calculated (LS) and Social Networks (SN) were used to calculate adaptive from the standardized value of each component also showed capacity (Equation 7). homogenous pattern among different villages (Table 4, Figure ∗ + ∗ 3 Dw 3 CVw 3(a)) with 0.3 in Bethan and 0.44 in Rampur. The spatial pat- Exposure = (5) w 6 tern shows that regions close to urban centers have higher values for this component as seen from the figure 3(a). Bethan Where Exposurew is the exposure index for ward w, Dw is the is close to Kathmandu, the capital city of Nepal, while part of average of disaster subcomponent for the ward w, and CVw is the Climate Variability sub component of ward w. Both of Ramechhap is the most densely populated urban center in this region. them were multiplied by 3 as each of them was calculated fi using three sub components. The resultant sum is divided by The livelihood diversi cation index measures the pro- the sum of their weight- 6, sum of their weight in this case. portion of households where family members are involved in more than one occupation. This value is 0.36 in Khaniyapani ∗ + ∗ + ∗ + ∗ 3 Hw 5 Fw 5 Ww 3 TOPOw to 0.63 in Rampur village. The percentage of households with Sensitivity = (6) w 16 agriculture as major occupation ranges from 85 in Ramechhap CLIMATE AND DEVELOPMENT 7

Table 4. Values of components, subcomponents and vulnerability index in different villages. Components and Sub-components Bethan Bhaluwajor Bhirpani Khaniyapani Majuwa Pakarbas Rakathum Ramechhap Rampur Sukhajor Percentage of household with female head 5.26 19.75 20.00 13.10 24.64 21.09 12.35 10.62 29.41 23.75 Age of household head 56.26 48.72 53.41 50.11 55.54 53.50 50.80 52.38 55.31 57.50 Education level of household head 36.84 58.02 34.12 50.00 62.32 43.75 53.09 39.82 62.35 30.00 Percentage of dependent population (less 30.24 32.40 34.31 39.75 38.15 37.46 37.42 35.50 35.35 36.18 than 15 and more than 65 years of age) Socio-Demographic Profile 0.31 0.38 0.34 0.36 0.44 0.38 0.37 0.33 0.44 0.36 Percentage of household with family 72.81 61.73 60.00 63.10 60.87 48.44 51.85 52.21 52.94 61.25 member working outside of the community Percentage of households dependent 94.74 93.83 98.82 100.00 97.10 100.00 100.00 85.84 100.00 88.75 solely on agriculture Livelihood diversification Index (1/(number 0.37 0.53 0.40 0.37 0.57 0.38 0.42 0.43 0.63 0.41 of occupation)) Livelihood Strategies 0.68 0.70 0.66 0.67 0.72 0.62 0.65 0.60 0.72 0.64 Proportion of household who didn’t get 20.18 23.46 25.88 38.10 17.39 55.91 62.96 36.61 68.24 37.50 help from their neighbor in past 12 months Proportion of people who have not 6.14 9.88 3.53 14.29 1.45 7.09 6.17 5.36 3.53 3.75 borrowed or lent money in past month Percentage of households that have not 76.32 51.25 94.12 95.24 37.68 89.76 76.54 83.93 25.88 82.50 asked government assistance Social Network 0.34 0.28 0.41 0.49 0.19 0.51 0.49 0.42 0.33 0.41 Percent of households solely dependent on 2.63 0.00 0.00 0.00 4.35 3.13 0.00 0.00 2.35 1.55 family farm for food Average number of months households 5.39 7.04 6.46 6.28 5.87 6.52 5.05 7.04 6.31 6.31 struggle to find food (12- own_production) Percent of households that do not save 91.23 97.53 97.65 97.59 95.65 95.20 95.06 96.43 96.47 96.71 crops Percent of households that do not save 3.57 35.00 0.00 1.20 8.70 10.40 3.70 13.39 2.35 4.28 seeds Average crop diversification index (1/ 0.33 0.40 0.35 0.33 0.23 0.33 0.38 0.41 0.48 0.42 (Number of crops grown +1) Food 0.35 0.46 0.37 0.37 0.36 0.39 0.36 0.42 0.40 0.39 Percent of households reporting water 63.16 43.21 42.86 53.57 40.00 58.59 70.37 19.09 61.18 22.50 conflicts Percent of households that utilize a natural 98.25 81.48 77.65 100.00 100.00 78.13 100.00 89.19 97.65 92.50 water source Average time to water source in minute 6.17 21.44 26.93 6.76 18.09 35.53 7.66 11.13 26.36 9.33 Percent of households that do not have a 71.05 28.40 51.76 54.76 69.57 26.56 74.07 75.68 83.53 81.25 consistent water supply Inverse of the average number of liters of 228.07 234.51 160.47 194.64 172.06 163.20 172.06 250.25 181.29 182.56 water stored per household. Water 0.63 0.49 0.55 0.59 0.61 0.54 0.67 0.54 0.69 0.57 Average number of floods, landslide and 0.40 0.80 0.00 0.02 1.91 0.01 0.15 0.08 1.05 0.38 drought events in past 6 years Percent of households that did not receive 0.00 0.00 0.00 0.00 0.00 1.60 0.00 0.00 0.00 0.00 a warning about the pending natural disasters Percent of households with an injury or 0.88 1.23 1.18 0.00 0.00 0.78 0.00 0.90 0.00 3.80 death as a result of the most severe natural disaster in the past 6 years. Disasters 0.07 0.13 0.00 0.00 0.32 0.33 0.03 0.01 0.17 0.06 Travel time in minutes to reach the nearest 42.81 77.22 85.44 53.94 76.45 67.40 52.28 49.40 76.65 56.39 health post. Percentage of household with a member 25.44 20.99 22.35 14.29 23.19 25.98 30.86 28.57 25.88 21.25 with chronic diseases Percentage of household with members 7.02 6.17 7.06 4.76 0.00 5.51 2.47 6.25 3.53 5.00 who left work or school due to illness in past week Health 0.23 0.31 0.34 0.21 0.29 0.29 0.26 0.25 0.31 0.24 Average annual precipitation from 1981 1219.44 1098.26 1293.19 1234.15 1310.99 1259.74 1202.29 1047.35 1151.78 1106.33 January to 2014* Coefficient of variation of monsoon 0.12 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 precipitation from 1981 to 2014 Rate of change of maximum temperature 0.03 0.03 0.03 0.04 0.03 0.03 0.04 0.03 0.02 0.03 from 1977 to 2012 Climate 0.28 0.27 0.29 0.31 0.32 0.29 0.31 0.27 0.26 0.27 The standard deviation of Normalized 0.10 0.11 0.11 0.14 0.14 0.10 0.09 0.12 0.10 0.13 Difference Vegetation Index of the average of July, August and September from 2000 to 2014. Land cover rank 3.34 3.11 3.31 3.08 3.19 3.50 3.51 3.09 3.05 2.99

(Continued) 8 J. MAINALI AND N. G. PRICOPE

Table 4. Continued. Components and Sub-components Bethan Bhaluwajor Bhirpani Khaniyapani Majuwa Pakarbas Rakathum Ramechhap Rampur Sukhajor Slope 24.97 23.72 24.92 24.97 27.19 20.45 22.65 23.03 26.05 24.08 Topography 0.38 0.37 0.39 0.41 0.43 0.36 0.37 0.38 0.38 0.39 Composite Livelihood Vulnerability Index 0.38 0.39 0.38 0.39 0.42 0.42 0.40 0.37 0.43 0.39 Exposure 0.17 0.20 0.15 0.16 0.32 0.31 0.17 0.14 0.22 0.17 Sensitivity 0.42 0.43 0.43 0.42 0.44 0.41 0.44 0.42 0.47 0.43 Adaptive Capacity 0.43 0.44 0.46 0.49 0.45 0.49 0.49 0.44 0.49 0.46 IPCC-Livelihood Vulnerability Index −0.11 −0.10 −0.13 −0.13 −0.05 −0.05 −0.13 −0.12 −0.13 −0.12 to 100 in four of the villages. The lowest percentage in Ramech- with only 1.44 in Majuwa to 14.28 in Khaniyapani. The percen- hap village is due to the factor that it is the most urbanized one tage of households that haven’t contacted their local govern- among the studied villages as it used to be a district headquarter ment for the help varies from 26 in Rampur to 95 in until 1998 (Pers. Comm.) This is declared a municipality Khaniyapani. The standardized average of the social network recently (Ramechhap Municipality, 2016). Percentage of the varies from the 0.18 in Majuwa to 0.58 in Pakarbas (Figure 3 households where at least one member has been to another (c)). The social network vulnerability scores are, however, community is also high in this region with 48 in Pakarbas to lower in the more remote region, as might be expected. 72 in Bethan (Table 4). The overall standardized average of live- Among the ten villages surveyed, all of the households in lihood component ranges from 0.6 in Ramechhap to 0.72 in Bhaluwajor, Rakathum, Ramechhap, Bhirpani, and Khaniyapni Rampur. The overall pattern of livelihood diversification were of the view that the food grown on their farm is not index which shows how diverse are the income source of sufficient to support their family. Among the others, the maxi- households, also reveals an urban to rural gradient with regions mum percentage was mere 4 in Majuwa village. The average close to the urbanized area having higher values than region number of months when households struggle to find food ran- farther (Figure 3(b)). ged from 5 in Rakathum to 7 in Bhaluwajor. There are many The percentage of households that neither received nor lent crops grown in this region with major staple crops being money to and from a neighbor ranged from 17 in Majuwa to 68 maize, rice, and wheat. Many different types of lentils are also in Rampur. But, the percentage of families who did not give or grown in this region. The average crop diversity index ranges receive monetary help from their neighbor and friends is low from 0.22 in Majuwa to 0.48 in Rampur. As most of the

Figure 3. Spatial variations of the livelihood vulnerability components. CLIMATE AND DEVELOPMENT 9 households are not able to support their family from farm ward number 8. The general spatial pattern of climate vulner- income alone, they also save little. The percentage of house- ability scores shows that northwest high elevation regions are holds who do not save the crop, ranges from 92 in Bethan to more vulnerable than other regions (Figure 3(h)). 98 in Bhirpani. The overall food vulnerability score ranges The variables in topographic components, however, vary from 0.35 in Bethan to 0.46 in Bhaluwajor (Figure 3(d)). The greatly. The topography component is aggregated at the ward north western regions have lower vulnerability scores, probably level. The average slope per ward ranges from 17 to 31 degree due to higher rainfall, cooler temperature, and better vegetation although per villages it is only 23 to 26 degrees (Table 4, Figure 3 cover in those regions. (i)). The standard deviation of normalized difference vegetation Availability of water is the major problem in this drought- index ranges from 0.065 to 0.23. The average land cover rank in prone region. Only 27% of household have 24-hour access to each village is 2.01 to 4.02 signifying that most of the area is water in Pakarbas village while it is 84 in Rampur. The majority mapped as agriculture land, shrubberies, and settlements. The of households use a natural source of water (river and natural average topographical vulnerability ranges from 0.43 in springs) with 77% in Bhirapni to 100% in Majuwa, Rakathum, Rakathum ward number 9 to 0.47 in Majuwa ward number and Khaniyapani. According to the central bureau of statistics, 8. The high elevation wards of Khaniyapani, Bhirpani, and only 45% of households use natural sources of water nationally Majuwa are highly exposed in terms of topography, and (Statistical Pocket Book, 2014). Average time to fetch water more so than the other regions. It must be noted from figure ranges from six minutes in Bethan to 36 min in Pakarbas. 3(i) that there are wards of higher vulnerability, topography The percentage of household reporting water conflict is 19 in wise, within a village especially due to higher variability of Ramechhap to 70 in Rakathum. The average amount of water elevation, slope, and land cover. collected by household ranges from 160 liters in Bhirpani to Using equation 3 we derived the composite livelihood vul- 250 liters in Ramechhap. The standardized water vulnerability nerability index of our ten village development committees. score ranges from 0.49 in Bhaluwajor to 0.69 in Rampur. The The detailed example of index calculation is given in sup- spatial pattern shows that the southeast and northwest regions plementary material (Supplementary Material 1). For the ten are more vulnerable than the region in the central region VDCs, the values ranged from 0.37 to 0.423. Among them, (Figure 3(e)). The villages in the central region are close to Rampur, Pakarbas and Majuwa villages are in the very high vul- Manthali, the district headquarters and therefore have better nerable category while whole Ramechhap village and part of access to resources to build water infrastructures. Bethan and Bhirpani are in the lower spectrum of our result The average number of households reporting disaster was (Figure 4). Our inclusion of high-resolution climate and topo- low in the study area with none in Bhirpani to 2% in Majuwa. graphic data has made it possible to detect vulnerability vari- We excluded the recent earthquake assuming that all of the ation among different wards of single villages. As this study households had a similar impact from the earthquake. The area is relatively homogenous, we find only a small range of injury with the disaster was recorded only in Pakarbas where values for most of the components. Nonetheless, we observe 2% households reported the injury (Figure 3(f)). The percen- significant spatial variations of different components. The tage of households that have access to an early warning system northeast and the southwest regions of our study area showed in the face of a pending natural disaster (both in the past and higher overall vulnerability values for most components. These presently) is very low with none in four villages to 4% in Sukha- high vulnerable regions are remote, lack road access, and are jor. The spatial pattern of disaster vulnerability score shows situated at higher elevations. that the Pakarbas and Majuwa villages are two highest vulner- able among 10 VDCs (Figure 3(f)). 3.2. IPCC livelihood vulnerability index The average time to reach the nearest health facility ranges from 42 min in Bethan to 85 min in Bhirpani. The percentage Nine different components were classified into three different of households with chronic illness ranges from 14 in Khaniya- IPCC vulnerability components: exposure, sensitivity, and pani to 31 in Rakathum. There were no households reporting adaptive capacity. The exposure component was derived as their member sick at the time we were there in Majuwa village an average of Disaster (D) and Climate Variability (CV) sub- while about 7% households reported so in Bethan and Bhirpani. components. The exposure index ranges from 0.14 in Ramech- The overall health vulnerability score ranges from 0.21 in Kha- hap ward number 7 to 0.32 in Majuwa ward number 8. The niyapani to 0.33 in Bhirpani. The spatial pattern of health vul- spatial pattern shows that it is high in Majuwa and Pakarbas nerability shows that the vulnerability is higher in medium villages. The average sensitivity component derived from distance villages to Manthali while those close to Kathmandu Health (H), Food (F), Water (W) and Topography (TOPO) are less vulnerable (Figure 3(g)). It can equally be due to the components ranges from 0.4 in Ramechhap ward number 4 small size and the accessible location of the local health post to 0.47 in Rampur ward number 6. Majuwa, Rakathum, and in the villages with lower health vulnerability. Rampur villages are among the highest sensitive villages in The climate component is relatively homogenous with 982 this region (Figure 5(b)). Socio-demography Profile (SDP), to 1338 millimeters of rainfall per year received among the Livelihood Strategies (LS), and Social Networks (SN) scores different wards surveyed. The coefficient of variation of precipi- were used to calculate adaptive capacity component. The adap- tation ranges from only 10 to 13% across our study area. The tive capacity score ranges from 0.43 in Bethan ward number 6 temperature trend ranges from 0.011 to 0.036 degree Celsius to 0.49 in Khaniyapani ward number 3. Khaniyapani, Pakarbas, per year. The average climate vulnerability score ranges from and Rampur are most vulnerable in terms of adaptive capacity 0.25 in Rampur ward number 2 and 9 to 0.33 in Majuwa (Figure 5(c)). The composite IPCC-livelihood vulnerability 10 J. MAINALI AND N. G. PRICOPE

Figure 4. Spatial pattern of Composite Livelihood Vulnerability Index.

Figure 5. Spatial patterns of different components of IPCC-LVI. CLIMATE AND DEVELOPMENT 11 index was calculated using equation 7. It ranges from −0.13 in 3.3. Contribution of different components to Rakathum ward number 4 to −0.048 in Pakarbas 8. The spatial vulnerability to drought pattern reveals that Pakarbas and Majuwa are among the high- In the overall livelihood vulnerability index result, the est in terms of vulnerability (Figure 5(d)). livelihood strategies and water component played the most Overall, climate vulnerability is a function of climatic and important roles with a vulnerability score higher than 0.7 hazard stressors typically conceptualized as exposure, the like- (Figure 7). The higher vulnerability score in the livelihood lihood of the system to be affected, conceptualized as sensitivity strategies component is due to the fact that most of the people and the capacity of a system to cope with or adapt to stressors in this region practice subsistence, rain-fed agriculture as a and exposure. In our study, the higher vulnerability observed in major occupation and livelihood strategy. They are highly sen- many of the sub-district-level wards is due to lack of adaptive sitive to recurring droughts given that very few households capacity (0.46) and sensitivity (0.42), and the lower value of have irrigation facilities. Our survey results show that although exposure (0.2) (Figure 6). The exposure component captures 88 to 100% of households’ major occupation is agriculture, food the severity of external hazards associated with the climate production from agriculture is sufficient for the households of variability, in this case primarily droughts (Gerlitz et al., less than 5% of the sampled households in most of the VDCs, 2017). Although we studied ‘drought vulnerability’, other stres- making this region generally food insecure. The drought-prone sors like landslide, flood, and change in temperature also act in smallholder farmers ’ vulnerability tends to be amplified by lack conjunction with drought to increase a household’s exposure to of employment diversification, access to markets, access or climatic hazards in this region. The adaptive capacity used in rights to land, and lack of relevant technological knowledge our analysis is a function of socio-economic capacity, social (eg. Ghimire, Shivakoti, & Perret, 2010). The reduced liveli- networks, socio-demographic profile, and livelihood strategies. hood diversification due to the lack of availability of non- Availability of multiple income sources, presence of strong gov- farm income is a major cause of heightened livelihood and ernmental, non-governmental, and social organizations, and food insecurity also (Bhandari & Grant, 2007; Gentle & Mara- community members who are active in the workforce help to seni, 2012) reduce the potential damage by a hazard and increase the The higher vulnerability score in the water component is household’s and community’s capacity to cope with the conse- due to the fact that this is one of the lowest rainfall receiving quences of exposure to hazards (Gerlitz et al., 2017, Hahn et al., regions of Nepal. The national mean annual precipitation is 2009). However, most of the households and communities in 1857.6 millimeters while this region receives less than our study fell short in all of these sub-components leading to 1000 mm on average during a climatologically normal year increased vulnerability. More vulnerable households are those (Marahatta, Dangol, & Gurung, 2009). Our survey results whose sensitivity is increased against climatic hazards by either show that 77 to 100% of households use a natural source of reduced water availability, lack of access to health services, or water and walk long distances to collect it during the dry season are located in high elevation steep slopes. Variations within (Table 4). The drinking water supply is also inconsistent these subcomponents define the degree to which any type of throughout the year, with about 75% of the households report- hazard is likely to affect households and communities. ing a lack of access to clean drinking water during the dry part As in of the majority of rural Nepal, this region’s vulner- of the year (Table 4). Previous studies have also found this ability is attributed more to the lower socioeconomic capacity region to be highly vulnerable in terms of water security, and inhospitable topography than the climatic exposure. In with water scarcity being the main driver of this region’s cli- most developing nations, lower adaptive capacity is the main mate-related vulnerability (Baral et al., 2012; Bhuju et al., 2013). cause of vulnerability (Füssel, 2010). The similar rural assess- Although the villages studied in this work lie along the Sun- ments in other parts of Nepal also find the lack of adaptive koshi River, parts of the villages surveyed have significant capacity as the main cause of vulnerability (Pandey & Bardsley, elevation ranges. Due to the lack of access to water-transfer 2015; Panthi et al., 2015). Mainali and Pricope (2017a) also technologies, most of these regions have historically not been found the lack of adaptive capacity as the most important con- able to move water from the river to high elevations where tributor to climate vulnerability for the entire country of Nepal. people live and farm. This renders households living far from the river vulnerable to loss of their ephemeral water resources, especially during prolonged drought periods. The steep slopes and variable elevations compounded by a lack of sufficient irri- gation make it difficult to grow crops as well. The midhill region’s topographically induced vulnerability can be reduced by diversifying livelihood options (Shukla, Sachdeva, & Joshi, 2016). Many of these regions cannot support forest cover and can only be vegetated with shrub lands especially on river- facing slopes (Bohara, Bhuju, & Mainali, 2015; Uddin et al., 2015). The rugged topography also makes it difficult for house- holds to access markets to sell any excess local products or transport food and other essential products to the locality. Although recent efforts by local governments have attempted Figure 6. Spider diagram showing different components of IPCC-Vulnerability. to improve road networks, most roads in the region remain 12 J. MAINALI AND N. G. PRICOPE

Socio-demography 0.8 0.7 Topography Livelihood Strategies 0.6 0.5 0.4 0.3 0.2 Climate Social Network 0.1 0.0

Health Food

Disaster Water

Figure 7. Spider diagram showing different components of livelihood vulnerability. impracticable year-round and are extremely unreliable and of social reciprocity are keys to the livelihoods of the rural dangerous (Pers. Comm.). poor. In this study also, we find that the overall condition of the provision of health, water, disaster assistance, and food components are affected by the climate, topography, socio- 3.5. Comparison of results demographic profile, livelihood strategies, and social network The spatial variation of the vulnerability index derived from the composition. Vulnerability sectors such as food, water, health, composite index approach and the IPCC approach produced a and disasters are interrelated (Figure 9). Our results reveal that similar pattern (Figure 8). Both of the assessments show Pakar- adaptation activities should focus on improving livelihood bas and Majuwa as the most vulnerable VDCs. The composite strategies, increasing access to water and health care, addres- index shows Rampur as another most vulnerable VDC. There sing accessibility issues brought by difficult terrain, and effec- are some discrepancies too, as the part of Rakathum and Bhir- tively communicating early warning for drought also. pani are in higher vulnerability category in the composite index Although early warning systems for flooding are being devel- while they are in lower vulnerability category in IPCC-LVI. The oped in Nepal, slow-onset disasters such as droughts have yet parts of Ramechhap and Rampur also show some discrepancies to catch up in terms of early warning systems and preparedness between the two aggregation methods. These discrepancies are (Shrestha et al., 2014). most likely due to the different variables included in creating In light of our findings, we posit that strategies for liveli- components and equations used in the calculation of vulner- hoods improvement should include creating employment ability index. opportunities at the local level so that people no longer need to be solely dependent on relatively unproductive and rain- fed agriculture. Addressing widespread intra-annual food inse- 3.6. Interconnections among different components curity and access to food will remain important aspects of Our analysis shows that different livelihood vulnerability com- adaptation activities in this region as it is dependent on many ponents are not mutually exclusive as one component can have factors like challenging terrain and transportation unavailabil- a certain effect in increasing the vulnerability of other com- ity, declining labor due to out-migration and low technological ponents in our study site (Figure 9). The interlinked nature capacity (Karki et al., 2015). This can be achieved by two basic of rural livelihoods is discussed in the literature as well; approaches: firstly, development of road and market infrastruc- examples include the complexity of vulnerability of South Afri- ture so that people can have access to affordable food due to can farming system (Neves & du Toit, 2013), where the inter- insufficient food supply throughout the year. The second linked nature of land employment and rural livelihoods were approach might consist of investing in infrastructure and studied. The authors reported that a combination of land- technology to upscale the current agricultural practices to based entitlements, informal farm and non-farm activities, increase the productivity of the land. Our analysis shows that state social activities, and state social assistance and practices most of the households perceive agriculture as their primary CLIMATE AND DEVELOPMENT 13

Figure 8. Spatial pattern of Composite-LVI and IPCC-LVI occupation; simultaneously, all of them are involved in other for expensive construction costs, and local people’s inability occupations too, as only a negligible number of households to access government machinery. In Nepal, inadequate accessi- can fulfill their household food requirement from their farm bility, poor and volatile governance are the main reasons production. behind water insecurity (Biggs, Duncan, Atkinson, & Dash, This is a relatively dry region in the middle hills of Nepal. 2013). Local people were of the view that the recent frequent drought Nepal’s healthcare facilities are also in poor state and they events are making water availability worse. Our analysis shows tend to be concentrated in major cities. The local health facili- that the majority of households still depend on natural sources ties, especially in rural regions, lack resources and expertise to of water for daily needs and there are growing issues of water- perform even basic health and client services (Nepal Health related conflicts among neighbors and inconsistent availability Facility Survey, 2015, pp. 8–9). Health facilities in our study of water in dry months. Although average daily water require- region are also very scarce and require a long travel time due ments of the households of this region are low (∼160–250 to inaccessibility of terrain and scattered settlements. The liters), even that small amount is not always reliably available local health posts in each village are understaffed and lack to many households. The global average of water use per house- medical resources. Our household survey also shows that it hold (assuming 6 persons in a household) for the dry region takes a few hours for many households in remote parts of the ranges from 360 to 480 liters per day (Gleick, 1996). The villages to reach the health posts and hospitals. Most of the main reason behind this water scarcity is the lack of resources local health posts only have paramedical facilities. The medical to construct drinking water infrastructure in this region. Our doctors are available only in a few hospitals in district head- results show that this lack of resources is, in turn, due to the quarters. On the other hand, people’s socio-demographic inaccessibility of the region because of lack of roads, scattered characteristics are also responsible for the high health-related settlements, low-income levels resulting in an inability to pay problem in this region as our study report a high proportion 14 J. MAINALI AND N. G. PRICOPE

Figure 9. Different vulnerability components and their interrelations resulting from this analysis. of the dependent population. Disaster occurrence and its aggregate level as can be observed based on our spatially explicit impacts are, therefore, amplified by the difficult terrain, poor mapping of the different components. This assessment further socio-demography, and lack of good governance. reiterates the fact that, in order to be effective on the ground, Due to the inherent interrelationships among several factors vulnerability assessments should go beyond coarse level admin- responsible for increasing vulnerability in this region, any istrative boundaries and should take into account topographic adaptation activities in this region require development initiat- variability as well since we find that the higher elevation regions ives, similar to other parts of Nepal which is built on overcom- with steep slopes are more vulnerable than lower elevation ing poverty by diversifying livelihood options, investing in regions. Our data show that there are strong interrelationships infrastructure especially for access roads, drinking water, irriga- among different components of livelihood vulnerability. Com- tion, and healthcare (eg. Gentle & Maraseni, 2012). ponents such as a community’s socio-demographic profile, live- lihood options, water, health, disaster, and topography affect each other and create a self-reinforcing downward spiral of vul- 4. Conclusions nerability that traps local communities in a state of heightened ffi We successfully apply the livelihood vulnerability index vulnerability, thus making it di cult to penetrate the situation approach to mapping the different climate components and and prioritize adaptation activities. Our results suggest that this the overall vulnerability in a continuous manner at very fine region may necessitate a set of carefully planned interventions which not only address the individual components but also spatial resolution. We show that national and global scale bio- ff physical data can successfully be used in congruence with takes into account the interconnections among the di erent locally collected data related to livelihoods and socioeconomic components of livelihood vulnerability. conditions in order to explore the spatial patterns of climate vulnerability at the lowest administrative level possible in a drought-prone mid-elevation region of a developing country. Acknowledgements ff We show that there are many factors a ecting drought vulner- This work was funded by International Foundation for Science (Grant ability with varying intensity in our case study region but lack Number W/5696). We thank Fulbright Commission for funding first of adaptive capacity and higher sensitivity because of the low author’s study at a US-based university. We would also like to thank Dr. water availability, a high percentage of dependent population, Douglas Gamble, who helped design this project in the beginning. We lack of education, dependence on agriculture, and difficult ter- greatly appreciate the help from local people of Ramechhap district, Nepal. We thank field support members Shyam Paudel, Mahesh Limbu, rain are the major causes of vulnerability in this region. Never- Jayram Ghimire, Dev Kumari Nepali, and Mina Dahal for their tremen- theless, even within this small region, there is a high degree of dous help. Mr. Jason Hess helped to improve language and grammar of heterogeneity among components both spatially and at the the manuscript. CLIMATE AND DEVELOPMENT 15

Disclosure statement Chambers, R., & Conway, G. R. (1991). Sustainable rural livelihoods: prac- tical concepts for the 21st century (IDS Discussion Paper 296). Retrieved fl No potential con ict of interest was reported by the authors. from http://opendocs.ids.ac.uk/opendocs/bitstream/handle/123456789/ 775/Dp296.pdf?seq Cutter, S., Boruff, B. J., & Shirley, W. L. (2003). Social vulnerability to Funding environmental hazards. Social Science Quarterly, 84, 242–261. This work was supported by International Foundation for Science [grant de Sherbinin, A., Chai-Onn, T., Giannini, A., Jaiteh, M., Levy, M., Mara, … number W/5696-1]. V., Trzaska, S. (2014). Mali climate vulnerability mapping. Burlington: USAID, African and Latin American Resilience to Climate Change Project. Retrieved from https://www.usaid.gov/sites/ Notes on contributor default/files/documents/1860/MALI%20CLIMATE% 20VULNERABILITY%20MAPPING.pdf Janardan Mainali’s research interest lies at the intersection of society de Sherbinin, A., Chai-Onn, T., Jaiteh, M., Mara, V., Pistolesi, L., Schnarr, and environment and he usually takes on the interdisciplinary research E., & Trzaska, S. (2015). Data integration for climate vulnerability map- projects. His past research includes national-level climate vulnerability ping in West Africa. ISPRS International Journal of Geo-Information, 4 mapping of Nepal, analysis of vegetation–climate relations in the Hima- (4), 2561–2582. doi:10.3390/ijgi4042561 layas, and water quality modelling of a river basin in South Korea. He DHM Nepal. (2016). Department of Hydrology and Meteorology. is currently a PhD student at Portland State University where he is looking Retrieved from http://dhm.gov.np/ at ways in which social and ecological perspectives can be incorporated in Field, C. B., Barros, V. R., Mastrandrea, M. D., Mach, K. J., Abdrabo, M.-K., the surface water quality modelling. Born and bred in Nepal, he came to Adger, N., … Yohe, G. W. (2014). Summary for policymakers. Climate the US to study MS in geography as a Fulbright student. He also has an Change 2014: Impacts, Adaptation, and Vulnerability. Part a: Global and MS degree in Botany from Tribhuvan University, Nepal. Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press. Retrieved from http://epic.awi. ORCID de/37531/. Flanagan, B. E., Gregory, E. W., Hallisey, E. J., Heitgerd, J. L., & Lewis, B. 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