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SchEMS

School of Environmental Science and Management

Pokhara University

Master Thesis No:

CLIMATE CHANGE VULNERABILITY ASSESSMENT AND

ADAPTATION OPTIONS: A CASE STUDY OF KARTIKSWAMI VDC,

JUMLA DISTRICT NEPAL

Karmath Subedi

2012

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Climate Change Vulnerability Assessment and Adaptation Options: A Case Study Of

Kartikswami VDC, Jumla District Nepal

By

Karmath Subedi

A thesis report submitted in partial fulfillment of the requirements for Degree of Master of

Science in Environmental Management

Examination Committee: Mr. Madhukar Upadhya (Advisor)

Mr. Ngabindra Dahal (External Examiner)

Prof. Dr.Ananda Raj Joshi (Director, Academic Department)

Sunita Khatiwoda

Previous Degree: Bachelor Degree in Environmental Science

Amrit Science College

Thamel, Kathmandu

School of Environmental Science and Management (SchEMS)

New Baneshwor

Kathmandu

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Certification

This is to certify that the thesis entitled “CLIMATE CHANGE VULNERABILITY

ASSESSMENT AND ADAPTATION OPTIONS: A CASE STUDY OF KARTIKSWAMI VDC,

JUMLA DISTRICT NEPAL” submitted by Mr. Karmath Subedi towards partial fulfillment of

Degree of Master of Science in Environmental Management is based on her original research and

study under the guidance of Mr. Madhukar Upadhya. The thesis in part or full is the property of

School of Environmental Science and Management (SchEMS) and thereof should not be used

for the purpose of awarding any academic degree in any other institution.

Mr. Madhukar Upadhya Date:

Faculty member, SchEMS

(Advisor)

Mr. Ngabindra Dahal Date:

(External Examiner)

Prof.Dr.Ananda Raj Joshi Date:

Director, Academic Department,

SchEMS

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Abstract

Reducing the impacts of climate change and variability is not as significant for the countries like

Nepal, which is unfortunately one of the most vulnerable countries in the world to the climate

change. The very sensitive mountain environment and the alpine ecosystem with less elasticity

towards the changing environment, complex geo-physical structure and poor socio-economic

status had made the people residing at higher altitude more vulnerable with less adaptive

capacity. The purpose of the study was to investigate the degree of vulnerability of the higher

altitude residents and find out the prevailing adaptation practices. Another important aim of the

study was to correlate the rice productivity with the changing climatic parameters i.e.

temperature and precipitation. Further this research also intends to assess the patterns and

trends of climate change and the socio-economic status of the community. The questionnaire

survey, key informant interview, focus group discussion were the major methods applied in the

field while the secondary hydro-meteorological data and the rice productivity data were analyzed

and interpreted. Upon the analysis of the primary and secondary data this study disclosed that

the people residing in the study area are moderately vulnerable to climate change and the socio

economic status are far below in compare to the other parts of the country. Further the

productivity of the rice plant has the positive impact with the change in temperature whereas it

responds inversely to the change in precipitation. From the study it can be concluded that there is

changing climate in the area and has a notable impact on people's daily life. In the village

adaptive capacity is low due to economic status and inequitable access to the resources. The

results also showed that the high variation in climatic pattern have significant impact on

livelihoods of people. The results are highly supported by hydro-meteorological data which

showed change in the weather condition of the study area. Moreover, rice cultivation in Jumla

was found to be associated with socio-cultural as well as special agronomic practices.

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Acknowledgement

I would never have been able to finish my dissertation without the guidance of my seniors, help

from friends, and support from my family.

I would like to express my deepest gratitude to my advisor, Mr.Madhukar Upadhya, for his

excellent guidance, caring, patience, and providing me with an excellent atmosphere for doing

research.

I would like to thank Nawraj Sapkota, who as a good friend was always willing to help and give

his best suggestions. Many thanks to Bikalpa Adhikari, Dibas Babu Panta and Nabin Bhattarai

for helping me to perform questionnaire survey and other data collection at the field. My

research would not have been possible without their helps.

Similarly, I am indebted to Prof. Dr. Ananda Raj Joshi (Academic Director, SchEMS), Prof. Dr.

Kunjani Joshi (Pokhara University) and Mr. Ajay Mathema (SchEMS) for their constructive

feedbacks and guidance during several stages of the research.

I would also like to thank my parents,Mr. Jeevan Prasad Subedi and Mrs Sumitra Subedi,

younger brother Mr. Nirbheek Kamal Subedi and elder sister Ms. Bandana Subedi. They were

always supporting me and encouraging me with their best wishes.

I am grateful to my good wishers Sanam Akchhya Raj, Ganesh Karki, Shanta Pandit, Som Nath

Bhattarai, Tahir Mohammad Miya, Nawaraj Neupane, Poojan Bhandari and all other friends

whose continuous concerns in my study and inspirational support always motivated me to

complete my research successfully.

Finally, I would like to thank my friend, Sunita Khatiwoda. She was always there cheering me up

and help me with every small and big problems I encounter with during my research.

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Contents Introduction ...................................................................................................................................... 9

1.1 Background ............................................................................................................................. 9

1.2 Statement of the Problem ..................................................................................................... 11

Literature Review ........................................................................................................................... 12

2.1 Global Context ...................................................................................................................... 12

2.2 United Nation's Framework Convention on Climate Change .............................................. 13

2.4 Nepal and Climate Change ................................................................................................... 13

2.4.1 Nepal in the UNFCCC ................................................................................................... 14

2.5 Climate Variability and Change ........................................................................................... 14

2.6 Impacts of Climate Change in Nepal .................................................................................... 16

2.6.1 Impacts on Water Resources ......................................................................................... 16

2.6.2 Impacts on Agriculture Sector ....................................................................................... 16

2.6.3 Impact of Climate Change on Natural Forest, Bio-diversity and Wildlife .................... 16

2.7 Vulnerability and Adaptation ............................................................................................... 16

2.8 Climate Change and other climatic study of Jumla .............................................................. 20

Rational of the study ....................................................................................................................... 22

Objectives, Scope and Limitation ................................................................................................... 23

4.1 General Objectives: .............................................................................................................. 23

4.2 Specific Objectives: .............................................................................................................. 23

4.3 Scope and Limitation ............................................................................................................ 23

Materials and Methods ................................................................................................................... 24

5.1 Study Area ............................................................................................................................ 24

5.1.1 Kartikswami VDC ............................................................................................................. 26

5.2 Research Design ................................................................................................................... 28

5.3 Sample Size and Sampling Technique ................................................................................. 28

5.4 Data Collection ..................................................................................................................... 29

5.4.1 Primary Data Collection ................................................................................................ 29

5.4.2 Secondary Data .............................................................................................................. 29

5.5 Data Analysis ........................................................................................................................ 29

Result and Discussion ..................................................................................................................... 31

6.1 Social and Economic Status ................................................................................................. 31

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6.2 Hydro-Meteorological Data Analysis: .................................................................................. 32

6.2.1 Temperature Analysis .................................................................................................... 32

6.2.2 Precipitation Analysis .................................................................................................... 37

6.3 Correlation between Rice Productivity and Temperature and Precipitation ........................ 42

6.3.1 Precipitation and Rainfall .............................................................................................. 43

6.3.2 Temperature and sunshine ............................................................................................. 43

6.4 Public Perception on Climate Change .................................................................................. 43

6.5 Climate Change Impacts and Adaptation practices .............................................................. 44

6.5.1 Agriculture ..................................................................................................................... 44

6.5.2 Water Resources ............................................................................................................ 44

6.5.3 Forest and Biodiversity .................................................................................................. 45

6.5.4 Human and Livestock Health ........................................................................................ 45

6.6 Vulnerability and Climate Change ....................................................................................... 45

Conclusions and Recommendation ................................................................................................ 47

7.1 Conclusion ............................................................................................................................ 47

7.2 Recommendation .................................................................................................................. 48

References: ..................................................................................................................................... 49

Appendix I: Questionnaire for Household Survey ......................................................................... 54

Appendix II: Checklist for focal group discussion ........................................................................ 59

Appendix III: Checklist for key informant interview ................................................................... 60

Appendix IV: Mean Maximum Temperature Data of Jumla ......................................................... 61

Appendix V: Mean Minimum Temperature of Jumla .................................................................... 62

Appendix VI: Precipitation Data of Jumla ..................................................................................... 63

Appendix VII: Vulnerability Analysis form ................................................................................... 64

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Table of Figure:

Figure 1 Map of Nepal.................................................................................................................... 25

Figure 2 : Annual mean temperature (1981 – 2010) ...................................................................... 32

Figure 3: Annual mean temperature with trend .............................................................................. 33

Figure 4: Seasonal mean temperature with trend ........................................................................... 33

Figure 5: Annual mean maximum temperature with trend ............................................................. 34

Figure 6: Seasonal mean maximum temperature with trend .......................................................... 35

Figure 7: Annual mean minimum temperature with trend ............................................................. 36

Figure 8: Seasonal mean minimum temperature with trend ........................................................... 36

Figure 9: Seasonal rainfall distribution (%) ................................................................................... 37

Figure 10: Mean monthly rainfall (1980 -2010) ............................................................................. 38

Figure 11: Annual mean rainfall with trend ................................................................................... 38

Figure 12: Mean monthly rainfall of wettest months with trend .................................................... 39

Figure 13: Mean monthly rainfall of driest months with trend ...................................................... 39

Figure 14: Total rainfall in winter season ....................................................................................... 40

Figure 15: Total rainfall in Pre monsoon season ............................................................................ 40

Figure 16: Total rainfall in monsoon season .................................................................................. 41

Figure 17: Total rainfall in post monsoon season ........................................................................... 41

List of Tables

Table 1: Demographic Distribution of Jumla District .................................................................... 26

Table 2: Demography of Kartikswami VDC .................................................................................. 27

Table 3: Ward wise Population Distribution of Kartikswami VDC ............................................... 27

Table 4: Average land holding size of households ......................................................................... 31

Table 5: Summary table on statistical analysis of annual and seasonal mean temperature ............ 34

Table 6: Summary table on statistical analysis of Maximum temperature ..................................... 35

Table 7: Summary table on statistical analysis of annual and seasonal mean temperature ............ 37

Table 8: Summary table on statistical analysis of Rainfall ............................................................ 41

Table 9: Annual temperature, precipitation and rice yield of Jumla from 1995 to 2008 ............... 42

Table 10 Vulnerability Calculation ................................................................................................ 45

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Introduction

1.1 Background

Reducing the impacts of climate change and the variability is very critical for the countries like

Nepal, which is unfortunately one of the most vulnerable countries in the world to the impacts of

climate change. Various factors including the topography, economy, social structure,

infrastructure for development make the communities vulnerable across the country but it is more

so in the high altitude areas where access to services, information, technology etc are lacking in

comparison to the other parts of the country. The complex geo-physical structures makes it poorly

accessible with the many part of the country, while makes people deprived of the taste of

development. Furthermore, the very sensitive mountain environment and the alpine ecosystem

with less flexibility towards the climatic variations make it one of the most vulnerable places

throughout the globe.

In general, there is lack of appropriate technologies to cope with those impacts all over the

country. In fact, the available technologies are also not adequate and are not sufficient to uplift

the living standard of the vulnerable to climate change communities. For Nepal, to minimize the

possible impacts of climate change, adaptation is the only option left. Smith and Wandela, 2006

state that Nepal has to focus more on adaptation approach rather than controlling emission,

because it contributes minimal to GHGs emission.

Widespread implications of climate change indicate that climate change is a complex and cross-

cutting issue as we are globally interconnected. Along with the mitigation approach to climate

change and associated risk, mainly for the developing countries, it is the coping mechanism

developed within the community that counts for risk reduction due to climate change.

The options and opportunities to adapt range from technological options, behavioral changes at

the individual level to other strategies including early warning systems for extreme events, better

water management, improved risk management, property and life insurance, bio-diversity

conservation, change in living style etc. The proper implementation of adaptation strategy at

community level is the best approach to reduce the impacts of climate change and climate

variability. (Agarawal, 2008)

Climate change is an issue of concern for Nepal as over two million Nepalese people depend

upon climate sensitive sectors like agriculture and forestry for their livelihood (Garg, Sukla and

Kapse, 2007). Least developing countries like Nepal are most susceptible to climate change and

its impacts due to their limited capacity to cope with hazards associated with the changes in

climate (Kates, 2000). Even a slight change on Nepal‟s climate may have a greater impact over

both the natural and human systems of Nepal. The share of Nepal in total emission is not so high

as compared to other developed countries. However, Nepal is likely to be affected highly which

means most of the communities of Nepal are vulnerable to climate change and its impacts.

Vulnerability is a subjective concept that includes three dimensions: exposure, sensitivity and

adaptive capacity of the affected system. IPCC, 2007 defines vulnerability of a system as “a

function of the character, magnitude and the rate of climate variation to which a system is

exposed.” In disaster planning, vulnerability is the social, economic and environmental exposure

and sensitivity.

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As already stated above, even though the share of Nepal in GHG emission is very less compared

to other developed nations, it is likely to be affected more from the climate induced disaster. The

complex topography and fragile economic status guide toward higher vulnerability. The country

can do very less towards decreasing the emissions due to its negligible contribution towards

climate change. Hence, it has to focus in adaptation.

Adaptation is all processes through which communities make themselves better able to cope with

an uncertain future climatic disaster. Adapting to climate change entails, making the right

measures to reduce the negative effects of climate change (also exploiting the positive ones) by

making the appropriate adjustment in natural and human systems to a new or changing

environment (Dow and Downing, 2006). Adaptive capacity is the ability to understand climate

change and hazards, to evaluate their consequences for vulnerable peoples, place and economies

and to moderate potential damages to take advantage of opportunities or to cope with the

consequences (Dow and Downing, 2006).

Rural livelihood is at risk as many natural systems are being affected by regional climate and

elevating temperature (IPCC, 2007). Predictions on climate change indicates that climate change

has its widespread implications on agriculture, water resources, fisheries, forestry, human health,

ecosystem as well as social, economic and political systems making climate much complex

global issue of social , economic and political dimension (Reilly,2001).

In order to assess the vulnerability to climate change it is necessary to strengthen the adaptive

capacity of the community to address these changes. Adaptation can be spontaneous or planned,

and can be carried out in response to or in anticipation of changes in climatic conditions (UKCIP,

2004). It is the property of a system to adjust its characteristics or behavior, in order to expand its

coping range under existing climate variability, or future climate conditions. The adaptive

capacity in actions lead to adaptations which enhance a system‟s coping capacity increasing its

coping range and reducing its vulnerability to climate hazards. The adaptive capacity inherent in a

system represents the set of resources available for adaptation, as well as the ability or capacity of

that system to use these resources effectively in the pursuit of adaptation. (UNDP, 2005).

To access the vulnerability to climate change the first step is to identify the vulnerable

community based on the topography, socio-economic scenario and the availability of the

resources. Keeping mind in all these factors Jumla district's Kartikswami VDC is chosen as the

study area of the study. Jumla district falls under Himalayan Region of Nepal with a variation of

altitude from 915m to 4679 m from the sea level. It is a unique place in Nepal where rice is

cultivated in the highest altitude in the world and the specific place Chhumchour (3000 m) has

got the privileged of rice cultivation in the highest altitude in the world (Paudel & Thakur, 2009).

The Himalayan region is more vulnerable to climate change and is warming 0.040C per year,

higher than any other region of Nepal (ICIMOD 2010). The Kartikswami VDC is situated in the

bank of Tila River. All the nine wards of the VDC have human settlement with almost all the

communities residing in the bank of Tila River.This study has focused on the effects of climate

change in the livelihood of the people of Kartikswami VDC of Jumla District. The vulnerability

due to climate change was calculated based on the sensitivity, exposure and adaptability of these

people towards these changes. Moreover, this study has co-related the impacts of the climate

change in the productivity of the traditional rice plant of the Jumla district.

The questionnaire survey, key informant interview, FGD and the secondary data analysis were

used to conduct this study. The one month stay in a field was a unique experience which helped

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to witness the complex socio-economic scenario of the remote part of the country. Out of the

total 434 households the 50 household questionnaire survey was conducted. The literacy rate was

good enough given all of its complex constraints. The people are helpful and curious about the

research and offer every possible help.

The study revealed that the people of this particular VDC are moderately vulnerable to climate

change which is mainly due to the increase in the access towards technology and development

because of the vibes of development in nearby district headquarter. The people are slowly

acquiring the knowledge about the climate change and its impacts along with the coping

mechanism. The hydro-meteorological data analysis showed that the both temperature and

precipitation pattern are changing in comparison to the past. Hence, the urgent need of the

availability of the adaptation measures to cope with these changes to minimize its probable future

impacts was clearly perceptible after this study.

1.2 Statement of the Problem

Climate change is the well accepted fact with the presence of the various evidences round the

globe. Its impacts are clearly visible in the vegetation, hydrology, productivity and availability of

an entire ecosystem. The unpredictable weather with the untimely and high intensity rainfall,

consequence with the flash floods and other severe impacts are more frequent these days.

Moreover, the drought in the Himalayan regions with the less snowfall and rising temperature

with the short and sudden high intensity rainfall have seriously affected the livelihood of the

people residing in these areas. Although, they have been adapting these erratic changes with their

own knowledge and skills, the current changes in the climate and its impacts will be clearly

irresistible to deal with.

The socio-economic scenario of the Karnali region is very woeful. The life of its resident have

already started being affected by the impacts of the climate change which is likely to go from bad

to worse in the near future if not addressed timely. For this, a scientific study of the vulnerability

to climate change of the residents of these areas along with the impact of climate change is very

vital. The impact of the changing climatic parameters in the productivity of the traditional rice

plants is very crucial to gauge the climate change effects as a whole.

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Chapter II

Literature Review

Climate includes the long-term averages of temperature, precipitation, atmospheric circulation

and atmospheric chemistry of a region as measured over a period of several seasons to decades.

Man and his environment are in crucial state of destruction and degradation. Men who are in

return hampering themselves directly or indirectly have degraded environment. Changes in the

Earth's climate are forced by a number of factors. The movement of the Earth's crust influences

climate over periods of millions of years through changes in the geographic arrangement of

oceans and continents. Climate change brings major new challenges to the environment. Any

systematic change in the long-term statistics of climate elements (such as temperature, pressure,

or winds) sustained over several decades or longer. Climate change may be due to natural

external forcing, such as changes in solar emission or slow changes in the earth's orbital elements,

natural internal processes of the climate system: or anthropogenic forcing.

Climate change is a phenomenon due to emissions of greenhouse gases from fuel combustion,

deforestation, urbanization and industrialization (Upreti, 1999) resulting variations in temperature

and precipitation. It is a real threat to the lives in the world that largely affects water resources,

agriculture, coastal regions, freshwater habitats, vegetation and forests, snow cover and melting

and geological processes such as landslide, desertification and floods, and has long-term effects

on food security as well as in human health.

Changes in the atmospheric concentrations of GHGs and aerosols, land cover alter the energy

balance of the climate system and are drivers of climate change. They affect the absorption,

scattering and emission of radiation within the atmosphere and at the Earth‟s surface. The

resulting positive or negative changes in energy balance due to these factors are expressed as

radioactive forcing, which is used to compare warming or cooling influences on global climate

(IPCC, 2007).

2.1 Global Context

The almost static environment throughout the earth only few century back was exploited

dramatically after the successful industrial revolution. The exponential growth in the population

and the successful scientific era lead to the intensive exploitation of the nature to full fill the

demand of growing population. In order to live a life with ease, the productions of luxurious

commodities accelerate demanding more use of natural resources as a raw material as well as

fuel. This eventually led to the emission of harmful gases and polluting the pristine environment.

The world didn't need long to witness the impact of these unwise exploitation of the nature. Soon,

some of the environment scientist started visualizing the future as a dead end if this abolish of the

nature is not stooped immediately. With this consensus the first historical conference on Human

Environment (Stockholm, 1972) was held which for the first time made people think about the

environmental issues. It was in this conference that the relationship between economic

development and environmental degradation was first place on international agenda. It paved the

way in succeeding years to integrate environmental concerns into national economic planning and

decision making (Hoksins, 2002)

The Brundtland Commission in 1983, put forwarded the concept of sustainable development as

an alternative approach based on economic growth and defined it as "the development which

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meets the need of the present without compromising the ability of future generations to meet their

needs." The commission published the report in 1987 which was primarily focused on securing

the global equity, redistributing resource towards poorer nations whilst encouraging their

economic growth. The report highlighted three fundamental components to sustainable

development: environmental protection, economic growth and social equity.

In 1992, the UN organized "The Earth Summit" in Rio de Generio of Brazil. The primary goal of

the summit was to come an understanding of "development" that will support the socio-economic

development as well as prevent the deterioration of the environment. The Rio conference

produced two international agreements, two statement of principles and a major action of agenda

on worldwide sustainable development (UNFCCC,1992).

2.2 United Nation's Framework Convention on Climate Change

The Convention on Climate Change sets an overall framework for intergovernmental efforts to

tackle the challenge posed by climate change. It recognizes that the climate system is a shared

resource whose stability can be affected by industrial and other emissions of carbon dioxide and

other greenhouse gases. The Convention enjoys near universal membership, with 192 countries

having ratified.

Under the Convention, governments should:

gather and share information on greenhouse gas emissions, national policies and best

practices;

launch national strategies for addressing greenhouse gas emissions and adapting to

expected impacts, including the provision of financial and technological support to

developing countries; and

cooperate in preparing for adaptation to the impacts of climate change

The Convention entered into force on 21 March 1994 at the global level. The ultimate objective

of this Convention and any related legal instruments that the Conference of Parties may adopt is

to achieve, in accordance with the relevant provisions of the Convention, stabilization of

greenhouse gas concentrations in the atmosphere at a level that would prevent dangerous

anthropogenic interference with the climate system. Such a level should be achieved within a

time-frame sufficient to allow ecosystems to adapt naturally to climate change, to ensure that

food production is not threatened and to enable economic development to proceed in a

sustainable development

2.4 Nepal and Climate Change

Nepal joined the climate change movement through submitting the Initial National

Communication (INC) document as a party of United Nations Framework Convention on Climate

Change (UNFCCC) in July 2004. Ratified Kyoto Protocol in 2005 and designated Ministry of

Environment as Designated Authority for Clean Development Mechanism (CDM). There is a

Climate Change Network representing government, NGOs, academicians, and donor agencies

working. National Climate Change Policy 2009 has been formulated by the government and other

climate change policies are either formulated or underway. A high level Climate Change Council

under the chairmanship of Prime Minister has been formulated. A National Committee for the

preparation COP 15/CMP 5 has been set up and ready to function. Nepal organized South Asian

Regional Conference on Climate Change „Kathmandu to Copenhagen‟ which came up with

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common stance among the countries of the region. It also formulated a consortium of the donor

countries for climate change. A regional conference of youth was also organized. National

Adaptation Program of Action (NAPA) is under preparation.

2.4.1 Nepal in the UNFCCC

The diverse topography and regional differences put Nepal under the special category as specified

in UNFCCC Principle 4.8 while considering both the adverse impacts of climate change and the

impact of implementation of response measures by the Parties. Principle 4.8 of the UNFCCC

states that:

In the implementation of the commitments (under Article 4.0), the Parties shall give full

consideration to what actions are necessary under the Convention, including actions

related to funding, insurance and the transfer of technology, to meet the specific needs and

concerns of developing country Parties arising from the adverse effects of climate change

and/or the impact of the implementation of response measures, especially on countries

with areas i) prone to natural disasters; ii) fragile ecosystems, including mountainous

ecosystems; and iii) land-locked countries.

Nepal thus realizing the special place and role to play among the global community in addressing

the global climate change issues ratified the UNFCCC in May 1994 as 70th country to ratify the

Convention and since then actively taking part in all the Conference of Parties (COP) meetings.

In September 2005, Nepal has accessed to the Kyoto Protocol and it has entered into force since

December 2005. The possibility of extending co-operation under both the UNFCCC and Kyoto

Protocol, thus have to do with the national capacity building, technology transfer and additional

new investments that Nepal can receive as specified under the Principle 4.8 and the Clean

Development Mechanism (CDM) in order to promote the adaptation and mitigation measures and

in the meantime achieving and sustaining high economic growth for fulfilling the basic needs of

the people.

2.5 Climate Variability and Change

Rapid changes in the altitude and aspect along the latitudes have made strong gradient in the

spatial as well as temporal patterns of climatic conditions in Nepal (Chalise, 1994). Therefore,

within a span of less than 200 km Nepal captures almost all types of climates, subtropical to

alpine/arctic. Physically, this is more apparent that the country has been home of diverse habitats,

vegetation and fauna. However, such climatic gradient along east-west direction is mainly due to

the seasonal atmospheric characteristics that determine climatic variations.

A number of studies related to the climate of Nepal have been carried out over the last four

decades (Chalise, 1994; Kripalani et al., 1996; Shrestha et al., 1999, 2000; Shrestha, 2000).

According to most of these studies, Summer Monsoon brings more than 80 percent of the annual

rainfall in Nepal during the rainy months, June through September. The remaining amount of

precipitation is predominantly released by the western disturbances, which influences rest of the

months (October through May). Spatial distribution of surface air temperature in Nepal nearly

follows the geographical configuration of the country. Chalise (1994) emphasized the role of

Himalayan regions as a climate barrier between the lower and mid-latitudes in the global

atmospheric circulation systems and therefore, the Himalayan regions are responsible for the

moist summers and mild winter in South Asia.

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Moreover, it was found that temperature is increasing at a higher rate in upland than in lowland

Nepal, and that erratic rainfall is characteristic of both regions. Chaulagain (2006) also found

temperature change to be more pronounced at higher altitudes than at lower altitudes. However,

the analysis of the stability of mean monthly rainfall, and monthly maximum and minimum

temperatures employing t-test does not show any significant trend. Ichiyanagi et al. (2007) found,

too, that winter rainfall over western Nepal has increased. The graph of total rainfall shows a

small increase in the last 30 years. Dey and Kumar (1983) found an inverse relationship (negative

correlation) between Indian Summer Monsoon (ISM) rainfall and the extent of snow cover in the

Himalayas, which suggests that there might be a link between increasing rainfall and decreasing

snowfall in the Mustang region. Arnell (1999) explained that a rise in temperature can lead to a

general reduction in the proportion of precipitation falling as snow, and a consequent reduction in

many areas in the duration of snow cover. Average annual maximum temperature data over a 30-

year period have revealed a significant rise in temperature in Marpha, which supports both

Arnell‟s statement concerning a link between rising temperatures and decreasing snowfall, and

farmers‟ perception of increasing temperatures in Mustang. Shrestha et al. (1999) stated that the

analysis of maximum temperatures from 1971 to 1994 showed an increasing trend after 1977,

ranging from 0.06 to 0.12 0C year in the Middle Mountain and Himalayan regions, while the

Siwalik (hills bordering the Gangetic plain with little potential for agricultural production) and

Terai regions show a comparatively lower increase in temperature. The chi-square test showed a

significant match between actual changes in temperature and farmers‟ perception of temperature

changes; the test for changes in rainfall, however, is not significant. Similarly, the t-test showed

an insignificant result for change in mean monthly maximum and minimum temperatures as well

as mean monthly rainfall. The comparison of farmers‟ perceptions and the climatic records has

revealed a close match between both, and provided evidence for the ability of farmers to

accurately observe and recall climatic events. This proves that farmers in both study areas are

aware of climate change. This ability combined with farming experience provides the basis for

their various endeavors to adapt their land use to the increasing impacts of climate change.

The impact of climate change on the Himalayan cryosphere is not understood sufficiently to be

able to estimate the full scale of the downstream impact of reduced snow and ice coverage.

Baseline studies are lacking for most areas and the varied Himalayan topography and climatic

variability mean that extrapolations from studies carried out elsewhere do not give an accurate

picture. In mountain areas the extreme topography causes temperature and precipitation to vary

over very short distances, which makes projections difficult. Equally, poor accessibility, low

population and poor infrastructure, means that little direct „data is collected‟. Three levels of

impact of climate change can be identified: i) local effects; ii) downstream effects; and iii) global

feedback effects. The development of adaptive strategies can be approached from the perspective

of each of these three different levels. All three levels are interlinked and interrelated – and also

full of uncertainty. Given the current state of knowledge about climate change, determining the

diversity of impacts is a challenge for researchers, and risk assessment is needed to guide future

action. The lack of data related to climate and water in the region hinders a comprehensive

assessment of changes in extreme climatic events. (ICIMOD, 2009)

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2.6 Impacts of Climate Change in Nepal

The ongoing climatic changes and changes those are projected to occur in the future are likely to

have impacts on different sectors of Nepal. Impacts on some sectors are likely to be more severe.

Following sections provides accounts of some observed impacts and impacts that are likely to

occur in the future in different sectors.

2.6.1 Impacts on Water Resources

Nepalese river basins are spread over such a diverse and extreme geographical and climatic

condition that the potential benefits of water are accompanied by risks. Though the available

surface water of Nepal (202 km3) could fulfill the demand of the country up to the end of 21st

century, the availability of only 26 km3 water in dry season shows that water scarcity is eminent

in Nepal unless water resources are properly managed. Besides, rising temperatures have caused

glaciers to melt and retreat faster. Receding glaciers mean an increased risk of the sudden

flooding following glacial lake outbursts.

2.6.2 Impacts on Agriculture Sector

NARC carried out impacts of climate change study on some of the cereal crops of Nepal.

Vulnerability assessment of Rice yield showed that at 4o C rise in temperature and 20 % increase

in precipitation could result in marginal increase yield from 0.09 to 7.5 % and beyond that the

yield would continue to decline. In case of wheat the actual yield of wheat showed increased

output in western region of Nepal with the rise of temperature but decline in other regions.

Similarly, temperature rise showed a decrease in maize yield with increase in temperature.

Though temperature rise had more negative effects on maize yield, the trend was almost similar

to wheat. However, rice, wheat and maize responded positively under double CO2. Wheat

potential went as high as 60 %, rice yield 21 % and maize yield 12 % under double CO2

condition.

2.6.3 Impact of Climate Change on Natural Forest, Bio-diversity and Wildlife

Rising temperatures, glacial retreat and changes in water availability will also lead to changes in

natural biodiversity. Nepal supports a disproportionately high number of globally important wild

animal and plant species and contributes to majority of people's livelihood. Global warming may

cause forest damage through migration of forests towards the polar region, change in their

composition, and extinction of species. Tropical wet forest and warm temperate rain forest would

disappear, and cool temperate vegetation would turn to warm temperate vegetation. Vegetation

pattern would be different under the incremental scenario (at 20C rise of temperature and 20%

rise of rainfall) than the existing types. Climate change will also have direct impact on wildlife.

Furthermore, migration of vegetation and decline in bio-diversity will have further adverse

impact on wildlife. (Initial National Communications, Nepal, 2004).

2.7 Vulnerability and Adaptation

Vulnerability is a term that describes the susceptibility of a group to the impact of hazards. It is

the degree to which a system is likely to experiences harm due to its exposure to hazard. It is

determined by the capacity of a system to anticipate, cope with, resist and recover from the

impact of hazard. Exposure to natural hazard of the community is increasing day by day, making

it more vulnerable with increasing global change and frequent extreme event (Tunner II, 2003).

17

Vulnerability is deeply rooted within the framework of societies, since it is determined by social

systems and power at a local, national and international level, not by the natural environment.

Brook (2003) classifies vulnerability as bio-physical vulnerability and social vulnerability.

Biophysical vulnerability is defined in terms of hazard and related to outcome of occurrence of

hazard or damage incurred by the system due to action of hazard upon the system. While social

vulnerability is defined independent of hazard and it is the inherent current state of the system or

communities. It may also be defined as one of the determinant of bio-physical vulnerability.

In context of climate change, O‟Brian et al. (2004) classifies vulnerability as „end-point‟ and

„starting point‟ interpretation of vulnerability. The „end point‟ interpretation mainly focuses on

climate change mitigation and compensation policy and technical adaptation. While, „starting

point‟ interpretation focuses on vulnerability of society to climatic hazards, adaptation policy and

sustainable development of societies.

Vulnerability is the degree to which a system is susceptible to, and unable to cope with, adverse

effects of climate change, including climate variability and extremes. Vulnerability is a function

of the character, magnitude, and rate of climate change and variation to which a system is

exposed, its sensitivity, and its adaptive capacity (IPCC, 2007a).

Vulnerability of a system refers to its physical, social, and economic aspects. According to IPCC,

vulnerability is a function of the character, magnitude and rate of climate variation to which a

system is exposed; its sensitivity; and adaptive capacity (IPCC, 2001).

From the above definition, we can note that IPCC uses three terms to define vulnerability

exposure, sensitivity, and adaptive capacity. Hence, vulnerability is a function of all the three

terms.

Mathematically, this can be denoted as

Vulnerability = (Exposure*Sensitivity/ Adaptive Capacity)

In other words, the greater the exposure or sensitivity, the greater is the vulnerability. However,

adaptive capacity is inversely related to vulnerability. So, the greater the adaptive capacity, the

lesser is the vulnerability.

Therefore, reducing vulnerability would involve reducing exposure through specific measures

like building a dyke in case of sea level rise, or increasing adaptive capacity through activities

that are closely aligned with development priorities. The terms exposure, sensitivity and adaptive

capacity is very crucial to determine the degree of vulnerability.

Exposure is defined as, "degree of climate stress upon a particular unit analysis; it may be

represented as either long-term changes in climate conditions, or by changes in climate

variability, including the magnitude and frequency of extreme events" (IPCC, 2001).

There are two main elements to consider in exposure.

Things that can be affected by climate change (populations, resources, property, and so on)

The change in climate itself (sea level rise, precipitation and temperature changes, and so on)

18

Sensitivity is the degree to which a system will be affected by, or responsive to climate stimuli

(Smith et al., 2001).

Sensitivity is basically the biophysical effect of climate change; but sensitivity can be altered by

socio-economic changes. For example, new crop varieties could be either more or less sensitive

to climate change.

Adaptive capacity refers to the potential or capability of a system to adjust to climate change,

including climate variability and extremes, so as to moderate potential damages, to take

advantage of opportunities, or to cope with consequences (Smit and Pilifosova, 2001). As the

name suggests, adaptive capacity is the capability of a system to adapt to impacts of climate

change.

Smit et al., 2001, have identified the following seven factors that determine adaptive capacity.

Wealth

Technology

Education

Institutions

Information

Infrastructure

Social capital

Nepal demonstrates diverse geo-physical and climatic conditions within relatively small areas

resulting vast biological diversity, therefore, it is an ideal place to study climate change impacts

on natural and socioeconomic spheres. In context of Nepal, a few studies have been carried out

on a vulnerability and risk assessment of natural hazards and most of them are based on the

available information of the past without or in only some extent to climate change and potential

future risk of climate change related disaster. Some of them are (Dhital, Khanal, & Thapa, 1993),

(Mool, Bajarachyra & Joshi 2001),(Khanal 2005), (ICIMOD 2007). Although Nepal is

responsible for only about 0.025%of total annual greenhouse gas emission of the world (Karkai,

2007), it is experiencing the increasing trends and the associated effects of global warming.

The response for country like Nepal to these consequences is adapting to these changes. The

capacity to adapt to climate hazards and stresses depends on country's wealth, resource and

governance (Kates, 2000).The climate change risk can be traced by studying the rate of

snowmelt, landslides, flooding and changing of vegetation. For vulnerable assessment, the place

based (micro scale) study is more relevant than the larger area if we give due consideration to

spatial variation of natural hazard and vulnerability (Tunner II, 2003 as cited in Sharma, 2009).

The degree of vulnerability for the livelihood will depend on availability and accessibility of

factors such as arable-land and water resources, farming technology and inputs, crop varieties,

knowledge, infrastructure, agricultural extension services, marketing and storage systems, rural

financial markets and wealth etc (Smit and Pilifosova, 2001). Regmi et al. (2008) state that the

poor and marginalized people are more vulnerable to impact of climate change as they are heavily

depended upon natural resources, lack access to technology, information and infrastructure.

Mirza (2003) studied vulnerability of communities to floods in Bangladesh and concluded that

vulnerability to extreme climatic hazards increases due to unemployment, high population

19

density, illiteracy, widespread poverty, enormous pressure upon rural land and economy

dominated by agriculture.

The vulnerability of rural communities can be reduced through effective governance over natural

resources (Cleaver and Schreiber, 1994), increasing access to market and enhancing human

capital (Paavola, 2008). Adaptation strategy can be of various types viz. shifting natural resources

management practices and agricultural practices, building institutions and strengthening

communities, raising awareness about climate change and possible ways to adapt, development of

new technology and better infrastructure etc. (Hedger et al., 2008). Depending upon degree of

severity of climate change on different sectors, different measures have to be considered. Despite

of availability of wide range of adaptation strategies, there are numerous barriers such as

technological, financial, behavioral, cognitive, social and cultural (IPCC, 2007a). The

international communities have important role to play for building adaptive capacity of least

developed countries. The costs required for adopting different adaptation measures ranges from

US$10-40 billion to US$ 86 billion. This amount equals to 0.2% GDP of developed country or

one tenth equivalent to their current military expenditure (UNDP, 2008). The economic affluence,

social capital, local and international networks, cultural and traditional values, perception and

recognition of risk are also important to improve adaptive capacity (IPCC, 2007a).

The adaptation measures complementary to development are desirable (Ribot et al., 1996). The

adaptation measures are aimed at reducing vulnerability of people to climate change and

necessary care should be taken that these measures do not have detrimental effect upon the

vulnerability (Hedger et al., 2008). However, some adaptation measures aggravate existing

vulnerabilities (Westerhoff and Smit, 2009). For example: the shrimp farming though has

economic and livelihood benefits against climate change but worsens vulnerability to sea-level

rise (Agrawala et al., 2005).

Adaptation refers to outcome of series of actions undertaken at different spatial scales to cope or

adjust in given new environment created due to stress or hazards or opportunity (Smit and

Wandel, 2006). Adaptation practices varies depending upon spatial and temporal scale, nature of

sector, actors involved, actions, climatic zone, socio-economic base or combination of these

factors and others (Yohe and Tol, 2002). The adaptation programs and policies should be

designed as such that they will address cross sectoral issues of poverty alleviation, bio-diversity

and ecosystem services conservation, reduction of land degradation and soil erosion and increase

food security enabling achievement of sustainable development at various scales (FAO, 2008).

Policies and plans like National Adaptation Plans of Action are regarded as anticipatory

adaptation measures (Bohle, 2001; Burton et al., 2003). Earlier, the adaptation studies were

scenario-driven based on Global Climate Models, also known as top-down approach. These

studies were limited to modeling the impact of temperature, rain and sea-level rise (Alast et al.,

2008); but did not take into account the adaptive capacity of the societies and individuals. The

bottom-up adaptation assessment approach involves local stakeholders in examining current

vulnerability to climatic variability and extremes and current adaptation measures and policies

(Alast et al., 2008).

20

2.8 Climate Change and other climatic study of Jumla

The poor people are more vulnerable to climatic extremes as well as gradual changes in climate

than the rich because they have less protection, less reserves, fewer alternatives and lower

adaptive capacity and because they are reliant more on primary production which affect their

health and livelihoods and undermines growth opportunities crucial for poverty reduction (IPCC,

2008).

Developing countries like Nepal are more susceptible to the climate change and its impacts due to

their limited capacity to cope with hazards associated with changes in climate. The impacts of

climate change are associated with water and food shortages, coastal inundation, distribution of

vector borne diseases such as malaria and dengue fever, and the rate of extinction of many

species (IPCC, 2001a).

Sapkota et.al, 2010: Jumla is the unique place where rice is cultivated in the highest altitude

(3000 m) in the world. To find out changes in rice production due to climatic effect, six VDCs of

Jumla were chosen for the study. Despite the replacement of local varieties by improved ones the

production trend seems to be stagnant. One of the factors on stagnant of rice production in Jumla

is due to the lack of desirable variety with genetic traits of early maturity, less nutrient

requirement compared to Jumli Marshi to cope up with the climate congruently supporting to

cultural practices regarding the unique rice production systems of Jumla.

Gentle et.al. 2012: the study based on the remote mountainous Jumla District of Nepal to explore

how climate change is affecting the livelihood of local communities and how different wellbeing

groups are differentially impacted. Effects of climate change tend to be more severe where people

rely on weather dependent rain-fed agriculture for their livelihoods. In rural mountain

communities with limited livelihood options, adaptive capacity is low due to limited information,

poor access to services, and inequitable access to productive assets. Looking from a wellbeing

lens, adaptation practices by households as well as local support mechanisms were explored to

predict the severity of effects now and into the future.

Action works Nepal in October 2010 conducted a detail study on the impact of climate change in

the livelihood of the people of Jumla district. The study revealed many evidences of climate

change and its impact on the life and livelihoods of people of Jumla district. The erratic and low

rain/snowfall, prolonged dry winters has already impacted on the production of crops and food

security. As the crop production, especially the cultivation of high altitude paddy, is a very

complex and weather based phenomenon, it has seriously affected due to delay and erratic

rainfall. Similarly, the winter crops like wheat, barley, millet and potato are also seriously

affected by low and erratic snowfall.

The secondary source of income as well as the means of transportation is based on livestock. The

livestock rearing has also affected by limited and less productive grazing lands due to low

moisture content and over exploitation of the resources.

Recently, the district has a road access to the district headquarters and it has made easy in the

transportation of food and other essential commodities. However with the road access, the

unplanned urbanization has been increased in and around the district headquarters and road sides.

It has already resulted in deforestation and extraction of stones and soil from the community and

government managed forests. Similarly, the community has reported the low availability of non-

21

timber forest products, which is another source of income. The community in Lamra VDC

reported the reduced availability of drinking and irrigation water sources.

The increased vulnerability and reduced livelihood options has also increased the off season

migration to India, which has increased the risks of indebtedness of the poor families and

additional burden to women, children and elder population to cope with extreme vulnerabilities.

The impacts of climate change are not evenly distributed between different communities. Poor

and marginalized communities, who often live in vulnerable areas with limited information,

limited livelihoods options and low adaptive capacity, are obviously most vulnerable to climate

change. Similarly, women are on the frontline of climate change due to their multiple burdens to

manage their livelihoods. The predicted impacts of climate change will heighten existing

vulnerabilities, inequalities and exposure to hazards.

The social and financial safety nets are not in an easy access to poor community. The institutions

like micro-finance groups, community forestry user groups although have a potential role in

providing social, environmental and financial safety nets, most of the groups are captured by

local elites.

It has been observed that the adaptation needs of marginalized community is far more than the

powerful groups so there is a strong rationale for pro-poor investments and international

cooperation for strengthening social protection safety nets for poor and marginalized community.

There is a need to mainstream climate change adaptation in the national development plans as

well as national and local level adaptation plan of action.

22

Chapter III

Rational of the study

Global climate change is burning issues in the world and it has been found that the rate of

warming is higher than average global warming rate in Hindu Kush Himalayas (Eastern

Himalayan Region). Nepal‟s greenhouse gas contribution to the atmosphere is not significant in

global terms but the impacts of climate change in the country‟s economy and local livelihoods are

significant. Based on available data it is found that the annual warming in annual temperature

between 1977 and 2000 was 0.060C/yr. (Shrestha et al., 1999). In such Scenario the high altitude

people will face problem in their lives and daily activities.

The sensitive sectors are agriculture, forestry, water and energy, health, urban and infrastructure,

tourism industry and overall livelihoods and economy. The analysis shows that Nepal is highly

vulnerable to climate change. The analysis also suggests that more than 1.9 million people are

highly vulnerable and 10 million are increasingly at risk, with climate change likely to increase

this number significantly in future. Jumla district is a highly vulnerable zone for Drought. The

overall climate change trend of the district is also very high. The agricultural sector is one of the

major sectors likely to have the impacts of climate change particularly the change in temperature

and precipitation. Jumla district is the high altitude district where precipitation is very less and

has moderate temperature. Most of the places is snow covered trough out the winter and have

very low temperature during this time. It is necessary to co-relate the impacts of these climatic

variables with the production of the major crop especially in the higher altitude district as Jumla.

Moreover, the economic condition of the district is very poor with the maximum population

living below the poverty line. In such circumstances, it is essential to study the socio-economic

condition, change in the climatic patterns and the vulnerability to climate change of the district

like Jumla. Hence, this study intends to access the vulnerability of the local people of Himalayan

region and co-relate the change in temperature and precipitation with the production of the major

crop i.e. rice.

23

Chapter IV

Objectives, Scope and Limitation

4.1 General Objectives:

The general objective of this study is to assess the vulnerability due to climate change and outline

the adaptive capacity of the people living in Himalayan region of Jumla district.

4.2 Specific Objectives:

To assess the pattern and trends of climate change based on secondary hydro-meteorological data

To co-relate the impact of climate change with the production of rice

To assess the socio-economic status, vulnerability and adaptation of a community

4.3 Scope and Limitation

This study was carried out in the month of the May 2012. It mostly focus on the livelihood of the

people of Kartikswami VDC which is nearer to the district headquarter in comparison to other

remote VDC of the district. This study can't figure up the vulnerability of the people living far

from headquarter of the district where in comparison has more facilities than the other places.

The secondary data are taken from the VDC office on the basis of the household survey in 2009,

any changes in the socio-economic factors beyond the date is not addressed by this study. The

hydro-meteorological data are taken from the district headquarter and the rice production data till

2008 were only used. The household survey was done using simple random sampling and the

socially active and older inhabitants were chosen as the key informant for the study purpose.

Hence, this study is limited to the perception of these people and may not address the voice of all

the population. The vulnerability calculations were done using the ranking which were ranked

according to the field area's socio-economical and geological background. The statistical

calculations were done using MS-Excel software. Moreover, this research has focused on the

impacts on the major agricultural product i.e. rice plant. The result of the impacts of climatic

factors on the productivity of rice is limited within the temperature and precipitation; however

other factors may be present which affects the productivity.

24

Chapter V

Materials and Methods

5.1 Study Area

The study area of this research is Kartikswami VDC which lies in the Jumla District of Karnali

zone in Mid-Western Development region. Jumla District is the zonal headquarter of Karnali

zone and has the altitude variation from 915m to 4679m above sea level. It is extended from

18005'08" to 29

016'31" N to 81

005'10"E to 82

011'00" E in the Himalayan Region of Nepal. It is

the gateway of Rara Trek (Rara National Park). Dolpa (in the east), Jajarkot (south), Kalikot

(west) and Mugu (north) are neighboring districts of Jumla.

It takes about two days bus to reach there from Surkhet, the nearest city. The air service is also

available but is not reliable because of unpredictable weather of the region.

It is a unique place in Nepal where rice is cultivated in the highest altitude in the world and the

specific place Chhumchour (3000 m) has got the privileged of rice cultivation in the highest

altitude in the world (Paudel & Thakur, 2009). The Tila valley as well as the Sinja Valley is

covered with paddy fields growing the „Kala Marci‟ rice variety, unique red rice that is sought

after for its special taste. The origin of Nepali language is Sinja of Jumla. Therefore, the Nepali

dialect "Khas Bhasa" is still spoken among the people in this region.

This region has a diverse climate due to the altitude variation and geographical structure. The

district constitutes higher mountains to lower river basin. The higher mountains have very low

temperature throughout the year while the mid hills have moderate climate with warm summer

and cold winter. The low river basins are hot in summer with the maximum temperature up to

32C while cool in winter. The average annual maximum temperature of the district is30C and

average annual minimum temperature is 12C. This district receives 800mm of mean annual

rainfall. In the winter season most of the parts of the district receives snow and the temperature

falls up to -14C. The maximum snowfall was recorded as 46.6 cm in 2054/55 B.S while the

minimum snowfall of 2.7 cm was recorded in 2055/56 B.S. (www.ddcjumla.gov.np )

Agriculture and animal husbandry is the main occupation nevertheless many youths come down

to Terai region (Nepalgunj) or even India for the part time job. The HDI is only 0.165 and is

ranked 70 which is very low with comparison to the number 1 ranked Kathmandu among the 75

districts of Nepal. (Asia-Pacific Population Journal) Vol. 10, No. 2 (1995), pp. 3-14)

25

Figure 1 Map of Nepal

26

Table 1: Demographic Distribution of Jumla District

Details Numbers

a. Total Population

b. Population Growth Rate

c. Female

d. Male

89426

1.63

45848

43579

Number of Household 15850

Average Family size 5.64

Population Density 35

Average Literacy Rate (Percentage)

a. Male

b. Female

32.5%

47%

16.8%

Ethnic Group (Percent)

a. Chhetri

b. Brahmin

c. Damai/Kami/Sarki

d. Rai/Limbu/Gurung/Tamang/Magar

e. Newar

f. Muslim

g. Thakuri

h. Others Dalit

i. Others

63.12%

9.54%

13.73%

1.07%

0.33%

0.04%

5.67%

2.25%

4.25%

Total 100%

Source: District Profile, Jumla

5.1.1 Kartikswami VDC

Kartikswami VDC was selected as the study area to study the adaptability, sensitivity and

exposure towards the climate change induced disaster in order to calculate the vulnerability of

this region due to climate change. The geographical location of the VDC is 29009' N to 29

016'N

latitude and 82008'E to 82

014'E longitude. This VDC is adjoined to neighboring VDC Depalgaun

VDC in east, Hanku VDC in west, Jajorkot District in south and Tila River in north. The total

population of the VDC is 2554. The table below shows the demographic composition of the

VDC.

27

Table 2: Demography of Kartikswami VDC

Details Numbers

Total Population

Female

Male

2554

1256

1298

Number of Household 437

Average Family size 5.8

Average Literacy Rate (Percentage)

Male

Female

79.7

84.62

74.71

Ethnic Group (Percent)Brahmin/Chhetri

Dalit

80.10

19.90

Total 100

Source: VDC Profile, 2066

Table 3: Ward wise Population Distribution of Kartikswami VDC

Ward No. Household Male Female Total Population

1 64 214 174 388

2 51 132 141 273

3 61 179 172 351

4 69 161 188 349

5 40 134 136 270

6 51 176 139 315

7 56 181 184 365

8 25 76 76 152

9 29 45 46 91

Total 437 1298 1256 2554

Source: VDC Profile, 2066

28

5.2 Research Design

Diagram shows the overall view of the study process in the form of step-wise box web.

.

5.3 Sample Size and Sampling Technique

The sampling technique used in this research was purposive random sampling in which

households were randomly selected. The total number of household in the Kartikswami VDC is

437 of which around 10% households were chosen randomly for the survey. For the key

informant interview the local residents for more than twenty years were chosen.

Super

vis

or

consu

ltat

ion

Vulnerability

Lit

erat

ure

Rev

iew

Secondary

Data Collection

Socioeconomic

Final Thesis

Thesis Preparation

Data Tabulation

And Analysis

Primary Data Collection

Field Visit and

Sampling Size Design

Site Selection

Methodology development

Research Proposal

Super

vis

or

consu

ltat

ion

Vulnerability

Lit

erat

ure

Rev

iew

Secondary

Data Collection

Socioeconomic

Final Thesis

Thesis Preparation

Data Tabulation

And Analysis

Primary Data Collection

Field Visit and

Sampling Size Design

Site Selection

Methodology development

Research Proposal

29

5.4 Data Collection

5.4.1 Primary Data Collection

For the collection of the primary data both closed and open ended questions were used to collect

information.

a) Household Survey: A representative sampling of 50 households on random basis was

surveyed in Kartikswami VDC to get the required data. For household survey random

sampling of household was done and the set of questionnaire was prepared. While

choosing the respondents, focuses was given for elder people.

b) Key Informant Interview (KII): To document the changes and adaptive practices, Key

informant survey was conducted by taking the key persons who have got knowledge of

the community, its changing situation and indicator of community exposure to climate

change and disasters. Local elder people and key person of village were taken as key

informant.

c) Focus Group Discussion: Discussion was conducted with local stakeholders to get

information about the past and present condition of climate, changes in the agriculture

product and pattern, their economic shifting, availability of water resources, condition of

forest and biodiversity, sources of energy etc. People who have been residing in the

community for at least twenty years were selected for this purpose and discussion was

carried on the basis of checklist prepared in order to extract required information.

5.4.2 Secondary Data

a) Hydro-meteorological data: The hydro-meteorological data (Temperature and

Precipitation) were collected from the department of hydrology and meteorology,

Kathmandu for the year 1987 to 2008 A.D for Jumla district.

b) Agricultural Productivity data: The agricultural productivity data from Ministry of

Agriculture and Co-operatives were used to correlate the rice productivity with change

in the climate i.e. change in temperature and precipitation

c) Desk Review: Various literature and dissertation regarding vulnerability due to

climate change and adaptation and socio-economic data were collected and reviewed

from internet, various NGOs, books, journals, newspaper etc. for study.

5.5 Data Analysis

Data obtained through different sources were proceed, analyzed and interpreted. All the data

were analyzed using Microsoft Excel Software. To determine the climatic trend, the time series

regression and correlation analysis of the hydro-meteorological data were done. Similarly, to

correlate the impact of the climatic parameters (Temperature and Precipitation) on the production

of rice crop, correlation between the temperature and productivity of rice and precipitation and

productivity of rice were calculated based on the Hydro-meteorological and crop productivity

data. Further, to calculate the vulnerability, the adaptability, exposure and sensitivity of the local

people were ranked with the suitable scale. The relation between adaptability, exposure and

sensitivity gives the vulnerability of the local people due to climate change. The relation is given

by,

Vulnerability = Exposure X Sensitivity/ Adaptability

30

To calculate Exposure, temperature trend analysis, precipitation analysis, physical change,

change in activities regarding livelihood and change in the hazard event were calculated giving

the suitable rankings. (Appendix-VII).

Similarly, for sensitivity, Impact of hazards in human and ecological parameters were ranked and

calculated using the appropriate scale. (Appendix-VII)

The adaptability was calculated using gateway system analysis. The primary, secondary and

tertiary indicators were ranked 1 to 4 based upon the access of the people towards those

indicators. The ranking were used to calculate the adaptability. (Appendix-VII)

The vulnerability thus calculated was thus ranked 1-4 with the criteria as below:

V1 = < 1.75

V2= 1.76 – 2.50

V3= 2.51 – 3.25

V4= 3.26 – 4

31

Chapter VI

Result and Discussion

6.1 Social and Economic Status

The socioeconomic conditions of Kartikswami VDC were reveled by Households survey. The

total number of 50 households was taken in this survey. Majority of the respondents are male and

most of them had formal education. Out of 50 respondents, 82.4 % were male whereas female

respondents accounted as 17.6%. Peoples from Brahmin and Chettri were the dominant group in

the community followed by Dalit. Out of the total population, Brahmin/Chettri constitute 75.6 %

whereas Dalit was found to be 24.2 %.

Most of the people live in single family. The average size of the family was found to be 7.57 per

households. The education level of the VDC was found to be good rather than other VDCs of

Jumla district. About 73.2% of the total respondents were literate; among them 57.1 % of male

and 42.9% of female were literate. The study reveled that 26.8% were illiterate. The illiteracy rate

was high among Dalit i.e. 92.5% where as 7.5% of Brahmin/Chettri were found to be illiterate.

Most of the respondents were engaged on agriculture for their subsistence livelihood. Agriculture

is also a major livelihood source of income for country i.e. more than 80% of the population is

involved in agriculture, which is the second largest contributor (33%) to GDP (ADB, 2009).

Agriculture (55.1%) and Government service (24.6 %) was the major source of income of the

respondents. Very few respondents were involved in business (3.7 %) and other activities (2.1%).

Rests of the peoples are used to go to India for Job. Most of the households have food sufficiency

for 7 month on average per year. Many people especially from Dalit community were used to go

India for Job (labor). Daily wage labor is used every year to meet the food deficit. Educated

people joint the civil service and rich people were migrated towards major city of the western

Terai.

Landholding of the respondents was categorized into four groups as almost landless (<1 ropani),

small (1-5 ropani), medium (5-10 ropani) and Large (>10 ropani). From the analysis it was found

that most of the respondents were medium landowners i.e. (1-5 ropani). The average landholding

size was 3.75 ropani per household. A total of 55% respondents have their cropland for cereals

production. Dalit people have very few agritural lands (<1 ropani) than other people on the

community. All the Khet lands were at the side of the Tila River.

Table 4: Average land holding size of households

S.N Land holding (in Ropani) % Of households

1 Almost landless (<1) 8

2 Small (1-5) 60

3 Medium (5-10) 16

4 Large (>10) 16

32

The major crops grown are paddy, wheat, maize, potato, millet, and cereals. They also grow

different seasonal vegetables. Production of apple is also another major source of income. Paddy

and Maize are the rainy season crops whereas wheat and cereals are winter season crops. The

paddy cultivation dominated other crops. Jumla, on the banks of the Tila River, is one of the

highest rice growing areas in the world. The Tila valley as well as the Sinja Valley is covered

with paddy fields growing the „Kala Marci‟ rice variety, unique red rice that is sought after for its

special taste.

Firewood is the major source of energy in the community used 4.25 bhari (106.25 kg) per month

on average. They were also used solar energy (16.5%) and micro-hydro power (23.1%) for

lighting that is not sufficient for daily practices. A total of 89% have an excess of toilet and 11 %

did not have.

The mean annual income was NRs. 15,074.00 (VDC Profile, 2066) per person, which was

comparatively less than the national average annual income of NRs. 80,000.00 (CBS, 2004) per

person.

6.2 Hydro-Meteorological Data Analysis:

6.2.1 Temperature Analysis

About 30 years data from 1980 on temperature and rainfall was collected from Department of

Hydrology and Meteorology. The recorded temperature data for 30 years (1981-2010) at Jumla

station was used for analyzing the temperature trend.

Figure 2 : Annual mean temperature (1981 – 2010)

The Annual mean temperature was 12.81 °C for the years 1981 to 2010 with the highest mean

temperature of 13.88 °C in the year 2006 and the lowest of 11.25 °C in the year 2000. Whereas

the highest mean maximum temperature was in the month of June (25.96 °C) similarly the lowest

mean minimum temperature was in January (-4.96 °C). Figure 2 showed that the annual mean

temperature trend for 29 years has increased to 1.16 °C i.e. at the rate of 0.40 °C per decade but it

is statistically insignificant. The mean temperature for monsoon and pre monsoon season is

10

15

20

25

30

35

40

45

1981 1986 1991 1996 2001 2006

Tem

per

atu

re (°C

)

Annual Mean Temperature

Mean

Mean Min

Mean Max

33

increasing at the rate of 1.23 °C and 0.96 °C per decade respectively whereas 0.45 °C and 1.7 °C

per decade for winter and post monsoon respectively. The annual mean temperature has positive

correlation with year i.e. 0.59. Similarly, pre monsoon, monsoon, post monsoon and winter

season also has positive correlation with year i.e. 0.57, 0.56, 0.30 and 0.42 respectively.

Figure 3: Annual mean temperature with trend

Figure 4: Seasonal mean temperature with trend

y = 0.0408x + 12.181

10

11

12

13

14

15

1981 1986 1991 1996 2001 2006

Tem

per

atu

re (°C

)

Annual Mean Temperature with Trend

y = 0.045x + 4.702

y = 0.095x + 16.57

y = 0.123x + 35.64

y = 0.169x + 45.72

0

10

20

30

40

50

60

1981 1986 1991 1996 2001 2006

Tem

per

atu

re(°

C)

Seasonal Mean Temperature with Trend

Post Monsoon

Monsoon

Pre Monsoon

Winter

34

Table 5: Summary table on statistical analysis of annual and seasonal mean temperature

Annual Winter Pre Monsoon Monsoon Post Monsoon

Mean (°C) 12.81 5.41 12.65 19.49 10.81

Standard deviation 0.61 0.96 0.95 0.53 0.72

Maximum (°C) 13.88 7.83 14.53 20.21 12.43

Minimum (°C) 11.25 3.53 10.37 17.69 9.58

Trend (°C/decade) 0.40 0.45 0.96 1.23 1.7

Correlation (r) 0.59 0.42 0.57 0.56 0.30

Figure 5: Annual mean maximum temperature with trend

y = 0.0569x + 19.919

18

19

20

21

22

23

1981 1986 1991 1996 2001 2006

Tem

per

ature

(°C

)

Annual Mean Maximum Temperature with Trend

35

Figure 6: Seasonal mean maximum temperature with trend

The mean maximum temperature was found to be 20.80 °C for the years 1981 to 2010 with the

highest mean maximum temperature 22.13 °C in the year 2006 and 2009 and the lowest of 19.60

°C in the year 1997. Figure 5 showed that the mean maximum temperature trend has increased to

1.65 °C between 1981 and 2010 i.e. at the rate of 0.57 °C per decade which is statistically

insignificant. Figure 4 showed that maximum temperature is in increasing trend. The post

monsoon season showed the greatest rate (2.4°C/decade) of change while the winter season

showed the lowest rate (0.72°C/decade) of change. Similarly, the rate of maximum temperature

change per decade on Pre monsoon and monsoon season is 1.42°C and 1.73°C respectively. The

annual mean maximum temperature showed highest positive correlation with the year i.e. 0.69.

Similarly, winter, pre monsoon, monsoon and post monsoon also showed positive correlation

with the year i.e. 0.47, 0.45, 0.23 and 0.26 respectively

Table 6: Summary table on statistical analysis of Maximum temperature

Annual Winter Pre Monsoon Monsoon Post Monsoon

Mean (°C) 20.80 14.99 21.43 24.95 20.28

Standard deviation 0.72 1.34 1.20 0.52 1.02

Maximum (°C) 22.13 18.33 24.20 25.90 22.10

Minimum (°C) 19.60 12.63 19.57 23.90 22.10

Trend (°C/decade) 0.57 0.72 1.42 1.73 2.4

Correlation (r) 0.69 0.47 0.45 0.23 0.26

y = 0.071x + 13.87

y = 0.142x + 34.21

y = 0.172x + 58.69

y = 0.24x + 77.92

0

10

20

30

40

50

60

70

80

90

100

1981 1986 1991 1996 2001 2006

Tem

per

ature

(°C

)

Seasonal Mean Maximum Temperature with Trend

Post Monson

Monsoon

Pre Monsoon

Winter

36

Figure 7: Annual mean minimum temperature with trend

Figure 8: Seasonal mean minimum temperature with trend

The mean minimum temperature was found to be 4.83°C for the year 1981 to 2010 with the

highest mean minimum temperature of 5.84°C in the year 1998 and the lowest of 2.13°C in the

year 2000. Figure 7 showed that the annual mean minimum temperature has increased to 0.71°C

between 1981 and 2010 i.e. at the rate of 0.25°C per decade which is also statistically not

significant. The minimum temperature is increasing for winter, pre monsoon, monsoon and post

monsoon i.e. at the rate of 0.19°C/decade, 0.75°C/decade, 0.99°C/decade and 0.45°C/decade

respectively. The rate of increase is more pronounced in monsoon season. The annual mean

minimum temperature showed positive correlation with the year i.e. 0.33. Similarly, winter, pre

monsoon, monsoon and monsoon season also showed positive correlation with year i.e. 0.20,

0.001, 0.28, 0.73 respectively.

y = 0.0246x + 4.4434

1

2

3

4

5

6

7

1981 1984 1987 1990 1993 1996 1999 2002 2005 2008

Tem

per

ature

(°C

)Annual Mean Minimum Temperature with Trend

y = 0.019x - 4.470

y = 0.044x - 3.539

y = 0.075x - 0.135

y = 0.099x + 13.53

-10

-5

0

5

10

15

20

25

1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

Tem

per

ature

(°C

)

Seasonal Mean Minimum Temperature with Trend

Monsoon

Pre MonsoonPost MonsoonWinter

37

Table 7: Summary table on statistical analysis of annual and seasonal mean temperature

6.2.2 Precipitation Analysis

About 30 (1980 – 2010) years rainfall data of Jumla station were used for analyzing the rainfall

pattern of Jumla. The rainfall contribution of monsoon season is highest among other season

accounting for about 58% of total rainfall whereas pre monsoon and winter season account 22%

and 11% respectively. The post monsoon rain is least accounting for about 9% of total rainfall. In

Nepal as a whole, monsoon season received about 79.6% of total annual precipitation whereas

pre monsoon, post monsoon and winter received only 12.7%, 4.2% and 3.5% respectively

(Marahatta et al., 2009)

Figure 9: Seasonal rainfall distribution (%)

Figure 10 showed that the mean rainfall was highest during the month of July followed by August

with the average rainfall of 183.3 mm and 177.73 mm respectively where as lowest in the month

11%

22%

58%

9%

Seasonal Rainfall Distribution (%)

Winter Pre Monsoon Monsoon Post Monsoon

Annual Winter Pre Monsoon Monsoon Post Monsoon

Mean (°C) 4.83 -4.18 1.33 3.87 14.04

Standard deviation 0.66 0.84 0.92 1.08 0.66

Maximum (°C) 5.84 -2.67 3.95 5.07 14.95

Minimum (°C) 2.13 -7.33 -0.95 -0.53 11.48

Trend (°C/decade) 0.71 0.19 0.75 0.99 0.45

Correlation (r) 0.33 0.20 0.01 0.28 0.73

38

of December with the average rainfall of 9.86 mm. The average annual rainfall of the station was

about 68.28 mm for the years 1980 to 2010 with the highest annual rainfall of 87.59 mm in the

year 1982 and the lowest of 52.35 in the year 1984. Figure 11 showed that the annual

precipitation trend has decreased to -11.79 mm i.e. at the rate of -0.39 mm per year for 30 years.

The annual rainfall data showed that there was more fluctuation showing erratic in yearly rainfall.

Figure 10: Mean monthly rainfall (1980 -2010)

Figure 11: Annual mean rainfall with trend

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Rainfall (mm) 27.8 39.2 55.8 41.6 57.0 76.4 183. 177. 102. 34.3 9.86 13.6

0

50

100

150

200

Rain

fall

(m

m)

Mean Monthly Rainfall

y = -0.3932x + 74.571

40

50

60

70

80

90

1980 1985 1990 1995 2000 2005 2010

Rai

nfa

ll (

mm

)

Annual Mean Rainfall with Trend

39

Figure 12: Mean monthly rainfall of wettest months with trend

Figure 13: Mean monthly rainfall of driest months with trend

July and August are the two wettest months for Jumla. Figure 12 showed that the rainfall trend

for July has decreased to -25.2 mm between 1980 and 2010 i.e. the rate of -0.84 mm per year.

Similarly, the rainfall trend for august has decreased to -23.7 mm between 1980 and 2010 i.e. at

the rate of -0.79 mm per year. November and December are the two driest months for Jumla. The

rainfall trend between 1980 and 2010 has decreased for November to -23.1 mm i.e. at the rate of -

0.77 mm per year while increased for December to 0.27 mm i.e. at the rate of 0.009mm per year.

y = -0.8402x + 196.74

y = -0.794x + 373.73

0

100

200

300

400

500

600

1980 1985 1990 1995 2000 2005 2010

Rai

nfa

ll (

mm

)

Mean Monthly Rainfall of Wettest Months with Trend

August

July

y = 0.0094x + 9.7077

y = -0.7714x + 35.806

0

20

40

60

80

100

120

140

1980 1985 1990 1995 2000 2005 2010

Rai

nfa

ll (

mm

)

Mean Monthly Rainfall of Driest Months with Trend

Dec

Nov

40

Figure 14: Total rainfall in winter season

Figure 15: Total rainfall in Pre monsoon season

y = -1.2058x + 99.957

0

50

100

150

200

250

1980 1985 1990 1995 2000 2005 2010

Rai

nfa

ll (

mm

)

Total Rainfall in Winter Season

y = -2.0054x + 186.58

0

50

100

150

200

250

300

350

400

1980 1985 1990 1995 2000 2005 2010

Rai

nfa

ll (

mm

)

Total Rainfall in Pre Monsoon Season

41

Figure 16: Total rainfall in monsoon season

Figure 17: Total rainfall in post monsoon season

The rainfall trend in pre monsoon season has most declined to -60.27 mm in between 1980 and

2010 i.e. at the rate of -2.009 mm per year. Similarly, monsoon season has decreased to -27.00

mm in between 1980 and 2010 i.e. at the rate of -0.900 mm per year while at winter rainfall has

decreased to -36.15 mm in between 1980 and 2010 i.e. at the rate of -1.205 mm per year. The

annual rainfall has negatively correlated with year i.e. -0.4. Similarly, monsoon and post

monsoon rainfall has also negative correlation with year i.e. -0.07 and -0.45 respectively while

winter, pre monsoon and monsoon has weak positive correlation with year i.e. 0.33 and 0.21

respectively.

Table 8: Summary table on statistical analysis of Rainfall

y = -0.9004x + 551.65

100

300

500

700

900

1980 1985 1990 1995 2000 2005 2010

Rai

nfa

ll (

mm

)

Total rainfall in Monsoon Season

y = -0.1327x + 46.352

0

20

40

60

80

100

120

140

160

180

200

1980 1985 1990 1995 2000 2005 2010

Rai

nfa

ll (

mm

)

Total Rainfall in Post Monsoon Season

42

Annual Winter Pre Monsoon Monsoon Post Monsoon

Mean (°C) 68.35 80.75 154.6 536.46 45.45

Standard deviation 9.00 46.61 71.39 94.64 45.36

Maximum (°C) 87.59 211.5 362.00 792.5 177.5

Minimum (°C) 52.35 14.00 38.00 359.70 ---

Trend (mm/year) -0.39 -1.20 -2.00 -0.90 -0.13

Correlation (r) -0.4 0.33 0.21 -0.07 -0.45

6.3 Correlation between Rice Productivity and Temperature and Precipitation

Table 9: Annual temperature, precipitation and rice yield of Jumla from 1995 to 2008

Source: #Department of Hydrology and Meteorology, Nepal 2008/009 ## MoAc, Nepal

2008/009

The above table shows the annual rainfall, annual temperature and annual production of the rice

in kilogram per hector from the year 1995 to 2008.

Year # T max

(°C)

# T min

(°C)

#Annual

Temp

(°C)

# Annual

rainfall

(mm)

## Rice

area (ha)

## Rice

yield (kg/ha)

1995 20.18 4.89 12.53 829.0 1371 1985

1996 20.52 4.78 12.65 832.8 1350 2000

1997 19.60 4.60 12.10 773.6 1400 1707

1998 21.33 5.84 13.59 833.7 1450 1761

1999 21.78 5.45 13.61 675.7 1500 1000

2000 20.36 2.13 11.25 886.6 1835 1460

2001 21.21 5.35 13.28 728.1 2800 1000

2002 20.68 5.18 12.93 842.2 2775 1457

2003 21.00 4.83 12.91 842.9 2800 1457

2004 21.48 5.27 13.38 685.4 2800 1579

2005 20.93 4.88 12.90 669.5 2850 1173

2006 22.13 5.63 13.88 747.7 2850 1700

2007 21.57 5.45 13.51 831.8 2850 1700

2008 21.43 5.21 13.32 966.7 2850 1700

43

6.3.1 Precipitation and Rainfall

It has been found that rainfall pattern of Jumla (1995 to 2008) has almost a smooth pattern that

coincide with the yield of rice however, after 1997 with the fluctuating rainfall the status of rice

yield is also following the rainfall trend as well.

There has been experience of inconsistent rainfall pattern with higher intensities of rain and less

number of rainy days in Jumla. This could be the effect of climate change. Such fluctuations in

rainfall pattern are responsible for excessive dryness during drought and damage to the rice field

during heavy down pours. These consequences have ultimately led to the problem of leaching and

damage to rice field at the river side field. On the other hand, decrease in soil moisture in the

sloppy area has less capacity to retain water. Basnet (2009) reported that to produce 1 kg rice

tentatively 3,000 liter of water is required whereas to grow rice in one hectare of land it needs

800,000 liter water. In Jumla, due to erratic rainfall there is uncertainty of irrigation facility to

rice cultivation. Such vagaries of rainfall and shortage of irrigation to rice are responsible for

environment degradation. Raut (2010) reported that emission of methane from rice field supplied

with 50% nitrogen fertilizer was 49kg/ha which is quite high in rain fed irrigation.

6.3.2 Temperature and sunshine

Rice plant is very much susceptible to temperature and it thrives fairly with temperature ranging

from 20° C to 40°C.The optimum temperature of 30°C during day time and 20°C during night

time seems to be more favorable for its development and growth. Rice yield has been fluctuating

along with the fluctuation of temperature. The yield of rice is influenced by the solar radiation

particularly during the last 35 to 45 days of its ripening period (www.Wikipedia, 2010). The

effect of solar radiation is more profound where water, temperature and nitrogenous nutrients are

not limiting. But in the case of Jumla nitrogen is a limiting factor for all crops and rice is no

exception because Jumla has been declared organic district in 2009 by the DDC, Jumla.

Therefore, after declaration of Jumla as organic district, there is no question of applying any

chemical fertilizers for the farming and rice could not be an exception until the declaration

prevails. In hills because of clouds during reproductive phase of rice, for example, rice varieties

Chandanath-1 and Chandannath-3 in Jumla face less sunshine duration during the grain filling

period resulting to low assimilation. However, as the crop advances there is bright sunshine

during ripening period of the crop that hastens development of carbohydrates in the grains despite

low temperature in high altitudes.

The correlation between the annual average temperature and the productivity of the rice is found

to be -0.13632 whereas the correlation between the annual precipitation and the productivity of

rice is found to be 0.535504. This implies that the increase in annual average temperature has

negative impact in the productivity of rice whereas the increase in the precipitation has positive

impact in the productivity of rice.

6.4 Public Perception on Climate Change

Focus group discussion, Key informants (socially active persons) and households' survey were

taken to collect the information about past evidence of climate and its impacts. Local adaptation

measures were also observed. Respondents showed their experience over past climatic events,

however 75 % of respondents were unclear about climate change and 95% people were well

satisfied with the present weather and climatic condition.

44

Very less respondents were well aware about climate change, its possible impacts and needs of

adaptation. But they have had experienced increase in temperature, erratic rainfall pattern, flash

flood and drought. Before 3 years ago they have the problem of drought at the community but in

the recent years they have sufficient rainfall at a time. A number of indicator have been used by

respondents to justify the rise in temperature like as diseases in rice plant (99%), elongated

growing periods (78.9%), early snow melt (66%), introduction of mosquito (98%), decreases

amount of water resources (78.6%), drought. In these years they were facing untimed rainfall and

big hailstorm.

Extreme rainfall and hailstorm occurs on post monsoon season. Regarding the perception on

changes in temperature, 95% showed in these years weather condition is much good than

previous years. They were agreed about the month of coldness and snow fall time has been

changed in the recent years. The most significant evidence of climate change is associated with

agriculture diseases.

6.5 Climate Change Impacts and Adaptation practices

6.5.1 Agriculture

Agricultural land at the bank of the Tila River is one of the highest productive lands for Jumla.

Most of the residents of the Kartikswami VDC have their agricultural land at the side of Tila

River. Rice, wheat, barley, potato, maize etc. are the major production. They have the traditional

and local practices of farming. According to respondents and key informants due to erratic

rainfall, hailstorm and increased temperature quickly affects the production of agriculture.

Especially production of local Rice (Orizya sativa) has been decreased by 50%.

Introduction of new diseases (blast) on rice were the major problem (Rice blast is caused by the

Ascomycota fungus). However, respondents said that they were using new varieties of rice seed

(Chandanath 1 & 3) have been good in recent years. In the recent days very few peoples have

grown high altitude local rice (Marci rice) because of their wellbeing only, said one of the

respondents. There were also reported that the production of barley, corn and potato had also

decreased due to early snowmelt, hailstorm and erratic rainfall. According to respondents the

quality of apple is also slightly decreasing in the recent years it is due to the less snowfall on

winter. Respondents also reported that there has been changing in cropping time by two weeks in

the recent years.

Due to the less production and crop failure in the community especially in Dalit community

seasonal migration to India and selling labor in local market and community is used as common

adaptation measures. According to the participants of focus group discussion in the village they

are taking loan for food security every year.

6.5.2 Water Resources

The area is not vulnerable to water scarcity and water related hazard. But according to

respondents the source of water in springs, ponds/wells are decreasing. They were also reported

that in the recent years the quantity of water in the river has been decreasing. In all focus group

discussion and key informant interview respondents reported, early snow melts, very less

snowfall at the mountains and low and erratic rainfall would be the causes of less water at the

river in dry season. The sources of drinking water near village were drying up and they have to go

far for drinking water in near future.

45

6.5.3 Forest and Biodiversity

Deforestation is the major problems of the district, human impact on forest had found very high.

Many wild species and medicinal plant were found at the jungle before 10 years but in the recent

years no wild species can be seen at the nearby jungle. Collection of NTFPs was the major source

of income for poor houses in the area has been decreasing every year. Out of total respondents

85% reported that there was the shifting of flowering time by one month of many plant species.

6.5.4 Human and Livestock Health

The village is not induced by climate related health hazard yet. In the focus group discussion, key

informant interview and households survey they were not reported any new diseases on human

and livestock health. There were the facilities of health post. But medicine and capable human

resources at the health post was the problem.

6.6 Vulnerability and Climate Change

Table 10 Vulnerability Calculation

See Appendix-VII

The Kartikswami VDC is found to be medium vulnerable to climate change and its induced

disaster with the 2.16 vulnerability ranking based on the adaptive capacity, exposure and

sensitivity towards climate change.

The adaptation capacity was medium with the ranking 2.58 as it has the modest access to the

primary, secondary and tertiary indicators of the adaptive capacity. With more than 85%

households having access to the drinking water facility, less than 10km distance for the access to

the district headquarter and with impressive literacy rate, sanitation rate and access to technical

Exposure (E)* Sensitivity (S)* Adaptability (A)* Vulnerability= E*S/A

2.5 2.23 2.58 2.16

Box 1.

Mr. Dal Bahadur Kami said,

"Due to the less production of crops, there was the pressure to harvest timber and non-

timber forest products whereas due to the less moisture, the rangelands have been less

productive in the recent years. Less snowfall and erratic rainfall have affected on

rangelands, grazing of livestock and collection of Non-Timber Forest Products."

46

services the VDC is moderately capable to adapt against any climate induced disaster in near

future.

The exposure towards the climate change and its induced disaster is also moderate with the

exposure ranking of 2.5 out of 4 in average. As there is no any significant change in the average

temperature and precipitation the exposure is quite normal. The flowering time of some species

and the harvest of some crops have changed with average 15 days, which can be the alarming for

future exposure.

Similarly, the sensitivity towards the climate change is also moderate with the value of just 2.23.

The increase in some diseases like asthma, dysentery etc in Humans and the introduction of

disease like blast in the rice is the area of concern. Since, all the cultivable land of this VDC are

situated in the bank of Tila River the VDC is likely to affected by the water induced hazard like

flooding, erosion etc in near future.

Although, the overall effect of the climate induced hazard on the Kartikswami VDC is moderate

still the exposure and sensitivity can accelerate in any time in future. Similarly, the adaptive

capacity also needs to be increased significantly to address the climate induced hazard in the

coming future.

47

Chapter VII

Conclusions and Recommendation

7.1 Conclusion

From the study it can be concluded that the climate of the area is changing and has a impact on

people's daily life. In the village adaptive capacity is low due to economic status and inequitable

access to the resources. The results showed that the high variation in climatic pattern have

significant impact on livelihoods of villagers. The results also supported by hydro-

meteorological data showed changing in weather condition of the study area. Moreover, rice

cultivation in Jumla was found to be associated with socio-cultural as well as special agronomic

practices. The rice yield kg/He was decreasing tremendously in the past few years which is

somewhat increasing in the present days. The traditional Marshi rice is being replaced with the

newer variety like Chandanath 1 and Chandanath 2. The temperature change has negative co-

relation with the productivity whereas the change in precipitation has positive co-relation with

the productivity.

The Kartikswami VDC is found to be medium vulnerable to climate change and its induced

disaster with the 2.16 vulnerability ranking based on the adaptive capacity, exposure and

sensitivity towards climate change. The adaptive capacity is not adequate and is unlikely to

support the climate induced disaster in the future. However, due to the availability of some of the

development infrastructure recently and based on the changing socio-economic scenario of the

district the condition is likely to improve in the near future.

After the study of the impact of climate change in the Kartikswami VDC it is found that due to

the less production and crop failure in the community especially in Dalit community seasonal

migration to India and selling labor in local market have risen up in recent days. According to the

participants of focus group discussion in the village they are taking loan for food security every

year. This represents the tragic situation of the Karnali as a whole. The responsible stake holders

need to come up with the detail planning and programs as soon as possible to stop the flowing of

people as a labor in India.

The study of the people's perception about climate change is very interesting and thinkable.

About 95% of the respondents believe that the temperature is rising in these days and they are

enjoying this risen temperature in compare to the past crispy and cold days. But, the extreme

rainfall and hailstorm occurrence on post monsoon season have been real problem and is proved

to be very damaging. The rainfall duration has decreased than past but the intensity has

increased.

48

7.2 Recommendation

The climatic impact on the livelihood of people at higher altitudes needs a multi dimensional

approach to tackle the issue. On the basis of the findings and observations of this study, the

following recommendations are made:-

1. There is a lack of sufficient study on the impacts of climate change in the higher altitude.

Hence, more study and research is recommended to know the consequences of these

changes in the livelihood of the people.

2. The research study focusing on the reasons behind the change in the climatic factors in

the higher Himalayas should be done elaborately.

3. The livelihood strategies need to be diversified to include other income sources besides

farming. This may include skill enhancement on adding values to the agriculture produce

such as fruits and vegetables for export by processing them.

4. Introducing drought resistant varieties of crops can help reduce loss of production due to

drought.

5. Micro finance schemes such as micro-credit, micro insurance etc with provisions for

providing loan with low rate of interest to the households suffering from losses due to

climatic condition is essential. Crop and livestock insurance scheme, agricultural subsidy

program should be launched by the government to the vulnerable communities.

6. Any new project that is implemented should include climate adaptation elements to

increase their resiliency.

7. The promotion of other crops like Marshi, Chino, Uwa, Barley, Millet etc. which have

higher calorie value than the rice plant is highly recommended to tackle with the existing

food security problem of the Karnali zone.

8. The access to the basic health facilities, education, transportation, communication etc.

needs to be increased which helps to upgrade the standard of living of the local people

making them more capable to face the climate induced disaster.

9. Karnali zone is very beautiful and appealing, having a huge potential of tourism in future.

The responsible stakeholders should promote the eco-tourism which will definitely help

the locals to enhance the resistivity against the climate change.

49

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54

Appendix I: Questionnaire for Household Survey

Interviewee: Sex: M/F Age:

Village:

Location:

Households Information:

1. Are you permanent resident of this locality?

Yes-1 No-2

If No:

From where you are migrated to this place?

Same locality-1 Same District-2

Same nation-3 Out of Nepal-4

If local migration (from same locality):

What is the cause of migration?

………………………………………………………………………………………………………

………………………………………………………………………………………………………

………………………………………………………………………………

How much time you have spent here?

Less than 10 years More than 10 years

2. How many members in your household are economically active?

55

3. What is your yearly income and expenditure (recent years)?

S.N Income

Expenditure

Sources Member involved Amount Sources Amount

1 Agriculture Food

2 Business Cloths

3 Gov service Education

4 Private service Health

5 Job abroad Occasion/Festival

6 Daily wages Agriculture

7 Others Other

Total Total

4. Do your family income generation activities changes these days?

Yes-1 No-2

If Yes:

What are your family income generation activities in past?

………………………………………………………………………………………………………

Agriculture:

1. Land Holding

Irrigated land Rainfed land Others

Khet

Bari

Pakha

2. How many seasons you conduct agricultural activities?

3. What are the major types of crops you cultivate?

4. What are the common Fertilizer uses?

5. What are the common pesticide/ chemical uses?

56

6. Are you facing any problem due to agro-chemicals in farm?

7. Do you know about organic farming?

8. Can you give the statistic of animal in this this area?

Buffalo ______________ Cows_________________ Goat_______________

Pig ____________________ Chicken_________________ Other_______________

Climate change Information:

1. Have you noticed any changes in your environment (weather, climate, temp, wind etc) over

the past 10-20 years?

Yes No Not Sure

If yes, can you tell me what changes you have noticed?

………………………………………………………………………………………………………

………………………………………………………………………………………………………

2. Before this interview, had you heard about climate change?

Yes No Don‟t Know

3. What is your source of hearing about?

a) Radio b) Newspaper

c) Television d) NGO/GO bodies

e) Others, specify plz

4. Which components of environment are affected?

a) Water Resources b) Agriculture

c) Environment and Sanitation d) Human settlement and Infrastructures

e) Others, Please specify,

57

5. Have you recently experienced any extreme/unusual weather events for example, extreme

flood, landslide, GLOF and Drought etc?

Yes No Don‟t Know

If “yes”, please describe the most recent significant event:

When did it happen?......................................

What happened?............................................................

6. Have you recently attended a consultation, workshop or school lessons on Climate Change?

Yes No Don‟t Know

If yes, who arrange the event?...................................................

7. What are your thoughts about the following statements about CC?

Agree Disagree Unsure

a) Climate Change is happening?

b) CC is affecting the people of this community already?

c) Every individual can do something to ADAPT to climate change?

d) Living for today is more important than worrying about the effects of Climate

Change.

e) CC will reduce the quality of life of your children & grandchildren in the future?

Adaptation:

Read out:

“Adaptation means doing something NEW or DIFFERENT to what you or your community did

in the past in order to adapt to climate change”

1. What have you done ALREADY to adapt to climate change?

2. Have you already planned to do any things in the FUTURE to adapt to climate change?

58

3. If necessary, in the long term, would you be prepared to move with your family to?

4. The central/local government is doing things to help you to ADAPT to CC locally?

Yes No

If “yes” please give the examples:

CAUSES OF CLIMATE CHANGE

1. What do you think what are the main cause of climate change?

a) Deforestation

b) Burning fossils fuels

c) Don‟t know

d) Others (please specify)

2. Do you have anything you would like to add about any climate change issues?

______________________________________________________________________________

______________________________________________________________________________

______________________________________________________________________________

______________________________________________________________________________

59

Appendix II: Checklist for focal group discussion

1. Have this community experienced any changes in temperature within last 30 years?

2. State what type of changes?

3. What do you think about the impacts of these changes in agriculture, water resources, and

diseases spreading?

4. What are the measures adopted to cope with these temperature changes?

5. Have this community experienced any changes in rainfall pattern within last 30 years?

6. State what type of changes?(increase/ decrease)

7. Do you fell the following?

a) Unusual rainfall

b) Heavy rainfall at once

c) Long rainy seasons

d) Delay in monsoon starting time (on set of monsoon)

e) Longer drought

f) Winter rainfall (increase/decrease)

g) Increase in hail storm

h) Wind storm

8. What do you think are the consequences of changed rainfall pattern in?

a) Agriculture (increase/decrease in agriculture yield)

b) Water resources (increase/decrease in water availability, flood frequency, drought)

9. What are you doing to cope with these rainfall changes? (Delay cropping, new variety of

plantation etc.)

10. Have you noticed any changes in wind pattern in this area?

11. Is there any long drought in this area in past years? How long was it? What are the impacts

and how you mitigate?

12. Enlisting new emerging problems relating to climate (agriculture, change in forest

characteristics, flowering and fruiting seasons, changes in water resources, health problem,

pest and diseases)

13. Measures adopted to cope with these problems (change in consumption pattern, use of

fertilizer and manure, migration)

14. Most significance disaster in this community? Its main cause?

15. What are the losses due to disaster within last 10 years? Any persons migrate from this area

due to disasters?

60

Appendix III: Checklist for key informant interview

1. Information on observed trend in rainfall, temperature and natural disasters.

2. Information on sources and use of water resources, extent of dependency and use of forest

and wetland resources, forest area of research area)

3. Recently observed change in forest, water and wetland resources.

4. Characteristics of agricultural crops (irrigation, temperature, fertilizer use)

5. Enlisting new emerging problems relating to climate (agriculture, change in forest

characteristics, flowering and fruiting seasons, changes in water resources, health problem,

pest and diseases)

6. Enlisting infrastructure in the area.

7. Any capacity development training especially for new agriculture practices, income

generation activities.

8. Profile on local government, NGO, INGO, Government office, other external organization.

61

Appendix IV: Mean Maximum Temperature Data of Jumla

Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

1981 12.50 15.30 16.10 21.30 24.00 26.70 23.70 23.70 23.40 22.10 17.20 15.30

1982 14.1 13.6 16.6 20.6 21.8 23.4 25 24.2 23.3 20.5 17.9 16.3

1983 13.9 13 17.9 19.8 22.4 25.3 25.1 25.2 24 20.6 17.3 14.4

1984 11.7 14.2 21.1 20.9 26.2 25.4 23.8 25 22.9 22.4 16.7 16.2

1985 10.8 17.5 21.5 22.2 24.1 25.8 23.5 24.8 23.4 20.1 17.8 16.4

1986 13.9 15.3 17.7 20.9 21.8 25.9 24.2 24.4 23.6 20.7 18.8 12.9

1987 14.9 15.9 18.8 20.7 22.1 27 24.8 25.2 25.4 21.9 20.8 18.6

1988 16.1 15.5 17 22.7 25.3 26.3 24.8 23.9 25.1 22.8 18.3 15.6

1989 12 14.2 18.8 22.1 24.7 26.1 26 24.5 24.3 22.3 17.3 16.1

1990 18.2 12.4 15.4 20.6 23.8 27.1 23.5 24.4 25.3 21.6 19.3 15.1

1991 12.2 14.3 16.5 19.9 24.7 24.9 25.9 24.5 24.6 22.7 17.2 13.9

1992 13.3 13.6 18.3 22.6 22.8 26 25.1 23.8 23.6 20.8 17.9 15.9

1993 12 15.9 14.4 20.4 23.9 24.4 25 24.8 22.3 22.1 18.7 16.5

1994 13.5 12.7 20.3 19.6 23.8 26.8 25.9 23.7 24.8 22.2 17.1 14.9

1995 10.5 12.8 17.1 20.9 25.5 26.3 24.9 24.1 24.3 23 18.1 14.6

1996 12.7 13.6 18.8 21.2 24.8 25.5 25.3 23.9 24.2 19.9 18.9 17.4

1997 12.4 13.7 18 19.3 22.9 25.7 26.7 24.5 24.5 19 16.7 11.8

1998 15 15.2 16.2 21.9 26 27.4 25.1 24.6 24.8 22.5 19.3 18

1999 14.4 17.7 21.4 25.3 25.9 25 25.1 24.4 24.5 21.5 19.4 16.7

2000 13.9 11.9 16.8 22.6 24.4 23.9 24.2 24.4 23.1 23.1 18.6 17.4

2001 14.8 16.5 17.7 20.8 23.8 24.3 25.4 25.3 25.7 23.5 19.8 16.9

2002 12.6 14.8 18.7 21.4 23.9 26.5 26.4 24.4 22.3 22 19 16.2

2003 15.7 14.6 18 22.1 24 26.1 24.6 24.9 23.9 23.3 19.5 15.3

2004 13.3 17.9 22.7 22.4 24.4 26.2 25.6 25 24.8 20.5 17.7 17.3

2005 11.8 12.9 19.1 21.7 24.3 28.1 24.5 25.4 24.5 22.8 19.9 16.2

2006 17.1 20.4 18.7 21.4 25.1 26.3 25.7 25.4 26 22.8 19.1 17.5

2007 16.6 14.2 18 23.9 24.5 26.7 24.8 24.9 25.2 23.3 19.5 17.2

2008 12.3 16.4 20.2 22.6 23.6 24.9 24.8 24.8 24.7 22.5 21.5 18.9

2009 18 18.2 20.3 24.2 24.2 27.9 26.4 25.7 23.6 21.8 18.9 16.3

2010

16.6 15.6 22.5 24.9 24.6 26.8 24.7 24.7 24.1 23.4 20.8 16.6

62

Appendix V: Mean Minimum Temperature of Jumla

Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

1981 -4.9 -2.7 1 5 8.3 9.2 15.9 15.5 11.7 4.7 -1 -3.3

1982 -3.7 -2.1 0.5 3.3 5.7 12.6 14.8 15.1 10.7 2.6 -0.5 -5.6

1983 -4.9 -4.3 -0.3 2.8 7.6 10.5 15.3 15.7 13.3 5.6 -2.3 -5.8

1984 -7.2 -4.5 0.5 4 9.2 15.1 15.3 14.7 10.2 3.7 -3 -4.8

1985 -4.2 -4 0.7 3.8 8 13.3 15.5 15.9 12.9 5.7 -1.6 -3.9

1986 -5.2 -3.5 0.1 3.4 6.1 12.9 15.4 14.8 12 4.2 -1.7 -4

1987 -4.6 -2.4 0.6 3.8 5.7 12.2 15.6 15.4 12.4 3.7 -2.5 -4.1

1988 -4.4 -2.7 0.6 3.7 7.7 12.7 16 15.3 11.9 3.1 -1.8 -3.8

1989 -5.8 -4.9 -1.7 1.7 7.2 12.7 15.3 14.7 13.1 3.6 -2.1 -4.4

1990 -5.1 -1.4 -0.1 2.9 9.2 14 15.4 14.9 13.3 3.8 -1.6 -4.4

1991 -6.2 -1.8 0.7 2.9 6.9 13.4 15.7 15.4 12 2.2 -2.1 -4.4

1992 -3.7 -4.3 0.3 2.8 6.6 11.7 14.5 15.1 12 5 -0.6 -5.1

1993 -4.4 -1.3 -1.1 3.7 8.3 13 15.7 15.5 10.5 0.8 -2.7 -5.8

1994 -4.3 -3.9 0.1 2.4 7.5 13.9 16.3 15.6 12.9 1.5 -1.9 -5.4

1995 -7 -3 0.7 2.7 8.1 15 15.7 15.3 12.9 4.4 -1.5 -4.6

1996 -4.8 -3 1.3 3.2 6.8 13.4 15.7 15.2 11.8 5.7 -2 -5.9

1997 -5.9 -3.6 0.6 4.1 5.4 11.9 16.5 14.8 12.6 3 -0.4 -3.8

1998 -5.1 -2.9 0.7 4.7 9.2 13.8 16.5 16 13.5 7.9 0 -4.2

1999 -4.8 -1.4 0 4.3 10.2 13.3 16 15.2 13.2 5.4 -1.9 -4.1

2000 -8.1 -9.1 -5.9 -0.5 4.8 9.2 11 14.4 11.3 3.6 -0.3 -4.8

2001 -5 -3.4 0.1 4.4 9.9 14.4 16.1 15.4 12.3 6.1 -1.3 -4.8

2002 -4.9 -2.6 1 4.4 9.3 13.3 16.1 15.4 11.6 4.6 -1.4 -4.6

2003 -5.4 -2.5 0.5 4.4 6.1 13.1 15.8 15.6 13.8 3.2 -2.1 -4.6

2004 -4.2 -3.5 0.8 5.5 8.3 13 15.3 15.1 13.4 4.7 -1.5 -3.7

2005 -3.9 -1.7 1.7 2.9 6.6 12.2 16.1 15.3 13.3 4.8 -3.3 -5.5

2006 -4.2 -0.6 0.8 3.8 10 12.8 16.6 15 12.8 4.2 -0.5 -3.2

2007 -5.8 -1.4 0.6 4.9 8.8 14 15.9 15.7 13.2 4.9 -1.3 -4.1

2008 -3.5 -3.6 0.8 4.1 8.1 14.9 16 15.4 10.4 4.4 -1.6 -2.9

2009 -3.3 -2.8 0.4 4.2 8.3 12.2 16.1 15.8 12.3 4 -1.4 -3.7

2010

-4.3 -2.7 1.8 5.3 8.1 12.6 16.1 16.1 13.4 4.7 0 -6.1

63

Appendix VI: Precipitation Data of Jumla

Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

1981 9.5 33.4 132 14.2 45.4 147 277.2 280.9 87.4 5.1 0 13.8

1982 27.8 26.2 73.5 27.4 50.8 DNA 268.9 192.2 201.8 0.3 45.4 10.6

1983 46.4 133.1 168 49.1 144.9 63.6 82.4 200.4 118.7 3 9.5 32

1984 34.9 14.2 16.8 89.5 86.4 73.3 104.4 191.3 160.9 112.3 0 11.3

1985 22.3 37.6 25.6 29 16.1 117.2 136 104 118.7 16.2 0 5.5

1986 42.4 2.1 27.5 55.1 45.1 47.8 170.1 128.8 143.1 147 0 67.6

1987 2.8 20.3 51.9 70.3 71.3 33.1 327.5 126.1 47.3 58.2 1.1 46.3

1988 10.6 26.7 19.5 76.3 109 57.6 166.8 89.9 45.4 78.8 0 16

1989 9.1 85.3 117.6 26.4 45.4 83.4 274.9 164.5 39.6 0.6 1 37.2

1990 9.1 8.8 34.4 8.6 31.5 51.5 249.7 190.4 81.5 26.8 8.6 2.9

1991 0 76.1 96 30.9 28.9 98.8 183.5 196.3 89.1 17.7 1 11

1992 54.1 0 146.8 88.1 88.4 73.4 155.8 224.2 31.1 0 9.8 21.2

1993 58.1 13.5 28.1 14.3 42.3 44.5 153.6 233.7 138.5 31 0 0

1994 40.2 41.3 136.8 27.5 63.3 116.7 107.1 133.6 154.8 0 0 0

1995 40.8 54.1 7.8 46.5 55.2 31.1 318.7 207 51.6 0 0.8 0

1996 79.5 44.4 45.1 18.3 6.8 100.6 199.4 159.1 98.6 0.6 73.8 2.8

1997 46.9 109.9 31.2 29.2 20.1 47 105 251.8 89.3 102.4 0 0

1998 57.1 20.9 18.1 40.5 36.1 40.1 173.8 146.4 66.7 42.9 35.8 95.2

1999 2 41.6 91.1 47 47.7 58.6 106.1 171 171.7 51.3 45.6 0

2000 15.9 17.6 4.1 6.5 27.4 113.1 194.9 97.2 131.8 65.7 0 1.5

2001 9.5 41 42.4 55.8 74.2 159.7 197 143.4 145.8 3.8 9.8 4.2

2002 28.7 45.4 50.7 40.2 84.7 181.9 108.9 130.5 44.3 11.6 1.2 0

2003 74.7 55.2 62 114.9 45.6 29.3 44.3 290.9 89.8 12.2 23.3 0

2004 44.4 65.1 43.2 48.1 31.3 63.6 171.2 236.3 121.4 10.3 0 8

2005 22.6 0.7 0 59.3 79.1 64.9 123.7 215.7 71.8 46.4 1.2 0

2006 37.9 51.5 33.9 17.4 26.9 12.7 302.4 85.9 80 8.5 0 12.4

2007 0 0.5 46.2 45.1 59.5 36.6 236.8 218.4 57.9 10 23.2 13.5

2008 1.4 44.7 119.6 23.4 48 83.1 206.3 144.8 136.6 19.5 0.4 4

2009 24.7 2.2 32.1 36.7 82 201.8 178.2 250.8 141.4 15.1 0 1.7

2010

1.3 34.6 15.9 20.7 70.3 12.1 168.2 82 112.7 163.4 14.1 1

64

Appendix VII: Vulnerability Analysis form

1. Gateway System Analysis (Adaptive Capacity)

A. Primary System (Core System)

VDC Energy Water Land Food

Sufficiency

* (%

households

Total

Cooking

(%

relying on

traditional

sources)

Lighting

(% with

access

to solar

and

electricity)

Access to

Drinking

Water

(no.

households

)

Irrigation

(Ha.

arable

land

irrigated)

Average

landholding

size (ha)

Disaster

-affected

land

(%)

86 (1) 80.1(3) 99 (4) 32.1 (1) 0.141 (1) 42(3) 13.5(1) (14)

* Food sufficient households indicates the households with enough food provision for 12 months

B. Secondary System

V.D.C Transportation Communication Livelihood Shelter Total

Average

distance

to

highway

(km)

Road density

(distance/area)

Households per

mobile

(no.)

Population

engaged in

agriculture

(%)

Households

affected by

climate hazards

(%)

Households

living in

cement

houses

(%)

<10 (4) <5(1) 100(3) 92.7(1) 30(3) <5(1) (13)

C. Tertiary System

V.D.C Literacy

(%)

Sanitation

Coverage*

(%)

Cooperatives

(no.)

Nearest

markets

(Km)

Local

organizations

(no.)

Distance to

nearest

government

office (km)

Access to

technical

service at

district(%)**

Total

79.7 (4) 53.1 (4) 2 (1) <10 (4) >7 (4) <5 (4) 100 (4) (25)

* Sanitation Coverage= no. of households with toilets

** Technical Service Provider= Agriculture, Forest, Health, Animals etc.

65

D. Criteria and Indicators for Adaptation Capacity Assessment

Adaptation

Capacity

Indicators Criteria: Rank (1-4)

Cooking % relying on traditional sources <50:4; 50-70:3;70-90:2;>90:1

%access to solar and electricity <20:1; 20-50:2;50-85:3;>85:4

Number of households with drinking water tube well <20:1; 20-50:2;50-85:3;>85:4

Irrigated land (Ha) <50:1; 50-150:2;150-250:3;>250:4

Land holding size (Ha) <0.5:1; 0.5-1.5:2;1.5-3:3;>3:4

Disaster affected land <30:4; 30-60:3;60-75:2;>75:1

Food sufficient households <15:1; 15-30:2;30-50:3;>50:4

Distance to Highway <10:4; 10-25:3;25-50:2;>50:1

Road Density <5:1; 5-15:2;15-50:3;>50:4

Household per mobile <50:1; 50-200:2;200-700:3;>700:4

Population engaged in agriculture % <50:4; 50-70:3;70-85:2;>85:1

Household affected by climate hazard <10:4; 10-30:3;30-60:2;>60:1

Households living in cemented house <5:1; 5-10:2;10-20:3;>20:4

Literacy % <30:1;30-50:2;50-70:3;>70:4

Sanitation % <10:1; 10-30:2;30-50:3;>50:4

Cooperatives <3:1; 3-5:2;5-9:3;>9:4

Distance to the nearest market <10:4; 10-30:3;30-50:2;>50:1

Number of local organizations <3:1; 3-5:2;5-7:3;>7:4

Distance to nearest government office <5:4; 5-10:3;10-30:2;>30:1

Access to technical services <10:1; 10-30:2;30-65:3;>65:4

66

2. Analysis of Climate Change Exposure

A. Temperature Trend Analysis

V.D.C Temperature Indicators and Scoring of Temperature Change Trend

Less

exposure

(1)

Moderate

Exposure

(2)

High

Exposure

(3)

Very High

Exposure

(4)

Remarks

Hot

temperature

or days

Very less

or no

change at

all

Normal

Change

(Hot days

rise by less

than 1

month)

Moderate

Change

(Hot days

rise by 1-2

months)

Remarkable

Change

(Hot days

rise by

more than 2

months)

2

Cold

temperature

or Days

Very less

or no

change at

all

Normal

Change

(Hot days

rise by less

than 1

month)

Moderate

Change

(Hot days

rise by 1-2

months)

Remarkable

Change

(Hot days

rise by

more than 2

months)

2

B. Precipitation Change Analysis

V.D.C Precipitation Indicators and Scoring of Temperature Change Trend

Less

exposure

(1)

Moderate

Exposure

(2)

High

Exposure

(3)

Very High

Exposure

(4)

Remarks

Monsoon

Rainfall

Very less

or no

change at

all

Normal

Change

(Rainfall

days rise

by less

than 1

month)

Moderate

Change

(Rainfall

days rise

by 1-2

months)

Remarkable

Change

(Rainfall

days rise by

more than 2

months)

2

Winter

Rainfall

Very less

or no

change at

all

Normal

Change

(Rainfall

days rise

by less

than 1

month)

Moderate

Change

(Rainfall

days rise

by 1-2

months)

Remarkable

Change

(Rainfall

days rise by

more than 2

months)

3

67

C. Changes in the behavior of Plants

V.D.C Plant's

behavior

Indicators and Scoring of Plant behavior Change Trend

Less

exposure

(1)

Moderate

Exposure

(2)

High

Exposure

(3)

Very High

Exposure

(4)

Remarks

Change in

the plant's

flowering

and fruiting

time

Very less

or no

change at

all (less

than 7

days)

Normal

Change (8

to 15 days)

Moderate

Change

(16 to 21

days)

Remarkable

Change

(more than

21 days)

3

Absence of

local

species/

Presence of

new species/

Presence of

new disease

of insects

Very less

or no

change at

all

Normal

Change (1

new

species)

Moderate

Change (2

new

species)

Remarkable

Change (3

or more

species)

3

D. Change in availability of Water Resource (Physical Change)

V.D.C Availability

of Water

resource

Less

exposure

(1)

Moderate

Exposure

(2)

High

Exposure

(3)

Very High

Exposure

(4)

Remarks

Decrease in

Water

Resource

Very less

or no

change at

all

Normal

Change

(Water

availability

decrease

by less

than 1

month)

Moderate

Change

(Water

availability

decrease

by 1-2

month)

Remarkable

Change

(Water

availability

decrease by

more than 2

months)

3

68

E. Change in the activities regarding livelihood

V.D.C Activities

regarding

Livelihood

Less

exposure

(1)

Moderate

Exposure

(2)

High

Exposure

(3)

Very High

Exposure

(4)

Remarks

Change in

the sowing

and

harvesting

of

agricultural

crops

Very less

or no

change at

all (less

than a

week)

Normal

Change (1-

2 weeks)

Moderate

Change (2-

3 weeks)

Remarkable

Change

(more than

3 weeks)

2

New disease

in Human

No new

disease

reported

1 new

disease

reported

2 new

disease

reported

More than

2 new

disease

reported

2

F. Change in the Hazard Events

V.D.C Rate of

Hazard

Less

exposure

(1)

Moderate

Exposure

(2)

High

Exposure

(3)

Very High

Exposure

(4)

Remarks

Increase in

the event,

number and

order of

Hazard

Very less

or no

change at

all (Once

in a 10

years)

Normal

Change (2-

3 in a 10

years)

Moderate

Change (4-

6 in a 10

years)

Remarkable

Change

(More than

10 in a 10

years)

3

69

3. Climate Change Impact's Order (Sensitivity)

A. Impact of Hazard (Humans)

V.D.C Human Loss

(Less-1

Very high-

4)

Loss in a

human

settlements and

physical infra

structure (Less-

1 Very high -4)

Loss in a

land

(Less-1

Very

high -4)

Impact on

Human

Health

(Less-1

Very high -

4)

Impact on

Refugee or

Migration

(Less-1

Very high -

4)

Total

Impact

(Less-1

Very high -

4)

1 2 2 2 2 9

B. Impact of Hazard (Ecological)

V.D.C Change in

Agricultural

Products, Bio-

diversity

(Less-1 Very

high-4)

Change in

Forest area,

Wildlife (Less-1

Very high -4)

Change in the

Water

resource and

Energy (Less-

1 Very high -

4)

Other Areas

(Graze land,

Aesthetic) (Less-

1 Very high -4)

Total

Impact

(Less-1

Very high -

4)

2 3 3 3 11

70

71