Vulnerability to climate change & variability : An Investigation into

The “State of DRR at the Local Level” A 2015 Report on the Patterns of Disaster Risk Reduction Actions at Local Level

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VULNERABILITY TO CLIMATE CHANGE & VARIABILITY:

AN INVESTIGATION INTO MACRO & MICRO LEVEL

ASSESSMENTS – A case study of agriculture sector in

Himachal Pradesh, India

Akshay Srivastava

Knowledge Manager, Centre for Good Governance

Masters of technology in Sustainable Development and Climate Change, CEPT University, Ahmedabad, India

Email: [email protected]

[Abstract]

There’s a growing recognition in the global environment change research community that climate

change impact studies must take into account the variations in its direct and indirect effects across

regions and sectors. Vulnerability analysis is one of such tools used by adaptation planners and policy

makers for prioritising actions for reducing vulnerability and improving resilience of the region. Yet

there is no systematic methodology to study climate change vulnerability which would incorporate the

context/specifics of the region for developing adaptation strategies. There are assessments which,

either, try to target multiple sectors at a grosser scale which makes it look comprehensive but seldom

succeed in developing strategies for specific sectors and specific locations, or, there are assessments

which are at a finer scale and bring out on-ground vulnerabilities which fail to develop strategies that

can be implemented through means of policies. There is an urgent need for engaging the

assessments in a manner that they not only address the climate change impacts on systems that

emerge at macro scale, but also they must not fail at capturing the mechanisms via which the

vulnerability manifests itself at micro scale. Using the example of Agriculture sector in Himachal

Pradesh, this paper presents an approach for investigating regional vulnerability to climate change.

This method, which combines both Quantitative mapping of macro level vulnerability and local level

case study to assess differential vulnerability for a particular sector within a region, can serve as a

basis for targeting policy interventions for adaptation. For policy relevance, both approaches have

their respective pros and cons and may be brought together for developing context specific adaptation

responses to climate change and variability.

Keywords: Vulnerability, Climate variability, Vulnerability Index, Adaptive Capacity, Adaptation

mailto:[email protected]


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1 Introduction & Background “Susceptibility to the highest forces is the highest genius” – Henry Adams

Post industrial revolution has advanced into globalization and never before have human activities had

caused so much environmental change as evident in our time. Climate change is one such global

environment change which is, if not proven the cause, exacerbated by anthropogenic activities.

Primarily, burning of fossil fuels and changes in land use and land cover has led to the increasing

concentrations of GHG gases in the atmosphere. These changes in the gases are projected to lead to

regional and global changes in temperature, precipitation and other climate variables. These changes

in the climate regime of a region are broadly termed as climate change.

It is well accepted that climate change will have a far more detrimental effect on developing countries

compared to developed countries; this is mainly because the capacity to respond to such changes is

the lowest in developing countries. Moreover, it seems clear that vulnerability to climate change is

closely related to poverty, as the poor are least able to respond to climatic stimuli. Also, certain

regions are more severely affected by climate change than others. Consequently, vulnerability and

adaptation to climate change are urgent issues among many developing countries.

India, being a developing country, is also going to be majorly impacted by climate change. The impact

will be more profound because of the heavy dependence on agriculture by a large percentage of the

population. According to recent government surveys, although agriculture contributes about 16% to

the Indian economy, it employs around 60% of the population. Agriculture is directly impacted by

climate change. This has prompted active research and analysis of the climate change for India. The

government’s 11th Five Year Plan (FYP; 2007-2012) clearly articulates the impact and implications of

climate change noted in the IPCC (Intergovernmental Panel on Climate Change) Assessment

Reports. In an address to the National Conference of Ministers of Environment and Forests in August

2009, the former Prime Minister, Dr Manmohan Singh, encouraged state governments to create state

level action plans on climate change consistent with the strategies of the National Action Plan on

Climate Change (NAPCC) which had been launched on 30 June 2008.

Most of the studies point out that the initial increase in CO2 concentrations and the reduced

damage from frost and cold at high altitudes and latitude will be beneficial to food production, as it

will lead to increases in yields for the most important cereals, namely wheat and rice. On the

other hand, the subsequent increase in temperature, pests and weeds, water scarcity and declining

soil fertility will most likely have a negative effect on crop yields, leading to an overall net decrease in

food production. According to a study conducted in 2000, part of the reason for the decline in

yields of rice and wheat in North West India is a rise in the frequency and intensity of extreme

events such as droughts, rainfall and floods1.

The Himalayas are extremely important for the region’s agriculture sector: while the arable

land accounts for only 10%, glaciers are in fact essential in providing water storage to the Indo -

Gangetic Plain, a key area for the country’s food security2. As previously noted, climate change will

negatively impact upstream snow and ice reserves and therefore the Himalayan basin’s capability of

support in seasonal water availability, with considerable effects on food production.

Given the broad implications of climate change for a range of economic sectors, human and

ecological communities, and geographic areas, there is room for assessments to target a broad range

of potential vulnerabilities. This research paper primarily focuses on elaborating and applying an

1Aggarwal, P. K. (2003). Impact of climate change on Indian agriculture. Plant Biology 2 Ibidem


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approach towards assessing vulnerability, which will be cross-scale & combine both quantitative and

qualitative analysis.

Accordingly, the following section provides the conceptual construct of vulnerability to climate change

and variability. The subsequent section describes the Macro Vulnerability assessment of the study

area using quantitative techniques with findings and outcomes. Further, micro vulnerability

assessment has been described for the selected case study of a village along with outcomes.

Discussions on the findings have been provided in the last section of the paper.

2 Conceptualization of Vulnerability

Scholars engaged in different knowledge domains define, conceptualize and apply vulnerability concepts differently. Definitions differ so widely that interdisciplinary use of the word is not possible without specifications. Vulnerability interpretations in the existing literature largely agree upon certain conceptual models which have differentiated significance in the fields they have been applied. The major models for conceptualizing vulnerability are discussed below:

In a review document of some existing research literature dealing with vulnerability concepts and approaches, Hans Martin Fussel, distinguishes between an internal and external side of vulnerability to environmental hazards. He also points out that several researchers distinguish between bio-physical and socio-economic vulnerability; even though there is no agreement on the meaning of the terms3. The paper gives a comprehensive insight into the nomenclature linked to vulnerability science and simplifies applicability of the terms and concepts in a wider context. In addition to this, HM Fussel puts a systems perspective towards explaining vulnerable situations for logical comparisons. A clear definition of a vulnerable situation with specifications helps in addressing, assessing and proposing strategies for reducing vulnerability in a contextual manner.

Also, in a review of climate change vulnerability assessments, two main vulnerability interpretations can be identified namely “end point” and “starting point”4. Vulnerability according to the end-point interpretation represents the (expected) net impacts of a given level of global climate change, taking into account feasible adaptations. This interpretation is most relevant in the context of mitigation and compensation policy, for the prioritization of international/national/sub national assistance, and for technical adaptations. Vulnerability according to the starting point interpretation focuses on reducing

3 Füssel, H. (2007). "Vulnerability: A generally applicable conceptual framework for climate change research.

Global Environment Change , 155-167. 4 Ibidem

Vulnerability Conceptual Models

Risk-hazard (RH) models that aim ‘‘to understand the impact of hazard as a function of exposure to the

hazard event and the dose–response (sensitivity) of the entity exposed.’’

Pressure-and-release (PAR) models in which ‘‘risk is explicitly defined as a function of the perturbation,

stressor, or stress and the vulnerability of the exposed unit.’’

Expanded vulnerability (EV) models that ‘‘direct attention to coupled human–environment systems, the

vulnerability and sustainability of which are predicated on synergy between the human and biophysical

subsystems as they are affected by processes operating at different spatiotemporal (as well as functional)

scales.’’

Political Economy Approach defines vulnerability as ‘‘the state of individuals, groups or communities in

terms of their ability to cope with and adapt to any external stress placed on their livelihoods and well-being.

Conceptualize vulnerability in terms of internal socio-economic factors”.


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internal socioeconomic vulnerability to any climatic hazards. This interpretation addresses primarily the needs of adaptation policy and of broader social development. It is largely consistent with the political economy approach mentioned above.

This paper adopts the conceptual framework developed by HM Fussel and focuses on starting point interpretation. The components of the conceptual framework help bring objectivity in framing the vulnerability assessments. The various domains of the conceptual framework have been applied to bring objectivity in the study context. Please see table 1.

Table 1 Conceptual Framework by HM Fussel Applied to Study Context

3 Study Area Himachal Pradesh takes its name from the mighty Himalaya ranges that dominate its topography,

climate, livelihoods and socio-economic trends. The state is predominantly a mountainous State

located in North – West India. It shares an international border with China. The State has highly

dissected mountain ranges interspersed with deep gorges and valleys. It is also characterized with

diverse climate that varies from semi tropical in lower hills, to semi arctic in cold deserts. Figure 1

shows the administrative boundaries and fact about the state of Himachal Pradesh.

Figure 1 District Map of Himachal Pradesh

DIMENSION(s) STUDY CONTEXT

TEMPORAL REFERENCE Current

SPHERE Internal And External

KNOWLEDGE DOMAIN Socioeconomic & Biophysical

VULNERABLE SYSTEM Agriculture In Himachal Pradesh

ATTRIBUTE OF CONCERN Production, Dependent Livelihoods

HAZARD Climate Variability

FACT SHEET

Area 55673 km2

Population 6856509 persons

Rural Population

6167805 persons

Urban Population

688704 persons

Districts 12

Blocks 77

Cities/Towns 59

Villages 20960


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4 Current Climate Variability in Himachal Pradesh The climate varies across the state with the altitude. In the lower lying regions, with altitudes of 400-

900m, the climate is of the hot sub humid type; regions from 900-1800m altitude are warm &

temperate, regions from altitudes 900-2400m are cool & temperate, while those regions that range

from 2400-4800m in altitude are cold alpine & glacial above those. Bilaspur, Kangra, Mandi, Sirmour,

and Una districts experience sub tropical monsoon, mild and dry winter and hot summer. Shimla

district has tropical upland type climate with mild and dry winter and short warm summer. Chamba

district experiences, humid subtropical type climate having mild winter, long hot summer and moist all

season. Kullu district experiences mainly humid subtropical type of climate with mild winter moist all

season, long hot summer and marine. During the period from January to February, heavy snowfall in

the higher regions creates conditions of low temperature throughout the state and a series of western

disturbances also affect the state.

The starting point of vulnerability assessment for the state of Himachal Pradesh is investigating the

current climate variability it faces. Rainfall and temperature are the two major climate variables

chosen for this study. Both rainfall and temperature are subjected to variability on spatial (space) and

temporal (time) scales. The variability in climate is likely to have significant impacts on Agriculture,

forest resources, water resources etc. It is needless to say that henceforth, the livelihood of people is

also going to get affected due to heavy dependence of population on these resources.

Climate variability refers to variations in the mean state (of temperature, monthly rainfall, etc.) and

other statistics (such as standard deviations, statistics of extremes, etc.) of the climate on all temporal

and spatial scales beyond that of individual weather events.

In this section, we focus on the current mean climate and climate variability in Himachal Pradesh at

district level and investigate how changes in them will alter HP’s vulnerability to climate change.

4.1 Data & Methodology Climatic Research Unit Time Series (CRU TS) version 2.10 on a 0.5° x 0.5° latitude and longitude

resolution monthly dataset spanning 102 years (1901-2002) for temperature and 40 years (1963-

2002) for precipitation are used. District-wise data is obtained by re-gridding the dataset to 0.1° lat. x

0.1° long and re-aggregating by districts to study the climate variability at district level. For studying

the rainfall variability only southwest monsoon (June, July, August & September) months have been

considered.

4.2 Rainfall Variability Information on spatial and temporal variations of rainfall is important in understanding the hydrological

balance on a regional scale. The distribution of precipitation is also important for water management

in agriculture, power generation and drought-monitoring.

The highest rainfall is seen in south interior region and central region of Himachal Pradesh.

Districts like Mandi, Sirmaur, Una, Solan have rainfall >25mm/day.

As we go up in the latitudes districts like Lahaul & Spiti, Kinnaur, Kullu, Chamba record

relatively lower rainfall <18mm/day. Lowest rainfall is recorded in Lahaul & Spiti with 13.4

mm/day.

Spatial variability of monsoon is moderate in the state.


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Coefficient of Variation (Table 2) is defined as the inter-annual variability (estimated as the standard

deviation) of rainfall over the region as a fraction of mean. Higher values of C.V. indicate larger inter-

annual variability and vice versa.

Table 2 District wise Rainfall Variability and trends in HP (1963 – 2002)

SI No. Districts Mean Rainfall

mm/day Standard Deviation

Coefficient of Variation %

Precipitation Trend mm/day/100 yr

1 Bilaspur 27.091 4.96 19 1.32

2 Chamba 19.345 4.60 24 6.8

3 Hamirpur 25.610 5.05 20 1.61

4 Kangra 23.430 4.98 21 4.86

5 Kinnaur 15.480 2.63 17 -1.07

6 Kullu 17.150 3.29 19 1.39

7 Lahaul&Spiti 13.410 3.01 22 3.24

8 Mandi 32.050 5.52 17 -0.43

9 Shimla 19.340 3.02 16 -2.08

10 Sirmour 25.760 4.24 16 -3.74

11 Solan 26.510 4.60 17 -2.09

12 Una 30.870 6.18 20 2.54

Overall inter annual variability is low for Himachal Pradesh. The coefficient of variation of

rainfall is low in all the districts and varies from 15% to 25%.

Chamba has the maximum coefficient of variation (24%) and Shimla and Sirmour have the

minimum (16%).

Precipitation trends over 100 years have positive values in Western & Central regions of the

state (Districts Bilaspur,Chamba, Kangra, Kullu) and acquire negative values in eastern and

North eastern & Southern regions (Districts Kinnaur, Sirmour, Solan,Shimla).

4.3 Temperature Variability In this section, the meteorological measurements of temperature for Himachal Pradesh are analyzed.

Table 3 shows the district wise variation of the annual mean minimum and maximum temperature

averaged for the period 1901–2002 derived from CRU-TS dataset.

Highest Mean Maximum temperature (>30 oC)& Highest Mean minimum temperature(>16 oC)

is noticed in most of the South & Southwest Himachal Pradesh districts – Sirmour, Solan,

Bilaspur,Una. These are districts in shiwalik ranges.

The lowest annual mean maximum temperature (<18oC) & lowest mean minimum

temperature (<=8oC) is observed over Lahaul & spiti and Kinnaur district. These districts lie in

Greater Himalayan ranges.

SI No.

Districts

Average Annual Maximum Temp.

Standard Deviation


Average Annual Minimum Temp.

Standard Deviation



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1 Bilaspur 30.340 0.47 1.70 16.910 0.47 3.01

2 Chamba 25.530 0.44 1.80 13.130 0.46 3.46

3 Hamirpur 29.810 0.47 1.50 16.650 0.46 2.70

4 Kangra 28.840 0.46 1.60 15.870 0.44 2.80

5 Kinnaur 17.360 0.50 2.90 8.040 0.54 6.70

6 Kullu 22.870 0.49 2.10 12.090 0.50 4.10

7 Lahaul&Spiti 14.230 0.50 3.50 3.620 0.52 14.40

8 Mandi 28.400 0.48 1.71 15.770 0.48 3.06

9 Shimla 26.440 0.50 1.80 14.710 0.51 3.40

10 Sirmour 30.660 0.49 1.60 17.350 0.49 2.80

11 Solan 31.060 0.50 1.60 17.610 0.49 2.80

12 Una 30.710 0.48 1.50 17.270 0.46 2.60

Table 3 District wise temperature variability in HP (1901-2002)

5 Climate related Risks in Himachal Pradesh The whole Indian subcontinent is at risk of climate change impacts and Himachal Pradesh is no

exception. The projected increase in average temperature and precipitation in Himalayan region, as

simulated by PRECIS model for 2030, is in the range of 1.7oC to 2.2oC and 5% to 13% respectively.

Also, minimum temperature and maximum temperature are projected to rise in the range of 1oC to

4.5oC and 0.5oC to 2.5oC respectively. Projections also indicate a 5-10 days rise in rainy days and an

increase in rainfall intensity by 1-2mm/day5. In this paper we look at the agriculture sector in Himachal

Pradesh

Climate related risks in Himachal Pradesh have been explained below:

Climate Change (Long term): With increasing temperatures, it is anticipated that there may be an

all-round decrease in horticultural and agricultural production in the region in long-term, and the line of

production may shift to higher altitudes. Apple production in the Himachal Pradesh region has

decreased between 1982 and 2005 as the increase in maximum temperature has led to a reduction in

total chilling hours in the region-a decline of more than 9.1 units per year in last 23 years has taken

place. Temperature Humidity Index (THI) is projected to rise in many parts of State during March–

September with a maximum rise during April–July in 2030s with respect to 1970s will lead to

discomfort of the livestock productivity and therefore will have negative impact on livestock

productivity6. Deglaciation occurring due to rise in temperatures is also going to affect downstream

flows and bring uncertainty in supply of irrigation water.

Climate Variability (Short term): With increased frequency of heavy precipitation and extreme

rainfall intensity there can be damage to crop and soils due to increased runoff. Increased variability

in rainfall patterns can cause major damage to non-irrigated crops, mainly due to erratic river flows.

Also, rainfall variability is likely to cause water shortages and drought like conditions during dry

season flows or drying up of springs etc. Increased temperatures are likely to alter plant morphology

and crop suitability in the region.

5 Ministry of Environment and Forests, Government of India. (2010). Climate Change and India: A 4X4

Assessment: A Sectoral and Regional Analysis. New Delhi: MoEF 6 Department of Environment Science & Technology, Government of Himachal Pradesh. (2012). State Strategy &

Action Plan on Climate Change. Retrieved August 2, 2013, from www.indiaenvironmentportal.org.in: http://www.indiaenvironmentportal.org.in/files/file/HPSCCAP.pdf


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6 Macro Vulnerability Assessment The macro vulnerability in this paper has been measured by computing a composite vulnerability

index, which provides a relative rank to different regions. The index has been computed by

aggregating the indicators of the analytical components of the vulnerability.

6.1 Analytical Framework for Vulnerability Assessment Vulnerability implies the susceptibility to damage or injury due to any negative impact. In the perspective of climate change, vulnerability here simply refers to the probability of being negatively affected by the variability in climate, including extreme climate events. Due to the intricate interactions between diverse components of the natural system along with human interventions, assessing vulnerability becomes a complicated job. Nevertheless, Vulnerability Assessment is significant as it is an important method in developing policies and adaptation plans for specific vulnerable groups and areas. It thereby forms the basis for establishing response mechanisms towards climate change risk reduction.

The Intergovernmental Panel on Climate Change (IPCC) defines vulnerability to climate change as a function of three factors7:

i) The types and magnitude of exposure to climate change impacts,

ii) The sensitivity of the target system to a given amount of exposure,

iii) The coping or adaptive capacity of the target system.

Exposure reflects factors external to the system of interest, such as changes in climate variability including extreme weather events or the rate of shifts in mean climate conditions.

Sensitivity and adaptive capacity reflect internal qualities, resilience and coping characteristics of the system of interest.

Adaptive capacity of a community depends on a combination of economic, social and technological factors such as extent of infrastructure development and distribution of resources. Depending on the system and regional differentials, these factors are quite dynamic and vary considerably.

6.2 Methodology for Computing Macro Vulnerability Index Computing vulnerability index involved three steps moving from indicators to components and ultimately to the final vulnerability index. The data for the indicators was normalized to bring consistency using the HDI (Human Development Index) formula. The normalized values of indicators, in turn were used as inputs for calculating the values for the three components: Exposure, Sensitivity and Adaptive Capacity. The vulnerability index for the region has been calculated by combining the values of these components. Steps mentioned below summarize the methodology which has been used for calculating the vulnerability index. The analysis presented in this report is based on the available secondary data and accordingly the results obtained are only for the purpose of getting insights on Vulnerability rather than drawing any strong conclusions on changes in the respective climate and non-climatic stressors.

Step 1: Indicators

Values for all the indicators are to be standardized for all the districts.

Indicator Index (Ix) ={ (Id – Imin/ Imax – Imin}

Where,

7 IPCC. (2007). Fourth Assessment Report: Climate Change. Retrieved from

http://www.ipcc.ch/publications_and_data/ar4/wg2/en/ch6s6-4-3.html


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Ix = Standardized value for the indicator; Id = Value for the indicator I for a particular district‘d’; Imax = Maximum value for the indicator across all districts; Imin = Minimum value for the indicator across all districts

Step 2: Components

Values of indicators are to be combined to get the value for that component.

Component (C) = (∑ni=1WPiIi)/ ∑n

i=1WPi

Where, WPi is the weightage of the component ‘i’.

Weightage of the component will depend upon the no. of indicators under it such that, within a component each indicator has equal weight.

Step 3: Vulnerability Index

The combination of the values of the three components will give the vulnerability Index.

Vulnerability Index = (Exposure – Adaptive Capacity) x Sensitivity

Scaling is done from -1 to +1 indicating low to high vulnerability.

6.3 Determinants and indicators of Vulnerability The following figure depicts the indicators and their linkages to conceptual framework, which shape the analytical components of vulnerability (Exposure, sensitivity & adaptive capacity, IPCC 2007) assessment. Indicators have been selected through a thorough literature review dealing with multi-scale indicators for climate change vulnerability assessments.

Figure 2 Indicators for Macro Vulnerability


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Table 4 Description & Rationale for indicators within each vulnerability component

COMPONENT INDICATORS DESCRIPTION/ RATIONALE SOURCE OF DATA

EXPOSURE

Coefficient Of Variation Precipitation

Variability in precipitation can alter the hydrology of the region and hence can have effects on agricultural productivity.

Calculated from CRU TS data sets

Coefficient Of Variation Average annual Max Temp.

Changes in temperature can have impacts on soil & plant morphology and also pressure on water resources.


Coefficient Of Variation Average annual Min Temp.


Projected Max Temp (2021 - 2050)

Changes in temperature can have impacts on soil & plant morphology and also pressure on water resources.

HP State Disaster Management Plan

Projected Min Temp (2021 - 2050)

HP State Disaster Management Plan

Flood frequency Extreme weather events can destroy crops and effect agricultural production on a large scale.


Drought frequency Calculated from CRU TS data sets

SENSITIVITY

Net sown area/total geographical area

Area under cultivation which is likely to get effected due to climate variables.

HP Statistical Abstract 2011-12

% rainfed area Captures the rainfall dependence of cultivated area in a region

HP District Agricultural Plans

Average Land Holding Captures the distribution of resources.


Area under apple production

Apples are likely to get impacted due to climate variability and change


Average Yield

Changes in temperature can have impacts on soil & plant morphology affecting the agricultural yield of the region.

HP District Agricultural Plans 2009-2010

ADAPTIVE CAPACITY

Cropping intensity

Crop intensity refers to percentage share of the area sown more than once. More the cropping intensity, better efficiency of land use.


Fertilizer Intensity Fertilizer intensity captures the soil nutrient availability.


% villages with access to roads

Access to infrastructure HP District Agricultural Plans 2009-2010

% villages with electricity Access to infrastructure HP District Agricultural Plans 2009-2010

% Irrigated Area of net sown area

Access to water resource during increased demands caused by variability in climate.



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Human Development Index Captures the health, income and demographic features of the region

State Development Report 2002

Livestock Population Provides significant energy inputs to croplands and a means for alternative livelihood.

HP District Agricultural Plans 2009-2010

6.4 Outcomes & Mapping of Macro Vulnerability After calculating the values for each component of vulnerability, maps of exposure, sensitivity and adaptive capacity were drawn in software ArcGiS ver. 10.0. This helped in separately analysing each component of vulnerability of how they contribute toward vulnerability and how they vary across the state. Finally, a map of overall Vulnerability index is drawn for identification and spatial distribution of vulnerable regions across the state.

6.4.1 Exposure index The district of Lahaul & Spiti has the highest exposure to climate variability and change in the state of Himachal Pradesh. Lahaul & Spiti demonstrates the highest variability in maximum mean temperature, minimum mean temperature and also in mean precipitation rate. Chamba, Kullu and Kinnaur fall in the range of highly exposed districts owing to high variability in precipitation and projected increase in minimum temperature. These districts also display susceptibility to drought occurrences. Kangra, Hamirpur, Bilaspur, Shimla and Mandi, together fall in the range of moderately exposed districts. These districts score low in projected increase in temperature and climate variability indicators; except Shimla which ranks the highest in projected increase in maximum temperature. On the other hand, these districts are prone to extreme weather events displayed by high flood and drought frequency indicator values. Lastly, Una, Solan and Sirmour are the least exposed districts as they score the lowest in almost all the exposure indicators; except Una which is highly susceptible to drought occurrences.

6.4.2 Sensitivity index Land holdings across the districts are marginal, which is a constraint for irrigation arrangements and also prevent economies of scale. Although, the indicator values of Average land holding do not have a major impact on the index. Due to high presence of agricultural land dependent on rainfall and highly fertile soils, Sirmour, Shimla and Mandi are the districts exhibiting very high sensitivity to climate variability. Kullu district also demonstrate high sensitivity due large tracts of land being utilised for apple production. Bilaspur, Una and Solan are moderately sensitive districts, mainly due less area being utilised for agriculture and apple production. Chamba, Kangra, Hamirpur, Lahaul & spiti and Kinnaur have low sensitivity to climate variability mainly due to soil types of low yield values and presence of irrigation infrastructure which brings down the rainfall dependence.

6.4.3 Adaptive capacity index In Himachal Pradesh, the highest adaptive capacity in agriculture sector is demonstrated by Una and Solan districts, mainly due to presence of electrified villages and road infrastructure. Also the cropping intensities and Human development index are fairly high in these districts. Kangra, Hamirpur and Shimla come next in the adaptive capacity index; owing to high cropping intensity and most of villages being electrified. High livestock density is demonstrated by Kangra & Hamirpur; but they score average in presence of irrigated land. Shimla on the other hand has very high fertilizer consumption. Bilaspur, Mandi, Kullu, Lahaul & Spiti and Sirmour have moderate adaptive capacities in comparison with other districts. Even though the cropping intensity and HDI values of the district are high, lower presence of irrigated land, electrified villages and road infrastructure pull these districts down in demonstrating their adaptive capacities.

From the assessment it turns out that Chamba and Kinnaur districts of Himachal Pradesh have the lowest capacities to adapt to climate variability and change. Chamba displays average cropping intensity and very low irrigated land under cultivation and fertilizer consumption. Chamba also depicts least connectedness with large proportion of villages not connected with roads. Apart from average


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values of access to electricity and roads, Kinnaur scores very low in rest of the indicators making it the least adaptive district.

6.4.4 Vulnerability index The final vulnerability index for the districts has been calculated by combining all the three components of exposure, sensitivity and adaptive capacity. The values lie between -1 and +1. Lesser the value, lower is the vulnerability of the district.

The final values have been divided into 4 classes. The districts having values between -0.2 to -0.1 form one class of districts which are least vulnerable. These districts are some of the urbanized districts of these two states. Owing to higher adaptive capacity these districts fall under this category. Most of the districts with higher altitude and latitude are highly or moderately vulnerable.

This is because their exposure and sensitivity levels are very high whereas the adaptive capacity levels are very low. There has been more climatic variability due to uncertain precipitation pattern and increasing temperature over the last 40 years. These together have resulted in high exposure values. The pressure on the agriculture is more in these districts with more land utilization, higher groundwater extraction and larger area under irrigation, which has made them more sensitive to any form of impacts in the context of climate variability.

Lower levels of development in the form of infrastructure and low levels of access to resources as well as assets have resulted in lower coping capacity of the people in these districts which makes them more vulnerable to any form of impacts occurring due to climate change.

The outcomes of the macro vulnerability have been compiled in Table 6, the districts have been categorised as High, Moderate and Low for each component of Vulnerability.

Table 5 Outcomes of Macro Vulnerability Assessment

Component Categorised Districts

High Moderate Low

Exposure Lahaul & Spiti, Chamba, Kinnaur, Kullu

Kangra, Hamirpur, Bilaspur, Mandi & Shimla

Una, Solan & Sirmour

Sensitivity Shimla, Sirmour, Mandi & Kullu

Bilaspur, Una & Solan

Lahaul & Spiti, Chamba, Kangra, Hamirpur & Kinnaur

Adaptive Capacity Solan, Una, Shimla, Kangra & Hamirpur

Lahaul & Spiti, Mandi, Bilaspur, Sirmour, Kullu

Chamba & Kinnaur

Composite Vulnerability Chamba. Lahaul & Spiti & Kinnaur

Mandi, Kullu, Kangra, Shimla, Hamirpur & Bilaspur

Una, Solan & Sirmour


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Figure 3 District Vulnerability Index values

Figure 4 Mapping of Vulnerability Index and its components

-0.30

-0.20

-0.10

0.00

0.10

0.20Bilaspur

Chamba

Hamirpur

Kangra

Kinnaur

Kullu

Lahaul-SpitiMandi

Shimla

Sirmaur

Solan

Una

HP

DISTRICT WISE VULNERABILITY INDEX


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7 Micro Vulnerability Assessment Local case studies are helpful in expanding the knowledge about how global, national or sub national

stresses diffuse at finer scales. In addition, they help in involving that knowledge in decisions made to

tackle the stresses, which are made at a grosser scale in forms of policies, programmes, schemes

etc. Climate change is such a global stress which is so dynamic (spatially and temporally) that it

requires adjustments in various coupled human-environment systems at different scales. For

adaptation planning and policy making, micro assessments help in understanding the needs of the

local stakeholders, the adjustments they already employ in face of stressed situations and developing

strategies and policies for reducing current and future vulnerabilities.

Agriculture system in Mandi district with its high vulnerability & sensitivity values (from macro

assessment) was selected for a rapid micro assessment. Apart from being highly vulnerable and

sensitive, Mandi district qualifies for local level case study because of the heavy dependence of its

population on agriculture as a livelihood activity. A village was picked up from a random sample of

villages in the district for micro vulnerability assessment.

This study analyses the on-ground situation, using data collected through household surveys and key

person interviews. Key person interviews and secondary data collection became the basis for

situation analysis of the village and household questionnaire. Apart from demographic information it

captured people’s perception of climate change and variability, current adaptation practices employed

and desired adaptation measures.

7.1 Methodology & Data Collection Qualitative assessment focused on getting insights about the current adaptive capacity of study

region and not on determining the internal coping capacity of households, representative of the

region. The study is descriptive in nature and is suitable for rapid assessments. Qualitative

assessments were done in two parts:

1) Unstructured Interviews with key government officials.

2) Household Level Questionnaire – Open ended Questions

Key Person interviews were kept semi structured, it was intended to gather socioeconomic

information required to evaluate the adaptive capacity of the region like, demographics, social

construct/ethnic composition (socially excluded population etc.), types of crops grown, presence of

financial institutions, government funds and schemes, access to information, etc. from the interviews.

A random sample of 30 respondents was used for Household level information, the questionnaire was

designed with open ended questions based on the fact that objective of the assessment is to

maximize the respondent’s view and minimize researcher’s preconception on the response.

Questionnaire was constructed in three parts: Part I dealt with questions regarding Household

information, Part II contained questions regarding perception of climate variability and long term

climate change. And the last part asked questions regarding adaptation measures taken. The

information from both the approaches was consolidated and represented as “Situation Analysis” for

the village.

Situation analysis basically breaks down adaptive capacity into function of endowment of resources.

Adaptive Capacity at local/village level is seen as endowment of certain capitals namely, Human &

Social Capital, Financial Capital, Physical Capital.


15

7.2 Case study area Chamyanu is a small village in Gopalpur block (Sarkaghat Tehsil) of Mandi district. It is a part of a

cluster of 7 villages namely RaswanTangri, Bastawa-Gyana, Daloli, God-ghulanu, Tatahar and

Nebahi. The Gram panchayat office of the circle is in Nebahi. Chamyanu lies in the south west region

of Mandi district and is characterized by medium hills and sandy loamy soil.

It is at an elevation of 1000 m above mean sea level and lies in the valley region of the district. This

region of the district receives around 1000mm rainfall annually. Location of the village with its fertile

valley soil and abundant rainfall makes it an agro-friendly village. Some of the important crops

cultivated in the village are wheat, maize, paddy, garlic, onion, few pulses like rajma, soyabean, urad

and vegetables like tomato lady finger, ridge god, and potato are also grown.

Figure 5 Map showing Location of Chamyanu Village in Mandi District

7.2.1 Socio-economic & Infrastructure profile Chamyanu has a population of 764 (Census 2011) of which 367 are males and 397 are females. This

shows that sex ratio favours women population in the region. Literacy was found to be around 55%

with the presence of 1 primary school in the village.

Total cultivated area in Chamyanu is 78 ha, non-cultivable area is 51 ha. Irrigated area is 25 ha and

non-irrigated area is 53 ha. What was important to note was that most of the landholdings were used

for practicing subsistence farming. Consequently, any damage to the agricultural production will have

implications on the village’s food security. Cropping system employed in Chamyanu is Maize –

Wheat in Kharif season and Wheat – Paddy in Rabi season. Livestock in the village is used for milk

production for self and community consumption.

Households in Chamayanu are made of good construction material (concrete, stones, wood etc) and

have access to basic amenities like drinking water, electricity, toilets. The nearest Primary health

center is in Sarkaghat which is around 5-6 kms from the village. Women cook in traditional firewood

stoves, wood is attained from the unarable land in the village which is used as a common property

resource by the villagers. The village is well connected with road which reduces a large amount of

vulnerability as it directly facilitates the movement of people and goods.There are no co-operative

societies (referred to as ‘depot’ by villagers) in the village wherein villagers can sell their surplus

produce in crisis.


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7.2.2 Climatic & Non climatic Stressors Climatic: Agriculture being practiced in Chamyanu is majorly rainfall dependent. Any delay in the

rainfall onset has major impacts on all the crops grown and disrupts the moisture available in the soil

for subsequent crop. Crops like potato and garlic are damaged due to late rains. Due to delay in

rainfall and during drought like situations the water in the water retention areas (traditionally known as

“Jhol”) is reduced which is the main source of water for drinking and irrigation. Kulhs are the

temporary structures constructed to divert water from water retention areas. Heavy rains or flood like

situations destroy the traditional irrigation systems.

Non-climatic: The fragmentation of land holdings creates challenges for irrigation and prevents

economies of scale. Farmers in the village don’t find it worthwhile to transport and sell their produce

due to lack of markets and co-operatives in the village. A Chamyanu village elder remembered that

during1960s, Chamyanu had a self-sufficient agriculture-based economy, with only one person in the

village working in the service sector. But now aspirations for city living standards and the lack of other

economic opportunities in the villages mean that the younger generation no longer considers

agriculture as a viable livelihood.

7.3 Micro Vulnerability Analysis and Outcomes At the micro level, analytical components of vulnerability assessment presume an altered character

and priority. The potential impacts of climate variability which are composed of exposure and

sensitivity are thoroughly included in the macro level assessments. At a finer resolution of scale,

where unit of analysis is a village, the ability or inability of the system to adapt, cope and adjust in

response to these impacts becomes more significant from an analytical point of view and hence for

designing context specific adaptation interventions.

The table below delineates the exposure and sensitivity of agriculture production of Chamayanu

village in Mandi district. The exposure can be seen as variations in local weather parameters (which is

a resultant of up-scaled variations in mean). The specific weather contingencies in Chamyanu are

erratic precipitation, early onset of droughts, increased intensities of rainfall (large amount of rainfall in

a short period of time). Chamyanu has also witnessed extreme weather events like droughts and

floods in past ten years.

The following table 7, compiles the capacity of the village with respect to access to different capitals and analyses it with respect to strengths and constraints.

CAPITAL SITUATION ADAPTIVE CAPACITY (STRENGTHS & CONSTRAINTS)

Human & Social Capital Chamayanu has a population of 764 (206 households).

Total Literacy is 55% (69% among the males and 41% among the females).

Due to lack of employment opportunities and inability of agriculture to sustain livelihoods, young generation migrates to nearby towns to gain income.

Knowledge among farmers about land management practices, how to cope with adverse climate and technologies

(+) High literacy rate contributes to agricultural practices. It’s a major strength for information dissemination.

(-) Restricted knowledge around agriculture and land management practices.

(-) Out migration weakens the human and social capital.


17

for irrigation is very low.

There are 3 self-help groups for women in the village, only few claim to be members and office remains closed.

Financial Capital Co-operative banks, Gramin bank, Punjab national bank, State bank of Patiala are certain credit instituitions which provide loans to farmers.

There is no market in the village where farmers can sell the surplus produce.

(-) Inactive self-help groups.

(+) Good amount of financial structures to help farmers.

(-) No market access

Natural Capital

Chamyanu is located in SikandraDhar ranges of the district, characterized by hilly terrain.

Of the total land (129 ha), 60% is cutivable land, 39% is non-cultivable land, 19% is irrigated and 41% is unirrigated land.

Groundwater is getting depleted at various sources as it was evident from 2 out of 5 handpumps going dry. And also put even more pressure on the existing water resources.

Hydrogeology of the region does not allow proper groundwater recharge.

Almost all the farmers are marginal landholders.

(-) Very high dependence on rainfall.

(+) Good fodder management practices.

(-) Ground water depletion is observed.

(-) Land holdings are fragmented which hampers productivity of the land and create irrigation challenges.

Physical Capital Poor quality seeds are made available from agriculture department.

No irrigation schemes exist in the village. Traditional irrigation systems called “kulhs” which are earthen channels exist in few households and are suceptible to damage in heavy rains.

3-4 rain water harvesting structures exist in the region but with very less water retention capacity.

Drinking water from the “nallahs” gets muddy during rainy season.

People cook in unimproved stoves (chulah) mainly with firewood which they obtain from unarable land in the village.

Electricity is 100% subsidized by the government.

(-) No seed treatments are done.

(-) Access to irrigation infrastructure is low due to lack of irrigation schemes

(-) Poor quality of drinking water from perennial ‘nallahs’ can have negative health effects.

(-) Poor water conservation infrastructure

(-) Traditional cooking practices put pressure on un-arable land and disrupt ambient air quality.

(+) Have access to mechanization.

Table 6 Vulnerability Situation Analysis

The strengths and constraints outcomes from situation analysis can form the basis for targeting

adaptation intervention in the case of Chamyanu village. Weak human, social and physical capitals


18

indicate a low adaptive capacity of the village to agricultural vulnerability in the region. The constraints

of various capitals which form adaptive capacity can be classified as follows:

Fewer income opportunities.

Lack of infrastructure such as irrigation, markets, schools etc.

Restricted knowledge of farmers regarding climate variability and change

Poor water resource management.

7.3.1 Community Perception on Climate change and Variability The households surveyed were asked about their perception of long term changes in climate. Specifically, farmers were asked “Have you noticed any long-term changes in the average temperature/rainfall/rainfall variability over the last 20 years? Outcomes show that overwhelming majority of farmers perceived the rainfall getting erratic (70%) and heavy (50%) over the last 20 years. Few farmers perceived the early and delayed onset of rainfall, 15% and 12% respectively. Changes reported less frequently included increase in temperature, decrease in temperature and more frequent floods.

Figure 6 Villagers' Perception of Climate Variability

The scientific data for climate variability (refer Table 2 and Table 3) analysed using legacy data of last

100 years demonstrate minor variability patterns in Mandi. The coefficient of variability value for

rainfall intensity in Mandi district is 17.25%. However, maximum villagers perceived that the rainfall

has become erratic in the past few decades. Fifty percent of the village respondents agreed to the

perception of delayed rainfall and instances of very high rainfall. Apart from capturing the perception

of the villagers, the study also culls out to the gap between science and perception where scientific

data opposes the public opinion.

7.3.2 Household Level Adaptation The farmers surveyed, reported a number of adaptation measures adopted by them in response to impacts of perceived climate change and variability. Since the agriculture produce is used for self-consumption, in case of damage of crops farmers employ certain measures for maintaining food security of the household. The most frequent measure adopted by farmers in Chamyanu is storage of food grains (55%), off farm labour (38%) and in less frequent adaptation measures farmers reported changing of crop types, receiving money from family members out of the village, water storage.

The figure 7 below shows different adaptation measures reported by farmers:

24 5 19 17 3 7 3 3

Erraticrainfall

Earlieronset ofrainfall

Delayedonset ofrainfall

Heavyrainfall

Morefrequent

floods

Morefrequentdroughts

Increase intemp.

Summers

Decreasein temp.winters

01020304050607080

PERCEPTION OF CLIMATE VARIABILITYPercent Multiple response


19

Figure 7 Household Adaptation measures taken by respondents in Chamyanu Village

Apart from the reported measures which can be categorized as adaptation measures, a key finding was the robustness of the public distribution systems on which the farmers rely during crisis. Public distribution systems function through fair price shops where food grains can be purchased at subsidized rates. For purchasing food grains from fair price shops farmers are given ration cards for claiming subsidized rates. Farmers find it easier to go for off-farm labour and purchase food grains from PDS fair price shops than take measures which may mitigate the situation. This also points out that agriculture is no longer a viable business to practice. This pushes the young generation to migrate and look for alternative employment opportunities, thus putting the agriculture sector in a vicious circle of not being a viable livelihood for future generations.

8 Discussion and way forward The study was formulated as a demonstration of Vulnerability assessment method wherein the

conceptualization of Climate change vulnerability at macro and micro scales has been emphasized.

The system’s perspective attained by adopting an analytical framework helped in gaining objectivity in

the assessment, which is imperative in climate change science because including all the sectors

would weaken the analysis and may result in developing weak and non-implementable strategies.

The macro level quantitative approach reveals that iff adaptation strategies formulated keeping in

view the macro-factors would be very generic in nature. Generic here means that they completely

ignore the context and specificity of differential vulnerable regions. In result to this, if only macro

assessment forms the basis of devising adaptation measures then implementing the strategies may

result in unintended consequences or complete ignorance of certain cases. Thus, emphasizing the

need for inclusion of bottom-up approach for developing context specific strategies. However, since

the interventions get formulated at a macro level as policies, schemes, programmes etc., vulnerability

index at the macro level provides a relative picture of vulnerable districts and can be useful to

prioritize the actions/interventions. Also, different components can be studied and analysed for

specific interventions in different spatial units (districts) in the region.

The qualitative approach for micro vulnerability assessment on the other hand reveals dynamics

which are of quite different nature than those revealed at the macro level. Water resource

management, skill development, economic non-viability of agriculture, migration, dependence on

public distribution systems etc are some of the dynamics which are revealed from the village level

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20

case study. These dynamics are completely masked while computing composite vulnerability index at

the macro level. So, clearly micro level assessment provides significant insights into interplay of

unrelated (to climate change) socio-economic conditions and policies which become constraints in

adaptive planning to climate change.

It can be argued that there are infinite contexts and specifics in any region or sector and targeting

each one will generate immense amount of information, nearly making it impossible to analyse. In

counter to that, context specific vulnerability profiles can be generated using the conceptual

framework to condense the large amount of information and can also be investigated for any recurring

pattern for further reduce the complexities.

Thus the analysis of the paper reveals and suggests that there is a requirement of bringing together

both the approaches for capturing the cross-scale factors and suggests an intermediate level

assessment which can bring together the approaches and help in developing robust, specific and

implementable adaptation strategies. It is recommended to undertake more micro assessments,

which will help in revealing more contexts to be targeted for adaptation and also it will help the macro

assessments to evolve and improve by including different socio-economic or bio-physical factors that

are revealed at the micro level.

9 Acknowledgments I wish to thank Prof. Dr. Prakash Rao, Symbiosis Institute of International Business, Prof. Mrs.Urvi

Desai, CEPT University, for inspiring, providing guidance and valuable comments throughout the

research work. I sincerely acknowledge the considerable amount of co-operation shown by the

personnel at various state departments of Government of Himachal Pradesh and also the residents of

Chamyanu village. The outcomes of this study are based on their commitment to participate in the

research work and openly sharing their views and the data required for the study, which are highly

appreciated.

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Documents

Vulnerability to climate change & variability : An Investigation into