Paper 9

Vulnerability to Drought, Cyclones and Floods in India

Sumana Bhattacharya and Aditi Das

Winrock International, India

September 2007

The BASIC Project is a capacity strengthening project – funded by the European Commission – that supports the institutional capacity of Brazil, India, China and South Africa to undertake analytical work to determine what kind of climate change actions best fit within their current and future national circumstances, interests and priorities. Additional funding for BASIC has also been kindly provided by the UK, Department for Environment, Food and Rural Affairs and Australian Greenhouse Office. For further information about BASIC go to http://www.basic-project.net/

B A S I C

About BASIC The BASIC Project supports the institutional capacity of Brazil, India, China and South Africa to undertake analytical work to determine what kind of national and international climate change actions best fit within their current and future circumstances, interests and priorities. BASIC has created a multi-national project team linking over 40 individuals from 25 research and policy institutions, the majority based in BASIC countries. Project activities comprise a mix of policy analysis, briefings, workshops, conferences, mentoring and training clustered around five tasks lead by teams as follows:

• Task 1 – Mitigation and sustainable development (China Team); • Task 2 – Adaptation, vulnerability and finance (India Team); • Task 3 – Carbon markets, policy coherence and institutional coordination (South Africa

Team); • Task 4 – Designing international climate change policy and enhancing negotiations

skills (Brazil Team); and • Task 5 – Creation of developing country expert group/mechanism on a long term basis

(All Teams). Funding for BASIC has been provided by Environment Directorate of the European Commission with additional support from the UK, Department for Environment, Food and Rural Affairs and Australian Greenhouse Office. For further information about BASIC go to: http://www.basic-project.net/ About this Paper The views and opinions expressed in this paper have been put forward by the BASIC Task 2 Team to advance discussions and contribute to capacity development and do not express the views or opinions of the funders or the BASIC Project Team as a whole. Task 2 is coordinated by the BASIC India Team which comprises: Sumana Bhattacharya and Aditi Dass, Winrock International, India, Amit Garg, Ashish Rana and PR Shukla, Energy Environment Analytics Ahmadabad, India, K Narayanan and D Parthasarathy, Indian Institute of Technology, Bombay, India, Manmohan Kapshe, Maulana Azad National Institute of Technology, India, Anand Patwardhan and Meeta Ajit, Technology Information Forecasting and Assessment Council, India. The authors would like to thank the BASIC Team and participants at the BASIC International Workshop, “Vulnerability and Adaptation to Climate Change: From Practice to Policy” held in India, in May 2006, for their contributions and comments to the work under Task 2. This does not imply support for the views expressed in this paper by these individuals and organizations. Other papers produced by BASIC Task Team 2 include:

• Handbook of Current and Next Generation Vulnerability and Adaptation Assessment Tools, Amit Garg, Ashish Rana and P.R. Shukla, Energy Environment Analytics Limited India, Manmohan Kapshe, Maulana Azad National Institute of Technology, India, K. Narayanan, D. Parthasarathy, and Unmesh Patnaik, Indian Institute of Technology, Bombay, India.

• Disaster Prevention, Preparedness and Management, and Linkages with Climate Change Adaptation, Anand Patwardhan and Meeta Ajit, Technology and Information Forecasting Assessment Council, India

• Lessons Learnt for Vulnerability and Adaptation Assessment from India’s First National Communication, Sumana Bhattacharya, Winrock International, India

• Proceedings of the BASIC India Workshop, Vulnerability and Adaptation to Climate Change: From Practice to Policy, May 2006, Winrock International, India

Abstract The growing interest in adaptation policies in the context of climate change, especially

among developing countries, is making vulnerability assessment a very active area of

research. Understanding present day vulnerabilities and adaptation potential among local

communities is increasingly being considered as a pre-requisite to such assessments. This

paper focuses on understanding and quantifying the vulnerability of India to three prominent

climate extremes that India is affected by repeatedly: drought, floods and cyclones. The

paper analyzes vulnerability “hot spots” in three states where these hazards are prevalent:

Andhra Pradesh, Uttar Pradesh and Orissa. Following the Intergovernmental Panel of

Climate Change definition of vulnerability, several studies have identified useful indicators of

vulnerability, but clarity is still missing on structured development of indicator sets and

appropriate aggregation mechanisms. This paper addresses precisely these two aspects.

Two aggregation procedures are illustrated – on based on simple averaging of normalized

indicators and another based on fuzzy inference system. Similarly two separate sets of

indicators – one based on structured characterization of vulnerability with focus on specific

stress and specific output sector, and another based on general vulnerability characterization

– are developed and used for vulnerability assessment. The empirical analysis is carried out

using state level data and vulnerability assessment is presented for the three chosen states

but could be undertaken at different levels and for other countries to provide a more detailed

and more useful assessment of vulnerability.

TABLE OF CONTENTS

1 Introduction 1

2 Study Focus 2

2.1 Droughts 3 2.2 Floods 3 2.3 Cyclones 3

3 Methodology 3

3.1 Conceptualizing Vulnerability 3 3.2 Operationalizing Vulnerability 4

4 Vulnerability Assessment – Fuzzy Inference System 6

4.1 Fuzzy Inference System 7 4.1.1 Fuzzification 7 4.1.2 Fuzzy Inference 8 4.1.3 Defuzzification 9

4.2 Indicators of Vulnerability 9 4.2.1 Agricultural Sensitivity 11 4.2.2 Demographic Sensitivity 11 4.2.3 Health Sensitivity 11 4.2.4 Economic Capacity 11 4.2.5 Human Capacity 12 4.2.6 Infrastructure Capacity 12

4.3 Sensitivity and Adaptive Capacity Indices 14 4.4 Vulnerability Assessment 17

5 Vulnerability Assessment – Developing Structured Indicator Set 19

5.1 Measures for Vulnerability Assessment 19 5.2 Vulnerability Assessment 29

6 Conclusions 34

Appendix – A 37 Table A1. State-wise Details Impacts of Drought: 1998–99 to 2000–01 37

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1 Introduction Over the past two decades a multitude of studies have been conducted aimed at

understanding how climate change might affect a range of natural and social systems, and at

identifying and evaluating options to respond to these effects. These studies have highlighted

differences between systems in what is termed “vulnerability” to climate change between

systems, although without necessarily defining this term. Most of the research so far has

focused on assessing potential impacts of climate sensitive systems/sectors together with a

brief sketch on possible adaptation strategies. In this approach vulnerability has been

characterized as an end-point (O’Brien et al., 2004a). In Indian context also a number of

studies followed this path and system specific vulnerabilities are assessed (see for example,

Kumar and Parikh, 2001a, 2001b).

Increasingly the research and policy community is showing interest in ‘starting-point’

characterization of vulnerability. The key difference between the two approaches is in terms

of the assessment of adaptive capacity. In the end-point interpretation, adaptive capacity has

been used as a measure of whether technological adaptations can be successfully

implemented. On the other hand, in the starting-point interpretation, adaptive capacity refers

to the present day ability to cope with and respond to stressors and secure livelihoods.

This second interpretation of adaptive capacity has significant policy relevance in the

ongoing discussion in policy circles on ‘mainstreaming’ climate policies. For vast majority of

developing countries climate change remains a distant and invisible threat whereas they are

presently exposed to a range of stresses. If climate change response strategies were to be

embraced by these countries it is imperative that such response strategies are aligned with

development agendas.

Also, more and more researchers recognize that for adaptation to be employed and to be

effective, they would need to be shown to be relevant to local people who should be

integrated into decision-making structures and processes. It is unrealistic to expect special

policy initiatives to deal with climate change adaptation by itself, especially when so many of

the suggested adaptation measures (such as drought planning, coastal zone management,

early warning etc.) are currently being addressed in other policies and programs. Thus as

argued by Smit and Benhin (2004), in order to mainstream, it is essential that analysts pay

serious attention to: (a) the climate related issues that matter now to communities; (b) the

management or coping strategies presently employed by local communities to deal with

those conditions; and (c) the policy structures that exist now to deal with such conditions.

Underlying this is the implicit assumption that adaptation strategies geared to cope with

large climate anomalies that society faces currently embrace a large proportion of the

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envelope of adjustments expected under long-term climate change. Against this background

the present study focuses on ‘present day’ climate extremes as a point of departure for

assessing vulnerability and adaptive capacity in India.

India is a vast country covering 3.28 million km2, occupying only 2.4 per cent of the

world’s geographical area but supporting 16.2 per cent of the global human population. It is

endowed with varied climate supporting rich biodiversity and highly diverse ecology. About

70 percent of its population is dependent on climate sensitive activities such as agriculture.

Climate change projections made up to 2100 for India, indicate an overall increase in

temperature by 2-4oC with no substantial change in precipitation quantity. However spatial

variation in rainfall may occur, with the western ghats, the central Indian region and the north

eastern parts projected to receive more rainfall compared to the other parts of India. An

increase in intensity and frequency of extreme events such as droughts, floods, and

hurricanes is also projected. Adverse impacts of these changes have been projected on

India’s water resources, agriculture, forests and other ecosystems, coastal zones, energy

and infrastructure and on human health. India is a developing country and has limited

resources, and therefore would need to mobilize substantial resources to build additional

capacity for addressing these projected adversities.

The paper is structured as follows: The specific focus regions of the study undertaken for

this paper are outlined in the next section, following which the overall methodology is

described. Section 4 describes the vulnerability assessment approach based on fuzzy

inference system and presents results specific to the three chosen states. In the next section

the indicators of exposure, sensitivity, and adaptive capacity are systematically developed

and vulnerability assessment based on averaging of normalized indicators. Finally section 6

sets out broader conclusions of the paper.

2 Study Focus This study focuses on three prominent climate extremes that India is repeatedly affected by –

namely, droughts, floods and cyclonic storms. While earthquakes constitute the most

prominent natural hazard, relatively higher frequency, significant damages and potential for

manageability makes climate hazards most important for vulnerability assessment. To limit

the scope of the present study an attempt has been made to identify the ‘hot-spot’ areas with

regard of each of these climate extremes and the vulnerability assessment limited to the

chosen areas only in three states where these hazards are prevalent.

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2.1 Droughts Table A1 shows the number of districts, human and livestock (cattle) population, and

cropped area affected by drought during the years 1998-1999 to 2000-01 in India. From the

table it can be seen that Rajasthan, Gujarat and Andhra Pradesh are most severely affected

states (based on various criteria). Historically also these states were among the most

frequently affected areas in India. Andhra Pradesh is selected for analysis given that the

state has initiated some innovative management practices in recent times to tackle the

recurring problem of drought. However, the vulnerability of affected population in Andhra

Pradesh is still considered high and hence it is considered useful to identify the potential

impediments in the implementation of the programs.

2.2 Floods Table A2 and A3 show state-wise average damages due to floods in India across states over

the period 1953-2000, and average area affected by floods across states in the past decade,

respectively. From the data it is clear that Uttar Pradesh is the most severely affected region

due to floods and is hence chosen for vulnerability analysis.

2.3 Cyclones The eastern states/districts in India are more adversely affected by the cyclonic storms than

the western states/districts (Kumar and Tholkappian, 2005). Among the eastern states

Orissa is most frequently affected by cyclonic storms and is chosen for vulnerability analysis.

During the period 1877 to 1990 the frequency of severe storms, storms and depressions was

highest in the districts of Puri, Cuttak and Balasore (Patnaik and Narayanan, 2005),

indicating the vulnerability of Orissa to cyclonic storms. Moreover the super cyclone in late

1990s exposed many mal-adaptation practices (such as destruction of mangroves) that

severely affected the people of Orissa and hence it may be helpful to analyze the post-super

cyclone response strategies that the state and people have undertaken.

3 Methodology The approach broadly would be to select a region for each of the climate extremes (viz.,

droughts, floods and cyclones) and assess present vulnerability as well as analyze the trend

over the past decade.

3.1 Conceptualizing Vulnerability Following Ionescu et al. (2005) vulnerability should ideally be characterized by three

primitives: (i) the entity that is vulnerable (represented as a dynamical system), (ii) the

stimulus to which it is vulnerable (the input to the system) and (iii) the preference criteria to

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evaluate the outcome of the interaction between the entity and the stimulus (represented by

a preorder relation). The following table illustrates such characterization with regard to the

three climate extremes being considered in this study.

Table 1. Characterization of Vulnerability Drought Flood Cyclone Entity Drought prone area Flood prone area Cyclone prone area Stimulus Increased dryness

and/or reduced rainfall

Increased frequency and intensity of rainfall

Increased frequency and intensity of cyclonic storms

Outcome a) Agricultural Production affected

b) Drinking water shortage

a) Aggregate flood damage on agriculture, housing, infrastructure, livestock etc.

b) Human and livestock casualties

a) Aggregate damages

b) Human and livestock casualties

Preference Criteria (example)

Loss beyond threshold production level not preferred

Loss of human and/or livestock lives above a threshold not preferred

Loss of human and/or livestock lives above a threshold not preferred

3.2 Operationalizing Vulnerability As mentioned above the effort should result in an operational approach that could help rapid

assessment of vulnerability and adaptive capacity. While there are a number of studies

assessing vulnerability in the recent past (see for example, O’Brien et al., 2004b; Brenkert

and Malone, 2004; Kumar and Tholkappian, 2005 for some of the recent India specific

studies), most of them use broad indicators to arrive at regional-level vulnerability to climate

change. Since it is not quite clear what exactly one means by climate change in these

studies it is difficult to interpret the overall vulnerability index developed. Hence the approach

of this study is to further narrow down and identify meaningful ‘stimulus’ that drive the

vulnerability assessment. That is, the study aims to assess vulnerability due to specific

stimulus like drought, floods and cyclones.

The study uses the vulnerability definition adopted by IPCC (McCarthy et al., 2001) –

namely, vulnerability of an entity is a function exposure, sensitivity and adaptive capacity,

which in turn are defined as:

Exposure – represents the magnitude and frequency of the stress experienced by

the entity

Sensitivity – describes the impact of stress that may result in the reduction of well-

being due to a cross over of a threshold (below which the entity experiences lower well-

being)

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Adaptive capacity – represents the extent to which an entity can modify the impact of

a stress to reduce its vulnerability

The following schematic diagram illustrates this approach: Figure 1: Conceptualization of vulnerability to climate change in the IPCC Third Assessment Report

The above mentioned studies have typically adopted simple aggregation procedure (that

is either geometric or arithmetic mean) to sum over various indicators while estimating the

vulnerability index. Fuzzy sets, on the other hand, allow for gradual transition from one state

to another while also allowing one to incorporate rules and goals, and hence are more

suitable for modeling preferences and outcome that are ‘ambiguous’. While the use of two-

valued logic would be limited to determining only whether vulnerability exists or not, a multi-

valued logic can be used to assess the ‘degree’ of vulnerability – that is, it is also possible to

attach linguistic values such as ‘low’, ‘moderate’, and ‘high’ to certain index value ranges.

Indices constructed based on fuzzy logic make quantitative inferences from linguistic

statements. Moreover, fuzzy inference system enables the modeler to analyze the interaction

among several indicators.

Given these advantages, this study demonstrates use of an alternative approach that is

based on fuzzy inference system, for developing vulnerability indices.

Another missing element in the literature is precise identification of the indicator set. The

present study attempts to systematically develop such an indicator set by precisely

identifying the cause of vulnerability of an entity and the outcomes with respect to which the

entity’s vulnerability status is determined. To operationalize vulnerability, it is defined in terms

of three ‘dimensions’ – exposure, sensitivity and adaptive capacity. These dimensions in turn

are characterized by various ‘components’ and finally various ‘measures’ are identified to

quantify the components.

Thus the contribution of the study is:

• To conceptualize vulnerability to climate stresses such as drought, cyclones and

floods using clearly defined outcome(s) of interest and the associated preference

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criteria. In the empirical analysis in the chosen hot-spot areas the impact of the

climate stresses on agricultural sector is considered as the outcome of interest.

Focus on specific outcome enables meaningful choice of indicators as illustrated

in the empirical analysis.

• Illustrate various aggregation procedures in the vulnerability assessment.

Specifically fuzzy inference system based aggregation are contrasted with simple

averaging (using either geometric and arithmetic) of normalized variables (similar

to the procedure adopted in the estimation of various human development

indices).

• Illustrate the contrast between vulnerability defied in ‘general’ terms and

vulnerability defined with reference to specific stress and outcome. Vulnerability

assessment is carried out for the same regions for comparability.

It may be noted that the analysis presented in this study is carried out using state level

data. Future refinements include, among other things, vulnerability assessment at a district

level. Such analysis would enable one to clearly identify which sub-regions of a hot-spot

state are making it remain as vulnerable.

4 Vulnerability Assessment – Fuzzy Inference System Quantifying vulnerability is difficult due to several reasons: (a) Many factors may contribute

towards the vulnerability and also in complex ways; (b) Knowledge about the determinants of

vulnerability is typically vague; (c) Possibility of non-linear relationships between the

determinants and vulnerability (for example, while a very high level of income inequality in

the society can be associated with vulnerability, a small decline in the inequality may not lead

to corresponding decline in the vulnerability); and (d) Lack of knowledge on weights to be

attached to these determinants. For these reasons the methodology adopted in this study

focuses on a range of determinants of the vulnerability and makes use of linguistic models of

vulnerability. Use of different factors for capturing the vulnerability is not new, but

identification and use of different measures as per the conceptualization of the vulnerability

outlined in the previous section is not very common (see Acosta-Michlik et. al, 2004, O’Brien

et al., 2004b, and Brenkert and Malone, 2004). Further, application of fuzzy set theory to

translate the inexact linguistic statements into quantitative estimates is relatively limited in the

vulnerability literature. This section describes the fuzzy inference system and illustrates its

use in assessing sensitivity and adaptive capacity indices and vulnerability to drought, flood

and cyclone in the three chosen states of analysis.

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4.1 Fuzzy Inference System Fuzzy set theory is useful to translate linguistic statements such as ‘high’ or ‘low’ into

numerical values. Use of fuzzy set theory in poverty analysis in economics1 is not new and

studies by Cerioli and Zani (1990) and Cheli and Lemmi (1995) provide fuzzy set theoretic

measures of poverty. Qizilbash (2002) extends the application of fuzzy set theory to capture

the notion of vulnerability to poverty. Use of fuzzy set theory in poverty is centered around

the idea that an entity could be considered definitely poor if her income is below a lower

threshold, and definitely non-poor if her income is above a higher threshold, with ambiguity

associated with income lying in between these two thresholds as with such income the entity

belongs to the set of poor people to some degree2. These studies essentially carry out what

is described below as ‘fuzzification’ to translate a crisp value into fuzzy number that falls in

the interval [0,1]. Qizilbash (2002) interprets such fuzzy numbers as entity’s level of

vulnerability to poverty – i.e., to indicate how close one is to being labelled as definitely poor.

The analysis presented in this study on the other hand while trying to assess welfare loss

due to a stress such as drought, cyclone and flood focuses on several dimensions (not just

consumption or income) and hence requires aggregation involving a set of crisp inputs going

through a fuzzification-defuzzification process to generate a crisp output. Martinetti (2006)

has proposed a similar approach for assessing multidimensional well-being of individuals.

The following section describes the elements of fuzzy inference system – namely,

fuzzification, fuzzy inference and defuzzification – using an illustrative example.

4.1.1 Fuzzification This involves translation of propositions into quantitative values using membership functions.

For instance consider the proposition: ‘if literacy rate is high, the vulnerability is low’. In binary

logic the levels such as ‘high’ and ‘low’ are assigned sharp boundaries, whereas in fuzzy

logic it is possible to assign non-sharp (or fuzzy) boundaries. As shown in figure 2 below the

membership functions defined in a fuzzy model describe the ‘degree of belief’ of a particular

value of a variable. As shown in the figure, literacy rate of say 35 percent need not be

assigned to either ‘low’ or ‘medium’ literate category, but can be member of both categories,

having a certain degree of membership in each category.

1 While fuzzy set theoretic tools are used in a range of disciplines in the context of vulnerability, the discussion here focused on economics literature given the end points are often economic in nature. 2 It is not necessary to define fuzzy set theoretic poverty measures on income dimension alone and studies cited considered multiple dimensions of poverty.

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Figure 2. An Example of Membership Functions in Fuzzy Model 4.1.2 Fuzzy Inference The variables are related to each other with a knowledge based rule system. A statement

about the resulting variable has to be made for all possible combinations of the categories of

all variables. Suppose the model consists of two variables, literacy and percentage share of

educational expenditure in the total expenditure. If each of these variables is defined in two

states (i.e., ‘low’ and ‘high’), then four rules are required to describe the resulting output

variable (say, human capability index). The rules describing the system can be:

Rule 1: If Literacy is low and Share of Educational Expenditure is low then the

human capability is low.

Rule 2: If Literacy is low and Share of Educational Expenditure is high then the

human capability is medium.

Rule 3: If Literacy is high and Share of Educational Expenditure is low then the

human capability is medium.

Rule 4: If Literacy is high and Share of Educational Expenditure is high then the

human capability is high.

For example, assume literacy rate and percentage share of educational expenditure in

the total expenditure in a region are such that the degree of membership to the ‘low’ and

‘high’ states are 0.7 and 0.3 (for literacy), and 0.4 and 0.6 (for share of educational

expenditure) respectively. Then using the above rule-base along with the intersection

operation (since the rules use ‘and’ conditions) the output variable (namely, human capacity

index) attains the following degrees of certainty under each rule:

Rule 1: Human Capability is low = min (0.7, 0.4) = 0.4

Rule 2: Human Capability is medium = min (0.7, 0.6) = 0.6

Rule 3: Human Capability is medium = min (0.3, 0.4) = 0.3

20 40 60 80 100

0.2

0.4

0.6

0.8

1.0 Low Medium High

Literacy Rate

Deg

ree

of

Mem

bers

hip

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Rule 4: Human Capability is high = min (0.4, 0.6) = 0.4

4.1.3 Defuzzification This final step of the fuzzy inference system is necessary for combining the results of each

rule into a single unique result. Although there are several mathematical approaches for

defuzzification, most commonly used approach is ‘centre of gravity’ method. A defuzzification

diagram (figure 3) containing membership functions for every category of the output variable

is used and the certainty of each inference result is represented as the area below the

corresponding membership function of the output variable. The final result is obtained as the

centre of gravity of the joined areas of all membership functions.

Figure 3. Rule Strength and Deffuzzification – Illustration

In the empirical analysis presented in the next sub-section two or three indicators at

several stages are clubbed together to generate various indices. In terms of procedure

described here this involved translating the input indicators in to fuzzy numbers using

fuzzification, fuzzy inference is then applied to relate the input indicators and output index,

and finally defuzzification is used to translate fuzzy output to crisp index.

4.2 Indicators of Vulnerability As mentioned above vulnerability of an entity is hypothesized to be a function of its exposure

(to the external stressor causing the vulnerability), sensitivity of the entity’s outcome to the

external stressor, and its adaptive capacity in overcoming the adverse impact of the stressor

on its outcome. Categorizing determinants of entity’s vulnerability among the sensitivity and

adaptive capacity sub-groups can be a demanding task as some indicators can be argued

both as sensitivity indicators and adaptive capacity indicators. Thus it is imperative to identify

guiding principles to categorize indicators under these two sub-groups of vulnerability.

Deg

ree

of M

embe

rshi

p

1

0.2

0.4

0.6

0.8

1.0 Low Medium High

Human Capability 0

Rule 1

Rule 2

Rule 3

Rule 4

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Since it is relatively easy to understand the notion of sensitivity and adaptive capacity

with reference to natural systems this sub-section describes the same using simple example

and extends the concept to social systems. Considering the example of vulnerability of

agricultural system rainfall fluctuations, the choice of a crop variety and seed commits the

farmer to certain impact on the farm yield due to the rainfall fluctuation (knowledge about

which is not available to the farmer at the time of planting). This could be described as the

sensitivity of the entity to rainfall fluctuation. More precisely sensitivity of the entity is

determined by its intrinsic characteristics on which the entity has no direct control. Once

faced with the prospect of adverse yield change farmer could employ a range of options at

her disposal – both before and after experiencing the yield loss, in order to protect herself

from the implications of the output change. Examples include use of irrigation, subscribing to

crop insurance and resorting to behavioural changes (say, reducing consumption). The

extent to which the entity could protect itself from the adverse impacts caused by the external

stressor can be described as its adaptation potential. The control that entity has over all the

options helpful in controlling the adverse impacts caused by the external stressor defines its

adaptive capacity. Naturally by definition the entity has more control over the factors

determining its adaptive capacity.

The insights from the above discussion could be translated into guiding principles to

allocate various factors among sensitivity and adaptive capacity categories in social systems:

• Both sensitivity and adaptive capacity deal with the outcome.

• Sensitivity is captured by indicators that represent the intrinsic features of the

system which define the impact of external stressor on the entity’s output. The

intrinsic features are those characteristics of the system that can’t be changed by

the entity in at least short and medium term. Adaptive capacity on the other hand

is captured by indicators that can be modified by the entity even in short and

medium term and hence can influence the shortfall in outcome caused by the

external stressor.

• Indicators capturing adaptive capacity can either contribute towards

compensating the adverse impacts caused by external stressor, or strengthen the

entity’s capacity to absorb the adverse implications of output change.

• While sensitivity is represented by indicators that reflect the state of the system,

adaptive capacity indicators are more like policy (or, control) variables.

In the analysis presented here these guiding principles are used in allocating various

indicators across sensitivity and adaptive capacity categories. Three sub-indices of sensitivity

and adaptive capacity are developed and are discussed here.

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4.2.1 Agricultural Sensitivity Sensitivity of agriculture is considered important given its role as livelihood provider for a

large section of population. Extent of agricultural sensitivity is measured through dependency

of a region on agriculture (share of agricultural GDP in overall GDP of the region) and access

to agricultural markets and public food distribution mechanisms (with per capita calorie

consumption of a region acting as a proxy). Both these indicators reflect the state of the

system which can not be changed in short to medium term by the entity. It may be noted that

per capital calorie consumption is determined among other things by the infrastructure and

institutions in a region and these cannot be modified by the individual in the short-run.

4.2.2 Demographic Sensitivity This sensitivity is included in the overall sensitivity measurement given varied population

characteristics of Indian states. Population density is used as a proxy to represent the access

to resources and also opportunities. Annual population growth rate on the other hand is a

forward looking indicator and is included to capture the ability of a region to effectively

allocate resources over time. Again both these indicators reflect the state of the system and

hence could be viewed as sensitivity measures.

4.2.3 Health Sensitivity The third sensitivity measure used in the analysis captures a dimension of social well-being,

namely health. Percentage of malnourished children among the children below 4 years of

age acts as proxy for life-cycle measure of health and human-capital because under-

nourishment leads to slow growth and lower level of cognitive development. Proportion of

health expenditure in total public expenditure reflects the concern for healthy and productive

society as such expenditure results in improvement of human capital3.

4.2.4 Economic Capacity Economic capacity is an important determinant of adaptive capacity as it represents the

availability of resources and scope of resource mobilization. The extent of economic capacity

is measured through per capita income – which represents the ability to access resources

that are useful for adaptation, and inequality measure (gini coefficient) – which represents

the degree of cohesiveness of society for adaptation. Both these indicators reflect entity’s

control on influencing the outcome (or shortfall in outcome induced by the shock) and hence

capture its adaptive capacity on economic dimension. 3 There is a case for this indicator to be labelled as adaptive capacity indicator (see discussion below on other expenditure indicators). However to effectively capture health sensitivity this expenditure indicator is used as sensitivity indicator.

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4.2.5 Human Capacity Human capacity is used as the second dimension of adaptive capacity as it captures the

inherent adaptive capacity of vulnerable population. The degree of human capacity is

assessed through percentage of literate population in the society – which indicates the

adaptability of population to both adverse impacts caused by shocks and the opportunities

created, and the proportion of expenditure on education in total public expenditure – which

represents the investment in human capital. Again both the indicators reflect the extent of

influence that an entity can exercise on its outcome and hence qualify as adaptive capacity

indicators.

4.2.6 Infrastructure Capacity The third and final dimension of adaptive capacity is the infrastructure capacity as it reflects

the availability of physical resources that enable adaptation. The infrastructure capacity is

measured through infrastructure development index – which is developed on the basis of a

range of physical resources and hence represents the accessibility of the same to the

vulnerable population, and proportion of expenditure on rural development in total public

expenditure – which captures the investment into the relatively backward sector of the

society (compared to the urban sector) that also provides livelihood for large proportion of

population.

In sum, the three dimensions of sensitivity capture influence of shock on outcome

through economic, demographic and social lenses. The three adaptive capacity dimensions

on the other hand reflect entity’s influence on outcome (or shortfall in the outcome caused by

the shock) through economic, human and physical resources at its disposal. Table 3

summarizes the rationale for use of these indicators under each dimension of vulnerability by

describing what each indicator represents and the expected functional relationship with the

respective component.

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Table 2. Sensitivity and Adaptive Capacity – Indicators and Functional Relationship

Dimension Components Measures Represents Functional Relationship

Sensitivity Agriculture Share of Agricultural GDP

Dependency on agriculture – the livelihood provider for majority population

Sensitivity increases as share of agricultural GDP increases

Per capita calorie consumption

Access to agricultural markets and other food distribution mechanisms

Sensitivity decreases as per capita calorie consumption increases

Demography Population density

Access to resources and opportunities

Sensitivity increases as population density increases

Annual population growth rate

Resource allocation from dynamic perspective – a forward looking indicator

Sensitivity increases as population growth rate increases

Health Percentage of malnourished children

Life-cycle measure of health and human capital

Sensitivity increases as proportion of malnourished children among children increases

Proportion of health expenditure in total expenditure

Concern for healthy and productive society

Sensitivity decreases as health expenditure increases

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Table 2. Sensitivity and Adaptive Capacity – Indicators and Functional Relationship (contd.)

Dimension Components Measures Represents Functional Relationship

Adaptive Capacity

Economic Per capita net state domestic product

Access to resources useful for adaptation

Adaptive capacity increases as per capital net state domestic product increases

Gini coefficient Degree of cohesiveness of society for adaptation

Adaptive capacity increases as inequity decreases

Human Literacy rate Human capital and adaptability of labour force

Adaptive capacity increases as literacy increases

Proportion of educational expenditure in total expenditure

Investment in human capital

Adaptive capacity increases as expenditure on education increases

Infrastructure Infrastructure development index

Physical resource capital for adaptation

Adaptive capacity increases with higher attainment of infrastructure development

Proportion of rural development expenditure in total expenditure

Investment in rural sector – backward sector with larger proportion of population

Adaptive capacity increases as rural development expenditure increases

4.3 Sensitivity and Adaptive Capacity Indices Data on all the above measures were collected for the three states (Andhra Pradesh, Orissa

and Uttar Pradesh) for two time points in the last decade and for each time period various

sensitivity and adaptive capacity indices were developed for each state by processing the

data through the fuzzy inference system described in section 4.1. The two time points

chosen for analysis were 1990-91 and 1999-2000. The data was collected through

secondary sources such as Reserve Bank of India Bulletins, CMIE reports, Central Statistical

Organization reports, and Census reports. The data on per capita calorie intake was

collected from Ray and Lancaster (2005), inequality index (gini) is from GoI (2001), relative

infrastructure index is from Ahluwalia (2001), percentage of malnourished children is from

IIPS (1994, 2000), and share of manufacturing investment is from Thomas (2002). It may be

15

noted that even though the study focuses on only three states (Andhra Pradesh, Uttar

Pradesh and Orissa) the analysis required data on all the indicators for all the major states of

India for each period of analysis. This is because the fuzzy membership functions are

developed using the variation observed across the sixteen major states on each indicator.

The analysis at this stage involved carrying out two levels of aggregation: (a) at the first

level ‘measures’ representing various ‘components’ are aggregated to arrive at various

sensitivity and adaptive capacity sub-indices; and (b) at the second level three sensitivity

sub-indices are combined to form sensitivity index, and three adaptive capacity sub-indices

are aggregated to form adaptive capacity index. Table 3 shows the total aggregations carried

out at each level of aggregation along with the information on membership functions and

number of rules used.

Table 3. Characteristics of Different Levels of Aggregation Aggregation

Level Number

of Inputs Membership Functions Number

of Rules Input Output

First – Six aggregations to generate seven sub-indices

2 3 – low, medium, high

4 – low, medium, high, very high

9 (for each index)

Second – Two aggregations to generate two indices

3 4 – low, medium, high, very high

6 – very low, low, fair, medium, high, very high

64 (for each index)

Figure 4 shows the performance of each of the three states in terms of sensitivity, and

adaptive capacity indices for two time periods (1990-91 and 1999-2000).

In Andhra Pradesh across the two time points, while the agricultural, health and

demographic sensitivities have shown declining trend, the economic, human and

infrastructure capacities have also shown declining trend. Thus the combined effect of these

two could imply either worsening or stable vulnerability status, which of course also depends

on the characteristics of the stress.

In Orissa while the sensitivities remained more or less similar across the two time points,

the adaptive capacities have shown slight improvement. This could imply declining

vulnerability status for the state provided the stress imposed is not exceptionally high.

In Uttar Pradesh the sensitivities have slightly increased over the two time points, and

despite improvement in human capacity significant decline in infrastructure (and to some

extent economic) capacity could lead to worsening vulnerability status.

16

Figure 4. Sensitivity and Adaptive Capacity Indices across States: 1990–91 and 1999–00

Uttar Pradesh

0

0.5

1AGRISENS

DEMOGSENS

HLTHSENS

ECONCAP

HUMCAP

INFRACAP

1990-91

1999-00

Andhra Pradesh

0

0.5

1AGRISENS

DEMOGSENS

HLTHSENS

ECONCAP

HUMCAP

INFRACAP

1990-91

1999-00

Orissa

0

0.5

1AGRISENS

DEMOGSENS

HLTHSENS

ECONCAP

HUMCAP

INFRACAP

1990-91

1999-00

17

4.4 Vulnerability Assessment To arrive at vulnerability indices exposure indices (defined with respect to several climate

stresses such as drought, flood and cyclones) are needed and they have to be integrated

with sensitivity and adaptive capacity indices through fuzzy inference system following similar

procedure as discussed above. Characterization of stress is not easy as it is often confused

with the impact of the stress. For the present analysis the probability of drought and floods

across states of India is used as exposure indicator of drought and flood, respectively. The

probability values are derived based on historic data of more than 125 years. The probability

values are as assessed at state level using meteorological sub-division specific data given in

Katyal (1996). For cyclones similar data could not be obtained and vulnerable population at

storm risk as given in Hossain and Singh (2002) is used as exposure indicator. Figure 5

shows the vulnerability for each of the three states at the two time points – 1990-91 and

1999-2000. The exposure indicator is kept constant across the two time points while the

sensitivity and adaptive capacity indices change over time. Thus the figure illustrates how the

overall vulnerability of these states (due to specific stresses) has changed over time.

In case of Andhra Pradesh a significant drop in sensitivity over time has enabled it to

overcome the decline in adaptive capacity and reduce its vulnerability to drought. Whereas in

the other two states the vulnerability level remained more or less similar across the two time

periods. In Uttar Pradesh almost proportional drop in sensitivity and adaptive capacity have

contributed towards unchanged vulnerability to floods. In Orissa despite a slight increase in

adaptive capacity, small increase in sensitivity has resulted in marginally increasing its

vulnerability to cyclone.

18

Figure 5. Vulnerability Assessment Using Fuzzy Inference System

Vulnerability to Drought - A.P.

0

0.5

1Sensitivity

AdapCap

Exposure

Vulner

1990-91

1999-00

Vulnerability to Flood - U.P.

0

0.5

1Sensitivity

AdapCap

Exposure

Vulner

1990-91

1999-00

Vulnerability to Cyclone - Orissa

0

0.5

1Sensitivity

AdapCap

Exposure

Vulner

1990-91

1999-00

19

5 Vulnerability Assessment – Developing Structured Indicator Set The previous section described the methodology that is based on fuzzy inference system, to

quantify vulnerability once a set of indicators is identified. However it is still not very clear the

exact rationale behind the choice of various indicators. Hence this section discusses the

criterion based on which the indicators should ideally be identified. The indicators once

identified are then aggregated using the simple averaging approach followed in the

estimation of the human development indices.

At the outset it may be noted that vulnerability of an entity can be hypothesized to

increase with increase in its exposure to the exogenous stress (such as drought, cyclones

and floods) and its sensitivity, but decrease with increase in its adaptive capacity in handling

the potential shortfall on the desired outcome caused by the exogenous stress. Thus as long

as this basic hypothesis is adhered to, it may not matter under which dimension a particular

measure is included. For instance income and inequality coefficient are used as measures of

entity’s economic capacity in the above section, but the same measures in the discussion

presented in this section are argued as representing an entity’s sensitivity.

5.1 Measures for Vulnerability Assessment The discussion here focuses on rationale behind the choice of various ‘measures’ in the

context of vulnerability to drought and summarizes the discussion in Table 4 where the

‘measures’ in the context of vulnerability to cyclones and floods are also shown. It may be

noted that in all the three cases (i.e., drought, cyclones and floods) the indicators are

identified in terms of agricultural system for ease of comparison and also for the simple

reason that agriculture continues to be the dominant sector in terms of population dependent

on it.

In terms of the conceptualization presented in section 3.1, drought is the stimulus that

causes vulnerability of an entity. The entity itself could broadly be the drought prone area (for

e.g., the state of Andhra Pradesh or a sub-region within that state), or more specifically a

target community – say for instance, the agricultural farmers, or users of drinking water. The

vulnerability could be assessed along the dimensions exposure, sensitivity and adaptive

capacity. Exposure components characterize the stresses and the entity that is stressed;

sensitivity components characterize the effect(s) of the stresses4; and adaptive capacity

components characterize responses of the entity to the effects of the stresses.

4 It is not necessary that the effect of drought is felt on a single outcome such as income, but it could be useful to think through such aggregate measure as shortfall in income would manifest in indirect effects on several dimensions that the entity may be interested.

20

o Exposure is better understood in terms of the components such as, (i) the

characteristics of the stimulus (namely drought); (ii) exposed population (in this case

agricultural farmers); and (iii) exposed activity (in this case agricultural activity). The

components can then be measured in terms of:

• Characteristics of the stimulus: (a) percentage area under stress – that is

say, where the ratio of water withdrawal to water availability exceeds a

norm; (b) rainfall deviation from the normal.

• Exposed population: (a) Rural population density; (b) percentage of

population engaged in farming activity (i.e., cultivators and agricultural

laborers).

• Exposed activity: (a) Percentage of rainfed agriculture; (b) size of

agricultural sector (i.e., percentage of GDP).

o Sensitivity can be described in terms of socio-economic features of the exposed

population and characteristics of the exposed activity. Thus sensitivity is better

understood in terms of the components such as, (i) Socio-economic characteristics;

(ii) Characteristics of existing technology; (iii) Characteristics of activity. Note that the

sensitivity (which is the effect of stresses) is captured through its effect on entity and

its present practice of water use. The components can then be measured in terms of:

• Socio-economic characteristics: (a) Income/Household expenditure; (b)

Inequality coefficient (say, gini coefficient); (c) Percentage of socially

(and/or economically) backward population in the total population.

• Characteristics of existing technology: (a) Fertilizer (total of N+P+K) use

per hectare; (b) Pesticide use per hectare; (c) Number tractors per

hectare.

• Characteristics of activity: (a) Percentage of HYV crop area in net sown

area; (b) Percentage of commercial crop area in net sown area.

o Adaptive Capacity can be described in terms the ability of the exposed population to

protect themselves, physical access to appropriate coping measures as well as

entity’s ability to avail such options. Thus adaptive capacity is best understood in

terms of the components such as, (i) Human capacity; (ii) Governance; and (iii)

Coping options. The components can then be measured in terms of:

• Human Capacity: (a) Percentage of literature population in the total

exposed population; (b) Percentage of expenditure on education in total

expenditure in the region; (c) Number of schools per 100 sq.km.

• Governance: (a) Tax revenue as percentage of total GDP of the region;

(b) Research and Development expenditure in agriculture as percentage

21

of GDP of the region; (c) Number of agricultural extension service centers

per 100 sq.km.; (d) Expenditure on drought management programs as

percentage of total expenditure of the region.

• Coping options: (a) Percentage of rural labor force in non-agricultural

sector; (b) Agricultural loans disbursed as percentage of total savings of

the region.

Extending the arguments for the other two climate extremes also the following table

summarizes various measures selected5 under several components of the three broad

dimensions (namely, exposure, sensitivity and adaptive capacity) of vulnerability.

5 The choice of measures (or indicators) is largely dictated by the data availability.

22

Table 4. Measures of Vulnerability to Drought, Cyclone and Flood

Measures Dimension/ComponentDrought Cyclone Flood

Exposure (i) Characteristics of the Stimulus/Stress

Probability of drought based on historic data

Percentage population at storm risk based on historic data

Probability of flood based on historic data

(ii) Exposed Population

(a) Percentage of population engaged in agricultural activity; (b) Share of rural population

(a) Percentage of population in agricultural activity in coastal areas; (b) Share of rural population

(a) Percentage of population in agricultural activity in flood prone areas; (b) Share of rural population

(iii) Exposed Activity (a) Size of agriculture (as percentage of regional GDP); (b) Percentage of area under rainfed cultivation

Size of agriculture (as percentage of regional GDP)

Size of agriculture (as percentage of regional GDP)

Sensitivity (i) Socio-economic Characteristics

(a) Income / Household Expenditure; (b) Coefficient of Inequality

(a) Income / Household Expenditure; (b) Coefficient of Inequality

(a) Income / Household Expenditure; (b) Coefficient of Inequality

(ii) Characteristics of Technology

(a) Fertilizer use per hectare; (b) Pesticide use per hectare; (c) Extent of mechanization (tractors per hectare)

(a) Fertilizer use per hectare; (b) Pesticide use per hectare; (c) Extent of mechanization (tractors per hectare)

(a) Fertilizer use per hectare; (b) Pesticide use per hectare; (c) Extent of mechanization (tractors per hectare)

(iii) Characteristics of Activity

(a) Share of fruits and vegetables in agricultural valued added; (b) Share of total oil seeds in agricultural value added

(a) Share of fruits and vegetables in agricultural valued added; (b) Share of total oil seeds in agricultural value added

(a) Share of fruits and vegetables in agricultural valued added; (b) Share of total oil seeds in agricultural value added

Adaptive Capacity (i) Human Capacity (a) Percentage of

literate population in total exposed population; (b) Percentage of expenditure on education in total expenditure

(a) Percentage of literate population in total exposed population; (b) Percentage of expenditure on education in total expenditure

(a) Percentage of literate population in total exposed population; (b) Percentage of expenditure on education in total expenditure

(ii) Governance Tax revenue as percentage of

Tax revenue as percentage of

Tax revenue as percentage of

23

GDP of the region GDP of the region GDP of the region (iii) Coping Options Percentage of

rural labor force in non-agricultural activities

Percentage of labor force in non-agricultural activities

Percentage of labor force in non-agricultural activities

Figure 6 illustrates the flowchart of the framework for assessing vulnerability to drought.

Table 5 shows the data collected for two time periods – 1990-91 and 1999-2000 – across

sixteen major states of India for the measures/indicators selected for assessing vulnerability

to drought. Similar data is assembled – for two periods and for all the sixteen major states –

for assessing vulnerability to flood and cyclone.

The functional relationships between various measures and vulnerability can be

described as follows (these relationships are also shown in the flowchart illustrated for the

drought case in figure 6): To start with vulnerability increases with exposure and sensitivity,

and declines with improvements in adaptive capacity. Exposure itself increases with increase

in stress, exposed population and exposed activity. In terms of the measures one can argue

that as probability of drought and rainfall anomaly6 increase the stress itself intensifies.

Similarly with increases in workforce dependent on agriculture and rural population, the

exposed population to the stress increases. As agricultural dependency of the economy

increases along with increase in rainfed agricultural, the activity affected by the stress

(namely, agriculture) increases. Sensitivity increases as socio-economic characteristics

deteriorate, and decreases as better technology is used for agriculture and more

commercialization takes place. Socio-economic characteristics improve with increase in

income, but decline with increase in inequality. Technology used in agriculture can be

hypothesized to improve with higher utilization of fertilizers, pesticides7, and tractors.

Increasing share of fruits and vegetables and total oilseeds in the agricultural produce

indicate greater commercialization of agriculture. Adaptive capacity increases with

improvements in human capacity, capacity of government and greater access to coping

options. Human capacity in turn increases with betterment of literacy rates and higher

spending on education by the state. Increasing tax revenue indicates growing capacity of the

government to handle stresses. Increase in percentage of labor force in household industries

indicates better access to coping options for the population that is otherwise dependent on

agriculture.

For the empirical analysis aggregation of indices is achieved following simple averaging

of the normalized measures. This is similar in spirit to the vast number of studies discussed 6 In the present analysis this measure is not used. 7 Higher use of pesticides need not imply better technology as intensive pesticide use has adverse environmental implications. In fact data shows that over the 1990s most states showed sharp decline in pesticide consumption per hectare possibly due to environmental pressures. Given this in the final analysis pesticide consumption is not included as an indicator of technology.

24

in section 3 of the report. Also such aggregation procedure is widely used in developing

various human development indices by the United Nations Development Program.

Normalization of each measure is carried out using the observed threshold values in the

sample of sixteen states. There are several averages that one can use and the analysis

presented here uses geometric and arithmetic means. The contrast between geometric and

arithmetic mean can best be illustrated using the example of poverty assessment. For

instance suppose poverty were hypothesized to be determined by achievements in several

dimensions. Use of geometric mean to assess overall poverty implies that non-achievement

in any one dimension is given high importance. On the other hand, use of arithmetic mean

implies that the entity is considered poor only if she fares badly on all the dimensions.

In the present context a similar reasoning is invoked and it is argued that all measures

representing a component and all components characterizing a dimension are individually

important. The dimensions on the other hand are collectively important for vulnerability

assessment. Thus, the measures representing any component are aggregated using

geometric mean, and the components themselves are also aggregated using geometric

mean. However the dimensions (namely, exposure, sensitivity and adaptive capacity) are

aggregated using arithmetic mean.

25

Figure 6. Framework for Assessing Vulnerability to Drought

Exposure ↑

Characteristics of Stress ↑

Exposed Population ↑

Exposed Activity ↑

Vulnerability To Drought Sensitivity

↑

Socio-economic Characteristics ↓

Characteristics of Technology

Characteristics of Activity ↓

Human Capacity ↑

Governance ↑

Coping Options ↑

Adaptive capacity ↓

Rainfall Anomaly ↑

Probability of Drought ↑

Labour Force in Agriculture ↑

Rural Population ↑

Size of Agricultural Sector ↑

% Area under Rainfed Agri ↑

Per-capita Income ↑

Inequality Index ↓

Fertilizer Use per Hectare ↑

Pesticide Use per Hectare ↑

Share of Fruits-Vegetables ↑

Share of Oil Seeds ↑

Tractor Use per Hectare ↑

Literacy Rate ↑

Share of Expenditure on Education ↑

Share of Tax Revenue in NSDP ↑

Labour Force in Non-agricultural Activities

26

Table 5. Indicators for Assessing Vulnerability to Drought: 1990–91 Exposure Sensitivity Adaptive Capacity Nature of Stress

Exposed Pop.

Exposed Act. Socio-Econ Character.

Character. Of Tech.

Character. Of Activity

Human Capacity

Governance Coping Options

State

Prob. Drgt.

Lab. In Agri.

Rural Pop.

Share of Agri

Unirri. Area

Percap Income

Gini Coeff.

Fert. Per Ha.

Tractor per Ha.

Sh. Of Frt/Veg

Sh. Of Oilseed

Literacy Sh. Of Edu. Exp.

Sh. Of Tax Rev. in NSDP

Lab. In Household Industries

AP 0.148 69.44 73.11 35.40 61.1 6873 0.318 123.0 4.46 10.00 17.78 44.09 15.4 7.5 3.43 Assam 0.034 67.32 88.90 40.67 94.1 5574 0.238 22.8 0.18 24.61 3.66 52.89 16.9 3.8 0.96 Bihar 0.069 81.94 86.86 44.81 65.3 4474 0.256 57.0 1.50 18.59 0.89 38.48 19.9 4.1 1.73 Guj 0.199 60.84 65.51 29.96 77.9 8788 0.255 68.0 6.02 9.89 22.50 61.29 16.7 7.9 1.42 Haryana 0.290 60.7 75.37 44.45 25.1 11125 0.289 98.0 26.69 3.49 7.91 55.85 13.8 7.3 1.48 HP 0.061 72.82 91.31 36.61 81.90 7618 0.291 58.2 2.67 36.65 0.81 63.86 16.9 5.1 1.22 Karnata 0.096 65.36 69.08 36.41 80.8 6631 0.300 71.0 3.20 35.76 15.91 56.04 16.1 9.3 1.87 Kerala 0.075 39.97 73.61 32.65 82.9 6851 0.331 83.0 0.66 46.55 24.79 89.81 23.5 8.1 2.65 MP 0.088 77.4 76.82 42.50 81.4 6350 0.296 36.0 3.78 7.89 16.67 44.2 16.3 4.9 2.47 Mah 0.130 62.15 61.31 23.00 88.3 10159 0.321 67.0 2.19 22.96 11.24 64.87 16.1 7.7 1.65 Ori 0.033 75.29 86.62 39.99 77.7 4300 0.275 21.0 0.24 38.99 16.26 49.09 15.1 5.9 3.24 Punj 0.300 56.33 70.45 44.61 5.9 11776 0.299 162.0 28.20 5.31 0.95 58.51 15.2 7 1.32 Raj 0.240 73.95 77.12 46.93 75.50 6760 0.308 20.0 7.23 1.76 19.13 38.55 17.5 5.1 1.79 TN 0.150 61.17 65.85 19.79 54.4 7864 0.334 129.0 4.17 20.08 18.53 62.66 19.5 9 3.6 UP 0.133 73.77 80.16 42.12 39.60 5342 0.293 62.8 13.50 8.56 4.85 41.6 17.5 5.2 2.39 WB 0.018 54.25 72.52 32.74 57.2 5991 0.279 91.0 0.76 33.80 4.81 57.7 22.9 5.7 4.24

27

Table 5 (contd.). Indicators for Assessing Vulnerability to Drought: 1999–2000 Exposure Sensitivity Adaptive Capacity Nature of Stress

Exposed Pop.

Exposed Act. Socio-Econ Character.

Character. Of Tech.

Character. Of Activity

Human Capacity

Governance Coping Options

State

Prob. Drgt.

Lab. In Agri.

Rural Pop.

Share of Agri

Unirri. Area

Percap Income

Gini Coeff.

Fert. Per Ha.

Tractor per Ha.

Sh. Of Frt/Veg

Sh. Of Oilseed

Literacy Sh. Of Edu. Exp.

Sh. Of Tax Rev. in NSDP

Lab. In Household Industries

AP 0.148 62.30 72.92 30.97 59.44 9445 0.260 158.0 6.67 14.2 8.7 61.1 14.5 7.2 4.5 Assam 0.034 52.65 87.28 41.00 79.16 5785 0.214 22.4 0.37 31.7 3.7 64.3 23.5 4.2 3.4 Bihar 0.069 72.84 84.46 31.25 50.61 4245 0.224 98.0 9.75 46.3 1.1 47.5 21.5 5.2 4.0 Guj 0.199 52.05 62.65 17.97 68.31 13490 0.254 88.0 13.91 16.9 18.1 70.0 15.8 7.7 1.9 Haryana 0.290 51.56 71.00 33.15 23.79 13308 0.252 148.0 32.13 8.6 6.2 68.6 14.9 7.2 2.5 HP 0.061 68.65 90.21 28.58 81.18 11051 0.251 141.7 7.31 38.9 1.0 77.1 17.4 5.2 1.7 Karnata 0.096 55.89 66.02 31.17 78.09 10912 0.268 103.0 5.06 24.3 8.7 67.0 16.8 8.1 4.0 Kerala 0.075 23.26 74.03 25.98 84.27 10178 0.285 71.0 0.70 30.7 25.8 90.9 20.2 8.3 3.5 MP 0.088 72.69 74.94 33.56 67.67 7850 0.260 47.0 9.81 11.2 24.1 64.1 16.8 5.6 3.3 Mah 0.130 55.41 57.60 16.93 85.64 15186 0.295 89.0 4.65 30.1 11.1 77.3 19.0 7.1 2.5 Ori 0.033 64.74 85.03 40.64 64.98 5735 0.250 44.0 1.92 56.6 2.8 63.6 19.1 4.4 4.8 Punj 0.300 39.36 66.05 42.09 7.06 14698 0.254 185.0 35.73 5.3 0.7 70.0 15.1 6.3 3.4 Raj 0.240 66.01 76.62 30.00 66.72 8550 0.227 39.0 16.21 3.8 24.4 61.0 19.0 5.8 2.7 TN 0.150 49.55 56.14 17.78 47.28 12144 0.323 165.0 7.64 26.8 16.8 73.5 19.2 8.6 5.2 UP 0.133 64.67 78.40 36.28 31.34 5787 0.263 98.6 26.10 16.5 3.7 57.4 16.5 5.5 4.8 WB 0.018 43.95 71.97 32.49 65.02 9320 0.254 136.0 1.93 38.3 3.0 69.2 22.0 4.0 7.3

Notes for Table 5: a) Probability of drought is based on data for the period 1871-1990; source: Katyal (1996) b) Labor force in agriculture is based on Census data – 1991 and 2001, includes cultivators and agricultural labor and expressed as

percentage of total workforce c) Rural population is based on Census data – 1991 and 2001, expressed as percentage of total population d) Share of agriculture is expressed as percentage share of valued added from agriculture in net state domestic product; source: CSO

28

e) Un-irrigated area is expressed as percentage of cropped area; 1990-91 data is based on gross irrigated and total cropped area from CSO; 1999-2000 data is based on Lok Sabha unstarred question 2394, 4/12/2000

f) Per capita NSDP is expressed in 1993-94 prices for both the years 1990-91 and 1999-2000; source: CSO g) Gini coefficient is obtained from National Human Development Report, GoI, 2001 h) Fertilizer used per hectare is from CSIR Industrial Data Book 2002-2003; expressed in kg/ha. i) Tractors per hectare is from www.Indiasta.com; expressed in numbers/ha. j) Share of fruits and vegetables and total oil seeds is calculated based on sector-wise value added data from CSO and

www.indiastat.com (all figures calculated based on values expressed in 1993-94 prices) k) Literacy, expressed in percentage, is from census data –1991 and 2001 l) Educational expenditure, expressed as percentage of total expenditure, is from India Development Report 2004-05 (Mahendra Dev

and J. Mooij, 2005) m) Tax revenue is expressed as percentage of GSDP; source www.indiastat.com n) Labor in household industries is expressed as percentage of total workers and is based on census data – 1991 and 2001

29

5.2 Vulnerability Assessment Figures 7, 8 and 9 show the vulnerability to drought in Andhra Pradesh, vulnerability

to floods in Uttar Pradesh, and vulnerability to cyclones in Orissa, respectively. In

each figure exposure, sensitivity and adaptive capacity dimensions are depicted

along with the overall vulnerability. The vulnerability calculations carried out for years

1990-91 and 1999-2000 are shown in these figures. Thus from these figures it is

possible to assess to what extent the vulnerability in each of the states has been

reduced (or increased) and which dimensions and components contributed to such

changes.

While reading the figures 7, 8 and 9 it may be noted that high value typically

denotes high realization, but there are exceptions to this general rule. High value on

characteristics of technology indicates lower technological sensitivity, which implies

greater penetration of technology. High value on characteristics of activity indicates

lower agricultural sensitivity as it captures higher commercialization in agriculture.

High value on overall sensitivity indicates lower sensitivity and similarly high value on

vulnerability represents lower vulnerability.

In Andhra Pradesh between the two time periods considered the exposed

population and exposed activity have both slightly increased leading to overall

increase in exposure. In terms of sensitivity despite decrease in both socio-economic

and technological sensitivity, the state witnessed increase in agricultural sensitivity

(i.e., relatively lower commercialization in 1999-00 compared to 1990-918) and as a

result the overall sensitivity only marginally decreased. In the adaptive capacity

dimension, while human and government capacity have improved over the period,

the coping options showed sharp decline resulting in a slight decrease in the overall

adaptive capacity. Put together slight increase in exposure and decrease in adaptive

capacity are countered partly by the marginal decrease in sensitivity and the

vulnerability level remained almost static over the period. This seems meaningful

given continuing concerns about the vulnerability of the state to drought. This

concern is also reflected through more and more assistance given by the central

government to the state government over the years for tackling drought problem.

However, this is in contrast to the result presented in section 4 where the

vulnerability to drought in Andhra Pradesh showed significant decline over the same

8 It must be kept in mind that the performance of any state is analyzed with reference to the performance of all other states. Hence it is feasible that Andhra Pradesh itself might have witnessed greater commercialization of agricultural sector over the period but when compared with the other states its performance may not have been that impressive.

30

period. The analysis presented in the previous section is based on an indicator set

that did not adequately capture the exposure, sensitivity and adaptive capacity

specific to drought. Thus relatively poor dataset, despite application of a much better

aggregation procedure, resulted in presenting a picture that appears to be

inadequately capturing the reality.

In Uttar Pradesh slight increase in exposed population is compensated by

decline in the exposed activity and the overall exposure decreased slightly. Marginal

decline in socio-economic sensitivity and significant decline in technological

sensitivity are negated slightly by the increase in the agricultural sensitivity. And the

overall sensitivity showed decline over the period. All components of adaptive

capacity dimension contributed positively and overall adaptive capacity showed

significant improvement over the period. Put together since all the three dimensions

contributed towards reducing the vulnerability the state witnessed drop in its

vulnerability to floods over the period. This is again in contrast to the result presented

section 4 for the U.P., where decline in sensitivity is countered by the drop in

adaptive capacity to leave vulnerability unchanged. Decline in vulnerability to floods

seems more realistic as corroborated by other studies.

In Orissa the exposure to cyclones remained high and in fact slightly increased

as both exposed population and exposed activity have increased. Reasonable

decline in socio-economic sensitivity coupled with a slight decline in technological

sensitivity (the state however continues to be one of the technologically most

backward states in the country), countered increased agricultural sensitivity to reduce

the overall sensitivity marginally. Impressive strides made in improving the human

capacity are negated by decline in government capacity and access to coping

options and the overall adaptive capacity registered drop over the period. Put

together the vulnerability of the state to cyclones remained more or less static over

the period and at alarmingly high level.

31

Figure 7. Vulnerability to Drought in Andhra Pradesh: 1990–91 and 1999–2000

Exposure to Drought

0

0.5

1Charact. Stress

Exposed Pop

Exposed Act.

Exposure

1990-91

1999-00

Sensitivity to Drought

0

0.5

1Socio-Econ Charact

Charact Tech

Charact Activ

Sensitivity

1990-91

1999-00

Adaptive Capacity to Drought

0

0.5

1Human Cap

Goverance

Coping Options

Adap Cap

1990-91

1999-00

Vulnerability to Drought

0

0.5

1Exposure

Sensitivity

Adap Cap

Vulnerability

1990-91

1999-00

32

Figure 8. Vulnerability to Flood in Uttar Pradesh: 1990–91 and 1999–2000

Exposure to Flood

0

0.5

1Charact. Stress

Exposed Pop

Exposed Act.

Exposure

1990-91

1999-00

Adaptive Capacity to Flood

0

0.5

1Human Cap

Goverance

Coping Options

Adap Cap

1990-91

1999-00

Sensitivity to Flood

0

0.5

1Socio-Econ Charact

Charact Tech

Charact Activ

Sensitivity

1990-91

1999-00

Vulnerability to Flood

0

0.5

1Exposure

Sensitivity

Adap Cap

Vulnerability

1990-911999-00

33

Figure 9. Vulnerability to Cyclone in Orissa: 1990-91 and 1999–2000

Exposure to Cyclone

0.2

0.7

1.2Charact. Stress

Exposed Pop

Exposed Act.

Exposure

1990-91

1999-00

Adaptive Capacity to Cyclone

0

0.5

1Human Cap

Goverance

Coping Options

Adap Cap

1990-91

1999-00

Sensitivity to Cyclone

-0.2

0.3

0.8Socio-Econ Charact

Charact Tech

Charact Activ

Sensitivity

1990-91

1999-00

Vulnerability to Cyclone

-0.1

0.4

0.9Exposure

Sensitivity

Adap Cap

Vulnerability

1990-91

1999-00

34

6 Conclusions This study has made an attempt to assess vulnerability to climate hazards such as

drought, floods and cyclones in order to get insights into the determinants of

vulnerability. Such insights are considered useful in identifying appropriate

intervention strategies aimed at improving adaptation to climate change. Focusing on

present day climate extremes is important as there is growing consensus in the

scientific community that current adaptation may overlap significantly with desired

adaptation under climate change conditions.

The study made an attempt to conceptualize vulnerability by appropriately

specifying the stress, outcome(s) of interest and associated preference criteria. While

operationalizing the concept of vulnerability with the help of various

indicators/measures, two specific issues were addressed in this study: (a) how

aggregation should be carried out; and (b) what is the basis on which the indicator

set is selected.

In the empirical analysis focusing on the states of Andhra Pradesh, Uttar

Pradesh and Orissa (for assessing vulnerability to drought, floods and cyclones,

respectively) and two time points (1990-91 and 1999-00) the aggregation issue was

addressed by using fuzzy inference based aggregation and simple averaging of

normalized indicators. To address the issue of basis for indicator set the empirical

analysis argued in favor of developing indicators in structured manner and illustrated

the approach for the three cases. The vulnerability analysis provided intuitively

meaningful results.

There are several directions in which future work could be carried out and these

include: (a) applying fuzzy inference based aggregation procedure over the indicator

set developed in structured manner and compare the results with simple aggregation

based results; (b) inclusion of several time points in the analysis to adequately

understand the trend in vulnerability and its determinants; (c) inclusion of more

indicators/measures to appropriately capture various components of vulnerability;

and (d) carry out disaggregated analysis within a state – possibly using district level

indicators, to fine-tune the hot-spot areas that need immediate interventions.

Acknowledgement We gratefully acknowledge the inputs provided by Prof. K. Kavi Kumar of Madras School of Economics, Chennai, India.

35

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37

Appendix – A Table A1. State-wise Details Impacts of Drought: 1998–99 to 2000–01

Districts Affected Human Population Affected (In Lakh)

Cattle Population Affected (In Lakh)

Cropped Area Affected (Lakh Ha.)

States 1998-99

1999-2000

2000-01 1998-99 1999-2000 2000-01 1998-99 1999-2000 2000-01

1998-99

1999-2000

2000-01

Andhra Pradesh - 18 22 - 413 NR - 125 NR - 26.52 18.54 Chhatisgarh - - 12 - - 94.08 - - 32.4 - - 11.36 Gujarat - 17 23 - 250 291 - 71.33 107 - NR 13.5 Himachal Pradesh - 12 12 - NR 46.64 - NR NR - 2.87 0.88 Jammu & Kashmir - 6 6 - NR NR - NR 37.98 - 2.96 NR Karnataka - 21 - - 220 - - 49.52 - - 18.48 - Kerala 14 - - NR - - NR - - 0.81 - - Madhya Pradesh 7 7 32 43.75 26.64 127.1 43.84 34.28 85.78 11.85 9.53 39.52 Maharashtra - - 26 - - 454.99 - - 2.58 - - 45 Orissa 19 - 28 12.33 - 119.5 NR - 65.54 10.66 - 11 Manipur - 5 - - NR - - NR - - 0.71 - Mizoram - 3 - - NR - - NR - - 0.51 - Rajasthan 17 26 31 199.86 262 330.41 281.73 345.6 399.69 61.57 78.18 89.47 Tripura - 4 - - NR - - NR - - 0.2 - West Bengal 10 10 - 25.24 NR - NR NR - 1.2 1.2 - Source: Lok Sabha Unstarred Question No. 84, dated 19.11.2001

38

Table A2. State-wise Details of Impacts of Floods: 1953–2002

Damage to Crops

Damage to Houses

States/UTs

Area Affected in m.ha.

Population Affected in Million

Area in m.ha.

Value in Rs. Crore Number

Value in Crore

Cattle lost Numbers

Human lives lost Numbers

Damage to public Utilities in Rs. Crore

Total Damages Crops, houses and Public Utilities in Rs. Crore

Andhra Pradesh 0.3 1.704 0.211 72.06 95714 19.467 28164 308 97.234 203.887 Assam 0.893 2.5 0.225 40.075 66949 7.053 10692 39 20.55 67.678 Bihar 1.35 6.615 0.62 82.618 150904 28.93 857 134 37.315 148.863 Goa 0 0.001 0 0.003 2 0.001 0 0 0 0.035 Gujarat 0.313 1.846 0.206 11.12 40253 7.095 13260 139 19.549 37.764 Haryana 0.168 0.454 0.109 12.871 29409 2.649 154 10 2.459 17.968 Himachal Pradesh 0.077 0.721 0.063 35.814 3342 9.628 748 38 33.815 79.257 Jammu and Kashmir 0.028 0.149 0.024 6.328 6742 4.167 4120 30 10.402 20.897 Karnataka 0.049 0.123 0.038 6.723 9873 2.134 459 21 8.802 17.677 Kerala 0.166 1.892 0.059 40.448 36314 11.905 1040 57 94.018 143.443 Madhya Pradesh 0.038 0.298 0.02 2.928 14439 0.541 2326 26 0.905 4.305 Maharashtra 0.04 0.301 0.036 4.083 24357 2.43 1765 82 9.121 16.557 Orissa 0.447 2.449 0.282 11.867 70869 3.456 3856 35 66.42 58.988 Punjab 0.239 0.576 0.162 26.157 56263 11.959 2776 58 25.628 63.744 Rajasthan 0.295 0.643 0.172 14.35 25486 7.363 2972 43 17.62 39.333 Tamil Nadu 0.041 1.063 0.032 5.647 47590 2.725 1395 49 17.016 25.388 Uttar Pradesh 1.986 7.618 1.105 146.252 260307 34.153 1641 290 69.345 249.75 West Bengal 0.818 3.565 0.27 72.546 243816 33.748 10894 162 23.661 129.955 Source: Rajya Sabha Unstarred Question No. 1526, dated 16.12.2003

39

Table A3. State-wise Average Area Affected due to Floods: 1991–2000

Average Area States/UTs (Million Hect.) Andhra Pradesh 0.354 Assam 0.728 Bihar 0.972 Goa 0 Gujarat 0.31 Haryana 0.073 Himachal Pradesh 0.234 Jammu & Kashmir 0.04 Karnataka 0.08 Kerala 0.266 Madhya Pradesh 0.042 Maharashtra 0.018 Orissa 0.304 Punjab 0.126 Rajasthan 0.213 Tamil Nadu 0.025 Uttar Pradesh 1.018 West Bengal 0.737 Source: Lok Sabha Unstarred Question No.3267, dated 19.03.2001