Document Control

Prof. A.K. Gosain

Dr. Sandhya Rao

Ms. Shradha Ganeriwala

Ms. Puja Singh

Ms. Anamika Arora

Mr. Ankush Mahajan

Disclaimer “The data and information used for preparing this report have been sourced from secondary sources including state government departments, published sources of Government of , and climate change assessment made by the consultants. While due care has been taken to ensure authenticity of the data and other information used, any inadvertent wrong data or information used is regretted. We are not liable to any legal or penal responsibilities arising from this and also from the use of this report by anyone.

Anupam Rajan, IAS Government of Principal Secretary Department of Environment Mantralaya, Vallabh Bhawan, -462004 (MP.) Tel. : (0755) 2460038

Preface

The study to assess the climate change risks and vulnerabilities of Madhya Pradesh was taken up under the collaborative project of the Ministry of Environment, Forest & Climate Change, Swiss Agency for Development & Cooperation, United Nations Development Programme (UNDP) and Government of Madhya Pradesh on Strengthening State Strategies for Climate Actions, understanding that Adaptation is the State’s key concern with respect to Climate Change.

Realizing that Vulnerability Assessment (VA) would support in formulating robust adaptation strategies for climate actions and in integrating climate change concerns in sub-national planning processes, the State Government agreed to undertake this study. This study incorporates recent advancements in climate science and the latest emission scenarios adopted by the Intergovernmental Panel on Climate Change (IPCC) in its 5th Assessment report.

Two key challenges before embarking on such a comprehensive State Level assessment was ensuring participation from stakeholders and collecting requisite data from different sectors for different timelines. Uncertainties and constraints of modelling, availability of data across sectors and different timelines, projections for different time scales etc. added to the complexities.

Nevertheless, I believe, a genuine effort has been made to overcome these aspects and meaningful inferences have been drawn from the Vulnerability Assessment study. Translating this academic activity into informed practices and establishing this as connect between science, policy and actions would be the task ahead for us. I am aware that there are ample scopes for further improvement and refinement of this report as the knowledge is ever evolving.

Despite all the barriers and limitations, I am sure that this report will serve the purpose of looking at the Climate Change related developmental issues in a congruent manner. I thank MoEFCC, SDC, EPCO and UNDP for taking up this study for Madhya Pradesh.

Anupam Rajan Director General Environment Planning and Coordination Organisation (EPCO) Bhopal

P.Narahari Environment Planning & Executive Director Coordination Organisation (An Autonomous organisation, under Govt. of M.P.) Paryavaran Parisar, E-5 Sector, Arera Colony,

Message

The impacts of climate change are increasingly being felt around the world. In the context of Madhya Pradesh, it may become a major environmental threat to the State’s development progress in the coming decades as it could have adverse impacts on food security, natural resources, human health and economic activities. With most of the population dependent on the climate sensitive sectors such as agriculture and forestry for livelihood, any adverse climatic impacts on these sectors will undermine the development efforts of the State. Hence, it becomes imperative to first develop a better scientific understanding of the climate change risks, vulnerability & associated impacts. In this context, I hope that the current vulnerability assessment study is an important milestone towards enhancing the understanding of climate change and its impacts on the key sectors which would assist the policymakers and other stakeholders in developing a range of adaptation options in the future.

I appreciate SKMCCC and UNDP for taking this initiative in developing this vulnerability assessment report.

P.Narahari Executive Director

Acknowledgement

The “Climate Change Vulnerability Assessment Report for Madhya Pradesh” represents a collaborative effort, made possible by the input and feedback received from experts in the field of climate change and from National and State Level Government partners working on issues to combat climate change. This report has undertaken climate change vulnerability assessment of select sectors of Water, Forest, Agriculture & Health for the state of Madhya Pradesh. The main purpose of vulnerability assessment is to identify and prioritize the regions and sectors which are likely to be adversely impacted by climate change so as to enable development of adaptation practices and strategies to help mainstream the climate change in to the broader developmental programs and projects. The report has been prepared under the project “Strengthening State Strategies for Climate Actions” being implemented by UNDP in partnership with MoEFCC and supported by Swiss Agency for Development and Cooperation (SDC).

UNDP extends special thanks to Mr. Ravi Shankar Prasad (IAS), Joint Secretary, MoEFCC, Mr. Anupam Rajan (IAS), Principal Secretary, Environment Department, Govt. of Madhya Pradesh, for their insights and guidance.

UNDP take this opportunity to also thank Mr. P. Narahari (IAS), Executive Director, Environmental Planning and Coordination Organisation (EPCO), Govt. of Madhya Pradesh, for his guidance and support in finalising the report. This report could not have completed in this form without the support of Mr. Lokendra Thakkar, Coordinator, SKMCCC, EPCO, Government of Madhya Pradesh.

Special thanks to the staff of Madhya Pradesh State Knowledge Management Centre on Climate Change (SKMCCC) Saransh Bajpai, Pratik Barapatre, Ramratan Simaiya, Ravi Shah and Raashee Abhilashi for their inputs and support in finalizing the report.

UNDP acknowledges the key role played by UNDP State and National teams for leading the process and hosting a number of workshops and meetings which facilitated collaboration and partnerships.

UNDP acknowledges the work of the key authors of the report Professor A.K.Gosain, IIT Delhi and Dr. Sandhya Rao, Integrated Natural Resource Management Consultants (INRM) and her team in carrying out the vulnerability assessment study and report writing.

Table of Contents

Climate Change Vulnerability Assessment for Madhya Pradesh

Introduction 1

Study Area 1 1Purpose of Vulnerability Assessment 2 Definition of Vulnerability to Climate Change 2

Approach and Methodology 3

Methodology 5

Selection of Indicators 5

Data Sources 6

Data Collection 7

Data Cleaning and Quality Checking 7

Limitations of Indicator Base Method 8

Strengths and Weaknesses of Principal Component Analysis (PCA) 8

Limitations 8

Limitations on Data 8

Madhya Pradesh District Vulnerability Profiles 9

Current and Projected Vulnerability Profile 9

Current Vulnerability Profile 9

Projected Vulnerability Profile 13

Disaggregated Sectoral Vulnerability 15

Social Vulnerability 15

Economic Vulnerability 16

Extreme Climate Vulnerability 18

Water Resource Vulnerability 20

Forest Vulnerability 22

Agriculture Vulnerability 24

Summary - District Vulnerability 26 Appendix I 35

List of Indicators for District Vulnerability Assessment 35 2 Tables for District Vulnerability 49 Appendix II 64

Software Used 64 3 Normalization of Indicator Data 64 Calculation of Weights 65

Calculation of Indices 65

Principle Component Analysis (PCA) 65

Cluster Analysis 66 List of Table

Table 1: District current composite vulnerability along with disaggregated sub components for districts of Madhya Pradesh 27 Table 2: Current and projected composite vulnerability for districts of Madhya Pradesh 30

List of Figures

Figure 1: Flowchart for construction of Composite Vulnerability Index 4 Figure 2: Current Composite Vulnerability map for Districts of Madhya Pradesh 11 Figure 3: Disaggregated sectoral vulnerability contributing to Current Composite Vulnerability for districts of Madhya Pradesh 12 Figure 4: Projected Composite Vulnerability map for districts of Madhya Pradesh for IPCC AR5 RCP4.5 and RCP8.5 scenarios 14 Figure 5: Social Vulnerability map for districts of Madhya Pradesh for current vulnerability 15 Figure 6: Economic Vulnerability map for districts of Madhya Pradesh for Current vulnerability 17 Figure 7: Extreme climate vulnerability map for districts of Madhya Pradesh under current and projected climate scenarios 18 Figure 8: Water resource Vulnerability map for districts of Madhya Pradesh under current and projected climate scenarios 20 Figure 9: Forest Vulnerability map for districts of Madhya Pradesh under current and projected climate scenarios 23 Figure 10: Agriculture Vulnerability map for districts of Madhya Pradesh under current and projected climate scenarios 25 List of Tables in Appendix

Table A- 1: Sectors and the associated indicators considered for climate change vulnerability assessment for districts of Madhya Pradesh 35 Table A- 2: Rationale for classifying indicators of vulnerability as Exposure, Sensitivity or Adaptive Capacity 41 Table A- 3: District wise Composite vulnerability index values, ranks and vulnerability category under current and projected climate scenarios 49 Table A- 4: District wise Social vulnerability Index values, ranks and category for current vulnerability 52 Table A- 5: District wise Economic vulnerability Index values, ranks and category for current vulnerability 53 Table A- 6: District wise Climate vulnerability Index values, ranks and vulnerability category under current and projected climate scenarios 54 Table A- 7: District wise Water resources vulnerability Index values, ranks and vulnerability category under current and projected climate scenarios 56 Table A- 8: District wise Forest vulnerability Index values, ranks and vulnerability category under current and projected climate scenarios 59 Table A- 9: District wise Agriculture vulnerability Index values, ranks and vulnerability category under current and projected climate scenarios 61 Abbreviations

Acronym Definition AGV Agriculture Vulnerability AGVI Agriculture Vulnerability Index BL Baseline CDD Consecutive Dry Days CLV Climate Vulnerability CLVI Climate Vulnerability Index CORDEX Coordinated Regional Climate Downscaling Experiment CRIDA Central Research Institute for Dryland Agriculture CV Composite Vulnerability CVI Composite Vulnerability Index CWD Consecutive Wet Days DACNET Department of Agriculture Cooperation DISE District Information System for Education EC End-Century ECV Economic Vulnerability ECVI Economic Vulnerability Index EH Extremely High FOV Forest Vulnerability FOVI Forest Vulnerability Index FSI Forest Survey Of India GDP Gross Domestic Product GHG Green House Gas GIS Geographic Information System H High IBIS Integrated Biosphere Simulator IFPRI International Food Policy Research Institute IIRS Indian Institute of Remote Sensing IITM Indian Institute of Tropical Meteorology INRM Integrated Natural Resources Management IPCC Intergovernmental Panel On Climate Change IVI Inherent Vulnerability Index L Low LPJ Lund-Postdam-Jena M Moderate MC Mid-century MDG Millennium Development Goal NAPCC National Action Plan on Climate Change NIPFP National Institute of Public Finance and Policy OECD Organization for Economic Co-operation and Development PCA Principle Component Analysis PET Potential Evapotranspiration RCP Representative Concentration Pathway SV Social Vulnerability SVI Social Vulnerability Index SWAT Soil And Water Assessment Tool THI Temperature Humidity Index UNICEF United Nations International Children's Emergency Fund VH Very High VL Very Low WMO World Meteorological Organization WRV Water Resources Vulnerability WRVI Water Resources Vulnerability Index WSSD World Summit on Sustainable Development WWF World Wildlife Fund Executive Summary

Vulnerability has been assessed for the state of climate, water resources and forest Madhya Pradesh at the district level. The sector vulnerability. vulnerability analysis has been based on o Panna has high social, economic, Composite Vulnerability Index (CVI) derived climate and agriculture sector using multivariate analysis for current and vulnerability. projected climate scenarios (under IPCC AR5 o has high social, economic, RCP4.5 and RCP8.5 climate scenario towards climate, water resources, forest and mid- and end-century). The IPCC working agriculture sector vulnerability. All six definition of vulnerability as a function of sectors contribute to its High vulnera - exposure, sensitivity, and adaptive capacity has bility. been used and identified 72 indicators have been categorized into adaptive capacity, o Alirajpur has high economic, climate sensitivity and exposure. Accordingly six and water resource sector vulnerability. sectoral vulnerability indices for social, • Seventeen districts namely, Dindori, economic, climate, water resources, forest and Ashoknagar, Shivpuri, Umaria, , agriculture have also been generated. The Chhatarpur, Tikamgarh, Khargone (West indices would facilitate the identification of Nimar), , Damoh, Guna, Satna, Dhar, districts, which are vulnerable to climate Katni, Sagar, and Rajgarh with change and need special attention towards ranks 42 to 26 in the given order fall under adaptation. high vulnerable category. Summary of the district composite vulnera- o Three districts namely, Bhopal, bility analysis is as follows: and districts with ranks 1, 2 and 3 Current Composite Vulnerability respectively are the least vulnerable districts. The main contributing factors • Eight districts namely, Singrauli, Jhabua, come from higher adaptive capacity Barwani, Panna, Morena, Sidhi, Rewa and due to higher social and economic Alirajpur with ranks 50, 49, 48, 47, 46, 45, 44 capital. and 43 respectively are the most vulnerable districts under current climate. Projected Composite Vulnerability Main sectors contributing to districts The overall Composite Vulnerability (CV) of the vulnerability in the CVI are: Madhya Pradesh districts is projected to o Singrauli with rank 50 is the most increase towards the mid-century as compared vulnerable district under current to the baseline, while decrease towards the conditions. end-century as compared to the baseline for both moderate and high emission scenarios of o Singrauli and Sidhi have high social, RCP4.5 and RCP8.5 respectively. District economic and agriculture vulnerability. vulnerability under RCP4.5 scenario is o Jhabua and Rewa have high social, projected to be higher as compared to RCP8.5 economic, climate, water resource and scenario towards both mid and end-century. agriculture sector vulnerability. • The overall climate and forest vulnerability o Barwani has high social, economic, of the districts is projected to increase towards mid-and end-centur y as Social Vulnerability compared to the baseline for both • Seven districts namely, Singrauli, Umaria, emission scenarios of RCP4.5 and RCP8.5. Dindori, Sheopur, Jhabua, Panna and • The overall water resources vulnerability of Sidhiwith ranks 50 to 44 respectively in the districts is projected to increase towards same order are currently the most mid-century while decrease towards the vulnerable districts. end-century under RCP4.5 and RCP8.5 o Higher decadal growth rate of climate scenarios as compared to current population and biomass dependency, conditions. relatively low literacy rate, poor • The overall agriculture vulnerability of the sanitation facilities and access to Madhya Pradesh districts is projected to transport and communication, low decrease towards mid-and end-century as access to electricity and level of compared to the current conditions for urbanization are the factors contribu - both emission scenarios. Districts ting to higher vulnerability of these vulnerability under RCP8.5 scenario is districts under current conditions. projected to be lower as compared to Singrauli also has the highest student- RCP4.5 scenario. teacher ratio. • Exposure to rainfall variability, drought • Bhopal, Indore, Jabalpur and with weeks and sensitivity to heat stress and ranks 1, 2, 3 and 4 respectively are the least seasonal crop water stress are projected to vulnerable districts. increase towards the mid-century as Economic Vulnerability compared to current conditions, thus, • Nine districts namely, Alirajpur, Rewa, contributing to increase in overall Singrauli, Panna, Damoh, Bhind, Composite Vulnerability (CV). Ashoknagar, Chhatarpur and Tikamgarh • CV improves towards end-century as districts with ranks 50, 49, 48, 47, 46, 45, 44, compared to current conditions as 43 and 42 respectively are the most exposure to drought weeks and sensitivity vulnerable districts. to seasonal crop water stress is projected to o Major contributing indicators include, decrease while yield for rice and soyabean low per capita income, households crops are projected to increase. Dry Teak availing banking services and access to Forests are the most vulnerable to impact total credit in banks. of climate change. • Indore with rank 1 belonging to Each sector considered in the analysis Plateau region of Madhya Pradesh is the contributes differently to the aggregated least vulnerable district as it has highest composite vulnerability index and district per capita income and higher number of rankings. The decomposition of the Composite commercial banks. Vulnerability Index can shed light on main factors contributing to overall vulnerability of a Extreme Climate Vulnerability particular district. Each sector used in CVI has • Jhabua, Alirajpur, Rewa and Dhar districts been further disaggregated to help with ranks 50, 49, 48 and 47 respectively understand the main factors driving to make are the most vulnerable districts. Exposure districts vulnerable. to extreme events like number of

ii consecutive dry days, less rainy days and exposure to drought weeks and higher higher sensitivity to heat stress are the seasonal crop water stress. Situation contributors. marginally improves towards end- • Anuppur, Seoni, Shahdol, Chhindwara and century as exposure to drought weeks Umaria with ranks 1 to 5 in the same order, and seasonal crop water stress is located in eastern and southern Madhya projected to decrease. Pradesh are the least vulnerable districts. Forest Vulnerability • The overall climate vulnerability of the • Seven districts namely, , Dhar, Madhya Pradesh districts is projected to Barwani, Rajgarh, Morena, and increase towards mid-and end-century as with ranks 50 to 44 in the same order compared to the current conditions for are the most vulnerable districts. both emission scenarios of RCP4.5 and • Sehore, Khandwa (East Nimar), Umaria, RCP8.5. Dindori, Bhopal districts with ranks 1, 2, 3, 4 o Factors contributing to projected and 5 respectively are the least vulnerable increase in climate vulnerability of districts. districts include projected increase in o Due to high biological richness, high rainfall variability and higher sensitivity canopy cover and low disturbance to heat stress. index. Water Resource Vulnerability • The overall forest vulnerability is projected • Ten districts namely, Barwani, Dhar, to increase towards mid-century and end- Alirajpur, Bhind, Khargone (West Nimar), century under RCP4.5 and RCP8.5 climate Morena, Burhanpur, Mandsaur, Indore and scenarios as compared to current with ranks 50 to 41 in the same conditions. order are the most vulnerable districts. • Dry Teak Forests are the most vulnerable to o These districts are projected to have impact of climate change followed by higher drought frequency, lower Southern Dry Mixed Deciduous Forest and surface, ground water availability and Northern Dry Mixed Deciduous Forest in high crop water stress in south west both the scenarios. monsoon season. • About 50% of the total forested grids • Narsinghpur, Anuppur, Dindori, Umaria (mostly located in the Western part of the and Harda districts of Madhya Pradesh state) under high emission scenario with ranks 1, 2, 3, 4 and 5 are the least (RCP8.5) in the long-term period are vulnerable districts. extremely vulnerable to climate change. • The overall water resources vulnerability of Agriculture Vulnerability districts is projected to increase towards • Nine districts namely, Dindori, Anuppur, mid-century while decrease towards the Mandla, Balaghat, Umaria, Singrauli, end-century under RCP4.5 and RCP8.5 Shahdol, Sidhi and Katni with ranks 50 to climate scenarios as compared to current 42 in the given order are currently very conditions. high vulnerable districts. o Projected increase towards mid- o Main contributing factors include lower century is attributed to likely increase in food grains yield, irrigated area, iii fertilizer consumption, net sown area RCP8.5. Districts vulnerability under and higher wasteland. RCP8.5 scenario is projected to be lower as compared to RCP4.5 scenario. The • Districts Harda, Hoshangabad, Sheopur projected decrease in vulnerability and with ranks 1, 2, 3 and 4 are towards end-century is more than that of the least vulnerable. mid-century for both the climate • The overall agriculture vulnerability of the scenarios. Madhya Pradesh districts is projected to o Increase in yield for rice and soya bean decrease towards mid-and end-century as crops are the main contributing factors compared to the current conditions for in reducing the agriculture vulnera - both emission scenarios of RCP4.5 and bility.

iv Climate Change Vulnerability Assessment for Madhya Pradesh

Introduction north-west regions receive only about 1000 mm. Climate change vulnerability assessment of select sectors of water, forest and agriculture The Narmada and Tapti rivers and their basins has been taken up for the state of Madhya divide the state in two, with the northern part Pradesh. The main purpose of vulnerability draining largely into the Ganga basin and the assessment is to identify and prioritize the southern part into the Godavari and Mahanadi regions and sectors which are likely to be systems. The , Sone, Betwa, Mahanadi adversely impacted by climate change so as to and Indravati rivers flow from the western side enable development of adaptation practices of the state to the east, while Narmada and and strategies to help mainstream climate Tapti flows from the eastern side to the west. change in to the broader developmental Madhya Pradesh is rich in natural resources, programs and projects. minerals, agriculture and biodiversity. The Study Area Economy of Madhya Pradesh depends mainly on the agricultural sector as more than 80% of The State of Madhya Pradesh lies in the center the people of the state depend on this sector of India between latitude 21°04'N to 26.87°N for their livelihood. The agricultural sector and longitude 74°02' to 82°49' E. It borders the contributes around 46% to the state's states of to the north-east, economy. There are 11 agro-climatic Chhattisgarh to the south-east, Maharashtra to conditions and a variety of soils available in the the south, Gujarat to the west, and to state to support cultivation of a wide range of 1 the northwest. Its total area is 308,245 km² crops, including rice, pulses, wheat, oilseeds, Madhya Pradesh is the second-largest state in grams, soybeans, and maize. Madhya Pradesh the country by area and fifth-largest state in also gets its revenue from the forest products India by population. Its capital is Bhopal and sector as the state has a forest cover of around the largest city is Indore. 1.7 million hectares. Madhya Pradesh has sub-tropical climate with Purpose of Vulnerability Assessment hot-dry summer (April-June) followed by the monsoon (June-September) season. Winter in A vulnerability assessment is the process of Madhya Pradesh is cool and dry. Average identifying, quantifying, and prioritizing (or annual rainfall in Madhya Pradesh is about ranking) the vulnerabilities in a system. 1300 mm, which is more in the eastern part Assessment of vulnerability to climate than the western part due to the movement of variability and change broadly helps in: moisture from east to west. There is a high • Understanding current vulnerability. spatial variability in rainfall in MP and districts • Identify the factors that render some located in the south-west receives more rain regions more vulnerable than others. (around 2100 mm), while districts located in • Inform and facilitate the decision-making

1Madhya Pradesh wiki

1 process. vulnerability -very low (VL), low (L), • Selection of adaptation strategies and moderate (M), high (H), very high (VH), and practices. extremely high (EH). Basic objectives for climate change vulnera - Definition of Vulnerability to Climate Change bility assessment of Madhya Pradesh State The Intergovernmental Panel on Climate include: Change (IPCC) is the leading scientific • Generating the Madhya Pradesh district international body for the assessment of 2 level Composite Vulnerability Index (CVI) climate change. According to the IPCC (2007 ) developed using multivariate analysis of definition, vulnerability in the context of individual sectoral indicators (social, climate change is the degree to which a system economic, climate, water resources, forest is susceptible to, and unable to cope with, and agriculture) that are vulnerable to adverse effects of climate change, including climate change. climate variability and extremes. Vulnerability • Generating sectoral vulnerability indices - is a function of the character, magnitude, and Social Vulnerability Index (SVI), Economic rate of climate change and variation to which a Vulnerability Index (ECVI), Climate system is exposed, its sensitivity, and its Vulnerability Index (CLVI), Water Resources adaptive capacity. Vulnerability Index (WRVI), Forest IPCC Fifth Assessment Report defines Vulnerability Index (FOVI) and Agriculture vulnerability to climate change broadly as "The Vulnerability Index (AGVI) for the districts propensity or predisposition to be adversely of Madhya Pradesh. The indices would affected. Vulnerability encompasses a variety facilitate the identification of districts, of concepts including sensitivity or which are vulnerable to climate change susceptibility to harm and lack of capacity to 3” and need special attention towards cope and adapt . adaptation. Climate vulnerability is characterized as a • Developing current vulnerability profile at function of both biophysical and socio- district level due to the current climatic economic vulnerabilities, each defined by the conditions. three dimensions of exposure, sensitivity and adaptive capacity. When combined with • Developing projected vulnerability profile specific likelihood of occurrence (either towards mid-century (2021-2050) and associated with biophysical changes or socio- end-century (2071-2100) for projected economic variables), climate vulnerability climatic conditions using IPCC AR5 RCP4.5 becomes climate risk (Preston and Stafford- (moderate emission scenario) and RCP8.5 4 Smith, 2009 ). (high emission scenario) scenarios. Approach and Methodology • Grouping the districts according to their degree of vulnerability using cluster There are several methods for evaluating the analysis, under 6 categor ies of level of vulnerability. Selection of a particular

2 IPCC, 2007. Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Annex I., M.L. Parry, O.F. Canziani, J.P. Palutikof, P.J. van der Linden and C.E. Hanson, Eds., Cambridge University Press, Cambridge, UK, 976pp.

2 method is determined by the context, purpose aggregated at these respective levels. and scale of analysis as well as by the availability of appropriate data. A common Composite index provide a starting point for method to quantify vulnerability to climate analysis. While it can be used as summary index change is by using a set or composite of proxy to guide policy work, it can also be indicators. Indicator approach constructs decomposed such that the contribution of comparative indicators which helps to subcomponents and individual indices can be 7 compare the vulnerability and adaptive identified (OECD, 2008 ). Composite index is capacity of different systems, groups or regions getting increasingly acknowledged as a tool for (Adger, W.N. et al., 2004 5). summarizing complex and multidimensional issues (Rovan, 20118). How the concepts of The vulnerability studies based on ranking and exposure, sensitivity, adaptive capacity, and comparing across regions, countries, and vulnerability has been translated into populations have increased in number during numerical indices; which variables have been the past decade. The main objective of these used and how they have been aggregated to kinds of studies is the allocation of resources for construct Composite Vulnerability Indices (CVI) vulnerability reduction by including decision has been described as follows. making authorities like government bodies and other organizations (Dinda, Soumy Vulnerability has been assessed at the district ananda, 20156 ). level. The vulnerability analysis has been based on Composite Vulnerability Index (CVI) derived For the present study relative vulnerability using multi variate analysis. Figure 1 shows the approach is followed as it is useful when flowchart for constructing CVI using the comparing vulnerability at a regional scale. identified indicators from the seven sectors Here, the inter district comparison can be done considered. The IPCC working definition of as to which is more vulnerable compared to the vulnerability as a function of exposure, other. If absolute vulnerability is calculated, the sensitivity, and adaptive capacity has been emphasis is on areas with the greatest number used and identified indicators have been of vulnerable people, not necessarily the most categorized into adaptive capacity, sensitivity vulnerable. Comparison of vulnerability within and exposure. the districts is not possible here, since, the required data/information available is

3 https://www.ipcc.ch/pdf/assessment-report/ar5/syr/AR5_SYR_FINAL_Glossary.pdf 4 Preston, B.L. & Stafford-Smith, M. 2009. Framing vulnerability and adaptive capacity assessment: Discussion paper. CSIRO Climate Adaptation Flagship Working Paper, No.1, CSIRO, Australia. 5 Adger, W.N., Brooks, N., Bentham, G., Agnew, M., Eriksen, S., 2004. New Indicators of Vulnerability and Adaptive Capacity. Technical Report 7, Tyndall Centre for Climate Change Research, University of East Anglia, Norwich. 6 Dinda, Soumyananda, 2015, Handbook of Research on Climate Change Impact on Health and Environmental Sustainability, IGI Global 7 OECD. 2008. Handbook on constructing composite indicators: methodology and user guide. Paris, OECD Publishing. 8 Rovan, Joze, 2011, "Composite Indicators","International Encyclopedia of Statistical Science", Springer Berlin Heidelberg

3 Figure 1: Flowchart for construction of Composite Vulnerability Index

Spatial, temporal and sectoral scope focused in availability and ground water recharge) the study includes: o Forest (Changes in forest types, · Spatial Scope of Assessment: productivity and Biodiversity) o District o Agriculture and Livestock (major crops: · Temporal Scope of Assessment: rice, wheat, soya bean and chick pea) o Historical period: 1981-2010. Methodology o For future climate projection – Mid- The steps involved in the vulnerability century between 2021– 2050 (2030s), assessment of the districts of Madhya Pradesh end-century between 2071 – 2100 are: (2080s), for two RCP (4.5 and 8.5) • Step 1: Identify set of indicators scenarios of IPCC AR5 recommendation, • Step 2: To classify indicators data based on CMIP5 and CORDEX regional (indicators) into three categories of climate model outputs. adaptive capacity, sensitivity and · Sector scope of Assessment: exposure. o Social (demographic and infrastruc- • Step 3: Secondary data collection for the ture) indicators, projection of the selected o Economic (income and banks) indicators for future using impact o Climate (extreme temperature and assessment models (for agriculture, rainfall indices) climate, water resource) carried out as a o Water Resources (Surface water part of the same study

4 • Step 4: Normalize indicators data values based on the availability of data across time to make the indicators comparable and space (districts), literature research (GIZ, since indicators are expressed in a 201411, CRIDA, 201312), consultation with variety of statistical units, ranges or domain experts (climate, water resources and scales. agriculture) and experiences drawn from • Step 5: Determine unbiased weights previously carried out climate change using statistical method of multi variate vulnerability assessment for the Indian states of 13 analysis (Principal component analysis, Madhya Pradesh (2014 ), West Bengal, PCA). Chhattisgarh, Jharkhand, Uttarakhand and Madhya Pradesh. • Step 6: Aggregation of weights and normalized values to derive Composite A set of 72 indicators have been identified for Vulnerability Index and the six sectors the purpose. Details of the list of the identified Vulnerability Indices viz. social, indicators, their sources and time period along economic, climate, water resources, with their functional relationship with forest and agriculture. vulnerability are given in Table A- 1of Appendix • Step 7: Rank districts based on the I. The indicators have been grouped under six calculated index values. Rank 1: least sectoral categories viz., social (31 indicators), vulnerable (Lowest index values), Rank economic (5 indicators), climate (8 indicators), 50 most vulnerable (Highest index water resources (8indicators),forest (5indica- values) tors) and agriculture (15 indicators). Further, the indicators have been classified into • Step 8: Perform cluster analysis9 on the exposure indicators (8 indicators), sensitivity calculated indices to group them in six indicators (29 indicators) and adaptive capacity vulnerable categories - Very low (VL), indicators (35 indicators). Rationale for the low (L), Moderate (M), High (H), Very same is given in Table A- 2of Appendix I. high (VH), and Extremely high (EH). Data Sources • Step 9: Visualization of the results using spatial maps. Data for 72 identified indicators for the Selection of Indicators vulnerability analysis have been collected from different sources drawn mainly from secondary Indicators should be selected on the basis of sources and impact assessment models. their analytical soundness, measurability, Sources include: spatial coverage, relevance to the pheno - menon being measured and relationship to • Census of India: Primary Census Abstract - each other (OECD, 200810). Madhya Pradesh. The indicators for this study have been selected • Census of India: C Series - Madhya

9 Cluster analysis helps to sort through the raw data and groups them into clusters. A cluster is a group of relatively homogeneous cases. 10 OECD, European Commission, Joint Research Centre (2008) Handbook on constructing composite indicators: methodology and user guide. OECD Publishing. 11 A Framework for Climate Change Vulnerability Assessments, Published by Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH, India Project on Climate Change Adaptation in Rural Areas of India (CCA RAI), September 2014. 12 Rama Rao, C.A.et al., Atlas on vulnerability of Indian agriculture to climate change. Central Research Institute for Dryland Agriculture, Hyderabad, 2013, p. 116;available at http://www.nicra- icar.in/nicrarevised/images/publicaitons/Vulerability_Atlas_web.pdf.

5 Pradesh. • Administrative Reports of Department of • Census of India: House listing and Animal Husbandry, GoMP. Housing Census - Madhya Pradesh. • SWAT Hydrological model outputs, • Madhya Pradesh State MDG Report-2014- CORDEX climate data-RCP 4.5, RCP 8.5 15: UNICEF, NIPFP. (IITM, Pune). • Rural Health Statistics in India 2015: • Analysis of CORDEX climate data (IITM, Ministry of Health and Family Welfare, Pune) - RCP4.5, RCP8.5. Government of India. • Forest impact assessment model generat- • Department of Public Health & Family ed indicators, LPJ dynamic vegetation Welfare, Government of Madhya Pradesh. model outputs. • District Information System for Education- • InfoCrop agriculture crop modelling DISE. outputs, RCP4.5, RCP8.5. • Development Commissioner (MSME) , • DACNET (Department of Agriculture Ministry Of Micro, Small & Medium Cooperation)- Landuse Statistics Report. Enterprises , Government of India. Data Collection • Annual health survey, Census of India, • The indicators data for the vulnerability Madhya Pradesh. assessment of Madhya Pradesh districts has been collected from various sources. A • National Vector Borne Disease Control set of 72 indicators have been identified for Programme, Ministry of Health & Family the analysis of Madhya Pradesh. Social, Welfare. Economic, Climate, Water Resources, • Indicus Analytics. Forest and Agriculture sectors data have • Reserve Bank of India: Quarterly statistics been collected. on deposits and credit of scheduled • Out of the 72 indicators, 47 socio- commercial banks. economic and agriculture indicators data • IIRS, Dehradun. have been collected from the secondary • FSI, Dehradun. sources while the rest 25 indicators data • Open access digital elevation data. are from the impact assessment models mainly for sectors namely, water resources, • Department of Agriculture and Cooperati- climate, forest and agriculture. on, Agriculture Census Database. • Extreme climate indices using CORDEX • Department of Animal Husbandry, climate data-RCP 4.5, RCP 8.5, SWAT Dairying & Fisheries, 19th Livestock Hydrological model, Forest dynamic Census District Wise Report 2012, 2013. vegetation models-LPJ and Agriculture, • Madhya Pradesh State Livestock and Info Crop model have been used to derive Poultry Development Corporation. indicator data.

13 Vulnerability Assessment of Madhya Pradesh towards Climate Change, 2014, Ministry of Environment & Forests and GIZ on Climate Change Adaptation in Rural Areas of India, www.epco.in/pdfs/ClimateChange/Vulnerability_Assessment_of_MP.pdf.

6 • The outputs from impact assessment uniform for all the indicators. Name of few models for water resources, forest and districts also changed over the years so agriculture yields were in gridded time that also needs to be taken care of. Eg: West series format. These were further Nimar district was previously known as processed to arrive at the district level data. Khargone while East Nimar as Khandwa. • Data collection and cleaning roughly took • Most importantly the indicators to be used about two months’ time starting from in vulnerability assessment have to be October, 2016. Public domain data relative values and not the absolute values. available from internet search has been To make the data as relative values for inter done to collect and tabulate the data. district comparison usually it’s divided by • From the Census of India website population, geographical area or demographic data for social indicators like household size. density of population, growth rate, sex For the present study relative vulnerability ratio, literacy rate, gender gap in literacy approach is followed as it is useful when rate, child population, scheduled caste and comparing vulnerability at a regional or global scheduled tribes population, households scale. Here the inter district comparison can be with access to drinking water, households done as to which district is more vulnerable with access to sanitation, households with compared to the other. access to electricity, households with Limitations of Indicator Base Method access to transport and communication, Indicator based assessment for ranking and workers data- work participation rate, comparing vulnerability across spatial units gender gap, the marginal workers, (districts)have following challenges and agriculture and cultivators main workers limitations mainly because of subjectivity data have been collected. involved in: Data Cleaning and Quality Checking • Selection and creation of appropriate • Initial processing was done to see if all the indicators. 50 districts data is available for all the • Classification of indicators into exposure, indicators. Data at times was available for sensitivity and adaptive capacity. 48 districts which needed to be extrapolated to 50 districts by area • Under lying assumptions in weighting weighted method. variables. • Data for few indicators were only available • Dynamic nature of vulnerability which for 48 districts since Singrauli and Alirajpur requires a constant updating of vulnerab- have been added in 2008 in Madhya ility scores. Pradesh. Therefore for these districts same Strengths and Weaknesses of Principal data is used as the parent district from Component Analysis (PCA) which they are carved, i.e. Sidhi and Jhabua PCA is a statistics technical and uses respectively. orthogonal transformation to convert a set of • Merging data from various sources had observations of possibly correlated variables issues like non-matching district names into a set of values of linearly uncorrelated which needed to be corrected and made

7 variables. PCA also is a tool to reduce the projection limitations of the socio multidimensional data to lower dimensions economic sector variables. Thus only the while retaining most of the information14. current vulnerability would be shown for Limitations the socio economic. • If the observed variables are completely • Some of the agriculture sector variables unrelated, PCA analysis is unable to have also not been projected due to the produce a meaningful result. projection limitations. • The weights change whenever data is • As of 2016, Madhya Pradesh has 51 updated or indicators are added or districts; however data for majority of the removed. indicators are available for 50 districts only, since has been formed Limitations on Data on 16th August 2013 from the Shajapur • Selection of indicators used in this analysis district. Thus 50 districts would be is based on the availability of the data. considered for the vulnerability analysis of • Same time period data was not available Madhya Pradesh state. for every indicator chosen for this study. • Data for few indicators are available for 48 • Most of the data used have been compiled districts since Singrauli and Alirajpur have from secondary sources available in public been added in 2008 in Madhya Pradesh. domain. Therefore for these districts same data is • The social and economic vulnerability used as the parent district from which they indices for the districts have not been are carved, i.e., Sidhi and Jhabua projected for the future unlike the other respectively. sectors indices used in the analysis due to

14Abdi, H., & Williams, L.J. (2010). Principal component analysis. Wiley Interdisciplinary Reviews: Computational Statistics, 2, 433- 459.

8 Madhya Pradesh District Vulnerab-ility Current and Projected Vulnerability Profile Profiles Current Vulnerability Profile In the present study, an assessment of the Very high vulnerability: Eight districts namely, overall implications of climate change and Singrauli, Jhabua, Barwani, Panna, Morena, vulnerability in Madhya Pradesh for six major Sidhi, Rewa and Alirajpur (ranks 50, 49, 48, 47, sectors social, economic, climate, water 46, 45, 44 and 43 respectively) are the most resources, forest and agriculture has been vulnerable districts under current climate as carried out to identify the vulnerable sectors can be seen in brown colour in Figure 2. Main and regions (districts) to climate change. sectors contributing to Very high vulnerability Using indicator based methodology; districts of these districts include: of Madhya Pradesh have been classified into • Singrauli and Sidhi have high social, various vulnerability categories. It is seen that economic and agriculture vulnerability. socio - economic and environmental (biophy- • Jhabua and Rewa have high social, sical) indicators vary widely within the districts economic, climate, water resource and of Madhya Pradesh. Composite Vulnerability agriculture sector vulnerability. Index (CVI) has been constructed across the 50 • Barwani has high social, economic, climate, districts of Madhya Pradesh using identified water resource and forest sector indicators (Table A- 1, Appendix I). Districts are vulnerability. ranked based on the calculated index values. • Panna has high social, economic, climate Higher index value represents high and agriculture sector vulnerability. vulnerability while lower index value represents low vulnerability for the districts. A • Morena has high social, economic, climate, rank value 1 indicates that the district is least water resource, forest and agriculture vulnerable to climate change and rank value 50 sector vulnerability. All six sectors indicates that it is the most vulnerable. The contribute to its high vulnerability. Composite Vulnerability analysis and the • Alirajpur has high economic, climate and disaggregated vulnerability analysis for social, water resource sector vulnerability. economic, climate, water resources, forest and It is observed that socioeconomic sector agriculture sectors has been presented in the contributes to very high vulnerability for all the following paragraphs. The disaggregated districts. Singrauli with rank 50 is the most indices help the decision makers to prioritize vulnerable amongst all districts of Madhya the development activities in any chosen Pradesh due to poor sanitation facilities, access district by identifying the sector which makes to safe drinking water, electricity, permanent that district vulnerable. Tables showing the houses, low literacy rate, access to bank credit, district wise index values, ranks and MSME units, food grains yield, net sown area vulnerability category for current and and irrigated area while it has higher student projected vulnerability for Composite teacher ratio, wasteland, growth rate, age Vulnerability and sectors individually are given dependency ratio and heat stress. from Table A- 3 to Table A- 9 in Appendix I.

9 High Vulnerability: Seventeen districts namely, include lack of proper access to drinking water, Dindori, Ashoknagar, Shivpuri, Umaria, permanent houses, livestock population and Sheopur, Chhatarpur, Tikamgarh, Khargone schools while relatively higher sex ratio, below (West Nimar), Bhind, Damoh, Guna, Satna, poverty line population, death rate and Dhar, Katni, Sagar, Mandla and Rajgarh with drought frequency. Low vulnerability: Eleven ranks 42 to 26 in the given order fall under High districts namely, Hoshangabad, Harda, vulnerable category as can be seen in red Narsinghpur, Gwalior, , Neemuch, Ujjain, colour in Figure 2. Main sectors contributing to Sehore, Chhindwara, Anuppur and Raisen with these districts' High vulnerability in the CVI can ranks 4 to 14 in the same order fall under low be observed from Figure 3. vulnerability. Mainly social, economic and • Damoh, Dindori, Umaria, Katni: social, agriculture sectors contribute to the low economic and agriculture sectors. vulnerability of these districts. • Ashoknagar: social, economic Very Low vulnerability: Three districts of Madhya Pradesh namely, Bhopal, Jabalpur and • Shivpuri, Chhatarpur: social, economic and Indore districts with ranks 1, 2 and 3 water resource sectors. respectively are the least vulnerable districts. • Sheopur: social, climate, water resource Sectors contributing to very low vulnerability and forest sectors. of the districts as can be seen from Figure 3 are: • Tikamgarh: social, economic, climate, • Bhopal has low social, economic, forest and water resource and forest sectors. agriculture sectors vulnerability. • Khargone: economic, climate, water • Jabalpur has low social, economic, climate, resource and forest sectors. water resource and agriculture sectors • Bhind: economic, climate and water vulnerability. resource sectors. • Indore has low social, economic and • Guna: social and, water resource sectors. agriculture sectors vulnerability. • Satna: climate, water resource and It is observed that though they are the least agriculture sectors. vulnerable districts, Bhopal and Indore have • Dhar: climate, water resource and forest high water resource sector vulnerability. The sectors. main contributing factors to least vulnerability • Sagar: economic and agriculture sectors. of these districts include high literacy rate, • Mandla: social and agriculture sectors. better access to improved source of drinking water, sanitation, electricity, transport and • Rajgarh: water resource and forest sectors. infrastructure, high level of urbanization, per Moderate vulnerability: Eleven districts capita income, egg production per capita, namely, Shahdol, Burhanpur, Ratlam, Khandwa commercial banks while lower gender gap in (East Nimar), Shajapur, Balaghat, Betul, Seoni, literacy rate, biomass dependency, age Vidisha, Mandsaur and Datia fall under dependency ratio, agriculture labourers and moderate vulnerability. They are depicted in cultivators, infant mortality rate, birth rate, yellow colour in Figure 2. The main death rate and total fertility rate. contributing factors to their vulnerability

10 Figure 2: Current Composite Vulnerability map for Districts of Madhya Pradesh

11 Figure 3: Disaggregated sectoral vulner ability contributing to current composit e vulnerability for districts of Madhya Pradesh

12 Projected Vulnerability Profile current conditions as exposure to drought The overall projected Composite Vulnerability weeks and sensitivity to seasonal crop (CV) of the all districts of Madhya Pradesh is water stress is projected to decrease while projected to increase towards the mid-century yield for rice and soyabean crops are as compared to the baseline while decrease projected to increase. towards the end-century as compared to the • Dry Teak Forests are the most vulnerable to baseline for both moderate and high emission impact of climate change followed by scenarios of RCP4.5 and RCP8.5 respectively. Southern Dry Mixed Deciduous Forest and District vulnerability under RCP4.5 scenario is Northern Dry Mixed Deciduous Forest in projected to be higher as compared to RCP8.5 both the scenarios. scenario towards both mid and end-century. RCP4.5,Mid-century: Six districts are projected • The overall climate and forest vulnerability to move to higher vulnerability category from of the districts is projected to increase current lower vulnerability category towards towards mid-century and end-century as mid-century. Anuppur is projected to move to compared to the current conditions for Moderate from current Low vulnerability, both emission scenarios of RCP4.5 and Shahdol to move to High from current RCP8.5. Moderate vulnerability, Dindori, Shivpuri and Umaria districts to move to Very high from • The overall water resources vulnerability of current High vulnerability and district Singrauli districts is projected to increase towards which has Very high current vulnerability is mid-century while decrease towards the projected to exacerbate further and fall under end-century under RCP4.5 and RCP8.5 Extremely high vulnerability category (Figure climate scenarios as compared to current 4). Climate, water resource and forest sector conditions. vulnerability for these districts is also projected • The overall agriculture vulnerability of the to increase towards mid-century thus increase Madhya Pradesh districts is projected to in the CV. decrease towards mid-century and end- Vulnerability category of rest 44 districts is century as compared to the current projected to remain unchanged and is conditions for both emission scenarios. projected to have same current vulnerability Districts vulnerability under RCP8.5 category as baseline. However, their index scenario is projected to be lower as values are projected to increase implying compared to RCP4.5 scenario. The increase in degree of vulnerability as can be projected decrease in vulnerability seen from Table A- 3 in Appendix I. towards end-century is more than that of mid-century for both the climate RCP4.5,End-century: Vulnerability of four scenarios. districts is projected to decline under RCP4.5 scenario towards end-century as compared to • Exposure to rainfall variability, drought current vulnerability. These districts are weeks and sensitivity to heat stress and Alirajpur (Very high to High), Dhar and Rajgarh seasonal crop water stress are projected to (High to Moderate) and Hoshangabad (Low to increase towards the mid-century as Very low vulnerability category). This is so compared to current conditions thus because exposure to drought weeks, sensitivity contributing to increase in overall to seasonal crop water stress is projected to Composite Vulnerability (CV). CV improves decrease while seasonal surface water towards end-century as compared to availability is projected to increase.

13 RCP8.5, Mid-century: Three districts are rainfall variability, sensitivity to heat stress and projected to move to higher vulnerability climate change impact on dry teak forests. category from current lower vulnerability RCP8.5, End-century: Vulnerability of seven category towards mid-century. Shahdol is districts is projected to decline under RCP8.5 projected to move to High from current scenario towards end-century as compared to Moderate vulnerability, to current vulnerability as water resource and move to Very high from current High agriculture sector vulnerability is projected to vulnerability and district Singrauli, which has decline for these districts. These districts are Very high current vulnerability is projected to Alirajpur and Rewa (Very High to High), Dhar exacerbate further and fall under Extremely and Rajgarh (High to Moderate), Mandsaur high vulnerability category (Figure 4). This is (moderate to low) and Harda, Hoshangabad mainly due to projected increase in exposure to (Low to Very Low vulnerability category).

Figure 4: Projected Composite Vulnerability map for districts of Madhya Pradesh for IPCC AR5RCP4.5 and RCP8.5 scenarios

14 Disaggregated Sectoral Vulnerability followed by discussion on contributing factors Social Vulnerability to district social vulnerability category. Due to projection limitations of the socio Very high vulnerability: Seven districts namely, economic indicators, only current socio Singrauli, Umaria, Dindori, Sheopur, Jhabua, economic vulnerability has been derived. Panna and Sidhi with ranks 50 to 44

Figure 5: Social Vulnerability map for districts of Madhya Pradesh for current vulnerability

Spatial representation of social vulnerability respectively in the same order are currently the distribution for districts is depicted in Figure 5 most vulnerable districts.

15 Higher decadal growth rate of population and Economic Vulnerability biomass dependency, relatively low literacy Similar to social vulnerability, only current rate, poor sanitation facilities and access to economic vulnerability has been analyzed due transport and communication, low access to to limitation on long term projections of electricity and level of urbanization are the economic indicators. Spatial representation of factors contributing to higher vulnerability of economic vulnerability distribution for districts these districts under current conditions. is depicted in Figure 6. Singrauli also has the highest student-teacher Very high vulnerability: Nine districts namely, ratio. Alirajpur, Rewa, Singrauli, Panna, Damoh, High vulnerability: 11 districts namely, Bhind, Ashoknagar, Chhatarpur and Tikamgarh Ashoknagar, Shivpuri, Mandla, Tikamgarh, districts with ranks 50, 49, 48, 47, 46, 45, 44, 43 Barwani, Rewa, Guna, Morena, Damoh, and 42 respectively are the Most vulnerable Chhatarpur and Katni with ranks 43 to 33 districts. Major contributing economic respectively in the same order are Highly indicators include, low per capita income, vulnerable districts as can be seen in red colour households availing banking services and in Figure 5. access to total credit in banks. Thus low Moderate vulnerability: 20 districts namely adaptive capacity makes these districts most Alirajpur, Sagar, Shahdol, Rajgarh, Satna, vulnerable. Vidisha, Khargone (West Nimar), Bhind, High vulnerability: Fourteen districts namely, Khandwa (East Nimar), Raisen, Shajapur, Seoni, Shivpuri, Sidhi, Seoni, Burhanpur, Balaghat, Betul, Dhar, Ratlam, Sehore, Burhanpur, Barwani, Sagar, Jhabua, Katni, Umaria, Anuppur, Datia and Harda fall under Moderate Khargone (West Nimar), Dindori, Datia and vulnerability, shown in yellow colour in Figure Morena fall under High vulnerability category. 5. However, these are relatively less vulnerable Low vulnerability: Six districts namely Jabalpur, than the Very high vulnerable districts. The Gwalior, Ujjain, Neemuch, Dewas and location of these districts can be seen in red Hoshangabad fall under Low vulnerability colour in Figure 6. category. Moderate vulnerability: Fifteen districts Very low vulnerability: Bhopal, Indore, Jabalpur namely, Narsinghpur, Rajgarh, Mandsaur, and Gwalior with ranks 1, 2, 3 and 4 respectively Chhindwara, Guna, Anuppur, Satna, Dhar, are the least vulnerable districts. Low Shahdol, Neemuch, Vidisha, Sheopur, Ratlam, vulnerability of these districts is associated Mandla and Raisen fall under Moderate with their relatively high literacy rate and low vulnerability category. The location of these gender gap in the same, lower age districts can be seen in Figure 6 represented in dependency ratio, better access to improved yellow colour. source of drinking water, sanitation, electricity, transport and infrastructure, high level of Low vulnerability: Eleven districts namely, urbanization, low biomass dependency, Bhopal, Hoshangabad, Harda, Sehore, relatively low agriculture and cultivators main Jabalpur, Dewas, Shajapur, Gwalior, Khandwa workers and low death rate and infant (East Nimar), Ujjain and Betul with ranks 2 to 12 mortality rate. in the same order are Low vulnerable districts.

16 Very low vulnerability: Indore with rank 1 commercial banks are contributing to enhance belonging to Malwa Plateau region of Madhya the adaptive capacity of Indore to make its Pradesh is the least vulnerable district. Highest vulnerability Very low. per capita income and higher number of

Figure 6: Economic Vulnerability map for districts of Madhya Pradesh for current vulnerability

17 Extreme Climate Vulnerability Spatial representation of extreme climate vulnerability distribution for districts is depicted in Figure 7.

Figure 7: Extreme climate vulnerability map for districts of Madhya Pradesh under current and projected climate scenarios

Current/Baseline Vulnerability Gwalior, Harda, Sheopur, Khargone (West Very high vulnerability: Jhabua, Alirajpur, Rewa Nimar) and Tikamgarh fall under High and Dhar districts of Madhya Pradesh with vulnerability category but they are relatively ranks 50, 49, 48 and 47 respectively are the less vulnerable than the Very high vulnerable most vulnerable districts. Exposure to extreme districts. The location of these districts can be events like number of consecutive dry days, seen in red colour in Figure 7. less rainy days and higher sensitivity to heat Moderate vulnerability: Fifteen districts stress contributes to make these districts the namely, Mandsaur, Neemuch, Ratlam, most vulnerable. Chhatarpur, Indore, Guna, Sidhi, Khandwa (East High vulnerability: Eleven districts namely, Nimar), Shivpuri, Ashoknagar, Sagar, Bhopal, Barwani, Bhind, Datia, Morena, Panna, Satna, Raisen, Burhanpur and Singrauli fall under

18 Moderate vulnerability category. The location vulnerability is projected for 9 districts namely, of these districts can be seen in Figure 7 Balaghat, Chhatarpur, Dindori, Jabalpur, Katni, represented in yellow colour. Ratlam, Shajapur, Sidhi and Umaria. They are Low vulnerability: Fifteen districts namely, projected to fall under Moderate, High and Dindori, Betul, Mandla, Narsinghpur, Katni, Very high vulnerability category respectively Jabalpur, Shajapur, Balaghat, Dewas, Damoh, from current Very low, Low and Moderate Hoshangabad, Vidisha, Ujjain, Sehore and vulnerability category. Three folds increase in Rajgarh with ranks 6 to 20 in the same order fall vulnerability is projected only for .district under Low vulnerability category. Damoh which is likely to fall under Very high vulnerability category from Low vulnerability Very low vulnerability: Anuppur, Seoni, category. The remaining 8 districts are Shahdol, Chhindwara and Umaria with ranks 1 projected to remain in the same vulnerability to 5 in the same order, located in eastern and category as current vulnerability (Figure 7). southern Madhya Pradesh are the least Damoh is projected to have less rainfall and vulnerable districts. Higher annual rainfall, rainy days while higher variability in rainfall, rainy days and lower number of consecutive consecutive dry days and heat stress towards dry days relative to the other districts are the MC RCP4.5 scenario as compared to the BL thus main factors contributing to low vulnerability higher vulnerability. for these districts. RCP4.5,End-century: Nineteen districts Projected Vulnerability namely, Balaghat, Barwani, Bhind, Burhanpur, The overall climate vulnerability of the Madhya Chhatarpur, Chhindwara, Damoh, Datia, Pradesh districts is projected to increase Khandwa (East Nimar), Panna, Ratlam, Seoni, towards mid-century and end-century as Shahdol, Shajapur, Sidhi, Singrauli, Tikamgarh, compared to the current conditions for both Ujjain and Umaria are projected to shift emission scenarios of RCP4.5 and RCP8.5. towards higher levels of vulnerability as Districts vulnerability under RCP8.5 scenario is compared to current vulnerability. While five projected to be higher as compared to RCP4.5 districts namely, Bhopal, Gwalior, Harda, Raisen scenario. The projected increase in and Sheopur are projected to reduce their vulnerability towards end-century is higher vulnerability under RCP4.5 scenario towards than that of mid-century for RCP8.5 scenario end-century as compared to current while the projected increase in vulnerability vulnerability as rainfall and rainy days are towards end-century is relatively lower than projected to increase while consecutive dry that of mid-century for RCP4.5 scenario. Factors days are projected to decline for them (Figure contributing to projected increase in climate 7). vulnerability of districts include projected RCP8.5,Mid-century: Thirty seven districts are increase in rainfall variability and higher projected to shift towards higher levels of sensitivity to heat stress. vulnerability under RCP8.5 mid-century RCP4.5,Mid-century: Forty two districts are scenario as compared to current vulnerability. projected to shift towards higher levels of Two fold increase in vulnerability is projected vulnerability under RCP4.5 mid-century for 13 districts namely Balaghat, Chhatarpur, scenario as compared to current vulnerability. Damoh, Guna, Indore, Khandwa, Mandsaur, Out of 42 districts, two folds increase in Neemuch, Rajgarh, Ratlam, Ujjain, Umaria,

19 Vidisha. current Very low and Low vulnerability RCP8.5,End-century: Forty five districts are category. Dewas vulnerability is projected to projected to shift towards higher levels of exacerbate further as it moves from Very high vulnerability under RCP8.5 end-century to Extremely high vulnerability category. 5 scenario as compared to current vulnerability. districts namely Alirajpur, Dhar, Harda, Indore Out of 45 districts, three folds increase in and Jhabua are projected to remain in the same vulnerability is projected for 9 districts namely, vulnerability category as current vulnerability Balaghat, Damoh, Dindori, Jabalpur, Katni, (Figure 7). Mandla, Seoni, Shahdol and Umaria falling in Water Resource Vulnerability South Eastern part of the State. They are Spatial representation of water resource projected to fall under High and Very high vulnerability distribution for districts is vulnerability category respectively from depicted in Figure 8.

Figure 8: Water resour ce vulnerability map for districts of Madhya Pradesh under current and projected climate scenarios

20 Current/Baseline Vulnerability Projected Vulnerability Very high vulnerability: Ten districts namely The overall water resources vulnerability of Barwani, Dhar, Alirajpur, Bhind, Khargone Madhya Pradesh districts is projected to (West Nimar), Morena, Burhanpur, Mandsaur, increase towards mid-century while decrease Indore and Ratlam with ranks 50 to 41 in the towards the end-century under RCP4.5 and same order are the most vulnerable districts RCP8.5 climate scenarios as compared to They fall in the agro-climatic zones of Grid current conditions. Projected increase in water Region, Nimar Plains and Malwa Plateau of resources vulnerability towards mid-century is Madhya Pradesh. Major Contributing factors attributed to likely increase in exposure to include higher drought frequency, lower drought weeks and higher seasonal crop water surface, ground water availability and high stress. Situation marginally improves towards crop water stress in South West Monsoon end-century as exposure to drought weeks and season. seasonal crop water stress is projected to High vulnerability: Nineteen districts namely, decrease. Gwalior, Ujjain, Neemuch, Jhabua, Dewas, RCP4.5,Mid-century: Seventeen districts are Khandwa (East Nimar), Betul, Sheopur, projected to shift towards higher levels of Shivpuri, Rajgarh, Guna, Shajapur, Rewa, vulnerability under RCP4.5 mid-century Chhindwara, Chhatarpur, Satna, Tikamgarh, scenario as compared to current vulnerability. Bhopal and Hoshangabad fall under High District Katni is projected two folds increase vulnerability category, seen in red colour in from current Low vulnerability to fall under Figure 8. High vulnerability category. Bhind and Morena Moderate vulnerability: Nine districts namely, are projected to fall under Extremely high Sagar, Panna, Datia, Vidisha, Ashoknagar, vulnerability from current Very high vulner- Balaghat, Sehore, Seoni and Sidhifall under ability category, Gwalior, Neemuch, Sheopur, Moderate vulnerability. The location of these Shivpuri, Tikamgarh and Ujjain to fall under districts can be seen in Figure 8 represented in Very high from current High and Datia, Panna, yellow colour. Low vulnerability: 7 districts Sagar and Vidisha districts to fall under High namely, Mandla, Jabalpur, Shahdol, Raisen, vulnerability from current Moderate category. Singrauli, Damoh and Katni with ranks 6 to 12 in Similarly, Damoh and Shahdol are likely to fall the same order are the low vulnerable districts. under moderate vulnerability category from Very low vulnerability: Narsinghpur, Anuppur, current Low vulnerability category while Dindori, Umaria and Harda districts of Madhya Dindori and Umaria are likely to fall under low Pradesh with ranks 1, 2, 3, 4 and 5 are the least vulnerability category from current Very low vulnerable districts. Very low vulnerability is vulnerability category Districts Hoshangabad attributed to highest seasonal per capita water and Indore being an exception are expected to availability and lower seasonal crop water decline their vulnerability marginally. The stress in South West Monsoon season as remaining 31 districts are projected to remain compared to other districts. in the same vulnerability category as current vulnerability (Figure 8).

21 RCP4.5,End-century: A mixed response to District Sagar being an exception is expected to climate change is projected with seven districts increase its vulnerability marginally. showing decrease in vulnerability while 4 Forest Vulnerability districts showed an increase in vulnerability Spatial representation of forest vulnerability under RCP4.5 scenario towards end-century as distribution for districts is depicted in Figure 9. compared to current vulnerability. Districts of Current/Baseline Vulnerability Alirajpur, Hoshangabad, Indore, Jhabua, Khargone (West Nimar), Ratlam and Sidhi Very high vulnerability: Seven districts namely which are currently under Moderate, High and Shajapur, Dhar, Barwani, Rajgarh, Morena, Very high vulnerability category are likely to Mandsaur and Ujjain with ranks 50 to 44 in the move to Low, Moderate and High vulnerability same order are the most vulnerable districts, category respectively. Districts Damoh, thus they should be on high priority. They are Gwalior, Katni and Sagar are expected to located in the Malwa Plateau zone excepting increase their vulnerability (Figure 8). which belongs to the Grid Zone RCP8.5,Mid-century: Vulnerability of fourteen of Madhya Pradesh. districts is projected to increase under RCP8.5 High vulnerability: Nine districts namely, scenario towards mid-century as compared to Neemuch, Ratlam, Khargone (West Nimar), current vulnerability. Districts of Bhind, Gwalior, Tikamgarh, Hoshangabad, Dewas, Damoh, Datia, Gwalior, Katni, Morena, Narsinghpur and Sheopur fall under High Neemuch, Panna, Sagar, Shahdol, Sheopur, vulnerability category, seen in red colour in Shivpuri, Umaria and Vidisha are likely to move Figure 9. to higher vulnerability category respectively as Moderate vulnerability: Twenty two districts as compared to current vulnerability. District can be seen in Figure 9 represented in yellow Indore being an exception is expected to colour fall under Moderate vulnerability. decline its vulnerability marginally (Figure 8). Low vulnerability: Satna, Anuppur, Burhanpur, RCP8.5,End-century:Vulnerability of twenty Rewa, Shahdol, Raisen and Mandla districts one districts is projected to decline under with ranks 6 to 12 in the same order are the Low RCP8.5 scenario towards end-century as vulnerable districts. compared to current vulnerability. These districts include, Alirajpur, Burhanpur, Dhar, Very low vulnerability: Sehore, Khandwa (East Indore, Khargone (West Nimar), Morena and Nimar), Umaria, Dindori, Bhopal districts with Ratlam (very high to high), Betul, Bhopal, ranks 1, 2, 3, 4 and 5 respectively are the least Chhatarpur, Chhindwara, Hoshangabad, vulnerable districts. Very low vulnerability is 15 Jhabua, Rewa, Satna and Tikamgarh (high to attributed to high biological richness , high moderate) and Datia, Panna, Sehore, Seoni, canopy cover and low disturbance index Sidhi (Moderate to Low vulnerability category). compared to other districts.

15 It is a composite indicator consisting of four parameters namely, species richness (SR), ecosystem uniqueness (EQ), terrain complexity (TC), biodiversity value (BV).

22 Figure 9: Forest vulnerability map for districts of Madhya Pradesh under curr ent and projected climate scenarios

Projected Vulnerability scenario (RCP4.5) the forests in the districts in The overall forest vulnerability of Madhya central region of the state are extremely Pradesh districts is projected to increase vulnerable in the MC (about 20% of the total towards mid-century and end-century under forested grids). While the forests in the district RCP4.5 and RCP8.5 climate scenarios as of Bhopal are least vulnerable, forests in compared to current conditions. Dry teak districts of Bhind, Datia and Gwalior are forests are the most vulnerable to impact of extremely vulnerable to climate change climate change followed by Southern dry impacts under RCP4.5 mid-century scenario. mixed deciduous forest and Northern dry The forests in the districts of Raisen, Shahdol, mixed deciduous forest in both the scenarios. Gwalior, Khandwa (East Nimar), Hoshangabad RCP4.5, Mid-century: Under low emission and Sehore are Extremely vulnerable (Figure 9).

23 RCP4.5, End-century: Under RCP4.5 about 35% Agriculture Vulnerability of the total forested grids of the state is Spatial representation of agriculture extremely vulnerable in the EC. While the vulnerability distribution for districts is forests in the district of Dindori are least depicted in Figure 10. vulnerable to climate change, forests in 17 Current/Baseline Vulnerability districts namely, Barwani, Bhind, Bhopal, Burhanpur, Datia, Dewas, Dhar, Khandwa, Very high vulnerability: 9 districts namely, Gwalior, Harda, Indore, Jhabua, Rajgarh, Dindori, Anuppur, Mandla, Balaghat, Umaria, Sehore, Shajapur, Ujjain, Khargone will be Singrauli, Shahdol, Sidhi and Katni with ranks impacted by climate change under RCP4.5 50 to 42 in the given order are currently Very end-century scenario. high vulnerable districts. The districts are located in the Eastern and South Eastern The forests in the districts of Raisen, Khandwa regions of Madhya Pradesh. Main contributing (East Nimar), Dewas and Khargone (West factors include food grains yield, irrigated area, Nimar) are Extremely vulnerable (Figure 9). wasteland, fertilizer consumption, net sown RCP8.5, Mid-century: Under high emission area and low rice and soya bean yield. scenario (RCP8.5) about 23% of the total forested grids of the state are extremely High vulnerability: Eight districts namely, vulnerable in the MC. The forests in district of Satna, Panna, Rewa, Damoh, Morena, Sagar, Sehore are least vulnerable to climate change Jhabua and Seoni fall under High vulnerability under RCP8.5 mid-century scenario, but eight category. Moderate vulnerability: Districts districts namely, Bhind, Burhanpur, Datia, namely Betul, Ashoknagar, Barwani, Dewas, East Nimar, Gwalior, Harda and Indore Chhatarpur, Narsinghpur, Guna, Khandwa, are going to be impacted by climate change. Khargone and Shivpuri have Moderate vulnerability. The forests in the districts of Khandwa (East Nimar), Dewas, Raisen, Khargone (West Nimar) Low vulnerability: Sixteen districts namely, and Gwalior are Extremely vulnerable (Figure Mandsaur, Shajapur, Datia, Gwalior, Ratlam, 9). Ujjain, Rajgarh, Dewas, Bhind, Indore, Dhar, RCP8.5, End-century: Under RCP8.5 the forests Shivpuri, Sehore, Raisen, Guna and Ashoknagar in the districts on the Western (including the located in north and western regions of North West and South West) region of the state Madhya Pradesh fall under Low vulnerability are Extremely vulnerable in the EC (about 50% category as can be seen in green colour in of the total forested grids). While the forests in Figure 10. the district of Dindori are least vulnerable to Very low vulnerability: Districts Harda, climate change, forests in 24 districts will be Hoshangabad, Sheopur and Neemuch with impacted by climate change under RCP8.5 ranks 1, 2, 3 and 4 are the least vulnerable. High end-century scenario. food grains yield, higher proportion of irrigated The forests in the districts of Sheopur, Shivpuri, area to net sown area, high fertilizer Raisen, Khandwa (East Nimar), Dewas and consumption and milk production per capita Khargone (West Nimar) are extremely contribute to render these districts least vulnerable (Figure 9). vulnerable.

24 Figure 10: Agriculture vulnerability map for districts of Madhya Pradesh under current and projected climate scenarios

Projected Vulnerability future. The overall agriculture vulnerability of the RCP4.5,Mid-century: Four districts are Madhya Pradesh districts is projected to projected to shift towards lower levels of decrease towards mid-century and end- vulnerability under RCP4.5 mid-century century as compared to the current conditions scenario as compared to current vulnerability. for both emission scenarios of RCP4.5 and Jabalpur from Low to Very low, Khargone from RCP8.5. Districts vulnerability under RCP8.5 Moderate to Low, Seoni from High to Moderate scenario is projected to be lower as compared vulnerability and Katni from Very high to High to RCP4.5 scenario. The projected decrease in vulnerability. District Indore being an vulnerability towards end-century is more than exception is expected to increase its that of mid-century for both the climate vulnerability marginally. The remaining 45 scenarios. Increase in yield for rice and soya districts are projected to remain in the same bean crops are the main factors contributing to vulnerability category as current vulnerability the decline in agriculture vulnerability in the (Figure 10).

25 RCP4.5, End-century: Twenty three districts (4 expected two folds decline in vulnerability as districts are same as those in MC) are projected compared to the current period (Figure 10). to shift towards lower levels of vulnerability Summary - District Vulnerability under RCP4.5 end-century scenario as Vulnerability has been assessed at the district compared to current vulnerability. The level. The vulnerability analysis has been based additional 19 districts in comparison to MC on Composite Vulnerability Index (CVI) derived whose vulnerability declines are Ashoknagar, using multi variate analysis for current and Balaghat, Barwani, Betul, Bhopal, Chhatarpur, projected climate (under RCP4.5 and RCP8.5 Damoh, Guna, Jhabua, Khandwa (East Nimar), climate scenario towards mid-century and Morena, Narsinghpur, Sagar, Shahdol, Shivpuri, end-century). The IPCC working definition of Sidhi, Singrauli, Umaria and Vidisha (Figure 10). vulnerability as a function of exposure, sensitivity, and adaptive capacity has been RCP8.5, Mid-century: Fifteen districts are used and identified 72 indicators have been projected to shift towards lower levels of categorized into adaptive capacity, sensitivity vulnerability under RCP8.5 mid-century and exposure. Accordingly six sectoral scenario as compared to current vulnerability vulnerability indices for social, economic, (Figure 10). climate, water resources, forest and agriculture RCP8.5, End-century: Thirty six districts are have also been generated. The indices would projected to shift towards lower levels of facilitate the identification of districts, which vulnerability under RCP8.5 end-century are vulnerable to climate change and need scenario as compared to current vulnerability. special attention towards adaptation. Table 1 Amongst the 36 districts, 3 districts namely, gives the overall summary of the vulnerability Katni, Sidhi (very high to moderate of Madhya Pradesh districts for the current vulnerability) and Sagar (high to low) are period.

26

Table 1: District current composite vulnerability along with disaggregated sub components for districts of Madhya Pradesh

Districts Rank CV SV ECV CLV WRV FOV AGV Districts Rank CV SV ECV CLV WRV FOV A GV

Bhopal 1 VL VL L M H VL L Rajgarh 26 H M M L H VH L

Jabalpur 2 VL VL L L L M L Mandla 27 H H M L L L VH

Indore 3 VL VL VL M VH M L Dhar 28 H M M VH VH VH L

Hoshangabad 4 L L L L H H VL Katni 28 H H H L L M VH

Harda 5 L M L H VL M VL Sagar 28 H M H M M M H

Narsinghpur 6 L L M L VL H M Satna 31 H M M H H L H

Gwalior 7 L VL L H H H L Guna 32 H H M M H M M

Dewas 8 L L L L H H L Bhind 33 H M VH H VH M L

Neemuch 9 L L M M H H VL Damoh 33 H H VH L L M H

Ujjain 10 L L L L H VH L Khargone 35 H M H H VH H M

Sehore 11 L M L L M VL L Chhatarpur 36 H H VH M H M M

Chhindwara 12 L L M VL H M L Tikamgarh 36 H H VH H H H L

Anuppur 13 L M M VL VL L VH Sheopur 38 H VH M H H H VL

Raisen 14 L M M M L L L Umaria 39 H VH H VL VL VL VH

Datia 15 M M H H M M L Shivpuri 40 H H H M H M M

Mandsaur 16 M L M M VH VH L Ashoknagar 41 H H VH M M M M

Seoni 17 M M H VL M M H Dindori 42 H VH H L VL VL VH

Vidisha 17 M M M L M M L Alirajpur 43 VH M VH VH VH M L

Betul 19 M M L L H M M Rewa 44 VH H VH VH H L H Balaghat 20 M L H L M M VH Sidhi 45 VH VH H M M M VH

27

Districts Rank CV SV ECV CLV WRV FOV AGV Districts Rank CV SV ECV CLV WRV FOV AGV Shajapur 21 M M L L H VH L Morena 46 VH H H H VH VH H Khandwa 22 M M L M H VL M Panna 47 VH VH VH H M M H Ratlam 23 M M M M VH H L Barwani 48 VH H H H VH VH M

Burhanpur 24 M M H M VH L L Jhabua 49 VH VH H VH H M H Shahdol 25 M M M VL L L VH Singrauli 50 VH VH VH M L M VH

SV: Social Vulnerability, ECV: Economic Vulnerability, CLV: Climate Vulnerability, WRV: Water Resources Vulnerability, AGV: Agriculture Vulnerability VL: Very Low, L: Low, M: Moderate, H: High, VH: Very High, EH: Extremely High

28 Sectors which contribute to make a district fall MSME units, food grains yield, net sown under a specific vulnerability category can be area and irrigated area while it has identified from Table 1, for example: Singrauli higher student teacher ratio, wastel - falls under very high vulnerability category and, growth rate, age dependency ratio (CVI) as vulnerability is high in social, economic and heat stress. and agriculture sectors. Though it is complex, • Seventeen districts namely, Dindori, one can broadly draw similar inferences for Ashoknagar, Shivpuri, Umaria, Sheopur, other districts as well. Chhatarpur, Tikamgarh, Khargone (West Summar y of the district composite Nimar), Bhind, Damoh, Guna, Satna, Dhar, vulnerability analysis is as follows: Katni, Sagar, Mandla and Rajgarh with • Eight districts namely, Singrauli, Jhabua, ranks 42 to 26 in the given order fall under Barwani, Panna, Morena, Sidhi, Rewa and High vulnerable category Alirajpur with ranks 50, 49, 48, 47, 46, 45, 44 • Three districts namely, Bhopal, Jabalpur and 43 respectively are the most and Indore districts with ranks 1, 2 and 3 vulnerable districts under current climate. respectively are the least vulnerable Main sectors contributing to districts districts. vulnerability in the CVI are: o The main contributing factors include o Singrauli and Sidhi have high social, high literacy rate, better access to economic and agriculture vulnerability. improved source of drinking water, o Jhabua and Rewa have high social, sanitation, electricity, transport and economic, climate, water resource and infrastructure, high level of urbanizati- agriculture sector vulnerability. on, per capita income, egg production per capita, commercial banks while o Barwani has high social, economic, lower gender gap in literacy rate, climate, water resources and forest biomass dependency, age dependency sector vulnerability. ratio, agriculture labourers and o Panna has high social, economic, cultivators, infant mortality rate, birth climate and agriculture sector vulnera- rate, death rate and total fertility rate. bility. Projected Composite Vulnerability o Morena has high social, economic, climate, water resource, forest and The overall Composite Vulnerability (CV) of the Madhya Pradesh districts is projected to agriculture sectors vulnerability. All six increase towards the mid-century as compared sectors contribute to its high vulnera- to the baseline while decrease towards the bility. end-century as compared to the baseline for o Alirajpur has high economic, climate both moderate and high emission scenarios of and water resource sector vulnerability. RCP4.5 and RCP8.5 respectively. District o Singrauli with rank 50 is the most vulnerability under RCP4.5 scenario is vulnerable amongst all districts of projected to be higher as compared to RCP8.5 Madhya Pradesh due to poor sanitation scenario towards both mid and end-century. facilities, access to safe drinking water, Table 2 gives the overall summary of the electricity, permanent houses, low current and projected composite vulnerability literacy rate, access to bank credit, of Madhya Pradesh districts.

29 • The overall climate and forest vulnerability towards end-century is more than that of of the districts is projected to increase mid-century for both the climate towards mid-century and end-century as scenarios. compared to the current conditions for • Exposure to rainfall variability, drought both emission scenarios of RCP4.5 and weeks and sensitivity to heat stress and RCP8.5. seasonal crop water stress are projected to • The overall water resources vulnerability of increase towards the mid-century as districts is projected to increase towards compared to current conditions thus mid-century while decrease towards the contributing to increase in overall end-century under RCP4.5 and RCP8.5 Composite Vulnerability (CV). CV improves climate scenarios as compared to current towards end-century as compared to conditions. current conditions as exposure to drought weeks and sensitivity to seasonal crop • The overall agriculture vulnerability of the Madhya Pradesh districts is projected to water stress is projected to decrease while decrease towards mid-century and end- yield for rice and soyabean crops are century as compared to the current projected to increase. conditions for both emission scenarios. • Dry Teak Forests are the most vulnerable to Districts vulnerability under RCP8.5 impact of climate change followed by scenario is projected to be lower as Southern Dry Mixed Deciduous Forest and compared to RCP4.5 scenario. The Northern Dry Mixed Deciduous Forest in projected decrease in vulnerability both the scenarios.

Table 2: Current and projected composite vulnerability for districts of Madhya Pradesh

RCP4.5 RCP8.5 Baseline Districts Mid- End- Mid- End- Rank Baseline Century Century Century Century Bhopal 1 VL VL VL VL VL Jabalpur 2 VL VL VL VL VL Indore 3 VL VL VL VL VL Hoshangabad 4 L L VL L VL Harda 5 L L L L VL Narsinghpur 6 L L L L L Gwalior 7 L L L L L Dewas 8 L L L L L Neemuch 9 L L L L L Ujjain 10 L L L L L Sehore 11 L L L L L Chhindwara 12 L L L L L Anuppur 13 L M L L L Raisen 14 L L L L L Datia 15 M M M M M Mandsaur 16 M M M M L

30 RCP4.5 RCP8.5 Baseline Districts Mid- End- Mid- End- Rank Baseline Century Century Century Century Seoni 17 M M M M M Vidisha 17 M M M M M Betul 19 M M M M M Balaghat 20 M M M M M Shajapur 21 M M M M M Khandwa 22 M M M M M Ratlam 23 M M M M M Burhanpur 24 M M M M M Shahdol 25 M H M H M Rajgarh 26 H H M H M Mandla 27 H H H H H Dhar 28 H H M H M Katni 28 H H H H H Sagar 28 H H H H H Satna 31 H H H H H Guna 32 H H H H H Bhind 33 H H H H H Damoh 33 H H H H H Khargone 35 H H H H H Chhatarpur 36 H H H H H Tikamgarh 36 H H H H H Sheopur 38 H H H H H Umaria 39 H VH H H H Shivpuri 40 H VH H H H Ashoknagar 41 H H H H H Dindori 42 H VH H VH VH Alirajpur 43 VH VH H VH H Rewa 44 VH VH VH VH H Sidhi 45 VH VH VH VH VH Morena 46 VH VH VH VH VH Panna 47 VH VH VH VH VH Barwani 48 VH VH VH VH VH Jhabua 49 VH VH VH VH VH Singrauli 50 VH EH VH EH VH

31 Each sector considered in the analysis Singrauli, Panna, Damoh, Bhind, Ashok contributes differently to the aggregated nagar, Chhatarpur and Tikamgarh districts composite vulnerability index and district with ranks 50,49, 48, 47, 46, 45, 44, 43 and rankings. The decomposition of the composite 42 respectively are the most vulnerable vulnerability index can shed light on main districts. factors contributing to overall vulnerability of a o Major contributing indicators include, particular district. Each sector used in CVI has low per capita income, households been further disaggregated to help availing banking services and access understand the main factors driving to make to total credit in banks. districts vulnerable. • Indore with rank 1 belonging to Malwa Summary of disaggregated sectoral vulnera- Plateau region of Madhya Pradesh is the bility for social, economic, climate, water least vulnerable district as it has highest resources, forest and agriculture is given per capita income and higher number of below: commercial banks. Social Vulnerability Climate Extremes Vulnerability • 7 districts namely, Singrauli, Umaria, • Jhabua, Alirajpur, Rewa and Dhar districts Dindori, Sheopur, Jhabua, Panna and with ranks 50, 49, 48 and 47 respectively Sidhiwith ranks 50 to 44 respectively in the are the most vulnerable districts. Exposure same order are currently the most to extreme events like number of vulnerable districts. consecutive dry days, less rainy days and o Higher decadal growth rate of higher sensitivity to heat stress are the population and biomass depende- contributors. ncy, relatively low literacy rate, poor • Anuppur, Seoni, Shahdol, Chhindwara and sanitation facilities and access to Umaria with ranks 1 to 5 in the same order, transport and communication, low located in eastern and southern Madhya access to electricity and level of Pradesh are the least vulnerable districts. urbanization are the factors contribu- • The overall climate vulnerability of the ting to higher vulnerability of these Madhya Pradesh districts is projected to districts under current conditions. increase towards mid-century and end- Singrauli also has the highest century as compared to the current student-teacher ratio. conditions for both emission scenarios of • Bhopal, Indore, Jabalpur and Gwalior with RCP4.5 and RCP8.5. ranks 1, 2, 3 and 4 respectively are the least o Factors contributing to projected vulnerable districts. increase in climate vulnerability of districts include projected increase in Economic Vulnerability rainfall variability and higher sensitiv- • Nine districts namely, Alirajpur, Rewa, ity to heat stress.

32 Water Resource Vulnerability Dindori, Bhopal districts with ranks 1, 2, 3, 4 • Ten districts namely, Barwani, Dhar, and 5 respectively are the least vulnerable Alirajpur, Bhind, Khargone (West Nimar), districts. Morena, Burhanpur, Mandsaur, Indore and o Due to high biological richness16, high Ratlam with ranks 50 to 41 in the same canopy cover and low disturbance order are the most vulnerable districts. index. o As they have higher drought • The overall forest vulnerability is projected frequency, lower surface, ground to increase towards mid-century and end- water availability and high crop water century under RCP4.5 and RCP8.5 climate stress in South West Monsoon season. scenarios as compared to current • Narsimhapur, Anuppur, Dindori, Umaria conditions. and Harda districts of Madhya Pradesh o Dry Teak Forests are the most with ranks 1, 2, 3, 4 and 5 are the least vulnerable to impact of climate vulnerable districts. change followed by Southern Dry • The overall water resources vulnerability of Mixed Deciduous Forest and Madhya Pradesh districts is projected to Northern Dry Mixed Deciduous increase towards mid-century while Forest in both the scenarios. decrease towards the end-century under • About 50% of the total forested grids RCP4.5 and RCP8.5 climate scenarios as (mostly located in the Western part of the compared to current conditions. state) under high emission scenario (RCP8.5) in the long-term period are o Projected increase in vulnerability extremely vulnerable to climate change. towards mid-century is attributed to likely increase in exposure to drought The present assessment shows that with an weeks and higher seasonal crop water average inherent vulnerability index (IVI) value stress. Situation marginally improves of 2.204 at district level, the inherent towards end-century as exposure to vulnerability of forests in MP is closer to the drought weeks and seasonal crop median of the range (between 1 and 4 water stress is projected to decrease. representing lowest and highest inherent vulnerability). Addressing the drivers of Forest Vulnerability inherent vulnerability in the priority districts • Seven districts namely, Shajapur, Dhar, would reduce forest vulnerability and enhance Barwani, Rajgarh, Morena, Mandsaur and their adaptive capacity under future stresses Ujjain with ranks 50 to 44 in the same order including climate change. It is necessary to are the most vulnerable districts. assess forest vulnerability at local as well as • Sehore, Khandwa (East Nimar), Umaria, larger scales concurrently.

16 It is a composite indicator consisting of five parameters namely, species richness (SR), ecosystem uniqueness (EQ), terrain complexity (TC), biodiversity value (BV).

33 Agriculture Vulnerability • The overall agriculture vulnerability of the • 9 districts namely, Dindori, Anuppur, Madhya Pradesh districts is projected to Mandla, Balaghat, Umaria, Singrauli, decrease towards mid-and end-century as Shahdol, Sidhi and Katni with ranks 50 to compared to the current conditions for 42 in the given order are currently very both emission scenarios of RCP4.5 and high vulnerable districts. RCP8.5. Districts vulnerability under o Main contributing factors include RCP8.5 scenario is projected to be lower as food grains yield, irrigated area, compared to RCP4.5 scenario. The wasteland, fertilizer consumption, projected decrease in vulnerability net sown area and low rice and soya towards end-century is more than that of bean yield. mid-century for both the climate scenarios. • Districts Harda, Hoshangabad, Sheopur and Neemuch with ranks 1, 2, 3 and 4 are o Increase in yield for rice and soya bean the least vulnerable. crops are the main factors .

34

Appendix I

List of Indicators for District Vulnerability Assessment

Table A- 1: Sectors and the associated indicators considered for climate change vulnerability assessment for districts of Madhya Pradesh

No Indicators Abbr Unit Functional Conceptual Data Source Time

eviati Relationship Basis Period

on with

vulnerability

Social

1 Density of Population DP Persons/Sq. Km Increase Sensitivity Census of India: Primary Census Abstract - 2011

Madhya Pradesh

2 Decadal growth rate of Population GR Percentage Increase Sensitivity Census of India: Madhya Pradesh 2011

3 Sex-ratio SR No of Increase Sensitivity Census of India: Primary Census Abstract - 2011

females/1000 Madhya Pradesh

males

4 Literacy Rate LR Perc entage Decrease Adaptive

Capacity

5 Gender gap in literacy rate GGLR Percentage Increase Sensitiv ity

6 Age Dependency ratio ADR Percentage Increase Sensitivity Census of India: C Series - Madhya Pradesh 2011

7 Child Population in the age group 0-6 CP Percentage Increase Sensitivity Census of India: Primary Census Abstract - 2011

Madhya Pradesh

8 Households with access to improved source of DW Percentage Decrease Adaptive Census of India: House listing and Housing 2011

drinking water (tap water, covered well, hand Capacity Census - Madhya Pradesh

pump, tube well)

9 Households having access to sanitation facility SF Percentage Decrease Adaptive

within the premises Capacity

10 Households having electricity as main source of EL Percentag e Decrease Adaptive

lighting Capacity 11 Households still dependent on biomass as fuel BM Percentage Increase Sensitivity

for cooking 12 Households living in Permanent houses PH Percentage Decrease Adaptive

35 No Indicators Abbr Unit Functional Conceptual Data Source Time eviati Relationship Basis Period on with vulnerability Capacity 13 Households with access to COM Percentage Decrease Adaptive communication/transport TR Capacity 14 Population Below Poverty Line BPL Percentage Increase Sensitivity Madhya Pradesh State MDG Report- 2011- 2014-15: UNICEF, NIPFP 12 15 Share of Marginal Workers MGW Percentage Increase Sensitivity Census of India: Primary Census 2011 16 Agricultural And Cultivators to Main Workers ACM Percentage Increase Sensitivity Abstract - Madhya Pradesh 2011 W 17 Total work participation rate TWPR Percentage Decrease Adaptive 2011 Capacity 18 Gender gap in work participation rate GWP Percentage Increase Adaptive 2011 R Capacity 19 Health Centres (Primary Health Centres, HC Number/Lakh Decrease Adaptive Rural Health Statistics in India 2015: 2015 Community Health Centres, Sub Centres, Sub of population Capacity Ministry of Health and Family Welfare, divisional and District hospitals) Government of India 20 Beds available in health institutions BHC Number/Lakh Decrease Adaptive Department of Public Health & Family 2009 of population Capacity Welfare, Government of Madhya Pradesh 21 Schools (primary/pre-primary junior basic EI Number/Lakh Decrease Adaptive District Information System for 2014- schools, high schools and higher secondary of population Capacity Education-DISE 15 schools) 22 Student Teacher Ratio ST Number Increase Sensitivity District Information System for 2014- Education-DISE 15 23 Level of urbanization UR Percentage Decrease Adaptive Census of India: Primary Census Abstract - 2011 Capacity Madhya Pradesh 24 Schedule Tribes and Scheduled Caste STSC Percentage Increase Sensitivity Census of India: Primary Census 2011 population Abstract - Madhya Pradesh 25 Road length RDEN Per 100 sq. km Decrease Adaptive Development Commissioner (MSME), 2015 Capacity Ministry Of Micro, Small & Medium Enterprises, Government of India 26 Crude Birth Rate CBR Percentage Increase Sensitivity Annual health survey, Census of India, 2012-

36 No Indicators Abbr Unit Functional Conceptual Data Source Time eviati Relationship Basis Period on with vulnerability 27 Crude Death Rate CDR Percentage Increase Sensitivity Madhya Pradesh 13 28 Infant Mortality Rate IMR Percentage Increase Sensitivity 29 Total Fertility Rate TFR Percentage Increase Sensitivity 30 Acute diarrhoea cases DH Persons Per Increase Sensitivity 100,000 Population 31 Annual Parasite Index - Malaria API Percentage Increase Sensitivity National Vector Borne Disease Control 2010 Programme, Ministry of Health & Family Welfare Economic 32 Per Capita Income (GDP) at current prices GDP Rs. '000 Decrease Adaptive Indicus Analytics 2013- Capacity 14 33 Households availing banking service s BNKS Percentage Decrease Adaptive Census of India: House listing and Housing 2011 Capacity Census - Madhya Pradesh 34 Commercial banks CBNK Number/Lakh Decrease Adaptive Reserve Bank of India: Quarterly statistics 2015- of population Capacity on deposits and credit of scheduled 16 commercial banks 35 Total Credit to Total Deposits in Banks TCTD Percentage Decrease Adaptive Reserve Bank of India: Quarterly statistics 2015- Capacity on deposits and credit of scheduled 16 commercial banks 36 Micro, Small & Medium Scale Industrial Units MSM Number/Lakh Decrease Adaptive Development Commissioner (MSME) , 2015 E of population Capacity Ministry Of Micro, Small & Medium Enterprises , Government of India Climate 37 Average annual rainfall RF mm Decrease Exposure Analysis of CORDEX climate data (IITM, 1981- 38 Standard deviation in annual rainfall STDR Increase Exposure Pune)-RCP4.5, RCP8.5 2010 F (BL), 2021- 39 No. of Rainy Days RD Number of Days Decrease Exposure 2050 40 Extremely Wet Days - Annual total rainfall when EWD mm Increase Exposure (MC),

37 No Indicators Abbr Unit Functional Conceptual Data Source Time eviati Relationship Basis Period on with vulnerability rainfall>99th percentile 2071- 41 Consecutive Dry Days - maximum number of CDD Number of Days Increase Exposure 2100 Consecutive Days With Rainfall Less Than 1 mm (EC) 42 Consecutive Wet Days -maximum number of CWD Number of Days Increase Exposure Consecutive Days 43 Heat Index HI Severity of days Increase Sensitivity SWAT Hydrological model outputs, CORDEX 44 Temperature Humidity Index THI Severity of days Increase Sensitivity climate data - RCP 4.5, RCP 8.5 (IITM, Pune ) Water Resource 45 Surface Water availability in South West SWS mm/lakh Decrease Adaptive SWAT Hydrological model outputs, CORDEX 1981- Monsoon season WM population Capacity climate data - RCP4.5, RCP8.5 (IITM, Pune) 2010 46 Surface Water availability in North East SWNE mm/lakh Decrease Adaptive (BL), Monsoon season M population Capacity 2021- 47 Ground Water availability in South West GWS mm/lakh Decrease Adaptive 2050 Monsoon season WM population Capacity (MC), 48 Ground Water availability in North East GWN mm/lakh Decrease Adaptive 2071- Monsoon season EM population Capacity 2100 49 Crop water Stress(ET/PET) in South West CWSS Ratio Increase Sensitivity (EC) Monsoon season WM 50 Crop water Stress(ET/PET) in North East CWS Ratio Increase Sensitivity Monsoon season NEM 51 Frequency of Drought DR Number of Increase Exposure weeks 52 Flood Discharge (1-2% probable flow) FL cumecs-day Increase Exposure Forest 53 Biological richness BR Number Decrease Adaptive IIRS, Dehradun 1961- Capacity 1990 54 Disturbance index DI Number Increase Sensitivity IIRS, Dehradun 1961- 1990 55 Canopy cover CC Percentage Increase Sensitivity FSI, Dehradun 2011

38 No Indicators Abbr Unit Functional Conceptual Data Source Time eviati Relationship Basis Period on with vulnerability 56 Slope S Degree Increase Sensitivity Open access digital elevation data 1961- 1990 57 Vegetation shift VS Number Increase Sensitivity Dynamic vegetation models - IBIS 2021- 2050 (MC), 2071- 2100 (EC) Agriculture 58 Rice yield RY Kg/hectare Decrease Adaptive InfoCrop Modelling, - RCP4.5, RCP8.5 1981- Capacity 2010 59 Wheat yield WY Kg/hectare Decrease Adaptive (BL), Capacity 2021- 60 Soya bean yield SY Kg/hectare Decrease Adaptive 2050 Capacity (MC), 61 Chickpea yield CHY Kg/hectare Decrease Adaptive 2071- Capacity 2100 (EC) 62 Food grains yield FY Kg/hectare Decrease Adaptive Department of Family Welfare and 2008- Capacity Agriculture Development, KRISHINET, 09 Government of Madhya Pradesh 63 Net Area Sown NSA Percentage of Decrease Adaptive DACNET (Department of Agriculture 2013- the district Capacity Cooperation)- Landuse Statistics Report 14 geographical 2013- area 14 64 Net Irrigated Area IA Percentage to Decrease Adaptive Net Sown Area Capacity 65 Fertilizer Consumption FC Kg/ha Decrease Adaptive Department of Family Welfare and 2006- Capacity Agriculture Development, KRISHINET, 07 Government of Madhya Pradesh 66 Cropping intensity CI Percentage Decrease Adaptive DACNET (Department of Agriculture 2013-

39 No Indicators Abbr Unit Functional Conceptual Data Source Time eviati Relationship Basis Period on with vulnerability Capacity Cooperation) - Landuse Statistics Repor t 14 67 Total Wasteland WL Percentage of Increase Sensitivity the district geographical area 68 Land Holdings area below 1 Hectare LH Percentage Increase Sensitivity Department of Agriculture and 2011 Cooperation, Agriculture Census Database 69 Livestock unit LP Number per Decrease Adaptive Department of Animal Husbandry, Dairying 2012 1000 Capacity & Fisheries, 19th Livestock Census District

households Wise Report 2012 70 Poultry Unit POP Number per Decrease Adaptive Department of Animal Husbandry, Dairying 2012 1000 Capacity & Fisheries, 19th Livestock Census District

households Wise Report 2013 71 Milk production per capita MP gms/day Decrease Adaptive Madhya Pradesh State Livestock and 2009 Capacity Poultry Development Corporation 72 Egg Production per capita EP eggs/year Decrease Adaptive Administrative Reports of Department of 2009 Capacity Animal Husbandry, GoMP

40 Table A- 2: Rationale for classifying indicators of vulnerability as exposure, sensitivity or adaptive capacity Indicators Rationale Reference Social Indicators A region with high population density is more sensitive to climate because more Glwadys Aymone Gbetibouo, Claudia Ringler, 2009, " Mapping South African Density of people are exposed and therefore the region will need greater humanitarian Farming Sector Vulnerability to Climate Change and Variability", Population assistance. High density leads to more problems related with security, water, natural Environment and Production Technology Division, International Food Policy (Sensitivity) resource stress, etc. Research Institute (IFPRI)

Decadal growth Growth of population reduces output by lowering the per capita availability of capital. http://www.economicsdiscussion.net/population-explosion/14-major- rate of Population Large population creates problem of unemployment and food scarcity, puts pressure negative-effects-of-population-explosion/4461 (Sensitivity) on the social infrastructure, etc. hence it’s treated as sensitivity.

Sex-ratio Women can have a more difficult time during recovery than men, often due to "Gender Equality in Education, Employment and Entrepreneurship: (Sensitivity) sector-specific employment, social and cultural structures that place them in inferior Final Report to the MCM 2012", Meeting of the OECD Council at Ministerial social positions, lower wages, education, public voice and family care responsibilities. Level, Paris, 23-24 May 2012

Increased overall literacy levels reduce vulnerability by increasing people’s capabilities Rama Rao, C.A.et al., " Atlas on vulnerability of Indian agriculture to climate Literacy Rate and access to information and thus their ability to cope with adversities, adapt change", Central Research Institute for Dryland Agriculture, Hyderabad, (Adaptive Capacity) better and also enhances their ability to diversify livelihoods14. 2013, p. 116;

Gender gap in Increased gap in literacy rate increases vulnerability. The low female literacy rate has P. Suma Latha: Asst. Prof. of ES, "Women Literacy and Development: literacy rate had a dramatically negative impact on family planning and population stabilisation Research Paper", Vignan’s Lara Institute of Technology and Science (Sensitivity) efforts in India.

Age Dependency It is calculated by adding together the percentage of children (aged under 15 years) Piya, L.; Maharjan, K.L.; Joshi, N.P. Vulnerability of Rural Households to ratio (Sensitivity) and the older population (aged 65+), dividing that percentage by the working-age Climate Change and Extremes: Analysis of Chepang Households in the population (aged 15-64 years), multiplying that percentage by 100 so the ratio is Mid-Hills of Nepal. In 28th International Conference of Agricultural expressed as the number of 'dependents' per 100. As the ratio increases there may Economists: The Global Bioeconomy; International Association of be an increased burden on the productive part of the population to maintain the Agricultural Economists: Foz Do Iguacu, Brazil, 2012; No. 126191. upbringing and pensions of the economically dependent.

The demographic group most affected by disasters, are children hence they are Child Population in A.P. Pandey and Surendra Kumar Gupta, 2010, Climate Change and Child most vulnerable. Child morbidity and mortality, including vector borne diseases, the age group 0-6 Health in India: Special Case 0-6 Year Old Children, Journal of Indian water borne disease mainly affect younger age groups; hence the total burden due (Sensitivity) Economics Association to climate change appears to be borne mainly by children

Households with Accessibility to improved water sources is of fundamental significance to https://blogs.unicef.org/blog/5-facts-on-water-and-climate-change/

41 Indicators Rationale Reference access to improved lowering the faecal risk and frequency of associated diseases. Its association with source of drinking other socioeconomic characteristics, including education and income, makes it a water (Adaptive good universal indicator of human development. Without clean water, children are Capacity) at risk of diseases such as diarrhea

Households having http://www.popclimate.net/indicators/view/25-chapter-ii-what-kind- Accessibility to adequate excreta disposal facilities is fundamental to decreasing access to sanitation of-census-data-can-be-used-and-why-mapping-vulnerabilities-is-relevant- the faecal risk and the frequency of associated diseases. facility within the to-climate-change-adaptation premises (Adaptive Capacity) Households having Access to affordable clean electricity is fundamental to daily life and any level of Peter Meisen and Irem Akin, "The Case for Meeting the Millennium electricity as main socioeconomic development. Because it is central to all aspects of our lives - lighting, Development Goals Through Access to Clean Electricity", Global Energy source of lighting heating, pumping and purification of water, agricultural productivity, refrigeration of Network Institute (Adaptive Capacity) food and medicines, sterilization of equipment and many others - there is an essential correlation between access to electricity and quality of life. Households Biomass dependency is an indicator of the reliance on natural resources for cooking. I http://earthobservatory.nasa.gov/Features/BiomassBurning/ dependent on n light of climate change, there might be an impact on these natural resources as well. biomass as fuel for If there is very high dependence on biomass, it would also mean a higher exploitation cooking (Sensitivity) of natural resources. In terms of emissions excessive biomass burning would mean high amounts of GHG emissions. It would also increase the levels of indoor pollution and subsequent health issues. The biomass here includes firewood, crop residue and cow dung cake.

Households living Those houses whose walls and roof are made of permanent materials are considered http://cdkn.org/wp-content/uploads/2013/01/Sheltering-from-a- in Permanent permanent houses. Permanent houses provide resilience to absorb climate change gathering-storm-Discussion-Paper-Series-Review-of-Housing- houses (Adaptive impacts. In case of disasters, such houses are most likely to withstand the shocks. Vulnerability.pdf Capacity) Higher percentage of permanent houses would indicate better coping capacity.

Households with Increased overall communication networks reduce people’s vulnerability by http://link.springer.com/article/10.1007/s13753-016-0085-6 access to early warning systems in disaster risk management strategies and access to communication/ information and thus their ability to cope with adversities. transport (Adaptive

42 Indicators Rationale Reference Capacity) Population below Adverse shocks occur more often among the poor because they encounter greater Barbara Parker and Valerie Kozel, "Understanding poverty and vulnerability poverty line risks in terms of dangerous working conditions, poor nutrition, lack of preventive in India's Uttar Pradesh and Bihar: a mixed method approach", World Bank (Sensitivity) health care, and exposure to environmental contaminants. Thus, not only are adverse shocks more likely to occur among the poor, but the impact of these shocks on overall well-being is generally greater among them, since any given emergency is likely to absorb a larger share of their limited resources in comparison to the more ample resources of the better-off. Share of Marginal Global warming is expected to heavily impact agriculture, the dominant source of A.P. Pradeepkumar, F.J. Behr, F.T. Illiyas and E. Shaji, "Proceedings of the Workers (Sensitivity) livelihood for the world's poor. Agricultural dependency is measured by the 2nd Disaster, Risk and Vulnerability Conference", University of Kerala, 2014 percentage of the workforce employed in agriculture. A high level of agricultural dependency will increase the regions vulnerability to climate variability and fluctuations in agricultural terms of trade. Agricultural And This indicates a relatively higher importance of agriculture in the livelihoods of Rama Rao, C.A.et al., " Atlas on vulnerability of Indian agriculture to climate Cultivators to Main population compared to other sectors. These households derive the bulk of their change", Central Research Institute for Dryland Agriculture, Hyderabad, Workers (Sensitivity) income from wage employment, as agriculture productivity resulting from adverse 2013, p. 116; climatic conditions decline their income declines.

Total work Work participation rates are calculated as the proportion of total workers Climate Change: Impacts, vulnerabilities and adaptation in developing participation rate (main + marginal) to total population. Higher proportion of work participation countries, United Nations Framework Convention on Climate Change (Adaptive Capacity) rate leads to economic growth of a region, thus greater income to sustain livelihood and thus higher adaptive capacity to adapt to climatic stresses.

Gender gap in Higher gap between male and female work participation rate implies higher "Gender at Work: A Companion to the World Development Report on Jobs” work participation sensitivity of a region to climate change. Reducing gender gaps of work can rate (Sensitivity) yield broad development dividends: improve capacity of women to better support their families and more actively participate in communities and societies, improve child health and education, enhance poverty reduction, and catalyze productivity.

Health Centers Health facilities are an important indication of health adaptive capacity in case of Divya Mohan and Shirish Sinha, "Facing the Facts: Ganga Basin's (Adaptive Capacity) disasters and other related health impacts. Sufficient number of hospitals and Vulnerability to Climate Change", WWF Report, 2011 health centers are a pre-requisite for providing proper medical services in a region. Beds available in The number of beds in allopathic and ayurvedic hospitals per lakhs of population health centers denotes the level of preparedness available to deal with health related impacts. (Adaptive Capacity) The projected changes in the climate might lead to an increase in the spread of some diseases. In such cases, better medical facilities

43 Indicators Rationale Reference would be required as a response measure to cope up with the health impacts. Schools (Adaptive Education level of a population is seen as an important determinant of its quality Robin Leichenko, Karen O’Brien, Guro Aandahl, Heather Tompkins, Capacity) of life. Higher education is critical in improving the health practices. The number of Akram Javed, "Mapping Vulnerability To Multiple Stressors: A Technical schools will show the extent to which a region is developed in terms of education. Lack of sufficient number of schools in the area would leave many students deprived of education. Quality of infrastructure is an important measure of relative adaptive capacity of a district, and districts with better infrastructure are presumed to be better able to adapt to climatic stresses. Student Teacher Student-teacher ratios are a general way to measure teacher workloads and http://edglossary.org/student-teacher-ratio/ Ratio (Sensitivity) resource allocations in public schools, as well as the amount of individual attention a child is likely to receive from teachers, thus higher number of students per teacher is considered as sensitive.

Level of Urban population have good quality homes and drainage systems that prevent Ulijana Urosevic, "Prevention of natural disasters due to unregulated urbanization flooding, by moving to places with less risk or by changing jobs if climate- change and indiscriminate urbanization", Environment Committee (Adaptive Capacity) threatens their livelihoods.

Scheduled Tribes SC/ST population is, in addition to being relatively poor, also less educated, Rama Rao, C.A.et al., " Atlas on vulnerability of Indian agriculture to and Scheduled high male unemployment, poorly integrated with main-stream economy and climate change", Central Research Institute for Dryland Agriculture, Caste population heavily-dependent on natural resources for their livelihoods tends to be highest Hyderabad, 2013, p. 116; (Sensitivity) amongst them. Thus districts with more tribal population are more sensitive to climate change impacts Road length This is indicator of market access as well as of better integration with the economy (Adaptive Capacity) and the associated spread effects of development14.

Crude Birth Rate The annual number of births per 1,000 total populations. When the population is https://www.boundless.com/sociology/textbooks/boundless- (Sensitivity) too large for the available resources, famine, energy shortages, war, and disease sociology-textbook/population-and-urbanization-17/population- can result. Higher birth rates leads to increasing population hence treated growth-122/implications-of-different-rates-of-growth-686-10354/ as sensitivity. Crude Death Rate The annual number of deaths per 1,000 total populations. In regions with poor Manish Tiwari, Dr .M K Mathur, Shiv Charan Mathur "Study on Inter Regional (Sensitivity) diets or a lack of food the death rate increases due to malnutrition. Economic Inequalities in Rajasthan", Social Policy Research Institute, 2010

Infant Mortality High IMR also depicts relative backward character of a region hence treated as Dr .M K Mathur: Shiv Charan Mathur, Manish Tiwari, "Study on Inter Rate (Sensitivity) sensitive. Regional Economic Inequalities in Rajasthan" , Social Policy Research Total Fertility The average number of children a woman would have assuming that current Institute: 2010

44 Indicators Rationale Reference Rate (Sensitivity) age-specific birth rates remain constant throughout her childbearing years (usually considered to be ages 15 to 49). TFR acts negatively in the process of development. A region showing a very high TFR generally experiences a high population growth rate and a low level of economic development.

Acute diarrhoea Due to heat stress increase in vector borne diseases are killing mostly children A.P. Pandey and Surendra Kumar Gupta, 2010, Climate Change and cases (Sensitivity) and the poor. Child Health in India: Special Case 0-6 Year Old Children, Journal of Indian Economics Association Annual Parasite Changes in temperature and rainfall may change the geographic range of Malaria WSSD Policy Brief Series: World Health Organization Index-Malaria vector-borne diseases such as malaria exposing new populations to it. Changes in (Sensitivity) temperature and rainfall may change the geographic range of vector-borne diseases such as malaria exposing new populations to it. Malaria affects primarily the rural poor, who can neither afford nor access effective protection or healthcare. Children, pregnant women and the displaced are particularly susceptible to the disease.

Economic Indicators

Per Capita Income Wealth enables communities to absorb and recover from losses more quickly Temesgen Deressa, Rashid M. Hassan, Claudia Ringler, "Measuring Ethiopian (GDP) at current due to insurance, social safety nets, and entitlement programs. The higher the Farmers’ Vulnerability to Climate Change Across Regional States", prices (Adaptive percentage of total population with asset ownership, and access to these income International Food Policy Research Institute, October 2008 Capacity) sources the lesser the vulnerability.

Households Banking Facilities are an important indicator of wealth and provide information Ajitava Raychaudhuri, Sushil Kr Haldar,"An Investigation into the availing banking related to the adaptive capacity of the region in extent of extreme events or climate Inter-District Disparity in West Bengal, 1991-2005" services (Adaptive related shocks. This variable includes the number of banks (public, private and foreign Capacity) banks), rural banks and non-commercial nationalized banks Cooperative Bank Branches and Cooperative agriculture and Village development branches.

Commercial banks Greater number of the banks per capita in a region implies easy access of S.Mahendra Dev, "Small Farmers in India: Challenges and Opportunities", (Adaptive Capacity) credit to small and marginal farmers. Indira Gandhi Institute of Development Research, Mumbai, June, 2012

Total Credit to Easy credit to small and marginal framers plays a pivotal role in the development http://shodhganga.inflibnet.ac.in/bitstream/10603/38509/7/07 Total Deposits in and transformation of the rural and agrarian economy. Credit plays an important _chapter%201.pdf Banks(Adaptive role in increasing agriculture productivity. Capacity) Micro, Small MSMEs are complementary to large industries as ancillary units and this sector Kalraj Mishra, Giriraj Singh"MSME at a glance 2016", Ministry of

45 Indicators Rationale Reference &Medium scale contributes enormously to the socio-economic development of the region. Micro, Small and Medium Enterprises, Government of India industrial units It not only play crucial role in providing large employment opportunities at (Adaptive Capacity) comparatively lower capital cost than large industries but also help in industrialization of rural & backward areas, thereby, reducing regional imbalances, assuring more equitable distribution of national income and wealth.

Climate Indicators

Temperature An increase in maximum temperature and frequency of hot days implies Rama Rao, C.A.et al., " Atlas on vulnerability of Indian agriculture to indices (Exposure) adverse effects on crop yields. An increase in minimum temperature implies adverse climate change", Central Research Institute for Dryland Agriculture, effects on yields, especially for rabi crops like wheat Hyderabad, 2013, p. 116; Rainfall indices An increase in extreme rainfall indicates the possibility of crop productivity (Exposure) getting affected. Increase in the intensity of such extreme rainfall event also means higher probability of floods with all the attendant problems.

Annual rainfall and The amount of rainfall received over an area is an important factor in assessing the Barakade Ankush Jagannath, "Rainfall Trend in Drought Prone Region in number of rainy amount of water available to meet the various demands of agriculture, industry, Eastern Part of Satara District of Maharashtra, India", Department of days (Exposure) irrigation, hydroelectric power generation, and other human activities. A region Geography, 2014 with high rainfall and rainy days is better off as compared to that with low rainfall and rainy days. Increase in number of rainy days implies a better distribution of rainfall.

Heat Index Higher HI impacts human health and can cause heat strokes, heat cramps and http://www.cdc.gov/niosh/topics/heatstress/ (Sensitivity) heat exhaustion. Exposure to extreme heat can result in occupational illnesses and injuries. Heat can also increase the risk of injuries in workers. Thus air coolers and ACs need rise to cool the temperature.

Temperature Higher THI leads to additional stress on the dairy animals. It directly affect feed intake http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4823286/ Humidity Index thereby, reduces growth rate, milk yield, reproductive performance, and even death in (Sensitivity) extreme cases hence considered as sensitivity indicator.

Water Resources Indicators

Surface Water The impacts of climate change on freshwater systems and their management are Leichenko, R., K. O’Brien, G. Aandahl, H. Tompkins, and A. Javed. availability mainly due to the observed and projected increases in temperature, sea level and 2004, "Mapping Vulnerability To Multiple Stressors: A Technical (Adaptive Capacity) precipitation variability. Areas with more productive soil and more groundwater Memorandum" CICERO: Oslo, Norway available for agriculture will be more adaptable to adverse climatic conditions and Ground Water better able to compete and utilize the opportunities of trade. If districts have larger availability availability of these water sources implies it is less vulnerable to climate change impact. (Adaptive

46 Indicators Rationale Reference Capacity) Crop water Stress Increasing crop water stress in a region impacts the productivity of the crops. Nitika Dangwal, "Detection of crop water stress and its impact on (ET/PET) (Sensitivity) productivity of cropland ecosystem", Agriculture and Soils Department Indian Institute of Remote Sensing, Indian Space Research Organization Frequency of The increase in natural disasters, primarily floods and droughts, further exacerbate Courtenay Cabot Venton, "Climate change and water resources: Drought issues over water availability and water quality directly affecting the life and ERM for WaterAid. Lead consultant", Environmental Resources (Exposure) livelihoods of humans. Incidence of more frequent droughts implies more risky Management, London agriculture and hence more sensitivity.

Flood discharge Increase in floods would add to stress on water resources, food security, Poverty and Climate Change Reducing the Vulnerability of the Poor (Exposure) human health, and infrastructure, constraining development. through Adaptation: African Development Bank, Asian Larger area susceptible to flood incidence implies high sensitivity. Development Bank, .et. al

Agriculture Indicators

Food grains Higher crop productivity and yield is directly proportional in reducing the Sehgal VK, Singh MR, Chaudhary A, Jain N and Pathak H (2013)," Yield (Adaptive vulnerability of districts by availability of more food being available and Vulnerability of Agriculture to Climate Change: District Capacity) more income for farmers and thus their ability to cope with adversities hence Level Assessment in the Indo-Gangetic Plains", Indian Agricultural it is taken under adaptive capacity. Research Institute, New Delhi p 74.

Net Area Sown The total land area on which crops are grown in a region is called net sown area. Rama Rao, C.A.et al., " Atlas on vulnerability of Indian agriculture to (Adaptive Capacity) Higher the area implies higher productivity of agriculture crops in a region hence climate change", Central Research Institute for Dryland Agriculture, it is taken under adaptive capacity. Hyderabad, 2013, p. 116; Net Irrigated Area Irrigation is the artificial application of water to the land or soil. It is used to Shaik Irfan Siddique, Pradeepa S,"Critical Analysis on the Effectiveness (Adaptive Capacity) assist in the growing of agricultural crops, trees, pastures and vegetation of of Water Supply System through Singataluru Lift Irrigation Scheme disturbed soils in dry areas and during periods of inadequate rainfall. Hence and Recommendations for Modernisation" International Journal of higher the net area irrigated better is the adaptive capacity of a region. Innovative Research in Science, Engineering and Technology, Vol. 5, Issue 2, Februray 2016

Fertilizer Higher use of fertilizers is an indicator of adoption of improved technologies, Rama Rao, C.A.et al., " Atlas on vulnerability of Indian agriculture to Consumption thus its use increases the productivity of a crop and hence indirectly increases climate change", Central Research Institute for Dryland Agriculture, (Adaptive Capacity) agriculture income14. Hyderabad, 2013, p. 116;

Cropping intensity Cropping intensity denotes the number of crops grown in a year on one piece of land. Sehgal VK, Singh MR, Chaudhary A, Jain N and Pathak H (2013) (Adaptive The agricultural land will be more vulnerable if it is not much used for growing crops. Vulnerability of Agriculture to Climate Change: District Level Capacity) In other words, the vulnerability is inversely proportional to the cropping intensity of Assessment in the Indo-Gangetic Plains. Indian Agricultural that land. Research Institute, New Delhi p 74.

47 Indicators Rationale Reference

Wasteland The increase in natural disasters, primarily floods and droughts, further exacerbate Rama Rao, C.A.et al., " Atlas on vulnerability of Indian agriculture to (Sensitivity) issues over water availability and water quality directly affecting the life and climate change", Central Research Institute for Dryland Agriculture, livelihoods of humans. Incidence of more frequent droughts implies more risky Hyderabad, 2013, p. 116; agriculture and hence more sensitivity.

Land Holdings Smaller farm size limits marketable surplus and also the opportunity to diversify area below 1 the cropping pattern and the low investment capacity of farmers make agriculture Hectare (Sensitivity) more sensitive to any climatic shock14.

Livestock unit This is an indicator of diversification of agriculture and livelihoods. (Adaptive Capacity) Although the income share earned from livestock directly is not high, livestock keepers obtain indirect benefits, such as the capacity of livestock to buffer against shocks such as drought or flood. Thus higher livestock population in a region implies greater ability to cope with climatic aberrations.

Poultry Unit Higher numbers indicate greater purchasing power of people of the given Frands Dolberg, "Poultry production for livelihood improvement and (Adaptive Capacity) region thus contributes to improved livelihoods and local economic development. poverty alleviation", University of Århus, Denmark

Milk and egg Higher temperatures or heat stress lead to reduction in milk or egg Das R, Sailo L, Verma N, Bharti P, Saikia J, Imtiwati, Kumar R (2016),"Impact production per production. Thus district with higher numbers are less impacted by heat of heat stress on health and performance of dairy animals: A review", capita(Adaptive stress so this is beneficial for the livestock thus treated as adaptive capacity. Veterinary World, 9(3): 260-268 Capacity) Forest Indicators Biological richness Value of BR denotes status of biodiversity and potential to host biodiversity; (Adaptive Capacity) higher potential stands for higher resilience and lower vulnerability. DI as part of BR accounts for level of stress on biological richness.

Disturbance Index DI as an independent indicator accounts for degradation in the structural and (Sensitivity) expanse attributes of forests compared to undisturbed situation; under disturbance species are under new order and competition; higher value of DI indicates higher inherent vulnerability. Canopy Cover Canopy cover regulates on-site conditions pertaining to temperature, desiccation, (Sensitivity) wind speed, light and invasive species; loss of canopy cover enhances the exposure and sensitivity of forests and adversely impacts adaptive capacity thus contributes to inherent vulnerability. Ground slope Propensity to landslides, soil disturbance and erosion due to higher ground slope (Sensitivity) enhances inherent vulnerability of forests.

48 Tables for District Vulnerability

Table A- 3: District wise composite vulnerability index values, ranks and vulnerability category under current and projected climate scenarios

Baseline RCP4.5 RCP8.5 Mid-Century End-Century Mid-Century End-Century Index Index Index Index Index Districts Rank value Category Rank value Category Rank value Category Rank value Category Rank value Category Bhopal 1 0.394 VL 2 0.399 VL 1 0.381 VL 1 0.397 VL 1 0.380 VL Jabalpur 2 0.401 VL 3 0.413 VL 2 0.402 VL 3 0.410 VL 3 0.406 VL Indore 3 0.415 VL 3 0.415 VL 2 0.400 VL 3 0.415 VL 1 0.391 VL Hoshangabad 4 0.440 L 4 0.442 L 3 0.428 VL 4 0.434 L 3 0.427 VL Harda 5 0.445 L 5 0.449 L 4 0.430 L 5 0.442 L 4 0.428 VL Narsinghpur 6 0.456 L 7 0.462 L 5 0.453 L 6 0.457 L 5 0.449 L Gwalior 7 0.457 L 11 0.475 L 7 0.457 L 10 0.471 L 6 0.454 L Dewas 8 0.462 L 9 0.466 L 6 0.455 L 8 0.462 L 5 0.448 L Neemuch 9 0.466 L 11 0.476 L 7 0.456 L 12 0.479 L 7 0.457 L Ujjain 10 0.469 L 12 0.477 L 9 0.465 L 11 0.475 L 7 0.455 L Sehore 11 0.471 L 14 0.478 L 9 0.461 L 12 0.474 L 9 0.462 L Chhindwara 12 0.479 L 12 0.481 L 10 0.475 L 11 0.476 L 12 0.478 L Anuppur 13 0.481 L 15 0.499 M 13 0.486 L 14 0.493 L 14 0.493 L Raisen 14 0.486 L 15 0.490 L 12 0.476 L 14 0.486 L 11 0.475 L Datia 15 0.504 M 22 0.523 M 16 0.506 M 20 0.514 M 15 0.502 M Mandsaur 16 0.505 M 19 0.512 M 14 0.497 M 19 0.512 M 14 0.495 L Seoni 17 0.506 M 20 0.512 M 19 0.511 M 17 0.506 M 21 0.515 M Vidisha 17 0.506 M 20 0.513 M 15 0.499 M 20 0.513 M 15 0.499 M Betul 19 0.509 M 19 0.510 M 15 0.497 M 17 0.504 M 17 0.502 M Balaghat 20 0.510 M 24 0.517 M 20 0.510 M 22 0.513 M 26 0.524 M Shajapur 21 0.512 M 26 0.525 M 20 0.510 M 24 0.518 M 17 0.501 M Khandwa 22 0.513 M 23 0.515 M 18 0.505 M 24 0.516 M 18 0.506 M

49

Baseline RCP4.5 RCP8.5 Mid-Century End-Century Mid-Century End-Century Index Index Index Index Index Districts Rank value Category Rank value Category Rank value Category Rank value Category Rank value Category Ratlam 23 0.519 M 26 0.529 M 22 0.515 M 24 0.524 M 18 0.509 M Burhanpur 24 0.521 M 26 0.527 M 23 0.518 M 25 0.525 M 24 0.523 M Shahdol 25 0.525 M 28 0.543 H 26 0.527 M 27 0.536 H 27 0.533 M Rajgarh 26 0.543 H 30 0.553 H 25 0.534 M 28 0.548 H 24 0.532 M Mandla 27 0.547 H 32 0.556 H 27 0.548 H 29 0.551 H 30 0.553 H Dhar 28 0.548 H 27 0.544 H 25 0.531 M 29 0.550 H 23 0.524 M Katni 28 0.548 H 37 0.565 H 29 0.549 H 35 0.562 H 32 0.554 H Sagar 28 0.548 H 33 0.556 H 26 0.542 H 32 0.554 H 26 0.540 H Satna 31 0.550 H 35 0.558 H 29 0.545 H 33 0.553 H 27 0.540 H Guna 32 0.552 H 37 0.560 H 28 0.543 H 35 0.555 H 28 0.543 H Bhind 33 0.553 H 44 0.575 H 36 0.559 H 41 0.569 H 32 0.552 H Damoh 33 0.553 H 40 0.565 H 32 0.552 H 36 0.559 H 32 0.552 H Khargone 35 0.558 H 33 0.554 H 29 0.545 H 36 0.560 H 27 0.542 H Chhatarpur 36 0.560 H 42 0.573 H 36 0.560 H 41 0.572 H 34 0.556 H Tikamgarh 36 0.560 H 41 0.571 H 37 0.562 H 40 0.568 H 35 0.557 H Sheopur 38 0.562 H 43 0.574 H 36 0.559 H 41 0.569 H 34 0.554 H Umaria 39 0.563 H 46 0.584 VH 40 0.566 H 44 0.578 H 40 0.567 H Shivpuri 40 0.570 H 44 0.583 VH 39 0.567 H 42 0.576 H 39 0.567 H Ashoknagar 41 0.572 H 42 0.576 H 37 0.562 H 41 0.571 H 38 0.563 H Dindori 42 0.573 H 48 0.595 VH 43 0.578 H 44 0.581 VH 44 0.581 VH Alirajpur 43 0.583 VH 44 0.586 VH 38 0.567 H 45 0.588 VH 37 0.565 H Rewa 44 0.587 VH 46 0.594 VH 43 0.584 VH 45 0.592 VH 41 0.578 H Sidhi 45 0.590 VH 47 0.600 VH 43 0.584 VH 46 0.593 VH 44 0.586 VH Morena 46 0.599 VH 48 0.615 VH 45 0.595 VH 48 0.608 VH 44 0.590 VH Panna 47 0.600 VH 48 0.607 VH 46 0.596 VH 48 0.606 VH 44 0.589 VH

50

51

ersa v vise and y abilit ulner V igher

H

implies implies index of values igh H igh H emely tr Ex EH: igh, H y er V VH: igh, H H: , e at oder M M: , w o L L: , w o L y er V VL: . y abilit vulner of ee r deg

in

change change the esents epr r olour c the but enarios sc oss acr t distric a or f change not y ma anks r that ed not be o t is t I t. distric a or f baseline

the

o o t ed ompar c as eased decr has y abilit vulner the that es indicat ank r ed olour c een r g while eased incr has y abilit vulner the that es

indicat

ank ank r ed olour c ed r , enarios sc oss acr and t distric a within omparison c ontal horiz or F ersa. v - e vic and y abilit vulner higher es indicat

value

index index igher H . anks r the not and e tur pic true the e iv g values index , enario sc a within ts distric the amongst omparison c tical er v or F e: Not

li rau Sing 0.616 50 0.616 50 H E 0.621 50 VH 0.613 50 H E 0.624 50 VH VH

ua ab h J 0.607 49 0.580 43 VH 0.612 50 VH 0.591 46 VH 0.606 49 VH VH

wani Bar 0.603 48 0.586 44 VH 0.608 49 VH 0.591 46 VH 0.597 47 VH VH

ts c i str Di y or ateg C ue al v ank R ank R y or ateg C ue al v ank R y or ateg C ue al v ank R y or ateg C ue al v ank R y or ateg C ue val

x x nde I x x nde I x nde I x nde I Index Index

y tur en C d- n E y tur en C d- i M y tur en C d- n E y tur en C d- i M

Baseline

RCP8.5 CP4.5 R Table A- 4:District wise social vulnerability Index values, ranks and category for current vulnerability

Baseline Districts Rank Index Category Districts Rank Index Category value value

Bhopal 1 0.292 VL Khargone 26 0.495 M

Indore 2 0.309 VL Vidisha 27 0.509 M

Jabalpur 3 0.332 VL Rajgarh 28 0.511 M

Gwalior 4 0.351 VL Satna 28 0.511 M

Neemuch 5 0.373 L Sagar 30 0.518 M

Ujjain 6 0.378 L Shahdol 30 0.518 M

Dewas 7 0.391 L Alirajpur 32 0.521 M

Hoshangabad 8 0.401 L Katni 33 0.528 H

Mandsaur 9 0.408 L Chhatarpur 34 0.535 H

Narsinghpur 10 0.421 L Damoh 35 0.537 H

Chhindwara 11 0.422 L Guna 36 0.544 H

Balaghat 12 0.425 L Morena 36 0.544 H

Harda 13 0.446 M Rewa 38 0.545 H

Datia 14 0.448 M Barwani 39 0.554 H

Anuppur 15 0.454 M Tikamgarh 40 0.555 H

Burhanpur 16 0.464 M Mandla 41 0.560 H

Sehore 17 0.465 M Shivpuri 42 0.570 H

Betul 18 0.467 M Ashoknagar 43 0.581 H

Dhar 18 0.467 M Sidhi 44 0.591 VH

Ratlam 18 0.467 M Jhabua 45 0.595 VH

Seoni 21 0.471 M Panna 45 0.595 VH

Shajapur 22 0.476 M Sheopur 47 0.607 VH

Raisen 23 0.477 M Dindori 48 0.608 VH

Bhind 24 0.487 M Umaria 49 0.610 VH

Khandwa 24 0.487 M Singrauli 50 0.645 VH

52 Table A- 5: District wise economic vulnerability Index values, ranks and category for current vulnerability

Baseline Districts Rank Index Category Districts Rank Index Category value value

Indore 1 0.280 VL Rajgarh 26 0.684 M

Bhopal 2 0.392 L Narsimhapur 27 0.692 M

Hoshangabad 3 0.455 L Datia 28 0.708 H

Harda 4 0.478 L Morena 28 0.708 H

Sehore 4 0.478 L Dindori 30 0.709 H

Jabalpur 6 0.498 L Khargone 31 0.713 H

Dewas 7 0.522 L Umaria 32 0.715 H

Shajapur 8 0.524 L Katni 33 0.720 H

Gwalior 9 0.525 L Jhabua 34 0.728 H

Khandwa 10 0.557 L Barwani 35 0.730 H

Ujjain 11 0.563 L Sagar 35 0.730 H

Betul 12 0.572 L Balaghat 37 0.746 H

Raisen 13 0.609 M Burhanpur 38 0.748 H

Mandla 14 0.611 M Seoni 39 0.752 H

Ratlam 15 0.612 M Sidhi 40 0.758 H

Sheopur 16 0.621 M Shivpuri 41 0.767 H

Vidisha 17 0.630 M Tikamgarh 42 0.781 VH

Neemuch 18 0.634 M Chhatarpur 43 0.790 VH

Shahdol 19 0.635 M Ashoknagar 44 0.793 VH

Dhar 20 0.638 M Bhind 45 0.796 VH

Satna 21 0.652 M Damoh 46 0.798 VH

Anuppur 22 0.665 M Panna 47 0.819 VH

Guna 23 0.669 M Singrauli 48 0.843 VH

Chhindwara 24 0.675 M Rewa 49 0.860 VH

Mandsaur 25 0.677 M Alirajpur 50 0.901 VH

53 Table A- 6: District wise climate vulnerability Index values, ranks and vulnerability category under current and projected climate scenarios

RCP4.5 RCP8.5 Baseline Mid-Century End-Century Mid-Century End-Century Index Index Districts Rank value Category Rank Index value Category Rank value Category Rank value Category Rank Index value Category Anuppur 1 0.284 VL 13 0.389 L 5 0.351 VL 4 0.343 VL 50 0.495 M Seoni 2 0.326 VL 15 0.39 L 29 0.424 L 8 0.368 L 50 0.511 H Shahdol 3 0.335 VL 50 0.443 L 12 0.388 L 11 0.387 L 50 0.516 H Chhindwara 4 0.358 VL 15 0.408 L 11 0.396 L 7 0.385 L 50 0.484 M Umaria 5 0.367 VL 50 0.488 M 18 0.413 L 41 0.456 M 50 0.528 H Dindori 6 0.384 L 50 0.515 H 20 0.425 L 20 0.425 L 50 0.557 VH Betul 7 0.389 L 33 0.462 M 12 0.408 L 28 0.445 M 50 0.533 H Mandla 8 0.397 L 27 0.457 M 17 0.431 L 17 0.431 L 50 0.553 VH Narsinghpur 8 0.397 L 41 0.487 M 16 0.427 L 23 0.438 L 50 0.503 H Katni 10 0.402 L 50 0.513 H 23 0.436 L 50 0.498 M 50 0.605 VH Jabalpur 11 0.403 L 50 0.511 H 26 0.438 L 37 0.473 M 50 0.565 VH Shajapur 12 0.415 L 50 0.506 H 24 0.449 M 41 0.489 M 34 0.480 M Balaghat 13 0.426 L 45 0.503 H 35 0.487 M 47 0.509 H 50 0.591 VH Dewas 14 0.427 L 36 0.486 M 19 0.435 L 31 0.474 M 32 0.479 M Damoh 15 0.429 L 50 0.553 VH 32 0.476 M 50 0.519 H 50 0.563 VH Hoshangabad 16 0.431 L 42 0.492 M 8 0.402 L 18 0.432 L 32 0.474 M Vidisha 16 0.431 L 30 0.469 M 20 0.435 L 50 0.519 H 50 0.518 H Ujjain 18 0.432 L 46 0.497 M 28 0.465 M 50 0.535 H 46 0.498 M

Sehore 19 0.433 L 45 0.496 M 11 0.411 L 41 0.489 M 50 0.534 H

Rajgarh 20 0.437 L 45 0.498 M 15 0.431 L 50 0.516 H 50 0.514 H

Singrauli 21 0.448 M 50 0.534 H 47 0.509 H 50 0.529 H 50 0.642 VH

Burhanpur 22 0.45 M 49 0.511 H 50 0.52 H 50 0.543 H 50 0.591 VH

Raisen 23 0.451 M 41 0.494 M 11 0.413 L 37 0.487 M 49 0.510 H Bhopal 24 0.463 M 29 0.48 M 11 0.416 L 48 0.516 H 47 0.513 H

54 RCP4.5 RCP8.5 Baseline Mid-Century End-Century Mid-Century End-Century Index Index Districts Rank value Category Rank Index value Category Rank value Category Rank value Category Rank Index value Category Sagar 25 0.465 M 50 0.540 H 23 0.461 M 50 0.544 H 50 0.528 H Ashoknagar 26 0.466 M 33 0.485 M 24 0.461 M 43 0.501 M 50 0.521 H Khandwa 27 0.47 M 50 0.529 H 44 0.503 H 50 0.548 VH 50 0.565 VH Shivpuri 27 0.47 M 50 0.517 H 22 0.453 M 31 0.483 M 50 0.551 VH Sidhi 29 0.484 M 50 0.572 VH 49 0.518 H 50 0.537 H 50 0.650 VH Guna 30 0.485 M 50 0.526 H 31 0.486 M 50 0.547 VH 50 0.545 H Indore 31 0.487 M 50 0.537 H 21 0.461 M 50 0.562 VH 35 0.495 M Chhatarpur 32 0.49 M 50 0.558 VH 50 0.54 H 50 0.583 VH 50 0.641 VH Ratlam 33 0.494 M 50 0.583 VH 50 0.537 H 50 0.586 VH 50 0.57 VH Neemuch 34 0.495 M 50 0.528 H 24 0.471 M 50 0.577 VH 50 0.566 VH Mandsaur 35 0.496 M 49 0.523 H 27 0.484 M 50 0.561 VH 50 0.552 VH Tikamgarh 36 0.507 H 46 0.528 H 50 0.569 VH 50 0.544 H 50 0.656 VH Khargone 37 0.510 H 50 0.536 H 50 0.539 H 50 0.579 VH 50 0.571 VH Sheopur 38 0.511 H 50 0.562 VH 35 0.500 M 50 0.548 VH 50 0.591 VH Harda 39 0.516 H 50 0.596 VH 28 0.488 M 45 0.529 H 50 0.540 H Gwalior 40 0.518 H 50 0.559 VH 31 0.497 M 50 0.546 H 50 0.61 VH Panna 41 0.519 H 50 0.577 VH 50 0.559 VH 50 0.606 VH 50 0.644 VH Satna 41 0.519 H 50 0.553 VH 50 0.546 H 50 0.548 VH 50 0.626 VH Morena 43 0.521 H 50 0.58 VH 47 0.531 H 50 0.553 VH 50 0.617 VH Datia 44 0.529 H 50 0.593 VH 50 0.555 VH 50 0.559 VH 50 0.659 VH Bhind 45 0.53 H 50 0.623 VH 50 0.571 VH 50 0.59 VH 50 0.668 VH Barwani 46 0.539 H 50 0.562 VH 50 0.587 VH 50 0.634 VH 50 0.58 VH Dhar 47 0.553 VH 50 0.589 VH 46 0.551 VH 50 0.642 VH 46 0.551 VH Rewa 48 0.567 VH 50 0.609 VH 50 0.614 VH 50 0.621 VH 50 0.699 EH Alirajpur 49 0.603 VH 50 0.679 EH 50 0.625 VH 50 0.717 EH 50 0.616 VH Jhabua 50 0.607 VH 50 0.653 VH 50 0.618 VH 50 0.733 EH 49 0.604 VH

55 Table A- 7:District wise water resources vulnerability Index values, ranks and vulnerability category under current and projected climate scenarios

RCP4.5 RCP8.5 Baseline Mid-Century End-Century Mid-Century End-Century Index Index Districts Rank value Category Rank Index value Category Rank value Category Rank value Category Rank Index value Category Narsinghpur 1 0.233 VL 2 0.242 VL 3 0.254 VL 2 0.249 VL 1 0.206 VL Anuppur 2 0.298 VL 5 0.359 VL 3 0.321 VL 5 0.352 VL 2 0.307 VL Dindori 3 0.341 VL 7 0.413 L 6 0.373 VL 5 0.367 VL 4 0.351 VL Umaria 3 0.341 VL 8 0.430 L 5 0.372 VL 7 0.407 L 4 0.349 VL Harda 5 0.360 VL 3 0.328 VL 3 0.31 VL 5 0.363 VL 2 0.281 VL Mandla 6 0.385 L 9 0.431 L 7 0.412 L 7 0.405 L 6 0.394 L Jabalpur 7 0.411 L 11 0.454 L 9 0.435 L 13 0.461 L 8 0.418 L Shahdol 8 0.439 L 19 0.506 M 11 0.463 L 19 0.505 M 10 0.459 L Raisen 9 0.445 L 13 0.465 L 10 0.458 L 13 0.467 L 8 0.434 L Singrauli 10 0.454 L 11 0.46 L 9 0.444 L 11 0.461 L 7 0.417 L Damoh 11 0.459 L 17 0.491 M 15 0.478 M 17 0.491 M 14 0.469 L Katni 12 0.462 L 25 0.531 H 17 0.491 M 25 0.530 H 12 0.463 L Sidhi 13 0.476 M 16 0.493 M 12 0.466 L 16 0.492 M 7 0.43 L Seoni 14 0.482 M 19 0.508 M 16 0.495 M 14 0.479 M 12 0.468 L Sehore 15 0.490 M 17 0.492 M 14 0.478 M 19 0.506 M 9 0.447 L Balaghat 16 0.491 M 22 0.516 M 18 0.496 M 15 0.480 M 18 0.497 M Ashoknagar 17 0.495 M 22 0.521 M 13 0.472 M 20 0.512 M 14 0.482 M Vidisha 18 0.504 M 25 0.537 H 18 0.503 M 23 0.534 H 17 0.501 M

56 RCP4.5 RCP8.5 Baseline Mid-Century End-Century Mid-Century End-Century Index Index Districts Rank value Category Rank Index value Category Rank value Category Rank value Category Rank Index value Category Datia 19 0.511 M 42 0.628 H 20 0.514 M 36 0.603 H 9 0.452 L Panna 20 0.519 M 27 0.556 H 20 0.517 M 25 0.545 H 8 0.45 L Sagar 21 0.522 M 27 0.553 H 25 0.538 H 26 0.551 H 22 0.529 H Hoshangabad 22 0.534 H 21 0.528 M 21 0.525 M 23 0.536 H 16 0.502 M Bhopal 23 0.544 H 28 0.572 H 21 0.532 H 27 0.566 H 17 0.511 M Tikamgarh 24 0.552 H 38 0.635 VH 26 0.563 H 37 0.627 H 17 0.507 M Satna 25 0.556 H 35 0.610 H 24 0.548 H 34 0.606 H 16 0.502 M Chhatarpur 26 0.557 H 38 0.628 H 28 0.565 H 37 0.621 H 18 0.512 M Chhindwara 27 0.560 H 27 0.558 H 23 0.539 H 22 0.534 H 20 0.527 M Rewa 28 0.561 H 34 0.599 H 28 0.56 H 34 0.602 H 14 0.49 M Shajapur 29 0.571 H 36 0.608 H 30 0.579 H 33 0.595 H 22 0.535 H Guna 30 0.573 H 35 0.603 H 25 0.545 H 33 0.594 H 27 0.556 H Rajgarh 31 0.583 H 38 0.62 H 29 0.573 H 37 0.611 H 25 0.551 H Shivpuri 32 0.588 H 45 0.668 VH 38 0.611 H 44 0.664 VH 31 0.581 H Betul 33 0.596 H 29 0.572 H 24 0.541 H 28 0.562 H 20 0.524 M Sheopur 33 0.596 H 43 0.667 VH 36 0.61 H 42 0.662 VH 23 0.537 H Khandwa 35 0.598 H 32 0.581 H 27 0.556 H 34 0.590 H 24 0.537 H Dewas 36 0.602 H 32 0.580 H 33 0.581 H 34 0.589 H 25 0.544 H Jhabua 37 0.604 H 31 0.578 H 21 0.521 M 33 0.589 H 12 0.471 M Neemuch 38 0.609 H 46 0.666 VH 35 0.595 H 46 0.668 VH 30 0.57 H Gwalior 39 0.620 H 50 0.757 VH 44 0.660 VH 50 0.747 VH 33 0.588 H Ujjain 39 0.620 H 42 0.637 VH 38 0.611 H 40 0.623 H 28 0.559 H Ratlam 41 0.646 VH 43 0.664 VH 39 0.626 H 41 0.649 VH 32 0.593 H Indore 42 0.648 VH 39 0.621 H 32 0.589 H 38 0.619 H 25 0.545 H Mandsaur 43 0.669 VH 45 0.710 VH 41 0.663 VH 44 0.702 VH 39 0.637 VH

57 RCP4.5 RCP8.5 Baseline Mid-Century End-Century Mid-Century End-Century Index Index Districts Rank value Category Rank Index value Category Rank value Category Rank value Category Rank Index value Category Burhanpur 44 0.686 VH 44 0.692 VH 41 0.649 VH 43 0.665 VH 35 0.603 H Khargone 45 0.711 VH 43 0.669 VH 39 0.631 H 44 0.703 VH 34 0.604 H Morena 45 0.711 VH 48 0.816 EH 44 0.706 VH 47 0.809 EH 39 0.632 H Bhind 47 0.719 VH 49 0.828 EH 48 0.746 VH 49 0.823 EH 42 0.662 VH Alirajpur 48 0.720 VH 47 0.708 VH 39 0.624 H 47 0.713 VH 35 0.603 H Dhar 49 0.725 VH 46 0.689 VH 42 0.646 VH 48 0.715 VH 38 0.619 H Barwani 50 0.742 VH 47 0.702 VH 44 0.661 VH 50 0.740 VH 42 0.641 VH

Note: For vertical comparison amongst the districts within a scenario, index values give the true picture and not the ranks. Higher index value indicates higher vulnerability and vice-versa. For horizontal comparison within a district and across scenarios, red coloured rank indicates that the vulnerability has increased while green coloured rank indicates that the vulnerability has decreased as compared to the baseline for a district. It is to be noted that ranks may not change for a district across scenarios but the colour represents the change in degree of vulnerability. VL: Very Low, L: Low, M: Moderate, H: High, VH: Very High, EH: Extremely High High values of index implies Higher Vulnerability and vise versa

58 Table A- 8:District wise forest vulnerability Index values, ranks and vulnerability category under current and projected climate scenarios

RCP4.5 RCP8.5 Baseline Mid-Century End-Century Mid-Century End-Century Districts Rank Index value Category Rank Index value Category Rank Index value Category Rank Index value Category Rank Index value Category Sehore 1 0.0154 VL 45 0.0845 EH 45 0.0688 EH 1 0.0154 VL 41 0.0568 VH Khandwa 2 0.0158 VL 47 0.1003 EH 49 0.0999 EH 50 0.1376 EH 47 0.0809 EH Umaria 3 0.0166 VL 15 0.0230 M 41 0.0643 M 2 0.0166 VL 38 0.0536 VH Dindori 4 0.0167 VL 33 0.0383 H 1 0.0167 H 3 0.0167 VL 1 0.0167 VL Bhopal 5 0.0168 VL 1 0.0168 VL 25 0.0307 VL 17 0.0230 M 22 0.0276 M Satna 6 0.0174 L 11 0.0209 L 18 0.0262 L 4 0.0174 L 15 0.0242 M Anuppur 7 0.0174 L 28 0.0305 M 2 0.0174 M 5 0.0174 L 2 0.0174 L Burhanpur 8 0.0176 L 36 0.0429 H 43 0.0670 H 45 0.0890 VH 40 0.0558 VH Rewa 9 0.0178 L 35 0.0398 H 12 0.0218 H 6 0.0178 L 10 0.0209 L Shahdol 10 0.0178 L 49 0.1067 EH 31 0.0468 EH 31 0.0326 H 27 0.0402 H Raisen 11 0.0179 L 50 0.1200 EH 50 0.1077 EH 48 0.1101 EH 48 0.0874 EH Mandla 12 0.0180 L 2 0.0180 L 3 0.0180 L 7 0.0180 L 3 0.0180 L Balaghat 13 0.0185 M 3 0.0185 L 4 0.0185 L 8 0.0185 L 4 0.0185 L Seoni 14 0.0189 M 4 0.0189 L 5 0.0189 L 9 0.0189 L 5 0.0189 L Chhindwara 15 0.0193 M 6 0.0194 L 7 0.0193 L 11 0.0194 L 7 0.0193 L Jabalpur 16 0.0193 M 5 0.0193 L 6 0.0193 L 10 0.0193 L 6 0.0193 L Indore 17 0.0195 M 34 0.0385 H 30 0.0467 H 39 0.0588 VH 28 0.0405 H Sagar 18 0.0195 M 32 0.0366 H 24 0.0299 H 32 0.0345 H 21 0.0276 M Alirajpur 19 0.0196 M 7 0.0196 L 37 0.0563 L 18 0.0242 M 32 0.0480 H Jhabua 19 0.0196 M 7 0.0196 L 37 0.0563 L 18 0.0242 M 32 0.0480 H Panna 21 0.0196 M 21 0.0269 M 14 0.0243 M 23 0.0260 M 13 0.0233 L Guna 22 0.0197 M 9 0.0197 L 10 0.0205 L 12 0.0197 L 44 0.0617 VH

59 RCP4.5 RCP8.5 Baseline Mid-Century End-Century Mid-Century End-Century Districts Rank Index value Category Rank Index value Category Rank Index value Category Rank Index value Category Rank Index value Category Damoh 23 0.0198 M 41 0.0672 VH 34 0.0485 VH 40 0.0614 VH 29 0.0420 H Harda 24 0.0200 M 44 0.0766 VH 40 0.0599 VH 43 0.0777 VH 36 0.0509 VH Shivpuri 25 0.0201 M 42 0.0689 VH 36 0.0546 VH 41 0.0629 VH 49 0.0928 EH Datia 26 0.0202 M 29 0.0313 M 20 0.0269 M 29 0.0300 M 17 0.0254 M Vidisha 27 0.0203 M 31 0.0356 H 32 0.0471 H 35 0.0411 H 31 0.0461 H Betul 28 0.0204 M 43 0.0703 VH 35 0.0506 VH 42 0.0642 VH 30 0.0438 H Ashoknagar 29 0.0204 M 10 0.0204 L 23 0.0294 L 13 0.0204 L 20 0.0275 M Sidhi 30 0.0205 M 37 0.0535 VH 8 0.0205 VH 36 0.0494 VH 8 0.0205 L Singrauli 30 0.0205 M 37 0.0535 VH 8 0.0205 VH 36 0.0494 VH 8 0.0205 L Katni 32 0.0208 M 30 0.0332 H 29 0.0418 H 30 0.0316 M 26 0.0370 H Chhatarpur 33 0.0210 M 13 0.0218 L 13 0.0220 L 14 0.0217 L 12 0.0218 L Bhind 34 0.0210 M 22 0.0272 M 15 0.0248 M 25 0.0265 M 14 0.0239 M Sheopur 35 0.0212 H 25 0.0296 M 19 0.0263 M 27 0.0285 M 50 0.1276 EH Narsinghpur 36 0.0217 H 12 0.0217 L 11 0.0217 L 15 0.0217 L 11 0.0217 L Dewas 37 0.0219 H 40 0.0620 VH 48 0.0967 VH 49 0.1300 EH 46 0.0798 EH Hoshangabad 38 0.0220 H 46 0.0939 EH 42 0.0655 EH 44 0.0850 VH 39 0.0557 VH Tikamgarh 39 0.0220 H 14 0.0226 L 16 0.0254 L 16 0.0225 L 16 0.0247 M Gwalior 40 0.0228 H 48 0.1029 EH 46 0.0713 EH 46 0.0930 EH 43 0.0604 VH Khargone 41 0.0233 H 17 0.0247 M 47 0.0752 M 47 0.0931 EH 45 0.0635 EH Ratlam 42 0.0244 H 16 0.0244 M 22 0.0289 M 20 0.0245 M 19 0.0261 M Neemuch 43 0.0247 H 18 0.0247 M 27 0.0323 M 21 0.0247 M 35 0.0505 VH Ujjain 44 0.0250 VH 19 0.0250 M 17 0.0261 M 22 0.0254 M 18 0.0259 M Mandsaur 45 0.0262 VH 20 0.0262 M 21 0.0273 M 24 0.0262 M 25 0.0344 H

60 RCP4.5 RCP8.5 Baseline Mid-Century End-Century Mid-Century End-Century Districts Rank Index value Category Rank Index value Category Rank Index value Category Rank Index value Category Rank Index value Category Morena 46 0.0270 VH 39 0.0617 VH 33 0.0480 VH 38 0.0574 VH 34 0.0483 VH Rajgarh 47 0.0277 VH 23 0.0277 M 28 0.0339 M 26 0.0277 M 24 0.0325 M Barwani 48 0.0282 VH 24 0.0282 M 44 0.0679 M 33 0.0354 H 42 0.0589 VH Dhar 49 0.0286 VH 27 0.0298 M 39 0.0574 M 34 0.0372 H 37 0.0509 VH Shajapur 50 0.0298 VH 26 0.0298 M 26 0.0310 M 28 0.0299 M 23 0.0307 M

Table A- 9:District wise agriculture vulnerability Index values, ranks and vulnerability category under current and projected climate scenarios RCP4.5 RCP8.5 Baseline Mid-Century End-Century Mid-Century End-Century Index Index Districts Rank value Category Rank Index value Category Rank value Category Rank value Category Rank Index value Category Harda 1 0.454 VL 1 0.453 VL 1 0.426 VL 1 0.435 VL 1 0.411 VL Hoshangabad 2 0.464 VL 2 0.453 VL 1 0.431 VL 1 0.438 VL 1 0.407 VL Sheopur 3 0.481 VL 3 0.479 VL 2 0.465 VL 2 0.466 VL 1 0.443 VL Neemuch 4 0.485 VL 4 0.486 VL 2 0.457 VL 3 0.479 VL 1 0.438 VL Jabalpur 5 0.506 L 4 0.493 VL 3 0.482 VL 4 0.49 VL 2 0.459 VL Vidisha 6 0.514 L 6 0.512 L 3 0.482 VL 4 0.495 VL 1 0.451 VL Bhopal 7 0.515 L 8 0.517 L 4 0.484 VL 5 0.496 VL 1 0.451 VL Gwalior 8 0.520 L 8 0.518 L 6 0.510 L 6 0.507 L 4 0.486 VL Dewas 9 0.525 L 10 0.526 L 5 0.501 L 7 0.513 L 2 0.471 VL Indore 9 0.525 L 8 0.52 L 5 0.498 L 6 0.511 L 2 0.468 VL Ratlam 11 0.527 L 10 0.525 L 6 0.501 L 6 0.51 L 3 0.479 VL Raisen 12 0.532 L 10 0.522 L 6 0.498 L 7 0.51 L 3 0.47 VL Mandsaur 13 0.533 L 12 0.532 L 7 0.507 L 10 0.522 L 4 0.482 VL Ujjain 14 0.534 L 15 0.535 L 7 0.510 L 10 0.519 L 3 0.477 VL

61 RCP4.5 RCP8.5 Baseline Mid-Century End-Century Mid-Century End-Century Index Index Index Districts Rank value Category Rank Index value Category Rank value Category Rank value Category Rank Index value Category Tikamgarh 15 0.535 L 14 0.534 L 9 0.515 L 11 0.520 L 6 0.489 VL Sehore 16 0.537 L 17 0.54 L 9 0.511 L 11 0.52 L 4 0.479 VL Shajapur 17 0.545 L 17 0.546 L 10 0.518 L 13 0.528 L 5 0.487 VL Datia 18 0.546 L 19 0.548 L 17 0.543 L 15 0.534 L 10 0.515 L Dhar 18 0.546 L 14 0.532 L 9 0.513 L 12 0.525 L 6 0.496 VL Bhind 20 0.547 L 21 0.549 L 19 0.545 L 18 0.540 L 11 0.516 L Burhanpur 21 0.548 L 20 0.546 L 14 0.525 L 18 0.538 L 18 0.54 L Alirajpur 22 0.551 L 19 0.542 L 12 0.519 L 15 0.532 L 13 0.524 L Rajgarh 23 0.558 L 23 0.558 L 14 0.525 L 18 0.538 L 8 0.498 L Chhindwara 24 0.561 L 22 0.552 L 18 0.538 L 21 0.549 L 14 0.525 L Shivpuri 25 0.573 M 25 0.573 M 22 0.556 L 22 0.556 L 15 0.532 L Khargone 26 0.575 M 25 0.565 L 19 0.543 L 23 0.559 L 16 0.534 L Khandwa 27 0.576 M 26 0.570 M 21 0.549 L 24 0.561 L 18 0.540 L Guna 28 0.578 M 28 0.581 M 23 0.553 L 24 0.558 L 13 0.523 L Narsinghpur 29 0.585 M 27 0.571 M 22 0.549 L 25 0.564 L 14 0.524 L Chhatarpur 30 0.586 M 29 0.577 M 25 0.561 L 27 0.568 M 16 0.527 L Barwani 31 0.589 M 28 0.575 M 24 0.557 L 28 0.575 M 23 0.549 L Ashoknagar 32 0.594 M 32 0.593 M 27 0.565 L 27 0.567 L 20 0.541 L Betul 33 0.599 M 31 0.585 M 27 0.565 L 28 0.571 M 23 0.548 L Seoni 34 0.616 H 32 0.602 M 30 0.589 M 32 0.600 M 29 0.585 M Jhabua 35 0.625 H 33 0.615 H 31 0.596 M 32 0.604 M 28 0.579 M Sagar 36 0.627 H 34 0.614 H 31 0.594 M 33 0.604 M 25 0.564 L Morena 37 0.628 H 36 0.619 H 34 0.606 M 34 0.606 M 30 0.586 M Damoh 38 0.634 H 36 0.620 H 33 0.602 M 35 0.610 H 28 0.572 M Rewa 39 0.645 H 39 0.639 H 35 0.615 H 37 0.628 H 31 0.589 M 62 RCP4.5 RCP8.5 Baseline Mid-Century End-Century Mid-Century End-Century Index Index Districts Rank value Category Rank Index value Category Rank value Category Rank value Category Rank Index value Category Panna 40 0.646 H 39 0.636 H 36 0.617 H 38 0.627 H 31 0.583 M Satna 41 0.647 H 40 0.638 H 37 0.616 H 38 0.625 H 32 0.586 M Katni 42 0.661 VH 41 0.653 H 39 0.635 H 40 0.647 H 34 0.603 M Sidhi 43 0.665 VH 43 0.665 VH 39 0.63 H 41 0.649 H 35 0.607 M Shahdol 44 0.671 VH 44 0.671 VH 41 0.647 H 43 0.665 VH 38 0.622 H Singrauli 45 0.674 VH 45 0.673 VH 40 0.643 H 43 0.66 VH 37 0.616 H Umaria 46 0.675 VH 46 0.674 VH 43 0.653 H 45 0.671 VH 38 0.624 H Balaghat 47 0.676 VH 44 0.663 VH 43 0.650 H 44 0.663 VH 45 0.667 VH Anuppur 48 0.693 VH 49 0.699 VH 47 0.679 VH 48 0.697 VH 44 0.657 VH Mandla 48 0.693 VH 48 0.685 VH 46 0.673 VH 48 0.687 VH 44 0.656 VH Dindori 50 0.716 VH 50 0.723 EH 49 0.706 VH 50 0.724 EH 48 0.678 VH

Note: For vertical comparison amongst the districts within a scenario, index values give the true picture and not the ranks. Higher index value indicates higher vulnerability and vice-versa. For horizontal comparison within a district and across scenarios, red coloured rank indicates that the vulnerability has increased while green coloured rank indicates that the vulnerability has decreased as compared to the baseline for a district. It is to be noted that ranks may not change for a district across scenarios but the colour represents the change in degree of vulnerability. VL: Very Low, L: Low, M: Moderate, H: High, VH: Very High, EH: Extremely High High values of index implies Higher Vulnerability and vise versa

63 Appendix II

Software Used relationship with vulnerability normalized • Statistical software STATA value for each of the indicator for each district is computed as: • GIS software ArcGIS • Microsoft Excel Normalization of Indicator Data Whenever an indicator has negative Normalization is done to convert raw data into relationship with vulnerability then the a normalized form. normalized value is calculated as: • To make the raw data unit free • To avoid one variable having an undue influence on the analysis(principal components), This is possible when, for example, higher literacy leads to lower vulnerability. Where, NV • To get the relative position of each district = Normalized value of X, X is an observed value in respect of the indicators. for the districts for a given variable, Max X is the • Normalized values always lie between 0 highest value of the variable across the district, and 1. Min X is the lowest value of the variable across Whenever an indicator has positive the district.

64 Calculation of Weights

In the development of aggregated index wiis the weights of the variables, Lij is the problems arise when the weights of each component loading of the ith variable on the indicator have to be selected. Components jth component; Ej is the Eigen value of the jth with Eigen value greater than 1 have been used component. Taking the Eigen values and to calculate the weights (Kaiser, 199017). In component loadings the weights of the essence this is like saying that, unless a factor indicators are derived according to the above extracts at least as much as the equivalent of equation19. one original variable, it is dropped. Each Calculation of Indices component is a linear combination of indicators multiplied by their loadings on that component. Large values of loadings of the indicators on the PCs (Principal Components) Where, i = 1 ……..n is the number of indicators, imply that the variable has a large bearing on w= weights, NV = Normalized value the creation of that component. The indicators are assigned different weights determined by Higher index value represents high Principal Component Analysis (PCA) to avoid vulnerability while lower index value the uncertainty of equal weighting given the represents low vulnerability. diversity of indicators used. Principle Component Analysis (PCA) The PCA (Principal Component Analysis) is PCA is a multi variate statistical technique used used to compute the factor loadings and to reduce the number of indicators in a data set weights of the indicators. In order to derive the into a smaller number of ‘dimensions’. In weights of the variable the matrix of loadings mathematical terms, from an initial set of n are rotated by Varimax Kaiser Normalization correlated indicators, PCA creates uncorrelated 18 criteria. indices or components, where each Absolute values of the eigenvectors or the component is a linear weighted combination loadings are considered in order to derive the of the initial indicators. Indeed, if the original weights. To derive the weights following indicators are uncorrelated then the analysis formula is used: does absolutely nothing. The best results are obtained when the original indicators are very highly correlated, positively or negatively.

17 Kaiser, H. F. (1960). The application of electronic computers to factor analysis. Educational and Psychological Measurement, 20, 141-151. 18 Varimax rotation is an orthogonal rotation of the factor axes to maximize the variance of the squared loadings of a factor (column) on all the indicators (rows) in a factor matrix, which has the effect of differentiating the original indicators by extracted factor. Each factor will tend to have either large or small loadings of any particular variable. A varimax solution yields results which make it as easy as possible to identify each variable with a single factor. This is the most common rotation option. 19 An Investigation into the Inter-District Disparity in West Bengal, 1991-2005: Ajitava Raychaudhuri, Sushil Kr Haldar

65 Each component is a linear combination of Cluster Analysis indicators (variables) multiplied by their Cluster analysis or clustering is the task of loadings on that component. Large values of assigning a set of objects into groups (called loadings of the variables (i.e. indicators) on the clusters) Cluster analysis is a class of statistical PCs imply that the indicator has a large bearing techniques that can be applied to data that on the creation of that component. Thus, the exhibit “natural” groupings. Cluster analysis most important indicators in each component, sorts through the raw data and groups them that best explain variance; will also be more into clusters. A cluster is a group of relatively useful in explaining variability between homogeneous cases or observations. Objects observations (i.e. districts). in the same cluster are more similar (in some sense or another) to each other than to those in The variance (λ) for each principal component i other clusters. is given by the eigenvalue of the corresponding eigenvector. The components Statistics associated with cluster analysis are ordered so that the first component (PC1) include: explains the largest possible amount of • Agglomeration schedule: An agglomera- variation in the original data. As the sum of the tion schedule gives information on the eigenvalues equals the number of indicators in objects or cases being combined at each the initial data set, the proportion of the total stage of a hierarchical clustering process. variation in the original data set accounted by • Cluster centroid: The cluster centroid is the each principal component is given by (λ)/n. The i mean values of the variables for all the second component (PC2) is completely cases or objects in a particular cluster. uncorrelated with the first component, and explains additional but less variation than the • Cluster centers: The cluster centers are the first component, subject to the same initial starting points in non-hierarchical constraint. Subsequent components are clustering. Clusters are built around these uncorrelated with previous components; centers, or seeds. therefore, each component captures an • Cluster membership: Cluster membership additional dimension in the data, while indicates the cluster to which each object explaining smaller and smaller proportions of or case belongs. the variation of the original indicators. The • Distances between cluster centers: higher the degree of correlation among the indicate how separated the individual pairs original indicators in the data, the fewer of clusters are. Clusters that are widely components required to capture common separated are distinct, and therefore information. desirable.

66