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District Level Vulnerability Assessment- 26th Annual Confernce of AERA (India) -: By Dr C A Rama Rao

  1. District Level Vulnerability Assessment 26th Annual Confernce of AERA (India) ICAR – National Dairy Research Institute, Karnal 14 - 17 November 2018 C A Rama Rao, ICAR-CRIDA, Hyderabad
  2. Introduction • Climate change has emerged as an important and potent threat to sustainable agriculture (and to sustainable development) • NICRA launched during 2011 with the over all objective of developing technologies and strategies for a more resilient Indian agriculture • Adaptation is a key aspect of dealing with climate change and vulnerability assessment (VA) is an important initial step in adaptation planning • VA was among the key activities of NICRA • Brought out the district level VA in the form of an Atlas; identified districts with varying vulnerability (Rama Rao et al., 2013; 2016) Chaturvedi et al., 2012
  3. Vulnerability and vulnerability assessment • A concept used in many different contexts with different meanings: geography, disaster management, economics, climate change, etc • Often used synonymously with susceptibility, frequency of occurrence of a shock, possibility of a negative outcome, etc. • Ex ante, what to what, negative outcome • Definition and conceptualizations of IPCC are more popular • Different methods: simulation, econometric, analytical descriptions of systems, indicator method • The degree to which a system is susceptible to, or unable to cope with, adverse effects of climate change, including climate variability and extremes. Vulnerability is a function of the character, magnitude, and rate of climate variation to which a system is exposed, its sensitivity, and its adaptive capacity [IPCC AR 3 and 4]
  4. Resilience and vulnerability – AC is the link • Resilience, vulnerability and adaptive capacity are interconnected concepts • Vulnerability – a more social concept • Resilience is a part of vulnerability • Resilience is reciprocal of vulnerability • Resilience is a positive concept • Resilience is closely related to adaptive capacity Resilience Vulnerability Adaptive capacity Source: Fellmann (2012)
  5. Revision of Vulnerability Assessment • Vulnerability atlas well accepted and being used by stakeholders • Vulnerability and risk are dynamic (Jurgilevich et al., 2017) • Two key developments since the last assessment: • different conceptualization of vulnerability in IPCC’s Fifth Assessment Report (AR5) compared to AR4: vulnerability assessment to risk assessment • CMIP5 model projections for different RCPs (in place of CMIP 3 with SRES) • A requirement for NATCOM • Third party evaluation of NICRA recommended to revise VA
  6. General logic of different assessment approaches Source: Kerstin et al., 2014
  7. Oppenheimer et al., 2014 Vulnerability Framework – IPCC AR5 Vulnerability considered as a determinant of risk
  8. Vulnerability: The propensity or predisposition to be adversely affected. Vulnerability encompasses a variety of concepts and elements including sensitivity or susceptibility to harm and lack of capacity to cope and adapt. Exposure: “The presence of people, livelihoods, species or ecosystems, environmental functions, services, and resources, infrastructure, or economic, social, or cultural assets in places and settings that could be adversely affected”. Hazard: “The potential occurrence of a natural or human-induced physical event or trend or physical impact that may cause loss of life, injury, or other health impacts, as well as damage and loss to property, infrastructure, livelihoods, service provision, ecosystems, and environmental resources. In this report, the term hazard usually refers to climate-related physical events or trends or their physical impacts”. Risk: “The potential for consequences where something of value is at stake and where the outcome is uncertain, recognizing the diversity of values. Risk is often represented as probability of occurrence of hazardous events or trends multiplied by the impacts if these events or trends occur”. “Risk results from the interaction of vulnerability, exposure, and hazard”. Definitions: IPCC AR5
  9. Difference between AR4 and AR5 vulnerability assessments  IPCC (2007) framework  Vulnerability conceptualized as residual impact  ‘Exposure’ represents the hazard to which the entity or system of interest is exposed to.  AR 4 climate projections for PRECIS A1B scenario  IPCC (2014) framework  Vulnerability conceptualized as predisposition or propensity to be harmed; viewed as internal system characteristic  ‘Exposure’ represents the characteristics (size, etc) of the system that is subjected to ‘hazard’  AR 5 climate projections: RCP based  Updated socio-economic data Vulnerability Sensitivity Exposure Adaptive Capacity Risk Vulnerability Sensitivity Adaptive capacity Exposure Hazard
  10. Indicators Normalization Aggregation Rescaling Aggregation of each component (E, V, H) Z score (Mean 0, SD 1); Range normalization (Zero-reference) into component index: vulnerability, exposure, hazard indices using weights: Expert, Factor Analysis, Equal of component indices into risk index using weights: Expert, Factor Analysis, Equal Building vulnerability and risk indices
  11. Vulnerability Exposure Hazard Historical Future (RCP 4.5 and 6.0) (Projected change in 2040-69 over 1976-2005) Normal Rainfall, mm NSA, %GA Mean cyclone rating Total rainfall, % Degraded land, % GA Rural population density, No/sqkm Flood prone area, % June rainfall, % AWHC, mm S&M farmers, % area Drought incidence, % severe drought July rainfall, % GW availability, ham/km2 SC-ST population, % Rainy days, Number Stage of GW development No. of cross bred cattle, % ACU Max T, 0C NIA, % Min T, 0C Livestock density, ACU/km2 Unusually hot days, Number Fertilizer use, kg/ha Unusually cold days, Number Literacy, % Frost days, Number Gender gap, % Drought incidence, % Social capital, % villages with SHGs Dry spells, Number/year Market density, No/lakh holdings 99 percentile rainfall, % Road connectivity, % villages Change in events with 100 mm rainfall in 3 days, % Electrification, % HHs Highest rainfall event Ag. Workers, % Rainfall in three consec. days DDP from Ag, % Income per capita, Rs Indicators selected for various dimensions of risk to agriculture
  12. Climate projections for ‘future hazard’ • Projections of rainfall, maxT and minT from an ensemble of 31 GCM models belonging to CMIP 5 for RCP 4.5 and 6.0 • Downscaled to 0.50 X 0.50 • Bias corrected following hybrid delta method • Thiessen polygons • Weighted average for using influential area of grid point as weight • Daily data converted into indicators for 2040-69 and for baseline 1976- 2005 • Changes in indicators computed
  13. Choice of RCP for future hazard • Which RCP to be selected: • RCP 8.5: Rising radiative forcing pathway leading to 8.5 W/m² in 2100 • RCP 6.0: Stabilization without overshoot pathway to 6 W/m² at stabilization after 2100; emissions to peak in 2080s • RCP 4.5: Stabilization without overshoot pathway to 4.5 W/m² at stabilization after 2100; emissions to peak in 2040s • RCP 2.6: Peak in radiative forcing at ~ 3 W/m² before 2100 and decline
  14. Indicators – Data – Tentative Outputs Note: Outputs are still being worked up on and hence not to be quoted or referred
  15. EXPOSURE INDICATORS
  16. VULNERABILITY INDICATORS
  17. HISTORICAL HAZARD INDICATORS
  18. Baseline RCP 4.5 RCP 6.0 Annual Rainfall Future Hazard Indicators
  19. Baseline RCP 4.5 RCP 6.0 Drought Proneness Future Hazard Indicators
  20. Minimum Temperature Baseline RCP 4.5 RCP 6.0 Future Hazard Indicators
  21. Categorization of districts Z score and Zero-reference normalization Index value Category E, V, HH (Z) FH, H (ZR) Risk (ZR) > 1.5. SD Very High Highly harmful High risk 0.5 SD to 1.5 SD High Moderately harmful Moderate risk - 0.5 SD to + 0.5 SD Medium Hazard Neutral Risk neutral - 0.5 SD to - 1.5 SD Low Moderately favourable Moderately advantageous < - 1.5 SD Very Low Highly favourable Highly advantageous
  22. Component indices – Z score, expert weights
  23. Hazard and Risk (RCP 6.0) – Zero reference, expert weights
  24. RCP 4.5 No of districts High risk 136 Moderate risk 220 Risk neutral 159 Moderately advantageous 53 Highly advantageous 5 RCP 6.0 High risk 198 Moderate risk 208 Risk neutral 135 Moderately advantageous 31 Highly advantageous 1 Distribution of districts in to risk categories
  25. Thank you!!
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