Climate Change: Vulnerability & Implications to Drought Programmes in India

1. Climate Change: Vulnerability & Implications to
Drought Programmes in India
Workshop on 'Climate Change Adaptation in Drought
Affected Areas: Policies, Programmes and Traditional
Coping Mechanisms‘
New Delhi
October 16, 2014
B M K Raju
Senior Scientist (Agrl. Statistics)
ICAR-Central Research Institute for Dryland Agriculture
Hyderabad

2. Climate change
Changing climate is now a reality; evidence unequivocal;
warming to continue
IPCC (2014)

3. 22.50
23.00
23.50
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25.00
25.50
YEAR
1903
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1996
1999
2002
2005
2008
2011
Trend of Mean Temperature in India (1901 to 2011)
Historical data for India shows rising trends in temperatures,
steeper in the recent decades
Climate change in India

4. Average Annual Rainfall (mm)
at 0.50 x 0.50 Grid (1971-2005)
Average Annual
Potential Evapo-transpiration (mm)
at 0.50 x 0.50 Grid (1971-2005)

5. Climate at 0.50 x 0.50 Grid (1971-2005)
Climatic Classification
Thornthwaite and Mather (1955) Raju et al. (2013)
simplified by Krishnan (1988)
MI = [(P-PE)/PE]100
Where P = Average annual Precipitation
PE = average annual Potential Evapo-transpiration
Value of MI Climatic zone
< -66.7 Arid
-66.6 to -33.3 Semi -arid
-33.3 to 0 Dry sub-humid
0 to + 20 Moist sub-humid
+20.1 to + 99.9 Humid
100 or more Per-humid

6. Climate at district level
1901-1950
Climate at district level
1971-2005
(Krishnan, 1988) Raju et al. (2013) in Current Science

7. Drought programmes
• DPAP: 1973-74; to address the problems in dryland areas that
suffer frequent droughts.
• DDP: 1977-78; to restore ecological balance in desert areas
• IWDP: 1992; to develop degraded non-forest wastelands
• IWMP (2009-10): Single umbrella
• Common Guidelines for Watershed Development Projects-
2008 (NRAA, 2011)
• RADP (2011): Implemented through RKVY to improve
productivity of rainfed areas

8. Implications to Drought programmes:
DPAP and DDP districts
Review of DPAP and DDP programmes by MoRD (1994)
C.H. Hanumantha Rao Committee
Criteria for identifying districts’ eligibility to DPAP and DDP
Programme
permissible
Ecosystem % Irrigated Area
(to NSA)
DDP Arid 50%
DPAP Semi-arid 40%
DPAP Dry sub-humid 30%

9. Percent net irrigated area to net sown area
(average of 1990-91 and 1991-1992)
Percent net irrigated area to net sown area
(average of 2007-08 and 2008-09)

10. DPAP & DDP Districts (in operation)
Eligibility of districts to
DPAP & DDP (2008-09) - Revisited
Source of data:
http://watershed.nic.in/QPRRep
ortingDistrictwise.asp
Venkateswarlu B, B M K Raju, K V Rao and C A
Rama Rao (2014) Revisiting Drought Prone Districts
in India. Economic & Political Weekly 49(25): 71-75

11. Shifts in eligibility of districts to DPAP and DDP
Revised
In operation
DPAP (121) DDP (22)
General
pool
DPAP (178) 92 3 83
DDP (40)
2 19 19
General
pool
27 0 326

12. Drought (meteorological)
Moderate drought: Deficit in rainfall is in the range 25-50% of
climatic normal
Severe drought : Deficit in rainfall is more than 50% of
climatic normal
Gore P.G., Prasad Thakur and Hatwar H.R. (2010) NCC
Research Report - Mapping of drought areas over India.
India Meteorological Department, Pune.
Map showing Probability (%) of Moderate drought for
different parts of country
Map showing Probability (%) of severe drought for different
parts of country

13. Probability of drought (%)
(Based on data of 1901-2000)
Moderate drought Severe drought

14. Drought Index
(combining probabilities of moderate and severe droughts)
Overall Drought index (D):
Single composite index of drought
was derived by assigning weights in
the ratio of 1:2 to moderate and
severe drought probabilities
D = [p(mod dr)/2 + p(sev dr)]
(in terms of severe droughts)
(two moderate droughts are
considered equivalent to one severe
drought)

15. Drought proneness
1971-1990
Drought proneness Change
1991-2010

16. Change in drought proneness
(based on A1B Scenario, PRECIS data)
Source: Rao et al. (2013) Atlas on Vulnerability of Indian Agriculture to Climate
Change

17. National Initiative on Climate Resilient
Agriculture (NICRA)
• Launched during February 2011
• To study the impact of climate change on Indian
agriculture and various mitigation and adaptation
options
• A more climate resilient Indian agriculture is the
overall goal

18. Activities under Basic & Strategic Research
• Vulnerability assessment
– Macro level (district)
– Micro level (household/farmer level)
• Simulation modeling, Agro-advisories and contingency crop planning
• Genetic improvement to climatic stresses
• Adaptation and mitigation through enhanced WUE, NUE, CA and AF
• GHG Emission Monitoring
• Pest and disease dynamics
• Understanding the climate-resilient traits of indigenous livestock
• Adaptation in livestock through nutrient and environment
management
• Spawning behaviour in marine and inland fisheries under elevated
temperature
• Socioeconomic impacts and community response

19. Why Vulnerability Assessment?
• Climate change - Affect on Agriculture Sector in India
• Will the impact be uniform across the country ?
Spatial variability in the impact
• Need for identifying ‘hot spots’ of vulnerability
• Need for identifying appropriate adaptation
interventions and their prioritization and resource
allocation accordingly
• Need for investing on building social and human
capital
• Need for identifying regional R & D priorities and
enhance resilience of agriculture

20. Defining Vulnerability
“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, 2001

21. Defining Vulnerability
• Exposure is defined as “the nature and degree to
which a system is exposed to significant climatic
variations”.
• Sensitivity is defined as “the degree to which a
system is affected, either adversely or beneficially, by
climate-related stimuli”.
• Adaptive capacity is “the ability of a system to adjust
to climate change, including climate variability and
extremes, to moderate potential damages, to take
advantage of opportunities, or to cope with the
consequences.
• Example: Rain

22. Defining Vulnerability
Components of vulnerability
Exposure
+
+
Vulnerability
Sensitivity
Impact Adaptive
Capacity

23. Methodology
I. District level analysis (572 districts)
II. Indicator method (Indicators selected to reflect
the three components of vulnerability)
III. Select relevant indicators, determine
relationship, normalize and aggregate into an
index using appropriate weights
IV. Latest available data for the indicators
V. Sort the districts in the ascending order of
vulnerability
VI. Divide them into five equal groups: Very low
(114), low (114), medium (114), high (115) and
very high (115) vulnerability

24. Components and indicators – basis of selection
Component Indicators
Sensitivity Those that reflect the extent of impact either
because of intensity of a problem or size of the
entity being affected
Exposure Change in different climate parameters (PRECIS
data for A1B scenario) for 2021-50 (and 2071-
98) relative to the baseline: 1961-90
Grid level daily data on rainfall, Max T and Min
T were converted to district level values for
computing the indicators chosen
Adaptive
capacity
Those that reflect the ability to adapt to or
cope with climate change/ variability; depends
on health, wealth, technology etc

25. Sensitivity (40) – Indicators included
Variable Expression/ unit Rel_vul Wt
Degraded & waste land %GA Direct 5
Annual rainfall (normal) mm Inverse 20
Cyclone proneness Composite index Direct 5
Area prone to flood
incidence
% GA Direct 10
Drought proneness % Direct 20
Available water holding
mm Inverse 5
capacity of the soil
Stage of GW
development
Draft relative to
availability
Direct 10
Rural Population density No/km2 Direct 5
Net sown area % GA Direct 15

26. Cyclone-proneness
Area prone to
floods (%GA)

27. Exposure (25) Indicators
Variable Expression/ unit
Annual rainfall Change relative to baseline (%)
June rainfall -do-
July rainfall -do-
Number of rainy days -do-
 MaxT relative to baseline (0 C)
 MinT relative to baseline (0 C)
 Heat wave frequency relative to baseline
 Cold wave frequency relative to baseline
 Frost occurrence relative to baseline

28. Exposure – Indicators
Variable Expression/ unit
Drought proneness Change relative to baseline
Dry spells of >= 14 days Change relative to baseline
Extreme rainfall events
99 percentile rainfall Change relative to baseline (%)
No. of events with >100 mm rainfall
Change relative to baseline (%)
in 3 consecutive days
Average highest rainfall in a single
event as % to annual normal
Change relative to baseline
Average highest rainfall in 3
consecutive days as % to annual
normal
Change relative to baseline

31. Indicators of Adaptive Capacity (35)
Variable Expression/ unit
Rural poor %
SC/ST Population %
Workforce engaged in agriculture %
Total Literacy %
Gender gap Tot lit– fem lit
Markets No/lakh holdings
Accessibility (Paved Roads) % villages
Rural Electrification % villages
Net Irrigated Area % NSA
Livestock population ACU/km2 GA
Fertilizer consumption Kg NPK/ha GSA
Ground water availability Ha m/km2
AgGDP/GDP %

32. Normalization
When the indicator is
positively related to the index
When the indicator is
negatively related to the index
min
X X
Z i

i 
X X
max min


X X
Z max
i
i 
X X
max min


36. Vulnerability profile of states
80
70
60
50
40
30
20
10
0
No. of distrcts
VH H M L VL

37. State-wise distribution of districts with
different levels of vulnerability
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
% districts
VH H M L VL

38. Scope and Limitations
• Covers 572 districts as per Census 2001; Each UT and Goa
treated as single district
• Wherever needed, apportioning or unapportioning done
• Data on different indicators do not refer to same year; latest
available data used
• Uncertainty associated with climate projections; used only
one model projection; Addressed by computing VI with
exposure eliminated or with least weight as this gives an idea
of how predisposed these districts are to CC vulnerability

39. District rankings

40. Adaptation Interventions
• Let us first target districts with Very high and High
vulnerability
• Identify the indicators responsible for higher
vulnerability
• Target feasible and economically viable options

41. Some smart practices for drought prone areas
• Rainwater harvesting through farm ponds,
check dams, community tanks
• Improved planting methods like broad bed
and furrow
• Short duration varieties for delayed planting
• Drought tolerant varieties

42. http://www.nicra-icar.in
Thank you
www.crida.in
bmkraju@crida.in

43. Drought prone districts
Source: Compendium of Environmental Statistics, 2003

44. Periodicity of Occurrence of droughts
Source: Drought Management Division, DAC, MoA, GoI