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Climate Change: Vulnerability & Implications to Drought Programmes in India
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Climate Change: Vulnerability & Implications to Drought Programmes in India
ICAR-Central Research Institute for Dryland Agriculture Hyderabad
Workshop on 'Climate Change Adaptation in Drought Affected Areas: Policies, Programmes and Traditional
Coping Mechanisms‘
New Delhi October 16, 2014
B M K RajuSenior Scientist (Agrl. Statistics)
Climate change
Changing climate is now a reality; evidence unequivocal;warming to continue
IPCC (2014)
YEAR 1905 1910 1915 1920 1925 1930 1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 201022.50
23.00
23.50
24.00
24.50
25.00
25.50
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
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)
Climate at 0.50 x 0.50 Grid (1971-2005)Raju et al. (2013) Thornthwaite and Mather (1955)
simplified by Krishnan (1988)
MI = [(P-PE)/PE]100
Where P = Average annual PrecipitationPE = 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
Climatic Classification
Climate at district level1901-1950
Climate at district level 1971-2005
Raju et al. (2013) in Current Science(Krishnan, 1988)
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
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%
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)
DPAP & DDP Districts (in operation)Eligibility of districts to
DPAP & DDP (2008-09) - Revisited
Source of data: http://watershed.nic.in/QPRReportingDistrictwise.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
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
Drought (meteorological)
Moderate drought : Deficit in rainfall is in the range 25-50%
of climatic normalSevere 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
Probability of drought (%) (Based on data of 1901-2000)
Moderate drought Severe drought
Drought Index (combining probabilities of moderate and severe droughts)
(two moderate droughts are considered equivalent to one severe drought)
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)
Drought proneness 1971-1990
ChangeDrought proneness 1991-2010
Change in drought proneness (based on A1B Scenario, PRECIS data)
Source: Rao et al. (2013) Atlas on Vulnerability of Indian Agriculture to Climate Change
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
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
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
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
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
Defining Vulnerability
Components of vulnerability
Exposure
+ +
Vulnerability
Sensitivity
Impact Adaptive Capacity
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 indicatorsV. Sort the districts in the ascending order of
vulnerabilityVI. Divide them into five equal groups: Very low
(114), low (114), medium (114), high (115) and very high (115) vulnerability
Components and indicators – basis of selection
Component IndicatorsSensitivity 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: 1961-90Grid 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
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 capacity of the soil
mm Inverse 5
Stage of GW development
Draft relative to availability
Direct 10
Rural Population density No/km2 Direct 5
Net sown area % GA Direct 15
Sensitivity (40) – Indicators included
Cyclone-proneness
Area prone to floods (%GA)
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
Exposure (25) 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 in 3 consecutive days
Change relative to baseline (%)
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
Exposure – Indicators
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 %
Indicators of Adaptive Capacity (35)
Normalization
When the indicator is positively related to the index
When the indicator is negatively related to the index
minmax
min
XX
XXZ ii
minmax
max
XX
XXZ ii
Vulnerability profile of states
Daman & Diu
Gujarat
Madhya Pradesh
Uttrakhand
Jharkhand
Chhattisgarh
Tamilnadu
PunjabAssa
m
West
Bengal
A & N Isl
ands
Dadra & Nagar H
aveli
Kerala
Meghalaya
NagalandSikkim
0
10
20
30
40
50
60
70
80
VH H M L VL
No.
of d
istr
cts
State-wise distribution of districts with different levels of vulnerability
Daman & Diu
Gujarat
Madhya Pradesh
Uttrakhand
Jharkhand
Chhattisgarh
Tamilnadu
PunjabAssa
m
West
Bengal
A & N Isl
ands
Dadra & Nagar H
aveli
Kerala
Meghalaya
NagalandSikkim
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
VH H M L VL
% d
istr
icts
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
District rankings
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
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
Thank you
www.crida.in
http://www.nicra-icar.in
Drought prone districts
Source: Compendium of Environmental Statistics, 2003
Periodicity of Occurrence of droughts
Source: Drought Management Division, DAC, MoA, GoI