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

bmkraju@crida.in

Drought prone districts

Source: Compendium of Environmental Statistics, 2003

Periodicity of Occurrence of droughts

Source: Drought Management Division, DAC, MoA, GoI