Y.S. RamakrishnaDirector

Central Research Institute for Dryland Agriculture Santosh Nagar, Hyderabad – 500 059

Drought Management

Probability of drought occurrence

Probability of occurrence of severe droughts

Impacts of drought

Environmental Societal

• Moisture stress• Drinking water shortage• Degradation of resources• Increased pollution (both air and water)

Economic

Social

• Fall in Ag. Production• Reduced income• Loss of livestock• Fall in output• Unemployment• Shortage of essential goods

• Malnutrition• Ill-health• Migration• Debts

METHODOLOGIES

FOR

ASSESSMENT

• This index is calculated on the basis of Thornthwait’s water balance.

• This is the ratio of water deficit (PET-AET) to water need (PET).

• The departure of the index from the normal expressed as percentage of the normal is called Aridity Anomaly index.

Aridity Anomaly Index (AI)

Methodologies

Palmer Drought Severity Index (PDSI)

• This index is based on a two layer water balance model.

• He introduced the concept of CAFEC (Climatically Appropriate For Existing Conditions) precipitation which is a normal value for the established human activities of the place.

• The anomaly between actual and CAFEC rainfall is used as a drought indicator.

• To make this anomaly comparable is space and time, it is multiplied by a weighting factor K which depends on average moisture demand and supply and mean of the absolute values of the anomaly of the place.

• He devised a scale which ranged from –4 to +4 on the basis of which droughts were classified.

Methodologies

Soil Moisture Deficit Index (SMDI)

• This index is based on crop specific soil water balance model.

• Soil moisture deficit ratio is calculated for a given period based on long term mean soil moisture, maximum and minimum available soil water.

• SMDI is calculated following Palmer’s (1965) procedure.

• This index can be classified into specific ranges as in the case of PDSI.

Methodologies

• Two parameter incomplete gamma distribution is fitted to the long term rainfall data to normalize the series.

• The cumulative probabilities are then transformed into standardized normal variables with mean of zero and standard deviation of one using inverse normal distribution, so the values of the SPI are in standard deviations.

• Positive values indicate greater than median precipitation and negative values indicate less than median precipitation.

• Being independent of the magnitude of mean rainfall, it is comparable over a range of climatic zones.

Standard Precipitation Index (SPI)Methodologies

Comparison of different indices

Index

Input requirements Remarks

PDSIWeekly / monthly rainfall &PET, AWC

Requires long series of data

AIWeekly / monthly rainfall & PET, AWC

Requires long series of data

SMDIDaily rainfall and PET crop coefficients, AWC

Requires long series of data

SPI

Weekly / monthly rainfall (can be calculated for multiple time scales)

Requires long series of data

Methodologies

Other drought indices

• R index R/PE less than 0.40

• Z index (R-Rca).w

• WRSI (FAO) WD/ TWR *100

• SDD Tc-Ta

• NDVI (IR-R)/(IR+R)

Early season: Delayed onset, prolonged dry spells after onset

Mid-season: Inadequate soil moisturebetween two rain events

Late season: Early cessation of rainsor insufficient rains

Types of Agricultural Droughts DROUGHT IN FIRST FORTNIGHT OF JULY

0

10

20

30

40

50

23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40

Standard Weeks

Rai

nfal

l

DROUGHT IN FIRST FORTNIGHT OF SEPTEMBER

0

10

20

30

40

50

23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38

Standard Weeks

Rai

nfal

l

DROUGHT IN FIRST FORTNIGHT OF AUGUST

0

10

20

30

40

50

23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38

Standard Weeks

Rai

nfal

l

Agricultural drought classification

AE/PE (%) inPhenophase

Drought intensity

CodeS V R

76 – 100 Nil S0 V0 R0

51 – 75 Mild S1 V1 R1

26 – 50 Moderate S2 V2 R2

25 or less Severe S3 V3 R3

S = Seedling; V = Vegetative; R = Reproductive

Source: Ramanarao et al, 1981

S1V3R2 = A2 (moderate)S0V1R1 = A1 (mild)

Drought Management A Case Study from Andhra

Pradesh: Approach

Criteria for identification of drought affected areas

• Various states have been following different norms for the declaration of drought.

• For example:

In A.P. following criteria has been used

i. Deficiency of rainfall

• Deficiency of rainfall of 25% and above in Mandals where the annual rainfall is more than 1000 mm.

• 20% and above, where the annual rainfall is 750 mm – 1000 mm.

• 15% and above where the annual rainfall is less than 750 mm.

Methodologies

ii. Compression / reduction in the cropped area of 50% and above under all principal crops.

iii. Normal reduction in crop yields of 50% and above in comparison with average yields of preceding 5 years.

iv. Dry spell and its impact on crops.

Methodologies

Any two of the remaining norms

 

 Parameters considered for prioritization of mandals for taking of drought mitigation activity

  

1.     Average Rainfall 2.     Coefficient of variation of rainfall3.     Meteorological drought frequency4.     Hydrological drought frequency5.     Agricultural drought frequency6.     Ground Water Status7.     Feed and Fodder availability8.     Percent Irrigated Area (Kharif)9.     Percent Irrigated Area (Rabi)10. Rural Water Supply (percent habitations fully covered)11. Drought Severity (agriculture)  Note: 1. The developmental / relief measures should be given preference as for the priority. The Priority I mandals should get maximum preference and Priority IV the least. 2. The ranks given to the individual parameters are flexible and the user has the option to change the ranks by using the Ranking Software.  3. Most of the mandals fall under Priority II and Priority III categories

N

EW

S

PRIORITIZATION OF DIFFERENT MANDALS OF A.P

PRIORITY-IPRIORITY-IIPRIORITY-IIIPRIORITY-IV

intensified

Deviation in vegetation index (NDVI)

FOREWARNINGFOREWARNING

 

INPUT

1.    Rainfall 2.  Pan evaporation (mm) maximum and minimum temperature (if pan evaporation is not available i.e. for calculation of potential evapotranspiration) 3.    Crop coefficient4.    Yield response factor5.    Available water holding capacity (AWC) of the soil6.    Latitude and longitude of the mandal (if pan evaporation is not available)7.    Maximum yield of crop 8.    Duration of the crop

OUTPUT

1.      Soil moisture2.      Runoff3.      Deep drainage4.      Moisture adequacy index5.      Water requirement satisfaction index6.      Yield compared to normal7.      Drought signals 

Forewarning SoftwareForewarning Software

Drought Signal

If MAI on any day is less than 0.75 it gives ALARMIf number of days with MAI < 0.75 is 10

↓Gives signal 1 i.e. mild drought signal

If number of days with MAI < 0.75 is 20 ↓

If number of days with MAI <0.5. is 10 ↓

Gives signal 2 i.e. moderate drought

If number of days with MAI <0.75 is 30

If number of days with MAI < 0.50 is 20 ↓

If number of days with MAI 0.25 is 10

Gives signal 3 i.e. severe drought

MITIGATION

BLACK GRAM

.

ALTERNATE LAND USE MODEL : DSS

Drought Mitigation Strategies

• Policy support at national and state level

• Developmental funding to rural development and special assistance during natural calamities

• Input supply, access to credit and marketing and price support

• Farm advisory services

Non-Government Agencies like:

NGO, CBO, SHG, Philanthropic bodies and aid agencies

Major focus:

• Education, building of awareness creation and community institutions and leadership

• Supplementing the Government effort in rural development

Short-term/Immediate measures

• Execution of labour-oriented schemes for employment

generation and implementation of relief and development

works – National Rural Employment Guarantee Act

(NREGA) programme

• Good weather code – encashing production from good

rainfall regions and managing low rainfall regions through

transport of food grains from high production areas

• Establishment of Fodder/Seed/Grain banks

• Establishment of Cattle camps near water points

Long-term measures

• Long-range forecasting of rains (2-4 months in advance)

• Regional analysis of rainfall patterns

• Crop weather watch programs

• Value-added Agro-Advisories

• Integrated watershed development

• Land use diversification

• Water harvesting

• In-situ moisture conservation

RISK TRANSFER

Risk Management and Crop Insurance

• Government sponsored National Agricultural Insurance Scheme (NAIS) in operation since Rabi 1999-2000.

• Farm Income Insurance Scheme (FIIS) implemented on pilot basis in Rabi 2003-04 and Kharif 2004. Discontinued w.e.f. rabi 2004-05.

• Insurance linked to crop loan• Varsha Bima Yojana (Rainfall

Insurance) being implemented by some insurance companies like ICICI- Lombard, IFFCO-Tokio, AIC on Pilot basis.

Weather-based Insurance • No state is immune to natural

calamities

• 12 million hectares of land damaged every year by natural calamities

• Agricultural Insurance is an important risk management tool

• Agricultural Insurance Company of India Ltd. (AIC) formed on January 1, 2006

Weather Index Insurance

High spatial and temporal viability in rainfall

Wide variation from village to village

Recording of rainfall at mandal level which is not representative

Mandals or group of mandals are considered as unit

Lack of transparency in recording and exchange of data

Constraints:

Weather Index Insurance - Rainfall

Advantages:

• Indemnity based on rainfall not on an individual

• Applicable to several crops

• Speedy settlement and transparency

• Weather indices could be deficit or excessive rainfall

Disadvantage:

• Recurring droughts would make it more expensive

Looking Ahead Scaling up to states most vulnerable to

drought namely Rajasthan, Karnataka, AP and Maharashtra

Inclusion of more partners and consortium approach

Consortium partners could be: CRIDA, CAZRI, SAUs

IIT-Bombay

NRSA, CGWB

State Remote Sensing Centers

Proposed source of funding to be:

NIDM