Agricultural Drought; Assessment and Monitoring using …ouat.nic.in/sites/default/files/drought_-13102017.pdf · Understanding Drought •Complex nonlinear interactions •Slow process

Agricultural Drought; Assessment and Monitoring using

Geospatial technologies

C.S. Murthy

Drought – a silent threat torural economy

unemployment Cattle - starvation

Fodder shortage

Non agricultural impact: Drinking water shortage

Agriculture – the immediate victim of drought

Consequences of crop failure

• 70% of population depend on agril.

• 68% of net sown area(142.2 M ha) is drought prone

• 50% of drought prone is severe in nature

Geographical Area328.7 M ha

Net Sown Area142.2 M haNet Irrigated Area55.10 M ha

Understanding Drought

• Complex nonlinear interactions • Slow process with multiple impact• No single index • Different states adopt different norms

Weather

Soil

Crop

Agricultural drought

Management

Drought Management

Short term Management

Long term Management

Monitoring & Assessment

Prediction & Early warning

Agro-advisories Crop damage

assessment

Vulnerability maps

Risk maps Prioritization Impact

monitoring

Meteorological drought: reduced rainfall met. indicators

Hydrological drought: reduced surface water hydrological indicators

Agricultural drought: reduced soil moisture crop stress indicators

Gu

idel

ines

fo

r d

rou

ght

man

agem

en

t in

Ind

ia Department of Agriculture, Cooperation and Farmers Welfare (DACFW), Govt. of India is the Nodal agency for drought management

Guidelines to states

• National Drought Manual 2009

• National Drought Manual 2016(www.agricoop.nic.in)

Manual provides•indices for drought monitoring•Drought declaration protocols•Relief management•Long term measures •Training to states

Agricultural DroughtCausative factors

Deficit rainfall

Deficit Soil moisture

Rise in temperature

Rise in water demand

Effect on agriculture

Delayed sowings Reduced sown area Poor germination Stressed crops Loss of crop yield

Indian Meteorological Department

Sparse observations Sharp variability in weather Physical nature of parameters partly related to biological nature of crops.

Aridity Anomaly

Non spatial and subjective manual observations.+

Inconsistency w.r.t to data collection and availability among the states.

No uniform criteria for drought assessment/drought declaration

State Depts. of Agriculture/Revenue/Relief

Weekly reporting of information• Rainfall• Crop sown areas (delay in sowings/reduction in sown area)• Reservoir levels• Manually observed agricultural situation at district/sub district level

Met. Drought season – if rainfall isless than 75 % of normal

Meteorological indicators

Rainfall – most common indicator

Standardized Precipitation index (SPI)

Indicators based on water balance

• Palmer drought severity index• Moisture Adequacy Index (MAI)• Aridity Index and its anomaly

Rainfall – in season+/- 20 % dev. Normal-20 to -60 % Deficit<-60 % dev. Scanty

SPI Values

2.0+ extremely wet

-.99 to .99 near normal

-1.0 to -1.49 moderately dry

-1.5 to -1.99 severely dry

-2 and less extremely dry

Palmer Classifications

4.0 or more extremely wet

0.5 to 0.99incipient wet

spell

0.49 to -0.49 near normal

-0.5 to -0.99incipient dry

spell

-1.0 to -1.99 mild drought

-2.0 to -2.99moderate

drought

-3.0 to -3.99 severe drought

-4.0 or lessextreme

drought

MAI Values

76-100 No drought

50-75 mild

25-49 moderate

< 25 severe

Aridity anomaly

0 Non arid

1-25 mild

25-50 moderate

>50 severe

Standardized Precipitation Index (SPI)

Reservoir Storage Index

Water levels in reservoirs in different yearsSurface water bodies mapping – water spread areaRunoff index using hydrological models

Ground water index

Observation wells – water level data

Hydrology indicators

Agricultural drought indicators

• Extent of reduction in crop planted area

• Extent of delay in crop planted area

• Crop stress at different growth states

• Crop yield loss

AGRICULTURAL DROUGHT – Satellite indices

Ground water Storage

Surface Storage

Runoff

Rain

Infiltration &Percolation

Satellite Sensors

Evapo transpiration

Biophysical Parameters

Vegetation Indices

Agricultural

Drought

Meteorological

Drought

Hydrological

Drought

Runoff

Conceptualisation, development, operational services and institutionalisation of a remote sensing application project

2012+

• Institutional Arrangement at Ministry of Agriculture, GOI

• Creation of MNCFC

• Transfer of NADAMS project to MNCFCApril 2012

• Enhanced end-use of NADAMS project

NADAMS ProjectA success story of

Institutionalization

• IRS WiFS based district / subdistrict level assessment • Participation of user departments1998+

• Use of multiple indices • IRS AWiFS based sub-district level assessment • Decision rules for drought warning & declaration• Enhanced content & frequency of reporting• Institutional participation & Capacity building

2004+

• Conceptualization of NADAMS project at NRSC• Development of Methodology

1988

• Supplementation of WiFS with MODIS • Agricultural area monitoring• Increased number of indices

2002+

2009+

• Operational Services from NRSC• NOAA AVHRR • Regional/district level assessment• Prevalence, intensity and persistence of agricultural

1989 +

• New approach for sowing-period drought assessment• Geospatial products on soil mositure and rainfall• New Indices – Shortwave Angle Slope Index

Institutionalisation phase

Traj

ecto

ry o

f N

AD

AM

S p

roje

ct-

Dev

elo

pm

en

t an

d o

per

atio

nal

ser

vice

s National Agricultural Drought Assessment and

Monitoring Systems (NADAMS)

NADAMS)

Coverage

Information reporting

Satellite derived Indicators/information• NDVI• NDWI/LSWI• EVI• SASI• AMSR E soil moisture

Ground data•Soil• Rainfall• Sown area• Cropping pattern• Irrigation support

OCM 2 NDVI

AWiFSNDVI

District levelSub-district level

Satellite data analysis – Resourcesat, Oceansat, NOAA, Terra, Aqua

NormalMildModerateSevere

End use: • Crop contingency plans• Drought declaration

• Ministry of Agriculture• State Depts. of Agril and Relief• Scientific Organizations

NDWI

Resourcesat AWiFS

AMSR E Soil moisture Spatial Water Balance

(Soil moisture estimation)

Crop SowingFavourable Area

Agricultural drought assessment

Data from multiple satellites

Combination of indices for assessment

Strong ground data base

Sub-district level assessment

Objective and user friendly information

Positive feedback from the User departments

Strengths of NADAMS project

Most commonly adopted index – NDVIa) chlorophyll based indexb) plant vigour and densityc) easy to compute and interpretd) robust indexe) Limitations – soil back ground, saturation, timelag etc.

Commonly used satellite indices

LSWI/NDWI

a) Plant moisture based indexb) NIR and SWIR basedc) No saturation issuesd) Immediate responsee) Sensitive to surface wetness during sowing period

Combination of NDVI and LSWIa) Overcomes limitations of either oneb) amplifies anomalies andc) more responsive to ground situation

Spectral response in V,

NIR region

Spectral response in the

SWIR region

Thermal response

Mainly using data from

polar orbiting satellites

Process based indices –

not operational

Weak forewarning and

preparedness capability

.

-100

-50

0

50

100

150

200

250

300

.

12/6 19/6 6/6 3/710/7 17/7 24/7 31/7 7/8 14/8 21/8 28/8 4/9 11/9 18/9 25/9

% d

evi

atio

n

NDVI response to Agricultural Drought

0

0.1

0.2

0.3

0.4

0.5

0.6

June July Aug. Sept. Oct. Nov.

Month

ND

VI

Normal delayed season Drought

(1) relative deviation from normal, (2) vegetation Condition Index, (3) in season rate of transformation

Integration with ground data

.

0

10

20

30

40

50

60

70

80

90

100

5 J

un

12

Ju

n

19

Ju

n

26

Ju

n

3 J

ul

10

Ju

l

17

Ju

l

24

Ju

l

31

Ju

l

7 A

ug

14

Au

g

21

Au

g

31

Au

g

11

Se

p

18

Se

p

25

Se

p

30

Se

p

% o

f n

orm

al

Seasonal NDVI profiles for drought assessmentWeekly deviations of rainfall

Weekly progression of sown areas

Tie up with ground depts.

Fortnightly NDVI Composites of Resourcesat-2 AWiFS over kharif crop area

Increasing NDVI (greenness)Non-crop area

Jun 01-15, 2016 Jun 16-30, 2016 Jul 01-15, 2016 Jul 16-31, 2016 Aug 01-15, 2016

Aug16-30, 2016 Sep 01-15, 2016 Sep 16-31, 2016 Oct 01-15, 2016 Oct 15-31, 2016

Nov 01-15, 2016 Nov15-31, 2016

NDVI anomaly Assessment(1) Relative dev.(2) VCI(3) In seasontransformation

Agricultural drought situation

Change in crop calendar

Lag between VI & rainfall

Abnormal weather eventsSuch as floods

Extent of VI anomaly

Extent of rainfalldeviation

Extent of sown areadeviation

Methodology for agricultural drought assessment

Normal

Watch

Alert

Drought warning(June, July, August)

Drought declaration(Sep, Oct)

Mild

Moderate

Severe

NDVI anomaly

% dev. from normal

(actual NDVI-normal NDVI)/normal NDVI*100

Selection of normal year –average of recent past normal years

NDVI is a conservative indicator and hence anomalies are not very high

Thumb rule:> 20% reduction in NDVI – drougtconditions

>30% reduction indicate moderate to severe drought conditions

Interpretation of NDVI changes to assess Agricultural drought

Block level crop condition – Anantpur districtComparison between normal year (2005) and drought year (2002)

Crop area affected by drought in kharif 2015, West Bengal

5

17

40

29

81

0

10

20

30

40

50

< 10 % 10-20%

20-30%

30-40%

40-50%

< 50 %

Agr

iltu

ral a

rea

Aff

ecte

d

(%)

% of reduction in crop condition

Normal

Mild Moderate

Severe

Normal

Mild Moderate

Severe

10

26

47

15

2 00

10

20

30

40

50

< 10 %10-20 %20-30 %30-40 %40-50 %< 50 %

Agr

iltu

ral a

rea

Aff

ecte

d

(%)

% of reduction in crop condition

Normal

Mild Moderate

Severe

23

3931

61 0

0

10

20

30

40

50

Agr

iltu

ral a

rea

Aff

ecte

d (

%)

% of reduction in crop condition

Purulia district Bankura district West Midnapur district

AWiFS derived crop condition anomalies showing agricultural drought situation in Andhra Pradesh, kharif 2011

Sep 2011 Sep 2010 NDVI anomaly Sep 2011

NDWI anomalyOct 2011

0.00

0.10

0.20

0.30

0.40

0.50

FN1-SEP FN2-SEP FN1-OCT FN2-OCT Nov

ND

VI

2010 2011

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

F1SEP F2SEP F1OCT F2OCT F1NOV

ND

WI

2011 2010

NDWI - Roddam mandal, AnantpurNDVI - Roddam mandal, Anantpur

164 mandals394 mandals

562 mandals

Assessment atsub-district level

Resourcesat AWiFS – A work-horse for monitoring Agriculture

AWiFS NDVIOct. 2010 (Drought year)

AWiFS NDVIOct. 2008 (Normal year)

1. Pas. Champaran2. Pur. Champaran3. Sheohar4. Sitamarhi5. Madhubani6. Supal7. Ararai8. Kishanganj9. Purnia10. Madhepura11. Saharsa12. Darbhanga13. Muzaffarpur14. Gopalganj15. Siwan16. Saran17.Vaishali18. Samastipur19. Begusarai

20. Khagaria21. Katihar22. Bhagalpur23. Banka24. Munger25. Lackeesarai26. Sheikhpura27. Nalanda28. Patna29. Bhojpur30. Buxar31. Bhabua32. Rhotas33. Aurangabad34. Jahanabad35. Gaya36. Nawada37. Jamui

-60

-40

-20

0

Jam

ui

Gay

a

Jahan

a…

Bu

xar

Aurang…

Samasti…

Bh

ojp

ur

Nal

and

a

Pat

na

Bhagalp…

% d

evia

tio

n f

rom

n

orm

al

Rainfall deficiency

0

50

100

Jam

ui

Gay

a

Jahan

a…

Bu

xar

Aurang…

Samasti…

Bh

ojp

ur

Nal

and

a

Pat

na

Bhagal…%

of

no

rmal

Crop sown area status

Crop areas affected by agriculturaldrought situation are showing lowerNDVI compared to normal, in kharif 2010in Bihar state.

Agricultural drought assessment

(based on multiple indices; NDVI, NDWI, SASI)

Moderate (103)Severe (55)

Moderate (103)Severe (55)

Satellite derived Area Favourable for Crop Sowing/Crop Sown Area (AFCS) , Lakh ha.

Kharif Nromal

Area

AFCS AFCS Unfavourable

Jul-10 Aug-10 area

37 19 23 14

Satellite based agricultural drought assessment

kharif 2010, West Bengal

0

100

200

300

400

500

-0.20

-0.15

-0.10

-0.05

0.00

0.05

0.10

0.15

0.20

09

Jun

25

Jun

11

Jul

27

Jul

12

Au

g

28

Au

g

13

Se

p

29

Se

p

So

wn

are

a 0

00’

ha

SA

SI

Weeks

SASI Sown area

Response of SASI to crop sown area

Features SASI value

Dry vegetation low negative

Moist veg. high negative

Shortwave Angle Slope Index (SASI)

βSWIR1 = cos-1 [ (a2 + b2 - c2) /

(2*a*b) ]

Slope = (SWIR2 − NIR)

SASI = βSWIR1 * Slope (radians)

where a, b and c are Euclidian

distances between vertices NIR

and SWIR1, SWIR1 and SWIR2,

and NIR and SWIR2, respectively

Chronological synchronization between(a) Decrease in SASI(b) Increase in rainfall(c) Increase in sown area

NADAMS projectConceptually and computationally simpleprocedures to discriminate the crop sowingfavorable areas at state level

Features SASI value

Dry soil highly positive

Wet soil low positive

-0.30

-0.25

-0.20

-0.15

-0.10

-0.05

0.00

0.05

0.10

0.15

0.20

09

Ju

ne

17

Ju

ne

25

Ju

ne

3 J

uly

11

Ju

ly

19

Ju

ly

27

Ju

ly

04

Au

g

12

Au

g

20

Au

g

28

Au

g

05

Se

p

13

Se

p

21

Se

p

29

Se

p

SASI

val

ue

s

2002 2006 2009

Seasonal dynamics of SASI

Before crop sowing

Crop growing and

maturity

Commencement of crop sowing

Area Favourable for Crop Sowing/Crop Sowan Area (AFCS)

Geospatial product on Area Favourable for Crop Sowing (AFCS)

using multi-criteria approach

Soil Texture

Rice area mask

Kharif areamask

SASIModelledsoil moisture

Input datasets

SeptemberAugust

JulyJune

State Kharif normal

AFCS M ha. Unfavorablearea

state June July Aug Sep

Andhra Pradesh 7.8 2.0 6.8 6.9 7.3 0.4

Bihar 3.7 0.7 3.6 3.7 3.7 0

Chhattisgarh 4.8 3.2 4.8 4.8 4.8 0

Gujarat 8.7 1.3 5.0 5.8 8.1 0.6

Haryana 2.8 0.6 1.6 2.8 2.8 0

Jharkhand 2.5 0.3 2.4 2.5 2.5 0

Karnataka 7.5 3.5 6.0 6.0 7.0 0.5

Madhya Pradesh 10.4 0.7 9.7 10.3 10.4 0

Maharashtra 14.0 5.5 13.2 13.8 13.8 0.2

Odisha 6.3 3.9 6.1 6.2 6.3 0

Rajasthan 14.3 0.2 4.4 11.7 13.6 0.8

Tamil nadu 2.4 1.1 1.8 2.0 2.0 0.4

Uttar Pradesh 9.3 2.8 8.7 9.2 9.3 0

Sub-Total 94.5 25.8 74.2 85.7 91.7 2.9

All India 108.6 34.2 87.0 97.7 105.5 3.1

Area Favourable for Crop Sowing (AFCS) derived from SASI and water balance methodology, Kharif 2012

Soil moisture

Satellite based• Large area, daily coverage• 25-50 km resolution• Increasing popularity Several microwave sensors• SMRR – 1978-1987• TRMM – TMI since 1997• Scatterometer – ERS 1 & 2• ASCAT – MetopA• AMSRE – 2002-2011• SMOS – 2009• SMAP - 2015Retrieval algorithms from passive systems• NASA• LPRM• PRI

Soil moisture important data for hydrology, agriculture, environment, climate system etc.

Sources of soil moisture data

I. Insitu measurements non-spatial data

Manual• accurate• inadequate coverageAutomatic systems• calibration related issues• large area coverage is

expensive

Spatial dataNon-spatial data

Hydrological models

• Mass balance approach• Profile level moisture• Parameterisation of

models – challenge

Soil moisture products from NRSC

• VIC hydrological models – daily soil moisture images

• AMSR 2 LPRM soil moisture 25 km, 2 day frequency

4_11 JUNE 12_18 JUNE 19_25 JUNE 26_2 JULY 3_9 JULY 10_16 JULY

Tracking the drought conditions of 2014 using LPRM Soil Moisture datasets of NRSC

17_23 JULY 24_30 JULY 31_06 AUGUST17_23 JULY

Soil moisture deviations from normal in 2014

0.000.050.100.150.200.250.300.350.40

01

jun

05

Ju

n

09

Ju

n

13

Ju

n

17

Ju

n

21

Ju

n

25

Ju

n

29

Ju

n

03

Ju

l

07

Ju

l

11

Ju

l

15

Ju

l

19

Ju

l

23

Ju

l

27

Ju

l

31

Ju

l

04

Au

g

08

Au

g

12

Au

g

Soil

mo

istu

re (

m3

/m3

)

LPRM_SM 2014 LPRM_SM 2013

Drought frequency in the sowing period

Field enumeration for drought impact assessment and relief management

Mobile Apps. Technology

Improved field data collection system

• Real-time field data collection,

robust & versatile system,

automation etc.

• Surveillance of events, automated

alerts generation and

dissemination

• Objective enumeration system

• Localised crop damages

Field Data Collection using Geo-ICT

Observation Transmission Information Decision Action

Field Data Collection using Geo-ICT Observation Transmission Information Decision Action

Mobile app. from NRSC/ISRO

that allows users to share,

access and upload natural

resources information on a near

real time basis, with Bhuvan

serving as the platform

Crowd sourcing approach with

open source tools like Open

layers, PHP, Geoserver and

Mapserver, etc. for

visualization and uploading

Immense use for agricultural

information collection/analysis

Provision to upload the

information through internet or

customized mobile which will

be geo-tagged for

visualization through Bhuvan

Portal

Geo-tagged in-season field

data enables developing a

repository of agriculture/crop

related data

Successful and On-going applications of FDC• Crop mapping• Pest/disease surveillance• Crop Insurance• Crop damage assessment enumerations• Disaster• Drought impact enumeration.

Exposure • Identification of factors

• Collection of data• Analysis of data• Construction of

composite Index

ExposureIndex (EI)

Sensitivity Index (SI)

AdaptiveCapacityIndex (AI)

Sensitivity

Adaptive Capacity

Agricultural Drought Vulnerability Index (ADVI)

ADVI = EI+SI-AI

Vulnerability framework

Agricultural drought vulnerability

• Degree of susceptibility of an area to agricultural drought due to variable exposure and coping abilities, Vulnerability map helps visualize the hazard and act before potential damage

• Vulnerability information is crucial for long term drought management• A quantitative and multi-dimensional approach for measuring crop-generic agricultural drought

vulnerability status at sub-district level

A. Exposure component1. Total season rainfall

2. Sowing period rainfall

3. Total season rainy days

4. Sowing period rainy days

B. Sensitivity component1. Season’s Integrated NDVI

2. Season’s Maximum NDVI

3. August NDVI

4. Cropping patternC. Adaptive capacitycomponent

1. Soil2. Irrigation support

3. Land holdings

Exposu

re I

ndex

Telangana state

Adap

tiv

e ca

pac

ity In

dex

Sen

siti

vit

y I

nd

ex

Agricultural Drought Vulnerability Index

87 90 9198

76

0

20

40

60

80

100

120

Less.vul Mod.vul. Vul. Highly vul. Very highlyvul.

No

. o

f M

and

als

Category

Telangana state

Agricultural drought vulnerability status of different blocksComposite Index based on exposure, sensitivity and adaptive capacity indices

29

115 114

49

7

020406080

100120140

No

of

blo

cks

0

2

4

6

8

10

12

14

No

of

Blo

cks

District

Less. Vul. Mod.Vul. Vul. High.Vul Very. High.Vul

District wise distribution of blocks in different vulnerability classes

Agricultural Drought Vulnerability Index – all India

0 600 1,200 1,800 2,400300KM

Regional analysis

Grid scale – 25*25 km

Scope for improvement withadditional indicators

Some drought monitoring products over Odisha State

AWiFS NDVI - Odisha – June to November 2016

Jun 01-15, 2016 Jun 16-30, 2016 Jul 01-15, 2016 Jul 16-31, 2016

Aug 01-15, 2016 Aug 16-31, 2016 Sep 01-15, 2016 Sep 16-30, 2016

Oct 01-15, 2016 Oct 16-30, 2016 Nov 01-15, 2016 Nov 16-30, 2016

Increasing NDVI (greenness) Non-crop area

Agricultural area NDVI (AWiFS) at Block levelAugust 2017 August 2016

August 2014

NDVI deviations

August 2017 compared to 2016 August 2017 compared to 2014

Block-wise rainfall status of Odisha state (June 2017)

RainfallRainfall deviation from the Normal

Block-wise Rainydays status of Odisha state (June 2017)

Rainy Days Rainy days deviation from the Normal

Block-wise rainfall status of Odisha state (July 2017)

Rainfall Rainy days deviation from the Normal

Block-wise Rainy days status of Odisha state (July 2017)

Rainy Days Rainy days deviation from the Normal

Block-wise rainfall status of Odisha state (Aug 2017)

RainfallRainfall deviation from the Normal

Cumulative Rainfall Aug2017 Deviation from Normal August Rainfall

Block-wise Rainydays status of Odisha state (Aug 2017)

Rainydays Rainydays deviation from the Normal

Rainy Days Aug2017Deviation from Normal August Rainy Days

Summary

Blocks having Deficit Rainfall (< 40 % from Normal) and Less Rainy Days (< 40% from Normal)

Blocks District

DARPAN JAJPUR

CHANDRAPUR RAYAGADA

BARANG CUTTACK

K. NUAGAON KANDHAMAL

KISHORENAGAR ANGUL

LAHUNIPARA SUNDARGARH

RAIRAKHOL SAMBALPUR

VYASANAGAR JAJPUR

SALEPUR CUTTACK

ULUNDA SONEPUR

August 2017

Blocks District

KISHORENAGAR ANGULKHAIRA BALASOREOUPADA BALASORE

RAJBARASAMBAR BARGARHTihidi BHADRAK

BANKI_DAMPARA CUTTACKBARANG CUTTACK

SALEPUR CUTTACKODAPADA DHENKANAL

NUAGADA GAJAPATIR. UDAYGIRI GAJAPATI

VYASANAGAR JAJPUR

THUAMUL-RAMPUR KALAHANDI

K. NUAGAON KANDHAMALAnandapur KEONJHAR

Patana KEONJHAR

KUDUMULGUMA MALKANGIRI

BISOI MAYURBHANJ

RARUAN MAYURBHANJ

TIRING MAYURBHANJ

NUAGAON NAYAGARH

DELANGA PURI

KANAS PURI

kOLNARA RAYAGADA

KOLNARA RAYAGADA

NUAGAON SUNDARGARH

June 2017 July 2017

Blocks District

KISHORENAGAR ANGUL

RAJBARASAMBAR BARGARH

BANKI CUTTACK

BARANG CUTTACK

GANDIA DHENKANAL

NUAGADA GAJAPATI

R. UDAYGIRI GAJAPATI

VYASANAGAR JAJPUR

K. NUAGAON KANDHAMAL

KUDUMULGUMA MALKANGIRI

BISOI MAYURBHANJ

RARUAN MAYURBHANJ

NUAGAON NAYAGARH

KHARIAR NUAPADA

kOLNARA RAYAGADA

KOLNARA RAYAGADA

NUAGAON SUNDARGARH

Dry Spells occurrence over Odisha

during 1 June - 31 August 2017

• A Dry Spell is an event of consecutive non-rainy days, i.e. rainfall less than or equal to 2.5 mm

• 10-day and 15-day dry spell events have been identified and mapped during 1 June- 31 August

2017, for 315 blocks of 30 districts of Orissa.

• Block wise daily rainfall provided by the State is the input data

• 103 blocks of 26 Districts were found affected by dry spell events of >=10 days.

• 12 blocks of 8 Districts were found affected by dry spell event of 15 or more days.

Blocks with one or more of dry spell events of >=10 days

Dry spell of >=10 days occurred in 103 blocks of 26 districts of Odisha between 1 June and 31 August 2017

Districts with no dry-spell1. Deogarh2. Kandhamal3. Malkangiri4. Nawarangapur

District

Number of Blocks

affected by dry spell

(>= 10 days)

ANGUL 2

BALASORE 5

BARAGARH 4

BHADRAK 6

BOLANGIR 9

BOUDH 1

CUTTACK 4

DHENKANAL 3

GAJAPATI 2

GANJAM 5

JAGATSINGHPUR 3

JAJPUR 7

JHARSUGUDA 3

KALAHANDI 5

KENDRAPARA 3

KEONJHAR 6

KHORDHA 1

KORAPUT 5

MAYURBHANJ 5

NAYAGARH 4

NUAPADA 4

PURI 7

RAYAGADA 2

SAMBALPUR 2

SUBARNAPUR 2

List of blocks that suffered any event of dry spell of >=10 days

District Blocks District Blocks District Blocks

Banki -Dampada Bolangir

Nischintakoi l i Puintala

Sa l ipur Gudvel la

Ja leswar Tangi -Choudwar Deogaon

Soro Lois ingha

Bahanaga Dhenkanal Patnagarh

Simul ia Gondia Titi lagarh

Khaira Bhuban Bangomunda

Sa intala

Ambabhona

Attabira

Gais i lete Padmapur Bal ikuda

Chhatrapur Naugaon

Bhadrak Ganjam Kujanga

Bhandaripokha

ri Khal ikot

Basudevpur Dharakote Jharsuguda

Bonth Sheragada Laikera

Chandbal i Lakhanpur

Tihidi

ANGULKahiha

BALASORE

DHENKANAL

BOUDH Kantamal

JHARSUGUDA

BARAGARH GAJAPATI

Kashinagar

GANJAM

BHADRAK

JAGATSINGHPUR

BOLANGIR

Chhendipada

CUTTACK

District Block District Block District Block

Karlamunda Gania Jajpur

Th.Rampur Nuagaon Dasarathpur

Junagarh Odagaon Korei

Jaipatna Ranpur Danagadi

Golamunda Sukinda

Boden Barachana

Mohakalpara Khariar Bari

Aul Komana

Rajnagar Nuapara

Banspal Astarang

Patna Brahmagiri

Saharpada Delang

Anandapur Gop

Ghasipura Kakatpur

Hatadihi Kanas

Nimapara

Raygada

Gunupur

Pottangi

Bandhugan Dhankuda

Nandapur Maneswar

Narayanpatna

Laxmipur Binika

Ullanda

Samakhunta

Betanati Hemagiri

Kuliana Subdega

Tiring Balisankara

Thakurmunda

JAJPUR

SAMBALPUR

SUBARNAPUR

MAYURBHANJ SUNDARGARH

KALAHANDI NAYAGARH

NUAPADA KENDRAPARA

KEONJHAR PURI

KHORDHA Begunia

RAYAGADA

KORAPUT

List of blocks that suffered any event of dry spell of >=10 days

Number of Dry Spell events (each event >=10 days) occurred between June and August 17

List of Blocks with a single event of Dry Spell (>= 10 days)

Chhendipada Laikera Gop

Kaniha Lakhanpur Kakatpur

Bahanaga Karlamunda Kanas

Simulia Th.Rampur Nimapara

Ambabhona Junagarh Raygada

Attabira Jaipatna Gunupur

Gaisilete Golamunda Dhankuda

Padmapur Mohakalpara Maneswar

Bhandaripokhari Aul Binika

Chandbali Rajnagar Ullanda

Bolangir Banspal Hemagiri

Puintala Patna Subdega

Gudvella Saharpada Balisankara

Deogaon Anandapur Naugaon

Loisingha Ghasipura Kujanga

Patnagarh Hatadihi Dasarathpur

Saintala Begunia Bari

Kantamal Pottangi Jharsuguda

Banki-Dampada Bandhugan Komana

Nischintakoili Nandapur Nuapara

Dhenkanal Laxmipur Astarang

Gondia Samakhunta Brahmagiri

Bhuban Betanati Delang

Kashinagar Kuliana Sheragada

Parlakhemundi Tiring Balikuda

Chhatrapur Gania Boden

Ganjam Nuagaon Khariar

Khalikot Odagaon

List of Blocks with a two events of Dry Spell (>= 10 days)

Jaleswar Tangi-Choudwar

Soro Jajpur

Khaira Korei

Bhadrak Danagadi

Basudevpur Sukinda

Bonth Barachana

Tihidi Narayanpatna

Titilagarh Thakurmunda

Bangomunda

Block Salepur of Cuttack district suffered 4 Dry Spell events of 10 or more days between June’17 and Aug’17.

Blocks with Dry spell of 15 or more daysDistrict

Number of Blocks with Dry spell (>= 15 Days)

ANGUL 1

BALASORE 2

BOLANGIR 3

BOUDH 1

KALAHANDI 1

KEONJHAR 2

MAYURBHANJ 1

NAYAGARH 1

District Block

ANGUL Kaniha

BALASORE Soro

Khaira

Bolangir

BOLANGIR Puintala

Gudvella

BOUDH Kantamal

KALAHANDI Th.Rampur

KEONJHAR Patna

Saharpada

MAYURBHANJ Betanati

NAYAGARH Odagaon

List of Blocks that suffered a dry spell of 15 or more days

12 Blocks of 8 Districts suffered a single event dry spell of 15 or more days.

Dry spell of >=15 days duration

Conclusion

• Development of drought manuals

• Strengthen the drought monitoring and declaration systems

• Adopt integrated approach

• Satellite indices have the potential to capture drought conditions

• Satellite data – free access, easy computations

• Immense scope for automation

• Technology support for drought resilient agriculture