Assessment of the Drought Conditions during Kharif 2016 for ......The drought is characterized based...

Research Article

February 2017

IJAR Journal Page | 19

Innovation: International Journal of Applied

Research; ISSN: 2347 9272 (Volume-5, Issue-1)

Assessment of the Drought Conditions during Kharif

2016 for Koppala District, Karnataka - A Case Study R. Reshma S. Emily Prabha Jothi G. S. Srinivasa Reddy

M.Tech-Geoinformtics,

University of Madras, Chennai,

Tamil Nadu, India

[email protected]

Project scientist, Karnataka State

Natural Disaster Monitoring Center,

Bengaluru, Karnataka, India

[email protected]

Director, Karnataka State

Natural Disaster Monitoring

Center, Bengaluru,

Karnataka, India

Abstract- Drought is a harmful hazard in the nature and it is also considered to be one among the most

complex natural hazards but it the least understood one. Drought is an impermanent instability, not like

aridity or even periodic aridity, which is an enduring feature of the climate. Enormous number of historic

data’s and the present data’s are essential to learn about the drought which includes difficult inter-

relationship between the hydrological, agricultural and meteorological data. The drought description are

area specific, marking the variance in climatic features along with the diverse physical, chemical, biological

and socio-economic influences. The assessment of drought condition was made using GIS and remote

sensing. The indicator such as Rainfall deviation, Dry Spell, SPI, NDVI deviation, Moisture Adequacy Index

and Reservoir storage index was considered and all the indicators/indices vary across one region to another

region depending on the geographical locations. Karnataka State stands second after Rajasthan in terms of

total area under arid/ semi-arid conditions. 17 taluks in Karnataka have drought vulnerability for 6 or more

consecutive years. The drought determination was done on Koppala district, Karnataka, India situated

between 15.09'00" to 16.03'30" North Latitude and 75.47'30" to 76.48'10" East Longitude. Koppala District

is one among the districts that has observed drought for 11 to 12 years. In 2005, 2007 and 2010 drought was

not observed. The result puts forward a conclusion that the methodologies are feasible to estimate the

drought condition for any given region.

Keyword: Drought, Rainfall deviation, Dry Spell, SPI, NDVI deviation, Moisture Adequacy Index.

I. INTRODUCTION

What is drought?

The question that first arise on the topic is what is drought? As such there is no definite universal

definition to state what a drought is. Drought is an impermanent instability, not like aridity or even periodic

aridity, which is an enduring feature of the climate. The drought description are area specific, markingthe

variance in climatic featuresalong withthediverse physical, chemical, biological and socio-economic influences,

it is typicallyhard to determinethe meaning of drought for one area to another. The drought vulnerability

assessment is made for identifying and arranging the vulnerability of the land in an order.The drought varies

from all other natural hazards for example cyclone, earthquake, volcanic eruption, floods and tsunami etc.

Types of drought

There are many types of droughts the most commonly categorized drought types are Metrological

drought, hydrological drought, Agricultural drought and Socio-Economic drought.

The Metrological drought is simply the rainfall deficiency, it have a lesser precipitation than the

average rainfall for a continued period of time.

The hydrological drought is said when the average amount of water in the source such as lakes and

reservoirs is not sufficient for any kind of activity.

The agriculture drought is which affects the crop growth by any form of causes such as the soil

conditions or the water supply etc.

The socio-economic drought is specifically dependent on the supply and production of goods

economically in relation with the metrological, hydrological and agricultural drought.

Reshma et al., Innovation: International Journal of Applied Research;

ISSN: 2347 9272 (Volume-5, Issue-1)


Fig 1.1: Sequence of drought occurrence and impact

Source: NDMC, 2007

Impacts of drought

Economic impacts-There are many economic impacts that are related to the agriculture and other

divisions. The impacts on drought is also the most growing problem. This may cause the wind erosion,

plant infection etc. This may also be related with the higher level of risk for the humans, flora and

fauna populations‟ altogether.

Environmental impacts-Environmental impacts results in the losses of biodiversity,wildlife habitat,

flora and fauna, water availability and soil erosion. Some of the effects are short-term and may also

lead to the rapid depletion and causes the drought. And, the effects are gradual and stay for a longer

period or become permanent.

Social impacts-The social impacts comprises of the safety, health issues, water usage, and disaster

mitigations. Many other impacts stated that the economic and environmental impacts also have social

components in addition.

Characteristics of Drought

The drought is characterized based on 4 seasons i.e. Pre-monsoon, South-west monsoon, Post-

monsoon, winter rains. In India, the situations of drought and when the drought occurs all are affected by a

number of factors and parameters. The rainfall precipitations and cropping patterns are different across one

region to another region depending on the geographical locations. It does not only depend on the deficiency of

rainfall, but also depends on the uneven and irregular distribution of rainfall pattern across the season or

monsoons. The detailsgiven below gives an idea of the different seasonal and the percentage of distribution of

rainfall from the normal rainfall in India.

Table 1.2: Seasonal Percentage of Distribution of Rainfall in India

Season Months Percentage of Distribution of Rainfall

Pre-monsoon March-May 10.4

South-west monsoon June-September 73.4

Post-monsoon October-December 13.3

Winter rains January-February 2.9

Source: Indian Meteorological Department




II. STUDY AREA

Koppal, a newly evolved district of Karnataka state, it is partitioned out of Raichur District and came

into existence on 01-04-1998. It is situated between 15.09'00" to 16.03'30" North Latitude and 75.47'30" to

76.48'10" East Longitude. It consists of four talukas they are: Koppal, Gangavathi, KushtagiandYelburga.

Koppal district is bounded by Raichur district in the east, Bellary district in the south, Bagalkot district in the

north, Gadag district in the West.

Fig2.1: Study Area-Koppal district, Karnataka.

III. METHODOLOGY

3.3: Rainfall related indices

Rainfall data

The IMD gathers data and information‟s on rainfall each daythroughout the rainy season. The IMD

preserves its linkage of data‟s from various weather stations located throughout the country. The actual rainfall

data is compared along with the Long-Term Average (LTA) i.e. the normal rainfall data, which has been evenly

maintained on a daily, weekly, monthly and annual basis and the comparison between the two data (actual and

normal data)gives information on the scanty, deficit, normal and excess of rainfall in a particular district, taluk

or hobli-wise for a certain period. According to IMD (Indian Meteorological Department), drought happens

when the deficiency of the rainfall at a meteorological sub-division level is greater or equal to 25 per cent of the

LTA (Long-Term Average)for a given period, the drought is said to be “moderate” only if the deficiency value

is in the middle of 26 and 50 per cent, and so called “severe” if the value is more than 50 per cent.

Rainfall deficiency

Rainfall is the utmostsignificant indicator of analyzing the drought condition. A departure in

percentage for the rainfall is calculated from its long-term averages (normal rainfall) and Actual rainfall and

should be considered as a basis for declaring the drought.

The departure is calculated using the formula,

𝐷𝑒𝑝 % = 𝐴𝑐𝑡𝑢𝑎𝑙 −𝑁𝑜𝑟𝑚𝑎𝑙

𝑁𝑜𝑟𝑚𝑎𝑙 ∗ 100,




The categories of the rainfall is made by calculating the Departure percentage from the above given

formula respectively,

Table 3.2: Rainfall departure categories.

Deviation from Normal Rainfall (%) Categories

>=20 Excess

+19 to -19 Normal

-20 to -59 Deficient

-60 to -99 Scanty

-100 No rainfall


Standardized precipitation index

The Standardized Precipitation Index (SPI) is a comparativelya new drought index which is based only on

precipitation. The SPI states the possibility of occurring wet conditions in one or more time balances and arid

conditions at other time balances. The SPI allotsall the precipitation observed a single numeric value, and will

beassociatedwith spatio-temporal data withsignificantly different climates. This spatial and temporal lets the SPI

to be used in both the short-term agricultural and long-term hydrological applications.

Table 3.3: Standard precipitation index (SPI) values categories.

SPI values Categories

<-2 Extremely dry

-1.99 to -1.5 Severely dry

-1.49 to -1.0 Moderately dry

-0.99 to 0 Mildly dry

0 to 0.99 Mildly wet

1.0 to 1.49 Moderately wet

1.5 to 1.99 Severely wet

>2.0 Extremely wet


Dry spell

A dry spell last for short duration, it has to be calculated regularlyfor 4 weeks (equal to 3 weeks in the

case of light soils), when it is low rainfall or if there is no rainfall. Therefore, repeatedly 3 to 4 weeks

subsequently to the due date of the onset of the monsoon equal to the rainfall that is less than 50% of the normal

rainfall in each of the week is defined as a Dry Spell. This indicator/indices is utmostvitaltoquantify the levelof

which the inter-season rainfall variesthat is so serious for the strength of crops and preservation of the soil and

watermanagementin the areasthat are generallyrelated with high rainfall (south-west monsoon with rainfall

greater than1400mm).

3.4: Remote Sensing based Vegetation Indices

Normalized Difference Vegetation Index Deviation

Normalized Difference Vegetation Index (NDVI) is based on the idea of vegetation strength and its

indication of water presence or absence. It allows us to analyze the effect of climatic condition on the vegetation

in an area in terms of the absorptive ability in the visible band and the near-infrared band. The difference of

visible and near-infrared reflectance represents photosynthetically effects and the lively vegetation, this will be

helpful in constructing the vegetation index. The NDVI value is given in value representing between -1 to +1.

The lower value in the vegetation index specifies wetness stress in the vegetation, resulting from prolonged

rainfall deficiency. The higher NDVI values indicates ideal climatic condition for the crop growing condition

and that shows the vegetation is higher.




𝑁𝐷𝑉𝐼 =𝑁𝐼𝑅 − 𝑅𝐸𝐷

𝑁𝐼𝑅 + 𝑅𝐸𝐷

Soil based crop situation differences which leads to the agriculture drought can be calculated using

NDVI deviations. The formula for calculation NDVIdev is given in the above definition respectively. The

deviation for NDVI can be identified by using,

𝑁𝐷𝑉𝐼𝑑𝑒𝑣 = 𝑁𝐷𝑉𝐼𝑖 – 𝑁𝐷𝑉𝐼𝑛

𝑁𝐷𝑉𝐼𝑛 ∗ 100

Where the „dev‟ represent deviation, „n‟ refers to normal value and „I‟ to current period.

If deviation of NDVI is -20% to -30% it represents “Moderate drought” and if lesser than or equal to -

30% it represents “Severe drought condition”. Although the values of deviation may vary from one region to

other region and also with the different cropping patterns.

3.5: Soil Moisture Related Indices

The amount of available soil moisture is the important parameter for identifying the drought. The data

on the following needs to be collected at weekly interval for computing soil moisture based indices with the aid

of agro-meteorological model.

1. Soil water constants

2. Climatic data

3. Weather parameter

4. Crop calendar

Moisture Adequacy Index

Moisture Adequacy Index (MAI), it is calculated by using the weekly water balance, that is equal to the

ratio (expressed as percentage) of Actual Evapotranspiration (AET) to the Potential Evapo Transpiration (PET)

following a soil–water balancing approach during a cropping season. MAI is obtained by using the following

equation:

𝑀𝐴𝐼 (%) = 𝐴𝐸

𝑃𝐸 ∗ 100

Where, AE is actual evapotranspiration and PE is potential evapotranspiration (in %) during different

phonological stages of a crop.

Water balance calculation takes into account the soil characteristic, crop growth period and water

requirements of major crops. Drought is specified crop-wise on a real-time basis.

Table 3.5: Moisture Adequacy Index (MAI) Classifications.


3.6: Hydrology Related Indices

Reservoir Storage Index

The state government collects the data on the level of storage water in important reservoirs through the

irrigation department. Reservoir storage level is a useful indicator of water shortages. As data on reservoir

shortage re available on a regular basis, these could provide accurate information on water shortages. The

Reservoir shortage index is calculated by using the Current level of water in the reservoir and the long term data

of water availability in the reservoir.

𝑅𝑆𝐼 = 𝐴𝑐𝑡𝑢𝑎𝑙 − 𝑁𝑜𝑟𝑚𝑎𝑙

𝑁𝑜𝑟𝑚𝑎𝑙 ∗ 100

Where, Actual is the current Reservoir storage and Normal is the average of Long term data of

Reservoir storage.

MAI (%) Agricultural drought class

76-100 Humid region

51-75 Agriculture favorable

26-50 Moderate drought

0-25 Severe drought




Table 3.6: Category of deficit based on percentage in live storage volume of reservoir.

Percentage deficit in live storage volume of

reservoir w.r.t. average storage of last 10 years

Categories of deficit

<20 Normal Deficit

20-30% Mild Deficit

30-40% Moderate Deficit

40-60% Severe Deficit

>60% Extreme Deficit

Source: Drought Manual 2016,GOI.

IV. DATA ANALYSIS

4.1: Rainfall deviation

4.1.1 4.1.2

4.1.3 4.1.4




Fig: Hobliwise Rainfall (mm) pattern (4.1.1) June, (4.1.2) July, (4.1.3) August, (4.1.4) September &(4.1.5)

October

4.2: Dry spell

Fig: (4.2) Hobliwise Dry Spell (mm) pattern

4.1.5

4.2




4.3: Moisture Adequacy Index

4.3.1 4.3.2

4.3.3 4.3.4




Fig: (4.3.1 to 4.3.5) Moisture Adequacy weekly maps from June to October respectively.

4.4: MODIS-Terra Normalized Difference Vegetation Index

4.3.5

4.4.1 4.4.2




Fig: (4.4.1 to 4.4.5) MODIS-Terra Normalized Difference Vegetation Index monthly maps from June to

October respectively.

4.4.3 4.4.4

4.4.5




4.5: Standard Precipitation Index (SPI)

-0.20.0

0.30.1

0.5

0.1

-0.2

0.7

-0.3

0.4

0.1

-0.6

-0.1

0.50.4

1.4

-0.2

0.2

0.50.5

-0.2

1.3

0.60.7

0.2

-0.5

-0.1

0.3

0.5

-0.1

0.5

-0.2

0.3

0.8

0.0

0.4

1.1

-0.8

0.30.5

0.60.7

-0.1-0.2

0.1

0.5

-0.8

0.5

0.0

1.1

0.4

-0.1-0.1

-0.8

0.2

-0.3-0.3

-1.0

-0.5

0.0

0.5

1.0

1.51

96

0

19

62

19

64

19

66

19

68

19

70

19

72

19

74

19

76

19

78

19

80

19

82

19

84

19

86

19

88

19

90

19

92

19

94

19

96

19

98

20

00

20

02

20

04

20

06

20

08

20

10

20

12

20

14

20

16

SPI V

ALU

ES

YEARS

SPI VALUE - GANGAVATHI TALUK

0.2

-0.6

0.20.1

0.60.8

-0.3

0.1

-0.1

0.50.4

0.0-0.3

0.3

0.80.7

-0.2-0.1

0.20.2

-0.1

0.5

0.10.10.1

0.0

-0.4

0.20.4

-0.4

0.20.3

-0.1

0.8

0.0

0.7

1.6

-0.1

1.0

0.50.60.6

-0.4-0.2

0.1

0.6

-0.2

0.9

-0.1

0.8

0.50.2

-1.0

0.1

0.40.2

-0.1

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

19

60

19

62

19

64

19

66

19

68

19

70

19

72

19

74

19

76

19

78

19

80

19

82

19

84

19

86

19

88

19

90

19

92

19

94

19

96

19

98

20

00

20

02

20

04

20

06

20

08

20

10

20

12

20

14

20

16

SPI V

ALU

ES

YEARS

SPI VALUE - KOPPALA TALUK

-0.2-0.3

0.1

0.7

0.9

0.6

-0.1-0.1

0.30.4

0.00.0

-0.2

0.4

0.80.9

-0.6

-0.3

-0.1

0.5

-0.4

0.8

0.0

0.50.5

-0.8

-0.3

0.4

0.1

-0.2

0.10.2

-0.1

1.0

0.10.0

0.9

-0.2

0.7

0.40.6

0.1

-0.2

-0.6

0.20.3

-0.4

1.0

0.2

1.0

0.5

-0.2-0.1

0.5

0.9

0.0

-0.1

-1.0

-0.5

0.0

0.5

1.0

1.5

19

60

19

62

19

64

19

66

19

68

19

70

19

72

19

74

19

76

19

78

19

80

19

82

19

84

19

86

19

88

19

90

19

92

19

94

19

96

19

98

20

00

20

02

20

04

20

06

20

08

20

10

20

12

20

14

20

16

SPI V

ALU

ES

YEARS

SPI VALUE - KUSHTAGI TALUK

4.5.1

4.5.2

4.5.3




Fig: Standardized Precipitation index (SPI) values Talukwise from 1960 to 2016 for (4.5.1) Gangavathi Taluk,

(4.5.2) Koppala Taluk, (4.5.3) Kushtagi Taluk & (4.5.4) Yelburga Taluk.

4.6: Reservoir Storage Index

Fig: (4.6.1) Reservoir Storage Index 2016 compared with average capacity (2005-2015) for south-west monsoon

Fig: (4.6.2)Cumulative inflow in Tungabhadra reservoir 2015 &2016 compared with average inflow (2004-

2015) for south-west monsoon

0.20.4

0.8

0.0

1.1

0.5

-0.2

0.1

0.50.50.6

-0.4-0.3

0.60.6

1.1

-0.3-0.1

-0.5

1.1

0.0

0.7

-0.2

0.30.3

-0.2-0.1

0.1

0.5

0.1

-0.1

0.10.0

0.7

0.20.1

0.6

-0.4

0.7

0.0

0.7

0.3

-0.4-0.1

0.60.3

-0.7

0.4

-0.2

1.1

0.4

-0.4

-1.1

0.50.40.3

-0.3

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

19

60

19

62

19

64

19

66

19

68

19

70

19

72

19

74

19

76

19

78

19

80

19

82

19

84

19

86

19

88

19

90

19

92

19

94

19

96

19

98

20

00

20

02

20

04

20

06

20

08

20

10

20

12

20

14

20

16

SPI V

ALU

ES

YEARS

SPI VALUE - YELBURGA TALUK 4.5.4




V. RESULTS

Fig 5.1: Drought affected KoppalaTaluks

The prolonged rainfall deficiency, the extended dry spell, the reservoir storage capacity, vegetation

condition and the moisture adequacy in the district from June to October 2016 resulted in severe

drought condition in the Koppala District. When analyzing for the district the whole district was termed

to be drought affected district in Karnataka during Kharif 2016.

Many of these Hoblis that are declared as drought affected has already been subjected to drought

affected area successively for 6 years or more. This has instigatedenormous economic loss to the

district in overall andto the communal in particular. The effect of repeated drought situation is vast on

the socio-economic condition of the societydependent on the farming and the similarzone.

Owing to deficit rainfall consecutively for more than 6 years hascaused in a criticaland no storage of

water in the reservoir and irrigation tanks and also the reduction of ground water which is

asignificantcause for drinking water for human population and as well as to livestock‟s in the district.

VI. CONCLUSION

The drought characteristic was observed using several indicators and indices among the agricultural,

meteorological and hydrological drought to assess and monitor the drought condition in Koppala district,

Karnataka. The indicator such as Rainfall deviation, Dry Spell, SPI, NDVI deviation, Moisture Adequacy Index

and Reservoir storage index was considered. Different analysis was performed to bring out the conclusion for

identifying the drought characteristics and its severity on the study area. The water storage capacity in the

Tungabhadra Reservoir which is in the southernmost part of Koppala district was also considered as a

hydrological parameter to analyze the amount of water the reservoir was holding along the South-West monsoon

in 2016 and its effect on Koppala District and it was observed that the reservoir had extreme deficit storage of

water that was not sufficient to supply to the district as a whole. The Moisture adequacy index was less than

50% in many Hoblis, the dry spell was observed from during 1st June to 28

th October 2016, which constitutes 22

standard weeks, as many as 19 Hoblis of 20 Hoblis have experienced dry spell for ≥4 consecutive weeks and the

district has been experiencing rainfall deficiency for the south-west monsoon in 2016. Based on the indicators




and indices the district is declared as drought affected as per the drought management guidelines 2016 as on the

end of October 2016.

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https://www.researchgate.net/profile/Nezar_Hammouri2

Assessment of the Drought Conditions during Kharif 2016 for ......The drought is characterized based...

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