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
Project scientist, Karnataka State
Natural Disaster Monitoring Center,
Bengaluru, Karnataka, India
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)
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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
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ISSN: 2347 9272 (Volume-5, Issue-1)
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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,
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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
Source: Indian Meteorological Department
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
Source: Indian Meteorological Department
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.
Reshma et al., Innovation: International Journal of Applied Research;
ISSN: 2347 9272 (Volume-5, Issue-1)
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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.
Source: Indian Meteorological Department
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
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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
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ISSN: 2347 9272 (Volume-5, Issue-1)
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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
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4.3: Moisture Adequacy Index
4.3.1 4.3.2
4.3.3 4.3.4
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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
Reshma et al., Innovation: International Journal of Applied Research;
ISSN: 2347 9272 (Volume-5, Issue-1)
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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
Reshma et al., Innovation: International Journal of Applied Research;
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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
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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
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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
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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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