1. 1 23
Arabian Journal of Geosciences
ISSN 1866-7511
Arab J Geosci
DOI 10.1007/s12517-014-1696-0
Spatio-temporal analysis of droughts in the
semi-arid Pedda Vagu and Ookacheti Vagu
watersheds, Mahabubnagar District, India
Sreedhar Ganapuram, R. Nagarajan,
G. Chandra Sehkar & V. Balaji
2. 1 23
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3. ORIGINAL PAPER
Spatio-temporal analysis of droughts in the semi-arid Pedda Vagu
and Ookacheti Vagu watersheds, Mahabubnagar District, India
Sreedhar Ganapuram & R. Nagarajan & G. Chandra Sehkar & V. Balaji
Received: 21 May 2014 /Accepted: 29 October 2014
# Saudi Society for Geosciences 2014
Abstract This paper presents spatio-temporal meteorological
drought analysis of Pedda Vagu and Ookacheti Vagu water-
sheds of Mahabubnagar and Ranga Reddy Districts of
Telangana state, South Central India. Rainfall anomaly index
(RAI) and run analysis have been leveraged to assess drought
characteristics at different stations in the basin. The study also
presents the interpolation of RAI values using spline tech-
nique in a geographic information system (GIS) environment
to map the spatial extent and variation of drought se-
verity in different time steps. The study reveals that the
occurrence, magnitude, and recurrence of drought varied
among the stations in the basin during an observed time
frame, i.e., 1986 to 2013. Significant variations in the
occurrences of number of drought events are observed
among the stations in the basin. The spline interpolated
rainfall anomaly index maps illustrated that some re-
gions experienced more severe drought while other re-
gions were well-off. This uncertainty in rainfall essen-
tially indicates that a finer scale of drought vulnerability
assessment is highly necessary for better drought man-
agement practices. Furthermore, empirical relationships
were developed between drought duration and magni-
tude to support decision-making during various agricul-
tural practices and water management.
Keywords Semi-arid tropics . Drought . Pedda Vagu .
Ookacheti Vagu . RAI . Run analysis
Introduction
Drought is a typical climatic natural disaster that occurs in any
climatic conditions. Drought is caused primarily due to defi-
ciency in precipitation over a span of time especially for a
season or more (Iglesias et al. 2009). It has critical impact on
the socio-economic aspects of the rural communities mainly
those dependent on agriculture, as it may last for few months
to several years with varying intensity and spatial extent. India
has a long history of drought events, with 22 major drought
years faced during the period 1871–2002 (Prabhakar and
Shaw 2008). The 2002 and 2004 droughts show clear evi-
dence of the inherent vulnerability of the Indian monsoon
system to the El Niño phenomenon, which was also demon-
strated with the linkage between El Niño and Southern
Oscillation and Indian food grain production (Saith and
Slingo 2006; Selvaraju 2003). Consequently, it is also evident
that agriculture is at the mercy of monsoon rainfall occurrence
and failure. India is the second most populated country in the
world, with over 69 % of the populations’ livelihood depen-
dent on agriculture and allied activities. India has a total
geographical area of 328 million hectares (Mha), out of which
the total cropped area is 174 Mha including 142 Mha of
rainfed area (Murali Krishna et al. 2009). Population growth
and the expansion of irrigation led to the scarcity of the water
in the Krishna river basin water (Gaur et al. 2007).
Additionally, climate variability adds pressure on the available
water resources making the basin much more vulnerable to
S. Ganapuram (*) :R. Nagarajan
Centre of Studies in Resources Engineering, Indian Institute of
Technology Bombay, Mumbai, India 400076
e-mail: g4sreedhar@gmail.com
R. Nagarajan
e-mail: rn@iitb.ac.in
G. C. Sehkar
Infosys Technology Limited, Bangalore, India
e-mail: goruganthu_c@infosys.com
V. Balaji
Technology & Knowledge Management, Commonwealth of
Learning, Vancouver, Canada
e-mail: vbalaji060@gmail.com
Arab J Geosci
DOI 10.1007/s12517-014-1696-0
Author's personal copy
4. drought. Between the years 2001 and 2004, Krishna basin
experienced severe droughts causing acute water shortages in
lower Krishna basin (Gaur et al. 2007; Biggs et al. 2007).
Although drought and variability in rainfall are not
predictive as most of its causes are natural, but the
impacts could be mitigated with prior awareness about
the possible vulnerable regions. The disaster risk miti-
gation (DRM) program initiated by the Government of
India in collaboration with the United Nations
Development Program (UNDP) envisages preparing di-
saster management plans for effective preparedness
against disasters at village, block, district, and provincial
levels (Prabhakar and Shaw 2008). Drought is mostly
analyzed using point rainfall data at different timescales
which is mapped at different spatial scales. The
Southeast Asia Drought Monitoring system developed
by the International Water Management Institute
(IWMI) provides drought information at the regional,
district/provincial, and pixel level and helps decision-
makers to monitor and mitigate the impact of drought.
Remote sensing-based applications invariably need
ground information such as meteorological and
agricultural data to make them more dependable
(Thenkabail et al. 2004). Suresh et al. (1993) studied
rainfall data of 26 years at Pusa, Bihar, by analyzing
the characteristics and variation in rainfall data with
respect to normal, abnormal, and drought months in a
year. It was reported that at 90 % probability level,
these regions’ expected annual rainfall obtained was
below the drought level and during rabi season the
situation was terrible. Several other studies were con-
ducted to analyze the rainfall data for drought assess-
ments and the variability and trends on annual, monthly,
seasonal, and weekly basis (Ankegowda et al. 2010;
Kwarteng et al. 2009; Srivastava et al. 2000; Rao
et al. 1998; Subudhi et al. 1996). Ankegowda et al.
(2010) analyzed rainfall data of Karnataka region for
23 years (1986–2008) and showed that 80.94 % of
rainfall occurred during June to September, and there
is no significant trend in mean annual rainfall. Kwarteng
et al. (2009) analyzed the characteristics of rainfall in
the semi-arid Sultanate of Oman using 27-year (1977–
2003) rainfall data. Statistics show a negative but insig-
nificant rainfall trends in this region.
Fig. 1 Location map of Pedda Vagu and Ookacheti Vagu watersheds
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5. Some studies concerned to Mahabubnagar district located
in lower Krishna basin include drought vulnerability assess-
ment using water deficit/surplus details (Sreedhar et al. 2013)
and physiographic parameters like rainfall, slope, drainage
density, etc. (Sreedhar et al. 2012). But detailed analysis of
meteorological droughts of this region using standard drought
indices are not available. RAI is a standardized drought index
used to recognize temporal droughts at various times scales
(Van Rooy 1965) and can be interpolated to assess spatial
extent of the droughts. Run analysis (Yevjevich 1967) helps in
the objective identification and characterization of drought
events (Sirdas and Sen 2003; Mishra and Nagarajan 2011).
The present study is conducted with an objective to determine
the spatial and temporal patterns of meteorological droughts in
Pedda Vagu and Ookacheti Vagu watersheds of Krishna river
basin. Additionally, empirical relationships are developed
using drought magnitude and length for objective identifica-
tion of droughts.
Research method
Study area
The study area (Fig. 1) consists of four watersheds of the
lower Krishna basin, located in Southern Telangana Agro-
climatic zone, India. The four watersheds consist of two
Pedda Vagu watersheds and two Ookacheti Vagu watersheds.
The study area lies between 77° 28′ 33.799″ to 78° 13′
31.134″ east longitude and 16° 11′ 45.63″ to 17° 8′ 23.744″
north latitude. The total geographical area of the basin is
4353 km2
spread in 31 mandals of Mahabubnagar district
and 3 mandals of Ranga Reddy district. The altitude of the
basin ranges from 191 to 637 m. The basin belongs to the
semi-arid tropics with distribution of rainfall mainly during
south-west (June–September) monsoon season. The average
annual rainfall of the basin is around 663 mm. The basin
consists of two medium reservoirs, namely Koil Sagar and
Sarla Sagar, and two small reservoirs Kanayapalli Cheruvu
and Raman Pahad. The climate of the area transits from
tropical to subtropical climate. The region has four distinct
climatic seasons like summer, winter, and south-west and
north-east monsoon. The summers are relatively hot, and the
period is from March to May with temperature ranging from
16.9 to 41.5 °C. The winter temperature ranges from 16.9 to
19.1 °C, i.e., from November to January. Agriculture and
livestock are the main livelihood opportunities of the rural
families in the basin. The region follows two agricultural
seasons, viz, kharif (June to October) and rabi (November to
March). Paddy is widely cultivated in the basin. Apart from
paddy, crops like sorghum, pearl millet, finger millet, maize,
groundnut, castor, vegetables, sunflower, chili, and red gram
are also being cultivated. Kharif crop cultivation is mostly
dependent on rainfall, whereas rabi crop is dependent on
Table 1 Details of location, observation period, and statistics of annual rainfall at various stations of the basins
S.
no
Station (observation
years)
Lat. Long. Altitude
(m)
Period Mean
(mm)
Standard
deviation
Minimum
(mm)
Median Maximum CS CK CV
1 Adakkal (23) 16.50 77.93 356 1989–2013 654.5 146.84 332 643.4 1001.1 0.129 0.563 0.224
2 Atmakur (18) 16.32 77.81 310 1996–2013 785.2 253.93 396.5 749.2 1242 0.246 −0.730 0.323
3 Bhoothpur (26) 16.71 78.05 444 1988–2013 617.4 140.02 369 604 925.3 0.565 0.035 0.227
4 C.C.kunta (28) 16.43 77.8 330 1986–2013 602.6 153.41 327.6 649.8 1002.6 0.060 0.285 0.255
5 Devarkadra (18) 16.60 77.85 370 1996–2013 646.5 137.71 477.5 588.15 903.8 0.679 −0.754 0.213
6 Dhanwada (18) 16.65 77.67 436 1996–2013 675.6 150.79 352.2 696.1 936.6 −0.291 0.223 0.223
7 Doulatabad (18) 17.01 77.58 532 1996–2013 757.7 238.79 447.2 690.35 1350.8 1.025 0.813 0.315
8 Gandeed (28) 16.91 77.81 510 1986–2013 621.7 156.83 351 619.4 927.7 0.115 −0.455 0.252
9 Ghanpur (28) 16.55 78.06 448 1986–2013 600.8 160.96 240 612.6 855.6 −0.337 −0.600 0.268
10 Gopalpet (28) 16.38 78.14 418 1986–2013 607.2 149.05 317 619 899.4 −0.157 −0.137 0.245
11 Hanwada (18) 16.80 77.91 442 1996–2013 678.7 156.58 346.2 685.7 917.4 −0.419 −0.010 0.231
12 Kodangal (28) 16.50 77.93 356 1986–2013 744.0 172.06 382 757 1000.8 −0.325 −0.782 0.231
13 Koilkonda (18) 16.75 77.79 442 1996–2013 555.6 141.27 346.8 561.6 916.2 0.758 1.093 0.254
14 Kosgi (27) 16.99 77.75 512 1986–2013 651.1 184.52 355.3 665.6 1046.4 −0.161 −0.617 0.283
15 Kothakota (23) 16.37 77.93 344 1991–2013 685.9 161.44 424.5 653 942.6 −0.125 −1.226 0.235
16 Kulkacherla (28) 17.01 77.86 566 1986–2013 792.3 180.98 283 838 1084.8 −1.254 1.862 0.228
17 Maddur (18) 16.85 77.63 488 1996–2013 501.8 126.54 268 496 733 −0.055 −0.670 0.252
18 Mahabubnagar (18) 16.72 77.99 474 1996–2013 803.5 196.79 473 814.45 1140.7 −0.253 −0.074 0.245
19 Peddamandadi (22) 16.41 78.03 383 1992–2013 561.7 139.51 244.7 547.05 833.2 −0.001 0.073 0.248
20 Wanaparthy (28) 16.34 78.07 445 1986–2013 718.0 163.36 395.9 710.9 982.1 −0.073 −0.902 0.228
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6. groundwater due to the depletion of water in surface water
bodies. Irrigation water during rabi season is obtained from
either canals or groundwater pumped from open wells that are
10 to 20 m deep or bore wells which are 80 to 100 m deep
installed with submersible pumps. The predominant soils in
the basin are clayey soils, cracking clay soils, gravelly clay
soils, gravelly loam soils, and loamy soils. Soil types include
Entisols and Vertisols (black cotton soils) and Alfisols (red
soils) with low water holding capacity.
Data
Rainfall data for 20 meteorological stations available for the
period 1986 to 2013 is collected from District Collectorate
office, Mahabubnagar District, and Directorate of
Economics and Statistics, Hyderabad, India (refer to
Table 1). The daily rainfall data analyzed in this study
is available for the period 1999 to 2013 for
Mahabubnagar and Ranga Reddy Districts. Several stan-
dard statistical parameters like mean, median, minimum
(Min), maximum (Max), standard deviation (SD), skew-
ness, kurtosis, and coefficient of variability mentioned
in Kwarteng et al. (2009) and Ankegowda et al. (2010)
are estimated for all the stations and presented in
Table 1.
Computation of rainfall anomaly index (RAI)
Rainfall measure is used in drought index calculations as it is
the most vital hydrological variable generally the only mete-
orological measurement made in semi-arid areas (Oladipo
1985; Tilahun 2006). Several indices are available to calculate
temporal meteorological droughts. In this study, RAI is mod-
ified to account for non-normality like SPI which is used
for the assessment of both temporal and spatial droughts
as it is independent of time and space. Hence, it is more
useful in semi-arid regions particularly India since at many
meteorological stations, the recorded rainfall data available
is less than 30 years, while most of the meteorological
drought assessment indices require more than 30 years of
data (Van Rooy 1965; Loukas et al. 2003). Additionally,
as the rainfall of the present study area is not normally
distributed, RAI is used in drought assessment of this
region. One more utility of this index is that it can be
computed at different time scales (1, 2, 3, 6, 9, and
12 months); short duration is useful in agriculture drought
monitoring while longer duration helps in water resource
assessment (Loukas et al. 2003). The recommended length
of data for any drought index is 30 years; however, RAI
is considered in this study due to above reasons. The
yearly and kharif rainfall variability is pursued using the
rainfall data details given in Table 1. RAI is used to
identify droughts by establishing some arbitrary values
for drought identification. The RAI is used to assess and
identify droughts, drought severity, and variability by com-
paring with some established arbitrary value. It is reported
that this index based on only rainfall as input performed
comparatively better than more complicated indices like
Palmer and Bholme-Mooley in depicting periods and den-
sity of droughts (Oladipo 1985; Tilahun 2006). Rainfall
anomaly index (RAI) is described as rainfall variability
over a time (Van Rooy 1965) and is estimated as below
for positive anomalies
RAI ¼ þ3 RF−MRF=MH10−MRF½ Š
and for negative anomalies
RAI ¼ −3 RF−MRF=ML10−MRF½ Š
where RAI represents the annual RAI,
RF is the actual rainfall for a given year,
MRF is mean rainfall of the total length of record,
MH10 is the mean of the 10 highest values of rainfall on
record, and
ML10 is the mean of the 10 lowest values of rainfall on
record.
A ranking of nine classes of rainfall abnormality ranging
from extremely wet to extremely dry and range of each
class is shown in Table 2 (Keyantash and Dracup 2002;
Roshan et al. 2012). If the purpose of the study is to
investigate dry periods, the negative prefixed RAI is
used, while positive RAI is used to study wet periods
(Hansel and Matschullat 2006). In the present study, negative
RAI is calculated for annual and kharif time scales for drought
assessment of all the stations.
Spatial drought severity assessment
Mapping of spatial extent of drought severity is essential to
know the drought severity of adjacent regions where point
Table 2 Classification of RAI ranges into drought classes
S. No. RAI Drought class
1 >3 Extreme wet
2 2.1 to 3 Severe wet
3 1.2 to 2.1 Medium wet
4 0.3 to 1.2 Weak wet
5 +0.3 to −0.3 Normal
6 −0.3 to −1.2 Weak drought
7 −1.2 to −2.1 Medium drought
8 −2.1 to −3 Severe drought
9 <−3 Extreme drought
Sources: Keyantash and Dracup 2002; Roshan et al. 2012
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7. estimates are not available. Interpolation is the most common-
ly used method to predict the values of attributes at unknown
points using measured points within the same area. The inter-
polation methods commonly used are inverse distance weight-
ed (IDW), co-kriging, and thin-plate smoothing splines;
among the three methods, thin-plate smoothing splines is
recommended for interpolating climate variables. Thin-plate
smoothing splines can be used for exact interpolation or for
smoothing to generate spatially coherent surface. Compared
to IDW, splining and co-kriging provides better spatial quality
of the prediction surfaces, but spline interpolation is preferred
over co-kriging as it is faster and easier to use (Hutchinson and
Gessler 1994; Hartkamp et al. 1999). The spatial extent of
drought severity is mapped by using annual and kharif season
RAI values in GIS environment using spline interpolation
method for years 1996 to 2013.
Objective identification of drought parameters using “run
theory”
Run theory is proposed and applied for the objective assess-
ment of drought parameters like drought duration, magnitude,
and intensity (Yevjevich 1967). To derive these parameters,
the threshold level approach is used, which is a constant or a
function of time. In run theory, a run is defined as a portion of
time series of drought variable which is either below or above
Fig. 2 Mean monthly rainfall distribution at different stations
Table 3 Descriptive statistics of monthly rainfall data of kharif (June–October) season
Station Maddur Mahabubnagar
Statistics Jun Jul Aug Sept Oct Jun Jul Aug Sept Oct
Mean 60.94 104.6 117.4 105.7 69.23 108.51 156.4 201.9 150.2 115.62
Median 49.70 75.20 115.8 101.5 65.20 108.60 139.80 206.90 136.50 94.40
Mini 11.00 8.20 14.00 36.00 0.00 17.00 41.60 45.30 31.60 36.60
Max 172.00 212.0 231.0 229.0 175.2 258.10 333.20 344.60 256.00 208.20
SD 40.74 64.21 65.25 54.29 54.59 62.04 83.57 96.19 70.05 59.72
CS 1.19 0.33 0.24 0.69 0.61 0.71 0.51 −0.13 −0.04 0.32
CK 1.81 −1.35 −0.88 0.07 −0.59 0.45 −0.48 −1.01 −1.09 −1.40
CV 0.67 0.61 0.56 0.51 0.79 0.57 0.53 0.48 0.47 0.52
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8. the threshold level, characterized as a negative or positive run,
respectively (Sirdas and Sen 2003). The threshold level is
achieved by dividing the average annual precipitation by the
mean number of rain days of the basin. Based on the threshold
value, a dry spell is recognized if seven consecutive days
receive rainfall lesser than the threshold value (Mishra and
Nagarajan 2011). The duration of a drought event is determined
as the time taken by a drought from the time it initiates to until it
terminates. Drought magnitude/severity is estimated as the sum
of cumulative deficiencies of precipitation that occurred below
threshold level, while drought intensity is obtained by dividing
the drought magnitude with drought duration.
Analysis and findings
Annual rainfall distribution and variability
Annual rainfall is the most vital climatic indicator of water
deficit or surplus in any region. The mean annual rainfall of
the basin is 663 mm, with mean kharif, rabi, and summer
rainfall of 599, 27.4, and 36.4 mm, respectively. The maxi-
mum annual rainfall of 1350 mm is recorded in 2010 at
Doulatabad and minimum of 240 mm in 2004 at Ghanpur.
The highest mean annual rainfalls are observed at
Mahabubnagar (803 mm), Kulkacherla (792 mm), Atmakur
(785 mm), and Doulatabad (757 mm) meteorological stations,
whereas the lowest mean annual rainfalls are observed at
Maddur (501 mm), Koilkonda (555 mm), and
Peddamandadi (561 tm) stations. The lowest mean rainfalls
of the basin which resulted in severe droughts are recorded in
1994, 1997, 1999, 2002, and 2004 (Gaur et al. 2007). Other
low rainfall years that resulted in various levels of drought
include 1992, 2001, 2003, 2006, 2008, 2011, and 2012 (refer
to Table 1). The mean annual rainfalls at all the meteorological
stations are quiet variable and irregular as compared to mean
annual rainfall of the basin. For instance, in 2010, Doulatabad
station recorded the highest annual rainfall of 1350 mm, but
other stations located at Ghanpur (499 mm), Gopalpet
(500 mm), Peddamandadi (568 mm), Gandeed (481 mm),
and Maddur (573 mm) have received very less rainfall
Fig. 3 Temporal variation of annual and kharif RAI of different stations
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9. accounting severe drought. Furthermore, the descriptive sta-
tistics like coefficients of variation (CV), skewness (CS), and
kurtosis (CK) are presented in Table 1; these coefficients are
highly variable from one station to another and infer high
annual rainfall variability among stations. The positive skew-
ness and kurtosis values indicate frequency of low precipita-
tions at Addakal and Koilkonda stations. A comparative
assessment of the probability distribution of rainfall was car-
ried out using Kolmogorov-Smirnov test hypothesis, and the
distribution with least p value is chosen as best fit. It is
observed that Gumbel distribution is the best fit for nine
stations while exponential distribution is the least suitable fit
for all stations. The details of the test data are provided as
additional data in Table 6 and from Figs. 13 and 14.
Fig. 4 Temporal variation of annual and kharif RAI of different stations
Fig. 5 Temporal variation of annual and kharif RAI of Peddamandadi and Wanaparthy stations
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11. Mean monthly rainfall distribution
The mean monthly rainfall distribution over the basin is quite
variable from station to station as shown in Fig. 2. During the
months of January and February, most of the regions are
completely dry without any rainfall. Monthly rainfall slightly
increases from March with nil or very less rainfall during
January and February. The rainfall increased gradually from
May to October, and again, there is a decrease in rainfall from
November to December. The monthly average rainfall account-
ing nearly 90 % is recorded mainly during June to October
(Kharif season) of the year at various stations. Major rainfall is
from south-west monsoon with August and September being the
peak months of rainfall. The lowest monthly rainfall is observed
from January to May and November to December accounting for
less than 10 % of annual rainfall. Hence, the months of June to
October are significant rainfall-contributing months to the annual
rainfall. Similar pattern of monthly rainfall distribution is ob-
served in other locations (Kwarteng et al. 2009; Ankegowda
et al. 2010). The descriptive statistics like mean, median, maxi-
mum, minimum, standard deviation (SD), coefficients of varia-
tion (CV), skewness (CS), and kurtosis (CK) of monthly rainfall
(June–October) for lowest mean rainfall region and highest mean
annual rainfall are presented in Table 3. The coefficients of
Table 5 Occurrences of number of drought events under different categories
Drought severity (range) Number of extreme
drought (<−3)
Number of severe
drought (−2.1 to −3)
Number of medium
drought (−1.2 to −2.1)
Total number of
drought events (n)
Frequency=n/
N
Stationscale Annual Kharif Annual Kharif Annual Kharif Annual Kharif Annual Kharif
Addakal 3 5 2 3 5 2 10 10 0.43 0.43
Atmakur 4 6 1 1 2 0 7 7 0.39 0.39
Bhoothpur 4 3 3 4 4 4 11 11 0.42 0.42
C.C.Kunta 5 6 3 1 2 4 10 11 0.36 0.39
Devarkadra 4 5 3 2 3 0 10 7 0.56 0.39
Dhanwada 4 5 2 1 0 1 6 7 0.33 0.39
Doulatabad 4 4 2 3 3 4 9 11 0.50 0.61
Gandeed 5 5 2 2 3 1 10 8 0.36 0.29
Ghanpur 3 3 5 5 2 3 10 11 0.36 0.39
Gopalpet 4 5 1 0 4 5 9 10 0.32 0.36
Hanwada 3 5 2 0 3 2 8 7 0.44 0.39
Kodangal 4 5 3 1 4 4 11 10 0.39 0.36
Koilkonda 5 3 2 2 1 4 8 9 0.44 0.50
Kosgi 4 4 3 3 1 3 8 10 0.30 0.37
Kothakota 4 6 3 3 3 2 10 11 0.43 0.48
Kulkacherla 4 5 1 1 1 3 6 9 0.21 0.32
Maddur 5 4 1 2 1 2 7 8 0.39 0.44
Mahabubnagar 4 4 0 1 1 1 5 6 0.28 0.33
Peddamandadi 4 3 3 5 2 1 9 9 0.41 0.41
Wanaparthy 4 6 4 1 1 3 9 10 0.32 0.36
Table 6 Rainfall distri-
bution details Station Distribution
1 Addakal Gumble
2 Atmakur Rayleigh
3 Bhoothpur Gumble
4 Chinnachintakunta Log normal
5 Devarkadra Gumble
6 Dhanwada Gumble
7 Doulatabad Rayleigh
8 Gandeed Gumble
9 Ghanpur Weibull
10 Gopalpet Gumble
11 Hanwada Log normal
12 Kodangal Gamma
13 Koilkonda Gumble
14 Kosgi Rayleigh
15 Kothakota Log normal
16 Kulkacherla Gamma
17 Maddur Rayleigh
18 Mahabubnagar Log normal
19 Peddamandadi Gumble
20 Wanaparthy Gumble
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12. variation show that rainfall distribution is quite variable from one
month to another in both regions. The positive skewness and
kurtosis values of monthly rainfall indicate that low precipitation
frequency is observed in June and September at Maddur station
while only June in Mahabubnagar station. It can be inferred that
regions which have good distribution of monthly rainfall like
Mahabubnagar have comparatively better annual rainfall as 90 %
rainfall occurs in kharif season.
Temporal drought severity assessment using RAI
The RAI values were computed for kharif (June–October) and
annual time scales for all the 20 meteorological stations of the
basin. The time series of RAIs computed for the two kharif
and annual scales are depicted in Figs. 3, 4, and 5. From the
figures, it is evident that annual and kharif rainfall varied at all
stations with time. In the RAI time series, the positive
ranges of RAI correspond to wet periods while the
negative ranges correspond to dry periods, i.e.,
droughts. The RAI time series show that different re-
gions experienced varying magnitudes (severity) of
droughts over the time. Based on the magnitude ranges
of RAI as shown in Table 2, the droughts are classified
into extreme, severe, and medium droughts. Table 4
shows the occurrences of drought under categories ex-
treme, severe, and medium drought at different meteo-
rological stations. The annual and kharif RAI ranges
less than −3 are categorized as extreme droughts, −3
to −2.1 as severe droughts, and −2.1 to −1.2 as medium
droughts. Visual interpretation of annual and kharif RAI
Fig. 6 Spatial variation of annual (top) and kharif (bottom) RAI interpolated drought severity, 1996 to 1998
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13. time series shows that both have the same pattern, but
the magnitude varied over the time at all the stations.
From the interpretation of graphs as well as from
Table 4, it is evident that all the regions experienced
extreme droughts of more than −3 magnitude in 1997,
1999, 2002, 2004, 2006, and 2008; besides this,
Chinnachintakunta, Peddamandadi, Doulatabad, and
Devarkadra regions have experienced extreme droughts
between 2011 and 2013. Moreover, all the regions have
also experienced either severe or medium droughts in
other years, and the details of occurrences of these
droughts are shown in Table 4.
Furthermore, Table 5 shows a matrix of number of drought
events that occurred under different drought categories at all
the stations. On the whole, there are around 3 to 6 drought
events under extreme drought category, while the occurrences
of number of drought events under severe and medium have
ranged from 0 to 5 at all the station. Comparison of annual and
kharif time series shows that kharif time scale experienced
more extreme and medium category drought events, whereas
annual time scale experienced more severe droughts. It is
observed that Addakal, Bhoothpur, Chinnachintakunta,
Ghanpur, Gopalpet, Kodangal, Kothakota, and Wanaparthy
regions experienced more number of drought events than
other regions. The frequency of droughts gives information
about the recurrence of a drought at a station over time.
The frequency of droughts is estimated by dividing the
total drought events with observation period (N) of the
station and presented in Tables 5 and 6. Frequencies
varied from 0.21 to 0.56 per year in annual time scale
Fig. 7 Spatial variation of annual (top) and kharif (bottom) RAI interpolated drought severity, 1999 to 2001
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14. and 0.29 to 0.61 per year for kharif time scale. It can be
inferred that Doulatabad, Devarkadra, and Koilkonda sta-
tions experience more frequently than other regions. Some
regions which experienced most vicious droughts of mag-
nitude ranging between −9 and −6 are in 1997 at
Dhanwada; 1999 at Addakal, Hanwada, and
Peddamandadi; 2002 at Kosgi and Kulkacherla; 2004 at
Maddur, Mahabubnagar, Kodangal, and Kulkacherla; and
2006 at Devarkadra and Koilkonda stations. It is observed
that in many regions, there has been recurrence of drought
events every 2 or 3 years. The above results show that the
droughts varied in space with time in the basin; as the
region is solely dependent on agriculture for livelihood, a
spatial assessment of drought extent and recurrence is
envisaged to provide better understanding about regional
drought development.
Spatial drought severity assessment spline interpolation RAI
values
Spatial variation of drought severity maps are derived from
the interpolated RAI time series data using spline interpolation
method in ArcGIS. Spatial drought severity maps were gen-
erated for kharif and annual RAI time series data only from
1996 to 2013 as the time series data for all the station is
available from 1996. Drought severity of kharif and annual
time series is classified into nine categories as per Table 2
(Keyantash and Dracup 2002; Roshan et al. 2012) and are
presented in Figs. 6, 7, 8, 9, 10, and 11. The tone of drought
severity are categorized as follows: red indicates extreme
droughts, orange indicates severe drought, yellow indicates
moderate drought, and light yellow indicates weak drought,
while light blue to dark blue indicates varying wet periods.
Fig. 8 Spatial variation of annual (top) and kharif (bottom) RAI interpolated drought severity, 2002 to 2004
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15. The colors indicate that red to yellow color regions would
suffer varying levels of droughts, while light blue to dark blue
would be comparatively wet.
From the visual interpretations of time series of RAI maps
(Figs. 6, 7, 8, 9, 10, and 11), it is obvious that the drought
severity varied in space and time from 1996 to 2013.
Moreover, the visual observation of these maps show that
the spatial extent and pattern of drought severity categories
varied in annual and kharif timescales to a certain extent due
to differences of magnitudes of two RAI time scales. From the
visual observation of maps, years 1997, 1999, 2002, 2004,
2006, and 2008 are identified to be extreme drought years and
years 2011 and 2012 as severe droughts. Similarly, the years
2001 to 2004 have been reported as critical drought years in
lower Krishna basin by Gaur et al. (2007), and the reason for
the droughts has been associated with the failure of Indian
monsoon system due to the El Niño phenomenon (Saith
and Slingo 2006; Selvaraju 2003). Even during 1996,
2001, 2003, 2007, 2009, and 2013, some regions have
experienced severe to extreme droughts. From the maps,
it is also evident that even during the years 1998, 2000,
2005, and 2010 when the rainfall is above mean rainfall
of the basin, some regions experienced medium to se-
vere droughts. The drought severity maps show a lot of
variation both in space and time from one region to
another as well as in the recurrence of droughts.
The study area is classified into southern Telangana (700 –
900 mm) and scarce rainfall (500–700) agro-climatic zones
based on rainfall (Valli et al. 2013). Atmakur, Doulatabad,
Kodangal, Kulkacherla, Mahabubnagar, and Wanaparthy re-
gions are categorized under the southern Telangana agro-
climatic zone; these regions experienced droughts only during
Fig. 9 Spatial variation of annual (top) and kharif (bottom) RAI interpolated drought severity, 2005 to 2007
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16. critical drought years like 2002 and 2004. Hence, these re-
gions are comparatively less prone to drought than the scarce
rainfall zones. The remaining 15 regions are categorized under
scarce rainfall zone; among these 15 stations, Maddur,
Koilkonda, Peddamandadi, Ghanpur, Goplapet, and
Chinnachintakunta regions are more prone to droughts. The
drought severity of both kharif and annual time scales showed
variations in the north, the center, and the south due to either
changes in the topography. The rainfall decreased from north
to center and to the south, similarly the drought severity
increased from north to center and towards south. It implies
that droughts increase with decrease in rainfall. The stations
Doulatabad, Kodangal, Kulkacherla, Mahabubnagar, and
Gandeed regions located in higher-elevation regions have
received better rainfall and experienced less drought events.
Central and lower part of the basin which is characterized with
lower elevations than the upper basin received lower rainfall
and is prone to more droughts. On the whole, spatial drought
assessment provides regional assessment and characterization
of drought events only, but the estimation of magnitude and
length of drought events are crucial for water resource assess-
ment and micro-level planning. For this purpose, run analysis
is carried out for objective assessment of droughts magnitude
and duration.
Objective assessment of drought magnitude and duration
using run analysis
Run analysis is used to study magnitude, duration, and inten-
sity of droughts in the present study. The mean rainy days of
Fig. 10 Spatial variation of annual (top) and kharif (bottom) RAI interpolated drought severity, 2008 to 2010
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17. the basin was observed to be 41 days. The threshold value of
16 mm for the basin was obtained by dividing mean annual
rainfall of 663 mm with mean rainfall days. Based on the
threshold value and rainfall, the water deficit and surplus
rainfall of region is determined for kharif season, i.e., June
to October only as 90 % of the rainfall occurs during this
period. The maximum duration of dry spells was 70 days
observed at Koilkonda and Ghanpur stations; consecutively,
63 days dry spells were observed at Atmakur, Devarakadra,
and Peddamandadi stations. Other dry spells of 56 days were
found at Chinnachintakunta, Kothakota, Peddamandadi, and
Gopalpet.
Additionally, it is observed that 42- and 35-day dry spells
are very common at all the stations in the basin. The years with
more number of dry spells during kharif season have resulted
in severe drought years like 1999, 2002, 2004, 2006, and
2008. The maximum drought magnitude of 126 mm was
observed at Ghanpur in 2009. Drought magnitudes of more
than 80 mm occurred mostly during drought years 2002,
2004, and 2006. The empirical relationships developed be-
tween drought magnitude and duration are presented using
scatter plots shown in Fig. 12. Such objective assessment of
past drought characteristics using statistical relationships pro-
vides valuable insights for what-if analysis of present and
future assessment of drought magnitude and helps in water
resource planning. Using these relationships, one can find the
water required for particular drought duration and can plan
water resource allocation accordingly from the water stored
during wet season or by transferring water from other catch-
ments (Figs. 13 and 14).
Fig. 11 Spatial variation of annual (top) and kharif (bottom) RAI interpolated drought severity, 2011 to 2013
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18. Conclusions
In this paper, the assessment of duration, magnitude, and
temporal and spatial drought occurrence of Pedda Vagu and
Ookacheti Vagu watersheds is presented. The spatial and
temporal characterization of droughts achieved can be used
in highlighting drought-prone regions or vulnerable regions.
The identification of highly vulnerable regions helps in plan-
ning drought-proofing measures. The drought events were
characterized by using rainfall anomaly index (RAI) applied
to kharif and annual time scales using monthly precipitation
data of 20 meteorological stations. Spline interpolation tech-
nique is used in identifying the spatial pattern of the RAI time
series. The study showed that the drought severity of the two
time scales varied at all the regions, north to south of the basin.
Years 1997, 1999, 2002, 2004, 2006, 2008, and 2011 have
showed high negative RAI values which varied in space and
time. It is also observed that droughts occur after every 2 or
3 years at all stations with varying frequencies. It is also
observed that the central part of the basin is more prone to
droughts. The main reason for the variation in the drought
severity is attributed to the topographical variation and varia-
tion in the rainfall abnormalities. From the time series plots of
RAI, it is observed that drought of either extreme or severe or
medium severity occurs every 2 or 3 years at all stations. The
spline interpolated rainfall anomaly index maps are useful for
regionalization of droughts which can be of high value in
classification of drought-prone areas as well as in planning
management measures. They also have shown that some
regions experienced more severe drought while other regions
were well-off. Furthermore, run analysis is employed on daily
rainfall for developing drought duration and magnitude em-
pirical relationships. These empirical relations are useful in
determining the water deficits based on duration during a
Fig. 12 Empirical relationships of drought magnitude and duration of some stations in the basin
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19. Fig. 13 Cumulative distribution function of different stations
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20. Fig. 14 Cumulative Distribution Function of different stations
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21. particular drought event and eventually help in planning the
water resources.
Acknowledgments The authors are grateful to Indian Institute of Tech-
nology Bombay, for the support and encouragement. The authors are also
grateful to District planning office, Mahabubnagar District, Regional
agriculture research station, Hyderabad and Directorate of Economics
and Statistics, Hyderabad for providing rainfall data. The authors are also
thankful to the valuable suggestions provided by the two anonymous
reviewers for the improvement of the manuscript.
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