Drought analysis

1. 1

2. Welcome
Drought indices:
A tools for drought
monitoring
RAHUL PATIL
PHD16AEG6047
2

3. OUTLINE
Introduction
Drought sequence
Drought indicators
Drought characterization using
drought indices
Taxonomy of drought indices
Drought indices
Case studies
References
3

4. Introduction
Drought is one of the world’s costliest natural disasters, causing an average US$6–8 billion in
global damages annually, and affecting more people than any other form of natural catastrophe
(Keyantash and Dracup 2002)
According to Dracup et al. (1980) the drought is defined as Lack of rainfall as great as so long
continued to affect injuriously to plant and animal life of a place and to deplete water supplies
both for domestic purposes and the operation of power plants especially in those regions where
rainfall is normally sufficient for such purposes
Agriculture sector is most affected by the onset of drought as it is highly reliable on the weather
and soil moisture etc.
Agriculture is the backbone of Indian economy since more than 70 per cent of the population
depends upon it for their livelihood
Since 70 per cent of the total cultivated area is rainfed, the major cultivated area is under dry
land conditions and it contributes 42 per cent of the total production. In spite of significant
efforts made since independence to bring more area under irrigation and high yielding crop
varieties, Indian agriculture is greatly dependent on monsoon rainfall and a very high
proportion of the total cultivated area still continues to be rainfed (Srinivasa reddy, 2009).
4

5. Drought has been a constant visitor to India since time immemorial. A study of drought in
India during the past one hundred years (1896-1995) reveals that the country experienced
nineteen droughts of which four were severe droughts.
Droughts are occurring in different regions of the world with increased frequency and
severity. In India, large parts of the country perennially reel under recurring drought. Over
68% of the total area is vulnerable to drought. The 'chronically drought-prone areas' is
around 33% , while 35%, classified as 'drought-prone'. The major drought years in India
were 1877, 1899, 1918, 1972, 1987 and 2002 (Moumita and Sujata 2013 )
The statistical details of the occurrence of droughts in India may be summarised as
follows:
 In India, on an average, one out of five years is a drought year.
 On an average a severe drought is likely to occur once in 25 years.
 In any 5-year period, the number of droughts have not exceed three in the recorded
history of past 125 years
 In any 10-year period, the number of droughts have not exceeded FIVE in the recorded
history of the past 125 years
 There is likelihood of 1 or 2 droughts in the next five years, as the statistics would
suggest.
Historically little attention has been given to drought forecasting aspect which is very
important from the point of view of drought preparedness and early warning. Because of
this emphasis on crisis management, many societies have generally moved from one
disaster to another with little, if any, reduction in risk (Wilhite, 2005).
5

6. History of Droughts in India
• During 1871–2015, there were 25 major drought years, 1873,
1877, 1899, 1901, 1904, 1905, 1911, 1918, 1920, 1941, 1951,
1965, 1966, 1968, 1972, 1974, 1979, 1982, 1985, 1986, 1987,
2002, 2009, 2014 and 2015.
• Among the many drought events since Independence, the one
in 1987 was one of the worst, with an overall rainfall
deficiency of 19% which affected 59–60% of the normal
cropped area and a population of 285 million.
• This was repeated in 2002 when the overall rainfall deficiency
for the country as a whole was 19%. Over 300 million people
spread over 18 States were affected by drought along with
around 150 million cattle.
• In 2009, the overall rainfall deficiency for the country as a
whole was 22%, which resulted in decrease of food grain
production by 16 million tonnes.
6
Source: Samra, 2004; NRAA, 2012, DAC&FW data

7. 7

8. 8

9. `
9

10. 10

11. The General sequence for the occurrence of different
drought types. Modified from NDMC (2006).
11

12.  Duration: Depending on the region, drought’s duration can vary
between a week up to a few years. Because of drought’s dynamic
nature, a region can experience wet and dry spells simultaneously
when considering various timescales (NCDC 2010).
 Magnitude: The accumulated deficit of water(e.g., precipitation, soil
moisture, or runoff) below some threshold during a drought period
 Intensity: The ratio of drought magnitude to its duration.
 Severity: Two usages are provided for drought severity:
 The degree of the precipitation deficit (i.e., magnitude), or the
degree of impacts resultant from the deficit (Wilhite 2004).
 Geographic extent: The areal coverage of the drought which is
variable during the event. This area can cover one or several pixels
(cells), watersheds or regions
12

13. Drought indicators
• Along with precipitation deficit, additional variables such as
evapotranspiration and stream flow are also used to more
comprehensively characterize drought.
• Using different models (e.g., water balance/hydrological models),
such variables or indicators are used in combination to derive a
drought index.
• Meteorological indicators include precipitation and cloud cover;
hydrological indicators include stream flow and groundwater level
• In practice, however, some indicators such as precipitation, potential
evapotranspiration, soil and vegetation-cover characteristics have
had wider applications and influence (Tsakiris and Vangelis, 2005).
13

14. Drought indices are quantitative measures that characterize drought
levels by assimilating data from one or several variables (indicators)
such as precipitation and evapotranspiration into a single numerical
value.
The nature of drought indices reflects different events and conditions;
they can reflect the climate dryness anomalies (mainly based on
precipitation) or correspond to delayed agricultural and hydrological
impacts such as soil moisture loss or lowered reservoir levels
Drought can generally be defined as the extreme persistence of
precipitation deficit (González and Valdés 2006) over a specific region
for a specific period of time.
Drought types and characteristics
 By implementing an operational definition of drought, three main
physical drought types were established
 meteorological, agricultural, and hydrological droughts
14

15. Drought characterization using drought indices
• Several methodologies for drought characterization exist; however,
using drought indices is prevalent (Tsakiris et al., 2007).
• Drought indices are calculated from assimilating drought indicators
into a single numerical value. A drought index provides a
comprehensive picture for drought analysis and decision-making
that is more readily useable compared with raw data from indicators
(Hayes 2006)
• More than 150 drought indices have been developed (Niemeyer
2008) and additional indices have recently been proposed (Cai et al.,
2011; Karamouz et al., 2009; Rhee et al., 2010; Vasiliades et al.,
2011; Vicente-Serrano et al., 2010).
15

16. Operationally, using an index for drought characterization
serves the following purposes
• Drought detection and real-time monitoring (Niemeyer 2008)
• Declaring the beginning or end of a drought period (Tsakiris
et al., 2007)
• Allowing drought managers to declare drought levels and
instigate drought responses measures
• Drought evaluation (Niemeyer 2008)
16

17. Taxonomy of drought indices
• Three popular categories are meteorological, agricultural and
hydrological drought indices.
• Niemeyer (2008) adds three categories to this list: comprehensive,
combined and remote-sensing-based drought indices.
• Comprehensive drought indices use a variety of meteorological,
agricultural and hydrological variables to draw a comprehensive
picture of drought
• Remote-sensing-based drought indices use information from
remote-sensing sensors to map the condition of the land (e.g., the
Normalized Difference Vegetation Index, NDVI, Tucker (1979))
• Combined (also termed hybrid and aggregate) drought indices are
derived by incorporating existing drought indicators and indices into
a single measure.
17

18. Drought indices
Major operational drought indices
• seven drought indices that are frequently used in forecasting,
monitoring, and planning operations.
 India Meteorological Department Method(IMD)
 Percent of normal
 Standardized Precipitation Index (SPI)
 Palmer Drought Severity Index (PDSI)
 Normalized Difference Vegetation Index (NDVI)
 Vegetation condition index(VCI)
 Standardized Precipitation Evapotranspiration Index (SPEI)
18

19. India Meteorological Department Method
• The method used by the IMD (Irrigation Commission Report, 1972)
is a simple procedure which assesses the drought on the basis of
percentage deviation of actual rainfall (P) from the long term mean
rainfall. The percentage deviation (Di)
19

20. Percent of normal
• The percent of normal precipitation is a meteorological drought
index that describes the drought as the precipitation deviation from
the normal (average).
• The normal usually corresponds to the mean of the past 30 years.
Percent of normal is calculated by dividing a given precipitation by
the normal.
• The time scale of the analysis can vary from a single month to a
year
• The main advantage of this index is its simplicity and transparency,
which makes it favourable for communicating drought levels to the
public (Keyantash and Dracup, 2002)
• The statistical construct of this index has been criticized for
inconsistency (Hayes, 2006).
20

21. Standardized Precipitation Index (SPI)
• SPI (McKee et al., 1993) is a popular meteorological drought index
that is also solely based on precipitation data.
• Similar to the percent of normal, SPI compares precipitation with
its multiyear average. SPI overcomes the discrepancies resulting
from using a non standardized distribution by transforming the
distribution of the precipitation record to a normal distribution.
• The precipitation record is first fitted to a gamma distribution that is
then transformed into a normal distribution using an equal-
probability transformation.
• The mean is then set to zero and as such, values above zero indicate
wet periods and values below zero indicate dry periods.
• If a value of less than zero is consistently observed and it reaches a value of
–1 or less, a drought is said to have occurred (McKee et al., 1993)
21

22. 22

23. • McKee’s index can be computed for any time period, however
typically it is applied for the 1, 3, 6, 12, 24, and 48 month periods
• For SPI, 30 years record is required but 50 years has been
recommended (Guttman, 1999). Currently, this index has been
widely adopted for research and operational modes.
Amin et al., 2011 23
Among users there is a general consensus about the fact that the SPI on shorter
time scales (say 3 or 6 months) describes drought events affecting agricultural
practices, while on the longer ones (say 12 or 24 months) it is more suitable for
water resources management purposes

24. • Since the gamma function is undefined for x=0 and a
precipitation distribution may contain zeros, the cumulative
probability becomes
• At least 30 years of continuous monthly precipitation data are
needed but longer-term records would be preferable.
• SPI timescale intervals shorter than 1 month and longer than
24 months may be unreliable.
• Its probability-based nature (probability of observed
precipitation transformed into an index) makes it well suited to
risk management and triggers for decision-making.
24

25. • The SPI can also be calculated using the following equation, written
as
• where, Xij is the seasonal precipitation at the ith rain-gauge station
and jth observation, Xim is its long-term seasonal mean and σ is its
standard deviation.
25

26. Procedure to calculate SPI on computer program 26

27. Notable drought events for Selangor basin
(Hazalizah et al., 2017)
27

28. • Lloyd-Hughes et al. (2002) found that the 2-parameters gamma
distribution seems to be the most appropriate approach to describe
monthly precipitation over Europe and to calculate the SPI index
• Ntale and Gan (2003) conducted a study in which they reviewed
various drought indicators and compared the performance of the
PDSI, Bhalme–Mooley Index (BMI) and standardized precipitation
index (SPI). Different indicators may yield different drought results.
SPI was recommended for eastern Africa.
• Chortaria et al. (2010) developed the SPI using 41 stations all over
Greece for a time period from 1989 to 1994 and concluded that it
described very closely the 1989–1990 severe drought.
28

29. Palmer Drought Severity Index (PDSI)
• PDSI (Palmer 1965) is a popular meteorological drought index,
especially in the US.
• The PDSI bases its concept of drought on water supply-and-demand
instead of precipitation anomaly. Emphasis is on abnormalities in
moisture deficiency rather than weather anomalies (Guttman 1999)
• PDSI uses precipitation, temperature, and the local available water
content (AWC) data for soil.
29

30. KEY STRENGTHS
• Effective in determining long-term droughts, especially over low
and middle latitudes
• By using surface air temperature and a physical water balance
model, the PDSI takes into account the basic effect of global
warming through potential evapotranspation.
• Takes precedent condition into account.
KEY LIMITATIONS
• Not as comparable across regions as standardized precipitation
index(SPI)
• Lacks multi timescale features of indices SPI.
• Does not account for snow or ice.
30
Alley [1984] there are a multitude of computations required, many of which follow somewhat
ambiguous procedures. Most of the studies that make use of the PDSI do not provide the
methods of calculation, so it is difficult to compute the PDSI independently when doing
research.

31. Normalized Difference Vegetation Index (NDVI)
• NDVI is a remote sensing-based index that measures vegetation
conditions (Rouse et al., 1974)
• NDVI uses the advanced very high resolution radiometer (AVHRR)
reflected red and near-infrared channels to calculate, if the
vegetation is healthy, or unhealthy and sparse (e.g., suffering from
drought or insect infestation).
• Under healthy conditions, chlorophyll (the green substance that
produces carbohydrates in plants) absorbs light, reflecting less R.
Lower R values result in higher NDVI value
31

32. • Unhealthy plants reflect higher R resulting in lower NDVI.
• NDVI deviation of -20 to -30% represents moderate drought
conditions and that of <-30% represents severe conditions
• NDVI has extensively been used as a base index for a number of
remote sensing indices that similarly measure vegetation conditions,
e.g., Vegetation Condition Index, VCI (Kogan 1990)
32
Corponicus global land services
USGS(Earth explorer)

33. • The correlation between LST and NDVI is -0.635 for the year 2002
and -0.586 for the year 2012. The LST when correlated with the
vegetation index it can be used to detect the agricultural drought of a
region (shruthi and Aslam 2015).
(shruthi and Aslam, 2015)Agricultural Drought Analysis Using the NDVI and Land Surface
Temperature Data; a Case Study of Raichur District
33Nivedha et al., 2017

34. Standardized Precipitation Evapotranspiration Index
• The role of warming-induced drought stress is evident in recent
studies that have analysed drought impacts on net primary
production and tree mortality (Williams et al., 2011; McGuire et al.,
2010).
• The strong role of temperatures on the drought severity was evident
in the devastating 2003 central European heat wave, in which
extreme high temperatures dramatically increased
evapotranspiration and exacerbated summer drought stress (Rebetez
et al., 2006)
• Empirical studies have demonstrated that higher temperatures
increase drought stress and enhance forest mortality under
precipitation shortages
34

35. • The use of drought indices which include temperature data in their
formulation (eg. PDSI) is preferable.
• However the PDSI lacks the multi-scalar character
• Therefore a new drought index, SPEI, was formulated based on
precipitation and PET.
• combines the sensitivity of PDSI to changes in evaporation demand
(caused by temperature fluctuations and trends) with the simplicity
of calculation and the multi-temporal nature of the SPI.
• The SPEI allows comparison of drought severity through time and
space, since it can be calculated over a wide range of climates, as
can the SPI.
• Keyantash and Dracup (2002) indicated that drought indices must
be statistically robust and easily calculated, and have a clear and
comprehensible calculation procedure.
35

36. • However, a crucial advantage of the SPEI over other widely
used drought indices that consider the effect of PET on
drought severity is that its multi-scalar characteristics enable
identification of different drought types and impacts in the
context of global warming.
36

37. Calculation of the SPEI
The computation of the SPEI is as follows:
• Potential evapotranspiration calculation (Thornthwaite, 1948):
• Deficit or surplus accumulation of a climate water balance at
different time scales with a value for PET, the difference
between the precipitation P and PET for the month i is
calculated using
37

38. • The calculated Di values are aggregated at different time
scales, following the same procedure as that for the SPI.
• Normalize the water balance into a log-logistic probability
distribution to obtain the SPEI index series.
• The log-logistic distribution was selected for standardizing the
D series to obtain the SPEI. The probability density function of
log-logistic distributed variable is expressed as:
• where α, β, and γ are scale, shape, and origin parameters
respectively.
Singh et al. (1993)
38

39. The analysis of trends of annual precipitation and SPEI suggest that
the changes are associated with changed in the temperature-based
ET component (Meixiu et al., 2014).
39

40. Advantages and disadvantages of
popular drought indices
DI, source and inputs Advantages Disadvantages
SPI (McKee et al., 1993)
Precipitation
Simplicity; SPI relies only on
precipitation data
As SPI is adaptable for the analysis
of drought at variable time scales; it
can be used for monitoring
agricultural and hydrological
Comparing precipitation departure
from normal for various regions
with highly different climates is
possible
Equally represents both wet and dry
climates and hence can be used for
monitoring wet periods
Uses only precipitation, loosely
connected to ground conditions
Potential evapotranspiration is a
valuable additional indicator (Hu and
Willson 2000; Tsakiris and Vangelis
2005; Vicente-Serrano et al., 2010)
Limitations of the precipitation data
including accuracy of measurements,
the number of gauging stations and
length of the record
40

41. NDVI (multiple)
Visible red band,
near infrared bands
While resolution is high (1 km)
(compared to weather stations) AVHRR
covers a large land area (L Ji and Peters
2003)
NDVI actually measures dryness (rather
than interpolation or extrapolation)
NDVI is sensitive to darker and wet soil
background (Huete et al., 1985). In wet
conditions, the reflectance may not be equal
in two bands and as such, the NDVI may
vary with soil moisture variations.
Atmospheric interference can contaminate
pixels. This contamination can be due to
cloud, seasonal smoke, aerosols, haze, etc.
Currently available algorithms are capable
of partially removing the contaminated
pixels
Vegetation stress is influenced by more
factors than moisture conditions alone.
41

42. Performance of drought indices
• Quiring and Papakryiakou (2003) compared four drought
indices: PDSI, Palmer's Z-Index, SPI and NOAA Drought
Index (Strommen et al., 1980) to find the SPI was most
suitable index to monitor agricultural drought in Canadian
prairies
•
42

43. Quiring (2009) (Keyantash and Dracup 2002; Narasimhan and
Srinivasan 2005) consider criteria for the evaluation of drought
indices.
Robustness
Tractability
Transparency
Sophistication
Extendability
43

44. Are droughts becoming more frequent or severe
in China based on the Standardized Precipitation
Evapotranspiration Index: 1951–2010 ?
Meixiu Yu, Qiongfang Li, Michael J. Hayes,
Mark D. Svobodab and Richard R. Heime
INTERNATIONAL JOURNAL OF CLIMATOLOGY
Int. J. Climatol. 34: 545–558 (2014)
44

45. Study area and data sources
• The monthly precipitation (mm) and air temperature (◦C) data during 1951–2010
from 752 meteorological stations in China were collected from the National
Climate Center of the China Meteorological Administration (CMA).
45

46. 46
Drought percentage area during different years for SPEI < -1

47. Changes and linear trends in dry areas (SPEI < −1) during 1951–2010 over
different regions of China
5.46%/10 yr 4.90%/10 yr
3.96%/10 yr3.80%/10 yr
47

48. 2.19%/10 yr2.51%/10 yr
1.86%/10 yr
48

49. Severe (SPEI < −1.5) and extremely (SPEI < −2.0) drought events
during 1951–2010
49
1966 1978
19891997

50. 50
2001
2008 2010
2006

51. Spatial distribution of drought duration (number of months) over China during
1951–2010.
51

52. Decadal spatial distributions of drought duration (number of months)
over China
52

53. • In WNW China, the drought frequency with an SPEI of <−1 increased to 4.91% during
1977–2010 from 1.92% over the period of 1951–1976, with the ratio to the value of
1.92% being 2.56.
• In ENW China, the drought frequency with an SPEI of<−1 also rose rapidly to 4.00%
during 1977–2010 from a value of only 0.32% over the 1951–1976 phase, with the ratio
being 12.5.
53

54. Drought frequency variations within separate regions for both
sub-periods
Year/region WNW ENW N NE E SW S Tibet
1977-2000 4.91 4.00 4.91 6.88 4.18 2.95 4.95 4.42
1951-1976 1.92 0.32 0.64 2.80 4.18 1.99 4.47 6.50
ratio 2.56 12.50 7.67 2.46 1 1.50 1.11 0.68
• This demonstrates that droughts have hit the regions of WNW, ENW, N, and
NE China more frequently during the past 30years
• droughts in E, SW, and S regions also increased to a lesser extent, but not
significantly; the climate in the eastern Tibetan Plateau is growing wetter
54

55. Conclusions
• A significant upward trend of dry conditions occurred in N China,
southwest parts of NE China, the central and east reaches of ENW China,
the central and southwest parts of SW China, and southwest and northeast
parts of WNW China; while significant trends towards wetter conditions
occurred in eastern parts of the Tibetan plateau.
• Comparison of the analysis of trends of annual precipitation and SPEI
suggest that these changes are associated with changed in the temperature-
based ET component
• Severe and extreme drought areas have increased since the late 1990s by
∼3.72% per decade. In addition, persistent, multiple-year severe droughts
have occurred more frequently in N, NE, and WNW during the period
1951–2010.
• N, WNW, and SW China had their longest drought durations occurring
mostly in the 1990s and 2000s.
• Drought occurrences have become much more frequent in WNW, ENW, N,
and NE regions over China during the past 30years and droughts in the E,
SW, and S regions also increased to a lesser degree.
55

56. Case study II
Analysis of Meteorological Drought Using
Standardized Precipitation Index – A Case Study
of Puruliya District, West Bengal, India
Moumita Palchaudhuri and Sujata Biswas
International Journal of Environmental and Ecological Engineering
7(3):167-174(2013)
56

57. STUDY AREA
• Puruliya lies between latitude 22° 36’ and 23° 30’ N, and longitude
85° 45’ and 86° 39’ E. The geographical area of the district is 6259
km².
• Average annual rainfall varies between 1100 and 1500 mm. The
relative humidity is high in monsoon season, being 75% to 85%. But
in hot summer it comes down to 25% to 35%. Temperature varies
over a wide range from 7°C in winter to 46.8°C in the summer
57

58. • The SPI is computed by fitting an appropriate probability
density function to the frequency distribution of precipitation
summed over the time scale of interest (usually 3, 6, 12, and
24 months). This is performed separately for each time scale
and for each location.
58

59. RESULTS AND DISCUSSION
The maximum SPI value (-2.52)
is for 6 months timescale in the
year 1998. For 3 and 12 months
timescale the SPI value is
maximum in the year 1998 (-1.76)
and 1983 (-2.36) respectively.
The maximum SPI value was
found in 1983 and the values are
-2.17, -2.3, -2.52 for 3, 6, 12
months timescale respectively
59
1983 1998
1983

60. • The maximum SPI value was
found in 1993 and the values are -
1.9, -3.14, -3.4 for 3, 6, 12 months
timescale respectively.
• the maximum SPI value (-2.24) is
for 6 months timescale, in the
year 1982. For 3 and 12 months
timescale the SPI values are
maximum in the year 1979
(-1.89) and (-2.47) respectively.
60
1993
19791982

61. Areal Extent of drought severity in the Puruliya
district based on 12-month SPI for September
61
37% of the years during the analysis period (1971–2005) are struck by drought
events

62. • It is also observed that more than 50% of the study area is
under the influence of drought in the years 1975, 1976, 1980,
1982, 1983, 1985, 1988, 1991, 1992, 1995, 1998, 2000 and
2001 (SPI < 0).
• This represents nearly 37% of the years during the analysis
period (1971–2005) are struck by drought events
• The years 1975, 1980, 1985, 1988, 1991, 1992, 2000, and
2001 mark the most critical drought years in the basin with
more than 75% of the total area under drought.
• Considering all the severity classes per year, 1976, 1979, 1980,
1982, 1983, 1985, 2001, 2003 are the worst years with nearly
100% of the total area under drought.
62

63. Study area showing drought severity
63

64. CONCLUSION
• From the areal extent graph, 1976, 1979, 1980, 1982, 1983,
1985, 2001, 2003 are the worst years with nearly 100% of the
total area under drought. Mild and moderate droughts occur in
the central portion of the study area.
• The northeast part of the study area is prone for severe
drought and extreme drought occurs in northwest and
southwest part of the study area.
• It is found that SPI is a good indicator of the drought
characteristics like severity and spatial extent.
64

65. Seminar conclusion
 Drought characterization is essential for drought management
operations.
 Using drought indices is a pragmatic way to assimilate large
amounts of data into quantitative information that can be used in
applications such as drought forecasting, declaring drought levels,
contingency planning and impact assessment.
65

66. Scope for seminar
66

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70. • Wilhite, D.A. 1993. The enigma of drought. Drought Assessment, Management,
and Planning: Theory and Case Studies. Kluwer Academic Publishers,
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•
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71. THANK YOU

72. Case study III
Analysis of Short-Term Droughts in the Mewar
Region of Rajasthan by Standard Precipitation
Index
K.A. Basamma, R.C. Purohit, S.R. Bhakar, Mahesh Kothari, R.R.
Joshi, Deepak Sharma, P.K. Singh and H.K. Mittal
Int.J.Curr.Microbiol.App.Sci (2017) 6(6): 182-192
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73. Study area
• Rajasthan is the most critical state in the country with highest
probabilities of drought occurrence and rainfall deficiencies
(Rathore, 2005)
• Mewar region which is selected as a Study area is located south of
the Great Indian Desert of Rajasthan, India with total area of 34437
km2. Located between 7205’ 32’E to 750 4’ 21‟ E longitude to 230
47’ 55’’ N to 250 57’ 58’’ N latitude and encompasses, broadly the
districts of Rajsamand, Udaipur, Bhilwara and Chittorgarh
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74. • The monthly rainfall data for the period of 34 years (1981-2014) of
17 rain gauge stations located in the Mewar was collected from the
website of Water Resource Department, Rajasthan.
• In this study, IDW approach is used for spatial interpolation of
rainfall and drought characteristics over the Mewar region (Mishraet
al., 2005). Total area of Mewar region is divided into grids of 30 ×
30 km
• Monthly rainfall recorded at 17 stations for 34 years (1981-2014)
were interpolated by ArcGIS 9.3, using Inverse Distance Weighing
(IDW) algorithm and gridded monthly rainfall was created.
74

75. Occurrence of drought categories (percentage) in the Mewar region
Monthly distributions of drought categories
Results and Discussion
75

76. Time series of SPI Values for 1 & 3-month timescale for Mewar region 76

77. Areal extent of drought categories for 1 & 3 month time scale 77

78. Conclusion
• SPI is used as a drought indicator in this study and its found effective in
analyzing the short term droughts, which cause significant impact on
agriculture.
• Analysis indicated that region experienced short term droughts frequently
during study period, in which mild droughts occur more frequently and
extreme droughts occur least frequently
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