PUBLICATIONS
Journal of Geophysical Research: Atmospheres
RESEARCH ARTICLE
10.1002/2015JD024310
Key Points:
• Variations of extreme Indian monsoon
precipitation intensity and spell are
investigated
• Notable change in monsoon system
with wet regions turning drier and dry
regions turning wetter
• Prolonged dry spells in wet regions
and frequent short wet spells in dry
regions in past decades
Supporting Information:
• Text S1 and Figures S1–S9
• Figure S1
• Figure S2
• Figure S3
• Figure S4
• Figure S5
• Figure S6
• Figure S7
• Figure S8
• Figure S9
Correspondence to:
C. T. Dhanya,
[email protected]
Citation:
Vinnarasi, R., and C. T. Dhanya (2016),
Changing characteristics of extreme
wet and dry spells of Indian monsoon
rainfall, J. Geophys. Res. Atmos., 121,
doi:10.1002/2015JD024310.
Received 4 OCT 2015
Accepted 7 FEB 2016
Accepted article online 11 FEB 2016
Changing characteristics of extreme wet and dry
spells of Indian monsoon rainfall
R. Vinnarasi1 and C. T. Dhanya1
1
Department of Civil Engineering, Indian Institute of Technology Delhi, New Delhi, India
Abstract
Modeling of extreme events and its dynamic behavior have always been an intriguing topic.
Increase in the magnitude and frequency of extreme events has widely been reported in recent decades,
which is attributed to abrupt changes in climate. Numerous studies on extreme Indian monsoon characteristics,
using a coarse-resolution data set, have pointed out significant changes in heavy precipitation pattern over
India. However, these studies differ in their conclusions, emphasizing the need for a fine-resolution analysis. The
present study aims to analyze the spatiotemporal variations and trends in the extreme (wet and dry) Indian
monsoon precipitation, using 0.25° × 0.25° high-resolution gridded data for a period of 113 years (1901–2013).
Significant increase in the maximum intensity of rainfall and spatial heterogeneity is observed over the past half
century. In addition, significant negative trends in wet spell durations and positive trends in dry spell durations
are observed over wet regions; whereas contrasting trends are observed over dry regions. A shift in the
frequency distribution of extreme events during the monsoon period is also noticed. The 50 year return level of
maximum intensity clearly shows positive trends over the past century. Though characteristics of extremes are
observed to be highly localized, apparent signs of wet regions turning drier and dry regions turning wetter
are obtained. A comprehensive insight into different characteristics (intensity, spell, onset, and frequency)
of Indian monsoon extremes is provided, which will help in effective water resources management and
flood/drought hazard preparedness.
1. Introduction
Extreme weather, though rare, has always been an intrinsic part of the climate system. The rarity and intensity
of such events often jeopardize the management policies undertaken. While for majority of the places
around the world, extremes are quite unexpected; it has become a unique characteristic as far as a few
regions are considered, having been identified as either a dry or a wet region. Such regions have already been
adapted to the historical occurrences of extremes. Nevertheless, such regions will also be under risk, if there is
any drastic change in the characteristics of extremes, thereby adversely affecting the system. It has been
reported that the distribution of extreme events is changing globally and their frequency of occurrence
has increased in recent decades [Gordon et al., 1992; Aguilar et al., 2005; Alexander et al., 2006; Kharin et al.,
2013]. As per the Intergovernmental Panel on Climate Change (IPCC) Special Report on Extreme (SREX), also
known as the IPCC-SREX, these changes can be attributed to the recent revelation of climate change
[Intergovernmental Panel on Climate Change (IPCC), 2012]. Moreover, heavy concentration of floods and
associated risks are expected over Asian regions in the future, especially over India, Bangladesh, and China
[IPCC, 2012].
India, whose economy is majorly driven by agriculture, largely depends on the summer monsoon rainfall
(June to September) to irrigate 70% of its farmland. While any significant variation in the monsoon, even a
well-predicted one, will adversely affect the economy; the impact of climate change further worsens these
effects. In India, the production of Kharif (monsoon) crops directly depends on the summer monsoon rainfall,
whereas that of Rabi (winter) crops depends on the soil moisture availability, which in turn depends entirely
on the summer monsoon rainfall [Prasanna, 2014]. Hence, the temporal and spatial patterns of Indian
monsoon rainfall are of particular interest.
©2016. American Geophysical Union.
All Rights Reserved.
VINNARASI AND DHANYA
Typically, prolonged dry days occurring in a monsoon season is termed as “break in monsoon,” and prolonged
wet days is called “active spell” [Annamalai and Slingo, 2001; Gadgil and Joseph, 2003]. A summary of numerous
studies [Gadgil and Joseph, 2003; Goswami et al., 2006; Rajeevan et al., 2006, 2010; Mandke et al., 2007;
Guhathakurta and Rajeevan, 2008; Krishnamurthy et al., 2009; Guhathakurta et al., 2010; Vittal et al., 2013; Singh,
2013; Singh et al., 2014] conducted on Indian monsoon extremes on different spatial domains, adopting various
CHANGING CHARACTERISTICS OF EXTREMES
1
VINNARASI AND DHANYA
Singh and Ranade
[2010]
Ghosh et al. [2011]
Singh [2013]
Vittal et al. [2013]
Ali et al. [2014]
Singh et al. [2014]
7
8
9
10
11
Dash et al. [2009]
4
6
Rajeevan
et al. [2008]
3
Krishnamurthy
et al. [2009]
Rajeevan et al.
[2008, 2010]
2
5
Goswami
et al. [2006]
Author and Year
1
Serial Number
CHANGING CHARACTERISTICS OF EXTREMES
Central India (18°N
to 28°N and 73°E
to 82°E)
57 major urban
regions in India
India (gridwise
analysis)
India (gridwise
analysis)
India (gridwise
analysis)
IMD 1° × 1° daily rainfall
gridded data for 1951
to 2011
IMD 0.25° × 0.25° daily
rainfall
gridded data for 1901
to 2010
IMD 1° × 1° daily rainfall
gridded data for 1901
to 2003
IMD 1° × 1° daily rainfall
gridded data for 1951
to 2003
IMD 1° × 1° daily rainfall
gridded data for 1951
to 2003
IMD 1° × 1° daily rainfall
gridded data for 1951
to 2007
IMD 1° × 1° daily rainfall
gridded data for 1951
to 2003
India (gridwise
analysis)
19 subregions
of India
IMD 1° × 1° daily rainfall
gridded data for 1951
to 2004
IMD 1° × 1° daily rainfall
gridded data for 1901
to 2004
IMD 1° × 1° daily rainfall
gridded data for 1951
to 2003
Indian Meteorological
Department (IMD) 1° × 1°
daily rainfall gridded data
for 1951 to 2000
Data and Resolution
Six homogeneous
monsoon regions
in India
Central India
(also called core
monsoon zone
18.0°N to 28.0°N
and 65.0°E to
88.0°E)
Central India
(bigger spatial
domain than that
considered in
Goswami et al.
[2006])
Central India (74.5°E
to 86.5°E and
16.5°N to 26.5°N)
Region
Table 1. Summary of Past Studies Conducted on the Characteristics of Indian Rainfall
Standardized rainfall anomaly of +1 is active
event and 1 is break event. Analysis done
pre-1980 and post-1980 separately
Four rainfall indices are defined
Intensity (50 year return level computed using
generalized Pareto distribution), duration,
and frequency is analyzed for the Peak Over
Threshold (POT) (rainfall > 95th percentile)
series. Change point analysis is also carried out.
Annual Maximum Series has been extracted to
compute 30 year and 100 year return level
using Generalized Extreme Value distribution
Standardized rainfall anomaly of +1 is active
event and 1 is break event. Analysis carried
out for pre-1975 and post-1977 separately
Significant increase in the frequency of dry spell
and intensity of wet spell
Significant trend shown only for four regions
Significant differences in the pattern of active
precipitation extremes in India during pre-1950
and post-1950. Sudden changes in the extreme
events happened after 1975 in majority of the
grids. Evidence suggest that this period
coincides with the beginning of urbanization
in India [Kishtawal et al., 2010]
Positive trend of short break spells and
moderate active spells in post-1977
is observed
Spatially nonuniform trend and also increase
in spatial heterogeneity is observed
Reduction in longest wet spell with highintensity rainfall and enhancement in longest
dry spell, which further indicates a drift
toward extreme of extremes
Spatially nonuniform trend is observed.
Moreover, field significance test revealed
insufficient evidence to reject null hypothesis
over a few grids in Central India
Intensity and frequency of rainfall above
90th and 99th
Onset, critical length, and number of wet
and dry events
Positive trend of dry spells observed in each
region Positive trend of heavy rainy days
observed in North West, Central North East
and North East, while negative trend is
observed in other regions
Confirmed the results from Goswami et al. [2006]
No significant trend has been observed
Positive trend in extreme active events and
negative trend in moderate active events
Major Results
Low, moderate, and heavy rainy days and
short, long, dry, and prolonged dry spells
are analyzed
Absolute threshold approach
(a) Heavy rainfall (≥100 mm/d)
(b) Moderate rainfall (≥5 and < 100 mm/d) and
(c) Very heavy rainfall (≥150 mm/d) events.
Standardized rainfall anomaly of +1 is taken
as active event and 1 is taken as break event
Absolute threshold approach
(a) Heavy rainfall (≥100 mm/d)
(b) Moderate rainfall (≥5 and < 100 mm/d) and
(c) Very heavy rainfall (≥150 mm/d) events
Analyzing Method
Journal of Geophysical Research: Atmospheres
10.1002/2015JD024310
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VINNARASI AND DHANYA
Negative trend in maximum duration
of dry spells and number of dry
spell over Northwest parts of
India, while observing an exactly
opposite behavior North Eastern
parts and Western Ghats
Wet conditions are observed for all
precipitation indices throughout the
last half century (1951 to 2003).
Rainfall below 1 and 3 mm/d
27 Climate Change Indices
(ETCCDMI—Expert Team on
Climate Change Detection,
Monitoring and Indices).
Among these, 11 indices
are defined to
analyze precipitation
13
Alexander
et al. [2006]
Globe
IMD 1° × 1° and Asian
Precipitation - HighlyResolved Observational
Data Integration
Towards Evaluation of
Water Resources
(APHRODITE) 0.5° × 0.5°
daily rainfall
gridded data for 1951
to 2003
Station data (1) data that
are freely available to the
international
community, (2) data
from all the ETCCDMI
workshops that were not
available previously, and
(3) data provided by
coauthors.
India (gridwise
analysis)
Sushama
et al. [2014]
12
Author and Year
Serial Number
Table 1. (continued)
Region
Data and Resolution
Analyzing Method
Major Results
Journal of Geophysical Research: Atmospheres
10.1002/2015JD024310
indices to identify active and break events is
given in Table 1. Some of these studies
assumed Central India to be homogenous
and considered it as a representative of
whole Indian region to examine the possible
trends [Goswami et al., 2006; Rajeevan et al.,
2006, 2008, 2010; Singh, 2013; Singh et al.,
2014]. However, the existence of distinctive
meteorological regions and heterogeneous
nature are neglected in these studies
[Satyanarayana and Srinivas, 2008].
The versatility of monsoon rainfall over India
and the diverse topography demands a comprehensive examination of rainfall characteristics all over the country. Details of a few
studies [Krishnamurthy et al., 2009; Ghosh
et al., 2011; Vittal et al., 2013], which have
accounted these factors by analyzing either
active events or break events, are also given
in Table 1. It is evident that while numerous
studies have already been carried out to
assess various characteristics of extremes
(in terms of intensity, spell, onset etc.), the
conclusions are not in accordance and coherent enough to recommend its employment
in any regional water resources management
studies. Contradicting nature of conclusions
from various studies may be attributed to
the study region, data sets, definitions of
extremes, and methodologies employed in
these studies. Besides, none of the above
studies have examined dry and wet spell
characteristics together and performed a
comparative analysis with a sufficiently long
data. Considering the emphasis on the highly
localized nature of extremes, as revealed by
many recent studies, a detailed investigation
of the changes (if any) happened on the historical extremes, in terms of various extreme
spell indices, is important for improving the
preparedness for the unexpected extremes
[Karl et al., 2008]. Therefore, in this study the
changes in the natural trends of the extreme
events over last century will be analyzed
using recently available high-resolution
(0.25° × 0.25°) daily rainfall data, which will
guide practitioners in water resources area,
who often demand reliable information in
finer spatial resolutions. The extreme indices
are defined based on the previous studies
conducted in this direction [Vittal et al.,
2013; Singh et al., 2014; Mondal and
Mujumdar, 2015].
CHANGING CHARACTERISTICS OF EXTREMES
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Journal of Geophysical Research: Atmospheres
10.1002/2015JD024310
Figure 1. (a) Spatial distribution of the mean annual rainfall (mm/d) for the period 1901 to 2013 and (b) homogenous
monsoon rainfall regions of India.
2. Data Used and Study Region
High-resolution (0.25° × 0.25°) daily gridded rainfall data [Pai et al., 2013] prepared by Indian
Meteorological Department (IMD) for a spatial domain of 6.5°N to 38.5°N and 66.5°E to 100°E, covering
the main land region of India, has been used in this study. The data are generated using daily rainfall
measurements obtained from over 6995 rain gauge stations for a period of 1901–2013, while previous
versions of gridded data had used less number of gauges (e.g., only 1803 stations used to create 1° × 1°
resolution data). Even though the spatial density of the station points used in the development of this
data set may not be uniform across the country, it is ensured to use a reasonably good number of stations representing most areas of the country while generating gridded data. Significant increase in the
spatial density of gauge stations brings out the spatial variations of rainfall across the country more
accurately, than that of other gridded data sets [Pai et al., 2014]. The station data are converted to
gridded data over the Indian region through spatial interpolation by employing the Inverse Distance
Weighted scheme [Shepard, 1968]. The climatological features like spatial distribution of mean annual
and seasonal rainfall over the country, annual cycle of mean monthly and daily rainfall averaged over
the country, and variability of daily rainfall derived from this data set are comparable with the existing
gridded daily rainfall data sets from IMD and APHRODITE [Pai et al., 2014]. In addition, the ability of this
data set in accurately bringing out the orographic influences in terms of the strong dependence
between orography and heavy rainfall in Western Ghats (WG) and North-Eastern regions, as well as
low rainfall in the leeward side of the Western Ghats due to its high-resolution and spatial density,
makes it highly reliable for regional studies related to water resources management and analysis
of extremes.
The entire region receives 80% of its total rainfall during the South-West monsoon rainfall, from June to
September. The all-India mean annual rainfall and South-West monsoon rainfall are approximately
3.0 mm/d and 6.9 mm/d, respectively, over the last 113 years, and the corresponding standard deviations
are 0.27 mm/d and 0.66 mm/d, respectively. The spatial distribution of mean annual rainfall over India
for the period 1901–2013 is shown in Figure 1a. Western Ghats and North Eastern region experience heavy
rainfall whereas leeward side of Western Ghats, North Western regions and Karakoram ranges experience
scanty rainfall. Based on the characteristics of South-West monsoon, India has been classified into six
homogeneous regions by Indian Institute of Tropical Meteorology (www.tropmet.res.in), Pune as shown
in Figure 1b. These regions are Hilly Region (Hilly Region in North India, HR1, and Hilly Region in North
East India, HR2), Central North East (CNE), North East (NE), North West (NW), West Central (WC), and
Peninsular (PI). As mentioned before, coastal parts of PI and WC (lying along the windward side of
Western Ghats), HR2, and some parts of NE receive high annual amount of rainfall (>10 mm/d) and are
denoted as “wet region” in this study. Similarly, northern parts of NW and southern parts of HR1 receive
low amount of rainfall (<1 mm/d) and are denoted as “dry region” in this study.
VINNARASI AND DHANYA
CHANGING CHARACTERISTICS OF EXTREMES
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Journal of Geophysical Research: Atmospheres
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Table 2. Indices Adopted to Define Various Characteristics of Extreme Spells
Abbreviations
NWD
NDD
FWS
FDS
LWS
LDS
DWS
IWS
OWS
ODS
Definitions
Units
Number of extreme wet days in a year
Number of extreme dry days in a year
Frequency (number of occurrence) of extreme wet spells in a year
Frequency (number of occurrence) of extreme dry spells in a year
Length of each extreme wet spell
Length of each extreme dry spell
Accumulated rainfall depth in each extreme wet spell
Maximum intensity of each extreme wet spell
Onset (starting period) of each extreme wet spell
Onset (starting period) of each extreme dry spell
Days
Days
Days
Days
mm
mm/d
-
3. Methodology
Different methods employed to extract the information of various characteristics of extreme spells and
indices defined are detailed below.
3.1. Extraction of Extreme Wet and Dry Spells
Two frequently adopted techniques for the extraction of extreme events from the observed time series are
Annual Maximum Series (AMS) through Block Maxima and Partial Maximum Series (PMS) through Peak
Over Threshold (POT). AMS approach considers only the annual maximum values and neglects all secondary
events, even if they exceed the annual maxima of other years [Ben-Zvi, 2009; Vittal et al., 2013]. An alternative
is POT approach, which considers all events above a fixed threshold. However, major challenges in POT
approach are (i) ensuring independence between the peaks and (ii) fixing an adequate threshold [Tanaka
and Kaoru, 2002]. Past studies have adopted two types of thresholds viz., absolute threshold [Goswami
et al., 2006; Sushama et al., 2014] and varying (percentile) threshold [Krishnamurthy et al., 2009; Vittal et al.,
2013; Mondal and Mujumdar, 2015]. Among these, varying threshold is most suitable to demarcate extreme
events in a gridwise analysis, since the characteristics of extreme events are highly influenced by the geographical locations in a heterogeneous rainfall region. A few studies have used precipitation anomalies to define
extreme wet and dry spells [Rajeevan et al., 2010; Singh et al., 2014]; however, such analyses failed to capture
the extreme events in every year. Threshold chosen, hence, should be sufficiently low (in case of extremely
low events) or high (in case of extremely heavy events) to distinguish the extremity and capture the extreme
events in all years [Katz, 2013]. Therefore, in this study, a suitable threshold is defined so as to include the
worst event of each year in the analysis; i.e., at least one event per year is considered in the analysis. Based
on this stipulation, the extreme wet day is defined as the day with rainfall value exceeding long-term 90th
percentile of daily rainfall, since it is found that this threshold (termed as wet threshold hereafter) ensures
the presence of at least one extreme event per year.
Similarly, the extraction of extreme dry days is done by adopting an absolute threshold value of 2.5 mm/d
(termed as dry threshold hereafter), since it is found reasonable for the classification and comparable with those
adopted by the previous studies. Most studies considered a threshold ≥ 2.5 mm/d to demarcate a rainy day,
assuming that a rainfall lesser than 1 mm/d contributes only to evaporation [Epifani et al., 2004; Tilya and
Mhita, 2007]. A few studies analyzing wet and dry spells considered even 1.5 mm/d [Harrington and
Flannigan, 1993], 2 mm/d [Perzyna, 1994], 2.5 mm/d [Ratan and Venugopal, 2013], and 3 mm/d [Sushama et al.,
2014] to avoid the possible underestimation of dry spell because of meagre rainfall events. In addition, a threshold of 2.5 mm/d has been frequently be used for defining a dry day in the context of agriculture and the onset of
monsoon [Pai and Rajeevan, 2007; Dash et al., 2009]. Rainfall lesser than 2.5 mm/d is defined as very little rain by
Indian Meteorological Department (IMD). Also, it is found that an adoption of any low percentile value will return
a threshold value of zero (more than 60% of daily rainfall series are zeroes for most of the grid points).
In this study, extremity is assessed through 10 extreme spell indices as described in Table 2, to analyze
various characteristics of extreme dry and wet spells. These indices are computed for South-West
monsoon rainfall season for a period of 113 years (1901 to 2013). An extreme spell is defined as the
prolonged period of extreme days. It is worthwhile to mention here the difference in the definition of
extreme spells adopted in this study and the past studies. Most of the past studies [Ratan and
VINNARASI AND DHANYA
CHANGING CHARACTERISTICS OF EXTREMES
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Journal of Geophysical Research: Atmospheres
10.1002/2015JD024310
Venugopal, 2013; Dash et al., 2009] have defined spells as the number of consecutive rainy days (e.g., rainy
day is a day with rainfall >2.5 mm), and extreme spells are fixed as those spells with length greater than
predefined threshold. However, in this study, extreme days are extracted based on dry (2.5 mm) and wet
thresholds (90th percentile), and subsequently, extreme spells are defined as the consecutive extreme
days. Hence, extreme spells with even a length of 1 day is considered in the analysis. This is to ensure
not to miss out any spell (with extreme rainfall magnitude), since rainfall of higher magnitude over shorter
durations may also be disastrous.
3.2. Trend Analysis and Testing of Significance
The presence of any trend, in each index (except last three indices listed in Table 2), is checked for each grid
using nonparametric Mann-Kendall (MK) test. MK test checks the existence of any monotonic upward or
downward trend of the variable of interest over time. Statistical significance of the trend is estimated at
10%, 5%, and 1% significance levels using standardized test static (Z) and p value. The rejection of null
hypothesis is based on two-tailed test. More details regarding this method can be obtained from Mann
[1945], Kendall [1975], Yue and Wang [2002], and Sonali and Nagesh Kumar [2013]. It is to be noted that this
approach assumes that the time series values are independent and identically distributed and therefore may
give spurious results or wrongly reject the null hypothesis, if the data series is serially correlated. Hence, in
order to account the temporal dependence in the time series, a nonparametric moving block bootstrap
approach [Khaliq et al., 2009] is used in this study. The estimation of trend is done in two ways: (i) long-term
trend (considering 113 years together) to analyze the long-term behavior of indices and (ii) time-varying
trend (considering 50 year moving windows with 10 year advance time step) to analyze the short-term variations of indices. Further, comparison and time evolution of trends are analyzed by considering three 50 year
windows—initial (1901–1950), middle (1931–1980), and last (1961–2010) with a sufficient 30 year time gap.
While considering IWS, the trend corresponding to the 50 year return level is computed, to enable comparison
of results with those from past studies. For the estimation of return level, 30 year moving window of IWS series
is modeled using Generalized Pareto Distribution (GPD), which is the best suited distribution for PMS/POT
[Coles, 2001; Katz et al., 2002; Chang et al., 2015]. Two parameters, i.e., location parameter and scale parameter
(σ) of GPD, are estimated using Method of Maximum Likelihood.
4. Result and Discussion
As a preliminary investigation, the annual maximum daily rainfall intensity for 113 years (1901 to 2013) is
computed for all grids over India (see Figure 2). The long-term average of annual maximum daily rainfall
intensity shown in Figure 2a depicts high-intensity rainfall over NE and WG regions, which can be attributed
to the combined effect of moisture-laden monsoon winds and the orographic influence of Himalayas and
WG, respectively. Low-intensity rainfall is observed in dry regions of NW, leeward side of WG, and HR1. The
spatial variability of annual maximum daily rainfall over 113 years in terms of variance and box plots of 10 year
windows shown in Figures 2b and 2c, respectively, reveals that there is a sudden intensification of spatial heterogeneity with respect to the rainfall extremes across India over the past half century. Spatial heterogeneity
is also highlighted in Ghosh et al. [2011] using a low-resolution rainfall data, which may be ascribed to regional drivers like topography, nonuniform population growth, and urbanization rather than the global drivers.
A trend analysis of annual maximum daily rainfall using MK test indicates a long-term positive trend in 26% of
grids (most of the grids lying in HR1) and negative trend in 12% of grids (Figure 2d). Grids showing a minimum of 10% significance level trend are only considered and shown in the figure. The long-term trend
(linear), as estimated by Mann-Kendall test, cannot capture the decadal variability, because linear trends
are sensitive to the boundary of time series. Therefore, the long-term trend is influenced by the changes in
extreme indices during the earliest or the latest decades. A time-varying trend analysis, adopting a 50 year
moving window, is conducted to discern any short-term temporal changes. Figure 2e shows the variation
in the number of grids showing positive and negative trends over the period 1901–2010, for different 50 year
windows (1901–1950, 1911–1960, 1921–1970, 1931–1980, 1941–1990, 1951–2000, and 1961–2010). It is
evident that majority of grids show positive trend after 1950 (Figure 2e). Though spatial trend of all 50 year
windows is investigated, the time evolution of spatial trend is illustrated through three 50 year windows over
1901 to 1950, 1931 to 1980, and 1961 to 2010, which indicate that the number of grids with positive trend in
annual maximum daily rainfall drastically increases in the last window (1961–2010) (refer to the supporting
VINNARASI AND DHANYA
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Journal of Geophysical Research: Atmospheres
10.1002/2015JD024310
Figure 2. Annual maximum daily rainfall intensity (a) average (in mm) for 1901 to 2013 and (b) spatial variance over India;
S1 and S2 indicates the linear trend for 1901 to 1950 and 1951 to 2013, respectively, and asterisk denotes significant trend,
(c) box plots corresponding to the spatial variance for each 10 year period, (d) long-term trend (positive and negative)
for 1901 to 2013. Statistical significance is estimated for different confidence intervals (99%, 95%, and 90%) using two-tailed
hypothesis test. Pie chart shows significant trend at 10% significance level, which corresponds to 90% confidence
interval and (e) time-varying trend using 50 year moving window in terms of variation in the number of grids showing
significant (10% significance level) positive and negative trends. In the box plots, the box represents the data lying between
the 25th and 75th percentile, i.e., interquartile range (IQR) of the data. Hence, the upper, lower, and middle lines of the
box denote 75th, 25th, and 50th (median) percentiles, respectively. The bottom whiskers represent the lowest datum
within 1.5 times the IQR of the lower quartile, and the top whisker represents the highest datum within 1.5 times the IQR of
the upper quartile. The plus signs denote the outliers falling outside these whisker limits.
information Figure S1). However, it is worthwhile to note that (i) grid points which show positive trends are
not always from same locations in three windows and (ii) evolution of definite positive trend is evident in HR1
region characterized for its scanty rainfall and positive trend is evident in NE wet regions. Though it is quite
evident from the above discussion that spatial heterogeneity is increasing, a clear conclusion cannot be
drawn pertaining to the possible changes in various characteristics of extremes. In addition, as discussed
before, annual maximum series neglects the second intense event or even any multiple occurrences of
events in a season [Coles, 2001]. An in-depth analysis is therefore carried out, which may provide more insight
into the fine-resolution spatiotemporal characteristics of daily rainfall.
4.1. Identification of Wet and Dry Events
As mentioned in section 3.1, the extreme spell characteristics are extracted using POT. The wet threshold
chosen to define extreme wet day, i.e., 90th percentile daily rainfall value for summer monsoon season for
the period 1901–2013, varies from 0.1 mm/d to 103 mm/d over India. As expected, while high wet thresholds
are observed in the wet regions (WG, parts of NE and HR2), dry regions (northern part of NW) exhibit low wet
thresholds. Moreover, it is observed that for around 14% of grids, the wet threshold (90th percentile) or the
mean is less than the dry threshold (2.5 mm/d). In those grid points, in order to draw a reasonable conclusion
from the analyses, dry threshold value is fixed as 0.1 mm/d. Evidently, the dry threshold adopted for drier
VINNARASI AND DHANYA
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Journal of Geophysical Research: Atmospheres
10.1002/2015JD024310
Figure 3. (a) Long-term trend for NWD, (b) long-term trend for FWS, (c) time-varying trend in terms of number of grids for NWD and FWS, (d) long-term trend for NDD,
(e) long-term trend for FDS, and (f) time-varying trend in terms of number of grids for NDD and FDS. Statistical significance is estimated for different confidence
intervals (99%, 95%, and 90%) using two-tailed hypothesis test. Pie chart and time-varying trend show significant trend at 10% significance level, which corresponds
to 90% confidence interval.
regions are 0.1 mm/d and wetter regions are 2.5 mm/d, which is suitable in the extraction of dry spells taking
into account the characteristics of the region. The varying thresholds adopted will help in identifying any
variations in the true nature of rainfall systems in various regions. These thresholds are kept the same for
moving window analyses also.
4.2. Spatiotemporal Variation of Extremes Days and Spells
The spatiotemporal variation and various characteristics of extremes are analyzed by computing the extreme
indices as defined in Table 2. Initially, the indices corresponding to number of extreme days and frequency of
extreme spells, i.e., NWD, NDD, FWS, and FDS are computed. The long-term trends of NWD and FWS as shown in
Figures 3a and 3b clearly show positive trend in 21% and 23% of grids, respectively, with most of the grids
exhibiting positive trend are in dry regions (HR1 and NW), while negative trend is predominant in grids over
wet regions like CNE and NE regions. This reveals the relative increase in the occurrence of frequent shortterm wet spells in dry regions. On the contrary, most of the earlier studies employing spatially averaged
rainfall have reported a positive trend in the NWD [Dash et al., 2009; Singh et al., 2014]. However, a highresolution gridwise analysis provides a clear understanding on the conspicuous spatial heterogeneity.
A time-varying trend analysis reveals that the number of grids exhibiting significant positive trend of NWD and
FWS decreases till the middle 1950s (1931 to 1980) but increases thereafter (Figure 3c). At the same time, the
grid count with negative trend shows contrasting behavior. Similar analyses on dry indices reveal negative
trend in NDD and positive trend in FDS, especially over dry regions like HR1 and NW, which in turn suggests
the discontinuities during dry spell, in the recent times. Whereas NE region exhibits positive trend in both
NDD and FDS, indicating the occurrence of frequent short dry spells. In addition, CNE displays positive trend
in NDD and negative trend in FDS demonstrating prolonged dry spells (Figures 3d and 3e). Besides, the grid
count with positive trend increases over the century, while grid count with negative trend decreases, as
depicted in Figure 3f. Hence, in general, wet spells are decreasing (Figure 3c) and prolonged dry spells are
increasing (Figure 3f).
Further, a detailed investigation on the dependence between number of extreme days and frequency of
extreme spells over the spatial domain is performed by estimating the short-term trend of the extreme
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Figure 4. Temporal mean of (a) ADWS in millimeters, (b) ALWS, and (c) ALDS for 1901 to 2013; long-term trend of (d) ADWS, (e) ALWS, and (f) ALDS, for 1901 to 2013. Pie
chart shows the statistical trend at 10% significance level; time-varying trend of (g) ADWS, (h) ALWS, and (i) ALDS, for 50 year moving window with 10 year difference.
Statistical significance is estimated for different confidence intervals (99%, 95%, and 90%) using two-tailed hypothesis test. Pie chart and time-varying trend
show significant trend at 10% significance level.
indices for three 50 year windows (refer to the supporting information Figures S2 and S3). Though reduction
in wet days and spells are evident, the reduction is relatively less in last window when compared with that of
middle, whereas drastic change is apparent between initial and middle windows. These changes are more
obvious in WC region (refer to the supporting information Figure S2). These trends clearly emphasize the
possibility of occurrence of wet spells with short durations in past half century.
Opposite pattern is visible in the short-term trend analysis of dry indices (refer to the supporting information
Figure S3). The predominant negative trend of dry days in initial window has shifted to dominant positive
trend in middle and final windows. It is worthwhile to note the reversal of trends in FDS from initial to last window, especially in PI, WC, and HR1. Most of the past studies also demonstrated that the positive trend of prolonged dry days happened in the past half century [Dash et al., 2009; Singh et al., 2014; Sushama et al., 2014].
The main factor attributing to these changes is the impact of global warming on the hydrological cycle and subsequent shift in the monsoon patterns [Giorgi et al., 2011]. Although the presence of short-duration extreme wet
spells and prolonged dry days, which will adversely affect the agricultural practices adopted in the country, is
discernible from the above analysis, a detailed probe into the variation of different spell durations (or length)
and corresponding accumulated depth may provide more insight about the changes in spell characteristics.
4.3. Spatiotemporal Variation of Accumulated Depth and Spell Length
Though intensification of dry days and wet spells over Indian region has been reported by many studies
[Giorgi et al., 2011; Vittal et al., 2013; Singh et al., 2014], an extensive attribute-wise inspection has not been
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Figure 5. Similarity between the ADWS and ALWS (a) 50 year moving window with 10 year difference and (b) spatial plot of
last 50 year window. Grouping is done into eight baskets, (A) no trend in ADWS and positive trend in ALWS, (B) no trend
in ADWS and negative trend in ALWS, (C) positive trend in ADWS and no trend in ALWS, (D) negative trend in ADWS and no
trend in ALWS, (E) positive trend in both ADWS and ALWS, (F) positive trend in ADWS and negative trend in ALWS, (G)
negative trend in ADWS and positive trend in ALWS, and (H) negative trend in both ADWS and ALWS.
carried out using high-resolution rainfall data. Therefore, the spatiotemporal variations of the main characteristics of extreme spells, i.e., accumulated depth (DWS), length (LWS) of each extreme wet spell, and length (LDS)
of each extreme dry spell are investigated. Since the number of occurrences of each of these indices per year
may vary, the analyses are done by categorizing the indices in two different ways: (i) by taking the average
value of the index (denoted as ADWS, ALWS, and ALDS) and (ii) by taking the maximum value of the index
(denoted as MDWS, MLWS, and MLDS).
The spatial distribution of long-term mean of ADWS, ALWS, and ALDS for 113 years are shown in Figures 4a
to 4c. High magnitude of accumulated depths are observed over wet regions, NE and WG, which matches
very well with the spatial pattern of annual maximum daily rainfall. Similar patterns are observed for
MDWS, MLWS, and MLDS (refer to the supporting information Figures S4a to S4c). ADWS varies from 4 mm to
320 mm, and ALWS varies from 1 to 3 days with a seemingly direct relation between ADWS and ALWS.
Subsequently, MDWS varies from 19 mm to 764 mm and MLWS varies from 2 to 5 days. Similarly, the temporal
mean of ALDS (average length of dry spell for each season) varies from 2 to 20 days and MLDS (maximum
length of dry spell for each season) varies from 4 to 52 days.
The long-term trend variation for the above indices are estimated as shown in Figures 4d to 4f. While majority
of grids show no trend, 19% of grids show significantly positive trend in ADWS and 15% of grids show significantly positive trend in ALWS. It is to be noted here that only grids showing trends with significance level
(>10%) are considered for the analyses. An inverse relation between wet and dry indices is quite apparent
in most of the grids over India. The positive trend of wet indices (ADWS and ALWS) and negative trend of
ALDS in HR1 affirms the previous results. In addition, the negative trend of wet indices and positive trend
of ALDS in NE and CNE regions are also worth mentioning. The reduction in ADWS in summer monsoon is
demonstrated by many studies [Goswami et al., 2006; Dash et al., 2007; Vittal et al., 2013], which is attributed
to the negative trend in monsoon depression (low-pressure areas) observed in the last century [Dash et al.,
2007], positive trend of aerosol optical depth, and increasing concentration of absorbing aerosol in the last
decades, which suppress the rainfall by burning off clouds [Ramachandran and Kedia, 2013]. Whereas, dry
spell index (ALDS) shows negative trend in dry regions such as NW and HR1, which is also agreed by
Alexander et al. [2006], and positive trends in wet regions like NE and CNE, as shown in Figure 4f. To summarize, majority of grids show negative trend in dry spell length. Moreover, similar kind of changes in long-term
trend has been observed in MDWS, MLWS, and MLDS (refer to the supporting information Figures S4d to S4f).
However, the present study emphasizes the highly localized nature of extremes, exhibiting more prominent
changes in distinguished dry and wet regions.
Spatially averaged behavior of indices in terms of number of grids as shown in Figures 4g and 4i further supports the occurrence of short wet spells over India. The time-varying trend analysis of ADWS demonstrates
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Figure 6. Most probable month of OWS for three 50 year windows (a) initial = 1901 to 1950, (b) middle = 1931 to 1980, and (c) final = 1961 to 2010. Most probable
month of ODS for three 50 year windows (d) initial = 1901 to 1950, (e) middle = 1931 to 1980, and (f) final = 1961 to 2010. Most probable month of (g) OWS and (h) ODS
for 50 year moving window with 10 year difference.
that the number of grids showing significant positive trend decreases whereas that of negative trend
increases. In the last three windows, the number of grids showing negative trend is approximately equal
to the number of grids showing positive trend. In case of ALWS, the number of grids showing positive and
negative trend is showing exactly opposite trend in the last 50 year window when compared to that of the
first 50 year window. In order to estimate the spatial distribution of these changes, the variation of these
indices for the three 50 year windows are depicted (Figure S5). Positive trend of ADWS is significant in HR1,
whereas negative trend is dominated in wet regions such as eastern parts of CNE and parts of HR2.
Likewise, ALWS shows negative trend in most of the regions in last window. In addition, the sign of the trends
seems to have changed in the initial and last windows. Similar changes are also observed for the maximum
value of each indices (refer to the supporting information Figure S6). However, for any reasonable conclusion to
be drawn, further verification is carried out pertaining to the similarity between ADWS and ALWS in various grids.
A similarity diagram is derived between ADWS and ALWS as shown in Figure 5 by grouping the gridwise trends
into eight baskets: (A) No trend in ADWS and positive trend in ALWS, (B) no trend in ADWS and negative trend
in ALWS, (C) positive trend in ADWS and no trend in ALWS, (D) negative trend in ADWS and no trend in ALWS,
(E) positive trend in both ADWS and ALWS, (F) positive trend in ADWS and negative trend in ALWS, (G) negative
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Figure 7. IWS of 50 year Return period. (a) Box plot of 10 year advance time step, which explains the spatial variance over India, (b) long-term trend for 1901 to
2013, (c) time-varying trend, in terms of the number of grids showing significant trend for 50 year moving window, and (d) similarity diagram for the successive 50 year moving window with 10 year difference. Statistical significance is estimated for different confidence intervals (99%, 95%, and 90%) using twotailed hypothesis test. Pie chart and time-varying trend show significant trend at 10% significance level, which corresponds to 90% confidence interval. In the
box plots, the box represents the data lying between the 25th and 75th percentile, i.e., interquartile range (IQR) of the data. Hence, the upper, lower, and
middle lines of the box denote 75th, 25th, and 50th (median) percentiles, respectively. The bottom whiskers represent the lowest datum within 1.5 times the
IQR of the lower quartile, and the top whisker represents the highest datum within 1.5 times the IQR of the upper quartile. The plus signs denote the outliers
falling outside these whisker limits.
trend in ADWS and positive trend in ALWS, and (H) negative trend in both ADWS and ALWS. The number of grids
in group E (positive trend in ADWS and ALWS) decreases over the 113 years, while the number of grids in group
B (no trend in ADWS and negative trend in ALWS) increases over the past 113 years. It is to be noted that the
number of grids in group H (Negative trend in both ADWS and ALWS) also increases in the last century. To
compare these results with the previous studies mentioned earlier, the spatial distribution of these groups
over India for the last window is shown in Figure 5b, which indicates that while group H is dominant in
wet regions like HR2, NE, and WG; groups B and C are dominant in Central India, the study region considered
by most of the past studies [Goswami et al., 2006; Singh et al., 2014]. Hence, the conclusions made by previous
studies on increase in the intensity of rainfall are applicable only to specific region and should not be generalized. These further highlight the occurrence of short-duration wet spells with high intensity and more frequent flood events, which subsequently affects agriculture and economy adversely.
Time-varying trend of ALDS and MLDS in terms of number of grids clearly shows that the grid count of positive
trend increases in the middle window (Figures 4i and S4i, respectively). Further, the spatial distribution of
time-varying trend of ALDS and MLDS (Figure S7) also reveals a trend reversal from first window to middle
window, while the last 50 year window shows significant trend in only a few grid points. This clearly indicates
that the characteristics of extremes vary significantly at fine scale, and hence, conclusions derived by previous
studies on drastic variations in rainfall characteristics cannot be considered as a generic statement for whole
Indian region. Overall, it is noted that majority of grids show wet spells of short duration and prolonged dry
spells, which signifies the intensification of hydrologic cycle induced by global warming, since warmer atmosphere can hold more moisture [Giorgi et al., 2011].
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4.4. Examination of Shift in Spatiotemporal Rainfall Pattern
Possible shift in pattern of rainfall is estimated by determining the changes in the onset and return levels.
Investigation on the spatiotemporal variation of onsets of extreme wet and dry spells (denoted as OWS and
ODS, respectively) is carried out, by identifying the onsets corresponding to each extreme spell, for all years.
The month in which onset is most probable to occur is selected and its spatial distribution is shown in
Figures 6a–6f. While, OWS was most probable in June and July in NE; July in WG, NW, CNE, and WC; and
August in HR1 during initial window, a gradual shift has been observed in NE, CNE, and Coastal parts of PI
in the middle window. The shift is more prominent in last window, with majority of the grids in CNE and
WC shifting from July to August. Similar analysis of ODS as shown in Figures 6d–6f also indicates a visible shift
in HR1, CNE, and WC. A comprised information on the occurrence of OWS and ODS in terms of number of grids
and corresponding onset month over India are shown in Figures 6g and 6h. A shift of OWS from July to August
and a shift of ODS from September to August are apparent, which indicates a shift in monsoon patterns across
the country.
Finally, the variation in trend, if any, for the 50 year return level (RL) of IWS has been investigated. The procedure adopted for computing 50 year RL is kept same as that of Vittal et al. [2013] considering 95% threshold
using 1° × 1° resolution of gridded daily rainfall. However, as defined earlier, since a threshold of 95% may not
include maximum intensity rainfall of all years, a threshold of 90th percentile is adopted in this study. Initially,
the 50 year return level is computed for 30 year moving window series using generalized Pareto distribution.
The spatial variation of 50 year RL for each window with 10 year difference as illustrated in Figure 7a, which
clearly shows that the spatial variability has increased in the past half century. Though Ghosh et al. [2011] also
indicated an increase in spatial variability using a low-resolution rainfall data, the magnitude of the variability
is found to be high in the case of fine-resolution data. Such an increase in heterogeneity further adds complexity to the modeling and adds uncertainty in forecasting [Mani et al., 2009]. Vittal et al. [2013] attributes
these changes to urbanization. Long-term trend of 50 year RL, as shown in Figure 7b, is indicated in around
55% of grids. Most of the grids are found to be shifting to a positive trend in maximum intensity rainfall as
evident from time-varying trend analysis (Figure 7c). A reversal in trend pattern is also apparent from
Figure 7d with numerous grids shifting from positive to negative trend or vice versa or even no trend to positive trend, which is noticeable in approximately 60% of grids. The spatial distribution of time-varying trends in
three windows (refer to the supporting information Figure S8) of 50 year RL shows that fraction of grid points
exhibiting positive trend in RL has increased from 32% (initial) to 46% (last).
Though a detailed analysis is required to attribute these changes to various physical mechanisms, a brief
analysis is carried out by relating the observed changes in daily precipitation with the local changes in surface
temperature. We found that the changes in the patterns of extreme events in recent decades, as found in this
study, can be attributed to the rise in the surface temperature, which in turn may be associated with the
anthropogenic activities and local influences (refer to the supporting information Text S1).
5. Conclusions
The present study focuses on the analysis of changing characteristics of extreme wet and dry spells over
Indian summer monsoon, with respect to various extreme indices, for the period from 1901 to 2013, using
0.25° × 0.25° high-resolution daily rainfall data. Spatial heterogeneity of annual maximum intensity of rainfall
over the past half century is found to have increased considerably. Variations in the different characteristics of
extremes are further investigated through various extreme indices employing POT analysis, adopting a
spatially varying wet and dry threshold. A direct relation between wet indices NWD and FWS indicates occurrence of frequent breaks in monsoon, thereby causing short frequent wet spells. On the other hand, inverse
patterns are prominent in dry indices—NDD and FDS—which indicate the presence of prolonged dry spell and
breaks in wet spells.
Moreover, four predominant combinations of DWS and LWS are noticed over India viz., (i) positive trend of
both DWS and LWS, with time-varying trend analysis showing a decline over the 50 year moving windows,
(ii) no trend in DWS while negative trend in LWS with number of grids showing an increase over the 50 year
moving windows, (iii) positive trend in DWS but no change in LWS, especially in HR1 and coastal part of
CNE, and (iv) negative trend in both DWS and LWS, especially in NE and WG in the last 50 year window. On
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the other hand, LDS shows positive trend in most of grids except for dry regions, HR1 and NW in middle
50 year window (1930 to 1980); but this significant trend pattern drastically reduces in the last 50 year window (1961 to 2010). These results clearly indicate that the so-called “natural system” of dry regions and
wet regions, started changing gradually in the recent half century. An intensification of wet spell and duration
of dry spell are observed, though these are highly localized phenomena and cannot be generalized.
Wet spell frequency peak is shifting toward the latter part of monsoon, while dry spells are moving toward
the early period of monsoon relatively, which establish an evident shift in monsoon patterns. A possible
change in the advance of monsoon across the country is indicated in the significant variation in the spatial
distribution of OWS and ODS. The frequent occurrence of heavy rainfall is discernible from the significant
positive trend in the 50 year return level in more than 55% grids. The detailed investigation on various characteristics of extreme events though provides sufficient proof regarding the abrupt variations in the monsoon rainfall distribution across the country; however, no generic statement of increase or decrease in
extremes in the country can be derived since the rainfall system seems to be significantly influenced by local
factors than any global influences. These local factors could be urbanization effects, anthropogenic influences, effect of aerosol concentration, land use land cover alterations, etc., which subsequently affect the true
behavior and response of the system. Moreover, the spatial variability further emphasizes the increase in the
heterogeneity of monsoon characteristics. Such an increase in heterogeneity further adds complexity to the
modeling and adds uncertainty to forecasting. The predictability, which otherwise would have been
improved upon spatial averaging, would be unaffected or even reduced because of the increase in heterogeneity. This further points out to the need in undertaking a high-resolution modeling incorporating these
local causal factors. Nevertheless, since most of the changes are found to be long term and significant, it
would be appropriate for the policy makers to be aware of the dynamic behavior of the rainfall pattern at
regional level and incorporate them in water resources modeling and management exercises.
Acknowledgments
The authors would like to express sincere
thanks to Indian Institute of Technology,
Delhi, for supporting this work. Highresolution daily gridded rainfall data used
in this study were purchased from Indian
Meteorological Department (http://www.
imdpune.gov.in/advts/Advt_spatial%
20resolution.pdf).
VINNARASI AND DHANYA
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