Proportional Trends of Continuous Rainfall in Indian Summer Monsoon

Abstract

A comprehensive study on the Indian summer monsoonal rainfall (ISMR) is performed in the light of decadal changes in the continuous rainfall events and the number of rainy days using 68 years (1951–2018) of gridded rain gauge data. Non-parametric Mann–Kendall’s test is applied on total rainfall amount, the number of rainy days, number of continuous rainfall events, and rainfall magnitude to find trends over different climatic zones of India for the two periods, 1951–1984 and 1985–2018. Our results found a decreasing trend for more than 4-days of continuous rainfall events during the recent 34 years (1985–2018) compared to 1951–1984. The rate of increase/decrease in extreme/continuous rainfall events does not follow a similar trend in number of continuous rainfall events and magnitude. Moreover, the rainfall is shifted towards a lesser number of continuous rainfall days with higher magnitudes during 1985–2018. During the crop’s sow season (i.e., the first 45 days from the onset date of Indian monsoon), the total number of rainy days decreased by a half day during the last 34 years. Over the Central and North East regions of India, the number of rainfall days decreased by ~0.1 days/yr and ~0.3 days/yr, respectively, during 1985–2018. Overall, the decreasing trends in continuous rainfall days may escalate water scarcity and lead to lower soil moisture over rain-fed irrigated land. Additionally, an upsurge in heavy rainfall episodes will lead to an unexpected floods. On a daily scale, rainfall correlates with soil moisture and evaporation up to 0.87 over various land cover and land use regions of India. Continuous light-moderate rainfall seems to be a controlling factor for replenishing soil moisture in upper levels. A change in rainfall characteristics may force the monsoon-fed rice cultivation period to adopt changing rainfall patterns.

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remote sensing
Article
Proportional Trends of Continuous Rainfall in Indian
Summer Monsoon
Vinay Kumar 1,* , K. Sunilkumar 2and Tushar Sinha 1


Citation: Kumar, V.; Sunilkumar, K.;
Sinha, T. Proportional Trends of
Continuous Rainfall in Indian
Summer Monsoon. Remote Sens. 2021,
13, 398. https://doi.org/10.3390/
rs13030398
Academic Editor: Simone Lolli
Received: 23 December 2020
Accepted: 21 January 2021
Published: 24 January 2021
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Attribution (CC BY) license (https://
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4.0/).
1Department of Environmental Engineering, Texas A&M University, Kingsville, TX 78363, USA;
tushar.sinha@tamuk.edu
2Indian Institute of Tropical Meteorology (IITM), Ministry of Earth Sciences, Pune 411008, India;
sunil.khadgarai@tropmet.res.in
*Correspondence: vinay.kumar@tamuk.edu
Abstract:
A comprehensive study on the Indian summer monsoonal rainfall (ISMR) is performed in
the light of decadal changes in the continuous rainfall events and the number of rainy days using
68 years (1951–2018) of gridded rain gauge data. Non-parametric Mann–Kendall’s test is applied on
total rainfall amount, the number of rainy days, number of continuous rainfall events, and rainfall
magnitude to find trends over different climatic zones of India for the two periods, 1951–1984 and
1985–2018. Our results found a decreasing trend for more than 4-days of continuous rainfall events
during the recent 34 years (1985–2018) compared to 1951–1984. The rate of increase/decrease in
extreme/continuous rainfall events does not follow a similar trend in number of continuous rainfall
events and magnitude. Moreover, the rainfall is shifted towards a lesser number of continuous
rainfall days with higher magnitudes during 1985–2018. During the crop’s sow season (i.e., the first
45 days from the onset date of Indian monsoon), the total number of rainy days decreased by a
half day during the last 34 years. Over the Central and North East regions of India, the number of
rainfall days decreased by ~0.1 days/yr and ~0.3 days/yr, respectively, during 1985–2018. Overall,
the decreasing trends in continuous rainfall days may escalate water scarcity and lead to lower soil
moisture over rain-fed irrigated land. Additionally, an upsurge in heavy rainfall episodes will lead
to an unexpected floods. On a daily scale, rainfall correlates with soil moisture and evaporation up
to 0.87 over various land cover and land use regions of India. Continuous light-moderate rainfall
seems to be a controlling factor for replenishing soil moisture in upper levels. A change in rainfall
characteristics may force the monsoon-fed rice cultivation period to adopt changing rainfall patterns.
Keywords: monsoon; Mann–Kendall test; rainy days trend; ISMR; LCLU; rice crop
1. Introduction
The summer monsoonal rainfall variability over different regions in India directly
affects the growth of rain-fed crops and socio-economic structure [
1
]. However, summer
(June to September, JJAS) is the only season that brings plentiful rainfall to most parts of
India. India receives about 75% to 80% of the total annual rainfall during JJAS, which is vital
for the irrigation of kharif crops (Crops that are sown in summer in the Indian subcontinent),
especially rice [
2
,
3
]. Every year, the plantation of the staple crop (rice) starts from May to
July in several parts of India. This plantation almost follows the climatological isochrones of
rainfall [
3
,
4
]. However, in fewer cases, the plantation of rice could be delayed up to August.
The germination and planting time of the summer crops mostly depend on the arrival of
the very first spell of monsoonal rainfall. Hence, the Indian Meteorological Department
(IMD) prioritized the daily rainfall prediction at the district level since 2000 [
5
]. Every
year, IMD issues rainfall prediction on vast spatial (all-India) scale and coarse temporal
scales (JJAS). However, the accuracy of rainfall prediction remains poor [
6
]. On a seasonal
scale, ISMR is declared normal or deficient/excess only by the end of each summer season.
Additionally, rainfall forecast at the district level, which is five days in advance, carries a
Remote Sens. 2021,13, 398. https://doi.org/10.3390/rs13030398 https://www.mdpi.com/journal/remotesensing

Remote Sens. 2021,13, 398 2 of 21
very low accuracy [
6
]. There is a need to predict rainfall a week to ten days in advance at
the district level, which is useful in agricultural and other sectors.
Monsoonal rainfall exhibits great spatial and temporal variability in total amount,
intensity, and duration. Every summer rainfall is compared to the long-term normal rainfall
(30-year climatological averages) and then declares as normal, drought, and flood year
based on total seasonal rainfall received. For instance, on a seasonal scale, the standard
deviation of
±
10% from the climatological rainfall determines if a year is to be declared a
flood or a drought year over India [
7
]. Both floods and droughts can significantly affect
the food production and gross domestic growth of a region and country [1,8]. In addition
to floods and droughts, extreme in spatio-temporal variability in rainfall characteristics
can also deteriorate crop production, predominantly over rain-fed agricultural regions.
To quantify such variability, a widely used metric, “active/break” is used in the summer
monsoonal rainfall. Active/break spell is declared when the daily rainfall is higher/lower
than climatological values for ≥3 consecutive days over Central India [9].
The Indian subcontinent has experienced several flood events in recent decades (e.g.,
26 July 2010; 16 June 2013, 5 September 2014). These events highlighted the need to better
understand the nature of heavy/extreme rainfall events and their frequency/duration [
10
].
Despite the advancements in computational resources and meteorological modeling,
the prediction of the Indian summer monsoonal rainfall (ISMR) is highly uncertain,
particularly for extreme rainfall events and their duration [
6
]. The understanding of
trends/characteristics in continuous rainfall days and extreme events will certainly help
the modeling community to better forecast such events.
Over the Indian region, extreme rainfall events, linked from local to remote climatic
conditions [
11
–
13
]. The inter-seasonal variability of the ISMR is associated with El Niño
southern oscillation (ENSO), the meridional gradient of temperature, European snow
cover, the Indian Ocean dipole (IOD), and the internal dynamics of monsoon itself [
14
].
Model simulations and observational datasets showed wet bias in the rainfall over North
East India during 1971–2005 [
15
]. However, ISMR predictions from several global climate
models show a dry bias, which underscores the need to understand better linkages between
local and exogenous climatic conditions [16].
Roxy et al. [2012] showed an increase in extreme rainfall events while a decrease in
annual rainfall over Central India from 1950 to 2015 [
17
]. The increase in moisture supply
from the Arabian Sea is the prime reason for an increase in extreme rainfall events over
Central India. The reduction in annual rainfall is attributed to the weakening of monsoon
circulation and decreasing low-pressure systems over the Indian region [
17
]. Another study
found a decreasing trend in annual ISMR amount by 2.6% (significant trend only over
3 stations out of 45 stations) over Central India based on 102 years (1901–2002) of rainfall
dataset [
18
]. A significant increase in the frequency of heavy rainfall days was found over
the northeast Indian region [
15
]. They further showed an increase in heavy rainfall over the
Indian region and a decrease in the duration of light to moderate rainfall during 1953–2002.
India witnessed below-normal to normal ISMR, most of the years during 1953–2002 [
15
]. A
good correlation is found between rainfall and the number of rainfall days over in Konkan
catchment area of India [
19
]. Though several studies exist in the literature regarding ISMR
rainfall trends, there is a gap in the trends of rainy days, continuous rainfall events and
their relation to the regional hydrology and crops. Although fewer studies have analyzed
trends in the ISMR over different regions of India, none of the studies have extensively
analyzed trends in continuous rainfall days over different climatic zones in India to the
best of the author’s knowledge. Low to moderate but continuous rainfall is particularly
crucial for enhancement of infiltration, which is critical for rain-fed agricultural regions
of India.
The objectives of this study are (a) Compare rainfall variability in the light of the
number of rainfall days, the number of continuous rainfall events and rainfall magnitude
in two periods of 34 years, 1951–1984 and 1995–2018, (b) Analyze spatial-temporal trends
and variability of continuous rainfall days over different climatic regions of India, (c)

Remote Sens. 2021,13, 398 3 of 21
Understanding the connection between continuous rainfall intensity to other hydrological
variables (e.g., soil moisture, evaporation), (d) Usefulness of June to mid-July rainfall
for the plantation of rice crop. Such information will be useful in developing adaptive
agricultural practices (e.g., planting and harvesting of crops), which needs to be updated
under changing climatic conditions and non-stationary climate.
2. Dataset and Methods
2.1. Datasets
Daily gridded rainfall data are obtained from the Indian Meteorological Department
(IMD) during 1951–2018. This dataset is generated based on the varying network of
6955 rain-gauges that collected consistent daily rainfall data over a longer period. They
used the spatial interpolation method (Shepard 1969) to create gridded datasets and applied
standard quality control e.g., missing data, duplicate station check, and extreme value
check. The gridded rainfall from the IMD is available at 0.25
◦×
0.25
◦
spatial resolution [
20
].
However, for a shorter duration (~20 years), rainfall datasets are available from other
sources as well, e.g., satellites, rain-gauges, and reanalysis products. We analyzed 68 years
of rainfall data for light to heavy rainfall and continuous rainfall days for different climatic
regions in India. In this study, we divided India into 5 homogenous climate regions because
of the complex variability of summer rainfall over various regions. We divided the total
period (1951–2018) into two equal durations 1951–1984 (referred to as Early, E, E-period,
hereafter), and 1985–2018 (referred to as Late, L, L-period hereafter) for comparison as
previous studies concluded that the long term trend of the ISMR is stable [
21
]. We found
that the two periods of 34-years are not biased towards drought or wet years and the mean
of the two periods are 759.01 mm and 749.53 mm respectively. On other possibilities of
the division of ISMR into two equal periods, we applied the abrupt trend change point
method [
22
]. This method divided the series of 68 years (1951–2018) into two periods
of 37 years and 31 years. The mean of these two periods is 750.63 mm and 758.62 mm,
respectively, while the mean of the total time series is 754.23 mm. The absolute difference
between parts of the two-time series and total remain ~4.4 mm, which is very small and
insignificant on a climate scale. Hydrological variables e.g., evaporation, soil moisture,
and total precipitation were obtained from ECMWF reanalysis on 0.25
◦×
0.25
◦
spatial
resolution [
23
]. TRMM dataset [
24
] was also used here from 2009 to 2018 to compare
against IMD rainfall.
2.2. Study Region
Monsoonal rainfall over India is spatially quite diverse [
25
]. We selected 5 regions
in Indian to perform regional-scale analysis. These selected regions almost represent the
climatological homogenous regions of India. Figure 1a displays different climatic zones:
North West (NW, 70–77
◦
E, 23–32
◦
N), West Coast (WC, 72–77
◦
E, 10–20
◦
N), South East (SE,
77–81
◦
E, 9–15
◦
N), North East (NE, 87–97
◦
E, 21–30
◦
N), and Central India (CI,
77–86◦E
,
20–27
◦
N). The annual mean rainfall approximately around 900 mm in India. The mean
summer rainfall peaks over topographic regions such as WC and NE, while ISMR is
moderate over CI. Dry regions such as NE and SE are less influenced by monsoon rainfall
bearing systems. In addition to large scale monsoon system, India also receives rainfall
from cyclones, thunderstorms and western disturbances.
The trends in mean rainfall do not provide exact information on spatio-temporal
variability of the ISMR in changing climatic conditions. We investigated how the magnitude
and number of continuous rainfall events changed in different climatic regions of India in
the past 68 years. However, the cases of extreme events over Indian regions came into the
focus in the last 20 years or so [10].

Remote Sens. 2021,13, 398 4 of 21
Remote Sens. 2021, 13, x FOR PEER REVIEW 4 of 22
(a) (b)
(c)
Figure 1. India Meteorological Department (IMD) gridded summer rainfall from 1951-2018 (a) Climatology of the number
of rainy days (where rainfall>0.5mm/day on each grid), (b) Rainfall climatology (cm), (c) Difference between the number
of rainy days from E-Period and L-Period. Black boxes numbered from 1 to 5 represent five homogenous regions of India
[1 North West (NW), 2 Central India (CI), 3 North East (NE), 4 West Coast (WC), 5 South East (SE)].
The trends in mean rainfall do not provide exact information on spatio-temporal var-
iability of the ISMR in changing climatic conditions. We investigated how the magnitude
and number of continuous rainfall events changed in different climatic regions of India in
the past 68 years. However, the cases of extreme events over Indian regions came into the
focus in the last 20 years or so [10].
2.3. Methodology
Non-parametric based Mann–Kendal (MK) test with Sen’s slope estimator was used
to compute trends for several qualitative and quantitative rainfall characteristics that were
derived using the daily gridded rainfall data. MK test and Sen’s Slope are insensitive to
missing data and data distribution since this test considers the median of all slopes and
rank of the data. Therefore, the MK test is robust and performs better than linear regres-
sion, particularly when data has outliers. MK trend statistic and Sen’s slope were esti-
mated using the equations described in earlier studies [26].
The statistics of the MK test and Sen’s slope for a time series is given by
S =∑.

 ∑.

 sign (xj-xk) (1)
Figure 1.
India Meteorological Department (IMD) gridded summer rainfall from 1951–2018 (
a
) Climatology of the number
of rainy days (where rainfall > 0.5 mm/day on each grid), (
b
) Rainfall climatology (cm), (
c
) Difference between the number
of rainy days from E-Period and L-Period. Black boxes numbered from 1 to 5 represent five homogenous regions of India [1
North West (NW), 2 Central India (CI), 3 North East (NE), 4 West Coast (WC), 5 South East (SE)].
2.3. Methodology
Non-parametric based Mann–Kendal (MK) test with Sen’s slope estimator was used
to compute trends for several qualitative and quantitative rainfall characteristics that were
derived using the daily gridded rainfall data. MK test and Sen’s Slope are insensitive to
missing data and data distribution since this test considers the median of all slopes and
rank of the data. Therefore, the MK test is robust and performs better than linear regression,
particularly when data has outliers. MK trend statistic and Sen’s slope were estimated
using the equations described in earlier studies [26].
The statistics of the MK test and Sen’s slope for a time series is given by
S=∑n−1
k=1·∑n
j=k+1·sign xj−xk(1)
Where xj−xk=


1 if xj−xk>0
0 if xj−xk=0
−1 if xj−xk<0
and j and k are numbers in series (2)
We applied the MK test and computed Sen’s slope on daily rainfall data during ISMR
at a confidence level of 95%. This study analyzed the trends in the number of rainy days
and continuous rainfall events ranging from 1 day to 11 days. Hereafter, in the manuscript

Remote Sens. 2021,13, 398 5 of 21
we referred continuous rainfall of 1 day, 2 days, 3 days, 4 days and N days as 1DAY, 2DAY,
3DAY and NDAY respectively. A day is considered as rainy day when the daily rainfall
exceeds or equal to 0.5 mm [
25
,
27
], while a light/heavy intensity rainfall is estimated when
the daily rainfall is below/above, 10th/90th percentile over each grid. IMD defines a rainy
day when the daily rainfall exceeds 2.5 mm over central India. However, we considered
a threshold of 0.5 mm in the present study since dry regions such as NW and SE often
might not meet the criteria of 2.5 mm. A continuous X-rainfall event is defined as an event
when the daily rainfall exceeds 0.5 mm over X consecutive day/s (where X = 1DAY, 3DAY,
5DAY, and more than 10DAY). For example, a 3DAY continuous rainfall event should have
>0.5 mm of rain over a grid, while a day preceding and following that event should have
less than 0.5 mm of rainfall over that grid. It is noted that the rainfall may be discontinuous
on an hourly basis. However, it was still considered as a continuous rainy day in this
study if the daily rainfall were higher than 0.5 mm for many days. However, consideration
of rainy days based on rainfall
≥
0.5 mm or rainfall
≥
2.5 mm does not influence the
rainfall trends shown in this manuscript but only the magnitude of rainfall (Figure S1).
The general formulae of rainfall frequency and fraction calculation are mentioned below.
X_event frequency over each grid is estimated by normalizing the total number of X_events
by the total period of ISMR, which is 122 days long from June 1 to September 30. Similarly,
the X_event intensity fraction was estimated by the ratio of total rainfall contributed from
X_event to total ISMR rainfall.
X_Event f requency =Tot al number o f X _events
122 (3)
X_Even t f ra ction =Rai n f all co ntribu ted f ro m X _events
Total amo unt o f I SMR (4)
where X = 1DAY, 2DAY, 3DAY, 4DAY, 5DAY up to 11DAY
3. Results
3.1. Trends in Number of Rainy Days and Rainfall
It is expected that the number of rainy days is higher over the regions that receive ex-
cessive rainfall than the areas with relatively low rainfall or drought-prone areas.
Figure 1a
shows the climatology of the number of rainy days (where rainfall > 0.5 mm/day on
each grid) over five selected climatic regions of India, e.g., NW, CI, NE, WC, and SE,
which almost replicates the spatial pattern of the ISMR climatology (Figure 1b). WC and
NE regions show the highest number of rainy days (Figure 1a), which resembles spatial
patterns with the composite of active-spells (Continuous rainfall of
≥
3 days over central
India of standard deviation > 0.7) of the ISMR. Hot-spot regions of maximum rainfall
and a higher number of rainy days, in general, experienced an increase in the number of
floods [
12
]. Additionally, NW and SE regions are drought-prone areas, particularly during
the summer monsoon season. Notably, the standard deviation of rainfall in regions that
receive low rainfall is higher than excessive rainfall [
28
]. Few studies have highlighted the
usefulness of rain harvesting, particularly in regions that receive moderate to low rainfall
so that the stored water could locally be utilized during monsoon breaks and droughts [
29
].
The decrease in rainfall trend can force people to preserve the water during active spells
of rainfall.
We calculate the difference between the number of rainy days for the E-period (1951–
1984) and the L-period (1985–2018) (Figure 1c). In the L-period, the number of rainy days
is higher over central India, while lesser over the Western Ghats. However, a decrease in
rainy days over the southern Western Ghats is unusual. The changes in the number of
rainfall days have implications for rain-fed agriculture, industries and socio-economics
of locals.
Total rainfall and its duration are an essential aspect of rainfall over a given region.
Significant differences are seen in the monthly mean rainfall trends during the two different
periods (Figure 2). All the summer months display an increasing trend of rainfall during

Remote Sens. 2021,13, 398 6 of 21
the E-period (Figure 2a–d). In contrast, June, July, and September months show a de-
creasing trend during L-period (Figure 2e–h). The rainfall contributions from the summer
months are as follows: July (~24%), August (~21%), September (~14%), and June (~14%),
respectively, to the annual rainfall [
30
]. In the recent 34 years, the decreasing trends of
rainfall in July and September months are much steeper than the other months (
Figure 2
).
Overall, the ISMR displays a clear contrast in the monthly rainfall trends in the two periods.
Remote Sens. 2021, 13, x FOR PEER REVIEW 6 of 22
decrease in rainfall trend can force people to preserve the water during active spells of
rainfall.
We calculate the difference between the number of rainy days for the E-period (1951-
1984) and the L-period (1985-2018) (Figure 1c). In the L-period, the number of rainy days
is higher over central India, while lesser over the Western Ghats. However, a decrease in
rainy days over the southern Western Ghats is unusual. The changes in the number of
rainfall days have implications for rain-fed agriculture, industries and socio-economics of
locals.
Total rainfall and its duration are an essential aspect of rainfall over a given region.
Significant differences are seen in the monthly mean rainfall trends during the two differ-
ent periods (Figure 2). All the summer months display an increasing trend of rainfall dur-
ing the E-period (Figure 2 a-d). In contrast, June, July, and September months show a de-
creasing trend during L-period (Figure 2 e-h). The rainfall contributions from the summer
months are as follows: July (~24%), August (~21%), September (~14%), and June (~14%),
respectively, to the annual rainfall [30]. In the recent 34 years, the decreasing trends of
rainfall in July and September months are much steeper than the other months (Figure 2).
Overall, the ISMR displays a clear contrast in the monthly rainfall trends in the two peri-
ods.
Figure 2. MK test and trends of rainfall (mm/month) on all India for summer months for E-Period
(a) June, (b) July, (c) August, (d) September, and for L-Period (L) (e) June, (f) July, (g) August, (h)
September. On each panel prefix, “E” means, data considered from 1951 to 1984, while prefix “L”
means, data consideration from 1985 to 2018 in this figure and the following figures. The blue line
h
f
d
g
e
c
b
a
Figure 2.
MK test and trends of rainfall (mm/month) over India for summer months for E-Period (
a
) June, (
b
) July, (
c
)
August, (
d
) September, and for L-Period (L) (
e
) June, (
f
) July, (
g
) August, (
h
) September. On each panel prefix, “E” means,
data considered from 1951 to 1984, while prefix “L” means, data consideration from 1985 to 2018 in this figure and the
following figures. The blue line shows the trend, while the red dotted lines show confidence at a 95% significance level here.
The same nomenclature is followed in all the figures.
Figure 3displays the trends of summer monsoonal rainfall days over various regions
in India (see Figure 1a). The number of rainfall days are notably declined over NE in
L-period as compare to E-period. Such a strange shift is observed over the NE region,
which is known for its dense forest region and the world’s wettest point, Cherrapunji.
Over CI the rainfall days are continuously decreasing (Figure 3). However, the regions,
e.g., NW, SE, and WC, show an increasing trend for the L-Period (Figure 3right column).
In contrast, for the E-Period, NW displays a decreasing trend, while SE and WC show
no trend (Figure 3left column). The number of rainy days over different climatic zones
demonstrates a remarkable difference in trends during E-period and L-periods. Overall,
the number of rainfall days, as well as rainfall quantity, declined over CI and NE India

Remote Sens. 2021,13, 398 7 of 21
during JJAS (Figures 2and 3) during L-period. It is interesting to note that the number of
rainfall days decreased over the southern region of WC (Figure 1c), while over total WC
number of rainfall days increased in L-period (Figure 3, bottom right). These results do not
dilute the fact that the cases of extreme rainfall events are increasing over Central India [
10
].
From Figures 2and 3, it seems the total number of rainfall days used to be linearly related
to the total rainfall during the E-Period, but this does not hold during L-Period. Figure S1
shows similar trends for two periods as in Figure 3, where we consider the IMD definitions
of rainy days (rainfall on each grid > 2.5 mm). Such criteria do not affect our analysis even
if we consider rainfall over a grid > 0.5 mm or 2.5 mm to define a rainy day.
Remote Sens. 2021, 13, x FOR PEER REVIEW 8 of 22
Figure 3. MK test for the number of rainy days over five homogenous regions of India North West
(NW), Central India (CI), North East (NE), West Coast (WC), South East (SE)]. On each panel pre-
fix, “E” means, data considered from 1951 to 1984, while prefix “L” means, data consideration
from 1985 to 2018. The blue line shows the trend, while the red dotted lines show confidence at a
95% significance level here.
The overall trend of the number of rainy days in each month and over each climatic
zone are shown in Table 1 for L-period. The number of rainfall days shows a decline dur-
ing July over all regions of India. Overall, the number of rainfall days exhibits a decreasing
trend in the summer season and months except for June over India. CI and NE exhibit a
decreasing trend in the number of rainy days for the summer season. August also experi-
enced a reduced number of rainy days over all regions, except for SE. The number of rainy
days decreased over NE and increased over WC in the L-period. The trends in rainy days
are significant at 95% e.g., an increasing trend over all India in June, increasing trend over
SE in August, and decreasing trend over WC in JJAS.
Table 2 shows the monthly and seasonal rainfall trends over selected climatic regions
of India for the L-period. There is an increasing rainfall trend in June over all regions ex-
cept over NE, a decreasing rainfall trend in July over all regions except over NW, a mixed
trend of rainfall in August over various regions, and an increasing rainfall trend in Sep-
tember over all regions except over CI (Table 2). Decreasing rainfall in July and increasing
rainfall in August over CI and NE, respectively, are significant trends at 95%. The Central
India region is being addressed in many studies, here, except in June, all the months show
Figure 3.
MK test for the number of rainy days over five homogenous regions of India North West
(NW), Central India (CI), North East (NE), West Coast (WC), South East (SE)]. On each panel prefix,
“E” means, data considered from 1951 to 1984, while prefix “L” means, data consideration from
1985 to 2018. The blue line shows the trend, while the red dotted lines show confidence at a 95%
significance level here.
Such declines in the ISMR will adversely affect soil moisture and irrigation in the early
summer period over India. Several earlier studies also reported similar trends in rainy
days over homogeneous regions. For example, annual rainfall from 1881 to 1970 showed
an increasing trend over North India and Central India while decreasing trend over the
North-Eastern region of India [
31
]. Over Central India, the decreasing trend of rainfall may
be due to the weakened relationship between the ISMR and the ENSO and a decrease in

Remote Sens. 2021,13, 398 8 of 21
the meridional temperature gradient across the Indian landmass and the Indian Ocean [
32
].
A significant decreasing trend is observed over NE and CI regions [
33
]. Few other studies
have reported mixed trends in rainfall (i.e., either increase or decrease) based on station
datasets [
34
], river basin scales [
35
], and regional levels [
30
]. The results in the current
study are in agreement with the previous findings on rainfall intensity and rainfall days.
However, the present study deals with different rainfall periods than other studies [
31
,
32
].
The overall trend of the number of rainy days in each month and over each climatic
zone are shown in Table 1for L-period. The number of rainfall days shows a decline during
July over all regions of India. Overall, the number of rainfall days exhibits a decreasing
trend in the summer season and months except for June over India. CI and NE exhibit
a decreasing trend in the number of rainy days for the summer season. August also
experienced a reduced number of rainy days over all regions, except for SE. The number of
rainy days decreased over NE and increased over WC in the L-period. The trends in rainy
days are significant at 95% e.g., an increasing trend over all India in June, increasing trend
over SE in August, and decreasing trend over WC in JJAS.
Table 1.
Trends in the number of rainy days from 1985 to 2018, where “*” shows the significant trends
at 95%. The up/down arrows show increasing/decreasing trends.
Number of Rainy Days
June July August September JJAS
Northwest ↑↓↓↓↓
Central India ↓↓↓↓↓
Northeast ↓↓↓↓↓
West coast ↑↓↓↑↓*
Southeast ↑ ↓ ↑ *↑ ↓
All India ↑↓↓↓↓
Table 2shows the monthly and seasonal rainfall trends over selected climatic regions
of India for the L-period. There is an increasing rainfall trend in June over all regions except
over NE, a decreasing rainfall trend in July over all regions except over NW, a mixed trend
of rainfall in August over various regions, and an increasing rainfall trend in September
over all regions except over CI (Table 2). Decreasing rainfall in July and increasing rainfall
in August over CI and NE, respectively, are significant trends at 95%. The Central India
region is being addressed in many studies, here, except in June, all the months show
decreasing rainfall trend. It seems that the number of rainfall days and monthly mean
rainfall are linearly varying over CI for the summer season. The decrease in July rainfall
will bring hardship to the farmers to irrigate their crop, a time of crop sustainability.
Table 2.
Trends in monthly mean rainfall from 1985 to 2018, where “*” shows a significant trend at
95%. The up/down arrows show increasing/decreasing trends.
Monthly Mean
June July August September JJAS
Northwest ↑↑↓↑↑
Central India ↑ ↓ *↓↓↓
Northeast ↓ ↓ ↑ *↑ ↓
West coast ↑↓↓↑↓*
Southeast ↑↓↑↑↑
All India ↓↓↓↑↓
3.2. Frequency of Continuous Rain Events and Their Magnitude
As a matter of fact, monsoon, a large-scale phenomenon where an episode of contin-
uous rainfall lasts for a couple of days to weeks. During active spells, about 70% of the
monsoonal rainfall is typically caused by strati-form clouds, which provide widespread

Remote Sens. 2021,13, 398 9 of 21
and continuous rainfall as compared to convective rainfall that drives extreme events [
36
].
Further, the low-pressure systems (e.g., lows, depressions, deep depressions) also retain
the rainfall over Central India [
37
]. Moreover, seasonal rainfall over southwest monsoonal
regions happens due to various rainfall bearing systems of different temporal and spe-
cial scales, e.g., monsoon lows, depressions, cyclones, and mesoscale convective systems
(MCS’s), and isolated convective systems. Extreme rainfalls due to low-pressure systems
are linked to global warming over the Indian subcontinent [
10
,
38
]. Considering spatiotem-
poral rainfall variability over India, such systems depict the asymmetric distribution of
rainfall over sub-regions (WC, CI, NE, SE, and NW). That is one of the reasons why rainfall
quantity and rainfall days showed quite strange results for two periods (Figures 2and 3).
Thus, it motivates us to find out the trends of continuous rainfall for 1DAY, 2DAY, 3DAY,
and more.
We evaluated the continuous rain events and continuous rainfall magnitude over
Indian region for two periods. The frequency of continuous rainfall events increased for
1DAY, 4DAY and
≥
6DAY for E-period (Figure 4a,d,f). However, it is decreased for day 2 of
rainfall (Figure 4b), and almost constant for 3DAY and 5DAY (Figure 4c,e). For, L-period,
trends in continuous rain events frequency escalated for 1 to 4DAY (Figure 4g–j), and
almost constant for 5DAY (Figure 4k), while decreased for >6DAY (Figure 4l). In recent
years, the frequency of continuous rain events de-escalated for a higher number of days
(>6DAY). The magnitude of Sens slope in E-period (0.01 for 1DAY and 0.004 for
≥
6DAY) is
smaller than the L-period (0.04 for 1DAY and
−
0.01 for
≥
6DAY) for rain event frequency.
A well-known local name for the frequency of continuous rainfall, Jhad, in northern Indian
villages, is fading in recent times. Some of the well-known recent flood events (such as
26–28 July 2010; 15–16 June 2013) over India lasted for 1DAY to 3DAY, which is also evident
from Figure 4g,i) during L-period.
The Sen’s slope calculated for JJAS seasonal and continuous rainfall day’s magnitude
ranging from 1 to 5DAY and more days (Figure 5), for both the periods. The number
of continuous rainfall magnitude remain almost constant for 1DAY, 2DAY, 4DAY, and
5DAY (Figure 5a,b,d,e), while decreasing and increasing trend for 3DAY and
≥
6DAY,
respectively (Figure 5c,f). In E-period magnitude of continuous rain magnitude with
≥
6DAY were increasing (Figure 5f) as compared to L-period (Figure 5l), where they are
decreasing. The magnitude of Sen’s slope for
≥
6DAY is 0.69 and
−
0.29 respectively for
E-and L-period. In the E-periods, the trends of continuous rainfall event and magnitude
are quite irregular for 1 to 6DAY, somewhat resembled with Figure 4a–f. In the case of
L-period continuous rain day’s magnitude increased from 1 to 3DAY, remain constant
for 4DAY while decreased for >5DAY. The frequency of continuous rainfall event and
magnitude decreased for
≥
6DAY for L-period (Figures 4and 5). It seems that the number
of continuous rainy events and corresponding rainfall quantities are in a linear relationship
over the Indian region (Figures 4and 5).
In particular, the magnitude of continuous rainfall is increasing for fewer days (i.e.,
extreme rainfall for 1 to 3DAY). There is an increase in rainfall magnitude of 1DAY continu-
ous rainfall by 1mm, 2DAY continuous rainfall by 2.7 mm and 3DAY continuous rainfall by
4.9 mm but reduced for
≥
6DAY by 10 mm during L-period. A reduction in the number of
continuous rainfall episodes for more than 6DAY is consistent with other studies highlight-
ing the influence of global warming over the Indian subcontinent. For example, in a study,
it is concluded that for every 1
◦
C warming, the atmosphere could hold extra 7% moisture.
This is one of the possible reasons for the extreme rainfall [
39
]. Based on Figures 4and 5,
it is inferred that the heavy rainfall for fewer continuous days (
≤
4) shows an increasing
trend as compared to higher numbers of days (≥5).

Remote Sens. 2021,13, 398 10 of 21
Remote Sens. 2021, 13, x FOR PEER REVIEW 10 of 22
Figure 4. MK test for continuous rainfall events over India for 1 DAY to 5 DAY and more, (a-f) E-
period, (g-l) L-Period. On each panel prefix, “E” means, data considered from 1951 to 1984, while
prefix “L” means, data consideration from 1985 to 2018. Continuous rainfall of 1 day, 2 days, 3
days, 4 days and N days are referred as 1DAY, 2DAY, 3DAY and NDAY respectively. The blue
line shows the trend, while the red dotted lines show confidence at a 95% significance level here.
We evaluated the continuous rain events and continuous rainfall magnitude over In-
dian region for two periods. The frequency of continuous rainfall events increased for
1DAY, 4DAY and ≥6DAY for E-period (Figure 4a,d,f). However, it is decreased for day 2
of rainfall (Figure 4 b), and almost constant for 3DAY and 5DAY (Figure 4c, 4e). For, L-
period, trends in continuous rain events frequency escalated for 1 to 4DAY (Figure 4g-j),
and almost constant for 5DAY (Figure 4k), while decreased for >6DAY (Figure 4l). In re-
cent years, the frequency of continuous rain events de-escalated for a higher number of
days (>6DAY). The magnitude of Sens slope in E-period (0.01 for 1DAY and 0.004 for ≥
6DAY) is smaller than the L-period (0.04 for 1DAY and -0.01 for ≥ 6DAY) for rain event
frequency. A well-known local name for the frequency of continuous rainfall, Jhad, in
northern Indian villages, is fading in recent times. Some of the well-known recent flood
events (such as July 26-28, 2010; June 15-16, 2013) over India lasted for 1DAY to 3DAY,
which is also evident from Figures (4g, 4i) during L-period.
The Sen’s slope calculated for JJAS seasonal and continuous rainfall day’s magnitude
ranging from 1 to 5DAY and more days (Figure 5), for both the periods. The number of
continuous rainfall magnitude remain almost constant for 1DAY, 2DAY, 4DAY, and
5DAY (Figure 5 a-b, d-e), while decreasing and increasing trend for 3DAY and ≥6DAY,
a
e
k l
c
d
b
g i
j
h
f
Figure 4.
MK test for continuous rainfall events over India for 1 DAY to 5 DAY and more, (
a
–
f
) E-period, (
g
–
l
) L-Period. On
each panel prefix, “E” means, data considered from 1951 to 1984, while prefix “L” means, data consideration from 1985
to 2018. Continuous rainfall of 1 day, 2 days, 3 days, 4 days and N days are referred as 1DAY, 2DAY, 3DAY and NDAY
respectively. The blue line shows the trend, while the red dotted lines show confidence at a 95% significance level here.
3.3. Variability of Light and Heavy Rainfall
Numerous studies showed that signatures of climate change are reflected in the
extreme side (heavy and low rainfall episodes) rather than in mean rainfall over the various
regions of the Indian domain [
40
]. Thus, we evaluated the possible changes in rainfall
magnitude, likely for heavy and light rainfall events. Figure 6a–d displays the trend in
heavy (greater than or equal to top 90th percentile) and light rainfall event’s magnitude
(less than or equal to lowest 10th percentile). The heavy rainfall events magnitude shows
an increasing marginal trend over India, while light rainfall events magnitude shows a
remarkable decreasing trend over India in L-period (Figure 6c,d). It is to be noted that
the cases of extreme rainfall events are influenced by the cyclonic systems, cloud bursts,
regional water bodies, and atmospheric rivers [
41
]. The heavy rainfall grows at the expense
of light rainfall, thus neutralizing such kind of trends in ISMR for 100 years [
10
]. During
an extreme rainfall event, an excessive quantity of rainfall is poured in a short period. In
contrast, a moderate to light rainfall event occur when almost a similar rainfall amount is
spread over a longer time. The regions NW and SE typically receive light rainfall while the
regions WC and NE receive relatively heavy rainfall (Figure 1b).

Remote Sens. 2021,13, 398 11 of 21
Remote Sens. 2021, 13, x FOR PEER REVIEW 11 of 22
respectively (Figure 5c, f). In E-period magnitude of continuous rain magnitude with
≥6DAY were increasing (Figure 5f) as compared to L-period (Figure 5l), where they are
decreasing. The magnitude of Sen’s slope for ≥ 6DAY is 0.69 and -0.29 respectively for E-
and L-period. In the E-periods, the trends of continuous rainfall event and magnitude are
quite irregular for 1 to 6DAY, somewhat resembled with Figure 4a-f. In the case of L-
period continuous rain day’s magnitude increased from 1 to 3DAY, remain constant for
4DAY while decreased for >5DAY. The frequency of continuous rainfall event and mag-
nitude decreased for ≥6DAY for L-period (Figures 4 and 5). It seems that the number of
continuous rainy events and corresponding rainfall quantities are in a linear relationship
over the Indian region (Figures 4 and 5).
Figure 5. MK test for continuous rainfall magnitude over India for 1DAY to 5DAY and more than
6 DAY. On each panel prefix, “E” means, data considered from 1951 to 1984, while prefix “L”
means, data consideration from 1985 to 2018. Continuous rainfall of 1 day, 2 days, 3 days, 4 days
and N days are referred as 1DAY, 2DAY, 3DAY and NDAY respectively. The blue line shows the
trend, while the red dotted lines show confidence at a 95% significance level here.
In particular, the magnitude of continuous rainfall is increasing for fewer days (i.e.,
extreme rainfall for 1 to 3DAY). There is an increase in rainfall magnitude of 1DAY con-
tinuous rainfall by 1mm, 2DAY continuous rainfall by 2.7mm and 3DAY continuous rain-
fall by 4.9mm but reduced for ≥6DAY by 10mm during L-period. A reduction in the num-
ber of continuous rainfall episodes for more than 6DAY is consistent with other studies
highlighting the influence of global warming over the Indian subcontinent. For example,
a
e
k l
c
d
b
g i
j
h
f
Figure 5.
MK test for continuous rainfall magnitude over India for 1DAY to 5DAY and more than 6 DAY. On each panel
prefix, “E” means, data considered from 1951 to 1984, while prefix “L” means, data consideration from 1985 to 2018.
Continuous rainfall of 1 day, 2 days, 3 days, 4 days and N days are referred as 1DAY, 2DAY, 3DAY and NDAY respectively.
The blue line shows the trend, while the red dotted lines show confidence at a 95% significance level here.
In the L-period, the magnitude of heavy rainfall frequency (HRF) increases throughout
India, at the cost of light rainfall frequency (LRF) (Figure 6c,d). However, the same logic
does not work for the E-period. The slopes of LRF in two periods (E and L) are almost the
same, except for the opposite sign (Figure 6b,d). In particular, the magnitude of light rainfall
dwindled in the L-period while the magnitude of heavy rainfall increased marginally. The
light rainfall is beneficial to grow crops by recharging the upper soil layer (1 to 10 cm).
The rainfall decreasing trend in the L-period for LRF may indicate an increased number of
non-rainy days. An increase in the number of non-rainy days may shift the total rainfall
distribution towards the heavy rainfall end.

Remote Sens. 2021,13, 398 12 of 21
Remote Sens. 2021, 13, x FOR PEER REVIEW 12 of 22
in a study, it is concluded that for every 1oC warming, the atmosphere could hold extra
7% moisture. This is one of the possible reasons for the extreme rainfall [39]. Based on
Figure 4 and 5, it is inferred that the heavy rainfall for fewer continuous days (≤4) shows
an increasing trend as compared to higher numbers of days (≥5).
3.3. Variability of
L
ight and
H
eavy
R
ainfall
Numerous studies showed that signatures of climate change are reflected in the ex-
treme side (heavy and low rainfall episodes) rather than in mean rainfall over the various
regions of the Indian domain [40]. Thus, we evaluated the possible changes in rainfall
magnitude, likely for heavy and light rainfall events. Figure 6a-d displays the trend in
heavy (greater than or equal to top 90th percentile) and light rainfall event’s magnitude
(less than or equal to lowest 10th percentile). The heavy rainfall events magnitude shows
an increasing marginal trend over India, while light rainfall events magnitude shows a
remarkable decreasing trend over India in L-period (Figure 6c,d). It is to be noted that the
cases of extreme rainfall events are influenced by the cyclonic systems, cloud bursts, re-
gional water bodies, and atmospheric rivers [41]. The heavy rainfall grows at the expense
of light rainfall, thus neutralizing such kind of trends in ISMR for 100 years [10]. During
an extreme rainfall event, an excessive quantity of rainfall is poured in a short period. In
contrast, a moderate to light rainfall event occur when almost a similar rainfall amount is
spread over a longer time. The regions NW and SE typically receive light rainfall while
the regions WC and NE receive relatively heavy rainfall (Figure 1b).
Figure 6. Rainfall magnitude (mm) as detected by (a,c) Extreme/Heavy events (top 90 percentile),
and (b,d) Rainfall magnitude as identified by (lowest 10 percentile) over India.
In the L-period, the magnitude of heavy rainfall frequency (HRF) increases through-
out India, at the cost of light rainfall frequency (LRF) (Figure 6c,d). However, the same
logic does not work for the E-period. The slopes of LRF in two periods (E and L) are almost
the same, except for the opposite sign (Figure 6b,d). In particular, the magnitude of light
rainfall dwindled in the L-period while the magnitude of heavy rainfall increased mar-
ginally. The light rainfall is beneficial to growing crops by recharging the upper soil layer
a
c d
b
Figure 6.
Rainfall magnitude (mm) as detected by (
a
,
c
) Extreme/Heavy events (top 90 percentile),
and (b,d) Rainfall magnitude as identified by (lowest 10 percentile) over India.
3.4. Spatial Variability of Rainfall Frequency and Fraction for Continuous Rainfall Days
As it was discussed in Section 3.2, selected homogenous regions depicted contrast
features in terms of continuous rainfall events and continuous rainfall magnitude in E-
period and L-periods. This section further discusses the spatial variability of continuous
rainfall frequency and fraction over the complete Indian region. Such attempt is informative
to detect the nature of dominant events over various climatic regions. The rainfall frequency
and fractions for the summer season for continuous rainfall for 1, 3, 6, and 11DAY are
displayed in Figure 7a–h, respectively. The frequency of 1DAY rainfall is boosted over the
desert (NW region e.g., Rajasthan) and rain shadow region (SE region e.g., Tamil Nadu).
The rainfall for continuous 11DAY is most dominant over the regions of maximum rainfall,
e.g., WC, NE, and a region of low-pressure-systems (area dotted by magenta in Figure 7d).
The spatial pattern of rainfall frequency for 1DAY rainfall (Figure 7a) is almost opposite to
the 11DAY (Figure 7d) over most parts of India.
The spatial pattern of 3 days continuous rainfall frequency shows a uniform pattern
over most parts of India, except over WC, western Rajasthan, and NE regions. Among all
panels, rainfall frequency for continuous 6 days seems to be uniform, except the drought-
prone regions and Jammu Kashmir region. The spatial pattern of rainfall fraction does not
follow a spatial pattern similar to rainfall frequency. Rainfall fraction is higher over NE and
NW and eastern coastal regions for continuous 1DAY, 3DAY, 6DAY rainfall, except over
WC and Jammu Kashmir region. There is an increase in rainfall intensity and frequency
of extreme rainfall events randomly on different spatial scales [
42
–
44
]. Rainfall fraction
for continuous 11DAY (Figure 7h) is similar to climatological rainfall (Figure 1b), where
WC, CI, and NE regions receive more rainfall than other regions of India in the summer
monsoon season. Monthly rainfall fraction differences between the two time periods (E and
L) are shown in Figure S2. In August and September, rainfall fractions increase everywhere
except over a few grid points here and there (Figure S2c,d), whereas it decreases over the
desert region and rain shadow regions in June. Rainfall fractions in September are opposite
to the June over most parts of India (Figure S1d). Notably, rainfall fractions are smoothened
in June over most parts of India except the NE region (Figure S2a).

Remote Sens. 2021,13, 398 13 of 21
Remote Sens. 2021, 13, x FOR PEER REVIEW 13 of 22
(1 to 10 cm). The rainfall decreasing trend in the L-period for LRF may indicate an in-
creased number of non-rainy days. An increase in the number of non-rainy days may shift
the total rainfall distribution towards the heavy rainfall end.
3.4. Spatial
V
ariability of
R
ainfall
F
requency and
F
raction for
C
ontinuous
R
ainfall
D
ays
As it was discussed in Section 3.2, selected homogenous regions depicted contrast
features in terms of continuous rainfall events and continuous rainfall magnitude in E-
period and L-periods. This section further discusses the spatial variability of continuous
rainfall frequency and fraction over the complete Indian region. Such attempt is informa-
tive to detect the nature of dominant events over various climatic regions. The rainfall
frequency and fractions for the summer season for continuous rainfall for 1, 3, 6, and
11DAY are displayed in Figure 7a-d, 7e-h, respectively. The frequency of 1DAY rainfall is
boosted over the desert (NW region e.g., Rajasthan) and rain shadow region (SE region
e.g., Tamil Nadu). The rainfall for continuous 11DAY is most dominant over the regions
of maximum rainfall, e.g., WC, NE, and a region of low-pressure-systems (area dotted by
magenta in Figure 7d). The spatial pattern of rainfall frequency for 1DAY rainfall (Figure
7a) is almost opposite to the 11DAY (Figure 7d) over most parts of India.
Figure 7. Spatial distribution of rainfall (mm) for continuous 1DAY, 3DAY, 6DAY, and 11DAY for L-period (a to d) rainfall
frequency (e and h) rainfall fraction. Low pressure is dotted by magenta in Figure 7d.
The spatial pattern of 3 days continuous rainfall frequency shows a uniform pattern
over most parts of India, except over WC, western Rajasthan, and NE regions. Among all
panels, rainfall frequency for continuous 6 days seems to be uniform, except the drought-
prone regions and Jammu Kashmir region. The spatial pattern of rainfall fraction does not
follow a spatial pattern similar to rainfall frequency. Rainfall fraction is higher over NE
and NW and eastern coastal regions for continuous 1DAY, 3DAY, 6DAY rainfall, except
over WC and Jammu Kashmir region. There is an increase in rainfall intensity and fre-
quency of extreme rainfall events randomly on different spatial scales [42-44]. Rainfall
fraction for continuous 11DAY (Figure 7h) is similar to climatological rainfall (Figure 1b),
where WC, CI, and NE regions receive more rainfall than other regions of India in the
summer monsoon season. Monthly rainfall fraction differences between the two time pe-
riods (E and L) are shown in Figure S2. In August and September, rainfall fractions in-
crease everywhere except over a few grid points here and there (Figure S2c and S2d),
whereas it decreases over the desert region and rain shadow regions in June. Rainfall frac-
tions in September are opposite to the June over most parts of India (Figure S1d). Notably,
a c d
b
e g h
f
Figure 7.
Spatial distribution of rainfall (mm) for continuous 1DAY, 3DAY, 6DAY, and 11DAY for L-period (
a
–
d
) rainfall
frequency (e–h) rainfall fraction. Low pressure is dotted by magenta in Figure 7d.
3.5. Decadal Analysis of Continuous Rainfall Days
To analyze rainfall contribution from prolonged continuous rainfall, we compared
the rainfall contribution (expressed in percentage) to the ISMR, up to continuous 10-days
rainfall from 1951 to 2018 (Figure 8a) for 3 decades. In the recent 10 years (2009–2018), ~60%
of rainfall was contributed from 2 to 6DAY of continuous rainfall, which is 20% more than
the rainfall contributed during 1951–1960 from the same number of continuous rainfall
days. Furthermore, it is obvious from the figure that rainfall contributed from a large
number of continuous rainfall days (>8DAY) decreased in recent years. On the average
of multi-years and 1–5 continuous rain days, rainfall percentage increased by 0.9% from
1951–1972 to 1995–2016, while the rainfall is decreased by 0.05% for 6–10 continuous rainfall
days (Figure 8b). The difference in the rainfall percentage is quite small for 6–10 continuous
rainfall days. Thus, for comparison, TRMM rainfall is compared for the recent decade,
which turns out that the TRMM underestimates the rainfall as compared to IMD rainfall
(Figure 8c). One known caveat of TRMM rainfall is underestimating orographic rainfall
(e.g., rainfall over the Western Ghats) [
25
]. Averaged rainfall over Central India shows
a strange trend (decreasing) when averaged for more than 20 years of span (
Figure S3
).
These results also support that the rainfall is shifting towards a lesser number of continuous
rainfall days with higher magnitudes. The change in rainfall duration and quantity also
supports that the heavy rainfall events only last for shorter durations.
Remote Sens. 2021, 13, x FOR PEER REVIEW 14 of 22
rainfall fractions are smoothened in June over most parts of India except the NE region
(Figure S2a).
3.5. Decadal analysis of
C
ontinuous
R
ainfall
D
ays
(a) (b) (c)
Figure 8. Rainfall contribution (in %) from the number of continuous rainfall days to the monsoon season over central
India (a) IMD rainfall for 3 decades, (b) 1 to 5DAY and 6 to 10 DAY continuous combined days, (c) IMD rainfall vs. TRMM
for the latest decade 2009-2018. Continuous rainfall of 1 day, 2 days, 3 days, 4 days and N days are referred as 1DAY,
2DAY, 3DAY and NDAY respectively.
To analyze rainfall contribution from prolonged continuous rainfall, we compared
the rainfall contribution (expressed in percentage) of the ISMR, up to continuous 10-days
rainfall from 1951 to 2018 (Figure 8a) for 3 decades. In the recent 10 years (2009-2018),
~60% of rainfall was contributed from 2 to 6DAY of continuous rainfall, which is 20% more
than the rainfall contributed during 1951-1960 from the same number of continuous rain-
fall days. Furthermore, it is obvious from the figure that rainfall contributed from a large
number of continuous rainfall days (>8DAY) decreased in recent years. On the average of
multi-years and 1-5 continuous rain days, rainfall percentage increased by 0.9% from
1951-1972 to 1995-2016, while the rainfall is decreased by 0.05% for 6-10 continuous rain-
fall days (Figure 8b). The difference in the rainfall percentage is quite small for 6-10 con-
tinuous rainfall days. Thus, for comparison, TRMM rainfall is compared for the recent
decade, which turns out that the TRMM underestimates the rainfall as compared to IMD
rainfall (Figure 8c). One known caveat of TRMM rainfall is underestimating orographic
rainfall (e.g., rainfall over the Western Ghats) [25]. Averaged rainfall over Central India
shows a strange trend (decreasing) when averaged for more than 20 years of span (Figure
S3). These results also support that the rainfall is shifting towards a lesser number of con-
tinuous rainfall days with higher magnitudes. The change in rainfall duration and quan-
tity also supports that the heavy rainfall events only last for shorter durations.
4. Hydrological and agricultural aspects of Surface Rainfall
South to northward propagation of rainfall over the Indian landmass is quite fasci-
nating but erratic. Sometimes, it progresses rapidly, while at other times, it is strangely
slower [45]. Climatologically, monsoon takes one and a half months (45 days) to reach
over the northwestern region of India. As the rainfall isochrones move northward, the
farmers start planting rice crops hoping that the monsoonal rain will frequently visit rain-
fed farming [3]. In the beginning, rice crop requires standing water for multiple days to
grow. The ISMR holds a good correlation with the summer season crop’s yield [46-47].
Figure 9 shows rainfall isochrones, rice production regions, and months of rice plantation,
growth and harvesting.
Uttar Pradesh, West Bengal, Andhra Pradesh, Punjab, and Odisha are the top five
states, and those grow staple rice crop. It is observed that in some of the states, plantation
of rice starts before the arrival of monsoon, which is counted in the category of an early
plantation of rice crops. Notably, all states in India grow rice, except western Rajasthan,
Figure 8.
Rainfall contribution (in %) from the number of continuous rainfall days to the monsoon season over central India
(
a
) IMD rainfall for 3 decades, (
b
) 1 to 5DAY and 6 to 10 DAY continuous combined days, (
c
) IMD rainfall vs. TRMM for the
latest decade 2009–2018. Continuous rainfall of 1 day, 2 days, 3 days, 4 days and N days are referred as 1DAY, 2DAY, 3DAY
and NDAY respectively.

Remote Sens. 2021,13, 398 14 of 21
4. Hydrological and Agricultural Aspects of Surface Rainfall
South to northward propagation of rainfall over the Indian landmass is quite fasci-
nating but erratic. Sometimes, it progresses rapidly, while at other times, it is strangely
slower [
45
]. Climatologically, monsoon takes one and a half months (45 days) to reach over
the northwestern region of India. As the rainfall isochrones move northward, the farmers
start planting rice crops hoping that the monsoonal rain will frequently visit rain-fed
farming [
3
]. In the beginning, rice crop requires standing water for multiple days to grow.
The ISMR holds a good correlation with the summer season crop’s yield [
46
,
47
]. Figure 9
shows rainfall isochrones, rice production regions, and months of rice plantation, growth
and harvesting.
Remote Sens. 2021, 13, x FOR PEER REVIEW 15 of 22
which is a desert region. In some years, the very first monsoonal rain reach over the north-
ern states quite late from the onset date of monsoon, which may delay rice plantation by
up to early August [48].
Figure 9. The date and line of the northern limit of the summer monsoon (climatological rainfall
isochrones, orange lines) and area of land under rice crop production in each state (modified after
[48]).
Figure 10. MK test for the number of rainy days over India from June 1 to July 15.
Figure 9.
The date and line of the northern limit of the summer monsoon (climatological rainfall isochrones, orange lines)
and area of land under rice crop production in each state (modified after [48]).
Uttar Pradesh, West Bengal, Andhra Pradesh, Punjab, and Odisha are the top five
states, and those grow staple rice crop. It is observed that in some of the states, plantation
of rice starts before the arrival of monsoon, which is counted in the category of an early
plantation of rice crops. Notably, all states in India grow rice, except western Rajasthan,
which is a desert region. In some years, the very first monsoonal rain reach over the
northern states quite late from the onset date of monsoon, which may delay rice plantation
by up to early August [48].
Moderate but continuous rainfall is beneficial for rice crop and groundwater recharge
because the soil gets enough time for infiltration/percolation to facilitate soil moisture
conservation for rain-fed agriculture. Generally, water from intense and heavy rainfall
over a short duration results in higher runoff into rivers and streams. Thus, we compared
the number of rainy days during the first 45 days of monsoonal rainfall (1 June to 15 July)
during the E-period and the L-period and found that the rainfall days declined in the
L-period (Figure 10) than the E-period (1951 to 1984). Overall, the rainfall trend from 1951

Remote Sens. 2021,13, 398 15 of 21
to 2018 decreased for the first 45 days (Figure S4). The magnitude of the Sen’s slope in
E-period and L-period are 0.006 and
−
0.023, respectively. The results from
Tables 1and 2
and Figure 10 for the recent 30-years indicate the number of rainfall days and rainfall
quantity increase in June while decreased in July over northern regions.
Remote Sens. 2021, 13, x FOR PEER REVIEW 15 of 22
which is a desert region. In some years, the very first monsoonal rain reach over the north-
ern states quite late from the onset date of monsoon, which may delay rice plantation by
up to early August [48].
Figure 9. The date and line of the northern limit of the summer monsoon (climatological rainfall
isochrones, orange lines) and area of land under rice crop production in each state (modified after
[48]).
Figure 10. MK test for the number of rainy days over India from June 1 to July 15.
Figure 10. MK test for the number of rainy days over India from 1 June to 15 July.
Timely information on hydrological variables (e.g., rainfall, soil moisture) in June is
crucial for the plantation of rice crops. Soil moisture, evaporation, and runoff are the impor-
tant hydrological aspects of monsoonal rainfall. The hydrological processes of the surface
and sub-surface depend on the land cover and land use (LCLU, Figure S5), which vary
erratically over the desert, forest, crops, shrubs, and bare land regions [
49
]. Hydrological
variables are being averaged over a uniform LCLU (Figure S5 and
Figure 11
) e.g., Agricul-
tural land over Punjab (lon = 75.15
◦
E, lon = 76
◦
E, lat = 30.1
◦
N,
lat = 30.4◦N
), shrub land
over Rajasthan (lon = 70
◦
E, lon = 70.75
◦
E, lat = 26.75
◦
N, lat = 27.25
◦
N), forest over western
Ghats (lon = 74
◦
E, lon = 75
◦
E, lat = 14
◦
N, lat = 15
◦
N), deciduous over Assam (
lon = 92.9◦E
,
lon = 93.4◦E
, lat = 26
◦
N, lat = 27
◦
N), crop land in Utter Pradesh (
lon = 78◦E
, lon = 79
◦
E,
lat = 27
◦
N, lat = 29.5
◦
N), and Lucknow in Utter Pradesh (
lon = 80.8◦E
,
lon = 81.25◦E
,
lat = 26.65◦N
, lat = 27
◦
N). LCLU does not change much over a period of 10 years. The
selected regions of uniform LCLU, well represent the 5-homogenous region (Figure 1a).
Figure 11a–f displays the daily variations of surface precipitation, volumetric soil moisture
for layer-1 (0–7cm), and evaporation over 6-selected regions dominated by cropland, decid-
uous, forest respectively for summer 2015 and 10 years (summer 2009–2018,
Figure 11g
)
over cropland of northern India. The volumetric soil moisture is associated with the soil
classification and soil layers/depths. On a daily scale, the rainfall and soil moisture cor-
relation varies from 0.50 to 0.87, while rainfall and evaporation correlate
−
0.46 to 0.87
(Table 3). There are quite variations in monsoonal rainfall (called an active-break cycle),
but the soil moisture keeps low variations. Soil moisture (slowly varying variable) varies
smoothly compared to rainfall and picks up suddenly on a daily time scale. For a longer
time scale (10 years, Figure 11e), we did not find any new insight than the individual years.
During extreme rainfall cases, the soil moisture does not increase in the same proportion as
low/moderate rainfall because runoff, evaporation, and impervious of soil/LULU limits
the infiltration of water into the soil (Figure 11). The 10-year time series of the hydrological
variables do not add much except yearly seasonal fluctuations (Figure 11g). Soil mois-
ture possess have a good correlation with rainfall over cropland, deciduous forest and
scrublands. Continuous rainfall for a short duration (e.g., 2 days, 3 days) enhances the soil
moisture dramatically. In contrast, small fluctuations in rainfall do not influence the slowly
varying soil moisture significantly. Heavy and extreme rainfall enhances flooding and
runoff as compared to soil moisture [
50
]. The percolation of water into the soil depends on
the rainfall duration, intensity, and continuity. A moderate to low rainfall will enhance the
soil moisture up to a great depth. However, it hard to relate soil moisture to continuous
rainfall for more than 2 days, because soil moisture values never reach zero, while a region
can receive zero rainfall, which is very common news.

Remote Sens. 2021,13, 398 16 of 21
Remote Sens. 2021, 13, x FOR PEER REVIEW 16 of 22
(a) (b)
(c) (d)
(e) (f)
(g)
Figure 11. Variations of hydrological parameters such as evaporation (Evapo), total precipitation (Tot-Pptn), and soil mois-
ture layer 1 (upper 7cm, SMOIS-1) for summer 2015 over selected regions of uniform land cover and land use (a) agricul-
tural land, (b) crop land (c) forest, (d) deciduous, (e) urban area, (f) scrub land, and (g) crop land for ten years (2008-2019).
Moderate but continuous rainfall is beneficial for rice crop and groundwater recharge
because the soil gets enough time for infiltration/percolation to facilitate soil moisture con-
servation for rain-fed agriculture. Generally, water from intense and heavy rainfall over a
short duration results in higher runoff into rivers and streams. Thus, we compared the
number of rainy days during the first 45 days of monsoonal rainfall (June 1 to July 45)
during the E-period and the L-period and found that the rainfall days declined in the L-
Figure 11.
Variations of hydrological parameters such as evaporation (Evapo), total precipitation (Tot-Pptn), and soil moisture
layer 1 (upper 7cm, SMOIS-1) for summer 2015 over selected regions of uniform land cover and land use (
a
) agricultural
land, (b) crop land (c) forest, (d) deciduous, (e) urban area, (f) scrub land, and (g) crop land for ten years (2008–2019).

Remote Sens. 2021,13, 398 17 of 21
Table 3. Correlation between total precipitations with hydrological parameters.
LCLU types and Regions Soil Moisture Evaporation Runoff
Agricultural land–Punjab (75.15◦E–76◦E, 30.1◦N–30.4◦N) 0.60 0.44 −0.36
Crop land–Utter Pradesh (78◦E–79◦E, 27◦N–29.5◦N) 0.67 0.51 −0.07
Forest–Goa–Karnataka (74◦E–75◦E, 14◦N–15◦N) 0.61 0.87 0.56
Deciduous forest–Assam (92.9◦E–93.4◦E, 26◦N–27◦N) 0.72 0.40 0.08
Urban area–Lucknow (80.8◦E–81.25◦E, 26.65◦N–27◦N) 0.50 0.66 −0.48
Scrub land–Rajasthan (70◦E–70.75◦E, 26.75◦N–27.25◦N) 0.87 −0.46 −0.08
5. Conclusions and Discussions
Indian summer monsoon rainfall does not show a significant trend over the long-term
periods of 70 years, 100 years, 120 years, and 140 years on monthly to seasonal scales [
7
,
10
].
On different time and spatial scales (over regions or megacities), monthly to seasonal
rainfall show mixed trends of decreasing and increasing trends in rainfall [
51
]. After a
rigorous analysis of rainfall data, this research work brought some of the interesting points
about spatial and temporal variability of the Indian summer monsoon rainfall in light of
the number of rainfall days and frequency of continuous rainfall events and magnitudes.
We applied a non-parametric Mann–Kendall test to daily gridded rain gauge data for trend
analysis. A detailed investigation was carried out on the climatological trend of the number
of rainy days and continuous rainfall events. Rainfall quantity and number of rainfall
days trends were calculated at 95% confidence level for two periods of 34 years (1951–
1984 and 1985–2018) utilizing the IMD gridded rainfall datasets. Though two different
periods indicate a marked change in the event magnitudes and types, the change in event
magnitude is less pronounced than the change in the number of events from the early
period to the late period. Continuous rainfall of 1 day, 2 days, 3 days, 4 days and N
days are referred as 1DAY, 2DAY, 3DAY and NDAY respectively. We analyzed the spatial
distribution of frequency and fraction of different rain events over different climatic zones.
Following are the major conclusion drawn from the study:
1.
The 1-day rainfall frequency and variability dominate over drought-prone regions
such as North West (NW) and South East (SE) parts of India.
2.
There is a decrease in the number of rainfall days (Table 1) over central India (CI)
and the Western Coast (WC) during recent years (1985–2018, L-period). However,
the southern region of WC (Southern Karnataka, the boundary of Kerala and Tamil
Nadu) shows a decrease in rainy days in recent years, which is an abnormality in the
rainfall over the Western Ghats range.
3.
In July and September, the number of rainfall days and monthly mean rainfall show
a decreasing and increasing trend respectively during the L-period over all climatic
regions of India. There are few exceptions in monthly rainfall such as an increase in
trend over NW in July and a decrease in trend over CI in September.
4.
The number of rainfall days and monthly mean rainfall decreased over NE in L-
period for the summer season. Further, the number of rainfall days decreased in all
the months over NE. The monthly mean rainfall shows a decreasing trend in June and
July while increasing in August (at 95% significant) and September. On a regional
scale, the number of rainfall days are decreased by ~0.1 days/yr and ~0.3 days/yr
over CI and NE, respectively, in L-period as compared to E-period.
5.
The continuous rain events escalated from 1 to 4DAY, while de-escalated for higher
number of days (
≥
6DAY) during L-period as compared to E-period. The de-escalation
of rainfall events for higher continuous days show that continuous rainfall shower for
a fewer number of rainfall days in L-period than E-period. The magnitude of
≥
6DAY
continuous rainfall decreased by 10 mm in L-period.
6.
The trends of light rainfall frequency (LRF) in two periods (E and L) are almost the
same, except for the opposite sign, while light rainfall frequency shows a drastic

Remote Sens. 2021,13, 398 18 of 21
decrease in recent decades, which may have implications associated with ground
discharge and agriculture.
7.
The rainfall for continuous day 11 is most dominant over the regions of maximum
rainfall, e.g., WC, NE, and a region of low-pressure-systems. The spatial pattern of
rainfall frequency for 1DAY rainfall is almost opposite to the 11DAY over most parts
of India. Rainfall fraction for continuous 11DAY is similar to climatological rainfall,
where WC, CI, and NE regions receive more rainfall than other regions of India in the
summer monsoon season.
8.
In the 10 years from 2009–2018, ~60% of rainfall is contributed from 2DAY to 6DAY of
continuous rainfall, which is 20% more than the rainfall contributed during 1951–1960
from the same number of continuous rainfall days. The rainfall contributed from
a large number of continuous rainfall days (>8DAY) decreased in recent years. For
1–5 continuous rainy days, the rainfall amount is increased by 0.9% from 1951–1972 to
1995–2016, while the rainfall is decreased by 0.05% for 6–10 continuous rainfall days.
9.
The rainfall is shifted towards a lesser number of continuous rainfall days with higher
magnitudes. A continuous rainfall of >5DAY decreased in the recent years.
10.
The rainfall trend from 1951 to 2018 displays a declining trend for the first 45 days (1
June to 15 July) from the onset of monsoon. The total number of rainy days in rice
crops season (i.e., first 45 days, crop season, from the onset of monsoon) is decreased
by half days during L-period than E-period over India.
11.
On a daily scale, the rainfall and soil moisture correlation varies from 0.50 to 0.87,
while rainfall and evaporation correlate at the range of
−
0.46 to 0.87 over selected
parts of Indian regions. Continuous light/moderate rainfall seems to be a controlling
factor for replenishing the soil moisture in upper layer.
The yield of the crops further depends on the inclusion of new technology, field &
irrigation management, soil moisture, fertilizer applications, crop hybrid management,
and many more parameters. Production of rice is well affected by rainfall, temperature,
and their interaction; or better to say climate change [
52
,
53
]. They further added that
temperature variability is an important factor for rice yield over highly irrigated and heavy
rainfall regions of India [
52
]. On a state level, extreme rainfall and drought affected the
rice yield in the rain-fed area, while drought had more impact than extreme rainfall [
53
].
Moreover, the rice harvest dates do not match with monsoon season, which makes it further
complex to reach a robust relationship between rice crop yield and trends in rainy days.
Additionally, the results support the fact that the increasing trend of extreme rainfall
over various climatic regions. Local, regional, or remote processes may influence long-term
trends of rainfall fraction and rainfall days. The possible impact of long-term changes in
rainfall days on LCLU, soil moisture, evaporation is presented. This study helps understand
how continuous rainfall days and magnitude are changing in different regions of India.
There is still a need to perform rigorous studies on developing a real-time forecasting system
that can be used for adaptive decision making. Further research should be advanced on
estimating ENSO’s effects on the number of rainfall days and applying regional scale
modeling aspects to investigate rainfall intensity, duration, and fraction. The constant
decline in the number of rainfall days may be linked to regional assistance and remote
teleconnection’s combined factors. Some of the factors that impact rainfall duration may
be the frequency of El Niño, weakening monsoonal circulation, increased air pollution, and
warming of the Indian Ocean.

Remote Sens. 2021,13, 398 19 of 21
Supplementary Materials:
The following are available online at https://www.mdpi.com/2072
-4292/13/3/398/s1, Figure S1: MK test for the number of rainy days (rainfall
≥
2.5mm) over
5 homogeneous regions of India. The Blue line shows the trend, while red dotted lines show
confidence at 95% significance level here and in the rest of the figures, Figure S2: Difference in
the rainfall fraction between two periods 1951–1984 and 1985–2018 (a) June, (b) July, (c) August,
(d) September, Figure S3: MK test for continuous rain days over India from 1 June to 15 July
during 1951–2018, Figure S4: Rainfall contribution to monsoon season (JJAS) in percentage from
the number of continuous rainfall days from IMD rainfall for almost average of two-decades over
Central India, Figure S5: Land Cover and Land Use (2015–16) for (a) Punjab, (b) Karnataka, (c)
Assam, (d) Utter Pradesh, (d) Rajasthan states of India. Figures were plotted online from https:
//bhuvan-app1.nrsc.gov.in/thematic/thematic/index.php#.
Author Contributions:
Conceptualization, V.K. and K.S.; methodology, K.S.; software, K.S. and V.K.;
validation, K.S., V.K. and T.S.; formal analysis, K.S.; investigation, K.S.; resources, K.S. and V.K.;
data curation, K.S.; writing—original draft preparation, V.K.; writing—review and editing, K.S., V.K.
and T.S.; visualization, K.S.; supervision, V.K., K.S.; project administration, K.S. and T.S.; funding
acquisition, V.K. All authors have read and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement:
We used observational datasets which is accessable to
everyone, with nominal charges from India Meteorological Department (IMD), India.
Informed Consent Statement:
“Not applicable” for studies, which do not involve human or aminals.
Acknowledgments:
The authors are thankful to Matlab, and Micro-Soft Package, which used in
this research work to draw the illustrations. We thank IMD to provide the gridded datasets for this
research work.
Conflicts of Interest: The authors declare no conflict of interest.
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