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Millions of people died due to famines in India in the 19th and 20th centuries; however, the relationship of historical famines with drought is complicated and not well understood. Using station‐based observations and simulations, we reconstruct soil moisture (agricultural) drought in India for the period 1870‐2016. We show that over this century and a half period, India experienced seven major drought periods (1876‐1882, 1895‐1900, 1908‐1924, 1937‐1945, 1982‐1990, 1997‐2004, and 2011‐2015) based on severity‐area‐duration (SAD) analysis of reconstructed soil moisture. Out of six major famines (1873‐74, 1876, 1877, 1896‐97, 1899, and 1943) that occurred during 1870‐2016, five are linked to soil moisture drought, and one (1943) was not. The three most deadly droughts (1877, 1896, and 1899) were linked with the positive phase of El Nino Southern Oscillation (ENSO). Five major droughts were not linked with famine, and three of those five non‐famine droughts occurred after Indian Independence in 1947.
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Drought and famine in India, 1870-2016
Vimal Mishra1, Amar Deep Tiwari1, Saran Aadhar1, Reepal Shah1, Mu Xiao2, D.S. Pai3,
Dennis Lettenmaier2
1. Civil Engineering, Indian Institute of Technology, Gandhinagar, India
2. Department of Geography, University of California, Los Angeles, USA
3. India Meteorological Department (IMD), Pune
Millions of people died due to famines in India in the 19th and 20th centuries; however, the
relationship of historical famines with drought is complicated and not well understood. Using
station-based observations and simulations, we reconstruct soil moisture (agricultural)
drought in India for the period 1870-2016. We show that over this century and a half period,
India experienced seven major drought periods (1876-1882, 1895-1900, 1908-1924, 1937-
1945, 1982-1990, 1997-2004, and 2011-2015) based on severity-area-duration (SAD)
analysis of reconstructed soil moisture. Out of six major famines (1873-74, 1876, 1877,
1896-97, 1899, and 1943) that occurred during 1870-2016, five are linked to soil moisture
drought, and one (1943) was not. The three most deadly droughts (1877, 1896, and 1899)
were linked with the positive phase of El Nino Southern Oscillation (ENSO). Five major
droughts were not linked with famine, and three of those five non-famine droughts occurred
after Indian Independence in 1947.
1.0 Introduction
Famine is defined as "food shortage accompanied by a significant number of deaths” (Dyson,
1991). India has a long history of famines that led to the starvation of millions of people
(Passmore, 1951). During the era of British rule in India (1765-1947), twelve major famines
occurred (in 1769-70, 1783-84, 1791-92, 1837-38, 1860-61, 1865-67, 1868-70, 1873-74,
1876-78, 1896-97, 1899-1900, and 1943-44) which lead to the deaths of millions people
(Maharatna, 1996). Many of these famines were caused by the failure of the summer
monsoon, which led to widespread droughts and crop failures (Cook et al., 2010). Although
no major famines have occurred since Indian independence in 1947, large-scale droughts in
the second half of the 20th and early 21st centuries have continued to have devastating effects
on India (Bhalme et al., 1983; Gadgil & Gadgil, 2006; Mishra et al., 2016; Parthasarathy et
al., 1987). Droughts in the late 18th and early 19th centuries had progressively more severe
effects due to a rising population, low crop yields, and lack of irrigation (FAO, 2014). Hence,
an understanding of historical famine and drought in India relates both to physical factors
associated with drought, and agricultural productivity and management.
Soil moisture drought affects crop production and food security in India especially in the
absence of irrigation (Mishra et al., 2014, 2018). Soil moisture droughts doubtless affected
food production and famines in India before the widespread advent of irrigation in the mid-
20th century. However, the crucial role of soil moisture in famines in India has received little
attention, perhaps due to the general absence of long-term observations. Most previous
attempts to study 18th and 19th-century droughts in India have been limited to meteorological
© 2019 American Geophysical Union. All rights reserved.
(Bhalme et al., 1983; Mooley & Parthasarathy, 1984) or paleoclimate reconstructions (Cook
et al., 2010) and mainly facilitate studies of the role of large-scale climate variability. Here,
we provide the first reconstruction of droughts based on soil moisture (their proximate link to
dryland agriculture) for the last century and a half (1870-2016) and their relationship to
famines. We use the Variable Infiltration Capacity (VIC) model to reconstruct soil moisture
using methods similar to those demonstrated previously for the conterminous U.S.
(Andreadis & Lettenmaier, 2006) and China (Wang et al., 2011).
2. Data and Methods:
We obtained 0.25° daily gridded precipitation data from the India Meteorology Department
(IMD) for the period 1901-2016 (Pai et al., 2015), which we regridded to 0.5°spatial
resolution by using synergic mapping (SYMAP) algorithm as described in Maurer et al.
(2002). Pai et al., (2015) developed the IMD gridded precipitation product using data from
6995 observational stations across India using inverse distance weighting (Shepard, 1984).
Orographic features and spatial variability in the Indian summer monsoon precipitation are
well captured by the gridded precipitation (Pai et al., 2015). Because the IMD gridded
precipitation product is available only for the post-1900 period, we developed a compatible
product at 0.5 degree using station observations for 1870-1900. Data availability and the
number of stations varied during this period; however, we were able to obtain reasonably
complete precipitation data from 1690 stations spread across India for most of the pre-1900
period. More detailed information on data preparation from observations, 20th Century
reanalysis (20CR) and Berkeley Earth and data evaluation can be obtained from supplemental
section S1(Compo et al., 2006; Dai et al., 2004; Wood et al., 2002).
We used the Variable Infiltration Capacity (Liang et al., 1994) macroscale hydrology model (
Section S2, Nijssen et al., 2001; Shuttleworth, 1993; Mishra et al., 2010), which simulates
water and energy fluxes by taking soil and vegetation parameters, and meteorological forcing
as inputs. The VIC model has been widely applied for soil moisture drought assessments at a
range of spatial scales (e.g., (Andreadis & Lettenmaier, 2006; Mishra et al., 2014, 2018; Shah
& Mishra, 2016, 2016a; Shah &Mishra, 2015; Shah&Mishra, 2014; Sheffield et al., 2004)).
We applied the VIC model at a daily time-step for each 0.5º grid for 1870-2016 (see
supplemental section S2 for more details). We aggregated daily soil moisture for each grid to
monthly to avoid the influence of precipitation and temperature variability within a month.
This is important as we resampled daily precipitation and temperature from the 20CR, for
which monthly aggregates are more accurate than daily values. We estimated soil moisture
percentiles (SMP) using the empirical Weibull plotting position method (Andreadis &
Lettenmaier, 2006). SMP less than 20 is categorized as drought (SMP 20-30: Abnormally
dry; 10-20: Moderate drought; 5-10: Severe drought; 2-5: Extreme drought; and less than 2:
Exceptional drought, (Svoboda et al., 2002)). We estimated monthly soil moisture percentiles
at 60 cm depth, which is a typical root-zone depth for most crops, following Mishra et al.
We used severity-area-duration (SAD) analysis as developed by Andreadis and Lettenmaier
(2006) and applied by Sheffield et al. (2009) and Wang et al. (2011) among others to identify
major droughts during 1870-2016. We identified drought periods in time and space using the
clustering algorithm of Andreadis and Lettenmaier, (2006). The algorithm considers drought
clusters with a minimum area threshold (0.1 million km2) for which drought duration and
© 2019 American Geophysical Union. All rights reserved.
severity are computed. In the SAD analysis, duration is the primary variable; for a given
duration of a prospective drought event, the SAD analysis performs a bivariate analysis of
severity and area. Severity and area in the SAD analysis clearly are linked, as the severity,
which is an average over the area, increases or decreases as the area contracts or expands. We
estimated monthly soil moisture percentiles using the empirical Weibull distribution. We
evaluated droughts of different duration, severity, and spatial extent (area) using SAD
analysis. The severity of a drought is defined as:
where S is drought severity, SMP is monthly soil moisture percentile (Sheffield et al. 2004),
and t is drought duration (months). We calculated drought severity for 3, 6, 12, 24, and 48
months duration. It should be noted that a region can experience a drought of short (3
months) or long (12-48 months) durations where the short duration is within the time span of
the longer duration, but the severity and areal extent for the “events” can be much different.
We identified seven major drought periods from the SAD analysis. We identified famines and
affected regions during the period of 1870-2016 from the literature (Table S1). We
constructed sea surface temperature (SST) anomalies for the monsoon season for the major
droughts that caused famines using NOAA’s extended reconstructed SST version 5 (Huang
et al., 2017).
3. Results and Discussion
3.1 All India droughts identified using SAD Analysis
We first identified the major soil moisture drought periods in India using the SAD analysis
applied to the 1870-2016 record (Fig. 1). We identified drought severity and area for 3-48
month durations as indicated above so as to include both short and long-term droughts in
India. Our analysis indicated that 1876-1882, 1895-1900, 1908-1924, 1937-1945, 1982-1990,
1997-2004, and 2011-2015 are the major periods for soil moisture droughts (Fig. 1). India
experienced 3 and 6-month soil moisture droughts during 1876-1882 while during 1895-1900
both short and long-term droughts occurred. Most severe and widespread (more than 2.0
million km2 area) soil moisture droughts occurred during 1895-1900 and 1908-1924 (Table
S2). In comparison to the 1895-1900 drought that covered almost the entire country (~65% of
total area), the 1876-1882 drought period was mainly located in the southern part of the
country and had a smaller extent (0.40 million km2) (Table S2). Among all of the seven major
drought periods, the most recent (2011-2015) was exceptionally severe (severity = 0.99) but
not widespread like the 1895-1900 drought (area = 0.13 million km2).
We further analyzed the ten months with the most widespread drought conditions within each
of the major drought periods (1876-1882, 1895-1900, 1908-1924, 1937-1945, 1982-1990,
1997-2004, and 2011-2015) obtained from the SAD analysis (see Table S3). We find that
during the 1876-1882 drought period, the most widespread drought conditions occurred in
August 1877 with coverage of 56.6% of the area of the country. From April to July of 1876,
more than 36% of the country’s area experienced soil moisture drought. Moreover, a major
part of the country remained under soil moisture drought until February 1878. Similarly, for
the 1895-1900 period, the most widespread extent of drought occurred in December 1896
(64.2%) followed by December 1899 (61.4%). More than 60% of India was under soil
moisture drought between October and December 1896 (Table S3).
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The 1908-1924 drought period was the most widespread in January 1908, October 1918,
December 1920, and May 1921 (Tables S2 and S3). Soil moisture drought in October 1918
and in December 1920 covered more than 65% of the country. The 1908-1924 drought period
was widespread; however, the drought was less severe than that of 1895-1900 (Fig. 1). The
1937-1945 drought period was of a lesser extent than 1895-1900 and 1908-1924. The month
with the largest extent (46.8%) during the 1937-1945 period was August 1941. Similarly,
during the 1982-1900 period, drought covered 47.3% of the country in August 1987 (Table
S3). During the 1997-2004 period, the drought was most widespread in February 2001 (56%)
and January 2003 (54.4%). The most recent drought period identified by the SAD analysis
occurred during 2011-2015, which had the largest extent (43%) in October 2015. Overall, the
SAD analysis shows that the frequency and severity of major soil moisture drought periods
was greatest before 1924.
3.2 Major Famines in India during 1870-2016
Next, we analyzed famines (Table S1) and associated causes during the century and a half
period 1870-2016. Our focus is on root-zone soil moisture (60 cm following (Mishra et al.,
2018)) from which we identified overlaps between famines and soil moisture droughts. In
particular, we find five major famines (1873-1874, 1876-1878, 1896-1897, 1899-1900, and
1943-1944) based on the past literature that occurred during the 1870-2016 record (Table S1).
Three of the famines are consistent with the drought periods identified by the SAD analysis.
The two exceptions are 1873-1874 and 1943-1944. Those two sequences of years were not
identified as drought periods in our SAD analysis likely because either a) they were too
localized to appear on the SAD envelope curves, or b) the famine was not coincident with
soil moisture deficits, and likely was caused by some other factor (e.g., failure of food
distribution systems). The identification of major droughts using the SAD analysis is for the
entire country while the famines were located in different regions of India (Fig. 2, Table S1).
Therefore, there inevitably is some disparity in the drought periods we identify from
continental scale soil moisture and the temporal extent of the famines.
The 1873-74 famine occurred in Bihar and Bengal, which were part of the northwestern
province and Oudh during the British period (Table S1). Long-term precipitation deficit
(based on 12-month anomaly based on moving average) of 13.5% caused soil moisture deficit
(~10%) in June 1873 (Fig. S8). The deficit in the monsoon season precipitation started in
1872 and continued until the monsoon season of 1874 (Fig. S8). Depleted soil moisture
primarily due to precipitation deficit created an exceptional drought (SMP <2.0) in Bengal
and the western part of Bihar during June 1873 (Fig. 2a and Fig. S8). Since the soil moisture
drought in 1873 was centered in a relatively small domain, it was not identified by the SAD
analysis (against the threshold of 0.1 million km2). During the 1873 famine, soil moisture
drought that affected more than 50% of Bihar and Bengal (Fig. 2 and Fig. S8), was caused by
a long-term precipitation deficit that started during the monsoon season of 1872, which was
further worsened by 25% below normal precipitation in the famine-affected region in June
1873. The precipitation deficit in June might have caused a reduction in the area under
cultivation and, the region did not get any relief in drought till the end of the monsoon season
of 1874 (Fig. S8). We find that precipitation deficit, rather than warmth, was the proximate
cause of the drought (Fig. 2 and Fig. S8). About 21.5 million people were affected by the
1873 famine, but little or no mortality was reported (Hall-Matthews, 2008; IGI, 1907). The
low mortality during the 1873 famine was mainly attributable to food imports from Burma
and timely relief aid provided by the British government (Hall-Matthews, 2008; IGI, 1907).
© 2019 American Geophysical Union. All rights reserved.
The famine was over in 1874 with 17% surplus monsoon precipitation (Fig. S8) and good
food production.
The second famine occurred during 1876-1878 (Table S1), which has also been called the
Great Famine of Southern India, or Madras Famine (Cook et al., 2010; Dyson, 1991;
Lardinois, 2009). Precipitation deficit started from 1875, which affected southern India until
mid-1878 (Fig. S8). In October 1876, the precipitation deficit was about 41% that created
significant soil moisture depletion (deficit of 12.5%) in the region (Fig. S9). As identified by
the SAD analysis, soil moisture deficits led to drought throughout much of southern India
(Fig 1, Fig. 2, and Fig. S9). The drought covered more than 85% of the famine-affected
region in October 1876 (Fig. 2), which remained under drought until October 1877. Soil
moisture drought in 1876 caused crop failures in South India (Roy, 2006). However, the
British government exported a substantial amount of wheat to England during this time,
which made the region especially vulnerable (Guha, 2006).
North India (and especially the central, north-western provinces, and Punjab) experienced an
extreme to exceptional soil moisture drought in 1877. Poor monsoon season precipitation in
1876 and 1877 led to an accumulated precipitation deficit of more than 27% in the famine
affected region in September 1877 (Fig. S10). The precipitation deficit caused anomalously
high (1.3ºC) air temperature that resulted in a soil moisture deficit of 13% in September 1877
(Fig. S10). In September 1877, about 48% of the country experienced soil moisture drought
(Table S3, Fig. 2). The famine-affected region had an extent of 79% and 78%, respectively in
August and September 1877. The 1876-1877 famine in the south and north India affected
more than 50 million people (IGI, 1907) of which about 6.1 to 10.3 million (Table S1)
perished (Davis, 2001; Fieldhouse, 1996).
The 1895-1900 drought period identified by the SAD analysis includes two famines: 1896-97
and 1899-1900. During October 1896 to January 1897, more than 57% of the country was
affected by soil moisture drought (Table S3). The 1896-97 famine was caused by a
precipitation deficit that started with a poor monsoon in 1895 and continued till the end of
1897 (Fig. S11). A large region was affected by the soil moisture deficit (11%), which was
caused by the combined impact of precipitation deficit (~ 17%) and above normal
temperature anomaly (1.0ºC) (Fig. S11). The famine of 1896-97 started in the Bundelkhand
area (Agra province in the British era) in north India (Fig. 2 and Fig. S11). More than 82% of
the famine-affected region was under soil moisture drought during October to December
1896, which overlaps with the major crop growing season (November to March) (Fig. S11).
The 1896-97 famine affected 69.5 million people in India (IGI, 1907) and caused the death of
5 million people (Table S1) as relief measures failed in the central province (Fieldhouse,
The population was still recovering from the 1896-97 famine when the 1899-1900 famine
started with a monsoon failure in central and western India (Fig. 2, Fig. S12). Below normal
monsoon season precipitation in 1898 affected the region and continued till the end of 1900
(Fig. S12). The combination of substantial (~46.7%) precipitation deficit and above normal
air temperature (1.2ºC) resulted in a widespread soil moisture deficit in the region, which
peaked (15.5% deficit) in September 1899 (Fig. S12). More than 56% of the country was in
soil moisture drought between September 1899 and February 1900 (Table S3). From July
1899 till June 1900, more than 50% of the famine-affected region experienced soil moisture
drought that peaked in September 1899 with 85% coverage (Fig. 2, Fig. S12). In the famine-
affected region, the monsoon season precipitation deficit was 57%, 67%, and 60%,
© 2019 American Geophysical Union. All rights reserved.
respectively in July, August, and September while air temperature was above normal starting
from July till December 1899 (Fig. 2). The soil moisture drought in 1899-1900 resulted in
major crop failure in the famine-affected region and food could not be exported from the
other regions of the country due to lack of transportation or availability of food (Dreze,
1988). The 1899 famine affected 59.5 million people (IGI, 1907) with mortality estimates of
1 to 4.5 million (Table S1) (Fagan, 2009; Fieldhouse, 1996). Deccan and Bombay had the
highest mortality rates (Attwood, 2005). Apart from human mortality, a large number of
cattle died due to acute shortage of fodder (IGI, 1907).
The last major famine in the British era occurred in 1943, which is also known as the Bengal
Famine. The famine resulted in 2-3 million deaths (Devereux, 2000). Our SAD analysis
identified 1937-1945 as a period under drought based on severity, area, and duration.
However, we find the drought was most widespread during August and December 1941
(Table S2 and S3) prior to the famine. This was the only famine that does not appear to be
linked directly to soil moisture drought and crop failures (Fig. S13, S14). The famine-affected
region received 15, 3, 9, and 4% above normal precipitation during June, July, August, and
September of 1943 (Fig. S13). We find that the Bengal famine was likely caused by other
factors related at least in part to the ongoing Asian Theater of World War II including
malaria, starvation and, malnutrition (Sen, 1976). In early 1943, military and political events
adversely affected Bengal’s economy (Tauger, 2009), which was exacerbated by refugees
from Burma (Maharatna, 1996). Additionally, wartime grain import restrictions imposed by
the British government played a major role in the famine (FIC, 1945). We note that aside
from the 1943 Bengal Famine, all the other famines in the 1870-2016 appear to be related at
least in part to widespread soil moisture drought.
3.3 Famines and Sea Surface Temperature Conditions
Out of six major famines we identified in our study period, five were caused by soil moisture
droughts, which were primarily driven by the monsoon (June to September) failures. As year-
to-year variability of the Indian summer monsoon is linked with SST anomalies in tropical
Pacific Ocean (Mishra et al., 2012), we constructed SST anomalies for the monsoon seasons
prior to the droughts that caused famines (Fig. 3). We find that the 1873 Bihar-Bengal and
1876 South India famines were not associated with the positive phase (El Nino) of El Nino
Southern Oscillation (ENSO) (Fig. 3a,b). In fact, these two (1873 and 1876) major droughts
that caused famines during negative phases (La Nina) of ENSO. The other three (e.g., 1877,
1896, and 1899) droughts occurred during strong El Ninos (Fig. 3c-e). The composite of all
five droughts that caused famine reveals a strong influence of El Nino that resulted in the
major monsoon failures (Fig. 3). Recently, Singh et al., (2018) reported that 1870-76 period
that had two (1873 and 1876) famines in India occurred during cool tropical Pacific
conditions. Our results show that the droughts occurred during 1873 and 1876 did not affect a
large area of India (Fig 2). All India monsoon season rainfall was 2% above the long-term
mean in 1873 while 6% below long-term mean in 1876. While El-Nino is the major driver of
monsoon season droughts (Mishra et al., 2012), precipitation anomalies in 1873 and 1876 are
not associated with the warm phase of ENSO.
3.4 Droughts that did not cause famine
Finally, we identify the major soil moisture drought periods in the post-1900 period that were
not coincident with famines (Fig. 4 and Table S1). For each post-1900 drought period
identified by the SAD analysis (e.g., 1908-1924, 1937-1945, 1982-1990, 1997-2004, and
© 2019 American Geophysical Union. All rights reserved.
2011-2015), we selected the month with the greatest areal coverage for our analysis. We find
that soil moisture droughts in October 1918 and December 1920 are comparable (based on
areal coverage) to those that occurred in December 1896 and December 1899, which were
associated with famines (Fig. 2). In October 1918, more than 65% of the country was affected
by soil moisture drought (Fig. 4a). Similarly, the 1908-1924 drought had covered a large area
(65.4%) in December 1920 (Fig. 4), which prominently affected central India. The 1937-1945
drought period had the largest extent in August 1941, which affected 46.8% (mainly
northwestern and southern regions) of India (Fig. 4c). Similarly, the drought period of 1982-
1990 affected 47.3% of the country in August 1987 (Fig. 4d, Table S3). The 1997-2004
drought period had more than 47% areal extent between November 2000 and March
2001(Table S3). The soil moisture drought (1997-2004) peaked in February 2001 and
affected 56% of the country (Fig. 4e, Table S3). The most recent drought period identified by
the SAD analysis occurred in 2011-2015 with the largest extent in October 2015 (Fig. 4f,
Table S3).
4. Discussion and conclusions
Limited irrigation (McGinn, 2009) and low crop yields almost certainly combined with soil
moisture droughts leading to crop failures and food shortages in the era of British rule. Soil
moisture droughts resulted in crop failures that not only affected food availability, but also
the livelihood of much of the population, especially given that a transportation system was
not in place to ship food from one place to another. Dreze, (1988) reported that British era
droughts resulted not only in massive crop failures and food shortages, but also they shattered
the rural economy. Among the six famines identified above, 1873 and 1943 provide some
important insights. For instance, despite the monsoon failure and drought in 1873 in Bihar
and Bengal provinces, there was minimal mortality (Hall-Matthews, 2008). Moreover, human
mortality was substantially higher in the other four droughts than in the 1873-74 famine,
which can be attributed to policy failures and mismanagement (Davis, 2001; Ferguson,
2004). The 1943 Bengal famine was not caused by drought rather but rather was a result of a
complete policy failure during the British era.
A series of famines from 1870 through 1943 killed well over ten million people in India. All
but one of the major famines in this period are linked to soil moisture drought. Out of five
major droughts that caused famines in India, three were driven by the positive SST anomalies
(El Niño) in the tropical Pacific Ocean. India has experienced soil moisture droughts that
were as severe as those that accompanied the deadly pre-1900 famines (for instance, 1918
and 1920). The fact that these droughts did not lead to famine deaths appears to be the result
mostly of more effective government responses. Despite substantial population growth
between 1900 and 2016, famine deaths have been essentially eliminated in modern India.
The primary reasons are better food distribution, and buffer food stocks, rural employment
generation, transportation, and groundwater-based irrigation (Aiyar, 2012). Rapid depletion
of groundwater in northern India (Asoka et al., 2017; Rodell et al., 2009) raises concerns for
food and fresh water security in India. Our results showing the linkage between droughts and
famine in India have implications for food and fresh water security of the region.
Acknowledgment: The work is financially supported by the grants from the Ministry of
Water Resources, India. All the data used in this study are freely available from IMD
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© 2019 American Geophysical Union. All rights reserved.
Figure 1. Severity-Area-Duration (SAD) curves for the major drought periods in India during
1870-2016 for (a) 3-month, (b) 6-month, (c) 12-month, (d) 24-month, and (e) 48-month
durations. Severity and area (million km2) for seven major drought periods were estimated
using SAD analysis.
© 2019 American Geophysical Union. All rights reserved.
Figure 2. Soil moisture droughts that were coincident with famines. (a) The extent of drought
during June 1873. Cyan polygon shows the famine-affected regions that were identified by
the British Provinces from the map of British India (W. H. Allen and Co. - Pope, G. U.
(1880)). (b) Monthly precipitation (blue, %), temperature (red, ºC) anomalies and areal extent
(green,%) of soil moisture drought. (c-j) same as (a,b) but for different famines during 1870-
1900 in India. The region with soil moisture percentile above 30 is shown with white in
(a,c,e,g,i) represents no drought condition. The drought extent and anomalies in (b,d,f,h, and
j) are estimated for the famine affected regions (shown as cyan polygons) in (a,c,e,g,i).
© 2019 American Geophysical Union. All rights reserved.
Figure 3. (a-e) Sea surface temperature (SST, ºC) anomaly for the monsoon season (JJAS) for
droughts that caused famines in India, and (f) SST anomaly composite for all five (1873,
1876, 1877, 1896, and 1899) droughts.
© 2019 American Geophysical Union. All rights reserved.
Figure 4. Major soil moisture droughts and their areal coverage (%) that were not associated
with famines. The region with soil moisture percentile above 30 is shown with white
represents no drought condition
... However, these previous studies have mainly focused on identifying factors affecting hydrological drought recovery, and factors affecting agricultural drought recovery have not been widely discussed. There is a tremendous need to deepen our understanding of the agricultural drought recovery process and its influencing factors, as agricultural droughts directly impact crop growth, which may lead to reduced agricultural productivity and in the worst case to famines (Mishra et al., 2019). Moreover, most of these studies were conducted at the basin scale, which means that the spatial variation of drought recovery characteristics and its influencing factors may be insufficient to be detected as drought recovery characteristics are spatially heterogeneous (Parry et al., 2016b;Yao et al., 2022). ...
... More recently, a groundbreaking work was done by Mishra et al. (2019) who used weather data to study soil moisture levels where they discovered that out of the six major famines between 1870 and 2016 in India, five were linked to soil moisture drought, but that the Bengal famine of 1943 was not caused by drought. Even the rainfall was also above average during that year. ...
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An estimated 3 million people died due to the Bengal famine of 1943. The purpose of this article is to theorize the Bengal famine through the lens of colonial biopolitics. The colonial strategies and utilitarian principles by the British authorities exacerbated the Bengal famine. Utilizing Foucault’s concept of biopolitics, I point out how the British viewed Indian bodies discursively. To reaffirm their sense of superiority, they reduced their Indian subjects to animal-like beings’ incapable of controlling their own reproduction. In order to fulfil British goals, Indian people were forced to participate in the war effort. This paper situates the local and global politics of the famine as they were wrapped up in the geopolitics of World War II, during which the British colonial authorities were far more concerned about a Japanese invasion of South Asia than they were with the lives of people dying of hunger. The article highlights how the implementation of racist policies worsened the famine since it was a product of wartime priorities and calculations. I argue that the Bengal famine of 1943 is a historic tragedy of the colonial past, which was transformed into a socially constructed catastrophe by the British colonizers.Geographers have never studied the Bengal famine of 1943, and one of the principal purposes of this paper is to fill this void.
... In the context of frequent climate extremes, recurrent and consecutive droughts have led to uncertainty about rainfed agriculture and its sustainability in India and Myanmar [45][46][47]. Meanwhile, Bangladesh is a flood-prone country and often experiences devastating floods during the monsoon season that cause damage to crops and property. ...
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South Asia, one of the most important food producing regions in the world, is facing a significant threat to food grain production under the influence of extreme high temperatures. Furthermore, the probability of simultaneous trends in extreme precipitation patterns and extreme heat conditions, which can have compounding effects on crops, is a likelihood in South Asia. In this study, we found complex relationships between extreme heat and precipitation patterns, as well as compound effects on major crops (rice and wheat) in South Asia. We also employed event coincidence analysis (ECA) to quantify the likelihood of simultaneous temperature and crop extremes. We used the Enhanced Vegetation Index (EVI) as the primary data to evaluate the distinct responses of major crops to weather extremes. Our results suggest that while the probability of simultaneous extreme events is small, most regions of South Asia (more than half) have experienced extreme events. The regulatory effect of precipitation on heat stress is very unevenly distributed in South Asia. The harm caused by a wet year at high temperature is far greater than that during a dry year, although the probability of a dry year is greater than that of a wet year. For the growing seasons, the highest significant event coincidence rates at a low EVI were found for both high- and low-temperature extremes. The regions that responded positively to EVI at extreme temperatures were mainly concentrated in irrigated farmland, and the regions that responded negatively to EVI at extreme temperatures were mostly in the mountains and other high-altitude regions. Implications can guide crop adaptation interventions in response to these climate influences.
... Large-scale intensive droughts have been observed in Australia (Tian et al. 2020;van Dijk et al. 2013), Brazil (Cunha et al. 2019), China (Yao et al. 2018), the United States (Rippey 2015), India (Mishra et al. 2019), and Russia (Cook et al. 2020) from 1960 to 2018. These severe droughts occur more frequently (He et al. 2020), which can significantly affect crop production and disrupt food stability (Kuwayama et al. 2019). ...
Emphasizing the role of water in human and ecosystem sustainability, this study defines water security and its associated aspects. It then reflects on the availability of water, water supply, water demand, and water consumption. The question of water scarcity and crisis around the world is addressed next. What are the causes of water scarcity if it exists and how can it be ameliorated? How does climate change impact water scarcity? These are critical issues that need urgent attention, for their importance transcends scientific and engineering boundaries and directly affects the society.
... Mann Kendall Test is a non-parametric test used to determine the presence or absence in a given time series data 31,32 . The null hypothesis, H 0 , states there is no monotonic trend, and this is tested against one of three possible alternatives hypothesis, H α : (1) there is a monotonic upward trend, (2) there is a monotonic downward trend, or (3) there is either a monotonic upward or a downward trend. MK test statistics can be defined as where n denotes the length of the dataset, X i and X j Represents data points in time series i and j, respectively (i < j). ...
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Drought is a natural disaster affects water resources, agriculture, and social and economic development due to its long-term and frequent occurrence. It is crucial to characterize and monitor drought and its propagation to minimize the impact. However, spatiotemporal assessment of drought characteristics over India at the sub-basin scale based on terrestrial water storage is unexplored. In this study, the Terrestrial water storage anomalies (TWSA) obtained from a Gravity Recovery and Climate Experiment and precipitation data are used to characterize the propagation of drought. Combined Climatological Deviation Index (CCDI) and GRACE-Drought Severity Index (GRACE-DSI) were computed as CCDI utilizes both precipitation and TWSA data while GRACE-DSI uses only TWSA data. Our results showed that GRACE-DSI exhibits significant negative trends over most of the Indian sub-basins compared to CCDI, indicating that most of the drought events are due to depletion of TWS. While other sub-basins show changing trends for GRACE-DSI and CCDI. The number of sub-basins showing significant negative trends for GRACE-DSI is more than that for CCDI. Hence TWS is depleting for most of the subbasins in India. Our results show that Indo-Gangetic plains face many drought events during 2002–2004, 2009–2014 & 2015–2017. Maximum drought duration and drought severity obtained for the area of North Ladakh (not draining into Indus basins) by GRACE-DSI are 26 months (2002–2004) and − 44.2835, respectively. The maximum drought duration and drought severity obtained for the Shyok sub-basin by CCDI is 17 months (2013–2015) and − 13.4392, respectively. Monthly trend analysis revealed that 39 & 23 no. of sub-basins show significant negative GRACE-DSI trends for October and CCDI for November, respectively. At the same time, the seasonal trend shows that total 34 and 14 sub-basins exhibited a significant negative trend at post-monsoon Kharif season for both the GRACE-DSI & CCDI, respectively.
The agricultural region of Marathwada in India incurred significant loss of crops and lives during the summer of 2015, which was characterised by persistent hot and dry conditions. We use observations and large ensembles of regional climate model simulations to understand and attribute this joint occurrence of deficient rainfall and high temperature in a novel, multivariate framework. Highly unlikely in a world without anthropogenic climate change (1-in-256 year), the event is found to be frequent (1-in-38 year) in the actual world. Thus, the risk of this event is found to be atleast quintupled due to anthropogenic factors, implying that the 2015 event is more likely due to anthropogenic climate change. Interestingly, the 2015 dry event is not unprecedented (∼1-in-15 year) based on observed records and for either of the model scenarios, suggesting that risk assessments based on rainfall alone may not be enough to reconcile the observed impacts. Further, such compound drought events are projected to become even more frequent under future end-of-the-century warming targets of 1.5 °C and 2 °C above pre-industrial levels, with expected doubling and tripling of the probability of the 2015 hot-dry conditions, respectively. Our findings highlight the role of human-induced warming on increased incidences of compound extreme events, thereby warranting adaptation strategies that aim at alleviating associated risks.
Drought is a frequently occurring hydrometeorological event, which is defined as a reduction in water availability in different hydrologic elements. Over the last century, the hydrologists around the world have put substantial efforts to improve the monitoring and prediction of droughts through the development of new drought indices and prediction models. However, the scarcity of site-based observations has constrained these efforts to date. Remote sensing has emerged as an alternative to supplement these observations and has enabled the progress in drought studies in data-scarce parts of the world. This chapter describes the applicability of remote sensing in evaluation and assessment of drought (i.e., meteorological, agricultural, and hydrological). We also discuss the limitations associated with remote sensing applications (resolution, continuity, and uncertainty) and future perspectives. Further, a case study on remote sensing application in assessment of drought impact on Net Primary Production (NPP) in India is also presented, which highlights the importance of remote sensing in providing information of ecohydrological variables that are difficult to monitor on ground.
Exceptional drought events, known as megadroughts, have occurred on every continent outside Antarctica over the past ~2,000 years, causing major ecological and societal disturbances. In this Review, we discuss shared causes and features of Common Era (Year 1–present) and future megadroughts. Decadal variations in sea surface temperatures are the primary driver of megadroughts, with secondary contributions from radiative forcing and land–atmosphere interactions. Anthropogenic climate change has intensified ongoing megadroughts in south-western North America and across Chile and Argentina. Future megadroughts will be substantially warmer than past events, with this warming driving projected increases in megadrought risk and severity across many regions, including western North America, Central America, Europe and the Mediterranean, extratropical South America, and Australia. However, several knowledge gaps currently undermine confidence in understanding past and future megadroughts. These gaps include a paucity of high-resolution palaeoclimate information over Africa, tropical South America and other regions; incomplete representations of internal variability and land surface processes in climate models; and the undetermined capacity of water-resource management systems to mitigate megadrought impacts. Addressing these deficiencies will be crucial for increasing confidence in projections of future megadrought risk and for resiliency planning. © 2022, This is a U.S. Government work and not under copyright protection in the US; foreign
Forest declines under global warming have received much attention in studies of forest ecology, yet such events in periods before climate warming have been less studied because of shortage in documentation of past decline events. Here we used dendroecological techniques to identify forest decline events in the past five and a half centuries for a juniper forest near Lhasa of Tibet, China. Data of tree ring-widths were obtained from 42 relatively old trees after sample collection, measurement and crossdating. Radial growth of these trees was significantly and positively correlated with total precipitation in May and June. Persistent and severe growth reductions, lasting for at least eight years, were identified for each sample. We found that greater than 35% of the trees exhibited persistent and severe growth reductions in the interval A.D. 1875–1883, suggesting a growth decline event in the forest. This growth decline was the most severe event in the past five and half centuries. The weakened Indian monsoon in A.D. 1875–1878, which would result in extreme and prolonged droughts at spatially large scale in the monsoon zone, was most likely the driving force for the forest decline event discovered in this study. Our results suggested that future risk of juniper forest declines in central Tibetan plateau will be related to extreme droughts which could be amplified by warming. The study highlighted the importance of examining growth trajectory of individual trees in assessing forest health in a long perspective.
Conference Paper
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The availability and depletion of groundwater resources - a possible threat to food and water security - are impacted by both pumping and climate variability, although the relative importance of these two drivers is rarely quantified. Here we show that long-term change in the monsoon precipitation is a major driver of groundwater storage variability in most parts of India either directly by changing recharge or indirectly by changing abstraction. GRACE and observation well data show that groundwater storage has declined in north India with a rate of 2 cm/year and increased in the south India by 1 to 2 cm/year during the period of 2002-2013. A large fraction of total variability in groundwater storage is influenced by precipitation in northcentral and southern India. Groundwater storage variability in the northwestern India is mainly explained by variability in abstraction for irrigation, which is influenced by precipitation. Declines in precipitation in north India is linked with the Indian Ocean warming, suggesting a previously unrecognised teleconnection between ocean temperatures and groundwater storage. These results have strong implications for management of groundwater resources under current and future climate conditions in India.
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From 1875 to 1878, concurrent multiyear droughts in Asia, Brazil, and Africa, referred to as the Great Drought, caused widespread crop failures, catalyzing the so-called Global Famine, which had fatalities exceeding 50 million people and long-lasting societal consequences. Observations, paleoclimate reconstructions, and climatemodel simulations are used 1) to demonstrate the severity and characterize the evolution of drought across different regions, and 2) to investigate the underlying mechanisms driving its multiyear persistence. Severe or record-setting droughts occurred on continents in both hemispheres and in multiple seasons, with the ''Monsoon Asia'' region being the hardest hit, experiencing the single most intense and the second most expansive drought in the last 800 years. The extreme severity, duration, and extent of this global event is associated with an extraordinary combination of preceding cool tropical Pacific conditions (1870-76), a record-breaking El Niño (1877-78), a record strong Indian Ocean dipole (1877), and record warm North Atlantic Ocean (1878) conditions. Composites of historical analogs and two sets of ensemble simulations-one forced with global sea surface temperatures (SSTs) and another forced with tropical Pacific SSTs-were used to distinguish the role of the extreme conditions in different ocean basins. While the drought in most regions was largely driven by the tropical Pacific SST conditions, an extreme positive phase of the Indian Ocean dipole and warm NorthAtlantic SSTs, both likely aided by the strong El Niño in 1877-78, intensified and prolonged droughts in Australia and Brazil, respectively, and extended the impact to northern and southeastern Africa. Climatic conditions that caused the Great Drought and Global Famine arose from natural variability, and their recurrence, with hydrological impacts intensified by global warming, could again potentially undermine global food security.
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India has witnessed some of the most severe historical droughts in the current decade, and severity, frequency, and areal extent of droughts have been increasing. As a large part of the population of India is dependent on agriculture, soil moisture drought affecting agricultural activities (crop yields) has significant impacts on socio-economic conditions. Due to limited observations, soil moisture is generally simulated using land-surface hydrological models (LSMs); however, these LSM outputs have uncertainty due to many factors, including errors in forcing data and model parameterization. Here we reconstruct agricultural drought events over India during the period of 1951–2015 based on simulated soil moisture from three LSMs, the Variable Infiltration Capacity (VIC), the Noah, and the Community Land Model (CLM). Based on simulations from the three LSMs, we find that major drought events occurred in 1987, 2002, and 2015 during the monsoon season (June through September). During the Rabi season (November through February), major soil moisture droughts occurred in 1966, 1973, 2001, and 2003. Soil moisture droughts estimated from the three LSMs are comparable in terms of their spatial coverage; however, differences are found in drought severity. Moreover, we find a higher uncertainty in simulated drought characteristics over a large part of India during the major crop-growing season (Rabi season, November to February: NDJF) compared to those of the monsoon season (June to September: JJAS). Furthermore, uncertainty in drought estimates is higher for severe and localized droughts. Higher uncertainty in the soil moisture droughts is largely due to the difference in model parameterizations (especially soil depth), resulting in different persistence of soil moisture simulated by the three LSMs. Our study highlights the importance of accounting for the LSMs' uncertainty and consideration of the multi-model ensemble system for the real-time monitoring and prediction of drought over India.
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India has witnessed some of the most severe droughts in the current decade and severity, frequency, and areal extent of droughts have been increasing. As a large population of India is dependent on agriculture, soil moisture droughts adversely affect agriculture and groundwater resources. Due to lack of observations, soil moisture is generally simulated using land surface hydrological models (LSMs), however, these LSMs have uncertainty due to model parameterization. Here we reconstruct agricultural drought events during the period of 1951–2015 based on simulated soil moisture from three LSMs, the Variable Infiltration Capacity (VIC), the Noah, and the Community Land Model (CLM). We find a higher uncertainty in soil moisture droughts over a large part of India during the major crop growing season (Rabi season, November to February: NDJF) than that of the monsoon season (June to September: JJAS). Moreover, uncertainty in drought estimates is higher for severe and localized droughts. Higher uncertainty in the soil moisture droughts is largely due to the difference in model parameterizations; resulting in different persistence of soil moisture simulated by the three LSMs. Our study highlights the importance of accounting for the LSMs uncertainty and consideration of multimodel ensemble for the real-time monitoring and prediction of drought over India.
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The depletion of groundwater resources threatens food and water security in India. However, the relative influence of groundwater pumping and climate variability on groundwater availability and storage remains unclear. Here we show from analyses of satellite and local well data spanning the past decade that long-term changes in monsoon precipitation are driving groundwater storage variability in most parts of India either directly by changing recharge or indirectly by changing abstraction. We find that groundwater storage has declined in northern India at the rate of 2 cm yr−1 and increased by 1 to 2 cm yr−1 in southern India between 2002 and 2013. We find that a large fraction of the total variability in groundwater storage in north-central and southern India can be explained by changes in precipitation. Groundwater storage variability in northwestern India can be explained predominantly by variability in abstraction for irrigation, which is in turn influenced by changes in precipitation. Declining precipitation in northern India is linked to Indian Ocean warming, suggesting a previously unrecognized teleconnection between ocean temperatures and groundwater storage.
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The 2015 drought in the Indo-Gangetic Plain posed new challenges related to food and water security and affected the lives of millions. All-India monsoon rainfall in 2015 was the 10th driest year on record (1906-2015) with a deficit of 14.5%, and the Indo-Gangetic Plain witnessed a rainfall deficit of 25.8% (3rd rank event). Drought severity was amplified by deficits from the previous years in the Indo-Gangetic Plain and other parts of India. The Indo-Gangetic Plain faced a two-year cumulative deficit of 51% and the drought of 2014-15 was unprecedented with a return period of 542 years. The GRACE data showed the occurrence of consecutive negative terrestrial water storage and groundwater anomalies in 2014 and 2015, mainly centered over the Indo-Gangetic Plain region. Notwithstanding uncertainty in future projections, the multi-year droughts in the same regions can pose challenges for water resources and agriculture.
Changes in precipitation, air temperature, and model-simulated soil moisture were examined for the observed (1950–2008) and projected (2010–99) climate for the sowing period of Kharif and Rabi [KHARIF_SOW (May–July) and RABI_SOW (October–December)] and the entire Kharif and Rabi [KHARIF (May–October) and RABI (October–April)] crop-growing periods in India. During the KHARIF_SOW and KHARIF periods, precipitation declined significantly in the Gangetic Plain, which in turn resulted in declines in soil moisture. Statistically significant warming trends were noticed as all-India-averaged air temperature increased by 0.40°, 0.90°, and 0.70°C in the KHARIF, RABI_SOW, and RABI periods, respectively, during 1950–2008. Frequency and areal extent of soil moisture–based droughts increased substantially during the latter half (1980–2008) of the observed period. Under the projected climate (2010–99), precipitation, air temperature, and soil moisture are projected to increase in all four crop-growing seasons. In the projected climate, all-India ensemble mean precipitation, air temperature, and soil moisture are projected to increase up to 39% (RABI_SOW period), 2.3°C, and 5.3%, respectively, in the crop-growing periods. While projected changes in air temperature are robust across India, robust increases in precipitation and soil moisture are projected to occur in the end-term (2070–99) climate. Frequency and areal extents of soil moisture–based severe, extreme, and exceptional droughts are projected to increase in the near- (2010–39) and midterm (2040–69) climate in the majority of crop-growing seasons in India. However, frequency and areal extent of droughts during the crop-growing period are projected to decline in the end-term climate in the entire crop-growing period because of projected increases in the monsoon season precipitation.
The monthly global 2° × 2° Extended Reconstructed Sea Surface Temperature (ERSST) has been revised and updated from version 4 to version 5. This update incorporates a new release of ICOADS release 3.0 (R3.0), a decade of near-surface data from Argo floats, and a new estimate of centennial sea ice from HadISST2. A number of choices in aspects of quality control, bias adjustment, and interpolation have been substantively revised. The resulting ERSST estimates have more realistic spatiotemporal variations, better representation of high-latitude SSTs, and ship SST biases are now calculated relative to more accurate buoy measurements, while the global long-term trend remains about the same. Progressive experiments have been undertaken to highlight the effects of each change in data source and analysis technique upon the final product. The reconstructed SST is systematically decreased by 0.077°C, as the reference data source is switched from ship SST in ERSSTv4 to modern buoy SST in ERSSTv5. Furthermore, high-latitude SSTs are decreased by 0.1°-0.2°C by using sea ice concentration from HadISST2 over HadISST1. Changes arising from remaining innovations are mostly important at small space and time scales, primarily having an impact where and when input observations are sparse. Cross validations and verifications with independent modern observations show that the updates incorporated in ERSSTv5 have improved the representation of spatial variability over the global oceans, the magnitude of El Niño and La Niña events, and the decadal nature of SST changes over 1930s-40s when observation instruments changed rapidly. Both long- (1900-2015) and short-term (2000-15) SST trends in ERSSTv5 remain significant as in ERSSTv4.