RESEARCH COMMUNICATIONS
Unprecedented drought in North East
India compared to Western India
Bikash Ranjan Parida1,* and
Bakimchandra Oinam2
1
Department of Civil Engineering, Shiv Nadar University,
Greater Noida 201 314, India
2
Department of Civil Engineering, National Institute of Technology,
Manipur, Imphal 795 001, India
The rainfall distribution over Western and North East
India during the southwest (SW) monsoon season is
geographically distinct with the heaviest seasonal
rainfall occurs over the North Eastern Region (NER),
while the lowest rainfall occurs over the Western region (Saurashtra and Kutch in Gujarat, and also in
Rajasthan). Gujarat is located in arid to semiarid
region and has more drought-prone areas. In contrast,
Assam and Meghalaya have humid climate and occurrence of drought is unusual. Here, we analyse the percentage departure of rainfall for nearly two decades
(1997–2014) along with crop statistics. Our results
indicate that the SW monsoon rainfall in the NER has
gradually dropped in recent years compared to the
1980s and 1990s. As a result, these regions have witnessed frequent unprecedented drought than Western
India. In NER, probability of drought occurrence was
54%, and it is 27% for Western India in the recent
decade (2000–2014). The frequent drought has caused
adverse agricultural impacts and our results show a
significant negative rice production anomaly during
drought years 2005–06 and 2009 in Assam. Drought
impacts were also reported from other states in NER
during 2010–11 and 2013. Drought associated with El
Niño was not so strong; however, increasing temperature and increased monsoon season rainfall variability
have an impact on global climate change. This may
cause warming-induced drought leading to adverse
impact on agriculture and food security in the NER.
Keywords: Crop production, meteorological and agriculture drought, monsoon season, rainfall departure.
DROUGHT is a hydro-meteorological natural hazard and
often catastrophic in nature causing widespread impact on
the society. It is defined as ‘a period of abnormally dry
weather sufficiently prolonged for the lack of water to
cause serious hydrologic imbalance in the affected area’.
On the basis of their nature and severity, droughts are
classified as meteorological, hydrological, agricultural,
socio-economic and ecological. According to India
Meteorological Department (IMD), meteorological
drought arises when actual rainfall over an area is significantly less than the climatological mean of that area. IMD
defines the rainfall categories for smaller areas like dif*For correspondence. (e-mail: bikashrp@gmail.com)
CURRENT SCIENCE, VOL. 109, NO. 11, 10 DECEMBER 2015
ferent meteorological districts or a sub-division by their
deviation from normal rainfall for a meteorological area –
excess (+20% or more above normal); normal (+19%
above normal to –19% below normal), deficient (–20% to
–59% below normal); and scanty (–60% or more below
normal). This may not cause agricultural drought but in
the event of extreme rainfall deficiency, agricultural
drought impacts are unavoidable. Agricultural drought
refers to a situation, in which the moisture in the soil is
no longer sufficient to meet the needs of the crops
growing in the area.
Droughts are recurring climatic events and are recognized as a major limiting factor to the regional economic
development by affecting agriculture, water resources and
food production. It produces widespread impacts on
society, especially in the agriculture sector, by a decrease
in foodgrain production depending upon the intensity,
duration and spatial coverage of drought stress. It can
lead to increased migration from rural to urban areas,
posing additional pressures on diminishing food production. Drought often hits the Indian subcontinent, causing
massive water shortages, financial losses and adverse social consequences. During the British rule, the Great Famine of 1876–1978 severely affected the entire Southern
peninsula of India and spread to Central and northern
parts of the country. The famine due to intense drought
was spread over 16.7 m ha and mortality was estimated as
5.5 million people. Davis1 explored the impact of colonialism and capitalism during the extreme climatic condition
‘El Niño Southern Oscillation (ENSO)’ drought-related
famines of 1876–1878, 1896–1897 and 1899–1902 in
India. In the second half of the 19th century, the India
subcontinent witnessed a near-permanent cycle of
droughts, meagre harvest and famine. Subsequently, the
Bengal famine of 1943–44 triggered crop failures and
food shortages and at least 3 million people died from
starvation, malnutrition and related illnesses during the
famine2.
During the last three decades, rapidly increasing populations have added to the growing demand for water, food
and other natural resources. The drought during 2000–
2004 across South Asia affected more than 462 million
people, with severe impacts in western India (Gujarat and
Rajasthan), Pakistan’s Sind and Baluchistan provinces
and in parts of Iran and Afghanistan 3. During the last
three decades (1980–2014), more than 832 million people
across south Asia affected by drought were forced to abandon their land4. Over this period, Emergency Events
Database (EM-DAT)4 reported seven occurrences (1982,
1987, 1993, 1996, 2000, 2002 and 2009) of drought in
India and about 751 million people were affected (i.e.
~90% of south Asia). According to EM-DAT4, since
1970 there have been 36 events of drought in south Asia.
India witnessed nine such events over the period 1970–
2014 and accounts for a quarter of the south Asian
events. However, many drought events observed in
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different districts and sub-divisions of the country were
not included in the report 4.
Generally, drought occurrence in India varies from
once in 2–3 years (Rajasthan and Gujarat) to once in 15
years (Assam and Meghalaya). Based on the literature,
drought-affected states (districts) in western India are
Gujarat (Ahmedabad, Amrely, Banaskantha, Bhavnagar,
Bharuch, Jamnagar, Kheda, Kutch, Mehsana, Panchmahal, Rajkot, Surendranagar) and Rajasthan (Ajmer, Banaswada, Barmer, Churu, Dungarpur, Jaisalmer, Jalore,
Jhunjunu, Jodhpur, Nagaur, Pali, Udaipur). In central and
peninsular India, the states affected are Madhya Pradesh,
Chhattisgarh, Andhra Pradesh, Karnataka and Tamil Nadu. In North East (NE) India, Assam (Bongaigaon, Cachar, Dhubri, Goalpara, Golaghat, Kailakandi, Jorhat,
Kanpur, Karbi-Anglong, Kokrajhar, Lakhimpur, Morigaon, Nagoan, Nalbari, Sivasagar, Sonitpur) and Meghalaya have experienced drought consecutively during 2005
and 2006 though this humid region is classified under
rare drought event and frequency is once in 15 years.
Subsequently in 2009, all the seven states in the North
Eastern Region (NER) were severely affected. In Assam,
about 10–14 districts were affected in three consecutive
years during 2009–2011 which suggests that frequent
droughts were observed in the recent decade5.
Deficient rainfall (–9%) during the kharif season 2014
also caused severe drought and affected 12–14 districts
across the state; it also severely affected crop production,
including tea production. Droughts were found to affect
more people compared to other hydro-meteorological
natural hazards, comprising about 50% of those affected
in the country. In view of larger impacts, assessment and
monitoring of drought is critical in most parts of the
country, as droughts are expected to rise here due to
climate change.
Drought in agricultural area leads to decline in food
grain production depending upon the intensity, duration
and spatial coverage of drought stress. It has been reported that about 68% of the area in India is prone to
drought and most of the areas are vulnerable under recurring drought. Drought-prone areas are mainly confined to
western and peninsular India – primarily arid, semi-arid
and sub-humid regions. Recently, India has faced the
worst drought episodes (2002 and 2009) in terms of magnitude, dispersion and duration, with an impact on human
and livestock as well as economic losses. Thus, monitoring drought is of utmost concern to decision-makers from
the viewpoint of food security and trade. The point-based
meteorological drought indices such as SPI, PDSI and
CMI have been extensively used for drought monitoring6,7. But sparse meteorological network and lack of
timely availability of weather data hinder the accurate
and timely monitoring of regional drought.
Innovations in remote sensing technology have provided new dimensions of spatial solutions to many environmental problems, including natural hazard monitoring.
2122
Satellite remote sensing has become crucial, particularly
for timely detection and monitoring of drought due
to timely availability of spatio-temporal data. The most
commonly used normalized difference vegetation index
(NDVI) from remote sensing is often not able to detect
drought events instantaneously because of a lagged vegetation response to drought8. On the other hand, surface
temperature (Ts) is sensitive to water stress and has been
identified as a good indicator of water stress9. Accurate
real-time drought monitoring, therefore, needs a combination of the thermal and visible/near-infrared wavelength to provide information on vegetation and moisture
conditions simultaneously. The Ts–NDVI space relationship has been widely exploited to derive various types of
hydrological information such as air temperature, evapotranspiration and soil moisture10. Many drought indices
like temperature vegetation dryness index (TVDI), water
deficit index (WDI) and the crop water stress index (CWSI)
have been explored for quantification and real-time monitoring of the spatial extent and magnitude of drought 11,12.
In an earlier study12, we have explored the potential of
TVDI from Terra-MODIS satellite to detect spatial distribution of 2002 agricultural drought in semi-arid regions
of western India (Gujarat) and 2006 agricultural drought
in humid regions of NE India (Assam). The spatial distribution of drought pattern over these two states was also
discussed12.
In this study, we have analysed nearly two decades of
rainfall departure data (IMD) and agricultural production
data (Ministry of Agriculture, Government of India) for
drought development over western India and NER. A
meteorological-based drought index called crop moisture
index (CMI) has been used to detect the 2002 drought in
Gujarat. Particularly, Assam has been considered from
NER, since this state has witnessed unprecedented
droughts during 2005–06 and subsequently in 2009–2011
that caused heavy damage to agricultural and horticultural
crops.
As a measure of meteorological drought, we have
analysed nearly two decades (1997–2014) of percentage
departure of rainfall for Gujarat region (south and north
Gujarat) and Saurashtra–Kutch region that represents
western India, and also for Assam and Meghalaya (henceforth A&M) that represents NER. The southwest (SW)
summer monsoon covers up to 80% of total annual rain
and thus the rainfall departure data were obtained for SW
monsoon season rainfall (June to September). As a measure of agricultural drought, CMI developed by Palmer13
was used. It measures the short-term changes in soil
moisture conditions of crops and gives the current status
of agricultural drought or moisture surplus, which can
change rapidly from week to week. It is normally calculated with a weekly time-step using the field-based
meteorological data like mean temperature, total precipitation for each week and CMI value from the previous
week.
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Figure 1. Time series of the percentage departure of seasonal rainfall (June–September) from long-period average for western and North
East India during 1997–2014. S&K, Saurashtra–Kutch region.
The long-term crop statistics was analysed to ascertain
the impact of drought on agriculture, and production
anomalies were computed as the difference between
observed production and long-term mean production.
Along with food grains and oilseeds, cotton is also an
important crop in Gujarat, which produces about onethird of the cotton in the country. Time series data of
food grains (rice, wheat, corn, coarse grains, viz. sorghum and millet, and pulses, viz. beans, dried peas and
lentils) and oilseeds (groundnut, sesamum, mustard and
castor) productivity (1981–2003) were linearly de-trended
to remove the effect of improvements in crop science
technology. The de-trended yields were derived by subtracting the per year yield variation from historical record
of food grain and oilseeds yields. The de-trended yield
anomaly (DYa) is computed as
DYai
Yai
1,
Yti
(1)
where DYai is de-trended yield anomaly for the ith year,
Yai and Yti are actual and 22 years time trend based yield
(Yt = a + b year) of ith year respectively. The slope b of
this regression line for each district has been used as an
indicator of the overall trend in productivity.
The seasonal rainfall over a district or sub-division is
classified as normal if the rainfall departure from the
long-period average is within –19% to +19%, excess
( 20%), deficient ( –20%) and scanty ( –60%).
Departure of rainfall during SW monsoon season over
S&K region shows that six years were normal and eight
years were excess, but seven years were observed to have
uninterrupted normal rainfall (2003–2011), except 2004
(Figure 1). Departure of rainfall was deficient for four
years (1999, 2000, 2002 and 2012). The pattern remains
CURRENT SCIENCE, VOL. 109, NO. 11, 10 DECEMBER 2015
the same for Gujarat region (N&S), except in 2009,
which was observed as deficient (–34% departure). For
the entire state, five years were observed as meteorological drought and within these 18 years (1997–2014), 2002
and 2009 drought years were associated with El Niño.
Over the period 1997–2014, five El Niño years were observed. Western India showed nearly 28% probability of
drought in 18 years, but 40% cases were associated with
El Niño.
The seasonal rainfall departure from the long-term
average rainfall over A&M shows that there is no single
year under excess rainfall, but ten years received normal
rainfall (1997–2000, 2003–2004, 2007–2008, 2012 and
2014). Departure of rainfall was deficient for eight years
(2001–2002, 2005–2006, 2009–2011 and 2013), and
2009–2011 were consecutive drought years. Within these
18 years, 8 years were associated with meteorological
drought giving a probability of drought as 44%, whereas
2002, 2006 and 2009 droughts were associated with El
Niño. In particular, for the recent decade (2000–2014),
27% cases from western India were observed as drought,
whereas 54% cases from NER were observed as drought,
suggestive of impacts of global climate change. The analysis reveals that the effect of El Niño on drought incidences was moderate (with 40–50% probability) in the
recent decade, which indicates that most of the drought
occurrences were linked with the variability of rainfall
during the SW monsoon season.
The occurrence of agricultural drought was analysed
for Gujarat (2001–2407N lat. and 6804–7404E
long.). Figure 2 shows a plot of CMI for two meteorological stations, namely Ahmedabad (North Gujarat) and Rajkot (Saurashtra region) for the two El Niño years,
wherein 2002 was a drought year and 2004 was a normal
year. The CMI plot shows that in both the stations, CMI
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values are negative (indicating lower soil moisture and
agricultural drought) for the year 2002, starting from the
beginning of the kharif season (Figure 2). In contrast,
CMI values are positive for the year 2004 throughout the
kharif season, indicating higher soil moisture or normal
year. These results suggest that CMI could detect agricultural drought stress on a weekly basis and can be further
used to ascertain the results obtained from satellite-based
indicator as discussed in Parida and Oinam12.
To assess the effect of the 2002 drought especially on
cotton production, we plot in Figure 3, production anomaly for the period 1998–2011. The figure reveals that district-level productions were adversely affected in 2000
and 2002 in both the districts and during 2002 production
anomalies were 3.11 104 and 1.78 105 tonnes in AMD
and RAJ respectively. For state-level, the cotton yield
was 175 kg/ha during 2002, whereas it was 421 kg/ha
during 2004.
The de-trended yield anomaly of food grains and oilseeds are negative for majority of districts in the state
during drought years 2000 and 2002. Yield anomaly of
AMD and RAJ districts for food grains is negative in the
drought years and positive in the non-drought years
(Table 1). The average yield anomalies of the state for
food grains is also negative in the drought years. Similarly, yield anomalies for oilseeds are negative in the
drought years. These negative anomalies provide evidence of the adverse effects of drought on crop productivity. The most affected districts are Ahmedabad, Rajkot,
Surendranagar, Banaskantha, Gandhinagar, Jamnagar,
Kutchh, Kheda, Sabarkantha, Surat, Dangs and Valsad.
For analysing agricultural drought in the NER, Assam
(24°50N–28°00N lat. and 89°42E–96°00E long.) was
chosen as a case study because drought is not a usual
phenomenon in these regions. Assam is surrounded by
hilly tracts with humid climate and the Brahmaputra River flows from east to west. The state has 27 districts and
paddy is the main food crop cultivated during winter,
summer and autumn season. Other major food crops
Figure 2. CMI plotted for the meteorological standard week between
27 and 43 weeks (i.e. 2 July to 28 October) representing kharif season
(rainy season). Week nos 35–43 (i.e. early September to end of October) indicate late stage of the crop. AMD and RAJ represent two meteorological stations located at Ahmedabad and Rajkot respectively. CMI
plotted for the year 2002 (red line) and 2004 (blue line).
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cultivated include jute, tea and pulses. Commonly, this
region witnesses floods nearly every year due to its geographical setting, high-intensity rainfall and inundation of
riverine areas caused by the Brahmaputra River. However, the scenario was just opposite in 2005–06 due to
scanty rainfall in many districts of Assam and subsequently during 2009–2011 and 2013 (compare Figure 1).
Assam received 30% less rainfall than normal during
monsoon season 2006 and consequently, the rainfed rice
crops were profoundly affected by drought. Districts critically affected were Dhubri, Nalbari, Jorhat, Morigaon,
Nowgong and Sonitpur.
Here, we have analysed the total rice production from
three seasons for two districts, namely Dhubri and Nalbari. Rice production anomaly plot (Figure 4) indicates
that production anomalies are negative in these districts;
they show a sharp dip during 2005 and 2006 and again
during 2009 compared to the normal years with positive
anomalies. Rice production anomalies for 2005–06 are
–4.71 104 and –3.29 104 tonnes for Nalburi and Dhubri respectively. In 2010 and 2012, these two districts
received annual rainfall of 2224–2730 mm, and because
of normal rainfall drought impacts on rice production
were not observed. However, in other districts the
impacts of drought were observed during 2010 and 2011
(ref. 5). The negative anomalies during drought years
provide corroboration of drought impacts on agriculture
in NER. Thereby, occurrence of intense drought in this
region may have an adverse effect on food security under
global climate change.
Our key finding on the unprecedented meteorological
drought in NER compared to western India is more
persistent in the recent decade (2000–2014). It was
observed that over these 15 years, probability of drought
was 27% and 54% in western India and NER respectively.
These findings suggest that NER experienced more frequent unprecedented droughts (two times more) than
western India. Drought probability associated with El
Niño was nearly 40–50% in western India as well as
NER, which indicates that most of the drought events
were associated with the variability of rainfall during the
SW monsoon season. The association between drought
Figure 3. Cotton production anomaly for Ahmedabad (primary axis)
and Rajkot (secondary axis) districts for the period 1998–2011.
CURRENT SCIENCE, VOL. 109, NO. 11, 10 DECEMBER 2015
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Figure 4. District-level rice productions anomaly during 1998–2011. The mean production is 1.52 105 ,
1.78 105 tonnes for Nalbari and Dhubri respectively.
Table 1.
District-level de-trended food grains and oilseeds yield anomaly during 2000–2003
Food grains yield anomaly
Ahmedabad
Rajkot
State level average
Oil seeds yield anomaly
2000
2001
2002
2003
2000
2001
2002
–46.31
–16.89
–28.17
48.36
84.01
31.45
–81.79
–27.60
–11.20
82.29
116.89
43.85
5.09
–11.46
–20.50
–35.94
–28.41
–36.22
–58.96
–30.59
–35.49
and El Niño over different metrological sub-divisions in
India was not strong, which is consistent with the previous findings14. Studies have shown that the SW monsoon
rainfall over India usually decreases during El Niño
years15, but recent studies indicate that their relationship
no longer holds and thus normal monsoon rainfall may
prevail16,17.
Deficient rainfall has led to agricultural drought, especially during 2002 in Gujarat and 2005, 2006, 2009–2011
and 2013 in Assam5,18. During these years, agricultural
production as well as yield at both district and state level
were lower compared to the normal years (Figures 3 and
4). Besides A&M, other states in the NER such as Manipur, Mizoram, Tripura and Arunachal Pradesh also witnessed unprecedented drought during 2009 (refs 5, 18).
As rice is the predominant crop in this region that occupies about 84% of cultivated area, rainfall deficiency at
transplanting stage can lead to a complete loss of crop.
Moreover, due to absence of irrigation facility, paddy
cultivation faces a serious threat of crop failure in this
region. According to the Department of Irrigation, Government of Assam, the overall irrigation implemented
across the state so far is only 25% (6.95 lakh ha) against
the irrigation potential of 66% (i.e. 27.00 lakh ha). Therefore, irrigation facilities with water management practices
could help overcome such situations of less rainfall
observed during the monsoon season.
In the contemporary period (2000–14), NER has witnessed more unexpected frequent drought, suggesting the
impacts of global climate change. This region falls under
the zone of high rainfall with subtropical and humid type
CURRENT SCIENCE, VOL. 109, NO. 11, 10 DECEMBER 2015
2003
–48.46
1082.85
85.15
of climate. However, under the influence of global climate change, the NER has witnessed drought-like situations in the current years (2000–11, 2013). The impacts
of climate change are well known, particularly in NER
which may exaggerate the vulnerability and risk of agriculture associated with variability of SW monsoon
rainfall. Since the Industrial Revolution, atmospheric
concentration of greenhouse gases, particularly CO2 has
increased significantly. The annual mean temperatures
are expected to further increase by 3–5C in NER19.
There is high confidence that projected rising temperatures and the resultant warming could lead to more
droughts and desertification. IPCC20 reported decreasing
trends in annual rainfall observed across NER. Change in
temperature owing to warming and increased summer
season rainfall variability may cause warming-induced
drought with severe impacts on agriculture and food
security.
With the shift in rainfall pattern and change in temperature effective drought management policies and
frameworks need to be formulated in India. In this context, the WMO, FAO and the UN Convention to Combat
Desertification (UNCCD), and other partners have formulated a National Drought Policy in 2013 to focus on
drought preparedness and management policies. This
emphasizes on quantification of drought impacts and
monitoring drought development. But the ability of
governments and international relief agencies to deal with
droughts is constrained by reliable data, and lack of technical and institutional capacities. Desertification or land
degradation is one of the most serious problems of the
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region and is often related to poor land-use practices.
Further drought can deepen the effect of land degradation. Declining vegetation cover due to drought stress
may enhance soil erosion and can lead to an irreversible
loss of nutrients and subsequently desertification. Hence,
modification of agricultural and water policies in the
drought-affected areas may require additional nationallevel actions and measures to mitigate the droughtaffected areas. While significant achievements have been
made in post-disaster response and reconstruction, there
are still challenges to reducing the risk of future disasters
as the frequency and intensity of droughts and extreme
weather events are expected to increase in the coming
decades. Thus, disaster management is becoming difficult
due to increasing population and climate change. The
only way to reduce such disasters is to improve disaster
and also better preparedness.
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ACKNOWLEDGEMENTS. We thank IMD Pune; Department of
Agrometeorology, AAU, Anand; Department of Agriculture, Gandhinagar, and The Directorate of Economics and Statistics for providing
meteorological and crop statistics data. We also thank Shiv Nadar University and NIT-Manipur for providing the necessary facilities.
Received 11 January 2015; accepted 10 August 2015
doi: 10.18520/v109/i11/2121-2126
Performance of residential buildings
during the M 7.8 Gorkha (Nepal)
earthquake of 25 April 2015
Durgesh C. Rai1,*, Vaibhav Singhal2 ,
S. Bhushan Raj1 and S. Lalit Sagar1
1
Department of Civil Engineering,
Indian Institute of Technology Kanpur, Kanpur 208 016, India
2
Department of Civil and Environmental Engineering,
Indian Institute of Technology Patna, Bihta 801103, India
The M 7.8 earthquake of 25 April 2015 was a significant event in the long seismic history of the Eastern
Himalayas, which caused more than 8000 casualties
and widespread destruction of various structures in
the western and central regions of Nepal. This article
discusses the general observations in the earthquake
affected regions, with special emphasis on the seismic
performance of residential structures in the Kathmandu
valley region. Widespread damage was observed in
*For correspondence. (e-mail: dcrai@iitk.ac.in)
CURRENT SCIENCE, VOL. 109, NO. 11, 10 DECEMBER 2015
Bikash Parida