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Unprecedented drought in South India and recent water scarcity

Vimal Mishra 4,1,2 , Kaustubh Thirumalai 3 , Sahil Jain 1 and Saran Aadhar 1

Published 16 April 2021 • © 2021 The Author(s). Published by IOP Publishing Ltd Environmental Research Letters , Volume 16 , Number 5 Citation Vimal Mishra et al 2021 Environ. Res. Lett. 16 054007 DOI 10.1088/1748-9326/abf289

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1 Civil Engineering, Indian Institute of Technology (IIT) Gandhinagar, Gandhinagar, Gujarat 382355, India

2 Earth Sciences, Indian Institute of Technology (IIT) Gandhinagar, Gandhinagar, Gujarat 382355, India

3 Department of Geosciences, University of Arizona, 1040 E. 4th Street, Tucson, AZ 85721, United States of America

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4 Author to whom any correspondence should be addressed.

Vimal Mishra https://orcid.org/0000-0002-3046-6296

Kaustubh Thirumalai https://orcid.org/0000-0002-7875-4182

Saran Aadhar https://orcid.org/0000-0003-1645-4093

• Received 25 November 2020
• Accepted 26 March 2021
• Published 16 April 2021

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Method : Single-anonymous Revisions: 1 Screened for originality? Yes

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Peninsular Indian agriculture and drinking water availability are critically reliant on seasonal winter rainfall occurring from October to December, associated with the northeastern monsoon (NEM). Over 2016–2018, moderate-to-exceptionally low NEM rainfall gave rise to severe drought conditions over much of southern India and exacerbated water scarcity. The magnitude and dynamics of this drought remain unexplored. Here, we quantify the severity of this event and explore causal mechanisms of drought conditions over South India. Our findings indicate that the 3-year cumulative rainfall totals of NEM rainfall during this event faced a deficit of more than 40%—the driest 3-year period in ∼150 years according to the observational record. We demonstrate that drought conditions linked to the NEM across South India are associated with cool phases in the equatorial Indian and Pacific Oceans. Future changes in these teleconnections will add to the challenges of drought prediction.

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1. Introduction

Deficiency of the summer monsoonal precipitation is one of the main drivers of meteorological drought in India, which if prolonged, can transform into more impactful agricultural and hydrological droughts (Mishra and Singh 2010 , Mishra et al 2010 , Mo 2011 ). Agricultural and hydrological droughts can pose lasting impacts on food production and water availability, respectively (Van Loon 2015 , Samaniego et al 2018 , Mishra 2020 ). India experiences two major monsoon seasons—the Indian summer monsoon (ISM), also known as the southwestern monsoon and the lesser-studied northeastern monsoon (NEM) or the winter monsoon (Gadgil and Gadgil 2006 , Rajeevan et al 2012 ). The ISM is the major source of precipitation for much of India over the period of June to September (hereafter JJAS) and has been the focus of extensive study (Gadgil and Gadgil 2006 , Singh et al 2019 ). On the other hand, the NEM is more important in selected parts of India and is associated with rainfall during the period between October and December (hereafter OND) (Kripalani and Kumar 2004 , Zubair and Ropelewski 2006 , Yadav 2012 ). In particular, the NEM significantly impacts peninsular India, where certain parts of South India receive a majority of their annual rainfall totals during the OND season (Rajeevan et al 2012 ). Despite lesser precipitation totals compared to the ISM, the NEM is critically important for water availability, agriculture, and the livelihood of millions of people residing in peninsular India.

Previously, studies have indicated that both monsoon seasons have experienced profound changes over the past few decades (Mishra et al 2012 , Rajeevan et al 2012 , Roxy et al 2015 , Singh et al 2019 ). For instance, seasonal mean precipitation associated with the ISM has shown a declining trend leading to more frequent monsoon-season deficits (Mishra et al 2012 , Christensen et al 2013 , Roxy et al 2015 ). Similarly, the increase in precipitation associated with the NEM over the last few decades has been attributed to the warming of the Indian Ocean (Mishra et al 2012 , Roxy et al 2015 ). Furthermore, the El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) phenomena are well-known drivers of deficits in monsoon rainfall (Ashok et al 2001 , Kumar et al 2007 ) and are also expected to undergo changes with ongoing increases in greenhouse gases (Cai et al 2018 , Timmermann et al 2018 ). Addressing the mechanisms of why and how these monsoon seasons are shifting under a warming world is critical for improving predictions of drought conditions in India.

Whereas previous studies have shown strong linkages between summer monsoon droughts in India and sea surface temperature (SST) variability in the equatorial Indian and Pacific Oceans (Kripalani and Kulkarni 1997 , Barlow et al 2002 , Niranjan Kumar et al 2013 , Roxy et al 2015 ), few studies have focused on the causes of rainfall deficits associated with the NEM (Dimri et al 2016 ). From 2016 to 2018, South India witnessed severe drought conditions, which significantly impacted agriculture and water availability in the region ('Chennai water crisis: City's reservoirs run dry,' BBC 2019 ). The densely populated states of Andhra Pradesh, Karnataka, and Tamil Nadu continuously declared drought in 2016, 2017, and 2018 related to the deficits in NEM precipitation. The drought caused water crises in both urban and rural areas (Aguilera 2019 ). Despite the profound impacts of the 2016–18 drought in South India, its magnitude, drivers, and mechanisms remain unexplored. In this study, we focus on the 2016–2018 drought, quantify its severity, and investigate its causes and relationships with regional and global ocean–atmosphere variability. We place this extreme event in the context of the previous droughts and conclude that its severity was unprecedented over the observational record.

2. Data and methods

The NEM (October–December) is a dominant source of rainfall in South India (Rajeevan et al 2012 ). South India (Latitude: 8°N–15°N; Longitude: 74°E–81°E)) comprises of five Indian states and three union territories. The region encompasses nearly 19% of India's area and harbors around 250 million people, which is one-fifth of the total population of India (Census of India 2011 ). South India is an agriculturally rich part of the country, with over 60% of its rural population engaged in agriculture (Aulong et al 2012 ). The population depends largely on the NEM for agricultural production. We used gridded daily precipitation data available at 0.5° spatial resolution for the period of 1870–2018 (Mishra et al 2019 ). Mishra et al ( 2019 ) used station observations from India Meteorology Department (IMD) to develop the gridded precipitation for the pre-1900 (1870–1900) period, which was merged with the gridded data available for the post-1900 period (1901–2018; (Pai et al 2014 )) from IMD. More details on the gridded precipitation data and evaluation of its quality can be obtained from Mishra et al ( 2019 ). The gridded data capture orographic precipitation along the Western Ghats, Northeast, and the foothills of Himalaya (Pai et al 2014 , Mishra et al 2019 ).

Total water storage (TWS) data were obtained from the Gravity Recovery and Climate Experiment (GRACE) and GRACE follow on (GRACE-FO) missions. TWS is available for the period April 2002 to June 2017 from the GRACE satellites. The GRACE-FO mission provides the data from June 2018 to present. Therefore, the TWS data for July 2017 to May 2018 is not available. We obtained TWS from GRACE and GRACE-FO from NASA's Jet Propulsion Lab (JPL: https://podaac.jpl.nasa.gov/dataset/TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06_V2 ) for the 2002–2019 period. The GRACE mascon product (RL06 V2) contains gridded monthly global water storage anomalies relative to mean, which is available at 0.5° spatial resolution (Wiese et al 2016 ). To remove the seasonal cycle from TWS, monthly mean TWS was removed from each month, and scale factors were applied.

To assess the influence of SSTs on the 2016–18 drought, we used monthly data from the HadSST dataset (Hadley Centre) for the period 1870–2018 at 2.0° spatial resolution (Rayner et al 2003 ). We obtained surface air temperatures (SATs) from Berkley Earth (Rohde et al 2013 ) to analyze anomalous temperature conditions during NEM droughts. Since SST data has a strong warming trend, we used Ensemble Empirical Mode Decomposition (EEMD; (Wu and Huang 2009 )) to remove the secular trend (Wu et al 2011 ) from SST time series as in Mishra ( 2020 ). The EEMD method has an advantage over conventional detrending as it removes both linear and non-linear trends (Mishra 2020 ). We estimated SST and precipitation anomalies for the NEM (October–December) to diagnose the linkage between precipitation and SST. To examine the coupled variability of precipitation and SST, we use maximum covariance analysis (MCA; (Bretherton et al 1992 )). In addition, we used empirical orthogonal function (EOF) analysis to obtain the dominant modes of variability in rainfall during the NEM when SST was not used. The MCA, performed on two fields (here precipitation and SST) together, identifies the leading modes of variability in which the variations of the two fields are strongly coupled (Mishra et al 2012 ). Sea level pressure (SLP) and wind fields (horizontal, u and meridional, v) were obtained from the European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA-5; (Hersbach and Dee 2016 )) for the period 1979–2018 to understand the mechanism of the northeast monsoon. Further, SLP and wind fields were regridded to 2° to make them consistent with SST.

Towards predictability of NEM rainfall, we employed univariate and multivariate techniques. We use the lagged relationship between SST anomalies and rainfall over South Asia during the NEM as a predictor of OND rainfall. We used SST anomalies from the Nino 3.4 region and over the northern Indian Ocean (NIO; 6°–24°N, 40°–100°E) as a predictor of monthly NEM precipitation using the following three equations:

3.1. Unprecedented recent failure of northeast monsoon rainfall

South India receives more than 40% of its total annual precipitation during the NEM season (figure S1 (available online at stacks.iop.org/ERL/16/054007/mmedia )), and thus deficits in NEM rainfall pose significant water-related challenges in the region. To investigate the long-term observational history of NEM rainfall in the region, we used rainfall observations from the IMD (Pai et al 2014 ), spanning from 1870 to 2018. Domain-averaged precipitation anomalies associated with the NEM indicate that most of South India experienced exceptional (>40%) precipitation deficits during 1874–1876 and 2016–2018 (figure 1 ). We calculated precipitation anomalies during the NEM for one, two, and three consecutive year durations over the 1870–2018 period to estimate abnormal deficit-years in the long-term record (figures 1 , 2 and S4, table 1 ). There are five pronounced periods of drought (>29% deficits) in the overall record including the recent drought of 2016–2018, the droughts during 2001–03, 1949–1951, 2002–04, and the well-known Great Drought of 1876–78 (Cook et al 2010 , Singh et al 2018 ), which was associated with the Great Madras Famine (Blanford 1884 , Mishra et al 2019 ). Among these events, our analysis indicates that the Great Drought and the recent event of 2016–18 are the most severe (figure 1 ). During 2016–18, South India experienced the worst NEM drought over the last 150 years with a precipitation deficit of 45%, whereas the 1874–76 drought was the second-worst, with a deficit of 37% (table 1 ). We note that the 1-year and 2-year duration NEM deficits for 1876 (69%) and 1876–77 (54%) were comparable to the deficits during 2016 (63%) and the 2016–17 (52%) durations (table 1 , figures S2–S4). However, the consecutive 3-year NEM deficit for 2016–18 was more significant than the Great Drought. We find that annual rainfall anomalies additionally indicate drought conditions in 2016, 2017, and 2018 (figure S5). Moreover, 2 and 3-year annual rainfall anomalies for 2016–17 and 2016–18 also show a major rainfall deficit in South India (figure S5). Thus, we conclude that the 2016–18 drought caused by the failure of the NEM also contained severe annual rainfall deficits.

Figure 1.  Three-year cumulative precipitation anomalies (mm) during the Northeast monsoon (NEM, October–December). (a), (b) The spatial pattern of 3 year cumulative precipitation anomalies (mm) during 1874–1876 and 2016–2018 periods, respectively, in southern India (denoted by the green box). (c) Area-averaged (over the green box) 3 year moving-mean precipitation anomalies (%) for the period 1870–2016. Red dots in (c) demarcate the two periods of interest, and show that the 2016–18 was the 1st and 1874–76 was the 2nd worst drought in last 150 years. Long-term precipitation data is based on station observations from the Indian Meteorological Department (IMD).

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Figure 2.  Total water storage (TWS) anomalies from the GRACE and GRACE–FO during 2002–2019. (a)–(c) TWS anomalies (cm) during December 2016, June 2017, and June 2019. (d) 12-month moving-sum precipitation anomalies (cm, in blue) and monthly TWS anomalies (cm, in red) aggregated over South India (south of 15°N). Note that the July 2017 to May 2018 period contains missing data as the GRACE-FO dataset is only available from June 2018 onwards. The Pearson correlation coefficient between TWS anomalies and precipitation anomalies is 0.63.

Table 1.  Top five driest years for one, two, and three-year cumulative northeast monsoon (OND).

Over individual NEM seasons, the two most extreme dry events occurred in 1876 and 2016 with precipitation deficits of 69% and 63%, respectively (table 1 ). The rainfall deficit in 2016 was more severe in comparison to the lack of precipitation in 2017 and 2018 (figure S2). The failure of the NEM in 2016 as well as relatively low rainfall totals over the consecutive years were the main causes behind the 2016–18 drought in South India (table 1 ). Overall, the 3-year NEM drought of 2016–2018 was more severe than the Great Drought of 1874–1876. Infamously, the 1876 drought resulted in famine and the deaths of millions of people (Mishra et al 2019 , Mishra 2020 ). The more recent 2016–18 NEM drought considerably influenced water availability in the region and caused a water crisis across South India ('Chennai water crisis: City's reservoirs run dry,' BBC 2019 ).

Furthermore, the 2016–2018 NEM drought in South India was unprecedented in the last 150 years and had severe implications for water availability. TWS from the GRACE and GRACE–FO satellites showed a considerable loss in South India due to the recent (2016–2018) drought (figure 2 ). Twelve months moving precipitation anomalies pinpoint the onset of drought in South India during October 2016 and show that it continued till October 2018 (figure 2 ). Although there was a weak recovery from drought conditions for two months in November and December 2018, these rainfall totals were not enough to negate the influence of the overall event (2016–2018), which continued till August 2019 (figure 2 ), and was only alleviated by stronger NEM rains later that year. We also note that 12-month precipitation anomalies and TWS anomalies are well-correlated ( r = 0.63), where local observations indicate that rainfall is the major contributor of TWS (Asoka et al 2017 ). Thus, we attribute the loss in regional TWS to the long-term 3-year drought, which was precipitated by the lack of NEM rainfall.

Total water loss in South India estimated from the GRACE satellite was 79 km 3 in December 2016 (figure 2 (a)). Similarly, GRACE–FO data reveal that total water loss in June 2017 and 2019 was 46.5 and 41.7 km 3 , respectively (figures 2 (b) and (c)). Recovery in TWS occurred in late 2019 due to improved NEM rainfall over the region. The 2016–2018 drought caused a significant loss in TWS, which also likely resulted in a significant depletion in groundwater across South India. We caveat that we did not estimate the overall loss in groundwater due to uncertainty in soil moisture (Long et al 2013 , Castle et al 2014 )—an estimate outside the scope of this work—however we suspect that the groundwater depletion was driven by the drought in addition to increased groundwater extraction (Thomas et al 2017 ) during the drought (Asoka et al 2017 ). Despite the uncertainty in the estimation of total water loss from GRACE satellites (Long et al 2013 ), the combined influence of depletion in surface-water and groundwater during this event led to unprecedented water scarcity in South India (Aguilera 2019 , 'Chennai water crisis: City's reservoirs run dry,' BBC 2019 ).

3.2. Mechanism of deficit during the Northeast monsoon

We examined circulation patterns to understand mechanisms behind variability in NEM rainfall. To do so, we first examined climatological surface temperatures (SAT and SST), sea-level pressure (SLP), and wind fields at 850 hPa during the OND season (figure 3 ). SLP and wind fields were taken from the ERA-5 reanalysis dataset (Hersbach and Dee 2016 ) whereas SSTs and SATs were taken from HadSST (Rayner et al 2003 ) and Berkley Earth (Rohde et al 2013 ), respectively. Climatologically during boreal fall, cooling SATs over the northwestern Pacific and northern latitudes alongside comparatively warmer mean-annual SSTs over the northern Indian Oceans set up easterly wind flow across the Bay of Bengal (figures 3 (a) and (b)). In particular, warm SSTs in the western Indian Ocean can elicit easterlies across the Indian Ocean and favor moisture transport from the Bay of Bengal into peninsular India. These moisture-bearing winds, which become northeasterly before landfall, bring NEM rainfall to South India (Rajeevan et al 2012 ). Strong winds from across the South China Sea, driven by the underlying SAT and SLP patterns ultimately facilitate NEM rainfall. Thus, El-Niño-like conditions in the Pacific with cooler SSTs in the northern portion of the western tropical Pacific Ocean, juxtaposed with cooler SSTs in the eastern Indian Ocean and warmer SSTs in the west (i.e. resembling positive IOD-like conditions), all serve to enhance NEM rainfall over South India. It is to be expected that circulation patterns which weaken these processes ought to yield diminished NEM rainfall.

Figure 3.  Atmospheric and oceanic patterns during the 2016–18 drought in South India. (a), (b) Climatological mean surface-air temperature (SAT, °C) and sea-surface temperature (SST, °C), mean sea-level pressure (SLP, Pa) and wind at 850 hPa (in (b)) during the October–December (OND) season. (c), (d) SST, SLP, and wind anomalies associated with the NEM during the OND season of 2016, (e), (f) 2017, and (g), (h) 2018. Mean SLP and wind fields were obtained from ERA-5 whereas SST was taken from HadSST and SAT from BEST.

To better understand the causes of rainfall deficits, we investigated anomalous patterns during the NEM season for 2016, 2017, and 2018 (figure 3 ). In 2016 and 2017, as expected, cool SST anomalies prevailed in the tropical Indo-Pacific and were associated with La Niña conditions in the central Pacific along with negative IOD-like conditions in the Indian Ocean (figures 3 (c)–(f)). Both years witnessed anomalously cooler SSTs in the eastern tropical Indian Ocean and western tropical Pacific, and warmer SSTs in the western Indian Ocean and central Pacific. These SST patterns, alongside SLP and adjacent continental SAT patterns, gave rise to anomalous westerlies in the equatorial Indian Ocean, which weakened moisture transport from the Bay of Bengal during the NEM season of both events (figures 3 (c)–(f)). Moreover, both years were associated with anomalously low SLP and cooler surface temperatures across the Indian sub-continent and Bay of Bengal, sustaining an anomalous anticyclonic pattern which inhibited moisture transport into South India (figures 3 (c)–(f)). In 2018, the rainfall deficit conditions were slightly alleviated due to favorable warm conditions in the western tropical Indian Ocean and cooling in the East (development of a positive IOD event) alongside the development of El-Niño-conditions in the Pacific. However, it should be noted that western Indian Ocean warming was not particularly pronounced that year and alongside cooler temperature anomalies in the northern Indian Ocean, resulted in an overall deficit in NEM rainfall that year.

Next, we analyzed surface temperature and precipitation anomalies for the five most severe dry events in South India over the 1870–2018 period during the NEM season (figure 4 ). The major droughts in South India occurred in 1876, 2016, 1938, 1988, and 1974 (in order of severity). Out of these five droughts, four occurred during La Niña conditions. In contrast, the well-studied drought of 1876 during the NEM was linked with El Niño (figure 4 )—a finding reported previously (Cook et al 2010 , Singh et al 2018 , Mishra et al 2019 ). However, it should be noted that cool SST conditions prevailed in the Pacific Ocean over the 1870–1876 period and the transition from the cool to warm phase occurred during the NEM season of 1876 (Singh et al 2018 ). Additionally, the western Indian Ocean was not anomalously warm as it typically is during El Niño years (figure 4 (a)). Nevertheless, temperature and SLP anomaly composites for the most severe dry and wet NEM years reveal a general propensity for cooler SSTs in the Indo–Pacific (i.e. La Niña conditions) to be associated with precipitation deficits over South India (figures S6 and S7). On the other hand, warming in the central Pacific and Indian Oceans is associated with a stronger NEM and surplus precipitation (figure S7). Overall, OND cooling in the Indian and central Pacific oceans results in lower SLP and weaker wind fields, which ultimately drive rainfall deficits in South India.

Figure 4.  Sea surface temperature (SST)/surface air temperature (SAT) and precipitation (P) anomalies for the top five droughts that occurred in South India during the northeast monsoon for 1870–2018 period. SST and SAT datasets were obtained from Hadley Center and Berkley Earth, respectively. SAT data over few regions are not available for 1876.

3.3. SST variability during Northeast Monsoon

To clarify the relationship between SST and precipitation anomalies associated with the NEM, we performed MCA, which helps delineate the leading patterns responsible for co-variability between South Indian NEM rainfall and tropical SSTs. The first leading mode exhibits typical ENSO-like patterns of covariance and explains 77.2% of total variance (figure 5 (a)). As demonstrated above with patterns of the major droughts (figure 4 ), MCA also indicates that negative SST anomalies over the central Pacific (i.e. La Niña) and Indian Oceans (negative IOD) result in below normal NEM precipitation over South India (figure 5 (b)). The second leading mode of MCA exhibits a relatively weaker relationship between precipitation and SST anomalies during the NEM (figure 5 ). The second mode fingerprints the role of SST warming in the Indian Ocean as a driver of increased NEM precipitation in South India (Roxy et al 2015 ). We also note that there appears to be a slight dichotomy between northern and southern South India, where NEM precipitation in the latter region is more strongly linked with ENSO (figure 5 ). On the other hand, precipitation over the northern parts of South India is more strongly associated with the second leading mode (figure 5 ). This finding might help explain some of the ambiguity surrounding the mechanisms of the impact of the 1876–78 Great Drought on South Indian rainfall. Overall, the leading mode of SST and precipitation variability during the NEM shows that cold SST anomalies in the Indo-Pacific facilitate drought conditions over South India.

Figure 5.  Links between South Indian precipitation and sea surface temperature (SST) during the Northeastern Monsoon season. (a), (b) Correlation patterns obtained from the first leading mode of maximum covariance analysis (MCA) performed between precipitation across South India (8°N–15°N and 74°E–81°E; see Green Box in figure 1 ) and SST during the October–November–December (OND) season over 1870–2018. (c), (d) Same as in the above panels but for the second leading mode of MCA. Rainfall was obtained from the IMD dataset whereas SST was retrieved from HadSST.

We performed EOF analysis to identify the dominant patterns of NEM rainfall in South India (figure 6 ). The first leading mode from the EOF analysis picks out rainfall variability across the entirety of South India and explains 50% of total variance (figure 6 (a)). The second leading mode reveals a bipolar rainfall pattern across the northern and southern parts of South India and explains 11% of the total variance (figure 6 ). We note that the characteristics of rainfall variability derived from the first and second modes of EOF analysis are consistent with the leading modes obtained from the MCA (figure 5 ). Taken together, our findings inferred from both EOFs and MCA show that the first leading mode affects rainfall across South India, whereas the second leading mode delineates opposing rainfall trends in the North versus the southern parts of South India (figure 6 ).

Figure 6.  The leading modes obtained from the empirical orthogonal function (EOF) analysis of rainfall during the NEM for the 1870–2018 period. (a) The first leading EOF mode of NEM, which explains 50.6% of the total variance in NEM rainfall in South India. (b) Lagged correlation between the first leading principle component (PC 1) and 3-month mean SST anomalies over different regions (Nino 3.4 (5°S–5°N, 120–170°W), North Indian Ocean (NIO; 6°–24°N, 40–100°E), North Pacific Ocean (NPO; 30°N–50°N, 120°E–175°W), North Atlantic Ocean (NAO; 6°–24°N, 10–60°W), Pacific Decadal Oscillation (PDO), and Southern Oscillation Index (SOI)). (c) and (d) same as (a) and (b) but for the second leading EOF mode and the corresponding PC 2. Year − 1, Year + 0, and Year + 1 represent the previous, current, and next year of the NEM season, respectively.

We calculated principal components (PCs) associated with the leading modes of variability derived from the EOF analysis (PC1 and PC2) to examine the predictability of NEM rainfall using SST anomalies (figure S8). We also computed the correlation between PC1 and SST anomalies in addition to oceanic indices (table S1) at different time lags (tables S2 and S3). We find that the first principal component (PC1) is strongly correlated ( r = 0.23, P -value < 0.05) to SSTs from April–June (AMJ) in the Nino 3.4 region (figure 6 ). However, PC2 is more appropriately delineated by ( r = 0.33, P -value < 0.05) SST anomalies from OND in Nino 3.4 and in the NIO (figure 6 ). We use this lagged relationship between oceanic indices and SST anomalies with PCs to establish a predictive model for NEM rainfall (as in Zhou et al 2019 ). Focusing on the first mode of variance, we used climatological Nino 3.4 SSTs from AMJ to predict rainfall in South India during OND (figure S9). We find that the OND rainfall is more skillfully predicted using AMJ Nino 3.4 anomalies in comparison to SST anomalies over OND NIO (figure S9). We also note that there is no significant increase in prediction skill when both AMJ Nino 3.4 and OND SST anomalies were used as opposed to Nino 3.4 SST anomalies alone (figure S9) due to high year-to-year variability between Nino 3.4 and NIO (figure S10). Overall, our analysis shows that SST anomalies at Nino 3.4 and over NIO can be used to predict rainfall during the NEM over South India with limited prediction skill.

4. Summary and conclusions

South India faced a severe water crisis during 2016–2018. In June 2019, a 'day zero' was declared in Chennai, Tamil Nadu, due to groundwater depletion and drying of four major reservoirs that supply water (Murphy and Mezzofiore 2019 ), largely induced by this event. We have shown that this extreme deficit was brought about by one of the worst droughts in the last 150 years. The 2016–2018 drought was worse than the 1874–1876 Great Drought, which was linked to the Great Madras famine and the deaths of several million in South India (Mishra et al 2019 ). The severity of the 2016–18 event during the NEM season peaked in 2016—the second singular driest year on record (after 1876). Dynamically, our study implicates negative IOD and La Niña conditions as facilitators for NEM rainfall deficits, where landward moisture transport from the Bay of Bengal into peninsular India is inhibited. The prevalence of La Niña throughout 2016 and 2017 (DiNezio et al 2017 ) further worsened the drought that started in 2016. Such rainfall deficits over consecutive years can result in multi-year drought, which have substantial and adverse impacts on surface and groundwater storage, and profoundly affect water availability and agriculture in the densely populated South Indian region. Although the intensity and timing of this recent event raise the possibility of anthropogenic forcing influencing NEM droughts, future work focusing on detection and attribution is required to separate the influence of natural variability (Thirumalai et al 2017 , Williams et al 2020 , Winter et al 2020 ). Moreover, potential changes in future patterns of SST variability in the Indian Ocean and tropical Pacific will add substantial uncertainty to projections and prediction of NEM rainfall.

Acknowledgments

We acknowledge the India Meteorological Department for providing the precipitation data. The last author appreciates financial assistance from the Indian Ministry of Human Resource Development (MHRD). The study is partially funded by the Ministry of Earth Sciences and Ministry of Water Resources forum projects. KT was supported by NSF Grant No. OCE-1903482 and acknowledges the University of Arizona and the Department of Geosciences for support.

Data availability statement

The data that support the findings of this study are available upon reasonable request from the authors.

Supplementary data

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India’s latest crisis: 600 million people struggle with drought

Cities have been forced to truck in drinking water, farms are failing, and the situation grows more desperate.

• Climate change

The agonising and often exhausting wait for the monsoon has long inspired India’s writers and poets. But it’s the country’s farmers who know all too well the impact a delayed or indeed a failed monsoon can have on millions of lives.

The monsoon is India’s life-giver, its rebirth and its life blood. Nearly 60% of India’s agriculture depends on the rains. Indeed as the environmental activist Sunita Narian claimed , “Indians know that the monsoon is the real finance minister of India”.

Today millions of farmers hit by drought and crop failure are struggling to stay alive.

Since 2015, India has been experiencing widespread drought conditions. In fact, some 600 million people in India are presently facing high to extreme water stress. According to the government’s own report, India is facing its worst ever water crisis . The report by premier policy research centre NITI Aayog says that by 2030 the country’s water demand is projected to be twice the available supply.

But all that is in the future. Today millions of farmers hit by drought and crop failure are struggling to stay alive. More than 80% of districts in the states of Karnataka and 70% in the state of Maharashtra have been declared drought affected. More than 6000 tankers supply water to nearly 15,000 villages and hamlets in Maharashtra alone.

This video of women from Phulambri in Maharashtra struggling to fill their utensils from a tanker sprinkling water over a newly constructed road, went viral on social media last month. It shows just how desperate people are.

Further south, in the state of Tamil Nadu, which in a good monsoon often floods, the four reservoirs that supply water to the capital Chennai has dropped below one per cent of their capacity. It’s shut down the city’s metro system and its hospitals have been forced to buy water for surgeries. Chennai is home to nine million people and there is no end in sight to the drought conditions. According to the South Asia Drought Monitor, Tamil Nadu along with other Indian states such as Karnataka, Andhra Pradesh and Maharashtra are trapped in a severe dry cycle that’s so far lasted six months.

The crisis is not confined to Chennai. Bangalore, Hyderabad and Delhi with a combined population of 60 million people are all facing the same fate. According to think tank World Resources Institute India, the last two decades have seen a rampant rise in environmental challenges that if left unchecked could lead to several cities becoming unliveable. The World Resources Institute cites rapid urbanisation, stress on natural resources and pollution as some of the challenges facing India’s continuing growth.

As for Chennai, its leaders have decided to spend nearly $10 million to transport tanks of so-called ‘’drinkable water” by rail from Vellore, a city nearly 200 kilometres away as a temporary solution. Small hotels and restaurants have shut shop and many residents are contemplating the unthinkable; leaving the city altogether.

The future doesn’t look too good.

India is the largest user of groundwater in the world and according to the government’s own report, by 2020 as many as 21 Indian cities could run out of ground water , and by 2030, nearly 40% of the country’s population may have no access to drinking water. Groundwater the source of 40% of India’s water needs is being depleted at an alarming rate.

This also has serious implications for India’s health. Currently nearly 200,000 people die every year due to inadequate access to safe water . With 70% of its water contaminated, India ranks 120th among 122 countries in a global water quality index. Water levels in India’s 90 major reservoirs have fallen to 20% of their capacity as of May. This is lower than the levels last year and is also less than the average levels in the past decade.

As the impact of climate change worsens , water is becoming a serious economic issue for one of the world’s largest economies. A study by the country’s environment ministry found that desertification, land degradation and drought cost India nearly 2.5% of GDP in 2014­–15. The recently returned administration of Prime Minister Narendra Modi has announced a water conservation awareness program this month. Modi also declared that his administration would aim to take piped drinking water to every household by 2024.

The announcement was received with rapturous applause in parliament. Outside though, the challenge was obvious. Where exactly would this water come from?

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India: Managing the Complex Problem of Floods and Droughts

• Floods and droughts are on the rise in India. But they need not become disasters. It depends on how society manages them.
• The World Bank has put forward the EPIC Response framework to better manage these climate extremes. It emphasizes that floods and droughts be addressed as different ends of the same spectrum, and the whole of society be involved in the response – including the government, private sector, local government, academia, and civil society.
• The Framework is now being piloted in India’s flood-prone state of Assam, along with a new tool that enables various agencies to assess the status of their flood and drought protection programs, identify where collaboration can be enhanced, and track progress over time.

Floods and droughts have long been a part of life in India. Almost 150 years ago, the Ganga canal system was developed to bring water to farmers in the fertile upper Gangetic Plain.  In southern India too, the early 20 th century Krishna Raja Sagar Dam and other systems helped manage floods and prevent crop failures.

Today, however, the challenges are of a different magnitude altogether. The monsoon has become more erratic and unpredictable, bringing extreme rainfall on the one hand and sudden drought on the other. Worryingly, India's drought-prone area has increased by 57 percent since 1997 1 , while instances of heavy rainfall have risen by almost 85 percent since 2012.  This can have far-reaching impacts, affecting several generations.

To mitigate the impacts of floods and droughts, India has launched many policies and programs to improve water security and build climate resilience – several with World Bank support. This includes advances in technologies such as flood forecasting models, hydromet services and early warning systems, greater dam safety, and a national plan for disaster management. Even so, these measures, although laudable, will not be enough to address the scale of India’s water woes.

Importantly, floods and droughts don’t need to become disasters. It depends on how society manages these climatic extremes. While national governments tend to deal with them in a siloed manner, what is needed is a paradigm shift in the way these events are managed altogether. It is a complex problem that requires a multi-sectorial approach to reduce the risks and impacts.

Given the urgency of staying ahead of the changing climate, the World Bank, with support from Deltares, a Netherlands-based research institute, has put forward a new perspective to better manage these risks – the EPIC Response framework  (Enable, Plan, Invest, Control).

The framework is being piloted by the World Bank supported Assam Integrated River Basin Management Program . The program seeks to reduce the vulnerability of Assam’s people to climate-related disasters and help the state develop its substantial water resources in an integrated and sustainable manner.  

Speaking of the program, Mr. Bhaskar Das, Chief Technical Officer, Flood and River Erosion Management Agency of Assam (FREMAA) pointed out: “The Water Resources Department and the State Disaster Management authorities are working in close coordination and in an integrated manner.”

To demonstrate the applicability of the EPIC Response framework in other Indian states, a workshop, co-organized by the Indian Water Partnership and the World Bank , was held in New Delhi in April 2023. Various central government agencies, leading experts and a mix of states that face recurrent floods and droughts participated.

While these states - Assam, Bihar, Karnataka, Kerala and Odisha - face many common challenges, they have different climatic, economic, and social conditions. In Karnataka, for instance, around 20,000 water bodies have dried up and, in many districts, groundwater is depleting by the day. In Assam, on the other hand, where the mighty Brahmaputra flows, floods, riverbank erosion, and encroachment of riverine areas are the major challenges. In Bihar, too, where many rivers flow down from the mountains of Nepal carrying large sediment loads, more than 70 percent of the land is flood prone. What’s more, 28 out of Bihar’s 38 districts are affected by either floods or droughts, and sometimes by both every year.

The workshop underscored three key messages:

·       Addressing floods and droughts as different ends of the same spectrum.

·       Collaboration between various water agencies and presenting a joint government response to the challenge.

·       Involving the whole of societ y in the response - private sector, local government, academia, and civil society.

Delivering his keynote address, Mr. Kushvinder Vohra, Chairman, Central Water Commission , stated: “Floods and droughts are one of the most pressing issues of our time.” Therefore, he stressed, it is essential to develop climate-resilient structural and non-structural solutions for effective water governance.

The newly developed EPIC Response Assessment Methodology (ERAM) tool that is being piloted in Assam was also presented at the workshop. The tool is a decision support system that enables various agencies to assess the status of their hydro-climatic risk management systems, identify areas where program components can be strengthened, ascertain where collaboration can be enhanced, and track progress over time. The results can facilitate a policy dialogue to generate a common understanding of their programs’ status, as well as the challenges and opportunities for enhancing flood and drought risk management systems.  

Commenting on the relevance of the EPIC framework in the Indian context, Mr. A.B. Pandya, Secretary General, International Commission on Irrigation & Drainage , said: “The EPIC framework serves as a good guideline and benchmark against which the readiness of the individual region or subregion can be assessed.”

Outlining the complex situation in Bihar, Mr. Pravin Kumar from the Bihar State Disaster Management Authori ty (BSDMA) spoke about their mandate to develop disaster management policy, lay down guidelines, approve plans across departments, coordinate implementation of plans, recommend funds for mitigation measures and review measures taken. He said that the EPIC Response Framework and the ERAM tool will be useful to assess these measures.

[1] “Drought in Numbers”, United Nations Convention to Combat Desertification 2022. 

The workshop, Improving Flood and Drought Governance: Applying the EPIC Response Framework, took place on April 28, 2023, in New Delh i.

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South India’s Two-Year Drought From 2016 Was Worst In 150 Years, Study Finds

DMK party workers stage a protest over the water crisis in Tamil Nadu in Chennai, June 24, 2019. Photo: R. Senthil Kumar/PTI

Southern India was hit by severe drought from 2016 to 2018 arising from low rainfall during the northeast monsoon, which occurs during the winter. So severe was the impact that a water crisis erupted in Chennai, India’s sixth-largest city of 11 million inhabitants, as four of the city’s major reservoirs went bone-dry and groundwater levels plummeted. In the summer of 2019, a “Day Zero” was declared and residents scrambled to obtain water from tankers.

Now, after examining rainfall data over the past 150 years, researchers in India and the US conclude that the 2016-2018 northeast monsoon drought was unprecedented with more than 40 percent deficit in northeast monsoonal rainfall during the three years.

The recent drought was worse than the Great Drought of 1874-1876, which led to crop failure and which in turn resulted in the Great Madras Famine of 1876 to 1878, claiming millions of lives. The team demonstrates that cool phases in the equatorial Indian and Pacific Oceans are associated with the rainfall deficit.

“The consecutive failure of the northeast monsoon can result in a water crisis in Southern India,” lead author Vimal Mishra, associate professor at Indian Institute of Technology, Gandhinagar, told Mongabay-India, adding that “it has considerable implications to agricultural productivity.”

While India receives most of its annual rainfall during the Indian summer monsoon (June to September), southern India receives about 40 percent of its rainfall from October to December in what is known as the northeastern monsoon (NEM) or the winter monsoon. It is crucial for drinking water and agriculture contributing to the livelihood of millions.

The southern Indian states of Andhra Pradesh, Karnataka and Tamil Nadu continuously declared drought from 2016 to 2018 linked to low northeast monsoonal rainfall. Over 60 percent of the rural population in southern India is engaged in agriculture and relies on rainfall from the winter monsoon.

Failure of northeast monsoon 

How severe was the recent drought compared to those Southern India has experienced in the past? What are the causes of the deficit in the northeast monsoon? Mishra’s team sought to answer these questions.

To investigate the long-term history of NEM droughts in the region, the team used rainfall observations from the India Meteorology Department from 1870 to 2018. Data on total water storage was obtained from NASA’s Gravity Recovery and Climate Experiment (GRACE) satellites for April 2002 to June 2017 while the GRACE Follow-On (GRACE-FO) mission provided data for 2018 onwards.

Over the past 150 years, there were five main periods of drought with more than 29 percent deficit in rainfall (1876, 2016, 1938, 1988, and 1974 in order of severity). Looking at single year rainfalls, 1876 was the driest year with a precipitation deficit of 69 percent followed by 2016 with a deficit of 63 percent. But when considering cumulative rainfall over three years, 2016 to 2018 was the worst NEM drought with a precipitation deficit of 45 percent while the 1874 to 1876 drought, or the Great Drought as it is known, was the second-worst with a deficit of 37 percent.

The GRACE satellite indicated that total water loss in Southern India in December 2016 was 79 cubic kilometres (km3) while the GRACE-FO data showed that the loss was 46.5 km3 in June 2017 and 41.7 km3 in June 2019. Loss in total water storage likely resulted in significant depletion of groundwater in the region, say the authors.

Associated factors

The team examined sea surface temperatures (SST), sea-level pressure and wind fields during the winter monsoon to understand how circulation patterns affect variability in northeast monsoonal rainfall. Sea surface temperature over the equatorial Indian and Pacific Oceans affects year-to-year variability of the northeast monsoon, explained Mishra. “SST anomalies cooler than normal are linked to a weak northeast monsoon.”

In 2016 and 2017, cool SST anomalies prevailed in the tropical Indo-Pacific Ocean and were associated with La Niña in the central Pacific, the researchers observed. La Niña is a climate pattern that occurs irregularly every two to seven years. During La Niña, the surface waters over the equatorial Pacific Ocean are cool and this affects global weather patterns.

At the same time, the researchers noted anomalous cooling was seen in the Indian Ocean. Such patterns along with those seen in sea-level pressure and surface-air temperatures gave rise to anomalous westerlies in the equatorial Indian Ocean, which weakened moisture transport from the Bay of Bengal during the northeast monsoon, explained the authors.

Interestingly, the study revealed that out of five of the major droughts that struck southern India over the past 150 years, four occurred during La Niña.

Deepti Singh, assistant professor at Washington State University, who was not connected with the study, notes that the paper “links the recent severe, multi-year drought primarily to La Niña conditions in the tropical Pacific Ocean in 2016-2017 and 2017-18.”

This finding “implies that there is potential to predict them a few months in advance since La Niña events can be predicted with some skill in the summer,” said Singh, adding that “this means that stakeholders can prepare for and mitigate their impacts.”

While the study does not explain what made the 2016-2018 drought one of the strongest on record, “it demonstrates that natural climate variability can lead to extreme events.” She stresses that a better understanding of these drivers can inform our ability to predict severe droughts in the future. “Timely predictions of such events can help better manage and potentially reduce their societal impacts,” Singh says.

“This is particularly important since extreme La Niña conditions are projected to become more frequent with warming and if this link holds, it might mean increasing drought risks to the region, which will likely be worsened by hotter conditions.”

This article was originally published by Mongabay India and has been republished here under a Creative Commons license.

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Drought and Socioeconomic Drivers of Crop Diversity in India: A Panel Analysis

• FULL-LENGTH RESEARCH ARTICLE
• Published: 07 August 2023
• Volume 12 , pages 450–461, ( 2023 )

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• Arup Jana   ORCID: orcid.org/0000-0001-5377-4614 1 &
• Aparajita Chattopadhyay   ORCID: orcid.org/0000-0002-1722-4268 1  

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Understanding the trends in crop diversity in context of changing socioeconomic and climatic factors are essential for implementing sustainable agricultural practices. The Shannon Index was adopted to calculate crop diversity across all districts of India from 2001 to 2021. High-resolution Standardized Precipitation Eevapotranspiration Index (SPEI) data was developed using the Climate Hazards Group InfraRed Precipitation (CHIRPS) and Global Land Evaporation Amsterdam Model's (GLEAM) data to capture climate variability. A panel regression was employed using ordinary least squares, fixed effects, and random effects models. Crop diversity in India experiences an increase of 2.6% between 2001 and 2021. During the study period, India experienced an increase in the proportion of land dedicated to non-food crops, rising from 19.79% to 22.80%. The area allocated to cereal and millet crops experienced a decline, decreasing from 54.51% to 50.61%. SPEI is a significant factor in diversified agriculture, showing a negative association. Higher urbanization, road density, number of markets, the presence of organic carbon in the soil, improved seeds, fertilizers, and credit facilities for farmers reduce crop diversity. Access to irrigation increases the likelihood of practicing in diversified crop. Increasing education level of farmers positively influences the practice of crop diversity in India. Farmers in droughtprone areas of India often adopted diversified cropping practices.

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Climate change and drought: a perspective on drought indices, availability of data and material.

The datasets used the study will be available from the corresponding author on reasonable request.

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Acknowledgements

We gratefully acknowledge the University Grants Commission (UGC) for providing the NET-JRF fellowship to the first author for conduction PhD work. The authors are thankful the Ministry of Agriculture and Farmers Welfare of India for making the agricultural census data available on public domain. We express our gratitude to the IIPS library for providing access to the open-access peer-reviewed journals. We are thankful to all the providers of the datasets used such as CHIPRS, GLEAM, SEDAC, GRIP, SRTM, HWSD, TCI and Agmarket in the study.

This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.

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Jana, A., Chattopadhyay, A. Drought and Socioeconomic Drivers of Crop Diversity in India: A Panel Analysis. Agric Res 12 , 450–461 (2023). https://doi.org/10.1007/s40003-023-00665-8

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The drought that hit Southern India from 2016 to 2018 was the worst in 150 years

The drought was unprecedented with more than 40% deficit in northeast monsoonal rainfall during the three years..

Southern India was hit by severe drought from 2016 to 2018 arising from low rainfall during the northeast monsoon, which occurs during the winter. So severe was the impact that a water crisis erupted in Chennai, India’s sixth-largest city of 1.1 crore inhabitants, as four of the city’s major reservoirs went bone-dry and groundwater levels plummeted. In the summer of 2019, a “Day Zero” was declared and residents scrambled to obtain water from tankers.

Now, after examining rainfall data over the past 150 years, researchers in India and the United States conclude that the 2016-2018 northeast monsoon drought was unprecedented with more than 40% deficit in northeast monsoonal rainfall during the three years.

The recent drought was worse than the Great Drought of 1874-1876 that led to crop failure, which in turn resulted in the Great Madras Famine of 1876 to 1878 that claimed millions of lives. The team demonstrates that cool phases in the equatorial Indian and Pacific Oceans are associated with the rainfall deficit.

“The consecutive failure of the northeast monsoon can result in a water crisis in South India,” lead author Vimal Mishra, associate professor at Indian Institute of Technology, Gandhinagar, told Mongabay-India , adding that “it has considerable implications to agricultural productivity”.

While India receives most of its annual rainfall during the Indian summer monsoon (June to September), southern India receives about 40% of its rainfall from October to December in what is known as the northeastern monsoon or the winter monsoon. It is crucial for drinking water and agriculture contributing to the livelihood of millions.

The South Indian states of Andhra Pradesh, Karnataka and Tamil Nadu continuously declared drought from 2016 to 2018 linked to low northeast monsoonal rainfall. Over 60% of the rural population in southern India is engaged in agriculture and relies on rainfall from the winter monsoon.

Northeast monsoon

How severe was the recent drought compared to those Southern India has experienced in the past? What are the causes of the deficit in the northeast monsoon? Mishra’s team sought to answer these questions.

To investigate the long-term history of northeastern monsoon droughts in the region, the team used rainfall observations from the India Meteorology Department from 1870 to 2018. Data on total water storage was obtained from NASA’s Gravity Recovery and Climate Experiment satellites for April 2002 to June 2017 while the Gravity Recovery and Climate Experiment Follow-On mission provided data for 2018 onwards.

Over the past 150 years, there were five main periods of drought with more than 29% deficit in rainfall (1876, 2016, 1938, 1988 and 1974 in order of severity). Looking at single year rainfalls, 1876 was the driest year with a precipitation deficit of 69% followed by 2016 with a deficit of 63%.

But when considering cumulative rainfall over three years, 2016 to 2018 was the worst northeastern monsoon drought with a precipitation deficit of 45% while the 1874 to 1876 drought, or the Great Drought as it is known, was the second-worst with a deficit of 37%.

The Gravity Recovery and Climate Experiment indicated that total water loss in Southern India in December 2016 was 79 cubic kilometres while the Gravity Recovery and Climate Experiment Follow-On data showed that the loss was 46.5 km3 in June 2017 and 41.7 km3 in June 2019. Loss in total water storage likely resulted in significant depletion of groundwater in the region, say the authors.

Factors causing deficits

The team examined sea surface temperatures, sea-level pressure and wind fields during the winter monsoon to understand how circulation patterns affect variability in northeast monsoonal rainfall.

Sea surface temperature over the equatorial Indian and Pacific Oceans affects year-to-year variability of the northeast monsoon, explained Mishra. “Sea surface temperature anomalies cooler than normal are linked to a weak northeast monsoon.”

In 2016 and 2017, cool sea surface temperature anomalies prevailed in the tropical Indo-Pacific Ocean and were associated with La Niña in the central Pacific, the researchers observed. La Niña is a climate pattern that occurs irregularly every two to seven years. During La Niña , the surface waters over the equatorial Pacific Ocean are cool and this affects global weather patterns.

At the same time, the researchers noted anomalous cooling was seen in the Indian Ocean. Such patterns along with those seen in sea-level pressure and surface-air temperatures gave rise to anomalous westerlies in the equatorial Indian Ocean, which weakened moisture transport from the Bay of Bengal during the northeast monsoon, explained the authors.

Interestingly, the study revealed that out of five of the major droughts that struck southern India over the past 150 years, four occurred during La Niña .

Deepti Singh, assistant professor at Washington State University, who was not connected with the study, notes that the paper “links the recent severe, multi-year drought primarily to La Niña conditions in the tropical Pacific Ocean in 2016-2017 and 2017-’18”.

This finding “implies that there is potential to predict them a few months in advance since La Niña events can be predicted with some skill in the summer,” said Singh, adding that “this means that stakeholders can prepare for and mitigate their impacts”.

While the study does not explain what made the 2016-2018 drought one of the strongest on record, “it demonstrates that natural climate variability can lead to extreme events”. She stresses that a better understanding of these drivers can inform our ability to predict severe droughts in the future.

“Timely predictions of such events can help better manage and potentially reduce their societal impacts,” Singh said. “This is particularly important since extreme La Niña conditions are projected to become more frequent with warming and if this link holds, it might mean increasing drought risks to the region, which will likely be worsened by hotter conditions.”

This article first appeared on Mongabay .

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Cool solution: how ice-cream saved drought-hit farmers in India

As the climate crisis forces people to abandon their land in Rajasthan, a new industry has sprung up in the desert state, with thousands of gaily decorated vans setting off to sell ice-cream across the country

T he parched villages of Gangapur in the desert state of Rajasthan have a new season in their calendar. Between November and February , car workshops along the town’s dusty mile-long market open before sunrise, cylindrical stainless-steel food containers are put on display, and traders stock up on chocolate and strawberry syrups.

Come March, the villagers start preparing to migrate. In the workshops, thousands of vehicles are converted into vans for selling a variety of ice-cream, from plain condensed milk flavoured with cardamom to chocolate, vanilla and pistachio, while local farmers turned dessert makers have their old mini-trucks serviced in readiness for the drive to distant towns and cities, where they will sell the sweet treat for the next nine months.

Ice-cream has become a lifeline for villagers in north-west India as decades of water scarcity have ruined farm yields and families’ livelihoods.

The ice-cream business has also spawned an entire industry in the town itself, yielding the rarest of all commodities: jobs. The number of vehicle repair shops and stores selling ice-cream paraphernalia has increased from 50 in 2015 to about 500 today.

Every year, an estimated 50,000 trucks are converted during the four-month season. Even the local printers are hiring staff to produce posters of ice-cream scoops against a backdrop of local temples and warrior kings.

There are an estimated 140 million internal migrants in India – people who leave their homes to find work at construction sites and factories, or as daily-wage workers in other states. Their decision to migrate is often rooted in mounting climate losses back home.

While remittances have bolstered household incomes and local economies, an entire industry growing out of a migration pattern is unusual.

“This market caters to 500 mini-trucks every day during the peak season from November to February,” Kalu Mohammad Pathan says in his workshop, as two workers slide under a truck for final checks before its owner leaves for Indore, an eight-hour drive away in the neighbouring state of Madhya Pradesh.

These trucks have become ubiquitous fixtures across cities in India, easily identifiable by their brightly coloured posters and neon lights.

“We earn enough during this season to survive the year,” says Pathan. “If there was no migration for this ice-cream business, people would have remained impoverished in these villages around Gangapur.

“There is no water here, no jobs,” he says. “And the landless find it difficult to migrate as setting up the business costs money, but now there is work for those left behind. Each workshop has created over 10 jobs.”

Rajasthan, known for its majestic forts, desert safaris, palaces and temples, is among the Indian states recording the highest migration numbers to other states, Indian statistics ministry data shows. The exodus from Rajasthan has been fuelled by the harsh climate of the arid region, with low rainfall leading to poor agricultural production.

People in Gangapur cannot remember the last good spell of rain. But the severe drought at the turn of this century – which destroyed crops of maize, peanuts and chillies, and left their animals starving – is still fresh in their memory. That was when the number of people seeking work outside Rajasthan began to swell.

Among those who left was Kanhaiya Prajapati, who was 16 in 2005 when he joined a fellow villager’s ice-cream truck travelling to Gorakhpur in the neighbouring northern Indian state of Uttar Pradesh.

“The farm yield had plummeted and there was no other income in sight. I returned home with 5,000 rupees after four months. It was decent money and the next year I decided to go on my own,” says Prajapati, sitting in front of his new ice-cream truck, an upgrade from the wooden cart he started his journey with almost 20 years ago.

In the past decade, about 100 people from Prajapati’s village of 350 have joined the same line of work, most driving the trucks.

They have all heard tales of the “snack culture” in the more prosperous states of Gujarat and Maharashtra, where families and friends visit roadside food carts until late at night, buying ice-creams, colas and ice lollies – unlike in their village, where the day ends at sunset.

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Bhairav Lal Dhangar, 31, was 14 years old when his father, tiring of travelling 95 miles (150km) to Madhya Pradesh to graze his cows and buffaloes, bought a secondhand mini-truck.

“I have rented a place in Manasa [a small temple town] and I make the ice-cream myself. I am able to save at least 15,000 rupees [£140] every month to send to my family, which I would not be able to in my village,” he says.

The remittances have helped people’s families at home build concrete houses and install wells for their homes and to irrigate their farms.

Inspired by the success of the migrating men, a growing number of villagers started taking ice-cream trucks to neighbouring districts such as Bhilwara, where demand for milk at the local state-run dairy shot up, officials at the dairy said.

About two hours from Gangapur, in the lake city of Udaipur, a car dealer says they sell up to 600 mini-trucks – colloquially called chhota haathi , meaning “small elephant” – during the four-month season, and most are for ice-cream trucks.

More than 600 miles away from Gangapur’s market, Shankar Singh sets up his truck near a temple in suburban Mumbai. His assistant cleans the counters, lights an incense stick and switches on the LED lights that spread a fluorescent blue and orange glow around the vehicle.

“My family is able to eat because of this business. I can’t close this shop even for a day,” says Singh, who like many others will skip the journey back to his village to vote in the elections.

He cites the loss of earnings over the peak summer time – as well as a lack of faith in any political party solving his area’s problems of water scarcity, poor public transport and inadequate hospitals – as reasons to avoid the trip home to vote. Nevertheless, Mumbai is just a workplace for Singh and many others like him.

A common poster on ice-cream trucks in Gangapur shows Rajasthan’s desert, camels and traditionally attired women, with an old tourist slogan Padharo mhare desh (“welcome to our land”) emblazoned in bold letters. It’s a message to their customers but also a proud reminder of home.

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• Open access
• Published: 15 March 2017

Droughts in India from 1981 to 2013 and Implications to Wheat Production

• Xiang Zhang 1 , 2 ,
• Renee Obringer 3 ,
• Chehan Wei 4 ,
• Nengcheng Chen 1 , 5 &
• Dev Niyogi 2 , 3  

Scientific Reports volume  7 , Article number:  44552 ( 2017 ) Cite this article

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Understanding drought from multiple perspectives is critical due to its complex interactions with crop production, especially in India. However, most studies only provide singular view of drought and lack the integration with specific crop phenology. In this study, four time series of monthly meteorological, hydrological, soil moisture, and vegetation droughts from 1981 to 2013 were reconstructed for the first time. The wheat growth season (from October to April) was particularly analyzed. In this study, not only the most severe and widespread droughts were identified, but their spatial-temporal distributions were also analyzed alone and concurrently. The relationship and evolutionary process among these four types of droughts were also quantified. The role that the Green Revolution played in drought evolution was also studied. Additionally, the trends of drought duration, frequency, extent, and severity were obtained. Finally, the relationship between crop yield anomalies and all four kinds of drought during the wheat growing season was established. These results provide the knowledge of the most influential drought type, conjunction, spatial-temporal distributions and variations for wheat production in India. This study demonstrates a novel approach to study drought from multiple views and integrate it with crop growth, thus providing valuable guidance for local drought mitigation.

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Introduction.

As an extreme event, drought severely affects global plant growth and food production 1 , 2 , 3 . Considering climate change and anthropogenic influences 4 , an overall enhanced drought risk for crop yield in the future is well documented 5 , 6 , 7 . In India, this risk is greater due to deviated monsoon rains 8 , 9 , 10 , depleted groundwater 11 , and the pressure of food demand from a population of 1.252 billion 12 , 13 .

Drought in India has been studied since the 1960 s 14 , 15 . With regard to the drought mechanism, it was found that prolonged ‘breaks’ in the southwest monsoon resulted in severe summer droughts in the Indian subcontinent due to upper tropospheric blocking ridges over East Asia 16 , 17 . With respect to drought monitoring, remotely sensed and in-situ data (e.g., precipitation, runoff, temperature, and vegetation data) have been used to assess drought condition alone 18 , 19 or in a combination approach 20 , 21 , 22 , 23 . In addition, reanalysis products, near real-time drought monitoring, and new drought indices have also been studied in India 24 , 25 , 26 . From the aspect of drought distribution and trend, several studies have found distinctive drought frequencies existed in different regions of India 27 , 28 , 29 , 30 . Besides that, Ojha et al . 31 predicted that drought events were expected to increase in the west central, peninsular, and central northeast regions of India in 2050–2099. Considering drought impact, Subash and Mohan 32 found that the monthly distribution of monsoon rainfall in terms of Standardized Precipitation Index (SPI) accounted for a 44% yield variability in rice. Similarly, SPI-7 in April and May was found to be substantially correlated with wheat production 33 .

One of our recent studies also analyzed drought trends and variability in India for the period 1901–2004 34 . Results indicated an increasing trend in drought severity and frequency. More regional droughts in the agriculturally important southern coast India, central Maharashtra, and Indo-Gangetic plains were also highlighted indicating higher food security and socioeconomic vulnerability. However, this preliminary study only focused on precipitation-based meteorological drought. In addition, it was recognized that while drought stress could be identified, the implications on crop production required a more comprehensive consideration of crop phenology.

Building off these studies, it is now possible to reconstruct major types of drought by using long-term multi-sensory datasets, including meteorological, hydrological, soil moisture, and vegetation droughts. Definitions of drought types can be found in Dracup et al . 35 and Wilhite and Glantz 36 , while in this study, we separate the conventional term of agricultural drought into two types of drought (i.e., soil moisture, and vegetation droughts). This new approach will help us to have a refined view on drought transformations. Therefore, it is timely to conduct a comprehensive analysis of the distribution, duration, severity, and trends of these droughts simultaneously as their interactions are still relatively unknown, especially in India. The relationship between these four different kinds of drought and crop production is also lacking in the previous studies 32 , 33 , 34 . For example, questions such as which type of drought has the most significant impact on wheat yield loss, and does this relationship vary with time, need to be addressed. Furthermore, as India has benefited from extensive irrigation, conventional indices such as soil moisture index will not be able to fully depict the water-stress condition. Here a comprehensive approach is presented to investigate multiple droughts in India so as to assess their influences on wheat production.

For the first time, four kinds of drought, including meteorological, hydrological, soil moisture, and vegetation were studied at the same time using occurrence, spatial-temporal evolution, severity, duration, and evolution from 1981 to 2013. Particular attention was offered to the drought evolution during the wheat growing (spanning from October through April). The specific goal of this study is to improve the understanding of different droughts and their influences on wheat yield from a finer and systemic view thereby increasing the efficiency of linking drought stress with impact on crop yield at a regional scale.

Results and Discussion

Analysis of retrospective droughts from 1981–2013.

Historical droughts were reconstructed using gridded observed precipitation, model-simulated total runoff, soil moisture, and remotely-sensed vegetation data. A time sequence of mean SPI, Standardized Runoff Index (SRI), Standardized Soil Moisture Index (SSI), and Vegetation Condition Index (VCI) in the study area for every month from 1981–2013 can be found in Supplementary Fig. S1 . It is not easy to determine whether the study area became drier or wetter by visual inspection alone. While the persistence of soil moisture and hydrological conditions is distinctive as these two exhibit less variability relative to precipitation. When precipitation anomalies occurred, corresponding changes in hydrology and soil moisture were often observed immediately. However, to quantify the above preliminary judgements and gain more precise knowledge, more quantitative analyses were conducted.

The occurrence of droughts for different years was listed first (details in Supplementary Tables S1–4 ). Years when at least three kinds of droughts occurred during the wheat growth season are 1985, 1990, 1993, 1997, 1999, 2000, 2001, 2004, 2006, and 2010. They were judged as significantly drought-impacted years for wheat production. 60% of years when meteorological droughts occurred were after 2000, with 91% of them concentrated in January to February while 43% of hydrological droughts occurred in the 1990 s, and 53% of vegetation droughts occurred before 1993. In addition, vegetation drought was more than double in February than in other months. It was also found that the severity of most meteorological and soil moisture droughts was equivalent of D1 (as used in the United States or global drought monitor), while the other two reaches D3 or even D4 (Severity thresholds are shown in Supplementary Table S8 ). For this study area, hydrological and vegetation droughts are more influential based on their level of severity, and supportive evidence of the areal extent is also provided.

The top drought year by spatial extent or severity are listed for all wheat growth months and all four drought types (shown in Tables 1 and 2 ). It is found that 19 out of 28 years with the largest spatial extent are well correlated to years with the most severe drought conditions. It is interesting to note that in October 2000, the largest area of meteorological, hydrological, and vegetation drought occurred at the same time as the most severe hydrological and vegetation drought. Corresponding to the two water-stress sensitive stages for crops (Heading and Anthesis), the most influential droughts occurred in 1985 and 2006 when at least four of the top droughts occurred with the maximum areal extent or highest severity (wheat phenology information is shown in Supplementary Table S10 ). In terms of severity, hydrological and vegetation droughts are usually more severe than meteorological and soil moisture droughts. This difference is also valid in terms of spatial extent. It is also notable that even the most severe meteorological droughts in study area are mainly featured by local and moderate impact, with averaged 44.8% of the whole spatial extent and D1 severity. In addition, severe meteorological droughts only occurred in January 2007, February 2006, and March 2004.

At pixel level (grid space is 0.5 degree), the number of years with meteorological, hydrological, soil moisture, and vegetation drought (severity of D1 or higher) was analyzed. As shown in Fig. 1 , the temporal extent of droughts provides the number of years under drought conditions for every month of the crop growth season. It was found that almost all study areas experienced more than 8 times the average number of droughts during January for the 33 year period, while occurrences of meteorological drought in November and December were less than 4. Regarding hydrological drought, there was little monthly variation showing only about 4–8 drought years. Soil moisture drought occurred more frequently (over 8 times) from October to February, while March and April had only about 4. The temporal extent of vegetation drought is notable due to the heterogeneous distribution of occurrences in contrast to the other three. There is no significant difference for different months, however, in some months, the occurrence varied greatly at different pixels (i.e., from under 4 to above 16). No obvious spatially concentrated region was found.

The temporal extent data was calculated by Matlab R2014b (Version 8.4, URL: http://www.mathworks.com ) [Software] with the method described in the next section. Then the data was input into ArcGIS Desktop (Version 10.2.3348, URL: http://www.esri.com ) [Software] to generate this color rendered map layer. Administrative boundary layer of the study area was obtained from DIVA-GIS (URL: http://www.diva-gis.org/Data ). DIVA-GIS provides free spatial data for geographical information system. Finally all these maps were organized and labeled in the Microsoft Visio Professional 2013 (Version 15.0.4569.1506, URL: https://products.office.com/en-us/visio ) [Software].

This result suggested that during the entire wheat growth season, there was no large monthly difference for hydrological and vegetation droughts. Meteorological droughts are particularly concentrated in January, and soil moisture droughts during October to February. The different number of years in which a grid cell was under drought conditions was spatially highlighted across the study domain, especially for vegetation drought. Overall, October, January, and February are judged as three drought-prone months when all four kinds of drought usually occur at the same time.

Concurrent droughts

In addition to the above retrospective analysis of different drought types, concurrent drought analysis was also conducted. The concurrent drought during the wheat growing season was analyzed from four aspects, including temporal distribution (year and month), spatial distribution, concurrent types, and frequency. The conjunctional feature of regional droughts in each month during the wheat growing season is provided from a regional perspective in Supplementary Table S5 . Firstly, it was found that 17 concurrent droughts occurred in 12 years during the wheat growth in 1981–2013. February was identified as a multi-drought prone month, with over 41% of the historical concurrent droughts. There were no concurrent droughts in November. Besides that, two-drought based conjunctions account for over 76% of concurrent types, and only February 1985 was impacted by all four kinds of droughts. The wheat production experienced the highest frequency of concurrent drought in 1993, with three times of vegetation drought in October, December, and February, respectively. Additionally, it was found that over 88% concurrent droughts included the hydrological drought. This result not only showed the high frequency of hydrological drought, but also suggested the important and interconnected role of not just precipitation but more so surface hydrology in the study area.

To understand the spatial distribution of concurrent droughts in all eleven kinds of conjunctions, the number of years under concurrent droughts in each grid was illustrated in Fig. 2 . Overall, the most number of years under concurrent drought ranges from one to eight. During wheat growing season, October, January, and February were found to have the most concurrent droughts regardless of the spatial location. November and December have three common combinations, including hydrological with soil moisture drought, hydrological with vegetation drought, and soil moisture with vegetation drought. These concurrent types are also the most common types when comparing with others. Besides that, it was found that no matter what kind of the concurrent drought occurred in April, most of them were usually distributed in the southern part of the domain. The conjunction of all four kinds of drought mostly occurred in October, January, and February.

Drought evolution

Based on monthly drought data, the evolution process is shown in Table 3 and Fig. 3 . It was interesting to find that there was no time lag between these four kinds of drought, except for the evolution from meteorological to vegetation drought. This result indicates that in the study area, the transformation between meteorological, hydrological, and soil moisture drought is typically within 1 month for the wheat belt. It also appears that it takes less than 1 month for soil moisture drought to become vegetation drought, but the complete drought evolution (from meteorological to vegetation drought) lasts about 1 month. This is to say, with a sharp decrease in rainfall, there can be a rapid evolution to meteorological, hydrological, and soil moisture droughts in the same month. Vegetation will show significant water stress only after this month. This suggests that for the future assessments, at least weekly drought data is needed in order to have a more detailed view of evolution.

Relationship between domain-mean monthly ( a ) SPI-SRI, ( b ) SPI-SSI, ( c ) SRI-SSI, ( d ) SPI-VCI, ( e ) SPI-VCI with a 1 month shift, and ( f ) SSI-VCI from 1981–2013. Linear regression equation with sample size (n) and coefficient of determination (R 2 ) were shown.

The reason for this rapid evolution of droughts in the study region is an interesting study question. There are several factors that can only be conjectured within the scope of the present study. The study region is already known as a global hotspot for land–atmosphere coupling in global climate model studies. Preliminary review of a short span of satellite and reanalyses data suggests that for drought to trigger the rainfall deficit occurs first, then this especially in crop growing region appears to a rapid evapotranspiration (ET) increase possibly as a vegetation growth and temperature feedback induced by rainfall anomaly. This then creates a larger effective precipitation deficit, which reflects in the soil moisture loss. These processes are typically a week-long time scale (but confounded within the monthly time scale analyzed in this study). The hydrological response appears to be a separate reduction and while the soil moisture and ET play a role they are likely more synergistic than causal. This conjecture and initial analysis will be assessed in a more detailed follow up study using higher temporal resolution soil moisture fields, coupled regional meteorological studies assessing the local moisture recycling potential, dynamic vegetation growth and reanalyses fields. The reduction in rainfall and the high demand of water from crop on the ground linked with the intensive water usage accentuates the drought evolutions. In reality this seems to be offset by the irrigation in this region (which is analyzed next).

Results from the linear regression analysis suggested 59% of hydrological variations can be explained by precipitation anomalies, 54% of soil moisture by hydrology, while the coefficient of determination between precipitation and soil moisture is only 0.22. This result indicates that runoff is primarily controlled by rainfall, and is the main water source for soil in this study area. Interestingly however, there are 31 months with hydrological drought but without soil moisture drought, but only 18 months with both kinds of drought. The former is about 72% more than the latter. Considering natural con-occurrence of the processes that cause these two droughts in the same month, their numbers were expected to be comparative. This result demonstrated the signature of ground water-based irrigation agriculture in the study area (after the Green Revolution in India in the 1960 s to 1970 s). The Green Revolution improved wheat production significantly in India by adopting high-yield variety seeds, chemical fertilizers, pesticides, and irrigation 37 . Our results are thus indicative of the irrigation activity where in more surface water had been pumped out for irrigation when dealing with the drought stress. Both soil moisture and precipitation have low correlations with simultaneous or 1 month shifted vegetation conditions. This result further revealed the considerable anthropogenic and other factor impacting crop management in this study area. Given these types of drought evolution characteristics, it can be suggested that rapid mitigation strategies would be required after meteorological drought occurrence in this region.

Drought trends

Results of drought trend analysis, including the statistical mean value of duration, frequency, areal extent with linear regression, Mann-Kendall analysis, and latitudinal variation are provided below (see more details in Supplementary Tables S6–8 and Fig. S2 ).

From the perspective of duration trends (see Supplementary Table S6 ), it was found that only meteorological drought lingered slightly longer since 1981, from 1 to 1.2 months per drought event. At the same time, the duration of the other three droughts shortened to 1.1, 1.4, and 1.3 months per event and the mean duration of soil moisture drought fell sharply by half. This result demonstrated the mean duration of all drought types was slightly longer than 1 month in the study area. In other words, more “flash droughts” occurred, compared with multi-months or years-long drought.

From the view of frequency (see Supplementary Table S7 ), which means how many times drought events occurred in every decade, meteorological and soil moisture drought exhibited an upward trend (from 2 to 11, and from 5 to 10), while the other two decreased (from 13 to 11, and from 20 to 9). That is to say, there are increased rainfall and soil moisture anomalies with fewer anomalies of runoff and vegetation. In the latest decade, there was an average of 10 times (with a standard deviation of 0.96) of every type of droughts.

Regarding the areal extent (see Supplementary Table S8 and Fig. S3 ), overall, only meteorological drought impacted increasingly larger areas, reaching 18.0% of the whole study area in the 2000 s from 12.7% in the 1980 s. In recent decades, the other three droughts are more likely to form as local drought events. The areal extent of every hydrological and soil moisture drought decreased slightly from 21.4% to 18.9%, and from 24.3% to 19.9%, respectively while the area of vegetation drought shrank from 32.9% to 23.2%. The mean areal extent for all four drought types is 20% in the latest decade with a standard deviation of 2.27.

Based on this statistical analysis, more prolonged, frequent, and larger area of meteorological drought was found, which is consistent with our previous study 34 . On the contrary, hydrological and vegetation droughts were relieved by shorter duration, less frequency, and smaller areal extent. Soil moisture drought occurred more frequently, but in a local and short-term manner.

For the spatial domain of monthly drought severity trends, the Mann-Kendall analysis results are shown in Fig. 4 . It is easy to find distinct contrasts between trends in different drought types during the wheat growth season. Meteorological drought generally became more serious in the northeastern areas in October and March, and in December and January for the central southern regions. In particular, the magnitude of change in January is remarkable, suggesting a much greater rainfall deficit. No significant trend was found in February and April. For hydrological and soil moisture droughts, part of the region near the upper boundary was notably relieved especially in October, November, and December, while other areas and other months did not show a significant severity trend. Due to the high sensitivity of soil moisture stress from December to March for wheat yield, this result suggested the soil water supply became more favorable in these sub-regions likely due to irrigation. Vegetation drought trends are significant due to the larger areal extent. Overall, vegetation became much drier in the northeast areas in November and April, and in December for the south, while other regions and other months became wetter or showed little trend. Therefore, in the last three decades, only meteorological and vegetation droughts increased in severity for certain sub-regions. This result highlighted different susceptible regions for each month to response more serious meteorological and vegetation droughts.

Severity trend of ( a ) meteorological, ( b ) hydrological, ( c ) soil moisture, and ( d ) vegetation drought for every month of wheat growth between 1981 and 2013. Significance level of 0.05 was applied in the Mann-Kendall analysis. The severity trend data was calculated by Matlab R2014b (Version 8.4, URL: http://www.mathworks.com ) [Software] with the method described in the next section, which was realized by Jeff Burkey (URL: https://www.mathworks.com/matlabcentral/fileexchange/11190-mann-kendall-tau-b-with-sen-s-method--enhanced ). Then the data was ingested into ArcGIS Desktop (Version 10.2.3348, URL: http://www.esri.com ) [Software] to generate this color rendered map layer. Administrative boundary layer of the study area was obtained from DIVA-GIS (URL: http://www.diva-gis.org/Data ). DIVA-GIS provides free spatial data for geographical information system. Finally all these maps were organized and labeled in the Microsoft Visio Professional 2013 (Version 15.0.4569.1506, URL: https://products.office.com/en-us/visio ) [Software].

To determine detailed latitudinal drought trends, change in the number of years under drought by latitudinal value since 1981 is shown in Supplementary Fig. S3 . Varied change patterns were found for meteorological drought, but overall, there was some tendency for a southern shift in January, while for other months, they are spatially concentrated in the north or south. No latitudinal movement was found in hydrological drought. Regions above 28°N experienced more serious soil moisture drought, and is obvious from October to December. Vegetation drought is spatially concentrated above 28°N in October to December, while below 28°N in February to March. Understanding the reason for this spatial discrimination is another notable feature that needs to be studied in a follow up study.

Relationship of drought with crop yield

The above analysis demonstrated the relationship between drought and wheat growth from the perspective of occurrence, distribution, and trend. The numerical relationship between them is also presented and shown in Table 4 and Supplementary Fig. S4 . Interestingly, it was found that during the entire wheat growth season, only soil moisture and vegetation drought correlated well with final wheat yields for certain months. Generally speaking, the soil moisture index in the Emergence stage (October and November) is significantly related to wheat yield anomaly (correlation coefficient r was 0.38 and 0.45, with the p value of 0.03 and 0.01). The vegetation condition index is much more closely correlated in the Anthesis stage (February and March), as the correlation coefficient r was 0.75 and 0.74, with both p values of 0.00. In addition, soil moisture and vegetation drought indices both have high correlation coefficients in October and February. No significant correlation was found in the Heading and Maturity stages (i.e., December, January, and April). The reason that correlation coefficients between vegetation drought and yield loss are low in April is probably related to the impact from harvest activity. Overall, these results demonstrated that VCI in the Anthesis stage was a good indicator for final yield loss. Alternative indicators are VCI in October and SSI in November. SPI is the default drought index used worldwide, especially in developing countries, and results indicate that it should be used with caution for agricultural drought assessment. These results also highlight the need to address drought stress and food security discussions for climate studies in a more comprehensive manner with explicit consideration of crop phenology and evolution of different drought types. Further, future studies and assessments should exercise caution in correlating rainfall deficits or SPI-like estimates for current and future climate to crop yield loss or food security. Consideration of the role of crop phenology, drought evolution, and local management practices is necessary in developing drought impact assessments in a more systems approach.

Data and Methods

Site description.

Next to rice, wheat is the most important food-grain of India and is the staple food of millions in that region. The Indo-Gangetic Plain (IGP) region of India has been referred to as the ‘bread basket’ or ‘food bowl’ of the country. Punjab, Haryana, Uttar Pradesh, and Bihar are the four prominent wheat producing states in IGP and were selected as the primary area of study ( Supplementary Fig. S5 ). These states mainly belong to the Northwestern and Northeastern Plains Zone based on agro-climatic conditions. They account for about 58% of wheat area and about 67% of the total wheat production in India in 2013–2014, according to the Department of Agriculture, India. In fact, these areas have earned the distinction of being called the “Granary of India”.

The area of wheat growth in the study area increased slowly from about 1,400 ha to 1,800 ha, but production rose to more than 60 million tonnes from about 25 million tonnes due to yield increases ( Supplementary Fig. S6 ). The overall yield trend was notable increasing from 45 to 85 million tons per million ha, although fluctuations were noted. From 1980–1990 and 2002–2014, the actual yield was below the trend, while from 1991–2001, the actual yield was higher than average.

Monthly mean air temperatures in the study area ranged from about 10 °C in December and January to more than 30 °C from May to August ( Supplementary Fig. S5 ). The temporal variation of rainfall was significant: 64.85% of the annual precipitation was concentrated in the monsoon season (July to September), while the total amount of precipitation was only 25.4 cm during the wheat growth season (i.e., October to April). However, the amount of rainfall required for wheat cultivation varies between 30 cm and 100 cm. Therefore, the study area was classified as a drought-prone area for wheat production, which is also highlighted in our previous study 34 . Since rainfall is not the only factor to influence wheat yield, this study will help determine the percentage of yield loss caused by different types of drought condition in India.

It is worth noting that the irrigation rate of this study area is over 40% in 2009–2010 according to the Open Government Data (OGD) Platform India. This is brought by the Green Revolution since 1960 s in India 37 . Due to this kind of human intervention, precipitation-only or soil moisture-only based drought index will not be able to truly capture the surface drought condition. Therefore, a multi-index approach is adopted to study the drought in this area.

Drought occurrence and severity

Four widely used drought indices were selected, including the Standardized Precipitation Index (SPI), Standardized Runoff Index (SRI), Standardized Soil moisture Index (SSI), and the Vegetation Condition Index (VCI) (see the Supplementary Text and Table S9 ). Monthly scales of SPI, SRI, SSI, and VCI from 1981 to 2013 were used to determine occurrence and severity of meteorological, hydrological, soil moisture, and vegetation droughts, respectively. By studying soil moisture drought and vegetation drought explicitly, we can quantify changes of specific environmental variables more directly, compared with a multi-variate integrated agricultural drought index (e.g., Vegetation Drought Response Index (VegDRI)).

To obtain the value of SPI, grid precipitation data from 1981 to 2013 was obtained from Global Precipitation Climatology Centre (GPCC) full data reanalysis version 7 products. The GPCC full data reanalysis monthly product is comprised of monthly totals on a regular grid with 0.5° spatial grid spacing. Based on 67200 stations worldwide, GPCC data was regarded with high accuracy 38 , 39 , 40 . Data input for calculating SRI and SSI came from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) product. MERRA-2 is the first long-term global reanalysis to assimilate space-based observations of aerosols and surface landscape and represent their interactions with other physical processes in the climate system 41 . In this study, the two-dimensional, monthly mean, and time-averaged land surface product (MERRA-2 tavgM_2d_lnd_Nx) was selected. Based on that, monthly runoff and root zone soil moisture values were obtained spanning from 1981 to 2013, with spatial resolution resampled to 0.5°*0.5° from 1/2°*2/3°.

In addition to the above data, VCI was also used to quantify the vegetation deficit 42 , 43 . VCI compares the current NDVI to the range of values observed for the same period in previous years. Unlike NDVI, VCI has the capability to separate short-term weather-related fluctuations from long-term ecological changes. Lower and higher VCI values indicate bad and good vegetation state conditions, respectively. To obtain VCI, the Global Inventory Modeling and Mapping Studies (GIMMS)-NDVI from NASA was used 44 , 45 , 46 . Details of VCI computation can be found in the Supplementary Text online. The latest version, termed the third generation NDVI data set (GIMMS NDVI3g) was selected for the period from July 1981 to December 2013, with a spatial resolution of 0.5° resampled from 1/12°. Bi-weekly GIMMS NDVI3g was also averaged to a monthly mean value to match the temporal resolution of precipitation, runoff, and soil moisture.

We acknowledge that station-based data is more direct and reliable to detect local extremes. However, due to the limited availability of station-based data and the need for having concurrent variables to assess drought evolution, the above grid datasets were adopted in this study. In addition, the suitability and reliability of the above grid datasets used in drought research are well documented 18 , 19 , 20 , 21 , 22 , 23 .

Areal extent, temporal extent, frequency, duration, and distribution of droughts

As described above, occurrences of meteorological, hydrological, soil moisture, and vegetation droughts were determined by SPI, SRI, SSI, and VCI respectively. Concurrent meteorological, hydrological, soil moisture, and vegetation droughts in the same month were determined by considering the SPI, SRI, SSI, and VCI values together. Then, the spatial/areal extent of the drought in each month was estimated by counting the total number of grid cells that experienced a drought and dividing that by the total number of grid cells in the study domain to estimate the percentage area under drought in a given period of time. To obtain the temporal drought extent, the number of years that each grid cell experienced a drought in a given month from 1981–2013 was counted. The temporal extent of concurrent droughts adopted the similar approach, while considering the occurrences of multi-droughts at the same time. For example, to calculate the concurrent meteorological drought and hydrological drought, the number of years each grid cell experienced these two droughts for the same month from 1981–2013 was counted.

The mean duration of drought in each decade was calculated as well. First the total number of drought events in one decade was counted. This is also called the frequency of drought in one decade. Then the duration of each drought event in this decade was summed up to get the total duration time. Finally, the mean duration of each drought in this decade was obtained by dividing the total duration time by drought occurrence numbers.

To analyze the latitudinal distribution of drought, every row of SPI/SRI/SSI/VCI data in the study area was first compressed to one mean value thereby transforming the drought map for every year into a column vector. The number of years under drought in each triennium was then obtained by summing up the column vectors for each three year period. Finally, all eleven triennium drought vectors were arranged by time to investigate the latitudinal trend.

Evolution process of drought

The evolution process is a qualitative process defined by the United States National Weather Service 47 and the National Drought Mitigation Center 48 as the formation process from meteorological to hydrological, then to soil moisture, and finally to vegetation drought. This multi-view process is valuable to probe into the water deficit transformation in different drought related variables. However, many current studies lack quantitative analysis about this feature. In this study, the theoretical analysis on how one kind of drought can influence the others is firstly shown in Supplementary Text and Fig. S7 . The time required for transformation between drought types, called time lags, was still unknown for this study area. So the cross-correlation analysis was adopted as the second step to obtain their evolution lags. To determine the degree of relevancy between the evolution process of these four kinds of droughts, linear regression analysis was also used ( Supplementary Text ). In this study, the anthropogenic signature of the extensive irrigation (brought by the Green Revolution in India) was evaluated by analyzing this evolution process as well. Besides that, a copula-based analysis 49 maybe also useful to study the relationship between different kinds of drought. Both of these assessments would be considered in our future study.

Drought trend by Mann-Kendall analysis

There are at least three different conclusions regarding drought trend: increase, decrease, and no change (see Dai 50 , Sheffield et al . 51 , and Mallya et al . 34 ). In this study, a more comprehensive analysis was conducted to answer this question specifically for India’s wheat belt. Besides the above statistical analyses about areal extent, frequency, and duration changes to estimate drought trends, we also used Mann-Kendall’s trend test at a significance level of 0.05 ( Supplementary Text ). The Mann-Kendall test 52 , 53 has been used in many previous studies for the detection of trends in hydrologic and climatic data 54 , 55 , 56 . In addition, Sen’s slope method 57 was also used to quantify the magnitude of trend ( Supplementary Text ).

Wheat is mainly a winter season (“Rabi”) crop in India and is usually planted in October and harvested in April. To evaluate the relationship between drought and wheat yield, the entire growing season was selected for this study. The phenology stages of wheat were listed as emergence, heading, anthesis, and maturity ( Supplementary Table S10 ). The sensitivity of wheat yield to soil moisture stress varied during different phenological stages ( Supplementary Table S10 ). Therefore, the relationship between drought and wheat yield for every month during the growing season was necessary for this study. This approach is different from a general drought study, such as our antecedent study 34 . The analysis of drought impacts on crop productivity was completed by using the drought indices from October to April for every year. Besides that, it is also noted that the potential of the crop to extract water from depths varies during different stages of the crop growth 58 . Therefore, considering the soil moisture in different depths will provide a finer approach to analyze the relationship between crop and soil moisture, such as the study by Narasimhan and Srinivasan 58 . To utilize that approach, gridded soil moisture is being developed as part of a regional reanalyses and will be included in our future work.

Crop yield data was available for wheat from 1980–2014 through the Directorate of Economics and Statistics, Department of Agriculture, India ( http://eands.dacnet.nic.in/ ) and Agricultural Statistics at a Glance 2014. These data are available by state for India. The yield anomalies index for every year was calculated as yield loss ( Supplementary Text ). Spearman correlation coefficients were then calculated calculated to identify relationships between the crop yield anomaly and drought indices.

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How to cite this article: Zhang, X. et al . Droughts in India from 1981 to 2013 and Implications to Wheat Production. Sci. Rep. 7 , 44552; doi: 10.1038/srep44552 (2017).

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Acknowledgements

Xiang was supported by the China Scholarship Council (CSC) under the State Scholarship Fund to pursue his study at Purdue University (No. 201506270080). DN acknowledges support from NSF CAREER AGS 0847472, USDA/NIFA grant on drought triggers and global trade 2011–67019–20042 and 2015–67023–23109, USDA NIFA Hatch project 1007699, and the financial support given by the Earth System Science Organization, Ministry of Earth Sciences, Government of India (Grant No./Project No. MM/SERP/CNRS/2013/INT-10/002) to conduct this research under Monsoon Mission. Nengcheng acknowledges support from the Union Foundation of Ministry of Education of the People’s Republic of China (6141A02231601) and the Project of Creative Research Groups of Natural Science Foundation of Hubei Province of China (2016CFA003). We thank the Global Precipitation Climatology Centre (GPCC) for GPCC v7 precipitation data, Global Modeling and Assimilation Office, NASA for MERRA-2 runoff and soil moisture data, Global Inventory Modeling and Mapping Studies, NASA for NDVI data, Department of Agriculture, India for wheat yield data, Jeff Burkey for Matlab code to compute Mann-Kendall test with Sen’s slope, Taesam Lee for Matlab code to compute SPI/SRI/SSI, which was also validated by SPI program provided by National Drought Mitigation Center, and Yuke Zhou for Matlab code to convert raw GIMMS NDVI3g data to Geotiff. We thank Dallas Staley for her professional help in editing the paper.

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X.Z. developed the method, experiment, and wrote the manuscript. R.O. and C.W. collected the data and assisted in the experiment. N.C. and D.N. assisted in the result analysis and provided overall guidance including conceiving the study. All authors reviewed and edited the manuscript.

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Zhang, X., Obringer, R., Wei, C. et al. Droughts in India from 1981 to 2013 and Implications to Wheat Production. Sci Rep 7 , 44552 (2017). https://doi.org/10.1038/srep44552

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By Somini Sengupta and Manuela Andreoni

An estimated 20 million people in southern Africa are facing what the United Nations calls “acute hunger” as one of the worst droughts in more than four decades shrivels crops, decimates livestock and, after years of rising food prices brought on by pandemic and war, spikes the price of corn, the region’s staple crop.

Malawi, Zambia and Zimbabwe have all declared national emergencies.

It is a bitter foretaste of what a warming climate is projected to bring to a region that’s likely to be acutely affected by climate change, though scientists said on Thursday that the current drought is more driven by the natural weather cycle known as El Niño than by global warming.

Its effects are all the more punishing because in the past few years the region had been hit by cyclones, unusually heavy rains and a widening outbreak of cholera.

‘Urgent help’ is needed

The rains this year began late and were lower than average. In February, when crops need it most, parts of Zimbabwe, Zambia, Malawi, Angola, Mozambique and Botswana received a fifth of the typical rainfall.

That’s devastating for these largely agrarian countries, where farmers rely entirely on the rains.

In southern Malawi, in a district called Chikwawa, some residents were wading into a river rife with crocodiles to collect a wild tuber known as nyika to curb their hunger. “My area needs urgent help,” the local leader, who identified himself as Chief Chimombo, said.

Elsewhere, cattle in search of water walked into fields still muddy from last year’s heavy rains, only to get stuck, said Chikondi Chabvuta, a Malawi-based aid worker with CARE, the international relief organization. Thousands of cattle deaths have been reported in the region, according to the group.

The first few months of every year, just before the harvest begins in late April and May, are usually a lean season. This year, because harvests are projected to be significantly lower , the lean season is likely to last longer. “The food security situation is very bad and is expected to get worse,” Ms. Chabvuta said.

Local corn prices have risen sharply. In Zambia, the price more than doubled between January 2022 and January of this year, according to the United Nations Food and Agriculture Organization . In Malawi, it rose fourfold.

The F.A.O. pointed out that, in addition to low yields, grain prices have been abnormally high because of the war in Ukraine, one of the world’s biggest grain exporters, as well as weak currencies in several southern African countries, making it expensive to buy imported food, fuel and fertilizers.

Why it’s happening

According to an analysis published Thursday by World Weather Attribution, an international coalition of scientists that focuses on rapid assessment of extreme weather events, the driving force behind the current drought is El Niño, a natural weather phenomenon that heats parts of the Pacific Ocean every few years and tweaks the weather in different ways in different parts of the world. In Southern Africa, El Niños tend to bring below-average rainfall.

El Niño made this drought twice as likely, the study concluded. That weather pattern is now weakening, but a repeat is expected soon.

The drought may also have been worsened by deforestation, which throws off local rainfall patterns and degrades soils, the study concluded.

Droughts are notoriously hard to attribute to global warming. That is particularly true in regions like Southern Africa, in part because it doesn’t have a dense network of weather stations offering detailed historical data.

Scientists are uncertain as to whether climate change played a role in this particular drought. However, there is little uncertainty about the long-term effects of climate change in this part of the world.

The average temperature in Southern Africa has risen by 1.04 to 1.8 degrees Celsius in the past 50 years , according to the Intergovernmental Panel on Climate Change, and the number of hot days has increased. That makes a dry year worse. Plants and animals are thirstier. Moisture evaporates. Soils dry out. Scientific models indicate that Southern Africa is becoming drier overall .

The Intergovernmental Panel on Climate Change calls Southern Africa a climate change “hot spot in terms of both hot extremes and drying.”

The costs of adaptation

To the millions of people trying to cope with this drought, it hardly matters whether climate change or something else is responsible for why the skies have gone dry.

What matters is whether these communities can adapt fast enough to weather shocks.

“It’s really important that resilience to droughts, especially in these parts of the continent, should really be improved,” said Joyce Kimutai, one of the authors of the study and a researcher at the Grantham Institute, a climate and environment center at Imperial College London.

There are existing solutions that need money to put into effect: early warning systems that inform people about what to expect, insurance and other social safety programs to help them prepare, as well as diversifying what farmers plant. Corn is extremely vulnerable to heat and erratic rains.

Golden Matonga contributed reporting.

Somini Sengupta is the international climate reporter on the Times climate team. More about Somini Sengupta

Manuela Andreoni is a Times climate and environmental reporter and a writer for the Climate Forward newsletter. More about Manuela Andreoni

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Covishield vaccine row: PIL in Supreme Court seeks medical experts to study risks

The petition also called for the centre to implement a vaccine damage payment system for citizens who are severely disabled as a result of the vaccination drive during the covid-19 pandemic..

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A Public Interest Litigation (PIL) was filed in the Supreme Court on Wednesday, urging the establishment of a medical experts’ panel to assess the risks associated with the Covishield vaccine's side effects .

Advocate Vishal Tiwari, who filed the PIL, also called for the Centre to implement a vaccine damage payment system for citizens who are severely disabled as a result of the vaccination drive during COVID-19.

The plea referred to UK court documents where pharmaceutical company AstraZeneca admitted that its COVID-19 vaccine has the potential to cause Thrombosis with Thrombocytopenia Syndrome (TTS) , a rare side effect linked to blood clotting. AstraZeneca's vaccine formula was licenced to the Pune-based Serum Institute of India (SII) for the production of the Covishield vaccine during the pandemic.

According to media reports cited in the petition, AstraZeneca acknowledged a connection between the vaccine and TTS, a medical condition characterised by low platelet levels and formation of blood clots. More than 175 crore doses of Covishield have been administered in India, as per the plea.

The PIL also seeks compensation for individuals severely disabled or deceased due to the side effects of COVID-19 vaccines administered during the pandemic. Additionally, it calls for strict guidelines and regulations to prevent the circulation and advertising of fake or counterfeit COVID-19 vaccines, with the committee overseeing these measures being led by a retired Supreme Court judge.

The plea urges the government to conduct awareness programmes on the dangers of counterfeit vaccines and ensure equitable distribution and affordable pricing of COVID-19 vaccines. It also advocates the enactment of strict laws against the criminal act of selling or circulating counterfeit vaccines.

The application highlights the increase in cases of heart attacks and sudden collapses post-COVID-19, particularly among younger individuals. "There have been a number of cases of heart attacks even in youngsters. Now, after the document filed in UK court by the developer of Covishield, we are compelled to think about the risks and hazardous consequences of Covishield vaccines which have been administered to the citizens in large numbers," the PIL stated.