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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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• ENVIRONMENT

India’s water crisis could be helped by better building, planning

Severe drought threatens the country, and poor infrastructure is making it worse. But there are potential solutions.

The parched bed of a reservoir on the outskirts of Chennai, India.

Once the wettest place on Earth, Cherrapunji, a town in northeastern India, has faced a drought each winter for the past few years. Kerala, a state in the southwest, flooded devastatingly in 2018, but saw its wells run dry soon after.

Chennai, a growing south-Indian metropolis, was inundated by rains in 2015 —but this summer, waiting for the monsoon, its 11 million residents have watched three of its four reservoirs run dry. Meanwhile, across India, the groundwater that provides an invaluable buffer between monsoons is severely depleted and in danger of being irreversibly lost.

Welcome to the new India: hot and desiccated and wet and flooded, all at once, with the fates of 1.3 billion people and rich biodiversity hotspots riding upon increasingly unpredictable rains.

The southwest monsoon, which usually drenches India from June to September, has come ten days late this year, bringing 30 percent less rain than normal for the month of June. In the north, Delhi has thus far seen almost no rain, while in southern India reservoir levels in southern India are running dangerously low. Headlines in newspapers scream “zero-day” and “running dry” and “historic drought.”

Chennai, a megacity now dependent on tanker trucks, leads the grim news. But Bangalore, India’s answer to Silicon Valley, is not far behind. There are murmurs that this burgeoning, bulging city will have used all its groundwater by 2020.

The dire predicaments of these two urban areas is a cautionary tale—a symptom of the larger malaise that plagues water management in a country that is soon to be the most populous on Earth.

In this photo taken on June 20, 2019, Chennai residents get water from a community well after reservoirs for the city ran dry. The drought is the worst in living memory for the bustling capital of Tamil Nadu state, India's sixth largest city.

Women from the aboriginal Kol community collect drinking water at a well in Nawargawa village in Madhya Pradesh state on June 16, 2019. Around 40 families in the village rely on the well for drinking water during the heatwave conditions in the remote region in central India.

Buy me a river

Bangalore sits smack in the center of the lower half of the Indian peninsula, about 3,000 feet above sea level. A city of 12 million, one of the fastest growing in the world, it contributes $110 billion to India’s GDP. “Bangalore deserves to be water-secure,” says S. Vishwanath, director of Biome Environmental Solutions , a local design firm.

Yet Bangalore has no perennial water source of its own. It has to pump water nearly 90 miles and up nearly 900 feet from the Cauvery, a sacred river that flows south of the city.

The city draws 1450 million liters per day (MLD), about 385 million gallons, from the Cauvery, with withdrawals slated to go up by 775 MLD (210 million gallons) in a couple of years, when new pipes will be laid. Still, the water does not reach everybody.

“Bangalore’s water woes are not as much a problem of supply, as it is of distribution,” Vishwanath says.

A quarter of Bangalore’s population, living mostly on its periphery, is not connected to the river water supply and is forced to mine groundwater to survive. This area of the city, replete with tech parks, is where most of the growth is.

Rampant groundwater extraction, unmonitored and unregulated, has caused Bangalore’s water table to plunge to depths of nearly 1,000 feet. Muddy and contaminated, the diminished resource threatens the future of the citizens who depend upon it.

Since in some places the groundwater is now below the level of the river itself, the river has begun to feed the groundwater. The Cauvery is thus being sucked dry from two sides—by the giant pipes that pump water into the city and by the bore wells that drive deeper each year, lowering the water table to levels below that of the river and forcing the river to feed it.

Water is pumped from the Bendsura reservoir to a tanker in this aerial photograph taken in Beed district, Maharashtra, India, on Sunday, Apr. 14, 2019.

Unreliable supply

Here’s what’s most unsettling: Bangalore’s current water distribution problems could evolve into a fundamental crisis of supply, because flows in the life-giving Cauvery are no longer secure.

Climate models predict a 5 percent increase in the river’s flow due to rising temperatures, which should bring more evaporation and heavier rains. However, that is not what recent trends in the basin show. Instead, dry season flows from the upstream regions of the river have been declining.

“These models don’t seem to be able to explain the recent past,” says hydrologist Veena Srinivasan, Fellow at the Ashoka Trust for Research in Ecology and the Environment (ATREE), Bangalore. The problem, she says, is that the models are not yet powerful and sophisticated enough to incorporate the effects on climate and weather of local changes in the landscape.

There have been plenty of changes. In the catchment regions of the Cauvery, widespread clear-cutting of forests to make way for transmission lines, coffee and palm-oil plantations, and other cash crops has affected the soil’s ability to retain and release water.

Research has also shown that large-scale deforestation affects South Asia’s monsoon, depressing precipitation levels—because fewer trees means fewer leaves transpiring water back to the atmosphere, resulting in warmer and drier conditions.

According to a study done by Coffee Agroforestry Network (CAFNET), this region has seen a decrease of 14 rainy days a year over the past three to four decades. Moreover, agricultural land-use has also changed near the river, with traditional paddy-fields giving way to coffee plantations. Paddy, with its requirement for standing water, meant that the fields traditionally acted as a spongesa sponge. Coffee and palm-oil plantations, in contrast, require heavy irrigation and see significant run-off.

“We don’t seem to be connecting the dots,” says Harini Nagendra , professor of sustainability at Azim Premji University in Bangalore. “Instead of trying to get more water into the Cauvery, we are investing time in fighting over its dwindling resources.”

If the Cauvery is dwindling, and groundwater is near exhausted, what options does Bangalore have?

First, catch the rain

Bangalore gets between 800 and 900 millimeters (32-36 inches) of rain in a year, which is hardly a meager amount. If the city were to catch half of that, it would translate to more than 100 liters (27 gallons) per capita per day, far more than adequate for domestic and drinking purposes. Rainwater harvesting has been slow to catch on in the city, in spite of laws requiring it, but it’s now steadily picking up.

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The next step would be to funnel some of the harvested rain via “recharge wells” back into groundwater, to begin to restore it. Two Bangalore NGOs, Biome Environmental Trust and Friends of Lakes, have launched a citizens’ initiative to dig a million recharge wells in the city—that’s about one every 90 feet. The idea is that these wells will pump back 60 percent of the rainwater into the shallow aquifer that has been depleted by overuse—and by the fact that only around 10 percent of rainwater currently can seep through cracks and gaps in the city’s pavement.

A local community of well-diggers called the mannu-vaddars man the effort. Using hand toolstools, they dig 30-to-40-foot-deep open wells that strike the shallow aquifer. Once it has been recharged the wells themselves become sustainable and cheap water source.

Vishwanath has other plans for recharging groundwater. Bangalore returns about 80 percent of its water to rural areas in the form of wastewater that flows in seasonal streams and rivers. Much of it is untreated—putrid, dark, and thick with sewage and hazardous heavy metals.

“Waste treatment plants are being put in place to treat 1440 MLD,” says Vishwanath. Once that begins to function, ‘fit for purpose’ water, cleaned of heavy metals but still rich in nutrients like phosphorus, will be returned to rural areas for use in agriculture. Vishwanath’s idea is to treat some of the wastewater further, to render it potable, and use that to recharge groundwater.

There is one gaping hole in the hopeful vision evoked by these ideas: India’s woeful lack of the institutions and governance needed to oversee such efforts and manage the water supply soundly, according to rules that are enforced.

A Chennai resident fills a pot with water from a tap on June 26, 2019. Chennai is getting less than two-thirds of the water it normally uses each day.

In Bangalore, the governmental department that oversees groundwater extraction has just six employees, and thus no earthly way to monitor and enforce the law in this sprawling city. One digger of illegal borewells freely admitted to me that he had paid a bribe to get a permit for a deep well—after he had already dug it.

At the larger river-basin level, there are no institutions in place to manage water resources at all.

“We are all fixated on technological solutions to the water crisis,” Vishwanath laments. “What we really need are robust institutions and governance at the river basin level so that we can understand what is happening to land-use change, sand-mining, forest cover, and groundwater extraction—all of which affects river flows.”

Without strong government involvement, known solutions to India’s water crisis won’t be implemented at a large enough scale to affect the bigger picture. Chennai is a case in point.

Chennai’s fossil water addiction

With a GDP of $78 billion, home to 40 percent of India’s automobile industry, Chennai has boxed itself into a corner as far as water resources go. Located on the east coast, it gets two-thirds of its rains from the northeast monsoon that arrives in November and December and only a third from the southwest monsoon—the one that this year has been less than enthusiastic.

The city depends on four small municipal reservoirs; it doesn’t have access to larger reservoirs, shared with agriculture, that it can borrow water from in a drought. Moreover, these four reservoirs service only 35 percent of the city’s water needs. Seven of 11 million people in Chennai depend upon fossil groundwater, w accessed either by privately dug borewells or by tank trucks that bring water in from distant borewells. The tankers are controlled by powerful water-mafias.

“The state [controlling only 35 percent of the water supply] becomes the minority, and its influence gets subverted as private [tanker] interests have captured value addition in water. They run it like a business,” says Vishwanath. The water mafias are well-connected to politicians, bureaucrats, and the police, making them difficult to dislodge. With taps running dry, voices are getting shrill and the government, as a band-aid measure, has set aside ten million dollars for a 50-wagon train to ferry 10 million liters (about 3 million gallons) of water every day from the Cauvery to Chennai.

Chennai, like many cities in India, has built over its wetlands . The pavement prevents groundwater from being recharged during rainy months—and instead causes low-lying areas to flood.

A man walks along a water pipe on the bottom of the dried-out Porur Lake in Chennai on Friday, July 5, 2019. Failed rains last year and delays in this year's annual monsoon have left nearly half of India facing drought-like conditions.

“The increasingly short attention span of policy makers, decision makers, the media, and even of citizens is frightening,” Nagendra says. “We focus on drought issues in the summer, and forget them once the monsoon strikes. We then focus on the challenges of too much water via flooding, and again forget this when the summer hits. Ironically the causes are much the same for both drought and floods—loss of wetlands, cutting of trees, building over lakes, rivers and interconnecting channels. But no one seems to make the link.”

The problem is nationwide; Chennai and Bangalore are just the leading edge. “The real crisis will come in the smaller cities and towns that are now fast urbanizing,” Nagendra says. “And the real hope of doing something may also lie there, as many of these areas have not yet faced ecosystem degradation and collapse at the scale that megacities have.”

Srinivasan is optimistic even for the degraded megacities.

“In a city with urban sprawl, the water problem is eminently solvable,” she says. “You build out your infrastructure for rainwater harvesting, you recharge the aquifers with treated wastewater, and make sure paving has enough give for rainwater to seep through.”

If India can just build the institutions to do all that, there will indeed be hope.

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• Published: 11 June 2024

Exploring short- and long-term meteorological drought parameters in the Vaippar Basin of Southern India

• Manikandan Muthiah 1 ,
• Saravanan Sivarajan 2 ,
• Nagarajan Madasamy 3 ,
• Anandaraj Natarajan 4 &
• Raviraj Ayyavoo 5  

Scientific Reports volume  14 , Article number:  13428 ( 2024 ) Cite this article

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• Climate change
• Climate sciences

Evaluating drought parameters at the basin level is one of the fundamental processes for planning sustainable crop production. This study aimed to evaluate both short-term and long-term meteorological drought parameters within the Vaippar Basin, located in southern India, by employing the standardized precipitation index (SPI). Gridded rainfall values developed from 13 rain gauge stations were employed to calculate the SPI values. Drought parameters, encompassing occurrence, intensity, duration, frequency, and trends, were assessed for both short-term and long-term droughts. The study findings indicated that the occurrence of short-term drought was 51.7%, while that of long-term drought was 49.82%. Notably, the basin experienced extreme short-term droughts in 1980, 1998 and 2016 and long-term droughts in 1981, 2013, and 2017. Utilizing an innovative trend identification method for SPI values, a significant monotonic upwards trend was identified in October and December for short-term drought and in December for long-term drought. This study defined the minimum threshold rainfall, which represents the critical amount required to prevent short-term drought (set at 390 mm) and long-term drought (set at 635 mm). The drought severity recurrence curves developed in this study indicate that when the SPI values fall below − 1.0, short-term drought affects 25% of the basin area, while long-term drought impacts 50% of the basin area at a 20-year recurrence interval. Additionally, the drought hazard index (DHI), which combines drought intensity and severity, demonstrated higher values in the northwestern regions for short-term drought and in the southern areas for long-term drought. The study's findings, highlighting areas of drought vulnerability, severity, and recurrence patterns in the basin, direct the attention for timely intervention when drought initiates.

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Introduction.

Drought is a natural disaster that inflicts the most extensive global losses and has the greatest impact among all natural disasters 1 . It is typically defined as a temporary meteorological event resulting from a prolonged absence of rainfall compared to long-term average conditions 2 , 3 , 4 . A deficiency in rainfall can lead to a range of consequences, affecting soil moisture, streamflow, reservoir storage, and groundwater levels. These factors, in turn, have significant repercussions on socioeconomic, agricultural, and environmental factors 5 .

Droughts develop slowly, making them difficult to detect and monitor 6 . The effectiveness of drought preparedness and mitigation efforts hinges largely on the timely acquisition of information regarding drought onset, progression, and extent. This crucial information is typically obtained through drought monitoring, which relies on drought indices. Drought indices offer decision makers insights into the severity of drought conditions and can serve as triggers for implementing drought contingency plans when available 7 . In essence, drought indices are developed based on various hydro-meteorological parameters, such as temperature, rainfall, evaporation, streamflow, and soil moisture, allowing for the assessment of different types of drought, including meteorological (related to precipitation), hydrological (related to streamflow), and agricultural (related to soil moisture) drought 8 .

Among all types of droughts, meteorological droughts are considered the most significant, as they can trigger other forms of droughts. Meteorological drought is referred as a shortage of rainfall in a specific region over a defined period 9 . Various indices have been developed to assess meteorological drought, including the Palmer modified drought index 10 , 11 , rainfall anomaly index 12 , standardized precipitation index (SPI) 13 , 14 , per cent of normal (PN) 15 , rainfall deciles 16 , 17 , effective drought index 18 , reconnaissance drought index 19 , and standardized precipitation evapotranspiration index (SPEI) 20 . Among these drought indices, the SPI is the most widely utilized. The World Meteorological Organization (WMO) has also endorsed the use of the SPI for drought characterization 21 .

The SPI, developed by Mckee et al. 13 , 14 , is employed to define, monitor, and assess drought conditions by identifying periods of insufficient rainfall across various timescales, including 1, 3, 6, 9, 12, 24, and 48 months. The National Drought Mitigation Center (NDMC) utilizes the SPI to monitor drought conditions and assess water storage conditions 22 . Numerous studies have explored the spatiotemporal variations in meteorological droughts across different regions, including Mexico 23 , Greece 24 , India 25 , 26 , China 27 , 28 , 29 , Turkey 30 , 31 , Iran 32 , 33 , the US 34 , Portugal 35 , Denmark 36 , Algeria 37 , Pakistan 38 , 39 , and the Kingdom of Saudi Arabia 40 . These studies assessed spatial and temporal variations in different drought severity categories using historical data, identifying the most vulnerable areas. These areas require increased attention and necessitate the implementation of effective mitigation strategies. The studies also focused on different dimensions of the drought parameters.

Indeed, the SPI has been employed in various dimensions of researching drought, covering analyses of drought occurrences, spatiotemporal assessment, frequency analysis, probabilistic characterization, trend identification, hazard evaluation, forecasting, and investigations related to climate change in India 41 , 42 , 43 . Moreover, many studies conducted in India in different basins have focused mainly on identifying the most vulnerable areas to different degrees of drought. For example, Patil et al. investigated the spatiotemporal characterization of droughts in terms of magnitude and severity at different return periods for various time scales in the Hyderabad Karnataka region of India 44 . Sharma et al. assessed the trends of rainfall indices and meteorological drought properties in the Mahi River basin, India 45 . The spatiotemporal drought parameters were analysed based on drought indices at different timescales over Uttar Pradesh, India, and trends were tested 46 . The spatiotemporal changes in meteorological drought were explored within the Luni River Basin in Rajasthan, India, utilizing the SPI. Drought trends were assessed through the application of the Mann‒Kendall (MK) test or the modified MK test, along with graphical innovative trend analysis (ITA) 47 .

Recently, the Innovative Trend Analysis (ITA) method, developed by Sen 48 , has garnered significant attention worldwide and has been rigorously tested and verified by numerous researchers, including Sanikhani et al. 49 and Singh et al. 50 . One of the primary advantages of the ITA method is its capability to analyse trends and present them graphically without being constrained by factors such as nonnormality, serial correlation, or the number of data in the series 51 . Furthermore, the ITA method delivers robust and accurate results with minimal error. It also allows for the detection of both monotonic and nonmonotonic trends, categorizing time series into different subcategories, such as high, medium, and low zones 52 .

The assessment of drought hazards holds paramount significance in the context of sustainable water resource planning and management. It necessitates a comprehensive understanding of both historical drought occurrences in the region and the various concepts associated with droughts. The drought hazard index (DHI), calculated by combining occurrences of various categories of drought severities, provide an in-depth assessment of a particular region's vulnerability to drought 53 . A specific weight is assigned to each drought severity category, and the features within each category are rated to estimate the drought hazard. Integrating all severity themes allows for the creation of prepared drought hazard maps 54 . Drought hazard maps have been developed to explore the spatial attributes of drought hazards in different regions, such as Bangladesh 53 , China 54 , India 55 and Iran 56 .

Drought is a recurring occurrence in India, predominantly impacting the Peninsular and Western regions of the country. Conversely, the central, northern, eastern, and southern parts of India experience relatively fewer instances of drought. Among India's total land area of 329 million hectares, Around 107 million hectares are experiencing different levels of water stress and drought conditions 25 . Over 100 districts spanning 13 states in India have been designated drought-prone districts, with approximately 8 of these districts located in Tamil Nadu 57 .

Drought is a natural aspect of the climate; its recurrence is an unavoidable part of our environment. Nevertheless, there is considerable confusion surrounding its characteristics. Research indicates that the inability to precisely identify drought conditions in specific scenarios has impeded our understanding of drought, resulting in uncertainty and a limited response from policymakers 3 . This study aims to investigate the short- and long-term drought parameters of the Vaippar Basin by employing the standardized precipitation index (SPI) as a drought analysis tool. The comprehensive analysis encompasses various aspects of drought, including (i) assessing drought parameters (occurrence, duration, severity), (ii) identifying historic trends, and (iii) establishing threshold rainfall values to mitigate drought, as well as (iv) developing a drought hazard index for demarcating weaker areas in the basin. This research is distinct in its approach, employing spatially interpolated gauge rainfall data at the micro-level to detect drought parameters and utilizing SPI values for innovative trend identification.

Materials and methods

Study area and data used.

The Vaippar River Basin, situated in the southern part of Tamil Nadu, India, holds significant importance in the region's geography. It is positioned between latitudes 8° 97′ N and 9° 78′ N and longitudes 77° 24′ E and 78° 37′ E, covering a total catchment area of 5320 square kilometers. This basin is bordered by the Western Ghats to the west, the Gulf of Mannar (Bay of Bengal) to the east, the Vaigai and Gundar basins to the north, and the Tamaraparani basin to the south. It spans four districts, with Virudhunagar accounting for 68%, Thoothukudi for 20%, Madurai for 7%, and Tirunelveli for 5% of the total area.

The Vaippar River originates from the Echamalai Mottai, Neduntheri Mottai, and Kiladiparai hill ranges of the Western Ghats, near Sivagiri in the Tirunelveli district, at the uppermost elevation of 165 m above mean sea level. It flows predominantly in an eastern and southeastern direction, covering a distance of 146 km before joining the Gulf of Mannar. Within the Vaippar basin's catchment area lies hilly regions such as KodaliparaiMottai, Vasudevanallur reserve forest, and Periyasudangi Malai, among others, which receive relatively low rainfall due to their location in the rain shadow regions of the Western Ghats.

The entire catchment area of the Vaippar Basin falls within the boundaries of Tamil Nadu State and is further subdivided into 13 subbasins, namely, Nichabanadhi, Kalingalar, Deviar, Nagariyar, Seperperiyar, Kayalkudiar, VallampattiOdai/Uppodai, Arjunanadhi, Kousiganadhi, SindapalliUppodai, Uppathurar (eastern side), Senkottaiyar, and Vaippar, as depicted in Fig.  1 a,b.

Geographical location of the study area with ( a ) raingage stations and ( b ) grid points in the subbasin.

This basin has a history of frequent drought occurrences, with droughts occurring every 4–8 years 58 . Approximately 74% of the basin's total geographical area is dedicated to agriculture, while forested areas account for 10%. Wasteland covers 8% of the land area, and settlements and water bodies combined occupy less than 8% of the basin's total area. Of the total agricultural land, 43% is cultivable land primarily utilized for water-intensive crops such as paddies, sugarcanes, and bananas 59 . In addition to these crops, cotton, nonpaddy crops, and dry crops are cultivated within the basin. Approximately 24% of the cultivable land is irrigated, with the remaining 76% relying heavily on rainwater for irrigation 58 .

In this study, daily rainfall data were collected from 13 rain gauge stations distributed across the Vaippar Basin. The data were sourced from the Tamil Nadu State Ground and Surface Water Resources Data Center, Public Works Department, Water Resources Organization in Chennai, Tamil Nadu, India. Table 1 provides the location details of each rain-gauge station and the period of data used in this study. Standard quality control measures were undertaken to identify outliers and errors in rainfall data for all 13 rain gauges. Any potential outliers, including missing data and instrument errors, were checked and corrected. To facilitate the analysis, the daily rainfall data for each station was processed to derive monthly data.

Methodology

The methodology involved several steps, including the creation of gridded rainfall data by the spatial interpolation method; the calculation of basin average monthly rainfall and gridded rainfall at the time scale of 3- and 12-month; the computation of short and long-term drought data at 3- and 12-month SPI values using both average and gridded rainfall data; a temporal assessment of drought, which included the classification of SPI values into drought categories using areal SPI values; the assessment of different drought parameters; the identification of trends in the SPI series; the development of drought severity recurrence curves; the creation of maps illustrating the occurrence of drought severities; the determination of threshold rainfall to avoid drought; and the construction of drought hazard maps based on the frequency of drought events. The entire workflow is illustrated in Fig.  2 .

The overall process involved in drought analysis in the Vaippar Basin.

Spatial interpolation method for gridded rainfall data

Spatial interpolation methods play a crucial role in estimating values at sites lacking observed data utilizing known data. Several methods, including inverse distance weighting (IDW), the kriging method and splines, are commonly employed for spatial investigations of various variables 23 , 36 , 60 , 61 . Among these methods, the IDW approach considers the proximity of known data points to the location of interest and assigns weights accordingly, ultimately producing an interpolated surface 25 , 62 .

In this study, the IDW technique was employed for estimating gridded rainfall across the basin. The Thiessen polygon technique often results in a coarse approximation of rainfall spatial variation due to attribute variations associated with each rain gauge station, with variations ranging from 14 to 30% 25 . To overcome this limitation, the Vaippar Basin was subdivided into smaller square grids. Given the constraints of rainfall data availability and the uneven distribution of rain gauge stations within the basin, spatial interpolation of data at smaller grids was deemed necessary to address these challenges and achieve a more accurate representation of rainfall patterns.

The entire Vaippar basin was subdivided into 26 grids, each covering 0.125° × 0.125° and approximately 205 km 2 . These grids accounted for approximately 3.85% of the total area, equivalent to 5320 km 2 . Monthly rainfall data recorded at 13 stations were subjected to spatial interpolation using the IDW method in QGIS 3.30.2 (QGIS is an official project of the Open Source Geospatial Foundation (OSGeo) licensed under the GNU General Public License), resulting in gridded rainfall data covering the period from 1971 to 2019. Subsequently, the gridded rainfall data were used to analyse drought during both the 3- and 12-month time periods. The areal average monthly rainfall for the Vaippar Basin was computed by averaging the gridded rainfall data. The procedure employed for calculating gridded SPI values in this study is known as the 'interpolate-calculate' method, wherein rainfall is spatially interpolated first, followed by the computation of SPI values 24 , 25 .

Assessing drought with the standardized precipitation index (SPI)

The standardized precipitation index (SPI), originally developed at Colorado State University in the United States, is a widely utilized tool for quantifying rainfall deficits and monitoring drought situations 13 , 14 . A drought event is defined as a period during which the SPI value consistently remains negative, and a drought event concludes when the SPI turns positive. The SPI can be calculated at various time periods, including 1, 3, 6, 9, 12, 24, and 48 months. Using multiple time periods enables the assessment of how a rainfall deficit impacts various water resource components, including groundwater, reservoir storage, soil moisture, and streamflow. For instance, the 3-month SPI for a specific month indicates the deviation in precipitation totals for that same month and the two preceding months, helping to gauge drought conditions.

The computation of the SPI entails modelling a probability density function to match the rainfall frequency distribution across the chosen time period. This process is conducted individually for each month (or the temporal basis of the raw rainfall time series) and for each location. Subsequently, each probability density function is standardized to a normal distribution with a mean value of zero and a standard deviation value of one. This normalization ensures that SPI values are expressed in standard deviations 62 , 63 . As a result, the SPI is standardized both in terms of location and time period, sharing the benefits of standardization similar to the Palmer Drought Severity Index (PDSI). Once standardized, anomaly severity was classified according to Table 2 . This table also provides the associated probabilities for each severity level, derived from the normal probability density function. For instance, at a specific location in a given month, the occurrence probability for moderate droughts (SPI ≤  − 1) is 15.9%, while for extreme droughts (SPI ≤  − 2), it is 2.3%. Extreme SPI values, by definition, occur with the same frequency across all locations. A drought event is considered to commence when the SPI value consistently remains negative and concludes when the SPI becomes positive. The detailed SPI computational methodology can be found in Guttman 64 , 65 , McKee et al. 13 , 14 , and Hayes et al. 6 . The SPI has been widely employed in numerous studies to analyse meteorological droughts 24 , 25 , 33 , 60 , 66 , 67 , 68 , 69 .

Temporal analysis of drought

Temporal analysis of drought was conducted in the Vaippar Basin using areal monthly SPI values and gridded SPI values. The areal monthly SPI values for short- and long-term droughts were calculated based on the areal average monthly rainfall at 3- and 12-month time periods. Following the SPI value classifications in Table 2 , these values were then categorized into four groups—D1 for mild drought, D2 for moderate drought, D3 for severe drought, and D4 for extreme drought—on a monthly basis. To determine the percentage of mild to extreme drought occurrence, the proportion of months experiencing drought conditions during each SPI time period relative to the total duration of the dataset was calculated 60 , 62 , 70 .

Monthly SPI values, calculated from gridded rainfall data for each grid, were utilized to categorize drought events for each month of the year. This study also analysed the spatial evolution of severely drought-stricken areas at various severity levels over time. The primary objective was to estimate the areas covered by mild to extreme drought categories according to the frequency of drought events. To accomplish this, the number of grids expressing mild or moderate or severe or extreme drought over the specified time intervals was determined using the respective SPI values 70 . This means that the number of grids exhibiting different drought categories associated with the SPI values was determined and mapped for the period from 1971 to 2019 to observe their geographical extent.

Analysis of drought parameters

Various drought parameters, including the most intensified drought month, annual accumulated drought severity, drought initiation and termination, drought duration, and drought severity, have been assessed for short- and long-term drought at timescales of 3- and 12-month based on run theory 71 , 72 , 73 . A drought event is characterized by the following parameters:

Most intensified drought refers to the most severe drought event during the analysis period. The deviation coefficient is determined by identifying the maximum negative departure of the SPI value from its normal value.

The annual accumulated drought severity is the cumulative sum of negative SPI values throughout the year.

The initiation time of a drought marks the onset of a drought event.

The termination time of a drought is when the drought conditions come to an end.

Drought duration, stated in months, signifies the consecutive period in which a drought parameter remains below the threshold level. It spans from the initiation to the termination of a drought event.

Drought severity is the aggregate deficit of a drought parameter below the threshold level, determined by summing negative SPI values during dry spells.

Drought intensity, calculated as the average value of a drought parameter below the critical level, is derived by dividing drought severity by its duration.

Developing drought severity recurrence curve

Regional drought analysis is valuable for identifying drought conditions and determining their severity within a specific year. A regional drought is considered to occur when a significant portion of the region's total area experiences drought, that is, when the cumulative area affected by local drought reaches a predefined areal threshold. Therefore, it is crucial to assess drought conditions throughout the entire region.

Solely focusing on the recurrence of drought occurrences is insufficient to gain a comprehensive understanding of droughts. It is crucial to establish quantitative relationships with other factors, such as the severity and spatial extent of droughts. This need has prompted the development of drought severity recurrence curves. These curves represent one of the most valuable methods for evaluating drought in a region. This approach was initially developed by Henriques and Santos 73 and has been adopted by subsequent researchers, including Kim et al. 23 , Loukas and Vasiliasdes 24 , Mishra and Desai 25 , and Zhang et al. 29 . In this study, drought severity recurrence curves were developed using the average annual severity through the following methodology:

Estimation of the annual average severity for each grid by dividing the annual sum of negative SPI values by 12 for each time scale.

Derivation of drought severity with respect to spatial extents (expressed as a percentage of the basin area) considering various spatial threshold levels.

Evaluation of drought severity using various probability distributions to identify the best fit for recurrence interval analysis. Subsequently, conducting recurrence interval analysis employing the chosen probability distribution for each percentage of drought spatial extent to establish the connection between severity and return periods.

Development of line curves for average annual severity corresponding to the recurrence interval and spatial extent.

This analysis assesses annual drought severity without considering the number or duration of monthly dry periods. As a result, an extreme drought lasting for a few months may be considered equivalent in representation to a prolonged moderate dry spell. Nonetheless, this analysis offers an assessment of the degree of dryness in a given year and can be used to evaluate the impact of drought duration.

Recurrence interval analysis of drought severity

Recurrence interval or frequency analysis is a common practice in hydrological and meteorological science and is used to assess the recurrence interval of specific events. This analysis involved the use of a selected probability distribution to estimate the recurrence interval of average annual drought severity. However, in this study, it is important to note that the annual average drought severity includes negative values. To make the model applicable for fitting into available distributions and to represent extreme conditions, these negative values of average annual drought severity were transformed into positive values. This transformation allows for the analysis of the associated risk of droughts using the exceedance probability.

Before fitting the drought severity data, various theoretical probability distributions underwent statistical testing. The most commonly used probability distributions, including normal, lognormal, gamma, and extreme value type I, were employed to determine the best-fit probability distribution for the average annual severity values of 12 month SPI values. These distributions were rigorously tested using both nonparametric Kolmogorov–Smirnov (K–S) tests and parametric chi-square tests at 5 and 1% significance levels.

The average annual drought severity (X T ), calculated for a given recurrence interval (T), can be represented as the average values (μ) plus the deviation (ΔX T ) of the variable from the average (X T  = μ + ∆X T ). The deviation can be calculated as the product of the standard deviation (σ) and the frequency factor (K T ), denoted as ΔX T  = K T σ. The values of deviation (ΔX T ) and the frequency factor (K T ) are dependent on the chosen recurrence interval and the type of probability distribution used in the analysis 74 . The expected annual severity for various recurrence intervals, such as 2, 3, 5, 10, 20, 25, 50, 75, and 100 years, was determined using the best-fit probability distribution.

Innovative trend identification method for short- and long-term SPI values

The innovative trend identification method has been successfully utilized for trend detection in hydrometeorological variables 48 , 51 . This method is simple, allowing for easy identification and visualization of trends in high, medium, and low datasets on the trend line 52 . Unlike nonparametric trend identification tests, this method does not require restrictive assumptions such as data series independence, normality, or data length 75 , 76 . All the data points of the time series are plotted in a Cartesian coordinate system and compared with a diagonal straight 1:1 line 49 , 50 , 77 . The computational procedure for the innovative trend method is provided below:

(a) Split the monthly SPI values into two equal halves for both the 3-month and 12-month time periods, and arrange each half series sequentially.

(b) Plot the series as a scatter diagram with the initial half of the series on the horizontal axis and the second half on the vertical axis.

(c) Draw a straight 45° line diagonally in the scatter graph representing a 1:1 relationship. Divide the plot into upper and lower half triangles.

(d) If the scattered data points align perfectly on the 45° line, the series does not exhibit a trend.

(e) When the scattered data points lie within the upper half triangle, the series is said to have an increasing trend. When the scattered points lie within the lower half of the triangle, the series is said to have a decreasing trend.

(f) A time series exhibits a monotonic trend if all scattered points are positioned either above or below the upper or lower half of the triangle. Nonmonotonic trends arise when scattered points are distributed in both the upper and lower halves of the triangle.

(g) Trends representing low, medium, and high values can be observed in the scatter graph.

(h) In assessing the significance of the trend, a null hypothesis (H0) is proposed: if the estimated slope value (S) falls below the critical threshold (S cri ), there is no significant trend. Conversely, the alternative hypothesis (Ha) posits that a significant trend exists when S > S cri .

(i) The trend slope (S) is computed using the following formula 78 :

where \({\overline{y} }_{1}\) and \({\overline{y} }_{2}\) represent the means of the I and II half of the SPI series, respectively, and n is the number of data points in the SPI timescales.

(j) The standard deviation (σ s ) of the slope of the trend is calculated using the following formula 78 :

where σ represents the standard deviation and \({\rho }_{{\overline{y} }_{1}{\overline{y} }_{2}}\) is the cross-correlation coefficient between the means of the two half series.

(k) The confidence limits (Scri) for a standard normal probability density function are utilized to calculate the confidence limits of the trend slope at a significance level of α, employing the following formula 78 :

If the slope falls outside the lower or upper confidence limits, the null hypothesis of no significant trend is rejected at the significance level of α. This methodology has been extensively applied to trend identification in rainfall and drought studies 50 , 79 , 80 . In this study, both short-term and long-term SPI values were employed to identify drought trends following the methodology described above.

Threshold rainfall for no drought conditions

The SPI method offers a notable advantage by not only calculating the SPI for a specific rainfall amount but also determining threshold rainfall values corresponding to various drought severity categories at different time scales. Threshold rainfall refers to the minimal moisture necessary to establish non-drought conditions 70 . This approach introduces a new perspective to drought studies, addressing drought vulnerability by establishing specific threshold rainfall conditions.

The threshold rainfall represents the minimum amount of rainfall required to prevent drought formation. In this context, threshold rainfall values are defined as the point where the SPI value reaches zero, marking the onset of drought 70 . When the SPI value reaches zero, the threshold rainfall for a particular month equals the average monthly rainfall over the period of analysis. It is important to note that SPI values below zero signify the presence of a drought, and as these values decreases below zero, the drought severity intensifies. After calculating the threshold rainfall values for respective grid, the values are mapped to illustrate their spatial extent. This mapping aids in identifying areas where a certain amount of rainfall is required to prevent the initiation of a drought event.

Drought hazard

The drought hazard index (DHI) was employed to assess the short- and long-term drought hazards in the Vaippar Basin over 3- and 12-month periods, aiming to facilitate mitigation plans. This index was calculated by incorporating the occurrence of moderate to extreme drought severity categories. Determination of drought severity occurrence is influenced by several key factors, including the duration of the drought period, its magnitude, and its spatial extent 70 , 81 . Subsequently, spatial maps based on the drought hazard index were developed to illustrate various severity levels and depict different facets of drought using GIS applications. The following methodology outlines the approach taken to create these spatial maps based on DHI values 53 , 54 , 55 , 56 .

Generate maps depicting moderate, severe, and extreme drought categories based on SPI values at 3- and 12-month intervals.

Classify the maps into three categories, low, moderate, and high, with each category receiving equal representation.

Subsequently, combine these maps individually to create drought hazard index maps.

Assign specific weights and ratings to each drought category, as outlined in Table 6 .

Calculate the Drought Hazard Index (DHI) for the integrated layer using the following formula: DHI = (Rating for moderate droughts × Weight assigned to moderate droughts) + (Rating for severe droughts × Weight assigned to severe droughts) + (Rating for extreme droughts × Weight assigned to extreme droughts).

Spatial drought analysis

Spatial drought analysis was conducted using gridded SPI values to visualize the spatial extent of drought occurrence, drought severity, and threshold rainfall to avoid drought and drought hazards for short- and long-term drought at 3- and 12-month timescales within a GIS environment. The analysis was performed using QGIS 3.30.2 and involved the application of IDW techniques.

Results and discussion

Rainfall characteristics.

The monthly rainfall data from 13 rain gauge stations were transformed into 26 gridded rainfall datasets via spatial interpolation methods within a GIS environment. The average areal monthly rainfall for the Vaippar Basin was calculated from these gridded monthly datasets, resulting in an average of 762.6 mm. Notably, 24 out of the 49 years (49%) of study recorded rainfall totals below this average.

The mean accumulated areal monthly rainfall distribution and monthly rainfall deviation from the average monthly rainfall are visually represented in Fig.  3 . The maximum rainfall, reaching 183.5 mm, was recorded in October, closely followed by November, during the northeast monsoon season. In contrast, the minimum monthly rainfall of 13.8 mm was observed in June. The rainfall distribution throughout the year is as follows: 41.2 mm (5.41%) during winter, 155.7 mm (20.42%) in summer, 148.5 mm (19.48%) during the southwest monsoon, and 417.1 mm (54.69%) during the northeast monsoon. The highest rainfall occurred during the northeast monsoon season, while the rainfall during the southeast monsoon and summer monsoon seasons was approximately equal. Generally, the winter season had the lowest and least frequent rainfall. In Fig.  3 , the monthly rainfall deviation from the average indicates positive values for September, October, and November, while the remaining months exhibit negative deviations.

The accumulated areal monthly rainfall (mm) for selected years and the percentage of monthly rainfall departure for the Vaippar Basin.

The mean accumulated areal monthly rainfall was compared with the accumulated areal rainfall in other years throughout the study period, and the results are depicted in Fig.  3 . This analysis revealed that the Vaippar Basin faced significant rainfall deficits during several decades, including the 1970s, 1980s, 1990s, and 2010s. Within these time spans, both monthly and annual rainfall often fall notably below the normal averages. Notably, in 1974, 1976, 1980, 1991, 2012, and 2016, rainfall totals were less than 75% of the average annual rainfall. Among these years, 2016, 1980, and 1991 were classified as I, II, and III extreme drought years, respectively.

Figure  4 a,b illustrates the annual rainfall in mm and annual rainfall deviations in percentage from the average, offering a clear visualization of both the overall variations in precipitation patterns over the study period and the gridwise distribution of these deviations. It becomes evident from the figure that the G17 to G26 grids exhibited negative values, with higher negative values corresponding to higher grid numbers. Approximately 34.6% of the rainfall grids exhibited annual rainfall exceeding 800 mm, which was predominantly concentrated in the eastern hilly region of the basin.

Gridwise ( a ) annual rainfall (mm) ( b ) deviation in percentage from average rainfall for the Vaippar Basin.

Temporal drought analysis

The areal monthly SPI values represent drought conditions across the entire basin. These indices were computed using average areal monthly gridded rainfall data to evaluate the temporal fluctuations in drought conditions. Figures  5 a,b depict the short- and long-term droughts, respectively, using the monthly SPI values at both the 3-month and 12-month time periods.

Monthly SPI values at ( a ) 3-month and ( b ) 12-month time periods for the Vaippar Basin.

The monthly SPI values illustrate that the region experienced frequent droughts during the period under drought analysis and reveal several extreme drought events. SPI_3 values are employed to assess short-duration droughts, which occur more frequently than other droughts and impact agricultural conditions. These short-term droughts are defined as instances when the annual rainfall for three consecutive months is less than the average annual rainfall.

SPI_12 values are used to assess long-term drought. These indices are analysed to evaluate the impact of hydrological drought on water resources. The examination of long-term droughts indicates that they happen less often than short-term droughts. Long-term droughts are characterized by instances where the annual rainfall falls below the average annual rainfall.

The figures also demonstrate that drought frequency changes with varying SPI time periods. In the shorter time period (SPI_3), there was a rise in drought frequency, yet the duration of drought events decreased, leading to shorter-lasting drought events. Conversely, at longer time periods (SPI_12), droughts occur less frequently but tend to persist for more extended periods.

An examination of the estimated SPI series indicates that the basin experienced droughts of varying magnitude and durations during the drought period, with notable drought years occurring in the 1970s, 1980s, 1990s, and 2010s. Specifically, 1976, 1980, 1991, 2012, and 2016 were identified as the extreme drought years in the basin. The areal monthly SPI values at 3- and 12-month time periods were analysed, focusing on two main aspects: (i) the occurrence of drought categories expressed as percentages and (ii) the assessment of drought parameters specific to the Vaippar Basin. Furthermore, with the aid of gridded SPI values, temporal analysis was extended to assess the frequency of drought severity and its areal extent.

Occurrence of drought severity categories

This study examined the occurrence of drought severity categories spanning from mild to extreme drought in the Vaippar Basin. This examination was carried out by calculating the proportion of each event's occurrence within its respective severity category in relation to the total number of droughts within that same severity category.

Table 3 presents the monthly distributions of occurrences of short- and long-term drought severity categories at 3- and 12-month time periods. The results suggest that mild droughts occur with the highest frequency, whereas extreme droughts are the least frequently observed for both short-term (SPI_3) and long-term (SPI_12) droughts. Furthermore, drought conditions were prevalent for almost half of the study period. The occurrence of drought severity levels, spanning from mild to extreme, demonstrates nearly similar values for both short-term and long-term droughts.

As shown in the table, it becomes evident that the basin faced frequent droughts throughout the year. The monthly distribution of drought occurrences during the 3-month time period indicates that negative SPI values were most frequent in February, while October recorded fewer instances of drought. On the other hand, when examining the 12-month SPI values, the occurrence of drought was greater in September, closely followed by October. Considering the significance of water resources and the predominant distribution of rainfall during the northeast monsoon, particular attention may be warranted for the month of October.

Annual distribution of drought severity categories

Monthly gridded SPI values were employed to evaluate the annual distribution of drought severity categories. For each year, for every grid, the number of months indicating mild to extreme drought conditions was used to illustrate the annual distribution of drought occurrences within different severity categories. Figure  6 a,b provide visual representations of the annual distributions of drought severity categories during the study period for short- and long-term droughts at 3- and 12-month time periods, respectively.

Annual distributions of drought occurrence categories (percentages) based on the ( a ) 3-month and ( b ) 12-month SPI series for the Vaippar Basin.

For short-term drought, it becomes evident that in the years 1978, 1982, 2009, and 2012, more than 70% of the time experienced mild to extreme drought conditions. Conversely, the years 1975, 1980, 1985, 1986, 1991, 2000, 2013, and 2016 exhibited mild to extreme drought durations ranging from 60 to 70%. In 2016, out of the total drought occurrences, which amounted to 69.55%, approximately 47.12% of the months across all grids experienced moderate to extreme drought conditions. Likewise, in 1980 and 1998, moderate to extreme drought conditions were observed, accounting for 39.42% of the total drought occurrences out of 63.78% and 33%, respectively, of the total drought occurrences.

For long-term drought, it was observed that in the years 1971, 1975, 1976, and 2013, drought occurred more than 90% of the time. In contrast, 1981, 1983, 1986, 1992, 1996, 2004, 2010, 2014, and 2017 experienced drought rates between 80 and 90%. The most severe drought years, namely, 1981, 2013, and 2017, experienced moderate to extreme drought conditions, accounting for 66.99% of 85.58%, 60.26% of 99.04%, and 60.26% of 86.86%, respectively, of the total occurrence of drought.

The short- and long-term drought parameters derived from areal SPI values at 3- and 12-month time periods are presented in Table 4 . For SPI_3, the most intensified drought, indicated by the minimum negative SPI value of − 3.14, occurred in October 1980. On the other hand, for SPI_12, the most intensified drought occurred in May and June 1981, with SPI values reaching − 2.89. The weighted annual accumulated drought severity calculated for short-term drought was greater in 1980, with a value of − 15.42, whereas for long-term drought, the severity was greater in 1981, with a value of − 21.28. This implies that during the long-term drought assessment, the severity of drought in the previous year had an impact on the following year.

The number of drought events, drought event duration and periods of occurrence for short- and long-term drought are presented in Table 4 . Drought events are defined as instances where negative SPI values persist continuously. There were 91 and 31 drought events for short- and long-term drought, respectively.

The average drought duration was computed by dividing the total number of drought months by the total number of drought events. This calculation, as shown in Table 4 for the SPI_3 and SPI_12 series, helps identify the types of droughts that occurred in the study area. Specifically, the average drought durations for short- and long-term droughts (SPI_3 and SPI_12) were 3.3 and 9.1 months, respectively, categorizing them as short- and long-term droughts, in line with the findings of Guttman 65 and Mishra and Desai 25 , 62 . The study of short-term droughts is crucial for agricultural interests, while long-term droughts have significant implications for water management.

The durations of prolonged drought events, along with their severity and intensity, were identified. The prolonged drought event duration was defined as the period during which the SPI values continuously remained negative. Drought severity was determined by summing the negative SPI values for the longest duration droughts. A prolonged drought duration and the highest severity of drought were observed at SPI_12, indicating that droughts became more severe over extended periods.

Furthermore, drought intensity, calculated by dividing drought severity by the duration of drought events, was estimated. The highest intensities of − 1.49 and − 0.97 for prolonged drought events were observed for short- and long-term drought, respectively. This analysis revealed that the basin experienced its longest-duration and highest-severity droughts during the 1970s, 1980s, 1990s, and 2010s.

Analysis of drought severity recurrence curves

Drought severity recurrence curves associated with different spatial extents serve as valuable tools for assessing both the spatial attributes and the recurrence of drought severity within a basin. These curves are particularly useful for estimating the recurrence interval associated with the average annual severity of disease in relation to its geographical extent. Furthermore, they prove instrumental in analysing the relationship between severity and recurrence interval and the spatial extent of historical droughts within the basin.

In the context of this study, drought severity recurrence curves were specifically developed based on the average annual severity for both the 3-month and 12-month time periods of SPI values. These curves are visually represented in Fig.  7 , with the X-axis denoting the spatial extent of droughts and the Y-axis denoting drought severity. The average annual drought severity is calculated as the sum of negative SPI values during dry months for various return periods.

Drought severity recurrence curves (in years) of the ( a ) 3-month and ( b ) 12-month SPI series for the Vaippar Basin.

Recurrence interval analysis was performed to identify the most suitable distribution for annual drought severity. In this study, the extreme value type I distribution was chosen for recurrence interval analysis because it successfully passed both tests for the SPI_12 time period across all grids. This distribution is characterized by two parameters, and its parameter values can be estimated with relatively lower uncertainty, which is particularly important given the limited sample size in this study. Furthermore, this approach has been extensively utilized in numerous studies focused on extreme drought analysis 23 , 24 , 25 , 73 .

Figure  7 a,b depict the drought severity recurrence curves for the average annual severity developed using 3-month and 12-month SPI values. The first three extreme drought years were identified based on the minimum average annual drought severity, and these years were included in the drought severity recurrence curves to identify recurrence patterns and the spatial extent of these selected drought years. Notably, most areas experienced drought in 1980, 2016, and 1998 due to short-term drought (SPI_3), with return periods of 50 years, and the spatial extent of these droughts increased over time.

The minimum average annual drought severity for long-term drought (SPI_12) occurred in 1981, 2017, and 1975. The figure illustrates that severe drought years are associated with return periods ranging from 20 to 75 years. Severe droughts (defined as SPI < − 1) exhibit lower recurrence rates and limited spatial extent.

Analysis of drought trends

Short- and long-term drought trends of areal SPI values were evaluated using the innovative trend identification method at 3- and 12-month time periods. The trend parameters for the monthly SPI series at 3- and 12-month time periods in the Vaippar Basin, as detected by the innovative trend method, are presented in Table 5 .

Based on the observations from the table, it is evident that the slope values of several monthly SPI series during the period 1971–2019 fall outside the lower and upper confidence limits (CL), indicating the presence of a significant trend in the rainfall pattern. Furthermore, there is a notable correlation between the two halves of the SPI series. Specifically, a significant upwards trend was identified in the months of March, April, May, August, October, and December, while a significant downwards trend was observed in January, June, and November, both at the 5% and 10% significance levels for short-term drought. Additionally, a significant upwards trend was noted in December at both the 5% and 10% significance levels for long-term drought.

Figure  8 a,b present a visual representation of the scattered SPI values of different months for short- and long-term droughts. The entire SPI series was split into two equal halves, with the first half plotted against the second half. One of the key advantages of this method is its ability to discern subtrends within an SPI series 48 . By examining the distribution of the data points on the ITA graph, the time series were categorized into three zones, namely, low, middle, and high, along the 1:1 line, facilitating detailed interpretation. Furthermore, the scatter plot technique allows for the identification of both monotonic and nonmonotonic upwards or downwards trends within a series by examining the positions of data points. A data point that aligns along the 1:1 line indicates the absence of a trend. However, if data points are situated in either the upper or lower triangle regions, this indicates a monotonic trend. On the other hand, if some data points cross the 1:1 line, it is considered a nonmonotonic trend. From the figures, it becomes evident that the majority of SPI points are situated in the upper triangle and are predominantly concentrated on the inner side of the graph. This pattern indicates a monotonic upwards trend in the data for most months.

( a ) Short-term drought trend determined by the innovative trend identification method for the Vaippar Basin. ( b ) Long-term drought trend determined by the innovative trend identification method for the Vaippar Basin. I and II halves are the SPI series of 1971–94 and 1995–2019, respectively; orange circle represents the SPI value.

Although estimating drought parameters at an areal scale offers valuable insights for water management, it is imperative to assess drought within a basin on a location-specific basis. Spatial drought analysis is essential for comprehending the spatial distribution of drought parameters and pinpointing the regions most impacted during a specific drought event.

In this study, spatial drought analysis was conducted using gridded SPI values estimated for short- and long-term droughts using the SPI_3 and SPI_12 time periods. The spatial analysis was carried out using QGIS 3.30.2, employing the IDW method to visualize the distribution of various drought parameters across the Vaippar Basin. The Vaippar Basin was divided into distinct regions: (a) the western side (Kalingalar, Deviar, and Nagariyar); (b) the central part (Sevalperiyar, Kayalkudiar, and SindapalliUppodai); (c) the southern side (Nichabanadhi, VallampattiOdai/Uppodai, and Uppathurar); (d) the northern side (Arjunanadhi and Kousiganadhi); and (e) the eastern side (Senkottaiyar and Vaippar).

Various types of maps were created to visualize the spatial distribution of drought parameters, including (i) occurrence of drought categories as a percentage, ranging from moderate to extreme severity (SPI < − 1); (ii) weighted annual accumulated severity and identification of the worst drought years; (iii) threshold rainfall values required to avoid drought; and (iv) the drought hazard index. These maps help provide a comprehensive understanding of the spatial dynamics of drought conditions within the Vaippar Basin.

Spatial analysis of occurrence of drought severity categories

Analysing the percentage occurrence of drought across different severity categories within the Vaippar Basin offers valuable insights for identifying regions that experience recurring droughts. The spatial analysis of the occurrence of drought severity categories as a percentage based on SPI values at 3- and 12-month time periods is depicted in Fig.  9 a,b. To create these maps, the percentage of severe drought occurrences was calculated by considering instances of moderate, severe, and extreme drought (SPI > − 1) categorizations.

Occurrence of drought for ( a ) 3-month and ( b ) 12-month SPI series in the Vaippar Basin.

In the case of short-term drought in the 3-month SPI series, drought was less prevalent on the northern side (Arjunanadhi and Kousiganadhi) than on the eastern side (Senkottaiyar and Vaippar). On the other hand, when examining long-term drought in the 12-month SPI series, drought occurrence was found to increase progressively from the western side (Kalingalar, Deviar, and Nagariyar) toward the eastern side (Senkottaiyar and Vaippar).

It has been observed that there is a distinct relationship between short-term and long-term droughts in most places, except for a few exceptions. In the southern mountainous region of the study area, rainfall typically exceeds normal levels compared to those in the central and northern parts of the basin. However, despite receiving more rainfall, these southern areas are more frequently affected by drought. This has significant implications for water resources, as it leads to a reduced water supply to the lower parts of the basin.

Spatial analysis of weighted annual accumulated drought severity

The weighted annual accumulated drought severity for every grid was computed by multiplying the accumulated severity (sum of all negative values) during monthly dry spells by the probability of occurrence of drought for each year and each grid. The probability of occurrence of drought for each year and each grid was estimated by dividing the number of drought months (i.e., months with negative SPI values) by twelve 23 , 24 , 25 , 29 . In this analysis, each instance of drought can be consistently linked to a particular year, preventing intermittence and implicitly accounting for the duration of dry spells within that specific year.

The spatial distribution of the worst drought years, as determined by the estimated weighted annual accumulated drought severity during the 3-month time period, is illustrated in Fig.  10 a–c. The worst drought year was selected based on the year with the lowest accumulated severity values. In particular, 1980 and 2016 emerged as the worst drought years during the 3-month time period. Drought severity exhibited higher values in the northern regions (Arjunanadhi and Kousiganadhi) as well as in the central region (SindapalliUppodai) of the basin. For the 1980 drought, severe drought was predominantly observed in the northern areas, while for the year 2016, drought severity was notably greater in the southwestern regions (Nichabanadhi, Kalingalar, Deviar, and Nagariyar) as well as in certain parts of the Vaippar and Arjunanadhi subbasins.

Weighted annual accumulated drought severity of short-term drought at 3-month time period for ( a ) annual minimum severity, ( b ) severity during 1980, and ( c ) severity during 2016 for the Vaippar Basin.

The spatial distributions of the extreme drought years and weighted annual accumulated drought severity during the 12-month time period are illustrated in Fig.  11 a–c. At the 12-month SPI time period, the years 1981 and 2017 exhibited heightened severity, attributed to the effects of the 1980 and 2016 droughts, along with accumulated rainfall from the preceding year. Weighted annual accumulated severity scores were notably greater for SPI_12 than for the SPI_3 series. For 1981, drought severity was most pronounced in the central and northern regions of the basin, while in the case of 2017, greater drought severity was observed in the lower regions of the basin.

Weighted annual accumulated drought severity of long-term drought at a 12-month time period for ( a ) annual minimum severity, ( b ) severity during 1981, and ( c ) severity during 2017 for the Vaippar Basin.

Spatial analysis of threshold rainfall for normal/nondrought conditions

The spatial analysis of the threshold rainfall for normal/nondrought conditions for short- and long-term drought events at 3- and 12-month time periods is presented in Fig.  12 a,b. By referring to these figures, it can be observed that the rainfall demand for normal/nondrought occurrences increases from east to west in the Vaippar Basin. Short-term drought occurred during the 3-month time period in December, which covers the northeast monsoon season from October to December over the Vaippar Basin, and the threshold rainfall values for normal conditions vary from 390 to 475 mm. Rainfall values increase from the eastern parts toward the western parts and reach their maximum in the southwestern parts of the basin.

Threshold rainfall (mm) for ( a ) 3-month and ( b ) 12-month SPI series of the Vaippar Basin.

The threshold rainfall values for long-term drought during the 12-month time period exhibit spatial patterns resembling those observed in short-term drought during the 3-month time period, with threshold rainfall ranging from 635 to 828 mm. It is evident that the geographical distribution of these threshold values displays moderate differences. For instance, approximately 828 mm of rainfall is necessary to avert drought in the western regions (Deviar, Nagariar, Kousiganadhi, and Arjunanadhi subbasins), whereas the rainfall requirement for normal/nondrought conditions decreases to 635 mm in the eastern regions (Vaippar subbasin).

Notably, the Deviar, Nagariar, Kousiganadhi, and Arjunanadhi subbasins, located in the hilly region of the Vaippar Basin, usually receive more rainfall than other regions. These areas are likely to be more susceptible to long-term droughts. Areas that typically receive more rainfall are more severely affected by drought events than are other areas that receive less rainfall.

Spatial analysis of drought hazard

The drought hazard index (DHI) was calculated by integrating the incidences of moderate, severe, and extreme drought categories for short- and long-term droughts at 3- and 12-month time periods. The integrated layers were then superimposed on the subbasin map of the Vaippar Basin. DHI values are categorized into three classes using the ratio 33.3:33.3:33.4 for both the 3- and 12-month time periods, as illustrated in Table 6 . The spatial extent of the DHI is presented in Fig.  13 a,b.

Drought hazard indices for the ( a ) 3-month and ( b ) 12-month SPI series of the Vaippar Basin.

For short-term droughts at 3 months, high DHI values covered 19.23% of the total land area, primarily in minor parts of the northeastern and northwestern areas (Arjunanadhi and Kousiganadhi) of the basin. Moderate hazards covered 61.54% of the total land area and were predominant in most parts of the basin. Low-hazard areas covered 19.23% of the total land area and were found mainly in very small sections.

After long-term drought for 12 months, higher DHI values were distributed across 26.92% of the total land area, largely in the lower parts of the basin (Uppathurar, Senkottaiyar, and Vaippar). Moderate drought hazards covered 42.31% of the total area and were distributed in the western and central regions. Low drought hazards covered 30.77% of the area and were primarily located in the western parts.

Assessing the spatial and temporal variability of short- and long-term meteorological drought parameters is the first step in managing drought-related risk 82 . This study revealed that droughts are recurrent phenomena in the Vaippar Basin. A decrease in the percentage of actual rainfall from normal rainfall in the rainy season has led to more severe and longer-lasting droughts. The present study revealed that for short-term drought, 1978, 1982, 2009, and 2012 had mild to extreme drought conditions for more than 70% of the time. For long-term droughts, drought occurred more than 90% of the time in 1971, 1975, 1976, and 2013. The weighted annual accumulated drought severity was greater in 1980 for SPI_3, with a value of − 15.42, whereas for SPI_12, the severity peaked in 1981, with a value of − 21.28. Notably, the highest intensity, at − 1.49, was observed in SPI_3 in comparison to SPI_12, which had an intensity of − 0.97. Most parts of the basin experienced drought in 1980, 2016, and 1998 due to short-term drought, with return periods of 50 years. Long-term droughts occurred in 1981, 2017, and 1975, with return periods ranging from 20 to 75 years. This study revealed a significant upwards monotonic trend in the monthly SPI series for both short- and long-term droughts utilizing an innovative trend identification method.

The spatial analysis revealed that the occurrence of drought was greater on the eastern side (Senkottaiyar and Vaippar) for both short- and long-term droughts. Drought severity exhibited higher values in the northern regions (Arjunanadhi and Kousiganadhi) for short-term drought, whereas severity scores were notably greater in the eastern parts of the basin for long-term drought. Threshold rainfall values to avoid drought increased from the eastern parts toward the western parts, reaching their maximum in the southwestern parts of the basin for both short- and long-term drought. Furthermore, the drought hazard index was greater in northwestern areas (Arjunanadhi and Kousiganadhi) for short-term drought and in southern parts (Uppathurar, Senkottaiyar, and Vaippar) for long-term drought.

The occurrence of drought in highly elevated eastern parts of the basin reduces the inflows to the lower parts of the basin and has a great impact on both rainfed and irrigated agriculture. On the other hand, increasing the occurrence of drought in the western parts of the basin, particularly in the rainfed areas of the basin, will further complicate agricultural-dependent farmers’ livelihoods. The increasing occurrence of meteorological droughts coupled with hydrological droughts in the upper Vaippar basin will have significant implications for the water supply in the lower basin. Access to site-specific data regarding drought occurrence, severity, threshold rainfall, and the drought hazard index could prove invaluable for decision-makers. This information can assist in identifying suitable mitigation measures for future drought events and in mitigating their adverse effects.

Studies of a similar nature, investigating rainfall pattern rainfall deficiency, have been previously conducted in India 45 , 46 , 47 , 83 . In this regard, drought analysis studies can provide valuable information for forecasting and mitigating drought impacts at the basin scale 23 , 84 , 85 , 86 . Studies also support the effectiveness of innovative trend analysis in detecting trends 46 . The drought vulnerability map developed in this study will be highly useful in identifying vulnerable areas within the study area for various degrees of drought, a finding also corroborated by previous research 56 . While our study assessed drought parameters using historical data, early warning systems that utilize real-time, high-resolution satellite rainfall products present a superior option for timely information on drought onset during various growing seasons 87 .

Conclusions

This study aimed to assess short- and long-term drought parameters in the Vaippar Basin using gridded rainfall data and the standardized precipitation index (SPI) at 3- and 12-month time periods. Temporal analysis included evaluating drought parameters such as the occurrence of drought severity categories, drought events, duration of drought event, severity, drought trend and drought severity recurrence curves. Spatial analysis utilizing gridded SPI values for short- and long-term drought encompassed the occurrence of drought categories, weighted annual accumulated severity, threshold rainfall values, and drought hazard indices. The results of the study showed that mild droughts were the most common droughts, while extreme droughts were the least common for both short-term and long-term droughts. Additionally, nearly half of the study period experienced drought conditions. The study showed that short-term drought events had an average duration of 3.3 months, while long-term droughts lasted for an average of 9.1 months. This study revealed a significant upwards monotonic trend in the monthly SPI series for both short- and long-term droughts utilizing an innovative trend identification method. The spatial analysis revealed that the occurrence of drought was greater on the eastern side for both short- and long-term droughts. Furthermore, the drought hazard index developed based on drought severity was greater in northwestern areas for short-term drought and in southern parts for long-term drought. Data pertaining to location-specific drought hazards, including information on drought occurrence, severity, threshold rainfall, and the drought hazard index, could prove instrumental for decision makers. Such information can aid in the identification of suitable mitigation strategies for future drought events and in mitigating their potential impacts. The core strength of this study lies in integrating all drought parameters to create a micro-level drought vulnerability map in the study area, alongside assessing trends. Advancing, the subsequent stage of this research will involve predicting drought trends, thus enabling better planning for the future within the basin. Additional research is necessary to gain a deeper understanding of the effects of droughts on water resources, encompassing streamflows and groundwater, and water storage structures throughout the basin.

Data availability

The data used to support the findings of this study are available from the corresponding author upon request.

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M.M, S.S and N.M conceived and designed the study. M.M, R.A and N.M collected and processed the data and prepared mappings. M.M, S.S and A.N analysed the data, and M.M, A.N, S.S and R.A prepared the manuscript writings. All authors reviewed the manuscript and approved the final draft version.

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Muthiah, M., Sivarajan, S., Madasamy, N. et al. Exploring short- and long-term meteorological drought parameters in the Vaippar Basin of Southern India. Sci Rep 14 , 13428 (2024). https://doi.org/10.1038/s41598-024-62095-y

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Drought characteristics over Deccan Plateau Region of India

Key messages.

• A persistent hazard - challenge to livelihoods of the most vulnerable communities, with cascading impacts on overall national development;
• Technical aspects – monitoring, early warning and improvements – ongoing and planned – need to focus on “practical” tools that can be embedded and sustained in operational systems that capture dynamic vulnerability and strengthening existing systems;
• Changing morphology of droughts in the Indian context – large-scale slow onset low-frequency to high-frequency localized impact – flash droughts;
• Institutional arrangements & challenges, relevance and consequences in the future context.

This case study is a contribution to the GAR Special Report on Drought 2021.

Document links last validated on: 16 July 2021

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Mongabay Series: Flood and drought

Flash droughts set to increase in India, finds study

• Flash droughts are droughts that intensify more rapidly than normal, posing a risk to agriculture, ecosystems and water availability.
• A new study predicts a 7-8 fold increase in the frequency of flash droughts in India due to concurrent occurrence of extreme dry and hot periods during the monsoon season and greenhouse emissions.
• An increased frequency of flash drought pose a major risk to crop production due to soil moisture depletion and intraseasonal monsoon variation.

A new study predicts an increase in the frequency of flash droughts in India towards the end of this century. Intraseasonal variability of the summer monsoon rainfall and anthropogenic warming have been found to amplify the risk of future flash droughts and this can have negative impacts on crop production, irrigation demands, and groundwater abstraction in India, according to the study.

Flash droughts are droughts that intensify more rapidly than normal,  posing a risk to agriculture, ecosystems and water availability. Conventional droughts take months and sometimes even years to develop to full intensity. Flash droughts on the other hand develop at an unusually fast rate due to extreme weather conditions and persist from a few weeks to some months. Such droughts can be localised to a specific region or can become widespread and affect a large part of the country.

The new study by researchers from the Indian Institute of Technology (IIT), Gandhinagar, investigated the causes of past flash droughts in India between 1951 and 2016. Based on the findings, the researchers predict an increase in the frequency of flash droughts in the future.

In India in recent years, flash droughts occurred in 1986, 2001 and 2015. The 2001 flash drought-affected north and central India while the 1986 and 2015 flash droughts were more widespread, impacting crop production. A 2020 study found that 10%-15% of rice and maize crop areas are affected by the flash droughts each year in India.

Ulka Kelkar, Director of Climate Program at the World Resource Institute (WRI) India, who was not involved in the study, said that as the climate warms, evaporation is likely to increase, and summer rainfall is expected to fall in a few intense spells. “We tend to think of drought as a slow-onset disaster. But many cropping practices are very tightly dependent on the timely arrival of the monsoon,” she said.

Though the impact may be lesser than a more severe conventional drought, frequent flash droughts can have a cumulative drastic effect on agriculture. The rapid onset of drought and lack of early warning does not give enough time for preparation and drought mitigation and could lead to extensive damage. For instance, in 2012, a flash drought in the United States of America impacted a large part of the country causing agricultural loss of over $30 billion.

Read more: Impact of drought and heat on forests

Identifying flash droughts

Flash droughts can be identified either by monitoring changes in precipitation, evapotranspiration, or soil moisture. In the current study, the researchers used a hydrology model to simulate soil moisture patterns across the country to identify the duration and extent of flash droughts. A defining feature of a flash drought is the rapid depletion of soil moisture in the topsoil layer which then moves deeper affecting the ‘root-zone’ of crops and vegetation.

Wind speed, daily precipitation, and maximum and minimum temperature data of the last 60 years were fed into the model to estimate soil moisture over 5-day periods. The onset of flash droughts was identified as the time when soil moisture dropped below a particular level (the 20 th percentile) over the 5-day period.

With these criteria, the researchers considered flash droughts with a minimum duration of 15 days and a maximum duration of 90 days and identified 15 flash drought events in the past. Of these, the flash drought in the year 1979 that affected 40% of the country, was observed to be the most severe.

The 1979 drought in India has been described as “the worst in this century nationwide” in a New York Times article . The hardest-hit areas received 75% less than normal rainfall and some states had reached ‘near-famine’ conditions. The reasons for the drought were not clear at that time but were thought to be due to odd-weather patterns and unusually hot temperatures.

This study offers some clues. On investigating the primary drivers of flash droughts, the researchers found that a simultaneous occurrence of extreme hot and dry periods resulting in depletion of soil moisture were the main causes.

Vimal Mishra, Associate Professor of Civil Engineering, IIT Gandhinagar and lead author of the study explains that in 1979, around 125 days after the onset of the monsoon season, the soil moisture was very high. But within 10 days, the soil moisture dropped very quickly as there was a dry spell and temperatures were high, with an average increase of more than 2 degrees centigrade.

He said, “Normally, conventional droughts take months to build up. But flash droughts can occur during the monsoon season when there are long breaks in the monsoon and the temperature is high. Soil moisture dries within 1-2 weeks, causing flash droughts.”

To gain an understanding of how flash droughts would occur in future climates, they used the same model but fed data from a global climate model, Community Earth System Model that provides temperature and precipitation data for the year 1900-2100, to estimate soil moisture.

The model predicts a 7-8 fold increase in the frequency of flash droughts in India similar to the one in 1979. Mishra said that although during the initial phase of the monsoon, there is a projected increase in rainfall, during the later season, rainfall is projected to decline. But temperatures are projected to increase throughout resulting in extreme hot and dry periods. This intraseasonal variation in monsoon along with warmer temperatures will increase the risk of flash droughts.

The influence of anthropogenic events such as greenhouse emissions, land use-land cover change and industrial aerosols were also investigated. The study found that greenhouse emissions will significantly increase the frequency of extreme hot and dry periods, which are the main drivers of flash droughts.

Raghu Murtugudde, professor of Atmospheric and Oceanic Science at the University of Maryland, USA, who was not involved in the study said, “The study looks at the past history of flash droughts in terms of the dynamic patterns before getting into future projections. This is a good way to do things. However, the models remain unreliable.”

He said that most models are unable to produce the historic trends in the monsoon and the projections of regional patterns for rain, for e.g. which month will have maximum rain, are different for each model.

Since flash droughts are a very regional phenomena, he added that it is critical to understand reliable regional patterns within the overall dynamic patterns.

“India is like a popcorn kettle that is getting hotter. But where the kernels will pop or not is a difficult process to model or project. So I tend to be very skeptical about projections beyond a decade or two,” he said.

But he expects that warming of the Indian Ocean and weather influences from the Atlantic and Pacific Oceans may produce large-scale extreme changes in monsoon which could cause flash droughts.

Impact on agriculture

The Indian summer monsoon varies across different time scales such as interannual or diurnal, but the intraseasonal variations are the most prominent and crucial ones that impact agriculture production, according to Arindam Chakraborty, Professor, Centre for Atmospheric and Oceanic Sciences, IISc, Bangalore.

Chakraborty who was not involved in the research said, “this study is essential to show how atmospheric forcings such as longer breaks (in monsoon) can impact the soil moisture and thus the agriculture production.”

Murtugudde added, “the impact on agriculture is even more complicated because just the warming will do a lot of damage even if rainfall doesn’t change. Droughts themselves get exacerbated because of the warming in terms of soil moisture loss.”

Read more: Making children resilient to droughts

Kelkar’s work in the Jalna district of Maharashtra has revealed that poorer farmers, usually from low caste or small farm holders are most vulnerable to drought. When droughts become severe or widespread, they impact agriculture as labour work is hard to find and even factories can shut down due to water shortage, said Kelkar.

“With climate change, we need to prepare for drought just as we would for a sudden flood or storm – with early warning systems, drought-tolerant crop varieties, hardy cattle breeds, cattle shelters, and crop insurance,” she warned.

Mishra, V., Aadhar, S., & Mahto, S. S. (2021). Anthropogenic warming and intraseasonal summer monsoon variability amplify the risk of future flash droughts in India.  npj Climate and Atmospheric Science ,  4 (1), 1-10.

Banner image: Soil cracked during drought. Photo by jodylehigh/Pixabay.

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India’s Longest Drought: 41-month-long Dry Spell From 2015-18 Was Longest in 150 years, Finds Study

Curated By : Aniruddha Ghosal

Last Updated: October 23, 2019, 15:15 IST

Representative image

The findings of the research conducted by an IIT professor suggest that such long-term droughts could impact “water security in one of the most populous regions in the world”.

As the dispute over their ancestral property was growing more acrimonious by the day, 40-year-old farmer Murat Singh Yadav and his brother Charan Singh decided a partition was the only way to tamp down the discord. But a bone of contention remained between these residents of Jhansi district in Uttar Pradesh. Charan wanted to use water from Murat’s well without paying for it, but an agreement could not be reached. On December 26, 2017, police in Madhya Pradesh’s Chhatarpur district got a call about a blindfolded man found abandoned with both his hands tied. It was Murat. He told the police that, angered at their wish not being granted, Charan, his sons, and relatives drugged him, took him to an undisclosed location and tortured him for over a week. Seeing his condition worsen, they dumped him where he was found.

The whole area falls in the Bundelkhand region, which straddles MP and UP. It is an arid territory that has been facing a crippling drought in recent years with water sources drying up even in mid-winter, forcing many locals to flee. Now, a scientific report published this month that reconstructed meteorological and hydrological droughts in India from 1870-2018 has found that the 41-month-long dry spell from 2015-18 was the longest. And, although not the deadliest, it had a “remarkable impact on reservoir and groundwater storage” suggesting that such long-term droughts could impact “water security in one of the most populous regions in the world”.

The study, “Long-term (1870-2018) drought reconstruction in the context of surface water security in India”, published in the Journal of Hydrology on October 14, was conducted by Vimal Mishra, associate professor in the civil engineering department at IIT Gandhinagar. The report identified “18 meteorological and 16 hydrological droughts”. The five most severe or “deadly” meteorological droughts occurred in 1899, 1876, 2000, 1918, and 1965, while the five major hydrological droughts occurred in 1899, 2000, 1876, 1965, and 1918, the study found. It added, “The meteorological and hydrological drought of 1899 was the most severe in the entire record of 1870-2018.”

A meteorological drought takes place when dry weather patterns dominate an area, and a hydrological drought takes place when low water supply becomes evident, in streams, reservoirs, usually after months of meteorological drought. India is particularly vulnerable to drought since a large section of the population is dependent on agriculture. And agriculture, in turn, is dependent on the monsoons when nearly 80 per cent of the total annual precipitation for the country takes place. The threat of drought is amplified by climate change and desertification and in India, nearly a third of the total area (effectively twice the size of Spain) has been degraded through deforestation, soil erosion or depleted wetlands. This, experts believe, reduces the country's GDP by 2.5 per cent annually.

The study also confirmed that each of the top five "meteorological droughts" were caused by the "positive SST (sea surface temperature) anomalies over the Pacific" and that "ENSO remains the major driver of the monsoon season precipitation in India". It added, "The role of SST variability in Indian and Atlantic Oceans has increased during the recent decades (1980-2018)."

The link between sea surface temperatures and droughts in India is one of concern. The special report by the United Nations Intergovernmental Panel on Climate Change released last month found that surface temperatures for the world’s oceans are rising at an alarming pace, causing marine “heatwaves” and accelerating sea levels. It noted, “Marine heatwaves have doubled in frequency since 1982 and are increasing in intensity” while adding that the global ocean temperatures have warmed every year since 1970, and the rate has more than doubled since 1993.

Although the “severity of the most recent drought (2015-2018)” was considerably lesser than previous droughts, it had the “longest duration (41 months) among all the major droughts in India”, the study found, and warned, “The 2015-2018 drought had a remarkable impact on reservoir and groundwater storage in India suggesting that long-term droughts can influence water security in one of the most populous regions of the world. In addition, rapid groundwater depletion combined with long-term droughts can pose serious challenges for water availability in India.”

The 2015-18 drought-impacted a fourth of India’s total population and affected “surface (reservoir storage) and groundwater availability in both southern and northern parts of India and was linked to El Nino and Indian Ocean Dipole”, it added.

The study utilises long-term data, from 1870-2018, to identify the top five or "deadly" meteorological or hydrological droughts based on overall severity score in the past 150 years.

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Follow our news, recent searches, rural india runs dry as thirsty megacity mumbai sucks water, advertisement.

Experts have warned of worsening water shortages in India. (Photo: AFP/Indranil Mukherjee)

NAVINWADI: Far from the gleaming high-rises of India's financial capital Mumbai, impoverished villages in areas supplying the megacity's water are running dry - a crisis repeated across the country that experts say foreshadows terrifying problems.

"The people in Mumbai drink our water but no one there, including the government, pays attention to us or our demands," said Sunita Pandurang Satgir, carrying a heavy metal pot on her head filled with foul-smelling water.

Demand is increasing in the world's most populous nation of 1.4 billion people, but supplies are shrinking - with climate change driving erratic rainfall and extreme heat .

Large-scale infrastructure for Mumbai includes reservoirs connected by canals and pipelines channelling water from 100km away.

But experts say a failure of basic planning means that the network is often not connected to hundreds of rural villages in the region and several nearby districts.

Instead, they rely on traditional wells.

But demand far outstrips meagre resources, and critical groundwater levels are falling.

"Our days and our lives just revolve around thinking about collecting water, collecting it once, and collecting it again, and again," Satgir said.

"We make four to six rounds for water every day ... leaving us time for nothing else".

HEATWAVES AND DRY WELLS

Climate change is shifting weather patterns, bringing longer-lasting and more intense droughts.

Wells rapidly run dry early in the extreme heat .

In the peak of summer, 35-year-old Satgir said she can spend up to six hours a day fetching water.

Temperatures this year surged above a brutal 45 degrees Celsius.

When the well dries, the village then relies on a government tanker with irregular supplies, two or three times a week.

It brings untreated water from a river where people wash and animals graze.

Satgir's home in the dusty village of Navinwadi, near the farming town of Shahapur, lies about 100km from the busy streets of Mumbai.

The area is also the source of major reservoirs supplying about 60 per cent of water to Mumbai, local government authorities say.

Mumbai is India's second-biggest and rapidly expanding city, with an estimated population of 22 million.

"All that water from around us goes to the people in the big city and nothing has changed for us," Satgir said.

"Our three generations are linked to that one well," she added. "It is our only source."

Deputy village head Rupali Bhaskar Sadgir, 26, said residents were often sick from the water.

But it was their only option.

"We've been requesting governments for years to ensure that the water available at the dams also reaches us," she said. "But it just keeps getting worse."

Government authorities both at the state level and in New Delhi say they are committed to tackling the problem and have announced repeated schemes to address the water crisis .

But villagers say they have not reached them yet.

Commentary: India’s scorching heat is making it unlivable

Extreme heat triggers novel payout for 50,000 women in India

"unsustainable rates".

India's government-run NITI Aayog public policy centre forecasts a "steep fall of around 40 per cent in freshwater availability by 2030", in a July 2023 report.

It also warned of "increasing water shortages, depleting groundwater tables and deteriorating resource quality".

Groundwater resources "are being depleted at unsustainable rates", it added, noting they make up some 40 per cent of total water supplies.

It is a story repeated across India, said Himanshu Thakkar, from the South Asia Network on Dams, Rivers and People, a Delhi-based water rights campaign group.

This is "typical of what keeps happening all over the country", Thakkar said, adding it represents everything "wrong with the political economy of making dams in India".

"While projects are planned and justified in the name of drought-prone regions and its people, most end up serving only the distant urban areas and industries," he said.

Prime Minister Narendra Modi, who began a third term in office this month , announced a flagship scheme to provide tapped water to every household in 2019.

But in Navinwadi village, residents are resigned to living on the strictly rationed supply.

When the water tanker arrives, dozens of women and children sprint out with pots, pans, and buckets.

Santosh Trambakh Dhonner, 50, a daily labourer, said he joined the scramble as he had not found work that day.

"More hands means more water at home", he said.

Ganesh Waghe, 25, said residents had complained and protested, but nothing was done.

"We are not living with any grand ambitions," Waghe said. "Just a dream of water the next morning."

Heatwave kills at least 56 in India, nearly 25,000 heat stroke cases from March to May

India's Odisha state records 8 deaths in 72 hours as heatwave persists

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Drought Atlas of India, 1901–2020

Dipesh singh chuphal.

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

Anuj Prakash Kushwaha

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

Saran Aadhar

3 Civil & Infrastructure Engineering, Indian Institute of Technology (IIT) Jodhpur, Jodhpur, India

Vimal Mishra

Associated data.

• Funk CC, 2014. A quasi-global precipitation time series for drought monitoring. Data Series. [ CrossRef ]
• Chuphal DS, Kushwaha AP, Aadhar S, Mishra V. 2023. Drought Atlas of India, 1901-2020. Zenodo. [ CrossRef ]

Code to estimate SPEI can be downloaded from: https://github.com/sbegueria/SPEI .

India has been considerably affected by droughts in the recent past. Despite the considerable impacts of droughts on agriculture and water resources, long-term datasets to examine droughts and their consequences at appropriate spatial and temporal scales have been lacking in India. Datasets that provide drought information are mostly available for a short period and at coarser resolutions, therefore, these do not comprehend the information regarding the major droughts that occurred in the distant past at administrative scales of decision-making. To fill this critical gap, we developed the high-resolution (0.05°) and long-term monthly precipitation and temperature datasets for the 1901–2021 period. We used long-term high-resolution precipitation and temperature to estimate droughts using standardized precipitation and evapotranspiration index (SPEI). As SPEI considers the role of air temperature in drought estimation, it can be used to examine meteorological, agricultural, and hydrological droughts. Using high-resolution SPEI, we developed drought atlas for India (1901–2020) that can provide comprehensive information on drought occurrence, impacts, and risks in India.

Background & Summary

Droughts are hydroclimatic extreme events that lead to prolonged periods of water scarcity, impacting agricultural production and food security worldwide 1 , 2 . Specifically, in monsoon-dominated regions like India, droughts have been recurrent 3 – 5 and caused major famines in the 19 th and 20 th centuries 6 . The southwest monsoon rainfall in India is the primary source of agricultural water 7 and groundwater recharge 8 , 9 , accounting for 80% of the total annual rainfall. Droughts in India due to the weakening of the southwest monsoon are closely linked to Indian Ocean warming and El Nino/Southern Oscillation (ENSO) 7 , 10 – 12 . Also, the diverse physiographic conditions and significant variability in rainfall patterns across India contribute to the varying intensities of drought events 13 .

India is highly vulnerable to drought with about two-thirds of its area prone to drought 14 – 16 . Being an agricultural-dominant country and home to 1.4 billion people, droughts in India profoundly impact agricultural productivity, water resource management, and socio-economic well-being. India has witnessed a rise in the frequency, severity, and duration of droughts over the recent decades, which is projected to be further exacerbated by climate change 4 , 10 , 17 – 19 . With the increasing food demand due to rising population and urbanization 20 , 21 , the impact of droughts is expected to become more severe in the future. Additionally, unsustainable pumping of groundwater adds further to the drought-induced challenges, increasing the risks in the future 22 , 23 .

Understanding the observed droughts and their patterns is crucial to reduce the vulnerability of India’s population to future drought events. Trends and variability of droughts in the Indian monsoon region have been greatly examined, however, mostly at a coarser spatial resolution 3 , 10 . Additionally, there have been studies on a particular region 24 , 25 and for a specific drought year 17 , 26 . While Aadhar & Mishra 27 developed high-resolution precipitation and temperature for monitoring droughts in South Asia, its temporal coverage is limited from 1981 to 2020. Therefore, the available high-resolution datasets do not provide information on the severe droughts that occurred in the distant past. Despite its importance for the climate change adaptation and decision making, the long-term (1901–2021) high-resolution (0.05°) drought product for India has been lacking. Long-term reconstruction of droughts at higher spatial resolution is crucial to understand the impacts of some of the worst droughts in the past at local and regional scales. In addition, high-resolution and long-term drought reconstruction can be valuable for climate change adaptation, providing insights for policy interventions. Most of the available drought-related data sets are at coarser spatial resolution or with limited temporal coverage. To fill these crucial research gaps that hinder the decision-making at a local scale (Taluk level), we develop a high-resolution and long-term gridded drought assessment product based on the Standardized Precipitation Evapotranspiration Index (SPEI) 28 , 29 spanning the period from 1901–2021. We developed the high-resolution and long-term monthly precipitation and air temperature datasets for the 1901–2021 period to estimate the SPEI, which overcomes the limitations of the Palmer Drought Severity Index (PDSI) 30 and Standardized Precipitation Index (SPI) 31 by taking into account the multi-scale characteristics of droughts and the influence of rising temperatures on atmospheric water demand. The high-resolution SPEI dataset is then used to develop a long-term drought atlas 32 – 34 of India, which can assist in policymaking, disaster-risk management, and climate change adaptation.

We developed a drought atlas of India using high-resolution (0.05°) precipitation and maximum and minimum temperatures. The existing observed precipitation and temperature for India are available at the coarser spatial resolution (0.25°) for the 1901–2021 period. We developed high-resolution gridded precipitation and temperature by integrating the high-resolution products available for shorter periods and using the Quantile-Quantile (QQ) mapping for bias-correction. The bias-corrected precipitation from CHIRPS 35 at 0.05° was used as the reference data for correcting the gridded precipitation from IMD 36 at 0.05°. Similarly, bias-corrected temperature from ERA5-Land reanalysis 37 was used to correct gridded temperature 38 , 39 at 0.05°. The performance of high-resolution data in terms of bias, seasonality, and spatial pattern was carefully examined against the bias-corrected CHIRPS precipitation and ERA5-Land temperature. The flow chart of the overall methodology to develop the drought atlas of India is shown in Fig.  1 .

Flow chart of the overall methodology used to develop drought atlas for India.

Development of high-resolution precipitation and air temperature dataset

We used satellite-based and reanalysis data products from CHIRPS and ERA5-Land to develop high-resolution precipitation and temperature. However, these hybrid datasets (CHIRPS and ERA5-Land) exhibit bias in space and time compared to observed datasets due to inadequate sampling, lack of ground-based observations, and error correction processes 40 , 41 . Consequently, the direct application of these datasets in studies related to climate change and hydroclimatic extremes may not be appropriate and straightforward. Several bias correction methods have been developed to address this challenge 42 – 47 . Bias correction involves a statistical transformation to modify the distribution of modelled data so that it closely resembles the observed data. We used the distribution (Quantile-Quantile) mapping bias correction method to reduce the bias in these datasets and making them consistent with the observed datasets. The distribution mapping method efficiently reduces bias for mean and interannual variations and also for extreme events 48 . Aadhar & Mishra 27 compared linear scaling 27 , 49 , 50 and distribution mapping 43 , 50 for the bias correction of precipitation and temperature over South Asia and demonstrated that distribution mapping performs better than the linear scaling. Detailed information on distribution mapping methods is available in previous studies 27 , 43 , 49 .

The high-resolution bias-corrected gridded precipitation was developed using gridded precipitation from India Meteorological Department (IMD) and CHIRPS. IMD precipitation is available for 1901–2021 at 0.25° spatial resolution, while CHIRPS precipitation is available from 1981 to 2021 at 0.05° spatial resolution. Since CHIRPS precipitation is a combined product of satellite observations, in-situ data, and observed climatology 35 , 51 , it has bias and random errors 27 , 52 . To remove the bias, first, we aggregated the CHIRPS precipitation from 0.05° to 0.25° spatial resolution to perform the bias correction. Next, we bias-corrected the aggregated CHIRPS precipitation (Raw data) using the IMD precipitation (Reference data) at 0.25° spatial resolution for the period 1981–2021. The bias correction of CHIRPS precipitation was performed using the distribution (Quantile-Quantile) mapping method as described in Aadhar & Mishra 27 . During the bias correction of CHIRPS precipitation at 0.25°, scaling factors (SF) were estimated for the distribution mapping. Further, these scaling factors estimated at 0.25° were also applied to bias-correct the CHIRPS precipitation data at 0.05° spatial resolution. Considering the bias-corrected CHIRPS precipitation at 0.05° as reference data, we bias-corrected the regridded IMD precipitation (Raw data) at 0.05° to construct the high-resolution and long-term precipitation data over India. The bias correction of IMD precipitation at 0.05° was performed using the same distribution mapping method. The stepwise description to construct the bias-corrected high-resolution precipitation data from 1901 to 2021 is shown in Fig.  2 . The overall methodology to develop high-resolution precipitation product is described in detail in Aadhar and Mishra 27 .

Steps to construct high-resolution (0.05°) precipitation data.

Next, we constructed the high-resolution and long-term maximum and minimum air temperatures over India using gridded temperatures from IMD, Princeton 38 , and ERA5-Land reanalysis. Maximum and minimum temperature from IMD is available for 1951–2021 at the spatial resolution of 0.25°. Gridded temperature from IMD is unavailable for the 1901–1950 period, therefore, we used the bias-corrected temperature from the Princeton database for the 1901–1950 period at 0.25° spatial resolution. The Princeton temperature data has been used in several hydrological applications in the Indian subcontinent 18 , 53 , 54 . The bias correction of Princeton temperature was performed using the same distribution mapping method 43 , 50 . The temperature data from Princeton was bias-corrected against IMD for the period 1951–2010 and scaling factors were estimated. The scaling factors were applied to bias-correct the Princeton temperature for the period 1901–1950 at the spatial resolution of 0.25°. Finally, the bias-corrected Princeton temperature for the 1901–1950 period and IMD temperature for the 1951–2021 period at 0.25° spatial resolution were used for further analysis.

To construct the high-resolution temperature data, we used the ERA5-Land temperature for the period 1951–2021 at 0.1° spatial resolution. ERA5-Land reanalysis is also a combined product of weather models and observations from the satellite and in-situ measurements 37 . Compared to the observed datasets, ERA5-Land reanalysis consists of bias in air temperature 48 . Therefore, the bias correction of ERA5-Land temperature (Raw data) was performed using the observed IMD temperature data (Reference data) at the spatial resolution of 0.25°. To perform the bias correction, the ERA5-Land temperature was aggregated from 0.1° to 0.25° spatial resolution. The correction in aggregated ERA5-Land temperature was performed using the distribution mapping and scaling factors were estimated at spatial resolution of 0.25°. Similar to precipitation, the scaling factors were applied to bias-correct the ERA5-Land temperature at 0.05°. We constructed the high-resolution ERA5-Land temperature at 0.05° from 0.1° spatial resolution using the elevation-based SYMAP algorithm 55 – 57 . The SYMAP algorithm 55 was also used to regrid the bias-corrected Princeton and IMD temperature data (Observed-Temperature data) at 0.05° from the spatial resolution of 0.25° for the period 1901–2021. Finally, we used the bias-corrected ERA5-Land temperature (Reference data) at 0.05° to bias-correct the regridded observed temperature data (Raw data) at the spatial resolution of 0.05° for the period 1901–2021 using the distribution mapping method. The stepwise description to construct the bias-corrected high-resolution temperature data from 1901 to 2021 is shown in Figure  S1 .

Development of high-resolution and long-term drought index

We estimated high-resolution and long-term (1901–1921) SPEI to analyze droughts in India. SPEI is a standardized index that depends on both precipitation and potential evapotranspiration (PET), incorporating the impact of temperature on atmospheric water demand 31 . SPEI primarily focuses on meteorological aspects and does not directly incorporate agricultural or hydrological factors, such as soil moisture or streamflow. However, SPEI at an appropriate duration can be well correlated with streamflow and soil-moisture based drought indicators. We used high-resolution bias-corrected maximum and minimum temperature data to estimate PET. We employed the Hargreaves method 58 for estimating PET due to the inadequacy of meteorological observations required for the Penman-Monteith method 59 . We fitted the log-logistic distribution to the data and estimated the SPEI values using the available SPEI package in R 60 . We categorized the SPEI values into distinct drought categories as abnormal drought (−0.8 to −0.5), moderate drought (−1.3 to −0.8), severe drought (−1.6 to −1.3), extreme drought (−2.0 to −1.6), and exceptional drought (less than −2.0) in our study 27 , 61 . The SPEI values greater than −0.5 indicate normal or wet conditions. PET based on the Hargreaves method can be estimated as:

where R A represents mean monthly extra-terrestrial radiation (MJm −2 /day), which is a function of latitude and day of the year 59 , T max represents monthly mean daily maximum temperature (°C), T min represents monthly mean daily minimum temperature (°C), and T represents monthly mean temperature (°C).

SPEI was estimated at 1-month, 4-month, and 12-month time scales. The 1-month SPEI is essential for assessing the short-term meteorological drought and supports immediate decision-making. The 4-month SPEI monitors seasonal drought or wet conditions, providing insights into agricultural droughts. In contrast, the 12-month SPEI is more suitable for assessing the impact of droughts on surface and groundwater resources. We used 1-month SPEI to estimate monthly drought conditions for the summer monsoon months (JJAS) individually. We used 4-month SPEI at the end of September and January to estimate drought conditions for the summer monsoon and winter monsoon (ONDJ), respectively. Moreover, 12-month SPEI at the end of December and May were used to estimate drought conditions for the calendar year (Jan-Dec) and water year (Jun-May), respectively. Further, the gridded SPEI was used to evaluate the mean SPEI for India at country, states (including union territories), districts, and taluka (sub-district) levels. We computed mean SPEI for grids corresponding to each geographical level (country, states, districts, and talukas).

Data Records

The drought atlas of India covering the period 1901–2020 at the taluka level has been made available through the Zenodo repository 62 . The repository also includes the gridded SPEI values at 1-month, 4-month, and 12-month time scales for India at 0.05° spatial resolution from 1901 to 2021. Moreover, standardized SPEI corresponding to different geographical levels has also been aggregated in the repository. Interested users can refer to the readme file available in the same repository for information regarding the data format and details.

Technical Validation

We bias-corrected the raw CHIRPS precipitation aggregated at 0.25° against the reference IMD precipitation for the period 1981–2016 (Figure  S2 ). The raw CHIRPS precipitation exhibited both dry and wet biases in the mean annual precipitation (Figure  S2A ). Raw precipitation underestimated rainfall in the Kutch region, lower Himalayas, and parts of the Western Ghats while overestimated in Northeast India and South India regions. The bias-corrected CHIRPS precipitation showed a considerably lower bias for most regions of India than the raw CHIRPS precipitation (Figure  S2A , B ). We compared the monthly mean climatology of raw (CHIRPS), reference (IMD), and bias-corrected (CHIRPS) precipitation (Figure  S2C ). The corrected precipitation showed a good agreement with reference precipitation (Figure  S2C ). We compared the mean annual IMD regridded precipitation (raw) and bias-corrected high-resolution precipitation (corrected) against the reference precipitation (bias-corrected CHIRPS) at 0.05° for the period 1981–2020 (Figure  S3 ). We note that the spatial variability of the reference precipitation was well represented in the bias-corrected high-resolution precipitation (Figure  S3B , C ), however, we find some differences in the raw precipitation (Figure  S3A ). Both datasets (reference and corrected) effectively captured the regions with high (North-East India, Western Ghats) and low (parts of Rajasthan and Western India) mean annual precipitation. Furthermore, we compared the mean monthly bias-corrected high-resolution precipitation against CHIRPS (already available high-resolution precipitation) data available at 0.05°.

We find a significant difference in all-India averaged monthly rainfall from 1981 to 2020 between the two precipitation datasets (Figure  S3d ). We quantified the performance improvement due to bias correction of the mean monthly precipitation over India by evaluating the Nash-Sutcliffe efficiency (NSE) 63 , coefficient of determination (R 2 ), and root-mean-square error (RMSE). We find an increase in NSE from 0.96 to 0.98, while the R 2 improved from 0.97 to 0.99 after the bias correction. Moreover, the RMSE for monthly precipitation was reduced from 12 to 8 mm/month after the bias correction. Evaluation of NSE, R 2 , and RMSE for the homogenous rainfall zones (Figure  S8 ) also showed significant improvements after the bias correction (Table  S1 ). For instance, in the case of Hilly regions, NSE increased from 0.36 to 0.78, R 2 increased from 0.73 to 0.79, and RMSE decreased from 57 to 33 mm/month.

Similar to precipitation, we bias-corrected the raw ERA5-Land temperatures (maximum and minimum) aggregated at 0.25° against the reference IMD temperature for 1981–2016 (Figure  S4 ). We observed a predominantly cold bias over the Indian region in ERA5-Land maximum temperature, except for the Kutch region (Figure  S4A ). In contrast, the ERA5-Land minimum temperature exhibited a warm bias in most areas (Figure  S4d ). Nevertheless, a significant reduction in bias was observed after the bias correction (Figure  S4B , E ). Additionally, we compared the monthly mean climatology of raw (ERA5-Land), reference (IMD), and bias-corrected (ERA5-Land) maximum and minimum temperatures (Figure  S4C , F ). We find that the corrected temperatures exhibited a good agreement with the reference IMD temperature. We also bias-corrected the Princeton temperature (maximum and minimum) before 1950 against IMD-Temperature (refer to Methods for detail) at 0.25° (Figure  S5 ). The mean annual Princeton-Temperature over India before 1950 showed a significant cold bias of 3 °C compared to IMD-Temperature after 1950 (Figure  S5A , C ). Nonetheless, a consistent temperature trend was observed between 1901–2010 after the bias correction (Figure  S5B , D ). The bias-corrected Princeton temperature (1901–1950) and IMD temperature (1951–2021) were regridded at 0.05° spatial resolution, which were used as raw data to construct the long-term high-resolution temperature data.

Next, we compared the mean annual regridded IMD (raw) temperatures (maximum and minimum) and bias-corrected high-resolution temperatures against the reference temperatures (bias-corrected ERA5-Land) at 0.05° for the period 1981–2016 (Figures  S6 , S7 ). The spatial variability of the reference temperature was well represented in the bias-corrected high-resolution maximum (Figure  S6B , C ) and minimum (Figure  S7B , C ) temperature. The significant difference in monthly mean ERA5-Land (already available high-resolution temperature data) and bias-corrected high-resolution temperatures over India at 0.05° is evident (Figures  S6D , S7D ). Similar to precipitation, we estimated the NSE, R 2 , and RMSE values for the bias-corrected mean monthly maximum and minimum temperatures across India (Tables  S2 , S3 ). The application of bias correction showed significant improvements in the skills. Furthermore, we evaluated the NSE, R 2 , and RMSE for the homogenous rainfall zones (Figure  S8 , Tables  S2 , S3 ) and found consistent improvements in the skills after the bias correction. The final bias-corrected high-resolution (0.05°) precipitation and temperature were used to estimate the SPEI drought index over India between 1901–2021.

To examine if the high-resolution dataset captures the spatial and temporal variability in major droughts, we used the time series of average SPEI over India to assess drought occurrences during the summer monsoon season, water year, and calendar year from 1901 to 2021 (Fig.  3 ). We calculated the standardized SPEI from the mean SPEI aggregated using the gridded data for an admirative region (state, district, and taluk). The summer monsoon of 2002 ranked as the most severe monsoon season drought followed by 1972, 1987, and 1918, based on SPEI values lower than −2.0 (Fig.  3A ). Similarly, the worst events for the water year drought were observed in 1965, 2002, and 1972 (Fig.  3B ). The droughts in 2002, 1965, 1972, 1918, and 2009 were identified as the five most exceptional calendar year droughts in India (Fig.  3C ). The occurrence of droughts exhibited fluctuations across different decades (Fig.  3 ). Between 1901 and 1920, there was one extreme/exceptional drought year (SPEI between −3.0 and −1.6). However, from 1921 to 1960, the incidence of drought decreased significantly, with no exceptional drought events recorded during this period. Most of the Indian monsoon region was wet during this period 3 . Subsequently, from 1961 to 1987, the frequency of droughts increased, which was associated with the influence of the El Nino Southern Oscillation 10 . We also estimated the annual drought area coverage (%) between 1901−2021 during the monsoon season, water year, and calendar year in India (Figure  S9 ). We considered the grids with SPEI values below −0.5 to calculate the total drought area. More than 60% of the total geographical area of India was under drought during the exceptional (SPEI less than −2.0) drought events (Figure  S9 ), which signifies the severity of these observed droughts in India.

Drought estimates in India based on interannual variability of SPEI. ( A ) Z-score of India’s average 4-month SPEI at the end of September (Summer monsoon: JJAS) for the period 1901–2021, ( B ) Z-score of India’s average 12-month SPEI at the end of May (Water year: June-May) for the period 1901–2020, ( C ) Z-score of India’s average 12-month SPEI at the end of December (Calendar year: January-December) for the period 1901–2021.

We examined the drought conditions for states, districts, and talukas during the worst monsoon season (2002), water year (1965), and calendar year (2002) droughts in India (Fig.  4 , S10 , S11 ). The peninsular and north-western parts of India were the most affected regions during the 2002 monsoon season drought, whereas the top northernmost part of India remained unaffected (Fig.  4A–C ). The drought situation affected more than 23 states, 522 districts, and 3623 talukas, with the SPEI ranging between −2.0 to −0.5 (Fig.  4D–F ). Similarly, the central and eastern parts of India were the most affected regions during the worst water year drought in 1965 (Figure  S10A – C ). More than 80% of the total states (27), districts (584), and talukas (3666) in India were under drought (Figure  S10D – F ). Moreover, the 2002 calendar year drought significantly affected the eastern, north-western, and southern parts of India (Figure  S11A – C ). During this period, over 70% of the total states (25), districts (548), and talukas (3676) experienced drought situations (Figure  S11D – F ). The 1965 water year drought was more severe in terms of areal coverage than the 2002 monsoon season and calendar year droughts (Figure  S9 ).

Worst summer monsoon season drought in India (2002) between 1901–2021 based on SPEI. ( A – C ) Spatial representation of Z-score of SPEI values across India at State, District, and Taluka (Sub-district) levels. ( D – F ) Distribution of States, Districts, and Talukas based on SPEI values for the year 2002.

As a next step of data validation, we analyzed the impacts of the summer monsoon season droughts of 2002 and 2009 on the major crop yield in India (Fig.  5 ). We obtained yearly crop data for Indian districts from the ICRISAT database ( http://data.icrisat.org/dld/ ), available from 1990 onwards and corresponding to India’s district boundaries before 2015. The years 2002 and 2009 witnessed two recent monsoon droughts of exceptional and extreme categories for which crop data is available in the ICRISAT database. The change in yield for a year is calculated by taking the difference between the yield of the current year and the yield of the previous year. We primarily focused on Rice and Maize, which are the two most essential rainy-season crops due to their higher water demands for growth. The impact of the summer monsoon season drought is evident in the production of these two crops (Fig.  5 ). The 2002 drought mainly affected the north-western, southern, and eastern regions of India, leading to substantial reductions in crop yield in those areas (Fig.  5A–C ). On the other hand, the monsoon drought of 2009 had a more pronounced impact on the east-central and north-western regions of India, resulting in a reduction in crop yield in these areas (Fig.  5D–F ). While Rice is not a significant crop in north-western India, including Rajasthan and Gujarat (Figure  S12A ), drought impact on its yield in this region was relatively insignificant. However, the decline in Maize yield in the same region was evident, as north-western states are significant producers of maize in India (Figure  S12B ). These results emphasize the effectiveness of the high-resolution data in capturing the drought events that cause significant crop loss in drought-affected regions of India.

Impact of drought on major crops in India. ( A ) Drought-affected districts in India during the 2002 summer monsoon based on SPEI (Z-score). ( B, C ) Change in the yield (Kilogram/hectare) of Rice and Maize in 2002 compared to 2001 at the district level. ( D ) Drought-affected districts in India during the 2009 summer monsoon based on SPEI (Z-score). ( E, F ) Change in the yield (Kilogram/hectare) of Rice and Maize in 2009 compared to 2008 at the district level. Crop production data was obtained from the ICRISAT database available from the year 1990. The grey colour in the Fig. ( B,C,E,F ) represents missing data. The year 2002 and 2009 were two recent monsoon season droughts (SPEI less than −2.0) in India.

To further demonstrate the effectiveness of high-resolution data, we analyzed the frequency of severe and exceptional drought events (SPEI less than −1.6) that occurred in India’s states, districts, and talukas between 1901–2021 (Fig.  6 ). At the state level, the northernmost part of India (Ladakh) has the least frequency of these events, while Himachal Pradesh (just below Ladakh) demonstrated the highest occurrence of such drought events (Fig.  6A ). Notably, a high spatial variability was observed within states when examined at the district and taluka levels (Fig.  6B,C ). Also, as we move to the higher spatial resolution, the frequency of drought events crossing the threshold (SPEI less than −1.6) increases (Fig.  6A–C ). This is because the averaging of SPEI values across larger spatial areas reduces variability, leading to higher z-scores (standardized values). The occurrences of drought events were predominantly clustered between 6 and 10 for the majority of states (Fig.  6D ). The number of drought events was concentrated between 6 and 10 for most of the states (Fig.  6D ). However, at the district level, the concentration of these events was observed between 5 and 11 occurrences and between 4 and 9 occurrences at the taluka level (Fig.  6E,F ).

Frequency of severe and exceptional droughts occurred in India. Number of drought events based on Z-score of SPEI values (SPEI less than −1.6) across India between 1901–2021 at ( A ) State, ( B ) District, ( C ) Taluka (Sub-district) levels. ( D – F ) Distribution of States, Districts, and Talukas based on the number of droughts events that occurred between 1901–2021.

As a next step of our high resolution data validation, we showed 64 significant impacts of the 2002 drought across various sectors in India (Fig.  7 ). During this drought, approximately 56% of India’s area experienced moderate to exceptional drought conditions, affecting 300 million people and 150 million cattle (Fig.  7 ). The economic impact of the drought was also substantial. The country experienced a reduction in per capita income due to the loss of over 1250 million person-days of employment. Additionally, an estimated economic loss of about 8.7 billion USD was reported due to crop damage, which reduced the country’s agricultural gross domestic product (GDP) by 3.1% (Fig.  7 ).

Impacts of the 2002 drought on different sectors of India.

Finally, using the high-resolution (0.05°) SPEI, we developed the Drought Atlas of India for each year between 1901 and 2020. The atlas includes the taluka-wise drought condition of summer monsoon, winter monsoon, calendar year, water year, and monsoon months (JJAS) for each year. As an example, we show drought condition for 1972 (Fig.  8 ), which was the second most exceptional monsoon season drought in India (Fig.  3A ). The severity of the 1972 drought was exceptionally high for all the selected seasons (except winter monsoon) and for all monsoon months (Fig.  8 ).

Drought condition in India for different seasons and time scales at taluka level. Drought condition based on SPEI (Z-score) for ( A ) summer monsoon (JJAS), ( B ) winter monsoon (ONDJ), ( C ) calendar year (January-December), ( D ) water year (June-May), ( E ) June, ( F ) July, ( G ) August, ( H ) September are represented at taluka level along with the total drought area in km 2 (for SPEI less than −0.5) and mean drought intensity.

Usage Notes

The gridded SPEI data are available at 0.05° spatial resolution from 1901 to 2021 at 1-month, 4-month, and 12-month scales. Gridded SPEI data and drought atlas plots can be accessed from the Zenodo repository 62 . Each year’s drought atlas plot shows drought-affected areas of different categories (Normal to Exceptional) across different talukas in India, highlighting the drought-prone areas, which can be directly used for future drought-related studies. High-resolution SPEI data can be used for analyzing the droughts at the basin and sub-basin levels.

We checked the accuracy of bias-corrected data against the reference data and noted significant improvements in its performance. However, despite the bias correction, potential bias may still exist 65 , 66 . The application of bias correction and interpolation techniques may also introduce random errors in the precipitation and temperature data 42 , 67 . Moreover, due to limited observations of climate variables, we estimated PET using the Hargreaves method, which may result in an overestimation of PET and drought 4 , 68 , 69 .

Supplementary information

Acknowledgements.

We appreciate data availability from India Meteorological Department (IMD): https://www.imdpune.gov.in/cmpg/Griddata/Rainfall_25_Bin.html ; ERA5-Land: https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land ; CHRIPS: https://data.chc.ucsb.edu/products/CHIRPS-2.0/ ; Sheffield: https://hydrology.soton.ac.uk/data/pgf/v3/0.25deg/daily/ . All the datasets are freely available and can be downloaded after registration.

Author contributions

V.M. designed the study. A.P.K., D.S.C. and S.A. performed analysis and wrote the first draft. All the authors contributed to the writing.

Code availability

Competing interests.

The authors declare no competing interests.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

These authors contributed equally: Dipesh Singh Chuphal, Anuj Prakash Kushwaha.

The online version contains supplementary material available at 10.1038/s41597-023-02856-y.

Living with droughts

As per a Global Assessment Report on Disaster Risk Reduction: Special Report on Drought 2021 , released by the UN Office for Disaster Risk Reduction (UNDRR) recently “the impact of severe droughts on India’s GDP is expected to be about 2-5% per annum, despite the decreasing contribution of agriculture in the country’s expanding economy.”

India’s economy is rightly called the ‘gamble of monsoon’ and the arid and semi-arid regions in the western, northern and peninsular parts experience more frequent droughts, a slow-onset disaster.

Droughts have deep impacts on societies, ecosystems and economies. The costs are borne disproportionately by the most vulnerable people. As per UNDP, the impact of severe droughts as in 2002 includes large-scale ecological damage, mass migration and death.

“The extensive impacts of drought are consistently underreported, even though they span large areas, cascade through systems and scales, and linger through time. They affect millions of people and many sectors and domains -- such as agricultural production, public water supply, energy production, waterborne transportation, tourism, human health and biodiversity -- contributing to food insecurity, poverty and inequality,” says the report.

In fact, in north-west­ern India, a combination of drought and groundwater overabstraction led to decreasing trends in groundwater levels and reduced resilience to future droughts (Pathak and Dodamani, 2019).

Impact of climate change

"Climate change is increasing temperatures and disrupting rainfall patterns, thus increasing the frequency, severity and duration of droughts in many regions. As the world moves towards being 2°C warmer, urgent action is required to better understand and more effectively manage drought risk to reduce the devastating toll on human lives and livelihoods," says the report commissioned by the UNDRR.

The report which incorporates inputs from the World Meteorological Organization (WMO) notes that the number of droughts will grow dramatically because of climate change, environmental degradation and demographic shifts. This will pose a threat to the achievement of the Sendai Framework for Disaster Risk Reduction, the Sustainable Development Goals and human and ecosystems health and wellbeing.

The biggest impacts of climate change have to do with water. Urgent action is therefore needed to improve drought management and prevention, such as through the development and the strengthening of Multi-Hazard Early Warning Systems (MHEWS) to enable multi-hazard, all-media emergency alerting. Impact based MHEWS help societies to prepare for, and respond to, all types of disasters, including those related to hydrometeorological hazards.

As drought is a slow onset event, early warning and alerting offers opportunities to enhance collective action that can save lives and minimize potential economic and environmental damages. A powerful way to adapt to climate change is to invest in early warning services and meteorological and hydrological services.

Case study: India

The report has a section on case studies that explores the countries’ capacities to respond to drought-related impacts vary. They highlight how limited knowledge on possible impacts, poor assessments of vulnera­bilities and costs, little coordination at national and regional levels, and lack of awareness on policy options are key impediments to effective drought management.

The India case study highlights the Deccan Plateau region (about 43% of southern and eastern India), which has faced major drought conditions in recent times in 2000–2003 and 2015–2018. Significant drought conditions occur once in 3 years (Mishra and Singh, 2010).

Rainfed agriculture is the dominant source of food production in this low rainfall area where droughts are embedded into society and the economy. “In terms of drought preparedness in agri­culture, crisis management plans and drought contingency plans are prepared each season, which, to varying extents, connect with coping strategies at farm level (e.g. choice of crop variety),” the report says.

The water demands of rapid urban­ization and industrialization in recent years have seen groundwater systems dry up without appro­priate aquifer replenishment.

Also, in India drought-related decisions and policies are made at national and state levels, the centre being the main authority and “drought declaration” being the most import­ant step in governmental response to a drought situation. However, institutions treat drought as discrete, episodic and outlier events, choosing to respond only when drought emergencies arise. This leads to perpetuation and aggravation of drought vulner­abilities, agrarian crisis and natural resources degradation.

The key aspects discussed in the India case study were drought impacts and risk governance; substantial variance in the quality of drought monitoring; exacerbation of pre-existing vulnerabilities during droughts. The case study notes that monitoring, early warning and technical improvements to drought manage­ment systems – ongoing and planned – need to focus on “practical” tools that can be embedded and sustained in operational systems that capture the dynamic vulnerability and strengthen existing systems.

“The hazard posed by drought can be compounded by exacerbating effects such as the co-occurrence of droughts and heatwaves, antecedent soil moisture deficits and the feedback and connections among droughts, heatwaves, wildfires and even floods,” as per the report.

Key recommendations

Addressing the full complexity of drought and reducing risk will require partnerships, greater public awareness and support, and participation and action at all levels.

The report calls for proactive and innovative approaches to drought risk management -- reflecting the long-held view of WMO which has campaigned for more proactive, coordinated and sustainable management policies to replace the current crisis-driven piecemeal response.

The report recommends the establishment of new coordination and collaboration mechanisms to rapidly advance the understanding and management of drought risk. This is the philosophy behind  WMO's Integrated Drought Management Programme  which is based on the  three pillars of monitoring and early warning; vulnerability and impact assessment; drought risk mitigation, preparedness and response .

Specific recommendations include -

• Prevention has far lower human, financial and environmental costs than reaction and response.
• Increased understanding of complex systemic risks and improved risk governance can lead to effective action on drought risk.
• Drought resilience partnerships at the national and local levels will be critical to managing drought in a warming world where rainfall will become ever more unpredictable and require practical solutions to tackle issues like deforestation, excessive use of fertilizers and pesticides, overgrazing, salination, waterlogging and soil erosion.
• A mechanism for drought management at the international and national levels could help address the complex and cascading nature of drought risk.
• Financial systems and services need to evolve to encourage cooperative approaches, to promote social protection mechanisms and to encourage risk transfer and contingent financing, so as to provide diversified adaptive support to drought risk management.

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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The impact of drought on the health and livelihoods of women and children in India: A systematic review

Climate change is expected to have severe consequences for the world, some of which are already being felt. According to projections, in some regions, droughts will be more frequent and intense in the 21st century. This calls for purposeful interventions by governments to mitigate the impacts. Drought-affected communities are more vulnerable to famine. The effects of drought are felt in people's education levels, nutrition, health, sanitation, and women and the safety of children in these communities. The impact of drought can be seen in the livelihoods of people affected by it. Against this backdrop, there is the need to document the effects of drought on women and children's health in the affected communities. Such a study calls for a systematic approach. This study explores the various dimensions of the effects of droughts. It accessed electronic databases, including Google Scholar, Scopus, Pub-Med, JSTOR to identify a substantial number of studies using key words and expressions. To begin with, the word drought was kept constant in all combinations of keywords and phrases. The search was then refined by using the word drought with keywords, such as livelihood, vulnerability, sustainable development, adaption and mitigation, migration, health impact, and risk management to search the required articles. Only studies conducted in the period 2000-2019 were considered for this review. The review's findings show that due to a lack of water during a drought, the burden of work on women and children increased considerably. Most faced severe health issues like malnutrition and anemia. The livelihoods of women were also affected because of which they were forced to adopt various strategies to overcome the problems posed by droughts. Droughts occur every year in different parts of India. Actions are required to mitigate the effects of drought, including the provision of drinking water, food, aid and relief aid to distressed farmers, employment support, support for changes in livelihoods, water security, and drought-proofing. State policies and actions must give particular attention to women and children because they are the most vulnerable. Employment-generation actions should also include youth by providing appropriate training for developing appropriate skills.

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Vinay Sehgal

"DROUGHT is a complex, slow-onset phenomenon of ecological challenge that affects people more than any other natural hazards by causing serious economic, social and environmental losses in both developing and developed countries. The period of unusual dryness (i.e. drought) is a normal feature of the climate and weather system in semi-arid and arid regions of the tropics, which covers more than one-third of the land surface and is vulnerable to drought and desertification. A drought is an extended period where water availability falls below the statistical requirements for a region. Drought is not a purely physical phenomenon, but instead is an interplay between natural water availability and human demands for water supply. There is no universally accepted definition of drought. It is generally considered to be occurring when the principal monsoons, i.e. southwest monsoon and northeast monsoon, fail or are deficient or scanty. Monsoon failure causing crop failure, drying up ecosystems and shortage of drinking water results in undue hardship to the rural and urban communities. Although droughts are still largely unpredictable; they are a recurring feature of the climate. Drought varies with regard to the time of occurrence, duration, intensity and extent of the area affected from year to year4. Land abuse during periods of good rains and its continuation during periods of deficient rainfall is the combination that contributes to desertification. Dry regions in India include about 94 mha and about 300 million people (one-third of India’s population) live in these areas; more than 50% of the region is affected by drought once every four years6. Different countries and states have developed codes, manuals, procedures, processes and policies for monitoring and management of drought with varying understanding. Over the years, India has developed a fairly elaborate governance system of institutionalized drought monitoring, declaration and mitigation at different levels. India’s response to the need for enhanced drought management has contributed to overall development. For example, the drought of 1965–1967 encouraged the ‘green revolution’, after the 1972 drought employment generation programmes were developed for the rural poor; the 1987–1988 drought relief effort focused on preserving the quality of life."

Post Graduate Thesis: Tata Institute of Social Sciences, Mumbai

Santosh Yadav

Drought as a word gives the impression that scarcity of something. This term has no fixed meaning or dominant conceptualization around it. Its meaning varies across the region and experiences. It is more about relative experiences than an absolute one (Bagchi, 1991). What is a drought for a farmer may vary from what is a drought for a woman, a laborer, a student, a bureaucrat, a meteorologist, or an entrepreneur. There are different articulations while understanding drought. Drought is multidimensional phenomena which creates a vicious cycle of different impacts which begin with crop failure and goes to lack of employment, migration, the outflow of assets, poor standard of living, poor health conditions, and affecting a most vulnerable group of the society which makes them prone to other risks (The World Bank, 2006). This study tries to focus on the impacts of drought on farmers, focused on Takwiki village. Socio-economic changes and coping mechanisms during drought years is analyzed based on primary data collected from farming households.

Dr. SRINIVASA REDDY MANDALA

Drought emerges as a major environmental and socio-economic problem with negative effects on the livelihood of populations in many developing countries. Although, drought may happen virtually in all climatic zones, yet, its characteristics differ considerably from one area to another. Rainfall, groundwater availability, reservoir levels and crop conditions determine the nature and extent of drought in a specified geographical area. Drought induced poverty leads to gross abuse of environmental resources, thereby generating land degradation that leads to desertification, which in turn accentuates poverty. Heavy dependence of the poor on the natural resources for livelihood that puts a disproportionate pressure on the land, thereby accelerating environmental degradation is also a problem of drought recurrent.

Rafiq Jaffer

In the protracted drought emergency in Balochistan province of Pakistan, the Education and Women and Child Protection working groups (with lead role from UNFPA and UNICEF) undertook an assessment in Balochistan province to ensure that interventions planned and implemented were evidence-based and responded to the education and protection needs of children, women and communities affected by drought. The assessment was conducted in schools, health facilities and communities in 5 communities (1 urban and 4 rural) in each of five districts, including Washuk, Chaghi, Jhal Magsi, Loralai and Killa Abdullah. Data was collected through 26 key informant interviews, need assessment of 34 schools, 51 assessments of priority issues, and 95 focus groups (25 women, 24 men, 24 girls, and 22 boys). The priority issues of the area identified through the assessment were drought and lack of clean drinking water. Lack of education and health facilities were also high priority issues, followed by the lack of a peaceful environment, rain-destroying crops and unemployment. All sections of the population were affected by most of these issues. The main impacts of drought included health problems, financial issues and migration, followed by food and water shortages. Drying up of trees and field and animal deaths, and schooling issues were also mentioned by a significant number of respondents. Drought and related activities also negatively affected the physical and psychological health of children, including helplessness, depression, aggressiveness, and spreading anarchy. In addition long travel for water and the threatening environment, labour work, and missing school due to drought affected some children. Most of the sampled 34 schools were functional, while a few were semi-functional, with very few teachers leaving schools due to drought. However, there was a 9.2% drop in enrolment between the start of the project and current enrolment, supporting the observations of school staff that school enrolments had been affected due to drought. The most frequently cited reasons for absence from school included domestic issues, labour, household work, poverty, and school-related factors (distance, facilities, fees). The steep drop in enrolment of girls in high schools can be attributed to cultural factors and lack of high schools for girls. Most respondents reported an increase in violence, including child marriages, physical abuse and harassment, particularly when women and children moved out of their own area - more women than men mentioned these forms of violence. Respondents attributed violence to various actors and factors, including society, parents and poverty. About half the respondents said that there was an increase in the number of women whose household heads left the area for earning, while half said there was no increase. Most respondents said that aid did not reach women-headed households and security measures did not improve after the drought. Only a fourth said that matters related to aid and security were brought to the attention of elders or local authorities. The most common suggestions for improving protection of women and children included provision of health, educational and basic facilities (water, electricity), while some respondents also mentioned employment opportunities, welfare institutions and services, rehabilitation of families, skill training, transportation, and food. Most water taps, hand pumps and wells in schools were non-functional, and only 25% staff said that the school had access to water. While 81% staff said that the water was usable, only 18% said that the water was drinkable. Half the staff said that children brought water from home. While three-fourths of staff said that the water source was within 30 minutes of the school, half said that the children did not feel safe covering the distance to fetch water, primarily due to the fear of violence or harassment. Only a third of staff said that a functional latrine was available. Not a single school had a basic hand washing facility, while only one school had a WASH Club. Most respondents said that health facilities were available in their district, particularly BHUs and DHQ hospitals, but two-thirds said that the facilities were more than an hour away, and only a third said that the facilty was active. Only half the respondents said that reproductive health facilities and female health service providers (mainly LHVs) were available, while a third said that emergency RH facilities and only a fifth said that referral services were available. The findings of the rapid assessment corroborate the findings of the recent drought assessment in Balochistan conducted by NDC, and fully justify a Drought Emergency Response for education, livelihood and food security, health and nutrition, WASH, and women and child protection. Some of the specific remedial measures that need to be urgently taken include: 1. Conduct of a household survey of the most affected regions of Balochistan in order to determine the losses suffered by households, and especially women and children, directly as a result of drought – such a survey would probably have to be conducted by the Balochistan government, since non-government organisations are not likely to get the required permission to conduct such a survey. 2. Identification of families which have migrated due to drought, determination of their current status, and the actions which would be required to bring these families back to their homes. 3. Taking measures to address the immediate needs of families, including provision of clean drinking water, plugging the gaps in educational and health facilities, and preparing compensation packages for those most affected by drought as well as crops-destroying rains. 4. Initiating a cash for work programme for the affected families, so that they can make a dignified return to their communities rather than being treated as victims. 5. Mobilise and build the capacity of welfare and rehabilitation institutions to organize counselling sessions for affected families, especially women and children. 6. Special attention will have to be given to female-headed households, including skill training for women so they could become self-employed. 7. In case of women protection, strengthening of local level protection mechanisms by providing safety nets, ensuring security arrangement, specific arrangements for aid distribution points to women, child or other vulnerable groups including women-headed households, establishment of safe places/houses for women and girls, ensuring mental health and psycho-social support services to women and children, and making referral mechanism/pathways stronger and available. 8. Promoting community organisations within affected communities to partner in the distribution of aid packages in order to ensure that the most deserving benefit on a priority basis. At the same time development issues not directly related to the drought should be addressed by the government with the help of UN and other development agencies through a medium-term framework, including school facilities, WASH clubs in schools, provision of clean drinking water, food, functional latrines and handwashing for children with the support of SMCs/PTAs, provision of reproductive health facilities, including appointment of LHVs, and improvement of emergency and referral services.

The Open Agriculture Journal

Parmeshwar Udmale

Annika Maniram

Drought is viewed as an important feature of climate change that results in extended periods of dryness, increasing temperatures and heatwaves (Orievulu, 2022). Additionally, drought is an extreme event in the hydrological cycle, and it is considered to be one of the most detrimental natural disasters occurring around the world. With the increasing impacts of climate change and anthropogenic activities, the seriousness and frequency of drought is expected to rise in the upcoming decades (Mandela, 2019). According to Brown (2016), a drought is a period of below-average precipitation which results in drier than normal conditions. This study will be focusing on socio-economic droughts. The main aim of this study is to determine the impacts of drought on the rural communities of Msinga in Kwa-Zulu Natal, South Africa. The objectives for this study are to determine the socio-economic impacts of drought, to examine the perceived seriousness and frequency of drought and to investigate the adaptation and mitigation strategies of drought. Globally, droughts are viewed as one of the most distressing natural disasters, which affects food production, water resources, biodiversity and livelihoods. Approximately, 1.5 billion people have been directly impacted by drought this century, whilst every year an estimated 55 million people are affected around the world (Harvey, 2021 & WHO, 2021). Droughts are a key feature of South African climatic conditions, because of its topography, location and below average rainfall. In 2015/2016, South Africa had experienced one of the worst droughts in 30 years because of the extreme weather system, El Nino. The South African drought had resulted in threatened livelihoods, water shortages, loss of agricultural production and increased food prices. Additionally, drought is one of the most difficult challenges affecting developing countries, with the most detrimental effects being felt by rural communities and subsistence farmers, since they mainly rely on rain-fed agriculture. This research study also focuses on a theoretical framework. It discusses the sustainable livelihoods approach and the drought perception theory. The SLA assumes that all individuals have assets and abilities that can improve their livelihoods, whilst the drought perception theory discusses how farmers perceive drought based on four elements. The data obtained for this research study is archival data that was collected in June 2019 till August 2019 at the Msinga Municipality in Kwa-Zulu Natal, South Africa. However, this research project was conducted over a period of three years during 2020 - 2022. The data that was used for this project was collected using a quantitative research method. Additionally, the collection of data was conducted using a purposive sampling method, which is utilised when the researcher uses their own judgement to choose a group of participants that requires the people with the most characteristics based on their relevance to the research study. Furthermore, the tools that were used in this study included a questionnaire which provided a deeper understanding of the community dynamics. Questionnaires are a research tool that consists of a series of questions that aim to collect data from a respondent. Furthermore, to analyse the data that was collected, a programme called Statistical Package for the Social Sciences (SPSS) was used. Data from the completed questionnaires were entered onto the SPSS programme. The demographic results have indicated that majority of residents within the Msinga Municipality were female, with a large portion of the surveyed population being single. The age distribution was disproportionate, with the older generation being the majority and the working-class population being the minority. The findings also showed a high level of uneducated residents, with majority of the population being unemployed and relying on social grants. The socio-economic impacts of droughts were also discussed, with the results showing high levels of malnutrition, food insecurity, limited food choices, crop failure, unemployment and poverty. The findings also presented adaptation and mitigation measures for dealing with drought, as well as strategies based on indigenous knowledge. The results also showed the different types of water that respondents used for irrigational purposes, as well as the perceived seriousness and frequency of droughts. Additionally, the results presented the percentages of respondents that received agricultural training and assistance from the government during a drought. It also discussed early warning systems and drought management programmes within the area.

Climate and Development

Anu Susan sam

Natural Resources and Conservation

DIPAK PANASKAR

International Journal of Disaster Risk Reduction

Balochistan Review

Dr. Siraj Bashir

Balochistan is critical region regarding drought and has been affected during 1998-2002. The citizen of this territory are engaged with agriculture and cultivating different plants for survival their better livelihood. The purpose of this article is to recognize the perception and comprehension regarding drought by the farming families, and their covering and adapting machinery. The research is existed on both primary and secondary collected information gathered from 150 respondent's family households following a structured questionnaire survey. The outcomes hint that farmers' discernment with respect to atmosphere changeability and drought are in the line of result obtained utilizing climatic information and data. Despite the fact that the respondents have religious faith in explaining climate related issues and a natural factors, for example, high temperature, low precipitation, change in the circumstance of stormy season, and different components like proper supply of energy for water system, over misuse of groundwater, population expansion and so on were additionally perceived and referenced by them that the drought seriousness in the territory. As a result of drought, a big losses in dates and fruits products cultivation. Animals are higher among weak and borderline land holding farmers. To adapt to the drought, they have adjusted various methodologies at farm and off-farm levels that include yield and water management practices, alteration in agrarian information sources, seeking off-farms government employment, resources exhaustion, utilization

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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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Spatiotemporal variabilities in evapotranspiration of alfalfa: a case study using remote sensing metric and ssebop models and eddy covariance.

1. Introduction

2. materials and methods, 2.1. description of the study area, 2.2. climate of the region, 2.3. et measurement using eddy covariance, 2.4. reference evapotranspiration, 2.5. satellite images and preprocessing, 2.6. remote sensing et models, 2.6.1. metric model, 2.6.2. ssebop model, 2.7. pixel selection, 2.8. statistical analysis, 3. results and discussion, 3.1. weather, 3.2. spatial distribution of et, 3.3. comparison of et estimates, 4. conclusions, author contributions, data availability statement, acknowledgments, conflicts of interest.

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Tawalbeh, Z.M.; Bawazir, A.S.; Fernald, A.; Sabie, R. Spatiotemporal Variabilities in Evapotranspiration of Alfalfa: A Case Study Using Remote Sensing METRIC and SSEBop Models and Eddy Covariance. Remote Sens. 2024 , 16 , 2290. https://doi.org/10.3390/rs16132290

Tawalbeh ZM, Bawazir AS, Fernald A, Sabie R. Spatiotemporal Variabilities in Evapotranspiration of Alfalfa: A Case Study Using Remote Sensing METRIC and SSEBop Models and Eddy Covariance. Remote Sensing . 2024; 16(13):2290. https://doi.org/10.3390/rs16132290

Tawalbeh, Zada M., A. Salim Bawazir, Alexander Fernald, and Robert Sabie. 2024. "Spatiotemporal Variabilities in Evapotranspiration of Alfalfa: A Case Study Using Remote Sensing METRIC and SSEBop Models and Eddy Covariance" Remote Sensing 16, no. 13: 2290. https://doi.org/10.3390/rs16132290

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