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Droughts pose significant challenges to food security, affecting millions of people and hectares of land in India. Despite their widespread impact, assessing drought patterns at a high spatial resolution remains a challenge, particularly in regions with poor data availability. Standardised Precipitation Evapotranspiration Index (SPEI) is one of the most widely accepted drought indices. The publicly available SPEI reanalysis dataset has a poor spatial resolution for regional drought studies. Land Surface Temperature (LST) has the potential to act as an effective proxy for SPEI and can be used for high-resolution drought studies. The study considered six homogeneous drought regions (NWH, CPR, NEI, NCP, NWI, and SPR) over India, and the recent trend in LST and SPEI over these regions was estimated using modified Mann-Kendell (MMK) trend and Sen's slope estimator at monthly, seasonal, and annual time scales. The correlation between SPEI and LST was also checked at a 95% confidence level. Results indicate statistically significant decreasing annual LST trends in NWH (slope = −0.10), CPR (slope = −0.09), and NWI (slope = −0.11) regions, while increasing SPEI trends were observed in NWI (slope = 0.06) and NCP (slope = 0.01) regions during 2001–2019. Additionally, NCP (p = 0.025), CPR (p = 0.002), and NWI (p = 0.020) exhibited significantly decreasing LST trends during winter, with a notable post-monsoon decrease observed in NWI. Significant correlations between annual LST and SPEI were observed only in CPR (r =−0.527) and NWI (r = −0.601), with seasonal associations found predominantly in winter across CPR, NEI, NCP, and NWI regions. Notably, a significant correlation was observed in all months except July over NWI, with consistent correlations during most of the winter months in NWH, CPR, NCP, and NWI regions. The observed trends and correlations can provide valuable insights for policymakers and stakeholders in formulating effective drought mitigation strategies. [ABSTRACT FROM AUTHOR]

This study attempts to develop a composite index by integrating meteorological, hydrological and agricultural droughts over semi-arid Banas River basin, Rajasthan, India. To affect this, the standardized precipitation index (SPI), streamflow drought index (SDI), and vegetation condition index (VCI) have been used at 1-, 3-, 5-, 9- and 12-month time scales using station and remote sensing-based data for the period 2000 to 2020. To identify the occurrence of above-stated droughts and most vulnerable drought period at different time scales (1-, 3-, 5-, 9- and 12-month) regarding SPI, SDI and VCI has been validated with foodgrains produced and occurrence of historical drought years. This validation has been found significant with SPI-3 (r = − 0.81), SDI-3 (r = − 0.78) and VCI-5 (r = − 0.80) time scales. Subsequently, these time scales have been coalesced using weights obtained from principal component analysis (PCA) to develop the composite drought index (CDI). The annual CDI developed this way has been further validated with foodgrains produced and occurrence of historical drought years. The results of CDI demonstrate the maximum area under mild drought (73 percent) followed by moderate (21 percent) and severe (4 percent), whereas minuscule area has been detected under wet conditions (2 percent). Finally, this study suggests that individual drought types (meteorological, hydrological, agricultural) do not appropriately arrest the drought severity, hence, the usage of multiple droughts based composite index can be more realistic for effective drought assessment and monitoring in hydrologic systems. [ABSTRACT FROM AUTHOR]

We used the Meteorological Research Institute Earth System Model to investigate land precipitation and net primary production (NPP, proxy for agricultural production here) in terms of food and water security. In the preindustrial state, the largest decrease in Southeast Asian precipitation (> 20%) occurs along with the largest El Niño and positive phase of the Atlantic multidecadal oscillation, consistent with the 1876-78 Great Drought, leading to the largest decrease in NPP (~ 20%); >2 standard deviations, extreme dryness and crop failure. In relatively arid regions (e.g., India and northeastern Brazil), the largest decreases in precipitation and NPP reach ≥ 50%, particularly 80 ~ 90% in Deccan, India. In the 21st century warming projection (Shared Socioeconomic Pathways 5-8.5, the largest CO2 emission scenario), the interannual variability of precipitation and NPP in Monsoon Asia increase under the influence of enhanced El Niño-Southern Oscillation. Although the increased precipitation variability indicates more frequent severer droughts than in the preindustrial state, this severity does not influence NPP so much: the increased NPP variability is negligible compared to the significant increase in normal NPP by CO2 fertilization effect, which indicates usually rich harvests. Compared to a recent severe crop failure (− 25% in Japan in 1993), the simulated preindustrial drought-induced NPP decreases of ≥ 50% in parts of low latitudes are extraordinarily large enough to correspond to devastating crop failures. The model indicates that great famines before the 20th century were natural in origin, and that manmade factors, such as harsh taxes and hoarding and export of grain, made the famines more devastating. [ABSTRACT FROM AUTHOR]

Drought is a natural and complex climatic hazard. It has both natural and social connotations. The purpose of this study is to use machine learning methods (MLAs) for drought vulnerability (DVM) in Uttar Pradesh, India. There were 18 factors used to determine drought vulnerability, separated into two groups: physical drought and meteorological drought. The study found that the eastern part of Uttar Pradesh is high to very highly prone to drought, which is approximately 31.38% of the area of Uttar Pradesh. The receiver operating characteristic curve (ROC) was then used to evaluate the machine learning models (artificial neural networks). According to the findings, the ANN functioned with AUC values of 0.843. For policy actions to lessen drought sensitivity, DVMs may be valuable. Future exploration may involve refining machine learning algorithms, integrating real-time data sources, and assessing the socio-economic impacts to continually enhance the efficacy of drought resilience strategies in Uttar Pradesh. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Agricultural drought refers to soil moisture deficit, which causes adverse effects on the crop production and economy of a nation. This work compared the capability of artificial neural network (ANN) and support vector machine (SVM) algorithm in predicting agricultural drought in the Palakkad district of Kerala, India. Also, the influence of various global climatic indices on soil moisture stress in the study area is assessed. Two models were developed to investigate the impact of global climatic indices. Model 1 considered only local meteorological variables as predictors, and model 2 included global climatic indices along with meteorological variables. The results showed that ENSO has commendable influence on the early prediction of agricultural drought in Palakkad and are more evident at higher lead times (2 to 4 months). For the first model of ANN and SVM, the R2 values at a 4-month lead range from 0.56 to 0.76 and 0.62 to 0.77, respectively. Similarly, for model 2, the R2 varies from 0.61 to 0.77 and 0.75 to 0.82 for ANN and SVM models, respectively. Further, the results indicated that the SVM model shows clear advancement in prediction over ANN especially at higher lead times, even though both show a comparable performance at 1-month lead time. The study provided useful information regarding the potential predictors of agricultural drought in the study area and suggest suitable models for the early prediction. This will support the decision makers in drought prevention and water resource management. [ABSTRACT FROM AUTHOR]

Uncertainty and uneven distribution of monsoonal rainfall and its consequences on crop production is a matter of serious concern in India, specifically, in the Indo-Gangetic plain region. In this study, drought patterns were investigated through standardised precipitation index (SPI) of varying timescales, using the India Meteorological Department (IMD) precipitation data (1901–2021). We analysed the spatio-temporal pattern of different drought characteristics (frequency, duration, severity, intensity) of the Indian Gangetic basin using run theory. The bivariate copula method has been incorporated to combine two drought properties (severity and duration). Copula integrates multivariate distribution and considers the dependency rate among the variables. The five most widely used copulas from various copula families, elliptical (normal, t-copula) and Archimedean (Clayton, Gumbel, Frank), were estimated for modelling, and the best fit copula was selected. The study revealed that seasonal drought is more frequent and intense in the Upper and Middle Gangetic Plain, whereas annual drought is quite scattered in nature. It is worthy to mention that downward drought trends were observed in this agricultural belts significantly after 1965; specifically, in the Upper, Middle, and Trans Gangetic Plain regions. With increasing drought duration and severity, the drought return period raised, but the frequency decreased gradually. Most of the droughts characterised by less duration and severity occurred with a return period below 10 years for the whole region. The major 100 + years return period droughts were to be found after 1960 and their frequencies were significantly higher after 2000. The most recent remarkable droughts with more than 100 years of return occurred during 2008–2011 and 2016–2018 in the Upper and Middle Gangetic plains, whereas in the Lower Gangetic plain, a hundred-year return period drought was occurred during 2010–2013. This study provides agroclimatic-zones-wise significant information of drought characteristics and its nature of occurrence in the Indian Ganga Basin. The results enhance the understanding of drought management and formulation of adaptive strategies to mitigate the adverse impact of droughts. [ABSTRACT FROM AUTHOR]

Current knowledge suggests a drought Indian monsoon (perhaps a severe one) when the El Nino Southern Oscillation and Pacific Decadal Oscillation each exhibit positive phases (a joint positive phase). For the monsoons, which are exceptions in this regard, we found northeast India often gets excess pre‐monsoon rainfall. Further investigation reveals that this excess pre‐monsoon rainfall is produced by the interaction of the large‐scale circulation associated with the joint phase with the mountains in northeast India. We posit that a warmer troposphere, a consequence of excess rainfall over northeast India, drives a stronger monsoon circulation and enhances monsoon rainfall over central India. Hence, we argue that pre‐monsoon rainfall over northeast India can be used for seasonal monsoon rainfall prediction over central India. Most importantly, its predictive value is at its peak when the Pacific Ocean exhibits a joint positive phase and the threat of extreme drought monsoon looms over India. Plain Language Summary: Monsoon brings rain over India. But some years are droughts. These drought monsoon years are historically associated with warmer sea surface temperatures (SSTs) in the eastern Pacific and cooler SST in the northern Pacific. This motivated scientists to predict drought monsoons when we observe a warm eastern and cold northern Pacific Ocean. However, in some years, the monsoon is not drought despite the SST anomalies in the Pacific suggesting so. We find that, in such years, rainfall over northeastern India during pre‐monsoon months is often excessive. So we argue that when the Pacific Ocean state suggests a drought monsoon over India (central region) but if pre‐monsoon rainfall over northeastern India is excessive, then we can rely less on the drought signal of the Pacific Ocean. Key Points: Pre‐monsoon rainfall over northeastern India is a potential indicator of false alarms of monsoon drought over central Indian regionAssociation between northeastern India pre‐monsoon rainfall and monsoon rainfall over central India oscillates multidecadallySea surface temperature anomalies in the Pacific are a key driver of pre‐monsoon rainfall over the northeastern India [ABSTRACT FROM AUTHOR]

Drought depends upon many factors such as lack of precipitation, sunshine duration, and altitude. In this study, future rainfall trend pattern was studied, and an attempt was made to quantify the amount of rainfall deficit to cause drought occurrence and corresponding required water for the recovery of future drought. The non-standardized anomaly index (AI) was used to quantify the water deficit for future drought event, which is based on CMIP 6 climatic model generated rainfall data under the four different shared socioeconomic pathways' (SSPs) scenarios (SSP 1–2.6, SSP 2–4.5, SSP 3–7.0, and SSP 5–8.5) for the chosen study area (the upper Tapti River basin in central India). Our study found out that the future rainfall pattern would be an increasing trend for all the SSPs scenarios up to the end of the twenty-first century. Particularly, SSP 5–8.5 scenario projected increasing rainfall pattern and more number of drought events than other SSPs. In specific season, the future monsoon season showed more number of drought events. The water required for drought recovery was found increased from SSP 1–2.6 to SSP 5–8.5 scenario. The results presented in this study are helpful to adopt drought management activity in response to changing rainfall pattern under climate change aspect. [ABSTRACT FROM AUTHOR]

Background: Drought is one of the important abiotic stresses that can significantly reduce crop yields. In India, about 24% of Brassica juncea (Indian mustard) cultivation is taken up under rainfed conditions, leading to low yields due to moisture deficit stress. Hence, there is an urgent need to improve the productivity of mustard under drought conditions. In the present study, a set of 87 B. carinata-derived B. juncea introgression lines (ILs) was developed with the goal of creating drought-tolerant genotypes. Method: The experiment followed the augmented randomized complete block design with four blocks and three checks. ILs were evaluated for seed yield and its contributing traits under both rainfed and irrigated conditions in three different environments created by manipulating locations and years. To identify novel genes and alleles imparting drought tolerance, Quantitative Trait Loci (QTL) analysis was carried out. Genotyping-by-Sequencing (GBS) approach was used to construct the linkage map. Results: The linkage map consisted of 5,165 SNP markers distributed across 18 chromosomes and spanning a distance of 1,671.87 cM. On average, there was a 3.09 cM gap between adjoining markers. A total of 29 additive QTLs were identified for drought tolerance; among these, 17 (58.6% of total QTLs detected) were contributed by B. carinata (BC 4), suggesting a greater contribution of B. carinata towards improving drought tolerance in the ILs. Out of 17 QTLs, 11 (64.7%) were located on the B genome, indicating more introgression segments on the B genome of B. juncea. Eight QTL hotspots, containing two or more QTLs, governing seed yield contributing traits, water use efficiency, and drought tolerance under moisture deficit stress conditions were identified. Seventeen candidate genes related to biotic and abiotic stresses, viz., SOS2, SOS2 like, NPR1, FAE1-KCS, HOT5, DNAJA1, NIA1, BRI1, RF21, ycf2, WRKY33, PAL, SAMS2, orf147, MAPK3, WRR1 and SUS, were reported in the genomic regions of identified QTLs. Conclusions: The significance of B. carinata in improving drought tolerance and WUE by introducing genomic segments in Indian mustard is well demonstrated. The findings also provide valuable insights into the genetic basis of drought tolerance in mustard and pave the way for the development of drought-tolerant varieties. [ABSTRACT FROM AUTHOR]

Sustainable water resource management is the immediate priority as the rapid depletion of water resources is aggravating drought conditions in many regions worldwide. One of the possible approaches to recover from such a condition is through water resource balancing. The water balance studies require knowledge of water availability for various sectors' present and future requirements to balance the water resource. The present study uses the Palmer Drought Severity Index (PDSI) for water balancing. It evaluates the soil moisture deficit or surplus with spatial and temporal changes for the Marathwada region consisting of eight districts for 1957–2017. The analyzis shows water surplus from July to September and water deficit from November to May. The study observes that the Marathwada is drought-free during the Kharif season and water deficit in Rabi season. This study analyzes the need for crop-shift to avoid crop damage during drought. Based on the analyzis, all the urban system sectors need to augment short-term and long-term policies to overcome the water crisis for a balanced water resource. The Self-Calibrated PDSI (ScPDSI) is calculated based on PDSI, which gives more freedom in analyzing the drought period. Highlights Water balancing approach can effectively help in modelling the rapid depletion of water resources. This work expands the use of drought indicators for water balancing by evaluating the soil moisture deficit or surplus with spatial and temporal changes for the Marathwada region for 1957–2017. Urban system sectors may augment short-term and long-term policies based on the framework to overcome the water crisis for a balanced water resource. [ABSTRACT FROM AUTHOR]

This study investigates the groundwater quality in the Kadiri Basin, Ananthapuramu district of Andhra Pradesh, India. Groundwater samples from 77 locations were collected and tested for the concentration of various physicochemical parameters. The collected data were assimilated in the form of a groundwater quality index to estimate groundwater quality (drinking and irrigation) using an information entropy-based weight determination approach (EWQI). The water quality maps obtained from the study area suggest a definite trend in groundwater contamination of the study area. Furthermore, the influence of different physicochemical parameters on groundwater quality was determined using machine learning techniques. Learning and prediction accuracies of four different techniques, namely artificial neural network (ANN), deep learning (DL), random forest (RF), and gradient boosting machine (GBM), were investigated. The performance of the ANN model (MEA = 11.23, RSME = 21.22, MAPE = 7.48, and R2 = 0.91) was found to be highly effective for the present dataset. The ANN model was then used to understand the relative influence of physicochemical parameters on groundwater quality. It was observed that the deterioration in groundwater quality in the study area was primarily due to the excess concentration of turbidity and iron values. The relatively higher concentration of sulfate and nitrate had caused a significant impact on the groundwater quality. The study has wider implications for modeling in similar drought-prone agricultural areas elsewhere for assessing the groundwater quality. [ABSTRACT FROM AUTHOR]

Compound dry hot events (CDHEs), where hot events and droughts coexist, have received a lot of attention lately due to their catastrophic effects on the economy, environment and human health. In this study, we use two CDHE indices, the Standardized Compound Event Indicator (SCEI) and the Standardized Dry and Hot Index (SDHI), to assess changes in CDHE characteristics (severity, frequency, spatial extent) over the historical past and future CMIP6 simulations across the Indian subcontinent. To understand the role of the drought index selected on CDHE characterization two drought indices namely the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) are employed in the calculation of the CDHE indices. Further, the role of climatic oscillations such as El Nino Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), Equatorial Indian Ocean Monsoon Oscillation and Indian Ocean Dipole in modulating CDHE characteristics have also been explored. Results show that SPI and SPEI based CDHE indices exhibit contrasting trends in northern India, while similar trends are observed in southern India in the historical past. Also, more frequent, extended, and severe CDHEs are reported by CDHE indices calculated using SPEI than by SPI. Temperature is found to be the dominant factor contributing to increases in CDHEs in the recent past and ENSO phases significantly modulate the severity and frequency of CDHE events in India. CMIP6 simulations generally report an increase in CDHE events for a 3°C global warming scenario. Overall, our findings show that the choice of the drought index has a greater impact on CDHE characterization than the choice of the CDHE index itself. Results from this study provide useful information towards understanding the risk of CDHEs in India under global warming and urge for the development and implementation of adaptation and mitigation measures. [ABSTRACT FROM AUTHOR]

Droughts are linked to tree die‐offs in the biodiverse humid tropics. We assessed drought response of a Dipterocarp Forest and found a marginal decrease in tree survival, indicating drought resistance. Understory and emergent species were sensitive to drought. Urgent focus is needed to understand drought impacts and plant physiological responses in South Asian forests. [ABSTRACT FROM AUTHOR]

The flash drought has gained widespread attention as a result of its rapid onset with severe impacts. However, a comprehensive assessment of different flash droughts based on various modulators is not carried out over India to date. Here, we attempt to answer the following research questions, (i) What is the status of different flash drought events due to soil moisture percentile drop (SMPD), standardized evaporative stress ratio (SESR), heat‐wave‐driven (HWD) and precipitation‐deficit‐driven (PDD) in India? (ii) How are these flash droughts distributed across India concerning occurrences, areal coverage, and persistence? (iii) What are the dominant factors that trigger different types of flash droughts? Lindeman, Merenda and Gold (LMG) method is used to examine the dominant factor. We find that PDD flash drought is noticed for the maximum time during 1959–2020. The SMPD flash drought occurrence is observed over northern parts of India. Mostly, Rabi and Zaid seasons experience the incidence of SMPD, SESR, and PDD flash drought conditions. However, HWD flash drought in more than 50% of the states is prominent in the Kharif season. The areal coverage shows a decreasing trend in the cases of SMPD, SESR, and PDD flash drought events. However, HWD flash drought condition has shown an increasing trend. The maximum percentage of SMPD and SESR flash drought events persisted for three to six pentads and five to eight pentads after the onset, respectively. For HWD and PDD flash droughts, most of the events last only for one to two pentads. The sensible heat flux and latent heat flux are responsible for triggering the flash drought events over most of parts in India. [ABSTRACT FROM AUTHOR]

This study attempts to build nonstationary indices for assessing meteorological drought in two different climate zones in India: the arid Saurashtra and Kutch and humid-tropical Coastal Karnataka. Time and climate indices are considered as covariates to develop nonstationary models using the generalized additive model in location, scale, and shape (GAMLSS) for the period, 1951–2004. A comparative study has been conducted to assess the statistical performance of stationary and nonstationary models on various time scales (3, 6, 12, and 24 months). The best model is selected to conduct copula-based bivariate drought analysis. For this purpose, drought properties such as drought severity, duration, and peak are calculated. The annual and seasonal rainfall departures are also analyzed, and more rainfall-deficient years are detected in Saurashtra and Kutch regions than in Coastal Karnataka. The nonstationary index performed better in capturing drought properties in statistical analysis over both the study areas at all time scales. The nonstationary drought index shows better consistency with historical drought and flood events than the stationary index. Cooccurrence and joint return periods are calculated and compared with univariate return periods. A significant difference is observed between bivariate and univariate return periods, and more risk is detected in Saurashtra and Kutch than in Coastal Karnataka. The impacts of rainfall and drought on the yield of major crops in study areas are also analyzed. The yield loss rate of bajra significantly correlates with the nonstationary standardized precipitation index (NSPI) in Saurashtra and Kutch, whereas rice yield has no significant correlation with the index in Coastal Karnataka. This new aspect of drought analysis provides feasible results in both arid and humid regions in a changing environment. El Niño-Southern Oscillation (ENSO) refers to changes in ocean temperatures and atmospheric pressure in the Pacific Ocean, while Indian Ocean Dipole (IOD) refers to differences in sea surface temperatures in the Indian Ocean. Both ENSO and IOD can affect rainfall and drought conditions in India, making them essential factors to consider in understanding and predicting drought events. The nonstationary drought index developed using climate indices could accurately assess the important aspects of drought, like how severe it was, how long it lasted, and when it reached its peak. By evaluating the return period of a specific drought severity and duration, decision makers can assess the likelihood of experiencing such an event in a given year or over a specific period. It helps to prioritize resources, plan adaptation measures, and design drought management strategies to mitigate potential impacts. If specific ENSO or IOD conditions are associated with prolonged drought periods, water resource managers can implement proactive measures like water conservation initiatives, irrigation scheduling, and crop diversification. This research examines the impact of rainfall and drought on major crop yields (bajra in arid regions and rice in humid-tropical regions). This knowledge can aid farmers and policymakers in predicting crop losses, optimizing irrigation strategies, and implementing timely interventions to minimize agricultural productivity losses. [ABSTRACT FROM AUTHOR]

In the present study, three cultivars of Cymbopogon species viz., Lemongrass var. CKP-25, Himrosa var. IIIM(J)CK-10 and Jamarosa var. RRL(J)CN-5 were assessed with five levels of irrigation frequencies viz., 0.20 IW, 0.40 IW, 0.60 IW, 0.80 IW and 1.00 IW, in three consecutive years (2019-2022). In the study, irrigation was scheduled at 5 cm depth and performed as per the IW: CPE ratio. The amount of water was applied as per the levels of irrigation frequency from lower to higher and to meet the ratio of cumulative pan evaporation (CPE). Furthermore, increase in the levels of irrigation frequency increased the essential oil yield. Interaction among the cultivars of Cymbopogon species with the different levels of irrigation frequencies, Lemongrass var. CKP-25 proved significantly the highest essential oil yield (209.97, 389.12 and 406.57 kg ha-1) during 1st, 2nd and 3rd year, respectively. Similarly, in 2nd and 3rd years the net returns were found to be highest in Jamarosa var. RRL(J)CN-5 (Rs. 454627.00 and 502990.30 ha-1), respectively at same levels of irrigation frequency. The benefit: cost ratio was found to be superior with treatment receiving irrigation at 1.00 IW. However, percentage of essential oil content, secondary metabolite production viz., Lemongrass, citral (citral a + citral b) content percentage (82.47 ± 2.12), Himrosa and Jamrosa, geraniol and geranyl acetate percentage (76.23± 1.46 and 8.49 ± 1.03) and (61.12 ± 1.58 and 25.88 ± 1.11), respectively, highest at 0.20 & 0.40 IW, while both decreases at higher levels of irrigation frequency (1.00 IW) in all the three consecutive years. [ABSTRACT FROM AUTHOR]

Crafting a comprehensive strategy to mitigate the impact of droughts, a complex geo-hazard profoundly affecting socio-economic aspects, entails the creation of a drought vulnerability map as a primary step. This study harmonizes geospatial techniques and the Fuzzy Analytical Hierarchy Process (fuzzy AHP) to formulate such a map for northwestern Odisha, India. From six principal drought-induced vulnerability parameters, namely physical attributes, water demand and usage, agriculture, land use, groundwater and population/development, 22 sub-parameters were selected. Spatial layers were generated for each sub-parameter, followed by their fuzzification using a fuzzy membership approach. Subsequently, AHP was employed to establish parameter weights through pair-wise comparisons. By applying the weighted overlay method, drought vulnerability maps were generated, classifying regions into five vulnerability levels: very high, high, moderate, low, and very low. The outcomes indicate that roughly 33% of the area is classified as having high drought vulnerability. Validation of the approach using statistical metrics, including accuracy, root mean square error and mean absolute error, demonstrates its efficacy in gauging drought vulnerability, thereby aiding planners in devising effective drought mitigation strategies. [ABSTRACT FROM AUTHOR]

Water/drought stress experiments are frequently conducted under imposed stress or rainout shelters, while natural drought hot-spot investigations are rare. The "drought hot spot" in Anantapur, Andhra Pradesh, India, is appropriate for drought stress evaluation due to its hot, arid environment, limited rainfall, with over 50% rainfall variability. According to reports, 30 out of 200 groundnut cultivars in India are supposed to possess drought-tolerant characteristics. However, these cultivars are yet to be evaluated in areas that are prone to drought. This study tested these drought-tolerant genotypes in naturally drought-prone areas of Anantapur under rainfed conditions from Kharif 2017 to 2019. Pod yield and rainfall-use-efficiency (RUE) were measured for these genotypes. Genotype and genotype*environment interactions affected pod yield and RUE (GEI). The AMMI model exhibits significant season-to-season variability within the same area with environmental vectors > 90° angles. GGE biplot suggested the 2018 wet season for drought-resistant cultivar identification. Kadiri5 and GPBD5 were the most drought-tolerant cultivars for cultivation in Anantapur and adjacent regions. These types could also be used to generate drought-tolerant groundnut variants for drought-prone regions. [ABSTRACT FROM AUTHOR]

Climate change can have an influence on rainfall that significantly affects the magnitude frequency of floods and droughts. Therefore, the analysis of the spatiotemporal distribution, variability, and trends of rainfall over the Mahi Basin in India is an important objective of the present work. Accordingly, a serial autocorrelation, coefficient of variation, Mann–Kendall (MK) and Sen's slope test, innovative trend analysis (ITA), and Pettitt's test were used in the rainfall analysis. The outcomes were derived from the monthly precipitation data (1901–2012) of 14 meteorology stations in the Mahi Basin. The serial autocorrelation results showed that there is no autocorrelation in the data series. The rainfall statistics denoted that the Mahi Basin receives 94.8% of its rainfall (821 mm) in the monsoon period (June–September). The normalized accumulated departure from the mean reveals that the annual and monsoon rainfall of the Mahi Basin were below average from 1901 to 1930 and above average from 1930 to 1990, followed by a period of fluctuating conditions. Annual and monsoon rainfall variations increase in the lower catchment of the basin. The annual and monsoon rainfall trend analysis specified a significant declining tendency for four stations and an increasing tendency for 3 stations, respectively. A significant declining trend in winter rainfall was observed for 9 stations under review. Likewise, out of 14 stations, 9 stations denote a significant decrease in pre-monsoon rainfall. Nevertheless, there is no significant increasing or decreasing tendency in annual, monsoon, and post-monsoon rainfall in the Mahi Basin. The Mann–Kendall test and innovative trend analysis indicate identical tendencies of annual and seasonal rainfall on the basin scale. The annual and monsoon rainfall of the basin showed a positive shift in rainfall after 1926. The rainfall analysis confirms that despite spatiotemporal variations in rainfall, there are no significant positive or negative trends of annual and monsoon rainfall on the basin scale. It suggests that the Mahi Basin received average rainfall (867 mm) annually and in the monsoon season (821 mm) from 1901 to 2012, except for a few years of high and low rainfall. Therefore, this study is important for flood and drought management, agriculture, and water management in the Mahi Basin. [ABSTRACT FROM AUTHOR]

Standardized precipitation index (SPI) is one of the frequently used meteorological drought indices. However, the time-varying characteristics observed in the historical precipitation data questions the reliability of SPI and motivated the development of nonstationary SPI. To overcome some of the limitations in the existing nonstationary drought indices, a new framework for drought index is proposed, incorporating the temporal dynamics in the precipitation. The proposed drought index is developed by coupling the joint deficit index with the extended time sliding window–based nonstationary modeling (TSW-NSM). The proposed nonstationary joint deficit index (NJDI) detects the signature of nonstationarity in the distribution parameter and models both long-term (i.e., trend) and short-term (i.e., step-change) temporal dynamics of distribution parameters. The efficacy of NJDI is demonstrated by employing it to identify the meteorological drought-prone areas over India. The changes observed in the distribution parameter of rainfall series reveal an increasing number of dry days in recent decades all over India, except the northeast. Comparison of NJDI and stationary joint deficit index (JDI) reveals that JDI overestimates drought when frequent severe dry events are clustered and underestimates when these events are scattered, which indicates that the traditional index is biased toward the lowest magnitude of precipitation while classifying the drought. Moreover, NJDI could closely capture historical droughts and their spatial variations, thereby reflecting the temporal dynamics of rainfall series and the changes in the pattern of dry events over India. NJDI proves to be a potentially reliable index for drought monitoring in a nonstationary climate. Significance Statement: Drought is one of the most severe natural disasters and is expected to intensify under a warming climate. There has been progress on developing newer methodologies to characterize drought severity under the changing climate. However, some limitations remain in capturing the temporal changes of the precipitation, especially the nonstationarity in variance (rapid increases in extreme precipitation versus the average precipitation). We propose an extension to the time sliding window approach to capture the nonstationarity in variance and mean while incorporating short-term (step-change) and long-term (trend) temporal dynamics. We apply the methodology to a subcontinent-sized heterogeneous country of India with gridded rainfall dataset (0.25° × 0.25°, 1901–2013) from the India Meteorological Department. We find that the majority of grids exhibit negative and positive trends in shape and scale parameters, respectively, which ultimately leads to an increase in the number of drier events, whereas a contradictory pattern is exhibited in the northwest Indian region (decrease in the number of dry days). The proposed index gives a potential framework for drought monitoring under the changing climate and can be extended to develop a multivariate nonstationary joint deficit index by incorporating other hydrological variables (e.g., soil moisture, diurnal temperature range, streamflow). [ABSTRACT FROM AUTHOR]

The agricultural drought significantly affects the socio-economic sectors in the agrarian country like India. Though there is a larger variability in the drought characteristics, the time to propagation from meteorological to agricultural drought is not investigated at regional scale in India. The Standardised Precipitation Evapotranspiration Index (SPEI), and Standardised Soil moisture Index (SSI) are computed incorporating large-scale climatic oscillations and regional hydro-meteorological variables. The time to propagation is calculated based on three different approaches. In addition, the internal characteristics of agricultural drought propagation is computed. The important findings from the study suggest that the time of propagation varies between 5 to 7 months for drought initiation, 9 to 15 months for drought peak, and 10 to 20 months for drought termination. The internal drought development and recover periods varies from 3.1 to 6 months. Over most of the area, the instantaneous drought development and recovery speed magnitude varies between 0.20 and 0.60. Lastly, it is observed that the exclusion of physical covariates leads to underestimation of agricultural drought propagation characteristics over India. The results of the current study can be used to guide future early warning and monitoring systems for agricultural drought as well as the study of agricultural drought at the regional level. [ABSTRACT FROM AUTHOR]

Prolonged and repeated drought, as seen in India and other parts of South Asia, is a symptom of climate change, which is partially the result of human interventions. The performance of the widely used drought metrics Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) are evaluated for 18 stations in Uttar Pradesh state for the period 1971 to 2018 in this study. Drought characteristics such as intensity, duration, and frequency of different categories are estimated and compared based on SPI and SPEI. In addition, station proportion is estimated at a different timescales, providing a better insight into temporal variability drought of a specific category. Spatiotemporal trend variability of SPEI and SPI was investigated at a significance level of 0.05 using the non-parametric Mann–Kendall (MK) test. SPEI adds the effect of temperature rise and deficit change on the drought occurrences of different classes. SPEI provides a better estimation of drought characteristics due to its consideration of temperature change in the drought severity. The more significant number of drying events accounted for a timescale of 3 months and 6 months, reflecting the higher variability of the seasonal fluctuation of water balance over the state. At 9-month and 12-month timescales, SPI and SPEI fluctuate gradually with considerable differences between the duration and severity of the drought event. This study reveals that there have been a substantial number of drought events over the state during the last two decades (2000 to 2018). The results conclude that the study area is at risk of erratic meteorological drought conditions where the western part of the study is worst affected compared to the eastern part of Uttar Pradesh (India). [ABSTRACT FROM AUTHOR]

Droughts and floods have been occurring at a higher frequency in recent decades. The rapid transition between them magnifies the socio-economic consequences of these catastrophes relative to the effects of the individual occurrences of the extreme event. This study examines the temporal variability of meteorological drought and wet event characteristics occurring over Mirzapur (Uttar Pradesh), India. The Standardized Precipitation Evapotranspiration Index (SPEI) is applied to monthly water balance at scales 3, 6, 9, and 12 months to estimate the meteorological drought and wet events from 1971 to 2018. Drought and wet event characteristics such as the number of drought/wet events, severity, duration, and intensity are estimated using run theory over SPEI output. While characterizing the pattern of trends over the historical time period, variable-sized cluster analysis (VSCA) allows the detection of multiple change points as opposed to the Mann-Kendall (MK) test, which produces a monotonic trend for the entire time period. The VSCA technique accounts for drought variability and depicts the pattern's evolution across the period under consideration. Stationscale drought data from Mirzapur, Uttar Pradesh (UP), India, were used in the procedure. VSCA allows for the detection of many change points while describing the pattern of drought trend throughout a historical period, as opposed to the usual Mann-Kendall (MK) test, which provides a monotonic trend for the whole time. As a result, VSCA demonstrated the MK test compatibility. [ABSTRACT FROM AUTHOR]

The incidence of droughts and their intensity in recent times are affected by climatic variability and change, consequently affecting the agro-based economy of red and laterite zone (RLZ) India. In the present study, changing characteristics of meteorological droughts have been investigated over the sub-humid RLZ of West Bengal, India, using the Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI). SPI and SPEI were computed over 1-, 3-, and 12-month time scales from monthly meteorological data from 1930 to 2019 to explore variations in drought frequency, intensity, duration, and spatial extent in the RLZ. It was observed that since the 1990s, the RLZ has been frequently affected by short-term extreme to severe drought, even in the wet-monsoon months. The frequency and intensity of droughts were observed to have increased in the recent period with a decrease in the duration. The Mann–Kendall test on the drought trend analysis of the region indicated a rising trend in monsoon months. The SPEI-monsoon was found to be more significantly correlated (r2 = 0.65) with the rainfed Kharif (monsoon)-rice production anomaly than the SPI. SPEI appeared to have a more pronounced impact on drought incidences in the region over the recent decades. Field surveys were conducted to validate the two recent drought occurrences and associated crop failure. A total of 95% of the farmers in the survey reported crop failure during the short-term meteorological droughts in monsoon months. It is therefore suggested to monitor changing patterns and extent of droughts, particularly in water-scarce RLZs, to design appropriate drought preparedness planning. [ABSTRACT FROM AUTHOR]

Regular monitoring of drought events, watershed characterization, management, and development efforts is crucial for future disaster predictions and mitigation strategies. The drought susceptibility investigation has been carried out in the Ken River Basin of Bundelkhand region, one of the most drought‐prone areas in India. Proxies used in the present study are long‐term climatological data (rainfall, standardized precipitation index, and aridity index), satellite data (slope, drainage density [DD], distance to river, and normalized difference vegetation index), lithology, lineament density, and groundwater depth. By the analytical hierarchy process, weightage of each factor is assigned according to its importance. Study shows that nearly 48% of the area of the basin experiences moderate to severe drought conditions. In the last five decades, there have been 22 years of extreme drought, with the most extended period being 1972–1974 and the driest year being 2006–2007. Sensitivity analysis reveals that lithology, slope, and DD are the most significant parameters in the susceptibility analysis. Model validation through an artificial neural network demonstrates the model's high accuracy (0.9) and sensitivity with minor errors. An integrated study of drought susceptibility and morphometry is useful for identifying the drought risk hotspots in the basin. The investigation will be helpful in river basin management and disaster management strategies. [ABSTRACT FROM AUTHOR]

The enormous perennial monocotyledonous herb banana (Musa spp.), which includes dessert and cooking varieties, is found in more than 120 countries and is a member of the order Zingiberales and family Musaceae. The production of bananas requires a certain amount of precipitation throughout the year, and its scarcity reduces productivity in rain-fed banana-growing areas due to drought stress. To increase the tolerance of banana crops to drought stress, it is necessary to explore crop wild relatives (CWRs) of banana. Although molecular genetic pathways involved in drought stress tolerance of cultivated banana have been uncovered and understood with the introduction of high-throughput DNA sequencing technology, next-generation sequencing (NGS) techniques, and numerous "omics" tools, unfortunately, such approaches have not been thoroughly implemented to utilize the huge potential of wild genetic resources of banana. In India, the northeastern region has been reported to have the highest diversity and distribution of Musaceae, with more than 30 taxa, 19 of which are unique to the area, accounting for around 81% of all wild species. As a result, the area is regarded as one of the main locations of origin for the Musaceae family. The understanding of the response of the banana genotypes of northeastern India belonging to different genome groups to water deficit stress at the molecular level will be useful for developing and improving drought tolerance in commercial banana cultivars not only in India but also worldwide. Hence, in the present review, we discuss the studies conducted to observe the effect of drought stress on different banana species. Moreover, the article highlights the tools and techniques that have been used or that can be used for exploring and understanding the molecular basis of differentially regulated genes and their networks in different drought stress-tolerant banana genotypes of northeast India, especially wild types, for unraveling their potential novel traits and genes. [ABSTRACT FROM AUTHOR]

This paper focus on the drought monitoring and forecasting for semi-arid region based on the various machine learning models and SPI index. Drought phenomena are crucial role in the agriculture and drinking purposes in the area. In this study, Standardized Precipitation Index (SPI) was used to predicted the future drought in the upper Godavari River basin, India. We have selected the ten input combinations of ML model were used to prediction of drought for three SPI timescales (i.e., SPI -3, SPI-6, and SPI-12). The historical data of SPI from 2000 to 2019 was used for creation of ML models SPI prediction, these datasets was divided into training (75% of the data) and testing (25% of the data) models. The best subset regression method and sensitivity analysis were applied to estimate the most effective input variables for estimation of SPI 3, 6, and 12. The improved support vector machine model using sequential minimal optimization (SVM-SMO) with various kernel functions i.e., SMO-SVM poly kernel, SMO-SVM Normalized poly kernel, SMO-SVM PUK (Pearson Universal Kernel) and SMO-SVM RBF (radial basis function) kernel was developed to forecasting of the SPI-3,6 and 12 months. The ML models accuracy were compared with various statistical indicators i.e., root mean square error (RMSE), mean absolute error (MAE), relative absolute error (RAE), root relative squared error (RRSE), and correlation coefficient (r). The results of study area have been showed that the SMO-SVM poly kernel model precisely predicted the SPI-3 (R2 = 0.819) and SPI-12 (R2 = 0.968) values at Paithan station; the SPI-3 (R2 = 0.736) and SPI-6 (R2 = 0.841) values at Silload station, respectively. The SMO-SVM PUK kernel is found that the best ML model for the prediction of SPI-6 (R2 = 0.846) at Paithan station and SPI-12 (R2 = 0.975) at the Silload station. The compared with SVM-SMO poly kernel and SVM-SMO PUK kernel was observed, these models are best forecasting of drought (i.e. SPI-6 and SPI-12), while SVM-SMO poly kernel is good for SPI-3 prediction at both stations. The results have been showed the ability of the SVM-SMO algorithm with various kernel functions successfully applied for the forecasting of multiscale SPI under the climate changes. It can be helpful for decision making in water resource management and tackle droughts in the semi-arid region of central India. [ABSTRACT FROM AUTHOR]

Changes in climate and socio-economic conditions pose a major threat to water security, particularly in the densely populated, agriculture-dependent and rapidly developing country of India. Therefore, for cogent mitigation and adaptation planning, it is important to assess the future evolution of drought hazard, vulnerability and risk. Earlier studies have demonstrated projected drought risk over India on the basis of frequency analysis and/or hazard assessment alone. This study investigates and evaluates the change in projected drought risk under future climatic and socio-economic conditions by combining drought hazard and vulnerability projections at a country-wide scale. A multivariate standardized drought index (MSDI) accounting for concurrent deficits in precipitation and soil moisture is chosen to quantify droughts. Drought vulnerability assessment is carried out combining exposure, adaptive capacity and sensitivity indicators, using a robust multi-criteria decision-making method called the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). In the worst-case scenario for drought hazard (RCP2.6-Far future), there is a projected decrease in the area under high or very high drought hazard classes in the country by approximately 7 %. Further, the worst-case scenario for drought vulnerability (RCP6.0-SSP2-Near future) shows a 33 % rise in the areal extent of high or very high drought vulnerability classes. The western Uttar Pradesh, Haryana and western Rajasthan regions are found to be high risk under all scenarios. Bivariate choropleth analysis shows that the projected drought risk is majorly driven by changes in drought vulnerability attributable to societal developments rather than changes in drought hazard resulting from climatic conditions. The present study can aid policy makers, administrators and drought managers in developing decision support systems for efficient drought management. [ABSTRACT FROM AUTHOR]

Climate change has increased the frequency of drought occurrence in various parts of the world. Drought as a complex phenomenon causes severe impacts on ecological and socio-economic status. Short-term and long-term occurrences of drought have made many regions vulnerable globally. This paper makes an attempt to assess drought vulnerability in Godavari Middle Sub-basin of India. Twenty-four site specific socio-economic and environmental factors were identified based on the extensive literature review. Drought frequency was assessed using standardized precipitation index (SPI). These datasets were divided into training (70%) and testing (30%) data. Frequency ratio (FR) model was utilized to establish relationship among drought conditioning factors and drought frequency. Weights obtained from the FR model were used as input to the adaptive neuro-fuzzy inference systems (ANFIS) model. Drought vulnerability results were validated using the testing data and receiver operating characteristic (ROC). The accuracy of ANFIS models for 1-month (0.957), 3-months (0.882), 6-months (0.964) and 12-months (0.938) showed high suitability of ANFIS model for the assessment of drought vulnerability. The findings revealed that very low normalized difference vegetation index (NDVI) and increasing trend of highest maximum and mean maximum temperature were major environmental factors which influenced high drought vulnerability in the sub-basin. High proportion of area under fallow land, high infant mortality rate (IMR) and moderate literacy rate were identified as major socio-economic factors making watersheds vulnerable during short and long-term droughts. Largest area of the sub-basin was found under high vulnerability for 3-months, followed by 6-months and 12-months droughts. Thus, the study calls for policy intervention towards lessening the impact of drought in highly vulnerable watersheds. [ABSTRACT FROM AUTHOR]

Drought is a natural and complex climatic hazard that has consequences for both natural and socio-economic contexts. The drought vulnerability assessment is an important contribution towards reducing its impact on the society and people. Although there has been significant research progress in the forecasting and prediction of droughts, but its vulnerability evaluation using high-precision deep learning algorithms has not yet been made. Keeping these things in mind, the present research is focused on preparing drought vulnerability map (DVM) for the monsoon climate dominant region of West Bengal, India. Fifty drought vulnerability determining variables were employed and divided into four categories based on the drought types: hydrological drought, agricultural drought, meteorological drought, and socio-economic drought. Then one deep learning model, i.e., Deep Learning Neural Network (DLNN) and two machine learning methods, i.e., Artificial Neural Network (ANN) and Multitask Gaussian Process (MGP) were used for preparing the drought vulnerability maps (DVMs). Results show nearly 24% of the study area is very highly vulnerable to drought. Several statistical performance indices such as the receiver operating characteristic (ROC) curve, mean-absolute-error (MAE), root-mean-square-error (RMSE), accuracy, Jaccard, F1 measure, MCC, and K-index were applied for judging the efficacy of the DVMs. The results show that the DLNN performed best, with an AUC of 94.8%, followed by ANN (89.66%) and MGP (87.08%). Hence, the deep learning algorithms performed better than the traditional machine learning models to prepare the DVMs. The combination of various parameters of different types of droughts can accurately predict the drought vulnerable zones with very high accuracy. This can be useful for policy-makers in managing drought severity in the south-western part of West Bengal. [ABSTRACT FROM AUTHOR]

The basic and derived meteorological parameters, are important factors that affect the epochal changes of monsoonal drought/flood events which consequently influence the socio‐economic aspects of India. The anomalous tricade departure of parameters namely rainfall, surface pressure, surface temperature, zonal and meridional wind, cloud cover (CC), outgoing longwave radiation, latent heat flux (LHF), sensible heat flux, net incoming shortwave radiation flux and net heat flux (NHF) have been considered between past, and recent tricade respectively drought and flood events. An anomalous decrease (−3 to −2 mm/d) in rainfall over the southern Arabian sea (AS), southern peninsular India and Bay of Bengal during past and recent tricade, on the other side increase (2–2.5 mm/d) in rainfall over the equatorial Indian ocean (EIO) during drought. The drought episode of the recent tricade is showing basin wise significant increase in LHF (20–30 W/m2) and decrease in NHF (−30 to −60 W/m2) from the Somali coast to Indonesian region which is linked with changing oceanic processes linked with the Indian ocean warming. The comparison of past and recent monsoonal flood has shown decrease in the CC over EIO and AS region which supported anomalous highest departure in LHF (12 W/m2) over EIO. A direct association of dry and moist static energy variations have been found to be linked with the surface fluxes during Indian summer monsoon season. Further, epochal differences of vertically integrated moisture flux convergence is consistent with flood/drought rainfall anomaly. Thus, radiative imbalance also plays a secondary role in determining epochal nature of drought/flood variability and its nature. Plain Language Summary: The epochal variation of basic meteorological parameters and surface energy fluxes in monsoonal drought and flood episodes have been analyzed from 1961 to 2020. In this study, over the southern Arabian Sea (AS), southern peninsular India, and the Bay of Bengal decreasing rainfall during past tricadal monsoonal drought events while an increase in rainfall over the Indian Ocean (IO) has been observed by a change in the amount of cloud cover and surface energy fluxes. The undulating behavior of convective heating is associated with a significantly changing pattern of surface heat fluxes observed in past and recent monsoonal drought and flood episodes. Determining the epochal nature of drought and flood variability and its nature. The epochal departure in net incoming shortwave radiation flux is a considerable factor in contrasting dynamical features of drought and flood events along with monsoon circulation over the Indian region. Key Points: The convective heating is a major role in changing surface fluxes during drought and flood eventsThe radiative imbalance plays a secondary role in determining drought and flood variability and its natureThe elevated value of net incoming shortwave radiation would cause more severe drought than flood in near future [ABSTRACT FROM AUTHOR]

As a natural hazard, drought is a complex multivariate phenomenon that does not depend on only one hydrometeorological variable, as well as, considering a particular kind of drought may not be effective for drought management. Considering this, many multivariate drought indices have been developed based on linearity assumptions or conventional copulas assuming symmetric relationships among univariate drought indices. In this study, D‐vine copula was applied to construct a four‐dimensional index, named as Integrated Drought Index (IDI), by combining four univariate drought indices (Standardized Precipitation Index [SPI], Reconnaissance Drought Index [RDI], Standardized Soil moisture Index [SSI] and Standardized stream flow Drought Index [SDI]) to better reflect many hydrometeorological variables (precipitation, evapotranspiration, soil moisture and stream flow) and different kinds of drought (meteorological, agricultural and hydrological) simultaneously. Vine copula was used to solve nonlinear and asymmetric relationships among drought indices due to its flexibility over the free selection of copula(s) in each step of hierarchical structure in high dimensional modelling. The IDI was constructed for 1‐ and 4‐month timescales for the upper Tapti basin of the central region in India. The performance of IDI was tested with dependence measures (Pearson's correlation coefficient, Mutual Information) and evaluated against the Terrestrial Water Storage Anomaly data derived from the Gravity Recovery and Climate Experiment (GRACE) mission. Spatial analysis of drought was carried out by fuzzy c‐means (FCM) clustering algorithm with IDI. IDI based on vine copula solved the nonlinear and asymmetric relationships among different variables associated with the occurrence of droughts effectively with a reduction of uncertainty as compared to the single drought indices for different kinds of droughts. Analysis revealed spatially different drought risks in the upper and lower river basins. In general, the vine copula addresses nonlinear and asymmetric relationships that exist between the variables associated with natural hazards like drought. [ABSTRACT FROM AUTHOR]

Depletion of water resources and soil moisture leading to drought is a global concern and the effective assessment and monitoring using a drought model has become essential. A detailed account of frequency, run length and temporal trend of the drought events are presented in the study from 1981 to 2019 at the 1-, 3-, 6-, 9-, 12- and 24-month timescale using the Standardized Precipitation Index and Standardized Precipitation Evapotranspiration Index. SPI recorded more drought months in the extreme category with 14 months at Gubbi station for example, while SPEI showed only 5 months. SPEI showed longer drought length in moderate and severe categories for agricultural and hydrological drought. At a 1- month timescale, both the indices reported extreme drought events where Tiptur station in May 2016 (–4.75) and Chikkanayakanahalli station in March 1992 (–2.76) were the worst-case scenarios. The study aims at providing practical results to facilitate decision-makers for drought risk management. [ABSTRACT FROM AUTHOR]

In this study, a detailed spatiotemporal assessment of meteorological drought characteristics (viz., persistence, severity, and frequency) is carried out for the Vidarbha Region of Maharashtra, India, which has been infamous for the increasing number of farmer suicides there in the recent past. Using the long-term rainfall data from 11 districts for 62 years (1951–2012), the droughts in different severity classes are characterized by SPI. A detailed investigation of SPI-3, SPI-6, and SPI-12 is also done to assess the dependence of the drought characteristics at different timescales. The results reveal that the Vidarbha region is prone to meteorological droughts with frequency from once in 5 to 8 years. However, the frequency and severity of droughts have significantly increased in 1982–2012 compared to 1951–1981. The persistence of droughts is well captured by SPI at longer timescales. The trend analysis reveals the drought severities to be increasing over 10 out of 11 districts. [ABSTRACT FROM AUTHOR]

Significant changes in the photosynthesis of terrestrial vegetation are observed in recent decades due to climate variability, land use and land cover changes, frequent disasters and anthropogenic forcing, but this situation in the Indian context remains largely unexplored. Here, therefore, we estimate the long–term changes in photosynthesis and its key climate drivers in India. The gross photosynthetic activity peaks (FPAR mean = 0.57) in post–monsoon (September, October and November) due to high soil moisture (87.26 kg/m² ), good precipitation (5.2 mm/day) and optimum temperature (21.46 C) conditions, whereas it is smallest (FPAR mean = 0.4) in summer (March, April and May) due to very high temperature (27.37 C) and lower soil moisture (55.45 kg/m2 ). The trend in FPAR over 2000–2019 reveals enhanced photosynthetic activity in India. In the northwest, both soil moisture and temperature have a positive influence on greening (i.e. moisture induced greening). Conversely, the unfavourable soil moisture and temperature conditions dominate in the peninsular region to trigger browning there (i.e. warming induced moisture stress). Our analyses suggest that the photosynthetic trend is primarily controlled by trend in the soil moisture (44.31%) and air temperature (32.54%). The warming induced moisture stress can be a serious threat in the future to drive browning in more areas. Extreme events such as droughts, landslides and fires are expected to increase in the hilly regions such as the northeast due to browning. Furthermore, as food production and forest resources influence the economy of a country like India, management and conservation of greenery are inevitable for a sustainable ecosystem. [ABSTRACT FROM AUTHOR]

Drought, a natural and very complex climatic hazard, causes impacts on natural and socio-economic environments. This study aims to produce the drought vulnerability map (DVM) considering novel ensemble machine learning algorithms (MLAs) in Jharkhand, India. Forty, drought vulnerability determining factors under the categories of exposure, sensitivity, and adaptive capacity were used. Then, four machine learning and four novel ensemble approaches of particle swarm optimized (PSO) algorithms, named random forest (RF), PSO-RF, multi-layer perceptron (MLP), PSO-MLP, support vector regression (SVM), PSO-MLP, Bagging, and PSO-Bagging, were established for DVMs. The receiver operating characteristic curve (ROC), mean-absolute-error (MAE), root-mean-square-error (RMSE), precision, and K-index were utilized for judging the performance of novel ensemble MLAs. The obtained results show that the PSO-RF had the highest performance with an AUC of 0.874, followed by RF, PSO-MLP, PSO-Bagging, Bagging, MLP, PSO-SVM and SVM, respectively. Produced DVMs would be helpful for policy intervention to minimize drought vulnerability. [ABSTRACT FROM AUTHOR]

In semi‐arid regions as in India, where agriculture relies on groundwater abstraction, increase of water resources availability through managed aquifer recharge (MAR) or rainwater harvesting (RWH) is often perceived as a major solution. Studies on these structures' efficiency exists but despite the interest, limited information is available on the temporal variation of their replenishment. In a monsoon driven climate, the inter‐annual variations are crucial to assess the potential of water storage and multi‐year management especially for these structures. Here, we aim at developing a methodology to reconstruct water storage of RWH tanks to further improve our understanding on long term efficiency and multi‐years drought management. To tackle this issue, long‐term monitoring of a RWH tank located in Telangana in Southern India is achieved by a combination of field monitoring over 2 years (tanks surface and water levels) and a daily water balance compared to LANDSAT measurements of the tank area. The procedure allows reconstructing the tank filling dynamic over a 14‐years period at a daily time step and show the extreme variability of the tank filling level. During this period, the yearly maximum tank volume ranges from 8650 to ~200 000 m3. On the 14‐years period, the tank reach its maximum capacity only once and, for 1/3 of the time, yearly maximum replenishment is below 15% of its capacity. The surface water availability remains limited in time since the tank dries‐up annually, except for 2 years. However, water percolation to the aquifer is slightly enhanced for some years. During this monitoring period, very few extreme raining events (6) contribute for more than 50% of the collected volume. This observation highlights (1) the dependency of the structure to extreme storm events, (2) the limited capacity for a multi‐year's management and (3) the farmers vulnerability to successive droughts. [ABSTRACT FROM AUTHOR]

Drought forecasting is essential for risk management and preparedness of drought mitigation measures. The present study aims to evaluate the effectiveness of the proposed hybrid technique for regional drought forecasting. Multiple Linear Regression (MLR), Artificial Neural Network (ANN), and two wavelet techniques, namely, Discrete Wavelet Transform (DWT) and Wavelet Packet Transform (WPT), were evaluated in drought forecasting up to a lead time of six months. Standard error metrics were used to select optimal model parameters, such as number of inputs, number of hidden neurons, level of decomposition, and number of mother wavelets. Additionally, the performance of various mother wavelets, including the Haar wavelet (db1) and 19 Daubechies wavelets (db1 to db20), were evaluated. The results indicated that the ANN model produced better forecasts than the MLR model, whereas the hybrid models outperformed both ANN and MLR models, which failed to predict the SPI values for a lead time greater than two months. The performance of all the models was found to improve as the timescale increased from 3 to 12 months. However, all the models' performances deteriorated as the lead time increased. The hybrid WPT-MLR was the best model for the study area. The findings indicated that a hybrid WPT-MLR model could be used for drought early warning systems in the study area. [ABSTRACT FROM AUTHOR]

Drought is considered among the most perilous events with catastrophic consequences, particularly from the agro-economic point of view. These consequences are expected to exacerbate under the increasing meteorological aberrations due to changing climate, which necessitates investigating drought variabilities. This study presents a thorough spatiotemporal assessment of drought trends and variabilities over the agriculture-dominated Marathwada Region, Maharashtra, India. The precipitation data is extracted from the India Meteorological Department (IMD) gridded product, whereas actual evapotranspiration (ET) and Evaporative Stress Index (ESI) are obtained from Global Land Evaporation Amsterdam Model (GLEAM) datasets. Standardized Precipitation Index (SPI) is used to characterize drought occurrences at multiple time frames, whereas non-parametric tests, i.e., modified Mann–Kendall (MMK) and Sen's slope (SS) tests, are employed to detect trends. The results reveal the region to be prone to droughts, and SPI at a longer time frame (i.e., 12-monthly moving frame) can capture drought occurrences better than the shorter time frames, which can be attributed to the lesser randomness in the time series in the longer frame. A mix of positive/negative trends of SPI series are found for the monsoonal months; however, they are relatively more concentrated towards negative ZMMK. Hence, the Marathwada Region can be inferred to have exhibited a relatively increased tendency towards drought occurrences. The seasonal differences in mean values and trends of rainfall, ET, and ESI are discussed in detail. Since the Marathwada Region has a monsoon-dominated climate with high agricultural importance, the information reported in this study will help in devising water management strategies to minimize the repercussions of droughts. [ABSTRACT FROM AUTHOR]

The present paper examines the significance of remittances by the male outmigrants on socio-economic development of the receipient households in a frequently drought affected Bankura district in the Rarh region of West Bengal State of India with particular emphasis on the varied impacts with respect to differential access to landholding or lack of it. As many as 185 out of a total of 756 households were randomly selected for collection of data with the help of a structured household schedule. Logistic regression model has been fitted to analyse the relation between the size of migrant remittances and important socio-economic determinants. The study revealed that the size of the remittance depends on nature and types of work undertaken by the migrants at the destination. Only a small proportion of the recipient migrant households utilized the remittances received by them for a single purpose though the majority used it for a multiple of purposes. Majority of the migrant households utilized the remittances for food consumption as well as to repay the loans taken by them before adopting migration as a strategy to improve their living. Outmigration for a short or longer period appears to be a successful strategy for these households to withstand chronic poverty suffered by the most marginalised segment while the somewhat better-off sections manages to further improve their living conditions with the help of remittances received. [ABSTRACT FROM AUTHOR]

The 2019 extreme positive Indian Ocean dipole drove climate extremes over Indian Ocean rim countries with unclear carbon‐cycle responses. We investigated its impact on net biome productivity (NBP) and its constituent fluxes, using the Global Carbon Assimilation System (GCASv2) product, process‐based model simulations from TRENDYv9, and satellite‐based gross primary productivity (GPP). By distinguishing two separate regions, the India‐Africa and Asia‐Pacific, GCASv2 indicated enhanced terrestrial carbon uptake of 0.23 ± 0.20 PgC and release of 0.38 ± 0.15 PgC, respectively, during September–December (SOND) 2019. These NBP anomalies had comparable magnitudes to those following the 2015 extreme El Niño which, however, caused the consistent carbon release in both regions. The TRENDYv9 model ensemble confirmed these NBP responses, albeit with smaller magnitudes. These regional NBP anomalies were related to soil moisture variations with a dominant role of GPP. Understanding the impact of IOD provides new insights into mechanisms driving interannual variations in regional carbon cycling. Plain Language Summary: The extreme Indian Ocean Dipole (IOD) can drive climate extremes, such as floods, heatwaves, droughts, and wildfires, over the Indian Ocean rim countries. However, responses of regional terrestrial carbon cycling to IOD remained unclear. We used the net biome productivity (NBP) from an atmospheric inversion and multiple terrestrial biosphere models to demonstrate an enhanced terrestrial carbon uptake and release over the India‐Africa and Asia‐Pacific regions, respectively, during the extreme positive IOD (September–December) in 2019. These IOD‐induced regional NBP anomalies showed comparable magnitudes but different patterns to those following the 2015 extreme El Niño. Along with the more frequent extreme IOD under future greenhouse warming, IOD will be an important mechanism driving interannual variations in regional carbon cycling. Key Points: The 2019 extreme positive Indian Ocean Dipole caused the enhanced land carbon uptake over India‐Africa and release over Asia‐Pacific during September–DecemberThese regional net biome productivity (NBP) anomalies were closely related to soil moisture variations with a dominant role of gross primary productivityThese Indian Ocean Dipole‐induced regional NBP anomalies showed comparable magnitudes but different patterns to those following the 2015 extreme El Niño [ABSTRACT FROM AUTHOR]

The study analyzes the individuals' risk management strategies and determinants of preparedness for drought in Madhya Pradesh, India. It employs the descriptive and binary logistic regression on primary data and show that more than one earning member, access to social safety schemes, migration, irrigation facilities and diversified employment are the important risk management strategies. Gender, social group, income, non-migration, employment status and the interaction of 'access to government schemes and gender' are the statistically significant determinants in order, increasing the odds of preparedness against droughts. However, in the sub-sample of farmers, the main predictor variables are gender, non-migration, social group, crop losses and income. The study is novel to generate the field evidences with respect to risk management and drought preparedness, making noteworthy contributions to the disaster literature. The results advocate to strengthen the outreach and access of the social safety schemes in the drought-affected areas. Individuals are expected to build self-resilience to cost-effectively supplement the government limited efforts and capacity. These findings are important and equally applicable to the governments, policymakers and individuals residing in the other drought-affected regions with similar socio-economic characteristics of respondents. [ABSTRACT FROM AUTHOR]

Vast stretches of India comes under meteorological drought this year or the other. A huge population base in rural India are rendered highly vulnerable to this drought because of their primary dependency on agriculture and in turn they may respond through temporary migration out of the drought affected rural areas in search of alternative livelihoods. This study aims to investigate the association between drought and temporary migration in rural India by fitting binary logistic regression models on a cross-sectional dataset involving both National Sample Survey Organization (NSSO) 64th round data and India Meteorological Department (IMD) rainfall data. The paper also examines whether this association varies across the different socio-economic groups. Out of the total temporary migrants generated in rural India in the study period, 99.46% migrated internally and 67.12% were rural to urban migrants. The study finds that there is a positive association between drought instances and probability of a household to have at least one temporary migrant member in rural India (OR 1.64 with p<0.001) while controlling all other covariates. The study also concludes that the probability of temporary migration on account of drought is more severe among the socio-economically marginalised sections of the rural population compared to their better-off counterparts. [ABSTRACT FROM AUTHOR]

A high yielding and early maturing kodo millet variety CKMV 1 was developed at the Centre of Excellence in Millets, Tamil Nadu Agricultural University, Athiyandal by pure line selection from DPS 63/58. It recorded an average grain yield of 2813 kg/ha and 6967 kg/ha of straw under rainfed conditions in all India trials, which was 14, 28, 34 and 16 per cent higher over the check varieties RK 390-25 (2465kg/ha), RBK-155 (2196kg/ha), GPUK-3 (2103kg/ha) and TNAU 86 (2420kg/ha), respectively. All India Coordinated trials were conducted in three years during kharif, 2017 to kharif, 2019. This variety has registered 18 and 16 per cent increased grain yield over the qualifying varieties KMV 546 and KMV 547, respectively in All India Coordinated trials. This variety has registered 22, 27, 23 and 10 per cent increased straw yield over the check varieties RK 390-25, RBK-155, GPUK3 and TNAU 86, respectively in All India Coordinated trials. This variety has registered 7 and 8 per cent increased fodder yield over the qualifying varieties KMV 546 and KMV 547, respectively in All India Coordinated trials. Being an early duration variety with 106-110 days of maturity, it is the best choice for rainfed sowing. It is a profusely tillering and non-lodging variety and is highly suitable for rainfed cultivation in India. The panicle is long, compact and grains are arranged in irregular rows. The grains are bold and dark brown in colour and nutritionally superior with high protein (8.5%) and Iron (3.8 mg/100g) of rice. The introduction of this high yielding, short duration and drought tolerant culture with nutritionally rich grains will fulfil the needs of the dry land, hill area and tribal farming tracts of India, where Kodomillet is grown predominantly under the rainfed situation. [ABSTRACT FROM AUTHOR]

Drought is a recurring hydroclimatic extreme whose frequency and intensity have increased over India in recent decades, with a detrimental effect on regional water resources. This study addresses the spatiotemporal variability of drought and its plausible mechanism over India from 1951 to 2018. Firstly, six drought‐homogeneous regions are adequately ascertained by applying rotated empirical orthogonal function analysis using the Standardized Precipitation Evapotranspiration Index that captures most of the reported drought and regional hydroclimatic patterns. For the study period, a drying trend is witnessed across the regions though not significant, whereas in recent decades, northeast India (NEI) and some parts of the Indo‐Gangetic plain (IGP) exhibit higher drought frequency. This anomalous drying is attributed to a weakening of the monsoon circulation and accelerated warming caused by changes in the land‐use pattern. The interplay between El Niño Southern Oscillation and the Indian Ocean Dipole largely affects drought's interannual variability, which shows a modified response in recent decades. However, the long‐term decadal drought pattern is found to be strongly teleconnected with a newly discovered Southern Atlantic Oscillation (SAO) index, which reveals a statistically quite significant relationship (>50%) with drought variability. The positive phase of the SAO index is generally associated with drought across the regions except for IGP and NEI. Despite the recent overall wetting trend, drought frequency has enhanced over most of the regions, modulating the regional hydrological cycle. [ABSTRACT FROM AUTHOR]

Drought poses a tremendous challenge to India's socioeconomic development, livelihood, agriculture, and water management. While existing drought monitoring systems have characterized drought impact at different scales, policymaking and management require drought assessment at sub-district or taluka (sub-district) levels. Here, we develop high resolution (250 m) agriculture drought indices for the Indian region to overcome the shortcomings of the coarse resolution datasets. We used the co-kriging to downscale the Land Surface Temperature (LST) from 1000m to 250m. The LST and Enhanced Vegetation Index (EVI) are obtained at 8-day intervals at 250m spatial resolution. The high-resolution datasets show significant improvement in identifying the severity and coverage of drought. Soil Moisture Agriculture Drought Index (SMADI), which accounts for water stress and vegetation lag response, shows high reliability in drought detection. We evaluated drought extent and severity using the newly developed dataset and found that the high-resolution dataset can be used to separate the irrigation impact on drought alleviation. The high resolution drought indices from SMADI and the Normalized Vegetation Supply Water Index (NVSWI) effectively represent the drought conditions at district and taluka levels that can be used in drought impacts assessments in India. [ABSTRACT FROM AUTHOR]

The soil moisture observation data from the Qinghai-Tibetan Plateau permafrost and alpine wetland (PAW) monitoring network and from the National Center for Environmental Prediction (NCEP) Final Analysis (FNL) from 1989 to 2019 were adopted to drive the Lagrangian backward trajectory model to simulate the water vapour transportation paths and source regions to the three-river source region during dry and wet episodes. The results showed that the water vapour transport paths mainly came from the land and ocean in the west during the extreme drought episodes based on the standardized precipitation index (SPI), including the northern African continent, the eastern European plains, and the mid-latitudes of the Atlantic Ocean. The water vapour source regions were mainly concentrated in the Kashmir region, northeast India and southern Motuo on the Qinghai-Tibet Plateau. When the SPI indicated an extremely wet state, the transport mainly occurred to the east and south, including the Pacific Ocean and the South China Sea, where water vapour entered the mainland from East/South China, passed through the Yangtze River Basin, and finally reached the three-river source region from east or south of the Qinghai-Tibet Plateau, while the water vapour from the Indian Ocean and Arabian Sea passed through the Indian Peninsula and the Bay of Bengal and entered the three-river source region from the western and southern sides of the Qinghai-Tibet Plateau. [ABSTRACT FROM AUTHOR]

This paper presents a composite approach for drought characterization and monitoring using in situ and remote sensing-based drought indicators. The study was carried out on one of the most drought-prone areas of India, i.e., the Marathwada region of Maharashtra. Meteorological, hydrological, and agricultural drought indices, namely standardized precipitation index (SPI), streamflow drought index (SDI), and vegetation condition index (VCI), respectively, were integrated to develop the composite drought index (CDI) using principal component analysis (PCA). SPI and SDI were computed using in situ precipitation and streamflow data, respectively, for 35 years (1980–2014), whereas VCI was computed using MODIS satellite data (500-m resolution) for 15 years (2000–2014) at 1-, 3-, and 5-month time scales. The time scales of drought indices were evaluated using historical drought years and foodgrain production of the region. The drought areas observed by SPI, SDI, and VCI at different time scales were correlated with foodgrain production during the kharif crop growing season for 15 years (2000–2014). The maximum correlation of foodgrain production was observed with 3-month SPI (r = − 0.72), 5-month SDI (r = − 0.40), and 5-month VCI (r = − 0.81) for meteorological, hydrological, and agricultural drought, respectively. Three-month SPI, 5-month SDI, and 5-month VCI were selected from each drought category to develop CDI. These drought indices were combined using weights derived through the PCA technique. The maximum weight was obtained for 3-month SPI (45.4%) followed by 5-month VCI (42.8%) and 5-month SDI (11.8%). The developed CDI products showed a strong relationship (r = − 0.85) with foodgrain production. The drought years observed by CDI were also closest to drought years declared by the State Government. The time series trend of the drought-affected area observed by CDI, 3-month SPI, and 5-month VCI resembled the drought patterns very closely, especially during the drought years. The spatio-temporal analysis of individual drought indices and CDI with foodgrain production deviation showed that CDI was better for capturing drought conditions than individual indicators. The study suggested that an individual or single indicator is not sufficient to capture the actual drought severity and its magnitudes; therefore, using a composite approach could be a good choice for effective drought assessment and monitoring in the region. [ABSTRACT FROM AUTHOR]

A framework was set up to monitor drought in the semi-arid regions of eastern Tamil Nadu, southern India, for the period of 2014–2018 CE with the application of the standardized precipitation index (SPI), the scaled drought-condition index (SDCI), and the standardized water-level index (SWI). The results emphasized that this region had a negative precipitation anomaly and vegetative stress, both of which triggered meteorological and agricultural droughts and caused significant losses in the farming sector. The distributions of extreme and high-level hydrological droughts were at their maximum in 2017 CE. The multi-drought severity index (MDSI), implemented to assess the combined impact and highlighting the gradient of affected areas, illustrated that the eastern region (i.e., Jayankondam block) was the most extremely affected, followed by the northern and southern regions (i.e., T.Palur and Andimadam), which were moderately affected by droughts. The extremely affected eastern region has less of an ability to overcome droughts due to its socio-economic vulnerability, with its greater population and household density leading to the over-exploitation of potential resources. Therefore, the focus of this study is on the monitoring of drought severity in micro-administrative units to suggest an appropriate management plan. Hence, the extreme-drought-prone block (Jayankondam) should be given high priority in monitoring and implementing long-term management practices for its conservation and resilience against the effects of severe droughts. [ABSTRACT FROM AUTHOR]