Petrographic and geochemical characterization of coal from Talcher coalfield,Mahanadi Basin,India

In the present study an attempt has been made to characterize the coals of Talcher coalfield employing petrographic and geochemical techniques on a large number of coal samples. Pillar coal samples were collected from all the six workable coal seams, which occur in the Karharbari (Seam-I) and the Barakar (Seams II, III, IV, V and IX) formations.     

Hydrochemical characteristics of groundwater in the plains of Phalgu River in Gaya, Bihar, India

Assessment of groundwater quality is essential to ensure sustainable use of it for drinking, agricultural, and industrial purposes. The chemical quality of groundwater of Gaya region has been studied in detail in this work to delineate the potable groundwater zones. A total of 30 groundwater samples and 2 surface water samples were collected in and around Gaya district of Bihar. The major cations follow the trend: Ca²⁺?>?Mg²⁺?>?Na⁺?>?K⁺. The domination of calcium ions in the groundwater is due to weathering of rocks. The K⁺ ranged between 0.2 and 47.95 ppm, suggesting its abundance the below desired limit; but some samples were found to be above permissible limit. K⁺ weathering of potash silicate and the use of potash fertilizer could be the source. The major anions abundance followed the order HCO ₃ ^? ?>?Cl^??>?SO ₄ ^2? ?>?NO ₃ ^? ?>?PO ₄ ^3? . Dissolution of carbonates and reaction of silicates with carbonic acid accounts for the addition of HCO ₃ ^? to the groundwater and oxidation of sulphite may be the source of SO ₄ ^2? . Principal component analysis was utilized to reflect those chemical data with the greatest correlation and seven major principal components (PCs) representing >80 % of cumulative variance were able to interpret the most information contained in the data. PC1, PC2 and PC3 reflect the hydrogeochemical processes like mineral dissolution, weathering and anthropogenic sources. PC4, PC5, PC6 and PC7 show monotonic, random and independent relationships.     

Drought hotspot analysis and risk assessment using probabilistic drought monitoring and severity–duration–frequency analysis

Drought hotspot identification requires continuous drought monitoring and spatial risk assessment. The present study analysed drought events in the agriculture‐dominated mid‐Mahanadi River Basin in Odisha, India, using crop water stress as a drought indicator. This drought index incorporated different factors that affect crop water deficit such as the cropping pattern, soil characteristics, and surface soil moisture. The drought monitoring framework utilized a relevance vector machine model‐based classification that provided the uncertainty associated with drought categorization. Using the proposed framework, drought hotspots are identified in the study region and compared with indices based on precipitation and soil moisture. Further, a bivariate copula is employed to model the agricultural drought characteristics and develop the drought severity–duration–frequency (S–D–F) relationships. The drought hotspot maps and S–D–F curves are developed for different locations in the region. These provided useful information on the site‐specific drought patterns and the characteristics of the devastating droughts of 2002 and 2012, characterized by an average drought duration of 7 months at several locations. The site‐specific risk of short‐ and long‐term agricultural droughts are then investigated using the conditional copula. The results suggest that the conditional return periods and the S–D–F curves are valuable tools to assess the spatial variability of drought risk in the region.