Ministry of Earth Sciences

The 2024 Asian–Australian monsoon (AAM) year, defined as April 2024 to March 2025, was notable. Based on available data, a prolonged rainy season was observed in most parts of the AAM region, except for the Meiyu Region, which corresponds to the area affected by the second stage of the East Asian summer monsoon. The rainy season also featured elevated near-surface air temperatures, a boreal summer rainfall surplus of approximately 20

This study investigates the relationship between earthquake magnitude and the amplitude–duration characteristics of the initial few seconds of the P-wave using 21,069 waveforms from 867 events recorded at 1,545 stations of the Japanese strong-motion network operated by the National Research Institute for Earth Science and Disaster Resilience (NIED), Japan. High-precision hypocentral information and associated displacement magnitudes were used, and P-phase arrivals were carefully picked using five different algorithms and validated against a velocity model. Four amplitude-based parameters, Peak Acceleration ( P_a ), Peak Velocity ( P_v ), Peak Displacement ( P_d ), and Peak Abasement ( P_a^* ), were considered in the analysis along with three amplitude-duration-based parameters, Cumulative Absolute Velocity (CAV), Cumulative Absolute Displacement (CAD), and Cumulative Absolute Abasement (CAA). Each parameter was computed over time windows ranging from 1 to 7 s. Outliers were removed using magnitude–distance binning with statistical methods appropriate for normally distributed and skewed datasets. The results show that parameters sensitive to high frequencies (CAV, P_a , P_v ) exhibit lower standard deviations with strong ground motion parameters, but show weaker correlation with magnitude. In contrast, parameters influenced by lower frequencies ( P_d , P_a^* , CAD, CAA) demonstrate strong and consistent linear relationships with magnitude, with the threshold magnitude increasing with longer time windows. Among all tested parameters, P_a^* and CAA consistently provide the most stable correlations for a given time window. These findings highlight that low-frequency amplitude–duration parameters are more suitable for robust magnitude estimation in EEW systems, while magnitude saturation is progressively postponed with increasing P-wave time window length rather than completely eliminated.

The present study is conducted to understand the rainwater chemistry at three different locations in India during the Southwest monsoon season from June to September 2018. The rainwater samples were analyzed for the pH, conductivity, along with the major anions (F−, Cl−, NO2−, NO3−, SO42−), and cations (Ca2+, Mg2+, NH4+, K+, Na+). The mean pH and conductivity shows a significant heterogeneity in rainwater samples with alkaline nature at Jaipur (7.09 and 43.46 µS/cm) and Varanasi (7.31 and 55.42 μ µS/cm) whereas acidic nature at Dhanbad (5.10 and 31.98 µS/cm). Moreover, the ionic compositions of rainwater were found to be drastically different at all the three stations, which are also reflected in the neutralization of rainwater. Neutralization factors of Ca2+, NH4+ and Mg2+ ions suggest that Ca2+ was the major neutralizing species in rainwater at Jaipur and Varanasi (but not at Dhanbad), with high neutralization factors of 3.10 and 2.65, respectively. The correlation among the measured ionic species indicates a significantly high correlation between SO42− and NO3−, Ca2+ and Mg2+ and Na+ and Cl− ions at all the three stations. Principal component analysis (PCA) was applied to identify the possible sources of rainwater constituents, explaining the crustal dust, biomass burning, fossil fuel combustion, agricultural emissions, and coal burning as possible sources of observed ions in rainwater. Further, the air mass back-trajectory clusters were also computed to estimate terrestrial influence on rainwater chemistry over these regions. Results suggest that the contribution from both local and regional sources significantly influenced monsoonal rainwater chemistry.

This study introduces a multi-metric diagnostic framework for district-level climate risk advisory in maize cultivation, using Telangana, India, as a case study. The framework combines correlation analysis, SHAP-based attribution, temporal validation, and operational diagnostics to identify key agro-climatic drivers and stratify forecast reliability. Across three seasonal windows, we found that minimum temperature during vegetative growth and early-season drought variability consistently emerged as dominant yield influencers. Districts such as Mancherial and Jangaon showed reliable forecast performance, while high-risk zones including Nirmal and Jayashankar exhibited unstable behavior and multiple critical warning events. In contrast, stable districts such as Jangaon and Kamareddy demonstrated consistent forecast reliability suitable for automated advisory integration. These findings highlight clear district-level contrasts between stable and high-risk zones and provide actionable insights for climate-smart agricultural planning. The proposed framework advances beyond conventional accuracy metrics, offering a reproducible tool for stratified model calibration, ensemble tuning, and adaptive district-specific planning under variable climatic regimes.

Delhi-National Capital Territory (NCT) is crucial due to the complex tectonics, varying sedimentary thickness, vicinity of active faults, and high population density. The Mahendragarh- Dehradun Fault (MDF) and Delhi-Sargodha Ridge Fault (DSRF) are the prominent tectonic features in the Delhi-NCT, which have produced a maximum moment magnitude of Mw=5.2 in the past. An integrated study comprising seismic hazard modeling (along these two prominent active faults), site amplification, empirical vulnerability functions, and geospatial building inventory data in the Delhi-NCT region, has been conducted to propose the possible damage potential ratio, aiding disaster preparedness and risk mitigation. The seismic hazard has been estimated using stochastic finite fault modeling technique, considering moderate earthquake along multiple ruptures along MDF and DSRF, respectively, incorporating the parametric uncertainty. The maximum peak ground acceleration (PGA) of the order of 415.36 cm/sec2 at Rohtak (Haryana) and 384.03 cm/sec2 at Sonipat have been calculated due to a moderate earthquake along DSRF and MDF, respectively. The PGA due to the earthquake along the reverse fault (DSRF) is found to be higher than the PGA along the normal fault (MDF). The damage potential of 16 major areas has been estimated using PGA and vulnerability function, for the year 2025 on the basis of the linear projection using the census data by Government of India for the year 2000 and 2011, estimating 8.9 million buildings. A total of 38