2353 papers
This study analyzes the 2020–2024 Noto Peninsula earthquake swarm to reveal a statistically significant magnitude–frequency kink in deep seismicity that is likely an artifact of magnitude bias rather than a physical phenomenon, thereby cautioning against interpreting single b-values from this catalogue without accounting for depth and magnitude range.
Analysis of the 2021–2023 Noto earthquake swarm reveals that while shallow events follow a standard Gutenberg–Richter distribution, deeper events exhibit a distinct magnitude–frequency break with a steep small-magnitude branch, suggesting a depth-dependent population structure or detection bias rather than a simple magnitude-scale artifact.
This study presents an optimized, environmentally friendly cloud-seeding formulation using surfactant-modified cellulose nanocrystals that significantly enhances cloud formation efficiency, alongside a machine learning model trained on global weather data to accurately identify spatiotemporal windows for successful deployment.
Analysis of 2012–2025 fire data in the West Kalimantan–Sarawak border region reveals that peat burning scales super-linearly with overall fire-season magnitude, causing haze burdens to increase disproportionately during El Niño years as a small fraction of recurrently burning peat cells drives the majority of cumulative emissions.
This study assesses the severe environmental degradation of Bangladesh's Pasur River caused by industrial effluents from nine facilities at Mongla Port, utilizing water quality indices and statistical analyses to identify specific pollution sources, establish a practical monitoring framework for regulators, and underscore the urgent need for treatment and enforcement to protect the Sundarbans ecosystem and public health.
This paper introduces an observation-constrained framework using synthetic global sea surface temperature ensembles to demonstrate that conventional moving-window methods for detecting temperature extremes introduce systematic biases, and it establishes that optimizing detection parameters—specifically recommending a 31-day window for satellite-era records—significantly improves the statistical reliability of marine heatwave metrics, particularly when derived from short observational records.
This study utilizes downscaled climate projections and hydrological modeling to assess how shifting flood frequencies under future climate scenarios impact the interconnected food, energy, and water (FEW) nexus, revealing that current single-sector policies are often insufficient to address these compounding risks and proposing integrated, cross-sectoral governance strategies to enhance regional resilience.
This systematic review and meta-analysis of 14 studies across Ethiopia demonstrates that Orthodox Church forests serve as critical refugia for native woody biodiversity, harboring a pooled mean of 74.8 species per forest with a 90.3% indigenous composition, thereby validating traditional management practices as essential for national conservation.
This study utilizes a 13-model CMIP6 ensemble under four SSP scenarios to project that Bangladesh will face a hydrological paradox characterized by intensified extreme rainfall and wet conditions in the north and northeast, increased dry spells in the south, and nationwide severe warming, providing critical spatial insights for adaptive disaster management.
This study establishes a framework for evaluating reconstructed prehistoric road networks under incomplete data by demonstrating that accuracy and reliability are governed by data sufficiency and settlement spatial patterns, revealing that hierarchical road structures in the Haidai region emerged gradually alongside increasing settlement complexity from the Houli to later Neolithic periods.