1447 papers
This study utilizes Multi-Angle Imaging Spectroradiometer (MISR) data to analyze the three-dimensional distribution and seasonal persistence of atmospheric turbidity in Iran, revealing that the country's average aerosol load is significantly lower than the global average with distinct regional and seasonal variations.
By analyzing an 8,300-year marine sedimentary ancient DNA record from the high Arctic, this study reveals how Holocene climate variability and the Arctic Oscillation drove heterogeneous functional trait diversity and temperature-size trade-offs in marine biota, offering unprecedented insights into ecosystem resilience across millennial timescales.
This study presents the first comprehensive long-term assessment (1950–2025) of tourism climate suitability along Türkiye's Black Sea coast using a novel weighted Holiday Climate Index, revealing that while summer conditions remain stable, climate change is significantly extending favorable tourism windows into spring and autumn, with improvements being more pronounced in the western provinces than in the humid east.
This study projects that heat-stress exposure at European professional football venues will intensify and spread by mid-century, revealing that while evening scheduling offers some protection, it is insufficient to counter climate-driven risks, thus necessitating integrated adaptation strategies beyond mere timing adjustments.
This study employs a log-transformation-based multivariate and GIS framework on multi-temporal data from the Tébessa-Morsott Basin to successfully distinguish between natural geogenic mineralization driven by carbonate and evaporite dissolution and anthropogenic nitrate pollution caused by agricultural and wastewater inputs in a semi-arid alluvial aquifer.
This paper argues that displacement in Assam's Brahmaputra floodplain is a gradual process driven by chronic land loss and livelihood erosion rather than just sudden floods, necessitating a shift from short-term relief to inclusive, long-term adaptive strategies for affected communities.
This study demonstrates that integrating Sentinel-2 satellite imagery with Random Forest machine learning algorithms provides an accurate and scalable method for estimating above-ground biomass in Trans-Himalayan rangelands, offering a vital tool for sustainable land management and pastoral livelihood protection.
This study utilizes a Random Forest machine learning model integrated with GIS and remote sensing data to map landslide susceptibility in Hanang District, Tanzania, achieving 93.5% accuracy and identifying NDVI as the primary predictor while revealing that nearly 44% of the district is highly vulnerable to future landslides.
This study analyzes the December 2025 Mw 7.4 earthquake off Aomori, Japan, revealing a unique width-dominant rupture pattern that deviates from standard fault scaling laws and suggests a complex plate interface geometry involving multiple asperities.
Using the deep-learning-based ACE2 emulator, this study reveals that an El Niño-like sea surface temperature pattern in the 15th Century drove a global decrease in tropical cyclone frequency through enhanced vertical wind shear and a drier midtroposphere, thereby demonstrating the utility of deep learning in paleoclimate analysis to improve future projections.