2443 papers
This study presents the first union-level heat risk assessment for Rajshahi Division, Bangladesh, utilizing a multiplicative Hazard-Exposure-Vulnerability framework integrated with remote sensing and census data to identify spatial risk clusters and inform targeted adaptation strategies across 654 administrative unions.
This study demonstrates that integrating satellite-derived, time-varying area-elevation curves into flood-routing models for the Golestan Dam reveals that significant sediment-induced morphological changes substantially alter peak outflows and flood risks, highlighting the necessity of dynamic geometry updates for accurate dam safety and flood management.
This study utilizes remote sensing and machine learning to reveal a 74% decline in forest cover in Baishari Bangdopa, Bangladesh, between 1988 and 2025, and projects that future forest stability depends critically on management interventions, with strict protection potentially tripling current cover by 2050 compared to a near-total collapse under high deforestation scenarios.
This study of peri-urban Oluyole, Nigeria, reveals that despite high environmental awareness, industrial pollution persists due to inadequate waste management infrastructure, sensory-driven risk perceptions, and weak institutional governance, compelling communities to adaptively tolerate pollution for livelihood security.
This study demonstrates that Indigenous and local oral histories generally complement instrumental climate records regarding temperature trends across three North American biosphere reserves, though discrepancies often arise from differences in spatial scale, livelihood-specific sensitivities, and data limitations.
This study demonstrates that a major sudden stratospheric warming in March 2025 influenced record-breaking surface wind extremes in Eastern Asia during April 2025 by descending easterlies that reflected planetary waves to intensify the East Asian trough, thereby enhancing short-range forecast skill when stratospheric data is incorporated into deep learning models.
This study presents a national assessment framework for Canada's blue carbon ecosystems, revealing significant carbon stocks and sequestration potential while demonstrating that expanding their protection to 30% by 2030 could yield substantial cumulative climate mitigation benefits by 2050.
This study integrates cloud-based remote sensing and a Cellular Automata-Artificial Neural Network (CA-ANN) model to analyze historical and project future Land Use and Land Cover (LULC) changes in Ethiopia's Gilgel Gibe I Watershed, revealing a significant decline in forest and grassland areas alongside rapid expansion of agriculture and built-up zones that threaten ecosystem services and environmental sustainability.
This study introduces a high-resolution modeling framework to demonstrate that future urban expansion will disproportionately intensify heat hazards in already hot, densely populated urban cores across North America, revealing a critical, localized vulnerability driven by the compounding effects of climate change, demographic shifts, and spatial development.
This study reveals that edible bivalves (*Pilsbryoconcha exilis*) from Makassar's Tallo coast are contaminated with microplastics, primarily blue fibers of PE, PET, and PVC, posing significant dietary health risks—particularly to children—as evidenced by risk quotients and excess cancer risk estimates exceeding safety thresholds.