Marine Heatwaves and Iceberg Melting in Polar Areas Intensify Phytoplankton Blooms

Global Change Biology, Journal Year: 2025, Volume and Issue: 31(3)

Published: March 1, 2025

ABSTRACT Climate change has led to increases in the intensity and frequency of marine heatwaves (MHWs). However, impact MHWs on phytoplankton at global scale remains unclear. The metaheuristic superlearner proposed this research indicates that occurrence weakens Fe limitation growth, leading intensified blooms. shock transmission effect analysis further reveals interactions among sea surface temperature (SST), iceberg melting, Fe, ammonium () nitrate (); namely, polar regions triggering a derivative melting. Compared with single event, dual disrupted effects limiting growth phytoplankton, resulting 54.90% increase rate massive reproduction regions. In addition, compared low‐emission scenario (SSP126), coverage area globally fragile respect blooms will by 5.84% under medium‐emission (SSP245) 9.29% high‐emission (SSP585). Specifically, Global South developing Pacific island countries are need scientific (marine protected guidance) financial (such as foundation for protection) assistance resist increasing expansion climate change.

Language: Английский

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Estimation of Forest Stand Volume in Coniferous Plantation from Individual Tree Segmentation Aspect Using UAV-LiDAR

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(15), P. 2736 - 2736

Published: July 26, 2024

The main problems of forest parameter extraction and stand volume estimation using unmanned aerial vehicle light detection ranging (UAV-LiDAR) technology are the lack precision in individual tree segmentation inability to directly obtain diameter at breast height (DBH) parameter. To address such limitations, study proposed an improved method combined with a DBH prediction model (H) for calculating trees, thus realizing accurate from aspect. involves following key steps: (1) local maximum variable window Gaussian mixture were used detect treetop position canopy removing pits. (2) measured H parameters sample trees construct optimal DBH-H model. (3) duality standing was calculate scale. results showed that: Individual based on accuracy, rate r, accuracy p, composite score F 89.10%, 95.21%, 0.921, respectively. coefficient determination R2 extracted 0.88, root mean square error RMSE 0.84 m. Weibull had fit predicted 2.28 cm, Using correctly detected estimated AE 90.86%. In conclusion, UAV-LiDAR technology, model, it is possible realize scale, which helps improve accuracy.

Language: Английский

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Evaluating the affecting factors of glacier mass balance in Tanggula Mountains using explainable machine learning and the open global glacier model

Journal of Mountain Science, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 8, 2025

Language: Английский

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Estimating the groundwater table threshold for mitigating soil salinization in the Songnen Plain of China

Journal of Hydrology Regional Studies, Journal Year: 2025, Volume and Issue: 59, P. 102326 - 102326

Published: March 25, 2025

Language: Английский

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Glacier Area and Surface Flow Velocity Variations for 2016–2024 in the West Kunlun Mountains Based on Time-Series Sentinel-2 Images

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(7), P. 1290 - 1290

Published: April 4, 2025

The West Kunlun Mountains (WKL) gather lots of large-scale glaciers, which play an important role in the climate and freshwater resource for central Asia. Despite extensive studies on glaciers this region, a comprehensive understanding inter-annual variations glacier area, flow velocity, terminus remains lacking. This study used deep learning model to derive time-series boundaries sub-pixel cross-correlation method calculate surface velocity region from 71 Sentinel-2 images acquired between 2016 2024. We analyzed spatial-temporal terminus. results indicate that, as follows: (1) area WKL remained relatively stable, with three expanding by more than 0.5 km2 five shrinking over (2) Five exhibited surging behavior during period. (3) Six velocities exceeding 50 m/y, have potential surge. (4) There were eight obvious advancing nine retreating Our demonstrates comprehensively monitoring changes mountain terminus, well identifying events regions beyond area.

Language: Английский

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Predicting Gross Primary Productivity under Future Climate Change for the Tibetan Plateau Based on Convolutional Neural Networks

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(19), P. 3723 - 3723

Published: Oct. 7, 2024

Gross primary productivity (GPP) is vital for ecosystems and the global carbon cycle, serving as a sensitive indicator of ecosystems’ responses to climate change. However, impact future changes on GPP in Tibetan Plateau, an ecologically important climatically region, remains underexplored. This study aimed develop data-driven approach predict seasonal annual variations Plateau up year 2100 under changing climatic conditions. A convolutional neural network (CNN) was employed investigate relationships between various environmental factors, including variables, CO2 concentrations, terrain attributes. analyzed projected from Coupled Model Intercomparison Project Phase 6 (CMIP6) four scenarios: SSP1–2.6, SSP2–4.5, SSP3–7.0, SSP5–8.5. The results suggest that expected significantly increase throughout 21st century all scenarios. By 2100, reach 1011.98 Tg C, 1032.67 1044.35 1055.50 C scenarios, representing 0.36%, 4.02%, 5.55%, 5.67% relative 2021. analysis indicates spring autumn shows more pronounced growth SSP3–7.0 SSP5–8.5 scenarios due extended growing season. Furthermore, identified elevation band 3000 4500 m particularly change terms response. Significant increases would occur east Qilian Mountains upper reaches Yellow Yangtze Rivers. These findings highlight pivotal role driving dynamics this region. insights not only bridge existing knowledge gaps regarding over coming decades but also provide valuable guidance formulation adaptation strategies at ecological conservation management.

Language: Английский

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The Importance of Solving Subglaciar Hydrology in Modeling Glacier Retreat: A Case Study of Hansbreen, Svalbard

Hydrology, Journal Year: 2024, Volume and Issue: 11(11), P. 193 - 193

Published: Nov. 12, 2024

Arctic tidewater glaciers are retreating, serving as key indicators of global warming. This study aims to assess how subglacial hydrology affects glacier front retreat by comparing two glacier–fjord models the Hansbreen glacier: one incorporating a detailed model and another simplifying discharge single channel centered in flow line. We first validate its channels with observations plume activity. Simulations conducted from April December 2010 revealed that position aligns more closely coupled than simplified version. Furthermore, mass loss due calving submarine melting is greater model, reaching 6 Mt end simulation compared 4 model. These findings highlight critical role predicting dynamics emphasize importance modeling understanding responses climate change.

Language: Английский

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Monitoring Cold-Region Water Cycles Using Remote Sensing Big Data

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(24), P. 4752 - 4752

Published: Dec. 20, 2024

In recent years, under the backdrop of global warming and intensifying extreme climates, water cycle processes in cold regions have been undergoing profound changes [...]

Language: Английский

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