Sustainability, Journal Year: 2025, Volume and Issue: 17(5), P. 2039 - 2039
Published: Feb. 27, 2025
In recent years, the rapid intensification of global warming has led to significant deterioration and disruption natural environment [...]
Language: Английский
Nature Reviews Earth & Environment, Journal Year: 2025, Volume and Issue: 6(1), P. 35 - 50
Published: Jan. 9, 2025
Language: Английский
Citations
17
Journal of Geophysical Research Atmospheres, Journal Year: 2025, Volume and Issue: 130(1)
Published: Jan. 2, 2025
Abstract Snowmelt and related extreme events can have profound natural societal impacts. However, the studies on projected changes in snow‐related extremes across Tianshan Mountains (TS) Pamir regions been underexplored. Utilizing regional climate model downscaling bias‐corrected CMIP6 data, this study examined snowmelt water available for runoff (SM ROS , rainfall plus snowmelt) during cold seasons these historical (1994–2014) future (2040–2060) periods under shared socioeconomic pathway (SSP) scenarios (SSP245 SSP585). The results demonstrated that accumulated was to rise by 17.98% 20.36%, whereas SM could increase 26.97% 28.95%, respectively, SSP245 SSP585 scenarios. Despite relatively minimal snowmelt, magnitude of daily maximum (10‐year return level) 28.04 mm expected 15.32% 15.31% scenarios, especially western TS exceeding 26%. Meanwhile, areas with a 50 over 13.5%. A notable its area occupation high intensity highlighted an increased risk rainfall‐driven events. absolute snowfall frequent snow‐rain phase transitions season warming (SSP245: 2.19°C SSP585: 2.22°C) benefits high‐intensity rain‐on‐snow events, leading augmentation. findings emphasize significant role rainfall‐trigger exacerbating climate.
Language: Английский
Citations
3
Water, Journal Year: 2024, Volume and Issue: 16(19), P. 2870 - 2870
Published: Oct. 9, 2024
Climate change affects the water cycle, resource management, and sustainable socio-economic development. In order to accurately predict climate in Weifang City, China, this study utilizes multiple data-driven deep learning models. The data for 73 years include monthly average air temperature (MAAT), minimum (MAMINAT), maximum (MAMAXAT), total precipitation (MP). different models artificial neural network (ANN), recurrent NN (RNN), gate unit (GRU), long short-term memory (LSTM), convolutional (CNN), hybrid CNN-GRU, CNN-LSTM, CNN-LSTM-GRU. CNN-LSTM-GRU MAAT prediction is best-performing model compared other with highest correlation coefficient (R = 0.9879) lowest root mean square error (RMSE 1.5347) absolute (MAE 1.1830). These results indicate that method a suitable model. This can also be used surface modeling. will help flood control management.
Language: Английский
Citations
10
Water Research, Journal Year: 2025, Volume and Issue: 276, P. 123240 - 123240
Published: Feb. 2, 2025
Language: Английский
Citations
1
Plant and Soil, Journal Year: 2025, Volume and Issue: unknown
Published: Feb. 3, 2025
Language: Английский
Citations
1
Global and Planetary Change, Journal Year: 2024, Volume and Issue: unknown, P. 104568 - 104568
Published: Sept. 1, 2024
Language: Английский
Citations
5
Published: Jan. 1, 2025
Snow leopards (Panthera uncia) are regarded as the most charismatic apex predator in alpine Asia, yet their populations under serious threat from human activities and habitat fragmentation. Ensuring effectiveness of current protected areas is critical for conservation, which necessitates a comprehensive understanding selection patterns at different spatial scales. Here, we conducted five-year camera trap survey snow Qilian Mountains used multi-scale modelling to investigate connectivity. Our results revealed scale-dependence leopard selection. We found that smaller scales, prey resource topographic variables were main factors determining leopards. Particularly, distribution probability primarily determined overall small scale. At larger however, there was stronger correlation between climate well impacts. The scale-optimized multivariate models indicated significant gaps protecting core habitats ensuring landscape More than 50% projected patches not included areas. Areas with highest number (Subei County) corridors (Tianjun also had least half area outside study provides insights conservation planning suggests prioritizing previously overlooked essential corridors.
Language: Английский
Citations
0
The Innovation Geoscience, Journal Year: 2025, Volume and Issue: unknown, P. 100113 - 100113
Published: Jan. 1, 2025
<p>The spatiotemporal patterns and driving factors of drought-flood abrupt alternations (DFAA) have been investigated across several regional watershed scales; however, comprehensive examination at the global scale is lacking. Here, we employed long period change index (LDFAI), derived from an ensemble 40 output datasets eight Coupled Model Intercomparison Project phase 6 (CMIP6) models, to assess patterns, drivers, future projections DFAA. The results indicate that DFAA are influenced by various anthropogenic forcings, greenhouse gas emissions exert most significant impact. changes in intensity (1950–2014), attributed natural forcing (NAT), aerosols (AER), (GHG) forcing, accounted for 5.65%, 14.57%, 33.55%, respectively. rates under shared socioeconomic pathways (SSPs) 2014 <styled-content style-type="number">2100</styled-content> were estimated be 21.73% (SSP1-2.6), 45.37% (SSP2-4.5), 63.1% (SSP3-7.0), 69.51% (SSP5-8.5). This means high radiative rivalry fossil-fuel development models will lead a increase These findings can aid adaptive policies related DFAA.</p>
Language: Английский
Citations
0
Global and Planetary Change, Journal Year: 2025, Volume and Issue: unknown, P. 104718 - 104718
Published: Jan. 1, 2025
Language: Английский
Citations
0
Science China Earth Sciences, Journal Year: 2025, Volume and Issue: unknown
Published: Jan. 9, 2025
Language: Английский
Citations
0