Investigating erosion and river sediment yield in high-mountain areas is crucial for understanding landscape and biogeochemical responses to environmental change. We compile data on contemporary fluvial suspended sediment yield (SSY) and 12 environmental proxies from 151 rivers in High Mountain Asia surrounding the Tibetan Plateau. We demonstrate that glaciers exert a first-order control on fluvial SSYs, with high precipitation nonlinearly amplifying their role, especially in high-glacier cover basins. We find a bidirectional response to vegetation's influence on SSY in the Eastern Tibetan Plateau and Tien Shan and identify that the two interacting factors of precipitation and vegetation cover explain 54% of the variability in SSY, reflecting the divergent roles of vegetation in promoting biogenic-weathering versus slope stabilization across bioclimatic zones. The competing interactions between glaciers, ecosystems, and climate in delivering suspended sediment have important implications for predicting carbon and nutrient exports and water quality in response to future climate change.
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http://dx.doi.org/10.1126/sciadv.ads6196 | DOI Listing |
Sci Total Environ
January 2025
National Laboratory for Agriculture and the Environment, Ames, IA 50011, USA.
Identifying the origins of storm fluvial particulate organic carbon (POC) provides information about the hydrological connectivity within the river corridor and the roles of the land-stream interface in the carbon cycle. However, current understanding of storm-induced POC source dynamics is constrained by observations limited in space and time. This study presents a unique approach integrating higher spatial and temporal resolution sampling with a multi-biomarker analysis to better understand POC source dynamics across scales.
View Article and Find Full Text PDFSci Total Environ
January 2025
School of Geography and Environmental Science, University of Southampton, UK.
Substantial amounts of mercury (Hg) are projected to be released into Arctic watersheds as permafrost thaws amid warmer and wetter conditions. This may have far-reaching consequences because the highly toxic methylated form of Hg biomagnifies rapidly in ecosystems. However, understanding how climate change affects Hg dynamics in permafrost regions is limited due to the lack of long-term Arctic Hg records.
View Article and Find Full Text PDFSci Data
January 2025
DiSTAR, University of Naples "Federico II", 80126, via Vicinale Cupa Cintia 26, Naples, Italy.
We present a new database, EutherianCoP, of fossil mammals which lived globally from the Late Pleistocene to the Holocene. The database includes 13,972 fossil occurrences of 786 extant or recently extinct placental mammal species, plus 155,198 current occurrences for those of them which survived to the present. The occurrences are correlated with radiometric age information.
View Article and Find Full Text PDFEcol Evol
January 2025
Department of Agricultural, Food and Environmental Sciences Università Politecnica delle Marche Ancona Italy.
This study investigates climate change impacts on spontaneous vegetation, focusing on the Mediterranean basin, a hotspot for climatic changes. Two case study areas, Monti Sibillini (central Italy, temperate) and Sidi Makhlouf (Southern Tunisia, arid), were selected for their contrasting climates and vegetation. Using WorldClim's CMCC-ESM2 climate model, future vegetation distribution was predicted for 2050 and 2080 under SSP 245 (optimistic) and 585 (pessimistic) scenarios.
View Article and Find Full Text PDFSci Rep
January 2025
Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research (IGSNRR), Chinese Academy of Sciences (CAS), Beijing, 100101, China.
Flash flood susceptibility mapping is essential for identifying areas prone to flooding events and aiding decision-makers in formulating effective prevention measures. This study aims to evaluate the flash flood susceptibility in the Yarlung Tsangpo River Basin (YTRB) using multiple machine learning (ML) models facilitated by the H2O automated ML platform. The best-performing model was used to generate a flash flood susceptibility map, and its interpretability was analyzed using the Shapley Additive Explanations (SHAP) tree interpretation method.
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