基流
比例(比率)
水流
环境科学
生态学
流域
水流
地理
水文学(农业)
地质学
生物
地图学
岩土工程
作者
Hao Chen,Saihua Huang,He Qiu,Yue‐Ping Xu,Ramesh S. V. Teegavarapu,Yuxue Guo,Hui Nie,Huawei Xie,Jingkai Xie,Yiting Shao,Yuping Han
标识
DOI:10.1016/j.ecolind.2025.113868
摘要
• Ensemble ML model effectively interpolates missing data, thereby enhancing the reliability of baseflow separation. • The ecological flow assessment is accurate using a conceptually simple a combined basin BFI and the Tennant method. • Ecological flow conditions are mostly good around the globe, except in Africa and Australia, where they are worsening. Understanding global baseflow dynamics is essential for sustainable water management and ecosystem resilience. This study introduces a comprehensive four-stage framework for quantifying novel spatiotemporal patterns in global baseflow variability and their critical implications for hydrological and ecological health. Key innovations include: (1) An ensemble machine learning approach demonstrating superior accuracy in runoff gap-filling compared to single-model methods; (2) Identification of distinct global decline patterns, revealing that 45.73 % of basins exhibit significant decreasing trends (p < 0.05), concentrated disproportionately in arid and warm temperate zones, suggesting heightened vulnerability linked to hydroclimate drivers; (3) Analysis establishing that baseflow provides a disproportionately vital contribution (>70 % in many basins) to dry-season streamflow globally, acting as a crucial ecological buffer; and (4) An integrated BFI-Tennant assessment revealing pronounced regional disparities in ecological flow health, with consistently good conditions in North America, South America, and Europe contrasting sharply with prevalent deficits in parts of Africa, Australia, and the Middle East, strongly correlating with water stress and climate regimes. Critically, our findings reveal emergent principles: the framework demonstrates that baseflow decline is not uniform but exhibits regionally accelerated vulnerability, and that its dry-season dominance underpins the stability of river ecosystems where it persists. This study presents a robust and transferable methodology, delivering new global insights essential for prioritizing conservation and management strategies in increasingly water-stressed basins.
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