Global dominance of seasonality in shaping lake-surface-extent dynamics

• 成果类型: Article
• 署名作者: Li, Luoqi; Long, Di; Wang, Yiming; Woolway, R. Iestyn
• 署名单位: Tsinghua University; Tsinghua University; Bangor University
• 刊物名称: Nature
• ISSN/ISSBN: 0028-2155
• DOI: 10.1038/s41586-025-09046-3
• 发表日期: 2025-06-12
• 关键词: water; climate; emissions; RIVERS; images; CHINA; AREA

摘要

Lakes are crucial for ecosystems1, greenhouse gas emissions2 and water resources3, yet their surface-extent dynamics, particularly seasonality, remain poorly understood at continental to global scales owing to limitations in satellite observations4,5. Although previous studies have focused on long-term changes6, 7-8, comprehensive assessments of seasonality have been constrained by trade-offs between spatial resolution and temporal resolution in single-source satellite data. Here we show that seasonality is the dominant driver of lake-surface-extent variations globally. By leveraging a deep-learning-based spatiotemporal fusion of MODIS and Landsat-based datasets, combined with high-performance computing, we achieved monthly mapping of 1.4 million lakes (2001-2023). Our approach yielded basin-level median user's and producer's accuracies of 93% and 96%, respectively, when validated against the Global Surface Water dataset7. Seasonality-dominated lakes constitute 66% of the global lake area and approximately 60% of total lake counts, with over 90% of the world's population residing in regions where such lakes prevail. During seasonality-induced extreme events, the impacts can exceed the combined magnitude of 23-year long-term changes and regular seasonal variations, doubling the contraction of 42% of shrinking lakes and fully offsetting the expansion of 45% of growing lakes. These results uncover previously hidden seasonal dynamics that are crucial for understanding hydrospheric responses to environmental changes9, protecting lacustrine systems10, 11-12 and improving global climate models13,14. Our findings underscore the importance of incorporating seasonality into future research and suggest that advancements in the fusion of multisource remote-sensing data offer a promising path forward.