湖面動態の季節性支配を解明（Professor Di Long’s team reveals global dominance of seasonality in shaping lake-surface-extent dynamics）

2025-06-16 清華大学

Fig. 1: Continuous and high-resolution monitoring of approximately 1.4 million global lakes achieved through deep-learning-based spatiotemporal fusion of multi-source satellite data

＜関連情報＞

• https://www.tsinghua.edu.cn/en/info/1245/14311.htm
• https://www.nature.com/articles/s41586-025-09046-3

湖面-湖水流出動態の形成における季節性のグローバルな優位性 Global dominance of seasonality in shaping lake-surface-extent dynamics

Luoqi Li,Di Long,Yiming Wang & R. Iestyn Woolway
Nature  Published:28 May 2025
DOI:https://doi.org/10.1038/s41586-025-09046-3

Abstract

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 observations^4,5. Although previous studies have focused on long-term changes^6,7,8, 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 systems^10,11,12 and improving global climate models^13,14. 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.