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<p>Transboundary lakes are highly sensitive to climate change, yet data scarcity has long constrained quantitative understanding of the driving mechanisms behind extreme hydrological events. This study presents an innovative integration of ICESat/ICESat-2 altimetry, Landsat imagery, and GRACE/GRACE-FO terrestrial water storage anomalies within a Partial Least Squares Structural Equation Modeling (PLS-SEM) framework, achieving the first monthly-scale water level reconstruction (2002–2023) for the transboundary Lake Khanka and quantifying multi-factor contributions. Our findings reveal that: (1) Lake Khanka exhibited a persistent upward trend post-2010, with a cumulative rise of 0.79 m during 2012–2023, wherein 23 extreme wet events between 2018–2023 drove a maximum water level increase of 0.74 m; (2) precipitation emerged as the dominant driver (PLS-SEM weight=0.826), overwhelmingly outweighing inflow, outflow, evaporation, and snowmelt. Results demonstrate that the recent rapid water level rise is primarily attributed to the concurrent intensification of extreme wet events in frequency, magnitude, and duration, rather than human interventions or reservoir operations. The open-source, reproducible workflow developed herein provides a transferable paradigm for deciphering climate-driven hydrological dynamics in transboundary lakes and furnishes a scientific foundation for adaptive management strategies in vulnerable freshwater systems facing amplified hydroclimatic extremes.</p>

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Persistent and rapid increases in water levels driven by extreme wet events in recent years in the largest freshwater lake in Northeast Asia

  • 期刊论文

  • 发表日期：

    2026年5月12日

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  • Bingbo Ni,
  • Qiusheng Ma,
  • Yanfeng Wu,
  • Xuemei Liu,
  • Y. Jun Xu,
  • Xiaofei Yu,
  • Kryukova Maria,
  • Irina Sergeevna Sinkova,
  • Oleg Igorevich Kaminsky,
  • Yuanchun Zou,
  • Ming Jiang

摘要

Transboundary lakes are highly sensitive to climate change, yet data scarcity has long constrained quantitative understanding of the driving mechanisms behind extreme hydrological events. This study presents an innovative integration of ICESat/ICESat-2 altimetry, Landsat imagery, and GRACE/GRACE-FO terrestrial water storage anomalies within a Partial Least Squares Structural Equation Modeling (PLS-SEM) framework, achieving the first monthly-scale water level reconstruction (2002–2023) for the transboundary Lake Khanka and quantifying multi-factor contributions. Our findings reveal that: (1) Lake Khanka exhibited a persistent upward trend post-2010, with a cumulative rise of 0.79 m during 2012–2023, wherein 23 extreme wet events between 2018–2023 drove a maximum water level increase of 0.74 m; (2) precipitation emerged as the dominant driver (PLS-SEM weight=0.826), overwhelmingly outweighing inflow, outflow, evaporation, and snowmelt. Results demonstrate that the recent rapid water level rise is primarily attributed to the concurrent intensification of extreme wet events in frequency, magnitude, and duration, rather than human interventions or reservoir operations. The open-source, reproducible workflow developed herein provides a transferable paradigm for deciphering climate-driven hydrological dynamics in transboundary lakes and furnishes a scientific foundation for adaptive management strategies in vulnerable freshwater systems facing amplified hydroclimatic extremes.