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<p>The Tibetan Plateau is a major regulator of Asian hydroclimate, yet rapid changes in atmospheric demand are reshaping its water balance. Here, we examine whether the evaporation-paradox pattern has reversed by analysing meteorological records from 2000 to 2024 across the Nyainqêntanglha Mountains. Potential evapotranspiration (ET<sub>0</sub>) increased across the region, but the mechanisms were strongly elevation dependent. In low-elevation valleys, ET<sub>0</sub> rebound was driven mainly by recovering wind speed and an expanding vapour pressure deficit, indicating increasing aerodynamic control. By contrast, high-altitude areas showed a cloud-brake effect, in which topographically enhanced cloudiness reduced radiation input and constrained ET<sub>0</sub> growth despite pronounced warming. Explainable machine-learning analyses showed that the dominant predictors identified by XGBoost and Random Forest were broadly consistent with Penman–Monteith sensitivity. Long Short-Term Memory models further reproduced ET<sub>0</sub> dynamics with strong predictive skill, supporting their use as virtual observation tools in data-sparse alpine environments. These findings indicate a transition from an energy-limited regime towards more dynamically controlled evaporative demand, with important implications for plateau water resources.</p>

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Altitude driven mechanisms and machine learning prediction of the evaporation paradox reversal in the Nyainqentanglha Mountains

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月11日

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  • Guangye Chen,
  • Tongliang Gong,
  • Yangzong Cidan,
  • Lin Zeng

摘要

The Tibetan Plateau is a major regulator of Asian hydroclimate, yet rapid changes in atmospheric demand are reshaping its water balance. Here, we examine whether the evaporation-paradox pattern has reversed by analysing meteorological records from 2000 to 2024 across the Nyainqêntanglha Mountains. Potential evapotranspiration (ET0) increased across the region, but the mechanisms were strongly elevation dependent. In low-elevation valleys, ET0 rebound was driven mainly by recovering wind speed and an expanding vapour pressure deficit, indicating increasing aerodynamic control. By contrast, high-altitude areas showed a cloud-brake effect, in which topographically enhanced cloudiness reduced radiation input and constrained ET0 growth despite pronounced warming. Explainable machine-learning analyses showed that the dominant predictors identified by XGBoost and Random Forest were broadly consistent with Penman–Monteith sensitivity. Long Short-Term Memory models further reproduced ET0 dynamics with strong predictive skill, supporting their use as virtual observation tools in data-sparse alpine environments. These findings indicate a transition from an energy-limited regime towards more dynamically controlled evaporative demand, with important implications for plateau water resources.