Physics-guided machine learning approach for reconstructing air temperature in warm permafrost on the Qinghai‒Xizang Plateau

High-resolution air temperature data are essential for quantifying eco-hydrological processes in climate-sensitive regions like the Qinghai‒Xizang Plateau. However, in-situ observations are frequently interrupted by extended data gaps. To address this, we developed a physics-guided machine learning...

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Detalhes bibliográficos

Principais autores:	Chen-Yang Peng, Dong-Liang Luo, Yu Sheng, Ji-Chun Wu, Jia Liu, Shi-Zhen Li, Ya-Juan Zao
Formato:	Artigo
Idioma:	Inglês
Publicado em:	KeAi Communications Co., Ltd. 2026-08-01
Colecção:	Advances in Climate Change Research
Assuntos:	Air temperature reconstruction Warm permafrost Machine learning Qinghai‒Xizang plateau
Acesso em linha:	http://www.sciencedirect.com/science/article/pii/S1674927826001012
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