Frontiers of Data and Computing ›› 2026, Vol. 8 ›› Issue (2): 3-14.

CSTR: 32002.14.jfdc.CN10-1649/TP.2026.02.001

doi: 10.11871/jfdc.issn.2096-742X.2026.02.001

• Special Issue: Key Technologies and Applications of Cryospheric Big Data Mining and Analysis • Previous Articles     Next Articles

Exploration of An Integrated “Big Data+AI+Modeling” Research Paradigm for Cryosphere Studies

ZHANG Yaonan1,2,*(),LIU Jingqi1,2,3,KANG Jianfang1,2,3,NAN Zhuotong4,TIAN Wenbiao4,MIN Yufang1,2,ZHAO Shuping4,Wang Baode5,6   

  1. 1 Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, Gansu 730030, China
    2 National Cryosphere Desert Data Center, Lanzhou, Gansu 730030, China
    3 University of Chinese Academy of Sciences, Beijing 100049, China
    4 Nanjing Normal University, Nanjing, Jiangsu 210023, China
    5 Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Chinese Academy of Sciences, Urumqi, Xinjiang 830011, China
    6 State Key Laboratory of Ecological Security and Sustainable Development in Arid Areas, Urumqi, Xinjiang 830011, China
  • Received:2025-10-10 Online:2026-04-20 Published:2026-04-23

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Abstract:

[Objective] As a vital component of Earth’s system, the cryosphere profoundly impacts global climate, hydrological cycles, and ecological security through multi-element nonlinear interactions, cross-sphere couplings, and long disaster chains. Traditional physical models exhibit limitations in characterizing multi-scale cryospheric changes, failing to meet practical needs for large-scale monitoring, rapid evolution analysis, and hazard early-warning. [Methods] This paper proposes a “Big Data+AI+Model” research paradigm for cryospheric studies and develops the Global Cryosphere research Engine (GCE) to support this framework. The GCE forms an integrated platform encompassing data management, model construction, AI algorithm application, and task workflow orchestration, with capabilities for multi-source heterogeneous data fusion, cross-language model collaboration, high-performance computing scheduling, and intelligent analysis. [Conclusions] Based on the GCE platform, model construction approaches such as “embedding AI into physical models”, “embedding physical models into AI”, and “data-driven parameter optimization coupled with AI” have been implemented. Focusing on soil moisture simulation experiments over the Tibetan Plateau, a novel multi-element interaction framework integrating “observation data+deep learning (hierarchical LSTM)+physical model (Noah-MP)” was developed. The average correlation between simulated and observed layered soil moisture exceeded 0.8, significantly outperforming the Noah model. The results demonstrate the feasibility of a research paradigm that combines data-driven approaches, physical model support, and artificial intelligence to effectively characterize cryospheric processes, while also validating the utility of the GCE platform. This framework can provide a supportive environment for new research paradigms in ecological environments and cryospheric disasters, promoting intelligent cryospheric research with cross-regional multi-element interactions.

Key words: cryosphere, big data, AI, model, GCE (Global Cryosphere research Engine)