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Simulation of soil moisture and drought prediction in middle reaches of the Yellow River based on machine learning

Author

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  • Yan, Siying
  • Weng, Baisha
  • Dong, Zhaoyu
  • Yan, Denghua
  • Fu, Qiang

Abstract

Under global climate change, agricultural and ecological droughts in the Middle Reaches of the Yellow River (MRYR) severely threaten agricultural production and ecological security. However, the lack of large-scale, high-spatiotemporal-resolution layered soil moisture data constrains the precise identification of droughts. This study innovatively integrates a Multi-Layer Perceptron (MLP) model with RegCM4 climate scenario data to generate a layered daily-scale soil moisture dataset for the 0–289 cm profile in the MRYR from 2001 to 2100 (Abbreviated as MLP_D). The MLP_D dataset features a spatial resolution of 0.01°× 0.01° and demonstrates superior performance to traditional data products across multiple metrics. Analysis of the MLP_D dataset reveals: During 2001–2022, surface soil moisture (0–7 cm) exhibited a slight, non-significant increasing trend at a rate of 0.0002 m³ /m³ /year, while soil moisture in layers below 7 cm declined, in 100–289 cm, the soil moisture decreasing significantly at 0.0016 m³ /m³ /year. Moreover, MLP_D data accurately captured typical drought events, demonstrating high consistency between simulated and actual observations. Future drought frequency and duration in the MRYR increase with more intense scenarios, under the RCP8.5 scenario, areas experiencing a significant increase in drought duration account for 71 % of the total region. By bridging a critical data gap in high-resolution, long-term, layered soil moisture data for the MRYR, this study provides pivotal insights into climate change impacts on soil moisture and drought regimes. It thereby serves as a scientific basis for enhancing precision agriculture and water management, with profound implications for mitigating drought risks and safeguarding regional agro-ecological security.

Suggested Citation

  • Yan, Siying & Weng, Baisha & Dong, Zhaoyu & Yan, Denghua & Fu, Qiang, 2026. "Simulation of soil moisture and drought prediction in middle reaches of the Yellow River based on machine learning," Agricultural Water Management, Elsevier, vol. 323(C).
    • Handle: RePEc:eee:agiwat:v:323:y:2026:i:c:s0378377425007826
    DOI: 10.1016/j.agwat.2025.110068
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    References listed on IDEAS

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    1. Li, Shenglin & Han, Yang & Li, Caixia & Wang, Jinglei, 2024. "A novel framework for multi-layer soil moisture estimation with high spatio-temporal resolution based on data fusion and automated machine learning," Agricultural Water Management, Elsevier, vol. 306(C).
    2. Yu Cai & Wengang Zheng & Xin Zhang & Lili Zhangzhong & Xuzhang Xue, 2019. "Research on soil moisture prediction model based on deep learning," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-19, April.
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