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Sub-seasonal soil moisture anomaly forecasting using combinations of deep learning, based on the reanalysis soil moisture records

Author

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  • Wang, Xiaoyi
  • Corzo, Gerald
  • Lü, Haishen
  • Zhou, Shiliang
  • Mao, Kangmin
  • Zhu, Yonghua
  • Duarte, Santiago
  • Liu, Mingwen
  • Su, Jianbin

Abstract

Sub-seasonal drought forecasting is crucial for early warning in estimating agricultural production and optimizing irrigation management, as forecasting skills are relatively weak during this period. Soil moisture exhibits stronger persistence compared to other climate system quantities, which makes it especially influential in shaping land-atmosphere feedback, thus supplying a unique insight into drought forecasting. Relying on the soil moisture memory, this study investigates the combination of multiple deep-learning modules for sub-seasonal drought indices hindcast in the Huai River basin of China, using long-term ERA5-Land soil moisture records with a noise-assisted data analysis tool. The inter-compared deep-learning models include a hybrid model and a committee machine framework. The results show that the performance of the committee machine framework can be improved with the help of series decomposition and the forecasting skill is not impaired with the lead time increases. Overall, this study highlights the potential of combining deep-learning models with soil moisture memory analysis to improve sub-seasonal drought forecasting.

Suggested Citation

  • Wang, Xiaoyi & Corzo, Gerald & Lü, Haishen & Zhou, Shiliang & Mao, Kangmin & Zhu, Yonghua & Duarte, Santiago & Liu, Mingwen & Su, Jianbin, 2024. "Sub-seasonal soil moisture anomaly forecasting using combinations of deep learning, based on the reanalysis soil moisture records," Agricultural Water Management, Elsevier, vol. 295(C).
    • Handle: RePEc:eee:agiwat:v:295:y:2024:i:c:s0378377424001070
    DOI: 10.1016/j.agwat.2024.108772
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    References listed on IDEAS

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    2. Yu, Jingxin & Zhang, Xin & Xu, Linlin & Dong, Jing & Zhangzhong, Lili, 2021. "A hybrid CNN-GRU model for predicting soil moisture in maize root zone," Agricultural Water Management, Elsevier, vol. 245(C).
    3. Corey Lesk & Pedram Rowhani & Navin Ramankutty, 2016. "Influence of extreme weather disasters on global crop production," Nature, Nature, vol. 529(7584), pages 84-87, January.
    4. Zhiyong Wu & Huihui Feng & Hai He & Jianhong Zhou & Yuliang Zhang, 2021. "Evaluation of Soil Moisture Climatology and Anomaly Components Derived From ERA5-Land and GLDAS-2.1 in China," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(2), pages 629-643, January.
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