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

Autor: Xiaoyi Wang, Gerald Corzo, Haishen Lü, Shiliang Zhou, Kangmin Mao, Yonghua Zhu, Santiago Duarte, Mingwen Liu, Jianbin Su
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Agricultural Water Management, Vol 295, Iss , Pp 108772- (2024)
Druh dokumentu: article
ISSN: 1873-2283
DOI: 10.1016/j.agwat.2024.108772
Popis: 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.
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