气候变化领域集成服务门户(CCPortal): Hybrid Theory-Guided Data Driven Framework for Calculating Irrigation Water Use of Three Staple Cereal Crops in China

CCPortal

DOI	10.1029/2023WR035234
	Hybrid Theory-Guided Data Driven Framework for Calculating Irrigation Water Use of Three Staple Cereal Crops in China
	Bo, Yong; Li, Xueke; Liu, Kai; Wang, Shudong; Li, Dehui; Xu, Yu; Wang, Mengmeng
发表日期	2024
ISSN	0043-1397
EISSN	1944-7973
起始页码	60
结束页码	3
卷号	60 期号:3
英文摘要	Current irrigation water use (IWU) estimation methods confront uncertainties warranting further attention, primarily stemming from constraints within model structure and data quality. This study proposes a hybrid framework that integrates multiple machine learning (ML) methods with theory-guided strategies to calculate IWU for three principal cereal crops within the Chinese agricultural landscape. We generated high resolution time series data sets of evapotranspiration and surface soil moisture (SM) using remote sensing resources. ML techniques, along with the Bayesian three-cornered hat ensemble, were employed to drive multiple remote sensing-derived data sets in IWU calculation. We applied two theory-guided mechanisms to quantify irrigation signals: first, converting original SM values into logarithmic terms, and second, extracting process-based SM residuals. Proposed framework has been validated at 12 field stations across China, yielding coefficient of determination (R2) ranging from 0.54 to 0.70, and root mean square error (RMSE) spanning 278-335 mm/yr. Our framework demonstrates considerable strength in IWU estimation when compared to reported IWU values form 341 cities across China. Specifically, for rice, wheat, and maize, the R2 values range from 0.78 to 0.83, 0.68 to 0.76, and 0.53 to 0.64, respectively, with corresponding RMSE measuring 0.22-0.25, 0.10-0.12, and 0.11-0.13 km3/yr, respectively. These findings highlight the effectiveness of theory-guided strategies in discerning irrigation-related information, thereby improving overall model performance. Attention should be directed toward the uncertainties in evapotranspiration and precipitation products on model performance, which remained modest, with a relative change of less than 5%. Hybrid framework is developed to estimate irrigation water use (IWU) for three staple cereal crops in China Machine learning is employed to drive multiple remote sensing-derived products for precise IWU estimation Proposed framework accurately estimates IWU and incorporates theory-guided module to reveal implicit irrigation signal
英文关键词	irrigation water use; data-driven; remote sensing; theory-guided framework; machine learning
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Marine & Freshwater Biology ; Water Resources
WOS类目	Environmental Sciences ; Limnology ; Water Resources
WOS记录号	WOS:001182618200001
来源期刊	WATER RESOURCES RESEARCH
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/304377
作者单位	Chinese Academy of Sciences; Aerospace Information Research Institute, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; China University of Geosciences; University of Pennsylvania; Nanjing University of Information Science & Technology; Northeast Normal University - China; China University of Geosciences
推荐引用方式 GB/T 7714	Bo, Yong,Li, Xueke,Liu, Kai,et al. Hybrid Theory-Guided Data Driven Framework for Calculating Irrigation Water Use of Three Staple Cereal Crops in China[J],2024,60(3).
APA	Bo, Yong.,Li, Xueke.,Liu, Kai.,Wang, Shudong.,Li, Dehui.,...&Wang, Mengmeng.(2024).Hybrid Theory-Guided Data Driven Framework for Calculating Irrigation Water Use of Three Staple Cereal Crops in China.WATER RESOURCES RESEARCH,60(3).
MLA	Bo, Yong,et al."Hybrid Theory-Guided Data Driven Framework for Calculating Irrigation Water Use of Three Staple Cereal Crops in China".WATER RESOURCES RESEARCH 60.3(2024).