Journal Article

Hybrid Theory‐Guided Data Driven Framework for Calculating Irrigation Water Use of Three Staple Cereal Crops in China

Wang, Shudong, Xu, Yu, Bo, Yong, Wang, Mengmeng, Li, Xueke, Liu, Kai, Li, Dehui

2024

Overview

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%. Key Points 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

• Publisher: John Wiley & Sons, Inc,Wiley
• Subject: Agricultural land / agricultural landscapes / Agriculture / Bayesian analysis / Bayesian theory / Cereal crops / China / Constraint modelling / Corn / Crops / data quality / Datasets / data‐driven / Evapotranspiration / hybrids / Hydrologic cycle / Hydrology / Irrigation / Irrigation water / irrigation water use / Machine learning / model validation / Performance evaluation / Precipitation / Probability theory / Remote sensing / rice / Root-mean-square errors / Soil moisture / Soil surfaces / soil water / Theories / theory‐guided framework / Time series / time series analysis / Uncertainty / Water / Water use / Wheat