Scale-dependent drivers of water use efficiency across China: integrating stable isotopes, remote sensing, and machine learning

IF 5.7 1区农林科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY

Catena Pub Date : 2025-09-09 DOI:10.1016/j.catena.2025.109403

Feng Jiang , Xiaoyi Shi , Fuxi Shi , Zhenyi Jia , Xin Song , Tao Pu , Yanlong Kong , Shijin Wang , Lizong Wu , Jia Jia , Zhenzhen Zhang , Jie Wang , Wenqing Han

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Abstract

Water use efficiency (WUE) serves as a crucial metric for terrestrial carbon–water coupling, yet systematic gaps persist in understanding the spatial patterns and drivers of leaf-level intrinsic WUE (iWUE) versus ecosystem-scale WUE (WUE_Eco). Combining machine learning with 1,446 leaf δ¹³C_p records, we investigated the spatial heterogeneity and main drivers of iWUE and WUE_Eco across different life forms and climate zones in China. Results showed that inverse spatial patterns, where iWUE peaked in arid northwestern grasslands (60.46 μmol mol⁻¹). In contrast, WUE_Eco exhibited maxima in humid southeastern forests (1.82 g C/kg H₂O). Hierarchical partitioning and structural equation modeling revealed that elevation indirectly influenced iWUE (17.72 %) and WUE_Eco (25.64 %) through its modification of climatic conditions. Vegetation factors (e.g., leaf area index) and climatic factors (e.g., relative humidity) emerged as key drivers of iWUE (24.06 %) and WUE_Eco (15.31 %), primarily through their regulation of photosynthesis–transpiration coupling processes. Among four machine learning models, Random Forest has the best performance in iWUE prediction (R² = 0.73, NRMSE = 0.122, MBE = − 0.078), providing a high-resolution national iWUE dataset. This study highlights the importance of scale in understanding carbon–water interactions and provides a valuable reference for water resource management under climate change.

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中国水资源利用效率的尺度驱动因素：稳定同位素、遥感和机器学习的整合

水分利用效率（WUE）是陆地碳-水耦合的重要指标，但在理解叶片级内在水分利用效率（iWUE）与生态系统级内在水分利用效率（WUEEco）的空间格局和驱动因素方面存在系统性差距。结合机器学习和1446个叶片δ13Cp记录，研究了中国不同生命形式和气候带的iWUE和WUEEco的空间异质性及其主要驱动因素。结果表明：西北干旱草原iWUE最高，为60.46 μmol mol−1；相比之下，WUEEco在潮湿的东南部森林中表现出最大值（1.82 g C/kg H2O）。分层划分和结构方程模型表明，高程通过对气候条件的改变间接影响iWUE（17.72%）和WUEEco（25.64%）。植被因子（如叶面积指数）和气候因子（如相对湿度）主要通过调控光合-蒸腾耦合过程成为iWUE（24.06%）和WUEEco（15.31%）的关键驱动因素。在四种机器学习模型中，Random Forest在iWUE预测方面表现最好（R2 = 0.73, NRMSE = 0.122, MBE = - 0.078），提供了高分辨率的全国iWUE数据集。该研究强调了尺度在理解碳水相互作用中的重要性，为气候变化下的水资源管理提供了有价值的参考。

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来源期刊

Catena 环境科学-地球科学综合

CiteScore

10.50

自引率

9.70%

发文量

816

审稿时长

54 days

期刊介绍： Catena publishes papers describing original field and laboratory investigations and reviews on geoecology and landscape evolution with emphasis on interdisciplinary aspects of soil science, hydrology and geomorphology. It aims to disseminate new knowledge and foster better understanding of the physical environment, of evolutionary sequences that have resulted in past and current landscapes, and of the natural processes that are likely to determine the fate of our terrestrial environment. Papers within any one of the above topics are welcome provided they are of sufficiently wide interest and relevance.