摘要
植物根系吸水过程是地下水-土壤-植被-大气连续系统中水分运移的关键环节。然而,受限于传统试验方法和数值模拟技术的局限性,根系吸水机制研究仍面临一定的挑战。本研究通过原位试验监测数据,提出了卷积神经网络-长短期记忆网络(CNN-LSTM)耦合模型,模拟典型沙生植物沙柳连续3个生长周期的根系吸水过程。结果表明:CNN-LSTM模型成功实现了根系吸水时空动态的高精度模拟,模型验证的决定系数保持在0.86以上,均方根误差均低于0.01,表现出优异的预测性能;与传统的数值模型相比,CNN-LSTM模型能够以较低的计算成本准确捕捉不同生长阶段的根系吸水过程,且具有较高的泛化性。研究成果可为植被根系吸水过程研究的方法创新提供重要参考。
The root water uptake process is a key link in the hydrological process within a continuous groundwater-soilvegetation-atmosphere system.However,the limitations of traditional methods such as experiments and numerical simulations still lead to great uncertainties in the study of the root water uptake process.Deep learning models have demonstrated the potential to surpass traditional methods in the field of hydrological process simulation,but relatively few studies have investigated the root water uptake process.A deep learning model(CNN-LSTM)combining a convolutional neural network(CNN)with a long short-term memory network(LSTM)was proposed in this study.The spatiotemporal distribution of root water uptake of a typical xerophytic plant,Salix,during three growth cycles was simulated with easily accessible hydrometeorological data.The CNN-LSTM model accurately characterized the spatiotemporal variation dynamics of root water uptake.The determination coefficient was not less than 0.86,and E_(RMS) was less than 0.01,significantly enhancing the prediction performance of root water uptake.The CNN-LSTM model can capture the spatiotemporal dynamic root water uptake processes with lower computational cost and higher accuracy.This model has a stronger generalizability and can adapt to conditions lacking basic data,such as soil hydraulic parameters and vegetation parameters.This study explored the application of deep learning methods in simulating complex root water uptake and achieved good results.This study provides reference data for theoretical research,method innovation for vegetation water consumption and ecological practices,and provides reference data for deep learning research on other hydrological problems.
作者
赵明
王文科
王一
赵永华
ZHAO Ming;WANG Wenke;WANG Yi;ZHAO Yonghua(School of Land Engineering,Chang’an University,Xi’an 710054,China;Shaanxi Key Laboratory of Land Reclamation Engineering,Xi’an 710054,China;Key Laboratory of Subsurface Hydrology and Ecological Effect in Arid Region of Ministry of Education,Xi’an 710054,China;School of Water and Environment,Chang’an University,Xi’an 710054,China)
出处
《水科学进展》
北大核心
2026年第1期120-133,共14页
Advances in Water Science
基金
国家自然科学基金项目(42407087)
中国博士后科学基金项目(2022M720535)。
关键词
根系吸水
深度学习
数值模拟
沙柳
毛乌素沙地
root water uptake
deep learning
numerical simulation
Salix
Mu Us Sandy Land*
