土壤学报2023，Vol.60Issue(6)：1543-1554,12.DOI:10.11766/trxb202203040090
基于数据同化的土壤水力参数反演方法:研究进展与展望
Data Assimilation for Soil Hydraulic Parameter Estimation:Progress and Perspectives
摘要
Abstract
The characterization of soil hydraulic parameters and their heterogeneity is related to many scientific problems in soil and groundwater fields.Due to the limitation of time and sampling cost,the traditional experimental approaches cannot address this issue adequately.With the development of Internet of Things technology,the state variables related to soil water movement(such as water content and pressure head)can be acquired in real time through sensors.This has sparked some debates about how to estimate the soil hydraulic parameters using these measurements.Data assimilation methods can estimate the soil hydraulic parameters by integrating the measurements into numerical models.This paper systematically analyzes the uncertainty sources and measurement approaches of soil hydraulic parameters,expounds on the basic principles of several common data assimilation methods and their applications in soil hydraulic parameter inversion,and discusses the latest advances in data assimilation methods from aspects of computational efficiency and accuracy.Finally,the development direction of data assimilation methods is provided.The results show that the data assimilation methods can break through the limitation of the traditional experimental approach,and thus are suitable for the characterization of soil hydraulic parameters and their heterogeneity.However,limitations such as the strong nonlinearity of the unsaturated flow model,spatial heterogeneity of soil and sparsity of in-situ measurements do exist.It is,therefore,essential for us to unfold in-depth research on soil hydraulic parameter inversion from the aspects of supervised dimension reduction method,multi-source and multi-scale data fusion,and coupling of machine learning with physical mechanisms,thereby assisting agricultural soil and water management as well as the prevention,control,and remediation of pollution in agroecosystems.关键词
数值模拟/土壤水力参数/参数反演/数据同化/优化试验设计/机器学习Key words
Numerical modeling/Soil hydraulic parameter/Parameter inversion/Data assimilation/Optimal experimental design/Machine learning分类
农业科技引用本文复制引用
满俊,张江江,郑强,尧一骏,曾令藻..基于数据同化的土壤水力参数反演方法:研究进展与展望[J].土壤学报,2023,60(6):1543-1554,12.基金项目
国家重点研发计划项目(2021YFC1808904)、国家自然科学基金项目(42107066)和江苏省自然科学基金项目(BK20201105)资助Supported by the National Key Research and Development Program of China(No.2021YFC1808904),the National Natural Science Foundation of China(No.42107066),and the Natural Science Foundation of Jiangsu Province(No.BK20201105) （2021YFC1808904）
