摘要
降雨量与径流的关系对农业地区的水资源调配及水土资源保护具有重要意义,但在小流域范围内,不同土地利用类型下降雨径流过程难以预测。基于LSTM模型、新安江模型以及EnKF技术,探讨数据驱动机器学习模型对不同土地利用方式下降雨径流过程的模拟效果,并与SWAT水文模型模拟效果进行对比;研究EnKF对新安江模型不同水文参数集合的估计效果和滤波估计参数的规律,并基于率定的参数对不同农业土地利用类型的径流过程进行模拟。结果表明,径流在土面坡度略小时的高径流情况以及在土面坡度较大时的低径流过程更易被学习到;SWAT模型模拟精度及稳定性比LSTM模型差,但其可以在一定程度反映当地土壤水文条件,便于进行成因分析;EnKF技术具有参数更新和参数估计功能,能够优化新安江水文模型的径流模拟效果。
The relationship between rainfall and runoff is of great significance for the allocation of water resources and the protection of water and land resources in agricultural areas,but it is difficult to deal with the rainfall-runoff process under different land use types in small watersheds.The long short-term memory model(LSTM)and the Xin'anjiang model combined with ensemble Kalman filter(EnKF)technology were used to explore the simulation effectiveness of data-driven machine learning(ML)model on rainfall-runoff process under different land use patterns,and the simulation effectiveness was compared with that of SWAT hydrological model.The estimation effectiveness of EnKF on hydrological parameters ensembles in the Xin'anjiang model and the patterns of filter-estimated parameters were studied,and the runoff processes for different agricultural land use types based on the calibrated parameters were simulated.The results showed that the runoff value was easier to learn in the case of high runoff with a slightly small slope and the low runoff process with a large slope.The simulation accuracy and stability of the SWAT model were not as good as those of the LSTM model,but SWAT model could reflect the local soil hydrological conditions to a certain extent,which was convenient for genetic analysis.The EnKF technology had the functions of parameter update and parameter estimation,which could optimize the runoff simulation effectiveness of the Xin'anjiang model.
作者
林琳
高肇天
丁一家
胡小龙
张中彬
LIN Lin;GAO Zhao-tian;DING Yi-jia;HU Xiao-long;ZHANG Zhong-bin(School of Water Resources and Hydropower Engineering,Wuhan University,Wuhan 430072,China;Institute of Soil Science,Chinese Academy of Sciences,Nanjing 211135,China)
出处
《湖北农业科学》
2025年第5期70-79,共10页
Hubei Agricultural Sciences
基金
国家自然科学基金黄河水科学研究联合基金项目(U2243235)
国家自然科学基金项目(52309058)。
