水利学报2024，Vol.55Issue(9)：1123-1134,12.DOI:10.13243/j.cnki.slxb.20230662
基于分布式产流要素和时空深度学习算法的径流后处理研究
Streamflow post-processing based on distributed hydrological fluxes and spatio-temporal deep learning algorithm
摘要
Abstract
Accurate simulation of streamflow is a crucial prerequisite for water resources management and regional integrated policy making.In order to improve the accuracy of streamflow simulation,this study takes Yonganxi Riv-er Basin in Taizhou,Zhejiang Province as the study area.A CNN-LSTM spatio-temporal post-processing model by coupling CNN with LSTM is proposed based on the measured daily discharge data at Baizhi'ao Station from 2010 to 2019 and hydrological fluxes simulated by the Grid-HBV model.We construct two post-processing models,namely CNN-LSTM with single flux(s-CNN-LSTM)and CNN-LSTM with double fluxes(bi-CNN-LSTM).Their performance is compared and analyzed with a benchmark model(s-LSTM).The results show that the NSE of the Grid-HBV model during the calibration and validation periods are 0.78 and 0.81,respectively,indicating an over-all good runoff simulation.However,there are underestimation in medium and high flow and overestimation in low flow simulations.After post-processing,the NSE of s-LSTM in the two study periods are 0.87 and 0.85,with an increase of 11.2％and 5.8％,and the NSE of s-CNN-LSTM are 0.90 and 0.89,with an increase of 14.6％and 10.9％.The NSE of bi-CNN-LSTM in the two study periods both reach 0.92,with an increase of 17.2％and 14.2％.Compared to the s-LSTM model,the bi-CNN-LSTM model presents a further enhancement of 6.0％and 8.4％in accuracy.In addition,the bi-CNN-LSTM model can markedly improve the defects of original simulation in the high,medium and low flows.For four typical flood events,the bi-CNN-LSTM model has the best post-processing effect,which reduces the flood peak error by 36.6％on average,the s-LSTM model and the s-CNN-LSTM model reduces the flood peak error by 19.3％and 30.3％on average.In summary,the CNN-LSTM model based on dis-tributed hydrological fluxes has a good ability of streamflow post-processing,which can significantly improve the streamflow simulations of hydrological models.关键词
径流后处理/CNN-LSTM/深度学习/网格化HBV水文模型/椒江流域Key words
post-processing/CNN-LSTM/deep learning/grid HBV hydrological model/Jiao River basin分类
建筑与水利引用本文复制引用
吴垚,许月萍,刘莉,何柯琪..基于分布式产流要素和时空深度学习算法的径流后处理研究[J].水利学报,2024,55(9):1123-1134,12.基金项目
浙江省重点研发项目(2021C03017) （2021C03017）
浙江省自然基金重点项目(Z20E090005) （Z20E090005）
国家重点研发计划项目(2021YFD1700802) （2021YFD1700802）
国家自然科学基金项目(52309038) （52309038）
