| 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 |
| 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 |
| 投稿时间：2023-10-25 |
| DOI：10.13243/j.cnki.slxb.20230662 |
| 中文关键词: 径流后处理 CNN-LSTM 深度学习 网格化HBV水文模型 椒江流域 |
| 英文关键词: post-processing CNN-LSTM deep learning grid HBV hydrological model Jiao River basin |
| 基金项目:浙江省重点研发项目(2021C03017);浙江省自然基金重点项目(Z20E090005);国家重点研发计划项目(2021YFD1700802);国家自然科学基金项目(52309038) |
|
| 摘要点击次数: 565 |
| 全文下载次数: 1461 |
| 中文摘要: |
| 准确的径流模拟是流域水资源管理和区域综合政策制定的重要前提。为提高径流模拟的精度，本文以浙江省台州市永安溪流域为研究区域，基于2010—2019年柏枝岙站出口断面的实测日径流数据和网格化HBV模型(Grid-HBV)的模拟结果，提出了一种耦合卷积神经网络CNN和长短期记忆网络LSTM的CNN-LSTM时空后处理模型;构建了基于单一产流要素的s-CNN-LSTM模型和基于两种产流要素的bi-CNN-LSTM模型，并与基准模型s-LSTM开展径流后处理和比较分析。研究结果表明：Grid-HBV模型率定期和验证期的纳什效率系数(NSE)分别为0.78和0.81，整体径流模拟效果较好，但存在中、高水低估和低水高估的不足。经s-LSTM模型后处理后，率定期和验证期NSE提升至0.87和0.85，提升幅度为11.2%和5.8%;s-CNN-LSTM模型后处理后NSE分别为0.90和0.89，提升幅度为14.6%和10.9%。bi-CNN-LSTM模型后处理后率定期和验证期NSE皆达到0.92，提升幅度为17.2%和14.2%，比s-LSTM模型的提升幅度分别大6.0%和8.4%，且该模型对原模拟径流高、中、低各流量等级中的局部缺陷有针对性改善。在4个典型洪水事件的分析中，bi-CNN-LSTM模型总体后处理效果最好，与Crid-HBV模型模拟结果相比，各洪峰误差平均减小36.6%，s-LSTM模型和s-CNN-LSTM模型则平均额外减小了19.3%和30.3%。基于分布式产流要素的CNN-LSTM模型具有较好的径流后处理能力，能够显著提高水文模型径流模拟效果，有助于流域水文水资源的科学管理。 |
| 英文摘要: |
| 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 River 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 overall 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 distributed hydrological fluxes has a good ability of streamflow post-processing,which can significantly improve the streamflow simulations of hydrological models. |
|
查看全文
查看/发表评论 下载PDF阅读器 |
| 关闭 |
|
|
|