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引用本文:范宏翔,何菡丹,徐力刚,张明睿,姜加虎.基于长短记忆模型的鄱阳湖流域径流模拟及其演变的归因分析.湖泊科学,2021,33(3):866-878. DOI:10.18307/2021.0319
Fan Hongxiang,He Handan,Xu Ligang,Zhang Mingrui,Jiang Jiahu.Simulation and attribution analysis based on the long-short-term-memory network for detecting the dominant cause of runoff variation in the Lake Poyang Basin. J. Lake Sci.2021,33(3):866-878. DOI:10.18307/2021.0319
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基于长短记忆模型的鄱阳湖流域径流模拟及其演变的归因分析
范宏翔1,2, 何菡丹3, 徐力刚1,4, 张明睿1,5, 姜加虎1
1.中国科学院南京地理与湖泊研究所, 中国科学院流域地理学重点实验室, 南京 210008;2.中国科学院大学, 北京 100049;3.江苏省水资源服务中心, 南京 210029;4.中国长江三峡集团有限公司长江生态环境工程研究中心, 北京 100038;5.安徽工业大学, 马鞍山 243002
摘要:
气候变化和人类活动直接或间接的影响着全球和区域水文循环过程,是导致水文水资源时空分布的主要因素,同时也是流域-湖泊水文情势变化的根本原因.本文基于长短记忆模型构建了鄱阳湖气象-径流模型,同时引入了基准期的概念,定量区分了导致鄱阳湖流域径流变化的主要影响因素.研究结果表明:在同时考虑计算效率和模拟效果的前提下,采用10 d预测窗口大小来构建鄱阳湖气象-径流模型能够很好地捕捉径流的极值,并且对径流的短期波动也能有很好的体现.训练期模型在各个子流域的纳什效率系数均高于0.94,而在验证期,模型在各个子流域的纳什效率系数均高于0.90.基于径流模拟结果,定量区分了人类活动和气候变化对鄱阳湖径流的影响,研究结果显示:人类活动对径流的影响主要发生在春、秋季,其中,人类活动在春季主要会造成径流的增加,平均增加幅度约为139.47 m3/s,而在秋、冬季,人类活动则会导致径流平均减少约34.37 m3/s.对比二者的相对贡献率,可以发现,春季人类活动对径流造成的影响较大,平均相对贡献率为77.26%.而在其余季节,鄱阳湖流域径流过程的改变主要是由于气候变化,平均相对贡献率约75.84%.研究结果能够为鄱阳湖流域水资源管理提供科学依据和理论指导.
关键词:  长短记忆模型  气候变化  人类活动  鄱阳湖  径流变化
DOI:10.18307/2021.0319
分类号:
基金项目:国家重点研发计划项目(2018YFE0206400,2018YFC0407606)、国家自然科学基金项目(41971137,41771235)、青海省科技支撑项目(2019-HZ-818)、中国科学院STS项目(KFJ-STS-QYZD-098)、中国长江三峡集团有限公司项目(201903145)、中国科学院南京地理与湖泊研究所引进人才启动项目(NIGLAS2019QD005)和中国科学院流域地理学重点实验室开放基金项目(WSGS2020003)联合资助.
Simulation and attribution analysis based on the long-short-term-memory network for detecting the dominant cause of runoff variation in the Lake Poyang Basin
Fan Hongxiang1,2, He Handan3, Xu Ligang1,4, Zhang Mingrui1,5, Jiang Jiahu1
1.Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, P. R. China;2.University of Chinese Academy of Sciences, Beijing 100049, P. R. China;3.Water Resources Service Center of Jiangsu Province, Nanjing 210029, P. R. China;4.Eco-Environmental Engineering Research Center, China Three Gorges Corporation, Beijing 100038, P. R. China;5.Anhui University of Technology, Maanshan 243002, P. R. China
Abstract:
Climate change and human activities directly or indirectly affect the global and regional hydrologic cycle, which is the main factor leading to the temporal and spatial distribution of water resources. Based on the long-short-term-memory (LSTM) model, this paper attributes the contribution of climate change and human activities on runoff variation in the Lake Poyang Basin. Results show that the runoff process was simulated well by LSTM framework with a window size of 10 days for both accuracy and computational efficiency. The overall performance of the model was good with Nash-Sutcliffe Efficiency varied from 0.94 to 0.95 in the training period and 0.90 to 0.98 in the test period, respectively. Based on the simulation results, the contribution of human activities and climate change on runoff change in Lake Poyang Basin was then quantified. Results show that the runoff increased by 139.47 m3/s due to human activities in spring, accounting for 77.26% of runoff change. Whereas climate change was the dominant factor in changing runoff in the other seasons, which led the runoff to decrease by 34.37 m3/s, accounting for about 75.84% of runoff changes. The results can provide scientific basis and theoretical guidance for water resources management in Lake Poyang Basin.
Key words:  Long-short-term-memory network  climate change  human activities  Lake Poyang  runoff
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