Estimation of Transport Trajectory and Residence Time in Large River–Lake Systems: Application to Poyang Lake (China) Using a Combined Model Approach
Estimation of Transport Trajectory and Residence Time in Large River–Lake Systems: Application to Poyang Lake (China) Using a Combined Model Approach
复制标题
大型河流-湖泊系统中的传输轨迹和停留时间的估计：使用组合模型方法在鄱阳湖（中国）的应用
作者：
李云良;姚静
许多湖泊的生化过程和水质主要取决于它们的输运行为。大多数现有的方法来调查运输行为是基于物理为基础的数值模型。鄱阳湖污染物的迁移轨迹和停留时间被认为对洪水期间水质的持续恶化和相关的快速环境变化具有重要意义。本研究采用水动力学模型（MIKE 21）结合传输和粒子跟踪子模型，提供了全面的调查在鄱阳湖的传输行为。模型模拟表明，湖泊的主要水流模式导致一个独特的运输轨迹，主要是从集水河口发展到下游地区沿着湖泊的主要流动渠道，类似于河流运输行为。粒子跟踪结果表明，7 - 9月，湖泊的平均停留时间为89天。长江（湖泊流出）对停留时间的影响大于汇水河流入。目前的研究代表了第一次尝试使用组合模型的方法来提供深入了解的运输行为的大型河流湖泊系统，提出了建议，以管理污染物的输入都直接到湖泊和集水河流。
The biochemical processes and associated water quality in many lakes mainly depend on their transport behaviors. Most existing methodologies for investigating transport behaviors are based on physically based numerical models. The pollutant transport trajectory and residence time of Poyang Lake are thought to have important implications for the steadily deteriorating water quality and the associated rapid environmental changes during the flood period. This study used a hydrodynamic model (MIKE 21) in conjunction with transport and particle-tracking sub-models to provide comprehensive investigation of transport behaviors in Poyang Lake. Model simulations reveal that the lake’s prevailing water flow patterns cause a unique transport trajectory that primarily develops from the catchment river mouths to the downstream area along the lake’s main flow channels, similar to a river-transport behavior. Particle tracking results show that the mean residence time of the lake is 89 days during July–September. The effect of the Yangtze River (the effluent of the lake) on the residence time is stronger than that of the catchment river inflows. The current study represents a first attempt to use a combined model approach to provide insights into the transport behaviors for a large river–lake system, given proposals to manage the pollutant inputs both directly to the lake and catchment rivers.
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DOI：
10.1016/j.amc.2013.06.052
发表时间：
2013-09
期刊：
Appl. Math. Comput.
影响因子：
--
作者：
Xinfeng Jia;F. Zeng;Yongan Gu
通讯作者：
Xinfeng Jia;F. Zeng;Yongan Gu
影响因子：
1.8
作者：
Zhongdao Wu;X. Lai;Liqing Zhang;Yongjiu Cai;Y. B. Chen
通讯作者：
Zhongdao Wu;X. Lai;Liqing Zhang;Yongjiu Cai;Y. B. Chen
影响因子：
2.8
作者：
J. Schoen;D. Stretch;K. Tirok
通讯作者：
J. Schoen;D. Stretch;K. Tirok
利用MODIS观测资料评估2000-2010年鄱阳湖淹没变化
影响因子：
13.5
作者：
Feng, Lian;Hu, Chuanmin;Gan, Wenxia
通讯作者：
Gan, Wenxia
DOI：
10.1175/jam2301.1
发表时间：
2005-11
期刊：
Journal of Applied Meteorology
影响因子：
--
作者：
L. Harris;V. Rao Kotamarthi
通讯作者：
L. Harris;V. Rao Kotamarthi