Water Quality Research Center, Korea Water Resources Corporation, 200 Sintanjin-Ro, Daedeok-Gu, Daejeon, K-water, South Korea.
University of California at Berkeley, Department of Civil and Environmental Engineering, Berkeley, CA 94720, USA.
Sci Total Environ. 2018 Sep 1;634:1042-1053. doi: 10.1016/j.scitotenv.2018.04.034. Epub 2018 Apr 11.
Fine particles or sediments have various effects on water quality and aquatic ecosystems. Thus, understanding the dynamics of these fine particles between water body and stream bed is an important issue in sediment research. Previous studies and analysis of empirical data suggest that fine particles are stored in the sediment bed in the low flow regime, where flow rate is smaller than the critical flow rate that mobilizes the sediment bed. These fine particles are re-suspended during flood events when the flow rate becomes larger than the critical flow rate that mobilizes bed material. The transition from pattern recognition to process analysis required incorporation of the dominant processes controlling fine particle dynamics within gravel-bedded streams into a model. The process analysis was performed using continuous flow and turbidity data at two locations on the Russian River in California to test process descriptions and then calibrate a quantitative model to represent those processes. The resulting process model coupled fine particle retention within the sediment bed by filtration and sedimentation with the release of accumulated fine particles in response to flood events. Model parameters, such as the critical flow rate required for initiating sediment bed fluidization, the maximum fine particle storage capacity within the sediment bed, and background particle concentration for the watershed, were estimated from the monitoring data. Model calibration optimized the filtration and the sediment bed fluidization parameters over two or three years of data. Overall, the difference between modeled and observed fine particle mass released from the sediment bed was within 20% of the measured mass.
细颗粒或沉积物对水质和水生生态系统有各种影响。因此，了解水体和河床之间这些细颗粒的动力学是沉积物研究中的一个重要问题。先前的研究和经验数据的分析表明，在低流量状态下，细颗粒储存在沉积物床中，此时的流速小于使沉积物床移动的临界流速。在流速大于使床物质移动的临界流速的洪水事件中，这些细颗粒会重新悬浮。从模式识别到过程分析的转变需要将控制细颗粒动力学的主要过程纳入模型中。使用加利福尼亚州俄罗斯河两个地点的连续流量和浊度数据进行了过程分析，以测试过程描述，然后校准定量模型以表示这些过程。所得过程模型将过滤和沉降过程中细颗粒在沉积物床中的保留与洪水事件中积累的细颗粒的释放相结合。模型参数，如启动沉积物床流化所需的临界流速、沉积物床中细颗粒的最大储存容量以及流域的背景颗粒浓度，都是从监测数据中估算出来的。模型校准在两到三年的数据基础上优化了过滤和沉积物床流化参数。总的来说，模型模拟和从沉积物床中释放的实测细颗粒质量之间的差异在测量质量的 20%以内。
