期刊
RIVER RESEARCH AND APPLICATIONS
卷 26, 期 9, 页码 1187-1197出版社
JOHN WILEY & SONS LTD
DOI: 10.1002/rra.1331
关键词
winter; anchor ice; stream heterogeneity; stream management
资金
- Norwegian University of Science and Technology (NTNU)
- Norwegian Research Council
During winter, different types of ice formation are commonly observed in northern boreal stream systems. Although largely overlooked today, river ice has profound effects on in-stream hydraulics and therefore ice processes should be considered in freshwater stream management and assessment. In particular, limited knowledge exists about the impacts of dynamic ice formation on stream environments. Results presented from the changes of in-stream heterogeneity in three steep stream environments caused by dynamic ice formation demonstrate that the formation of anchor ice and anchor ice dams may induce significant backwater effects by increasing wetted areas (maximum 43%) and water depths (maximum 241%) and reducing water velocities (maximum 70%); independent of minimal changes in discharge. Consequently, stream environments are transformed from fast-flow to slow-flow areas, even on a short temporal scale (< 12 h). Furthermore, the anchor ice build-up initiated static (surface) ice formation due to reduced local water velocities upstream ice dams. Thus, dynamic ice formation plays a key role in the balanced ice regime in steep stream environments and contributes largely to stable static ice cover in these environments. Observations from the present study suggest that the current paradigm emphasizing the role of discharge as the main controller of in-stream heterogeneity may call for a modification in steep streams that experience seasonal ice formation. This is particularly important if future hydraulic-/habitat models and assessment tools are to be implemented in freshwater management to realistically characterize steep stream environments in cold climate regions on a seasonal scale. Copyright (C) 2009 John Wiley & Sons, Ltd.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据