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首页> 外文期刊>River Research and Applications >WHY DO FISH STRAND? AN ANALYSIS OF TEN YEARS OF FLOW REDUCTION MONITORING DATA FROM THE COLUMBIA AND KOOTENAY RIVERS, CANADA
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WHY DO FISH STRAND? AN ANALYSIS OF TEN YEARS OF FLOW REDUCTION MONITORING DATA FROM THE COLUMBIA AND KOOTENAY RIVERS, CANADA

机译：鱼干为什么？来自加拿大哥伦比亚河和科滕尼河的减流监测数据的十年分析

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Stranding of fish due to flow reductions has been documented in the near shore of the Columbia and Kootenay Rivers, Canada, and can result in sub-lethal or lethal effects on fish. Ten years (1999-2009) of monitoring data have been collected at sites below two hydro-electric dams (Hugh-L-Keenleyside and Brilliant Dam) following flow reductions. A generalized linear mixed effects model analysed the probability of a stranding event in relation to environmental and operational variables including the rate of change in the water levels, the duration of shoreline inundation prior to a reduction (wetted history), the river stage, the magnitude of the reduction, distance downstream from the dam, time of day, day of year (season) and whether a site had been physically altered to mitigate stranding. The results demonstrated statistically significant effects on stranding risk from minimum river stage, day of the year and whether a site had been physically re-contoured. The combination of investigated factors giving the highest probability of stranding was a large magnitude reduction completed in the afternoon in midsummer, at low water levels when the near shore had been inundated for a long period. This research is significant in its approach to assessing years of ecosystem scale monitoring data and using the modelling results to determine ways for these findings to be applied in regulated river management to minimize fish stranding. It also highlighted data gaps that require addressing and provides ecosystem scale results to compare with stranding studies carried out in mesocosms.
机译：在加拿大的哥伦比亚河和库特尼河的近岸，由于流量减少而导致的搁浅鱼类记录在案，可能对鱼类造成致命性或致命性影响。随着流量的减少，在两个水电大坝（休-L-肯利赛德和辉煌水坝）下方的站点收集了十年（1999-2009年）的监测数据。广义线性混合效应模型分析了与环境和运营变量相关的搁浅事件的概率，包括环境水位的变化率，水位下降之前的淹没持续时间（湿润的历史），河段，水位减少量，距大坝下游的距离，一天中的时间，一年中的某天（季节）以及是否已对场地进行了物理改造以减轻搁浅的情况。结果表明，从最小河段，一年中的一天以及是否对场地进行了物理重新轮廓处理，对搁浅风险的统计显着影响。导致搁浅的可能性最高的已调查因素的结合是，盛夏时节在近水淹没了很长时间的低水位下，在午后大幅度减少。这项研究的意义在于评估多年的生态系统规模监测数据，并使用建模结果来确定将这些发现应用于调节河流管理以最大程度地减少鱼类搁浅的方式。它还强调了需要解决的数据缺口，并提供了生态系统规模的结果，以便与中观宇宙中进行的搁浅研究进行比较。

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