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Nutrient dynamics and the eutrophication of shallow lakes Kasumigaura (Japan), Donghu (PR China), and Okeechobee (USA)

机译:霞浦(日本),东湖(中国)和奥基乔比(美国)的浅水湖泊的营养动态和富营养化

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摘要

We compared the nutrient dynamics of three lakes that have been heavily influenced by point and non-point source pollution and other human activities. The lakes, located in Japan (Lake Kasumigaura), People's Republic of China (Lake Donghu), and USA (Lake Okeechobee), all are relatively large (> 30 km~2), very shallow (<4 m mean depth), and eutrophic. In all three lakes we found strong interactions among the sediments, water column, and human activities. Important processes affecting nutrient dynamics included nitrogen fixation, light limitation due to resuspended sediments, and intense grazing on algae by cultured fish. As a result of these complex interactions, simple empirical models developed to predict in-lake responses of total phosphorus and algal biomass to external nutrient loads must be used with caution. While published models may provide 'good' results, in terms of model output matching actual data, this may not be due to accurate representation of lake processes in the models. The variable nutrient dynamics that we observed among the three study lakes appears to be typical for shallow lake systems. This indicates that a greater reliance on lake-specific research may be required for effective management, and a lesser role of inter-lake generalization than is possible for deeper, dimictic lake systems. Furthermore, accurate predictions of management impacts in shallow eutrophic lakes may require the use of relatively complex deterministic modeling tools.
机译:我们比较了三个受点,面源污染和其他人类活动严重影响的湖泊的养分动态。位于日本(霞浦湖),中华人民共和国(东湖湖)和美国(奥基乔比湖)的湖泊都相对较大(> 30 km〜2),非常浅(<4 m平均深度),并且富营养的。在这三个湖泊中,我们都发现了沉积物,水柱和人类活动之间的强烈相互作用。影响养分动态的重要过程包括固氮,由于沉积物重悬而造成的光限制以及养殖鱼类对藻类的强烈放牧。由于这些复杂的相互作用,必须谨慎使用简单的经验模型来预测湖中总磷和藻类生物量对外部养分负荷的响应。尽管已发布的模型可能会在模型输出与实际数据匹配方面提供“良好”的结果,但这可能不是由于模型中湖泊过程的准确表示所致。我们在三个研究湖泊中观察到的可变养分动态似乎是浅湖系统的典型特征。这表明,对于更有效的管理,可能需要更多地依赖于湖泊的研究,而湖间泛化的作用则要比更深的,仿照的湖泊系统的作用要小。此外,对浅水富营养化湖泊中管理影响的准确预测可能需要使用相对复杂的确定性建模工具。

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