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Using the General Lake Model (GLM) to simulate water temperatures and ice cover of a medium-sized lake: a case study of Lake Ammersee, Germany

机译:使用通用湖模型(GLM)来模拟中型湖的水温和冰盖:以德国阿默湖为例

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Thermal dynamics of lakes has a key role in chemical and biological processes in lakes including nutrient distributions and phytoplankton growth. Applications of hydrodynamic models to lakes can provide insights into possible future alterations in thermal dynamics induced by climate change. In this study, we present the calibration and validation of the newest version of the open-source hydrodynamic model GLM (General Lake Model) to the dimictic Lake Ammersee, located in southeast Germany. The simulation of lake water temperatures for the calibration period revealed an overall root mean square error of 0.65 degrees C and a mean error of 0.08 degrees C. The seasonal stratification pattern and the annual thermal structure of this dimictic lake were reproduced by the model. The model simulated the presence of winter ice cover for the only year out of 8 years simulated, when ice cover was observed. Elevated lake water temperatures were also reproduced in model simulations during a period in 2003 of unusually high air temperatures. Statistical analysis of the model calibration results for Lake Ammersee indicates a fit comparable to or better than most other well-established hydrodynamic models and provides an opportunity for continuous simulations through periods of ice cover. Our results indicate a major improvement in GLM compared with earlier model versions and demonstrate the applicability of GLM for limnophysical studies, particularly for altered forcing conditions such as climate change.
机译:湖泊的热力学在湖泊的化学和生物过程(包括养分分布和浮游植物生长)中起着关键作用。水动力模型在湖泊中的应用可以提供对气候变化引起的热动力未来可能变化的见解。在这项研究中,我们向位于德国东南部的仿生的Ammersee湖提供了最新版本的开源水动力模型GLM(通用​​湖模型)的校准和验证。在校准期间对湖水温度的模拟显示,总的均方根误差为0.65摄氏度,平均误差为0.08摄氏度。该模型再现了该干燥湖泊的季节分层模式和年热结构。当观察到冰盖时,该模型模拟了在模拟的8年中只有一年的冬季冰盖的存在。在2003年异常高的气温期间,模型模拟还再现了升高的湖水温度。对Ammersee湖模型校准结果的统计分析表明,其拟合度可与大多数其他公认的水动力模型相媲美或更好,并且为整个覆冰期进行连续模拟提供了机会。我们的结果表明,与早期模型版本相比,GLM有了重大改进,并证明了GLM在线粒体研究中的适用性,特别是对于改变的强迫条件(例如气候变化)。

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