A multi-lake comparative analysis of the General Lake Model (GLM): Stress-testing across a global observatory network
A multi-lake comparative analysis of the General Lake Model (GLM): Stress-testing across a global observatory network
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DOI：
10.1016/j.envsoft.2017.11.016
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发表时间：
2018-04-01
影响因子：
4.9
通讯作者：
Hipsey, Matthew R.
中科院分区：
文献类型：
--
作者：
Bruce, Louise C.;Frassl, Marieke A.;Hipsey, Matthew R.
由于建模方法和湖泊的多样性，建模界已经确定了湖泊模型集成和评估的挑战。在这项研究中，我们开发并评估了一个一维湖泊模型，并将其应用于全球观测站网络中的 32 个湖泊。该数据集包括广泛纬度、气候带、大小、停留时间、混合方式和营养级的湖泊。使用多个误差评估指标评估模型性能，并对控制表面热交换和混合效率的九个参数进行敏感性分析。输入数据不确定性与模型性能之间的相关性较低，温度预测对模型参数的敏感度低于温跃层深度和施密特稳定性的预测。该研究为一般模型方法和相关假设的适用范围以及需要调整模型参数化和/或结构的情况提供了指导。 (c) 2017 年由爱思唯尔有限公司出版。
The modelling community has identified challenges for the integration and assessment of lake models due to the diversity of modelling approaches and lakes. In this study, we develop and assess a one-dimensional lake model and apply it to 32 lakes from a global observatory network. The data set included lakes over broad ranges in latitude, climatic zones, size, residence time, mixing regime and trophic level. Model performance was evaluated using several error assessment metrics, and a sensitivity analysis was conducted for nine parameters that governed the surface heat exchange and mixing efficiency. There was low correlation between input data uncertainty and model performance and predictions of temperature were less sensitive to model parameters than prediction of thermocline depth and Schmidt stability. The study provides guidance to where the general model approach and associated assumptions work, and cases where adjustments to model parameterisations and/or structure are required. (c) 2017 Published by Elsevier Ltd.