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Long-term light environment variability in Lake Biwa and Lake Kasumigaura, Japan: modeling approach

机译：日本琵琶湖和霞浦湖的长期光环境变化：建模方法

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Light environment variability was investigated in the two Japanese lakes Biwa and Kasumigaura, which offer a broad range of optical conditions in the water bodies due to their diverse morphometries and limnological characteristics. To elucidate their light environments, Secchi depths (SDs) were related to long-term monitored datasets of concentrations of optically active substances (OASs) using two approaches based on statistical and mechanistic models. A good estimate for the nonphytoplanktonic suspended solids (NPSS) concentration gained using a monthly factor δ (which represents the phytoplanktonic portion in total suspended solids) from a long-term analysis was utilized to develop robust models. Using the mechanistic model, the contribution of each OAS to the SD can be understood and investigated in more detail than possible with a statistical approach, but the statistical model yields better results in terms of SD prediction. Based on the results of an analysis of the contribution of each OAS to the SD, it was clear that NPSS was the component that exerted the most influence on the light environments in the two lakes; in this respect, this study agrees with other studies that show the importance of suspended inorganic particles as the main contributor to the SD in inland waters. Using ANOVA, we analyzed how specific inherent optical properties may have changed spatially and temporally, and the results indicated that the temporal (monthly) effect was primarily responsible for the loss of accuracy in the models. In addition, the ANOVA analysis suggested that grouping the data improved the predictive performances of the statistical models. Finally, we concluded that combining the two models yields the most reliable results in terms of SD prediction and determining the contribution of each OAS to the SD at present.
机译：在两个日本琵琶湖和霞浦湖中研究了光环境的可变性，这两个湖由于其形态和形态学特征的多样性而在水体中提供了广泛的光学条件。为了阐明其光照环境，Secchi深度（SD）与基于统计和机理模型的两种方法对光学活性物质（OAS）浓度的长期监测数据集相关。利用长期分析得出的月度因子δ（代表总悬浮固体中植物浮游植物部分）获得的非植物浮游生物悬浮固体（NPSS）浓度的良好估算值可用于开发鲁棒模型。使用机械模型，可以比采用统计方法更详细地了解和研究每个OAS对SD的贡献，但是根据SD预测，统计模型会产生更好的结果。根据每个OAS对SD的贡献的分析结果，很明显，NPSS是对两个湖泊的光环境影响最大的组件。在这方面，本研究与其他研究相吻合，其他研究表明，悬浮无机颗粒是内陆水域SD的主要贡献者。使用方差分析，我们分析了特定的固有光学特性如何在空间和时间上发生变化，结果表明，时间（每月）效应是造成模型准确性下降的主要原因。此外，ANOVA分析表明，对数据进行分组可以改善统计模型的预测性能。最后，我们得出结论，就SD预测而言，结合这两个模型可得出最可靠的结果，并确定当前每个OAS对SD的贡献。

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