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利用开放数据存储库评估欧洲湖泊浊度和营养状态的时间变异性。

Assessing temporal variability of lake turbidity and trophic state of European lakes using open data repositories.

机构信息

Hellenic Centre for Marine Research, Institute of Marine Biological Resources and Inland Waters, 46.7 km of Athens-Sounio Ave., 19013 Anavyssos, Attica, Greece; Department of Biology, University of Patras, University Campus Rio, GR 26500 Patras, Greece.

Hellenic Centre for Marine Research, Institute of Marine Biological Resources and Inland Waters, 46.7 km of Athens-Sounio Ave., 19013 Anavyssos, Attica, Greece.

出版信息

Sci Total Environ. 2023 Jan 20;857(Pt 3):159618. doi: 10.1016/j.scitotenv.2022.159618. Epub 2022 Oct 21.

Abstract

Water turbidity is one of the more important water quality parameters that is strictly linked with the productivity of the lake and is commonly used as an indicator of the trophic state. However, limited field data availability across wide geographic gradients may hinder the conduction of large scale longitudinal studies. In this study, time series of lake turbidity and trophic state index (TSI) between 2002 and 2012 were obtained from the Copernicus Lake Water products to create a large longitudinal dataset of lake variables for 22 European lakes. The dataset was combined with estimates of nutrient concentrations and surface water temperature obtained from the Hydrological Predictions for the Environment (HYPE) and ERA5-Land data repositories, that were used as environmental predictors. Hence, the validity of the lake water quality parameters was tested by a) exploring their spatial and temporal variability and b) identifying associations with the environmental predictors. For this purpose, seasonal Mann-Kendall tests were applied to find significant inter-annual trends of turbidity and TSI for each lake, and generalized additive models (GAMs) were employed to identify the main parameters that shape their temporal dynamics. Although we did not find significant inter-annual changes, our findings highlighted the strong influence of seasonality and surface water temperature in defining the temporal variability patterns in most of the lakes. In addition, the importance of nutrients varied among lakes as several lakes exhibited narrow nutrient gradients reflecting relatively stable nutrient conditions during the examined period. Other lake intrinsic factors, such as local climate and biotic interactions, are important drivers of shaping turbidity and nutrient dynamics. This study highlighted the usefulness of combining lake data from large repositories in conducting large scale spatial studies as a valuable asset for future lake research and management purposes.

摘要

水浊度是水质参数中较为重要的参数之一,与湖泊生产力密切相关,通常被用作指示湖泊富营养状态的指标。然而,在广泛的地理梯度上,有限的现场数据可用性可能会阻碍大规模纵向研究的进行。在这项研究中,我们从哥白尼湖泊水质产品中获取了 2002 年至 2012 年期间的湖泊浊度和营养状态指数(TSI)时间序列,为 22 个欧洲湖泊创建了一个大型的湖泊变量纵向数据集。该数据集与从 Hydrological Predictions for the Environment (HYPE) 和 ERA5-Land 数据存储库中获取的营养物浓度和地表水温度估计值相结合,这些数据被用作环境预测因子。因此,我们通过以下方式测试了湖泊水质参数的有效性:a)探索其时空变异性;b)识别与环境预测因子的关联。为此,我们应用季节性 Mann-Kendall 检验来发现每个湖泊浊度和 TSI 的年际变化趋势,并使用广义加性模型(GAMs)来识别塑造其时间动态的主要参数。尽管我们没有发现显著的年际变化,但我们的研究结果突出了季节性和地表水温度在定义大多数湖泊时间变化模式方面的强烈影响。此外,营养物的重要性因湖泊而异,因为一些湖泊表现出狭窄的营养物梯度,反映了在所研究期间相对稳定的营养条件。其他湖泊内在因素,如当地气候和生物相互作用,是塑造浊度和营养动态的重要驱动因素。本研究强调了结合大型存储库中的湖泊数据进行大规模空间研究的有用性,这是未来湖泊研究和管理的宝贵资产。

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