使用高频传感器数据,了解水质变量与淡水流中的酸盐度之间的联系
Claire Kermorvant1, Benoit Liquet1,2,3, Guy Litt4
1Le CNRS et l'Université de Pau et des Pays de l'Adour, Laboratoire de Mathématiques et de leurs Applications de Pau, Anglet, France.
PloS one
|June 30, 2023
概括
高频水质监测揭示了不同流域中酸盐和其他变量之间的一致关系. 这一发现使得河流和小溪的有效,经济高效的酸盐管理策略成为可能.
科学领域:
- 环境科学 环境科学
- 水资源管理 水资源管理
- 生态监测 生态监测
背景情况:
- 实时,现场传感器提供高频水质数据,生成大型数据集进行高级分析.
- 了解酸盐动态对于有效的流域管理至关重要,因为它在水生系统中的反应性.
- 美国国家生态观测网络 (NEON) 提供了来自不同美国流域的宝贵数据.
研究的目的:
- 分析高频水质数据,以了解酸盐和其他关键变量之间的关系.
- 开发不同环境和气候区域中酸盐度的预测模型.
- 为了确定具有成本效益的水质变量,用于监测酸盐动态.
主要方法:
- 利用通用添加混合模型 (GAMMs) 来分析酸盐和导电性,度,溶解氧气,温度和海拔等变量之间的非线性关系.
- 使用自动回归移动平均线 (ARIMA) 模型来考虑数据中的时间自相关性.
- 在三个不同的NEON站点进行了变量重要性和模型性能比较.
主要成果:
- 模型解释了所有地点酸盐度的高百分比总偏差 (99%).
- 尽管在变量重要性和参数方面存在特定地点的差异,但同一组解释变量始终解释了酸盐的最大变化.
- 导电性,度,溶解氧气,水温和海拔等水质关键变量是酸盐度的重要预测因素.
结论:
- 使用一套一致的水质变量组的统一建模方法是有效的,以了解酸盐动态,即使在环境多样化的流域.
- 这些发现支持选择具有成本效益的监测变量来进行全面的空间和时间酸盐评估.
- 该研究提供了基于强大的酸盐监测模型的河流和溪流水质量适应性管理的框架.
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