Abstract Hansen CH, Burian SJ, Dennison PE, Williams GP. 2019. Evaluating historical trends and influences of meteoorological and seasonal climate conditions on lake chlorophyll a using remote sensing. Lake Reserv Manage. 36:45–63. Evaluations of long-term water quality trends and patterns in lakes and reservoirs are often inhibited by irregular historical records. This study uses historical Landsat satellite imagery to construct a more complete historical record of algal biomass (measured via chlorophyll a [Chl-a]) and presents a framework for developing seasonal algal estimation models using open source tools for processing and model development. This approach is both physically based (using observed patterns of variability and algal succession in the lake) and data driven (relying on statistical methods for model development). We use a generalized linear regression modeling technique to develop lake-specific, seasonal models for each lake in the multilake Great Salt Lake system in Utah. The 32-yr constructed history of estimated Chl-a enables analysis of long-term trends within the lake system as well as evaluations of local climate influences on Chl-a concentrations. The estimated historical record exhibits a shift in seasonality (i.e., maximum Chl-a occurs earlier in the growing season), as well as increasing trends of extreme Chl-a concentrations. We also evaluated relationships between meteorological conditions and Chl-a using the enhanced historical record and found localized sensitivity to short-term weather events such as high wind, high temperatures, or precipitation events. Seasonal climate conditions including high winter precipitation, summer temperatures, and early spring snow water equivalent are consistent with higher Chl-a extremes in the historical record. Improved understanding of the trends and climate influences provides useful context and guidance for future monitoring efforts and management strategies.

中文翻译：

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利用遥感评估气象和季节性气候条件对湖泊叶绿素a的历史趋势和影响

摘要 Hansen CH, Burian SJ, Dennison PE, Williams GP。2019. 使用遥感评估气象和季节性气候条件对湖泊叶绿素 a 的历史趋势和影响。湖泊保护区管理。36:45-63。对湖泊和水库中长期水质趋势和模式的评估常常受到不规则的历史记录的阻碍。本研究使用历史 Landsat 卫星图像构建更完整的藻类生物量历史记录（通过叶绿素 a [Chl-a] 测量），并提出了使用开源工具进行处理和模型开发来开发季节性藻类估计模型的框架。这种方法既是基于物理的（使用观察到的湖中变化和藻类演替模式），也是数据驱动的（依赖于模型开发的统计方法）。我们使用广义线性回归建模技术为犹他州大盐湖多湖系统中的每个湖泊开发特定于湖泊的季节性模型。估计 Chl-a 的 32 年构建历史能够分析湖泊系统内的长期趋势以及评估当地气候对 Chl-a 浓度的影响。估计的历史记录显示季节性变化（即最大 Chl-a 出现在生长季节的早期），以及极端 Chl-a 浓度的增加趋势。我们还使用增强的历史记录评估了气象条件与 Chl-a 之间的关系，并发现了对短期天气事件（如大风、高温或降水事件）的局部敏感性。季节性气候条件，包括冬季高降水量、夏季气温、和早春雪水当量与历史记录中较高的 Chl-a 极端值一致。对趋势和气候影响的更好理解为未来的监测工作和管理战略提供了有用的背景和指导。