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Evaluating historical trends and influences of meteorological and seasonal climate conditions on lake chlorophyll a using remote sensing

机译:利用遥感评估历史趋势以及气象和季节性气候条件对叶绿素a的影响

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摘要

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. XX:XX-XX. 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.
机译:汉森(Hansen CH),布里亚恩(Burian SJ),丹尼森(Denison)体育,威廉姆斯(Williams)GP。 2019年。使用遥感技术评估历史趋势以及气象和季节性气候条件对叶绿素a的影响。湖后备管理。 XX:XX-XX。湖泊和水库的长期水质趋势和格局的评估常常受到不规则的历史记录的阻碍。这项研究使用Landsat卫星历史图像来构建藻类生物量的更完整历史记录(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。这种方法既基于物理(使用在湖中观察到的变异性和藻类演替模式),也基于数据驱动(依靠用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多湖大盐湖系统中的每个湖开发特定于湖的季节性模型。估计的Chl-a的32年历史记录可以分析湖泊系统内的长期趋势,并评估当地气候对Chl-a浓度的影响。估计的历史记录显示出季节性变化(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(如强风,高温或降水事件)的局部敏感性。季节性气候条件包括较高的冬季降水,夏季温度和早春的雪水当量,与历史记录中较高的Chl-a极端值一致。更好地了解趋势和气候影响为将来的监测工作和管理策略提供了有用的背景和指导。

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