Thermal regimes of rivers and streams profoundly affect aquatic ecosystems, but are poorly described and classified in many areas due to the limited availability of annual datasets from extensive and representative monitoring networks. By mining a new temperature database composed of >23,000 site records that spans the western United States (U.S.), we extract annual monitoring records at 578 sites on perennial streams to describe regimes in this diverse region. Records were summarized using 34 metrics that described regime aspects related to magnitude, variation, frequency, duration, and timing. The metrics were used in a multivariate cluster analysis to classify streams into seven distinct regime types and in a principal components analysis (PCA) to examine patterns of redundancy among metrics. The PCA indicated that 2–5 orthogonal PC axes accounted for 74%–89% of the variation in thermal regimes at the monitoring sites. Most of the variation in PC scores that defined the two dominant axes was in turn predictable from a suite of geospatial covariates in multiple linear regressions that included elevation, latitude, riparian canopy density, reach slope, precipitation, lake prevalence, and dam height. Our results have parallels to previous flow regime analyses that describe the utility of small numbers of PCs or allied metrics in regime characterization, and can be used to better understand and parsimoniously represent thermal regimes in the western U.S.

中文翻译：

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美国西部多年生河流和河流的热状况

河流和河流的热力状况对水生生态系统产生了深远的影响，但由于来自广泛且具有代表性的监测网络的年度数据集的可用性有限，因此在许多地区的描述和分类都比较差。通过挖掘一个新的温度数据库，该数据库由横跨美国西部的超过23,000个站点记录组成，我们提取了多年流中578个站点的年度监测记录，以描述这一不同地区的状况。使用34个度量标准对记录进行了汇总，这些度量标准描述了与震级，变化，频率，持续时间和时序有关的制度方面。指标用于多变量聚类分析以将流分类为七个不同的方案类型，而主成分分析（PCA）则用于检查指标之间的冗余模式。PCA表明，在监测点，2-5个正交PC轴占热态变化的74％-89％。定义两个主导轴的大部分PC得分变化可通过一系列线性回归的地理空间协变量来预测，这些线性回归包括海拔，纬度，河岸冠层密度，坡度，降水，湖泊患病率和大坝高度。我们的结果与先前的流态分析相似，后者描述了少量PC或相关指标在态表征中的效用，可用于更好地理解和简化美国西部的热态。定义两个主导轴的大部分PC得分变化可通过一系列线性回归的地理空间协变量来预测，这些线性回归包括海拔，纬度，河岸冠层密度，坡度，降水，湖泊患病率和大坝高度。我们的结果与先前的流态分析相似，后者描述了少量PC或相关指标在态表征中的效用，可用于更好地理解和简化美国西部的热态。定义两个主导轴的大部分PC得分变化可通过一系列线性回归的地理空间协变量来预测，这些线性回归包括海拔，纬度，河岸冠层密度，坡度，降水，湖泊患病率和大坝高度。我们的结果与先前的流态分析相似，后者描述了少量PC或相关指标在态表征中的效用，可用于更好地理解和简化美国西部的热态。