Stream temperature (ST) is a key driver of water quality and ecosystem health, and the analysis and forecasting thereof benefit from the availability of high spatiotemporal resolution ST datasets. However, such datasets are limited spatially and temporally across the CONUS, particularly for small, remote streams. Available models are limited in domain (regional), spatial resolution (≥10 km), temporal resolution (monthly), or a combination thereof. We address these limitations by developing a satellite remote sensing-based spatial-statistical model, TempEst 2 (“stream TEMPerature ESTimation, version 2”), to estimate daily mean and maximum temperatures at 1 km resolution for ungaged streams of any size across the CONUS. This contrasts with an earlier version, TempEst 1, which used a random forest algorithm at monthly resolution. TempEst 2 also improves over TempEst 1 in interpretability and computational efficiency.

中文翻译：

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CONUS 尺度的每日溪流水温基于卫星的空间统计建模

溪流温度 （ST） 是水质和生态系统健康的关键驱动因素，其分析和预测受益于高时空分辨率 ST 数据集的可用性。但是，此类数据集在 CONUS 中的空间和时间上受到限制，特别是对于小型远程流。可用模型在域（区域）、空间分辨率（≥10 公里）、时间分辨率（每月）或它们的组合方面受到限制。我们通过开发基于卫星遥感的空间统计模型 TempEst 2（“stream TEMPerature ESTimation，第 2 版”）来解决这些限制，以 1 公里分辨率估计整个 CONUS 中任何大小的未覆盖溪流的日平均和最高温度。这与早期版本的 TempEst 1 形成鲜明对比，后者使用每月分辨率的随机森林算法。TempEst 2 在可解释性和计算效率方面也比 TempEst 1 有所提高。