Quantifying stream thermal regimes at multiple scales: Combining thermal infrared imagery and stationary stream temperature data in a novel modeling framework
Quantifying stream thermal regimes at multiple scales: Combining thermal infrared imagery and stationary stream temperature data in a novel modeling framework
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量化多尺度流热状况：在新颖的建模框架中结合热红外图像和静止流温度数据
DOI：
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
G. Poole
中科院分区：
文献类型：
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作者：
Shane Vatland;R. Gresswell;G. Poole
由于河流温度在空间和时间上往往是不均匀的，因此准确地量化河流的热状态可能具有挑战性。在这项研究中，我们提出了一种新的建模框架，该框架结合了在空间或时间上连续的流温度数据集。具体来说，我们将热红外（TIR）图像的精细空间分辨率与美国MT大洞河100公里部分的10个固定温度记录仪的每小时数据合并在一起。这种组合使我们能够在夏季最温暖的部分以相对精细的空间分辨率（每~ 100米的溪流长度）在大范围的溪流（~ 100公里的溪流）中估计夏季的热条件。严格的评估，包括内部验证，从朗朗日参考框架收集的空间连续河流温度测量数据的外部验证以及敏感性分析，表明该模型能够准确估计该系统夏季河流温度的纵向模式（验证rmse < 1°C）。结果表明，夏季河流温度具有明显的时空异质性，并强调了在相对精细的时空尺度上评估热状态的价值。保存非生物流数据的时空变异和结构为理解流动流生物动态的、多尺度的栖息地需求提供了重要的基础。同样，加强对动态水质属性的时空变化的理解，包括时间序列和空间安排，可以指导监测设备的战略放置，这些设备随后将捕获与研究和管理目标直接相关的环境条件的变化。
Accurately quantifying stream thermal regimes can be challenging because stream temperatures are often spatially and temporally heterogeneous. In this study, we present a novel modeling framework that combines stream temperature data sets that are continuous in either space or time. Specifically, we merged the fine spatial resolution of thermal infrared (TIR) imagery with hourly data from 10 stationary temperature loggers in a 100 km portion of the Big Hole River, MT, USA. This combination allowed us to estimate summer thermal conditions at a relatively fine spatial resolution (every ∼100 m of stream length) over a large extent of stream (∼100 km of stream) during the warmest part of the summer. Rigorous evaluation, including internal validation, external validation with spatially continuous instream temperature measurements collected from a Langrangian frame of reference, and sensitivity analyses, suggests the model was capable of accurately estimating longitudinal patterns in summer stream temperatures for this system (validation RMSEs < 1°C). Results revealed considerable spatial and temporal heterogeneity in summer stream temperatures and highlighted the value of assessing thermal regimes at relatively fine spatial and temporal scales. Preserving spatial and temporal variability and structure in abiotic stream data provides a critical foundation for understanding the dynamic, multiscale habitat needs of mobile stream organisms. Similarly, enhanced understanding of spatial and temporal variation in dynamic water quality attributes, including temporal sequence and spatial arrangement, can guide strategic placement of monitoring equipment that will subsequently capture variation in environmental conditions directly pertinent to research and management objectives.
影响因子：
6.3
作者：
S. Krause;T. Blume;N. Cassidy
通讯作者：
S. Krause;T. Blume;N. Cassidy
影响因子：
1.8
作者：
Gudmundsdottir, Rake;Gislason, Gisli Mar;Moss, Brian
通讯作者：
Moss, Brian