美国太平洋西北部自由流动溪流的每日溪流温度预测

Jesse E. Siegel, A. Fullerton, A. FitzGerald, Damon M. Holzer, Chris E. Jordan
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引用次数: 1

摘要

支持可持续的激流生态系统和热栖息地需要在空间和时间上对溪流温度进行高范围和高分辨率的估计。我们结合并增强了现有流温度模型的元素,以产生一个新的统计模型来满足这一需求。与以前的模型相比,以前的模型估计了更粗略的指标,如每月或季节性的河流温度,或关注单个流域,我们为广泛的地理区域建模了整个日历年的每日河流温度。该模型反映了在广义相加模型框架中使用公开可用的气候和景观协变量的机制过程。我们允许协变量相互作用,同时考虑时间和空间协变量之间的非线性关系,以更好地捕捉季节模式。为了表示对气候敏感性的变化,我们使用了与区域标准化流量相关的可变持续时间内前期气温的移动平均值。对于以雪为主的水文河段,移动平均窗口大小较长,尤其是在流量较高的情况下,而对于以雨为主的水文河道,窗口大小相对恒定且较低。我们的模型能够捕捉融雪的随时间变化的影响,这提高了它多年来预测不同地理区域河流温度的能力。我们将该模型与1993-2013年的溪流温度进行了拟合,并预测了1990-2021年美国太平洋西北部约261200条自由流动溪流的日溪流温度。我们的每日模型拟合良好(RMSE=1.76;MAE=1.32°C)。交叉验证表明,该模型在不同景观和气候条件下的未采样位置产生了有用的预测。这些溪流温度预测将有助于自然资源从业者在激流生态系统中进行有效的保护规划,并管理太平洋鲑鱼等物种。我们的方法很简单,可以适应新的空间区域、时间段或场景,例如预计融雪量会随着气候变化而减少。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Daily stream temperature predictions for free-flowing streams in the Pacific Northwest, USA
Supporting sustainable lotic ecosystems and thermal habitats requires estimates of stream temperature that are high in scope and resolution across space and time. We combined and enhanced elements of existing stream temperature models to produce a new statistical model to address this need. Contrasting with previous models that estimated coarser metrics such as monthly or seasonal stream temperature or focused on individual watersheds, we modeled daily stream temperature across the entire calendar year for a broad geographic region. This model reflects mechanistic processes using publicly available climate and landscape covariates in a Generalized Additive Model framework. We allowed covariates to interact while accounting for nonlinear relationships between temporal and spatial covariates to better capture seasonal patterns. To represent variation in sensitivity to climate, we used a moving average of antecedent air temperatures over a variable duration linked to area-standardized streamflow. The moving average window size was longer for reaches having snow-dominated hydrology, especially at higher flows, whereas window size was relatively constant and low for reaches having rain-dominated hydrology. Our model’s ability to capture the temporally-variable impact of snowmelt improved its capacity to predict stream temperature across diverse geography for multiple years. We fit the model to stream temperatures from 1993–2013 and predicted daily stream temperatures for ~261,200 free-flowing stream reaches across the Pacific Northwest USA from 1990–2021. Our daily model fit well (RMSE = 1.76; MAE = 1.32°C). Cross-validation suggested that the model produced useful predictions at unsampled locations across diverse landscapes and climate conditions. These stream temperature predictions will be useful to natural resource practitioners for effective conservation planning in lotic ecosystems and for managing species such as Pacific salmon. Our approach is straightforward and can be adapted to new spatial regions, time periods, or scenarios such as the anticipated decline in snowmelt with climate change.
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