Can air temperature be used to project influences of climate change on stream temperature?
Can air temperature be used to project influences of climate change on stream temperature?
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DOI：
10.1088/1748-9326/9/8/084015
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发表时间：
2014-08-01
影响因子：
6.7
通讯作者：
Johnson, Sherri L.
中科院分区：
文献类型：
--
作者：
Arismendi, Ivan;Safeeq, Mohammad;Johnson, Sherri L.
在世界范围内，缺乏数据流温度的动机，使用基于回归的统计模型来预测流温度的基础上更广泛的可用数据的空气温度。这些模型已被广泛应用于预测气候变化下河流温度的响应，但这些模型的性能尚未得到充分评价。为了解决这一知识差距，我们研究了两种广泛使用的线性和非线性回归模型的性能，这些模型根据空气温度预测气流温度。我们使用11-44年的流温数据评估了一系列调节和非调节流中的模型性能和模型参数的时间稳定性。虽然这种模型在预测与用于开发它们的数据相对应的时间跨度内的流温度时可能具有有效性，但是模型预测不能很好地转移到其他时间段。验证模型预测最近的流温度，空气温度流温度的关系，从以前的时间段的基础上往往表现出较差的性能时，与观察到的流温度。总体而言，模型预测不太稳健的调节流，他们经常无法检测到所有网站内的最冷和最热的温度。在许多情况下，这些预测的误差幅度福尔斯在一个范围内，等于或超过未来预测的幅度与气候有关的变化，流温度报告的地区，我们研究（0.5和3.0摄氏度之间，到2080年）。基于回归的统计模型准确预测河流温度随时间变化的能力有限，这可能源于这样一个事实，即起作用的基本过程，即空气和水的热收支，在每种介质中都是独特的，并且在不同的地方和不同的时间。
Worldwide, lack of data on stream temperature has motivated the use of regression-based statistical models to predict stream temperatures based on more widely available data on air temperatures. Such models have been widely applied to project responses of stream temperatures under climate change, but the performance of these models has not been fully evaluated. To address this knowledge gap, we examined the performance of two widely used linear and nonlinear regression models that predict stream temperatures based on air temperatures. We evaluated model performance and temporal stability of model parameters in a suite of regulated and unregulated streams with 11-44 years of stream temperature data. Although such models may have validity when predicting stream temperatures within the span of time that corresponds to the data used to develop them, model predictions did not transfer well to other time periods. Validation of model predictions of most recent stream temperatures, based on air temperature-stream temperature relationships from previous time periods often showed poor performance when compared with observed stream temperatures. Overall, model predictions were less robust in regulated streams and they frequently failed in detecting the coldest and warmest temperatures within all sites. In many cases, the magnitude of errors in these predictions falls within a range that equals or exceeds the magnitude of future projections of climate-related changes in stream temperatures reported for the region we studied (between 0.5 and 3.0 degrees C by 2080). The limited ability of regression-based statistical models to accurately project stream temperatures over time likely stems from the fact that underlying processes at play, namely the heat budgets of air and water, are distinctive in each medium and vary among localities and through time.