Can air temperature be used to project influences of climate change on stream temperature?
By I. Arismendi, M. Safeeq, J. B. Dunham, S. Johnson. 2014
165 citations recorded by the source corpus.
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 °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.
Subfields: Environmental Engineering, Nature and Landscape Conservation, Water Science and Technology
View Paper: https://www.semanticscholar.org/paper/3136f5281b1c9a70184410b71b321590dce4d67b
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Environmental Engineering
In Nature and Landscape Conservation
In Water Science and Technology
Citations in the corpus
- A hybrid model for river water temperature as a function of air temperature and discharge (2015, methodology): “In the end, we were able to address the main question raised by Arismendi et al (2014) by showing that air temperature can be satisfactorily used as the sole input if the model is suitably designed (as in the 5-parameter version), and that the role of discharge becomes influential when snowmelt…”