Can air temperature be used to project influences of climate change on stream temperature?

Abstract

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.

Waning habitats due to climate change: the effects of changes in streamflow and temperature at the rear edge of the distribution of a cold-water fish

“…Their main objections to regressive methods arose when modelling reaches of regulated rivers, but this is not our case. In addition, our model improves the models that were tested in both studies (Arismendi et al, 2014;Piccolroaz et al, 2016). Performance indicators of our models produce good results, showing that the models are sufficiently competent.…”

“…Arismendi et al (2014) hold that regression models based on air temperature can be inadequate for projecting future stream temperatures because they are only surrogates for air temperature, whereas Piccolroaz et al (2016) argued that the adequacy depends on the hydrological regime, type of model and the timescale analysis. Their main objections to regressive methods arose when modelling reaches of regulated rivers, but this is not our case.…”

“…Regression models are substantially site-specific compared to deterministic approaches (Arismendi et al, 2014). However, the parameters of these regression approaches are still physically meaningful, and these models require fewer variables that can limit the applicability of more complex models in areas where data are scarce.…”

Simulation of the hydrodynamic behaviour of a Mediterranean reservoir under different climate change and management scenarios

“…Although water-air temperature regressions have often been used to predict climate change effects on freshwater temperatures (Mohseni et al, 1999;Rübbelke and Vögele, 2011), Arismendi et al (2014) cautioned against their use to predict temperatures outside the calibration period. To test the validity of using regressions, we compared the regression predictions to those of the processbased model CALNAT (Gosse et al, 2008).…”

Can riparian vegetation shade mitigate the expected rise in stream temperatures due to climate change during heat waves in a human-impacted pre-alpine river?

“…air temperature, water temperature of the previous days or streamflow). Use of air temperature as a surrogate for future water temperature can lead to errors when linear (Erickson and Stefan, 2000;Webb and Nobilis, 1997) or non-linear (Mohseni et al, 1998) regression models are applied (Arismendi et al, 2014). Stochastic models used to determine the long-term annual component of temperatures and their short-term residuals separately yield good results (Caissie et al, 2001).…”