Technical note: Fourier approach for estimating the thermal attributes of streams
Temperature models that directly predict ecologically important thermal attributes across spatiotemporal scales are still poorly developed. This study developed an analytical method based on Fourier analysis to estimate seasonal and diel periodicities, as well as irregularities in stream temperature...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2016-08-01
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| Series: | Hydrology and Earth System Sciences |
| Online Access: | http://www.hydrol-earth-syst-sci.net/20/3411/2016/hess-20-3411-2016.pdf |
| Summary: | Temperature models that directly predict
ecologically important thermal attributes across spatiotemporal scales are
still poorly developed. This study developed an analytical method based on
Fourier analysis to estimate seasonal and diel periodicities, as well as
irregularities in stream temperature, at data-poor sites. The method
extrapolates thermal attributes from highly resolved temperature data at a
reference site to the data-poor sites on the assumption of spatial
autocorrelation. We first quantified the thermal attributes of a glacier-fed
stream in the Swiss Alps using 2 years of hourly recorded temperature. Our
approach decomposed stream temperature into its average temperature of 3.8 °C,
a diel periodicity of 4.9 °C, seasonal periodicity
spanning 7.5 °C, and the remaining irregularity (variance) with
an average of 0.0 °C but spanning 9.7 °C. These
attributes were used to estimate thermal characteristics at upstream sites
where temperatures were measured monthly, and we found that a diel
periodicity and the variance strongly contributed to the variability at the
sites. We evaluated the performance of our predictive mechanism and found
that our approach can reasonably estimate periodic components and extremes.
We could also estimate the variability in irregularity, which cannot be
represented by other techniques that assume a linear relationship in
temperature variabilities between sites. The results confirm that spatially
extrapolating thermal attributes based on Fourier analysis can predict
thermal characteristics at a data-poor site. The R scripts used in this
study are available in the Supplement. |
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| ISSN: | 1027-5606 1607-7938 |