Journal metrics

Journal metrics

  • IF value: 4.256 IF 4.256
  • IF 5-year value: 4.819 IF 5-year 4.819
  • CiteScore value: 4.10 CiteScore 4.10
  • SNIP value: 1.412 SNIP 1.412
  • SJR value: 2.023 SJR 2.023
  • IPP value: 3.97 IPP 3.97
  • h5-index value: 58 h5-index 58
  • Scimago H index value: 99 Scimago H index 99
Volume 20, issue 8 | Copyright
Hydrol. Earth Syst. Sci., 20, 3411-3418, 2016
https://doi.org/10.5194/hess-20-3411-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Technical note 24 Aug 2016

Technical note | 24 Aug 2016

Technical note: Fourier approach for estimating the thermal attributes of streams

Masahiro Ryo1,2,3, Marie Leys1,3, and Christopher T. Robinson1,3 Masahiro Ryo et al.
  • 1Department of Aquatic Ecology, EAWAG, 8600 Duebendorf, Switzerland
  • 2Department of Civil Engineering, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, 152-0033 Tokyo, Japan
  • 3Institute of Integrative Biology, ETH-Zürich, 8092 Zürich, Switzerland

Abstract. 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.

Download & links
Publications Copernicus
Download
Short summary
We developed an analytical method to estimate thermal attributes (seasonal and diel periodicities as well as irregularities) in stream temperature at data-poor sites. We extrapolated the thermal attributes of a glacier-fed stream in the Swiss Alps using 2 years of hourly recorded temperature to the data-poor sites. The R scripts used in this study are available in the Supplement.
We developed an analytical method to estimate thermal attributes (seasonal and diel...
Citation
Share