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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/hess-2018-601
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2018-601
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Technical note 07 Dec 2018

Technical note | 07 Dec 2018

Review status
This discussion paper is a preprint. It has been under review for the journal Hydrology and Earth System Sciences (HESS). The manuscript was not accepted for further review after discussion.

Technical note: Decomposing a time series into independent trend, seasonal and random components

Dongqin Yin1,2, Hannah Slatford1, and Michael L. Roderick1,3 Dongqin Yin et al.
  • 1Research School of Earth Sciences, Australian National University, Canberra, ACT 0200, Australia
  • 2Australian Research Council Centre of Excellence for Climate System Science, Canberra, Australia
  • 3Australian Research Council Centre of Excellence for Climate Extremes, Canberra, Australia

Abstract. Many time series observations in hydrology and climate show large seasonal variations and it has long been common practice to separate the original data into trend, seasonal and random components. We were interested in using that decomposition approach as a basis for understanding variability in hydro-climatic time series. For that purpose, it is desirable that the trend, seasonal and random components are independent so that the variance of the original time series equals the sum of the variances of the three components. We show that the resulting decomposition with the trend component traditionally estimated either as a linear trend or a moving average does not produce components that are independent. Instead we introduce the rarely adopted two-way ANOVA model into studies of hydro-climatic variability and define the trend as equal to the annual anomaly. This traditional approach produces a decomposition with three independent components. We then use global land precipitation data to demonstrate a simple application showing how this decomposition method can be used as a basis for comparing hydro-climatic variability. We anticipate that the three-part decomposition based on the two-way ANOVA approach will prove useful for future applications that seek to understand the space-time dimensions of hydro-climatic variability.

Dongqin Yin et al.
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Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
Interactive discussion
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
Dongqin Yin et al.
Data sets

Updated high-resolution grids of monthly climatic observations–the CRU TS3. 10 Dataset I. Harris, P. D. Jones, T. J. Osborn, and D. H. Lister https://doi.org/10.1002/joc.3711

Dongqin Yin et al.
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Short summary
We focused on seeking for a decomposition approach which can produce independent decomposed components as a basis for understanding variability in hydro-climatic time series. We find that the rarely adopted two-way ANOVA model in hydro-climatic variability (rather than the traditional methods using linear trend or moving average) will produce independent components. This method is further applied to explore the variability in global precipitation and is expected to be useful for other variables.
We focused on seeking for a decomposition approach which can produce independent decomposed...
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