Details:

Journal Title:
Hydrology and Earth System Sciences
Personal Author:
Zhang, Qian ; Harman, Ciaran J. ; Kirchner, James W.
Zhang, Qian ; Harman, Ciaran J. ; Kirchner, James W. Less -
NOAA Program & Office:
OAR (Oceanic and Atmospheric Research) ; Sea Grant
OAR (Oceanic and Atmospheric Research) ; Sea Grant Less -
Sea Grant Program:
MDU (Maryland Sea Grant)
Description:
River water-quality time series often exhibit fractal scaling, which here refers to autocorrelation that decays as a power law over some range of scales. Fractal scaling presents challenges to the identification of deterministic trends because (1) fractal scaling has the potential to lead to false inference about the statistical significance of trends and (2) the abundance of irregularly spaced data in water-quality monitoring networks complicates efforts to quantify fractal scaling. Traditional methods for estimating fractal scaling – in the form of spectral slope (β) or other equivalent scaling parameters (e.g., Hurst exponent) – are generally inapplicable to irregularly sampled data. Here we consider two types of estimation approaches for irregularly sampled data and evaluate their performance using synthetic time series. These time series were generated such that (1) they exhibit a wide range of prescribed fractal scaling behaviors, ranging from white noise (β  =  0) to Brown noise (β  =  2) and (2) their sampling gap intervals mimic the sampling irregularity (as quantified by both the skewness and mean of gap-interval lengths) in real water-quality data. The results suggest that none of the existing methods fully account for the effects of sampling irregularity on β estimation. First, the results illustrate the danger of using interpolation for gap filling when examining autocorrelation, as the interpolation methods consistently underestimate or overestimate β under a wide range of prescribed β values and gap distributions. Second, the widely used Lomb–Scargle spectral method also consistently underestimates β. A previously published modified form, using only the lowest 5 % of the frequencies for spectral slope estimation, has very poor precision, although the overall bias is small. Third, a recent wavelet-based method, coupled with an aliasing filter, generally has the smallest bias and root-mean-squared error among all methods for a wide range of prescribed β values and gap distributions. The aliasing method, however, does not itself account for sampling irregularity, and this introduces some bias in the result. Nonetheless, the wavelet method is recommended for estimating β in irregular time series until improved methods are developed. Finally, all methods' performances depend strongly on the sampling irregularity, highlighting that the accuracy and precision of each method are data specific. Accurately quantifying the strength of fractal scaling in irregular water-quality time series remains an unresolved challenge for the hydrologic community and for other disciplines that must grapple with irregular sampling.
Keywords:
[+]
Rivers Water Quality
Source:
Hydrol. Earth Syst. Sci., 22, 1175–1192
DOI:
https://doi.org/10.5194/hess-22-1175-2018
Sea Grant Document Number:
MDU-R-18-002
Document Type:
Journal Article
Funding:
Grant no. NA10OAR4170072 ; Grant no. NA14OAR1470090
Grant no. NA10OAR4170072 ; Grant no. NA14OAR1470090 Less -
Rights Information:
CC BY
Compliance:
Library
Main Document Checksum:
[+]
urn:sha256:48ab00b386c6d56f45ef19442a11982ecbdd3c5cabcd35339aefff6b6c48dba6
Download URL:
https://repository.library.noaa.gov/view/noaa/40051/noaa_40051_DS1.pdf
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