Temporal scaling phenomena in groundwater-floodplain systems using robust detrended fluctuation analysis

Habib, Abrar; Sorensen, James P.R.; Bloomfield, John P. ORCID: https://orcid.org/0000-0002-5730-1723; Muchan, Katie; Newell, Andrew J.; Butler, Adrian P.. 2017 Temporal scaling phenomena in groundwater-floodplain systems using robust detrended fluctuation analysis. Journal of Hydrology, 549. 715-730. https://doi.org/10.1016/j.jhydrol.2017.04.034

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Official URL: https://doi.org/10.1016/j.jhydrol.2017.04.034

Abstract/Summary

In order to determine objectively the fractal behaviour of a time series, and to facilitate potential future attempts to assess model performance by incorporating fractal behaviour, a multi-order robust detrended fluctuation analysis (r-DFAn) procedure is developed herein. The r-DFAn procedure allows for robust and automated quantification of mono-fractal behaviour. The fractal behaviour is quantified with three parts: a global scaling exponent, crossovers, and local scaling exponents. The robustness of the r-DFAn procedure is established by the systematic use of robust regression, piecewise linear regression, Analysis of Covariance (ANCOVA) and Multiple Comparison Procedure to determine statistically significant scaling exponents and optimum crossover locations. The MATLAB code implementing the r-DFAn procedure has also been open sourced to enable reproducible results. r-DFAn will be illustrated on a synthetic signal after which is used to analyse high-resolution hydrologic data; although the r-DFAn procedure is not limited to hydrological or geophysical time series. The hydrological data are 4 year-long datasets (January 2012 to January 2016) of 1-min groundwater level, river stage, groundwater and river temperature, and 15-min precipitation and air temperature, at Wallingford, UK. The datasets are analysed in both time and fractal domains. The study area is a shallow riparian aquifer in hydraulic connection to River Thames, which traverses the site. The unusually high resolution datasets, along with the responsive nature of the aquifer, enable detailed examination of the various data and their interconnections in both time- and fractal-domains.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1016/j.jhydrol.2017.04.034
UKCEH and CEH Sections/Science Areas:	Rees (from October 2014)
ISSN:	0022-1694
Additional Keywords:	GroundwaterBGS, Groundwater, Aquifer characterisation, Catchment processes, Groundwater monitoring, Surface water interaction, robust detrended fluctuation analysis, detrended fluctuation analysis, fractal behaviour, Hurst phenomenon, time series analysis, high resolution hydrological data
NORA Subject Terms:	Hydrology
Date made live:	26 Jun 2017 12:37 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/517217

Item Type:

Publication - Article

Digital Object Identifier (DOI):

https://doi.org/10.1016/j.jhydrol.2017.04.034

UKCEH and CEH Sections/Science Areas:

Rees (from October 2014)

ISSN:

0022-1694

Additional Keywords:

GroundwaterBGS, Groundwater, Aquifer characterisation, Catchment processes, Groundwater monitoring, Surface water interaction, robust detrended fluctuation analysis, detrended fluctuation analysis, fractal behaviour, Hurst phenomenon, time series analysis, high resolution hydrological data

NORA Subject Terms:

Hydrology

Date made live:

26 Jun 2017 12:37 +0 (UTC)

URI:

https://nora.nerc.ac.uk/id/eprint/517217