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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
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Volume 23, issue 4
Nonlin. Processes Geophys., 23, 269–284, 2016
https://doi.org/10.5194/npg-23-269-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: Multifractal analysis in soil systems

Nonlin. Processes Geophys., 23, 269–284, 2016
https://doi.org/10.5194/npg-23-269-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 15 Aug 2016

Research article | 15 Aug 2016

Fractal behavior of soil water storage at multiple depths

Wenjun Ji1, Mi Lin1, Asim Biswas1, Bing C. Si2, Henry W. Chau3, and Hamish P. Cresswell4 Wenjun Ji et al.
  • 1Department of Natural Resource Sciences, McGill University, 21111 Lakeshore Road, Ste-Anne-de-Bellevue, Québec, H9X3V9, Canada
  • 2Department of Soil Science, University of Saskatchewan, Saskatoon, Saskatchewan, S7N5A8, Canada
  • 3Department of Soil and Physical Sciences, Lincoln University, P.O. Box 85084, Lincoln, 7647 Christchurch, New Zealand
  • 4CSIRO Land and Water, 2601 Canberra, ACT, Australia

Abstract. Spatiotemporal behavior of soil water is essential to understand the science of hydrodynamics. Data intensive measurement of surface soil water using remote sensing has established that the spatial variability of soil water can be described using the principle of self-similarity (scaling properties) or fractal theory. This information can be used in determining land management practices provided the surface scaling properties are kept at deep layers. The current study examined the scaling properties of sub-surface soil water and their relationship to surface soil water, thereby serving as supporting information for plant root and vadose zone models. Soil water storage (SWS) down to 1.4 m depth at seven equal intervals was measured along a transect of 576 m for 5 years in Saskatchewan. The surface SWS showed multifractal nature only during the wet period (from snowmelt until mid- to late June) indicating the need for multiple scaling indices in transferring soil water variability information over multiple scales. However, with increasing depth, the SWS became monofractal in nature indicating the need for a single scaling index to upscale/downscale soil water variability information. In contrast, all soil layers during the dry period (from late June to the end of the growing season in early November) were monofractal in nature, probably resulting from the high evapotranspirative demand of the growing vegetation that surpassed other effects. This strong similarity between the scaling properties at the surface layer and deep layers provides the possibility of inferring about the whole profile soil water dynamics using the scaling properties of the easy-to-measure surface SWS data.

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We measure soil water at points (point scale) and try to understand how they vary over the landscape. Previous studies identified a statistical relationship between these scales only at the surface and not at depths. This study found that the relationship stands at different depths. The relationship was very similar at different depths in drier season or in late summer and fall. A less similar relationship was observed between surface and subsurface layers in spring or in wetter seasons.
We measure soil water at points (point scale) and try to understand how they vary over the...
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