Revealing Multi-Variate And Multi-Scale Controls Of Soil Water

Bing Si1, Wei Hu1 and Asim Biswas2, (1)University of Saskatchewan, Saskatoon, SK, Canada, (2)McGill University, Montreal, QC, Canada

Contact First Author: Bing Si; bing.si@usask.ca

Previously Published Material: Part of the presentation was given at Soil Science Society of America Annual meeting in 2014 and some material was submitted to WRR.

Abstract ID#: 36241

 

English Abstract:
Soil water varies spatially and temporally within the landscape. The variability is controlled by a complex suite of environmental factors and processes acting in different intensities over a variety of scales. While modeling approach has made progresses in this aspect, but its application has been hindered by the lack of detailed measurement of soil hydraulic properties and biophysical properties of land surface. There is a need for developing new statistical methods for revealing this multivariate, nonstationary, potentially nonlinear variability and its dominant controls at multiple scales in the landscapes. The objective of this study is to summarize recent progresses in methodology and a few recent applications of the methods on understanding of the soil water dynamics and the underlying processes causing the variability. Soil water storage (SWS) was measured down to 1.4 m (0.2 m depth interval) at 128 regularly spaced locations along a transect over five years from the Hummocky landscape of central Canada. The locations and the scales of the most persistent spatial patterns over time and depth were quantified using the wavelet coherency. The variability in SWS spatial patterns was controlled by different factors at different scales. Scale specific dominant controls were identified after using the Hilbert-Huang transform, structural equation model and multivariate wavelet coherency. The large scale macro-topographical control and medium scale landform control were much stronger than very large scale soil textural control on SWS. The scale-specific relationship with controlling factors improved the prediction of SWS.