Spatial relationships between soil moisture patterns and topographic variables at multiple scales in a humid temperate forested catchment

Genevieve A. Ali, Andre G. Roy, Pierre Legendre

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32 Citations (Scopus)


New tools are needed in hydrology to improve our understanding of process heterogeneity and its relationship to catchment topography. We tested the distance-based Moran's eigenvector maps (DBMEM) method, which models patterns using a combination of positively and negatively autocorrelated structures, searching for soil moisture characteristic scales in a temperate humid forested system. We focused on three questions: (1) What are the characteristic spatial scales of shallow soil moisture? (2) Is there a strong relationship between soil moisture patterns and topographic variables at these scales? and (3) Which hydro-meteorological variables influence soil moisture scales and topographic controls in a significant way? Data consisted of 16 surveys of soil moisture at depths of 5, 15, 30, and 45 cm in the 5.1 ha Hermine catchment (Laurentians, Canada). The global DBMEM model explained 21 to 96% (adjusted R square) of the spatial variations in soil moisture apportioned into decreasing fractions over six spatially nested, additive submodels: very large (0.85-1.4 ha), large (0.54-0.85 ha), meso (0.50-0.54 ha), fine positive (0.22-0.50 ha), fine negative (0.10-0.22 ha), and very fine (0.02-0.10 ha). The effects of catchment topography (e. g., slope and contributing area) on soil moisture were significant at large and very large scales. Moisture patterns at these scales were dependent on previous storm properties and were good predictors of catchment response. The DBMEM approach provided insightful quantitative evidence regarding the temporal dependency of the relationships between dynamic soil moisture content and static topographic variables across scales.

Original languageEnglish
Article numberW10526
Number of pages17
JournalWater Resources Research
Publication statusPublished - 15 Oct 2010


  • neighbor matrices PCNM
  • ecological data
  • wetness index
  • water content
  • hydrology
  • field
  • variability

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