Abstract

[1] In a recent paper, Famiglietti et al. [2008] analyzed more than 36,000 ground-based soil moisture measurements to characterize soil moisture variability across spatial scales ranging from 2.5 m to 50 km. They concluded that the relationship between soil moisture standard deviation versus mean moisture content, sq (hqi), has a convex upward behavior with maximum values occurring at mean moisture contents of 0.17 cm cm 3 and 0.19 cm cm 3 for the 800-m and 50-km scale, respectively. On the basis of these data, they derived empirical relationships between the coefficient of variation and the mean soil moisture content in order to estimate the uncertainty in field observations of mean moisture content. The authors are to be commended for providing this valuable database to the scientific community. We agree with the authors that such data are important in improving our understanding about the importance of subgrid moisture variability in the parameterization and simulation of land surface processes. However, the authors limited themselves to an empirical description of the observed data by fitting exponential relationships to the mean moisture content versus coefficient of variation (CV) data. We feel that this is a missed opportunity and would like to argue that an interpretation based on established theories and concepts in soil hydrology and upscaling theories could provide alternative methods and new insights for interpreting such data sets. Specifically, it can be shown from soil physical concepts that for a homogeneous soil, the shape of the moisture retention curve can largely explain observed variations in surface soil moisture, at any specific observation scale. For heterogeneous soils, stochastic upscaling theories may be used to relate sq (hqi) to spatial variability in soil hydraulic properties. These theories can be used to predict sq (hqi) and to examine the sensitivity of this function with respect to soil hydraulic properties.

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