Elsevier

Journal of Hydrology

Volume 516, 4 August 2014, Pages 154-160
Journal of Hydrology

Effect of soil hydraulic properties on the relationship between the spatial mean and variability of soil moisture

https://doi.org/10.1016/j.jhydrol.2014.01.069Get rights and content

Highlights

  • There exists a potential for estimating soil hydraulic properties from soil moisture data.

  • van Genuchten’s n parameter strongly related to the dry part of soil moisture spatial variability relationship.

  • Ks variability influences the n parameter effect on soil moisture variability.

  • Climate affects maximum moisture variability but not its corresponding average.

Summary

Knowledge of spatial mean soil moisture and its variability over time is needed in many environmental applications. We analyzed dependencies of soil moisture variability on average soil moisture contents in soils with and without root water uptake using ensembles of non-stationary water flow simulations by varying soil hydraulic properties under different climatic conditions. We focused on the dry end of the soil moisture range and found that the magnitude of soil moisture variability was controlled by the interplay of soil hydraulic properties and climate. The average moisture at which the maximum variability occurred depended on soil hydraulic properties and vegetation. A positive linear relationship was observed between mean soil moisture and its standard deviation and was controlled by the parameter defining the shape of soil water retention curves and the spatial variability of saturated hydraulic conductivity. The influence of other controls, such as variable weather patterns, topography or lateral flow processes needs to be studied further to see if such relationship persists and could be used for the inference of soil hydraulic properties from the spatiotemporal variation in soil moisture.

Introduction

Understanding topsoil water content variability is critical for improving the performance of hydrologic and atmospheric models and for up- and down-scaling remotely sensed soil moisture (Vereecken et al., 2008). Surface soil moisture variability has been shown to be related with spatially-averaged soil moisture content and that has been demonstrated at different scales (Choi et al., 2007, Famiglietti et al., 2008, Famiglietti et al., 1999, Martinez-Fernández and Ceballos, 2003, Mittelbach and Seneviratne, 2012, Rosenbaum et al., 2012, Teuling and Troch, 2005, Vereecken et al., 2007).

Soil water content spatial variability was shown to be affected by several local and non-local factors (Grayson et al., 1997). Such controls are: vegetation (Teuling and Troch, 2005), climate (Teuling et al., 2007a), soil hydraulic properties (Vereecken et al., 2007), topography (Grayson et al., 1997) and antecedent soil moisture (Ivanov et al., 2010). Contradictory reports have been published on the shape of the relationship between the spatial mean soil moisture (〈θ〉) and its variability (σθ). Works can be found that report an increasing variability with decreasing mean moisture (Famiglietti et al., 1999), decreasing variability with decreasing mean moisture (Martinez-Fernández and Ceballos, 2003) and an increase up to a certain value of 〈θ〉 followed by a decrease (Brocca et al., 2010, Brocca et al., 2012, Rosenbaum et al., 2012). The range of soil moisture measured in each case (dry or wet states or the full range of soil moisture) can be one of reasons for such differences. The body of literature that addressed this topic for more than a decade (from Famiglietti et al., 1998 to Rosenbaum et al., 2012) generally shows that the graph of this relationship is typically convex (Choi et al., 2007, Rosenbaum et al., 2012, Teuling and Troch, 2005). Regression models for the ‘σθ–〈θ (referred as σθ from here after) relationship have been proposed, including an exponential model (Famiglietti et al., 2008), a third-order polynomial (Rosenbaum et al., 2012) and a linear equation for the dry-end (Teuling et al., 2007b).

Soil properties, and more specifically soil hydraulic properties-related parameters, often had the largest influence on the variability of soil moisture (Choi et al., 2007). The dependence of the standard deviation of soil moisture σθ on average soil moisture as affected by soil hydraulic properties was previously studied by Vereecken et al. (2007) using an analytical solution of a stochastic steady state flow model. They used the Brooks-Corey moisture retention characteristic parameters, the saturated hydraulic conductivity and joint-Gaussian spatial distribution of hydraulic parameters with exponential covariance functions and negligible correlation between the hydraulic parameters. They found that the mean water content at which the standard deviation became maximal depended on the shape parameters of the moisture retention characteristic. More specifically, on the parameter describing the pore-size distribution of soils. This work was based on results of the work of Zhang et al. (1998), which stemmed from strong assumptions of stationary flow, gravity-dominated flow, and spatial autocorrelation of parameters. These assumptions are hardly appicable to dry conditions, and using modeling of non-stationary flow with evaporation dominating most of the time may provide more realistic information about soil moisture variability in time and space. In dry conditions, there exists a decoupling of the rate of drying and the vertical profiles of soil moisture in the topsoil (Capehart and Carlson, 1997). This type of conditions are predominant in arid and semiarid conditions.

The objective of this work was to examine the effects of soil texture and climate in the σθ for a non-stationary flow model framework. We provide also an explanation to the differences observed in the literature regarding the positive or negative relationship between σθ and 〈θ〉. Finally, we show that the linearization of the dry part of the relationship σθ may be useful to evaluate and estimate soil hydraulic properties and more specifically the spatial variability of Ks and the parameter “n” that measures the pore-size distribution in the van-Genuchten model.

Section snippets

Simulations setup

We used the HYDRUS code (Šimůnek and van Genuchten, 2008) to simulate water flow by solving the Richard equation numerically. Time-dependent atmospheric boundary conditions were imposed at the soil surface and a constant head boundary condition was imposed at the bottom of a 3-m depth profile. The Initial condition was obtained from a spin up model run of 1 year. Simulations were performed in a 1-D soil profile with homogeneous properties. The profile was deep enough to make the soil moisture of

Results and discussion

The typical convex shape of σθ shown in some of the field data of Brocca et al., 2012, Rosenbaum et al., 2012 could be observed by running a non-stationary flow model with an ensemble of variable Ks in most of the soils studied (Fig. 2). A clear peak could not be seen with the LS and SL textures as previously reported for the steady-state flow case (Vereecken et al., 2007) The textures with a high percentage of sand show a linear increase of σθ with increasing 〈θ〉 where no peak could be

Conclusion

We reproduced the shape of σθ presented in several field studies by running ensemble simulations of a non-stationary flow model and variable soil hydraulic parameters and climate conditions. For bare soil conditions we were able to show the effect of σlnKs on the slope of the σθ relationship in its dry end and on the maximum value of σθ. In vegetated soils, root water uptake is a factor that affects the σθ relationship giving flatter shapes than the bare soil case and increased the maximum σθ.

Acknowledgements

This study was partially supported by US Department of Agriculture and US Nuclear Regulatory Commission Interagency Agreement IAA-NRC-05-005 on “Model Abstraction Techniques to Simulate Transport in Soils”. The first author wishes to thank the Spanish Ministry of Education for the mobility Grant EX2009-0433. The authors would like to thank the valuable and useful contribution of two anonymous referees.

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