Modeling the impacts of soil hydraulic properties on temporal stability of soil moisture under a semi-arid climate
Introduction
Soil moisture is a key state variable that is linked to a range of hydrological, ecological, climatic, and geological processes. At regional and global scales, soil moisture affects precipitation and land evapotranspiration (Eltahir, 1998, Koster et al., 2004, Jung et al., 2010). At catchment and field scales, soil moisture partly controls the partitioning of precipitation and net radiation, evapotranspiration, groundwater recharge, and subsurface solute transport (Robinson et al., 2008, Vereecken et al., 2008). Meanwhile, soil moisture has also been used for validating spatially distributed land surface and hydrological models (Houser et al., 1998, Nijssen et al., 2001, Vereecken et al., 2008).
As a result of complex interactions among different controlling factors and processes (e.g., soil, topography, vegetation, and climate), soil moisture exhibits significant spatial and temporal variability, which presents a great challenge for utilizing soil moisture data for different research and application purposes. To overcome this issue, there has been a rising interest in studying the temporal stability of soil moisture (TS SM). As first introduced by Vachaud et al. (1985), the phenomenon of TS SM has been widely observed in field experiments across various spatial (from tens of m2 to thousands of km2) and temporal (from days to years) scales (see the review by Vanderlinden et al. (2012)). By examining soil moisture data collected from three field plots, Vachaud et al. (1985) showed that soil moisture contents at certain locations were closer to and thus more representative of the areal average moisture condition, while other locations exhibited consistently either wetter or drier conditions compared to the areal average. Moreover, Vachaud et al. (1985) found a temporal persistence in the spatial pattern of soil moisture. Since the seminal work of Vachaud et al. (1985), a large body of studies have been generated on the topic of TS SM for different research and application purposes, such as identifying representative locations, optimizing monitoring schemes, filling missing data, scaling soil moisture contents, improving the performance of hydrological models, and delineating water management zones (e.g., Grayson and Western, 1998, Cosh et al., 2004, Jacobs et al., 2004, Pachepsky et al., 2005, Starr, 2005, Guber et al., 2008, Brocca et al., 2010).
Depending on local conditions, TS SM has been shown to be related to soil properties and depth, topography, vegetation, and moisture state (Vachaud et al., 1985, Grayson and Western, 1998, Gomez-Plaza et al., 2000, Mohanty and Skaggs, 2001, Martinez-Fernandez and Ceballos, 2003, Jacobs et al., 2004, Hu et al., 2010, Heathman et al., 2012, Zhang and Shao, 2013). However, previous field studies also led to some contradictory findings about the controlling factors on TS SM. For instance, Jacobs et al. (2004) found a positive correlation between TS SM and clay contents; whereas, Mohanty and Skaggs (2001) showed that TS SM was more pronounced in coarser soils. Moreover, there is still a debate as to whether TS SM is higher at dry conditions or wet ones (Gomez-Plaza et al., 2000, Martinez-Fernandez and Ceballos, 2003, Zhao et al., 2010, Jia et al., 2013, Zhang and Shao, 2013). With limited observational data, those contradictory results stem mainly from the lack of understandings of the factors and processes that control TS SM at different temporal and spatial scales (Vanderlinden et al., 2012).
To further understand and more importantly quantify those controlling factors on TS SM, numerical models may offer an alternative way to complement field studies. Modeling approaches have been long used for studying spatial variability of soil moisture fields (Entekhabi and Rodriguez-Iturbe, 1994, Teuling and Troch, 2005, Ivanov et al., 2010). By comparison, modeling studies on TS SM are still very limited (Martinez et al., 2013, Martinez et al., 2014). By employing a 1-D vadose zone model to simulate soil moisture dynamics, Martinez et al. (2013) attempted to quantify the impact of soil saturated hydraulic conductivity (KS) on TS SM along with the consideration of root water uptake, and found a negative linear relationship between the mean relative difference of soil moisture and lnKS. The same modeling approach was also used by Martinez et al. (2014) to further examine the impact of KS on TS SM under different climate regimes. For both studies, log-normal distributions of KS were generated to represent the spatial variability in soil hydraulic properties. However, as also noticed by Martinez et al. (2013), the simulated patterns of TS SM deviated from commonly observed patterns of TS SM in field studies. It was most likely due to the simplistic approach used to generate log-normally distributed KS for representing spatial variations in soil hydraulic properties. In reality, other soil hydraulic properties also vary in space, which may lead to spatial and temporal variability in soil moisture. Moreover, in a modeling study by Wang et al. (2009a), KS was shown to be less important in controlling groundwater recharge and actual evapotranspiration, compared to the shape factors in soil water retention and hydraulic conductivity functions, indicating a minor role of KS in affecting soil moisture dynamics and thus TS SM.
Given the potential use of modeling approaches for resolving the existing contradictory findings about TS SM, there is still a need to use more realistic combinations of soil hydraulic parameters for examining the effects of soil hydraulic properties on TS SM. To this end, the soil dataset generated by Wang et al. (2009a) was used for the simulations of soil moisture dynamics. The soil dataset contained correlated soil hydraulic parameters for sandy soils. The main objectives of this study were to (1) examine whether more reasonable patterns of TS SM could be generated using the soil dataset of Wang et al. (2009a), (2) assess the impacts of different soil hydraulic parameters on TS SM, and (3) probe the possibility of using modeling approaches to explain some of the observed TS SM patterns for sandy soils in field experiments.
Section snippets
Simulation of soil moisture dynamics
A commonly used vadose zone model, Hydrus-1D (Simunek et al., 2005) was selected in this study for simulating soil moisture dynamics, as the accuracy of its numerical algorithm has been tested by analytical solutions (Zlotnik et al., 2007). The Hydrus-1D model simulates soil moisture movement in vadose zone by solving 1-D Richards’ equation:where θ [L3/L3] is volumetric soil moisture content, t [T] is time, x [L] is spatial coordinate, h [L] is water pressure head, S
Overall simulated pattern of MRD and SDRD
The correlated soil hydraulic parameters for the 200 samples were used to represent the variations in soil hydraulic properties. Simulated daily moisture contents of the 200 samples were then used to calculate MRD and associated SDRD under different surface conditions. The ranked MRD and SDRD under vegetated conditions are plotted in Fig. 3 for the depths of 25, 50, and 100 cm. The statistical summary of those MRD and SDRD values is given in Table 2. By only varying soil hydraulic parameters in
Conclusions
In this study, the impacts of soil hydraulic properties on the temporal stability of soil moisture (TS SM) were investigated using a 1-D vadose zone model and a soil dataset with correlated soil hydraulic parameters for sandy soils. Compared to the results of previous modeling studies, more reasonable patterns of mean relative difference (MRD) and standard deviation of relative difference (SDRD) that resembled field observations were produced by varying all the parameters in soil water
Acknowledgments
The authors would like to thank the High Plain Regional Climate Center for providing the hydrometeorological data at the Barta Brothers Ranch site and two anonymous reviewers for their comments that led to improvements of this work.
References (46)
- et al.
Watershed scale temporal and spatial stability of soil moisture and its role in validating satellite estimates
Remote Sens. Environ.
(2004) Assessing temporal stability and spatial variability of soil water patterns with implications for precision water management
Agric. Water Manag.
(2005)- et al.
Towards areal estimation of soil water content from point measurements: time and space stability of mean response
J. Hydrol.
(1998) - et al.
Temporal stability in soil water content patterns across agricultural fields
Catena
(2008) - et al.
Analytical framework for the characterization of the space-time variability of soil moisture
Adv. Water Resour.
(1994) - et al.
Surface and profile soil moisture spatio-temporal analysis during an excessive rainfall period in the Southern Great Plains, USA
Catena
(2009) - et al.
Multi-scale temporal stability analysis of surface and subsurface soil moisture within the Upper Cedar Creek Watershed, Indiana
CATENA
(2012) - et al.
Watershed scale temporal stability of soil water content
Geoderma
(2010) - et al.
SMEX02: Field scale variability, time stability and similarity of soil moisture
Remote Sens. Environ.
(2004) - et al.
Hillslope scale temporal stability of soil water storage in diverse soil layers
J. Hydrol.
(2013)