scholarly journals Impacts of Soil Properties, Topography, and Environmental Features on Soil Water Holding Capacities (SWHCs) and Their Interrelationships

Land ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1290
Author(s):  
Hyunje Yang ◽  
Hyeonju Yoo ◽  
Honggeun Lim ◽  
Jaehoon Kim ◽  
Hyung Tae Choi

Soil water holding capacities (SWHCs) are among the most important factors for understanding the water cycle in forested catchments because they control available plant water that supports evapotranspiration. The direct determination of SWHCs, however, is time consuming and expensive, so many pedotransfer functions (PTFs) and digital soil mapping (DSM) models have been developed for predicting SWHCs. Thus, it is important to select the correct soil properties, topographies, and environmental features when developing a prediction model, as well as to understand the interrelationships among variables. In this study, we collected soil samples at 971 forest sites and developed PTF and DSM models for predicting three kinds of SWHCs: saturated water content (θS) and water content at pF1.8 and pF2.7 (θ1.8 and θ2.7). Important explanatory variables for SWHC prediction were selected from two variable importance analyses. Correlation matrix and sensitivity analysis based on the developed models showed that, as the matric suction changed, the soil physical and chemical properties that influence the SWHCs changed, i.e., soil structure rather than soil particle distribution at θS, coarse soil particles at θ1.8, and finer soil particle at θ2.7. In addition, organic matter had a considerable influence on all SWHCs. Among the topographic features, elevation was the most influential, and it was closely related to the geological variability of bedrock and soil properties. Aspect was highly related to vegetation, confirming that it was an important variable for DSM modeling. Information about important variables and their interrelationship can be used to strengthen PTFs and DSM models for future research.

2019 ◽  
Vol 62 (2) ◽  
pp. 289-301
Author(s):  
Amjad T. Assi ◽  
Rabi H. Mohtar ◽  
Erik F. Braudeau ◽  
Cristine L. S. Morgan

Abstract. The purpose of this study was to evaluate the use of the pedostructure concept to determine the soil available water capacity, specifically the field capacity (FC). Pedostructure describes the soil aggregate structure and its thermodynamic interaction with water. Specifically, this work compared the calculation of soil water-holding properties based on the pedostructure concept with other standard methods for determining FC and permanent wilting point (PWP). The standard methods evaluated were the FAO texture estimate (FAO method), the Saxton-Rawls pedotransfer functions (PTFs method), and the water content at predefined soil suction (330 and 15,000 hPa) as measured with a pressure plate apparatus (PP method). Additionally, two pedostructure methods were assessed: the thermodynamic water retention curve (TWRC method) and the thermodynamic pedostructure (TPC method). Undisturbed loamy fine sand soil from a field in Millican, Texas, was analyzed at both the Ap and E horizons. The results showed that the estimated water content at FC and PWP for the three standard methods and for the TWRC method were in relative agreement. However, the TPC method used characteristic transition points in the modeled contents of different water pools in the soil aggregate and was higher for the Ap horizon, but in agreement with the other methods for the E horizon. For example, for the Ap horizon of the soil analyzed in this study, the FC estimated with the standard and TWRC methods ranged from 0.073 to 0.150 m3H2O m-3soil, while the TPC method estimate was 0.221 m3H2O m-3soil. Overall, the different methods showed good agreement in estimating the available water; however, the results also showed some variations in these estimates. It is clear that the TPC method has advantages over the other methods in considering the soil aggregate structure and modeling the soil water content within the aggregate structure. The thermodynamic nature of the TPC method enabled the use of both the soil shrinkage curve and the water retention curve in a weakly structured soil. It is expected that the TPC method would provide more comprehensive advances in understanding the soil water-holding properties of structured soils with higher clay contents. Keywords: Available water, Field capacity, Pedostructure, Pedotransfer functions, Permanent wilting point.


2012 ◽  
Vol 76 (3) ◽  
pp. 829-844 ◽  
Author(s):  
Feng Pan ◽  
Yakov Pachepsky ◽  
Diederik Jacques ◽  
Andrey Guber ◽  
Robert L. Hill

2014 ◽  
Vol 38 (3) ◽  
pp. 730-743 ◽  
Author(s):  
João Carlos Medeiros ◽  
Miguel Cooper ◽  
Jaqueline Dalla Rosa ◽  
Michel Grimaldi ◽  
Yves Coquet

Knowledge of the soil water retention curve (SWRC) is essential for understanding and modeling hydraulic processes in the soil. However, direct determination of the SWRC is time consuming and costly. In addition, it requires a large number of samples, due to the high spatial and temporal variability of soil hydraulic properties. An alternative is the use of models, called pedotransfer functions (PTFs), which estimate the SWRC from easy-to-measure properties. The aim of this paper was to test the accuracy of 16 point or parametric PTFs reported in the literature on different soils from the south and southeast of the State of Pará, Brazil. The PTFs tested were proposed by Pidgeon (1972), Lal (1979), Aina & Periaswamy (1985), Arruda et al. (1987), Dijkerman (1988), Vereecken et al. (1989), Batjes (1996), van den Berg et al. (1997), Tomasella et al. (2000), Hodnett & Tomasella (2002), Oliveira et al. (2002), and Barros (2010). We used a database that includes soil texture (sand, silt, and clay), bulk density, soil organic carbon, soil pH, cation exchange capacity, and the SWRC. Most of the PTFs tested did not show good performance in estimating the SWRC. The parametric PTFs, however, performed better than the point PTFs in assessing the SWRC in the tested region. Among the parametric PTFs, those proposed by Tomasella et al. (2000) achieved the best accuracy in estimating the empirical parameters of the van Genuchten (1980) model, especially when tested in the top soil layer.


2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Glécio Machado Siqueira ◽  
Jorge Dafonte Dafonte ◽  
Montserrat Valcárcel Armesto ◽  
Ênio Farias França e Silva

The apparent soil electrical conductivity (ECa) was continuously recorded in three successive dates using electromagnetic induction in horizontal (ECa-H) and vertical (ECa-V) dipole modes at a 6 ha plot located in Northwestern Spain. One of the ECadata sets was used to devise an optimized sampling scheme consisting of 40 points. Soil was sampled at the 0.0–0.3 m depth, in these 40 points, and analyzed for sand, silt, and clay content; gravimetric water content; and electrical conductivity of saturated soil paste. Coefficients of correlation between ECaand gravimetric soil water content (0.685 for ECa-V and 0.649 for ECa-H) were higher than those between ECaand clay content (ranging from 0.197 to 0.495, when different ECarecording dates were taken into account). Ordinary and universal kriging have been used to assess the patterns of spatial variability of the ECadata sets recorded at successive dates and the analyzed soil properties. Ordinary and universal cokriging methods have improved the estimation of gravimetric soil water content using the data of ECaas secondary variable with respect to the use of ordinary kriging.


2020 ◽  
Vol 53 (7) ◽  
pp. 941-949
Author(s):  
M. I. Makarov ◽  
R. V. Sabirova ◽  
M. S. Kadulin ◽  
T. I. Malysheva ◽  
A. I. Zhuravleva ◽  
...  

2011 ◽  
Vol 51 (No, 7) ◽  
pp. 296-303 ◽  
Author(s):  
T. Behrens ◽  
K. Gregor ◽  
W. Diepenbrock

Remote sensing can provide visual indications of crop growth during production season. In past, spectral optical estimations were well performed in the ability to be correlated with crop and soil properties but were not consistent within the whole production season. To better quantify vegetation properties gathered via remote sensing, models of soil reflectance under changing moisture conditions are needed. Signatures of reflected radiation were acquired for several Mid German agricultural soils in laboratory and field experiments. Results were evaluated at near-infrared spectral region at the wavelength of 850 nm. The selected soils represented different soil colors and brightness values reflecting a broad range of soil properties. At the wavelength of 850 nm soil reflectance ranged between 10% (black peat) and 74% (white quartz sand). The reflectance of topsoils varied from 21% to 32%. An interrelation was found between soil brightness rating values and spectral optical reflectance values in form of a linear regression. Increases of soil water content from 0% to 25% decreased signatures of soil reflectance at 850 nm of two different soil types about 40%. The interrelation of soil reflectance and soil moisture revealed a non-linear exponential function. Using knowledge of the individual signature of soil reflectance as well as the soil water content at the measurement, soil reflectance could be predicted. As a result, a clear separation is established between soil reflectance and reflectance of the vegetation cover if the vegetation index is known.


1988 ◽  
Vol 18 (4) ◽  
pp. 427-434 ◽  
Author(s):  
Richard Barry ◽  
André P. Plamondon ◽  
Jean Stein

An analysis of hydrologic soil properties and the prediction of volumetric soil water content during four summers have been done for a site located in the balsam fir (Abiesbalsamea (L.) Mill.) forest of the Lac Laflamme watershed. The hydrologic properties were used to identify three different soil layers, THIRSTY, a soil moisture model using the Penman evapotranspiration formula, was applied to predict daily volumetric water content of these layers. Predictions of soil moisture with the calibrated model were close to the observed data for the median layer (20–60 cm from the soil surface) and less accurate for the surface layer (0–20 cm) where important transpiration activities take place. The model appeared unreliable for predicting soil water content of the bottom layer (60–100 cm) which was often saturated by groundwater. The calibration of the model required modifications of the observed values of the available water content at field capacity and the relative root density factor and was adjusted with the crop coefficient of the Penman evapotranspiration formula. These modifications of observed physical parameters raise the question of the feasibility of extrapolating the model to other sites without extensive calibration. The high sensitivity to variations of the crop coefficient applied to the evapotranspiration equation indicated that a more physically based transpiration model, supported by field-oriented process studies, would be required to improve the model's performance.


Soil Research ◽  
2014 ◽  
Vol 52 (5) ◽  
pp. 431 ◽  
Author(s):  
K. Liao ◽  
S. Xu ◽  
J. Wu ◽  
Q. Zhu

Hydrological, environmental and ecological modellers require van Genuchten soil-water retention parameters that are difficult to measure. Pedotransfer functions (PTFs) are thus routinely applied to predict hydraulic parameters (θs, ln(α) and n) from basic soil properties (e.g. bulk density, soil texture and organic matter content). This study investigated the spatial variations of van Genuchten parameters via geostatistical methods (e.g. kriging and co-kriging with remote-sensing data) and multiple-stepwise-regression-based PTFs with a limited number of samples (58) collected in Pingdu City, Shandong Province, China. The uncertainties in the spatial estimation of van Genuchten parameters were evaluated using bootstrap and Latin hypercube sampling methods. Results show that PTF-estimated parameters are less varied than observed parameters. The uncertainty in the parameter estimation is mainly due to the limited number of samples used for deriving PTFs (intrinsic uncertainty) and spatial interpolations of basic soil properties by (co)kriging (input uncertainty). When considering the intrinsic uncertainty, 36%, 29% and 47% of measurements are within the corresponding error bars (95% confidence intervals of the predictions) for the θs, ln(α) and n, respectively. When considering both intrinsic and input uncertainties, 86%, 66% and 88% of observations are within the corresponding error bars for the θs, ln(α) and n, respectively. Therefore, the input uncertainty is more important in the spatial estimation of van Genuchten parameters than the intrinsic uncertainty. Measurement of basic soil properties at high resolution and properly use of powerful spatial interpolation approach are both critical in the accurate spatial estimation of van Genuchten parameters.


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