Estimation Of Soil Water Retention And Hydraulic Properties Pdf

estimation of soil water retention and hydraulic properties pdf

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Water retention curve

Estimation of water retention and availability in soils of Rio Grande do Sul 1. Pesquisador do CNPq. E-mails: reichert ccr. E-mail: albuquerque pq. E-mail: kaiser mail. E-mail: felurach gmail. E-mail: carlesso ccr. Dispersed information on water retention and availability in soils may be compiled in databases to generate pedotransfer functions. The objectives of this study were: to generate pedotransfer functions to estimate soil water retention based on easily measurable soil properties; to evaluate the efficiency of existing pedotransfer functions for different geographical regions for the estimation of water retention in soils of Rio Grande do Sul RS ; and to estimate plant-available water capacity based on soil particle-size distribution.

Two databases were set up for soil properties, including water retention: one based on literature data entries and the other with soil data from an irrigation scheduling and management system entries. From the literature database, pedotransfer functions were generated, nine pedofunctions available in the literature were evaluated and the plant-available water capacity was calculated.

The coefficient of determination of some pedotransfer functions ranged from 0. Pedotransfer functions generated based on soils from other regions were not appropriate for estimating the water retention for RS soils.

The plant-available water content varied with soil texture classes, from 0. These variations were more related to sand and silt than to clay content.

Index terms: pedotransfer functions, texture class, water retention curve, mineralogy. Plant-available water in the soil is essential for adequate crop growth and development and depends on the soil properties.

For plants under water stress, the molecular and physiological processes are impaired Ramos et al. Plant-available water is measured directly, by the determination of gravimetric soil water content in a laboratory drying-oven or by indirect methods, with equipments such as the neutron probe and reflectometers.

The accuracy of these methods is good, but they are very time-demanding or require the availability of expensive equipment, creating barriers to a large-scale use. To overcome these difficulties, some researchers have proposed mathematical models to estimate soil water retention Meng et al.

These models estimate water retention by means of soil properties that are more easily measurable or available in the literature and related to water retention, and which are generally related to capillarity and water adsorption phenomena Rawls et al. The water retention curve expresses the soil water content based on its energy state at a given potential.

The water retained at lower tensions has a greater relation to soil structure, while at higher tensions it is related to particle size distribution and soil mineralogy. In Brazil, some pedotransfer functions have already been established for estimating soil water retention Arruda et al. The objectives of this study were: to generate pedotransfer functions to estimate soil water retention at different tensions based on easily measurable soil properties; to evaluate the efficiency of pedotransfer functions generated in other regions for the estimation of water retention in soils of Rio Grande do Sul RS ; and to calculate plant-available water capacity based on soil particle-size distribution of RS soils.

These studies were based on samples collected from various representative soil classes and horizons in different regions of the State, resulting in a total of datasets, which include water retention curves, organic matter, clay, silt and sand content, and bulk and particle density.

Data of water retention were available for the tensions of 1, 6, 10, 33, , , , , , 1,, and 1, kPa. In some studies the retention curve was determined for up to eight tensions, while in others there is only one tension for water retention. The water retained at the tension of 10 kPa was denominated as field capacity and that of 1, kPa as permanent wilting point. The option was made to standardize the estimation of water retention at 10 kPa, determined in the laboratory, although the concept of field capacity for a given tension is questionable, as laid out by Hillel , who argues that, in addition to soil properties such as texture and mineralogy, the effects of slope, sequence of the layers or horizons and other soil properties influence water retention as well.

For all samples, the particle size distribution clay, silt and sand fractions data is available, but in some studies the data are incomplete regarding organic matter content, bulk density, particle density or total porosity.

In figure 1 the ample distribution of particle sizes in the dataset can be visualized, with samples in all textural classes, except for the silt textural class. There was a greater concentration in some classes such as loam, sandy loam, clay loam and clay. Based on the database, multiple regression analyses were performed for the pedofunctions using the "stepwise" option SAS, This method selects the independent variables: sand, silt, clay, organic matter, bulk density, particle density and the sum of the clay fractions plus silt soil properties and generates the respective coefficients that compose each pedofunction to estimate the water content retained by the soil at the tensions of 6, 10, 33, , and 1, kPa.

Pedofunctions to estimate water retention for the tensions of 10, 33 and 1, kPa were also generated based on particle size distribution data only, which is necessary for databases that do not include the organic matter content and the bulk and particle densities. For the determination of the multiple regressions, the complete database was used, because the separation in subsets did not improve the accuracy of the equations.

Oliveira et al. The pedotransfer functions were tested by comparing the water content estimated by the proposed equations and those estimated by the pedofunctions proposed by Oliveira et al. In addition, a dataset of an irrigation scheduling and management system www. This database contains the properties particle size distribution and water retention at the tensions of 33 and 1, kPa. To evaluate the accuracy of other available equations, those that estimate the gravimetric soil water content were used, such as those proposed by Arruda et al.

The estimated soil water content for each model was correlated with the one measured. Water content at field capacity kPa , at the permanent wilting point -1, kPa and plant-available water capacity between and -1, kPa were calculated for each sample. The results were grouped by textural class and the mean of each class was presented in a textural triangle.

For these properties, regression analysis was performed using program SAS and path analysis with the program Genes , with result interpretion according to Cruz In this analysis, the data were submitted to descriptive statistics, Pearson correlation analysis and multicollinearity.

Variables with high and severe multicollinearity were not included in the path analysis. Clay contents varied from 0. The organic matter content was 0. Thus, water retention also varied, as exemplified for the tension of 1, kPa, with levels of 0. These differences reflect the parent material and the degree of weathering, and consequently the physical, chemical and mineralogical properties of the soil. Water retention is positively correlated with the clay content Table 3 , because this fraction favors the occurrence of micropores and menisci that generate capillary forces.

In addition, clay increases the specific surface area of the soil matrix and, consequently, water adsorption Hillel, These two phenomena, capillarity and adsorption, determine the matric potential and are responsible for soil water retention. Consequently, soils whose constituents or structure favor the appearance of these two phenomena will retain a greater amount of water. Estimation of water retention and validation of pedofunctions.

Due to the existence of soil variables in the RS soil database with direct and indirect relationships to water retention, it was possible to estimate water retention by pedotransfer functions Table 4 , as shown in figure 2. In the model of van den Berg et al. The coefficients of determination of the proposed pedofunctions varied from 0. The underestimation of the equations at high tensions was caused primarily by the presence of soils with a wide variation in mineralogy, since that to generate the pedofunctions all data collected in the literature were included.

This same observation was reported by Tomasella et al. The coefficients of determination of pedofunctions generated only with data of particle size distribution were 0. These authors also observed considerable differences in the cation exchange capacity of clay in the Vertisol horizons, from 56 cmol c kg -1 in the C to 18 cmol c kg -1 in the A horizon. The role of mineralogy in water retention was already discussed by Woodruf , based on the particle diameter of the different minerals.

Therefore, in addition to particle size distribution, water retention is highly dependent on soil mineralogy, which varies among soils and horizons. In a study by Puckett et al. The equations generated with soil samples collected in Brazil, as well as the expression of van den Berg et al. Of these five models, that of Masutti at a tension of 33 kPa and of Oliveira et al.

The model of Arruda et al. In figure 3b , results of the estimations with models developed with soils from Mexico and the USA were presented. Nevertheless, the b coefficient of the equation was 0. This indicates an underestimation of water retention at low tensions. With the exception of the model of Saxton et al. This may be due to differences in mineralogy between the soils in tropical and temperate climate regions.

The observations based on analysis of the models from the literature and of the model proposed in this study, clearly show the need for specific equations for soils with more homogeneous characteristics, as described by Arruda et al.

Nevertheless, for the data available, the grouping of soils by texture classes did not increase the coefficients of determination between water retention and soil properties data not shown. To evaluate the accuracy of the model proposed, the estimated results were compared with those estimated by the models of Oliveira et al. The water retention estimated by the proposed model, compared to that estimated by the model of Oliveira et al.

For the model of Masutti , the coefficients of determination were 0. In addition, the angular coefficient at 1, kPa was only 0. With the objective of making equations available when there is only information regarding particle size distribution, three water retention equations were generated for the tensions of 10, 33 and 1, kPa Table 4.

For the tensions of 33 and 1, kPa, it was possible to evaluate the equations with the data available for soils from an irrigation system. It was observed that at a tension of 33 kPa, the coefficient of determination between the estimated and the measured soil water content was 0. For soils with low water retention, the estimated is greater than the measured soil water content. In addition, the slope of the straight line is different from the line. For retention at 1, kPa the estimated is greater than the measured soil water content in all samples.

This may be the case because the soils underlying the proposed model have a mineralogy containing oxides, kaolinite and smectite, while in the database with the soils of the irrigation system the mineralogy consists predominantly of kaolinite and iron oxides. Nevertheless, the estimation of soil water retention at 33 kPa, depending less on soil mineralogy and more on structure, was satisfactory.

Therefore, dispersion is greater and accuracy of water retention lower when we use equations generated from the database with predominance of soils of certain classes, or soils with characteristics that differ considerably from the soils where the model is being evaluated.

Thus, the equations will only efficiently express water retention for soils that are similar in regard to their genesis and mineralogy Williams et al. To overcome this limitation, it is necessary to work with a large database to allow a division of the soils into more homogeneous classes. Using path analysis, the direct and indirect effects of soil properties on water retention were evaluated Table 6. For water retention at field capacity 10 kPa , direct and positive effects of clay and silt are observed, and a negative effect of bulk density.

In denser soils, the volume of larger pores diminishes, affecting water retention at field capacity. The organic matter content had a total positive effect on water retention at field capacity, with a correlation coefficient of 0. Similar effects to those discussed for field capacity were observed for the permanent wilting point, however with different correlation coefficients Table 6 , primarily due to the lower direct effect of the silt fraction.

The water content retained at field capacity varied from 0.

Estimation of Soil Water Properties

A computer program for estimating soil hydraulic parameters with hierarchical pedotransfer functions. Information about the predicted hydraulic parameters. Mathematical models have become increasingly popular in both research and management problems involving flow and transport processes in the subsurface. The unsaturated hydraulic functions are key input data in numerical models of vadose zone processes. These functions may be either measured directly or estimated indirectly through prediction from more easily measured data based using quasi-empirical models.

Alternatively, indirect estimation techniques are becoming increasingly popular, so that parameters of soil hydraulic functions can be estimated from other, easier to measure soil physical properties such as particle size distributions and soil structural characteristics. Alternatively, functional descriptors of water flow and transport can be derived from the parameters of soil hydraulic functions, such as in the use of pedotransfer functions to define land or soil quality indicators. Measurement of soil hydraulic properties using the multi-step outflow technique B. Neural network prediction. In the past 10 years or so, our laboratory with various graduate students and collaborators has developed the so-called multi-step outflow method, to estimate the soil hydraulic functions, i. After demonstrating the successful application of the inverse modeling approach to estimate laboratory-measured soil hydraulic functions, we have suggested to use this technique to estimate the capillary pressure and permeability functions for multi-fluid systems such as for soils that include oil, air and water.

To simulate soil water content, and and their associated parameters should be determined prior to solving the above equation. In addition, a more realistic simulation should account for spatial variability of these parameters in both vertical different layers of the soil column and horizontal directions their geographic variability. However, soil hydraulic properties are highly heterogeneous in space, and their estimates are dependent on local soil characteristics. There is no unique way to relate soil hydraulic properties and soil characteristics. For this reason, various researchers proposed different empirical relationships of soil hydraulic parameters and soil characteristics, referred to as pedotransfer functions PTFs.


models are the hydraulic conductivity and soil water retention functions. 2) estimate infiltration parameters based on soil properties, and. 3) test their utility to​.


Estimation of the soil hydraulic properties from field data by solving an inverse problem

Jaiswal, T. Thomas, R. The unsaturated hydraulic functions are key input data in numerical models of vadose zone processes.

Unsaturated Flow in Hydrologic Modeling pp Cite as. Water flow in soils can be characterized, in principle, for many boundary and initial conditions by solving the proper governing differential equations. There are several reasons why this state-of-the-art technology is not yet fully utilized. One reason may be complexity and expense of computer based numerical solutions.

Soil Water Retention Modeling Using Pedotransfer Functions

Estimation of water retention and availability in soils of Rio Grande do Sul 1. Pesquisador do CNPq. E-mails: reichert ccr. E-mail: albuquerque pq.

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References

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Estimation of Soil Water Retention and Hydraulic Properties

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Mathilde B.

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Crop transpiration needs depend not only on the existing environmental conditions, but also on the rate of water supply to the roots.

Leah O.

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D Corresponding author.

Staranawar1983

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This curve is characteristic for different types of soil, and is also called the soil moisture characteristic.

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