Estimation Of Soil Water Retention And Hydraulic Properties Pdf

estimation of soil water retention and hydraulic properties pdf

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D Corresponding author. Emails: MinhPhuong. Nguyen ugent.

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.

Estimation of Soil Water Properties

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. Land surface modelers have employed different PTFs to estimate soil hydraulic parameters.

This study aims to develop a dataset of soil hydraulic parameters over regions of China for different PTFs. We selected the most frequently used empirical functions for and , that is, those given by Clapp and Hornberger , as well as those by van Genuchten and Mualem The functions of van Genuchten and Mualem hereafter FGM have been favored by soil scientists and hydrologists e.

Direct measurement of parameters associated with and is difficult and in most cases impractical. It is also impossible to obtain sufficient numbers of direct measurements across a region to adequately reflect the spatial heterogeneity of soils. Thus, most soil databases do not provide soil hydraulic parameters associated with and. Instead, they are usually obtained by PTFs from soil properties that are easily measured and widely available, such as particle size distribution, bulk density BD , and soil organic matter SOM content.

In regional and global applications, the soil hydraulic parameters are all derived from PTFs. Furthermore, the soil properties required by PTFs are developed by linking soil survey maps with representative soil profiles. Land surface models for numerical weather prediction, climate modeling, and hydrological modeling usually use lookup tables of mean values of hydraulic parameters based on soil textural classes or continuous PTFs provided by Clapp and Hornberger and Cosby et al.

Table 1 provides an overview of the PFTs and global soil datasets of the seven most widely used LSMs in the hydrometeorology community. Most of them use the taxonomy-based PTFs and expert rules based on soil parameter estimation.

During the past decades, many new PTFs and soil datasets have been developed. Table 1. A large number of studies have been performed recently to develop PTFs Vereecken et al. Most published PTFs provided a very limited description of the functions and where they can potentially be used McBratney et al. The accuracy of a PTF outside of its development dataset is generally unknown.

A PTF may perform well in one region where it was developed and tested but perform relatively poorly in other regions. In past decades, there were many evaluations of the PTFs using different datasets, including those of PTFs for the soil moisture retention curve Cornelis et al. However, it is not clear that a best set of PTFs for both the soil moisture retention curve and hydraulic conductivity exists. The inability and uncertainty of PTFs in the estimation of soil hydraulic parameters can be attributed to factors as follows: 1 the intrinsic inability i.

Chirico et al. They found the simulated evaporation to be much more affected by the PTF intrinsic inability than by errors due to uncertainty in the input data Vereecken et al. Therefore, using ensembles of PTFs to estimate soil hydraulic properties may be a practical approach in land surface modeling Guber et al.

Global or regional datasets of hydraulic parameters have recently been compiled using soil profile attributes or PTFs. However, most of the datasets of the hydraulic parameters are only usable for simple bucket models with a specified water-holding capacity, such as available water capacity AWC and saturated volumetric water content Table 2. Table 2. Other abbreviations as in Table 1. Another limitation of soil datasets listed in Table 2 is a dearth of information on vertical variability of soil profiles.

In most datasets, the properties of the A horizon or surface—0. However, the attributes of the underlying horizons in most soils are significantly different from those of the surface layer Williams et al.

Besides uncertainty in meteorological forcing data, the inaccuracy in land hydrometeorological modeling can be attributed in part to inadequate land surface hydrology parameterizations, including poor estimates of soil hydraulic parameters. So far, there is not a soil hydraulic parameter dataset available for hydrometeorological modeling from catchments to global scales because soil hydraulic parameters have not been derived from soil profile databases to adequately reflect spatial heterogeneity of the soil.

As land modelers, we expect that the dataset of the hydraulic parameters associated with FCH and FGM developed in this study could 1 describe spatial variability of the soil hydraulic characteristics in both vertical and horizontal directions, 2 provide multiple choices for users to use either a single set or an ensemble of hydraulic parameters associated with different PTFs, and 3 provide a benchmark dataset the median values from multiple PTFs or as a reference dataset.

The dataset includes the mean values of the hydraulic parameters derived with each PTF and their statistics, that is, values of median and coefficient of variation. Our effort is unique in that the science community will have access to a dataset of soil hydraulic parameters specifically designed for land modeling applications.

The generation of soil hydraulic parameters requires a soil property dataset and appropriate PTFs. The soil property dataset should include the percentages of sand, silt, and clayin addition to bulk density, and soil organic matter in the profiles.

At present, it is the most detailed digitized national soil map of China. This spatial dataset is based on the Genetic Soil Classification of China GSCC , consisting of 12 orders, 61 great groups, subgroups, families, and 11 nonsoil map units i. However, there are only soil map units in the soil map, and each map unit has only one soil type at family, subgroup, and great group levels. Not all soil types appear in the soil map, which is delineated into 94 map polygons.

We collected representative soil profiles with 33 soil horizons from the literature of the Second National Soil Survey of China. The soil profiles were digitized from published books, including soil books at the national and provincial level and at prefectural and county levels of Tibet Shangguan et al.

The information collected for each profile includes classification under the GSCC; physical and chemical properties of each horizon including soil particle size distribution, SOM, and BD, which are used as inputs to the PTFs in this study. The spatial distribution of soil properties are derived using a polygon linkage method from the soil map of China and soil profile database Shangguan et al. The polygon linkage method links soil polygons and soil profiles taking the distance between soil profiles and soil polygons into account to preserve spatial variations of a soil type.

The limits of sand, silt, and clay fractions are between 2 and 0. The soil characteristics of soil profiles are standardized into seven layers 0—0.

However, in the deepest layer, there is little information of the needed input properties, so it was not included in our study.

Field capacity is the amount of water content retained in soil after excessive water has drained away under gravity. Gravitational drainage usually lasts for 2—3 days after a rain or irrigation in pervious soils of uniform structure and texture, and the drainage rate decreases substantially.

Permanent wilting point, or wilting point, is defined as the minimal point of soil moisture the plant requires not to wilt. FCH and FGM have shown great values in the widespread applications of water flow models at field and larger scales. The PTFs have been developed from easily measured and widely available soil properties such as sand, silt, and clay percentages; bulk density; or organic matter content.

This rule is used to avoid confusion in the physical definition. The physical meanings of , , and in different models are similar, but their values may be different for a given soil. This is partly due to the subtle differences in the definition of saturation.

However, the databases with fewer samples typically have provided better PTFs, since additional samples may create additional variability, and smaller databases have typically used the same measurement methodology for all water retention curves Vereecken et al. PTFs should have more positive evaluations. The PTFs developed by Cosby et al.

The PTFs are listed in the appendix. The data products developed in this study include 1 the resulting hydraulic parameters from individual PTFs and 2 statistics of the parameters from multiple PTFs, that is, median and coefficient of variation CV. Median values were taken as the best estimations as these can avoid excessive influence of extreme values.

In this section, we present an overview of the spatial variations of the estimated hydraulic parameters and a comparison of the lookup tables of parameters with previous estimations from U.

As an example, we only show the horizontal distribution of the median and CV of the soil hydraulic parameters of the second land model standardized soil layer 0. The values associated with FCH and FGM have a similar spatial pattern, with higher values in mountainous areas Tibetan Plateau and southern and northeastern China and lower values in northern arid and semiarid areas as well as central and northern alluvial plains.

The spatial variation of agrees well with that of the soil bulk density BD ; that is, higher areas correspond well to the lower BD areas and higher SOM areas Shangguan et al. The CV values of are lower in most areas, implying that various PTFs are consistent in the estimations. The PTF estimations of are scattered in a range of 0. The slope, , of FCH is the slope of the retention curve on a logarithmic graph. Low corresponds to high soil water retention, and has a good inverse correlation with the percentage of clay Shangguan et al.

Lower areas are in southern and northeastern China, where the soils are well developed or formed. The estimation of of FCH has a good correlation with the percentage of sand Shangguan et al.

The saturated hydraulic conductivities in FCH and FGM have a similar spatial pattern in the deserts and on the Tibetan Plateau, and lower values spread over southern China and the northern plains.

The other three parameters of FGM i. In general, the CV is large when values of a parameter are small, and vice versa. One exception is that L is rather high in the desert areas of the north. Citation: Journal of Hydrometeorology 14, 3; The spatial distributions of and of the second land model standardized soil layer 0. Higher values of and are in southern China, while lower values are in the arid and semiarid areas in northwestern China.

In this subsection, we take the soil hydraulic parameters of the functions of Clapp and Hornberger as an example to show the vertical variation of soil hydraulic parameters Fig. In almost all areas, decreases with depth, with an exception that layer 2 has slightly smaller values than layer 6 in some areas of southern and central China. In most areas, generally decreases with depth, and layer 2 has much higher values in the east part of the Tibetan Plateau and the south.

There are some areas with lower values of in the western part of Tibetan Plateau and the northeast and northwest of China. The saturated capillary potential, , increases with depth in almost all areas and decreases with depth in some areas of the northwest, whereas decreases with depth in most areas and increases with depth in some areas of the northwest and the northeast; has the largest vertical variation among all the parameters.

Layer 2 is 0. Layer 2 has a much lower from FCH in the northeast and a much higher from FCH in the desert areas of the north; of FCH has higher values in the northwest and the Sichuan Basin, while it has lower values in most of the other areas and much lower values in the south.

Table 3 shows the median and standard deviation of the median of hydraulic parameters estimated by PTFs of FCH for each texture class using the China database. These values are quite different from the popular lookup table developed by Cosby et al. The values of in China are about 2 to 5 times smaller than those in the United States. Except for sandy loam, sand, and loamy sand, the values of in China are significantly smaller than those in the United States, especially for the texture classes with high clay content.

The parameters and are quite similar, except that sand soil has a much higher median of 0.

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

Crop transpiration needs depend not only on the existing environmental conditions, but also on the rate of water supply to the roots. Both water supply to plants and substrate aeration are estimated from the substrate hydraulic properties Da Silva et al. Consequently, it is very important to determine the magnitude and the range of such changes. In contrast to mineral soils, much less is known about the hydraulic properties of substrates. Efforts have been made from many researchers to study the hydraulic properties of substrates and to apply mathematical functions to estimate their hydraulic properties Bilderback et al.

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.

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. However, a more important reason is the difficulty of obtaining the primary input which is the relationship between matrix potential and hydraulic conductivity as a function of soil water content.


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Estimation of Soil Water Retention and Hydraulic Properties

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Water retention curve

Soil Water Retention Modeling Using Pedotransfer Functions

This curve is characteristic for different types of soil, and is also called the soil moisture characteristic. It is used to predict the soil water storage, water supply to the plants field capacity and soil aggregate stability. Due to the hysteretic effect of water filling and draining the pores, different wetting and drying curves may be distinguished. At potentials close to zero, a soil is close to saturation, and water is held in the soil primarily by capillary forces. Sandy soils will involve mainly capillary binding, and will therefore release most of the water at higher potentials, while clayey soils, with adhesive and osmotic binding, will release water at lower more negative potentials. At any given potential, peaty soils will usually display much higher moisture contents than clayey soils, which would be expected to hold more water than sandy soils. The water holding capacity of any soil is due to the porosity and the nature of the bonding in the soil.

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. Although scientists were initially reluctant to accept this indirect measurement approach, an increasing number of laboratories worldwide are acknowledging the benefits of this technique, and are adapting our techniques.

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