In this article we will discuss about how to determine and estimate erodibility factor(k) of soil.
The first study on erodibility of materials was done by Hjulstrom. They developed a curve, called Hjulstrom’s diagram which includes three sectors (erosion, transport and sedimentation) depending on water velocity and the diameter of soil particles. Analysis of the erosion sector shows that the diameter of particles of the most fragile matter is about 100 microns, i.e., line sand.
In finer matter, the cohesion develops simply as the surfaces of the clays rub together, while coarser clumps become increasingly heavy and therefore harder to transport. This relates the resistance to the erosive force of runoff in a wet environment. (Fig. 8.2)
This diagram casts following informations:
1. The material most easily dislodged by the runoff, is related to the texture close to line sand. More clayey material is stickier. The coarser materials are bigger in size therefore they require high flow velocity for movement.
2. As long as the flow is slow (25 cm/s), it cannot erode.
3. Fine clays and loam particles are easily transported to a longer distance even at low speeds, but the coarser particles are to the shorter distance than the fine sands from erosion site to sedimentation site.
The evaluation of soil erodibility is done on the basis of long-term study using natural runoff plot. However, there is very few studies have been reported so far in this aspect. The use of simulated rainfall for study is very common because it can be used throughout year without any constraints, while by natural rainfall it not so, because of uncertainty in rainfall occurrence of desired characteristics.
The essential requirements of runoff plot studies for determining the soil erodibility factor are outlined as under:
i. The land should be fallow and tilled, immediately before and during the observation to stipulate the removal of natural degradation of all surface and subsurface plant residues that remained after crop harvesting.
ii. The adequacy of observation period should be based on the climatic conditions.
iii. The prediction of reliable K-value under runoff plot study needs uniformity in soil and its topography within the plot. The uniformity in topographic is essential to avoid the soil deposition or acceleration in soil erosion.
iv. The plots should have a standard length and steepness to avoid errors in soil-loss adjustments with topographic factors. Actually, the 9%-slope steepness is not rationally based, but was selected as an average gradient of runoff plots on which early erosion studies in the United States were conducted.
Similarly, 22.1 m plot length was on the basis of selection of 1/1250 ha plot area, giving a two-row of 1.83 m plot width. Errors may be introduced in K-factor determinations for soils with incomplete removal or degradation of surface and subsurface residues or for soils with incorrect C-factor adjustments.
v. With the use of rainfall simulation in determination of appropriate K-factor, the selection of weighting factors for soil losses on different antecedent soil-water conditions is very important. Romkens (1985) observed that the K values for different antecedent moisture levels need to be weighted in proportion of the runoff and erosion in different climates.
The soil erodibility expresses an inherent resistance to particle detachment (degradation) and transport by the rainfall. It is determined by the cohesive force between the soil particles; that may vary depending on the presence or absence of plant cover, the soil water content and the development of its structure.
In computing the factor-K of the Universal Soil Loss Equation (USLE), Wishmeier and Smith (1978) took into account the sill content, very line sand content, clay content and organic matter, structure of the surface layer and permeability of the soil profile. The equation for factor-K based on above parameters is given below. This equation can be used to generate the erodibility classes (Table 8.5).
K = [27.66 x m1.14 x 10–8 x (12–a)] + [0.0043 x (b–2)] + [0.0033 x (c–3)] … (8.15)
K = soil erodibility factor (t.ha/MJ–1/mm–1)
m = [silt (%) + very fine sand (%)][100–clay (%)]
a = organic matter (%)
b = structure code – (1) very structured or particulate; (2) fairly structured; (3) slightly structured and (4) solid.
c = profile permeability code – (1) rapid; (2) moderate to rapid; (3) moderate; (4) moderate to slow; (5) slow and (6) very slow.
Nomograph of factor-K is presented in Fig. 8.1.
The procedures of determining various parameters of above equation are described as under:
Topsoil Texture (M):
For determining the soil texture (M), the data on percent clay, silt and silty sand particles are required. In which, the percent clay is the content of grains < 0.001 mm in diameter. However, in the methodology for evaluation of K-factor the clay content is delimited by the grain size <.0.002 mm.
The limits of the grain size of silt and silty sand (0.002-0.1 mm) are also different from the original limits used (0.001-0.01 mm for medium and fine silt; 0.01-0.05 mm for coarse silt; 0.05 to 0.25 mm for fine sand and 0.25-2.0 mm for medium sand).
Due to this reason, it is necessary to change the old textural categories into the new ones. The new categories can be evaluated graphically by the computer program MS EXCEL using grain size curve to the nearest 0.5%.
The parameter “a” is computed by multiplying a constant to the total oxidisable carbon content (Cox), i.e. 1.724 Cox. The constant 1.724 is the Welte’s coefficient, which is based on the assumption of 58% carbon content in humus.
For calculation of factor-K, four classes of topsoil texture are used, shown in Table 8.6. The soil structure is described by means of a code. If the structure type in the database is identical to the given in Table 8.7, then corresponding class number is attributed to it for calculation of factor-K.
If the structure of a given soil is not appearing in Table 8.7, or the soil does not involve recognizable structure then soil type and geology base are considered (Tables 8.8.). The light soils (p-sandy, hp-loam sandy) are placed in class 1; medium soils (ph-sandy-loam, h-loamy) in class 3; medium soils on light substrates in class I and heavy soils (jh-clay-loamy, jv, j-clay) in class 4. This classification is done on the basis of information’s on various types of soil structure in relation to soil type and soil forming substrate.
To estimate this parameter the categorization of main soil unit is done into hydrological grouping according to the soil classification unit (soil type, sub-type, and variety), texture composition, water and air regime, parent material, skeleton content and the depth of soil profile. If the data on percent silt, very fine sand or clay are lacking, then an average value can be calculated on the basis of similar-textured soils. Similarly, if data on structure is lacking, then structure codes can be deduced from the texture of the surface layer.
The erodibility of organic soils can be assumed to be negligible. The nomograph was derived from the rainfall- simulation data from mid-western, mostly (81%) medium-textured surface soils. More than 60% of these soils had an aggregation index smaller than 0.3. The nomograph is well suitable for the less aggregated medium- textured surface soils of the Mid-west.
The relationship for volcanic soils in Hawaii is given by the following expression –
K = – 0.03970 + 0.00311 x1 x 0.00043 x2 + 0.00185 x3 + 0.00258 x4 – 0.00823 x5 … (8.16)
x1 = unstable aggregate size fraction (percent) less than 0.250 mm
x2 = product of % modified silt (0.002-0.1 mm) and % modified sand (0.1-2 mm)
x3 = % base saturation
x4 = silt fraction (0.002-0.050 mm) in percent
x5 = modified sand fraction (0.1-2 mm) in percent
Young and Mutchler (1977) developed following expression for the soils in the upper Mid-west –
K = – 0.204 + 0.385 x6 + 0.013 x7 + 0.247 x8 + 0.003 x2 – 0.005 x9 … (8.17)
x6 = aggregation index
x7 = percent montmorillonite in the soil
x8 = bulk density of the soil at 50-125 mm depth
X9 = dispersion ratio
The montmorillonite is the clay mineral. Presence of this clay mineral significantly affects the aggregation and granulation characteristics of the soils; and thus causing detachment during drying, and transport in subsequent storm events.
For clay sub-soils in Midwest, the following relationship was introduced by Romkens et al. (1977) can be used for determining the value of K factor –
K = 0.004 + 0.00023 x10 – 0.108 x11 … (8.18)
x10 = parameter M
x11 = Citrate-dithionite-bicarbonate (CDB) cxtractablc percent of A120 plus Fe203.
This relationship is suitable for the soil having the particle size between 0.002 and 0.1 mm in sub-soils. For highly weathered or cemented soils, the equation has not been tested and presumably needs modification.
Recently, all the available published data at global level of about 225 soils of measured K values, obtained from natural or simulated-rainfall studies, are pooled and grouped into textural classes.
Only the soils with less than 10% of rock fragments by weight (> 2 mm) are considered. The mean values of the soil-erodibility factor for soils within these size classes are related to the mean geometric particle diameter of that class.
The following expression shows the resulting relationship:
This relationship is useful for predicting the K values of those soils, for which,
i. Data are limited, i.e. no information about the very-fine-sand fraction or organic-matter content.
ii. The textural composition is given in different classification system.
The above methods/formulae for estimating the soil-erodibility factor have their specific suitability for few certain conditions. For example, the nomograph (Fig. 8.1) appears to be the best predictive tool for medium-textured soils or poorly aggregated soils of temperate zones. For tropical soils of volcanic origin, the equation 8.16 may be suitable. For soils or sub-soils containing clay minerals with 2:1 expanding lattices, the equation 8.17 or 8.18 can be used.
If the soil properties do not fit with any of the described formulae (methods) or there is incomplete information, i.e., the particle-size distribution and organic matter content are not available, then broadly based relationships (equations 8.19 and 8.20) can be used.
Estimation of Seasonal K:
The computation of seasonal soil erodibility factor (K) is difficult, mainly because of antecedent soil-water and soil-surface conditions and seasonal variations in soil properties. Since, these conditions and properties tend to be consistent for a season, therefore the seasonal K values can reduce errors in soil-loss estimates.
Based on this fact, Mutchler and Carter (1983) in the United States and Zanchi (1983) in Italy computed the monthly K values. They proposed a periodic function for K value, given as under –
Kr = 1 + a cos (bt – c) … (8.21)
In which, Kr is the ratio of average seasonal (monthly) K value over the average annual K value; t is the mean monthly temperature; and a, b, and c are the location specific constants. El-Swaify and Dangler (1976) and Hosoyamada (1986) introduced wet/dry K values in Hawaii and cold-warm K values in Japan, respectively.
Variations in seasonal K values is mainly related to the following factors:
i. Soil freezing
ii. Soil texture, and
iii. Soil water.
Of these factors, the soil-freezing effect is probably the most difficult to evaluate because of limited understanding about the processes and temporary change occurring in the soil properties and in the soil profile during the cycles, as well.
Although, no relationships have been developed so far by considering the soil freezing and thawing effects on soil erodibility. The properties such as soil structure, hydraulic conductivity, bulk density, aggregate stability and soil strength get affect by the freezing/thawing actions.
The soil-water content at initial freezing, the rate of soil freezing and the number of freeze-thaw cycles affect the soil aggregation and aggregate stability in spring at the time of thawing, significantly. Freeze-thaw cycle generally causes reduction in bulk density of the surface soil.
As result, the low density and high soil water content make the soil very susceptible to get detach and transport due to effects of external forces. In this way, the freezing and thawing actions tend to increase the soil-erodibility factor.
The high soil-water content also forms concrete frost, which is generally impermeable; this phenomenon causes reduction in soil erodibility. The numbers of freeze-thaw cycles also affects the soil erodibility. If there is greater number of freeze-thaw cycles, then the erosion resistance of soil becomes minimum.
Also, during thawing period the soil is extremely susceptible to erosion due to snowmelt and rainfall. In the regions where winter soil temperatures is around the freezing point, the soil surface undergoes many freeze-thaw cycles throughout the winter, which cause the soil erosion at high rate during this period.
Investigations have reported that the rate of change in soil erodibility varies with the types of soil or soil textures. The relationship between soil erodibility and the soil texture can be adequately determined using the soil-erodibility nomograph. The ratio of Kmax (the maximum value of soil erodibility for a given soil) to Knom (soil erodibility determined from the nomograph) be constant for a given soil texture. In this way, the magnitude of Kmax also becomes a function of soil texture.
The soil erodibility appears to vary with the soil water at the time of rainfall event. The probability of soil being wet at any lime is the function of timing and amount of annual precipitation. The average erodibility (Kav) will normally differ slightly from Knom, and can be estimated from the following relationship –
Kav = Σ (EIi) Ki/100