Soil saturated conductivity

Even if a strictly mono-dimensional solution, with no account for downstream and lateral capillary effects nor scale effects, is roughly representative of the field physical phenomenon, we attempted to use the Darcy's method especially for the experimental sites (Melezzo Occidentale River basin and Anza River basin) whose water retention relationships were unknown.

In the investigated cases by observing that, after the long imbibition, the soil around the infiltrometer could be considered almost saturated, a piezometric head p/ym = D (see Figure 9.1 and Section 9.2.2) was assumed downstream the infiltrometer. The hypothesis was tested during the laboratory analysis verifying that the 98% of the soils reached saturation at the end of the modified-moisture infiltration tests, and the other soils were close to the saturation. The regression to determine Ks was therefore applied to the data of the final stage of the modified-moisture cumulative infiltration curve.

This method was then applied to the upper soils of the Toce River and of the Mella River basin and compared with the GA (Toce River basin) method and with the RE method (Toce and Mella River basin). In Figure 9.5, the scatter of the soil saturated conductivity of the Toce River basin is represented: it can be seen that the Darcy's method provides an estimate of Ks on average about 1 order of magnitude higher than the RE method, on average. The same behaviour, with a regression coefficient R2 = 0.4705, was observed for the saturated conductivity of the Mella River basin soils. On the other hand, by taking into account the matric potential (GA method) at the wetting front, a better estimate of Ks is provided even using a mono-dimensional representation. In particular, the GA method was applied to the first set of data (about 15 minutes' recording) of the natural-moisture cumulative infiltration curve, until it could be hypothesised that the process was mainly mono-dimensional (see also Section 9.2.2 for details). The RE method, on the other hand, was applied to the final limb of the natural-moisture cumulative infiltration curve in order so that the hypothesis of having an almost constant internal pond was more realistic than at the beginning of the test. Because a soil sample was taken before the beginning of the tests, a reliable estimation of the initial saturation se was available. There is a slight difference between the experimental geometry and that proposed by the authors.

A quite good agreement on average, even with a high dispersion, was found (Figure 9.6) between the estimate of the soil saturated conductivity after laboratory experiments and field data (GA method and RE method). The soil volume, and the surface area as well, investigated by the infiltrometer is greater than that of the soil cores for the laboratory measurements. As a consequence, it is more likely that macropores are included in the infiltrometer soil. However, a systematic bias between the laboratory and the RE estimates is not evident. The high dispersion of the data and the limited number of samples for each soil type discourages the attempt to estimate a scale factor (see e.g. Focardi etal. 1997, Merz et al. 2002) characterising the whole experimental set. In Figure 9.7, the geometric average for each soil class (Toce River basin) is represented, and the RE field method, the Darcy's method and the laboratory estimates are compared.

Table 9.4 Surface soil saturated conductivity for the Mella River basin derived using the Darcy and the Reynolds and Elrick (1991) method. In brackets, the number of the measurements used for the estimation of the saturated conductivity is reported (f = arithmetic average, J = geometric average)

Class

Ks [m/s]

Ks [m/s]

Ks,max [m/s]

Ks,mm [m/s]

Ks [m/s]

Ks [m/s]

Ks,max [m/s]

Ks,mm [m/s]

(Darcy f)

(Darcy J)

(Darcy)

(Darcy)

(RE f)

(RE J)

(RE)

(RE)

Discontinuous urban fabric

3.68E-04

(6)

2.67E-04

6.33E-04

2.84E-05

6.20E-06

(6)

4.11E-06

1.55E-05

7.04E-07

Cultivated and wooden agricultural areas

9.78E-04

(3)

2.39E-04

2.76E-03

3.52E-05

5.14E-06

(2)

3.54E-06

8.87E-06

1.42E-06

Grass cov. debris and slope deb.

7.03E-04

(6)

3.45E-04

2.27E-03

1.85E-05

8.83E-05

(6)

1.54E-05

4.57E-04

1.26E-06

Forest cov. debris and slope deb.

1.24E-03

(11)

7.46E-04

2.58E-03

1.67E-05

1.59E-05

(5)

1.31E-05

2.83E-05

4.94E-06

Grass-covered recent alluvia

8.47E-04

(7)

6.73E-04

1.58E-03

1.72E-04

2.81E-05

(6)

1.25E-05

5.41E-05

2.59E-07

Forest-covered recent alluvia

6.71E-04

(7)

1.51E-04

2.99E-03

3.49E-06

8.51E-06

(5)

3.08E-06

3.22E-05

2.50E-07

Grass-covered eluvial-colluvial dregs

5.96E-04

(13)

2.78E-04

3.27E-03

2.23E-05

2.32E-05

(10)

8.32E-06

1.36E-04

8.01E-07

Forest-covered eluvial-colluvial dregs

9.80E-04

(3)

5.73E-04

2.35E-03

2.19E-04

9.51E-06

(1)

9.51E-06

9.51E-06

9.51E-06

Grass-covered glacials

2.57E-04

(3)

1.84E-04

4.07E-04

4.88E-05

4.54E-06

(2)

4.04E-06

6.62E-06

2.47E-06

Grass-covered alluvial fans

1.26E-03

(2)

9.44E-04

2.10E-03

4.25E-04

1.49E-04

(1)

1.49E-04

1.49E-04

1.49E-04

Forest-covered alluvial fans

5.61E-04

(2)

5.51E-04

6.67E-04

4.56E-04

3.67E-05

(1)

3.67E-05

3.67E-05

3.67E-05

Grass-covered conglomerates

1.25E-06

(1)

1.25E-06

1.25E-06

1.25E-06

1.29E-07

(1)

1.29E-07

1.29E-07

1.29E-07

Forest-covered conglomerates

8.37E-04

(2)

6.90E-04

1.31E-03

3.63E-04

2.43E-05

(2)

2.18E-05

3.51E-05

1.36E-05

Grass-covered sand-stones

1.18E-03

(3)

1.05E-03

2.04E-03

7.14E-04

2.34E-05

(2)

2.34E-05

2.35E-05

2.33E-05

Grass cov. limest., dolom. limest., dolost.

4.06E-04

(3)

3.96E-04

5.37E-04

3.20E-04

6.95E-06

(3)

6.54E-06

1.05E-05

5.08E-06

Forest cov. limest., dolom. limest., dolost.

2.34E-03

(5)

1.87E-03

5.12E-03

5.74E-04

5.52E-05

(3)

5.37E-05

7.44E-05

4.36E-05

Grass-covered gneiss

1.21E-03

(3)

1.69E-04

3.33E-03

4.78E-06

3.18E-06

(2)

2.81E-06

4.68E-06

1.69E-06

Ks (Reynolds and Elrick, 1991) (m/s) Figure 9.5 Comparison of different estimates of the saturated conductivity after field data for some soils of the Toce River basin

"to

1.0E-03

1.0E-04

1.0E-05

1.0E-06

1.0E-07

1.0E-08

1.0E-04

1.0E-05

1.0E-06

1.0E-07

3 0 -

O"

h aVi 0*

3 / ♦ / ♦

• V

1 *

o •

-♦ -

+ Ks (R • Ks (G Ks (D

eynolds and E reen and Amp arcy, 1856)

Irick, 1991) t, 1911)

Ks after laboratory data (m/s)

1.0E-04

1.0E-03

Figure 9.6 Comparison of different estimates of the saturated conductivity after field and laboratory data for some soils of the Toce River basin

Figure 9.7 Comparison between different estimates of the soil saturated conductivity for the pedogenetic and land-use classes of the Toce River basin map

Figure 9.7 Comparison between different estimates of the soil saturated conductivity for the pedogenetic and land-use classes of the Toce River basin map

In order to characterise the behaviour of water in the upper soil layers, in particular, after a long imbibition process as happens during heavy rainfall events when the soil on the surface can be considered almost saturated, the saturated conductivity of the first layers of the soil was investigated. The soil saturated conductivity is generally expected to decrease with the depth of the upper soil layers. The trend is due to the presence of an impervious layer of non-completely decayed organic matter and eluvial particles, altered by the physical and chemical processes on the soil surface, and to the finer texture of the soil particles. Such results, which are often assumed by hillslope hydrological models (Beven and Kirkby 1979), were also found during these field campaigns by comparing the estimate of the upper layer with the lower layers saturated conductivity. In Figure 9.8, the lower layers Ks (Darcy method), normalised using the surface saturated conductivity, are presented for the soils of the Melezzo Occidentale River basin and of the Anza River basin. So it can be expected that once the soil surface is saturated, such as at the end of heavy rainfall events, the response of the soil to the rainfall is mainly governed by the lower layers with lower soil saturated conductivity.

As often assumed in modelling the hillslope runoff response (Beven and Kirkby 1979, Kirkby 1985), an exponential decay of the saturated conductivity with depth can be used to fit the observations:

where z is positive upward as previously assumed, and Ks(0) is the surface soil saturated conductivity. From our data over vertical saturated conductivity, a value of the exponential decay constant 1// = 0.19 m was found. This value is consistent with the range of the decay constant of the saturated lateral conductivity (between 0.2 and 0.4 m) estimated by Beven (1983) over a 27 soils set. However, the high dispersion of data keeps in evidence the importance of the depth of the single horizons due to the different local pedogenetic processes and the degree of development of the soil. Moreover, these inhomogeneities point out the difficulty, for applications involving the investigated areas, to extend a theoretical framework of the hillslope runoff process to the whole basin.

Finally, because of the competition between erosional, transport and sedimentation processes, soils are expected to be coarser as the altitude increases, and so also the soil saturated conductivity, mainly due to the macropores between the soil particles, is expected to increase. The

Figure 9.8 Comparison between the saturated conductivity in the upper soil layers (field data of the soils of the Anza River basin and of the Melezzo Occidentale River basin)

Figure 9.8 Comparison between the saturated conductivity in the upper soil layers (field data of the soils of the Anza River basin and of the Melezzo Occidentale River basin)

saturated conductivity of the soils of the Toce River basin, estimated after laboratory analysis, is plotted versus the altitude of the experimental site. In a qualitative agreement with results reported for another mountain basin of the central Italian Alps (Orlandini et al. 1999), an increase in soil saturated conductivity of about 1 order of magnitude over a 2000-m altitude increase can be observed in Figure 9.9. The slope and the correlation of the regression line of the logarithms of Ks versus altitude is different from zero with a 0.05 significance, although the spread of the data is very high.

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