the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Prediction of the absolute hydraulic conductivity function from soil water retention data
- 1Division of Soil Science and Soil Physics, Institute of Geoecology, Technische Universität Braunschweig, Germany
- 2Department of Earth Sciences, Utrecht University, Netherlands
- 3Department of Nuclear Engineering, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
- 1Division of Soil Science and Soil Physics, Institute of Geoecology, Technische Universität Braunschweig, Germany
- 2Department of Earth Sciences, Utrecht University, Netherlands
- 3Department of Nuclear Engineering, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
Abstract. For modelling flow and transport processes in the soil-plant-atmosphere system, knowledge of the unsaturated hydraulic properties in functional form is mandatory. While much data is available for the water retention function, the hydraulic conductivity function often needs to be predicted. The classical approach is to predict the relative conductivity from the retention function and scale it with the measured saturated conductivity, Ks. In this paper we highlight the shortcomings of this approach, namely that measured Ks values are often highly uncertain and biased, resulting in poor predictions of the unsaturated conductivity function.
We propose to reformulate the unsaturated hydraulic conductivity function by replacing the soil-specific Ks as a scaling factor with a generally applicable effective saturated tortuosity parameter τs and predicting total conductivity using only the water retention curve. Using four different unimodal expressions for the water retention curve, a soil-independent general value for τs was derived by fitting the new formulation to 12 data sets containing the relevant information. τs was found to be approximately 0.1.
Testing of the new prediction scheme with independent data showed a mean error between the fully predicted conductivity functions and measured data of less than half an order of magnitude. The new scheme can be used when insufficient or no conductivity data are available. The model also helps to predict the saturated conductivity of the soil matrix alone, and thus to distinguish between the macropore conductivity and the soil matrix conductivity.
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Andre Peters et al.
Status: open (until 10 Mar 2023)
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RC1: 'Comment on hess-2022-431', Gerrit H. de Rooij, 16 Jan 2023
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The paper is generally well-written and clear, and the contribution to soil physics is relevant and suitable for HESS. Below are a few (somewhat) major comments. These, in addition to minor comments, also appear in the annotated manuscript.
The Introduction is well-written and convincingly argued. I think the paper can be embedded in the literature a bit better. I provide two additional references that themselves have additional references that may be worthy of inclusion. I have been following the work of some of the authors, so I know they are well aware of developments in the literature. Perhaps they can use the depth of that awareness to add a few relevant papers. There is no need for a full-blown review though.
L. 201-202
Does the hypothesis of a mildly varying tau_sub_s not implicitly require that the conductivity of a soil that is so wet that all but the largest pores (whose size and shape are determined almost entirely by the soil macrostructure) are filled does not vary much for different textures? I am not convinced that is the case, but have to admit that my reservations are based more on intuition than hard data. I understand the proof will come later in the paper.
Still, I would be interested in a more elaborate treatment of the implications of the range of tau_sub_s for the range of Ksc in Eq. (20). It seems to me that the additional variability in Ks must stem from the other terms in Eq. (20) except the constant beta. Perhaps dot plots of those terms for the soils for which you predicted Ksc could help. I am not sure if that is the best way to explore this, but the interaction between the three non-constant terms in Eq. (20) is of interest but largely neglected.
Eq. (20)
The presence of a residual water content confuses me a little given the tendency in the past 10 - 15 years or so to get rid of it. As a case in point, you quote Schneider and Goss, who provided a finite matric potential for oven-dryness. Does this not contradict the existence of a non-zero residual water content?
L.228
Please add some explanation for your choices for the capillary saturation functions. There are many alternatives, some of which are part of a set of curves that distinguish between adsorbed water and capillary-bound water. There are also versions without asymptote at zero or a non-zero residual water content. Given the argumentation in the Introduction, I did not expect equations with an asymptote to be included. Perhaps you indeed used the non-asymptotic version of Fredlund and Xing? If I recall correctly, they propose several functions, some of which have an asymptote at zero water content.
L. 263
Basically, you fit tau-s instead of Ks to fit the data everywhere except in the range near saturation. In effect you are still scaling a conductivity curve, you just call the scaling parameter something else.
The new element is that you use the fitted value as a predictor for soils where you did not fit it to. These soils each have their own values of the residual and saturated water contents, F(1), and F(Gamma-0), from which emerges an individual value of Ksc using Eq. (20).
Is this a correct description of the procedure? If so, it makes any relationships/correlations between the three non-constant terms of Eq. (20) even more interesting (see my earlier comment).
Figs. 5 and 6
Due to ‘space limitations’ you present only 6 of the predicted curves. Particularly in the box plots of Figs. 5 and 6, there seems to be plenty of space for more red dots. I would not mind seeing a few more, perhaps even all of them. But what is the meaning of the red cross in the vGc column of the left panel in Fig. 5?
Andre Peters et al.
Andre Peters et al.
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