Predicting the soil water retention curve from the particle size distribution based on a pore space geometry containing slit-shaped spaces

Chang, Chen-Chao; Cheng, Dong-Hui

doi:https://doi.org/10.5194/hess-22-4621-2018

Articles | Volume 22, issue 9

https://doi.org/10.5194/hess-22-4621-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-22-4621-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 22, issue 9

Research article

|

04 Sep 2018

Research article |

| 04 Sep 2018

Predicting the soil water retention curve from the particle size distribution based on a pore space geometry containing slit-shaped spaces

Chen-Chao Chang and Dong-Hui Cheng

Download

Final revised paper (published on 04 Sep 2018)
Supplement to the final revised paper
Preprint (discussion started on 05 Dec 2017)
Supplement to the preprint

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

- Printer-friendly version

- Supplement

RC1: 'Interactive comment on Predicting the soil water characteristic curve from the particle size distribution based on a pore space geometry containing slit-shaped spaces by Chen-chao Chang and Dong-hui Cheng', Fatemeh Meskini-Vishkaee, 17 Jan 2018
- AC1: 'Responses to comments posted by F. Meskini-Vishkaee', Dong-hui Cheng, 15 Feb 2018
  - AC3: 'author's changes in manuscript', Dong-hui Cheng, 03 May 2018
RC2: 'Referee Comments', Anonymous Referee #2, 14 Apr 2018
- AC2: 'Interactive comment on “Predicting the soil water characteristic curve from the particle size distribution based on a pore space geometry containing slit-shaped spaces” by Chen-chao Chang and Dong-hui Cheng', Dong-hui Cheng, 03 May 2018
- AC4: 'author's changes in manuscript', Dong-hui Cheng, 03 May 2018

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

ED: Reconsider after major revisions (further review by editor and referees) (08 May 2018) by Roberto Greco

AR by Donghui Cheng on behalf of the Authors (27 May 2018) Author's response Manuscript

ED: Referee Nomination & Report Request started (11 Jun 2018) by Roberto Greco

RR by Fatemeh Meskini-Vishkaee (01 Jul 2018)

Suggestions for revision or reasons for rejection

Interactive comment on predicting the soil water characteristic curve from particle size distribution based on a pore space geometry containing slit-shaped space by Chen-chao Chang and Dong-hui Cheng

The authors propose a modified approach to estimate the soil moisture characteristic curve (SMC) from the measurement of the particle size distribution (PSD) and specific surface area (SSA). The authors used an empirical power equation with two fitting parameters to estimate SSA proposed by Sepaskhah et al. (2010). Subsequently, estimated SSA is used to estimate two other fitting parameters ( and ), and at the end estimated  and  are used to estimate the slit pore volume fraction. In spite of adding seven soil samples with more clay content in the validation dataset and Comparing proposed model with the scaling approach proposed by Meskini-Vishkaee et al. (2014), result and discussion part of manuscript must be extended.

The minimal requirements for possible publication mandate the following revisions:

i: In response to reviewer's comment "Specific surface area (SSA) is a required parameter to obtain the values of α and β. The authors used a power equation with two fitting parameters (Eqn. 10) to estimate SSA proposed by Sepaskhah et al. (2010). Sepaskhah et al. (2010) used twenty soil samples from a depth of 0–30 cm were collected from different locations in Fars province, in the south of Iran to calibrate the power equation. In addition, a different set of data was used to validate the calibrated model. Their results indicated that in the range of around 20 up to 200 m^2 g^-1 the values of measured SSA were in quite a good agreement, while for SSA greater than 200 m^2 g^-1, the deviations increase distinctly. As respects higher SSA is related to finer texture soils that usually have underestimation problem of estimated SWCC from PSD, Indeed, I think use power model to estimate SSA cannot be useful to improve estimated SWCC in fine-textured soils. ", the authors mentioned that in proposed method, the values of parameter α and β were firstly figured out using SSA and the measured SWC, and then these parameters were used for predicting SWC as input parameters. For the predicted SWCs of fine-textured soils which calculated from the parameter α and β, the errors from estimated SSA, to some extend, could been offset by the parameter α and β. Besides, the parameter α and β were main used to estimate the volume fraction of the slit-shaped spaces, thus the estimation accuracy of SSA influence the estimation of the volume fraction of the slit-shaped spaces, consequently the degree of improvement of predicted SWC.

The main objective to estimate SMC from PSD is to have SMC data when this data is not available. However, as mentioned above, SMC data is needed to develop proposed model for the accurate estimation especially for the fine-textured soils.

ii: regardless good performance of proposed model, SSA is used to develop model and SSA measurement is very difficult, thus it must be estimated. The proposed model is an estimation method that needs to estimate its input parameters. It is a disadvantage of proposed model.

The authors noted that the results illustrated that the improved method here applied well to a wide range of soils, while the scaling approach performed better for fine- and medium-textured soils. The validation results illustrate that the SMC predicted using the proposed method provided the best predictions of the SMC, closely followed by the scaling approach, and the traditional method performed worst. Did the authors do any statistical analysis between the performances of three models? Is there any significant difference between three models? The authors could perform a paired T-test analysis between proposed and Meskini-Vishkaee models for mean model performance in different soil textural classes.

Even if there was a significant difference between two desired models, the proposed model is a complex model with some input parameters that is need to estimate from some difficult properties such as SSA. It is correct that the assumption of pore space geometry containing slit-shaped spaces may be affected on the accuracy of the estimation, but on the other hand, this assumption could be increased the model inputs and complexity.

I think that the authors have to add some more discussion to explain the advantage and disadvantages of proposed model. Moreover, performing statistical analysis between model performances is necessary and must be added to the manuscript text.

Minor revision:
The authors provided detailed information of both validation and calibration data sets in two table in response to reviewers. I think these tables have to add in main text of manuscript. Note: offering of the statistical criteria for each soil textural classes is not necessary. Presenting of the mean, max and min of all data for both dataset is enough.

Referee Report: PDF

Hide

RR by Anonymous Referee #2 (02 Jul 2018)

Suggestions for revision or reasons for rejection

General comment
In the revised version of the manuscript, most of my concerns have been addressed, and thus I have no more reservations about the possible publication of this manuscript. However, there are still some minor points that should be improved (see “minor points” below) and, more important, the English language throughout the entire manuscript is quite poor, and the help of a native speaker is definitely needed before publication.
Minor points
Throughout the entire manuscript, I strongly suggest using “soil water retention curve”, rather than “soil water characteristic curve”, as it is by far the more established (and clear) name for the curve you are estimating (indeed, usually two soil water characteristic curves are considered: water retention curve and hydraulic conductivity curve). Consistently, I would adopt the acronym SWRC (or simply WRC) instead of SWC.
Section 3.1
This modified section now gives more information about how water potential in silt shaped voids has been calculated. However, this section should be rewritten and better organized:
- Page 5, line 20: the values of the parameters of Table 2 are here discussed before introducing Table 2 and the way such parameters were estimated. Section 3.1 should contain only general (theoretical) arguments, and the eventual confirmation given by the estimated parameters should be discussed afterwards.
- Page 5, equation (7): I don’t see the necessity of writing this equation, as it is exactly the same as equation (4) (cos  = 1), written in terms of water potential rather than in terms of suction head. You should simplify your discussion by simply stating that you are using exactly the same equation in circular voids and in slits.
- Page 5, lines 24-26: you state that 6502 is smaller than 5000, while it is obviously not so. This is again the issue of the intrinsic negative values of water potential that I already raised in my previous report. You should decide if you want to refer to suction head (as you write in your answer to my previous comment), or to water potential (as in equation 7), and then stick consistently to this choice throughout the entire manuscript.
- Page 5, lines 25-26: I don’t agree with this statement. The values of suction head do not demonstrate anything about the dimensions of the voids where the meniscus is supposed to be located. They are, instead, a consequence of the dimensions, which are in turn a consequence of the estimated values of the parameters  and β, that you are discussing here, before explaining how they were estimated and which results you obtained (see my previous comment in this respect).
- Page 5, line 27: the word “included”, instead of “contained”, would be more appropriate and would make the concept clearer to the reader.
My concerns about soil surface area and soil specific surface area have been addressed in section 4.3.2. Few minor issues in the newly added parts:
- Page 10, line 10: The meaning of the sentence “This effect may contribute to the lower SSA value for this texture than the fine-textured soil” is obscure. Please, reformulate it, or delete it.
- Page 10, line 13: What does it mean “soil media data”?
- Page 10, lines 19-21: The syntax of the two sentences, from “Overall” till the end of the section, is wrong.
Section 4.3.3 is called “Physical meanings of the parameters”, while it actually gives no physical interpretation of the obtained values. Just a comparison with the results of the conceptually similar model by Or and Tuller (1999) is proposed. I also observe that the adopted model of the pore geometry is just conceptual (and not physical at all), so I would refrain from claiming that the obtained parameter values have any physical meaning, and I would use another name for this section.

Hide

ED: Publish subject to minor revisions (review by editor) (10 Jul 2018) by Roberto Greco

AR by Donghui Cheng on behalf of the Authors (27 Jul 2018) Author's response Manuscript

ED: Publish subject to technical corrections (08 Aug 2018) by Roberto Greco

AR by Donghui Cheng on behalf of the Authors (12 Aug 2018) Author's response Manuscript

Post-review adjustments

AA: Author's adjustment | EA: Editor approval

AA by Donghui Cheng on behalf of the Authors (27 Aug 2018) Author's adjustment Manuscript

EA: Adjustments approved (02 Sep 2018) by Roberto Greco

Short summary

The soil water retention curve (SWRC) is fundamental to researching water flow and chemical transport in unsaturated media. However, the traditional prediction models underestimate the water content in the dry range of the SWRC. A method was therefore proposed to improve the estimation of the SWRC using a pore model containing slit-shaped spaces. The results show that the predicted SWRCs using the improved method reasonably approximated the measured SWRCs.