Mapping the suitability of groundwater-dependent  vegetation in a semi-arid Mediterranean area

Gomes Marques, Inês; Nascimento, João; Cardoso, Rita M.; Miguéns, Filipe; Condesso de Melo, Maria Teresa; Soares, Pedro M. M.; Gouveia, Célia M.; Kurz Besson, Cathy

doi:https://doi.org/10.5194/hess-23-3525-2019

Articles | Volume 23, issue 9

https://doi.org/10.5194/hess-23-3525-2019

© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-23-3525-2019

© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 23, issue 9

Research article

|

03 Sep 2019

Research article |

| 03 Sep 2019

Mapping the suitability of groundwater-dependent vegetation in a semi-arid Mediterranean area

Inês Gomes Marques, João Nascimento, Rita M. Cardoso, Filipe Miguéns, Maria Teresa Condesso de Melo, Pedro M. M. Soares, Célia M. Gouveia, and Cathy Kurz Besson

Download

Final revised paper (published on 03 Sep 2019)
Preprint (discussion started on 22 May 2018)

Interactive discussion

Status: closed

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

- Printer-friendly version

- Supplement

RC1: 'Review of Gomes Marques et al. "Mapping the suitability of subsurface groundwater dependent vegetation in a semi-arid Mediterranean area"', Anonymous Referee #1, 04 Jul 2018
- AC1: 'Response to Reviewer1', Inês Marques, 25 Nov 2018
RC2: 'Referee Comment', Anonymous Referee #2, 04 Sep 2018
- AC2: 'Response to Reviewer2', Inês Marques, 25 Nov 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) (07 Dec 2018) by Miriam Coenders-Gerrits

AR by Inês Marques on behalf of the Authors (19 Jan 2019) Author's response Manuscript

ED: Referee Nomination & Report Request started (25 Jan 2019) by Miriam Coenders-Gerrits

RR by Anonymous Referee #2 (11 Mar 2019)

Suggestions for revision or reasons for rejection

The manuscript has been quite significantly altered from its previous version, with many relevant and good aspects added to the model development, suitability map building, map evaluation and sensitivity assessment, in terms of methodology, results and discussion. It really has become an interesting paper to read, with very good use of references throughout. I maintain my opinion that the strongest part is the regression model, and that this model can be applied very well in the future for scenario analysis within the same study area. I continue to believe that the sustainability map is less interesting, because it basically uses the regression model that was already locally optimised using GWR to calculate a new map, but it is only applicable to the study area, precisely due to the local nature of the regression coefficients. Moreover, it does not fit the original vegetation (Kernel density) map as well as the authors claim based on their validation. Please allow me to start with these two main concerns I would like to see addressed in the discussion.

1. The calculation of R-squared of the GWR model provides very good results. Nothwithstanding, the resulting local coefficients vary largely, from highly positive to highly negative. Moreover, the variations sometimes occur on very small distances. This means that the effect of Ai or groundwater depth on groundwater dependent vegetation can vary from highly positive to highly negative throughout the area and even within very short distances. This seems purely a statistical exercise with apparently little physical meaning and needs to be addressed in the discussion. What does it mean? How then is this method valid and applicable elsewhere?

2. Given the high weight of the aridity index (Ai) in the regression map, the groundwater dependent vegetation (GDV) suitability map now closely follows the Ai categorised map (Fig. B1a), as also mentioned by the authors. The good agreement observed by the authors between the suitability map and the groundwater depth map, in my view is a coincidence, as the groundwater depth map in fact follows the aridity index map. In other words, in the more humid areas the groundwater level seems shallower, and vice-versa. In addition, the GDV suitability map does not show a good correspondence with the GDV occurrence map (Fig. 1), unlike the previous suitability map that was produced in the first version of the manuscript. In the former map (version 1 of the manuscript) soil type was the most important parameter, but that parameter was now taken out. As a direct consequence, the highest GDV density in the central north now occurs in an area of very poor to poor mapped GDV suitability, whereas in the southeastern area the GDV density is very low in an area of very good suitability. I acknowledge that the reality is always more complex and that the authors already refer to this in their discussion, but please also address the issues I have mentioned. The fact that the suitability maps fits well with the NDWI map, could be a logical consequence of the fact that the latter represents moisture content in vegetation. Why would the highest stress be indicative for groundwater dependency? Wouldn’t you expect stress to decrease if the trees have access to groundwater?

Some other comments are given below:

The abstract is well written.

The introduction provides a very good overview on the need of study, but could mention the other work/studies carried out so far in the field. That is currently limited to one sentence (ln 127-129), so that the paper does not show the added value of the implemented methodology as compared to existing studies, some of which are actually referred to later on in the manuscript (e.g. Barron et al., 2014; Condesso de Melo, 2015; Costa et al., 2008; Doody et al., 2017). Therefore, no new references are needed.

In material and methods, section 2.3.1, attributing a low GDV suitability score to soils of high clay content can be debated. Soils of a finer texture will have large extinction depths due to an increased capacity of capillary rise. I would expect coarser soils to have vegetation of lower groundwater dependency. Please briefly elucidate on this aspect.

In the model development (material and methods, section 2.5), how many data points are used (and what is the search radius) for the calculation of local model coefficients?

In section 2.7 of material and methods briefly explain for what purpose the NDWI anomaly map was calculated.

Please explain why you select slope (s) rather than soil thickness (S), if the latter has a higher correlation with principle component axis 2 (PC2).

What happens to R-squared when reducing the set to four or even three variables? Given the large weight of Ai and O4 I would expect the impact to be small. Have you considered using a reduced set? This would largely facilitate the application of the method in other areas.

Other minor comments and technical corrections:

Ln 17: delete the word “scenarios”
Ln 19: delete the words “the density of”
Ln 25: “closely followed”: this is not true. The other three parameters (groundwater depth, drainage density and slope) follow at a large distance, i.e. they are of much lower importance in the regression model.
Ln 28: “relative proportion”. Please briefly clarify what it means. Is it the local coefficient divided by sum of local coefficients? When negative, do you use absolute values (which would make sense)? This needs to be explained in detail in section 2.6 (pg 11 ln 329-341).
Ln 60: include
Ln 61: “subsurface groundwater” seems a pleonasm, although I understand what you mean, when comparing it to “surface groundwater”. Perhaps you could consider using the terms “emerging groundwater” vs. “resident groundwater”.
Ln 62: “a visible source”
Ln 64-65: place the references after GDE
Ln 74: “relying on”, perhaps use “entirely relying on”
Ln 76: “root system”
Ln 115: “rising temperature”
Ln 129-130: rephrase “coefficients proportions”, e.g. to “coefficients as proportion of total sum of absolute coefficients”.
Ln 184: “low drainage capacity”, “high clay fraction”
Ln 325-328: lower drainage density leads to higher suitability, which is correct, but the explanation is incorrect, as the explanation in fact suggests the opposite, or so it seems.
Ln 342-343: I suggest using “representing” instead of the word “referred”.
Ln 402: and in the south?
Ln 422: the maximum value on the map seems much higher than the value indicated in the text (0.714).
Ln 488: I suggest changing to: “poor suitability to GDV, corresponding to”
Ln 572: “did not only allow”

Figure 3: What are the units in this figure?
Figure 4: The reference to the different maps in the figure title is incorrect. Figure 4a is aridity index, not soil type, etc.
Figure 10: I would not use green to indicate highest stress.
Table 4: Values for slope and aridity index are incorrect in the table (the order of the scores 1-3 is inversed, as can be seen in the maps of Fig. B1, which are correct).

Hide

RR by Anonymous Referee #3 (27 Apr 2019)

ED: Publish subject to revisions (further review by editor and referees) (10 May 2019) by Miriam Coenders-Gerrits

AR by Inês Marques on behalf of the Authors (21 Jun 2019) Author's response Manuscript

ED: Publish subject to minor revisions (further review by editor) (08 Jul 2019) by Miriam Coenders-Gerrits

ED: Publish as is (09 Jul 2019) by Miriam Coenders-Gerrits

AR by Inês Marques on behalf of the Authors (20 Jul 2019) Manuscript

Short summary

Mediterranean cork woodlands are very particular agroforestry systems present in a confined area of the Mediterranean Basin. They are of great importance due to their high socioeconomic value; however, a decrease in water availability has put this system in danger. In this paper we build a model that explains this system's tree-species distribution in southern Portugal from environmental variables. This could help predict their future distribution under changing climatic conditions.