the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 02 Nov 2023
Regional patterns and drivers of water flows along environmental, functional and stand structure gradients in Spanish forests
Abstract. The study of the water cycle in the forest at large scales, such as countries, is challenging due to the difficulty of correctly estimating forest water flows. Hydrological models can be coupled to extensive forest data sources, such as national forest inventories, to estimate the water flow of forests over large extents, but so far the studies conducted have not analysed in detail the role of stand structure variables or the functional traits of the forest on predicted blue/green water flows. In this study, we modelled the water balance of Spanish forests using stand structure and species data from forest inventories to understand the effects of climate, stand structure, and functional groups on blue water flows. We conducted model simulations across a gradient of different climatic biomes and predominant functional trait species. We calculated both the blue water (surface runoff and drainage to groundwater) and green water (evapotranspiration). Our analysis focused on relative blue water, the ratio between blue water and total precipitation. Climatic, stand structure, and topographic variables were used to interpret the determinants of variation of relative blue water. The results showed that higher relative blue water is mainly concentrated in the wetter regions of Spain and during the autumn-winter season. Leaf Area Index (LAI) of the forest stand is the most important predictor of relative blue water, exhibiting a negative effect until it reaches a plateau at higher levels. Deciduous forests showed a greater relative blue water content than evergreen functional groups, primarily due to leaf fall during the autumn-winter season. This study highlights the significant importance in a Mediterranean region of seasonal distribution in water flow and how seasonal LAI can act as a crucial filter for excess water.
- Preprint
(1057 KB) - Metadata XML
- BibTeX
- EndNote
Status: final response (author comments only)
-
RC1: 'Comment on hess-2023-255', Anonymous Referee #1, 05 Dec 2023
This work can represent a very significant contribution to improve our knowledge about the effect of forest structure and climate on water resources (or blue and green waters) in a very heterogenous context of climates and forest typologies such as Spain. Although it is true that it has been proposed at country level, in my opinion, the results presented are those expected in terms of the differences observed between the biomes and the types of forests present in the study area, which gives credibility to the results from modelling apart from other about the validation of results based on field data (transpiration, water in the soil, runoff, etc.). Its subsequent use in studies at higher spatial resolution (at the basin scale, for example) could help managers to better define forest management with a strong hydrological foundation (to improve green water, blue water, or both) and therefore promoting ecohydrology-based management to improve water resource availability at regional scales.
I value very positively the great effort made in terms of obtaining information for the characterization of the forest inventory plots, as well as other information necessary to apply the model on a scale as ambitious as Iberian Spain, with such diverse forest typologies and climates and extension.
However, I believe that the work still needs to be greatly improved in order to be finally published in a journal like HESS. In this sense, I consider very important to justify the model performance to produce realistic estimates for all the forest typologies addressed in the work. Although it is true that a recent work is indicated (De Cáceres et al., 2023) where the validation for some species with experimental field data is presented, this work should at least indicate how the latter has addressed the calibration and validation of the model, and not only relegating this information to the conclusions section. As I indicated, I understand this is not straightforward given you have not experimental plots in all the forest typologies, but I understand more efforts should be carried out to justify the use of the model for the entire territory of Spain and the validity of their results. In this sense, maybe you can consider available databases to discuss about the model performance (such as that for sapflow from https://essd.copernicus.org/articles/13/2607/2021/) when comparing your estimates to some hydrological component such as transpiration or runoff. On the other hand, although the fundamentals of the model are understood quite clearly, I believe that the work should not simplify relevant information to understand how the calculations have been carried out in the study, either in the materials and methods section or as additional information. For example, it is not explained how the LAI is calculated or how the data obtained for the IF3 plots are regionalized or how the modeled fluxes are extrapolated to the entire Spanish forest territory (except for the Canary Islands). In my opinion, this aspect must be clearly addressed, since in previous works related to your model performance, I am not able to understand how this is addressed. I believe this work is the correct place to give a proper explanation about how spatial dimension is considered within your model framework.
I believe English flows well and the manuscript is easy to be followed due to ist clearity and well structure. I have, however, several suggestions and comments which have been added as comments within the manuscript. In this sense, apart from considerations about the modelling approach, I would recommend reviewing the introduction section for improving it (see please my comments on it).
- AC1: 'Reply on RC1', Jesus Sánchez, 01 Feb 2024
-
RC2: 'Comment on hess-2023-255', Anonymous Referee #2, 20 Dec 2023
Sánchez-Dávila and co-authors present an exhaustive modelling exercise to quantify green and blue water fluxes across Spanish forests (excluding the Canary Islands). These estimations are based on a very detailed forest ecosystem model, which has been evaluated against ecophysiological data at the plot level and against forest dynamics in Spain, with good results. In this paper, the results on blue water (BW) and %BW and the patterns associated with climate and functional groups are somewhat expected, but nevertheless valuable, as they had not been examined with this level of detail before in Spain, an area with diverse forests and where water management for ecosystems and society is a pressing issue. The text and figures are generally clear, although I provide some suggestions to improve clarity in my specific comments below.
My major criticism of the paper is that it could have gone beyond a modelling exercise and aim at gathering more evidence that could have provided (i) a more robust spatial evaluation of the model or (ii) additional support to the paper’s conclusions. I’m aware that there are no eddy flux or sap flux stations with a sufficient geographic coverage to be representative of the entire country, precluding an extensive model evaluation. However, I wonder whether products such as FLUXCOM ET, with a relatively good spatial resolution (Jung et al. 2019), could be used to address some of the questions in the paper. This is a gridded product which I believe would be relatively straightforward to use in the paper and it also provides estimates of GPP, which aid in the interpretation of some of the results (see my specific comments below). I think that showing results from the modelling alongside FLUXCOM would make this a more valuable contribution.
If the authors decide to stick to the model results, then I think that the title of the paper should explicitly say that it’s a modelling exercise. For example ‘Regional patterns and drivers of modelled water flows along environmental, functional and stand structure gradients in Spanish forests’. Or maybe mention the combination of modelling and forest inventory data, which, to me, is also a nice contribution of the paper and that would remain hidden if only ‘model’ appears in the title. Anyway, these are suggestions that put the focus more on the methodology and tools, which seems to align with what the paper currently shows. If, alternatively, the focus is indeed on the actual results and geographical patterns, the additional suggested analyses above would make this contribution more valuable, in my opinion.
Specific comments
The abstract reads a bit vague, I think it that someone skimming the abstract would expect some overall quantification of green and blue water for different forest types and different seasons for example. Lines 12-19 simply describe methods and it feels a bit excessive, so you could make these descriptions a bit more compact and free some space to provide more quantitative results. Also, the last sentence of the abstract, especially the last part is not very clear to me. Why do you say LAI is a filter for excess water? Do you mean that LAI influences the partitioning of blue and green water, for example?
- L. 49. The Jasechko study was a bit controversial (Coender-Gerrits et al. 2014), I would suggest you use other references to support the dominant role of transpiration in terrestrial ET for specific biomes (Schlesinger & Jasechko 2014; Wei et al 2017). Also, please be sure to provide the correct numbers for the estimations of green water when comparing with estimations that only provide transpiration,or T/ET. I understand that green water also includes evaporation from vegetated and other surfaces.
- L. 61 - 62. I think that the focus here is more on ‘water saver’ vs ‘water spender’ species, which is not always related to isohydric-anisohydric dimension (Martínez-Vilalta & Garcia-Forner 2017). I would simply mention the distinction between water spender/water savers or loose vs strict stomatal regulation of transpiration.
- L. 72. When reading here ‘forest species traits’ one would expect that this is dealt with in this paper, but this is not the case. It’s more a functional type approach.
L.133. Is it E-OBS or Worldclim data you are using? As I understand, E-OBS contains already the daily data you need, and the link is not the same as the official E-OBS one (https://www.ecad.eu/download/ensembles/download.php); could you clarify this?
- L. 136. Provide website URL for IGN.
- L. 138 - 141. I would use ‘climatic moisture’, to differentiate from ‘soil moisture’, for example. And for ‘seasonality’ use ‘precipitation seasonality’, also in figures and other instance throughout the text (abbreviated, if needed).
- L. 173. These are aTmax and bTmax in De Cáceres et al 2023, right? As it’s written, it’s not very clear how maximum transpiration is estimated.Given the relevance of LAI in these calculations (which impact on the results you observe), maybe it’s worth including the equations relating max transpiration and LAI here, and explain clearly how species-specific parameters are obtained.
- L. 191-193. Please provide the source of these allometries, if published, or provide them in a supplementary table if you have derived them for this study. If the latter, please also explain briefly which data sources were employed and the overall data design (number of tree/plot replicates, geographical scope – Spain or also data from elsewhere).
- L. 209.’functional group’
- L. 229. How was this R2 estimated?
- L. 241. Readers not familiar with Spain’s geography will not know where these ranges are. I would suggest providing a more complete figure A1, with several informative layers, as well as the current climatic biomes. These would need to include topography, distribution of forest types and also the important spatial predictors in your models from Fig 5 (i.e. multiple small maps in fig A1).
Figure 2 and Figure 3. I think it would be informative to show in these figures also the % of soil evaporation and interception. Even Figure 4 could show the contributions of T vs interception + soil evaporation (these two could be combined for clarity) by showing this within the ‘blue water’ bar.
Related to this, I also wonder whether you could check whether the results agree with the global patterns observed for the partitioning of terrestrial evaporative fluxes: a plot of interception, transpiration and soil evaporation, as a fraction of rainfall following Good et al. 2017 paper (Fig. 1), as a function of climatic aridity.
- L. 350.’water evapotranspired or intercepted by the canopy’
- L . 365-366. The model provides the ET partitioning so you could show T,soil evaporation and interception to support this interpretation, I believe.
- L. 382. This statement on the anisohydry is too general to be used here and I would be more cautious; moreover, I don’t think this pattern emerges from Klein’s paper. Deciduous species such as Populus or Quercus robur (Martínez-Vilalta et al. 2014; Urli et al 2014) can hardly be considered isohydric, and some evergreen species within Juniperus can be quite anisohydric.
- L. 404. ‘produce more blue water’
- L. 428. ‘ a poor predictor’
- L. 441 - 443. But in fact this detailed partitioning of the fluxes has not been addressed in the paper.
- L. 450-460. I don’t find this part of the conclusions particularly meaningful. In fact,most of the conclusions before this section revolve around the limitations of your approach (a model, not observations) and I think you could highlight better what are the broader implications of your exercise, both in methodological terms and for the results you obtain. What is the power of combining actual forest structure data with a process based model to estimate forest water fluxes in time? i.e. incorporating variation in forest structure through repeated NFI or other airborne/remote sensing surveys, for example.
References
Coenders-Gerrits, A. M. J., van der Ent, R. J., Bogaard, T. A., Wang-Erlandsson, L., Hrachowitz, M., and Savenije, H. H. G.: Uncertainties in transpiration estimates, Nature, 506, E1–E2, https://doi.org/10.1038/nature12925, 2014.
Good, S. P., Moore, G. W., and Miralles, D. G.: A mesic maximum in biological water use demarcates biome sensitivity to aridity shifts, Nature Ecology & Evolution, 1, 1883–1888, https://doi.org/10.1038/s41559-017-0371-8, 2017.
Jung, M., Koirala, S., Weber, U., Ichii, K., Gans, F., Camps-Valls, G., Papale, D., Schwalm, C., Tramontana, G., and Reichstein, M.: The FLUXCOM ensemble of global land-atmosphere energy fluxes, Scientific Data, 6, 74, https://doi.org/10.1038/s41597-019-0076-8, 2019.
Martínez-Vilalta, J. and Garcia-Forner, N.: Water potential regulation, stomatal behaviour and hydraulic transport under drought: deconstructing the iso/anisohydric concept, Plant Cell Environ, 40, 962–976, https://doi.org/10.1111/pce.12846, 2017.
Schlesinger, W. H. and Jasechko, S.: Transpiration in the global water cycle, Agricultural and Forest Meteorology, 189–190, 115–117, https://doi.org/10.1016/j.agrformet.2014.01.011, 2014.
Urli, M., Lamy, J.-B., Sin, F., Burlett, R., Delzon, S., and Porté, A. J.: The high vulnerability of Quercus robur to drought at its southern margin paves the way for Quercus ilex, Plant Ecol, 1–11, https://doi.org/10.1007/s11258-014-0426-8, 2014.
Wei, Z., Yoshimura, K., Wang, L., Miralles, D. G., Jasechko, S., and Lee, X.: Revisiting the contribution of transpiration to global terrestrial evapotranspiration, Geophys. Res. Lett., 2016GL072235, https://doi.org/10.1002/2016GL072235, 2017.
Citation: https://doi.org/10.5194/hess-2023-255-RC2 - AC2: 'Reply on RC2', Jesus Sánchez, 01 Feb 2024
Viewed
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 304 | 73 | 22 | 399 | 13 | 14 |
- HTML: 304
- PDF: 73
- XML: 22
- Total: 399
- BibTeX: 13
- EndNote: 14
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
Jesús Sánchez-Dávila
Miquel De Cáceres
Jordi Vayreda
Javier Retana
Forest blue water is determined by climate, functional traits and stand structure variables. LAI is the main driver in the trade-off between the blue and green water. Blue water is concentrated in the autumn-winter season and deciduous trees can increase the relative blue water. The leave phenology and seasonal distribution are determinants in the relative blue water.
Forest blue water is determined by climate, functional traits and stand structure variables. LAI...