the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 29 Jan 2024
High Resolution Land Surface Modelling over Africa: the role of uncertain soil properties in combination with temporal model resolution
Abstract. Land surface modelling runs with CLM5 over Africa at 3 km resolution were carried out and we assessed the impact of different sources of soil information and different upscaling strategies of the soil information, also in combination with different atmospheric forcings and different temporal resolutions of those atmospheric forcings. FAO and SoilGrids250m were used as soil information. SoilGrids information at 250 m resolution was upscaled to the 3 km grid scale by three different methods: (i) random selection of one of the small SoilGrids250m grid cells contained in the model grid cell; (ii) arithmetic averaging of SoilGrids soil texture values and (iii) selection of the dominant soil texture. These different soil model inputs were combined with different atmospheric forcings model inputs, which provide inputs at different temporal resolutions: CRUNCEPv7 (6-hourly input resolution), GSWPv3 (3-hourly) and WFDE5 (hourly). We found that varying the source of soil texture information (FAO or SoilGrids250m) influences model water balance outputs more than the upscaling methodology of the soil texture maps. However, for high temporal resolution of atmospheric forcings (WFDE5) the different soil texture upscaling methods result in large differences in simulated evapotranspiration, surface runoff and subsurface runoff at the local and regional scales related to the higher resolution representation of rainfall intensity in the model. The upscaling methodology of fine scale soil texture information influences land surface model simulation results, but only clearly in combination with high temporal resolution atmospheric forcings.
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CC1: 'Comment on egusphere-2023-3132', Zhenyu Zhang, 01 Mar 2024
This research conducted a comprehensive study by integrating two distinct soil mapping approaches, FAO and SoilGrids, into CLM modeling in Africa. Additionally, it investigated the impact of different soil texture upscaling methods.
The findings reveal that the origin of soil texture exerts a significant influence on simulated ET, runoff, and soil moisture content, surpassing the impact of the chosen upscaling method. This phenomenon is particularly pronounced as variations in soil properties directly affect surface water distribution. Our prior publication, which integrated soil texture into fully coupled modeling, corroborates this observation and underscores potential feedback loops between soil uncertainty and atmospheric dynamics in the African region.
Zhang, Z., Laux, P., Baade, J., Arnault, J., Wei, J., Wang, X., Liu, Y., Schmullius, C., & Kunstmann, H. (2023). Impact of alternative soil data sources on the uncertainties in simulated land-atmosphere interactions. Agricultural and Forest Meteorology, 339(March), 109565. https://doi.org/10.1016/j.agrformet.2023.109565
All these studies underscore the importance of accurately representing soil texture within land surface models, as well as in coupled land-atmosphere and earth system models. It highlights the necessity for further research to refine these soil representations for improved modeling skills
Citation: https://doi.org/10.5194/egusphere-2023-3132-CC1 - RC2: 'Reply on CC1', Anonymous Referee #2, 27 Apr 2024
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RC1: 'Comment on egusphere-2023-3132', Anonymous Referee #1, 03 Mar 2024
This study uses the CLM5 model over Africa with different atmospheric forcings and soil texture inputs and explores their role in estimates of ET, runoff and soil moisture. Overall, there is a larger effect from the forcing dataset than from the soil texture inputs.
General comments
In my view, the paper in its current form requires major revisions before it can be considered for publication. The authors should specifically address the following limitations:
- Since the study period is relatively short (two years): how can we know the results are generalizable to other years? Would it be an option to extend the study period?
- The study misses an evaluation with reference data of ET, soil moisture, and runoff to better appreciate the impact of the work: which forcing and soil texture input combination do the authors recommend the community to use in future work? Section 3.3 (local results) could be dropped in favor of this.
- Often small impacts are extensively discussed, but it's not clear whether they are significant or not. Statistical tests should be performed to verify this.
- I have methodological concerns about using the "random selection" upscaling technique for a single grid cell and then comparing the results with other upscaling techniques. Such results are not reproducible, so how should they be interpreted?
- The Methdology is too detailed in terms of equations that are used by the LSM. These equations are only useful if they are referenced to in the text to explain or discuss the results, which is currently not the case.
- The DIscussion is too descriptive and fails to convincingly explain some of the results.
Specific comments
- The title hints an impact of "temporal model resolution", but this should be the resolution of the atmospheric forcing dataset (input for the model). Temporal model resolution is always 30 minutes and its impact was not examined.
- L62-69: Part of this paragraph should go to the Methodology, for example, how the dataset was upscaled to the model resolution.
- L70: many new acronyms which should be written in full the first time they are introduced.
- L83: "the novelty of the work lies in ..."; this is more or less a repetition of line 58.
- L88: the Introduction is finished with raising two main research questions on which the authors want to find the answer. I expect to explicitly find an answer to question 1 and question 2 somewhere in the Discussion or (preferably) the Conclusions.
- L94: Add a citation to CLM5.0. Additionally; be consistent in the use of CLM5.0 and CLM5: both are currently found throughout the text.
- L101: "CLM5.0 therefore has features of great interest for land surface modelling over Africa at a high resolution". Why specifically Africa?
- L106: Acronym for CLM5 should be written out in full the first time it is used (in the Introduction).
- L107: Change "The total porosity is given by" to "The total porosity for level i is given by".
- L117: Citation for the Brooks and Corey model? What is the physical interpretation of "the exponent B"?
- Overall, the introduction has too many equations which are not referenced anywhere in the text. So it is not clear what their added value is. Either they should be used in the Discussion to support in explaining the observed results, or they should be replaced by a citation of the CLM5 paper in which they can probably also be found?
- L162: Add that the time step Δt is expressed in seconds.
- L167: What is the native resolution of IGBP-DIS? Is it 3km? If not, why is only SoilGrids250m upscaled?
- L170: "The 10 upper CLM5 soil layers": how many are there in total? This type of information on the model should be in section 2.1, rather than the equations which are currently there.
- L181-182: Too detailed and not relevant for this study.
- L200: Is method (iii) the novelty of this paper? If yes, it should be mentioned explicitly and motivated (why is a third new method necessary if two other already exist). If no, a citation to another study using this approach is missing.
- Table 1: the longitudinal extent is not relevant for this study: all three are global and thus cover the study domain.
- L256: "A spatially varying soil thickness dataset": which one?
- L270: "the hourly WFDE5 forcings were also aggregated to 3 hours and used in new simulations". Perhaps this should go to section 2.4. Why not 6h, so all three forcings are comparable?
- L276: "A simulated variable for a certain atmospheric forcings- soil texture map combination at a given time step is denoted by M1(t), M2(t), M3(t) and M4(t)." Don't you have 12 different of such combinations? Or do you mean that you have 4 such combinations for each atmospheric forcing? Please be more specific in the definition. Also, define t as the time step.
- L290: ET is evapotranspiration, not evaporation.
- L298: "marginally" implies that you performed a statistical test. One should actually be performed to assess the significance of the differences in mean/max ET (and other variables), depending on the texture, forcing and their interaction.
- L330-332: again, the statements made here require hypothesis testing.
- L335: WFDE5 are not hourly because they have been aggregated to 3h (L270)?
- L337: "The increased surface runoff in the WFDE5 forced simulations reduces the availability of water for ET processes"; can this claim be made with certainty if it also rains more, so more water is coming in (cfr. Fig. 2)?
- L339: "A higher temporal resolution of the atmospheric forcings as for WFDE5 will result in higher peaks of precipitation intensity, whereas a coarser resolution of 6 hours like for CRUNCEP will average out intensive precipitation over longer time periods with less high peaks in precipitation intensity"; but wasn't this effect to be mitigated by aggregating the hourly dataset (L270)?
- L353: "significantly influenced": be careful using this word.
- L355: "The most substantial differences in simulated subsurface runoff are observed between the FAO soil map the SoilGrids250m maps"; actually the forcings still result in the largest differences.
- L364: Fig. 4 should be referenced to at the start of the section.
- L371: It's confusing to talk about ET in the section on runoff, save this for the Discussion.
- L382: "weighted average": how are the weights defined? Perhaps this is something to be described in the Methodology.
- L394: This statement doesn't hold for the mean: similar effect when varying the forcing and the texture.
- Figure 5:
- Would it make sense to also look at min soil moisture, next to mean and max?
- Units of the colorbar are mm³/mm³, while elsewhere in the text cm³/cm³ is used. Be consistent. Also, consider using more sensible intervals for the colorbar ticks.
- Perhaps one could keep the first column as is, and the other columns could show the difference w.r.t. the first column? Now all 12 maps look identitical and it's hard to see where the differences are.
- Section 3.2: Consider moving the definitions of the regions to the Methodology.
- L410: Motivate the choice for Sahara and Central Africa (dry and wet region?).
- L417: This statement is true on average (it may well exceed this in certain pixels).
- Figure 7:
- "Soil water content", "soil moisture content", "soil moisture" are all used interchangeably. Be consistent.
- Dominant and Random are very similar, as can be expected. The first one has already been used in literature. Please motivate (in the Methodology) the advantage of Random to justify it as a new technique (e.g., are there computational gains?). Also: which one is best? Why was no evaluation performed? There are many global grid-based ET products available that can serve as reference.
- L430: Why is the long name for ET suddenly introduced here?
- L434: "Subsurface runoff shows a decreasing trend, which is attributed to initially higher groundwater levels"; was it already decreasing during the spinup? In this case, the spinup time of 1 year may not have been sufficient. What spinup times are used in similar studies?
- L443: Again, I am confused that WFDE5 would have an hourly temporal resolution while it is states in the Methodology that it was aggregated to 3h.
- Figure 8: can we conclude from this that one shouldn't use mean soil texture properties, as it is the only one deviating from all others? Of course, this claim would be stronger if an external product were used for validation.
- L465: "... variations in ET values across different soil texture maps", not really: only a single one shows a variation (and this is probably also the limitation of your average margin metric, which isn't able to identify this "outlier"). The following paragraph fails to mention or discuss this fact.
- L481: "We selected one location for each of the eight climate regions"; only two are in the main text.
- L490-492: Add a paragraph on the crop model/irrigation model in CLM5 to the Methdology.
- L494-495: Not clear from the Figure.
- L499: "While slight variations in surface runoff are found ..."; again, be more specific which soil texture maps differ, since they don't all differ from one another!
- Figure 9:
- Panel "S.Runoff-WFDE5": is the Mean on top of the other textures, or the Rand?
- What is the FAO/Dom soil texture type for this grid cell?
- The Rand soil texture may be very different when the experiment is performed again, no? Since this is only a single pixel. Hence, the study is actually not reproducible in this sence. Also see Fig. 10: here Rand just happens to coincide with Dom in terms of soil texture class, so the lines overlap. But run the experiment again and the results may be completely different. I suggest performing "Rand" several times and reporting an ensemble average to resolve this issue.
- L519-529: Same remark as earlier: the difference in soil texture maps is always just cause by Mean vs. all the rest.
- Table 3: Not clear what is presented here: difference between which two things? Units?
- Table 5:
- How come the SGMean column has a specific soil texture class? I thought this approach created a "new" class with averaged soil metrics.
- Is referenced nowhere in the text. Please use it to discuss for example the bottom row of Fig. 9. Overall, the Discussion describes the figures and results too much, rather than explaining what we see in them (and why!).
- Conclusion: misses outlook discussing how future research can benefit from the results of the study.
Technical corrections
- L55: "0.5o" instead of "0.5°"
- L162: "kg/m²s" instead of "kg/(m²s)"
Citation: https://doi.org/10.5194/egusphere-2023-3132-RC1
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