the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A first attempt to model global hydrology at hyper-resolution
Abstract. Global hydrological models are one of the key tools that can help meet the needs of stakeholders and policy makers when water management strategies and policies are developed. The primary objective of this paper is therefore to establish a first of its kind, truly global hyper-resolution hydrological model that spans a multiple-decade period (1985–2019). To achieve this, two key limitations are addressed, namely the lack of high resolution meteorological data and insufficient representation of lateral movement of snow and ice. Thus a novel meteorological downscaling procedure that better incorporates fine-scale topographic climate drivers is incorporated, and a snow module capable of lateral movement of frozen water resembling glaciers, avalanches and wind movement is included. We compare this global 30 arc-seconds version of PCR-GLOBWB to previously published 5 arc-minutes and 30 arc-minutes versions by evaluating simulated river discharge, snow cover, soil moisture, land surface evaporation, and total water storage against observations. We show that hyper-resolution provides a more accurate simulation of river discharge, this is especially true for smaller catchments. We highlight that although global hyper-resolution modelling is possible with current computational resources and that hyper-resolution modelling results in more realistic representations of the hydrological cycle; our results suggest that global hydrological modelling still needs to incorporate landcover heterogeneity at the sub-grid scale and include processes relevant at the kilometre scale in search of better predictive capacity to provide more accurate estimates of soil moisture and evaporation fluxes.
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RC1: 'Comment on egusphere-2024-1025', Anonymous Referee #1, 16 May 2024
General comments
The paper “A first attempt to model global hydrology at hyper-resolution” by van Jaarsveld and colleagues uses the recent global kilometer scale dataset CHELSA (Brun et al., 2022) as a reference to run the global hydrological model PCR-GLOBWB, using a new downscaling method of the forcing W5E5 (Lange et al., 2021) and introducing a new scheme for lateral transfers of snow.
It addresses important scientific questions in the current state of hydrological research and high-resolution simulation challenges. Their paper is well written, easy to read, with the method very well explained and detailed.
They point out the challenges linked to hyper-resolution modelling such as the calculation time and here develop a very clear and detailed spin-up and parallelisation approach. It allows them to keep the simulation time reasonable (about 17 days for the 30 arc-seconds resolution for the period 1985 - 2019).
Their downscaling method relies on the construction of monthly climatologies: the recently available CHELSA dataset is used to construct monthly kilometric scale climatologies as references, then the forcing data are downscaled by bilinear interpolation. Monthly climatologies are calculated from these interpolated fields and a correction factor is calculated comparing them to CHELSA climatologies, then temporally linearly interpolated to get a correction factor by Julian day for precipitation, temperature and potential evaporation.
Their lateral transport of snow scheme is relatively simple, introducing a frozen water threshold. When this threshold is reached, the lateral transport is activated, through a function of steepness towards neighbouring cells.
Their main results show that the hyper-resolution modelling allows for a better simulation of discharge, especially over smaller catchments that are not represented at coarser resolution. However, not all variables show a significant improvement, such as evaporation which is even worse at higher resolution when compared to MODIS data. The authors hypothesised that this is mainly due to how the land cover is handled at high resolution.
The results are clear but I feel the discussion would benefit from being expanded, with a more critical discussion on the limit of the downscaled climate variables and with more links between the different variables analysed. For instance, the river discharge tends to be underestimated for the 30 arc-second resolution: is it logical since the evaporation is overestimated? Also, maybe some regional comparisons in performances and more discussion on the hypotheses behind some limitations would be interesting. To explain the poor performance of evaporation for instance.
Specific comments
The main questions and remarks I have relate to the downscaling method and to the discussion regarding the poor performance of evaporation.
At such a resolution, climate variables will highly depend on altitude and topographic features, as underlined l. 415, and a bilinear interpolation will not be sufficient to maintain spatial coherence between all the climatic variables. I feel this should explain most of the poor performances on evaporation at high resolution since it is the climate variable most non-linearly dependent on a variety of climate factors. I understand you used the only global scale kilometric meteorological data available to you at such a scale and this is a good start but I feel this limitation should be introduced sooner and highlighted more.
I am not sure I fully understand what they did for the part “4.3 Untangling Model Scaling and Forcing Downscaling Affects” p23.
They conclude that since both downscaling method lead to similar differences in simulations, these differences should be mostly due to parametrisation. Could neither downscaling method be sufficiently good to keep the spatial coherence of climate variables to calculate a correct evaporation? Could neither downscaling technique allow for an accurate disaggregation of the forcing variables?
It seems in Table 6 that there are smaller gaps between simulations than there are in Table 3 (I would add the ratios here to help the comparison). I would be interested in knowing the precipitation/evaporation ratio over Europe compared to the world wide results, Europe may not be representative of the limitations occurring everywherein the world. Since there is an issue with the representation of evaporation, if it is linked to the downscaling of potential evapotranspiration, the results should change depending on how much of the water cycle is driven by evapotranspiration. If it depends on the land cover types, comparing regions with very different land cover dominant types would be interesting.
Here are some minor comments:
In the results, I would introduce the relative values analysed in the text in the table!
And, as previously said, the discussion needs to be expanded a little.
Maybe comment on the discrepancies in the validation over different regions? (Appendix maps)
In the explanation of downscaling precipitation p5:
- Why use a multiplicative correction factor while an additive one was used for the other two other climatic variables? Is it to handle differently the issue with the variance conservation? I feel this should be at least justified.
Paragraph on evaporation and soil moisture evaluation p10:
How did you use the simulation data there? Are the station which have observations located and the nearest grid point value taken? How was handled the difference in resolution?
What are the optimum targets for rho, beta, and alpha there? It could help to understand the analysis later on.
l 233: Comparison with MODIS: Do the results differ if all the data are aggregated to the 30 arc-minutes resolution instead? Because the downscaling algorithm here may have an impact.
Paragraph 3.2: Since the relative values are analysed, I would introduce them in Table 3 (and Table 6 later for comparison).
For the analysis of the snow cover p18, I had some questions:
- Are the differences significant? They seem very small.
- Are the results different in different regions?Technical corrections
l 16: “cross-cutting”??
l 48: “How ever valuable” and not “However, valuable...”, it has a different meaning
l 53: Missing verb? “lead to unrealistic accumulation...”
l 126: “in each month of ...”
l 194: Issue with the structure of the paper: you have a 2.2.1 but no 2.2.2
Equ. 11 and 13: you should explain the meaning of the terms that were not introduced before, even if most of them are classic notations.
l 219: “were used” and not “we used”
l 233-234: I don’t understand the link between the two part of this sentence. I believe the second part would go better with the next sentence. Maybe present the resolution of MODIS for comparison?
l 240: “snow events to be correctly forecasted” ?
L 241: not “where” but “are incorrectly”; issue with “since where...”; “ the the”; “information” and not “info”; issue with the end of the last sentence, missing a verb?
l 258: “relatively” and not “relative”
l 259: “simulations”; “where” ?
l 274: “does, however, ...”
l 289: 4) ?? (Fig. 4)
l. 290: “The simulated total water storage changes are comparable between the three resolutions”, seems contradictory with the absolute values of storage changes presented in table 3. So where do you get that from?
l 290: “When comparing the total water storage the GRACE satellite observations.” is not a full sentence.
l 297: sentence not clear: “ 50% of 1 676 stations (Fig. A1) display a KGE greater than -0.41 for all simulations”
l 298: sentence also not clear: “ correlation and variability increases yet this is offset by a reduction in bias”, isn’t a correlation increase AND a reduction in bias what we are looking for? Offset by a variability increase?
l 316: “skillful”
l 326: issue with “like asynchronous many tasks”
l 337: issue with “that is that”
l 419: Issue with second sentence, missing verb?
l 422: “As has been done for smaller scale studies.” Not a sentence.
l 434, last sentence of the conclusion, issue with the sentence. But important message.
Citation: https://doi.org/10.5194/egusphere-2024-1025-RC1 -
RC2: 'Comment on egusphere-2024-1025', Anonymous Referee #2, 20 May 2024
OVERVIEW
The paper describes a first attempt to model global hydrology at 1 km resolution, daily, by using the well-developed PCR-GLOBWB model. Results are assessed by comparison with different data sources for soil moisture, groundwater storage, evaporation and river discharge. The comparison of model results at different resolutions is also discussed.
GENERAL COMMENTS
The paper is well written and clear, and the topic is undoubtedly relevant to the ESD readership. However, there are some major points that require further discussion and careful attention.
- MAJOR: The authors should better discuss WHY we want to develop high-resolution simulations of the hydrological cycle. If we model hydrology at 1 km scale, we need to consider processes that are relevant at that scale, and if I well understood are not included in the simulations performed in the paper. I refer, for instance, to the human intervention on the water cycle through irrigation, reservoirs, water diversions. Additionally, at km-scale processes like surface runoff might be highly relevant. These are only some examples. I believe an improved discussion on these aspects should be added in the paper.
- MODERATE: A further crucial point for discussion is the manner in which such simulations should be evaluated. While a comparison with in situ data from across the globe is undoubtedly relevant, it is not a suitable approach for performing a reliable assessment. As previously stated, it is necessary to ascertain whether the model is genuinely capable of simulating the processes at high resolution in both space and time. The comparison with river discharge for small basins is useful, but it would be beneficial to conduct stress testing of such modelling simulations in highly disturbed basins, for instance, or in basins affected by processes acting at high resolution, e.g., basins characterised by complex topography. While this is not the focus of this paper (although I would be interested to see some examples), it should be highlighted in the discussion.
- MAJOR: High-resolution meteorological forcing is obtained by downscaling coarse resolution forcing with climatology (CHELSEA). While this approach is interesting, it is evident that it will not reproduce the high-resolution day-to-day variation of meteorological forcing, particularly precipitation, which is the crucial variable for hydrological modelling. This is briefly mentioned in the discussion but should be better analysed and potential way forward should be highlighted.
- MAJOR: Several typos are present in the text, and some sentences seem to be quickly written, of course the simulation effort was huge, but the writing needs more attention. I spotted some of them in the text, but a carefully re-reading and check is highly necessary. For instance, tables and figures should be put after they are cited in the text, not before.
- MODERATE: Figure 4 is reported in the text but not discussed and analysed. Please revise.
- MODERATE: To me, the only suitable metric to be used for the comparison between modelled soil moisture and in situ observations should be the correlation (see Figure 6). The bias and the variability ratio depend too much on the climatology that is not relevant. In terms of correlation, the 30 arc-seconds simulations provide much better results, and to me it is highly interesting and it should be better highlighted and discussed.
- MODERATE: It is not clear whether the model has been calibrated against observations, typically in situ river discharge. Is there a calibration for each resolution of the model? I don't think so, and it's strange to see the much better results of the 30 arc-seconds simulation for simulating river discharge. It is not mentioned at all in the paper, which needs to be clarified.
In the sequel, a number of specific comments to be addressed is reported, but not a comprehensive list.
SPECIFIC COMMENTS (L: line or lines)
L251: Please quantify how much similar should be, e.g., as percentage difference.
L256-257: The sentence is not clear and it should be revised.
L274: Typo.
Table 2: What is NA?
Table 3: Why is precipitation higher in the 30 arc-seconds simulation? The downscaling should not produce higher precipitation. Am I wrong? Please clarify.
L286: Typo.
L287: Should be Table 3 instead of Table 1.
L291: Typo-
L291: The R^2 of the comparison of modelling simulations with GRACE data is quite low, so any conclusion from this comparison is not reliable. Please revise.
Figure 6: The legend is missing.
L326: The sentence is not clear and it should be revised.
L323-343: The high-resolution hydrology community should benefit from the remote sensing community who is working for making accessible large datasets (big data) through cloud computing and storage facilities. It can be included in the paper.
L362: Snow cover should be reported after soil moisture and evaporation as in the results.
L395-398: The percentage of forests for each spatial resolution can be easily computed. It should be quantified to test if it should be the reason for the higher evaporation in the 30 arc-seconds simulation.
L399: Typo.
L399-400: The sentence is not clear and it should be revised.
L424: There is no Sentinel ESA-CCI high-resolution soil moisture product. For a list of high-resolution soil moisture products freely available check this preprint here: https://dx.doi.org/10.2139/ssrn.4737858
L434: Typo.
RECOMMENDATION
Based on the above comments, I suggest the paper needs a major revision before its publication.
Citation: https://doi.org/10.5194/egusphere-2024-1025-RC2 -
RC3: 'Comment on egusphere-2024-1025', Anonymous Referee #3, 11 Jun 2024
Jaarsveld et al present ‘a first attempt to model global hydrology at hyper-resolution’ using a 30 arc-second version of the PCR-GLOBWB model. Overall I think this is an interesting proof of concept for scaling the global model and there are some interesting results. However I do have some concerns that need to be addressed:
- I take this paper to be a rough proof of concept in terms of the scaling abilities of this model. I think that is a very valuable exercise and is worthy of publication. However, the results themselves show a lot of concerning model behavior and low KGE scores which would be very concerning if this model were used to make any hydrologic evaluations. I understand that is not really the goal of this paper. However, I think it needs to be stated very clearly in both the abstract and conclusions that this model has not been validated for scientific use and should not be used to draw any scientific conclusions. Without these very clear usage notes I worry that this could be applied in very concerning ways.
- I would have liked to have seen some comparisons to existing high resolution products. The authors cite many continental scale products that exist for hydrology and snow at 1km resolution. Comparing to point observations is valuable but I would like to know if this model is also capturing the spatial heterogeneities that other well validated models show. I think this is especially important for snow since a new approach is being presented here.
- While I think the scalability of this model is a promising step I need to acknowledge that there are a lot of results that are concerning, for example the lack of sensitivity of soil moisture and the low KGE values across the board. To me these indicate that this model is not ready to be used at this resolution even if it is computationally possible (i.e. my first comment). Still though I wonder if the authors could provide some references for the performance of other high resolution models to put this performance in context?
- No information is provided on how topography or the stream routing approach was scaled. The assumptions made here would have a big impact on the results I would think. Please provide more information on how the static geo-fabrics for the model were scaled.
Specific comments:
- Line 221: The KGE threshold of -0.41 seems arbitrary and also very low. Please explain more why this was chosen.
- Figure 8 should list the variable that is being compared explicitly in the caption.
Citation: https://doi.org/10.5194/egusphere-2024-1025-RC3
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