the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 08 Sep 2022
Global soil moisture storage capacity at 0.5° resolution for geoscientific modelling
Abstract. Soil moisture storage capacity (SMSC) links the atmosphere and terrestrial ecosystems, which is required as spatial parameters for geoscientific models. However, there are currently no available common datasets of the SMSC on a global scale, especially for hydrological models since conventional evapotranspiration-derived estimates cannot represent the extra storage capacity for the lateral flow and runoff generation. Here, we produce a dataset of the SMSC parameter for global hydrological models. Joint parameter calibration of three commonly used monthly water balance models provides the labels for a deep residual network. The global SMSC is constructed based on the deep residual network at 0.5° resolution by integrating 15 types of meteorological forcings, underlying surface properties, and runoff data. SMSC products are validated with the spatial distribution against root zone depth datasets and validated in the simulation efficiency on global grids and typical catchments from different climatic regions. We provide the global SMSC parameter dataset as a benchmark for geoscientific modelling by users.
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Status: closed
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RC1: 'Comment on essd-2022-217', Anonymous Referee #1, 08 Oct 2022
This paper developed global soil moisture storage capacity (SMSC) map at 5° × 5° grid scale, which provide a great improvement on the further application of hydrologic model in ungagged area. The new SMSC data was generated by the joint calibration of three hydrologic model and expand to global by deep learning networks, and was evaluated in 20 watersheds from 5 different climate regions. Overall, this manuscript is reasonably organized, and I think this manuscript is acceptable for publication with minor revision.
Specific comments
Line 27: “SMSC[L]” to “SMSC”
Line 99-100: According to Table 1, “1902 to 2014” means January 1902 to December 2014, hence there are 113 years in total. But in line 100, “first year…, 80 years…, 30 years…” only 111 years in all. Besides, does it enough to have only one year warming-up period? I suggest to have 3-5 years for warming-up.
Line 152: Please specify the calculation method of the Em.
Line 161: “SC” to “SMSC”
Line 290: In figure 2(d), there is an increasing trend from -30° to -50° latitude. It’s not decreasing towards the South Pole. Could you explain it?
Line 334: “snowbelt” to “snowmelt”
Line 647: Table 4, could you add a column to list the climate zone of each catchment?
Citation: https://doi.org/10.5194/essd-2022-217-RC1 -
AC1: 'Reply on RC1', Pan Liu, 13 Oct 2022
Dear Anonymous Referee #1,
Thanks very much for your great efforts to assess our manuscript. We have studied your comments carefully and will make corrections/revisions as suggested. Please check the detailed replies in the supplement. A revised manuscript which specifies the adjustments based on your comments will be provided at a later stage, awaiting the editor’s decision.
We are looking forward to your further assessment.
With best regards,
Kang Xie and co-authors
-
AC1: 'Reply on RC1', Pan Liu, 13 Oct 2022
-
RC2: 'Comment on essd-2022-217', Anonymous Referee #2, 23 Oct 2022
-
AC2: 'Reply on RC2', Pan Liu, 27 Oct 2022
Dear Anonymous Referee #2,
Thanks very much for your great efforts to assess our manuscript. We have studied your comments carefully and will make corrections/revisions as suggested. Please check the detailed replies in the supplement. A revised manuscript which specifies the adjustments based on your comments will be provided at a later stage, awaiting the editor’s decision.
We are looking forward to your further assessment.
With best regards,
Kang Xie and co-authors
-
AC2: 'Reply on RC2', Pan Liu, 27 Oct 2022
-
CC1: 'Comment on essd-2022-217', Chian Phạm, 29 Oct 2022
Xie et al present a dataset of global soil water storage capacity. This paper is unique in that it attempts to construct soil water storage capacity parameter. However, I personally find it to be a premature dataset of limited value. I’m not sure this paper and dataset would be of value to the broader community.
I could not find value in this dataset except for three hydrologic models with this “soil water storage capacity" parameter. That is, the paper does not show its value to the scientific community. I would like to know what the purpose of such a dataset is and why there are data gaps on "soil water storage capacity"? Is it due to technical problems or is the dataset not useful? If the former, it feels like directly calibrating three hydrologic models with "soil storage capacity" parameters against the GRUN runoff data to obtain this variable, without any new advanced methods. From my standpoint, these results are not convincing enough and the quality of the developed dataset remains questionable and unreliable due to its great uncertainty. The three hydrological models with "soil water storage capacity" parameters were calibrated against the GRUN runoff dataset (0.5 degree) and then the "soil water storage capacity" parameters were generated from the calibrated models. Next, the "soil water storage capacity" parameters were reconstructed globally using the CNN method, which takes various data as input. The final "soil water storage capacity" parameters were used for large catchments and showed good results in terms of KGE. However, it is not convincing that these parameters have been optimized on a grid scale and are very likely to produce reliable discharges for these large catchments. It feels like that authors re-run the model to prove that the parameters are reliable. I apologize for any misunderstanding of the dataset (comments above and below).
Some specific comments:
Please clarify "soil water storage capacity" and "root zone water capacity" as well as "root depth". Here, I would like to point out a very serious flaw in your manuscript, where in Figure 3, the authors refer to the "root zone water capacity" of (Wang et al., 2016) and the "root depth" of (Schenk et al., 2009) as the "soil water storage capacity"! and made a direct comparison in their discussion, without distinguishing between these three terms.
Why did you choose the dataset listed in Table to reconstruct the parameters from the calibrated hydrological model? The uncertainty of the dataset you developed is not deeply discussed! Why did you choose CNN? The authors admit that their dataset has a large uncertainty (lines 401-402).
Descriptions of "soil water storage capacity" and "root zone water storage capacity" and "root depth" are missing from the presentation, as well as the methods and detailed applications for generating these variables.
The uncertainty of the developed dataset is not discussed, nor is it further explored by testing the sensitivity of the CNN, or by using other input datasets. Why not use soil bulk data? or ESA WorldCover 2020? or other datasets? Considering that your manuscript is also very short, could you please show some specific applications of your proposed dataset.
Citation: https://doi.org/10.5194/essd-2022-217-CC1
Status: closed
-
RC1: 'Comment on essd-2022-217', Anonymous Referee #1, 08 Oct 2022
This paper developed global soil moisture storage capacity (SMSC) map at 5° × 5° grid scale, which provide a great improvement on the further application of hydrologic model in ungagged area. The new SMSC data was generated by the joint calibration of three hydrologic model and expand to global by deep learning networks, and was evaluated in 20 watersheds from 5 different climate regions. Overall, this manuscript is reasonably organized, and I think this manuscript is acceptable for publication with minor revision.
Specific comments
Line 27: “SMSC[L]” to “SMSC”
Line 99-100: According to Table 1, “1902 to 2014” means January 1902 to December 2014, hence there are 113 years in total. But in line 100, “first year…, 80 years…, 30 years…” only 111 years in all. Besides, does it enough to have only one year warming-up period? I suggest to have 3-5 years for warming-up.
Line 152: Please specify the calculation method of the Em.
Line 161: “SC” to “SMSC”
Line 290: In figure 2(d), there is an increasing trend from -30° to -50° latitude. It’s not decreasing towards the South Pole. Could you explain it?
Line 334: “snowbelt” to “snowmelt”
Line 647: Table 4, could you add a column to list the climate zone of each catchment?
Citation: https://doi.org/10.5194/essd-2022-217-RC1 -
AC1: 'Reply on RC1', Pan Liu, 13 Oct 2022
Dear Anonymous Referee #1,
Thanks very much for your great efforts to assess our manuscript. We have studied your comments carefully and will make corrections/revisions as suggested. Please check the detailed replies in the supplement. A revised manuscript which specifies the adjustments based on your comments will be provided at a later stage, awaiting the editor’s decision.
We are looking forward to your further assessment.
With best regards,
Kang Xie and co-authors
-
AC1: 'Reply on RC1', Pan Liu, 13 Oct 2022
-
RC2: 'Comment on essd-2022-217', Anonymous Referee #2, 23 Oct 2022
-
AC2: 'Reply on RC2', Pan Liu, 27 Oct 2022
Dear Anonymous Referee #2,
Thanks very much for your great efforts to assess our manuscript. We have studied your comments carefully and will make corrections/revisions as suggested. Please check the detailed replies in the supplement. A revised manuscript which specifies the adjustments based on your comments will be provided at a later stage, awaiting the editor’s decision.
We are looking forward to your further assessment.
With best regards,
Kang Xie and co-authors
-
AC2: 'Reply on RC2', Pan Liu, 27 Oct 2022
-
CC1: 'Comment on essd-2022-217', Chian Phạm, 29 Oct 2022
Xie et al present a dataset of global soil water storage capacity. This paper is unique in that it attempts to construct soil water storage capacity parameter. However, I personally find it to be a premature dataset of limited value. I’m not sure this paper and dataset would be of value to the broader community.
I could not find value in this dataset except for three hydrologic models with this “soil water storage capacity" parameter. That is, the paper does not show its value to the scientific community. I would like to know what the purpose of such a dataset is and why there are data gaps on "soil water storage capacity"? Is it due to technical problems or is the dataset not useful? If the former, it feels like directly calibrating three hydrologic models with "soil storage capacity" parameters against the GRUN runoff data to obtain this variable, without any new advanced methods. From my standpoint, these results are not convincing enough and the quality of the developed dataset remains questionable and unreliable due to its great uncertainty. The three hydrological models with "soil water storage capacity" parameters were calibrated against the GRUN runoff dataset (0.5 degree) and then the "soil water storage capacity" parameters were generated from the calibrated models. Next, the "soil water storage capacity" parameters were reconstructed globally using the CNN method, which takes various data as input. The final "soil water storage capacity" parameters were used for large catchments and showed good results in terms of KGE. However, it is not convincing that these parameters have been optimized on a grid scale and are very likely to produce reliable discharges for these large catchments. It feels like that authors re-run the model to prove that the parameters are reliable. I apologize for any misunderstanding of the dataset (comments above and below).
Some specific comments:
Please clarify "soil water storage capacity" and "root zone water capacity" as well as "root depth". Here, I would like to point out a very serious flaw in your manuscript, where in Figure 3, the authors refer to the "root zone water capacity" of (Wang et al., 2016) and the "root depth" of (Schenk et al., 2009) as the "soil water storage capacity"! and made a direct comparison in their discussion, without distinguishing between these three terms.
Why did you choose the dataset listed in Table to reconstruct the parameters from the calibrated hydrological model? The uncertainty of the dataset you developed is not deeply discussed! Why did you choose CNN? The authors admit that their dataset has a large uncertainty (lines 401-402).
Descriptions of "soil water storage capacity" and "root zone water storage capacity" and "root depth" are missing from the presentation, as well as the methods and detailed applications for generating these variables.
The uncertainty of the developed dataset is not discussed, nor is it further explored by testing the sensitivity of the CNN, or by using other input datasets. Why not use soil bulk data? or ESA WorldCover 2020? or other datasets? Considering that your manuscript is also very short, could you please show some specific applications of your proposed dataset.
Citation: https://doi.org/10.5194/essd-2022-217-CC1
Data sets
Global soil moisture storage capacity at 0.5° resolution for geoscientific modelling Kang Xie https://zenodo.org/record/5598405
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