Evaluation of root-zone soil moisture over the Huai river basin

Liu, En; Zhu, Yonghua; Calvet, Jean-Christophe; Lü, Haishen; Bonan, Bertrand; Zheng, Jingyao; Gou, Qiqi; Wang, Xiaoyi; Ding, Zhenzhou; Xu, Haiting; Pan, Ying; Chen, Tingxing

doi:https://doi.org/10.5194/egusphere-2023-1597

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https://doi.org/10.5194/egusphere-2023-1597

Preprints

22 Sep 2023

| 22 Sep 2023

Evaluation of root-zone soil moisture over the Huai river basin

En Liu, Yonghua Zhu, Jean-Christophe Calvet, Haishen Lü, Bertrand Bonan, Jingyao Zheng, Qiqi Gou, Xiaoyi Wang, Zhenzhou Ding, Haiting Xu, Ying Pan, and Tingxing Chen

Abstract. Root zone soil moisture (RZSM) is critical for water resource management, drought monitoring and sub-seasonal flood climate prediction. RZSM is not directly observable from space, but several RZSM products are available and widely used at global and continental scales. This paper presents a comprehensive quantitative evaluation of eight RZSM products over the Huai River Basin (HRB) in China. A direct validation is performed using observations from 58 in situ soil moisture stations from 1 April 2015 to 31 March 2020. Attention is drawn to the potential factors that increase the uncertainties of model-based RZSM, such as errors in atmospheric forcing (precipitation, air temperature), soil properties, and spatial scale mismatch. The results show that the Global Land Data Assimilation System Catchment Land Surface Model (GLDAS_CLSM) performs best among all RZSM products with the highest correlation coefficient (R) and the lowest unbiased root mean square error (ubRMSE): 0.69 and 0.018 m³ m⁻³, respectively. All RZSM products tend to overestimate in situ soil moisture values, except for the Soil Moisture and Ocean Salinity (SMOS) L4 product, which underestimates RZSM. The underestimation of Surface Soil Moisture (SSM) in SMOS L3, caused by underestimated physical surface temperature and overestimated ERA interim soil moisture, triggers the underestimation of RZSM in SMOS L4. The overestimation of RZSM by the other products can be explained by the overestimation of precipitation, the frequency of precipitation events (drizzle effects) and the underestimation of air temperature. In addition, the overestimation of soil clay content and the underestimation of soil sand content in different LSMs lead to higher soil moisture values. The intercomparison of the eight RZSM products shows that MERRA-2 and SMAP L4 RZSM have the highest correlation, which can be attributed to the fact that both products use the catchment land surface model and the atmospheric forcing provided by the Goddard Earth Observing System Model, version 5 (GEOS-5), although the versions differ slightly.

Received: 12 Jul 2023 – Discussion started: 22 Sep 2023

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Journal article(s) based on this preprint

05 Jun 2024

Evaluation of root zone soil moisture products over the Huai River basin

En Liu, Yonghua Zhu, Jean-Christophe Calvet, Haishen Lü, Bertrand Bonan, Jingyao Zheng, Qiqi Gou, Xiaoyi Wang, Zhenzhou Ding, Haiting Xu, Ying Pan, and Tingxing Chen

Hydrol. Earth Syst. Sci., 28, 2375–2400, https://doi.org/10.5194/hess-28-2375-2024,https://doi.org/10.5194/hess-28-2375-2024, 2024

Short summary

En Liu, Yonghua Zhu, Jean-Christophe Calvet, Haishen Lü, Bertrand Bonan, Jingyao Zheng, Qiqi Gou, Xiaoyi Wang, Zhenzhou Ding, Haiting Xu, Ying Pan, and Tingxing Chen

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2023-1597', Anonymous Referee #1, 07 Nov 2023
The authors attempted to evaluate the predictive performance of eight satellite data-derived root-zone soil moisture data. The authors provided a quantitative statistical analysis of each RZSM product based on average values of in-situ, and remote sensing estimates for the Huai River Basin. The authors concluded that the GLDAS CLSM RZMS products outperform other RZSM products. While the information contained in the manuscript is quantitative, I ended up questioning the potential contribution of this paper to the readers. I have tried to address why I think
- Major comments:
Although the study provides a significant amount of comparative analysis between remote sensing-derived RZSM products as well as against observational data, the mechanistic understanding and explanation of each result are widely missing across the manuscript. Therefore, the author's rationale about the causes of differences was often too obvious or uncertain.

Also, the authors argued that GLDAS CLSM-derived RZSM outperforms other RZSM estimates. However, rather than trying to explain the different predictive performances of each RZSM product (against in-situ observations) in relation to soil properties, land cover/use, and vegetation in the study catchment, the authors just used the average of 58 in-situ data as well as satellite products, resulting in 'all-lumped' single time series for each dataset. Thus, it is not convincible to say a certain remote sensing-derived estimates outperform others since the performance differences can be revealed differently depending on soil properties, vegetation, land cover/use, etc.

It is also not indicated how each satellite-based soil moisture (at multiple depths) and RZSM 'with different spatiotemporal resolutions were aggregated (again, spatially and temporally) to come up with the sets of time series that require consistent temporal scales between them. The method used for spatial aggregation of the gridded-RZSM also needs to be manifested (i.e., methods).

As this is site-specific, it sounds even less convincing that CLSM-derived soil moisture products outperform, and thus it gets more confusing what the authors want to argue from the RZSM products comparison.

There is significant inconsistency (due to the randomness in estimating RZSM from the remote sensing data) between RZSM estimation methods. For example, the authors tried to estimate RZSM using a depth-weighted method, but equation 1 used for in-situ RZSM is different from equation 2, which was used for RZSM estimation from satellite-derived modeled soil moisture.

-Specific comments:
line 39-40: is this sentence needed?
line 42: duplicate definition of RZSM?
line 99-100: by this sentence, do you intend not to include any process-based explanation for the soil moisture products? What about attempting to explain the performance differences found among the RZSM products (as this is essentially modeled data) in relation to model structure? Why does CLSM outperform other land models in terms of RZSM products?
Chapter 2.4: the information on the spatial and temporal resolution of each data needs to be revisited and clearly indicated.
Chapter 3.2: why did you estimate satellite-derived RZSM different from in-situ RZSM? Why equation 1 and 2 are different? How convincing are the RZSM comparisons based on equation 1 and 2?
line 293: instead of averaging all in-situ stations, can you think of disaggregating the study basin (and stations) using any available information such as surface soil properties, orography (e.g., slope, and elevation), land cover, and/or vegetation? That will help the readers get more generalizable information and references.
line 306-309: This needs to be rephrased. It is hard to understand what is meant.
line 311: can you explain why SMOS L4 showed less rapid changes and smoother trends?
line 321: can you explain why they did a better job in the wet season compared to the dry season?
line 360: can you explain why individual satellite-based RZSM products showed different probabilistic distributions? Some are log-normal and the others are normal. Can you add more explanation on this matter?
line 375: how does this ground-based observation of precipitation (840 mm/year) represent the average precipitation of the basin area? You also compared gridded-precipitation with this in-situ precipitation observation (line 430). Can you clarify how solid the comparison of this in-situ precipitation with gridded precipitation is?
line 432: do you think MERRA-2 and GLDAS-CLSM would outperform other satellite-derived RZSM in other basins (or area) as well? What if you perform a continental-scale study, will you still think there will be a certain winner? If not, how can you limit the scale of this sort of comparison study to be meaningful and convincing?
line 436-438: the sentences need to be re-structured to clarify the argument.
line 453: in-situ RZSM observation does not capture irrigation effect? Can you explain how the irrigation water supply does not impact the soil moisture content?
line 485-489: can you add more information on how the soil properties could end up in certain ranges of soil moisture values?
Citation: https://doi.org/10.5194/egusphere-2023-1597-RC1
- AC1: 'Reply on RC1', Yonghua Zhu, 02 Dec 2023
  
  Thank you so much to the Reviewer for the constructive comments which helped us to significantly improve the manuscript. Please see our detailed response to all comments in the attached pdf file.
  
  Citation: https://doi.org/10.5194/egusphere-2023-1597-AC1
- AC2: 'Reply on RC1', Yonghua Zhu, 02 Dec 2023
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-1597/egusphere-2023-1597-AC2-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2023-1597-AC2
RC2:
'Comment on egusphere-2023-1597', Anonymous Referee #2, 10 Nov 2023

The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-1597/egusphere-2023-1597-RC2-supplement.pdf

Citation: https://doi.org/10.5194/egusphere-2023-1597-RC2
- AC3: 'Reply on RC2', Yonghua Zhu, 02 Dec 2023
  
  Thank you so much to the Reviewer for the constructive comments which helped us to significantly improve the manuscript. Please see our detailed response to all comments in the attached pdf file.
  
  Citation: https://doi.org/10.5194/egusphere-2023-1597-AC3
RC3:
'Comment on egusphere-2023-1597', Anonymous Referee #3, 23 Nov 2023

See supplement

Citation: https://doi.org/10.5194/egusphere-2023-1597-RC3
- AC4: 'Reply on RC3', Yonghua Zhu, 02 Dec 2023
  
  Thank you so much to the Reviewer for the constructive comments which helped us to significantly improve the manuscript. Please see our detailed response to all comments in the attached pdf file.
  
  Citation: https://doi.org/10.5194/egusphere-2023-1597-AC4

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2023-1597', Anonymous Referee #1, 07 Nov 2023
The authors attempted to evaluate the predictive performance of eight satellite data-derived root-zone soil moisture data. The authors provided a quantitative statistical analysis of each RZSM product based on average values of in-situ, and remote sensing estimates for the Huai River Basin. The authors concluded that the GLDAS CLSM RZMS products outperform other RZSM products. While the information contained in the manuscript is quantitative, I ended up questioning the potential contribution of this paper to the readers. I have tried to address why I think
- Major comments:
Although the study provides a significant amount of comparative analysis between remote sensing-derived RZSM products as well as against observational data, the mechanistic understanding and explanation of each result are widely missing across the manuscript. Therefore, the author's rationale about the causes of differences was often too obvious or uncertain.

Also, the authors argued that GLDAS CLSM-derived RZSM outperforms other RZSM estimates. However, rather than trying to explain the different predictive performances of each RZSM product (against in-situ observations) in relation to soil properties, land cover/use, and vegetation in the study catchment, the authors just used the average of 58 in-situ data as well as satellite products, resulting in 'all-lumped' single time series for each dataset. Thus, it is not convincible to say a certain remote sensing-derived estimates outperform others since the performance differences can be revealed differently depending on soil properties, vegetation, land cover/use, etc.

It is also not indicated how each satellite-based soil moisture (at multiple depths) and RZSM 'with different spatiotemporal resolutions were aggregated (again, spatially and temporally) to come up with the sets of time series that require consistent temporal scales between them. The method used for spatial aggregation of the gridded-RZSM also needs to be manifested (i.e., methods).

As this is site-specific, it sounds even less convincing that CLSM-derived soil moisture products outperform, and thus it gets more confusing what the authors want to argue from the RZSM products comparison.

There is significant inconsistency (due to the randomness in estimating RZSM from the remote sensing data) between RZSM estimation methods. For example, the authors tried to estimate RZSM using a depth-weighted method, but equation 1 used for in-situ RZSM is different from equation 2, which was used for RZSM estimation from satellite-derived modeled soil moisture.

-Specific comments:
line 39-40: is this sentence needed?
line 42: duplicate definition of RZSM?
line 99-100: by this sentence, do you intend not to include any process-based explanation for the soil moisture products? What about attempting to explain the performance differences found among the RZSM products (as this is essentially modeled data) in relation to model structure? Why does CLSM outperform other land models in terms of RZSM products?
Chapter 2.4: the information on the spatial and temporal resolution of each data needs to be revisited and clearly indicated.
Chapter 3.2: why did you estimate satellite-derived RZSM different from in-situ RZSM? Why equation 1 and 2 are different? How convincing are the RZSM comparisons based on equation 1 and 2?
line 293: instead of averaging all in-situ stations, can you think of disaggregating the study basin (and stations) using any available information such as surface soil properties, orography (e.g., slope, and elevation), land cover, and/or vegetation? That will help the readers get more generalizable information and references.
line 306-309: This needs to be rephrased. It is hard to understand what is meant.
line 311: can you explain why SMOS L4 showed less rapid changes and smoother trends?
line 321: can you explain why they did a better job in the wet season compared to the dry season?
line 360: can you explain why individual satellite-based RZSM products showed different probabilistic distributions? Some are log-normal and the others are normal. Can you add more explanation on this matter?
line 375: how does this ground-based observation of precipitation (840 mm/year) represent the average precipitation of the basin area? You also compared gridded-precipitation with this in-situ precipitation observation (line 430). Can you clarify how solid the comparison of this in-situ precipitation with gridded precipitation is?
line 432: do you think MERRA-2 and GLDAS-CLSM would outperform other satellite-derived RZSM in other basins (or area) as well? What if you perform a continental-scale study, will you still think there will be a certain winner? If not, how can you limit the scale of this sort of comparison study to be meaningful and convincing?
line 436-438: the sentences need to be re-structured to clarify the argument.
line 453: in-situ RZSM observation does not capture irrigation effect? Can you explain how the irrigation water supply does not impact the soil moisture content?
line 485-489: can you add more information on how the soil properties could end up in certain ranges of soil moisture values?
Citation: https://doi.org/10.5194/egusphere-2023-1597-RC1
- AC1: 'Reply on RC1', Yonghua Zhu, 02 Dec 2023
  
  Thank you so much to the Reviewer for the constructive comments which helped us to significantly improve the manuscript. Please see our detailed response to all comments in the attached pdf file.
  
  Citation: https://doi.org/10.5194/egusphere-2023-1597-AC1
- AC2: 'Reply on RC1', Yonghua Zhu, 02 Dec 2023
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-1597/egusphere-2023-1597-AC2-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2023-1597-AC2
RC2:
'Comment on egusphere-2023-1597', Anonymous Referee #2, 10 Nov 2023

The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-1597/egusphere-2023-1597-RC2-supplement.pdf

Citation: https://doi.org/10.5194/egusphere-2023-1597-RC2
- AC3: 'Reply on RC2', Yonghua Zhu, 02 Dec 2023
  
  Thank you so much to the Reviewer for the constructive comments which helped us to significantly improve the manuscript. Please see our detailed response to all comments in the attached pdf file.
  
  Citation: https://doi.org/10.5194/egusphere-2023-1597-AC3
RC3:
'Comment on egusphere-2023-1597', Anonymous Referee #3, 23 Nov 2023

See supplement

Citation: https://doi.org/10.5194/egusphere-2023-1597-RC3
- AC4: 'Reply on RC3', Yonghua Zhu, 02 Dec 2023
  
  Thank you so much to the Reviewer for the constructive comments which helped us to significantly improve the manuscript. Please see our detailed response to all comments in the attached pdf file.
  
  Citation: https://doi.org/10.5194/egusphere-2023-1597-AC4

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload

ED: Publish subject to revisions (further review by editor and referees) (11 Dec 2023) by Hongkai Gao

AR by Yonghua Zhu on behalf of the Authors (26 Dec 2023) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (04 Jan 2024) by Hongkai Gao

RR by Anonymous Referee #2 (12 Jan 2024)

RR by Anonymous Referee #3 (31 Jan 2024)

RR by Anonymous Referee #1 (02 Feb 2024)

Suggestions for revision or reasons for rejection

While the revised manuscript has been improved in the sense that the objective can be better interpreted, I found the whole manuscript is still relatively complex to understand mainly due to 1) many awkward/incomplete sentences, 2) figures/tables are not well organized and indicated (e.g., labels, units, captions). I am afraid the readers might get confused due to missing information in the figures. Please check my specific comments below:

Title: Should the title include 'remote sensing RZSM'?

Figure (all): Please increase labels and units and add more explanation to the captions to help understand more correctly.

Table 1: It is nice to have this organized table for the spatiotemporal resolution of each product. Can you add a plot showing how in-situ stations overlap with each RZSM product?

Line 354: Didn't > did not (or change the sentence)

Line 446-448: Richards' equation is just an equation; what would be the way for Richards's equation to produce different soil moisture dynamics? Do you mean retention curve? or changing boundary conditions?

Line 499: Are these soil properties data compared with observations? Is it overestimation (compared to soil properties in-situ) or just higher estimates?

Line 532: I do not expect significant spatial heterogeneity for precipitation in a watershed. And what do you mean by temporal heterogeneity? Accuracy for precipitation forcing has always been a major challenge.

Line 536: Does not make any sense! "overestimation of in situ observations by LSM-based RZSM products" ???

Fig. 10: So what is this figure for? Is this observation and what sites? Why didn't you plot this within Fig. 9?

Line 618-621: The sentence needs to be restructured.
Line 622: Why is line 618-621 the specific reason for the difficulty of vegetation parameterization? That is a very general theory and not appropriate to be selected as a specific reason as it does not stem from the model structure.

Line 666-670: The whole sentence needs to be rephrased.

While I didn't compile the whole list of awkward/unclear sentences, I do express my concern about the clarity of this paper due to the components that I tried to address here. I do encourage authors to review and revise the paper thoroughly to make sure the paper conveys what they want to convey.

Hide

ED: Publish subject to revisions (further review by editor and referees) (03 Feb 2024) by Hongkai Gao

AR by Yonghua Zhu on behalf of the Authors (18 Feb 2024) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (21 Feb 2024) by Hongkai Gao

RR by Anonymous Referee #2 (12 Mar 2024)

Suggestions for revision or reasons for rejection

I believe that the manuscript has been improved based on the reviewer comments. I still have difficulty in grasping the key messages and contributions from the paper. I think this can be overcome by critically removing text that does not directly contribute to the main conclusion. This would make the paper more readable. Besides, I think that strengthening (and shortening) the abstract and conclusion can definitely help to better convey the main message. See below some specific comments:
- The abstract is relatively long and not entirely understandable by itself. For example L28: 'The seven model based products' raises questions by the reader; too many details on SMOS for the abstract; a lot of emphasis on the limitations. I believe that addressing these issues in the abstract will make it more clear and impactful.
- L28: we have eight products, but only seven model-based products? I assume SMOS is not considered as model based? Please clarify this throughout the text and in Table 1
- L266 doesn't -> does not
- Table 2: which correlation coefficient? Pearson?
- Fig 4: Why are the outliers red, and not the same color as the corresponding box?
- 5.3 title: capital W in 'What'
-L740-743: this would fit better at the end of the conclusion
- The authors have put a lot of effort in evaluating potential causes for mismatches between the different datasets, could the authors also put this into perspective in the conclusion? I mean: what is needed to overcome the major issues?
- Can you put the potential causes for the mismatches into perspective while considering the spatial resolution of the models assessed here? For example, it is not likely that the models will ever cover all the details on the soil textures as presented in Sect. 5.2. The answer to the section title: 'are the soil properties correctly represented' is obviously no. Can you refer to literature that is trying to overcome this issue in the model world?

Hide

RR by Anonymous Referee #3 (24 Mar 2024)

ED: Publish subject to minor revisions (review by editor) (26 Mar 2024) by Hongkai Gao

AR by Yonghua Zhu on behalf of the Authors (04 Apr 2024) Author's response Author's tracked changes Manuscript

ED: Publish as is (08 Apr 2024) by Hongkai Gao

AR by Yonghua Zhu on behalf of the Authors (09 Apr 2024) Author's response Manuscript

Journal article(s) based on this preprint

05 Jun 2024

Evaluation of root zone soil moisture products over the Huai River basin

En Liu, Yonghua Zhu, Jean-Christophe Calvet, Haishen Lü, Bertrand Bonan, Jingyao Zheng, Qiqi Gou, Xiaoyi Wang, Zhenzhou Ding, Haiting Xu, Ying Pan, and Tingxing Chen

Hydrol. Earth Syst. Sci., 28, 2375–2400, https://doi.org/10.5194/hess-28-2375-2024,https://doi.org/10.5194/hess-28-2375-2024, 2024

Short summary

En Liu, Yonghua Zhu, Jean-Christophe Calvet, Haishen Lü, Bertrand Bonan, Jingyao Zheng, Qiqi Gou, Xiaoyi Wang, Zhenzhou Ding, Haiting Xu, Ying Pan, and Tingxing Chen

Supplement

https://doi.org/10.5194/egusphere-2023-1597-supplement

Data sets

In situ root-zone soil moisture measurements En Liu https://doi.org/10.6084/m9.figshare.23497502

ERA5 reanalysis datasets Hourly 0.25 x 0.25 degree| ECMWF Hans Hersbach https://doi.org/10.24381/cds.adbb2d47

GES DISC Dataset: MERRA-2 tavg1_2d_lnd_Nx (M2T1NXLND 5.12.4) (nasa.gov) Global Modeling and Assimilation Office (GMAO) https://doi.org/10.5067/VJAFPLI1CSIV

CISL RDA: NCEP Climate Forecast System Version 2 (CFSv2) 6-hourly Products (ucar.edu) Suranjana Saha and coauthors https://doi.org/10.5065/D61C1TXF

GES DISC Dataset: GLDAS Noah Land Surface Model L4 3 hourly 0.25 x 0.25 degree V2.1 (nasa.gov) H. Beaudoing, M. Rodell, and NASA/GSFC/HSL https://doi.org/10.5067/E7TYRXPJKWOQ

GES DISC Dataset: GLDAS Catchment Land Surface Model L4 daily 0.25 x 0.25 degree GRACE-DA1 V2.2 (nasa.gov) B. Li, H. Beaudoing, M. Rodell, and NASA/GSFC/HSL https://doi.org/10.5067/TXBMLX370XX8

China Meteorological Administration Land Data Assimilation System (CLDAS v2.0) Product Dataset (cma.cn) China Meteorological Administration http://data.cma.cn/en/?r=search/uSearch&keywords=cldas

SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data, Version 5 | National Snow and Ice Data Center (nsidc.org) R. Reichle, G. De Lannoy, R. D. Koster, W. T. Crow, J. S. Kimball, and Q. Liu https://doi.org/10.5067/9LNYIYOBNBR5

L4 Land research products-Centre Aval de Traitement des Données SMOS (CATDS) Centre Aval de Traitement des Données SMOS http://dx.doi.org/10.12770/316e77af-cb72-4312-96a3-3011cc5068d4

En Liu, Yonghua Zhu, Jean-Christophe Calvet, Haishen Lü, Bertrand Bonan, Jingyao Zheng, Qiqi Gou, Xiaoyi Wang, Zhenzhou Ding, Haiting Xu, Ying Pan, and Tingxing Chen

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Short summary

Among 8 root zone soil moisture (RZSM) products, the GLDAS_CLSM product performs best over the Huai River basin in China. In situ observations show that most products tend to overestimate RZSM. This can be attributed to (1) underestimation of air temperature, (2) overestimation of precipitation amount and frequency of atmospheric forcing, (3) the fact that in situ observations do not capture irrigation.


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