the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A global analysis of water storage variations from remotely sensed soil moisture and daily satellite gravimetry
- 1HafenCity University Hamburg, Hamburg, Germany
- 2Helmholtz Centre Potsdam GFZ German Research Centre for Geosciences, Potsdam, Germany
- 3University of Potsdam, Institute of Environmental Sciences and Geography, Potsdam, Germany
- 1HafenCity University Hamburg, Hamburg, Germany
- 2Helmholtz Centre Potsdam GFZ German Research Centre for Geosciences, Potsdam, Germany
- 3University of Potsdam, Institute of Environmental Sciences and Geography, Potsdam, Germany
Abstract. Water storage changes in the soil can be observed on a global scale with different types of satellite remote sensing. While active or passive microwave sensors are limited to the upper few centimeters of the soil, satellite gravimetry can detect changes of terrestrial water storage (TWS) in an integrative way but it cannot distinguish between storage variations in different compartments or soil depths. Jointly analyzing both data types promises novel insights into the dynamics of subsurface water storage and of related hydrological processes. In this study, we investigate the global relationship of (1) several satellite soil moisture products and (2) non-standard daily TWS data from the GRACE and GRACE-FO satellite gravimetry missions on different time scales. The six soil moisture products analyzed in this study differ in post-processing and the considered soil depth. Level-3 surface soil moisture data sets of SMAP and SMOS are compared to post-processed Level-4 data products (surface and root zone soil moisture) and the ESA CCI multi-satellite product. On a common global 1 degree grid, we decompose all TWS and soil moisture data into seasonal to sub-monthly signal components and compare their spatial patterns and temporal variability. We find larger correlations between TWS and soil moisture for soil moisture products with deeper integration depths (root zone vs. surface layer) and for Level-4 data products. Even for high-pass filtered sub-monthly variations, significant correlations of up to 0.6 can be found in regions with large high-frequency storage variability. A time-shift analysis of TWS versus soil moisture data reveals the differences in water storage dynamics with integration depth.
Daniel Blank et al.
Status: open (until 25 Feb 2023)
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RC1: 'Comment on hess-2022-398', Anonymous Referee #1, 11 Dec 2022
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OVERVIEW
The study investigates the relationship between soil moisture and satellite gravimetry total water storage variations at daily scale and on a global scale. Multiple soil moisture products have been analysed, both for the surface layer and the root zone. The correlation and the time shift among satellite gravimetry total water storage and soil moisture products have been investigated in depth.
GENERAL COMMENTS
The paper is well written and clear. The investigation of daily terrestrial water storage (TWS) variations from GRACE(-FO) has been carried out only in a very limited number of studies and hence their global analysis is surely of interest for the readership of Hydrology and Earth System Sciences. However, I have found some major comments that needs to be addressed carefully.
- MAJOR: In the analysis of different soil moisture products, any disagreement with TWS variations from GRACE is attributed to error in soil moisture product. For instance, when SMOS L4 product has positive time shift with GRACE it is attributed to errors in the algorithm for obtaining root zone soil moisture from SMOS, but it might be an error on GRACE (of course, particularly at daily temporal resolution). Instead of only identifying the area of disagreement, a more detailed discussion should be carried out to shed light on the potential causes for that.
- MAJOR: The investigation with SMAP L4 product should be carried out separately from the other products. SMAP L4 is mostly a modelled product, the contribution of SMAP data is quite limited as highlighted in the analysis for the pixel in India. All satellite soil moisture products are able to identify the irrigation signal, whereas it is not the case for SMAP L4 as it is mostly modelled and it does not include the irrigation component. A paper clearly showing this aspect is going to be published soon. I believe the analysis with SMAP L4 should be likely removed, or considered completely separately (note that many other modelled products can be considered as well).
- MINOR: Throughout the paper many acronyms are present without the definition, please add.
- MODERATE: At line 192 it reads that the linear trend is removed. I believe it would be very interesting to compare the products in terms of their long-term trends. Can the authors add this analysis?
In the specific comments I have added several suggestions to improve the manuscript.
SPECIFIC COMMENT (L: line or lines)
L39: Soil moisture can be obtained from microwave but also optical data. If GNSS is mentioned, also optical data should be.
L53-55: Currently, well established approaches have been exploited for estimating root zone soil moisture from satellite surface soil moisture data. For instance, the operational service under Copernicus providing the Soil Water Index and the EUMETSAT H SAF root-zone soil moisture products. These products should be mentioned and I believe the sentence should be revised.
Figure 2: I would change the text in the legend for “soil moisture”. For instance, “committed area”, or something similar.
L241: These evaluations are valid for the analysed pixel, it should be clear. It seems to read general results.
L256: It’s not merely extrapolation, there’s a physically approach for getting root-zone soil moisture from surface data.
L260-261: It clearly shows that SMAP L4 is a modelled product not including irrigation, should be considered apart.
Figure 3: In the figures the anomalies are shown. It should be clarified in the y-axis labels.
L288: Exactly, GRACE TWS cannot be considered as a reference.
L294-295: SMAP L4 does not remove the noise, it is simply a modelled product.
L329-333: Deficiencies might be due also in GRACE data, right?
Figure 7: It is not readable, please improve.
Figure 8: In the caption it reads “Data gap between …” Not clear, please revise.
L364-365: It seems to me the authors are overselling the results, the correlations in the high-pass filtered signal are very low. Only relatively better with SMAP L4, but it’s not a satellite-based product.
Figure 9: The range of the colorbar should be reduces. Otherwise the figure provides little information.
RECOMMENDATION
Based on the above comments, I suggest a major revision before the possible publication on Hydrology and Earth System Sciences.
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CC1: 'Comment on hess-2022-398', Abhishek Abhi, 05 Jan 2023
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I read through this interesting manuscript focused on assessing relationships between soil moisture (SM) and GRACE-based daily TWSA on a global scale. Thanks to the authors for this great contribution to the literature. I have two comments/suggestions that the authors may find relevant while revising their manuscript.Lines 62-64. Daily TWSA has also been successfully employed to analyze the development and propagation of the water extremes using standardized drought and flood potential index (SDFPI). Please see Xiong et al. 2022a.More importantly,Lines 388-389: From these lines, I understood that climate is (as portrayed herein) the major factor for a strong correlation between TWS and SM. [Line 401: not only surface water bodies but also human activities such as groundwater extraction in north India can affect these relationships significantly]. In my understanding, the larger the groundwater extraction for irrigation, the more positive will be the trends in SM, hence the more declining trends in TWS [due to the eventual loss of irrigated GW as runoff, evapotranspiration, and atmospheric moisture content]. Please see Xiong et al., 2022b (third paragraph of section 3.2). How do the authors relate the effect of such human-induced activities to their analysis? Additionally, how this human-related part (e.g., irrigation) is reflected in various SM products as we go deeper.Overall, I could not find a sufficient description of human activities in the manuscript (though partly touched upon in line 260), which I think should be accommodated, at least as the explicit uncertainty discussion in the analysis and/or future research directions.ReferencesXiong et al., 2022a. A Novel Standardized Drought and Flood Potential Index Based on Reconstructed Daily GRACE Data. Journal of Hydrometeorology. https://doi.org/10.1175/JHM-D-22-0011.1Xiong et al. 2022b. Leveraging machine learning methods to quantify 50 years of dwindling groundwater in India. Science of the Total Environment. https://doi.org/10.1016/j.scitotenv.2022.155474
Daniel Blank et al.
Daniel Blank et al.
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