A global analysis of water storage variations from remotely sensed soil moisture and daily satellite gravimetry
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: final response (author comments only)
RC1: 'Comment on hess-2022-398', Anonymous Referee #1, 11 Dec 2022
- AC1: 'Reply on RC1', Daniel Blank, 20 Mar 2023
CC1: 'Comment on hess-2022-398', Abhishek Abhi, 05 Jan 2023
- AC3: 'Reply on CC1', Daniel Blank, 20 Mar 2023
RC2: 'Comment on hess-2022-398', Anonymous Referee #2, 14 Feb 2023
- AC2: 'Reply on RC2', Daniel Blank, 20 Mar 2023
Daniel Blank et al.
Daniel Blank et al.
Viewed (geographical distribution)
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.
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.
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.
Based on the above comments, I suggest a major revision before the possible publication on Hydrology and Earth System Sciences.