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Remote Sensing
The Gravity Recovery and Climate Experiment (GRACE) satellite provides time-varying gravity field models that can detect total water storage change (TWSC) from April 2002 to June 2017, and its second-generation satellite, GRACE Follow-On (GRACE-FO), provides models from June 2018, so there is a one year gap. Swarm satellites are equipped with Global Positioning System (GPS) receivers, which can be used to recover the Earth’s time-varying gravitational field. Swarm’s time-varying gravitational field models (from December 2013 to June 2018) were solved by the International Combination Service for Time-variable Gravity Field Solutions (COST-G) and the Astronomical Institute of the Czech Academy of Sciences (ASI). On a timely scale, Swarm has the potential to fill the gap between the two generations of GRACE satellites.
- GRACE
- Swarm
- GRACE follow on
- gap
- TWSC
- global basins
1. Introduction
The Gravity Recovery and Climate Experiment (GRACE) satellite is the first satellite mission dedicated to Earth gravity sounding, launched by the National Aeronautics and Space Administration (NASA) and the German Aerospace Center (DLR). In the decade since its launch in March 2002, GRACE has been widely used to detect Earth-mass transport, including total water storage change (TWSC) [1,2], changes in the Antarctic and Greenland ice caps [3,4], and global sea-level changes [5,6], making important contributions to Earth science-related research and functioning as an important tool for estimating changes in terrestrial water reserves. However, in September 2017, one of the batteries in the GRACE-2 satellite failed, and its mission was successfully ended in mid-October 2017 [7,8]. Now, the GRACE time-varying gravity field model provided by the three major international centers, the Jet Propulsion Laboratory (JPL), the University of Texas Space Research Institute (CSR), and the German Geosciences Research Center (GFZ), is currently up to date only as of June 2017. The successor to the GRACE mission, GRACE Follow-On (GRACE-FO), was successfully launched on 22 May 2018 in California, USA, and its measurement principle is similar to that of GRACE, so its model can be used to continue the study of TWSC. However, the GRACE-FO time-varying gravity field model data are now published from June 2018, which means that there is a one-year gap between GRACE and GRACE-FO, so, valid and reliable data need to be found to fill this gap and ensure the consistency of the time-varying gravity field information time series.
On 22 November 2013, the European Space Agency (ESA) successfully launched an Earth observation satellite constellation, Swarm, consisting of three satellites, similar to the Challenging Mini-satellite Payload (CHAMP) mission. Although its mission is mainly to monitor the Earth’s magnetic field variations, it can also be applied to study the time-varying gravity field because it carries high-precision Global Navigation Satellite System (GNSS) receivers and other key gravity detection equipment, thus filling the observation gap between GRACE and GRACE-FO [9]. The published Swarm time-varying gravity field models are the model from December 2013 to June 2019, solved by COST-G, and the model from December 2013 to October 2018, solved by ASI. The Swarm of both institutions allows the continuity of GRACE and GRACE-FO observations on a time scale, so it is particularly important to determine the feasibility and effectiveness of the Swarm-based model to recover changes in terrestrial water storage. In recent years, several scholars have used the Swarm time-varying gravity field model to detect water storage changes in basins. Lück et al. (2018) studied the possibility of Swarm bridging GRACE and GRACE-FO, and the possibility of using Swarm time-varying gravity field with significantly lower resolution to replace GRACE time-varying gravity field in missing months [10]. Meyer et al. (2019) provided a long-term time series of monthly gravity field solutions by combining laser satellite data, GPS and K/Ka band observations of GRACE mission and GPS observations of three Swarm satellites. In their study, the lunar gravity field from Swarm was used to fill the gap between GRACE and GRACE-FO tasks [11]. Li et al. (2019) used the Swarm time-varying gravity field to estimate terrestrial water storage changes in the Amazon Basin and the water storage deficit caused by the 2015/2016 drought event. Comparing GRACE data, hydrological models, and hydrological station data, they found that the Swarm results were in good agreement with GRACE, hydrological models, and virtual hydrological station estimates, providing a new and effective way to detect terrestrial water storage changes and drought events. It also has the potential to replace the GRACE satellite to detect extreme droughts and floods in the Amazon basin [12]. Cui et al. (2020) compared Swarm with the GRACE/GRACE-FO models in terms of model accuracy, observation noise, and inverted TWSC and the results verified that Swarm time-variable gravity field has the potential to extract TWSC signals in the Amazon River Basin and can serve as a complement to GRACE/GRACE-FO data for detecting TWSC in local areas [13]. Forootan et al. (2020) applied time-variable gravity fields (2013 onward) from the Swarm Earth explorer mission with a low spatial resolution of ∼1500 km. A novel iterative reconstruction approach was formulated based on independent component analysis (ICA) combining GRACE and Swarm fields. The reconstructed TWSC fields of 2003–2018 were compared with a commonly applied reconstruction technique and GRACE-FO TWSC fields, and the results indicated considerable noise reduction and improved long-term consistency of the iterative ICA reconstruction technique. These models were applied to evaluate trends and seasonal mass changes (for 2003–2018) within the world’s 33 largest river basins [14]. However, all the research does not define the best Swarm data processing and does not estimate the potential of Swarm worldly. Therefore, how to preserve the original Swarm signal as much as possible and how to better detect water storage changes in more basins will be the focus of ongoing Swarm-based research.
This paper targets 26 regions worldwide (see Figure 1 and Table 1) and explores regional water storage change time series between December 2013 and June 2017 from two institutions (ASI and COST-G) under different treatment strategies by computing the results of GRACE (GRACE-TWSC) and comparing them with the limits of Swarm in water storage detection and the optimal processing strategy. Finally, the TWSC of the Amazon, Volga, and Zambezi Basins is constructed to demonstrate the potential of Swarm to fill the gap between the two generations of GRACE missions.
2. Applicability Analysis of Swarm-TWSC
Based on the optimal data processing strategy of the Swarm model for detecting water storage variability in terrestrial areas obtained in Section 3.1, Swarm-TWSC was calculated for 26 areas and compared with GRACE-TWSC in terms of correlation coefficient and root mean square error to evaluate the capability of the Swarm model for water storage detection.
The magnitude and accuracy of Swarm’s water storage potential are closely related to the characteristics of the area under study. To this end, this paper is based on water storage trends detected by the GRACE time-varying gravity field model for 26 major global basins between December 2013 and June 2017, i.e., GRACE-TWSC, and the basin area, average annual runoff within the basin, and annual and instantaneous changes in basin water storage are calculated for each basin. The results can be seen in Figure 7 and Table 14.
Figure 7. GRACE-TWSC and Swarm-TWSC time series and long-term (December 2013 to June 2017) trend plots for 26 areas.
Table 14. Statistical table of water storage change information in 26 basins.
| NO | Basin | Area (10,000 km2) | Runoff (km3) |
GRACE-Trend (cm/Year) |
Average Mass Change (km3) |
Swarm-Trend (cm/Year) |
Correlation Coefficient (%) | RMSE (cm) |
|---|---|---|---|---|---|---|---|---|
| 1 | Yukon | 83.5 | 200.6 | −1.69 | −14.11 | −0.77 | 62.44 | 4.03 |
| 2 | Mackenzie | 180.5 | 357.2 | −1.1 | −19.86 | 0.47 | 55.97 | 4.45 |
| 3 | Nelson | 115 | 74.7 | −1.21 | −13.91 | 2.68 | −1.62 | 5.88 |
| 4 | Mississippi | 323 | 599.5 | 1.02 | 32.95 | 1.64 | 58.3 | 3.94 |
| 5 | St Lawrence | 30 | 332.39 | 0.9 | 2.7 | 2.77 | 29.14 | 5.95 |
| 6 | Amazon | 691.5 | 6906.38 | −2.11 | −145.91 | −2.59 | 93.55 | 4.92 |
| 7 | Parana | 310.3 | 800 | 2.79 | 86.57 | 0.40 | 42.85 | 6.29 |
| 8 | Niger | 209 | 200 | −0.26 | −5.43 | −0.10 | 58.86 | 3.12 |
| 9 | Lake Chad Basin | 100 | 450 | −0.23 | −5.06 | 0.50 | 61 | 5.43 |
| 10 | Nile | 335 | 81 | −0.6 | −20.1 | −0.48 | 70.14 | 4.38 |
| 11 | Congo | 401 | 1292.98 | −0.07 | −2.807 | −0.67 | 57.66 | 3.46 |
| 12 | Zambezi | 138 | 311.1 | −1.68 | −23.18 | −0.27 | 71.56 | 6.86 |
| 13 | Orange | 102 | 15.45 | −0.2 | −2.04 | −0.15 | 5.36 | 5.65 |
| 14 | Danube | 81.7 | 203 | −0.31 | −2.53 | 1.61 | 32 | 4.96 |
| 15 | Euphrates and Tigris | 104.8 | 62.06 | 4.91 | 51.46 | −0.87 | 39.45 | 4.39 |
| 16 | Volga | 138 | 254.18 | 1.43 | 19.73 | 1.19 | 81 | 3.56 |
| 17 | Ob | 297 | 385 | 1.97 | 58.51 | 0.86 | 77.13 | 3.89 |
| 18 | Yenisey | 260.5 | 625.36 | −0.75 | −19.54 | −0.62 | 74.67 | 3.22 |
| 19 | Lena | 249 | 540 | −0.41 | −10.21 | −0.5 | 57.62 | 4.16 |
| 20 | Kolyma | 64.4 | 123 | 0.14 | 0.90 | −0.42 | 39.37 | 5.62 |
| 21 | Amur | 185.5 | 346.5 | −0.89 | −16.51 | 0.52 | 3.64 | 4.34 |
| 22 | Huang He | 79.5 | 58 | −0.93 | −7.39 | 0.12 | −8.31 | 4.79 |
| 23 | Yangtze | 180 | 1160 | 0.75 | 13.5 | −0.33 | 53.41 | 4.03 |
| 24 | Ganges and Brahmaputra | 132.6 | 165.4 | −3.09 | −40.97 | −2.09 | 73.56 | 6.05 |
| 25 | Indus | 116.55 | 207 | −0.63 | −7.34 | −0.65 | 52.06 | 4.73 |
| 26 | Murray Darling | 100 | 5.99 | 0.63 | 6.3 | −1.58 | −1.68 | 5.26 |
From Figure 7 and Table 14, we can find that the accuracy of Swarm is different in different basins. To get the result more clearly, we analyze it in three aspects which are trend, correlation classification and cycle repetition time. We can get the long-time accuracy of Swarm by compared the TWSC trend with GRACE, get the total accuracy of Swarm by compared the correlation coefficient with GRACE, and get the periodic accuracy of Swarm by summed the similar period with GRACE-TWSC time series.
From the perspective of long-term trends (see Figure 7 and Table 14), Swarm-TWSC and GRACE-TWSC show the same trend of increased and decreased water storage in basins 1, 4–8, 10–13, 16, 17, 19, 24, and 25, and the other basins have the opposite results.
In order to reflect the closeness of the correlation between variables, we use the correlation coefficient in this paper (see Table 15). The correlation coefficient is calculated by the product-difference method based on the deviation of two variables from their respective means, and reflects the degree of correlation between them by multiplying the two deviations. To get the periodic accuracy of Swarm-TWSC in 26 basins, we get the cycle repetition time of each basin between GRACE-TWSC and Swarm-TWSC (see Table 16).
Table 15. Correlation classification.
| Correlation Classification | Negative Strongly | Negative Weakly | Irrelevant | Positive Weakly | Positive Strongly |
|---|---|---|---|---|---|
| Correlation Coefficient (%) | [−100, 80) | [−80, 30) | [−30, 30] | (30, 80] | (80, 100] |
Table 16. Statistical table of cycle repetition time of 26 basins (December 2012 to June 2017).
| NO | Basin | Cycle Repetition Time (Year) | NO | Basin | Cycle Repetition Time (Year) | NO | Basin | Cycle Repetition Time (Year) |
|---|---|---|---|---|---|---|---|---|
| 1 | Yukon | 3 | 10 | Nile | 3 | 19 | Lena | 3 |
| 2 | Mackenzie | 2.5 | 11 | Congo | 3 | 20 | Kolyma | 2.5 |
| 3 | Nelson | 2.5 | 12 | Zambezi | 3 | 21 | Amur | 1 |
| 4 | Mississippi | 3 | 13 | Orange | 0.5 | 22 | Huang He | 0.5 |
| 5 | St Lawrence | 1.5 | 14 | Danube | 3 | 23 | Yangtze | 2.5 |
| 6 | Amazon | 3.5 | 15 | Euphrates and Tigris | 2.5 | 24 | Ganges and Brahmaputra | 3 |
| 7 | Parana | 3 | 16 | Volga | 3.5 | 25 | Indus | 2.5 |
| 8 | Niger | 3 | 17 | Ob | 3 | 26 | Murray Darling | 1 |
| 9 | Lake Chad Basin | 2.5 | 18 | Yenisey | 3 |
From the perspective of correlation coefficient statistics (see Table 17), the region with a strong positive correlation between Swarm-TWSC and GRACE-TWSC is basin 6; the watersheds with weak positive correlation are basins 1, 2, 4–12, 14, 15, 17–20, 23, 24, and 25; and the watersheds that are not relevant are basins 3, 5, 9, 13, 16, 21, 22, and 26.
Table 17. Statistics of accuracy indicators of Swarm-TWSC in 26 watersheds.
| NO | Basin | Trend | Relevance | Similar Period Ratio |
|---|---|---|---|---|
| 1 | Yukon | Same | Positive Weakly | 86 |
| 2 | Mackenzie | Conversely | Positive Weakly | 71 |
| 3 | Nelson | Conversely | Irrelevant | 71 |
| 4 | Mississippi | Same | Positive Weakly | 86 |
| 5 | St Lawrence | Same | Irrelevant | 43 |
| 6 | Amazon | Same | Positive Strong | 100 |
| 7 | Parana | Same | Positive Weakly | 86 |
| 8 | Niger | Same | Positive Weakly | 86 |
| 9 | Lake Chad Basin | Conversely | Positive Weakly | 71 |
| 10 | Nile | Same | Positive Weakly | 86 |
| 11 | Congo | Same | Positive Weakly | 86 |
| 12 | Zambezi | Same | Positive Weakly | 86 |
| 13 | Orange | Same | Irrelevant | 14 |
| 14 | Danube | Conversely | Positive Weakly | 86 |
| 15 | Euphrates and Tigris | Conversely | Positive Weakly | 71 |
| 16 | Volga | Same | Positive Strongly | 100 |
| 17 | Ob | Same | Positive Weakly | 86 |
| 18 | Yenisey | Same | Positive Weakly | 86 |
| 19 | Lena | Same | Positive Weakly | 86 |
| 20 | Kolyma | Conversely | Positive Weakly | 71 |
| 21 | Amur | Conversely | Irrelevant | 29 |
| 22 | Huang He | Conversely | Irrelevant | 14 |
| 23 | Yangtze | Conversely | Positive Weakly | 71 |
| 24 | Ganges and Brahmaputra | Same | Positive Weakly | 86 |
| 25 | Indus | Same | Positive Weakly | 71 |
| 26 | Murray Darling | Conversely | Irrelevant | 29 |
From Figure 6, we can compare the performance of Swarm-TWSC and GRACE-TWSC in terms of periodicity (see Table 16 and Table 17). By counting the periodic repetition time periods of the two results and calculating their repetition time ratios, we can see that Swarm performs better in basins 1–4, 6–12, 14–20, and 23–25, with the same periodic repetition ratio above 70%, and performs worse in basins 5, 13, 21, 22, and 26.
The long-term trend of water storage changes in land areas is the combination of the two satellite sounding results, and to some extent covers abrupt errors at certain points in time (which can be considered coarse deviations, such as those created by unspecified instrumentation failure, etc.); the correlation between the two results can assess the reliability of the Swarm sounding results. The degree of deviation can measure the accuracy of the Swarm composite value, i.e., the accuracy of the detected water storage height variation value, and the validity of the detection results can be measured by comparing the same length of variation of Swarm-TWSC with the periodic fluctuation of GRACE-TWSC and the increased or decreased time of water storage variation, thus calculating the similar proportion of its periodic variation.
Comparing these three measures, among the 26 major global land basins studied in this paper (see Table 17), we can get the conclusions (Figure 8), Swarm has the best performance in basins 6, 12, and 16 and the second-best accuracy in basins 1, 4, 7, 8, 10, 11, 17, 18, 19, 24 and 25, and can be used when the GRACE series satellites are not available. Swarm could replace GRACE to detect water storage changes in the above basins. The accuracy of Swarm-TWSC is very bad in basins 3, 5, 13, 21, 22, and 26, so it is not recommended to use the original Swarm satellite time-varying gravity field to recover the water storage changes in these basins. For regions 2, 9, 14, 15, 20, and 23, on the whole, Swarm can detect the periodic change of water reserves certain completely and correctly. However, because the change value of water reserves detected by Swarm may have gross errors at some time points, Swarm-TWSC and GRACE-TWSC have opposite long-term change trends of water reserves. If these gross errors are eliminated, such as basin 2, and if only Swarm-TWSC between 2015 and 2017 is used, the change of water reserves during this period can be detected correctly. Therefore, this paper suggests that the Swarm time-varying gravity field can be selectively used to detect changes in water reserves in these basins if there are no GRACE series satellites or other effective means of detection.
Figure 8. The accuracy classification map of water storage change detection in 26 basins by Swarm. Among them, red represents the area where Swarm is fully available, green represents the area where Swarm is available, cyan represents the area where Swarm can be selectively used, and orange line represents the area where Swarm is not available.
3. Reasons for Applying Swarm-TWSC
Swarm satellites have constant accuracy in detecting water storage changes in different basins and different detection capabilities in different basins, which is caused by the different characteristics of the basins. The size of the watershed affects the number of Swarm-TWSC statistical grid points, and the regional water storage variation we obtained is the sum of water storage variation for all grid points. According to statistical theory, in general, the more statistics of equal precision are introduced, the more reliable the results. Therefore, the size of the watershed area affects the accuracy of Swarm detection of regional water storage. In general, the most important factor that causes mass changes in basins is changes in water, and surface water is the main component of the total water, while the size of annual runoff represents the total amount of annual surface water in basins. The quality change of basins detected by Swarm has a certain relationship with the size of runoff, so we also included it in the factors that cause good or bad effects of water storage detection by Swarm. Swarm detects total water storage variation in basins, so it is necessary to analyze this indicator to study the applicability of Swarm. Based on the trend of water storage changes in basins detected by GRACE, the average annual change of water storage can be obtained, combined with the size of the basin, and the applicability of Swarm can be assessed by this indicator. In addition, it is necessary to analyze the degree of water storage change in each basin when assessing the detection capability of Swarm in different basins.
To synthesize the above analysis, in order to evaluate the capability of Swarm to detect water storage changes in terrestrial areas, this paper studied four aspects: area of each watershed, annual runoff volume, annual mass change of water storage, and transient change of water storage, as shown in Table 19. The table shows the size and area ranking of each watershed, the size and ranking of annual runoff in each watershed, the size and ranking of overall quality change in each watershed, and the size and ranking of the instantaneous change in water storage in each watershed.
Table 19. Statistical table of watershed area, annual runoff, annual change, instantaneous change information and ranking for 26 watersheds.
| NO | Basin | Area (10,000 km2) |
Rank | Runoff (km3) |
Rank | Average Mass Change (km3) |
Rank | Instantaneous Change (cm) | Rank | Result Rank |
|---|---|---|---|---|---|---|---|---|---|---|
| 6 | Amazon | 691.5 | 1 | 6906.38 | 1 | −145.91 | 1 | 13.66 | 1 | 1 |
| 16 | Volga | 138 | 14 | 254.18 | 14 | 19.73 | 10 | 4.61 | 5 | 2 |
| 12 | Zambezi | 138 | 13 | 311.1 | 13 | −23.18 | 7 | 9.96 | 2 | 3 |
| 7 | Parana | 310.3 | 5 | 800 | 4 | 86.57 | 2 | 4.83 | 4 | 4 |
| 17 | Ob | 297 | 6 | 385 | 9 | 58.51 | 3 | 3.8 | 8 | 5 |
| 18 | Yenisey | 260.5 | 7 | 625.36 | 5 | −19.54 | 11 | 3.38 | 12 | 6 |
| 24 | Ganges and Brahmaputra | 132.6 | 15 | 165.4 | 19 | −40.97 | 5 | 8.94 | 3 | 7 |
| 10 | Nile | 335 | 3 | 81 | 21 | −20.1 | 8 | 3.75 | 9 | 8 |
| 1 | Yukon | 83.5 | 22 | 200.6 | 17 | −14.11 | 13 | 4.22 | 6 | 9 |
| 8 | Niger | 209 | 9 | 200 | 18 | −5.43 | 20 | 1.97 | 22 | 10 |
| 4 | Mississippi | 323 | 4 | 599.5 | 6 | 32.95 | 6 | 3.59 | 10 | 11 |
| 11 | Congo | 401 | 2 | 1292.98 | 2 | −2.81 | 22 | 3.02 | 18 | 12 |
| 19 | Lena | 249 | 8 | 540 | 7 | −10.21 | 16 | 2.57 | 19 | 13 |
| 25 | Indus | 116.55 | 16 | 207 | 15 | −7.34 | 18 | 3.1 | 16 | 14 |
| 9 | Lake Chad Basin | 100 | 20 | 450 | 8 | −5.06 | 21 | 3.35 | 13 | 15 |
| 2 | Mackenzie | 180.5 | 11 | 357.2 | 10 | −19.86 | 9 | 2.75 | 21 | 16 |
| 23 | Yangtze | 180 | 12 | 1160 | 3 | 13.5 | 15 | 3.15 | 15 | 17 |
| 15 | Euphrates and Tigris | 104.8 | 18 | 62.06 | 23 | 51.46 | 4 | 3.06 | 17 | 18 |
| 20 | Kolyma | 64.4 | 25 | 123 | 20 | 0.90 | 26 | 3.35 | 14 | 19 |
| 14 | Danube | 81.7 | 23 | 203 | 16 | −2.53 | 24 | 3.83 | 7 | 20 |
| 5 | St Lawrence | 30 | 26 | 332.39 | 12 | 2.7 | 23 | 3.47 | 11 | 21 |
| 13 | Orange | 102 | 19 | 15.45 | 25 | −2.04 | 25 | 1.08 | 26 | 22 |
| 21 | Amur | 185.5 | 10 | 346.5 | 11 | −16.51 | 12 | 1.6 | 24 | 23 |
| 3 | Nelson | 115 | 17 | 74.7 | 22 | −13.91 | 14 | 2.69 | 20 | 24 |
| 26 | Murray Darling | 100 | 21 | 5.99 | 26 | 6.3 | 19 | 1.76 | 23 | 25 |
| 22 | Huang He | 79.5 | 24 | 58 | 24 | −7.39 | 17 | 1.52 | 25 | 26 |
According to the ranking of Swarm detection results, Swarm can be used to detect water storage changes in the first 14 basins. In terms of basin area assessment, there are 11 watersheds in the top 14. Therefore, it can be judged that basin area size is a factor that affects the Swarm detection results. However, it does not mean that the larger the watershed, the stronger the swarm detection ability. For example, watershed 21 ranks 10th in area, but Swarm cannot detect its changes accurately. On the other hand, basin 1 ranks 22nd in area, but it has better Swarm detection results (9th). Therefore, it can be determined that other factors also affect the Swarm detection results.
It can be seen from the influence of annual runoff on Swarm’s detection ability that 9 of the top 14 basins have the best detection effect, which indicates that annual runoff does affect Swarm’s ability to detect regional water reserves. However, similar to the analysis of basin areas, the size of annual runoff is not the only factor that affects the detection results. For example, although the annual runoff of the Yangtze River Basin ranks third, its Swarm detection results were poor (17th), and although the runoff of Nile ranks 21st, its detection results were better (8th).
In analyzing whether the Swarm’s ability to detect regional water reserve changes is related to the total change of annual water reserve of the basin itself, among the basins with a Swarm detection effect, there are 10 in the top 14. Similar to the analysis of the first two factors, the total change of annual water reserve can indeed affect Swarm’s detection ability, but it is not the only factor. For example, the annual change of water reserves in watershed 15 is very large (ranking 4th), but Swarm’s detection effect is poor (18th), and the annual change of water reserves in watershed 11 is small (22nd), but the detection result is good (12th).
The instantaneous change of water reserves in a basin in numerical value is the standard deviation and in graphical form is the amplitude of GRACE-TWSC. According to the statistical results, among the watersheds with good Swarm detection effect, 10 watersheds rank in the top 14 in terms of instantaneous variation of water reserves. Similar to the analysis of the first three factors, the instantaneous change of water reserves can indeed affect Swarm’s detection ability, but it is not the only factor. For example, the annual change of water reserves in watershed 11 is small (ranked 22nd), but Swarm’s detection results are better (ranked 12th), and the instantaneous water reserves in watershed 14 are large (7th), but Swarm’s detection ability is poor (20th).
Combining the above analyses, the four factors all influence Swarm’s ability to detect changes in water storage in basins. In order to quantify the degree of influence of various factors, we calculated the correlation coefficients between the rankings of various factors and the Swarm detection effect so as to count the proportion of influence of the factors on the detection results (see Table 20).
Table 20. Statistics on the degree of influence of different factors on Swarm-TWSC in 26 watersheds.
The results show that Swarm detects regional water storage changes on land mainly related to transient changes in regional water storage, followed by total mass change, the area of basins, and finally annual runoff.
4. Long-Time GRACE-Swarm-GRACE-FO-TWSC
Based on the results above, we use GRACE, Swarm and GFO to construct the long time series of about 17 years in the Amazon basin, the Volga basin and the Zambezi basin (Figure 9).
Figure 9. The GRACE-Swarm-GFO-TWSC time series and long-term (April 2002 to June 2019) in the Amazon basin, the Volga basin and the Zambezi basin. The blue line is the GRACE-TWSC time series, the red line is the Swarm-TWSC, the orange line is the GFO-TWSC and the green line is the long time TWSC trend of each basin.
The results show that the GRACE-Swarm-GFO-TWSC time series in these three basins with good continuity. The TWSC in the Amazon basin is increased by 0.38 cm per year, in the Volga basin is 0.21 cm per year and 0,18 cm per year in the Zambezi basin.
5. Discussions
In this paper, we first calculated seven GRACE-TWSCs based on seven GRACE time-varying gravity field models, and then used the weighted average method to obtain the time series of water storage changes in 26 major basins around the world to represent the true values of regional water storage changes. Although each GRACE model was checked for accuracy and can be used to detect regional TWSC, there are differences among the seven results and it is difficult to say which model is the best. In this paper, in order to explore the potential of Swarm to detect water storage, we tried to find a GRACE-TWSC with the highest accuracy as the true value, so a weighted average method was used to determine the average of the seven models’ results. Although this approach may weaken the accuracy of the optimal model for part of the time period, it takes into account the combined detection capability of the seven results as much as possible, which is more convincing for multiple regions and long time periods.
Based on the data processing experience of GRACE-TWSC, the optimal filter radius, truncation order, coefficient replacement method, and filtering method of the two Swarm models were analyzed for Swarm-TWSC, and the results show that the optimal data processing strategy is to replace the COST model of order 10 with the C1,0 term of the SLR model when the Swarm model is used to detect water storage changes in land areas, and then use 1000 km Gaussian filtering. This conclusion is different from the classical data processing strategy of using the GRACE model to detect water storage changes, which may be related to the different principles, satellite configurations, satellite trajectories, and measurement accuracy of the two satellites in measuring the Earth’s time-varying gravity field.
Based on the optimal data processing strategy of the Swarm time-varying gravity field model, Swarm-TWSC was calculated for 26 basins and compared with GRACE-TWSC, and the applicability of Swarm in detecting water storage changes in each basin was analyzed by comparing several accuracy indices (correlation coefficient, root mean square error, and period repetition rate) to determine the credibility of Swarm-TWSC in each basin. The results demonstrate that Swarm-TWSC is fully usable in 3 of the 26 basins worldwide, usable in 11, appropriately usable in 6, and not usable in 6. In this paper, the overall water storage changes in the whole basin are analyzed, but not from a spatial perspective; however, this conclusion does not hinder the utility of reference for other scholars.
Based on the accurate performance of Swarm in detecting water storage changes in 26 watersheds around the world, this paper conducted a statistical analysis in four aspects, watershed area, runoff magnitude, total annual mass change, and transient change, and found that the accuracy of Swarm-TWSC is related to all four factors, with the transient change of watershed mass as the main factor. This finding is convenient for scholars to compare the usability of Swarm when they use it for other studies of new areas.
In this paper, only Swarm-TWSC is compared with GRACE-TWSC, because both exploration models essentially represent water storage changes calculated using a time-varying gravity field model from satellite measurements, and in terms of results, both calculate the total regional mass change. In summary, this paper gives an optimal data processing strategy to systematically explore the potential of Swarm in detecting regional water storage changes and analyzes the reasons for the differences in its performance accuracy in different basins. This paper provides some guidance for future research on Swarm in water storage detection.
Although some conclusions have been obtained in the study of TWSC in 26 basins or other regions, there are still some shortcomings. For the regions with insufficient precision of Swarm-TWSC, the next step is to use GRACE-TWSC as the true value to explore the correlation with Swarm-TWSC, and establish the system difference model of the two types of satellite detection results according to the correlation, then the accuracy of swarm TWSC can be improved.
This entry is adapted from the peer-reviewed paper 10.3390/rs13142659
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