# Using Swarm to Detect Total Water Storage Changes in 26 Global Basins (Taking the Amazon Basin, Volga Basin and Zambezi Basin as Examples)

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## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Materials

#### 2.1.1. GRACE

#### 2.1.2. GRACE Follow-On

#### 2.1.3. Swarm

#### 2.2. Methods

#### 2.2.1. Estimation of TWSC Using GRACE and GRACE-FO Gravity Field Models

_{2,0}term of the GRACE time-variable gravity field models was replaced by the satellite laser ranging (SLR) observation data to improve the accuracy of the second order of the spherical harmonic coefficients [18]. Second, the glacial isostatic adjustment (GIA) was removed using the ICE-5G (VM2) model [19]. Third, the north–south strips and high-degree noises [20] in the GRACE and GRACE-FO monthly time-variable gravity field models were removed by de-stripping (P5M8) and 300 km Gaussian filtering [21]. Then, the GRACE and GRACE-FO TWSC in the basins was calculated using the following formula:

#### 2.2.2. Estimation of Average GRACE-TWSC Using Three-Cornered Hat (TCH)

#### 2.2.3. The Optimal Postprocessing Method of Swarm-TWSC

#### The Optimal Filtering Radius

#### The Optimal Order Selection

#### The Optimal Coefficient Substitution

_{2,0}term provided by SLR was used to replace the original C

_{2,0}term of the GRACE time-varying gravity field, the accuracy of GRACE-TWSC was improved. Both Swarm and GRACE mission satellites are gravity satellites, which can provide monthly resolution gravity field models and have similar components. Based on this, it is necessary to analyze the coefficient replacement strategy of the Swarm time-varying gravity field when exploring its potential to detect changes in regional water reserves and the accuracy of Swarm-TWSC.

_{1,0}coefficient or the C

_{2,0}coefficient or not make any coefficient replacement. In order to keep the mathematical analysis principle of a single variable and the original information of the Swarm time-varying gravity field as much as possible, other variables were set as truncation order of 40, no other filtering method was used, and the filtering radius was set as 600 km. As shown in the figure below, ASISwarm-TWSC and COSTSwarm-TWSC with different coefficient replacement strategies were obtained and compared with GRACE-TWSC. In order to quantify the accuracy of Swarm-TWSC, we also calculated the annual trends of Swarm-TWSC and GRACE-TWSC, as well as the correlation coefficient and root mean square error between them, taking watershed 1 as an example (see Figure 4 and Table 4).

#### The Optimal Filtering Method

## 3. Results

#### 3.1. The Optimal Postprocessing Method of Swarm-TWSC

#### 3.1.1. The Optimal Filtering Radius

#### 3.1.2. The Optimal Order Selection

#### 3.1.3. The Optimal Coefficient Substitution

_{2,0}term of the ASI and COST models are more suitable for Swarm. For the model itself, the accuracy of replacing the C

_{2,0}term of the SLR model is reduced, and replacing the C

_{1,0}coefficient of the original Swarm model with the C

_{1,0}term of the SLR model can slightly improve the accuracy of the Swarm model. Comparing the two models, the comprehensive performance of COST is better, and the basic effect of the COST model is the same as that of replacing the C

_{1,0}coefficient, only without any coefficient replacement, taking into account the average performance of 26 watersheds. As a result, we used SLR to replace the C

_{1,0}item of the COST model when evaluating the optimal coefficient selection of the Swarm time-varying gravity field model to detect changes in water reserves in land areas.

#### 3.1.4. The Optimal Filtering Method

_{1,0}term of the COST model of order 10 with the C

_{1,0}term of the SLR model, and then do 1000 km Gaussian filtering. The Swarm-TWSC in this paper was obtained by this processing strategy.

#### 3.2. Applicability Analysis of Swarm-TWSC

#### 3.3. Reasons for Applying Swarm-TWSC

#### 3.4. Long-Time GRACE-Swarm-GRACE-FO-TWSC

## 4. Discussions

_{1,0}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.

## 5. Conclusions

_{1,0}term of the COST model of order 10 with the C

_{1,0}term of the SLR model, and then do 1000 km Gaussian filtering.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**Location map of 26 regions worldwide (The boundary file is from http://hydroweb.theia-land.fr/?lang=en&basin, accessed on 5 July 2021).

**Figure 2.**The Swarm-TWSC time series and the long-time trend with different filtering radius and GRACE-TWSC’s in Basin 1. The left one is Swarm from ASI and the right one is Swarm from COST.

**Figure 3.**The Swarm-TWSC time series and the long-time trend with different order and GRACE-TWSC’s in Basin 1. The left one is Swarm from ASI and the right one is Swarm from COST.

**Figure 4.**The Swarm-TWSC time series and the long-time trend with different coefficients and GRACE-TWSC’s in Basin 1. The left one is Swarm from ASI and the right one is Swarm from COST.

**Figure 5.**The Swarm-TWSC time series and the long-time trend with different filtering methods and GRACE-TWSC’s in Basin 1. The left one is Swarm from ASI and the right one is Swarm from COST.

**Figure 6.**The processing flow of this research. Firstly, get the GRACE-TWSC and GRACE-FO-TWSC, secondly, based on GRACE-TWSC, compared different results from different Swarm models and different postprocessing methods, get the Optimal Swarm-TWSC, thirdly, combined GRACE-TWSC, Swarm-TWSC and GRACE-FO-TWSC, get the GRACE-Swarm-GFO-TWSC.

**Figure 7.**GRACE-TWSC and Swarm-TWSC time series and long-term (December 2013 to June 2017) trend plots for 26 areas.

**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.

**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.

NO | Basin | Location | NO | Basin | Location | NO | Basin | Location |
---|---|---|---|---|---|---|---|---|

1 | Yukon | North America | 10 | Nile | Africa | 19 | Lena | Asia |

2 | Mackenzie | North America | 11 | Congo | Africa | 20 | Kolyma | Asia |

3 | Nelson | North America | 12 | Zambezi | Africa | 21 | Amur | Asia |

4 | Mississippi | North America | 13 | Orange | Africa | 22 | Huang He | Asia |

5 | St Lawrence | North America | 14 | Danube | Europe | 23 | Yangtze | Asia |

6 | Amazon | South America | 15 | Euphrates and Tigris | West Asia | 24 | Ganges and Brahmaputra | Asia |

7 | Parana | South America | 16 | Volga | Asia | 25 | Indus | Asia |

8 | Niger | Africa | 17 | Ob | Asia | 26 | Murray Darling | Australia |

9 | Lake Chad Basin | Africa | 18 | Yenisey | Asia |

**Table 2.**The results of Swarm-TWSC of different filtering radius compared with GRACE-TWSC in Basin 1.

Model | ASI | COST | ||||||
---|---|---|---|---|---|---|---|---|

Filtering Radius (km) | 400 | 600 | 800 | 1000 | 400 | 600 | 800 | 1000 |

Trend (−1.69) | −9.83 | −7.99 | −5.09 | −3.3 | −10.95 | −7.21 | −4.25 | −2.62 |

Correlation Coefficient (%) | 28.93 | 48.35 | 59.2 | 63.84 | 28.7 | 53.44 | 66.44 | 68.4 |

RMSE (cm) | 37.66 | 18.07 | 11.22 | 8.71 | 32.44 | 15.23 | 10.17 | 8.37 |

Model | ASI | COST | ||||||
---|---|---|---|---|---|---|---|---|

Order | 10 | 20 | 30 | 40 | 10 | 20 | 30 | 40 |

Trend (−1.69) | −1.53 | −7.25 | −8.53 | −7.99 | −1.06 | −6.11 | −7.41 | −7.21 |

Correlation Coefficient (%) | 64.13 | 50.4 | 50.22 | 48.35 | 61.43 | 62.47 | 55.93 | 53.44 |

RMSE(cm) | 8.85 | 15.55 | 17.83 | 18.07 | 8.18 | 13.29 | 14.83 | 15.23 |

Model | ASI | COST | ||||
---|---|---|---|---|---|---|

Coefficient | C_{1,0} | C_{2,0} | NO | C_{1,0} | C_{2,0} | NO |

Trend (−1.69) | −7.79 | 14.41 | −7.99 | −7.01 | 14.58 | −7.21 |

Correlation Coefficient (%) | 49.07 | −5.19 | 48.35 | 54.71 | −11.79 | 53.44 |

RMSE(cm) | 18.03 | 28.52 | 18.07 | 15.07 | 24.94 | 15.23 |

**Table 5.**The results of Swarm-TWSC of different filtering methods compared with GRACE-TWSC in Basin 1.

Model | ASI | COST | ||||
---|---|---|---|---|---|---|

Filtering Method | P4M15 | SWEN | NO | P4M15 | SWEN | NO |

Trend (−1.69) | −4.86 | −20.24 | −7.99 | −3.41 | −15.31 | −7.21 |

Correlation Coefficient (%) | 50.35 | 34.42 | 48.35 | 54.84 | 26.81 | 53.44 |

RMSE(cm) | 14.85 | 55.25 | 18.07 | 12.93 | 38.24 | 15.23 |

**Table 6.**The average results of Swarm-TWSC of different filtering radius compared with GRACE-TWSC in 26 basins.

Model | ASI | COST | ||||||
---|---|---|---|---|---|---|---|---|

Filtering Radius (km) | 400 | 600 | 800 | 1000 | 400 | 600 | 800 | 1000 |

Correlation Coefficient (%) | 21.92 | 34.13 | 39.99 | 43.85 | 26.96 | 39.36 | 44.45 | 46.36 |

RMSE (cm) | 25.72 | 12.14 | 8.26 | 6.67 | 19.91 | 10.24 | 7.38 | 6.17 |

Optimal | Basins |
---|---|

COST-1000 | 1, 2, 3, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 24 |

ASI-1000 | 4, 5, 23, 25, 26 |

COST-800 | 12 |

**Table 8.**The average results of Swarm-TWSC of different order compared with GRACE-TWSC in 26 basins.

Model | ASI | COST | ||||||
---|---|---|---|---|---|---|---|---|

Order | 10 | 20 | 30 | 40 | 10 | 20 | 30 | 40 |

Correlation Coefficient (%) | 44.32 | 32.31 | 34.92 | 34.28 | 45.81 | 38.12 | 40.16 | 39.37 |

RMSE (cm) | 7.12 | 11.48 | 12.10 | 12.14 | 6.68 | 9.90 | 10.27 | 10.24 |

Optimal | Basins |
---|---|

ASI-10 | 1, 4, 11, 23 |

COST-10 | 2, 3, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 24, 25, 26 |

**Table 10.**The average results of Swarm-TWSC of different coefficients compared with GRACE-TWSC in 26 basins.

Model | ASI | COST | ||||
---|---|---|---|---|---|---|

Coefficient | C_{1,0} | C_{2,0} | NO | C_{1,0} | C_{2,0} | NO |

Correlation Coefficient (%) | 34.60 | 17.24 | 34.13 | 39.73 | 17.61 | 39.37 |

RMSE(cm) | 12.12 | 19.29 | 12.14 | 10.23 | 18.31 | 10.24 |

Optimal | Basins |
---|---|

COST-C_{1,0} | 1, 2, 3, 5, 12, 13, 15, 16, 17, 18, 19, 20 |

COST-NO | 4, 6, 7, 8, 9, 10, 11, 14, 21, 22, 23, 24, 25, 26 |

**Table 12.**The average results of Swarm-TWSC of different filtering methods compared with GRACE-TWSC in 26 basins.

Model | ASI | COST | ||||
---|---|---|---|---|---|---|

Filtering Method | P4M15 | SWEN | NO | P4M15 | SWEN | NO |

Correlation Coefficient (%) | 29.69 | 22.06 | 34.13 | 34.38 | 26.98 | 39.37 |

RMSE (cm) | 14.82 | 25.28 | 12.14 | 12.16 | 18.64 | 10.24 |

Optimal | Basins |
---|---|

COST-P4M15 | 1, 2, 5, 13, 23 |

COST-NO | 3, 4, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 24, 25, 26 |

NO | Basin | Area (10,000 km^{2}) | Runoff (km ^{3}) | GRACE-Trend (cm/Year) | Average Mass Change (km ^{3}) | 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 |

Correlation Classification | Negative Strongly | Negative Weakly | Irrelevant | Positive Weakly | Positive Strongly |
---|---|---|---|---|---|

Correlation Coefficient (%) | [−100, 80) | [−80, 30) | [−30, 30] | (30, 80] | (80, 100] |

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 |

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 |

NO | Basin | Result | NO | Basin | Result |
---|---|---|---|---|---|

6 | Amazon | Fully available | 25 | Indus | Available |

16 | Volga | Fully available | 9 | Lake Chad Basin | Applicable |

12 | Zambezi | Fully available | 2 | Mackenzie | Applicable |

7 | Parana | Available | 23 | Yangtze | Applicable |

17 | Ob | Available | 15 | Euphrates and Tigris | Applicable |

18 | Yenisey | Available | 20 | Kolyma | Applicable |

24 | Ganges and Brahmaputra | Available | 14 | Danube | Applicable |

10 | Nile | Available | 5 | St Lawrence | Not available |

1 | Yukon | Available | 13 | Orange | Not available |

8 | Niger | Available | 21 | Amur | Not available |

4 | Mississippi | Available | 3 | Nelson | Not available |

11 | Congo | Available | 26 | Murray Darling | Not available |

19 | Lena | Available | 22 | Huang He | Not available |

**Table 19.**Statistical table of watershed area, annual runoff, annual change, instantaneous change information and ranking for 26 watersheds.

NO | Basin | Area (10,000 km ^{2}) | Rank | Runoff (km ^{3}) | Rank | Average Mass Change (km ^{3}) | 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 |

**Table 20.**Statistics on the degree of influence of different factors on Swarm-TWSC in 26 watersheds.

Area | Yearly Runoff | Total Mass Change | Instantaneous Mass Change | |
---|---|---|---|---|

Correlation Coefficient (%) | 58.75 | 52.33 | 60.96 | 77.8 |

Impact ratio (%) | 23.66 | 20.99 | 24.45 | 31 |

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**MDPI and ACS Style**

Wang, Z.; Tian, K.; Li, F.; Xiong, S.; Gao, Y.; Wang, L.; Zhang, B.
Using Swarm to Detect Total Water Storage Changes in 26 Global Basins (Taking the Amazon Basin, Volga Basin and Zambezi Basin as Examples). *Remote Sens.* **2021**, *13*, 2659.
https://doi.org/10.3390/rs13142659

**AMA Style**

Wang Z, Tian K, Li F, Xiong S, Gao Y, Wang L, Zhang B.
Using Swarm to Detect Total Water Storage Changes in 26 Global Basins (Taking the Amazon Basin, Volga Basin and Zambezi Basin as Examples). *Remote Sensing*. 2021; 13(14):2659.
https://doi.org/10.3390/rs13142659

**Chicago/Turabian Style**

Wang, Zhengtao, Kunjun Tian, Fupeng Li, Si Xiong, Yu Gao, Lingxuan Wang, and Bingbing Zhang.
2021. "Using Swarm to Detect Total Water Storage Changes in 26 Global Basins (Taking the Amazon Basin, Volga Basin and Zambezi Basin as Examples)" *Remote Sensing* 13, no. 14: 2659.
https://doi.org/10.3390/rs13142659