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.
NO | Basin | Area (10,000 km | 2 | ) | Runoff (km | 3 | ) | GRACE-Trend (cm/Year) |
Average Mass Change |
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NO | Basin | Area (10,000 km | 2 | ) | (km | 3 | ) | Swarm-Trend (cm/Year) |
Correlation Coefficient (%) | RMSE (cm) |
---|---|---|---|---|---|---|---|---|---|---|
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) |
---|---|---|---|---|---|---|---|---|
Rank | Runoff | (km | ||||||
1 | Yukon | 3 |
NO | Basin | Trend | Relevance | Similar Period Ratio | 3 | ) | Rank | Average Mass Change (km | 3 | ) | Rank | Instantaneous Change (cm) | Rank | Result Rank |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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 | |||||||||
1 | Yukon | 83.5 | 200.6 | −1.69 | −14.11 | −0.77 | 62.44 | 4.03 |
1 | Yukon | Same | Positive Weakly | 86 | |||||||||||||||
6 | Amazon | 691.5 | 1 | 6906.38 | 1 | −145.91 | 1 | 13.66 | 1 | 1 | |||||||||
2 | Mackenzie | 180.5 | 357.2 | −1.1 | −19.86 | 0.47 | 55.97 | 4.45 | |||||||||||
2 | Mackenzie | Conversely | Positive Weakly | 71 | |||||||||||||||
16 | Volga | 138 | 14 | 254.18 | 14 | 19.73 | 10 | 4.61 | 5 | 2 | 3 | Nelson | 115 | 74.7 | −1.21 | −13.91 | 2.68 | −1.62 | 5.88 |
3 | Nelson | Conversely | Irrelevant | 71 | |||||||||||||||
12 | Zambezi | 138 | 13 | 311.1 | 13 | −23.18 | 7 | 9.96 | 2 | 3 | 4 | Mississippi | 323 | 599.5 | 1.02 | 32.95 | |||
7 | 1.64 | 58.3 | 3.94 | ||||||||||||||||
Parana | 310.3 | 5 | 800 | 4 | 86.57 | 2 | 4.83 | 4 | 4 | 5 | St Lawrence | 30 | 43 | ||||||
17 | Ob | 297 | 332.39 | 0.9 | 2.7 | 2.77 | 29.14 | 5.95 | |||||||||||
6 | 385 | 9 | 58.51 | 3 | 3.8 | 8 | 5 | 6 | Amazon | 691.5 | 6906.38 | −2.11 | 6 | Amazon | Same−145.91 | −2.59 | 93.55 | 4.92 | |
Positive Strong | 100 | ||||||||||||||||||
18 | Yenisey | 260.5 | 7 | 625.36 | 5 | −19.54 | 11 | 3.38 | 12 | 6 | 7 | Parana | 310.3 | 800 | 2.79 | 86.57 | 0.40 | 42.85 | 6.29 |
7 | Parana | Same | Positive Weakly | ||||||||||||||||
24 | 86 | ||||||||||||||||||
Ganges and Brahmaputra | 132.6 | 15 | 165.4 | 19 | −40.97 | 5 | 8.94 | 3 | 7 | 8 | Niger | 8209 | 200 | −0.26 | −5.43 | −0.10 | 58.86 | Niger3.12 | |
Same | Positive Weakly | 86 | |||||||||||||||||
10 | Nile | 335 | 3 | 81 | 21 | −20.1 | 8 | 3.75 | 9 | 8 | 9 | Lake Chad Basin | 100 | 450 | −0.23 | −5.06 | 0.50 | 61 | 5.43 |
9 | Lake Chad Basin | Conversely | Positive Weakly | 71 | |||||||||||||||
1 | Yukon | 83.5 | 22 | 200.6 | 17 | −14.11 | 13 | 4.22 | 10 | Nile | 335 | 81 | −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 | |||||||||
25 | |||||||||||||||||||
22 | Huang He | 79.5 | 24 |
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 | ||||||||||||||||||
6 | ||||||||||||||||||||||
−0.6 | ||||||||||||||||||||||
−20.1 | −0.48 | 70.14 | 4.38 | |||||||||||||||||||
10 | Nile | Same | Positive Weakly | 86 | ||||||||||||||||||
9 | ||||||||||||||||||||||
8 | Niger | 209 | 9 | 200 | 18 | −5.43 | 20 | 1.97 | 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 | Danube | 81.7 | 203 | −0.31 | −2.53 | 1.61 | 32 | 4.96 | |||||||
15 | ||||||||||||||||||||||
4 | Mississippi | Same | Positive Weakly | 86 | ||||||||||||||||||
5 | St Lawrence | 6.86 | ||||||||||||||||||||
Same | Irrelevant | 13 | Orange | 102 | 15.45 | −0.2 | −2.04 | −0.15 | 5.36 | 5.65 | ||||||||||||
22 | 10 | 11 | Congo | Same | Positive Weakly | 86 | ||||||||||||||||
4 | Mississippi | 323 | 4 | 599.5 | 6 | 32.95 | 6 | 3.59 | 10 | 11 | 12 | Zambezi | Same | Positive Weakly | 14Euphrates and Tigris | 104.8 | 62.06 | 4.91 | 51.46 | −0.87 | 39.45 | 4.39 |
16 | Volga | 138 | ||||||||||||||||||||
11 | 86 | |||||||||||||||||||||
Congo | 401 | 2 | 1292.98 | 2 | 13 | Orange | Same | Irrelevant | 14 | |||||||||||||
14 | Danube | Conversely | Positive Weakly | 86 | ||||||||||||||||||
15 | Euphrates and Tigris | Conversely | Positive Weakly | 71 | 254.18 | 1.43 | 19.73 | |||||||||||||||
16 | Volga | Same | 1.19 | 81 | 3.56 | |||||||||||||||||
Positive Strongly | 100 | 17 | Ob | 297 | 385 | |||||||||||||||||
17 | Ob | Same | 1.97 | 58.51 | 0.86 | 77.13 | 3.89 | |||||||||||||||
Positive Weakly | 86 | 18 | Yenisey | 260.5 | 625.36 | −0.75 | −19.54 | −0.62 | 74.67 | 3.22 | ||||||||||||
18 | Yenisey | Same | Positive Weakly | 86 | 19 | Lena | 249 | 540 | −0.41 | −10.21 | −0.5 | |||||||||||
19 | Lena | Same | Positive Weakly | 86 | 57.62 | 4.16 | ||||||||||||||||
20 | Kolyma | 64.4 | 123 | 0.14 | 0.90 | −0.42 | 39.37 | 5.62 | ||||||||||||||
20 | Kolyma | Conversely | Positive Weakly | 71 | ||||||||||||||||||
14 | Danube | 81.7 | 23 | 203 | 16 | −2.53 | 24 | 3.83 | 7 | 20 | 21 | Amur | 185.5 | 21 | Amur | Conversely346.5 | −0.89 | −16.51 | 0.52 | 3.64 | 4.34 | |
Irrelevant | 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 | ||||||||||||||
29 | ||||||||||||||||||||||
5 | St Lawrence | 30 | 26 | 332.39 | 12 | 2.7 | 23 | 3.47 | 11 | 21 | 22 | Huang He | Conversely | Irrelevant | 14 | |||||||
13 | Orange | 102 | 23 | Yangtze | Conversely | Positive Weakly | 24 | Ganges and Brahmaputra | 132.6 | 165.4 | −3.09 | −40.97 | −2.09 | 73.56 | 6.05 | |||||||
71 | ||||||||||||||||||||||
24 | Ganges and Brahmaputra | Same | Positive Weakly | 86 | 25 | Indus | 116.55 | 207 | −0.63 | −7.34 | −0.65 | 52.06 | 4.73 | |||||||||
25 | Indus | Same | Positive Weakly | 71 | 26 | Murray Darling | 100 | 5.99 | 0.63 | 6.3 | −1.58 | −1.68 | 5.26 | |||||||||
26 | ||||||||||||||||||||||
Murray Darling | ||||||||||||||||||||||
Conversely | ||||||||||||||||||||||
Irrelevant | ||||||||||||||||||||||
29 | ||||||||||||||||||||||
NO | Basin | Location | NO | Basin | Location | NO | Basin | Location | ||||||||||||||
1 | Yukon | North America | 10 | Nile | Africa | 19 | Lena | Asia | ||||||||||||||
2 | Mackenzie | North America | 11 |
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 | |
58 | 24 | −7.39 | 17 | 1.52 | 25 | 26 | ||||
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 |
Correlation Classification | Negative Strongly | Negative Weakly | Irrelevant | Positive Weakly | Positive Strongly |
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