Spatiotemporal Variation and Driving Analysis of Groundwater in the Tibetan Plateau Based on GRACE Downscaling Data
Abstract
:1. Introduction
2. Study Area and Data
2.1. Study Area
2.2. GRACE Data
2.3. GLDAS Data
2.4. Precipitation Data
2.5. Groundwater Observation Data
2.6. Teleconnection Data
3. Method
3.1. Calculation of Groundwater Storage
3.2. Phased Statistical Downscaling Model
3.3. Spatiotemporal Analysis Method
3.3.1. Extreme-Point Symmetric Mode Decomposition
3.3.2. The Modified Mann–Kendall Trend Test
3.4. Groundwater Storage Changes Attribution Analysis
3.5. Cross Wavelet Transform
4. Results
4.1. Evaluation of Downscaled Results
4.1.1. Comparison of GRACE-Based and Downscaled GTWSA
4.1.2. Accuracy of Downscalsed GWSA
4.2. Spatial and Temporal Characteristics of GWSA
4.2.1. Spatial and Temporal Variations of GWSA
4.2.2. Periodic Characteristics of GWSA
4.2.3. Trend Characteristics of GWSA
4.3. GWSA Change Attribution Analysis
4.4. Teleconnection Driving Forces on GWSA
5. Discussion
6. Conclusions
- (1)
- The correlation coefficients of the data before and after downscaling exceed 0.99 in the all-time series, and the RSME is low, while the data before and after downscaling in the 12 sub-basins also show high correlation. The groundwater levels measured at the Lhasa and Maoxian stations and the GWSA after downscaling of the corresponding grid show consistent trends, and are significantly correlated at the 0.01 level.
- (2)
- From the perspective of long time series, the GWSA in the Tibetan Plateau shows a downward trend (−0.45 mm/yr) from 2002 to 2020, but experiences a trend of increasing, then decreasing, and finally stabilizing. The variation trend of the GWSA in the Tibetan Plateau shows significant spatial heterogeneity, and the GWSA in the Inner, Tarim, and Yangtze basins indicate a significant upward trend, while those in the western part of the Tibetan Plateau and the eastern region, except the Yangtze basin, show a significant downward trend, in which the Brahmaputra basin has the most obvious downward trend, reaching −13.84 mm/yr.
- (3)
- The GWSA changes in the Tibetan Plateau are mainly dominated by natural factors, which is in line with the characteristics of “vast land and sparsely populated” in the Tibetan Plateau. However, the impact of human activities can not be ignored in individual sub-basins. The results of cross wavelet transform show that ENSO has the greatest influence on the GWSA on the Tibetan Plateau.
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Climate Factor | Long Name | Data Source |
---|---|---|
AOI | Arctic Oscillation Index | https://www.ncdc.noaa.gov/teleconnections/ao/ |
ENSO | El Nino-Southern Oscillation Index | http://www.esrl.noaa.gov/psd/data/correlation/nina34.data |
NAOI | North Atlantic Oscillation Index | https:/www.ncdc.noaa.gov/teleconnections/nao/ |
PDOI | Pacific Decadal Oscillation Index | http://www.ncdc.noaa.gov/teleconnections/pdo/ |
IMF1 | IMF2 | IMF3 | IMF4 | IMF5 | R | |
---|---|---|---|---|---|---|
Period | 0.43 | 0.55 | 1.03 | 2.65 | 4.65 | |
Variance contribution | 23.14% | 12.59% | 8.58% | 7.42% | 37.58% | 10.70% |
Correlation coefficient | 0.48 ** | 0.38 ** | 0.30 ** | 0.23 ** | 0.57 ** | 0.27 ** |
Basins | R2 | Evaporation | Precipitation | Runoff | Night Lights | Natural | Human |
---|---|---|---|---|---|---|---|
Tibetan Plateau | 0.08 | 32% | 5% | 24% | 39% | 61% | 39% |
AmuDayra | 0.32 | 33% | 36% | 11% | 20% | 80% | 20% |
Brahmaputra | 0.79 | 9% | 11% | 30% | 50% | 50% | 50% |
Ganges | 0.71 | 29% | 6% | 27% | 39% | 61% | 39% |
Hexi | 0.69 | 46% | 32% | 3% | 20% | 80% | 20% |
Indus | 0.73 | 23% | 18% | 15% | 43% | 57% | 43% |
Inner | 0.83 | 26% | 7% | 32% | 35% | 65% | 35% |
Mekong | 0.53 | 42% | 2% | 25% | 30% | 70% | 30% |
Qaidam | 0.89 | 8% | 7% | 24% | 61% | 39% | 61% |
Salween | 0.67 | 31% | 16% | 20% | 33% | 67% | 33% |
Tarim | 0.21 | 32% | 11% | 24% | 33% | 67% | 33% |
Yangtze | 0.41 | 26% | 9% | 17% | 48% | 52% | 48% |
Yellow | 0.44 | 17% | 38% | 2% | 43% | 57% | 43% |
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Gao, G.; Zhao, J.; Wang, J.; Zhao, G.; Chen, J.; Li, Z. Spatiotemporal Variation and Driving Analysis of Groundwater in the Tibetan Plateau Based on GRACE Downscaling Data. Water 2022, 14, 3302. https://doi.org/10.3390/w14203302
Gao G, Zhao J, Wang J, Zhao G, Chen J, Li Z. Spatiotemporal Variation and Driving Analysis of Groundwater in the Tibetan Plateau Based on GRACE Downscaling Data. Water. 2022; 14(20):3302. https://doi.org/10.3390/w14203302
Chicago/Turabian StyleGao, Guangli, Jing Zhao, Jiaxue Wang, Guizhang Zhao, Jiayue Chen, and Zhiping Li. 2022. "Spatiotemporal Variation and Driving Analysis of Groundwater in the Tibetan Plateau Based on GRACE Downscaling Data" Water 14, no. 20: 3302. https://doi.org/10.3390/w14203302